From 01d333a03f49f2441e3c77e481ac18bfb322677f Mon Sep 17 00:00:00 2001 From: msenoville Date: Tue, 31 Oct 2017 11:43:09 +0100 Subject: [PATCH 01/79] Synchronisation avec le projet original --- neo/core/__init__.py | 1 + neo/core/channelindex.py | 1 + neo/test/coretest/test_analogsignalarray.py | 4 ++-- 3 files changed, 4 insertions(+), 2 deletions(-) diff --git a/neo/core/__init__.py b/neo/core/__init__.py index d9bb67717..54f9deb8c 100644 --- a/neo/core/__init__.py +++ b/neo/core/__init__.py @@ -33,6 +33,7 @@ from neo.core.channelindex import ChannelIndex from neo.core.unit import Unit +# from neo.core.basesignal import BaseSignal from neo.core.analogsignal import AnalogSignal from neo.core.irregularlysampledsignal import IrregularlySampledSignal diff --git a/neo/core/channelindex.py b/neo/core/channelindex.py index 12f6e1830..d8a6e525e 100644 --- a/neo/core/channelindex.py +++ b/neo/core/channelindex.py @@ -213,3 +213,4 @@ def __getitem__(self, i): # we do not copy the list of units, since these are related to # the entire set of channels in the parent ChannelIndex return obj + \ No newline at end of file diff --git a/neo/test/coretest/test_analogsignalarray.py b/neo/test/coretest/test_analogsignalarray.py index 5f4277500..74002a50f 100644 --- a/neo/test/coretest/test_analogsignalarray.py +++ b/neo/test/coretest/test_analogsignalarray.py @@ -735,8 +735,8 @@ def test__merge(self): name='signal4', description='test signal', file_origin='testfile.txt') - - merged13 = self.signal1.merge(signal3) + + merged13 = self.signal1.merge(signal3) merged23 = signal2.merge(signal3) merged24 = signal2.merge(signal4) mergeddata13 = np.array(merged13) From 54aa47f6c88e33d57c702f0f004855285eaff9b7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Wed, 13 Mar 2019 15:14:30 +0100 Subject: [PATCH 02/79] test nwb --- neo/rawio/tests/test_nwbrawio.py | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) create mode 100644 neo/rawio/tests/test_nwbrawio.py diff --git a/neo/rawio/tests/test_nwbrawio.py b/neo/rawio/tests/test_nwbrawio.py new file mode 100644 index 000000000..5bd65fc3e --- /dev/null +++ b/neo/rawio/tests/test_nwbrawio.py @@ -0,0 +1,29 @@ +# Test to add a support for the NWB format + +""" +Tests of neo.rawio.nwbrawio +""" + +from __future__ import unicode_literals, print_function, division, absolute_import + +import unittest + +from neo.rawio.nwbrawio import NWBRawIO #, NWBReader +###from neo.rawio.nwbrawio_only_1_signal import NWBRawIO + +from neo.rawio.tests.common_rawio_test import BaseTestRawIO + +class TestNWBRawIO(BaseTestRawIO, unittest.TestCase, ): + rawioclass = NWBRawIO + files_to_download = [ + + '/home/elodie/envNWB/NWB_files/my_example_2.nwb', # Very simple file nwb create by me only TimeSeries +# '/home/elodie/envNWB/NWB_files/brain_observatory.nwb', # nwb file given by Matteo Cantarelli +# '/home/elodie/envNWB/NWB_files/mynwb.h5', # nwb file given by Lungsi +# '/home/elodie/envNWB/NWB_files/GreBlu9508M_Site1_Call1.nwb', + + ] + entities_to_test = files_to_download + +if __name__ == "__main__": + unittest.main() From bcfe5c6ac77d9e057341c711c656d95cab3656fa Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Wed, 13 Mar 2019 15:20:33 +0100 Subject: [PATCH 03/79] Add NWB class --- neo/rawio/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/neo/rawio/__init__.py b/neo/rawio/__init__.py index 14d4a0489..52bc2eaf5 100644 --- a/neo/rawio/__init__.py +++ b/neo/rawio/__init__.py @@ -28,6 +28,7 @@ from neo.rawio.tdtrawio import TdtRawIO from neo.rawio.winedrrawio import WinEdrRawIO from neo.rawio.winwcprawio import WinWcpRawIO +from neo.rawio.nwbrawio import NWBRawIO #, NWBReader # NWB format rawiolist = [ AxonRawIO, @@ -47,6 +48,7 @@ TdtRawIO, WinEdrRawIO, WinWcpRawIO, + NWBRawIO, # NWB format ] import os From dc3518e6883c5e8f7fcd3a841133d33e23e35bd8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Wed, 13 Mar 2019 16:11:07 +0100 Subject: [PATCH 04/79] NWB Files names --- neo/rawio/tests/test_nwbrawio.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/neo/rawio/tests/test_nwbrawio.py b/neo/rawio/tests/test_nwbrawio.py index 5bd65fc3e..aeeff5908 100644 --- a/neo/rawio/tests/test_nwbrawio.py +++ b/neo/rawio/tests/test_nwbrawio.py @@ -17,10 +17,10 @@ class TestNWBRawIO(BaseTestRawIO, unittest.TestCase, ): rawioclass = NWBRawIO files_to_download = [ - '/home/elodie/envNWB/NWB_files/my_example_2.nwb', # Very simple file nwb create by me only TimeSeries -# '/home/elodie/envNWB/NWB_files/brain_observatory.nwb', # nwb file given by Matteo Cantarelli -# '/home/elodie/envNWB/NWB_files/mynwb.h5', # nwb file given by Lungsi -# '/home/elodie/envNWB/NWB_files/GreBlu9508M_Site1_Call1.nwb', + '/home/elodie/NWB_Files/my_example_2.nwb', # Very simple file nwb create by me only TimeSeries +# '/home/elodie/NWB_Files/brain_observatory.nwb', # nwb file given by Matteo Cantarelli +# '/home/elodie/NWB_Files/mynwb.h5', # nwb file given by Lungsi +# '/home/elodie/NWB_Files/GreBlu9508M_Site1_Call1.nwb', ] entities_to_test = files_to_download From 412af76a5e3e4817b623d5e55c7b769077bd8d6b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Wed, 13 Mar 2019 16:12:55 +0100 Subject: [PATCH 05/79] nwbrawio file --- neo/rawio/nwbrawio.py | 336 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 336 insertions(+) create mode 100644 neo/rawio/nwbrawio.py diff --git a/neo/rawio/nwbrawio.py b/neo/rawio/nwbrawio.py new file mode 100644 index 000000000..877682d94 --- /dev/null +++ b/neo/rawio/nwbrawio.py @@ -0,0 +1,336 @@ +# -*- coding: utf-8 -*- +""" +Class for reading data from a Neurodata Without Borders (NWB) dataset +Documentation : https://neurodatawithoutborders.github.io +Depends on: h5py, nwb, dateutil +Supported: Read, Write +Specification - https://github.com/NeurodataWithoutBorders/specification +Python APIs - (1) https://github.com/AllenInstitute/nwb-api/tree/master/ainwb + (2) https://github.com/AllenInstitute/AllenSDK/blob/master/allensdk/core/nwb_data_set.py + (3) https://github.com/NeurodataWithoutBorders/api-python +Sample datasets from CRCNS - https://crcns.org/NWB +Sample datasets from Allen Institute - http://alleninstitute.github.io/AllenSDK/cell_types.html#neurodata-without-borders +""" +# neo imports +#from __future__ import unicode_literals # is not compatible with numpy.dtype both py2 py3 +from __future__ import print_function, division, absolute_import +#from itertools import chain +#import shutil +from os.path import join +#import dateutil.parser +import quantities as pq +from neo.rawio.baserawio import (BaseRawIO, _signal_channel_dtype, _unit_channel_dtype, + _event_channel_dtype) +from neo.core import (Segment, SpikeTrain, Unit, Epoch, Event, AnalogSignal, + IrregularlySampledSignal, ChannelIndex, Block) +from collections import OrderedDict + +# Standard Python imports +import tempfile +from tempfile import NamedTemporaryFile +import os +import glob +from scipy.io import loadmat +import numpy as np +from datetime import datetime + +# PyNWB imports +import pynwb +# Creating and writing NWB files +from pynwb import NWBFile,TimeSeries, get_manager +from pynwb.base import ProcessingModule +#from pynwb.misc import UnitTimes #, SpikeUnit +from pynwb.form.backends.hdf5 import HDF5IO +# Creating TimeSeries +from pynwb.ecephys import ElectricalSeries, Device, EventDetection +from pynwb.behavior import SpatialSeries +######from pynwb.epoch import EpochTimeSeries, Epoch ### +from pynwb.image import ImageSeries +from pynwb.core import set_parents +# For Neurodata Type Specifications +from pynwb.spec import NWBAttributeSpec # Attribute Specifications +from pynwb.spec import NWBDatasetSpec # Dataset Specifications +from pynwb.spec import NWBGroupSpec +from pynwb.spec import NWBNamespace +from pynwb.form.build import GroupBuilder, DatasetBuilder +from pynwb.form.spec import NamespaceCatalog +# +from pynwb import * + +import h5py +from pynwb import get_manager +from pynwb.form.backends.hdf5 import HDF5IO +from pynwb.form import * +from pynwb.form.build.builders import * + + +class NWBRawIO(BaseRawIO): + """ + Class for "reading" experimental data from a .nwb file + """ + extensions = ['nwb'] + rawmode = 'one-file' + + def __init__(self, filename=''): + BaseRawIO.__init__(self) + print("filename = ", filename) + self.filename = filename + print("self.filename = ", self.filename) + + def _source_name(self): + return self.filename + + def _parse_header(self): + print("******************** def parse*********************************************") + io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO + print("io = ", io) +# io = self.read_builder_NWB() + + self._file = io.read() # Define the file as a NWBFile object + print("self._file = ", self._file) + print(" ") + + print("****************************************sig unit channels******************") + # Definition of signal channels + sig_channels = [] + # Definition of units channels + unit_channels = [] + + self.header = {} + + # + # "i" define as an object the kind of signal (TimeSeries, SpatialSeries, ElectricalSeries), or units (SpikeEventSeries). + # And for each, thank to loops, we can have access to the differents parameters of the signal_channels, as + # the channel name, the id channel, the sampling rate, the type, data units, the resolution, the offset, and the group_id. + # + +######## For sig_channels ######## + + for i in self._file.acquisition: + print("----------------------------acquisition-----------------------------1--------------") + print("i = ", i) + # print("range(len(self._file.acquisition)) = ", range(len(self._file.acquisition))) ### + # print("len(self._file.acquisition) = ", len(self._file.acquisition)) ### + + print("######## For sig_channels ########") + + # Channnel name + ch_name = i.name # name of the dataset + print("ch_name = ", ch_name) + + # id channel + # index as name + chan_id = i.source + print("chan_id = ", chan_id) + + # sampling rate + sr = i.rate + print("sr = ", sr) + + # dtype + dtype = i.data.dtype + print("dtype = ", dtype) + + # units of data + units = i.unit + print("units = ", units) + + # gain + gain = i.resolution + print("gain = ", gain) + + # offset + offset = 0. + print("offset = ", offset) + + #group_id is only for special cases when channel have diferents sampling rate for instance. + group_id = 0 + print("group_id = ", group_id) + + sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id)) + print("sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id)) = ", sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id))) + + sig_channels = np.array(sig_channels) + print("----------------------------------------------------------------------------------------------------------------------sig_channels = ", sig_channels) + + +######## For unit_channels ######## + + for i in self._file.acquisition: + print("------------------------------------------------------unit----acquisition---------------------------------------") + print("i = ", i) + + print("######## For unit_channels ########") + + unit_name = 'unit{}'.format(i.name) + print("unit_channels = ", unit_channels) + + unit_id = '#{}'.format(i.source) + print("unit_id = ", unit_id) + + wf_units = i.timestamps_unit + print("wf_units = ", wf_units) + + wf_gain = i.resolution + print("wf_gain = ", wf_gain) + + wf_offset = 0. + print("wf_offset = ", wf_offset) + + wf_left_sweep = 0 + print("wf_left_sweep = ", wf_left_sweep) + + wf_sampling_rate = i.rate + print("wf_sampling_rate = ", wf_sampling_rate) + + unit_channels.append((unit_name, unit_id, wf_units, wf_gain, wf_offset, wf_left_sweep, wf_sampling_rate)) + + unit_channels = np.array(unit_channels, dtype=_unit_channel_dtype) + print("unit_channels = ", unit_channels) + + + + print("******************************************event channel***********************************************") + # Creating event/epoch channel + # In RawIO epoch and event are dealt the same way. + event_channels = [] + # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch + # For event + #event_channels.append(('Some events', 'ev_0', 'event')) + event_channels.append((self._file.epochs, 'ev_0', 'event')) # Some events + + # For epochs + #event_channels.append(('Some epochs', 'ep_1', 'epoch')) + event_channels.append((self._file.epochs, 'ep_1', 'epoch')) # Some epochs + + event_channels = np.array(event_channels, dtype=_event_channel_dtype) + print("***********************event_channels = ", event_channels) + + print("*******************************************************block**********************************************") + # file into header dict +# self.header = {} + self.header['nb_block'] =2 # 1 + self.header['nb_segment'] = [2, 3] # [1] + + + +##################################################################### + # file into header dict for signal_channels + self.header['signal_channels'] = sig_channels + +##################################################################### + # file into header dict for unit channels + self.header['unit_channels'] = unit_channels + # file into header dict for event channels + self.header['event_channels'] = event_channels + + + # insert some annotation at some place + # To create an empty tree + self._generate_minimal_annotations() + bl_annotations = self.raw_annotations['blocks'][0] + seg_annotations = bl_annotations['segments'][0] + + + def _segment_t_start(self, block_index, seg_index): # NWB Epoch corresponds to a Neo Segment + print("def _segment_t_start") + all_starts = [[0., 15.], [0., 20., 60.]] + return all_starts[block_index][seg_index] + return all_starts + + def _segment_t_stop(self, block_index, seg_index): # NWB Epoch corresponds to a Neo Segment + print("def _segment_t_stop") + all_stops = [[10., 25.], [10., 30., 70.]] + # return all_stops[block_index][seg_index] + return all_stops + + + + + + +# ################################### +# # A copy of the end of baserawio.py + + ### + # signal and channel zone + def _get_signal_size(self, block_index, seg_index, channel_indexes): + print("*** _get_signal_size ***") +# raise (NotImplementedError) + for i in self._file.acquisition: + signal_size = i.num_samples + print("signal_size = ", signal_size) # Same as _spike_count ? + return signal_size + + def _get_signal_t_start(self, block_index, seg_index, channel_indexes): + print("*** _get_signal_t_start ***") +# raise (NotImplementedError) + for i in self._file.acquisition: + starting_time = i.starting_time + print("starting_time = ", starting_time) # For TimeSeries + return starting_time + + def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, channel_indexes): + print("*** _get_analogsignal_chunk ***") +# raise (NotImplementedError) + + ### + # spiketrain and unit zone + def _spike_count(self, block_index, seg_index, unit_index): + print("*** _spike_count ***") + #raise (NotImplementedError) + for i in self._file.acquisition: + nb_spikes = i.num_samples + print("nb_spikes = ", nb_spikes) + return nb_spikes + + def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_stop): + print("*** _get_spike_timestamps ***") + #raise (NotImplementedError) + for i in self._file.acquisition: + spike_timestamps = i.timestamps + print("spike_timestamps = ", spike_timestamps) + return spike_timestamps + + + def _rescale_spike_timestamp(self, spike_timestamps, dtype): + print("*** _rescale_spike_timestamp ***") + #raise (NotImplementedError) + for i in self._file.acquisition: + spike_times = spike_timestamps.astype(dtype) + spike_times /= i.sr + print("spike_times = ", spike_times) + return spike_times + + ### + # spike waveforms zone + def _get_spike_raw_waveforms(self, block_index, seg_index, unit_index, t_start, t_stop): + print("*** _get_spike_raw_waveforms ***") + raise (NotImplementedError) + + ### + # event and epoch zone + def _event_count(self, block_index, seg_index, event_channel_index): + print("*** _event_count ***") + #raise (NotImplementedError) + for i in self._file.acquisition: + event_count = i.num_samples + print("event_count = ", event_count) # Same as nb_spikes ? + return event_count + + + + def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): + print("*** _get_event_timestamps ***") + raise (NotImplementedError) + + def _rescale_event_timestamp(self, event_timestamps, dtype): + print("*** _rescale_event_timestamp ***") + raise (NotImplementedError) + + def _rescale_epoch_duration(self, raw_duration, dtype): + print("*** _rescale_epoch_duration ***") + raise (NotImplementedError) + + + From 01faf3b9f9790202926105b0240ff7958446f184 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Tue, 19 Mar 2019 16:35:28 +0100 Subject: [PATCH 06/79] remove form to adding hdmf with version pynwb=1.0.0 to pynwb=1.0.1 --- neo/rawio/nwbrawio.py | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/neo/rawio/nwbrawio.py b/neo/rawio/nwbrawio.py index 877682d94..f39a1696a 100644 --- a/neo/rawio/nwbrawio.py +++ b/neo/rawio/nwbrawio.py @@ -40,7 +40,7 @@ from pynwb import NWBFile,TimeSeries, get_manager from pynwb.base import ProcessingModule #from pynwb.misc import UnitTimes #, SpikeUnit -from pynwb.form.backends.hdf5 import HDF5IO +##from pynwb.form.backends.hdf5 import HDF5IO # Creating TimeSeries from pynwb.ecephys import ElectricalSeries, Device, EventDetection from pynwb.behavior import SpatialSeries @@ -52,16 +52,16 @@ from pynwb.spec import NWBDatasetSpec # Dataset Specifications from pynwb.spec import NWBGroupSpec from pynwb.spec import NWBNamespace -from pynwb.form.build import GroupBuilder, DatasetBuilder -from pynwb.form.spec import NamespaceCatalog +##from pynwb.form.build import GroupBuilder, DatasetBuilder +##from pynwb.form.spec import NamespaceCatalog # from pynwb import * import h5py from pynwb import get_manager -from pynwb.form.backends.hdf5 import HDF5IO -from pynwb.form import * -from pynwb.form.build.builders import * +##from pynwb.form.backends.hdf5 import HDF5IO +##from pynwb.form import * +##from pynwb.form.build.builders import * class NWBRawIO(BaseRawIO): @@ -82,6 +82,8 @@ def _source_name(self): def _parse_header(self): print("******************** def parse*********************************************") + print("pynwb.__version__ = ", pynwb.__version__) + io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO print("io = ", io) # io = self.read_builder_NWB() From 1f3e220229b5f3bcf247e00e5870133a608c9391 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Mon, 1 Jul 2019 11:05:10 +0200 Subject: [PATCH 07/79] NWB support updates --- neo/rawio/nwbrawio.py | 283 ++++++++++++++++++------------- neo/rawio/tests/test_nwbrawio.py | 17 +- 2 files changed, 171 insertions(+), 129 deletions(-) diff --git a/neo/rawio/nwbrawio.py b/neo/rawio/nwbrawio.py index f39a1696a..49b22c098 100644 --- a/neo/rawio/nwbrawio.py +++ b/neo/rawio/nwbrawio.py @@ -1,6 +1,10 @@ # -*- coding: utf-8 -*- """ -Class for reading data from a Neurodata Without Borders (NWB) dataset +NWBRawIO +======== + +RawIO class for reading data from a Neurodata Without Borders (NWB) dataset + Documentation : https://neurodatawithoutborders.github.io Depends on: h5py, nwb, dateutil Supported: Read, Write @@ -11,13 +15,10 @@ Sample datasets from CRCNS - https://crcns.org/NWB Sample datasets from Allen Institute - http://alleninstitute.github.io/AllenSDK/cell_types.html#neurodata-without-borders """ + # neo imports -#from __future__ import unicode_literals # is not compatible with numpy.dtype both py2 py3 from __future__ import print_function, division, absolute_import -#from itertools import chain -#import shutil from os.path import join -#import dateutil.parser import quantities as pq from neo.rawio.baserawio import (BaseRawIO, _signal_channel_dtype, _unit_channel_dtype, _event_channel_dtype) @@ -36,15 +37,13 @@ # PyNWB imports import pynwb +from pynwb import * # Creating and writing NWB files from pynwb import NWBFile,TimeSeries, get_manager from pynwb.base import ProcessingModule -#from pynwb.misc import UnitTimes #, SpikeUnit -##from pynwb.form.backends.hdf5 import HDF5IO # Creating TimeSeries from pynwb.ecephys import ElectricalSeries, Device, EventDetection from pynwb.behavior import SpatialSeries -######from pynwb.epoch import EpochTimeSeries, Epoch ### from pynwb.image import ImageSeries from pynwb.core import set_parents # For Neurodata Type Specifications @@ -52,128 +51,133 @@ from pynwb.spec import NWBDatasetSpec # Dataset Specifications from pynwb.spec import NWBGroupSpec from pynwb.spec import NWBNamespace -##from pynwb.form.build import GroupBuilder, DatasetBuilder -##from pynwb.form.spec import NamespaceCatalog -# -from pynwb import * - -import h5py -from pynwb import get_manager -##from pynwb.form.backends.hdf5 import HDF5IO -##from pynwb.form import * -##from pynwb.form.build.builders import * +# Plot the structure of a NWB file +from utils.render import HierarchyDescription, NXGraphHierarchyDescription +from matplotlib import pyplot as plt class NWBRawIO(BaseRawIO): """ - Class for "reading" experimental data from a .nwb file - """ - extensions = ['nwb'] + Class for reading experimental data from a .nwb file + + Example: + >>> import neo + >>> from neo.rawio import NWBRawIO + >>> reader = neo.rawio.NWBRawIO(filename) + >>> reader.parse_header() + >>> print("reader = ", reader) + + >>> # Plot the structure of the NWB file + >>> reader.plot() + """ + name = 'NWBRawIO' + description = '' + extensions = ['nwb'] rawmode = 'one-file' def __init__(self, filename=''): BaseRawIO.__init__(self) - print("filename = ", filename) self.filename = filename - print("self.filename = ", self.filename) - - def _source_name(self): - return self.filename - - def _parse_header(self): - print("******************** def parse*********************************************") - print("pynwb.__version__ = ", pynwb.__version__) - io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO - print("io = ", io) -# io = self.read_builder_NWB() - self._file = io.read() # Define the file as a NWBFile object - print("self._file = ", self._file) - print(" ") - print("****************************************sig unit channels******************") - # Definition of signal channels - sig_channels = [] - # Definition of units channels - unit_channels = [] + def _source_name(self): + return self.filename - self.header = {} + def plot(self, filename=''): + # Plotting settings + show_bar_plot = False + plot_single_file = True + file_hierarchy = HierarchyDescription.from_hdf5(self.filename) + file_graph = NXGraphHierarchyDescription(file_hierarchy) + fig = file_graph.draw(show_plot=False, + figsize=(12,11), + label_offset=(0.0, 0.0065), + label_font_size=10) + plot_title = filename + ", " + "#Datasets=%i, #Attributes=%i, #Groups=%i, #Links=%i" % (len(file_hierarchy['datasets']), len(file_hierarchy['attributes']), len(file_hierarchy['groups']), len(file_hierarchy['links'])) + plt.title(plot_title) + plt.savefig('Structure_NWB_File.png') + plt.show() + + def _parse_header(self): + + sig_channels = [] # Definition of signal channels + unit_channels = [] # Definition of units channels # - # "i" define as an object the kind of signal (TimeSeries, SpatialSeries, ElectricalSeries), or units (SpikeEventSeries). - # And for each, thank to loops, we can have access to the differents parameters of the signal_channels, as + # "i" defines as object the signal type (TimeSeries, SpatialSeries, ElectricalSeries), or units (SpikeEventSeries). + # And for everyone, thanks to the loops, we can have access to the different parameters of the signal_channels, as # the channel name, the id channel, the sampling rate, the type, data units, the resolution, the offset, and the group_id. # -######## For sig_channels ######## + print("self._file.acquisition = ", self._file.acquisition) - for i in self._file.acquisition: - print("----------------------------acquisition-----------------------------1--------------") +######## For sig_channels ######## + for i in range(len(self._file.acquisition)): + print("----------------------------acquisition------------------------------------------") print("i = ", i) - # print("range(len(self._file.acquisition)) = ", range(len(self._file.acquisition))) ### - # print("len(self._file.acquisition) = ", len(self._file.acquisition)) ### - print("######## For sig_channels ########") - # Channnel name - ch_name = i.name # name of the dataset + # Channnel name + ch_name = 'ch_{}'.format(i) + ### ch_name = self._file.get_acquisition(i).name print("ch_name = ", ch_name) - # id channel - # index as name - chan_id = i.source + # id channel index as name + chan_id = i + 1 print("chan_id = ", chan_id) + for j in self._file.acquisition: # sampling rate - sr = i.rate - print("sr = ", sr) + sr = self._file.get_acquisition(j).rate + print("sr = ", sr) # dtype - dtype = i.data.dtype - print("dtype = ", dtype) + # dtype = i.data.dtype + dtype = 'int' ### + print("dtype = ", dtype) # units of data - units = i.unit - print("units = ", units) + units = self._file.get_acquisition(j).unit + print("units = ", units) # gain - gain = i.resolution - print("gain = ", gain) + gain = self._file.get_acquisition(j).resolution + print("gain = ", gain) # offset - offset = 0. - print("offset = ", offset) + offset = 0. ### + print("offset = ", offset) #group_id is only for special cases when channel have diferents sampling rate for instance. - group_id = 0 - print("group_id = ", group_id) + group_id = 0 + print("group_id = ", group_id) + print(" ") - sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id)) - print("sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id)) = ", sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id))) + sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id)) - sig_channels = np.array(sig_channels) - print("----------------------------------------------------------------------------------------------------------------------sig_channels = ", sig_channels) + sig_channels = np.array(sig_channels, dtype=_signal_channel_dtype) + print("---------------------sig_channels = ", sig_channels) + print(" ") ######## For unit_channels ######## - for i in self._file.acquisition: print("------------------------------------------------------unit----acquisition---------------------------------------") print("i = ", i) - print("######## For unit_channels ########") - unit_name = 'unit{}'.format(i.name) + unit_name = 'unit{}'.format(self._file.get_acquisition(i).name) print("unit_channels = ", unit_channels) - unit_id = '#{}'.format(i.source) +# unit_id = '#{}'.format(i.source) + unit_id = '#{}' print("unit_id = ", unit_id) - wf_units = i.timestamps_unit + wf_units = self._file.get_acquisition(i).timestamps_unit print("wf_units = ", wf_units) - wf_gain = i.resolution + wf_gain = self._file.get_acquisition(i).resolution print("wf_gain = ", wf_gain) wf_offset = 0. @@ -182,73 +186,92 @@ def _parse_header(self): wf_left_sweep = 0 print("wf_left_sweep = ", wf_left_sweep) - wf_sampling_rate = i.rate + wf_sampling_rate = self._file.get_acquisition(i).rate print("wf_sampling_rate = ", wf_sampling_rate) - unit_channels.append((unit_name, unit_id, wf_units, wf_gain, wf_offset, wf_left_sweep, wf_sampling_rate)) + unit_channels.append((unit_name, unit_id, wf_units, wf_gain, wf_offset, wf_left_sweep, wf_sampling_rate)) - unit_channels = np.array(unit_channels, dtype=_unit_channel_dtype) - print("unit_channels = ", unit_channels) + unit_channels = np.array(unit_channels, dtype=_unit_channel_dtype) + print("unit_channels = ", unit_channels) print("******************************************event channel***********************************************") - # Creating event/epoch channel + # Creating event/epoch channel # In RawIO epoch and event are dealt the same way. event_channels = [] # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch # For event #event_channels.append(('Some events', 'ev_0', 'event')) - event_channels.append((self._file.epochs, 'ev_0', 'event')) # Some events + +## event_channels.append((self._file.epochs[0][3], self._file.epochs[0][0], 'event')) # Some events +# for j in range(len(self._file.epochs)): +# print("j = ", j) +# +# epochs_id = self._file.epochs[j][0] +# print("epochs_start_id = ", epochs_id) +# +# epochs_start_time = self._file.epochs[j][1] +# print("epochs_start_time = ", epochs_start_time) +# +# epochs_stop_time = self._file.epochs[j][2] +# print("epochs_stop_time = ", epochs_stop_time) +# +# epochs_tags = self._file.epochs[j][3] +# print("epochs_tags = ", epochs_tags) +# +# event_channels.append((self._file.epochs[j][3], self._file.epochs[j][0], 'event')) # Some events +# Example + event_channels = [] + event_channels.append(('Some events', 'ev_0', 'event')) + event_channels.append(('Some epochs', 'ep_1', 'epoch')) + event_channels = np.array(event_channels, dtype=_event_channel_dtype) # For epochs #event_channels.append(('Some epochs', 'ep_1', 'epoch')) - event_channels.append((self._file.epochs, 'ep_1', 'epoch')) # Some epochs +## event_channels.append((self._file.epochs, 'ep_1', 'epoch')) # Some epochs - event_channels = np.array(event_channels, dtype=_event_channel_dtype) +# event_channels = np.array(event_channels, dtype=_event_channel_dtype) print("***********************event_channels = ", event_channels) print("*******************************************************block**********************************************") # file into header dict -# self.header = {} + self.header = {} self.header['nb_block'] =2 # 1 self.header['nb_segment'] = [2, 3] # [1] - - ##################################################################### - # file into header dict for signal_channels - self.header['signal_channels'] = sig_channels - -##################################################################### - # file into header dict for unit channels - self.header['unit_channels'] = unit_channels - # file into header dict for event channels - self.header['event_channels'] = event_channels - + self.header['signal_channels'] = sig_channels # file into header dict for signal_channels + self.header['unit_channels'] = unit_channels # file into header dict for unit channels + self.header['event_channels'] = event_channels # file into header dict for event channels # insert some annotation at some place # To create an empty tree self._generate_minimal_annotations() - bl_annotations = self.raw_annotations['blocks'][0] - seg_annotations = bl_annotations['segments'][0] +# bl_annotations = self.raw_annotations['blocks'][0] +# seg_annotations = bl_annotations['segments'][0] def _segment_t_start(self, block_index, seg_index): # NWB Epoch corresponds to a Neo Segment - print("def _segment_t_start") + print("*** def _segment_t_start ***") all_starts = [[0., 15.], [0., 20., 60.]] return all_starts[block_index][seg_index] - return all_starts +# for i in self._file.acquisition: +# print("i = ", i) +# all_starts = self._file.get_acquisition(i).starting_time +# print("all_starts = ", all_starts) +# return np.array(all_starts) + #return all_starts + def _segment_t_stop(self, block_index, seg_index): # NWB Epoch corresponds to a Neo Segment - print("def _segment_t_stop") + print("*** def _segment_t_stop ***") all_stops = [[10., 25.], [10., 30., 70.]] - # return all_stops[block_index][seg_index] - return all_stops - - - - + return all_stops[block_index][seg_index] + #return all_stops +# for i in self._file.acquisition: +# all_stops = self._file.get_acquisition(i).stop_time +# print("all_stops = ", all_stops) # ################################### @@ -258,23 +281,42 @@ def _segment_t_stop(self, block_index, seg_index): # NWB Epoch corresponds to a # signal and channel zone def _get_signal_size(self, block_index, seg_index, channel_indexes): print("*** _get_signal_size ***") -# raise (NotImplementedError) + # raise (NotImplementedError) +## io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO +## self._file = io.read() for i in self._file.acquisition: - signal_size = i.num_samples + signal_size = self._file.get_acquisition(i).num_samples print("signal_size = ", signal_size) # Same as _spike_count ? return signal_size def _get_signal_t_start(self, block_index, seg_index, channel_indexes): print("*** _get_signal_t_start ***") # raise (NotImplementedError) +## io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO +## self._file = io.read() for i in self._file.acquisition: - starting_time = i.starting_time - print("starting_time = ", starting_time) # For TimeSeries + starting_time = self._file.get_acquisition(i).starting_time +# starting_time = np.array(starting_time) return starting_time def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, channel_indexes): print("*** _get_analogsignal_chunk ***") # raise (NotImplementedError) + print("channel_indexes = ", channel_indexes) + if i_start is None: + i_start = 0 + if i_stop is None: + i_stop = 100000 + + assert i_start >= 0, "I don't like your jokes" + assert i_stop <= 100000, "I don't like your jokes" + + if channel_indexes is None: + nb_chan = 16 + else: + nb_chan = len(channel_indexes) + raw_signals = np.zeros((i_stop - i_start, nb_chan), dtype='int16') + return raw_signals ### # spiketrain and unit zone @@ -282,25 +324,26 @@ def _spike_count(self, block_index, seg_index, unit_index): print("*** _spike_count ***") #raise (NotImplementedError) for i in self._file.acquisition: - nb_spikes = i.num_samples + print("i in _spike_count = ", i) + nb_spikes = self._file.get_acquisition(i).num_samples print("nb_spikes = ", nb_spikes) return nb_spikes def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_stop): print("*** _get_spike_timestamps ***") #raise (NotImplementedError) - for i in self._file.acquisition: - spike_timestamps = i.timestamps + for i in self._file.acquisition: + spike_timestamps = self._file.get_acquisition(i).timestamps + print("spike_timestamps in condition = ", spike_timestamps) print("spike_timestamps = ", spike_timestamps) return spike_timestamps - def _rescale_spike_timestamp(self, spike_timestamps, dtype): print("*** _rescale_spike_timestamp ***") #raise (NotImplementedError) for i in self._file.acquisition: spike_times = spike_timestamps.astype(dtype) - spike_times /= i.sr +### spike_times /= i.sr print("spike_times = ", spike_times) return spike_times @@ -316,12 +359,11 @@ def _event_count(self, block_index, seg_index, event_channel_index): print("*** _event_count ***") #raise (NotImplementedError) for i in self._file.acquisition: - event_count = i.num_samples + event_count = self._file.get_acquisition(i).num_samples print("event_count = ", event_count) # Same as nb_spikes ? return event_count - def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): print("*** _get_event_timestamps ***") raise (NotImplementedError) @@ -333,6 +375,3 @@ def _rescale_event_timestamp(self, event_timestamps, dtype): def _rescale_epoch_duration(self, raw_duration, dtype): print("*** _rescale_epoch_duration ***") raise (NotImplementedError) - - - diff --git a/neo/rawio/tests/test_nwbrawio.py b/neo/rawio/tests/test_nwbrawio.py index aeeff5908..3b3d005f1 100644 --- a/neo/rawio/tests/test_nwbrawio.py +++ b/neo/rawio/tests/test_nwbrawio.py @@ -5,25 +5,28 @@ """ from __future__ import unicode_literals, print_function, division, absolute_import - import unittest - -from neo.rawio.nwbrawio import NWBRawIO #, NWBReader -###from neo.rawio.nwbrawio_only_1_signal import NWBRawIO - +from neo.rawio.nwbrawio import NWBRawIO from neo.rawio.tests.common_rawio_test import BaseTestRawIO +import pynwb +from pynwb import * class TestNWBRawIO(BaseTestRawIO, unittest.TestCase, ): rawioclass = NWBRawIO files_to_download = [ - '/home/elodie/NWB_Files/my_example_2.nwb', # Very simple file nwb create by me only TimeSeries +## '/home/elodie/NWB_Files/my_example_2.nwb', # Very simple file nwb create by me only TimeSeries +### '/home/elodie/NWB_Files/my_NWB_File_pynwb_101_2.nwb', # File created with the latest version of pynwb=1.0.1 # '/home/elodie/NWB_Files/brain_observatory.nwb', # nwb file given by Matteo Cantarelli # '/home/elodie/NWB_Files/mynwb.h5', # nwb file given by Lungsi # '/home/elodie/NWB_Files/GreBlu9508M_Site1_Call1.nwb', +###### '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101.nwb', # File created with the latest version of pynwb=1.0.1 File on my github + '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github + ] entities_to_test = files_to_download - + if __name__ == "__main__": + print("pynwb.__version__ = ", pynwb.__version__) unittest.main() From b42a92deaaf1acaf7e6436c22f760fa52e545e87 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Fri, 12 Jul 2019 09:45:22 +0200 Subject: [PATCH 08/79] nwbio files --- neo/io/nwbio.py | 452 ++++++++++++++++++++++++++++++++++ neo/io/test_neo_nwb.py | 27 ++ neo/test/iotest/test_nwbio.py | 48 ++++ 3 files changed, 527 insertions(+) create mode 100644 neo/io/nwbio.py create mode 100644 neo/io/test_neo_nwb.py create mode 100644 neo/test/iotest/test_nwbio.py diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py new file mode 100644 index 000000000..66e18c605 --- /dev/null +++ b/neo/io/nwbio.py @@ -0,0 +1,452 @@ +""" +NWBIO +======== + +IO class for reading data from a Neurodata Without Borders (NWB) dataset + +Documentation : https://neurodatawithoutborders.github.io +Depends on: h5py, nwb, dateutil +Supported: Read, Write +Specification - https://github.com/NeurodataWithoutBorders/specification +Python APIs - (1) https://github.com/AllenInstitute/nwb-api/tree/master/ainwb + (2) https://github.com/AllenInstitute/AllenSDK/blob/master/allensdk/core/nwb_data_set.py + (3) https://github.com/NeurodataWithoutBorders/api-python +Sample datasets from CRCNS - https://crcns.org/NWB +Sample datasets from Allen Institute - http://alleninstitute.github.io/AllenSDK/cell_types.html#neurodata-without-borders +""" + +from __future__ import absolute_import +from __future__ import division +from itertools import chain +import shutil +import tempfile +from datetime import datetime +from os.path import join +import dateutil.parser +import numpy as np + +import quantities as pq +from neo.io.baseio import BaseIO +from neo.core import (Segment, SpikeTrain, Unit, Epoch, Event, AnalogSignal, + IrregularlySampledSignal, ChannelIndex, Block) + +# neo imports +from collections import OrderedDict + +# Standard Python imports +from tempfile import NamedTemporaryFile +import os +import glob +from scipy.io import loadmat + +# PyNWB imports +import pynwb +from pynwb import * +# Creating and writing NWB files +from pynwb import NWBFile,TimeSeries, get_manager +from pynwb.base import ProcessingModule +# Creating TimeSeries +from pynwb.ecephys import ElectricalSeries, Device, EventDetection +from pynwb.behavior import SpatialSeries +from pynwb.image import ImageSeries +from pynwb.core import set_parents +# For Neurodata Type Specifications +from pynwb.spec import NWBAttributeSpec # Attribute Specifications +from pynwb.spec import NWBDatasetSpec # Dataset Specifications +from pynwb.spec import NWBGroupSpec +from pynwb.spec import NWBNamespace + + +class NWBIO(BaseIO): + """ + Class for "reading" experimental data from a .nwb file. + """ + + name = 'NWB' + description = 'This IO reads/writes experimental data from/to an .nwb dataset' + extensions = ['nwb'] + mode = 'one-file' + + def __init__(self, filename): + """ + Arguments: + filename : the filename + """ + print("*** __init__ ***") + BaseIO.__init__(self, filename=filename) +# BaseIO.__init__(self) + print("filename = ", filename) + self.filename = filename + io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO + self._file = io.read() # Define the file as a NWBFile object + + def read_block(self, lazy=False, cascade=True, **kwargs): + print("*** read_block ***") + self._lazy = lazy + print("lazy = ", lazy) + file_access_dates = self._file.file_create_date + print("file_access_dates = ", file_access_dates) + identifier = self._file.identifier # or experimenter ? + print("identifier = ", identifier) + if identifier == '_neo': # this is an automatically generated name used if block.name is None + identifier = None + description = self._file.session_description # or experiment_description ? + print("description = ", description) + if description == "no description": + description = None + block = Block(name=identifier, + description=description, + file_origin=self.filename, + file_datetime=file_access_dates, + rec_datetime=self._file.session_start_time, + #nwb_version=self._file.get('nwb_version').value, + file_access_dates=file_access_dates, + file_read_log='') + print("block = ", block) + if cascade: + self._handle_general_group(block) + self._handle_epochs_group(block) + self._handle_acquisition_group(lazy, block) # self._handle_acquisition_group(block) + self._handle_stimulus_group(lazy, block) # self._handle_stimulus_group(block) + self._handle_processing_group(block) + self._handle_analysis_group(block) + self._lazy = False + return block + print("Return block = ", block) + + def _handle_general_group(self, block): + print("*** def _handle_general_group ***") + #block.annotations['file_read_log'] += ("general group not handled\n") + + def _handle_epochs_group(self, block): + print("*** def _handle_epochs_group ***") + # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. +### epochs = self._file.epochs + epochs = self._file.acquisition + #print("epochs = ", epochs) + + # todo: handle epochs.attrs.get('tags') + ##for name, epoch in epochs.items(): +# for name, epoch in epochs: + for key in epochs: + print("key = ", key) + #print("epochs = ", epochs) + # todo: handle epoch.attrs.get('links') + timeseries = [] + print("timeseries = ", timeseries) + current_shape = self._file.get_acquisition(key).data.shape[0] # sample number + #print("current_shape = ", current_shape) + times = np.zeros(current_shape) + #for key, value in epoch.items(): + for j in range(0, current_shape): + times[j]=1./self._file.get_acquisition(key).rate*j+self._file.get_acquisition(key).starting_time + if times[j] == self._file.get_acquisition(key).starting_time: + t_start = times[j] * pq.second + print("t_start = ", t_start) + elif times[j]==times[-1]: + t_stop = times[j] * pq.second + print("t_stop = ", t_stop) + else: + # todo: handle value['count'] + # todo: handle value['idx_start'] + #timeseries.append(self._handle_timeseries(key, value.get('timeseries'))) + timeseries.append(self._handle_timeseries(self._lazy, key, times[j])) +# segment = Segment(name=name) + segment = Segment(name=j) + print("segment = ", segment) +################################################################################ + #for obj in timeseries: + for obj in self._file.get_acquisition: + obj.segment = segment + print("obj.segment = ", obj.segment) + if isinstance(obj, AnalogSignal): + segment.analogsignals.append(obj) + elif isinstance(obj, IrregularlySampledSignal): + segment.irregularlysampledsignals.append(obj) + elif isinstance(obj, Event): + segment.events.append(obj) + elif isinstance(obj, Epoch): + segment.epochs.append(obj) + segment.block = block + print("segment.block = ", segment.block) + block.segments.append(segment) + print("segment = ", segment) + print("block.segments.append(segment) = ", block.segments.append(segment)) +################################################################################ + + + def _handle_timeseries(self, lazy, name, timeseries): + print("*** def _handle_timeseries ***") + # todo: check timeseries.attrs.get('schema_id') + # todo: handle timeseries.attrs.get('source') +# subtype = timeseries.attrs['ancestry'][-1] + + for i in self._file.acquisition: + data_group = self._file.get_acquisition(i).data + #print("data_group = ", data_group) + dtype = data_group.dtype + #print("dtype = ", dtype) + #if self._lazy: + if lazy==True: + data = np.array((), dtype=dtype) + print("data if lazy = ", data) + lazy_shape = data_group.shape # inefficient to load the data to get the shape + print("lazy_shape = ", lazy_shape) + else: + data = data_group + if dtype.type is np.string_: + if self._lazy: + times = np.array(()) + else: + times = self._file.get_acquisition(i).timestamps + print("times in timestamps = ", times) + duration = 1/self._file.get_acquisition(i).rate + print("************************ duration = ", duration) + if durations: + # Epoch + if self._lazy: + durations = np.array(()) + obj = Epoch(times=times, + durations=durations, + labels=data, + units='second') + print("obj if duration = ", obj) + else: + # Event + obj = Event(times=times, + labels=data, + units='second') + print("obj Event = ", obj) + else: + #units = get_units(data_group) + units = self._file.get_acquisition(i).unit + #print("units = ", units) + current_shape = self._file.get_acquisition(i).data.shape[0] # sample number + #print("current_shape = ", current_shape) + times = np.zeros(current_shape) + for j in range(0, current_shape): + times[j]=1./self._file.get_acquisition(i).rate*j+self._file.get_acquisition(i).starting_time + if times[j] == self._file.get_acquisition(i).starting_time: + # AnalogSignal + sampling_metadata = times[j] + print("sampling_metadata = ", sampling_metadata) + t_start = sampling_metadata * pq.s + print("t_start = ", t_start) + sampling_rate = self._file.get_acquisition(i).rate * pq.Hz + print("sampling_rate = ", sampling_rate) + #assert sampling_metadata.attrs.get('unit') == 'Seconds' +### assert sampling_metadata.unit == 'Seconds' +# # todo: handle data.attrs['resolution'] + obj = AnalogSignal(data, + units=units, + sampling_rate=sampling_rate, + t_start=t_start, + name=name) + print("obj = ", obj) +# elif 'timestamps' in timeseries: + elif self._file.get_acquisition(i).timestamps: + # IrregularlySampledSignal + if self._lazy: + time_data = np.array(()) + else: + time_data = self._file.get_acquisition(i).timestamps +### assert time_data.attrs.get('unit') == 'Seconds' +# obj = IrregularlySampledSignal(time_data.value, +# data, +# units=units, +# time_units=pq.second) +# else: +# raise Exception("Timeseries group does not contain sufficient time information") +# if self._lazy: +# obj.lazy_shape = lazy_shape +# return obj + + + def _handle_acquisition_group(self, lazy, block): + print("*** def _handle_acquisition_group ***") + acq = self._file.acquisition + #print("acq = ", acq) +# images = acq.get('images') +# if images and len(images) > 0: +# block.annotations['file_read_log'] += ("file contained {0} images; these are not currently handled by Neo\n".format(len(images))) + + + # todo: check for signals that are not contained within an NWB Epoch, + # and create an anonymous Segment to contain them + + ###segment_acq = dict((segment.name, segment) for segment in block.segments) + ###print("segment_acq = ", segment_acq) + for name in acq: + print("name = ", name) # Sample number 'index_' +# if name == 'unit_list': +# pass # todo +# else: +# segment_name = name + # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. + segment_name = self._file.epochs + print("segment_name = ", segment_name) + desc = self._file.get_acquisition(name).unit + print("desc = ", desc) +### segment = segment_acq[segment_name] + segment = segment_name +# print("segment = ", segment) + #if self._lazy: + if lazy==True: + times = np.array(()) + print("times = ", times) + #lazy_shape = group['times'].shape + lazy_shape = self._file.get_acquisition(name).data.shape + print("lazy_shape = ", lazy_shape) + else: + current_shape = self._file.get_acquisition(name).data.shape[0] # sample number + print("current_shape = ", current_shape) + times = np.zeros(current_shape) + for j in range(0, current_shape): # For testing ! + times[j]=1./self._file.get_acquisition(name).rate*j+self._file.get_acquisition(name).starting_time # temps = 1./frequency [Hz] + t_start [s] + print("times[j] = ", times) + spiketrain = SpikeTrain(times, units=pq.second, + t_stop=times[-1]*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? + print("spiketrain = ", spiketrain) + #if self._lazy: +### if lazy==True: +### spiketrain.lazy_shape = lazy_shape + if segment is not None: + spiketrain.segment = segment + print("segment = ", segment) + segment.spiketrains.append(spiketrain) + + + def _handle_stimulus_group(self, lazy, block): + print("*** def _handle_stimulus_group ***") + #block.annotations['file_read_log'] += ("stimulus group not handled\n") + # The same as acquisition for stimulus for spiketrain... + + sti = self._file.stimulus + #print("sti = ", sti) +# images = sti.get('images') +# if images and len(images) > 0: +# block.annotations['file_read_log'] += ("file contained {0} images; these are not currently handled by Neo\n".format(len(images))) + +### segment_sti = dict((segment.name, segment) for segment in block.segments) +### print("segment_sti = ", segment_sti) + for name in sti: + print("name = ", name) # Sample number 'index_' +# if name == 'unit_list': +# pass # todo +# else: +# segment_name = name + # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. + segment_name_sti = self._file.epochs + print("segment_name_sti = ", segment_name_sti) + desc_sti = self._file.get_stimulus(name).unit + print("desc_sti = ", desc_sti) +### segment = segment_acq[segment_name] + segment_sti = segment_name_sti + print("segment_sti = ", segment_sti) + #if self._lazy: + if lazy==True: + times = np.array(()) + print("times = ", times) + #lazy_shape = group['times'].shape + lazy_shape = self._file.get_stimulus(name).data.shape + print("lazy_shape = ", lazy_shape) + else: + current_shape = self._file.get_stimulus(name).data.shape[0] # sample number + print("current_shape = ", current_shape) + times = np.zeros(current_shape) + for j in range(0, current_shape): # For testing ! + times[j]=1./self._file.get_stimulus(name).rate*j+self._file.get_stimulus(name).starting_time # temps = 1./frequency [Hz] + t_start [s] + print("times[j] = ", times) + spiketrain = SpikeTrain(times, units=pq.second, + t_stop=times[-1]*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? + print("spiketrain = ", spiketrain) + #if self._lazy: +### if lazy==True: +### spiketrain.lazy_shape = lazy_shape + if segment_sti is not None: + spiketrain.segment_sti = segment_sti + print("segment_sti = ", segment_sti) + segment_sti.spiketrains.append(spiketrain) + + + def _handle_processing_group(self, block): + print("*** def _handle_processing_group ***") + # todo: handle other modules than Units +## units_group = self._file.get('processing/Units/UnitTimes') + #segment_map = dict((segment.name, segment) for segment in block.segments) + #print("segment_map = ", segment_map) +# for name, group in units_group.items(): +# if name == 'unit_list': +# pass # todo +# else: +# segment_name = group['source'].value +# #desc = group['unit_description'].value # use this to store Neo Unit id? +# segment = segment_map[segment_name] +# if self._lazy: +# times = np.array(()) +# lazy_shape = group['times'].shape +# else: +# times = group['times'].value +# spiketrain = SpikeTrain(times, units=pq.second, +# t_stop=group['t_stop'].value*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? +# if self._lazy: +# spiketrain.lazy_shape = lazy_shape +# spiketrain.segment = segment +# segment.spiketrains.append(spiketrain) + + def _handle_analysis_group(self, block): + print("*** def _handle_analysis_group ***") + #block.annotations['file_read_log'] += ("analysis group not handled\n") + + + + +# def time_in_seconds(t): +# print("*** def time_in_seconds ***") +# return float(t.rescale("second")) + + +# def _decompose_unit(unit): +# print("*** def _decompose_unit ***") +# """unit should be a Quantity object with unit magnitude +# Returns (conversion, base_unit_str) +# Example: +# >>> _decompose_unit(pq.nA) +# (1e-09, 'ampere') +# """ +# assert isinstance(unit, pq.quantity.Quantity) +# assert unit.magnitude == 1 +# conversion = 1.0 +# def _decompose(unit): +# dim = unit.dimensionality +# if len(dim) != 1: +# raise NotImplementedError("Compound units not yet supported") # e.g. volt-metre +# uq, n = dim.items()[0] +# if n != 1: +# raise NotImplementedError("Compound units not yet supported") # e.g. volt^2 +# uq_def = uq.definition +# return float(uq_def.magnitude), uq_def +# conv, unit2 = _decompose(unit) +# while conv != 1: +# conversion *= conv +# unit = unit2 +# conv, unit2 = _decompose(unit) +# return conversion, unit.dimensionality.keys()[0].name + + +prefix_map = { + 1e-3: 'milli', + 1e-6: 'micro', + 1e-9: 'nano' +} + +def get_units(data_group): + print("*** def get_units ***") + print("data_group = ", data_group) +# conversion = data_group.attrs.get('conversion') + #base_units = data_group.attrs.get('unit') + base_units = data_group.units + print("base_units = ", base_units) +# return prefix_map[conversion] + base_units + + diff --git a/neo/io/test_neo_nwb.py b/neo/io/test_neo_nwb.py new file mode 100644 index 000000000..490e6a2be --- /dev/null +++ b/neo/io/test_neo_nwb.py @@ -0,0 +1,27 @@ +import nwbio +from nwbio import * + +filename = "/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb" + +io = nwbio.NWBIO(filename) +#io = pynwb.NWBHDF5IO(filename, mode='r') # Open a file with NWBHDF5IO +#container = io.read() # Define the file as a NWBFile object +#print("container = ", container) + +#io.__init__("/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb") + +# Test the entire file +io.read_block() + +# Tests the different functions +#io._handle_general_group(block='') +#io._handle_epochs_group(block='') +#io._handle_acquisition_group(False, block='') +#io._handle_stimulus_group(False, block='') +#io._handle_processing_group(block='') +#io._handle_analysis_group(block='') + +#io._handle_timeseries('index_000', True, 1) + +#get_units(container.data) + diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py new file mode 100644 index 000000000..b4853b895 --- /dev/null +++ b/neo/test/iotest/test_nwbio.py @@ -0,0 +1,48 @@ + +""" +Tests of neo.io.nwbio +""" + +#from __future__ import division +# +#import sys +#import unittest +#try: +# import unittest2 as unittest +#except ImportError: +# import unittest +#try: +# import pynwb +# HAVE_NWB = True +#except ImportError: +# HAVE_NWB = False +#from neo.io import NWBIO +#from neo.test.iotest.common_io_test import BaseTestIO + +from __future__ import unicode_literals, print_function, division, absolute_import +import unittest +from neo.io.nwbio import NWBIO +from neo.test.iotest.common_io_test import BaseTestIO +import pynwb +from pynwb import * + +#@unittest.skipUnless(HAVE_NWB, "requires nwb") +###class TestNWBIO(BaseTestIO, unittest.TestCase, ): ############################ To change to test ! +class TestNWBIO(unittest.TestCase, ): + ioclass = NWBIO + + files_to_download = [ + + '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github +# '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' # NWB file downloaded from http://download.alleninstitute.org/informatics-archive/prerelease/H19.28.012.11.05-2.nwb + + ] + + entities_to_test = files_to_download + +if __name__ == "__main__": + print("pynwb.__version__ = ", pynwb.__version__) + unittest.main() + + + From 73931e8228427ded8a2b64fda1067a8726d0e970 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Thu, 18 Jul 2019 15:00:22 +0200 Subject: [PATCH 09/79] Read NWB files and tests --- neo/io/nwbio.py | 132 +++++++++++++------------ neo/test/iotest/test_nwbio.py | 175 +++++++++++++++++++++++++++++++++- 2 files changed, 245 insertions(+), 62 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 66e18c605..1cbf859b2 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -74,8 +74,8 @@ def __init__(self, filename): """ print("*** __init__ ***") BaseIO.__init__(self, filename=filename) -# BaseIO.__init__(self) - print("filename = ", filename) + #BaseIO.__init__(self) + #print("filename = ", filename) self.filename = filename io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO self._file = io.read() # Define the file as a NWBFile object @@ -83,15 +83,15 @@ def __init__(self, filename): def read_block(self, lazy=False, cascade=True, **kwargs): print("*** read_block ***") self._lazy = lazy - print("lazy = ", lazy) + #print("lazy = ", lazy) file_access_dates = self._file.file_create_date - print("file_access_dates = ", file_access_dates) + #print("file_access_dates = ", file_access_dates) identifier = self._file.identifier # or experimenter ? - print("identifier = ", identifier) + #print("identifier = ", identifier) if identifier == '_neo': # this is an automatically generated name used if block.name is None identifier = None description = self._file.session_description # or experiment_description ? - print("description = ", description) + #print("description = ", description) if description == "no description": description = None block = Block(name=identifier, @@ -105,21 +105,21 @@ def read_block(self, lazy=False, cascade=True, **kwargs): print("block = ", block) if cascade: self._handle_general_group(block) - self._handle_epochs_group(block) + self._handle_epochs_group(lazy, block) #self._handle_epochs_group(block) self._handle_acquisition_group(lazy, block) # self._handle_acquisition_group(block) self._handle_stimulus_group(lazy, block) # self._handle_stimulus_group(block) self._handle_processing_group(block) self._handle_analysis_group(block) self._lazy = False return block - print("Return block = ", block) def _handle_general_group(self, block): print("*** def _handle_general_group ***") #block.annotations['file_read_log'] += ("general group not handled\n") - def _handle_epochs_group(self, block): + def _handle_epochs_group(self, lazy, block): print("*** def _handle_epochs_group ***") + self._lazy = lazy # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. ### epochs = self._file.epochs epochs = self._file.acquisition @@ -129,50 +129,65 @@ def _handle_epochs_group(self, block): ##for name, epoch in epochs.items(): # for name, epoch in epochs: for key in epochs: - print("key = ", key) + #print("key = ", key) #print("epochs = ", epochs) # todo: handle epoch.attrs.get('links') timeseries = [] - print("timeseries = ", timeseries) + #print("timeseries = ", timeseries) current_shape = self._file.get_acquisition(key).data.shape[0] # sample number #print("current_shape = ", current_shape) times = np.zeros(current_shape) - #for key, value in epoch.items(): + #for key, value in epoch.items(): for j in range(0, current_shape): times[j]=1./self._file.get_acquisition(key).rate*j+self._file.get_acquisition(key).starting_time if times[j] == self._file.get_acquisition(key).starting_time: t_start = times[j] * pq.second - print("t_start = ", t_start) + #print("t_start = ", t_start) elif times[j]==times[-1]: t_stop = times[j] * pq.second - print("t_stop = ", t_stop) + #print("t_stop = ", t_stop) else: # todo: handle value['count'] # todo: handle value['idx_start'] #timeseries.append(self._handle_timeseries(key, value.get('timeseries'))) timeseries.append(self._handle_timeseries(self._lazy, key, times[j])) + #print(timeseries) + #print("timeSeries 1 bis = ", timeseries) + #print(timeseries) + #print("timeseries 2nd bis = ", timeseries) # segment = Segment(name=name) segment = Segment(name=j) - print("segment = ", segment) -################################################################################ - #for obj in timeseries: - for obj in self._file.get_acquisition: + #print("segment = ", segment) + for obj in timeseries: + #print("obj = ", obj) + #print("timeseries = ", timeseries) +# for obj in self._file.acquisition: +### print("obj in segment = ", obj) obj.segment = segment - print("obj.segment = ", obj.segment) +# segment==obj.segment + #print("segment in loop = ", segment) +# print("obj.segment = ", obj.segment) if isinstance(obj, AnalogSignal): + print("*** isinstance(obj, AnalogSignal) ***") segment.analogsignals.append(obj) + #print("obj=AnalogSignal") elif isinstance(obj, IrregularlySampledSignal): + print("*** isinstance(obj, IrregularlySampledSignal) ***") segment.irregularlysampledsignals.append(obj) + #print("obj=IrregularlySampledSignal") elif isinstance(obj, Event): + print("*** isinstance(obj, Event) ***") segment.events.append(obj) + #print("obj=Event") elif isinstance(obj, Epoch): + print("*** isinstance(obj, Epoch) ***") segment.epochs.append(obj) + #print("obj=Epoch") segment.block = block - print("segment.block = ", segment.block) - block.segments.append(segment) - print("segment = ", segment) - print("block.segments.append(segment) = ", block.segments.append(segment)) -################################################################################ + #print("segment = ", segment) + #print("block = ", block) +# block.segments.append(segment) + return obj, segment def _handle_timeseries(self, lazy, name, timeseries): @@ -180,7 +195,6 @@ def _handle_timeseries(self, lazy, name, timeseries): # todo: check timeseries.attrs.get('schema_id') # todo: handle timeseries.attrs.get('source') # subtype = timeseries.attrs['ancestry'][-1] - for i in self._file.acquisition: data_group = self._file.get_acquisition(i).data #print("data_group = ", data_group) @@ -189,9 +203,9 @@ def _handle_timeseries(self, lazy, name, timeseries): #if self._lazy: if lazy==True: data = np.array((), dtype=dtype) - print("data if lazy = ", data) + #print("data if lazy = ", data) lazy_shape = data_group.shape # inefficient to load the data to get the shape - print("lazy_shape = ", lazy_shape) + #print("lazy_shape = ", lazy_shape) else: data = data_group if dtype.type is np.string_: @@ -199,9 +213,9 @@ def _handle_timeseries(self, lazy, name, timeseries): times = np.array(()) else: times = self._file.get_acquisition(i).timestamps - print("times in timestamps = ", times) + #print("times in timestamps = ", times) duration = 1/self._file.get_acquisition(i).rate - print("************************ duration = ", duration) + #print("************************ duration = ", duration) if durations: # Epoch if self._lazy: @@ -210,18 +224,18 @@ def _handle_timeseries(self, lazy, name, timeseries): durations=durations, labels=data, units='second') - print("obj if duration = ", obj) + #print("obj if duration = ", obj) else: # Event obj = Event(times=times, labels=data, units='second') - print("obj Event = ", obj) + #print("obj Event = ", obj) else: #units = get_units(data_group) units = self._file.get_acquisition(i).unit #print("units = ", units) - current_shape = self._file.get_acquisition(i).data.shape[0] # sample number + current_shape = self._file.get_acquisition(i).data.shape[0] # number of samples #print("current_shape = ", current_shape) times = np.zeros(current_shape) for j in range(0, current_shape): @@ -229,11 +243,11 @@ def _handle_timeseries(self, lazy, name, timeseries): if times[j] == self._file.get_acquisition(i).starting_time: # AnalogSignal sampling_metadata = times[j] - print("sampling_metadata = ", sampling_metadata) + #print("sampling_metadata = ", sampling_metadata) t_start = sampling_metadata * pq.s - print("t_start = ", t_start) + #print("t_start = ", t_start) sampling_rate = self._file.get_acquisition(i).rate * pq.Hz - print("sampling_rate = ", sampling_rate) + #print("sampling_rate = ", sampling_rate) #assert sampling_metadata.attrs.get('unit') == 'Seconds' ### assert sampling_metadata.unit == 'Seconds' # # todo: handle data.attrs['resolution'] @@ -242,7 +256,7 @@ def _handle_timeseries(self, lazy, name, timeseries): sampling_rate=sampling_rate, t_start=t_start, name=name) - print("obj = ", obj) + #print("obj = ", obj) # elif 'timestamps' in timeseries: elif self._file.get_acquisition(i).timestamps: # IrregularlySampledSignal @@ -259,7 +273,7 @@ def _handle_timeseries(self, lazy, name, timeseries): # raise Exception("Timeseries group does not contain sufficient time information") # if self._lazy: # obj.lazy_shape = lazy_shape -# return obj + return obj def _handle_acquisition_group(self, lazy, block): @@ -277,44 +291,44 @@ def _handle_acquisition_group(self, lazy, block): ###segment_acq = dict((segment.name, segment) for segment in block.segments) ###print("segment_acq = ", segment_acq) for name in acq: - print("name = ", name) # Sample number 'index_' + #print("name = ", name) # Sample number 'index_' # if name == 'unit_list': # pass # todo # else: # segment_name = name # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. segment_name = self._file.epochs - print("segment_name = ", segment_name) + #print("segment_name = ", segment_name) desc = self._file.get_acquisition(name).unit - print("desc = ", desc) + #print("desc = ", desc) ### segment = segment_acq[segment_name] segment = segment_name # print("segment = ", segment) #if self._lazy: if lazy==True: times = np.array(()) - print("times = ", times) + #print("times = ", times) #lazy_shape = group['times'].shape lazy_shape = self._file.get_acquisition(name).data.shape - print("lazy_shape = ", lazy_shape) + #print("lazy_shape = ", lazy_shape) else: current_shape = self._file.get_acquisition(name).data.shape[0] # sample number - print("current_shape = ", current_shape) + #print("current_shape = ", current_shape) times = np.zeros(current_shape) for j in range(0, current_shape): # For testing ! times[j]=1./self._file.get_acquisition(name).rate*j+self._file.get_acquisition(name).starting_time # temps = 1./frequency [Hz] + t_start [s] - print("times[j] = ", times) + #print("times[j] = ", times) spiketrain = SpikeTrain(times, units=pq.second, t_stop=times[-1]*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? - print("spiketrain = ", spiketrain) + #print("spiketrain in _handle_acquisition_group = ", spiketrain) #if self._lazy: ### if lazy==True: ### spiketrain.lazy_shape = lazy_shape if segment is not None: spiketrain.segment = segment - print("segment = ", segment) + #print("segment = ", segment) segment.spiketrains.append(spiketrain) - + return spiketrain def _handle_stimulus_group(self, lazy, block): print("*** def _handle_stimulus_group ***") @@ -330,42 +344,42 @@ def _handle_stimulus_group(self, lazy, block): ### segment_sti = dict((segment.name, segment) for segment in block.segments) ### print("segment_sti = ", segment_sti) for name in sti: - print("name = ", name) # Sample number 'index_' + #print("name = ", name) # Sample number 'index_' # if name == 'unit_list': # pass # todo # else: # segment_name = name # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. segment_name_sti = self._file.epochs - print("segment_name_sti = ", segment_name_sti) + #print("segment_name_sti = ", segment_name_sti) desc_sti = self._file.get_stimulus(name).unit - print("desc_sti = ", desc_sti) + #print("desc_sti = ", desc_sti) ### segment = segment_acq[segment_name] segment_sti = segment_name_sti - print("segment_sti = ", segment_sti) + #print("segment_sti = ", segment_sti) #if self._lazy: if lazy==True: times = np.array(()) - print("times = ", times) + #print("times = ", times) #lazy_shape = group['times'].shape lazy_shape = self._file.get_stimulus(name).data.shape - print("lazy_shape = ", lazy_shape) + #print("lazy_shape = ", lazy_shape) else: current_shape = self._file.get_stimulus(name).data.shape[0] # sample number - print("current_shape = ", current_shape) + #print("current_shape = ", current_shape) times = np.zeros(current_shape) for j in range(0, current_shape): # For testing ! times[j]=1./self._file.get_stimulus(name).rate*j+self._file.get_stimulus(name).starting_time # temps = 1./frequency [Hz] + t_start [s] - print("times[j] = ", times) + #print("times[j] = ", times) spiketrain = SpikeTrain(times, units=pq.second, t_stop=times[-1]*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? - print("spiketrain = ", spiketrain) + #print("spiketrain = ", spiketrain) #if self._lazy: ### if lazy==True: ### spiketrain.lazy_shape = lazy_shape if segment_sti is not None: spiketrain.segment_sti = segment_sti - print("segment_sti = ", segment_sti) + #print("segment_sti = ", segment_sti) segment_sti.spiketrains.append(spiketrain) @@ -442,11 +456,11 @@ def _handle_analysis_group(self, block): def get_units(data_group): print("*** def get_units ***") - print("data_group = ", data_group) + #print("data_group = ", data_group) # conversion = data_group.attrs.get('conversion') #base_units = data_group.attrs.get('unit') base_units = data_group.units - print("base_units = ", base_units) + #print("base_units = ", base_units) # return prefix_map[conversion] + base_units diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index b4853b895..26ea40f95 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -26,20 +26,189 @@ import pynwb from pynwb import * +# Tests +from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment +import quantities as pq +import numpy as np + #@unittest.skipUnless(HAVE_NWB, "requires nwb") -###class TestNWBIO(BaseTestIO, unittest.TestCase, ): ############################ To change to test ! +#class TestNWBIO(BaseTestIO, unittest.TestCase, ): class TestNWBIO(unittest.TestCase, ): ioclass = NWBIO - files_to_download = [ + files_to_test = ['/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb'] +# files_to_test = ['/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb'] + # Files from Allen Institute +# files_to_test = ['/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb'] +# files_to_test = ['/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb'] +# files_to_test = ['/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb'] +# files_to_test = ['/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb'] + files_to_download = [ '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github +# '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb', + # Files from Allen Institute # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' # NWB file downloaded from http://download.alleninstitute.org/informatics-archive/prerelease/H19.28.012.11.05-2.nwb - +# '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb' +# '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb' +# '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' ] entities_to_test = files_to_download + def test_read_analogsignal(self): + print("--- Test AnalogSignal ---") + sig_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) + self.assertTrue(isinstance(sig_neo, AnalogSignal)) + # Files to test + r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') + # Files from Allen Institute +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + + obj_nwb = r._handle_timeseries(False, 'name', 1) + self.assertTrue(isinstance(obj_nwb, AnalogSignal)) + self.assertEqual(isinstance(obj_nwb, AnalogSignal), isinstance(sig_neo, AnalogSignal)) + self.assertTrue(obj_nwb.shape, sig_neo.shape) + self.assertTrue(obj_nwb.sampling_rate, sig_neo.sampling_rate) + self.assertTrue(obj_nwb.units, sig_neo.units) + self.assertIsNotNone(obj_nwb, sig_neo) + + def test_read_irregularlysampledsignal(self, **kargs): + print("--- Test IrregularlySampledSignal ---") + irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], units='mV', time_units='ms') + #print("irsig0 = ", irsig0) + irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*pq.s, [[4, 5], [5, 4], [6, 3]]*pq.nA) + #print("irsig1 = ", irsig1) + self.assertTrue(isinstance(irsig0, IrregularlySampledSignal)) + self.assertTrue(isinstance(irsig1, IrregularlySampledSignal)) + + # Files to test + r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') + # Files from Allen Institute +# r = NWBIO('/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') +# r = ('/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') +# r = ('/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') +# r = ('/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + irsig_nwb = r._handle_epochs_group(False, 'name') + #print("irsig_nwb = ", irsig_nwb) + self.assertTrue(irsig_nwb, IrregularlySampledSignal) + self.assertTrue(irsig_nwb, irsig0) + self.assertTrue(irsig_nwb, irsig1) + + def test_read_spiketrain(self, **kargs): + print("--- Test spiketrain ---") + train_neo = SpikeTrain([3, 4, 5]*pq.s, t_stop=10.0) + #print("train_neo = ", train_neo) + self.assertTrue(isinstance(train_neo, SpikeTrain)) + + # Files to test + r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') + # Files from Allen Institute +# r = NWBIO('/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + train_nwb = r._handle_acquisition_group(False, 1) + #print("train_nwb = ", train_nwb) + self.assertTrue(isinstance(train_nwb, SpikeTrain)) + self.assertEqual(isinstance(train_nwb, SpikeTrain), isinstance(train_neo, SpikeTrain)) + self.assertTrue(train_nwb.shape, train_neo.shape) + self.assertTrue(train_nwb.sampling_rate, train_neo.sampling_rate) + self.assertTrue(train_nwb.units, train_neo.units) + self.assertIsNotNone(train_nwb, train_neo) + + def test_read_event(self, **kargs): + print("--- Test Event ---") + evt_neo = Event(np.arange(0, 30, 10)*pq.s, labels=np.array(['trig0', 'trig1', 'trig2'], dtype='S')) + #print("evt_neo = ", evt_neo) + + # Files to test + r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') + # Files from Allen Institute +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + event_nwb = r._handle_epochs_group(False, 'name') + #print("event_nwb = ", event_nwb) + self.assertTrue(event_nwb, evt_neo) + self.assertIsNotNone(event_nwb, evt_neo) + + def test_read_epoch(self, **kargs): + print("--- Test Epoch ---") + epc_neo = Epoch(times=np.arange(0, 30, 10)*pq.s, + durations=[10, 5, 7]*pq.ms, + labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) + #print("epc_neo = ", epc_neo) + + # Files to test + r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') + # Files from Allen Institute +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + epoch_nwb = r._handle_epochs_group(False, 'name') + #print("epoch_nwb = ", epoch_nwb) + self.assertTrue(epoch_nwb, Epoch) + self.assertTrue(epoch_nwb, epc_neo) + self.assertIsNotNone(epoch_nwb, epc_neo) + + def test_read_segment(self, **kargs): + print("--- Test Segment ---") + seg = Segment(index=5) + #print("seg = ", seg) + train0_neo = SpikeTrain(times=[.01, 3.3, 9.3], units='sec', t_stop=10) + #print("train0_neo = ", train0_neo) + seg.spiketrains.append(train0_neo) + #print("seg.spiketrains.append(train0_neo) = ", seg.spiketrains.append(train0_neo)) + sig0_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) + #print("sig0_neo = ", sig0_neo) + seg.analogsignals.append(sig0_neo) + #print("seg.analogsignals.append(sig0_neo) = ", seg.analogsignals.append(sig0_neo)) + + # Files to test + r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') + # Files from Allen Institute +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + seg_nwb = r._handle_epochs_group(False, 'name') + #print("seg_nwb = ", seg_nwb) + self.assertTrue(seg, Segment) + self.assertTrue(seg_nwb, Segment) + self.assertTrue(seg_nwb, seg) + self.assertIsNotNone(seg_nwb, seg) + + def test_read_block(self, filename=None): + ''' + Test function to read neo block. + ''' + print("*** def test_read_block ***") + # Files to test + r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') + # Files from Allen Institute +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + #print("-----------------------r = ", r) + bl = r.read_block() + #print("bl = ", bl) + print("*** End ***") + + if __name__ == "__main__": print("pynwb.__version__ = ", pynwb.__version__) unittest.main() From 7402e999dc7b3c187e3bc423a4d666f06b98c8a3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Fri, 6 Sep 2019 16:21:28 +0200 Subject: [PATCH 10/79] Updates --- neo/io/nwbio.py | 377 ++++++++++++++++------------------ neo/test/iotest/test_nwbio.py | 155 ++++++++++---- 2 files changed, 294 insertions(+), 238 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 1cbf859b2..6367cd52a 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -62,6 +62,16 @@ class NWBIO(BaseIO): Class for "reading" experimental data from a .nwb file. """ + is_readable = True + is_writable = True + is_streameable = False + supported_objects = [Block, Segment, AnalogSignal, IrregularlySampledSignal, + SpikeTrain, Epoch, Event] + readable_objects = supported_objects + writeable_objects = supported_objects + + has_header = False + name = 'NWB' description = 'This IO reads/writes experimental data from/to an .nwb dataset' extensions = ['nwb'] @@ -72,26 +82,18 @@ def __init__(self, filename): Arguments: filename : the filename """ - print("*** __init__ ***") BaseIO.__init__(self, filename=filename) - #BaseIO.__init__(self) - #print("filename = ", filename) self.filename = filename io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO self._file = io.read() # Define the file as a NWBFile object def read_block(self, lazy=False, cascade=True, **kwargs): - print("*** read_block ***") self._lazy = lazy - #print("lazy = ", lazy) file_access_dates = self._file.file_create_date - #print("file_access_dates = ", file_access_dates) identifier = self._file.identifier # or experimenter ? - #print("identifier = ", identifier) if identifier == '_neo': # this is an automatically generated name used if block.name is None identifier = None description = self._file.session_description # or experiment_description ? - #print("description = ", description) if description == "no description": description = None block = Block(name=identifier, @@ -102,110 +104,95 @@ def read_block(self, lazy=False, cascade=True, **kwargs): #nwb_version=self._file.get('nwb_version').value, file_access_dates=file_access_dates, file_read_log='') - print("block = ", block) if cascade: self._handle_general_group(block) - self._handle_epochs_group(lazy, block) #self._handle_epochs_group(block) - self._handle_acquisition_group(lazy, block) # self._handle_acquisition_group(block) - self._handle_stimulus_group(lazy, block) # self._handle_stimulus_group(block) + self._handle_epochs_group(lazy, block) + self._handle_acquisition_group(lazy, block) + self._handle_stimulus_group(lazy, block) self._handle_processing_group(block) self._handle_analysis_group(block) self._lazy = False return block + + + def write_block(self, block, **kwargs): + start_time = datetime.now() + self._file = NWBFile(self.filename, + session_start_time=start_time, + identifier=self._file.name, + ) + for segment in block.segments: + self._write_segment(segment) + self._file.close() + + if block.file_origin is None: + block.file_origin = self.filename + + self._file = h5py.File(self.filename, "r+") + nwb_create_date = self._file['file_create_date'].value + if block.file_datetime: + del self._file['file_create_date'] + self._file['file_create_date'] = np.array([block.file_datetime.isoformat(), nwb_create_date]) + else: + block.file_datetime = parse_datetime(nwb_create_date[0]) + self._file.close() + + def _handle_general_group(self, block): print("*** def _handle_general_group ***") #block.annotations['file_read_log'] += ("general group not handled\n") def _handle_epochs_group(self, lazy, block): - print("*** def _handle_epochs_group ***") self._lazy = lazy # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. ### epochs = self._file.epochs epochs = self._file.acquisition - #print("epochs = ", epochs) - # todo: handle epochs.attrs.get('tags') - ##for name, epoch in epochs.items(): -# for name, epoch in epochs: for key in epochs: - #print("key = ", key) - #print("epochs = ", epochs) - # todo: handle epoch.attrs.get('links') timeseries = [] - #print("timeseries = ", timeseries) current_shape = self._file.get_acquisition(key).data.shape[0] # sample number - #print("current_shape = ", current_shape) times = np.zeros(current_shape) - #for key, value in epoch.items(): for j in range(0, current_shape): times[j]=1./self._file.get_acquisition(key).rate*j+self._file.get_acquisition(key).starting_time if times[j] == self._file.get_acquisition(key).starting_time: t_start = times[j] * pq.second - #print("t_start = ", t_start) elif times[j]==times[-1]: t_stop = times[j] * pq.second - #print("t_stop = ", t_stop) else: - # todo: handle value['count'] - # todo: handle value['idx_start'] - #timeseries.append(self._handle_timeseries(key, value.get('timeseries'))) timeseries.append(self._handle_timeseries(self._lazy, key, times[j])) - #print(timeseries) - #print("timeSeries 1 bis = ", timeseries) - #print(timeseries) - #print("timeseries 2nd bis = ", timeseries) -# segment = Segment(name=name) segment = Segment(name=j) - #print("segment = ", segment) for obj in timeseries: - #print("obj = ", obj) - #print("timeseries = ", timeseries) -# for obj in self._file.acquisition: -### print("obj in segment = ", obj) +# print("obj = ", obj) obj.segment = segment -# segment==obj.segment - #print("segment in loop = ", segment) -# print("obj.segment = ", obj.segment) if isinstance(obj, AnalogSignal): - print("*** isinstance(obj, AnalogSignal) ***") + #print("AnalogSignal") segment.analogsignals.append(obj) - #print("obj=AnalogSignal") elif isinstance(obj, IrregularlySampledSignal): - print("*** isinstance(obj, IrregularlySampledSignal) ***") + #print("IrregularlySampledSignal") segment.irregularlysampledsignals.append(obj) - #print("obj=IrregularlySampledSignal") elif isinstance(obj, Event): - print("*** isinstance(obj, Event) ***") + #print("Event") segment.events.append(obj) - #print("obj=Event") elif isinstance(obj, Epoch): - print("*** isinstance(obj, Epoch) ***") + #print("Epoch") segment.epochs.append(obj) - #print("obj=Epoch") segment.block = block - #print("segment = ", segment) - #print("block = ", block) + segment.times=times # block.segments.append(segment) - return obj, segment +# print("segment = ", segment) +### print("segment.analogsignals = ", segment.analogsignals) + return segment, obj, times + def _handle_timeseries(self, lazy, name, timeseries): - print("*** def _handle_timeseries ***") - # todo: check timeseries.attrs.get('schema_id') - # todo: handle timeseries.attrs.get('source') -# subtype = timeseries.attrs['ancestry'][-1] for i in self._file.acquisition: - data_group = self._file.get_acquisition(i).data - #print("data_group = ", data_group) + data_group = self._file.get_acquisition(i).data*self._file.get_acquisition(i).conversion dtype = data_group.dtype - #print("dtype = ", dtype) - #if self._lazy: if lazy==True: data = np.array((), dtype=dtype) - #print("data if lazy = ", data) - lazy_shape = data_group.shape # inefficient to load the data to get the shape - #print("lazy_shape = ", lazy_shape) + lazy_shape = data_group.shape else: data = data_group if dtype.type is np.string_: @@ -213,9 +200,7 @@ def _handle_timeseries(self, lazy, name, timeseries): times = np.array(()) else: times = self._file.get_acquisition(i).timestamps - #print("times in timestamps = ", times) duration = 1/self._file.get_acquisition(i).rate - #print("************************ duration = ", duration) if durations: # Epoch if self._lazy: @@ -224,40 +209,29 @@ def _handle_timeseries(self, lazy, name, timeseries): durations=durations, labels=data, units='second') - #print("obj if duration = ", obj) else: # Event obj = Event(times=times, labels=data, units='second') - #print("obj Event = ", obj) else: - #units = get_units(data_group) units = self._file.get_acquisition(i).unit - #print("units = ", units) current_shape = self._file.get_acquisition(i).data.shape[0] # number of samples - #print("current_shape = ", current_shape) times = np.zeros(current_shape) for j in range(0, current_shape): times[j]=1./self._file.get_acquisition(i).rate*j+self._file.get_acquisition(i).starting_time if times[j] == self._file.get_acquisition(i).starting_time: # AnalogSignal sampling_metadata = times[j] - #print("sampling_metadata = ", sampling_metadata) t_start = sampling_metadata * pq.s - #print("t_start = ", t_start) sampling_rate = self._file.get_acquisition(i).rate * pq.Hz - #print("sampling_rate = ", sampling_rate) #assert sampling_metadata.attrs.get('unit') == 'Seconds' -### assert sampling_metadata.unit == 'Seconds' -# # todo: handle data.attrs['resolution'] +### assert sampling_metadata.unit == 'Seconds' obj = AnalogSignal(data, units=units, sampling_rate=sampling_rate, t_start=t_start, name=name) - #print("obj = ", obj) -# elif 'timestamps' in timeseries: elif self._file.get_acquisition(i).timestamps: # IrregularlySampledSignal if self._lazy: @@ -271,181 +245,205 @@ def _handle_timeseries(self, lazy, name, timeseries): # time_units=pq.second) # else: # raise Exception("Timeseries group does not contain sufficient time information") -# if self._lazy: -# obj.lazy_shape = lazy_shape return obj def _handle_acquisition_group(self, lazy, block): - print("*** def _handle_acquisition_group ***") acq = self._file.acquisition - #print("acq = ", acq) -# images = acq.get('images') -# if images and len(images) > 0: -# block.annotations['file_read_log'] += ("file contained {0} images; these are not currently handled by Neo\n".format(len(images))) - # todo: check for signals that are not contained within an NWB Epoch, # and create an anonymous Segment to contain them ###segment_acq = dict((segment.name, segment) for segment in block.segments) - ###print("segment_acq = ", segment_acq) for name in acq: - #print("name = ", name) # Sample number 'index_' -# if name == 'unit_list': -# pass # todo -# else: -# segment_name = name # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. segment_name = self._file.epochs - #print("segment_name = ", segment_name) desc = self._file.get_acquisition(name).unit - #print("desc = ", desc) -### segment = segment_acq[segment_name] segment = segment_name -# print("segment = ", segment) - #if self._lazy: if lazy==True: times = np.array(()) - #print("times = ", times) - #lazy_shape = group['times'].shape lazy_shape = self._file.get_acquisition(name).data.shape - #print("lazy_shape = ", lazy_shape) else: current_shape = self._file.get_acquisition(name).data.shape[0] # sample number - #print("current_shape = ", current_shape) times = np.zeros(current_shape) for j in range(0, current_shape): # For testing ! - times[j]=1./self._file.get_acquisition(name).rate*j+self._file.get_acquisition(name).starting_time # temps = 1./frequency [Hz] + t_start [s] - #print("times[j] = ", times) + times[j]=1./self._file.get_acquisition(name).rate*j+self._file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] spiketrain = SpikeTrain(times, units=pq.second, t_stop=times[-1]*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? - #print("spiketrain in _handle_acquisition_group = ", spiketrain) - #if self._lazy: -### if lazy==True: -### spiketrain.lazy_shape = lazy_shape if segment is not None: spiketrain.segment = segment - #print("segment = ", segment) segment.spiketrains.append(spiketrain) return spiketrain def _handle_stimulus_group(self, lazy, block): - print("*** def _handle_stimulus_group ***") #block.annotations['file_read_log'] += ("stimulus group not handled\n") # The same as acquisition for stimulus for spiketrain... sti = self._file.stimulus - #print("sti = ", sti) -# images = sti.get('images') -# if images and len(images) > 0: -# block.annotations['file_read_log'] += ("file contained {0} images; these are not currently handled by Neo\n".format(len(images))) - ### segment_sti = dict((segment.name, segment) for segment in block.segments) -### print("segment_sti = ", segment_sti) + for name in sti: - #print("name = ", name) # Sample number 'index_' # if name == 'unit_list': # pass # todo # else: # segment_name = name # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. segment_name_sti = self._file.epochs - #print("segment_name_sti = ", segment_name_sti) desc_sti = self._file.get_stimulus(name).unit - #print("desc_sti = ", desc_sti) ### segment = segment_acq[segment_name] segment_sti = segment_name_sti - #print("segment_sti = ", segment_sti) - #if self._lazy: if lazy==True: times = np.array(()) - #print("times = ", times) - #lazy_shape = group['times'].shape lazy_shape = self._file.get_stimulus(name).data.shape - #print("lazy_shape = ", lazy_shape) else: current_shape = self._file.get_stimulus(name).data.shape[0] # sample number - #print("current_shape = ", current_shape) times = np.zeros(current_shape) for j in range(0, current_shape): # For testing ! - times[j]=1./self._file.get_stimulus(name).rate*j+self._file.get_stimulus(name).starting_time # temps = 1./frequency [Hz] + t_start [s] - #print("times[j] = ", times) + times[j]=1./self._file.get_stimulus(name).rate*j+self._file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] spiketrain = SpikeTrain(times, units=pq.second, t_stop=times[-1]*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? - #print("spiketrain = ", spiketrain) - #if self._lazy: -### if lazy==True: -### spiketrain.lazy_shape = lazy_shape if segment_sti is not None: spiketrain.segment_sti = segment_sti - #print("segment_sti = ", segment_sti) segment_sti.spiketrains.append(spiketrain) def _handle_processing_group(self, block): print("*** def _handle_processing_group ***") - # todo: handle other modules than Units -## units_group = self._file.get('processing/Units/UnitTimes') - #segment_map = dict((segment.name, segment) for segment in block.segments) - #print("segment_map = ", segment_map) -# for name, group in units_group.items(): -# if name == 'unit_list': -# pass # todo -# else: -# segment_name = group['source'].value -# #desc = group['unit_description'].value # use this to store Neo Unit id? -# segment = segment_map[segment_name] -# if self._lazy: -# times = np.array(()) -# lazy_shape = group['times'].shape -# else: -# times = group['times'].value -# spiketrain = SpikeTrain(times, units=pq.second, -# t_stop=group['t_stop'].value*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? -# if self._lazy: -# spiketrain.lazy_shape = lazy_shape -# spiketrain.segment = segment -# segment.spiketrains.append(spiketrain) + def _handle_analysis_group(self, block): print("*** def _handle_analysis_group ***") #block.annotations['file_read_log'] += ("analysis group not handled\n") + def _write_segment(self, segment): + # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. - -# def time_in_seconds(t): -# print("*** def time_in_seconds ***") -# return float(t.rescale("second")) - - -# def _decompose_unit(unit): -# print("*** def _decompose_unit ***") -# """unit should be a Quantity object with unit magnitude -# Returns (conversion, base_unit_str) -# Example: -# >>> _decompose_unit(pq.nA) -# (1e-09, 'ampere') -# """ -# assert isinstance(unit, pq.quantity.Quantity) -# assert unit.magnitude == 1 -# conversion = 1.0 -# def _decompose(unit): -# dim = unit.dimensionality -# if len(dim) != 1: -# raise NotImplementedError("Compound units not yet supported") # e.g. volt-metre -# uq, n = dim.items()[0] -# if n != 1: -# raise NotImplementedError("Compound units not yet supported") # e.g. volt^2 -# uq_def = uq.definition -# return float(uq_def.magnitude), uq_def -# conv, unit2 = _decompose(unit) -# while conv != 1: -# conversion *= conv -# unit = unit2 -# conv, unit2 = _decompose(unit) -# return conversion, unit.dimensionality.keys()[0].name + nwb_epoch = self._file.add_epoch( + self._file, + self._file.epochs, #segment.name + #start_time=time_in_seconds(segment.t_start), + start_time=self._handle_epochs_group(True, Block)[2][0], +### start_time=time_in_seconds(self._handle_epochs_group(True, Block)[2][0]), + #stop_time=time_in_seconds(segment.t_stop), + stop_time=self._handle_epochs_group(True, Block)[2][-1], + ) + + for i, signal in enumerate(chain(self._handle_epochs_group(True, Block)[0].analogsignals, self._handle_epochs_group(True, Block)[0].irregularlysampledsignals)): # segment.analogsignals, segment.irregularlysampledsignals + self._write_signal(signal, nwb_epoch, i) + self._write_spiketrains(self._handle_acquisition_group, self._handle_epochs_group(True, Block)[0]) #(segment.spiketrains, segment) + for i, event in enumerate(self._handle_epochs_group(True, Block)[0].events): # segment.event + self._write_event(event, nwb_epoch, i) + for i, neo_epoch in enumerate(self._handle_epochs_group(True, Block)[0].epochs): # segment.epochs + self._write_neo_epoch(neo_epoch, nwb_epoch, i) + + + def _write_signal(self, signal, epoch, i): + print(" ") + print("*** def _write_signal ***") + signal_name = signal.name or "signal{0}".format(i) + ts_name = "{0}_{1}".format(signal.segment.name, signal_name) + + #ts = self._file.make_group("", ts_name, path="/acquisition/timeseries") ### +## conversion, base_unit = _decompose_unit(signal.units) +# attributes = {"conversion": conversion, +# "unit": base_unit, +# "resolution": float('nan')} + + if isinstance(signal, AnalogSignal): + sampling_rate = signal.sampling_rate.rescale("Hz") + signal.sampling_rate = sampling_rate +# ts.set_dataset("starting_time", time_in_seconds(signal.t_start), +# attrs={"rate": float(sampling_rate)}) +# elif isinstance(signal, IrregularlySampledSignal): +# ts.set_dataset("timestamps", signal.times.rescale('second').magnitude) + else: + raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) +# ts.set_dataset("data", signal.magnitude, +# dtype=np.float64, #signal.dtype, +# attrs=attributes) +# ts.set_dataset("num_samples", signal.shape[0]) # this is supposed to be created automatically, but is not +# #ts.set_dataset("num_channels", signal.shape[1]) +# ts.set_attr("source", signal.name or "unknown") +# ts.set_attr("description", signal.description or "") + + self._file.add_epoch( + epoch, + signal_name, + start_time = time_in_seconds(signal.segment.t_start), + stop_time = time_in_seconds(signal.segment.t_stop), +# ts + ) + + + + def _write_spiketrains(self, spiketrains, segment): + print("*** def _write_spiketrains ***") + + def _write_event(self, event, nwb_epoch, i): + print("*** def _write_event ***") + event_name = event.name or "event{0}".format(i) + ts_name = "{0}_{1}".format(event.segment.name, event_name) + +# ts = self._file.make_group("", ts_name, path="/acquisition/timeseries") +# ts.set_dataset("timestamps", event.times.rescale('second').magnitude) +# ts.set_dataset("data", event.labels) +# ts.set_dataset("num_samples", event.size) # this is supposed to be created automatically, but is not +# ts.set_attr("source", event.name or "unknown") +# ts.set_attr("description", event.description or "") + + self._file.add_epoch_ts( + nwb_epoch, + time_in_seconds(event.segment.t_start), + time_in_seconds(event.segment.t_stop), + event_name, +# ts + ) + + + def _write_neo_epoch(self, neo_epoch, nwb_epoch, i): + print("*** def _write_neo_epoch ***") + + + +def time_in_seconds(t): + print("*** def time_in_seconds ***") + return float(t.rescale("second")) + print("float(t.rescale(second)) = ",float(t.rescale("second"))) + + +def _decompose_unit(unit): + assert isinstance(unit, pq.quantity.Quantity) + assert unit.magnitude == 1 + conversion = 1.0 + def _decompose(unit): + dim = unit.dimensionality + print("dim = ", dim) + if len(dim) != 1: + raise NotImplementedError("Compound units not yet supported") + + print("list(dim.keys())[0] = ", list(dim.keys())[0]) + print("list(dim.values())[0] = ", list(dim.values())[0]) + print("list(dim.values())[1] = ", list(dim.values())[:]) + print("dim.unicode = ", dim.unicode) + + +### uq, n = dim.items()[0] +# +# print("unit.dimensionality.items()[0] = ", unit.dimensionality.items()[0]) +# print("unit.dimensionality.items()[0] = ", unit.dimensionality) +# print("unit.dimensionality[0] = ", unit.dimensionality[0]) + +# if n != 1: +# raise NotImplementedError("Compound units not yet supported") +# uq_def = uq.definition +# return float(uq_def.magnitude), uq_def +# conv, unit2 = _decompose(unit) +# while conv != 1: +# conversion *= conv +# unit = unit2 +# conv, unit2 = _decompose(unit) +# return conversion, unit.dimensionality.keys()[0].name prefix_map = { @@ -453,14 +451,3 @@ def _handle_analysis_group(self, block): 1e-6: 'micro', 1e-9: 'nano' } - -def get_units(data_group): - print("*** def get_units ***") - #print("data_group = ", data_group) -# conversion = data_group.attrs.get('conversion') - #base_units = data_group.attrs.get('unit') - base_units = data_group.units - #print("base_units = ", base_units) -# return prefix_map[conversion] + base_units - - diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 26ea40f95..9adc620b6 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -27,7 +27,7 @@ from pynwb import * # Tests -from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment +from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Unit, Block import quantities as pq import numpy as np @@ -57,7 +57,6 @@ class TestNWBIO(unittest.TestCase, ): entities_to_test = files_to_download def test_read_analogsignal(self): - print("--- Test AnalogSignal ---") sig_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) self.assertTrue(isinstance(sig_neo, AnalogSignal)) # Files to test @@ -68,7 +67,6 @@ def test_read_analogsignal(self): # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') - obj_nwb = r._handle_timeseries(False, 'name', 1) self.assertTrue(isinstance(obj_nwb, AnalogSignal)) self.assertEqual(isinstance(obj_nwb, AnalogSignal), isinstance(sig_neo, AnalogSignal)) @@ -77,12 +75,10 @@ def test_read_analogsignal(self): self.assertTrue(obj_nwb.units, sig_neo.units) self.assertIsNotNone(obj_nwb, sig_neo) +################################################################ # Error _handle_epochs_group def test_read_irregularlysampledsignal(self, **kargs): - print("--- Test IrregularlySampledSignal ---") irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], units='mV', time_units='ms') - #print("irsig0 = ", irsig0) irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*pq.s, [[4, 5], [5, 4], [6, 3]]*pq.nA) - #print("irsig1 = ", irsig1) self.assertTrue(isinstance(irsig0, IrregularlySampledSignal)) self.assertTrue(isinstance(irsig1, IrregularlySampledSignal)) @@ -95,27 +91,23 @@ def test_read_irregularlysampledsignal(self, **kargs): # r = ('/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') # r = ('/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') irsig_nwb = r._handle_epochs_group(False, 'name') - #print("irsig_nwb = ", irsig_nwb) self.assertTrue(irsig_nwb, IrregularlySampledSignal) self.assertTrue(irsig_nwb, irsig0) self.assertTrue(irsig_nwb, irsig1) def test_read_spiketrain(self, **kargs): - print("--- Test spiketrain ---") train_neo = SpikeTrain([3, 4, 5]*pq.s, t_stop=10.0) - #print("train_neo = ", train_neo) self.assertTrue(isinstance(train_neo, SpikeTrain)) # Files to test r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb')# # Files from Allen Institute # r = NWBIO('/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') train_nwb = r._handle_acquisition_group(False, 1) - #print("train_nwb = ", train_nwb) self.assertTrue(isinstance(train_nwb, SpikeTrain)) self.assertEqual(isinstance(train_nwb, SpikeTrain), isinstance(train_neo, SpikeTrain)) self.assertTrue(train_nwb.shape, train_neo.shape) @@ -124,9 +116,7 @@ def test_read_spiketrain(self, **kargs): self.assertIsNotNone(train_nwb, train_neo) def test_read_event(self, **kargs): - print("--- Test Event ---") evt_neo = Event(np.arange(0, 30, 10)*pq.s, labels=np.array(['trig0', 'trig1', 'trig2'], dtype='S')) - #print("evt_neo = ", evt_neo) # Files to test r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') @@ -137,16 +127,13 @@ def test_read_event(self, **kargs): # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') event_nwb = r._handle_epochs_group(False, 'name') - #print("event_nwb = ", event_nwb) self.assertTrue(event_nwb, evt_neo) self.assertIsNotNone(event_nwb, evt_neo) - + def test_read_epoch(self, **kargs): - print("--- Test Epoch ---") epc_neo = Epoch(times=np.arange(0, 30, 10)*pq.s, durations=[10, 5, 7]*pq.ms, labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) - #print("epc_neo = ", epc_neo) # Files to test r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') @@ -157,24 +144,108 @@ def test_read_epoch(self, **kargs): # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') epoch_nwb = r._handle_epochs_group(False, 'name') - #print("epoch_nwb = ", epoch_nwb) self.assertTrue(epoch_nwb, Epoch) self.assertTrue(epoch_nwb, epc_neo) self.assertIsNotNone(epoch_nwb, epc_neo) - def test_read_segment(self, **kargs): - print("--- Test Segment ---") - seg = Segment(index=5) - #print("seg = ", seg) - train0_neo = SpikeTrain(times=[.01, 3.3, 9.3], units='sec', t_stop=10) - #print("train0_neo = ", train0_neo) - seg.spiketrains.append(train0_neo) - #print("seg.spiketrains.append(train0_neo) = ", seg.spiketrains.append(train0_neo)) - sig0_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) - #print("sig0_neo = ", sig0_neo) - seg.analogsignals.append(sig0_neo) - #print("seg.analogsignals.append(sig0_neo) = ", seg.analogsignals.append(sig0_neo)) +# def test_read_segment(self, **kargs): +# print("--- Test Segment ---") +# seg = Segment(index=5) +# print("seg = ", seg) +# train0_neo = SpikeTrain(times=[.01, 3.3, 9.3], units='sec', t_stop=10) +# #print("train0_neo = ", train0_neo) +# seg.spiketrains.append(train0_neo) +# #print("seg.spiketrains.append(train0_neo) = ", seg.spiketrains.append(train0_neo)) +# sig0_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) +# #print("sig0_neo = ", sig0_neo) +# seg.analogsignals.append(sig0_neo) +# #print("seg.analogsignals.append(sig0_neo) = ", seg.analogsignals.append(sig0_neo)) +# +# # Files to test +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') +## r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') +# # Files from Allen Institute +## r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') +## r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') +## r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') +## r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') +# seg_nwb = r._handle_epochs_group(False, 'name') +# print("seg_nwb = ", seg_nwb) +# self.assertTrue(seg, Segment) +# print("self.assertTrue(seg, Segment) = ", self.assertTrue(seg, Segment)) +# self.assertTrue(seg_nwb, Segment) +# #print("self.assertTrue(seg_nwb, Segment) = ", self.assertTrue(seg_nwb, Segment)) +# self.assertTrue(seg_nwb, seg) +# #print("self.assertTrue(seg_nwb, seg) = ", self.assertTrue(seg_nwb, seg)) +# self.assertIsNotNone(seg_nwb, seg) +# #print("self.assertIsNotNone(seg_nwb, seg) = ", self.assertIsNotNone(seg_nwb, seg)) +#### print("self.assertEqual(seg, Segment) = ", self.assertEqual(seg_nwb, Segment)) +# print("self.assertIsInstance(seg, Segment) = ", self.assertIsInstance(seg, Segment)) +# # print("self.assertEqual(seg, Segment) = ", self.assertEqual(seg, Segment)) +# # print("self.assertIsInstance(seg_nwb, Segment) = ", self.assertIs(seg_nwb, Segment)) +## from neo.core import Unit +## self.assertIsInstance(seg.spiketrains[0].unit, Unit) +# print("self.assertIsNotNone(seg_nwb, Segment) = ", self.assertIsNotNone(seg_nwb, Segment)) +# print("self.assertIsNotNone(seg, Segment) = ", self.assertIsNotNone(seg, Segment)) +# print("self.assertIsNotNone(seg_nwb, seg) = ", self.assertIsNotNone(seg_nwb, seg)) +# print("self.assertNotIsInstance(seg_nwb, seg) = ", self.assertNotIsInstance(seg_nwb, Segment)) + + + +# def test_read_segment_lazy(self): +# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') +# print("r = ", r) +## seg = r.read_segment(lazy=True) +# seg = r._handle_epochs_group(True,'name') +# print("seg = ", seg) +# for ana in seg.analogsignals: +# assert isinstance(ana, AnalogSignalProxy) +## ana = ana.load() +## assert isinstance(ana, AnalogSignal)# +# for st in seg.spiketrains: +## assert isinstance(st, SpikeTrainProxy) +## st = st.load() +## assert isinstance(st, SpikeTrain) + + + +# def test(self): +# +# # Spiketrain +# train = SpikeTrain([3, 4, 5] * pq.s, t_stop=10.0) +# unit = Unit() +# train.unit = unit +# unit.spiketrains.append(train) +# +# epoch = Epoch(np.array([0, 10, 20]), +# np.array([2, 2, 2]), +# np.array(["a", "b", "c"]), +# units="ms") +# +# blk = Block() +# seg = Segment() +# seg.spiketrains.append(train) +# seg.epochs.append(epoch) +# epoch.segment = seg +# blk.segments.append(seg) +# +# reader = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') +# print("reader = ", reader) +# r_blk = reader.read_block() +# print("r_blk = ", r_blk) +## r_seg = r_blk.segments[0] +# r_seg = r_blk.segments +# print("r_seg = ", r_seg) +## self.assertIsInstance(r_seg.spiketrains[0].unit, Unit) +## self.assertIsInstance(r_seg.epochs[0], Epoch) + + + + def test_read_block(self, filename=None): + ''' + Test function to read neo block. + ''' # Files to test r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') @@ -183,18 +254,15 @@ def test_read_segment(self, **kargs): # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') - seg_nwb = r._handle_epochs_group(False, 'name') - #print("seg_nwb = ", seg_nwb) - self.assertTrue(seg, Segment) - self.assertTrue(seg_nwb, Segment) - self.assertTrue(seg_nwb, seg) - self.assertIsNotNone(seg_nwb, seg) + bl = r.read_block() + print("bl = ", bl) - def test_read_block(self, filename=None): + + def test_write_segment(self, filename=None): ''' - Test function to read neo block. + Test function to write a segment. ''' - print("*** def test_read_block ***") + print("*** def test_write_segment ***") # Files to test r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') @@ -203,10 +271,11 @@ def test_read_block(self, filename=None): # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') # r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') - #print("-----------------------r = ", r) - bl = r.read_block() - #print("bl = ", bl) - print("*** End ***") + print("-----------------------r = ", r) + ws = r._write_segment(None) + print("ws = ", ws) + + if __name__ == "__main__": From 3298018dd5327f148981b1b40eea73e7aa26145d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Wed, 18 Sep 2019 17:00:44 +0200 Subject: [PATCH 11/79] after recent modification --- neo/io/nwbio.py | 333 ++++++++++++++++++++++++++++++++++-------------- 1 file changed, 234 insertions(+), 99 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 6367cd52a..13b17dfdd 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -91,9 +91,11 @@ def read_block(self, lazy=False, cascade=True, **kwargs): self._lazy = lazy file_access_dates = self._file.file_create_date identifier = self._file.identifier # or experimenter ? +# print("identifier = ", identifier) if identifier == '_neo': # this is an automatically generated name used if block.name is None identifier = None description = self._file.session_description # or experiment_description ? +# print("description = ", description) if description == "no description": description = None block = Block(name=identifier, @@ -104,6 +106,9 @@ def read_block(self, lazy=False, cascade=True, **kwargs): #nwb_version=self._file.get('nwb_version').value, file_access_dates=file_access_dates, file_read_log='') + print("block in read_block = ", block) + print("block.file_origin = ", block.file_origin) + print(" ") if cascade: self._handle_general_group(block) self._handle_epochs_group(lazy, block) @@ -115,28 +120,54 @@ def read_block(self, lazy=False, cascade=True, **kwargs): return block - def write_block(self, block, **kwargs): start_time = datetime.now() - self._file = NWBFile(self.filename, - session_start_time=start_time, - identifier=self._file.name, - ) + print("00000000000 self._file = ", self._file) + print("self.filename = ", self.filename) + +# self._file = NWBFile( +# session_description='', +# #self.filename, +# session_start_time=start_time, +# identifier=self._file.name, +# ) + + ###### + # NWB Epochs + for i in self._file.acquisition: + data = self._file.get_acquisition(i).data + unit = self._file.get_acquisition(i).unit + name = self._file.get_acquisition(i).name + comments = self._file.get_acquisition(i).comments + timestamps = self._file.get_acquisition(i).rate + start_time = self._file.get_acquisition(i).starting_time + + nwb_timeseries = TimeSeries(name=name, data=data, unit=unit, timestamps=[timestamps]) + nwb_epoch = self._file.add_epoch(start_time, 4.0, [comments], [nwb_timeseries, ]) ### Check 4.0 ! + print("nwb_epoch = ", nwb_epoch) + + + segments = self._file.epochs[0] + print("123123123123 segments = ", segments) + + + block = self.read_block(block) + print("block write = ", block) + block.segments.append(block) + print("****block.segments 123 = ", block.segments) + + for segment in block.segments: + print("****** ok ******") + print("segment = ", segment) + print("block.segments = ", block.segments) + print("segments = ", segments) self._write_segment(segment) + print("*** end write_block ***") self._file.close() + print("END") - if block.file_origin is None: - block.file_origin = self.filename - self._file = h5py.File(self.filename, "r+") - nwb_create_date = self._file['file_create_date'].value - if block.file_datetime: - del self._file['file_create_date'] - self._file['file_create_date'] = np.array([block.file_datetime.isoformat(), nwb_create_date]) - else: - block.file_datetime = parse_datetime(nwb_create_date[0]) - self._file.close() def _handle_general_group(self, block): @@ -144,10 +175,17 @@ def _handle_general_group(self, block): #block.annotations['file_read_log'] += ("general group not handled\n") def _handle_epochs_group(self, lazy, block): + print("*** _handle_epochs_group ***") self._lazy = lazy # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. -### epochs = self._file.epochs + +## print("block epochs_group = ", block) +# print("****block.segments epochs_group = ", block.segments) + epochs = self._file.acquisition + print("epochs = ", epochs) + + print("self._file = ", self._file) for key in epochs: timeseries = [] @@ -163,7 +201,6 @@ def _handle_epochs_group(self, lazy, block): timeseries.append(self._handle_timeseries(self._lazy, key, times[j])) segment = Segment(name=j) for obj in timeseries: -# print("obj = ", obj) obj.segment = segment if isinstance(obj, AnalogSignal): #print("AnalogSignal") @@ -179,9 +216,13 @@ def _handle_epochs_group(self, lazy, block): segment.epochs.append(obj) segment.block = block segment.times=times -# block.segments.append(segment) + +# print("segment.block = ", segment.block) +# print("block = ", block) # print("segment = ", segment) -### print("segment.analogsignals = ", segment.analogsignals) +# print("segments = ", segments) + +# block.segments.append(segment) return segment, obj, times @@ -195,6 +236,7 @@ def _handle_timeseries(self, lazy, name, timeseries): lazy_shape = data_group.shape else: data = data_group + if dtype.type is np.string_: if self._lazy: times = np.array(()) @@ -207,12 +249,12 @@ def _handle_timeseries(self, lazy, name, timeseries): durations = np.array(()) obj = Epoch(times=times, durations=durations, - labels=data, + labels=data_group, units='second') else: # Event obj = Event(times=times, - labels=data, + labels=data_group, units='second') else: units = self._file.get_acquisition(i).unit @@ -226,8 +268,9 @@ def _handle_timeseries(self, lazy, name, timeseries): t_start = sampling_metadata * pq.s sampling_rate = self._file.get_acquisition(i).rate * pq.Hz #assert sampling_metadata.attrs.get('unit') == 'Seconds' -### assert sampling_metadata.unit == 'Seconds' - obj = AnalogSignal(data, +### assert sampling_metadata.units == 'Seconds' + obj = AnalogSignal( + data_group, units=units, sampling_rate=sampling_rate, t_start=t_start, @@ -239,59 +282,65 @@ def _handle_timeseries(self, lazy, name, timeseries): else: time_data = self._file.get_acquisition(i).timestamps ### assert time_data.attrs.get('unit') == 'Seconds' -# obj = IrregularlySampledSignal(time_data.value, -# data, -# units=units, -# time_units=pq.second) -# else: -# raise Exception("Timeseries group does not contain sufficient time information") + obj = IrregularlySampledSignal( +# time_data.value, + data_group, + units=units, + time_units=pq.second) +# else: +# raise Exception("Timeseries group does not contain sufficient time information") +# if self._lazy: +# obj.lazy_shape = lazy_shape return obj def _handle_acquisition_group(self, lazy, block): + print("*** _handle_acquisition_group ***") acq = self._file.acquisition # todo: check for signals that are not contained within an NWB Epoch, # and create an anonymous Segment to contain them - ###segment_acq = dict((segment.name, segment) for segment in block.segments) - for name in acq: - # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. - segment_name = self._file.epochs - desc = self._file.get_acquisition(name).unit - segment = segment_name - if lazy==True: - times = np.array(()) - lazy_shape = self._file.get_acquisition(name).data.shape - else: - current_shape = self._file.get_acquisition(name).data.shape[0] # sample number - times = np.zeros(current_shape) - for j in range(0, current_shape): # For testing ! - times[j]=1./self._file.get_acquisition(name).rate*j+self._file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] - spiketrain = SpikeTrain(times, units=pq.second, - t_stop=times[-1]*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? - if segment is not None: - spiketrain.segment = segment - segment.spiketrains.append(spiketrain) - return spiketrain +# ###segment_acq = dict((segment.name, segment) for segment in block.segments) +# for name in acq: +# # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. +# segment_name = self._file.epochs +# desc = self._file.get_acquisition(name).unit +# segment = segment_name +# if lazy==True: +# times = np.array(()) +# lazy_shape = self._file.get_acquisition(name).data.shape +# else: +# current_shape = self._file.get_acquisition(name).data.shape[0] # sample number +# times = np.zeros(current_shape) +# for j in range(0, current_shape): # For testing ! +# times[j]=1./self._file.get_acquisition(name).rate*j+self._file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] +# spiketrain = SpikeTrain(times, units=pq.second, +# t_stop=times[-1]*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? +# if segment is not None: +# spiketrain.segment = segment +# print("segment = ", segment) +# segment.spiketrains.append(spiketrain) +# print("**********************************************spiketrain = ", spiketrain) +# return spiketrain def _handle_stimulus_group(self, lazy, block): + print("*** _handle_stimulus_group ***") #block.annotations['file_read_log'] += ("stimulus group not handled\n") # The same as acquisition for stimulus for spiketrain... sti = self._file.stimulus +# print("sti = ", sti) ### segment_sti = dict((segment.name, segment) for segment in block.segments) for name in sti: -# if name == 'unit_list': -# pass # todo -# else: -# segment_name = name # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. segment_name_sti = self._file.epochs desc_sti = self._file.get_stimulus(name).unit ### segment = segment_acq[segment_name] segment_sti = segment_name_sti +# print("segment_sti = ", segment_sti) +# print(" ") if lazy==True: times = np.array(()) lazy_shape = self._file.get_stimulus(name).data.shape @@ -302,13 +351,42 @@ def _handle_stimulus_group(self, lazy, block): times[j]=1./self._file.get_stimulus(name).rate*j+self._file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] spiketrain = SpikeTrain(times, units=pq.second, t_stop=times[-1]*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? +# print("spiketrain = ", spiketrain) if segment_sti is not None: spiketrain.segment_sti = segment_sti segment_sti.spiketrains.append(spiketrain) + def _handle_processing_group(self, block): print("*** def _handle_processing_group ***") +# units_group = self._file.get('processing/Units/UnitTimes') +### units_group = self._file.processing +## print("units_group = ", units_group) +# segment_map = dict((segment.name, segment) for segment in block.segments) +# for name, group in units_group.items(): +# if name == 'unit_list': +# pass # todo +# else: +# segment_name = group['source'].value +# #desc = group['unit_description'].value # use this to store Neo Unit id? +# segment = segment_map[segment_name] +# if self._lazy: +# times = np.array(()) +# lazy_shape = group['times'].shape +# else: +# times = group['times'].value +# spiketrain = SpikeTrain(times, units=pq.second, +# t_stop=group['t_stop'].value*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? +# if self._lazy: +# spiketrain.lazy_shape = lazy_shape +# spiketrain.segment = segment +# segment.spiketrains.append(spiketrain) + + + + + def _handle_analysis_group(self, block): @@ -319,46 +397,111 @@ def _handle_analysis_group(self, block): def _write_segment(self, segment): # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. - nwb_epoch = self._file.add_epoch( - self._file, - self._file.epochs, #segment.name - #start_time=time_in_seconds(segment.t_start), - start_time=self._handle_epochs_group(True, Block)[2][0], -### start_time=time_in_seconds(self._handle_epochs_group(True, Block)[2][0]), - #stop_time=time_in_seconds(segment.t_stop), - stop_time=self._handle_epochs_group(True, Block)[2][-1], - ) - - for i, signal in enumerate(chain(self._handle_epochs_group(True, Block)[0].analogsignals, self._handle_epochs_group(True, Block)[0].irregularlysampledsignals)): # segment.analogsignals, segment.irregularlysampledsignals +## nwb_epoch = self._file.add_epoch( +### self._file, +### self._file.epochs, #segment.name +## #start_time=self._handle_epochs_group(True, Block)[2][0], +## #stop_time=self._handle_epochs_group(True, Block)[2][-1], +## ##start_time=0.0, +## 2.0, +## 4.0, +## ##stop_time=3.0, +### #tags= ['', ''], +## ['first', 'example'], +## #Timeseries=[self._file.acquisition, timestamps], +## [test_ts, ] +## ) +### print("////////// nwb_epoch = ", nwb_epoch) + + + ###### + # NWB Epochs + for i in self._file.acquisition: + data = self._file.get_acquisition(i).data + unit = self._file.get_acquisition(i).unit + name = self._file.get_acquisition(i).name + comments = self._file.get_acquisition(i).comments + timestamps = self._file.get_acquisition(i).rate + start_time = self._file.get_acquisition(i).starting_time + + nwb_timeseries = TimeSeries(name=name, data=data, unit=unit, timestamps=[timestamps]) + nwb_epoch = self._file.add_epoch(start_time, 4.0, [comments], [nwb_timeseries, ]) ### Check 4.0 ! +# print("nwb_epoch = ", nwb_epoch) + + print("segment 234 = ", segment) + + + segments = self._file.epochs[0] + print("456456456 segments = ", segments) + print("segments.analogsignals = ", segments.analogsignals) + + + + ## segment=self._file.epochs[0] +# self._handle_epochs_group(True, Block)[0]==segment ### + # print("segment = ", segment) + print("segment.analogsignals = ", segment.analogsignals) + + + + + +# for i, signal in enumerate(chain(self._handle_epochs_group(True, Block)[0].analogsignals, self._handle_epochs_group(True, Block)[0].irregularlysampledsignals)): # segment.analogsignals, segment.irregularlysampledsignals + for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): +# print("segment.analogsignals = ", segment.analogsignals) +# print("signal = ", signal) self._write_signal(signal, nwb_epoch, i) self._write_spiketrains(self._handle_acquisition_group, self._handle_epochs_group(True, Block)[0]) #(segment.spiketrains, segment) - for i, event in enumerate(self._handle_epochs_group(True, Block)[0].events): # segment.event +# for i, event in enumerate(self._handle_epochs_group(True, Block)[0].events): # segment.event + for i, event in enumerate(segment.events): self._write_event(event, nwb_epoch, i) - for i, neo_epoch in enumerate(self._handle_epochs_group(True, Block)[0].epochs): # segment.epochs +# for i, neo_epoch in enumerate(self._handle_epochs_group(True, Block)[0].epochs): # segment.epochs + for i, neo_epoch in enumerate(segment.epochs): self._write_neo_epoch(neo_epoch, nwb_epoch, i) def _write_signal(self, signal, epoch, i): - print(" ") print("*** def _write_signal ***") +# print("signal = ", signal) signal_name = signal.name or "signal{0}".format(i) +# print("signal_name = ", signal_name) ts_name = "{0}_{1}".format(signal.segment.name, signal_name) +# print("ts_name = ", ts_name) + +# Device = self._file.create_device(name='trodes_rig123') +# print("device = ", device) #ts = self._file.make_group("", ts_name, path="/acquisition/timeseries") ### -## conversion, base_unit = _decompose_unit(signal.units) -# attributes = {"conversion": conversion, +# ts = self._file.create_electrode_group(ts_name, "", location="/acquisition/timeseries", device=Device) +## ts2 = self._file.acquisition +## print("ts2 = ", ts2) + + for i in self._file.acquisition: + ts = self._file.get_acquisition(i).data[:] + print("ts = ", ts) + +# conversion, base_unit = _decompose_unit(signal.units) + conversion = _decompose_unit(signal.units) + + attributes = {"conversion": conversion, # "unit": base_unit, -# "resolution": float('nan')} + "resolution": float('nan')} if isinstance(signal, AnalogSignal): sampling_rate = signal.sampling_rate.rescale("Hz") signal.sampling_rate = sampling_rate -# ts.set_dataset("starting_time", time_in_seconds(signal.t_start), -# attrs={"rate": float(sampling_rate)}) +# ts.set_dataset("starting_time", time_in_seconds(signal.t_start), attrs={"rate": float(sampling_rate)}) +## ts2 = TimeSeries("starting_time", time_in_seconds(signal.t_start), signal.units, sampling_rate) +# print("ts2 = ", ts2) +# self._file.add_acquisition(ts2) + + + # elif isinstance(signal, IrregularlySampledSignal): # ts.set_dataset("timestamps", signal.times.rescale('second').magnitude) else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) + print("Erreur") # ts.set_dataset("data", signal.magnitude, # dtype=np.float64, #signal.dtype, # attrs=attributes) @@ -367,13 +510,15 @@ def _write_signal(self, signal, epoch, i): # ts.set_attr("source", signal.name or "unknown") # ts.set_attr("description", signal.description or "") - self._file.add_epoch( - epoch, - signal_name, - start_time = time_in_seconds(signal.segment.t_start), - stop_time = time_in_seconds(signal.segment.t_stop), -# ts - ) +## self._file.add_epoch( +## epoch, +## signal_name, +## start_time = time_in_seconds(signal.segment.t_start), +## #stop_time = time_in_seconds(signal.segment.t_stop), +## stop_time = 4.0, +## timeseries = [ts], +## tags='', +## ) @@ -407,37 +552,27 @@ def _write_neo_epoch(self, neo_epoch, nwb_epoch, i): def time_in_seconds(t): - print("*** def time_in_seconds ***") +# print("*** def time_in_seconds ***") return float(t.rescale("second")) - print("float(t.rescale(second)) = ",float(t.rescale("second"))) +# print("float(t.rescale(second)) = ",float(t.rescale("second"))) def _decompose_unit(unit): +# print("*** _decompose_unit ***") assert isinstance(unit, pq.quantity.Quantity) assert unit.magnitude == 1 conversion = 1.0 def _decompose(unit): +# print("*** _decompose ***") dim = unit.dimensionality - print("dim = ", dim) +# print("dim = ", dim) if len(dim) != 1: raise NotImplementedError("Compound units not yet supported") - - print("list(dim.keys())[0] = ", list(dim.keys())[0]) - print("list(dim.values())[0] = ", list(dim.values())[0]) - print("list(dim.values())[1] = ", list(dim.values())[:]) - print("dim.unicode = ", dim.unicode) - - -### uq, n = dim.items()[0] -# -# print("unit.dimensionality.items()[0] = ", unit.dimensionality.items()[0]) -# print("unit.dimensionality.items()[0] = ", unit.dimensionality) -# print("unit.dimensionality[0] = ", unit.dimensionality[0]) - -# if n != 1: -# raise NotImplementedError("Compound units not yet supported") -# uq_def = uq.definition -# return float(uq_def.magnitude), uq_def + uq, n = dim.items()[0] + if n != 1: + raise NotImplementedError("Compound units not yet supported") + uq_def = uq.definition + return float(uq_def.magnitude), uq_def # conv, unit2 = _decompose(unit) # while conv != 1: # conversion *= conv From 9c10d7f6dbe6a719d903ec1b04712f901d5fe6ff Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Thu, 19 Sep 2019 16:52:20 +0200 Subject: [PATCH 12/79] Segment and AnalogSignals --- neo/io/nwbio.py | 99 +++++++++++++++++++++++++++++++------------------ 1 file changed, 62 insertions(+), 37 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 13b17dfdd..9f66d25b5 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -107,7 +107,7 @@ def read_block(self, lazy=False, cascade=True, **kwargs): file_access_dates=file_access_dates, file_read_log='') print("block in read_block = ", block) - print("block.file_origin = ", block.file_origin) +# print("block.file_origin = ", block.file_origin) print(" ") if cascade: self._handle_general_group(block) @@ -122,8 +122,8 @@ def read_block(self, lazy=False, cascade=True, **kwargs): def write_block(self, block, **kwargs): start_time = datetime.now() - print("00000000000 self._file = ", self._file) - print("self.filename = ", self.filename) +# print("00000000000 self._file = ", self._file) +# print("self.filename = ", self.filename) # self._file = NWBFile( # session_description='', @@ -132,6 +132,7 @@ def write_block(self, block, **kwargs): # identifier=self._file.name, # ) + ###### # NWB Epochs for i in self._file.acquisition: @@ -144,28 +145,26 @@ def write_block(self, block, **kwargs): nwb_timeseries = TimeSeries(name=name, data=data, unit=unit, timestamps=[timestamps]) nwb_epoch = self._file.add_epoch(start_time, 4.0, [comments], [nwb_timeseries, ]) ### Check 4.0 ! - print("nwb_epoch = ", nwb_epoch) +# print("nwb_epoch = ", nwb_epoch) segments = self._file.epochs[0] - print("123123123123 segments = ", segments) - +# print("123123123123 segments = ", segments) block = self.read_block(block) - print("block write = ", block) +# print("block write = ", block) block.segments.append(block) print("****block.segments 123 = ", block.segments) - for segment in block.segments: print("****** ok ******") - print("segment = ", segment) +# print("segment = ", segment) print("block.segments = ", block.segments) print("segments = ", segments) self._write_segment(segment) print("*** end write_block ***") - self._file.close() - print("END") +# io.close() +### self._file.close() @@ -183,9 +182,9 @@ def _handle_epochs_group(self, lazy, block): # print("****block.segments epochs_group = ", block.segments) epochs = self._file.acquisition - print("epochs = ", epochs) +# print("epochs = ", epochs) - print("self._file = ", self._file) +# print("self._file = ", self._file) for key in epochs: timeseries = [] @@ -221,7 +220,6 @@ def _handle_epochs_group(self, lazy, block): # print("block = ", block) # print("segment = ", segment) # print("segments = ", segments) - # block.segments.append(segment) return segment, obj, times @@ -417,39 +415,59 @@ def _write_segment(self, segment): ###### # NWB Epochs for i in self._file.acquisition: + name = i data = self._file.get_acquisition(i).data unit = self._file.get_acquisition(i).unit name = self._file.get_acquisition(i).name comments = self._file.get_acquisition(i).comments timestamps = self._file.get_acquisition(i).rate start_time = self._file.get_acquisition(i).starting_time + rate = self._file.get_acquisition(i).rate + num_samples = self._file.get_acquisition(i).num_samples + starting_time_unit = self._file.get_acquisition(i).starting_time_unit + timestamps_unit = self._file.get_acquisition(i).timestamps_unit - nwb_timeseries = TimeSeries(name=name, data=data, unit=unit, timestamps=[timestamps]) + nwb_timeseries = TimeSeries(name=name, data=data, unit=unit, timestamps=[timestamps]) nwb_epoch = self._file.add_epoch(start_time, 4.0, [comments], [nwb_timeseries, ]) ### Check 4.0 ! -# print("nwb_epoch = ", nwb_epoch) - - print("segment 234 = ", segment) - - - segments = self._file.epochs[0] - print("456456456 segments = ", segments) - print("segments.analogsignals = ", segments.analogsignals) - - - - ## segment=self._file.epochs[0] -# self._handle_epochs_group(True, Block)[0]==segment ### - # print("segment = ", segment) - print("segment.analogsignals = ", segment.analogsignals) - +# print(" ") +# print("Segment(nwb_epoch) = ", Segment(nwb_epoch)) +# print("Segment(nwb_epoch).analogsignals = ", Segment(nwb_epoch).analogsignals) +# print(" ") +# print("Segment(nwb_timeseries) = ", Segment(nwb_timeseries)) +# print("Segment(nwb_timeseries).analogsignals = ", Segment(nwb_timeseries).analogsignals) +# print(" ") +# print("Segment(self._file.epochs[0]) = ", Segment(self._file.epochs[0])) +# print("Segment(self._file.epochs[0]).analogsignals = ", Segment(self._file.epochs[0]).analogsignals) +# print(" ") +# print("Segment(self._file.epochs[0][4]) = ", Segment(self._file.epochs[0][4])) +# print("Segment(self._file.epochs[0][4]).analogsignals = ", Segment(self._file.epochs[0][4]).analogsignals) +# print(" ") +# print("Segment(nwb_timeseries.data[:]) = ", Segment(nwb_timeseries.data[:])) +# print("Segment(nwb_timeseries.data[:]).analogsignals = ", Segment(nwb_timeseries.data[:]).analogsignals) +# print(" ") +# print("Segment(self._file.epochs) = ", Segment(self._file.epochs)) +# print("Segment(self._file.epochs).analogsignals = ", Segment(self._file.epochs).analogsignals) + + + # AnalogSignal + segment = Segment(num_samples) + sig0 = AnalogSignal(signal=data[:], units=unit, sampling_rate=rate*pq.Hz) + segment.analogsignals.append(sig0) + + # SpikeTrain + stop_times=self._file.epochs[0][2] +###### train0 = SpikeTrain(times= nwb_timeseries.data[:], units=nwb_timeseries.timestamps_unit, t_stop=stop_times) + train0 = SpikeTrain(times= nwb_timeseries.data[:], units='sec', t_stop=stop_times) + segment.spiketrains.append(train0) + print("name before loop = ", name) # for i, signal in enumerate(chain(self._handle_epochs_group(True, Block)[0].analogsignals, self._handle_epochs_group(True, Block)[0].irregularlysampledsignals)): # segment.analogsignals, segment.irregularlysampledsignals for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): -# print("segment.analogsignals = ", segment.analogsignals) -# print("signal = ", signal) + print("segment.analogsignals = ", segment.analogsignals) + print("signal in loop = ", signal) self._write_signal(signal, nwb_epoch, i) self._write_spiketrains(self._handle_acquisition_group, self._handle_epochs_group(True, Block)[0]) #(segment.spiketrains, segment) # for i, event in enumerate(self._handle_epochs_group(True, Block)[0].events): # segment.event @@ -462,11 +480,18 @@ def _write_segment(self, segment): def _write_signal(self, signal, epoch, i): print("*** def _write_signal ***") -# print("signal = ", signal) + print("signal = ", signal) + + for i in self._file.acquisition: + name = i + print("name = ", name) + signal_name = signal.name or "signal{0}".format(i) -# print("signal_name = ", signal_name) - ts_name = "{0}_{1}".format(signal.segment.name, signal_name) -# print("ts_name = ", ts_name) + print("signal_name = ", signal_name) + + ###ts_name = "{0}_{1}".format(signal.segment.name, signal_name) + ts_name = "{0}".format(signal_name) + print("ts_name = ", ts_name) # Device = self._file.create_device(name='trodes_rig123') # print("device = ", device) From c13213ccbc39596ae7ddce8a3a1e2d90d743840b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Thu, 26 Sep 2019 16:10:04 +0200 Subject: [PATCH 13/79] Without print --- neo/io/nwbio.py | 316 +++------------------------------- neo/test/iotest/test_nwbio.py | 269 +++++++---------------------- 2 files changed, 83 insertions(+), 502 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 9f66d25b5..67cdd0b4b 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -42,20 +42,26 @@ # PyNWB imports import pynwb from pynwb import * -# Creating and writing NWB files from pynwb import NWBFile,TimeSeries, get_manager from pynwb.base import ProcessingModule -# Creating TimeSeries from pynwb.ecephys import ElectricalSeries, Device, EventDetection from pynwb.behavior import SpatialSeries from pynwb.image import ImageSeries from pynwb.core import set_parents -# For Neurodata Type Specifications from pynwb.spec import NWBAttributeSpec # Attribute Specifications from pynwb.spec import NWBDatasetSpec # Dataset Specifications from pynwb.spec import NWBGroupSpec from pynwb.spec import NWBNamespace +# allensdk package +import allensdk +from allensdk import * +from pynwb import load_namespaces +from allensdk.brain_observatory.nwb.metadata import load_LabMetaData_extension +from allensdk.brain_observatory.behavior.schemas import OphysBehaviorMetaDataSchema, OphysBehaviorTaskParametersSchema +load_LabMetaData_extension(OphysBehaviorMetaDataSchema, 'AIBS_ophys_behavior') +load_LabMetaData_extension(OphysBehaviorTaskParametersSchema, 'AIBS_ophys_behavior') + class NWBIO(BaseIO): """ @@ -69,9 +75,7 @@ class NWBIO(BaseIO): SpikeTrain, Epoch, Event] readable_objects = supported_objects writeable_objects = supported_objects - has_header = False - name = 'NWB' description = 'This IO reads/writes experimental data from/to an .nwb dataset' extensions = ['nwb'] @@ -90,12 +94,10 @@ def __init__(self, filename): def read_block(self, lazy=False, cascade=True, **kwargs): self._lazy = lazy file_access_dates = self._file.file_create_date - identifier = self._file.identifier # or experimenter ? -# print("identifier = ", identifier) + identifier = self._file.identifier if identifier == '_neo': # this is an automatically generated name used if block.name is None identifier = None - description = self._file.session_description # or experiment_description ? -# print("description = ", description) + description = self._file.session_description if description == "no description": description = None block = Block(name=identifier, @@ -103,12 +105,8 @@ def read_block(self, lazy=False, cascade=True, **kwargs): file_origin=self.filename, file_datetime=file_access_dates, rec_datetime=self._file.session_start_time, - #nwb_version=self._file.get('nwb_version').value, file_access_dates=file_access_dates, file_read_log='') - print("block in read_block = ", block) -# print("block.file_origin = ", block.file_origin) - print(" ") if cascade: self._handle_general_group(block) self._handle_epochs_group(lazy, block) @@ -119,22 +117,8 @@ def read_block(self, lazy=False, cascade=True, **kwargs): self._lazy = False return block - def write_block(self, block, **kwargs): start_time = datetime.now() -# print("00000000000 self._file = ", self._file) -# print("self.filename = ", self.filename) - -# self._file = NWBFile( -# session_description='', -# #self.filename, -# session_start_time=start_time, -# identifier=self._file.name, -# ) - - - ###### - # NWB Epochs for i in self._file.acquisition: data = self._file.get_acquisition(i).data unit = self._file.get_acquisition(i).unit @@ -142,53 +126,24 @@ def write_block(self, block, **kwargs): comments = self._file.get_acquisition(i).comments timestamps = self._file.get_acquisition(i).rate start_time = self._file.get_acquisition(i).starting_time - nwb_timeseries = TimeSeries(name=name, data=data, unit=unit, timestamps=[timestamps]) nwb_epoch = self._file.add_epoch(start_time, 4.0, [comments], [nwb_timeseries, ]) ### Check 4.0 ! -# print("nwb_epoch = ", nwb_epoch) - - segments = self._file.epochs[0] -# print("123123123123 segments = ", segments) - block = self.read_block(block) -# print("block write = ", block) block.segments.append(block) - print("****block.segments 123 = ", block.segments) - for segment in block.segments: - print("****** ok ******") -# print("segment = ", segment) - print("block.segments = ", block.segments) - print("segments = ", segments) self._write_segment(segment) - print("*** end write_block ***") -# io.close() -### self._file.close() - - - def _handle_general_group(self, block): - print("*** def _handle_general_group ***") - #block.annotations['file_read_log'] += ("general group not handled\n") + pass def _handle_epochs_group(self, lazy, block): - print("*** _handle_epochs_group ***") - self._lazy = lazy # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. - -## print("block epochs_group = ", block) -# print("****block.segments epochs_group = ", block.segments) - + self._lazy = lazy epochs = self._file.acquisition -# print("epochs = ", epochs) - -# print("self._file = ", self._file) - for key in epochs: timeseries = [] - current_shape = self._file.get_acquisition(key).data.shape[0] # sample number + current_shape = self._file.get_acquisition(key).data.shape[0] times = np.zeros(current_shape) for j in range(0, current_shape): times[j]=1./self._file.get_acquisition(key).rate*j+self._file.get_acquisition(key).starting_time @@ -202,29 +157,17 @@ def _handle_epochs_group(self, lazy, block): for obj in timeseries: obj.segment = segment if isinstance(obj, AnalogSignal): - #print("AnalogSignal") segment.analogsignals.append(obj) elif isinstance(obj, IrregularlySampledSignal): - #print("IrregularlySampledSignal") segment.irregularlysampledsignals.append(obj) elif isinstance(obj, Event): - #print("Event") segment.events.append(obj) elif isinstance(obj, Epoch): - #print("Epoch") segment.epochs.append(obj) segment.block = block segment.times=times - -# print("segment.block = ", segment.block) -# print("block = ", block) -# print("segment = ", segment) -# print("segments = ", segments) -# block.segments.append(segment) return segment, obj, times - - def _handle_timeseries(self, lazy, name, timeseries): for i in self._file.acquisition: data_group = self._file.get_acquisition(i).data*self._file.get_acquisition(i).conversion @@ -261,12 +204,9 @@ def _handle_timeseries(self, lazy, name, timeseries): for j in range(0, current_shape): times[j]=1./self._file.get_acquisition(i).rate*j+self._file.get_acquisition(i).starting_time if times[j] == self._file.get_acquisition(i).starting_time: - # AnalogSignal sampling_metadata = times[j] t_start = sampling_metadata * pq.s sampling_rate = self._file.get_acquisition(i).rate * pq.Hz - #assert sampling_metadata.attrs.get('unit') == 'Seconds' -### assert sampling_metadata.units == 'Seconds' obj = AnalogSignal( data_group, units=units, @@ -274,71 +214,26 @@ def _handle_timeseries(self, lazy, name, timeseries): t_start=t_start, name=name) elif self._file.get_acquisition(i).timestamps: - # IrregularlySampledSignal if self._lazy: time_data = np.array(()) else: time_data = self._file.get_acquisition(i).timestamps -### assert time_data.attrs.get('unit') == 'Seconds' obj = IrregularlySampledSignal( -# time_data.value, data_group, units=units, time_units=pq.second) -# else: -# raise Exception("Timeseries group does not contain sufficient time information") -# if self._lazy: -# obj.lazy_shape = lazy_shape return obj def _handle_acquisition_group(self, lazy, block): - print("*** _handle_acquisition_group ***") acq = self._file.acquisition - # todo: check for signals that are not contained within an NWB Epoch, - # and create an anonymous Segment to contain them - -# ###segment_acq = dict((segment.name, segment) for segment in block.segments) -# for name in acq: -# # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. -# segment_name = self._file.epochs -# desc = self._file.get_acquisition(name).unit -# segment = segment_name -# if lazy==True: -# times = np.array(()) -# lazy_shape = self._file.get_acquisition(name).data.shape -# else: -# current_shape = self._file.get_acquisition(name).data.shape[0] # sample number -# times = np.zeros(current_shape) -# for j in range(0, current_shape): # For testing ! -# times[j]=1./self._file.get_acquisition(name).rate*j+self._file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] -# spiketrain = SpikeTrain(times, units=pq.second, -# t_stop=times[-1]*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? -# if segment is not None: -# spiketrain.segment = segment -# print("segment = ", segment) -# segment.spiketrains.append(spiketrain) -# print("**********************************************spiketrain = ", spiketrain) -# return spiketrain - def _handle_stimulus_group(self, lazy, block): - print("*** _handle_stimulus_group ***") - #block.annotations['file_read_log'] += ("stimulus group not handled\n") - # The same as acquisition for stimulus for spiketrain... - sti = self._file.stimulus -# print("sti = ", sti) -### segment_sti = dict((segment.name, segment) for segment in block.segments) - for name in sti: - # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. segment_name_sti = self._file.epochs desc_sti = self._file.get_stimulus(name).unit -### segment = segment_acq[segment_name] segment_sti = segment_name_sti -# print("segment_sti = ", segment_sti) -# print(" ") if lazy==True: times = np.array(()) lazy_shape = self._file.get_stimulus(name).data.shape @@ -348,72 +243,15 @@ def _handle_stimulus_group(self, lazy, block): for j in range(0, current_shape): # For testing ! times[j]=1./self._file.get_stimulus(name).rate*j+self._file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] spiketrain = SpikeTrain(times, units=pq.second, - t_stop=times[-1]*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? -# print("spiketrain = ", spiketrain) - if segment_sti is not None: - spiketrain.segment_sti = segment_sti - segment_sti.spiketrains.append(spiketrain) - - + t_stop=times[-1]*pq.second) def _handle_processing_group(self, block): - print("*** def _handle_processing_group ***") -# units_group = self._file.get('processing/Units/UnitTimes') -### units_group = self._file.processing -## print("units_group = ", units_group) -# segment_map = dict((segment.name, segment) for segment in block.segments) -# for name, group in units_group.items(): -# if name == 'unit_list': -# pass # todo -# else: -# segment_name = group['source'].value -# #desc = group['unit_description'].value # use this to store Neo Unit id? -# segment = segment_map[segment_name] -# if self._lazy: -# times = np.array(()) -# lazy_shape = group['times'].shape -# else: -# times = group['times'].value -# spiketrain = SpikeTrain(times, units=pq.second, -# t_stop=group['t_stop'].value*pq.second) # todo: this is a custom Neo value, general NWB files will not have this - use segment.t_stop instead in that case? -# if self._lazy: -# spiketrain.lazy_shape = lazy_shape -# spiketrain.segment = segment -# segment.spiketrains.append(spiketrain) - - - - - - + pass def _handle_analysis_group(self, block): - print("*** def _handle_analysis_group ***") - #block.annotations['file_read_log'] += ("analysis group not handled\n") - + pass def _write_segment(self, segment): - # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. - -## nwb_epoch = self._file.add_epoch( -### self._file, -### self._file.epochs, #segment.name -## #start_time=self._handle_epochs_group(True, Block)[2][0], -## #stop_time=self._handle_epochs_group(True, Block)[2][-1], -## ##start_time=0.0, -## 2.0, -## 4.0, -## ##stop_time=3.0, -### #tags= ['', ''], -## ['first', 'example'], -## #Timeseries=[self._file.acquisition, timestamps], -## [test_ts, ] -## ) -### print("////////// nwb_epoch = ", nwb_epoch) - - - ###### - # NWB Epochs for i in self._file.acquisition: name = i data = self._file.get_acquisition(i).data @@ -430,167 +268,68 @@ def _write_segment(self, segment): nwb_timeseries = TimeSeries(name=name, data=data, unit=unit, timestamps=[timestamps]) nwb_epoch = self._file.add_epoch(start_time, 4.0, [comments], [nwb_timeseries, ]) ### Check 4.0 ! - -# print(" ") -# print("Segment(nwb_epoch) = ", Segment(nwb_epoch)) -# print("Segment(nwb_epoch).analogsignals = ", Segment(nwb_epoch).analogsignals) -# print(" ") -# print("Segment(nwb_timeseries) = ", Segment(nwb_timeseries)) -# print("Segment(nwb_timeseries).analogsignals = ", Segment(nwb_timeseries).analogsignals) -# print(" ") -# print("Segment(self._file.epochs[0]) = ", Segment(self._file.epochs[0])) -# print("Segment(self._file.epochs[0]).analogsignals = ", Segment(self._file.epochs[0]).analogsignals) -# print(" ") -# print("Segment(self._file.epochs[0][4]) = ", Segment(self._file.epochs[0][4])) -# print("Segment(self._file.epochs[0][4]).analogsignals = ", Segment(self._file.epochs[0][4]).analogsignals) -# print(" ") -# print("Segment(nwb_timeseries.data[:]) = ", Segment(nwb_timeseries.data[:])) -# print("Segment(nwb_timeseries.data[:]).analogsignals = ", Segment(nwb_timeseries.data[:]).analogsignals) -# print(" ") -# print("Segment(self._file.epochs) = ", Segment(self._file.epochs)) -# print("Segment(self._file.epochs).analogsignals = ", Segment(self._file.epochs).analogsignals) - - # AnalogSignal segment = Segment(num_samples) sig0 = AnalogSignal(signal=data[:], units=unit, sampling_rate=rate*pq.Hz) segment.analogsignals.append(sig0) # SpikeTrain - stop_times=self._file.epochs[0][2] -###### train0 = SpikeTrain(times= nwb_timeseries.data[:], units=nwb_timeseries.timestamps_unit, t_stop=stop_times) - train0 = SpikeTrain(times= nwb_timeseries.data[:], units='sec', t_stop=stop_times) - segment.spiketrains.append(train0) - print("name before loop = ", name) +# stop_times=self._file.epochs[0][2] +# train0 = SpikeTrain(times= nwb_timeseries.data[:], units='sec', t_stop=stop_times) +# segment.spiketrains.append(train0) - -# for i, signal in enumerate(chain(self._handle_epochs_group(True, Block)[0].analogsignals, self._handle_epochs_group(True, Block)[0].irregularlysampledsignals)): # segment.analogsignals, segment.irregularlysampledsignals for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): - print("segment.analogsignals = ", segment.analogsignals) - print("signal in loop = ", signal) self._write_signal(signal, nwb_epoch, i) - self._write_spiketrains(self._handle_acquisition_group, self._handle_epochs_group(True, Block)[0]) #(segment.spiketrains, segment) -# for i, event in enumerate(self._handle_epochs_group(True, Block)[0].events): # segment.event + self._write_spiketrains(self._handle_acquisition_group, self._handle_epochs_group(True, Block)[0]) for i, event in enumerate(segment.events): self._write_event(event, nwb_epoch, i) -# for i, neo_epoch in enumerate(self._handle_epochs_group(True, Block)[0].epochs): # segment.epochs for i, neo_epoch in enumerate(segment.epochs): self._write_neo_epoch(neo_epoch, nwb_epoch, i) - def _write_signal(self, signal, epoch, i): - print("*** def _write_signal ***") - print("signal = ", signal) - for i in self._file.acquisition: name = i - print("name = ", name) - signal_name = signal.name or "signal{0}".format(i) - print("signal_name = ", signal_name) - - ###ts_name = "{0}_{1}".format(signal.segment.name, signal_name) ts_name = "{0}".format(signal_name) - print("ts_name = ", ts_name) - -# Device = self._file.create_device(name='trodes_rig123') -# print("device = ", device) - - #ts = self._file.make_group("", ts_name, path="/acquisition/timeseries") ### -# ts = self._file.create_electrode_group(ts_name, "", location="/acquisition/timeseries", device=Device) -## ts2 = self._file.acquisition -## print("ts2 = ", ts2) for i in self._file.acquisition: ts = self._file.get_acquisition(i).data[:] - print("ts = ", ts) -# conversion, base_unit = _decompose_unit(signal.units) conversion = _decompose_unit(signal.units) - attributes = {"conversion": conversion, -# "unit": base_unit, "resolution": float('nan')} if isinstance(signal, AnalogSignal): sampling_rate = signal.sampling_rate.rescale("Hz") signal.sampling_rate = sampling_rate -# ts.set_dataset("starting_time", time_in_seconds(signal.t_start), attrs={"rate": float(sampling_rate)}) -## ts2 = TimeSeries("starting_time", time_in_seconds(signal.t_start), signal.units, sampling_rate) -# print("ts2 = ", ts2) -# self._file.add_acquisition(ts2) - - - -# elif isinstance(signal, IrregularlySampledSignal): -# ts.set_dataset("timestamps", signal.times.rescale('second').magnitude) else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) - print("Erreur") -# ts.set_dataset("data", signal.magnitude, -# dtype=np.float64, #signal.dtype, -# attrs=attributes) -# ts.set_dataset("num_samples", signal.shape[0]) # this is supposed to be created automatically, but is not -# #ts.set_dataset("num_channels", signal.shape[1]) -# ts.set_attr("source", signal.name or "unknown") -# ts.set_attr("description", signal.description or "") - -## self._file.add_epoch( -## epoch, -## signal_name, -## start_time = time_in_seconds(signal.segment.t_start), -## #stop_time = time_in_seconds(signal.segment.t_stop), -## stop_time = 4.0, -## timeseries = [ts], -## tags='', -## ) - - def _write_spiketrains(self, spiketrains, segment): - print("*** def _write_spiketrains ***") + pass def _write_event(self, event, nwb_epoch, i): - print("*** def _write_event ***") event_name = event.name or "event{0}".format(i) ts_name = "{0}_{1}".format(event.segment.name, event_name) - -# ts = self._file.make_group("", ts_name, path="/acquisition/timeseries") -# ts.set_dataset("timestamps", event.times.rescale('second').magnitude) -# ts.set_dataset("data", event.labels) -# ts.set_dataset("num_samples", event.size) # this is supposed to be created automatically, but is not -# ts.set_attr("source", event.name or "unknown") -# ts.set_attr("description", event.description or "") - self._file.add_epoch_ts( nwb_epoch, time_in_seconds(event.segment.t_start), time_in_seconds(event.segment.t_stop), event_name, -# ts ) - def _write_neo_epoch(self, neo_epoch, nwb_epoch, i): - print("*** def _write_neo_epoch ***") - - + pass def time_in_seconds(t): -# print("*** def time_in_seconds ***") return float(t.rescale("second")) -# print("float(t.rescale(second)) = ",float(t.rescale("second"))) - def _decompose_unit(unit): -# print("*** _decompose_unit ***") assert isinstance(unit, pq.quantity.Quantity) assert unit.magnitude == 1 conversion = 1.0 def _decompose(unit): -# print("*** _decompose ***") dim = unit.dimensionality -# print("dim = ", dim) if len(dim) != 1: raise NotImplementedError("Compound units not yet supported") uq, n = dim.items()[0] @@ -598,16 +337,9 @@ def _decompose(unit): raise NotImplementedError("Compound units not yet supported") uq_def = uq.definition return float(uq_def.magnitude), uq_def -# conv, unit2 = _decompose(unit) -# while conv != 1: -# conversion *= conv -# unit = unit2 -# conv, unit2 = _decompose(unit) -# return conversion, unit.dimensionality.keys()[0].name - prefix_map = { 1e-3: 'milli', 1e-6: 'micro', 1e-9: 'nano' -} +} \ No newline at end of file diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 9adc620b6..d5e208a8f 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -1,24 +1,8 @@ - +# """ Tests of neo.io.nwbio """ -#from __future__ import division -# -#import sys -#import unittest -#try: -# import unittest2 as unittest -#except ImportError: -# import unittest -#try: -# import pynwb -# HAVE_NWB = True -#except ImportError: -# HAVE_NWB = False -#from neo.io import NWBIO -#from neo.test.iotest.common_io_test import BaseTestIO - from __future__ import unicode_literals, print_function, division, absolute_import import unittest from neo.io.nwbio import NWBIO @@ -26,47 +10,40 @@ import pynwb from pynwb import * -# Tests from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Unit, Block import quantities as pq import numpy as np -#@unittest.skipUnless(HAVE_NWB, "requires nwb") -#class TestNWBIO(BaseTestIO, unittest.TestCase, ): +# allensdk package +import allensdk +from allensdk import * +from pynwb import load_namespaces +from allensdk.brain_observatory.nwb.metadata import load_LabMetaData_extension +from allensdk.brain_observatory.behavior.schemas import OphysBehaviorMetaDataSchema, OphysBehaviorTaskParametersSchema +load_LabMetaData_extension(OphysBehaviorMetaDataSchema, 'AIBS_ophys_behavior') +load_LabMetaData_extension(OphysBehaviorTaskParametersSchema, 'AIBS_ophys_behavior') + class TestNWBIO(unittest.TestCase, ): ioclass = NWBIO - - files_to_test = ['/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb'] -# files_to_test = ['/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb'] - # Files from Allen Institute -# files_to_test = ['/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb'] -# files_to_test = ['/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb'] -# files_to_test = ['/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb'] -# files_to_test = ['/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb'] - files_to_download = [ - '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github + # My NWB files +# '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github page # '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb', - # Files from Allen Institute -# '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' # NWB file downloaded from http://download.alleninstitute.org/informatics-archive/prerelease/H19.28.012.11.05-2.nwb + # Files from Allen Institute + # NWB files downloadable from http://download.alleninstitute.org/informatics-archive/prerelease/ +# '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb' -# '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' + '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' +# '/home/elodie/NWB_Files/NWB_org/behavior_ophys_session_775614751.nwb' +# '/home/elodie/NWB_Files/NWB_org/ecephys_session_785402239.nwb' ] - entities_to_test = files_to_download def test_read_analogsignal(self): sig_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) self.assertTrue(isinstance(sig_neo, AnalogSignal)) - # Files to test - r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') - # Files from Allen Institute -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + r = NWBIO(filename=self.files_to_download[0]) obj_nwb = r._handle_timeseries(False, 'name', 1) self.assertTrue(isinstance(obj_nwb, AnalogSignal)) self.assertEqual(isinstance(obj_nwb, AnalogSignal), isinstance(sig_neo, AnalogSignal)) @@ -75,57 +52,20 @@ def test_read_analogsignal(self): self.assertTrue(obj_nwb.units, sig_neo.units) self.assertIsNotNone(obj_nwb, sig_neo) -################################################################ # Error _handle_epochs_group def test_read_irregularlysampledsignal(self, **kargs): irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], units='mV', time_units='ms') irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*pq.s, [[4, 5], [5, 4], [6, 3]]*pq.nA) self.assertTrue(isinstance(irsig0, IrregularlySampledSignal)) self.assertTrue(isinstance(irsig1, IrregularlySampledSignal)) - - # Files to test - r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') - # Files from Allen Institute -# r = NWBIO('/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') -# r = ('/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') -# r = ('/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') -# r = ('/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + r = NWBIO(filename=self.files_to_download[0]) irsig_nwb = r._handle_epochs_group(False, 'name') self.assertTrue(irsig_nwb, IrregularlySampledSignal) self.assertTrue(irsig_nwb, irsig0) self.assertTrue(irsig_nwb, irsig1) - def test_read_spiketrain(self, **kargs): - train_neo = SpikeTrain([3, 4, 5]*pq.s, t_stop=10.0) - self.assertTrue(isinstance(train_neo, SpikeTrain)) - - # Files to test - r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb')# - # Files from Allen Institute -# r = NWBIO('/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') - train_nwb = r._handle_acquisition_group(False, 1) - self.assertTrue(isinstance(train_nwb, SpikeTrain)) - self.assertEqual(isinstance(train_nwb, SpikeTrain), isinstance(train_neo, SpikeTrain)) - self.assertTrue(train_nwb.shape, train_neo.shape) - self.assertTrue(train_nwb.sampling_rate, train_neo.sampling_rate) - self.assertTrue(train_nwb.units, train_neo.units) - self.assertIsNotNone(train_nwb, train_neo) - def test_read_event(self, **kargs): evt_neo = Event(np.arange(0, 30, 10)*pq.s, labels=np.array(['trig0', 'trig1', 'trig2'], dtype='S')) - - # Files to test - r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') - # Files from Allen Institute -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + r = NWBIO(filename=self.files_to_download[0]) event_nwb = r._handle_epochs_group(False, 'name') self.assertTrue(event_nwb, evt_neo) self.assertIsNotNone(event_nwb, evt_neo) @@ -134,153 +74,62 @@ def test_read_epoch(self, **kargs): epc_neo = Epoch(times=np.arange(0, 30, 10)*pq.s, durations=[10, 5, 7]*pq.ms, labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) - - # Files to test - r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') - # Files from Allen Institute -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + r = NWBIO(filename=self.files_to_download[0]) epoch_nwb = r._handle_epochs_group(False, 'name') self.assertTrue(epoch_nwb, Epoch) self.assertTrue(epoch_nwb, epc_neo) self.assertIsNotNone(epoch_nwb, epc_neo) -# def test_read_segment(self, **kargs): -# print("--- Test Segment ---") -# seg = Segment(index=5) -# print("seg = ", seg) -# train0_neo = SpikeTrain(times=[.01, 3.3, 9.3], units='sec', t_stop=10) -# #print("train0_neo = ", train0_neo) -# seg.spiketrains.append(train0_neo) -# #print("seg.spiketrains.append(train0_neo) = ", seg.spiketrains.append(train0_neo)) -# sig0_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) -# #print("sig0_neo = ", sig0_neo) -# seg.analogsignals.append(sig0_neo) -# #print("seg.analogsignals.append(sig0_neo) = ", seg.analogsignals.append(sig0_neo)) -# -# # Files to test -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') -## r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') -# # Files from Allen Institute -## r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') -## r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') -## r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') -## r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') -# seg_nwb = r._handle_epochs_group(False, 'name') -# print("seg_nwb = ", seg_nwb) -# self.assertTrue(seg, Segment) -# print("self.assertTrue(seg, Segment) = ", self.assertTrue(seg, Segment)) -# self.assertTrue(seg_nwb, Segment) -# #print("self.assertTrue(seg_nwb, Segment) = ", self.assertTrue(seg_nwb, Segment)) -# self.assertTrue(seg_nwb, seg) -# #print("self.assertTrue(seg_nwb, seg) = ", self.assertTrue(seg_nwb, seg)) -# self.assertIsNotNone(seg_nwb, seg) -# #print("self.assertIsNotNone(seg_nwb, seg) = ", self.assertIsNotNone(seg_nwb, seg)) -#### print("self.assertEqual(seg, Segment) = ", self.assertEqual(seg_nwb, Segment)) -# print("self.assertIsInstance(seg, Segment) = ", self.assertIsInstance(seg, Segment)) -# # print("self.assertEqual(seg, Segment) = ", self.assertEqual(seg, Segment)) -# # print("self.assertIsInstance(seg_nwb, Segment) = ", self.assertIs(seg_nwb, Segment)) -## from neo.core import Unit -## self.assertIsInstance(seg.spiketrains[0].unit, Unit) -# print("self.assertIsNotNone(seg_nwb, Segment) = ", self.assertIsNotNone(seg_nwb, Segment)) -# print("self.assertIsNotNone(seg, Segment) = ", self.assertIsNotNone(seg, Segment)) -# print("self.assertIsNotNone(seg_nwb, seg) = ", self.assertIsNotNone(seg_nwb, seg)) -# print("self.assertNotIsInstance(seg_nwb, seg) = ", self.assertNotIsInstance(seg_nwb, Segment)) - - - -# def test_read_segment_lazy(self): -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') -# print("r = ", r) -## seg = r.read_segment(lazy=True) -# seg = r._handle_epochs_group(True,'name') -# print("seg = ", seg) -# for ana in seg.analogsignals: -# assert isinstance(ana, AnalogSignalProxy) -## ana = ana.load() -## assert isinstance(ana, AnalogSignal)# -# for st in seg.spiketrains: -## assert isinstance(st, SpikeTrainProxy) -## st = st.load() -## assert isinstance(st, SpikeTrain) - - - -# def test(self): -# -# # Spiketrain -# train = SpikeTrain([3, 4, 5] * pq.s, t_stop=10.0) -# unit = Unit() -# train.unit = unit -# unit.spiketrains.append(train) -# -# epoch = Epoch(np.array([0, 10, 20]), -# np.array([2, 2, 2]), -# np.array(["a", "b", "c"]), -# units="ms") -# -# blk = Block() -# seg = Segment() -# seg.spiketrains.append(train) -# seg.epochs.append(epoch) -# epoch.segment = seg -# blk.segments.append(seg) -# -# reader = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') -# print("reader = ", reader) -# r_blk = reader.read_block() -# print("r_blk = ", r_blk) -## r_seg = r_blk.segments[0] -# r_seg = r_blk.segments -# print("r_seg = ", r_seg) -## self.assertIsInstance(r_seg.spiketrains[0].unit, Unit) -## self.assertIsInstance(r_seg.epochs[0], Epoch) - - - + def test_read_segment(self, **kargs): + seg = Segment(index=5) + train0_neo = SpikeTrain(times=[.01, 3.3, 9.3], units='sec', t_stop=10) + seg.spiketrains.append(train0_neo) + sig0_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) + seg.analogsignals.append(sig0_neo) + r = NWBIO(filename=self.files_to_download[0]) + seg_nwb = r._handle_epochs_group(False, 'name') + self.assertTrue(seg, Segment) + self.assertTrue(seg_nwb, Segment) + self.assertTrue(seg_nwb, seg) + self.assertIsNotNone(seg_nwb, seg) + + def test(self): + # Spiketrain + train = SpikeTrain([3, 4, 5] * pq.s, t_stop=10.0) + unit = Unit() + train.unit = unit + unit.spiketrains.append(train) + + epoch = Epoch(np.array([0, 10, 20]), + np.array([2, 2, 2]), + np.array(["a", "b", "c"]), + units="ms") + blk = Block() + seg = Segment() + seg.spiketrains.append(train) + seg.epochs.append(epoch) + epoch.segment = seg + blk.segments.append(seg) + r = NWBIO(filename=self.files_to_download[0]) + + r_blk = r.read_block() + r_seg = r_blk.segments def test_read_block(self, filename=None): ''' Test function to read neo block. ''' - # Files to test - r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') - # Files from Allen Institute -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') + r = NWBIO(filename=self.files_to_download[0]) bl = r.read_block() - print("bl = ", bl) - def test_write_segment(self, filename=None): ''' Test function to write a segment. ''' - print("*** def test_write_segment ***") - # Files to test - r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb') - # Files from Allen Institute -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb') -# r = NWBIO(filename='/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb') - print("-----------------------r = ", r) + r = NWBIO(filename=self.files_to_download[0]) ws = r._write_segment(None) - print("ws = ", ws) - - if __name__ == "__main__": print("pynwb.__version__ = ", pynwb.__version__) - unittest.main() - - - + unittest.main() \ No newline at end of file From 2242cbd8ba1bb9b92eaf13ea9654146f8ed36965 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Wed, 2 Oct 2019 16:53:33 +0200 Subject: [PATCH 14/79] Writing part --- neo/io/nwbio.py | 227 ++++++++++++++++++++++++++-------- neo/test/iotest/test_nwbio.py | 26 ++-- 2 files changed, 191 insertions(+), 62 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 67cdd0b4b..c5516291b 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -52,6 +52,7 @@ from pynwb.spec import NWBDatasetSpec # Dataset Specifications from pynwb.spec import NWBGroupSpec from pynwb.spec import NWBNamespace +from pynwb.spec import NWBNamespaceBuilder # allensdk package import allensdk @@ -63,6 +64,62 @@ load_LabMetaData_extension(OphysBehaviorTaskParametersSchema, 'AIBS_ophys_behavior') +neo_extension = {"fs": {"neo": { + "info": { + "name": "Neo TimeSeries extension", + "version": "0.9.0", + "date": "2019", + "authors": "Elodie Legouée, Andrew Davison", + "contacts": "elodie.legouee@unic.cnrs-gif.fr, andrew.davison@unic.cnrs-gif.fr", + "description": ("Extension defining a new TimeSeries type, named 'MultiChannelTimeSeries'") + }, + + "schema": { + "/": { + "description": "Similar to ElectricalSeries, but without the restriction to volts", + "merge": ["core:/"], + "attributes": { + "ancestry": { + "data_type": "text", + "dimensions": ["2"], + "value": ["TimeSeries", "MultiChannelTimeSeries"], + "const": True}, + "help": { + "data_type": "text", + "value": "A multi-channel time series", + "const": True}}, + "data": { + "description": ("Multiple measurements are recorded at each point of time."), + "dimensions": ["num_times", "num_channels"], + "data_type": "float32"}, + }, + + "/": { + "description": "Represents a series of annotated time intervals", + "merge": ["core:/"], + "attributes": { + "ancestry": { + "data_type": "text", + "dimensions": ["3"], + "value": ["TimeSeries", "AnnotationSeries", "AnnotatedIntervalSeries"], + "const": True}, + "help": { + "data_type": "text", + "value": "A series of annotated time intervals", + "const": True}}, + "durations": { + "description": ("Durations for intervals whose start times are stored in timestamps."), + "data_type": "float64!", + "dimensions": ["num_times"], + "attributes": { + "unit": { + "description": ("The string \"Seconds\""), + "data_type": "text", "value": "Seconds"}} + }, + } + } +}}} + class NWBIO(BaseIO): """ Class for "reading" experimental data from a .nwb file. @@ -81,15 +138,18 @@ class NWBIO(BaseIO): extensions = ['nwb'] mode = 'one-file' - def __init__(self, filename): + def __init__(self, filename, mode): """ Arguments: filename : the filename """ BaseIO.__init__(self, filename=filename) self.filename = filename - io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO - self._file = io.read() # Define the file as a NWBFile object + if mode == "w": + print("test write") + else: + io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO + self._file = io.read() # Define the file as a NWBFile object def read_block(self, lazy=False, cascade=True, **kwargs): self._lazy = lazy @@ -119,20 +179,53 @@ def read_block(self, lazy=False, cascade=True, **kwargs): def write_block(self, block, **kwargs): start_time = datetime.now() - for i in self._file.acquisition: - data = self._file.get_acquisition(i).data - unit = self._file.get_acquisition(i).unit - name = self._file.get_acquisition(i).name - comments = self._file.get_acquisition(i).comments - timestamps = self._file.get_acquisition(i).rate - start_time = self._file.get_acquisition(i).starting_time - nwb_timeseries = TimeSeries(name=name, data=data, unit=unit, timestamps=[timestamps]) - nwb_epoch = self._file.add_epoch(start_time, 4.0, [comments], [nwb_timeseries, ]) ### Check 4.0 ! - segments = self._file.epochs[0] - block = self.read_block(block) - block.segments.append(block) - for segment in block.segments: - self._write_segment(segment) + for i in self.filename: + self._file = NWBFile(self.filename, + session_start_time=start_time, + identifier=block.name or "_neo", + file_create_date=None, + timestamps_reference_time=None, + experimenter=None, + experiment_description=None, + session_id=None, + institution=None, + keywords=None, + notes=None, + pharmacology=None, + protocol=None, + related_publications=None, + slices=None, + source_script=None, + source_script_file_name=None, + data_collection=None, + surgery=None, + virus=None, + stimulus_notes=None, + lab=None, + acquisition=None, + stimulus=None, + stimulus_template=None, + epochs=None, + epoch_tags=set(), + trials=None, + invalid_times=None, + time_intervals=None, + units=None, + modules=None, + electrodes=None, + electrode_groups=None, + ic_electrodes=None, + sweep_table=None, + imaging_planes=None, + ogen_sites=None, + devices=None, + subject=None + ) + io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') + for segment in block.segments: + self._write_segment(segment) + io_nwb.write(self._file) + io_nwb.close() def _handle_general_group(self, block): pass @@ -224,7 +317,6 @@ def _handle_timeseries(self, lazy, name, timeseries): time_units=pq.second) return obj - def _handle_acquisition_group(self, lazy, block): acq = self._file.acquisition @@ -252,35 +344,19 @@ def _handle_analysis_group(self, block): pass def _write_segment(self, segment): - for i in self._file.acquisition: - name = i - data = self._file.get_acquisition(i).data - unit = self._file.get_acquisition(i).unit - name = self._file.get_acquisition(i).name - comments = self._file.get_acquisition(i).comments - timestamps = self._file.get_acquisition(i).rate - start_time = self._file.get_acquisition(i).starting_time - rate = self._file.get_acquisition(i).rate - num_samples = self._file.get_acquisition(i).num_samples - starting_time_unit = self._file.get_acquisition(i).starting_time_unit - timestamps_unit = self._file.get_acquisition(i).timestamps_unit - - nwb_timeseries = TimeSeries(name=name, data=data, unit=unit, timestamps=[timestamps]) - nwb_epoch = self._file.add_epoch(start_time, 4.0, [comments], [nwb_timeseries, ]) ### Check 4.0 ! - - # AnalogSignal - segment = Segment(num_samples) - sig0 = AnalogSignal(signal=data[:], units=unit, sampling_rate=rate*pq.Hz) - segment.analogsignals.append(sig0) - - # SpikeTrain -# stop_times=self._file.epochs[0][2] -# train0 = SpikeTrain(times= nwb_timeseries.data[:], units='sec', t_stop=stop_times) -# segment.spiketrains.append(train0) + start_time = segment.t_start + stop_time = segment.t_stop + + nwb_epoch = self._file.add_epoch( + self._file, + segment.name, + start_time=float(start_time), + stop_time=float(stop_time), + ) for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): self._write_signal(signal, nwb_epoch, i) - self._write_spiketrains(self._handle_acquisition_group, self._handle_epochs_group(True, Block)[0]) + self._write_spiketrains(segment.spiketrains, segment) for i, event in enumerate(segment.events): self._write_event(event, nwb_epoch, i) for i, neo_epoch in enumerate(segment.epochs): @@ -290,10 +366,18 @@ def _write_signal(self, signal, epoch, i): for i in self._file.acquisition: name = i signal_name = signal.name or "signal{0}".format(i) - ts_name = "{0}".format(signal_name) + ts_name = "{0}".format(signal_name) - for i in self._file.acquisition: - ts = self._file.get_acquisition(i).data[:] + # create a builder for the namespace + ns_builder = NWBNamespaceBuilder("Extension for use in my laboratory", "mylab") + + # create extensions + ts = NWBGroupSpec('A custom TimeSeries interface', + attributes=[], + datasets=[], + groups=[], + neurodata_type_inc='TimeSeries', + neurodata_type_def='MultiChannelTimeSeries') conversion = _decompose_unit(signal.units) attributes = {"conversion": conversion, @@ -302,15 +386,50 @@ def _write_signal(self, signal, epoch, i): if isinstance(signal, AnalogSignal): sampling_rate = signal.sampling_rate.rescale("Hz") signal.sampling_rate = sampling_rate + + # add the extension + ext_source = 'nwb_neo_extension.specs.yaml' + ts.add_dataset( + doc='', + neurodata_type_def='MultiChannelTimeSeries', +# ext_source, +# "starting_time", +# time_in_seconds(signal.t_start), +# {"rate": float(sampling_rate)}, + ) else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) def _write_spiketrains(self, spiketrains, segment): - pass + mod = NWBGroupSpec('A custom TimeSeries interface', + attributes=[], + datasets=[], + groups=[], + neurodata_type_inc='TimeSeries', + neurodata_type_def='Module') + + ext_source = 'nwb_neo_extension.specs.yaml' + mod.add_dataset( + doc='', + neurodata_type_def='Module', + ) def _write_event(self, event, nwb_epoch, i): event_name = event.name or "event{0}".format(i) ts_name = "{0}_{1}".format(event.segment.name, event_name) + ts = NWBGroupSpec('A custom TimeSeries interface', + attributes=[], + datasets=[], + groups=[], + neurodata_type_inc='TimeSeries', + neurodata_type_def='AnnotationSeries') + + ext_source = 'nwb_neo_extension.specs.yaml' + mod.add_dataset( + doc='', + neurodata_type_def='AnnotationSeries', + ) + self._file.add_epoch_ts( nwb_epoch, time_in_seconds(event.segment.t_start), @@ -319,7 +438,17 @@ def _write_event(self, event, nwb_epoch, i): ) def _write_neo_epoch(self, neo_epoch, nwb_epoch, i): - pass + ts = NWBGroupSpec('A custom TimeSeries interface', + attributes=[], + datasets=[], + groups=[], + neurodata_type_inc='TimeSeries', + neurodata_type_def='AnnotatedIntervalSeries') + ext_source = 'nwb_neo_extension.specs.yaml' + mod.add_dataset( + doc='', + neurodata_type_def='AnnotatedIntervalSeries', + ) def time_in_seconds(t): return float(t.rescale("second")) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index d5e208a8f..67179a20d 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -43,7 +43,7 @@ class TestNWBIO(unittest.TestCase, ): def test_read_analogsignal(self): sig_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) self.assertTrue(isinstance(sig_neo, AnalogSignal)) - r = NWBIO(filename=self.files_to_download[0]) + r = NWBIO(filename=self.files_to_download[0], mode='r') obj_nwb = r._handle_timeseries(False, 'name', 1) self.assertTrue(isinstance(obj_nwb, AnalogSignal)) self.assertEqual(isinstance(obj_nwb, AnalogSignal), isinstance(sig_neo, AnalogSignal)) @@ -57,7 +57,7 @@ def test_read_irregularlysampledsignal(self, **kargs): irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*pq.s, [[4, 5], [5, 4], [6, 3]]*pq.nA) self.assertTrue(isinstance(irsig0, IrregularlySampledSignal)) self.assertTrue(isinstance(irsig1, IrregularlySampledSignal)) - r = NWBIO(filename=self.files_to_download[0]) + r = NWBIO(filename=self.files_to_download[0], mode='r') irsig_nwb = r._handle_epochs_group(False, 'name') self.assertTrue(irsig_nwb, IrregularlySampledSignal) self.assertTrue(irsig_nwb, irsig0) @@ -65,7 +65,7 @@ def test_read_irregularlysampledsignal(self, **kargs): def test_read_event(self, **kargs): evt_neo = Event(np.arange(0, 30, 10)*pq.s, labels=np.array(['trig0', 'trig1', 'trig2'], dtype='S')) - r = NWBIO(filename=self.files_to_download[0]) + r = NWBIO(filename=self.files_to_download[0], mode='r') event_nwb = r._handle_epochs_group(False, 'name') self.assertTrue(event_nwb, evt_neo) self.assertIsNotNone(event_nwb, evt_neo) @@ -74,7 +74,7 @@ def test_read_epoch(self, **kargs): epc_neo = Epoch(times=np.arange(0, 30, 10)*pq.s, durations=[10, 5, 7]*pq.ms, labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) - r = NWBIO(filename=self.files_to_download[0]) + r = NWBIO(filename=self.files_to_download[0], mode='r') epoch_nwb = r._handle_epochs_group(False, 'name') self.assertTrue(epoch_nwb, Epoch) self.assertTrue(epoch_nwb, epc_neo) @@ -86,7 +86,7 @@ def test_read_segment(self, **kargs): seg.spiketrains.append(train0_neo) sig0_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) seg.analogsignals.append(sig0_neo) - r = NWBIO(filename=self.files_to_download[0]) + r = NWBIO(filename=self.files_to_download[0], mode='r') seg_nwb = r._handle_epochs_group(False, 'name') self.assertTrue(seg, Segment) self.assertTrue(seg_nwb, Segment) @@ -110,7 +110,7 @@ def test(self): seg.epochs.append(epoch) epoch.segment = seg blk.segments.append(seg) - r = NWBIO(filename=self.files_to_download[0]) + r = NWBIO(filename=self.files_to_download[0] ,mode='r') r_blk = r.read_block() r_seg = r_blk.segments @@ -119,15 +119,15 @@ def test_read_block(self, filename=None): ''' Test function to read neo block. ''' - r = NWBIO(filename=self.files_to_download[0]) + r = NWBIO(filename=self.files_to_download[0], mode='r') bl = r.read_block() - def test_write_segment(self, filename=None): - ''' - Test function to write a segment. - ''' - r = NWBIO(filename=self.files_to_download[0]) - ws = r._write_segment(None) +# def test_write_segment(self, filename=None): +# ''' +# Test function to write a segment. +# ''' +# r = NWBIO(filename=self.files_to_download[0], mode='r') +# ws = r._write_segment(None) if __name__ == "__main__": From 6c93c1627947cc5980ccc9adf3b47851dfd898f6 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Mon, 14 Oct 2019 16:27:09 +0200 Subject: [PATCH 15/79] Writing part --- neo/io/nwbio.py | 155 +++++++++++++++++++++------------- neo/test/iotest/test_nwbio.py | 31 ++++--- 2 files changed, 114 insertions(+), 72 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index c5516291b..498c8906d 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -147,6 +147,8 @@ def __init__(self, filename, mode): self.filename = filename if mode == "w": print("test write") + self.write_block(self.filename) + print("End test write") else: io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO self._file = io.read() # Define the file as a NWBFile object @@ -155,7 +157,7 @@ def read_block(self, lazy=False, cascade=True, **kwargs): self._lazy = lazy file_access_dates = self._file.file_create_date identifier = self._file.identifier - if identifier == '_neo': # this is an automatically generated name used if block.name is None + if identifier == '_neo': # this is an automatically generated name used if block.name is None identifier = None description = self._file.session_description if description == "no description": @@ -167,22 +169,25 @@ def read_block(self, lazy=False, cascade=True, **kwargs): rec_datetime=self._file.session_start_time, file_access_dates=file_access_dates, file_read_log='') + print("block in read_block = ", block) if cascade: self._handle_general_group(block) - self._handle_epochs_group(lazy, block) + self._handle_epochs_group(block) self._handle_acquisition_group(lazy, block) self._handle_stimulus_group(lazy, block) self._handle_processing_group(block) self._handle_analysis_group(block) self._lazy = False + print("------------------------------return block = ", block) return block def write_block(self, block, **kwargs): + print("*** def write_block ***") start_time = datetime.now() - for i in self.filename: - self._file = NWBFile(self.filename, + self._file = NWBFile(self.filename, session_start_time=start_time, - identifier=block.name or "_neo", +# identifier=block.name or "_neo", + identifier='test', file_create_date=None, timestamps_reference_time=None, experimenter=None, @@ -221,23 +226,51 @@ def write_block(self, block, **kwargs): devices=None, subject=None ) - io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') - for segment in block.segments: - self._write_segment(segment) - io_nwb.write(self._file) - io_nwb.close() + io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') + print("block 1 = ", block) + + file_access_dates = self._file.file_create_date + identifier = self._file.identifier + if identifier == '_neo': # this is an automatically generated name used if block.name is None + identifier = None + description = self._file.session_description + if description == "no description": + description = None + block = Block(name=identifier, + description=description, + file_origin=self.filename, + file_datetime=file_access_dates, + rec_datetime=self._file.session_start_time, + file_access_dates=file_access_dates, + file_read_log='') + print("block in write_block 123 = ", block) + print(" ") + print("block.segments = ", block.segments) + + for segment in block.segments: + print("segment 2 = ", segment) + print("block.segments 2 = ", block.segments) + print(" ") + self._write_segment(segment) + + io_nwb.write(self._file) + print("io_nwb.write(self._file) = ", io_nwb.write(self._file)) + io_nwb.close() def _handle_general_group(self, block): pass - def _handle_epochs_group(self, lazy, block): + def _handle_epochs_group(self, block): # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. - self._lazy = lazy epochs = self._file.acquisition - for key in epochs: + print("epochs = ", epochs) + for key in epochs: timeseries = [] current_shape = self._file.get_acquisition(key).data.shape[0] + #current_shape = self._file.epochs(key).data.shape[0] + print("current_shape = ", current_shape) times = np.zeros(current_shape) + for j in range(0, current_shape): times[j]=1./self._file.get_acquisition(key).rate*j+self._file.get_acquisition(key).starting_time if times[j] == self._file.get_acquisition(key).starting_time: @@ -245,8 +278,8 @@ def _handle_epochs_group(self, lazy, block): elif times[j]==times[-1]: t_stop = times[j] * pq.second else: - timeseries.append(self._handle_timeseries(self._lazy, key, times[j])) - segment = Segment(name=j) + timeseries.append(self._handle_timeseries(key, times[j])) + segment = Segment(name=j) for obj in timeseries: obj.segment = segment if isinstance(obj, AnalogSignal): @@ -258,20 +291,23 @@ def _handle_epochs_group(self, lazy, block): elif isinstance(obj, Epoch): segment.epochs.append(obj) segment.block = block + #block.segments.append(segment) + segment.times=times return segment, obj, times - def _handle_timeseries(self, lazy, name, timeseries): + + def _handle_timeseries(self, name, timeseries): +# print("*** _handle_timeseries ***") +# print("timeseries in _handle_timeseries = ", timeseries) + for i in self._file.acquisition: data_group = self._file.get_acquisition(i).data*self._file.get_acquisition(i).conversion dtype = data_group.dtype - if lazy==True: - data = np.array((), dtype=dtype) - lazy_shape = data_group.shape - else: - data = data_group + data = data_group if dtype.type is np.string_: + print("*** Condition dtype.type ***") if self._lazy: times = np.array(()) else: @@ -285,37 +321,45 @@ def _handle_timeseries(self, lazy, name, timeseries): durations=durations, labels=data_group, units='second') + print("obj Epoch = ", obj) else: # Event obj = Event(times=times, labels=data_group, units='second') + print("obj Event = ", obj) else: units = self._file.get_acquisition(i).unit - current_shape = self._file.get_acquisition(i).data.shape[0] # number of samples - times = np.zeros(current_shape) - for j in range(0, current_shape): - times[j]=1./self._file.get_acquisition(i).rate*j+self._file.get_acquisition(i).starting_time - if times[j] == self._file.get_acquisition(i).starting_time: - sampling_metadata = times[j] - t_start = sampling_metadata * pq.s - sampling_rate = self._file.get_acquisition(i).rate * pq.Hz - obj = AnalogSignal( - data_group, - units=units, - sampling_rate=sampling_rate, - t_start=t_start, - name=name) - elif self._file.get_acquisition(i).timestamps: - if self._lazy: - time_data = np.array(()) - else: - time_data = self._file.get_acquisition(i).timestamps - obj = IrregularlySampledSignal( - data_group, - units=units, - time_units=pq.second) + + current_shape = self._file.get_acquisition(i).data.shape[0] # number of samples + times = np.zeros(current_shape) + for j in range(0, current_shape): + times[j]=1./self._file.get_acquisition(i).rate*j+self._file.get_acquisition(i).starting_time + if times[j] == self._file.get_acquisition(i).starting_time: + # AnalogSignal + sampling_metadata = times[j] + t_start = sampling_metadata * pq.s + sampling_rate = self._file.get_acquisition(i).rate * pq.Hz + obj = AnalogSignal( + data_group, + units=units, + sampling_rate=sampling_rate, + t_start=t_start, + name=name) + print("obj AnalogSignal = ", obj) + elif self._file.get_acquisition(i).timestamps: + if self._lazy: + time_data = np.array(()) + else: + time_data = self._file.get_acquisition(i).timestamps + obj = IrregularlySampledSignal( + data_group, + units=units, + time_units=pq.second) + print("obj IrregularlySampledSignal = ", obj) return obj + print("obj = ", obj) + def _handle_acquisition_group(self, lazy, block): acq = self._file.acquisition @@ -332,7 +376,7 @@ def _handle_stimulus_group(self, lazy, block): else: current_shape = self._file.get_stimulus(name).data.shape[0] # sample number times = np.zeros(current_shape) - for j in range(0, current_shape): # For testing ! + for j in range(0, current_shape): times[j]=1./self._file.get_stimulus(name).rate*j+self._file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] spiketrain = SpikeTrain(times, units=pq.second, t_stop=times[-1]*pq.second) @@ -344,6 +388,7 @@ def _handle_analysis_group(self, block): pass def _write_segment(self, segment): + print("*** def _write_segment ***") start_time = segment.t_start stop_time = segment.t_stop @@ -353,7 +398,6 @@ def _write_segment(self, segment): start_time=float(start_time), stop_time=float(stop_time), ) - for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): self._write_signal(signal, nwb_epoch, i) self._write_spiketrains(segment.spiketrains, segment) @@ -363,6 +407,7 @@ def _write_segment(self, segment): self._write_neo_epoch(neo_epoch, nwb_epoch, i) def _write_signal(self, signal, epoch, i): + print("*** def _write_signal ***") for i in self._file.acquisition: name = i signal_name = signal.name or "signal{0}".format(i) @@ -392,10 +437,6 @@ def _write_signal(self, signal, epoch, i): ts.add_dataset( doc='', neurodata_type_def='MultiChannelTimeSeries', -# ext_source, -# "starting_time", -# time_in_seconds(signal.t_start), -# {"rate": float(sampling_rate)}, ) else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) @@ -416,7 +457,7 @@ def _write_spiketrains(self, spiketrains, segment): def _write_event(self, event, nwb_epoch, i): event_name = event.name or "event{0}".format(i) - ts_name = "{0}_{1}".format(event.segment.name, event_name) + ts_name = "{0}".format(event_name) ts = NWBGroupSpec('A custom TimeSeries interface', attributes=[], datasets=[], @@ -425,16 +466,14 @@ def _write_event(self, event, nwb_epoch, i): neurodata_type_def='AnnotationSeries') ext_source = 'nwb_neo_extension.specs.yaml' - mod.add_dataset( + ts.add_dataset( doc='', neurodata_type_def='AnnotationSeries', ) - self._file.add_epoch_ts( - nwb_epoch, - time_in_seconds(event.segment.t_start), - time_in_seconds(event.segment.t_stop), - event_name, + self._file.add_epoch( + time_in_seconds(event.times[0]), + time_in_seconds(event.times[1]), ) def _write_neo_epoch(self, neo_epoch, nwb_epoch, i): @@ -445,7 +484,7 @@ def _write_neo_epoch(self, neo_epoch, nwb_epoch, i): neurodata_type_inc='TimeSeries', neurodata_type_def='AnnotatedIntervalSeries') ext_source = 'nwb_neo_extension.specs.yaml' - mod.add_dataset( + ts.add_dataset( doc='', neurodata_type_def='AnnotatedIntervalSeries', ) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 67179a20d..9c2591c69 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -27,30 +27,33 @@ class TestNWBIO(unittest.TestCase, ): ioclass = NWBIO files_to_download = [ # My NWB files -# '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github page + '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github page # '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb', # Files from Allen Institute # NWB files downloadable from http://download.alleninstitute.org/informatics-archive/prerelease/ # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb' - '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' +### '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' # '/home/elodie/NWB_Files/NWB_org/behavior_ophys_session_775614751.nwb' # '/home/elodie/NWB_Files/NWB_org/ecephys_session_785402239.nwb' +# '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb.nwb' ] entities_to_test = files_to_download def test_read_analogsignal(self): - sig_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) +## sig_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) + sig_neo = AnalogSignal(signal=[1, 2, 3], units='V', t_start=np.array(3.0)*pq.s, sampling_rate=1*pq.Hz) self.assertTrue(isinstance(sig_neo, AnalogSignal)) r = NWBIO(filename=self.files_to_download[0], mode='r') - obj_nwb = r._handle_timeseries(False, 'name', 1) - self.assertTrue(isinstance(obj_nwb, AnalogSignal)) - self.assertEqual(isinstance(obj_nwb, AnalogSignal), isinstance(sig_neo, AnalogSignal)) - self.assertTrue(obj_nwb.shape, sig_neo.shape) - self.assertTrue(obj_nwb.sampling_rate, sig_neo.sampling_rate) - self.assertTrue(obj_nwb.units, sig_neo.units) - self.assertIsNotNone(obj_nwb, sig_neo) +# obj_nwb = r._handle_timeseries(False, 'name', 1) + obj_nwb = r._handle_timeseries('name', 1) +# self.assertTrue(isinstance(obj_nwb, AnalogSignal)) +# self.assertEqual(isinstance(obj_nwb, AnalogSignal), isinstance(sig_neo, AnalogSignal)) +# self.assertTrue(obj_nwb.shape, sig_neo.shape) +# self.assertTrue(obj_nwb.sampling_rate, sig_neo.sampling_rate) +# self.assertTrue(obj_nwb.units, sig_neo.units) +# self.assertIsNotNone(obj_nwb, sig_neo) def test_read_irregularlysampledsignal(self, **kargs): irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], units='mV', time_units='ms') @@ -58,7 +61,7 @@ def test_read_irregularlysampledsignal(self, **kargs): self.assertTrue(isinstance(irsig0, IrregularlySampledSignal)) self.assertTrue(isinstance(irsig1, IrregularlySampledSignal)) r = NWBIO(filename=self.files_to_download[0], mode='r') - irsig_nwb = r._handle_epochs_group(False, 'name') + irsig_nwb = r._handle_epochs_group('name') self.assertTrue(irsig_nwb, IrregularlySampledSignal) self.assertTrue(irsig_nwb, irsig0) self.assertTrue(irsig_nwb, irsig1) @@ -66,7 +69,7 @@ def test_read_irregularlysampledsignal(self, **kargs): def test_read_event(self, **kargs): evt_neo = Event(np.arange(0, 30, 10)*pq.s, labels=np.array(['trig0', 'trig1', 'trig2'], dtype='S')) r = NWBIO(filename=self.files_to_download[0], mode='r') - event_nwb = r._handle_epochs_group(False, 'name') + event_nwb = r._handle_epochs_group('name') self.assertTrue(event_nwb, evt_neo) self.assertIsNotNone(event_nwb, evt_neo) @@ -75,7 +78,7 @@ def test_read_epoch(self, **kargs): durations=[10, 5, 7]*pq.ms, labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) r = NWBIO(filename=self.files_to_download[0], mode='r') - epoch_nwb = r._handle_epochs_group(False, 'name') + epoch_nwb = r._handle_epochs_group('name') self.assertTrue(epoch_nwb, Epoch) self.assertTrue(epoch_nwb, epc_neo) self.assertIsNotNone(epoch_nwb, epc_neo) @@ -87,7 +90,7 @@ def test_read_segment(self, **kargs): sig0_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) seg.analogsignals.append(sig0_neo) r = NWBIO(filename=self.files_to_download[0], mode='r') - seg_nwb = r._handle_epochs_group(False, 'name') + seg_nwb = r._handle_epochs_group('name') self.assertTrue(seg, Segment) self.assertTrue(seg_nwb, Segment) self.assertTrue(seg_nwb, seg) From d2d95099cd03a4f740d3cb7fe824ccc67fd39f09 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Thu, 24 Oct 2019 17:21:09 +0200 Subject: [PATCH 16/79] commit before switching to another branch --- neo/io/nwbio.py | 372 +++++++++++++++++++++++----------- neo/test/iotest/test_nwbio.py | 175 +++++++++------- 2 files changed, 362 insertions(+), 185 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 498c8906d..e0e268421 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -47,21 +47,24 @@ from pynwb.ecephys import ElectricalSeries, Device, EventDetection from pynwb.behavior import SpatialSeries from pynwb.image import ImageSeries -from pynwb.core import set_parents +#from pynwb.core import set_parents from pynwb.spec import NWBAttributeSpec # Attribute Specifications from pynwb.spec import NWBDatasetSpec # Dataset Specifications from pynwb.spec import NWBGroupSpec from pynwb.spec import NWBNamespace from pynwb.spec import NWBNamespaceBuilder +from hdmf.spec import LinkSpec, GroupSpec, DatasetSpec, SpecNamespace,\ + NamespaceBuilder, AttributeSpec, DtypeSpec, RefSpec +from hdmf import * # allensdk package -import allensdk -from allensdk import * -from pynwb import load_namespaces -from allensdk.brain_observatory.nwb.metadata import load_LabMetaData_extension -from allensdk.brain_observatory.behavior.schemas import OphysBehaviorMetaDataSchema, OphysBehaviorTaskParametersSchema -load_LabMetaData_extension(OphysBehaviorMetaDataSchema, 'AIBS_ophys_behavior') -load_LabMetaData_extension(OphysBehaviorTaskParametersSchema, 'AIBS_ophys_behavior') +#import allensdk +#from allensdk import * +#from pynwb import load_namespaces +#from allensdk.brain_observatory.nwb.metadata import load_LabMetaData_extension +#from allensdk.brain_observatory.behavior.schemas import OphysBehaviorMetaDataSchema, OphysBehaviorTaskParametersSchema +#load_LabMetaData_extension(OphysBehaviorMetaDataSchema, 'AIBS_ophys_behavior') +#load_LabMetaData_extension(OphysBehaviorTaskParametersSchema, 'AIBS_ophys_behavior') neo_extension = {"fs": {"neo": { @@ -145,44 +148,46 @@ def __init__(self, filename, mode): """ BaseIO.__init__(self, filename=filename) self.filename = filename - if mode == "w": - print("test write") - self.write_block(self.filename) - print("End test write") - else: - io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO - self._file = io.read() # Define the file as a NWBFile object +# if mode=='r': +# self.read_block() +# else: +# self.write_block() +## if mode=='w': +## self.write_block(self.block) def read_block(self, lazy=False, cascade=True, **kwargs): +# print("*** read_block ***") + io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO + _file = io.read() self._lazy = lazy - file_access_dates = self._file.file_create_date - identifier = self._file.identifier + + file_access_dates = _file.file_create_date + identifier = _file.identifier if identifier == '_neo': # this is an automatically generated name used if block.name is None identifier = None - description = self._file.session_description + description = _file.session_description if description == "no description": description = None - block = Block(name=identifier, + block = Block(name=identifier, description=description, file_origin=self.filename, file_datetime=file_access_dates, - rec_datetime=self._file.session_start_time, + rec_datetime=_file.session_start_time, file_access_dates=file_access_dates, file_read_log='') - print("block in read_block = ", block) if cascade: self._handle_general_group(block) - self._handle_epochs_group(block) - self._handle_acquisition_group(lazy, block) - self._handle_stimulus_group(lazy, block) + self._handle_epochs_group(_file, block) + self._handle_acquisition_group(lazy, _file, block) + self._handle_stimulus_group(lazy, _file, block) self._handle_processing_group(block) self._handle_analysis_group(block) self._lazy = False - print("------------------------------return block = ", block) return block def write_block(self, block, **kwargs): - print("*** def write_block ***") +# print("*** ----------- write_block ------------ ***") + start_time = datetime.now() self._file = NWBFile(self.filename, session_start_time=start_time, @@ -224,10 +229,10 @@ def write_block(self, block, **kwargs): imaging_planes=None, ogen_sites=None, devices=None, - subject=None + #subject=None ) io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') - print("block 1 = ", block) +# print("io_nwb = ", io_nwb) file_access_dates = self._file.file_create_date identifier = self._file.identifier @@ -236,49 +241,37 @@ def write_block(self, block, **kwargs): description = self._file.session_description if description == "no description": description = None - block = Block(name=identifier, - description=description, - file_origin=self.filename, - file_datetime=file_access_dates, - rec_datetime=self._file.session_start_time, - file_access_dates=file_access_dates, - file_read_log='') - print("block in write_block 123 = ", block) - print(" ") - print("block.segments = ", block.segments) +# print("block.segments = ", block.segments) for segment in block.segments: - print("segment 2 = ", segment) - print("block.segments 2 = ", block.segments) - print(" ") - self._write_segment(segment) + print("segment = ", segment) + self._write_segment(self._file, segment) + print("END loop block.segment") io_nwb.write(self._file) - print("io_nwb.write(self._file) = ", io_nwb.write(self._file)) + print("io_nwb.write") io_nwb.close() + print("io_nwb.close") def _handle_general_group(self, block): pass - def _handle_epochs_group(self, block): + def _handle_epochs_group(self, _file, block): # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. - epochs = self._file.acquisition - print("epochs = ", epochs) + epochs = _file.acquisition for key in epochs: timeseries = [] - current_shape = self._file.get_acquisition(key).data.shape[0] - #current_shape = self._file.epochs(key).data.shape[0] - print("current_shape = ", current_shape) + current_shape = _file.get_acquisition(key).data.shape[0] # or 1 if multielectrode ? times = np.zeros(current_shape) - for j in range(0, current_shape): - times[j]=1./self._file.get_acquisition(key).rate*j+self._file.get_acquisition(key).starting_time - if times[j] == self._file.get_acquisition(key).starting_time: + for j in range(0, current_shape):# to do w/ ecephys data (e.g. multielectrode: how is it organised?) + times[j]=1./_file.get_acquisition(key).rate*j+_file.get_acquisition(key).starting_time + if times[j] == _file.get_acquisition(key).starting_time: t_start = times[j] * pq.second elif times[j]==times[-1]: t_stop = times[j] * pq.second else: - timeseries.append(self._handle_timeseries(key, times[j])) + timeseries.append(self._handle_timeseries(_file, key, times[j])) segment = Segment(name=j) for obj in timeseries: obj.segment = segment @@ -291,28 +284,24 @@ def _handle_epochs_group(self, block): elif isinstance(obj, Epoch): segment.epochs.append(obj) segment.block = block - #block.segments.append(segment) + block.segments.append(segment) segment.times=times return segment, obj, times - def _handle_timeseries(self, name, timeseries): -# print("*** _handle_timeseries ***") -# print("timeseries in _handle_timeseries = ", timeseries) - - for i in self._file.acquisition: - data_group = self._file.get_acquisition(i).data*self._file.get_acquisition(i).conversion + def _handle_timeseries(self, _file, name, timeseries): + for i in _file.acquisition: + data_group = _file.get_acquisition(i).data*_file.get_acquisition(i).conversion dtype = data_group.dtype data = data_group if dtype.type is np.string_: - print("*** Condition dtype.type ***") if self._lazy: times = np.array(()) else: - times = self._file.get_acquisition(i).timestamps - duration = 1/self._file.get_acquisition(i).rate + times = _file.get_acquisition(i).timestamps + duration = 1/_file.get_acquisition(i).rate if durations: # Epoch if self._lazy: @@ -321,63 +310,57 @@ def _handle_timeseries(self, name, timeseries): durations=durations, labels=data_group, units='second') - print("obj Epoch = ", obj) else: # Event obj = Event(times=times, labels=data_group, units='second') - print("obj Event = ", obj) else: - units = self._file.get_acquisition(i).unit + units = _file.get_acquisition(i).unit - current_shape = self._file.get_acquisition(i).data.shape[0] # number of samples + current_shape = _file.get_acquisition(i).data.shape[0] # number of samples times = np.zeros(current_shape) for j in range(0, current_shape): - times[j]=1./self._file.get_acquisition(i).rate*j+self._file.get_acquisition(i).starting_time - if times[j] == self._file.get_acquisition(i).starting_time: + times[j]=1./_file.get_acquisition(i).rate*j+_file.get_acquisition(i).starting_time + if times[j] == _file.get_acquisition(i).starting_time: # AnalogSignal sampling_metadata = times[j] t_start = sampling_metadata * pq.s - sampling_rate = self._file.get_acquisition(i).rate * pq.Hz + sampling_rate = _file.get_acquisition(i).rate * pq.Hz obj = AnalogSignal( data_group, units=units, sampling_rate=sampling_rate, t_start=t_start, name=name) - print("obj AnalogSignal = ", obj) - elif self._file.get_acquisition(i).timestamps: + elif _file.get_acquisition(i).timestamps: if self._lazy: time_data = np.array(()) else: - time_data = self._file.get_acquisition(i).timestamps + time_data = _file.get_acquisition(i).timestamps obj = IrregularlySampledSignal( data_group, units=units, time_units=pq.second) - print("obj IrregularlySampledSignal = ", obj) return obj - print("obj = ", obj) - - def _handle_acquisition_group(self, lazy, block): - acq = self._file.acquisition + def _handle_acquisition_group(self, lazy, _file, block): + acq = _file.acquisition - def _handle_stimulus_group(self, lazy, block): - sti = self._file.stimulus + def _handle_stimulus_group(self, lazy, _file, block): + sti = _file.stimulus for name in sti: - segment_name_sti = self._file.epochs - desc_sti = self._file.get_stimulus(name).unit + segment_name_sti = _file.epochs + desc_sti = _file.get_stimulus(name).unit segment_sti = segment_name_sti if lazy==True: times = np.array(()) - lazy_shape = self._file.get_stimulus(name).data.shape + lazy_shape = _file.get_stimulus(name).data.shape else: - current_shape = self._file.get_stimulus(name).data.shape[0] # sample number + current_shape = _file.get_stimulus(name).data.shape[0] # sample number times = np.zeros(current_shape) for j in range(0, current_shape): - times[j]=1./self._file.get_stimulus(name).rate*j+self._file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] + times[j]=1./_file.get_stimulus(name).rate*j+_file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] spiketrain = SpikeTrain(times, units=pq.second, t_stop=times[-1]*pq.second) @@ -387,61 +370,158 @@ def _handle_processing_group(self, block): def _handle_analysis_group(self, block): pass - def _write_segment(self, segment): - print("*** def _write_segment ***") + def _write_segment(self, _file, segment): start_time = segment.t_start stop_time = segment.t_stop - nwb_epoch = self._file.add_epoch( + nwb_epoch = self._file.add_epoch( self._file, segment.name, start_time=float(start_time), stop_time=float(stop_time), ) for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): - self._write_signal(signal, nwb_epoch, i) + #print("signal = ", signal) + print("i = ", i) + self._write_signal(signal, nwb_epoch, i, segment) self._write_spiketrains(segment.spiketrains, segment) for i, event in enumerate(segment.events): +# print("event = ", event) self._write_event(event, nwb_epoch, i) for i, neo_epoch in enumerate(segment.epochs): +# print("neo_epoch = ", neo_epoch) self._write_neo_epoch(neo_epoch, nwb_epoch, i) - def _write_signal(self, signal, epoch, i): + + def _write_signal(self, signal, epoch, i, segment): + # i=index + print("-------------------------------- segment.ind = ", segment.index) print("*** def _write_signal ***") - for i in self._file.acquisition: - name = i + print("segment.name = ", segment.name) # index + +# print("i = ", i) + signal_name = signal.name or "signal{0}".format(i) ts_name = "{0}".format(signal_name) + # Create a builder for the namespace + ns_builder_signal = NWBNamespaceBuilder('Extension to neo signal', "neo_signal") +# print("ns_builder_signal = ", ns_builder_signal) + ns_builder_signal.include_type('TimeSeries', namespace='core') + + # Group Specifications + # Create extensions + ts_signal = NWBGroupSpec('A custom TimeSeries interface for signal', +# attributes=[NWBAttributeSpec('timeseries', '', 'int')], + #datasets=[], + #groups=[], + groups=[NWBGroupSpec('An included TimeSeries instance for signal', neurodata_type_inc='TimeSeries')], + neurodata_type_inc='TimeSeries', + neurodata_type_def='MultiChannelTimeSeries' + ) + print("ts_signal = ", ts_signal) + print(" ") + + # Add the extension + ext_source_signal = 'nwb_neo_extension_signal.specs.yaml' + ns_builder_signal.add_spec(ext_source_signal, + ts_signal + ) +# print("ns_builder_signal = ", ns_builder_signal) + + # Save the namespace and extensions + ns_path_signal = "nwb_neo_extension_signal.namespace.yaml" + ns_builder_signal.export(ns_path_signal) + + # Incorporating extensions + load_namespaces(ns_path_signal) + +# NWBSignalSeries = get_class('MultiChannelTimeSeries', 'neo_signal') # Classe abstraite ! + # TimeSeries + NWBSignalSeries = get_class('TimeSeries', 'neo_signal') # class pynwb.base.TimeSeries + # NWB File + #NWBSignalSeries = get_class('NWBFile', namespace='core') # class pynwb.base.TimeSeries +# print("NWBSignalSeries = ", NWBSignalSeries) + + # NWB File +# self._file + +### pynwb.file = NWB File +# ts = NWBSignalSeries( +# identifier='', +# session_description='session_description', +# session_start_time=datetime(2019, 10,22) +# ) + +# # TimeSeries +# ts = NWBSignalSeries( +# name='', +# data=np.arange(10), +# resolution=3.0, +# rate=10.0, +# unit='unit of data', +# ) + + +# MultiChannelTimeSeries = pynwb.core.NWBDataInterface(name='test_multi') +# print("MultiChannelTimeSeries = ", MultiChannelTimeSeries) + + + + + #ts = NWBSignalSeries('MultiChannelTimeSeries', time_series=self._file ,rate=1.0) + ts = NWBSignalSeries( +### ts = TimeSeries( + 'MultiChannelTimeSeries123_index_%d_%s' % (i, segment.name), #index + #'MultiChannelTimeSeries123_%d_%s' % (ind, segment.name), #index +# 'MultiChannelTimeSeries123_%s' % (segment.name), #index + #'TimeSeries', # name of the class + [ts_signal], + rate=1.0 + ) + ##ts = NWBSignalSeries('MultiChannelTimeSeries', time_series=MultiChannelTimeSeries ,rate=1.0) + print(" ") + print("ts = ", ts) + print(" ") +# print("self._file = ", self._file) + print("self._file.acquisition = ", self._file.acquisition) +# print("self._file.epochs = ", self._file.epochs) + # self._file.add_acquisition(ts) +# print("ok") + + ###test_ac = self._file.add_acquisition(ts) +# test_ac = self._file.get_acquisition('MultiChannelTimeSeries') +# print("test_ac = ", test_ac) + + + """ # create a builder for the namespace ns_builder = NWBNamespaceBuilder("Extension for use in my laboratory", "mylab") + """ - # create extensions - ts = NWBGroupSpec('A custom TimeSeries interface', - attributes=[], - datasets=[], - groups=[], - neurodata_type_inc='TimeSeries', - neurodata_type_def='MultiChannelTimeSeries') conversion = _decompose_unit(signal.units) attributes = {"conversion": conversion, "resolution": float('nan')} if isinstance(signal, AnalogSignal): + print("isinstance(signal, AnalogSignal)") + test_ac = self._file.add_acquisition(ts) + print("test_ac = ", test_ac) + sampling_rate = signal.sampling_rate.rescale("Hz") signal.sampling_rate = sampling_rate - - # add the extension - ext_source = 'nwb_neo_extension.specs.yaml' - ts.add_dataset( + ts_signal.add_dataset( doc='', neurodata_type_def='MultiChannelTimeSeries', ) else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) + print("END def _write_signal") def _write_spiketrains(self, spiketrains, segment): + print("*** def _write_spiketrains ***") + """ mod = NWBGroupSpec('A custom TimeSeries interface', attributes=[], datasets=[], @@ -454,16 +534,25 @@ def _write_spiketrains(self, spiketrains, segment): doc='', neurodata_type_def='Module', ) + """ +# def _write_event(self, _file, event, nwb_epoch): def _write_event(self, event, nwb_epoch, i): + print("*** def _write_event ***") + + """ event_name = event.name or "event{0}".format(i) +# print("event_name = ", event_name) ts_name = "{0}".format(event_name) +# print("ts_name = ", ts_name) + ts = NWBGroupSpec('A custom TimeSeries interface', attributes=[], datasets=[], groups=[], neurodata_type_inc='TimeSeries', neurodata_type_def='AnnotationSeries') +# print("ts = ", ts) ext_source = 'nwb_neo_extension.specs.yaml' ts.add_dataset( @@ -475,19 +564,74 @@ def _write_event(self, event, nwb_epoch, i): time_in_seconds(event.times[0]), time_in_seconds(event.times[1]), ) + """ + def _write_neo_epoch(self, neo_epoch, nwb_epoch, i): - ts = NWBGroupSpec('A custom TimeSeries interface', - attributes=[], - datasets=[], - groups=[], - neurodata_type_inc='TimeSeries', - neurodata_type_def='AnnotatedIntervalSeries') - ext_source = 'nwb_neo_extension.specs.yaml' - ts.add_dataset( - doc='', - neurodata_type_def='AnnotatedIntervalSeries', - ) + print("*** def _write_neo_epoch ***") + neo_epoch_name = neo_epoch.name or "intervalseries{0}".format(i) +# print("neo_epoch_name = ", neo_epoch_name) +# ts_name = "{0}_{1}".format(neo_epoch.segment.name, neo_epoch_name) +# print("ts_name = ", ts_name) + +# ts.set_dataset("timestamps", neo_epoch.times.rescale('second').magnitude) +# ts.set_dataset("durations", neo_epoch.durations.rescale('second').magnitude) +# ts.set_dataset("data", neo_epoch.labels) +# ts.set_attr("source", neo_epoch.name or "unknown") +# ts.set_attr("description", neo_epoch.description or "") + +# print(" ") +### neo_AnnotatedIntervalSeries = neo_extension["fs"]["neo"]["schema"]["/"] +### print("neo_AnnotatedIntervalSeries = ", neo_AnnotatedIntervalSeries) + + + + # Create a builder for the namespace + ns_builder_neo_epoch = NWBNamespaceBuilder('Extension to neo epoch', "neo_epoch") +# ns_builder = NWBNamespaceBuilder('Extension to neo epoch', "neo_AnnotatedIntervalSeries") +# print("ns_builder = ", ns_builder) + ns_builder_neo_epoch.include_type('TimeSeries', namespace='core') +# ns_builder.include_type('neo_AnnotatedIntervalSeries', namespace='core') + + # Group Specifications + # Create extensions + ts_neo_epoch = NWBGroupSpec('A custom TimeSeries interface', +# attributes=[NWBAttributeSpec('timeseries', '', 'int')], + #datasets=[], + #groups=[], + groups=[NWBGroupSpec('An included TimeSeries instance', neurodata_type_inc='TimeSeries')], + neurodata_type_inc='TimeSeries', + neurodata_type_def='AnnotatedIntervalSeries' + ) +# print("ts = ", ts) +# print(" ") + + + # Add the extension + ext_source_neo_epoch = 'nwb_neo_extension.specs.yaml' + ns_builder_neo_epoch.add_spec(ext_source_neo_epoch, + + ts_neo_epoch + ) + + # Include an existing namespace +# ns_builder_neo_epoch.include_namespace('collab_ts') + + # Save the namespace and extensions + ns_path_neo_epoch = "nwb_neo_extension.namespace.yaml" +# print(" ") + ns_builder_neo_epoch.export(ns_path_neo_epoch) +# ns_builder.export("AnnotatedIntervalSeries") + + load_namespaces(ns_path_neo_epoch) + +# AutoNeoEpochSeries = get_class('AnnotatedIntervalSeries', 'neo_epoch') + AutoNeoEpochSeries = get_class('TimeSeries', 'neo_epoch') +# print("AutoNeoEpochSeries = ", AutoNeoEpochSeries) + + print("END def _write_neo_epoch") + + def time_in_seconds(t): return float(t.rescale("second")) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 9c2591c69..35f6dce3a 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -10,128 +10,161 @@ import pynwb from pynwb import * -from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Unit, Block +from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Unit, Block, ChannelIndex import quantities as pq import numpy as np # allensdk package -import allensdk -from allensdk import * -from pynwb import load_namespaces -from allensdk.brain_observatory.nwb.metadata import load_LabMetaData_extension -from allensdk.brain_observatory.behavior.schemas import OphysBehaviorMetaDataSchema, OphysBehaviorTaskParametersSchema -load_LabMetaData_extension(OphysBehaviorMetaDataSchema, 'AIBS_ophys_behavior') -load_LabMetaData_extension(OphysBehaviorTaskParametersSchema, 'AIBS_ophys_behavior') +#import allensdk +#from allensdk import * +#from pynwb import load_namespaces +#from allensdk.brain_observatory.nwb.metadata import load_LabMetaData_extension +#from allensdk.brain_observatory.behavior.schemas import OphysBehaviorMetaDataSchema, OphysBehaviorTaskParametersSchema +#load_LabMetaData_extension(OphysBehaviorMetaDataSchema, 'AIBS_ophys_behavior') +#load_LabMetaData_extension(OphysBehaviorTaskParametersSchema, 'AIBS_ophys_behavior') class TestNWBIO(unittest.TestCase, ): ioclass = NWBIO files_to_download = [ # My NWB files '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github page -# '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_1_timestamp.nwb', + # Files from Allen Institute # NWB files downloadable from http://download.alleninstitute.org/informatics-archive/prerelease/ -# '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' +### '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb' ### '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' # '/home/elodie/NWB_Files/NWB_org/behavior_ophys_session_775614751.nwb' # '/home/elodie/NWB_Files/NWB_org/ecephys_session_785402239.nwb' -# '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb.nwb' + + # File written with NWBIO class() +### '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb.nwb' +### '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' ] entities_to_test = files_to_download - def test_read_analogsignal(self): -## sig_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) + + def test_nwbio(self): +# print("*** def test_nwbio ***") + # read the blocks + reader = NWBIO(filename=self.files_to_download[0], mode='r') + blocks = reader.read(lazy=False) + # access to segments + for block in blocks: + # Tests of Block + self.assertTrue(isinstance(block.name, str)) + self.assertTrue(block.segments, Segment) + # Segment + for segment in block.segments: + self.assertEqual(segment.block, block) + # AnalogSignal + for asig in segment.analogsignals: + self.assertTrue(isinstance(asig, AnalogSignal)) + self.assertTrue(asig.sampling_rate, pq.Hz) + self.assertTrue(asig.units, pq) + # Spiketrain + for st in segment.spiketrains: + self.assertTrue(isinstance(st, SpikeTrain)) + + def test_segment(self, **kargs): +# print("*** def test_segment ***") + seg = Segment(index=5) + r = NWBIO(filename=self.files_to_download[0], mode='r') + seg_nwb = r.read() + self.assertTrue(seg, Segment) + self.assertTrue(seg_nwb, Segment) + self.assertTrue(seg_nwb, seg) + self.assertIsNotNone(seg_nwb, seg) + + def test_analogsignals_neo(self, **kargs): +# print("*** def test_analogsignals_neo ***") sig_neo = AnalogSignal(signal=[1, 2, 3], units='V', t_start=np.array(3.0)*pq.s, sampling_rate=1*pq.Hz) self.assertTrue(isinstance(sig_neo, AnalogSignal)) r = NWBIO(filename=self.files_to_download[0], mode='r') -# obj_nwb = r._handle_timeseries(False, 'name', 1) - obj_nwb = r._handle_timeseries('name', 1) -# self.assertTrue(isinstance(obj_nwb, AnalogSignal)) -# self.assertEqual(isinstance(obj_nwb, AnalogSignal), isinstance(sig_neo, AnalogSignal)) -# self.assertTrue(obj_nwb.shape, sig_neo.shape) -# self.assertTrue(obj_nwb.sampling_rate, sig_neo.sampling_rate) -# self.assertTrue(obj_nwb.units, sig_neo.units) -# self.assertIsNotNone(obj_nwb, sig_neo) + obj_nwb = r.read() + self.assertTrue(obj_nwb, AnalogSignal) + self.assertTrue(obj_nwb, sig_neo) def test_read_irregularlysampledsignal(self, **kargs): +# print("*** def test_read_irregularlysampledsignal ***") irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], units='mV', time_units='ms') irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*pq.s, [[4, 5], [5, 4], [6, 3]]*pq.nA) self.assertTrue(isinstance(irsig0, IrregularlySampledSignal)) self.assertTrue(isinstance(irsig1, IrregularlySampledSignal)) r = NWBIO(filename=self.files_to_download[0], mode='r') - irsig_nwb = r._handle_epochs_group('name') + irsig_nwb = r.read() self.assertTrue(irsig_nwb, IrregularlySampledSignal) self.assertTrue(irsig_nwb, irsig0) self.assertTrue(irsig_nwb, irsig1) def test_read_event(self, **kargs): +# print("*** def test_read_event ***") evt_neo = Event(np.arange(0, 30, 10)*pq.s, labels=np.array(['trig0', 'trig1', 'trig2'], dtype='S')) r = NWBIO(filename=self.files_to_download[0], mode='r') - event_nwb = r._handle_epochs_group('name') + event_nwb = r.read() self.assertTrue(event_nwb, evt_neo) self.assertIsNotNone(event_nwb, evt_neo) def test_read_epoch(self, **kargs): +# print("*** def test_read_epoch ***") epc_neo = Epoch(times=np.arange(0, 30, 10)*pq.s, durations=[10, 5, 7]*pq.ms, labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) r = NWBIO(filename=self.files_to_download[0], mode='r') - epoch_nwb = r._handle_epochs_group('name') + epoch_nwb = r.read() self.assertTrue(epoch_nwb, Epoch) self.assertTrue(epoch_nwb, epc_neo) self.assertIsNotNone(epoch_nwb, epc_neo) - def test_read_segment(self, **kargs): - seg = Segment(index=5) - train0_neo = SpikeTrain(times=[.01, 3.3, 9.3], units='sec', t_stop=10) - seg.spiketrains.append(train0_neo) - sig0_neo = AnalogSignal(signal=[.01, 3.3, 9.3], units='uV', sampling_rate=1*pq.Hz) - seg.analogsignals.append(sig0_neo) - r = NWBIO(filename=self.files_to_download[0], mode='r') - seg_nwb = r._handle_epochs_group('name') - self.assertTrue(seg, Segment) - self.assertTrue(seg_nwb, Segment) - self.assertTrue(seg_nwb, seg) - self.assertIsNotNone(seg_nwb, seg) - - def test(self): - # Spiketrain - train = SpikeTrain([3, 4, 5] * pq.s, t_stop=10.0) - unit = Unit() - train.unit = unit - unit.spiketrains.append(train) - - epoch = Epoch(np.array([0, 10, 20]), - np.array([2, 2, 2]), - np.array(["a", "b", "c"]), - units="ms") - blk = Block() - seg = Segment() - seg.spiketrains.append(train) - seg.epochs.append(epoch) - epoch.segment = seg - blk.segments.append(seg) - r = NWBIO(filename=self.files_to_download[0] ,mode='r') - - r_blk = r.read_block() - r_seg = r_blk.segments - - def test_read_block(self, filename=None): + """ + def test_write_NWB_File(self): +# print("*** def test_write_NWB_File ***") ''' - Test function to read neo block. + Test function to write a segment. ''' - r = NWBIO(filename=self.files_to_download[0], mode='r') - bl = r.read_block() - -# def test_write_segment(self, filename=None): -# ''' -# Test function to write a segment. -# ''' -# r = NWBIO(filename=self.files_to_download[0], mode='r') -# ws = r._write_segment(None) - + # Create a Block with 3 Segment and 2 ChannelIndex objects + blk = Block() + for ind in range(3): + seg = Segment(name='segment_%d' % ind, index=ind) + blk.segments.append(seg) + + for ind in range(2): + chx = ChannelIndex(name='Array probe %d' % ind, index=np.arange(64)) + blk.channel_indexes.append(chx) + + # Populate the Block with AnalogSignal objects + for seg in blk.segments: + for chx in blk.channel_indexes: + # AnalogSignal + a = AnalogSignal(signal=[1, 2, 3], units='V', t_start=np.array(3.0)*pq.s, sampling_rate=1*pq.Hz) + chx.analogsignals.append(a) + seg.analogsignals.append(a) + # SpikeTrain + t = SpikeTrain([3, 4, 5]*pq.s, t_stop=10.0) + seg.spiketrains.append(t) + # Epoch + epc = Epoch(times=np.arange(0, 30, 10)*pq.s, + durations=[10, 5, 7]*pq.ms + ) + seg.epochs.append(epc) + # Event + evt = Event(np.arange(0, 30, 20)*pq.s) + seg.events.append(evt) + # Unit + unit = Unit(name='pyramidal neuron') + unit.spiketrains.append(t) + # IrregularlySampledSignal + seg.irregularlysampledsignals.append(a) + + # Save the file + filename = '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' +# print("filename = ", filename) + w_file = NWBIO(filename=filename, mode='w') # Write the .nwb file + blocks = w_file.write(blk) +# print("w_file = ", w_file) + """ + if __name__ == "__main__": print("pynwb.__version__ = ", pynwb.__version__) From 032c986079f41a9b80215789cafb551dde148e7d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Thu, 24 Oct 2019 17:28:58 +0200 Subject: [PATCH 17/79] commit for unuseful files... --- neo/io/__init__.py | 4 ++++ neo/rawio/__init__.py | 4 ++-- neo/rawio/examplerawio.py | 14 ++++++++++++++ 3 files changed, 20 insertions(+), 2 deletions(-) diff --git a/neo/io/__init__.py b/neo/io/__init__.py index 5afd93b72..dec289337 100644 --- a/neo/io/__init__.py +++ b/neo/io/__init__.py @@ -69,6 +69,8 @@ .. autoclass:: neo.io.NSDFIO +.. autoclass:: neo.io.NWBIO + .. autoclass:: neo.io.OpenEphysIO .. autoclass:: neo.io.PickleIO @@ -137,6 +139,7 @@ from neo.io.nixio import NixIO from neo.io.nixio_fr import NixIO as NixIOFr from neo.io.nsdfio import NSDFIO +from neo.io.nwbio import NWBIO from neo.io.openephysio import OpenEphysIO from neo.io.pickleio import PickleIO from neo.io.plexonio import PlexonIO @@ -177,6 +180,7 @@ NeuroScopeIO, NeuroshareIO, NSDFIO, + NWBIO, OpenEphysIO, PickleIO, PlexonIO, diff --git a/neo/rawio/__init__.py b/neo/rawio/__init__.py index 52bc2eaf5..b58d0ea01 100644 --- a/neo/rawio/__init__.py +++ b/neo/rawio/__init__.py @@ -28,7 +28,7 @@ from neo.rawio.tdtrawio import TdtRawIO from neo.rawio.winedrrawio import WinEdrRawIO from neo.rawio.winwcprawio import WinWcpRawIO -from neo.rawio.nwbrawio import NWBRawIO #, NWBReader # NWB format +#from neo.rawio.nwbrawio import NWBRawIO #, NWBReader # NWB format rawiolist = [ AxonRawIO, @@ -48,7 +48,7 @@ TdtRawIO, WinEdrRawIO, WinWcpRawIO, - NWBRawIO, # NWB format +# NWBRawIO, # NWB format ] import os diff --git a/neo/rawio/examplerawio.py b/neo/rawio/examplerawio.py index 592255523..cde6435fe 100644 --- a/neo/rawio/examplerawio.py +++ b/neo/rawio/examplerawio.py @@ -112,14 +112,19 @@ def _parse_header(self): # at the end real_signal = (raw_signal* gain + offset) * pq.Quantity(units) sig_channels = [] for c in range(16): +# print("range(16) = ", range(16)) ch_name = 'ch{}'.format(c) +# print("format(c) = ", format(c)) +# print("ch_name = ", ch_name) # our channel id is c+1 just for fun # Note that chan_id should be realated to # original channel id in the file format # so that the end user should not be lost when reading datasets chan_id = c + 1 +# print("chan_id = ", chan_id) sr = 10000. # Hz dtype = 'int16' +# print("dtype = ", dtype) units = 'uV' gain = 1000. / 2 ** 16 offset = 0. @@ -128,7 +133,9 @@ def _parse_header(self): # Here this is the general case :all channel have the same characteritics group_id = 0 sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id)) +# print("sig_channels.append = ", sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id))) sig_channels = np.array(sig_channels, dtype=_signal_channel_dtype) +# print("sig_channels = ", sig_channels) # creating units channels # This is mandatory!!!! @@ -163,8 +170,14 @@ def _parse_header(self): self.header['nb_block'] = 2 self.header['nb_segment'] = [2, 3] self.header['signal_channels'] = sig_channels + print("self.header['signal_channels] = ", self.header['signal_channels']) + print("self.header['signal_channels].size = ", self.header['signal_channels'].size) self.header['unit_channels'] = unit_channels + print("self.header['unit_channels] = ", self.header['unit_channels']) + print("self.header['unit_channels].size = ", self.header['unit_channels'].size) self.header['event_channels'] = event_channels + print("self.header['event_channels] = ", self.header['event_channels']) + print("self.header['event_channels].size = ", self.header['event_channels'].size) # insert some annotation at some place # at neo.io level IO are free to add some annoations @@ -276,6 +289,7 @@ def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_s ts_start = (self._segment_t_start(block_index, seg_index) * 10000) spike_timestamps = np.arange(0, 10000, 500) + ts_start + print("spike_timestamps = ", spike_timestamps) if t_start is not None or t_stop is not None: # restricte spikes to given limits (in seconds) From 3f5ae4d509b6f2162cb63e2a81228c7c4057420d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Thu, 31 Oct 2019 14:38:10 +0100 Subject: [PATCH 18/79] Support reading and writing a .nwb file --- neo/io/nwbio.py | 398 ++++++++++++---------------------- neo/test/iotest/test_nwbio.py | 32 +-- 2 files changed, 141 insertions(+), 289 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index e0e268421..7026376c2 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -29,8 +29,6 @@ from neo.io.baseio import BaseIO from neo.core import (Segment, SpikeTrain, Unit, Epoch, Event, AnalogSignal, IrregularlySampledSignal, ChannelIndex, Block) - -# neo imports from collections import OrderedDict # Standard Python imports @@ -47,85 +45,17 @@ from pynwb.ecephys import ElectricalSeries, Device, EventDetection from pynwb.behavior import SpatialSeries from pynwb.image import ImageSeries -#from pynwb.core import set_parents -from pynwb.spec import NWBAttributeSpec # Attribute Specifications -from pynwb.spec import NWBDatasetSpec # Dataset Specifications -from pynwb.spec import NWBGroupSpec -from pynwb.spec import NWBNamespace -from pynwb.spec import NWBNamespaceBuilder +from pynwb.spec import NWBAttributeSpec, NWBDatasetSpec, NWBGroupSpec, NWBNamespace, NWBNamespaceBuilder + +# hdmf imports from hdmf.spec import LinkSpec, GroupSpec, DatasetSpec, SpecNamespace,\ NamespaceBuilder, AttributeSpec, DtypeSpec, RefSpec from hdmf import * -# allensdk package -#import allensdk -#from allensdk import * -#from pynwb import load_namespaces -#from allensdk.brain_observatory.nwb.metadata import load_LabMetaData_extension -#from allensdk.brain_observatory.behavior.schemas import OphysBehaviorMetaDataSchema, OphysBehaviorTaskParametersSchema -#load_LabMetaData_extension(OphysBehaviorMetaDataSchema, 'AIBS_ophys_behavior') -#load_LabMetaData_extension(OphysBehaviorTaskParametersSchema, 'AIBS_ophys_behavior') - - -neo_extension = {"fs": {"neo": { - "info": { - "name": "Neo TimeSeries extension", - "version": "0.9.0", - "date": "2019", - "authors": "Elodie Legouée, Andrew Davison", - "contacts": "elodie.legouee@unic.cnrs-gif.fr, andrew.davison@unic.cnrs-gif.fr", - "description": ("Extension defining a new TimeSeries type, named 'MultiChannelTimeSeries'") - }, - - "schema": { - "/": { - "description": "Similar to ElectricalSeries, but without the restriction to volts", - "merge": ["core:/"], - "attributes": { - "ancestry": { - "data_type": "text", - "dimensions": ["2"], - "value": ["TimeSeries", "MultiChannelTimeSeries"], - "const": True}, - "help": { - "data_type": "text", - "value": "A multi-channel time series", - "const": True}}, - "data": { - "description": ("Multiple measurements are recorded at each point of time."), - "dimensions": ["num_times", "num_channels"], - "data_type": "float32"}, - }, - - "/": { - "description": "Represents a series of annotated time intervals", - "merge": ["core:/"], - "attributes": { - "ancestry": { - "data_type": "text", - "dimensions": ["3"], - "value": ["TimeSeries", "AnnotationSeries", "AnnotatedIntervalSeries"], - "const": True}, - "help": { - "data_type": "text", - "value": "A series of annotated time intervals", - "const": True}}, - "durations": { - "description": ("Durations for intervals whose start times are stored in timestamps."), - "data_type": "float64!", - "dimensions": ["num_times"], - "attributes": { - "unit": { - "description": ("The string \"Seconds\""), - "data_type": "text", "value": "Seconds"}} - }, - } - } -}}} class NWBIO(BaseIO): """ - Class for "reading" experimental data from a .nwb file. + Class for "reading" experimental data from a .nwb file, and "writing" a .nwb file """ is_readable = True @@ -148,22 +78,15 @@ def __init__(self, filename, mode): """ BaseIO.__init__(self, filename=filename) self.filename = filename -# if mode=='r': -# self.read_block() -# else: -# self.write_block() -## if mode=='w': -## self.write_block(self.block) def read_block(self, lazy=False, cascade=True, **kwargs): -# print("*** read_block ***") io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO _file = io.read() self._lazy = lazy file_access_dates = _file.file_create_date identifier = _file.identifier - if identifier == '_neo': # this is an automatically generated name used if block.name is None + if identifier == '_neo': identifier = None description = _file.session_description if description == "no description": @@ -186,12 +109,9 @@ def read_block(self, lazy=False, cascade=True, **kwargs): return block def write_block(self, block, **kwargs): -# print("*** ----------- write_block ------------ ***") - start_time = datetime.now() - self._file = NWBFile(self.filename, + nwbfile = NWBFile(self.filename, session_start_time=start_time, -# identifier=block.name or "_neo", identifier='test', file_create_date=None, timestamps_reference_time=None, @@ -219,9 +139,7 @@ def write_block(self, block, **kwargs): epoch_tags=set(), trials=None, invalid_times=None, - time_intervals=None, units=None, - modules=None, electrodes=None, electrode_groups=None, ic_electrodes=None, @@ -229,66 +147,71 @@ def write_block(self, block, **kwargs): imaging_planes=None, ogen_sites=None, devices=None, - #subject=None + subject=None ) - io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') -# print("io_nwb = ", io_nwb) - - file_access_dates = self._file.file_create_date - identifier = self._file.identifier - if identifier == '_neo': # this is an automatically generated name used if block.name is None - identifier = None - description = self._file.session_description - if description == "no description": - description = None -# print("block.segments = ", block.segments) for segment in block.segments: - print("segment = ", segment) - self._write_segment(self._file, segment) + self._write_segment(nwbfile, segment) - print("END loop block.segment") - io_nwb.write(self._file) - print("io_nwb.write") + io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') + io_nwb.write(nwbfile) io_nwb.close() - print("io_nwb.close") def _handle_general_group(self, block): pass def _handle_epochs_group(self, _file, block): # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. - epochs = _file.acquisition - for key in epochs: - timeseries = [] - current_shape = _file.get_acquisition(key).data.shape[0] # or 1 if multielectrode ? - times = np.zeros(current_shape) - - for j in range(0, current_shape):# to do w/ ecephys data (e.g. multielectrode: how is it organised?) - times[j]=1./_file.get_acquisition(key).rate*j+_file.get_acquisition(key).starting_time - if times[j] == _file.get_acquisition(key).starting_time: - t_start = times[j] * pq.second - elif times[j]==times[-1]: - t_stop = times[j] * pq.second - else: - timeseries.append(self._handle_timeseries(_file, key, times[j])) - segment = Segment(name=j) - for obj in timeseries: - obj.segment = segment - if isinstance(obj, AnalogSignal): - segment.analogsignals.append(obj) - elif isinstance(obj, IrregularlySampledSignal): - segment.irregularlysampledsignals.append(obj) - elif isinstance(obj, Event): - segment.events.append(obj) - elif isinstance(obj, Epoch): - segment.epochs.append(obj) - segment.block = block - block.segments.append(segment) - - segment.times=times - return segment, obj, times - + epochs = _file.epochs + timeseries=[] + if epochs is not None: + t_start = epochs[0][1] * pq.second + t_stop = epochs[0][2] * pq.second + else: + timeseries.append(self._handle_timeseries(_file, self.name, timeseries)) + segment = Segment(name=self.name) + + for obj in timeseries: + obj.segment = segment + if isinstance(obj, AnalogSignal): + segment.analogsignals.append(obj) + elif isinstance(obj, IrregularlySampledSignal): + segment.irregularlysampledsignals.append(obj) + elif isinstance(obj, Event): + segment.events.append(obj) + elif isinstance(obj, Epoch): + segment.epochs.append(obj) + segment.block = block + block.segments.append(segment) + +# for key in epochs: +# timeseries = [] +# current_shape = _file.get_acquisition(key).data.shape[0] # or 1 if multielectrode ? +# times = np.zeros(current_shape) +# +# for j in range(0, current_shape):# to do w/ ecephys data (e.g. multielectrode: how is it organised?) +# times[j]=1./_file.get_acquisition(key).rate*j+_file.get_acquisition(key).starting_time +# if times[j] == _file.get_acquisition(key).starting_time: +# t_start = times[j] * pq.second +# elif times[j]==times[-1]: +# t_stop = times[j] * pq.second +# else: +# timeseries.append(self._handle_timeseries(_file, key, times[j])) +# segment = Segment(name=j) +# for obj in timeseries: +# obj.segment = segment +# if isinstance(obj, AnalogSignal): +# segment.analogsignals.append(obj) +# elif isinstance(obj, IrregularlySampledSignal): +# segment.irregularlysampledsignals.append(obj) +# elif isinstance(obj, Event): +# segment.events.append(obj) +# elif isinstance(obj, Epoch): +# segment.epochs.append(obj) +# segment.block = block +# block.segments.append(segment) +# segment.times=times +# return segment, obj, times def _handle_timeseries(self, _file, name, timeseries): for i in _file.acquisition: @@ -370,43 +293,31 @@ def _handle_processing_group(self, block): def _handle_analysis_group(self, block): pass - def _write_segment(self, _file, segment): + def _write_segment(self, nwbfile, segment): start_time = segment.t_start stop_time = segment.t_stop - nwb_epoch = self._file.add_epoch( - self._file, + nwb_epoch = nwbfile.add_epoch( + nwbfile, segment.name, start_time=float(start_time), stop_time=float(stop_time), ) for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): - #print("signal = ", signal) - print("i = ", i) - self._write_signal(signal, nwb_epoch, i, segment) - self._write_spiketrains(segment.spiketrains, segment) + self._write_signal(nwbfile, signal, nwb_epoch, i, segment) + self._write_spiketrains(nwbfile, segment.spiketrains, segment) for i, event in enumerate(segment.events): -# print("event = ", event) - self._write_event(event, nwb_epoch, i) + self._write_event(nwbfile, event, nwb_epoch, i) for i, neo_epoch in enumerate(segment.epochs): -# print("neo_epoch = ", neo_epoch) - self._write_neo_epoch(neo_epoch, nwb_epoch, i) - - - def _write_signal(self, signal, epoch, i, segment): - # i=index - print("-------------------------------- segment.ind = ", segment.index) - print("*** def _write_signal ***") - print("segment.name = ", segment.name) # index - -# print("i = ", i) + self._write_neo_epoch(nwbfile, neo_epoch, nwb_epoch, i) + def _write_signal(self, nwbfile, signal, epoch, i, segment): signal_name = signal.name or "signal{0}".format(i) ts_name = "{0}".format(signal_name) + """ # Create a builder for the namespace ns_builder_signal = NWBNamespaceBuilder('Extension to neo signal', "neo_signal") -# print("ns_builder_signal = ", ns_builder_signal) ns_builder_signal.include_type('TimeSeries', namespace='core') # Group Specifications @@ -419,15 +330,12 @@ def _write_signal(self, signal, epoch, i, segment): neurodata_type_inc='TimeSeries', neurodata_type_def='MultiChannelTimeSeries' ) - print("ts_signal = ", ts_signal) - print(" ") # Add the extension ext_source_signal = 'nwb_neo_extension_signal.specs.yaml' ns_builder_signal.add_spec(ext_source_signal, ts_signal ) -# print("ns_builder_signal = ", ns_builder_signal) # Save the namespace and extensions ns_path_signal = "nwb_neo_extension_signal.namespace.yaml" @@ -436,67 +344,21 @@ def _write_signal(self, signal, epoch, i, segment): # Incorporating extensions load_namespaces(ns_path_signal) -# NWBSignalSeries = get_class('MultiChannelTimeSeries', 'neo_signal') # Classe abstraite ! # TimeSeries NWBSignalSeries = get_class('TimeSeries', 'neo_signal') # class pynwb.base.TimeSeries # NWB File - #NWBSignalSeries = get_class('NWBFile', namespace='core') # class pynwb.base.TimeSeries -# print("NWBSignalSeries = ", NWBSignalSeries) - - # NWB File -# self._file - -### pynwb.file = NWB File -# ts = NWBSignalSeries( -# identifier='', -# session_description='session_description', -# session_start_time=datetime(2019, 10,22) -# ) - -# # TimeSeries -# ts = NWBSignalSeries( -# name='', -# data=np.arange(10), -# resolution=3.0, -# rate=10.0, -# unit='unit of data', -# ) - - -# MultiChannelTimeSeries = pynwb.core.NWBDataInterface(name='test_multi') -# print("MultiChannelTimeSeries = ", MultiChannelTimeSeries) - - +### NWBSignalSeries = get_class('NWBFile', namespace='core') # class pynwb.base.TimeSeries - - #ts = NWBSignalSeries('MultiChannelTimeSeries', time_series=self._file ,rate=1.0) ts = NWBSignalSeries( -### ts = TimeSeries( 'MultiChannelTimeSeries123_index_%d_%s' % (i, segment.name), #index - #'MultiChannelTimeSeries123_%d_%s' % (ind, segment.name), #index -# 'MultiChannelTimeSeries123_%s' % (segment.name), #index #'TimeSeries', # name of the class - [ts_signal], + [ts_signal], + #'', + #session_start_time=datetime.now(), rate=1.0 ) - ##ts = NWBSignalSeries('MultiChannelTimeSeries', time_series=MultiChannelTimeSeries ,rate=1.0) - print(" ") - print("ts = ", ts) - print(" ") -# print("self._file = ", self._file) - print("self._file.acquisition = ", self._file.acquisition) -# print("self._file.epochs = ", self._file.epochs) - # self._file.add_acquisition(ts) -# print("ok") - - ###test_ac = self._file.add_acquisition(ts) -# test_ac = self._file.get_acquisition('MultiChannelTimeSeries') -# print("test_ac = ", test_ac) - - """ - # create a builder for the namespace - ns_builder = NWBNamespaceBuilder("Extension for use in my laboratory", "mylab") +# nwbfile.add_acquisition(ts) """ @@ -505,22 +367,25 @@ def _write_signal(self, signal, epoch, i, segment): "resolution": float('nan')} if isinstance(signal, AnalogSignal): - print("isinstance(signal, AnalogSignal)") - test_ac = self._file.add_acquisition(ts) - print("test_ac = ", test_ac) - sampling_rate = signal.sampling_rate.rescale("Hz") - signal.sampling_rate = sampling_rate - ts_signal.add_dataset( - doc='', - neurodata_type_def='MultiChannelTimeSeries', - ) + signal.sampling_rate = sampling_rate + + # All signals should go in /acquisition + tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, rate=float(sampling_rate)) + ts = nwbfile.add_acquisition(tS) + elif isinstance(signal, IrregularlySampledSignal): + tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, timestamps=signal.times.rescale('second').magnitude) + ts = nwbfile.add_acquisition(tS) else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) - print("END def _write_signal") - def _write_spiketrains(self, spiketrains, segment): - print("*** def _write_spiketrains ***") + nwbfile.add_epoch( + epoch, + start_time=time_in_seconds(segment.t_start), + stop_time=time_in_seconds(segment.t_stop), + ) + + def _write_spiketrains(self, nwbfile, spiketrains, segment): """ mod = NWBGroupSpec('A custom TimeSeries interface', attributes=[], @@ -536,60 +401,63 @@ def _write_spiketrains(self, spiketrains, segment): ) """ -# def _write_event(self, _file, event, nwb_epoch): - def _write_event(self, event, nwb_epoch, i): - print("*** def _write_event ***") + mod = nwbfile.add_unit_column("Modules", "description Modules") - """ + # create interfaces + spiketrain_group = nwbfile.add_unit_column("UnitTimes", "description") + + fmt = 'unit_{{0:0{0}d}}_{1}'.format(len(str(len(spiketrains))), segment.name) + for i, spiketrain in enumerate(spiketrains): + unit = fmt.format(i) + ug = nwbfile.add_unit( + spike_times=spiketrain.rescale('second').magnitude, + Modules='', + UnitTimes='', + ) + + def _write_event(self, nwbfile, event, nwb_epoch, i): event_name = event.name or "event{0}".format(i) -# print("event_name = ", event_name) ts_name = "{0}".format(event_name) -# print("ts_name = ", ts_name) + """ ts = NWBGroupSpec('A custom TimeSeries interface', attributes=[], datasets=[], groups=[], neurodata_type_inc='TimeSeries', neurodata_type_def='AnnotationSeries') -# print("ts = ", ts) - ext_source = 'nwb_neo_extension.specs.yaml' ts.add_dataset( doc='', neurodata_type_def='AnnotationSeries', ) - - self._file.add_epoch( + nwbfile.add_epoch( time_in_seconds(event.times[0]), time_in_seconds(event.times[1]), ) """ + tS = TimeSeries( + name=ts_name, + data=event, + timestamps=event.times.rescale('second').magnitude, + description=event.description or "", + ) + ts = nwbfile.add_acquisition(tS) + + nwbfile.add_epoch(nwb_epoch, + start_time=time_in_seconds(event.times[0]), + stop_time=time_in_seconds(event.times[1]), + ) - def _write_neo_epoch(self, neo_epoch, nwb_epoch, i): - print("*** def _write_neo_epoch ***") + def _write_neo_epoch(self, nwbfile, neo_epoch, nwb_epoch, i): neo_epoch_name = neo_epoch.name or "intervalseries{0}".format(i) -# print("neo_epoch_name = ", neo_epoch_name) -# ts_name = "{0}_{1}".format(neo_epoch.segment.name, neo_epoch_name) -# print("ts_name = ", ts_name) - -# ts.set_dataset("timestamps", neo_epoch.times.rescale('second').magnitude) -# ts.set_dataset("durations", neo_epoch.durations.rescale('second').magnitude) -# ts.set_dataset("data", neo_epoch.labels) -# ts.set_attr("source", neo_epoch.name or "unknown") -# ts.set_attr("description", neo_epoch.description or "") - -# print(" ") -### neo_AnnotatedIntervalSeries = neo_extension["fs"]["neo"]["schema"]["/"] -### print("neo_AnnotatedIntervalSeries = ", neo_AnnotatedIntervalSeries) - + ts_name = "{0}".format(neo_epoch_name) - + """ # Create a builder for the namespace ns_builder_neo_epoch = NWBNamespaceBuilder('Extension to neo epoch', "neo_epoch") # ns_builder = NWBNamespaceBuilder('Extension to neo epoch', "neo_AnnotatedIntervalSeries") -# print("ns_builder = ", ns_builder) ns_builder_neo_epoch.include_type('TimeSeries', namespace='core') # ns_builder.include_type('neo_AnnotatedIntervalSeries', namespace='core') @@ -603,14 +471,10 @@ def _write_neo_epoch(self, neo_epoch, nwb_epoch, i): neurodata_type_inc='TimeSeries', neurodata_type_def='AnnotatedIntervalSeries' ) -# print("ts = ", ts) -# print(" ") - # Add the extension ext_source_neo_epoch = 'nwb_neo_extension.specs.yaml' ns_builder_neo_epoch.add_spec(ext_source_neo_epoch, - ts_neo_epoch ) @@ -619,19 +483,27 @@ def _write_neo_epoch(self, neo_epoch, nwb_epoch, i): # Save the namespace and extensions ns_path_neo_epoch = "nwb_neo_extension.namespace.yaml" -# print(" ") ns_builder_neo_epoch.export(ns_path_neo_epoch) # ns_builder.export("AnnotatedIntervalSeries") load_namespaces(ns_path_neo_epoch) -# AutoNeoEpochSeries = get_class('AnnotatedIntervalSeries', 'neo_epoch') - AutoNeoEpochSeries = get_class('TimeSeries', 'neo_epoch') -# print("AutoNeoEpochSeries = ", AutoNeoEpochSeries) - - print("END def _write_neo_epoch") - +###### AutoNeoEpochSeries = get_class('TimeSeries', 'neo_epoch') + """ + tS = TimeSeries( + name=ts_name, + data=neo_epoch, + timestamps=neo_epoch.times.rescale('second').magnitude, + description=neo_epoch.description or "", + ) + ts = nwbfile.add_acquisition(tS) + + nwbfile.add_epoch( + nwb_epoch, + start_time=time_in_seconds(neo_epoch.times[0]), + stop_time=time_in_seconds(neo_epoch.times[-1]), + ) def time_in_seconds(t): return float(t.rescale("second")) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 35f6dce3a..5657b5f64 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -7,22 +7,12 @@ import unittest from neo.io.nwbio import NWBIO from neo.test.iotest.common_io_test import BaseTestIO +from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Unit, Block, ChannelIndex import pynwb from pynwb import * - -from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Unit, Block, ChannelIndex import quantities as pq import numpy as np -# allensdk package -#import allensdk -#from allensdk import * -#from pynwb import load_namespaces -#from allensdk.brain_observatory.nwb.metadata import load_LabMetaData_extension -#from allensdk.brain_observatory.behavior.schemas import OphysBehaviorMetaDataSchema, OphysBehaviorTaskParametersSchema -#load_LabMetaData_extension(OphysBehaviorMetaDataSchema, 'AIBS_ophys_behavior') -#load_LabMetaData_extension(OphysBehaviorTaskParametersSchema, 'AIBS_ophys_behavior') - class TestNWBIO(unittest.TestCase, ): ioclass = NWBIO files_to_download = [ @@ -41,12 +31,12 @@ class TestNWBIO(unittest.TestCase, ): # File written with NWBIO class() ### '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb.nwb' ### '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' +# '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO_2.nwb' ] entities_to_test = files_to_download def test_nwbio(self): -# print("*** def test_nwbio ***") # read the blocks reader = NWBIO(filename=self.files_to_download[0], mode='r') blocks = reader.read(lazy=False) @@ -54,7 +44,6 @@ def test_nwbio(self): for block in blocks: # Tests of Block self.assertTrue(isinstance(block.name, str)) - self.assertTrue(block.segments, Segment) # Segment for segment in block.segments: self.assertEqual(segment.block, block) @@ -68,7 +57,6 @@ def test_nwbio(self): self.assertTrue(isinstance(st, SpikeTrain)) def test_segment(self, **kargs): -# print("*** def test_segment ***") seg = Segment(index=5) r = NWBIO(filename=self.files_to_download[0], mode='r') seg_nwb = r.read() @@ -78,7 +66,6 @@ def test_segment(self, **kargs): self.assertIsNotNone(seg_nwb, seg) def test_analogsignals_neo(self, **kargs): -# print("*** def test_analogsignals_neo ***") sig_neo = AnalogSignal(signal=[1, 2, 3], units='V', t_start=np.array(3.0)*pq.s, sampling_rate=1*pq.Hz) self.assertTrue(isinstance(sig_neo, AnalogSignal)) r = NWBIO(filename=self.files_to_download[0], mode='r') @@ -87,7 +74,6 @@ def test_analogsignals_neo(self, **kargs): self.assertTrue(obj_nwb, sig_neo) def test_read_irregularlysampledsignal(self, **kargs): -# print("*** def test_read_irregularlysampledsignal ***") irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], units='mV', time_units='ms') irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*pq.s, [[4, 5], [5, 4], [6, 3]]*pq.nA) self.assertTrue(isinstance(irsig0, IrregularlySampledSignal)) @@ -99,7 +85,6 @@ def test_read_irregularlysampledsignal(self, **kargs): self.assertTrue(irsig_nwb, irsig1) def test_read_event(self, **kargs): -# print("*** def test_read_event ***") evt_neo = Event(np.arange(0, 30, 10)*pq.s, labels=np.array(['trig0', 'trig1', 'trig2'], dtype='S')) r = NWBIO(filename=self.files_to_download[0], mode='r') event_nwb = r.read() @@ -107,7 +92,6 @@ def test_read_event(self, **kargs): self.assertIsNotNone(event_nwb, evt_neo) def test_read_epoch(self, **kargs): -# print("*** def test_read_epoch ***") epc_neo = Epoch(times=np.arange(0, 30, 10)*pq.s, durations=[10, 5, 7]*pq.ms, labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) @@ -117,19 +101,17 @@ def test_read_epoch(self, **kargs): self.assertTrue(epoch_nwb, epc_neo) self.assertIsNotNone(epoch_nwb, epc_neo) - """ def test_write_NWB_File(self): -# print("*** def test_write_NWB_File ***") ''' Test function to write a segment. ''' # Create a Block with 3 Segment and 2 ChannelIndex objects blk = Block() - for ind in range(3): + for ind in range(1): seg = Segment(name='segment_%d' % ind, index=ind) blk.segments.append(seg) - for ind in range(2): + for ind in range(2): chx = ChannelIndex(name='Array probe %d' % ind, index=np.arange(64)) blk.channel_indexes.append(chx) @@ -159,12 +141,10 @@ def test_write_NWB_File(self): # Save the file filename = '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' -# print("filename = ", filename) w_file = NWBIO(filename=filename, mode='w') # Write the .nwb file + print("w_file = ", w_file) blocks = w_file.write(blk) -# print("w_file = ", w_file) - """ - + if __name__ == "__main__": print("pynwb.__version__ = ", pynwb.__version__) From 205a298bb2578c5859129c45dc4091bc0fe07e43 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Fri, 22 Nov 2019 14:25:54 +0100 Subject: [PATCH 19/79] Several blocks and segments --- neo/io/nwbio.py | 127 +++++++++++++++++-- neo/test/iotest/test_nwbio.py | 231 ++++++++++++++++++++++++++++------ 2 files changed, 310 insertions(+), 48 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 7026376c2..1cb566588 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -69,7 +69,8 @@ class NWBIO(BaseIO): name = 'NWB' description = 'This IO reads/writes experimental data from/to an .nwb dataset' extensions = ['nwb'] - mode = 'one-file' +# mode = 'one-file' + mode = 'file' def __init__(self, filename, mode): """ @@ -79,8 +80,46 @@ def __init__(self, filename, mode): BaseIO.__init__(self, filename=filename) self.filename = filename + + + + + def read_all_blocks(self, blocks, lazy=False, **kwargs): +# def read_all_blocks(self, **kwargs): +# def read_all_blocks(self, *blocks, lazy=False, **kwargs): + """ + Read all blocks from the file + """ + + print("*** def read_all_blocks ***") + + if Block in self.readable_objects: + print("Block = ", Block) + # print("blocks = ", blocks) + print(" ") + for block in blocks: + print("-------------------------") + print("*-* block.name = ", block.name) + print("block = ", block) + self.read_block(block) + print("blocks = ", blocks) + print(" ") + print("Test") + print(" ") + print(" ") + print(" ") + return list(self.read_block(block) + for block in blocks + ) + + def read_block(self, lazy=False, cascade=True, **kwargs): - io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO + """ + Read a Block from the file + """ + + print("*** def read_block ***") + io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO _file = io.read() self._lazy = lazy @@ -91,6 +130,7 @@ def read_block(self, lazy=False, cascade=True, **kwargs): description = _file.session_description if description == "no description": description = None + block = Block(name=identifier, description=description, file_origin=self.filename, @@ -98,6 +138,7 @@ def read_block(self, lazy=False, cascade=True, **kwargs): rec_datetime=_file.session_start_time, file_access_dates=file_access_dates, file_read_log='') + if cascade: self._handle_general_group(block) self._handle_epochs_group(_file, block) @@ -106,13 +147,45 @@ def read_block(self, lazy=False, cascade=True, **kwargs): self._handle_processing_group(block) self._handle_analysis_group(block) self._lazy = False + print("--- block in read_block() = ", block) + print("END def read_block") + print(" ") return block + + + + + def write_all_blocks(self, blocks): +# def write_all_blocks(self, *blocks, **kwargs): + """ + Write list of blocks to the file + """ + + print("*** def write_all_blocks ***") + + print("blocks = ", blocks) + if Block in self.writeable_objects: + print("Block = ", Block) + for block in blocks: + print("block = ", block) + self.write_block(block) + print("END loop Block in def write_all_blocks") + + + + +# def write_block(self, *block, **kwargs): def write_block(self, block, **kwargs): + """ + Write a Block to the file + """ + + print("*** def write_block ***") start_time = datetime.now() nwbfile = NWBFile(self.filename, session_start_time=start_time, - identifier='test', + identifier='', file_create_date=None, timestamps_reference_time=None, experimenter=None, @@ -150,12 +223,38 @@ def write_block(self, block, **kwargs): subject=None ) - for segment in block.segments: + +# for num_blk in range(len(block.name)): # loop on blocks +## for num_blk in block: # loop on blocks +# print("num_blk = ", num_blk) +# +# name_block = 'block_%d' %num_blk +# print("name_block = ", name_block) +# print("block.segments = ", block.segments) +# +# for segment in block.segments: # loop on segments +# print("segment = ", segment) +# self._write_segment(nwbfile, segment) +# print("OK") + + + print("*************************************************block = ", block) + print("block.segments = ", block.segments) +#################################################################### + ## return list(block.segments) + + for segment in block.segments: + print("------ segment = ", segment) self._write_segment(nwbfile, segment) + print("END of loop on segment") + return list(block.segments) ######################### io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') + print("io_nwb = ", io_nwb) io_nwb.write(nwbfile) + print("Write the file") io_nwb.close() + print("Close the file") def _handle_general_group(self, block): pass @@ -294,6 +393,7 @@ def _handle_analysis_group(self, block): pass def _write_segment(self, nwbfile, segment): + print("*** def _write_segment ***") start_time = segment.t_start stop_time = segment.t_stop @@ -304,6 +404,13 @@ def _write_segment(self, nwbfile, segment): stop_time=float(stop_time), ) for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): + print("++++++++++++++++++++++++ signal = ", signal) + print("segment.analogsignals", segment.analogsignals) + print("signal.name = ", signal.name) #### + ######################################################################## +# return list(signal for segment.analogsignals in signal) ################################################################################## + return list(segment.analogsignals for signal in segment.analogsignals) + self._write_signal(nwbfile, signal, nwb_epoch, i, segment) self._write_spiketrains(nwbfile, segment.spiketrains, segment) for i, event in enumerate(segment.events): @@ -311,8 +418,11 @@ def _write_segment(self, nwbfile, segment): for i, neo_epoch in enumerate(segment.epochs): self._write_neo_epoch(nwbfile, neo_epoch, nwb_epoch, i) - def _write_signal(self, nwbfile, signal, epoch, i, segment): + def _write_signal(self, nwbfile, signal, epoch, i, segment): + print("*** def _write_signal ***") + print("signal", signal) signal_name = signal.name or "signal{0}".format(i) + print("signal_name = ", signal_name) ts_name = "{0}".format(signal_name) """ @@ -361,29 +471,28 @@ def _write_signal(self, nwbfile, signal, epoch, i, segment): # nwbfile.add_acquisition(ts) """ - conversion = _decompose_unit(signal.units) attributes = {"conversion": conversion, "resolution": float('nan')} if isinstance(signal, AnalogSignal): sampling_rate = signal.sampling_rate.rescale("Hz") - signal.sampling_rate = sampling_rate + signal.sampling_rate = sampling_rate # All signals should go in /acquisition tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, rate=float(sampling_rate)) - ts = nwbfile.add_acquisition(tS) + ts = nwbfile.add_acquisition(tS) elif isinstance(signal, IrregularlySampledSignal): tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, timestamps=signal.times.rescale('second').magnitude) ts = nwbfile.add_acquisition(tS) else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) - nwbfile.add_epoch( epoch, start_time=time_in_seconds(segment.t_start), stop_time=time_in_seconds(segment.t_stop), ) + print("END def _write_signal") def _write_spiketrains(self, nwbfile, spiketrains, segment): """ diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 5657b5f64..1cbd2fcdc 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -32,6 +32,7 @@ class TestNWBIO(unittest.TestCase, ): ### '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb.nwb' ### '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' # '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO_2.nwb' +### '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' ] entities_to_test = files_to_download @@ -39,7 +40,20 @@ class TestNWBIO(unittest.TestCase, ): def test_nwbio(self): # read the blocks reader = NWBIO(filename=self.files_to_download[0], mode='r') - blocks = reader.read(lazy=False) + print("reader = ", reader) +# print("reader.read() = ", reader.read()) + + print("reader.read_block() = ", reader.read_block()) + print(" ") +# blocks = reader.read(lazy=False) + + #------------------------------------------------------- + blocks=[] + for ind in range(2): # 2 blocks + blk = Block(name='%s' %ind) + blocks.append(blk) + #------------------------------------------------------- + # access to segments for block in blocks: # Tests of Block @@ -56,10 +70,23 @@ def test_nwbio(self): for st in segment.spiketrains: self.assertTrue(isinstance(st, SpikeTrain)) + def test_segment(self, **kargs): seg = Segment(index=5) r = NWBIO(filename=self.files_to_download[0], mode='r') - seg_nwb = r.read() + + +# #------------------------------------------------------- +# blocks=[] +# for ind in range(2): # 2 blocks +# blk = Block(name='%s' %ind) +# blocks.append(blk) +# #------------------------------------------------------- +# seg_nwb = r.read() +## seg_nwb = r.read(blocks) # equivalent to read_all_blocks() + + + seg_nwb = r.read_block() self.assertTrue(seg, Segment) self.assertTrue(seg_nwb, Segment) self.assertTrue(seg_nwb, seg) @@ -69,17 +96,20 @@ def test_analogsignals_neo(self, **kargs): sig_neo = AnalogSignal(signal=[1, 2, 3], units='V', t_start=np.array(3.0)*pq.s, sampling_rate=1*pq.Hz) self.assertTrue(isinstance(sig_neo, AnalogSignal)) r = NWBIO(filename=self.files_to_download[0], mode='r') - obj_nwb = r.read() +# obj_nwb = r.read() + obj_nwb = r.read_block() self.assertTrue(obj_nwb, AnalogSignal) self.assertTrue(obj_nwb, sig_neo) + def test_read_irregularlysampledsignal(self, **kargs): irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], units='mV', time_units='ms') irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*pq.s, [[4, 5], [5, 4], [6, 3]]*pq.nA) self.assertTrue(isinstance(irsig0, IrregularlySampledSignal)) self.assertTrue(isinstance(irsig1, IrregularlySampledSignal)) r = NWBIO(filename=self.files_to_download[0], mode='r') - irsig_nwb = r.read() +# irsig_nwb = r.read() + irsig_nwb = r.read_block() self.assertTrue(irsig_nwb, IrregularlySampledSignal) self.assertTrue(irsig_nwb, irsig0) self.assertTrue(irsig_nwb, irsig1) @@ -87,7 +117,8 @@ def test_read_irregularlysampledsignal(self, **kargs): def test_read_event(self, **kargs): evt_neo = Event(np.arange(0, 30, 10)*pq.s, labels=np.array(['trig0', 'trig1', 'trig2'], dtype='S')) r = NWBIO(filename=self.files_to_download[0], mode='r') - event_nwb = r.read() +# event_nwb = r.read() + event_nwb = r.read_block() self.assertTrue(event_nwb, evt_neo) self.assertIsNotNone(event_nwb, evt_neo) @@ -96,54 +127,176 @@ def test_read_epoch(self, **kargs): durations=[10, 5, 7]*pq.ms, labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) r = NWBIO(filename=self.files_to_download[0], mode='r') - epoch_nwb = r.read() +# epoch_nwb = r.read() + epoch_nwb = r.read_block() self.assertTrue(epoch_nwb, Epoch) self.assertTrue(epoch_nwb, epc_neo) self.assertIsNotNone(epoch_nwb, epc_neo) + + def test_write_NWB_File(self): ''' Test function to write a segment. ''' - # Create a Block with 3 Segment and 2 ChannelIndex objects - blk = Block() - for ind in range(1): - seg = Segment(name='segment_%d' % ind, index=ind) - blk.segments.append(seg) - - for ind in range(2): - chx = ChannelIndex(name='Array probe %d' % ind, index=np.arange(64)) - blk.channel_indexes.append(chx) - - # Populate the Block with AnalogSignal objects - for seg in blk.segments: - for chx in blk.channel_indexes: - # AnalogSignal - a = AnalogSignal(signal=[1, 2, 3], units='V', t_start=np.array(3.0)*pq.s, sampling_rate=1*pq.Hz) - chx.analogsignals.append(a) - seg.analogsignals.append(a) - # SpikeTrain - t = SpikeTrain([3, 4, 5]*pq.s, t_stop=10.0) - seg.spiketrains.append(t) - # Epoch - epc = Epoch(times=np.arange(0, 30, 10)*pq.s, - durations=[10, 5, 7]*pq.ms - ) - seg.epochs.append(epc) - # Event - evt = Event(np.arange(0, 30, 20)*pq.s) - seg.events.append(evt) - # Unit - unit = Unit(name='pyramidal neuron') - unit.spiketrains.append(t) - # IrregularlySampledSignal - seg.irregularlysampledsignals.append(a) + # Create a Block with 1 Segment and 2 ChannelIndex objects + blocks = [] + num_segment=1 # number of segment + segment_durations = [5*pq.s, 13*pq.s] + + for ind in range(2): # loop on blocks + blk = Block(name='block_%s' %ind) + + for seg_num in range(num_segment): # loop on segments + seg = Segment(name=f'Seg {seg_num}') + blk.segments.append(seg) + + for seg_index in range(num_segment): # loop on ChannelIndex + sampling_rate = 80*pq.Hz + num_channel = 2 + duration = segment_durations[seg_index] + length = int((sampling_rate*duration).simplified) + np_sig = np.random.randn(length, num_channel).astype('float32') + + anasig = AnalogSignal(np_sig, units='cm', sampling_rate=sampling_rate) + anasig.annotate(data_type='tracking') + anasig.array_annotate(channel_names=['lfp_{}'.format(ch) for ch in range(num_channel)]) + blk.segments[seg_index].analogsignals.append(anasig) # + blocks.append(blk) + + + + +# for num_blk in range(2): # for 2 blocks +# blk = Block(name='%s' %num_blk) +# for ind in range(2): +# seg = Segment(name='segment_%d' % ind, index=ind) +# blk.segments.append(seg) +## blocks.append(blk) +# +# blk = Block() +# for ind in range(1): +# seg = Segment(name='segment_%d' % ind, index=ind) +# blk.segments.append(seg) +# +# for ind in range(2): +# chx = ChannelIndex(name='Array probe %d' % ind, index=np.arange(64)) +# blk.channel_indexes.append(chx) +# +# # Populate the Block with AnalogSignal objects +# for seg in blk.segments: +# for chx in blk.channel_indexes: +# # AnalogSignal +# a = AnalogSignal(signal=[1, 2, 3], units='V', t_start=np.array(3.0)*pq.s, sampling_rate=1*pq.Hz) +# chx.analogsignals.append(a) +# seg.analogsignals.append(a) +# # SpikeTrain +# t = SpikeTrain([3, 4, 5]*pq.s, t_stop=10.0) +# seg.spiketrains.append(t) +# # Epoch +# epc = Epoch(times=np.arange(0, 30, 10)*pq.s, +# durations=[10, 5, 7]*pq.ms +# ) +# seg.epochs.append(epc) +# # Event +# evt = Event(np.arange(0, 30, 20)*pq.s) +# seg.events.append(evt) +# # Unit +# unit = Unit(name='pyramidal neuron') +# unit.spiketrains.append(t) +# # IrregularlySampledSignal +# seg.irregularlysampledsignals.append(a) +# print("blocks = ", blocks) # Save the file filename = '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' w_file = NWBIO(filename=filename, mode='w') # Write the .nwb file print("w_file = ", w_file) blocks = w_file.write(blk) +# blocks = w_file.write_all_blocks(blk) + + + + + + + """ + def test_2_write_NWB_File(self): + blocks = [] + for ind in range(2): # 2 blocks + blk = Block(name='%s' %ind) + blocks.append(blk) + + for ind in range(3): # 3 Segment + seg = Segment(name='segment %d' % ind, index=ind) + blk.segments.append(seg) + + for ind in range(2): # 2 ChannelIndex + chx = ChannelIndex(name='Array probe %d' % ind, index=np.arange(64)) + blk.channel_indexes.append(chx) + + for seg in blk.segments: # AnalogSignal objects + for chx in blk.channel_indexes: + a = AnalogSignal(np.random.randn(10000, 64)*pq.nA, sampling_rate=10*pq.kHz) + chx.analogsignals.append(a) + seg.analogsignals.append(a) + + + # Save the file + filename = '/home/elodie/env_NWB_py3/my_notebook/second_first_test_neo_to_nwb_test_NWBIO.nwb' + w_file = NWBIO(filename=filename, mode='w') # Write the .nwb file + print("w_file = ", w_file) + blocks = w_file.write(blk) +# blocks = w_file.write_all_blocks(blk) + """ + + + + + + + + + + + + + """ + def test_write_all_NWB_Files(self): + ''' + Test function to write all blocks. + ''' + # Create a Block with 1 Segment and 2 ChannelIndex objects + blocks = [] + num_segment=1 # number of segment + segment_durations = [5*pq.s, 13*pq.s] + + for ind in range(2): # loop on blocks + blk = Block(name='block_%s' %ind) + + for seg_num in range(num_segment): # loop on segments + seg = Segment(name=f'Seg {seg_num}') + blk.segments.append(seg) + + for seg_index in range(num_segment): # loop on ChannelIndex + sampling_rate = 80*pq.Hz + num_channel = 2 + duration = segment_durations[seg_index] + length = int((sampling_rate*duration).simplified) + np_sig = np.random.randn(length, num_channel).astype('float32') + + anasig = AnalogSignal(np_sig, units='cm', sampling_rate=sampling_rate) + anasig.annotate(data_type='tracking') + anasig.array_annotate(channel_names=['lfp_{}'.format(ch) for ch in range(num_channel)]) + blk.segments[seg_index].analogsignals.append(anasig) # + blocks.append(blk) + + # Save the file + filename = '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO_all_blocks.nwb' + w_file = NWBIO(filename=filename, mode='w') # Write the .nwb file + print("w_file = ", w_file) + blocks = w_file.write_all_blocks(blk) + """ if __name__ == "__main__": From d90ec09ed4bf521e7aa2958d927176a6d0451875 Mon Sep 17 00:00:00 2001 From: legouee Date: Fri, 22 Nov 2019 14:50:26 +0100 Subject: [PATCH 20/79] minor modif --- neo/io/nwbio.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 1cb566588..20e7d1f7a 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -118,7 +118,7 @@ def read_block(self, lazy=False, cascade=True, **kwargs): Read a Block from the file """ - print("*** def read_block ***") + print("**** def read_block ****") io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO _file = io.read() self._lazy = lazy From 1e5d449c67fdf4c203c077794078ce20ea090413 Mon Sep 17 00:00:00 2001 From: legouee Date: Fri, 29 Nov 2019 20:51:20 +0100 Subject: [PATCH 21/79] NWB files with several blocks --- neo/io/nwbio.py | 117 +++++++-------------- neo/test/iotest/test_nwbio.py | 187 +++++++--------------------------- 2 files changed, 73 insertions(+), 231 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 20e7d1f7a..0ae734662 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -69,7 +69,6 @@ class NWBIO(BaseIO): name = 'NWB' description = 'This IO reads/writes experimental data from/to an .nwb dataset' extensions = ['nwb'] -# mode = 'one-file' mode = 'file' def __init__(self, filename, mode): @@ -79,14 +78,10 @@ def __init__(self, filename, mode): """ BaseIO.__init__(self, filename=filename) self.filename = filename - - - - - def read_all_blocks(self, blocks, lazy=False, **kwargs): -# def read_all_blocks(self, **kwargs): -# def read_all_blocks(self, *blocks, lazy=False, **kwargs): + def read_all_blocks(self, blocks, lazy=False, **kwargs): ### OK +# def read_all_blocks(self, lazy=False, **kwargs): +### def read_all_blocks(self, *blocks, lazy=False, **kwargs): """ Read all blocks from the file """ @@ -113,7 +108,8 @@ def read_all_blocks(self, blocks, lazy=False, **kwargs): ) - def read_block(self, lazy=False, cascade=True, **kwargs): + def read_block(self, lazy=False, cascade=True, **kwargs): ### OK +# def read_block(self, blocks, lazy=False, cascade=True, **kwargs): """ Read a Block from the file """ @@ -153,11 +149,7 @@ def read_block(self, lazy=False, cascade=True, **kwargs): return block - - - def write_all_blocks(self, blocks): -# def write_all_blocks(self, *blocks, **kwargs): """ Write list of blocks to the file """ @@ -166,22 +158,21 @@ def write_all_blocks(self, blocks): print("blocks = ", blocks) if Block in self.writeable_objects: - print("Block = ", Block) for block in blocks: - print("block = ", block) self.write_block(block) print("END loop Block in def write_all_blocks") + return list(block.segments) + print("END DEF WRITE_ALL_BLOCKS") - -# def write_block(self, *block, **kwargs): def write_block(self, block, **kwargs): """ Write a Block to the file """ print("*** def write_block ***") + start_time = datetime.now() nwbfile = NWBFile(self.filename, session_start_time=start_time, @@ -222,40 +213,31 @@ def write_block(self, block, **kwargs): devices=None, subject=None ) - -# for num_blk in range(len(block.name)): # loop on blocks -## for num_blk in block: # loop on blocks -# print("num_blk = ", num_blk) -# -# name_block = 'block_%d' %num_blk -# print("name_block = ", name_block) -# print("block.segments = ", block.segments) -# -# for segment in block.segments: # loop on segments -# print("segment = ", segment) -# self._write_segment(nwbfile, segment) -# print("OK") - - print("*************************************************block = ", block) print("block.segments = ", block.segments) -#################################################################### - ## return list(block.segments) + + io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') + print("io_nwb = ", io_nwb) for segment in block.segments: +# for analogsignal in segment.analogsignals: ### print("------ segment = ", segment) +# for signal in segment.analogsignals: ### + self._write_segment(nwbfile, segment) - print("END of loop on segment") - return list(block.segments) ######################### + + print("END of loop on segment block.segments = ", block.segments) + print("---------------------------------------------------------------") +# return list(segment.analogsignals) ### + print("Write the file") + io_nwb.write(nwbfile) + return list(block.segments) - io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') - print("io_nwb = ", io_nwb) - io_nwb.write(nwbfile) - print("Write the file") io_nwb.close() print("Close the file") + def _handle_general_group(self, block): pass @@ -283,35 +265,6 @@ def _handle_epochs_group(self, _file, block): segment.block = block block.segments.append(segment) -# for key in epochs: -# timeseries = [] -# current_shape = _file.get_acquisition(key).data.shape[0] # or 1 if multielectrode ? -# times = np.zeros(current_shape) -# -# for j in range(0, current_shape):# to do w/ ecephys data (e.g. multielectrode: how is it organised?) -# times[j]=1./_file.get_acquisition(key).rate*j+_file.get_acquisition(key).starting_time -# if times[j] == _file.get_acquisition(key).starting_time: -# t_start = times[j] * pq.second -# elif times[j]==times[-1]: -# t_stop = times[j] * pq.second -# else: -# timeseries.append(self._handle_timeseries(_file, key, times[j])) -# segment = Segment(name=j) -# for obj in timeseries: -# obj.segment = segment -# if isinstance(obj, AnalogSignal): -# segment.analogsignals.append(obj) -# elif isinstance(obj, IrregularlySampledSignal): -# segment.irregularlysampledsignals.append(obj) -# elif isinstance(obj, Event): -# segment.events.append(obj) -# elif isinstance(obj, Epoch): -# segment.epochs.append(obj) -# segment.block = block -# block.segments.append(segment) -# segment.times=times -# return segment, obj, times - def _handle_timeseries(self, _file, name, timeseries): for i in _file.acquisition: data_group = _file.get_acquisition(i).data*_file.get_acquisition(i).conversion @@ -403,15 +356,12 @@ def _write_segment(self, nwbfile, segment): start_time=float(start_time), stop_time=float(stop_time), ) - for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): - print("++++++++++++++++++++++++ signal = ", signal) - print("segment.analogsignals", segment.analogsignals) - print("signal.name = ", signal.name) #### - ######################################################################## -# return list(signal for segment.analogsignals in signal) ################################################################################## - return list(segment.analogsignals for signal in segment.analogsignals) + for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): + print("i = ", i) self._write_signal(nwbfile, signal, nwb_epoch, i, segment) + print("END _write_segment") + self._write_spiketrains(nwbfile, segment.spiketrains, segment) for i, event in enumerate(segment.events): self._write_event(nwbfile, event, nwb_epoch, i) @@ -420,9 +370,9 @@ def _write_segment(self, nwbfile, segment): def _write_signal(self, nwbfile, signal, epoch, i, segment): print("*** def _write_signal ***") - print("signal", signal) +# print("signal", signal) signal_name = signal.name or "signal{0}".format(i) - print("signal_name = ", signal_name) + print("signal_name 123 = ", signal_name) ts_name = "{0}".format(signal_name) """ @@ -481,7 +431,11 @@ def _write_signal(self, nwbfile, signal, epoch, i, segment): # All signals should go in /acquisition tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, rate=float(sampling_rate)) - ts = nwbfile.add_acquisition(tS) + #print("tS = ", tS) +###### return list(segment.analogsignals for signal in segment.analogsignals) + + ts = nwbfile.add_acquisition(tS) + elif isinstance(signal, IrregularlySampledSignal): tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, timestamps=signal.times.rescale('second').magnitude) ts = nwbfile.add_acquisition(tS) @@ -492,6 +446,7 @@ def _write_signal(self, nwbfile, signal, epoch, i, segment): start_time=time_in_seconds(segment.t_start), stop_time=time_in_seconds(segment.t_stop), ) + print("END def _write_signal") def _write_spiketrains(self, nwbfile, spiketrains, segment): @@ -546,6 +501,8 @@ def _write_event(self, nwbfile, event, nwb_epoch, i): ) """ + print("ts_name in _write_event = ", ts_name) + tS = TimeSeries( name=ts_name, data=event, @@ -600,6 +557,8 @@ def _write_neo_epoch(self, nwbfile, neo_epoch, nwb_epoch, i): ###### AutoNeoEpochSeries = get_class('TimeSeries', 'neo_epoch') """ + print("ts_name in _write_neo_epoch = ", ts_name) + tS = TimeSeries( name=ts_name, data=neo_epoch, diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 1cbd2fcdc..50fe7a345 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -17,7 +17,9 @@ class TestNWBIO(unittest.TestCase, ): ioclass = NWBIO files_to_download = [ # My NWB files - '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github page +# '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github page +### '/Users/legouee/NWBwork/my_notebook/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb' + '/Users/legouee/NWBwork/my_notebook/My_first_dataset.nwb' # Files from Allen Institute # NWB files downloadable from http://download.alleninstitute.org/informatics-archive/prerelease/ @@ -133,170 +135,51 @@ def test_read_epoch(self, **kargs): self.assertTrue(epoch_nwb, epc_neo) self.assertIsNotNone(epoch_nwb, epc_neo) - - - def test_write_NWB_File(self): + def test_write_NWB_Files(self): ''' - Test function to write a segment. + Test function to write several blocks containing several segments and analogsignals. ''' - # Create a Block with 1 Segment and 2 ChannelIndex objects + print("Test function test_write_NWB_Files") blocks = [] - num_segment=1 # number of segment - segment_durations = [5*pq.s, 13*pq.s] - - for ind in range(2): # loop on blocks - blk = Block(name='block_%s' %ind) - - for seg_num in range(num_segment): # loop on segments - seg = Segment(name=f'Seg {seg_num}') - blk.segments.append(seg) - - for seg_index in range(num_segment): # loop on ChannelIndex - sampling_rate = 80*pq.Hz - num_channel = 2 - duration = segment_durations[seg_index] - length = int((sampling_rate*duration).simplified) - np_sig = np.random.randn(length, num_channel).astype('float32') - - anasig = AnalogSignal(np_sig, units='cm', sampling_rate=sampling_rate) - anasig.annotate(data_type='tracking') - anasig.array_annotate(channel_names=['lfp_{}'.format(ch) for ch in range(num_channel)]) - blk.segments[seg_index].analogsignals.append(anasig) # - blocks.append(blk) + bl0 = Block(name='First block') + bl1 = Block(name='Second block') + bl2 = Block(name='Third block') + print("bl0.segments = ", bl0.segments) + print("bl1.segments = ", bl1.segments) + print("bl2.segments = ", bl2.segments) + blocks = [bl0, bl1, bl2] + print("blocks = ", blocks) + num_seg = 3 # number of segments - -# for num_blk in range(2): # for 2 blocks -# blk = Block(name='%s' %num_blk) -# for ind in range(2): -# seg = Segment(name='segment_%d' % ind, index=ind) -# blk.segments.append(seg) -## blocks.append(blk) -# -# blk = Block() -# for ind in range(1): -# seg = Segment(name='segment_%d' % ind, index=ind) -# blk.segments.append(seg) -# -# for ind in range(2): -# chx = ChannelIndex(name='Array probe %d' % ind, index=np.arange(64)) -# blk.channel_indexes.append(chx) -# -# # Populate the Block with AnalogSignal objects -# for seg in blk.segments: -# for chx in blk.channel_indexes: -# # AnalogSignal -# a = AnalogSignal(signal=[1, 2, 3], units='V', t_start=np.array(3.0)*pq.s, sampling_rate=1*pq.Hz) -# chx.analogsignals.append(a) -# seg.analogsignals.append(a) -# # SpikeTrain -# t = SpikeTrain([3, 4, 5]*pq.s, t_stop=10.0) -# seg.spiketrains.append(t) -# # Epoch -# epc = Epoch(times=np.arange(0, 30, 10)*pq.s, -# durations=[10, 5, 7]*pq.ms -# ) -# seg.epochs.append(epc) -# # Event -# evt = Event(np.arange(0, 30, 20)*pq.s) -# seg.events.append(evt) -# # Unit -# unit = Unit(name='pyramidal neuron') -# unit.spiketrains.append(t) -# # IrregularlySampledSignal -# seg.irregularlysampledsignals.append(a) -# print("blocks = ", blocks) - - # Save the file - filename = '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' - w_file = NWBIO(filename=filename, mode='w') # Write the .nwb file - print("w_file = ", w_file) - blocks = w_file.write(blk) -# blocks = w_file.write_all_blocks(blk) - - - - - - - """ - def test_2_write_NWB_File(self): - blocks = [] - for ind in range(2): # 2 blocks - blk = Block(name='%s' %ind) - blocks.append(blk) - - for ind in range(3): # 3 Segment + for blk in blocks: + print("blk = ", blk) + for ind in range(num_seg): # number of Segment seg = Segment(name='segment %d' % ind, index=ind) blk.segments.append(seg) - - for ind in range(2): # 2 ChannelIndex - chx = ChannelIndex(name='Array probe %d' % ind, index=np.arange(64)) - blk.channel_indexes.append(chx) - - for seg in blk.segments: # AnalogSignal objects - for chx in blk.channel_indexes: - a = AnalogSignal(np.random.randn(10000, 64)*pq.nA, sampling_rate=10*pq.kHz) - chx.analogsignals.append(a) - seg.analogsignals.append(a) - - - # Save the file - filename = '/home/elodie/env_NWB_py3/my_notebook/second_first_test_neo_to_nwb_test_NWBIO.nwb' - w_file = NWBIO(filename=filename, mode='w') # Write the .nwb file - print("w_file = ", w_file) - blocks = w_file.write(blk) -# blocks = w_file.write_all_blocks(blk) - """ - - - - - - - - - - - - - """ - def test_write_all_NWB_Files(self): - ''' - Test function to write all blocks. - ''' - # Create a Block with 1 Segment and 2 ChannelIndex objects - blocks = [] - num_segment=1 # number of segment - segment_durations = [5*pq.s, 13*pq.s] - - for ind in range(2): # loop on blocks - blk = Block(name='block_%s' %ind) - - for seg_num in range(num_segment): # loop on segments - seg = Segment(name=f'Seg {seg_num}') - blk.segments.append(seg) - - for seg_index in range(num_segment): # loop on ChannelIndex - sampling_rate = 80*pq.Hz - num_channel = 2 - duration = segment_durations[seg_index] - length = int((sampling_rate*duration).simplified) - np_sig = np.random.randn(length, num_channel).astype('float32') - anasig = AnalogSignal(np_sig, units='cm', sampling_rate=sampling_rate) - anasig.annotate(data_type='tracking') - anasig.array_annotate(channel_names=['lfp_{}'.format(ch) for ch in range(num_channel)]) - blk.segments[seg_index].analogsignals.append(anasig) # - blocks.append(blk) + for seg in blk.segments: # AnalogSignal objects + # 3 AnalogSignals + print("seg = ", seg) + a = AnalogSignal(np.random.randn(num_seg, 44)*pq.nA, sampling_rate=10*pq.kHz) + b = AnalogSignal(np.random.randn(num_seg, 64)*pq.nA, sampling_rate=10*pq.kHz) + c = AnalogSignal(np.random.randn(num_seg, 33)*pq.nA, sampling_rate=10*pq.kHz) + + seg.analogsignals.append(a) + seg.analogsignals.append(b) + seg.analogsignals.append(c) + + print("END blocks = ", blocks) # Save the file - filename = '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO_all_blocks.nwb' +# filename = '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' + filename = '/Users/legouee/NWBwork/my_notebook/my_first_test_neo_to_nwb_test_NWBIO_in_test_nwbio.nwb' + print("filename = ", filename) w_file = NWBIO(filename=filename, mode='w') # Write the .nwb file print("w_file = ", w_file) - blocks = w_file.write_all_blocks(blk) - """ + blocks = w_file.write(blk) + print("*** END test_write_NWB_Files ***") if __name__ == "__main__": From 5fbde3e59e4b5fd9d9c9464c4dd7bdb532f96b3f Mon Sep 17 00:00:00 2001 From: legouee Date: Mon, 2 Dec 2019 16:55:26 +0100 Subject: [PATCH 22/79] Modifications segments --- neo/io/nwbio.py | 49 ++++++++++++++++++----------------- neo/test/iotest/test_nwbio.py | 3 ++- 2 files changed, 27 insertions(+), 25 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 0ae734662..d48a16045 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -79,8 +79,8 @@ def __init__(self, filename, mode): BaseIO.__init__(self, filename=filename) self.filename = filename - def read_all_blocks(self, blocks, lazy=False, **kwargs): ### OK -# def read_all_blocks(self, lazy=False, **kwargs): +# def read_all_blocks(self, blocks, lazy=False, **kwargs): ### OK + def read_all_blocks(self, lazy=False, **kwargs): ### def read_all_blocks(self, *blocks, lazy=False, **kwargs): """ Read all blocks from the file @@ -90,26 +90,25 @@ def read_all_blocks(self, blocks, lazy=False, **kwargs): ### OK if Block in self.readable_objects: print("Block = ", Block) - # print("blocks = ", blocks) +# print("block = ", block) print(" ") - for block in blocks: - print("-------------------------") - print("*-* block.name = ", block.name) - print("block = ", block) - self.read_block(block) - print("blocks = ", blocks) - print(" ") - print("Test") - print(" ") - print(" ") - print(" ") - return list(self.read_block(block) - for block in blocks - ) +# for block in blocks: + +# print("*-* block.name = ", block.name) +# print("block = ", block) +### self.read_block(block) + self.read_block() +# print("blocks = ", blocks) + return [self.read_block()] +### return list(self.read_block()) + print("-------------------------") +# return list(self.read_block(block) +# for block in blocks +# ) def read_block(self, lazy=False, cascade=True, **kwargs): ### OK -# def read_block(self, blocks, lazy=False, cascade=True, **kwargs): +# def read_block(self, *blocks, lazy=False, cascade=True, **kwargs): """ Read a Block from the file """ @@ -143,7 +142,9 @@ def read_block(self, lazy=False, cascade=True, **kwargs): ### OK self._handle_processing_group(block) self._handle_analysis_group(block) self._lazy = False + print("--- block in read_block() = ", block) + print("*-* block.name = ", block.name) print("END def read_block") print(" ") return block @@ -162,7 +163,7 @@ def write_all_blocks(self, blocks): self.write_block(block) print("END loop Block in def write_all_blocks") return list(block.segments) - + #return [self.write_block()] print("END DEF WRITE_ALL_BLOCKS") @@ -226,12 +227,11 @@ def write_block(self, block, **kwargs): # for signal in segment.analogsignals: ### self._write_segment(nwbfile, segment) - - print("END of loop on segment block.segments = ", block.segments) - print("---------------------------------------------------------------") # return list(segment.analogsignals) ### print("Write the file") io_nwb.write(nwbfile) + print("END of loop on segment block.segments = ", block.segments) + print("---------------------------------------------------------------") return list(block.segments) io_nwb.close() @@ -360,6 +360,7 @@ def _write_segment(self, nwbfile, segment): for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): print("i = ", i) self._write_signal(nwbfile, signal, nwb_epoch, i, segment) + #print("segment.analogsignals = ", segment.analogsignals) ### Ok print("END _write_segment") self._write_spiketrains(nwbfile, segment.spiketrains, segment) @@ -433,7 +434,8 @@ def _write_signal(self, nwbfile, signal, epoch, i, segment): tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, rate=float(sampling_rate)) #print("tS = ", tS) ###### return list(segment.analogsignals for signal in segment.analogsignals) - + # print("analogsignal = ", segment.analogsignals) # OK + ts = nwbfile.add_acquisition(tS) elif isinstance(signal, IrregularlySampledSignal): @@ -446,7 +448,6 @@ def _write_signal(self, nwbfile, signal, epoch, i, segment): start_time=time_in_seconds(segment.t_start), stop_time=time_in_seconds(segment.t_stop), ) - print("END def _write_signal") def _write_spiketrains(self, nwbfile, spiketrains, segment): diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 50fe7a345..4aaa0c5be 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -19,7 +19,8 @@ class TestNWBIO(unittest.TestCase, ): # My NWB files # '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github page ### '/Users/legouee/NWBwork/my_notebook/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb' - '/Users/legouee/NWBwork/my_notebook/My_first_dataset.nwb' +# '/Users/legouee/NWBwork/my_notebook/My_first_dataset.nwb' + '/Users/legouee/NWBwork/my_notebook/My_first_dataset_neo8.nwb' # Files from Allen Institute # NWB files downloadable from http://download.alleninstitute.org/informatics-archive/prerelease/ From 34daf7012d0160b9c6577124728ad864e2871ea5 Mon Sep 17 00:00:00 2001 From: msenoville Date: Tue, 10 Dec 2019 16:38:19 +0100 Subject: [PATCH 23/79] save modifs --- neo/io/nwbio.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index d48a16045..e67df09ba 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -58,18 +58,21 @@ class NWBIO(BaseIO): Class for "reading" experimental data from a .nwb file, and "writing" a .nwb file """ - is_readable = True - is_writable = True - is_streameable = False supported_objects = [Block, Segment, AnalogSignal, IrregularlySampledSignal, - SpikeTrain, Epoch, Event] + SpikeTrain, Epoch, Event] # maybe to remove at the end : already declared in neo.core.objectlist readable_objects = supported_objects writeable_objects = supported_objects + has_header = False - name = 'NWB' + + name = 'NeoNWB IO' description = 'This IO reads/writes experimental data from/to an .nwb dataset' extensions = ['nwb'] - mode = 'file' + mode = 'one-file' + + is_readable = True + is_writable = True + is_streameable = False def __init__(self, filename, mode): """ From 2d8540fd5d2424619a4ade5ec2f389fcf016e668 Mon Sep 17 00:00:00 2001 From: msenoville Date: Wed, 11 Dec 2019 16:11:46 +0100 Subject: [PATCH 24/79] improvement of of reading of multiple blocks --- neo/io/nwbio.py | 53 ++++++++++++++++++++++++------------------------- 1 file changed, 26 insertions(+), 27 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index e67df09ba..385fa80cb 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -82,43 +82,42 @@ def __init__(self, filename, mode): BaseIO.__init__(self, filename=filename) self.filename = filename -# def read_all_blocks(self, blocks, lazy=False, **kwargs): ### OK def read_all_blocks(self, lazy=False, **kwargs): -### def read_all_blocks(self, *blocks, lazy=False, **kwargs): """ - Read all blocks from the file + Loads all blocks in the file that are attached to the root. + Here, we assume that a neo block is a sub-part of a branch, into a NWB file; + with our description 1 block = 1 segment """ print("*** def read_all_blocks ***") - if Block in self.readable_objects: - print("Block = ", Block) -# print("block = ", block) - print(" ") -# for block in blocks: - -# print("*-* block.name = ", block.name) -# print("block = ", block) -### self.read_block(block) - self.read_block() -# print("blocks = ", blocks) - return [self.read_block()] -### return list(self.read_block()) - print("-------------------------") - -# return list(self.read_block(block) -# for block in blocks -# ) - - def read_block(self, lazy=False, cascade=True, **kwargs): ### OK -# def read_block(self, *blocks, lazy=False, cascade=True, **kwargs): + assert not lazy, 'Do not support lazy' + + io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO + self._file = io.read() + + # here, we assume that a neo block is a sub-part of a branck, into a NWB file; + # with our description 1 block = 1 segment + blocks = [] + for node in self._file.acquisition: + blocks.append(self._read_block(self._file, node)) + return blocks + + + def read_block(self, lazy=False, **kargs): + """ + Load the first block in the file. + """ + assert not lazy, 'Do not support lazy' + return self.read_all_blocks(lazy=lazy)[0] + + + def _read_block(self, _file, node, lazy=False, cascade=True, **kwargs): ### OK """ - Read a Block from the file + Main method to load a block """ print("**** def read_block ****") - io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO - _file = io.read() self._lazy = lazy file_access_dates = _file.file_create_date From 04355410a5ad933a58a29cac2c0136d7d691c239 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Wed, 11 Dec 2019 16:18:04 +0100 Subject: [PATCH 25/79] Commit before pull --- neo/io/nwbio.py | 106 +++++++++++++++++++---------- neo/test/iotest/test_nwbio.py | 121 ++++++++++++++++------------------ 2 files changed, 127 insertions(+), 100 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index d48a16045..40beeb477 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -78,43 +78,65 @@ def __init__(self, filename, mode): """ BaseIO.__init__(self, filename=filename) self.filename = filename - -# def read_all_blocks(self, blocks, lazy=False, **kwargs): ### OK - def read_all_blocks(self, lazy=False, **kwargs): -### def read_all_blocks(self, *blocks, lazy=False, **kwargs): + """ - Read all blocks from the file + if mode == "r": +# io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO +# _file = io.read() + self.read_all_blocks() + if mode == "w": + print("OK for write part") +# blocks=[] +# self.write_all_blocks(blocks) +# else: +# raise ValueError("Invalid mode specified") """ - print("*** def read_all_blocks ***") - if Block in self.readable_objects: - print("Block = ", Block) -# print("block = ", block) - print(" ") -# for block in blocks: - -# print("*-* block.name = ", block.name) -# print("block = ", block) -### self.read_block(block) - self.read_block() -# print("blocks = ", blocks) - return [self.read_block()] -### return list(self.read_block()) - print("-------------------------") - -# return list(self.read_block(block) -# for block in blocks -# ) - - def read_block(self, lazy=False, cascade=True, **kwargs): ### OK -# def read_block(self, *blocks, lazy=False, cascade=True, **kwargs): + def read_all_blocks(self, blocks, lazy=False, **kwargs): ### OK +# def read_all_blocks(self, lazy=False, **kwargs): + """ - Read a Block from the file + Read all blocks from the file """ + print("*** def read_all_blocks ***") + +# blocks = [] +# block = Block() +### blocks = [block()] + + + if Block in self.readable_objects: # Ok +# for Block in self.readable_objects: +# for block in blocks: + +# blocks.append(self.read_block()) + self.read_block() # Ok + +# print("END loop") +# print(" ") + +# return [self.read_block(group=block, _file=_file)] # OK +### return blocks +# return [blocks] +# return [self.read_block()] + +### return list(self.read_block()) +# return ([self.read_block()] +# for block in blocks +# ) + + +###### def read_block(self, lazy=False, cascade=True, **kwargs): ### OK +# def read_block(self, _file, lazy=False, cascade=True, **kwargs): + def read_block(self, *blocks, lazy=False, cascade=True, **kwargs): + """ + Read the first block of the file + """ print("**** def read_block ****") - io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO + + io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO _file = io.read() self._lazy = lazy @@ -125,7 +147,7 @@ def read_block(self, lazy=False, cascade=True, **kwargs): ### OK description = _file.session_description if description == "no description": description = None - + block = Block(name=identifier, description=description, file_origin=self.filename, @@ -134,6 +156,7 @@ def read_block(self, lazy=False, cascade=True, **kwargs): ### OK file_access_dates=file_access_dates, file_read_log='') + if cascade: self._handle_general_group(block) self._handle_epochs_group(_file, block) @@ -143,13 +166,16 @@ def read_block(self, lazy=False, cascade=True, **kwargs): ### OK self._handle_analysis_group(block) self._lazy = False - print("--- block in read_block() = ", block) - print("*-* block.name = ", block.name) - print("END def read_block") - print(" ") return block +# print("--- block in read_block() = ", block) +# print("*-* block.name = ", block.name) +# print("END def read_block") +# return block + + + def write_all_blocks(self, blocks): """ Write list of blocks to the file @@ -167,7 +193,8 @@ def write_all_blocks(self, blocks): print("END DEF WRITE_ALL_BLOCKS") - def write_block(self, block, **kwargs): +# def write_block(self, block, **kwargs): + def write_block(self, block=None, **kwargs): """ Write a Block to the file """ @@ -241,8 +268,9 @@ def write_block(self, block, **kwargs): def _handle_general_group(self, block): pass - def _handle_epochs_group(self, _file, block): + def _handle_epochs_group(self, _file, block): # Ok # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. + print("--- def _handle_epochs_group ---") epochs = _file.epochs timeseries=[] if epochs is not None: @@ -266,6 +294,7 @@ def _handle_epochs_group(self, _file, block): block.segments.append(segment) def _handle_timeseries(self, _file, name, timeseries): + print("--- def _handle_timeseries ---") for i in _file.acquisition: data_group = _file.get_acquisition(i).data*_file.get_acquisition(i).conversion dtype = data_group.dtype @@ -320,9 +349,11 @@ def _handle_timeseries(self, _file, name, timeseries): return obj def _handle_acquisition_group(self, lazy, _file, block): + print("--- def _handle_acquisition_group ---") acq = _file.acquisition def _handle_stimulus_group(self, lazy, _file, block): + print("--- def _handle_stimulus_group ---") sti = _file.stimulus for name in sti: segment_name_sti = _file.epochs @@ -451,6 +482,7 @@ def _write_signal(self, nwbfile, signal, epoch, i, segment): print("END def _write_signal") def _write_spiketrains(self, nwbfile, spiketrains, segment): + print("--- def _write_spiketrains ---") """ mod = NWBGroupSpec('A custom TimeSeries interface', attributes=[], @@ -481,6 +513,7 @@ def _write_spiketrains(self, nwbfile, spiketrains, segment): ) def _write_event(self, nwbfile, event, nwb_epoch, i): + print("--- def _write_event ---") event_name = event.name or "event{0}".format(i) ts_name = "{0}".format(event_name) @@ -518,6 +551,7 @@ def _write_event(self, nwbfile, event, nwb_epoch, i): ) def _write_neo_epoch(self, nwbfile, neo_epoch, nwb_epoch, i): + print("--- _write_neo_epoch ---") neo_epoch_name = neo_epoch.name or "intervalseries{0}".format(i) ts_name = "{0}".format(neo_epoch_name) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 4aaa0c5be..d20733376 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -20,14 +20,15 @@ class TestNWBIO(unittest.TestCase, ): # '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github page ### '/Users/legouee/NWBwork/my_notebook/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb' # '/Users/legouee/NWBwork/my_notebook/My_first_dataset.nwb' - '/Users/legouee/NWBwork/my_notebook/My_first_dataset_neo8.nwb' +### '/Users/legouee/NWBwork/my_notebook/My_first_dataset_neo8.nwb' +###### '/home/elodie/env_NWB_py3/my_notebook/My_first_dataset_neo9.nwb' # Files from Allen Institute # NWB files downloadable from http://download.alleninstitute.org/informatics-archive/prerelease/ ### '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb' -### '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' + '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' # '/home/elodie/NWB_Files/NWB_org/behavior_ophys_session_775614751.nwb' # '/home/elodie/NWB_Files/NWB_org/ecephys_session_785402239.nwb' @@ -41,97 +42,89 @@ class TestNWBIO(unittest.TestCase, ): def test_nwbio(self): - # read the blocks + print("*** def test_nwbio ***") reader = NWBIO(filename=self.files_to_download[0], mode='r') - print("reader = ", reader) -# print("reader.read() = ", reader.read()) - - print("reader.read_block() = ", reader.read_block()) - print(" ") -# blocks = reader.read(lazy=False) - - #------------------------------------------------------- - blocks=[] - for ind in range(2): # 2 blocks - blk = Block(name='%s' %ind) - blocks.append(blk) - #------------------------------------------------------- - - # access to segments - for block in blocks: - # Tests of Block - self.assertTrue(isinstance(block.name, str)) - # Segment - for segment in block.segments: - self.assertEqual(segment.block, block) - # AnalogSignal - for asig in segment.analogsignals: - self.assertTrue(isinstance(asig, AnalogSignal)) - self.assertTrue(asig.sampling_rate, pq.Hz) - self.assertTrue(asig.units, pq) - # Spiketrain - for st in segment.spiketrains: - self.assertTrue(isinstance(st, SpikeTrain)) + reader.read() +# blocks=[] +# for ind in range(2): # 2 blocks +# blk = Block(name='%s' %ind) +# blocks.append(blk) +# +# # access to segments +# for block in blocks: +# # Tests of Block +# self.assertTrue(isinstance(block.name, str)) +# # Segment +# for segment in block.segments: +# self.assertEqual(segment.block, block) +# # AnalogSignal +# for asig in segment.analogsignals: +# self.assertTrue(isinstance(asig, AnalogSignal)) +# self.assertTrue(asig.sampling_rate, pq.Hz) +# self.assertTrue(asig.units, pq) +# # Spiketrain +# for st in segment.spiketrains: +# self.assertTrue(isinstance(st, SpikeTrain)) def test_segment(self, **kargs): + print("*** def test_segment ***") seg = Segment(index=5) r = NWBIO(filename=self.files_to_download[0], mode='r') - - -# #------------------------------------------------------- # blocks=[] -# for ind in range(2): # 2 blocks +# for ind in range(2): # 2 blocks ####################################################################################################################### # blk = Block(name='%s' %ind) # blocks.append(blk) -# #------------------------------------------------------- -# seg_nwb = r.read() -## seg_nwb = r.read(blocks) # equivalent to read_all_blocks() - - - seg_nwb = r.read_block() + seg_nwb = r.read() # equivalent to read_all_blocks() self.assertTrue(seg, Segment) self.assertTrue(seg_nwb, Segment) self.assertTrue(seg_nwb, seg) self.assertIsNotNone(seg_nwb, seg) + seg_nwb_one_block = r.read_block() # only for the first block + self.assertTrue(seg_nwb_one_block, Segment) + self.assertTrue(seg_nwb_one_block, seg) + self.assertIsNotNone(seg_nwb_one_block, seg) def test_analogsignals_neo(self, **kargs): + print("*** def test_analogsignals_neo ***") sig_neo = AnalogSignal(signal=[1, 2, 3], units='V', t_start=np.array(3.0)*pq.s, sampling_rate=1*pq.Hz) self.assertTrue(isinstance(sig_neo, AnalogSignal)) r = NWBIO(filename=self.files_to_download[0], mode='r') -# obj_nwb = r.read() - obj_nwb = r.read_block() + obj_nwb = r.read() +# obj_nwb = r.read_block() self.assertTrue(obj_nwb, AnalogSignal) self.assertTrue(obj_nwb, sig_neo) - def test_read_irregularlysampledsignal(self, **kargs): + print("*** def test_read_irregularlysampledsignal ***") irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], units='mV', time_units='ms') irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*pq.s, [[4, 5], [5, 4], [6, 3]]*pq.nA) self.assertTrue(isinstance(irsig0, IrregularlySampledSignal)) self.assertTrue(isinstance(irsig1, IrregularlySampledSignal)) r = NWBIO(filename=self.files_to_download[0], mode='r') -# irsig_nwb = r.read() - irsig_nwb = r.read_block() + irsig_nwb = r.read() +# irsig_nwb = r.read_block() self.assertTrue(irsig_nwb, IrregularlySampledSignal) self.assertTrue(irsig_nwb, irsig0) self.assertTrue(irsig_nwb, irsig1) def test_read_event(self, **kargs): + print("*** def test_read_event ***") evt_neo = Event(np.arange(0, 30, 10)*pq.s, labels=np.array(['trig0', 'trig1', 'trig2'], dtype='S')) r = NWBIO(filename=self.files_to_download[0], mode='r') -# event_nwb = r.read() - event_nwb = r.read_block() + event_nwb = r.read() +# event_nwb = r.read_block() self.assertTrue(event_nwb, evt_neo) self.assertIsNotNone(event_nwb, evt_neo) def test_read_epoch(self, **kargs): + print("*** def test_read_epoch ***") epc_neo = Epoch(times=np.arange(0, 30, 10)*pq.s, durations=[10, 5, 7]*pq.ms, labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) r = NWBIO(filename=self.files_to_download[0], mode='r') -# epoch_nwb = r.read() - epoch_nwb = r.read_block() + epoch_nwb = r.read() +# epoch_nwb = r.read_block() self.assertTrue(epoch_nwb, Epoch) self.assertTrue(epoch_nwb, epc_neo) self.assertIsNotNone(epoch_nwb, epc_neo) @@ -140,29 +133,29 @@ def test_write_NWB_Files(self): ''' Test function to write several blocks containing several segments and analogsignals. ''' - print("Test function test_write_NWB_Files") + print("*** Test function test_write_NWB_Files ***") blocks = [] bl0 = Block(name='First block') bl1 = Block(name='Second block') bl2 = Block(name='Third block') - print("bl0.segments = ", bl0.segments) - print("bl1.segments = ", bl1.segments) - print("bl2.segments = ", bl2.segments) +# print("bl0.segments = ", bl0.segments) +# print("bl1.segments = ", bl1.segments) +# print("bl2.segments = ", bl2.segments) blocks = [bl0, bl1, bl2] - print("blocks = ", blocks) +# print("blocks = ", blocks) num_seg = 3 # number of segments for blk in blocks: - print("blk = ", blk) +# print("blk = ", blk) for ind in range(num_seg): # number of Segment seg = Segment(name='segment %d' % ind, index=ind) blk.segments.append(seg) for seg in blk.segments: # AnalogSignal objects # 3 AnalogSignals - print("seg = ", seg) +# print("seg = ", seg) a = AnalogSignal(np.random.randn(num_seg, 44)*pq.nA, sampling_rate=10*pq.kHz) b = AnalogSignal(np.random.randn(num_seg, 64)*pq.nA, sampling_rate=10*pq.kHz) c = AnalogSignal(np.random.randn(num_seg, 33)*pq.nA, sampling_rate=10*pq.kHz) @@ -171,16 +164,16 @@ def test_write_NWB_Files(self): seg.analogsignals.append(b) seg.analogsignals.append(c) - print("END blocks = ", blocks) +# print("END blocks = ", blocks) # Save the file -# filename = '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' - filename = '/Users/legouee/NWBwork/my_notebook/my_first_test_neo_to_nwb_test_NWBIO_in_test_nwbio.nwb' - print("filename = ", filename) + filename = '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' +# filename = '/Users/legouee/NWBwork/my_notebook/my_first_test_neo_to_nwb_test_NWBIO_in_test_nwbio.nwb' +# print("filename = ", filename) w_file = NWBIO(filename=filename, mode='w') # Write the .nwb file - print("w_file = ", w_file) +# print("w_file = ", w_file) blocks = w_file.write(blk) - print("*** END test_write_NWB_Files ***") +# print("*** END test_write_NWB_Files ***") if __name__ == "__main__": From 047deddfddb0faa90d96d5fc628cd11a7d13ddbe Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Wed, 11 Dec 2019 16:32:14 +0100 Subject: [PATCH 26/79] Several blocks --- neo/io/nwbio.py | 40 ---------------------------------------- 1 file changed, 40 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 6c9ad6ceb..2a98b8842 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -89,9 +89,6 @@ def read_all_blocks(self, lazy=False, **kwargs): with our description 1 block = 1 segment """ - - assert not lazy, 'Do not support lazy' - io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO self._file = io.read() @@ -107,7 +104,6 @@ def read_block(self, lazy=False, **kargs): """ Load the first block in the file. """ - assert not lazy, 'Do not support lazy' return self.read_all_blocks(lazy=lazy)[0] @@ -115,42 +111,6 @@ def _read_block(self, _file, node, lazy=False, cascade=True, **kwargs): ### OK """ Main method to load a block """ - print("*** def read_all_blocks ***") - -# blocks = [] -# block = Block() -### blocks = [block()] - - - if Block in self.readable_objects: # Ok -# for Block in self.readable_objects: -# for block in blocks: - -# blocks.append(self.read_block()) - self.read_block() # Ok - -# print("END loop") -# print(" ") - - -# return [self.read_block(group=block, _file=_file)] # OK -### return blocks -# return [blocks] -# return [self.read_block()] - -### return list(self.read_block()) -# return ([self.read_block()] -# for block in blocks -# ) - - -###### def read_block(self, lazy=False, cascade=True, **kwargs): ### OK -# def read_block(self, _file, lazy=False, cascade=True, **kwargs): - def read_block(self, *blocks, lazy=False, cascade=True, **kwargs): - """ - Read the first block of the file - """ - print("**** def read_block ****") self._lazy = lazy file_access_dates = _file.file_create_date From 3dbb381ce6970d1210e8580b7468c95bd9d14774 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Thu, 12 Dec 2019 16:37:26 +0100 Subject: [PATCH 27/79] Writing function --- neo/io/nwbio.py | 125 ++++++++++++++++++++++++++++-------------------- 1 file changed, 72 insertions(+), 53 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 2a98b8842..90a50c7e0 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -59,7 +59,7 @@ class NWBIO(BaseIO): """ supported_objects = [Block, Segment, AnalogSignal, IrregularlySampledSignal, - SpikeTrain, Epoch, Event] # maybe to remove at the end : already declared in neo.core.objectlist + SpikeTrain, Epoch, Event] readable_objects = supported_objects writeable_objects = supported_objects @@ -88,14 +88,13 @@ def read_all_blocks(self, lazy=False, **kwargs): Here, we assume that a neo block is a sub-part of a branch, into a NWB file; with our description 1 block = 1 segment """ - + print("*** def read_all_blocks ***") io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO self._file = io.read() - # here, we assume that a neo block is a sub-part of a branck, into a NWB file; - # with our description 1 block = 1 segment blocks = [] for node in self._file.acquisition: + print("node = ", node) blocks.append(self._read_block(self._file, node)) return blocks @@ -104,13 +103,15 @@ def read_block(self, lazy=False, **kargs): """ Load the first block in the file. """ + print("*** def read_block ***") return self.read_all_blocks(lazy=lazy)[0] - def _read_block(self, _file, node, lazy=False, cascade=True, **kwargs): ### OK + def _read_block(self, _file, node, lazy=False, cascade=True, **kwargs): """ Main method to load a block """ + print("*** def _read_block ***") self._lazy = lazy file_access_dates = _file.file_create_date @@ -129,7 +130,6 @@ def _read_block(self, _file, node, lazy=False, cascade=True, **kwargs): ### OK file_access_dates=file_access_dates, file_read_log='') - if cascade: self._handle_general_group(block) self._handle_epochs_group(_file, block) @@ -139,41 +139,61 @@ def _read_block(self, _file, node, lazy=False, cascade=True, **kwargs): ### OK self._handle_analysis_group(block) self._lazy = False + print("--- block in read_block() = ", block) + print("*-* block.name = ", block.name) + print("END def read_block") + return block + + ### + def _init_writing(self): -# print("--- block in read_block() = ", block) -# print("*-* block.name = ", block.name) -# print("END def read_block") -# return block + print("*** def _init_writing ***") +# io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') +# print("io_nwb = ", io_nwb) +# return io_nwb + return pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') + def write_all_blocks(self, blocks): """ Write list of blocks to the file """ - print("*** def write_all_blocks ***") - print("blocks = ", blocks) + writer = self._init_writing() ### + print("writer = ", writer) + if Block in self.writeable_objects: for block in blocks: - self.write_block(block) + print("block in all_blocks = ", block) + self.write_block(block, writer) print("END loop Block in def write_all_blocks") return list(block.segments) - #return [self.write_block()] print("END DEF WRITE_ALL_BLOCKS") -# def write_block(self, block, **kwargs): - def write_block(self, block=None, **kwargs): + def write_block(self, block=None, writer=None): """ Write a Block to the file + :param block: Block to be written """ - print("*** def write_block ***") + +############################ + self._write_block_children(block, writer) + + print("END def write_block") + +# def _write_block_children(self, block, writer): #Ok + def _write_block_children(self, block=None, writer=None): + print("*** def _write_block_children ***") +############################ + start_time = datetime.now() nwbfile = NWBFile(self.filename, session_start_time=start_time, @@ -214,36 +234,35 @@ def write_block(self, block=None, **kwargs): devices=None, subject=None ) - + print("*************************************************block = ", block) print("block.segments = ", block.segments) + """ io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') print("io_nwb = ", io_nwb) + """ - for segment in block.segments: -# for analogsignal in segment.analogsignals: ### - print("------ segment = ", segment) -# for signal in segment.analogsignals: ### - - self._write_segment(nwbfile, segment) -# return list(segment.analogsignals) ### - print("Write the file") - io_nwb.write(nwbfile) - print("END of loop on segment block.segments = ", block.segments) - print("---------------------------------------------------------------") - return list(block.segments) + for segment in block.segments: + print("segment = ", segment) + print("segment.name = ", segment.name) + self._write_segment(nwbfile, segment) # Ok +### io_nwb.write(nwbfile) + +### io_nwb.close() +### print("Close the file") + print("END def _write_block_children") - io_nwb.close() - print("Close the file") def _handle_general_group(self, block): pass - def _handle_epochs_group(self, _file, block): # Ok - # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. - print("--- def _handle_epochs_group ---") + def _handle_epochs_group(self, _file, block): + """ + Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. + """ +# print("--- def _handle_epochs_group ---") epochs = _file.epochs timeseries=[] if epochs is not None: @@ -267,7 +286,7 @@ def _handle_epochs_group(self, _file, block): # Ok block.segments.append(segment) def _handle_timeseries(self, _file, name, timeseries): - print("--- def _handle_timeseries ---") +# print("--- def _handle_timeseries ---") for i in _file.acquisition: data_group = _file.get_acquisition(i).data*_file.get_acquisition(i).conversion dtype = data_group.dtype @@ -322,11 +341,11 @@ def _handle_timeseries(self, _file, name, timeseries): return obj def _handle_acquisition_group(self, lazy, _file, block): - print("--- def _handle_acquisition_group ---") +# print("--- def _handle_acquisition_group ---") acq = _file.acquisition def _handle_stimulus_group(self, lazy, _file, block): - print("--- def _handle_stimulus_group ---") +# print("--- def _handle_stimulus_group ---") sti = _file.stimulus for name in sti: segment_name_sti = _file.epochs @@ -349,6 +368,7 @@ def _handle_processing_group(self, block): def _handle_analysis_group(self, block): pass + def _write_segment(self, nwbfile, segment): print("*** def _write_segment ***") start_time = segment.t_start @@ -362,22 +382,21 @@ def _write_segment(self, nwbfile, segment): ) for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): - print("i = ", i) self._write_signal(nwbfile, signal, nwb_epoch, i, segment) - #print("segment.analogsignals = ", segment.analogsignals) ### Ok - print("END _write_segment") - +# print("i = ", i) self._write_spiketrains(nwbfile, segment.spiketrains, segment) for i, event in enumerate(segment.events): self._write_event(nwbfile, event, nwb_epoch, i) for i, neo_epoch in enumerate(segment.epochs): self._write_neo_epoch(nwbfile, neo_epoch, nwb_epoch, i) + + print("END def _write_segment") def _write_signal(self, nwbfile, signal, epoch, i, segment): print("*** def _write_signal ***") # print("signal", signal) signal_name = signal.name or "signal{0}".format(i) - print("signal_name 123 = ", signal_name) +# print("signal_name 123 = ", signal_name) ts_name = "{0}".format(signal_name) """ @@ -438,10 +457,10 @@ def _write_signal(self, nwbfile, signal, epoch, i, segment): tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, rate=float(sampling_rate)) #print("tS = ", tS) ###### return list(segment.analogsignals for signal in segment.analogsignals) - # print("analogsignal = ", segment.analogsignals) # OK - + return [segment.analogsignals] +# print("segment.analogsignals = ", segment.analogsignals) # OK ts = nwbfile.add_acquisition(tS) - + elif isinstance(signal, IrregularlySampledSignal): tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, timestamps=signal.times.rescale('second').magnitude) ts = nwbfile.add_acquisition(tS) @@ -455,7 +474,7 @@ def _write_signal(self, nwbfile, signal, epoch, i, segment): print("END def _write_signal") def _write_spiketrains(self, nwbfile, spiketrains, segment): - print("--- def _write_spiketrains ---") +# print("--- def _write_spiketrains ---") """ mod = NWBGroupSpec('A custom TimeSeries interface', attributes=[], @@ -471,10 +490,10 @@ def _write_spiketrains(self, nwbfile, spiketrains, segment): ) """ - mod = nwbfile.add_unit_column("Modules", "description Modules") +###### mod = nwbfile.add_unit_column("Modules", "description Modules") # create interfaces - spiketrain_group = nwbfile.add_unit_column("UnitTimes", "description") +###### spiketrain_group = nwbfile.add_unit_column("UnitTimes", "description") fmt = 'unit_{{0:0{0}d}}_{1}'.format(len(str(len(spiketrains))), segment.name) for i, spiketrain in enumerate(spiketrains): @@ -486,7 +505,7 @@ def _write_spiketrains(self, nwbfile, spiketrains, segment): ) def _write_event(self, nwbfile, event, nwb_epoch, i): - print("--- def _write_event ---") +# print("--- def _write_event ---") event_name = event.name or "event{0}".format(i) ts_name = "{0}".format(event_name) @@ -508,7 +527,7 @@ def _write_event(self, nwbfile, event, nwb_epoch, i): ) """ - print("ts_name in _write_event = ", ts_name) +# print("ts_name in _write_event = ", ts_name) tS = TimeSeries( name=ts_name, @@ -524,7 +543,7 @@ def _write_event(self, nwbfile, event, nwb_epoch, i): ) def _write_neo_epoch(self, nwbfile, neo_epoch, nwb_epoch, i): - print("--- _write_neo_epoch ---") +# print("--- _write_neo_epoch ---") neo_epoch_name = neo_epoch.name or "intervalseries{0}".format(i) ts_name = "{0}".format(neo_epoch_name) @@ -565,7 +584,7 @@ def _write_neo_epoch(self, nwbfile, neo_epoch, nwb_epoch, i): ###### AutoNeoEpochSeries = get_class('TimeSeries', 'neo_epoch') """ - print("ts_name in _write_neo_epoch = ", ts_name) +# print("ts_name in _write_neo_epoch = ", ts_name) tS = TimeSeries( name=ts_name, From c4bd09fdf91bd9ed05dee8e59983fe0fa7ef14a4 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Fri, 13 Dec 2019 16:55:03 +0100 Subject: [PATCH 28/79] writing part --- neo/io/nwbio.py | 212 +++++++++++++++++++++++++++++++++++++----------- 1 file changed, 164 insertions(+), 48 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 90a50c7e0..5363a1136 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -93,9 +93,10 @@ def read_all_blocks(self, lazy=False, **kwargs): self._file = io.read() blocks = [] - for node in self._file.acquisition: + for node in self._file.acquisition: print("node = ", node) - blocks.append(self._read_block(self._file, node)) + ###blocks.append(self._read_block(self._file, node)) # Ok + blocks.append(self._read_block(self._file, node, blocks)) return blocks @@ -107,11 +108,12 @@ def read_block(self, lazy=False, **kargs): return self.read_all_blocks(lazy=lazy)[0] - def _read_block(self, _file, node, lazy=False, cascade=True, **kwargs): +### def _read_block(self, _file, node, lazy=False, cascade=True, **kwargs): # Ok + def _read_block(self, _file, node, blocks, lazy=False, cascade=True, **kwargs): """ Main method to load a block """ - print("*** def _read_block ***") + #print("*** def _read_block ***") self._lazy = lazy file_access_dates = _file.file_create_date @@ -130,7 +132,12 @@ def _read_block(self, _file, node, lazy=False, cascade=True, **kwargs): file_access_dates=file_access_dates, file_read_log='') +# print("block = ", block) +# print("blocks = ", blocks) + + #for block in blocks: ### New if cascade: + #print("cascade") self._handle_general_group(block) self._handle_epochs_group(_file, block) self._handle_acquisition_group(lazy, _file, block) @@ -138,17 +145,19 @@ def _read_block(self, _file, node, lazy=False, cascade=True, **kwargs): self._handle_processing_group(block) self._handle_analysis_group(block) self._lazy = False + #print("block.segments = ", block.segments) + #return list(block.segments) - print("--- block in read_block() = ", block) - print("*-* block.name = ", block.name) - print("END def read_block") +# print("--- block in read_block() = ", block) + # print("*-* block.name = ", block.name) + # print("END def read_block") return block ### + """ def _init_writing(self): - print("*** def _init_writing ***") # io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') @@ -156,27 +165,29 @@ def _init_writing(self): # return io_nwb return pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') - + """ + - def write_all_blocks(self, blocks): + def write_all_blocks(self, blocks, **kwargs): """ Write list of blocks to the file """ print("*** def write_all_blocks ***") - writer = self._init_writing() ### - print("writer = ", writer) +# writer = self._init_writing() ### +# print("writer = ", writer) if Block in self.writeable_objects: for block in blocks: print("block in all_blocks = ", block) - self.write_block(block, writer) +# self.write_block(block, writer) + self.write_block(block) print("END loop Block in def write_all_blocks") return list(block.segments) print("END DEF WRITE_ALL_BLOCKS") - def write_block(self, block=None, writer=None): + def write_block(self, block, **kwargs): """ Write a Block to the file :param block: Block to be written @@ -185,12 +196,15 @@ def write_block(self, block=None, writer=None): ############################ - self._write_block_children(block, writer) +# self._write_block_children(block, writer) + self._write_block_children(block) print("END def write_block") # def _write_block_children(self, block, writer): #Ok - def _write_block_children(self, block=None, writer=None): +### def _write_block_children(self, block=None, writer=None): # Ok 2 +# def _write_block_children(self, block=None, writer=None, **kwargs): + def _write_block_children(self, block=None, **kwargs): print("*** def _write_block_children ***") ############################ @@ -237,24 +251,107 @@ def _write_block_children(self, block=None, writer=None): print("*************************************************block = ", block) print("block.segments = ", block.segments) - - """ + io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') print("io_nwb = ", io_nwb) - """ + for segment in block.segments: print("segment = ", segment) print("segment.name = ", segment.name) self._write_segment(nwbfile, segment) # Ok -### io_nwb.write(nwbfile) + io_nwb.write(nwbfile) -### io_nwb.close() + io_nwb.close() ### print("Close the file") print("END def _write_block_children") + + + """ + ### Ok before + + def write_all_blocks(self, blocks, **kwargs): + print("*** def write_all_blocks ***") + + if Block in self.writeable_objects: + for block in blocks: + self.write_block(block) + print("END loop Block in def write_all_blocks") + return list(block.segments) + + + def write_block(self, block, **kwargs): + print("*** def write_block ***") + start_time = datetime.now() + nwbfile = NWBFile(self.filename, + session_start_time=start_time, + identifier='', + file_create_date=None, + timestamps_reference_time=None, + experimenter=None, + experiment_description=None, + session_id=None, + institution=None, + keywords=None, + notes=None, + pharmacology=None, + protocol=None, + related_publications=None, + slices=None, + source_script=None, + source_script_file_name=None, + data_collection=None, + surgery=None, + virus=None, + stimulus_notes=None, + lab=None, + acquisition=None, + stimulus=None, + stimulus_template=None, + epochs=None, + epoch_tags=set(), + trials=None, + invalid_times=None, + units=None, + electrodes=None, + electrode_groups=None, + ic_electrodes=None, + sweep_table=None, + imaging_planes=None, + ogen_sites=None, + devices=None, + subject=None + ) + + print("*************************************************block = ", block) + print("block.segments = ", block.segments) + + io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') + print("io_nwb = ", io_nwb) + + for segment in block.segments: + print("segment = ", segment) + print("segment.name = ", segment.name) + self._write_segment(nwbfile, segment) # Ok + io_nwb.write(nwbfile) + + io_nwb.close() + print("END def _write_block_children") + """ + + + + + + + + ### + + + def _handle_general_group(self, block): pass @@ -268,8 +365,8 @@ def _handle_epochs_group(self, _file, block): if epochs is not None: t_start = epochs[0][1] * pq.second t_stop = epochs[0][2] * pq.second - else: - timeseries.append(self._handle_timeseries(_file, self.name, timeseries)) +### else: +### timeseries.append(self._handle_timeseries(_file, self.name, timeseries)) segment = Segment(name=self.name) for obj in timeseries: @@ -285,6 +382,8 @@ def _handle_epochs_group(self, _file, block): segment.block = block block.segments.append(segment) + + """ def _handle_timeseries(self, _file, name, timeseries): # print("--- def _handle_timeseries ---") for i in _file.acquisition: @@ -339,6 +438,7 @@ def _handle_timeseries(self, _file, name, timeseries): units=units, time_units=pq.second) return obj + """ def _handle_acquisition_group(self, lazy, _file, block): # print("--- def _handle_acquisition_group ---") @@ -374,29 +474,48 @@ def _write_segment(self, nwbfile, segment): start_time = segment.t_start stop_time = segment.t_stop - nwb_epoch = nwbfile.add_epoch( - nwbfile, - segment.name, - start_time=float(start_time), - stop_time=float(stop_time), - ) +###### nwb_epoch = nwbfile.add_epoch( +# nwb_epoch = nwbfile.add_acquisition( +# nwbfile, +# segment.name, +# # start_time=float(start_time), +# stop_time=float(stop_time), +# ) + + + + tS_seg = TimeSeries( + name=segment.name, +# data=neo_epoch, +# timestamps=neo_epoch.times.rescale('second').magnitude, + timestamps=[1], + description="", + ) + + nwb_epoch = nwbfile.add_acquisition(tS_seg) + + for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): - self._write_signal(nwbfile, signal, nwb_epoch, i, segment) -# print("i = ", i) + #tS_seg = TimeSeries(name=segment.name, data=signal, timestamps=[1], description="") + print("segment.analogsignals = ", segment.analogsignals) + self._write_signal(nwbfile, signal, nwb_epoch, i, segment) # Ok + #print("i = ", i) + self._write_spiketrains(nwbfile, segment.spiketrains, segment) for i, event in enumerate(segment.events): self._write_event(nwbfile, event, nwb_epoch, i) for i, neo_epoch in enumerate(segment.epochs): self._write_neo_epoch(nwbfile, neo_epoch, nwb_epoch, i) - - print("END def _write_segment") +# print("END def _write_segment") + + - def _write_signal(self, nwbfile, signal, epoch, i, segment): + def _write_signal(self, nwbfile, signal, epoch, i, segment): # Ok print("*** def _write_signal ***") # print("signal", signal) signal_name = signal.name or "signal{0}".format(i) -# print("signal_name 123 = ", signal_name) + print("signal_name = ", signal_name) ts_name = "{0}".format(signal_name) """ @@ -454,23 +573,20 @@ def _write_signal(self, nwbfile, signal, epoch, i, segment): signal.sampling_rate = sampling_rate # All signals should go in /acquisition - tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, rate=float(sampling_rate)) + tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=segment.analogsignals, rate=float(sampling_rate)) #print("tS = ", tS) -###### return list(segment.analogsignals for signal in segment.analogsignals) return [segment.analogsignals] -# print("segment.analogsignals = ", segment.analogsignals) # OK - ts = nwbfile.add_acquisition(tS) - elif isinstance(signal, IrregularlySampledSignal): tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, timestamps=signal.times.rescale('second').magnitude) - ts = nwbfile.add_acquisition(tS) + return [segment.irregularlysampledsignals] else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) - nwbfile.add_epoch( - epoch, - start_time=time_in_seconds(segment.t_start), - stop_time=time_in_seconds(segment.t_stop), - ) +# nwbfile.add_epoch( +# epoch, +# start_time=time_in_seconds(segment.t_start), +# stop_time=time_in_seconds(segment.t_stop), +# ) + ts = nwbfile.add_acquisition(tS) print("END def _write_signal") def _write_spiketrains(self, nwbfile, spiketrains, segment): @@ -537,7 +653,7 @@ def _write_event(self, nwbfile, event, nwb_epoch, i): ) ts = nwbfile.add_acquisition(tS) - nwbfile.add_epoch(nwb_epoch, + nwbfile.add_epoch(nwb_epoch, start_time=time_in_seconds(event.times[0]), stop_time=time_in_seconds(event.times[1]), ) @@ -594,7 +710,7 @@ def _write_neo_epoch(self, nwbfile, neo_epoch, nwb_epoch, i): ) ts = nwbfile.add_acquisition(tS) - nwbfile.add_epoch( + nwbfile.add_epoch( nwb_epoch, start_time=time_in_seconds(neo_epoch.times[0]), stop_time=time_in_seconds(neo_epoch.times[-1]), From 39fc11dd7212a921e10caf10ffea4843f871a822 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Wed, 18 Dec 2019 15:41:25 +0100 Subject: [PATCH 29/79] Improvements writing several blocks - wip --- neo/io/nwbio.py | 440 +++------------------------------- neo/test/iotest/test_nwbio.py | 110 +-------- 2 files changed, 36 insertions(+), 514 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 5363a1136..c0c6b9d6f 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -55,9 +55,8 @@ class NWBIO(BaseIO): """ - Class for "reading" experimental data from a .nwb file, and "writing" a .nwb file + Class for "reading" experimental data from a .nwb file, and "writing" a .nwb file from Neo """ - supported_objects = [Block, Segment, AnalogSignal, IrregularlySampledSignal, SpikeTrain, Epoch, Event] readable_objects = supported_objects @@ -85,35 +84,27 @@ def __init__(self, filename, mode): def read_all_blocks(self, lazy=False, **kwargs): """ Loads all blocks in the file that are attached to the root. - Here, we assume that a neo block is a sub-part of a branch, into a NWB file; - with our description 1 block = 1 segment + Here, we assume that a neo block is a sub-part of a branch, into a NWB file; """ - print("*** def read_all_blocks ***") io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO self._file = io.read() - + blocks = [] - for node in self._file.acquisition: + for node in self._file.acquisition: print("node = ", node) - ###blocks.append(self._read_block(self._file, node)) # Ok blocks.append(self._read_block(self._file, node, blocks)) return blocks - def read_block(self, lazy=False, **kargs): """ Load the first block in the file. """ - print("*** def read_block ***") return self.read_all_blocks(lazy=lazy)[0] - -### def _read_block(self, _file, node, lazy=False, cascade=True, **kwargs): # Ok - def _read_block(self, _file, node, blocks, lazy=False, cascade=True, **kwargs): + def _read_block(self, _file, node, blocks, lazy=False, cascade=True, **kwargs): """ Main method to load a block """ - #print("*** def _read_block ***") self._lazy = lazy file_access_dates = _file.file_create_date @@ -131,13 +122,7 @@ def _read_block(self, _file, node, blocks, lazy=False, cascade=True, **kwargs): rec_datetime=_file.session_start_time, file_access_dates=file_access_dates, file_read_log='') - -# print("block = ", block) -# print("blocks = ", blocks) - - #for block in blocks: ### New if cascade: - #print("cascade") self._handle_general_group(block) self._handle_epochs_group(_file, block) self._handle_acquisition_group(lazy, _file, block) @@ -145,69 +130,12 @@ def _read_block(self, _file, node, blocks, lazy=False, cascade=True, **kwargs): self._handle_processing_group(block) self._handle_analysis_group(block) self._lazy = False - #print("block.segments = ", block.segments) - #return list(block.segments) - -# print("--- block in read_block() = ", block) - # print("*-* block.name = ", block.name) - # print("END def read_block") - return block - - ### - """ - def _init_writing(self): - print("*** def _init_writing ***") - -# io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') -# print("io_nwb = ", io_nwb) -# return io_nwb - - return pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') - """ - - def write_all_blocks(self, blocks, **kwargs): """ Write list of blocks to the file """ - print("*** def write_all_blocks ***") - -# writer = self._init_writing() ### -# print("writer = ", writer) - - if Block in self.writeable_objects: - for block in blocks: - print("block in all_blocks = ", block) -# self.write_block(block, writer) - self.write_block(block) - print("END loop Block in def write_all_blocks") - return list(block.segments) - print("END DEF WRITE_ALL_BLOCKS") - - - def write_block(self, block, **kwargs): - """ - Write a Block to the file - :param block: Block to be written - """ - print("*** def write_block ***") - - -############################ -# self._write_block_children(block, writer) - self._write_block_children(block) - - print("END def write_block") - -# def _write_block_children(self, block, writer): #Ok -### def _write_block_children(self, block=None, writer=None): # Ok 2 -# def _write_block_children(self, block=None, writer=None, **kwargs): - def _write_block_children(self, block=None, **kwargs): - print("*** def _write_block_children ***") -############################ - start_time = datetime.now() nwbfile = NWBFile(self.filename, session_start_time=start_time, @@ -248,109 +176,27 @@ def _write_block_children(self, block=None, **kwargs): devices=None, subject=None ) - - print("*************************************************block = ", block) - print("block.segments = ", block.segments) - - io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') - print("io_nwb = ", io_nwb) - - - for segment in block.segments: - print("segment = ", segment) - print("segment.name = ", segment.name) - self._write_segment(nwbfile, segment) # Ok - io_nwb.write(nwbfile) - - io_nwb.close() -### print("Close the file") - print("END def _write_block_children") - - - - - - """ - ### Ok before - - def write_all_blocks(self, blocks, **kwargs): - print("*** def write_all_blocks ***") + io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') if Block in self.writeable_objects: for block in blocks: - self.write_block(block) - print("END loop Block in def write_all_blocks") + print("block in all_blocks = ", block) + self.write_block(nwbfile, block) + io_nwb.write(nwbfile) return list(block.segments) + io_nwb.close() - - def write_block(self, block, **kwargs): - print("*** def write_block ***") - start_time = datetime.now() - nwbfile = NWBFile(self.filename, - session_start_time=start_time, - identifier='', - file_create_date=None, - timestamps_reference_time=None, - experimenter=None, - experiment_description=None, - session_id=None, - institution=None, - keywords=None, - notes=None, - pharmacology=None, - protocol=None, - related_publications=None, - slices=None, - source_script=None, - source_script_file_name=None, - data_collection=None, - surgery=None, - virus=None, - stimulus_notes=None, - lab=None, - acquisition=None, - stimulus=None, - stimulus_template=None, - epochs=None, - epoch_tags=set(), - trials=None, - invalid_times=None, - units=None, - electrodes=None, - electrode_groups=None, - ic_electrodes=None, - sweep_table=None, - imaging_planes=None, - ogen_sites=None, - devices=None, - subject=None - ) - - print("*************************************************block = ", block) - print("block.segments = ", block.segments) - - io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') - print("io_nwb = ", io_nwb) + def write_block(self, nwbfile, block, **kwargs): + """ + Write a Block to the file + :param block: Block to be written + """ + self._write_block_children(nwbfile, block) + def _write_block_children(self, nwbfile, block=None, **kwargs): for segment in block.segments: - print("segment = ", segment) print("segment.name = ", segment.name) - self._write_segment(nwbfile, segment) # Ok - io_nwb.write(nwbfile) - - io_nwb.close() - print("END def _write_block_children") - """ - - - - - - - - ### - - + self._write_segment(nwbfile, segment) def _handle_general_group(self, block): pass @@ -359,14 +205,11 @@ def _handle_epochs_group(self, _file, block): """ Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. """ -# print("--- def _handle_epochs_group ---") epochs = _file.epochs timeseries=[] if epochs is not None: t_start = epochs[0][1] * pq.second t_stop = epochs[0][2] * pq.second -### else: -### timeseries.append(self._handle_timeseries(_file, self.name, timeseries)) segment = Segment(name=self.name) for obj in timeseries: @@ -382,70 +225,10 @@ def _handle_epochs_group(self, _file, block): segment.block = block block.segments.append(segment) - - """ - def _handle_timeseries(self, _file, name, timeseries): -# print("--- def _handle_timeseries ---") - for i in _file.acquisition: - data_group = _file.get_acquisition(i).data*_file.get_acquisition(i).conversion - dtype = data_group.dtype - data = data_group - - if dtype.type is np.string_: - if self._lazy: - times = np.array(()) - else: - times = _file.get_acquisition(i).timestamps - duration = 1/_file.get_acquisition(i).rate - if durations: - # Epoch - if self._lazy: - durations = np.array(()) - obj = Epoch(times=times, - durations=durations, - labels=data_group, - units='second') - else: - # Event - obj = Event(times=times, - labels=data_group, - units='second') - else: - units = _file.get_acquisition(i).unit - - current_shape = _file.get_acquisition(i).data.shape[0] # number of samples - times = np.zeros(current_shape) - for j in range(0, current_shape): - times[j]=1./_file.get_acquisition(i).rate*j+_file.get_acquisition(i).starting_time - if times[j] == _file.get_acquisition(i).starting_time: - # AnalogSignal - sampling_metadata = times[j] - t_start = sampling_metadata * pq.s - sampling_rate = _file.get_acquisition(i).rate * pq.Hz - obj = AnalogSignal( - data_group, - units=units, - sampling_rate=sampling_rate, - t_start=t_start, - name=name) - elif _file.get_acquisition(i).timestamps: - if self._lazy: - time_data = np.array(()) - else: - time_data = _file.get_acquisition(i).timestamps - obj = IrregularlySampledSignal( - data_group, - units=units, - time_units=pq.second) - return obj - """ - def _handle_acquisition_group(self, lazy, _file, block): -# print("--- def _handle_acquisition_group ---") acq = _file.acquisition def _handle_stimulus_group(self, lazy, _file, block): -# print("--- def _handle_stimulus_group ---") sti = _file.stimulus for name in sti: segment_name_sti = _file.epochs @@ -455,7 +238,7 @@ def _handle_stimulus_group(self, lazy, _file, block): times = np.array(()) lazy_shape = _file.get_stimulus(name).data.shape else: - current_shape = _file.get_stimulus(name).data.shape[0] # sample number + current_shape = _file.get_stimulus(name).data.shape[0] times = np.zeros(current_shape) for j in range(0, current_shape): times[j]=1./_file.get_stimulus(name).rate*j+_file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] @@ -468,102 +251,30 @@ def _handle_processing_group(self, block): def _handle_analysis_group(self, block): pass - def _write_segment(self, nwbfile, segment): - print("*** def _write_segment ***") start_time = segment.t_start stop_time = segment.t_stop -###### nwb_epoch = nwbfile.add_epoch( -# nwb_epoch = nwbfile.add_acquisition( -# nwbfile, -# segment.name, -# # start_time=float(start_time), -# stop_time=float(stop_time), -# ) - - - - tS_seg = TimeSeries( + for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): + self._write_signal(nwbfile, signal, i, segment) + tS_seg = TimeSeries( name=segment.name, -# data=neo_epoch, -# timestamps=neo_epoch.times.rescale('second').magnitude, + data=signal, timestamps=[1], description="", ) - - nwb_epoch = nwbfile.add_acquisition(tS_seg) - - - - for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): - #tS_seg = TimeSeries(name=segment.name, data=signal, timestamps=[1], description="") - print("segment.analogsignals = ", segment.analogsignals) - self._write_signal(nwbfile, signal, nwb_epoch, i, segment) # Ok - #print("i = ", i) - self._write_spiketrains(nwbfile, segment.spiketrains, segment) for i, event in enumerate(segment.events): self._write_event(nwbfile, event, nwb_epoch, i) for i, neo_epoch in enumerate(segment.epochs): self._write_neo_epoch(nwbfile, neo_epoch, nwb_epoch, i) -# print("END def _write_segment") - - + nwbfile.add_acquisition(tS_seg) - def _write_signal(self, nwbfile, signal, epoch, i, segment): # Ok - print("*** def _write_signal ***") -# print("signal", signal) - signal_name = signal.name or "signal{0}".format(i) + def _write_signal(self, nwbfile, signal, i, segment): + signal_name = signal.name or "signal%d" % i print("signal_name = ", signal_name) ts_name = "{0}".format(signal_name) - """ - # Create a builder for the namespace - ns_builder_signal = NWBNamespaceBuilder('Extension to neo signal', "neo_signal") - ns_builder_signal.include_type('TimeSeries', namespace='core') - - # Group Specifications - # Create extensions - ts_signal = NWBGroupSpec('A custom TimeSeries interface for signal', -# attributes=[NWBAttributeSpec('timeseries', '', 'int')], - #datasets=[], - #groups=[], - groups=[NWBGroupSpec('An included TimeSeries instance for signal', neurodata_type_inc='TimeSeries')], - neurodata_type_inc='TimeSeries', - neurodata_type_def='MultiChannelTimeSeries' - ) - - # Add the extension - ext_source_signal = 'nwb_neo_extension_signal.specs.yaml' - ns_builder_signal.add_spec(ext_source_signal, - ts_signal - ) - - # Save the namespace and extensions - ns_path_signal = "nwb_neo_extension_signal.namespace.yaml" - ns_builder_signal.export(ns_path_signal) - - # Incorporating extensions - load_namespaces(ns_path_signal) - - # TimeSeries - NWBSignalSeries = get_class('TimeSeries', 'neo_signal') # class pynwb.base.TimeSeries - # NWB File -### NWBSignalSeries = get_class('NWBFile', namespace='core') # class pynwb.base.TimeSeries - - ts = NWBSignalSeries( - 'MultiChannelTimeSeries123_index_%d_%s' % (i, segment.name), #index - #'TimeSeries', # name of the class - [ts_signal], - #'', - #session_start_time=datetime.now(), - rate=1.0 - ) - -# nwbfile.add_acquisition(ts) - """ - conversion = _decompose_unit(signal.units) attributes = {"conversion": conversion, "resolution": float('nan')} @@ -571,46 +282,18 @@ def _write_signal(self, nwbfile, signal, epoch, i, segment): # Ok if isinstance(signal, AnalogSignal): sampling_rate = signal.sampling_rate.rescale("Hz") signal.sampling_rate = sampling_rate - # All signals should go in /acquisition tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=segment.analogsignals, rate=float(sampling_rate)) - #print("tS = ", tS) + #ts = nwbfile.add_acquisition(tS) return [segment.analogsignals] elif isinstance(signal, IrregularlySampledSignal): tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, timestamps=signal.times.rescale('second').magnitude) return [segment.irregularlysampledsignals] else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) -# nwbfile.add_epoch( -# epoch, -# start_time=time_in_seconds(segment.t_start), -# stop_time=time_in_seconds(segment.t_stop), -# ) - ts = nwbfile.add_acquisition(tS) - print("END def _write_signal") + #ts = nwbfile.add_acquisition(tS) def _write_spiketrains(self, nwbfile, spiketrains, segment): -# print("--- def _write_spiketrains ---") - """ - mod = NWBGroupSpec('A custom TimeSeries interface', - attributes=[], - datasets=[], - groups=[], - neurodata_type_inc='TimeSeries', - neurodata_type_def='Module') - - ext_source = 'nwb_neo_extension.specs.yaml' - mod.add_dataset( - doc='', - neurodata_type_def='Module', - ) - """ - -###### mod = nwbfile.add_unit_column("Modules", "description Modules") - - # create interfaces -###### spiketrain_group = nwbfile.add_unit_column("UnitTimes", "description") - fmt = 'unit_{{0:0{0}d}}_{1}'.format(len(str(len(spiketrains))), segment.name) for i, spiketrain in enumerate(spiketrains): unit = fmt.format(i) @@ -621,37 +304,14 @@ def _write_spiketrains(self, nwbfile, spiketrains, segment): ) def _write_event(self, nwbfile, event, nwb_epoch, i): -# print("--- def _write_event ---") event_name = event.name or "event{0}".format(i) ts_name = "{0}".format(event_name) - - """ - ts = NWBGroupSpec('A custom TimeSeries interface', - attributes=[], - datasets=[], - groups=[], - neurodata_type_inc='TimeSeries', - neurodata_type_def='AnnotationSeries') - ext_source = 'nwb_neo_extension.specs.yaml' - ts.add_dataset( - doc='', - neurodata_type_def='AnnotationSeries', - ) - nwbfile.add_epoch( - time_in_seconds(event.times[0]), - time_in_seconds(event.times[1]), - ) - """ - -# print("ts_name in _write_event = ", ts_name) - tS = TimeSeries( name=ts_name, data=event, timestamps=event.times.rescale('second').magnitude, description=event.description or "", ) - ts = nwbfile.add_acquisition(tS) nwbfile.add_epoch(nwb_epoch, start_time=time_in_seconds(event.times[0]), @@ -659,56 +319,14 @@ def _write_event(self, nwbfile, event, nwb_epoch, i): ) def _write_neo_epoch(self, nwbfile, neo_epoch, nwb_epoch, i): -# print("--- _write_neo_epoch ---") neo_epoch_name = neo_epoch.name or "intervalseries{0}".format(i) - ts_name = "{0}".format(neo_epoch_name) - - """ - # Create a builder for the namespace - ns_builder_neo_epoch = NWBNamespaceBuilder('Extension to neo epoch', "neo_epoch") -# ns_builder = NWBNamespaceBuilder('Extension to neo epoch', "neo_AnnotatedIntervalSeries") - ns_builder_neo_epoch.include_type('TimeSeries', namespace='core') -# ns_builder.include_type('neo_AnnotatedIntervalSeries', namespace='core') - - # Group Specifications - # Create extensions - ts_neo_epoch = NWBGroupSpec('A custom TimeSeries interface', -# attributes=[NWBAttributeSpec('timeseries', '', 'int')], - #datasets=[], - #groups=[], - groups=[NWBGroupSpec('An included TimeSeries instance', neurodata_type_inc='TimeSeries')], - neurodata_type_inc='TimeSeries', - neurodata_type_def='AnnotatedIntervalSeries' - ) - - # Add the extension - ext_source_neo_epoch = 'nwb_neo_extension.specs.yaml' - ns_builder_neo_epoch.add_spec(ext_source_neo_epoch, - ts_neo_epoch - ) - - # Include an existing namespace -# ns_builder_neo_epoch.include_namespace('collab_ts') - - # Save the namespace and extensions - ns_path_neo_epoch = "nwb_neo_extension.namespace.yaml" - ns_builder_neo_epoch.export(ns_path_neo_epoch) -# ns_builder.export("AnnotatedIntervalSeries") - - load_namespaces(ns_path_neo_epoch) - -###### AutoNeoEpochSeries = get_class('TimeSeries', 'neo_epoch') - """ - -# print("ts_name in _write_neo_epoch = ", ts_name) - + ts_name = "{0}".format(neo_epoch_name) tS = TimeSeries( name=ts_name, data=neo_epoch, timestamps=neo_epoch.times.rescale('second').magnitude, description=neo_epoch.description or "", ) - ts = nwbfile.add_acquisition(tS) nwbfile.add_epoch( nwb_epoch, diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index d20733376..ab46f3ef5 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -16,65 +16,24 @@ class TestNWBIO(unittest.TestCase, ): ioclass = NWBIO files_to_download = [ - # My NWB files -# '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github page -### '/Users/legouee/NWBwork/my_notebook/NWB_File_python_3_pynwb_101_ephys_data_bis.nwb' -# '/Users/legouee/NWBwork/my_notebook/My_first_dataset.nwb' -### '/Users/legouee/NWBwork/my_notebook/My_first_dataset_neo8.nwb' -###### '/home/elodie/env_NWB_py3/my_notebook/My_first_dataset_neo9.nwb' - - # Files from Allen Institute - # NWB files downloadable from http://download.alleninstitute.org/informatics-archive/prerelease/ -### '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' +# Files from Allen Institute : +# NWB files downloadable from http://download.alleninstitute.org/informatics-archive/prerelease/ +# '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb' - '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' -# '/home/elodie/NWB_Files/NWB_org/behavior_ophys_session_775614751.nwb' -# '/home/elodie/NWB_Files/NWB_org/ecephys_session_785402239.nwb' - - # File written with NWBIO class() -### '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb.nwb' -### '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' -# '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO_2.nwb' -### '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' +# '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' +# File created from Neo (Jupyter notebook "test_nwbio_class_from_Neo.ipynb") + '/home/elodie/env_NWB_py3/my_notebook/My_first_dataset_neo9.nwb' ] entities_to_test = files_to_download - def test_nwbio(self): - print("*** def test_nwbio ***") reader = NWBIO(filename=self.files_to_download[0], mode='r') reader.read() -# blocks=[] -# for ind in range(2): # 2 blocks -# blk = Block(name='%s' %ind) -# blocks.append(blk) -# -# # access to segments -# for block in blocks: -# # Tests of Block -# self.assertTrue(isinstance(block.name, str)) -# # Segment -# for segment in block.segments: -# self.assertEqual(segment.block, block) -# # AnalogSignal -# for asig in segment.analogsignals: -# self.assertTrue(isinstance(asig, AnalogSignal)) -# self.assertTrue(asig.sampling_rate, pq.Hz) -# self.assertTrue(asig.units, pq) -# # Spiketrain -# for st in segment.spiketrains: -# self.assertTrue(isinstance(st, SpikeTrain)) - def test_segment(self, **kargs): - print("*** def test_segment ***") seg = Segment(index=5) r = NWBIO(filename=self.files_to_download[0], mode='r') -# blocks=[] -# for ind in range(2): # 2 blocks ####################################################################################################################### -# blk = Block(name='%s' %ind) -# blocks.append(blk) seg_nwb = r.read() # equivalent to read_all_blocks() self.assertTrue(seg, Segment) self.assertTrue(seg_nwb, Segment) @@ -86,96 +45,41 @@ def test_segment(self, **kargs): self.assertIsNotNone(seg_nwb_one_block, seg) def test_analogsignals_neo(self, **kargs): - print("*** def test_analogsignals_neo ***") sig_neo = AnalogSignal(signal=[1, 2, 3], units='V', t_start=np.array(3.0)*pq.s, sampling_rate=1*pq.Hz) self.assertTrue(isinstance(sig_neo, AnalogSignal)) r = NWBIO(filename=self.files_to_download[0], mode='r') obj_nwb = r.read() -# obj_nwb = r.read_block() self.assertTrue(obj_nwb, AnalogSignal) self.assertTrue(obj_nwb, sig_neo) - + def test_read_irregularlysampledsignal(self, **kargs): - print("*** def test_read_irregularlysampledsignal ***") irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], units='mV', time_units='ms') irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*pq.s, [[4, 5], [5, 4], [6, 3]]*pq.nA) self.assertTrue(isinstance(irsig0, IrregularlySampledSignal)) self.assertTrue(isinstance(irsig1, IrregularlySampledSignal)) r = NWBIO(filename=self.files_to_download[0], mode='r') irsig_nwb = r.read() -# irsig_nwb = r.read_block() self.assertTrue(irsig_nwb, IrregularlySampledSignal) self.assertTrue(irsig_nwb, irsig0) self.assertTrue(irsig_nwb, irsig1) def test_read_event(self, **kargs): - print("*** def test_read_event ***") evt_neo = Event(np.arange(0, 30, 10)*pq.s, labels=np.array(['trig0', 'trig1', 'trig2'], dtype='S')) r = NWBIO(filename=self.files_to_download[0], mode='r') event_nwb = r.read() -# event_nwb = r.read_block() self.assertTrue(event_nwb, evt_neo) self.assertIsNotNone(event_nwb, evt_neo) def test_read_epoch(self, **kargs): - print("*** def test_read_epoch ***") epc_neo = Epoch(times=np.arange(0, 30, 10)*pq.s, durations=[10, 5, 7]*pq.ms, labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) r = NWBIO(filename=self.files_to_download[0], mode='r') epoch_nwb = r.read() -# epoch_nwb = r.read_block() self.assertTrue(epoch_nwb, Epoch) self.assertTrue(epoch_nwb, epc_neo) self.assertIsNotNone(epoch_nwb, epc_neo) - def test_write_NWB_Files(self): - ''' - Test function to write several blocks containing several segments and analogsignals. - ''' - print("*** Test function test_write_NWB_Files ***") - blocks = [] - - bl0 = Block(name='First block') - bl1 = Block(name='Second block') - bl2 = Block(name='Third block') -# print("bl0.segments = ", bl0.segments) -# print("bl1.segments = ", bl1.segments) -# print("bl2.segments = ", bl2.segments) - blocks = [bl0, bl1, bl2] -# print("blocks = ", blocks) - - num_seg = 3 # number of segments - - for blk in blocks: -# print("blk = ", blk) - for ind in range(num_seg): # number of Segment - seg = Segment(name='segment %d' % ind, index=ind) - blk.segments.append(seg) - - for seg in blk.segments: # AnalogSignal objects - # 3 AnalogSignals -# print("seg = ", seg) - a = AnalogSignal(np.random.randn(num_seg, 44)*pq.nA, sampling_rate=10*pq.kHz) - b = AnalogSignal(np.random.randn(num_seg, 64)*pq.nA, sampling_rate=10*pq.kHz) - c = AnalogSignal(np.random.randn(num_seg, 33)*pq.nA, sampling_rate=10*pq.kHz) - - seg.analogsignals.append(a) - seg.analogsignals.append(b) - seg.analogsignals.append(c) - -# print("END blocks = ", blocks) - - # Save the file - filename = '/home/elodie/env_NWB_py3/my_notebook/my_first_test_neo_to_nwb_test_NWBIO.nwb' -# filename = '/Users/legouee/NWBwork/my_notebook/my_first_test_neo_to_nwb_test_NWBIO_in_test_nwbio.nwb' -# print("filename = ", filename) - w_file = NWBIO(filename=filename, mode='w') # Write the .nwb file -# print("w_file = ", w_file) - blocks = w_file.write(blk) -# print("*** END test_write_NWB_Files ***") - - if __name__ == "__main__": print("pynwb.__version__ = ", pynwb.__version__) unittest.main() \ No newline at end of file From 78a026c54c28d003d9691b1c8f3cf66a07722922 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Mon, 27 Jan 2020 15:36:58 +0100 Subject: [PATCH 30/79] Neo SpikeTrain Epoch --- neo/io/nwbio.py | 146 +++++++++++++++++++++------------- neo/test/iotest/test_nwbio.py | 18 +++++ 2 files changed, 107 insertions(+), 57 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index c0c6b9d6f..2bfc1d89c 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -88,11 +88,11 @@ def read_all_blocks(self, lazy=False, **kwargs): """ io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO self._file = io.read() - + blocks = [] - for node in self._file.acquisition: + for node in (self._file.acquisition, self._file.units, self._file.epochs): print("node = ", node) - blocks.append(self._read_block(self._file, node, blocks)) + blocks.append(self._read_block(self._file, node, blocks)) return blocks def read_block(self, lazy=False, **kargs): @@ -101,7 +101,7 @@ def read_block(self, lazy=False, **kargs): """ return self.read_all_blocks(lazy=lazy)[0] - def _read_block(self, _file, node, blocks, lazy=False, cascade=True, **kwargs): + def _read_block(self, _file, node, blocks, lazy=False, cascade=True, **kwargs): """ Main method to load a block """ @@ -127,7 +127,7 @@ def _read_block(self, _file, node, blocks, lazy=False, cascade=True, **kwargs): self._handle_epochs_group(_file, block) self._handle_acquisition_group(lazy, _file, block) self._handle_stimulus_group(lazy, _file, block) - self._handle_processing_group(block) + self._handle_processing_group(_file, block) self._handle_analysis_group(block) self._lazy = False return block @@ -179,8 +179,8 @@ def write_all_blocks(self, blocks, **kwargs): io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') if Block in self.writeable_objects: - for block in blocks: - print("block in all_blocks = ", block) + for i, block in enumerate(blocks): + block_name = block.name or "blocks%d" % i self.write_block(nwbfile, block) io_nwb.write(nwbfile) return list(block.segments) @@ -194,9 +194,23 @@ def write_block(self, nwbfile, block, **kwargs): self._write_block_children(nwbfile, block) def _write_block_children(self, nwbfile, block=None, **kwargs): - for segment in block.segments: - print("segment.name = ", segment.name) - self._write_segment(nwbfile, segment) + for i, segment in enumerate(block.segments): + self._write_segment(nwbfile, block, segment) + segment_name = segment.name + seg_start_time = segment.t_start + seg_stop_time = segment.t_stop + tS_seg = TimeSeries( + name=segment_name, + data=[segment], + timestamps=[1], + description="", + ) + + nwbfile.add_epoch( + float(seg_start_time), + float(seg_stop_time), + tags=['segment_name'], + ) def _handle_general_group(self, block): pass @@ -207,15 +221,14 @@ def _handle_epochs_group(self, _file, block): """ epochs = _file.epochs timeseries=[] - if epochs is not None: - t_start = epochs[0][1] * pq.second - t_stop = epochs[0][2] * pq.second segment = Segment(name=self.name) + segment.epochs.append(Epoch) for obj in timeseries: obj.segment = segment if isinstance(obj, AnalogSignal): segment.analogsignals.append(obj) + segment.epochs.append(obj) elif isinstance(obj, IrregularlySampledSignal): segment.irregularlysampledsignals.append(obj) elif isinstance(obj, Event): @@ -245,36 +258,60 @@ def _handle_stimulus_group(self, lazy, _file, block): spiketrain = SpikeTrain(times, units=pq.second, t_stop=times[-1]*pq.second) - def _handle_processing_group(self, block): - pass + def _handle_processing_group(self, _file, block): + segment = Segment(name=self.name) def _handle_analysis_group(self, block): pass - def _write_segment(self, nwbfile, segment): + def _write_segment(self, nwbfile, block, segment): start_time = segment.t_start stop_time = segment.t_stop - for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): - self._write_signal(nwbfile, signal, i, segment) - tS_seg = TimeSeries( - name=segment.name, + block_name = block.name or "blocks %d" % i + segment_name = segment.name + + for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): # analogsignals + self._write_signal(nwbfile, block, signal, i, segment) + analogsignal_name = signal.name or ("analogsignal %s %s %d" % (block_name, segment_name, i)) + tS_signal = TimeSeries( + name=analogsignal_name, data=signal, timestamps=[1], description="", ) - self._write_spiketrains(nwbfile, segment.spiketrains, segment) + for i, train in enumerate(segment.spiketrains): # spiketrains + self._write_spiketrains(nwbfile, train, i, segment) + spiketrains_name = train.name or ("spiketrains %s %s %d" % (block_name, segment_name, i)) + ts_name = "{0}".format(spiketrains_name) + tS_train = TimeSeries( + name=spiketrains_name, + data=train, + timestamps=[1], + description="", + ) for i, event in enumerate(segment.events): self._write_event(nwbfile, event, nwb_epoch, i) - for i, neo_epoch in enumerate(segment.epochs): - self._write_neo_epoch(nwbfile, neo_epoch, nwb_epoch, i) - nwbfile.add_acquisition(tS_seg) + for i, neo_epoch in enumerate(segment.epochs): # epochs + self._write_neo_epoch(nwbfile, neo_epoch, i, segment) + epochs_name = neo_epoch.name or ("neo epochs %s %s %d" % (block_name, segment_name, i)) + ts_name = "{0}".format(epochs_name) + tS_epc = TimeSeries( + name=epochs_name, + data=signal, + timestamps=signal.times.rescale('second').magnitude, + description=signal.description or "", + ) - def _write_signal(self, nwbfile, signal, i, segment): - signal_name = signal.name or "signal%d" % i - print("signal_name = ", signal_name) - ts_name = "{0}".format(signal_name) + nwbfile.add_acquisition(tS_signal) # For analogsignals + nwbfile.add_acquisition(tS_train) # For spiketrains + nwbfile.add_acquisition(tS_epc) # For Neo segment (Neo epoch) + def _write_signal(self, nwbfile, block, signal, i, segment): # analogsignals + block_name = block.name or "blocks %d" % i + segment_name = segment.name + signal_name = signal.name or ("signal %s %s %d" % (block_name, segment_name, i)) + ts_name = "{0}".format(signal_name) conversion = _decompose_unit(signal.units) attributes = {"conversion": conversion, "resolution": float('nan')} @@ -291,17 +328,20 @@ def _write_signal(self, nwbfile, signal, i, segment): return [segment.irregularlysampledsignals] else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) - #ts = nwbfile.add_acquisition(tS) - - def _write_spiketrains(self, nwbfile, spiketrains, segment): - fmt = 'unit_{{0:0{0}d}}_{1}'.format(len(str(len(spiketrains))), segment.name) - for i, spiketrain in enumerate(spiketrains): - unit = fmt.format(i) - ug = nwbfile.add_unit( - spike_times=spiketrain.rescale('second').magnitude, - Modules='', - UnitTimes='', - ) + ####ts = nwbfile.add_acquisition(tS) + + def _write_spiketrains(self, nwbfile, spiketrains, i, segment): # spiketrains + spiketrain = segment.spiketrains + for i, train in enumerate(segment.spiketrains): # spiketrains + spiketrains_name = train.name or "spiketrains %d" % i + ts_name = "{0}".format(spiketrains_name) + tS_train = TimeSeries( + name=spiketrains_name, + data=train, + timestamps=[1], + description="", + ) + return [segment.spiketrains] def _write_event(self, nwbfile, event, nwb_epoch, i): event_name = event.name or "event{0}".format(i) @@ -313,26 +353,18 @@ def _write_event(self, nwbfile, event, nwb_epoch, i): description=event.description or "", ) - nwbfile.add_epoch(nwb_epoch, - start_time=time_in_seconds(event.times[0]), - stop_time=time_in_seconds(event.times[1]), - ) - - def _write_neo_epoch(self, nwbfile, neo_epoch, nwb_epoch, i): - neo_epoch_name = neo_epoch.name or "intervalseries{0}".format(i) - ts_name = "{0}".format(neo_epoch_name) - tS = TimeSeries( - name=ts_name, - data=neo_epoch, - timestamps=neo_epoch.times.rescale('second').magnitude, - description=neo_epoch.description or "", + def _write_neo_epoch(self, nwbfile, neo_epoch, i, segment): # epochs + for i, epoch in enumerate(segment.epochs): # epochs + epochs_name = epoch.name or "epochs %d" % i + ts_name = "{0}".format(epochs_name) + tS_epc = TimeSeries( + name=epochs_name, + data=epoch, + timestamps=[1], + description="", ) + return [segment.epochs] - nwbfile.add_epoch( - nwb_epoch, - start_time=time_in_seconds(neo_epoch.times[0]), - stop_time=time_in_seconds(neo_epoch.times[-1]), - ) def time_in_seconds(t): return float(t.rescale("second")) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index ab46f3ef5..c6bf4f877 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -52,6 +52,24 @@ def test_analogsignals_neo(self, **kargs): self.assertTrue(obj_nwb, AnalogSignal) self.assertTrue(obj_nwb, sig_neo) + def test_spiketrains_neo(self, **kargs): + train = SpikeTrain(times=[1, 2, 3]*pq.s, t_start=1.0, t_stop=10.0) + self.assertTrue(isinstance(train, SpikeTrain)) + r = NWBIO(filename=self.files_to_download[0], mode='r') + obj_nwb = r.read() + self.assertTrue(obj_nwb, SpikeTrain) + self.assertTrue(obj_nwb, train) + + def test_epochs_neo(self, **kargs): + epc = Epoch(times=np.arange(0, 30, 10)*pq.s, + durations=[10, 5, 7]*pq.ms, + labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) + self.assertTrue(isinstance(epc, Epoch)) + r = NWBIO(filename=self.files_to_download[0], mode='r') + obj_nwb = r.read() + self.assertTrue(obj_nwb, Epoch) + self.assertTrue(obj_nwb, epc) + def test_read_irregularlysampledsignal(self, **kargs): irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], units='mV', time_units='ms') irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*pq.s, [[4, 5], [5, 4], [6, 3]]*pq.nA) From 68f3d631da8756df352a5ae4cbb2ebc3cfc1d919 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Mon, 17 Feb 2020 15:16:05 +0100 Subject: [PATCH 31/79] Calcium imaging data --- neo/io/nwbio.py | 255 ++++++++++++++++++++++++++++++---- neo/test/iotest/test_nwbio.py | 24 +++- 2 files changed, 251 insertions(+), 28 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 2bfc1d89c..adf54167e 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -24,11 +24,12 @@ from os.path import join import dateutil.parser import numpy as np +import random import quantities as pq from neo.io.baseio import BaseIO from neo.core import (Segment, SpikeTrain, Unit, Epoch, Event, AnalogSignal, - IrregularlySampledSignal, ChannelIndex, Block) + IrregularlySampledSignal, ChannelIndex, Block, ImageSequence) from collections import OrderedDict # Standard Python imports @@ -44,8 +45,11 @@ from pynwb.base import ProcessingModule from pynwb.ecephys import ElectricalSeries, Device, EventDetection from pynwb.behavior import SpatialSeries +from pynwb import image from pynwb.image import ImageSeries from pynwb.spec import NWBAttributeSpec, NWBDatasetSpec, NWBGroupSpec, NWBNamespace, NWBNamespaceBuilder +from pynwb.device import Device +from pynwb.ophys import TwoPhotonSeries, OpticalChannel, ImageSegmentation, Fluorescence # For calcium imaging data # hdmf imports from hdmf.spec import LinkSpec, GroupSpec, DatasetSpec, SpecNamespace,\ @@ -58,7 +62,7 @@ class NWBIO(BaseIO): Class for "reading" experimental data from a .nwb file, and "writing" a .nwb file from Neo """ supported_objects = [Block, Segment, AnalogSignal, IrregularlySampledSignal, - SpikeTrain, Epoch, Event] + SpikeTrain, Epoch, Event, ImageSequence] readable_objects = supported_objects writeable_objects = supported_objects @@ -78,6 +82,10 @@ def __init__(self, filename, mode): Arguments: filename : the filename """ + if not pynwb: + raise Exception("Please install the pynwb package to use NWBIO") + if not hdmf: + raise Exception("Please install the hdmf package to use NWBIO") BaseIO.__init__(self, filename=filename) self.filename = filename @@ -91,7 +99,6 @@ def read_all_blocks(self, lazy=False, **kwargs): blocks = [] for node in (self._file.acquisition, self._file.units, self._file.epochs): - print("node = ", node) blocks.append(self._read_block(self._file, node, blocks)) return blocks @@ -129,6 +136,7 @@ def _read_block(self, _file, node, blocks, lazy=False, cascade=True, **kwargs): self._handle_stimulus_group(lazy, _file, block) self._handle_processing_group(_file, block) self._handle_analysis_group(block) + self._handle_calcium_imaging_data(_file, block) # Calcium imaging data self._lazy = False return block @@ -182,7 +190,8 @@ def write_all_blocks(self, blocks, **kwargs): for i, block in enumerate(blocks): block_name = block.name or "blocks%d" % i self.write_block(nwbfile, block) - io_nwb.write(nwbfile) + self.write_calcium_imaging_data(nwbfile, block, i) + io_nwb.write(nwbfile) return list(block.segments) io_nwb.close() @@ -197,8 +206,7 @@ def _write_block_children(self, nwbfile, block=None, **kwargs): for i, segment in enumerate(block.segments): self._write_segment(nwbfile, block, segment) segment_name = segment.name - seg_start_time = segment.t_start - seg_stop_time = segment.t_stop +# print("segment_name = ", segment_name) tS_seg = TimeSeries( name=segment_name, data=[segment], @@ -206,12 +214,6 @@ def _write_block_children(self, nwbfile, block=None, **kwargs): description="", ) - nwbfile.add_epoch( - float(seg_start_time), - float(seg_stop_time), - tags=['segment_name'], - ) - def _handle_general_group(self, block): pass @@ -264,23 +266,192 @@ def _handle_processing_group(self, _file, block): def _handle_analysis_group(self, block): pass - def _write_segment(self, nwbfile, block, segment): - start_time = segment.t_start - stop_time = segment.t_stop + def _handle_calcium_imaging_data(self, _file, block): + """ + Function to read calcium imaging data. + """ +# print("*** def _handle_calcium_imaging_data ***") + pass + + +############################# + def write_calcium_imaging_data(self, nwbfile, block, i): + """ + Function to write calcium imaging data. This involves three main steps: + - Acquiring two-photon images + - Image segmentation + - Fluorescence and dF/F response + + Adding metadata about acquisition + """ + name_imaging_device = "imaging_device %s %d" % (block.name, i) + device = Device(name_imaging_device) + + nwbfile.add_device(device) + + # To define the manifold + l = [] + for frame in range(50): + l.append([]) + for y in range(100): + l[frame].append([]) + for x in range(100): + l[frame][y].append(random.randint(0, 50)) + + # OpticalChannel + name_optical_channel = "optical_channel %s %d" %(block.name, i) + optical_channel = OpticalChannel( + name = name_optical_channel, + description = 'description', + emission_lambda = 500.) # Emission wavelength for channel, in nm + + name_imaging_plane = "imaging_plane %s %d " %(block.name, i) + + imaging_plane = nwbfile.create_imaging_plane( + name_imaging_plane, # name + optical_channel, # optical_channel + 'a very interesting part of the brain', # description + device, # device + 600., # excitation_lambda + 300., # imaging_rate + 'GFP', # indicator + 'my favorite brain location', # location + l[frame][y].append(random.randint(0, 50)), # manifold + 1.0, # conversion + 'manifold unit', # unit + 'A frame to refer to' # reference_frame + ) +# print("imaging_plane = ", imaging_plane) + + """ + Adding two-photon image data + """ + name_twophotonseries = "two_photon_series %s %d" %(block.name, i) + image_series = TwoPhotonSeries( + name=name_twophotonseries, + dimension=[2], + external_file=['images.tiff'], + imaging_plane=imaging_plane, + starting_frame=[0], + format='tiff', + starting_time=0.0, + rate=1.0 + ) +# print("image_series = ", image_series) + + nwbfile.add_acquisition(image_series) + + """ + Storing image segmentation output + """ + name_processing_module = "processing_module %s %d" %(block.name, i) + mod = nwbfile.create_processing_module( + name_processing_module, # Example : 'ophys' + 'contains optical physiology processed data' + ) + + img_seg = ImageSegmentation() + mod.add(img_seg) + + name_plane_segmentation = "plane_segmentation %s %d" %(block.name, i) + ps = img_seg.create_plane_segmentation( + description = 'output from segmenting my favorite imaging plane', + imaging_plane = imaging_plane, # link to OpticalChannel + name = name_plane_segmentation, + reference_images = image_series # link to TwoPhotonSeries + ) +# print("ps = ", ps) + + """ + Add the resulting ROIs + """ + w, h = 3, 3 + pix_mask1 = [(0, 0, 1.1), (1, 1, 1.2), (2, 2, 1.3)] + img_mask1 = [[0.0 for x in range(w)] for y in range(h)] + img_mask1[0][0] = 1.1 + img_mask1[1][1] = 1.2 + img_mask1[2][2] = 1.3 + ps.add_roi(pixel_mask=pix_mask1, image_mask=img_mask1) + + pix_mask2 = [(0, 0, 2.1), (1, 1, 2.2)] + img_mask2 = [[0.0 for x in range(w)] for y in range(h)] + img_mask2[0][0] = 2.1 + img_mask2[1][1] = 2.2 + + ps.add_roi(pixel_mask=pix_mask2, image_mask=img_mask2) + + + """ + Storing fluorescence measurements + """ + # Create a data interface + fl = Fluorescence() + mod.add(fl) + + # Reference to the ROIs + rt_region = ps.create_roi_table_region( + 'the first of two ROIs', + region=[0] + ) + + # RoiResponseSeries + data = [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.] + timestamps = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] + rrs = fl.create_roi_response_series( + 'my_rrs', + data, + rt_region, + unit='lumens', + timestamps=timestamps + ) +# print("rrs = ", rrs) +############################# + + + def _write_segment(self, nwbfile, block, segment): block_name = block.name or "blocks %d" % i segment_name = segment.name for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): # analogsignals self._write_signal(nwbfile, block, signal, i, segment) analogsignal_name = signal.name or ("analogsignal %s %s %d" % (block_name, segment_name, i)) +# print("analogsignal_name = ", analogsignal_name) tS_signal = TimeSeries( - name=analogsignal_name, - data=signal, - timestamps=[1], - description="", - ) - for i, train in enumerate(segment.spiketrains): # spiketrains + name=analogsignal_name, + data=signal, + timestamps=[1], + description="", + ) + +############################# + if ImageSequence: + imagesequence_name = ("ImageSequence %s %s %d" % (block_name, segment_name, i)) +# print("imagesequence_name = ", imagesequence_name) + sampling_rate = signal.sampling_rate.rescale("Hz") + image = pynwb.image.ImageSeries( + name=imagesequence_name, + data=[[[column for column in range(2)]for row in range(3)] for frame in range(4)], + unit=None, + format=None, + external_file=None, + starting_frame=None, + bits_per_pixel=None, + dimension=None, + resolution=-1.0, + conversion=float(1*pq.micrometer), + timestamps=None, + starting_time=None, + rate=float(sampling_rate), + comments='no comments', + description='no description', + control=None, + control_description=None + ) +# print("image 2 = ", image) +############################# + + for i, train in enumerate(segment.spiketrains): self._write_spiketrains(nwbfile, train, i, segment) spiketrains_name = train.name or ("spiketrains %s %s %d" % (block_name, segment_name, i)) ts_name = "{0}".format(spiketrains_name) @@ -292,23 +463,24 @@ def _write_segment(self, nwbfile, block, segment): ) for i, event in enumerate(segment.events): self._write_event(nwbfile, event, nwb_epoch, i) - for i, neo_epoch in enumerate(segment.epochs): # epochs + for i, neo_epoch in enumerate(segment.epochs): self._write_neo_epoch(nwbfile, neo_epoch, i, segment) epochs_name = neo_epoch.name or ("neo epochs %s %s %d" % (block_name, segment_name, i)) ts_name = "{0}".format(epochs_name) tS_epc = TimeSeries( name=epochs_name, - data=signal, - timestamps=signal.times.rescale('second').magnitude, - description=signal.description or "", + data=neo_epoch, + timestamps=neo_epoch.times.rescale('second').magnitude, + description=neo_epoch.description or "", ) nwbfile.add_acquisition(tS_signal) # For analogsignals nwbfile.add_acquisition(tS_train) # For spiketrains nwbfile.add_acquisition(tS_epc) # For Neo segment (Neo epoch) + nwbfile.add_acquisition(image) # for ImageSequence def _write_signal(self, nwbfile, block, signal, i, segment): # analogsignals - block_name = block.name or "blocks %d" % i + block_name = block.name or "blocks %d" % i segment_name = segment.name signal_name = signal.name or ("signal %s %s %d" % (block_name, segment_name, i)) ts_name = "{0}".format(signal_name) @@ -323,6 +495,37 @@ def _write_signal(self, nwbfile, block, signal, i, segment): # analogsignals tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=segment.analogsignals, rate=float(sampling_rate)) #ts = nwbfile.add_acquisition(tS) return [segment.analogsignals] + +######################### # ImageSequence + elif isinstance(signal, ImageSequence): # ImageSequence + imagesequence_name = "ImageSequence %d" % i + sampling_rate = signal.sampling_rate.rescale("Hz") + signal.sampling_rate = sampling_rate + # All signals should go in /acquisition + + image = pynwb.image.ImageSeries( + name=imagesequence_name, + data=[[[column for column in range(2)]for row in range(3)] for frame in range(4)], + unit=None, + format=None, + external_file=None, + starting_frame=None, + bits_per_pixel=None, + dimension=None, + resolution=-1.0, + conversion=float(1*pq.micrometer), + timestamps=None, + starting_time=None, + rate=float(sampling_rate), #sampling_rate + comments='no comments', + description='no description', + control=None, + control_description=None + ) +# print("image = ", image) +######################### + + elif isinstance(signal, IrregularlySampledSignal): tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, timestamps=signal.times.rescale('second').magnitude) return [segment.irregularlysampledsignals] diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index c6bf4f877..27e9a8a4d 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -7,7 +7,7 @@ import unittest from neo.io.nwbio import NWBIO from neo.test.iotest.common_io_test import BaseTestIO -from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Unit, Block, ChannelIndex +from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Unit, Block, ChannelIndex, ImageSequence import pynwb from pynwb import * import quantities as pq @@ -23,7 +23,8 @@ class TestNWBIO(unittest.TestCase, ): # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' # File created from Neo (Jupyter notebook "test_nwbio_class_from_Neo.ipynb") - '/home/elodie/env_NWB_py3/my_notebook/My_first_dataset_neo9.nwb' +### '/home/elodie/env_NWB_py3/my_notebook/My_first_dataset_neo9.nwb' + '/home/elodie/env_NWB_py3/my_notebook/My_first_dataset_neo10.nwb' ] entities_to_test = files_to_download @@ -52,6 +53,25 @@ def test_analogsignals_neo(self, **kargs): self.assertTrue(obj_nwb, AnalogSignal) self.assertTrue(obj_nwb, sig_neo) + def test_ImageSequence_neo(self, **kargs): + img_sequence_array = [[[column for column in range(2)]for row in range(3)] for frame in range(4)] + image_neo = ImageSequence(img_sequence_array, units='V', sampling_rate=1*pq.Hz, spatial_scale=1*pq.micrometer) + self.assertTrue(isinstance(image_neo, ImageSequence)) + r = NWBIO(filename=self.files_to_download[0], mode='r') + obj_nwb = r.read() + self.assertTrue(obj_nwb, ImageSequence) + self.assertTrue(obj_nwb, image_neo) + + +# def test_calcium_imaging_data_neo(self, **kargs): +# self.assertTrue(isinstance(image_neo, ImageSequence)) +# r = NWBIO(filename=self.files_to_download[0], mode='r') +# cid_nwb = r.read() + + + + + def test_spiketrains_neo(self, **kargs): train = SpikeTrain(times=[1, 2, 3]*pq.s, t_start=1.0, t_stop=10.0) self.assertTrue(isinstance(train, SpikeTrain)) From 244282bdb3147d75235fb54b44f59d3e7e96c458 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Fri, 21 Feb 2020 15:19:12 +0100 Subject: [PATCH 32/79] Calcium imaging data test --- neo/test/iotest/test_nwbio.py | 17 ++++++++--------- 1 file changed, 8 insertions(+), 9 deletions(-) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 27e9a8a4d..3d5d97e3e 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -62,15 +62,14 @@ def test_ImageSequence_neo(self, **kargs): self.assertTrue(obj_nwb, ImageSequence) self.assertTrue(obj_nwb, image_neo) - -# def test_calcium_imaging_data_neo(self, **kargs): -# self.assertTrue(isinstance(image_neo, ImageSequence)) -# r = NWBIO(filename=self.files_to_download[0], mode='r') -# cid_nwb = r.read() - - - - + def test_calcium_imaging_data_neo(self, **kargs): + img_sequence_array = [[[column for column in range(2)]for row in range(3)] for frame in range(4)] + calcium_imaging_data_neo = ImageSequence(img_sequence_array, units='V', sampling_rate=1*pq.Hz, spatial_scale=1*pq.micrometer) + self.assertTrue(isinstance(calcium_imaging_data_neo, ImageSequence)) + r = NWBIO(filename=self.files_to_download[0], mode='r') + cid_nwb = r.read() + self.assertTrue(cid_nwb, ImageSequence) + self.assertTrue(cid_nwb, calcium_imaging_data_neo) def test_spiketrains_neo(self, **kargs): train = SpikeTrain(times=[1, 2, 3]*pq.s, t_start=1.0, t_stop=10.0) From f1fb32e9b02ed379fd046f7ffffa2beeac59647e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Elodie=20Legou=C3=A9e?= Date: Fri, 28 Feb 2020 17:10:27 +0100 Subject: [PATCH 33/79] NWB --- neo/io/nwbio.py | 44 +++++++---------------------------- neo/test/iotest/test_nwbio.py | 3 +-- 2 files changed, 10 insertions(+), 37 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index adf54167e..ce48ea412 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -136,7 +136,7 @@ def _read_block(self, _file, node, blocks, lazy=False, cascade=True, **kwargs): self._handle_stimulus_group(lazy, _file, block) self._handle_processing_group(_file, block) self._handle_analysis_group(block) - self._handle_calcium_imaging_data(_file, block) # Calcium imaging data + self._handle_calcium_imaging_data(_file, block) self._lazy = False return block @@ -206,7 +206,6 @@ def _write_block_children(self, nwbfile, block=None, **kwargs): for i, segment in enumerate(block.segments): self._write_segment(nwbfile, block, segment) segment_name = segment.name -# print("segment_name = ", segment_name) tS_seg = TimeSeries( name=segment_name, data=[segment], @@ -270,11 +269,8 @@ def _handle_calcium_imaging_data(self, _file, block): """ Function to read calcium imaging data. """ -# print("*** def _handle_calcium_imaging_data ***") pass - -############################# def write_calcium_imaging_data(self, nwbfile, block, i): """ Function to write calcium imaging data. This involves three main steps: @@ -321,7 +317,6 @@ def write_calcium_imaging_data(self, nwbfile, block, i): 'manifold unit', # unit 'A frame to refer to' # reference_frame ) -# print("imaging_plane = ", imaging_plane) """ Adding two-photon image data @@ -337,7 +332,6 @@ def write_calcium_imaging_data(self, nwbfile, block, i): starting_time=0.0, rate=1.0 ) -# print("image_series = ", image_series) nwbfile.add_acquisition(image_series) @@ -360,7 +354,6 @@ def write_calcium_imaging_data(self, nwbfile, block, i): name = name_plane_segmentation, reference_images = image_series # link to TwoPhotonSeries ) -# print("ps = ", ps) """ Add the resulting ROIs @@ -371,17 +364,14 @@ def write_calcium_imaging_data(self, nwbfile, block, i): img_mask1[0][0] = 1.1 img_mask1[1][1] = 1.2 img_mask1[2][2] = 1.3 - ps.add_roi(pixel_mask=pix_mask1, image_mask=img_mask1) pix_mask2 = [(0, 0, 2.1), (1, 1, 2.2)] img_mask2 = [[0.0 for x in range(w)] for y in range(h)] img_mask2[0][0] = 2.1 img_mask2[1][1] = 2.2 - ps.add_roi(pixel_mask=pix_mask2, image_mask=img_mask2) - """ Storing fluorescence measurements """ @@ -405,18 +395,14 @@ def write_calcium_imaging_data(self, nwbfile, block, i): unit='lumens', timestamps=timestamps ) -# print("rrs = ", rrs) -############################# - def _write_segment(self, nwbfile, block, segment): block_name = block.name or "blocks %d" % i segment_name = segment.name - for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): # analogsignals + for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): self._write_signal(nwbfile, block, signal, i, segment) analogsignal_name = signal.name or ("analogsignal %s %s %d" % (block_name, segment_name, i)) -# print("analogsignal_name = ", analogsignal_name) tS_signal = TimeSeries( name=analogsignal_name, data=signal, @@ -424,10 +410,8 @@ def _write_segment(self, nwbfile, block, segment): description="", ) -############################# if ImageSequence: imagesequence_name = ("ImageSequence %s %s %d" % (block_name, segment_name, i)) -# print("imagesequence_name = ", imagesequence_name) sampling_rate = signal.sampling_rate.rescale("Hz") image = pynwb.image.ImageSeries( name=imagesequence_name, @@ -448,8 +432,6 @@ def _write_segment(self, nwbfile, block, segment): control=None, control_description=None ) -# print("image 2 = ", image) -############################# for i, train in enumerate(segment.spiketrains): self._write_spiketrains(nwbfile, train, i, segment) @@ -479,7 +461,7 @@ def _write_segment(self, nwbfile, block, segment): nwbfile.add_acquisition(tS_epc) # For Neo segment (Neo epoch) nwbfile.add_acquisition(image) # for ImageSequence - def _write_signal(self, nwbfile, block, signal, i, segment): # analogsignals + def _write_signal(self, nwbfile, block, signal, i, segment): block_name = block.name or "blocks %d" % i segment_name = segment.name signal_name = signal.name or ("signal %s %s %d" % (block_name, segment_name, i)) @@ -493,15 +475,12 @@ def _write_signal(self, nwbfile, block, signal, i, segment): # analogsignals signal.sampling_rate = sampling_rate # All signals should go in /acquisition tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=segment.analogsignals, rate=float(sampling_rate)) - #ts = nwbfile.add_acquisition(tS) return [segment.analogsignals] -######################### # ImageSequence - elif isinstance(signal, ImageSequence): # ImageSequence + elif isinstance(signal, ImageSequence): imagesequence_name = "ImageSequence %d" % i sampling_rate = signal.sampling_rate.rescale("Hz") signal.sampling_rate = sampling_rate - # All signals should go in /acquisition image = pynwb.image.ImageSeries( name=imagesequence_name, @@ -516,26 +495,22 @@ def _write_signal(self, nwbfile, block, signal, i, segment): # analogsignals conversion=float(1*pq.micrometer), timestamps=None, starting_time=None, - rate=float(sampling_rate), #sampling_rate + rate=float(sampling_rate), comments='no comments', description='no description', control=None, control_description=None ) -# print("image = ", image) -######################### - elif isinstance(signal, IrregularlySampledSignal): tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, timestamps=signal.times.rescale('second').magnitude) return [segment.irregularlysampledsignals] else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) - ####ts = nwbfile.add_acquisition(tS) - def _write_spiketrains(self, nwbfile, spiketrains, i, segment): # spiketrains + def _write_spiketrains(self, nwbfile, spiketrains, i, segment): spiketrain = segment.spiketrains - for i, train in enumerate(segment.spiketrains): # spiketrains + for i, train in enumerate(segment.spiketrains): spiketrains_name = train.name or "spiketrains %d" % i ts_name = "{0}".format(spiketrains_name) tS_train = TimeSeries( @@ -556,8 +531,8 @@ def _write_event(self, nwbfile, event, nwb_epoch, i): description=event.description or "", ) - def _write_neo_epoch(self, nwbfile, neo_epoch, i, segment): # epochs - for i, epoch in enumerate(segment.epochs): # epochs + def _write_neo_epoch(self, nwbfile, neo_epoch, i, segment): + for i, epoch in enumerate(segment.epochs): epochs_name = epoch.name or "epochs %d" % i ts_name = "{0}".format(epochs_name) tS_epc = TimeSeries( @@ -568,7 +543,6 @@ def _write_neo_epoch(self, nwbfile, neo_epoch, i, segment): # epochs ) return [segment.epochs] - def time_in_seconds(t): return float(t.rescale("second")) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 3d5d97e3e..861c97ca8 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -22,8 +22,7 @@ class TestNWBIO(unittest.TestCase, ): # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' -# File created from Neo (Jupyter notebook "test_nwbio_class_from_Neo.ipynb") -### '/home/elodie/env_NWB_py3/my_notebook/My_first_dataset_neo9.nwb' +# File created from Neo (Jupyter notebook) '/home/elodie/env_NWB_py3/my_notebook/My_first_dataset_neo10.nwb' ] entities_to_test = files_to_download From 67505347cdb7dc86100255fbf98c5bb29b78c662 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Wed, 4 Mar 2020 17:13:33 +0100 Subject: [PATCH 34/79] fix 'rescale' method for Event and Epoch --- neo/core/epoch.py | 16 +++++++++++++--- neo/core/event.py | 14 +++++++++++++- 2 files changed, 26 insertions(+), 4 deletions(-) diff --git a/neo/core/epoch.py b/neo/core/epoch.py index de1d6914a..bc96fdb8e 100644 --- a/neo/core/epoch.py +++ b/neo/core/epoch.py @@ -187,9 +187,19 @@ def rescale(self, units): Return a copy of the :class:`Epoch` converted to the specified units ''' - - obj = super(Epoch, self).rescale(units) - obj._durations = obj.durations.rescale(units) + # Use simpler functionality, if nothing will be changed + dim = pq.quantity.validate_dimensionality(units) + if self.dimensionality == dim: + return self.copy() + + # Rescale the object into a new object + obj = self.duplicate_with_new_data(times=self.view(pq.Quantity).rescale(dim), + durations=self.durations.rescale(dim), + labels=self.labels, + units=units) + + # Expected behavior is deepcopy, so deepcopying array_annotations + obj.array_annotations = deepcopy(self.array_annotations) obj.segment = self.segment # not sure we should do this return obj diff --git a/neo/core/event.py b/neo/core/event.py index 027d786b3..99b1f4e2d 100644 --- a/neo/core/event.py +++ b/neo/core/event.py @@ -164,7 +164,19 @@ def rescale(self, units): Return a copy of the :class:`Event` converted to the specified units ''' - obj = super(Event, self).rescale(units) + # Use simpler functionality, if nothing will be changed + dim = pq.quantity.validate_dimensionality(units) + if self.dimensionality == dim: + return self.copy() + + # Rescale the object into a new object + obj = self.duplicate_with_new_data(times=self.view(pq.Quantity).rescale(dim), + labels=self.labels, + units=units) + + # Expected behavior is deepcopy, so deepcopying array_annotations + obj.array_annotations = deepcopy(self.array_annotations) + obj.segment = self.segment return obj From 15839666b686a4942e0ab51cded38d3634ecd0ce Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Wed, 4 Mar 2020 17:13:47 +0100 Subject: [PATCH 35/79] wip --- neo/io/nwbio.py | 557 ++++++++++++++++++---------------- neo/test/iotest/test_nwbio.py | 226 ++++++++------ 2 files changed, 435 insertions(+), 348 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index ce48ea412..db22bf7ea 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -8,8 +8,8 @@ Depends on: h5py, nwb, dateutil Supported: Read, Write Specification - https://github.com/NeurodataWithoutBorders/specification -Python APIs - (1) https://github.com/AllenInstitute/nwb-api/tree/master/ainwb - (2) https://github.com/AllenInstitute/AllenSDK/blob/master/allensdk/core/nwb_data_set.py +Python APIs - (1) https://github.com/AllenInstitute/nwb-api/tree/master/ainwb + (2) https://github.com/AllenInstitute/AllenSDK/blob/master/allensdk/core/nwb_data_set.py (3) https://github.com/NeurodataWithoutBorders/api-python Sample datasets from CRCNS - https://crcns.org/NWB Sample datasets from Allen Institute - http://alleninstitute.github.io/AllenSDK/cell_types.html#neurodata-without-borders @@ -22,6 +22,7 @@ import tempfile from datetime import datetime from os.path import join +import json import dateutil.parser import numpy as np import random @@ -36,25 +37,33 @@ from tempfile import NamedTemporaryFile import os import glob -from scipy.io import loadmat # PyNWB imports -import pynwb -from pynwb import * -from pynwb import NWBFile,TimeSeries, get_manager -from pynwb.base import ProcessingModule -from pynwb.ecephys import ElectricalSeries, Device, EventDetection -from pynwb.behavior import SpatialSeries -from pynwb import image -from pynwb.image import ImageSeries -from pynwb.spec import NWBAttributeSpec, NWBDatasetSpec, NWBGroupSpec, NWBNamespace, NWBNamespaceBuilder -from pynwb.device import Device -from pynwb.ophys import TwoPhotonSeries, OpticalChannel, ImageSegmentation, Fluorescence # For calcium imaging data +try: + import pynwb + from pynwb import * + from pynwb import NWBFile, TimeSeries, get_manager + from pynwb.base import ProcessingModule + from pynwb.ecephys import ElectricalSeries, Device, EventDetection + from pynwb.behavior import SpatialSeries + from pynwb.misc import AnnotationSeries + from pynwb import image + from pynwb.image import ImageSeries + from pynwb.spec import NWBAttributeSpec, NWBDatasetSpec, NWBGroupSpec, NWBNamespace, NWBNamespaceBuilder + from pynwb.device import Device + from pynwb.ophys import TwoPhotonSeries, OpticalChannel, ImageSegmentation, Fluorescence # For calcium imaging data + have_pynwb = True +except ImportError: + have_pynwb = False # hdmf imports -from hdmf.spec import LinkSpec, GroupSpec, DatasetSpec, SpecNamespace,\ - NamespaceBuilder, AttributeSpec, DtypeSpec, RefSpec -from hdmf import * +try: + from hdmf.spec import LinkSpec, GroupSpec, DatasetSpec, SpecNamespace,\ + NamespaceBuilder, AttributeSpec, DtypeSpec, RefSpec + from hdmf import * + have_hdmf = True +except ImportError: + have_hdmf = False class NWBIO(BaseIO): @@ -75,32 +84,57 @@ class NWBIO(BaseIO): is_readable = True is_writable = True - is_streameable = False + is_streameable = False - def __init__(self, filename, mode): + def __init__(self, filename, mode='r'): """ Arguments: filename : the filename """ - if not pynwb: + if not have_pynwb: raise Exception("Please install the pynwb package to use NWBIO") - if not hdmf: - raise Exception("Please install the hdmf package to use NWBIO") + if not have_hdmf: + raise Exception("Please install the hdmf package to use NWBIO") BaseIO.__init__(self, filename=filename) self.filename = filename - - def read_all_blocks(self, lazy=False, **kwargs): + self.blocks_written = 0 + + def read_all_blocks(self, lazy=False, **kwargs): """ - Loads all blocks in the file that are attached to the root. - Here, we assume that a neo block is a sub-part of a branch, into a NWB file; + """ io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO self._file = io.read() - blocks = [] - for node in (self._file.acquisition, self._file.units, self._file.epochs): - blocks.append(self._read_block(self._file, node, blocks)) - return blocks + file_access_dates = self._file.file_create_date + identifier = self._file.identifier + if identifier == '_neo': + identifier = None + description = self._file.session_description + if description == "no description": + description = None + + self._blocks = {} + self._handle_acquisition_group(lazy=lazy) + self._handle_units(lazy=lazy) + self._handle_epochs_group(lazy) + + # block = Block(name=identifier, + # description=description, + # file_origin=self.filename, + # file_datetime=file_access_dates, + # rec_datetime=_file.session_start_time, + # file_access_dates=file_access_dates, + # file_read_log='') + # self._handle_general_group(block) + + # self._handle_acquisition_group(lazy, _file, block) + # self._handle_stimulus_group(lazy, _file, block) + # self._handle_processing_group(_file, block) + # self._handle_analysis_group(block) + # self._handle_calcium_imaging_data(_file, block) + # self._lazy = False + return list(self._blocks.values()) def read_block(self, lazy=False, **kargs): """ @@ -108,47 +142,16 @@ def read_block(self, lazy=False, **kargs): """ return self.read_all_blocks(lazy=lazy)[0] - def _read_block(self, _file, node, blocks, lazy=False, cascade=True, **kwargs): - """ - Main method to load a block - """ - self._lazy = lazy - - file_access_dates = _file.file_create_date - identifier = _file.identifier - if identifier == '_neo': - identifier = None - description = _file.session_description - if description == "no description": - description = None - - block = Block(name=identifier, - description=description, - file_origin=self.filename, - file_datetime=file_access_dates, - rec_datetime=_file.session_start_time, - file_access_dates=file_access_dates, - file_read_log='') - if cascade: - self._handle_general_group(block) - self._handle_epochs_group(_file, block) - self._handle_acquisition_group(lazy, _file, block) - self._handle_stimulus_group(lazy, _file, block) - self._handle_processing_group(_file, block) - self._handle_analysis_group(block) - self._handle_calcium_imaging_data(_file, block) - self._lazy = False - return block - def write_all_blocks(self, blocks, **kwargs): """ Write list of blocks to the file """ + # todo: allow metadata in NWBFile constructor to be taken from kwargs start_time = datetime.now() nwbfile = NWBFile(self.filename, session_start_time=start_time, identifier='', - file_create_date=None, + file_create_date=None, # use current date? timestamps_reference_time=None, experimenter=None, experiment_description=None, @@ -186,13 +189,20 @@ def write_all_blocks(self, blocks, **kwargs): ) io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') - if Block in self.writeable_objects: - for i, block in enumerate(blocks): - block_name = block.name or "blocks%d" % i - self.write_block(nwbfile, block) - self.write_calcium_imaging_data(nwbfile, block, i) - io_nwb.write(nwbfile) - return list(block.segments) + nwbfile.add_unit_column('_name', 'the name attribute of the SpikeTrain') + #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') + nwbfile.add_unit_column('segment', 'the name of the Neo Segment to which the SpikeTrain belongs') + nwbfile.add_unit_column('block', 'the name of the Neo Block to which the SpikeTrain belongs') + + nwbfile.add_epoch_column('_name', 'the name attribute of the Epoch') + #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') + nwbfile.add_epoch_column('segment', 'the name of the Neo Segment to which the Epoch belongs') + nwbfile.add_epoch_column('block', 'the name of the Neo Block to which the Epoch belongs') + + for i, block in enumerate(blocks): + self.write_block(nwbfile, block) + #self.write_calcium_imaging_data(nwbfile, block, i) + io_nwb.write(nwbfile) io_nwb.close() def write_block(self, nwbfile, block, **kwargs): @@ -200,67 +210,129 @@ def write_block(self, nwbfile, block, **kwargs): Write a Block to the file :param block: Block to be written """ - self._write_block_children(nwbfile, block) - - def _write_block_children(self, nwbfile, block=None, **kwargs): + if not block.name: + block.name = "block%d" % self.blocks_written for i, segment in enumerate(block.segments): - self._write_segment(nwbfile, block, segment) - segment_name = segment.name - tS_seg = TimeSeries( - name=segment_name, - data=[segment], - timestamps=[1], - description="", - ) - - def _handle_general_group(self, block): - pass + assert segment.block is block + if not segment.name: + segment.name = "%s : segment%d" % (block.name, i) + self._write_segment(nwbfile, segment) + self.blocks_written += 1 + + def _get_segment(self, block_name, segment_name): + # If we've already created a Block with the given name return it, + # otherwise create it now and store it in self._blocks. + # If we've already created a Segment in the given block, return it, + # otherwise create it now and return it. + if block_name in self._blocks: + block = self._blocks[block_name] + else: + block = Block(name=block_name) + self._blocks[block_name] = block + segment = None + for seg in block.segments: + if segment_name == seg.name: + segment = seg + break + if segment is None: + segment = Segment(name=segment_name) + segment.block = block + block.segments.append(segment) + return segment + + def _handle_epochs_group(self, lazy): + start_times = self._file.epochs.start_time[:] + stop_times = self._file.epochs.stop_time[:] + durations = stop_times - start_times + labels = self._file.epochs.tags[:] + segment_names = self._file.epochs.segment[:] + block_names = self._file.epochs.block[:] + epoch_names = self._file.epochs._name[:] + + unique_epoch_names = np.unique(epoch_names) + for epoch_name in unique_epoch_names: + index = (epoch_names == epoch_name) + epoch = Epoch(times=start_times[index] * pq.s, + durations=durations[index] * pq.s, + labels=labels[index], + name=epoch_name) + # todo: handle annotations, array_annotations + segment_name = np.unique(segment_names[index]) + block_name = np.unique(block_names[index]) + assert segment_name.size == block_name.size == 1 + segment = self._get_segment(block_name[0], segment_name[0]) + segment.epochs.append(epoch) + epoch.segment = segment + + def _handle_acquisition_group(self, lazy): + acq = self._file.acquisition + for timeseries in acq.values(): + hierarchy = json.loads(timeseries.comments) + block_name = hierarchy["block"] + segment_name = hierarchy["segment"] + segment = self._get_segment(block_name, segment_name) + if isinstance(timeseries, AnnotationSeries): + event = Event(timeseries.timestamps[:] * pq.s, + labels=timeseries.data[:], + name=timeseries.name, + description=timeseries.description) + segment.events.append(event) + event.segment = segment + elif timeseries.rate: + signal = AnalogSignal( + timeseries.data[:], + units=timeseries.unit, + t_start=timeseries.starting_time * pq.s, # use timeseries.starting_time_units + sampling_rate=timeseries.rate * pq.Hz, + name=timeseries.name, + file_origin=self._file.session_description, + description=timeseries.description, + array_annotations=None) # todo: timeseries.control / control_description + segment.analogsignals.append(signal) + signal.segment = segment + else: + signal = IrregularlySampledSignal( + timeseries.timestamps[:] * pq.s, + timeseries.data[:], + units=timeseries.unit, + name=timeseries.name, + file_origin=self._file.session_description, + description=timeseries.description, + array_annotations=None) # todo: timeseries.control / control_description + segment.irregularlysampledsignals.append(signal) + signal.segment = segment + + def _handle_units(self, lazy): + for id in self._file.units.id[:]: + spike_times = self._file.units.get_unit_spike_times(id) + t_start, t_stop = self._file.units.get_unit_obs_intervals(id)[0] + name = self._file.units._name[id] + segment_name = self._file.units.segment[id] + block_name = self._file.units.block[id] + segment = self._get_segment(block_name, segment_name) + spiketrain = SpikeTrain( + spike_times, + t_stop * pq.s, + units='s', + #sampling_rate=array(1.) * Hz, + t_start=t_start * pq.s, + #waveforms=None, + #left_sweep=None, + name=name, + #file_origin=None, + #description=None, + #array_annotations=None, + #**annotations + ) + segment.spiketrains.append(spiketrain) + spiketrain.segment = segment - def _handle_epochs_group(self, _file, block): - """ - Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch. - """ - epochs = _file.epochs - timeseries=[] - segment = Segment(name=self.name) - segment.epochs.append(Epoch) - - for obj in timeseries: - obj.segment = segment - if isinstance(obj, AnalogSignal): - segment.analogsignals.append(obj) - segment.epochs.append(obj) - elif isinstance(obj, IrregularlySampledSignal): - segment.irregularlysampledsignals.append(obj) - elif isinstance(obj, Event): - segment.events.append(obj) - elif isinstance(obj, Epoch): - segment.epochs.append(obj) - segment.block = block - block.segments.append(segment) - - def _handle_acquisition_group(self, lazy, _file, block): - acq = _file.acquisition def _handle_stimulus_group(self, lazy, _file, block): - sti = _file.stimulus - for name in sti: - segment_name_sti = _file.epochs - desc_sti = _file.get_stimulus(name).unit - segment_sti = segment_name_sti - if lazy==True: - times = np.array(()) - lazy_shape = _file.get_stimulus(name).data.shape - else: - current_shape = _file.get_stimulus(name).data.shape[0] - times = np.zeros(current_shape) - for j in range(0, current_shape): - times[j]=1./_file.get_stimulus(name).rate*j+_file.get_acquisition(name).starting_time # times = 1./frequency [Hz] + t_start [s] - spiketrain = SpikeTrain(times, units=pq.second, - t_stop=times[-1]*pq.second) + pass def _handle_processing_group(self, _file, block): - segment = Segment(name=self.name) + pass def _handle_analysis_group(self, block): pass @@ -396,156 +468,116 @@ def write_calcium_imaging_data(self, nwbfile, block, i): timestamps=timestamps ) - def _write_segment(self, nwbfile, block, segment): - block_name = block.name or "blocks %d" % i - segment_name = segment.name + # if ImageSequence: + # imagesequence_name = ("ImageSequence %s %s %d" % (block.name, segment.name, i)) + # sampling_rate = signal.sampling_rate.rescale("Hz") + # image = pynwb.image.ImageSeries( + # name=imagesequence_name, + # data=[[[column for column in range(2)]for row in range(3)] for frame in range(4)], + # unit=None, + # format=None, + # external_file=None, + # starting_frame=None, + # bits_per_pixel=None, + # dimension=None, + # resolution=-1.0, + # conversion=float(1*pq.micrometer), + # timestamps=None, + # starting_time=None, + # rate=float(sampling_rate), + # comments='no comments', + # description='no description', + # control=None, + # control_description=None + # ) + + def _write_segment(self, nwbfile, segment): + # maybe use NWB trials to store Segment metadata? for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): - self._write_signal(nwbfile, block, signal, i, segment) - analogsignal_name = signal.name or ("analogsignal %s %s %d" % (block_name, segment_name, i)) - tS_signal = TimeSeries( - name=analogsignal_name, - data=signal, - timestamps=[1], - description="", - ) - - if ImageSequence: - imagesequence_name = ("ImageSequence %s %s %d" % (block_name, segment_name, i)) - sampling_rate = signal.sampling_rate.rescale("Hz") - image = pynwb.image.ImageSeries( - name=imagesequence_name, - data=[[[column for column in range(2)]for row in range(3)] for frame in range(4)], - unit=None, - format=None, - external_file=None, - starting_frame=None, - bits_per_pixel=None, - dimension=None, - resolution=-1.0, - conversion=float(1*pq.micrometer), - timestamps=None, - starting_time=None, - rate=float(sampling_rate), - comments='no comments', - description='no description', - control=None, - control_description=None - ) + assert signal.segment is segment + if not signal.name: + signal.name = "%s : analogsignal%d" % (segment.name, i) + self._write_signal(nwbfile, signal) for i, train in enumerate(segment.spiketrains): - self._write_spiketrains(nwbfile, train, i, segment) - spiketrains_name = train.name or ("spiketrains %s %s %d" % (block_name, segment_name, i)) - ts_name = "{0}".format(spiketrains_name) - tS_train = TimeSeries( - name=spiketrains_name, - data=train, - timestamps=[1], - description="", - ) + assert train.segment is segment + if not train.name: + train.name = "%s : spiketrain%d" % (segment.name, i) + self._write_spiketrain(nwbfile, train) + for i, event in enumerate(segment.events): - self._write_event(nwbfile, event, nwb_epoch, i) - for i, neo_epoch in enumerate(segment.epochs): - self._write_neo_epoch(nwbfile, neo_epoch, i, segment) - epochs_name = neo_epoch.name or ("neo epochs %s %s %d" % (block_name, segment_name, i)) - ts_name = "{0}".format(epochs_name) - tS_epc = TimeSeries( - name=epochs_name, - data=neo_epoch, - timestamps=neo_epoch.times.rescale('second').magnitude, - description=neo_epoch.description or "", - ) - - nwbfile.add_acquisition(tS_signal) # For analogsignals - nwbfile.add_acquisition(tS_train) # For spiketrains - nwbfile.add_acquisition(tS_epc) # For Neo segment (Neo epoch) - nwbfile.add_acquisition(image) # for ImageSequence - - def _write_signal(self, nwbfile, block, signal, i, segment): - block_name = block.name or "blocks %d" % i - segment_name = segment.name - signal_name = signal.name or ("signal %s %s %d" % (block_name, segment_name, i)) - ts_name = "{0}".format(signal_name) - conversion = _decompose_unit(signal.units) - attributes = {"conversion": conversion, - "resolution": float('nan')} + assert event.segment is segment + if not event.name: + event.name = "%s : event%d" % (segment.name, i) + self._write_event(nwbfile, event) - if isinstance(signal, AnalogSignal): - sampling_rate = signal.sampling_rate.rescale("Hz") - signal.sampling_rate = sampling_rate - # All signals should go in /acquisition - tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=segment.analogsignals, rate=float(sampling_rate)) - return [segment.analogsignals] + for i, epoch in enumerate(segment.epochs): + if not epoch.name: + epoch.name = "%s : epoch%d" % (segment.name, i) + self._write_epoch(nwbfile, epoch) - elif isinstance(signal, ImageSequence): - imagesequence_name = "ImageSequence %d" % i + def _write_signal(self, nwbfile, signal): + hierarchy = {'block': signal.segment.block.name, 'segment': signal.segment.name} + if isinstance(signal, AnalogSignal): sampling_rate = signal.sampling_rate.rescale("Hz") - signal.sampling_rate = sampling_rate - - image = pynwb.image.ImageSeries( - name=imagesequence_name, - data=[[[column for column in range(2)]for row in range(3)] for frame in range(4)], - unit=None, - format=None, - external_file=None, - starting_frame=None, - bits_per_pixel=None, - dimension=None, - resolution=-1.0, - conversion=float(1*pq.micrometer), - timestamps=None, - starting_time=None, - rate=float(sampling_rate), - comments='no comments', - description='no description', - control=None, - control_description=None - ) - + tS = TimeSeries(name=signal.name, + starting_time=time_in_seconds(signal.t_start), + data=signal, + unit=signal.units.dimensionality.string, + rate=float(sampling_rate), + comments=json.dumps(hierarchy)) + # todo: try to add array_annotations via "control" attribute elif isinstance(signal, IrregularlySampledSignal): - tS = TimeSeries(name=ts_name, starting_time=time_in_seconds(signal.t_start), data=signal, timestamps=signal.times.rescale('second').magnitude) - return [segment.irregularlysampledsignals] + tS = TimeSeries(name=signal.name, + data=signal, + unit=signal.units.dimensionality.string, + timestamps=signal.times.rescale('second').magnitude, + comments=json.dumps(hierarchy)) else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) - - def _write_spiketrains(self, nwbfile, spiketrains, i, segment): - spiketrain = segment.spiketrains - for i, train in enumerate(segment.spiketrains): - spiketrains_name = train.name or "spiketrains %d" % i - ts_name = "{0}".format(spiketrains_name) - tS_train = TimeSeries( - name=spiketrains_name, - data=train, - timestamps=[1], - description="", - ) - return [segment.spiketrains] - - def _write_event(self, nwbfile, event, nwb_epoch, i): - event_name = event.name or "event{0}".format(i) - ts_name = "{0}".format(event_name) - tS = TimeSeries( - name=ts_name, - data=event, + nwbfile.add_acquisition(tS) + return tS + + def _write_spiketrain(self, nwbfile, spiketrain): + nwbfile.add_unit(spike_times=spiketrain.rescale('s').magnitude, + obs_intervals=[[float(spiketrain.t_start.rescale('s')), + float(spiketrain.t_stop.rescale('s'))]], + _name=spiketrain.name, + #_description=spiketrain.description, + segment=spiketrain.segment.name, + block=spiketrain.segment.block.name) + # todo: handle annotations (using add_unit_column()?) + # todo: handle Neo Units + # todo: handle spike waveforms, if any (see SpikeEventSeries) + return nwbfile.units + + def _write_event(self, nwbfile, event): + hierarchy = {'block': event.segment.block.name, 'segment': event.segment.name} + tS_evt = AnnotationSeries( + name=event.name, + data=event.labels, timestamps=event.times.rescale('second').magnitude, description=event.description or "", - ) + comments=json.dumps(hierarchy)) + nwbfile.add_acquisition(tS_evt) + return tS_evt + + def _write_epoch(self, nwbfile, epoch): + for t_start, duration, label in zip(epoch.rescale('s').magnitude, + epoch.durations.rescale('s').magnitude, + epoch.labels): + nwbfile.add_epoch(t_start, t_start + duration, [label], [], + _name=epoch.name, + segment=epoch.segment.name, + block=epoch.segment.block.name) + return nwbfile.epochs - def _write_neo_epoch(self, nwbfile, neo_epoch, i, segment): - for i, epoch in enumerate(segment.epochs): - epochs_name = epoch.name or "epochs %d" % i - ts_name = "{0}".format(epochs_name) - tS_epc = TimeSeries( - name=epochs_name, - data=epoch, - timestamps=[1], - description="", - ) - return [segment.epochs] def time_in_seconds(t): return float(t.rescale("second")) + def _decompose_unit(unit): assert isinstance(unit, pq.quantity.Quantity) assert unit.magnitude == 1 @@ -553,12 +585,19 @@ def _decompose_unit(unit): def _decompose(unit): dim = unit.dimensionality if len(dim) != 1: - raise NotImplementedError("Compound units not yet supported") + raise NotImplementedError("Compound units not yet supported") # e.g. volt-metre uq, n = dim.items()[0] if n != 1: - raise NotImplementedError("Compound units not yet supported") + raise NotImplementedError("Compound units not yet supported") # e.g. volt^2 uq_def = uq.definition return float(uq_def.magnitude), uq_def + conv, unit2 = _decompose(unit) + while conv != 1: + conversion *= conv + unit = unit2 + conv, unit2 = _decompose(unit) + return conversion, unit.dimensionality.keys()[0].name + prefix_map = { 1e-3: 'milli', diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 861c97ca8..f2ecd8222 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -12,6 +12,8 @@ from pynwb import * import quantities as pq import numpy as np +from numpy.testing import assert_array_equal, assert_allclose + class TestNWBIO(unittest.TestCase, ): ioclass = NWBIO @@ -23,98 +25,144 @@ class TestNWBIO(unittest.TestCase, ): # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' # File created from Neo (Jupyter notebook) - '/home/elodie/env_NWB_py3/my_notebook/My_first_dataset_neo10.nwb' +# '/home/elodie/env_NWB_py3/my_notebook/My_first_dataset_neo10.nwb' ] entities_to_test = files_to_download - def test_nwbio(self): - reader = NWBIO(filename=self.files_to_download[0], mode='r') - reader.read() - - def test_segment(self, **kargs): - seg = Segment(index=5) - r = NWBIO(filename=self.files_to_download[0], mode='r') - seg_nwb = r.read() # equivalent to read_all_blocks() - self.assertTrue(seg, Segment) - self.assertTrue(seg_nwb, Segment) - self.assertTrue(seg_nwb, seg) - self.assertIsNotNone(seg_nwb, seg) - seg_nwb_one_block = r.read_block() # only for the first block - self.assertTrue(seg_nwb_one_block, Segment) - self.assertTrue(seg_nwb_one_block, seg) - self.assertIsNotNone(seg_nwb_one_block, seg) - - def test_analogsignals_neo(self, **kargs): - sig_neo = AnalogSignal(signal=[1, 2, 3], units='V', t_start=np.array(3.0)*pq.s, sampling_rate=1*pq.Hz) - self.assertTrue(isinstance(sig_neo, AnalogSignal)) - r = NWBIO(filename=self.files_to_download[0], mode='r') - obj_nwb = r.read() - self.assertTrue(obj_nwb, AnalogSignal) - self.assertTrue(obj_nwb, sig_neo) - - def test_ImageSequence_neo(self, **kargs): - img_sequence_array = [[[column for column in range(2)]for row in range(3)] for frame in range(4)] - image_neo = ImageSequence(img_sequence_array, units='V', sampling_rate=1*pq.Hz, spatial_scale=1*pq.micrometer) - self.assertTrue(isinstance(image_neo, ImageSequence)) - r = NWBIO(filename=self.files_to_download[0], mode='r') - obj_nwb = r.read() - self.assertTrue(obj_nwb, ImageSequence) - self.assertTrue(obj_nwb, image_neo) - - def test_calcium_imaging_data_neo(self, **kargs): - img_sequence_array = [[[column for column in range(2)]for row in range(3)] for frame in range(4)] - calcium_imaging_data_neo = ImageSequence(img_sequence_array, units='V', sampling_rate=1*pq.Hz, spatial_scale=1*pq.micrometer) - self.assertTrue(isinstance(calcium_imaging_data_neo, ImageSequence)) - r = NWBIO(filename=self.files_to_download[0], mode='r') - cid_nwb = r.read() - self.assertTrue(cid_nwb, ImageSequence) - self.assertTrue(cid_nwb, calcium_imaging_data_neo) - - def test_spiketrains_neo(self, **kargs): - train = SpikeTrain(times=[1, 2, 3]*pq.s, t_start=1.0, t_stop=10.0) - self.assertTrue(isinstance(train, SpikeTrain)) - r = NWBIO(filename=self.files_to_download[0], mode='r') - obj_nwb = r.read() - self.assertTrue(obj_nwb, SpikeTrain) - self.assertTrue(obj_nwb, train) - - def test_epochs_neo(self, **kargs): - epc = Epoch(times=np.arange(0, 30, 10)*pq.s, - durations=[10, 5, 7]*pq.ms, - labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) - self.assertTrue(isinstance(epc, Epoch)) - r = NWBIO(filename=self.files_to_download[0], mode='r') - obj_nwb = r.read() - self.assertTrue(obj_nwb, Epoch) - self.assertTrue(obj_nwb, epc) - - def test_read_irregularlysampledsignal(self, **kargs): - irsig0 = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3], units='mV', time_units='ms') - irsig1 = IrregularlySampledSignal([0.01, 0.03, 0.12]*pq.s, [[4, 5], [5, 4], [6, 3]]*pq.nA) - self.assertTrue(isinstance(irsig0, IrregularlySampledSignal)) - self.assertTrue(isinstance(irsig1, IrregularlySampledSignal)) - r = NWBIO(filename=self.files_to_download[0], mode='r') - irsig_nwb = r.read() - self.assertTrue(irsig_nwb, IrregularlySampledSignal) - self.assertTrue(irsig_nwb, irsig0) - self.assertTrue(irsig_nwb, irsig1) - - def test_read_event(self, **kargs): - evt_neo = Event(np.arange(0, 30, 10)*pq.s, labels=np.array(['trig0', 'trig1', 'trig2'], dtype='S')) - r = NWBIO(filename=self.files_to_download[0], mode='r') - event_nwb = r.read() - self.assertTrue(event_nwb, evt_neo) - self.assertIsNotNone(event_nwb, evt_neo) - - def test_read_epoch(self, **kargs): - epc_neo = Epoch(times=np.arange(0, 30, 10)*pq.s, - durations=[10, 5, 7]*pq.ms, - labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S')) - r = NWBIO(filename=self.files_to_download[0], mode='r') - epoch_nwb = r.read() - self.assertTrue(epoch_nwb, Epoch) - self.assertTrue(epoch_nwb, epc_neo) - self.assertIsNotNone(epoch_nwb, epc_neo) + def test_roundtrip(self): + + # Define Neo blocks + bl0 = Block(name='First block') + bl1 = Block(name='Second block') + bl2 = Block(name='Third block') + original_blocks = [bl0, bl1, bl2] + + num_seg = 4 # number of segments + num_chan = 3 # number of channels + + for blk in original_blocks: + + for ind in range(num_seg): # number of Segment + seg = Segment(index=ind) + seg.block = blk + blk.segments.append(seg) + + for seg in blk.segments: # AnalogSignal objects + + # 3 Neo AnalogSignals + a = AnalogSignal(np.random.randn(44, num_chan) * pq.nA, + sampling_rate=10 * pq.kHz, + t_start=50 * pq.ms) + b = AnalogSignal(np.random.randn(64, num_chan) * pq.mV, + sampling_rate=8 * pq.kHz, + t_start=40 * pq.ms) + c = AnalogSignal(np.random.randn(33, num_chan) * pq.uA, + sampling_rate=10 * pq.kHz, + t_start=120 * pq.ms) + + # 2 Neo IrregularlySampledSignals + d = IrregularlySampledSignal(np.arange(7.0)*pq.ms, + np.random.randn(7, num_chan)*pq.mV) + + # 2 Neo SpikeTrains + train = SpikeTrain(times=[1, 2, 3] * pq.s, t_start=1.0, t_stop=10.0) + train2 = SpikeTrain(times=[4, 5, 6] * pq.s, t_stop=10.0) + # todo: add waveforms + + # 1 Neo Event + evt = Event(times=np.arange(0, 30, 10) * pq.ms, + labels=np.array(['ev0', 'ev1', 'ev2'])) + + # 2 Neo Epochs + epc = Epoch(times=np.arange(0, 30, 10) * pq.s, + durations=[10, 5, 7] * pq.ms, + labels=np.array(['btn0', 'btn1', 'btn2'])) + + epc2 = Epoch(times=np.arange(10, 40, 10) * pq.s, + durations=[9, 3, 8] * pq.ms, + labels=np.array(['btn3', 'btn4', 'btn5'])) + + seg.spiketrains.append(train) + seg.spiketrains.append(train2) + + seg.epochs.append(epc) + seg.epochs.append(epc2) + + seg.analogsignals.append(a) + seg.analogsignals.append(b) + seg.analogsignals.append(c) + seg.irregularlysampledsignals.append(d) + seg.events.append(evt) + a.segment = seg + b.segment = seg + c.segment = seg + d.segment = seg + evt.segment = seg + train.segment = seg + train2.segment = seg + epc.segment = seg + epc2.segment = seg + + # write to file + test_file_name = "test_round_trip.nwb" + iow = NWBIO(filename=test_file_name, mode='w') + iow.write_all_blocks(original_blocks) + + ior = NWBIO(filename=test_file_name, mode='r') + retrieved_blocks = ior.read_all_blocks() + + self.assertEqual(len(retrieved_blocks), 3) + self.assertEqual(len(retrieved_blocks[2].segments), num_seg) + + original_signal_22b = original_blocks[2].segments[2].analogsignals[1] + retrieved_signal_22b = retrieved_blocks[2].segments[2].analogsignals[1] + for attr_name in ("name", "units", "sampling_rate", "t_start"): + retrieved_attribute = getattr(retrieved_signal_22b, attr_name) + original_attribute = getattr(original_signal_22b, attr_name) + self.assertEqual(retrieved_attribute, original_attribute) + assert_array_equal(retrieved_signal_22b.magnitude, original_signal_22b.magnitude) + + original_issignal_22d = original_blocks[2].segments[2].irregularlysampledsignals[0] + retrieved_issignal_22d = retrieved_blocks[2].segments[2].irregularlysampledsignals[0] + for attr_name in ("name", "units", "t_start"): + retrieved_attribute = getattr(retrieved_issignal_22d, attr_name) + original_attribute = getattr(original_issignal_22d, attr_name) + self.assertEqual(retrieved_attribute, original_attribute) + assert_array_equal(retrieved_issignal_22d.times.rescale('ms').magnitude, + original_issignal_22d.times.rescale('ms').magnitude) + assert_array_equal(retrieved_issignal_22d.magnitude, original_issignal_22d.magnitude) + + original_event_11 = original_blocks[1].segments[1].events[0] + retrieved_event_11 = retrieved_blocks[1].segments[1].events[0] + for attr_name in ("name",): + retrieved_attribute = getattr(retrieved_event_11, attr_name) + original_attribute = getattr(original_event_11, attr_name) + self.assertEqual(retrieved_attribute, original_attribute) + assert_array_equal(retrieved_event_11.rescale('ms').magnitude, + original_event_11.rescale('ms').magnitude) + assert_array_equal(retrieved_event_11.labels, original_event_11.labels) + + original_spiketrain_131 = original_blocks[1].segments[1].spiketrains[1] + retrieved_spiketrain_131 = retrieved_blocks[1].segments[1].spiketrains[1] + for attr_name in ("name", "t_start", "t_stop"): + retrieved_attribute = getattr(retrieved_spiketrain_131, attr_name) + original_attribute = getattr(original_spiketrain_131, attr_name) + self.assertEqual(retrieved_attribute, original_attribute) + assert_array_equal(retrieved_spiketrain_131.times.rescale('ms').magnitude, + original_spiketrain_131.times.rescale('ms').magnitude) + + original_epoch_11 = original_blocks[1].segments[1].epochs[0] + retrieved_epoch_11 = retrieved_blocks[1].segments[1].epochs[0] + for attr_name in ("name",): + retrieved_attribute = getattr(retrieved_epoch_11, attr_name) + original_attribute = getattr(original_epoch_11, attr_name) + self.assertEqual(retrieved_attribute, original_attribute) + assert_array_equal(retrieved_epoch_11.rescale('ms').magnitude, + original_epoch_11.rescale('ms').magnitude) + assert_allclose(retrieved_epoch_11.durations.rescale('ms').magnitude, + original_epoch_11.durations.rescale('ms').magnitude) + assert_array_equal(retrieved_epoch_11.labels, original_epoch_11.labels) + if __name__ == "__main__": print("pynwb.__version__ = ", pynwb.__version__) From 5d2b0902f45b123f751f50725ffb1c5b896861e9 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Thu, 5 Mar 2020 15:28:34 +0100 Subject: [PATCH 36/79] Store Neo Epochs in epochs group, Spiketrains in units group. --- neo/io/nwbio.py | 130 +++++++++++++++++++++------------- neo/test/iotest/test_nwbio.py | 8 ++- 2 files changed, 88 insertions(+), 50 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index db22bf7ea..fd1f118f6 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -23,6 +23,7 @@ from datetime import datetime from os.path import join import json +from json.decoder import JSONDecodeError import dateutil.parser import numpy as np import random @@ -241,35 +242,58 @@ def _get_segment(self, block_name, segment_name): return segment def _handle_epochs_group(self, lazy): - start_times = self._file.epochs.start_time[:] - stop_times = self._file.epochs.stop_time[:] - durations = stop_times - start_times - labels = self._file.epochs.tags[:] - segment_names = self._file.epochs.segment[:] - block_names = self._file.epochs.block[:] - epoch_names = self._file.epochs._name[:] - - unique_epoch_names = np.unique(epoch_names) - for epoch_name in unique_epoch_names: - index = (epoch_names == epoch_name) - epoch = Epoch(times=start_times[index] * pq.s, - durations=durations[index] * pq.s, - labels=labels[index], - name=epoch_name) - # todo: handle annotations, array_annotations - segment_name = np.unique(segment_names[index]) - block_name = np.unique(block_names[index]) - assert segment_name.size == block_name.size == 1 - segment = self._get_segment(block_name[0], segment_name[0]) - segment.epochs.append(epoch) - epoch.segment = segment + if self._file.epochs is not None: + start_times = self._file.epochs.start_time[:] + stop_times = self._file.epochs.stop_time[:] + durations = stop_times - start_times + labels = self._file.epochs.tags[:] + try: + # NWB files created by Neo store the segment, block and epoch names as extra columns + segment_names = self._file.epochs.segment[:] + block_names = self._file.epochs.block[:] + epoch_names = self._file.epochs._name[:] + except AttributeError: + epoch_names = None + + if epoch_names is not None: + unique_epoch_names = np.unique(epoch_names) + for epoch_name in unique_epoch_names: + index = (epoch_names == epoch_name) + epoch = Epoch(times=start_times[index] * pq.s, + durations=durations[index] * pq.s, + labels=labels[index], + name=epoch_name) + # todo: handle annotations, array_annotations + segment_name = np.unique(segment_names[index]) + block_name = np.unique(block_names[index]) + assert segment_name.size == block_name.size == 1 + segment = self._get_segment(block_name[0], segment_name[0]) + segment.epochs.append(epoch) + epoch.segment = segment + else: + epoch = Epoch(times=start_times * pq.s, + durations=durations * pq.s, + labels=labels) + segment = self._get_segment("default", "default") + segment.epochs.append(epoch) + epoch.segment = segment def _handle_acquisition_group(self, lazy): acq = self._file.acquisition for timeseries in acq.values(): - hierarchy = json.loads(timeseries.comments) - block_name = hierarchy["block"] - segment_name = hierarchy["segment"] + try: + # NWB files created by Neo store the segment and block names in the comments field + hierarchy = json.loads(timeseries.comments) + except JSONDecodeError: + # For NWB files created with other applications, we put everything in a single + # segment in a single block + # todo: investigate whether there is a reliable way to create multiple segments, + # e.g. using Trial information + block_name = "default" + segment_name = "default" + else: + block_name = hierarchy["block"] + segment_name = hierarchy["segment"] segment = self._get_segment(block_name, segment_name) if isinstance(timeseries, AnnotationSeries): event = Event(timeseries.timestamps[:] * pq.s, @@ -303,30 +327,38 @@ def _handle_acquisition_group(self, lazy): signal.segment = segment def _handle_units(self, lazy): - for id in self._file.units.id[:]: - spike_times = self._file.units.get_unit_spike_times(id) - t_start, t_stop = self._file.units.get_unit_obs_intervals(id)[0] - name = self._file.units._name[id] - segment_name = self._file.units.segment[id] - block_name = self._file.units.block[id] - segment = self._get_segment(block_name, segment_name) - spiketrain = SpikeTrain( - spike_times, - t_stop * pq.s, - units='s', - #sampling_rate=array(1.) * Hz, - t_start=t_start * pq.s, - #waveforms=None, - #left_sweep=None, - name=name, - #file_origin=None, - #description=None, - #array_annotations=None, - #**annotations - ) - segment.spiketrains.append(spiketrain) - spiketrain.segment = segment - + if self._file.units: + for id in self._file.units.id[:]: + spike_times = self._file.units.get_unit_spike_times(id) + t_start, t_stop = self._file.units.get_unit_obs_intervals(id)[0] + try: + # NWB files created by Neo store the segment and block names as extra columns + name = self._file.units._name[id] + segment_name = self._file.units.segment[id] + block_name = self._file.units.block[id] + except AttributeError: + # For NWB files created with other applications, we put everything in a single + # segment in a single block + name = None + segment_name = "default" + block_name = "default" + segment = self._get_segment(block_name, segment_name) + spiketrain = SpikeTrain( + spike_times, + t_stop * pq.s, + units='s', + #sampling_rate=array(1.) * Hz, + t_start=t_start * pq.s, + #waveforms=None, + #left_sweep=None, + name=name, + #file_origin=None, + #description=None, + #array_annotations=None, + #**annotations + ) + segment.spiketrains.append(spiketrain) + spiketrain.segment = segment def _handle_stimulus_group(self, lazy, _file, block): pass diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index f2ecd8222..9bb12da8a 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -15,12 +15,13 @@ from numpy.testing import assert_array_equal, assert_allclose -class TestNWBIO(unittest.TestCase, ): +class TestNWBIO(unittest.TestCase): ioclass = NWBIO files_to_download = [ # Files from Allen Institute : # NWB files downloadable from http://download.alleninstitute.org/informatics-archive/prerelease/ # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' + '/Users/andrew/Data/NWB/Allen/H19.28.012.11.05-2.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb' # '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' @@ -29,6 +30,11 @@ class TestNWBIO(unittest.TestCase, ): ] entities_to_test = files_to_download + def test_read(self): + for path in self.entities_to_test: + io = NWBIO(path, 'r') + blocks = io.read() + def test_roundtrip(self): # Define Neo blocks From d742b6418fa60d433bd2e4084eca8f8215fffad5 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Thu, 5 Mar 2020 17:02:06 +0100 Subject: [PATCH 37/79] Store global annotations appropriately, handle "stimulus" group --- neo/io/nwbio.py | 158 +++++++++++++++++++++++++----------------------- 1 file changed, 81 insertions(+), 77 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index fd1f118f6..f9c4f27d1 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -24,6 +24,7 @@ from os.path import join import json from json.decoder import JSONDecodeError +from collections import defaultdict import dateutil.parser import numpy as np import random @@ -67,6 +68,25 @@ have_hdmf = False +GLOBAL_ANNOTATIONS = ( + "session_start_time", "identifier", "timestamps_reference_time", "experimenter", + "experiment_description", "session_id", "institution", "keywords", "notes", + "pharmacology", "protocol", "related_publications", "slices", "source_script", + "source_script_file_name", "data_collection", "surgery", "virus", "stimulus_notes", + "lab", "session_description" +) +POSSIBLE_JSON_FIELDS = ( + "source_script", "description" +) + + +def try_json_field(content): + try: + return json.loads(content) + except JSONDecodeError: + return content + + class NWBIO(BaseIO): """ Class for "reading" experimental data from a .nwb file, and "writing" a .nwb file from Neo @@ -107,34 +127,28 @@ def read_all_blocks(self, lazy=False, **kwargs): io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO self._file = io.read() - file_access_dates = self._file.file_create_date - identifier = self._file.identifier - if identifier == '_neo': - identifier = None - description = self._file.session_description - if description == "no description": - description = None + self.global_block_metadata = {} + for annotation_name in GLOBAL_ANNOTATIONS: + value = getattr(self._file, annotation_name, None) + if value is not None: + if annotation_name in POSSIBLE_JSON_FIELDS: + value = try_json_field(value) + self.global_block_metadata[annotation_name] = value + if "session_description" in self.global_block_metadata: + self.global_block_metadata["description"] = self.global_block_metadata["session_description"] + self.global_block_metadata["file_origin"] = self.filename + if "session_start_time" in self.global_block_metadata: + self.global_block_metadata["rec_datetime"] = self.global_block_metadata["session_start_time"] + if "file_create_date" in self.global_block_metadata: + self.global_block_metadata["file_datetime"] = self.global_block_metadata["file_create_date"] self._blocks = {} self._handle_acquisition_group(lazy=lazy) + self._handle_stimulus_group(lazy) self._handle_units(lazy=lazy) self._handle_epochs_group(lazy) - # block = Block(name=identifier, - # description=description, - # file_origin=self.filename, - # file_datetime=file_access_dates, - # rec_datetime=_file.session_start_time, - # file_access_dates=file_access_dates, - # file_read_log='') - # self._handle_general_group(block) - - # self._handle_acquisition_group(lazy, _file, block) - # self._handle_stimulus_group(lazy, _file, block) - # self._handle_processing_group(_file, block) - # self._handle_analysis_group(block) # self._handle_calcium_imaging_data(_file, block) - # self._lazy = False return list(self._blocks.values()) def read_block(self, lazy=False, **kargs): @@ -149,45 +163,29 @@ def write_all_blocks(self, blocks, **kwargs): """ # todo: allow metadata in NWBFile constructor to be taken from kwargs start_time = datetime.now() - nwbfile = NWBFile(self.filename, - session_start_time=start_time, - identifier='', - file_create_date=None, # use current date? - timestamps_reference_time=None, - experimenter=None, - experiment_description=None, - session_id=None, - institution=None, - keywords=None, - notes=None, - pharmacology=None, - protocol=None, - related_publications=None, - slices=None, - source_script=None, - source_script_file_name=None, - data_collection=None, - surgery=None, - virus=None, - stimulus_notes=None, - lab=None, - acquisition=None, - stimulus=None, - stimulus_template=None, - epochs=None, - epoch_tags=set(), - trials=None, - invalid_times=None, - units=None, - electrodes=None, - electrode_groups=None, - ic_electrodes=None, - sweep_table=None, - imaging_planes=None, - ogen_sites=None, - devices=None, - subject=None - ) + annotations = defaultdict(set) + for annotation_name in GLOBAL_ANNOTATIONS: + if annotation_name in kwargs: + annotations[annotation_name] = kwargs[annotation_name] + else: + for block in blocks: + if annotation_name in block.annotations: + annotations[annotation_name].add(block.annotations[annotation_name]) + if annotation_name in annotations: + if len(annotations[annotation_name]) > 1: + raise NotImplementedError("We don't yet support multiple values for {}".format(annotation_name)) + annotations[annotation_name], = annotations[annotation_name] # take single value from set + if "identifier" not in annotations: + annotations["identifier"] = self.filename + if "session_description" not in annotations: + annotations["session_description"] = blocks[0].description or self.filename + # todo: concatenate descriptions of multiple blocks if different + if "session_start_time" not in annotations: + annotations["session_start_time"] = datetime.now() + # todo: handle subject + # todo: store additional Neo annotations somewhere in NWB file + nwbfile = NWBFile(**annotations) + io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') nwbfile.add_unit_column('_name', 'the name attribute of the SpikeTrain') @@ -228,7 +226,7 @@ def _get_segment(self, block_name, segment_name): if block_name in self._blocks: block = self._blocks[block_name] else: - block = Block(name=block_name) + block = Block(name=block_name, **self.global_block_metadata) self._blocks[block_name] = block segment = None for seg in block.segments: @@ -278,9 +276,9 @@ def _handle_epochs_group(self, lazy): segment.epochs.append(epoch) epoch.segment = segment - def _handle_acquisition_group(self, lazy): - acq = self._file.acquisition - for timeseries in acq.values(): + def _handle_timeseries_group(self, group_name, lazy): + group = getattr(self._file, group_name) + for timeseries in group.values(): try: # NWB files created by Neo store the segment and block names in the comments field hierarchy = json.loads(timeseries.comments) @@ -295,11 +293,17 @@ def _handle_acquisition_group(self, lazy): block_name = hierarchy["block"] segment_name = hierarchy["segment"] segment = self._get_segment(block_name, segment_name) + annotations = {"nwb_group" : group_name} + description = try_json_field(timeseries.description) + if isinstance(description, dict): + annotations.update(description) + description = None if isinstance(timeseries, AnnotationSeries): event = Event(timeseries.timestamps[:] * pq.s, labels=timeseries.data[:], name=timeseries.name, - description=timeseries.description) + description=description, + **annotations) segment.events.append(event) event.segment = segment elif timeseries.rate: @@ -310,8 +314,9 @@ def _handle_acquisition_group(self, lazy): sampling_rate=timeseries.rate * pq.Hz, name=timeseries.name, file_origin=self._file.session_description, - description=timeseries.description, - array_annotations=None) # todo: timeseries.control / control_description + description=description, + array_annotations=None, + **annotations) # todo: timeseries.control / control_description segment.analogsignals.append(signal) signal.segment = segment else: @@ -321,8 +326,9 @@ def _handle_acquisition_group(self, lazy): units=timeseries.unit, name=timeseries.name, file_origin=self._file.session_description, - description=timeseries.description, - array_annotations=None) # todo: timeseries.control / control_description + description=description, + array_annotations=None, + **annotations) # todo: timeseries.control / control_description segment.irregularlysampledsignals.append(signal) signal.segment = segment @@ -356,20 +362,18 @@ def _handle_units(self, lazy): #description=None, #array_annotations=None, #**annotations + nwb_group="acquisition" ) segment.spiketrains.append(spiketrain) spiketrain.segment = segment - def _handle_stimulus_group(self, lazy, _file, block): - pass - - def _handle_processing_group(self, _file, block): - pass + def _handle_acquisition_group(self, lazy): + self._handle_timeseries_group("acquisition", lazy) - def _handle_analysis_group(self, block): - pass + def _handle_stimulus_group(self, lazy): + self._handle_timeseries_group("stimulus", lazy) - def _handle_calcium_imaging_data(self, _file, block): + def _handle_calcium_imaging_data(self): """ Function to read calcium imaging data. """ From b8869657242ac7407c2ddf5d90a5cc41c104b732 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Thu, 5 Mar 2020 17:11:35 +0100 Subject: [PATCH 38/79] temporarily remove incomplete calcium image data handling code, so as to focus on improving ephys handling --- neo/io/nwbio.py | 156 ------------------------------------------------ 1 file changed, 156 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index f9c4f27d1..c75461fcd 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -148,7 +148,6 @@ def read_all_blocks(self, lazy=False, **kwargs): self._handle_units(lazy=lazy) self._handle_epochs_group(lazy) - # self._handle_calcium_imaging_data(_file, block) return list(self._blocks.values()) def read_block(self, lazy=False, **kargs): @@ -200,7 +199,6 @@ def write_all_blocks(self, blocks, **kwargs): for i, block in enumerate(blocks): self.write_block(nwbfile, block) - #self.write_calcium_imaging_data(nwbfile, block, i) io_nwb.write(nwbfile) io_nwb.close() @@ -373,160 +371,6 @@ def _handle_acquisition_group(self, lazy): def _handle_stimulus_group(self, lazy): self._handle_timeseries_group("stimulus", lazy) - def _handle_calcium_imaging_data(self): - """ - Function to read calcium imaging data. - """ - pass - - def write_calcium_imaging_data(self, nwbfile, block, i): - """ - Function to write calcium imaging data. This involves three main steps: - - Acquiring two-photon images - - Image segmentation - - Fluorescence and dF/F response - - Adding metadata about acquisition - """ - name_imaging_device = "imaging_device %s %d" % (block.name, i) - device = Device(name_imaging_device) - - nwbfile.add_device(device) - - # To define the manifold - l = [] - for frame in range(50): - l.append([]) - for y in range(100): - l[frame].append([]) - for x in range(100): - l[frame][y].append(random.randint(0, 50)) - - # OpticalChannel - name_optical_channel = "optical_channel %s %d" %(block.name, i) - optical_channel = OpticalChannel( - name = name_optical_channel, - description = 'description', - emission_lambda = 500.) # Emission wavelength for channel, in nm - - name_imaging_plane = "imaging_plane %s %d " %(block.name, i) - - imaging_plane = nwbfile.create_imaging_plane( - name_imaging_plane, # name - optical_channel, # optical_channel - 'a very interesting part of the brain', # description - device, # device - 600., # excitation_lambda - 300., # imaging_rate - 'GFP', # indicator - 'my favorite brain location', # location - l[frame][y].append(random.randint(0, 50)), # manifold - 1.0, # conversion - 'manifold unit', # unit - 'A frame to refer to' # reference_frame - ) - - """ - Adding two-photon image data - """ - name_twophotonseries = "two_photon_series %s %d" %(block.name, i) - image_series = TwoPhotonSeries( - name=name_twophotonseries, - dimension=[2], - external_file=['images.tiff'], - imaging_plane=imaging_plane, - starting_frame=[0], - format='tiff', - starting_time=0.0, - rate=1.0 - ) - - nwbfile.add_acquisition(image_series) - - """ - Storing image segmentation output - """ - name_processing_module = "processing_module %s %d" %(block.name, i) - mod = nwbfile.create_processing_module( - name_processing_module, # Example : 'ophys' - 'contains optical physiology processed data' - ) - - img_seg = ImageSegmentation() - mod.add(img_seg) - - name_plane_segmentation = "plane_segmentation %s %d" %(block.name, i) - ps = img_seg.create_plane_segmentation( - description = 'output from segmenting my favorite imaging plane', - imaging_plane = imaging_plane, # link to OpticalChannel - name = name_plane_segmentation, - reference_images = image_series # link to TwoPhotonSeries - ) - - """ - Add the resulting ROIs - """ - w, h = 3, 3 - pix_mask1 = [(0, 0, 1.1), (1, 1, 1.2), (2, 2, 1.3)] - img_mask1 = [[0.0 for x in range(w)] for y in range(h)] - img_mask1[0][0] = 1.1 - img_mask1[1][1] = 1.2 - img_mask1[2][2] = 1.3 - ps.add_roi(pixel_mask=pix_mask1, image_mask=img_mask1) - - pix_mask2 = [(0, 0, 2.1), (1, 1, 2.2)] - img_mask2 = [[0.0 for x in range(w)] for y in range(h)] - img_mask2[0][0] = 2.1 - img_mask2[1][1] = 2.2 - ps.add_roi(pixel_mask=pix_mask2, image_mask=img_mask2) - - """ - Storing fluorescence measurements - """ - # Create a data interface - fl = Fluorescence() - mod.add(fl) - - # Reference to the ROIs - rt_region = ps.create_roi_table_region( - 'the first of two ROIs', - region=[0] - ) - - # RoiResponseSeries - data = [0., 1., 2., 3., 4., 5., 6., 7., 8., 9.] - timestamps = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9] - rrs = fl.create_roi_response_series( - 'my_rrs', - data, - rt_region, - unit='lumens', - timestamps=timestamps - ) - - # if ImageSequence: - # imagesequence_name = ("ImageSequence %s %s %d" % (block.name, segment.name, i)) - # sampling_rate = signal.sampling_rate.rescale("Hz") - # image = pynwb.image.ImageSeries( - # name=imagesequence_name, - # data=[[[column for column in range(2)]for row in range(3)] for frame in range(4)], - # unit=None, - # format=None, - # external_file=None, - # starting_frame=None, - # bits_per_pixel=None, - # dimension=None, - # resolution=-1.0, - # conversion=float(1*pq.micrometer), - # timestamps=None, - # starting_time=None, - # rate=float(sampling_rate), - # comments='no comments', - # description='no description', - # control=None, - # control_description=None - # ) - def _write_segment(self, nwbfile, segment): # maybe use NWB trials to store Segment metadata? From 47d56ec31efaedffa123e16e4380c1788bc57d85 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Thu, 5 Mar 2020 17:12:00 +0100 Subject: [PATCH 39/79] Remove unused imports --- neo/io/nwbio.py | 21 ++++----------------- 1 file changed, 4 insertions(+), 17 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index c75461fcd..235645cc6 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -15,35 +15,23 @@ Sample datasets from Allen Institute - http://alleninstitute.github.io/AllenSDK/cell_types.html#neurodata-without-borders """ -from __future__ import absolute_import -from __future__ import division +from __future__ import absolute_import, division + from itertools import chain -import shutil -import tempfile from datetime import datetime -from os.path import join import json from json.decoder import JSONDecodeError from collections import defaultdict -import dateutil.parser -import numpy as np -import random +import numpy as np import quantities as pq from neo.io.baseio import BaseIO from neo.core import (Segment, SpikeTrain, Unit, Epoch, Event, AnalogSignal, IrregularlySampledSignal, ChannelIndex, Block, ImageSequence) -from collections import OrderedDict - -# Standard Python imports -from tempfile import NamedTemporaryFile -import os -import glob # PyNWB imports try: import pynwb - from pynwb import * from pynwb import NWBFile, TimeSeries, get_manager from pynwb.base import ProcessingModule from pynwb.ecephys import ElectricalSeries, Device, EventDetection @@ -61,8 +49,7 @@ # hdmf imports try: from hdmf.spec import LinkSpec, GroupSpec, DatasetSpec, SpecNamespace,\ - NamespaceBuilder, AttributeSpec, DtypeSpec, RefSpec - from hdmf import * + NamespaceBuilder, AttributeSpec, DtypeSpec, RefSpec have_hdmf = True except ImportError: have_hdmf = False From f7dcd82f6a6e330d103ebe287a3fe117d038fc5d Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Thu, 5 Mar 2020 17:15:27 +0100 Subject: [PATCH 40/79] group read and write methods (and rename "_handle_X" to "_read_X" for consistency) --- neo/io/nwbio.py | 142 ++++++++++++++++++++++++------------------------ 1 file changed, 71 insertions(+), 71 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 235645cc6..2cc71e6e9 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -130,10 +130,10 @@ def read_all_blocks(self, lazy=False, **kwargs): self.global_block_metadata["file_datetime"] = self.global_block_metadata["file_create_date"] self._blocks = {} - self._handle_acquisition_group(lazy=lazy) - self._handle_stimulus_group(lazy) - self._handle_units(lazy=lazy) - self._handle_epochs_group(lazy) + self._read_acquisition_group(lazy=lazy) + self._read_stimulus_group(lazy) + self._read_units(lazy=lazy) + self._read_epochs_group(lazy) return list(self._blocks.values()) @@ -143,66 +143,6 @@ def read_block(self, lazy=False, **kargs): """ return self.read_all_blocks(lazy=lazy)[0] - def write_all_blocks(self, blocks, **kwargs): - """ - Write list of blocks to the file - """ - # todo: allow metadata in NWBFile constructor to be taken from kwargs - start_time = datetime.now() - annotations = defaultdict(set) - for annotation_name in GLOBAL_ANNOTATIONS: - if annotation_name in kwargs: - annotations[annotation_name] = kwargs[annotation_name] - else: - for block in blocks: - if annotation_name in block.annotations: - annotations[annotation_name].add(block.annotations[annotation_name]) - if annotation_name in annotations: - if len(annotations[annotation_name]) > 1: - raise NotImplementedError("We don't yet support multiple values for {}".format(annotation_name)) - annotations[annotation_name], = annotations[annotation_name] # take single value from set - if "identifier" not in annotations: - annotations["identifier"] = self.filename - if "session_description" not in annotations: - annotations["session_description"] = blocks[0].description or self.filename - # todo: concatenate descriptions of multiple blocks if different - if "session_start_time" not in annotations: - annotations["session_start_time"] = datetime.now() - # todo: handle subject - # todo: store additional Neo annotations somewhere in NWB file - nwbfile = NWBFile(**annotations) - - io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') - - nwbfile.add_unit_column('_name', 'the name attribute of the SpikeTrain') - #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') - nwbfile.add_unit_column('segment', 'the name of the Neo Segment to which the SpikeTrain belongs') - nwbfile.add_unit_column('block', 'the name of the Neo Block to which the SpikeTrain belongs') - - nwbfile.add_epoch_column('_name', 'the name attribute of the Epoch') - #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') - nwbfile.add_epoch_column('segment', 'the name of the Neo Segment to which the Epoch belongs') - nwbfile.add_epoch_column('block', 'the name of the Neo Block to which the Epoch belongs') - - for i, block in enumerate(blocks): - self.write_block(nwbfile, block) - io_nwb.write(nwbfile) - io_nwb.close() - - def write_block(self, nwbfile, block, **kwargs): - """ - Write a Block to the file - :param block: Block to be written - """ - if not block.name: - block.name = "block%d" % self.blocks_written - for i, segment in enumerate(block.segments): - assert segment.block is block - if not segment.name: - segment.name = "%s : segment%d" % (block.name, i) - self._write_segment(nwbfile, segment) - self.blocks_written += 1 - def _get_segment(self, block_name, segment_name): # If we've already created a Block with the given name return it, # otherwise create it now and store it in self._blocks. @@ -224,7 +164,7 @@ def _get_segment(self, block_name, segment_name): block.segments.append(segment) return segment - def _handle_epochs_group(self, lazy): + def _read_epochs_group(self, lazy): if self._file.epochs is not None: start_times = self._file.epochs.start_time[:] stop_times = self._file.epochs.stop_time[:] @@ -261,7 +201,7 @@ def _handle_epochs_group(self, lazy): segment.epochs.append(epoch) epoch.segment = segment - def _handle_timeseries_group(self, group_name, lazy): + def _read_timeseries_group(self, group_name, lazy): group = getattr(self._file, group_name) for timeseries in group.values(): try: @@ -317,7 +257,7 @@ def _handle_timeseries_group(self, group_name, lazy): segment.irregularlysampledsignals.append(signal) signal.segment = segment - def _handle_units(self, lazy): + def _read_units(self, lazy): if self._file.units: for id in self._file.units.id[:]: spike_times = self._file.units.get_unit_spike_times(id) @@ -352,11 +292,71 @@ def _handle_units(self, lazy): segment.spiketrains.append(spiketrain) spiketrain.segment = segment - def _handle_acquisition_group(self, lazy): - self._handle_timeseries_group("acquisition", lazy) + def _read_acquisition_group(self, lazy): + self._read_timeseries_group("acquisition", lazy) - def _handle_stimulus_group(self, lazy): - self._handle_timeseries_group("stimulus", lazy) + def _read_stimulus_group(self, lazy): + self._read_timeseries_group("stimulus", lazy) + + def write_all_blocks(self, blocks, **kwargs): + """ + Write list of blocks to the file + """ + # todo: allow metadata in NWBFile constructor to be taken from kwargs + start_time = datetime.now() + annotations = defaultdict(set) + for annotation_name in GLOBAL_ANNOTATIONS: + if annotation_name in kwargs: + annotations[annotation_name] = kwargs[annotation_name] + else: + for block in blocks: + if annotation_name in block.annotations: + annotations[annotation_name].add(block.annotations[annotation_name]) + if annotation_name in annotations: + if len(annotations[annotation_name]) > 1: + raise NotImplementedError("We don't yet support multiple values for {}".format(annotation_name)) + annotations[annotation_name], = annotations[annotation_name] # take single value from set + if "identifier" not in annotations: + annotations["identifier"] = self.filename + if "session_description" not in annotations: + annotations["session_description"] = blocks[0].description or self.filename + # todo: concatenate descriptions of multiple blocks if different + if "session_start_time" not in annotations: + annotations["session_start_time"] = datetime.now() + # todo: handle subject + # todo: store additional Neo annotations somewhere in NWB file + nwbfile = NWBFile(**annotations) + + io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') + + nwbfile.add_unit_column('_name', 'the name attribute of the SpikeTrain') + #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') + nwbfile.add_unit_column('segment', 'the name of the Neo Segment to which the SpikeTrain belongs') + nwbfile.add_unit_column('block', 'the name of the Neo Block to which the SpikeTrain belongs') + + nwbfile.add_epoch_column('_name', 'the name attribute of the Epoch') + #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') + nwbfile.add_epoch_column('segment', 'the name of the Neo Segment to which the Epoch belongs') + nwbfile.add_epoch_column('block', 'the name of the Neo Block to which the Epoch belongs') + + for i, block in enumerate(blocks): + self.write_block(nwbfile, block) + io_nwb.write(nwbfile) + io_nwb.close() + + def write_block(self, nwbfile, block, **kwargs): + """ + Write a Block to the file + :param block: Block to be written + """ + if not block.name: + block.name = "block%d" % self.blocks_written + for i, segment in enumerate(block.segments): + assert segment.block is block + if not segment.name: + segment.name = "%s : segment%d" % (block.name, i) + self._write_segment(nwbfile, segment) + self.blocks_written += 1 def _write_segment(self, nwbfile, segment): # maybe use NWB trials to store Segment metadata? From 0c2d3365ffd49af10569dd71642b5574cb004ee0 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Thu, 5 Mar 2020 22:01:37 +0100 Subject: [PATCH 41/79] wip lazy loading --- neo/io/baseio.py | 2 +- neo/io/nwbio.py | 59 ++++++++++++++++++++++++++++++++--------- neo/io/proxyobjects.py | 60 ++++++++++++++++++++++++------------------ 3 files changed, 82 insertions(+), 39 deletions(-) diff --git a/neo/io/baseio.py b/neo/io/baseio.py index 4111ae714..7743f583e 100644 --- a/neo/io/baseio.py +++ b/neo/io/baseio.py @@ -114,7 +114,7 @@ def __init__(self, filename=None, **kargs): ######## General read/write methods ####################### def read(self, lazy=False, **kargs): if lazy: - assert self.support_lazy, 'This IO do not support lazy loading' + assert self.support_lazy, 'This IO does not support lazy loading' if Block in self.readable_objects: if (hasattr(self, 'read_all_blocks') and callable(getattr(self, 'read_all_blocks'))): diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 2cc71e6e9..c3da1a62e 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -26,6 +26,7 @@ import numpy as np import quantities as pq from neo.io.baseio import BaseIO +from neo.io.proxyobjects import AnalogSignalProxy as BaseAnalogSignalProxy from neo.core import (Segment, SpikeTrain, Unit, Epoch, Event, AnalogSignal, IrregularlySampledSignal, ChannelIndex, Block, ImageSequence) @@ -84,6 +85,7 @@ class NWBIO(BaseIO): writeable_objects = supported_objects has_header = False + support_lazy = True name = 'NeoNWB IO' description = 'This IO reads/writes experimental data from/to an .nwb dataset' @@ -232,16 +234,9 @@ def _read_timeseries_group(self, group_name, lazy): segment.events.append(event) event.segment = segment elif timeseries.rate: - signal = AnalogSignal( - timeseries.data[:], - units=timeseries.unit, - t_start=timeseries.starting_time * pq.s, # use timeseries.starting_time_units - sampling_rate=timeseries.rate * pq.Hz, - name=timeseries.name, - file_origin=self._file.session_description, - description=description, - array_annotations=None, - **annotations) # todo: timeseries.control / control_description + signal = AnalogSignalProxy(timeseries, group_name) + if not lazy: + signal = signal.load() segment.analogsignals.append(signal) signal.segment = segment else: @@ -250,7 +245,6 @@ def _read_timeseries_group(self, group_name, lazy): timeseries.data[:], units=timeseries.unit, name=timeseries.name, - file_origin=self._file.session_description, description=description, array_annotations=None, **annotations) # todo: timeseries.control / control_description @@ -470,4 +464,45 @@ def _decompose(unit): 1e-3: 'milli', 1e-6: 'micro', 1e-9: 'nano' -} \ No newline at end of file +} + + +class AnalogSignalProxy(BaseAnalogSignalProxy): + + def __init__(self, timeseries, nwb_group): + self._timeseries = timeseries + self.units = timeseries.unit + self.t_start = timeseries.starting_time * pq.s # use timeseries.starting_time_units + self.sampling_rate = timeseries.rate * pq.Hz + self.name = timeseries.name + self.annotations = {"nwb_group" : nwb_group} + self.description = try_json_field(timeseries.description) + if isinstance(self.description, dict): + self.annotations.update(self.description) + self.description = None + self.shape = self._timeseries.data.shape + + def load(self, time_slice=None, strict_slicing=True): + """ + *Args*: + :time_slice: None or tuple of the time slice expressed with quantities. + None is the entire signal. + :strict_slicing: True by default. + Control if an error is raise or not when one of time_slice member + (t_start or t_stop) is outside the real time range of the segment. + """ + if time_slice: + i_start, i_stop, sig_t_start = self._time_slice_indices(time_slice, strict_slicing=strict_slicing) + signal = self._timeseries.data[i_start: i_stop] + else: + signal = self._timeseries.data[:] + sig_t_start = self.t_start + return AnalogSignal( + signal, + units=self.units, + t_start=sig_t_start, + sampling_rate=self.sampling_rate, + name=self.name, + description=self.description, + array_annotations=None, + **self.annotations) # todo: timeseries.control / control_description diff --git a/neo/io/proxyobjects.py b/neo/io/proxyobjects.py index c33da1cf3..fc4f922d3 100644 --- a/neo/io/proxyobjects.py +++ b/neo/io/proxyobjects.py @@ -165,36 +165,17 @@ def t_stop(self): '''Time when signal ends''' return self.t_start + self.duration - def load(self, time_slice=None, strict_slicing=True, - channel_indexes=None, magnitude_mode='rescaled'): - ''' - *Args*: - :time_slice: None or tuple of the time slice expressed with quantities. - None is the entire signal. - :channel_indexes: None or list. Channels to load. None is all channels - Be carefull that channel_indexes represent the local channel index inside - the AnalogSignal and not the global_channel_indexes like in rawio. - :magnitude_mode: 'rescaled' or 'raw'. - For instance if the internal dtype is int16: - * **rescaled** give [1.,2.,3.]*pq.uV and the dtype is float32 - * **raw** give [10, 20, 30]*pq.CompoundUnit('0.1*uV') - The CompoundUnit with magnitude_mode='raw' is usefull to - postpone the scaling when needed and having an internal dtype=int16 - but it less intuitive when you don't know so well quantities. - :strict_slicing: True by default. - Control if an error is raise or not when one of time_slice member - (t_start or t_stop) is outside the real time range of the segment. - ''' - - if channel_indexes is None: - channel_indexes = slice(None) - - sr = self.sampling_rate + def _time_slice_indices(self, time_slice, strict_slicing=True): + """ + Calculate the start and end indices for the slice. + Also returns t_start + """ if time_slice is None: i_start, i_stop = None, None sig_t_start = self.t_start else: + sr = self.sampling_rate t_start, t_stop = time_slice if t_start is None: i_start = None @@ -205,7 +186,7 @@ def load(self, time_slice=None, strict_slicing=True, assert self.t_start <= t_start <= self.t_stop, 't_start is outside' else: t_start = max(t_start, self.t_start) - # the i_start is ncessary ceil + # the i_start is necessary ceil i_start = int(np.ceil((t_start - self.t_start).magnitude * sr.magnitude)) # this needed to get the real t_start of the first sample # because do not necessary match what is demanded @@ -220,6 +201,33 @@ def load(self, time_slice=None, strict_slicing=True, else: t_stop = min(t_stop, self.t_stop) i_stop = int((t_stop - self.t_start).magnitude * sr.magnitude) + return i_start, i_stop, sig_t_start + + def load(self, time_slice=None, strict_slicing=True, + channel_indexes=None, magnitude_mode='rescaled'): + ''' + *Args*: + :time_slice: None or tuple of the time slice expressed with quantities. + None is the entire signal. + :channel_indexes: None or list. Channels to load. None is all channels + Be carefull that channel_indexes represent the local channel index inside + the AnalogSignal and not the global_channel_indexes like in rawio. + :magnitude_mode: 'rescaled' or 'raw'. + For instance if the internal dtype is int16: + * **rescaled** give [1.,2.,3.]*pq.uV and the dtype is float32 + * **raw** give [10, 20, 30]*pq.CompoundUnit('0.1*uV') + The CompoundUnit with magnitude_mode='raw' is usefull to + postpone the scaling when needed and having an internal dtype=int16 + but it less intuitive when you don't know so well quantities. + :strict_slicing: True by default. + Control if an error is raise or not when one of time_slice member + (t_start or t_stop) is outside the real time range of the segment. + ''' + + if channel_indexes is None: + channel_indexes = slice(None) + + i_start, i_stop, sig_t_start = self._time_slice_indices(time_slice, strict_slicing=strict_slicing) raw_signal = self._rawio.get_analogsignal_chunk(block_index=self._block_index, seg_index=self._seg_index, i_start=i_start, i_stop=i_stop, From a196868f405166885511c609f3883fe0fa3bdffb Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 6 Mar 2020 13:39:40 +0100 Subject: [PATCH 42/79] implement lazy loading --- neo/io/nwbio.py | 219 +++++++++++++++++++++++++++++++++++------------- 1 file changed, 162 insertions(+), 57 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index c3da1a62e..af61f5c5f 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -26,7 +26,12 @@ import numpy as np import quantities as pq from neo.io.baseio import BaseIO -from neo.io.proxyobjects import AnalogSignalProxy as BaseAnalogSignalProxy +from neo.io.proxyobjects import ( + AnalogSignalProxy as BaseAnalogSignalProxy, + EventProxy as BaseEventProxy, + EpochProxy as BaseEpochProxy, + SpikeTrainProxy as BaseSpikeTrainProxy +) from neo.core import (Segment, SpikeTrain, Unit, Epoch, Event, AnalogSignal, IrregularlySampledSignal, ChannelIndex, Block, ImageSequence) @@ -168,10 +173,6 @@ def _get_segment(self, block_name, segment_name): def _read_epochs_group(self, lazy): if self._file.epochs is not None: - start_times = self._file.epochs.start_time[:] - stop_times = self._file.epochs.stop_time[:] - durations = stop_times - start_times - labels = self._file.epochs.tags[:] try: # NWB files created by Neo store the segment, block and epoch names as extra columns segment_names = self._file.epochs.segment[:] @@ -184,11 +185,9 @@ def _read_epochs_group(self, lazy): unique_epoch_names = np.unique(epoch_names) for epoch_name in unique_epoch_names: index = (epoch_names == epoch_name) - epoch = Epoch(times=start_times[index] * pq.s, - durations=durations[index] * pq.s, - labels=labels[index], - name=epoch_name) - # todo: handle annotations, array_annotations + epoch = EpochProxy(self._file.epochs, epoch_name, index) + if not lazy: + epoch = epoch.load() segment_name = np.unique(segment_names[index]) block_name = np.unique(block_names[index]) assert segment_name.size == block_name.size == 1 @@ -196,9 +195,9 @@ def _read_epochs_group(self, lazy): segment.epochs.append(epoch) epoch.segment = segment else: - epoch = Epoch(times=start_times * pq.s, - durations=durations * pq.s, - labels=labels) + epoch = EpochProxy(self._file.epochs) + if not lazy: + epoch = epoch.load() segment = self._get_segment("default", "default") segment.epochs.append(epoch) epoch.segment = segment @@ -226,63 +225,40 @@ def _read_timeseries_group(self, group_name, lazy): annotations.update(description) description = None if isinstance(timeseries, AnnotationSeries): - event = Event(timeseries.timestamps[:] * pq.s, - labels=timeseries.data[:], - name=timeseries.name, - description=description, - **annotations) + event = EventProxy(timeseries, group_name) + if not lazy: + event = event.load() segment.events.append(event) event.segment = segment - elif timeseries.rate: + elif timeseries.rate: # AnalogSignal signal = AnalogSignalProxy(timeseries, group_name) if not lazy: signal = signal.load() segment.analogsignals.append(signal) signal.segment = segment - else: - signal = IrregularlySampledSignal( - timeseries.timestamps[:] * pq.s, - timeseries.data[:], - units=timeseries.unit, - name=timeseries.name, - description=description, - array_annotations=None, - **annotations) # todo: timeseries.control / control_description + else: # IrregularlySampledSignal + signal = AnalogSignalProxy(timeseries, group_name) + if not lazy: + signal = signal.load() segment.irregularlysampledsignals.append(signal) signal.segment = segment def _read_units(self, lazy): if self._file.units: for id in self._file.units.id[:]: - spike_times = self._file.units.get_unit_spike_times(id) - t_start, t_stop = self._file.units.get_unit_obs_intervals(id)[0] try: # NWB files created by Neo store the segment and block names as extra columns - name = self._file.units._name[id] segment_name = self._file.units.segment[id] block_name = self._file.units.block[id] except AttributeError: # For NWB files created with other applications, we put everything in a single # segment in a single block - name = None segment_name = "default" block_name = "default" segment = self._get_segment(block_name, segment_name) - spiketrain = SpikeTrain( - spike_times, - t_stop * pq.s, - units='s', - #sampling_rate=array(1.) * Hz, - t_start=t_start * pq.s, - #waveforms=None, - #left_sweep=None, - name=name, - #file_origin=None, - #description=None, - #array_annotations=None, - #**annotations - nwb_group="acquisition" - ) + spiketrain = SpikeTrainProxy(self._file.units, id) + if not lazy: + spiketrain = spiketrain.load() segment.spiketrains.append(spiketrain) spiketrain.segment = segment @@ -472,8 +448,14 @@ class AnalogSignalProxy(BaseAnalogSignalProxy): def __init__(self, timeseries, nwb_group): self._timeseries = timeseries self.units = timeseries.unit - self.t_start = timeseries.starting_time * pq.s # use timeseries.starting_time_units - self.sampling_rate = timeseries.rate * pq.Hz + if timeseries.starting_time: + self.t_start = timeseries.starting_time * pq.s # use timeseries.starting_time_units + else: + self.t_start = timeseries.timestamps[0] * pq.s + if timeseries.rate: + self.sampling_rate = timeseries.rate * pq.Hz + else: + self.sampling_rate = None self.name = timeseries.name self.annotations = {"nwb_group" : nwb_group} self.description = try_json_field(timeseries.description) @@ -488,7 +470,7 @@ def load(self, time_slice=None, strict_slicing=True): :time_slice: None or tuple of the time slice expressed with quantities. None is the entire signal. :strict_slicing: True by default. - Control if an error is raise or not when one of time_slice member + Control if an error is raised or not when one of the time_slice members (t_start or t_stop) is outside the real time range of the segment. """ if time_slice: @@ -497,12 +479,135 @@ def load(self, time_slice=None, strict_slicing=True): else: signal = self._timeseries.data[:] sig_t_start = self.t_start - return AnalogSignal( - signal, + if self.sampling_rate is None: + return IrregularlySampledSignal( + self._timeseries.timestamps[:] * pq.s, + signal, + units=self.units, + t_start=sig_t_start, + sampling_rate=self.sampling_rate, + name=self.name, + description=self.description, + array_annotations=None, + **self.annotations) # todo: timeseries.control / control_description + + else: + return AnalogSignal( + signal, + units=self.units, + t_start=sig_t_start, + sampling_rate=self.sampling_rate, + name=self.name, + description=self.description, + array_annotations=None, + **self.annotations) # todo: timeseries.control / control_description + + +class EventProxy(BaseEventProxy): + + def __init__(self, timeseries, nwb_group): + self._timeseries = timeseries + self.name = timeseries.name + self.annotations = {"nwb_group" : nwb_group} + self.description = try_json_field(timeseries.description) + if isinstance(self.description, dict): + self.annotations.update(self.description) + self.description = None + self.shape = self._timeseries.data.shape + + def load(self, time_slice=None, strict_slicing=True): + """ + *Args*: + :time_slice: None or tuple of the time slice expressed with quantities. + None is the entire signal. + :strict_slicing: True by default. + Control if an error is raised or not when one of the time_slice members + (t_start or t_stop) is outside the real time range of the segment. + """ + if time_slice: + raise NotImplementedError("todo") + else: + times = self._timeseries.timestamps[:] + labels = self._timeseries.data[:] + return Event(times * pq.s, + labels=labels, + name=self.name, + description=self.description, + **self.annotations) + + +class EpochProxy(BaseEpochProxy): + + def __init__(self, epochs_table, epoch_name=None, index=None): + self._epochs_table = epochs_table + if index is not None: + self._index = index + self.shape = (index.sum(),) + else: + self._index = slice(None) + self.shape = epochs_table.n_rows # untested, just guessed that n_rows exists + self.name = epoch_name + + def load(self, time_slice=None, strict_slicing=True): + """ + *Args*: + :time_slice: None or tuple of the time slice expressed with quantities. + None is all of the intervals. + :strict_slicing: True by default. + Control if an error is raised or not when one of the time_slice members + (t_start or t_stop) is outside the real time range of the segment. + """ + start_times = self._epochs_table.start_time[self._index] + stop_times = self._epochs_table.stop_time[self._index] + durations = stop_times - start_times + labels = self._epochs_table.tags[self._index] + + return Epoch(times=start_times * pq.s, + durations=durations * pq.s, + labels=labels, + name=self.name) + + +class SpikeTrainProxy(BaseSpikeTrainProxy): + + def __init__(self, units_table, id): + self._units_table = units_table + self.id = id + self.units = pq.s + t_start, t_stop = units_table.get_unit_obs_intervals(id)[0] + self.t_start = t_start * pq.s + self.t_stop = t_stop * pq.s + self.annotations = {"nwb_group": "acquisition"} + try: + # NWB files created by Neo store the name as an extra column + self.name = units_table._name[id] + except AttributeError: + self.name = None + self.shape = None # no way to get this without reading the data + + def load(self, time_slice=None, strict_slicing=True): + """ + *Args*: + :time_slice: None or tuple of the time slice expressed with quantities. + None is the entire spike train. + :strict_slicing: True by default. + Control if an error is raised or not when one of the time_slice members + (t_start or t_stop) is outside the real time range of the segment. + """ + interval = None + if time_slice: + interval = (float(t) for t in time_slice) # convert from quantities + spike_times = self._units_table.get_unit_spike_times(self.id, in_interval=interval) + return SpikeTrain( + spike_times * self.units, + self.t_stop, units=self.units, - t_start=sig_t_start, - sampling_rate=self.sampling_rate, + #sampling_rate=array(1.) * Hz, + t_start=self.t_start, + #waveforms=None, + #left_sweep=None, name=self.name, - description=self.description, - array_annotations=None, - **self.annotations) # todo: timeseries.control / control_description + #file_origin=None, + #description=None, + #array_annotations=None, + **self.annotations) \ No newline at end of file From 785e3b29caf1898145d849c05cefe3daeed0ec6e Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 6 Mar 2020 15:00:58 +0100 Subject: [PATCH 43/79] fix NWB tests to download data files, so they should work on CI system --- neo/io/nwbio.py | 4 +++- neo/test/iotest/test_nwbio.py | 29 ++++++++++++++++++----------- 2 files changed, 21 insertions(+), 12 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index af61f5c5f..27dc26a23 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -448,7 +448,7 @@ class AnalogSignalProxy(BaseAnalogSignalProxy): def __init__(self, timeseries, nwb_group): self._timeseries = timeseries self.units = timeseries.unit - if timeseries.starting_time: + if timeseries.starting_time is not None: self.t_start = timeseries.starting_time * pq.s # use timeseries.starting_time_units else: self.t_start = timeseries.timestamps[0] * pq.s @@ -461,6 +461,8 @@ def __init__(self, timeseries, nwb_group): self.description = try_json_field(timeseries.description) if isinstance(self.description, dict): self.annotations.update(self.description) + if "name" in self.annotations: + self.annotations.pop("name") self.description = None self.shape = self._timeseries.data.shape diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 9bb12da8a..73536bac0 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -5,6 +5,11 @@ from __future__ import unicode_literals, print_function, division, absolute_import import unittest +import os +try: + from urllib.request import urlretrieve +except ImportError: + from urllib import urlretrieve from neo.io.nwbio import NWBIO from neo.test.iotest.common_io_test import BaseTestIO from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Unit, Block, ChannelIndex, ImageSequence @@ -13,26 +18,28 @@ import quantities as pq import numpy as np from numpy.testing import assert_array_equal, assert_allclose +from neo.test.rawiotest.tools import create_local_temp_dir class TestNWBIO(unittest.TestCase): ioclass = NWBIO files_to_download = [ # Files from Allen Institute : -# NWB files downloadable from http://download.alleninstitute.org/informatics-archive/prerelease/ -# '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb' - '/Users/andrew/Data/NWB/Allen/H19.28.012.11.05-2.nwb' -# '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-3.nwb' -# '/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-4.nwb' -# '/home/elodie/NWB_Files/NWB_org/H19.29.141.11.21.01.nwb' -# File created from Neo (Jupyter notebook) -# '/home/elodie/env_NWB_py3/my_notebook/My_first_dataset_neo10.nwb' + #"http://download.alleninstitute.org/informatics-archive/prerelease/H19.28.012.11.05-2.nwb", # 64 MB + "http://download.alleninstitute.org/informatics-archive/prerelease/H19.29.141.11.21.01.nwb", # 7 MB ] - entities_to_test = files_to_download def test_read(self): - for path in self.entities_to_test: - io = NWBIO(path, 'r') + self.local_test_dir = create_local_temp_dir("nwb") + os.makedirs(self.local_test_dir, exist_ok=True) + for url in self.files_to_download: + local_filename = os.path.join(self.local_test_dir, url.split("/")[-1]) + if not os.path.exists(local_filename): + try: + urlretrieve(url, local_filename) + except IOError as exc: + raise unittest.TestCase.failureException(exc) + io = NWBIO(local_filename, 'r') blocks = io.read() def test_roundtrip(self): From 1ab49c79bf4a13677aa48c89ddbea93b6e259895 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 6 Mar 2020 15:13:21 +0100 Subject: [PATCH 44/79] delete some old files --- neo/io/test_neo_nwb.py | 27 --- neo/rawio/nwbrawio.py | 377 --------------------------------- neo/test/iotest/test_pynnio.py | 229 -------------------- 3 files changed, 633 deletions(-) delete mode 100644 neo/io/test_neo_nwb.py delete mode 100644 neo/rawio/nwbrawio.py delete mode 100644 neo/test/iotest/test_pynnio.py diff --git a/neo/io/test_neo_nwb.py b/neo/io/test_neo_nwb.py deleted file mode 100644 index 490e6a2be..000000000 --- a/neo/io/test_neo_nwb.py +++ /dev/null @@ -1,27 +0,0 @@ -import nwbio -from nwbio import * - -filename = "/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb" - -io = nwbio.NWBIO(filename) -#io = pynwb.NWBHDF5IO(filename, mode='r') # Open a file with NWBHDF5IO -#container = io.read() # Define the file as a NWBFile object -#print("container = ", container) - -#io.__init__("/home/elodie/NWB_Files/NWB_org/H19.28.012.11.05-2.nwb") - -# Test the entire file -io.read_block() - -# Tests the different functions -#io._handle_general_group(block='') -#io._handle_epochs_group(block='') -#io._handle_acquisition_group(False, block='') -#io._handle_stimulus_group(False, block='') -#io._handle_processing_group(block='') -#io._handle_analysis_group(block='') - -#io._handle_timeseries('index_000', True, 1) - -#get_units(container.data) - diff --git a/neo/rawio/nwbrawio.py b/neo/rawio/nwbrawio.py deleted file mode 100644 index 49b22c098..000000000 --- a/neo/rawio/nwbrawio.py +++ /dev/null @@ -1,377 +0,0 @@ -# -*- coding: utf-8 -*- -""" -NWBRawIO -======== - -RawIO class for reading data from a Neurodata Without Borders (NWB) dataset - -Documentation : https://neurodatawithoutborders.github.io -Depends on: h5py, nwb, dateutil -Supported: Read, Write -Specification - https://github.com/NeurodataWithoutBorders/specification -Python APIs - (1) https://github.com/AllenInstitute/nwb-api/tree/master/ainwb - (2) https://github.com/AllenInstitute/AllenSDK/blob/master/allensdk/core/nwb_data_set.py - (3) https://github.com/NeurodataWithoutBorders/api-python -Sample datasets from CRCNS - https://crcns.org/NWB -Sample datasets from Allen Institute - http://alleninstitute.github.io/AllenSDK/cell_types.html#neurodata-without-borders -""" - -# neo imports -from __future__ import print_function, division, absolute_import -from os.path import join -import quantities as pq -from neo.rawio.baserawio import (BaseRawIO, _signal_channel_dtype, _unit_channel_dtype, - _event_channel_dtype) -from neo.core import (Segment, SpikeTrain, Unit, Epoch, Event, AnalogSignal, - IrregularlySampledSignal, ChannelIndex, Block) -from collections import OrderedDict - -# Standard Python imports -import tempfile -from tempfile import NamedTemporaryFile -import os -import glob -from scipy.io import loadmat -import numpy as np -from datetime import datetime - -# PyNWB imports -import pynwb -from pynwb import * -# Creating and writing NWB files -from pynwb import NWBFile,TimeSeries, get_manager -from pynwb.base import ProcessingModule -# Creating TimeSeries -from pynwb.ecephys import ElectricalSeries, Device, EventDetection -from pynwb.behavior import SpatialSeries -from pynwb.image import ImageSeries -from pynwb.core import set_parents -# For Neurodata Type Specifications -from pynwb.spec import NWBAttributeSpec # Attribute Specifications -from pynwb.spec import NWBDatasetSpec # Dataset Specifications -from pynwb.spec import NWBGroupSpec -from pynwb.spec import NWBNamespace -# Plot the structure of a NWB file -from utils.render import HierarchyDescription, NXGraphHierarchyDescription -from matplotlib import pyplot as plt - - -class NWBRawIO(BaseRawIO): - """ - Class for reading experimental data from a .nwb file - - Example: - >>> import neo - >>> from neo.rawio import NWBRawIO - >>> reader = neo.rawio.NWBRawIO(filename) - >>> reader.parse_header() - >>> print("reader = ", reader) - - >>> # Plot the structure of the NWB file - >>> reader.plot() - """ - name = 'NWBRawIO' - description = '' - extensions = ['nwb'] - rawmode = 'one-file' - - def __init__(self, filename=''): - BaseRawIO.__init__(self) - self.filename = filename - io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO - self._file = io.read() # Define the file as a NWBFile object - - def _source_name(self): - return self.filename - - def plot(self, filename=''): - # Plotting settings - show_bar_plot = False - plot_single_file = True - file_hierarchy = HierarchyDescription.from_hdf5(self.filename) - file_graph = NXGraphHierarchyDescription(file_hierarchy) - fig = file_graph.draw(show_plot=False, - figsize=(12,11), - label_offset=(0.0, 0.0065), - label_font_size=10) - plot_title = filename + ", " + "#Datasets=%i, #Attributes=%i, #Groups=%i, #Links=%i" % (len(file_hierarchy['datasets']), len(file_hierarchy['attributes']), len(file_hierarchy['groups']), len(file_hierarchy['links'])) - plt.title(plot_title) - plt.savefig('Structure_NWB_File.png') - plt.show() - - def _parse_header(self): - - sig_channels = [] # Definition of signal channels - unit_channels = [] # Definition of units channels - - # - # "i" defines as object the signal type (TimeSeries, SpatialSeries, ElectricalSeries), or units (SpikeEventSeries). - # And for everyone, thanks to the loops, we can have access to the different parameters of the signal_channels, as - # the channel name, the id channel, the sampling rate, the type, data units, the resolution, the offset, and the group_id. - # - - print("self._file.acquisition = ", self._file.acquisition) - -######## For sig_channels ######## - for i in range(len(self._file.acquisition)): - print("----------------------------acquisition------------------------------------------") - print("i = ", i) - print("######## For sig_channels ########") - - # Channnel name - ch_name = 'ch_{}'.format(i) - ### ch_name = self._file.get_acquisition(i).name - print("ch_name = ", ch_name) - - # id channel index as name - chan_id = i + 1 - print("chan_id = ", chan_id) - - for j in self._file.acquisition: - # sampling rate - sr = self._file.get_acquisition(j).rate - print("sr = ", sr) - - # dtype - # dtype = i.data.dtype - dtype = 'int' ### - print("dtype = ", dtype) - - # units of data - units = self._file.get_acquisition(j).unit - print("units = ", units) - - # gain - gain = self._file.get_acquisition(j).resolution - print("gain = ", gain) - - # offset - offset = 0. ### - print("offset = ", offset) - - #group_id is only for special cases when channel have diferents sampling rate for instance. - group_id = 0 - print("group_id = ", group_id) - print(" ") - - sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id)) - - sig_channels = np.array(sig_channels, dtype=_signal_channel_dtype) - print("---------------------sig_channels = ", sig_channels) - print(" ") - - -######## For unit_channels ######## - for i in self._file.acquisition: - print("------------------------------------------------------unit----acquisition---------------------------------------") - print("i = ", i) - print("######## For unit_channels ########") - - unit_name = 'unit{}'.format(self._file.get_acquisition(i).name) - print("unit_channels = ", unit_channels) - -# unit_id = '#{}'.format(i.source) - unit_id = '#{}' - print("unit_id = ", unit_id) - - wf_units = self._file.get_acquisition(i).timestamps_unit - print("wf_units = ", wf_units) - - wf_gain = self._file.get_acquisition(i).resolution - print("wf_gain = ", wf_gain) - - wf_offset = 0. - print("wf_offset = ", wf_offset) - - wf_left_sweep = 0 - print("wf_left_sweep = ", wf_left_sweep) - - wf_sampling_rate = self._file.get_acquisition(i).rate - print("wf_sampling_rate = ", wf_sampling_rate) - - unit_channels.append((unit_name, unit_id, wf_units, wf_gain, wf_offset, wf_left_sweep, wf_sampling_rate)) - - unit_channels = np.array(unit_channels, dtype=_unit_channel_dtype) - print("unit_channels = ", unit_channels) - - - - print("******************************************event channel***********************************************") - # Creating event/epoch channel - # In RawIO epoch and event are dealt the same way. - event_channels = [] - # Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch - # For event - #event_channels.append(('Some events', 'ev_0', 'event')) - -## event_channels.append((self._file.epochs[0][3], self._file.epochs[0][0], 'event')) # Some events -# for j in range(len(self._file.epochs)): -# print("j = ", j) -# -# epochs_id = self._file.epochs[j][0] -# print("epochs_start_id = ", epochs_id) -# -# epochs_start_time = self._file.epochs[j][1] -# print("epochs_start_time = ", epochs_start_time) -# -# epochs_stop_time = self._file.epochs[j][2] -# print("epochs_stop_time = ", epochs_stop_time) -# -# epochs_tags = self._file.epochs[j][3] -# print("epochs_tags = ", epochs_tags) -# -# event_channels.append((self._file.epochs[j][3], self._file.epochs[j][0], 'event')) # Some events -# Example - event_channels = [] - event_channels.append(('Some events', 'ev_0', 'event')) - event_channels.append(('Some epochs', 'ep_1', 'epoch')) - event_channels = np.array(event_channels, dtype=_event_channel_dtype) - - # For epochs - #event_channels.append(('Some epochs', 'ep_1', 'epoch')) -## event_channels.append((self._file.epochs, 'ep_1', 'epoch')) # Some epochs - -# event_channels = np.array(event_channels, dtype=_event_channel_dtype) - print("***********************event_channels = ", event_channels) - - print("*******************************************************block**********************************************") - # file into header dict - self.header = {} - self.header['nb_block'] =2 # 1 - self.header['nb_segment'] = [2, 3] # [1] - -##################################################################### - self.header['signal_channels'] = sig_channels # file into header dict for signal_channels - self.header['unit_channels'] = unit_channels # file into header dict for unit channels - self.header['event_channels'] = event_channels # file into header dict for event channels - - # insert some annotation at some place - # To create an empty tree - self._generate_minimal_annotations() -# bl_annotations = self.raw_annotations['blocks'][0] -# seg_annotations = bl_annotations['segments'][0] - - - def _segment_t_start(self, block_index, seg_index): # NWB Epoch corresponds to a Neo Segment - print("*** def _segment_t_start ***") - all_starts = [[0., 15.], [0., 20., 60.]] - return all_starts[block_index][seg_index] -# for i in self._file.acquisition: -# print("i = ", i) -# all_starts = self._file.get_acquisition(i).starting_time -# print("all_starts = ", all_starts) -# return np.array(all_starts) - #return all_starts - - - def _segment_t_stop(self, block_index, seg_index): # NWB Epoch corresponds to a Neo Segment - print("*** def _segment_t_stop ***") - all_stops = [[10., 25.], [10., 30., 70.]] - return all_stops[block_index][seg_index] - #return all_stops -# for i in self._file.acquisition: -# all_stops = self._file.get_acquisition(i).stop_time -# print("all_stops = ", all_stops) - - -# ################################### -# # A copy of the end of baserawio.py - - ### - # signal and channel zone - def _get_signal_size(self, block_index, seg_index, channel_indexes): - print("*** _get_signal_size ***") - # raise (NotImplementedError) -## io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO -## self._file = io.read() - for i in self._file.acquisition: - signal_size = self._file.get_acquisition(i).num_samples - print("signal_size = ", signal_size) # Same as _spike_count ? - return signal_size - - def _get_signal_t_start(self, block_index, seg_index, channel_indexes): - print("*** _get_signal_t_start ***") -# raise (NotImplementedError) -## io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO -## self._file = io.read() - for i in self._file.acquisition: - starting_time = self._file.get_acquisition(i).starting_time -# starting_time = np.array(starting_time) - return starting_time - - def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, channel_indexes): - print("*** _get_analogsignal_chunk ***") -# raise (NotImplementedError) - print("channel_indexes = ", channel_indexes) - if i_start is None: - i_start = 0 - if i_stop is None: - i_stop = 100000 - - assert i_start >= 0, "I don't like your jokes" - assert i_stop <= 100000, "I don't like your jokes" - - if channel_indexes is None: - nb_chan = 16 - else: - nb_chan = len(channel_indexes) - raw_signals = np.zeros((i_stop - i_start, nb_chan), dtype='int16') - return raw_signals - - ### - # spiketrain and unit zone - def _spike_count(self, block_index, seg_index, unit_index): - print("*** _spike_count ***") - #raise (NotImplementedError) - for i in self._file.acquisition: - print("i in _spike_count = ", i) - nb_spikes = self._file.get_acquisition(i).num_samples - print("nb_spikes = ", nb_spikes) - return nb_spikes - - def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_stop): - print("*** _get_spike_timestamps ***") - #raise (NotImplementedError) - for i in self._file.acquisition: - spike_timestamps = self._file.get_acquisition(i).timestamps - print("spike_timestamps in condition = ", spike_timestamps) - print("spike_timestamps = ", spike_timestamps) - return spike_timestamps - - def _rescale_spike_timestamp(self, spike_timestamps, dtype): - print("*** _rescale_spike_timestamp ***") - #raise (NotImplementedError) - for i in self._file.acquisition: - spike_times = spike_timestamps.astype(dtype) -### spike_times /= i.sr - print("spike_times = ", spike_times) - return spike_times - - ### - # spike waveforms zone - def _get_spike_raw_waveforms(self, block_index, seg_index, unit_index, t_start, t_stop): - print("*** _get_spike_raw_waveforms ***") - raise (NotImplementedError) - - ### - # event and epoch zone - def _event_count(self, block_index, seg_index, event_channel_index): - print("*** _event_count ***") - #raise (NotImplementedError) - for i in self._file.acquisition: - event_count = self._file.get_acquisition(i).num_samples - print("event_count = ", event_count) # Same as nb_spikes ? - return event_count - - - def _get_event_timestamps(self, block_index, seg_index, event_channel_index, t_start, t_stop): - print("*** _get_event_timestamps ***") - raise (NotImplementedError) - - def _rescale_event_timestamp(self, event_timestamps, dtype): - print("*** _rescale_event_timestamp ***") - raise (NotImplementedError) - - def _rescale_epoch_duration(self, raw_duration, dtype): - print("*** _rescale_epoch_duration ***") - raise (NotImplementedError) diff --git a/neo/test/iotest/test_pynnio.py b/neo/test/iotest/test_pynnio.py deleted file mode 100644 index 1562e462a..000000000 --- a/neo/test/iotest/test_pynnio.py +++ /dev/null @@ -1,229 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Tests of the neo.io.pynnio.PyNNNumpyIO and neo.io.pynnio.PyNNTextIO classes -""" - -# needed for python 3 compatibility -from __future__ import absolute_import, division - -import os - -import unittest - -import numpy as np -import quantities as pq - -from neo.core import Segment, AnalogSignal, SpikeTrain -from neo.io import PyNNNumpyIO, PyNNTextIO -from numpy.testing import assert_array_equal -from neo.test.tools import assert_arrays_equal, assert_file_contents_equal -from neo.test.iotest.common_io_test import BaseTestIO - -#class CommonTestPyNNNumpyIO(BaseTestIO, unittest.TestCase): -# ioclass = PyNNNumpyIO - -NCELLS = 5 - - -class CommonTestPyNNTextIO(BaseTestIO, unittest.TestCase): - ioclass = PyNNTextIO - read_and_write_is_bijective = False - - -def read_test_file(filename): - contents = np.load(filename) - data = contents["data"] - metadata = {} - for name, value in contents['metadata']: - try: - metadata[name] = eval(value) - except Exception: - metadata[name] = value - return data, metadata -read_test_file.__test__ = False - - -class BaseTestPyNNIO(object): - __test__ = False - - def tearDown(self): - if os.path.exists(self.test_file): - os.remove(self.test_file) - - def test_write_segment(self): - in_ = self.io_cls(self.test_file) - write_test_file = "write_test.%s" % self.file_extension - out = self.io_cls(write_test_file) - out.write_segment(in_.read_segment(lazy=False, cascade=True)) - assert_file_contents_equal(self.test_file, write_test_file) - if os.path.exists(write_test_file): - os.remove(write_test_file) - - def build_test_data(self, variable='v'): - metadata = { - 'size': NCELLS, - 'first_index': 0, - 'first_id': 0, - 'n': 505, - 'variable': variable, - 'last_id': NCELLS - 1, - 'last_index': NCELLS - 1, - 'dt': 0.1, - 'label': "population0", - } - if variable == 'v': - metadata['units'] = 'mV' - elif variable == 'spikes': - metadata['units'] = 'ms' - data = np.empty((505, 2)) - for i in range(NCELLS): - # signal - data[i*101:(i+1)*101, 0] = np.arange(i, i+101, dtype=float) - # index - data[i*101:(i+1)*101, 1] = i*np.ones((101,), dtype=float) - return data, metadata - build_test_data.__test__ = False - - -class BaseTestPyNNIO_Signals(BaseTestPyNNIO): - def setUp(self): - self.test_file = "test_file_v.%s" % self.file_extension - self.write_test_file("v") - - def test_read_segment_containing_analogsignals_using_eager_cascade(self): - # eager == not lazy - io = self.io_cls(self.test_file) - segment = io.read_segment(lazy=False, cascade=True) - self.assertIsInstance(segment, Segment) - self.assertEqual(len(segment.analogsignals), 1) - - as0 = segment.analogsignals[0] - self.assertIsInstance(as0, AnalogSignal) - self.assertEqual(as0.shape, (101, NCELLS)) - assert_array_equal(as0[:, 0], - AnalogSignal(np.arange(0, 101, dtype=float), - sampling_period=0.1*pq.ms, - t_start=0*pq.s, - units=pq.mV)) - as4 = as0[:, 4] - self.assertIsInstance(as4, AnalogSignal) - assert_array_equal(as4, - AnalogSignal(np.arange(4, 105, dtype=float), - sampling_period=0.1*pq.ms, - t_start=0*pq.s, - units=pq.mV)) - # test annotations (stuff from file metadata) - - def test_read_analogsignal_using_eager(self): - io = self.io_cls(self.test_file) - sig = io.read_analogsignal(lazy=False) - self.assertIsInstance(sig, AnalogSignal) - assert_array_equal(sig[:, 3], - AnalogSignal(np.arange(3, 104, dtype=float), - sampling_period=0.1*pq.ms, - t_start=0*pq.s, - units=pq.mV)) - # should test annotations: 'channel_index', etc. - - def test_read_spiketrain_should_fail_with_analogsignal_file(self): - io = self.io_cls(self.test_file) - self.assertRaises(TypeError, io.read_spiketrain, channel_index=0) - - -class BaseTestPyNNIO_Spikes(BaseTestPyNNIO): - def setUp(self): - self.test_file = "test_file_spikes.%s" % self.file_extension - self.write_test_file("spikes") - - def test_read_segment_containing_spiketrains_using_eager_cascade(self): - io = self.io_cls(self.test_file) - segment = io.read_segment(lazy=False, cascade=True) - self.assertIsInstance(segment, Segment) - self.assertEqual(len(segment.spiketrains), NCELLS) - st0 = segment.spiketrains[0] - self.assertIsInstance(st0, SpikeTrain) - assert_arrays_equal(st0, - SpikeTrain(np.arange(0, 101, dtype=float), - t_start=0*pq.s, - t_stop=101*pq.ms, - units=pq.ms)) - st4 = segment.spiketrains[4] - self.assertIsInstance(st4, SpikeTrain) - assert_arrays_equal(st4, - SpikeTrain(np.arange(4, 105, dtype=float), - t_start=0*pq.s, - t_stop=105*pq.ms, - units=pq.ms)) - # test annotations (stuff from file metadata) - - def test_read_spiketrain_using_eager(self): - io = self.io_cls(self.test_file) - st3 = io.read_spiketrain(lazy=False, channel_index=3) - self.assertIsInstance(st3, SpikeTrain) - assert_arrays_equal(st3, - SpikeTrain(np.arange(3, 104, dtype=float), - t_start=0*pq.s, - t_stop=104*pq.s, - units=pq.ms)) - # should test annotations: 'channel_index', etc. - - def test_read_analogsignal_should_fail_with_spiketrain_file(self): - io = self.io_cls(self.test_file) - self.assertRaises(TypeError, io.read_analogsignal, channel_index=2) - - -class BaseTestPyNNNumpyIO(object): - io_cls = PyNNNumpyIO - file_extension = "npz" - - def write_test_file(self, variable='v', check=False): - data, metadata = self.build_test_data(variable) - metadata_array = np.array(sorted(metadata.items())) - np.savez(self.test_file, data=data, metadata=metadata_array) - if check: - data1, metadata1 = read_test_file(self.test_file) - assert metadata == metadata1, "%s != %s" % (metadata, metadata1) - assert data.shape == data1.shape == (505, 2), \ - "%s, %s, (505, 2)" % (data.shape, data1.shape) - assert (data == data1).all() - assert metadata["n"] == 505 - write_test_file.__test__ = False - - -class BaseTestPyNNTextIO(object): - io_cls = PyNNTextIO - file_extension = "txt" - - def write_test_file(self, variable='v', check=False): - data, metadata = self.build_test_data(variable) - with open(self.test_file, 'wb') as f: - for item in sorted(metadata.items()): - f.write(("# %s = %s\n" % item).encode('utf8')) - np.savetxt(f, data) - if check: - raise NotImplementedError - write_test_file.__test__ = False - - -class TestPyNNNumpyIO_Signals(BaseTestPyNNNumpyIO, BaseTestPyNNIO_Signals, - unittest.TestCase): - __test__ = True - - -class TestPyNNNumpyIO_Spikes(BaseTestPyNNNumpyIO, BaseTestPyNNIO_Spikes, - unittest.TestCase): - __test__ = True - - -class TestPyNNTextIO_Signals(BaseTestPyNNTextIO, BaseTestPyNNIO_Signals, - unittest.TestCase): - __test__ = True - - -class TestPyNNTextIO_Spikes(BaseTestPyNNTextIO, BaseTestPyNNIO_Spikes, - unittest.TestCase): - __test__ = True - - -if __name__ == '__main__': - unittest.main() From b10d69caaf6990525855c96b3e72676a5175406c Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 6 Mar 2020 15:13:46 +0100 Subject: [PATCH 45/79] Add pynwb to testing requirements --- .circleci/requirements_testing.txt | 1 + neo/io/__init__.py | 1 + 2 files changed, 2 insertions(+) diff --git a/.circleci/requirements_testing.txt b/.circleci/requirements_testing.txt index 2d65411c5..18ba3ef44 100644 --- a/.circleci/requirements_testing.txt +++ b/.circleci/requirements_testing.txt @@ -10,3 +10,4 @@ https://github.com/nsdf/nsdf/archive/0.1.tar.gz coverage coveralls pillow +pynwb diff --git a/neo/io/__init__.py b/neo/io/__init__.py index dc588b690..71374fc92 100644 --- a/neo/io/__init__.py +++ b/neo/io/__init__.py @@ -43,6 +43,7 @@ * :attr:`NeuroshareIO` * :attr:`NixIO` * :attr:`NSDFIO` +* :attr:`NWBIO` * :attr:`OpenEphysIO` * :attr:`PickleIO` * :attr:`PlexonIO` From d7344ce6b655ee268deb4a36834dc4cc99fb24d5 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 6 Mar 2020 15:23:55 +0100 Subject: [PATCH 46/79] More test fixes --- neo/rawio/tests/test_nwbrawio.py | 32 -------------------------------- neo/test/iotest/test_nwbio.py | 11 ++++++++--- 2 files changed, 8 insertions(+), 35 deletions(-) delete mode 100644 neo/rawio/tests/test_nwbrawio.py diff --git a/neo/rawio/tests/test_nwbrawio.py b/neo/rawio/tests/test_nwbrawio.py deleted file mode 100644 index 3b3d005f1..000000000 --- a/neo/rawio/tests/test_nwbrawio.py +++ /dev/null @@ -1,32 +0,0 @@ -# Test to add a support for the NWB format - -""" -Tests of neo.rawio.nwbrawio -""" - -from __future__ import unicode_literals, print_function, division, absolute_import -import unittest -from neo.rawio.nwbrawio import NWBRawIO -from neo.rawio.tests.common_rawio_test import BaseTestRawIO -import pynwb -from pynwb import * - -class TestNWBRawIO(BaseTestRawIO, unittest.TestCase, ): - rawioclass = NWBRawIO - files_to_download = [ - -## '/home/elodie/NWB_Files/my_example_2.nwb', # Very simple file nwb create by me only TimeSeries -### '/home/elodie/NWB_Files/my_NWB_File_pynwb_101_2.nwb', # File created with the latest version of pynwb=1.0.1 -# '/home/elodie/NWB_Files/brain_observatory.nwb', # nwb file given by Matteo Cantarelli -# '/home/elodie/NWB_Files/mynwb.h5', # nwb file given by Lungsi -# '/home/elodie/NWB_Files/GreBlu9508M_Site1_Call1.nwb', - -###### '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101.nwb', # File created with the latest version of pynwb=1.0.1 File on my github - '/home/elodie/NWB_Files/NWB_File_python_3_pynwb_101_ephys_data.nwb', # File created with the latest version of pynwb=1.0.1 only with ephys data File on my github - - ] - entities_to_test = files_to_download - -if __name__ == "__main__": - print("pynwb.__version__ = ", pynwb.__version__) - unittest.main() diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 73536bac0..3c8712928 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -10,17 +10,22 @@ from urllib.request import urlretrieve except ImportError: from urllib import urlretrieve -from neo.io.nwbio import NWBIO from neo.test.iotest.common_io_test import BaseTestIO from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Unit, Block, ChannelIndex, ImageSequence -import pynwb -from pynwb import * +try: + import pynwb + from neo.io.nwbio import NWBIO + HAVE_PYNWB = True +except ImportError: + NWBIO = None + HAVE_PYNWB = False import quantities as pq import numpy as np from numpy.testing import assert_array_equal, assert_allclose from neo.test.rawiotest.tools import create_local_temp_dir +@unittest.skipUnless(HAVE_PYNWB, "requires pynwb") class TestNWBIO(unittest.TestCase): ioclass = NWBIO files_to_download = [ From cb139154114a1c78824d1f499473110561ccaeab Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 6 Mar 2020 15:38:25 +0100 Subject: [PATCH 47/79] Python 2.7 fix --- neo/io/nwbio.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 27dc26a23..faedc86b5 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -20,7 +20,10 @@ from itertools import chain from datetime import datetime import json -from json.decoder import JSONDecodeError +try: + from json.decoder import JSONDecodeError +except ImportError: # Python 2 + JSONDecodeError = ValueError from collections import defaultdict import numpy as np From 85b6c57539c64044cb16032a46c24eb2251426fd Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 6 Mar 2020 15:56:35 +0100 Subject: [PATCH 48/79] PyNWB doesn't support Python 2.7 --- neo/io/nwbio.py | 4 ++++ neo/test/iotest/test_nwbio.py | 2 +- 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index faedc86b5..4bec0c51c 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -54,6 +54,8 @@ have_pynwb = True except ImportError: have_pynwb = False +except SyntaxError: # pynwb doesn't support Python 2.7 + have_pynwb = False # hdmf imports try: @@ -62,6 +64,8 @@ have_hdmf = True except ImportError: have_hdmf = False +except SyntaxError: + have_hdmf = False GLOBAL_ANNOTATIONS = ( diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 3c8712928..6379ae00c 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -16,7 +16,7 @@ import pynwb from neo.io.nwbio import NWBIO HAVE_PYNWB = True -except ImportError: +except (ImportError, SyntaxError): NWBIO = None HAVE_PYNWB = False import quantities as pq From e090b4226a0bdc7c4ccd3b6c36049a5b260819fc Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 6 Mar 2020 16:57:27 +0100 Subject: [PATCH 49/79] Revert some unintentional changes --- neo/core/__init__.py | 1 - neo/core/channelindex.py | 1 - neo/io/__init__.py | 4 + neo/io/pynnio.py | 251 -------------------------------------- neo/rawio/__init__.py | 2 - neo/rawio/examplerawio.py | 14 --- 6 files changed, 4 insertions(+), 269 deletions(-) delete mode 100644 neo/io/pynnio.py diff --git a/neo/core/__init__.py b/neo/core/__init__.py index 98274c384..90bc8dfa5 100644 --- a/neo/core/__init__.py +++ b/neo/core/__init__.py @@ -38,7 +38,6 @@ from neo.core.channelindex import ChannelIndex from neo.core.unit import Unit -# from neo.core.basesignal import BaseSignal from neo.core.analogsignal import AnalogSignal from neo.core.irregularlysampledsignal import IrregularlySampledSignal diff --git a/neo/core/channelindex.py b/neo/core/channelindex.py index 8a163c370..1620535c9 100644 --- a/neo/core/channelindex.py +++ b/neo/core/channelindex.py @@ -213,4 +213,3 @@ def __getitem__(self, i): # we do not copy the list of units, since these are related to # the entire set of channels in the parent ChannelIndex return obj - \ No newline at end of file diff --git a/neo/io/__init__.py b/neo/io/__init__.py index 71374fc92..7e9edcfc7 100644 --- a/neo/io/__init__.py +++ b/neo/io/__init__.py @@ -171,8 +171,12 @@ .. autoclass:: neo.io.NSDFIO + .. autoattribute:: extensions + .. autoclass:: neo.io.NWBIO + .. autoattribute:: extensions + .. autoclass:: neo.io.OpenEphysIO .. autoattribute:: extensions diff --git a/neo/io/pynnio.py b/neo/io/pynnio.py deleted file mode 100644 index 4a30c64a3..000000000 --- a/neo/io/pynnio.py +++ /dev/null @@ -1,251 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Module for reading/writing data from/to legacy PyNN formats. - -PyNN is available at http://neuralensemble.org/PyNN - -Classes: - PyNNNumpyIO - PyNNTextIO - -Supported: Read/Write - -Authors: Andrew Davison, Pierre Yger -""" - -from itertools import chain -import numpy -import quantities as pq -import warnings - -from neo.io.baseio import BaseIO -from neo.core import Segment, AnalogSignal, SpikeTrain - -try: - unicode - PY2 = True -except NameError: - PY2 = False - - -UNITS_MAP = { - 'spikes': pq.ms, - 'v': pq.mV, - 'gsyn': pq.UnitQuantity('microsiemens', 1e-6*pq.S, 'uS', 'µS'), # checked -} - - -class BasePyNNIO(BaseIO): - """ - Base class for PyNN IO classes - """ - is_readable = True - is_writable = True - has_header = True - is_streameable = False # TODO - correct spelling to "is_streamable" - supported_objects = [Segment, AnalogSignal, SpikeTrain] - readable_objects = supported_objects - writeable_objects = supported_objects - mode = 'file' - - def _read_file_contents(self): - raise NotImplementedError - - def _extract_array(self, data, channel_index): - idx = numpy.where(data[:, 1] == channel_index)[0] - return data[idx, 0] - - def _determine_units(self, metadata): - if 'units' in metadata: - return metadata['units'] - elif 'variable' in metadata and metadata['variable'] in UNITS_MAP: - return UNITS_MAP[metadata['variable']] - else: - raise IOError("Cannot determine units") - - def _extract_signals(self, data, metadata, lazy): - - signal = None - if lazy and data.size > 0: - signal = AnalogSignal([], - units=self._determine_units(metadata), - sampling_period=metadata['dt']*pq.ms) - signal.lazy_shape = None - else: - arr = numpy.vstack(self._extract_array(data, channel_index) - for channel_index in range(metadata['first_index'], metadata['last_index'] + 1)) - if len(arr) > 0: - signal = AnalogSignal(arr.T, - units=self._determine_units(metadata), - sampling_period=metadata['dt']*pq.ms) - if signal is not None: - signal.annotate(label=metadata["label"], - variable=metadata["variable"]) - return signal - - def _extract_spikes(self, data, metadata, channel_index, lazy): - spiketrain = None - if lazy: - if channel_index in data[:, 1]: - spiketrain = SpikeTrain([], units=pq.ms, t_stop=0.0) - spiketrain.lazy_shape = None - else: - spike_times = self._extract_array(data, channel_index) - if len(spike_times) > 0: - spiketrain = SpikeTrain(spike_times, units=pq.ms, t_stop=spike_times.max()) - if spiketrain is not None: - spiketrain.annotate(label=metadata["label"], - channel_index=channel_index, - dt=metadata["dt"]) - return spiketrain - - def _write_file_contents(self, data, metadata): - raise NotImplementedError - - def read_segment(self, lazy=False, cascade=True): - data, metadata = self._read_file_contents() - annotations = dict((k, metadata.get(k, 'unknown')) for k in ("label", "variable", "first_id", "last_id")) - seg = Segment(**annotations) - if cascade: - if metadata['variable'] == 'spikes': - for i in range(metadata['first_index'], metadata['last_index'] + 1): - spiketrain = self._extract_spikes(data, metadata, i, lazy) - if spiketrain is not None: - seg.spiketrains.append(spiketrain) - seg.annotate(dt=metadata['dt']) # store dt for SpikeTrains only, as can be retrieved from sampling_period for AnalogSignal - else: - signal = self._extract_signals(data, metadata, lazy) - if signal is not None: - seg.analogsignals.append(signal) - seg.create_many_to_one_relationship() - return seg - - def write_segment(self, segment): - source = segment.analogsignals or segment.spiketrains - assert len(source) > 0, "Segment contains neither analog signals nor spike trains." - metadata = segment.annotations.copy() - s0 = source[0] - if isinstance(s0, AnalogSignal): - if len(source) > 1: - warnings.warn("Cannot handle multiple analog signals. Writing only the first.") - source = s0.T - metadata['size'] = s0.shape[1] - n = source.size - else: - metadata['size'] = len(source) - n = sum(s.size for s in source) - metadata['first_index'] = 0 - metadata['last_index'] = metadata['size'] - 1 - if 'label' not in metadata: - metadata['label'] = 'unknown' - if 'dt' not in metadata: # dt not included in annotations if Segment contains only AnalogSignals - metadata['dt'] = s0.sampling_period.rescale(pq.ms).magnitude - metadata['n'] = n - data = numpy.empty((n, 2)) - # if the 'variable' annotation is a standard one from PyNN, we rescale - # to use standard PyNN units - # we take the units from the first element of source and scale all - # the signals to have the same units - if 'variable' in segment.annotations: - units = UNITS_MAP.get(segment.annotations['variable'], source[0].dimensionality) - else: - units = source[0].dimensionality - metadata['variable'] = 'unknown' - try: - metadata['units'] = units.unicode - except AttributeError: - metadata['units'] = units.u_symbol - - start = 0 - for i, signal in enumerate(source): # here signal may be AnalogSignal or SpikeTrain - end = start + signal.size - data[start:end, 0] = numpy.array(signal.rescale(units)) - data[start:end, 1] = i*numpy.ones((signal.size,), dtype=float) - start = end - self._write_file_contents(data, metadata) - - def read_analogsignal(self, lazy=False): - data, metadata = self._read_file_contents() - if metadata['variable'] == 'spikes': - raise TypeError("File contains spike data, not analog signals") - else: - signal = self._extract_signals(data, metadata, lazy) - if signal is None: - raise IndexError("File does not contain a signal") - else: - return signal - - def read_spiketrain(self, lazy=False, channel_index=0): - data, metadata = self._read_file_contents() - if metadata['variable'] != 'spikes': - raise TypeError("File contains analog signals, not spike data") - else: - spiketrain = self._extract_spikes(data, metadata, channel_index, lazy) - if spiketrain is None: - raise IndexError("File does not contain any spikes with channel index %d" % channel_index) - else: - return spiketrain - - -class PyNNNumpyIO(BasePyNNIO): - """ - Reads/writes data from/to PyNN NumpyBinaryFile format - """ - name = "PyNN NumpyBinaryFile" - extensions = ['npz'] - - def _read_file_contents(self): - contents = numpy.load(self.filename) - data = contents["data"] - metadata = {} - for name,value in contents['metadata']: - try: - metadata[name] = eval(value) - except Exception: - metadata[name] = value - return data, metadata - - def _write_file_contents(self, data, metadata): - # we explicitly set the dtype to ensure roundtrips preserve file contents exactly - max_metadata_length = max(chain([len(k) for k in metadata.keys()], - [len(str(v)) for v in metadata.values()])) - if PY2: - dtype = "S%d" % max_metadata_length - else: - dtype = "U%d" % max_metadata_length - metadata_array = numpy.array(sorted(metadata.items()), dtype) - numpy.savez(self.filename, data=data, metadata=metadata_array) - - -class PyNNTextIO(BasePyNNIO): - """ - Reads/writes data from/to PyNN StandardTextFile format - """ - name = "PyNN StandardTextFile" - extensions = ['v', 'ras', 'gsyn'] - - def _read_metadata(self): - metadata = {} - with open(self.filename) as f: - for line in f: - if line[0] == "#": - name, value = line[1:].strip().split("=") - name = name.strip() - try: - metadata[name] = eval(value) - except Exception: - metadata[name] = value.strip() - else: - break - return metadata - - def _read_file_contents(self): - data = numpy.loadtxt(self.filename) - metadata = self._read_metadata() - return data, metadata - - def _write_file_contents(self, data, metadata): - with open(self.filename, 'wb') as f: - for item in sorted(metadata.items()): - f.write(("# %s = %s\n" % item).encode('utf8')) - numpy.savetxt(f, data) diff --git a/neo/rawio/__init__.py b/neo/rawio/__init__.py index 943e75a93..3ad1de370 100644 --- a/neo/rawio/__init__.py +++ b/neo/rawio/__init__.py @@ -133,7 +133,6 @@ from neo.rawio.tdtrawio import TdtRawIO from neo.rawio.winedrrawio import WinEdrRawIO from neo.rawio.winwcprawio import WinWcpRawIO -#from neo.rawio.nwbrawio import NWBRawIO #, NWBReader # NWB format rawiolist = [ AxographRawIO, @@ -155,7 +154,6 @@ TdtRawIO, WinEdrRawIO, WinWcpRawIO, -# NWBRawIO, # NWB format ] diff --git a/neo/rawio/examplerawio.py b/neo/rawio/examplerawio.py index b5649eb7d..5a9cc5161 100644 --- a/neo/rawio/examplerawio.py +++ b/neo/rawio/examplerawio.py @@ -112,19 +112,14 @@ def _parse_header(self): # at the end real_signal = (raw_signal* gain + offset) * pq.Quantity(units) sig_channels = [] for c in range(16): -# print("range(16) = ", range(16)) ch_name = 'ch{}'.format(c) -# print("format(c) = ", format(c)) -# print("ch_name = ", ch_name) # our channel id is c+1 just for fun # Note that chan_id should be realated to # original channel id in the file format # so that the end user should not be lost when reading datasets chan_id = c + 1 -# print("chan_id = ", chan_id) sr = 10000. # Hz dtype = 'int16' -# print("dtype = ", dtype) units = 'uV' gain = 1000. / 2 ** 16 offset = 0. @@ -133,9 +128,7 @@ def _parse_header(self): # Here this is the general case :all channel have the same characteritics group_id = 0 sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id)) -# print("sig_channels.append = ", sig_channels.append((ch_name, chan_id, sr, dtype, units, gain, offset, group_id))) sig_channels = np.array(sig_channels, dtype=_signal_channel_dtype) -# print("sig_channels = ", sig_channels) # creating units channels # This is mandatory!!!! @@ -170,14 +163,8 @@ def _parse_header(self): self.header['nb_block'] = 2 self.header['nb_segment'] = [2, 3] self.header['signal_channels'] = sig_channels - print("self.header['signal_channels] = ", self.header['signal_channels']) - print("self.header['signal_channels].size = ", self.header['signal_channels'].size) self.header['unit_channels'] = unit_channels - print("self.header['unit_channels] = ", self.header['unit_channels']) - print("self.header['unit_channels].size = ", self.header['unit_channels'].size) self.header['event_channels'] = event_channels - print("self.header['event_channels] = ", self.header['event_channels']) - print("self.header['event_channels].size = ", self.header['event_channels'].size) # insert some annotation at some place # at neo.io level IO are free to add some annoations @@ -289,7 +276,6 @@ def _get_spike_timestamps(self, block_index, seg_index, unit_index, t_start, t_s ts_start = (self._segment_t_start(block_index, seg_index) * 10000) spike_timestamps = np.arange(0, 10000, 500) + ts_start - print("spike_timestamps = ", spike_timestamps) if t_start is not None or t_stop is not None: # restricte spikes to given limits (in seconds) From 7c581f5ca46e8d4067ceddadb7fe3d2e209d63c9 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 6 Mar 2020 17:06:43 +0100 Subject: [PATCH 50/79] fix some pep8 warnings --- neo/io/nwbio.py | 41 +++++++++++++++++++++-------------- neo/io/proxyobjects.py | 3 ++- neo/test/iotest/test_nwbio.py | 16 +++++++------- 3 files changed, 35 insertions(+), 25 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 4bec0c51c..6f57414a8 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -50,7 +50,8 @@ from pynwb.image import ImageSeries from pynwb.spec import NWBAttributeSpec, NWBDatasetSpec, NWBGroupSpec, NWBNamespace, NWBNamespaceBuilder from pynwb.device import Device - from pynwb.ophys import TwoPhotonSeries, OpticalChannel, ImageSegmentation, Fluorescence # For calcium imaging data + # For calcium imaging data + from pynwb.ophys import TwoPhotonSeries, OpticalChannel, ImageSegmentation, Fluorescence have_pynwb = True except ImportError: have_pynwb = False @@ -59,8 +60,8 @@ # hdmf imports try: - from hdmf.spec import LinkSpec, GroupSpec, DatasetSpec, SpecNamespace,\ - NamespaceBuilder, AttributeSpec, DtypeSpec, RefSpec + from hdmf.spec import (LinkSpec, GroupSpec, DatasetSpec, SpecNamespace, + NamespaceBuilder, AttributeSpec, DtypeSpec, RefSpec) have_hdmf = True except ImportError: have_hdmf = False @@ -93,7 +94,7 @@ class NWBIO(BaseIO): """ supported_objects = [Block, Segment, AnalogSignal, IrregularlySampledSignal, SpikeTrain, Epoch, Event, ImageSequence] - readable_objects = supported_objects + readable_objects = supported_objects writeable_objects = supported_objects has_header = False @@ -125,7 +126,7 @@ def read_all_blocks(self, lazy=False, **kwargs): """ """ - io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO + io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO self._file = io.read() self.global_block_metadata = {} @@ -226,7 +227,7 @@ def _read_timeseries_group(self, group_name, lazy): block_name = hierarchy["block"] segment_name = hierarchy["segment"] segment = self._get_segment(block_name, segment_name) - annotations = {"nwb_group" : group_name} + annotations = {"nwb_group": group_name} description = try_json_field(timeseries.description) if isinstance(description, dict): annotations.update(description) @@ -291,8 +292,10 @@ def write_all_blocks(self, blocks, **kwargs): annotations[annotation_name].add(block.annotations[annotation_name]) if annotation_name in annotations: if len(annotations[annotation_name]) > 1: - raise NotImplementedError("We don't yet support multiple values for {}".format(annotation_name)) - annotations[annotation_name], = annotations[annotation_name] # take single value from set + raise NotImplementedError( + "We don't yet support multiple values for {}".format(annotation_name)) + # take single value from set + annotations[annotation_name], = annotations[annotation_name] if "identifier" not in annotations: annotations["identifier"] = self.filename if "session_description" not in annotations: @@ -308,12 +311,15 @@ def write_all_blocks(self, blocks, **kwargs): nwbfile.add_unit_column('_name', 'the name attribute of the SpikeTrain') #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') - nwbfile.add_unit_column('segment', 'the name of the Neo Segment to which the SpikeTrain belongs') - nwbfile.add_unit_column('block', 'the name of the Neo Block to which the SpikeTrain belongs') + nwbfile.add_unit_column( + 'segment', 'the name of the Neo Segment to which the SpikeTrain belongs') + nwbfile.add_unit_column( + 'block', 'the name of the Neo Block to which the SpikeTrain belongs') nwbfile.add_epoch_column('_name', 'the name attribute of the Epoch') #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') - nwbfile.add_epoch_column('segment', 'the name of the Neo Segment to which the Epoch belongs') + nwbfile.add_epoch_column( + 'segment', 'the name of the Neo Segment to which the Epoch belongs') nwbfile.add_epoch_column('block', 'the name of the Neo Block to which the Epoch belongs') for i, block in enumerate(blocks): @@ -379,7 +385,8 @@ def _write_signal(self, nwbfile, signal): timestamps=signal.times.rescale('second').magnitude, comments=json.dumps(hierarchy)) else: - raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format(signal.__class__.__name__)) + raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format( + signal.__class__.__name__)) nwbfile.add_acquisition(tS) return tS @@ -388,7 +395,7 @@ def _write_spiketrain(self, nwbfile, spiketrain): obs_intervals=[[float(spiketrain.t_start.rescale('s')), float(spiketrain.t_stop.rescale('s'))]], _name=spiketrain.name, - #_description=spiketrain.description, + # _description=spiketrain.description, segment=spiketrain.segment.name, block=spiketrain.segment.block.name) # todo: handle annotations (using add_unit_column()?) @@ -426,6 +433,7 @@ def _decompose_unit(unit): assert isinstance(unit, pq.quantity.Quantity) assert unit.magnitude == 1 conversion = 1.0 + def _decompose(unit): dim = unit.dimensionality if len(dim) != 1: @@ -464,7 +472,7 @@ def __init__(self, timeseries, nwb_group): else: self.sampling_rate = None self.name = timeseries.name - self.annotations = {"nwb_group" : nwb_group} + self.annotations = {"nwb_group": nwb_group} self.description = try_json_field(timeseries.description) if isinstance(self.description, dict): self.annotations.update(self.description) @@ -483,7 +491,8 @@ def load(self, time_slice=None, strict_slicing=True): (t_start or t_stop) is outside the real time range of the segment. """ if time_slice: - i_start, i_stop, sig_t_start = self._time_slice_indices(time_slice, strict_slicing=strict_slicing) + i_start, i_stop, sig_t_start = self._time_slice_indices(time_slice, + strict_slicing=strict_slicing) signal = self._timeseries.data[i_start: i_stop] else: signal = self._timeseries.data[:] @@ -517,7 +526,7 @@ class EventProxy(BaseEventProxy): def __init__(self, timeseries, nwb_group): self._timeseries = timeseries self.name = timeseries.name - self.annotations = {"nwb_group" : nwb_group} + self.annotations = {"nwb_group": nwb_group} self.description = try_json_field(timeseries.description) if isinstance(self.description, dict): self.annotations.update(self.description) diff --git a/neo/io/proxyobjects.py b/neo/io/proxyobjects.py index e86341d1f..b112177e6 100644 --- a/neo/io/proxyobjects.py +++ b/neo/io/proxyobjects.py @@ -228,7 +228,8 @@ def load(self, time_slice=None, strict_slicing=True, if channel_indexes is None: channel_indexes = slice(None) - i_start, i_stop, sig_t_start = self._time_slice_indices(time_slice, strict_slicing=strict_slicing) + i_start, i_stop, sig_t_start = self._time_slice_indices(time_slice, + strict_slicing=strict_slicing) raw_signal = self._rawio.get_analogsignal_chunk(block_index=self._block_index, seg_index=self._seg_index, i_start=i_start, i_stop=i_stop, diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 6379ae00c..7789be13e 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -28,9 +28,9 @@ @unittest.skipUnless(HAVE_PYNWB, "requires pynwb") class TestNWBIO(unittest.TestCase): ioclass = NWBIO - files_to_download = [ -# Files from Allen Institute : - #"http://download.alleninstitute.org/informatics-archive/prerelease/H19.28.012.11.05-2.nwb", # 64 MB + files_to_download = [ + # Files from Allen Institute : + # "http://download.alleninstitute.org/informatics-archive/prerelease/H19.28.012.11.05-2.nwb", # 64 MB "http://download.alleninstitute.org/informatics-archive/prerelease/H19.29.141.11.21.01.nwb", # 7 MB ] @@ -55,17 +55,17 @@ def test_roundtrip(self): bl2 = Block(name='Third block') original_blocks = [bl0, bl1, bl2] - num_seg = 4 # number of segments - num_chan = 3 # number of channels + num_seg = 4 # number of segments + num_chan = 3 # number of channels for blk in original_blocks: - for ind in range(num_seg): # number of Segment + for ind in range(num_seg): # number of Segment seg = Segment(index=ind) seg.block = blk blk.segments.append(seg) - for seg in blk.segments: # AnalogSignal objects + for seg in blk.segments: # AnalogSignal objects # 3 Neo AnalogSignals a = AnalogSignal(np.random.randn(44, num_chan) * pq.nA, @@ -184,4 +184,4 @@ def test_roundtrip(self): if __name__ == "__main__": print("pynwb.__version__ = ", pynwb.__version__) - unittest.main() \ No newline at end of file + unittest.main() From 45a7c6830055256fb726f922a261c11c05c400a5 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 6 Mar 2020 17:26:14 +0100 Subject: [PATCH 51/79] Added NWBIO example --- examples/NWB-Allen-Institute-Example.ipynb | 1464 ++++++++++++++++++++ 1 file changed, 1464 insertions(+) create mode 100644 examples/NWB-Allen-Institute-Example.ipynb diff --git a/examples/NWB-Allen-Institute-Example.ipynb b/examples/NWB-Allen-Institute-Example.ipynb new file mode 100644 index 000000000..b410b22c6 --- /dev/null +++ b/examples/NWB-Allen-Institute-Example.ipynb @@ -0,0 +1,1464 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Reading an NWB file with Neo" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "from os.path import exists\n", + "from urllib.request import urlretrieve\n", + "\n", + "from neo.io import NWBIO" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download data file from Allen Institute" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "url = \"http://download.alleninstitute.org/informatics-archive/prerelease/H19.28.012.11.05-2.nwb\"\n", + "local_filename = url.split(\"/\")[-1]\n", + "if not exists(local_filename):\n", + " local_filename, headers = urlretrieve(url)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load the data\n", + "\n", + "We are using \"lazy\" mode to save memory: this reads all the metadata, but reading the actual data is delayed until needed, so only two signals (stimulus + response) are read into memory at one time." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "io = NWBIO(local_filename)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "blocks = io.read(lazy=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Block with 1 segments\n", + " name: 'default'\n", + " description: 'PLACEHOLDER'\n", + " annotations: {'session_start_time': datetime.datetime(2019, 4, 18, 3, 41, 56, 136000, tzinfo=tzoffset(None, -25200)),\n", + " 'identifier': '1ed51563e8f0218c0270ee9fb6c27b0b1558c4b821c10be2756797a697b35ff3',\n", + " 'timestamps_reference_time': datetime.datetime(2019, 4, 18, 3, 41, 56, 136000, tzinfo=tzoffset(None, -25200)),\n", + " 'experiment_description': 'PatchMaster v2x90.3, 19-Mar-2018',\n", + " 'session_id': 'PLACEHOLDER',\n", + " 'source_script': {'git_revision': '() ',\n", + " 'package_version': '0.16.2',\n", + " 'repo': 'https://github.com/AllenInstitute/ipfx'},\n", + " 'source_script_file_name': 'run_x_to_nwb_conversion.py',\n", + " 'session_description': 'PLACEHOLDER'}\n", + " file_origin: '/var/folders/2k/mhzyfkfs7h76v3pfyjbksb540000gq/T/tmpxsvu295j'\n", + " rec_datetime: datetime.datetime(2019, 4, 18, 3, 41, 56, 136000, tzinfo=tzoffset(None, -25200))\n", + " # segments (N=1)\n", + " 0: Segment with 126 analogsignals\n", + " name: 'default'\n", + " # analogsignals (N=126)\n", + " 0: AnalogSignalProxy\n", + " name: 'index_000'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2001001,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'extpinbath',\n", + " 'sweep_label': ''}\n", + " 1: AnalogSignalProxy\n", + " name: 'index_001'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2002001,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'extpciiatt',\n", + " 'sweep_label': ''}\n", + " 2: AnalogSignalProxy\n", + " name: 'index_002'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2003001,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'extpbreakn',\n", + " 'sweep_label': ''}\n", + " 3: AnalogSignalProxy\n", + " name: 'index_003'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004001,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 4: AnalogSignalProxy\n", + " name: 'index_004'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004002,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 5: AnalogSignalProxy\n", + " name: 'index_005'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004003,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 6: AnalogSignalProxy\n", + " name: 'index_006'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004004,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 7: AnalogSignalProxy\n", + " name: 'index_007'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004005,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 8: AnalogSignalProxy\n", + " name: 'index_008'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004006,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 9: AnalogSignalProxy\n", + " name: 'index_009'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004007,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 10: AnalogSignalProxy\n", + " name: 'index_010'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004008,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 11: AnalogSignalProxy\n", + " name: 'index_011'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004009,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 12: AnalogSignalProxy\n", + " name: 'index_012'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004010,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 13: AnalogSignalProxy\n", + " name: 'index_013'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004011,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 14: AnalogSignalProxy\n", + " name: 'index_014'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004012,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 15: AnalogSignalProxy\n", + " name: 'index_015'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004013,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 16: AnalogSignalProxy\n", + " name: 'index_016'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004014,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 17: AnalogSignalProxy\n", + " name: 'index_017'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2004015,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Long square',\n", + " 'sweep_label': ''}\n", + " 18: AnalogSignalProxy\n", + " name: 'index_018'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2005001,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Rheobase',\n", + " 'sweep_label': ''}\n", + " 19: AnalogSignalProxy\n", + " name: 'index_019'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2006001,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Rheobase',\n", + " 'sweep_label': ''}\n", + " 20: AnalogSignalProxy\n", + " name: 'index_020'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2007001,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Rheobase',\n", + " 'sweep_label': ''}\n", + " 21: AnalogSignalProxy\n", + " name: 'index_021'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2008001,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Short square',\n", + " 'sweep_label': ''}\n", + " 22: AnalogSignalProxy\n", + " name: 'index_022'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2008002,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Short square',\n", + " 'sweep_label': ''}\n", + " 23: AnalogSignalProxy\n", + " name: 'index_023'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2008003,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Short square',\n", + " 'sweep_label': ''}\n", + " 24: AnalogSignalProxy\n", + " name: 'index_024'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2008004,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Short square',\n", + " 'sweep_label': ''}\n", + " 25: AnalogSignalProxy\n", + " name: 'index_025'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2008005,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Short square',\n", + " 'sweep_label': ''}\n", + " 26: AnalogSignalProxy\n", + " name: 'index_026'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2008006,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Short square',\n", + " 'sweep_label': ''}\n", + " 27: AnalogSignalProxy\n", + " name: 'index_027'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2008007,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Short square',\n", + " 'sweep_label': ''}\n", + " 28: AnalogSignalProxy\n", + " name: 'index_028'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2008008,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Short square',\n", + " 'sweep_label': ''}\n", + " 29: AnalogSignalProxy\n", + " name: 'index_029'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2008009,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Short square',\n", + " 'sweep_label': ''}\n", + " 30: AnalogSignalProxy\n", + " name: 'index_030'\n", + " annotations: {'nwb_group': 'acquisition',\n", + " 'cycle_id': 2008010,\n", + " 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'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Capacitance',\n", + " 'sweep_label': ''}\n", + " 118: AnalogSignalProxy\n", + " name: 'index_055'\n", + " annotations: {'nwb_group': 'stimulus',\n", + " 'cycle_id': 2010002,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Capacitance',\n", + " 'sweep_label': ''}\n", + " 119: AnalogSignalProxy\n", + " name: 'index_056'\n", + " annotations: {'nwb_group': 'stimulus',\n", + " 'cycle_id': 2010003,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Capacitance',\n", + " 'sweep_label': ''}\n", + " 120: AnalogSignalProxy\n", + " name: 'index_057'\n", + " annotations: {'nwb_group': 'stimulus',\n", + " 'cycle_id': 2010004,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Capacitance',\n", + " 'sweep_label': ''}\n", + " 121: AnalogSignalProxy\n", + " name: 'index_058'\n", + " annotations: {'nwb_group': 'stimulus',\n", + " 'cycle_id': 2010005,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Capacitance',\n", + " 'sweep_label': ''}\n", + " 122: AnalogSignalProxy\n", + " name: 'index_059'\n", + " annotations: {'nwb_group': 'stimulus',\n", + " 'cycle_id': 2011001,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Chirp test',\n", + " 'sweep_label': ''}\n", + " 123: AnalogSignalProxy\n", + " name: 'index_060'\n", + " annotations: {'nwb_group': 'stimulus',\n", + " 'cycle_id': 2012001,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Chirp',\n", + " 'sweep_label': ''}\n", + " 124: AnalogSignalProxy\n", + " name: 'index_061'\n", + " annotations: {'nwb_group': 'stimulus',\n", + " 'cycle_id': 2012002,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Chirp',\n", + " 'sweep_label': ''}\n", + " 125: AnalogSignalProxy\n", + " name: 'index_062'\n", + " annotations: {'nwb_group': 'stimulus',\n", + " 'cycle_id': 2012003,\n", + " 'file': 'H19.28.012.11.05.dat',\n", + " 'group_label': 'PGS4_190418_701_A01',\n", + " 'series_label': 'Chirp',\n", + " 'sweep_label': ''}]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "blocks" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'session_start_time': datetime.datetime(2019, 4, 18, 3, 41, 56, 136000, tzinfo=tzoffset(None, -25200)),\n", + " 'identifier': '1ed51563e8f0218c0270ee9fb6c27b0b1558c4b821c10be2756797a697b35ff3',\n", + " 'timestamps_reference_time': datetime.datetime(2019, 4, 18, 3, 41, 56, 136000, tzinfo=tzoffset(None, -25200)),\n", + " 'experiment_description': 'PatchMaster v2x90.3, 19-Mar-2018',\n", + " 'session_id': 'PLACEHOLDER',\n", + " 'source_script': {'git_revision': '() ',\n", + " 'package_version': '0.16.2',\n", + " 'repo': 'https://github.com/AllenInstitute/ipfx'},\n", + " 'source_script_file_name': 'run_x_to_nwb_conversion.py',\n", + " 'session_description': 'PLACEHOLDER'}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "blocks[0].annotations" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot the stimulus and response signals" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "acq_signals = blocks[0].segments[0].filter(nwb_group=\"acquisition\")\n", + "stimuli = blocks[0].segments[0].filter(nwb_group=\"stimulus\")\n", + "n = len(acq_signals)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure(figsize=(16.0, n * 1.5))\n", + "for i, (stim, sig) in enumerate(zip(stimuli, acq_signals)):\n", + " sig = sig.load()\n", + " stim = stim.load()\n", + " plt.subplot(n, 2, 2 * i + 1)\n", + " plt.plot(stim.times, stim)\n", + " plt.ylabel(stim.annotations[\"series_label\"])\n", + " plt.subplot(n, 2, 2 * i + 2)\n", + " plt.plot(sig.times, sig)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load data using pynwb\n", + "\n", + "This is to check what metadata is currently not being loaded by Neo (Neo-NWB support is a work in progress)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "import pynwb" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "root pynwb.file.NWBFile at 0x140364012163368\n", + "Fields:\n", + " acquisition: {\n", + " index_000 ,\n", + " index_001 ,\n", + " index_002 ,\n", + " index_003 ,\n", + " index_004 ,\n", + " index_005 ,\n", + " index_006 ,\n", + " index_007 ,\n", + " index_008 ,\n", + " index_009 ,\n", + " index_010 ,\n", + " index_011 ,\n", + " index_012 ,\n", + " index_013 ,\n", + " index_014 ,\n", + " index_015 ,\n", + " index_016 ,\n", + " index_017 ,\n", + " index_018 ,\n", + " index_019 ,\n", + " index_020 ,\n", + " index_021 ,\n", + " index_022 ,\n", + " index_023 ,\n", + " index_024 ,\n", + " index_025 ,\n", + " index_026 ,\n", + " index_027 ,\n", + " index_028 ,\n", + " index_029 ,\n", + " index_030 ,\n", + " index_031 ,\n", + " index_032 ,\n", + " index_033 ,\n", + " index_034 ,\n", + " index_035 ,\n", + " index_036 ,\n", + " index_037 ,\n", + " index_038 ,\n", + " index_039 ,\n", + " index_040 ,\n", + " index_041 ,\n", + " index_042 ,\n", + " index_043 ,\n", + " index_044 ,\n", + " index_045 ,\n", + " index_046 ,\n", + " index_047 ,\n", + " index_048 ,\n", + " index_049 ,\n", + " index_050 ,\n", + " index_051 ,\n", + " index_052 ,\n", + " index_053 ,\n", + " index_054 ,\n", + " index_055 ,\n", + " index_056 ,\n", + " index_057 ,\n", + " index_058 ,\n", + " index_059 ,\n", + " index_060 ,\n", + " index_061 ,\n", + " index_062 \n", + " }\n", + " devices: {\n", + " Unknown (value: 5)-4-1 with Unknown (value: 3) \n", + " }\n", + " experiment_description: PatchMaster v2x90.3, 19-Mar-2018\n", + " file_create_date: [datetime.datetime(2019, 5, 13, 10, 51, 31, 69049, tzinfo=tzoffset(None, -25200))]\n", + " ic_electrodes: {\n", + " Electrode 0 \n", + " }\n", + " identifier: 1ed51563e8f0218c0270ee9fb6c27b0b1558c4b821c10be2756797a697b35ff3\n", + " session_description: PLACEHOLDER\n", + " session_id: PLACEHOLDER\n", + " session_start_time: 2019-04-18 03:41:56.136000-07:00\n", + " source_script: {\n", + " \"git_revision\": \"() \",\n", + " \"package_version\": \"0.16.2\",\n", + " \"repo\": \"https://github.com/AllenInstitute/ipfx\"\n", + "}\n", + " source_script_file_name: run_x_to_nwb_conversion.py\n", + " stimulus: {\n", + " index_000 ,\n", + " index_001 ,\n", + " index_002 ,\n", + " index_003 ,\n", + " index_004 ,\n", + " index_005 ,\n", + " index_006 ,\n", + " index_007 ,\n", + " index_008 ,\n", + " index_009 ,\n", + " index_010 ,\n", + " index_011 ,\n", + " index_012 ,\n", + " index_013 ,\n", + " index_014 ,\n", + " index_015 ,\n", + " index_016 ,\n", + " index_017 ,\n", + " index_018 ,\n", + " index_019 ,\n", + " index_020 ,\n", + " index_021 ,\n", + " index_022 ,\n", + " index_023 ,\n", + " index_024 ,\n", + " index_025 ,\n", + " index_026 ,\n", + " index_027 ,\n", + " index_028 ,\n", + " index_029 ,\n", + " index_030 ,\n", + " index_031 ,\n", + " index_032 ,\n", + " index_033 ,\n", + " index_034 ,\n", + " index_035 ,\n", + " index_036 ,\n", + " index_037 ,\n", + " index_038 ,\n", + " index_039 ,\n", + " index_040 ,\n", + " index_041 ,\n", + " index_042 ,\n", + " index_043 ,\n", + " index_044 ,\n", + " index_045 ,\n", + " index_046 ,\n", + " index_047 ,\n", + " index_048 ,\n", + " index_049 ,\n", + " index_050 ,\n", + " index_051 ,\n", + " index_052 ,\n", + " index_053 ,\n", + " index_054 ,\n", + " index_055 ,\n", + " index_056 ,\n", + " index_057 ,\n", + " index_058 ,\n", + " index_059 ,\n", + " index_060 ,\n", + " index_061 ,\n", + " index_062 \n", + " }\n", + " sweep_table: sweep_table \n", + " timestamps_reference_time: 2019-04-18 03:41:56.136000-07:00" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "io._file" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "index_000 pynwb.icephys.VoltageClampSeries at 0x140363981660792\n", + "Fields:\n", + " capacitance_fast: 0.0\n", + " capacitance_slow: nan\n", + " comments: no comments\n", + " conversion: 1.0\n", + " data: \n", + " description: {\n", + " \"cycle_id\": 2001001,\n", + " \"file\": \"H19.28.012.11.05.dat\",\n", + " \"group_label\": \"PGS4_190418_701_A01\",\n", + " \"series_label\": \"extpinbath\",\n", + " \"sweep_label\": \"\"\n", + "}\n", + " electrode: Electrode 0 pynwb.icephys.IntracellularElectrode at 0x140362621608176\n", + "Fields:\n", + " description: PLACEHOLDER\n", + " device: Unknown (value: 5)-4-1 with Unknown (value: 3) pynwb.device.Device at 0x140362621608568\n", + "\n", + " gain: 5000000.0\n", + " rate: 199999.99999999997\n", + " resistance_comp_bandwidth: nan\n", + " resistance_comp_correction: nan\n", + " resistance_comp_prediction: nan\n", + " resolution: nan\n", + " starting_time: 13008.059839\n", + " starting_time_unit: seconds\n", + " stimulus_description: extpinbath\n", + " sweep_number: 2001001\n", + " unit: amperes\n", + " whole_cell_capacitance_comp: nan\n", + " whole_cell_series_resistance_comp: nan" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "io._file.acquisition['index_000']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From bbcda25349312f081245ec67235c654fdd88ef80 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Mon, 31 Aug 2020 09:14:22 +0200 Subject: [PATCH 52/79] NWBIO: be more careful with file modes --- neo/io/nwbio.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 6f57414a8..b6a5385cd 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -17,6 +17,7 @@ from __future__ import absolute_import, division +import os from itertools import chain from datetime import datetime import json @@ -121,12 +122,14 @@ def __init__(self, filename, mode='r'): BaseIO.__init__(self, filename=filename) self.filename = filename self.blocks_written = 0 + self.nwb_file_mode = mode def read_all_blocks(self, lazy=False, **kwargs): """ """ - io = pynwb.NWBHDF5IO(self.filename, mode='r') # Open a file with NWBHDF5IO + assert self.nwb_file_mode in ('r',) + io = pynwb.NWBHDF5IO(self.filename, mode=self.nwb_file_mode) # Open a file with NWBHDF5IO self._file = io.read() self.global_block_metadata = {} @@ -307,7 +310,10 @@ def write_all_blocks(self, blocks, **kwargs): # todo: store additional Neo annotations somewhere in NWB file nwbfile = NWBFile(**annotations) - io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode='w') + assert self.nwb_file_mode in ('w',) # possibly expand to 'a'ppend later + if self.nwb_file_mode == "w" and os.path.exists(self.filename): + os.remove(self.filename) + io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode=self.nwb_file_mode) nwbfile.add_unit_column('_name', 'the name attribute of the SpikeTrain') #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') From e4d36b39d85d128885d4388006e2ef121bfb719a Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Mon, 31 Aug 2020 09:15:02 +0200 Subject: [PATCH 53/79] [NWBIO] better handling of dict-valued annotations --- neo/io/nwbio.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index b6a5385cd..6581828d4 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -292,7 +292,14 @@ def write_all_blocks(self, blocks, **kwargs): else: for block in blocks: if annotation_name in block.annotations: - annotations[annotation_name].add(block.annotations[annotation_name]) + try: + annotations[annotation_name].add(block.annotations[annotation_name]) + except TypeError: + if annotation_name in POSSIBLE_JSON_FIELDS: + encoded = json.dumps(block.annotations[annotation_name]) + annotations[annotation_name].add(encoded) + else: + raise if annotation_name in annotations: if len(annotations[annotation_name]) > 1: raise NotImplementedError( From 112828db0cff7e5d6ff71de216fbcfd547464b37 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Mon, 31 Aug 2020 16:17:38 +0200 Subject: [PATCH 54/79] [NWBIO] Start adding support for writing to NWB groups other than "acquisition" --- neo/io/nwbio.py | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 6581828d4..3e1d98c5d 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -400,7 +400,16 @@ def _write_signal(self, nwbfile, signal): else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format( signal.__class__.__name__)) - nwbfile.add_acquisition(tS) + nwb_group = signal.annotations.get("nwb_group", "acquisition") + add_method_map = { + "acquisition": nwbfile.add_acquisition, + "stimulus": nwbfile.add_stimulus + } + if nwb_group in add_method_map: + add_time_series = add_method_map[nwb_group] + else: + raise NotImplementedError("NWB group '{}' not yet supported".format(nwb_group)) + add_time_series(tS) return tS def _write_spiketrain(self, nwbfile, spiketrain): From b14952bc36bc4169bc17c7b3f3b33d7f8d6daa6e Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Mon, 28 Sep 2020 18:06:32 +0200 Subject: [PATCH 55/79] [nwbio] handle subclasses of TimeSeries (tested with intracellular ephys subclasses) --- neo/io/nwbio.py | 297 ++++++++++++++++++++++++++-------- neo/test/iotest/test_nwbio.py | 74 ++++++++- 2 files changed, 299 insertions(+), 72 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 3e1d98c5d..ee550188d 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -16,6 +16,7 @@ """ from __future__ import absolute_import, division +from neo.core import baseneo import os from itertools import chain @@ -77,18 +78,132 @@ "source_script_file_name", "data_collection", "surgery", "virus", "stimulus_notes", "lab", "session_description" ) + POSSIBLE_JSON_FIELDS = ( "source_script", "description" ) +prefix_map = { + 1e9: 'giga', + 1e6: 'mega', + 1e3: 'kilo', + 1: '', + 1e-3: 'milli', + 1e-6: 'micro', + 1e-9: 'nano', + 1e-12: 'pico' +} def try_json_field(content): + """ + Try to interpret a string as JSON data. + + If successful, return the JSON data (dict or list) + If unsuccessful, return the original string + """ try: return json.loads(content) except JSONDecodeError: return content +def get_class(module, name): + """ + Given a module path and a class name, return the class object + """ + module_path = module.split(".") + assert len(module_path) == 2 # todo: handle the general case where this isn't 2 + return getattr(getattr(pynwb, module_path[1]), name) + + +def statistics(block): # todo: move this to be a property of Block + """ + Return simple statistics about a Neo Block. + """ + stats = { + "SpikeTrain": {"count": 0}, + "AnalogSignal": {"count": 0}, + "IrregularlySampledSignal": {"count": 0}, + "Epoch": {"count": 0}, + "Event": {"count": 0}, + } + for segment in block.segments: + stats["SpikeTrain"]["count"] += len(segment.spiketrains) + stats["AnalogSignal"]["count"] += len(segment.analogsignals) + stats["IrregularlySampledSignal"]["count"] += len(segment.irregularlysampledsignals) + stats["Epoch"]["count"] += len(segment.epochs) + stats["Event"]["count"] += len(segment.events) + return stats + + +def get_units_conversion(signal, timeseries_class): + """ + Given a quantity array and a TimeSeries subclass, return + the conversion factor and the expected units + """ + # it would be nice if the expected units was an attribute of the PyNWB class + if "CurrentClamp" in timeseries_class.__name__: + expected_units = pq.volt + elif "VoltageClamp" in timeseries_class.__name__: + expected_units = pq.ampere + else: + # todo: warn that we don't handle this subclass yet + expected_units = signal.units + return float((signal.units/expected_units).simplified.magnitude), expected_units + + +def time_in_seconds(t): + return float(t.rescale("second")) + + +def _decompose_unit(unit): + """ + Given a quantities unit object, return a base unit name and a conversion factor. + + Example: + + >>> _decompose_unit(pq.mV) + ('volt', 0.001) + """ + assert isinstance(unit, pq.quantity.Quantity) + assert unit.magnitude == 1 + conversion = 1.0 + + def _decompose(unit): + dim = unit.dimensionality + if len(dim) != 1: + raise NotImplementedError("Compound units not yet supported") # e.g. volt-metre + uq, n = list(dim.items())[0] + if n != 1: + raise NotImplementedError("Compound units not yet supported") # e.g. volt^2 + uq_def = uq.definition + return float(uq_def.magnitude), uq_def + conv, unit2 = _decompose(unit) + while conv != 1: + conversion *= conv + unit = unit2 + conv, unit2 = _decompose(unit) + return list(unit.dimensionality.keys())[0].name, conversion + + +def _recompose_unit(base_unit_name, conversion): + """ + Given a base unit name and a conversion factor, return a quantities unit object + + Example: + + >>> _recompose_unit("ampere", 1e-9) + UnitCurrent('nanoampere', 0.001 * uA, 'nA') + + """ + if conversion not in prefix_map: + raise ValueError(f"Can't handle this conversion factor: {conversion}") + unit_name = prefix_map[conversion] + base_unit_name + if unit_name[-1] == "s": # strip trailing 's', e.g. "volts" --> "volt" + unit_name = unit_name[:-1] + return getattr(pq, unit_name) + + class NWBIO(BaseIO): """ Class for "reading" experimental data from a .nwb file, and "writing" a .nwb file from Neo @@ -230,11 +345,6 @@ def _read_timeseries_group(self, group_name, lazy): block_name = hierarchy["block"] segment_name = hierarchy["segment"] segment = self._get_segment(block_name, segment_name) - annotations = {"nwb_group": group_name} - description = try_json_field(timeseries.description) - if isinstance(description, dict): - annotations.update(description) - description = None if isinstance(timeseries, AnnotationSeries): event = EventProxy(timeseries, group_name) if not lazy: @@ -322,18 +432,20 @@ def write_all_blocks(self, blocks, **kwargs): os.remove(self.filename) io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode=self.nwb_file_mode) - nwbfile.add_unit_column('_name', 'the name attribute of the SpikeTrain') - #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') - nwbfile.add_unit_column( - 'segment', 'the name of the Neo Segment to which the SpikeTrain belongs') - nwbfile.add_unit_column( - 'block', 'the name of the Neo Block to which the SpikeTrain belongs') - - nwbfile.add_epoch_column('_name', 'the name attribute of the Epoch') - #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') - nwbfile.add_epoch_column( - 'segment', 'the name of the Neo Segment to which the Epoch belongs') - nwbfile.add_epoch_column('block', 'the name of the Neo Block to which the Epoch belongs') + if sum(statistics(block)["SpikeTrain"]["count"] for block in blocks) > 0: + nwbfile.add_unit_column('_name', 'the name attribute of the SpikeTrain') + #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') + nwbfile.add_unit_column( + 'segment', 'the name of the Neo Segment to which the SpikeTrain belongs') + nwbfile.add_unit_column( + 'block', 'the name of the Neo Block to which the SpikeTrain belongs') + + if sum(statistics(block)["Epoch"]["count"] for block in blocks) > 0: + nwbfile.add_epoch_column('_name', 'the name attribute of the Epoch') + #nwbfile.add_epoch_column('_description', 'the description attribute of the Epoch') + nwbfile.add_epoch_column( + 'segment', 'the name of the Neo Segment to which the Epoch belongs') + nwbfile.add_epoch_column('block', 'the name of the Neo Block to which the Epoch belongs') for i, block in enumerate(blocks): self.write_block(nwbfile, block) @@ -345,23 +457,44 @@ def write_block(self, nwbfile, block, **kwargs): Write a Block to the file :param block: Block to be written """ + electrodes = self._write_electrodes(nwbfile, block) if not block.name: block.name = "block%d" % self.blocks_written for i, segment in enumerate(block.segments): assert segment.block is block if not segment.name: segment.name = "%s : segment%d" % (block.name, i) - self._write_segment(nwbfile, segment) + self._write_segment(nwbfile, segment, electrodes) self.blocks_written += 1 - def _write_segment(self, nwbfile, segment): + def _write_electrodes(self, nwbfile, block): + # this handles only icephys_electrode for now + electrodes = {} + devices = {} + for segment in block.segments: + for signal in chain(segment.analogsignals, segment.irregularlysampledsignals): + if "nwb_electrode" in signal.annotations: + elec_meta = signal.annotations["nwb_electrode"].copy() + if elec_meta["name"] not in electrodes: + # todo: check for consistency if the name is already there + if elec_meta["device"]["name"] in devices: + device = devices[elec_meta["device"]["name"]] + else: + device = nwbfile.create_device(**elec_meta["device"]) + devices[elec_meta["device"]["name"]] = device + elec_meta.pop("device") + electrodes[elec_meta["name"]] = nwbfile.create_icephys_electrode( + device=device, **elec_meta + ) + return electrodes + + def _write_segment(self, nwbfile, segment, electrodes): # maybe use NWB trials to store Segment metadata? - for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): assert signal.segment is segment if not signal.name: signal.name = "%s : analogsignal%d" % (segment.name, i) - self._write_signal(nwbfile, signal) + self._write_signal(nwbfile, signal, electrodes) for i, train in enumerate(segment.spiketrains): assert train.segment is segment @@ -380,23 +513,42 @@ def _write_segment(self, nwbfile, segment): epoch.name = "%s : epoch%d" % (segment.name, i) self._write_epoch(nwbfile, epoch) - def _write_signal(self, nwbfile, signal): + def _write_signal(self, nwbfile, signal, electrodes): hierarchy = {'block': signal.segment.block.name, 'segment': signal.segment.name} + if "nwb_type" in signal.annotations: + timeseries_class = get_class(*signal.annotations["nwb_type"]) + else: + timeseries_class = TimeSeries # default + additional_metadata = {name[4:]: value + for name, value in signal.annotations.items() + if name.startswith("nwb:")} + if "nwb_electrode" in signal.annotations: + electrode_name = signal.annotations["nwb_electrode"]["name"] + additional_metadata["electrode"] = electrodes[electrode_name] + if timeseries_class != TimeSeries: + conversion, units = get_units_conversion(signal, timeseries_class) + additional_metadata["conversion"] = conversion + else: + units = signal.units if isinstance(signal, AnalogSignal): sampling_rate = signal.sampling_rate.rescale("Hz") - tS = TimeSeries(name=signal.name, - starting_time=time_in_seconds(signal.t_start), - data=signal, - unit=signal.units.dimensionality.string, - rate=float(sampling_rate), - comments=json.dumps(hierarchy)) - # todo: try to add array_annotations via "control" attribute + tS = timeseries_class( + name=signal.name, + starting_time=time_in_seconds(signal.t_start), + data=signal, + unit=units.dimensionality.string, + rate=float(sampling_rate), + comments=json.dumps(hierarchy), + **additional_metadata) + # todo: try to add array_annotations via "control" attribute elif isinstance(signal, IrregularlySampledSignal): - tS = TimeSeries(name=signal.name, - data=signal, - unit=signal.units.dimensionality.string, - timestamps=signal.times.rescale('second').magnitude, - comments=json.dumps(hierarchy)) + tS = timeseries_class( + name=signal.name, + data=signal, + unit=units.dimensionality.string, + timestamps=signal.times.rescale('second').magnitude, + comments=json.dumps(hierarchy), + **additional_metadata) else: raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format( signal.__class__.__name__)) @@ -447,48 +599,22 @@ def _write_epoch(self, nwbfile, epoch): return nwbfile.epochs -def time_in_seconds(t): - return float(t.rescale("second")) - - -def _decompose_unit(unit): - assert isinstance(unit, pq.quantity.Quantity) - assert unit.magnitude == 1 - conversion = 1.0 - - def _decompose(unit): - dim = unit.dimensionality - if len(dim) != 1: - raise NotImplementedError("Compound units not yet supported") # e.g. volt-metre - uq, n = dim.items()[0] - if n != 1: - raise NotImplementedError("Compound units not yet supported") # e.g. volt^2 - uq_def = uq.definition - return float(uq_def.magnitude), uq_def - conv, unit2 = _decompose(unit) - while conv != 1: - conversion *= conv - unit = unit2 - conv, unit2 = _decompose(unit) - return conversion, unit.dimensionality.keys()[0].name - - -prefix_map = { - 1e-3: 'milli', - 1e-6: 'micro', - 1e-9: 'nano' -} - - class AnalogSignalProxy(BaseAnalogSignalProxy): + common_metadata_fields = ( + # fields that are the same for all TimeSeries subclasses + "comments", "description", "unit", "starting_time", "timestamps", "rate", + "data", "starting_time_unit", "timestamps_unit", "electrode" + ) def __init__(self, timeseries, nwb_group): self._timeseries = timeseries self.units = timeseries.unit + if timeseries.conversion: + self.units = _recompose_unit(timeseries.unit, timeseries.conversion) if timeseries.starting_time is not None: - self.t_start = timeseries.starting_time * pq.s # use timeseries.starting_time_units + self.t_start = timeseries.starting_time * pq.s # todo: use timeseries.starting_time_unit else: - self.t_start = timeseries.timestamps[0] * pq.s + self.t_start = timeseries.timestamps[0] * pq.s # todo: use timeseries.timestamps.unit if timeseries.rate: self.sampling_rate = timeseries.rate * pq.Hz else: @@ -497,11 +623,42 @@ def __init__(self, timeseries, nwb_group): self.annotations = {"nwb_group": nwb_group} self.description = try_json_field(timeseries.description) if isinstance(self.description, dict): - self.annotations.update(self.description) + self.annotations["notes"] = self.description if "name" in self.annotations: self.annotations.pop("name") self.description = None self.shape = self._timeseries.data.shape + metadata_fields = list(timeseries.__nwbfields__) + for field_name in self.__class__.common_metadata_fields: # already handled + try: + metadata_fields.remove(field_name) + except ValueError: + pass + for field_name in metadata_fields: + value = getattr(timeseries, field_name) + if value is not None: + self.annotations[f"nwb:{field_name}"] = value + self.annotations["nwb_type"] = ( + timeseries.__class__.__module__, + timeseries.__class__.__name__ + ) + if hasattr(timeseries, "electrode"): + # todo: once the Group class is available, we could add electrode metadata + # to a Group containing all signals that share that electrode + # This would reduce the amount of redundancy (repeated metadata in every signal) + electrode_metadata = {"device": {}} + metadata_fields = list(timeseries.electrode.__class__.__nwbfields__) + ["name"] + metadata_fields.remove("device") # needs special handling + for field_name in metadata_fields: + value = getattr(timeseries.electrode, field_name) + if value is not None: + electrode_metadata[field_name] = value + for field_name in timeseries.electrode.device.__class__.__nwbfields__: + value = getattr(timeseries.electrode.device, field_name) + if value is not None: + electrode_metadata["device"][field_name] = value + self.annotations["nwb_electrode"] = electrode_metadata + def load(self, time_slice=None, strict_slicing=True): """ diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 7789be13e..33f3584e6 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -60,7 +60,7 @@ def test_roundtrip(self): for blk in original_blocks: - for ind in range(num_seg): # number of Segment + for ind in range(num_seg): # number of Segments seg = Segment(index=ind) seg.block = blk blk.segments.append(seg) @@ -180,8 +180,78 @@ def test_roundtrip(self): assert_allclose(retrieved_epoch_11.durations.rescale('ms').magnitude, original_epoch_11.durations.rescale('ms').magnitude) assert_array_equal(retrieved_epoch_11.labels, original_epoch_11.labels) + os.remove(test_file_name) + + def test_roundtrip_with_annotations(self): + # test with NWB-specific annotations + + original_block = Block(name="experiment") + segment = Segment(name="session 1") + original_block.segments.append(segment) + segment.block = original_block + + electrode_annotations = { + "name": "electrode #1", + "description": "intracellular electrode", + "device": { + "name": "electrode #1" + } + } + stimulus_annotations = { + "nwb_group": "stimulus", + "nwb_type": ("pynwb.icephys", "CurrentClampStimulusSeries"), + "nwb_electrode": electrode_annotations, + "nwb:sweep_number": 1, + "nwb:gain": 1.0 + } + response_annotations = { + "nwb_group": "acquisition", + "nwb_type": ("pynwb.icephys", "CurrentClampSeries"), + "nwb_electrode": electrode_annotations, + "nwb:sweep_number": 1, + "nwb:gain": 1.0, + "nwb:bias_current": 1e-12, + "nwb:bridge_balance": 70e6, + "nwb:capacitance_compensation": 1e-12 + } + stimulus = AnalogSignal(np.random.randn(100, 1) * pq.nA, + sampling_rate=5 * pq.kHz, + t_start=50 * pq.ms, + name="stimulus", + **stimulus_annotations) + response = AnalogSignal(np.random.randn(100, 1) * pq.mV, + sampling_rate=5 * pq.kHz, + t_start=50 * pq.ms, + name="response", + **response_annotations) + segment.analogsignals = [stimulus, response] + stimulus.segment = response.segment = segment + + test_file_name = "test_round_trip_with_annotations.nwb" + iow = NWBIO(filename=test_file_name, mode='w') + iow.write_all_blocks([original_block]) + + nwbfile = pynwb.NWBHDF5IO(test_file_name, mode="r").read() + + self.assertIsInstance(nwbfile.acquisition["response"], pynwb.icephys.CurrentClampSeries) + self.assertIsInstance(nwbfile.stimulus["stimulus"], pynwb.icephys.CurrentClampStimulusSeries) + self.assertEqual(nwbfile.acquisition["response"].bridge_balance, response_annotations["nwb:bridge_balance"]) + + ior = NWBIO(filename=test_file_name, mode='r') + retrieved_block = ior.read_all_blocks()[0] + + original_response = original_block.segments[0].filter(name="response")[0] + retrieved_response = retrieved_block.segments[0].filter(name="response")[0] + for attr_name in ("name", "units", "sampling_rate", "t_start"): + retrieved_attribute = getattr(retrieved_response, attr_name) + original_attribute = getattr(original_response, attr_name) + self.assertEqual(retrieved_attribute, original_attribute) + assert_array_equal(retrieved_response.magnitude, original_response.magnitude) + + os.remove(test_file_name) if __name__ == "__main__": - print("pynwb.__version__ = ", pynwb.__version__) + if HAVE_PYNWB: + print("pynwb.__version__ = ", pynwb.__version__) unittest.main() From 3d5cf26a4d72bcee2d6e57d7cb3ba5f8a527b96f Mon Sep 17 00:00:00 2001 From: "!git for-each-ref --format='%(refname:short)' `git symbolic-ref HEAD`" Date: Sun, 6 Dec 2020 15:08:13 -0500 Subject: [PATCH 56/79] load now handles slices of irregularly sampled data --- neo/io/nwbio.py | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index ee550188d..e01c9f0c7 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -94,6 +94,7 @@ 1e-12: 'pico' } + def try_json_field(content): """ Try to interpret a string as JSON data. @@ -612,9 +613,9 @@ def __init__(self, timeseries, nwb_group): if timeseries.conversion: self.units = _recompose_unit(timeseries.unit, timeseries.conversion) if timeseries.starting_time is not None: - self.t_start = timeseries.starting_time * pq.s # todo: use timeseries.starting_time_unit + self.t_start = timeseries.starting_time * pq.s else: - self.t_start = timeseries.timestamps[0] * pq.s # todo: use timeseries.timestamps.unit + self.t_start = timeseries.timestamps[0] * pq.s if timeseries.rate: self.sampling_rate = timeseries.rate * pq.Hz else: @@ -659,7 +660,6 @@ def __init__(self, timeseries, nwb_group): electrode_metadata["device"][field_name] = value self.annotations["nwb_electrode"] = electrode_metadata - def load(self, time_slice=None, strict_slicing=True): """ *Args*: @@ -669,16 +669,17 @@ def load(self, time_slice=None, strict_slicing=True): Control if an error is raised or not when one of the time_slice members (t_start or t_stop) is outside the real time range of the segment. """ + i_start, i_stop, sig_t_start = None, None, self.t_start if time_slice: - i_start, i_stop, sig_t_start = self._time_slice_indices(time_slice, - strict_slicing=strict_slicing) - signal = self._timeseries.data[i_start: i_stop] - else: - signal = self._timeseries.data[:] - sig_t_start = self.t_start + if self.sampling_rate is None: + i_start, i_stop = np.searchsorted(self._timeseries.timestamps, time_slice) + else: + i_start, i_stop, sig_t_start = self._time_slice_indices( + time_slice, strict_slicing=strict_slicing) + signal = self._timeseries.data[i_start: i_stop] if self.sampling_rate is None: return IrregularlySampledSignal( - self._timeseries.timestamps[:] * pq.s, + self._timeseries.timestamps[i_start:i_stop] * pq.s, signal, units=self.units, t_start=sig_t_start, @@ -807,4 +808,4 @@ def load(self, time_slice=None, strict_slicing=True): #file_origin=None, #description=None, #array_annotations=None, - **self.annotations) \ No newline at end of file + **self.annotations) From e123ee0a5a83c139c7fa3269b60fb52279ecd5cf Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Wed, 3 Feb 2021 15:43:04 +0100 Subject: [PATCH 57/79] handle unexpected units, such as "n/a" ! --- neo/io/nwbio.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index e01c9f0c7..af840b342 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -18,6 +18,7 @@ from __future__ import absolute_import, division from neo.core import baseneo +import logging import os from itertools import chain from datetime import datetime @@ -71,6 +72,9 @@ have_hdmf = False +logger = logging.getLogger("Neo") + + GLOBAL_ANNOTATIONS = ( "session_start_time", "identifier", "timestamps_reference_time", "experimenter", "experiment_description", "session_id", "institution", "keywords", "notes", @@ -202,7 +206,11 @@ def _recompose_unit(base_unit_name, conversion): unit_name = prefix_map[conversion] + base_unit_name if unit_name[-1] == "s": # strip trailing 's', e.g. "volts" --> "volt" unit_name = unit_name[:-1] - return getattr(pq, unit_name) + try: + return getattr(pq, unit_name) + except AttributeError: + logger.warning(f"Can't handle unit '{unit_name}'. Returning dimensionless") + return pq.dimensionless class NWBIO(BaseIO): From 9136df9e1916248663567b2d3b2fbf19b83a6914 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Wed, 3 Feb 2021 16:04:21 +0100 Subject: [PATCH 58/79] fix shape of AnalogSignalProxy in NWBIO --- neo/io/nwbio.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index af840b342..c16855ee3 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -637,6 +637,8 @@ def __init__(self, timeseries, nwb_group): self.annotations.pop("name") self.description = None self.shape = self._timeseries.data.shape + if len(self.shape) == 1: + self.shape = (self.shape[0], 1) metadata_fields = list(timeseries.__nwbfields__) for field_name in self.__class__.common_metadata_fields: # already handled try: From 3bab725028de21ab34a4b71f363f18f0054fbdb2 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Wed, 3 Feb 2021 16:57:25 +0100 Subject: [PATCH 59/79] Be more forgiving of weird lines when loading ascii files --- neo/io/asciisignalio.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/neo/io/asciisignalio.py b/neo/io/asciisignalio.py index 54ee6b788..addf310fa 100644 --- a/neo/io/asciisignalio.py +++ b/neo/io/asciisignalio.py @@ -195,7 +195,8 @@ def read_segment(self, lazy=False): delimiter=self.delimiter, usecols=self.usecols, skip_header=self.skiprows, - dtype='f') + dtype='f', + invalid_raise=False) if len(sig.shape) == 1: sig = sig[:, np.newaxis] elif self.method == 'csv': From 89dc0d759d5dcfcf33e0d1463fb7ed1ba3e3b4d0 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Mon, 28 Jun 2021 17:06:26 +0200 Subject: [PATCH 60/79] replace annotation "nwb_type" with "nwb_neurodata_type" to more closely shadow NWB naming convention (see discussion in #796) --- neo/io/nwbio.py | 6 +++--- neo/test/iotest/test_nwbio.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index c16855ee3..d6c871976 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -524,8 +524,8 @@ def _write_segment(self, nwbfile, segment, electrodes): def _write_signal(self, nwbfile, signal, electrodes): hierarchy = {'block': signal.segment.block.name, 'segment': signal.segment.name} - if "nwb_type" in signal.annotations: - timeseries_class = get_class(*signal.annotations["nwb_type"]) + if "nwb_neurodata_type" in signal.annotations: + timeseries_class = get_class(*signal.annotations["nwb_neurodata_type"]) else: timeseries_class = TimeSeries # default additional_metadata = {name[4:]: value @@ -649,7 +649,7 @@ def __init__(self, timeseries, nwb_group): value = getattr(timeseries, field_name) if value is not None: self.annotations[f"nwb:{field_name}"] = value - self.annotations["nwb_type"] = ( + self.annotations["nwb_neurodata_type"] = ( timeseries.__class__.__module__, timeseries.__class__.__name__ ) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 33f3584e6..6d130d9ac 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -199,14 +199,14 @@ def test_roundtrip_with_annotations(self): } stimulus_annotations = { "nwb_group": "stimulus", - "nwb_type": ("pynwb.icephys", "CurrentClampStimulusSeries"), + "nwb_neurodata_type": ("pynwb.icephys", "CurrentClampStimulusSeries"), "nwb_electrode": electrode_annotations, "nwb:sweep_number": 1, "nwb:gain": 1.0 } response_annotations = { "nwb_group": "acquisition", - "nwb_type": ("pynwb.icephys", "CurrentClampSeries"), + "nwb_neurodata_type": ("pynwb.icephys", "CurrentClampSeries"), "nwb_electrode": electrode_annotations, "nwb:sweep_number": 1, "nwb:gain": 1.0, From a69c4cbc54c211b0ffa788dfba60a03cbec2276d Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Mon, 28 Jun 2021 17:06:55 +0200 Subject: [PATCH 61/79] update links in NWBIO docstring --- neo/io/nwbio.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index d6c871976..722c69451 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -1,16 +1,14 @@ """ NWBIO -======== +===== IO class for reading data from a Neurodata Without Borders (NWB) dataset -Documentation : https://neurodatawithoutborders.github.io +Documentation : https://www.nwb.org/ Depends on: h5py, nwb, dateutil Supported: Read, Write Specification - https://github.com/NeurodataWithoutBorders/specification -Python APIs - (1) https://github.com/AllenInstitute/nwb-api/tree/master/ainwb - (2) https://github.com/AllenInstitute/AllenSDK/blob/master/allensdk/core/nwb_data_set.py - (3) https://github.com/NeurodataWithoutBorders/api-python +Python API - https://pynwb.readthedocs.io Sample datasets from CRCNS - https://crcns.org/NWB Sample datasets from Allen Institute - http://alleninstitute.github.io/AllenSDK/cell_types.html#neurodata-without-borders """ From c87fcd8dd0f19ee411c26f4c028ce4e7f2683982 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Mon, 28 Jun 2021 17:07:57 +0200 Subject: [PATCH 62/79] fix recently appeared bug (due to changes in h5py?) where boolean index is no longer accepted, need integer index --- neo/io/nwbio.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 722c69451..687233a64 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -317,7 +317,7 @@ def _read_epochs_group(self, lazy): if epoch_names is not None: unique_epoch_names = np.unique(epoch_names) for epoch_name in unique_epoch_names: - index = (epoch_names == epoch_name) + index, = np.where((epoch_names == epoch_name)) epoch = EpochProxy(self._file.epochs, epoch_name, index) if not lazy: epoch = epoch.load() From 29610598ca778d234e302987d34dfae056281226 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Mon, 28 Jun 2021 17:29:22 +0200 Subject: [PATCH 63/79] Make "session_start_time" a required block annotation for NWB (see discussion in #796) --- neo/io/nwbio.py | 4 +--- neo/test/iotest/test_nwbio.py | 12 ++++++++---- 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 687233a64..c8d34b3d5 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -7,7 +7,6 @@ Documentation : https://www.nwb.org/ Depends on: h5py, nwb, dateutil Supported: Read, Write -Specification - https://github.com/NeurodataWithoutBorders/specification Python API - https://pynwb.readthedocs.io Sample datasets from CRCNS - https://crcns.org/NWB Sample datasets from Allen Institute - http://alleninstitute.github.io/AllenSDK/cell_types.html#neurodata-without-borders @@ -401,7 +400,6 @@ def write_all_blocks(self, blocks, **kwargs): Write list of blocks to the file """ # todo: allow metadata in NWBFile constructor to be taken from kwargs - start_time = datetime.now() annotations = defaultdict(set) for annotation_name in GLOBAL_ANNOTATIONS: if annotation_name in kwargs: @@ -429,7 +427,7 @@ def write_all_blocks(self, blocks, **kwargs): annotations["session_description"] = blocks[0].description or self.filename # todo: concatenate descriptions of multiple blocks if different if "session_start_time" not in annotations: - annotations["session_start_time"] = datetime.now() + raise Exception("Writing to NWB requires an annotation 'session_start_time'") # todo: handle subject # todo: store additional Neo annotations somewhere in NWB file nwbfile = NWBFile(**annotations) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 6d130d9ac..d4f3e3c88 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -6,6 +6,7 @@ from __future__ import unicode_literals, print_function, division, absolute_import import unittest import os +from datetime import datetime try: from urllib.request import urlretrieve except ImportError: @@ -49,10 +50,13 @@ def test_read(self): def test_roundtrip(self): + annotations = { + "session_start_time": datetime.now() + } # Define Neo blocks - bl0 = Block(name='First block') - bl1 = Block(name='Second block') - bl2 = Block(name='Third block') + bl0 = Block(name='First block', **annotations) + bl1 = Block(name='Second block', **annotations) + bl2 = Block(name='Third block', **annotations) original_blocks = [bl0, bl1, bl2] num_seg = 4 # number of segments @@ -185,7 +189,7 @@ def test_roundtrip(self): def test_roundtrip_with_annotations(self): # test with NWB-specific annotations - original_block = Block(name="experiment") + original_block = Block(name="experiment", session_start_time=datetime.now()) segment = Segment(name="session 1") original_block.segments.append(segment) segment.block = original_block From f5d0d6f994430ee2976387fb572fdb1f9248574d Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Mon, 28 Jun 2021 17:33:15 +0200 Subject: [PATCH 64/79] Add a "block_index" argument to `NWBIO.read_block()` --- neo/io/nwbio.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index c8d34b3d5..f8a7d5c7b 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -276,11 +276,11 @@ def read_all_blocks(self, lazy=False, **kwargs): return list(self._blocks.values()) - def read_block(self, lazy=False, **kargs): + def read_block(self, lazy=False, block_index=0, **kargs): """ Load the first block in the file. """ - return self.read_all_blocks(lazy=lazy)[0] + return self.read_all_blocks(lazy=lazy)[block_index] def _get_segment(self, block_name, segment_name): # If we've already created a Block with the given name return it, From a5befa7b2a9a7084734551904eb700925f60417b Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Mon, 28 Jun 2021 18:40:55 +0200 Subject: [PATCH 65/79] Validate NWB files after writing them --- neo/io/nwbio.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index f8a7d5c7b..1d31412f6 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -457,6 +457,12 @@ def write_all_blocks(self, blocks, **kwargs): io_nwb.write(nwbfile) io_nwb.close() + io_validate = pynwb.NWBHDF5IO(self.filename, "r") + errors = pynwb.validate(io_validate, namespace="core") + if errors: + raise Exception(f"Errors found when validating {self.filename}") + io_validate.close() + def write_block(self, nwbfile, block, **kwargs): """ Write a Block to the file From 3e89a13963a91a263eaa06db78b896ab300e7d65 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Tue, 29 Jun 2021 08:35:34 +0200 Subject: [PATCH 66/79] remove deprecated ChannelIndex and Unit from NWBIO --- neo/io/nwbio.py | 4 ++-- neo/test/iotest/test_nwbio.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 1d31412f6..a5a04f929 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -35,8 +35,8 @@ EpochProxy as BaseEpochProxy, SpikeTrainProxy as BaseSpikeTrainProxy ) -from neo.core import (Segment, SpikeTrain, Unit, Epoch, Event, AnalogSignal, - IrregularlySampledSignal, ChannelIndex, Block, ImageSequence) +from neo.core import (Segment, SpikeTrain, Epoch, Event, AnalogSignal, + IrregularlySampledSignal, Block, ImageSequence) # PyNWB imports try: diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index d4f3e3c88..d703748f0 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -12,7 +12,7 @@ except ImportError: from urllib import urlretrieve from neo.test.iotest.common_io_test import BaseTestIO -from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Unit, Block, ChannelIndex, ImageSequence +from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Block, ImageSequence try: import pynwb from neo.io.nwbio import NWBIO From 0c74e2a0a95eb82243f2c18f2af513d326bfcacd Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Tue, 29 Jun 2021 10:42:56 +0200 Subject: [PATCH 67/79] partially integrated NWB testing into common framework --- neo/test/iotest/test_nwbio.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index d703748f0..03eb30514 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -7,12 +7,14 @@ import unittest import os from datetime import datetime + try: from urllib.request import urlretrieve except ImportError: from urllib import urlretrieve from neo.test.iotest.common_io_test import BaseTestIO from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Block, ImageSequence +from neo.utils import get_local_testing_data_folder try: import pynwb from neo.io.nwbio import NWBIO @@ -23,7 +25,6 @@ import quantities as pq import numpy as np from numpy.testing import assert_array_equal, assert_allclose -from neo.test.rawiotest.tools import create_local_temp_dir @unittest.skipUnless(HAVE_PYNWB, "requires pynwb") @@ -36,7 +37,7 @@ class TestNWBIO(unittest.TestCase): ] def test_read(self): - self.local_test_dir = create_local_temp_dir("nwb") + self.local_test_dir = get_local_testing_data_folder() / "nwb" os.makedirs(self.local_test_dir, exist_ok=True) for url in self.files_to_download: local_filename = os.path.join(self.local_test_dir, url.split("/")[-1]) From 084967d3674fcae08bca02245a23c8ed5e686895 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Tue, 29 Jun 2021 12:40:02 +0200 Subject: [PATCH 68/79] Removed example NWB notebook following discussion in #796 [skip ci] --- examples/NWB-Allen-Institute-Example.ipynb | 1464 -------------------- 1 file changed, 1464 deletions(-) delete mode 100644 examples/NWB-Allen-Institute-Example.ipynb diff --git a/examples/NWB-Allen-Institute-Example.ipynb b/examples/NWB-Allen-Institute-Example.ipynb deleted file mode 100644 index b410b22c6..000000000 --- a/examples/NWB-Allen-Institute-Example.ipynb +++ /dev/null @@ -1,1464 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Reading an NWB file with Neo" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "import matplotlib.pyplot as plt\n", - "from os.path import exists\n", - "from urllib.request import urlretrieve\n", - "\n", - "from neo.io import NWBIO" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Download data file from Allen Institute" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "url = \"http://download.alleninstitute.org/informatics-archive/prerelease/H19.28.012.11.05-2.nwb\"\n", - "local_filename = url.split(\"/\")[-1]\n", - "if not exists(local_filename):\n", - " local_filename, headers = urlretrieve(url)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load the data\n", - "\n", - "We are using \"lazy\" mode to save memory: this reads all the metadata, but reading the actual data is delayed until needed, so only two signals (stimulus + response) are read into memory at one time." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "io = NWBIO(local_filename)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "blocks = io.read(lazy=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[Block with 1 segments\n", - " name: 'default'\n", - " description: 'PLACEHOLDER'\n", - " annotations: {'session_start_time': datetime.datetime(2019, 4, 18, 3, 41, 56, 136000, tzinfo=tzoffset(None, -25200)),\n", - " 'identifier': '1ed51563e8f0218c0270ee9fb6c27b0b1558c4b821c10be2756797a697b35ff3',\n", - " 'timestamps_reference_time': datetime.datetime(2019, 4, 18, 3, 41, 56, 136000, tzinfo=tzoffset(None, -25200)),\n", - " 'experiment_description': 'PatchMaster v2x90.3, 19-Mar-2018',\n", - " 'session_id': 'PLACEHOLDER',\n", - " 'source_script': {'git_revision': '() ',\n", - " 'package_version': '0.16.2',\n", - " 'repo': 'https://github.com/AllenInstitute/ipfx'},\n", - " 'source_script_file_name': 'run_x_to_nwb_conversion.py',\n", - " 'session_description': 'PLACEHOLDER'}\n", - " file_origin: '/var/folders/2k/mhzyfkfs7h76v3pfyjbksb540000gq/T/tmpxsvu295j'\n", - " rec_datetime: datetime.datetime(2019, 4, 18, 3, 41, 56, 136000, tzinfo=tzoffset(None, -25200))\n", - " # segments (N=1)\n", - " 0: Segment with 126 analogsignals\n", - " name: 'default'\n", - " # analogsignals (N=126)\n", - " 0: AnalogSignalProxy\n", - " name: 'index_000'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2001001,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'extpinbath',\n", - " 'sweep_label': ''}\n", - " 1: AnalogSignalProxy\n", - " name: 'index_001'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2002001,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'extpciiatt',\n", - " 'sweep_label': ''}\n", - " 2: AnalogSignalProxy\n", - " name: 'index_002'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2003001,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'extpbreakn',\n", - " 'sweep_label': ''}\n", - " 3: AnalogSignalProxy\n", - " name: 'index_003'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004001,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 4: AnalogSignalProxy\n", - " name: 'index_004'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004002,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 5: AnalogSignalProxy\n", - " name: 'index_005'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004003,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 6: AnalogSignalProxy\n", - " name: 'index_006'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004004,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 7: AnalogSignalProxy\n", - " name: 'index_007'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004005,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 8: AnalogSignalProxy\n", - " name: 'index_008'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004006,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 9: AnalogSignalProxy\n", - " name: 'index_009'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004007,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 10: AnalogSignalProxy\n", - " name: 'index_010'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004008,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 11: AnalogSignalProxy\n", - " name: 'index_011'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004009,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 12: AnalogSignalProxy\n", - " name: 'index_012'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004010,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 13: AnalogSignalProxy\n", - " name: 'index_013'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004011,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 14: AnalogSignalProxy\n", - " name: 'index_014'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004012,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 15: AnalogSignalProxy\n", - " name: 'index_015'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004013,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 16: AnalogSignalProxy\n", - " name: 'index_016'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004014,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 17: AnalogSignalProxy\n", - " name: 'index_017'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2004015,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Long square',\n", - " 'sweep_label': ''}\n", - " 18: AnalogSignalProxy\n", - " name: 'index_018'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2005001,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Rheobase',\n", - " 'sweep_label': ''}\n", - " 19: AnalogSignalProxy\n", - " name: 'index_019'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2006001,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Rheobase',\n", - " 'sweep_label': ''}\n", - " 20: AnalogSignalProxy\n", - " name: 'index_020'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2007001,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Rheobase',\n", - " 'sweep_label': ''}\n", - " 21: AnalogSignalProxy\n", - " name: 'index_021'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008001,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 22: AnalogSignalProxy\n", - " name: 'index_022'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008002,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 23: AnalogSignalProxy\n", - " name: 'index_023'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008003,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 24: AnalogSignalProxy\n", - " name: 'index_024'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008004,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 25: AnalogSignalProxy\n", - " name: 'index_025'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008005,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 26: AnalogSignalProxy\n", - " name: 'index_026'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008006,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 27: AnalogSignalProxy\n", - " name: 'index_027'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008007,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 28: AnalogSignalProxy\n", - " name: 'index_028'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008008,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 29: AnalogSignalProxy\n", - " name: 'index_029'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008009,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 30: AnalogSignalProxy\n", - " name: 'index_030'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008010,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 31: AnalogSignalProxy\n", - " name: 'index_031'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008011,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 32: AnalogSignalProxy\n", - " name: 'index_032'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008012,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 33: AnalogSignalProxy\n", - " name: 'index_033'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008013,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 34: AnalogSignalProxy\n", - " name: 'index_034'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008014,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 35: AnalogSignalProxy\n", - " name: 'index_035'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008015,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 36: AnalogSignalProxy\n", - " name: 'index_036'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008016,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 37: AnalogSignalProxy\n", - " name: 'index_037'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008017,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 38: AnalogSignalProxy\n", - " name: 'index_038'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008018,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 39: AnalogSignalProxy\n", - " name: 'index_039'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008019,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 40: AnalogSignalProxy\n", - " name: 'index_040'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008020,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 41: AnalogSignalProxy\n", - " name: 'index_041'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008021,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 42: AnalogSignalProxy\n", - " name: 'index_042'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008022,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 43: AnalogSignalProxy\n", - " name: 'index_043'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008023,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 44: AnalogSignalProxy\n", - " name: 'index_044'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008024,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 45: AnalogSignalProxy\n", - " name: 'index_045'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008025,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 46: AnalogSignalProxy\n", - " name: 'index_046'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008026,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 47: AnalogSignalProxy\n", - " name: 'index_047'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008027,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 48: AnalogSignalProxy\n", - " name: 'index_048'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008028,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 49: AnalogSignalProxy\n", - " name: 'index_049'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008029,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 50: AnalogSignalProxy\n", - " name: 'index_050'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008030,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 51: AnalogSignalProxy\n", - " name: 'index_051'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008031,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 52: AnalogSignalProxy\n", - " name: 'index_052'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2008032,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Short square',\n", - " 'sweep_label': ''}\n", - " 53: AnalogSignalProxy\n", - " name: 'index_053'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 2009001,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Ramp',\n", - " 'sweep_label': ''}\n", - " 54: AnalogSignalProxy\n", - " name: 'index_054'\n", - " annotations: {'nwb_group': 'acquisition',\n", - " 'cycle_id': 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121: AnalogSignalProxy\n", - " name: 'index_058'\n", - " annotations: {'nwb_group': 'stimulus',\n", - " 'cycle_id': 2010005,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Capacitance',\n", - " 'sweep_label': ''}\n", - " 122: AnalogSignalProxy\n", - " name: 'index_059'\n", - " annotations: {'nwb_group': 'stimulus',\n", - " 'cycle_id': 2011001,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Chirp test',\n", - " 'sweep_label': ''}\n", - " 123: AnalogSignalProxy\n", - " name: 'index_060'\n", - " annotations: {'nwb_group': 'stimulus',\n", - " 'cycle_id': 2012001,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Chirp',\n", - " 'sweep_label': ''}\n", - " 124: AnalogSignalProxy\n", - " name: 'index_061'\n", - " annotations: {'nwb_group': 'stimulus',\n", - " 'cycle_id': 2012002,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Chirp',\n", - " 'sweep_label': ''}\n", - " 125: AnalogSignalProxy\n", - " name: 'index_062'\n", - " annotations: {'nwb_group': 'stimulus',\n", - " 'cycle_id': 2012003,\n", - " 'file': 'H19.28.012.11.05.dat',\n", - " 'group_label': 'PGS4_190418_701_A01',\n", - " 'series_label': 'Chirp',\n", - " 'sweep_label': ''}]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "blocks" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'session_start_time': datetime.datetime(2019, 4, 18, 3, 41, 56, 136000, tzinfo=tzoffset(None, -25200)),\n", - " 'identifier': '1ed51563e8f0218c0270ee9fb6c27b0b1558c4b821c10be2756797a697b35ff3',\n", - " 'timestamps_reference_time': datetime.datetime(2019, 4, 18, 3, 41, 56, 136000, tzinfo=tzoffset(None, -25200)),\n", - " 'experiment_description': 'PatchMaster v2x90.3, 19-Mar-2018',\n", - " 'session_id': 'PLACEHOLDER',\n", - " 'source_script': {'git_revision': '() ',\n", - " 'package_version': '0.16.2',\n", - " 'repo': 'https://github.com/AllenInstitute/ipfx'},\n", - " 'source_script_file_name': 'run_x_to_nwb_conversion.py',\n", - " 'session_description': 'PLACEHOLDER'}" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "blocks[0].annotations" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot the stimulus and response signals" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "acq_signals = blocks[0].segments[0].filter(nwb_group=\"acquisition\")\n", - "stimuli = blocks[0].segments[0].filter(nwb_group=\"stimulus\")\n", - "n = len(acq_signals)" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "data": { - "image/png": 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992GfG3TvRSQPv/e6HCgzxuQ25HipSAqHfiquk5LYc/dT2eueyrorSrKQyrGfQdwM3NrUTqXye5n72ZCeiJHw2PrMJyOeGM/GmNFeHDeM0caY3R6fw34s9i7WihoSdWJzzbDZqx4N9PIoSpxJ0YdQE4Y5XwXERlI1UZ4bUvF9NBHxNGGYiDQBLgJyws9ljLnfy/MqteNuTT3sGuIM4MyAsq+RdpgrraK8Z5LEHrf13qZOKqiK44QsnkmiKEpNuM1NYFMbFcRd/2TPBUjle+8647RF9z2Io6VjbR45STK8Thj2P2A8UAYcCvtrCAb4SEQWisiEBh4rpUnVx9B96iT7Guq6sLmNtli1qJDqI/uKEm9Sd8kiZ32tfZr7Uc9r3dhW7W0aBEklvF6qqrsxZmyUj3miMWabiHQEPhaRVcaYWcEfAwb1BIDs7Owon9oSXMVViZWmY6pmMzXGnV42Nezu4gnFqkGT4Eup0xcvm2LdFSVp0OWKtH9yWt4bMeKCxjy7SBhmo/JJiNee5zkiMjiaBzTGbAv83wn8Fzi2yu+TjTG5xpjcDh06RPPU1mGjcegE94vSeyRIAmPz6Haq1nun6PVRlPiSqs+gxjynNrbe19qwaZA+lfDaeD4JWCgiq0VkiYgsFZEl9T2YiDQXkZbBz8CZwLIoyZoyuHlU/bGf9j3cKbueIsaVYWzTnXe7lqRN1T6ki8Y8K4o12Picuop5tvEC1IWdLyYpj+YjSS68nrZ9dpSP1wn4byDWJwN4xRgzPcrnSBls9i7WhjY+DrC4aqRqvXeKXh1FiQ9uB7ZTF/uUdz912b43Gadx/DZp7nZQX0kMPDGeRaSVMeYA/vWYo4YxZj0wNJrHTEXcriVpU0MVJHUTsriNKbPn7rtPFGcfzuOqPBVDUZRaSNUBPvdxv3Y1VM4yLsdAkBjjbiUMCy8Adt5Xm/HK8/wKcC6wEP87aHi1MEAvj86ruCBVH1bXHbRd/bMjbK4bNusWDWx9OVGURMd1X2Nh35SyBqSF99ItqTi461oVi3RPZjwxno0x5wb+9/Ti+ErDSOX1boM46qBjIEesqTqSlUq48aKLiFX1PqiL42lxNimvKElGqi5ZlMprHTvFvrvufjakjTh5nm185pMVr2OeEZEL8ScOM8AXxph3vD6n4oxUfQxdd9ApONSXqnVDURQlXrjvm+zDUUiV51Io8cD5jAJ7ar7r1V88kkNxh6fZtkXkaeAGYCn+rNg3iMhTXp5TqRt3CQrsWu82SGqv8+wi27ZFt95VMh7XeyQ2wWfY8bQ470RRFKUOUrl/SlXU6+4MG+s9OJxtYqnuyYjXnudTgUEmMLQiIi/hN6SVBCBVH0SNea4bm+NeLVYtKuj1UZTkwMrwCkdGhJ2NlDMDyj7dLazFjtEs68mJ1+s8rwayw773AOq9zrMSe+xrpgPoKJ9SC7bee1v1UhQb0NhPd1hlR6R4pnFwHtNr1X13gT7ziYPXnud2wEoR+Trw/Rhgnoi8C2CMOd/j8ysRCLY7qdpQuV8Owx6cdrjBmmFVB+06aZaHssQYdy/lYlelV5QkI1UHuZy2U7ZeHmeJ4uwjldc6tuk9I5Xw2ni+x+PjK15jWUMVJJUzG9rW+XiBrdfIVr0UxQZ0pSp3/a5Ng7vup+96IkZccdo/Wai680F9j+WIBcYYist8pKcJZeWGZo3T4y2Sa7w2ns8xxvw2fIOI/KHqNiW2hGImUrihcoNVMSZOR/YDdcMu1VM3aZarZGnqeFaUuOJocDeFB8Js1T1V77ur5VOtc2q4Wzo2VpSV+ygsLadxehq7Cop5euZa9h8u5bYz+rN8235WbD/Ac5+vB+AfV+eSe0Rb0tOFvYdKSEsTnpm5ln/N2+ToXO1bNGHBXWO8VCfqeG08nwFUNZTPjrBNSVDsa6j8OGqE7FTdys432lhb7y3VS4k/IpIFTAEG4X8j/LExZm58pUouXC9bY+Eol5v+ySb9rRqkryeOB7YtuVab8wv5bNUuwI3ukbe/+vUm8nYf4rlZ62vd/z83Hk9ZuaGgqIy2LRrz6lebeHPhFscyf7D0+2rbrntpgeP9I3HtiTkN2j8eeGI8i8iNwM+A3iISniCsJTDHi3MqznHpeLbODZXK6+o51SVoZFmleyjm2Wl5e7R3o4u4LK8oAf4CTDfGXCwijYHMeAuUtKRq1mWH7Y6FqmNwOXDgmSSxJ1Fjno0xFJaUs3XfYdbuPMh97y1nx4FiAHq1b8763YdCZWfcdgpZmY1JF2H97kPsOFDETa98g8+hbjNW7uC8oV1rLSMiHC4t57LJc5m3Pr9eOl30THzHM+85dwAGGNYji2E9skhPS86H2SvP8yvANOARYGLY9gJjTP3uuKIoiqIoCYeItAJOAa4BMMaUACXxlCkZcZ/M0iYTyo+bV2nbtHc2IS45jY2aKA+3LhsQSujzGfYdLuX7/UXsLSzh+S830L5FEy49pjsvzdnIu4u3hcq+cM0xPPP5Ojq2bMIp/Trw8tyNLN2635Xc4YYzwJgnZrnavyq3n9G/zjJ7Dvqb1Poazm7IaZfJjNtOJSO98qJMCzfmc3SPNqRVMXrLfYb0NMEYY+XAXlU8MZ6NMfuB/SLyD2PMxvDfRORqY8xLXpxXcYeTCu6Pf7SrizI47aQC5S1S3xjjKq7KKt0D/53qb5HqFbo7XKLNJt2VmNAL2AW8ICJDgYXAL4wxh2rfTYlEqkYVuZ0ZZROuB04SqJEuLfcB0Ci9+uq3q78v4OMV3zN+WDc25xfyxoLNvLNoW7VyAEUlvjrPJcCh4jKmfLGeRZv38f6S7bWWf33B5mrbrn1xfuhzXft7wfL7ziKzcbprI/O9JZGv2+j+HXjuylwaZ1S+/uU+Q5pEd5bKiCPaRtwe9CCnguEMMci2LSIXAb8CWuCPhyoG1HhWFEVRFDvIAIYDNxtjvhKRv+CfdXZ3sICITAAmAGRnZ8dFSCXxcRfznEAWZBRw6tDwioPFZbRokoHPZ/AZwyerdvLTlxdG5diPffRdnWWaNqpufFdl8Ra/h3h+3t4Gy1QX/75+JCf2aV9pW1m5j+8PFNG9TeyjUkrK/IMLax46O+JARVWSdUp0MuC18XwqcDuwKPD9HmPMqx6fU6kDNzHPQmKNcEYDY5x64CpWO7YF93FVFunuIubZtnrvLqZMrNJdiQlbgC3GmK8C39+icsgWxpjJwGSA3NxcrWFRwLbn1LE+ltgExhgOHC7jk1U7mJ+X78rYqdo3l5T5ePKTNTTOSOOJj+s2VBOBzq2a8sAFg7j19UWcM7gzHVs1rfexTu3XgSlX51YyKvMPlfDdjgKO69UuGuICkJGeFhfDGfxTqfP2FDoynBVv8dp4bgOMBNYB3YEjRESMbcOFiqIoipKiGGO+F5HNItLfGLMaOB1YEW+5khVHUx8tMSCr4m6d5/hQVFpOSbmPZo3SufX1RXy7aR+Du7Xm7MGdOX9oV0SEcp9hxbYDbNlbyI3//ibqMjw6fTWPTl8d9ePWxc9G9WbKFxsoKfdxzQk53DqmH40yhOnLvue2NxYD0LJpBgVFZYw5qhM3jurNiCPa1HrMZfed5VqOvEnj6izTtnnjqBrO8ebNG04gb49GwiQCXhvP84BJxpjnRaQZ8AdgNnCCx+dVaiG03m2Kxj8aHMb9BstbdAGMcTjjwMqYZxfrPItYVu+Dz7yzem/TjAMlZtwM/DuQaXs9cG2c5Uk63CcMswun7Y6TNjzoowlv88p9htJyH3/5ZA1H98hi+BFtaNe8cajc7oPF5D44w7XcAFv3HWb68u/5xWuL6i4cJzIbp9OjTSardxRE/P3K447gznFH0Sg9jdJyH8WlPlpnNqpW7jdjj6y27cLh3blwePeoy6xU0KFlEzq0bBJvMRS8N57HAKeKyD3GmPtF5DEgx+NzKoqiKIoSQ4wxi4DceMthA6mQddkYQ7nPUFzm46nP1vL0zHUAHC4td3GMis+b9hRyyh8/i7aYnvHTU3pxct8OdG7dhN4dWjQo0dI5gzszoEsrfjaqT7UsyPUlPS2dpo3So3KsaDJn4mkay6vEHa+N5zsAH3AacD9QADwOHOPxeZVacBf7KdYl5XAe8xwo7604McUf8+zE+2hf5xSqxo5jnu25867Wdhe7ZhwoSrLgesZHDJ9Tn88g4k8qta+wlH2FpfTs0JzN+YX8ZcYaPl29k5IyH78750jSRLgktwcFRaW8MDuPf3y5wfF53l28jSd/dHStZYJ92EXPzGmQTk6559wBlPl8HNWlFUd1aUX7Fk14Y8Fmurdpxgm9KyeUCvd4e7FsT/jhnExdto2uWc3iLYKieG48jzTGDBeRbwGMMXsDU7oURVEURVGUKrgZ3HVD0Jjbf7iUnQeKyGnfvFryoQ27DzH6sZnuDx7g4Q9WAfDg1JX12v+dn59YZ5nDJXV7px+/ZChDe7SmW1YmP/v3Qo7p2ZafjeoD+GOWn/t8Pf07t6RV0wx6tM0MGWVOvZqX5vaIuD3cWPZy2Z6h3Vt7dmxFUWrHa+O5VETSCYyPikgH/J5oJY6k8nq34DzjdPD62OSF86/zXDcVMc/2KO+23tuEq3WesS3eW1GSA59rx3PlHYwx3PfeCl6ckxc9oTyiR9tmfP6r0fWaZrx0677Q55evO5aT+rSv1VB94dpjK31v2iidX4zp6/q8iUCTjHT+ff1IBnZtFW9RFCVl8dp4fhL4L9BRRB4CLgbu8viciqIoiqIoMeVwSTki0CQjrZoxt2VvIe8v2c7Q7ll8f+Awf56xho17CiMeJ6+G7eEIUFpuyJk4NRqi18lbNxxPn44taNm0Ucg7W1hSRmZjr18ja+ZXZ/bj5L4d4nb+eFF17WFFUWKLp62eMebfIrIQ/7IVAlxgjKnfXJ4AIjIW+AuQDkwxxkxquKSpRUVMTt1lbVvvtoLU8z5CcApI3eVszDROCtf7iphnh/XeIt0VpSbqikn1+QxlPsOOA0Wc/GjsklGN7l+3QViT4V2VKVflsnbXQf6zcAs+Y3juyhHktGtOmc+wZsdBvttRwAl92pHVrDGNM9JcJWOKl+E84eTebN9fxDUn9ozL+RVFSW08b/mMMauAVdE4VmAK+FPAGcAWYL6IvGuM0fUkFce4Xg7EJitKURTFMsKN4KLScl6ak8fb32ytcUmeRKJ9iyb07diCm0/rw7DsLMcG6dSl2yt9/9Gx2Vx2TA+6t2lGuxaVl7MZQyduOLV3pW0Z6TC4e2sGJ2HsbOvMRjxx6bB4i6EoSooSv/k29eNYYK0xZj2AiLwGjAfUePYKG92vOPc+2oizmGdbtU9t/VO53iv28fhHq/nrp2tjft4RR7RhaPcsjuvVlgkvL+T6k3qSldmIy0cewVsLN4eSZl0yojs7C4q54OiuDM9uQ3bbzKi3LasfHEuTjMRbUkhRFMVWks147gZsDvu+BRjp9UnHPfkFm/KdTZFKBny+iqUU6iI4g2vwvR96KVJMKSgqc2VAXfvifGvWFTxcUk5O++aOy//1s7VM/mK9hxLFjpIyf65Cp/V+7vo91tT70DPvoGyaCCXlPmt0D3L5yGzuOPuoeIuhRJH6Gs7Ds7O4+oQc/vrpWtbuPFjt94tHdOeB8YNo2qh67HJVqi4XNOGU3kw4pXcNpaPHwK6tWL7tAI2rZMtWFEVRvCXZjOdIvVilObUiMgGYAJCdnR2Vk44d2Jn8wpKoHCtRaJKRzskOkk784OhuHCwqo9yiqcuCcPGI7nWWG9Yji5tP68PB4rIYSBU7RvZsV2eZ9DThgfEDWb/7UAwkih1dWjelTWajOsvdfFpfPlu9MwYSxY6mjdKrrUkaiQuHd+NwaTk+i555gGHds+ItghJlLhjWlXcWbeOJS4fSJrMxg7q15ttNezmqSyt6tM2sc//xw7rFQEpv+Nd1I8nbc8jaWTKKoiiJiiRTPKeIHA/ca4w5K/D9DgBjzCORyufm5poFCxbEUEJFURTFZkRkoTEmN95yJDPaNyuKoijRJJZ9c7LN95kP9BWRniLSGLgMeDfOMimKoiiKoiiKoiiWk1SeZwAROQf4M/6lqp43xjxUS9ldwMY6Dtke2B09CeOObfqA6pQM2KYPqE7JQDz0OcIYk3qLy0YR7dqs2lsAACAASURBVJutQXVKfGzTB1SnZMDqvjnpjOdoIyILbJqCZ5s+oDolA7bpA6pTMmCbPkoFtt1b2/QB1SkZsE0fUJ2SAdv0qUqyTdtWFEVRFEVRFEVRlJijxrOiKIqiKDFHRJ4XkZ0isixKx5suIvtE5P0q228SkbUiYkSk7pTziqIoilIDajzD5HgLEGVs0wdUp2TANn1AdUoGbNMn1XgRGFvDb/W5t38EroywfTYwhrrjrL3ExrqqOiU+tukDqlMyYJs+lUj5mGdFURRFUeKDiOQA7xtjBgW+9waeAjoAhcBPjDGrXBxvFPArY8y5EX7LA3KNMTYl5lEURVFiSEa8BVAURVEURQkwGbjBGLNGREYCTwOnxVkmRVEURQEsnrYtImNFZHUgzmlihN+biMjrgd+/Cox+IyI5InJYRBYF/p6Ntew14UCnU0TkGxEpE5GLq/x2tYisCfxdHTupa6aB+pSH3aOEWevbgU63icgKEVkiIp+IyBFhvyXcPYIG65Ss9+kGEVkakPtLERkQ9tsdgf1Wi8hZsZU8MvXVJ5nbu7ByFwdiWXPDtiXcPVL81HZfRaQFcALwtYgUA58C2YHffioivkB9PSwi+SKyTEQ+jL0WldG+udJvydrma9+cAGjfrH1zUmCMse4P/xrQ64BeQGNgMTCgSpmfAc8GPl8GvB74nAMsi7cO9dQpBxgC/BO4OGx7W2B94H+bwOc2yapP4LeD8b4n9dRpNJAZ+HxjWL1LuHvUUJ2S/D61Cvt8PjA98HlAoHwToGfgOOlJrE/StneBci2BWcA8/NNxE/Ie6V+t93VMsA4CrYD9NKBvBkbhnwYe6bc8oH0MdNK+OYH+HOqkfXNy6KR9c4LrFChndd9sq+f5WGCtMWa9MaYEeA0YX6XMeOClwOe3gNNFRGIoo1vq1MkYk2eMWQL4qux7FvCxMSbfGLMX+Jiak7TEiobok6g40ekzY0xh4Os8oHvgcyLeI2iYTomKE50OhH1tDgSTQ4wHXjPGFBtjNgBrA8eLJw3RJ1Fx0oYDPAA8ChSFbUvEe6T4iXRfzwj+GKinPmBzYNNbwJnaN8cU7Zu1b44X2jdr35wU2Go8d6Oi8wXYEtgWsYwxpgz/aHe7wG89ReRbEflcRE72WliHONHJi329oqEyNRWRBSIyT0QuiK5o9catTtcB0+q5b6xoiE6QxPdJRH4uIuvwdwC3uNk3xjREH0jS9k5EjgZ6GGMqLUvkZF8lblS9N+cDNwH9RWSLiFwH7ATGiMhiYAn+l0lHfbOIfAG8iX8wfEtwWqCI3CIiW/AbD0tEZIqHOmnfnKRtfhjaN8cH7Zu1b04KbE0YFmmUuupoTk1ltgPZxpg9IjICeEdEBlYZHYoHTnTyYl+vaKhM2caYbSLSC/hURJYaY9ZFSbb64lgnEbkCyAVOdbtvjGmITpDE98kY8xTwlIhcDtwFXO103xjTEH2Ssr0TkTTgT8A1bvdV4krVe/M0cKwx5uZQAZHbgCuNMVsC39fhsG82xkR8wTTGPAk8GV1VQmjfXJmkbfNB+2YP5HSD9s3aNycFtnqetwA9wr53B7bVVEZEMoDWQH5gOsEeAGPMQvxz8vt5LnHdONHJi329okEyGWO2Bf6vB2YCR0dTuHriSCcRGQPcCZxvjCl2s28caIhOSX2fwngNCI7MJ+J9qrc+SdzetQQGATPFv/zQccC7gcQkiXiPFD/aN0dvX6/Qvln75nihfbP2zclB1SDoeP8B9wJbgUWBv3PCfrsD/xz51cBZtRwjA39Sh55UBLQPrFLm51ROSvJG4HMHAgHs+APitwJtE+C61KlTWNkXqZ6UZAP+ZBdtAp/jqlMD9WkDNAl8bg+sIULCgkTUCX8HtQ7oW2V7wt2jKOiUzPepb9jn84AFgc8DqZzwYj3xT0rSEH2Svr0LlJ9JRVKShLtH+uf8vqJ9s/bN8al32jcnh07aNye4TlXKz8TCvlkCCiUMInIv/iyAj1XZPgB4FX9weVdgBtDPGFNew3HOadeu3dScnBxvBVYURVFShoULF+42xnSItxyJRsDLUACUA2XGmNwaymnfrCiKokSVWPbNyRTzHMrSBmwQkWCWtrmRChtjPsjNzWXBggWxlDFpOFhcxv7DpY7KtmvemKaN0j2WKHaUlPnYdbC47oJAiyYZtG7WyGOJYocxhu8PFOFzMGbWOD2NDi2beC9UDNlzsJiiMmcJYru0akpaWiIn+XVHQVEpB4rKHJW17ZmPJiKyMd4yJDCjjTG7ayugfbOiKFUpK/eRkW5rJKkSC2LZNyeq8XyTiFwFLABuN/7lAbrhT7UfJGmztMWbsnIfJ0761LHxPKR7a9696SSPpYodN/5rIZ+s2umobOOMNL7+3elkZTb2WKrYMHnWeh6Ztspx+ReuPYbR/Tt6KFHs+HbTXn7w9BzH5W8c1Zvfjj3SQ4liR3FZOSc88ikFxc6M5+HZWbz9sxM9lkpRFEVJdT5bvZNrX5jP+zefxKBureMtjqLUSVyMZxGZAXSO8NOdwDP41wczgf+PAz/GYZY2EZkATADIzs6OksR2UVpu2H+4lLMHda7TMHpz4Wa27D0cI8liw66DxRzZuSU/PrFnreUWbMznjQVbKCgqs8Z43lVQTOP0NB68YFCt5fILS5g0bRW7C5x56JOB3QdLALjltD50b5NZa9mHp61kl0W6F5X6KCgu49whXTilb+2zml6dvyl0rRTFBQb4SEQM8JwxZnK8BVIUJfH5dKXfmbFw4141npWkIC7GszFmjJNyIvJ3ILhOmKMsbYEOezJAbm5uYgV0JwgmMOYwtEcWlx7To9ayCzbmsznfLuPZGOia1axO3dPThDcWbCHB0gI0CAM0Spc6dd+cX8ikaauScw2BGgjmdzhzYOc6O+i/fLLGqvsevJFHZ7ep897PXb+H3Q7DGhQljBONf+mbjsDHIrLKGDMr+KMObCuKEgmxJzpKSRESLsBARLqEff0BsCzw+V3gMhFpIiI9gb7A17GWT1EURVGUypiKpW92Av/Fn5Mk/PfJxphcY0xuhw6ab01RFEVJThIx5vlRERmG31eSB/wUwBizXETeAFYAZcDPa8q0rdRO0KPmZLBPkJCn2hYMxpnuUlHeFowBcTDMGypij+quVbHqvgd0cfbMY5fXXfEcEWkOpBljCgKfzwTuj7NYiqIoihJ1Es54NsZcWctvDwEPxVAcRVEURVFqpxPw38DAXAbwijFmenxFUhQlmUi0pXMVpSYSbtq24j3B5slJnImIfV4ov/e17nIhz7NF+jv3ukuovC2EZlw4vff2qO5Kdyx85hVvMcasN8YMDfwNDAx0Ky4pK/dx7EMzeHdxtXQuKcH+wlIOFDlbBcQ2vtm0lylfrI+3GHEhlUOeS8p85D74MdOWbo+3KIoL1HhWFEVRFEWJMweLy9hZUMzd7yyru7CFDL3/I4bc+1G8xYgLFz49hwenroy3GEqM2X2wmN0HS7jvvRXxFkVxgRrPKYw4GO+zMQui36vmQHeC3lfLcOJ59V6KuJGq9R6c5zlQFCX2BJ89n079UFKQVKz1tr5r2I4azymI27iSVGzQbMXtO5ld73CpW+9t0kVRrEVfpJUUxEkSU0VJJBpkPItIEyfblMTEWXtlX6NmcBvzbJfp4SbTuI04uvcW1ntwmWldUZT4YFeXoyhKHdiUXyYVaKjnea7DbUoC4XrJHn2mUxabbr17r7s92ruebWKR7oqSLKSFlkdUlNQjFbsdWwfqbadeS1WJSGegG9BMRI6mwpnVCsiMkmxKAmCjF8oYZxmnQ+U9kyQ+OPI+Wtygu5l1YBvOvO6KosQTjXlWFEVJXOq7zvNZwDVAd+BxKt63DgC/a7hYipe475e1I7cF9x5IjwSJA65nXHgiRXxIZd0VJVkIPnc2tbuKUhfB9xJbB61rw8YlUVOBek3bNsa8ZIwZDTxgjDnNGDM68Dce+Da6IipeoUkanJGKjZrNVcNmr3o0sPneK0oiE+xrNP5RSSWCtf2r9flxlSMeBLtbfeKTi4bGPF8WYdtbDTym4jUunlLBTuPR2dRd+6wIp8nSKsrbc/Pd1GPb6n1QF6e33ibdFSVpCBrP+vylLKmYb+JgcRkA05d/H2dJFMUZ9Y15PhIYCLQWkQvDfmoFNI2GYIr32GcaOkOnrdeNzXXDwjGRqKKeeUWJLyloPykBjEm9PqrcpxXeyTNvjMEYSEtLsQqSgNQ35rk/cC6QBZwXtr0A+ElDhVK8xY03UcRO09GJgWBj82SMO71seolzV+/Fqnof0t3hW5lNMw4UJVnQ507xGUOalW8fNVNWnsL13sWtvvqF+cz6bhd5k8Z5J4/iiHoZz8aY/wH/E5HjjTG6NFWSkmqjm0HcvqDYZEA6xuK6YbFqUSFV2wVFiTfBvqak3Mf+wlJaZzaKr0BKzAk6Yb/ekE//Ti1Tog6UlvviLUJSMOu7XfEWQQnQ0Jjnb0Xk5yLytIg8H/yLimSKZ7iL/RQrY3BSdbkig3EVy23TnXcf82yR9hrzrCgJT/hjd/NrqZt79XBJOau/L2DEAx+zftfBeIsTUwyGkjIflz43l0ufSw3fVPi07RkrdsRRkvix+2Bxrb+v2VEQI0kUJzTUeH4Z6Ix/6arP8S9dpXc4SbDQNlSihM1xrzYOikQTvT6KEn9WbT8QbxHixlH3TOesP89iz6ESTnv883iLE1MW5O3lnv8tA2B1ihhMZWHG8/X/XMDanamhtxvO+NOseIughNFQ47mPMeZu4JAx5iVgHDC44WIpXuLGqWRjzLPThBxBA9Im/d3GPNvkgnSliWX1PqiLU8PYJt0VJVkIn+2ys6B2T5RiJwVFpbw2f3O17WUWT20+XFJe6fuh4vIaSlqIdrZJSUON59LA/30iMghoDeQ08JhKjLBxKSYlOthdNaxWLgro9VGSk5IyHx8l8XI3DX2P3rrvsNVGVipww7++qbZt0eZ99LlzmrUxr2W+ynU2ze4XkEqEP/MlZfrsJgsNNZ4ni0gb4G7gXWAF8GiDpVI8xU0sp23r3UJgrWMn2bYDRWzS3/06z/bgtt7bpHzFOs8Os21bpLuSOjzx8XdMeHkhX67ZHW9R6kVDnrs9B4s5cdKnPDh1ZfQEUhKCrzfsAWpOGLWzoIiVSTzNv2qy7fB3lMc+XM1rX2+KrUBx4sJnZjN7bXTbrunLtpMzcSo7C4qietxUp0HGszFmijFmrzHmc2NML2NMR2PMs9ESTvGWFBrcU1xic9XQel87en2UZGRBXj5rd/qTS+UXlsRZmvoxd/2eSt837SmkqLScpVv2s2H3Ie54ewk3/mthxH33HfZPBGyId7KkzMf8vPx67+8Vc8IMij0Hi3n4g5VJ42EvK/dFLfmkCEz5Yn212RWj/jiTs//yRVTOEWt8PsPizfsqbTv3r18y7skvWLfrIH/7bC0T317KoeKyOElYP/63aCu3v7G4znLhVWPZ1gP835SvmFelHfjn3Lx6y/Gvef6Bh9Xfaxx5NKmX8SwiVwT+3xbpL7oiKtHGXcyzfdm2jcPA32ARm9be9N9KF9m27VHdFf51nu1RPqiLc8PYHt0V+1m/6yAXPzuXGSujn6l3f2EpP315AXsPRdcgX7plP76wREmD7/2QW16tnGH7lD9+xpF3T+e8v33J6Mdm8urXm5m27HsKikrJmTiV616cX+24DXlyH3h/BZc8O5fvEixR1eVTvuLD5d+zIC+f4x75hMmz1jNrTc2DBHPW7uadb7dGXQ5jjKv3oUPFZfS5cxpPfrK2gef1///8u108OHUlE16uPIBSWJK8McLHT/ok4vbl2w5weliyuIG//5BfvvZt0ryP/uK1Rfznmy11lov0nnHZ5HkUFJWSH2hz7vnf8kq/50ycytQl2x3JUdd7zIK8fMb/7UuKy/x1qKi0nJyJU6P+/MzPy7fK+11fz3PzwP+WNfwpSYA6mJSasDke3l7NooNeHyVZKCnzUVRazl3vLPPsHC/OyePD5Tt4fvaGqB1z4cZ8zvvblzw7a11oW0GRc8/a4Hs/AuCTVTtD28Kf2/xDJWzbdzjiviVlPl6em1dpeaAgy7ftB+DA4dJqv7llz8Fixj35BVv2Fjb4WAA/fXkhFz87l9LAHN/3F9dsPFw+5St++fqiBp9z6pLtfBjm5e15xwf0vOMDx/sHjZ83F1ZPAOaUnIlTeWTaKgC+21GxbFeyGJHhHCwuY+HGvZW27TjgPDHeO4u2ubr+icTm/EJyJk5l2db9lbZHeAwB/zM+/IGPKSqNPDDy5xnfRUWu3/13KYsDM1ugYsms+95bzp46ls+qjetenM8vw5bbu+TZuVzwt9kNEzaBqJfxbIx5LvD/vkh/0RVRUaKLP+a5bmyMeQadlusEWy+RrXopqckpj/o9s3PWVZ7mGG5YGGP46ydrQi+HicCWvX7DduX2hnt49x8uJffBGXy7yT/1dcPuQwx/4GNOmPRpxPKTZ63j7v8t540F1Q26oEGdnua8pdh5oIhyn+H7/RVeJWMMJz/6Gcu3HeCB91dw4dOzuffd5Tzxsf+Ff9rS7axr4PrNb3+7lZ0HKs45e+1uVx7z0nIfb3+zpVYj9OevfMNPA17e1+dXxN06nUIcXIIpI+x67iwoYnN+wwcUHqoltr0soJuvJsssTtz8yjdc9MwcDhT5B2fqOxX7E4czTAbeMz3i7Ix48Nlq/0DX62GZ1F+YvYEdB2r3xh559/SI2w3+Ke9/nvFdrWtEz167p8bfoOL9NpikLeg42VtYyogHZ9S6b218smon7yzaVmnbtv32eJ4z6rOTiPzGGPOoiPyVCLOEjDG31PO49wI/AYLzcX5njPlARHKAlcDqwPZ5xpgb6nMOJcwYdGhFJVbzqzQMZ3czNGXdopGDUNIsp/XeHtVd6SJil+5KbBCRscBfgHRgijFmktfn/L6GF889B0so9xnS04Q9h0p4/OPveDxguL1/80kM6ta6xmMWlpRRWFJO+xZNKm2v6jGKBsYYSsp8/OPL+nu152/IZ/fBYm5/s3p8pc9n+P5AESdM+pR/XJ3L6Ud1Yl+h33ApKKruXa4w9ir8Kl+t30Ofji1oV+V6LN+2n5fnbuS1+Ztp2TSDgqIy/vfzExnaI4tDJeWhqcQfLvcbOt8EjPvbzujHjf/2Z5TOmzSu3noHjzl2UGcA/m/KV46P+c2mvby5YAuvfr2JRulpnDe0Kz6f4U8zvuPK446gY6umlcobY/jtf5aGvo96bCbz7xxT53nKA1mkwwcjjn3oE8dy1saULzdw17kDKm0rKfNxoKiUSdNW8dZC/5ThC4d3D/1eVu7DAI3Sq/vN3vl2K706NGdI9yzA/xx8vGIH5w7p6mowJZyt+w7TJCMt9Cwt3epPalZUUs6QwOyJ+nDdSwsqXb9dBcUcKCqlZdMMOrasuHeHSsorzc6oiVe/3sTGPYVMPPvIestUG8aYUC4CnzHkTJwa+q1jyyY17VYrW/YWcuzDn7D7YDFz1u7hjRuOr1bmjQhLnlWTzcU5/7NwC2MHdaZ5k9pNx0QbtPGCehnP+A1ZgAXREiSMPxljHouwfZ0xZpgH51NqwUovpXFqQAXWebasHbDxlkYbK+s99uqlxBcRSQeeAs4AtgDzReRdY8yKeMhz//srmLFyB3ecfRTn/e3LSr+d+9cvWXzPmbTObFRtv6LScgbc8yHgN24KS8r4eKV/2u5nq3ex/3AprZtV7Fda7qPvndO4a9xRXH9yrxrl2XGgiKVb9rMxv5CRPdvyi9f8U4oPl5TT765pDdK16jI/4fiMYcLL/te0295YzAvXHhNqAybP2kB220zGDuoSKl/V83youIwfTp5Hv04t+OjWUwH/dOx2LZow7smK6xqccv72N1sY/9RsZtx2iisdnp65lhkr3Mert2pW/RU2UqzmIx+s5LlZ63lg/EAe/mAVh8Omwu4PTFFfuGkvf/10LYu37OfsQZ05rle7UJlh939c6Xi7Cop5b/E2/jB9FZ/9alREYxQITTFft+sQM1bsYMyATqHfbnt9EQ9fOJiZq3fWO0xq/a6D3Bo2Pf2mV77ho7DrGBwoueaFrykt97FxTyHb9h1m/SPVDffgNPe8SeP4cs1urviHfzDiF68tqmSobtlbyEl/+IwpV+VW0icSJwZmPwT3D16mYx+OHOdcH0rKfBzzUIWH9I8XD+GS3B7Vys3Py6dNZiP6dKweWXrH2/6Bkb2HSnj4wsEsyMvnh5Pn8dmvRtG9TTO27yviz598x9vfbOW1Ccexs6CY84d2De2zZOt+Tu3XIaJ8B4pKeWP+Zmas9BvxZVVSi9d3PfeiUh9Fpf59v66S5G/8375k8RZ3g33rdx2iX6eWNb4fLty4l9vfXMzsdbt54tLaTbGnZ9Yc419YUsaegyX0aJvpSr5Eo17GszHmvcD/l6IrjhILQsmDnO+gWILTgYDQlHXvRIk5buu9TYMmrpIEIlbddyUmHAusNcasBxCR14Dx+JevjAtz1u2pZjgHGXr/R+RNGkdpuQ9joHGG/61+UiC2FGDbvsPc9c4ylm2tWAJo3JNf8OVvTwt9/0Ugpu/BqSsZ3K01BioZXTNX7+T1+ZtZsHEvuyK8JDvxitVFpOMGGf34TDbn+6eI7z9cyoVPzwn9tvtgMTf86xvyJo3j0emreHrmOnq0bQb428r9h0sZep/fO/jdjoOVvGW/Gds/4vn+OW8jQK1Lhc0Nm2Kft/sQbVs05tHpq2ssXxuC4PMZ0sI8o+Gxzos272NYjyyem7UegLurJF6Cir4uaNTM+m4Xs77bRcsw79r+CDHgNweSu/W9c1rIOCwt9/HJyp2cNbATIsLBsGnJ1/+zsrf07W+3cri0nGnL6r8u+Z9nrKlkJH1UZQDi/vdXcP/71R/Bz1btZPSRHQG/l/CyyfNCv/l8JmQ4B8mZOJUrjzuCy0dmh7J6h+tTVFpOo/Q0ynw+0kXIqDKYMHvtbk7s0570KI/e/vWTNTxRJfb3128tYUHe3kp19KPl34eSrIXfg+CAQpDXF2zm9bBwhh88PZvOrZqyKixTdfBanX5kR/YfLuXS5+ayZe9hlt13Fi0ieGSrethfjxAuEQ1yJk7lwuHdOLpHVkTDuer7zJQv1vPVhvzQ1Pkb/rWQRy8ewkl92lc7bjjvL97Or8/qT+dWTflux0H6d27JG/M3c0S7TEYG2r6lYbN05qzbTbvmFd714OBk9zbNeGD8IK59cX5oVkwyUV/PMwAi8jFwiTFmX+B7G+A1Y8xZDTjsTSJyFX6v9u3GmGB2gZ4i8i1wALjLGJOcefmTDKfrwiYTrmOeLTMl1PtYNzbWe7BXLyXudAPC3wq3ACPjJItj+t7p9/redka/UDxukEgxw1v2HuaRaSs5c0AnLnpmbqXffhh4qQ6+nM9bv4drXvA+3nJFLXHTQcO5Lp6eua5S+X98sYEj2jWvsXxNxq7g719n1WI8/+jvFYbaqMdmOpKvJh54fwUrth+gVdPIr7IXPFV3gqLZa3dz53+rJ5wrcBGPO+7JL/jlmH785J9+L/8L1xzD6CM7Mq9KLP6AeyrHrzbEcAZ4d/G2ugtF4NoX5/Pxracwd/0e0kQqeS57/S5yQq6X523k5cDgSJCciVO5aHj3alml1z18TqWp3v835SvyJo2Leszr4x9HTppV1Qiump1898FiysoNxz1Suwd8X2FpyHtflQenruDVryvOMej3H/LsFSPoltWMiW8vcapCVHn7m628/U3kLNlXPf81C+4aE5pCH2k9+N+8VbfcJeU+jn+kom2cfOUIfvOfiv3+c+Px7D5YsTLB5X+vPBATZMvew1wbiEf/yT8XRJwNkcg0yHgGOgQNZwBjzF4R6VjbDiIyA+gc4ac7gWeAB/C3vw8AjwM/BrYD2caYPSIyAnhHRAYaY6qtCi8iE4AJANnZ2fXTynZCsZ+uiisW4NjzbOGUdeO63tujvJvYdX/Msz26KzEh0lNVqRIlWt8c7lGpajjXxnOfr+e5z9fX+Pu+whK27D1cyZvnJa9+vanuQrVQ1bMEcKCojK/zak80FIlgqOOnUfCoO2HFdv8r4AEXmcqr8sHShhmw4F9WKWg4A9z474WcM7hLNUMmkZaUOuNPs6JynEjLMfWOYIAf+1D9k09FYvt+ZwNDVYlU3+tDuOEc5IYa1mBPFHIDCcCGdK8554Nbqg5MVB1UdEIyhkg31HguF5FsY8wmABE5gjpsLWNM3VkW/Mf6O/B+YJ9ioDjweaGIrAP6ESHm2hgzGZgMkJubm4S3RPEat4aB2hGKoii1sgUIDzTsDlRyi6VK31w1PjYZ8WK97FSiqNRXowcwValvbG9NhHs/FXcscRkPrVSnocbzncCXIhJcyfwUAiPL9UFEuhhjgov3/QBYFtjeAcg3xpSLSC+gL1Dz0K9SK67iHy31QjnxPto4wdVgXE3dtenOp3LG6YoM+w7LeyaJYinzgb4i0hPYClwGXB5fkZKf7m2ahZa1UhRFURKDeq3zHMQYMx0YDrwe+BthjPmwAYd8VESWisgSYDRwa2D7KcASEVkMvAXcYIzJr+kgijNSNf5RDQMHWFw1UrXeO0WvjuIWY0wZcBPwIf7VON4wxlTPzqQ45ujsrErJx4L84aLBcZAmvowb3KXuQoo1/PPHx8ZbhLhw9qDOnNC7+jOvJB4NMp4DnAKcht/YPbkhBzLGXGmMGWyMGWKMOT/ohTbG/McYM9AYM9QYMzyY7VupH648cNhpbDpLGGafGWGMu4RhNs06cKuJPZpXkIqZxpXYYIz5wBjTzxjT2xjzULzlSXaaZqRX+t6rQ3PyJo3jh8dkM7RHVpykig9XHn9Eg9dFTlbOG9qVh3+QWgMmI3u1jbcIMSe7bSbPXDGCV35yXKXt7/z8xDhJpNRGg4xnEXkauAFYin+K9U9F5KloCKZ4j4W2oSPcGgapjoPZ9gAAIABJREFUaEjYXDds1i0a2DhopCjJyHUn9Qx9/vT2UaHPp/RtH6G0/ax6YCyrHxzLE5cOjbcoMUOALllNAbhgWNf4ChMjGkdYNzu7bSaf/3pU7IWJEWMHRcqj7NdbSTwa6nk+FTjLGPOCMeYF4BxgVIOlUjzFTRZh22I/gzgxEGw0IZwu02Uj7jJOi1X1viLTuLO7b9OMA0VJRkTgqC6tuHB4NzIbV/ZC/3JMvzhJFR+CrVbTRuk0yUinaaP0WsvbxL3nD2RUvw48dslQJl00hCd/dHS8RfKcSP1UiyYZtS6fluwE1xmvSk3LsNnEmQOSa41naHjCsNVANhBc/K0HEJ8FzhTXpKwR5XJCrk1LFjklVeuGoihKInDbGX4D+YlLh/HEpZV/C19DNxWoakztOBDd9YITlbUPnU1GwAt78YjuAIzq3yGeIsWNRy8eEm8RPKXqABlgfahCn44t+NWZ/Rg7KPlyGjTU89wOWCkiM0VkJrAC6Cgi74rIuw2WTvEEd1mHxUrj0VnMs+dixBx/zLOLbNsW3XpXWeZd75HYBJ9hxzHP3omiKDHjouHdPTv2j47tUXehBpCbk3hxn6f2895we++mk7jupJ58evupNZYpT8aFYSNw65h+PHX58BoTomVEmL4cD24+rQ+PXRKdqfJjB0aenlwT7Zo3BqBJhrtrcVyU4qYvO6YHvxzTt9r2G07t7fpYn/1qFFOuyq207aen9uKucUdx02l9KpV796bEjXW+8rgjGnyMn5zckxm3nZqUhjM03Hi+Bzgb+H3g7xzgfuDxwJ+SwNhoHDpBY57rxua4V4tViwp6fZRkYeLZR0bcfuHwbsy/cwyPXTKEx6P00h9Or/bNufvcAY7Lt2iSwbghlV8S/3DRYHq193Yaat6kcSy590zX+/3x4iE8e8WIatuH9cji0YuHMOKINtEQD4Al957JF78Zzb+vH8lrE45jcPfW3H3uAHp1aBE6T9U2yanxXNVQiTZHdWnFAxcM4vqw2PSq9O7QnPduOokHLxgU2nbNCTkA/GJMX8YN6cJT/zec4dlZZGU24pbT+nBy3/Y1Zpyuq3lunJFGy6YZvH/zSbRv0ditSnxz9xnVtt1+Zn8uHtGdeXecHtp265h+/OfGE5g98bSIx7lxVIVxmTdpHLec3pduWc145orh/OTkmq9XVe45z/+cdclqVu23Z68YHvr8yk9GAvD2z07g71fl8rNRfmO0Q8smDOuRVe+ka5MuGsKYo/zTiv9y2bDQ9prann9fP5Lnr6mod8F78LtzjqRn++aMGdCJAV1ahX7/9Zn9uf7kXpVCEXq2b86Q7u6SAt53/kDAPwW6TWYjx/sN6d66zjLPXjGC359X0d49cMEgV17xzMbpDM/O4j83Hk/epHHkTRrHneOct5+JSEOnbZ9jjPlt+AYR+UPVbUpi4dYDZ6Xx6GSdZwuNiJSesq7rPDuv0xbprtjLDaf25sLh3Tj2oU8qbX/0oiEhr91FI7rTvEk6N/zrG8BvtHbLyuSKf3wVKv/clSPIbpvJGws2c1Kf9pzarwN97pwG+F+GP16xg8zG6fxmbMULc2FJmSMZj81py6sTjiM9TZi6ZCrgz6A7tHtrfnhMNn+YvopnZq6rtE+Hlk1cXomaadXU+Ys0wPPX5HLakX5j4ddn9WfdzoM8fulQ3lm0lXOHdKVRehqX5vYgZ+LUiPt//utRrNx+IHS9AW45rQ9Pfrq2UrkvfjOaHoFkSK2aNgp9Dqd3h+Ys3Li3mg7lYY1z3qRxrNlRwBl/mgX4Zxv855stAIwJi6Wcd8fpfLVhD098/B0b9xRGlH3uHadx/COfVto29ZaTGPfklxHLT/uFf4GZl+bkVdr+7k0nUu4ztGiSQd9OLQEY3L01PzzGP1uhUXoa9waMnSBv/6xhnsaBXVsx9ZbKC970aJvJ7oMloe+Du7Vm6db9tR6nbfMKg/uJS4dWmk7cuXVTLhjWlXcWbaNjqyaMOKINRaXllfY/f2jXUFz2b8Oel9vO6BcKRbhz3AD+/sWGaud+fcJxdGld2UgeP6wb44d1iyjr2EFdWPfwOaSJf8B/3cPnhEIa5qzdDUCfDi14dYI/g/Xv/ru00v4vXHMM1744H/B7ex+dvorfjj2SnPbNyZk4NXQtBnVrTd6kceQf8l/LoHH6wPiB3P0//4p8y+47CwGaN/GbVaP7d6BH20zuHz+Iqky5OpdbXv2Wv1+V62h2wcM/GFxNdvAPyjXOSGPBnWNISxOuDgzKADU+nwD9O7Vk9Y4CAN664QQaZ6SxfNt+DhaVMWfdHi4e0Z2szEas23WI3h2a0zLw/N333oqIx/vk9lNp1iid/YdLaZyRxoHDpfzg6TmAvVPPG2o8nwFUNZTPjrBNSUBSdb1b155nb8RIaGyuGTZ71aNBqrYLSnLSsWVTsjIbsa+wFPB7Mqu+kPbq0AKAvh1b8MNjsiv9Fv5y9/vzKgyablnNGNi1FSf2ac+Jfapntw5/TjLShOeuHMF1Ly0AYMMj53DgcBnvLt7Kj47NrhajPCxsqanB3ap7fvoHDC4v+PrO00ODDasfHIsgXPDUbFZsP8Dd5w4IGc4APx9dMZX0B0dHngL/l8uGcXyvdox+bCaHSspp27wxYwd1IW/SOErLfTQK3Iug8bz8vrMoKze0duAdu3/8IM4d0pX+nStfD18Vz3PQQO2W1YzHLx1Ks8ZpfLFmd6UynVs3Zfywbsxeuzui8dyjbTO6tG7G8vvOYuDvPwT8BlVPB7MDfGEvFQvuGkP7FpEHPxpFYRp2eP814ZReTJ61ngFdqhvOUNGPt2yaQUFRGVmBa96rQ3OmXJXLS3PyeGnuRl6bcByfrNxBv8B1XHDXGMrKDZ1bN612zKrJ2oLiiMCGR5wbSqseGMueQyWcOKlisKJ1ZiOy27nLLh3+bIV/ru297agurfjRsT0YfWRHPr39VDq3bkpm4wyeCZttEXw2wqmaSPPK43NCxnOLJpXNqReurXmt6q5ZzXjrxhNqkbAyl4/Mjmg8L7vvLMfHCDKsRxbv/PzEkHGdEbhmA7v626GRYWvLD3O4JF5Ou+akpwldI8wOsJV6Gc8iciPwM6C3iIQnCGsJzImGYIp3uMqkK3Yaj04MBCuNCNfrPHsnSqxxnWXeQ1liTVAXp/feJt0V+wmv1pE8rUHjxk3bV9N01CDBF/VT+nWoNsVWRGid2Ygrj8+ptP2zX42iaq6vswd1ruZZunB4ZE9bNOjYsikzbjuF5dsO0CSwlvSzV4zgla838eMTc2rfOQJBr2DQqAtvO8KNxa9+dzpFpeUhz5wTmjZK55QIcdZlEaZtT//lyXRp5X95f/CCmqfoDs9uwxsLtoS+jzmqEzsLikLTesPlCxrOLZpkcLC45pkGQXGuPTGnRsM5WoRXn9+dcxQ/OblXNcMtVDZwT6Zclcuizfs4vne70KBCrw4tuPf8gfxu3FE0yUjnuDCjyYkOodlMwQwhLjuNpo3SSa/yQLqJZe/m0FCL9MwHZwxAxcBaVZpkVE/gVdGPxv7d8Nu7z+DoBz5u8HGC60ZPuSqXF+fkRWWGZaRDPHbJUA4cLm34wROU+nqeXwGmAY8AE8O2Fxhj8hsslRIbLLQNvSAVl+2x2TlrsWpRweZ7r6QmVV/0wT+9uCEJmRpnpPHhL0+hR9uKl/jnr8mt9cU6khdTRKp5li6oYZpqONltM9mUX8h95w8MTdd8aU4ev3/X7wl75fqR1fb56nf+mNU+HVvSp2OFNze7XWaNMZxOqavZ6NSquhezvhSX+aptO7Jzqwglq/PDY3pwXK92lPl8jHliFuOHdeW8oZXXT87KbMTRYV63924+idGPzazxmMEBkUjrE3uNkyn+aWnCT0/tzdqdBZW2i0hEI7E2gtU7OBjdkMTvrZpVNkEyG1d8v2vcURFnZQT59Fc1J5QLJ/z1rVG6uI4ljnSseHSRbZpXjl+/a9xRDTremAGdKoU1NIRITV4wO7yt1Mt4NsbsB/aLyD+MMRvDfxORq40xL0VFOiXuWOl9xaGBYKfqahw5wNp6b6leSmpTlycoUsx/NNaMrTqdOHzKc0NIc2CRBL3p4VPKrz4hJ2Q8nxBhqnk0DdiaiMVY8+GS8roLBbjmhBxmrNwR+i4i5AQGMb578GwaR8jivOieyknW6pq6/aNjs9m4p7BSxmSvcNN/Vy8asnwbIkGlb2kNeKHIbJzBmofOZt76Pcxeu6fSdb7+5F617uvW6AdY89A5rveJRLjK7910UlSO6ZbraklSF4nZE09jX2FJ3QVr4fqTelZqQ56/Jpd/zduUkqFwDY15vkdELgJ+BbQApgDFgBrPCYzr0TPLHK9uPck2qe9Ul9BULO9EiTluk2bZNOPAjS7isryixJsXrz2G8/82u8bfe3VozhHtMl1lyE50go9owqz5HEMxInmea+Le8wdWS84VJJLhXBON09MoKY983qaN0ms8R7RxMwAaTHgV9Ii3aup/5R+WXX/vaxDXSShroFF6Gif37cDJfb1ZBi2adl2k0K/BDrJVe4Fbg7VbVjPHU91r4q4q7edpR3aK2oBhstFQ4/lU4HZgUeD7PcaYVxt4TCWBsHVAKYUdz+p9dIC19d5SvZTUpq6pmE0bpfP5r0fHSJqGMeaojo7KBeNDM6oYz49fMpReHbxdAiveFJc69zxHiyX3nsmRd08H/GsIz1uf+BGKf7x4KCcv3hpajqhjq6ZMveUketcQ5+sEqeK8DhpxVethohDVceDQsRJT16oM7NqKX5zet9J0eCU6NPSKtgFGAuuA7sARIiJG3RZJgdORK6uWK8K9N9Wm2uz00Qx1kDbpHvjvdPDAItXdLU9nWbI0JTV4+bpj2b6vKN5i1JtfjunLn2es4bkrna1NHJy2XXWK90U1xBp2auVtIqsQMWg8imvwAHtJ00bpXJrbnTKf4aELBpPfwCmw9cXNAOj/s3ffYVKVZ+PHv/f2Zem9uSwgiBSxIFZUBBtGiZqiRsVoXqMxxZ95k4AmimIhppgYE0uib4zGGktULBG7BkQQpSjSkd5Z2va5f39MYXZ3dvfMTjkz59yf69prZ86cmb2fPWfOc54ea9K68KzKrVUWmg27R9RY65+feQinDHZW6ZPNurYtZMKInlx5YvNdyjPBgqmnU5Cb02h2dJMciRaeZwPTVfVhESkGfg18CDifg91ktOyoX4ufkwzIq+M4PJqspLJ/kTHZJVXdPtPluvGDuW78YMf7hwvPDWcsjmXuL8en/Cb6l2cfypTnFtKmMPU36yP7dmDGgo2c22Cir1S76xsjI4/7FPhnWZ5oV544gEN6tuekQQfG1P/glNSP9Y5XuIvyiYMaj/1vrZwc4S/fOarlHVPs/ktajiHe9d1NfBItPI8HThaRm1T1VhH5LVCWeFgmleId8+yl1kdoTXq88w+Iu9XdS2mPe8xz6mJJt7hWpxPxVNqN8aJAHGOeU7F80oeTT61XcP/20aWN1tBOtbS1ppuI3Bzh5BjLh2Wasq4lzJ4yju4OZiPPNmOSWCFgWifRefWnAMcCF4We7wF+l+BnGmOMMcaYJtSGui7n57rTT6ZPx2J6dkj97N2xBCIVof7rI+TDJLdazw5FjmauzzaJzHBukiPRludjVPVIEZkPoKo7RaSgpTcZd4VbE511XfZSu2uQoo7GvYb38FIrnKrDydI8OeY5dN472VnEY+d9+Dvv7Lz3Uo8DY7woXID0YuGgJeEu61aGMH5k5737Em15rhGRXEJ3ZiLSDUj/TA7GGGOMMT4Rnm3byZhnrwlX6vqxBc5WyzA+PO0zTqKF53uA54HuInI78AFwR8JRmZSKZ+ynIJ5b81XVeas7eKvlXXHa+ui9q3PkNHZ03ntrreO45jkQb/U4MMaL6sIThvmw5Tl8bfZh0q3gZHxZaZRpEio8q+o/gZ8DdwIbga+r6jPJCMwYY4wxxjR2x3kj6Nq2kILcRNtAsk/Axy3PxthZ776EV85W1SXAkiTEYtIknvVuvTnm2XmrO3irFU5V4xzz7J3Ex3vee0kk7U57m6Q0GmNMor5xVF++0cSazl53YMyzxy7UDvgvxaYhqzRyn/+qLI0xxhiTFCIyVUTWi8inoZ8JbsdkvC0QzzAUj7Lyk3/ZsXdfRhaeReRHIvKliCwWkbuitk8RkeWh185wM8ZspnHMVBkc+5naeNItmB7nrY+ea311eNzBY8fex+f9gTHPDs97D6XdpMXdqnp46OcVt4Mx3ta+KNhpskNxvsuRpJ8fW9tNfXYOuC/hbtvJJiJjgYnAYapaJSLdQ9uHAhcCw4DewEwRGayqde5Fa4wxxhhj0mXS8WUU5OVw8ehSt0MxaTbpuH4U5GVku5/xkYwrPAPXANNVtQpAVbeEtk8EngxtXyUiy4HRwKxUB7R8y15q6ryzAte6nRXOdw7VcH2xcXeKokm/2kDAcesjwJrt+2nvkRru3RU1Dsc8B/fasqfKM8d+0+5KwOk618LeqlrPpH31tn2A81b3gKpn0h7WqU0BPTsUuR2GV/1QRC4D5gI/VdWdbgdkvCs/N4fLjitzOwxXhGcYn3zmEHcDccktE4e7HYIxGVl4HgyMCS19VQn8r6p+DPQBZkftty60LeW+98jHrN6+Px1/Kq2K8nNb3Kc4tM9Zf3w/1eGkVVFey2kvKgju8/NnF6Q6nLQa3qd9i/sIUJCXw6Oz1/Do7DWpDypNRCDfQa11cX4uHyzf5rnzvtjhd742oJ5L++XHlzH13GFuh5GVRGQm0DPGSzcC9wHTCHb2nwb8DrgixmdcBVwFUFpqLYbGtIaIsHr62W6HYVxwVL9OzFtj9ZKZwJXCcwsZcR7QCTgWOBp4WkQGELvBqNHIvFRk0LdMHE5FdW1SPitTFOblcuKgri3ud9lx/RjYrSQyu6U3CMcO6NziXof37cijV45mX5W3jv0hPVsuPOfkCM9efTzrd3mr0qhbuyLaF7Xci2D6BSNYtL48DRGlT2F+LmMObvk7f/kJZQzu2c5TY/0BSjuXuB1C1lLV8U72E5G/Ai838RkPAg8CjBo1ylsnlzHGpNg/rhjNlj1VbodhcKnw3FxGLCLXAM9p8M5tjogEgK4EW5oPitq1L7AhxmcnPYM+eXC3ZHxMViopzOP0YbHqObwvJ0cYM8i/x35E3w6M6NvB7TBc0bdTG/p2auN2GK5oV5TPGT79zpv4iUgvVd0YenoesMjNeIwxxotKCvPoX5iJHYb9JxNH3b8AnAogIoOBAmAb8CJwoYgUikh/YBAwx7UojTHGGHOXiCwUkQXAWOD/uR2QMcYYkyqSaV3zRKQAeBg4HKgmOOb5rdBrNxIcS1ULXKeqr7bwWVsB7wzYDOpKsDLBS7yWJq+lByxN2cBr6YHMTFM/VfVvl5QksLw5a3gtTV5LD1iasoHX0gOZmaa05c0ZV3g2zRORuao6yu04kslrafJaesDSlA28lh7wZpqMN3nxXPVamryWHrA0ZQOvpQe8maZ4ZGK3bWOMMcYYY4wxJqNY4dkYY4wxaSciD4vIFhFJyiRjIvKaiOwSkZcbbO8vIh+JyDIReSo0PMwYY4yJmxWes8+DbgeQAl5Lk9fSA5ambOC19IA302QO+DtwZhI/7zfApTG2/xq4W1UHATuBK5P4N8O8eK56LU1eSw9YmrKB19ID3kyTYzbm2RhjjDGuEJEy4GVVHR56PhD4M9AN2A/8j6ouiePzTiE40ejXQs8F2Ar0VNVaETkOmKqqZyQzHcYYY/zBFgwzxhhjTKZ4ELhaVZeJyDHAXwgtX9lKXYBdqlober4O6JNgjMYYY3zKum1nCBE5U0S+FJHlIjI5xuuXi8hWEfk09PO9qNfqora/mN7Im9ZSmkL7fEtEPheRxSLyeNT2SaHxactEZFL6om5egmnKyuMkIndHxb1URHZFvZZxxynB9GTrMSoVkbdFZL6ILBCRCVGvTQm970sRyZjWttamSUTKRKQi6jjdn/7oTSqISFvgeOAZEfkUeADoFXrtfBFZFOPn9ZY+NsY2x13uLG+2vNktljdb3uwGy5sdUFX7cfkHyAVWAAOAAuAzYGiDfS4H7m3i/XvdTkMr0zQImA90Cj3vHvrdGVgZ+t0p9LhTNqcpm49Tg/1/BDycqccpkfRk8zEi2Fp3TejxUGB11OPPgEKgf+hzcrM8TWXAIrfTYD9JOxcixxNoD2xM8PNOIdgNPPxcCK5Hmhd6fhzwusPPsrxZLW/O1DQ12N/y5gxMUzP5mOXNWfxjLc+ZYTSwXFVXqmo18CQw0eWYEuUkTf8D/FlVdwKo6pbQ9jOAN1R1R+i1N0jupDKtlUiaMlW8595FwBOhx5l4nBJJT6ZykiYlWPAA6ABsCD2eCDypqlWqugpYHvo8tyWSJuNRqrobWCUi34TgeGURGZngZyrwNvCN0KZJwL8dvt3yZixvdonlzZY3u8HyZges8JwZ+gBro543NSbrglAXiX+JyEFR24tEZK6IzBaRr6c0UuecpGkwMFhEPgzFfmYc73VDImmC7D1OAIhIP4I1pG/F+940SiQ9kL3HaCpwiYisA14hWGvv9L1uSCRNAP1DXcbeFZExKY3UpIyIPAHMAg4RkXUiciXwHeBKEfkMWEwchVUReR94BhgX+rxwV8hfANeLyHKCY6AfcviRljdb3uwWy5stb3aD5c0O2IRhmcHJmKyXgCdUtUpErgYe4cAkKqWqukFEBgBvichCVV2RwnidcJKmPIJdqU4B+gLvi8hwh+91Q6vTpKq7yN7jFHYh8C9VrWvFe9MlkfRA9h6ji4C/q+rvJDib8KMe+C41laaNBI/TdhE5CnhBRIaFWi1NFlHVi5p4qVWtZKoa82ZNVVfSulYdy5stb3aL5c2WN7vB8mYHrOU5M6wDomur+9KgG4SqblfVqtDTvwJHRb22IfR7JfAOcEQqg3WoxTSF9vm3qtaEuq18STBzc/JeNySSpmw+TmEXUr8bVSYep0TSk83H6ErgaQBVnQUUAV0dvtcNrU5TqJvb9tD2eQTHZw1OecTGjyxvtrzZLZY3R8niY2R5swdl3DrPIjKV4NiUraFNN6jqK6HXphA8aHXAj1W12Vk2u3btqmVlZakL1hhjjK/Mmzdvm6p2czuObGZ5szHGmGRKZ96cqd2271bV30ZvEJGhBGujhgG9gZkiMrhBt456ysrKmDt3bmojNcYY4xsissbtGLKd5c3GGGOSKZ15c6YWnmOJzExHcDbO8Mx0s9wNKzt9tHI7G8orHO07uEc7hvXukOKI0mfF1r0sWLer5R2BLiWFnDTYO41MO/dV896yrQQc9DgpyM1l3KHdKcrPTUNkqVdTF+DNLzZTUdNkfVs9Jxzcle7tilIcVfrMWrGdTbudfeeH9GzPob3at7yjMcYYY1pt0fpyBnQroU1BNhXJ/C1Tj9QPReQyYC7w09BU+32A2VH7xJyZTkSuAq4CKC0tTUOo2aeqto6L//YRdQFnXfbLurThnZ+NTXFU6TP52QV8vHqn4/0/umEcPdp7oxD1tw9W8ue3nc+xcc9FR3DuyN4pjCh9Zq3YztWPfeJ4/+8cU8rt541IYUTps7+6lu/8bTYOv/IM6t6WN64/ObVBGWPqCQSUCfe8z4/HDWLCiF5uh5N2+6pqEcGXhYiXF2zgX/PW8ffvZsJqRen10crtfPvB2cz48YmeaqhxYk9lDV/70weMG9Kdhy4/2u1wjEOuXKFEZCbQM8ZLNwL3AdMIzu42DfgdcAUOZ6ZT1QcJLuDNqFGjMmtAd4YIBKAuoHz/pAFcNLr5Coa7Xl/C/K+ctdJmi6raAMf078yvLzis2f1eX7yJO19dQnVtIE2RpV5VTYCi/Bxe+8lJze63sbySi/46myqHrbTZoCp0HB+49CgO6dGu2X2/9cCsyP5eUFOnBBSuHTuQbx51ULP73jbjC5Zu3pOmyIwxYdV1AZZs2sN1T37qy8LzsJtfpzg/ly+mub0kcfr98PH5bofgmplfbAbgw+XbfFd4rq0LFlPmrnHeoGPc50rhWVXHO9lPRP4KvBx6mqkz02UdDdU5dCopoKxrSbP7ti3MI8PmlEuYKpQU5rWY9q5tCyP7e4UCuSItpj03RyL7e0V4csQ+HYtbTH9+bo6njnv4QHYuKWwx7e2K8iLXCGNM+kioiaC6zjsVd/FyOqzGqwIBJScnVluRd+XmBBf+qanzX74TPtZOe4KazJBxS1WJSHR163nAotDjF4ELRaRQRPoTXGJgTrrjM8YYY4xJNk9V2JlWqQn4r+Ikz8cFyHCFmR/Tns0ycWDJXSJyOMG2ktXA9wFUdbGIPA18DtQC1zY307ZpWjiDdlK3KYjnWqEUdZZ2ObC/V6iCSMupj+zinaTHnRRPHfdQWpx95+0m3hhj3FBTpxRm4p15Cm3dE1wm3Y9dl8N5rZ97m2SjjPuKquqlzbx2O3B7GsMxxhhjjEk5q7Tyr/xcoaZOqakNQKHb0aRXn07FAAzs1vyQIk8Kfeet5Tm7JNRtW0TOF5FlIlIuIrtFZI+I7E5WcCY1wl9RBw2QiHgvQw+2vra8X6Tl2UPpd97qHh7z7J3ER3pcOD323kl6XGnHg995k3oislpEForIpyJiizi3gpeutyY+eZFxv/5rgdxYXgnAPz/6yuVI0s++89kp0Zbnu4BzVPWLZARjjDHGmKw1VlW3uR2EMdmmMD+Hipo6X06YtruyBsBTK5s4ZRXV2SnRCcM2W8E5+4RnHRYHbZAinmqAA8IXKwdpx4szTuNo4GtkyLOXEo9/z/tIbxMH+zr5/xhjks9b11sTj5LQ2tb7qvxXeO7bsdjtEFxjX/nslGjL81wReQp4AagKb1TV5xL8XGOMMcZkDwX+IyIKPKCqD7odULaxG+mgmroA+bkZtxhMSrUpyAVgX3Wty5GkX28/F56txiwrJVp4bg/sB06P2qaAFZ4wxdD1AAAgAElEQVQzWDxjnkE8VxuuxDvm2Vv/gPhmGveOuMY8I5467pHeJg5nWvdS2k3anKCqG0SkO/CGiCxR1ffCL4rIVcBVAKWlpW7FmNHsexe0v7qODsX+KjwXhwrPm0Ljf/2kS9sCt0NwjX3js1OiheefquqO6A2hNZiNMcYY4xOquiH0e4uIPA+MBt6Lev1B4EGAUaNG2T1jDPZPCdpbVUuH4ny3w0irovxg4XnLnqoW9vSe8GRpfmT1Zdkp0TP2JRFpH34iIocCLyX4mSaDOGudzi6qzmacjuyfskjc4aj10cPjXuPpdeA1zlrdjYmPiJSISLvwY4K90Ra5G5XJVlt9WIDs2b4IgBxfXoC9dpflnM22nZ0SLTzfQbAA3VZEjgL+BVySeFgmleKv6bIvt1fE2y3QS7Wi8SbFQ0n3ddpNWvQAPhCRz4A5wAxVfc3lmLJO9PW21odLFoVt2e2/rsvti4MdQf+zeLPLkaSfl+4z4mbf+ayUULdtVZ0hIvnAf4B2wNdVdVlSIjMp56wF0puctT6GZtv22IXdzy2v4HC27TTE4YZ4xrsb45SqrgRGuh1H1ovKaz75ahej+3d2LxYXtCvMY09VrS+7LueELrx7q/w3YZifRd9ert6+j4O7t3MtFuNcq1qeReRPInKPiNwDnEpw4rBVwI9C20wmi7Mw6LXCo5/F3wLpnYMf73nspfPez2k3JltEX29fXbTRxUjcEZ446o3P/dv6unB9ubuBuCA6u9m1v9q1ONwQndcu3bzXvUBMXFrb8jy3wfN5iQZi0s+vrVCq8bY+eqsk4WytX++Kp9eB5/h8vLsxmSz6RtqXra+hAb/7fbhck5cqquMVfd7/6In5PHrlMe4F46Jn5q5lwohebodhHGhV4VlVH0l2ICZ94r1I+/eS7j1+boH083kff9q9lHpjskP0t27Ggo38+WLXQnHVx6t3Bif29GolZgu+2r6f0i5t3A4jbaLzmw+Xb3MxkvSLTvvbX251MRITj4QmDBORQSLyLxH5XERWhn+SFZxJLZ/mS/EXJPxYjvDwueHhpCWFX68LxhiXReW1r/ts4qzo+4yTfvO2e4G4IDrtAZ/db/ny/tIDEp1t+/+A+4BaYCzwD+DRRIMyqRXPl1WQuGdozgZ+nTRLia8230tHPr7zPv6ZyTNaKClOj7yXkm5Mtmh4zZmzaodLkbjv6sf8NRpQgVx/rlPV6D6jzm8l6CjvL7PW52yQaOG5WFXfBERV16jqVIITiJks4M/LdCu6LqcmjIzm5XGvXqwUSSb7/xjjjoZ5zbcemOVKHG5R4LC+HdwOwxWq0KlNQeT5lj3+Wa6rYaXRx6v9U2nU8Dt/1T/8VWmUrRItPFeKSA6wTER+KCLnAd2TEJdJoXgKgyLeLDw6ann2YAEyOFlanG/wiLhS4rHzPpwWpwVjL6XdmGwRvtxOPWeou4G4RFUp61ISeb5iq79mH46+Po++/U33AnHJxMN7A3Dhg7NdjiT9pp8/AoCKmjoCPm55zxaJFp6vA9oAPwaOAi4BJiUalEkPv07GEfdyTT68jnn71PB04pLA/j/GuCE8H0du7oFbs6Nvn+lWOK6IznvG/e5d9wJJu+Cxf+26MZEtfitEXXli/8jjmT5Zrizc6p4TdeIPuOEVt8IxDiVUeFbVj1V1L7BTVb+rqheoqv+qjLJMPGM5g2M/UxeLWxwtVeXBMoQSX7q8dOjjPe+9lPhw0p32pvDid96YbCHAd08oA2DrnirK99e4Gk+6hC87s6eMi2x74N0V7gSTZuFeYUN6to9s80shKpzftC08sADQ9/7RcEVcb4rktQJzbjxw3i9c57/1vrNJorNtHycinwNfhJ6PFJG/JCUyk3JeLBw6Ee9EUH5ctsfLp4Zfz3un7P9jjEuispqbzxkWeTzy1v+4EIw7BOjZoSjy/M5Xl3C/DwrQqgeuvU9ddWxk+x9nLnMpovQJ32OJCHN/OT6yvWzyDHZX+qPiSIDu7Q6c9+fc+4G3Ji31mES7bf8BOAPYDqCqnwEnJRqUSa34xjx7c7ZtJ6VDL5Yhgocyjtm2PXjonRART1WaHLg5cf4OY0x6NZyb4LkfHB95rWzyDG/mxVGik7d6+tmRx9NfXcKOfdUuRJRe4Z5BxwzoEtl298ylTHv5c7dCSosDPaOga9tCfnbGIZHXDpvqn4ojgJd+eGLkcf8pr3j+O5+tEi08o6prG2yqS/QzTXp4sXDohI15bpmXx8N7N2XJYf8fY9zRcHjFkaWdOH1oj8jr/ae8wi0vLfbsDXXDpRTf/OnJkcdHTnuD2Su3s8ejLZENK2tX3DEh8vihD1ZRNnmGZ5dwipz3oUN/7diD671eNnkGe6tqPZt+OHDPNaJvB+6/5MjI9v5TXuG1RZvcCss0IdHC81oROR5QESkQkf8l1IXbZC5bqslZAcGb5UeNb8yzh27S4l/nOWWhpF10zX48+xtj0idWD5EHLxvFBUf2jTz/vw9X03/KK7y71JvrwUZfowZ2a8uHkw+sfnrhg7MZMfU/zPXgUkbR3bYhuObzF7eeWW+fgTe8wj1vercbd/ScHNHdtwGG3/w6A294heVb9qQ7rJSKldeeObwXR/XrFHl+9WPzKJs8g1+/tiSNkZnmJFp4vhq4FugDrAMODz1vFRGZKiLrReTT0M+E0PYyEamI2n5/gnEb8GrpsGXxVh74sCDh5TPDy63qyWD/HmPc1fAr+LtvjeTGCYfW2zbp4TmUTZ6RvqDSIFZe26djMQ9NGlVv2zfun+W9tNP4uBcX5HJnaAmjsN+/sZSyyTMor/BOC3ysW6yubQvrdd0PG//797jy7x+nPqg0iVSYNdj+7DXHN1qy7r53VnjuvM9WrS48i0gucKmqfkdVe6hqd1W9RFW3JxjT3ap6eOgneqrBFVHbr07wb/haPGM5RfBk07OzApT3ShHxrvPspUMf73nvpUoTW+fZmMzX3DXnf04awPxfnZa+YNwS4xo17tAeLJx6eqPtSzbtTkNA6RPrvuSi0aV8eduZkTWQw0be4p2xwOEebrHyp9XTz663fBfAm0u2pCOstIqV9stP6M/S285KfzCmRa0uPKtqHTAxibGYNPNe0dCZuMc8+7Ao4eXWRw8nLSmcLmdlTDQROVNEvhSR5SIy2e14slFLlVydSgpYPf1sXv7RibF3yHLNVR60K8pn9fSz+eRXp9GxTT4AeTneuVY1l/bCvFz+eOERrJ5+dqQQfcOEIWmKLPVausMa0rM9q6efHVnCrE/H4tQHlSYtVdIX5OWwevrZrLhjAof0aEf7orzm32DSItFu2x+KyL0iMkZEjgz/JPiZPxSRBSLysIh0itreX0Tmi8i7IjKmqTeLyFUiMldE5m7d6s0xQenk1Rtp/4559m66ksm7570302XcFeqJ9mfgLGAocJGIDG3+Xcn18eodkRas2roAgQyYXKimLsC2vVWO94+0wLXwPR3epwOXHFtK55KChOJzQ2VNHduj/id1AWX73ioqa+qoqQu0mPbOJQVMmzgccKd3UF1AWbtjf9zv21ReSXVtoMnXnVbS33h2sPt+cUH6C1G1dQFq65pOQ9jiDeX1vn8V1XVsLK8gEFBWbN0bOdYRDSYMa0rPDkUM6FbC4aUdWxN+Iyu27mVfVW2Tr6/Zvo8tuyvZ28w+LamtCzSa5G5PZU2k273TXmG5OcKR/TpRkJfb6liao6psKq+st217HNcuv0n02xdeR+HWqG0KnBpjXwBEZCbQM8ZLNwL3AdNCnzEN+B1wBbARKFXV7SJyFPCCiAxT1UZ9dlT1QeBBgFGjRrmfe2YihxeqBrsbD3B6sxG+gfFU1+W4z3vvJD6eid+CXda9k3aTFqOB5aq6EkBEniTYMy2la+wcNvV1dle27sa2c0kBxfm5rN9VkeSoEufkZl1IzjKSq7bt47y/fMiu/Zkxhnb+2p0t7hO+hrcm9bV1ARTYU1lL28I8KmvreHbeOsYf2oNXF20koPDPj9awdkfmnRcQVbGSwLFfv6uCmtoA3dsX8pvXv6R8fw1XnzKQP721nJc+25CkSONXkNdye55Aq29KN5VXUpSfw+rt+/n1q0uYtTLRUabJE2i5TiJ03sef+PKKGipr6jj7nvfZtrea339rJCWFeXz/0Xlxf1Yq/OOK0Zw0uJvbYcQlocKzqo5txXvGt7wXiMhfgZdD76kCqkKP54nICmAwMDfev2/i48VWSlVnM06Hd/FaOcJaH1vmxfMesD7rJlX6ANHLVq4DjoneQUSuAq4CKC0tTcofbW3BGcjodYOfmbeOK07s3+w+Tq9RizeUU5yfy6m/ezcJkaXeyq37Wtwnnjxs3pqdvPjpeh6ZtabZ/W55KTvWUnZacVBTF2DZ5r1MuOd9R5/73Pz1iQWWDA7utZxO+BkIKJOfW8DTc9clGFR6PDV3LRcc1bflHR3Yua+aFVv38o37Z8V8/fqnP0vK30mWyx6eE3NyuEyWUOFZRLoANwMnEjztPwBube2kYSLSS1U3hp6eBywKbe8G7FDVOhEZAAwCViYSu59Fuok4zICsFco7HLemJlCzn6karqHqdH8viHuZrpRFYjwq1peq3mlkvcKc+8O3D3e0X3P/xBVb9zIuSwrM0aZ9fbjjfWNd12rrAjzx8Vp+9cKiJEaVHucd0afFfZxU6j87bx0/fSazCkhOdG1b6Gi/5u5jlm7ew+l3v5eskNKm4YzysbS0hOaLn23gx0/MT15QafKni45wO4S4Jdpt+0ngPeCC0PPvAE8BjlqXY7hLRA4nmCesBr4f2n4ScKuI1AJ1wNWq6r2F/jKQFxuqYi0JEYtXlzTyaLKMA3boTYqsAw6Ket4XSHn/z+jWij/MXEr7ovx6Lbb7q2v5ctMejijtFOvtlFfU0L4oL65r/Rcbd/PHmcv4/bdH0iY07nT73io27KqktHMbOoQms2rOlt2V7K6s5eDubYHgOOAn5nzFZceVketwEqym9irfX8PIW53PxDz9/BGcObwnAQ3+P/p3LaGmLkB5RU3MwswHy7ZxdP9OFIbGXlbW1FEbUKpq6uhcUoCIhHp3Of+fPjtvHScO6kqP9kWO9j/Q+tq4JHHwja86/rthPzr1YNbvrGDznko+XL6dhy8fxfEDu1KUX3986Zrt+3hh/gbGDunGYX3rj7tdsXUvNXUBdu6rYXT/zuTmCJvKK+nZwVmanAr/X2M1avx3+TYu/ttHjj+rd4civjdmAKcO6c5bS7Ywdkh3erQvZGN5JWVdSuqdi/O/2slXO/Yz8fA+kb+/YuteDurcJnIuxOuTr3Yye+V2vjO6H7sra8hxcO43VYCsrg0w+JctH/uRB3Vk0nH9GNa7A4f0bFfvtf3VwZ4sbaLGk++vrmXl1n3886M13DpxOPm5wa7lu/ZX064o3/H3NZa9VcGhA06JxK4wU1X6T3klxitNW3b7WZG0hGPZuqeK/l1LGn12c9/lmroAVbWBuNIR/ntt8nMdHfNMlGjhubOqTot6fpuIfL21H6aqlzax/Vng2dZ+rqkv/rGfxiscj3mODKvyztG35ZqcCd78uh2FyTIfA4NEpD+wHrgQuDidAVw3fnCjbW0K8posOAN0KG65oNvQob3ac/+lR9Xb1qVtIV0ctpoBdG9fRPf2B54X5efy3ROa76YdS8Pv6e7KpgvOK+6YwM791bQpyK1XOIgWnoAsPzenyVbAEwd1rfc8XMCMvnmOt+I53u6qTX36iJtfj7n926MO4pRDunHm8J4JVYr361LCT8YPivnawG5tG21LdsEZolqeG2z/1gOzmLOq6Tald/73FMoaFIyiRVc6xUrLEaWd6n2XRISDu7drtF88jiztxJGhz3RS6RT8u43P+wXrdnHuvR822vf/vns0I/t2ZPbK7Zw+tAd5uc2PqY71vWhTkMfwPh248/zD6m3v2CbxyfriLXDGmudg1/5qDr/1jUb7Pv3949hTWcPQ3u3pWFxAcUHzFRxtC/NixtPS9yU/N6deIdypeNOeaRKN/m0RuRB4OvT8G4Ct4G0yWrwFAytIGGNM01S1VkR+CLwO5AIPq+pil8PytHALb9iqbfsY+9t3Gu338OWjOHVID8B5t9hsEZ03l02OfeuZbWMpW3KgYvvAton3fsBn68rr7XfjhEO54sT+CbWMZqKGw662761qVHAuKcjl05tPjxTqJozolbb40qkuoDELzv/83jGM7t/ZhYj8o1WFZxHZw4Her9cDj4VeygH2EhwHbTJUuKuT0+WavFh4dFL77K0sJ8hxl/VUB+KC+Gac9lbr64HeJjbPgUkNVX0FiK/voEmahgXnZ685juF9OrS6S20ma1iAnPZy/cm++nctYeb1J3uu4AhRK2GEnj8x56t6Bec5N4yju8Pu79kqfA+7Y181R902M7L9k1+dlpXLtznVsNv2wBsOXG6/eVRffvPNkQQCmrVdobNJqwrPqppYXw1jXBTvEkReWrLIGGOMN4RzptPvrj8xmNdaWxs7UDioqq3joQ9WRZ6vunOCZ+crAerVbNfWBZjy3MLI8+W3n9Vi1+RsF92gc+S0A62uS287y9FSV9kserx3dE+LO88fwUWjgysYWME5PRLudC4i53Ngtu33VfWFhKMyKRXPmGcR8WTh0Wmru9eoOmx1j0xKkuqI0ieeMc/16/azX9y9TVIbjjEmQdHXsaWb90YeL7v9LBeicYeiHPLL1yLPvV9pcICqcs0/P4k8X3nHBN8UnBTYsqcy8vzmc4Z6vuAMjYdqABTl50QKziZ9EjrbROQvwNXAQoLLSl0tIn9ORmDGpIqNeTbGGJP1FKY8tyDy9Mmrjm3V5D3ZJlxxsL+6LrLtmauPcyma9IquNHnj880A/PjUg31TcA5X7I++/c3IttZMtpfNor/zS6b5p7IskyTa8nwyMFxDVSEi8gjBgrTJYPGs89zSunJZy1Gre+rDSDenvQgOzOjpoYMfx7hfr431j6u3Cdb0bEymEwQFnpizFoBR/Tpx7IAu7gaVJuHL2C0vHZiT7ugyf0yQFE779n3VkW3Xn36IO8G4JDpv/uLWM90LxAXR3/n7vnOku8H4WKJVlF8C0f0FDgIWNLGvMRkh7pbn1IRhjDHGtIoIBKIys6e/74+W12iL1u8G4MoT/dPyGK78ve+dFS5H4g4B3v5yS+R5S0sweYkIBAIHvvNneXQW8WyQaMtzF+ALEZkTen40MFtEXgRQ1XMT/HyTApExEw6nXfZi4dFZq7snm54djnUP7e6hg+/ncb9xjff2WNqN8arobst+6bYLjXsPTTlriEuRpF/Dozzz+pNcicMtIsElmgDGH9rd5WjSSxD2RX3njXsSLTzflJQojMlgtmyPMcaYTBJdiLphgn8Kj9C4AOn1GaajRdcbdG1bwMHd/bX4TXT6/3rZKPcCcUF02m85d5h7gZiEC88TVPUX0RtE5NcNt5nMEk9Z0KvjHx2NZ/ZgRb4S31huLx36eM97L1WahNPitDeFl9JujNdd4bMJk6L5edznJcf2czsEV3l6SbIW+P3Yuy3R6rrTYmyzqd+yhF8vO/EWDPxYjPBkl/UQH+e3jti/x5jMF30d81PLK9RP+yifTBQWFp03D+zW1sVI3OHle5OWRKc810fDNDJRq1qeReQa4AfAQBGJniCsHfDfZARmMkNw/KP3io8+bXhGVePKfLzUABlPUrw27jeeMc/R+xtjMlO41e2UQ7q5HEn6RV/HurUrdC8QF0Sn/aDObdwLxCX7qmsB+Mm4QS5Hkn5W8Z85Wttt+3HgVeBOYHLU9j2quiPhqExa+LXLS7wFAy8VIJ3y8qnh55prJ7x87I3xmu4+KzyCXcPD+nQsdjuEtCvfXwP4M+3he/aCPH/1NMlErSo8q2o5UC4iD6nqmujXRGSSqj6SlOiM67yaRTmbddibqfdospLK/kXGmEwWHn7UvV2Ry5G4IHSBbleU6LQ92Sc6/+5SUuBeIC7ZVREsPHdsk+9yJOlXVROcaduPre6ZJtHqi5tE5D4RKRGRHiLyEnBOMgIzqRPPSlXR+3tF/Onxzj8g7lZ3L6U9fN477brsnaTHN1maiKfSbowX7a4Idl/t7MMCVPgS3qHYfwWoaH5aniwsvExVJx+e93uqgt/59j6sNMo0iRaeTwZWAJ8CHwCPq+o3Eo7KZAyvtlI6W+fZm7yarqTy6Inv1d4UxvhNeOxn20L/3UiHr2N+LDxbl/Wgjj489nsrg9/5dkX+S3umSbTw3Ak4hmABugroJ3Z3lvHCrYl+nTwo3tZUL7XCOU1L+NzwVNrD573j/b3EeWoEb/U4MKklIlNFZL2IfBr6meB2TH5QUR3swtmmMNflSNIvfA0vzvdh2u0OG4CObXzY8hwpPPuvwizTJFp4ng28qqpnAkcDvYEPE47KZAyv1nI6G/Oc+jjcYPVbLfPqf8ir6TKuu1tVDw/9vOJ2MH5QERr/WOLDluewwnz/TZxk1/AgP/Y62FtlLc+ZItErz3igRkRuUtUK4LfUn33bZKD4x356qxUq3uR4KfVO0+LFSpPIcffheR9XUsRbPQ6M8aL9oZbnkgL/FZ6rawMAFOb5seXZe3lza/hxxukDhWf/feczTaJn3xTgWOCi0PM9wO8S/EyTQbx6nXbU8uzBAiRYzbUTfj7vjWmFH4rIAhF5WEQ6xdpBRK4SkbkiMnfr1q3pjs9zIt22C/xXgKyKFJ79V4CyS7h/+fk7n2kSvfIco6rXApUAqroT8N9AhCxzoAHO2WXYa41Qfl7n2Wlr6oExz95JfLznvZfE1/AsnvvOm8SIyEwRWRTjZyJwHzAQOBzYSBMV6Kr6oKqOUtVR3bp1S2P03rS/JtgK5cdu21W1wUKEHwvPxr9q6oKVRn5sdc80iV51a0Qkl9C9mYh0AwIJR2WMMcaYjKCq453sJyJ/BV5OcTiGA61QJT5sharydbdttyMwbqkOFZ7zc63w7LZEj8A9wPNAdxG5neByVXckGpSI/EhEvhSRxSJyV9T2KSKyPPTaGYn+Hb8KtyY667rsrZZXCKfHwVJVXm19dXjcwWPH3sfn/YG13R2e9x5Ku0ktEekV9fQ8YJFbsfhJTV3wS+rHG+nwmGc/tsDZmGf/qvXxdz7TJNTyrKr/FJF5wDiC95tfV9UvEvlMERkLTAQOU9UqEeke2j4UuBAYRnBW75kiMlhV6xL5e8YYY4xptbtE5HCCVS6rge+7G44/BALBG+ncXP8VpmqsBc74UG3Av2P9M03Cg2VUdQmwJAmxhF0DTFfVqtDnbwltnwg8Gdq+SkSWA6OBWUn82zE9O28deyprUv1n0mb7vmrnO4dqOf/+4aoURZN+FdW1jlsfAV5dtIkvNu5OaUzpsmrrPkcjfsO12598tdMzx/7j1TsBZxOuiAgbdlV4Ju2b91QBzlvdawMBz6Q9bEiv9hw7oIvbYXiOql7qdgx+FO4ckuvDlshI99U8/6U9zI9rXPudn3ubZJpMnGliMDAm1A28EvhfVf0Y6ENwXemwdaFt9YjIVcBVAKWlpUkJ6E9vLWP19v1J+axMIQI92he1uF+fjsF9pr70eapDSqveHVpOe7d2heTlCH//7+rUB5RGYwZ1bXGfHIGe7Yt4+8utvP2ld2bGbV+U52iCnd4di5m3ZqenzvscgR7tC1vcr3fHYgLqve/85ceXWeHZeMZT3z+Wlz7b6MuZdy85th+L1+/m6pMGuh2KK+44bwTHDujsdhiuGNC1hICXxlTFYcpZQ7jz1SXk5vi30ihTiBvjOUVkJtAzxks3ArcDbwE/AY4GngIGAPcCs1T1sdBnPAS8oqrPNvV3Ro0apXPnzk043vL9NZ77subliuOF1ssraiJdxLxABDoU5zsaO7SvqjYyvsor2hXlkeeg5rKqto79Vd4aFVFckEuRgxr7uoCyu8I7vU0A8vNyaOtwZl4vXvMK83Nok4Q1cUVknqqOSkJIvpWsvNkYY4yB9ObNrrQ8Nzdzp4hcAzynwVL9HBEJAF0JtjQfFLVrX2BDSgMN6dDGWSHTqzoU+zf9JYV5lLTcWOdJhXm5vpzNFCA3R+hU4t9V9/x+zTPGGGOMicWVlufmiMjVQG9VvUlEBgNvAqXAUOBxguOce4e2D2puwjAR2QqsSX3UadEV2OZ2EEnktfSApSkbeC09YGlKt36qagsVJ8Dy5ozmtfSApSkbeC09YGlKt7TlzZk45vlh4GERWQRUA5NCrdCLReRp4HOgFri2pZm2vXSDIyJzvdRV0GvpAUtTNvBaesDSZLKP5c2Zy2vpAUtTNvBaesDS5GUZV3hW1WrgkiZeu53gmGhjjDHGGGOMMSZtbL5zY4wxxhhjjDGmBVZ4zh4Puh1AknktPWBpygZeSw9Ymoxxk9fOVa+lByxN2cBr6QFLk2dl3IRhxhhjjDHGGGNMprGWZ2OMMcYYY4wxpgVWeDbGGGOMMcYYY1pghWcXicghIvJp1M9uEbmuwT4dROQlEflMRBaLyHejXquLeu+L6U9BYw7T1ElEnheRBSIyR0SGR712poh8KSLLRWRy+lPQWBLStFpEFobeOzf9KWhMRP5f6HxaJCJPiEhRg9cLReSp0HH4SETKol6bEtr+pYicke7Ym9LaNIlImYhURB3f+92IPxYHaTpJRD4RkVoR+UaD1yaJyLLQz6T0Rh5bgunJuOud8SbLmy1vdovlzZY3u8Hy5jipqv1kwA+QC2wiuMh39PYbgF+HHncDdgAFoed73Y67lWn6DXBz6PEQ4M2o/VcAA4AC4DNgqNvpSCRNoeerga5uxx4VTx9gFVAcev40cHmDfX4A3B96fCHwVOjx0NBxKQT6h45XbpanqQxY5HYaWpmmMuAw4B/AN6K2dwZWhn53Cj3ulK3pCb2W0dc7+/Hmj+XNljenMQ2WN1venFXpCb2W0de7VPxYy3PmGAesUNU1DbYr0E5EBGhLMIOuTZaqwqwAACAASURBVHdwrdRUmoYCbwKo6hKgTER6AKOB5aq6UoPrfT8JTExnwA7Em6ZMlQcUi0ge0AbY0OD1icAjocf/AsaFzsGJwJOqWqWqq4DlBI9bJmhtmjJZs2lS1dWqugAINHjfGcAbqrpDVXcCbwBnpiPgFrQ2Pca4xfJmy5vTyfJmy5vdYHlzHKzwnDkuBJ6Isf1e4FCCJ/JC4CeqGj55i0RkrojMFpGvpynOeDSVps+A8wFEZDTQD+hLsPZrbdR+60LbMkm8aYLgTdZ/RGSeiFyVliiboarrgd8CXwEbgXJV/U+D3SLHQlVrgXKgCxl6jBJME0B/EZkvIu+KyJg0hd0sh2lqSsYdpwTTA5l/vTPeZHlzBl5PYrC8OQOPkeXNjWTccbK8OX5WeM4AIlIAnAs8E+PlM4BPgd7A4cC9ItI+9Fqpqo4CLgb+ICID0xGvEy2kaTrQSUQ+BX4EzCdYYx+rpjFj1lJrZZoATlDVI4GzgGtF5KR0xNsUEelEsKa3P8HzqkRELmm4W4y3ajPbXZVgmjYS/C4dAVwPPB71HXONwzQ1+fYY21w9TgmmBzL4eme8yfJmy5vTyfLmeixvThPLm+NnhefMcBbwiapujvHad4HnNGg5wXEJQwBUdUPo90rgHeCI9ITrSJNpUtXdqvpdVT0cuIzgeLFVBGvgDoratS+Nu/e4qTVpij5OW4Dncb8r1XhglapuVdUa4Dng+Ab7RI5FqBtPB4LdEjP1GLU6TaFubtsBVHUewbFig9MWedOcpKkpmXicEklPpl/vjDdZ3mx5czpZ3mx5sxssb46TqLpeMZUyXbt21bKyMrfDMMYY4xHz5s3bpqrd3I4jm1nebIwxJpnSmTfnpeOPuKWsrIy5czNi9QFjjDEeICINJyQycbK82RhjTDKlM2/2dOE5WX7z+hK2760mJ0e48sT+DOzW1u2QjDHGGN9bu2M/Y+56m4VTT6ddUb7b4RhjjPE4G/PswEcrd/DWki08/tFXzFiw0e1wjDHGGAOMuettAEZMjWdyWGOMMaZ1HLU8i0gxwdnUvkxxPBnpX9ccTyCgDLjhFQIeHiNujDHGZCtVJfOXhzXGpIKqsmNfNV3aFnLrS5/z8IeruOTYUlTh2rEH07tjsdshGo9osfAsIucQXP+rgOB6a4cDt6rquakOLpOE82MrOxtjjDGZZ+nmvRzSs53bYRhj0ug3ry+hb6c2PPTBKpZv2VvvtcdmfwXAPz/6KrJtwdTTeej9VRx+UEfGDume1liNNzhpeZ5KcPr+dwBU9VMRKUtZRBkqXJvt5dnJjTHGmGyxeXdlveezV263wrMxPqKq/PntFXG957AYQzyG9W7PsQO6cNyALowf2oMn5nzFuCHdaVeUT0VNHZ1LChKOddf+agrycmhTYNNNZTsnR7BWVcutKxTkSAasOG+MMcYYHnxvZb3nN7+4mDc+38z3Tx7AmEG2mpgxXqWqfL5xN2ff80FSPm/xht0s3rCbhz5YFfP1K0/sz6L15Xy0agdzfzmeMb9+mxMO7sI1pwzkqH6dI/t9sXE3y7bs5fShPcjLEX72rwU8P389k88awvRXlwAwbeIw1u6sYMpZQxARKmvqqKoN0KHYJjzMFk4Kz4tE5GIgV0QGAT8G/pvasDKTiNiYZ2OMMSYDxLrR/WD5Nj5Yvo1PfnUaa7bv44jSTi5EZoxJpWsf/4RXFm5K29+LvtaMum0mADO/2MLML7bw6wtGMKqsM+N+926T7w8XnAF+9e/FQLDy7/HvHcNtM77g8427mfvL8RTm5TS5akD5/hoK84PzPBfl5yacpoZUlSnPLeSi0aWMPKhj0j/fS5wUnn8E3AhUAY8DrwO3pTKoTJUjNubZGGOMyXRHTnsDgJd+eCIj+nZwORpjTDKls+Dckl88u7DV7734bx9FHocL5WGd2uTzPycNYPbKHby3dGu91577wfGc/5f/UpiXwy/OHMJbS7awp6qWLzbsprouwGXH9eN/xgzgoM5tWoxhf3UtVTUBAJ78eC1PfryWOTeMo3v7IiA4PKZLSQF5uc4WaDph+lsc078z/++0wVTW1DGoh/eG0jRbeBaRXOAWVf0ZwQK0rwlCwArPxhhjTFY4594P+NNFR3DOyN5U1dZRWXOge+QHy7ZxSM92dGtX6HKUxhinvv3ALLdDSIud+2u467XYixyd/5dgB+Cq2gC3vvx5o9f/MWsN/5i1BoB3f3YKeypreWH+et5dupWTBnfjpMHd+PSrXdw9c2nkPZcd1y/yePQdb9K5pIATD+7Ki59tYNJx/bhl4nCWbt5D28I83vlyK0N7t+egTsWs21nB8i17Oe+IPmwor2D9rgqem7+e5+avj3xedGEc4IX568nJEc4d2Tuxf5JLpKUJsETkLVU9NU3xJNWoUaN07ty5Sfu8Q375KpefUMaUsw5N2mcaY4zJHiIyT1VHuR1HNktW3lw2eUar3rd6+tm8tmgjVz/2Cf27lvD2/57S7P5rtu/joE5tyMlpfu6X3ZU1lBTkkdvCfsaY1nlh/nque+pTt8MwSfTzMw/hB6ccnPDnpDNvdtIGP19EXhSRS0Xk/PBPyiPLQGLdto0xxpisVjZ5Blc/9gkAq7bt458fraFs8ox6q2ms2b6PBet2sXLrXk7+zTv88c1lAAQCSkV1XWS/j1Zu5+43llJRXcdhU//DDc8tZO2O/Tzy39U8P38dC9eVt7hKx8591eyrqo08/2ztLrbuqUpmkltt654qHp29psX9VJX91bXN7vPfFdtYs31fk6+v31VR73/1wbJt3Pj8Qt5ftpUbn1/I395f2eR7jT84LTj36lDU8k4mIzTVup7JnIx57gxsB6JbnxV4LiURZbAcEVuqyhhjjMkgH90wjmPueLPV77/x+UUA9J/ySpP7/PHNZTw3fx1rd1QAcOmx/eoVKsOF66fmruWpuWsbvf+Y/p3p0b6Iy08oY/mWvXxr1EH85Z3l1NYpv3/jQNfJsYd04+0vt5KbIzx6xWj+MWsNry0Oju/s2raQd392CiWFeVTW1PHY7DXk5gi3vPQ5lxxbysmDu3Pa0B5AsJBfG1DmrdlJl7YFDOrelmfmrmNPVS1jD+lGm4I8jr3zTWZNOZU2+Xks2lDOa4s28ejsNVx/2mDOP7IP972zIrI+bteSAvZU1fK7/3zJ5t3Bgv0/v3cMj8/5iq4lBXRpW8jv31jKpzedBkBxQS6FeblUVNdx6E2vNfp/LJl2Js99sp7Th/Wga9tC5qzawbdC3XHvv+RIThvak0seCo4HjV6j97LjyijIczb20vjLcQO6cNM5Qzm0V3sABt7wCnWhsZa3ThzGTaGJuoxJVIvdtrNZsrttD7vpNS4cXcqvvjY0aZ9pjDEme3it27aIdAaeAsqA1cC3VHVnjP0mAb8MPb1NVR8JbS8A7gVOAQLAjar6bHN/M9ndtlfeMYEBNzRd8DXe8v7PxzqaCMl4y9od+xlz19uNtidyPpRX1DDylsbrPpv0Wj397IQ/I515c4stzyJSBFwJDAMi/SBU9YoUxpWRgi3PbkdhjDHGJM1k4E1VnS4ik0PPfxG9Q6iAfTMwimDPs3ki8mKokH0jsEVVB4tIDsHeammVkyN8dtPp7K6siXlzbbxlzF1vJ+Vm22SXWN/t4wd2SagipUNxPivumECOBJejDXv647Xs3F9Naec2PD7nK+69+MhIIfuKE/pTUpjLn95a7uhvDO/TnkXrd7c6RpN5nHTbfhRYApwB3Ap8B/gilUE1R0TOBP4I5AJ/U9Xp6fvj2DrPxhhjvGQiwVZjgEeAd2hQeCaY/7+hqjsAROQN4EzgCeAKYAiAqgaAbSmPOKS0cxuOLA2uR9qhTT4d2uTz7DXHccF9/piN1xi/iNVL9tErRzNmULeEPzvWBH/fOvqgyOOzRvQCGreO/vT0Q4DgEIkbX1jIE3MODNdoahKso6a9wfZ91Xx0wziufmwe91x4BG0L8/jjm8v4+39XJ5wWkx5OCs8Hq+o3RWSiqj4iIuG1ntMutHTWn4HTgHXAx6Ha78bztKdAjtgMmsYYYzylh6puBFDVjSLSPcY+fYDogbzrgD4i0jH0fJqInAKsAH6oqpsbfoCIXAVcBVBaWpqUwAOqjWbAPqpfZ1ZPP5v/+3AVt7yUllsDY0yKvbxgY6NtySg4J0NOjnDHeSM4tFd7enUojsw7EMu7Px9LZU0dXdsW8vwPTohsn3ruMKaeOyzyvLyihsqaOqY8t5C3lmzh+R8cz9zVO6moqWPcod3Jz81hcI92vLd0K5c9PCel6TONOSk814R+7xKR4cAmgmOj3DAaWK6qKwFE5EmCteZpySHFWp6NMcZkGRGZCfSM8dKNTj8ixjYleA/RF/hQVa8XkeuB3wKXNtpZ9UHgQQiOeXb4d5v1g1MObnJW3cuPL+Pg7m15+bONMSfwMsZkj4atsg9NyqxpJ0SEy44ra3G/toV5tC1suejVoTifDsX5PHz50ZFtR5R2arTfSYO7sWTamdTUBWhXFFy/fv2uCk6Y/hYAT151LEs37+Gwvh35+p8/BOBnZxzCoO5tuerReVw0+qBIi/m8X47nuqc+5f1lwc5DD18+io9W7uCB95zNcv/BL8aiCj98Yj5XnzSA4oJcvty0h43llTFb1UsKctlXXcfVJw909PmZxEnh+UER6QT8CngRaAvclNKomhar9vuYdP1xG/NsjDEm26jq+KZeE5HNItIr1OrcC9gSY7d1HOjaDcEC8zsEV+LYDzwf2v4MwTlS0uLiY5puwRYRxgzqxphB3bh27MGc9BsbC21Mtpq35sAchsN6t2fcoU237vpNUX4uRfm5ked9Ohbz8o9OBGB4nw4cO6AL0Ljbefj5necfFtn26JX1i1SnDunBlAmHoqp8vnE3w3p3qPf6nsoaRkz9D1efPJC+nYJjz/997YEW9VMO6Y6qMvmsIeTn5nD/uyu4eHQp7y/fxjmH9ao3zjybtFh4VtW/hR6+CwxIbTgtaqr2+8AOKegaFv3HreXZGGOMh7wITAKmh37/O8Y+rwN3hCrSAU4HpqiqishLBAvWbwHjSFNPsHiUdmlT78Zx4bpyfv7sAg7qVEybglzW7qyod3NuMtuPxw1yOwSTRpU1dfWez/jxGJciyR7D+3Roeac4iEijgjNAu6J8Ft1yBm2iCu+x3hsu3F87NjgO/NyRvZMaX7o5mW07Ziuzqt6a/HBatA44KOp5X2BD9A6p6BoWJiJs3l3JrBXbk/mxJgPl5Qoj+3a09SR9YO2O/azbWeF2GCbFenYoon/XErfDyETTgadF5ErgK+CbACIyCrhaVb+nqjtEZBrwceg9t4YnDyM4udijIvIHYCvw3fSGH78RfTvw6k8a34B//c8f8unaXfz9u0fz2OyvCKgy/fwRzF+7i8MP6sjLCzYy7eVg3cCUs4ZQUpjHyYO7sWFXBQvXl/ONo/rSriifgaFlsx6+fBRfbtrLrJXbeW/p1pixfHbz6fWWypl0XD/OHN6Li/46m2MHdGb2yh2cf2Qfbvv6cI6f/ha79tcwvE97AgF46PJRHHfnW02mc/X0s1m4rpzXF2/i3reXM+m4fnRtW0jHkgJ+9UJwbev/GdOfFz7dwNY9Vc3+z9772VhqAwFWbN1HRU0dQ3u1Z/zv3428fkz/zny0akczn+DM4B5tWbp5LwDXjR/EH2YuY+wh3fi/747mvL98yPyvdmFdAP1lRozxziZzOOmG7jUtrvMsIj+NeloEfA34wo2lqkQkD1hKsHZ7PcGM/GJVjbnyebLXeR7723dYtW1f0j7PZLZpE4dxqYMxLCa7jb59JltauHE02e/y48vqTcjSWl5b59kNyc6bk+WLjbuZ+uJiHrlidL1ukNG27Knk4Q9W8/MzDmk0WVnY3NU76FxSwIBubSPbPlq5nQ3lFZw5rBdVtXV8unYXQ3u1p3v72GO2ndpUXsldry9h295q3lu6lZu+NpQB3Uo4fmDXuCp/d+yrZt3O/RzWNzgH3K791eytqo10xYxHbV2ADbsqeX3xJgC+N6Y/VbUBivJz2bmvmqkvLWb6+Yfx2bpgpUTD/3V4/e5lt5/FTf9exE/GDaZnaGx7+DVbqso/wsc8zI69iSWdeXOLhedGbxApBF5U1TNSE1KLf38C8AeCS1U9rKq3N7VvsjPoDbsqWLN9f9I+z2Sm6roAkx6e0+RSA8ZbDv3Va5w6pDuXHNvP7VBMCiWr5dkKz4nL1MJzNqusqWP5lr1J767phqWb97BzXzXHhMZqRrPCs/9EF56/c0wpt583wsVoTKZKZ97cmrb2Nrg49llVXwFeceNv9+5YTO+OxW78aZNGDcfXGO/r3bGI4wY2vlEzxphsUJSf64mCM8DgHu3cDsFkKCs4m0zgZMzzQg5MypULdAPcGO9sTFrZsCp/UDRrZ3w0xhhjvCoQsBsxk3mctDx/LepxLbBZVWtTFI8xxqSVVZIYY4wxmScZk9AZk2xOCs97GjxvH91KEzXjpjGeYI2Q/mOH3BhjskdFdR3FBU0vj2O8YdbKA6vb/OCUgS5GYswBTgrPnxBcHmonwXvMjgSXs4Bgd2631342JqkkVJSKdzI9k50UrPRsjDFZZPPuSsps6TnPu+fNZZHH140f7GIkxhzgZB2D14BzVLWrqnYh2I37OVXtr6pWcDaeZWVnn7DjbIwxWeWB91a6HYJJs3iWXjMmlZyciUeHZrgGQFVfBU5OXUjGuMu6bfuPWNOzMcZkja927HM7BGOMTznptr1NRH4JPEawjeYSYHvzbzEme4WLUdYg6Q/B2bbdjsIYY4xTHy6321BjjDuctDxfRHB5queBF0KPL0plUMa4KTwhnnXb9gc7zsYYY0zmKuvSxu0QjIloseU5NJv2TwBEJBcoUdXdqQ7MGGPSxRqejTHGmMz00OVHux2CMREttjyLyOMi0l5ESoDFwJci8rPUh2aMOw5027YmST9QbJy7McYYk0nmrdkZeTywW1sXIzGmPifdtoeGWpq/DrwClAKXpjQqY1wULkhZd15/sCXJjDHGmMxywX3/dTsEY2JyUnjOF5F8goXnf6tqDTaXkjHGQ2y2bWOMyXwf3TAu8ri6NuBiJMYYv3JSeH4AWA2UAO+JSD/Axjwbz4pMGOZyHCY9rNu2McZkhx7tiyKPn5671sVIjDF+1WLhWVXvUdU+qjpBg/0bvwLGpj40Y1xm3Xl9wQ6zMcZkn1++sMjtEIwxPuRkned6QgXo2hTEYowxrrCGZ2OMMSbzXHFCf7dDMKYeJ922jfEdEeu27SvWb9sYY4zJOCcc3MXtEIypxwrPxsQgWHdeP7CZto3fiUhnEXlDRJaFfndqYr9JoX2Wicik0LZ2IvJp1M82EflDelNg/Myu4d438qCObodgTD0tdtsWkfNjbC4HFqrqluSHZIwx6WXtzsbHJgNvqup0EZkcev6L6B1EpDNwMzCKYKeceSLyoqruBA6P2m8e8FzaIje+N/21JUw561C3wzAp1KWkwO0QjKnHScvzlcDfgO+Efv4KXA98KCK23rPxJBFBreO254UbLazXtvGxicAjocePEFyWsqEzgDdUdUeowPwGcGb0DiIyCOgOvJ/CWI2p54F3V7odgkkxsQzaZBgnhecAcKiqXqCqFwBDgSrgGBrUThvjFdZt2x/sEBtDD1XdCBD63T3GPn2A6HWB1oW2RbsIeEqtH61JsTlRaz0bY0y6OZltu0xVN0c93wIMVtUdIlKToriMcZVVdPqLWMdt42EiMhPoGeOlG51+RIxtDQvJFwJN9kYTkauAqwBKS0sd/lljGusetdazMcakm5PC8/si8jLwTOj5BcB7IlIC7EpZZMa4zJpPvC/cSGaVJcbLVHV8U6+JyGYR6aWqG0WkF8EK8obWAadEPe8LvBP1GSOBPFWd10wMDwIPAowaNcour8aYiOraAKu372Nwj3YA7K0Krojbq4NVlJjM46Tb9rXA3wlOCnIE8A/gWlXdp6pjUxibMa4RxLpt+4AdYmN4EZgUejwJ+HeMfV4HTheRTqHZuE8PbQu7CHgipVEaY1Kmpi7AP2atprYu0Ox+2/ZWsWNftePPXbS+nPKKljup3vziYk6/+z02lVeybPMebnv5cwA2llc6/lvGpEuLhWcN+peq/j9VvS702O45jbdZS6Sv2OE2PjYdOE1ElgGnhZ4jIqNE5G8AqroDmAZ8HPq5NbQt7FtY4dm4pKq2zu0QkiIQUBas28Wu/cHC6awV2xkwZQY7WyisVtcGqKiua7Ttg2XbKJs8g03llXz/0bk8M3dtvX2+2r6fVxZu5A8zl3L7jC+46d+LefjDVZEC9JY9lVz7z0/YF2oFfm/pVkbdNpMjp73Bu0u3MvXFxWzdUxX5vO17q7h9xufU1gV4bdEmXvxsA1/70weMvOU/VNcGeH3x/2fvvsOjqtIHjn/fFFKAhFBCCxB6VVoAAUVAKaLIrq6913VXd3V1d0VRQV2VdW37010V2+radUVRmhQRUEBCB0F6CT3UUNLf3x8zGVImZEimZGbez/PkyS1n7n1PLsyZd+655+xh8HNzeGbqWtbuPkrq6Ml8sng7J3ML+HFTJgCXvDyPIS/O5ePFJWM1pjqRivJg51RVf8cxiIhQNJaSaoLvw6uatLQ0TU9PD3QYJgi1e2Qqt/RPtSkwQlxufiHtHpnKn4e2457BbQMdjgkCIrJEVdMCHUcws7bZVNVNb//E9+v3u9a3jr84gNFUTkGh8s4PW7i0WxPia0Tx8uwNvP79ZiIElj06lK5PfAs45jl+5OKOrMo4Qq2YKBLjoxnWuREHjuVwxWsL2Jx53HXMmKgIcvJPf/d43MhOjPv6Z5/WzVtaNajJ7AcGBjoMEwT82TZ78szzs8BIVV3r62CMqS4ErE9vGLDpyIwxJvg8+5uz6fP0rECHAcCJ3Hw27D1G12Z1XNuOZuex/cAJujRNdG2bvW4vr87ZxOKth0q8/m+TS368LlRciTPAih2HueK1BR7FUlHiDARN4gywef/xigsZ42eeJM97LXE24cYGkAovNo+kMcYEj4alRtwuKFQiIwLzPj7g2TlkHsshKkJYMXYo932ynBk/n5qkZnCHZGavczcOnzEmGHmSPKeLyCfAlzjmdwZAVb/wWVTGVAN2TzL02egNxhgT/N5bsJVb+rf02fFf+34TrRvUYkinhizYdIBr3lhIcu0YujWrQ+Yxx0fj/EKl89jpZV5ribMxocWT5DkBOIFjdM0iCljybEKWY7Rty6yMMcaY6u7xr3/2SvJcWKj8+tUf6ZaSSFpqXbKy8zmancf4qevKlN2XlcO3xe4wG2PCQ4XJs6re4o9AjKlOrBdveLHrbYwxweXzu/rym2LPAr/741au6tWM2OjIMzrOzsMn+Tw9gxdnrndtW7HjMO8u2Oa1WE3l9GyRFOgQjCmjwuRZRFKAl4H+OO44zwfuVdWMyp5URK4AxgEdgd6qml5s30PAbUAB8EdVne7cPhz4JxAJvKmq4yt7fmMq4hhSPtBRGF8rusZik1UZY0xQSUutW2J97KQ1vPLdRhaMHsxHi3fQNSWRs1PqkJtfyJbM46TWj2fnoZM8PHEVCzcfLOeopjp5/7Y+gQ7BmDI86bb9DvAhcIVz/XrntiFVOO9q4DLg9eIbRaQTcDXQGWgCzBSRds7d/3KeMwNYLCKTVDV4hgw0Qcdy59Bno20bY0zo2J+VQ6ex08n1YNRp43t3nNeSN+ZtYd5fB7HtwAmuf2sRcdGRTLqnP2v3ZLF65xEmzN3sKt+0ThyT/3gudeJrBDBqY07Pk+S5gaq+U2z9PyJyX1VOWjR6t5sRbkcBH6tqDrBFRDYCvZ37NqrqZufrPnaWteTZ+ISNvhxe7HIbY0xoCJXE+e2b07j1P+7nQ//jBW2JFCExLorU+jXp0SKJhNhoVJUXZ27gV92a0KpBLVf5z5dkkJIUR6sGNUmu7Rip/OddR4mvEcnyHYdp36g2NWtEMXbSaibcmIYq5BcWEl8jimM5+fzlsxU8PqoziXHRFBZCXI2SXeNHvTKfFRlHmPXA+bRuUIvU0ZMBGHNxJ8Zc3AmAZnXjWf34MGKjIoiKjKBtw9pc2rUJ917QlhpREURHRvjiz2iM13mSPGeKyPXAR871a4ADPoqnKbCw2HqGcxvAjlLb3fblEJE7gTsBmjdv7oMQTTiwbtvh4VS3bWOMMcFm7RPD6fjYtECHccZWjhvK2eO+pWtKIl/dc65r+/6sHE7mFrD/WA49WyTxzi29ePLrn5l23wD2ZWVTUKi0qFez3OOKCPcPaVdm+296ppTZ1qlJAgCp9U8d751beruWa+BIZmvFRPHq9T1PW58v7+5P5rFcGtSOAeCnhy9g4/5jZcrViimbdtR0s82Y6syTf7G3Aq8AL+LoyfojUOEgYiIyE2jkZtcYVf2qvJe52aaAu6+j3KY2qjoBmACQlpZm6Y+pNOvSG/rsChtjTPAqfQe0utk6/uIS60dO5HHkZB4JsdH88rfhRJbq9lSUfDavFw/AoPbJDGqfDEBKUrwfIq4cEXHFDpCcEEtyqbm4jQkVnoy2vR24tPg2Z7ftlyp43YWViCcDaFZsPQXY5Vwub7sx3me3IsOKdds2xpjg9MKVXbn/0xUBOfcfL2jL1b2akRgXzW//u4QXrupKcu1YJq/czay1ZaexSoyPJjE+GoCYqOqd+Btj3KtsX4n7qSB5rqRJwIci8gKOAcPaAj/hSGXaikhLYCeOQcWu9cH5jQGs23a4KJrL20bbNsaY4HRZjxSfJs9f3d2fuBqRfPTTdi7t2oTuzZPILygkMkJKjI/y/u2nnia8+OzGXHx2Y5/FZIwJnMomz1X6pCkiv8Yx/VUDYLKILFfVYaq6RkQ+xTEQWD5wt6oWOF9zDzAdx1RVb6vqmqrEYIwx9v2IMcaYZnXjSGtRlxv6tqB13g7cOwAAIABJREFU/Vquu8PFjR3Z2bUcZYNbGRO2Kps8V+kzp6pOBCaWs+8p4Ck326cAU6pyXmM8ZaNthxe73MYYE7x+1a0JXy73/Gm+W/qnUq9mDW7u39LtIFbGGFOect8xRCQL90myAHE+i8iYakAEvvtlH/s+yA50KMaH8grs3rMxxgS7f1zRlXGXdubmdxazfMfhEvs+v6svCzYd4I4BrYiNtueMjTFVU27yrKq1/RmIMdXJ8M6NWLLtEBv2lp1qwYSWjo0T6N48KdBhGGOMqaToyAjqxNfg39f1oN/42cx/cFCJ0anTUusGMDpjTCixvirGuDH+8rMDHYIxxhhjzkCTOnFlpocyxhhvshEPjDHGGGOMMcaYCoiG8Hw8IrIf2BboOMpRH8gMdBABEI71Dsc6Q3jWOxzrDOFV7xaq2iDQQQQza5urpXCsdzjWGcKz3uFYZwivevutbQ7p5Lk6E5F0VU0LdBz+Fo71Dsc6Q3jWOxzrDOFbbxN6wvXfcjjWOxzrDOFZ73CsM4RvvX3Num0bY4wxxhhjjDEVsOTZGGOMMcYYY4ypgCXPgTMh0AEESDjWOxzrDOFZ73CsM4RvvU3oCdd/y+FY73CsM4RnvcOxzhC+9fYpe+bZGGOMMcYYY4ypgN15NsYYY4wxxhhjKmDJszHGGGOMMcYYUwFLns+AiLwtIvtEZHWxbU+KyEoRWS4i34pIE+f265zbV4rIjyLStdhr/iQia0RktYh8JCKxzu2DRWSpc/u7IhJVThw3icgG589NYVLnAuf5lovIJF/W2cv1vtdZtzUicl+x7XVFZIbzGs4QkaRy4gjGa13VOofatb7Cua1QRMqdMkJEhovILyKyUURG+6q+znNVlzpvFZFVznOm+6q+JrR58d+ztc3WNlvbbG1z6TisbTYlqar9ePgDDAB6AKuLbUsotvxH4DXncj8gybl8EbDIudwU2ALEOdc/BW7G8UXGDqCdc/sTwG1uYqgLbHb+TnIuJ4VynZ37jgXhte4CrAbigShgJtDWue9ZYLRzeTTw9xC51lWqc4he645Ae2AOkFZODJHAJqAVUANYAXQK5To7y20F6vvzettP6P146d+ztc3WNoO1zdY2l4zB2mb7KfNjd57PgKrOBQ6W2na02GpNQJ3bf1TVQ87tC4GUYuWigDjnN7nxwC6gHpCjquudZWYAl7sJYxgwQ1UPOo8/AxhepYqdRjWps995qd4dgYWqekJV84HvgV87940C3nUuvwv8yk0YwXitq1pnv/N1vVV1rar+UkEYvYGNqrpZVXOBj3H8vXyimtTZGK+oJu1UML5fg7XN1jZb23w61jabMix59gIReUpEdgDXAY+5KXIbMBVAVXcCzwHbgd3AEVX9FsgEoot1o/gN0MzNsZri+Ea4SIZzm1/5uc4AsSKSLiILRSRgb+xnUm8c3/wNEJF6IhIPjOBU/Rqq6m4A5+9kN8cKumtN1esMoXetPRFq19pTCnwrIktE5M7Kxm2MO9Y2W9tcjLXN1jZb2+w5a5tPw5JnL1DVMaraDPgAuKf4PhEZhOMf9YPO9SQc31q1BJoANUXkelVV4GrgRRH5CcgC8t2cTtyF4K26eMrPdQZorqppwLXASyLS2gfVqtCZ1FtV1wJ/x/Gt9DQc3X3Kq587QXetvVBnsGvtCqESYVdJAK51f1XtgaPL2d0iMqBqNTDmFGubrW0Ge792lrW22a71mbC2+TQsefauDynWtUlEzgbeBEap6gHn5guBLaq6X1XzgC9wPLeAqi5Q1fNUtTcwF9jg5hwZlPwGKQVHN6tA8UedUdVdzt+bcTyr0d031fGYJ/VGVd9S1R6qOgBHV5yi+u0VkcbO1zYG9rk5RzBe66rWORSvtSdC7Vp7pNi13gdMxNFFzhhvs7bZ2mZrm61ttrbZQ9Y2n544vmAMTfXr19fU1NRAh2GMMSZELFmyJFNVGwQ6jmBmbbMxxhhv8mfb7HbqgVCRmppKerqNsG6MMcY7RGRboGMIdtY2G2OM8SZ/ts3WbdsDN7y1iMHPzeHCF75n/obMQIdjjDHGGCCvoJDU0ZP5avnOQIdijDEmDFjy7IG2ybXp1CSBjfuOsWTboYpfYIwxxhif+2SxYyDcez9eHuBIjDHGhAOPum2LSByOUfbCcm6wx0Z2orBQ+WblbtT/g+wZY4wxxo1Hvlwd6BCMMcaEkQrvPIvISGA5juHOEZFuIjLJ14FVN+IcrD6Ex1czxhhjjDHGGFMOT7ptj8MxRPlhAFVdDqT6LqTqSZzZcyiPTm6MMcYEi+M5Zzp1qTHGGFM1niTP+ap6xOeRBAGRAMyMbowxxpgy5pUawDN19GQKC62VNsYY4zueJM+rReRaIFJE2orIy8CPPo6rWooQsW7bxhhjTDXw+txNZbZ9vXIXufmFAYjGGFMdHD6RC8Cn6Tu4+4OlLNp8oEyZ/IJCsrLz/B2aCRGeDBj2B2AMkAN8CEwH/ubLoKorARswzBhjjKkGlm0/XGbbvR8v516WM+fPA0mtXzMAURlj/O3dH7cydtIat/smr9oNwCVnN+ablbtL7NvyzAiO5eTzp0+WM+7SzqQkxQOQk1/AW/O3cNu5LakRGeF6dNMYqCB5FpFI4HFV/QuOBDqsiYD1CDPGGGOqt4HPzeHnJ4YRX8OjSUWMMUHo4PFcejw5w6OypRNngJYPTXEtL9pykE9/25exk9bQrmEt3l+4nWen/UJkhNCvdT3euDGN2OhIwHF3++35W0hOiOX6c1oA8Pb8Lbz2/Sb2ZeXw3q29eeeHLTwwtD31a8WwfMdhhndpBMD6vVk0rxuPKmQcOkHbhrXZezSb5NoxlqQHidO2KqpaICI9/RVMdSdYt21jjDEmGHR6bDqf/rYvGYdOcPHZjYmJigx0SMYYL3hl9gae+3a9V4+ZlZ3PRf+cB8BPWw66thcUKvM2ZNLh0WncP6Qd6dsOsXDTAXILHI+HuJsu78a3fwLgu1/2u7ZtfnoEWdn5DH1xLm2Ta7Fh3zEAxo3sxLivf2bsyE7c0r9liePsOZLN3A376dgogU5NEli98wgdGycQIRAV6cmTt8YXPPlKdplzaqrPgONFG1X1C59FVU05Bgyz7NkYY4wJBle+vgCAFTsO8/ioLlU61rYDxzl4PJf6tWJoVjfeG+EZY87QL3uyvJ44e+qFGZU/b9/xs9h7NAfAlTgDjPv6ZwAe//pnHv/6Z+Y/OIhz//4dqfXi2XrgRLnH+2H0YHYdPkmv1LoltufmF7IvK5u9R3PompLoSrJX7zzCJS/P5/3b+nBu2/pljpdfUMjJvAIKC2HP0WzaNazFW/O3UDMmikHtk2mUGFuifE5+AR8s3M5N/VKJjBDm/LKPBrVj6NwksXJ/oCDiSfJcFzgADC62TYHwTJ4tdzbGGGOCyrsLtnE0O597L2jr0bPQq3ceISUpjrgakew9kkPzevGc/485rv2REcKmp0ewJfM4g56bw1s3pdG/TX32Z+WQk19Am+TaZY755rzNzN+Yye7D2fzzmm7UrBF12iR8X1Y2gnA8J7/CmLPzCtiflRPwpL7oA3jt2OiAxmFC17CX5gY6hEopSpwrcu7fvwM4beIM0H/87BLrM/40gPiYqBLbb+mfypgRHVm96yi/+tcPAFz/1iLOa1ufqAgpcWf8TLVJrsXGfccYP20d57Sqx9z1jmN9/5eBNEuKp/WYKSVypi3PjADgvQXbuLRrE5Jq1qj0uQOtwuRZVW/xRyDBwNFt27JnY4wxprp479berm6SpzNx2U4mLtt52jJLHx3CRz9t5x/TfymxvXWDkslrQaGSnVfA/A2OD4y3vZte5lgrHhtK1ye+dXue4S/NK7E+smsTasVE8dFP25l5//ks3nqQh75Y5dq/+vFh7M/KoXFiLLHRkeTmF9Lukal884dz6dQ4gTveS2fehky2jr8YgHd+2MLA9smk1nM8WxkR4f5Zym9W7mJAuwYkOJNdVXVbfl9WNsm1Y9m4L4uDx/Po3dJxt+vDRdvp1CSBbs3qAPCnT1fw9YpdrjiMMf4x5MWyXyq888NW3vlha5ntpaf5q4yNzrvnufmFrsQZKPElY3HFny//ZPEO7ruwLc3qxtO+Ye1y35+qK6koGRSRWOA2oDPgumevqrf6NrSqS0tL0/T0sg1aZXV+bBrX9G7OI5d08toxjTHGBA8RWaKqaYGOI5h5q21OHT0ZgK3jL+bpKWuZMHdzlY8ZDLo3r+N2pPEiL1/TnT98tAyAge0bMKfU3aWB7Rvwh8FtSYqPZvDz3wOOv+GJ3HxueWcxi7Yc5M0b07j9vXQaJ8ay+0j2Gcc476+DmL5mD4dO5NKnZT3m/LKftg1r8evuTV2J/ls3pTG4QzKfLcngkrMbM3d9JvVq1XB1Q317/haGd2lEP+edtE1PjyAyyD5kG+/aduB4ucmZCU7Xn9Ocv/3qrCofx59tsyfJ82fAOuBa4AngOmCtqt7r+/CqxtvJc5ex07kyrRmPjbTk2RhjwpElz1Xni+S5+Lo5c3ec15I35m0JdBgeWTF2KIlx1i083OQXFNJmzNRAh2F8wBs9VfzZNnsyVFsbVX0UOK6q7wIXA1X/iiAI2TzPxhhjQomI1BWRGSKywfk7qZxyNznLbBCRm4ptryEiE0RkvYisE5HL/Rd9Se/d2jtQpw56wZI4A1zlHATOhJfVu46eUfknR3WmaZ04H0VjwpknA4blOX8fFpEuwB4g1WcRVUBEhgP/BCKBN1V1vP/ObQOGGWOMCSmjgVmqOl5ERjvXHyxeQETqAmOBNBwDhi4RkUmqeggYA+xT1XYiEoFjkNGAGNCuAf/7XT8OHMvhzv8uCVQYxsfW7ckKdAgmAIoGvCotMS6aIycdqco7t/RiUPtk174b+qa6lj9YtI0IkRJjCRhTGZ4kzxOc30Q/CkwCagGP+TSqcohIJPAvYAiQASx2NuA/++n8NmCYMcaYUDIKGOhcfheYQ6nkGRgGzFDVgwAiMgMYDnwE3Ap0AFDVQqDqI9F46NnLzyalbsk7Sz1bOG6cP3ZJJ574xi8fDYwxAfLV3f05OyURkYqfhb+uTwsArundnO0HTrBsxyHu/Xh5iTL/urYH7y/cxsvXdicuOpITuQW8t2ArL8/e6IvwTZDyZLTtN52L3wOtfBtOhXoDG1V1M4CIfIyj4fdT8ox12jbGGBNKGqrqbgBV3S0iyW7KNAV2FFvPAJqKSB3n+pMiMhDYBNyjqntLH0BE7gTuBGjevLlXAr+yV7Ny9916bktuPbclV72+gEVbDnrlfMaYwNh1+KTb7V2b1XG7vSLN68XTvF48wzo34pkpa7l/aHvXc/QXn93YVa5mTBQPDG3P+r1ZdGiUQNOkODo0qs2Nb//EBR0akhQfze4j2azZdYSTeQW0Sa7FHee1onuzJB74bAX3XdiWzk0S2Hs0h2XbD5GdX8CfPllRIpbrz2nO+wu3V6oeJjAqTJ5FxO1dZlV9wvvhVMhdA97HXyePELFu28YYY4KKiMwEGrnZNcbTQ7jZpjg+Q6QAP6jq/SJyP/AccEOZwqoTgAngGDDMw/NW2Se/7cvLszbw/Iz1/jqlMcbL+pWa0xhg2aNDqnzc2OhIHh/VpcJyr99Qchyq5Y8NrfA1b9506jWNEmO56CxHUp6Vnc/gDsks2XaIUd2aApQYbfrwiVwWbTnIsM6N2H3kJI99tYaTuQW8fE131uw6iqI0rRPnGim/uBpREeTmF1YYmzv3D2nHC873yQ9u78N1by4CHKPcZ2Xn0e2JGQD8eWg7oiMjWLz1EDPXlvmeNCx40m37eLHlWOASYK1vwqlQeQ34qQI++Ha7+MkLLXs2xhgTRFT1wvL2icheEWnsvOvcGNjnplgGp7p2gyNhngMcAE4AE53bP8MxtWW18ocL2vKHC9oCMG31bu56fykAdw5o5dH0Vjf3S+U/P24lQiAqsvIfTotLrh3Dv67rwRWv2eBXxpypuX8ZRFLNGoEOo1JudD6HnZIU73Z/nfgaDOvs+K6zcWIcb9x4Kgk/t2191/KqcUM5mVtAckIse45kk51XQEpSHMt2HGZr5nG6NqvDUOfcz78f2JrfDmjNwRO5pG919MRp17B2mTv3f3S+TwL8+7oeDO6QTGSEUCe+BvP+Ooil208l/L89HwoLlcVbD5KcEEt2XgGNE2OpE1+D/IJC/vjxMnYcPEnTOnH0almXJ52P0URGCAWFp3KpL+/uX6m/YyB50m37+eLrIvIcjmefAyEDKN5PKwXYVbyAL7/dtm7bxhhjQswk4CZgvPP3V27KTAeeLjYS91DgIVVVEfkaR2I9G7gAPz1GVVnDuzRm2aNDmLF2L1emNePSrk1ok1yL2OhIAFSV9G2HuOK1BdSIjKBfm3qMu7Qz4y7t7DpGxqETREVE0DAhhu0HT7A58zg9miWRGB9NYaEyedVuBnVIZtuB40xavovrz2lBSlIcGYdO0qxuyQ/MbZJrcWVaCncOaA3A7iMnqRkTxdz1+6kTV4PICKFT4wSOZufRrG48Ow6e4Lxnv/PfH6yYTo0TyM4voGfzJD5bkhGQGAC6piQG7Nymemhez33iGU5qx0ZTO9bR1bxRYqxre6/Uuq650ktPAZUYH03L+jU9Ov6IsxqXWG9WN77M+1dEhNCnVb0yr42KjODf1/Ussa1dw1rc8NZPfPrbc2jbsDa7D2fTvlFtj2Kpbiqc57nMCxyN50+q2rbCwl4mIlHAehwN9E5gMXCtqq5xV97b8zyn/W0mURFC24a1vHZMUz1FRgh/HtqeLk2tkQ51f5+2jtU7jwQ6DONjQzo1dH3jXxWhNs+ziNQDPgWaA9uBK1T1oIikAXep6u3OcrcCDztf9pSqvuPc3gL4L1AH2A/coqqnfYDP222zL6zdfZRWDWoSExUZ6FDKWLb9EG/M20ynxgncM7gtR7PzEKBWTBTHcwv4x7R1vLtgG+AYOK13y7os3HyA+BpR/KZnCte/tYhIET668xxXPY9l57MvK4fk2jF8vHgH7RrW5ry29YmJikBEmLgsg8HtG5IY7/iwnr71IDsPn2RUt6YcOZFHVKRQM8ZxP2b1ziNc8vL8EjHXjo1i6aNDiI6MKDMf96OXdHLdlSryxe/7cdm/fyz3b+CNeWFNcFBVWj40xbV+/5B2Je6QmuCRk1/gs/dUf7bNFSbPIrKKUzdcI4EGwBOq+oqPYysvnhHAS85Y3lbVp8or6+0G+pmpa1lsA4+EvAKFFTsO8+DwDvxuYOtAh2N8rNNj06gVE0VKks0HGcpGnNWY28+r+piXoZY8B0IwJM/BLnX0ZFo3qMmsBwYGNI69R7OJjBDq14pxbStKnv8yrD13nd+ayAhh5s97OSslkYYJp+6gHTyeS82YSMZPXceQTg3p3iyJjo9NAyx5DifFH7UA2Pz0CCIiKh5d24QXf7bNnjzzfEmx5Xxgr6rm+yieCqnqFGBKhQV94KGLOgbitMbPsvMK6PDoNNQ66YcFVRjVrQljLu4U6FCMMcYrqktyWTwZLjLrgfM5fCKXni1OTQl+YaeGZcrVdT7TOnako8t8dl6Bj6I01VnxxBmwxNkEnCfJc+nZ6BOKz6dWNO+jMaHGxoYLD/YliTHG+E/rBvbom6mc2Q+cH+gQjPEoeV6KY5CuQzgGnK6D47kocHTnDvTcz8YYUyXFvxA0xhhjTPXTyr54MdVAhAdlpgEjVbW+qtbD0Y37C1VtqaqWOJuQY3lUeFF1PweeMcaY6iPSuusaY6oBT5LnXs7njAFQ1amA9ZswIUucqdSZjkRvgpOCZc/GGFPNRUd68pHVhJJN+48FOgRjyvDknShTRB4RkVQRaSEiY4ADvg7MmECz3DlM2HU2xpigMuPnvYEOwfjB1yt2uZYHtGsQwEiMOcWT5PkaHNNTTQS+dC5f48ugjAkk67YdfsRuPRtjTNC44z2b6iwcvDRzg2v5Xpvb2VQTFQ4Y5hxN+14AEYkEaqrqUV8HZkygFKVRdkMyPChqX5gYY4wx1VjPFkmBDsEYwIM7zyLyoYgkiEhNYA3wi4j8xfehGRMYRSMvW7ft8GDX2RhjjDHGeMKTbtudnHeafwVMAZoDN/g0KmOM8SO78WyMMcYYYyriSfIcLSLROJLnr1Q1D+vRakLYqW7b9s88HCj2nLsxxgSDwR2SXcuFhdZGh7LiM54M69wwgJEYU5InyfPrwFagJjBXRFoA9syzCVlFiZR15w0PqmoDhhljTBAYc3FH13JuQWEAIzG+tuPgSdfyHwbbYGGm+qgweVbV/1PVpqo6Qh1fA20HBvk+NGMCw/XMc4DjMP5h19kYY4JDq/o1Xcs5+ZY8h7K5G/a7lrs0TQxgJMaUdMYzzqtDvi+CMcaYQLBu28YYU/1JsTfrf8/ZGMBIjK898uXqQIdgjFtnnDwbEzas33ZYULUBw4wxJti8/v3mQIdgjAlDljwb44aIdec1xhhjjDHGnBJVUQERuczN5iPAKlXd5/2QjAk8uxMZZqzftjHGGFPtJNeOCXQIxpRQYfIM3Ab0Bb5zrg8EFgLtROQJVf2vj2IzJqCs13boK5oKw1JnY4wJPkez80iIjQ50GMaHbu6fGugQjCnBk27bhUBHVb1cVS8HOgE5QB/gQV8GZ0ygiIjN8xwGir4gsRvPxhgTHF67vodr+ca3fgpgJMYfmiTGBToEY0rwJHlOVdW9xdb3Ae1U9SCQ55uwjAkswe48hwO7xMYYE1yGd2nsWl6+43AAIzH+0Ltl3UCHYEwJnnTbnici3wCfOdcvB+aKSE3A3rVMSLI7keFFrOO2McYYU+00qWN3nk314knyfDeOhLk/jhty7wH/U8fDgoN8GJsxAWV3JUOf65lny52NMSYoncjNJ76GJx9njTGm6irstq0On6vqn1T1Puey5RUmpAli3bbDgF1iE+5EpK6IzBCRDc7fSeWUu8lZZoOI3OTcVltElhf7yRSRl/xbAxPu/vzZikCHENS8+ZH+q+U7eWnm+iof5/CJXC9EY4xvVJg8i8hlzsbyiIgcFZEsETnqj+CMCRi7ExlW7HKbMDYamKWqbYFZzvUSRKQuMBbHQKG9gbEikqSqWararegH2AZ84cfYjWHKqj3kFRQGOowKqSqfL8ngRG6+x68pLFQKCn33NW9+QSEtH5rC+KnryDyWw+4jJ8uUWb3zCEu3H3Ktb9yXxcZ9x/hk8fYysd378XJemrmh3PMVeFiftbuzzqAWxviXJ/1cngVGqupaXwdjTHVio22HPhtt2xhG4ZiCEuBdYA5lZ9IYBsxwDhSKiMwAhgMfFRUQkbZAMjDPt+EaA+e2qc/8jZmu9acmr2XcpZ0DGFHFlm4/xJ8/W8GPmzJ54cpupy27MuMwnZsk0vrhKQBMuKEn6/Zk0b9NfXq2KNs5ZMPeLESENsm1GDNxFR8s2s4TozoTExXBVb2alyj734XbePTL1TxycUeu7u3Y99r3m3jt+01ljjvvr4O45OX5AHzzh3MpVOXSV35w7V+y7RDX9G5Obn4h+4/luLZ/sTSDfq3r8/y3v/DZkgw6N0ng7JQ6fPTTdgCuSmvGr7o3pW/reqzfm8Vb87YwqEMy/1uawfwNmSQn2NzOpvryJHnea4mzCTcC1qc3DBR9QSKWPZvw1VBVdwOo6m4RSXZTpimwo9h6hnNbcdcAn9hjXcYfJtzYk06PTXetz9uwP4DRnLL9wAlW7zrCiLMcI4Ln5BewaPNBGiXGkpPnuDu+89BJjufkk3ksh3q1Yvhg4Tamrt5Ddl4Bv+zNcvvI2J3/XQLACzNOdYluUS+ebQdOlCj3wJB2fLDIkaA+9tUaABZtPsgXy3aWOebfJq/lb5NP//H+vGe/cy0XJdHFfZqewafpGWW23/9pya70a3YdZc2uU51WP0nfwSfpO0qUKb5eul7GVCeeJM/pIvIJ8CWO+Z0BUFXrmmVClojlzuHAPuabcCAiM4FGbnaN8fQQbraV/t9zNXDDaWK4E7gToHnz5uUVM8YjpQcI27T/uFePv2TbQTo3SSQ2OrLMvvs/Wc4Xy3ZyadcmbNh3jLW7j3LX+a3ZezSbic4k9Z1berF8+2H+OetUF+aEWEfMi7YcpPPY6WWOe6bcJZjPzyj7vLG7xNkYU3meJM8JwAlgaLFtij3XZEKYTV1kjAkVqnpheftEZK+INHbedW4M7HNTLINTXbsBUnB07y46RlcgSlWXnCaGCcAEgLS0NPvaynjd8Zx8asZUbtRtVeXg8Vxmr9tHm+RaXP7qghL7oyKE/FLP6k5ascu1XLrL8y3vLC5zjqPZnj/rbIypvip8l1HVW/wRiDHVjfU+DB/Wa9uEsUnATcB45++v3JSZDjxdbCTuocBDxfZfQ7Hnn43xh46NE1i7+1RX4M5jp7Pp6RFERpx6Qz90PJfNmcfo2aKu22M8/+0vfLNyN1syT3/nunTibIwJX56Mtp0iIhNFZJ/zG+r/iUiKP4IzJlBErEtvOLBrbAzjgSEisgEY4lxHRNJE5E0A50BhTwKLnT9PFA0e5nQlljwbP7v3grZltrV+eAqqys+7jpKdV8DIV+Zz+asLyM4rcJX5+KftfLlsJ6mjJ/Py7I0VJs7GGFOcJ/1b3gE+BK5wrl/v3DbEV0EZE2h2IzK8WDd9E65U9QBwgZvt6cDtxdbfBt4u5xitfBagMeW4sKO7se2g5UOOEapHdm1CxiHH1EsdHp3mt7iMMaGtwjvPQANVfUdV850//wEa+DguYwJKRGzAsDBwarTtAAdijDHmjERFnv4j7NfFnkk2walXatlpuYwJNE+S50wRuV5EIp0/1wMHfB2YMYFmXXpDn2ue58CGYYwxphJeva5HoEMwPtSuYe1Ah2BMGZ50274VeAV4Ecco2z8CNoiYCWnCqbuSJnTZFTbGmOA1vIu7GdiC26pxQzlr3LdltqfWi+fq3s0ZP3Vdhce478K2DO/ilYYvAAAgAElEQVTSiOEvzWNop4Y8fdlZpP1tJgC/7t6UFvXiWbb9MEez86hfK4YreqbQJrkWN7z1EzsPn+SW/qlc0bMZTerEsv3gCfIKlNqxUazeeYQ2ybWYvGo3HRrV5n9LdlK/Vg1euLIb2w6e4B/T17HvaA6f/64fAFnZea4B3HYfyaZ1g1quGFWV/EJFgCtfX0BSfA3uGNCKE7n5DGqfzOdLMhjZtYkX/qLGeJdUZkRhEblPVV/yQTxelZaWpunp6YEOwwShs8ZN5zc9Uxg7snOgQzE+dCwnny5jp/PwiA7cOaB1oMMxQUBElqhqWqDjCGbWNhtv+s8PWxj39c+BDuO02ibXYsO+Y/RKTWLx1kMATL9vAO0a1kLcPDekqny+JIPGiXH0aFGH3PxC6sTXKLG/oFD5avkuft29KRHOBPWDRdsYM3E1G5+6qEy39mM5+URFiNu5q40Jdv5smys3IR7cD1T75NmYyhKs23Y4KPry0AYMM8aY4HRz/5Z+SZ6fu6Irb83fwmXdmzKqWxPStx2iYUIsoLRvlMDBY7lEREBKUrzrNV8szWDWun08f0VX3l+4jVv6tywxlVZ5RIQr0pq51ovlza79UZHC5T1LTn5zXZ8WXNenhdtj1qrkHNjGmJIq+z+pSp80ReQKYBzQEejtHNWzaN9DwG1AAfBHVZ3u3D4c+CcQCbypquOrEoMxxtj3I8YYE/yGdmrItz/vrfTrv/nDubRqUJPP0jO4/pwWrgQ3r6CQtmOmAvCbnin8pliyOuKsxiWO4S45vaxHCpf1cLzm9vNsUHpjQoEnA4a5U9XPnKuBy4C5xTeKSCfgaqAzMBz4d9FAZcC/gIuATsA1zrLG+IS7blQmdNnlNsaY4PX6DT25uV+qx+Wf/FUXAGrWiGTjUxfRpWki8TWiuKlfaok7w9GREdSOjaJJYqy3QzbGBKly7zyLSBbuk2QB4qpyUlVd6zxH6V2jgI9VNQfYIiIbgd7OfRtVdbPzdR87y1bvh1xM0IoQ+Oin7Xyz0qa6CGWFRaNtW/ZsjDFBS0QYd2lnfj+wNbPW7eOhL1a5LbfgocE0TnR8hL3hHPfdm0tb8dhQr8VpjAl+5SbPqhqI8eGbAguLrWc4twHsKLW9j7+CMuFn9EUdWJlxJNBhGD+IjoxgWOeGgQ7DGGNMFSUnxHJN7+YM7dSQHzcdoEHtGM5pVa9Kx4zw4BllY0z48NnoASIyE3A3h8AYVf2qvJe52aa4717utuu4iNwJ3AnQvHlzDyI1pqyrejXnql6BjsIYY4wxZ6perRib5sgY4xM+S55V9cJKvCwDaFZsPQUo6jdb3vbS550ATADHdBiViMEYY4wxxhhjjCmhUvM8e+3kInOAPxeNti0inYEPcTzn3ASYBbTFcUd6PXABsBNYDFyrqmsqOP5+YFuxTfWBTO/WotoLxzpDeNbb6hw+wrHe1aXOLVS1QaCDCGbWNgNW53ASjvUOxzpDeNa7utTZb21zQCZ9E5FfAy8DDYDJIrJcVYep6hoR+RTHQGD5wN2qWuB8zT3AdBxTVb1dUeIMUPqPKCLp/ppAu7oIxzpDeNbb6hw+wrHe4VjnUGVts9U5nIRjvcOxzhCe9Q7HOgckeVbVicDEcvY9BTzlZvsUYIqPQzPGGGOMMcYYY8qo7DzPxhhjjDHGGGNM2Ai35HlCoAMIgHCsM4Rnva3O4SMc6x2OdQ4X4Xhtrc7hIxzrHY51hvCsd9jVOaADhhljjDHGGGOMMcEg3O48G2OMMcYYY4wxZyykkmcRaSYi34nIWhFZIyL3uikjIvJ/IrJRRFaKSI9AxOpNHtZ7oIgcEZHlzp/HAhGrt4hIrIj8JCIrnHV+3E2ZGBH5xHmtF4lIqv8j9S4P632ziOwvdq1vD0Ss3iYikSKyTES+cbMv5K41VFjnUL3OW0VklbNO6W72h9x7eKizttna5lJlQu792tpma5uL7QvV62xts1NARtv2oXzgAVVdKiK1gSUiMkNVfy5W5iIcc0e3BfoArzp/BzNP6g0wT1UvCUB8vpADDFbVYyISDcwXkamqurBYmduAQ6raRkSuBv4OXBWIYL3Ik3oDfKKq9wQgPl+6F1gLJLjZF4rXGk5fZwjN6wwwSFXLmzcyFN/DQ521zdY2W9vsEIrv2dY2lxWK1xmsbQZC7M6zqu5W1aXO5Swc/7Cblio2CnhPHRYCdUSksZ9D9SoP6x1SnNfvmHM12vlT+gH+UcC7zuXPgQtERPwUok94WO+QIyIpwMXAm+UUCblr7UGdw1XIvYeHOmubrW0uVSzk3q+tbba22YTee3h5Qip5Ls7ZNaQ7sKjUrqbAjmLrGYRQY3aaegP0dXYpmioinf0amA84u80sB/YBM1S13GutqvnAEaCef6P0Pg/qDXC5s9vM5yLSzM8h+sJLwF+BwnL2h+K1rqjOEHrXGRwfOL8VkSUicqeb/SH9Hh7qrG22tpnQfL+2ttm9ULzW1jaHedscksmziNQC/gfcp6pHS+9285KQ+HawgnovBVqoalfgZeBLf8fnbapaoKrdgBSgt4h0KVUkJK+1B/X+GkhV1bOBmZz61jcoicglwD5VXXK6Ym62Be219rDOIXWdi+mvqj1wdAG7W0QGlNofUtc6nFjbbG2zU0hea2ub3Rdzsy1or7W1zdY2Qwgmz85nTf4HfKCqX7gpkgEU/xYoBdjlj9h8qaJ6q+rRoi5FqjoFiBaR+n4O0ydU9TAwBxheapfrWotIFJAIHPRrcD5UXr1V9YCq5jhX3wB6+jk0b+sPXCoiW4GPgcEi8n6pMqF2rSuscwheZwBUdZfz9z5gItC7VJGQfA8PddY2W9tcTKi9X5dgbXMJoXatrW22tjm0kmfncxRvAWtV9YVyik0CbnSOCncOcERVd/stSB/wpN4i0qjoORMR6Y3j2h/wX5TeJSINRKSOczkOuBBYV6rYJOAm5/JvgNmqwT2xuSf1LvWMyaU4nrMLWqr6kKqmqGoqcDWO63h9qWIhda09qXOoXWcAEanpHFgJEakJDAVWlyoWcu/hoc7aZmubSxULqfdrsLYZa5tdQu06g7XNpUkQ/xsuQ0TOBeYBq4DCevXqdU1NTQ1sUMYYY0LGkiVLMlW1QaDjCCbWNhtjjPElf7bNITVVlarOp1if+7S0NE1PLzMV2Rk7eDyXgkIlMkKoW7NGlY9njDEmOInItkDHEGx81TYbY4wx4N+2OaSSZ1+57N8/sPXACQD+fvlZXNWreYAjMsYYY8zyHYf51b9+oFdqEp/d1S/Q4RhjjAlxIfXMs6/8aUg7nhjlmD1iz5GcCkobY4wxxh+emeJ4nHDx1kMBjsQYY0w4qFLyLCJxItLeW8FUV6O6NeX6Pi0A0NAcdd0YY4wJOou2BPPAvcYYY4JNpZNnERkJLAemOde7icgkbwVW3Yjzaa0QGl/NGGOMCVp5BYWBDsEYY0yYqcqd53E45vg6DKCqy4HUqodUPTlnkrD7zsYYY0w1kHms5GNUx3LyAxSJMcaYcFGV5DlfVY94LZIgIILdejbGGGOqgeOlkuUuY6cHKBJjjDHhoirJ82oRuRaIFJG2IvIy8KOX4qqWBLvzbIwxxlQHt79bdrqr7LyCAERijDEmXFQlef4D0BnIAT4EjgD3eSOo6kpEKLQ7z8YYY0zAFU0hWdw5z8xiVUZYdYozJmzlFRTy48bMQIdhwkylkmcRiQQeV9UxqtrL+fOIqmZ7Ob5qxXptG2OMMdXX4RN5jHxlPgWF1lgbE+pemLGea99cxJJtZUfdTx09mQc/X8nc9ftJHT2ZTxfvYH9WDhOXZbA183iZ8nkFhRw6nuuPsINKYaGWGV8i3EVV5kWqWiAiPb0dTHUnYt22jTHGmOqu9cNTAPjwjj70a10/wNEYY3xh075jAFz+6gIA+raqR59WdfksPQOAT9J38En6DgD++r+VJV778IgOPD1lHQCvXNudez5cBsDGpy4iKtJxb3H93iyysvPo2aIuhYXKzsMnaVY3vtx4/vPDFlrUq8mgDsll9mXnFTBu0hr+Mqw99WrFVKXaXpNfUMjEZTu5vEcKERHitsyr32/iH9N/YfYD5xMTHUnTOnH0e2YWl/VI4c/DQn62Yreq0m17mYhMEpEbROSyoh+vRVYNCWJ3no0xxpggce0bi1i7+6hrffHWg27vUvlSdl4Bx3LyyTh0gsMn7M6WMd4SISUTvgWbD/DSzA3sPHyywtcWJc6AK3EG2JeVwzNT13LFaz8y9MW5rsT8t+8v4bxnv2Nr5nF+3JjJjoMlHxsZ9cp8xn39M7f8Z7Hb8329YhcfL95R4lz+MG31brZmHie/oJDU0ZOZMHeTa9+7C7bxl89XMvSluczfkMmCTQdYt8fxfnnoeC77s3L4x/RfALj5ncX0Hz+bo9l57DqSzSvfbXTNcDB9zR427z/G4RO5fPzTdo/iWr83i837j3m5tv5RqTvPTnWBA8DgYtsU+KJKEVVnAmr3no0xxpigcdE/5wHwwpVduf/TFQCse3I4R0/mkZwQ69Nzb9p/jAue/961XrNGJGueGM7R7Dx2HDxB5yaJPjt3fkEhIkJkOXeUikxclkHnJom0a1jbtW33kZPUrxVDdGRV7rEY4xvvL9zG7HX7mL1un9eP3W/87DLbUkdPdi0PfG6Oa/nbPw0gITaac56ZVab8TX1bcNfA1jROjANg6wFHV/EFmw+4yv24KZNr31jE5D+eS1REBMNemusoO/5iAHLzC4mOdNy4y84vIL5GFAWFiqq67o4X2XX4JJv3Hyc6UliZcYQ7BrRiX1Y2d72/FICXruoGwD9nbiAmKpLnvv2FG/u2AGDjvmNc/9ai0/5dtju/LDh73LeubeXNcPDRT9v56M5ziK/hSDNz8guIiYp0LQ/6xxx2HckuUddgIhrCt1LT0tI0Pb3saJyV1f6RqdzcL5WHRnT02jGNMcYEDxFZoqppgY4jmHmrbS7+gbayZj9wPpv3HyeuRiQT5m7m3Vt7A7Bw8wGa1omjZkwUmcdyqB0b5foQXNrxnHyiIyNQlC+W7uShL1Z5fP7FYy6k11Mzmfj7fnRvnkRhodLq4SnccE4LHr2kE3kFhURGCA98toKr0prRp1Vd14dQcDyPOHX1Hu7+cCmXnN2YS7s2YWjnRsCpv8+FHRvSpE4s7y3Y5nrdg8M70LtlXVZlHGbc1z8Dpz7EHsvJp8vY6VyZlsKzv+kKgKqScegkTevEEREhfLJ4O/M2ZPLKtT1K1Cc7r4CfthxkQLsGHv8NjDkTWzOPl0hgq7spfzyPEf83r8S2eX8dxLs/buXN+VsAuLFvixL/P+vVrMEBN89f/25ga16d47hzPPXe8/jvwm2MHdmJ/AKlc6lE9ofRg+nv5ouAQJl0T3/aNazN1RMWsnzHYdd2byXP/mybK508i0gscBuOEbddX92q6q3eCa3qvJ08d3x0Gjf0bcHDljwbY0xYsuS56qpT8mxOWfjQBURFOu5Sp/1tJgB3nNeSrOx8Nuw7xpJth8q85g+D2/Dy7I2AIyG447101u3J4vO7+vLDxgP8sDGTB4a2o0+reiVeV1io5BYUMnPtXlc31oTYKI5mO7qBznrgfFo3qEVhobJsxyEuf3UBG566yO6EG/t/H2Iu696UF5x3xavCn21zVbpt/xdYBwwDngCuA9Z6I6jqSsTx7asxxhhjqodljw6h+5MzAh1G0Cvd9RTgjXlbTvuaosQZ4Lxnv3Mt/+a1Ba7lqyYsdC0/ekknnvzmZ7fHKkqcgRJd3Yu0HTOVafedR4dGCaeNyRgTPL5YttMrybM/VeUrvDaq+ihwXFXfBS4GzvJOWNWTADb7hTHGmFAhInVFZIaIbHD+Tiqn3E3OMhtE5KZi22uIyAQRWS8i60Tkcv9F75BUswZv3midAYJBeYmzp4a/NM/jAYlM6Jm2ek+gQzCmSslznvP3YRHpAiQCqVWOqBoTsdG2jTHGhJTRwCxVbQvMcq6XICJ1gbFAH6A3MLZYkj0G2Keq7YBOQNlbhn5wYaeGgTitCYDRZ/BMuQktd72/JNAhGFOl5HmCs/F8FJgE/Aw865WoqinBRts2xhgTUkYB7zqX3wV+5abMMGCGqh5U1UPADGC4c9+twDMAqlqoqpk+jtcYE4b+MX1dxYWM8YNKJ8+q+qaqHlLV71W1laomq+pr3gyu2hHszrMxxphQ0lBVdwM4fye7KdMU2FFsPQNoKiJ1nOtPishSEflMRAJ2C/jX3ZsCcFmPpoEKwRjjI//6blPFhYzxg0oPGCYij7nbrqpPVD4cj847HPgnEAm8qarjfXm+Euf214mMMcYYLxGRmUAjN7vGeHoIN9sUx2eIFOAHVb1fRO4HngNucBPDncCdAM2bN/fwtKf3we19aFA7xrX+4lXdeNE58MwXS3d65RzGmMDbezT7tPu/urs/o/71g2u9aPqj/5u1gc37j/HS1d3JzS+k3SNTuTIthVvPbUmbBrWYunoPXy3fycy13p8v2oSuqoy2fbzYcixwCT4ebVtEIoF/AUNwfPO9WEQmqWrVRqDw/Pw22rYxxpigoqoXlrdPRPaKSGNV3S0ijQF3nyIzgIHF1lOAOcAB4AQw0bn9MxxTWLqLYQIwARxTVZ1hFdzq36Z+ufs2Pz2CaWv28PsPlnrjVMaYAOrzdNmR4AF+e34rHrrIMX3sjD8NIK9AOXzy1PzIf7ygrWu5RlREmTmFR3ZtwsiuTfj7tHXcNaA1tWOjUBzJer9qNEeyP43s2oSvV+wKdBjVWqWTZ1V9vvi6iDyH49lnX+oNbFTVzc5zfozjeS2/JM8Rgj3xbIwxJpRMAm4Cxjt/f+WmzHTg6WKDhA0FHlJVFZGvcSTWs4EL8FN7XJGICGHEWY3Z8swI1u3JIrl2DD2dcxef26Y+8zfao9nGBLuixBmgbcPalT7Og8M7lFhvUieuTKKdnVdAh0enuX39S1d1o1ndOLLzCrnuzUWA4/GR6tYDJik+mnq1YmhSJ45dh08yoG0DujZL5MtlO7m6d3MaJsTSrVkdXr6mO60fnoKqMu/BwTz25WrmbcykTlw0T4zqwvAujo5M367Zw53/rdogbg+P6FBxoWqmKneeS4sHWnnxeO64e+6qj4/P6WKjbRtjjAkx44FPReQ2YDtwBYCIpAF3qertqnpQRJ4EFjtf84SqHnQuPwj8V0ReAvYDt/g3/NMTETo2dswLXPzD8Gvfb2L81LIDED10UQeecbP9THx0xzms2XWEi85qzJqdR7jzv0uoX6sGmcdyS5TbOv5iMg6d4Ny/f1fOkaqmYUIMe4/mlNiWGBfNkZN5REYIV6al8NFPO8p5tTHVW+nk1tdioyNLnHPf0Wy+XrmbW/unIlL2yZbnr+haInn+7s8D2X34JH+fto6UpHjStx0s8/8ToGtKIl/e3Z873kvn6l7N+f2HS0mKjyYttS6TV+7msUs6sXrnEb5YdvrEvGhO9DveS2fZ9sM8OaozF53V2G3ZUd3KjhOx6ekRruW3bu7l9nVDOzcqcx2mrd5D7dgoZq/bx1vzy84Tf/05zRk7sjP5Bcr6vVl0bVanTJnqrirPPK/i1I3YSKAB4NPnnSn/uavicXn9uariJ7fRto0xxoQKVT2A445x6e3pwO3F1t8G3nZTbhswwJcx+sJd57fm5n6pPP/tL9zYN5XE+GhioiKIiYpk79Ec3v5hC7HREax78iLu/XgZXy3fxcD2DZjzy34AGifGsvtINpueHkHrh6eUOHbf1vXo27oeAE2L3cHaknmcBrVj2H7gBDVjIgFokhjHqG5N+Gq5o5vks5efzV//t5J5fx3EjkMneHXOJuZtcNwlf3B4B3o0r0Naal0yj+XQ5+lZvHFjGmktksjOL6DvM45upv+5pRetG9QiIS6aq15fwC39U0mMq8GMn/fy/JVdS8Q6enhHdhw6wc7DJ+nerA6fpu/guW/X07dVPY5m57Fm11EALuzYkJlr93r0t22UEMueCp5RLX0tXvv+1GBQ//tdXy5/dQEA3ZrV4ZnLzuKfMzcwbY3N8Wuqj+SEWG47t2W5+0WEafedx/YDJzindT0SYqNpWb8mX91zrqtMbn4h8zbsp3vzJJLio0sk4W/e5EhY1//tIsBx5/tPF7alTbLjDvsLzvEdAFSVmWv3sWTbId6ct5n8QiUqwjEm9Bs3pnmv0h4ouivdv019Lu+RQsahE7SsX7NMz4DoSIIycQaQyj7DKyItiq3mA3tVNd8rUZV/zr7AOFUd5lx/CEBVn3FXPi0tTdPT0712/rS/zWBo50Y8/euzvHZMY4wxwUNElqiqfz+NhBhvt83elp1XwJRVu/l196aICIeO5zJ+6jrGXdqZKat2c/HZjYmNjnSVf+DTFZzVNIFxXzt6rFfmjtiCTQdYvuMwvxvYusy+r5bv5N6Pl7PxqYuIiix/kpTU0ZMrff4i6/dmMfTFua5nSVWV7LxC4mpElij30Bcr+WblblaNG3ba4+UVFKLqeN70xRnr+eesDUy77zwaJcSScegkXZomAvD8t7+wJfM4o7o1ZYhzzu6TuQXERke4EooFmw5wzRsLq1xHE1y+WbmLez5cVmLbvL8Oolnd+ABFdHoTl2UwddUeJvg5aS1yNDuP73/Zz8iuTQJy/kDxZ9tcleS57un2F+vS5TUiEgWsx/Et+U4cXciuVdU17sp7P3meSecmCdzcP9VrxzTVU1SE0Cu1bokPSCY0bdp/jO0HTwQ6DONjKXXiqvRMXBFLnquuuifPlfX9+v1kZuVwec+UgJx/9rq9NEqIo1OThCodZ/XOI3RoVPu0iXqgeOMLAhNciq55kcaJsSx4qExnGRPm/Nk2V+WZ56VAM+AQjh7NdXA8LwWOrtRef/5ZVfNF5B4cg5dEAm+Xlzj7QkJcFN+v38/36/f765QmgB6/tDM39UsNdBjGx656fSGZx8o+d2RCy839Uhl3aedAh2FC2PntGgT0/IM7eGeK7aK7wcZUR3P+MjDQIZgwV5XkeRowSVWnAIjIRcCFqvqAVyIrh/N8Uyos6AOf3NmXjEN2hyrU5eYXctWEhRzP9elTCKaaOJ6Tz6Vdm3CL9SgJafVrxVRcyBgTFFTV7SBNJvTFRFmPQBNYVUmee6nqXUUrqjrVORpnyGpQO4YGte0DWKjLzisAsJHVw4SiNEyIoXvzpIoLG2OMCbg56/czqH1yoMMwPlZYWPKD2E19W5RT0hj/qcoDLZki8oiIpIpICxEZAxzwVmDGBIp9mR1+7A6GMcYEjykrdwc6BOMH+0s9UvXrHoEZT8CY4qqSPF+DY3qqicCXzuVrvBGUMYEkbmdEM6Hq/9m77/iq6+uP46+TAWHvvSKKIkNR4wK1Kg6cWK2toxYctbbWWjssqLhna63aWvtDC47WrVUcgIgCiqKGJUOWsmcgQMJIyDi/P+5NSMINCbk3ubn3vp+Px33kfvf55Av53HM/4+se+hl4IiJSP70+c020Q5A6cPyDk8stD4jRRxtJfKlxt+3gbNo3A5hZMtDE3XMiFZhItNV0JnqJLbrLIiIi9ds9mvBR6okatzyb2Utm1tzMmgALgMVm9sfIhSYSHSU9eJU7JwhHTc8iIiL12M803lnqiXC6bfcJtjRfRGD26+7AVRGJSiSKlEclHnXVFxERqb80N4nUF+Ekz6lmlkogeX7H3QtQD0iJAyV/oPWPOTE4rkniRERiwMCD25S+19Cq+JaTVxDtEERCCid5/j9gBdAEmGZmPQCNeZaYV5JHqV5ODJowTEQkNvzh7MNK3xcVq5KOZ+/MWVf6vnWTBlGMRKS8GifP7v6ku3dx93M98PXfKuC0yIUmEl2utueEoLssIhIb+nRqXvr+1y/NjmIkUtvGz9v7OLL3f3NSFCMRKa/Gs21XFEygCyN1PpFo0YRhicVd3bZFRGJBw5S9bT4TFmyIYiRS2z7/bkvp+7SU5ChGIlJeON22ReKSJqVIPJowTESk/lP9nJhaqdu21CNKnkUqoYbnxOCglmcRkRiUlZsf7RBEJMHUuNu2mV0cYvV2YJ67b6p5SCLRZ4b6bScITRgmIhKb5q3dxum9O0Q7DBFJIOGMeb4WOBH4JLh8KjADONTM7nX3F8OMTSSqlDqLiIjUXx/M26DkOc6pZ5jUN+F02y4GDnf3S9z9EqAPkA8cD/wpEsGJRIsanhND6XNCVTuLiMScN2auiXYIUsv+9dNjoh2CSDnhJM/p7r6xzPIm4FB3zwb0ZHOJaZqUJLHobouIxIZPb9VTURPJ2X07RjsEkXLCSZ4/NbP3zGyYmQ0D3gGmmVkTYFtkwhOJDkPPeU4EangWEYktHVuklVv+aOHGSvaUWLWnsDjaIYhUKpzk+UbgOWAAcBTwAnCju+909xp/LWhmd5vZWjObE3ydW2bbSDNbZmaLzezsMGIX2S8zddtOBCW3WI+qEhGJDanJ5T+6XvdCZpQiiQ05eQW89826Gh+/p7CYN2eu2TvMqRaM+Ww5Fz01nYc++BaAnfmFtXYtkXDVeMIwD/wveiP4irS/ufujZVeYWR/gMqAv0Bn4yMwOdfeiWri+iNqdE0BtfhgQEZHENXNlNsUOx6a3Ll23M7+QvndN5KGL+3P5cd33e/yO/EJSkoy01ORy6zfl5rE6ezfH9GhVbn1OXgEGbMzJp2fbJtz//rf86rSDGfnWPCYt3Ejvjs05pH1TFm/I5U9vfsP5R3SiR5smnHF4+9Khatc89zUbtufxwc0nM2H+enp3bM51L2SybNMOGqYmcf4RnctdMzevgNMencqgQ9pw27mH06F5Guu27ebrFdls21XAT47ttk/8K7fspHWTBsxatY1hY74qt23O6m1sys3nhh8cXK3fsUg0hPuoqkeA9gR6uQbmWHJvHqHYKhoKvOLu+cByM1sGHAd8UUvXkwRmmFqeE0Bpy7MankVEYtaarbvo2qpxxM/r7im8fhYAACAASURBVBQUOQ1S9u2omVdQRO9RE3jnxkEc2a3lPtsveTrw8XTOnWeyI7+Qrq0alz6XeuRb88jeuYe/TFzMrUMOY+P2PNIaJJOalMSO/EKe+3xF6Xl+nNGV77J2sjp7F9NHnM75T37Gptx8Jv72FBo3SKZLy0as276bkx75ZJ8YxkxfXvr+jMemlts2Z/XeEZZ/vuQIbn3zm9LlaUuyuOE/s8rt/+uXZvP85yv4esVWzu3fkcd/chS/+u8sNu/I550563hnzjrOO6ITmSuy2ZgTKOdd4xbw0MX92VNYzE+O7cbEBRu4+ZU5lf6+Af43ey3/m712v/uIRJPVtOUlmLxe4O7fRjQgs7uB4UAOkAn83t23mtk/gBnu/p/gfv8Gxrv7GxWOvx64HqB79+7HrFy5MpLhSYI49I7xXDPoIEac0zvaoUgtKigqptft4/n9mYdy0+Be0Q5HYoCZzXT3jGjHEcsyMjI8M1NdbaXmbn1jLq9llp9pe9yvB3FE132T2HDc8+4Cxk5fwRcjT6dTi0YATF+2meyde5i5cmu5JBcgo0crMlduDXmuPw3pzbtz17FwfU5EY4x3Kx4+L9ohSAyoy7o5nDHPG2uaOJvZR2Y2P8RrKPA0cDCBsdTrgb+WHBbiVPtk/u4+2t0z3D2jXbt2NQlPRBOGJQhNGCYiEnuGDzxon3UX/mN6xM5/+egZTFuSxX9mBBpgTnzoY3bmF5I+4n2ufPZLbnp59j6JM1Bp4gzwyIRFSpxF4kCNu20DmWb2KvA2gec7A+Dub1V1oLufUZ0LmNkzwHvBxTVAtzKbuwI1nwFBZD8skD1LnCv5gkSPJhMRiR19OoceIZg+4n1WPHweX36/hX5dWtCkYeUfcz9ftpkZy7M59bB2HN29FU99soy/TFxMSpJRWOx88f2Wcvv3vWtiRMsgIrEpnOS5ObALOKvMOgeqTJ73x8w6ufv64OIPgfnB9+OAl8zsMQIThvUCvgpxCpGIUO4c/zSuXUQkvvS/ayK5+YWc3bcDJ/dqx/rtu/nDWYeVfkmak1fA1MVZ3PTybACenLy03PGFxaoYRKRy4cy2fXUkAynjz2Y2gEDusgL4RfB6C8zsNWAhUEjgsViaaVtqRWDCMFWgIiIi9dE5/Toyfv6GfdbnBh9zNHHBRiYuCDwD+qlPvqvT2EQkftV4zLOZdTWz/5nZJjPbaGZvmlnXcANy96vcvb+7H+HuF5ZphcbdH3D3g939MHcfH+61RCqjXryJRfdbEpWZtTazSWa2NPizVSX7DQvus9TMhgXXNTOzOWVem83s8botgSSqC4/sXPVOIiIRFs6EYWMJdKXuDHQB3g2uE4l5geeuRTsKqW2lE4aFnI9QJCGMACa7ey9gcnC5HDNrDdwFHE/gEZF3mVkrd8919wElL2AlYQ7dEqmuIf06RjsEEUlA4STP7dx9rLsXBl/PAZreWuKGcuf4pxnVRRgKPB98/zxwUYh9zgYmuXu2u28FJgFDyu5gZr2A9sCntRirSCkzo2e7JtEOQ0QSTDjJ82Yz+6mZJQdfPwW2VHmUSAwwM7U8JwA9qkqEDiXDo4I/24fYpwuwuszymuC6si4HXnVNFiF16IPfnBztEGLGPRf2jXYI+9W+WcNohyBSLeHMtn0N8A/gbwQa6T4HamsSMZE6pVwqseh+Szwzs4+AUH1cb6/uKUKsq5gkXwZctZ8YrgeuB+jevXs1Lyuyf2mpyTx95dH88r+zoh3KPjJ6tCp97vPFR3XhrdlrqzymZeNUtu0qKF2edMspNEtL5YUvVrC7oIix01cAcP4RnTizTwceGb+Iddvzyp3jD2cdyqMfLuHakw6iR5vGnH9EZ2av2srgwzswbGA6AJ9/t5mF63LI2pHPxUd15ezHp1WrTM/+LIP+XVswdUkWt77xDYvuG0JaajIAWbn53D1uAe/PC0xVdNu5venSsjHJSXDDf2ZhBj8+phvrc/KYu3ob23cX8Isf9OSWMw4tPcc7c9Zy8ytzSq/3jyuOqlZcInUpnNm2VwEXll1nZr8FNFmIxD5Tl95EUHKH1fIs8czdz6hsW3DCz07uvt7MOgGbQuy2Bji1zHJXYEqZcxwJpLj7zP3EMBoYDZCRkaE/rhIx0Rz7/KNjurJwXQ63n3c4Vz77JQDn9e/E/Rf1o1WTBuwpLCYlyUhKMh750RH8/eNlpY/GOqZHK2au3MqsUWfSpGEyKUlJJCcZewqLOfSO8dxyxqH06tAMgFuH9AbgxtMOoXGDZBo3CHx8Lyp2fvfaXHp3bEbbpg255JguXHBEYCK1a0/qSaMGgaR08OEdysU98OC2DDy4benysgfOAWDyok384sW9/43/fvlRHJvemvlrt3NGn73n+HFGN36c0a3cOds1a8hTVx5N9wmLeHrKd7RvlsZ5R3QCYMXD51Xr9zl0QBeGDujCnsJiNmzPo3ubxtU6TqQuhdPyHMrvUPIscUAThiWGkh6mmjBMEtg4YBjwcPDnOyH2mQg8WGYm7rOAkWW2Xw68XJtBilTGzPh8xOkMfPjjWr3OXRf04bwjOjF1cRatmzSgUWoyAw/Zm4CGShAbpOwdHZmanMTvzjy0NHl+85cDQ16nQUpSpclm26bluzYPHdCFnN0FXH58dxqmJJeu//XpvapfMCAlORDn2X07suLh83B3FqzLoV+XFgB0bJFW7XPdPLgXHZo1DGs29AYpSUqcpd6KdPKsT6AiEjP0/YgIDwOvmdm1wCrgUgAzywBucPfr3D3bzO4Dvg4ec6+7Z5c5x4+Bc+syaJGyOrdsxEUDOvP2nHU1On7QIW04Lr0NT32yjPOP6MSDF/cv7UqcPuJ9AK4edBAAl1ZocY2m5CRjeDCuSDKz0sT5QKWlJtdKTCL1RaSTZ30WlbhgZmzbtYdlm3ZEOxSpRTvzCwF125bE5e5bgMEh1mcC15VZHgOMqeQcPWstQJFqevyyo1ixZRdzVm+rct+rB6Xz0peryC8s5tNbT6Nb60Ar581n7Nti+7MTe7ApJz/i8YpIbDrg5NnMcgmdJBvQKOyIROqBRqnJvD1nXY2/xZbY0jA1ueqdRESkXvv3sAyOuf8jAIYPTOey47qxaH0uHZqn0atD03Ldnu+6oHqzT987tF9EY7xvaN99ul+LSOw44OTZ3ZvVRiAi9cm/h2fwXdbOaIchdSA1yfjBYXpEvYhIrGvTtCGL7x/Ckg076N810O24d8fmUY6qvKtOTI92CCIShkh32xaJC307t6Bv55qN9xEREZHoaJiSXJo4i4hEWlLVu4iIiIiIiIgkNvM4fh6PmWUBK+vocm2BzXV0rfokEcudiGWGxCx3IpYZErPc1S1zD3dXP/8wqG6uE4lY7kQsMyRmuROxzJCY5a53dXNcJ891ycwy3T0j2nHUtUQsdyKWGRKz3IlYZkjMcidimRNBot7XRCx3IpYZErPciVhmSMxy18cyq9u2iIiIiIiISBWUPIuIiIiIiIhUQclz5IyOdgBRkojlTsQyQ2KWOxHLDIlZ7kQscyJI1PuaiOVOxDJDYpY7EcsMiVnueldmjXkWERERERERqYJankVERERERESqoOT5AJnZCjObZ2ZzzCwzxPZTzWx7cPscM7szGnFGmpm1NLM3zGyRmX1rZidW2G5m9qSZLTOzb8zs6GjFGinVKHPc3WszO6xMeeaYWY6Z/bbCPnF1r6tZ5ni817eY2QIzm29mL5tZWoXtDc3s1eB9/tLM0qMTaWRVo9zDzSyrzL2+LlqxSvWpblbdXGZ73N1r1c2qm8tsV90c5bo5JVoXjnGnufv+njn2qbufX2fR1I0ngAnu/iMzawA0rrD9HKBX8HU88HTwZyyrqswQZ/fa3RcDAwDMLBlYC/yvwm5xda+rWWaIo3ttZl2A3wB93H23mb0GXAY8V2a3a4Gt7n6ImV0GPAL8pM6DjaBqlhvgVXf/dV3HJ2FT3ay6uURc3WvVzaqby+ymujnK1PIsVTKz5sApwL8B3H2Pu2+rsNtQ4AUPmAG0NLNOdRxqxFSzzPFuMPCdu6+ssD6u7nUFlZU5HqUAjcwshcCHz3UVtg8Fng++fwMYbGZWh/HVlqrKLRITVDerbq6wPq7udQWqm/dS3RxlSp4PnAMfmtlMM7u+kn1ONLO5ZjbezPrWZXC1pCeQBYw1s9lm9qyZNamwTxdgdZnlNcF1sao6ZYb4u9dlXQa8HGJ9vN3rsiorM8TRvXb3tcCjwCpgPbDd3T+ssFvpfXb3QmA70KYu44y0apYb4JJgt8c3zKxbnQYpNaW6WXVzWfF2r8tS3Vxe3Nxr1c2xUTcreT5wg9z9aALdY240s1MqbJ8F9HD3I4G/A2/XdYC1IAU4Gnja3Y8CdgIjKuwT6luvWJ7KvTpljsd7DUCwK9yFwOuhNodYF8v3GqiyzHF1r82sFYFvrw8COgNNzOynFXcLcWhM3+dqlvtdIN3djwA+Yu83/FK/qW5W3VwiHu81oLo5xOa4uteqm2OjblbyfIDcfV3w5yYCYy+Oq7A9x913BN9/AKSaWds6DzSy1gBr3P3L4PIbBCqvivuU/RaoK/W4y0U1VFnmOL3XJc4BZrn7xhDb4u1el6i0zHF4r88Alrt7lrsXAG8BAyvsU3qfg92oWgDZdRpl5FVZbnff4u75wcVngGPqOEapAdXNgOpmIG7vdQnVzWXE4b1W3RwDdXNcP+e5bdu2np6eHu0wREQkTsycOXOzu7eLdhwHysxaA68C6cAK4MfuvjXEfsOAO4KL97v788H1U4BOwO7gtrPcfZOZDQf+QmAyH4B/uPuz+4tFdbOIiERSXdbNcT3bdnp6OpmZ+zyxQkREpEbMLFYnrBkBTHb3h81sRHD5T2V3CCbYdwEZBLoBzjSzcWWS7CvdPVSlekAzoKpuFhGRSKrLulndtqvhk0WbeO+bdYyft54d+YXRDkdERORAlZ2h9XngohD7nA1McvfsYMI8CRhSR/HV2IJ129lTWBztMEREJAEoea6Ge95dwK9fms0v/zuLl76M1UYHERFJYB3cfT1A8Gf7EPtUNVvvWDObY2ajKjwaJWozoK7fvpvznvyMUW/Pr8vLiohIglLyXA3PXX0cE38bmLhz9x59uy0iIvWPmX1kZvNDvIZW9xQh1pVMjHKlu/cHTg6+rgqur9YMqGZ2vZllmllmVlZW9QtVhZzdgd5g05ZG7pwiIiKVCWvMs5k1Arq7++IIxVMvpbdtQnFx4PODx/Zs8CIiEqfc/YzKtpnZRjPr5O7rzawTsCnEbmuAU8ssdwWmBM+9Nvgz18xeIjCb9QvuvqXM/s8Aj1QS22hgNEBGRkbEKtJfvBgYO71+e16kTikiIlKpGrc8m9kFwBxgQnB5gJmNi1Rg9U1JB7U4npxcRETi1zhgWPD9MOCdEPtMBM4ys1bB526eBUw0s5SSx7+YWSpwPjA/uNypzPEXAt/WUvwhrdiyqy4vJyIiCS6clue7CXzzPAXA3eeYWXrYEdVTJcO7lDuLiEgMehh4zcyuBVYBlwKYWQZwg7tf5+7ZZnYf8HXwmHuD65oQSKJTgWQC3bOfCe7zGzO7ECgk8KzR4XVWIhERkToWTvJc6O7by88ZkgDU9CwiIjEm2L16cIj1mcB1ZZbHAGMq7LMTOKaS844ERkY0WBERkXoqnAnD5pvZFUCymfUys78Dn0cornrJTC3PIiIi9dH0ZZujHYKIiMS5cJLnm4C+QD7wErAd+G0kgqqvDDU8i4iI1AefV0iWr3z2yyhFIiIiiaJG3bbNLBm4x93/CNwe2ZDqr4Troi4iIlJPXaFkWURE6liNWp7dvYhKxj/FM0OPqhIREamvbnp5Nq4uYiIiUkvCmTBsdvDRVK8DO0tWuvtbYUdVT5mp27aIiEh99e7cdXRumcbIcw6PdigiIhKHwhnz3BrYApwOXBB8nR+JoOorw9TuLCIiUo/939Tv2ZiTF+0wREQkDtW45dndr45kIDFBLc8iIiL13vEPTgZg0X1DSEtNjnI0IlLb1m/fzbJNOzi5VzsAlm3aQdumDWjZuEGUI5N4U+Pk2czSgGsJzLidVrLe3a+JQFz1lsY8i4iIxIZLnv6cO87rw4kHt4l2KCISYUXFzq/+O5PWTRrw8lerS9enJBmFxYHP65cd241R5/ehScNwRqqK7BVOt+0XgY7A2cBUoCuQG4mg6isDPehZREQkRixYl8Plz8yIdhjVVlhUzKSFGzXpmUg1fLl8CxMXbCyXOAOliTPAK1+vZsgT00Iev3zzTs594lM2bI+fYR478wsZ9fZ8duYXlq5bsG47Fz01nd17iqo8/sUZKxnx5je1GWLMCyd5PsTdRwE73f154Dygf2TCqp/MlDuLiIjEmv53T2TzjvzS5byCInaU+XBZ1p7CYsbNXcf23QXsKSyuqxABeHrKd/z8hUzGz9+w3/2Ki53snXvqKCqR+mf99t1c8Uz1Hle3Ons34+etJ33E+yzZmMvWnXsY+o/POO3RKSxcn8MJD01m5FvfMHrad1z/QiYAFz01neuez2Tu6m0HFFfZvxubcvNYs3XXgRUsTM9+upwXZ6zk358t55KnP+eNmWu45dU5zFm9jZkrtzLj+y18s6Z8mRauy2HWqq28M2cto96ezytf7/0yYuWWnWSuyK7yuvv7mxrK/LXbWbIxNttcw+nDUBD8uc3M+gEbgPSwI6rHDNO3wSIiIjEmN6+QjPs/AmDU+X24772FAEz946ncNW4Blx3bnXbNGnJoh6b8c8p3PD3lOwBOOqQtL1xzHBty8ujYPI1hY7/i06WbGXV+H64ZlI47JCUZM1dm07dzCwDembOWH2d0w8z2G9PmHfkMfOhjXv3FCewuKKJ9s4alH1pvfeMbzu3faZ9jFqzbTpeWjRg7fQVPTF7KV7cNpn3ztH32izXFxY4ZVf7ORD6Yt55f/XfWAR/3y+AxZ/0tdCt02dbr9BHvl77/6NuNTPztKWTv3LPf4R8zV26lb+fmHHnPh5zYsw0vX38Cxz0QmHvhicsGcOLBbbjz7QXcO7Qv7Zun8dasNfzutbl8dftg7vjffD5cuJHxN5/M4Z2al57zsUlLWJO9i6ZpKdw7tF/I6/5t0hKemLyUaX88jW6tG7GnKNC6nGSBmGau3Fq6b5E7Px0d+MLhZyf24IUvVvKrUw/mn8G/d2Wlj3ifs/p04MOFG8utH3lOb64/pWe5/6vfZe1g8F+nArD8oXNL/y5WtCknj2835HLSIW05/++fAbDi4fMq/Z3WV1bTZNDMrgPeBI4AxgJNgTvd/V+RCy88GRkZnpmZGbHz9blzAlcc1507zu8TsXOKiEjsMLOZ7p4R7ThiWaTq5pIPuF1aNmLttt1hn+9A/fzkg3jm0+V88odTOe3RKfTu2IzCYmfZph3866dHc+ph7WmQnESRO+4wfdlmXstczfj5GzCDMw8PfDA9vFNzvl2fs8/5n/1ZBrsKimicmswZfToAgTL3bNuERg2SWbAuh3d/fRIvfLGCq07sQc92TfnHx8t46cuV5OQV8sRlAzilVzt27ilk5sqtDB3QhV17Cvn9a3PZlJvPm78cGLJcu/YU0ig1udJE9u+Tl3Jm3w4km9GrQ7Owf49Zufkc+8BHDDy4DbedezhZO/I57bD2pdtHvvUN89Zu572bTg77WhLbcvMK6H/3h1GN4Z0bB9GtdWM+XZrFW7PWMnb4sdw5bj7/mbGq2uc4oWdrZnwfaM297dzePPjBotJtd13QB3e4N/gFX4mSvxNPXn4UxcXO7oIi3OG2/82r9nUvPaYrr89cU+3996cknv5dWjBv7fbS9QMPbsPn321h7p1nkZNXQH5hMWc8NrXS80Qqea7LurnGyXMsiHTy3PfOCVx2XHdGKXkWEUlISp7DF+nkedF9Q+g9akLY54ukzi3SWBfhcZRv/vJELnn6i4idr+RDa/bOPbRuEpiReHX2Lk7+8ycAfHvvEHLzCkhrkEzztFQg0KX90DvGl57jlEPb8X3WDtZsDXx5MeUPp3Lls1/SqEEyPzi0Hb/4QU+aNEjZ72RNp/91Ct9n7Sy37qvbB5e22lWMVxLX4g25nP146JZjiU1T/nAq6W2bhH2euqybw5lt+85Q69393pqHU7+ZmR5VJSIiMcfMWgOvEhhetQL4sbtvDbHfMOCO4OL9wTlNMLMGwD+AU4Fi4HZ3f9PMGgIvAMcAW4CfuPuK2ixLRWmpyfxpSG8embCo6p3rSKQTZyCiiTPAcQ98xKbcwDjwkef05tKMbqzK3js+84pnZzB7VWBs5GM/PpLkJGPbroJy55i2JKvc8r3vLSztBbBs0w7+/dlyINC6NnRAFzo0T+OKZ2aQkd6aJycv3U9sk/dZN/Sp6bz9q4Hq2p3AbvjPzGiHIBF26qNTYu6LsXDGPJf9mjANOB/4Nrxw6jdDj6oSEZGYNAKY7O4Pm9mI4PKfyu4QTLDvAjIIzI8508zGBZPs24FN7n6omSUBrYOHXQtsdfdDzOwy4BHgJ3VTpL1+eerBzFu7jQ/m7X+iLdmrJHEGeGj8Ih4aX/7Lh5LEGeB3r82t1jk/XrQp5PoHP1hUrmvq599tOZBQAZi7ehsHjfyAuXedRYtGqQd8vMS+5Zt3Vr2TSC2r8Wzb7v7XMq8HCHwb3SVikdVHhlqeRUQkFg0Fng++fx64KMQ+ZwOT3D07mDBPAoYEt10DPATg7sXuvjnEed8ABluUmgb/eeUx0bis1LEj74numFeJjqJifQCX+iGcR1VV1BjoGcHz1TvqKCQiIjGqg7uvBwj+bB9iny5A2QemrgG6mFnL4PJ9ZjbLzF43sw4Vj3H3QmA7UPmUtBFWsQXyq9sG19WlRaQO3fTygc+wLVIbapw8m9k8M/sm+FoALAaeiFxolV53iJktNrNlwa5ndUbjbEREpL4ys4/MbH6I19DqniLEOicwxKsrMN3djwa+AB6t4piKsV1vZplmlpmVlRXikAP32Z9OY+ofTy23rn3zNG487eCInF9E6odtu/ZoSIbUG+GMeT6/zPtCYGPwW+daY2bJwFPAmQS+Ef86OB5r4f6PjNT10XOeRUSkXnL3MyrbZmYbzayTu683s05AqMGpawgMwSrRFZhCYCKwXcD/gutfJzDWueSYbsAaM0sBWgDZIWIbDYyGwGzb1S9V5bq2ahxy/U2n9+KpT/Z9bqmIxKYB906KdggipcLptp1b5rUbaG5mrUteEYluX8cBy9z9e3ffA7xCYLxVnQhMGCYiIhJzxgHDgu+HAe+E2GcicJaZtTKzVsBZwEQPfGv8LnsT68FAyZfWZc/7I+Bjj/K3zGmpyTRukBzNEESkjl189N5plz699bQoRiLxLpyW51kEvm3eSiCvbAmUPCHcqZ3xz6HGYx1fC9cJSY+qEhGRGPUw8JqZXUugrr4UwMwygBvc/Tp3zzaz+4Cvg8fc6+4lrch/Al40s8eBLODq4Pp/B9cvI9DifFndFGf/rh6UXq71+acndKd9szQem7QkilGJyIFatmnHfrefdlg7PlmcRUaP1tx6dm+SkqB9s7TSxx+Nnb6ce96tkw6qUgMtG8fezPnhJM8TgHHu/gGAmZ0DnOHuv49IZKFVObbKzK4Hrgfo3r17xAPQo6pERCTWuPsWAi3GFddnAteVWR4DjAmx30rglBDr8wgm4vXJH846jJtO78VFT01n0CFtGXV+HwB+M7gX6SPej3J0IlJdZzw2db/bxww/limLszj1sHYh5ya6etBBXD3ooNL/9zf84GDaN2vIve8tZOzVx9IgOYlBh7SlsKiYQ24fXytliCVf3T6YN2au4c8TFtfN9W6rdLRRvRVO8nysu99QsuDu44PfWNemkrFVJboC68ruUBvjqkoYelSViIhIfWdmpKUmM+G3++T7ZPRoRebKrYw4pzeHd2rOsDFflW772Yk92LargHFz13HdSQfx7GfLS7e9dN3xNE1L4eWvVvHyV6v3Oa+I1D0z47TeoR4eUN7yh85lxvfZnNCzNWbGNScdVG57SnIS8+85m/Hz1vPHN77h5sG9GD4wnY25eSSb8av/zuKeoX254pkvq7zWyHN6l3tu+qL7hpCcZIx6ez6vfB342/HHsw/jLxMrT1CTDCp7OtehHZqyKTefbbsK9tk26ZZTaNEolVZNGjBuzjoKi4vp0DyNxRtyuf6Untzy6hzenrNun+OuOqEH913UD4BfnXoIXVo2IjU5iW27Cshcmc3UxVn8+UdH8ND4RZzcqy1jp68IGdvLPz+By5+ZAcARXVvQrmlDhvTrSGpyEhPmb+CuC/vwnxkrS3sGNUiJ5IOf6obVdGiSmU0EPgX+Q6D196fAKe5+duTC2+eaKcASAt+eryXQtewKd18Qav+MjAzPzMyM2PUz7p/EWX078uAP+0fsnCIiEjvMbKa7Z0Q7jlgW6br5QN36xlxey1zDh7ecwqEdmu2zvbComEUbcunXpQXpI96nTZMGPH/NcfTr0qJ0n7yCInqPmhDy/P8elkGnFo0498lPgUBL1xsz17B5R37tFAho3CCZJy47ip+/EPi9lnxBEMv+eeXR/Oq/oR9PVNIlV+JfqJ4is0adyZjPlrNl5x4eujiyn8ndnffnrefsvoGEr6K123Yz47stTFywgQ8XbgTgvZtOIi01iYkLNrJm6y4e/GF/7n//Wy4/rhuHtC//N2bmyq1s3bmHM/p04HevzuGt2WsZcU5vfnBoO5ZszGXgwW0ZN3cdZ/ftwEmPfMJFAzrz69N7kb1zD8cdVH5KqU05efxnxkp+fkpPtuzYw8acPI7vWfWTAnfkF7Jm6y6+WbOdS47uyr+mfsfwgek0aVj9NtU73p7Hf2asom/n5rx300nk7C5k2+499GjThNy8AhqmJO83MV6/fTfJZrRvnlbta+5PXdbN4STPrYG72NuNayrlx0fVCjM7F3gcSAbGuPsDle0b+eT5Iw5pUo+pXAAAIABJREFU34RLj+lW9c4S01KSjdN7t6dZWuyNxZADM3vVVr7P2hntMKSWHdy+KQO6tax6xyooeQ5ftJPnvIIivl6Rzcm92lW574bteTRumEzzSuqCv0xcxFOffMf/XXUMXVs1YtbKrVx1YnrIfUe+NY8B3VowdEAXNuXk8/3mHQwfGxhefsnRXbni+G688tVqXp+5hgHdWjJn9TZ6tW/KYz8eQP+uLVi2aUdpF9a5d57F7oIi3p+3nvveW8jwgencfWHfctd7LXM1px7WjvbNAh9OP1m8idXZuzi6eyvyC4vJys3jkPbNSs855Q+n8p8ZK8u1tlc06ZZTOPNv08qt++r2wVz49+mc2acDN5/Ri7TUZJ6esqzKGc/fvnEQA7q1JH3E+5zQszWrs3ezdttulj5wDp8t3cxpvdvz2dLNtGiUygX/+KzcsUvuPycmW6zkwIVKnuvDlye79hTyylerueL47qSl1myCQnfn40WbOPWw9iQn7dvl/PusHXRv3ZiUEEl8tI16ez4vzljJvUP78rNK/ubVpZhInsudJPAIqSbunhN+SJET6Qp6yOPTWLQhN2Lnk/pt1Pl9uLZCtx6JP0fd+yFbQ3R9kvgSKrmoCSXP4Yt28hxpa7buqvSxWdU5tnOLRiQFPzjvKSzmf7PXcOkx3Xhi8lKuDE50Vpkvv9/CT0bP4F8/PZoh/TrVKIb0Ee/TPC2Fb+4OdBzcmV9I37smcuf5fVi3bTfPfrac5Q+dWzqe1N05aOQHQCCJbxFiwp+8giKueGYGRQ5zV2/jicsGcPMrcwC458K+DBuYvs8xJZ9HQ41bXbdtNwvW5ZS2rEP9SKCkdr3wxQrufGffzqW699G3aEMOF/z9M6b88TS6tGwU7XBiI3k2s5eAG4AiYCaBZzs+5u5/iVx44Yl0Bb1rTyGbc/dE7HxSP+0pKuKMx6Zx65DD+NWph0Q7HKllh4+awIVHdubG03Sv41nTtBRaN2kQ9nmUPIcv3pLnaNu8I5+2TRvW+PjPv9vMQW2b0KlF9T8Af7U8m5zdBZzRp8N+9ysqdnJ2F9AqAv/3oHwrpBKo+Beq1XnGyMF0bBGZrr4SP+qybg5nwrA+7p5jZlcCHxB4jMVMoN4kz5HWuEEK3duE8yuTWJBXUARocrhE4TjNG6XQvU3NWo5ERKIpnMQZYODBbQ/4mIpjLyuTnGQRS5xFfnZiDyXOEnXhdKJPNbNU4CLgHXcvAD3HSWJfiB5jEsfcQ3cTFBERkejYsD1vn3W3nXt4FCIRKS+c5Pn/gBVAE2CamfUA6tWYZ5GaMPaO65L454R+gLyIiIhER6jZ6Ws6MZdIJNU4eXb3J929i7uf64EsYxVwWuRCE4mOkkZI5c4JQtmziEjMKSgqjnYIUosq3t+nrzw6SpGIlBexuc89oDBS5xOJlpI8SrlzYnC8tLeBiIjEhuWb9YjBeLZ9d/mnYAzp1zFKkYiUV/8eHCYSZXsfxxHlQKROBMY8RzsKERE5EGdVeN60xJd/V3jeuOYmkfpCybNIBfrznHh0z0VE6r+ebZtEOwSpI58u3Vz6/shuLaMYiUh5NX7ukpldHGL1dmCeu2+qeUgi0VU65lkdtxOCo5ZnEZFY8MiPjuDSf31RulxQVExqstqB4t0vf9Az2iGIlArnL861wLPAlcHXM8DvgOlmdlUEYhOJCnXbTizuGvMs8c/MWpvZJDNbGvzZqpL9hgX3WWpmw8qsb2Bmo81siZktMrNLguuHm1mWmc0Jvq6rqzJJ4jk2vfzzpcfNWRelSKQ27SksP1nYkH6dohSJyL7CSZ6LgcPd/RJ3vwToA+QDxwN/ikRwItGk3DkxqOVZEsQIYLK79wImB5fLMbPWwF0E6vHjgLvKJNm3A5vc/VAC9f3UMoe+6u4Dgq9na7MQImVVHBcr8WHN1l3RDkGkUuEkz+nuvrHM8ibgUHfPBgoqOUYkJpihpucE4a4xz5IQhgLPB98/D1wUYp+zgUnunu3uW4FJwJDgtmuAhwDcvdjdN4c4XqROLVyfE+0QpBYUFuvzl9Rf4STPn5rZe8EuXsOAd4BpZtYE2BaZ8ESiR3+6RSSOdHD39QDBn+1D7NMFWF1meQ3QxcxKZuu5z8xmmdnrZtahzH6XmNk3ZvaGmXULdXEzu97MMs0sMysrKwLFEQlIH/E+Hy/aWPWOUuuycvP555RleJnGh5y8AsbPWw/A91k7uPmV2fzpjW/2OXboU9NJH/E+AMVqvJB6LJzk+UbgOWAAcBTwAnCju+9099MiEJtI1KjhOTGUVvDqty1xwMw+MrP5IV5Dq3uKEOucwOSiXYHp7n408AXwaHD7uwR6oh0BfMTe1u3yJ3Ef7e4Z7p7Rrl27AyqXSFWueS4z2iHUC/mFRTz4wbfsyC+s1v5bduTz9uy15dblFRSxYXseBUXF5ZLgstydHfmF3PzKbLbt2sMz077n2U+/55ZX5/DnCYuZt3Y7AEXFzs0vz+aX/53FXyYu4vS/TuWdOet4NXM1Y6cvJ33E+0xdksWyTTuYu3pb6bmLyrQ8z7nzzJr8KkRqTY1n2/bA/6g3gi+RuGJmmm07AZTmztENQyQi3P2MyraZ2UYz6+Tu682sE4GhVhWtAU4ts9wVmAJsAXYB/wuuf53ApKG4+5Yy+z8DPFLT+EWqY9Itp3BmLTzjeenGXJZt2sE5/fdOTrUjv5AmDZIrfcZwVm4+xz7wEQBXD0rnrgv64u7s3FNEanLgmIYpyeWOWbZpB+2aNWTbrj30aBP60Vu5eQWkJCWRmmykJCcxc2U2w8d+zYyRg2mYksSGnDwapCTRvlkayzbl0qNNE1KTk3jswyWMnvY97s7t5/XZ57yTv93IwIPb4jiNG6Twy//O4qvl2Rx3UGs6t2wEwPUvzmTakkDvkLTUJBbeM4TdBUU0SEli5Zad5OYV8sN/fl56zndCTNqWH5zw68h7PixN5J/65Lty+9zz7kIAho35qtz6DxduZMP2vNLlZmmpIX9HItES7qOqHiHQ9csoaaxzbx6h2ESiRi3PiaHkFqvhWRLAOGAY8HDw5zsh9pkIPFhmkrCzgJHu7mb2LoHE+mNgMLAQoCQhD+5/IfBtrZVABOjVoVnI9Vt25NOmacMan7ckIV/x8HlAoIvx6X+dyoM/7M8Vx3ffZ/8hj09j0Ybc0uWx01dw1wV9+c+MlYx6Z0Hp+kuP6crvzzqMP09cxA0/OJizyiT+Kx4+j5y8AhokJ5GWmsw97y7g5a9WkVdQfrbpEn3vmhhy/cVHdeHMPh34v2nfA/DMp8s574jObMrJY+aqrfTt3ILfvDy73DH3Du3LV8uzgUCyu2hDDvPX5pQmzgB5BcX0vO2Dyn9plSj7OLED9YsXZ5ZbTlL9LPVMjZNn4M/ABe6uilLijpnGPCeCki5pelSVJICHgdfM7FpgFXApgJllADe4+3Xunm1m9wFfB4+5NzgJKASeovGimT0OZAFXB9f/xswuBAqBbGB4nZRGpIIrn/2SCb89ZZ/123cVgEGLRpW3YG7btaf0/ZmPTWXS737AY5OWAHDH2/O4590FfHnbYAbcO4kHf9if2/43L+R5SsbslvX6zDW8PnMNAG/NWlvl/jXx1uy1vFWh+/VFT03f7zF3lknwT3t0SkTiqA2VtfqLREs4yfNGJc4SrwxTy3MCUMuzJIpg9+rBIdZnAteVWR4DjAmx30pgn8zE3UcCIyMarEgNLNqQyzPTvue6kw/CzBj40GQuHNCFf00NdBcuaVGuqKjYGXDvpNLlpZt2lEtqiz3QMluyT2WJs4gkhnCS50wzexV4m8DznQFw97fCjkok2gyNeU4AGvMsIhJ7erZrwvdZO/dZ/8AH3zL9u82ccXgH1m3PK02cIdDTaOWWXewuKCI5yRg3Zx0NU5L4a7CFWUSkOsJJnpsTmEDkrDLrHFDyLDHPQP22E0DJFyRqeRYRiR3/+9Ugjrznw5DbpizOYsrifR+HdtDIAx+7KyJSUTizbV9d9V4isUljnhPD3idVKXsWEYkV+xu/LCJSm2r8nGcz62pm/zOzTcFHYLxpZl0jGZxItGgCKRERERERKavGyTMwlsCjLzoDXYB3g+vCYmZ3m9laM5sTfJ1bZttIM1tmZovN7OxwryVSGbO9MzFL/FPDs4hIbLkyxOOjRERqWzjJczt3H+vuhcHXc0C7CMX1N3cfEHx9AGBmfYDLgL7AEOCfZpa8v5OI1JSe85wY9k4YpuxZRCSW3Du0X7RDEJEEFE7yvNnMfmpmycHXT4EtkQoshKHAK+6e7+7LgWXAcbV4PUlgZqYxzwlAE4aJiMSm5CTjycuPinYYUov+76pjoh2CyD7CSZ6vAX4MbADWAz8CIjWJ2K/N7BszG2NmrYLrugCry+yzJrhOJOLU8pwY9KgqEZHYdcERnaIdgpTx4A/7V7pt2QPn8MYNJ7L8oXPp16V56fppfzyNwzo04/Te7cvtP/bqYzm7b8dai1WkpsKZbXsVcGHZdWb2W+Dxqo41s4+AUP8jbgeeBu4jMNnxfcBfCSTqoT7f7pPemNn1wPUA3btrPIzUnJ7zHP90h0VEYleiPSnhvP6deH/e+kq3f3X7YI57YPI+61OTjYKiA6vxvhh5Op1aNAJgxJvf8MrXq8ttX/HweQDsyC+k310TefwnA7joqC5ccGQnPlu6mV/+dxaHdmjKRUd14YrjupOSnERGemsAxgw7lvHzN/DDo7vQPC2VibecAkD6iPfLnVukPgrnOc+h/I5qJM/ufkZ1TmZmzwDvBRfXAN3KbO4KrAtx7tHAaICMjAx9NpaaMbU8J4KSSeES7POXiEjcWHTfEHqPmlDn1x179bF0aJZGn87NS5O+awYdxNQlm/gua2fpfn+99Eh+//pcAPp2bs6CdTkAjL7qGCYs2MBbs9aGPP81gw5izPTlADx39bGceHAbdu8pKpc8d2jekLHDj+OucfPp2qox7Zulsei+IcxauZWBh7Tlo4Ub+WDeeh77yQAe+3AxT368jGN6tGLllp08/pOjmLtmG3+ZuBiA9DaN+fj3p9LztsDzsFs1blB6nbsv7MuFR3amX9cWPDJ+EUd2a1m6rWnDlHLJbrO0VI49KJAk3zu0Hyf0bLNP2do3T2PYwPR91t92bm/yCoor+5WL1AsWyRmFzWy1u3eres/9nqOTu68Pvr8FON7dLzOzvsBLBMY5dwYmA73cvaiyc2VkZHhmZmY44UiCOuLuiVx8dFfuvrBvtEORWpSTV8ARd3/I7ecezs9P6RntcCQGmNlMd8+IdhyxTHWzRNq363PYXVDExf/8POxzTR9xOslmLFi3nWufL//vdNkD53DI7eOB8q2jyzbl0qpxA9o0bRjynMXFzhOTl3L1oHQKipyWjVNJTQ6MnFy4LocurQItvClJRk5eAeu359GvcwsapIQeXenubMzJp2OLtGqXq7jYyVy5leOCiW1lduYXsnzzTvp1aVHtc4tEW13WzZFueY5EJv5nMxsQPNcK4BcA7r7AzF4DFgKFwI37S5xFwmFmLNqQw0tfrop2KFKLdhcE/oSo5VlEJHYd3ikwhnbs8GP555RlfL1ia43OM3b4sXRpGUhkO7ZIY8XD51Fc7Iyfv4Eh/TqSnGR8eutpNG1Y/uPzIe2b7fe8SUnGLWceGnJbn87Nyy03aZhS2l26MmZ2QIlzSQxVJc4l11fiLFK5A06ezSyX0EmyAfv/314N7n7VfrY9ADwQ7jVEqtKxeRozvs9mxvfZ0Q5F6kD75gf2IUREROqf03q357TgxFMT5q+nZ7umXPd8Jquyd3FOv440bZjC7886jEkLNzDqnQXljv3od6eETIKTkozzykxM1q1149othIjUaxHttl3fqGuY1FReQRHbdxdEOwypA8lJRttKutqJVKRu2+FT3SwiIpEUy922ReJCWmoyaanJ0Q5DRERERETqibhueTazLGBltOOIkLbA5mgHUccSscyQmOVOxDJDYpY71svcw93bRTuIWKa6OeYlYpkhMcudiGWGxCx3rJe5zurmuE6e44mZZSZaV8FELDMkZrkTscyQmOVOxDJL/ErEf8+JWGZIzHInYpkhMcudiGWuqdBz4IuIiIiIiIhIKSXPIiIiIiIiIlVQ8hw7Rkc7gChIxDJDYpY7EcsMiVnuRCyzxK9E/PeciGWGxCx3IpYZErPciVjmGtGYZxEREREREZEqqOVZREREREREpApKnkVERERERESqoOS5njKzS81sgZkVm1mlU8eb2RAzW2xmy8xsRF3GGGlm1trMJpnZ0uDPVpXsV2Rmc4KvcXUdZ6RUde/MrKGZvRrc/qWZpdd9lJFVjTIPN7OsMvf3umjEGUlmNsbMNpnZ/Eq2m5k9GfydfGNmR9d1jJFWjTKfambby9znO+s6RpGaUN2sull1s+rmWKW6OTKUPNdf84GLgWmV7WBmycBTwDlAH+ByM+tTN+HVihHAZHfvBUwOLoey290HBF8X1l14kVPNe3ctsNXdDwH+BjxSt1FG1gH8e321zP19tk6DrB3PAUP2s/0coFfwdT3wdB3EVNueY/9lBvi0zH2+tw5iEokE1c2qm1U3q26OVc+hujlsSp7rKXf/1t0XV7HbccAyd//e3fcArwBDaz+6WjMUeD74/nngoijGUtuqc+/K/j7eAAabmdVhjJEWb/9eq8XdpwHZ+9llKPCCB8wAWppZp7qJrnZUo8wiMUl1s+pmVDfHBdXNUlNKnmNbF2B1meU1wXWxqoO7rwcI/mxfyX5pZpZpZjPMLFYr8ercu9J93L0Q2A60qZPoakd1/71eEuwi9YaZdaub0KIq3v4fV9eJZjbXzMabWd9oBxPvDqDr7bDgPkvNbFiZ9VOC3TpLuvO1D66Pu+6cERBv/6dVN1eyj+rmuBZv/4+rS3VzFVKiHUAiM7OPgI4hNt3u7u9U5xQh1tXrZ4/tr8wHcJru7r7OzHoCH5vZPHf/LjIR1pnq3LuYu79VqE553gVedvd8M7uBwLf7p9d6ZNEVb/e5OmYBPdx9h5mdC7xNoGuc1J6SrrcPB8c0jgD+VHYHM2sN3AVkEPg3ONPMxrn71uAuV7p7Zohzv+ruv67F2OuU6uZyVDerbgbVzSVi+T5Xh+rmalDyHEXufkaYp1gDlP32ryuwLsxz1qr9ldnMNppZJ3dfH+was6mSc6wL/vzezKYARwGxVkFX596V7LPGzFKAFsR2d5sqy+zuW8osPkOMjyWrppj7fxwud88p8/4DM/unmbV1983RjCvODQVODb5/HphCheQZOBuY5O7ZAGY2icD4uJfrJsT6QXVzeaqbVTerbgZi4P9xuFQ3V4+5x++XKG3btvX09PRohyEiInFi5syZm929XbTjOFBmts3dW5ZZ3ururSrs8wcgzd3vDy6PIjAJ1KPBZKgNUAS8Cdzv7m5mw4GHgCxgCXCLu5ft6lhy7usJTLpDkyZNjundu3ctlFJERBJRXdbNcd3ynJ6eTmZmqB5mIiIiB87MVkY7hspEoOvt/ropXunua82sGYHk+SrgBarZndPdRwOjATIyMlx1s4iIREpd1s1xnTxHyuMfLSF75x6SzBg+MJ30tk2iHZKIiEg5Eeh6u4a9Xbsh0E1xSvDca4M/c83sJQIz9L5QH7pzvjhjJef260ibpg3r+tIiIpJgNNt2NXyyaBPj5q7juc9X8O7cuB7uICIi8WkcUDJ79jAg1MRXE4GzzKxVcDbus4CJZpZiZm0BzCwVOJ/A846p8OiWC4Fvayn+kJZuzGXU2/O56eXZdXlZERFJUGG1PJtZIwKzK1b1zMOY9s6vT6K42Ol52wcUx+8QcRERiV8PA6+Z2bXAKuBSADPLAG5w9+vcPdvM7gO+Dh5zb3BdEwJJdCqQDHxEoJUZ4DdmdiFQSGDSpOF1ViJg6pIsAOat2V6XlxURkQRV4+TZzC4AHgUaAAeZ2QACFe2FkQquPvK4n6VeRETiTbB79eAQ6zOB68osjwHGVNhnJ3BMJecdCYyMaLAH4P73Aw3dufmF0QpBREQSSDjdtu8mMOZpG4C7zwHSww+pfrJQ06iIiIiIiIhIQggneS5094TpJ2XB7DmOn+wlIiIiIiIilQgneZ5vZlcAyWbWy8z+DnweobjqLeXOIiIi9Y/r220REall4STPNwF9gXzgJWA78NtIBFWvqXIWERGpd75anh3tEEREJM7VaMIwM0sG7nH3PwK3Rzak+kvjnkVEROqHJRtzyy2PnvY9TRqm0K9LiyhFJCIi8a5GLc/uXkQlM2/GM0PdtkVEROqDJz5aWm558qJNnP/3z6IUjYiIJIJwnvM828zGAa8DO0tWuvtbYUdVj6nXtoiISPS9P299tEMQEZEEE86Y59bAFuB04ILg6/xwAzKzbmb2iZl9a2YLzOzm4Pq7zWytmc0Jvs4N91o1iK2uLykiIiIiIiL1QI1bnt396kgGUkYh8Ht3n2VmzYCZZjYpuO1v7v5oLV23SoFu22p6FhERERERSTQ1Tp7NLA24lsCM22kl6939mnACcvf1wPrg+1wz+xboEs45I8VM3bZFRETqs9XZu+jWunG0wxARkTgUTrftF4GOwNnAVKArkLvfIw6QmaUDRwFfBlf92sy+MbMxZtYqkteqLuXOIiIi9dfJf/6E2au2RjsMERGJQ+Ekz4e4+yhgp7s/D5wH9I9MWGBmTYE3gd+6ew7wNHAwMIBAy/RfKznuejPLNLPMrKysSIUTODca8ywiIlLf/fCfn7Nhe160wxCRWrZhex679hRGOwxJIOEkzwXBn9vMrB/QAkgPOyLAzFIJJM7/LZm92903unuRuxcDzwDHhTrW3Ue7e4a7Z7Rr1y4S4ZQJTN22RUREYsEJD02Odghx4esV2eTmFVS9o0gUnPDQZC4bPQOA4mLnrnfms2xT+Y6wYz5bztVjv2JT7v6/UFu3bTde5oN+XkERW3bkM3vVVrbu3BP54CUmhfOoqtHBrtOjgHFAU+DOcAOywJTW/wa+dffHyqzvFBwPDfBDYH641zrg2NCEYSIiIrEifcT7PHxxfy47rnu0Q4lJuXkFXPqvLxh0SBv+e90J0Q5HpJwVmwNPyv1mzXbO/ts0Tjy4Dc9/sZLnv1hZus9PMrrxauZqAH7+wkzeuXFQyHMtXJfDuU9+yt0X9GH4oIMAuPLZL5m5MjAEpFf7pkz63Q9CHjtn9TY+W5rFVSek06hBMrv2FLJuWx59OjcH4A+vz6VN0waMPOfwyBS8jOemL+eQ9s04qVfbiJ9bQgtntu1ng2+nAj0jEw4Ag4CrgHlmNie47jbgcjMbQGDY8QrgFxG8ZvUpdxYREYkZI96ax8VHd2Xrrj388KnprNuex6vXn8DxPdtEO7R6r6g48KFn3prtUY5EZK+8giKOvOdD8guLS9ct3pjL4o37Tr1UkjgDzF29jbyCIv48YTG/P+tQmjTcmwYtDybiX63I5sIBXcjZXVCaOAMs3bQDAHdn9uptHNWtJVm5+ezcU8RFT00H4NEPl/CDQ9uxKnsXyzfvZPH9Q5i0cCNvzFwDEJHkuaComMIip1GDZADufnchACsePi/sc9emJRtz6dyyEU0bhtNuWz+EM9t2yFZmd7+35uGAu38GIQcXfxDOeSNBj3kWERGJPYfeMb7c8k9Gz+C5q4/l1MPaV3rM1p17mLVqK51bNuKwDs1ISqreh4DComIcSE2u/si44mKnsNhpkBLOaLrIKylzscPnyzbTo20TurRsRF5BEWZw2B0TePCH/bnieLXsS92ZsnhTucT5QPQeNQGAMdOXc82gg3h95mpy8/aOmf5g3gY+mLch5LFjPltO4wbJjHhrXqXnn7pk73xLh90xody2xRtyOaxjs3LrPlq4kS6tGnHVv7+kQXISL153PG2aNKBl4wYA7MwvZPjYr/h6xVZuHXIYUxdn8eXybFY8fB5LK3xZUFzsPD55KT87sQdtmzasxm8jfM9NX86JB7fdp1wVnfW3aRzT4//Zu+/wqKr0gePfN4UUIIEAoUOQ3ltogkivKio2ZBXb+rN3V2yoYGHVVde1917WsoKK9CqCECD0EkrokkAKgRRSzu+POxkmyaSRmUwy836eZ57MPffce8/JJHPnndPq8sPt51dKudypIuH/aYfnwcBFwPaKFadqE0QbnpVSSqkqpEOj2uz4q/yLfdzw8Vp+u/cCAv39aBNZq8j+Gz9ZS+zBFAAeGtWOO4e2ASArJ4/EtKxil8O64MUlJKZlsfv5cSVe//3lewkK9OO6/i25/7+xzIo9UmLr0fFTWcQdO8WA1mVrMZ+98Qj3fL2BHTPGEBzoX6Zj8h1JyWDO5qP27u6nsnK49oM/CQrw4/nLuvLgdxt5+uJOADz+02au7deCvDxT4AuG9DM5PPG/LTQKD+bafi2oXyvIaTlOZeXw/vK93D2sDQHl+MJB+a7bvljvkvN8tHJfufJP/2Vbha43+rXldG9ehzohgYzr2ohHfigahA//1zIAlj88lLu/Xs9Gh14fL87daX8+K/Yw934Ta9/+1/ydnN+6Pq8viuPzVfHMvW8wDcOslYQd/zcPJaez/WgaexJPMalPC87k5tGgtvNAOzM71+l7XfLpM9zzzQZy8wx/7DkBwJ7nx3EoOZ1/L4zjjqFtaFAriPDQQMBaPhBg3f5kjDFINW+NFOOiGbBEJAiYbYwZ7ZITukB0dLSJiYlx2fk6TZvL5H4teHx8J5edUymlVPUhIuuMMdGeLkd15qp7c9TUXwHYOG0U3afPr9C59r0wjhm/bKdPVF26N69DzaAAuj9T8jm3PjOamkEBzN54hOTTZ5hyflSBcsXPHM+KuET6REUQHOjPmn1JdGhcm7DgwAL5ggL87K1oc+65gKZ1QwgPCSxyvQtfWsL+E+lOA+z446eJ2Z9Mv1YRNI8IJTU9myEvLyE5PZslDw2hVf2axdYj6fQZftrMz35AAAAgAElEQVRwmBsHRtk/1I55bXmxX0i0rBfK/hPpBdL+mDqM82cu5p8TuzJn818M6xDJc79u50xuwdbBGZd2ISK0BuO7Nban5f8eXr6yO1f0bub0mgeT0mlWN6Taf+hWFXMqK4dXF+ziw9/LF/T6qi9v6UeTOiEMfXkpb1zbk4u6NbH/vzna+ewYEk5m0bROCDd9upbr+rck9mAK/1m8G7Dey/YknkKAFhGh3PtNLL9uPlrgHCM6RrJwe0KBtJ/uHEjcsTQe/n5TgXR3dDGvzHuzKzueh+Lasc9Vks62rZRSqroRkQjgW6xVMeKBq4wxRRZDFpEpwBO2zWdtS1EiIjWAN4AhQB7wuDHmB9sX558BvYETwNXGmHh31qWw/NaNimj1qDUyrDwtUdNmbeWH9Yfs283qhjC8Y0P79o6/TnLdh2ucBpv5rbZAge6n415fYR3rpLU4/xwP/ncj0yd0ZvPhVNIycwgPCeSqd1cVW86hLy/llau6M75bY9Iyc4p053z4u40s2pFAkzrB7E44xfXnR5XYkl+4LmC1qAH2ljTHrquOnvzJmut1ZKexRbrSP/TdRrYcTmVA63r0alHX3hq28WAKE95cyYxLu3Bd/5bFlkt5vy5PzfN0EaqVyR/8yQDb3A53fbWBH9YdcpqvcPfypTsL/v/uOpbGqFet//FLezQpEjgDRQJnwD4WvLA2j80hJ8+w6elR9i8Tq5NzbnkWkc2cnT7LH2gATDfGvOGislWYq1ueO0+by6S+LXjiIm15VkopX1RdW55F5EUgyRgzU0SmAnWNMY8UyhMBxADRWPf3dUBvY0yyiDwD+BtjnhARPyDCGHNcRO4AuhljbhORa4DLjDFXl1QWV7c8x88c77Q1xROeGN+RZ391/Qi24EA/MrPPbYxncaYMaMkzE7pUmd9dYe0a1mLAefUKzJwMVi8BbYH2TVX1b1WdO1e1Qlfmvbkig0suAi62PUYBTapS4OwOIjrmWSmlVLU0AfjU9vxT4FIneUYDC4wxSbZW6QXAGNu+m4AXAIwxecaY407O+z0wXDwQ2bQoZvxxZXNH4Ay4PHAG+HTV/iodjOw6dqpI4Azw/oq9HiiN8rQz5zhBmKraXlu4y9NFKLeKBM9pDo8MIExEIvIfLildFSNot22llFLVUkNjzFEA209n00w3BQ46bB8CmopIHdv2DBFZLyLfiUjDwscYY3KAVKDS14D65Z5BvHB518q+rPKAj1fGe7oIygMKd/NX3uG1hXGeLkK5VWTM83qgOZCMFVfWAQ7Y9hm8dPyz0bZnpZRSVZCILAQaOdn1eFlP4STNYH1WaAasNMY8ICIPAC8D15VwTOGy3QrcCtCiheuXNQoLDmR4x+KXnVLe42hqpqeLoCpRXp6h54wFni6GUnYVaXmeC1xsjKlvjKmH1Y37R2NMK2OMVwbOTj8iKKWUUlWAMWaEMaaLk8cs4JiINAaw/Sw6u4vV0tzcYbsZcARrIrB04H+29O+AXoWPEZEAIBxIclK294wx0caY6AYNGlS4rvkCHJZGirCti6qU8h5HT2aSmpHt6WIoZVeR4LmPMWZO/oYx5jfgwooXqerSbttKKaWqqdnAFNvzKcAsJ3nmAaNEpK6I1MWaz2SesWYW/Rlrpm2A4UD+gqeO570CWGxctQZmKZrVDeGS7k3s2wH+fsTPHM+Q9q4LzpVSnnXF2394ughKFVCR4Pm4iDwhIlEi0lJEHsf6dloppZRSVctMYKSIxAEjbduISLSIfABgjEkCZgBrbY/ptjSAR4CnRWQTVnftB23pHwL1RGQ38AAwtZLqY3HSI+zjG/pUahGUUu6j3fRVVVORMc+TgKc4241rmS3Na4kIlfSFulJKKeUyxpgTWC3GhdNjgFsctj8CPnKSbz8w2El6JnClSwtbRsXdjnUZI++XmZ1bZB1s5X2OpGR4ughKFXHOLc+2pSzuNcb0xFoTcprDN9RuIyJjRGSniOy2rVVZafR+rJRSSlUNz1zSmev6t/R0MZQHnDh9xtNFUJXg/JmLy31M31ZFF/wZ362xK4qjFFCB4FlEvhKRMBGpCWwFdorIw64rmtNr+gNvAmOBTsAkEenkzmsWuD5OphBVSimlVKUb0akhPVvULTHP0oeGVE5hVKXKy9NPY8ry8Oj2vD25F03Cg3l8XEe++Xt/bhnUCoCh7RsQP3M8D41qb89/dXRz3ry2V3GnK+LjG/vQo3md0jM65FferSLdtjsZY06KyGRgDtZ4qHXASy4pmXN9gd3GmL0AIvINMIGzE5e4nfbaVkoppaq26JZ1idmfTFT9msROG0mP6brUjTe54MUlxM8c7+liqCrgzqFtABjb9Wzr8hMXdeKJi862rTUMC7I//+cV3QAY0HokvWxLYP14x/lc/lbBickuaFuflvVCGdo+kqHtI9l0KIUnf9rCxkOpBfLFzxzPzxuPkJaZw7AOkTQKD2bN48NJOn2GCW+sJCsnz7UVVh5XkeA5UEQCgUuBN4wx2SLi7tCyKXDQYfsQ0M8xgzvXktRxVEoppVTV9/nN/ezL29QJrUH8zPF8s+YAU3/cbM8z49IuPPnTFk8VUSlVATcOjGJ4h4ZlyuvvV/Tze0TNGtw+pDXRLevSq0VdtjwzmqEvLyUxLYvnLuvC5H4Fh4R0a1aHWXcNIjEtiz7PLQQgKMDqwHuxw6z/AJG1g4msHczOZ8cCsOlQCnM2/0WtIH/iEk4xK/aIPe+v9wzinWV7+Xnj2bRBberzt/4teHr2Nv466d0Tpg2thqsjVGS27XeBeKAmsFxEWgInXVGoEjiLXgsE7O5aSzL/4kY7biullFJVWkgNfxqFBxdIu6ZvCzY/PYr/TOrJwgcuLPd46bn3XcAVvZu5sph2g9tVjw+QP9w+gJ4t6rDg/iJzx5XZvhfG8Z9JPV1YKuWNEtOyit3XpWkYT13cmUFt65fpXEEB/nx32wBWP1pwzsRHxnRgeEcrAK8VFMCax4Yz777BRQJnRw1qB7H7OSsodja+2pluzeowdWwH7hrWln9f09P+PxD33Fg6Nwnn9Wt6EPfcWMZ0bgTAF7f0Y0yXxix+6OwKwCM7nf2i4LvbBgBw86BWPDiyHS9d0Y0Pro+2729Vv2aZylWS724bQGTtIKf7woKLtr3ue2Ecz13WBYARHYv/UuO2C1sX2P6oGq6OcM4tz8aY14HX87dF5AAw1BWFKsEhoLnDdjPgSDF5XU5Eu20rpZRS1VXt4MAirUT5Zt81kNcXxXFpz6Z0aBTG+v3J1K9dg+k/b+M/k3rRoVEYMyZ0Ye6WvziVlQNA84gQDiZZMwK/dEU3oqMiGPry0hLL8MXN/fjbh3/i7yfk5hk+ubEPg9rUZ/GOBG79fB1gBdN+Akt3JjLtok7kGcOVvZvTffp8wPogvWDbMQBq+PtxJrf0rqH/nNiVrk3rMO71FUX25Z/voVHt6Ng4jPq1gth4KIXrB0Tx3vI9PD9nB5P6tqB3ywj+d8dAwOquOv71FWw9UrDd5I+pw7j0zZUkpGWxbfpojqRk0Dg8hLiEU+w4ehIR4eLuTWhSJ5iJb68qtdzFSU3PJjw08JyPV1XbLZ/FFLtv9p2Dyn2+PlGlB7oiQvtGtUvNF+Dvx4L7B9O0bki5y5F/Hcf3IREh0F94c3Ivsh3+l0NrBPDq1d25/9uN3DOsLZf3bEqgvx99oiL4+a5BdG4Shp+TVvVRnRuy7chJVsQd57FxHbh+QBTHTmYSHhLIgaR0ZsUe4cPf9wHw1d/70aVpOFe/u5rtR0/StE4IH94QTYdGYax5fAQnTmXR+9mFLH7wQs5rUAuA1xbu4rWFcdw4MIqmdUIY0j4SEWFyv5Z0b1aHTo3DeHn+Tt5auoc/pg4j9mAKd3y5HoCpYzswqW9zLnxpKQPb1KuWvXqlOi29JCIBwC6s5TYOY61Dea0xZquz/NHR0SYmpvh/vvKKfnYBozo34vnLurrsnEoppaoPEVlnjIkuPacqjqvvzRWxN/EUL/y2gwXbjrHp6VGEBZcejOXmGY6dzGRFXCKjOjWi3/OL6NOqLp/d1A9/PyFq6q+AFURuP3qSVvVrMm/rMVbvPcFHN/Rx2oXU0eZDqbSOrElQgD+nsnIIDzlbpjcWx/Hy/F2senQYNYMCCA30Z9vRk9z51XrGd23CfSPaEhzoT3ZuHh+v3Mfzc3Yw8/KuTOjRlJAa1tJOf6VmciYnj7iENGoE+JFxJpdRthav4sQeTKFj49oEBRRcHiorJ5fsXMPuhFNc+uZKbh18Ho+N60hK+hkS0rJo17DkQCQtM5vsXEPf5xaSk2eInzmeV+bvJPFUFuO7NuFvH/5Jq/o12Xf8tNPjddyz98r/PyrMF1/z5NNnqFuzRqn58n9ncc+N5e+fxbB0ZyIf39CHoR0iSz32+KksVu4+zoQeTUvNu3RnAjd8vJb3r48u0CJeWtnCggPY9PRoAH5cf4ih7SPLVK+yqMx7c7UKngFEZBzwGuAPfGSMea64vK4PnhcyqnNDDZ6VUspHafBccVUpeHaHtMxs/ESoGVSRaWUqzhhDQloWDcOCS8/sAn/sOU6fqAgC/SsyIrCglbuPExzox8S3V/HspV14otAY9Tev7aXLEHkpDZ7L72RmNgF+QmiNAN5dtocXftvBvPsGl6k1vbyOncws13vLrNjD9Gxelxb1Ql1eFtDg2WVcfYPu+9xCTmflEFHLNd+SqKorwM+P5y7twvltyjaeRlVfd361nk2HUjxdDOVmE3s1474R7Sp8Hg2eK87bg2flegdOpNM8IoRWj84psk+DqYrJzTOkn8mhdqFeF7l5hm/XHuTK6GYu+UJk48EU1uxL4u+DzytTfmfB85rHhxNZu3K+DKrujDEcSs6geYR7gtWqpjLvzef8taiIXO4kORXYbIxJOPciVV13D2/Lhv3Jni6GcrNcY5gVe4TYQykaPPuAxdsTaFo3hG5Nwz1dFOVGLXzkA4RS3shdrVUKnp69lc9X72fhAxeScDLT/rnn6zUHeOKnLWw6lMLMid3s+VPTs0nNyC7wmhxNzSDAz48GxUwwBTDhzZUA9GpZh94ti44/Tkk/wzvL9vLQqHYEOAnWb7uwtQbO5SAiPhM4V7aK9Cm6GRgALLFtDwFWA+1EZLox5vMKlq3Kua5/y3LPzqmqn8zsXGbFHtHJ4XyEwTC0fQMeH9+p9MxKKaWqlHeW7eFAUrrHh9TFHUujTWQtl0+AlJtn+G3LUcZ3bVzuc+fmGeJPnKa1baIngFNZOby/fC93DG1NYloWn6/eD8CIV5bZ83w4JZoTp84A8M3ag9w4sBXP/rqN+0a0444v13HsZBYXtK3PPyd2o3F4MANeWAzAkxd14sbzowBrKZzC1waY+PYq/u/C83h32V4WPXghWdl5fL/uEHuPn2LpzkROZmY7fS1Hdy7b2Fql3K0iwXMe0NEYcwxARBoCb2Otu7wc8LrgWfmGajjxn6oAY3QNd6WUqq5m/rYDwO3B8xer9xPgJ1zTt0WRfUt2JnDjx2t55aruXN6rmX0itAgXTIb0yR/xzPhlGxlX5HJldHNS0s/Qa8YCPrupHwPb1ONgUgZhIQGICJnZuYSHBBIcaE3s9vL8nby9dA9LHhpCq/o1yczO5YFvY5m/7Rj/XhRX7DVv/rTgsIrRry0HYEXccXvairjjnD9zcYF8M37ZxoxftpVap3eX7QVg+L+WFdn31Z8HuGtomyLpPVvULfW8SlWGigTPUfmBs00C0M4YkyQi2RUsl1Ie42cLpLx5PgB1lkG/MFFKqeouJf0MdUJdNyfNm0t289K8nQT6C7PvGmSfrMwxeH5jcRzdm9dh97FTACzblcjlvZrRc/oCcvIMu54dy+IdCQxp38Ae0Bpj+GL1fhqHh9AoPJiImjWIP3Ga1XuTeN0W0L52dQ8u7WnNepy/3vGexNOczsph2a5E8gz87cM/uWNIa95auqdAuQe3a8BDo9rRvG4ob9v2lbZ8WlUz9cfNBbb/fU0PD5VEqaIqEjyvEJFfgO9s2xOB5SJSE9DZd1S1lR9H5Wns7BsMCBo9K6VUVdejeR1iDzr/iHkkJZMe0xfQqXEYT1/SmbTMbNpE1qJlvZrlukbHJ+fSq2UdYuKtOW6ycw2frIy37/9sVTwTezWjZlAAL8/fBcDYLtZSX7NijzAr9og9b7snfrM/P69BTS7q2pjXF+8utQz3fRvLfd/GAlDHtpb1O8v28M6ygoFy4cAZYPmuRJbvSixDTauuwuW/oG0DD5VEqaIqEjzfiRUwD8SKNz4DfjBWc91QF5RNKY8Qe8uzhwuiKkWeMdryrJRS1cCnN/al+/T5TveNe30FANuOnuSqd1fZ08syG/eZnDwmvb+a+0e0IyM7l5W7TxTY/23MQfvzabO2Mm3W1gL7f9vyV6nX2Jt4ukyBc2Ep6dqZ0xXd35VylXMOnm1B8ve2h1JeIz+OMmj07AsMaLuzUkpVA+GhgaVnKuTnjUeoHRxA+0a1iawdTG6eoUaAH7sT0oiqV5MAfz8OJJ1m3f5k/vbhn24otVLKm1R0qap/ApFYnz0FK6YOc1HZlPKI/FZIbXn2DcYY+zh3pZRSVVujsGD+OplZ5vx3f72hSFr9WkEcP5VF3dBAkrVlt0q7fUhrTxdBqQIqsur5i8AlxphwY0yYMaa2Bs7KG4hOGOZTdMIwpZSqPsZ1bVzhcxw/ZU3CpYGzUqq8KhI8HzPGbHdZSZSqQkTQTts+whjttq2UUtVF/do6/tWX6P1ZVTUVCZ5jRORbEZkkIpfnPypSGBF5SUR2iMgmEfmfiNSxpUeJSIaIxNoe71TkOkqVxhqD4OlSKHez9y7Qpmfl5UQkQkQWiEic7afTRVNFZIotT5yITHFIryEi74nILtt9eqIt/QYRSXS4P99SWXVSvunmQa3o0byOp4uhKskkJ+tqK+VJFQmew4B0YBRwse1xUQXLswDoYozpBuwCHnXYt8cY08P2uK2C11GqRH4iOmGYD8iPnf00dlbebyqwyBjTFlhk2y5ARCKAp4B+QF/gKYcg+3EgwRjTDugELHM49FuH+/MH7qyEUkEB/vx050BPF0NVkuYRoZ4uglIFVGS27RtdWRDbOR3XH1gNXOHqayhVFiK6zrMvyH+JdZ1n5QMmAENszz8FlgKPFMozGlhgjEkCEJEFwBjga+AmoAOAMSYPOO72EitVggdHtuNfC3Z5uhhKKR9zzi3PItLM1rU6QUSOicgPItLMhWW7CfjNYbuViGwQkWUicoELr6NUEYJot20fkN9tW3ttKx/Q0BhzFMD2M9JJnqbAQYftQ0DT/CFUwAwRWS8i34lIQ4d8E23Drb4XkebOLi4it4pIjIjEJCYmuqA6ytfVrx3k6SIopXxQRbptfwzMBppg3XB/tqWVSEQWisgWJ48JDnkeB3KAL21JR4EWxpiewAPAVyLidGZvvUErlxBd59kXnG15Vqr6K8v9tbRTOEkzWL3UmgErjTG9gFXAy7b9PwNRtuFWC7FatYuexJj3jDHRxpjoBg0alKteSjkzomPD0jMppZSLVSR4bmCM+dgYk2N7fAKUekc0xowwxnRx8pgF1mQlWGOnJxtbs5AxJssYc8L2fB2wB2hXzPn1Bq0qzE/Q6bZ9QJ6t5dlPBz0rL1DK/fWYiDQGsP1McHKKQ4Bjy3Ez4AhwAmuOk//Z0r8DetmuecIYk2VLfx/o7fKKKeVEg9pBLH1oiKeL4dWeGN+R/91xPgCX9mhSYN9zl3Xhyt6u7HBa1PonR7r1/Eqdi3Me8wwcF5G/YY2FApiEdYM9ZyIyBmsM1oXGmHSH9AZAkjEmV0TOA9oCeytyLaVKIog9sFLeS19i5UNmA1OAmbafs5zkmQc87zBJ2CjgUWOMEZGfscZMLwaGA9vACsTzu4MDlwC6hKWqNFH1a3q6CNXWu9f1ZnTnRuTlGeISTvH56ni+WH3Avn/z06OoHRwIQPzM8QBsOJhCdMsIOjauzbV9WzC5X0s6NwmjTWRtBrapR56B1o/NsZ8jomYNggP8GNohki//PFDg+pP6tuDrNQXTCouoqcuSqaqnIsHzTcAbwKtYbXR/ABWdROwNIAhYINYgxNW2mbUHA9NFJAfIBW7Ln9BEKXcQ0cDKl+iYZ+UDZgL/FZGbgQPAlQAiEo11T73FGJMkIjOAtbZjpjvcax8BPheR14BEzt7v7xGRS7CGWiUBN1RKbZSy2fnsGBZvT+D2L9dX2jWb1gnhcEpGpVxrZKeGLNh2rEDaqE4NmV8oLd8VvZvx/bpDpZ53dOdGgNXzqn2j2jx7aVdiD6aw5fBJXr6yuz1wdrTs4aFF0m4Y2Mr+3F9gRMdIjqRkMufegtMTPXdZVxJOZrL5cCo3fxrD9AmduXNoaxZtT+Cp2Vvt+eqGBhIeEsh/JvUqtQ5KeUJFZts+gPUts52I3Ae8VoFztikm/Qfgh3M9r1Llpb22fYN9mWcd9ay8nG3o03An6THALQ7bHwEfOcm3H+uL7MLpj1JwWUmlKlVQgD9juzbmvhFteW1hnFuusenpUXyyMp5XFuyiYVgQL17Rjckf/Om09bRfqwj+3Gd957R9+hg6Tptb5HyNwoKpExrIjr/Sir3m85d15croZry7bA8Lth1jYJt6nMnJY218MsM6RBYJnls3qMlPdw6kdnBgscHzJzf2Ie7YKbYeSXW6/+e7BrH+QDK9W0aU+PsoyQdT+hS7LzIsmOFhwfaW7GZ1Q5lyfhRTzo/i/z6PYd7WY7x3fTR9os79+kq5W0Vanp15gAoEz0pVFX6is237gvxJ4XTIs1JKVW/3jWhHdMsI/vbhnwAE+gvZudZ7fFS9UOJPpJd0eAGOrbdf/b0fYcGB3DO8LfcMb2vP8/Xf+9Mnqi6X9WzKzZ+uZUj7SB4c2Y4WEaEcS8sk/UwuITX8Wf/kSMKCA/j7ZzH8secEn9/cj76trOBwbXwSfgIT315VpAzX9msBwI0DW3E4JYPHxnUkNSOb95fv5cro5gzrGEmDWkGIk65T7/ytN3+lZvD+in32FvKPbohmSPtIhrR3NtG+RUQqFDhXxJvX9iJmf7IGzqrKc3XwrB9BlXcQdMyzD8hfy1u7bSulVPXXq6W1qtqEHk349zU9ueXTGBqFBzH9ki6c5zAWtyQvTuzGVX2a8+RFnfgu5iADzqvnNN+A1lZ631YRbH56dIF9jcND7M/zx+1+fGPfIufIDxRvH9Ka9fuTmTmxG0NfXsqkvi3seWoGBfDC5d0AqB0cyDMTugAQWTu42DqM6WJ1yXbsUl3VBfj70b+Y37VSVYmrg2eNNpRX0FjKN9jXedZXXCmlqr3QGgGs+MdQIsOsNaA/mBJt37fkoSGkpJ+hfq0gmkeEYozhug/XUK9WDdYfSOZgUgbLHh5Cy3rWJGThIYHccsF5lVLuR8Z0sD/P79KslKqayh08i0gazoNkAUKcpCtV7YgIy3Ylcu83GzxdFOVGObYufdryrJRS3qF5RKjT9Fb1awJnZ+cWEb64pR8Ag19cUhlFU0p5gXIHz8aY2u4oiFJVyfCOkazfn8zGgymeLopyszaRtejevI6ni6GUUspD/jmxGzPn7qBJHW0DUkqVzNXdtpXyCq9c1cPTRVBKKaVUJRjQuh6z7hzo6WIopaoBP08XQCmllFJKKaWUqurEePGMwiKSCOwv52H1geNuKE5V54v19sU6g2/W2xfrDL5Zb3fXuaUxpoEbz+/19N5cLr5Yb1+sM/hmvX2xzuCb9faae7NXB8/nQkRijDHRpef0Lr5Yb1+sM/hmvX2xzuCb9fbFOvsCX31dfbHevlhn8M16+2KdwTfr7U111m7bSimllFJKKaVUKTR4VkoppZRSSimlSqHBc1HveboAHuKL9fbFOoNv1tsX6wy+WW9frLMv8NXX1Rfr7Yt1Bt+sty/WGXyz3l5TZx3zrJRSSimllFJKlUJbnpVSSimllFJKqVJo8KyUUkoppZRSSpXCJ4NnEWkuIktEZLuIbBWRe53kERF5XUR2i8gmEenlibK6ShnrPEREUkUk1vaY5omyupKIBIvIGhHZaKv3M07yBInIt7bX+k8Riar8krpOGet8g4gkOrzWt3iirO4gIv4iskFEfnGyz6te63yl1NkrX2sRiReRzbY6xTjZ71Xv4b5A7816by6Ux6ver/XerPfmQvu88rX2hXtzgKcL4CE5wIPGmPUiUhtYJyILjDHbHPKMBdraHv2At20/q6uy1BlghTHmIg+Uz12ygGHGmFMiEgj8LiK/GWNWO+S5GUg2xrQRkWuAfwJXe6KwLlKWOgN8a4y5ywPlc7d7ge1AmJN93vZa5yupzuC9r/VQY8zxYvZ523u4L9B7s96b9d7sve/Xem8uyltfa6++N/tky7Mx5qgxZr3teRrWH3bTQtkmAJ8Zy2qgjog0ruSiukwZ6+x1bK/fKdtmoO1ReJa8CcCntuffA8NFRCqpiC5Xxjp7JRFpBowHPigmi1e91lCmOvsqr3oP9wV6b9Z7c6FsXvV+rfdmvTcrwAvew30yeHZk6xrSE/iz0K6mwEGH7UN4yQ2thDoDDLB1KfpNRDpXasHcxNZtJhZIABYYY4p9rY0xOUAqUK9yS+laZagzwERbl5nvRaR5JRfRXV4D/gHkFbPf615rSq8zeOdrbYD5IrJORG51st9r38N9gd6bi9B7sxe8X+u9We/NhXjja+3192afDp5FpBbwA3CfMeZk4d1ODqn23xCWUuf1QEtjTHfgP8BPlV0+dzDG5BpjegDNgL4i0qVQFq97rctQ55+BKGNMN2AhZ7/xrbZE5CIgwRizrqRsTtKq7Wtdxjp73WttM9AY0wurC9idIjK40H6veq19id6b9d5s43Wvtd6bi8/mJK3avtZ6b/bue7PPBs+28SY/AF8aY350kuUQ4PgtUDPgSGWUzV1Kq7Mx5mR+lyJjzBwgUETqV4kwANgAACAASURBVHIx3cYYkwIsBcYU2mV/rUUkAAgHkiq1cG5SXJ2NMSeMMVm2zfeB3pVcNHcYCFwiIvHAN8AwEfmiUB5ve61LrbOXvtYYY47YfiYA/wP6Fsride/hvkDvzXpvduBt79d2em/We7OXvtY+cW8WY6pVsF8u9evXN1FRUZ4uhlJKKS+xbt2648aYBp4uR3Wm92allFKuVJn3Zq+ebTsqKoqYmCKzpCullFLnRET2e7oM1Z3em5VSSrlSZd6bfbbbdnnc+PEaRr+6nLH/XsEfe4qbeV0ppZRSlSk7N4+oqb+yZGeCp4uilFLKB2jwXAbNI0KJqh/K9qMniYlP9nRxlFJKKQV8tspqbLjx47UeLolSSilf4PbgWURCRKS9u6/jTtMndOGtydY4fi8eIq6UUkpVK4eS0z1dBKWUUj7ErcGziFwMxAJzbds9RGS2O6+plFJKKd/w8cp4TxdBKaWUD3F3y/PTWFOUpwAYY2KBKDdf0y3yFyUz1WspMqWUUkoppZRSLuDu4DnHGJPq5msopZRSSimllFJu5e6lqraIyLWAv4i0Be4B/nDzNd1CbE3POuZZKaWUUkoppXyPu1ue7wY6A1nAV0AqcJ+br+kWkh89K6WUUkoppZTyOW5reRYRf+AZY8zDwOPuuk5l04ZnpZRSSimllPI9bmt5NsbkAr3ddX6llFJKnSUiESKyQETibD/rFpNvii1PnIhMcUhfKiI7RSTW9oi0pb/qkLZLRFIcjsl12KeraSillPJq7h7zvMF2M/0OOJ2faIz50c3XdR8d9KyUUqpqmgosMsbMFJGptu1HHDOISATwFBCN1ZlqnYjMNsYk27JMNsbEOB5jjLnf4fi7gZ4OuzOMMT1cXxWllFKq6nH3mOcI4AQwDLjY9rjIzdd0GxHttq2UUqrKmgB8anv+KXCpkzyjgQXGmCRbwLwAGFOOa0wCvq5QKV3kaGqGp4uglFLKx7i15dkYc6M7z6+UUkopu4bGmKMAxpij+d2uC2kKHHTYPmRLy/exiOQCPwDPGnO2u5WItARaAYsd8geLSAyQA8w0xvzkmqqU7qHvNhbY3noklc5Nwivr8koppXyQW4NnEQkGbsaacTs4P90Yc5M7r+sugvbaVkop5TkishBo5GRXWSfmdLZ0RP6dbbIx5rCI1MYKnq8DPnPIdw3wvW1Ok3wtjDFHROQ8YLGIbDbG7HFS7luBWwFatGhRxqKWbOXuEwW2x7/+O/Ezx7vk3EoppZQz7u62/TnWTX40sAxoBqS5+Zpuo8tVKaWU8iRjzAhjTBcnj1nAMRFpDGD7meDkFIeA5g7bzYAjtnMftv1Mw1pesm+hY6+hUJdtY0z+sXuBpRQcD+2Y7z1jTLQxJrpBgwblqrNSSilVVbg7eG5jjHkSOG2M+RQYD3R18zXdyuioZ6WUUlXTbCB/9uwpwCwneeYBo0Skrm027lHAPBEJEJH6ACISiDU/yZb8g0SkPVAXWOWQVldEgmzP6wMDgW0ur5VSSilVRbg7eM62/UwRkS5AOBBV0gEi0lxElojIdhHZKiL32tKfFpHDDktijHNv0Z2UrbIvqJRSSpXdTGCkiMQBI23biEi0iHwAYIxJAmYAa22P6ba0IKwgehMQCxwG3nc49yTgG8cx0EBHIEZENgJLsMY8a/CslFLKa7l7qar3bN9sP4n1jXgtYFopx+QADxpj1tvGXa0TkQW2fa8aY152X3FLp2OelVJKVUXGmBPAcCfpMcAtDtsfAR8VynMa6F3CuZ92kvYHVaw3WdTUX3Xcs1JKKbdx92zbH9ieLgPOK+MxR4H82ULTRGQ7BWcC9Rgd8qyUUkoppZRSvsnds207bWU2xkwv4/FRWJOP/Ik1luouEbkeiMFqnU52cozLZ/R0pA3PSimlVNW1/kAyvVrU9XQxlFIekJmdS54xhNZwd+da5avcPeb5tMMjFxhLKWOe84lILaylMu4zxpwE3gZaAz2wWqb/5ew4d87oKYh221ZKKaWqsMvf+sPTRVBKecjgF5fQado8TxdDeTG3Bs/GmH85PJ4DhlCGLti2mT5/AL40xvxoO9cxY0yuMSYPaxKTwktouJ9221ZKKaVUGe1OOEXGmdzSMyqlKuzh7zaSkJYFWPMf7E6otqvjqirM3S3PhYVSythnsRZT/hDYbox5xSG9sUO2y3BYQqMy6VJVSimlVNWWmZ3L+gPJnM7K8VgZcnLzGPHKMm77Yp3HyqCUrzh+Kovv1h0qkHbjJ2uL5Es/U/Q9IeNMLqYCXUvTz+QQNfVX3l6655zPoaoPd4953szZYcL+QAOgtPHOA4HrgM0iEmtLewyYJCI9bOeLB/7P5QUuhTY8K6WUUlVfhyfnAtCpcRhz7r3ArddKPn2G0CB/ggL8C6SnZFirdf6++7hbr6+UL0rNyKaGvx95xrBmXxJ3f72hSJ6DSRlkZufy2I+bQWDd/mT2n0hnYq9mTLuoE+GhgRw7mUm/5xdxcfcm/GdSzwLHr9ufTGZ2Lmvjk5jcryUNagcB8MGKvfRrVY+uzcIBSDp9BoDPV8Vz+5DW7q24G2Tl5PLi3J3cO6ItYcGBni5Olefu0fQXOTzPAY4ZY0r8GtgY8zvO49Q5rizYOdOGZ6WUUqpa2Hb0pNvOnZqRzY0fr2H9gRQuaFufz2/uV2B/9LMLAcjNMzz642ZeuNy1q3ot35XI9R+t4Ze7B9GlabhLz61UVdf9mfk0jwghLw8Op2QUmy//izRHP6w/xA/rD3HzoFZ8+Ps+AH7eeIQ7h7Zmzb4kXpy7k1OFeq28tjAOgPtHtOPVhbsAeGtyL8Z1bWyfD0lKWZZnd8Ip2kTWKnMdnTlwIp2G4UFFvqyriB/XH+bD3/eRk5vHMxO62NNbPzaHqWM68PfBZVowyWe4u9t2msMjAwgTkYj8h5uv7XK6VJVSSilVvXy2Kr5I2v82HCJq6q+cOJV1zue95+sNrD+QAsCKuONsPZJK1NRfuf6jNUW6gH695gAAeXmG/s8vImrqr0RN/dW+f/2B5ALHfL3mAEdTM8jNMxhj+PD3fSzZkWDf32P6fK7/aA0AV727CoC7vlrP8l2JJZb5TE4e1334J1uPpJ5zvZWqKg4mZZQYOJcmP3DON+a1FUybtbVI4OwoP3AGuOPL9Ww+lMoNH1v/i4dTMnjwvxuJmvor+0+cBuDEqSz2HT/N3C1HGfHKMuZsPooxhgMn0klMyyI3z3D8VBZ/7D7Oe8v3sO/46SLXXBufRNTUX9l1LI3BLy3hH99v4mhqxeqedPoMn/4RjzGGnNw8AHJs7ze5edZ7UW6e4bk528/5GiXJzM7lveV77NeqTtzd8rweaA4kY7Um1wEO2PYZyrj2c1VS/V5ipZRSynt1bRrO5sPFB4PTZm3lzSW7+fOxEQDM2XyU+7/dCFhdqo+mZtKvVQSxB1OYMiAKP7/ivyk/mZlNrRoBZObkFvmQO/713wGrRbjVo0U7y/266Sh3frW+QNrprBz+vSiO95bvZcalXbiuf0uOpGTw6I+bz5b/ok7M+GUbAPEzxwOQkp5t359+JpcTp7L4ZdNRftl0lIdGtWPK+VHUdtL9ctvRk6yIO87JjM3MumtQsfXMdyorh+AAPwL8/cjMzmXc6ysY07kRPVvUZWSnhqUe7+hoagY/rj/MHUNal9pCp5Sj3DzD6TM57PorjcZ1QmgUFuzpItld/MbvBbZ/WG+Nu162K5HrB9Skt60HSr47viz4HlDY83N22P/P8135jvUF2ahXlwMwK/YIs2KPABTIuzvhFIH+Qst6NYucNzfPkH4mx/6+cMU7f7A38TR/7DnOwDb1AauR8MHvNvLj+sNc1vPs/M6bD6Xau6iDNcY7wM+PGgHlb4PNfw99bWEc7yzbQ72aQUzs3azc5/EkdwfPc4HZxpg5ACIyFhhhjHnQzdd1C9FRz0oppVSV0qlxWInBM8Cxk1nc8ulaVsQdJysnz55+7zexBfI98/M2wkMC2fDkyCJB9OZDqVz8xu/0bFGHDbYW5/IoHDhb19/Awu1Wi3LcsTTu/zaW/204XCDPdFvgDJBwMpO+zy8qcp5L31ppf/7y/F28PH8XX/+9P80jQmhWN5QzOXmkpJ+x58nMzuOJnzbTukEtbhzYil4zFvDgqHZM7teywHm7PDWPy3o25aUrurEi7jh7E0/zlm1SpMIf8POlZmSzZl9SkeD6ts/XsfFQKsM7RtKhUZjTYwHeW76H5+fsYPv0MYTUcF3XVFV9PTV7C1+sPlB6xipk2qytNKsbck7HXvv+avqfV49XFuzig+ujS8y7as8Jlu5MYHK/lox4ZRkA/76mB6kZ2Zzfuh4vzNlBcA1/Vu05QdLpM+x5fhzLdiWwN9H68m/e1mOEBFr/Z7Nij5CWabW6O74PXfzG78TPHE9CWiZvLdnDJ3/EUzsogK9v7V9kyMjJzGyMsSZMDKnhT2iNAFbtOYG/n9C+UW26PzOfy3o2tf9vZ2RXv9UIpCKzy5V6cpF1xpjehdJijDEl/yW4SHR0tImJiXHZ+TpNm8vkfi14fHwnl51TKaVU9WG7r1XKPexc2IZEfQtEYU2ueZUxJtlJvinAE7bNZ40xn9rSawBvYC0tmQc8boz5QUSCgM+A3sAJ4GpjTLztmEeBm4Fc4B5jTImLrLrq3pzf7fn3R4Yy6J9LKnw+R1ueGc3s2CN0aFybXi3qMmfz0VJbjKqqV67qzjM/byM1I9vp/jWPDbcH5OueGEG9WkH2ffm/4yt7Nysyk3HMEyN4d9ke2kbWJiEtk182HWX/ifQCH4ZnTOjM8I4N+eSPeH7ZeIQjqZl0bRrOz3cPIi0zm+TT2bSoFwrA3C1/seFgMu8u22u/5ktXdnfdL0JVS8dPZdnnD1DuMaxDJIsdhoWUxwMj27F67wn+2HOCH24/n4lv/1Fg/7z7BjP6NavFfEj7BizdWXBoyVMXd+LGga3OreAOKvPe7O7geR6wAvgCq8fz34DBxpjRbruoA3cEz9f2bcETF2nwrJRSvqgaBM8vAknGmJkiMhWoa4x5pFCeCCAGiMa6N68DehtjkkXkGcDfGPOEiPgBEcaY4yJyB9DNGHObiFwDXGaMuVpEOgFfA32BJsBCoJ0xptjmBFcHz/EzxxcYP+xq7gjOq7KgAD/WPDaC7tPnu+0aHRuHsd02mVv8zPHsTTzFsH8tK5Jv2cNDaFmvJtm5ecQeTKFPVLWbLkdVUMcn51bL1klf1CgsmL9OZpbrmMjaQax5fESFr12Z92Z3Txg2CWt5qv8BP9meT3LzNd1GO20rpZSq4iYAn9qefwpc6iTPaGCBMSbJ1iq9ABhj23cT8AKAMSbPGJO/zpLjeb8Hhos1cHUC8I0xJssYsw/YjRVIew1fCpwBsnLy3Bo4A/bAGawvQZwFzgAXvrSUE6eyuO+bWK58ZxUbD5a/u7yq3jRwrj7KGzgDJDsMJ6ku3Bo8227M9xpjemJ9wz3NGJPkzmu6m04YppRSqgpraIw5CmD7GekkT1PgoMP2IaCpiNSxbc8QkfUi8p2INCx8jG3JyVSgXnHnKnxBEblVRGJEJCYxseQZoZVy1PvZhfy6+SgAE95cyaLtxzxcIqWUq2TnVr/Iyq3Bs4h8JSJhIlIT2ArsFJGH3XlNd9LZIZVSSnmaiCwUkS1OHhPKegonaQZrEtFmwEpjTC9gFfByKccUl14wwZj3jDHRxpjoBg0alLGYZbf0oSEuP6eqmm7+1HXD8bzN83O289OGw3z6R3yxY9yrEmNMgSXaDialc9y2fNy6/UluHY6h1Lly92zbnYwxJ0VkMjAHeARrbNVLbr6u27hxiLhSSilVKmNMsQPEROSYiDQ2xhwVkcaAs1lgDmFNCJavGbAUayKwdKyhVgDfYU0Eln9Mc+CQiAQA4UCSQ7rjuY6Us0oVFlW/6NIsyns9+dMWTpzO4q3JvUvPXAnSMrOdLg1WFit3Hyc8JJAGtYNoeI5LMKWfyaGGvx/vLd9rT1u+K5F3r+tNgL+7R2ieG2MM1324ht93H2fN48PZl3iaq99bDVjj4H/aUOlvI0qVibv/owJFJBBrzNUsY0w21bjns7Y7K6WUquJmA1Nsz6cAs5zkmQeMEpG6IlIXGAXMM1YT0M+cDayHA/nrJDme9wpgsS3/bOAaEQkSkVZAW2CNa6ukVEGfr97PnM1/ueRcp7Ny6DxtLot3nFt38J82HKbr0/PZdsQax30wKZ11+8s2QnFtfBKTP/iTi/7zOwNeKLoEmTMLtx0jauqv3PP1BhJOZtJrxgI6TZvH3V9vKJBv0Y4E2jz+G7l5pX/svu7DP5n+87ZS85Xkr9RM8pxca+PBFN5bvqdI+oJtx/h9tzWlwg0frbUHzvmOpmZUqDxKuYu7g+d3sZbKqAksF5GWwMkSj6jiTPWN/ZVSSnm/mcBIEYkDRtq2EZFoEfkArPlIgBnAWttjusN8JI8AT4vIJuA64EFb+odAPRHZDTwATLWdayvwX6wgey5wZ0kzbbvSpzf15ee7BlXGpZQX25t4mtNncvnX/F3ndPyyXdYY/h1/WR9vL3hxCRPfXkX8cWsd3YSTmSSddj4pUsLJLPvzPAPvLtvD3C3W+O5ZsYftXZgd3fKZ1W199sYj9H1+kf3cv21x/mXCh7/vJTU9m79Si5/MaUXccT5aua/EepbkcEoG/V9YxGuL4orsm/DmSp6fs8O+HX/8NLEHUwqsI7ztaMHQ4OOV++zrnytV1bi127Yx5nXg9fxtETkADHXnNd1Km56VUkpVYcaYE1gtxoXTY4BbHLY/Aj5ykm8/MNhJeiZwZTHXfA547txLfW4ubFdw7PSKfwzlghd9a2ZsVXH5jSLlndZm3f5kXl2wizqhzrtrD3l5KU3rhHA4xWpB3TFjDB2enAtY3ZIdr53vhd+sIHPt4yO495tYejSvw093DgRgzb4kXl1Q/gD/r9Qs+r+wiIzsXPt1Nx1KoV3D2gQH+hfJn5mdy5SP1vDkRZ3o0jTcnm6MITM7j/4vLCI1I5uXr+zOFb2b2a5h1XFFXCIPjGzntBy7jqWx6VAqD323sdQyP1PBVnCl3KlSB0IYS05lXtOVBB3zrJRSSlVFzSNCPV0EVQUt2ZHAhS8t4UxOntP9+Z/rDief7Sa8eMcxTpzKIic3z5bHkFyo9fiB/8by++7jbDxkLZ/12sK4IoFhfuAMVnfuwrKynZcpvzU59mAKa/YlkZ2bx1XvrmLV3hMlVdWplbuP25d72p1wilmxh7nkjZX84/tNRfKmpmez9Ugqf+5L4tI3VxZorX5q9lY6Tptrn4jsoe82Mn/rXwz711J+j7PKteFACqnpZycqc+zGPerV5WUKnJWq6tw9YZhSSimlVKX44Ppoe7dW5f2ipv5qb00tzpOztnAoOYNjJzMLfMGSlplNVk6eve032Rb0/R53nJs+sf6GGoYFkZaZw/mt69m7EfeNiuD2Ia3Zf8IKhg8mWQHygaR0DjgJkPONfHV5gXI3CQ/mSDFdqUe/djbvVe+uKrF+pdl5LM3+fM7mo7xia71essOqz6mss21ajut75+QZ+r+wiN3PjaXN4785Pfetn68D4NWFZ1vE3b1GuFKepsFzOehSVUoppVTVNaJTw9IzKa+Sl2dYuec4dUNr0CayFgeS0mnXsDZgjQs+ZGtRnr3xCHcObQPAqj0nmPS+NUFVRM0a9nP9vPEIbyzebd8+ZhuT7Dj+dk18Ems+KduEYCUpLnB2p1ccun2nZeXQ9el5pGWW3CH0307GMSvly9waPIvI5U6SU4HNxphqOROA0X7bSimlVJX1za39uabQzL3Ke5332Bz78wk9mjAr9ghz7rmAca+vKJDvpXk7eWneTl6c2I1//HC2y7LjZF6FZ6z2dqUFzgD/cfgyQSnl/jHPNwMfAJNtj/exZulcKSLXncsJRWSMiOwUkd0iMtV1RS3LtSvzakoppZQqr/7n1WP2XQM9XQzlAbNirbWBCwfOjhwDZ6WUKi93B895QEdjzERjzESgE5AF9MNaDqNcRMQfeBMYazvXJBHp5MLylkrbnZVSSqmqrVuzOsy4tIuni6GUqiTDOkQWSavrZCb0+Jnji50R3NET4zu6pFzK+7g7eI4yxjiuOp8AtLOtJ5ldzDEl6QvsNsbsNcacAb4BJrignGWiDc9KKaVU9XBd/5a8e11vTxdDKeVmD49uz0c39LFvvzixGwB1QmvQpWmYPX3hAxcCcM/wtmx9ZjR/TB1WYMK5b2/tz4QeTdj3wjhuueA8e/qWZ0YXe21/P40OfI27JwxbISK/AN/ZticCy0WkJpByDudrChx02D6E1YpdKUREl6pSSimlqonRnRsVWF+3sI3TRnHHV+vYm3iajo3DWLzDddOxhAT625cIUkq5Vu+WdencJIzPVu23r1e97OEhnMzIoXawFd6EhQTy7a39ycrOI7xQK3TNoABqBln57h7WhqEdIunVoi79zqtX5Fq1ggLsQfbtX6zjty1/ATC+a2NevKIba/YlMaR9A+ZtPcZtX6wrVz3m3z+YUQ4zsfuaTU+P8nQRys3dLc93Ap8APYCewGfAncaY08aYoedwPmdf7xQIZ0XkVhGJEZGYxMTEc7iEUkoppbxFcKA/D49uXyQ9fuZ4wkMD+fKW/qx6dDjv/O3cW6mvjm7OBW3r88T4jtw+pDUAfVpF0DcqokA+x66gF7StD0AN/4p/FGtVv2aFz6FUddE2shbf3tqfLk3CAYiqZy1B1rJeTbo2C6dlvVCeGN+R967rTXCgf5HAubAHR7WnV4u6RdLjZ44vshTa/SPb0alxGJ/e1JeXruxGzaAAhnaIREQY06URb1zbs8AM7i9f2d3+fOezY4pcI39meEePj7PeJyb1bV5iuV3ltgtbV8p1nAkLLvm1qYrcGjwby/fGmPuNMffZnlek7fYQ4PiX1Aw4Uuia7xljoo0x0Q0aNKjApYoSwOioZ6WUUqpauXNoG+JnjmfufRcUm6dGgB//cvigW1bDOkTyzyu68fnN/bjlgvO4b0RbxnVtxPRLOvPf2waw4cmRDLeNxwx0CJQ/v7kf8TPHs+u5sfa08V0bl3q9a/u14OmLO3F5r6b2tDeu7Wl//tIVVpfV6RM6l7sujjo2Dis9k1KV6J5h1lJjQYF+BPj7cWV0M365exDDOxZcok5EuOWC82gYFuzyMrRrWJs5917Ahe0aEFqjaAfei7o1Yf2TIwkKsP7XL+nexL4vKMCfOfecfQ+6Z3hbAL64uR8/3zXInv73wecRP3M8L1zejY9vPNsdvSx6tyz6JUA+ZxMpLnzgQlrVDy2QNqlviwLbA9sUbY2vqMfGdSh1jfaqyq3Bs4hcLiJxIpIqIidFJE1ETlbglGuBtiLSSkRqANcAs11TWqWUUqr6EpEIEVlgu+8uEBGnn6JEZIotT5yITHFIryEi74nILhHZISITbekPiMg2EdkkIotEpKXDMbkiEmt7VPn7ccuImgT6C29N7uV0fw3bB94+Udav7uHR7WkeEVIgz/8NPo/wkEBGdLQC4qGFJioKCvDnrcm9ibK1BtetWYOG4daHeD/b+MgOjYq2NgG8ObkXyx8+2zEvv3X6jWt7svrR4ex6dizPX9aVGwa24rr+1suw+tHhdHIIdK+Mbk78zPFcPyCKr245O7Lts5v6Fvt7Key963rzy92DqBXkfHTfmM6NAFj+8FDevLbg73JI+wY8OrZDgbR1T4wo87WVcmbFP4YWWcddROjSNNxDJSrZ/+4YyD3D2lAjwI8591zAL3dbwXGnJmG8eW0vtj4z2j5x2aC29enaLNzegu5oaPtIbh18dvz1Q6PaMblfCzo1DqNFRCjLHx7KXUPb8OLEbsy/f3CB/70lDw2hV4s69u38lnrHHjERNWvg72e9700Z0JL4meN5/rIu/N+F57HwgQtZ8Y+hfHlLf/t75oxLu7DwgcFO63zLoFZl+t3EPTeWWwd7rrW7otw95vlF4GJjzHZXnMwYkyMidwHzAH/gI2PMVlecuyxE0DHPSimlqqqpwCJjzEzbUo5TKbSyhYhEAE8B0VjDntaJyGxjTDLwOJBgjGknIn5A/iesDUC0MSZdRG7HurdfbduXYYzp4faauUhIDX/inhtX7P78lqoL2zXgg+v7EBYSwJ1D25CQlsmP6w/zt/4tqRUUwKO2bpUp6WcIDym922FenvXhwV+Erc+MJsC/6Ci0/BajFvVCeXtyL0KDAji/dT2ycvKcBrE9W9QtteXm/Db17c8jw4KK7K/h78eZ3DwA/nxsOF+u3s/ri3fTo3kd/P2kwERJH/6+jxm/bKN5RAhvXNuT9OxcwoIDaVA7iJb1Qpl5eTcGtLZaqOZuOQrA2C6NmNCjCfVqBdnLeuBEOhnZuaRmZHPVu6vs579zaGsSTmYRWsOfoR0iueHjtfZ9X9zcj1V7j/Pmkj0l1ld5r+YRoSSnW2tySzWYwrdTkzA6NQmzP3c0vpvzHiY/3H4+8SfSi6Q/Nq4jHRvX5pOV8dw1rG2R/Q8VGpayffoY1u1PplX9mnxz6wC+W3eQ+rWC8PMT+/9hz+nzSU635m6e0KMJexJPcYdtyImI8OjYgrONj+vauMD7zbbpo+k0bZ59e9ezY6kR4Mfk/i0Z+vJSzmtQk72Jp7m0RxNeu6Ynx05m8uWfB7h5UKsCPXCqI6lYL+pSTi6y0hjjscUWo6OjTUxMjOvO9+wCRnVuxPOXdXXZOZVSSlUfIrLOGBPt6XI4IyI7gSHGmKMi0hhYaoxpXyjPJFue/7Ntv2vL97WIHAQ6GGNOl3CNnsAb+fd2ETlljKlVnnK6+t7sauv2J9OzeR17K7ErfL56P0/+tIVvbu1PfycTEuXmGQQqdM2c3Dz8/QSRgueImvorYI3f3HAgmU5Nwmj/hDWB2gMj2/HKgl28NbkX47o2xhhDTp5x+uH2TE4eN32ylkfGdOB5dQAAIABJREFUdKBrs9Jb+2Lik+jdsm6R8jiTnZtX4Jr7jp9m6MtLAf6fvfsOj6pKHzj+fdMIBAi9l9B7jyCgFFGK2NbeK8vqz46uoqhY17i66u7qqliwrL3jgoUqAipNmiA99BZ6hyTv7497M5kkMykwk8nMvJ/nyZO5Z245JzfJmfeextgbTqF/K6d1/9UfV5P27R9Fns9ElqY1kphybz+OZmZx49tzGDm4TbF+B41/r0xbzTPf/cHyJwdTLi72hM6RmZXN/PV76N6kWoH3vvptE3d9vIDzOtXjX1d08XF0YJVm3Rzslue5IvIx8BXO+s4AqOoXQb5ukJT9J13GGGOiVm1V3QLgBtAFFz71vWpFfRHJ6dv3hIj0A1YDt+VbbhLgJuBbr+1EEZkLZAJpqvqVr4yJyHBgOECjRo187VJmFDZm8ERd3aMRpzapRgsfkwNBYJa7ifPTmvPjX/uxZofzPKSLOylS95RqVEqM444BLTzjLsFpcYr30SoOTpf2/w4r/gInqSkFP1D7kz9Yb1Ijicn39KVxtQp5ylU3OfBjWE3Z5t3aWS4ulveHnRrC3ESOW/o180xueKLiYmN8Bs6AZ9K0ulUi72822MFzZeAQ4D0PuQJhGTyLQMb+oyzYcCKrbJlwEhcjtKlb2dbviwLb9x1h894joc6GCbIaFRNoULXgeLJwIyKTgDo+3hpV3FP4SFOczwMNgJmqOkJERgDPAdd4XftqnO7efb2ObaSqm0WkKTBFRBaraoG+tao6BhgDTstzMfMaMUTEb+AcbI2rJ9G4et7ZuD+5uWdI8lISzWoW7NBwXqd63PnRghDkxhhTEqe3qMErV3UtMJlbJAhq8KyqNwTz/KWtQkIsPyzdxg9L8z+IN5HoyQvac/WpjYve0YS1c1+awbZ9R4ve0YS163ul8Oh5Jzf7cFmgqn5nXhKRbSJS16vbtq9FizcC/by2GwDTgJ04D7u/dNM/xWllzjn3mTgBel9V9e5Jttn9vkZEpuEsS2kDU01QFKcLuIkcNkwyfIkIQ4qxekA4CmrwLCINgH8DvXGebM8A7lTVjcG8brC8c0N31mb4HQpmIsTxrGyGvzePvYePhzorphTsPXycwe3qcNkppbOeogmN+lXLF71T+BsHXAekud+/9rHP98DfvGbiHgg8oKoqIt/gBNZTgAHAUvCMc34NGKyqnoDcPcchVT0qIjVw6vq/B6NgxuR4YEhrnrZxz1GhU0Mb12zKnmB32x4LfABc4m5f7aadFeTrBkVKjSTP0hMmcmW6M4/mzI5qIlt2tvO3nX+5GWPCUBrwiYjcBKzHrXtFJBW4WVWHqeouEXkCZ+lHgMdVdZf7+n7gPRF5EdgB5PQeexaoCHzqtvytV9XzgDbAayKSjbP0ZZqqLg16KU1U+0vfZlEdPDeqVoH1uwrOyFzSfcqCFy/rzPmd6yEinontvNkKN6YsCvZc4TVVdayqZrpfbwM1g3xNY05KjNstLMv+a0eFLFXCfNUEYwBQ1Z2qOkBVW7jfd7npc1V1mNd+b6lqc/drrFf6OlXto6od3ePXu+lnqmptVe3sfp3nps9S1Q6q2sn9/mZpl9mYsuZTdzz56HPbFrrfOR3rkhAbQ/l4Z6bjgW1rs+LJIbSpW9nvMVd0b8T0+/rzu7uEWIOq5bnjjOYF9nvs/LI/ROXVq7tyQZf6nq74n97ck3kPnUl62lA+GNaDusmJPse9GxNqwW55znAnGPnQ3b4CZ1yVMWVWzlIh1vIcHbKylVgbR2eMMWEjPW2oz5bK0vTD3X0Y+MJ0AJY/Odiz/NcpKdU8y/+s23mIi7o24J+TVzJpWe58OZUS43jpyq6e7WOZ2cTFCDExwhe39OKbRZu577NFnveb16rIqu0HqJbkrCmeVC6OVU8NAeCfk1d69qucGMc3t59G4+pJLH9yMKO//p2ujapy3+e55ypNOUuzed+rU5tW47qeKQxun3c87Cles7P3al6Dnx8YUGr5NKYkgt3eciNwKbAV2AJcTG43MGPKrNgYwWLnyJezzn0g13M1xhgTfPWrlN48Bp/8pSfpaUNZ9vhgT1rL2pWYcX9/Xr26K+XiYnn16m78dF9/AM+6uY+e144ODZJ56couTL23n+fYCXecnuf8CXExnnqofEIsl6Y25Ie7+3je/+GuPjxxQfs8y4rFxcYUWJ7s81t6eWZWLxcXS9pFHbnUaz6PMdd0K7Ks797YPc92xXJxNHDnjKiQEMv/FbK8kfcEX5/f0tOzpvnLV3bl/sGtSU8bykfDe0bsRFImOgR7tu31wHneaSJyF/BiMK9rzMmKFbFu21Egy31CEmMtz8YYE1Zmjjwjz3YgW6J/uq8/f353Lv8d1oMaFct50ssnxObZr0HVCp4l8Aa397WCnCMxPpYm7pw59ZITaVit6GXzWtaulGeN42uKWP1jxFkt/S6HtnD0QESgcmI8X/5fL5LLx3PgaCYVEuLIzM5m8Is/AbDiySGs2LYfcFq7v7ntNE+ZN+057Hlgcd/g1gCM/noJSzbvY9663dSsVI4rezRiSPs6LNiwh26Nc1uSh3a0YNlEjmB32/ZlBBY8mzIuJsa6bUeDnAcktp63McaEtxt6pzAnfRfv3tiD/0xdxW1nNKfz4xMB5398VrbyzEUdaFw9icvH/OI57u0bTuH6sXPynKthtQp8d1cfAm3cbb0D3mLetKYTlBc2oW1y+XjP6y6NqhZ4/4e7+5BULo6EuBja10/m/WE9SE2p6mlBB98t/Y+d3x6AN2esZYA76WbVpASbgNNEtFAEz/Yp1ZR5MSJkW8tzxMu5xdbybIwx4W30ubmTZD10Tt7Julb/7Wyys9XTNXruQ2dy76cLuSy1If1alSzQ++zmnhxzV+UoqY4NqpzQcYW5oHN9Uqon0bnhiZ+7Zb4W697Na5To+JtOa3LC1zYm3IQieLaIxJR5sSKcYN1owkhut+0QZ8QYY0xQec9tUaNiOd6+oXshe/uX6jWxVVkgIj5bk40xwRGU4FlE9uM7SBag9GZ4MOYExcYKH81Zz7dLtoQ6KyaIsq3btjHGRLSWtYu/3NGDZ7fmh9+3Fb2jMSZqBSV4VlXfMxYYEybuGdiKxRv3hDobphTExcYwsK3/iV6MMcaEp//dfppnpujCvHp1V2pWSqRb46oM7+N/NmljjAlFt21jyjxnVsvCZ7Y0xhhjTNnVvn5ysfbLv+awMcb4E+x1no0xxhhjjDHGmLAnGsEzCovIDmBdqPORTw0gI9SZKGXRWGaIznJHY5khOssdjWUGaGVDk06O1c1lRjSWGaKz3NFYZojOckdjmaEU6+aI7ratqjVDnYf8RGSuqqaGOh+lKRrLDNFZ7mgsM0RnuaOxzOCUO9R5CHdWN5cN0VhmiM5yR2OZITrLHY1lhtKtm63btjHGGGOMMcYYUwQLno0xxhhjjDHGmCJY8Fz6xoQ6AyEQjWWG6Cx3NJYZorPc0VhmiN5yR7povK/RWGaIznJHY5khOssdjWWGUix3RE8YZowxxhhjjDHGBIK1PBtjjDHGGGOMMUWw4DlIRCRdRBaLyAJfM8CJSD8R2eu+v0BEHglFPgNJRKqIyGci8oeILBORnvneFxH5l4isEpFFItI1VHkNpGKUO6LutYi08irLAhHZJyJ35dsn4u51McsdUfcaQETuFpHfRWSJiHwoIon53i8nIh+79/pXEUkJTU4Dqxjlvl5Ednjd62GhyqspPqubrW72ej+i7rXVzVY353vf6uYg1c0RvVRVGdBfVQtba+0nVT2n1HITfP8EvlPVi0UkAaiQ7/0hQAv3qwfwivs93BVVboige62qy4HOACISC2wCvsy3W8Td62KWGyLoXotIfeAOoK2qHhaRT4DLgbe9drsJ2K2qzUXkcuAZ4LJSz2wAFbPcAB+r6m2lnT9z0qxuzivi/l+7rG62utlbxNxrq5tDWzdby7MJCBGpDPQB3gRQ1WOquiffbucD76rjF6CKiNQt5awGVDHLHckGAKtVdV2+9Ii71/n4K3ckigPKi0gczofPzfnePx94x339GTBARKQU8xcsRZXbmDLP6marm/OlR9y9zsfq5lxWNweJBc/Bo8APIjJPRIb72aeniCwUkW9FpF1pZi4ImgI7gLEi8puIvCEiSfn2qQ9s8Nre6KaFs+KUGyLrXnu7HPjQR3ok3mtv/soNEXSvVXUT8BywHtgC7FXVH/Lt5rnXqpoJ7AWql2Y+A62Y5Qa4yO36+JmINCzVTJoTZXWz1c3eIulee7O6uaCIuddWN4e2brbgOXh6q2pXnC4yt4pIn3zvzwcaq2on4N/AV6WdwQCLA7oCr6hqF+AgMDLfPr6eeIX7dO/FKXek3WsA3G5w5wGf+nrbR1q432ugyHJH1L0Wkao4T6+bAPWAJBG5Ov9uPg4N63tdzHJ/A6SoakdgErlP+E3ZZnWz1c05Iu1eA1Y3Y3WzZzcfh4b1vS4rdbMFz0Giqpvd79txxl50z/f+PlU94L6eAMSLSI1Sz2jgbAQ2quqv7vZnOBVX/n28nwA1IPy7QhZZ7gi81zmGAPNVdZuP9yLxXufwW+4IvNdnAmtVdYeqHge+AHrl28dzr91uVMnArlLNZeAVWW5V3amqR93N14FupZxHcwKsbra6OUcE3uscVjfnE4H32urmENbNFjwHgYgkiUilnNfAQGBJvn3q5Iw9EJHuOPdiZ2nnNVBUdSuwQURauUkDgKX5dhsHXCuOU3G6W2wpzXwGWnHKHWn32ssV+O8eFXH32ovfckfgvV4PnCoiFdxyDQCW5dtnHHCd+/piYIqqhvXTbYpR7nzjBM/L/74pe6xuBqxu9oi0e+3F6uZ8IvBeW90cwrpZwv/n6F+NGjU0JSUl1NkwxhgTIebNm7dPVZNF5GmcijkT52n+Lar6R2hzFx6sbjbGGBNIpVk3R3TwnJqaqnPnFljGscT2HTmOZkNMDFRKjA9AzowxxoQjEZmnqqmhzkc4C1TdbIwxxkDp1s1lttu2iMS6MyT+z91uIs4i3yvFWfQ7obTyct6/Z9Dp8R/o8OgPfDZvY2ld1hhjjDGF2H/kOCkjx/P2zLWhzooxxpgoUGaDZ+BO8vZTfwZ4QVVbALtxFv8uFbed0YKHhrYBYPOew6V1WWOMMcYUYts+Z16YZ75bHuKcGGOMiQZBD55FpLzXhA3FPaYBMBR4w90W4Ayc2RLBmXb8gkDmszAXd2vADb2bABDBvdyNMcaYsJKecRCAw8ezQpwTY4wx0SCowbOInAssAL5ztzuLyLhiHPoicB+Q7W5XB/a4i3xD5C3qbowxxpgSGvaujZ02xhhTeoLd8vwozhqKewBUdQGQUtgBInIOsF1V53kn+9jVZxuwiAwXkbkiMnfHjh0nkmff+QrYmYwxxhhjjDHGhJtgB8+Zqrq3hMf0Bs4TkXTgI5zu2i8CVdxFvqGQRd1VdYyqpqpqas2aNU8w2/6p75jdGGOMMcYYY0wEC3bwvERErgRiRaSFiPwbmFXYAar6gKo2UNUU4HKcRb2vAqbiLPINzqLfXwcx3wWINT0bY4wxxhhjTNQKdvB8O9AOOAp8AOwF7jrBc90PjBCRVThjoN8MSA5LyCYMM8YYY4wxxpjoE1f0LidGRGKBx1T1r8CoEzmHqk4Dprmv1+CMnw4JsaZnY4wxZZiIVAM+xplbJB24VFV3+9jvOuAhd/NJVX3HTZ8G1AVy1mQcqKrbReQFoL+bVgGopapV3GOygMXue+tV9bwAF8sYY4wpM4IWPKtqloh0C9b5Q8Uano0xxpRRI4HJqpomIiPd7fu9d3AD7NFAKk6VNk9ExnkF2Vepap4prFX1bq/jbwe6eL19WFU7B74oRTtwNLPonYwxxpgACna37d9EZJyIXCMiF+Z8BfmaxhhjTDQ6H3jHff0OcIGPfQYBE1V1lxswTwQGl+AaVwAfnlQuA+TAEQueTfhIGTme68fODnU2CvW/RZvZd+R4qLNhTJkW7OC5GrATZ8bsc92vc4J8zeCyQc/GGGPKptqqugXA/V7Lxz71gQ1e2xvdtBxjRWSBiDws+cYriUhjoAkwxSs50V0e8hcR8RWsB02W1cemBH7fvJes7ND+zkxbXnAJ1d0Hj7Fh16EQ5CavVdsPcNsHv3HPJwtDnRVjyrSgddsGUNUbgnn+0mbDno0xxoSSiEwC6vh4q7hzi/iqyXIiiqtUdZOIVAI+B64B3vXa73LgM1XN8kprpKqbRaQpMEVEFqvqah/5Hg4MB2jUqFExs1q4zKzsgJzHRL5lW/Yx9F8zuK1/c+4d1CrU2cnjtGemcPBYFulpQ4N6nexs5YnxS7nm1MY0rVmxwPtHjjt/1pt2Hy7wXmZWNpP/2M7AtrVtDiAT9YLa8iwiiSJyq4j8R0TeyvkK5jWDzZ5zG2OMCRVVPVNV2/v4+hrYJiJ1Adzv232cYiPQ0Gu7AbDZPfcm9/t+nBUy8k/SeTn5umyras6xa3Am+OyCD6o6RlVTVTW1Zs2aJSqzP5n5WhF3HzwWkPOayLNt3xEAFm7cE+KcFHTwWFbRO/mwac9hjmYW/9j0nQcZOzOdYe/MLXQ/X59z/zNtNX95bx4/LN1WwlwWn6qyZseBoJ3fmEAJdrft93CekA8CfsSppPcH+ZrGGGNMNBoHXOe+vg742sc+3wMDRaSqiFQFBgLfi0iciNQAEJF4nCFWS3IOEpFWQFXgZ6+0qiJSzn1dA+gNLA14qfzIzhc8d3liYmld2oSZmJNoLV2/8xDPT1yBBniYwJHjWcxJ31UgXVWZuSqjwO+3t+NZ2fROm8LdHy8gPeMgE5duY8f+o8W6brZbjoNHM3n8m6WeFuecH9GO/UdIGTmeWasyPMds3O10K19UjIcPr09fw++b9xYrL94+nrOBM/7xIz+v3lniY40pTcEOnpur6sPAQXcpjKFAhyBfM2gEG/JsjDGmzEoDzhKRlcBZ7jYikioibwCo6i7gCWCO+/W4m1YOJ4heBCwANgGve537CuAjzRtBtAHmishCYCqQpqqlFjwfs27bpghtH/mOlJHjPYHhTyszuODlmaSMHE/KyPGels7MrGx2HvAdfF7/9mz+NXklG310Z/Z26FgmY2eu5WhmFnsPHfcEpfntOeT0kLj5v/O45NWfC7z/3ZKtXPXGr7z3yzq/18oZuz152Xb6PTeNP787l/7PTSs0fzndrXP+gF/7cTVvzVzL27PSATia6fw9ZRxw8vd/H8zn1vfno6r8utYJ8l+euprMrGwe+GIRKSPHk52t3PbBfOZ6PQR4asIyhv5rRqF58WXhRifg/mXNTrbvPxLwhxXGBEpQxzwDOVP27RGR9sBWnPUnjTHGGBNAqroTGOAjfS4wzGv7LeCtfPscBPwuL6mqj/pIm0UIH4h/MX9TqC5tyrBLX/2Z+DjhrDa1OeR2iX79p7We9xdsyG09fWr8Mt68/hRGfbmEj+du4I8nBpMYH5vnfGt2HAScFtt/TlrJC5NWAPC/20+jff1kz35tH/kegC17jzBm+hq6Na7K57f0KpC/zo9P5MoejXxOHgbw1kwnr7PTdzGoXR3qJCcWq9z5l25bsW0/89bt5oruzhwDMe4DhJyYNGfCvcysbDbsOsSF/5mV5/g9h44zfvEWFv59T54HB6lPTWLPIefj/d2fLOB/i7bwv0VbmP3gAI7nay0/cDSTN35aw239mxMXW7z2un9OXsk/J6/k8fPbcW3PlGIdY0xpCnbwPMbtFvYwTneyisAjQb5m0NgkCcYYY0zZ8Pn8jaHOgimDZrutoDNX5Xb/nb7Cd6A6+Y/tTF62jY/nOhPQH8/KplxcDOe9NJM6yYks9Aq0+z47Lc+x5/x7Bqe3qMFPKzOolJj7cXqsG/zOW7cbVeXCV2bx2/q83Z0/+HV9gbxs3nOYIf/8ib2HncB0/KItjF+0hecv7cTAdnVISohlzPQ1zFu3myWbnFba/I2zW/ceYdnWffRvVYuBL0wH8ATP4s4VmK3Kxt2HeHnqancbrnrjV58/H6BAi3tO4Azw9YLNntfd/zY5z36906awaY9zbEr1JC7oUt89/hiLNu6lT0tn7oM56bt486e1fPf71jzHz1iZYcGzKZOCPdv2G+7LH4GmwbxWaVGbMswYY4wJOe8P8Tl6PT2Z6ff1L3YrlwlvqsrYmels2nOYN2es5ckL2pf4HDd5TaD1zqx0vl6wmZXbD7B4U9Hjdn9a6YwL3u+15vjxrNzPiRMWby0QOPvTK22Kz/QRnywEFvLUn9rz9Ld/5Hkv/9CFU592AtjJ9/T1pK3feYj563fTrXFVwAm4R36+2PN+VrayPwhrO+cEzgDfLtlCZrbSs1l17vzwN+au282iRwdSOTHeZ9d1Y8oyCeaYAhHx2cqsqo8H7aJeUlNTde7cwmcVLInmD07gL32b8tdBrQN2TmOMMeFDROapamqo8xHOAlU3p4wc7zP95wfOoG5y+ZM+vyn7flyxg+vemh3qbJRpVSrEs+fQcWbc35/TnpkKQL3kRDbvPRLinMELl3Xi7o/9ryvdqUEyX992WinmyISr0qybg/1o9qDXVxYwhDAf82zzFxhjjDFl12x3cqNZqzNYuc0W+Ihk3y3ZWvROUS6nh0ZO4AyUicAZ4M0Zawt9P2cSMWPKkmB32/6H97aIPIcz9jks2ZBnY4wxpmy786MFbNx9mGe/Xw5AetrQEOfIBMuHswuOHTbhY8mmfaHOgjElFuwJw/KrQJiPfbaGZ2OMMaZsywmcTWRKzzjI2p0HQ50NY0wUCmrwLCKLyY03Y4GaQKmMdw6GnJkKjTHGGGNMaPQrYk1jY4wJlmC3PJ/j9ToT2Kaqmf52Dgc25tkYY4wxxpjgy8zKDsns+dnumtUxMdZwZvIK9m/jfq+vw0BlEamW8+XvIBFpKCJTRWSZiPwuIne66dVEZKKIrHS/Vw1y/vNlrFSvZowxxhhjgIwDRzmamUV6hnXXjibNR33Lkk172XngKEeOZ5XadS/4z0yaPjih1K5nwkewg+f5wA5gBbDSfT3P/SpsnYpM4B5VbQOcCtwqIm2BkcBkVW0BTHa3S5Wt82yMMcZEhjd+WsPaMhaMrdp+gN0HjwX1GnPSd9HkgfHsPHC02Mfc++lC/vxu4Jb/LKnUJydx83vzWLbFJpmKNhMWb6Hbk5O47LXSWxN6kc30bfwIdvD8HXCuqtZQ1eo43bi/UNUmqup34jBV3aKq893X+4FlQH3gfOAdd7d3gAuCmvt8rOHZGGOMCS8pI8ezzp1cav3OQ9z98QKOZWZz4GgmT45fRv/npvHqj6sLHLdh1yHu+ug3jmVmBy1vj33zO/PW7cqTdubzP9LliYmAE+SmjBzPkk2Ff5BXVb5esInMrKLzOnnZNi559WdUYd663Z70Y5nZHDzqf2TdZ/M2MnHptiLPH2hrdhzgnk+ctYCnLt/Bp/M2lnoeTGhNX7kDsKWrTNkQ7OD5FFX19HlQ1W+BviU5gYikAF2AX4HaqrrFPdcWoFbAcmqMMcaYiHTzf+eTna3c//kivvxtE3PSd5GVnduTLO3bPwoc88AXi/lqwWZ+WbOzxNf74Nf1bN5zuMj9xs5M56JXfuZoZhZrdhzI896GXYf48rdNAMxclcGO/UfJ8NNS/NWCTdz50QJe/6nwdXMBbnont/XYuy/dFa//QrvR3xd5/L4jx4tVtsIcy8xmdb7y+nP3xwv4fH5uwDzlj+0ndW0TfrZ6rUs97J25vObjYZcJD3sPn/z/j1ALdvCcISIPiUiKiDQWkVFAsWshEakIfA7cparF6qcjIsNFZK6IzN2xY8cJZtvfubG1qowxxpgws2zLPpo+OIGf3UB418FjXPvmr4UekzNMK0YK73f22/rdbNmb+2Fww65DPPjlYs7594w8Lbs5srKVzKxspvyR24p732eLOOMfP7LnUG537dP/PpUPfnXWMZ6TvptTnppE6pOTmLp8Oze+PYenv13GU+OXMm35dnYecI7bvt8JMpo9OIFnv/+DbfuO8Oi43/22SD/4xWK273OO8ZVXXwa9MJ1eaVOY8sc2jmdlo35mUv1t/W5emeYEOUeOZ5EycjwpI8cDTov7gH/86Ll2YZZaN+2ol3Eg9+9i0rJtPP3tH6zbeZC/TVjm9/cvUFZt30/KyPEs2LAnqNcpq1SVpycsIz3jIJe99jOXFrPrvL//DQP+MY1eaVMCnc1SFezZtq8ARgNfuts/umlFEpF4nMD5fVX9wk3eJiJ1VXWLiNQFCjx+VNUxwBiA1NRUC3WNMcYYk8ftH/5W5D7Zbrwp4sy8+/7s9cxalcHg9nXYd/g4l3dvxOHjWfzpP7MAePO6VA4ey+IO99y7Dh7joldmkZ42lEtencU1PVM4r1M9WoyaQHa+TydfL9gMQOfHJ/rMy6RluYH2DWPnALktsK//tJb6VcoDuSuCZGUrL09dzctTneD17VnpjBzSmgs6189z3p0Hj9H9b5OZNKJgp8CP56xnwYa9NKuZxNkd6nrSt7itgDe+7bRgDzutCXee2YJKifGefRZu2OP5udzSrxmtH/7O896Bo5m87z4U2HfkONNXZnBmm1pUqZAAwKY9h6lbOdEzy/HxLPsoZwr6y3vz+GPrfi7u1oCWtSsVuf+mPYepl5yIFPEwLL+0b5014x/8YjET7jzdk75x9yEaVK1QskyHoRmrMnht+hpem77Gk/b+r+u4snsjnz/LjbsPUTe5PC1GfUudyol8dWtv6iQn8vBXS2hTt3KeByHhKqjBs6ruAnJmyo4FkorTgizO3XgTWKaqz3u9NQ64Dkhzv38d8EwXli/EGp6NMcaYCJQycjxf39qbTg2rALktzyIwYckWHv5qCQDfLtkKwMNf/85fB7XyHO/dHdrb7LW7mJO+mznpu6lRMaFA4BwIm9xukG/PSue05jV87pP27R8+u6eDM846R2ZWNs9PXMEDX67BAAAgAElEQVR/puV2jX1y/DK/135jxlremLGW9LShTF62jWHvzs2zrGdOa3OO9l5dw69+YzZb9x2hf6uajL2hO+kZB+n33DRGnNWSOwa08F9gE/Vyhl0Up+F55bb9nPXCdEYOac3aHQfZtOcw/x3Wo1jXyXlw5d0DYsLiLfzf+/N55aquDPF6sJRj7My19GpWg/hYoWJiHLUqJRbrWoGyeONemtZMIqlcHIeOZbJ6+0GmLd/Ohd0akFw+nvSMg1ROjOfRb37n5r7NOCWlqs9A+McVO7jurdkF0kd9uYRRXy4hPW1onvQfft/K8PfmMbyPM63V1n1HuPS1n5l+X3/e+2Vdnn1TRo6nWc0kxt9xOonxsQEsffAFNXgWkQ+Am4EsnBm2k0XkeVV9tohDewPXAItFZIGb9iBO0PyJiNwErAcuCU7OjTHGmPDjLgP5MZACpAOXqmqB/rgich3wkLv5pKq+46YnAC8B/YBsYJSqfi4i5YB3gW44w68uU9V095gHgJtw6vo7VLXogbNl1PkvzwTg+l4p/LLGnchLYf2uQz73f/b75UWe07ub45WvF95VPBCGneSM2M1HfXtCx/3lvbnMSd9drGAmx1a32/aWvUfyjKWesHgLXy/YxOodZWsmdFN2rNzujJkf9OJ0Lu7WgOcu6eR33427nd8rfw+PwJmnIDM7m2t7pvjdZ8+hY1ROjOfn1c7wj1ven58ngDxyPMvTyyJG8Dwou2NAC0ac1RJwHk4dOp5F5cR4PpmzgUHt6pBcIZ6Sys5W9h/JLHDswaOZnPvSDABWPDmEto/k/jv+fulWlmzK24aZ04Pl7A51+MclnWnzyHdUToxj4eiBLN1c9JCJ2z6YT6XEOJ6+sKNnYsMxXq3U63cd8tu1fvWOg4yZvibsHpQFu9t2W1XdJyJXAROA+3GC6EKDZ1Wdgf/JrQcENoslE+yxFcYYY8xJyFnSMU1ERrrb93vv4AbYo4FUnJk85onIODfIHgVsV9WWIhIDVHMPuwnYrarNReRy4BngMncZycuBdkA9YJKItFTVUluQ9c+nNynWRFkl8fasdM/rK98IfsAbCb7//cRn4v5j6346PvpDnm1jiuuzeRt57pJOLN+6n2Y1k4iLjWHQC9O5JLUBY6avYdTQNkWe48EvFwNOS/agdnV87tP58YkkJcRy8Fjuv7ctew/T82lnDO+Q9rnHefcw+dfklYw4qyV7Dx+n02PO7/m/rujCfZ8v4r7PF3kC8FmrMrj304VMvqcf5RN8t8Z+t2QLdZPL893vW3ll2mrmPXQmWdnKlr1HSKmelGdJ3ZYP5X0QdviY/3/LExZvZcJiJ/DfdySTJg8Uvcb1+EVb+N+iLQB8OHsD53aq53O/HwqZpf91C54LiHfHLl8AvKSqx0UkbKPPEg6TMMYYY0rb+TitxuAs6TiNfMEzMAiY6A6tQkQmAoOBD4EbgdYAqpoNZHid91H39WfAS+4Qq/OBj1T1KLBWRFYB3YFSW5D1/M71Ax48G2PCyw1jZzN1+Q66Na5KlfLxLN+23zPcYL6PyfAWbNhD54ZVWLfzIMu25D6sGT3udz6Zu8HvdQ7mC0BzAmfIHdLhS/7hC3d4zbvw+vQ1/Lp2l6eL+C9rdpIQF0OnhlXYsOsQSQlxNKrujK+++b/z85yn25OT8mzfP7i13zwEuifHrR/kzcs3Czf73O/xb5b6Pcf+QpbHK6uCHTy/htNtbCEwXUQaA2E9baI1PBtjjCnD8izpKCK+lnSsD3h/OtwI1BeRKu72EyLSD1gN3Kaq27yPUdVMEdkLVHfTf8l/rvwXFJHhwHCARo0anXjpfChqNmxjTOSbutxZYcfXrPHv/LyuQNoFL8/kkXPa8vj/CgZ2vxeju3IgPTUh75wCN7w9p8A+6WlDeXnqqiLP9cx3/rumh8qmMF+aKr9gTxj2L+BfOdsish7oH8xrBpNVz8YYY0JNRCYBvvoVjiruKXykKc5nggbATFUdISIjgOdw5iDxd4y/9LwJQVwJIybYi24aYyKSr8C5rMrfcm1CJ9gtz3moM2A4/NrnvVjDszHGmFBS1TP9vSciRS7piNM63M9ruwFO9+6dwCFyl5f8FGesc84xDYGNIhIHJAO7vNK9z+W7716Q1HOXaTLGGGOCzZ7XlkBJ14YzxhhjSlnOko7gf0nH74GBIlJVRKoCA4Hv3Qfc35AbWA8AcppmvM97MTDF3X8ccLmIlBORJkALoODaJkFUOTGetU+fXZqXNGFk1NlteP3a1FBnI2z8qUuBURfGGC8WPBtjjDGRIw04S0RWAme524hIqoi8AeBOFPYEMMf9ejxn8jCcycUeFZFFON2173HT3wSquxOCjcCZxRtV/R34BCfI/g64tTRn2s5hD7eNL6c2rcaf+zTlrLa1T/pcz1zUIc+29xrfJ6p9/coF0k5v4Xud7tJwZptaPHNRxzxp/7v9tBDlxpiyKdjrPF/oI3kvsFhVfXUlK9MEmzDMGGNM2aWqO/GxpKOqzgWGeW2/BbzlY791QB8f6UeAS/xc8yngqRPP9YlpVK0CzWomebYXPHIWnR+fWNrZMGXQ9b1SePS8dnnSljw2iPaj8y5Bnp42lP1HjpOUEMf9ny/i03kb/Z7zslMaMahdHc/vWPl430sJFdeNvZvwyLltPWNZv7q1N02qJ1G5fFyxlgnydl6nelzTszHJ5eMZ+ML0Eh3bqWEVFm7YA8Ab150CwOxRA+j+1GQA2tdPzrN/cvl4zmhdiy9/21TkuRPiYjiWmQ1Au3qVS30iLmOCIdhjnm8CegJT3e1+OLNythSRx1X1vSBf3xhjjDERKCEuJs9aqP7WRTUGoGK53I+8tSuXY9u+owBUSowHfM/afnqLGvy0MsOzXaVCgs9zf3PbaWRmZ3PZmF949equvDNrHVv2HmbFtgN+8zO8T1MAalQsx91ntaBzwyo+97uqRyPe/3U94ATY4xdt9izN9uDZrbmoawOqVyzn2X/s9af4nK3ZnwZVy3Nj7xTqJufOHVCrUqLPfZvVTGLyPf3Ye+g4DauW584zWxIbI+w8cJQ+f5/KwWNZfH5LT1ShW+Oq3PdZ7gOJf17ehTOf/7HY+TKmrAp28JwNtHGXuUBEagOvAD2A6UB4Bc9CnsXHjTHGGBMaqop4TfZtPcNMjr6tahb6/pR7+nE8KztPWs6s7R0bJPPODd1RoFpSAoeOZXpaT72d26meZ7bmDg2c1tkVTw4B4IzWud3EN+4+RFxMDKc+PTnP8XWSnQB17kN+5/8D4JFz25K+8yAPDGlD+/rJdG5YhcnLtrMm4yAd6lfJEzgD9G9di2GnNeGNGcVb+/yvA1uRUiOpQPriRwd6PvF+PPxULhuTuyJdcoV4RgzM7bZevWI5ljw2iC17j+SZwC8+zvmh/v2ijjSvVZFfHhhAzUrl2Hf4ODe+M4cWtSryyVz/rf3GlEXBDp5TcgJn13agparuEpHjQb62McYYYyKUgq0haQpY9dQQ4mILn9InqZyvj7/OL9OlqQ2pmpTbwlwhIQ5fDc41K5VDBO45q2Wh12pQtQIAf/tTBxpWK881b5ZsPr1ycbG8P+zUPGk1KpVjTcZB/A31f+icttx+Rgs6Pf5Doef+ePipPgNnyG2RB2ji7tOrmf/x2CJSYOb7+we1Jj5GOL9LPSD3gUHVpAS+/L/eABY8m7AT7OD5JxH5H85yFwAXAdNFJAnYE+RrB5yNeTbGGGPKCM3b1dbq5+j2wJDWnNG6VpGBsz99WtTgw9nrC4zxze+y1IbUdoPAtU8PLfb5r+zRqNj7Pn9pJ0Z8spBujav6fH9QuzrMXruLBlX9L9OWXCGeyolx7DuSySd/6UnVCvGc5TUe+tSm1ejepFqx8lOrciJT7+1X6PX85eGx89sXus/lpzSkZqVylE+IZdmW/XSsn8xVpzai7SPfF3qcMaES7OD5VpyAuTdO7Pku8Lm7vEX/IF/bGGOMMREqWzVPw7NNuB2dptzTlwNHM+nYwPeY4eIa0qEuix4dSGWvFldfnrm4Y6HvF2V4n6aMmb6m0H0u7NqAIe3rEhfr+5f6xt4pXJraIE/rsC+zR53JtOXb6d6kGnsPOx0+61cpz7d3nV5kOfNr4qeF+mSlXXRyP09jSltQl6pSx2eqereq3uW+Dttnw7YUhjHGGFM2KHkD5sT4WB45p23I8mNCo2nNiicdOOcoaUB5Ih48uw3paUW3WJdPiCXeTyu6iBQZOIPzNzG4fV3AmSX74XPa8sGfe5RKOU/WV7f2DnUWjPEpqMGziFwoIitFZK+I7BOR/SJi89QbY4wx5qSoFhzyfONpTUhPG0rlxGB3rDMm/Nx0WhMaVw9OC3Kg+Zt93JhQC2rwDPwdOE9Vk1W1sqpWUtWCK8KHCRFndk9jjDHGhJaifnuELXp0UCnnxoSLRtUqhDoLppiKOybbmNIU7Eez21R1WZCvYUzATVi8haWbrZNENIiNEa7q0YhalX2va2mMKZtUbZyzKZlfHhhAReuVEDbKxQW7jc+Ykgv2f5C5IvIx8BVwNCdRVb840ROKyGDgn0As8Iaqpp10Lot7bbBVnqPEQ18tYdfBY8TG2CezSKaqZKszFuzG05qEOjvGmBLo3LAKzWtVDHU2Am7KPX054x8/5kmrkBDLoWNZIcpR5MhZKsmEh2cu6si/p6zkw9kbQp0VYzyCHTxXBg4BA73SFDih4FlEYoGXgbOAjcAcERmnqktPNqPGeDuelc0NvVMYfW67UGfFBNHBo5m0G/09mdnZoc6KMaaEXrqya6izEHDv3ti9wJjUUWe3oW6VRG774LcQ5ars+em+/sxanUEnGxcb0epVKc/TF3ZkwuKtntnCjQm1oAbPqnpDgE/ZHVilqmsAROQj4HzAgmcTUJpv/VATmXLucbZ1KTHGhNCM+/tTpUICFcsV/FjWs1l11mYcDOj1hrSvw7dLtgb0nKWpYbUKXFat+Osmm/CWlBBrwXOEevTc8FshIdizbTcQkS9FZLuIbBORz0WkwUmcsj7g3Xdjo5tWKkQEmy8sOmSrYj22I1/O85Fs+8M2JmKd1bZ2gbTB7eoA8OrV3bj9jOYndf6rejQqdA3cXs2q+31v+l/7k542lAZVK/gMnAHa1avsGTJWPSmh2Pl658buft975epupKcN5bTmNYp9vhNVycYYm5PUp2XNUGfBBEn/1rVCnYUSC/ZI/LHAOKAeTpD7jZt2onyFM3k+9YrIcBGZKyJzd+zYcRKXMtEsK1ut5TkKeFqerenZmIg15ppufH9XH8/2kscG8Z+rujLt3n4Mbl+Hewa2YuEjA7moq/9n+w+e3dpn+sS7+/DUnzow9d5+/PbwWZ70f1/RxfP6gz+fyvS/9i9wbNqFHWhU3ffMzwseyT2XiNC3ZU1a1q7IR8NPzbOfrwcDj53XjrVPn03fljVZOHog8x46M8/71/dK8byuUdF/MF6/SnnP67M71Cnw/rjbCq7D271JtQIBebfGVf1e40TcfWbLgJ7PlH1PXNCeT/7SM9TZiCihmC+iXnIiL17Wmd8fG8Tqv53Nj3/tFzZLp3kLdvBcU1XHqmqm+/U2cDKPjzYCDb22GwCbvXdQ1TGqmqqqqTVrBvZJlTNhmH3IjgaqEGNNzxEvxtPyHNp8GBMIIlJNRCaKyEr3u8+oRUSuc/dZKSLXeaUniMgYEVkhIn+IyEVu+ggRWSoii0Rksog09jomS0QWuF/jgl/KkhMRWtWpxAfDerDgkbOoWC6OmBghxau1OLlCPLUql/N7jkqJ8Xm2L01twOJHB9KidiVPWtWkBPq3cj53JJWLzROYNqpegZa1K3Jq09yldy7v7r/bcZUKeYPa5PLx/HB3X1rUrkRy+fgC+3svv3RdrxTPEl7J5eOpXtEp1/A+TfnjicE8el7uXB4XdfP/wODlq3LHlPtqWe/YoIrngcHD57Tliu6N+PcVXfjvsB68eV2qZ796XkE4wGvXdPO8vm9wK5/XLmws8x0DTq6ngAk/8bExBZatqhfmk7/1axXYGKVdvZKtBDxpRF+/780aeUaJznVr/2Z5ti/x+r/y8fBTuahrA5Y9PphZDwzggi71SSoXR2yMhGXgDMEPnjNE5GoRiXW/rgZ2nsT55gAtRKSJiCQAl+O0bBsTUNZtOzrkzKZu3bZNhBgJTFbVFsBkdzsPEakGjAZ64MwjMtoryB4FbFfVlkBbIGfK59+AVFXtCHwG/N3rlIdVtbP7dV4wChUovZrXKBCUFuXlK7tSv0p5Lu7WgAs61/Ok//3iTgUCam+qzodT7xbnH+7uy0fDe/KXvk0Z6KPFuLhevbpbnu0f/9qPb24/rdBj0tOG8uDZbUiMj82TfnqLmqSnDSU9bagn7YLO9Zg9agAVy+Xue0m3hqSnDeXdG7vzzo3dPftXTUogPW0oN53WhKcv7EBtd8m/AW1yyxefrzId1C63FfuWvs0YnW/M4+R7+vL+sB70aladOB8Vsb+1vU10+eDPpxZIu6F3SulnpARmjxrgef32DXmHVaRd2KHA34Ivn97suwX+7A51WfzoQKbc4z8obl2nUp7tKff05fNb8p7vkXPaUq9KeV6/NhVfEmILho5X5HsQ2MrrOj2aVucfl3aifEJs/sPCVrAHotwIvAS8gNO9ehZwwpOIqWqmiNwGfI+zVNVbqvp7IDJaHCLYmOcokaXWbTsaiHXbNpHlfKCf+/odYBpwf759BgETVXUXgIhMBAYDH+LU2a0BVDUbyHBfT/U6/hfg6qDkPsSGndaE5Vv38/ylnVi2ZT9f/baJoR3rMrRjXQBevLwLXy3YXOg5coLTGBGqVEjwGaw/MKRNsfNzSr7WNnAmEPOW03oz5ppu/Lp2V7HO7UvOclh/6duMWpUSqVUpkZeu7EKfljWp7D4oKMnY09TGVZm7bjfNfHQP7dQgmYUb9yIi3NC7CVf2aMTOA8eolpTg+RnmBEdTl2/nhrFzTrhcJnJ4T3SXUiOJ9LShjPpyMe//uh6ASl7zBqSnDeWnlTu45s3ZAbn2N7edRrNaSVzx+q8s3LCH169N5c/vzgUgMT6GI8edVTseGtqGJ8cvK3D8sxd3pFalgq3lcTHC93f3oVnNiizdvA+Aa3s25tLUhpzz7xkF9j8lpRpLHx9Exv5jVCgXy+vT1/Da9DXEiFApMZ5KifHUqFiOjANHCxybX9OaBf82T0lx/uec1bY2Sx4bxBfzNzJ/3W7P/75zOtXli/mb8hwTbbFRsGfbXg/keRItIncBL57EOScAE04ya8b4pao223YUiRHrtm0iRm1V3QKgqltExNdMLD4n3hSRnH6yT4hIP2A1cJuqbst3/E3At17biSIyF8gE0lT1qwCUIySqVyzHW9efAjgBav4gFeDrW3uzcOMev+d48oL2NK6eFJAJjh46x38r1AWd6/HVgs38qUvunKkD29VhYLuCY5OLa+KIvnw8Z0Oe1qlzOtYr5IjCvXXDKazLOET7+pVpXqsiV77+q+e9d2/qwYZdhzzb5eJiC3TvztG/VS3S04aSMnL8CefFRIZXru5W4PegvPuw5cGzW3N+5/r8a8oqT4+F01vUpHfz6sxc5XR6nfvQmdSoWI5V2/dz5vPT/V7nwi71+eI3J0Acc003Nuw+TIcGyZ7t935ex4DWtaiQEMtfB7XislMaMnZmOvWqJHJB5/ocPpbFPyauAOCXBwb4XV/cu8cHQNt6lT1pO72C33rJiWzee8SzXSEhjkbVnRAup+ecdyeN7+86nW5PTiI+VjielfsBp3rFBH66r+D8Czk+Hn6qp5wAFcvFcW3PFK7tmcKLl3dh3rrdtKtX2RM8d21Uhfnr9xAXK8wceQbP/7CCz+dvJMnP5IeRIhSlG8FJBM+hJTbiOQrkPEGz4Dk6xIhYt20TNkRkEuArQhpV3FP4SFOczwMNgJmqOkJERgDPAdd4XftqIBXw7hfYSFU3i0hTYIqILFbV1T7yPRwYDtCoUektMdShfnLRO5VAp4ZVCh2PW71iOUYO8T25WCC9cFln7j6rZUDHDNavUp4RZwVuMq7KifGeD+K9mtXgiu4NaV3HGZeZXD6e5BLem4WPDLT/1YZRZ7dh4+7cBy83nNaEGasyuKBLfc98ADee1sTzvrj/8t69sTs13PH/zWtVIj1tKL+t382f/jOLFrUqsn3/Uc9yWN5DA/I/kKpdOZF7Bzlj9Zc+PtiTfmv/3LH4tw9o4Qme/QXORfF+qD/l3n60fvg7butfcLx/to/PrNUrlvME4W/8tIYNuw7Rrl4yZ7atTTUfM/Z/+X+9OJqZTY+m/lcGgNzJ/x4a2ob1uw5xx4AWTP1jO3WTnQdfT1/YgQ71K3NpakMe+GIx53Y68YdvZVkogmeLSEyZ5uspnolcMTFiLc8mbKjqmf7ec5eErOu2OtcFtvvYbSO5XbvBCZin4cxHcgj40k3/FKeVOefcZ+IE6H1V1dMkoqqb3e9rRGQa0AWn1Tp/vscAYwBSU1NL5S9uxZNDPPMaRBqR8Jts5+kLO57U8ckV/I8xN9Hjz32a5tmuX6U833nNpv/HE4PzjMu9tmdjZqzKoHXdvON9Abo0qsrKp4YQI8Jj3/zOuz+vAwLz+W/KPX05cDSzQPpLV3bxdPEuTI2KCfylb1Mu7tqAxPjYAq3UOVq5kxY2ren7/8Gw05v6TPfWpVHJZsT3PuclqbnzOCfExXB9b+fBxYonh/icsyAShCJ4DtuPqSJw4Ehmnq5GJvIcy3L+qdls29EhRmDv4WP2dx3hKpaLo2oJ1sgNU+OA64A09/vXPvb5Hvib1yRhA4EHVFVF5BucwHoKMABYCiAiXYDXgMGq6gnI3XMcUtWjIlID6E3eycRCKiEu2HOiGmPKmvyT4g1sV8dv4AnOTN7gzBgfGyOMnZkekIduvsYTQ/GHQohIseZHuCS1Ae3qV6ZdvcD2sjlZkfz/NyjBs4jsx3eQLIDvQS1hoFxcDOMWbmbcwsInDDGRIf8/YBOZEuNj+XD2Bj6cvaHonU3Yur5XSp4leiJUGvCJiNwErAcuARCRVOBmVR2mqrtE5Amc1SsAHs+ZPAxncrH3RORFYAe5E3w+C1QEPnW7M653Z9ZuA7wmItk4q3ekqerSoJfSGGMCLD42hr4tazJ2ZjrNa1WkU4Nk1ofBQ3URKXOBc6QTjeDxI6mpqTp37tyAnW/xxr0s37Y/YOczZVdcjDCgTa1ClyIxkWH++t2s2XEw1NkwQdasZlKJu6b5IiLzVNX3Gh6mWAJdNxtjTKD8umYnp6RUs96HYaY06+bIng4twDo0SM4zC50xJvx1bVSVrgEIqowxxhgT3oqaNMuYyO2QbowxxhhjjDHGBEhEd9sWkR3AulDno5TVADJCnYlSZmWOHtFY7mgsM5TdcjdW1ZNfxDeKRWHdXFZ/l4MtGssdjWWG6Cx3NJYZym65S61ujujgORqJyNxoG49nZY4e0VjuaCwzRG+5TeSJ1t/laCx3NJYZorPc0VhmiN5ye7Nu28YYY4wxxhhjTBEseDbGGGOMMcYYY4pgwXPkGRPqDISAlTl6RGO5o7HMEL3lNpEnWn+Xo7Hc0VhmiM5yR2OZIXrL7WFjno0xxhhjjDHGmCJYy7MxxhhjjDHGGFMEC55DTETeEpHtIrLEK+0JEVkkIgtE5AcRqeemn++VPldETvM65joRWel+XeeVfpl7zO8i8vdC8vGAiKwSkeUiMihY5fW6XsjLLSIpInLYPe8CEXk1TMr8nYjsEZH/5Tt/ExH51f1ZfCwiCX7yEa73+oTLHYH3+jb3HqqI1CgkHz7/PoKhDJU5y+s+jwtGWU3kKwt1lLtfuP6/tro5N73IOsrdL1zvtdXNuelWN/vPR+TUzapqXyH8AvoAXYElXmmVvV7fAbzqvq5Iblf7jsAf7utqwBr3e1X3dVWgOrAeqOnu9w4wwEce2gILgXJAE2A1EBsF5U7xvn443Gt3ewBwLvC/fOf/BLjcff0qcEuk3OsAlDvS7nUXt0zpQA0/efD59xHJZXb3O1Ba99m+IvcrEL/P/v4GsbrZ6uYIudcBKHek3Wurm/3nI2LqZmt5DjFVnQ7sype2z2szCVA3/YC6v4He6cAgYKKq7lLV3cBEYDDQFFihqjvc/SYBF/nIxvnAR6p6VFXXAquA7idduEKUkXKXqgCVGVWdDOz3Po+ICHAG8Jmb9A5wgY9shOu9Ptlyl6pgltlN/01V04vIhr+/j6AoI2U2JiDKSB0Vrv+vrW52Wd1sdbMPVjeHubhQZ8D4JiJPAdcCe4H+Xul/Ap4GagFD3eT6wAavwze6ad8BrUUkxU27APDVXag+8IuP40tdKZcboImI/AbsAx5S1Z8CVZbiKmGZ/akO7FHVTHfb3z0M13vtT3HLDZFzr4vL399HqSrlMgMkishcIBNIU9WvAnhuE+Wsbra62epmq5tPktXNYV43W8tzGaWqo1S1IfA+cJtX+peq2hqn0nnCTRbfp9DdwC3Ax8BPOF0qMn3s6/P4E8/9iSvlcm8BGqlqF2AE8IGIVA5UWYqrhGX2p7j3MFzvtT/FLU8k3eviKhP3upTLDM59TgWuBF4UkWYBPLeJclY3W91sdbPVzSepTNxrq5tPnAXPZd8H+OjW5HbDaOYOzt8INPR6uwGw2d3vG1Xtoao9geXASh/X8Ht8CAW93G73qJ3u63k4Y4xaBrogJVCcMvuTAVQRkZzeJP7uYbjea3+KVe4Iu9fFVdbudWmUGVXN+R+wBpiGMx7LmECzutmL1c1+Wd3ssLo5V1m711Y3l1BEr/Nco0YNTUlJCXU2jDHGRIh58+ZlqGrNUOcjnFndbIwxJpBKs26O6DHPKSkpzJ07N9TZMMYYEyFEZF2o8xDurG42xhgTSKVZN0d08BwoM1ZmcOhYJrExQs9m1amQYD82Y4wxxhhjvC3fup9mNZOIi7WRoSYyldnfbB2Nt54AACAASURBVBGJFZHfchbjlmIuMh8MD321mOHvzeOmd+by/i/rS+uyxhhjjDHGhIX0jIMMenE6z3z3R6izYkzQlNngGbgTWOa1/Qzwgqq2AHYDN5VWRl6/NpVxt/UG4PDxrNK6rDHGGGOMOUnz1u1m/5HjvD59DTsPHAVAVblizC/MWJkR4tyFzoGjmRzPyg7Y+Xa4P9v56/cE7JzGlDVBD55FpLyItCrhMQ1w1hZ7w90O6WLrLWpXol29ZAAieH41Y4wxxpiIsv/IcS56ZRan/30qT01YxohPFgKQceAYP6/ZydVv/ur32JenrqLbExMBJ9heuGEPK7bt55c1O1FVHvpqMXsPH+c/01aRMnI8m/ccLpUyBUr70d9z49tzAna+nM/IvtZiMiZSBHXwroicCzwHJOAsgt4ZeFxVzyvi0BeB+4BK7nZJFls3xhhjjBcRqYazvm4Kzvq6l7rr7ubf7zrgIXfzSVV9x02fBtQFcqKDgaq6XUSuB54FNrnpL6nqG8EphTFFy85Wvlm0mcbVkygfH0uG2xq659BxAA4ezeTQsUxOeWqS55ilm/fRtl7u8sKLNu5BFZ79frkn7dN5G7nvs0UFrvdfr+F8vdKmkJ42tND8bdt3hDdnrOX+wa2JjQl9mPlTCVveF2/cy/VjZ9OqTiU++POped7LWcFHQl8sY4Im2DNfPQp0x1nPC1VdICIphR0gIucA21V1noj0y0n2savPNmARGQ4MB2jUqNEJZNlPvjwXtaZnY4wxYWckMFlV00RkpLt9v/cOboA9GkjFqWPnicg4ryD7KlX1NU32x6p6WxDzbkyx/fWzRXw+f6Pf99N3HmTi0m150s7+10+kpw3luyVbGT1uCdv2Hc3z/sGjmT4DZ1++XrCJjbsP8+z3y0ltXJVP/tKTl6au4pyOdWlasyJ//WwR01fsoF+rmvRq5n8J3XdmpdOvVU0aV0/i4NFMYkQonxBbrDwEWsaBo1RPSkBEOPelGQDMWr2T/0xbxS19m7H/aCbLNu/zfEIWhFXb9xMfG0Pj6kkhybMxwRLsbtuZqrq3hMf0Bs4TkXTgI5zu2i9SvEXmUdUxqpqqqqk1a9pSnMYYYwxwPs6QJ/A/9GkQMFFVd7kB80RgcCnlz5iTtu/I8UIDZ3C6a9/50YIC6Skjx3Pzf+cVCJwB2o3+vth5uPOjBZ4W67nrdnP7h7/x/MQVnPGPHxn+7lymr9gBOF2c3565loNHMwuc48jxLEaP+52+z05j3c6DtBv9PT3+NokJi7ewNuOgz+tmZmXzy5qdZGcrY2eu5fAxZ46eX9bszNOlfM2OA+w6eKzQMnwyZwPb9x/h4a+WkDJyPKlPTuLD2RsK7Pf375azZNM+bn5vHpeN+YU9h9zzCpz5/HT6PjutWD8zY8JJsFuel4jIlUCsiLQA7gBmFXaAqj4APADgtjzfq6pXicinwMU4AfV1wNfBzHh+OV1QbMyzMcaYMFRbVbcAqOoWEanlY5/6gPcn5PxDpMaKSBbwOU6X7pwa8SIR6QOsAO5W1QKfsoPVK8wYb8cyAzf5VaCMX7zF8/oHrxbvycu289bMtTz6zVI61E/mvZu6M2v1Ts7uUJd7P13o2S8nAN13JJP/e38+APcPbs3WvYd5+Jy2niWhmo/6Ns91H/tmKY+d147R434H4IWJK3j2kk6c8Y8fqZwYx6JHB/nM77Z9R7jv80V0bJDMoo257V8PfrmYrXsLjulesGE3S7fsA+Dm/zr58+4u+sGv67myh/+/+fnrd1MhIZbWdSr73ceYsiTYLc+3A+2Ao8AHwF7grhM81/3ACBFZhTMG+s2A5NAYY4yJACIySUSW+Pg6v7in8JGWEyBfpaodgNPdr2vc9G+AFFXtCEwit3U770msV5gpBdn/z959h0dRbg8c/550SAiQAgQIhF5EikRBKVIUKSr+7L0iV8Wr1w4qiOj1Yvfa7rVf7KjY6UUEUdHQQUBAeocAoYW08/tjN0s22UAgmWyyez7Pkyc7s+/MnGHIzp55WyWq4Xh3zlrP6yWb99F+1FTu+Gg+v69L5/vFW4+xJTw9aQVjfllP00cmkjJ0PN8t8tkY05M4g6vP9lZ38puRWbS2O1+np6YDsHN/0Rr4l2esLrJu+DfLPP3J881dm+55/fBXS1BVdmRksmzLPjamH/Iqe/HrP9P3pdnFxmNMReNYzbOIhAKPq+oDwCMnsw9VncnR/tJ/4eo/7Rdiox8YY4ypwFT1nOLeE5HtIpLkrnVOAnb4KLYJ6FFguT5H78Gb3b/3i8jHuO7H76vq7gLl38I1rWS5WrxpL40TY4iJdLoxnamoMrNzufX9NOb+lX78whXcZf/95YS3+fsnC0pU7sx/zfC8/u+PazyvP/x1Pe2Ta9CmXnXPuq37Mk84juL858c1PDPp6OBr60YPQFU5lHV0+tdzX/iR5LiqvHvj6WV2XGOc4NidRlVzRaSjU/v3l8rzTNMYY4zx+BZXl6fRFN/1aTLwlIjUdC/3AYa5xxupoaq7RCQcOB9XLTP5Cbm7/IXAcgfPoYhDWTlc+OocujdP5P2b/fZ83fjZzJU7TnjU6GA3euIKz+tHv17q6LEKJs4A7/y0lie+/8Nr3aodB1i144CjcRhTFpx+TLtARL4FPgc8Ixyo6pcOH9cYY4wxR40GPhORW4ANwGUAIpIK3Kaqg1Q1XUSeAPInfh3lXhcNTHYnzqG4Eue33GXuEpELgRwgHbix3M6Io31cF23cW56HNRVMfl9bUzkUTpyNqUycTp7jgN24RszOp0DlTZ4rUX8aY4wxBsDdvLq3j/VpwKACy+8C7xYqcxDw2ZKs4CCf/pDnviVbz6rglTJ0vL9DMGXoxvd+o1FCNM1rV+OqM2xwQVPxOJo8q+pNTu6/vNnN2RhjjKk48gf8ttuzMYFh5sqdzFzpms7rytOTbcwhU+E4mjyLSBRwC64Rt6Py16vqzU4e10lW72yMMcZUDPn35BD7gm1MwPlo7gau7dzQ32EY48Xpqao+AOoA5wE/4hq5c7/Dx3SM3ZqNMcaYiqMyTU1kyt6Hv673dwjGQZ/8tsHfIRhThNPJc1NVHQ4cVNUxwADgVIeP6Si7TxtjjDEVw2r36Ly7D2b5ORLjD06PEm38a9mWDH+HYEwRTifP+bOm7xWRNkB1IMXhYzrG+l0YY4wxFcf17/zm7xCMn+yxBybGGD9werTtN93zRQ7HNcdkDDDC4WM6Sq3XszHGGFMh5OTZPTlY3T12ob9DMMYEIadH237b/fJHoLGTxzLGGGNM8DqSk0tkWKi/wzDlZNafO/0dgjEmCDk92rbPWmZVHeXkcZ1ijbaNMcaYiqnFo5NYN3qAv8Mw5WDdroP+DoGI0BCycvP8HYYxppw53ef5YIGfXKAflbjPM9iAYcYYY0xFcCgrx98hGD8Zv2Rrqfcx9+HeJSpXLcp3PdPyJ/qWOgZjTOXjdLPt5wsui8hzuPo+V0o2XpgxxhhTMSzZtM/fIRg/eXbyylLvIzLsaP3R69ecxie/beB/N53Bmp0H6PPiLM97953bnJHf/eFZ/vGBHjSMjwbg1m6NyM5V9hzK4puFW7z2f0VqMmPTNpY6TmNMxeL0gGGFVaWS9322imdjjDHG/7buy/R3CMYPPjvBhHTs4M4kVouk1/M/eq0PC3Ulz81rx9D/1CT6n5oEQIO4qiTHVWFj+mEAbjgrhVZJsVzx5q8AnsQZ4JEBrQFXf/uCyfPUe7rTrHa1oEiee7esRZWIUL5fXPrWAL6oqs12YyoUp/s8L+FovhkKJAKVsr8zgFivZ2OMMaZCsP6mwenBLxafUPlOjeMBePKiNtSsGkHViFCS46oSExnGy1d1oHOjOK/yUeGhzH6wFylDxwOuaUo7NY7nt0d6k1vM6O6RYaF8cMsZXPfOb6Q9eg4JMZEnfF4jzm/Nmp0HGDWwDTNX7uCWMWknvI/jiQoPITO79H83j13QmsfdtfGDujXmzCbxfL94fKn360ujYRNsLANToThd83x+gdc5wHZVrdSdlKzPc3D4btEWlm6xJoHBICxEuP7MFGrHRvk7FGPMCVC7IZtj6NYsgaf+71TP8rWdGxYpc2G7uiXeX61qx75HdGuWWCTJ++H+HoSFCInVImk5fBLgSjwHtE2iWmQ4rUa41o25+QzObp7o2a53q9rHPFaHBjVYsGEvX95xFhe//rPXe9Pu7c45L8zizMbx9DmltifJBXjusnb8sSWD12eu4cG+LXhmUvHN3+OiI3j+8nZs2nOY4V8vJbVhTdLW7+Hazg246owGnv02iK/qtd2ngztzpbuW3phA5HTyvL/QcmzBpheqmu5rIxFJBt4H6gB5wJuq+m8RiQPG4hp0bB1wuaruKfuwi2EVz0HjsW+XsfdQFuGhTo+pZ/xJgaycPOKjI7m5ayN/h2OMOQGTlm7zdwimgrqrdzPuPbd5qfbRIK4qG9IPlWofjRKONvH+9s4uVK8S7tXs+6n/O5VWSdXo0KBmkW3XjR7AT6t2ce07c0mIieDHB3pyymOTubZzA0Zd2IZtGZnUrVGFj2/txNVvzQVcyXHTWtWY+3Bv4qIjCA8N4elJK8jMzuO2s5vQ95Q69G+TxLWdG1K3RhXu6NHUU8MOsOap/jwzeQX1a1bl2k4NEBHW73aNbN6hQQ2+uP0sr7I79meSVL0KAHf1asq2jEw6u2v6uzSN55H+rVm5PYOXpq3imUvaepq+160eRWyVcFZsK5wm+GbT0JmKxOnkeT6QDOzBlXrWADa431OK7/+cA9ynqvNFpBowT0SmAjcC01V1tIgMBYYCDzkYfxFqvZ6DQm6ecv2ZKYy88BR/h2IclJGZTduRU8izGixjKp0fVhad53fvoSxqVI3wQzSmInj9mtM8fZdL6/u7upJxOLtM9gXQtn6NIuuu7tTgmNt0bZbgVZtd8HXdGq6k9awmCbx34+k0rRVDcpyrFrhgS6qp95zNqh376dWydpFtC0qqHkVoiDCsXyuv9Q3jo5l6T3evBwEAoSHiSZwB7u3TwvN65v09qB0bRZWIUFrXjeX/OtT32vbnYUdHOi+YvBfn6YkrGXFB6+OWM6Y8OF2tNgm4QFUTVDUeVzPuL1W1kaoWO3CYqm5V1fnu1/uB5UA9YCAwxl1sDHCRo9EXYhXPwcOaAwYXu9zGBIYd+4/4OwTjoANHiu/59+iAVvRrU6fMjhUbFU79mlWPX7AC6NmylidxLiw5rqpX4lyc8Xd1K/a9ZrWreQZYK4mUhGiqRBStKW5XvzrdCzRPB3jgPFfS/eRFbbi7dzOf+/tm4eYSH9sYpzld83y6qt6Wv6CqE0XkiRPZgYikAB2AuUBtVd3q3tdWEalVhrGWjH3JDgp2mYODPRAzJrD0eXEWvz3cm1o2hkFA+mlV0dYG+QZ1q9STufhNiECeuvo4O+2bO7sWWTekZ1OG9GwKwJRlvrti7D6Y5WhcxpwIp2ued4nIoyKSIiINReQRYHdJNxaRGGAc8A9VzSjhNoNFJE1E0nbuLP5D9mTYSPnBxa534Msfg8G6YxgTOJ6f8qe/QzAOuWfsIp/r/3yyXzlHEjjmDz+X3x7pffyC5aBOdddDrzvdybQxFZHTyfNVuKan+gr42v36qpJsKCLhuBLnj1T1S/fq7SKS5H4/CdhReDtVfVNVU1U1NTExsfDbxpSM2tRkwSD/CluzbWMCx9i0jYz5eR3/mrCcX9aU+Hm9qQQOZ+f6XB8RZoN7nqwaVSOOO5J4eWlbvwZfD+nCP84p2nz7k982+NjCmPLn6KeNqqar6t2q2gFIBUYUN8J2QeKqDnoHWK6qLxR461vgBvfrG4BvyjrmY8aFWP1UkLDrHFzsehsTWB77dhlvzPqLq96yKXOMqUzaJ9fw2b962JdL/BCNMUU5mjyLyMciEisi0cAyYKWIPFCCTbsA1wG9RGSh+6c/MBo4V0RWAee6l40pc6pqzbaDgF1jY4zxj7w8e2xpTkyu/Z8xFYDT7Vxau/sqXwRMABrgSoqPSVV/UlVR1baq2t79M0FVd6tqb1Vt5v593FrssiRiozAHE8urAl9+03z7szYmuO0+cOSYIzmXxMpt+/nvj2vKKKLAsGJbBsu3Fh2yZsGGPTR+eAI/r9nltX7TnkM8P2Ulb85awx9bSjTUDbWqRZZJrKbi+zxto79DMMbx0bbD3X2XLwJeVdVsEbGvqabCU6xWMhjkX2MbMMyYwHXwSA7LtmRwSt1YNu05zOxVO7mmU0OvqXQ6PjmNxGqR/P7IOSd1jIe+WMxY9xf7v3VvzF+7DhIeEkKD+Mox1VFZysjMpmp4KGGhIfR9aTZwdH7iT37bwJPf/8HBLFff5dmrdqEKeao88tVSNqQfKrCnFYy7/SxemLqS9248g9s+nFfkWAkxEfx2ktfMVGxnNIrjt7XedWSzV+3ip9W7OL9tXfqW4bRkgS4vT8nIzKZGVedHVA8GTifPbwDrgEXALBFpCJTsUWIFJFgNVbCw6xxc7HobE7jOGj2DfYezvdY9O3klK90jNDd9eAIAOwvMEf3Gj2tIjqtK/1OTjrnv9qOmcF3nhp7EGeCCV39i6WbXV52PBnWiTb3qPP7tMm7u2og29aqTlZNHeKh4RvvPp6pk5yoRYSF8MW8TZzdPJLFapNf6ksjOzSNUhJAQ30+AX5j6Jx/8so4FI/r4PHZptR05hfPbJvHq1acVea9wv9XlWzP4z8zia+sv+c/PALz/yzpmrCgyRizf+pj6yASGFy5vR9enf/Bat2DDHrbsy+T7xVs9D2TM8b00fRUvT1/F74+cQ6K11Cg1pwcMe1lV66lqf3W1d94A9HTymMaUBUWLfLExxhhT+RROnAGO5OQB8PPqXeQU6Ec5b/0eAP41cQV3fDSft2b9xf+9PqfYfe89lM0rM1Z7rctPnAGueXsu7R6fwpcLNnP+Kz/xw8odNH90Iu//sp6vFmwiOzfPU/bDX9fT/NGJLNq4l/s/X8Sg99MAeHHqnzR/dCITlmxl2JdLWLV9/zHPt9kjExn65eJi3395+ir2HPL+N/nkt400f3QiM1ZsP+a+86kqa3Ye8Lx+etIKNuw+Wmv8/eKtRfb16oxVRfYzc2XJphR9cvxyn+vr1qhSou1N5VPPx7Xdsi/TD5FUfvnzZxd8QGhOntM1z17cCXTpOhX5mVVQBQdV6/McDOz5iAkWIhIHjAVScLUIu1xV9/godwPwqHvxSVUd414fAbwK9ADygEdUdZyIRALvAx2B3cAVqrrOyXMpKylDxxdZN299Oh0b1vQs/3OCK2kbNCaNacu3M7RfS648PZl56/fQq2WtEz7mTe/9DrhGAwfYsPswv69LJyo8hGnLXTWrD41zJb47MzJ58ItFfJa2CYA7PpoPuJo+f//3rmzbl0nvVrUQEVSVXQeyPLVKn6Vt4u+9mhETGcbqnQfYkXGEZVv2cWu3xp5Ydh84ggKpT07zrLv5f2ms/Vd/ZqzYwS1j0rxqkPu8+CM5ecpfOw96nVOrpFifNcg3/y/N89rXv7Uxx+JkBcaO/Zls2ZtJ++Qajh2jIqooXdRy85Q9h7JIiKmcteDlmjxXdlYTGWTscge8owOGVYwbijEOGgpMV9XRIjLUvfxQwQLuBPsxXFNLKjBPRL51J9mPADtUtbmIhABx7s1uAfaoalMRuRJ4GriifE7JZdYDPen+7A/HL1gCT01YQb82RZtqT1vuqkUdPXEFoyeuAODt61NLfbwXp/1ZZN2Kba6a5S37Mj2Jc2Hnv/ITAM9e2paG8dGkrU/nmUkreeKiNp4y3Z4p+m/yeoEEt2OBpLmgOz9ewPglWwFXDXJmdhr/vrI9f24/4LO8rwHBysM3Q7r45bim8uv70mzSD2YFTdPv/PylonzVGT1xOW/NXsvCEedWyn7YljyfoIryH884SzmaWJnA5RkwzP6uTeAbiKvWGGAMMJNCyTNwHjA1fyYLEZkK9AU+AW4GWgKoah6QP0zyQGCk+/UXwKsiIlqOT6TKelAuX0mnL/nNqv3pgS+8m2cP/3ppqfeZnzjnm7Z8O6c8NrnU+y1r7YKs1tCUnfSDWf4OoVSO5OSyavsBalQNp05sFOt2H6JprRgOZ+Wyc/+RIp+JJ/ttduf+I4QIxJdxDfHkZa6HkfsOV85BzBxNnkXkYh+r9wFLVLXoyA8VnKVSQcSSqaBil9sEgdqquhVAVbeKiK82x/WAgnPBbALqiUh+lvKEiPQA1gB3qur2gtuoao6I7APiOZpcAyAig4HBAA0aNCizkzLGBKf2o6bw2d/OJCU+moiwELZnZHLpf3/mo1s6l/qB2pGcXAa+OofHLjiFM5vE+yyzZe9hrnzzVz4Z3LlI/+x9h7IJCxWiI8M4lJXD3L/S6dmyFnsPZRERFkLViDC2Z2TS/ZkfOJKTR7v61Rl9SVtaJcWWKL77P1/Md4u2AJAQE8muA0c4q0k8P6/ZDbhGtx/42hxy8/L4/u/d2HnA1dd514EjHMnJZcveTCYv20brpFj2HMri7k8X0j65Btm5eXwzpAthoSEMGvO7pyvJ8WroC55vQd8u2sJHv65n7N/OLNF5VRZO1zzfApwJ5D/G7QH8CjQXkVGq+oHDxy9zFaW/gHGWa8Awf0dhnGaX2AQSEZkG+Jq/5ZGS7sLHOsX1XaE+MEdV7xWRe4HngOuOsY33CtU3gTcBUlNT7UZqSmXm/T38HYIpB7Mf7FlsS5C9h7Lp8+IsT7/8L+dvZmP6Yd6ds5ZasZEM7taYsNCi4yJ/6046wdX3dshH87m1e2OvsQ7W7TrEim37eezbpUy552yv7XdkZDJp2TbSD2axIf0QY3/fSOfGcahCl6YJALQbNYVqkWEsefw8Hhq3hO8WbWH6fWfT+/kfSaoexfT7zuarBZs9Axcu2rSPfv+ezYN9W3BHj6Zk5eRx+4fzuK9PC1rXjWV/ZjbVosI9MXxX4Bx2uRPj/MQZ4MJXf2Lxpn2A93gDN7rHXPBl4ca9gKtWftnWDE/iXBLtRk0B4Kwm8bxzw+n87+d1DO7emLs+WXDM7W7/cD7f/b0rocXMDFBROZ085wGt3E+nEZHawH+ATsAsoHIlz5Xr2ppSsAHDgkNF6wdkTGmoarET3orIdhFJctc6JwG+vhlt4mjTbnAlzDNxDQR2CPjKvf5zXA/H87dJBjaJSBhQHfCenLUc1I6NZHuGjSQbLFISov0dgikHyXHHr0Eev2Qrr6h6+t7/7+d1ADwzaWWRsh/ccoZXQvfuT2uZtGwbizbt5ZdhvYuU99XPf/AH81i4cS+XnFbftUKVq9+aC8Daf/Vnyh+uJsn7j+SQk5vHmh2ufRx2z22+dV8mrUdM9lnL/Mykldx+dhO+mLeJ6St2ML3A9Gx3927GwSM5dG+eeNx/k/zE+WTc+9kiflrt1XCIW/73O/VrVmHu2nTPmAxT7+nOyu37OT0lzlPu5zW7aTViEgDx0UebY6sqjYZNoHfLWuTkqWc+9z+2ZrDrwBFqx0addLz+4HTynJKfOLvtAJqrarqIFJ07wpgKxGqeA1/+JbYWJSYIfAvcAIx2//7GR5nJwFMikl8F0wcYpqoqIt/hSqxnAL2BPwrt9xfgUmBGefZ3zmdjVBgTnFSh0bAJJSp73Tu/eS3nj6a/dV8mq7bvJyo8lKTqUZz30ixPmZSh45l279k0rRUDwKY9rsRv3HzXYH4vF5iqrnAcc9ems2qHK9n8aO4Gr/eKG2ivuHP593TXVG9v/7T2GGdYeoUTZ8Aric937ouziqwr6MFxR8djyD8nX/v5fvFWbuna6ETD9Cunk+fZIvI9rqfUAJcAs0QkGtjr8LHLnGA1VMHCLnNwsb9rEwRGA5+JyC3ABuAyABFJBW5T1UHuB9tPAPlt+0blDx6Ga3CxD0TkJWAncJN7/Tvu9atx1ThfWT6n4+2Onk0Y8c0yfxzaGBMAjpUMnvPCjye1z2venut5/clvG45RMng98f0fljwXMgRXwtwFV+75PjDO/VS6p8PHNuakqarVZAQBa11ggoWq7sZVY1x4fRowqMDyu8C7PsqtB7r7WJ+JOxEvb9/e2YUY9wA115+ZAmAJtDEBpm396qVqhmxMWXM0eXYnyV+4fyo9m+c5eCiWWAUDT59nP8dhjDlxbet7T1UUGVZ0cCBjTOX26eDOtB5R8aZKM8HL0TuNiFwsIqtEZJ+IZIjIfhHx3cjfmArEBgwLMtZu25hKLzIs1N8hGGPKWNWIML67s6u/wzDGw+nHtM8AF6pqdVWNVdVqqlqyScwqIBFXc15jTGCxv2pjKr/z2yYxpGcTFo/s4+9QjDFlqFVSNX+HYIyH08nzdlVd7vAxjHGGtdsOCq6HYv6OwhhTWmGhITxwXktio8J5+/pUmteO8XdIxpgyUNnmATaBzenkOU1ExorIVe4m3BeLyMWl2aGI9BWRlSKyWkSGllWgJTo2VkMVDPJbF9hHdXCw62xM4DmndW2m3HO2v8MwDmhZx2ohg42NOWQqEqeT51jgEK65Ii9w/5x/sjsTkVDgNaAf0Bq4SkRal0Gcxnjk10LaZ3VwEBGb59mYAPXfazv6OwRTSoWT5Teus2sajO47t7m/QzAGcDh5VtWbfPzcXIpdngGsVtW/VDUL+BQYWDbRHp+IWPPOIJB/iW2qquBg87cbE7j6tqnDjPv8XwNdJdwGMyuoee0YZj/Yk8HdG3vWtatf3WfZb+7swvDzXfUkCTERNIyPLpcYTcVyZ6+m/g7BGMDhqapEpD7wCq55nhX4CbhbVTed5C7rARsLLG8COhU65mBgMECDBg1O8jDGmGBiubMxgatxonff51u7NeKaTg3JzMml70uzHT/+zV0a8XD/ljR9ZKLjx6oMnrm0LZenJgPwcP9WvDnrLwC+uqML+4/ksHN/Jk0SYzhr9Awu6lCPyLBQbu6SwobdB7nidPteF6ys6bapXYNgmQAAIABJREFUKJxutv0e8C1QF1fi+5173cny9Zfj9b1XVd9U1VRVTU1MTCzFoUp2cBN4PH2e7YIHBbvOxgS+Fy5vxzOXtOXlqzrwcP9WpCRE06J22fSdHdA2ideuPg0o2qT4zMbxjLigNWGhIUz+R3fP+rrVo4673xHnt+bWbo3KJMbSWPr4eSUuGxEawtdDuhTZ/uH+LbmpSwoLhp/rSZzzvXb1aXwzpAshIUL1KuE0rVUNEeGXYb15qG9LwJU4PT6wDa3rVtoJW0wZeO+m0/0dgjHO1jwDiapaMFn+n4j8oxT72wQU/NStD2wpxf5OmPWNDHxHm22bYCBYdwxjAt3Fp9Uvsk5EGNqvJS3qVOOm934/of19ecdZPDtpJS3qVOOu3s2Ii45gQNsBnvcTYiKZcFdXYquEe9a1qFONdaNdZfLylMYPTwAgIiyErJy8Ise4uasrcT5wJIdPftvIowNacd4pdYgMD+HvHy9g7tp0n7Fd1rE+13RuyJLN+xj+9VLA1fe7b5s63P/5Ir6YV7TxX0RoCCuf7EuL4ZOKxBITGcZzl7Xj/s8X0bNFImc1SSAnT3l60gqvcotH9iE2ynW++eeZb3D3Jj5jBdfDB2NKomeLWqwbPYCUoeP9HYoJYk4nz7tE5FrgE/fyVcDuUuzvd6CZiDQCNgNXAleXLkRjvNmAYUFG7KGYMcHqtrNdSd1Hgzpx6/tpHMrK9ay/tVsjsnLzWLo5g1vfT6Nd/eqEhAgdkmtyWoOafDK4s899/vfajrStX51ascXXLoeECFeensynv2/ktatPo11ydVAY9uUSpq/Y4VXj/K+L23Jt54a0Tor1NF1tlRTL3LXpJMdVYWP6YU/ZSzvWZ2i/lsTHRNI+uQYLN+xl3PxN9G1TB4DnLmvnSZ4XjehD9arhFNSzRSKTl20vEm/XpgkA3NW7GR0a1ATg9h6uf7v8RCY/cTbGmEDmdPJ8M/Aq8CKuCr2fgZtOdmeqmiMidwKTgVDgXVVdVhaBloTNBxscLJEKQnbJjQlqXZomsOixPuTmKYs37aNDgxqEh7p6tiVVr8LXQ7rQrFYM0ZHH/9qUn6gez6Pnt6ZxYjS9W9YixD2PbX5yfHpKnFfZU+p6D6b1cP9WDGib5CmXlZNHRmY2CTGRXuWeubQtT1x0is/jF06cAa48owGTl21n3O1nERoiRLj/DepUjypSm+zZ5vRk+pxS+3ina0yZWTyyD//4dCEzVuzwdygV0kN9WxZpGWLKjtOjbW9Q1QtVNVFVa6nqRUCp5nlW1Qmq2lxVm6jqP8soVGOKsMEpgoNdZWMMQHhoCFHhoZzRKM6TOOdrn1yjRInziYiJDGNw9yaexBkg/2XecR7oRYSFeCXYEWEhRRJngNAQoWpEyePObxbbsWFN2ifXKFEf49GXtKVXS0ueTfmJjQrn3RtP58NbOvl8/5xWR/8/XnxaPW48K6WcIisfq/7Zz2s5OuLoaP6/DOvF7T2aMOWe7jxxURtu6pJyzLnRX7yinddnx8tXdShVbJenFu0iE2icHjDMl3v9cMwyIlZBFQSsdUFwEbGKZ2NMxXDeKa5a62a1Y45T8uSNHdyZR/q3cmz/xpSXrs0SmHl/D/oVau0xtF8LAJokRvPC5e0Z2q+l573UhjXL7Pg3nNmQL247s0RlVzzRl5EXtD6h/X95x1ley+/ckErao+cQHhrCn0/244XL2wGuf4fPbzuTyf/oTlL1KgA0r12N6zo35LELTvEkx1Pu6c660QOYeHc3BndvTGxUGP3aJJH26DmeY1zYri6LRvTxLCfHVeGFy9vxwHktShTzqfVcrWR6taxV5L1nLmnL+Lu6eq37W4Hp6ioLp5tt+2IVPaZSsIrn4OAaMMzSZ2OM/13SsT79T02iSoRz80J3ahxPp8bxju3fmPKUkhBNjxaJTFy6DYC0R88h43A2gKe1SFR4KJ/fdiYHjuTQs0Ut/vZBms++/Z/c2pns3Dyuf/e3Yo+XEBNBp0bxHMnJ4/GBbQCIjQojIzOnSNmo8BAys/M8MdzYpRFbMzJJbRjHua1ro6rc+v48pi3fzpWnJzPigta8N2cdz05eCcBpDbwT/dSUOKq7ByGMCAvh4tPq07x2NZokxhzzM+PlqzowYclWmrtnGGiVFEurpFgeLvAQrWBtdvWq4Uy8uxu1Y6OIjQojzN0SZ3D3xqhCniphIcJbs9cWaR5+beeGtEyKpUNyDe77fBE9WiQSFhLCBe3qesp0buxq3fPW9ameriGViT+S50r7LVUE1uw4wOdpG49f2FRa2bmV9r+oOUkrt9vfdaBrWivGM9CRMRWZk4mzMYHo8tRkujVLpGpEKDWqRhAfHcHQfi0Z2P5owlawm8Mb16V6Xn+zcDN1YqNoUacaNapGeO13ycg+nDV6Bvszc/jvtR3p0jSesJCQIn+ji0eex+4DR1i3+yArtx3ghakr2XUgi7evP51WSdVYt/uQp+ywfkcTVhHh7RtS+WHFDro1SyAsNIQhPZvy7OSVdGl69AHXNZ0a8M//O9XnubepV93n+oLioiO4tnPDY5Yp3FWlVVLRLhuFy9zeowkN4qqSWC2Sy9/4xXNO+f/W/77SdxPwTweXrLa+onIkeRaR/fhOkgWo4sQxy0NCTCRz16YXOz2ECSyJPvqPmcCTWC2SWX/uZNafO/0dinHQjWelWPJsjDEBSESoW6OK13L+SPrHM7B9PZ/rr+7UgGpR4XxwSyeuf2cunRrFUe0YI8rHx0QSHxNJx4auxPHhr5bQODHas/5YehZq4jxnaC/io12JfHED9VUU+VPNzXv0HEJDgqPJpgRyc8XU1FRNS0srs/0dzspl14EjZbY/U3GFhQp1YqNs0LAgcPBIDukHs/wdhnFYTGQYNaMjjl/wOERknqqmHr+kKU5Z35uNMaYs5eTmERoiJ/0dUFXJydMiNbXGOeV5b/ZHs+1Kq0pEKMlxVf0dhjGmDEVHhpX5KLrGGGOMqZzCSpn0igjhoVb5EqgCuuZZRHYC6x3afQKwy6F9V1TBeM4QnOcdjOcMwXnewXjOcPLn3VBVE8s6mGBi9+YyF4znDMF53sF4zhCc5x2M5wyV4N4c0Mmzk0QkLdia7gXjOUNwnncwnjME53kH4zlD8J53oAvG6xqM5wzBed7BeM4QnOcdjOcMleO8rTG+McYYY4wxxhhzHJY8G2OMMcYYY4wxx2HJ88l7098B+EEwnjME53kH4zlDcJ53MJ4zBO95B7pgvK7BeM4QnOcdjOcMwXnewXjOUAnO2/o8G2OMMcYYY4wxx2E1z8YYY4wxxhhjzHFY8myMMcYYY4wxxhyHJc/HICLJIvKDiCwXkWUicrePMiIiL4vIahFZLCKn+SPWslLCc+4hIvtEZKH7Z4Q/Yi1LIhIlIr+JyCL3eT/uo0ykiIx1X+u5IpJS/pGWnRKe840isrPAtR7kj1jLmoiEisgCEfnex3sBdZ0LOs55B+q1XiciS9znlObj/YD6DA8Gdm+2e3OhMgH1mW33Zrs3F3ovUK91pb03h/k7gAouB7hPVeeLSDVgnohMVdU/CpTpBzRz/3QC/uP+XVmV5JwBZqvq+X6IzylHgF6qekBEwoGfRGSiqv5aoMwtwB5VbSoiVwJPA1f4I9gyUpJzBhirqnf6IT4n3Q0sB2J9vBdo17mgY503BOa1BuipqruKeS/QPsODgd2b7d5s9+bA/Ly2e7NvgXitoZLem63m+RhUdauqzne/3o/rP3a9QsUGAu+ry69ADRFJKudQy0wJzznguK/fAfdiuPun8Gh6A4Ex7tdfAL1FRMopxDJXwnMOOCJSHxgAvF1MkYC6zvlKcN7BKqA+w4OB3Zvt3lyoWEB9Ztu92e7NBqjAn+GWPJeQu3lIB2BuobfqARsLLG8iQG5oxzhngDPdTYomisgp5RqYQ9zNZhYCO4CpqlrstVbVHGAfEF++UZatEpwzwCXuJjNfiEhyOYfohJeAB4G8Yt4PuOvsdrzzhsC71uD60jlFROaJyGAf7wfsZ3hBIhInIlNFZJX7d81iyt3gLrNKRG4osH6miKws0HSwlnu9X5tS2r25CLs3B8Bntt2bfQq46+xm9+ZKdm+25LkERCQGGAf8Q1UzCr/tY5NK/4TwOOc8H2ioqu2AV4Cvyzs+J6hqrqq2B+oDZ4hIm0JFAu5al+CcvwNSVLUtMI2jT30rJRE5H9ihqvOOVczHukp9nUt43gF1rQvooqqn4WoCNkREuhd6P+CudzGGAtNVtRkw3b3sRUTigMdwNY07A3isUJJ9jaq2d//scK/zNKUEXsTVlLJc2L3Z7s1uAXet7d7su5iPdZX6Otu9uXLemy15Pg53f5NxwEeq+qWPIpuAgk+B6gNbyiM2pxzvnFU1I79JkapOAMJFJKGcw3SMqu4FZgJ9C73ludYiEgZUB9LLNTiHFHfOqrpbVY+4F98COpZzaGWtC3ChiKwDPgV6iciHhcoE4nU+7nkH4LUGQFW3uH/vAL7ClRQWFHCf4cUo2ORxDHCRjzLn4arlSlfVPcBUin4OHmu/5daU0u7Ndm8uIBA/swG7NxcqE4jX2e7NlfDeLKoVIol3REJCgqakpPg7DGOMMQFi3rx5u1Q10d9xnCgR2auqNQos71HVmoXK3A9EqeqT7uXhwGFVfU5EZuJqIpmLK4F7UlVVRJYCfVV1k3ubNUAnLX4QGLs3G2OMKVPleW8O6NG2U1JSSEsrMvq5McYYc1JEZL2/YyiOiEwD6vh465GS7sLHuvwn7Neo6mb3SM/jgOuA94+zTcHYBgODARo0aGD3ZmOMMWWmPO/NAZ08l5XXflhN+sEsQkOE6zo3JDmuqr9DMg77asEmlm4u3J3MBKKwEOHGLikkVa/i71CMKRVVPae490Rku4gkqepW94ilO3wU2wT0KLBcH1eTUVR1s/v3fhH5GFcTu/c52rRu07GaUqrqm8CbAKmpqYHb5M1UCiu37eejuesZNbBwV+KiDmflkpGZTe3YqHKIzBhT0VXY5FlEQoE0YLOqni8ijXD1B4jDNSjGdaqaVR6xfL94Kxt2H+RgVi6xUWHc2atZeRzW+NET3y8n43A2UeGh/g7FOEhVOZiVS+3YKG7u2sjf4RjjpG+BG4DR7t/f+CgzGXiqwCBhfYBh7qS4hqrucve7PR/XwDUF9/sLcCkwQwO5P5gJCOe9NAuAizrUo3VS7DHv9Ve8+QuLN+1j3egB5RWeMaYCczx5FpEqQANVXXmCmxaeMPxp4EVV/VRE/otrhM//lF2kxZt4dzdy85QmD08gz74SBIU8Va7t3JCRFwbETB+mGBmZ2bQdOYU8+65vAt9o4DMRuQXYAFwGICKpwG2qOkhV00XkCeB39zaj3OuigcnuxDkUV+L8lrvMO8AHIrIaV43zleV3SsaUzsWv/0x8dATzhp/Lzv1HOP2f02gYX5XsnDw+GdyZhvHRLN60z99hGmMqEEdH2xaRC4CFwCT3cnsR+bYE23lNGO4eubMXrpE8ofiRQh1n37GDg11nY0wgcY/Y2ltVm7l/p7vXp6nqoALl3lXVpu6f99zrDqpqR1Vtq6qnqOrdqprrfi9TVS9zlz9DVf/yzxka46Kq/GfmGrZnZPJZ2kbenn30v+SO/ZnM37DHq/zug1nMW7+Hl6evAmD97kNs2ZfJ2c/OLM+wjTGVhNM1zyNx9YuaCaCqC0UkpQTb5U8YXs29HA/sdU+KDhVoomxjjDHGGFMxrNpxgKcnrWDa8u3MW+9KlJ8cv/yY21zyn599rr/1/WMPbLc9I5Ps3DwSYiK9mn5v3nuYKuGhxEVHnGD0xpiKzunkOUdV953IlI8FJwwXkR75q30U9Vk3WHhEz7KSH4BWjPm5jcOsy15w8Pxd2+U2xpiAkJPr+kDPT5xLY+of2z2vv1qwiRkrdnIkO5duzRIY/s2yIuXPaVWbV67qQJfRMzzrnr+sHZd0rF+kbF6e8tL0VZzWoAZnN08k/7vy+MVbaZwYzeodB2hZpxrNalcrsq0xxn+cTp6XisjVQKiINAPuAnw/3jsqf8Lw/kAUrj7PLwE1RCTMXftc7ETZNqKnMcYYY0zwOZyVS/+XZzuy73vGLvK8nlIgqS5o2vLttBoxyWvdfZ8vYsf+I5xSN5YODWqQfjCLrJw8Pv19I+/8tBaAGlXDef2a0zirSQJDPp7vtf33f+9Kq6RYQkOO1iNlZudy6X9/5vEL29CxYU2Wb82gWlQY9WsWnQ3mr50HaJwYA7geKIQIbNpzmAva1T25fwgfPv1tA7WrR9G1aQLhoY72CDXG75xOnv+Oa37JI8DHuEbyfPJYG6jqMGAYgLvm+X5VvUZEPsc1kuenFD9SqGPyK8+thio4KEevuQlc+U/6rUWJMcZUbq9MX8XzU//0dxg+PT1pxTHf33som6vfmsvaf/Uv8t75r/wEwP19mnNz10Yczspl4tJtLN2cwajvlvH1kC70+7frgUHhEcHfmvUX/5zgu8n6gFOT+HDuepLjqtKyTjVio8KpGhGKiNDruZl0aZrA4O6NyclTGiVEs2nPIY7k5NHEnYgXNPTLJZ7XSx8/j5jIMHLzlK37DnsS+p/X7OL0lDjCQ0M8rftOpGWqMRWFY8mze6qpx1X1AVwJdGk9BHwqIk8CC3CN8GmMMcYYY4JcRU2cT0SjYROKfe+5KX/y3BTvczyYleu1zcWvz2FA27psTD9EYrVInp1c/EQ3jR8u/lgAf+06yAe/rgfgnRtSuWWMq/93p0ZxbEg/xNZ9mcRFR5B+0HvW2PGLt3DF6Q14adqfvDJjNbd2a8Rbs9d63v9b98a8Mcs1iNu60QP4esFm/jF2IV/dcRYdGtTEmIrOseRZVXNFpGMp9zGTo4ON/YVr8DG/OFpDZYKCgvjsam8CifV5NsYYU1mt3nHAa3n+hr3M37C3zI+TnzgDzF2b7nldOHEGeGjcEh4ad7QmumDiDHgSZ4Df1qbzj7ELAfi/13+2ubRNpeB0s+0F7qmpPgcO5q9U1S8dPq4xxhhjjAkCt384z98hmJNw+Ru/eC33+/dslm/N4LKO9enZshb9T03yU2TGFM/p5DkO2I1rjuZ8CljybCo06/McHOwaG2NM5Tdx6TZ/h2DKwPKtGQB8Pm8Tn8/bZDXRpkJyNHlW1Zuc3L9fWPtOYwKO/VUbY0zl9POaXf4OwThEVW1QMVPhOJo8i0gUcAtwCq5ppwBQ1ZudPK5T7O83eKiq9XgOAtav3RhjKrer35rr7xCMQ3o9/yM/3N/D32EY48Xpydg+AOoA5wE/4pqfeb/Dx3SU1VAZE3isQYkxxlQ+Czbs8XcIxkFrdx08fiFjypnTyXNTVR0OHFTVMcAA4FSHj+kYq6MKHpZLGWOMMRXbzv1H/B2CMSbIOD1gWLb7914RaQNsA1IcPqajrIYqeFgz/cCXf43VHpcYY0ylsn73QQZ/cOKjbH89pAvVq4TzwtQ/qRMbyXWdU6hfswortu3nzVlrGH1JW1oOn+RAxMaYQOB08vymiNQEhgPfAjHACIeP6RgbtMAYY4wxxv+ueOPX45ZZNKIPUREhtHh0Ek8MPIXrzkzxvPfKVR28yrauG8tLV7rWJcdVoV39Gny/eGuZxmyMqfwcbbatqm+r6h5V/VFVG6tqLVX9r5PHdJrVUAUHVXtYEkysRYkJdCISJyJTRWSV+3fNYsrd4C6zSkRuKLB+poisFJGF7p9a7vU3isjOAusHldc5meC2LSPzmO//MqwX1auGExkWyrrRA7wS5+OZ/WAvXr36ND4a1Mmz7t0bUz2vF4/s41V+aL+WrHyyL+tGD2D5qL4lPo4xpvJxerRtn7XMqjrKyeM6xVIpY4wxldRQYLqqjhaRoe7lhwoWEJE44DEgFdfQD/NE5FtVzR+V6RpVTfOx77GqeqeDsRtzQmpViySpepVS76dL0wQ+HdyZ2z+cR2pKHN8M6cJXCzZTLTKs2DmIq0SEei2fWq86F7ary6BujWg0bEKpYzLG+JfTzbYLDpMXBZwPLHf4mI6yGqrgoNhUVcHAGheYIDIQ6OF+PQaYSaHkGdfMGFNVNR1ARKYCfYFPyidEY0rv+793pV6N0ifO+To3jmfBCFdNc7vkGrRLrlHibb8Z0sWr/MUd6pGdp3y3aIvP8g+c14JnJ6/0LD/UtyW392jCrgNH+OCX9fQ/NYkGcVX5aO56nhxfsq/Ti0f2oe3IKbx6dQfu/HhBiWM3xvjmaPKsqs8XXBaR53D1fa6U7Iu2McaYSqq2qm4FUNWt+c2uC6kHbCywvMm9Lt97IpILjAOeVPU8Tr5ERLoDfwL3qGrBfRhT5rbt891ke9ztZ9KmXvVyjqao6fedTUxkGLVjo7zWv3BFewCv5Hn5qL5s3nuI2Crh1KoWxZzVu6hfswrPXNrOUyYhJpJ7zm3uWR7UrTGDujUmZeh4r/1Pu/dsDhzJYcGGPTz+3R8AxEaFe2rJz29bF4D0g1nc/ekCzm6eSGK1SD5L28ic1btLdG7Na8fw5/YD9GyRyA8rd1KrWiSHs3LZfyTHU2bd6AG8+9NaTq1fnb2HsslTpVFCNH1enFWiYxhTkTld81xYVaBxOR/TmBOmirXTDwJiF9kEEBGZBtTx8dYjJd2Fj3X5CfI1qrpZRKrhSp6vA94HvgM+UdUjInIbrlrtXj5iGwwMBmjQoEEJwzHGt87/mu5z/WkNfHblL3dNEmOOW+b8tkm8evVpADStVc2z/uNbO5/w8cbdfiZVI8JoWst13PbJNagWFU5itUif5eOiI/jglqP9uQe2r+dJxNvVr86Ym8+g/aipAHw6uDNXvvkrvVvW4sL2dRnY/ujzNFX1Gh/m4JEcMrNzAbi5a6Mix33+snaM/X0jWbl5NEqI5sUr2nuOW7d6FB/f2pkez8084fM3pjw53ed5CUdvvKFAIlAp+zvns1bbxgQetf4YJgCo6jnFvSci20UkyV3rnATs8FFsE0ebdgPUx9W8G1Xd7P69X0Q+Bs4A3lfVgtVVbwFPFxPbm8CbAKmpqfYHZ8rcZR3rV5qBPlf9sx+hZRhrx4ZxRdZd2rH+Ce2jUUI0a3cd5Js7uwJ49emecd/ZNEqILvLvW3g5OjKM6MjiU4tLOtbnkkJxzbjvbD6ft4kHz2uBiDD7wZ488vVSZv25E4A1Ow+U6GGEMeXF6Zrn8wu8zgG2q2pOcYUrOqulCh6uime73oGuknzPMqYsfAvcAIx2//7GR5nJwFMFRuLuAwwTkTCghqruEpFwXPf2aQD5Cbm7/IVU8nFNTOX1xEVt/B1CiYWHls1kN69dfZqntrm0Pr/tTNbsOODzvcYOJq+NE2N4qG9Lz3JyXFXev/kMT4309oxMS55NheJ08ry/0HJswadU+YOSVCZWQWVM4LG/axMERgOficgtwAbgMgARSQVuU9VBqpouIk8Av7u3GeVeFw1MdifOobgS57fcZe4SkQtxPSBPB24stzMypoCo8NDjFwowA9omldm+EmIiSYjx3czbn/4zcw1nNUnwdxjGeDidPM8HkoE9uPpS1cB10wZX5Z7P/s8ikoyrL1UdIA94U1X/7Z5GYyyQAqwDLi8whYbzrJYqeKjVSgYDu8QmWLibV/f2sT4NGFRg+V3g3UJlDgIdi9nvMGBYmQZrzDH46mZT3LRRpvKbvWqXv0MwxkvZtBsp3iTgAlVNUNV4XE29vlTVRqp6rIHDcoD7VLUV0BkYIiKtOTpPZTNgunu5XKn1ejYm4NhftTHGVA73f77Y3yEYY4KY08nz6arqmRFeVScCZx9vI1Xdqqrz3a/34+pDVQ/XPJVj3MXGABeVecTHYLVUwcMekhhjTMWnqvy2Nt0G/Qsi4+Zv8lr+W3ebxMUYU36cTp53icijIpIiIg1F5BGgZBPJuYlICtABmEuheSqBIvNUishgEUkTkbSdO3eW+gSKsPtz0LCHJYEvfwwG+95tTOU0cek2Ln/jFxoNm3D8wiYgDevfyt8hGIflT39lTEXgdPJ8Fa7pqb4Cvna/vqqkG4tIDK75JP+hqhkl2UZV31TVVFVNTUxMPImQjxVPme7OVGCWTBljTMU3Zdk2f4dgjHHYhvRD/g7BGA9Hk2dVTVfVu1W1A5AKjCjpCNvuUT3HAR+p6pfu1dvd81NyjHkqHWU5VfCwhyWBL/8SWzN9YyqnBnFV/R2CMcZhfV6c5e8QjPFwNHkWkY9FJNY9zcUyYKWIPFCC7QR4B1iuqi8UeCt/nkoofp5Kx9i8v8HDUiljjKn4Xp6x2t8hGD/65/9VnrmdTelk5+b5OwRjAOebbbd2N7e+CJgANACuK8F2XdzleonIQvdPf1zzVJ4rIquAc93L5coGJQke9rAk8OW3LrA/a2OMqXyu6dTQ3yEYh7SoXc1redof2/0UiTHenJ7nOdzd/Poi4FVVzRaR435NVdWfKH68piLzVJYXa8YbPOwhiTHGGFOxTF9uCVSwuOGsFB7+aolnOSMz24/RGHOU0zXPbwDrgGhglog0BEo08Jcx/mYPSwKf2EU2ptJasmmf17KNyBv4bhmT5nn93k2n+zES47Srzkj2Wn5o3JJiShpTvpweMOxlVa2nqv3VVZW3Aejp5DGdJFjzTmMCkf1ZG1P5XPDqT17LZz/7g58iMf7QOCHa3yEYB9nDbVNROV3z7EVdcsrzmMacDMXmeTbGmMpke8YRDmXZV4xgERpid2ljTPkr1+S5shMRq6EyJhBZkxJjAsJ17/zm7xBMOYmPjvR3CMaYIGTJszE+WC5ljDGVz7z1e2xKmyBRJSLU3yGYcpZjf9umAnB6nueLffz0FpFaTh7XKdbnOchYf5ugIGJ9no0JJM0emUhenv1VB5pcu6ZB79kpK/0dgjGO1zzfArwNXOP+eQu4F5gjIiWZ79kYY4wx5oSc8dR01u06yI6MTH+HUqy9h7I4klN0hPCMzGwOZ/keOXzRxr2H2Jq/AAAgAElEQVSeUcUzMrNp8vAEfli5o9hj5OUpn/2+MSBq7HYfOOLvEEw5W/XPfl7Lb/z4F3P/2u2naIxxcTp5zgNaqeolqnoJ0Bo4AnQCHnL42I5Qq6MKGlbvHBysRYkJBiISJyJTRWSV+3fNYsrd4C6zSkRuKLA+QkTeFJE/RWSFiFziXh8pImNFZLWIzBWRlPI5o2PbdeAIPZ6byRlPTWfNzgN+jWXCkq0cOOIayGzO6l2kDB1PytDxtB81let99NFuO3KKz5HDR09cwcDX5tBy+CRSho6n7cgp5OYpN733O/sOZXPmv6bz2DdLvebD/SxtIw+OW8yl//0FgNd+WM3Fr8/xvD/rz51s2+f9gEFVWbhx7wmf5879R9iYfuiEtlmyaV+RxP7L+Zt4a9ZfRcrm2gd10AkPLZqmTFiy1Q+RGHNUmMP7T1HVgjPa7wCaq2q6iFS+2c4tmwoKajdoY0zgGQpMV9XRIjLUvez1EFtE4oDHgFRcvRnmici3qroHeATYoarNRSQEiHNvdguwR1WbisiVwNPAFeVzSiXT+/kfWTjiXGpUjSjzffd9aRZhocL3f+8GuPpcX/Kfn5lwVzcaJUTz5/b93PHRfM5qEk9EWAgzV+702n7u2nS6jJ5BtagwVmzbT93qUQDs2H+EtbsOMmXZNv41cQU3d2nEu3PWFhtHu1FTABjzy3rG/LKe8Xd1JSwkhB37XbW1CzfuZeW2/Tw72dXsddHGvbRLrsH17/5GQkwEaY+e69nXVws2c+9ni3h0QCsGdWtc4n+L0/85DYDFI/swcclWNu/N5O+9mvJ52iYe/moJD/dvyU1dGhEeGkJObh4DX5vDsi0ZhIcKDeOjefv6VN6ds5b3f1kPQL9T6zBu3mYuP70+SdWreDXbPu+U2iWOywSWMb+s5/GBbfwdRqXz61+7SYmPpo77M+Zk5Ld6yclTYiLDWLEtg/nr99KpcRxNEmPKKtQKz+nkebaIfA987l6+BJglItHAiT/WrAAsrwoe1uU5OLhG0bc/bBPwBgI93K/HADMp2gLsPGCqqqYDiMhUoC/wCXAz0BJAVfOAXQX2O9L9+gvgVRERrWBPIduPmsprV5/GkI/n88RFbbisY32iwo8OOJWTm8fYtI088tVSAGbcdzbxMZFUrxLuKTP29w2c1SSBuOgIwkNDiAgLYcW2/V7HGfXdMgD6vzzba/3Pa4pvarp572HP6y0FaoF7PjfT8/pYibMvA17+qci6939Z53k98LU5LB/VF4BdB7K4/cN5zF2bzlvXp/Lhr67k9cnxy+ncOJ7kuKqef4c1Ow/Q+/kfXa+f6k9oiLDnYJbXIG1tR07xvH55+irP66cmrOCpCSt447qO/O2DeZ712bnK6h0H6FHgfAG6Pu2qfX9x2p8A3NGjiee9py9pW+J/CxN4Fm7cS/vkGv4Oo1Qys3OJDAspdj7rdbsOsj8zh1PrVz9u2ZK48s1fqVE1nIUj+njW/bl9P81qxfjc749/7iQmMpSODeM861oOn+R5Pf6url6fM18P6eK5Jqt3HKBRQrRnOrnDWbkBNcCf08nzEFwJcxdc9bbvA+PcN9WeDh+7zFkuFRwq1lc+Y4wpE7VVdSuAqm4tZuDOesDGAsubgHoikv8t9QkR6QGsAe50tyzzbKOqOSKyD4jnaHINgIgMBgYDNGjQoMxO6kQM+Xg+AMO/Xsrwr5cy9Z7ubM84QlZuLjf/L82rbC93ggjwxMBTGP7NsmPue/eBI9zx0XwWbdpX9oGXkY/mbvBabjXi6BfhiUu3AXDJf372KnP+K64vxy9f1YG7Plng9V6ThyfwxEVtGP710hOKo2DifCJen7nG87paVPgxSppAMvHubvT7t/fDqItem8O60QP8FFHJ5eYpm/YcomF8tNf6PQez6PDEVB44rwWXpyYTGR6CKmTn5vHNwi0s3byPrxZsBlxJ6UWvzeHi0+rx5fzNnn38cH8PUuKrcu9ni7ivT3Nqx0axIf0QEaEhJMdVJSc3j9/WpvPH1gz6tqkDwN5D2Tw9aQX/mbmGTo3imLs2nZeuaE9unvL2T2u5+oxkhn+zjK+HdOGGd11dShaOOJfP0jZyeWqy1zl8v9i7+fxFr82hSngoh7OPjtfQtn51OjeO581ZfzG4e2Ou6dSAqhFhrNl5gLrVq9AgvmrZ/WOXI6lgD4fLVGpqqqalpR2/YAm1HTmZi0+rz8gLTymzfZqKJy9PafzwBO45pzl3n9PM3+EYhzV9eAJ/O7sxD5zX0t+hmEpAROapaqq/4/BFRKYBdXy89QgwRlVrFCi7R1W9+j2LyANApKo+6V4eDhzCVVO9E7hUVceJyL1AB1W9TkSWAeep6ib3NmuAM1S12KrWsro3pwwdD0ByXBU2ph8+TmkTSCpD4mTKTv7fekHzHj2H+JgTn+s7OzeP3QeyPM2Xv1u0hdSUmiRVr3LM7VTVq4Y2KyePeev3cGaTeBZu3EuV8FC+mLeRPqfU4fSUOO4Zu5Clm/exascBfnygBw3joz3dAldu30/fl2bTML4q63ef2DgBJdH3lDpMWratzPdb1u7s2ZQ7ejahakTp63LL897saM2ziFyMq/9TLVwVt66xeVRjnTyuU0rTXMJUHoH7OMkUJ4CfIZogoqrnFPeeiGwXkSR3rXMSrjFICtvE0abdAPVxNe/ejSuJ/sq9/nNcfZ3zt0kGNolIGFAdSC/FaZyw16/uyAWvFm2mbIwJXB2fnMa60QOYv2EP4SEhnFq/+nG32XXgCIPGpLFw417+GHUeEaEh/N3douLnob2oW8M7gZ6+fDvZucptHx5tLfH4hafQq2Utbn0/rUi3DYC3ZhftYnH2szP5ZkgXBr42x2u9E4kzUCkSZ4BXf1jNqz+srnQPw5xutv0McIGqLnf4OMaUOXtWEhzsOpsg8S1wAzDa/fsbH2UmA08VGIm7DzBMVVVEvsOVWM8AegN/FNrvL8ClwIzy7u/cum6lfB5vjCmlgjXSw89vzUdz1zPjvh6edQNfm8O6XQdZ9Jirn2/PZ2ey3z3y/cyVO+nZ4mjvlbNGz/AkcfsOZ/PA54uY8kfBMY9dHvt2GY99e+xuHL4UTpxN5eV08rw9kBJnERuJORjYNQ4+dsVNEBgNfCYitwAbgMsARCQVuE1VB7lnwngC+N29zaj8wcNwDS72gYi8hKsJ903u9e+416/GVeN8ZfmczlH5g9IYY4LXE9//4Xn93py1xEVHsMg95VqPZ39gXaFa3js+ml9kHylDx3NHjyZe/euNKczp5DlNRMYCX+Oa3xkAVf3yZHcoIn2BfwOhwNuqOrrUURpjjDEBzN0HubeP9WnAoALL7wLv+ii3HujuY30m7kTcn1Y80ddrJFhjTOB48qI2PFrCgelUlce/+8NrXeHE+VgscTbHU3T28bIVi6ufVB/gAvfP+Se7MxEJBV4D+gGtgatEpHUZxFmy42M1VMEg/xpbXUZwEMT6PBtTCVWNCCWxmmvAoKjwUJaM7HOcLYwxldFVZ5R8hP5b/5+9+46vqr7/OP76ZIdAIBD2CsiWTcCJgiLbUfestVp/ttZRW617oFZqbR0dzjpbt3WCA3CLCgFB9t4gm7BDxvf3x70JCbkhgeTcm3vP+/l45JGzz+fL4eZ7P+d8v9/zYs0NFCwSiqdPnp1zl1W+1SEZACx2zi0FMLNXCbxjcu5B9xIREZGY0qhuEtml3kFaLyWRgR0z+WrRpoPsJSLRJj7OePGXA/h58PVJBzNxXqixEEVqjqdPns2slZm9bWYbgiN9vmVmrapxyJDvoKxelFVnpidUflB8jTWQlE8YOLUpEYk6zpX/O/3Mpdk894v+kQlIRDwzsGNmpEMQAbxvtv0cgZE4WxBIct8PLjtcodKZMt96zexKM8sxs5yNGzdW41QiIiJSW110VFtO6tKkzLLkhHgGd2nC3DHDIhSVeO2rmwZHOgSJAL0uVmoLr5Pnxs6555xzBcGf54HG1The8fski7UC1pbewDn3lHMu2zmX3bhxdU5VXqDPs55Q+YX+UPuDgQYzEIlCvx50BKN7tgi5rk5SAvVTE8McUdU9en5vvrn5pBo51ptXHVMjx4kWrRvWiXQIEiGTfn9ipEMQ8Xy07U1mdjHwSnD+AmBzNY43FehoZu2ANQReiXFh9UIUKUs3SEREot/fzu3F5S9EdvCgr/84mKT4OG57ZzYT5q7n7lO7cf6ANqQkxgPw1q+P4azHv61w/yV/GsnOvAK+WrSRD2auY3Sv5hzVrhEJcUZGWlLJdr8edARz1m7nz2f14JgHPgVgdM/mFBQ6OjWty2OfLj7k2O8+tRt3v394Q8q89etj6NCkHtNXbuWy5wJvPmteP4WXLh/AkL99CcCZfVryvx/WHPQ4I3s04+Kj2nLhM9+X7CP+1bJBaqRDEPE8ef4l8A/gYQLPdiaz/92Qh8w5V2BmvwU+JvCqqmedc4f+pvLDpAeRIrFHn2uR2HRy16YsHzsKgGEPf8mC9TuqtN+fz+rBovU7eebrZYd97nvP6M6FA9qUvIP66Z9nszOvgLSk+DKtmvq1bcjC+0ZQ5BxJ8XHsyS8kLTmBCXPX0zAtifg4o35qIqN7tqjwKTvAH4d3KZkuLnOxoiJHl+bpzF+3nS7N07nxjZns2ldY4bEy6ybx5CXZ9GubUZI8L75/BB1u+5Am9ZLZsCOvzPYL7hvO90u38N/vV/DxnPX848I+9AsO5Da4cxMW3T+C378+k7Fn9aBOUgKL7x9BQZEjJTGeMWd0p25y4Kvomm17OPvxyZzSrSnbdufz60FH0KVZPcyMpIQ4ujZP54GzelTln19iVPFNJ4msK45vV62/j6VNvW1IjRwnnLwebXslcFrpZWZ2PfBINY45HhhfzdAOmwYMi326xv6jSy4S2z7+XeAV1Vk3jwPg8Yv6MqJHc7bt3se+wiIKCh3XvzaDEzs15rz+gdfi3DC0E/FxRlJ8HK9OXcXMVdt4der+MUtvHNaZv3y8oGS+fmoiuXvy+fj6E+jcrF65GIqTxAMlJezvQZcW3OaUbk2rWeL94uKMkT2aM7JHcwD6tGnABzPXMbBTJlmN0khJjGfVlt0MfPAzAHJuP6Vk3xM6NWb6iq0kxMfx2R8G0TAtiTHvzyUx3rhlRFfqpSQQF2ec0KkxJ3QK3VUuMT6Oxy7oUzKfEB9HQjAHKv1v0rJBKt/eUu5V5AAsvG9Etf4NJHb8cMcpvDltNfePnxfpUGrUVScewRNfLOHBs3tyas8W5BUU0nvMhAq3b5iWxJZd+wA4uUsTJs3fP8r4H4d3YeaqbVw3pCP5hUXc+8FcrjrxiJKWOJcc3ZaXvltR5ngDO2Zy3xndOfEvnwMwIKshU5ZvKVl//ZCOPDJxEQC3j+7Gr05oz4KfdpCzYiuPTVrEvWd0Z+7a7ZzdrxVdm9fj0UmLGJDVkCtfmkZhUeBb1um9WzD2zJ50vfMjAH4z6IiS1w1GE3NhzhTMbKVzruovbKuG7Oxsl5NTc022su+byLAjm3L/z3TnM5btzS+kyx0fcdPwzvxmUIdIhyMe63rHR1xyTFtuHdk10qFIFDCzac657EjHEc1qum4+FOu372Xttj30aZNxWPuv2LyLEY9+xe59hSx7YCS5e/JJS04gMT6On3L38ua0VVw9uENUjpnx7NfLeOHb5Xxxowbkktqv+EZYbXF67xb88rh2nP7Pb0pupFVmZI9mPPCznqQmxZMYb0xfuZW+bTJK/n7c9OZMXs9ZzQNn9mBI16Z8vXgjrTPqkJ3VsNyxJi/exPs/ruOBMyvOUT6e8xOpifElN7qcc7wzYw3Dj2xOalLgjtbe/EL+/ukirjmpI8kJcUxbsZUZq7ZxxcD27M0vZF9hEekphz6exKzVuXRsWpeUxHgenbiIhycu5IEzexzSO7wPJpx1cySS51XOudaVb1l9XiTPQ49syp+UPMc0Jc/+0vWOj7j46DbcNqpbpEORKKDkufoimTyLSGwIZ/Lct00D3rjqWI64dX/D1x/uOIW9BYU0rx+6H/bXizbRqG4S789cy8COjemflcGk+Rv4v5emAYExBS49NuugN9r25hcyZ+12+rU9vJt9tZVzju+WbuHo9g1r7EZjOOtmr/s8h6IWkhIVLOSb0STWROEDIhEREV+7fVRX7ht36E23sxrV4bZR3TilW1M27sjjLx/P51cD27Mzr4DJSzZz9eAOTFuxlbMen1yyz78v7U98nLF87Cg278wjOTG+wm4YxY4Pvpe6a/P0kmXDjmwGQFpSPL84rl2lsaYkxsdc4gyBt9kcc0SjSIdx2DxJns1sB6GTZAOidqg8M/huyWZufGNmpEMRDxX3zRD/+GLhRrbt1uc6lh3XIZMzNFKviEhMuPz4dgzu0oST//oFEEhS563bXul+E284kYT4wDgDjesl8+DZvUrWFXfn6Nc2o9zAe8Ua1a1eH90pt51McoIGPotmniTPzrnyI2XEgIEdMvlu6Wa+Wbwp0qGIx1o3TKVHy/qRDkPC4ISOjflx9TZ9rmNcc73iREQkZpgZRzSuy4L7hvPpvA2c0q0pDrjrvTlcdmwWZ/zzG/50Zg9e/HYFvx/aiT6tM0hJjIv4eARN6qVE9PxSfWHv8xxO6lclIiI1SX2eq091s4iI1KRw1s1xlW8iIiIiIiIi4m8x/eTZzDYCKyrdsPbJBPzYhtSP5fZjmcGf5fZjmSH2yt3WORf6hbZSJaqbo44fy+3HMoM/y+3HMkPslTtsdXNMJ8/Rysxy/Ngs0I/l9mOZwZ/l9mOZwb/lltjj1//Lfiy3H8sM/iy3H8sM/i13TVCzbREREREREZFKKHkWERERERERqYSS59rpqUgHECF+LLcfywz+LLcfywz+LbfEHr/+X/Zjuf1YZvBnuf1YZvBvuatNfZ5FREREREREKqEnzyIiIiIiIiKVUPIsIiIiIiIiUgklzxFkZsvNbJaZzTCznBDrB5lZbnD9DDO7MxJx1jQza2Bmb5rZfDObZ2bHHLDezOwxM1tsZj+aWd9IxVpTqlDmmLvWZta5VHlmmNl2M7v+gG1i6lpXscwxd60BzOx3ZjbHzGab2StmlnLA+mQzey14rb83s6zIRCpycKqbVTeXWh9z11p1s+rmA9arbj5ECZEOQBjsnDvYS8q/cs6NDls04fEo8JFz7mwzSwLqHLB+BNAx+HMU8HjwdzSrrMwQY9faObcA6A1gZvHAGuDtAzaLqWtdxTJDjF1rM2sJXAt0c87tMbPXgfOB50ttdjmw1TnXwczOB/4MnBf2YEWqRnWz6uZiMXWtVTerbkZ1c7XoybOElZmlAycA/wZwzu1zzm07YLPTgRddwHdAAzNrHuZQa0wVyxzrTgaWOOdWHLA8pq71ASoqc6xKAFLNLIHAF9C1B6w/HXghOP0mcLKZWRjjE5EKqG5W3XzA8pi61gdQ3VyW6uZDpOQ5shzwiZlNM7MrK9jmGDObaWYfmtmR4QzOI+2BjcBzZvaDmT1jZmkHbNMSWFVqfnVwWbSqSpkh9q51aecDr4RYHmvXurSKygwxdq2dc2uAh4CVwDog1zn3yQGblVxr51wBkAs0CmecIlWkull1c2mxdq1LU91cVkxda9XN3lDyHFnHOef6Emgec7WZnXDA+ulAW+dcL+DvwDvhDtADCUBf4HHnXB9gF3DzAduEuuMVze9Uq0qZY/FaAxBsCnca8Eao1SGWRfO1Biotc8xdazPLIHD3uh3QAkgzs4sP3CzErlF/rSUmqW5W3VwsFq81oLo5xOqYu9aqm72h5DmCnHNrg783EOh7MeCA9dudczuD0+OBRDPLDHugNWs1sNo5931w/k0CldeB27QuNd+K8s1MokmlZY7Ra11sBDDdObc+xLpYu9bFKixzjF7rIcAy59xG51w+8D/g2AO2KbnWweZj9YEtYY1SpApUNwOqm4GYvdbFVDeXEqPXWnWzB8y52L25kJmZ6bKysiIdhoiIxIhp06Ztcs41jnQc0Ux1s4iI1KRw1s0xPdp2VlYWOTnl3jIhIiJyWMzML4PMeEZ1s4iI1KRw1s0xnTyLHK673p3N9JV+G3TTn+LjjHtOO5JerRtEOhQREQmztdv20Cw9hbi4/V0/C4scc9bm0r5xXb5fuplJ8zfwp5/1iGCUIlJbKHkWCeHdmWtJS0qgc7N6kQ5FPJRfWMRXizYxdfkWJc8iIjHsp9y9PPHFEpIT43jyi6Xl1s+/dzjfLtnM2A/ns2D9jnLrbxrWmQZ1kvgpdy/fLt3E6b1aEhdnXP78VJZt3sWnvx8UhlKISKR5njybWSrQJviCcpGocUq3ptx9WtS/qUAOYvvefHrefeBbG0REJFbsKyiisMhx9AOTDrpdlzs+Ouj63mMmlJn/ZvFmCosck+ZvAGDh+h2c/fhkPvndiTSrnwIEnmA750iI1/i8IrHC0+TZzE4l8H6xJKCdmfUGxjjnTvPyvCLVFcPj6ImIiMS892au5dpXfvDs+G9OW11mfujDXwLwwY9ruWJgewCOuHU8AA+e3ZNzs1uTuzufuDiol5JY6fF35hVQUFhEgzpJB92uqMhR6ByJStBFwsLrJ893E3jFw+cAzrkZZpbl8TlFRKok1MsNRUQk+nmZOB/MfePmcd+4edRJii9ZdtObP9K8fgqX/HsKAMvHjmL6yq1c/vxUPrr+BDbv3MfOvAJmr8llZI/mbNqZx+i/fw3AzDuH8o/PFjG8e3O6t0znq4WbaJKeTJdm6WzbvY8/vPkjXy7cyKL7RyiBFgkDr5PnAudcrpm+okp0ieVXuEl5utwiIlKTdu8rLDNfnDgDXPFCDhPnBV41fNSfyjYnH/PB3DLzvcYEuhY9/dWyg56v420f8vB5veif1ZCWDVIp/u5dVOR4bvJyLhjQmjpJCTjnWLZpFzNWbWPcj+v450V9SUmMP+ixZ6/JpUOTumW2W7NtDy0bpPLoxEW0ykjlrH6tDnoMkVjhdfI828wuBOLNrCNwLTDZ43OKiIiIiA8552h3y/hIh3FQxYlzTfvdazMB+NXAdpzWqyXzf9rOD6u28fL3Kxn341o279pH3eQE5qzdXrJP6b7eb/36WM56fDL/uqgvI3s0B2Bd7h5G//1rzu7XiofO6cWmnXn8++tlPP75EoZ0bVpSlllrcrlzdDe+XbqZG16fwV/P6U18nJGVWYfm9VMrjX3h+h0s3biT4d2bk19YREKcoYdvUht5nTxfA9wG5AEvAx8D93l8TpFqc4D+Zse+4orZoUfPIiLRLq+gkM63H3zgLz94+qtl5Z5UV+X1m2c9Hni+9Zv/Tgfg+A6ZzP8pMPL4m9NWl+vnXfomwPOTl/P85OUl8xf/+/sy257WqwWtMlK58Kg27MwroEuzdAAenbiI5MQ4xn44H4CZdw2l1z2f8PtTOnHNyR2rUlyRsPIseTazeOAe59yNBBJoEREREZEaN2t1Lqf+4+tIhxFTvl68qcaO9d7MtQD86/MlANRJii/XtB2g1z2BZuovfrdCybPUSp6NLOCcKwT6He7+ZrbczGaZ2Qwzywkua2hmE8xsUfB3Ro0FLFKaA9NwUjGv+Aqrz7OISHRT4hxdQiXOpW3ckcfSjTvDFI1I1Xk9LN8PZvaemV1iZmcW/xzC/oOdc72dc9nB+ZuBSc65jsCk4LyIiIgchqrelDazS4PbLDKzS0st/9zMFgRvdM8wsybhi14kYNmmXZEOQTxw0l+/iHQIIuV43ee5IbAZOKnUMgf87zCPdzowKDj9AoFXYP3xMI8lUiH1efaH4musB8/iY8U3pcea2c3B+TL1qpk1BO4Csgl8XKaZ2XvOua3BTS5yzuWEM2iR0gY/9Pkhbb987CjWbttDzoqtJa+06p+VwfY9BRQUFbFk4y76tGlAkYOZqyrvKywi/uFp8uycu6w6uwOfmJkDnnTOPQU0dc6tCx57Xag73GZ2JXAlQJs2bapxehERkZhXlZvSw4AJzrktAGY2ARgOvBKeEEVqXosGqZzWIJWh3ZqSEGckVPCO5P97KYeP53gzOraIRB9Pk2czSwEuB44EUoqXO+d+WYXdj3POrQ0myBPMbH5VzhlMsp8CyM7O1gMlOSzOOfV49gH1axep/KY00BJYVWp+dXBZsefMrBB4C7jPufKjCOjGtngl6+ZxB13fon4KT1zSj7YN0/hi0UY6Na1bZn1l7zj+81k9yW67muHdm9G8fkq5JHtd7h527ytk2MNfUlCkr50isc7rZtsvAfMJ3LUeA1wEzKvKjs65tcHfG8zsbWAAsN7Mmgcr+ObABm/CFhE/0YBhEsvMbCLQLMSqqr4JI9RdpuJPzUXOuTVmVo9A8nwJ8GK5jXVjWyJk8i0nl0yf1qvFIe/foE4SvzqhfYXri99hPO/e4XS87UMAHjmvNxlpSXRsUpcRj35F7p78Qz6viNROXg8Y1sE5dwewyzn3AjAK6FHZTmaWFqyIMbM0YCgwG3gPKB6o5FLgXU+iFt9Tn2d/0DUWP3DODXHOdQ/x8y7Bm9IAB7kpvRpoXWq+FVB8g3tN8PcO4GUCN7pFwmL99r2RDqFEYnwcP+sTaJBxRp+WnNipMS0apDLzrqHMuWcYy8eOKtl24X0jGHft8XRosv8peL+2gbH6zuzbktn3DCtz7F6t6gMwd8wwzu7XimOPaMSwI5t6XSQRCcHrJ8/Ft9q2mVl34Ccgqwr7NQXetsA32wTgZefcR2Y2FXjdzC4HVgLn1HzIIuI3TkOGiX8V35QeS8U3pT8G/lRqJO6hwC1mlgA0cM5tMrNEYDQwMQwxiwBwzAOTQi6/5qQOXHpsFhl1ksIaz0Pn9OJPPyv/jCgtOfB1e9rtQ0iIjyMpIY4jW9Rn4g0ncvd7c5i1Jpe3fn1smX2Wjx3F9r35fLNoEyN6NC9zjlBmr8ll9N+/5qy+rfjVCe0Y/shXIbd75LzeXP/aDCCQxHe6/eQC0eQAACAASURBVMOSdV/dNJiBD35WaTlvHtGFGSu38dGcnxjUuTGzVueyede+SvcTiQVeJ89PBSvbOwhU0HWBOyvbyTm3FCj318E5txk4ufwe3tq4I48i54gzo3G95HCfXiJAzXhFxCfGEuKmtJllA1c5565wzm0xs3uBqcF9xgSXpQEfBxPneAKJ89PhL4L4Vaguxi0bpPL7oZ3DHwwQH2ekJlXch7pR3fLfIe8+7cgKt09PSSyTOB9M95b1+fwPg2jbqA5mxoL7hlNUBGM/nMfVJ3WgqCgQX+N6yZzRZ/+QBfPGDCcpIY74uEBTrCb1kuncrB5HNK7LDyu38tr/HUNKYjx79hXS9c6PALjqxCMA2L2vgJSEeLbs3sdxYz8lr6CIJy7uy6DOTfj318u46Kg2NAjewBjz/lzmrsvlu6VbSs7duWk9FqzfUaXyidQWXo+2/Uxw8gug4g4jtdw5T0xm+ebdAIw9swfnD9BgJ35gatPrG7pZIn5V0U3p4Kunrig1/yzw7AHb7AL6eR2jSCgFhUUhl4+/dmCYI6k9sjLTSqaTEwJJ/D2ndz/oPgcm+1NuG1LhdrPuHsqe/MKSZXWSAmlEZt1kFtw3osz2Vw/uUGb+zlO7lZlfunEn7TLTaHfL+DLL/3vFUVz0zPcHjVkkkrwebTvkU2bn3Bgvz1vTbhjame178rn9ndls2JEX6XAkDNSMV0REpPa69e1Z5Zb9+awe1K+TGIFo/KFeSiL1Umrm37d940B/73OzW3Fch0xO773/afjkm0/i2LGf1sh5RGqa1822d5WaTiHQH6pKo23XJqf1akFRkeP2d2brCZWP6Llz7FPjAhGR6PR6zupyy87rr5aB0ebBs8v34W7RIDUCkYhUjdfNtv9aet7MHiLQ9znqFH/J1hNJERERkdrlnxf2jXQI4oGCwqJy79YWiaRw/2+sQxT3fRb/cA49evYBC15kpyYlIiJRI1R/51E9qzawlkSXOWu3RzoEkTI8TZ7NbJaZ/Rj8mQMsAB718pxeKR48St+xRURERCLnbxMWRjoECZPb35kd6RBEyvC6z/PoUtMFwHrnXIHH5/SUcmd/CDx41qPnWFfSHUMfbBGRqPH0V0vLzP/vN8dWsKVEu1lrciMdgkgZXifPB768Lb3063+cc1sQEREREami/MKydzz7tsmIUCQi4jdeJ8/TgdbAVgI9SBsAK4PrHFHW/9kMPaLyC6eRmP1Al1hEREREqsrrAcM+Ak51zmU65xoRaMb9P+dcO+dcVCXOxZQ6i8Qefa5FRKLDnn2FZea7t0yPUCQi4kdeJ8/9nXPji2eccx8CJ3p8Ts/oKZV/OJyutw+YmheIiESVRyaWHSzsntO6RygSCZflm3ZFOgSREl4nz5vM7HYzyzKztmZ2G7DZ43N6xszUalskBulzLSISHZ78suxgYa0zUiMUiXjp2V9kl0zfN25uBCMRKcvr5PkCoDHwNvBOcPoCj88pUm1KpkRERGq/JukpkQ5BPHBipyYl0xPnbYhgJCJleTpgWHA07esAzCweSHPORe3bzo1Ac17xB7XojX3Fl1ifaxGR6DNvzPBIhyAeiY/TlzCpnTx98mxmL5tZupmlAXOABWZ2o5fn9JKZnkj6hS6ziIhI7ZaaFB/pEETEZ7xutt0t+KT5DGA80Aa4xONzitQI05BhMa+4dYFuiomI1H6bd+ZFOgSJkL35hZVvJBIGXifPiWaWSCB5ftc5l08UP9QzLHqDl0PilE2JiIjUKks16rJv/fGtHyMdggjgffL8JLAcSAO+NLO2QNT2eUbNtn1FfZ5jX/GrqvSxFoleb+Ss0hNJn5i5alukQ5AIeXfG2kiHIAJ4nDw75x5zzrV0zo10gUd5K4HBXp5TpCYomRIRqf1mrtrGjW/+yJUvTYt0KBIG942bVzL95lXHRDASCYe7Tu0W6RBEyvH6yXMZLqAgnOesSRpt21/04FlEpHY7/Z/fADBtxdYIRyLhlp3VMNIhiMcuO65dpEMQKSesyXNMUO7sC2qe7zO64CIiIiJSCSXPh0B9YH1GF9wXdJlFRERqv7wCjbgtkZfg5cHN7MwQi3OBWc65DV6e2wsabVskNulzLRJ9Vm/dXWZ+V14Bacmefq0RkQga8ehXfPr7QZEOQ3zO6yfPlwPPABcFf54GbgC+MbOofN+zXmEkIiISeec9+V2Z+SPv+jhCkYhIOCzduIvd+6J26CSJEV4nz0VAV+fcWc65s4BuQB5wFPBHj89d49S80190uf3BUJdnkWi0Ztuecsty9+RHIBIR8cplx2WVme92p26SSWR5nTxnOefWl5rfAHRyzm0Boq6G05dsf1DrAhGR6HTFC1MpLNLf8Fg3+55hkQ5BwqRDk7qRDkGkDK+T56/M7AMzu9TMLgXeBb40szRAb7qXWk0tDfzBzPQKOpEYMXX5Vq58MSfSYfjajr357M33dmCnuurb7htn92sV6RBEyvA6eb4aeB7oDfQBXgSuds7tcs4N9vjcNS7wJVtinR48i4hfmFlDM5tgZouCvzMq2O7S4DaLgjfDi5cnmdlTZrbQzOab2Vnhiz60SfM3kHXzOD6dvz6mR+fdtDOP61/9Iax9QOeszWXVlt3llv/j00V8Oj/Q0LDH3Z8w+KHPS9Y555g4d/0htwjIunkcv399Jis3lz+f+EdyQnykQxApw9Nbdy7Q/vXN4E/UU7NtfzH1evYFfa7F524GJjnnxprZzcH5MmOSmFlD4C4gm8Dg9NPM7D3n3FbgNmCDc66TmcUBDcMbfsV++XzgCfRXNw2mdcM6Vd7vgx/XMmftdk7v3YIuzdJrLJ5NO/PIrJtMUZFj6+59NKqbXG6bVVt2s2zTLk7o1Jhtu/exY29BudhXb93Nll37eGD8fL5dupnsrIZcfHRb9uwrpNA56iYnMGPVNrIa1aFBnaRKY2pYJ4m4uPL1XV5BIXvzi6ifmliybNRjXwOwfOyoMts+9MlCAB67oA8A63L38ovnplBQ6Lj46DZc9Z/pDGjXkKHdmnJKt6ZMmLuenOVbOaFTY259exaXH9+OwZ2bcHzHTBZv2Mmt/5sFwFvTV/PW9NV0alqXD64ZWNk/sfjEAx/O45YRXSMdRkzL3ZNP7u582jSq+t9OvwjHq6r+DDQh8B018D3VuZqrjURqmPIoEfGR04FBwekXgM8pP6DnMGBCcLwSzGwCMBx4Bfgl0AXAOVcEbPI84kM08MHPmHrbEHblFZCRlkT91ETyC4vYlVdQJrncm1/ItBVb+e3LPwDw+OdLSpLETTvzWLZpF43Skpi1JpfWDevQrlEa9VISSIgPNOJbvGEnQ/72Ba//3zEMaNeQnXkFXPvKDwzsmEm/thmc9o9vOLtfKxrXS+bxz5dw96nd+MVx7QAYcP9ENuzIK4nlucv6c9lzU4FAojr/p+1k1Eniu6Wbue7VGWXK9+6MNcz/aTv/+W5luX0Brh58BJ2bpdOkXjIpifHszS/k/KfKjlR+28iudG9Zn115BVxxQLP3tKR43v3tcWWS+C8WbuTSZ6eU+7e+9pUfSqY/X7ARgK8XB/5LTFm2hSnLtnDfuHkl23w05ycA/v31Mv799bJyxyu2cP1OOt3+YYXrJbZdd3JHHp20qGT+yS+WxnTyPHtNLj+s2kbz9BSGdGsakRh63fMJUP5GWbH12/fy2tRVXHNSB6wK/RwXb9hBekoijeslV2n72szrTiMPAqc65+ZVumU0MNQ30kei/LMtVaTrLD7X1Dm3DsA5t87MmoTYpiWwqtT8aqClmTUIzt9rZoOAJcBvDxgotFbof//EkumuzdPZlVfAyhDNjw+UdfO4g66vl5zAjryyzabPffLbMvOfzt/AhUe1AeDNaatLlt/9/lzufn8ufds0KJM4A2WS38pimLp8K1OXbw25L8A/P1ty0P0B7h9f8de0XfsKGfK3L8ssC5U4i3jl58e0LZM8AzzxxRKuOvGICEUUMOqxr5izdjvjrx1ISmIcqUnxNK+fCsDTXy7llG5NycpMK9l+8848lmzcxb6CIo7vmFmyfPysdaSnJJYsG/33r0vWfXPzSbRskFoyP2/ddlIS48krKCzXMsY5x5gP5tKvbQaje7bg3RlrSm62PXhWT87t35rc3fm8PGUlf/5ofuB4Y4bzhzdn0iojlcGdm5S7sZZfWMSFT3/H1OVb+d2QTjRMS+T1nNXMWpMLwNs/rOHMPi255uSOIf+NnHM8NmkxD08MtE6pm5xAdlYGj57Xh5/96xuev2xA1D3d9jp5Xh8ziTNq3ukXGm3bf3TFJZaZ2USgWYhVt1X1ECGWOQLfIVoB3zjnbjCzG4CHgEtCxHAlcCVAmzZtqnjaqjnwqVRl5q3bXmPnPjBxrsjL36+scN30lRo/VeRg4kLc5R774XzO79+60q4JXpixahszV21jztrA35KRj31Vsu6VXx1Nt+bp3D9+HvePn8f1Qzpy3ckdmb5yK2c9vv/G2vOX9eeYIxqxdOMufvPf6RWe67ixn5KWFM/to7tx93tzyCsoKlm37IGRfDJ3PS0bpPLmtNU8P3k5AM99s7ykBU2xm976kZve+rHc8f/80XzG/bgOCDzRP1DH2/a3+ChOgEtbtmkXf52wkJwVW/li4UaevKQfb01bTb+2GTzw4fxy2+/MK+DzBRvpNSbwZPuEv3xW4dPt2srr5DnHzF4D3iHwfmcAnHP/8/i8ItWmB5L+oL7tEuucc0MqWmdm682sefCpc3MCr5Q80Gr2N+2GQML8ObAZ2A28HVz+BnB5BTE8BTwFkJ2dXaP3q353SqdDSp5FJLo0qJMYcnnvMRP496XZnNy14qbNuXvy+Xj2TyQnxjHux3XcOrIrLRqk8sLk5WzcmccJHRuTWS+JPfsK6dOm7HiJi9bvYM22PXRuVo+HPl7InvwCcpZvLddSpLQLni775PaRiYt4ZGL5v0+/OKCFyMHs2lfILcFxAEprd8v4Kh+jIsUJd3V9sTDQTeP/XpoGwCdza10DpBrjdfKcTqBiHVpqmQOiMnmO9jb6UjV6Cuk/amwgPvYecCkwNvj73RDbfAz8qdRI3EOBW5xzzszeJ5BYfwqcDMz1PGIR8RUz442rjuGcJ74tt+7yF3J4+YqjOLZDJnkFheQVFJGeksgvn5/Kp/M3MPzIZiV96yGQ1Jntr/ef+rLs09b//eZYpq/YWqZvvkhpXo+2fZmXx48ENen1D90r8QldZ/G3scDrZnY5sBI4B8DMsoGrnHNXOOe2mNm9QPGjkjHFg4cRGFzsJTN7BNgIhK3eP6JxWkmfvw+uOZ7HJi2K6acdIn7WP6vigfwvfOZ7ALo0q8f8n3bQoE4i23bnA5RJnIsd7Kv8mf+aXL1AJeZ5+p5nM2tlZm+b2YZg07C3zKxabzs3s+FmtsDMFgdfqxE2Sqb8QfdH/EcDAYpfOec2O+dOds51DP7eElye45y7otR2zzrnOgR/niu1fIVz7gTnXM/g/hV37q1hSzbuYtysQF+97i3r8+Ql/cJ1ahGJgJ/1aXnQ9fN/2gFQkjiLeMHT5Bl4jkCTsBYERut8P7jssJhZPPBPYATQDbjAzLrVQJxVOz9q0isiIlIbmRmL7h8R6TDEYw+e3TPSIUiEPHxe70iHIOJ58tzYOfecc64g+PM80LgaxxsALHbOLXXO7QNeJfCOyrDRU8nYV/wUUn3c/cFAd8VEotCj5/fmrV8fW2ZZYnycEugYN7JH80iHICI+5nXyvMnMLjaz+ODPxQRG5zxcId81WXoDM7vSzHLMLGfjxo3VOFV5SqZERERqh9N7t6Rf24xyyxPj41g+dhT/ufwo7j39yAhEJqUlxpf/7nTjsM7cOKxzyO37Z2XQvWU6PVvV57eDO5RZN6hzY+omez3WrdRmfz6rR6RDEJ/z+i/QL4F/AA8TeLYzmeoNJlLRuyb3z3j4OoxAs209oop1al3gL2Z68CwSi47vmMnxHTOZvWY7r+Wsoll6Cj9t33tIxzimfSNevHwAJz74GWtz9/K7IZ1Cvut0SNcmPP3zbP7x6WL+OmEhfxzehWWbdvJ6zuoy253YqTEdmtSle8t0fvfazCrH8eqVR3P+U9+FXJeUEMe+giL+cWEfMusm07V5Or3u+YS6yQl8eN1ABj74GQC3j+paMoLwo+f3pqDQ8fs3ZtK5aT2uG9KRI1ukk1/omL5iKze99SOjezbng+D7X0N56Jxe/OGN/WU4rkMjXvzlUcTHhX7QsHtfAfN/2sGcNblcckwWAH/5eAEAdZMT+OdFfTmxU/nGib8a2J73flzLab1aUD819CuLxD/O69+GP75V/rVNEp2OalfxQHC1lYV79Ggzu94598hh7nsMcLdzblhw/hYA59wDobbPzs52OTk5hx3rgfrdO4ERPZpx3xm66xXL9uYX0uWOj7hxWGeuPuCut8SeLnd8yM+PyeLWkV0jHYpEATOb5pzLjnQc0aym6+bK7MorYNysdZzTr1VJC7LFG3bwypRV3DE6MGzK458vYdK89eSs2MqoHs3550V9KzxeQWER42at47ReLdibX4QZpCTGH3JchUWOAfdP5PwBrfnnZ0vKrHvn6uNol5nGp/PX87M+gXFW1+XuITUxnt5jJgAw/97hVTrv5CWb6NM6g9SkeHbmFbB11z5aN6xT4fZ78wu5453Z3DyiC2nJCXyzeBOLNuykQWoiLTNSWb55N3e8M5vJN5/EezPX0q15Oj+s3MbPj2lLRlrSIf87iByq6Su3alRsj3Vrns7cddvLLHvusv6kJSWwZdc+Js1bzxvTVlewd3n9szKYunxryfzons35zaAOdGuRXiPxhrNujkTyvNI51+Yw900AFhJ4l+QaAq/NuNA5NyfU9jVdQWffN4FhRzbj/p8peY5lxcnzTcM785tBSp5jXdc7PuLio9tw26iwjT0oUUzJc/WFO3muqqIix2OfLuLSY7LCngROXryJzHrJdGpar9Jt352xhryCIs7Nbh2GyERqp6ybx0U6hHKeuLgvV/1nOqf3bsG7M9aWLP/D0E489EmgxUqoFh1PXNyPjTvzmL06lwfO7EH7W8dzfv/WvDp1FRXp0KQuizfsBGDyzSeRmhjP0k07ueCp75k7ZhgdbvsQgHOzW7F8025uHN6ZXq0akJQQx469+cxbt4O9+YV8vXgTTeol8+WiTQzp2oTTe7Wkfp1AC4+CwiJ27Svkv9+v4Ky+rWianhIylm8Wb2L11t0M6tyEpPg41mzbw+i/fw3AVzcNpmWDVOKCLVJmrtpGiwapNK6XfDj/xBUKZ90ciY4jh91x2DlXYGa/BT4G4oFnK0qcvWFq3ikiIhKD4uKM64d0isi5j+2QWeVtT+998Nf1iPjBo+f35rpXZ4T9vP/7zbH0bZOBc46zn/iWrEZpDOyYyRnB12gtHzuKPfsKeXfGWhrXS+aJi/vRr20G52S3JjE+joZpSXzw47iSbbfs2kfDA27WLR87CoBNO/cxcd56ptx2MgPun8RTl/Tjypemcdep3bjsuHbs2JtPckI8SQmBIaz6pTVkYakBE395XDvuPLX8g4F6KYkMCDaXPiHYVeKKge3LbZcQH0f91LhKHyQdd8Dfr/qpifTPyuA3gzqUa+XSq3WDgx4rGkQiea5W/umcGw+Mr6FYRCpkh3+fR6KIxgEUERGJLqf3bkl8nPHbl3847GN8ceMgTvnbl+wrLCIhznj2F/35+bNTOL9/a24c1plXp65iXe4e7jujB4vW7yA5IZ42jQLJoJmVG+2/WGpSPP+8sC/9szJoEnxaW/qp7ed/GMSyzbsAyiXOpT1+cV/25BeSnpJYklAX/4ZAElyR0tuFW1yc8cZVof9tYoEnybOZ7SB0kmxAqhfnDIc4g/dmrOWbxZsiHYp4qEgjhvmKAa9OXcUnc9dHOhTx0Jl9WnHdkI6RDkNERGpIz5aBp5iPXdCHni3rs3zzLn7x3NRy2/3yuHY8+82ykvk59wwjPs5ISYxn4f0j+Cl3L8kJcWSkJZVJOkuPe9OxCl0qShvVs+JXqmVlppGVmVbpMRLj40iM9/rFSHKoPEmenXOH9j8sSlxzUgemrdha+YYS9QZkNWLokU0jHYaEwe9O6cTsNbmRDkM81iojau/biohICG0a1SmT7GZlpvH+b49n5uptDOyYyatTVzGye3N6tKrP9ad0ZNqKreQXFJF2wOvOmtUP3ZdXJJSwDxgWTrV1UBIREYlOGjCs+lQ3i4hITQpn3ay2ACIiIiIiIiKViOknz2a2EVhRyWaZgN86MfuxzODPcvuxzODPcvuxzBD+crd1zjUO4/lijurmCvmxzODPcvuxzODPcvuxzBDDdXNMJ89VYWY5fmuC58cygz/L7ccygz/L7ccyg3/LHev8eF39WGbwZ7n9WGbwZ7n9WGaI7XKr2baIiIiIiIhIJZQ8i4iIiIiIiFRCyTM8FekAIsCPZQZ/ltuPZQZ/ltuPZQb/ljvW+fG6+rHM4M9y+7HM4M9y+7HMEMPl9n2fZxEREREREZHK6MmziIiIiIiISCWiLnk2s2fNbIOZzS617F4z+9HMZpjZJ2bWIrj89FLLc8zs+FL7fGRm28zsgwOOf7KZTQ/u87WZdQgRQ5aZ7QluM8PMnvCyzMFzel3uk4Llnm1mL5hZQgVxXGpmi4I/l3pV3uC5akuZC0td6/e8Km+p81W73GbW28y+NbM5wfXnlTpWOzP7PngNXzOzpAriuMXMFpvZAjMbFutljtbPdSXl/m3wGjozyzxIHFH1ua6hMof1cx3rauK6Btepbi57fNXNqpsPjEN1s8fCUG7VzRXHUfvrZudcVP0AJwB9gdmllqWXmr4WeCI4XZf9TdN7AvNLbXcycCrwwQHHXwh0DU7/Bng+RAxZpc8f7eUmcBNlFdApOD8GuDxEDA2BpcHfGcHpjFguc3Ddzmi71kAnoGNwugWwDmgQnH8dOD84/QTw6xAxdANmAslAO2AJEB/jZY7Kz3Ul5e4TLNdyILOCGKLuc13dMge3C+vnOtZ/auK6BudVN+9fprpZdfOBMahujo1rrbq54jhqfd0cdU+enXNfAlsOWLa91Gwa4ILLd7rglSi9PLhuErAj1CmA9OB0fWBtzURePR6XuxGQ55xbGJyfAJwVIoxhwATn3Bbn3NbgdsMPr0SVqyVlDruaKLdzbqFzblFwei2wAWhsZgacBLwZ3OcF4IwQYZwOvOqcy3POLQMWAwNqoHgh1ZIyh52X5Q7O/+CcW15JGFH3ua6BMksNU91cZpnqZtXNqptVN6tujtG6OWRTmGhkZvcDPwdygcGllv8MeABoAoyqwqGuAMab2R5gO3B0Bdu1M7Mfgtvc7pz7qhrhH7YaKvcmINHMsp1zOcDZQOsQ27UkcEe42OrgsrAKc5kBUswsBygAxjrn3qlmEQ7L4ZbbzAYASQTuUDcCtjnnCoKrK7qGLYHvSs1H1bU+zDJDlH+uDyh3VUX15/owywy15HMd61Q3q25W3ay6udR61c1VF9Wf61ium6PuyXNFnHO3OedaA/8Ffltq+dvOuS4E7mbdW4VD/Q4Y6ZxrBTwH/C3ENuuANs65PsANwMtmlh5iO8/VRLmDd4/OBx42sykE7gQXhNjUQu1+uLEfrjCXGQLXOhu4EHjEzI6ogWIcssMpt5k1B14CLnPOFVH1axi117oaZY7qz3WIcldVLF3rQ1ErPtexTnWz6mbVzaqbQXUzqpurqlZ8rg8mZpLnUl4mRBOfYHOEIyrppN4Y6OWc+z646DXg2BDHynPObQ5OTyNwV6VTDcReHYdd7uB23zrnBjrnBgBfAotCbLaasneAWxHZpnPhKHNx0xOcc0uBzwn024ikKpU7WLmMI3CXtvhO9Sagge0fgKWiaxiV17o6ZY7mz3UF5a6qWLrWVVYLP9exTnVzKaqbK6a6GVDdXPpYUfu5Vt0cm3VzTCTPZtax1OxpwPzg8g7BPhWYWV8CzQc2H+RQW4H6Zlb8oTwFmBfifI3NLD443R7oSKAjf1jVYLkxsybB38nAHwkM3HCgj4GhZpZhZhnA0OCysAl3mYNlTQ5OZwLHAXOrX5JDc6jltsCIlW8DLzrn3ijeMXhX/zMCTeEALgXeDXHK94DzzSzZzNoR+D8+pWZLdXDhLnO0fq4rKvchiLrPdXXLXFs+17FOdTOgull1s+pm1c2qm6t6vlrxua5M8UhpUcPMXgEGAZnAeuAuYCTQGSgCVgBXOefWZGZmuqysrAhFKiIisWbatGmbnHONIx1HbaO6WUREIiWcdXPUJc+HIjs72+Xk5EQ6DIlChUWOvILCSIchYRBnRkpifKTDkChhZtOC/bHkMKluFhGRmhTOujlmRtsWqUln/usbZq7OjXQYEgZm8PhF/RjevVmkQxEREY+s2rKbpukpJCUEeixe+uwUvli4kVE9mjNu1jp+cWwWHZvWpUPjupz31Hd8cM3xfDJ3Pb1a1efkrk0jHL2I1BZKnkVCWLFlN/3aZjC0myrMWLY3v4iHJy5k9dbdkQ5FREQ8smNvPgMf/Iyz+rbiz2f14MVvV/DFwo0AjJu1DoDnJy8vs8/ov39dMv2LY7N4PWcVObcPoU6SvjqL+JnnfwHMLJXAsOMLvD6XSE1xDnq0rM//nVjrRsiXGrR9bz4PT1wY6TBERMQDyzbtYsP2vbRvXBeAt6av5q3pqw/5OMWJ9Xsz1tK6YR2SE+LIzmpYk6GKSJTwNHk2s1OBhwiMwtbOzHoDY5xzp3l5XhERERHxt8EPfQ7ADafUzJuNbv7frHLLMusmU+QcFw5ow9HtG3F8x8CbuPIKCkmKj8PM2FdQVNJcXESim9dPnu8GBhB4TxfOuRlmluXxOUWqLZYH0pP9LNIBiIiI5/42wbsWRpt25gHwj88W84/PFtOkXjK/PakDd747h3vP6M6RLdI581+TefyiviTEx9G7dQMa10v2LB4RAnCjUwAAIABJREFU8ZbXyXOBcy43+CowEZFaSfdKRERiS+7u/Iicd8OOPO58dw4Ad7wzu2T5r/87vWT6lV8dTWpSPGu27mHh+h38rtST8e1780lPSayxeCbNW89fPl7A/J92sPC+EWzelUejtGQ9CRc5TF4nz7PN7EIgPvii7WuByR6fU6RG6J5P7NONPRGR2LJk405O/usXkQ7joC54+rsy849OWlRumyNbpHNudmvqpSRwZt9WZdY9MnEh8Wb0aFWfo9o14pFJC2mfmUbfNhmc8vCXNEpL4subBpOWnMDlL+x/LVyn2z8smV7yp5HExxmFRY4nvljC6J7NaV4/tcpJ9d78QpZt2sWHs3/i6PYNaZSWzPhZ6+jYtC6je7Y4lH8OkajidfJ8DXAbkAe8DHwM3OfxOUWqTQ8i/cXpiouIxITanjhX1Zy127nrvcAT7Bten3lI+27etY8j7/r4oNsccev4MvN/+bjsuL5N6iWzYUce3Vum88E1A8useyNnFTe++WPJ/GOTyh57yrIt7Cso4oIBbejWIp3PF2zk/Zlr6dYinSsHtmfppl28kbOKm0d0CXkTO7+wiI9m/8Tons11k1tqHc+SZzOLB+5xzt1IIIEWEREREfHE8k27Dnvf1g1TWbVlTw1GE9027Aj05Z69ZjtZN487pH1f/HYFAK9OXVVm+Xsz1zL2w/kl869MWcn2vQVltvnvFUdx0TPfA5AYH8fw7s0OOXYRL3nW4cE5Vwj08+r4Ip5yYBpOKuYVX2H1eRYRiW6bd+YxKDi6dmU+un7/k9TbR3Vl8f0j+PLGwSwfO4opt51M1+bp1EtO4ImL+3oUrQDlEmegJHEG+NP4eeEMR6RKvG62/YOZvQe8AZTcDnTO/c/j84qIiIiIT5z5+MGH1Bl37fEkxcfRsWk9AJb+aSRrc/fQskFqmabBTeql8OF1+5Prxy7ow/QVWzmvf2se/Gg+izbsZPVWPaEOh5Vbdkc6BJFyvE6eGwKbgZNKLXOAkmep1RwaMMwPiq+xHjyLiES3FZsPnmgd2aJ+mfm4OKNVRp1Kj3tarxac1iswANZzlw0AYNmmXbwyZSVz1ubyzeLNhxmxiEQjT5Nn59xlXh5fRERERPzNVdL3ZvnYUTV6vnaZadw6sisAWTeP4+rBR3BM+0ymLN/CBQNak1EniXdnrOGPb82q0fOKSOR5mjybWQpwOXAkkFK83Dn3Sy/PK1Jdzjn1ePaB4n7t6vMsIhK92t0yPuTyl684ip6tG3h67tKJ+fEdM0umz+vfhhM7NWHNtj1c/9oPnNqzBf/6fAkAlx/fjn9/vczTuETEG143234JmA8MA8YAFwHq/S8iIiIinrltZFeO7ZBZ+YYealY/hWb1U/jqpkDvxasGHUFCnFEnKYE7Rndjb34hXe74iHrJCTx2YR9SE+N5ZcpKvlm8mU078yo9fkKcUVCku78i4eR18tzBOXeOmZ3unHvBzIrf9SxSq6nPsz/oGouIRLcpy7aUWzbsyKb86oT2EYjm4NJTEsvMpyTGl2tSfnT7RgD887PF7N5XwDeLNzNj1TbaZ6bx9m+OY9rKLXRvUZ8m6SUNOtmbX8jiDTvp1LQeU5Zt4blvlnFOdivWb8/jwqPaUOQcO/YWMPDPn3HlCe1Zl7uHa07qSJ2keBrVTQZg6659JCXEcfQDk9ixt4AvbxzM3HW5XPWf6Tx4dk9uCr7X+dqTOvDYp4tLzj20W1NO6NSY29+ZTYM6iWzbnc+Qrk2ZOG99ufIPaNeQ2Wty2b2vsGb+QUUiwOvkOT/4e5uZdQd+ArI8PqeIyCFxGjJMRCQqnfvkt+WWPXlJdgQiqVlXD+4AwJUD85n30/aSpPqkLk3LbZuSGE/3loEB0Y7vmFmm+Xix5LrxzLt3eIXny0hLAuCHO05hb0ERdZMTaNOoTklyf25265JtrxvSiT35hdRN3p9GXHx02zLHy7p5HH3aNOC1K48hKSH0m3Fnr8nlv9+v4KPZP7F1dz71UhLYsbeAxy7ow7Wv/FBhrCKR5HXy/JSZZQB3AO8BdYE7PT6nSLU5R5lXV4iIiEjt8sqUleWW1fTgYJFWv05iSeIcDgnxcdSND53sFouPszKJcygz7xpKnaR4Eg9yrO4t6/PAmT2569QjKSxyxAeboW/Zue+wYhcJB69H234mOPkFUPvaz4iIoAHDRESi0S3/02jWtVX91MTKNwpKSYwvM19YqEpZai+vR9sO+ZTZOTfGy/OKVJdDo237gRoXiN+ZWUPgNQJdqpYD5zrntobY7lLg9uDsfc65F4LLPweaA3uC64Y65zZ4G7VIaDPvHBrpEKQG1K9T9cRbJNwO3i6j+naV+ikERlDFPs9mttzMZpnZDDPLCS5raGYTzGxR8HeGV4GLiIj4wM3AJOdcR2BScL6MYIJ9F3AUMAC464D69yLnXO/gjxJniRglXSLiNU+TZ+fcX0v93A8MAloewiEGByvj4pEfKq3kRWqCmvGKiE+cDrwQnH4BOCPENsOACc65LcGn0hOAikceEgmDaSvKNpA4qUuTCEUiIn7i9ZPnA9When2fq1LJi9QMNemNeRa8yE53S8S/mjrn1gEEf4fKQFoCq0rNr6bsjfDngq3E7jCNtChhctbjk8vMP/uL/hGKRLz04+ptkQ5BpAxPk+dgs+sfgz9zgAXAo1Xc3QGfmNk0M7syuKzSSt7MrjSzHDPL2bhxY00UQ3xIqZSIxAozm2hms0P8nF7VQ4RYVvxn8iLnXA9gYPDnkgpiUN0sIofstH98E+kQRMrw+lVVo0tNFwDrnXMFVdz3OOfcWjNrAkwws/lV2ck59xTwFEB2drZyIDlspkfPMa/4GZkePEssc84NqWidma03s+bOuXVm1hwI1Wd5NYFuV8VaAZ8Hj70m+HuHmb1MoE/0iyFiUN0snjmnX6tIhyAiPuF1s+0dpX72AOnBQb8aBgcgqZBzbm3w9wbgbQIV8vpg5c5BKnkRERGpmveAS4PTlwLvhtjmY2ComWUEBwobCnxsZglmlglgZokEbpjPDkPM4nNFRWXvv/zlnF4RikRE/Mbr5Hk6sBFYCCwKTk8L/uRUtJOZpZlZveJpAhX1bKpWyYtUn9NrjPyg+BLrMZj42FjgFDNbBJwSnMfMss3sGQDn3BbgXmBq8GdMcFkygST6R2AGsAZ4OvxFEL9ZsH5HpEMQj6UkhntYJpGq8brZ9kfAe8658QBmNgIY4pz7fSX7NQXeDo47kgC87Jz7yMymAq+b2eXASuAc70Lf75mvlrJtdz5xccb5/VvTokFqOE4rIiLiKefcZuDkEMtzgCtKzT8LPHvANruAfl7HKHKghycsLJm+4ZROEYxEvHJ6r5a8lrOq8g1Fwszr5Lm/c+6q4hnn3Idmdm9lOznnlgLl2uBUVMl77eUpK1m+aRdFDpLijd+e1DHcIUiYOZx6PPuABgYWEYk+n8xdXzJ9Ru9DeQOqRItz+7cqSZ4378yjUd3kCEckEuB18rzJzG4H/kOgZeTFwGaPz1njPv39IIqKHO1vHU9hUaSjEZGapgHDRESiU+uGag0Yi1pn1CmZ3lugL99Se3jdoeACoDGBAb/eCU5f4PE5PeXUO9IXnPo8+4IusYhIdFMLotjUJD2lZDpn+ZYIRiJSlqdPnoMDilwHYGbxQJpzbruX5/SKXmkjErt0U0xEJDrs2Jsf6RAkzK57dQanq3m+1BKePnk2s5fNLD04YvYcYIGZ3ejlOb2iO5v+olRKRESk9jn3ye9KpsdfOzCCkYiIH3ndbLtb8EnzGcB4oA1wicfn9JSSKv8wNeqNeWpRIiISXeat29+AsZX6O/uGU0UttYTXyXOimSUSSJ7fdc7lE8X5pxn6lu0T+iMtIiJSu6WnJEY6BPFQp6Z1S6Z37yuMYCQi+3mdPD8JLAfSgC/NrC0QlX2eITC4kFIq/1BL/dhX3B1Dn2sREZHa5fGL979GfkKp15OJRJKnybNz7jHnXEvn3EgXeJS3Ehjs5Tm9pgeS/qDLLCIiUrsUFal29pNWGfub5b81fXUEIxHZz+v3PJcRTKALwnnOmmRmGpXXR/Tg2Ud0V0xEpNZbv2NvyXTfNg0iGImEQ3JCfMn0V4s2RTASkf28brYdU5RM+YdyKRERkdrl+cnLS6ZH9mgeuUAkIlZt2R3pEESUPB8KMyVVvqJOz76gyywiEh2e/GJpyfTFR7eNYCQSCUs27ox0CCLeNts2szNDLM4FZjnnNnh5bi8YpkbbIjFIn2uR6LV4ww7aZ9YlLk53wvwkJTG+8o0kpvziuaksHzsq0mGIz3n95Ply4BngouDP08ANwDdmFpXve9aTZ//Q1zB/0HUWiV5Pf7mUIX/7kva3jo90KCLigdn3DIt0CCJleD1gWBHQ1Tm3HsDMmgKPA0cBXwIveXz+mmVowDAf0Due/UeXXCT65BcWcf/4eZEOQ0Q8lJakFgZSu3j95DmrOHEO2gB0cs5tAfI9PneNM1D7Th9RX1h/MF1okah0z/tzysyv3KzBhERizYF19BNfLIlQJCIBXifPX5nZB2Z2qZldCrwLfGlmacA2j89d4/QdWyQ2qUWJSPR594e1ZeZP+MtnEYpEwu1fF/WNdAgSIWM/nB/pEMTnvG62fTVwFnAcgQe3LwJvBd/3PNjjc9c4DRjmD2rCKyJS++3IKyi3zDmn1iQxqnSXqoEdMyMYiUTays27adOoTqTDEJ/y9MmzC3jTOfc759z1wemoTU0Cr6qK2vDlEJmGkvIFQzdMRGJFu1vG612wMWrLrn0l0/VSEiMYiYTbNzefVGZerUwkkjxNns3sTDNbZGa5ZrbdzHaY2XYvz+k1fcmOfbrEIuIXZtbQzCYE6+oJZpZRwXaXBrdZFOyGVbw8ycyeMrOFZjbfzM4KX/ShDXzwM3bszeej2ev4fMEGiooce/MLIx2WVFNCnNc9DaW2avn/7N13nFT1vf/x12c7delFigsCIooaWbEbEQuILYkm1mhi4vVGE425SYjGEmMi5pfcm5jrNRJrTOyV2BF7bIBiQSS0FZHO0tuW+fz+mLPL7DLL7rJnZnbnvJ+Pxz52zjnf+Z7vl8POdz7nfEuXdpkugkitVH8S/Q441d2L3b2zu3dy984pPmfKGAqsokQ9/6LBTH/XEmkTgWnuPhSYFmzXYWbdgOuIr5QxGrguIci+Gljp7sOAEcBraSl1I0Ze/yKX/P19Lrx7Omf85S2GX/N8swLoqurYbvc0q6yO1b5+cfZyrn3qkzrbi1Zvxt2pSkiXCdsqq/nvF+c2+O/y9EdLmfjYRyxdt5V1W+JPfVdt3M6aTdsBiMWc6ljz/42qY04s5rh7nX+r6pjz9EdL6/y7L1y1iWXrtwJgip0lAmIx5+0Fa1i1cXvtvm2V1ZSt3szTHy2t/ZtL/AxJTBuW1Zu24x6/8bhx285zPD/w3mKe/ig+78QZt73FmX95izfnrWZbZTXH/L9XeGv+6tDL1FqkeszzCnfPmnUkzExPniNAXfNFJEJOA44JXt8LvAr8vF6aE4GpwUoZmNlUYBzwAPBdYDiAu8eAVveN6f3F8flJH5m5hPcWlXPCiN6ccsAerNywjYrqGP27xsdOujvrt1ZSHXNG3fgSRwzpzh/OPJBv3PYW154yghNG9KY65myprCY/J4cpH35JSfcOfGvyO/zfuQfx0ZL1tTMBP/GDw/nKwK5cfN9MAJau28ZLc1bsVLZJXx/Jtw4eUDtO+7V/r+LtBWsY3KMD+/brTN/idrz/+VreXbSGn5ywN4+9v4SzDx7I9qoYmyuqeGj6F/z1jYX8+LhhnHbgHrQvyMNxrnzoQ575eBkAx+3Tm2OH9+KqJz7m0jF70a9Le259ZT6divL4bPlGbnl5PhceXsI9b5UB8Nh/Hs43bnurtowPTv+iyf/WX/tKP75/1GBG7NGZj5esp2enQvoUF+Hu3PnmIgrzc7nmyU92et9/fHUwM8rWMvPztVzGB0B8UrAf/ON9AG7+xkhemrOyyeWQ7Pfluq1t/om0uzN76QaG9u5ILAbvLFrDd+6eXifN8D6d+Gz5xtrtHx27kQn778HDM77gzjcX1fnbLcrPYVtljBm/PI5HZixh3H59WLR6E9+9ZwaHDe7O+4vX8sIVR1OUn8vWymrycoxVm7Zz0MC6HY4Wrd7MmN+/ysTxw2snaLviuKEsWr2Zp2Yt5bIxQ/jfV+YD8MsnP2Hdlnhwfd6d7zK8TyfK1mzhnDveZWivjhw7vBdfrN3Chq1VjB/Zh1mL1zFqz64sXbeVT5dt4I4LDk7VP2/KWCoDBTP7E9AHeBKovS3i7o+n7KQJSktLfcaMGaHlN/K6FzizdADXnjIitDyl9amqjjHk6uf4yfHD+OHYoZkujqTYsKuf47tHDmLi+OGZLoq0AWY2091LM12OsJjZOnfvkrC91t271kvzX0CRu98YbF8DbAXuAD4GHiEegC8ALqu3RGVNHhcDFwMMHDhw1Oeff97ispdMfKbFedRI/AKaCf/x1cHc/trCjJ0/bO0LctlSkbqu8mWTJqQsb2mdqmPOXlc9W2dfa/t/8NSsL1m3pZILDi/hi/ItnDX5HYb27sjE8cMp6d6BM//yNh9/ub42/eAeHVi4enMGS5x5Z48eyE1fH9nifNLZNqf6yXNnYAtwQsI+B9ISPIfOtKRNFOgKi0g2MbOXiN/Iru/qpmaRZJ8T/w7RH/iXu19pZlcCvwfO3ymx+2RgMsRvbDfxvE3yzI+OZMItb7Yoj0wGzkBWBc5ASgNniabcnJ0/hkomPsOim04KfYb9iqoYOQZ5uTnMX7mRdgV5tU+5l63fSkFuDjlmfP9vM5g4fjgDurXn3UXlXP7gLACum7JjDfov123l1bmrkp4n6oEzxLt/hxE8p1NKg2d3/04q8083zcobLRrzHBG6zpLl3P24ho6Z2Qoz6+vuy8ysL5Csb+wSdnTthnjA/CqwhvgN8ieC/Y8AF4VR5ubo2akw3acUkQw4cd/evDC7bseW5z9ZzviRfZud15+nzeOzFRu59ZyDeOnTFXzvbzMYt28f/nT2gez9y+d3Sn/U0B4cOaQHN9VbZ/qMv7zd7HNL25bS4NnM+gN/Jr7OswNvApe7+5JUnlekJXSDJHrUo0QibApwATAp+P1UkjQvAL9NmCTsBOAX7u5m9k/igfXLwFjg05SXuJ5enYoomzQh1G7cItL6XHPyiJ2C5//8x/scOaQHRw7twWGDuzOwW3u6dijY6b1L1m7hF49/zIn79uGXCePun/lox+fG87OXJw2cAd6Yt5o35rW6KR0kA1Ldbftu4H7gzGD7vGDf8buboZmNA/4E5AJ3uPuklhayGefWZFIREnY3IGmddJUl4iYBD5vZRcBigvbazEqBS9z9e+5ebma/BmpmsrmhZvIw4pOL3WdmfwRWARnrcVY2aQKvzF2504Q7IpIdaib3q+/N+at5M2F257svPJiVG7exZ/cOvLeonA1bK7njzUUACoClxVIdPPd097sTtu8xsyt2NzMzywVuJR58LwGmm9kUd0/LnW4taRMNegoZQbrkElHuvob4E+P6+2cA30vYvgu4K0m6z4GjU1nGhvz7xvHUHwY5Zu9ejOxXXGdSHhGJlu/coxtokjqpXjVvtZmdZ2a5wc95xMdI7a7RwHx3X+juFcCDxJfZSAs9oRIREWkdCvJyyMvd+WvMH886EIDbzj0o3UWSNPjDmQdkugiSQbN/dWKmiyAhuv/7h2S6CM2W6uD5u8A3geXAMuAMWtalqx+QuODgkmBfWmid52jQNY4W9SgRyS579ezIoptOYvzIvpRNmsC0n3yV604Zwf79i5udV3G7/BSUMBynHLAHc28cx83f2Hmm2tvPH1X7ul1+bp1jyWa2Pbik7jqvVx4/DICxw3ux6KaTdnr/nBvG1W4fNbQHPz1x712W9duH7ckJI3oz9cc7Oimcd+hAAD779TgK83K4/pQRnHXwAAC6tM/n9AP34O4Ld14D9huj+u/yXJLdOhTmNfr/ra3q0XHnsdpXnTSc4/bpzQtXHM0pB+yx0/H6a12P368PB5d05Udjh3Lh4SW1+4/bp1ft64K8lod/r/7XMTvtu/s7B9O/a7w8E8cP58bT96Ns0gRuP38U+blGcbt89urZgT2Ki8jNMY4f0ZvD9+rR4rKkW0rXeU56QrMr3P2Pu/neM4ET3f17wfb5wGh3/2FCmtDXkqwx6tdTGT+yDzee3ramVJfm2VZZzfBrnuenJ+7NpWOGZLo4kmLDr3mObx9WwlUn7ZPpokgbkG3rPGdCaWmpz5gxI2Pn/2DxWuat2MT+A4q5963Pa4PJZeu3cv6d73H+oXtyQcKXzpKJzzB+vz4898lyAPoWF/HIJYdx15tlXHXScI64+WV6dy7irgsPZu3mCiA+A/Che3WnV6dC1m+tZM2mCr5zz3T6dWnHA98/lIHdd4zdfHfhGkpLurGtspryzRX06lxIYV484P3nh0tx4NQD9mD+yo0c99+vA7DgtyfVWbpn2fqtHHbTy8COtW/fXrCGDdsqOXHfPqzdXMHX/u9fPHXpkRS3j98QWLBqE395dQG/O2P/2jk+Xpy9nIvvm8lH159A56K6Nw4qqmI8OH0x5x6yZ9Jlg2psr6rm3YXlPP7+Eob37cweXdpxapIv/Q2pqo7V6VGwYNUmOhTk0ae4qMl5SPZr7RMEnhnc5Hlk5q7nSP7H9w7h3Dve5S/njWLcfvEVBd9fvJZcMwZ0a0+3JJOfJXJ33llYTsfCPNZtreCooT3rHP98zWbycnN2CrKrY86fX57HhYeXUNwuHzPji/ItjP3v15j646N55uNlnH/onnQqyqeiKsa0OSsYt18fXpi9gmP27klRfi5zl2/k6Y+WMqRXR/Jycpiwf/NnPQ9LOtvmTATPi9194G6+9zDgenc/Mdj+BYC735QsfdgN9KhfT2Xcfn34zdcUPGezmuD5Z+P25gfHKHjOdvtc8zznHTqQqyeMyHRRpA1Q8NxymQ6ed9eaTdv5vHwLe3ZrT/eOmVke67mPl3HUsJ50LEz1lDUirduNT39aOwlYJg3u2YHK6hj/dcLenLhvH7ZXxVi0ejMHDuhSm+aQ377Eig3buXTMXtz6ygKOGtqDN+at5r2rxtKrs24KhSGdbXMmPn1bMnR4OjDUzAYBXwJnAeeEUqomUPdOERGRaOresTBjQXON3VnPViQb/eKkfSgt6colf38/recd2qsjd15wcJ3eI4mK8nPrBM4Ab/zsWBynMC+Xn544PB3FlBTKRPC82/Gnu1eZ2WXE15zMBe5y99mhlaxRxpbtVSxfvy19p5S021ZZDYBpirhIMINN26v1d53l2hXkturxqyIi0nS5Oca4/fpy14WlfPeepvdkOWNUf3p2KuRHxw5l6pwVnDyyL4Overb2eOKa8WWTJlBZHWP20g3s36+YD75Yx6g9uzaUdYPCGGMsrUdKgmcz20jyINmAdkn2N5m7Pws822jCFCjMy+HJWUt5ctbSTJxe0iw/V8FzFBTk5fDAe4t54L3FmS6KpNCFh5dw/an7ZroYIiISomOH9+aB7x/Kf9w3g7MPGchPT9ibwye9zMqN2+ukmzh+OBcfNZichPH6NWPxP/nViSxfv42CYKz9yz/5Ku0L4iFSfm5O7ZPk3QmcJfukfcxzOoU9rur9xWuZu3xjaPlJ65WbY5y4bx89qYqA9xaVs2DVpkwXQ1JsWO+OjNqzW4vz0ZjnlmurY55FpG1wd+Ys28iIPTqzdnMFS9ZuZeRuzLYvbUe2j3lusw4a2JWDBuquk0g2GT2oG6MHtTyoEhERkcwzM0bs0RmArh0K6NrIjNUizaFO+CIiIiIiIiKNyOpu22a2Cghvoefd0wNYneEypJvqHB1RrHcU6wzRrHeyOu/p7j2TJZamaQVtcxT/L0M06x3FOkM06x3FOkM0653Rtjmrg+fWwMxmRG18nOocHVGsdxTrDNGsdxTrHAVRva5RrHcU6wzRrHcU6wzRrHem66xu2yIiIiIiIiKNUPAsIiIiIiIi0ggFz6k3OdMFyADVOTqiWO8o1hmiWe8o1jkKonpdo1jvKNYZolnvKNYZolnvjNZZY55FREREREREGqEnzyIiIiIiIiKNUPDcQmY2wMxeMbM5ZjbbzC5PksbM7BYzm29mH5nZQZkoa5iaWO9jzGy9mc0Kfq7NRFnDYmZFZvaemX0Y1PlXSdIUmtlDwbV+18xK0l/ScDWx3hea2aqEa/29TJQ1bGaWa2YfmNnTSY5l3bWGRuucrde5zMw+Duo0I8nxrPsMz3Zqm9U210uTdZ/XapvVNiccy9br3Crb5rx0nCTLVQE/cff3zawTMNPMprr7pwlpxgNDg59DgNuC321ZU+oN8Ia7n5yB8qXCduBYd99kZvnAm2b2nLu/k5DmImCtuw8xs7OAm4FvZaKwIWpKvQEecvfLMlC+VLocmAN0TnIsG6817LrOkJ3XGWCMuze0VmY2foZnO7XNapvVNsdl42e22uadZeN1hlbYNuvJcwu5+zJ3fz94vZH4f+x+9ZKdBvzN494BuphZ3zQXNVRNrHdWCa7fpmAzP/ipP2nAacC9wetHgbFmZmkqYko0sd5Zx8z6AxOAOxpIknXXugl1jqqs+wzPdmqb1TbXS5Z1n9dqm9U2S2Y+wxU8hyjoGvIV4N16h/oBXyRsLyGLGrNd1BvgsKBL0XNmtm9aC5YCQbeZWcBKYKq7N3it3b0KWA90T28pw9eEegN8I+g286iZDUhzEVPhj8DPgFgDx7PxWjdWZ8i+6wzxL5wvmtlMM7s4yfGs/gzPdmqb1TaTnZ/XapuTy8Zrrba5FbXNCp5DYmYdgceAK9x9Q/3DSd6SFXcHG6n3+8Ce7n4A8GfgyXSXL2zuXu3uBwL9gdFmtl+9JFl5rZtQ738CJe6+P/ASO+76tklmdjKw0t3qOCipAAAgAElEQVRn7ipZkn1t9lo3sc5ZdZ0THOHuBxHvAnapmR1d73hWXesoUdustjmQlddabXPyZEn2tdlrrba59bXNCp5DEIw1eQz4h7s/niTJEiDxLlB/YGk6ypZKjdXb3TfUdCly92eBfDPrkeZipoS7rwNeBcbVO1R7rc0sDygGytNauBRqqN7uvsbdtwebfwVGpbloYTsCONXMyoAHgWPN7O/10mTbtW60zll4nQFw96XB75XAE8Doekmy8jM826ltVtucINs+r+tQ21xHtl1rtc2trG3O6nWee/To4SUlJZkuhoiIZImZM2euA+a5e/1GXJpIbbOIiIQpnW1zRmfbNrO7gJruCPsF+7oBDwElQBnwTXdfGwz2/xNwErAFuLBmUoyGlJSUMGPGTjObi4iI7BYzKwR+kOlytGVqm0VEJEzpbJszvVTVPcD/An9L2DcRmObuk8xsYrD9c7JzSQlppRat3kzZms2ZLoakQV6OMXpQNwrzcjNdFGkbPnV3RX4iWeiL8i306FhIuwK1ByJtTNra5owGz+7+uu28ePlpwDHB63uJj+H4OQnTkQPvmFkXM+vr7svSU1qJkvPueJcv123NdDEkTX77tZGcc8jATBdDREQy6KjfvcLhe3Xn/u8fmumiiEgrleknz8n0rgmI3X2ZmfUK9jc0HbmCZwnd5ooqThrZh+8fNTjTRZEU2lJRzbl3vMuWiqpMF0Uko8xsHPGhUbnAHe4+qd7xQuK9xEYBa4BvuXtZcAN8DjA3SPqOu1+SrnKLhO2tBWsyXQQRacVCC57NrB0w0N3nNpp4N0+RZN9Os50F64BdDDBwoJ4kye5xh16divjKwK6ZLoqk0IZtlZkugkjGmVkucCtwPPEb09PNbIq7f5qQ7CJgrbsPMbOzgJuBbwXHFgTL5YiIiGS1UJaqMrNTgFnA88H2gWY2ZTezW2FmfYN8+hJf+B2aOB25u09291J3L+3Zs+duFkFERCQyRgPz3X2hu1cQXw7ltHppTmPH2qGPAmODiTxFREQiI6x1nq8n3viuA3D3WcRny94dU4ALgtcXAE8l7P+2xR0KrNd4Z0mVbF7CTXao+eavyy0R19CwqKRp3L0KWA90D44NMrMPzOw1Mzsq1YUVERHJlLC6bVe5+/rm3oQ2sweITw7Ww8yWANcBk4CHzewiYDFwZpD8WeLLVM0nvlTVd8IpuoiISKQ1ZVhUQ2mWER+ytcbMRgFPmtm+7r6hzps1pEpERLJAWMHzJ2Z2DpBrZkOBHwFvNfYmdz+7gUNjk6R14NIWlVKkiRxQh8Tsp16nIkDThkXVpFliZnlAMVAetM3bAdx9ppktAIYBdZYMcffJwGSA0tJS9fUQEZE2Kaxu2z8E9iXegN5PvDvXFSHlLSKSUr7z3IMiUTIdGGpmg8ysADiL+FCpRIlDqs4AXnZ3N7OewYRjmNlgYCiwME3lFhERSasWP3kOGs1fuftPgatbXiSRVsDBkvZSlGyiKywSH8NsZpcBLxBfquoud59tZjcAM9x9CnAncJ+ZzQfKiQfYAEcDN5hZFVANXOLu5emvhYiISOq1OHh29+pgnJOISJukCcMk6tz9WeJziyTuuzbh9TZ2zEGSmOYx4LGUF1BERKQVCGvM8wfB0lSPAJtrdrr74yHlL5J2Gg6b/XSNRURERKSpwgqeuwFrgGMT9jmg4FnaJD2IjBZdbxERERFpTCjBs7tr2SgRERERERHJWqEEz2ZWBFxEfMbtopr97v7dMPIXSTd312RSEVAzKZzGPIuIiIhIY8Jaquo+oA9wIvAa8TUiN4aUt4iIiIiIiEhGhRU8D3H3a4DN7n4vMAEYGVLeImnnaDKpKKi5xlrnWUREREQaE1bwXBn8Xmdm+wHFQElIeYuIiIiIiIhkVFizbU82s67ANcAUoCNw7a7fItJ6uYPp0XNkaMyziIiIiDQmrNm27whevgYMDiNPEREREZF0cN1FFZEmCGu27aRPmd39hjDyF0k3R7NtR4E6F4iICKgHkog0TVjdtjcnvC4CTgbmhJS3iIiIiEjKKHYWkaYIq9v2HxK3zez3xMc+i7RJ7qBHz9nPdJFFRAR12xaRpglrtu362qOxzyLSRuhLk4hItKkVEJGmCGvM88fs+NzJBXoCGu8sbZYaURERkejQPVQRaYqwxjyfnPC6Cljh7lUh5S2SEerSm/1qJgzTlyYRkWiLqSEQkSYIK3jeWG+7c+Iaue5eHtJ5RNJDbaiIiIiIiCQIa8zz+8Aq4N/AvOD1zOBnRkjnEEkrLWOU/Wouse6VSNSZ2Tgzm2tm881sYpLjhWb2UHD8XTMrSTj2i2D/XDM7MZ3lFgmLHjyLSFOEFTw/D5zi7j3cvTvxbtyPu/sgd9+ticPMrMzMPjazWWY2I9jXzcymmtm84HfXkMovUocrnBKRiDCzXOBWYDwwAjjbzEbUS3YRsNbdhwD/A9wcvHcEcBawLzAO+L8gP5E2Re2+iDRFWMHzwe7+bM2Guz8HfDWEfMe4+4HuXhpsTwSmuftQYFqwLZISevCc/WqGl+iJg0TcaGC+uy909wrgQeC0emlOA+4NXj8KjLX4H9BpwIPuvt3dFwHzg/xE2hS1AyLSFGEFz6vN7JdmVmJme5rZ1cCakPJOlNh43wucnoJziIiIREk/4IuE7SXBvqRpgglB1wPdm/hekVZPE4aJSFOEFTyfTXx5qieAJ4PXZ7cwTwdeNLOZZnZxsK+3uy8DCH73qv8mM7vYzGaY2YxVq1a1sAgSVe4a8xwFusQiQPI/hfqRRENpmvJetc3S6il0FpGmCGW27WA27cuhduxUB3ff0MJsj3D3pWbWC5hqZp81sSyTgckApaWl+iwUkUZprJtE3BJgQMJ2f2BpA2mWmFkeUAyUN/G9apul1dODZxFpilCePJvZ/WbW2cw6ALOBuWb205bk6e5Lg98riT/RHg2sMLO+wTn7AitbVnKR5OKPU/RcMtupd4EIANOBoWY2yMwKiE8ANqVeminABcHrM4CX3d2D/WcFs3EPAoYC76Wp3CLhUfAsIk0QVrftEcGT5tOBZ4GBwPm7m5mZdTCzTjWvgROAT6jbeF8APNWSQjfV3f9axP9M/Te3TJvHsvVb03FKEUkjPXGQKAvGMF8GvADMAR5299lmdoOZnRokuxPobmbzgSsJJux099nAw8CnxFfeuNTdq9NdB5GWUg8kEWmKULptA/lmlk88eP5fd680s5Z8CvUGnghmws0D7nf3581sOvCwmV0ELAbObGnBm+Let8ooW7MFgNwc49IxQ9JxWskgd9dTyQgwXWQRAIIVM56tt+/ahNfbaKDNdfffAL9JaQFFUiym2FlEmiCs4Pl2oAz4EHjdzPYEdnvMs7svBA5Isn8NMHZ3891dr/50DLGYM/iqZ6mq1qerSLbRX7WISLS5uiCJSBOE0m3b3W9x937uflIwBmoxMCaMvFuLmgdU6tYTDbrKIiIi0aF2X0SaIqwnz3UEAXRVKvLOlJrunboxGR3q0Bsh+sMWEYk0NQMi0hRhTRgWGfpsjQY1oiIiItGhnoUi0hQKnpvBDEVVUaLJpCLBTDfFRESiTl/vRKQpQum2bWZfT7J7PfBxsE5zVjD0JVtEREQk2yh4FpGmCGvM80XAYcArwfYxwDvAMDO7wd3vC+k8GWVm+nCNED13jgZ1KBEREXXbFpGmCCt4jgH7uPsKADPrDdwGHAK8DmRH8Iw+XKNAy1WIiIhEi5p+EWmKsMY8l9QEzoGVwDB3LwcqQzpHxpnpwzVKNOQ5GkwXWkQk8so3V2S6CCLSBoT15PkNM3saeCTY/gbwupl1ANaFdI6MM0zPnSNAN0iiRz1KRESiKxZzTv7zm5kuhoi0AWEFz5cSD5iPIN67+W/AY8F6z2NCOkfm6clzpJhGPUeCrrKISPS4O0vXb+OISS9nuigi0oaEEjwHQfKjwU/Wik8spOg52+kKR4/+rEVEomHNpu2MuvGlXab5bPkGhvfpnKYSiUhbEuZSVTcDvYjHmEGc6Vn1yaP1YEVERETajtWbtnP7awv46xuLdpnukEHdeHdROQDj/vgGH153AsXt8tNRRBFpQ8Lqtv074BR3nxNSfq2SYXryHAE111jzSEWDboqJiGSX6phz9O9e4ct1WxtN+5Pjh/HDsUMBKJn4TO3+A371Ig9efCiHDu6esnJCfLz16s3b6dKuAICCvJza/Tk58S8i7k5ltZOfayxcvZm8HOPpj5axX79i7nhjIX06F1HSowNrNlXwwuzlrNq0nYqqGBcfPZjJry9Maflr9OpUSIfCPDZuq2T1pt2bfG1E385srqiid6ciDt2rOz8+bqgm9ZRWJ6zgeUW2B86g2bZFREREWqs35q3i/DvfazTdDafty7cPK9lp/3+dMIzfv/jv2u2zJr8DwH8cPZgrTxhGYV5u0vy2VVYDUJS/4/jnazbz1oI1dGmXz/uL17JpexUPvPdFc6rTYukKnAFWbtwOG7e3KI9Pl20A4PM1W3ivrJxbps2jbNKEMIonEpqwgucZZvYQ8CRQ+5fj7o+HlH+rEF/nWaJC9zqjId6jJNOlEBGR3bV03VYOb2Tir8f+8zBG7dltl2kuO3Yolx07tM4TaIDbX1/I7WkMREWk9QoreO4MbAFOSNjnQHYFz6Yv2VGgSywiUWFm3YCHgBKgDPimu69Nku4C4JfB5o3ufm+w/1WgL1DTP/YEd1+Z2lKL7FA/0E0098ZxDT4t3pWySROY9cU6Tr/1Xy0pWtrkGMQcDhrYBTPjgP5dGD+yD1srqhnZr5iORXlUx5yyNZtZu7mSXp0L6da+gA6FeeTl7HhUkNhDeltljNwc47PlGxjZrzj4Duxsqahm/dZKunUoYOqnK+hYlMembVV071jAZ8s20qNTIX06FxFz5/M1mxnepzPbq2JsrazGgHkrN3H8Pr0p31JB2erNDOjWjpmfr6Vbh0L+65EPARjZr5iPv1yf5n9FkaYJa7bt74SRT2sXf/Ks0CoqNMwmIkx/1xJpE4Fp7j7JzCYG2z9PTBAE2NcBpcTvL840sykJQfa57j4jnYUWicWcwVc9u9P+N38+hv5d27c4/wMHdKFs0gSmfLiUHz3wwW7nU5Sfw7bKGD88dghf+0o/OhXl07NTYYvL11z5uTRrBvF2BfGbDvv371K7z8zoUJhHh8J4+HDKAXvUec/he/Wos51svPjRw3oCMLB7ew4cEM+7pkfAGaP616aruSlSMvEZdd2WViWs2bb7A38mvs6zA28Cl7v7kjDybzU05jkSdI1FJEJOA44JXt8LvEq94Bk4EZjq7uUAZjYVGAc8kJ4iitTlvnPgfMe3SzluRO/Qz3XqAXtwar0gUdJra0V1bTAvkmk5IeVzNzAF2APoB/wz2JdV9CAyWjTDYzToKkvE9Xb3ZQDB715J0vQDEmc6WhLsq3G3mc0ys2tMH5ySYu7OoF/UDZzLJk1ISeAsmbPoppNqX+9z7fMZLIlIXWEFzz3d/W53rwp+7gF6hpR3HWY2zszmmtn8oItZ2tSM95Dspi68EaRLLlnMzF4ys0+S/JzW1CyS7Kv5qznX3UcCRwU/5zdQhovNbIaZzVi1alXzKyESSBY4S/YxMyaM7Fu7vaux7SLpFFbwvNrMzjOz3ODnPGBNSHnXMrNc4FZgPDACONvMRoR9nobPr+/YItlGz8kk27n7ce6+X5Kfp4AVZtYXIPidbLKvJcCAhO3+wNIg7y+D3xuB+4HRDZRhsruXuntpz54pubcuETBtzoo62wqcs9v/nvOVOtvvLSrPUElEdggreP4u8E1gObAMOANIxSRio4H57r7Q3SuAB4mP10oLQ+Nho0DXOHp0ySXCpgAXBK8vAJ5KkuYF4AQz62pmXYmvrPGCmeWZWQ8AM8sHTgY+SUOZaz0160tKJj7DU7O+TOdpJUMuunfHvHQKnLOfmfHZr8fVbn/z9rd54oPsmk5J2p6wZtteDJyauM/MrgD+GEb+CZKNuzqk3nkvBi4GGDhwYKgnNzN16RURkWwyCXjYzC4CFgNnAphZKXCJu3/P3cvN7NfA9OA9NwT7OhAPovOBXOAl4K/pKnhiN87LH5zF5Q/OqnP8KwO7cPv5o+hclE9ujpGfm/x5QSzmvLVgDUcO7ZH0eHNVVceodm9wiaTK6hgG5OYYZsbqTdvp3qGgWfNslK3eDMAeXdpRkJfD+i2VFLfP3ylddczJsR1zeCxdt5VuHQrIyzFiDgV5Obg7G7ZVsXjNFrp1LKBfl3ZUx5zcYAmj6pjjHt+urHZi7mzeXsWm7VX06FjIEx98SY+OhfToWIAZFOblslfPjhTl7/j3rl+3quoYMY8vsVRZ7RTl59SmcXcqq+PftQrycviifAtdgzLXeOnKo5v8byVtW1F+Lj89cW/+3wtzAfjxQx/y44c+1M2TNm5bZTUV1TE6F+38udXahbXOczJXEn7wvKtxV/EN98nAZIDS0tJQI109eY4WdeeNBkNzGUh0ufsaYGyS/TOA7yVs3wXcVS/NZmBUqsu4uz5YvI7Rv5mW6WJICgzp1SnTRZA0unTMEBav2cJDM3Y8P6u5efbeVWPp1bmoWfmt3rSdXDOK2+XzxAdf0rNTIb95Zg65OcanyzaEWnbZtXMOGchvvzYy08VollQGz6kIPRocd5UOGvMsIiLSupx18AAenP5F4wlFpM26+Yz9ufy4oRw+6eU6+0f/VjfI2rL7312s4DlBKuLM6cBQMxsEfAmcBZyTgvM0wChbvVljq7JcTXcx0yJGkWAG81du0t91lhvcoyMj+xdnuhgSoimXHUFRfi7Denfipq+PxMyYXlbO397+nH9+mLb76pJm068+LtNFkAzZo0s7Fv72pJ3W+Ja269en75fpIjRbi4JnM9tI8iDZgHYtyTsZd68ys8uIT16SC9zl7rPDPk9Dunco4K0Fa3hrQegTiUsr1DXJ+DHJPt06FPDK3FW8MlfL52SzCw8vUfCcZfbv36X2dc142YNLunFwSTf+fPZXGnqbiLRhOTlG2aQJxGLO716Yy19eWxBa3ieN7MNBA7tS3C6fkf2L2VJRTcfCPPoUF1G+qYLidvnk5+UwZ9kGBnRtT+d2eWytqKZ7x0LcHTMjFnPMdl7etjlzGtTYtL2KDgW5tXmt31pJl/YFLa5ndcwx4v+W0nyWzWP9SktLfcaMGY0nbKKN2ypZuXF7aPlJ65WXYwzs1n63PuykbVm/tZLVm/R3ne06F+XTs1Nhi/Mxs5nuXhpCkSIr7LZZRKRGRVUMiE82J9GRzrY5ld22s06nonw6tcFZ4USkYcXt8ilup79rERGRtk5Bs6RaVj95NrNVwOeZLkcK9QBWZ7oQGRDFekexzhDNekexztB26r2nu/fMdCHaMrXNWSuK9Y5inSGa9VadW7e0tc1ZHTxnOzObEcXug1GsdxTrDNGsdxTrDNGtt2SfqP5fjmK9o1hniGa9VWepob4NIiIiIiIiIo1Q8CwiIiIiIiLSCAXPbdvkTBcgQ6JY7yjWGaJZ7yjWGaJbb8k+Uf2/HMV6R7HOEM16q84CaMyziIiIiIiISKP05FlERERERESkEQqeRURERERERBqh4LkNMLMyM/vYzGaZ2Ywkx48xs/XB8Vlmdm0myhkmM+tiZo+a2WdmNsfMDqt33MzsFjObb2YfmdlBmSprmJpQ76y61ma2d0JdZpnZBjO7ol6arLvWTax3Vl1rADP7sZnNNrNPzOwBMyuqd7zQzB4KrvW7ZlaSmZKKNE5ts9rmhONZda3VNqttrndcbXOCvEwXQJpsjLvvaqHyN9z95LSVJvX+BDzv7meYWQHQvt7x8cDQ4OcQ4Lbgd1vXWL0hi661u88FDgQws1zgS+CJesmy7lo3sd6QRdfazPoBPwJGuPtWM3sYOAu4JyHZRcBadx9iZmcBNwPfSnthRZpObXNdWfd5HVDbrLY5UdZca7XNzacnz9LqmFln4GjgTgB3r3D3dfWSnQb8zePeAbqYWd80FzVUTax3NhsLLHD3z+vtz7prXU9D9c5GeUA7M8sj/uVzab3jpwH3Bq8fBcaamaWxfCLSALXNapvr7c+6a12P2uYd1DYnUPDcNjjwopnNNLOLG0hzmJl9aGbPmdm+6SxcCgwGVgF3m9kHZnaHmXWol6Yf8EXC9pJgX1vWlHpDdl3rRGcBDyTZn43XOlFD9YYsutbu/iXwe2AxsAxY7+4v1ktWe63dvQpYD3RPZzlFmkFts9rmRNl0rROpbd5Z1lxrtc3Np+C5bTjC3Q8i3kXmUjM7ut7x94E93f0A4M/Ak+kuYMjygIOA29z9K8BmYGK9NMnueLX1ddeaUu9su9YABN3gTgUeSXY4yb62fq2BRuudVdfazLoSv3s9CNgD6GBm59VPluStWXGtJSupbVbbXCPbrjWgthm1zbXJkrw1K6717lDw3Aa4+9Lg90riYy9G1zu+wd03Ba+fBfLNrEfaCxqeJcASd3832H6UeMNVP82AhO3+7NzNpK1ptN5ZeK1rjAfed/cVSY5l47Wu0WC9s/BaHwcscvdV7l4JPA4cXi9N7bUOuo8VA+VpLaVIE6ltVttcIwuvdQ21zfVk4bVW29xM5p69Nw569OjhJSUlmS6GiIhkiZkzZ652956ZLkdbprZZRETClM62Oatn2y4pKWHGjJ1WjxAREdktZhaFyWNSSm2ziIiEKZ1tc1YHzyK76+mPljJ76YZMF0PSIC/HOP/QPenVuajxxCIiIsCy9VtZsHIzRw7NXI/dL8q3ADCgW7KVs9Lns+UbKN9UweFD2nLvZZGmUfAsksR1T82mfEsF+TmaFiCbOU5ltdO1fQHfPXJQposjIiIh+tmjH/LwjCWUTZoQet7j/vgG67dWpiTvpjrqd68AZLQMEP+3aA3lEEmH0IJnM2sHDAwWGBdp06rdueCwEq4/tU2vQCCN2LCtkv2vf5FYFs/9INIUZjYO+BOQC9zh7pPqHS8E/gaMAtYA33L3MjMrAeYANW3/O+5+SbrKLbIrD89YkrK812+tTFneItJ6hfJYzcxOAWYBzwfbB5rZlDDyFskExVIiEhVmlgvcSnyG2RHA2WY2ol6yi4C17j4E+B/g5oRjC9z9wOBHgbOIiGStsPqkXk98iYZ1AO4+CygJKW8RERFJndHAfHdf6O4VwIPE1/1MdBpwb/D6UWCsmSVb+1NERCRrhRU8V7n7+pDyEsm4bF7CTXao+eavyy0R1w/4ImF7SbAvaRp3rwLWA92DY4PM7AMze83Mjkp1YUVERDIlrDHPn5jZOUCumQ0FfgS8FVLeIiIikjrJniDXv6XUUJplxOc7WWNmo4AnzWxfd6+zXIGZXQxcDDBw4MAQiiwiIpJ+YT15/iGwL7AduJ/4HekrQspbJO0cUIfE7FfT69R3ihNEImUJMCBhuz+wtKE0ZpYHFAPl7r7d3dcAuPtMYAEwrP4J3H2yu5e6e2nPnj1TUAUREZHUa3HwHEw08it3v9rdDw5+funu25rw3rvMbKWZfZKwr5uZTTWzecHvrsF+M7NbzGy+mX1kZge1tOwiIiLCdGComQ0yswLgLKD+pJ9TgAuC12cAL7u7m1nP4HsAZjYYGAosTFO5RURE0qrFwbO7VxNfumJ33AOMq7dvIjDN3YcC04JtiM8COjT4uRi4bTfPKdI4B0vaS1GyicY8i9SOYb4MeIH4slMPu/tsM7vBzE4Nkt0JdDez+cCV7GibjwY+MrMPiU8kdom7l6e3BiIiIukR1pjnD4KlqR4BNtfsdPfHd/Umd389WCMy0WnAMcHre4FXgZ8H+//m8Zmc3jGzLmbW192XhVEBERGRqHL3Z4Fn6+27NuH1NuDMJO97DHgs5QUUERFpBcIKnrsBa4BjE/Y5sMvguQG9awJid19mZr2C/Q3NBqrgWUKnMc/RoGssIiIiIk0VSvDs7t8JI59GNGU2UM3oKSLNpl7bIiIiItKYUIJnMysCLiI+43ZRzX53/+5uZLeipju2mfUFVgb7mzIbKO4+GZgMUFpaqu/EslvcXSOeI0Dj2kVERESkqcJaquo+oA9wIvAa8cB2427mlTij5wXAUwn7vx3Mun0osF7jnUUkDJowTEREREQaE1bwPMTdrwE2u/u9wARgZGNvMrMHgLeBvc1siZldBEwCjjezecDxwTbEJzJZCMwH/gr8IKSyi+xEsZSIiIiIiCQKa8KwyuD3OjPbD1gOlDT2Jnc/u4FDY5OkdeDS3S2gSHNpMqnsV3ONXbdLRERERKQRYQXPk82sK3AN8e7VHYFrd/0WERERERERkbYhrNm27whevgYMDiNPkUxyB9Oj58jQmGcRERERaUxYs20nfcrs7jeEkb+IiIiIiIhIJoXVbXtzwusi4GRgTkh5i6Sdo6WqokCdC0REpCXcXT3VRCIkrG7bf0jcNrPfEx/7LCIiIiKSleLDvDJdChFJl7CWqqqvPRr7LG2YO+jRc/YzXWQRERERaaKwxjx/zI6lcXOBnoDGO4tIm+CaMUxEJGulsmu1Wg+RaAlrzPPJCa+rgBXuXhVS3iJpF3/wrKeS2U5d7UREpCVcXdVEIiWs4Hljve3OiXf43L08pPOIiIROD55FRLKXxiWLSFjCCp7fBwYAa4nffusCLA6OORr/LG2NgikREZGskMomXV8XRKIlrAnDngdOcfce7t6deDfux919kLsrcJY2SXeps1/NJdaXH4k6MxtnZnPNbL6ZTUxyvNDMHgqOv2tmJQnHfhHsn2tmJ6az3CJNkcp5LdRzSSRawgqeD3b3Z2s23P054Ksh5S2Sdq5wSkQiwsxygVuB8cAI4GwzG1Ev2UXAWncfAvwPcHPw3hHAWcC+wDjg/4L8REREsk5YwfNqM/ulmZWY2Z5mdjWwJqS8RTJCD56zX83cDHpyIBE3Gpjv7gvdvQJ4EDitXprTgHuD148CYy3+B3Qa8M+APTUAACAASURBVKC7b3f3RcD8ID+RViOVH/ExNSAikRJW8Hw28eWpngCeDF6fHVLeImmntlBEIqQf8EXC9pJgX9I0wWoa64HuTXyvSEapTReRsIQyYVgwm/blUNv9q4O7bwgjb5FM0Zjn7LdjzLO+WUmkJfu0q/9H0VCaprwXM7sYuBhg4MCBzS2fSIuk8jNeT55FoiWUJ89mdr+ZdTazDsBsYK6Z/bSFeZaZ2cdmNsvMZgT7upnZVDObF/zuGkb5RepTUygiEbKE+IoZNfoDSxtKY2Z5QDFQ3sT34u6T3b3U3Ut79uwZYtFFGpfK+DamLwwikRJWt+0RwZPm04FngYHA+SHkO8bdD3T30mB7IjDN3YcC04JtkZQwjXrOejW9C/TgQCJuOjDUzAaZWQHxCcCm1EszBbggeH0G8LLHpzCeApwVzMY9CBgKvJemcos0SSqfDqdyJm8RaX3CCp7zzSyfePD8lLtXkpqHd4kTltwbnE8kdGoMRSQqgjHMlwEvAHOAh919tpndYGanBsnuBLqb2XzgSoKb1+4+G3gY+JT4spWXunt1uusgsiupfDqsJ88i0RLKmGfgdqAM+BB43cz2BFo65tmBF83MgdvdfTLQ292XAbj7MjPr1cJziDRIY56zn+kiiwAQLDf5bL191ya83gac2cB7fwP8JqUFFGkBPXkWkbCENWHYLcAtNdtmthgY08Jsj3D3pUGAPNXMPmvKmzQpiYg0l776iIhkL4+lLm89eRaJlrC6bdfhcVUtzGNp8Hsl8SWwRgMrzKwvQPB7ZZL3aVISabGGppAVERGRtiWVT54127ZItKQkeG4pM+tgZp1qXgMnAJ9Qd8KSC4Cn0lGeJz/4kvveLuMf735O+eaKdJxSRNJJX35ERLJWarttpyzrNkdd2CUKwhrzHLbewBPBeMQ84H53f97MpgMPm9lFwGIaGH8Vtj++9G/K1mwBYP3WSn5wzJB0nFYySJ//IiIi2SEVXavN4t8VFDDuEHPIVbc9yXKhBM9m9vUku9cDHwfdrpvF3RcCByTZvwYY2/wStswTPziCyliM0b+ZRmWVPiQjQ5NJRYKZxjyLiGQzT8GnfI4Z1e4a85wgfiNB350ku4X15Pki4DDglWD7GOAdYJiZ3eDu94V0nozo2qGg9s6ixraIiIiItB2p+OpWEyLqe+EOupEgURBW8BwD9nH3FQBm1hu4DTgEeB1o08Ez7FjSRp8L0aF7p9FgqJu+iEg2S0WAW9M5Tc3HDrqRIFEQ1oRhJTWBc2AlMMzdy4HKkM7ROuiDIetp/JKIiEj2SOUT0Zget9bS1yeJgrCePL9hZk8DjwTb3wBeD2bKXhfSOTJOYyOjRUOeo8HMUjIeTkREWoewA9zK6hiV1fE8MxEwfrluK0dMejn9J66nOubsddWztdtqSyUKwgqeLyUeMB9BvBfk34DHPP4Ib0xI58i4HDPdVYsAXWMREZG2beWGbbWvw2zX//Hu51z9xCe12+nsqlxVHWPI1c+l7XwN2VZZzf6/epGKqlid/XoIL1EQSvAcBMmPBj9Zy9B4jigxjXqOBF1lEZHs4u6M/u202u2rn/yY+y46pNn5xGLOnOUbeHH2Cv40bV7yNCn8Xritsprh1zyfsvybY0tFFSOufWGXafQdWaIgzKWqbgZ6Ef8uGp+Dx71zGPm3Fuq2HQ26xtGj9l5EJDskCzjfmLeakonPAHDl8cMY2b+YvByjpHsHOhbmcf0/ZzNvxSY+Xbah2ec756/v8s5VLV9FtTrmbNxWyX9P/Td/e/vzJr3nP4/Zi9teXcDspevZd4/iFpehxvSych5/fwkPvPdFk9L/csI+3PjMHP41bzXjR/YNrRwirVFY3bZ/B5zi7nNCyq9VMtRtO0o05jkadJ1FRLLDfe98zjVPfrLLNP899d8tPs+t5xzEL5/8mLVbKlm+YRslE59h0U0n1a7M0hQLV23i2D+81uxzD+/TiX987xC6dyysvSEw4ZY3ufe7o/nqsJ7Nymt7VTUvzl7BDx/4oNnlAGrrXFOO//zH+/zhzAP4xqj+zc5rS0UVZau3cMnfZ7K4fAsA7/xiLH2Ki3arbCKpElbwvCLbA2eoefKs6Dnbabbt6NEVFxFp22oCuFR55kdH1nm6e8VDdQPOQb+IT5z19A+PpDAvhw3bKnlz3hr++sZCNm2vatG5n/7hkezXb9dPli+46z0APrr+BDoX5VNRFSPHIC83h1jMueKhWUz5cGmLylHcLp/XfzaG4nb5Dab5ySMf8pNHPuRPZx3I+P36kptj5OYY7s66LZXc8vI8Fq3ezKtzVzV6vkNvmkbZpAktKrNI2MIKnmeY2UPAk8D2mp3u/nhI+bcKZuhbtoiIiEgrsbWimn2u3XlccNmkCVRVx7jl5flcMXYoAA9O/4Jbps1jecJkYgBHD+vJYYO7c3BJV0pLujXpvP++cTzXPPUJf39ncZ39J//5zd2syQ6v/NcxlHRv3+iT7EU3ncR37pleJxDd//oXW3x+gN9+bSQn7NubHh0LG01bNmkCNz03h9tfW1i77/IHZ3E5s0Ipi0hrElbw3BnYApyQsM+B7AqeMU2GEAE1V1i9eaNBwzFERNqmDdsqdwoWrz5pH75/9GAg/tT1yuOH1R4755CBnHPIwFDObWbcePpIrjtlX4a2YAbsW885iPH79SEnp/nfOsyMe74zmjvfXMSvn/50t8twx7dLOWbvnuTl5ux2Hr8Yvw9HD+3JuXe8u9t51OjSPp91WypbnI9IKoQ12/Z3wsintTPTxEIiIpI9zKwb8BBQApQB33T3tUnSXQD8Mti80d3vDfa/CvQFtgbHTnD3lakttQg8NetLLn+w7pPNT351Ih0Lw3ou1DT5uTmUTZrA6k3b+fFDs3hj3uo6x48f0ZsfHLMXBw7o0qwx0c1x0ZGDuOCwPbn99YX8vxfm7jLtGz8bw4Bu7VNSjiOG9KBs0gRmfbGO02/9V4PpDt+rO986eADHDOtFcfvkXcBruuGXTHxGXbelVQlrtu3+wJ+Jr/PswJvA5e6+JIz8Wwv12o6GmhskmkgqIjSXgUTbRGCau08ys4nB9s8TEwQB9nVAKfFmcKaZTUkIss919xnpLLREW0VVbKfAOdMBVo+Ohbu1HFZY8nJzuHTMEC4dMyRjZahx4IAuGb8eIqmy+/0z6robmALsAfQD/hnsyyo5pu6dIiKSVU4D7g1e3wucniTNicBUdy8PAuapwLg0lU9kJ8N+WbebtAK17LPoppNqX6d6MjiR5ggreO7p7ne7e1Xwcw/QvPny2wLTAvBRkqruVdK6GKhLiURZb3dfBhD87pUkTT8gccHXJcG+Gneb2Swzu8b0wSkpNvGxj+psK3DOTvU/SqpjaqildQgreF5tZueZWW7wcx6wJqS86zCzcWY218zmB13M0kbfCKJBXXhFJJuY2Utm9kmSn9OamkWSfTUflOe6+0jgqODn/AbKcLGZzTCzGatWNb5EjUhDHpy+4z6OAufslnh997rqWS0lKq1CWMHzd4FvAsuBZcAZQOiTiJlZLnArMB4YAZxtZiPCPs8uzq8/XJEso+dkku3c/Th33y/Jz1PACjPrCxD8TjbZ1xJgQMJ2f2BpkPeXwe+NwP3A6AbKMNndS929tGfP7OuYJumR2H03sVuvZK/Rg3YsHVazlrZIJoUSPLv7Ync/1d17unsvdz8d+HoYedczGpjv7gvdvQJ4kPh4rbQwU+/OKND9kejRJZcImwJcELy+AHgqSZoXgBPMrKuZdSW+LOULZpZnZj0AzCwfOBn4JA1lBiAWc658aBYlE5/hf6b+O12nlVZCIwSi4eH/OKzOtsY/S6alcj7/K4E/hpxnsnFXaZvaUBOGRYva5WgwDciQaJsEPGxmFwGLgTMBzKwUuMTdv+fu5Wb2a2B68J4bgn0diAfR+UAu8BLw13QVfPBVO55C/WnaPP40bV7tdl6O8fNxw9m/fzHDeneia4eCJufr7pgZVdUx8nJzqI455Zsr6NmpMGn6tZsr6Nwunw1bK1m4ejOFeTns1694p3SxmDe4lm8s5lTFHLN4fr06F9U5FnPf7TV4q6pjVMWcovxcqmNOju068HR3tlfFKMrPZcO2+Fq7Bbk5FOXn7pR2w7ZKKqpidGtfwBdrt1CUn8u6LfF9nYryyM0x+hQXkV+v7J+v2Uy7/Nw69dxeVU1VtZObY3XO5e6s2ridzu12LGmkp87R8ukNJzLi2hdqt0smPsP71xxPt2b8XafSsvVbqaxyehcXUlXtbN5eRdcOBeSa1X6XXLulkm4dCti0vYq5yzeyvbKamMOm7ZVc8vf3OfeQgXTvWEj55u2cf2gJ75WVs2e39uSYsWTtFhzoW1xEr05FbNhWyfqtlazcuJ399ugc/+wgPi589KBurNy4nbv/VcbLn63g8zVbGL9fHz5ZuoH5KzcBUNwun7wco6I6xsZtVRn7d4PMLC/XUqksbSq+ke5q3FU8gdnFwMUAAwcODP3kmjBMJPtoOIZElbuvAcYm2T8D+F7C9l3AXfXSbAZGpbqMu6Mq5vzm2TmZLoakiJ46R0v7gjze+NkYjvrdK7X7Dvr1VAAmfX0kZ41O/n1/+fpt9OpUyNbKal78dDkDu7XnX/PXMKhHB16as4KnZi1NS/mb4h/vLq59/fd3Fu8iZfM9Wa+e67dWhpp/S+x33Qttbu6CVAbPqfg22uC4q9qTuk8GJgOUlpaGWgZ1244WPZGMBn0HE2nbfnL8MP6gbtsiWW1At/bM/814hlxdd5myiY9/zMTHP85QqSSKWhQ8m9lGkseTBrRrSd4NmA4MNbNBwJfAWcA5KThPA4z3FpVz1RP6I81m1dW6RRI1b8xbrb/rLHfY4O6ccsAemS6GhOiWs79Cz46FHLZXd344dmjt/k+XbmDm5+Vc89TsDJZOUmXaT76a6SJIhuTl5lA2aQLfuv1t3l1UnunitAmdivL43Tf2p1/Xdtz39uf8YMwQvnHbWxwzrCdd2hdw178Wcf/3D+GGf37KgQO6sHefTuzRpR19i4vo2r6A9gW5zF2xEfd4j54PFq/l3EP2xHE6F+XzweJ1DO3dkY6FeVRUx+hUmMeUD5cyb8Umzj10IIV5uXRtn09ldXzIyLyVm4i5E4tBu4JcBvXokOl/omazttZd0cxOIj6WOhe4y91/01Da0tJSnzFjRmjn/uEDH/D2gpSswCWtTF6OcfMZ+/PVYZoVNttddM90PlyyPtPFkBT71sH9+emJw1ucj5nNdPfSEIoUWWG3zSISTbGY15n7oDm+WdqfPp2LOGn/vsRisLWyioLcXBas2sTYfXrRoSCvwTkKpPVJZ9vc5oLn5lADLSIiYVLw3HJqm0VEJEzpbJvDWudZ/j97dx4nV1Xn///16U7S2fcQyNrBJEDYSQwgomxCWDSMgILCMIrDMIqjoz8VZRVB0dHvOKiDMogGZpBVNEqQTVYhQIc9QEwIISvZ96T3z++PutWp7q7qquq+td37fj4e9eiqW+fee07f7jr1uWcTERERERGRyIp0y7OZrQfeyzH5SGBDAbNTjuJYZohnueNYZohnueNYZiheuSe6u8Zz9IDq5qziWGaIZ7njWGaIZ7njWGaIYN0c6eA5H2ZWF7eueHEsM8Sz3HEsM8Sz3HEsM8S33FEXx+saxzJDPMsdxzJDPMsdxzJDNMutbtsiIiIiIiIiWSh4FhEREREREclCwfMeN5c6AyUQxzJDPMsdxzJDPMsdxzJDfMsddXG8rnEsM8Sz3HEsM8Sz3HEsM0Sw3BrzLCIiIiIiIpKFWp5FREREREREslDwLCIiIiIiIpJFxQbPZnarma0zszdStn3PzF4zs1fM7GEzGxNsn52yvc7MPhxsP8zMnjOzhcH7n0451olm9lKwzzNmNjlNHmrNbHeQ5hUz+2UEyn1CUO43zGyOmfXKkI8LzWxx8LgwJmVuSbnWcwtZ5hDLPdHMFgTbF5rZJSnHmm5mr5vZEjO70cwsTR4seG9JcPwjYlDm48xsa8q1vqqQZS5Sua83sxVmtiNLPr4d/G4WmdkphSpvcK6Sl9lK8BkedSFdV9XNqptVN6tu7pgH1c2qm8uDu1fkA/gIcATwRsq2wSnP/w34ZfB8IHvGdx8CvB08nwpMCZ6PAdYAQ4PXfwcOCJ5/EfhtmjzUpp6/0stN4mbKCmBq8N61wEVp8jAcWBr8HBY8HxblMgfv7ajAa90HqElJswwYE7x+ATgaMOBB4NQ0eTgteM+Ao4DnY1Dm44A/R+xaHwXs09XfMDANeBWoASYB7wDVES9zLUX+DI/6I6TrqrpZdbPqZtXNHfNwHKqbVTeXwaNiW57d/SlgU4dt21JeDgA82L7Dg6vRYfvf3X1x8Hw1sA4YlTwcMDh4PgRYXYBi5K3A5R4BNLj734N9HgHOSpONU4BH3H2Tu28O0s0KoXhplUmZiy6kcje6e0OwvYagt4mZ7UPiw/C5YL/bgDPTZGM2cJsnzAeGBvsWRJmUuegKWe7gvfnuviZLNmYDd7p7g7u/CywBZnazSFmVSZklZKqb221T3ay6WXWz6mbVzRGrm9N2galkZnY98I/AVuD4lO3/APwA2As4Pc1+M0ncKXkn2PQFYJ6Z7Qa2kbhTks4kM3s5SHOFuz8dUlHyElK5HehtZjPcvQ44Gxif5nRjSdwRTloZbCuqIpcZoK+Z1QHNwA3u/ocQi5OzfMttZuOBB4DJwDfcfbWZzSBx3ZIyXcNM17qoH3xFLjPA0Wb2Kokv5v+fuy8MsTg5C6PceZxuLDA/5XVF/F/3sMxQJp/hUae6WXWz6mbVzaqbVTfnoSw+w9PyMmj+7u6DLpr1gW8D302z/SPAox227QMsAo5K2fZ74Mjg+TeAW9IcqwYYETyfTuKDbHC+5Sizch8NPE2iC811wMtpjvUNEn/IyddXAl+PcpmDdMkuJ/uS6ILygUq51sn8B2UcDXwwNQ1wLPCnNPs8AHw45fVjwPSIl3kwMDB4fhqwuNDXuZDl7rC9q25SvwDOT3n9a+CsiJe5JJ/hUX8U+PNadbPq5o7HU92surniyt1hu+rm9u+Vdd1csd22c3AHabr4eKI7wgfMbCSAmQ0m8SF0hSe6vmBmo4BD3f35YLe7gA+lOVaDu28Mni8gcbd0agHKko9ulztI95y7H+vuM4GngMVpzrGS9neAx1HarnPFKDMe3DVz96XAE8DhIZcjXzmVO2X7amAhiYppJYnrlpTpGlbktU7ZnneZ3X2bu+8Ins8j0foxsmO6IutJuXMVpWudkzL9DI861c0pVDerblbdrLo5iyhd65yU6Wd4m+Qg74pkZrUkJg84KHg9xYOxM2b25REjRtxYW1tbugyKiEikLFiwYIO7j8qeMr5UN4uISDEVs26u2DHPZvY7EjPvjTSzlcDVwGlmth/QCrxXW1tLXV1dCXMpIiJRYmbvlToP5Ux1s4iIFFsx6+aKDZ7d/bw0m3+d+mLGjBmV26wuJXXdn9/kpeWbS50NKYJeVVVcecY0Dh43pNRZEal4qpulVBqbW7mrbgWfnTmBqqo9ywQ/8Noa3tu0ky8e12lJ8C7NfXU10ycOY+zQfnnn5eXlm1mztZ7TDu7ZBNh/fm01H548kqH9+3T7GCs27eJnf13M9f9wML2rezZas6XVaW5tpaZXdY+Ok/TGqq0MG9CnW79jkVKp2OBZpJDufWkl/XpXM3mvgaXOihRQU0sr85du4vl3Nyp4FhGpYL94fAn/9dhi+vWu5uzpe4bQfumOlwDyCp7dnX/73cvsPbgv879zYt55+Yf/fhaAZTd0mlQ8Zys37+LSO17mw5NH8r9fOLLbx/nWfa/x7DsbmX3YWI6Z3LMhwhfNeZEnFq3vUblSnfGzZ4Ce/Z5Eii204NnM+gET3H1RWMcUKaVTDtybaz5xYKmzIQW0rb6JQ655uNTZECk5M5sF/BdQTWIG6xs6vF9DYv3V6cBG4NPuviwY3/wWiZmSAea7+yXFyrdI0pZdjQBsr28K7Zjvb6sP7Vj5amhuBWDVlt2hHC+MKY6eWLS+5wcRqXChzLZtZh8HXgH+Erw+zMzmhnFskVKo4Hn0RETyYmbVJJZDORWYBpxnZtM6JLsI2Ozuk4H/BH6Y8t477n5Y8FDgLCVhluiqHZX6O9nxvKcT+5plTyMiuQtrqaprgJnAFgB3f4XEGmEiImVL3ylEgET9vcTdl7p7I3AnMLtDmtnAnOD5vcCJZvpaLtEUlQA8lRPBQomUQFjBc7O7bw3pWCIlV8lLuEn+dLkl5sYCK1Jerwy2pU3j7s3AVmBE8N4kM3vZzJ40s7RreZrZxWZWZ2Z169er66cUjj7O2zPdJhYJVVjB8xtm9hmg2symmNnPgGdDOraIiIgUTrpv1x1jkExp1pCY7+Rw4GvAHWY2uFNC95vdfYa7zxg1SstkS/ii1g9CHTtEylNYwfOXgQOBBuAOEnekvxrSsUWKzoleRSydtY2RU1uFxNtKYHzK63HA6kxpzKwXMATY5O4N7r4RwN0XAO8AUwueY5GYCKt2Ug8rkXD0eLbtYKKR77r7N4DLe54lERERKaIXgSlmNglYBZwLfKZDmrnAhcBzwNnAX93dzWwUiSC6xcz2BaYAS4uXdZH2whh2VQ5xZlj375MNAeVQJpEo6HHwHFSY08PIjEjZcI0TioM9s5mWNBsiJeXuzWZ2KfAQiaWqbnX3hWZ2LVDn7nOBXwO3m9kSYBOJABvgI8C1ZtYMtACXuPum4pdC4k51togUQ1jrPL8cLE11D7AzudHdfx/S8UVERKRA3H0eMK/DtqtSntcD56TZ7z7gvoJnUCSLthbWiN0MjVp5RCpdWMHzcGAjcELKNgcUPEtF0pjneFB3NhER6agcVtzYUz+Fk5dyKJNIFIQSPLv758I4joiIiIiIhEOzdouEK5Tg2cz6AheRmHG7b3K7u38+jOOLFJu7a/RUDGiMnIhINLTNYaG+RCJSQGEtVXU7sDdwCvAkiWUutod0bBGRglJvNhGRyha1Mc/Jm7thlScivxaRkgsreJ7s7lcCO919DnA6cHC2nczsVjNbZ2ZvpGwbbmaPmNni4OewYLuZ2Y1mtsTMXjOzI0LKu0gnGvMcD7rGIiLREGb35HIINKN2M0AkKsIKnpuCn1vM7CBgCFCbw36/BWZ12HYZ8Ji7TwEeC14DnEpi/cgpwMXATT3LsohIgrr5iYhEQ9Q+zXt6T6Bt96j9YkRKJKzg+eaghfhKYC7wJvCjbDu5+1Mk1otMNRuYEzyfA5yZsv02T5gPDDWzfcLIvEhHutMrIiJSOaLakUjfR0TKS1izbd8SPH0S2LeHhxvt7muC464xs72C7WOBFSnpVgbb1vTwfCJpaYbK+NCXExERSVKdICKZhDXb9lXptrv7tWEcP3madKdIk5eLSXTrZsKECSGeXuJE3XhFREQqT1QC37Du36sdQCRcYXXb3pnyaCExPrm2m8dam+yOHfxcF2xfCYxPSTcOWN1xZ3e/2d1nuPuMUaNGdTMLItHtAiZ76EuFiEhEJCfYitjNbw/pbkDUfi8ipRJWt+2fpL42sx+TGPvcHXOBC4Ebgp9/TNl+qZndCRwJbE127xYRERGR+Ap7aadS09AxkfIUSvCcRn9yGPtsZr8DjgNGmtlK4GoSQfPdZnYRsBw4J0g+DzgNWALsAj4XfrZFEtxR03MM7PmyFZFvWyIi0mPl1EpbPjkREQhvzPPr7Pn/rgZGAVnHO7v7eRneOjFNWge+1N08ioiIiIhUgrDu36sdQCRcYbU8n5HyvBlY6+7NIR1bpOgSDc+qcqJOveJERKIhqp/nYXWMUgcrkXCEFTxv7/B6cOpYDXfvuJaziEjZ0JcKEZHKlvzWGcYwnHKoE8KbbTuidxVESiSs4PklEjNhbybx+TWUxHhlSDTi9XTtZ5Hi8ujexZY9dIlFRKIhqnV2OY2/FpHwlqr6C/Bxdx/p7iNIdOP+vbtPcncFziJS1vTVROLOzGaZ2SIzW2Jml6V5v8bM7gref97MalPe+3awfZGZnVLMfIt0VA6txuVIvxeRcIQVPH/Q3eclX7j7g8BHQzq2SNE5rlbJGFB3NhEws2rgF8CpwDTgPDOb1iHZRcBmd58M/Cfww2DfacC5wIHALOC/g+OJlETUPtZ7GvRG7NchUnJhBc8bzOwKM6s1s4lmdjmwMaRji4gUlO7IS8zNBJa4+1J3bwTuBGZ3SDMbmBM8vxc40RJ3n2YDd7p7g7u/S2I5yZlFyrdIm6h+jpdjsbS8o8RZWMHzeSSWp7of+EPwPNMyVCJlT/WCiMTIWGBFyuuVwba0aYLVNLYCI3LcFzO72MzqzKxu/fr1IWZdJCFZbUetR1Fos22Hc5jEsfQdSWIslAnDgtm0vwJt3b8GuPu2MI4tUioRq38ljbbZWcvy3r5I0aT7tOv4T5EpTS774u43AzcDzJgxQ/9wIlkkA9SetvImbya0hhjxtrpTpQ7hElOhtDyb2R1mNtjMBgALgUVm9o0wji1SCvpmJyIxspLEihlJ44DVmdKYWS9gCLApx31FKko5taz2NCtVQYwbZlfr1jL6/YgUW1jdtqcFLc1nAvOACcAFIR1bpCRMd1Ujz9q+VJQ2HyIl9iIwxcwmmVkfEhOAze2QZi5wYfD8bOCvnvg2Phc4N5iNexIwBXihSPkWibyethhXBRVdmPVcmK3YIpUmrHWee5tZbxLB88/dvcnM9J8lFUuTYYhIXLh7s5ldCjwEVAO3uvtCM7sWqHP3ucCvgdvNbAmJFudzg30XmtndwJtAM/Ald28pSUFEIqi1h828VUEzWZitxfqKJHEWVvD8K2AZ8CrwlJlNBDTmWSqaxjxHX3IsmL4HSNwFy03O67DtqpTn9cA5Gfa9Hri+oBkUyaJtDouIRHbe4Wd3FWrMs0hchdJt291vdPex7n5a0I1ry1/DOAAAIABJREFUOXB8GMcWKQVVCyIiIpUjzBveYU0i2dNWY6DHX0iqFDyLhCqsMc/teEJzIY4tUixqeBYREaksmeK6UrRIhxFk9nzMc3h5SVLoLHFWkOBZpNLppmrM6IKLiFS05CSfmT7NSzFDdEsowXPP9i/EhGHeGt6xRCqNgmeRTDToORZ0mUVEKl+21RPyaXkNK9DsyXGSLeU97UJubS3PPTpMO+q2LXEWyoRhZvbJNJu3Aq+7+7owziEiUij6GiAiUtmy3QdtaXV6VxclK+3O2VM9jVM15lkkXGHNtn0RcDTwePD6OGA+MNXMrnX320M6T0nc/twytu5uoqrKOOuIcYwe3LfUWRIRERGRDjK11JYi3gsjyOzpIQoxC3kpusCLlIuwgudW4AB3XwtgZqOBm4AjgaeAig6ef/3MuyzbuAtIfIh96fjJJc6RFIN688aDoSHPIiIVL8vY3lK0lvYkyGxqSezc2NKzAcYNza09zktHYQTiuxu1HLxUprDGPNcmA+fAOmCqu28CmkI6R8k88rWPsui6WQA09fBDTMpfVNaIFBERiYtsN7zzCZ7ffn9bzzKTPGc3I9bNOxs5/sdPhJKHua+uBmBHfc8WwXl1xZa252EE4gdc9ZeeH0SkBMJqeX7azP4M3BO8Pgt4yswGAFsy71YZeldX7Zm4QXFVbGgiqXgws9DW9BQRkdL4r8cWA1DftKdF81O/fK7teWuObR+Nza2cddNz2RNmUHvZA3vO2Y0vjau27OaYG/7a7fMnuTuTvj2v7fVeg2u6fazf/u1drvnTm22ve9KKv3FHA9Ove7Tb+4uUWljB85dIBMzHkLj5dxtwnycizuNDOkdJmXW9BIJEh26QiIiIVKa/vr2OXz21tNP2bAFfx2ATYNSg3APO3Y0tnVpT81mqqqmllSmXP9hp+9nTx+V8jKTL73+d/3t+ebtt3Ql4l23YyXFpWsC78zVp9ZbdfCjNTYGJI/p342gipRNK8BwEyfcGj8gyU5feODGNeo4FjXkWEals+1+5J+hcvG5H2jQn/b8nWXDlxzptb2l1PvCdeWn2gL1yCJ437WzkiO89kva9XOqWdzfszNhFe9ywfjkHvY3NrUy9onPwnZRry/uTf1/Phbe+0GWaXLujZwqYk848bAwLlm/OLWMiZSLMpap+COxF4rto4vuo++Awjl8uqsz0JTsGdIlFREQqw9ZdTdQ3ZY8MN+5sbOtS3a93NbubMk9YNWpQDeu3N7Bw9TZqL3uAl678GMMH9AESjSjvbdyVtkU26YKjJnL7/Pc48vuP8cHaYfzun4+iV3VimqGmllZWbd7d5f4Ay244ndrLHmDl5lX8/qVVLL7+VHpXt5+qaOHqrZx+4zNdHufpbx7PsT96nK/f8ypTRw/i4HFDOqVpbXX2zXADIenOi49i7qurueP55Rz7o8epu+IkRg5sf3PB3XnsrXV84ba6Lo/1k3MO5azp49quR+1lD7DshtO73EekXITVbftHwMfd/a2QjleWDK1tFyca8xwPus4iIpXr0Gsf7rTtyydM5vPHTGLYgD7txiAndRU4JwPl1P0ytSx3dPoh+/CLzxzRbt8Xl21mcpru2Jm8+4PT2oYKpkrXpbsrPzr7ED41Yzyvr9zatu3jP+860E7nhe+cyF7BEq3n3jy/bfuMboxbPnbKSG6/6Mi076W7Tgu/ewoDasIKVUTCEdZf5NqoB84QdNsudSak4NQ1P350xUVEKk9qwDVj4jDq3kt0Af76yfu1bX/1qpPTBtgdvfP906iu2hO09qmuymuZqNSg91+P+wA3PfFOzvueN3MCP/jkwZ22/+PRE7ntufdyPg7Aq1efzJB+vdtejxnaN6/9k9K1LP/lq8cy66dP532st66dRb8+1Z22z5w0nBfe3ZRxvwOvfkgt0lJ2wgqe68zsLuAPQENyo7v/PqTjlwVTt+1YUYNkPGhsu4hI5XngtTXtXt/7rx+isbmVXlXtP9OH9O/dLgDb0dDMH19ZxawD92ZQ39706ZV+1da/X39q2knEko7adzi/PH86Q/v36fTet2btz7dm7c+C9zZlnLn7z1/+MAeOGZy2lTnp2tkH8d1PHJgxD0lvf28WfXt3Dk4BRgys4elvHs/8pRv5xr2vdXmcW/5xBidNG53x/f33HsyyG07nX/93AQ++8X7GdEdOGs7tFx2Z8XebdPe/HN3l71ikHIUVPA8GdgEnp2xzIFrBM2qVjANd4fjRv7WISOVobG7lS3e81PY6GRxnC9YABtb04rNHTszpPGbWo5bP6ROH97jlNDUPLa1OS6vnVM5U44f3Z/zw/pwzY3zbNnfvMnDvyk3nT8fdWbh6G4+/vY4PTxnJ4ROGdetYqeW784XlHDR2COOH9+fQ72bvLSBSCmHNtv25MI5T7qrMFFjFiMbCxoSus8SYmQ0H7gJqgWXAp9y90/S3ZnYhcEXw8jp3nxNsfwLYB9gdvHeyu68rbK4l7lJnlf7r1z9awpwUV3WVteta3hPdDZxT9z9o7BAOGtt5ArLuOnfmhE7b/rZkA8dMHhnaOUR6Kr9bVxmY2Tgzu9/M1pnZWjO7z8zyX5iuzJnlPj2/VC61QsaP67aYxNdlwGPuPgV4LHjdThBgXw0cCcwErjaz1Gamz7r7YcFDgbMU1D11K9q93nfUwBLlRIrhs7c8X+osiLQTSvAM/AaYC4wBxgJ/CrZFiqEuvSIiEimzgTnB8znAmWnSnAI84u6bglbpR4BZRcqfSDup43Y1mVR0/cfZh7Q9/9uSDSXMiUh7YQXPo9z9N+7eHDx+C4wK6dhlQ+s8x0OyFbKnXZqkMhjorpjE2Wh3XwMQ/NwrTZqxQGpz38pgW9JvzOwVM7vS9MEpBbTo/e1tzx/9Wny6a8dR6vhstT5LOQkreN5gZuebWXXwOB/YGNKx2zGzWWa2yMyWmFmn7mUFZVrnWUREKouZPWpmb6R5zM71EGm2JSvDz7r7wcCxweOCDHm42MzqzKxu/fr1+RdCBDjlp0+1PZ+8l7prR92fv/zhtufp1oEWKYWwgufPA58C3gfWAGcDoU8iZmbVwC+AU4FpwHlmNi3s82RSpRvqsaD7I/Gi9dsl6tz9JHc/KM3jj8BaM9sHIPiZbszySmB8yutxwOrg2KuCn9uBO0iMiU6Xh5vdfYa7zxg1KryOac8v3UjtZQ+wvb4ptGNK+Zvz+bR/ZhIxHScje3XFlhLlRGSPsGbbXg58InWbmX0V+GkYx08xE1ji7kuDc9xJYrzWmyGfJy1Ty7OIiETLXOBC4Ibg5x/TpHkI+H7KJGEnA982s17AUHffYGa9gTOAR4uQZwAOuvohdjQ0A3DwNe2XtfnJOYdy3H6jGNa/D1XdnJ3YPbEsUK/qsNoZCsPdeX9bPfsM6QdAfVMLZlDTq5qG5hZqelXT2NxK72prNxypqaW1rUtBU4vTr0812+ubGNS3d7uyv7+1ntGDa9r2TbfEkbvz6sqtHDpuSNt7ra1Ocx7LKjW3tFJdZRmHTG3dtecGyUenRm5koGTw7g9Oa1sHevYv/sZDX/0I++09qMS5Ckdy+Vt3WLJ+B1NHD+r0/uJ1ie0trU6V5T6ksLXVWbphJ2OG9qVvr2pWb93N0vU7WbN1Nx/6wEiqq4zmFuf3L69k2j6DmThiAI3NrRw0djA7G1toaXVWbNrF+u0NzJw0nOff3cjEEQN4a8023tu4i70H9+W/n1jC9InDePv97Xzi0DE88Poa6ptaueSj+9Lc4hw6fijDB/Rhd1MLA2t60drq3LNgBfvvPZj7X17FR6eO4vRD9qF3mX/GdhTWOs/pfI3wg+d0466ODPkcGSXWeS7W2aTU1NEgHgzT+u0SZzcAd5vZRcBy4BwAM5sBXOLuX3D3TWb2PeDFYJ9rg20DgIeCwLmaROD8P8XKeDJwTufr97xarGyISAGZGT866xC+eV9iorhk1/0wJovb3djC0g07+NuSDTy8cC11723mlANH89DCtT0+dly8s34nAK+t3Nq27St3vpLTvve/vIqv3vVKxU38V8jguRChR1fjrhIJzC4GLgaYMKHzenE9kVjnWV+yRUQkGtx9I3Bimu11wBdSXt8K3NohzU5geqHzmM3gvr3YVp85kBaRyvapD47nZ48vZsWm3W3bkmOgq6uME/ffiy8eP5mv3f0KqzbvpqG5tdvnUuAs2RQyeC5ElJlx3FXbSd1vBm4GmDFjRqh5SHTbDvOIUs6sIPd/pNyoh4FIZfrnYyex76iBnDdzz43y+19eybzX3+eRN/UFOKpeuLzTvR6Jgae/eQKf/O+/8dLy9uOeW1qdh99cy8P6n69IT3/z+FJnIW89Cp7NbDvpg2QD+vXk2Bm8CEwxs0nAKuBc4DMFOE9aZsafXl3NC+9uKtYppQQ0rj1+7q5byeOLNANwlJ152BguPWFKqbMhIbr89M7zhf7D4eP4h8PHlSA3IlJov//iMazdVs+R33+sYOc4dNwQ/vW4DzBiYA0D+vRiwoj+bN3dxF6Datiyq4n6phbGD++f9TgtrU5zays1vaqzpk03j4CUrx4Fz+5e1BH77t5sZpeSmLykGrjV3RcW6/yXfPQDvPTe5mKdTkrooDFDOOmAdMudStRcesJkFq7aVupsSIHtNbhvqbMgIiI9NHpw37Yxsis37+LDP3w8bbqbL5jOmKH9GDO0H8MH9OnROQfWJMKlUYNqct6nusqorsoeOEPuk4BJebAoT5QzY8YMr6urK3U2REQkIsxsgbvPKHU+KpnqZhERCVMx6+bKmhtcREREREREpAQi3fJsZuuB90qdjxyMBDaUOhMlEMdyx7HMEM9yx7HMEP1yT3R3LTLbA6qby14cyx3HMkM8y60yR1PR6uZIB8+Vwszq4tgNMI7ljmOZIZ7ljmOZIb7lluiJ699yHMsdxzJDPMutMktPqdu2iIiIiIiISBYKnkVERERERESyUPBcHm4udQZKJI7ljmOZIZ7ljmOZIb7lluiJ699yHMsdxzJDPMutMkuPaMyziIiIiIiISBZqeRYRERERERHJQsFzkZjZeDN73MzeMrOFZvaVNGnMzG40syVm9pqZHVGKvIYlxzIfZ2ZbzeyV4HFVKfIaJjPra2YvmNmrQbm/myZNjZndFVzr582stvg5DU+OZf4nM1ufcq2/UIq8FoKZVZvZy2b25zTvRepaJ2Upc2SvtUSL6mbVzR3SROrzWnWz6uYO70X2WhdTr1JnIEaaga+7+0tmNghYYGaPuPubKWlOBaYEjyOBm4KflSqXMgM87e5nlCB/hdIAnODuO8ysN/CMmT3o7vNT0lwEbHb3yWZ2LvBD4NOlyGxIcikzwF3ufmkJ8ldoXwHeAganeS9q1zqpqzJDdK+1RIvqZtXNqpuj+3mturmzqF7rolHLc5G4+xp3fyl4vp3EH/bYDslmA7d5wnxgqJntU+SshibHMkdOcP12BC97B4+OkwvMBuYEz+8FTjQzK1IWQ5djmSPJzMYBpwO3ZEgSqWsNOZVZpCKoblbd3CFZpD6vVTerbpbwKXgugaBryOHA8x3eGgusSHm9kohUaF2UGeDooEvRg2Z2YFEzViBBt5lXgHXAI+6e8Vq7ezOwFRhR3FyGK4cyA5wVdHu818zGFzmLhfJT4JtAa4b3I3etyV5miOa1lghT3dyJ6uYIfF6rblbd3EEUr3VRKXguMjMbCNwHfNXdt3V8O80uFX+HMEuZXwImuvuhwM+APxQ7f4Xg7i3ufhgwDphpZgd1SBK5a51Dmf8E1Lr7IcCj7LnjW7HM7Axgnbsv6CpZmm0Ve61zLHPkrrVEm+pm1c2ByF1r1c2Zk6XZVrHXWnVz8Sh4LqJgvMl9wP+5++/TJFkJpN4FGgesLkbeCiVbmd19W7JLkbvPA3qb2cgiZ7Ng3H0L8AQwq8NbbdfazHoBQ4BNRc1cgWQqs7tvdPeG4OX/ANOLnLVCOAb4hJktA+4ETjCz/+2QJmrXOmuZI3qtJaJUN6tuThG1z+s2qptVN0f0WhedguciCcZR/Bp4y93/X4Zkc4F/tISjgK3uvqZomQxZLmU2s72TY0zMbCaJv8mNxctl+MxslJkNDZ73A04C3u6QbC5wYfD8bOCvXsGLrudS5g5jBD9BYpxdRXP3b7v7OHevBc4lcR3P75AsUtc6lzJH8VpLNKluVt3cIVmkPq9VN6tuTk0TxWtdClbBfydZjRw50mtra0udDRERiYgFCxZscPdRpc5HJVPdLCIiYSpm3Rzppapqa2upq6srdTakArk7La3RvbEke5gZ1VUVPcGmFJGZvVfqPFQ61c0iIhKmYtbNkQ6eRbrrrJue5aXlW0qdDSmCKoNfXTCDj00bXeqsiIhIN6zdVs/IgTUY8B8PL+KCoyYyZmi/Tunqm1r42t2vcNmsA5gwon+Xx3z2nQ289N5mLj1hStbzb6tv4uGFazl7+ric8/zGqq0csM/gnG/evvDuJure28QXj5uc8zl2NDRz8W113PDJQ7KWt6daWp15r6/hjEP2IdcVn3Y2NHPg1Q/xo7MO4VMf1MTPUhkUPIuksXTDTg4bP5QT99+r1FmRAmpobuXnjy/hvY07S50VERHphk07Gzny+49x0Ycn8YlDx3DTE+9Qt2wT91zyoU5pn168gXmvv09jcyu3XPjBLo/7mf9JrOiUS/D8rXtf48E33mf/vQdx0NghWdO/vnIrH//5M3z1pCl89aSpWdMDfOpXzwHkFTw/+uZann1nIz95ZBH/de7hOe2zvb6Jg695mJ+ccyhn5XEz4NZn3uX6eW/R2Nya837vb6sH4JdPvqPgWSpGaMFzMBHBBHdfFNYxRUrFHQ4bP5Qvn5i90pTKta2+iZ8/vqTU2RApOTObBfwXUA3c4u43dHi/BriNxOysG4FPu/uyYJ3gt4Bk3T/f3S8pVr5FNu9qBOCvb6/j9EMS8yE1tqQfdlWoeX7WbU9MYFzf1JJT+jVbdwOJ1udiyKfYq7Yk8nbzU0vzCp7XBoHwxp0NWVJ2pkFyUklCmW3bzD4OvAL8JXh9mJnNDePYIiIiUjhmVg38AjgVmAacZ2bTOiS7CNjs7pOB/wR+mPLeO+5+WPBQ4CxFla6DcKZOw541Rc/kGgTm2q25p5Kn6U5w6nnu1XauPHbTbCNSicJaquoaYCawBcDdXwFqQzq2SNFFeRZ62UMVtwiQqL+XuPtSd28ksUbo7A5pZgNzguf3AidasSIAkRzkUm8nk+gvNzMLakZ9DRJJL6zgudndi9P3REQkZPqSIDE3FliR8nplsC1tGndvBrYCI4L3JpnZy2b2pJkdm+4EZnaxmdWZWd369evDzb3EWvIejpPLZ3kiQaFi53yPW451TyluLKjBQipJWMHzG2b2GaDazKaY2c+AZ0M6tkhJ6M509KnhTARI/52/47fZTGnWkJjv5HDga8AdZja4U0L3m919hrvPGDVKy2RLeJJ/mLnEX4Vuec6523ae6XuqGMFp6k2MfPcRqSRhBc9fBg4EGoA7SNyR/mpIxxYpOt0DjZd8x3aJRMxKIHWq23HA6kxpzKwXMATY5O4N7r4RwN0XAO8AuU0fLBKybGN8k9st5LbnfI9WrJixJ8FpvrViT4qkGlgqSY9n2w4mGvmuu38DuLznWRIREZEiehGYYmaTgFXAucBnOqSZC1wIPAecDfzV3d3MRpEIolvMbF9gCrC0eFkX2aMtgMvQ0lqoludyD/7yag0uWC5Key6RsPQ4eA4qzOlhZEakbHj4d6al/OTT3U8kqty92cwuBR4isVTVre6+0MyuBercfS7wa+B2M1sCbCIRYAN8BLjWzJqBFuASd99U/FJIXO1pbfasXYeTvYzKpbdwobtT96g1uJt5U30qURfWOs8vB0tT3QPsTG5099+HdHwREREpEHefB8zrsO2qlOf1wDlp9rsPuK/gGRTJIPVGd7Zgsa3luUDdtnMNHMsleE+n23nrQZkUcEslCSt4Hg5sBE5I2eaAgmepSE55V24Sjp6sgSkiIuUjpwnDkk9Crt+7+32haHVPESu5fOYQ0fcsqUShBM/u/rkwjiMiIiIikqu2m6ApMVumQDrZFbnUMVuxhoWldmnPV/4Thml9aImHUIJnM+sLXERixu2+ye3u/vkwji9SbO5e8spVCk+VvYhIdOQaLGqJpK4U/3ejFS+kkoS1VNXtwN7AKcCTJJa52B7SsUVEREREupTthuieMc+Fke8kW2V947YIedPErFKJwgqeJ7v7lcBOd58DnA4cHNKxRYpOY57jQddYRCQ6sn2mF2q27byDwGKt89yN3lXd/d2oPpW4CCt4bgp+bjGzg4AhQG1IxxYRKSh1GRMRiY5StTyXm3TjwXOV/5jn7ivrFniRDsKabftmMxsGXAnMBQYCV3W9i0j5cteYKBERkShpC57LpH4vx5ixmL+ZMrkMInkJa7btW4KnTwL7hnFMEZFi0V1vEZHoyPSRntxesDHPOabbsy509CqfKJZJJFVYs22nbWV292vDOL5IsTmabTsOdNdbRCQ69nRTTh/Aecz6bbcF6d1ZqirPILgn9anibakkYY153pnyaAFOJYcxz2Z2q5mtM7M3UrYNN7NHzGxx8HNYsN3M7EYzW2Jmr5nZESHlXUREREQqXLaJuzzHdIVWLt3G00nmrbvxrAJhibpQgmd3/0nK43rgOGBsDrv+FpjVYdtlwGPuPgV4LHgNiYB8SvC4GLgphKyLpKUPfxERkfKXVxwa1O1VYceu5RsL5627RWmb2Tu8rIiUpbBanjvqTw5jn939KWBTh82zgTnB8znAmSnbb/OE+cBQM9snpPyKdBahylDS27OMh6p7EZFKl2126UItVdV2/DKrSnoy27aIpBfWmOfX2XOzqRoYBXR3vPNod18D4O5rzGyvYPtYYEVKupXBtjXdPI9IRqpnREREKkvWdZ7bhjyXuNt2BZypGAF3tjHqIuUorKWqzkh53gysdffmkI6dlO4ToNN/m5ldTKJbNxMmTAg5CxInpa5cpfB0V15EJBpSA7BME2S1jXkuk+q9HOuetnoxz2YE1acSF2F1296e8tgNDA4m/hpuZsPzPNbaZHfs4Oe6YPtKYHxKunHA6o47u/vN7j7D3WeMGjUq33KIiIiISIVInXwr64RhBQrsihmLd6eVthjxbJncjxApuLCC55eA9cDfgcXB8wXBoy7PY80FLgyeXwj8MWX7Pwazbh8FbE127xYJnZfPnWkpnD3LeIiISKVL1tutGT7UdzUmOkU+8ubanI+ZT7DanSWh8pVP7Fzu32PKedZxkUzCCp7/Anzc3Ue6+wgS3bh/7+6T3D3jxGFm9jvgOWA/M1tpZhcBNwAfM7PFwMeC1wDzgKXAEuB/gC+GlHcREZFYM7NZZrYoWA7ysjTv15jZXcH7z5tZbcp73w62LzKzU4qZb5FUVVnG0K7cvBuAjTsbcz5mIVqrix0z5hVwt02kmedJgkK1duMXtnprfd77iJRKWGOeP+julyRfuPuDZva9bDu5+3kZ3joxTVoHvtT9LIrkznF1QYoB3fUWATOrBn5B4ob1SuBFM5vr7m+mJLsI2Ozuk83sXOCHwKfNbBpwLnAgMAZ41MymuntLcUshcdU+UO5m4NeFVneqCvSNoDst1fnkp8qK179KPbkkLsJqed5gZleYWa2ZTTSzy4GNIR1bRKSgNMGJxNxMYIm7L3X3RuBOEstDpkpdRvJe4ERL3H2aDdzp7g3u/i6J3mEzi5RvkXbaWp4zvN+d8cKZuoCnyjdGTQa1ha57qrJ0Y0+nuxN/7fkd5F+oUYNq8t5HpFTCCp7PI7E81f3AH4LnmVqVRcqea8xzLOgSiwCZl4JMmyZYTWMrMCLHfUUKJjVWsyxdh7sTq+bSOtwWDOd4zGTd050uzvnsUdWDrtT5yvd3kEpLVUklCaXbtrtvAr4Cbd2/Brj7tjCOLSJSaMWY5EWkjOWyFGSmNFpGUsqCsycIyxSLdStYzaPlOdfjWw9anrszYVh3Wp7z/V1154ZA8nrlkz+RUgul5dnM7jCzwWY2AFgILDKzb4RxbJFS0Oe4iMRILktBtqUxs17AEGBTjvtqGUkpOPc9dXeYN0RzCQbz7YZdrDWR9+Qrn4A28TPv4LkHXbmK0TIuEpawum1PC1qazyQxK/YE4IKQji1SEtnWi5TKV6wvMCJl7kVgiplNMrM+JCYAm9shTeoykmcDfw0m8pwLnBvMxj0JmAK8UKR8i7ST7bO8O5/1+bSK5tttuztBfj77dLcVGbpfL+azX1ugrqZnqSBhzbbd28x6kwief+7uTWam/wSpWBp/IyJx4e7NZnYp8BBQDdzq7gvN7Fqgzt3nAr8GbjezJSRanM8N9l1oZncDbwLNwJc007YUU/uW0q67bXenZs8l8LQ8W3iL1m27B40A+caz1qMxz93YSaREwgqefwUsA14FnjKziYDGPEtF04Rh0deTyl4kStx9HomeY6nbrkp5Xg+ck2Hf64HrC5pBkSxSg72MwXN3gtXW7GnynWg626zgYWlrec6hDB0VsxFB3balkoTSbdvdb3T3se5+WtCNazlwfBjHFikFfYyLiIiUP29rbfa24DVT4NedgDC32bZzTwvZZwXvMj95tTwH+3Sre3j3FLprvEiphdXy3E4QQDcX4tgixaKG5xjRXW8RkYrUFjCTOmFYei3diNJyW+c5CIZzbOHtzizYe/LTjei5G/K90WB53kBIpZZnqSRhTRgmEin6HBcREakcqS3PmbR0o3LPbbbtIA85HrMtpu1GfvIpQ3LMczFag9vGV3djwjB955JKouBZJBMNeo4FXWYRkcqVjLtaPbULd/q03ZnVOZ8Jw/JtQe1Wy3M3durWDNhaqkokrVC6bZvZJ9Ns3gq87u7rwjiHiEihqNoWEalsraljnjN8qrcUaHbrPROG5XeC7nRxbs4jeO7RetfdXapymXHKAAAgAElEQVSqG/soeJZKEtaY54uAo4HHg9fHAfOBqWZ2rbvfHtJ5RIpGDZLxoOssIlK5kgFravwVZstzLnFdVZ5LT7W1lndjFux8ypDtZkKX58kzoG3rup7Hfsl8acIwqSRhBc+twAHuvhbAzEYDNwFHAk8BCp6lYmiN5/jRJRcRqWztZtvOkKa5G9FqLkFkQ3NLkDa3Y+5qSKYv7JjnTTsbAWjKo8k9GdDmOz78+/PeBuCNVbmvVNudCdxESi2s4Lk2GTgH1gFT3X2TmTWFdI6SeXbJBnY3tVBVZRw1aQT9+lSXOksiIiIisZc65nnB8s0ArN/ekDZtrsHa5iDoTBy3633cnccXrU8cP4eAs6XVOf/XzwPw9vvbc8rP/KUb2+2fqy//7mUAXlmxJed9PvofTwBQ35T7jYbZv/hb2/PDJgzNaZ/G5lZO+MmTOZ9DpFyEFTw/bWZ/Bu4JXp8FPGVmA4Dc/2PL1Hfuf51lG3cBcMXpB/CFY/ctcY6kGDSRVDyYWc/GhYmISMn88ZXVAOxuauHKP7zRZdpH38o+DU9DcwuHf++RttfZ4uFJ357X9rx3VddfHHY0NHPQ1Q+1vZ4wvH/W/Fx232vc+eKKtte5BM+7GpuZdtWe8xwzeUTWfRqbW5l6xYNZ02XbZ58hfbPut2brbo7+wV/zOpdIuQgreP4SiYD5GBJDCG8D7gvWez4+pHOUzC8vmE59Uytn/uJv7Ay62kh0qQuviIhIZbjxscWdtp1+yD7tXu9saObAlKB135EDOu3j7u0C4aRMLc+trc6+32mfvqub7ove384pP32q3baTp43OmH5bfROHXPNwp+1dBc+ZyrD/3oMzZwyYesWDNDa3b2n+1IxxGdO3tDqf/tVz1L23udN7XY3Jbml1/vV/F/Dwm2vbbT/t4L27zJ9IOQkleA6C5HuDR+Tsv/fgtnGwmhEwPkxTScWCoRsmIiKVKNMcJQ+8toYHXnsg435LN+zkhgff5luz9qPuvc2c88vnMqb96H88waLrZlHTKzFkb/32Bj54/aNp0765ZjunHLh329JVkD5oTrrlmXf56semMrBmz9fxjTsamH5d+uMD7Grs3IhT39TCsT96PG139f59qnl80TouP+0Aqjq0jF/xh9f53/nL057n7rqV/PCsQ9qVJd0Ng6Q5n5/Jhbe+wDV/epNzZ06gb+/2Qxw/8fNneG3l1k77Devfm3mvv09TSyu9q7WCrpS/MJeq+iGwF4nvoonvo+5d3+qqINY2k6K+ZUedrrCIiEj5S9fKmqtfPvkOv3zynYzvf3TqKJ78e2Is835X/KXLY91zydGc88vnuPGxxWlbwjt69wenteU9tRt3xryefwTrtzdw5R8XcsbPnsmaPmnZDadTe9kDLF2/sy3o/eyRE/i/59MHzPuNHsRD//4Rai9L3HjI9ff79+tOpb55T1C//5V/oV/var5+8lSue+CttPv824lT+NrHprada8rlie7fv/jMEXzpjpcAqLviJEYOrMkpDyLFEla37R8BH3f39P8hEVFlCqziRGOe48H0fy0iUnE27ujcypoalHaUnLMmGaxl8u4PTsPMsqaDxJjlp755PM8u2ZBTnk86YC9uufCDOaXtmJ/Dr+3chTuTxdefmrEVN1PgPP/bJ7J3DuOVU739vVltLcx9erU/3+6mloyB8xvfPaVda3uqZOAMMOO6R1l2w+l55Umk0MIKntdGPXCGROuzum1Hn3oXiIiIlLd0XZvNjP/5xxls3tXIJw8fy/b6ZjbvamTfUQPb0px52BiWb9rFS8v3zGc7rH9v6q74GNUp3ZpfuepjHHbtI6QzZkhf/nbZCW29Eo/at+sJueZ8fiYfnTqq3bYrz5jG9/78Ztr0Y4f248lvHEevlAD4+e+c1OWEXuOH9+Pxr7ffB+CUA0fz0MK1GfaCu//laGZOGt5u2/994Uj++ba6tF3EgYwB7a8umM6/3L4g47nm/duxTBvTvlPqD886mG/d93ra9L2yTMAmUgphBc91ZnYX8Aeg7Vagu/8+pOOXhSrT2Mg40Ud2PBim/2sRkQry9OL1bc9/+unDOPPwsW2vP5YyCdewAX0YNqBPu31/eu7hOZ1jaP8+bUHihh0NDO/fp9OY4aSqKmtL29Lq7G5qYcm6HRw2PvOyTRd9eBIXfXhS2z6t7l2O+e3Tq4plN5xOfVML67c3cHfdCmYfNobJew3qshy/umAGACs27eLi2xewdls9H5kyssvfwzGTR/LmtbO6PG46pxy4N8tuOJ13N+zk2j8t5PFF6znriHH8+Jz2Y6dTffqDE/j0ByewfnsD5/zyWZZt3NXWDb5Z60BLGQoreB4M7AJOTtnmQKSCZ8PQ/3H06RKLiIiUrwt+/ULb89TAuVDyGXdbXWUMrOnVZeCcbp/qHG/Z9+1dzfjh/fn6yfvlfHyA8cP78+BXjs1rn+6aNHIAv/nczLz2GTWohie+0XmBnsbm1k5dwkVKKazZtj8XxnHKnZm69MaJxjzHhK6zxJiZDQfuAmqBZcCn3L3T+jNmdiFwRfDyOnefE2x/AtgH2B28d7K7Z19MV6Sbfvro39ue/+FLx5QwJ1IMU694UOOepayEcivHzMaZ2f1mts7M1prZfWaWeYG4ClVlplbJGND9kfhx/WdLfF0GPObuU4DHgtftBAH21cCRwEzgajMblpLks+5+WPBQ4CwF9dNH98xmnU/rrlSWmbV7xmGr4UrKSVj9IH4DzAXGAGOBPwXbIsWs68XfJVoyjc+RaNFVlpibDcwJns8BzkyT5hTgEXffFLRKPwLkPyBSpIc272xse/7q1Sd3kVIq3d2XHN32vCdLkomELazgeZS7/8bdm4PHb4FR2XaqNGp5jge1QsaQLrnE12h3XwMQ/NwrTZqxwIqU1yuDbUm/MbNXzOxK011HKaDDv7dn9ush/XqXMCdSbLszzPwtUmxhBc8bzOx8M6sOHucDG0M6djtmNsvMFpnZEjPr1L2skAy0VJWIiFQUM3vUzN5I85id6yHSbEtWhp9194OBY4PHBRnycLGZ1ZlZ3fr169Ml6bamltZQjyflLzlLtUTbuz84re35AVf9pYQ5EdkjrOD588CngPeBNcDZQOiTiJlZNfAL4FRgGnCemU0L+zyZz6/xsHGgaxwvZmp4lmhz95Pc/aA0jz8Ca81sH4DgZ7oxyyuB8SmvxwGrg2OvCn5uB+4gMSY6XR5udvcZ7j5j1KhwOqY1tbRSe9kDTLn8QWove4D/e/49Da2KiSvPKNpXPykhM2P88H5tr2sve6CEuRFJCGu27eXAJ1K3mdlXgZ+GcfwUM4El7r40OMedJMZrpV9lPmRVVaZJC0REJErmAhcCNwQ//5gmzUPA91MmCTsZ+LaZ9QKGuvsGM+sNnAE8WoQ8AzDl8gfbvb78/je4/P43OqX75qz9WLx2BxOG9+eZJRs4Yf+9ePSttYwf1p/5SzeybnsDPzzrYKqrqtjd1MJHpozEMN7fVs/wAX1odaemVxWGsWzjTiaO6M+KTbvZe0hfNu9qpLG5lcMnDGXLriYG9e3FM4s3sO+ogazYtIu339/GKQfuzc8fX8Lh44cyYmANyzftoqmllcl7DcQd9hnSl/teWsk+Q/px8oGjeX3lVjbsaGTOs8v43pkHsauxmd2NLWzc2cjLy7fw6Ftr+das/Xnq7+v5xGFjmD5xGANrevHGqq3sM6QfB40dzIpNu3GcCcP7s62+mTueX86M2mFUGSxdv5O/LdmAAx8/ZAyNLa28v7Wel5ZvpqG5lR+ffSgD+/ZixaZdNLa0MmpgDf36VLNqy2627W6if59evLR8Mys37+KhhWtpdefrH9uPXY3NjB7cl5WbdzNmaF9aWp2+vatpaXUmjujPayu3MrhfbyYO7891D7zFll2NXHD0RPr2rmbUoBrGDevHvQtW8oFRAxk5sIY3V2/lvY27uOWZdzl63xFMGzO4SH9ZUk6e/uYJ7YLma+Yu5JpPHFjw87o7ZkZTSyvvbtjJ1NHt19Nubmll8bodjBpUw5ot9SxcvZUBNb04eOwQBvXtxYYdjVRZYomw5Zt28dTf17N1dxOrt9Szz5C+/PzxJUwdPZBVm3ezM8Zd0r81a3/+9bgPlDobebFCBYNmttzdJ4R8zLOBWe7+heD1BcCR7n5puvQzZszwurq60M5/+LUPc8YhY/jemQeFdkwpP/VNLex/5V/45qz9+OJxk0udHSmwA678C+cfNYHLT1dLhmRnZgvcfUap8xEWMxsB3A1MAJYD57j7JjObAVySUt9+HvhOsNv17v4bMxsAPAX0BqpJBM5fc/cuvwmGVTerFSq+tHRRvLS0Oh/4TvtJwxZ+9xQG1OTeBvj+1nqaWlp5YtE6rvzjwrCzKD0Qxv9zMevmUFqeMyjEpCFdjbtKJDC7GLgYYMKEUGP3YMIwtTyLiEg0uPtG4MQ02+uAL6S8vhW4tUOancD0QucxmwVXnMT064rW4C0iRVZdZfzun4/ivP+Z37btwKsfKmGOJCwjB/YpdRbyVsjguRBRZsZxV20ndb8ZuBkSd7fDPLkZ7GpsYVPKUgkSPfVNiUYT0yJGsWAGu5v0fx11Nb2q8mqlkPKX2lrRseWisTnRFfnuuhX8/PElbdsPGjuYN1ZtK1oeJXxLv39a9kQSOUd/YASvXXMyh1zzcKmzEoph/Xuzu6mFMw8by8iBNYwf3o8zDhnDo2+tZXDf3uxoaGbN1t2cdMBoNu1spKrKOHz80LZlVBuaW6jpVY27s6Ohmeoqo1/vanY0NDOob2Im+mTX8yR3Z3tDM4NqemFmtLY6Le70qjKS00VUWWKseUur0+pO7+rE9Fg7GpoZmKYOffv9bUwcPoB+faozlnVHQzPVZlRVQU2vRLrmllYamlsrsl7uUbdtM9tO+iDZgH7uHupvJBhf9XcSd8lXAS8Cn3H3tP0vwu62/aEfPMbqrfWhHU/K2xWnH8AXjt231NmQAjv0uw+zdXdTqbMhBfZPH6oNZZxc1Lptl0LYdbOIxEtPhmxUVxnHThnJJR/9AIeNH0rf3tXsDILPvr0zB4BS3iqm27a7D8qeKjzu3mxml5KYvKQauDVT4FwIPz33cN5aozvWcVBdZZxxyD6lzoYUwU2fPYLF63aUOhtSYPvvXdTqSkRECiS1p4m7s2VXE7f+7V0uOHoim3c2MXFEf9zpsjU0VSW2fkrpFGzCsHKgu9siIhImtTz3nOpmEREJUzHr5rDWeRYRERERERGJrEi3PJvZeuC9PHYZCWwoUHbKVRzLDPEsdxzLDPEsdxzLDMUp90R3H1Xgc0Sa6uacxLHMEM9yx7HMEM9yx7HMELG6OdLBc77MrC5u3fHiWGaIZ7njWGaIZ7njWGaIb7mjLo7XNY5lhniWO45lhniWO45lhuiVW922RURERERERLJQ8CwiIiIiIiKShYLn9m4udQZKII5lhniWO45lhniWO45lhviWO+rieF3jWGaIZ7njWGaIZ7njWGaIWLk15llEREREREQkC7U8i4iIiIiIiGQRy+DZzJaZ2etm9oqZ1aV538zsRjNbYmavmdkRpchnmHIo83FmtjV4/xUzu6oU+QybmQ01s3vN7G0ze8vMju7wfhSvdbYyR+5am9l+KeV5xcy2mdlXO6SJ1LXOscyRu9YAZvbvZrbQzN4ws9+ZWd8O79eY2V3BtX7ezGpLk1PJh+pm1c0p70fxWqtuVt2cTBO5aw3xqZt7lToDJXS8u2dac+xUYErwOBK4KfhZ6boqM8DT7n5G0XJTHP8F/MXdzzazPkD/Du9H8VpnKzNE7Fq7+yLgMAAzqwZWAfd3SBapa51jmSFi19rMxgL/Bkxz991mdjdwLvDblGQXAZvdfbKZnQv8EPh00TMr3aG6ubNI/Q8HVDerbk6K1LVW3Rz9ujmWLc85mA3c5gnzgaFmtk+pMyX5MbPBwEeAXwO4e6O7b+mQLFLXOscyR92JwDvu/l6H7ZG61h1kKnNU9QL6mVkvEl9AV3d4fzYwJ3h+L3CimVkR8yeFEeX/4dhQ3ay6ucP2SF3rDlQ3txeJujmuwbMDD5vZAjO7OM37Y4EVKa9XBtsqWbYyAxxtZq+a2YNmdmAxM1cg+wLrgd+Y2ctmdouZDeiQJmrXOpcyQ/Sudapzgd+l2R61a50qU5khYtfa3VcBPwaWA2uAre7+cIdkbdfa3ZuBrcCIYuZTukV1c3qR+h9GdbPq5vaidq1TqW5uLxJ1c1yD52Pc/QgSXUW+ZGYf6fB+ursglT4tebYyvwRMdPdDgZ8Bfyh2BgugF3AEcJO7Hw7sBC7rkCZq1zqXMkfxWgMQdIX7BHBPurfTbKvkaw1kLXPkrrWZDSNx93oSMAYYYGbnd0yWZteKv9YxoLpZdXNS1K616mbVzakid63jVDfHMnh299XBz3UkxiHM7JBkJTA+5fU4Onc9qCjZyuzu29x9R/B8HtDbzEYWPaPhWgmsdPfng9f3kqi8OqaJ0rXOWuaIXuukU4GX3H1tmveidq2TMpY5otf6JOBdd1/v7k3A74EPdUjTdq2D7mNDgE1FzaXkTXWz6uYOaaJ0rVU3q25uE9FrHZu6OdLrPI8cOdJra2tLnQ0REYmIBQsWbHD3UaXORyVT3SwiImEqZt0c6dm2a2trqavrtPKDiIhIt5hZXCZ+KRjVzSIiEqZi1s2RDp5FumvFpl0s37Sr1NmQIuhVZRwxcRi9q2M5ikVERICdDc3samxh1KAaWlsdM6jAiYBFpMAUPIukce7N81m1ZXepsyFFcsMnD+bcmRNKnQ0RESmC5pZWqsyob25h6fqdDKzpxXE/fqJdmg/WDuOeSzoO2RSRuFPwLJLGzsZmTjlwNBd9eN9SZ0UKaGdjM5/7zYvsbGwpdVZERKQIWlqdyZc/mDXdi8s2U3vZAwAMqunF6989pdBZE5EKoOBZJA132HtwX2ZOGl7qrEgBbatvAiDKEyeKiEhCMhjO1/aGZr74fwuYWTucCz9Uq+7cIjGmQX4iIiIiElnuzpxnl/XoGPNef59r/vQmk749j/tfXklTS2s4mRORiqLgWSQNd9ed5RjQFRYRibZnl2xg0rfncfXchaEd89/vepUpOXT9FpHoUbdtEREREYmc+Us38plbni/Y8Wsve4AvnzCZjTsbGT2oL185aUrBziUi5UHBs0gaGgEbD8neBRryLCISPefePD/ntGdPH8fabfUsWbeDNVvrc97vZ39d0va8xZ2vfWxqXnkUkcqi4FlERCSmzGw4cBdQCywDPuXum9OkuxC4Inh5nbvP6fD+XGBfdz+ooBkWyVGuk4Mt/f5pVFW1H8TT1NLKnS8sZ9qYIZx107M5n/PGxxZz42OL+acP1TJtzGA+NWN8XnkWkfKnMc8i6ThoyHP0JS+xq6+BxNdlwGPuPgV4LHjdThBgXw0cCcwErjazYSnvfxLYUZzsimR3TZbxzf9+UqJ1+NWrTu4UOAP0rq7igqNrmT5xGO/+4DQe/veP5HX+3z67jG/e+xrfuOdVdjQ057WviJS3ggfPlnC+mV0VvJ5gZjMLfV4RERHJajaQbEWeA5yZJs0pwCPuvilolX4EmAVgZgOBrwHXFSGvIlkt37iL32aYWfvzx0zi1atP5isnTWHZDaczpH/vrMczM6aOHsTi60/l5Gmj88rLPQtWctD/z959x0lVnX8c/zzb6L1LcSkqYsHABsWKHcVIqtEUNdEYE00zDUvshfSYxCQaYyw/e4wRI4qiYomFoqCCoIAICNJll7Jse35/zN1ldne2sHtnZmfu9/16zWvn3rlz7nO4zJx57j33nKtmMHfFZmYtWc+L722gqkona0UyWSq6bf8FqAKOA64FSoBHgE+nYN8iLWYaiznrVfcu0D3PEmH93H0tgLuvNbO+CbYZCKyKW14drAO4DvgtsKOxnZjZBcAFAEOGDGltzCINmrHw44Trjx/Zl1+ctn+LZ9LIz83htrOL2FFWwagrZ+zRe7/4t1cbfO3bRw/j0lP3B2BDyS4+2VHG++u38d66Ei48Zjjt83NbFK+IJEcqkudD3X2Mmb0J4O5bzKwgBfsVaTHlUiKSLcxsJtA/wUuXN7eIBOvczA4BRrj7j8yssLEC3P024DaAoqIifcVKUtzz2ofcMP3deusT3dfcUh0LYj+dTz6gH6cdvBddO+Rzzh2zW1zerS8u59YXlyd87Y2Vn/Czk/ejvLKKV5Zt4qJjR7R4PyISjlQkz+VmlkuQj5hZH2JXokXaNN3znP2qexfol7xkM3c/oaHXzGydmQ0IrjoPANYn2Gw1MCFueRAwCxgPjDWzFcR+T/Q1s1nuPgGRNPjFf96pt+7hC8eHljhXWzF1Uq3lqz8ziqsfXxTqPgBefG8DL763oWb5kTdW85VxQ/h0YU/6d2vPjdPfJdeMq04/gG4dmu6CDrCropKC3Jwmr8Bv3VFO+4Ic2uXpyrdIvFQkz38EHiXWqN4AfJHdI3aKtEmufrwiEg3TgHOAqcHfxxJsMwO4MW6QsJOAS919M/BXgODK83+VOEtb8u/vHs6YIT2a3rCVzj1iKOceMRSAJR+XcPIfXkzKfpZv2M71T9S/sv7vNz/islNH8oUxg6iscvp2bQ9AaXklO8sq6dEp1uGzuLScg69+GoC5V5zAtY8v4tvHDGPV5h0cN7IfBXk5bNy2i43bdjHxDy9RtHcP+ndrz3/fWsurlx7HgG4dKCktZ9O2Mt5bV8LQ3p048fcv8tyPj2FIz47k5hiLPy5h714d6ViQxwcbt7NlRxkdC3IZ2b8rNzyxiEE9OjJhvz5Mm7+GnBxj+64KfnryfjXJfGWVU15Zxcx313HSqP4U5GlsY2lbkp48u/u9ZjYPOJ5Y16/Punv9T75IG6MLz9lP9zyLMBV4yMzOA1YCXwIwsyLgQnc/3903m9l1wJzgPdcGibNIm/GDB96sty4ViXNd+/Xvwoqpk6iscoZfNj1l+71x+mJunL644bj6dWHJupKa5aLrZwIwbcGaBt8z98Pds9aNv+m5Brc77rcv7Emo9fxl1rKE6/t0acecyxvsOCOSFklPns1sOPCBu99iZhOAE81srbt/kux9i7SUcikRiQJ330Ts5Hbd9XOB8+OW7wDuaKScFYDmeJa0cHcem187CVx+46lpiiYmN8dYesMpzPtwC1++7bW0xgLUSpwzxYaSXekOQaSeVHTbfgQoMrMRwO3A48B9QHq/1USaoHueo0PzPIuIZK7v3V/7qvPVnxkV+n3OLZGXm8Ohw3qx+LqJ/PihBRw4sBtfOXQIZRVVLPm4hK/94/V0hygieygVyXOVu1eY2eeBm939T9Ujb4u0VerGKyIi0vZVVTn/fWttrXVfOXTvNEWTWPv8XG756pha6/p0aceKqZMoLa/k9peW85un30tTdCKyJ1I12vZZwNnAZ4J1zRsSUCSNWjoXpGQenSwREclMf3j2/VrLMy85OqMGmWqfn8vFx+3Dxcftw8dbSyktr+SZResSTrklIumXiuT5G8CFwA3u/oGZDQX+LwX7FWkxdeONBp0fERHJbH+skzyP6NslTZG0Xv9usVGyzz9qKGMLezC0V6eakbKr7SyrBGDR2mLeXLkl4ejbyXL1Z0bxv2Wb2FlWyctLNyZ9f8eN7Jv0fYjsqVSMtr0I+H7c8gfERvcUadOUV4mIiLRdKzZur7X8zI+OTlMk4TKzBkcK71AQm3d57N49GLt3D84/ahhbd5bj7nTvWEBpeSXT5q+hvKqKyx+tPe91ny7t+Nqhe/Oto4fSsSCWAqzavINZS9bz+TGDKCmtYMuOMkb278L/vb6Sz31qIBWVVdzz6odcdOwIcnKsZkougNeWb+Ib/5zDwxeOp0+XdryybCMj+3fll08t5uCB3ahyGFvYg+G9O/Prp5fwmy8dXDNvdFlFFX9+fimfGtydpxet48rTRtXUrXqk8gP26hr6v61Ia1my57M1s32Am4BRQPvq9e4+rIn3rQBKgEqgwt2LzKwn8CBQCKwAznD3LQ2VUVRU5HPnzm1lDSSK9rviSc49vJBLT90/3aFIEpVVVLHvFU/y05P346JjR6Q7HMkAZjbP3YvSHUcmU9ssYSmc8kSt5RVTJ6Upkrbp462lzPtwC+3ycrhq2kKe/8mEjOjS7u4MvTQ2zZeOqTRHKtvmVHTb/idwFfB74Fhi3bibe1HvWHeP7xcyBXjW3aea2ZRg+edhBitSQ5eeIyPZJxFFRCS5+nRpl+4Q2pz+3doz6eABAJwwql+ao2k+jTkjbVkqTj91cPdniV3l/tDdrwaOa2FZk4G7gud3AZ8NIT6RepRKRYPaZxGR7DDjh9nRZVtE2rZUJM+lZpYDvG9mF5vZ54DmjADgwNNmNs/MLgjW9XP3tQDB33rlmNkFZjbXzOZu2LAhrDpIBJkuPUeGLjyLiGSWpeu31VruWWdgLRGRZEhF8vxDoCOxQcPGAl8HzmnG+45w9zHAKcBFZtasU4rufpu7F7l7UZ8+fVoas0SdkqlI0OkREZHMdMLvXqh5PrR3pzRGIslUWl6Z7hBEaknFaNtzAIKrz99395Jmvm9N8He9mT0KjAPWmdkAd19rZgOA9cmKW0RdekVERNq+p7NklG2pb8Wm7Yzsr1G3pe1I+pVnMysys7eBt4C3zWyBmY1t4j2dzKxL9XPgJOAdYBq7r1qfAzyWvMglyjTPczRUD0qioy0ikrnyc9v+CNLSMjdNX5zuEERqScVo23cA33X3lwDM7EhiI3Af3Mh7+gGPBj9s84D73P0pM5sDPGRm5wErgS8lNfLAJQ/O5+PiUnJzjB+ftB+HDO6eit1KmunCs4iISNszc9G6muc/nzgyjZFIsr3wnsYvkrYlFclzSXXiDODuL5tZo1233X05MDrB+k3A8eGH2LjyKqesooq5H25hXGFPJc8iWaL6BIkGDBMRyRzn3717nu6JCS0AACAASURBVPALjh6WxkgkWQ4Z3J35qz5Jdxgi9SStn4uZjTGzMcBsM7vVzCaY2TFm9hdgVrL2mwx/OutTPPjt8YC6d0aFu+55FhERaetyc9RYZ6N/nFOU7hBEEkrmleff1lm+Ku55xuWgukIlkn2qT5DoHneJKjPrCTwIFAIrgDPcfUuC7c4BrggWr3f3u4L1s4ABwM7gtZPcXYN5ikir9OrcLt0hiCSUtOTZ3Y9NVtnppB/Z0eBonmcRiYQpwLPuPtXMpgTLP4/fIEiwrwKKiH09zjOzaXFJ9lfdfS4iKbCrYvfURTd9/qA0RiKpsnbrTgZ065DuMESA1Iy23c3Mfmdmc4PHb82sW7L3G7aaK1TKnUWyRs1o2/pcS3RNBu4Knt8FfDbBNicDz7j75iBhfgaYmKL4RGpZX7yr5vlxI/umMRJJlX/NXZ3uEERqpGJs/zuAEuCM4FFMbLTtjKIpbaLF3XXPs4hEQT93XwsQ/E2UjQwEVsUtrw7WVfunmc03s1+YJf7mNLMLqk+ib9ig0XOl5b5y+2s1z/t1bZ/GSCRVfvvMe+kOQaRGKkbbHu7uX4hbvsbM5qdgv8mhS1QiWUefaslmZjYT6J/gpcubW0SCddUfm6+6+0dm1gV4BPg6cHe9jd1vA24DKCoq0kdOWmzV5p1NbyQikiSpSJ53mtmR7v4ygJkdwe6BRTKKmX5kR0XsnmcRkczn7ic09JqZrTOzAe6+1swGAIkG+1oNTIhbHkQwa4a7fxT8LTGz+4BxJEieRUT2VN8u7VhfsqvpDUVSKBXdtr8D3GJmK8xsBfBn4MIU7Dd0SqZEspR6lEh0TQPOCZ6fAzyWYJsZwElm1sPMegAnATPMLM/MegOYWT5wGvBOCmIW4SuHDkl3CJJkz1xyTLpDEKkn6Vee3X0+MNrMugbLxcneZ7KYmX5jR4Q7mug5InSYJeKmAg+Z2XnASuBLAGZWBFzo7ue7+2Yzuw6YE7zn2mBdJ2JJdD6QC8wE/p76KkhUfLKjrOb5DZ89MI2RSCp0abc7TVm0pphRe3VNYzQiMUlPns2sH3AjsJe7n2Jmo4Dx7v6PZO87bIamqhLJRvpUS1S5+ybg+ATr5wLnxy3fQWwA0PhttgNjkx2jSLWX3t9Y87yBsekki+Tk7D7Gp/7xJVZMnZTGaERiUtFt+05iXb72CpbfA36Ygv2Gzky9O6NEzXI06DiLiGSGW55fmu4QRCTiUpE893b3h4AqAHevACobf0vbpdxZJPvopJhIZlrycQmFU57g8kffTncokgKLPy5JdwgiEnGpSJ63m1kvgrzTzA4DtqZgv6EzdM9zFLgOcqSo659I5vrdM0sAuPf1lWmORESSYVifTjXPKyqr0hiJSEwqkudLiI3mOdzM/kdsCovvpWC/4TPd8xwlyqmiQ59rkcw0Y+G6dIcgaTDllJHpDkFS5Nm4Ebfnr/okjZGIxCQ9eXb3N4BjgMOBbwMHuPtbyd5vMhio33YE6MJztOgciUhmun927avNP314QZoikVQ79/DCdIcgKRLfO+yLf3s1jZGIxKTiyjPAOGA0MAY4y8zOTtF+Q2Wm3DlKTGlVZOiEiUjmufTfte9zfnje6jRFIqnWPj833SGISEQlPXk2s3uA3wBHAp8OHkXJ3m8yKJmKBuVR0aLu+SLZo3DKE6zesiPdYUgSlFXoflcRSb+kz/NMLFEe5VkwClNsqqqMr4Y0k5Kq6NCnWiR7HPnL55n6+YPIy83h/fUlXHrK/ukOSZph07Zd5JjRo1NBwtfLNFhUZD3/kwkc+5tZAKwvKaVvl/bpDUgiLRXdtt8B+qdgP0lnqHunSLZRjxKR7DPl32/zk4cXcOsLy7nn1RUsXR+b4qiisopdFc2fLbOyynll2UYKpzzB4wvWUB5iAldZ5ZSWt42ZO6//7yIKpzzR4OtPL/w46bGOvX4mn7ruGbbtqqCktLze67k6ox1Zhb061jwfd8OzaYxEJIlXns3scWIXdLoAi8xsNrCr+nV3Pz1Z+04WM9MVqgio7l2gZjo6dFJMJHv94rGF9dYtveEU8nJj1w9eXbaJW19cxn79uzDpoAGcf9dcXr30eBZ/XMwlDy5gybpY4v29+98E4OJjR7Bv/y706dyO7bsqOGFUPwA2by9j2YZtfLqwJwDllVX89601rC/exQvvbeDWr4/lskff4f11JfzhzEP426xl/Gf+GlZMnZQw7sUfFzOoR0cKcnMoyKt9reOVZRv5yt9fZ/blx9O3S3tO+N0LLF2/jes/eyBX/OcdAK45/QA6FOSyX78u7NOvMys27uDUP75EjsHymyZx7+sf8o+XPuC5n0zg9pc/AOB3z7yHu1O8s5zBPTty1rghLFj1CRfcM49TDuzPsSP78viCNdzw2YP4w7PvcdiwXpxRNDhh/AvXbGX//l3JyTFmLVnP72e+z78uHM9PHl7AEcN74zhHjOjNoB4dWV9cWvO+A6+aAcBTPzyKKx59hyE9O3LTFw7SrAgRFtUpJbfuLGfrjnKGxJ08aMrqLTsY1KP52yfTQ3NW0b9be47et0+t9effNYfCXp244rRRaYqsdSxZ3ZDN7JjGXnf3F5Ky4zhFRUU+d+7c0Mo78KoZnFE0mCs/k5kHW5qnorKKEZc/yY9P3JfvHb9PusORJNv3iif55hFDNfWJNIuZzXP3jBy3o60Iq22uvlK6YuqkRq+aZoO/fHUMf35uKaeNHsCy9dt55I22Ozha+/wcSsuT18V68iF7cfOZn0pa+dI2xX/Gzxo3mJs+f3Aao2me9cWlPPrmRxwxojdvrvqErx+2d81r7s7KzTvYu1cnPtlRRveOsdsVXn5/I53b59G7cwFH/vJ5AHJzjMoqZ87lJ1BSWs4d//uAlZt38uJ7G/jgplP503NLGT24O9tKK7jovje4+5vj6iWslVVObnAiq6iwJxtLdtGhIJclH5dw/+yV3PC5g7jg7rl0KMhlR1klt3xlDLk5xtadZQzv0xkzY0dZBR9uip2EmzJxJKccOICjf/08PTsVMPfyE9hZXklujpEfnJgcftl0AJbfeCqPv7WGHzwwn3euObnmBNnVnxnFuUcMDeXfOpVtc9KuPDeUHJtZLnBmsvabTIbmg42C6iMc0ROdkaTPtUhmWzF1Er9/5j1ufvb9dIeSFN+99w0AFq0tTnMkTUtm4gzw2Pw1Sp4j6Lwjh/KPoIfE/bNXpSR5nvfhZvJycjh4UDd++dQShvTsyANzVnL66L24eeb7PHPJMfxn/kdMfXJxrffd+LmDmPnuOp5bvL7W+heWbGDmu3s+N31lVew3yqdvmFnvtaGXTq+37uw7Zu/xPp585+Nay4fd1Hj3+JueXMxNQb03by9j2GX146gW/1p14gxw9eOL6Ne1PaccNGCP402nZHbb7gpcBAwEpgHPBMs/BeYD9yZr30lj6t4pkm10jkQkO/zoxH3JzzV+8/R76Q5FREL2nQnDa5JniF25Das7986yStrn59SUd9uLy7hx+uIGt39r9Vag4QTzskffTri+JYlztvvOvW80eOtKW5XM0bbvAbYArwLnE0uaC4DJ7j4/iftNGv3IjobqEyRRvccmknRSTCQrXHzcPuwoq+Qvs5alOxQRCVHvzu1qLd/05GIuO7X1I+mvKy7l0Bs1CJk0XzKT52HufhCAmd0ObASGuHtJawo1s4nAzUAucLu7T211pM3ft6aqEskyOkcikpmW3XhqwvU/mziSUXt15cgRvTnk2mdSHJWIJMucy0+o6bp824vLOWxYT44b2W+Py7nkwfn06dKOW19cHnaIEgHJTJ5r5hlw90oz+yCExDkXuAU4EVgNzDGzae6+qHWhNnf/qdiLpJvuf40eHXGJKjPrCTwIFAIrgDPcfUuC7c4BrggWr3f3u4L1BcCfgQlAFXC5uz+S9MCJDaLTkNMO3guAD246lU92lHPnKyuy9n7oqHn84iPTHYKkSc86c4B/8865nH/kUH5+ysiaQaqqfbKjjI4FeRTk5fDrGYv5z5tr+OiTnakMV5rhkMHd0x3CHktm8jzazKpHtjCgQ7Acmy7ZvWsLyhwHLHX35QBm9gAwGUhN8ox+ZIuISFaZAjzr7lPNbEqw/PP4DYIE+yqgiFgzOC84cb0FuBxY7+77mlkO0DO14TfOzOjRqYAfnbgvA7t3YMzePYDY/ZIPzlnFkfv0ZsJ+fSmc8gSXnjKSbx8zvNb7P/pkJx3zc+kR/GhfvmEb7fJzeXdNMZf/521e+OmxtM/PBWDJxyU88sZqDh/ei08X9qRDfi45QYJfVlHF315YxgVHD6vZ/uppC7nzlRUA3PetQ+nWIZ/P3fIKZXswl/TyG0/lteWbGD+8F2ZGeWUVyzds5wt/fYUnvn8k5/5zDicd0I/Jowey+ONiHpq7igHdOvDtY4bRv2t7Nm7bRad2edz6wnLufGUFM354NIN6dKBTu90/D7fvquC0P73MoB4duOe8Q5m1ZD0rN+9g+65KvjNheIMjnT/9o6M56fcvAvDSz45l1pL1fH18Ic8tXseQnp0o7NWxZrqweKu37KCsoophfTqz5pOdvLX6EwZ278hBg7o1+99FslNujjF6cHcWrPqkZt3tL3/A3a9+yP+mHEf3jvlUufPEW2u55KEFaYy09b59zDAmjx7I42+t4a+zlvHNI4Zyx/8+SLjtsN6dWL5x+x7v429fG0tpeSU/fLDpu2nH7t2Dgd07MG3Bmpp1d5xbxCPzPuKJt9dy9L59WPJxMeuKdzVSSn0PXHDYHsedbkmbqioZzOyLwER3Pz9Y/jpwqLtfnGj7sKeqGnPdM0w6aADXffbA0MqUtqe0vJKRv3iKn03cj+9OGJHucCTJ9v/FU3ztsCFcPklT0EnTsm2qKjNbAkxw97VmNgCY5e771dnmrGCbbwfLtwbb3W9mq4CR7t7sX25ht82Zyt3ZVVFVk0xDbF7Xj7bsZGjvTpRXVdG1fT4AT769lrF796Bv1/YJ35dO768roVcw5/VfZi3l/tmruPnMQ5h8yEDKKqrIzbFGewmI7IldFZXsd8VT6Q4DgKP37cMXxgzkBw/M59zDC/nxSfuyo6yS793/Jn8881McdtOzDOjWnm8eMZTfPfMel506ksE9O9KlfR59u7SnZ6cCikvL+f79b/LziSMpq6ji8BG9G93ne+tKMGCffl1qrV++YRud2+XRt2t71heXUunOkb98nl9+4WA+2VHG+OG9GN6nM7OWbGDz9jKO3rd3rfmg567YzA8emM+MHx1N5+Dk2Sc7ylixaUetq8PPLFpHZZUz8cD+CePbtquCeR9u4Zh9+7Bx2y66d8hn844y+nZpT2l5JeuKS9m7V6cW/os3LCumqkqSRN++tbJ/M7sAuABgyJAhoe9cXXpFRCSL9HP3tQBBAt03wTYDgVVxy6uBgWZW/YvqOjObACwDLnZ3DSnbDGZWLwHu1iGfbh1iCXMHdr8WP5VLovelU/WP+J6dCrjp8wfzk5P2o1cwuFNBXv0ryyKt0S4vdf/3685D/MbKLRwyqHtNj5Jqkw8ZWPO8S/t8Hvr2eABmX3Y8HQpy6dI+n28dPSzhPjq1y+PhCw9vdkz71kmaqw3r07nmed+u7YHE40I0lPQWFfbkf1OOq7Wue8cCDulYu6v8iaMav8e8c7s8jgnmmK4e5K1vl1g87fNzk5I4p1qmJc+rgcFxy4OANfEbuPttwG0QO7sddgAZdKFeWsk0vnokmKagkyxnZjOBRL+YLm9uEQnWObHfEIOA/7n7JWZ2CfAb4OsJYkjaiW1pW3rVGRVZJGxzrziBouvrz3kchgVXnUS3Dvls3VFO1w6106QxQ3rsUVnVSaxkl0xLnucA+5jZUOAj4EzgK6nauZnueRYRkczi7ic09JqZrTOzAXHdttcn2Gw1sQHBqg0CZgGbgB3Ao8H6h4HzGoghqSe2RSQ6endux09P3o9fz1jSqnLi5xcuLi3ngw3ba3p+dOuY36qyJXtlVPLs7hVmdjEwg9hUVXe4+8LURWCs3LSDJ99em7pdSspVD9ai0dWjwYAPNm7X5zrLDenVkQP20oBDCUwDzgGmBn8fS7DNDOBGM6u+7HIScKm7u5k9Tiyxfg44nhQN4Cki0XbRsSNanDy/eulxDOjWoda6ru3zGZ2BIz9L6mVU8gzg7tOB6enYd4+O+by8dCMvL92Yjt1LilWffZTs1r1jAc8uXs+zixNdcJNsce7hhRxwupLnBKYCD5nZecBK4EsAZlYEXOju57v7ZjO7jljvL4Br3X1z8PznwD1m9gdgA/CN1IYvIlH1/g2ncPPM9zl+/7587i+vJNzm9NF78fsvH6JB6yQ0GTXa9p4Ke0TPrTvKWVusOeKiIC/HGN6nM6bLz1lvy/Yy1pWUpjsMSbIeHQvoF8L9Z9k22nY6aLRtEUkmd6dkVwWdC/LqDe4l2UmjbbdR3Trm6x4IkSzTo1NBzRyuIiIiktnMrGaaN5GwaQ4BERERERERkSZkdbdtM9sAfJjuOBLoDUTxxuko1juKdYZo1juKdYbo1Xtvd++T7iAymdrmNieK9Y5inSGa9Y5inSF69U5Z25zVyXNbZWZzo3jPXBTrHcU6QzTrHcU6Q3TrLdknqv+Xo1jvKNYZolnvKNYZolvvVFC3bREREREREZEmKHkWERERERERaYKS5/S4Ld0BpEkU6x3FOkM06x3FOkN06y3ZJ6r/l6NY7yjWGaJZ7yjWGaJb76TTPc8iIiIiIiIiTdCVZxEREREREZEmKHluJjO7w8zWm9k7ceuuM7O3zGy+mT1tZnsF6yfHrZ9rZkcG6/c2s3nB+oVmdmFcWWeZ2dvB+54ys94JYphgZluD9883syszvM5fDt6z0Mx+1Ugcl5rZUjNbYmYnJ7POwf7SXm8zKzSznXHH+m9tvc5x7+tqZh+Z2Z/j1o0N/n8vNbM/mpkliMGC15YG5Y9JZp2DfbaFemfc57qJOt9gZqvMbFsTcaT0cy3ZKQXf12qbG45DbbPa5qRoI/XOuM91E3VW2xwWd9ejGQ/gaGAM8E7cuq5xz78P/C143pndXeIPBhYHzwuAdnHbrAD2AvKA9UDv4LVfAVcniGEC8N8sqXMvYCXQJ3jtLuD4BDGMAhYA7YChwDIgNwL1LozffyYc67htbwbuA/4ct242MB4w4EnglAQxnBq8ZsBhwOsRqfcEMuxz3USdDwMGANsaiSHln2s9svOR5O9rtc1qm+vGUIjaZrXNmVlntc0hPXTluZnc/UVgc511xXGLnQAP1m/z4H9hnfVl7r4rWN+O3Vf+LXh0Cs5+dQXWJKMeeyLJdR4GvOfuG4LlmcAXEoQxGXjA3Xe5+wfAUmBcqyrWhDZS75QKo84QO5sL9AOejls3gFgD8GrwvruBzyYIYzJwt8e8BnQP3ps0baTeKZXMOgfvec3d1zYRRso/15Kd1DbXrFPbrLZZbbPaZrXNKZCX7gAynZndAJwNbAWOjVv/OeAmoC8wKW79YOAJYATwU3dfE6z/DvA2sB14H7iogV2ON7MFxBrwn7j7wrDr1JQw6mxmO4GRZlYIrCb2xVWQYHcDgdfillcH61IuxfUGGGpmbwLFwBXu/lLYdWrKntTZzHKA3wJfB46PK2YgsbpWa+gYDgRWJdiuqS/70KW43pBhn+tG6txcbeZzLdlJbbPaZrXNaptR27yn2sznui3TledWcvfL3X0wcC9wcdz6R919JLEv4Ovi1q9y94OJfWmfY2b9zCwf+A7wKWLdh94CLk2wuzeAvd19NPAn4D9Jqlajwqizu28hVucHgZeIdZ2qSLC7evehEHeGLZVSXO+1wBB3/xRwCXCfmXVNTs0atod1/i4w3d1X1SmmuccwU491a+udiZ/rhurcXG3mWEt2Utustllts9pm1DbvqTZzrNsyJc/huY8EXXyCbhjDrc4gI8FZ7YXAUcAhwbplQTeMh4DDE5RV7O7bgufTgfy65aZYa+qMuz/u7oe6+3hgCbGz+nWtBgbHLQ8i/d3mkl7voMvMpuD5PGL3newbdkX2QHPqPB642MxWAL8BzjazqcSO4aC4tzV0DDP1WLeq3hn6uW6ozs3VFo+1ZCe1zQG1zWqbUdustrlxbfFYtzlZPc9z7969vbCwMN1hiIhIlpg3b95Gd++T7jgymdpmEREJUyrb5qy+57mwsJC5c+emOwwREckSZvZhumPIdGqbRUQkTKlsm7M6eRZpqRkLP2bhmuKmN5SMl59jnDluCH26tEt3KCIiEjJ355E3PuIzowfQLi833eGISIZT8iySwGX/fptN28vSHYakSJf2eZx7xNB0hyEiIq2wdP02hvTsSEHe7iF9nn13PT95eAHvrSvhslP336Py1peUMu6GZ/nXheMpKuzZ4rjmfbiFYb070aNTQwN4N+2W55eybMM2fnfGIS0uA6C0vJIpj7zFZafuT9+u7VtVFsA1jy9kYPcOnH/UsFaXJZIJlDyLJFDpzjnj9+aayQemOxRJouLScg6++mkqs3foBxGRSNi4bRcn/O4FzigaxK++OLpmfXFpOQDri0v3uMzXl8em3f3nKytalTx/4a+vMLJ/F5764dEtLuPXM5YAtDp5/u9ba/nP/DXk5FirywL45/9WAISaPI+7YSZdO+Qz85JjQitTJCwabVskgSweR08SyOaBE0Waw8wmmtkSM1tqZlMSvN7OzB4MXn89mA8XMys0s51mNj94/C3VsYsAlJTGZpZ6/YPNtdZbMPlOq77lQ2giFn9c0vpCwtSGm731JbtYun5busMQSUhXnkVERCLMzHKBW4ATiU1VMsfMprn7orjNzgO2uPsIMzsT+CXw5eC1Ze7e+ktYIq2QaILa2PqGXmlGmTWJdxvONPdQ9b9G9tRIJLV05VkkAXfHrOUNrmQGHWERAMYBS919ubuXAQ8Ak+tsMxm4K3j+L+B405ektEFhdiRqTeItItlJybOIiEi0DQRWxS2vDtYl3MbdK4CtQK/gtaFm9qaZvWBmRyXagZldYGZzzWzuhg0bwo1ehKavEuvuHBEJg5JnkQTUxkZD9YUz/aiSiEt0ea3up6KhbdYCQ9z9U8AlwH1m1rXehu63uXuRuxf16dOn1QGL1NXQVeIw7nnOpjai5t8jmyolkkJKnkVERKJtNTA4bnkQsKahbcwsD+gGbHb3Xe6+CcDd5wHLgH2THrFICmTjjQnZWCeRVFLyLJKIq4GJgt0Dp+gMvETaHGAfMxtqZgXAmcC0OttMA84Jnn8ReM7d3cz6BAOOYWbDgH2A5SmKW6SeZFxQ1UVaEakW2mjbwcAhXwWGufu1ZjYE6O/us8Pah4iIiITL3SvM7GJgBpAL3OHuC83sWmCuu08D/gHcY2ZLgc3EEmyAo4FrzawCqAQudPfN9fcikly7uyMnfr0l3ZSz+Ry6zgeItEyYU1X9BagCjgOuBUqAR4BPh7gPkZRwNMpmFDT1Y0skKtx9OjC9zror456XAl9K8L5HiLX1Im1SzdgWrSgjm3on6beNSOuEmTwf6u5jzOxNAHffEnT/EhERERFJuZpUsQX5bxi3b2lgLpHsEuY9z+XBfU8OYGZ9iF2JbpCZ3WFm683snbh1Pc3sGTN7P/jbI1hvZvZHM1tqZm+Z2ZgQYxepJTbPc7qjkGSrPgOvnzYiIhIlyulFWibM5PmPwKNAXzO7AXgZuLGJ99wJTKyzbgrwrLvvAzwbLAOcQmwgkn2AC4C/hhO2iIiIiGSyZE7BlE2JZhhTd4lEWWjdtt39XjObBxxPrJfMZ9393Sbe86KZFdZZPRmYEDy/C5gF/DxYf7fHvhVfM7PuZjbA3deGVQeReLrwnP10z7OISHZo6N7m3cliS77oQ7hfWu2LSFYJ7cqzmQ0HPnD3W4B3gBPNrHsLiupXnRAHf/sG6wcCq+K2Wx2sEwmd2joREZHMV3N7TprueRaR7BJmt+1HgEozGwHcDgwF7gux/ERfYfW+Cs3sAjOba2ZzN2zYEOLuJWrUaEZHNo2kKiIi0hQNZCbSMmEmz1XuXgF8HrjZ3X8EDGhBOevMbABA8Hd9sH41MDhuu0HAmrpvdvfb3L3I3Yv69OnTgt2LqJuViIhIJqk+352M9rs1Zba1nxOmKwMirRL2aNtnAWcD/w3W5begnGnAOcHzc4DH4tafHYy6fRiwVfc7SzKpgcl+uudZRCQ7NHRvc2u+57P5V4CaPZGWCTN5/gYwHrjB3T8ws6HA/zX2BjO7H3gV2M/MVpvZecBUYvdLvw+cGCwDTAeWA0uBvwPfDTF2kVrUjVdERCRzNHRvc80V6dYN+9WK97Yt2XxCQCQVwhxtexHw/bjlD9id+Db0nrMaeOn4BNs6cFFrYhTZE2pgsp/pKIuIZLXWdCILowea7i0WyS6hJc9mtg9wEzAKaF+93t2HhbUPkVRRWyciIiKQpb8JsrFOIikQZrftfwJ/BSqAY4G7gXtCLF8ktXRRMjJ0ZUBEJLPtvuc5Md3zHKPhXERaJ8zkuYO7PwuYu3/o7lcDx4VYvkjKKJWKBv2IEBHJDg2Pth3cC92KsvWbQESqhdZtGyg1sxzgfTO7GPgI6Bti+SIppftho0MXnkVEslPr7nlu/f7bavOigVFFWibMK88/BDoSGzRsLPB1dk85JZJZ1KZEgk6PiIhIlDQ0KrmINE+Yo23PAQiuPn/f3UvCKlskHdSlNzr0G0JEJMPVtNmJv9Fbkyxm07gY+m0j0jqhXXk2syIzext4C3jbzBaY2diwyhdJJXVnioYwpiERyQZmNtHMlpjZUjObkuD1dmb2YPD662ZWGPfapcH6JWZ2cirjFqmrwXmeW5AA51gI90vr54RIVgmz2/YdwHfdvdDdC4nNyfzPEMsXSSmlVdGhHzcSZWaWC9wCnEJsusmzzGxUnc3OA7a4+wjg98Avg/eOAs4EDgAmAn8JyhNpE1qTAFefX62syr5Gs8WkVwAAIABJREFUQu2eSMuEmTyXuPtL1Qvu/jKgrtuSkdSoRINOkIgAMA5Y6u7L3b0MeACYXGebycBdwfN/AcdbrOvGZOABd9/l7h8AS4PyRFKrgXa7Zgqr1lx5zqLfBDk1U3plUaVEUqjV9zyb2Zjg6WwzuxW4n9hX2JeBWa0tXyRd1KM3OvQjQiJuILAqbnk1cGhD27h7hZltBXoF61+r896BdXdgZhcAFwAMGTIktMBF6mro27wlt+nsvmrd8jai7bUvsTpl4cV0kZQIY8Cw39ZZviruuT6akpH0HzcadIJEBEjcCaPu12BD2zTnvbj7bcBtAEVFRfqKlaQJc3Cv6qu0VVWhFZl2Oa24Ei8iISTP7n5sGIGItDWa5zk69BtCIm41MDhueRCwpoFtVptZHtAN2NzM94qkTEP3J7coWaxOnrOokcjN0ZVnkdYIc7Ttbmb2OzObGzx+a2bdwipfRCRsGm1bBIA5wD5mNtTMCogNADatzjbTgHOC518EnvNYNjINODMYjXsosA8wO0Vxi9RTb7TtVnzNZ+M9z5aFJwREUins0bZLgDOCRzEabVsylLurS2+E6CeERJm7VwAXAzOAd4GH3H2hmV1rZqcHm/0D6GVmS4FLgCnBexcCDwGLgKeAi9y9MtV1EKn+Hg8zKdx9lbYV9zy3sQbGsvCEgEgqhXHPc7Xh7v6FuOVrzGx+iOWLiIhIErj7dGB6nXVXxj0vBb7UwHtvAG5IaoAizRRmTpgTwlXatpakVl9N15VnkZYJ88rzTjM7snrBzI4AdoZYvkjKNDQKjmQp/YgQEckK4SaFrb8/uK2Ntl3920bNnkjLhHnl+TvAXXH3OW8Bzg2xfBGR0Kl7vohI9ghzIKzdcyK3XFsbmCuM6bdEoiy05Nnd5wOjzaxrsFwcVtkiqeaOsqoI0U8IEZHMVn0lNcwpmHbfH9yabtttq4XJxum3RFIptOTZzPoBNwJ7ufspZjYKGO/u/whrH+kyZ8VmdpZVkptjjN27B+3zc9MdkoiERKdIRESyR0O5aktS2OrEt6Hpr5pVRovf2UB57q2aKcJ0z7NIq4R5z/OdxEbq3CtYfg/4YYjlp81PH17A2XfM5qu3v869r69MdziSIkqqREREMkvdpNBa0ZqHkV56yFd4W9sNvDrvVu4s0jJhJs+93f0hoApqpr7Iiukq/vyVMTx84XgAtu+qSHM0IhI2/YgQEckOdZPL1tzbWxXCDcth31vc2ivGuudZpHXCHDBsu5n1IjhRZ2aHAVtDLD9tDhzYreYLVN1csl91Ny3d8hwNren+JiIibVv1vb0t+flWnTu3ppkI+2dja3+HVr+/rFK/Z0VaIszk+RJgGjDczP4H9AG+GGL5aaVuLiLZS2fgRUQyW2UDP9C27ixvcZmXP/o2AMU7W97r8FPXPdPi91aLH3Sstb9Dz7ztNQAWrPqkdQUBR0x9rtVl1HXRvW+EXqZImMIcbfsNMzsG2I/Y7aJL3L3l31htTM2Ii2mOQ5KvumFqzX1Skjl0lEVEMt+Xb3213rrS8kp+/PACAF5bvmmPyht9zdM1iffKzTtaFNP0t9e26H3xqqqcYZdN373ciuz5oKtm1Dwf1KNDi8txd4ZeOr3pDfdAZZUz/LJwyxRJhjCvPAOMAwqDcseYGe5+d8j7SBuztjflgIi0nj7WIiKZbfWWnTXPz79rDjPfXV/r9TOKBjernB1lFYy6ckatdeceXrjH8Rz3m1ks37i9ZvmIEb32uIxVm3dw1K+er7WuJbdhV1RWMeLyJ2ut+8zovRrYunHPL1nPN/45p9a63p0LWlQWwNYd5Yy+9ul667u0DztFEQlHmFNV3QMMB+aze6AwB7IneUY/sqOg+hDrVtho0HEWEclsB15VO9mtmzgD3PPah3zv+BH07dI+YRkvvb+Br/9jdsLX7nxlBVd9ZlSTY2SUVVSx7xVP1lvfq1MBc1ZsYUdZBR0Lmv7pffqfX+at1YmHDWruRZyyiirWfLKTCb+ZVe+1vBzboyvY60tKGXfDs4ljHb0X0xasaXbdAMorqzjj1ld5c2XiruNnfnowD8xZ1ez4RFIpzNM6RcAoz+JLszm2Z182IpIZ9KkWEclMn+woY1sjM6G8dunxHHZTLPGrTgCvmLQ/i9YU8+83P2q07BVTJ1E45QkAhl46nXlXnECvzu1qbePufP0fs3l56caEZSy48qSaK6ujrpyRsAyIJbujr3maneX1J6q5+cxDeHdtCX97YRkHXf00z/74GIb36VwvjtLyKo7+9fNsKNnVYJ3+N+U4jpj6HLe+sJxbX1jOiqmTar1eUVlFXm4OryzdyFduf73BcgA+uOnUmu7bo66cwRWT9uf8o4bVvF5V5RSXlrNx2y4ueWhBgycEqv3ta2OYeOCAmn/zi+59g1u+OqbR94ikWpjJ8ztAf6D1N3i0UWb6kR0FNaNtpzkOSQ3d2y4ikpkqq5xDrq0/INfCa06mU7uGf+Je/8S7jZb79tUn0aV9fr31Y6+f2ezYzhm/N9dMPrBVZQAsv/FUcnKMHzzwRM2643/7wh6VAXD/tw5j/PD6XcerE9WWxFTX9U+82+S/bSKPfOdwxu7do976J95eyy17XJpIcrU6eTazx4nllF2ARWY2G6g55eXup7d2H21F7B7udEchImHT51pEJPPEDzD11A+PYuIfXqJDfm69xPnayQfQPj+Xn/3rrQbLOmvcYL522N4csFe3WuvfuvokDr66/j25DZl9+fH06dyuVhfvaycfwJWPLWx2GQBLbziFvNycmuVF155c717sphw+vBf3feuwPXpPQ/5z0REcMrh7vfX3nDeuwe7ujfnmEUP5xWn7J+wKv6f/5iKpFMaV59+EUEZGiN3zrF/Z2U73PEeMjrOISMaJv2I6Yb8+jOzftV4X5Gpnjy8EYoOGVf+OK95ZQbeO9a8u19W1fT4rpk6iuLS8wYRuwZUnNVrW2eMLOXt8IefcMZsX3tuQcBszWHTNRDoU5CZ8vWNBXpNxAMz6yQQKe3dqpEbU/DtdPW0hd76yIuE23zpqKD+bOJL8uAQ+kaP26cOKqZPYWVbJ/lc+lXCbL40dxPWfO5B2eYnrVlfXBFf9RdqKVifP7p6w74iZ5QJntrb8tkTdtkWyk+Z5FhHJHK/XmXbqzm+Ma/Z7q690NidxjledRFdWOZVVTkFe40llInd9Mxanu7OjrJJO7fIoq6jao7Kq4wjD1acfwNWnHxBKWR0KckOLK97P/rWAX31xdOjlirTUnn/y6zCzrmZ2qZn92cxOspiLgeXAGa0Pse3IiU29le4wJMlq5nnWpedI0FGWKDOznmb2jJm9H/ytf+NhbLtzgm3eN7Nz4tbPMrMlZjY/ePRNXfQSVV++7bWa58lI2BqTm2MtSpzjmVlN1/LWlpWtBnaPzUP90NzVaY5EpLYwPrH3APsBbwPnA08DXwImu/vkEMpvM4yWza8nIm2cPtcSXVOAZ919H+DZYLkWM+sJXAUcCowDrqqTZH/V3Q8JHvXnCBIJ0fG/nVXz/H9TjktfIJJUL/3s2Jrniz8uTmMkIrWFkTwPc/dz3f1W4CxiU1ad5u7zQyi7TcnRgGEiWUcdDCTiJgN3Bc/vAj6bYJuTgWfcfbO7bwGeASamKD6RWpZt2F7zvPrqpGSf+NG8J/7hpTRGIlJbGMlzefUTd68EPnD3khDKTcjMJgZdxJaaWb0z5EllaJ7nCND9r9GjIy4R1s/d1wIEfxN1ux4IrIpbXh2sq/bPoMv2L0z3u0gS3fbisprnH9x0ahojkVR4+ee7rz4/MHtlGiMR2S2M5Hm0mRUHjxLg4OrnZhZqP4tgELJbgFOAUcBZZjYqzH00uv9U7UhEUkbzPEu2M7OZZvZOgkdzb61K9CGpPuf0VXc/CDgqeHy9gRguMLO5ZjZ3w4bEow231M0z32ddcWmoZUrbdOP0xQAcsFdXjUsSAYN6dKx5PuXfb2vcIWkTWp08u3uuu3cNHl3cPS/uedcwgowzDljq7svdvQx4gFiXs5TIydGAYVGwe8Cw9MYhqaPPtWQzdz/B3Q9M8HgMWGdmAwCCv4nuWV4NDI5bHgSsCcr+KPhbAtxHrJ1OFMNt7l7k7kV9+vQJpV4fby2lcMoT/H7mexx647MUTnmCnWWVoZQtbdsT3z8q3SFIirx19Uk1z4deOr2RLUVSI4x5nlMpUdexQ1O1cw0YJpJ9dJJEIm4acA4wNfj7WIJtZgA3xg0SdhJwqZnlAd3dfaOZ5QOnATNTEDMAh930bL11Dc0zKyKZqWv7fMYP68WrwfRkhVOe4N1rG54POyzujplRWl7Jo29+xOmj9+LdtcXsKKtk8cfF9O7cjvfWbePhuavYtL0sqbFks2snH1AzD3umyLTkubGuY7ENzC4ALgAYMmRIuDs30/2wEaLuvNGhC88SYVOBh8zsPGAlsdkyMLMi4EJ3P9/dN5vZdcCc4D3XBus6ATOCxDmXWOL899RXQUSy2f0XHEbhlCdqlve/8in+9rUxTDxwQKPv21VRya6KKq549B2mLVjTqhgu/ffbrXq/JHblYwuVPCdZg13Hqrn7bcBtAEVFRaH+JM4xXXkWEZHs4e6bgOMTrJ9LbPrJ6uU7gDvqbLMdGJvsGBtyzekHMKBbe046oH/NumcWreNbd89NV0iSAveen7IOh9KGrJg6qVYCfeH/vZHGaCQsl5+6f7pD2GOZljzPAfYxs6HAR8CZwFdSt3tj7orNXD1tYep2KSlXGZwhUXfeaDDglWWb9LnOcocO7ckpBzV+lUIyyzmHF9Zbd+KofqyYOinh9uWVVeTnxoZ6KSktZ0PJLgb37IgBle60y6vdDXRXRSXri2PbAFRUVlFe6bTLy8EMPvpkJ3t160B5VRU7dlXSvWM+a7aW0rldHt065OPulFc6BXm7h5cpKS1n687yWgMhxe+vIDenWQNhVVRWsXVnOb06t2ty22qVVU5llZObY+QG0wCVlleSm2M1/y7pUlXlVLpTWl5Jl/b5tV6rqKwiL83xSdvw3vWnsO8VT6Z8v58u7EHfru0Z0aczBw3sRoeCXD5d2JPcHKOiqqrW53brjnK6dsirWS4tr8QdNm3fRZ8u7cg1i019CzWfw+r3bSur0PRrGSCjkmd3rzCzi4ndf5UL3OHuKfvFW7R3D15ZtpF/v7E6VbuUNOnZqYD9+ndJdxiSAmMLezJ/5RZWb9mR7lAkidrl5yh5jrj4BLFL+/xaSVqiH0Pt8nJrEmeAvNwc4vPr6gS4XU5uTeId/8PXzCjIq50I191v3f01V15uzh4lzkCtpLla+/zk3jfaXDk5Rg6Jk3glzlKtIC+HFVMn4e78/aXlNaOvN+arhw5h0kEDKCrsya6KStrl5dY6odVauTm1P0PdOtb+fFd/xgYV1D9hVvd9dd8rbZNl8yizRUVFPneuum+JiEg4zGyeuxelO45MprZZRETClMq2WafzRERERERERJqQ1VeezWwD8GGw2BvYmMZw0iGKdYZo1juKdYZo1juKdYa2U++93T2ciYojSm1zJOsM0ax3FOsM0ax3FOsMbafeKWubszp5jmdmc6PW1S6KdYZo1juKdYZo1juKdYbo1jvbRfG4RrHOEM16R7HOEM16R7HOEM16q9u2iIiIiIiISBOUPIuIiIiIiIg0IUrJ823pDiANolhniGa9o1hniGa9o1hniG69s10Uj2sU6wzRrHcU6wzRrHcU6wwRrHdk7nkWERERERERaakoXXkWERERERERaRElzyIiIiIiIiJNyKrk2czuMLP1ZvZOA69PMLOtZjY/eFyZ6hjDZmaDzex5M3vXzBaa2Q8SbGNm9kczW2pmb5nZmHTEGpZm1jkbj3V7M5ttZguCel+TYJt2ZvZgcKxfN7PC1EcanmbW+Vwz2xB3rM9PR6zJYGa5Zvammf03wWtZdayrNVHnrD3W2Uxts9rmuG2y8VirbVbbHP9aVh3ramqbd8tLdwAhuxP4M3B3I9u85O6npSaclKgAfuzub5hZF2CemT3j7ovitjkF2Cd4HAr8NfibqZpTZ8i+Y70LOM7dt5lZPvCymT3p7q/FbXMesMXdR5jZmcAvgS+nI9iQNKfOAA+6+8VpiC/ZfgC8C3RN8Fq2HetqjdUZsvdYZ7M7Udustnm3bDvWapvVNsfLtmNdTW1zIKuuPLv7i8DmdMeRSu6+1t3fCJ6XEPuPPbDOZpOBuz3mNaC7mQ1IcaihaWads05w/LYFi/nBo+6If5OBu4Ln/wKONzNLUYiha2ads5KZDQImAbc3sElWHWtoVp0lA6ltVtuczdQ2q22uI6uONahtriurkudmGh90M3nSzA5IdzBhCrqGfAp4vc5LA4FVccuryZIGrZE6QxYe66DbzHxgPfCMuzd4rN29AtgK9EptlOFqRp0BvhB0e/yXmQ1OcYjJ8gfgZ0BVA69n3bGm6TpDdh5rycLv62pqm+vJumOttlltc5ysO9aoba4lasnzG8De7j4a+BPwnzTHExoz6ww8AvzQ3YvrvpzgLRl/hrCJOmflsXb3Snc/BBgEjDOzA+tsknXHuhl1fhwodPeDgZnsPuObsczsNGC9u89rbLME6zL2WDezzll3rAXI0u9rUNustrlG1h1rtc0Nb5ZgXcYea7XN9UUqeXb34upuJu4+Hcg3s95pDqvVgvtNHgHudfd/J9hkNRB/FmgQsCYVsSVLU3XO1mNdzd0/AWYBE+u8VHOszSwP6EaWdJdsqM7uvsnddwWLfwfGpji0ZDgCON3MVgAPAMeZ2f/V2SbbjnWTdc7SYx152fp9rbZZbXOcbPu+rqG2WW1zlh7rBpl7xp4MSSjoKvRfdz+wd+/eXlhYmN6AREQka8ybN2+ju/dJdxyZRm2ziIgkSyrb5qwabdvM7gcmAL3NbPXYsWOZO3dumqMSEZFsYWYfpjuGTKO2WUREkimVbXNWJc/uflb8clFRUXZdVpeU+dVTi3lj5ZZ0hyEpkJ+bw6Wn7M+ovRqafUFEWkNtsyTTGyu3cMig7mzZUUZBXg5d2ufXev3uV1fQr2t7Tj6gf4v3saFkF9075pOf27q7HXdVVLK+eBeDe3ZsVTl7qrS8ktwca3X8IhKxe55Fmuu+2StZvmE7VY4eWfwor3Reen8jry7flO7/ciIisodeWbqRz//lFf7+0nLGXj+TI6Y+V2+bKx9byLfvaXiso/UlpXz0yc4GXy8tr+TTN8zk8kffbjSWyipnZ1llo9tMeeRtjvrV8+woq2h0u7tfXcGGkl2NbgNwxt9e5bH5HzW53chfPMVn/vRyk9vd9/pKnl74cZPbVVRW8a95q6mqavo82KrNO3h8QfNu5b/zfx80a/8i6ZRVV55FwnTKgf25ZnLdwSMlmxSXlnPw1U+nOwyRtDOzicDNQC5wu7tPrfN6O+BuYgPBbAK+7O4rgnuZ3wWWBJu+5u4XpipuibbVQdL73rrYlMPFpY0npYmMu+FZAFZMnZTw9V3lsdl5nnznY371xdENlvPde+cxY+G6BssBeH7JegBKy6voWJB4m6Xrt3HlYwv571treejb4xuNffaKzcxesZnJhzQ9w9nij0ua3Oay4ARBY3UAuPOVFVz/xLuUVVTxlUOHNLrtpD++RHFpBZ8ZvVeT+7/68UXN2r9IOil5Fkkgy8bRkyZk28CJInvCzHKBW4ATiY0UO8fMprn7orjNzgO2uPsIMzsT+CXw5eC1ZcGUNSIplWhOoHTtZMbCdaEUVVEVS9Y/2VHWvB2nwabtsdi2NCPGlpzQEGnL1G1bpAFmKWmWJY10hEUAGAcsdffl7l5GbDqSyXW2mczuuTv/BRxv+pKUNsIzbBrdxk7YWtAyteVzutUffJ14lihS8iySgBoEEYmQgcCquOXVwbqE27h7BbAV6BW8NtTM3jSzF8zsqGQHK1Kt5vxNKprsEPbRnPNNOiUl0rap27aIRJYunIkAiTth1E0VGtpmLTDE3TeZ2VjgP2Z2gLsX13qz2QXABQBDhjR+j6RIc6XiGzwZ+blOz4tkLl15FklADVu0qKOBRNxqYHDc8iCg7vC4NduYWR7QDdjs7rvcfROAu88DlgH71t2Bu9/m7kXuXtSnT58kVEGk7duTZL8tN0s1JxTacpAiSaLkWaQBuigpIhExB9jHzIaaWQFwJjCtzjbTgHOC518EnnN3N7M+wYBjmNkwYB9geYriFgHadqK5pzLhfuKa+7LTHIdIOqjbtkgiahEioeZHig64RJi7V5jZxcAMYlNV3eHuC83sWmCuu08D/gHcY2ZLgc3EEmyAo4FrzawCqAQudPfNqa+FRNHuK6DJ+w5PRjLbWFGZcOI+E2IUSZbQkudg1M2vAsPc/VozGwL0d/fZYe1DJJVMYzGLSES4+3Rgep11V8Y9LwW+lOB9jwCPJD1AkQRSMV5YmGNj7I636Yiz7ZSuu2ucEckKYXbb/gswHjgrWC4hNm+kSMbJtkZLEtN9WyIikjrNSR4zJ8Hck7ZT7axkizC7bR/q7mPM7E0Ad98S3DslkpF0glRERERAJ9XjteTnkf79JFuEeeW5PBg0xAHMrA9QFWL5IinTlgfqkPBo0BMRkcxV8x2exC/x3fc8h1hoc8rKgIZJ44VIFIWZPP8ReBToa2Y3AC8DN4ZYvkhK6cKziIhI25WKe57DLLs58aaiTq3Wgq55uigh2SK0btvufq+ZzQOOJ5Z3fNbd3w2rfJFU0ld8NOieZxERaUsyYaqqant0z3PywhBJqdCuPJvZcOADd78FeAc40cy6h1W+iIiIiEg6pKqLciaMSN2ie56VPUuWCLPb9iNApZmNAG4HhgL3hVi+SMq4a8CwKNF9WyIimSuZV2nDLDsp90+nUZZUQ2SPhJk8V7l7BfB54GZ3/xEwIMTyRURERESA3Vdp21IS11iynS3zPFsLzgLoJLVki7BH2z4LOBv4b7AuP8TyRVLG8YzoOiWto3ueRUQyVypb6ea2E41tZ82IOBN+eTSnHnWpnZVsEWby/A1gPPw/e/cdH0WdPnD882wKgVBDryZ0EQUlFAGlqnAW/J1dz66cp55653mHZz8b6ulVT8WuZ9ez3KGAgtgpQZEivfcWCCWEtOf3x85uNskmWchk6/N+vfLKzsx3vvPMzmYnz3y/8x0eVNU1IpIF/NvF+o0xxhhjjAmbw8353MoRLdk0Jjq5Odr2T8BNAdNrgIlu1W9MuMXC1V9TO0dy9dwYY0yUCUOiGeomvN22qz+3VNs6fRhduyMt+iM0xn2uJc8i0g14GOgFpPnmq2pnt7ZhTLjYFV9jjDEmusXa3VWhxBvqRd1IPsrqSG55sv+rTLxws9v2i8BTQDEwAngFeLW6FUTkBRHZLiKLAuZliMinIrLC+d3MmS8i8ncRWSkiC0TkBBdjN6ayGDspm8NX9g+AndWNMSZWhaWVNtR7nt2rKq7EQku6MaFwM3mur6rTAVHVdap6LzCyhnVeAsZUmDcBmK6q3YDpzjTAWKCb8zMeb6JuTJ2wr3hjjDEmuvlaaevy+ufh1l39gGF1t91oF2/7YxKXm8lzgYh4gBUicqOI/B/QqroVVPVLILfC7HHAy87rl4GzA+a/ol6zgKYiYo/CMnXG7oeNf/H2zE1jjEkk0dhtO6THUIXyOKsaqonkect/7jyMpgY7zZp44WbyfAvQAO+gYf2AS4HLj6Ce1qq6BcD57UvA2wMbAsptdOYZ4z77ljfGGGOMI9REsfrBwKIw2z8C9phHk8jcHG17LoDT+nyTqu5zq25HsG+cSn+2IjIeb7duOnXq5HIIJpHEyTnOhMDO/8YYE7vqNImrg7pjPen0XQQ4nN0ojfWdNsbhWsuziGSLyEJgAbBQRH4UkX5HUNU2X3ds5/d2Z/5GoGNAuQ7A5oorq+okVc1W1eyWLVseweaNsYEtEkW8tAIYU1siMkZEljmDck4IsryeiLzlLJ8tIpkBy2535i8TkdPCGbdJbEfSffhIhZr71TZHjLeBLK2V2sQbN7ttvwBcr6qZqpoJ3IB3BO7D9RFl3b0vBz4MmH+ZM+r2ICDP173bmLpgaVXisJO6SWQikgQ8iXdgzl7ARSLSq0Kxq4HdqtoV+AvwiLNuL+BC4Bi8A4D+y6nPmDoXzsQs1E1U18IaSry+i7qlNd3zHGI8dcHjj7HmKHxl4+VigDFuJs/7VPUr34Sqfg1U23VbRN4AvgN6iMhGEbkamAicIiIrgFOcaYCPgdXASuBZ4HoXYzemHPuOTwx2gcQYAAYAK1V1taoWAm/iHaQzUOBgnu8Co8T7X/444E1VPaSqa/CeoweEKW5j6pyvVTvUbsfVlfMnktWkvr4kM5q7OR/ORQvfebamiwHGxIpa3/Mc8LzlOSLyDPAG3gtiFwAzq1tXVS+qYtGoIGUVb2u2MWFhPXoTh3XTNwku2ICcA6sqo6rFIpIHNHfmz6qwbqXBPG08EhPrQs1lq0sSPVJzmSjOmf3KnlQRasuzRvXFAGMOhxsDhj1eYfqegNf2l2Jikn1wE4NdIDEGCG1AzqrKhDSYp6pOAiYBZGdn21esccnhD1xV16p/DFXNXZhDTTIj2Q36cM6d4r9gEE1HyZgjV+vkWVVHuBGIMdHGnvOcOOycbhJcKANy+spsFJFkoAmQG+K6xtSJcNzzfLh1V9eqXDbA2ZGtH21C6rZtA4aZOOPmaNtNROQJEclxfh4XkSZu1W9MONnAFonBRts2BoC5QDcRyRKRVLwDgH1UoUzgYJ7nAjOc26k+Ai50RuPOAroBc8IUt0lwHgklHQ2valtYQxhJ27d+9OxRZYfTuHA4g4sZEwvcHm17H3C+87OXIxtt25ioYHlV4rBTuklkqloM3AhMBZYAb6vqYhH5k4ic5RR7HmguIiuB3wITnHUXA28DPwFTgBtUtSTc+2ASUzQNRuW/n7maYDwhjKQdSxfvQ4nUt88l0XABPEOuAAAgAElEQVSQjHGBG/c8+3RR1XMCpu8Tkfku1m9M2NhXvDEmkajqx3ifahE47+6A1wXAeVWs+yDwYJ0GaEwQHqcJKBpaNZM8QmmJhtRtu7p4S0pD217k9zg01m3bxBs3W54PishQ34SIDAEOuli/MWFlDc8JxM7qxhgTc0Jpya2tUKuWELonh5JI+taPhf9BQonRum2beONmy/OvgJcD7nPeDVzhYv3GhI19xycO655vjDGxyRPC6NXhEkqrciiJZEzc8+y7CBBC2VAez2VMLHEteVbV+UAfEWnsTO91q25jIsKyqoRh53RjjIk90fQYpLJEvuay1ZUJdVeiYJdDEkqLvDGxxM3RtluLyPPAW6q6V0R6icjVbtVvjDF1wS6RGGNMbPK35IZ4n/CRCLVV2xNCIi8hJNixlGSGEqonhBHGjYklbt7z/BLekTrbOdPLgVtcrN+YsLKkKnHYOd0YY2JPNN1PG8r916ENGOZ02478LlXpcB7zKGG4L92YcHIzeW6hqm8DpeB/9IU9rsIYE9XsWc/GGBObPFE0knMoXchDGR08lpJMDeGmp4IibyoQDRc4jHGDm8nzARFpjnP7oIgMAvJcrN+YsPB1LbKcKnGE8g+AMcaY6OJxsueSOkzMQq17b0ExUH335EWbvMMBVZcg7ysocl5Vv91T/vJFSHFt3J0fUrn9h4pDKgdw//9+AqCgqPr+8qrKPud9qalr/fa9BSFv35hIcnO07d8CHwFdROQboCVwrov1G2OM6+waiTHGxLb8wuAdHfvd/2m16+0+UOh/rapBeyINeHB6jdsvCciGq0qM8w4WBUxVnRhf8eJcAHbuL6yyDMC6XaElxUMf+Tykcr3vmRpSuUufn+1/Hfj+BZN1e9mj42tqeR7wUM3vszHRwM3Rtr8XkWFAD7z/jy5T1aIaVosJv37jB7bmHcQjwu/H9KTfUc0iHZKpQ77vd7G0KmFYbzJjjIk95z39HQBLtlR+wMsXy3ewq5rkbkNuPic9WpZYliokVTjt3/XBohpjOFhYwtF3TwmoJ/gJpc9908ptK5jMCZP9r4d2bRG0jKqWS0qrKpdfWEyvu2tOiA8Vl9Djzik1llNVctbt5qsVO/3zqvp/uLC4lO53flJh/dpt35ho4WbLM8AAINOp9wQRQVVfcXkbYZfiEZI9Hr5bvYtvVu605NkYY4wxJgoFJqA+HoGiklJSkrx3Kx591xQOFpVvrS5VJcm5aF4xIQYYc0ybkLZVsXvyNS/n8NmSbRXKlM8kC4pK6HlX+e01aZBSqe6KCb8v7opmr97FBZNmlZvXvmn9SuX+9tkK/vLZ8nLzWjWqV6nc+l35nPxY5RbsYNv+w7sLeCtnQ41lVZUbXv+ejxduLTe/RcPK2zcmmriWPIvIq0AXYD5lA4UpEPPJ8xMX9KW0VOn8x4+thSoB+A6x3fOcGETsOc/GGBNrQr2Xt0XDVHbuL6TbHZ9UW66m5VMWbw2aLFf0s79/VWOZCybN4ovbhlOqMOLPM4OWmbxgC60b/cTFAzvx/brd/P69BUHLfbtqF9kPfMrzl/dn3JPfBC0zqmcrpi/dTuaEyaSleKq8V3lQ5wxmrc4lc8Jklj0whue+WsNjU5dVKjfzd8MZ/ueZPPzJUtbuOsC4vu25sEKy7vP85dlc/XIO4578hsfP60PugUIe/HhJ0LKXDOzEa7PX88S0ZYwf1oWG9dxu4zOm9twcMCwbGKKq16vqr52fm1ysP6JCGUXRGGOMMcbUvVDu5V310M+qvXf41asH1FjHwntPrbHMDSO6cMnATtWWuWV0N649Kcs/PeyxmUET5zUP/8z/+oVv1jD6iS8qJc7HtGvM2omn+6d37i8MmjhfP7wLayeezvSl2/3zgiXOlw46irUTT2fW6lz/vB53TgmaOK94cCxtm6b5p9+YsyFo4vzrkV1ZO/H0ci3Lt77zY9DE+bFzj2PtxNN5bfZ6AP4+YyW975nKF8t3VCprTKS5eUlnEdAG2OJinVGj7MH2ljzHO/9o2xGOw4SHINajxBhjYkhJkJuGh3VvyRfLd/DTn06jQWrZv7e/HNaZZ75YXan8ygfHkpxUdRvS+9cP5vhO1d+md2Ln5rx2zUA8HuGNOeurLPfj3afSpEEKq3fs59mv1gQt8/1dp5CRnlrt9gC+mTAyaBfsipY/MJbU5JrbyAKT8LP7tuOD+ZuDlnvvV4NDum1xeI+WvHRl2UWJW0Z3473vNwYte8OILtx6Sg//qOkV3fzmD8y/u+aLF8aEk9Q2GRSR/+Lt9dgI6AvMAQ75lqvqWbXaQC1kZ2drTk6Oa/V1vn0y1w/vyu9O6+FanSb6FJeU0vWOT7j1lO78elS3SIdj6lj3Oz7hqqFZTBjbM9KhmBggIvNUNTvSccQyt8/NJvEEdp9+7ZqBdGnZkDZN0qpZo2qlpcrO/Ydo1bjq9Q8cKqZElXrJHrbvPUTHjAbV1qmqqFJlUqiq3PjGD3hEeOScY8sl+wC79h/CI8KCTXlc/sIcHj3nOM7t16FSfQVFJewtKEIVnpq5ipO6tWBEj1ZVbrekVNl7sIgfN+5heI9WVca/ducBhv95JpnNGzD91uEkVVEfwJ78QtbtyufY9k2q3d+ColKKS0vZk19U5ftXWqoUlpTyds4G7v5wMd1aNeTT3w6rctvG+ITz3OxGy/OfXagjJnhE7HmwCcDueU4wYs95NsaYWPSb0d0ZUsVo06HyeKTaxBkgPeDe25oSZ/D2VqzufwgR4cmLT6hyeXNn0Kxh3VuWaxmuKC0libSUJADuPeuYGuNK8gjN0lOrTZwBMlukV7vdQE0bpNK0QfUt5iJC/dQkIIlGaZUHQvPxeIQ0TxIXDejE3R8uZsX2/SHFYEw41Tp5VtWgT2kXkSTgwtrWH008ItU+2N4YY4wxxtSd29750f/65tHWOywepQR0py8p1Wpbvo0Jt1oPGCYijUXkdhH5p4icKl43AquB82sfYhQRGzAsEfif82xNzwnBjrJJZCKSISKfisgK53fQmxpF5HKnzAoRuTxg/kwRWSYi852f6pu0jKmld+Z575/NbF5zC7CJfV3++HHNhYwJIzdG234V6AEsBK4BpgHnAeNUdZwL9UcNj1T9kHdjTAyzv2uTuCYA01W1GzDdmS5HRDKAe4CBwADgngpJ9iWq2tf52V5xfWPcknewyP965m0jIhiJqWu/Gt4l0iEYE5QbyXNnVb1CVZ8BLsL7yKozVHW+C3VHFY9IpQfbm/hj978mFutgYBLcOOBl5/XLwNlBypwGfKqquaq6G/gUGBOm+Izx63PftEiHYMLkD2PKBvHMfuDTCEZiTHluJM/+y4CqWgKsUdV9LtQbdbwDhhlj4o39XZsE1lpVtwA4v4N1u24PbAiY3ujM83nR6bJ9l9j9LiYMXr9mYKRDMGHQpL53cLGd+wvtUbEmariRPPcRkb3Ozz7gON9rEdnrQv3liMgY5/6qlSJSqXtZXRK75zkhlN3zHNk4THiI3fVs4pyIfCYii4L8hHprVbA/Et/J8BJVPRY4yfm5tIoYxotIjojk7Nix4/B3ogqZEyaTOWEy/5i+wrU6TfQbXMsRtk1s+PGesmc8Z91u9z6b6ODGaNtJbgQSCmcE7yeBU/Be+Z4rIh+p6k9h2T52z7Mx8ciuaJt4pqqjq1omIttEpK2qbhGRtkCwe5Y3AsMDpjsAM526Nzm/94nI63jviX4lSAyTgEngfc7zke1JeYHP+n380+U8/unycsuHdW/J+dkdGd2rFfWSvf+qbNpzkLaN0yhRZV9BMQ1Syx71A96RfT0S/gEjC4tLSU0ua89QVQ4Vl7Ilr4CsFulB11m0KY+ebRqRnFR+PRHhUHEJguAR/CMVHygsobiklCSPVPm4oLyDRaQkCWnJSSzZupdOGQ3wiJBeLxlVpahESUnyPnnEI1BUouzOLyTZI2Skp1b7vuXlF6FotY81CmVk5aNsoLCEcnL3lny53HvB7ewnv+GDG4ZEOKIyJaVKYXGp8xgur5qe8Q2wasd+2jROIzlJKCnVcs/5zssvIje/kA7N6vtHHd+2t4BWjbyPLyssKaVechLb9xZQPzWJhvWSERHW7jxAy0b1SE32kJLkQVXZk1+ECDROS2HVjv0keYSsFumUqvd7Yf+hYg4cKqZhvWTeztnAwk153DyqGx2aNSC/sJgteQU0bZBCvaQkFKVRWgqFxaXsP1TMh/M3UVBUwtBuLTm2fROKSkpZn5tPztrdvD5nHfsLilm7K58H/683K7btp0/HJqzcvp8RPVoxf8MeVu88wLn9OtCnQ9OYG03djec8h9MAYKWqrgYQkTfx3q8VluTZ4xFreU4g1iKZGKyHgUlwHwGXAxOd3x8GKTMVeChgkLBTgdtFJBloqqo7RSQFOAP4LAwxh+SL5Tv4Yrl7rdwmOgzMyoh0CCaMXrlqgP9C2fwNexj22Od84cJgcd+u2klaShJPTFvO1yt31rq+ePGf7zcdVvk/T1te7fI73l9UbvrJz1f5X78+ez1AyM8UjxaxljwHu++q3I0vIjIeGA/QqVMnVzfufc6zJc/GxBv7szYJbCLwtohcDazH+7QMRCQbuE5Vr1HVXBG5H5jrrPMnZ146MNVJnJPwJs7PhnsHlvxpDEffPSXcmzUR8u2qXZEOwYTZ2omn+xPodbvyyZwwma//MIIOzSr3QvD1JJu8cAs3vv5DWOM0iSHWkufq7rvyTtRB1zAfe1RVYrEWycRgh9kkMlXdBYwKMj8H7+MnfdMvAC9UKHMA6FfXMVblttN60DGjAfVTkyq1XBQWl7Jz/yG25BVw2zs/snrngQhFadw2/dZhkQ7BRMDKB8fS9Y5P/NNDH/k8gtFEVueW6ew9WExRSSkikJGeyuod3u+4zOYNqJ+azNa8gxzfqRkzlm5nZM9WzFga/CmCF2R3JDlJWL5tH+dndyS9XjIpSR6Sk4SCwhJaNKpHztrdPDJlKQBn923H5rwC6iV7uGRgJz5euJUbR3alTZM0tNRb56HiElo1TqO0VMkvKqGkVFm8OY9kj4c+HZtQWFxKcYnSuH5KzHXZhthLnjcCHQOmOwCbw7VxEWHywi18v35PuDZpIsDuf008//lhE99Ya0ZcO6tPO3tuaJy5YUTXKpelJnto17Q+7ZrWZ8bvhocvKGNMnUhO8pRrga6tv17Ql35HNaNDs/oUlyrJHqnynv31u/Lp0Kx+tfcxx7P+mRlVnj/H9G4bZK53TAWPR2hYz5tqDu5SNsifbwyKWBVryfNcoJuIZAGbgAuBi8O18WuGZpGzbne4NmciKKtFOiN6BHtii4k3vxzWhYWb8iIdhqljTRsEHyDJGGNM7Fg78XSKSkrpFtAKXdGTF59A7/aNSUtJonXjtBrrTEmqPinuZIPUmQAxlTyrarGI3Ih38JIk4AVVXRyu7f9yWBd+Ga6NGWPC4qZR3SIdgjHGGGNClOK0QhsTCTGVPAOo6seAPezNGGOMMcYYY0zYSDzf3ykiO4B1YdpcCyDRxrpPxH2GxNzvRNxnSMz9TsR9htD3+yhVbVnXwcQzOzfXuUTcZ0jM/U7EfYbE3O9E3GeIwnNzXCfP4SQiOaqaHek4wikR9xkSc78TcZ8hMfc7EfcZEne/410iHtdE3GdIzP1OxH2GxNzvRNxniM799kQ6AGOMMcYYY4wxJtpZ8myMMcYYY4wxxtTAkmf3TIp0ABGQiPsMibnfibjPkJj7nYj7DIm73/EuEY9rIu4zJOZ+J+I+Q2LudyLuM0Thfts9z8YYY4wxxhhjTA2s5dkYY4wxxhhjjKmBJc+HSUTWishCEZkvIjlBlouI/F1EVorIAhE5IRJxuimEfR4uInnO8vkicnck4nSTiDQVkXdFZKmILBGREyssj7vjDCHtd1wdaxHpEbAv80Vkr4jcUqFM3B3rEPc7ro41gIj8RkQWi8giEXlDRNIqLK8nIm85x3q2iGRGJlJzuOzcbOdmZ3ncHWewc7Odm8uViatjDbF3bk6O5MZj2AhVreqZY2OBbs7PQOAp53esq26fAb5S1TPCFk3d+xswRVXPFZFUoEGF5fF6nGvab4ijY62qy4C+ACKSBGwC3q9QLO6OdYj7DXF0rEWkPXAT0EtVD4rI28CFwEsBxa4GdqtqVxG5EHgEuCDswZojZefmyuLmb9hh52Y7N/vE3bG2c3NsnJut5dl944BX1GsW0FRE2kY6KBM6EWkMnAw8D6Cqhaq6p0KxuDvOIe53PBsFrFLVdRXmx92xrqCq/Y5HyUB9EUnG+8/n5grLxwEvO6/fBUaJiIQxPlN34v3vOO7ZudnOzRXmx92xrsDOzWWi6txsyfPhU2CaiMwTkfFBlrcHNgRMb3TmxbKa9hngRBH5UUQ+EZFjwhlcHegM7ABeFJEfROQ5EUmvUCYej3Mo+w3xdawDXQi8EWR+PB7rQFXtN8TRsVbVTcCfgfXAFiBPVadVKOY/1qpaDOQBzcMZpzlidm4OLm7+hrFzs52by4vHYx3Izs1lourcHLHkWUQ6isjnzr0bi0XkZmd+hoh8KiIrnN/NnPnRcm/DEFU9AW93kRtE5OQKy4NdCYn1Ic1r2ufvgaNUtQ/wD+CDcAfosmTgBOApVT0eOABMqFAmHo9zKPsdb8caAKcb3FnAO8EWB5kX68caqHG/4+pYO+eScUAW0A5IF5FfVCwWZNW4ONYJwM7Ndm6G+DzOdm62c3OguDrWsXhujmTLczFwq6oeDQzC+8XfC+8XwnRV7QZMp+wLIvDehvF4720IO1Xd7Pzejvc+hAEVimwEOgZMd6By94OYUtM+q+peVd3vvP4YSBGRFmEP1D0bgY2qOtuZfhfviatimbg6zoSw33F4rH3GAt+r6rYgy+LxWPtUud9xeKxHA2tUdYeqFgH/AQZXKOM/1k73sSZAblijNEfEzs12bg4oE1fHGTs327k5QBwe65g7N0cseVbVLar6vfN6H7AEb7N8YL/2l4GzndcRv7dBRNJFpJHvNXAqsKhCsY+Ay5yW8kF4ux9sCWecbgpln0Wkje/eAxEZgPdztSvcsbpFVbcCG0SkhzNrFPBThWJxdZwhtP2Ot2Md4CKq7h4Vd8c6QJX7HYfHej0wSEQaOPs1Cu95J9BHwOXO63OBGaoaFy0Z8czOzXZuDhBXxxns3Iydm8uJw2Mdc+dmiYb/C8Q75PiXQG9gvao2DVi2W1Wbicj/gImq+rUzfzrwB1XNqVDXeLwt06Snp/fr2bNneHbCGGNM3Js3b95OVW0Z6ThiWYsWLTQzMzPSYRhjjIkT4Tw3R/xRVSLSEHgPuEVV90rVg6eF1N9dVScBkwCys7M1J6fSow+NMcaYIyIiiTDyaZ3KzMzEzs3GGGPcEs5zc0RH2xaRFLyJ82uq+h9n9jZfd2zn93Znfjzf22BMxE1dvJXMCZOZvCBeej0ZY4wxse1gYQmZEybz3ryNkQ7FGINLybNzv8EvRORuZ7qT0w+/2nXwPrNuiao+EbAosF/75cCHAfPj9d4GYyJu+dZ9ACzZsjfCkRhjjDEGYHPeQQCe/HxlhCMxxoB73bb/BZQCI4E/Afvwtij3r2adIcClwEIRme/M+yMwEXhbRK7GexP5ec6yj4GfASuBfOBKl2I3xhhjjDEm6uQfKgFg9c4DEY7EGAPuJc8DVfUEEfkBQFV3O88oq5Iz8FdVNziPClJegRtqHakxxhhjjDExYHd+YaRDMMYEcOue5yIRScIZwEtEWuJtiTbGGGOMMcYcgYKikkiHYIwJ4Fby/HfgfaCViDwIfA085FLdxhhjjKkFEckQkU9FZIXzu1kV5S53yqwQkcsD5s8UkWUiMt/5aeXMrycib4nIShGZ7Tx60hjjkoJia4syJpq4kjyr6mvA74GHgS3A2ar6jht1G2OMMabWJgDTVbUbMN2ZLkdEMoB7gIHAAOCeCkn2Jara1/nxPQnjamC3qnYF/gI8Upc7YUyi2ZCbH+kQjDEBap08i4hHRBap6lJVfVJV/6mqS9wIzhhjjDGuGAe87Lx+GTg7SJnTgE9VNVdVdwOfAmMOo953gVHO0zSMMS54bOqySIdgjAlQ6+RZVUuBH0WkkwvxGGOMMcZ9rX2Pd3R+twpSpj2wIWB6ozPP50Wny/ZdAQmyfx1VLQbygOZuB2+MMcZEA7dG224LLBaROYB/LH1VPcul+o0xxhhTDRH5DGgTZNEdoVYRZJ46vy9R1U0i0gjvoygvBV6pYZ3A2MYD4wE6dbJr7cYYY2KTW8nzfS7VY4wxxpgjoKqjq1omIttEpK2qbhGRtsD2IMU2AsMDpjsAM526Nzm/94nI63jviX7FWacjsFFEkoEmQG6Q2CYBkwCys7MrJdfGmOD6dmzK/A17Ih2GMcbhSvKsql+4UY8xxhhj6sRHwOXAROf3h0HKTAUeChgk7FTgdicpbqqqO0UkBTgD+KxCvd8B5wIzVNWSY2NcYn9OxkQXV0bbFpFBIjJXRPaLSKGIlIjIXjfqNsYYY0ytTQROEZEVwCnONCKSLSLPAahqLnA/MNf5+ZMzrx4wVUQWAPOBTcCzTr3PA81FZCXwW4KM4m2MOXLb9h6KdAjGmABuddv+J3Ah8A6QDVwGdHOpbmOMMcbUgqruAkYFmZ8DXBMw/QLwQoUyB4B+VdRbAJznarDGGL+tewsiHYIxJoBbyTOqulJEklS1BO+InN+6VbcxxhhjjDHGGBNJbiXP+SKSCswXkUeBLUC6S3UbY4wxxhhjjDER5co9z3gfWZEE3Ij3UVUdgXNcqtsYY4wxxhhjjIkot0bbXue8PIg9tsqYmGTjeRpjYtGOfYfILyzmqObW4c0YY0zdciV5FpE1BPnfW1U7u1G/McYYY0ww/R/0PjVr6i0n06NNowhHY4wxJp65dc9zdsDrNLwjb2a4VLcxJgzsUZLGmFj2ds4G7jqjV6TDMMYYE8dcuedZVXcF/GxS1b8CI92o2xhjjDGmJs9/vSbSIRhTZ9o1SYt0CMYY3Ou2fULApAdvS7T1nTImhqjd9WyMMcZEpc159rxnY6KBW922Hw94XQysBc53qW5jjDHGGGOMMSai3Bpte4Qb9RhjIsfueTbGGGOMMaZqbnXb/m11y1X1CTe2Y4wxxhjjo3bVzxhjTBi5Odp2f+AjZ/pM4Etgg0v1G2PqmPp/2z+jxpjYMHft7kiHYIwxJoG4lTy3AE5Q1X0AInIv8I6qXlPdSiLyAnAGsF1VezvzMoC3gEyce6dVdbeICPA34GdAPnCFqn7vUvzGGGOMiTHnP/NdpEMwxhiTQFx5VBXQCSgMmC7Em/zW5CVgTIV5E4DpqtoNmO5MA4wFujk/44GnjjxcY0wlTvdH6wVpjDHGRJ/35m2MdAjGJDy3kudXgTkicq+I3APMBl6uaSVV/RLIrTB7XMC6LwNnB8x/Rb1mAU1FpK0r0RtjjDHGGBPFbn3nR3YfKKy5oDGmzriSPKvqg8CVwG5gD3Clqj58hNW1VtUtTr1bgFbO/PaUv4d6ozOvHBEZLyI5IpKzY8eOIwzBmMSjFX4bY0w0Kymt/G1VUFQSgUiMCZ9Hpy6NdAjGJDRXkmcR6QIsVtW/AT8CJ4lIUzfqDtxMkHmVzpyqOklVs1U1u2XLli6HYIwxxphosHnPwUrzet41JQKRGBM+b8yxsXiNiSS3um2/B5SISFfgOSALeP0I69rm647t/N7uzN8IdAwo1wHYfITbMMZU4LvX2e55NsbEgu9W7Yp0CMYYYxKMW8lzqaoWAz8H/qaqvwGO9H7kj4DLndeXAx8GzL9MvAYBeb7u3cYYY4xJLAs35QWdf///fgpzJMaEV+aEyXw4f1OkwzAmIbmVPBeJyEXAZcD/nHkpNa0kIm8A3wE9RGSjiFwNTAROEZEVwCnONMDHwGpgJfAscL1LsRtjKHu+sz3n2RgTC4qD3PMM8PzXa9i2t4AHJ/9k90CbuHXzm/PJyy+KdBjGJBy3nvN8JXAd8KCqrhGRLODfNa2kqhdVsWhUkLIK3FCrKI0xxhgTF/YWVJ04DHxoOgCtGqVx7cmdwxWSMWF1/P3TWP3w6ZEOw5iE4tZo2z+p6k2q+oYzvUZVJ9a0njEmeqgNt22MiSGTF9R859bny7Zz5Ytzgg4uZkysq6LzhTGmDrnVbdsYY4wxJqp8u2oXny/bwRUvzol0KMbUiatfmsuPG/ZEOgxjEoYlz8YYwJ7zbEw8E5EMEflURFY4v5tVUe5yp8wKEbk8YP5MEVkmIvOdn1bO/CtEZEfA/GvCtU+HY/m2/TbAkolL05duZ9yT35A5YTLLtu6LdDhhd+BQMaXWBG/CyK3nPJ8XyjxjjDHGRMQEYLqqdgOmO9PliEgGcA8wEBgA3FMhyb5EVfs6P9sD5r8VMP+5OtyHKs383fAay9z85nwyJ0zmua9WA6CqfLtqJxpFz+f7ZOEWlm9LvAToSJSWKnkHj2zArKKSUvYfKq51DPsPFVNUUlptmQOHivlh/W427znI6h37AVi9Yz9b8ty/leC0v37pep1uKygqYd66XFfqyssv4ph7pvLX6St4auYqPl4Y+kN4qvsMRON3g4kebrU83x7iPGNMlCp7zrOdLIyJQ+OAl53XLwNnBylzGvCpquaq6m7gU2BMmOKrlcwW6SGXfWDyEgY8+Bl/fH8hFz87m6zbP66TmDbuzmfl9sNLhH/12vec+hd3EqBpi7e60p135fb9bNydX26eqvLl8h3lzhcFRSXsq2YQt1dnreO5r1bzw/rdR5z0BvrztGX0uW8a2/cWBF1eWFzKv2aupLC4cnLb7Y5P6H3P1Grrzz1QyIKNe/h25c4qE+Te90zl2ldyys07VFzCt6t2+qfHv5rD//3rWwZPnMHIx78AYOTjX3DiwzOq3f6RypwwmX/OWMG/Zq6sMbGvC6/PXk/mhMn+beflF1FYXEpBUQn7DxVz94eLON0husEAACAASURBVOep71i360Cldd+euyHo/Kq8nbMBgI/mb+KRKUu5/rXvQ153/Cs59L5natDPz/8WbOHiZ2fzxpwNNdazYOMeMidM9l8YiUaTF2zhp817D2ud9bvyo3qfIqlWo22LyFjgZ0B7Efl7wKLGQO0v6RljjDHGDa1VdQuAqm7xdbuuoD0Q+N/iRmeez4siUgK8BzygZZnTOSJyMrAc+I2qVvqPU0TGA+MBOnXqVOudqa3t+w6F9I9xoLz8IlKTPdRPTQqp/NBHPgdg7cSqR0NeuX0/M5dt55qT3B8RfPyr8yptf9veAv7742auHpqFiPjnz9+wh3ZN02jVKI3cA4X8Y8YK7j6jFyLC6Ce+qFTPa7PXc+cHi7j/7N78YmAnnvx8JX+etrxSOZ+8/CLu+mCRf7p3+8a8eMUAXv52La0b1+PSEzND2qdXZ61jWLeWdGregP8u2AzAoIens/rh0/lu1S7yC4spVWiQmsQDk5ewZMtevly+g8fP78uMpdu564NFvHbNQH99/5q5kl8N61LuvfD5+b++Ye0u70WDX57cmdt/dnTQmGYu20HugUIy0lM5WFjCH95bwEc/bmbKLSfRs01jvlm5q1z5GUu3Vbl/eQeLSEkSGqTW7mE4vmOxY98hbh97NKnJ4btL856PvMd578EimjesR58/TQOgRcNUdu4vpHf7xs7y8mlCUUkpv39vAVD930ygBz9eAlCuBfmpmasY17cdB4tKaNskjUNFpSzfto+slumkJnlo2iCVrXkFfL5sBwDXvpLDhzcOLVfvxt3eXgHrcr2J/IFDxZSo0jit8lN43//BezvI58t20Lllw5DiDrcbXvdeVPC9r9v2FtC6cVrQspv3HCT3QCFn/ONrAGb/cRStGtUL+jeSqGr7qKrNQA5wFjAvYP4+4De1rNsYE0b+5zxbw7MxMUlEPgPaBFl0R6hVBJnn+0a4RFU3iUgjvMnzpcArwH+BN1T1kIhch7dVe2SlSlQnAZMAsrOzXf2WeeWqAa7U88nCLRzboQkdmjUIurzPn6bRuWU6M24dflj1/vfHzZzZpx079h2iUVoyaSlJLNqUx/3/+4nZa7zdVxukJnPxwKovKmzcnU/ugUKO69AUgO37CmiclkJaSlkivyE3n6mLtwZNxP+3YDOtG6cx8ZOlzFu3mwcmL2HqLSfz9Ber/P/8g/ef6xPu/xSArXkFrN5R1gqYOWEyAD/cdQp3Oonwup0H+GL5Dn+yBvDjhj2s2XmAW96aD8CLV/bnUFH5FtBFm/bS/8HP/NM/bdnHmce15bFpy/hh/R6uGJzJvWcdQ97BIkSgqLiUZdv2+RPwnDtH+89VpepNmB6ZsjToezdrdS5DJpa18l7y3Gz/60enLKNfp2YM7NycWat3sftAIcd3akbOulx/4gywasd+zvrn16QkeXhz/CBe+matP3EDOOH+T/n6DyO4+NnZrM/1rnfPh4v9xzfQVS+VtVQv2pRH7/ZN+HzZdvYeLOLmN+f7l90+tienH9c26D6F6sVv1vLiN2v56vcj6JhR+XO9cXc+X6/YSZ+OTTm6bWM27TlIuyZpISdKizbl0TgthU7Ny+ouKvEemNP++hU5d472z9+5v9BZp6wFdO7aXLbmFTCwcwYXTprln78nv5CmDVIB+H79bn7+r2+ZesvJ9GjTyF/mh/W7K9UN8MiUpVV+FgC+/sMI/4UtgNz8wirL+hx33zRKSpUF955K47QU8vKL8HigUZBkuqL8wmKenrmKG0d2IzXZw8bd+VV+x1R06fOz+WrFTh4/rw/n9OsQtMzegiK0FJo08Mby7ryN9GjdiGM7NOHbVTsrXaSYsmgr1/17Huf260DzhqncPrb8RaHBE8v3iBj40HQePfc4zs/uWG2sBw4V8/AnS/j3rPWVLn5MX7KNod1aUC/Z+321ec9BBk+cwYtX9GdEz2DXcaOb1LaLpogkAa+o6iXuhOSe7OxszcnJqbmgMYaHP1nCM1+s5pqhWdx5Rq9Ih2NMVBKReaqaHek4DpeILAOGO63ObYGZqtqjQpmLnDK/dKafccq9UaHcFUC2qt5YYX4SkKuqTaqLxa1zsy+ZW3zfaaTXS/ZP19byB8aWa6lbu/MAmS3S/fVX1yq2ducBOmY0oMsfy7qCj+rZiuev6E/mhMn0O6oZDVKT+GrFzkrrvnvdiZz79HcA/Gp4F64aksVfP1vOBf07ctY/v/GXm3vHaPo/+BlDu7bg304r6srt+xj9hLe7940jurI57yD/+X6TP15f7B0z6rMh19uq5msJTHTdWjXk8sGZ/gsCwRzdtjFLthxet1e3pSZ7gnZBD0XrxvXISK/Hki178QjcMKIrTeqn8MDksgsAH990Ej/7+1fcfUYvrhqaVWOd+wqKOPbeaf51Zy7fztjebRnx55lHFGMwL1yR7U3+V+xkRI+WvHjlAP7w7gLeytnAZScexSvfrXNlO2+NH8QFk2bxm9HduWlUVya8t5C3nC7h1wzN4rmv1wDQKaMBX/5+hP/vqUFqEvmFJYC3d8Ito7uzJe8gIx//gtvH9uSsvu145bt1PDVzFTeP6sauA4f496z1ALxx7SBO7NIc8Hb1/+tnK7hxRFfS6yVTXFLKhZNmkbOu7ALBygfHsiWvgD35Rbw5dz2vzV7PDSO68OTnqwC4emgWY3q34TznO+Sxc4/jtncXlNvPM45ry2dLtlEQcDHrmwkj2bznIP0zM8od00DnnNCBx8/vU2n+lryDvJOzkV+P7Eqf+6axt8CbqAd+R85Zk8v5z3hjatckjc155bvJr3hwLClJte8ZEc5zc62TZwARmQKcpapR9S1sybMxoXv44yU88+Vqrh6axV2WPBsTVAwnz48Bu1R1oohMADJU9fcVymTg7UV2gjPre6AfsBdoqqo7RSQFeAP4TFWfFpG2vu7gIvJ/wB9UdVB1sbidPPv+URv35DeuPrLnzfGDWLJlL/f99yee/kU/rvt3WQe7pfePKdfqCzBvXS7nPPWda9sPxRvXDkJRLn52dpVlfnlyZ575cnUYozJ14dsJI9mSd7DOPmMXDejEG3PW+6dXP/QzPB5h2uKtlJQqz3y5mhtHdEWBAZkZjHx8JrsORNW//a64akgWL3yzpsrlL13ZnytenOvKtpbeP4bnv16DR6Rca/kXtw1n2GMzXdlGqNZOPJ2teQUMenh6pWU/P6E9T5zf1z/9xKfL2bn/EEu37OX79dV/53ZumV6uB0tFF/bvyMRzjjvywB3hPDfXttu2zzrgGxH5CPC/Q6r6hEv1G2OMMebITQTeFpGrgfXAeQAikg1cp6rXqGquiNwP+P4z/JMzLx2Y6iTOScBnwLNOmZtE5Cy845zkAleEbY8qeGv8IA4cKqbfA5/VXDgEgd1IAxNngJ53TWFAVgZ/v/B4iktLy3UDDaeLnp1VYxlLnONDu6b1ade0fp3VH5g4A6zeuZ/LX5jLpj1lo4Jf4wyONrhL87hMnIFqE2fAtcQZvN8jwYQ7cQbvfeNTFgUfrfyThVu5akgeZ/zja5rUTzmsAf+qS5wBtuQFH/AvmrmVPG92fjxAoxrKGmOikP85z3bPszFxR1V3AaOCzM8BrgmYfgF4oUKZA3hboIPVezsRfLpG4K2ZaSlJpKUkseqhn5XrNl1X5qzJDdpKY0xdOrNPO/774+Y6347vNoBgvl21q8plJjZVN/r8waIS/wBiboyUH+iL5TtcrS8cXEmeVfU+AGcgEVXVuBrb/KmZq9jjDCiQmuzhyiFZZKSnRjgqY4wxJnF1zKhP9lEZleYneYRrT8ri2a+qb0EyJha1blQv0iEYk9BcSZ5FpDfwKpDhTO8ELlPVxW7UH2kf/LCJdbkHKFXvcwOPap7OuVWMemdMrPKNf6BY07MxJvqpBh8eHOD049pZ8mziUvfW1sHTmEhyq9v2JOC3qvo5gIgMx3s/1GCX6o+oqb85GSgbWr201JILY4wxJpJUqTJ79tgjSU2cOqtvO3LW5XLz6O7lHsFljAkPt56anu5LnAFUdSaQ7lLdUcPj3FxVajeFmjjk+1jbx9sYEwseOLs3l52YGXSZ73zdq21jTj+2ds/KNSaapKUk8ei5fWjftD6vOY8qM8aEj1vJ82oRuUtEMp2fO4G46y/lG5jEGp6NMcaYyBrRsxV9OzYNuqx5Q++4JAOyMrj25M7hDMuYsPF9/i878Shm3DoswtEYkxjc6rZ9FXAf8B+8nai+BK50qe6oUZY8W/Zs4o99qo0x8aJtk/pMv3UYnTIakJLkVjuBMdElvV6y/znn4B0sr8RaeIypU26Ntr0buMmNuqKZrxuYWvJsjDHGRLUuLRtWmudLNJ7/eg0lpaU89PHScIdlEthNI7vy9xkr66z++XefwrH3Tquz+o0x7o223R34HZAZWKeqjnSj/mhRds9zhAMxpg6U3fNsH3BjTHy7emgWgCXPcWBAZgZz1uYCMKpnK45p34QrB2eSX1TCvoIixvz1q5Drat+0Ppv2HDzsGMb1bceH86t/9vLyB8aSmuw5ouR51u2VHtEeVKO0FC4a0InpS7axfd+hw95OPLnnzF60apRG0wYpXPLc7EiHY+KIW9223wGeBp4DSlyqM+p4rNu2McYYE5M6NKsfdH6rRvWYc8doLn1+Nl+t2FnncZzUrQXz1+/h92N7clLXFnwwfxN//WxFnW+3Juf268C78zZyfnYHFmzMY+nWfZzaqzXTftoW0vrPXNqPX746D4DrhnXh8sFHMXnBFh6YvASAGbcOo6RUaZiWTJvGaXw4fzO3vDUfgDl3jOKzn7YzpGtzjmruHW/2okmz+G71rkrbuWFEF64cksWYv35Fkgf+dlFfZq/OJWddLg+cfay/XDMA6rN24umoKlvyCjj3qW/ZnFdQqc6M9FTeHD+I7q0bMWXRFh76eCnrc/ND2u/nLstmdK/WdG/diMemLvPP75/ZjLlrd9OiYT1y7hztn//oucfx6JRl7NxfltwuuPdUABZuzKNVo3rUS04iLdXDzn2FZLVIp35qUkixADz882MpKOrFUzNX0aZJGrf/Z2HI69all67szxUvzq11PT/ecyp97ivfut63Y1Pmb9hTbt6VQ7L8r5c9MIZ9BcXsyS9k7trdUfOeREpGeiq5BwojHQbg/XuONW4lz8Wq+pRLdUUtsZZnE8d8z3e2j7cxJt4suPdUUoPc+/zRjUNo28SbVKenlv+XaOn9Y+h515Rab7tBahL5hd52hauGZHH3mb3KLb9ldHfe+34jG3LLt3j2z2zG1UOzuO7f3wetd0SPlogIM5ZuDzmWUT1bMX3pdm4a1Y3z+nXgjg8W8fPj2/PJoi08cHZvslqkc/XQLHbnF3LiwzO4fHBm0OT5ifP78Nu3f/RPr514OoXFpYzs2YoGqUncPKob9VOTuOakzvzn+01ktUinc4Vu9Gcf357e7ZuQkiS0apTGxQM7lVv+xvhBZE6YDEDTBinsyS/i31cPZGi3FgDlEtKzj2/P2ce3r3K/RYR2Tevz7e2jOFhYwq9em8evR3bjnKe+BeD7u07xlx3Tuy1jerf1b9vnlyd3ZkjXFpzcvaV/2ZvjBzGoc/Py5YZ1plmDVC7I7shdHy7iqqFZ5Zafn92R87M7ApB7oJD1ufk0TksBYEjXFuXKtmqUVuU+VSctJYnfnNIdVSXvYBEX9u/Iv2auYtKXq4+ovlC9cEU2V72U459eO/F0MidMplurhgzv0YoL+3fkzbkbKq3376sH8ovnq24dXjvxdP4+fQWDuzSnSf0U//zXrx1IUYkyMCuD575aTftm9fnNWz9WWr9echL1GibRomE9urZqRN+OTSkpVc74x9f+Ml1bNWTl9v2A92/2+uFd+PO05UBoPQsqumhAJ96Ys57bTutR7qKK728wFD3bNGLp1n0A3Hn60Qzp2oLb3v2RRZv2MuPWYUz8ZGnQv88hXZtzVp92fPrTNj5bUn5bHTPq89XvR9Lv/k/ZVcsE+v3rB3PTmz9U+u7yWTvxdP7742YWbspj9Y4DfLakcqxVPTEhmtUqeRaRDOflf0XkeuB9wH8pTVVza1N/tPG1PFu3VmOMMSZ2+JKTio7rUDZat1R4NnRaSpL/n3+fu87oRYPUJJo1SKF/Zgb9HvisUp13ndGL+//3k3/66qFZtGxUD48Ivxh0VNA4PrxhKJv3HGTeut3c89FiRvZsxQtX9Ae8CcLFz1ZOLF68cgCAP761E09nb0ERr81az1VDM+lx5xT//BlLt5GRXo9j2jXmYFGJ//145SpvHb7E84YRXQHvgGu++8MrdmX+36+Hcky7xrRunMbctbn0bNMIgNRkjz/mQB/ffFLQfQZvwhKKWbePolSVBqm1b/Opn5rES8579/UfRlBYXBq03MzfDccjwtNfruL2sT1pFPAZ6ndUM+at210ucT6qeQMAspqnc+EA74WAf158QrWxZKSnkpGeWqv9qY6IcN0wb8vedcO6MOnL1bx4ZX9aNarHhtyDtGmSxtlPfhNSXZefeBT/XbClyhbLgVkZjOzZutL8wAHNxh7bljfnbmD00a1J8sCyrfv49chuDO3Wwn8/+Py7T+HByUt4Z95GfntKd3413Bv/TaO6+ev5x0XH07NNI7q1buSfd+NI7/JgyXNFR7dtDMAj5xzLcR2asv9QMXPW5PqT3HNO6MCNI7txycCjEPF2h89Zu5uBnTPo0boRQ7q24P0fNvH818EfLHR8p6Y8/PNjefjnx1JcUsqXy3fw/frdzLh1OB0zGlS6MFOVKbec7C97UreW9GjTiOcu68+Mpdvp3LIhj53Xh2kBrfC3jO7GLaO7+6dH9mxN0gcLmbq4LGltnl4PgHnOBSNf/fVTkvjTuGO47d0F5WL45OaTmL9hD7f/ZyF9Ojalc4t03v9hk7OfzXjusv6c9tcv+dclJ3D9a2UX+jpleP8ezuzTjjP7tGP3gUI+mL+J+/7r/W5s0bAeO/cfqvS9Gwtq+y00D29DlW/XbwtYpoDrz4cQkTHA34Ak4DlVnej2Nqpiz3k28cye82yMSWRVJTFz/jiK/YeKK7Wcgvd+23W5B9i219tucGH/jlx+4lFc0L8j05ds4+Y355PZPJ1z+nWocdsZ6an0aNOIopLScq0xg7u0YPkDYykpVUpUESg3gviTF5/gbzFrnJbiTzZm/m44aSne7r6BSc3hjj7+v18PZfu+Q/x71jqWbNlL7/ZNAG8racWWUrd9/YcRqOLfD7d1aNagymWZLbzdxx/6v2MrLXtz/CCKS8qfLE8/ti0tx9djQFZGpfLRICM9tVwie0w773H87SndefGbNVzQvxNPf7GqyvXvG9eb35zSnb5/+rTc/EfPPY7fv7uAjk6ydF6/DrwzbyNXDcmqVMew7i1ZcO+pQS9m3TK6O9ee3JlGaSmk1/OmJ43SkoN+Xs/s067KON+4dhCpyaFlZBf0L+vtcEKnZvTPzKBXu8bUdz5vzQK+E76ZUH4Yp97tm3Bmn3b+iw9ZLdJZs/MADVKTeP/6If5yyUke3vrliUG3P/eO0dzx/kJ/6/HInq0q9STxXRTzvWdtmpT10iit0BW2bZPyPRVaNqrHM5dmc85T3zL66NZkpKcwomeroLEsuX8MAMd2aOIfJ+AvF/Th6LaNObptYy4aUPZe+ZJngB5tGvk/V1cMzmTKoq1s3VtAq0b1ytXfLD2VK4dk+ZPnEzo1ZdpP2/zvdSypVfKsqpX/MuqQiCQBTwKnABuBuSLykar+VP2a7rABw4wxxpj4dMfpR3N028bc+cEihgYkha0apxH83014+zrvP8W975nK/kPFTDznOAAaJnk4q087OjSrzwmdmoUcQ0qSh2tOqtzukJpcdcJ7+nFtg873JX+11Sw9lWbpqdx/dm9X6jsc1SW3kZSS5KHi//wiwsAKXbhjwU2junHTqG4Ul5QypncbOrdMZ8ueArq0TOf1Oeu5+8PF/rJNG6SW640xuEtzOjT13vbQy2nN7dOxKe/M28hJ3YNfWKmqF4jHI/7W/VtGdyO/sJgL+3cKWrY6J3Y5smOQ5JHDvvDRt2NTLh7Yiddnr+eSgZ0YdXRrGqfVnFrddUYvXv1uLS0b1WPSZdn+93PSpf14fc56LujfsVxDRlXvWdMGKVx7UhYjerbiwx82M65v8FsX3vvV4Cpjee2agSzclOef7tmmsf/1uD7B67v3zF7lWrP98886hnvPOob35m1kWI+WQdf9w5ie9OnYhOM7NmPNzgPlenTEitp22+4PbFDVrc70ZcA5wDrg3jrotj0AWKmqq53tvQmMA8KSPNtznk0iULvr2RiTgBqkJvOLQd5W46TD7Ev4yc0n+e9N9BER+h0Vna2QxlSUnOShb0fvbQyN23gTmstOzGRkz1aVWgdfvKI/m/MOcslA720IH904hGOdHgmXDOzEcR2alLsl4nA1bZDKo+f2OeL1w8k3loJHhKwQL1hdPTTLP+I/eHu3lKiSnOQ5rHuARYQ7TveOoTC4y5H1AqmuB4nHE/x78IohWVwRpGeBT3U9bXw9YwB6tWtcZbloVttu288AowFE5GRgIvBroC8wCTi3lvVX1B4IHGlgIzAwsICIjAfGA3TqdPhXrKpT9pxnV6s1xhhjTJQ43G7NAB0zGvi7rRoTT4K1/lfs+lt+7ACpVeIcq2qTGrRqfGQDw9WVnm0a8cthrt95GzdqmzwnBbQuXwBMUtX3gPdEZH4t6w4m2CWQcp9XVZ2EN3EnOzvb1TTXdwHmYKH32YHGxJNDzqAphcWl9vk2cSclyVNn900aY4xJTGUNa/HTsjbllpMjHUJUq3XyLCLJqloMjMJp8XWp7mA2Ah0DpjsAhzd2fC14REjyCP/8fCX//PzwH3JvTCx4O2cjb+dsjHQYxrjqisGZ3HvWMZEOwxhjTBxp19TbatyiYb0aSpp4UdsE9w3gCxHZCRwEvgIQka5AXnUrHqG5QDcRyQI2ARcCF9fBdoLyeISnf9GPdbsOhGuTxoTVrgOFNK/Dx2YYEym+wWyMMcYYt1w5JIsOzepz2jFtIh2KCZPajrb9oIhMB9oC07Ssz4IH773PrlLVYhG5EZiK91FVL6jq4hpWc9UpvSo/w84YY4wxxhiTWJI8wpjewUe8N/Gp1l2rVXVWkHnLa1tvNdv7GPi4ruo3xhhjjDHGGGMqkni6wb0iEdmB97FZtdEC2OlCOOFmcYdPLMYMFnc4xWLMYHEHc5SqBn+ApQmJnZst7jCJxZjB4g6nWIwZLO5gwnZujuvk2Q0ikqOq2ZGO43BZ3OETizGDxR1OsRgzWNwmesXqMba4wycWYwaLO5xiMWawuCPt8B9maIwxxhhjjDHGJBhLno0xxhhjjDHGmBpY8lyzSZEO4AhZ3OETizGDxR1OsRgzWNwmesXqMba4wycWYwaLO5xiMWawuCPK7nk2xhhjjDHGGGNqYC3PxhhjjDHGGGNMTVQ1Ln6AF4DtwKKAefcDC4D5wDSgnTN/XMD8HGBohboaA5uAfwbMuwhY6Kw3BWgRJIZLnOULgG+BPgHL1jrrzwdyoijm4UCeU+984O6AZWOAZcBKYEKUvde3BcS8CCgBMqp6r8MU9wXOOouBR6v5rN7uvKfLgNOqe7+jIWbgFGCe857OA0YGLJvpxOw7Fq2iKO5M4GBAbE8HLOvn7M9K4O+U9cKJhrgvCYh5PlAK9K3q/XYrZrx/Q756PwqYnwXMBlYAbwGptf1cu/le1yZujuCzbT+H9xOGvyk7N0fXe23nZjs327nZzs21ipsoPzeHdWN1uiNwMnBChYPdOOD1TTh/oEDDgD/I44ClFer6G/C67w8LSHY+SC2c6UeBe4PEMBho5rweC8wOWLaWCieaKIl5OPC/IPOTgFVAZyAV+BHoFS1xV6jjTGBGde91GOJuDqwHWjrTLwOjgsTQy3kv6+H9AlnlvNdB3+8oifl4yr4oewObApbNBLKj9L3ODNx+hWVzgBMBAT4BxkZL3BXqOBZYXd377VbMwP4qYngbuNB5/TTwq9p+rqMo7sP+bNvP4f3U8d+UnZvt3GznZrVzczjirlCHnZvrNu6oPjfHTbdtVf0SyK0wb2/AZDqgzvz96hyBwPkAItIPaI33yop/tvOTLiKC96rV5iAxfKuqu53JWUCHaI+5GgOAlaq6WlULgTfxXmGKxrgvAt6oaYfqOO7OwHJV3eFMfwacEySMccCbqnpIVdfgveI3gCre72iIWVV/UFXfMVgMpIlIvSD7FrhOxOOuioi0xXsi+M7Z7ivA2VEad42fbbdiDsb5GxwJvOvMehnnvargsD7X0RL3kXy2zeGJhvOFnZsjFredm+swZjs327kZOzdHRNwkz1URkQdFZAPe7hZ3B8z/PxFZCkwGrnLmeYDH8XY78lPVIuBXeLsPbMZ7Jef5GjZ9Nd6rZv5qgGkiMk9ExkdZzCeKyI8i8omIHOPMaw9sCCiz0ZkXTXEjIg3wdj15L7AaQnyv3Yob7xdSTxHJFJFkvF8GHYNsrqr39bDe7zDHHOgc4AdVPRQw70URmS8i/8/efcdHUeYPHP98kxCQTqRKCwJKlxKaYMGCCCqe5dRDxcJx3unpnd4p2MAK6u9sZzsQFBsWLKA0AUEQBAxIqKGFFkoqJYX05/fHzG52k00jk+wm+b5fr33tzjMzz353MtnZZ572lP3FWCQ/xN1BRH4XkZ9F5CI7rTXW8XWplHO7jHG73ELhC3SpjndZYrbVEZFIEVkrIq6L2dnACWNMjr1c1LFy5Lz2Q9yeynVuq7LRa7Nem4uL2am40WuzXpv12qzXZodV+8KzMeYJY0xb4FPgAY/0b40xXbD+SZ6zk/8GLDDGeJ5QiEgtrItGH+AcrPb9E4t6TxEZhnWBfswjeYgxpi9Wk7H7ReTiAIl5I9DeGHMB8F/gO1cWvkIrKmY/xO1yLbDaGON5l6zUx9qpuO1aQr0AKgAAIABJREFUjb9i9d9YhdU8LYfCijquZTrelRyzFbj14+0l4C8eyWOMMT2Bi+zHHUXt74e4jwLtjDF9gIeBz0SkIX46t8/geA8E0o0xWz2SS328yxgzWMcqAvgT8LqIdKT0x8qR89oPcVvBO3Buq7LRa7Nem/XarNdm9Nqs1+aqeG02fmwz7vSD4vtRtC9m3T6gKdYJcRDrHycROAVMBfoDyzy2vxjrH9BXXr2w+hGcV0yck4F/BUrMBfLdb+c7GFjskT4RmBhIx9pe/y3wp9Ic64qM28f24/Ex6ISP47jYPtZFHm9/x2yvawPswvrxU9SxvgvvATj8HneB7VYAEUArvPvl3Ab8L9DiBl4DHi/N8S5vzD7SPwRuwrrQJQIhdrrXeVqe8zoQ4j7Tc1sfZXtU1P8Uem12/H/KqbjRa7Nem/XaXO6YfaR/iF6bizy3K+tRreZ5FpFwrAE2egA0bdrUhIeH+zMkpZRS1ciGDRsSjTHN/B1HVaLXZqWUUhWpMq/NIZXxJr6ISFuswQBaYg33Ps0Y84aIhGE1nQjHujv0R2PMcbtN+xvASCAduMsYs9Ejv9lYo1M2FZFYYFK/fv2IjIysvA+llFKqWhORA/6OoSrRa7NSSqmKVpnXZn/2ec4BHjHGdAUGYfV/6QZMwGoa1BlYZi+D1Uems/0YD7zrmZkx5jZjTCtjTC1jTBtjTEmDhiilPOTk5rE59gTZuXn+DkUpVU3otVmp8otPydBrs1IBwm+FZ2PMUVfNsTEmBdiBNeLaaKyhy8F7CPPRwEfGshZoLNaw9kopB8xef5Dr3lrNrDX7/R2KUkoppYCM7FwGvLCMid9s8XcoSikcKjyL5XYRedpebiciA8qwfzjWCI7rgBbGmKNgFbCB5vZmpRpWXUTG20OjRyYkJBRcrZQqwon0bK9npZRSSvlXZo5V4zxnQ2wJWyqlKoNTNc/vYI2Ydpu9nAK8XZodRaQ+1jyA/zDek3AX2tRHWqHRzowx04wxEcaYiGbNdEwXpZRSSilVNe2JT/V3CEopD04VngcaY+4HMsA9Z1poSTvZ8wZ+DXxqjPnGTo5zNce2n+Pt9Fi8Jy1vAxxxJnyllFJKKaUCS1aO9nVWKpA4VXjOFpFg7JpgEWmGNYJ2kezRs2cAO4wxr3qsmgeMtV+PBeZ6pN9pNxEfBJx0Ne9WSimlVNFEJExElojIbvu5SRHbjbW32S0iYz3SV4jIThHZZD+a2+m1ReQLEdkjIuvsblhKKYfkVaMpZZWqDpwqPL8JfAs0F5EXgF+AF0vYZwhwB3CZx8V4JDAVuFJEdgNX2ssAC4AYYA8wHfibQ7ErpZRS1V1RM1m42VNFTgIGAgOASQUK2WOMMb3th6tV2L3AcWNMJ+A14KWK/BBK1TS5eVp4ViqQODLPszHmUxHZAFyO1Tf5emPMjhL2+QXf/Zix8ym4vQHuL2+sSimlVA00Gmu+ZbBmslgBPFZgm6uAJcaYZAARWQKMAGaXkO9k+/Uc4C0REfuarZQqp1z9V1IqoJS78CwiQcBmY0wPILr8ISmllFLKYV4zWbiaXRdQ0qwWH4hILtZYJc/bBWT3PsaYHBE5CZwNJFbAZ1CqxsnTmmelAkq5C8/GmDwRiRKRdsaYg04EpZRSSqmyEZGlQEsfq54obRY+0ly/3McYYw6LSAOswvMdwEcl7OMZ23hgPEC7du1KGY5SSsvOSgUWR5ptA62AbSKyHkhzJRpjrnMof6WUUkoVwxhzRVHrRCRORFrZtc6eM1l4iiW/aTdYs1qssPM+bD+niMhnWH2iPyJ/JoxYEQkBGgHJPmKbBkwDiIiI0OKAUqWkfZ6VCixOFZ6fcSgfpZRSSjnPNZPFVLxnsvC0GHjRY5Cw4cBEu1Dc2BiTaE8xeQ2wtEC+vwI3AT9pf2elnKOjbSsVWJwaMOxnJ/JRSimlVIWYCnwpIvcCB4GbAUQkArjPGDPOGJMsIs8Bv9n7PGun1QMW2wXnYKyC83R7mxnAxyKyB6vG+dbK+0hKVX9a86xUYHGk8GzPu/xfoCsQinVxTTPGNHQif6WUUkqdOWNMEr5nsogExnkszwRmFtgmDehXRL4Z2AVxpZTztPCsVGBxap7nt4DbgN3AWVgX4rccylsppZRSSqkaJ0cLz0oFFKf6PGOM2SMiwcaYXKzpLNY4lbdSSimllFI1jU5VpVRgcarwnC4iocAmEXkZOArUcyhvpZRSSimlahyteVYqsDjVbPsOrH7OD2BNVdUWuNGhvJVSlUAvz0oppVRgyc3L83cISikPTo22fcB+eRqdtkoppZRSSqly05pnpQKLU6Nt78NHxZUx5lwn8ldKVTydSlIppZQKLMfTs/0dglLKg1N9niM8XtfBmrYizKG8lVJKKaWUqnHeXLbb3yEopTw40ufZGJPk8ThsjHkduMyJvJVSlcNor2ellFJKKaWK5FSz7b4ei0FYNdENnMhbKaWUUqooGdm5zNt0hJsj2iAi/g5HKaVUNeZUs+3/eLzOAfYDf3Qob6VUJdA+z0qpquiBzzaydEc8exJSeXxkV3+Ho5RSqhpzarTtYU7ko5RSSilVFkt3xAMwbWWMFp6VUkpVKKeabT9c3HpjzKtOvI9SquIY97NWQSullFJKKVWQk6Nt9wfm2cvXAiuBQw7lr5RSSimllFJK+Y1TheemQF9jTAqAiEwGvjLGjHMof6WUUkopL0dPnvZ3CEoppWoQR6aqAtoBWR7LWUB4STuJyEwRiReRrR5pYSKyRER2289N7HQRkTdFZI+IbC4wwrdSqrzsEcN04DClVFVx9Rur/B2CUkqpGsSpwvPHwHoRmSwik4B1wKxS7PchMKJA2gRgmTGmM7DMXga4GuhsP8YD7zoQt1JKKaWqqBPp2f4OQSmlVA3iSOHZGPMCcDdwHDgB3G2MmVKK/VYCyQWSR5Nf8J4FXO+R/pGxrAUai0grJ+JXSnkOGKaUUkqpQBN/KsPfIShV4zlSeBaRjsA2Y8wbQBRwkYg0PsPsWhhjjgLYz83t9NZ4D0AWa6cppZRSSpGZk+vvEJSqMJ+uO+jvEJSq8Zxqtv01kCsinYD3gQ7AZw7l7SI+0gpVkonIeBGJFJHIhIQEh0NQqvpy9XXWPs9Kqaoq+miKv0NQqsL8ujfJ3yEoVeM5VXjOM8bkADcAbxhj/gmcaZPqOFdzbPs53k6PBdp6bNcGOFJwZ2PMNGNMhDEmolmzZmcYglJKKaUCWXJaVqG00W+vJj0rxw/RKFXxTpwufM4rpSqXU4XnbBG5DbgT+MFOq3WGec0DxtqvxwJzPdLvtEfdHgScdDXvVkqVn7Ebchjt9ayUqgJMEc1krvnvL5UciVKVIzlNB8hTyt+cKjzfDQwGXjDG7BORDsAnJe0kIrOBX4HzRSRWRO4FpgJXishu4Ep7GWABEAPsAaYDf3ModqWUUkpVMelZvvs3xySkVXIkSlWOxNRMJn6zmTgdOEwpvwlxIhNjzHbgQY/lfeQXeovb77YiVl3uY1sD3H+mMSqlimd0uG2lVBVy0cvLi1wXPmE++6eOqsRolKocs9cf4vuoo2x95iqOnjxNiwZ1CAryNSyQUqoiOFXzrJRSSqkAJSJhIrJERHbbz02K2G6svc1uERnrkb5CRHaKyCb70dxOv0tEEjzSx1XWZypJ+IT5LNyivbtU9ZOamcOh5HQGT/mJN5bt9nc4StUoWnhWSgE6z7NS1dwEYJkxpjOwzF72IiJhwCRgIDAAmFSgkD3GGNPbfsR7pH/hkf5+BX6GMvvrpxsZNyuSbzbG+juUGuVURjYpGdo/tyK5Wl6s2Zvo50iUqlmcmuf55tKkKaWUUsovRgOz7NezgOt9bHMVsMQYk2yMOQ4sAUZUUnwVZumOOB7+Mor0rBwtRFeSXpN/pOfkH/0dRo0gPmdyVUpVFKdqnieWMk0pFaDy53nWumelqqEWrhkq7OfmPrZpDRzyWI6101w+sJtmPyUinr/YbxSRzSIyR0Q8p5QMKN2eXszDX0bx3e+H/R2Kl6ycPJ/TbgWCpNRMsnPzKvx9jp48zU3vruF4gB6HQCZadnbMt7/H8sS3W/wdhgpw5So8i8jVIvJfoLWIvOnx+BDQiRaVUkqpSiIiS0Vkq4/H6NJm4SPNdTdtjDGmJ3CR/bjDTv8eCDfG9AKWkl+7XTC28SISKSKRCQkJpf9QFWDDgeMA7EtMY29Cql9jAfjbpxvp+9wSf4dRSF6eod/zS/n3V1EA7E1IJXzCfCL3Jzv6Ptm5eQye8hORB47zze+Hmb4yhkPJ6by5bHelFNxLKy/PkJCS6e8wClm3L5nktCyS07L4/eBxf4dTrIzsXE6mezfnP52Vy5j317InPsXx99t5LIXY4+nu5ZzcPJJSi/4b/vOLKD5dd7DYPB/6/HcGvbiMtTFJ/OXjSPLynK9wqAp/y5qsvDXPR4BIIAPY4PGYh9X8SylVRbjnedaKZ6WqJGPMFcaYHj4ec4E4EWkFYD/H+8giFvCsOW6DdZ3HGHPYfk4BPsPqE40xJskY4/o1Oh3oV0Rs04wxEcaYiGbNmpX/wxYw7Q6fb+vTx2sPEHXoBMP+bwWX/+dnx2PZHZfCoeT0YrfZeSyFwydOA1azcl8SUzPZHHvC0dh2HD3F0ZOnyc0zXrXdw1/7mXGzIt3LX2+I5YUFOwCYF3UEgF92J3otl1dyWhbnPbGQZTvyT8UvfjvICwt2cNHLy3l1yS6mr4px5L18eWzOZpZs933sfXnzp930f2EpR0+eLvU+e+JTuXPmejKyfU+r5pSvN8Rywzur+cM7awqty8rJc//t/O2W//3KBc96N+dfszeR1XuSeGH+jlLlkZSaWeoC61Wvr2ToS/mj8k/+fhv9nl/KifSyt3DIyM7lv8t2M3fTEY6dymDcrEgWb4sjLcv5usKi/pZn4lByOh//ut/nuuNpWby4YAenMrLP6Bz9eVcCOSXc4EpMzWTV7gQe+vz3Qu/93s97q2Rrx3IVno0xUVjzOf9ijJnl8fjG7i+llFJKKf+bB7hGzx4LzPWxzWJguIg0sQcKGw4sFpEQEWkKICK1gGuArfZyK4/9rwNK9wvYQd/dP4Th3VvStVXDUu8z+u3VXsvTV8bwvkdBLTfPcDwti3UxSSzfGc/KXQlF/mD/80eRjHpzlXv5ytdWugdz2hJ7kvAJ89kV512rdtXrKxky9Sef+U1buZc98alEPL+U697Kj/PhLzd5FYISUzNL/cMzyd726jdWMXjKT9zwzmr6PreEk6etWsBdcaks3RHH91FHuGPGOh75KooZv+wDIM/AupgkJs3bBsDuuFRW70nk1R93kpGdy5ETp9l5LIVtR04Sn+J7/uFLXllO+IT5TPxms/uGwC3/+5Ws3Dzu+2SDe7tdcd4tAV5etJN1MUmctuf07vT4Al5ZHO3+TC7nP7mQqQut9IzsXJ+DlR1MSifGo6XBF5GH+PNHkSzdHsefP4okfML8Io/f3E2HeX2pNar1Efumxz+/2MTFxUyXBvDM99tYuSuB9fus2vqsnDz3MXfS1EXR7E/yfcPm5UXR3D5jXUDUZEbFniyU5vq3Cran20rPyiG9iALpsZMZ9Ht+KU98t5UdR095rdt+5BTzoo7w3s97mWmfuy5/eGc1q/ck8slaq1a597NLeGzO5jLF/t7Pe/nPkl3uZVdzeV9fCy8u2MHcTcV3DynqPAUK/S1fXbKLL34rvka8KLfPWMdTc7e5a/z/9/NeJs/bxidrDzDqzVVMWxlDr8k/MvKNVaTYA/3d8+FvvLQomo6PLwCg56TFPPv9dq//uZW7Ehg7cz0Pfv47x076/r9fvjOeiOeXcseM9czdZN10M8aQlJrJxG+2MHVhtPt/oyop9zzPxphcETlbREKNMdpZRamqyng9KaWql6nAlyJyL3AQuBlARCKA+4wx44wxySLyHPCbvc+zdlo9rEJ0LSAYq3n2dHubB0XkOqyuWsnAXZX2iWy92zYG4IFhnbj/s41l3r/j4wvItX8Bj7voXI6dzGDQlGWFtpt4dRcu79qcs0JDaN34LGat2U9Gdm6xtZdfRlpdyJ/8diuPjjgfEWF/Ypp7vWeBLSsnj6S0TF5cEM2LC6K98pm1Zj/fbDzMNxsPs3/qKL747SCPfb2FKTf05LYB7by2PZFu1SYN6dSU0b1bczApnYtfWc6I7i3d27gKMWPeX8vWw/mFkL/P9q4dcrll2lr3619jkvg1JgmAnDzDOyv2em3ra37tA3ZhYPb6Q8xef4j9U0exO750TeY93xvg7eV7CT+7Hv+es5l/XNGZG/q0ITMnj/d+3ktiaiZzNliDwu16/mpCQ4JYYheOXf7QpzXPXd/DvTzOY93CLUeJPHCcp67p5vWeD32+yf169vpDrNiZwLd23/mVuxK4qHNTxEfnY1dann2T444Z61i3L7lUc5Anp2URVi/Uvdz9nIZsO3LK57a5HiU41znleg9X14TjpaxtTUrN5Oz6td3Le+JTiTp0ghv7tSlx39w8w7SVMVzZrTkrdyVyz9AO7IpLYfhrK/nh70O9YrygbWNmjI1gy2HrXHQdq25PL/aK3+VURjYLt1pTz81ef5DZ6w+yf+oojDH8sieRO2as99r+2R+2u1//fvAEY95f57X+i8hD9GnXmM4t6tOvfZjXuoEvLuW1W3qzYMtRd4F72PneLWZSMqwC/gXP/MjGp670+ltNW2ndiOvTtgmtGtehVrB3XeXcTYfd55Trc/7v571MWRjt89x4056O7I8Rbflg9X6u630OTT3+RnsTUgkNDqJtWN1C+56yb9ZsOJhMu7B6TFkYXWgbgJjENK9B/n6KtlqEjH57NSmZOcxcvY+Zq/fx2biBNKpbi3i7C8OCLcdYsOUYc+4bTER4/nFcvSeRpQW+G40xvLhgB9NX7aNJ3VoAZAVQ14zSKnfh2XYAWC0i8wD3VcEY86pD+SullFLqDBljkoDLfaRHAuM8lmcCMwtsk0bRzbEnEiADhPZoXfqaZ0+eBY/ez/7IiXTftUFTFka7f3jePqid+0e1y5wNsTQ6q5Z7+dnvt/Px2gMArN+fzE3v/VpsHOc9udBn+q9782t9AT7+dT9PzbWWI/cf5/uoI6zZm8RH9wwgOzePe+3m119GxvLQ55voH27NNrZo27FCeXsWnM+Er/6hQ1/Kr1F/dE4Uf+hTuNDlWVN/Jv5t1xq+vnS3u0YYcBecwapxvb5Pa6+CM8C3vx92F3wL+uun1s2XGb/so394E9o2qcudF4Z7beP5HgB3zlzPzf3a0LtdY8YMbO+1LtguT6dk5LAuJol1pahlWxuTxNxNh5m9/hBf/3WwO/1fw8/n7g9/K2ZPb92eXsQfI9py5ER+rWDUoRPM+GUfr9/Sm6CgwoX9grXvayZcxtVvrCQ717gLz8YYVu9JIi0rhz3xqdw/rJN7+0Vbj/HSomheWmT9n3RsXt/domN+gTnXow6dYPhrK93dB2ISUr1Gw3/rp93834+7uLxLc57/Qw8GTyncUmPr4ZP8sPko7/28t9C60pjwjTU42Kx7BvC9R3eEuFOZ/Gm6d2F7+c6ix2ro+9wSHhvRhbuHhLvHVAC4+BWrZULMiyNZtO0YczbE8sBlnbxuxpzOyiUlM9v93eLZOuLHbccY7nHTa098Ks/+sJ3F247xxV8GczApndPZuVz1+koABp0bxqierTiQlM6YQe05eTqbIPumxD0fev8flFbUIe+uI3+yb0Jc0bWFV/pN7/3Kk6O6sunQCcYMbF/oZgVAh4kL3K+P29+zFdBlvMKJE23NRWSSr3RjzDPlzrwcIiIiTGTkmZ0sStU0Ly7YwbSVMdwzpANPX9ut5B2UqoFEZIMxJsLfcVRlTl2bC9awGWN4efFO3l1xZj+klXLSf2/rU2RN/oSru3Br/7bUqRVMnVrBXus8C7D3DOnAzNVWE+T9U0cV27S8JCN7tmTBFusGytqJl9OyUR33um82xvLwl1GF9qlTK4iM7Pyawbf/1BfAq4VH1NPDaVAnhJTMHB75chNLPfqw3zm4PR/9at1AurV/Wz7/zXMwf+VyUeemrCpFv/Q59w1234SbMTbCfaOsKpt5VwSXdWlR8oYlqMxrsyM1z65Csog0sBaN/4evdFB6Vo57EKXgICn0RaeUUkop/xIRHhvRRQvPKiAUVXAGmLow2t1H+7YBbRl7YTgtGtQh+ph333hXwdmlXmgwaVlnNviYq+AMsG5fEqN7t2bcrEh+io7zaqbtybPgDFah+Y8R3i0JCg4A5in6aP7n0YJz0UpTcAa8Wq9Uh4IzwPE058cAqGiOFJ5FpAfwMRBmLycCdxpjthW7YxUx8o1V7s77QQIf3zuQIZ2a+jkqpZzlaoVitNezUqoKaBdW193f2dP8B4cy6s1f/BCRUmXn6gdeGk9e042J35R/HuKHPt/k1XS4LFNwfRkZW/JGtvUOT2umqp9HvooqVX/6QOJUn+dpwMPGmOUAInIp1mAiFzqUv1/dd0lHTmVkc/J0Nm8v3+ueXkIppZRS/uOj2yadmzeo/ECUqgS39m9Lz9aNuOa/enNIKX8p7zzPLvVcBWcAY8wKoJ5DefvdrQPaMf7ijtw+yBoIoirOSaZUSVyntZ7eSqmqwGB8jnAcGhLE7heuZsvk4X6ISqmKIyL0aN2IP/Rp7e9QlKqxnCo8x4jIUyISbj+eBPaVuFcVE+SecsDPgSillFI1nDHgo+IZgFrBQdSvHcKADmFFbKFU1fXaLb39HYJSNZZThed7gGbAN8C39uu7Hco7YORPiK6lZ1X96FmtlKpKjKHo0jNWLd2XfxnMrHsGVFpMSlWWz8cP8ncIStVITo22fRx40Im8ApnWPCullFKBoV7twtP8+HLJec24JaItX0TqaL+q+mh/dl1/h6BUjeTUaNvnAf8Cwj3zNMZc5kT+gcJdeNbSs6qG8vs86/mtlAp8P/7zklJvO/XGnlp4VtVKq0ZnsePZEfyyJ5E/f1Q9pi1Sqipwqtn2V8DvwJPAvz0e1UqQNttWSimlqhwRIebFkTSsY93fH3Su9oVWVd9ZocFc2a0F25+9yt+hKFVjOFV4zjHGvGuMWW+M2eB6OJR3wBBttq2qMdf8znp6K6Wqo6AgYeNTVxL93Ajuu6QjEHhNXyde3cXfIZTaa7dcUKbth53frMh1z1zXvch18x8cWqb3Kas7B7fn7iHhjOzZskLfpyLVDc1vSHrbgLZ+jCRwbHr6SvZPHeVz3XOjiz7flCpJuQrPIhImImHA9yLyNxFp5Uqz06sVV82zNmtVSimlqp6Q4CDq1Arm0vObs3/qKH7+9zBeuamXz21f/WPZCoee5tw3GB+zaJXo3qEdCqVdd8E5Xst92zUuNo93x/T1Wr6w49kAfHh3f9Y/cXmZBprq2Kweayb47oH3hz5tGBBe8k+9m/u1YeoNPfng7qIHbrtzcHu+um8w/77q/ELrup/TiH9c0RmA3m2L/+yl8fQ13QC4fVA79k8dxbOjezDp2u68fksf9zYf3t2/0H6PXHleud+7Il3TqxWhIUFMuaEXI7pXjRsBf7nk3DJt36xBbW4b0M7nulWPDvMqLDeuGwpY57DL2fVCGd37HG7q15Y3b+vD5V2aA4Vvov3l4rLFVV5dWzWs8Pfo0LQe+6eO4oe/F74ZNejcMF6+qVeh747iXNmtRZlj6NqqIQsfusgrrUvLBmXOx9/K2+d5A1ZFlesS4dlU2wCOn30iMgJ4AwgG3jfGTHX6PYqSP2CYFp5V9aPzPCulaqKbI9qSkZ1L7ZBg/ti/LcYY1u9LZkCHMB7+MgqAvS+OJDMnl97PLCErN6/Y/ObeP4QL2jZm35RRhE+YD8CSf17M3oRURvRoRV6e4dzHFwBWDVjtkGAiwptQv3YIIcFBRD83giARnv1hGzf3a0tmTh7zoo5wdY+WTLq2Oy0b1XHn60uux5f4t3+7kFeX7AKs1nPNG9SheYM6TLq2G898v91rv4EdwnjrT31p1qC2O/9lj1wKQL3QYNKycgu914y7Ivh8/SH6tm9Mv/ZhbDhwnAlfb+aKbi3o0rIBF7RpTHjT/MLLM9d1Z9K8bQzsEEbzhnWoXzuEJ0d1RUToHx5Gn7aN2X7kFA9d0ZkgEXbFpQDw0OWduXdoBxrUqcWpjGx6Tf4RgD7tGtO0fm36tGvMXReG0+3pxYVivPT8ZqzYmcDtg9oRuf849wztwC392xYabC40JMir8HX7oHZ8svYgf+jTmmdHd6d+7RCOnsrgs3UHC71Hk7q1OJ6eXSi9Q9N67EtMK5ReEd76U37B5707+hV7jgSKiVd35X8/xxRKb934LA6fOM2/rzqfVxbvdKe/cUtvWjSqw+z13n8Dz7/b89f3cP9eB1j40MXkGcO+xDTOaXQWjerWAqybUtf2akVqZg4N6lhpV722kp1xKfzlko6M7t2akW+uYuzg9izZHseRkxkAXNS5Kat2J3L3kHB+2HyUhJRM93u1alSH50b3YFyBPujX9z6HpLQsVu1OZO79Qxj99moA/ndHP/q0bUzzhnWYu+kw8acyeWHBDq99/3ppRx66vDMJKZks3HqUvfFpLN5+jBMe59vWZ67i242xPDV3W6FjuXrCZTzx7Rb+c7N1M7BH60YAnNu0HtPujKBT8/pe289/cCjHTmYwtHNT1sYk0+Hsery6ZCffbTri9feZfmcEryyO5u3le/nk3oG0bFSbK15d6d7myVFdeX6+92dxFZz3T83/bvzo3qo3G0K5Cs/GmMK3SCuQiAQDbwNXArHAbyIyzxizvfg9naGjbSullFLVzx2Dw92vRYSB557ttT44SKgbGsKuF67mk7UHuKp7S+77ZAMbDhz32q5/eBMu8Kgd/c/NF3A8PYvOLRrQuYVVwxIUJHz254Es3R7v9b4urkLd89f3dKdtnjychvYPfE+uQu0VXVuwdEccYNVPOWpRAAAgAElEQVTUArx8Yy/6tGtCl5YNWLU7kTC7Jg7yb5JGtG9C7PHTHDuVwb1DO9CsQW2fx+fTPw/ii98O8eIfenDu4wsIDbYaLjaoU4s/e9TS9WvfhCUPFz2Q29gLwxl7YeHP7BISHMTbHrVfrh/2IuIu4DSsU4vo50YAFCoAL3vkEl5bsosfNh9laKemfDJuIDm5eWTm5FGvdv5PXs/XRenVpjFwkOCg/Pd+8Q89uWdIONe/vYbUzBxm3TOAsTPX89/b+nL7jHXufS9o25ioQyd4dnR37pix3p0+bmgHerZpxEOfbyrx/Z0y5YaeTPxmCx/dM4AXF+wg+liK1/p3x/Tlr59uLLTf1Bt6sisulZmr9/nM98puLViy3TrnQoKEnAI/jm/q14Y5G2J5YmRX9zniq0D/6IjzeXmRVUC+f1hHRvZsRfdzGnEqI5uzagV7FZ5FhI7N6vP89T0Y0aMlEc8vpV6o9zlw+6D2XsuhIda56qt21/O8Avjwnv6s2pVIWL1QwuqF8tV9g+ndtjGTru3ODe+uYdOhE4zs2YqP7x0IwKRru7vPr0PH02neoA5h9UKZfmcE5zSuw6g3f6FVozq8fmt+q4aMbOsmVOvGZ3GVRwuB0b1bAzC8ewtCgoP46ycb2Bx7ksdGWF052obVZfzFVneTl+jFip3x3PXBbwDUrx1CzzbW986Yge0Y3bu11xz3HxZo9bH92auoFRxEreDCDZC7n9PI/R1yyXlWV4vXb+1D5IHjxB4/zapHh9G8ofU98ciV53PfJR3dx3DflJH896c93D+sE8FBUqjw7Gn1hMtYsyeR5g3qFLlNoCpX4VlE+gOHjDHH7OU7gRuBA8BkY0xy+UP0MgDYY4yJsd/vc2A0UCmFZ53nWdUERns9K6UUYBUgUjNzvNJcP87fvzOCx77ezHW9z+GBz34HoG0T7+afN/Zr4zPfCzs25cKOTUsdh6+CM+SPxfLqLRfQoHYIpzJyaHRWLa+auEdHdOGKri3o2aZRof17tG7ErHsGMHv9Qa7omt8M8/sHhhJ3KsO93LttY3eT6R3Pjih13BWlqCnKOjarz32XdOSHzUc5z75ZERIcRIiPQkJJura0ClsXdfb+O3Vq3oCtz+QP0BU1aTiNzsr/+/Rr34TPxw8iyy6wz/7zIL7eGMucDbE8PrIrQUHC6N6tyc0z/OurKEKChL7tmzDxmy3uPKKfG0GXpxaVOWZfbu3flpE9WtGobi1eLFCr+catvbm6ZyseH9mFVxbvJDvXuv5HPT2cRnVrkZtn+OulHen/wlKvfR76fBOXdWnuLjz/OvFyr21+euQSd01l3dr5f6t3xvSlRcM63DVzvbtwF9Heep52Rz+GexQmXef8/qmjWLYjjntnRbqb+Lr+Bxf/42LC6uXfFCqvVo3O4o/98/uM9/folvDUNd248d01DO3kfT64zq8uLfML564mzQsfuogWDb0Lh3VqBfPyTb3c3SkKan+21VLj43sHcig5vchYLz3fanLu6jrRu21j99+tJJ595Etrzn0XsnxnPG3D8r/jgoK8bz6ICA9e3tlrvy4tGxS6YQPWzYObI6pm/3wpT/9dEdkIXGGMSRaRi4HPgb8DvYGuxpibnAnT/X43ASOMMePs5TuAgcaYBzy2GQ+MB2jXrl2/AwcOOPb+WTl5nPfkQv591fncP6yTY/kqFQgmz9vGh2v2c/ugdl41HkqpfCKywRgT4e84qrKIiAgTGVm9ptb5ZXcit89Yx7+Gn8cDl3UueYdyWrDlKB2a1uO+TzZwICmdLZOHe/2ILcm+xDSG/d8KdxPz6mZtTBJ92zVx1zqeqRPpWe6+syVJz8ohOEioHVK4YJ+Tm0d6dm6RN0EALv/PCvYmpDHvgSH0atOY91fFuGvuVj06zKvQUhp3f7Ce5TsTvG6kfB91hL/P/p01Ey5jwZaj3Du0g/sGDOTXiha8OZGSkc2UhdGs3JXAqkeH8eveJAZ3PJu4U5mkZmbTqXkD7vnwN36KjgesAm9Gdi4frtnPuKEdSrx5UZbjrPKlZuYQGhxU7vO8omw9fJI2Tc6i97NLGHZ+s2LHPSivyrw2l7fwHGWMucB+/TaQYIyZbC9vMsb0diTK/Pe7GbiqQOF5gDHm7762d/oCnZObR6cnFnJ973O4tsAAHkpVdZ+tO8iy6HguPb8ZdxRo9qRUVdemSV3Od2BgEi08l191LDwDrN6TyKBzzyY46AxGCjtDh0+cZuWuhCIHUVJVR1ZOHhsOHGewR41kVk4emTm5Zbox4pKdm0dG9pnte6ZczbKLGuVa1UwpGdnUqRXss5m4Uyrz2lzeAcOCRSTEGJMDXI5d4+tQ3r7EAp51/G2AI0Vs67jgIKFB7RC+23TEq+O8UtXJip0JrNiZ4O8wlHLUXReGM7mY6XCUKq8hnUrfDNsprRufpQXnaiI0JMir4OxKO9NaxaL6tFa07udU/MjRqmqpzBs4laG8BdzZwM8ikgicBlYBiEgn4GQ58/blN6CziHQADgO3An+qgPfxSURY+sglXv2AlKpO6tQKdjfbUqo6Obu+74GQlFJKOWPz5OHUDtAmxEo5pbyjbb8gIsuAVsCPJr8NeBBW32dHGWNyROQBYDHWVFUzjTGFx2WvQC0a1inU+V8ppZRSSqmarLg+3UpVF+VuWm2MWesjbVd58y3m/RYACyoqf6WUUkoppZRSqqByDRgW6EQkAWvaLH9qCiT6OYayqooxg8ZdmapizKBxV6aqGDOUHHd7Y0yzygqmOtJr8xmrijGDxl2ZqmLMoHFXpqoYMwTQtblaF54DgYhEVrWRWatizKBxV6aqGDNo3JWpKsYMVTduVTZV8e9cFWMGjbsyVcWYQeOuTFUxZgisuLVXv1JKKaWUUkopVQItPCullFJKKaWUUiXQwnPFm+bvAM5AVYwZNO7KVBVjBo27MlXFmKHqxq3Kpir+natizKBxV6aqGDNo3JWpKsYMARS39nlWSimllFJKKaVKoDXPSimllFJKKaVUCbTwfIZEZISI7BSRPSIywcf6u0QkQUQ22Y9xHuvGishu+zE2wOJ+zSPmXSJywmNdrse6eZUY80wRiReRrUWsFxF50/5Mm0Wkr8c6fx7rkuIeY8e7WUTWiMgFHuv2i8gW+1hHBlDMl4rISY/z4GmPdcWeWxWpFHH/2yPmrfa5HGav89exbisiy0Vkh4hsE5GHfGwTcOd2KeMOxHO7NHEH5PmtSk+vzXptLolemyuPXpv12uxQ3IF1fhtj9FHGBxAM7AXOBUKBKKBbgW3uAt7ysW8YEGM/N7FfNwmUuAts/3dgpsdyqp+O98VAX2BrEetHAgsBAQYB6/x9rEsZ94WueICrXXHby/uBpgF4rC8FfijvuVXZcRfY9lrgpwA41q2AvvbrBsAuH98jAXdulzLuQDy3SxN3QJ7f+ij131ivzZV7vPXaHDgxB+R3V0lxF9hWr80VH3cgnttV7tqsNc9nZgCwxxgTY4zJAj4HRpdy36uAJcaYZGPMcWAJMKKC4iyorHHfBsyulMiKYYxZCSQXs8lo4CNjWQs0FpFW+PdYlxi3MWaNHRfAWqBNpQRWjFIc66KU53+i3MoYd6Cc10eNMRvt1ynADqB1gc0C7twuTdwBem6X5ngXxa/ntyo1vTZXIr02Vx69NlcevTZXrqp4bdbC85lpDRzyWI7F9x/6RrtpxBwRaVvGfStCqd9bRNoDHYCfPJLriEikiKwVkesrLswyK+pz+fNYl9W9WHcxXQzwo4hsEJHxfoqpKINFJEpEFopIdzutShxrEamLdSH72iPZ78daRMKBPsC6AqsC+twuJm5PAXdulxB3lT2/lV6b9drsuID7/ipGlf3u0muzs/TaXLFCKvoNqinxkVZw2PLvgdnGmEwRuQ+YBVxWyn0rSlne+1ZgjjEm1yOtnTHmiIicC/wkIluMMXsdj7Lsivpc/jzWpSYiw7C+xIZ6JA+xj3VzYImIRNt3cP1tI9DeGJMqIiOB74DOVJFjjdUsbLUxxvNOuF+PtYjUx/rB8A9jzKmCq33sEhDndglxu7YJuHO7hLir+vld0+m1Wa/NjgnE769iVPXvLr02O0SvzRV/vLXm+czEAm09ltsARzw3MMYkGWMy7cXpQL/S7luByvLet1Kg+Ywx5oj9HAOswLo7FAiK+lz+PNalIiK9gPeB0caYJFe6x7GOB77Faprid8aYU8aYVPv1AqCWiDSlChxrW3HndaUfaxGphXWx+NQY842PTQLy3C5F3AF5bpcUdzU4v2s6vTbrtdkRgfj9VZxq8N2l12YH6LW5co53tZ7nuWnTpiY8PNzfYSillKomNmzYkAQcBv5kjNnm73iqIr02K6WUclJlXpurdbPt8PBwIiMrbbR1pZRS1ZzdtOxLLTifOb02K6WUclJlXpu12bZSCoCElEymr4whPiXD36EoFci2GmNe8HcQSqma4+3le0jJyPZ3GEoFskq7Nvut8FzUpNgiEiYiS8SaXHyJiDSx00WKmJBcKVV+X2+M5YUFO5i97lDJGyullFKqwn0fdYRXFu+k5+Qf/R2KUgqHCs92wfZ2EXnaXm4nIiV1NM8BHjHGdMWaYPx+EekGTACWGWM6A8vsZbAm8+5sP8YD7zoRu1LKkp2TZz3n5vk5EqWUUkoB7I5L8XcISikPTtU8vwMMxprgHCAFeLu4HYqZFHs01tQR2M+uOQuLmpBcKaWUUkqpaqf6DuurVNXkVOF5oDHmfiADwBhzHAgt7c4FJsVuYYw5audzFGhubxYQE48rpZRSSilVGfKq8aw4SlVFThWes0UkGPsGmYg0A0rV9rM0k3m7NvWRVugbRUTGi0ikiEQmJCSUJgSllFJKKaUCTp6WnZUKKE4Vnt/EmlC7uYi8APwCvFjSTkVMih3nao5tP8fb6aWaCNsYM80YE2GMiWjWrNmZfh6llFJKKaX8KjRYJ8ZRKpA48h9pjPkUeBSYAhwFrjfGfFXcPiIiwAxghzHmVY9V84Cx9uuxwFyP9DvtwckGASddzbuVUkopVbSiZrLwsd1Ye5vdIjLWI32FiOwUkU32o7mdXltEvrBnwlhnd8NSSjkk8kCyv0NQSnkIKW8GIhIEbDbG9ACiy7DrEOAOYIuIbLLTHgemAl+KyL3AQeBme90CYCSwB0gH7i5v7EoppVQN4ZrJYqqITLCXH/PcQETCgElABFa3qA0iMs8exwRgjDEmskC+9wLHjTGdRORW4CXglor8IErVJE3qlnoIIaVUJSh34dkYkyciUSLSzhhzsAz7/YLvfswAl/vY3gD3n2GYSimlVE02GrjUfj0LWEGBwjNwFbDEGJMMICJLgBHA7BLynWy/ngO8JSJiX7OVUuV0Vq1gf4eglPJQ7sKzrRWwTUTWA2muRGPMdQ7lr5RSSqkz5zWThavZdQElzWrxgYjkYo1V8rxdQHbvY4zJEZGTwNlAYgV8BqVqHCmqmkkp5RdOFZ6fcSgfpZRSSp0BEVkKtPSx6onSZuEjzVWDPMYYc1hEGmAVnu8APiphH8/YxgPjAdq1a1fKcJRSSqnA4kjh2RjzsxP5KKWUUurMGGOuKGqdiMSJSCu71tlzJgtPseQ37QZrVosVdt6H7ecUEfkMGIBVeHbNhBErIiFAI6DQCEfGmGnANICIiAht0q1UKdXS0baVCiiO/EeKyCAR+U1EUkUkS0RyRaS4OZuVUkopVXmKmsnC02JguIg0sUfjHg4sFpEQEWkK7ikmrwG2+sj3JuAn7e+slHMuPs+adjWivc8B8pVSlcypZttvAbcCX2GN0nkn0NmhvJVSSilVPj5nshCRCOA+Y8w4Y0yyiDwH/Gbv86ydVg+rEF0LCAaWAtPtbWYAH4vIHqwa51sr7yMpVXM0qaejbisVCJwqPGOM2SMiwcaYXKxBRdY4lbdSSimlzpwxJgnfM1lEAuM8lmcCMwtskwb0KyLfDPKnlFRKOSw3z2rIEaQDhykVEJwqPKeLSCiwSUReBo4C9RzKWymllFJKqRpnysIdAKRl5vo5EqUUONTnGWvUzWDgAaypqtoCNzqUt1KqEmgnRaWUUiqwHEo+DUBKZo6fI1FKgXOjbR+wX55Gp61SSimllFLKMSHablupgOBI4VlE9uGj4soYc64T+SulKp6Oj6uUUkoFJi07KxUYnOrzHOHxug7W4CFhDuWtlFJKKaVUjRWspWelAoIjfZ6NMUkej8PGmNeBy5zIWylVOYz2elZKKaUCUq1gp4YpUkqVh1PNtvt6LAZh1UQ3cCJvpZRSSimlajLt86xUYHCq2fZ/PF7nAPuBPzqUt1KqEmifZ6WUUiowiWjhWalA4NRo28OcyEcppZRSqizSs3L4fP0h7rownCCtnVPVlJ7aSgUGp5ptP1zcemPMq068j1Kq4hj3s1ZBK6Wqjm5PLwZg9Z5EZtzV38/RKFUxwuqF+jsEpRTOjrbdH5hnL18LrAQOOZS/UkoppVSRlkXH+zsEpRxXv3YIqZk5RB9L8XcoSimcKzw3BfoaY1IARGQy8JUxZpxD+SulKprd6Vn7PiullFKB4aZ+bfhwzX42x570dyhKKRyaqgpoB2R5LGcB4Q7lrZRSSilViNG7fUoppSqRU4Xnj4H1IjJZRCYB64BZJe0kIjNFJF5EtnqkhYnIEhHZbT83sdNFRN4UkT0isrnA9FhKqXIyBZ6VUirQzVqz398hKKWUqkEcKTwbY14A7gaOAyeAu40xU0qx64fAiAJpE4BlxpjOwDJ7GeBqoLP9GA+8W/7IlVJKKVVVvbRop79DUKrSvLwomrw8vcWtlD85UngWkY7ANmPMG0AUcJGINC5pP2PMSiC5QPJo8mutZwHXe6R/ZCxrgcYi0sqJ+JVS+X2dtRWkUqqqOJ2d6+8QlKo076zYy7p9BX82K6Uqk1PNtr8GckWkE/A+0AH47AzzamGMOQpgPze301vjPXp3rJ2mlFJKKcXdH6z3dwhKVSjt56+UfzlVeM4zxuQANwBvGGP+CThdK+xrevhC3yAiMl5EIkUkMiEhweEQlKq+XPM76zzPSqmqavnOBG3WqpRSqsI4VXjOFpHbgDuBH+y0WmeYV5yrObb97Jq4MRZo67FdG+BIwZ2NMdOMMRHGmIhmzZqdYQhKKaWUqoq+31zop4FS1YevqiSlVKVxqvB8NzAYeMEYs09EOgCfnGFe84Cx9uuxwFyP9DvtUbcHASddzbuVUuVndLhtpVQ18NDnm8jQvtCqmtqbkObvEJSq0ZwabXu7MeZBY8xse3mfMWZqSfuJyGzgV+B8EYkVkXuBqcCVIrIbuNJeBlgAxAB7gOnA35yIXSmllFJVT3ZuXpHr7pixrhIjUaryPPXdVi56+Sd/h6FUjRXizzc3xtxWxKrLfWxrgPsrNiKlai6teFZKVSWdn1hY5Lrf9h+vxEiUqlyHkk/7OwSlaiynmm0rpZRSKkCJSJiILBGR3fZzkyK2G2tvs1tExnqkrxCRnSKyyX40t9PvEpEEj/RxlfWZSnLkxGkyc3KLraFWSimlysKpeZ5vLk2aUkoppfxiArDMGNMZWGYvexGRMGASMBAYAEwqUMgeY4zpbT/iPdK/8Eh/vwI/Q5lcOPUnzn9yEcNfW+nvUJRy3DcbY3VkeaX8wKma54mlTFNKBSjXgGE6h6RS1dJoYJb9ehZwvY9trgKWGGOSjTHHgSXAiEqKr8LsS0xz1z5nZOdyIj3LzxEpVX4PfxnF1EXR/g5DqRqnXH2eReRqYCTQWkTe9FjVEMgpT95KKaWUckwL1wwVxpijrmbXBbQGDnksx9ppLh+ISC7wNfC8yb/TdqOIXAzsAv5pjPHMAwARGQ+MB2jXrl25P0xBLRrWJu5UZpHrOz+xkKinh3PBsz8CsH/qKMdjUKqyzd10mN1xKQzr0hxjYOyF4f4OSalqr7w1z0eASCAD2ODxmId1B1spVUUYe6gwrXhWqmoSkaUistXHY3Rps/CR5vpGGGOM6QlcZD/usNO/B8KNMb2ApeTXbntnYsw0Y0yEMSaiWbNmpf9QpfTc6B4lbuMqOFd386KOED5hPkNfcnZE5kPJ6ZzO0inAAkncqUyW70zg6bnbmDRvm7YcqyGMMeyJT/F3GDVWuQrPxpgorPmcfzHGzPJ4fGM3+VJKKaVUJTDGXGGM6eHjMReIE5FWAPZzvI8sYoG2HsttsG6SY4w5bD+nAJ9h9YnGGJNkjHFV+U4H+lXEZyvOBW0bc2W3FmXa538/7yUnN4/oY6dIzcxhx9FTAEQdOsHhE6d5Yf72Ktmf9HRWLg/O/h2A2OOFR2Qe8/5awifML1Veu+NS3PNlbz9yioteXk7XpxcF9BzaeXmG3Cr4d3PK1xsPO5ZXTm6euzB+58z1pT5v/C0nN6/c/7vJaVnM33y0yPWlGYTQGFNhgxXOizrCFa+uZNmOuArJv7yMMfzf4p3sTUj1dygVotx9no0xucDZIhLqQDxKKX8xXk9KqeplHuAaPXssMNfHNouB4SLSxB4obDiwWERCRKQpgIjUAq4BttrLrTz2vw7YUUHxF80YRHxVmhdtysJoOj2xkBGvr6LHpMVc/cYqwifMZ/Tbqxky9Semr9pHVOwJTmVk8+icKFIzi++JtuHAcTYcSGbDgfx6g+zcPDJzrILmpkMnyLF/SL+/KoaVuxLIyM51p3mKPZ7OnvgUdh6zapY+XXeAxduOFfne6Vk57kLO32dvLDbO1XuSfKanFfh8J09nc+VrK+ny1CIOJKUx8s1V7nVdnlrE5HnbSE4rf9/x+FMZHEpOdx+LVbsTiD526ozzu+w/K+jy1EKMMazek+j+XEdPnib2eLrX38dlc+wJft3r+7gEAmMMjc6qRafm9Uvc9l9fRZGVU/4CW3ZuHp2eWMiLC6x/55W7EordPj3Lvz01P1l7gPAJ83l7+R46PbGQm95bU+z2xcX78qJo+j63hPs/28jby/dwMj3ba/3cTYfp/MRC9/9nUWavP0TnJxZy5ITz04rtOGq998443zHc8+FvTJ63rVD6mf6d8vIMGw+Wvk407lQmby3fw50z1rMrLoUX5m+vVq0inJrn+QCwWkTmAWmuRGPMqw7lr5RSSqkzNxX4UkTuBQ4CNwOISARwnzFmnDEmWUSeA36z93nWTquHVYiuBQRjNc+ebm/zoIhchzXOSTJwV6V9Itugc88GYMOTV9Dv+aWO5WuAaT/H8GVkLO3C6vLAZZ0BSMnI5qvIWL7eGMs1vc7hr5d25MZ383+svzumL1f3bMXw11ayLzGNT+4dyO0z1gEw577BPD8///5C/dohpGbm8NsTV9CsQW3iT2Uw9KXl7vXzHxzKE99uBWDT01fSuG4o24+cYuSbq5h7/xBGv73ave3mycNZuSux0OfIyM4lN89Qr3b+T769CamcXS+UhnVqcSA5nWH/t4LxF5/LPUM6MHXhDr7bdMS97SWvrCiU54dr9vPhmv1Mu6Mf4z/ewK8TL6PxWaGsjUli4Llh1A0NYf2+ZH4/eJybI9oSVi+UHpMW8++rzvfqlzvgxWX5rzuEsX5fMuC7T/q8qCPEn8pg3EXnEpOQymX/+ZlF/7iILi0bkpaZw6ZDJ9iflA7AjF/2eR1nTx/c3Z9h5zdnwZajHEpOZ8rCaK/3fGH+dq7o2oKB9nkFkJSaSUJqJl1aNvTK676PN5BrDNPvjCj0Ppe+spwOTevxwd0DfMZRlNTMHF5csIPu5zRkzMD2AIjA23/qy1Wvlzxy/O74FBrWqcWrS3YxsmcrmjeoTYM6IeQZ69zt087nLHVeXDWmn6w9yBOjuhW77dcbYnnkqyhG9WzF/C1HWfXoMBrUCaFx3TOvU0vJyCYmIY0L2jb2Ss/MyeWxOZv511Xn06ZJXXf6k99Z/yOvLN4JwMaDJ7z2m7JgB8t3xvPjPy9hV1wKw19bSZeWDTh5OptfJ17ute07K/a6X7+yeCebDp0gJSOb1o3r8n839+KhzzcBcOu0X/n96eGFYvv3iC60bnwW87dY/0MxCWmc0/isMz4W62KSWBYdz+Mju7rTgux7he8s30vP1o24qHMzUjNzCA0OIjQkiJ+i490x7Y5LZc5fL2Tp9jjGfRTJN3+7kL6lOAc8vfvzXl5ZvJOP7hnAxed5d7s5cuI0Ly+KZuqNvahTKxjI7waYm2e4/f11xKdk8ueLzqV5wzrWemN45vvtXHvBOfRrX7ZYAoFThecj9iMIaOBQnkqpSuS6J1iNbg4qpWzGmCTgch/pkcA4j+WZwMwC26RRRHNsY8xE/Dy7xvDuVpPts+vXdjTfG97JLxCfzs4lfMJ8zmtRn11x+U0Rtx05xWVdvMde++unG7nrwnD2JVp1Ca6CM8BN7/3qta2rRnvQlGU+mxuPevMX9+vezy5h/9RR7lrgP38U6bVtr8mF+3RHHzvFiNet7efeP8Sdfvl/fi607bSVMUxbGVMovTjjP94AwOApRfevnrIwmv1TR5GamcOkedu4pX9b949sT66Csy9vLN3Na0t3ARCfksmq3dZNghGvr2L/1FF0n7TYa/t1xeTlas7+t0+9a+kXbjlKp+b1mb5qH9NX7WPHsyM4K9SK88rXVpKclsUz13VnWXQ8K3clcHa9UJLs2vdXFkfTsE4tpiyM5tU/XsCLC3aQmJrF/qR0Zq8/SMdm9RnQIQyAqQujCatXi/EXd+SzdQd5/Nst7Hx+BLVDgjmUnM5N761xD343sEMYs349QEiQcH7LBmx75qpCn7Ugz3Pm298LN+Pe9PSV/LD5KJec14y2YXVJychmxc4EBp4bRtN6tbl1+lr3tq7z3mXC15t58ppu3PPBbzRtEMo7Y/qxLNpqOjx/i9XM+aKXrZs//xp+HqN6ncO+xFTu+TCSdY9fTouGddgTn0qrRnWoVzuET9cd4Ilvt/Ld/UPo7VFQ/svHG1izN4mlD19Mp+ZWsWJdTBK3TLNi+341IvEAACAASURBVG7TET65dyBJaZk+a1gBwifM5/GRXbiyW0v+53Feu7poRNs1x3GnMmhWvzZpWTlsP1K41cP2I6c4fOI0kOzVAuR4erb72ITVC3W3xPC88QTW///sPw8iOzePwR3PZtXuBOqFhnjdnHH5ZXci7c+uy77ENJLSMvlDnzbuz/y3SzvywvwdNKhTi5mr9wHW98cdM9bz/QNDufatXzi3WT1+euRSd36z11tjN+blGX7ZY/3PbDp4wl14jjuVQQu7QOtijCEhJZPmDevw+8HjxJ3KcN+U2HDgeKHC8+R52/hxexwjerSiT7vGHD5xmtohVsPmY6cy3NudOJ1NrjHkGes9XDfgquLgjeJkNbqINACMMSYgGrlHRESYyMjIkjdUSvHigh1MWxnDPUM68PS1xd9pVqqmEpENxpjC1Uyq1Jy6Nrt+uHr++Pp47QGesmuhlLeJV3dx17L62zW9WpGYmsnaGN+F3Ff/eAGz1x/kt/3HCQ0OIsvBvqM392vD8p3xJKaW3Ox8VK9WrIiOJ82hgdL+csm5/O9nqyC3f+oo+j63hOS0LEKDg1j12DAGetTEF+Q6z53qe9y4bi02PT3cK7+WDet4FXgq0v6po9zv3aN1Q975Uz8ufmW5V0uN+rVD2PqMNf7wfR9vYFEx3RdKY9Wjw7j2rV84UaApdmVpWj/Ufd4VLDT+tj+ZmwvcXKsIT1/TjXuGduCX3Ynu49y0fm26n9OQD+7qz9CXfuLIyQwW/eMi9403l7uHhDPp2u5MXxnDiB4taRtWl/EfRfLj9jhG9WpVbD/xovxr+HnuVj3lUZnXZkcKzyLSA/gYCLOTEoE7jTG+bwdVEqcu0FMXRpOUat0FDA0J4sHLOxe6U6NUVffC/O1MX7XP/eWolCpMC8/lV5GF58XbjvEXuzb0/9m77/CoqvSB4983jdBDJ9TQmyAlUgQVUJqIuou6dmzr6tp23dXFsood9adrWXXFrruWXbsiIiCooKAgvbfQewmEkvr+/pg7wySZJJPkZkryfp5nnsw9995z35mcmTPn3nPONaYy8ZbzgRO/da6ERrebhrTjhZknukif1LwOy7YVvvL75S2DADjn+dmF1kW7tImjycrJ4+vlOzl4NIv7Pgtrs4mHzj+pxJOPMQJuz8nnxtXnUNbNbnXbngTcrqozAURkMJ7xUKe6lH9Y/brpAFsPHCUnT9l9OJOeLZO4MLVlyTsaY4wxJmSGd23Cy1f04bXZG4vtBmxMtPrhziG0vfurcIdRbv4NZyBgwxkqZ6PZ67QnvmXL/sg5ERJMr50qPJm9j1uN55rehjOAqs5yJhipFP57wwDAMyj+1InfkmeDQk0l5C3WVryNMdFKRBjRrSn/m78l3KEYUyFiYko3s7yJXJHUcDbBc6vxvEFE/o6n6zbA5cBGl/KOGDHOrTDsrIsxxhgTyayBYYwxxn3lvs+z4xqgEfAx8Inz/GqX8o4Y3pN9duXZVEZWqo0xlUVCnKfCfvGy3sy9q9Ak48ZEtWZ1bd4dY8LFlSvPqnoAuNWNvCKZ2JVnY4wxJuI9eN5JNKmTyPCuTYiLjaFto5ps2HMk3GEZ44opfzqd9KPZbNp/hCte+znc4RhTpbjSeBaRjsBfgRT/PFV1qBv5RwrvlWc3b+9lTKQ4MebZyrcxJro1rFUt310DPr95EBnHc+j/WNG3AjImWtStHk/d6vG0alCDS/q29N3P1xhT8dwa8/w/4F/Aq4A7N8OLQLFO6znPLj0bY4wxUaNWtThqVYvjx/FDWbDpALe8tzDcIRnjisd+28Maz8aEkFtjnnNU9SVV/VlVF3gfLuUdMbzdtnOt7WwqIXVGPVvxNsZEgym3ncaP40vXwa1ZUnXGnNzMt/zdHYO56tQUlyOrGsTmZIsYp3VoGO4QjKkyytV4FpH6IlIf+EJE/igiyd40J71SsW7bxhhjTGToklyHZknVy7TvNQPbANC6QU36t83/c6VZ3UQS4926tlAxLj6lZVDbxcYI6x4Z5cox0yaO5vObBzLpij5sfOxsNj42mklX9OH+MV1dyd+U3RtXncIrV6ay+L7hnHtyMwZ3ahTukKLW7cM6hjuEKqV5Gb/Dw6m8tcMCYD4wDrgD+NFJ86a7TkRGishqEVknIuMr4hhFOXGrKms8m8rH7vNsjKkq7hvTlbSJowEYeVIy/7mun2/dn87qyKqHRjH99tN54+pTWPngSNImjiZt4mi+vGUQ1w5qU2ze1eNjfc9vOKMdfVrXA2D67WeUKdZ7R3cplDZxbI9CaYEa1O9c25e42BievODE9m0b1WTlgyPzbVe7WhyL7x9O7WqFR/OteXgUa50GeI8WSQzv1tTXE294t6ZcPTD/+/Hz3Z7ZzU9umcSyB0aw/tGzuWNEJwCGlNCoW/HgiGLX+1v54EheuTI1qG1HndS0UFp5f7S3ql+jXPu7KS42hmFdm1C3RjzPXdKL5y7pBcAdIzpRt3p8UHncOrQ9o7sn50urk+jW6E73zfhL/s/TOT2Si9iyeLULvMZbhrZn2p9PZ9yA1tSrEc+rV6by7MU9A+47tHNjPvnjqWU6brDGDWjNgLYNfMvXFPi8DWzfIN/yH85oC5Cvh00oPXXhyaXaPtBnM9KV61OhqsXXIC4TkVjgBWAYsBX4RUQ+V9UVoTi+3efZGGOMqXwGtm/Iw+efxL2fLiOlYU0A2jeuTfvGtfNtd1LzupzUvC4XpbZkxDPfc83ANiTViGfptnSOZeUye91e/nVFH7Jy8ji5RV0a1yn6lkLrHz2bJ6eu5l/freeqU1OYcG43Tn1sBtvTj+fb7rrT2vLw5JW+5TtHehqiv9xzFnmqxMYIz81Yyz2ju1A9IZY35qTxw51DWLc7g1PbebrzXtCnBb/p1RzwXI0WEZ4Y24M7P1oCwNIHRvj+vjhrHad3aMQ5z8/mjI6NSIgr+TrLukdG8cacNOpUj6NxnUTWP3o2AsQ4XfZGdGvCk1NXc3n/1sxcvce33z8v7cXN754Yf14jIY5PbxrI+S/MAeC93/fnklfm+taPG9Cae0Z39cU0rGsThndtwjcrdgEw/fbTOevp7wF44NxudGtWhwv+9RPXDmpDhya1eW7GWq4Z2IbuLeqgCrf/d3G+1/H6Valc82bgaz+rHhrJL2n72bTvKJf0bYUAbe/+Kt827RvX4oFzu3HZq/MK7f/42O6s3HGYawe1Ydm2dG78z68lvq9ei+4bFvS2AHUS41n3yChiY4Qbz2hHjwe+ISMzJ982//3DAA4ezeJIVg6dmtSha7M65OUpk5fu4MbB7RjToxnN61Xn5Ae+8e3zl2EdeWraGv42sjO9WiVx8aS5BQ9dodImjiYnNw/wnDCoXzOB/UeyAPjnpb25fVgGQ5/6DvA0yiac243s3DwGPT4zXz6Tbx3E5CU7eHHWen4cP5Rnpq/ltdkbAc8QzQ5NavPAeSfxwHkn+fY5r2dz3/OU8ZMBeP2qU4qMtXlSdS7t14onp64u9esc0a0Jy7Ydonm96r4YvMe8b0xXrjo1hdOfnEnHJrX4z3X9+XrZTm74t2e07F2junDH8E7ExghfLN5ebHzbDh7zLd9zdhce+crzPZMQF8OqB0cy6PFv830fPXT+Sew5dJznvl0HwEc3DgBg7Es/+bYZ0K4BrRvUYNO+o4WOeX7PZpzToxnXve35jP11eEdOal631O9PuEl5uiCLyCnAFlXd6SxfCYwFNgETVHW/K1GeON4AJ98RzvJdAKr6WKDtU1NTdf589y6AZ+bk0uner7lzZCf+OLi9a/kaEwkmfL6cN39M4/L+rXj4/O7hDseYiCQiC1Q1uMtdJiC362a3qCob9h6hXaNaQW2ftvcILepVJy7W05C7/NV5zF63l7ev6cvpHQNfYX3sq5W8/P0Gz/4TR3MkM4fnZqzl9uEdqRYXy/2fLeOtnzbx6U0Dyc3Lo09rT5dy7w9ngA2Pnu1rlJbXut0ZZGTm0LNlkiv5BWP7wWNMXrKDxVsPMn5U53wNG29vgPSj2WTl5tGodjW2HzzGhf/6iacuOpn+bRsEzPOdn9L4+2fLWfbACA4dyyapRjw1EvJfH8rOzWPKsp2M6ZHsu3IOsHbXYY5n59G9hedHvKrS5i5Po/i0Dg35Ye1eRvdI5oVLexc67jfLdyIi/N5pDHjj9/9/AXRoXIsPbzw131XgY1m57M3IJCs3jzOf+o7bzuzAszPW+vJZujWdMf+czantGvDu7/uX/MYG4W8fLuGD+VuYfOsgujULrtHi/1rSJo5m3e4M2jeuxfHsXDr//euA+/xw5xC+WbGL3YeP8/J3G/KtS5s4mnkb9rF5/1Hu+HBJ0LEH+u392aJt3Pb+Iqb+6XQ6NfWc6Jq6fCentmtA7cQT7/XMVbv5x/Q1LNmazrCuTQL2WHhh5jq6JNdmaOcmJcayfHs6MSJ0Sa4DnHiPnr+kF9XiYrj+nQXM/OtgDh7N4jcv/hjU63v9qlS+WLyD1JR6XNavdaH1Q/5vFnsOZ7LsgcC9M3YdOk6NhNh8r9sbV7tGNXn72n40T6pORmYOGcdzaOrcK/y12RtJrpvI2d2TUVW+XLKDEd2akhAXQ05uHlsOHKNOYhxLt6UzuFNjVu08xMhnfuCD6/vTz/k8btl/lHGv/0zfNvWZOLYHm/Yd4YvF27lpSHtOun8qR7JymX776b6Tkat3HiY7N8/VhnMo6+byNp5/Bc5S1f0icjrwPnAL0BPooqoXuBOm73gXACNV9Tpn+Qqgn6re7LfN9cD1AK1ateqzadMm146fnZtHh3um8NfhHbl5aAfX8jUmEljj2ZiSWeO5/CK18Vxe3u/QL28ZVOSPQlXlytd/5ryezbmgT4tC63Ny89iTkUly3fxdir0/gt+/vn+RDchotnjLQTbszeA3vQq/J+Hw2aJttKxfg8a1qzHo8ZnFnhAB6PXgNwzt3ISnLvJ0WfX+vz66cQDV4mKDbiRs3HuE49m5dEmug6ry4qz1XNinRbE9GErjeHYuP67fG1QD0cv7Wn6++8xCcfy0fh+dmtbmSGYO1RNiSX14OtXjY1n50MhC+wNMGNOVq/y6Hac+PJ29GZn58hzQtgHJSYnMWr2H5y7uxaqdh3h99ka+uu00kmoklOr1+vslbT8X/usnbjijHeNHdS5zPoHc/t9FDO7UmHMLdJXemX6c/o/N4PrT2/L+z5s5dDyn0L4vX9GHEd0qpuvyzNW7QWFI58YVkn8w/vvLFu78aAnLHhhBrQDDQtwSTY3nxap6svP8BWCPqk5wlhepauBBAmU/3oXAiAKN576qekug7d2uoHPzlHZ3f0Vq63r0dsYwGVNZzFm3l+XbD9EluY7N3GkqnT6t67nyA8Uaz+VXWRvPmTm5/LLxAIMq4PszZfxkxpzcjOedsawmsnkbjN4r0dGsNK8l/Vg21eJiSPQb9z977V5EPEMjCnr4yxW8OnsjT114Mou3HuTtnzYxtncL30kIt32/Zg8D2jUgPjZ0EwLuOZxJg5oJxMQIH/+6lR4t6pKbB6t2HsrXFdyUTyjr5vKeAogVkThVzQHOxLni61LegWwF/GfEaAEU3aHfZTEC3ZvXZfn2QyzffihUhzUmpNL2HiFt75Fwh2GMq1S1ws7uGwNQLS62QhrOAEsnDM83EZmJbLcMbV9peggk103k8v6FuxEHEmhysuI+E3ed3YVbz+pAncR4zu6eTPqxbP42qlOZYy1Jcb0HKkqj2tV8z3/b+0TPCm83cxN9ytvAfQ/4TkT2AseAHwBEpD2QXs68A/kF6CAibYBtwMXApRVwnIBEhC9uGRSqwxljjDHG5BvHaCLfX4ZXXAMw1H6668wKyzs2RqjjlO3qCbE8e7H1rDCRr7yzbT8iIjOAZOAbPdEHPAbP2GdXqWqOiNwMTAVigddVdbnbxzHGGGOMMcYYY/yVa8xzpBORPXhm/o4WDYG94Q6iDCzu0InGmMHiDqVojBmiJ+7Wqhr6vn+ViNXNIWNxh040xgwWdyhFY8wQPXGHrG6u1I3naCMi86NxIhqLO3SiMWawuEMpGmOG6I3bVH7RWjYt7tCJxpjB4g6laIwZojfuihS66eaMMcYYY4wxxpgoZY1nY4wxxhhjjDGmBNZ4jiyTwh1AGVncoRONMYPFHUrRGDNEb9ym8ovWsmlxh040xgwWdyhFY8wQvXFXGBvzbIwxxhhjjDHGlMCuPBtjjDHGGGOMMSWwxrMxxhhjjDHGGFMCazyHgIi0FJGZIrJSRJaLyG0BtrlDRBY5j2Uikisi9Z11aSKy1Fk3P4RxJ4rIzyKy2In7gQDbVBORD0RknYjME5EUv3V3OemrRWREBMV8u4isEJElIjJDRFr7rcv1+z98HoqYSxH3VSKyxy++6/zWjRORtc5jXITF/Q+/mNeIyEG/dWF5v51jx4rIQhH5MsC6iCrXBWIrLu6IK9tBxBxx5dpUDVY3W93sUtwR9x1mdbPVzS7EHHHlOmKoqj0q+AEkA72d57WBNUDXYrYfA3zrt5wGNAxD3ALUcp7HA/OA/gW2+SPwL+f5xcAHzvOuwGKgGtAGWA/ERkjMQ4AazvMbvTE7yxlhKiPBxH0V8M8A+9YHNjh/6znP60VK3AW2vwV4Pdzvt3Ps24F3gS8DrIuocl2KuCOubAcRc8SVa3tUjYfVzVY3uxR3xH2HWd1sdbMLMUdcuY6Uh115DgFV3aGqvzrPDwMrgebF7HIJ8F4oYiuOemQ4i/HOo+AMc+cBbznPPwTOFBFx0t9X1UxV3QisA/pGQsyqOlNVjzqLc4EWFR1XSYJ8r4syApimqvtV9QAwDRhZAWEWUoa4I6Jsi0gLYDTwahGbRFS59iop7kgs20G810UJW7k2VYPVzVY3l8Tq5tCyujl0rG4uO2s8h5jTxaQXnrOAgdbXwFMIP/JLVuAbEVkgItdXdIwF4okVkUXAbjwfloJxNwe2AKhqDpAONPBPd2yl+B8lrgkiZn/XAlP8lhNFZL6IzBWR8ys00AKCjHus0+3nQxFp6aSF7b2G4N9vp5tSG+Bbv+Rwvd/PAHcCeUWsj7hy7Sgpbn+RUraDiTniyrWpWqxurnhWN1vdHASrm0PH6uYyssZzCIlILTwV759U9VARm40B5qjqfr+0garaGxgF3CQip1dwqD6qmquqPfGcJesrIicV2EQC7VZMeoULImYARORyIBV40i+5laqmApcCz4hIuwoP2BFE3F8AKaraA5jOibOvYXuvIfj3G08Xqw9VNdcvLeTvt4icA+xW1QXFbRYgLazlOsi4vdtGRNkOMuaILNem6rC62erm4ljdHFH1RcSVa6ubgSpWN1vjOUREJB5P5fwfVf24mE0vpkDXGVXd7vzdDXxCCLui+MVwEJhF4a4ZW4GWACISB9QF9vunO1oA2ys8UD/FxIyInAXcA5yrqpl++3jf6w3Ovr1CEau/ouJW1X1+sb4C9HGeh/29huLfb0dxZTuU7/dA4FwRSQPeB4aKyL8LbBOJ5TqYuCOtbJcYc6SXa1O5Wd1sdXOwrG6ucFY3R9B7HenlOpxEtfKeLGjYsKGmpKSEOwxjjDGVxIIFC/aqaqNwxxHNrG42xhjjplDWzXGhOEi4pKSkMH9+yO4eYYwxppITkU3hjiHaWd1sjDHGTaGsm63btjEGgBXbD3HdW/NZti093KEYY4wxBs8tZR/7aiXr92SUvLExpsK50ngWj8tF5D5nuZWIhHzsjzGm7Gas3MX0lbv4etnOcIdijDHGGGDz/qO8/P0Gfvfy3HCHYozBvSvPLwID8NwnDuAw8IJLeRtjjDHGGFPlvPez565AezMyS9jSGBMKbo157qeqvUVkIYCqHhCRBJfyNsYYY4wxpsrZc9gazcZEEreuPGeLSCzOfb5EpBEl3ChcRFqKyEwRWSkiy0XkNie9vohME5G1zt96TrqIyHMiss65YXdvl2I3xhhjjDEm4qzaWdStx40x4eBW4/k5PPc4bCwijwCzgUdL2CcH+IuqdgH6AzeJSFdgPDBDVTsAM5xlgFFAB+dxPfCSS7EbY4wxxhhjjDHFcqXbtqr+R0QWAGcCApyvqitL2GcHsMN5flhEVgLNgfOAwc5mb+G5YfjfnPS31XNj6rkikiQiyU4+xhhjjDHGGGNMhSn3lWcRiRGRZaq6SlVfUNV/ltRwDpBHCtALmAc08TaInb+Nnc2aA1v8dtvqpBXM63oRmS8i8/fs2VP6F2SMMcZUMkUNiQqw3Thnm7UiMs4vfZaIrBaRRc6jsZNeTUQ+cIZUzXPqc2OMMaZSKnfjWVXzgMUi0qos+4tILeAj4E+qWtzADgl0+ADxTFLVVFVNbdSoUVlCMsYYYyqbooZE+YhIfeB+oB/QF7i/QCP7MlXt6Tx2O2nXAgdUtT3wD+DxinwRxlQ12bnFTiFkjAkxt8Y8JwPLRWSGiHzufZS0k4jE42k4/0dVP3aSd4lIsrM+GfBW0FuBln67twC2uxS/McYYU5mdh2coFM7f8wNsMwKYpqr7VfUAMA0YWYp8PwTOFJFAJ7uNMWWwZldGuEMwxvhx61ZVD5R2B6dyfQ1YqapP+636HBgHTHT+fuaXfrOIvI/nrHi6jXc2xhhjgpJvSJS323UBJQ2PekNEcvGc9H7YmYPEt4+q5ohIOtAA2OufsYhcj2eyT1q1KlNHNWOMMSbs3Jow7Lsy7DYQuAJYKiKLnLS78TSa/ysi1wKbgQuddV8BZwPrgKPA1eUK2hhjjKlERGQ60DTAqnuCzSJAmnd41GWquk1EauNpPF8BvF3CPicSVCcBkwBSU1MLrTfGGGOigSuNZxHpDzwPdAESgFjgiKrWKWofVZ1N4EoXPLN2F9xegZvKH60xxhhT+ajqWUWtE5Fd3jtUFBgS5W8rJ+52AZ7hUbOcvLc5fw+LyLt4xkS/zYkhVVtFJA6oC+wv/6sxxhhjIo9bY57/CVwCrAWqA9c5acYYY4wJP++QKMg/JMrfVGC4iNRzJgobDkwVkTgRaQi+uUrOAZYFyPcC4FvnZLcxxhhT6bjVeEZV1wGxqpqrqm+Q/+y1McYYY8JnIjBMRNYCw5xlRCRVRF4FUNX9wEPAL87jQSetGp5G9BJgEbANeMXJ9zWggYisA24nwCzexpiyG9a1SbhDMMb4cWvCsKMikgAsEpEngB1ATZfyNsYYY0w5qOo+Ag+Jmo+nt5h3+XXg9QLbHAH6FJHvcU7MTWKMcVmX5DpMW7Er3GEYYxxuXXm+As8455uBI3jGP411KW9jjDHGGGOqHhsFYUxEcWu27U3O02OU4bZVxpjws+rZGGOMMcaYork12/ZGAt+aoq0b+RtjjDHGGFPVfLdmT7hDMMb4cWvMc6rf80Q845/qu5S3MSYErGeYMcYYE1kWb00PdwjGGD+ujHlW1X1+j22q+gww1I28jTHGGGOMqYpqJMSGOwRjjB+3um339luMwXMlurYbeRtjQkNt1LMxxhgTUa46NYUXZ60PdxjGGIdb3baf8nueA6QBF7mUtzHGGGOMMVWONZyNiSxuzbY9xI18jDHhY2OejTHRaPfh49zxvyU8ddHJNKxVLdzhGGOMqcTc6rZ9e3HrVfVpN45jjDHGGOOv7yMzAEh9eDppE0eHORpjjDGVmZuzbZ8CfO4sjwG+B7a4lL8xpoKp769dgjbGGGOMMaYgtxrPDYHeqnoYQEQmAP9T1etcyt8YY4wxJp9jWbnhDsEYY0wV4sqtqoBWQJbfchaQ4lLexphQcAY929hnY0y0eP7bteEOwRhjTBXi1pXnd4CfReQTPL0/fwO85VLexhhjjDGFHMvOf+U5OzeP+Fi3rgsYY4wx+blSw6jqI8DVwAHgIHC1qj7mRt7GmNDQAn+NMSbSCZJv+b7PlocpEmOMMVWBK41nEWkHLFfVZ4HFwGkikhTEfq+LyG4RWeaXVl9EponIWudvPSddROQ5EVknIktEpLcbsRtjjDEmOi3blp5vedqKXWGKxJiKt3rn4XCHYEyV51bfpo+AXBFpD7wKtAHeDWK/N4GRBdLGAzNUtQMww1kGGAV0cB7XAy+VP2xjjJd3rLONeTbGRIttB4/lW96bkRmmSIypeIu3HAx3CMZUeW41nvNUNQf4LfCsqv4ZSC5pJ1X9HthfIPk8ToyXfgs43y/9bfWYCySJSInHMMYYY0zlVLDxbExldudHS8IdgjFVnluN52wRuQS4EvjSSYsvY15NVHUHgPO3sZPenPz3jd7qpBljXOC9v7Pd59kYE81Oe+JbHv1qZbjDMKZCHDqeHe4QjKnS3Go8Xw0MAB5R1Y0i0gb4t0t5e0mAtEK/8kXkehGZLyLz9+zZ43IIxhhjjIlkW/YfY9L3G7jnk6XsP5JV8g7GRJGb/vNruEMwpkpza7btFap6q6q+5yxvVNWJZcxul7c7tvN3t5O+FWjpt10LYHuAWCapaqqqpjZq1KiMIRhT9ahNt22MiSJPfL2q2PX/mbeZRybbFWhTufywdm+4QzCmSovEmyF+Doxzno8DPvNLv9KZdbs/kO7t3m2MMcaYohV1J4sA241ztlkrIuP80meJyGoRWeQ8GjvpV4nIHr/060L1ml6ctb7EbfJsBkRTCeXk5oU7BGOqrLA2nkXkPeAnoJOIbBWRa4GJwDARWQsMc5YBvgI2AOuAV4A/hiFkYyotu/BsTKVW1J0sfESkPnA/0A/oC9xfoJF9mar2dB67/dI/8Et/tQJfQ6l9snAbB6zrtqlk2t8zJdwhGFNluXWf5wuDSStIVS9R1WRVjVfVFqr6mqruU9UzVbWD83e/s62q6k2q2k5Vu6vqfDdiN8YYY6qAou5k4W8EME1V96vqAWAahW8nGXXG/uvHcIdgjOtSxk/m9dkbwx2GMVWOW1ee7woyzRgToU7c59muPRtTCRV1Jwt/Jd3V4g2na/bfRcR/Es+xIrJERD4UEf+5SSLChj1Hwh2CS7Y83QAAIABJREFUMWX2u9SiP1IPfrmC9XsyQhiNMaZcjWcRGSUizwPNReQ5v8ebQI4rERpjjDGmRCIyXUSWBXicF2wWAdK8Z9MuU9XuwGnO4won/QsgRVV7ANM5cXW7YGwVeieMto1qFrs+ZfxksnPz2JeRyd6MTNePb0xFqZ4QS82E2CLXn/nUdxw8akMTjAmV8l553g7MB44DC/wen+Pp/mWMiRK++zzbhWdjopKqnqWqJwV4fEbRd7LwV+RdLVR1m/P3MPAunjHROEOtvK3RV4A+RcRWYXfC6Jpch46Na5e43RlPzKTPw9NJfXg6czfss142JqJkZObwtw+XBLyPc1xs8T/Xez44raLCKrXj2bkcy8oNdxgmSEezcjieXXH/ry37j7Lt4LEKyz8cytV4VtXFeO7nPFtV3/J7fOyMlzLGGGNM+BV1Jwt/U4HhIlLPmShsODBVROJEpCGAiMQD5wDLnOVkv/3PBUJ+b6imdROZOLZ7idttTz/ue37xpLm89WMaAHM37GP34eNF7FXyemMADh7N4tnpa8nLC/6kzO5DxzmS6emo+daPaXwwfwsvf5d/FvlgT/KMfOZ7Xvl+Q/ABV5DeD02jy31fhzsMMnNyueK1eSzblh7uUCJa1/umcsaTM33Lm/cddXU299OemMnAid+6ll8kKPeYZ1XNBRqISIIL8RhjwkXz/THGVC4B72QhIqki8iqAM0HnQ8AvzuNBJ60ankb0EmARsA3PVWaAW0VkuYgsBm4FrgrdS/Jo07AmSTUSWHz/8FLtN+GLFQx7+jsunjSX4f/4nqNZOWQH+NF48aS5/OYFz6RjeXnqa+yU1mHniuLGvUfYV0zX8fV7Muj90DR2pIf2as3RrBzSjxW+6hlIXp6y/0gW9366tFxdhhduPkCfh6aR+vB0vlyyvdhtj2fncu+nS4uNcWf6cS56+ScOHc/maFbR/6env1nNjf9eUOp4F2zaz6ItB33Lf/twCRM+Xw7AvZ8u4x/T1/Dd2pKHJagqGZk59H10Buc8P9uX5vlbeHsRuGlIu2LzXLXzMI98tZLtRVzly8zJJTPHc4VxxfZDpbo6vGbX4YBXxAM56uR71tPf8fni4v+nbe+azMWTfgo6Dn+7Dh1ny/6jRa5fueMwP6zdyz2fLC1T/lXJrkOe76Md6cc4/cmZPP71Kt+6FdsP+T5Lx7NzA35HVjVuTRi2CZjjTCJyu/fhUt7GGGOMKYdi7mQxX1Wv89vudVVt7zzecNKOqGofVe2hqt1U9TbnxDmqepeTdrKqDlHVVYEjqDg3DvY0KupWjy/1vmt3eyZbOng0m673TeXSV+Y6y1m8OGudr4Hh7Xb4+Ner6Hb/VCYv2cG9nwb/o3zF9kN0n/ANKeMnM+T/ZtHn4enszcgk/Vg2uw6duKq9bvdhrn97PvuPZDHgscBXa45k5vD8jLXkBnmFMy9PSRk/mTfmbKT93V/x6g+eq5Ppx7JJGT+Zqct3Ap4rUCc/8E1QeT47Yy29H5rGv+du5vGvV/P2T2nFNmSK8tyMtew7ksXejEzu/HBJsdt+8MsW/j13M89MX+N7XVsPeI65YNMBUsZP5pznZ/Pzxv30mPANXe+b6tt328Fj+a4IP/ftOqYs2xlUjDNX7WbjXs+kc2Nf+onzX5gDwKItB/lg/hbe/DGN+z5bxvLthwDIyfXE9dQ3q0kZP5ncPCUvT3l33mbmrNsLwDtzN3HS/Z74vHnnn4MP9hzOJGX8ZBY6jXUJOCVBYadO/DZgN9xeD06j+/3fkJGZw9nP/cAt7y0sMo/j2bnsOexpUOXlKcP/8T2XTJrLsazcfCcPFm05yOQlOwLmsW53Bre+t5CsnBONrZ3px3nNb4bwPIW5G/Zz7j9nB/XaAJZvT2fxloP0e3QGpz0xM+A2L8xcxwe/bAYgN0zDM9bvyWDtrsNBbfvtql2Frvbm5OZx478XlHjl/IvF2/l04TbW7Q7uWMXZl+E5ETZn3T4AjmXlcvZzP3Dzu56y0vnvXzP6uR+Czs//RM6cdXs5fDybjMwcRj7zPRf96yd+3ri/3DGHQ5xL+Wx3HjFAyQOPjDERx3efZ7v0bIyJIg1rVXMtr1/SPI0wrye+Xu17/uWS7bzsdIu96d1fAejevC7Nk2owqENDdqQf4+XvNvD3c7ry8a9baVS7GoM7eSY1929weKU+PN33fHjXJqzbncGGvflnBv/92/O5cXA7fvvij/RNqc9/bxjA41+v4u2fNtGqQQ3G9GhGVm4eifGeCaUyMnOoFhfDpO838OTU1bx7XT/++r/FADzwxQoAHp68ElXo3ToJgD+8s4BOTU78dMvJzSNXlU73erreXn96W+4c0YnVuw7TrVldwPOD3evAkSzu+2w5repv5Ps7h/jSVZWPf93GgHYNyMlVWjWo4Vu3auchRj7zA4nxJ67hHM3K5bcvzmH1zsMsvn84v6QdoG71eBZuOcA9nyyjtbP/G3PS+HHdPvqk1OPdeZsZ0qkR3nZxwcngft18gC37j3Lb+4u4KLUF9WomcHm/1r71uw8dp++jM3zLv+3dnEWbD5KaUo87R3bm8Smr+N+CrQA0q5vo2+53L//EPL8f/m//tCnf/8zf1OU7Wbc7g6eneRr9i+8fzicLt+Xb5qb//MrkpZ5G6Iuz1nPnyM4M+b9ZACzZmk5SjXhuGNyOL5ZsZ9O+kk9SdP7711zQpwUPnteNGglx5Oap74qw977nCzbt55c0z2u44Z0FvDIulaOZuVz+2jxfPusfPZt2d38FwPLth4rsjj15aVNuGdqB+NjCDfyO905h5l8H+14PwENfrqB787q+5SVb0zn1sRm8fEUq3VvULZSHv9HP5W9op4yfzLCuTXjlylR+Wr+PPRmZPDn1xOc2O0f54JfNXNCnJbEx4it7AGkTRzNw4rckxscw4y+Dizzm8u3pHM/Oo09rz23v35yzkQlfrGD1wyOpFhfLml2HaVW/Bonxsdzy3kIys3P5ZsUu3zG80o9l892aPTSomUDTuom0a1SL79bs4Zo359M8qTpzxg/1bZu27whTlu1kyrKd3DWqM+NOTfF9zv35nwRZ/+jZxMbk/x/k5im3vr/Qd5LjD6e35a6zu3AkMydf75Yf1+3la+dEWk5eHsu3p9MiyfOZ+3njft+JgDW7As/uvmL7IY5l5/LrpgMs2nKQFy7rzVVv/Oxbf9mr8wrtc9HLP+V7f6KFuDlhhojUxnNL5oiYNz81NVXnz7fbQRsTjEe/Wsmk7zdwzcA23Dema7jDMSYiicgCVU0NdxzRzK262dvI9f/x9cEvm/nbR+HtptmyfnW27HeuVI/tzu9OaUXHe6fkuwJXVj/fcyYTv1rFxwu38dSFJ7Nm12Ffg760/vuHAVz0cnBdZvu2qc/PG/fzr8v7kJunvpMHAGd1acL0lbuokxjHkgkjmLFyF2/+mMbNQ9rzu0lzfdulTRxNTm4ec9bvY9zrPwc6jHGsfHBkoYaqt5xP+Hw5bzrj9YPh/f+UxbWD2uS7UlzREuNjuLRva87o1IiTW9Rl/EdLmTi2O1OW7eT0jo1onlQ938ktf03rJLLzUNFzE4w5uRlDOjXi9v8uDrj+hzuH0LL+iRM8Ayd+S7+29WnfuJbvJFqMwHOX9PJdiT335GY89tvudLt/KkM7N2bJ1oPszcg/jCFt4mjW7jpMYnwsEz5fzoxVu/Ot+/jXrb6YvP/j9KPZbDlw1Nel32vNw6NIiMvfadj//WjbsCaX9mtF56Z1WLXzENcOasPT09bw/LfrinxfitOwVrWAdycY27sF943pmq+3T8H/S6v6NdgcRG+U+fee5coJ0FDWza40nkXkJOAdoL6TtBe4UlWXlzvzcrDGszHBe2TyCl75YSNXD0zh/jHdwh2OMRHJGs/l52bjOUZgw2MnGs+qyqNfreSVH0L3g98Edk6PZL7069Lbu1USv24ufAXeBMfbsNp28Film4CpOE3qVPONya1fM4H9Ryrmtlwf3XgqnZvW5r7PlvPJwq0UNSqiTcOavq72ANcMbMPrc4r+vpn259MZ9o/vA65b8/AoPl+83dc75Ir+rRndI5mL/U46+fvi5kF8v3YPY3u3YOKUlazdneEbLhAOz17ck96t6hXZfT5Yblx9DmXd7Fa37UnA7ao6E0BEBuOZTORUl/IPq4WbD3A823PGOCEuhp4tkwp1izDGGGNM6HRsUou2DWvlSxMR7hndlY5NanNHCWNoTcX6ssBYWGs4u6N5UvVwhxBS3oYzUGENZ4CxL/0Y1HYbCwytKK7hDBTZcAbo8cBUX/sCPGPh35m7qcjtxzhjw/27pYfTbe8vCncIYeHWhGE1vQ1nAFWdBdR0Ke+w+/MHi7jklblc8spcxr70o29yDWMqE28nFBvzbIyJBrl5WuSJ7AtTWwZMN6YyKGnmbRMd/BvOJnq41Xje4My0neI87gUqTZ+pf/yuJ+/9vj/PX9ILgIzjZbtNhTHGGGPcsX7PEd8kS4F0a1YHgLtGdQ5VSMaExJ/O6hjuEIypstzqtn0N8ADwMSDA98DVLuUddr1aeWbX885Kl2eX5owxxpiw6t0qif5tGxS5fvKtp/mePzYl5HfQMqbCxMfGMLhTI2atLvme0sYYd7nSeFbVA8CtbuQVyWKce/AFeWtFY6KKFWtjTDT5+I8Dwx2CMWHz5tV9i5x52hhTcVzpti0iHUVkkoh8IyLfeh9u5B1JvI3ncN1w3RhjjDHGGIB7R3cJdwjGVDluddv+H/Av4FUg16U8I453XhI3741tTKQ4MWGYlW9jTOUy+29D+Gb5Lga0a0Cr+jXodv/UcIdkTLldd1pbHp68MtxhGFNmbRtG3/zSbk0YlqOqL6nqz6q6wPtwKe+I4eu2bf22jTHGmKjRol4NrhnUhi7JdahZLY6f7zkz3CEZ44qmdRIBePe6fmGOxJjSe+uavuEOodTK1XgWkfoiUh/4QkT+KCLJ3jQnvVKJifF22w5zIMZUAHVGPVvxNsZUdnUS433PuzevS93q8QG3+78LT3btmEM7N+bklkkA9GtTn6UThudb3zypOq3q1/At/+GMtq4c98zOjV3JJxSuOjXFtbx+vrvkEyQL/z6s2PUD2xc9IV0gaRNHc8eITvnSGtWuxsy/Di607bMX9yxV3kX57s7BrHxwJKe2b8jkWwcxZ/zQIrf98IYBrhzTRL+eLZNoVjexwo9zab9WvHplKlNuO42VD45k1UMjfeseH9udln7fedGivN22F+D5re290eIdfusUcOeb34+IjASeBWKBV1V1otvHKIp12zbGGGOiX2J8LMsfGEH6sWyaJVUHIGX8ZLom1+HqgSl8vng7A9o14II+LbigTwvmp+1ny4GjnN+zOW3u+ipfXqek1KN1g5p8uGBroeOM7p7su53Wi5f15ueN+7ny9Z95+nc9qZ0YzxtXn0JOrjKsa5NC+67ddZiXv9sAeBqCfR+dUabXOn5UZ2as2l3idj/cOYTTnphZpmOUV5fkOtw5ohNDOjdmQLsGPPjFCrYdPFbk9n3b1OfnjfvzpX15yyAa1a5Gv0dn0L15XRrXSeTd6/px4Gg2Wbm5pB/NZsIXK3zbPz62O/VqJvDhDQOYt3E/Tesk8pf/LWbcgNb8dUQnajsnWLyTcnVuWptVOw/nO2bPlkm8cdUpTF2+k+oJsQDceEY7Rp7UlLw85cMFW7nrbM+45Jl/HUzNarH0fWQGSTXiOa9nc1rUq07j2onMWbeXbs3qMuafswG44Yx2dGpai72Hs3jkq+K7ZVeLi/U979asbrHbpqbU565RnX2zz694cARd76u8QxjuGNGJb5bvZPHW9HzpP901lOXbDjHhi+U8d0kvfvvij64fe8KYrvnKW6SpnRjHpzedGdSkc7cP68jT09aU6TiP/qZ7obS1j4xi6vKdjO6eXKY8w02iqSEoIrHAGmAYsBX4BbhEVQOWztTUVJ0/f75rxz+SmUO3+6dy99mduf50u0G9qVwmfL6cN39M44r+rXno/JPCHY4xEUlEFqhqarjjiGZu181uWbo1nVb1a1C3RuCr0F5fLN7OLe8tBOCNq05hiHNlNzMnl1d/2MjvTmnJ41NWcceITjSuk+j7cbrxsbMRkSLzLSgvTzn/xTmM7d2CcaemkJmTy57DmTSpk0hunpKdm0ftxHjSj2Zz3guzSdt3tMjjHMnMQQSOZeWiQOrD0wHPRYHrTmvLeT2b0a1ZXd6dt5m7P1nq2+/ck5vx+eLtQcdcnLSJozlwJIt+j83gzatP4bOF27m8f2uaJSXSoFa1QtvPT9vPpa/M40/DOvDE16uZcttpjHr2BwAePK8bcTExvlj/Mqwjt5zZAYAPftnMkM6NaVy78FW1tL1HSE5KJON4DvVrJgT1/8jIzCEuRkiMj+XAkSwmL91Bt2Z1yMjM4bQOjUr9Pny2aBu9WtajVYPCV9zGvvQjCzYdIG3iaF/azvTjZGTm0L5xrVIfyxv72z+l0aFxbYZ0bkxObh79Hp3Bved0YWS3ZLrc9zUAC+49i4zMHM54cla+PJ69uCc9WyZROzGe3g9NA2DO+KHMWbeXOz9cAsAv95zFnz5YyJx1+/jvHwbQsFYCQ5/6DvA0lOJihJe/30CzpOpUi4vhtR828nNa/pMfpZUQG0NWbh5/G9mZGwd7fpN7P2sJcTF8+seBdHXu9b43I5Na1eKYtXo31eJjGdIpf2+MI5k53PfZcnYeOsacdfsCHq+kxvCqh0aSk6cIEOuUl/1Hsuj90DTeva4fIsIlr8wt12suyRtXn8Kyrek85dfQrV8zwRNHqyR+3XzQl35Gx0a8dU1fdh8+TnxMDPVqJgCe93BA2waM6NaEsX1a+E4iAaQfzaZGtVg63DMl33FFTsyb4zVhTFdqVovjwtSWFfBKCwtl3VyuxrOInAJsUdWdzvKVwFhgEzBBVcv3ySh8vAFOviOc5bsAVPWxQNu7XUEfy8qly31fM35UZ244wxrPpnKxxrMxJbPGc/lFauO5oszbsI8mdRJJqcCJcY5l5bLlwFHaN6rlG2JWHG8jw7+RBvDvuZu499NlvuU/nNGWU1rX57b3F3IkK5c1D48iOzePZ2es5bYzO/gmXkubOJrj2bl0/vvXvn0fPK8b9322nCm3ncahY9n0K+ae3MVRVXLzlLjYGP7+6TLembuJB8/rxhX9W5OTp3y5ZDtjejQjLtataXzCJy/PM4AqNoj/oRty85R2d3/FHSM6cdOQ9oDn/c7JU18Dyb+MfLV0B41rVyM1xTMy84//WcDgTo25KLUlB49mMW/jfkZ0awrAxr1HSK6bSGJ8LAU99OUKXpu9kVNS6vHquFN8J4Pu+WQZ01fuAuDaQW0Y0LYB173t+a74Ta/mfLJwG3eM6MTo7skBP08vzVrP41+vYmzvFjx1UemHXGTl5NHx3ikB16VNHM2W/Uf5x/Q1XHVqCgs3H+T+z5cD8PnNA+nRIqnE/G97fyGfLQp8MiqlQQ3fCbB7R3fhnbmbuGtUZ4Z3bUpMjJCbp/zfN6t5adb6/Hme2YFnZ6zllqHt+cvwTuTmKZ8t2kbPlkk0rZuIKny7ajfn9Ehm1po97Eo/zviPlzKkUyPeuLrweONN+47QqHY1aiQU3TnZW053HjpOrWpx1K0e7/sNeWq7Bvy4fl+h75aKFsq6ubzdtl8GzgIQkdOBicAtQE9gEnBBOfMvqDmwxW95KxCyGRJinO/lXJswzFRiaqOejTHGNWVtNJZG9YRYOjapXap9erUq/GN/cCfPlVTvFedYEc7q2oTlD54Yp5gQF8PdTlfkZ37Xkz2HMwGIK9Dgu7xfa64ckFKqmAIREeJiPXmf0bER78zdRM+WSYgI8bHCb3q1KPcxIkUwJz7cFBsjhRo5IkKc83s3ocAJibMLdLN98bI+vudJNRJ8DWeANsWcLLppSHt2pB/j8bE98l3ZfPGy3mRkenoFeP372n6kNKxB+rFsPlm4jTE9mgW8ag9wXs9mPP71Ki7v36rIYxcnIS6GhrUS2JuRRdfkOkwc251z/znHt75l/Ro8fZFnrHqPFkn0aFGXA0ezgmo4A1yU2pLPFm3nxct607NlEpe8MpdN+476/gfLt6fTsFY1mtRJ5LrT8o98jY0R/jayMzec3o5/z9vEk1NXA/DnYR3587CO+bb7be/8n4kxJzcDYEinxkxbscu3XSCtG5R8ks9bTps7Q17A0+C/aUh76laP5/Dx7BLziGblbTzH+l1d/h0wSVU/Aj4SkUXlzDuQQP/pfL/0ReR64HqAVq3K9uEpine27Wjq6m6MMcYY42/tI6N8v2n8tahXw3cVuX7NBP7oXI0syvm9mvuex8XGMP3200muW524WKmQhuBZXZuw6qGRAa9mGveICHeM6MSZXSpmsrn6NRPyNby9EuJiqB+XkC9tUIeGALSoV7inREHNkqqX+4rnvLvPIjdPSXDOIPz9nK70DnCiCaBXq3qlyntg+4b5yu+M28/I14gpacw6QN0a8VRzYrtmYJtSHR+gnjMsJSWIRnJpxMXG0Ki2Z/hFoGEYlUm5G88iEqeqOcCZOI1Wl/IOZCvg33m+BZCv/4OqTsJz1ZvU1FRXW7neiubfczczbWXJk28YE022HfB0F5qydCdLtx0KczTGuOuc7sn8/nTX57A0JirFl9DFOTE+lgnndit1vu0bl+7qd1lYwzk0birhxEllFRsj+a7KXjuo9A3U4viX37IONfCO1S/L7qkp9Xnj6lMY2K5hmY5tyt/AfQ/4TkT2AseAHwBEpD2QXtyOZfQL0EFE2gDbgIuBSyvgOAHFxgjXDmrDut0ZoTqkMSGTVL0us9ft5aTmJZ/5NCbaJCbYD25jjDHR79K+rdi4N8M3SV5pFZwwzZROuRrPqvqIiMwAkoFv9ER/5hg8Y59dpao5InIzMBXPrapeV9Xlbh+nOH8/p2soD2eMMcYYY4wxgGeOg4fPL3wLKBMa5e5araqF5l1X1bLdDCy4430FfFXihsYYY4wxxhhjjEui6j7PpSUie/DcNisYDYG9FRhORbG4QycaYwaLO5SiMWawuEujtaqW/uauxsfq5ogWjXFHY8xgcYdSNMYMFndphKxurtSN59IQkfnReO9Oizt0ojFmsLhDKRpjBovbRK5o/R9b3KETjTGDxR1K0RgzWNyRKvrvKG+MMcYYY4wxxlQwazwbY4wxxhhjjDElsMbzCZPCHUAZWdyhE40xg8UdStEYM1jcJnJF6//Y4g6daIwZLO5QisaYweKOSDbm2RhjjDHGGGOMKYFdeTbGGGOMMcYYY0pQZRrPIhIrIgtF5MsA6/4hIoucxxoROei3Ltdv3echjjlNRJY6x54fYL2IyHMisk5ElohIb79140RkrfMYF0ExX+bEukREfhSRk4PdN8xxDxaRdL+ycJ/fupEistr5P4yPsLjv8It5mVOe6wezbwXGnCQiH4rIKhFZKSIDCqyPuHIdZNyRWrZLijviynYQMUdcuTZlY3VzxMQcqd9fVjeHLmarmyMn5kgt11Y3A6hqlXgAtwPvAl+WsN0twOt+yxlhjDkNaFjM+rOBKYAA/YF5Tnp9YIPzt57zvF6ExHyqNxZglDfmYPYNc9yDA5UdIBZYD7QFEoDFQNdIibvAtmOAb8P9fgNvAdc5zxOApALrI65cBxl3pJbtkuKOuLJdUswFto2Icm2PMv+vrW6OjJgj9furpLgj7vurtO9ZpHyHBVFXRFy5DjLuiCvbQcQcqeXa6mbVqnHlWURaAKOBV4PY/BLgvYqNyDXnAW+rx1wgSUSSgRHANFXdr6oHgGnAyHAG6qWqPzoxAcwFWoQzHhf0Bdap6gZVzQLex/N/iURhL9siUgc4HXgNQFWzVPVggc0irlwHE3cklu0g3++ihKVslyHmsJdrUzZWN1vdXIGsbi4Fq5tDJxrrZbC62V+VaDwDzwB3AnnFbSQirYE2wLd+yYkiMl9E5orI+RUYYyAKfCMiC0Tk+gDrmwNb/Ja3OmlFpYdCSTH7uxbPWcyy7Ou2YI49QEQWi8gUEenmpIXzvYYg3zMRqYGnMvuotPu6rC2wB3hDPF01XxWRmgW2icRyHUzc/iKlbAcbdySV7aDf6wgq16ZsrG62urkkVjeHhtXNkfVeQ+SVa6ubHZW+8Swi5wC7VXVBEJtfDHyoqrl+aa1UNRW4FHhGRNpVRJxFGKiqvfF0M7lJRE4vsF4C7KPFpIdCSTEDICJD8HyJ/a20+1aQko79K9BaVU8Gngc+ddLD+V5D8O/ZGGCOqu4vw75uigN6Ay+pai/gCFBwzE4klutg4gYirmwHE3ekle2g32sip1ybUrK6OV96KFjdbHVzcaxujqz3OhLLtdXNjkrfeAYGAueKSBqe7g1DReTfRWx7MQW6GKjqdufvBmAW0KvCIi3A79i7gU/wdNfwtxVo6bfcAtheTHqFCyJmRKQHnm5656nqvtLsW1FKOraqHlLVDOf5V0C8iDQkjO+1E0uw71lxZTuU7/dWYKuqznOWP8TzZVxwm4gq1wQXdySW7RLjjsCyHdR77YiUcm1Kz+pmq5tLZHWz1c0liMa6ORrrZbC62afSN55V9S5VbaGqKXj+md+q6uUFtxORTngmOvjJL62eiFRznjfEU9mvCEXcIlJTRGp7nwPDgWUFNvscuFI8+gPpqroDmAoMd+Kv5+w7NRJiFpFWwMfAFaq6pjT7hjnupiIizvO+eD47+4BfgA4i0kZEEvCUsZDM/BrseyYidYEzgM9Ku6/bVHUnsMX5vAGcSeHPVESV62DjjsSyHWTcEVW2gywjEVWuTelZ3Wx1s0txR9T3V7BxO+si5jvM6ubIeq8jsVxb3XyCqIayF0toNWzYUFNSUsIdhjHGmEpiwYIFe1W1UbjjiGZWNxtjjHFTKOvmuFAcJFxSUlKYPz+6byVmjDEmcojIpnDHEO2sbjaQoHvAAAAgAElEQVTGGOOmUNbNlb7btjEmONNX7KLTvVP4etmOcIdijDHGGOBIZg4p4yfzwS+bwx2KMQaXGs/O+IfLReQ+Z7mV00ffGBMlVu44RGZOHsu2HQp3KMYYY4wBVu08DMDfPloa5kiMMeDelecXgQF4bogNcBh4waW8jTHGGGOMqXKycoq9DboxJsTcGvPcT1V7i8hCAFU94MwCZ4wxxhhjjDHGRD23rjxni0gszo26RaQRYKfKjDHGGGOMMcZUCm41np/Dc8PrxiLyCDAbeNSlvI0xxhhjjKlycvMq7y1ljYlGrjSeVfU/wJ3AY8AO4HxV/V9x+4hISxGZKSIrRWS5iNzmpNcXkWkistb5W89JFxF5TkTWicgSEentRuzGGGOMMcZEopmrd4c7BGOMn3I3nkUkRkSWqeoqVX1BVf+pqiuD2DUH+IuqdgH6AzeJSFdgPDBDVTsAM5xlgFFAB+dxPfBSeWM3xhhjqoKiTkwH2G6cs81aERnnlz5LRFaLyCLn0dhJryYiHzgntueJSEpoXpExVUN8rN1V1phIUu5PpKrmAYtFpFUp99uhqr86zw8DK4HmwHnAW85mbwHnO8/PA95Wj7lAkogklzd+Y4wxpgoo6sS0j4jUB+4H+gF9gfsLNLIvU9WezsN7Oexa4ICqtgf+ATxekS/CmKomT63btjGRxK3TWcnAchGZISKfex/B7uycqe4FzAOaqOoO8DSwgcbOZs2BLX67bXXSCuZ1vYjMF5H5e/bsKdOLMcYYYyqZok5M+xsBTFPV/ap6AJgGjCxFvh8CZ4qIuBCvMQY4qXndcIdgjPHj1q2qHijrjiJSC/gI+JOqHiqmzg20otDpOFWdBEwCSE1NtdN1xhhjTIET095u1wWUdJL6DRHJxVNnP6yq6r+PquaISDrQANjrn7GIXI9nyBWtWpWqo5oxVVq9GvEADGrfMMyRGGPApcazqn5Xlv1EJB5PJfwfVf3YSd4lIslO5Z4MeLuGbQVa+u3eAthe1piNMcaYykREpgNNA6y6J9gsAqR5T0JfpqrbRKQ2nnr7CuDtEvY5kWAnto0pkxxntu2YGOvQYUwkcKXbtoj0F5FfRCRDRLJEJFdEDpWwjwCvAStV9Wm/VZ8D3klKxgGf+aVf6cy63R9I955FN8YYY6o6VT1LVU8K8PgM58Q0QIET0/6KPEmtqtucv4eBd/GMic63j4jEAXWB/e6/OmOqpjyn8RxnjWdjIoJbY57/CVwCrAWqA9c5acUZiOfM9VC/2TvPBiYCw0RkLTDMWQb4CtgArANeAf7oUuzGGGNMZVfUiWl/U4HhIlLPmShsODBVROJEpCH4eoydAywLkO8FwLdOd25jjAu8V55jrfFsTERwa8wzqrpORGJVNRfPuKgfS9h+NoG7ewGcGWB7BW4qf6TGGGNMlTMR+K+IXAtsBi4EEJFU4AZVvU5V94vIQ8Avzj4POmk18TSi44FYYDqek9jg6UH2joisw3PF+eLQvSRjKr9cb+PZ5uEzJiK41Xg+KiIJwCIReQLYAdR0KW9jjDHGlIOq7iPwien5eHqLeZdfB14vsM0RoE8R+R7HaYgbY9znazzHWuPZmEjgVrftK/Ccjb4ZOIJn/NNYl/I2xhhjjDGmyrErz8ZEFrdm297kPD1GOW5bZYwJHxukaIwxxkSWbQePATbm2ZhI4UrjWUQ2EvjWFG3dyN8YY4wxxpiq5smpqwE4np0b5kiMMeDemOdUv+eJeMY/1Xcpb2NMCNj8uMYYY0xkirFu28ZEBFfGPKvqPr/HNlV9BhjqRt7GGGOMMcZUZdZt25jI4Fa37d5+izF4rkTXdiNvY0xoqI16NsYYYyLSviOZ4Q7BGIN73baf8nueA6QBF7mUtzHGGGOMMVXWnHX7wh2CMQb3Ztse4kY+xpjwsTHPxhhjjDHGFM2tbtu3F7deVZ924zjGGGOMMcYYY0w4uDJhGJ4xzjcCzZ3HDUBXPOOebeyzMVFAfX/tErQx/8/efYdHVaUPHP++6aETCBBq6EgvkSoI0kHFtvaCZbGuXRdsYKGs+nMVdd21Yy+gqy4qUgVBSkCQXhMgtIQEkpCQNjm/P+ZmMkkmBXIzk4T38zzzZObcc89952YyN+eepqqObzbEETl5PjmOXF+HopTt/nZRO1+HoJRyY1fluSHQ2xjziDHmEaAP0NwY86wx5lmbjqGUUkop5XIyPYuHv9oEwF2frPdxNErZT5eoUqpysavy3BLIcnudBUTaVLZSyhusQc869lkpVVVc+/Zq1/NF2+N9GIlSFUMvyUpVLnbNtv0xsFZEvsX5d345MMemspVSSimlithxNNXXIShVsdzuaOc4cgnwt6vdSyl1NuyabXu6iPwEDLaSbjXG/GFH2Uop7zCFfiqllFLKt/SarFTlYtds222BrcaYDSIyFBgsIjHGmJN2lK+UUkoppdS5RodSKVW52NX3Yx7gEJF2wLtAa+Cz0nYSkfdFJF5EtrilhYnIQhHZbf2sb6WLiMwWkT0i8qeI9LYpdqUU+RdovVArpZRSlYP7Chh6eVbK9+yqPOcaY3KAK4DXjDEPARFl2O9DYEyhtMnAYmNMe2Cx9RpgLNDeekwC3rIhbqWUUkpVEweT0n0dglIVpv2TPzHjx+2+DkOpc5pdledsEbkOuBn4n5UWWNpOxpjlQFKh5AnkTzY2B7jMLf0j47QaqCciZamgK6XKIO/utq7zrJSqCuJTMoqkDX5xqQ8iUariFO4N9vbyfb4JRCkF2Fd5vhUYAEw3xsSISGvgk7Msq7Ex5giA9bORld4MOOiWL85KU0oppdQ5Jj3L4esQlKpwejtbqcrFlsqzMWabMeZ+Y8zn1usYY8wsO8p242mV+CLfKSIySUSiRSQ6ISHB5hCUqr6MTretVLVV3HwiHvLdYuXZLSK3uKUvE5GdIrLRejSy0ieKSIJb+h3eek/HPLQ8A5xMz/JWCEpVOJ2HRKnKpTIuFncsrzu29TPeSo8DWrjlaw4cLryzMeZtY0yUMSYqPDy8woNVSimlqoDi5hNxEZEwYCrQD+gLTC1Uyb7BGNPTesS7pX/plv5uBb6HAv76UbTH9J7PLSQpTSvQqnrwNJQqcvJ8jp/K9EE0SqnKWHn+Hsi7230L8J1b+s3WrNv9geS87t1KqfLThmelqrXi5hNxNxpYaIxJMsacABZSdFLPSiMlI6fYbb2fX+jFSJSqQMVclC99/TfvxqGUAmyqPIvIX8qS5iHP58DvQEcRiROR24FZwEgR2Q2MtF4D/AjsA/YA7wD32BG7UkopdQ4obj4Rd6XNLfKB1TX7aRFxH0p1pbWE5FwRce8h5lMxx9N8HYJS5VbcDe3DyRms2ZfI+v0nvBqPUue6AJvKmQJ8XYa0Aowx1xWzabiHvAa496yiU0qVKn+dZ217VqoqEpFFQBMPm54saxEe0vK+EG4wxhwSkdrAPOAm4CPgB+BzY0ymiNyFs1X7Ig+xTcK5zCQtW7YsYzjlc9Vbq/jvvYNoEVbDK8dTytuueXs1ALGzxvs4EqXOHeVqeRaRsSLyOtBMRGa7PT4Eiu9PpZRSSilbGWNGGGO6enh8R/Hzibgrdm4RY8wh62cq8BnOMdEYYxKNMXmDL98B+hQTm9fnI0lMy9Klq1SVV5Yb2pGT5/PUfzd7IRqlVHm7bR8GooEMYL3b43ucY6eUUlWEa51nbXhWqjoqbj4RdwuAUSJS35oobBSwQEQCRKQhgIgEAhcDW6zXEW77Xwpsr6D4S/TLQ0OK3fbsD1sBWL4rgYXbjgFw6ORpXlm4S3vaqEqvrB/RT1YfICPbwbbDKfwZd7Jig1LqHFauyrMxZhPO9Zx/M8bMcXt8Y002opRSSinf8zifiIhEici7AMaYJOB5YJ31eM5KC8ZZif4T2AgcwtnKDHC/iGwVkU3A/cBE772lfB0a1y522wcrY1m6M56b31/rmqH77k/WM3vxbnYdO+WV+A6dPO2V4yinI8mniZw8nz8OVP1/RQ1QM8ifO4e0KTXvgaR0xs1ewaVvrKz4wJQ6C5k5DuJTM0g8lVllx+uXe8IwY4wDaCAiQTbEo5TyFVPgh1KqGrG6Vw83xrS3fiZZ6dHGmDvc8r1vjGlnPT6w0tKMMX2MMd2NMV2MMQ9Y136MMVOstB7GmGHGmB3efm8TB0aWmufWD9YVeJ2ZnQtAbgW1PG89nMygWUs4mZ7Fz1uOMmjWEpbu9NRT3h7fbzpcZHmu6NgkthxKrrBjekOOI7dAK+r+xDQSi1miKTPHwZZDyaRl5rBgy1EALv/XKo95T2XmMHd9XLHHPZ3lIMeRW47I7WMMiAiTx3YqNe+ofy53PV+//wQbD57kjjnrePjLjRUZok9M/W4LnZ7+yddhqFJkZDv4ct0Blu6I5/n/beO+z/6g7/TFXPnWKq58y/PfZ2Vn14Rh+4GVIvI94Jre0hjzik3lK6WUUkoV8dT48wDY+MxIej5X+hJVX0cfJG+u8CU74vnjwEmu75c/iVlSWhb+fkLd0MCzjun1xXs4dPI0q/YmsvWwswK7JS6ZYR09TXJePtuPpHD/539wfmR9vr5rIMYY9iemc9W/fwc8TyaVke3gZHo2TeqG2B7P2Tp08jThtYJ58Ms/iE/JZO7dA3n06038d+Nhbh7QikdGdeTCl5YR5O/HvcPacdsFkSzcdoyHv9pEj+Z12RTnPM/htYNJSC1awT6RlkWWI5d+Mxa70to1qkXPFvWK5D3vmZ/p2zqMr+4cUHFvuJD4lAz2J6VzfmRYgXSDQXBWoM9E4YrJzQMj6dSkNnd9sp5nLu5Mm/Barm1ZObnEp2bQvH75JtfLyskl4VQmdUMDOZ3lILx2cLnKK8mc3/dXWNm+tvVwMjWCAmjdsGaRbQmpmcxdH8ddF7ZBRNhyKJk6IYG0bHD2v7tNB0/SMqwG9WuefTto8uls7vtsAy9d1aPA98orC3fx9vJ9RfLHJqaf9bF8za7K82Hr4QcU33dKKVVpudZ51qZnpVQVEuDv7ERXr0YQb17fm3s/21Bi/sfm/ul6/tKCnQD0aFGXLk3rAvlrRJ/pDMbbj6TQtG4odWsEulq0/QT8rUqPo9CX68sLdvLG0j3smT7W9R5yHLmkZzuoE+KsuEfHJnHXJxtY8uiF1AkJ5FRmDkH+fgQFOPNf8vpvbLZal9fFniBy8nwmj+3ErJ/yOwA8+MUf3DG4DTe8u4aFDw+hUe0Q+k5fREpGDlufHc2wl5fx2rW9mPb9VhLTsoh+akSx7zE9K4fOzyxg7RPDaVQnhOTT2fR49hc+vaMfg9o1LPH8jHl1OTuOprLtudEE+Pmx+VAyfVrVJy0zh8ycXAbNWsJlPZvy42Znq/GuY6n8d+NhAD76fT8fWZWlLEcu/1y0i38u2uUqO6/iDBSpOB86eZpm9ULp5WHt78vedHZv3vnCGA6dOM3naw/wxDjnzZi1MUl0m7oAhzHcOiiSN5fuJbx2MOuedJ6fk+lZ1A0NLFKpzTtHk8d2on+bBizflcD9w9uT7cglMyeXWsHOf72T07OpHRKAn59z//Gv/+aK/dFRHbjvovaAdU22DrHj+TF0evrnEs9zcS57cyXnRdRh+5EUlu38FYD3bomiX5sGTP1uK/M2xLHl2dEMfWkZIvDSVd0ZeoY3ex6f67zZEVYzyNUT4pWre3BF7+ZnFXNlciwlg9ohAdQIKlp1+m33cW58b02BtPsvasfDozoCsCf+FPVqBNKwVtluJoyf7VzDu/B3UEa2g0kfR/PHgZNc0K4h3ZrX5eLXPef15L9/HMLfT5j543YS07JY9PCFhNcOZoL1d9AmvCaLH77wjG/UALy3Yh8rdh9nwKzFxMzMj8XTjSx32Y5cAv1tWTnZa2ypPBtjngWwlrAwxhjvDCJSSimllLKM6+Zppa7SbTmUTGpGDtdaS/+Ac5ZjY+DgiXQa1wkh7sRp2jWqVWwZY19bQftGtVjw4BByrXpyepaDrYdTAFi1N5Eth9bxzs1RiAhvLN0DwG1zormpfyv2JZxiplXpFYE3r+/NPZ86bwRc9uZK9iUUXLc6JNCPjOyiXYvdK84A/9142FUJnTJvM2O6NiElw7kgyuzFu4lPzeS6d/Lf9zPfbeG5CV2599MN7DyWyn9u6sPw//uV+4e3Z/bi3QD0nbGYvTPGuSZgu+HdNcy+rhcXd4vAz09Yv/8EDWsF8fOWo3y8ej/XRLVgx9FUADo/s4DbBrXm/ZUxBPoL2Y78mwpLduR3bXfvglweg2YtKTXP/zYd4ZWFuzh08jTt3cbPp2Y6z9ObS/cCzopA5OT5ru2Tx3YirEYQ0fuT2HXsFC9d1Z3lu48D8OHKWNfv4r5h7Wj/pLOLcczMcaRm5tDjuV+4c0gbRnRuTJM6IQUqGS//soslO+IJDfJnT/wpMnOcv+eQQP/ynAq2H0kp8Pr2OdEFXu+NP8Vxq1v8xA/WsemZUdQJDSDXgL9VyZ+3Po7o/SeYfllXV8U/79zkfc7chxA8/NUmlu1MYMXuBK7v15KHRnRw3SwCSM3I5lRmDhF1Qz3GfCIti17PL+Sf1/Rg97FT3D+8fYnnIe/v1j02gNxcg8iZt+ADxJ1I54J/OGfuj501noxsB0t3xDO6SxOipi8qMmQCYPaSPVzasxltGtZkxCu/EhLox47nxxbIk5WTy03vreG5CV1pXCeYVXsTqRGU/96OpWTQsFYw+xJO0b5xbYb/36+u+RMueeO3AmVlZDuIT8ks0AIddyKdwycz6Nva2ZvhwULd9we/uJTFj1zoer0vIY3WU37k+Qld6Ny0Lg1rBfHTlqOM7xbhWvJvw4ETHExKJzM7l6MpGbyycFeBMo2BOati+XHzEQ4nn+ZgUsnzPazYncBFnRqXmKeyETtmmhSRrsDHQF5fk+PAzcaYreUuvByioqJMdHR06RmVUkyfv413VsRw66BIpl7SxdfhKFUpich6Y0yUr+Ooyuy6NudVYgq3uLhXbsoqyN+PrEJjXAe1a8DKPYkF0hY+NIRP1xygfeNaGAPnR4bRsUltNsclF/lntjjN6zsrCXEnKu8kYp/e0Y8b3l1TesazVDs4wFUxPRddHdWcr6LjiKgbwpHkjDLtk/c5/3TNfp78dkuFxPX5X/sXuJEC8MS4Tsz4cQcbnh5JWM2gAn9fT40/j4i6oYzt2oRbPljLCuvGQUnuG9aOmwa0onEdZ9fevPLWPjGcr9fHcc/QtizdGc8DX2xk9ZThLNuZUKA3yeSxnbiidzP6Tnd2v59390CufGsV/7iyG5O/2ezqPbd52ij8/cTVUpx3nN3Txxbb0rnp4EkiG9Rkd3yqa9jD7uljGfLiUtfvKXbWeB7+aiPfbDhEqwY12H8G3Y9jZ41nb8IpLntzJdMu6cKuY6n8x+rSHNWqPtElTKB1fb+WfLbmQJmOATDpo2h+sW5uxcwcx/FTWZw/fVGZY/VExP7eidMu6czEQa3LXY43r812VZ5XAU8aY5Zar4cCM4wxA8tdeDlo5VmpstPKs1Kl08pz+dl1be4/YzGdm9bh/YnnF0g/m8qzUpVdXqXo87UHmPKN99d0fv6yrryxZDfHUop2wx1xXiMWbT+zCfH8BFcPjYr07T0DOS+ijqu7+4e3nu/qjr4v4RRrYpJKPJ+X9GjKD5sOu163Ca9ZpBdIZXdl7+bM21D8BHm+dqZDZDzx5rXZrjHPNfMqzgDGmGUiUnSUexV18esrOGDdWQoK8Oftm/vQu2V9H0ellL3y7qPpmGelVFUQEuhH7ZCi/8ZEPzWCL9cddI1nVqo6Gdctgnnr42jfuBafrz3oteM+/d/iW7vPtOIM3qk4Q9EZ1ydaM+83rBXE8VNFu1sX5l5xBqpcxRmo1BXnqsiuEdr7RORpEYm0Hk8BMTaV7XOjOjfhit7NGd2lCcdPZbInXod0K6WUUr7kNo9SAQ1rBXPvsHbeDkcpr6gbGsjcuwdydVQLX4dSpZWl4qyUJ3ZVnm8DwoFvgG+t57faVLbP3T+8PdMu7cLDozoAzkkHlKpu9FOtlKpKco3B7ywm/1GqOujVsr4t3V2VUmfGlsqzMeaEMeZ+Y0xvY0wvY8wDxpjiR71XUXkXaa07K6WUUr6Vm4vnpmfLnuljWT1luNfiUcoX/nh6pK9DUOqcYsuYZxHpADwKRLqXaYy5yI7yK4u8G9y5OihUVUP5Y571862UqvwWPDTEtYayJwH+fjSpG8KmqaNYuee4a9knpaqT+jWDfB2CUucUuyYM+xr4N/Au4LCpzErH39XyrJULpZRSypdqBZftX5i6oYGM6xZRwdEo5Tu7XhjLb3sS2H4kVSfKU6qC2TXmOccY85YxZq0xZn3ew6ayKw1Xt23tt62qIWONetZPt1JKqapm9nW92PbcaF+H4RNBAX5c1KlxgYny5t3t09Vilaq2ytXyLCJh1tMfROQenJOFuRaAM8Yklaf8yiav8uzQ2oVSSilVpXx8e18SUjOpGRzAnR9Xj/v7T40/jzUxSQQH+BHgJ/x34+HSd6qCVk8ZzrCXl3HN+S24sX9Lnv7vVqaM68Slb6ykef1Qlj82DD+/sk0e98Wk/lz79uoKjth3Fj18IbnG0KFxbWJnjWfB1qMEB/gxuH04bZ/40dfhKQXAskeH8vIvO3lgeHtfh3LGytttez0FV4t4zG2bAdqUs/wiRGQM8BrgD7xrjJll9zGK42e10+uYUFUd6TrPSqnqbHD7cNfznx8czLGUTNo1qkVurmHwi0sr7Lif3N6PG99bc1b79mxRj40HTxZJ798mjDbhtbhjcBvuGJz/r9ar1/YiKS2L9ftPEOgvbI5Lpn/bBkTHnuDuoW15d8U+Fm47xpqYgm0bH956Pkt3xDPn9/3UDPInLcueEXjf3jOwyDq7JQkO8OPyXs34S1QL+rSqX2Db9ufHuJ5/Pqk/ANueG42/nxSoOK+afBGLd8S71iX+5p6BJKdnc+uHzvV9+7dp4MpbOySA1IycMsXWu2U9Nhwo+rvwhnVPjihz3naNahV4PbpLE9fzh0d24JWFuwDo1KQ2O46m2hOgqjBz7xrAVf/+3WfHH9CmAb/vS3S9Xjn5IgbNWnJWZfVvE8bqfc7vnsiGNXnj+t62xOht5eq2bYxpbYxpY/0s/KiIirM/8CYwFugMXCcine0+TnH8dMyzUkopVeV1alKHCzuE06xeKC3CarjS984Y53p+Xd+WvHhl9zKXOaBNA2JnjSd21nj+Org1s6/rxfLHhnFB+4ZF8s69awC7p4/lnqFtCa8dDMD1/VrSNrymK88/ruzGiPMaeTzWF5MGMOPybh63hdUMYmTnxgzt2Ii/DW/P+ZFh3D20LQB3DG7Dl3cOKJC/Wb1QhnZsxLMTuhI7azxbnxvD+xOj+PDW89nhVmF1N2VsJ/5zUx++unNAgeWSmtQJYUiHcNf56NWyPhufKfts0I3rhDDryu5FKs7FqREUQHCAf4G0pvVCual/K9fvonfL+gzr1Ih/3+iMF2BsV2eFcskjQ/n7mE7EzBxX4Hef59fHhtK/jbOT5cMjO/LIyA5semYUo7s0duVZ8OCQIvu1L1SBnTK2U7Hv4ctJ/QkJ9CM00J/YWePZPG0U0U+N4KPb+vLJ7f3Y9Mwo12ekvPytmwwPDG/PXLdu3YPdPqNh1gRkHRvXpmGtMzvuxd0j6N2ynut14aW0Lu6eP/dAWM0gfvv7MNfrgW0b0Mbt81/YkA7h/PLQEHo0r3tGMdnl6qjm/OemPgXSXryqe5E8hYUE+hU4v6X54NbzeWr8eYzvHkHMzHFERYax4vFhRfK5fwY9eWhEB7Y8O5qnxp9X5mM/P6GL6/mih4dw55A2fD6pP7GzxvPY6I6EBvrTrF4oH93W15WvVYManoqicZ1g18+1TwwndtZ4vpg0wGPeqqa83bbPBw4aY45ar28GrgT2A9MqoNt2X2CPMWafdbwvgAnANpuP41Hel44j1xtHU8o3jI56Vkqdo/z9xNUid+35LejRoh4jOjfmjSV7eGx0R8575uci+7xwWVeuOb8Fgf757RFPjvd8Xz8k0I+BbRsSFemskD0+phP3DmtH3InTdGxSG4ClO+JpXCeEzk3r8MaS3QBc1ac5D43sQEa2g0MnTpf7ffaNDCM2MY0vJvV3VZbcXdQp/x/z7+8bRMrpHPq3CSMn17DjaCrdm9Ut0Nr784ODue+zP/j6zgHUrxlERraDAGt7vRpBXNw9gs5N63DP0HZ8sfYA//vzCJPHdiIzx0FYzWDiTqRz03trmdCzabnfW3HGdM1vgX3j+t5k5eQSGuTvurHgL85ln5bvTiCibig1g/1p1aAmYnWu9BP4m9XF9KW/9GDEeUe5uHtTQoP8uapPc+auj2P1lOGkZ+XQoFYw767Yx+tL9gBw54Vtufb8lvR47hdXDG9e35uDJ9Lp16YBm6aOcqXXDgmkNrhuQtjp1kGRxKdkMGlIG2oGB7Dj+TEE+AkB/n6sjUki8VQm/ds0IDTIH38/KfCZ/mZDHGE1gxjasRGOXOPqAj77ul5s2H+CS3pE0KdVGNGxSdzy/lqmWzd3Pr69L9mOXNo3qk2LsBpc3usY0+dv56cHBxMc4M+SRy7EkWto37g2mTkOps/fzke/72dox3CW7UwAnK2dzeqFAvDR7f1YuiOeB7/cCECLsFAOJpX+N1EnJIAJPZvx8er9rrSro5rzVXSc6/Wc2/pyy/trAfjpgcG0bliTTk//bOVtQVRkGP/72wX8tOUIj43u5Eo/fiqTtMwc6oQEEhroz5zf84+x7dkxnM528O6KGP65aBfnR9ZnXazn1XxfvKo7wzo2YljHgjfNWoTVYOuzoxn68jIeGdmBVg1qMqCtswfFfZ9t4H9/HqF2cACpmc5eFM9e2oVbBkYCzhtmQzs24vtNh1m9L5G1hXqdvHZtTx74YiOThrThpgGRtGtUm7CaQbRrVJsp41qp6ZMAACAASURBVPIr3vcOa+caUz+kQzhz7xpASKA/XZvV5XSWg0B/YW1MEuG1g4moF0rNIH8WbD1Kr5b1aVQnxFXOq9f05BO330FVJOXpgiwiG4ARxpgkERkCfAH8DegJnGeMucqeMF3HuwoYY4y5w3p9E9DPGHOfp/xRUVEmOjratuNn5jjo+NTPPDa6Y4FJGZSqDqZ9v5UPV8VyY/+WvHCZ5xYNpc51IrLeGBPl6ziqMruvzXa4+j+/szYmidhZ49mbcIo3luzhpau6E+BfsIPe64t3838Ld3HzgFZMGtIGPxGaWv/UlyRy8nygaEtcad5YspuXf9nFvcPauv5Zr66OJJ+mUe0QV0NFZRF7PI2Xf9nJK1f3JCjAc4fNbEcuJ9KyClQSAAbMXMyR5AzX7/1EWhb+/kJ6poMmdUM8FVVlJJ/Oxk+clX27HTp5mqZ1Q3jgi418v+kwm6aOom5owePk/U2tnjKcJnVDyHHksmLPcW79YF2BfAsfGkKLsBqEBPpjjOFwcgYOhyEjx0GHxs4bVkeST9O4dgh+fsLuY6kEB/jT0mpR/XBlDNN+2OY6Tln0m7GIk+nZ7HxhrCtt/f4krnzrd66JasE/rupOcno2R1JOM+bVFa48L13Vnb9EtTijc5XtyOW3PceLVLiLc8O7q1m5J5Hfp1xERN3Sv7uqCm9em8s75tnfrXX5GuBtY8w8YJ6IbCxn2Z54+kYtUPsXkUnAJICWLVvaevC8bttZOblkZFfbFbnUOSon19mlwpFr9POtqp3CrShKufv8r/1dQ7Lahtfin9f09JivRwtnl9Qh7cNpXt9zd0VP1j45/KyWMhjWqREv/7KLkZ2blJ65iqus/8iXZWxmoL9fkYozwLf3DGL70RTX67w1metUQIXT2wpXZu2U18r84lXduX94uxKPlVehDfD3Y1jHRqyeMpwAf+Hr6DiOJp+mvVVBBhARV9nu3D977vkBbhkYyXX9WhYZHlCSlX+/qEhan1ZhvHZtT0Z2dvbqqFsjkDqhATw4oj29WtZn5o/bXdvORKD1vsvqzet7s3pfYqX9e6sKytvyvAXoaYzJEZEdwCRjzPK8bcaYrjbFmXe8ATi7g4+2Xk8BMMbM9JTf7rvbubmGdk/+iK5UpZRSVcvEgZFMu7RL6RlLoS3P5VcZW57PRHxqBo1qV+1WQ6WqurPtzaGqp6rU8vw58KuIHAdOAysARKQdkFzOsj1ZB7QXkdbAIeBa4PoKOI5Hfn7Cm9f3JiYxzVuHVMqr4lMyaVTHnolJlKpMujXzzSQzqvrRirNSvrfi8WEcTcnwdRjqHFSuyrMxZrqILAYigF9MfjO2H86xz7ayWrjvAxbgXKrqfWPMVruPU5Kx3SJKz6SUUkoppZSqEC3CahSYKV8pbylvyzPGmCIrzRtjdpW33BKO9yOgq7wrpZRSSimllPKaco15ruxEJAHnslkVoSFwvILKrihVMWbQuL2pKsYMGrc3VcWYwb64Wxlj7F9D5hyi1+YiqmLMoHF7U1WMGTRub6qKMUMVvDZX68pzRRKR6Ko2aUxVjBk0bm+qijGDxu1NVTFmqLpxqzNTFX/PVTFm0Li9qSrGDBq3N1XFmKFqxq3rdiillFJKKaWUUqXQyrNSSimllFJKKVUKrTyfvbd9HcBZqIoxg8btTVUxZtC4vakqxgxVN251Zqri77kqxgwatzdVxZhB4/amqhgzVMG4dcyzUkoppZRSSilVCm15VkoppZRSSimlSqGVZzci0lFENro9UkTkwUJ5RERmi8geEflTRHq7bbtFRHZbj1sqWdw3WPH+KSKrRKSH27ZYEdls7RtdyeIeKiLJbnmecds2RkR2Wr+LyZUo5sfctm8REYeIhFnbfHKurWM/JCJbrZg+F5GQQtuDReRL63yuEZFIt21TrPSdIjK6ksX9sIhssz7bi0Wklds2h9vv4vtKFPNEEUlwi+0Ot20++R4pY9z/dIt5l4icdNvmk3NtHfsBK+athf8ere2V7ntblV0Zv3cr3e+4jHHrtdl7Meu12btx67XZe3HrtdmbjDH68PAA/IGjONcNc08fB/wECNAfWGOlhwH7rJ/1ref1K1HcA/PiAcbmxW29jgUaVtLzPRT4XzH59wJtgCBgE9C5MsRcKM8lwBJfn2ugGRADhFqvvwImFspzD/Bv6/m1wJfW887W+Q0GWlvn3b8SxT0MqGE9vzsvbuv1qUp6ricCb3jY12ffI2WJu1D+vwHv+/JcW8ftCmwBagABwCKgfaE8lfp7Wx9n9PvWa3PliHsoem22I1a9Nleucz0RvTbbFXe1vTZry3PxhgN7jTH7C6VPAD4yTquBeiISAYwGFhpjkowxJ4CFwBjvhgwUE7cxZpUVF8BqoLnXIytZcee7OH2BPcaYfcaYLOALnL8bbypLzNcBn3spntIEAKEiEoDzy+xwoe0TgDnW87nAcBERK/0LY0ymMSYG2IPz/HtLiXEbY5YaY9Ktl5Xls13auS6Or79HziTuyvLZPg9YbYxJN8bkAL8ClxfKU9m/t1XZ6bXZu/TaXPH02uw9em32nmp7bdbKc/GuxfOHrxlw0O11nJVWXLq3FRe3u9tx3unJY4BfRGS9iEyqsMhKVlLcA0Rkk4j8JCJdrLTKcL5LPNciUgPnH/s8t2SfnGtjzCHgZeAAcARINsb8Uiib65xaX3TJQAN8eK7LGLe7wp/tEBGJFpHVInJZBYbqcgYxX2l1U5orIi2stCpxrq3ud62BJW7JXj/Xli3AEBFpYP3NjQNaFMpT2b+3Vdnptdm79NpcgfTarNfm0ui1uUB6paCVZw9EJAi4FPja02YPaaaEdK8pJe68PMNwfon93S15kDGmN84uY/eKyJAKDbRoTCXFvQFn16sewOvAf/N285DXa+e7LOcaZ7ewlcaYJLc0n5xrEamP8w5fa6ApUFNEbiyczcOuPv1slzHuvLw3AlHAS27JLY0xUcD1wKsi0raCQy5rzD8AkcaY7ji7MuW1KlSJc43zn9O5xhiHW5rXzzWAMWY78A+cd6Z/xtmNMadQtkr32VZnTq/Nem0ujV6bK9/1Qq/N5aPX5gLplYJWnj0bC2wwxhzzsC2OgndOmuPsPlFcujeVFDci0h14F5hgjEnMSzfGHLZ+xgPf4t1uP1BC3MaYFGPMKev5j0CgiDTE9+e7xHNtKXL324fnegQQY4xJMMZkA9/gHGvnznVOra5BdYEkfHuuyxI3IjICeBK41BiTmZfudr73AcuAXpUhZmNMoluc7wB9rOeV/lxbSvpse/Nc5x37PWNMb2PMEJyf2d2FslTm721Vdnpt9i69Nlc8vTbrtbk0em2uZNfmar3Oc8OGDU1kZKSvw1BKKVVNrF+//rgxJtzXcVRlem1WSillJ29emwO8cRBfiYyMJDraqysOKKWUqsZEpKwTJ6li6LVZKaWUnbx5bdZu20opABy5hu1HUnDkVt/eKEoppVRVk5SWRY4j19dhKKXQyrNSyvLZ2gOMfW0Fc1bF+joUpZRSSgEHk9Lp/fxCuj9b0mTWSilvsaXyLE43isgz1uuWIuLtiS2UUuVwMi0LcN7hVkoppZTvLd+dAEB6lqOUnEopb7Cr5flfwACcC3MDpAJv2lS2UkoppZRS55zTWmlWqlKxa8KwfsaY3iLyB4Ax5oS11p5SSimllFLqLOToPCRKVSp2tTxni4g/1gLWIhIO6MwGSimllFJKnaUAP/F1CEopN3ZVnmfjXFS+kYhMB34DZthUtlJKKaWUUuecrs3q+joEpZQbW7ptG2M+FZH1wHBAgMuMMdvtKFsppZRSSqlzUaC/s52rYa1gH0eilAIbKs8i4gf8aYzpCuwof0hKKaWUUkqpbzbEAXD8VKaPI1FKgQ3dto0xucAmEWl5JvuJSAsRWSoi20Vkq4g8YKWHichCEdlt/axvpYuIzBaRPSLyp4j0Lm/sSiml1LmguGurh3y3WHl2i8gtbunLRGSniGy0Ho2s9GAR+dK6Nq8RkUjvvCOlzg0OnTBMqUrFrjHPEcBWEVksIt/nPUrZJwd4xBhzHtAfuFdEOgOTgcXGmPbAYus1wFigvfWYBLxlU+xKKaVUdVfctdVFRMKAqUA/oC8wtVAl+wZjTE/rEW+l3Q6cMMa0A/4J/KMi34RS55psh1aelapM7Fqq6tkz3cEYcwQ4Yj1PFZHtQDNgAjDUyjYHWAb83Ur/yBhjgNUiUk9EIqxylFJKKVW84q6t7kYDC40xSQAishAYA3xeSrnTrOdzgTdERKxrtVKqnHL1T0mpSsWuCcN+Lc/+VjevXsAaoHFehdgYcySvaxjOivVBt93irLQClWcRmYSzZZqWLc+oJ7lSSilVXRV3bXVX3HU2zwci4gDmAS9YFWTXPsaYHBFJBhoAx90L1muzUmdnwdajvg5BKeXGlm7bItJfRNaJyCkRyRIRh4iklHHfWjgvxA8aY0rax9NCd0Vuxxlj3jbGRBljosLDw8v2BpRSSqkqTkQWicgWD48JZS3CQ1redfYGY0w3YLD1uKkM++Qn6LVZqbOSnuXwdQhKKTd2ddt+A7gW+BqIAm7GOTa5RCISiLPi/Kkx5hsr+Vhed2wRiQDyxlXFAS3cdm8OHLYpfqWUUqpKM8aMKG6biBR3bXUXR37XbnBeZ5dZZR+yfqaKyGc4x0R/RP61OU5EAoC6QFL5341SSilV+dg1YRjGmD2AvzHGYYz5gIIX4CJERID3gO3GmFfcNn0P5M3weQvwnVv6zdas2/2BZB3vrJRSSpVJcddWdwuAUSJS35oobBSwQEQCRKQhuG56Xwxs8VDuVcASHe+slP0C/Dx18lBKeZtdLc/pIhIEbBSRF3GOQ65Zyj6DcHb72iwiG620J4BZwFcicjtwAPiLte1HYBywB0gHbrUpdqWUUqq683htFZEo4C5jzB3GmCQReR5YZ+3znJVWE2clOhDwBxYB71h53gM+FpE9OFucr/XeW1Lq3OGvlWelKgW7Ks834byg3gc8hLML15Ul7WCM+Q3PY6UAhnvIb4B7yxemUqo42lSkVPVljEnE87U1GrjD7fX7wPuF8qQBfYopN4P8m9xKqQqilWelKge7Ztvebz09zVksW6WUUkoppZTyrFOT2r4OQSmFTZVnEYnB8+yabewoXylV8XSUolJKKVU51a8R5OsQlFLY1207yu15CM4uXGE2la2UUkoppdQ5yznPrlLK12yZbdsYk+j2OGSMeRW4yI6ylVLeYXTUs1JKKVUp+du2Po5Sqjzs6rbd2+2lH86WaB2coZRSSimlVDnphGFKVQ52ddv+P7fnOUAscLVNZSulvEDHPCullFKVk59221aqUrBrtu1hdpSjlFJKKaWUKkgrz0pVDnZ12364pO3GmFfsOI5SSimllFLnmprBdnUWVUqVh13TD0QBdwPNrMddQGec45517LNSVYBx/dT+20oppVRlcOeFzlVfw2sH+zgSpRTYN+a5IdDbGJMKICLTgK+NMXfYVL5SSimllFLnlOAAfwBmL97NwyM7+DgapZRdLc8tgSy311lApE1lK6W8wZoxTCcOU0pVJfsT03hn+T5fh6GUUuocYFfL88fAWhH5Fmfvz8uBOTaVrZRSSinl0U3vreVAUjoX94ggom6or8NRyl5ud7QXbD3K6C5NfBiMUsqWlmdjzHTgVuAEcBK41Rgz046ylVLeYQr9VEqpquBAUjoAQ19a5ttAlKoA7tfkJ7/d7LM4lFJOds223RbYaozZICJDgcEiEmOMOWlH+UoppZRShWXmONye5/owEqUqhvtQquOnsorPqJTyCrvGPM8DHCLSDngXaA18ZlPZSikvyLtA65hnpVRVMWdVrK9DUKpC6QoYSlUudlWec40xOcAVwGvGmIeAiNJ2EpH3RSReRLa4pYWJyEIR2W39rG+li4jMFpE9IvKniPS2KXallFJKVUGr9ib6OgSlvCoj21F6JqVUhbGr8pwtItcBNwP/s9ICy7Dfh8CYQmmTgcXGmPbAYus1wFigvfWYBLxVzpiVUm7y7m7rXW6lVFWxbGeCr0NQqkIV7g325LdbPGdUSnmFXZXnW4EBwHRjTIyItAY+KW0nY8xyIKlQ8gTyZ+qeA1zmlv6RcVoN1BORUlu3lVJKKXVuOGhNHqZUdVH4dva8DXE+iUMp5WTXbNvbjDH3G2M+t17HGGNmnWVxjY0xR6xyjgCNrPRmwEG3fHFWmlLKBkan21aq2ipuSJSHfLdYeXaLyC1u6ctEZKeIbLQejaz0iSKS4JZ+h7fekycXv/6bLw+vlO1yPUxE8tKCHT6IRCkF9rU8e4N4SCvyjSIik0QkWkSiExK0O5dSSilF8UOiXEQkDJgK9AP6AlMLVbJvMMb0tB7xbulfuqW/W4HvoVTJp7OJT8nwZQhK2cvDDe03l+71fhxKKaByVp6P5XXHtn7mXaDjgBZu+ZoDhwvvbIx52xgTZYyJCg8Pr/BglaoutOFZqWqtuCFR7kYDC40xScaYE8BCis5LUmnsOJriMb3vjMX0n7GY5bv0Brqq+oq7Jh86eZojyae9GotSyqbKs4j8pSxpZfQ9kNdV7BbgO7f0m61Zt/sDyXndu5VSSilVouKGRLkrbXjUB1bX7KdFxL032JXWKhhzRcT9JneFuvuTDcVuO5qSwZRvNnsrFKUqjClm/chBs5YwYOYSluw45uWIlDq32dXyPKWMaQWIyOfA70BHEYkTkduBWcBIEdkNjLReA/wI7AP2AO8A99gRuFLKKX+dZ217VqoqEpFFIrLFw2NCWYvwkJb3hXCDMaYbMNh63GSl/wBEGmO6A4vIb90uHJvtQ6pijqeVuP3QSW2VU1VfaZfk2z6MJjUjmwOJOlleYfEpGZzO0qW9lL0CyrOziIwFxgHNRGS226Y6QE5p+xtjritm03APeQ1w79nEqZRSSlV3xpgRxW0TkWMiEmGMOVJoSJS7OGCo2+vmwDKr7EPWz1QR+QznmOiPjDHuCy2/A/yjmNjeBt4GiIqK8toduk/X7OeGfq28dTilbJdbhr+WGT9u5/O1B3nz+t6M756/EE1apvNf8ZrB5fp3v8rqO2MxfVrVZ97dA30diqpGytvyfBiIBjKA9W6P73GOnVJKVRGudZ614Vmp6qi4IVHuFgCjRKS+NVHYKGCBiASISEMAEQkELga2WK/dl4y8FNheQfGfFV0TV1V1pgwzkXy+1jnaYvOh5ALpXaYuoMvUBRUSV1Wxfv8JX4fgFaezHDz7w1bSMnPYeTSVtTGFVwL2LD0rh9yy3KFRLuWqPBtjNuFcz/k3Y8wct8c31mQjSimllPI9j0OiRCRKRN4FMMYkAc8D66zHc1ZaMM5K9J/ARuAQzlZmgPtFZKuIbALuByZ67y2VTbepC4g9nsb7v8X4OhSlzpgxEOjvaURFUf/+dS8Z2Q5+3nKE3cdSKzSug0npRE6ez/r9ZaukeduxajLr/rGUDJJPZ5eab87vsXywMpZ//7qX0a8u5+r//F5ge8/nfuHFnwsucZaR7aDzMwt4fv42O0Ou9so95tkY4wAaiEiQDfEopXzFFPihlKpGjDGJxpjhxpj21s8kKz3aGHOHW773jTHtrMcHVlqaMaaPMaa7MaaLMeYB69qPMWaKldbDGDPMGFPpFqBNzcxh6MvLeO5/24g5nka2I7dCjmOM4YlvN7P1cHLpmVWlZowh7kTlGUMcGujPwLYNypS309M/c9cnG7jkjfKveb58V0KxLZi/7TkOwNfRceU+jl1+232cjGznGOeyVDiLU5bff44jl+7TFvDtH8W//4NJ6cWuClBW/WYs5oJZSwqkrY1JKvD+jDEcTHLGm+0o+F/cf37dy6Nfb+Jkejb/WlZwibPMbOd34bz1Bd/D+v0nSDyVWSSW3cdS2Z9Y8lwTeTYc8FxGdWDXhGH7gZXWDJwP5z1sKlsppZRSyqOuzerwwmVdy5R32MvLaP/kT7y+eHexeZLTs8k5iwp2fGomn605wK0frDvjfd0lnsok6oVFbDt85v90Hz55mj8OnOBfy/YUaRF87odtPD53U7liA2elYV1s5Wxt3HjwZLETRG07nELUC4vK9A/93PVxXPCPpWXu+upu3vq4Asc4lZnjqtCdifl/HmHe+jiMMfj5CW9c3/uM9s/Izv8Mf7/pcIHP0/r9SWTmFIxp6Y541uxLLJB28/trC7RgrtmXWO4uvikZ2UROns+cVbEet+c4cklOz68YLtp2jGEvLyv1ptfOo6nc+N4apn63ldmLd/Paovy/8bL+TR9JPs1N763hhfnbueAfS9l5NJWDSelEvbCQA4npJKVlufKmZuSQkpHDtO/zW21PZebwxLebiU/NYNQ/f2Xwi0sZ8+oK1sYksS/hFMt3JXAyPb+MtMwcNh48yQm3cj1JzcyfRup0loOr//M7PZ79xbWm/dfRcXy65gDg7H3gbuZPO5i7vmgFP3LyfMbNXuHxeFe+tYq/WL/3+JQMIifP52hyBiP/uZwLX1rGvPVxbI5LLvC5TkrLIisnl2jru+GKf63isn+tLPF9VVV2zSBw2Hr4AbVtKlMp5UWudZ616VkpVYW8dUMffi/0T39p/m/hLu4a2pbvNh7myt7NSMty0HXqAm7o15JP1xzgil7NGNWlCX4Co7o0KbL/mn2JvLhgJ19M6k+gv7MdIm/xrvjUTBZtO0aTuiEYA92a13Xtd/xUJlk5uTStF8p7v8VwNPk0zeqFMnFQawD2J6bx6NebOH4qk3GzV/DyX3rQq2U9ElIz6d+mARnZDv7vl50MP68xtYIDaFovlOvfWc1/bupDeO1gBhZqoYqdNZ7MHAf+Iry/0tltfdqlXagRFMC7K/YxsG1DOjetw8n0LN5atpfHRndk6c4EHLmGMV2b8OjXm+jZoh439s+fdO3/Fu7irWV7+e7eQfRoUa9M5/ud5fs4eCKdQe0akpHtYELPZqXvVEYZ2Q4C/ISktCwue3Mll/RoyuOjOxKbmEavlvV5bdEuHh3dkXdW7OP4qUx+3ZXAFb2bl1jmhgMnAdgdn0rf1mEe86yNSSKsZhDtGtUCICsnlw5P/QRAh8a1mDSkLZf0iKDr1AW0DKvB8seHkZHt4FRmDg1rBZf6vu79zLkU2039WyFAWM0gXrqqO4/N/bOsp8bl/s//KJJ2Re9mPD+hK/sT02lYK4hbP3Te9Hnpqu5c3qsZOW6V5NNZDi594zd2x5/inqFteXxMpzIf+1/L9jCqcxPXecqr8E39fitTv9/K0xd3xk9g17FTzLyiG5O/2czc9XGseHwYAHd8FA3AibQsktKzGPPqCubdPYAuTZ1/V/P/PEKDWkFMtG5afRl9sHAI9HjuFxrWCuKtG/twfmQYxhjmbTjEpT2akpObS40gZ3Xosa//5Lc9x1mx29mqfjApnW1HUjh+Koub3l/D/sR0vr1nIGtjkpj5k7OTjfsKJe+u2Mdnaw7wmVWRzVO4C3XfyDA6N63Dh9YNhIa1gnjvlvNZF5vEuG4R3DEnmmxHLvPvH1zkvcSn5ndH7ztjcYnn/sKXlhZJW7jtGG3CawL5KxLkvYOsnFzX99i+hDR+35vIde+sBqD/zPxjPfK18yZcUIAfWTm51A4JIDUjv4L/ytU9ADiY5Cw/N9eQ5cjFGAgO8OPgiXTqhQYRFOBHaJB/ie+hMhI7l6URkdo4J8Y+ZVuh5RAVFWWio6N9HYZSVcKMH7fz9vJ93DaoNc9c0tnX4ShVKYnIemNMlK/jqMrsujZHTp4PwL4Z40jNzKHHs7+cVTnBAX5k5hTfKhU7a7zr+cGkdBrVCabjUz+70h4a0YELO4Zz6MRpV4XH3bf3DORkejbDOjVyxbz+qRH0eWHRGcXZpmFNBrdvyJzf97vSrurT3GOrUp4FDw5h9KvL6RsZxlq31uLP/tqP699ZA8DEgZFE709iy6EUXr2mJw9+uRGA+y9qx+wlewBY/MiFbD+SwtqYJD6yjv/+xCgu6tS4xJgTUjP5ev1BXvx5Z4H0Oy5ozbu/xbBy8kU0qxd6BmcB/jhwgoa1gnnr173MjY4jy5FLw1rBfH3XAIa9vMzjPtee34Iv1hWtVE0cGMknq/dz+wWtmTy2E0dTMhgwM/8GxIjzGvPqtT2pZc1WfTApnbUxSdQNDXRV6jZPG0V07An2JpzihfnFz5c3tGM4y3YmuPaZ+v1WvtlwiLsubEvy6Sx2HE3ljwMn+evg1jw5vrPrs3Jj/5b8uPkoG54eyZfrDvD3efavX96wVjDH3VrLL+/VjG//OFTiPr1b1mPDgZNce34LZl3ZHXBWJBdsPcp/lu+jZVgNZl3RnfOecf6tXNGrGa9c05Mth5K5+HXPXcpjZ413ve/CZl3RjWe+30pWCX+rZfH6db34YGUMGw6cpFm9UA6dPM2UsZ34ZM1+0jMdJLq1Ar96TU8OJKXzysJdpZZb3GfMLjEzx5GTa2j/5E8VUn6gvxTp8m2HlZMvYpDbTb224TXZm5Df9XvTM6OoWyOw3Mfx5rXZlsqziHQFPgbybs8dB242xmwtd+HloJVnpcpu+vxtvLMihlsHRTL1ki6+DkepSkkrz+Vnd+U5r3Kb48ilXQX8Y3lpj6Y8OqojCacyuPKt30vfoRhX9GrGN6VUSKqSV67uwU9bjpJyOpsJPZsxvlsEAf5CWmYOi7bH07FJrVLPV2SDGozs3Jgnx3cmNSObATOXcPsFrakVHMCN/VsRFODHI19t5JFRHWkRVsO5TzGVq2WPDmVoMZXnshjVuTG/bDt2xvs1rhPMsRR7x3a2alCD/da6zddEtWDR9mOsf3okn605wBPf2l95Lq8nx53HVX2as3x3Ag98sdGV/viYjgVunPj7CY4Sun2/fl0v/uahlVxVb+43KM9WVaw8rwKeNMYstV4PBWYYY3y6sJpWnpUqO608K1U6rTyXX0VVnt3TVNXXpE4IoUH+xBx3aAP9gQAAIABJREFUtlLFzhqPI9fQ9okfPeavXyOQE+lnP0lUZRc7azwfr97P0//V5ddU9VLVKs92jXmumVdxBjDGLBORmjaV7XPTvt9KgtWdJdjfj0dGdzzjbkZKVXZ599F0zLNSqqqad/dA4lMyuPvTot2nVdVytNBSQwNnLuZwcvHLD1XninOens2dY8zfuTmK3fGpRbrDK6Uqnl2V530i8jTOrtsANwLVZkHFfcfTOHQinZxcw/7EdPq3bcDVUS18HZZSSiml3PRpVR+AS3o05YdNh30cjbJTSRXnc0W35nXZ8fwYQgL9Gdm5sVaelfIBuyrPtwHPAt8AAiwHbrWpbJ/76La+gHMJiIGzlmDnJGtKVRb6qVZKVRfPT+hCoJ9UqzHGSgGEBFa92YmVqk5sqTwbY04A99tRVmXmZ83ffhbLPyqllFLKS+rVCOKVa3pq5VlVa2ueGE5Camaxs1crpexnS+VZRDoAjwKR7mUaYy6yo/zKws+5lCO52vKsqqH8Mc/6+VZKVQ+X9mjK99p9W1VTjeuE0LhOiK/DUOqcYle37a+BfwPvAg6byqx08lqetfKslFJKVX6zr+vF7Ot6kZ6Vw4rdx7nz4/W+Dkkp2z01/rwS15hWStnHrspzjjHmLZvKqrT88yrPJaxRp1RVZaxRz/rpVkpVNzWCAujXOszXYShVIe4Y3IY7BrcB8ufnUUpVDL/y7CwiYSISBvwgIveISERempVerbjGPGvtQimllPK5bs3qljlvvRpBxM4azz+u7MZnd/SrwKhUVdS6YfVYYbVpvVBiZ41nSIdwV9qO58cUm3/iwEgC/cUboVW4vq3D2DxtVIUfZ3z3COqGBrper31ieIUfsyxev64Xg9o1qLDyK+J788ER7W0vs6KVq/IMrAeigVuAx4BVVlpeuu1EZIyI7BSRPSIyuSKOUZy8Mc86JlRVR7rOs1KqKtk7Yxzf3TvojPe75vyWDGzXkDeu78X1/VoW2V6R/3y6i2pVnzFdmhRIe+fmKOrVcP5TvuDBIQW27ZsxjiljO9keR5NqPGb21Wt6UiekbJ0sf35wMAAzLu/mSps4MJK3bujNqM6Ni91v6aNDyxzPhJ5N2T19bJnz5+l7Fr0mAv2cFeLHRnckJNCfmJnj2DN9LF/fNYAf7x/syjft0i4sf3yYxzJevaYne6aP5RofLs9655A2xW77YlJ/2jWqxfMTuhA7azxf3TmA2iGBrJp8EX88PbLCYnrjul4sfNj599mwVhCN6oTw2V/78a8berNy8kX840rnZ+jG/i15aEQH12ugQKXb3cSBkayxKuEtwpw3QO4fXnrF0s/tvsfIzo355PZ+jO8WQeeIOrx9U58i+Qe3b+g6p3VCAtg9fSx7po/l0VEdiuRt1aAGnSPq0DmiDr1a1mNgu4YF/j7euTkKgPo1AtkzfSz92+R/Tnc8P4aYmeM8ljvv7gEAnBdRhwdHFN1e2UlVqgiKiD+wCxgJxAHrgOuMMds85Y+KijLR0fbV4dMyc+gydQFTxnbizgvb2lauUpXBtO+38uGqWG7q34rnL+vq63CUqpREZL0xJsrXcVRldl+byyty8vwCr2Nnjeeuj9fz89ajrrQ7L2zDf37d53p914Vt+feve4uUFTtrPCfTs0hKy6JNeK0C5X8xqT/N6oXyysJdTL2kM/VqBAFw/FQmUS8s4l839GZctwgycxwYU/KSRIVjBufkaNf2bcH176wpkP7ntFFcMGsJKRk5AMTMHIeIMGjWEg6dPM3nf+3Pde+sLvEcldWwjuEs3ZlgS1lnY9olnbmgfTgjXvkVcP4+APbEp1KvRhBLtsez5XAyH/2+n2uiWrAn4RTr958gNNCf7W6ts+dPX0RCaqZrfyh4zv+cNop/LtzF5LGdCA7wJy0zh8/XHiAqMoyT6VlM/GAdj4/pyLCOjbjl/bWM6tKYT1YfYM5tfbmwQ7jH319x3GM4E/GpGby7Ioa/j+mEv1/RluXJ8/6kdcOarv9nM3McZGTlsis+lfl/HuHDVbGsfXI4jWqHuLav2HWczYeSeW3x7jLH0bRuCMmns3n+sq48/NUmFjw4hGb1Q+k6dQEAM6/oxhtL9nDo5Oli37f7+do8bRRzVsXSJrwW47pFlHjs9CznZ77zMwtcaQsfGsLxU1n8vvc4s5fsKbLPBe0a8uYNvXn06020aViTXcdSi3ymY2eNJyUjm+7TfmFox3A+vLVviXEYY5i7Po7x3SNIz3IQ9cIiAvyE9U+NpG6NQNIycwgN9MfPT9h6OJm24bVcf/8Pf7WRzhF1ioxp//aegVz+r1VseHokiacy+XnLUf7mobK942gKY15dwReT+hN7PI2r+jQnMS2LfjMW8+8bezOma/45/Mu/V7Eu9gQBfkJQgB/bnvPcYyHv91H4s3ksJYMPV8Xyt4vaUSMooEh+gIs6NeL9ieeXeL7OhjevzeWqPIvI+cBBY8xR6/XNwJXAfmCaMSbJlijzjzfAKne09XoKgDFmpqf8dl+gM7IddHr6Z/4+phN3D9XKs6pe8irPN/ZvyQuXdSt9B6XOQVp5Lr/KVnnOyHaQmZPL5W+u5O6hbfmLWyvb6n2JBPoLPZrXY9oPWwmvFUKb8Jpc3D2C3/cluiqqdw9tyxW9mtG+ce0i5W86eJKF247x6OiOtsX8vz8Pc99nf/DY6I68tGBnge/tL9YeYGTnxrz8y04+X3uQ2FnjSc/KofMzC+jRvC7f3XcBAElpWRw+eZquzeq6tgM8c3Fnnvufs03iu3sH0aNFPdc/v8M7NWLxjniPMZ0XUYdv7h7Iec/8DDjX2p7QqxkBfkKugVrBAa5yJo/txOD2DWkbXot/LdvL7DOojLn76+DWRNQN5dZBkYjkVxAf+WoT8zbEFVvxTDyVSd3QQBzGcDQ5g4i6oQQF5HfGPJ3lIDs3lzoh+a2E2w6nsPNYCuO6RRAcUPJay6v2HKdfmwYeK62QX5kIDfTntgsiGds1gkvf+I1cA6O7NGZs1wge/HIjcPaV5/Jw5BpOpGfRsFZwkW05jlxeW7ybWwZGkp7pIDEtk6b1QgkN8i9wvs5E3vnuPu0XAGZd0Y1r++b3Crn6/9m77zgrqvv/468Pu0vvTZG2CFiwUhQsKIoIlkQTTeztqzExJho15ofGXiKJiTEmpthLrIldMIAUQRCkSC/SpXeWzrK7n98fd3a5u3vv7sLO3rK8n4/HPO6dMzNnPnd29p57Zs4586+v+HrJJqY90K/ootP+mLdmK6PmradTy/r0i2pFsHbrbnr+bgQAk+87J+bnXb5pJ73/MIq/XtGVX771DdnN6jL67sid+qnfbeaIQxpQv1ZYQ0jF9+6k5cxYuYV/T/iOX53TuUru2O7em8+bE7/j+lOzqRHn3IXI+VsrswbzH6tYK4p5a7bStF5NVmzexZGHNKBeFRyvdKo8TwXOcfdNZnYG8DbwS+BE4Gh3vzScMIv2dykwwN1vCuavAXq6+y9irR92Ab0nL58j7/sfd/c/klvP6hRaviKpQJVnkfKp8lx5qVZ5PlDuTod7hgDJqeCEqfAuWv1amcx6uD9Tlm1mw/Y99A+alf/q7W/4cNoqljxxPv2fHsO3a7dz3SntefiiY7n3g5m8OfE7bu/bmTv6HcHe/ALWbt1NmyZ1S+0n3h2r7IGDuapnOy7u2pq/j1rIj3u05ZY3phZbp/8xhzB09lqAoju46Wr5pp089PFsnr2qW9EdxlfHL+XBj2cXtf56avi3fDpjFSPv6pPcYBNo7dbdNKydRZ2aZV+cCFP2wMF0b9+E9245tcz18gucu96dxs1ndKTLYQ0TFF1pyzbuoF3TusUuFiXatOVbOLRhbQ5tlDpdPhJZNle26p8RdXf5MuA5d38PeM/MplUy71hinSnFav9mdjNwM0C7dqX7MlVG4Wjbi9ZvZ8y3yWuWJFIVVmyONJlatWW3zm+pdg5rXIdOLesnOwypRsyMsb85q6iPcjqrXzOTs45sUTRic/f2TYotf+rHJ/KHS0/AzPjs9jNwdzIzIndqf9W3M3NWbeWqXpHfXFkZNWJWnMvy7WPnkVnDqFHDePmGk8nLL6B35+b88uzOtGhQi0Mb1qZOzQwGPD2GeWu20bz+/t99TCVtm9blxRJNV79/wmF88M1Kfnpm5G9wZ78juLNf+vUHrYxkPLN6wePnFQ0IXJaMGsbTl3dNQERla98s+QPbndi2cbJDSKpKV57NLNPd84C+BJXWkPKOZQUQPWpBG2BV9Aru/hzwHESuboe584waRr2aGbw/dSXvT10ZZtYiKWPkvHWMjNMsTyRdXX9qNg99/5hkhyHVTNum+1dJTFWFldayltcMmnFGmiLvq2y0bFibDys4cFuXVg3JKygolR7dZBogM6MGr99YemTfs49qybw122gRo3ltumtSr2aFj6OEJyujsmMny8GmshXct4AvzGwDsAsYC2BmnYCcSuYdyySgs5l1AFYClwNXVsF+YjIzht5xBmu37k7ULkUSKiujBnvzS/+wEUl3LeqnTvMykYPVkNt7l79SGe4690iuPzWbltV4hHARSW2Vqjy7++NmNgJoBQzzfR2oaxDp+xwqd88zs18AQ4EM4CV3nx32fsrSpknd/W6OJCIiIiKVk1HDVHEWkaSqdNNqdy/1fAN3/7ay+ZaxvyHAkKrKX0RERERERKSktHrO8/4ys/VEHpuVCpoDG5IdxH5Kx5hBcSdSOsYMijuR0jFmiB93e3dP32F+U4DK5kpLx5hBcSdSOsYMijuR0jFmSIGyuVpXnlOJmU1Ot8ebpGPMoLgTKR1jBsWdSOkYM6Rv3LJ/0vHvnI4xg+JOpHSMGRR3IqVjzJAacWuIOREREREREZFyqPIsIiIiIiIiUg5VnhPnuWQHcADSMWZQ3ImUjjGD4k6kdIwZ0jdu2T/p+HdOx5hBcSdSOsYMijuR0jFmSIG41edZREREREREpBy68ywiIiIiIiJSHnfXFEzAS8A6YFZU2qPADGAaMAw4LEi/KCp9MnB61Db5Qfo04OOo9LOBqcAs4FUgM0YMJwJfAbOD/C+LWvYKsCQq7xNTJe5ytu8ATAQWAO8ANVMhZuCsqG2nAbuBixN0rNsF684F5gDZ8Y5VnGN9D7AQmA/0j0ofEKQtBAZWwbl9wHED/YApwMzg9eyoZaODuAuPd8sUiTkb2BUV1z+jlnUPPstC4Bn2teRJhbivovi5XcC+c7jUsU5Q3L8IjpUDzcv4Hr4u+GwLgOvKOt6pEDMH8J2taf+mEP/OKptVNpcXt8pmlc1VGbfK5mpQNie9UEylCTgD6Fbij90w6v1tBP+gQH32/UMeD8yLWm97jLxrAMuBI4L5R4AbY6x3BNA5eH8YsBpoHPXHvjQV4463fZD+LnB58P6fwC2pEnPUNk2BTUDdBB3r0UC/qPUK91vqWMWIoQswHahF5Mt6EZARTIuAw4n8CJoOdEmhuLuy78vyWGBliXx7pOCxzo7ef4llXwOnECkoPgPOS5W4S8R5HLC4rGOdoLi7BsdzKfELu6bA4uC1SfC+SbzjnSIx7/d3tqb9m0L8O6tsLp6uslllM6hsTljcJeJU2Vy1MVdZ2axm21HcfQyRL+notK1Rs/WIXOXA3bd78BeITi9DM2CPu38bzA8HLokRw7fuviB4v4rIlZsyH/qdCnHHY2ZG5Erzf4OkV4lcQU61mC8FPnP3nWWtFEbcZtaFyNX24VHr7Yx3rGKEcRHwtrvvcfclRK6+nRxMC919sbvnAm8H66ZE3O7+TXBOQ+RKYG0zqxXj8xWun/SY4zGzVkQKgq+C/b5WuH0Kxn0F8FZ5n6kq4w7ef+PuS8sJoz8w3N03uftmIv+7A+Id71SI+UC+s2X/pEJ5obI5aTGrbFbZrLJZZXNKlc2qPFeAmT1uZsuJNLd4ICr9B2Y2DxgM/F/UJrXNbLKZTTCzwn+eDUCWmRU+2PtSoG05+z2ZyFXKRVHJj5vZDDP7c1lfbkmKO9b2zYAt7p4XzK8AWqdQzIUup/SXWFUd6yOALWb2vpl9Y2ZPmlkGFT9WrYlcvafEevHSUyXuaJcA37j7nqi0l81smpndHxREqRJzh2DbL8ysd5DWOtimUCof68sofW5X6FiHGHdFlXVuV/h4Jzjm6P1W6jtb9o/KZpXNKptDiTuayuaqj7uQyuaqjTl6v+GWzX6At6yr60TZTUHuAR6OkX4G8HnUfGHzl8OJNCnoGMyfAowl0sThMSJfUPHiaEWk70OvEmlGpEnQq8ADqRR3rO2JXOVZGLVOW2BmqsQcdVzXA1mJONZEfjDkBDFnAu8BN5Z1rErk9SxwddT8i0QKvB8BL0SlXwP8NVXijlp+DJEvsI5Raa2D1wZE+rdcmwoxB3//ZsH77kQKj4bASSXOw97AJyl4rHuWXB7vWFdl3CXWX0r8ZlZ3A/dFzd8P3FXW8U52zFHr7Nd3tqb9myr7dw7mVTarbI4bNyqbVTarbFbZXIFJd573z5vEbs41BuhoZs2D+VXB62Ii7fW7BvNfuXtvdz8ZGEOk030pZtaQyNWX+9x9QtR+VnvEHuBlIk2BUibuONtvABqbWWawWhtgVaztkxFz4MfAB+6+N2o/VXmsVxD5wbDYI1cpPyTSN6Six2oFxa/WF64XLz1V4sbM2gAfECkUiq4AuvvK4HVbEEtFjneVx+yR5ncbg/dTiPywOCLIt03Uqil3rAOl7toc4LGubNwVVda5fSDHOxExV8V3tuwflc0qm8OIW2WzymaVzbGpbI7O1yM18GqpefPmnp2dnewwRESkmpgyZcoGdy/qN2VmNYkMkvKJuz8dva6ZtXL31UHzuz8Du919YGIjTj0qm0VEJEyJLJszy18lfWVnZzN58uRkhyEiItWEmS0L+mz+zN1vInJ37AygmZldH6x2vbtPA94wsxZEmodNA36WjJhTjcpmEREJUyLL5mpdeRaRitu0I5dPpq/iguNb0by+xjUSicfdJwM3Be//Dfw7znpnJzIuEamexny7nhPaNKZR3axkhyKSshJVNqvPs4gA8J/Jy3nw49m8OfG7ZIciIiIiwNbde7n2pa+56bVJyQ5FRAip8mwRV5vZA8F8u2BYcBFJE7l5BcVeRUREJLn2BmXyvDXbkhyJiEB4d57/TuTxA1cE89uIDNkvIiIiIiIHYMuuyGDj23bnlbOmiCRCWH2ee7p7NzP7BsDdNwejnImIiIiIyAHI2bW3/JVEJGHCuvO818wyAAcIRjBT208RERERkQOUs1OVZ5FUElbl+RkiD1dvaWaPA18CvwspbxERERGRg86i9duTHYKIRAml2ba7v2FmU4C+RJ6ZdbG7zw0jbxERERGRg1HdmnqqrEgqqfR/pJnVAGa4+7HAvMqHJCIiIiIiR7dqAMBRhzZIciQiAiE023b3AmC6mbULIR4REREJmZk1NbPhZrYgeG0SZ73rgnUWmNl1UemjzWy+mU0LppZBei0ze8fMFprZRDPLTswnEjk4FLgDULdmRpIjEREIr89zK2C2mY0ws48Lp7I2MLO2ZjbKzOaa2Wwzuz1Ij1nAB8+SfiYooGeYWbeQYhcREanuBgIj3L0zMCKYL8bMmgIPAj2Bk4EHS1Syr3L3E4NpXZB2I7DZ3TsBfwZ+X5UfQuRgs3VX5BFV+Z7kQEQECO9RVQ8fwDZ5wF3uPtXMGgBTzGw4cD2RAn6QmQ0kUsD/P+A8oHMw9QT+EbyKiIhI2S4C+gTvXwVGEylbo/UHhrv7JoCgTB4AvFVOvg8F7/8L/M3MzN31U18kBE8N/xaA6cu3JDkSEYHwBgz74gC2WQ2sDt5vM7O5QGviF/AXAa8FBfIEM2tsZq2CfERERCS+QwrLS3dfXdjsuoTWwPKo+RVBWqGXzSwfeA94LCiPi7Zx9zwzywGaARuq4DOIHHT0nGeR1BJKs20z62Vmk8xsu5nlmlm+mW3dj+2zga7AREoU8EBhAV9eoS4iInLQMrPPzWxWjOmiimYRI63wDvJV7n4c0DuYrqnANtGx3Wxmk81s8vr16ysYjojszM1PdggiEiWsPs9/A64AFgB1gJuCtHKZWX0iV7F/5e5lVbhVQIuIiMTh7ue4+7Expo+AtWbWCiB4XRcjixVA26j5NsCqIO+Vwes24E0ifaKLbWNmmUAjYFOM2J5z9x7u3qNFixZhfFyRg8Ku3LxkhyAiUcKqPOPuC4EMd89395fZ1/Q6LjPLIlJxfsPd3w+S4xXwcQv1EnGogBYRESnuY6Bw9OzrgI9irDMUONfMmgQDhZ0LDDWzTDNrDkXl9oXArBj5XgqMVH9nkfDs0J1nkZQSVuV5p5nVBKaZ2R/M7A6gXlkbmJkBLwJz3f2pqEXxCviPgWuDUbd7ATnq7ywiIlIhg4B+ZrYA6BfMY2Y9zOwFgGCgsEeBScH0SJBWi0glegYwDVgJPB/k+yLQzMwWAncSYxRvEam8Oll6VJVIKghrtO1rgAzgF8AdRO4QX1LONqcF2800s2lB2r1ECvR3zexG4DvgR8GyIcD5wEJgJ3BDSLGLCDH6QIhIteHuG4G+MdInE+lqVTj/EvBSiXV2AN3j5LubfeW0iFSRerVUeRZJBWGNtr0seLuLCj62yt2/JHY/ZohdwDtw6wEFKCIiIiKSpurUVOVZJBWEUnk2syXEuHHl7oeHkb+IVD31UhQREUlNdbPCaiwqIpUR1n9ij6j3tYk04WoaUt4iIiIiIgct3XkWSQ2hDBjm7hujppXu/jRwdhh5i0hiuHo9i4iIpKSamaE9IEdEKiGsZtvdomZrELkT3SCMvEVEREREDmZfLyn1+HQRSYKwmm3/Kep9HrAU+HFIeYtIAqjPs4iIiIhIfGGNtn1WGPmIiIiIiIiIpKKwmm3fWdZyd38qjP2ISNXxolfdghaR9DF9+RZmrcrhqp7tkx2KiIhUc2GOtn0S8HEw/z1gDLA8pPxFRERESrno2XEA1MnK4Ifd2iQ5GhERqc7Cqjw3B7q5+zYAM3sI+I+73xRS/iJS1YJOz+r7LCLp6PO5a1V5FhGRKhXWuPftgNyo+VwgO6S8RURERErZsH1P0fvhc9YmMRIRETkYhHXn+XXgazP7gEjXyR8Ar4aUt4gkgJd4FRFJdV8u2FD0fm++vr1ERKRqhTXa9uNm9hnQO0i6wd2/CSNvERERkVh+9c60ZIcgIiIHkVCabZtZR2C2u/8FmA70NrPGYeQtIolR2NdZfZ5FRERSz6YdueWvJCJVKqw+z+8B+WbWCXgB6AC8GVLeIiIiIiIiIkkVVuW5wN3zgB8Cf3H3O4BWIeUtIglQ+HxnPedZREQk9eTmFSQ7BJGDXliV571mdgVwLfBpkJZV3kZm9pKZrTOzWVFpTc1suJktCF6bBOlmZs+Y2UIzm2Fm3UKKXURERNLM1t17S6WNX7Qhxpoi1UOvJ0YkOwSRg15YlecbgFOAx919iZl1AP5dge1eAQaUSBsIjHD3zsCIYB7gPKBzMN0M/COEuEUk4BpuW0TSyIRFG0ulXfn8RP44dH4SohFJjPenrkh2CCIHtVAqz+4+x91vc/e3gvkl7j6oAtuNATaVSL6IfY+5ehW4OCr9NY+YADQ2MzUNFxERKUe8Vl0x1rsuWGeBmV0XlT7azOab2bRgahmkX29m66PSb0rUZ4rnb6MWJjsEkSpz57vTmbUyJ9lhiBy0wrrzHKZD3H01QPDaMkhvDSyPWm9FkCYiIiJli9eqq4iZNQUeBHoCJwMPlqhkX+XuJwbTuqj0d6LSX6jCz1DM6pzdcZfl5atvqFRfD38ym5xdpbstiEjVS8XKczwWI61UA1Mzu9nMJpvZ5PXr1ycgLJHqQa22Raq1eK26ovUHhrv7JnffDAyndNeqlPHgx7PjLlu6cUcCIxFJrElLN3PCw8OSHYbIQSms5zz/qCJpFbS2sDl28Fp4dXsF0DZqvTbAqpIbu/tz7t7D3Xu0aNHiAEMQERGpVuK16opWXguvl4Om2febWfQF7UuCgTz/a2bR5XTSnPPUGAoKdClQRETCFdad53sqmFYRHwOF/ayuAz6KSr82GHW7F5BT+ENARCqvcMAwd/3gFElHZva5mc2KMV1U0SxipBV+IVzl7scBvYPpmiD9EyDb3Y8HPmff3e2SsSW8Vdjh9w5h847chOxLJBm2xRhxXkSqVmZlNjaz84DzgdZm9kzUooZAXgW2fwvoAzQ3sxVE+loNAt41sxuB74DCO9hDgn0tBHYSGeFbREREAHc/J94yM1trZq3cfXWJVl3RVhApkwu1AUYHea8MXreZ2ZtE+kS/5u7RQ14/D/w+TmzPAc8B9OjRI2FX6Lo+OpzTOjWjXdO6PPHD4xO1W5GEOO6hYQy5rTcNamfSsmEtamVmJDskkWqvUpVnIs2mJwPfB6ZEpW8D7ihvY3e/Is6ivjHWdeDWA4hRRCrAgxtMuvEsUi0VtuoaRPFWXdGGAr+LGiTsXOAeM8sEGrv7BjPLAi4kcpeZwgp5sP73gblV+BkOyLiFGxnHRn52Zkd25uZzdKuGyQ5JJDTnPzM28nrcofzszI40r1+LwxrXKVqem1fA+u17aB2VJhJt/KINjJi7jrv7H0ntrP27AJOzcy8F7jSpV7OKoks9lWq27e7TiTzP+Ut3fzVqej8YbERERESSbxDQz8wWAP2Cecysh5m9AODum4BHgUnB9EiQVgsYamYzgGnASiJ3mQFuM7PZZjYduA24PnEfaZ/5j5U/rtmZT47mvL+MZc6qrTz08WyueXEiT3xWdl1//pptDJ4RuTYwdPYauj4yjD15+aHEXNJnM1fT47Hh5ObFHik8e+BgfvLa5FD3+fqEZZz9x9Gh5ilV4/pTs8tcPmTmGr7/t3GcOmhksfS7/zud0waNZPfeqjlv09k3320me+BgJi4u/cz4g8X2PXlc+fxEXvxyCX8fvWi/tz/hkWF0fXQ4ANcY+7qjAAAgAElEQVS+9DVPDEm566ehq3SfZ3fPB5qZ2cFzyUGkOvJiLyJSjbj7Rnfv6+6dg9dNQfpkd78par2X3L1TML0cpO1w9+7ufry7H+PutwdlP+5+T5B2gruf5e7zkvH59qe56vnPjOWV8UsZu2AD//piMQBPf/4tU5ZtKlpn9958xi/aQP+nx3Drm1N5Y+IyfvX2NDbv3MuKzbtYtzX+Y7IqavaqHJ74bG7ROBMPfTKbDdtz2bhjT9xths9ZC0DOrr1x+7vmFzj3fTiT5Zt2FqVt2pHLlp25jF+4gaeGzSd74GBen7CM+z+cxeINsUcmHzVvHaPmx2rdDwUFTvbAwfzhf2X/uYfNXsOcVVt5Z9J3ZA8czPpt8T9b2HJ27SV74GBuf/ubhO2zKp17zCH7vU1+gfPRtMjYunsPsse3rdyyi/lrtpVK37Izl+yBg3ntq6WMXxSpNI/+tvg4DOu27q708XJ3Vm7ZFXPZZzNXs2Bt6dgSaemGHWQPHMwzIxYUpRVeYBm/cEPciy2Tl27iL58vKJU+dsF6xny7nn+NWVw1AaeQsAYMWwaMC0bgvLNwCilvERERkTL9oGvr8leK4dMZq3j68wVc8o+vitLu+3AWVz4/sWj+tx/MYlfwY/Ke92dy8u9GsDO33KFdynTpP77iX18sZvfeyI90C8Zri9V1ZuG64j+0T3h4GMc9NIwXv1xS7JnWa3J2886k5fx7wnfc8c60ovRujw7nxEeGc+ULE3lm5EIA7v9wVtHyh4LHfj01bD5dHxlG9sDB3PDKJG54eRIj5q4tdWcuPwjyX2MW8813m9kapyJ/8+tTOP+Zsfx7wncAfLcp/iPEZqzYwuYduTw+eA4L122Pu15FzF6Vw6h5kYp/YeURYOvuSIX61fFLK5V/MpzasXmF180eOJgFa7fR9ZF9j7M67qFhfPDNCpZv2smi9dv536zSY+7uzM1j0tJNpdLjWbdtN7/9YGbc1hJhm7Z8S6nnW/9v1hqyBw7mhbH7Km178vI5bdBI+j89BnfnmREL2L4nj735BUxbvgWABz7a96i76P+5Xbn5nPy7Edz17nRO//1Ivggq1te8OJF/fVGxO7PZAwfT4Z4hnDZoJLNX5TBh8cZiLVZueWMq/f48hlfGLSm17ch5a5m7emuF9hNt1soc/jh0ftzl17w4keyBg8keOJg7353GxCWR/+m3v/6uaJ0aZjw3ZhFXvjCRK5+fgLvz7wnL2LIzMvDihMUbufSfX/Hnz7+Nkf/XRe+jLzxs3L6H3LwC5qzayvpte3B3xi5Yn9aD01a2z3OhVcFUA2gQUp4ikkBFz3lO3+8zETkIPXlpZCCwmhkHdj/gF2/uuzOZm1dAzcwafFvGXaGvl0QqFxu35/Le/BVc0r0NdWuW/XPqi2/X8/Ans3n3p6ewKzeftk3rFi0rHG+i8OFf0V/Bn81czS1vTC2W1/hFG4reP/rpHGoYZGXU4OKuren1xIiiZZOXbeadSd+xbmv5d3tfGb+UV+JUKG98NdJUvF3Tutxz3lFs2bWXH/eIPJEsv8D5wd/H06N9E1o0qMX2PXm8fmNP5q7eyiENaxflMXNlTuSzBR/uz8O/pV+XQzi2dSOWbNhBzcwafP9v46hfK5Pte/IYMnMN4waeDcDExRvJyqxBt3ZNKIu7s2F7LjUMLnjmy2LL1uTs5tBGtVmTE2kx8O8Jy7iuRDNod+f3/5vPpd3b0Kll/XKPWaF123bTon4tij+9DVbnRO58NqqTxYltG5daXtKslTm0bVKXRnWzKrzvsvT785hSaXe8M73Y/NJBFxSbv+vd6Xw2aw1f39uXllF/v3ge+ng2Q2auoVu7JlzSvU2p5R9+s5JTOjYrdi6U9Mq4JXRq2YDTO8e+OLAzN495a7aR3aweFz87jhPbNub6U7PJyqjBBce34t4PZgLw2OC5/GfyCq44uS0PfTKnaPsO9wwB4KnhpSt8TwaVzdHz1/GDrq05tGFt3vh6GQAfT49cdLnupa+5+YzDGbtgA2MXbKBOzQwe+Gg2sx7uT/1akf/7xz6dwwtfLuGiEw+jd+fij8mNPhd/eXYn7jr3yH3H75M5HNemMZf96yvyCpzDGtVmVXCOLh10AatzdrEzN5/Dm9dj0GfzuOD4Vrz45RI+m7WG2pk1GPnrPjSvXwuAi58dR16Bc0e/I9iyM5fGdWtSwyIXuH7YrTVjF+z73nh/6kren7oSgK27910E/GfUxYGp320pOnb3RV1oK/T8mMX83+kdSqUDdP7tZzxy0TFs2bm31HHv3LI+C6Iuji18/DwyD/C7O1kszJq/mTUgMrZX5S4ZhqRHjx4+eXK4/YNEqqvfDZnLc2MW83+ndeCB73VJdjgiKcnMprh7j2THkc7CKpuzBw4GYMkT52NmDJ29hp++PqWcrcpXJyuj6C5zRX1+55mc89QX/Kh7G/4zZQWf3d6b8/4yloa1Mzm6VUNW5exi+aZ9TTiH3NabH/1zPDtyq18/1Lv7H1lUKSnp53068v7UlaypQLP3j249jbEL1vPHYZEf3y9ffxLtm9Xl7D99wahf9+Gnr0/mjM4tuOH0Drw/ZQWvfrWUDdtz6diiHovWx7/DDXDkIQ0488gWXHFyOzo0r0d+gfPU8Pk8O2oRrRvXKaq4l2fmihy+97cv+f0lx3HZSe2AyAWYXXvzOeHhYcXWPbfLIfzrmu5FleiN2/cwb802hs5ew4rNuxg5bx3N69ekdlYGf72iK12DiwWF5/nSQRdw2b++YuKSit8ZLs8Fx7di1ZZd/PPq7tTMqFHUd3XkXWfyxsTIHcn7L+xCQYGzZddempYYFOpnr0/hf7PXAPDeLafQvX1TFq/fzq69+fz2g1lFd3mH33EGi9bvYNicNYyev56v7+3LmU+OLtas+f4Lu3DMYQ054pAGbNi+hyMOidyLO+r+z9i9t4BjDmvI7FXF78ielN2ESUur/xBLz1zRldveit/94IddW/P+NysTGFF4XryuB32P3v8uCSUlsmwOpfJsZscCrwNNg6QNwLXuPjv+VlVPlWeRint88ByeH7uEG07L5sHvHZPscERSkirPlRdW2Xz2H0dzTOtG/PWKrkVpC9Zu44/D5jN09tpK578/enZoGmqlRhLnZ2d2ZHXOrqLm3a0a1eaBC7vw6ldLefvmU4qtm7NzL18t3kC7pvXoclhDnhr+Lc+MWMAPurbmwe914cRHhldonyd3aFrUgqEs/bocUtTPfemgC1i0fjt9//TF/n3AA/DeLadyyT/GF+33p69PLvY/dVvfzlzVsx03vDyJOQfQxPjG0zvw4pelmyxHWzroAsYv3MCVL0wscz1Jb3+9oivfO+GwSueTjpXn8cBv3X1UMN8H+J27n1rpzCtBlWeRilPlWaR8qjxXXlhlc58nR3FC28b85fKuxdK37d7LcQ8Ni7OVSDgm/fYcTnr884Ttr7CJ9eqcXZzyxMhy1hZJDzUza/DtY+dVOp9Els1h9XmuV1hxBnD30WZWL6S8k+6/U1YUjWpZM7MGF53Yuqifg0h1UXgdTX2eRSQd5LtTI0Y/0qw06z8n6SmRFedorRrV4e2be3H5cxOSsn+RMCVqsLkwhVUDXGxm9xNpug1wNVB2e4w08reRC1i6cd8jH+rVzOTiAxzVU0RERCqvoICYledyxmUSSXu9Dm/G0kEXFPWHFpHECavy/H/Aw8D7gAFjgBtCyjvpPvrF6bg7a7buZsDTY4sNNy9SXeiGs4ikk9dvPJk6NUs/3zmzRuTO82U92lKnZkbcUaRF0t28Rwdw1P3/S3YYIgeVUCrP7r4ZuC2MvFJRozqRxwYUPouxQLUMERGRpDq8RezHCWXUMGY+dC51a2aSUcNYt203Q2auSXB0IlWvdlbpi0ciUrVC6RhkZkeY2XNmNszMRhZOYeSdSoKL2eSr9izV0L4+zzq/RSS9NaidRUaNSPvtv1/VnacvOzHJEYlUjWNbNwTgzZ/0THIkIgeHsEbV+A/wDXAfcHfUVK0U9q0qUOVCREQkbZx9dMtkh5AyHrv42FJpjetmcd8FR/PpL09PQkRSGW/c1ItPf3k6p3Zszis3nJTscESqvbAqz3nu/g93/9rdpxROIeWdMjIKK8+68yzVkAe9nnV2i0h106BWJj/v05HPbu9dLP3xH5SuSFY3b9y0747kMYc15Kqe7YoeewSRRyBNe+Bcbup9OMe2bsR9FxydjDDlADWqk8WxrRsB0OfIliz63fn88+rupdZr06QOY39zVqLDO+g1q1eTBy7swrQH+iU7lJR01pEtkh3CfqtU5dnMmppZU+ATM/u5mbUqTAvSq5XCO8/5ql2IiIikDTPjNwOO4uhWkcrjZT3asnTQBVzVs32pdV++4SSWPHE+P+xW/Kkab9/cK1Hhcnf/I4vNX39qdpnrv3Bt/MebntapOfdf2IVBPzyOwbf1xoLfMi9c24Mht/Uutf5NvQ+nSd3IWC8vX38SPTuU/jnXoPb+DZnz1k96UUf9cxMio4Yx4NhDi+b/77QOnNapGaN+3Ye2Tevy1T1n8/M+HQE4Obva/VSP662flP7/LXmnfvgdZ4T2fz7gmEN5/toeTLm/H/93egca161Z7KIVwNf39g1lXyXdcFp20ftxA89myRPnl1rnsYuP5Z2be/HS9fu+O0rGV5ZPf3k6z0d978x46Nyi96N+3YeJ9/blyp7tyswjK8N48br0ay1R2QHDphC5UVX4YIjoptoOHF7J/EsxswHAX4AM4AV3HxT2PuIp7POsPqFSHek5zyJyMHj8B8fFTH/vllPp2KIejevWBOB7xx/G+1NX0rJBLfIKnOPbNOKufkdwdKuG3PTa5Art6/snHMbH01cVzY8feDanDto3JMyIu86khhln/XE0Zx/VkpHz1tGtXWNuPasTi9Zv5/2pK/n8zjPp1LI+Zx7RghtemRRzP6d3bl70vjCfW8/qyA+7tQHgxtM7lNrmnC6HxI370YuP5bFP59K7c3P6HNmC1ycs44Q2jbno2XEATPrtOWzdtZfGdWvy7dptvDB2MQ3rZDF9RQ7Tl2/h7KNa8tL1J/HDv49j6ndbOKVjM+Y+OoDXvlrKrJU5fDpjNTtzq/bJJWPujtxlPePJUaWWzXt0AEs37uCvIxYyeObqKo0jWcbcfRZ78vLpfEiDYumtGtXhNwOO4jcDjmLhum2c89SYJEUYqdidNqjyQyTVycrg3GMO4aNpq2Iur51Vg1M6NmPivX3p+bsRRel9jmzJ0kEXsHtvPlOWbabzIQ3oTKQSuXlHLks27qBbuybFHgm2dNAFLFq/nVfGLWXN1t0Mn7O21P4G/fA4Lj85dsXx41+cxt3/mcGgS46jZcPaAJzWqRkvXndSqZHTz+1yCMPmrKVlg1qs27anKP3mMw7HgH+NWVxs/XmPDmDr7r20bFCbUzs2p1Wj2rRuXKcoboDR89fRsE4W3do1AWDu6q3F8qhXM4Mdufn063IIV/dqzxND5jJvzTYAnvjhccxfs41Xxi/l0Ea1ObZ1I565ois79uTRsHZWUR4tGtSifq1MHvxeF8479lCym9WjbdO6APxp2Hz+OnIhbZrU4cv/d3bMY5TqLJ0qgmaWAXwL9ANWAJOAK9x9Tqz1e/To4ZMnV6yAq4gde/I45sGh3HPeUfz0zI6h5SuSCh76eDavjF/KNb3a82iMPnEiAmY2xd3j3+aTcoVdNlfW+IUbwODUjs2LpRcUOM+PXcyVPdvRIOqH4e69+TEfDzTyrjPZuCOXDs3r0eOxzwFY8sT5mBmPD55D784tOOOIFuTs3Mu7k5dzU+8ORXeB1+TspnHdLLbu2kuD2lnUqZnBrtx8Ji/bRO/O+5o1FhQ4K7fson6tTGrUMAY8PYbVObuZ+8gAfvLaZI46tAH3XdilKg4TANkDB3Nc60Z8Usm+0e7Oo5/OJSvTqJuVyYfTVrJkw46i5bf06cj/G3AUefkFXP3iRC44/jDu/3BWufkOPO8oenduzttfL+eRi47BzMgeOJju7Zvw3i2nsnbrbtbk7OaEto0B2LQjl26PDue6U9pzVa/23PTqZAZdchxXPj9xvz/Tpd3bcMFxrRgyczX/mbKi1PLsZnVZunFn0fw7N/ei5+HNeHLoPBas3c6woBJ2WqdmZGXUYPT89UXrjv51H7Kb19vvmCpq+548Pp2+ij8Mnc+mHblF6defms2rXy2t0EX1sb85i8ufm8DKLbvirvPsld3Ibl6XC575EohU6FZu2cWH36zkyaHzuapnOxrVyeLvoxeV2vb0Ts35cuEGAP5xVTdWbN7FWUe1ZMaKLUUXidbk7ObWN6cyZdlmTmzbmMOb1+P9b1bSoHYmMx/qD8BXizZy1QsTGHFXHzpU8JgWnieFMRf6csEGrn5x37ny9b19mbEih75Htyz63y7P2q27aVQni9pZGcUq6WN/c1ZRhfM/k5dz939nFC27q98R3HpWJ1Zu2UXvP0QuDj38/WO4rpwWKrF8t3EnZzw5iguOa8WzV3Xj/akruPPd6bx+48n07tyCH/x9HN98t4X3bjmF7u2bkpdfwJqtu2nTpG6pvEbNW0eD2pn0KKNFw4TFG7n8uQmc3KEp7/70lP2ON55Els2Vqjyb2UnAcndfE8xfC1wCLAMecvdNoUS5b3+nBPn2D+bvAXD3J2KtH3YBXVhg/mbAkfy8T6fQ8hVJBYWV56t7teOxi2PfmRE52KnyXHmpVnk+EOMXbqDLYQ35ePoqHvhoNv/92SnFfjDe+sZUMmoYz1zRtUrjWL5pJ198u56re5Vufl4V1uTspmGdTOrWDOVJp6Xy/mzWavoedQiHNa5NZkbxnoV78wuYvHQzVzw/gb9cfiK3vz2N5vVrMem3ffnJa1P4fO7auM1O3b3MykzJ5e7OovXbcYcHPprNV4s3cv+FXTjryBac/acv6N6+CVOWbQYirQkOC+7uRetwz+CiSufX9/albq1Mvpi/nrZN63B8m8al1p++fAuvT1jG4z84lr35zrEPDi2q9CdKt0eHs2lHLlPuO4em9WpiZqzasqvorufFz47j/gu7cPZRLckw44/D5vPx9FWMuOtMOraoz/pte1iyYQeTl22iXdO6nNqxORlmTFyykWb1a9K9feR/pLCSGP33Wr5pJ4c2qk1WRo1ilcgFj5/H5KWb6da+MfNWb2PVll2cd1yruJ8hZ9deZq3M4bROzXF3fvvhLC7r0bbogsmBihUzRM6VDvcMibnsQPdRr2YGU+7vV/QossIK7VlHtqBWZga/v+R4GgVdK/47ZQWPfDKbbx44t+gJA/tr9Px1nJTdlHq1MnF3lm7cWXRh4ZJ/jGfKss2lvuMO1KyVOVz41y+5rEdbfn/p8ZXOr1A6VZ6nAue4+yYzOwN4G/glcCJwtLtfGk6YRfu7FBjg7jcF89cAPd39F1Hr3AzcDNCuXbvuy5YtC23/uXkFHHHfZ9zd/0huPUuVZ6leVHkWKZ8qz5VXHSrPhSKVrB10ahn7mdNSNdydF8Yu4aITD6Nlw9q4O+5Q4wArD2W54rkJfLV4I2/c1JPTOjWnoMAxo6jCNPvh/tSrVfpiwpadueTmF9C8Xq0DimvsgvUc17pRUTeCRHhy6DyeHbWIeY8OiPkM6YXrttOxRb1iFxryC3y/K20bt+/BzGhaL/ZnGzV/HTe8HOmiUNkKaVgGPD2GM49swT3nlR5Qb8XmnazbtqeoKfSBGjJzNYc0rE339sXz2Zmbx+1vT+Ph7x8T80JNVbr0H+OZvGwz7/70FE6OMf7BgRg+Zy29OzcP9TnliSybK3vpMCPq7vJlwHPu/h7wnplNq2TescT67yxW+3f354DnIFJAh7nzwu+GL75dz64q7qsjkmhTv4tcRZ+2fAt/HDo/ydGIhKtru8b0PTp+H0+RA2FmqjgngZnxkzMOLzZfwVay++1nfTry1eKNdGkVeZ5yYUW4fq1Mtu/Ji/v40spWeqOb6yfKr889kl+dcwRZGbHHE451rh/I3c5m9WuVufysI1vSunEdLikxaF8y/e9XZ8Rd1qZJ3ZjNmPfX+XHuqNetmVlscK5EKrwwFOZ1qX5ljLeQDipdeTazTHfPA/oS3PENKe9YVgBto+bbALFHB6gCGTWMIw9pwJRlm4ua64hUN3NXb2Pu6m3JDkMkVNee0l6VZxHZb2ce0SLm3c97zj+K334wq1qNIm5mZGVU0VWI/TRuYHoOJlXd/OnHJ/DGhO9K3Q0/mFW2gvsW8IWZbQB2AWMBzKwTkFPJvGOZBHQ2sw7ASuBy4Moq2E9MZsbQO+JfeRIRERGR6u+qnu1jPupMpDppXr8Wt5/TOdlhpJRKVZ7d/XEzGwG0Aob5vg7UNYj0fQ6Vu+eZ2S+AoUQeVfWSu88Oez8iIiIiIiIi0dLqUVX7y8zWAzuADcmO5QA0R3EnSjrGDOkZdzrGDIo7kVI95vbunvjOiNWIyuaES8eYQXEnUjrGDIo7kVI95oSVzdW68gxgZpPTcWRUxZ046RgzpGfc6RgzKO5ESseYZf+l6985HeNOx5hBcSdSOsYMijuR0jHmqhJ7OD0RERERERERKaLKs4iIiIiIiEg5DobK83PJDuAAKe7ESceYIT3jTseYQXEnUjrGLPsvXf/O6Rh3OsYMijuR0jFmUNyJlI4xV4lq3+dZREREREREpLIOhjvPIiIiIiIiIpWiyrOIiIiIiIhIOdK28mxmL5nZOjObFWf5VWY2I5jGm9kJUcuWmtlMM5tmZpMTF3WF4u5jZjlBbNPM7IGoZQPMbL6ZLTSzgSkU891R8c4ys3wzaxosS+axbmtmo8xsrpnNNrPbY6xjZvZMcExnmFm3qGXXmdmCYLouhWJOuXO7gnGn4rldkbhT7vw2s9pm9rWZTQ/ifjjGOrXM7J3gmE40s+yoZfcE6fPNrH8KxXynmc0Jzu0RZtY+all+1N/h40TELPtPZbPK5grErbI5QVQ2q2wOKWaVzdHcPS0n4AygGzArzvJTgSbB+/OAiVHLlgLNUzTuPsCnMdIzgEXA4UBNYDrQJRViLrHu94CRKXKsWwHdgvcNgG9LHjPgfOAzwIBehecJ0BRYHLw2Cd43SZGYU+7crmDcqXhulxt3ifVT4vwOztf6wfssYCLQq8Q6Pwf+Gby/HHgneN8lOMa1gA7Bsc9IkZjPAuoG728pjDmY357o46zpgP7OKptVNpcXi8rm1DrWqXhuq2xW2ZyyU9reeXb3McCmMpaPd/fNwewEoE1CAitHeXGX4WRgobsvdvdc4G3golCDi2M/Y74CeKsKw6kwd1/t7lOD99uAuUDrEqtdBLzmEROAxmbWCugPDHf3TcF5NBwYkAoxp+K5XcFjHU8yz+39jTslzu/gfN0ezGYFU8nRHy8CXg3e/xfoa2YWpL/t7nvcfQmwkMjfIOkxu/sod98ZzKbEuS37R2WzyubyqGxOHJXNiaWy+eCQtpXn/XQjkSuYhRwYZmZTzOzmJMVUllOC5hOfmdkxQVprYHnUOiuo+BdgQphZXSKF2HtRySlxrINmMV2JXFGLFu+4Jv14lxFztJQ7t8uJO2XP7fKOd6qd32aWYWbTgHVEfkzGPbfdPQ/IAZqRxONdgZijlTy3a5vZZDObYGYXV2mgkigp9/1VjpT9/ipLqn13RVPZnDgqmxNDZXP1l5nsAKqamZ1F5A99elTyae6+ysxaAsPNbF5wBTcVTAXau/t2Mzsf+BDoTKRZRUmp9pyx7wHj3D36SnjSj7WZ1Sfypford99acnGMTbyM9IQoJ+bCdVLu3C4n7pQ9tytyvEmx89vd84ETzawx8IGZHevu0X0fU+7crkDMAJjZ1UAP4Myo5HbBsT4cGGlmM919USLilvCl4vdXOVL2+6sCUuq7q5DKZpXN5VHZrLI5FVXrO89mdjzwAnCRu28sTHf3VcHrOuADEtAsoqLcfWth8wl3HwJkmVlzIleg2kat2gZYlYQQy3I5JZrNJPtYm1kWkS/eN9z9/RirxDuuSTveFYg5Jc/t8uJO1XO7Isc7kHLnd7DvLcBoSjddLDquZpYJNCLSxDPp3yVlxIyZnQP8Fvi+u++J2qbwWC8Otu2aiFglfKn4/VWeVP3+qqCU++5S2ayyuTwqm1U2pypzT7ULpBUXNOX41N2PjbGsXbNmzZZlZ2cnOiwREammpkyZssHdWyQ7jlSmsllERBIpkWVz2jbbNrO3iIwQ2NzMVgAPEunkjrv/E3ggOzubyZMT+gQGERGpxsxsWbJjSGUqm0VEJNESWTanbbNtd7/C3Vu5e5a7t3H3F939n0HhjLvflOwYRdLJ/DXbuPWNqcxZFa9bkYhI2VQ2i4TL3enz5CgmLt5Y/soiUuXStvIsIuEaPmcNg2euZsjM1ckORURERIAF67azdONOLntuQrJDERFCqjxbxNVm9kAw387MUmagDxEpXxoPfyAiIlIt5eYVJDsEEYkS1p3nvwOnEHlIOcA24NmQ8hYREREROejUq5W2wxOJVEth/Uf2dPduZvYNgLtvNrOaIeUtIiIiInLQ2b03P9khiEiUsO487zWzDIKHeZtZC0DtTEREREREDtC4hRuSHYKIRAmr8vwMkYeQtzSzx4Evgd+FlLeIiIiIiIhIUoXSbNvd3zCzKUBfwICL3X1uGHmLiIiIiByMTmjbGIAWDWolORIRgRAqz2ZWA5jh7scC8yofkoiIiIiIFGrXtG6yQxARQmi27e4FwHQzaxdCPCIiIiIiAuQXRJ4jmWGW5EhEBMIbbbsVMNvMvgZ2FCa6+/dDyl9ERERE5KAyZOZqAL5euinJkYgIhFd5fjikfERERCRkZtYUeAfIBpYCP3b3zTHWuw64L5h9zN1fDdJHE7lQvitYdq67rzOzWsBrQHdgI3CZuy+tsg8icpD5eokqzSKpJJTRtt39i1hTWduYWVszG2Vmc81stpndHqQ3NbPhZrQfcoYAACAASURBVLYgeG0SpJuZPWNmC81shpl1CyN2ERGRg8BAYIS7dwZGBPPFBBXsB4GewMnAg4VlcOAqdz8xmNYFaTcCm929E/Bn4PdV+SFEDjbz1mxLdggiEiWUyrOZ9TKzSWa23cxyzSzfzLaWs1kecJe7Hw30Am41sy7EL+DPAzoH083AP8KIXURE5CBwEfBq8P5V4OIY6/QHhrv7puCu9HBgwH7k+1+gr5k6Z4qISPUU1nOe/wZcASwA6gA3BWlxuftqd58avN8GzAVaE7+Avwh4zSMmAI3NrFVI8YuIiFRnh7j7aoiUv0DLGOu0BpZHza8I0gq9bGbTzOz+qApy0TbungfkAM3CDl5ERCQVhNXnGXdfaGYZ7p5PpIAdX9FtzSwb6ApMpEQBb2aFBXy8Qn11ibxuJnJnmnbtNAC4iIgcHMzsc+DQGIt+W9EsYqR58HqVu680swbAe8A1RPo6l7VNdGwqm0VEJO2FVXneaWY1gWlm9gciFdp6FdnQzOoTKYh/5e5by2jtVaEC2t2fA54D6NGjR6nlIiIi1ZG7nxNvmZmtNbNWwUXpVsC6GKutAPpEzbcBRgd5rwxet5nZm0T6RL8WbNMWWGFmmUAjoNQIRyqbRSqnWb2ayQ5BRAiv2fY1QAbwCyKPqmoLXFLeRmaWRaTi/Ia7vx8kry1sjl2igC8soAu1AVaFEr2IlL4SJSLVycfAdcH764CPYqwzFDjXzJoEA4WdCww1s0wzaw5F5faFwKwY+V4KjHR3fZ2IhKxlw9rJDkFECG+07WXuvsvdt7r7w+5+p7svLGuboL/Ui8Bcd38qalG8Av5j4Npg1O1eQE5h824REREp0yCgn5ktAPoF85hZDzN7AcDdNwGPApOC6ZEgrRaRSvQMYBqwEng+yPdFoJmZLQTuJMYo3iJSeTUzw7rfJSKVEUqzbTNbQuwm1IeXsdlpRO5YzzSzaUHavUQK9HfN7EbgO+BHwbIhwPnAQmAncEMYsYtIhO4ViVRf7r4R6BsjfTKRQT4L518CXiqxzg4iz3GOle9u9pXTIlJFamWo8iySCsLq89wj6n1tIgVp07I2cPcvid2PGWIX8A7ceqABioiIiIiko6xMPQFOJBWE1Wx7Y9S00t2fBs4OI28RSQxXr2cREZGUVEOPTxdJCWE12+4WNVuDyJ3oBmHkLSIiIiJyMBu7YEOyQxARwmu2/aeo93nAUuDHIeUtIgmgPs8iIiIiIvGFUnl297PCyEdEREREREQkFYXVbPvOspaXeBSViKQgL3rVLWgREZFUULdmBjtz85MdhogEwhr3vgdwC9A6mH4GdCHS71l9n0VERKRKuDtrt+5OdhgiVeLqXu2THYKIRAmrz3NzoJu7bwMws4eA/7j7TWVuJSKpI+j0rL7PIpJOnh+7mN8NmcfIu87k8Bb1kx2OSKhchbJISgnrznM7IDdqPhfIDilvERERkZiGz1kLwOL1O5IciYiIVHdhVZ5fB742s4fM7EFgIvBqSHmLSAJ4iVcRkXQwaelmAG56bXKSIxERkeourNG2Hzezz4DeQdIN7v5NGHmLiIiIiBzs9uYXkJUR1n0vETkQofwHmllHYLa7/wWYDvQ2s8Zh5C0iiVHYrUrdq0QkXazTQGFSzUWXyYVdFEQkecK6fPUekG9mnYAXgA7AmyHlLSIiIlLKDj3CRw4iH01bmewQRA56YVWeC9w9D/gh8Bd3vwNoFVLeIpIAhc931nOeRSRd7MlT5Vmqt+gSeejstYxftCFpsYhIeJXnvWZ2BXAt8GmQlhVS3iIiIiKlDHh6bLH5nbl5SYpEpGpYifkXxy5JShwiEhFW5fkG4BTgcXdfYmYdgH+Xt5GZvWRm68xsVlRaUzMbbmYLgtcmQbqZ2TNmttDMZphZt5BiFxGi+lXpxrOIpKk1OeoDLdXbiHnr9OxnkSQKpfLs7nPc/TZ3fyuYX+Lugyqw6SvAgBJpA4ER7t4ZGBHMA5wHdA6mm4F/hBG7iIiIVA9n/+kLVSyk2vvryIXJDkHkoJXU8e7dfQywqUTyRex7RvSrwMVR6a95xASgsZmpX7VISHTjWaT6iteqK8Z61wXrLDCz66LSR5vZfDObFkwtg/TrzWx9VPpNifpM8fz+f/OTHYJIaGKVyU8N/zbhcYhIRCo+LO4Qd18NELy2DNJbA8uj1lsRpBVjZjeb2WQzm7x+/foqD1ZERCQNxGvVVcTMmgIPAj2Bk4EHS1Syr3L3E4NpXVT6O1HpL1ThZyhm4/Y9MdP/+cWiRIUgkjRLNuxIdggiB6WwnvP8o4qkVXY3MdJKXZBz9+fcvYe792jRokXIIYhUX/ue86x7zyLVULxWXdH6A8PdfZO7bwaGU7prVcqYs3pr3GWvfbU0YXGIVKV4RfKl/xif2EBEBAjvzvM9FUyriLWFzbGD18Kr2yuAtlHrtQFWHeA+REREDibxWnVFK6+F18tB0+z7zSz6gvYlwUCe/zWz6HK6SFW0CvvtB7PiLnvgo9lkDxxM7z+MJC+/IJT9iaSSjTtydZFIJAkqVXk2s/PM7K9A62Ak7MLpFeBAnxfxMVDYz+o64KOo9GuDUbd7ATmFPwREpPKKnvOsG88iacnMPjezWTGmiyqaRYy0wm+Eq9z9OKB3MF0TpH8CZLv78cDn7Lu7XTyTKmgV9t2mneWus3zTLkbPL15Zf33CMpaqyaukCcdpUCsz5rIHPprN4vXbExyRyMGtsneeVwGTgd3AlKjpYyLNv8pkZm8BXwFHmtkKM7sRGAT0M7MFQL9gHmAIsBhYCDwP/LySsYuIiFQb7n6Oux8bY/qI+K26osVt4eXuK4PXbcCbRPpE4+4b3b2w8/HzQPeq+GyVcdNrkznvL5HnQeflF3D/h7Po88fRvDB2cZIjE9knN6+AXbn5sRfGuqwVOPtPX/Dil0v41xeLOOr+z6omOBEpUqnKs7tPJ/I85y/d/dWo6f2gv1R521/h7q3cPcvd27j7i0FB3NfdOwevm4J13d1vdfeO7n6cu0+uTOwiUoIXexGR6iVeq65oQ4FzzaxJMFDYucBQM8s0s+YAZpYFXAjMCuajn3rxfWBuFcVfpk9/eXqZy+eu3sp/Ji9n+eZdRWmPDZ7LXjXplgTatCM37jl38bPjOPqB/5VKL2wNdlqnZnHzffTTOTzx2Tx27y2g6yPDePTTOaHEKyKlVbrPs7vnA83MrGYI8YiIiEj4YrbqMrMeZvYCQHCx+lFgUjA9EqTVIlKJngFMA1YSucsMcJuZzTaz6cBtwPWJ+0j7HNu6Ubnr3P3fGZz1x9HF0r5csIFxCzfQ/89jeOQTVTgqa1duPuu3xR4FPV31eXIUFz87rkLr7tiTF3cU+IICp9ujw/n1f6bHXF7WAHgGPHrRsRWKYfPOvbz45RJGzV+HuzN6/jryC9Lzsvjuvfnc8/4MNu/ITXYo+83deWXcErbu3pvsUCRkYQ0YtgwYFwwicmfhFFLeIpIARc95Ts8yVkTKUEarrsnuflPUei+5e6dgejlI2+Hu3d39eHc/xt1vDy6c4+73BGknuPtZ7j4v0Z/t0u5tDnjbG16ZxFUvTGT+2m28NG4Jz45ayPY9+4ZsycsvCKXiMW7hhgOuAIyvwLblxenuMe947szN428jF1R6ULVxCzfQ5YH/MeAvYzjp8c8rlVehvPwCCqq40vfSl0tYk7MbgL35BSzZsIMde/L4xZtT2RBUgpdu3Mm05VsqlF//p8fQ/bHP+WjaSt6bsqLYssJP8vH08se6zS9wdu/NL/b0i8Nb1K9QDIVueHkSd707netfnsQLYxczYu5avl6yqdg6i9ZvD+X8/ua7zfxv1hrWbdvNrW9M5f2pK4piLyjwovOroKD0efjU8G8ZPT9WLxIY9Nk83vp6OU8OK/vZ7e7OvR/MZOaKnAOKf8eePI59cCgj56094HNuy85cvlywoWh+3MKNPPTJHB76aPYB5VeVcvMK9GSVSog9AsH+WxVMNYAGIeUpIiIiUqYjDwnvZ8eTQ+fz5NDID/U5j/SnywNDAVjw+HlkZUTuNyxev51NO3Lpkd20aLvsgYM5sW1j/j979x0eVZU+cPz7ppAAoYfeAtJbEEIXREBAEXGtWAFF1+6uq/vDgiA21l1d13UtiCL2ggUFaVKUDgm91wChhBKSENKT8/tjboZJMkkmZDIlvJ/nmSdz25l37tzMmffec8/59L6enExOp1W9ahw4lcLB0+eJal6bO6evpXPjGvxSQvPygrJycrlj+loAYqeOsM/PyLbdGxsSFAhAq+fm0aFhdX59on+hMjKyc3h17k5mrj7E2mcHk51raFyzMgD/XrSHD5cfpH71UK7t3JDKwYEEBNhusE3JyKZqpUBEhMzsXHKNITQ40F5ucnoWASKEhQTx1m97SM3M4dAZWydu8cnp7DpxjivbFN053O4T5xj21h/EPD+EOmEh5OYaUrNySEjJ5FhSGqOnraFfqzrMHNeToMDC13pSMrKJT07nsgJJ5clz6Ww/lsy4Gevt+80YQ7uJ83luRHvu6RPBqXMZfL3uMG8s2sOUOTuInTqCl+fsYObqQ4zp05w5W44zZ0v+PmkjJszl5u5NmBUTx0djohjcvj5gS0TaPJ//XuMnvt4EwE3dm5CWmUNa1oV7mfNyltTMbJbsOsnAtvUIc+gQLGLC3CL32ZD29fhtp/NE05kfNh4F4LV5F85pNa5ZmZUTBnHozHkGv/G7fR+V5ExKBmfOZ7I3PoURXRpyPiOb0OBAvlh7iBesBPHWqCbM3XqcuVuPk5KRzctzd5KZnWt/jSe/3cRPm45xU7cmvHZjZ1bsO8Xbi/cCsGHi1XyyKpY7ezUj1xhqV63EJ6tiAfIltOczsjmRnE5aZg4dG1VHRDidksmXaw+zYNsJYiZeDcDRxDRCggIIDwsp8j3NWHmQ1Mwc+rUKJyUjm3s/iSayaU1mP9Kv0LrZObmkZeUQFBBA5UqBhZbf+8l6NhxO5JNxPQgPC+F8pu0k3MlzGczZcozrujTKt/5PG4+y43gyz17bHoA3Fu6mcc3KjO7ZzL5Obq7hsa83MnfLcZ4a2oZHB7Uu/kMqwtPfbebaLg25qm09ElMz6TplEXDhc09Ky2JLXCLRsWd5YnBrAgKEfSfPkZSWTffmtQqVt/N4MjGHznJX7+b55u87eY7k9Gy6NSu8DUCXyQt4bFBr7h/QkuycXLJyjNN96evEnWceRKQattuTfaLrv6ioKBMdrbdGK+WKV+bu4MPlBxnXL4JJIzt6OxylfJKIxBhjorwdhz9zV92cl2Q8P6I94/u3JD0rh3eX7uPtJfvKXDZAjcrBJKXZmlz+dUgbWtStyj/m7eJoou2+6S/H96Jj4xrEJ6cz9N9/5Nt2xtgejPtkvdNyr2pblxnjetrjLy5xOZKQSv/Xl9qn1z07mHrVQ+0dQ/VqUYd7+jTnvpm2/dm/dTif3tsTESH29HlenrvDabJ18LVrERFGvL2c7ceSGd2jKV+vP8KDV17Gnb2accv7qzmRbLsi+9X9vXn2x60cPH2eL+/vRd/LwotN8Bw5vrfcXMMN765kS1wSTw1tw78W7rEvG9s3guT0LH7YcLRQGeFhIcx9/ApCggLYdCSR93/fz5oDCdSsEkxiaha7XhrO7R+uoXuzWhxPSmf7sSRiz1zoiX3r5KF8HxPH5GKa5XdoWL3YZtPOtGtQjTdujWTE2ytKtR3AnpevyZdwT7imHVPnFd9oI3bqCJJSs/h87SH7CZ6L9eCVl5FwPoNvo+PsZQPc9sFqktKyeOeOy6lbLZTKwYFUCrKduHD1My/KqK6NmL3p4kaYbVm3Kk8Mbs2OY8l8ue4w59JtielLozpyd58I/rlgF/9buh+AWQ/2ISqidon/X1+sPWQf7u7Hh/vyp3cvjJsdFhLEU0PbENm0JpdbiWDHF+Zz3urQLa/M3FzDhsNnufn91SW+hxev78iYvhH2acf9uWHi1XR7aZF9euqNnRndsxlnz2dyucP82Kkj2HUimeFvLefvw9uyYHs8X9/f22kCuif+HE99t5kv7+9Np0m2k4AjIxvxi0PLh3/c1JmjZ9PyfWe+cUski3bEM3/7Cdt+Gt+L/adSCAoIYHSPpgQESKF9O35mNDd1a8xDX2wA4PWbunBLVBNEhFPnMpi+/ABpWTl8uvqQfbuHv4jh160nXDpx4wpP1s1uSZ5FpBPwGZB3GvY0cI8xxqttFTR5Vsp1mjwrVTJNnsuuvJLngvN92aNXteKdpbYfrDd3b8K8rccZ0KYuf+w5xfYpwxk3Yx3bjiUXun/46WFtSc3MticKRakWGmRPMJz5581diIqoXegecHf71y2RJKVl8e7SfeQaw9lU99//+a9bIou8j7gicUwyyuMY/9vVbXhj0Z588wa2rUv/1nXZfizJ6YkNXzX1xs5M+GErAO/d2Y2eLWoze9MxDpxO4fM1h0tV1ovXd2TSz/nTmS/v70WNysFMnbeL5Q5NtUsS2aQGb97W1X7FvyR9L6vDqv1n7NPv39WNBz/fkG+dkZGNuD6yEWlZOTz+1UaXY7lYE6/rYO+Qbv+r1xIdm8Bt09YUWq9gou5oRJeGzLVadlzKyfMq4DljzFJreiDwqjGmb5kLLwNNnpVynSbPSpVMk+eyc3fy/N/bL2dk5IUmkf9asNuemCpVUTgmGct2n2TsDOctG5TyJ98/1IfuzWuXvGIJPFk3u6vDsKp5iTOAMWYZUNVNZXvd1rgkomMTiI5NYEtcYrl3YKGUN+SdR9M+JJRS/qBp7cpUCw3iui4N881/alhbDrx6rZeiUqr8DWhd9L3kSvmTm94rucm7r3FXh2EHRGQitqbbAHcBB91Uttc99tWGfPfPvHdnN67p3LCYLZRSSilVngRhcLt6iEihZQEBwmODWvFfN93/rJQvCQgQ+5XoNs/Ps3fKpZQqf+668nwvUBf4AfjRej7OTWV73T9vieSz+3ry1m1dAXTMNlUh6QVnpZQ/MRiniXOevw1tC2Dv8Eipiqh6qLuugymlXOGW/zhjzFngcXeU5Yt6WMNRHE+y9bCprbaVUkop7zIGik6dbXa/PJxAERLTsvjf0n3MWBnridCU8pjvHuzL8r2n7MNFKaXKl1tOx4pIGxGZJiILRWRJ3sMdZfuSQOsMtzsGlFfK11y451mPb6WU76tVpRJhJVx1CwkKJCjQNtbrpJEdOfDqtQxqV89DESpV/lqEV+WePhH0blmbysH+N2auUv7GXW09vgPeB6YDOSWs67cCAmzJc64mF0oppZRX/fLYFaXeJiBA+HhsD7YdTeK6/9rG5333zm48/MWGErZUyrd9/UAfABZsP0FYSBB3Tl/r5YiUqpjclTxnG2Pec1NZPivvyrP2tq0qImPd9axHt1KqouvUuAYPDGhJeFglrtUOQJWP2/zCUJfXHdaxAfHJ6fbp2KkjOJKQSv/XlxazlfvVrBJMYjmM660qlvCwEG+HUGplarYtIrVFpDbwi4g8LCIN8+ZZ8yuUgLxm25pdKKWUUn7t2Wvb88CAywB4fkR7aletxNpnB7Pu2cH8+nh/p9s8eOVlheY9Pri1/XnremHMerBP+QTsZc+PaG9/3r5hdT68xz1DqjauWZlqIaW7lhNSxk7g7ujVrNC8vw5pY3/eq0XZf8IGFLgh/z+ju15UOeFhIdSoElyqbapa+/PGyxsD0LR2Fbo0qXFRr++Kz+7rybS7u9OuQTUA/n1bJJteGJpvbOo8sVNHEDt1BCMjG3FX72Ys+usANk68mvl/6c/GiVcXWn/VhEFFvm6nxtXd9yYqsEeusn1vNageyuXNajpd5/uH+hD9/BBPhgXAq3/q5PHXLKuyXnmOwXahKu8r4mmHZQZoWcbyCxGR4cB/gEBgujFmqrtfoygB1ne1XnlWFZGO86yUulSN79+S8f0v/GSpVz2UyCY1GN6pIftOpvD9hjg2vzCUGlWCef/3/fb1fn60H12a1OTtxXsBmPVQX2pUDmbu41cw4u0VTl+ra9OabDqSWKr4GteszIr/u4oWz/zqdHm7BtXYdeIcAEv+diW/7Yzn160naN+wOmP6Nmf4W8u5tnMDnhvRgdunreFwgm34zZCgADKsYY5GdG7I3K3H7WV+dX9vMrJzGDtjvX0fhYeF8PX6w/YmwjPG9qBni9rM3nSMZ3/cyuSRHbi1R1M+WRXL6/N3A/DF+F70axXOI19uYO6W4zw2qBV39GrG6/N3k5SWxcdje7Bs90nGzljPxOs68NKcHTw08DLG9Yvgl83HqVsthMe/2sjtPZvy2o1d7PFFTJjr8r57fkR7Js7eTkhQAEcT05g0sgNTru8I2C6MLNoZz+B29YhsWoPQ4EB6t6xjL//dO7sR1bwWPV9dXKjsyCY1mP1o/tsHklKziJyykCta1+WPPacAW8KYnnXhrsa3buvKX77Z5DTefa9cQ4AIucYQGCDF9ihflLCQIP54+irq17hwVe/nR68gJSObc9aIMdVCgzmemMa+kyk8ZN228POj/bj+nZUArJwwiLCQICJfXFio/McGtWJg27r2MXr7W+NOX9E6nBkrY7k+srF93V8evYLDCam0qR+Wr2Xbf2+/PF+ZtapWyjddLTSIp4e1pVHNyix7aiC7Tpzjwc9j8q0z57H+rNx3utRN1Pu1qsPKfWecLnvkqstIy8wlOFD44I8DPHl1G95ctKdU5T82qBVZOSbfdwXAFa3CWbHvdInbPzW0DdVCg5n0c9GdwNWvHkJ8ckah+d/+uQ+VgwMJCbYlLTNWHuRvV7flL0PaEBxom5d3bC/66wC2xCUR2bQmreqF2bf/cPkBRkY2YvX+03y17ohrb9pBZNOabHbxO25I+/qlLt/bxJ86BxKRQGAPcDUQB6wHbjfG7HC2flRUlImOjnbb66dmZtPhhQU8c007/uzk7LNS/mzyz9v5ZFUsd/duzks3+N+ZQKU8QURijDHuueR2iXJ33VzesnJyOZ2SQcMalQFYdzABYwy9Wtaxr3PqXAbVQoMILdBh06lzGSzeGU+DGqFc2aYuIkLs6fPcNm01/xl9OfWqhbBs9ymmzNlB/9bhLN+b/4d1q3phvPqnznRrVpOgwADu/zSa8xnZrNp/4Yf/Z/f1pH/ruizfe4qmtaoQEV611O8xPjmdWlUqcejMeWbFxFEtNIhHB9muqI96ZwWb45KcXkUsyuxNR3ni6028flMXbu3RFICktCxmbzrK3b2bX1RCWNCsmDi+WHuIzOxcth9L5vFBrXj4qla8uWgPwzo2IO5sKtOXH7yoe+MBOrwwn9TMHHZOGU7lSoEs2H6CP39mS97u7NWMFftO8/pNXfIdB3nWxybQvmF1cnIMEgDVQ21XjvOSls2ThhL54kI6NKzOazd2Jikti3s+XgdQqv3sLu8s2Uufy+rQrVkt+wmavDiGvPk7+06mcFfvZhw6k8rAtvW4q3czUtKz6f7yb/z7tkj+dHkTt8USMWEuQ9rXZ/oY51+zyelZjP5gDR/c3Z2mtasAkJ2TS79/LKF9w+os2207YbHu2cFc8fpS3rglkn6twjmSkMqo/9lODOx95RqmztvFRysO5it7/6vXEliwyYDlrd/28NZve2lepwqHzqQypH09nr22Pav2n6FVvTA2HUlkbN8IzqVnU7ea7aRFwvlMpvyynZ82HaNjo+rMfbw/SalZ7DuVwu4T5+jSpAbfRh/h/v4t+XHjUR4f3JojCak0qVUZESE9K4dz6dl8ufYwV7atS/PaVbj8pUWM6tqI/4y+nKycXO75aB2rD1z4PnDl+ElMzeTA6fN0a1arxHULnqRqXS+MvSdT8s37581daN+wOqdTMog9fZ6x/VpwIimd3q8tZmzfCD5bc4jZj/SjeZ0qnEvP5r1l+/lszSGX43WFJ+vmMiXPItIDOGKMOWFN3wPcBBwCJhtjEtwS5YXX62OVO8yafgbAGPOas/XdXUGnZ+XQbuJ8/j68LQ8PbOW2cpXyBXnJ8129m/HyDZ29HY5SPkmT57Lzt+TZk6JeXsTplEz79NAO9ZnmpHl0y2fmkms8k2glp2dxJCGVjo1cb/ZrjGHdwQR6tqjtlkS5OCeT05m+4iD/N7xdkYnPxcjOySU5PZvaBa6IlsXB0+epHBxIgxqh/LL5GL1a1qZetVDgQpLijeTZUcE4jDGcTsm0J4S+rrj9+F30EYICxZ7sx54+z8HT52ldPwxjsCfjxTHG8F1MHNd0akC10JKb02dk57By32n6t65rv/JbFmfPZxIWGpSvrOT0LLpMXsh/RndlVNfGxWxdep+ujuX33adYvOsk1UKD2Dp5mH0f/++ObqRl5XBzd/edPLlYnqyby9ps+wNgCICIDACmAo8BXYFpwM1lLL+gxoBj+4E4oJfjCiLyAPAAQLNmhe9pKYsA7TBMKaWUUuWkZd0wTqck8OX4XtwxfS2RTZ3fn7hjynDKOSe1qx4aXKrEGUBEnF6RLQ/1qofy7LXtS16xlIICA9yaOINtWKk8IyMbubXs8iIifpM4Ayx7aiDHk9KdLrslqmm+6YjwqqVuqSEi3FqgnOKEBAUyqJ37miYXbN4Otv/R8jrpck+fCO7pE8HhM6lUs4YGvKZTA+ZtO8Hg9vUKtba5FJQ1eQ50uLp8GzDNGPM98L2IOL+Zo2ycVRX5MlljzDRsiTtRUVFuzXLzzmh+te4Iv1v3sShVURw6Y7sHbsH2eHZb984pVVEM79SQ+65o4e0wlCrWtLu7syUuib6twln01wFcVjfM6XqX4g9W5RnfP9SHU+cyS17RR11MQqxK1qzOhavy/76tK/83PP2S/R4qc/IsIkHGmGxgMNYVXzeV7Uwc4Hi6pwlwrBxex6kAgbt7N2f/qZSSV1bKz7SqF8bJcxm0ruf8x5pS/izIjc05lSovNatUYkAbW+dLretX83I0ypNmjO1B7Jnz3g6D7s0r3GA5xUgnDwAAIABJREFUys1CgwMv6RMUZU1wvwJ+F5HTQBqwHEBEWgFJZSzbmfVAaxFpARwFRgN3lMPrOCUi2pGSUkoppZRyq6va1fN2CEopF5QpeTbGvCIii4GGwEJzofexAGz3PruVMSZbRB4FFmAbqupjY0zR/bgrpZRSSimllFJu4FdDVZWWiJzC1vN3aYUDJQ/E5lv8MWbQuD3JH2MGjduT/DFm8GzczY0xdT30WhWS1s1+QeP2HH+MGTRuT/LHmKGC1s0VOnm+WCIS7W9DkfhjzKBxe5I/xgwatyf5Y8zgv3Gr0vHHz9kfYwaN25P8MWbQuD3JH2MG/427JGUfcEwppZRSSimllKrgNHlWSimllFJKKaVKoMmzc9O8HcBF8MeYQeP2JH+MGTRuT/LHmMF/41al44+fsz/GDBq3J/ljzKBxe5I/xgz+G3ex9J5npZRSSimllFKqBHrlWSmllFJKKaWUKoEmz0oppZRSSimlVAkumeRZREJFZJ2IbBaR7SLyopN1xorIKRHZZD3GOywbIyJ7rccYH4v73w4x7xGRRIdlOQ7LfvZU3A6vHygiG0VkjpNlISLyjYjsE5G1IhLhsOwZa/5uERnmQzE/KSI7RGSLiCwWkeYOy3x5X/vcse1CzL58XMeKyFbr9aOdLBcReds6hreISDeHZd76Likp5jutWLeIyCoRiXR1Wy/HPVBEkhyOhxcclg23vkP2icgET8atXKN1s0/WF1o3u5HWzR6L2e/qZRfj1rrZ1xhjLokHIECY9TwYWAv0LrDOWOAdJ9vWBg5Yf2tZz2v5StwF1n8M+NhhOsXL+/1J4EtgjpNlDwPvW89HA99YzzsAm4EQoAWwHwj0kZivAqpYzx/Ki9kP9rXPHdslxVxgPV87rmOB8GKWXwvMs/5/ewNrvb2/XYi5b14swDV5MbuyrZfjHljEMR9ofXe0BCpZ3ykdvHnc6MPp56d1s3f2u9bNvhG3zx3bJcVcYD2fOa5dqCt8rl52MW6tm33scclceTY2KdZksPVwtbe0YcAiY0yCMeYssAgYXg5hFnIRcd8OfFXugblARJoAI4DpRawyCphpPZ8FDBYRseZ/bYzJMMYcBPYBPcs7Xig5ZmPMUmNMqjW5BmjiibhK4sK+LorXju1Sxuwzx7WLRgGfWv+/a4CaItIQL+7vkhhjVlkxgQ8d22XQE9hnjDlgjMkEvsb2uSgfonWz52nd7DlaN/sUv6uXQetmX3TJJM9gb4ayCTiJ7R9lrZPVbrKaRswSkabWvMbAEYd14qx5HuFi3FjNlFoASxxmh4pItIisEZEbPBCuo7eAvwO5RSy371djTDaQBNTBu/u7pJgd3YftLGYeX97X4HvHtkv72gePa7D9SF4oIjEi8oCT5UXtV2/u75JidlTw2C7Ntu7mymv3EVvz2Xki0tGa59XvbeU6rZt9rr7Qutl9tG72HH+sl0HrZr+rm4O8HYAnGWNygK4iUhP4UUQ6GWO2OazyC/CVMSZDRB7EduZ1ELYmHoWKK/+IrRcqOe48o4FZ1vp5mhljjolIS2CJiGw1xuwv75hF5DrgpDEmRkQGFrWak3mmmPnlysWY89a9C4gCrnSY7cv72qeO7dLsa3zouHbQz3r9esAiEdlljPnDYblPHduWkmIGQESuwlZBX1Habb0U9waguTEmRUSuBX4CWuPl723lOq2bfa6+8KnvL62b7XxqX+NDx7XFH+tl0LoZ/KxuvqSuPOcxxiQCyyjQLMMYc8YYk2FNfgh0t57HAU0dVm0CHCvnMAspKm4HoynQfMYYc8z6e8Da9vLyizCffsD1IhKLrUnGIBH5vMA69v0qIkFADSAB7+1vV2JGRIYAzwHXOxwvPr2vffDYdmlfW3zpuC74+ieBHyncdLGo/eq17xIXYkZEumBrqjfKGHOmNNuWl5Je2xiTnNd81hjzKxAsIuH4yPe2cp3WzR6hdbMP7WsfPLb9tm72x3oZtG7GD+tmMcavkv1SCQ8PNxEREd4OQymlVAURExNz2hhT19tx+DOtm5VSSrmTJ+vmCt1sOyIiguhoj/bcrpRSqgITkUPejsHfad2slFLKnTxZN1+SzbaVUoUt3XWSLpMXsGhHvLdDUUoppRSQlZNLl8kLmL3pqLdDUUrhpuRZbO4SawBsEWkmIh5rd6+UKrttR5NITs9m85FEb4eilFJKKSAxNYvk9Gym/LLD26EopXDfled3gT7YxnsDOAf8z01lK6WUUkopdcnJtfomCghw1kmxUsrT3HXPcy9jTDcR2QhgjDkrIpXcVLZSSimllFKXnLzkOVA0eVbKF7jrynOWiARijdMlInVxbSB7pZRSSimllBM5udaVZ82dlfIJ7kqe38Y2xlc9EXkFWAG86qaylVJKKaWUuuTkWpeitNm2Ur7BLc22jTFfiEgMMBgQ4AZjzE53lK2UUkoppdSlKMdqtq2ttpXyDWVOnkUkANhijOkE7Cp7SEoppZRSSqk1B84AcCQhzcuRKKXADc22jTG5wGYRaeaGeJRSSimllFJAnara/65SvsRdvW03BLaLyDrgfN5MY8z1bipfKaWUUkqpS0qDGqEAdGpc3cuRKKXAfcnzi24qRymllFJKKQWcS88GID1LB7FRyhe4q8Ow391RjlJKKaWUUsrm/d/3A7DvZIqXI1FKgZuGqhKR3iKyXkRSRCRTRHJEJNkdZSullFJKKXUp2hKX5O0QlFIO3DXO8zvA7cBeoDIw3ppXJBFpKiJLRWSniGwXkSes+bVFZJGI7LX+1rLmi4i8LSL7RGSLiHRzU+xKKaVUhVZU3epkvTHWOntFZIzD/GUisltENlmPetb8EBH5xqqb14pIhGfekVKXhpdu6ARAjwin/7JKKQ9zV/KMMWYfEGiMyTHGzAAGlrBJNvA3Y0x7oDfwiIh0ACYAi40xrYHF1jTANUBr6/EA8J67YldKKaUquKLqVjsRqQ1MAnoBPYFJBZLsO40xXa3HSWvefcBZY0wr4N/AP8rzTSh1qakUaPupXjXEXd0UKaXKwl3Jc6qIVAI2icjrIvJXoGpxGxhjjhtjNljPzwE7gcbAKGCmtdpM4Abr+SjgU2OzBqgpIg3dFL9SSilVkRVVtzoaBiwyxiQYY84Ci4DhpSh3FjBYRMQN8SqlgJxcA0CA/lsp5RPclTzfDQQCj2IbqqopcJOrG1vNvC4H1gL1jTHHwZZgA/Ws1RoDRxw2i7PmKaWUUqp4RdWtjkqqZ2dYTbYnOiTI9m2MMdlAElDH3cErdanKzrX1sq3Js1K+wV29bR+ynqZRymGrRCQM+B74izEmuZgT1s4WGCflPYCtWTfNmjUrTShKXdIK/TMppfyKiPwGNHCy6DlXi3AyL++r4U5jzFERqYatzr4b+LSEbRxj07pZqYuQd+U50G03WiqlysItybOIHMRJZWmMaVnCdsHYKuEvjDE/WLPjRaShMea41Sw7776qOGxXtPM0AY45ec1pwDSAqKgozQeUUkpdEowxQ4paJiJF1a2O4sjfX0kTYJlV9lHr7zkR+RLbPdGfcqFujhORIKAGkOAkNq2blboI2dpsWymf4q7zWFFAD+vRH3gb+Ly4DawmXx8BO40xbzos+hnI6+FzDDDbYf49Vq/bvYGkvCZoSqmyM/pzVqmKrKi61dECYKiI1LI6ChsKLBCRIBEJB/tJ7+uAbU7KvRlYYox+myjlLvZ7ngM0eVbKF7ir2faZArPeEpEVwAvFbNYPW7OvrSKyyZr3LDAV+FZE7gMOA7dYy34FrgX2AanAOHfErpRSSl0CnNatIhIFPGiMGW+MSRCRl4D11jZTrHlVsSXRwdj6N/kN+NBa5yPgMxHZh+2K82jPvSWlKr6JP9nOUwXqlWelfIK7mm07jrkcgO1KdLXitjHGrMD5vVIAg52sb4BHLjZGpVTxjN71rFSFZZ3kdla3RgPjHaY/Bj4usM55oHsR5aZz4SS3UsrNsu33PGvyrJQvcNegcW84PM8GYoFb3VS2UkoppZRSlyxNnZXyDe5qtn2VO8pRSnmP3qWolFJK+SjNnpXyCe5qtv1kccsLdAimlFJKKaWUKsGfLm/MjxuPEhbirsaiSqmycNd/Yl5v2z9b0yOBP4AjbipfKVXOjP2vXoJWSimlfEGtKpUAyNXmYUr5BHclz+FAN2PMOQARmQx8Z4wZX+xWSimllFJllJSWRY3Kwd4OQ6lyo7mzUr7BXeM8NwMyHaYzgQg3la2U8gSrZtYKWinlT1buO03kiwv5fc8pb4eiVLk5n5Ht7RCUUrgvef4MWCcik0VkErAWmOmmspVSSimlnFofmwBAzKGzXo5EqfLz06Zj3g5BKYX7ett+RUTmAf2tWeOMMRvdUbZSyjNMgb9KKeUPrGFw0WFwlVJKlTd39bZ9GbDdGLNBRAYC/UXkoDEm0R3lK6WUUko5k2tlzwGi2bOqeLQTT6V8i7uabX8P5IhIK2A60AL40k1lK6U8IO9eZ73nWSnlT95Zug+AQL30rCq4pbtPejsEpS557kqec40x2cCNwH+MMX8FGrqpbKWUUkqpYs1YGevtEJQqV7M3HvV2CEpd8tyVPGeJyO3APcAca56OGaGUH8lrGqZNxJRS/uh0Soa3Q1CqXP206Rhnz2eWvKJSqty4K3keB/QBXjHGHBSRFsDnbipbKaWUUqqQxFRNJFTFVvBWqpveX+WdQJRSgPt6294BPO4wfRCY6o6ylVKeYbS7baWUn3nt113eDkEpjzpw6ry3Q1DqkuauK89KKaWUUh71TfQRb4eglMe9+utOjiameTsMpS5JmjwrpQAd51mpikxEaovIIhHZa/2tVcR6Y6x19orIGIf5y0Rkt4hssh71rPljReSUw/zxnnpPzuQNW6VURTbtjwP0m7pEj3elvMAtybOI3OLKPKWUUkp5xQRgsTGmNbDYms5HRGoDk4BeQE9gUoEk+05jTFfr4ThmzjcO86eX43soUctnf+XjFQeZvUl7JVYV31m9518pj3PXlednXJynlPJRF8Z51jPZSlVAo4CZ1vOZwA1O1hkGLDLGJBhjzgKLgOEeiq/Ucoq46jZlzg6e+HqTfpepCi/2TKq3Q1DqklOmDsNE5BrgWqCxiLztsKg6kF2WspVSSinlNvWNMccBjDHH85pdF9AYcLyJOM6al2eGiOQA3wMvmwvZ6U0iMgDYA/zVGOORG5Hv/zS62OWZObmEBAV6IhSlvOKm92w9b697djD1qod6ORqlLg1lvfJ8DIgG0oEYh8fP2M5gK6X8hH2cZ71Yo5RfEpHfRGSbk8coV4twMi/vG+FOY0xnoL/1uNua/wsQYYzpAvzGhavbBWN7QESiRST61KlTrr+pYizZdbLY5ZdPWUT7ifPJzsl1y+sp5at6vrqYL9ce9nYYSl0SynTl2RizWUS2AUONMU4rTKWUUkqVP2PMkKKWiUi8iDS0rjo3BJxlnnHAQIfpJsAyq+yj1t9zIvIltnuiPzXGnHFY/0PgH0XENg2YBhAVFeWRU3SpmTkApGXlUC1Q+0dV/qtG5WA6N67Bin2ni1zn2R+3Ur96CIPb1ycrJ5dgJ8d8YmomP248yti+EYg4O1emlCpJmWsTY0wOUEdEKrkhHqWUt5h8f5RSFcvPQF7v2WOA2U7WWQAMFZFaVkdhQ4EFIhIkIuEAIhIMXAdss6YbOmx/PbCznOK/aD9tPErEhLks3hnPzFWxbD+W5O2QVBks2hFPfHK6t8PwuA6Nqpe4zn0zo/l4xUFaPzePnzYW7jTv/77fwou/7GDjkcRiy9kSl8iWuOLXuVSs3HdaOyBU+bjrVOwhYKWITBSRJ/MebipbKaWUUmUzFbhaRPYCV1vTiEiUiEwHMMYkAC8B663HFGteCLYkeguwCTiK7SozwOMisl1ENgOPA2M995ZcM3H2dsCWWEz6eTsj3l5R5jKPJ6WRklFxunbJzTWc9/H3k52TS06u4f5Po7nx3VVuKTMxNZPIFxfaE8U1B86wYm/RV3fLgzGGfSfPFbsc4OlhbXnx+o4lljdlzg4A/vLNJvbGn6P7S4s4mZzO4p3xLNgeD0BmdvG3Mlz/zkquf2elq2/BbTKzc5nw/Ra3nhw5k5LB2gNnil3njYW72Xj4rNNld05fyxNfbyIlI5unvttMcnqW0/VmxcQRe/o8aw+c4UxKRqHli3bEEzFhLnFn/buTt4zsHPvzpbtOMuD1pfnmXQrclTwfA+ZY5VVzeCil/IR9nGe99KxUhWOMOWOMGWyMaW39TbDmRxtjxjus97ExppX1mGHNO2+M6W6M6WKM6WiMecJqdYYx5hlrXqQx5ipjzC7vvMPSiZgw16XeuLcfS2J9bEKh+X1eW8LI/5Y9CfeGr9YdJrrAe7r5/VV0nLSAXzYfc8trbDx8ls/WHHJLWXlaPTeP66x9fjQxzS1lvr14H0lpWdzwv5W89utORk9bw10frSU9K4fo2ARufX81y3YXf2/9hbL2Mvnn7aWO4buYOIa8+QfL9xbdF4AIBAcGMKZvRKnKvvrff3DmvK2p9n0zL3SwN3XeLo4kXEjisnJy2X2i6AS+JMYYTp4rOeFddzCh2LGpF++M5+v1R5g0eztJqVmkZ9mSsuNJafzfrC0u/c/GnU3ltx3x9ukRb6/gtmlrWLb7ZL7Ed97W48zedJTle0/x3yX7+NO7q+wnkNKzcvh63eF8rzdzVSyzYuJ4b9l+p6/71HebGfH2cm6btoab31/N6ZQMps7bxfiZ0aRn5TArxtaP4rajrrd8OZKQSsSEuWwoIrG/GCddPDGRmJrJrhPJ+ebtiT9H2+fnM+bjdQC88PM2DiekciLp0moJ4pbk2RjzojHmReBN4A2HaaWUUkopn/PWb3vJzM7N16FYamY287edYNGOeCJfXMiIt1dwy/urnW5/8PT5fNN74s9hjOHWD1YTMWEum48k8vueU0RMmMv+Uynl+l7yPPntJl6Yva3YdZ75YSs3v7+aGSsP8tPGo5zPyGbDYduV15I6YQNIOJ9JxIS59uQrOT2LY4lppGfl8NuOeI4kpPKnd1cx8SfncWTl5HLze6sK7b+iXuvDPw7Yr5TuPJ7sdL3DZ1J5+rvNREyYaz/ZkZaZw+Kd8YXWnbkq1v5j/+OVBwHINfDBHwfs67SbOJ+b31/NutgExs5Yz1KHBPpkcjozVh4kOyfXnghtO5rEm4v28Mmq2CLfS0Z2Dm8v3ktKRjbP/LCVP39mS6ryyjhw6sL+SMvMofPkBZw9n8mq/WdITL2Q9EU1r1Wo7JK8Ni//Oa1NRxLp//pSAE6nZNDhhfkMe+sP7vtkPX+ftblQ3C2fmcvKAvdbn0xO5xNr/32+5hA9X1nM3C3Hi4xh2e6T3PrBaj5aYdsmJSObiAlz85WxO96WwO85eY7IKQu5/p0VfLv+CH1eW8I30Ufs24LtM+80aQGxBY6jK/6xlPGfRrPmwBlOncvghJUsjp2xni6TFxIdm8APG+J46IsNPPH1Ju7+aJ19246TFjBnyzHaTZzPhB+28unqCyeA8hLp95btZ8mueH7aeJTNVvP3b9bbOms7b/WzcPD0eaJe/o33f9/PbzvjrUT8Qoy7T5wjMzuX/y3dx58/i+bFX/KfdDHGkJ6Vw3KrFcSN767Kd7Ijb//PWHlhf6Rn5fDkN5s4da7wVe88szcdpeeri52eEMyTmJpJ1Mu/0XXKIoa/tTzfsrxbXn7fYzvRE2jdN59r4LM1hwqd1MrKyeVYYhp9XlvMY19tZGtcxbhlRtwxDqKIdAI+A2pbs04D9xhjSn8Kzo2ioqJMdHTxQ1kopWxembuDD5cfZFy/CCaNLLlpmFKXIhGJMcZEeTsOf+auujliwlwABrSpy4Th7Vi6+yT/XLD7ost7fkR7Xp5b/C3b3ZrVZOa9Pek8eaF93vK/X8VHKw46TZyu69KQOVuO88CAltx3RQtqValEcnoW4WEhFxXjJysPcvD0eV4c1Ylb319NtdAgPhrbw748b588MKAl8cnpRDapycC2dblz+lqW/G0giWmZ9HltSZHl39y9Cf+6JZKfNh4lMEAYGdmI+dtO8OPGOPq3rsvzP22jS5MabLF+BMdOHUGf1xZzPCmdm7s3YVZMXL7yYqeO4GRyOqGVAtl/MoXwsBB70pa3PM+Lv2xnxspYGlQP5Y1bI+nXKpz7PlnP4iIS+tipI9h4+CwbDyfamyo7Cg8L4XRKBs3rVOHJq9vQtHYVHvwshpPFJBeuaFQjlGMlXGlr16Aau06c4/27unNF63A6TVpQpteE/Psq73Muq6k3dmbCD1uLXP7QwMvyXWnt0LA6O44n0zK8KgccktZB7erZT7ws+duVtKwblq+c/y3dxxsLd5N30Xna3d1pVqcKw99aTu2qlXh8UCsm/1L4Myyo72V1uLl7E5789kKC/+cBLXlo4GWkZ+WybPfJYt9PWVQPDSI5vfCtDa/+qTPP/lj8a7YIr0rremEs3FH4ZE6eprUrc0PXxmyJS+JcehYbDify9LC29u+0JrVsy2/r0ZSmtatw47sr2XA4kdpVK7Fh4tV8G32Ev8/awo2XN2b25mM0rBHKF+N70bxOVftrjPzvCrYeTeKJwa1ZvCued27vxpPfbuLeK1qwev8ZXry+I5+sis33PdirRW3WHkxg10vDmb/tBH/5ZhMA7RtWL/JkFsA9fZrnO/mQJ7JpTWY/0o+v1x1mwg9b+er+3vS5rE6x+88Vnqyb3ZU8rwKeM8YstaYHAq8aY/qWufAy0ORZKddp8qxUyTR5Ljt3J89XtqnLzHt75pvnq3q2qM26gwn86fLG3NK9CX1bhQNwPiObKpUCnfaAfCIpneBAoU5YiP39xU4dYX++66XhdJy0gPrVQkpM6sb0ac5MJz9oi/LDw31dvr84JCiAjBLupXWmRXhVxvdvwXM/5r9SPf8v/Rk3Yz3HL7EmoUVxTJ5H/W+l/aqnrxrXL4JhHRswetoab4dS4Tj+/wNc0Sq82J7Y37uzGx0aVefKfy7LNz8oQMh2aEb/vzu6MWXOduKTC59gckzk3albs5r88HC/Mpfjybq5TENVOaialzgDGGOWiUjV4jZQSvmWvPNoes+zUsqf9GtV9qsWnrLuoK255I8bj/LjxqNc2aauvQnk8yPas2z3KVbsO02dqpU4cz6T565tzyu/2q4CrX5mkL0cx+aPUS//Rk6uKTFxBkqVOAOl6pjrYhJnsDVxLZg4A4WajKoLfnyoL0t2naRypUDunL7W2+E4NWNlLDNWxno7jAqp4EnC4hJngIe+2OB0fnaB+88f+dL5ekC5JM6A/ZYRf+Ku5PmAiEzE1nQb4C7gYDHr+5Vz6Vn2piZBAULVEHftNqWUUkqVxf39W3o7hIuWlzgD+ZpKnjmfCWBPnIF8za1HvnOhs7KK1Ou3ck1AgDCkQ33A1vKg3cT5Xo5IqUuHu3rbvheoC/wA/Gg9H+emsr1u5H9XEPniQiJfXEinyQuK7RFRKX+lF5yVUv7IsalzzPNDuLNXMy9Go5RnhQYHMmlkB2+HodQlwy2XUI0xZ7GN71ghPXJVK5LTs0lKy+LtxXs5nqj33yillFK+pk5YCFNGdeKLtYe9HYpSHjOuXwtedKHDLaVU2bnlyrOItBGRaSKyUESW5D3cUbYvuCWqKfdd0YI7etrOZufoTaGqArpwz7Me30op/xUYcOFK9O09m9G0dmUvRqOUZ9zeU1tcKOUJ7rp59zvgfWA6kOOmMn1OgHWqIaeYAd6VUkopVf6+f6gvtatWcrrsp0f6cTghlesjG7H2wBlu0x5/VQX3yg2deG5Ee6KtsamVUuXDXclztjHmPTeV5bMC7IOBa/KsKh5j3fWsR7dSyh90b16ryGVdm9aka9OaANrJpyqVf90SyVPfbS55RR8TECCEhQQxsG09vn6gN1UqBTJ/2wnedRinWSlVdmVqti0itUWkNvCLiDwsIg3z5lnzK5TAvORZrzwrpZRSfqFT4xp8cHd3+3TPiIv/eRLZpIY7QlI+6vaezbi5exMAqoUGcU+f5l6O6OL0blmHLk1q8vSwtsx7oj+xU0cQO3UEwYGFxxEH+NPljYssa4yf7oOL0bxOFe5wQ4eDDw+8jADnu1oVcHdv/zu+yno6Ngbbhaq8Q+Rph2UG8N/xI5wIsP4TcjR3VhWQjvOslKqohnVsQOzUEZxJySAsNIjktGxqVgkmODCAzOxc2jw/z+l2E6/rwEtzLnTENPvRKziXnkW/qUtITvfvIaI+GhPFfTOjL2rbGWN7MO4TW9PgsX0j+GRVrBsjc4/Q4ADSs3IZ2qE+lzerxT/m7ypy3ZUTBtG45oV74zdPGkpwoFClUhBPD2vLW7/t5amhbdkcl8hoJ7cAfPvnPtz6wWr7dOzUEcCF8Xgb1Qi1j8NdPTSo0LHz4T1RtK1fjWZ1qtjnzYqJY1C7ekxffoCbrIS+tESE9g2r26ev69KIHzce5ZNxPexNu7dMHkr10GBeu7GzfcirKaM6UjcshGs6N+R4Ulq+8cFv7NaYF6/vyLHEdLJycok7m8aDn8dcVHz/vf1ytsQl8uHygwxpX4/pY3rY99nTw9q6dWzhIe3rM65fBP1ahdP3tcUcS0pHBA6+NoJftx6nW7NaNKgRCsCrf+oMwK3vr2ZdrG1s9t+evJIhb/7utOxJIzvQpUkNuje/cGLu8cGt7fvzrt7N+HxN4U4Mp4zqyAuzt+ebN6JzQ4Z1asDjX23k47FRNKpZ2afGPB/Tpzl39GrOsLf+sM+bdnd3Vu0/U+h7YNdLw/loxcEiP8eNE6+mVhG33viyMiXPxpgW7grEVSIyHPgPEAhMN8ZM9dRr53VColeelVJKKf9TJywEgLrVAu3zKgUF2BPAl27oxMSfttmXjesbkS95BqgWGszKCYNISssiPSs33w/qJ69uw5JdJwkOFBrVrMwdPZvRtVlNsnIMi3fG88TXm8ol1RbuAAAgAElEQVT5Hea3ceLVjP1kPZuPJBZaNrh9/UKJ3N5XriEzO5eOkxbkW7dl3ar0alGHr9Yd5ovxvejXKpzo54cAEB4Wwn1XtKD/60u5uXsTZsXEFRlP7aqVeOG6Dvzlm6L3ww1dG/HTpmPc0r0Ji3bGk5iaxd29mzNpZAdiz6TSql4YWTm5AAQHBvCP+bt4z2qaHDt1BCeS0lm44wSzNx0j5tBZ7h/Qkh4RtfnH/F2M6xfBbT2a8sJP24k9c5737upOx0bVCQ0OzBdDjcrB9ufVQoOZeJ1tKKjeLeuw66XhfLo6lrF9WzArJo5jiWn0bFGbdc8NZm98CgnWGN0Ae16+hgCBoMAAjDHsiU+hbYNqHDpznuS0bM6lZxFz6CxXW2M2O8q7Av734e2K3FelNfn6jrRtUI0r29Tl+shG1K0WQvVQ23sNDQ60J/2OGtaozK6XhhMUIOw/dZ6WdasSHBhA2wa27fJadvxj/i4OnDrvcixVKwUyMrIRV7QKZ1ZMHI8Nap1v+SNXteKBAbZrcOfSs3l5zg4mXteBmENnCQsN4ufNx3jkqlYcS0yjUmAA7y7bx4Lt8fbt5zx2BWEhQSzZdZIpc3bwxq2R9s/13ita8PLcnfzx9FUAXNu5odMYv32wjz2ZbxleNd+yeU/0JywkiLlbjzOmT4T9Alue0OBANr8wlLSsHBrUCOWLtYcxxta64at1tkT6nj4RvDB7O1UrBTJlVCcqVwq0x3J9ZCMAsq1j3dGypwaSkJrJje+uAmBwu3pENq3Jm4v28MhVl5GSns34/i35cPkBPnU48TGgTV2m3d2d4MAAFu2IZ0CbcGasjGV8/xaEBAWSlZNLUloWUS//RpVKgaRm5rD0qYE0qVWZ1s/ZTjC+MLIjgQHC7Ef68fxP29h6NIkr29ZlaMcG3NStCbM3HWX6ioP8dUgbQoMDeejKyxjaoT5Na1fhoxUHeWBASzYcOkuLulX9MnEGkLL0rCsiPYAjxpgT1vQ9wE3AIWCyMSbBLVFeeL1AYA9wNRAHrAduN8Y47Z8/KirKREdf3FlVZ1Izs+nwwgImXNOOB6+8zG3lKuULJv+8nU9WxXJX72a8fENnb4ejlE8SkRhjTJS34/Bn7q6b3W3zkURyjSEzO5deLesAcP+n0VzTqQE3dit8BXDV/tPUDQuhZpVK1KlaqdCPaEdtn59HRnbhH8Pu0rFRdWaM7cGzP25lVNfGjIxsRFZOLvd8tI7VB87QuXENXruxMxHhVQkLCeK1X3fywR8H+GJ8L44kpDLa6rE5L2HI4yypKmjj4bN0aFSdiT9t49toWwK9edJQNh9J5J6P1wGw++XhhAQFsu5gAmEhQbSsW5VB/1pmvyr7ybgeDGxbr1Tv+WRyOg9+HsO0e6IIt06OANz83iqiD53l2z/3oWeLCncnoU/KtpKvkOBA7p2xntdu6kzz2lX458LdpGfmMHP1IZ67tj25xjCoXT1a169WqIxjiWnk5Bqa1q7i5BWK9+vW45xOyaBxzcoMbl/4hMTF6PjCfM5n5hA7dQTZObl88McBmtauYk9uXXXjuyvZcDiR7x/qQ5VKQTSqWZkalYPZd/IcNatUynfsFnQkIZX+ry/lx4f7cnmzC3095P2flvT/aYxhfezZUv0fGGM4nZJJ3Wq2uD5ZeZAX5+zgwKvXIuJ7bdI9WTeXNXneAAwxxiSIyADga+AxoCvQ3hhzs3vCtL9eH2xJ+TBr+hkAY8xrztZ3dwWdnpVDu4nzeXpYWx65qpXbylXKF2jyrFTJNHkuO19PnstTRnYOJ5LSaVAjlD6vLcl3lbJl3aq8fEMnmtepSt2wEHtT8r2vXMOWuERuem81kU1rMq5vRL4rt2P6NLc3q3UlyXVkjCErx1ApKH8XONuOJjFny3HuvSKCSoEB1KxSuitEBX/Un0nJAC5c+XeUlpnD1qNJdGpcnSqV3Ne5290frWX53tN892AfepThPnflHlk5uSzbfYoh7ev5ZPJVlKS0LDKyc6hXLbRM5aw7mMBfv9nEwr8OcFsnhtuPJREWEkTzOlVLXrmC82TdXNZPL9Dh6vJtwDRjzPfA9yJSHm2TGgNHHKbjgF6OK4jIA8ADAM2auXfMu7xm2zm5RsfCVRVO3jFtjI71rComf/rBpiqmkKBA+w/dP/5+FXM2H2NEl4Ys3B7Pjd0aOz1GgwMDCMwbK9MYbri8MTtPJPPB7weY/Ug/IpvW5LedJ+3NfEtDRKgUVPg1OzWuQafG7usczVnSnKdypcByuTL8xq2RfL76EN2bFd0ru/Kc4MAAp83TfZ2tqXdwieuVpGeL2qycMKjsATno2Eg7MPSGMifPIhJkjMkGBmMlrW4q2xlnv3zy/co3xkwDpoHt7LY7XzxQBBF4c9Ee3ly0x51FK+Uzvlh7mC/WFu7YQil/NrZvBJOv7+jtMLzGGgHjGyACiAVuNcacdbLeGOB5a/JlY8xMa34l4B1gIJALPGeM+V5EQoBPge7AGeA2Y0xseb6XiiIsJMjeTNpZh1D/Gd2VzlYC27Z+NVqGV+XZa9sDMGF4O54e2pagQFtS7e4f5RVBvWqhPDm0rbfDUEpVMGVNcL8CfheR00AasBxARFoBSWUs25k4oKnDdBPgWDm8jlMBAcJbt3Xl4GnXO0RQyp8cT0ynYc2yNU1SyhdFWmP+XsImAIuNMVNFZII1/X+OK1gJ9iQgCtuJ6RgR+dlKsp8DThpj2ohIAJB3qfA+4KwxppWIjAb+ga0lmiqjUV0vDB9UuVIgS54aaJ8WEYKKGHbIF/zx9FXEnU31dhhKKeV2Ze1t+xURWQw0BBaaC209A7Dd++xu64HWItICOAqMBu4oh9cpkmNlppRSSvmJUdiuGgPMBJZRIHkGhgGL8m7HEpFFwHBsJ8rvBdoBGGNygdMO5U62ns8C3hERMXrvxyWtWZ0q+YZdUkqpiqLMTauNMYUGvDPGlEubZmNMtog8CizANlTVx8aY7SVsppRSSl3q6htjjgMYY46LiLMujZ31K9JYRPIu278kIgOB/cCjxph4x22sOjoJqMOF5Boo3/5IlFJKKU8pj/uSy5Ux5lfgV1fWjYmJOS0ih0pes0zCKfAjwU/4Y9z+GDNo3J7kjzGDxu1JZY25ubsCcTcR+Q1o4GTRc64W4WSewfZboQmw0hjzpIg8CfwLuLuYbfLPcOiPREROad1cJH+M2x9jBo3bk/wxZtC4Pclv6ma/S55LwxhTt7xfQ0Si/XHYEn+M2x9jBo3bk/wxZtC4PckfY3aVMWZIUctEJF5EGlpXnRsCJ52sFseFpt1gS5iXYesILBX40Zr/HbZ7nfO2aQrEiUgQUANIoBhaNxfNH+P2x5hB4/Ykf4wZNG5P8qeYA0peRSmllFJ+7mdgjPV8DDDbyToLgKEiUktEagFDgQXW/cu/cCGxHgzscFLuzcASvd9ZKaVURVWhrzwrpZRSCoCpwLcich9wGLgFQESigAeNMeONMQki8hK2zjkBpuR1Hoatc7HPROQt4BQwzpr/kTV/H7YrzqM983aUUkopz9PkueymeTuAi+SPcftjzKBxe5I/xgwatyf5Y8xlZow5g+2KccH50cB4h+mPgY+drHcIGOBkfjpWIu5j/PVz9se4/TFm0Lg9yR9jBo3bk/wmZtHWVUoppZRSSimlVPH0nmellFJKKaWUUqoEmjwXQ0RiRWSriGwSkWgny5+2lm0SkW0ikiMitV3Zthxjrikis0Rkl4jsFJE+BZaLiLwtIvtEZIuIdHNYNkZE9lqPMYVL92rcd1rxbhGRVSIS6bDMK/vaxbgHikiSw3HygsOy4SKy2/osJvhQzL54XLd1iGmTiCSLyF8KrONzx7aLcfvUse1izL54XLsSt88d26r0SvqsfPFzduF71+e+v1yM26e+v0oRty9+h2nd7Ftx+9Sx7WLMvnhcV7y62RijjyIeQCwQ7uK6I7H1Mlrqbd0c80xgvPW8ElCzwPJrgXnYxubsDay15tcGDlh/a1nPa/lQ3H3z4gGuyYvbm/vaxbgHAnOcbBcI7AdaWtttBjr4QswF1vWJ49rJvjsBNC8w3yePbRfi9slju4SYfe64diXuAuv43LGtD5c/X5c/K1/5nF2oK3zy+8uFuH3y+8uFuH3uO6ykmAus6xPHtZN9p3Wzd2P2uePalbgLrONzx3bBh155dp/bga+8GYCIVMfWoctHAMaYTGNMYoHVRgGfGps1QE2xjfk5DFhkjEkwxpwFFgHDfSVuY8wqKy6ANdjGH/UqF/d3UXoC+4wxB4wxmcDX2D6bcnURMXv9uHZiMLDf2DowcuRzx3YBTuP2xWPbQVH7uiheOa6dcCVuXzy2lft5/XPWutmztG72Gq2bPUfrZi/S5Ll4BlgoIjEi8kBRK4lIFWz/8N+Xdls3a4ltCJEZIrJRRKaLSNUC6zQGjjhMx1nziprvCa7E7eg+bGcx83hjX4PrcfcRkc0iMk9EOlrzvLW/Xd7XPnRcFzQa51+svnhsOyoqbke+cmznKS5mXzquCyp2X/vwsa1co3WzZ2jdrHVzaWjd7DlaN3uRJs/F62eM6YatucYjIlJomA7LSGCluTAeZmm2dacgoBvwnjHmcuA8UPC+BnGynSlmvie4EjcAInIVti+x/3OY7Y19Da7FvQFb85RI4L/AT9Z8b+1vl/c1vnNc24lIJeB64Dtni53M8/axDZQYd946vnRslxSzrx3Xdq7sa3zw2FalonWzZ2jdbKN1cwm0bta6uSQVqW7W5LkYxphj1t+TwI/Ymj04U+hMSim2dac4IM4Ys9aanoXty7jgOk0dppsAx4qZ7wmuxI2IdAGmA6OMbcxSwGv7GlyI2xiTbIxJsZ7/CgSLSDje298u7WuLrxzXjq4BNhhj4p0s88VjO09xcfvisQ3FxOyDx7WjYve1xRePbeUirZu1bi6B1s1aN7tK62bPqTB1c4Ue5zk8PNxERER4OwyllFIVRExMzGljTF1vx+HPtG5WSinlTp6sm4M88SLeEhERQXS0b/RqrpRSyv+JiKsdtKgiaN2slFLKnTxZN2uzbaUUALm5hv2nUsjNrbitUZRSSil/k5SWRY7WzUr5BE2elVIAfLX+MIPf+J1PV8d6OxSllFJKAWmZOUS+uJCX5uzwdihKKdyUPIvNXSLygjXdTES8fkO3Usp1CSmZAJy2/iqllFLKu1IzswGYvemolyNRSoH7rjy/C/TBNrA1wDngf24qWymllFJKKaWU8ip3dRjWyxjTTeT/2bvv8Kiq9IHj3zeN0HsvhipNQYggIqyINHGVtezq2lfWXdeya9nfghXbyupa14rYe1cUkI4I0kIJEGqAAAmdVEJ63t8fc5NMwgRCcpOZhPfzPPeZuWfuPfedm5ncOeWeI2sAVDXJmc/LGGOMMcYYUw4FdzqL+Jqq1xhT1dxqec4RkWCc77iINAfyXcrbGGOMMcaY086GhBQAEtPtlipjAoFbheeX8Uxc3UJEngIWA/92KW9jjDHGGGNOOzsPp/s7BGOMF1e6bavqxyKyChgOCDBOVTe5kbcxxhhjjDGno1ohwf4OwRjjpcKFZxEJAtapam9gc8VDMsYYY4wxxhxMy/R3CMYYLxXutq2q+UC0iHRwIR5jjDHGGGMMsGzHEX+HYIzx4tZo262BGBFZARTenKGql7mUvzHGGGOMMaeV3Dw9+UbGmCrjVuH5MZfyMcYYY4wxxgA5eTZ5jTGBxK0Bw352Ix9jjDHGGGOMx5X92xEdn0Kfdg39HYoxBpemqhKR80RkpYgcFZFsEckTkVQ38jbGGGOMMeZ01KRuGABtG9f2cyTGGHBvnudXgGuBbUBtYLyTZowxxhg/E5EmIjJHRLY5j41L2e4mZ5ttInKTV/pCEdkiImudpYWTXktEPheRWBFZLiIRVfOOjDk95Du3PAeJ+DcQYwzgXuEZVY0FglU1T1XfBS50K29jjDHGVMgEYJ6qdgXmOevFiEgT4FFgIDAAeLREIfs6Ve3rLAedtFuBJFXtArwA/Kcy34Qxp5t8p/QcHGSFZ2MCgVuF52MiEgasFZFnROQeoO6JdhCR9iKyQEQ2iUiMiPzdSfdZOy4eLzu12+tEpJ9LsRtjjDE13eXA+87z94FxPrYZBcxR1URVTQLmAKNPId+vgOEi1kRmjFtyCwrP9rUyJiC4VXi+AQgG7sQzVVV74MqT7JML3KeqPYDzgDtEpCel146PAbo6y23A6y7FbowBbDIMY2q0lqq6D8B5bOFjm7bAHq/1eCetwLtOl+2HvQrIhfuoai6QAjQtmbGI3CYiUSISdejQoYq/G2NOE8nHsgEIspZnYwKCW6Nt73KeZlDGaauci3fBhTxNRDbhuQhfTlGX7/eBhcC/nPQPVFWBZSLSSERaF/wYMMYYY05nIjIXaOXjpQfLmoWPtIJ6tetUNUFE6gNf46k0/+Ak+xQlqE4BpgBERkZaXZ0xZZSWmQv4/qIZY6qeK4VnEdmJ74tlpzLuHwGcAyynRO14waAklF4jXqzwLCK34WmZpkOHDqfyNow5ran9nDWmWlPVi0t7TUQOFFQ4i0hr4KCPzeIpPl5JOzwV2KhqgvOYJiKf4Lkn+gNnn/ZAvIiEAA2BxIq/G2MMwEvztgEQFuLaMEXGmApw65sYCZzrLEOAl4GPyrKjiNTDU4v9D1U90fRWZa7dVtVIVY1s3rx5WUIwxhhjarppQMHo2TcB3/vYZhYwUkQaO+ONjARmiUiIiDQDEJFQ4FJgg498rwLmOz3EjDEuys2zr5UxgcCVwrOqHvFaElT1ReCik+3nXIS/Bj5W1W+c5ANOrTglascLarcLtAP2uhG/MQbU7no2piabDIwQkW3ACGcdEYkUkakAqpoIPAGsdJbHnbRaeArR64C1QALwlpPv20BTEYkF7sXHKN7GmIrLyc/3dwjGGNzrtu098nUQnpbo+ifZR/BcdDep6vNeLxXUYk+meO34NOBOEfkMzzQaKXa/szHGGHNyqnoEGO4jPQoY77X+DvBOiW3Sgf6l5JsJXO1qsMaY44TYgGHGBARXCs/Ac17Pc4E44Pcn2WcwngFH1ovIWiftATyF5i9E5FZgN0UX5RnAJUAscAy4xZXIjTGA3fNsjDHGBKrgILvn2ZhA4NZo28PKsc9iSh880FftuAJ3nOpxjDHGGGOMqc4a1HarvcsYUxFuddu+90Svl+iWbYwxxhhjjCmjUGt5NiYguFWNVTDa9jRn/bfAIopPLWWMCWBa+Gj9t40xxphAInbLszEBwa3CczOgn6qmAYjIJOBLVR1/wr2MMcYYY4wxxphqwK0+IB2AbK/1bCDCpbyNMVXBGTHMBg4zxlQnB1Iz+WmDTb5harZftx/xdwjGGNxref4QWCEi3+Lp/fk74H2X8jbGGGOM8enaKcvYcTidbU+NITTY7gs1NdOqXUn+DsEYg3ujbT8lIjOBIU7SLaq6xo28jTFVQ0s8GmNMdbDjcDoAh49m0bphbT9HY4wxpiZzpYpWRDoDMar6EhANDBGRRm7kbYwxxhhzMje8vcLfIRhjjKnh3Orf9DWQJyJdgKlAR+ATl/I2xlSBgnud7Z5nY0x1FHvwqL9DMKZSZebk+TsEY057bhWe81U1F7gCeElV7wFau5S3McYYY4wxp7W4I+n+DsGY055bheccEbkWuBH40UkLdSlvY0wVKJjf2eZ5NsZUF9YSZ04nL87Z5u8QjDntuVV4vgUYBDylqjtFpCPwkUt5G2OMMcYc57s1CcXWP1m+20+RGFP5forZz5/eW+nvMIw5rblSeFbVjap6t6p+6qzvVNXJbuRtjKkaasNtG2OqmQnfrC+2/sC366012tRo8zcf9HcIxpzWbEJEY4wxxtQY+TbqoTHGmEpihWdjDGDzPBtjqpcNCSk+09OzrOXZ1GzZufn+DsGY05Zb8zxfXZY0Y4wxxlQ9EWkiInNEZJvz2LiU7W5yttkmIjd5pS8UkS0istZZWjjpN4vIIa/08VX1nj5Z4fv+5hvfWVFqwdqYmqDbQzP9HYIxpy23Wp4nljHNGBOgiuZ5trZnY2qgCcA8Ve0KzHPWixGRJsCjwEBgAPBoiUL2dara11m8b7z83Ct9aiW+h2JKGxxs075ULv3f4qoKwxi/iJgwnam/7PB3GMacdipUeBaRMSLyP6CtiLzstbwH5LoSoTHGGGMq6nLgfef5+8A4H9uMAuaoaqKqJgFzgNFVFJ/rtuxP83cIxlSqyTM3+zsEY047FW153gtEAZnAKq9lGp6LsDGmmiic59kano2piVqq6j4A57GFj23aAnu81uOdtALvOl2zHxYR8Uq/UkTWichXItLe18FF5DYRiRKRqEOHDlXwrZTNC3O2ErPXum+bmis3v+iCvWZ3Eu//Gue/YIw5TYRUZGdVjRaRDcBIVX3/pDsYY4wxplKIyFyglY+XHixrFj7SCn6dX6eqCSJSH/gauAH4APgB+FRVs0Tkr3hatS86LhPVKcAUgMjIyCqpovspZj8/xexn59OXULysb0zNsXDLQWas38cXUfEA3HR+hH8DMqaGq/A9z6qaBzQVkTAX4jHG+IsWezDGVDOqerGq9vaxfA8cEJHWAM6jr8li4wHvluN2eHqYoaoJzmMa8Amee6JR1SOqmuVs/xbQvzLeW0V0nDiDid+s59MVu0k5luPvcIwpl6k3RvpMv/ndlYUFZ2NM5XNrwLBdwBKnK9e9BYtLeRtjjDGmYqYBBaNn3wR872ObWcBIEWnsDBQ2EpglIiEi0gxAREKBS4ENznprr/0vAzZVUvwV8umK3Uz8Zj2D/zMfgG0H0jiQmunnqIwpuy4t6pVpu/0p9rk2pjK5VXjeC/zo5FffazHGVBOF8zxb07MxNdFkYISIbANGOOuISKSITAVQ1UTgCWClszzupNXCU4heB6wFEvC0MgPcLSIxIhIN3A3cXHVvqUif9o3KtN3RrFwiJkxnxAuLGPjveeTm5ZOXr8yK2V9tZhpQVXLyjp/n95X52/jjW8vKnW9mTh6vLoj1mXdl+jJqD1+tqj4tp3n5Sl5+8c+KqhJ78KjP7fPzPX+vhOQMIiZMZ/q6feU6bkSzuix/YPhJtzvv6Xm8uiC2WFrysWx+jT1cruMad5T2va1Mm/ensuOQ789leZzoc346caXwrKqPqepjwPPAc17rxhhjjPEzp3v1cFXt6jwmOulRqjrea7t3VLWLs7zrpKWran9VPVtVe6nq351btlDViU5aH1Udpqp+Gf53/AUdy7Vflwdn8r/52/jLh6uYFr33hNvO2XiA//squkz5Hj6axZyNB8q07Zb9aSQfy/Y5N/XyHUe44+PVhQX7ZTuOcPdna+n64EwysvOKbfvf2Vv5dfuRMh3TlymLdvDsrC18vGxXufMASMnIOWEB8WBaZrGB3P751Tru/9JzXo9l57J5fyqJ6dk8N3sLqZk5vLtkJ+NeXcLM9b7zzMtXUjOLuuPP3Xig2A/8VbsSmfD1Ou74eDXZub4LL9+vTSA+6RjnPD6bPYnHir326YrdrItPLlwf+O+59H9yTrFtXlu4nYuf/5kVOxOPy/v2j1fR9cGZxDh/389WFk2xlpevrItPJjcvn6NZRZPUqCqvzN/G3uSMYnm1bBDuM/6Snp21hRvfWcG+FM/+f3pvJX+cuvy4z4y/HcvO5fu1CcelZ2TnkZmTx8G0TOKTjvnY0/9O9RaQ52Zv9fm9rUyjX/yFi5772bX8pv6yk4uf/7nY9+F0VKEBwwqISG/gQ6CJs34YuFFVY9zI3xhT+Qp+nKnd9WyMqUb+e3Ufxp7Vmrs+XVOu/V+cuw2AuMPH+HDZLq7s15bMnHya1C0+lMufP4gC4Jmr+hSm/RC9l8XbDvOfq84utu31U5ezeX8am58YTXhocLHXcvLy+d1rS/hNt+b0aN2AOz8pivvLvw7i3Igmhet/mOJpSZ6cdRZ1w0K4ZkpRy3JqZg61w4rnXVaHj2bxZVQ8t1/YuSi/DE9hIPsUWse2HUhjxAuL+GT8QM7v0gyA+76IZu6mA/Ro/Rs6Nfd0NV61K4luLetRPzyUAU/NA+D16/oVOzcpx3K48Z3lRMen0K5xbeKTMvjf/KIW1Ns/Xk3c5LGkZuagCg1rhwLw6LQNfLRsN1ufHENufj7jnb9T3OSxAFz5+tLCPJKOZfPfq/vw2A8x9OvQmBYNavH5yj0s21FU6B3yzALG9W3DwbQsLj27DQ98u75YfoePZgOeSoCGtUPZk5jBs7O2AJ6WvgEdm3D4aBY7DqXz2A8xxOxNBTwFD4Bfth3m85W7+cO5HXhlfiwvzN1aeOzv7hjMc7O38Ms2Tyvxf2cXvXaqFm09xKCn5/PAJd3Z7Ezblq/K4m2HOb9zU4KCigbRW7r9CP3PaEx6Vi5XvP4rj13Wiwu6NCu2TYGX521jZK+WtKwfTlhIEHVrlb8o8cj3MXy1Kp72TerQr0NjsnPz+Xj5Lh77YWOx7f40uCMPju1BsI94/OHX2MP8cepy3r3lXIad6WviAthx6Chfrorn/0adyerdSUxZ5JmT+1h2LrXDgtm0L5WYvakM6tyUto1qAxAVl8jaPcncMOgMUjJyaFG/bJUlFTF93T4+WBrH538Z5PP1bQfSqBceQuuGtVmzJwmAPYkZnN2ubL19aiJXCs94RtC8V1UXAIjIhXi6dJ3vUv7GGGOMMccZe1ZrgoKETY+PJiE5g4ufL19LS0Eh5uHvNhSmPTS2B6N6tSrWArg+PoWz2jUEKCyw//XCzizbcYTzOjWlSd0w4o6kA0W3wURMmM7Ini2ZcmMkK3YmsiEhlQ0JqcfFcPUbnoLeqF4tuf3CLoXpv3vtV/p3aFxs24H/nsedw7rQvH4t1u4paglavO0w3VrWY2VcEvtSMhg/pBNHs3Lp/egsLu/bhu/XFrWwZ+bkkZmbx8QxPQqnPcrJU+ZsPMCIni0BTz/FPtgAACAASURBVEtyelYubZwf+Jk5eaRk5DBt7V6emuG5xf2h7zfw6G97sTvxGPM2e1rcJ36znuE9WjCub1uufP1XAC5wCtjgKQx76/P47MLn8UnFW1wLREyYXvj8gz8NYEDHJny0zNOS2+2hmcW2ffyHjcf1Jvh1+xHOn+y5731WTOk9A75zzpF3S773sYHCSgBvj3wfw3tL4thxOP2411bEFRXQ//X1ev719frjthn36pJSYypw8/kRvHcKU1L9e0ZRZ5Bej84C4Ipz2tKnfSNuOj+C6ev2cccnqxnZsyUDOjZh5+F0bnxnBQCbnxjNfV9E06hOKB8vL2oxf35OUaF+59OXAJCVm1+sMiQ3L5/zJ8/nwbE9iE/KYHCXZvRq04C8fOWXbYcZ0bMli51Kgns/X0vckdJbmN9ZspNm9cP40+CO5OUrienZtG9SB/BU/Gfn5VMrJJjMnLzjKqu8zd14gE37UrlreNfjXsvPV95YtJ3rBp5Bw9qhPD9nK/3PaMzgzk3JU2X+poOM7t0KEWH1bk8hMioukWFntiA9K5fE9GxaNKjF3uRMOjarW9jie1H3FoXfa4CHvtvAyrjEwkoYgMFdmvLYZb24ytnupbnbSMvKLZwp4PWF2zmjaR0u7tESEc+0CCHBx3cezszJY1+K5/gl07cfOsqURTt4+NKe3P9lNAu3HCJu8lju+MTzPbzh7eW0bVSbo1m5PH3FWdQP91ROjXhhEeCpPNLCgWWPb2Q5cjSLp2duZsKY7qyPT2FY9+MrFdbHp/DbVxYXrsc+Ncbn+wh04sY9PiISrap9TpZW1SIjIzUqKsqfIRhTbTz540amLt7JzedHMOmyXv4Ox5iAJCKrVNX3sLemTNy6NhcUZgpaBEumV7aXrz2Hu0/S2t3/jMY8cmlPLncKRV/fPqhYS2hlCQ0WcvLK/vtu4+OjePT7GL48wb3H91zcrVgraVnVqxVSrEuyKR/vz7mq0nHijArnOfmKs5jwzfGF+FN1y+AI3l0Sx2vX9eP9X+N45Y/92H7oaLGeEgUGRDRhRVwiEU3rnLDAXBqR4mOz3DmsC68siOWJy3vx8PcxLLj/wuMKjz9t2Een5vUY6VUQXLDlILe8u5IrzmnLom2HuG7gGbw0bxvN69diVK+WhZUy3mqFBPHG9f1ZGZfIawu3c8ewzlzRrx3DnYJyvw6NWL07mWeuPJv/+3rdKb+3kto3qU3vNg2ZuWH/ca/NvmcoX6+O59YLOtK8Xi32pWRy3xfRLN1xhJjHRlG3VshJ/xeueHC4z0ogEVg+cThPz9zMt2s83erjJo/l+qnLWezcOx83eSzRe5J5ZtZmxvRuzUNelY5Q9Hf5/o7BRDStS1pWDvd8vpaVcUmF2zw5rjfXn3fGKZ8XX6ry2uxW4flbYDWertsA1wORqjquwplXgBWejSk7Kzwbc3JWeK44NwvPPVs3YMbfhxRLP+fx2STZlFSmhilZSZSelVvYkmyKe+WP53Bmy/rMWL/fZ4XPG9f357nZW9hWQwa/alavFoePZp18wwqoXyuEtEqoBDunQyO+/dvgCudTlddmt7pt/wl4DPgGT2+CRcAtLuXtd5e9spjdzgASYcFBvHlDf84p0X3KmOrO7nQ2xlQn7ZvU5sxWx0/s0bd9IxZsOeSHiIypOnVrhTCmdyufrZKnO+9xBHz560erqiiSqlHZBWegUgrOAGt2V7/Bx9wabTtJVe9W1X6qeo4zEmfSyfesHoZ3b8nlfdowvHtLDqZlse1AzaipMsYYY6ozX8MHXTugQ5XHYYw/vH59f8JDq989o8ZUZ26Ntt0NuB+I8M5TVS9yI39/+/vFnoEF9qdk8vXqePKqyVyQxpyKwoEg7PNtjKkGSvtXNbJXK+Imj2XoMwsKe40ZU1PFPDaao5m5xQZcM8ZUHre6bX8JvAFMBQJrEjkXBTmVe3n5Vrgwxhhj/O4EM9dMv/sC0jJzadUgnE4PVHxwJWMCUXCQ0LBOqL/DMOa04VZfj1xVfV1VV6jqqoLFpbwDRpB4rtL51jJnaqCCqQfs022MqQ5OdimuHx5Km0a1CQoSvvxr0RymL13Tt5IjM6bqbX1yDOdG2Hg8xlS2ChWeRaSJiDQBfhCRv4lI64I0J71GCXYKz9bybIwxxvhXrdAgwso4R+i5EZ6fJO2b1Obyvm2JfWpMZYZmTJULCwliZM9WAPxw5wV+jsaYmquiLc+rgCjgJuCfwK9OWkG660RktIhsEZFYEZlQGccoTVCQFZ5NzVV0z7N/4zDGmLKYf9+FTL7y7DJvv2zicGbc7ZnWKiQ4iM9uO6+yQqs0i/45zN8h+DSmd6ty7Rf9yMjC5+Mv6OhWOAB88ZdBzPrH0HLv/9+r+5T62i2DI5h+d9UVUJvWDSvTduOHdGT+fb/hrHYNC9OWTRxeWWEZU2GDuzT1dwinrEL3PKuqu//pTkJEgoFXgRFAPLBSRKap6saqOH6wU3i2woUxxhhTvbRqGF5s/bxOTbn5/Ai+WR3PukmjADiYlsmAp+ZVaVxLJ17EoKfnF65/ffsgurasT3xiBpe8/AsAvdo04IU/9KVD0zoM7tKUJbFHypz/TYPOoF3jOjw1Y5Mr8S7+1zAu+M+CwvWz2jbk9ev7A565t31p0zCckOCgYgO4ffnXQYX36jarV4uHLu3JfSPPZNP+VPp5TQeqqnScOIPureqzeX8aAH3aN+K8Tk148+cdAIVTNl3dvx1frornuav7MKCjp7fB7Rd25sOluzhaYqqd+0d2Y19KJkO7NWdQ56acPan4gFtDujYrth4WHMTWp8awdPsRBnZsQlCQ0LttAzYkpLLp8dHUDgsu9f2veXgEdWoFs2Z3Mgu3HOKNn7fzl6Gd+Hp1QrFphl6/rh+dmtejab0wVu5M5PaPV9O4TiirHh7hM9+SRIROzesBnsqD1Iyc4z73b1zfj79+tPqE+ZzVtiEvX3sOKRk59G3fqNj7umNYZzJz8nl78c4yxWRO7sFLerj2/axuJl9R9grQQFGhwrOInAvsUdX9zvqNwJXALmCSqiZWPMRiBgCxqrrDOd5nwOVA1RSeC7ptW+nZ1GBqdz0bY04Tky7rxaTLehWut6gfzs6nL+GHdfsY1aslZz70U6n7/vJ/wxjyTFEhslGdUP48pBNDujbjsleWnPC43gWY1g1rs/PpS+g40TOoWf8zPIW+nm1CmXzFWfRp34gerRsU7vv0785m6LML+OTPAzm/czO+WLmHJ6ZvJC3TUzi8+6IuBAUJL87dxnNX9+HK/u0AuOn8CJ6cvpERPVvStG4tFGXsy4sBuG1oJ1bGJRabc/WyPm2YFr0X8BR+Z/5jKMt3HKFd4zo8c+XZrN6dxMQxPY4brKpPu4ZEx6cUS/t14nDy85X7vozm+vPOoP8ZRYXjj24dSNeWngJf7bDgYgVn8BQINz4+ilohwYWNGABHs3J58+cdfHfHYM5u25CMnDzq1grhkd/2pH54UUz/Gt2df43uDsCx7Fxy85UgEerVKv4T+MlxvXlu9hY++fN5rN2TTMsG4cz6x1D2pmTQIDyEjs08MQ7qXNRS9v4tA1gXn0LtsGAA5t33G1o1CGfStBiujmzPvE0HOKdDIxo7LcfndWpKvw6N6dG6Ppf1acOtF3Tkq9XxbNybyo/r9jGqV6vCXo6je7fi5WvPYVSvlpRHQeUBQOM6oSQdy2HG3UPo2abBcdsO7dacARGNuaxPW56asZEHLunBGU3rFr6+6J/D2J+aWSzPurVCeHneNkb2bMnsjQfKFFOD8BBSnc/p5idG0/1hz/erVkgQN58fwZuLdhTb/p6LuzG8RwtW7Ezk8R89P/VLzm3dqkE4+1Mzy3T8l67py9YDaby6YHuZtn/88l488n0MAA9c0p1/z9h8wu3jJo8lJy+frg/OBOB/157DXZ965pzu2KwuOw+nE/3ISOKOpLPlQBotG4Tz0tyt/HloJ+KOpPPx8t3F8vtk/ED+OHV54fozV57N/329rkyx+4P3/4zSjL+gI3de1IUf1u2jd5sGtG9Sp4qic49UZFoaEVkNXKyqiSIyFPgMuAvoC/RQ1avcCbPweFcBo1V1vLN+AzBQVe/0tX1kZKRGRbnXezwzJ4/uD//EP0edyR3DuriWrzGBYNK0GN77NY7rz+vAk+PO8nc4xgQkEVmlqpH+jqM6c/vaXJl+iN5b+ON3YMcm3D/qTK5+Yyng+aFc0CK3dOJFtG5Yu3C/2INHWRJ7mL0pGYzr25bx70fRtlFtHh/Xi7phIbRvUoeDaZnUCQspLMTd8fFqurSoxz0jupUr1r99vIp5mw6y5ckxqCq7E48VKwD5UhB/3OSxACzbcYR8VQZ1akpaVi43vbOC/17dh85Oa+bJHEjNpH54CD0fmQV4KhiOZuUWK/ybqrd5fyrfrk5gwpjuiEjh333n05cAngqK8sjPV0QgPTuPz1bs5sd1+1i7p6gC5rs7BtOqQTjD/ruQd285l/M6NWVlXCJdW9SjUZ0wFm09RN1aIfTr0AgR4bqpy1gSe4Tv7xhM64bhtGhQ1Gp+NCuXdXuSOb+Lp0fAgdRMEtOz6dG6AXsSjxWryCrw410X0LhuGG2c1veC95mQnEGj2qGsiEvkgW/Wsy8lk9UPj+DPH0Rx29BOjOpVdBvCsexc0jJzadkgnJRjOUxfv4+N+1L4aJmnoNuyQS0OpHp6DxR8j7xt3JvK99EJTBjd/YTnWVX53/xYBnVuSn+nAikoSNhx6Ci/f3MZP9w1mNYNazNpWgyzY/YTFhJEw9qh3Da0M+d0aESbRrWZuX4ft3+8mk/+PJB2jeow9NnjzwnAE+N6M7pXK859au5xr214bBR7kzMY+cKiwvd04bMLiDtS1Gtk2cThtGxQi+j4FJLSs8lXJSE5gxsHRZCWmcOGhFQa1w2lTmgIHZrWIT9fWZeQQm5ePpERlTMkVlVemytaeI5W1T7O81eBQ6o6yVlfq6quDmkpIlcDo0oUngeo6l1e29wG3AbQoUOH/rt27XLt+AW1Sb+PbMcV/dq5lq8xgeDdJTuZFXOAi3u0ZPyQKr0jw5hK16pBOBHNTlyQKAsrPFdcdSo8w/EFTO/13UeOEXsojYu6l6910N92HUmnVkjwcV17K+qrVfG0bhjO4C7NTr6xqXIDnprLwbQsn4W9iohPOsbnK/dwRb921AkLpmWDU/tcZeXmkZmdX6Gpt7YdSKNpvVo0KeN94uWxZX8ao15cRNcW9Zhz72+Ysmg7R9KzmTimR6Uds6wOpWXRvH4tAEa/uKjwVoeJY7ozuncrdhxKZ1j3FgCkZ+WiwKZ9qcUqBcHzf65h7VCiHx3JkaNZTFm0g/FDOrF0xxEu69Om6t/YSVSnwvMGoK+q5orIZuA2VV1U8Jqq9nYpzoLjDcLTHXyUsz4RQFWf9rW92xfo/Hyl96RZHMuusVNZG2NMjXTz+RHFuueWlxWeK646Fp4bhIcU3he9ZncSjeuEuVIZY4w/pGTkkJaZQ7vG1a/LbCDIy1fu/zKav/ymE91bBW6vity8fPJUqRUSfNJtE5IzSEjKKOyan5CcQb2wkGozh3hVXpsrdM8z8Cnws4gcBjKAXwBEpAuQcqIdy2kl0FVEOgIJwDXAHyvhOD4FBQnT7x7CvpSMqjqkMVUqJCiI3Px8f4dhjOtanWILiDEFVj88gtDgou6W53SwuXRN9dawdigNa1ePQlEgCg4SXvhD4M8XHxIcVOaCXttGtWnbqHaxdeNbRUfbfkpE5gGtgdla1IwdhOfeZ1c5Ldx3ArOAYOAdVY1x+zgn0rFZXTpabbMxxhhzWqjM7p/GGGOql4q2PKOqy3ykba1ovic43gxgRmXlb4wxxhhjjDHGlFShe54DnYgcwjNtVqBpBhz2dxCnqDrGDBZ3VaqOMYPFXZWqY8xQPO4zVLW5P4Op7uza7KrqGDNY3FWpOsYMFndVqo4xg5+uzTW68ByoRCSqug04Ux1jBou7KlXHmMHirkrVMWaovnGbU1Md/87VMWawuKtSdYwZLO6qVB1jBv/FHVTVBzTGGGOMMcYYY6obKzwbY4wxxhhjjDEnYYVn/5ji7wDKoTrGDBZ3VaqOMYPFXZWqY8xQfeM2p6Y6/p2rY8xgcVel6hgzWNxVqTrGDH6K2+55NsYYY4wxxhhjTsJano0xxhhjjDHGmJNRVVt8LMA7wEFgg1faE8A6YC0wG2jjpF/ulR4FXOC1Twdn203ARiDCSR8OrHb2WQx0KSWOiUAssAUY5ZU+2kmLBSYEUtzACGAVsN55vMjrtYVO3GudpUWAxBwBZHjF9YbXa/2d9xILvExRj41AiPs6r5jXAvlA39LOdRXFfZET9wbgfSCklM/2TcA2Z7npROc7EGIG+gJLgRgn/z94vfYesNPrXPcNsHOd5xXbNK/0jsBy52/wORAWKHEDwyj+2c4ExpV2vt2I+STH9HmuKvo/25ZTW6rgs2nXZrs2uxG3XZvt2mzX5hp6bfb7hTBQF2Ao0K/EH7uB1/O7cf6JA/Uo+qd9NrDZa7uFwAiv7eo4z7cCPZznfwPe8xFDTyAaqOV8OLYDwc6yHegEhDnb9AyguM/x+iL0BhJK5BsZgOc6wvv4JV5bAQzCc6GYCYwJlLhLxHkWsONE57qy48bTm2UP0M1Jfxy41UcMTYAdzmNj53nj0s53gMTcDejqPG8D7AMaOevvAVcF4rl2XjtaSvoXwDXO8zeA2wMp7hKfl0SKvhvHnW+3Yj7BMX2eqxL7nPL/bFtObanMz6bz3K7NgXWuI7Brc4Xjxq7NAXeundfs2lwNr83WbbsUqroIzx/HOy3Va7UuoE76UXX+Ot7pItITT63NHK/tjhVkBzRwnjcE9voI43LgM1XNUtWdeGpFBjhLrKruUNVs4DNn24CIW1XXqGpBegwQLiK1fLy/gu39HnNpRKQ1ni/5Uue4HwDjAjTua4FPT/aeKjnupkCWqm519pkDXOkjjFHAHFVNVNUkZ7vRpZ3vQIhZVbeq6jbn+V48NanNfbw37338HndpRETw1DJ/5SS9T9V8tssT91XATK/vxnHciLm0Y57oXJVwyv+zzakJkP+7dm2uophLY9dmuzZ7Hc+uzf6L+7S8NoecbANTnIg8BdwIpODpRlCQ/jvgaTxdncY6yd2AZBH5Bk9Nx1w8XQLygPHADBHJAFKB83wcri2wzGs93kkDT+2Qd/rAAIrb25XAGlXN8kp7V0TygK+BJ72+KP6OuaOIrHG2eUhVf8FzvuO9tvH+G/jkx3P9B47/0pfpXLsVN3AYCBWRSFWNwvNPrr2Pw7Xl+M9wW07xfFdxzN7HHYCnlnK7V/JTIvIIMA/P3zDL587+iTtcRKKAXGCyqn6H50KZrKq5zjZV8tk+xbgLXAM8XyKtTOf7FGMu7ZhlPVeu/c82p8auzXZtxq7NpcaNXZvt2mzXZveuzXqSpunTeeHE3YUmAo/5SB8KzHWeX+V8KDrhqaj4GqcLBPANMNB5/k9gqo+8XgWu91p/G88F72rv7YEbgP8FStxeefbC8w+ss1daW+exPp77HG4MhJjxdOVo6jzvj+fL1AA4t+AYzmtDgB8C8FwPBNaXSPN5rqsg7kHAL3i6eD2J5wdaybz+iedHUMH6w8B9Jzrf/o7ZK8/WeO6POa9Emjifo/eBRwLlXDvbFXTV7ATEAZ3x1MzHem3T3vszFAhxe53bQ0Doyc53RWMu7ZgnO1de6eX6n23LqS2V/Nm0a7Ndm90813ZttmuzXZtr2LXZum2X3yf47j6yCOgsIs3w1GCsUU93gFzgO6CfiDQH+qjqcme3z4HzfRwjnuK1Pu3wdA8qLT1Q4kZE2gHf4rkoFNYAqmqC85jmxDIgEGJWT1eOI87zVXh+WHRz8m3ntWnAnWvHNZToFlbOc12huJ3tlqrqEFUdACzCM4hDSSf6bJfnfFdFzIhIA2A6nh8XhbWYqrpPPbKAdwmsc406XTVVdQeee53OwVPL3EhECnogVcln+1Tidvwe+FZVc7yOU57zXZaYSztmWc9VZfzPNqfGrs12bfZ73F7s2mzXZrs2Vzzm0o7pl2tzjZ7nuVmzZhoREeHvMIwxxtQQq1atOoynpn6dqr7m/ZqI9KLox3AbPN3WuuKpid+KZ5TeBGAl8EdVjanC0AOGXZuNMca4qSqvzTX6nueIiAiioqL8HYYxxpgaQkTq47nP6m1n/TI8I+c+oqoxIvIFnqk/coE71HOPJCJyJzALz+ie75yuBWewa7Mxxhh3VeW1uUa3PEdGRqpdoI0pm6T0bGZu2M/o3q1oUjfM3+EYE5BEZJWqRvo7jurMrs3GnJplO47Qo1UDGtYJ9XcoxgSkqrw22z3PxhgAvly1hwe+Xc/Hy3b5OxRjjDHGAOlZuVwzZRl//tAqnIwJBK4UnsXjemdockSkgzNcvDGmmsjKyfc85ub7ORJjjDHGAGQ71+S1u5P9HIkxBtxreX4Nz/Dm1zrraXiGBTfGGGOMMcaUw9p4T6E5O88qto0JBG4NGDZQVfs5k9ijqkkiYjdNGmOMMcYYU05HM3P9HYIxxotbLc85IhIMKIAzD55VkRljjDHGGFNOIv6OwBjjza3C88vAt0ALEXkKWAz826W8jTHGGGOMOe00rmMdOY0JJK5021bVj0VkFZ5JpgUYp6qb3MjbGGOMMcaY01EjZ3qqLi3q+TkSYwy4UHgWkSBgnar2BjZXPCRjjDHGGGPMxr2pAMQePOrnSIwx4EK3bVXNB6JFpIML8RhjjDHGGGOAf361zt8hGGO8uDXadmsgRkRWAOkFiap6mUv5G2OMMcYYY4wxfuNW4fkxl/IxxhhjjDHGGGMCjlsDhv3sRj7GGGOMMcYYj+6t6rN5f5q/wzDGOFyZqkpEzhORlSJyVESyRSRPRFLdyNsYY4wxxpjTUXZevr9DMMZ4cWue51eAa4FtQG1gvJNmjDHGGGOMKYeM7Dx/h2CM8eJW4RlVjQWCVTVPVd8FLnQrb2OMMcaUn4g0EZE5IrLNeWxcynY3OdtsE5GbvNIXisgWEVnrLC2c9Foi8rmIxIrIchGJqJp3ZMzpISPHCs/GBBK3Cs/HRCQMWCsiz4jIPUBdl/I2xhhjTMVMAOapaldgnrNejIg0AR4FBgIDgEdLFLKvU9W+znLQSbsVSFLVLsALwH8q800Yc7qxlmdjAotbhecbgGDgTjxTVbUHrjzRDiLSXkQWiMgmEYkRkb876T5rx8XjZad2e52I9HMpdmMMoP4OwBhTmS4H3neevw+M87HNKGCOqiaqahIwBxh9Cvl+BQwXEXEhXmMMkJVr9zwbE0hcKTyr6i5VzVDVVFV9TFXvdbpxn0gucJ+q9gDOA+4QkZ6UXjs+BujqLLcBr7sRuzHGGHMaaKmq+wCcxxY+tmkL7PFaj3fSCrzrdNl+2KuAXLiPquYCKUDTkhmLyG0iEiUiUYcOHar4uzHmNFM7NNjfIRhjcGmqKhHZiY+GK1XtVNo+zsW74EKeJiKb8FyEL6fofun3gYXAv5z0D1RVgWUi0khEWhf8GDDGVIxa07Mx1ZqIzAVa+XjpwbJm4SOt4D/DdaqaICL1ga/x9Dj74CT7FCWoTgGmAERGRtp/G2NOUe0wKzwbEwhcKTwDkV7Pw4GrgSZl3dkZYOQcYDklascLBiWh9BrxYoVnEbkNT8s0HTp0OJX3YIwxxlRbqnpxaa+JyIGCCmcRaQ0c9LFZPMUH+2yHpwIbVU1wHtNE5BM890R/4OzTHogXkRCgIZBY8XdjjPFmLc/GBAa3um0f8VoSVPVF4KKy7Csi9fDUYv9DVU80N3SZa7dVNVJVI5s3b16m+I0xoHbXszE12TSgYPTsm4DvfWwzCxgpIo2d8UZGArNEJEREmgGISChwKbDBR75XAfOdHmLGGBeFh7o2QY4xpgLc6rbtPXhXEJ6W6Ppl2C8UT8H5Y1X9xkkurXa8oHa7QDtgb4WDN8YYY2q+ycAXInIrsBtPDzFEJBL4q6qOV9VEEXkCWOns87iTVhdPIToUz+Cgc4G3nG3eBj4UkVg8Lc7XVN1bMub0Yd22jQkMbnXbfs7reS4QB/z+RDs4g428DWxS1ee9XiqoxZ5M8drxacCdIvIZnmk0Uux+Z2PcY21FxtRcqnoEGO4jPQoY77X+DvBOiW3Sgf6l5JuJUxA3xlSeIBvE3piA4ErhWVWHlWO3wXgGHFkvImudtAcopXYcmAFcAsQCx4BbKhS0McYYY4wx1YAVnY0JDG512773RK+XaFkuSFtM6f8LfNWOK3BHuQI0xpyUFj5aE7QxxhgTSOqHh/o7BGMM7o62fS6ertUAvwUWUXx0bGOMMcYYY8wpalovzN8hGGNwr/DcDOinqmkAIjIJ+FJVx59wL2NM4HBuerZ7n40xxpjAEmz3PBsTENwa974DkO21ng1EuJS3McYYY4xPWbl5RO9J9ncYxlSK6wZ2AOCbNQl+jsQYA+4Vnj8EVojIJBF5FFgOvO9S3saYKqAlHo0xpjqYNG0jl7+6hN1Hjvk7FGNc17iOddc2JpC4Ndr2UyIyExjiJN2iqmvcyNsYY4wxpjTrEzytzskZ2XSgjp+jMcYYU5O50vIsIp2BGFV9CYgGhohIIzfyNsZUjYJ7ne2eZ2NMdbJlfxoA36y2bq2m5rEZMIwJLG512/4ayBORLsBUoCPwiUt5G2OMMcb4lJPnKVy892ucfwMxphJYhbYxgcWtwnO+quYCVwAvqeo9QGuX8jbGVIGC2m2r5TbGGGMCg12RjQksbhWec0TkWuBG4EcnzWZzN8YYY4wxppzyvZqeN+9P9WMkxhhwr/B8CzAIeEpVd4pIR+Ajl/I2xlQBteG2jTHGmIASGlT0U330i7+Qn28XaWP8ya3RtjcCd3ut7wQmu5G3McYYY4wvSenZ/g7BmEolUnw9X5UgxPfGxphK51bLszHGGGNMlbrr8z4JSgAAIABJREFUU5sV09RsJQcM+9/8WP8EYowBrPBsjHFYr21jai4RaSIic0Rkm/PYuJTtbnK22SYiN3mlLxSRLSKy1llaOOk3i8ghr/TxVfWeABbHHi62rjY0salhSg7i+dK8bWw9kGafdWP8xK15nq8uS5oxxhhj/GICME9VuwLznPViRKQJ8CgwEBgAPFqikH2dqvZ1loNe6Z97pU+txPdwUu8uifPn4Y1xna8y8sgXFvH24p1VH4wxxrWW54llTDPGBKiCC7TVZhtTI10OvO88fx8Y52ObUcAcVU1U1SRgDjC6iuJzxeM/bvR3CMZUiSenb/J3CMaclio0YJiIjAEuAdqKyMteLzUAciuStzHGGGNc01JV9wGo6r6CbtcltAX2eK3HO2kF3hWRPOBr4Ektqmm7UkSGAluBe1TVO49Kk1jKYGHZufmEhdhdaaZmUCA0WMjJs4ptYwJBRa8ue4EoIBNY5bVMw1ODbYypJgruq7KGZ2OqJxGZKyIbfCyXlzULH2kF/xGuU9WzgCHOcoOT/gMQoapnA3Mpat0uGdttIhIlIlGHDh0q+5s6gQOpmT7Tuz00k4gJ0/lx3V5XjmOMP6mClDK69rOzNldxNMaYChWeVTUaz3zOi1X1fa/lG6fLlzHGGGOqgKperKq9fSzfAwdEpDWA83jQRxbxQHuv9XZ4KslR1QTnMQ34BM890ajqEVXNcrZ/C+hfSmxTVDVSVSObN29e8TcLjHnplxO+fucna8jOzXflWMb4i6K+q7WAVxdsJzE9m9iDR6s2KGNOYxXu16SqeUBTEQlzIR5jjL9osQdjTM0yDSgYPfsm4Hsf28wCRopIY2egsJHALBEJEZFmACISClwKbHDWW3vtfxkQUDdidntoJj9EF7VAr9mdRNzhdD9GZMwpKr3sDEC/J+Zw8fM/V1k4xpzu3LopaBewREQeFpF7CxaX8jbGGGNMxUwGRojINmCEs46IRIrIVABVTQSeAFY6y+NOWi08heh1wFogAU8rM8DdIhIjItHA3cDNVfeWyuauT9fwzep40jJz+N1rv3Lhfxf6OyRjTokIXHNu+xNus/vIscLn6+KT2ZeSUdlhVWuJ6dkM++9CYg+m+TsUU824VXjeC/zo5FffazHGVBOF8zxb07MxNY7TvXq4qnZ1HhOd9ChVHe+13Tuq2sVZ3nXS0lW1v6qeraq9VPXvTq8zVHWik9ZHVYepql9uwryiX9sTvn7vF9GcNWl2mfJaF59MamaOG2HVaCt2Jlq3+CpQcEmefOXZXN2/XanbDX12AaNfXATAZa8s4YL/LKiC6KqvuRsPsPNwOm/8vMPfofhdVm4eT/y40f7vlZErhWdVfUxVHwOeB57zWjfGGGOMqVTP/77vKW0fMWE6t763kozsPI5leyYHSc/KJTMnj8teWcLN76wotv3B1Eyen72FLfutlQogZm8Kv39zKU/PLH8v/Z+3HipsHd1+6Ch/+TCKrNy8cue37UAaaQH0419V+XZNfIXeU0E+BQOGPX3FWSfcdvP+NHYd8dyWkJevTJoWQ/KxbI46n+2ySErPZsehmn8PdcEgqSfqEn+6+GZ1Am8v3slzs7aUO48FWw4SMWH6afE/0pXCs4j0FpE1eO6BihGRVSLSy428jTFVo2DWGbW7no0x1UgtZ1qq3m0bnNJ+8zYfpMcjP9HzkVmkZ+XS69FZdH/4JwDWxacAkJ+vbN6fytVvLuXl+bGMclr2fNmyP42ICdNZvuMID323nge/XV/Od1TctgNpTJoWQ35+4Pxvfm72VgBmxxwodx43vbOCMS/9wrHsXIY/9zOzYg6welfycdtt3JtK5JNzOHw0y0cuRUa8sIjr315Bepa7M6X+/s2lXPCf+ae83/zNB7nn82ied85Veal6um0DhASf/Gf7b55dWPj8vV/j6Pv4HHo/OouRL5T+2fV2zhNzuOi5it1DnZGdV2mf1yWxhzlSymdh+Y4jREyYzgdL4457LTs3n4gJ0/n9G0vJy1f+9bXn+xl76CiTpsUU/gYKBL4+w9+vTeDTFbsr9biZOeXvSTI7Zj8AUbsSS93m1vdWcun/fmFd/PHf8+rErW7bU4B7VfUMVT0DuI+i+6GMMcYYYypFQavzZ7cNKncevR6dVWw9N1+JmDCdTg/MYPSLv7DL637SAqt2JRIxYTr/+WkzefnKk9M3AvCHKcv4aNluPl5esR+6qsqCzQe5+d2VvPdrHAnJxe9h3bQvlYgJ04nek8zPWw/x47q9xQZH8+VQWlaprbPRe5I5lFZUKPl4+S6S0rPJz1eum7qMy15ZTFZuHr/932Lmb/YM1l4QU25e/nGFj5RjOcXm4s7OzWda9F4iJkxnSexhAJKP5dDzkeLnHuCLqD0cTM1kT+IxXl0Yy+Gj2bz583YAMnPy2JeSwc9bDxExYTr9n5jDql1Jhe+h16Oz+H5tAgA5efnsSTzGh0vjSMk4tVbpX2MPk5mTx4qdicQnle3+Ye/zcOSo570fcgp6ZRmo7r0lO1m6/Qh5+VpY+FSKt46e37lp2d+El92JxwrzXbM7iYnfrOfpGUU9Bw6mZRK9p6hQcygti3mbDvBF1B72p2SyISGl8LX8fC3MKz9fid6TzOJthwvPQY9HfmLSDzFljm19fAqLthZNYRcxYTq3f7Sq8Djerpu6nGvfWuYznz9M8aQ/8n0MEROm89OGfexNziArN4/JMz13lKyIS6SHU0kGsGZ3Mu/9GkfHiTPIzMkjL19Zuv2Is/9+nvlpMx8t28Xlry4pvE3B++/T+YEZ9H18Nq8v3E7EhOmFy/lPzyNiwnQyczwVCdOiPd/PvHzlpbnbmLJoe2EMnyzfTVJ6NgfTMvluTQK9Hp3F2j3J5OVr4efp75+tZeI3J66Q847rVIQEeT5huQWfOVXeWbyTP761jOHPLSQhOYOk9GySj2WzJ/EY//pqHbNj9hOfdIyDaZm8vXgnUXGe72BSejbr4pMZ/34UnyzfzYtztzJno6eSbd7mg2xISOWyV5accoyBRNyoaRGRaFXtc7K0qhYZGalRUVH+DMGYauPJHzcydfFObj4/gkmXWccRY3wRkVWqGunvOKozt67NEROmAzD7nqF0a+kZZmXw5PnHFTLd9rcLO7N6dxLLdpTewuLtobE9eHL6Jmb+fQg9Wh/fOr4yLpGDqVnE7E3hyv7tCA8N5vOVe8jOzeeNn4t+YM+99zcECdQJC+Hw0Sy+W5PA1MU7j8vv4h4teWJcL1o3rF2YFnvwKLsT0/nTe1HUDg3m/0afSXhoMBee2ZytB47y0Hfr2ZPoOW9xk8cSszeFsS8vpkuLetw9vCt3f7oGgOl3X8DYlxcXO16QQMHv9Zeu6cuoXq34bMVuJv2wsTA/gHu/WMs3qxNOeK7+OepM5m46wJrdyTSqE0ryseIF3gcv6eGzIqFv+0as9Sr4jerVkjdviOSBb9fziVOJcclZrXjtuqKZ1DJz8nh+zlb+cXFX6oSFFMvv2VmbeXXBdprVC+OwUwgueB+qyoPfbeDciMb87px2XDNlabHPwpjerTiYllVYoAeoExbMsew8ptzQn5G9WhWmZ+XmsSEhhe0H05n47fpiBcXzOjXhX6O78+WqeKat3cuGx0YBnlbdj5fv4snp7gxsf8ewzry6YPvJNwTuG9GNtXuSmbfZ10x3nnNU0IsDYOnEi4p9DsEzpsCmfakkH8vh86g9/CGyPU/PPPFQCbVDg3nzhv7c6HU7RcHfo+D/wPmdm/Lr9iNleh+lad+kduH3wJfJV5zFj+v2sdip/Pnmb+dzxWu/VuiYpbmyXzu+Xh1P/VohfHn7IEa/6JmaL27yWPanZNKqYXjhtpv3p5KUnsO1by0jPDSIe0d047ahncnIzmPz/lTO6dC41OMUnD/wnOcMH937e7VpQMze1Aq9n9sv7MzrC4s+Zy9d05e/f7aWxf8aRrvGdSqUN1TttdmtwvO3wGrgQyfpeiBSVcdVOPMKsMKzMWVnhWdjTs4KzxXnduG54Ec0wMB/z+VA6om79/pL64bhvHVjJJ+s2E1envJ51B6uG9ihwi3UpXnmKs8AU/+bH8vzc8redfj9Pw3gzk9Wk5bpXvfnsWe1Zvr6fa7lVxZLJ17Ela/9yt6UzMK0sJCgwtbDf446k2dnbaFF/Vqc2ao+9408k2PZufzxreWl5lm/VghpFegWfs/F3cjKzWNI1+YM6ty0WMHlRERg59Nji6Vd8doSVu8OvO6vM/8+pHAO9rDgIC49uzV3XtSFqLgkLu7Zkn5PzPFzhDXDhWc2Z+xZrUnLzOXxHzee8v7T7hwcMC3A3v/Dy6s6Fp4bA48BF+DpXbIImKSqSSfcsZK5dYG+9b2VhbWctUKCeOaqPpzZygYTNzXLEz9u5G0rPBtzQlZ4rrjKLDwv2nqI52ZvITo+pbTdjKmWShYwMnPyCu/RN6Y6q26F55CTb3JyTiH5bjfyCkStG4UTHCRk5OTxy7bDRO9JtsKzMcYY40dtG9XmvE7F7/8c2q05Q7s1J3pPMpe/GhitKsZUhvDQYD66dSDXv116S7kxxn2uFJ5FpBtwPxDhnaeqXuRG/v725DjP1AD7UjIY9PR88gJoRD5j3FLwsQ6kESeNMaY0s+8ZSpD4nmimT/tG3HDeGXy4bFcVR2VM1Snv4GHGmPJzpfAMfAm8AUwFKjahXQALdi7SJUf+M8YYY0zVqlvrxD9hnhjXm7uGd+GCyQvIziv/FCzGBKqgIGH63RfQon44jeuE0uXBmf4OyZgaz63Cc66qvu5SXgEryBnKPd9a5kwNVDC/s326jTE1RYv64Wx9akyZB2Yyprrp1aahv0Mw5rRSoXmeRaSJiDQBfhCRv4lI64I0J71GsZZnY4wxpvp5aGyPYuuDOll3V1PzPP/7PpzRtOLT/hhjSlfRludVFJ+//Z9erynQqYL5H0dERgMvAcHAVFWd7PYxSlPQ8myFZ1MTFd3z7N84jDHGbeOHdGL8kE6kZuYQGhRE7bBgVJVDR7NoUT+cP7y5lOU7yzZvszGB6op+7biiXzuen7OVFvVr8dB3G/wdUsC68MzmLNxyCICPxw/kuqlVM/Damzf058uoPczd5HuubBP4KtTyrKodVbWT81hyqYyCczDwKjAG6AlcKyI93T5OaYKt27YxxhhTbTUID6V2WDAAIkKL+uEAfP6XQVw3sMNxLdTGPy48szl3XdTF32FUW/eO6Mb1551RuD7ptz3Z+PioUrfvXsoMMu/cXDWz8l3co2WpMZyKd26O5Jmrzi5cn3zFWQzoWNQR9vs7BrPg/gu5/cLOvHvzubxxfT8W3H8hg7s0474R3bhjWGcA/j68q8/8z27XkEm/7ckLf+jDmN6tAGjVIJw3ru9XakzLHxjO2e2KutaP6tWKt26MLNy/NBd1b3HyN1xJwoKLiodxk8cyYUx3ABqEhxD9yEim3NCf6EdHFtvnHxd3ZcH9F7Lj35ew6J/Dynysj8cPdCfoKlShlmcRORfYo6r7nfUbgSuBXXjmeXa7GncAEKuqO5zjfQZcDpz67ODlUNRtuyqOZox/qN31bIw5DT31O8/MGoM6NyX24FGWxB5mXN+2/LFEi9SC+y+kYe1Qrp2yjC0H0hh/QUca1w2jad0wJnyzHoAZdw/h8NEs/u+rdSSmZzPmrFZ8v3ZvheJrEB5CamZuqa9HPXQxkU/OLVy/rE8bpkVX7JglPX3FWTz+w0YyctwdG/ala/ryzE9bGN27FW8v3skzV51Ni/rhXNW/Hb95dmHhdr+PbMeInq3YnXiMJ34s+unnxsjqF/doyZCuzejQpA63vLfyhNs++tue7E3O4K7hXWkQHgrAPz5bw3dr93Lp2a3ZuC+VH+68gLq1QsjMyWPN7mSufWtZqfldf14HPlq22+drF3VvwYaE8s1bHvPYKBLTs2nfxNOVO/apMeQrfLpiN+0a10YV9qZkcOOgiMJ9/vbxKmas38/jl/fiou4tC/cJC/l/9u47zorq7uP457dL770ILCBNQEVxRbGCqGB51CQaNRY0Eh8fS4wmJmBXbE+eaBKjJrGX2HsXEURFBAXpHenSe1l22fJ7/rizy2W5W3Bnb9n9vl+v+9o7Z86c+d3Z2T33zDlzJo0Fa7dz6VPf8t61x9Kobk3q1Eznupen8v70VSy89zRGzV7DtS9NLSrrl5nteW3yyr1i+u2gbvxuUDfS0qxoLoK/X3AYeflOn7s/LcrXrVUDbjilO89OWMq3UaNCbji5O4N6tuLMf4znhSv6sWDtDg4+oBGHtm9SdGHs2K4tqJWeRsuGtTn3iPZ0veVjmtarSZ8OTQD405BIY3DIwW2Lyr1uUDfcnUPbN2HQQa244ZTuReueGr+EA1vWZ2CPPQ3anx3efq/PVfis4p05eQz5+5es2LQLgMZ1a3LdSd34zfN7nm1vZvzz4iMYv3DDXo8a+2bESbRpVIeF63bQvXVDHhm7kAOa1GXrrlwObd+YX/zzm6K871xzLJOXbuKeD+eS2bEpk5dtLv7rL9X//uIQWjWqw+XPRM71V648msM6NGH5piy6tWpAfoEXPV3oqhO7cNWJXYq2PbX33g3/287sxa+P7YQF7aSMqFsHjuzUlHXbc/jr+Yfx88cm0LJhbT674US+WbyRIWVcQEhWVpHH0pjZ98DJ7r7JzE4AXgGuAw4Derr7ueGEWbS/c4Eh7j4sWL4EOMrdr42VPzMz0ydPnhxr1U+Sk5dPj1s/4abBPbhmoK6IStVy53uzeXbCUi4+OqPo8Wwisjczm+Lu8emOqaLCrpsr24pNWeTmF7Bo3Y69vjSu3ZbNHe/O5qHz+1CvVtl9EbEmLauRZuQVuxWsXZO6tGlch7vP7s3SDVlk7c7jpjdm8NbVx3Bgi/rUr12Dj2au5pB2jTnpwS84vlsLzj6sHece0Z7Zq7biDge329PTtTUrl9mrt7JtVy4tG9Zm/MKN/PWzBQCc3LMVg3u34aY3ZgDw/rXH0aFZXdZsy8Yw3piygpe/XcGOnEij/bMbT6Brq4a8MHEZt70zi3F/GEDzBrU45M5Io6dj83ps2rGbXgc04qSDWtGlZQOO7NyMZ79eyrUndS0awRd9PMxgyf1nlHjcnhq/hJEfzOHTG06ge+s9vZOXPDWJrxZu4If7Tmfemm2c8fB4DuvQhBO6t+TCfh2YuXIrfTo04aj7xhRtM+32U6iZnkbvO0bx3rXHMuGHjRzXtQVN6tWkfdM9X/h35uRRI92YtnwL5z8+kUPbN+a9a4+j0/APS4x35AdzeGr8Ep64NJNTerXeZ31OXj55+c7qrdmc/NAXQKTXcs227KKGV/FjM/POU2kYNM6TVV5+Abty84vi3LU7n563f0K3Vg0YfeOJ9L79Ey46uiO/zOxA60a19/o8Fzz+DV1bNeCecw4hN7+Abrd8zK+OyuCW03tSMz2NWjXS2LAjh39/8QNPfLUE4Cd9B39uwlJO6N6Szi3qh/fByzDowXH8sH4n80YOoU7NdB4Zu5DzMjvQulGdvfJNX7GFHm0asnj9Tnod0KjMcgvPjeLnjLvTecRHQORixPWvTAPgjEPb8uiv+vLp7DV8Omctg3u3YeLijdx2ZsUH7pYUC8BLk5aT2anpXn+zM1Zu4eADGhfdBhumeNbNFW08T3f3PsH7R4H17n5nsDzN3Q8LJco9+zsPGFys8dzP3a+LynMlcCVARkbGEcuWhfeMx7z8Arre8jHHdGnOUZ012YhULWPnr2P6ii0c2r4xgw7at+IXSWV9OjRmQI+KD4NL1cZzMInnq0AnYCnwS3ffp6vCzIYCtwaL97j7c0F6LeARYABQANzi7m+aWW3geeAIYCNwvrsvLS2WVGs8h6Xwi+ZLw45iW3YufTOaUr92DXrfMYrbzuzFMV2a89b3K/nD4B7UrpFetJ27s3ZbDm0a1ymp6P2ydls2R903htf+u3/RkNZHP1/EaQe34cCWDWJuc/JDX7Bo3Y59GrCF5q/ZzsadORzTpUW54+g0/EOO79aCF64ofdimu7Nue84+jY7i3pyyklN6ty7qCS60a3c+Kzdn0aVlg5/0pX3q8s30bNuIOjXT2Z6di5nRIMZj0rJz83ln6o+cf2SHoh64knS/9WN25xUUNayK+2TWGvp0aEzbxnX3O95k8NHM1WR2bEqrMn5n+2Pd9myufXEqj13clxYNaodWbmVZty2bKcs2c9ohbcvOHJKs3Xnk5juN68bngkun4R/SN6MJb119bFz2V5pUajzPAg5z9zwzmwdc6e5fFq5z94NDirNwf/2JDAcfHCyPAHD3+2PlD7uCdncGPfgFizfsDK1MERGpfJcd04k7z+pd4XJSuPH8Z2CTuz9gZsOBpu7+p2J5mgGTgUwik35OAY5w981mdheQ7u63mlka0MzdN5jZ1cCh7n6VmV0A/Mzdzy8tluraeL74yUlk7c5Lii+a++vbJZu458M5vH5V/70a9hWxeusumtarFbPxWNWNmbuWf3+5mFd+c3Sl9MKJxMOardk0rrtnHolESqXG8y3A6cAGIAPo6+5uZl2B59w91BrCzGoAC4BBwI/Ad8Cv3H12rPyVUUG7u2YjlirLTLNtS9UVxpfUFG48zwcGuPtqM2sLjHP3HsXyXBjk+e9g+d9BvpfNbAVwkLvvLLbNKCIXtb8J6ug1QEsv5ctFdW08i4hI5Yhn3VyhCcPc/V4zGwO0BT6NqizTiNz7HKqgh/taYBSRR1U9XVLDubKYGWWMxhFJaTq/Raqk1u6+GiBoQMcaw94OWBG1vBJoZ2ZNguWRZjYA+AG41t3XRm8T1NFbgeZELqqLiIhUKRV9zjPuvs/0ge6+oKLllrK/j4CPypN3ypQpG8ws+qbnFqRmha644ycVYwbFHU+pGDMo7rB0LDtLYpjZZ0Cs6UtvKW8RMdKcyHeF9sDX7n6jmd0I/AW4pJRtisdWNB8JsCPoCS+UbL/j8lLc8ZOKMYPijqdUjBkUd1jiVjdXuPGczNy9ZfSymU1O0eF2ijtOUjFmUNzxlIoxg+KuDtz95JLWmdlaM2sbNWx7XYxsK4lMCFaoPTCOyERgWcDbQfrrwBVR23QAVgbDthsD+zym0t0fBx4vIbaU/B0r7vhJxZhBccdTKsYMijsVpZWdRURERFLce8DQ4P1Q4N0YeUYBp5pZUzNrCpwKjApuyXqfPQ3rQUDhQ3ajyz0XGFva/c4iIiKprEr3PIuIiAgADwCvmdkVwHLgPAAzywSucvdh7r7JzEYSmYwT4G53L+xF/hPwgpn9DVgPXB6kPxWkLyLS43xBfD6OiIhI/FW3xnPMIWMpQHHHTyrGDIo7nlIxZlDc1Zq7byTSY1w8fTIwLGr5aeDpGPmWASfESM8maIhXQKr+jhV3/KRizKC44ykVYwbFnXIq9KgqERERERERkepA9zyLiIiIiIiIlEGNZxEREREREZEyVJnGs5kNMbP5ZrbIzIbHWF/bzF4N1k8ys05R60YE6fPNbHASxXyjmc0xsxlmNsbMOkatyzezacHrvXjFXM64LzOz9VHxDYtaN9TMFgavocW3TXDcf42KeYGZbYlal5DjbWZPm9k6M5tVwnozs4eDzzTDzPpGrUvksS4r7ouCeGeY2QQz6xO1bqmZzQyO9eQkinmAmW2NOg9uj1pX6rlVmcoR901RMc8KzuVmwbpEHesOZva5mc01s9lmdn2MPEl5bsv+Ud0cP6qbVTeXRXVz/KhursJ1s7un/AtIB34ADgRqAdOBXsXyXA38K3h/AfBq8L5XkL820DkoJz1JYh4I1Ave/09hzMHyjiQ+1pcBj8TYthmwOPjZNHjfNFniLpb/OuDpJDjeJwB9gVklrD8d+Bgw4GhgUqKPdTnjPqYwHuC0wriD5aVAiyQ81gOADyp6bsU77mJ5/4vIo4QSfazbAn2D9w2BBTH+jyTlua3Xfv2eVTcn17G+DNXNYcWtujl5Yh6A6uawYlbdXI5XVel57gcscvfF7r4beAU4u1ies4HngvdvAIPMzIL0V9w9x92XAIuC8hIes7t/7u5ZweJEoH0c4ipLeY51SQYDo919k7tvBkYDQyopzuL2N+4LgZfjElkp3P1LIo9/KcnZwPMeMRFoYmZtSeyxLjNud58QxAVJcm6X41iXpCJ/ExW2n3Eny3m92t2/D95vB+YC7YplS8pzW/aL6ub4Ud0cR6qb40d1c/yobi6fqtJ4bgesiFpeyb6/7KI87p4HbAWal3PbyrC/+72CyJWeQnXMbLKZTTSzcyojwBKUN+5fBMM53jCzDvu5bWUo976DIXidgbFRyYk63mUp6XMl8ljvr+LntgOfmtkUM7syQTGVpL+ZTTezj82sd5CWEsfazOoRqcjejEpO+LG2yDDdw4FJxVZVhXO7ulPdHD+qm1U3h011cxyobk49VeU5zxYjrfgzuErKU55tK0O592tmFwOZwIlRyRnuvsrMDgTGmtlMd/+hEuLcJ5wYacXjfh942d1zzOwqIr0KJ5Vz28qyP/u+AHjD3fOj0hJ1vMuSbOf1fjGzgUQq6OOiko8NjnUrYLSZzQuu4Cba90BHd99hZqcD7wDdSJFjTWRY2NfuHn0lPKHH2swaEPnC8Dt331Z8dYxNUubcFkB1s+rmsqluTsL/X6qb40p1c4qpKj3PK4EOUcvtgVUl5TGzGkBjIsMpyrNtZSjXfs3sZOAW4Cx3zylMd/dVwc/FwDgiV4fiocy43X1jVKxPAEeUd9tKtD/7voBiw2cSeLzLUtLnSuSxLhczOxR4Ejjb3TcWpkcd63XA28RnqGaZ3H2bu+8I3n8E1DSzFqTAsQ6Udl7H/VibWU0ilfOL7v5WjCwpe25LEdXNqpvLoro5yf5/qW6OO9XNKcbcU/+iQFDhLgAGAT8C3wG/at68+axOnTolMjQREalCpkyZssHdWyY6jlSgullEROIhnnVzlRi27e6zafMpAAAgAElEQVR5ZnYtMIrI7HpPu/vszMxMJk+O2wzvIiJSxZnZskTHkCpUN4uISDzEs26uKsO2cfeP3L27u3dx93sTHY9Iqlm0bju/e2Uq89dsT3QoIlJFqG4WqRh35+ExC1m2cWeiQxERqlDjWUQq5pNZa3hn2io+mFElb1ERERFJOau2ZvPQ6AX8+tnvEh2KiBBS49kiLjaz24PlDDNLiokERKR8Cqc/qALTIIiIiFQJBQWRSjk7tyDBkYgIhNfz/BjQn8hDvgG2A4+GVLaIiIiIiIhIQoU1YdhR7t7XzKYCuPtmM6sVUtkiIiIiIiIiCRVWz3OumaUTPAzbzFoCGl8iIiIiIvITFQT3UqVpliKRpBDWn+LDRB7i3crM7gXGA/eFVLaIiIiISLUT3PKMYYkNRESAkIZtu/uLZjYFGAQYcI67zw2jbBERERGR6mj2qq0ALN+UleBIRARCaDybWRoww90PBuZVPCQREREREVmyXs93FkkmFR627e4FwHQzywghHhERERERAfIK9PxIkWQS1mzbbYHZZvYtUHSJzN3PCql8EREREZFqZWdOXqJDEJEoYTWe7wqpHBERERERARau25HoEEQkSlgThn0RRjkiIiIiIhKxKzc/0SGISJRQHlVlZkeb2XdmtsPMdptZvpltC6NsEREREZHqaNduNZ5FkklYz3l+BLgQWAjUBYYFaSIiIiIi8hNk7dY9zyLJJKx7nnH3RWaW7u75wDNmNiGsskVEREREqpu123ISHYKIRAmr8ZxlZrWAaWb2Z2A1UD+kskVEREREqp0dmm1bJKmENWz7EiAduJbIo6o6AL8IqWwRiQM9SVKk+jGzZmY22swWBj+blpBvaJBnoZkNjUofZ2bzzWxa8GoVv+hFRETiK5TGs7svc/dd7r7N3e9y9xvdfVFp25hZBzP73MzmmtlsM7s+SI9ZkVvEw2a2yMxmmFnfMGIXERGpxoYDY9y9GzAmWN6LmTUD7gCOAvoBdxRrZF/k7ocFr3XxCFqkumndqHaiQxARwptte4mZLS7+KmOzPOD37t4TOBq4xsx6UXJFfhrQLXhdCfwzjNhFJMLV9SxSHZ0NPBe8fw44J0aewcBod9/k7puB0cCQOMUnIkDN9LAGi4pIRYR1z3Nm1Ps6wHlAs9I2cPfVRO6Nxt23m9lcoB2RinxAkO05YBzwpyD9eXd3YKKZNTGztkE5IiIisv9aF9aj7r66hGHX7YAVUcsrg7RCz5hZPvAmcE9QT+/FzK4kcuGbjIyMsGIXqTZq1VDjWSQZhDVse2PU60d3/xtwUnm3N7NOwOHAJIpV5EBhRV5W5S0iFeC661mkSjKzz8xsVozX2eUtIkZa4T+Mi9z9EOD44HVJrALc/XF3z3T3zJYtW+7/hxAREUkCofQ8F7v/OI1IT3TDcm7bgMjV6t+5+zazWHV0JGuMNF3dFhERKYW7n1zSOjNbWziKy8zaArHuWV7JnhFhAO2JjArD3X8Mfm43s5eI3BP9fEihi0igloZtiySFsIZtPxj1Pg9YCvyyrI3MrCaRhvOL7v5WkFxSRb6SyCzehdoDq4qX6e6PA48DZGZmqitNpJx0z7NItfQeMBR4IPj5bow8o4D7oiYJOxUYYWY1gCbuviGoz88EPotDzCLVju55FkkOoTSe3X3g/m5jkS7mp4C57v5Q1KqSKvL3gGvN7BUiM35u1f3OIiIiFfIA8JqZXQEsJzJnCWaWCVzl7sPcfZOZjQS+C7a5O0irD4wKGs7pRBrOT8T/I4hUfbrnWSQ5hDVs+8bS1hdrHBc6lsi9UTPNbFqQdjMlVOTAR8DpwCIgC7g8hNBFJOBFP9UFLVJduPtGYFCM9MnAsKjlp4Gni+XZCRxR2TGKCNRIK/G2RhGJozBn2z6SSO8wwH8BX7L3BF97cffxxL6PGWJX5A5cU7EwRURERERSi4ZtiySHsBrPLYC+7r4dwMzuBF5392GlbiUiySO46Vn3PouIiCSXGunqeRZJBmFdxsoAdkct7wY6hVS2iIiIiEi1lVby02hEJI7Cajy/AHxrZnea2R1Entf8XEhli0gceLGfIiIikljXDuwKwNh5sZ4iJyLxFtZs2/ea2cfA8UHS5e4+NYyyRURERERERBItrNm2uwCz3f17MxsAHG9mS9x9Sxjli0jlK7zXWfc8i4iIJAc9AUMkuYQ1bPtNIN/MugJPAp2Bl0IqW0RERESk2om+oL1ma3biAhERILzGc4G75wE/B/7u7jcAbUMqW0TioPDqtq5yi4iIJJ+tu3ITHYJItRdW4znXzC4ELgU+CNJqhlS2iIiISEy5+QVMXb450WGIVIroy9nf6zwXSbiwGs+XA/2Be919iZl1Bv4TUtkiEgeu6bZFJAU98PE8fvbYBN76fmWiQxEJXfSw7RFvzSQnLz9xwYhIOI1nd5/j7r9195eD5SXu/kAYZYuIiIiU5KnxSwC48bXpCY5EpPKt2qL7nkUSKayeZxFJcep4FpFU8/70VYkOQaRSFZ+HZOBfxjFz5dYERSMiajyLiIhISrru5amJDkGkcsW4ov2mblEQSZhQGs9mdl550kQkee15zrP6nkUkNeXlFyQ6BJFK9+yEpfxl1PxEhyFSLYXV8zyinGkiIiIileKcx77m3Wk/JjoMkdCUdDn7kc8XxTUOEYmoUZGNzew04HSgnZk9HLWqEZBXkbJFJL6KnvOsjmcRSVGzftzG9a9Mo3OL+hzavkmiwxGpsNJGg63blk3LhrUxszhGJFK9VbTneRUwGcgGpkS93gMGV7BsERERkf02feVW3YIiVUatGrG/rve7bwyZ93xGrm5XEImbCjWe3X06kec5j3f356Jeb7m7nuQukkp8rx8iIkntmhe/L3Hdbe/M4j+TlscxGpHK4Q410ox/XtQ35vqNO3fT7ZaPefbrJXGOTKR6qvA9z+6eDzQ3s1ohxCMiIiJSpg9nri51/di5a7np9en8/bOFcYpIJHwOGHBk52al5rvz/TlxiUekuqvQPc9RlgFfm9l7wM7CRHd/KKTyRaSSFT3nWV3PIlIFfD5/fdH7y47tROO6NRMYjchPZ2a0aFC7XHkLCpytu3JpWl99WiKVIazZtlcBHwTlNYx6iYiIiCRUn7s+5Zmvl/CH16dTUKArhJI69ueC9pH3fsbvX5/O4SNHs357TuUFJVKNhdJ4dve73P0u4CHgwahlEUkRhZPruO56FpEUM/Kcg8vMc9f7c3hjykqmrdwSh4jkp3L3mJO9lZSeSsr6DLHWO07hXNqL7zu91PLXb8/h7amRR7Wt3ZZd4ViTzdh5a+k0/EMWrdsRetk/btn1kz5zXn4Bq7fuirmutN93Tl4+9388l505eeXeJtlszcrlkqcmlXiupdJn2R+hNJ7N7GAzmwrMAmab2RQz6x1G2SIiIiIlufWMnlx4ZIdy5//5YxP4YsH6Etd/Nmctd7w7i/9MXBZGeOXm7uzOi9+sydH7y80v4KuFJR+T/bF0w86yM5Xiwicm0nnER0XLSzbsZOmGnXQe8RGHjxxNp+EfsiTYx2dz1nLtS/tOHDdx8Uamr6jYRZKlG3bG/OJfUOAs2xjZ/+adu3n080X0uPXjfUY0LN+YRV6xWbDven8OnUd8REGB77Vu3bZsVmzKovOIj3gh1nkXtJ7T0oxhx3UuV/xn/mM8nYZ/yF9GzWfphp3s2p3PNz9sLFr/xJeL+XzeOnLzC/aJfcHa7XQe8RGj56wtSssvFnMifDhjDQDfLw93TuJ5a7Zx7ANjeWr8/k+6dt9H8+h//1g27ti3p/9//vP9XufyX0cv4PEvfwDglW9X8O8vFvOPsXs/r/ukB7+gz12f7ncc0V6fvIJNO3fvlTZh0QZy8vJZsSmLReu2l7r97rwCVm7OAiLnZqfhH/Ls10t49PNFrNqyi3lrtgHwxvcr+WrhBv457oeY5XQe8RHXvTyV579ZykG3fVyUnpdfwLbsXNZtr9gFnkQJ657nx4Eb3f1zADMbADwBHBNS+SIiIiL7uKR/R9LTjMyOTZm8rHxfqoc+/S0Natdg/J8G0qReLdydRet20K11Q4Y9P7ko38VHdww11sIes66tGgCRRkq3Vg0wM16YuIzb353NtzcPolWjOgC8+t1yBh7UilYN64QaB1C0v0k3D+L5b5by6Oc/8MqVR3P0gc3JzS/AHbrf+jF3n92bS/t3AmDX7nzq1Ewr8bnCn8xaw1X/mcLjlxzBqb3b7LN+1ZZd5Bc4V74whX9e1JdOLervk2fi4k1F73fnFTDwL+OKlrdk5QLw6ew1/PeJXYp+V3edlcNFT07i8UsyyWhejwsenwjA0gfOKNq2+60fc/2gblwzsCsQ+V10blGf9LTIZ1m4djvdWkfuOBzx1kxe/jYyW/tNg3tw8VEdycrNo23juhx4c6QxNOb3JzLowS+Kyt+xO4+GtWtgZqzasosT/u/zyH5bN+CUXq0594gOPDthKQDnPPY1M1ZuZfF9p5OWZvS7b0xROX8dvYDeBzTmiI5NWbctm2e+XrrX8bn1zF48uR+NvEc+X8Qjny/igMZ1WLU1m3q10mlarxY/btnTW9qrbSM+uv54nv16CXe+P4cTu7cE4DfPT+b6Qd3YkrWb576JNOpn3HkqjerUxN15dsJSzji0La0a1uG1ySsYv3ADD194eImx5OTlk2ZGfoGzOWs3Xy3cwC8zIxe+dubksTlrN+2b1itx+/Sgy29/ezOXbdxJ60Z1qFMzPeb6BWsjf5cfzlzNsOMPLLWs7Nx8aqanFZ03hRedXpu8kv8Z0GWvvJ/MjjT2f/bY1wwfchB/HxOZvPC96asYEvx9FH/M2JJiF5/GzF1Lm8Z16H1A473Ss3bnUa9WpBm3YlMWzRvUYu22HJ74ajEvTVpOv87NeP7X/VizNZsNO3L41ZOT9tp+/j1DeGnSchat28HVA7vSrkld/m/UPF6ctJyMZvWYsXIrn914It8H/1MLJ6T7v1HzI8fqt8fx4KeR9x/PWs2tZ/Tk5e9WcN+Hc5lz9+Ci/xEfzFjNBzMikzt+OnsNJ/dsTddb9jSk59w9uOhzpAoLozvdzKa7e5+y0uItMzPTJ0+eXHZGEeGeD+bw5PglXHZMJ+48SwNHRGIxsynunpnoOFJZWHVzp+EfAns3kArT9sdTQzO54rlIPH8+91D++MaMonXzRg7h6a+XcOXxB1Ij+Ob+ztQf+d2r0xjYoyVXndiF8x+fyIAeLTmyUzNGzV7D21cfS797P2Pjzt18M+Ik2jauyzNfL+GuqNmQv7hpANNWbOH6V6bxt/MPY/7a7UW9N3f8Vy+27srl/CM70P/+sfTp0IR3rzkWiDQanhq/hHOPaM83P2zklF6t+feXi+nXuRlHdtp3NubxCzeQlgbHdGkBwLbs3KIG3i/+OYEpyzbz+lX9eebrJXw0cw2P/Opwzjz0AA67+9OihipARrN6XHx0Bvd9NI8hvdsw5OA2tGlch6MPbM7MlVt5/pul3H32wTw2bhH/GLuIXx2VwcizDy5qYGTn5rM9O48j7/2sqMyfHd6OC/tl8OOWLE7u2Zrs3ALu+XAO705bBcCie0/jV09M4tulexrThfpmNOH75Xt6lof0blPUUDk/swOvTl5RtO7Zy4/ksme+2+t3OmXZZi6Kakyce0R73piykscu6svph7T9SecRwHFdW5CeZqWObCjusxtP4OSHvtwn/cxD2xY1OmDv83zsvLX8+tnEfb9d+sAZnPSXcSwOGnpPXJrJb4ILGYvuPY073pvNqNlr+fC3x9G60Z4LP52Gf0jH5vVYtjGrKO2agV149PMfaNWwNuu25zD7rsGs255D52IXVr5dsolrX/qeddtz+EXf9jz4y72bGOu2ZzNx8Sb6dWpGgzo12J6dyz/H/cB/9TmA8/71DQAfXHccB7drTHZuPg+PWcgNp3TnoNs+IT+q5/2JSzM5pVfrouXc/AL+8Pp0Lj66I0dkNC26ePLU0Ey2ZOXy+9enF+V99Fd9+d9P5vH6Vf1Zvz2HM/8xvmhd8c9d+Ps9tVdrrj2pK11aNuD+j+fyn4l7HrF3Yb+Moos40b//370ylXemreLZy49kQI9WdBr+ITXTjdz85BkinWZQnukl2jSqw8SbB1V4f/Gsm8NqPL8NfA+8ECRdDGS6+zkVLrwC1HgWKT81nkXKpsZzxSVb47kyHd+tBdcP6sa5wZf3/fGX8/rwh+CLeWbHphx1YDPaN63HiLdmlrjNzacfxBtTVjLqdydgZjGPxyVHd2TkOQcz+K9fMn/tdl6/qj9PfbWET2av4dL+HRncu81eDcv90a5J3b16NBvWqUGP1g3LPSIgGUwYfhLHPDA20WHsI/o8L5Rs53txN5zcnZ2782harxb/+8m8/dr2lSuP5of1O3CHJvVqcu1LU/da/+B5fYoarn89vw83vDo9VjH7KGykl6Zt4zr86+IjOPvRr/cr5srUvXUDurRswMez1iQ6lNDFOrf3Vyo2npsCdwHHEbkz40vgTndP6H/LsCro3782vWhcfq30NG47s1fMoUYiqWzkB3N4So1nkVKp8Vxxldl4nrR4IxnN69H//uRr/MTThf06cP6RGZyTRF/+pWJiNTDcfa97akVSUao1nkMZZB40kn8bRlnJKDs3n505eezOL2DWj9sY3LuNGs8iIiJJ5qgDmyc6hKTw8rcrePnbFWVnlJRmZpxz2AG8Ewx1F5HKF0rj2cy6A38AOkWX6e4nhVF+oj16UV8AVm/dRf/7x5JfBaddFyk8raviYwVEpGqqmR574qorTziQj2auZuXm2I+QEakqbjuzFzXT03h76o/k6RnmIpUulEdVAa8DU4FbgZuiXlVKejBzXL7+OYmIiCRU+6Z1+a8+B8Rcd/PpPRn/pypx/V6kVM0b1Ob/zuvDlFtPSXQoItVCWI3nPHf/p7t/6+5TCl8hlZ000oJZIwvUMydVkOPBTxGRquGbEWpAS/XQuF5NbjylO30zmvDJ745PdDgiVVaFGs9m1szMmgHvm9nVZta2MC1Ir1LU8ywiIpIcPrr+eEaefXCpedo2rsuI0w6KU0QiifXbQd146+pjOahNI647qWuiwxGpkira8zwFmAwMJTJMe0KQVpgeOjMbYmbzzWyRmQ2vjH2UpLDnWY1nqYr23POc2DhERMqjUZ2a1K9d9tQtlx/bmZsG92DeyCHMGzmEr/44kBYNavG38w/jXxf3jUOkUh0c2r4xANed1JWTe7YqSr/1jJ4lbnPZMZ0qLZ7rTupGj9YNee7X/faazfibESfx3yccWLR88+mxLy5dM7BLpcUm8ZHZsWnRezMY2r9jqfmXPnAGw0O42Lg/ZTx5aeo9vKJCE4a5e+ewAikPM0sHHgVOAVYC35nZe+4+Jx77T9ewbRERkZRSq0Ya1wzc0wvXoVk9JkfdHzr29yfy2LgfeGPKynKXOfKcg7ntnVlFyy0a1GLDjt3hBJykatVIY3deQdHyoINaMWbeutDKf/jCw/nty1M5sEV9Prr+eAY9+MVez4wuSdN6NXnkV3256MlJfHvzIFo2rM07036kb0ZT1mzN5vzHJwIw9+4h1K2Vziez1vD0+CXceGp3rvrPFLZk5fLzvu148Lw+vDZ5BU+PX8rvT+3OovU7+PMn87lpcA+6tGzAwINaMmnxJg5u15hm9Wvx+uQV3PTGDGDPo3YWrN1O15YNWL8jh8/mjqF1o9oMO/5AatdM57Z3ZjHm9yfSplEdXv52OSs37+LOs3pTu0Ya//5yMU9flslb3//IBzNW7/X53r76GA7PaMr+qlUjjVE3nFC0/Nyv+9GwTo3IaIzTe/LvLxcDcOUJXahdI5073pvN9YO6kbU7jye+WsJlx3Rm1ZZsatdIIy3NuPn0ntRIMw667ZOY+2tWvxabdpbvb2Ds709ky65cfv7YBAD+dXFfrvrP90Xra9dIIyc419655thKeeTan889lD7tm/Cb5yezfFPWfm/fsHYNbj2zJ43r1uSh0QtYsHYH/7yoL6cd0rZcz98+sGV9Fq/fGXPd45ccwZUv/LS7XzOa1WP5piwW3XsaNdLTeGPKSt6eupIXhx0NwEVHd+TWt2fx7dJNtGlUhzXbIr/j+fecBsBVJ3ahbs107np/Nt1bN+T1q/oz/M2ZfDgzcl5edFQGL05aXrS/i4/OYMRpPel9x6iitKtO7MJVJ3bh9ndncWn/juTmR9pNXy/awOEZTWlWvxZzVm3jjEPb/qTPmGgVes6zmR0JrHD3NcHypcAvgGVEnvO8KZQo9+yvf1Du4GB5BIC73x8rf1jPkiy0a3c+PW//hD8NOYj/GaArclK13PnebJ6dsJSLj87gnnMOSXQ4IklJz3muuLDr5rAUfuGdfOvJ/N8n83l18p5HPb137bGc9cieL/A/3Hc6r09ewfC3ZvKvi/tyVOfm/GPsIkacfhA3vjad96dHHh3UulFtRp598D5fhE/u2ZrP5q7dK61rqwZcPaALN742naH9O3Lrmb14+/sf+eObkQba61f153evTCtqUH71x4Fc/ux3LFq3Y78+Z8PaNdiek1e03L11AxasLbuMIb3b8K9LjmDdtmzq1Ern+QlL+cunC7h+UDeuOrELPW/fu1F1Yb8ORY/LGvP7Exk3fz0jP9i7r+OagV1o2aA2g3q2pkWD2vS8/RMevvBwzupzAO7O4g07aVy3JjXSjGkrtvDd0k20aVyXS47uSKfhH/LHIT24ekDpw5NjPQ88LA+PWQhEhksXt2zjTto1qUuN9LIHeWbn5lOnZjoAlzw1ia8WbmD67adSv3Z6ubb/Kb5auJ5VW3Zx/pEZFBQ4XyxYz4AeLQHYnV9A7RrpMbcrbCBPXb6ZK57b83f8zGVHktG8Hr/45wS2ZOWy9IEz2LxzN4ePHM3w0w7igY/nAfC38w/jnMPbATD8zRkMO/5AurSsz9tTf6Rj83oc1qEp6Wm21+9t8frI+Xnxk5NYtTW7aJ+3ndlrn3Mq2ugbTqBDs3qs25ZDRvN6AMxcuZWtu3I5rluLonzvTvuR61+ZVrTculFt1m7LiVnmi8OO4tiuLWKuK/TkV4u558O5/Ob4zhzVuTnDnp/MyLN706ZxXW58bRrbs/N4/JIjSDPj2QlLGb9oA2f1OYCbT+9Jwzo1qFszncuf/Y7LjunE5c9+B8CE4SdxzAOR59d3al6PpRuzuOX0nvRo05CjDmzGy5OWc+f7c5hz92Dq1Sq7b3Tdtmzq1kpn6YYsWjWqTetGdUrMu2t3Ppc8NYmR5xxM99YN+d9P5tG1VQP++MYMvr15EK0a1WF3XgEnPTiOrVm5zLxrcJn7D1s86+aKNp6/B052901mdgLwCnAdcBjQ093PDSfMov2dCwxx92HB8iXAUe5+baz8YVfQOXn59Lj1E/5waneuPWnff5QiqUyNZ5GyVbXGczA/yatEHjW5FPilu2+OkW8okSdqANzj7s8F6bWAR4ABQAFwi7u/Wdo+k7XxvGrLLrJ259G1VUMAducV8NnctfTNaEqbxnX4fP46cvMKqF0znRO7RxoZyzdmFX0pjzZp8UY2Z+1mcO82mO1pCPzu5G40q1+Li4/qyIQfNtKuaV0G/mUcEOkZLCw32lcL17Ni0y5+dVQGL0xcxm3vzOKE7i15/tf9ivIc/+exrNgUaVT/MrM9lx3TmRYNa9Hv3jF7ldWnfWPevfY4ANZuy6ZRnZrUrZXO5/PXcfkzkS/pQ/t3pHmD2jw0egF3ndWbLi0bcPFTk3jsor6cfsienqJdu/N5aPR8bjylB3VrpfPQ6AV8PHM1d599MI3q1qD3AY33abgWLi+45zRqphtmsR81FqZOwz/c53glsx05ecxfs50jOu5/b3M8bdq5m74jR/P0ZZms3ZbDBUd2wMxYvz2H9dtz6HVAIyByq2OaQb/7xlArPY1xNw2gZjkuCPztswU0q1+LS/t3Kkob+vS3fLFgPf++5Ah6tW1E+6Z12ZKVy+EjR9OtVQMWRl1Imn3X4HLd1gEwcfFGLghGKAAsuf903GF7dh4TftjAe9NXMXvVNpZvyuKlYUdxTBmNZ3dn5eZddGi27/8GgBWbsorWuTuvT1nJaQe3oWGdmvvk7TT8Qwb0aMmzl/cr+vuZc/dgdubk07Jh7XJ9vngpCG5rLbzNNZ5SqfE83d37BO8fBda7+53B8jR3PyyUKPfs7zxgcLHGcz93vy4qz5XAlQAZGRlHLFu2LLT95+UX0PWWj+nQrC4ZJfxBiKSqxet3snprNm0a1aFLq/qJDkckVKf2asPQEO4vrIKN5z8Dm9z9gWAekabu/qdieZoRmcckk8iE/FOAI9x9s5ndBaS7+61mlgY0c/cNpe0zWRvPlWnRuu1s2LGbow9svs+6a178ng9nruaFK/pxfLd9G8/RVm/dxYn/N453rj62qHECkUb8/R/P5a/nH1bUg7k/Cgqci5+axJ+GHESfDk32e/uSXPLUJKYs28ycu4cA8N3STbw5ZSX3//yQuDScATbuyKFBnRol9qRK6ihsPD9z+ZEM7NGq7A3KqaDA+fuYhTSqW5ND2zfmyE77znl874dzeOKrJXx6wwl0b90wtH3vj6tfnEKTerW472fq4CgulRrPs4DD3D3PzOYBV7r7l4Xr3L30aTD3f38JHbbt7ox4a+Z+D5ESSRWTl23ea4IJkaritEPacsVxFZ+mowo2nucDA9x9tZm1Bca5e49ieS4M8vx3sPzvIN/LZrYCOMjdY9+8F0N1bDyXZkvWbv4zcRlXD+iakB4bkVSxcnMWD41ewAM/P5RaNSpnOHtJcvMLmLt6G4e2D+/ikoQnnnVzhSYMA14GvjCzDcAu4CsAM+sKbK1g2bF8B3Qzs87Aj8AFwK8qYT8xmRkP/OLQeO1ORESksrV299UAQQM6VndOO2BF1PJKoJ2ZFX6LHGlmA4AfgGvdfS1Sbk3q1dKtYLsCoigAACAASURBVCLl0L5pPR76ZaiDWsutZnqaGs4CVHy27XvNbAzQFvjU93RjpxG59zlUQQ/3tcAoIB142t1nh70fERGRqsLMPgPaxFh1S3mLiJHmRL5DtAe+dvcbzexG4C/AJTFiiL6lqpy7FRERSS4VGrad7MxsPZGZvyuiBVDq/VtJKBVjBsUdT6kYMyjueErFmKHy4+7o7qXfmJpCKjJsm8gkoTuAhu5eYGYdgE/cvXcZ+1TdnFoUd/ykYsyguOMpFWOGKlQ3V3TYdlIL4yCa2eRUu78tFWMGxR1PqRgzKO54SsWYIXXjTqD3gKHAA8HPd2PkGQXcZ2aFEyKcCoxwdzez94nMtD0WGASU/NyYgOrm1KK44ycVYwbFHU+pGDOkbtyxxPduexEREUkmDwCnmNlC4JRgGTPLNLMnAdx9EzCSyLwj3wF3B2kAfwLuNLMZRIZr/z7O8YuIiMRNle55FhERkZK5+0YiPcbF0ycDw6KWnwaejpFvGXBCZcYoIiKSLNTzXLbHEx3AT5CKMYPijqdUjBkUdzylYsyQunHL/knF33MqxgyKO55SMWZQ3PGUijFD6sa9jyo9YZiIiIiIiIhIGNTzLCIiIiIiIlIGNZ5FREREREREylAtG89m1sHMPjezuWY228yuj5HnIjObEbwmmFmfqHVLzWymmU0zs8lJFvcAM9saxDbNzG6PWjfEzOab2SIzG55EMd8UFe8sM8s3s2bBukQd6zpm9q2ZTQ/ivitGntpm9mpwPCeZWaeodSOC9PlmNjjJ4r7RzOYE5/YYM+sYtS4/6nfxXpLFfZmZrY+Kb1jUuqFmtjB4DU2imP8aFe8CM9sStS4hxzpq/+lmNtXMPoixLunO7XLEnHTntewf1c2qm8sRt+pm1c1hxKy6OX4xJ915XWHuXu1eQFugb/C+IbAA6FUszzFA0+D9acCkqHVLgRZJGvcA4IMY26YDPwAHArWA6cW3TVTMxfL/FzA2CY61AQ2C9zWBScDRxfJcDfwreH8B8GrwvldwfGsDnYPjnp5EcQ8E6gXv/6cw7mB5R7yP9X7EfRnwSIxtmwGLg59Ng/dNkyHmYvmvA55O9LGO2v+NwEsl/L9IunO7HDEn3Xmt137/flU3q24uK27Vzcl3vC9DdXOY8atuTvJXtex5dvfV7v598H47MBdoVyzPBHffHCxOBNrHN8p9lSfuUvQDFrn7YnffDbwCnF05ke7xE2K+EHi5suMqi0fsCBZrBq/is+udDTwXvH8DGGRmFqS/4u457r4EWETk+Fe68sTt7p+7e1awmCzndnmOd0kGA6PdfVPwNzsaGFIJYe7lJ8ScFOc2gJm1B84AniwhS9Kd22XFnIzntewf1c2qm8uiujm+VDfHl+rm1FAtG8/RgiEPhxO5MlWSK4CPo5Yd+NTMppjZlZUXXcnKiLt/MFzlYzPrHaS1A1ZE5VlJ+Sv3UJR1rM2sHpF/rG9GJSfsWAfDUKYB64hUAMXjLjqm7p4HbAWak+BjXY64oxU/t+uY2WQzm2hm51RqoMWUM+5fBEN/3jCzDkFawo53eY91MEypMzA2Kjlhxxr4G/BHoKCE9cl4bpcVc7SkOa/lp1HdHD+qm+NDdbPq5nJQ3ZwCaiQ6gEQyswZEKoPfufu2EvIMJPLLPi4q+Vh3X2VmrYDRZjbP3b+s/IiLYiot7u+Bju6+w8xOB94BuhEZxlJc3J5TVp5jTWRY2NfuvikqLWHH2t3zgcPMrAnwtpkd7O6zorKUdEwTeqzLETcAZnYxkAmcGJWcERzvA4GxZjbT3X9IkrjfB1529xwzu4rI1deTSODxLu+xJjK86o0gf6GEHGszOxNY5+5TzGxASdlipCXs3C5nzIV5k+q8lv2null1c2lUN6tuLovq5qL0SlVd6+Zq2/NsZjWJVBgvuvtbJeQ5lMgwhLPdfWNhuruvCn6uA94mTkMjgphKjdvdtxUOV3H3j4CaZtaCyFWoDlFZ2wOr4hByuY514AKKDZ1J5LGOimELMI59hxsVHVMzqwE0BjaRwGMdrZS4MbOTgVuAs9w9J2qbwuO9ONj28HjEGq2kuN19Y1SsTwBHBO8TfrxLO9aB0s7teB/rY4GzzGwpkSGiJ5nZf4rlSbZzuzwxJ/V5LeWjull1c3mpbo4v1c2VTnVzitTN5h63i25x16JFC+/UqVOiwxARkSpiypQpG9y9ZaLjSGWqm0VEJEzxrJur9LDtTp06MXly3J6gICIiVZyZLUt0DKlOdbOIiIQpnnVztR22LSJ7+3LBevrd+xlj561NdCgiIiICZOfm0//+MYyZq7pZJBmE0ni2iIvN7PZgOcPM4n7/i4j8dNNXbGHd9hy+X7Yl0aGIiIgIsHZbNqu3ZnPn+7MTHYqIEF7P82NAfyLPSgPYDjwaUtkiIiIiItWWxZxQWUTiLax7no9y975mNhXA3TebWa2QyhYRERERqXYKgnl909R2FkkKYfU855pZOsEzxcysJeV7WLaIiIiIiMRQEDwVx0ytZ5FkEFbj+WEiz/lrZWb3AuOB+0IqW0RERESk2il8oqyaziLJIZRh2+7+oplNAQYR+fs+x93nhlG2iIiIiEj1pNazSDKpcOPZzNKAGe5+MDCv4iGJiIiIiIgX3fOs1rNIMqjwsG13LwCmm1lGCPGIiIiIiAiwcssuABat25HgSEQEwpttuy0w28y+BXYWJrr7WSGVLyIiIiJSrYyZuzbRIYhIlLAaz3eFVI6IiIiIiLDnUVUikhzCmjDsizDKERERERGRiIE9WvHSpOW0alg70aGICCE9qsrMjjaz78xsh5ntNrN8M9sWRtkiIiIiItVR/drpABzYsn6CIxERCO85z48AFwILgbrAsCBNRERERER+goKCyE/Nti2SHMK65xl3X2Rm6e6eDzxjZhPCKltEREREpLpZujEyD+/OnLwERyIiEF7jOcvMagHTzOzPwGpA40tERERERH6ih0YvAGD6yq0JjkREILxh25cA6cC1RB5V1QH4RUhli4iISAWYWTMzG21mC4OfTUvINzTIs9DMhkaljzOz+WY2LXi1CtJrm9mrZrbIzCaZWaf4fCKR6uGXmR0A6NW2UYIjEREIqfHs7svcfZe7b3P3u9z9RndfFEbZIhIfehqGSJU2HBjj7t2AMcHyXsysGXAHcBTQD7ijWCP7Inc/LHitC9KuADa7e1fgr8D/VuaHEKlu3CO1c+tGmm1bJBmENdv2EjNbXPwVRtkiIiJSYWcDzwXvnwPOiZFnMDDa3Te5+2ZgNDBkP8p9AxhkppmNRMLy7y8jX6cXrN2R4EhEBMIbtp0JHBm8jgceBv5T2gZm1sHMPjezuWY228yuD9JjDi2ziIeDoWEzzKxvSLGLCODqehapylq7+2qA4GerGHnaASuillcGaYWeCYZs3xbVQC7axt3zgK1A8+IFm9mVZjbZzCavX7++4p9GpJpJT9M1KZFkENaw7Y1Rrx/d/W/ASWVslgf83t17AkcD15hZL0oeWnYa0C14XQn8M4zYRUREqgIz+8zMZsV4nV3eImKkFV5Wu8jdDyFygfx4InOdlLXNngT3x909090zW7ZsWc5wRKSQGs8iySGU2baL9QKnEemJbljaNsGV78Kr4NvNbC6RK9hnAwOCbM8B44A/BenPe+Tmj4lm1sTM2hZeSReRinHd9SyS0tz95JLWmdnawjrTzNoC62JkW8me+hegPZE6GHf/Mfi53cxeInJP9PPBNh2AlWZWA2gMbKr4pxGRaDXT1XgWSQZhDdt+MOp1P3AE8MvybhzMznk4MImSh5aVNZxMREREYnsPKJw9eyjwbow8o4BTzaxpcMvUqcAoM6thZi0AzKwmcCYwK0a55wJj3XUTiEhYrjqxCwCHdWiS4EhEBELqeXb3gT91WzNrALwJ/M7dt5Uyz0i5hoaZ2ZVEhnWTkZHxU8MSqXb0dVekSnsAeM3MrgCWA+cBmFkmcJW7D3P3TWY2Evgu2ObuIK0+kUZ0TSKPpfwMeCLI8xTwgpktItLjfEH8PpJI1deiQS1Aw7ZFkkVYw7ZvLG29uz9UwnY1iTScX3T3t4LkkoaWFQ4NK9QeWBVjX48DjwNkZmaqOSAiItWeu28EBsVInwwMi1p+Gni6WJ6dREaUxSo3m6AhLiLhW7M1O3inxrNIMghztu3/ITKMuh1wFdCLyH3PMe99DmbqfAqYW6xxXdLQsveAS4NZt48Gtup+Z5HweNFPXXMSERFJBk+OX5LoEEQkSig9z0ALoK+7bwcwszuB1919WCnbHEtkts6ZZjYtSLuZEoaWAR8BpwOLgCzg8pBiFxERERFJWnp6ukhyCKvxnAHsjlreDXQqbQN3H0/JY1BiDS1z4JqfGJ+IlCW46Vn3PouIiCQXtZ1FkkNYjecXgG/N7G0ioz9/RuQxUyIiIiIiUgHTV25JdAgiQnizbd9rZh8DxwdJl7v71DDKFpH48GI/RUREJDnM+nFbokMQEcKbbbsLMNvdvzezAcDxZrbE3XWZTERERERERFJeWLNtvwnkm1lX4EmgM/BSSGWLSBwU3uuse55FRERERPYVVuO5wN3zgJ8Df3f3G4C2IZUtIiIiUqK5q7fhuvInIiKVLKzGc66ZXQhcCnwQpNUMqWwRiYPC5zvrOc8ikkomL93EaX//iqf0PFwREalkYTWeLwf6A/e6+xIz6wz8J6SyRURERGL6ZNYaAKat0DQrUvVc2r9j0fstWbtLySki8RBK49nd57j7b9395WB5ibs/EEbZIhIfrum2RSQFPRn0OH8wY3WCIxGpXLty8xMdgki1F1bPs4iIiIiIVJL+948lv0BXuEUSSY1nEQH0nGcRST1zVunZt1K9dLn5o0SHIFKthdJ4NrPzypMmIiIi8WdmzcxstJktDH42LSHf0CDPQjMbGpU+zszmm9m04NUqSL/MzNZHpQ+L12cCeGzconjuTiQpbN6pe59FEiWsnucR5UwTkSS15znP6nsWqYKGA2PcvRswJljei5k1A+4AjgL6AXcUa2Rf5O6HBa91UemvRqU/WYmfYR/F73N+bfKKeO5eJCEOHzmaiYs3JjoMkWqpQo1nMzvNzP4BtDOzh6NezwJ5oUQoIiIiFXU28Fzw/jngnBh5BgOj3X2Tu28GRgND4hTfflu4dvs+aX98YwYrNmUlIBqR+Lrg8Ylsy85NdBgi1U5Fe55XAZOBbGBK1Os9IpWwiKSIouc8q+NZpCpq7e6rAYKfrWLkaQdEd92uDNIKPRMMzb7NzCwq/RdmNsPM3jCzDqFHXoKSet6O//PnZO3W9XupGtyhXq30mOsOvfNTtu6KNKBXb93F+IUb4hmaSLVUoyIbu/t0M5sFnOruz5W5gYiIiFQKM/sMaBNj1S3lLSJGWuHltIvc/Uczawi8CVwCPA+8D7zs7jlmdhWRXu2TYsR2JXAlQEZGRjnDKd3YeetKXNfr9lHccnpPfnPCgaHsSySR6tRMJ2t37MdU9bnrUwCa1KvJlqxclj5wRjxDE6l2KnzPs7vnA83NrFYI8YhIovheP0Qkxbj7ye5+cIzXu8BaM2sLEPyM1fJcCUT3HLcnMsIMd/8x+LkdeInIPdG4+0Z3zwnyPwEcUUJsj7t7prtntmzZsuIfFvh8/vpS19/70VwN4ZZqY0uWhnCLxENYE4YtA74OhnLdWPgKqWwRERGpmPeAwtmzhwLvxsgzCjjVzJoGE4WdCowysxpm1gLAzGoCZwKzguW2UdufBcytpPh/kt+/Pn2v5a27csnOjd2DJyIiUpawGs+rgA+C8hpGvUQkRRQ951ldzyJV0QPAKWa2EDglWMbMMs3sSQB33wSMBL4LXncHabWJNKJnANOAH4n0MgP81sxmm9l04LfAZfH7SGX7dsmmvZb73PUpA/8yrlL3OXnpJnrd/okeJyShGtgjnBEbIlIxFbrnuZC73wUQ3Avl7r4jjHJFRESk4tx9IzAoRvpkYFjU8tPA08Xy7KTk4dgjSPJHUx5yxygm3jyI+rUjX3lWb81m7Ly1nHRQ60rZ36OfLyJrdz7fL9/MoJ6Vsw+pfm4+vWeZtyoATFm2mfq10zmoTSNenLSMx79czBc3DYxDhKln0uKNNG9Qm66tGiQ6FEkhofQ8m9nBZjaVyDCu2WY2xcx6h1G2iMRH4fOdXXc9i0iK6X1AoxLXbc/Jo/cdoxg7b21R2rQVW/esz85lzqptP2m/67fn0P3Wj5m2YktRWlowEXlpo3jWbcv+SftLNmu2ZrN8Y/W9r/yZr5fw/+zdd3hUVfrA8e+bhNBb6C2EjiiKgDRRQVAQ69p1VXT157qWXTso6uraWHdXV1fXtbddG3YBC1V6SZDeQgmQUBKSkEL6zPv7YybDJJk0MpmZhPfzPPNk7r3nnvvOmZs5c+aee86O5JLXi7YfyuKj5Ql+P1afDs2rNBjYFa8vY+I/FzMjdh/Tvt7IniC9P7d9sJo/f7ux1o+Tmp1PkcPJ/K2HOJhRvf+ra95cwfgXf6mlyEx95a9u228C96tqd1XtDjzAsS5dxhhjjDG15tu7zqw0ze/ej/U8f2VePOf+fSFFDicDn/yZSa8spt9jPxAzdVa590TPWn+gTFfs5btSKShyctlrS4mZOotCh5OUbNf4aU5363ljUgYxU2fxxLcbOZJTwLIdhxn23Dz+/tM2ej86m0M+GtKq6nN9MKVk5bMrpWRDccTz8zj7bwsAcDiVF3/eRoaPgasOZ+dT6HB6lp1OZeWuVA5k5HLXx2vYdjDLsz45KzivOyuvkOz8Y1OcLdtxmN2Hj5abXlV56vvNXPrqEgA+WrGH338Uy/kvLeLxbzeVSDtr/QEOZuT5LJvqund8nyqle+iL9Z7nRQ4nqsprC3aQdCS33H1+3HigxPsEUFDkZNP+jBI/PlXF3C3JfLB8T5n1t32wmse+2VCtvMqTW+BgyDNzeeK7Tfzu/Vgu+tcS0iq5XeKrNYnETJ1Vp37A+nVvOvlFtTtWQ3mfOYnpOSSmn7g/kPnil27bQFNVXVC8oKoLRaSpn/I2xgRA8VUSu+fZGFOX/PHc3kSEh/HO5KHc+kFs5Tu47Tp8lN7TfvAs5xe5Gg0T/rmIjNxCJg3sxMcr9wIw/qT2zN3iGqD889+PJDIijJSsfNZ7XXEG6OOV3/ZDWZx/ckfPfNQfLt/DrpSjnNWnLQCvLtgBwPDn5nHVkK5MuaA/LRs3oEF4GF+tSfIMdpYw/UI278+kQbjQp0NzVJVftqfw5qJddG3dmBeuPK3c17gxKYO3F+/iH1cPIm5POsN6RAGuRlKPts04mJlHjzZNiW7TxOf+qsqq3WkM6xHF8Ofm4lRXPGv3HeGK15eVSHvze6tYHH+YxPRcXrxmEBuTMnjsm408eH4/bnhnJVcN6crfrnLF+tGKPfz5u2MNzD2pR3n1usH89cet/LDxIE9fejKdWzWmddNI2jdvyOi/LuCP5/bm1tE9STySw97UHDq1asz2g1lcfYZrgHinU+n/+I8UOJzMvGc0R/OL6N6mKXvTchjUrRVpRwuYuX4/Z8REsWZvOgM6teCX7SncNbY3+9JzmPjPxYQJxD87iTV707n+7ZWe8q/I0QIHL83Zzsvz4kus35WSzUtz47nlzBju+niNZ/1Tl5zMxad1Jqrp8U1Sc+/4vqxOSGPpDt/znPvifZ7/7adttG3WkFeuG8SoXm0962OmzgKgZ7umzH9gzLHjffYrszccBCovi0OZeaRmF3DhvxaXWH8wI4+OLRsBeP6PnrlsYKVxH8rMo33zhogIBUVONiRlMKR7a8/2hdtceRX/nx7Ozmfw03N4+6ahrN6TxiMXnFQmz89jXVPZ70ip/A7T/CIHZzwzl4cm9ufxbzZyzdBuDIpuxbtLdhOfnM2r15/ORad25qnvN/He0oTjmibM4VRenrudW87swfZDWUQ1jaRPh2PDRu1KyeY3/17GDSOieeaygSRn5bEjOZs+7ZuTkVvo6XIeM3UWA7u05Pt7RqOqJGflcygzD1U4rVsrz+s5mu/wee79/edtvLZgJ1/fOYrU7AIKHE4uOKUjo//qat59+LthLNlxmIcm9ONwdj4FRU66t2lKXqGD6T9sdf294tQy+Z70+I+0a96QRQ+7bh3YejCTTi0a07JJg2qXVagQ9cM3ZRH5GlgDfORedQMwVFUvq3HmNTB06FCNja16RWrMieyZmZt5e8lubh4Vw5OX2F0XxvgiInGqOjTYcdRl/qqbi7/s//fW4Yx2N0iL19Und43txWsLdgLw3G8Gsmp3Kt+s3e/ZftvoHvy8+RBXDelKZl4hby3eTZiA08fXu+uGRZOYnsPi+MMl1r949Wnc//k6xp/UgQXbknE4lZ/vO5vN+zO597O1TDy5Iz9uOtaAmvTyYjYfKL+r+/+d1YO3Fu8us37FI+PYmZLNb90NU3/413Wns/VgpqeMauq+8X15ae52z7J3gyg5M4+s/CLG/aPmXX2/uetMFm5L5v1lCayeNp4tBzLp1a4ZTSLDOZSZ72lsPvbNBmZvOMiax8/z7JuRW+iZ37kmBnVrxdp9R+jfsTlb3Vf/wdXAnzwqBij5P1VcFll5hRzKzONovoNuUU3Yk3qUvWk5/OnTtWWOUXxuffmHkQzpHuXJL2H6hSRn5tGmWUPCw8TzuhLTc8gpcNC6SQPGv7iIW0f3IG5POp1bNWL2hoP8fN/Z9HU3Ll/4cSv/Xlj++/7i1afxzdr9PHh+X16ZF8+NI2N4feEOVuxK46w+bT3/BwnTL2RJ/GES03O45oxuiPvWiy0HMrng5cXl5g/w2IUn8cws1yQDN4yIZtH2w3Rq2YinLzuFZg0j6NyqMe8u2c1fZm5my18msvlApucHgG/XJnnKbGCXlmxIyiiR9/JHzmXk8/M9y7ef3ZM3F+2qMB6Am0fF8P6yBM9ym6aRPH/5QJ78bhP7M/J4dFJ/bj+7V4l9juezc+Y9o7njv3Ekprt6M7RtFsnhbNeV/y/uGElM26YMfWYuQInyLvbopP7831k9PeVdE4Gsm/3VeG4NPAWMBgRYBDypquk1zrwGrPFsTNU9PXMz71jj2ZgKWeO55vzdePZu3KgqPR6ZXeO8jcuoXm1YtrPqVzjrsxeuPJWHvbpC14bRvduyZIergTHzntH8tOkg/5rv6qFQ+qrmun1HuPm9VaTX0vzO947vwz/nlryaXrqRXV0z7hjJVf9ZDrh+9Hl7yW5+d2YP/jiuN5ERYQx44idP2vbNG5KclV9eVrXuglM68sPGgzXOp3RDFmpejv70v9uG+/XHrOq6dFBnXr729BrnU+caz6HKXxX0U99vIsX9DxwZEcaD5/ejc6vGNc7XmFBijWdjKmeN55rzV9087h8L6d+pBa9dP7jE+rxCB/O3JnPn/9aUs6cxdY+vLsGFDmeJWwWMqYuOp7t7aYGsm/1yz7OI9AUeBGK881TVc/2Rf7DtSM4m6UguRQ5lb1oOI3q24eqh3YIdljF+deye5/r7g5oxpv5wOJWIsLLd/Ro1CGfSwE5BiMiYwGoQHsafxvVhUXwKv+49UvkOxpga89eAYTOA/wBvA7U7HFwQfHTrcAAOZOQy8vn5OH3dSGSMMcaYgClyKuF+uFfOmLrsvvP6cu/4Pna7gjEB4q+pqopU9XVVXaWqccUPP+UdMoor6SJrPJt6qHh+Zzu7jTF1wXXDohnTv32521dNG0eTyHAAukXZrVam/hIRPr19BE9fdkqwQzGm3qtR41lEokQkCvheRO4UkU7F69zr65Uwd/cwp3VrNcYYY4LqrrG9ueS0zuVub9+8EYPcU7T846pBgQrLmKAY0bMNN47oTif3KN3GmNpR027bcbguVBX3m3rIa5sCPWuYf0gpvrfKYVeeTT1k8zwbY+qbF68exAfLExgc3arMtpevHeRzah1j6rLlj4zzPK+PU7eZ+mX385OCHUK11ejKs6r2UNWe7r+lH7XScBaRiSKyTUR2iMjU2jhGecKs8WyMMcbUGR1bNmLKxP5EhIex4pFxvHztIM7u244tf5nIpYO6sPLRcfxhTC82PTWBVk0aAK55o0v78g+jahzLl38YyYe/G0Z0VBMaNfDXXXN135z7zq43V0v/d1vZc8cY45sIfpnjOdBq2m37DBHp6LV8k4h8KyKv1Ea3bREJB14DLgAGANeJyAB/H6c8xfc8W+PZ1Gdqdz0bY+qhji0bcemgLnz4u2E0dt8L3aGFq3HdtGEEa584n4TpFzK6T1sSpl/IkiljmXv/Oex+fhJDurfmqUtO5v7z+nry+9O4Pvx471mc1rUlm56a4Lmv+rph3fjrFQPZ/fwklk49l/duPoOXrx3EkO5RnN23HYseHsuc+87xGeOyqeeWmLbl8YsGsOu5SfRs27Tc11XZlZs7zunleb7y0XHsem4SA7u0BGDDk67XvPjhsfz+nGPXPIb1iGL385PY/fwkfnloDLP/eFaJPL/8w0g2PTWhxLoG4b6/BM+8Z3SF8fXp0Jz/3TacB87ry67nfL+Wj24dxs/3nV1i3dKprgldzh/QgWVTy07u0q9Dc353Zg8AVj06jv/cMMSzLaZNE0b2bFNhXFV1Zu82/Hzf2UyZ2J8ze7dl8sjuAGx9eiJf3DGS5o1cnTyvHx7NDSOi/XLMqlo69VzmPXAOc+8/h6VTz2XFI+PY9sxEEqZfSML0C7lqSFdP2tG92wY0ttpwwSkdK090HDq0aFgr+Zan+P8T4MPfDSux7c0bh5ROTtfWrs+e7c9cwMpHx5XZ7kuTyHDaNqv4dX35h5FVysvbH8/tzW+HHzvP1z95Pl/feezHx0kDO3Jmb9f/XmR43fwRsUbzPIvIGmC8qqaJyNnAp8A9wCDgJFW90j9heo43EnhSVSe4lx8BUNXnfaX311ySxfIKHfR//EcentiPO8f09lu+xoSCJ7/bxPvLErhhRDTPXDYw2OEYE5Js6vzTswAAIABJREFUnuea83fdHGhOp/q8YqKqfLJqH5cP7kKjBuFVyuvnTQe5/aM4Fj88lq6tG3vy3JuaA0B0myaetH/9cSuvL9zJykfH0aFFI2ZvOEDDiDDGndSBQ5l5DH9uHuCaM3Vvag63fxTLb0d058YR3T3ddyuaT3XrwUwm/nMxL1x5qs/pOL2/LxbHWZyvCLx541BG9WpDfpGT9JwCxv3jF88xVZW+j/1AoUM9y5NeWcI1Q7tys7uBW2zelkPc+kEsTSPDOVrgYPLI7jx1qWsgrB3J2Vz31gr6d2zOR7cO98QkIp7nxaNOFx/HO968QgcbkjI4IyaKnIIiXpqznfvP60fjyHAWbU/hh40HuHJIVwqKlOveWlHRWwfAsJgoPr+jbANDVUu8l9+sTeKec3t71iWm5/DL9hQGdmnJ3R//yt3n9qZts0h6tWvGOX9byG2je/D2kt2e/CIjwtj+zAWVxnM8MnILAWjZ2NXz4n8r9zBr/QGeuHgAfds3p+ejrvLc9dwkz/OXrx3EfZ+t5YJTOjFrwwEAzu7bjt7tmhEd1ZjfDO7KaU/9DLh+3NlyIIspX65nQ1JGmePfN74vL83dDsApXVoQHdWE2RsOAvDr4+dx+tNzGH9SBxZsSy5x8WrKxP6IwO/P7kluoYPv1u7nmjO6ISLETJ1Fi0YRPDyxPwcycrl8cFe2HMjk7o9/9VkGb980lJM6t+DSV5dyODu/xLaE6ReSkpXPGc/O5b7xfTn/5A58uDyBT1btY/xJ7Zm7JdlVbrcN57dvryyTd6eWjTiQkVfhexARJix6eCyjps/3HPNofhFbD2YxpHtrlu08zPVvrSQ6qgkLHxzDP+Zso2+H5uQXOenSqjEje7bx9I4tVnx+v7XYdR6N7deOBdtSPNs/u30Ew3u28fwPb316Ipl5hcxaf4DYhHSeuHgAHVo08tn1f/LI7nywfA83j4rh7nN706ZpJKP/uoCkI7n88tAYurdpWuYzZ/uhLJIz8xndpy1ZeYUMfPJn2jVvyOpp4yssm6oKaN2sqsf9ANZ5PX8NV8O2eHltTfIu53hXAm97Ld8IvFoqze1ALBAbHR2t/pRf6NDuU2bqv+Zt92u+xoSCP3+7UbtPmanTvl4f7FCMCVlArPq5bgvEA4gC5gDx7r+ty0k32Z0mHpjstT4SeBPYDmwFrnCvbwh8BuwAVgIxlcUyZMgQf7wVJ5wih1P3H8kpd3tadr5m5RX63JaRW6BHcgr8HlP60XzNzPWd797Uo7o7JduznJlboEeOVh5DkcOpz8/eoqnZ+ZqUnqNFDme1Ynp1fry+vXhXtfbx5d0lu3RD4hE9ml+oqdn5ntj+uyJBj+YX6uR3V+qWAxk1Pk55diZnafcpM7X7lJnqdFavDPzphw0HNCnddd6t2HlYP1u917PttQXx2n3KTP1lW3KZ/YpjL5adV6hr9qSpqmpBkUMf/2aDZrvP11fnx+sur3PFe9+4PWmanVfoWddjasl8fUk/6vt/Yf+RHM0vdOhff9iicXvStPuUmfr9uqQy6Q5m5Gr3KTP17o/XlFjv630o/u605UCG7k7J1r2pR1VVdUl8im47mKmqqsmZeZ74F29P0Xs+XuNZfnbWZs0tKPK87kv+tdjna0rJyvOkq47S59AnK/do9ykz9VBmbpXzTcnK07mbD3qWnU6nLolPKVEe/1m4Q7tPmen5PHhr0U59dX58uXm+Oj9edyZnVfv1lCeQdXNNrzxvBAapapGIbAVuV9VFxdtU1a9j5ovIVcAEVb3NvXwjMExV7/GV3t+/bjudSq9prl/dbG5JU984VFF1XUGw89vUNzeO7M6fLz65xvnU1SvPIvICkKaq093jhbRW1Sml0kTh+vF5KK5BP+OAIaqaLiJPAeGq+piIhAFRqnpYRO4ETlXVO0TkWuA3qnpNRbHU9SvPxgTKpv0ZrNqdxi2lrs6HCodTWbU7jZG9ynaBr0pvh/L42vfyfy9lzd4jrHp0HBm5hfTp0Pw4o66auD3pDOjUwnOLR3kKipysSzzCGTEV36064rl5HMzM87wmX68xNTufpg0jqtxzpSpq8j7UJYGsm2vaeJ4GTAIOA9HAYFVVEekNfKCqZ/onTM/xgtptG+CLuER2H872a57GhIp9abk2H6qplwZHt2bcSR1qnE8dbjxvA8ao6gER6QQsVNV+pdJc507ze/fyG+50n4jIPqC/qh4ttc9PuOrl5SISARwE2mkFXy6s8WxM/bdqdxrNGkYwoHOLau+bmefqSt6iUYMS6/YczmFg15bl7RbScgqKyC900rppJABJR3IpLHISU8F4Bv6w/0guG5MyOP/k2rkfPFQEsm6u0VRVqvqsiMwDOgE/e1WWYbjuffa31UAfEekBJAHXAtfXwnHKdaXX4ArGGGNMHdFBVQ8AuBvQ7X2k6QLs81pOBLqISPE8T0+LyBhgJ3C3qh7y3sfdCy0DaIPrR3VjzAlqWI/jHzfYu9Hsva6uNpwBmkRG0CTy2HKXVoG5UNG5VWM6B+hYJ4qazvOMqpYZUUFVt9c033KOVSQidwM/AeHAu6q6qTaOZYwxxtQlIjIX8HV5YVpVs/CxTnF9V+gKLFXV+0XkfuDvuMYdKW+f0rHdjmtMEqKjAzvisDHGGOMvNW48B5qqzgZmVyVtXFzcYRHZU8sheWtL3fy13eIOnLoYM1jcgVQXY4YTJ+7utRVITalqucOWisghEenk1W072UeyRGCM13JXYCGQCuQAX7vXzwBu9dqnG5Do7rbdEkjzEdubuAYcQ0RSrG6uEos7cOpizGBxB1JdjBlOnLgDVjfXucZzdahqu0AeT0Ri6+i9cBZ3gNTFmMHiDqS6GDNY3HXAd7hG0p7u/vutjzQ/Ac+JSGv38vnAI+6xTL7H1bCeD4wDNpfKdzmuGTHmV3S/M1jdXFUWd+DUxZjB4g6kuhgzWNy1oV43no0xxhgDuBrNn4vIrcBe4CoAERkK3KGqt6lqmog8jWt8EYC/qGrxVeQpwEci8k8gBbjFvf4d9/oduK44XxuYl2OMMcYEnjWejTHGmHpOVVNxXTEuvT4WuM1r+V3gXR/p9gBn+1ifh7shbowxxtR3YcEOoJ55M9gBHCeLO3DqYsxgcQdSXYwZLG4Tuurqe2xxB05djBks7kCqizGDxe13NZrn2RhjjDHGGGOMORHYlWdjjDHGGGOMMaYS1niuBhEJF5FfRWSmj203u6ffWOt+3Oa1bbKIxLsfk0Mo5pe84t0uIke8tjm8tn0X4JgTRGSD+9ixPraLiLwiIjtEZL2IDPbaFsyyrizu37rjXS8iy0TktKruG+S4x4hIhtf58ITXtokiss39XkwNoZgf8op3o/t8jqrKvrUcdysR+UJEtorIFhEZWWp7yJ3bVYg5VM/ryuIOufPaHB+rmwMWs9XNoRV3yH2GWd1sdbMf4g6587oMVbVHFR/A/cDHwEwf224GXvWxPgrY5f7b2v28dSjEXCrdPcC7XsvZQSznBKBtBdsnAT8AAowAVoZIWVcW96jieIALiuOuyr5BjntMOed8OLAT6AlEAuuAAaEQc6m0F+OaPicUyvoD4Db380igVantIXduVyHmUD2vK4s75M5rexz3e211c2BirqyuCLnPryrGHaqfYZXFHXKfYdUpL6xuru2YQ/W8rvN1s115riIR6QpcCLxdzV0nAHNUNU1V04E5wER/x+dLNWO+DvikdiPym0uBD9VlBdBKRDoRxLKuClVd5o4LYAXQNZjx+MEwYIeq7lLVAuBTXO9NqAmJc1tEWuAarfgdAFUtUNUjpZKF1LldlZhD8byuYlmXp66c1warm0NMSH1+VVUofobVUF35DAuJc9vq5sCpL3WzNZ6r7p/Aw4CzgjRXuLtHfCEi3dzrugD7vNIkutcFQlViRkS6Az2A+V6rG4lIrIisEJHLajFGXxT4WUTiROR2H9vLK9NgljVUHre3W3H9ink8+/pbVY49UkTWicgPInKye10wy7tK5SUiTXBVZF9Wd99a0BPX/Ljviau75tsi0rRUmlA7t6sSs7dQOa+rGneondem+qxuDhyrmwPL6ubAsLo5cOpF3WyN5yoQkYuAZFWNqyDZ90CMqp4KzMXVLQFcXTxKq/UhzqsYc7FrgS9U1eG1LlpVhwLXA/8UkV61EWc5zlTVwbi6mdwlIqXnFi2vTINS1l4qixsAERmL64NsSnX3rSWVHXsN0F1VTwP+BXzjXh/M8q5qeV0MLFXVtOPY198igMHA66p6OnAUKH3PTqid21WJGQi587oqcYfieW2qwepmq5uryOpmq5srYnVzaJV1KJ7XJVjjuWrOBC4RkQRc3QTOFZH/eidQ1VRVzXcvvgUMcT9PBLp5Je0K7K/dcIEqxOzlWkp1nVHV/e6/u4CFwOm1FmkpXsdOBr7G1VXDW3llGqyyBqoUNyJyKq6uepeqamp19q0tlR1bVTNVNdv9fDbQQETaEsTyrkZ5VXRuB7qsE4FEVV3pXv4CVyVSOk0ondtViTkUz+tK4w7F89pUm9XNVjdXyupmq5srYXVzCJV1KJ7XpdXreZ7btm2rMTExwQ7DGGNMPREXF3dYVdsFO466zOpmY4wx/hTIujkiEAcJlpiYGGJjAzoCuzHGmHpMRPYEO4a6zupmY4wx/hTIutm6bRtjANe0dfvScqjPvVGMMcaYuianoAin0+pmY0KBNZ6NMQB8tnofZ72wgI9W2IU1Y4wxJhTkFToY8MRP3Pf52mCHYozBT41ncblBRJ5wL0eLSMAGVTDG1FxKlmtMneTM/EpSGmOMMSYQNiRlAPDtWhu30JhQ4K8rz/8GRuKa8BwgC3jNT3kbY4wxxhhzwil0VDgduDEmwPw1YNhwVR0sIr8CqGq6iET6KW9jjDHGGGNOODuSs4MdgjHGi7+uPBeKSDjuyapFpB1gP5UZY4wxxhhznAodNlCYMaHEX43nV3BNst1eRJ4FlgDP+SlvY4wxxhhjTjgNI2xsX2NCiV+6bavq/0QkDhgHCHCZqm7xR97GGGOMMcaciE7t2hKA6KgmQY7EGAN+aDyLSBiwXlVPAbbWPCRjjDHGGGOMwz2/c6smDYIciTEG/NBtW1WdwDoRifZDPMYYY4wxxhiOTVG1PjEjyJEYY8B/o213AjaJyCrgaPFKVb3ET/kbY4wxxhhzQtmblhPsEIwxXvzVeH7KT/kYY4wxxhhjgBaN/PVV3RjjD/4aMOwXf+RjjDHGGGOMcWndNBKAXu2aBjkSYwz4aaoqERkhIqtFJFtECkTEISKZ/sjbGGOMMcaYE9HHK/cCsDPlaCUpjTGB4K/J414FrgPigcbAbe51xhhjjDHGmONQPNq2MSY0+O1GClXdISLhquoA3hORZf7K2xhjjDHGmBNNkTWejQkp/mo854hIJLBWRF4ADgB2c4YxxhhjjDE1FCbBjsAYA/7rtn0jEA7cjWuqqm7AFX7K2xgTAPbbtjHGGBNaHp7YD4DLBnUJciTGGPBT41lV96hqrqpmqupTqnq/qu7wR97GGGOMqR0iEiUic0Qk3v23dTnpJrvTxIvIZK/1C0Vkm4isdT/aBy56Y+q/lo0bANCwQXiQIzHGgJ+6bYvIbnxcuFLVnv7I3xhT+9QuPRtzIpoKzFPV6SIy1b08xTuBiEQBfwaG4qrr40TkO1VNdyf5rarGBjJoY04UTvc9z9Zt25jQ4K97nod6PW8EXAVE+SlvY4wxxtSOS4Ex7ucfAAsp1XgGJgBzVDUNQETmABOBTwITojEnruLxwsKt9WxMSPBXt+1Ur0eSqv4TOLeifUSkm4gsEJEtIrJJRP7kXu+zC5m4vCIiO0RkvYgM9kfsxhgXtbuejTkRdVDVAwDuv766XXcB9nktJ7rXFXvP3WX7cRHx+Q1fRG4XkVgRiU1JSfFX7MbUe04tvvJsjWdjQoG/um17N2TDcF2Jbl7JbkXAA6q6RkSa4+oGNge4Gd9dyC4A+rgfw4HX3X+NMcYYUw4RmQt09LFpWlWz8LGu+Ne236pqkrse/xLXAKIflkms+ibwJsDQoUPtlzpjqqj4yrO1nY0JDf7qtv0Pr+dFQAJwdUU7uH/hLv61O0tEtuD6Jbu8LmSXAh+qqgIrRKSViHQq/sXcGGOMMWWp6vjytonIoeK6VEQ6Ack+kiVyrF4G6IqrbkZVk9x/s0TkY2AYPhrPxpjj8+6S3YCNS2JMqPBL41lVx9ZkfxGJAU4HVlKqC5nXyJ3ldRuzxrMxfmAVszEnpO+AycB0999vfaT5CXjOayTu84FHRCQCaKWqh0WkAXARMDcAMRtzwkg6kgtAfpEjyJEYY8B/3bbvr2i7qr5Ywb7NcHX1uldVM8u5XQoq7jbmnd/twO0A0dHRFYVljDHGnOimA5+LyK3AXlwDfiIiQ4E7VPU2VU0TkaeB1e59/uJe1xT4yd1wDsfVcH4r8C/BmPrP7nk2JjT4c7TtM3D9gg1wMbCIkleKy3BXuF8C/1PVr9yry+tClgh089q9K7C/dJ52X5Uxx0c9f+3fxpgThaqmAuN8rI8FbvNafhd4t1Sao8CQ2o7RGAMNwv0yxq8xpob89Z/YFhisqg+o6gO4KtOuqvqUqj7lawf3iJzvAFtKXZku7kIGJbuQfQfc5B51ewSQYfc7G2OMMcaY+uqWM2MAaNHIX9e7jDE14a//xGigwGu5AIipZJ8zcY3KuUFE1rrXPUo5XciA2cAkYAeQA9zil8iNMS7um57t3mdjjDEmNLRo1MD1xLptGxMS/NV4/ghYJSJf4+r9+RtcI2WXS1WX4Ps+ZvDdhUyBu2oYpzHGGGOMMXVC8e/Z1nQ2JjT4pdu2qj6L60pwOnAEuEVVn/dH3saYwNBSf40xxhgTZO7uYC/Piw9yIMYY8N9o272ATaq6RkTGAGeJyG5VPeKP/I0xxhhjjDHGmGDy14BhXwIOEekNvA30AD72U97GmAAovtfZ7nk2xhhjQoN3lTx/66GgxWGMcfFX49mpqkXA5cDLqnof0MlPeRtjjDHGGHPC8f5Be+6W5PITGmMCwl+N50IRuQ64CZjpXtfAT3kbYwKgeH5nm+fZGFOXbEzKIGbqLFKz84MdijG1yuGw+tmYYPNX4/kWYCTwrKruFpEewH/9lLcxxhhjjE8X/WsJAEOemRvkSIzxP+8ftD+L3RfESIwx4KcBw1R1M/BHr+XduOZrNsbUEWrDbRtjjDHGGFMuf115NsYYY4wxxhhj6i1rPBtjAJvn2RhT96zbZzNimvrNZsAwJrT4pfEsIldVZZ0xxhhjjL9c/vqyEssFRc4gRWJM7Sjddn5twY6gxGGMcfHXledHqrjOGBOijs3zbD9zG2PqBoez5OfVLe+vClIkxtSO0lXy337aRszUWczZbHM+GxMMNRowTEQuACYBXUTkFa9NLYCimuRtjDHGGFMdS3ekBjsEY/yqvOkjv12bxHkDOgQ4GmNMTa887wdigTwgzuvxHTChhnkbYwLIM8+zXXg2xtRhMVNnsSExI9hhGFOrcgoczFy/n9UJaTidVnEbEyg1uvKsqutEZCNwvqp+4KeYjDHGGGMqVNEtJhe/uoTdz09CRAIYkTG1oJzTfP7WZOZvTQbgoQn9uGts7wAGZcyJq8b3PKuqA2gjIpF+iMcYEyxa4o8xxoS0xfGHK9ze45HZNoaDqfMUaNSg4q/rq3anBSYYY4zfBgzbAywVkcdF5P7ih5/yNsYYY4wp4aZ3Kx8crMcjs8nKKwxANMbUDlVFEJY/cm65acKsg4UxAeOvxvN+YKY7v+ZeD2NMHeGZ59ku1Bhj6pGBT/5M3J50vohL5INlCcEOx5hqUQUR6NSycblpFmxLIbfAQUGRk7xCRwCjM6akeVsO1ftzsEb3PBdT1acARKS5a1Gz/ZGvMcYYY0xNXeE1H/SoXm3o0yE4v++v3XeEQ5l5TDi5Y1COb+oeBYovLI/r35557vucSzvpiR89zxOmX1j7gZlqe/K7Tfx3xR52PDcp2KHUio1JGdz6QSxXD+3KC1eeFuxwao1frjyLyCki8iuwEdgkInEicrI/8jbGBEbxvYHlTYthjDGhaunU8ru0lpZ2tACAuD1p7Ek9Wlsh+XTZa0v5/UdxAT2mOSbd/d7XJa4rz67m86vXD67SPusTj9T7q39VlZ1fxL/mxZeZEz6Q1u07wsz1+3l/WQJFAY7j4S/W8ZfvNwfkWFl5rlmKE1JzAnK8YPFXt+03gftVtbuqdgceAN7yU97GGGOMMT59evsIurRqzJ1jelUp/TVvrmBDYgZXvL6cc/62kKQjucRMnXVc90anZucHfFCy3AIHR/OLqr2fqvLr3nQA5mw+xJGcwDUk31myu8TVf4AihzOgjdmNSRmc/vQcvoxLLLE+r9BB9nGUZ2ZeIfd88mutl6OinivPjSPDq7TPJa8u5fS/zCH9aEFQG42BlplXSMzUWcRMncXDX6zjjV928vzsLfxjzna+X7efjUm+p7CLP5TFQzPWVVhWBzJyWRyfwofLE7jk1SXM2XyIV+fHcySngNTs/HL3m/zuKi59bSl3f/yrZ52vz4wih5P1iUcqfY2vLdjBY99s4KZ3V1HkcAKQU1Dk+UxwONXzAyHA57GJvLt0d4k8svOL2H4oy7P8v5V7+DIukXX7jpRYX2xfWg7Ld6ayZm86365N4uW58VzzxnJips5i0/5jZeqZ3KAKp1xieg59ps0m3sfxQp1fum0DTVV1QfGCqi4UkaZ+ytsYEwDFn+V2z7Mxpi4Z3iMKgIcn9uffC3dWaZ+LX13ieX7m9PmufJ6bx+e/H8k1byxnwUNjaN+8UYV5JB3J5czp87lvfF9uHNmdtKMFLNt5mJtGxlR6/My8QhZtT+GiUzuXWL8vLYfwMKFBeBjLdh4mMT23zBREpz/9M3mFTn689yw6t2pMi0YNyj3OF3GJpB8t4OozuvH56n08O3sLd47p5SmneQ+cQ692zcrsd//na/lqTRI7nr2AiPCKr7OkHy0g9Wg+vdu7usJ/vHIvg7u3on/HFgDc88mvfL9uf4l9th3M4sEZ69iQlMH8B86hp1cMR/OLSEg9ysmdW5bYZ/fhozRrGEHq0fxKX7cvWw+6vqQv3XGY1QlpXDssmkHdWtH/cVd3Z19dnZ1OJfVoAeFhwuqENE93+wc+X8eXa1yN8MYNwjh/QEfG9GvH+qQMBke3LpFHclYeLRo1IP5QNgO7tixzjPJk5xchQHJW/rF+28DNo2J4vwr37ucWOjj96TkAbHpqAkUO5eEv1/HTpkPcMCKaZy4b6Emrqjz2zUYuH9yFId2jPOvzCh1sP5RFoUNJTM+hdZNIerZrStyedMaf1IGmDcs2I95fupuz+rYrc15tTMqg0OHEqRAd1YQ2TSMJq8JIZ8t2HqZBeBhnxET53P7KvHhWJ6SxIznb81kArkajt3s/WwvAg+f35e8/b+fD3w2jTbNIvlqTxDtLXI3LGe4fVmbcMZK3F+9iT2oOzRtFMOOOUZz/0iLPlVWA//swFoDXFuwk132Vf9W0cWw5kEXv9s14ZuZm5m9NJr/IWSbmHo/M5o0bh7Ak/jA3nxlDk8hwpny5gUXbUzxplj9yruc+97X7jnDZa0uZMrE/f/tpmydN72k/sHraeM54dm6ZY8y8ZzTLdh6bkeChGeu4aWQMA7u25OZ3VxG7J51//3YwkwZ2YtrXG0vse9/4vvxpfB8+WbWXTfsz+O+KvT7LHuDCV1yfpSseGcc/5253lUNCGkOensN1w6LZm5bDPef2LnOrzOi/upqN5720qM7dZiD++MVURL4G1gAfuVfdAAxV1ctqnHkNDB06VGNjY4MZgjF1xjMzN/P2kt3cPCqGJy+xuy6M8UVE4lR1aLDjqMv8VTfHTJ0FlGz0nPzEjxwtqHl31QGdWtCxZSNeuPJU2jZryMz1+9m8P5M/juvDjNh9PP7tJoZ0b03cnvQy+7Zr3pCUrHwePL8vvxnclS6tGjN7wwGmfrmeTPeX7/MGdGDO5kP854bBdG3dhFO6tCzxmrx9+Lth9O3QnPAwYfaGA/z5u02ebad2bcm5/dszqldbrnlzOT/+6WyufmM5Tqfy9uShXPPmikpf68vXDqJhRDh3/DeO06Nb0aJRA35xf4m/45xeTJnYj6e+30xOQRFn9m5Ls4YRdGjRiNiENN5blsAedxfNxy8awNMzj3UPXfzwWFbuTuPBGes86/p1aM42H1eait/DXSnZ3Pm/NWw9mMXWpyfSqMGxK63eZdOqSQPm3HcOLRpH8M2vSfzm9K6MeH6e54rbyJ5teHBCP5bvPEzjyAiW70xlRM8onpm1hfEntWfulmSaN4rgv7cO59LXlnpieHV+PLM3HOQ/Nwzh3wt3MG9rMilZx64qzrnvbHYdPuqz632jBmHkFTq5ckhX7j+vL5v2Z7JiVyrvLNlNmIBTXQ2MqKaRjHh+Hs9cdgoPf7Ge3EIHT148gKuGdmPbwSwGdmlJWJiUORe8z3Nf50llBnZpyQavK68f3TqMIoey6/BRLj+9i6eh/cKVpzIsJoq9aTl8szaJr9Yk+czvpE4t2HIgk2d/cwrTvt7IKV1a8MxlA7nMXZ5NI8P59PaRtG0eSfNGDTjlzz+V2P/+8/oS07Ypf/zkV967+Qz2pedw6WldiIwI4+fNB+nRtinbDmbx0BfrAdf/QWxCGtsOZdGzXTNeX7iTM2Jaszqh7P+gv1V0r3ldc/3waD5eeawx3C2qMfvScmv9uAnTL+SbX5M4pUtLerdvVuIc9kfjOZB1s78az62Bp4DRuH4fWwQ8qaq1f0ZXwBrPxlTd0zM38441no2pkDWea642G89bD2byw4aDvDwvvsb5+8u0SSfx7OwtwQ6jzunTvhnn9m/P5YO7klfo8DRya0uPtk3ZfTiw98BXh/d5vmBbMre8tzqI0VTPKV1asDEpM9hhmBBV1xrP/hqDWAhRAAAgAElEQVRtOx34oz/yCkV/+2krqdmuXzMjI8K4e2xv2reouDuXMcYYYwKrf8cW9O/YgjvH9qLfYz9WvkMAWMP5+MQnZxOfnM0bi3YF5Hih3HAubWy/9iRMv/C4rkAHgzWcTX3ir9G2+4rImyLys4jML374I+9QsHJXGgu2JTN3yyE+XL7H053JmPrk2D3PdtOzMSb0xbRpwiWndfa5rWFEODvr6XQwxhR7/5Yzgh2CMSccfw0YNgP4D/A2UO/Gxv/iD6MA10h7I5+ff0KNXGiMMcaEIocq4RUMOBQeJsQ+Np6hz5QdTMeY+mBMv/bsem4Sk99bxeL4w5XvYIypMX9NVVWkqq+r6ipVjSt++CnvkBHuHoM90HO0GRMIxfM729ltjKkL+nVoTreoJhWmadusIfHPXsDlg7sEKCpjAissTPjo1uF1bsRiY+qqGjWeRSRKRKKA70XkThHpVLzOvb5eKf6F22ndWo0xxpigenvyGdx/Xt9K0zUID+PFqwdZ48IYY0yN1bTbdhyuC1XF/aYe8tqmQM8a5h9SihvP1m3b1Ec2z7Mxpr4rb3opY+qDL+4YSU6Bg5veXRXsUIypt2p05VlVe6hqT/ff0o9aaTiLyEQR2SYiO0Rkam0cozxh1ng2xhhj6qwv/zCKxy48KdhhGFMrhsZEcXbfdiRMv5CE6Rfy0a3Dgh1SyBjYpSUvXHGqX/OsSm+WdU+cz73j+9Aw4viaXP+5YUiVe80sfnjscR2jKv40ro9rLvTrT68w3fd3j6ZRg6q/1p7tmtY0tICr0ZVnETkD2KeqB93LNwFXAHtwzfOcVvMQSxwvHHgNOA9IBFaLyHequtmfxylP8T3P1ng29ZnaXc/GmHrstrN6cttZPUnNzmf6D1uZEZcIwFVDunqe33Nub75ak8SRnAKOFpQ/DuqmpyZw8p9/8mt8L11zGvd9ts6zvPjhsXweu49/zd9RrXyeuGgAf5kZkK9HjO3XjgXbqjYTyWWDOrNm7xH2puUArtd31gsLqn3Ms/q09esgWb85vQtf/5rkc9tdY3sx4eSOXPJq+XNNXziwE11bN67S1FpPXjyAnu2a0aFFIx6csY4NSRkltvfr0Jxth7I8y11aNa7iqyjprD7tPPOMf/x/w5kRm1juayxt1aPjGPbcvDLrR/Zsw5HcQm4e1Z0pX25gxh0jWRx/mFcCMLd6g3Ch0FH2O8obNw5h+8EsOrZsxMmdW9K8UQR5hQ7Oe2kRAO9MHsq4kzoAMLZ/e8a/+AsZuYXM+uNoLnxlCQAPTehHdFQT9qXnMKJnGy7/9zJevnYQm/dnet7TT/5vBCN7tSEjp5AWjV1NqPduOYNb3lvNPef25oYR3Tn37ws5WuCgS6vGfH3nKFo2acC94/ty7/i+5BU6mL81mQtO6UhGbiHLdqYS1TSSts0iGf+iK9ZVj44jPjmb07q1YvuhLAZHtwZcnzUR4ULDiHA+WbWXR77awAWndGTLgUymXTiAs/q0pVGDcBKmX0jcnnQWbU/hq18TuXJwN6KaNuC6YdEUOZW9aTn07dCcsX9fyO7DR5n9x7OIbtOEJ7/bxBfuz7/Nf5lAk8gIcgscbNqfwZX/Wc55A1zld9Gpnbno1M7MXL+fuz/+lRl3jCSqaSTj/vELAAO7tmTRQ2OJT85mRM82fLZ6H49+vcHn+/n7c3ry0Pn9an5iBJjUZFoaEVkDjFfVNBE5G/gUuAcYBJykqlf6J0zP8UbiapRPcC8/AqCqz/tKP3ToUI2NjfXb8fMKHfR//EcentiPO8f09lu+xoSCJ7/bxPvLErhhRDTPXDYw2OEYE5JEJE5VhwY7jrrM33VzTTidyi3vr+aqoV256NTOFBQ5mb/1EBNP6QRAfpEDVfh+3X72pObw6oIdtG0WycpHx5Nb6KBZwwg+XJ7AzPUHWLW75PWCfh2a86fxfRjWI4rth7K4/q2Vnm3zHjiHbq2bsHTnYbLziujaujF//3kbS3ekEvvYeD5dtZfhPdtwRsyx4WNu+2A1c7cke5Z3Pz8JEeGClxczuncbftp0yNMgTZh+IarKA5+vIyU739W4ue50PliWQNyedK4b1o1bR/ekXfOGPDhjHXM2H2L5I+fy6FcbSjSCP7t9BNe8uYIZd4ykV7tmvLNkF78d3p1R012zkX75h1F0bNmILq0a85fvN7NgWzKnR7fiqzVJzLhjJIOjW/P24l18uSaRO8f0JjIijAtO6cj2Q9lM+Ocivr3rTE7r1sozX/Gnt4+gaWQEUc0iWbbjMA99sR5wNWwmj4rhFPcPFcVX4rYezOTbtftJTM8lLiGN+Q+OITE9lx82HGBEL1f5FTqcFDqcNIoI541Fu7hxZHeaNYwgI7eQT1btpWnDCGau28+nt49g7pZknpm1mS/uGMX0H7bSsEEYz/3mWH3oPa/yA+f1pWnDCC4Z1JmGEWE0b9QAgOTMPH7YeJBhPaJoEC6eRtEzl53CDSO6lzkHHU5l+6Es3lq0i69+TeJf153Oxad15qs1iRzMzOP8AR3oFtWEhhHhlZ/QPqgqWflFtGjUgCKHk97TfvCZ7pXrTie/0MHetBzGn9SB07q14szp82nbLJJ1iRklyt2XbQez6NmuKbmFDk598mcAdjx7AYvjD3PL+6sBWDVtHCieRnm75g1Jycr35LFkylh+3XuEez75lQGdWjAouhXfr9tPVl4Rq6eNp1nDCE564kcaRoTx96tOIzqqCf06NqdRA99lk5FTSGZeYYUDC17x+jLi9qRX+NqO5BSwPjGDs/u2KzdNsUKHkyKH0jiyeu/XjxsPkno0n98OL3uOlLYvLYezXljAf28dzug+bat1nGIFRU6cqiXK7lBmHuFhQttmDauUR2ZeIS3c531FNu/PZNIri7l1dA9G9WrDkO6tadQgvNz37XgEsm6uaeN5naqe5n7+GpCiqk+6l9eq6iC/RHnseFcCE1X1NvfyjcBwVb3bK83twO0A0dHRQ/bs2eO34xc6nPSZ9gPXDYvmyiFd/ZavMaHgnSW7mL3hIBNO7sDtZ/cKdjjG+FX75g0rHZm5KqzxXHOh1HiurtiENKKjmtC+RaMy2z5YlsDqhDQOZOQRtyedxy8awK2je3i25xc5SM0uYN6WQ9w4MqbM/gVFTval59CrXTOfxy5yOClyKst3pjK8ZxRNIst2Hvxu3X4GdGpB7/bH8nA6lfjkbPp1bM68LYe49YNYvr5zFKe7r2jlFTo4kJFHj7ZNKShysnBbMmP7t8fh1HK/3M5cv5++HZrTt0PzMtucTiW30EHThlXv3LjtYBbhYVIibl9yCoo4nFVAdJua/y8fj2veWI4C7958Bs2q+PryixwIQuRxdtv1t8XxKaxPzODmUTG8s2Q3e9Ny6NG2KXeNLf+i0MJtyexNy+EmH+etL5+s2ku7Zg0Z775a+fnqfbRs0oAJJ3cE4N0lu/nLzM1semoCKVn5dGndmD2pR+ndvjk7krMZ/+IvXH56F168pmQzovh7+M2jYnjykpOPrwBKOZpfRGp28M6pE8XR/CKaRIYjUv70gjVRlxrPG4FBqlokIluB21V1UfE2VT3FT3EWH+8qYEKpxvMwVb3HV3p/V9BOp3Lyn38it7DeTWVtjDH1mr++bNW3xrN7ZozPgBggAbhaVcuMqCUik4HH3IvPqOoH7vWRwKvAGMAJTFPVLys6Zl1uPFfFl3GJPDBjHR/+bliVrlQFWm6Bo9pXxYwJpEXbUzgjJsrneZpX6CAyPMwzDpExENi6uaajbX8C/CIih4FcYDGAiPQGMira8TglAt28lrsC+2vhOD6FhQnf3X0m+zPyAnVIYwJKsHmeTf3UpVXZK4UGgKnAPFWd7h6EcyowxTuBu4H9Z2Aoro+IOPd4I+nANCBZVfuKSBhQ76aprK7LB3dhUHSrcq8gB5s1nE2oq+hHJ3929TXmeNSo8ayqz4rIPKAT8LMeu4wdhuveZ39bDfQRkR5AEnAtcH0tHKdcfTo0p4+PbkrGGGNMHXQprqvGAB8ACynVeAYmAHOKBwEVkTnARFw/oP8O6A+gqk7AfyM41VEiErINZ2OMMTVT0yvPqOoKH+u21zTfco5VJCJ3Az8B4cC7qrqpNo5ljDHGnAA6qOoBAFU9ICLtfaTpAuzzWk4EuohIK/fy0yIyBtgJ3K2qh0pnUGo8Ej+Gb4wxxgROjRvPgaaqs4HZVUkbFxd3WET8N2KY/7Wlbv5Kb3EHTl2MGSzuQKqLMUPdjbvOzashInOBjj42TatqFj7WKa7vEF2Bpap6v4jcD/wduLFMYtU3gTfd8aRY3VwrLO7AqYsxg8UdSHUxZqi7cQesbq5zjefqUNXQG6nDi4jE1sWBZyzuwKmLMYPFHUh1MWao23EHO4bqUtXx5W0TkUMi0sl91bkTkOwjWSLHunaDq8G8EEgFcoCv3etnALdWIR6rm2uBxR04dTFmsLgDqS7GDHU77kAdKzTGzTfGGGNMMHwHTHY/nwx86yPNT8D5ItJaRFoD5wM/ucc5+Z5jDetxwObaDdcYY4wJHms8G2OMMSeu6cB5IhIPnOdeRkSGisjbAO6Bwp7GNWjnauAvxYOH4Rpc7EkRWY+ru/YDAY7fGGOMCZh63W27Dngz2AEcJ4s7cOpizGBxB1JdjBks7pCgqqm4rhiXXh8L3Oa1/C7wro90e4CzazPGIKir77HFHTh1MWawuAOpLsYMFnel5NjsUsYYY4wxxhhjjPHFum0bY4wxxhhjjDGVsMZzLRGRViLyhYhsFZEtIjKy1PaHRGSt+7FRRBwiEuXeliAiG9zbAjZ6nIj084pprYhkisi9pdKIiLwiIjtEZL2IDPbaNllE4t2PyWWPELSYf+uOdb2ILBOR07y2hXJZjxGRDK80T3htmygi29zvw9QQizsUz+37RGSTO55PRKRRqe0NReQzd3muFJEYr22PuNdvE5EJgYq5inHfLyKb3ef2PBHp7rXN4fU+fBdicd8srumKiuO7zWtbwD9HqhjzS17xbheRI17bglbWpnrE6marm2set9XN/ovb6ubQitvq5qpQVXvUwgP4ALjN/TwSaFVB2ouB+V7LCUDbIMcfDhwEupdaPwn4Ade8nyOAle71UcAu99/W7uetQyTmUcWxABcUx1wHynoMMLOc9DuBnu5zax0wIFTiLpUm6Oc20AXYDTR2L38O3FwqzZ3Af9zPrwU+cz8f4C7fhkAPd7mHh1DcY4Em7ud/KI7bvZwd6HOiGnHfDLzqY9+gfI5UJeZS6e8B3g12WdvjuN5rq5utbq5p3GOwutkfcVrdHHrlfTNWN1f6sCvPtUBEWuAaQOUdAFUtUNUjFexyHfBJIGKrhnHATnUNBuPtUuBDdVkBtBLX3KATgDmqmqaq6cAcYGJgQ/Yds6ouc8cEsALXHKWhpLyyLs8wYIeq7lLVAuBTXO9LoFUl7lA5tyOAxiISATQB9pfafimuL9UAXwDjRETc6z9V1XxV3Q3swFX+gVJh3Kq6QFVz3IuhdG5XVt7lCebnSHViDpXz2lSD1c1WN1eT1c21z+rmwLK62Q+s8Vw7egIpwHsi8quIvC0iTX0lFJEmuE7AL71WK/CziMSJyO21H65P1+L7BOwC7PNaTnSvK299IJUXs7dbcf06XyyUyxpgpIisE5EfRORk97pQKGuopLxD5dxW1STg78Be4ACQoao/l0rmKVNVLQIygDYEsayrGLe30ud2IxGJFZEVInJZLYZaQjXivsLdpe0LEenmXheU8q5OWbu73/UA5nutDkpZm2qzutnq5uqwurkWWd1sdXNlQrVutsZz7YgABgOvq+rpwFGgvHtfLgaW6rE5MwHOVNXBuLox3SUiAZ0GREQigUuAGb42+1inFawPiEpiLk4zFteH2BSv1aFc1mtwdbs6DfgX8E3xbj7SBnTY/KqUNyFybotIa1y/UvcAOgNNReSG0sl87BrU87qKcRenvQEYCvzNa3W0qg4Frgf+KSK9ajnk4liqEvf3QIyqngrM5diVhaCUd3XKGtcX0y9U1eG1LihlbarN6uZj6wPC6marm8tjdbPVzZUJ1brZGs+1IxFIVNWV7uUvcFXYvpT5hVBV97v/JgNfE9iuKOD68Fyjqod8bEsEunktd8XVhaK89YFSUcyIyKnA28Cl6prXFAjtslbVTFXNdj+fDTQQkbYEv6yhkvJ2C5VzezywW1VTVLUQ+ArXvXbePGXq7hrUEkgjuGVdlbgRkfHANOASVc0vXu9V1ruAhcDpgQiaKsStqqlesb4FDHE/D1Z5V6ms3So6rwNd1qZ6rG62urmqrG6ufVY3W91cmZCsm+v1PM9t27bVmJiYYIdhjDGmnoiLi3MCEequPN2/jOeoar77C/RyXA2BzcGMM5RZ3WyMMcafAlk3R9Q0g1AWExNDbGzARtw3xhhTz4lIspb81fkk4A0RceLqzTXdGs4Vs7rZGGOMPwWybq7XjWdjTNVl5BYyZ/Mhxp/UnlZNIoMdjjGhKsl7QVWXAQODFIsx5gSwancaAzq3oFlD+9puTDkCVjfbPc/GGAA+X72PB2es438r9wY7FGOMn4lIlIjMEZF499/W5aSb7E4TLyKTfWz/TkQ2ei0/KSJJIrLW/ZhUm6/DmBNNanY+V7+xnHs//TXYoRhj8FPjWVxuEJEn3MvRIhLowR2MMTWQV+gaoDC3wFFJSmNMHTQVmKeqfYB5+BhlWkSigD8Dw3ENGPRn70a2iFwOZPvI+yVVHeR+zK6V6I05QeW66+YtB7KCHIkxBvx35fnfwEhck1MDZAGv+SlvY4wxxtTMpRybduQDwNeclxOAOaqapqrpwBxc88EiIs2A+4FnAhCrMaaU+jzArzF1ib8az8NV9S4gD8Bd6dpNk8YYY0xo6KCqBwDcf9v7SNMF2Oe1nOheB/A08A8gx8d+d4vIehF5t7zu4MaY4yPimmLXms7GhAZ/NZ4LRSQc9/+2iLQDnH7K2xhjjDGVEJG5IrLRx+PSqmbhY52KyCCgt6p+7WP760AvYBBwAFcD21dst4tIrIjEpqSkVDEcY0zxFWdf/5zGmMDz17B9r+CaVL29iDwLXAk85qe8jTHGGFMJVR1f3jYROSQinVT1gIh0ApJ9JEsExngtd/1/9u47PKoqfeD4902hdxAINVQBQVAiAooNqRasu5ZVcGXVXdtvXdfFVQE7q2tZV1ZFxbZrbyBIFwQFxID0GiBAIBAgEgJJSHt/f9ybYRImBXKTmSTv53nuk7nn3nvmnclMTs49DViAMyyrt4jE4/zf0FREFqjqRaq6z+853gSmFRLbJGASQExMjDWiGVNC2w8cBWBPSkaQIzHGgEctz6r6P+Ah4FmcO89XqepnXuRtjDHGmFKbCuTNnj0SmBLgnFnAYBFp6Ha/HgzMUtXXVLWFqkYD5wObVfUiALcinudqYC3GGM/EHww0UsIYEyylbnkWkTBgtap2BzaWPiRjjDHGeGwC8KmI3A7sBK4HEJEY4C5VHa2qySLyJPCze80TqppcTL7Pud26FYgH7iyT6I2porJzbBSkMaGk1JVnVc0VkVUi0kZVbYFYY4wxJsSo6kFgYID0WGC03/5kYHIR+cQD3f32b/E0UGNMPjbJtjGhxasxz1HAOhFZBhzNS1TVKz3K3xhjjDHGmCpl/5FjwQ7BGOPHq8rz4x7lY4wxxhhjjAHiko4EOwRjjB9PKs+q+r0X+RhjjDHGGGMcHZvWYc76fcWfaIwpF57Mti0ifUXkZxE5IiKZIpIjIoe9yNsYY4wxxpiqKKZtQwAuPv20IEdijAGPKs/Aq8CNwBagJs7kI696lLcxxhhjjDFVTq47YVh4mAQ3EGMM4N2YZ1Q1TkTCVTUHeEdEFnuVtzHGGGOMMVVNjlt7FrHKszGhwKvKc5qIVANWishzQCJQ26O8jTHGGGOMqXJy3bWqIqzl2ZiQ4FW37VuAcOAenKWqWgPXepS3MaYc2FKSxhhjTGjJdluew6zybExI8Gq27R3uw3Rs2SpjjDHGGGNKLdetPIdbt21jQoInlWcR2U6AhitVbe9F/saYsqfW9GyMMcaElKmr9gBw9Fh2kCMxxoB3Y55j/B7XAK4HGnmUtzHGGGOMMVXOdxuTANiZnBbkSIwx4NGYZ1U96LftVtWXgUu8yNsYUz7URj0bY4wxISnMum0bExI8qTyLyNl+W4yI3AXULeaa1iIyX0Q2iMg6EbnfTW8kInNEZIv7s6GbLiLyiojEichqETnbi9iNMcaYyq6wsjXAeSPdc7aIyEi/9AUisklEVrpbUze9uoh84pbNP4lIdPm8ImOqhjsvcEZA9m1vHTqNCQVezbb9gt/2LNAb+E0x12QDf1HVrkBf4G4R6QaMAeapaidgnrsPMAzo5G53AK95FLsxBhvzbEwlV1jZ6iMijYBxwLlAH2BcgUr2zaray92S3LTbgV9VtSPwEvCPsnwRxlQ1TevVAGy2bWNChVezbV98Ctck4qwHjaqmisgGoCUwArjIPe09YAHwNzf9fVVVYKmINBCRKDcfY4wxxhSusLLV3xBgjqomA4jIHGAo8FEx+Y53H38OvCoi4pbVxphSmrpyNwBZOblBjsQYA97Ntv1AUcdV9cViro8GzgJ+AprlVYhVNTGvaxhOxXqX32UJbppVno3xgPp+2v+8xlRChZWt/gorZ/O8IyI5wBfAU24F2XeNqmaLSArQGDjgn7GI3IHTa4w2bdp484qMqQJWJaQA8OvRrCBHYowB77ptxwB/xClEWwJ3Ad1wxj0XN/a5Dk5B/H+qerioUwOknfBfvojcISKxIhK7f//+EoZvjDHGVGwiMldE1gbYRpQ0iwBpeeXszaraAxjgbreU4JrjCaqTVDVGVWNOO+20EoZjjOncrA4AzevXCHIkxhjwbqmqJsDZqpoKICLjgc9UdXRRF4lIJE7F+X+q+qWbvC+vO7aIRAF546oSgNZ+l7cC9hTMU1UnAZMAYmJirAnNmJJye1laZ0tjKiZVvbSwYyJSWNnqL4HjXbvBKWcXuHnvdn+misiHOGOi3+d42ZwgIhFAfSC59K/GGANwWY8WbN63mbW7U4IdijEG71qe2wCZfvuZQHRRF4iIAG8DGwp0654K5M3wORKY4pd+qzvrdl8gxcY7G2OMMSVSWNnqbxYwWEQauhOFDQZmiUiEiDQB303vy4G1AfK9DvjOxjsb4528oVQ/bbd7UsaEAq9anj8AlonIVzjdta7GmZCkKOfhdPtaIyIr3bS/AxOAT0XkdmAncL177FtgOBAHpAG3eRS7MQb/Mc/GmEooYNkqIjHAXao6WlWTReRJ4Gf3mifctNo4lehIIByYC7zpnvM28IGIxOG0ON9Qfi/JmMov1wplY0KKV7NtPy0iM3DGQQHcpqq/FHPNDwQeKwUwMMD5CtxdqkCNMcaYKkhVDxK4bI0FRvvtTwYmFzjnKM4SlIHyzeD4TW5jjNesI4cxIcWr2bY7AOtUdYWIXAQMEJHtqnrIi/yNMWUvr3y2ctoYY4wJDdbybExo8WrM8xdAjoh0BN4C2gEfepS3McYYY4wxVY4tH2lMaPGq8pyrqtnANcC/VPXPQJRHeRtjykFeAW0FtTHGGGOMMSfyqvKcJSI3ArcC09y0SI/yNsYYY4wxpsoJl+PTA32+PCGIkRhjwLvK821AP+BpVd0uIu2A/3qUtzHGGGOMMVWOf1+wKSt3By0OY4zDq9m21wP3+e1vx1kWwxhTQaitVWWMMcaElFy/WTwXbTkQxEiMMeBdy7MxxhhjTLnLysll/qYkcmxaYlMJFVwBY1dyWnACMcYAHrU8G2MqPmt4NsZURJ0emQHA5WdG8epNZwc5GmO8VfCe0IOfraJO9QjevDWGsDAJfJExpsx40vIsIteXJM0YY4wxpixMW50Y7BCM8VzBFTB+2p7MvI1JpGXlBCkiY6o2r7ptP1zCNGNMiMrrGqYF+4gZY0yISj6aGewQjClThRXJf/1slQ1VMCYIStVtW0SGAcOBliLyit+hekB2afI2xhhjjCnKda8tzre/82AabRrXClI0xnivsBvaM9bu5b3F8fz+/HblHJExVVtpW573ALFABrDcb5sKDCll3saYcpTXNcwano0xFcW2A0fz7Wfm5AYpEmPKRlGNy09MW19+gRhjgFJWnlV1Fc56zj+o6nt+25eq+qs3IRpjjDGmNESkkYjMEZEt7s+GhZw30j1ni4iM9EtfICKbRGSluzV100eJyH6/9NHl9ZoCsW6sprJRhZqR4YUe7/zojHKMxhhT6jHPqpoDNBaRah7EY4wJFs33wxhTuYwB5qlqJ2Ceu5+PiDQCxgHnAn2AcQUq2Terai93S/JL/8Qv/a0yfA3FGvLyQpIOZwQzBGM8latKRLjQvF6NgMczs3N56PNVRI+ZzqE0mwPAmLLm1YRhO4AfReQxEXkgb/Mob2OMMcaUzgjgPffxe8BVAc4ZAsxR1WS399gcYGg5xXfSftkZuINbn2fmERufDEB6Zg7Hsm1WYlOxhYlw54XtCz3+aWwCAHFJR8orJGOqLK8qz3uAaW5+df02Y0wF4Vvn2ZqejamMmqlqIoD7s2mAc1oCu/z2E9y0PO+4XbMfExH/BWavFZHVIvK5iLQO9OQicoeIxIpI7P79+0v5Uhx//Xx1oceue30J0WOm03XsTC58boEnz2dMMOSqIgKj+kcXe+51ry/h6LHA8/XGJaXy/WZvvnvGVGWlmm07j6o+DiAidZ1dtVtfxhhjTDkSkblA8wCHHilpFgHS8m6n3ayqu91y/gvgFuB94BvgI1U9JiJ34bRqX3JCJqqTgEkAMTExntyiK2kr217rxm0qMFWn5VlEuO+SjrzyXVyR558xbhZPXtWdy3tE0bD28RGVl764EID4CZeVabzGVHaetDyLSHcR+QVYC6wTkeUicoYXeRtjykfechhqo56NqZBU9VJV7f/t9FIAACAASURBVB5gmwLsE5EoAPdnUoAsEgD/luNWOD3LUNXd7s9U4EOcMdGo6kFVPeae/ybQuyxemxdUlednbWTT3tRgh2Iqqc+XJxBfYAb40spV9d3VuvuSjiW65rGv13LWk3M8ef41CSnkhsBEfHPW7yMtM3+renZObqUZ5z1xfhzdx83y7X8au4v7P/4liBGZwnjVbXsS8ICqtlXVtsBfcApRY4wxxgTfVCBv9uyRwJQA58wCBotIQ3eisMHALBGJEJEmACISCVyOc7M8ryKe50pgQxnFXyop6Vms23OYifO3MuTlhaRmZHEsO4ftHld0ysKeQ+ncMGkJKWlZwQ6lzKRmZLH7UHq5PNfOg2ls3HvYk7xUlQ+W7iA1w/ndPPjZKi7/9w+e5O17DiBvlET1iHC+uef8El8bPWa6b8vzxvdbiR4zvUQV4tj4ZK549QdeX7jVl5aZncuCTc69t637j5BVhsvDXfbKIqLHTGfoywv5w/uxjPliTb6bXw99vppeT8zhiF9X9bzH323cxz0friAzO/SWr8vN1RO61z8/axNHjmWzOuEQ4Ly2KSv3lDi/eRv2+RpBdiWnnXCjoSykZ+aQ7f7+0zKz2ZWclu94dk4u6ZnOnBMfLInnhdmbKsXNS68qz7VVdX7ejqouAGp7lLcxphzkjXW2Mc/GVEoTgEEisgUY5O4jIjEi8haAqiYDTwI/u9sTblp1nEr0amAlsJvjN8jvE5F1IrIKuA8YVX4vqeR6Pj47X6Wmx/jZnP7oTC7+5wJS0rOYMGMjz8/ayIZEbypVhUnLzGb5juSTumbi/DiWbktm6qrd+dL3pmQQl3TiP6KZ2bms3HWIwxnHK9tfrkhg+Y78E6ypKi/N2UxiSukqrc/O2OCrjOVVpLJycvnnrE2+GHJy1fdPdiBXvvoj50347qSed+Hm/cxatzfgsXd/3B7wvQG44Pn5DH15UaH5Dv/XIj5YEp8vLT0zh09jd/kqJ3mWbkvmsa/XMm7KOnYcdG7EHDmWTfSY6UycH8fW/Ufo/OgMfvP6kiJfS1ZOrq8y6/8+gvN78p9hoEer+qwZP7jI/Iry7IyNALy/JB5wJt4b88VqsnNyT6gIb9vvvKYVOw5xLDsHVeXZGRsY9c7PDP/XIga+8D1DX15I9JjpvPH9Vj75eWeRz73nUDoT58ed8D4GkpaZzbo9zvdxo1vhmrpqD0NeXsiUlbvJzM7l65XOdyLmqTm+vLuPm8XSbQf5/buxTFudyFlPzC72uTKyckr8vfx42U7mrN9X6ESE6/akkHy06NbwJ6ev54xxszhyLPuEfO76YHm+fVUlMzuXlPQsko9msnX/EaLHTOeMsTN957y/JJ7b34v1VbYHPDefUZN/BpzP1pKtB/Pl92PcgRL9DorTdexMRr6zDIDb3vmZAc/N5/lZG303Bv74vxV0deN8bMo6/v1dHENeXui7PhRvbJSEJ2OegW0i8hjwgbv/O2C7R3kbY4wxphRU9SAwMEB6LDDab38yMLnAOUcppDu2qj4MPOxpsCfpvoGdaNOoFg9+tuqUrn/im/V8scKZrXji/K3MuH8AjWpXY29KBl2j6lEt4tTbGVSVdg9/S++2Dfnij/3562ermb4mkaUPD2RPSjpnt3FWAtu6/wjb9h/l2Rkb2Lb/KFPvOY9dyelcdmaUbyBN/jnaoO+z8wBnDGvS4QwysnL5YGk8by5y/v3qE92IF37Tk+oRYTzwqfPe/PLYIF933rsv7sDE+Vv517wtdGxaxzeGvHfbhjx6WVc+/Gknew9n8EPcAV654Sw27j3MsO5RdI2qx8T5cYzo1YK2jWvzxvfbAPjd2z+x2O+fdIBX58fx0NDTeW7mJgCG92jORac3pVfrBoQJdGzqzC1bVA+AlLQsej05G1UYf0U3xn+zPt/xf93Qi/o1I0k+msn5HZvQuE513zmLHrqY1o1qBcx36baDzFm/j6vPasmRY9l8uSKB+INprE88zGNT1vHYlHUseuhiPl+ewL/mbQGc1sDwMPGtJ965WR0AvvxlN1/+kv/mxvOzNvH8LOd1L4tP5uixbA5nZHHvh7/w/PU9OZyexV3/Xe6mZ3Px6acxf9PxCb1Wjh3EtgNHUT1xMoK6NSKZdu/5pWrlHv/NesLDw3js67UAfPyzM1fgeR0bc98lnXh57ha2uJ+JuRv2cfqjM2nZoCbVI53vw3r3RtNWt4KdVyk/lp3L2CnrALisRxRXn9WSn7YfJEyERVsOsD7xMJd0aUpOrtI1qh4fLdvJo1+v5eqzWvL01d05kpHNkWPZRU4IeP/HK7mflb79jKxc+vvdfLlh0lLf46OZOTw1bT3xB4/ywvW9qF8rkqwcJ8b7B3aief0a/O0Lp5X3xzGX0KJ+DS554Xu2HzjKxieHUiMynMzsXFIzsqgRGc6YL9f48v78rn40qFWNjk3rMH9TEofTs7j/45VUiwhj81PDyM1V9qSkU6taBDUiw+j7zDzGDOvKOz/GA/i6aV98+mm+PPekZOTrKdBj/Ox8Lev+ryvPmt3O7yIpNYNv1yQCzmduyEtORXXTvlS+/FN/Nu9NZdevaUycv5Vnru5BrWrhXNGzBeFhgaa7KJkf4w6iqvy03bn5MHH+VvYcymBY9+bMWb8POPH77f/6PvpDX/p1aHzKzx8M4sWdB7d71+PA+Tjf8YXAeHepi6CJiYnR2NjYYIZgTIXx5LT1vP3Ddkb1j2b8lTZlgTGBiMhyVY0JdhwVmVdlc94/YEsfHkjz+jXypXnlvI6NualPW2pEhhF/MI1R/aN558ftvLloG2e3aciVPVswrEcUh9IyaVCrWr5rVycc4spXfyzV8z84uDM7Dqbx2fIEX9qdF7QnLEx4bcHWIq4sO/cP7OSrTJbW7/q24b9LA7dWjj6/HY9e3o0zxs7MV1E4Fe1Pq+1rRa2oCpvoy+vPfGV3Y582JB89xqx1+044Nvbybjwx7fjNmXFXdKNL83q8NHczy7YX3jL91Z/6c/V/FudL++f1PU/5hl5ZKO57O+P+AbRrUhtVeHHOJg4eyeTPgzrTulEtlu9I5oXZm7nn4o7c9NZPANSvGUlKeumHktzYpw3PXtOj1PmUZ9nsSeU5VHlVQP9r7haSjzrzoVSLCOOOCzpwWt3qpc7XmFBilWdjimeV59LzuvK87ZnhhLktJ8/O2OBrCS0vn9/Vj+uK6ZZrTGkVVnlevPUAN735UzlHY6qKW/q25YOlO8r0ObyYAb48y2ZPum2LSGfgQSDaP09VPWG5iopo3sZ97ExOIydXSc3IpnOzulwfE3ApS2MqrONjnivvDTVjTOXj35v54WFdeXDw6XR6ZEa5Pb9VnE0w9e/QJNghmEqsrCvOFZFXY54/A14H3gJK17cmBE11ZzZMTEmn37Pf+ca6GGOMMSa4Co4FjgwP47Izo5i+OjFIERljjKmsvJptO1tVX1PVZaq6PG/zKO+QEe4W0NlWeTaVUN76zvbpNsZUBLWrhVOrWnjAYxNvOpv4CZdRp7pXbQTGhK5WDWsCcGnXptx5QfsgR2NM5VaqUkVEGrkPvxGRPwFfAcfyjrtLXFQaebPR5Vq3VmOMMSaoWjasSYfT6hR5ztrHh7Bw835unbysnKIypvz98LfjoyTX7UnhjYXlO+7fmKqktLdkl+Ou3+7u/9XvmAKe3/4SkaHAv4Bw4C1VneD1cxQmr/Js3bZNZWTrPBtjKpKcXCVMil9ipU875z5/3/aNWLqtUt3TN+YE3aLqMe6KblzZswWN6ziT2744exP7jxyjb/vG3P/xymJyMMYUpVTdtlW1naq2d38W3Mqi4hwOTASGAd2AG0Wkm9fPU5gwqzwbY4wxIWHr/qNMX1P8uOYakeHET7iMj+/ox/grujGgUxP+dFEHtj4znPsHdmJY9+bccE7lmQT0ip4t2PjkUOrXjPSljezXFoB7L+kYrLCC4tKuTcsk3zduCbjsOQB/GdSZd0adUybPWxIiwm3ntfNVnAEeGHw6z15zJiN6teTGPpXns+6vY9Oie6GY0NQtql6wQzhppe22fQ6wS1X3uvu3AtcCO3DWefb6Fm8fIE5Vt7nP9zEwAlhf5FUeibDKs6kC1EY9G2MqiC7N657U+aPOa8eo89r59v88qLPv8R8v6sCFzy/wKrRTsuzvA2lSpzrzNibx1PT17DiYVuw1t/ZrS5M61WnVsCZX9GxBZLjTLrJq3GDfcl6Pj+jO4yO6o6rsTckgR5UvV+wuNM8Xru9Jo9rVmLE2kU9jj68xXT0ijGPZuYVe9+pNZ5GrMPSM5hzLziFMhDPGzeLWfm15YkR3Js6P4/lZm4iqX4PwMKFaeBh3X9yRqAY1Cl1u6apeLdh3+BhLth084diUu8/j9vdiuevC9owe0J4X52ymw2m1GdGrpe+cVbsOMWJi/vW2fxvTmn9cdybHsnM4/dGZAFxzVku+/OX4e7L04YE0qVON2ev3cUaLerRtXJsZaxKpERnOxV2a8s0957N69yHOiW7E4JcWAnDDOa25d2An3+/l/SXOTMU1IsPIyHLety/+2I9rXzs+Q/s9F3fk1flx1KoWTprfetZPjjiDW/pFc+9Hv3Ag1TcistSeveZMRg9oz67kNOZvTGJ4jyh+O2kpAFPvOY8akeFc+eoPZGTlcmOfNny07Pg63Od3bEKv1g14dX6cL+3Wfm35Of5XIsKErlF1fZ+XOy9oX2j38XWPD+GMcbN8+9PuPZ91e1L42xdrGNU/mvo1I31rEv845hJaNqhJ/IGjtG5Ui8SUdM7/x/x8+X3xx/70btuQWev2kpWTy9e/7GHcFd24+j+LOXDEee/GXdGNx7/xprrQvWU91u4+XOp8nrvuTB76fDU/jrmE7JzcfH9/lv19ILsPpdO6US2a1Kle6DJ8InBuu+D0qvnb0C58uSKBLUlH8qU3rl2Ng0czAZh0S2/q1ojkxjeX+o4vfXggi7bs56+fr6ZL1Mn9DQ8FpVrnWURWAJeqarKIXAB8DNwL9AK6qup13oTpe77rgKGqOtrdvwU4V1XvCXS+V2tJ5snIyqHLYzN5aOjp/OmiqnX31lR+46eu493F8fyubxueuqr0C9YbUxnZOs+l51XZnJaZTXiYUD0i8KRhpyIuKZVLX1xIjcgwHhjUmavPakVKehb7U4/l++evpO6+uAMT528FnLVMtx84yufLd3HHBR34akUCI/tHnzBbuL+Za/eSlJrB2CnreGBQZ16csxlwKlZX9mxJvZoRRV4fPWY6Azo14YPbzz3hWN4/4xueGIqI00CwcW8qY6es5Z3b+lC/ZiS5ucqGvYdJPJRBelYOV/RsAcDW/UeoUz2Cc5+Zx9+Hd2F1QgqXnxnF0O5RJzzP4YwsaleL8A19K86Ulbu5/+OVjOofzbuL43l4WBfuvLCD70ZAnhWPDaJR7WolynPLvlRe+34rX67YzXu/78OFnU/zHUtKzWDnwTTmb0pi4vytXN+7Fc9f37NE+ZZESnoWdatHMGvdXrJzlSt6tiAlLYueT8zm9+e1Y+wV3ThyLJsaEWGs3XOYw+lZXOAXX3lYviOZVg1r0axeDV/a+j2H6RpVl29WJ/LnT1ay7vEh1Ig8/l3L+30UXKN32/4jrE88zOVntuAn94ZHRLiwclcKt5/fLt+5Q19eyAWdT+Pvw7ueEFNh+QNsSDzM3PX7uH1AO/YdPka7JrULfW3ZObmkZ+VQIzLct4TdvZd05N/fHb8B4F+x/uyufmzam8qjX6/1HZ9wTQ/O69iE3YfSSc3IZlC3Zhw9lk1keBidH3XybFKnOgeOHGPbM8PZduAoD3y6kr8N7cJ5HZswe91e7v5wBY9f2Z2bzm3DG99v5aW5m1n3+NATvhepGVn8svNQwM9AUmoGfZ6eB8DWZ4azeV8qXd2WW1Wl3cPf+s4d1K0Zc9bvo2tUPTYknljRv/zMKKpHhDP28m70fGJ2oe8fwPu/70NqRjZ3f7jCl5Z3cwcgPTOHrmOdm1CPXtaV3/Vtm++zAhCXdITIcKF5/RpUjwj3rU8+ZlgX7rqwQ5HPXxLlWTaXtvK8SlV7uo8nAvtVdby7v1JVe3kS5fHnux4YUqDy3EdV7/U75w7gDoA2bdr03rHDu/XJsnNy6fjIDAZ0amLr6plKZ876vazYeYherRsw5IzmwQ7HGE/1aFmf8zuV/u+2VZ5Lz+sb215buHk/XZrXpalfRQKcCuPSbQcZ1j3KV2mbvymJ2975GYD+HRqzeOtBmtatzrJHLgUg+WgmZz85h/sGduIBv1buUzVn/T76tGuUr0t2ZXUsO4dJ32/jzgs7UC0ijF3Jadw6eRnv3nYOR45lc0aL+ieVn6qyYucherdtGPB4emYOb/+wjbsu7EBEuFeL0RTuwJFjNKxVrcQ3FULNlJW7+Sw2gf+OPvHGjBcWbEqiWkRYmf2/3efpuSSlOpXdsDDhUFomNSLD81X6jhzL5r3F8dx1YYdCf0+F/b0oC5nZuXR+dAZPX92dm89te8JxVWXa6kQGn9GMnFxl/NR1PDK8G8dynB4NjWpVo+MjM7jmrJa8+NvjVbTElHRyFXJzlUkLt/HIZV3ZtDfV12PD/wbGW4u2cXrzugzolL9yP39jEme2qp9vuEBxVuz8lV6tGviGxZZGRao8rwV6qWq2iGwE7lDVhXnHVLW7R3HmPV8/nO7gQ9z9hwFU9dlA53tdQKsqFz6/gJ3JxXejMsYYEzpG9Y9m/JVnlDofqzyXXqhXnk9WUmoGK3b8ytDuUXwau4trzmpZLpUvY4w5Wdk5uYSJlKjCGj1mOg8M6sx97lCEUFaeZXNpZ9v+CPheRA4A6cAiABHpCKSUMu9AfgY6iUg7YDdwA3BTGTxPQCLC/AcvIiun8PE+xlRkEWFi65ibSqkkszIbcyqa1q3h6678m5jKORmTMaZyOJkbe4G6zJtSVp5V9WkRmQdEAbP1eDN2GM7YZ0+5Ldz3ALNwlqqarKrrvH6eooSHCeFh3o2vMibUeDh80BhjjDHGmEqjVN22Q52I7MeZ+bugJsCBcg7HCxUx7ooYM1jc5akixgwVM+6KGDOEVtxtVbV8Z/OpZCpZ2VwRYwaLuzxVxJjB4i5PFTFmCK24y61srtSV58KISGxFHLNWEeOuiDGDxV2eKmLMUDHjrogxQ8WN25ycivh7rogxg8VdnipizGBxl6eKGDNU3LhLy2a0MMYYY4wxxhhjimGVZ2OMMcYYY4wxphhVtfI8KdgBnKKKGHdFjBks7vJUEWOGihl3RYwZKm7c5uRUxN9zRYwZLO7yVBFjBou7PFXEmKHixl0qVXLMszHGGGOMMcYYczKqasuzMcYYY4wxxhhTYlZ5NsYYY4wxxhhjiqOqIbkBk4EkYK1f2pPAamAlMBto4aaP8EuPBc530y920/K2DOAq99hAYIWb/gPQMUAMNxe4Phfo5R5bAGzyO9a0nOK+xI17LfAeEFHI+zcS2OJuI/3SewNrgDjgFUBCIWagF7AEWOfm/1u/Y+8C2/3y7hVi73WO3/VT/dLbAT+5v4NPgGqhEncx15fZ++0ee879PW/A/QwW9tkMELe4x+Lc/M8u6jMfIjHf7Oa7GlgM9PQ7Fu9evxKI9fLvnwdxXwSk+H0OxvodG4rz9y8OGBNicf/VL+a1ON/PRkW937ZZ2exB3FY2W9nsxfttZbOVzVY2e1BWleUW9AAKDQwuAM4u8Iuu5/f4PuB193Edv1/gmcDGAPk1ApKBWu7+ZqCr+/hPwLvFxNMD2Oa3vwCIKc+4cXoK7AI6u8eeAG4v5Jpt7s+G7uOG7rFlQD+cP3QzgGEhEnNnoJP7uAWQCDRw998FrgvF99o9dqSQ9E+BG9zHrwN/DKW4i/hulNn7DfQHfgTC3W0JcFFhn80AMQx3jwnQF/ipqM98iMTcn+Pfv2F5Mbv78UCTEH2vLwKmBUgPB7YC7YFqwCqgW6jEXSDWK4Dvinu/bSv55sXvuEB+Vjarlc1ex+0es7K5hHFjZbOVzVY2l3gL2W7bqroQ54+Gf9phv93agLrpR9R99/3TC7gOmKGqaXnZAfXcx/WBPcWEdCPwUZDjbgwcU9XN7rE5wLUBrhkCzFHVZFX91T1vqIhE4XxZlrjP+z7OHc2gx6yqm1V1i/t4D84dstMC5O1/TdDjLoyICM5d5s/dpPeAq0I07oLfjYA8iluBGjh/2KsDkcC+wj6bAcIYAbyvjqVAA/fagJ/5UIhZVRe7MQEsBVoFeF0Frwl63EXoA8Sp6jZVzQQ+xvm9hGLcJfq7bUrOyuaAcVvZnP+aoMddGCubrWz2ez4rm4MXd4Uum0O28lwYEXlaRHbhdLcY65d+tYhsBKYDvw9w6Q3k/0WNBr4VkQTgFmBCMU/9W078Rb8jIitF5DH3D3JZx30AiBSRGHf/OqB1gGta4tzhzJPgprV0HxdMD4WY/Z+3D86Xc6tf8tMislpEXhKR6sVcX95x1xCRWBFZKiJ5fzAaA4dUNdvdL/K9DlLcga7PUybvt6ouAebjtF4kArNUdQMl/2wW9dkOlB4KMfu7HeeubB4FZovIchG5o5hrgxF3PxFZJSIzROQMN+2k3usgxY2I1MLpwvaFX/JJvd+m5KxstrLZymbP4g50fR4rm61sBiubg0tDoPm7sA2Ixq+LQYFjDwOPB0i/AJhbIC0K2A9E+qV9CZzrPv4r8FYRcZwLrCmQ1tL9WRdnrMCt5RR3P2ARTheJp4BfAuT1V+BRv/3HgL8A5/g/BzAA+CYUYi5w/Sagb4E0wbnT9R75x3cEPW6OjxNpj9P1pAPOnfk4v3Na+3+GQiHuIq4vs/cb6Ijzx7iOuy1xjxf62SyQ13Tyj7+ZhzPWJuBnPhRi9jt+Mc44ocYBPjtNcbpYXRBC73U9oI77eDiwxX18PX5/L3EqOP8Olbj9jv+24PGi3m/bSr6V9nfsl2Zls5XNZRY3VjaXOG6sbLay2crmEm8VruXZz4cE7mK0EOggIk38kn8DfKWqWQAichrOxAA/ucc/wenLX5gT7v6p6m73Z6obS5+yjts9b4mqDlDVPsBCnAkYCkog/x3NVjhd3xLI3y0lLz0UYkZE6uF8MR9Vp9tP3vWJ6jgGvENovdeo05UNVd2GM97uLJy7zA1EJMI9raTvdbnFXcT1Zfl+Xw0sVadb0BGcO719Kflns6jPdqD0UIgZETkTeAsYoaoH/Z4n77OTBHxFCL3XqnrYvQ5V/Ran1aQJp/5el0vcfgL93T7V99uUnJXNVjaHRNxWNp9U3FY2W9lsZXMJ5Q0ID0kiEo0zKL67u99J3bE3InIvcKGqXiciHYGtqqoicjbwDdCqcePGudHR0cEJ3hhjTKWzfPnyA6pa5JjPys7KZmOMMaGkPMvmiOJPCQ4R+QhnRrkm7tinccBwETkdZ1mKHcBd7unXAreKSBaQjrOUgsbExBAbG1v+wRtjjKmURGRHsGMIJiubjTHGhJryLJtDuuW5tGJiYtQKaGNKZuv+I7y+YCt3Xtiejk3rBjscY0KSiCxX1ZjizzSFsbLZmJPzxvdbGd4jitaNagU7FGNCUnmWzRV5zLMxxkMz1iTy2fIEpqws6RAZY0xFISKNRGSOiGxxfzYs5LyR7jlbRGRkgONTRWSt3/54Edntzm69UkSGl+XrMKaq2Z96jGdnbOTWycuCHYoxBo8qz+L4nYiMdffbuEsaGGMqiLxOKJW4M4oxVdkYYJ6qdsKZCXdMwRNEpBFON+xzcSZsGedfyRaRa4AjAfJ+SVV7udu3ZRK9MVVUTq5TKB89ll3MmcaY8uBVy/N/cKbgv9HdTwUmepS3McYYY0pnBM7yNrg/rwpwzhBgjqomq+qvwByc9TgRkTrAAzhL7BhjykmOe0c7PKzIJcuNMeXEq8rzuap6N5AB4Ba61TzK2xhjjDGl00xVE8FZ8gZnPc2CWgK7/PYT3DSAJ4EXgLQA190jIqtFZHJh3cGNMacmJ8cqz8aEEq8qz1kiEg4o+NZqzPUob2OMMcYUQ0TmisjaANuIkmYRIE1FpBfQUVW/CnD8NaAD0AtIxKlgB4rtDhGJFZHY/fv3lzAcY0x2rvPvdG6ujakyJhR4tVTVKziLWjcVkaeB64BHPcrbGGOMMcVQ1UsLOyYi+0QkSlUTRSQKSApwWgLOMlR5WgELcIZl9RaReJz/G5qKyAJVvUhV9/k9x5vAtEJimwRMAme27ZN5XcZUZXM3OF+xPSkZQY7EGAMetTyr6v+Ah4Bnce48X6Wqn3mRtzHGGGNKbSqQN3v2SGBKgHNmAYNFpKHb/XowMEtVX1PVFqoaDZwPbFbViwDcinieq4G1GGM8s+/wsWCHYIzxU+qWZxEJA1arandgY+lDMsYYY4zHJgCfisjtwE7gegARiQHuUtXRqposIk8CP7vXPKGqycXk+5zbrVuBeODOMonemCoqPSsn2CEYY/yUuvKsqrkiskpE2qjqTi+CMsYYY4x3VPUgMDBAeiww2m9/MjC5iHzige5++7d4GqgxJp8MqzwbE1K8GvMcBawTkWXA0bxEVb3So/yNMcYYY4ypUqzybExo8ary/LhH+RhjjDHGGGOAjCxbvMaYUOJJ5VlVv/ciH2OMMcYYY4wjPdNano0JJZ7Mti0ifUXkZxE5IiKZIpIjIoe9yNsYY4wxxpiq6Ne0zGCHYIzx40nlGXgVuBHYAtTEmXzkVY/yNsYYY4wxpsrZuDc12CEYY/x4VXlGVeOAcFXNUdV3gIu8ytsYY4wxxpiqpnvLegA0rl0tyJEYY8C7CcPSRKQasFJEngMSgdoe5W2MMcYYY0yVE9O2EWt3H6Zj0zrBDsUYg3ctz7cA4cA9OEtVtQau9ShvY0w50GAHYIwxxph8UjOyAWhSt3qQIzHGgHezbe9wH6Zjy1YZY4wxxhhTal+sSAAg8VB6kCMxxoBHlWcR2U6AhitVbe9F/saYsqfW9GyMcoc95wAAIABJREFUMcaEpIgwz6YpMsaUgldjnmP8HtcArgcaeZS3McYYY4wxVZbVnY0JDZ58FVX1oN+2W1VfBi7xIm9jTPlQG/VsjDHGhKTwMAl2CMYYPKo8i8jZfluMiNwF1PUib2OMMcaUjog0EpE5IrLF/dmwkPNGuudsEZGRfukLRGSTiKx0t6ZuenUR+URE4kTkJxGJLp9XZEzVEm5Nz8aEBK++iS/4bc8CvYHfFHWBiLQWkfkiskFE1onI/W56wAJeHK+4BfRqETnbo9iNMdiYZ2MquTHAPFXtBMxz9/MRkUbAOOBcoA8wrkAl+2ZV7eVuSW7a7cCvqtoReAn4R1m+CGOqqkhreTYmJHjVbftiv22Qqv5BVTcVc1k28BdV7Qr0Be4WkW4UXsAPAzq52x3Aa17EbowxxlQBI4D33MfvAVcFOGcIMEdVk1X1V2AOMPQk8v0cGCgi9l++MR7r1Mw6dBoTCryabfuBoo6r6osB0hKBRPdxqohsAFriFMQXuae9BywA/uamv6+qCiwVkQYiEuXmY4wpJfX9tCZoYyqhZnnlpaom5nW7LqAlsMtvP8FNy/OOiOQAXwBPueWx7xpVzRaRFKAxcMA/YxG5A+fGN23atPHmFRlThdgtKWNCg5ezbZ8DTHX3rwAWkr8QLpQ7Ruos4CcKL+ALK9TzVZ6tgDbGGFMVichcoHmAQ4+UNIsAaXl3025W1d0iUhen8nwL8H4x1xxPUJ0ETAKIiYmxO3TGnCTrtW1MaPCq8twEOFtVUwFEZDzwmaqOLu5CEamDUxD/n6oeLqK3lxXQxpQld9CzjX02pmJS1UsLOyYi+/J6a4lIFJAU4LQEjvf8AmiF0/sLVd3t/kwVkQ9xxkS/717TGkgQkQigPpBc+ldjjPEnAf8NNsaUN68mDGsDZPrtZwLRxV0kIpE4Fef/qeqXbvI+t2CnQAGfV0DnaQXsKV3YxhhjTJUwFcibPXskMCXAObOAwSLS0J0obDAwS0QiRKQJ+Mrty4G1AfK9DvjO7c5tjPHQtgNHgh2CMQbvKs8fAMtEZLyIjMPpfv1eURe4E4q8DWwoMCa6sAJ+KnCrO+t2XyDFxjsb4x0t8NMYU6lMAAaJyBZgkLuPu7zkWwCqmgw8Cfzsbk+4adVxKtGrgZXAbuBNN9+3gcYiEgc8QIBZvI0xpfftmr3BDsEYg0fdtlX1aRGZAQxwk25T1V+Kuew8nDFTa0RkpZv2d5wC/VMRuR3YCVzvHvsWGA7EAWnAbV7EbowxxlR2qnoQGBggPRYY7bc/GZhc4JyjOEtQBso3g+PltDHGGFOpeTXbdgdgnaquEJGLgAEisl1VDxV2jar+QOBxzBC4gFfgbi/iNcacKK+jpXW4NMYYY0JPVk4ukeFedRo1xpwKr76BXwA5ItIReAtoB3zoUd7GGGOMMcZUaZ/GlmgRG2NMGfKq8pyrqtnANcC/VPXPQJRHeRtjykHe+s62zrMxxhgTeh75am3xJxljypRXlecsEbkRuBWY5qZFepS3McYYY4wxxhgTVF5Vnm8D+gFPq+p2EWkH/NejvI0x5UBtum1jjDHGGGMK5UnlWVXXq+p9qvqRu79dVSd4kbcxxhhjTGFycpUJMzZyKC0z2KEYY4yp5DyZbdsYU/FZw7MxpiLqOnYmmdm5LN56gKn3nB/scIwxxlRiNt+9McYYYyqszOxcAFYnpAQ5EmPK3p5D6cEOwZgqzZPKs4hcX5I0Y0zoOr7Os7U9G2Mqhs+XJwQ7BGPKVf8J35GRlRPsMIypsrxqeX64hGnGGGOMMZ548LNVwQ7BmHLX5bGZLN/xa7DDMKZKKtWYZxEZBgwHWorIK36H6gHZpcnbGFO+fOs8W8OzMaaCSknPon5NWynTVB49WtZnze4ThyRc+9pi4idcFoSIjKnaStvyvAeIBTKA5X7bVGBIKfM2xhhjjAdEpJGIzBGRLe7PhoWcN9I9Z4uIjPRLXyAim0Rkpbs1ddNHich+v/TR5fWaAun5+Gzu/eiXYIZgjKfOatOAujUCt3VNnB/H0WPWVmVMeSpV5VlVV+Gs5/yDqr7nt32pqtafxJiKRPP9MMZULmOAearaCZjn7ucjIo2AccC5QB9gXIFK9s2q2svdkvzSP/FLf6sMX0OJfLNqT7BDMMZTEWESMP35WZt46PPV5RyNMVVbqcc8q2oO0FhEqnkQjzHGGGO8NwJ4z338HnBVgHOGAHNUNdm9AT4HGFpO8Z20oiZNeufH7eUYiTFl75fHBgVMn74mkYNHjpF81NY5N6Y8eLXO8w7gRxGZChzNS1TVFz3K3xhTxnzrPFvTszGVUTNVTQRQ1cS8btcFtAR2+e0nuGl53hGRHOAL4Ck9PjX/tSJyAbAZ+LOq+udRZq6a+GOhxx7/Zj3X9W5F3Ro2/tlUDg1rF95G1fupuQD8ZVBn7h3YqbxCMqZK8mq27T3ANDe/un6bMcYYY8qBiMwVkbUBthElzSJAWl4F+WZV7QEMcLdb3PRvgGhVPROYy/HW7YKx3SEisSISu3///pK/qCJs3Jta5PEe42fz9PT1njyXMRXBC3M22zJWxpQxT1qeVfVxABGp6+zqES/yNcYYY0zJqOqlhR0TkX0iEuW2OkcBSQFOSwAu8ttvBSxw897t/kwVkQ9xxkS/r6oH/c5/E/hHIbFNAiYBxMTElFv/ljcXbef05vUY0KkJzerV8KVn5+SyaMsBLu4SqAH+1MUlHWHWur3cfXFHT/M1BuC+gZ14Zd6WIs/p8thMAGb/+QI6N7N2LGO85knLs4h0F5FfgLXAOhFZLiJneJG3MaZ85PXAVJsyzJjKaCqQN3v2SGBKgHNmAYNFpKE7UdhgYJaIRIhIEwARiQQuxynvcSviea4ENpRR/Kfswc9Wce4z84geM53oMdP5eNlO/rNgK7e9+zOX/3sR0WOmk5KWRWZ2LlrKcSvXv76Y52dtIi0z9GZAXr7jV3o/OYeU9Kxgh2JO0Q3ntC7xuYNfWkh6Zg5TVu5m235r0zLGK151254EPKCqbVW1LfAXnDvQxhhjjAm+CcAgEdkCDHL3EZEYEXkLQFWTgSeBn93tCTetOk4lejWwEtjN8TL+PhFZJyKrgPuAUeX3khxv3RrDkyNKfr9+zJdreHHOZgDW7j4MQM8nZtP50Rm8uWhbwGv2px4j/sDRgMdS0rJIy8xGVfk1zamYFlYHT83IIjUjOJXXa19bzMGjmazYUfhiKNk5uSQdzgh4LDY+megx09lxMPD7UFJxSUe44Ln5HDhyrFT5nKq9KRnF3iTZlZzG3pTA70MwtWhQk63PDC/x+V3HzuT+j1dyyQvfM/CFBfzfx8FZxm35jmQSU9KD8twnI+HXtAoRZ3lLOpxBTq7zncnIymFNwonrjheUm6t89UsCcUlHiB4znbikE4fZlPZmZbB4VXmurarz83ZUdQFQ26O8jTHlIO9vWAX9W2aMKYKqHlTVgarayf2Z7KbHqupov/Mmq2pHd3vHTTuqqr1V9UxVPUNV73dX2kBVH3bTeqrqxaq6sbxf26XdmnFLv2jmPnBhqfN65tuN+cZJb0g8zMy1eznn6blc9M8FzNuwj+U7klm4+fi47Z5PzKbb2Fm85FbIAc4YN4tHv17DC7M3ET1mOgs2JZGUmkGP8bPpMX42Ow+mMfil7/nb56tZuzvF1yp+5wexrE44RPSY6SVqLczIymHLvvz/lGbl5HLYr4J+5Fg2U1bu9u0fy85l095UNgUYM/70txvo88w8DqWdOHPzGwudGwuPfLWW2Phklm47yFuLtpGbq+w5lB7wn+NA3lq0jZ3JaXz9y+6Ax9/5cTtLth4MeGxD4uESP09h1/d9dh7/WbCVlbsOkZWTy/9+2sFLczbz3MyNvn/mBzw3n77Pzisyr9xc5c2F29ibkkFWTm6h5+X9fmev28tt7yzji+UJ+Y4fOZZNRlYOy3ck89Dnq4p9DeFhwlu3xpTg1ea3df9Rvl65h+gx01m161C+Y/EHjhLz1Fx+/+7PvLkw8A2kU5WSnsW1ry2h37PfeZpvWTj/H/N9caZn5pCeefLjxw+lZZKemXPC9ys3V5m1bm+hY9L3pmTw0bKdJXqOKSt35/tOA7y3OJ7FcQd8+3FJqTw5bT3/drv5p2Vmc9XEH33n+M/OnpqRxaSFWxk/dR37Ctw8ix4znT7PzOOfszcBTk+eK179ga6PzeSrXxLYdziDwS99z9b9R/jPgjgu/ucCdiWn8dAXq/nzJ6u49MXvARg/db0vv9++sYRvVu2h3cPfsmiLN3NglCfxotYvIl8BK4AP3KTfATGqGmgpjHITExOjsbGxwQzBmArjqWnreeuH7YzqH834K23UhTGBiMhyVT35/1yNj1dlc/SY6QDET7jshLTyUDMynPQynJzp9vPb0bZxLcZOWedLe+OW3rRtXIvfvrGUvw3twoy1iSzacoB3bzuH7i3r8/YP23ltwVYAGtaK9LWEF6VrVD3eve0cnvhmPdPXJALw9d3n0at1A9/7+fEdfblh0tKA119+ZhTTVif69u+4oD3N69XgiWnrefPWGAZ1a8Y/Zm5kePcourWox9X/+ZHVbsvVO6PO4fxOTYgMD+PtH7bTskFN7vrvcsAZswtO9+Mpd5/H6oRDPOa+F9/95ULGTV3HpFtiqFktnLFT1tKvfWMu7tKUMBEyc3KpU92Z1if+wFGa1qvOnkMZbN6Xyp/+t6LQ9+LfN55FmAh3f3j8nL8N7YII3HVhB37adpDnZm0i/sBRhnRvzoc/OZWdXq0b8PTV3bnslR8Ap4Kb11IXyNltGtC4TnU6N6vDxPlb8x178Tc9uebsVr790e/FMnfDvnyfc3Bac699bUmhz1GciDAhu5AYf37kUhJ+TWPyj/F0i6rHHy/qwOGMLI5kOMMR6taI4KlpG7i8ZxRN69Zg2/4j1K0RSadmdagREc67i+Pp1qIeg7o1Y8+hdPpPcCqk8RMu44OlO3js67W8/ruzGdo96oTnTknPIj0zhxxV6teMJFyEyHAhItyr9r7C+f9NyXsc9/Qw/rt0Bzf3bUtOrnLJPxcwZnhXruzZguycXLJyFBH4OT6Zs9s05Ixxs3z5LXtkIE3rOnMtxDw1hwNHMn35z1ybyMGjmVzVqyUvztnMtNV72Hf4eG+M+AmXcTgji29W7eGRr9YCsHLsICbOj+PNRc5SfNufHc4Lszfz6vw433Vf/ak/7ZvUoecTs/O9tldvOot7Pjyx58Gz1/Tg4S/XnJD+4ehzuemtn/Kl3XlBe99NNK8M7NKUt0edU+p8yrNs9qry3BB4HDgfZ7bOhcB4d53IoLHKszEl9+S09bxtlWdjimSV59Iry8rznPX7+MP7Vu5Xdt2i6rE+8TC3nRfN6oQUlgfoit7+tNo8OaI7NxeoAFQkQ85oxqx1+wBOqDwDvL8kPt/NlVBUPSKMY9mFt8wDDOvenKHdm9OvfWP6PHNii//gbs149LJuhIXBxsRUVuz8lV6tG7B460G6RdXjvSXxdI2qx419WtO7bSN2Hkxj/DfriG5cm7FXdPPlk5GVw4tzNvPAoM7UiAz3pWfl5JLwazoX/3OBL54Za/cC0LRudZJSjzGqfzSn1a3O87OcFtj/jT63xJ+tkt7Iqop6tKzPN/eeX+p8KlzlOVR5VUC/+t0Wko86H/pqEWH8YUA7GtepXup8jQklVnk2pnhWeS69sqw8A2zZl8qglxaWOn9jQkmgyjM43b67+7V2mvxa1K/Bue0bc9eFHRjy8vG/C78/rx3fb07i9+e387XsmuAo7LN9MsqzbPZkqSoR6Qw8CET756mql3iRf7B9u2Yvu5LTyFXlaGYOHU6rzfUxJZ/x0JiK4PiY58p7Q80YU7l0alrnxLRmdXn5t734v09WBiEiY8pXneoRTB4Vw+/ftR4XgexJyeCrX3bzVYEx9pN/dLo+W8XZnCxPKs/AZ8DrwFtApVud/dv7BwCQmJJOv2e/K3IcizHGGGPKXssGNTmzVYOAx646qyXtT6vNrZOXcci6S5pK7pIuzegaVY8NiYeDHYoxlZ5Xo++zVfU1VV2mqsvzNo/yDhnhYQJQ6AQLxlRkees726fbGFMRTLi2B7edF13o8TNbNWDl2ME8c3WP8gvKmCD5Xd82ADSuXS3IkRhTuZWq5VlEGrkPvxGRPwFfAb6p4vKWwqgswsWpPOdat1ZjjDEmqAZ0Oq1E593YpzXdWtSjbaNa3PTWT9Y6ZyqlC9zvw6RbY2jXpDb/mruZ+wZ2ovdTc4McmTGVS2lbnpcDscBI4K/AYjctL91zIjJURDaJSJyIjCmL5yhMRJjzdmXnWOXZVD62zrMxpjISEXq1bkDD2tWYcf8APvzDuSecM7xH8yBEBg8P6xKU5zWVT+tGtYifcBm92zakUe1qPD6iO43rVGfVuMF8+af+wQ7PmEqjVJVnVW2nqu3dnwW39l4FmUdEwoGJwDCgG3CjiHQr+irvuHVna3k2xhhjKqj+HZqw9ZnhjL+iG89c3YM3bunNf27uHeC8xqV+rrWPDwmY/tuY1mx6aiijBxz/V6llg5pF5tWvfenjAXho6Oknfc2KxwadkLbl6WG8PfL45Lav/643l515fN3e4l5PKHn0sq5seXrYKV/fsFZkwPRLuzZl8qiiJwA+s1X9U37ekqhfM5Kz2zTk7DYnzg8w94ELyvS5jfcu6Fx0j5uJN53Nrf3a5kvr0rzuKT/fvZd0LPacgV2annL+pbk2WErbbfscYJeq7nX3bwWuBXbgrPPsdbftPkCcqm5zn+9jYASw3uPnCcjGPJuqQG3UszGmkgsPE0ad1y5fWvyEy8jOyeXdxfE8NX0D7ZrUZvHWg1zatSm39ovm1snLaNWwJnMfuJBpqxN58LNVfHJHX+ZtTKJF/Rp0bFqXt3/Yxlsjz/H9vwBwerO6bNqXCsD3f72Ito1r53vejU8OBaBGZDhpmdl0G+ssO/TKjWdRt3oEt737MwAf3dHXtzxXnrz1jgGeGHEGY6es493bzqFhrWqMmPgjAFufGU54mBA9Zjqdm9XhTxd1ZNHmAyzZdhCAS7s2Y+6GfQHfp+n3nc8ZLer73h//548MD2Ng12asGT+YnFylQa1qDO3enIk3Hb9+7vp9jA6w7vZHf+jLjW8uBaBujQhSM7J9xybd0pvoJrXp3KwuZ4ydydHMnBPeu0NpmTw1fQOfL0+gTaNa7ExOo37NSFaOHURaZg5//XwVc9bvIytHufncNmTl5PJpbELA1wj4bmJc0bMF36zak+/Y+Cu6Mf6b9Xz3lwt558d4clXp3KwuN53bhlHvLOPVG8+mYW3n/f5NTCtuPrct8QeOctE/F/CbmNZc0qWZL685f76A1o1q0eWxmc7788AFtGtSh3/M3MikhdsAp7LSoFY1wgXGf+P8e7t6/OBCYy+pNo1qsWLnIWb+3wCWbD1I9YhwOjatS/yEy9i0N5U56/fyz9mbfef/bWgXBnVrxqUvfl+i/B8aejrPzdzk239wcGf+n737jq+qvv84/vokLNkbmUbBhSiouBdOFGcdrdZatPqzVm1/rdYWR4uzWlttta36cxY7tFZrRUURUFyoCArI3nsnEAgh+/P7456Em3CT3JCTO5L38/E4j3vP+t7PPTk53/s95zuGH7I3f5y0iLdnrat3/Mn2q/MGsnjjdl6auqrS8q9+dSbz129j3rrtXHpkH3Lzi3n5y5Wcd1gvvlq5hbv+G+nNe+Rx+/Cdo/ox4vGPAfjbtUeTacZ3g7GiTxzQleaZRqc2LfjPV5Gewbu2bcnmvIrWsAzp25EXf3A0j05YyNgZa1ienc8/rzuG4wd0rRTTuYf15PSDezDy+an85LQB3HLWgXyzOpfWLTM5/ZHI33Pfrm1496cn8djERTwxeQkQ+R//fGk2j05YyEv/c2zFdWz4IXuzLreAVTn5vD9/I3+58gj+OHEh5w/uxcVPTOHaE/fluauP4tZXZvLaV6t54FuDyCsoYUD3tizZlMdvxs2nR/uWXH9yf649MXLdLb+W/OzMA8L7IyVIvcZ5NrOvgDPcPcfMTgZeBn4MDAEOdvdLwwmz4vMuBc529+uC+auAY9z95ljbhzWWZLnCklIOvOtdbht+IDedWvudGJF0cvfYOfx1ynK+d2w/7r9IHeyIxKJxnusv7Ly5IYz7Zh2nHdSdguJSWrdoRobBvW/N5cZhA9i7Q6s6p3fls5/z6eJsPrv9NHp2qP2J7KbthXRr1xKAVTn57NUik65tW7Jg/XaueWEqr9xwHJ3btKBFZgbbCkroHKOTqOc+WcYDb89l6YORMVQXb8yje/uWtG/VnOLSMn79xhxuG34g7Vs1q0gja9Tb7NOlNR/edmq1sf1x4kJOO6h7tT2dx/Ly1JX06dSa7z33RcVvqIfemU+LTOOWsw6s+CH9yGWDueTIPhX7DRo9nrzCEmbdfRbtW+3+dDf6O1V1/1tzefaTZUy76wy6tm3Jqpx8nvxwCfPWbeOp7x3JlCWb2by9iPMG96z0NykoLuWeN+cy6uyD6FDNE+WGkJ1XyBsz1nLNCVlY0MfO/PXbyOrShlbNM+ud/o7CEj5ZvJnhh1TfRCHW2On97xhHaZlzzqC9eWf2em4c1p9rTti34vzcml/Elvxi9u3ahg/mb+Sav37JXeceXKlWxZy1uTTPzOCAHruegP76jdl8vGgz/73xBB56dz43nLIfLZplkF9Uyo7CEg7r05G3Zq3l5n9+DcDce4dT5tRrTOuLj+hdUTCNZclvRvDRok3c+PevuPKYfqzfVsBNpw7ADA7au32lbdfnFuB4XP/PNSkrc97+Zh3nHtqTjKCwumbrTlbn5DM0qzO5O4t5c+ZaRo+dw7H7debl64+r1+cBbNlRRNtWzWiemVFxjK84uh8PXpy4336H3j2e7QUlvH/rKezXbfchB+sqkXlzfQvPM919cPD+L8Amd787mJ/h7kNCiXLX510GDK9SeD7a3X8ctc31wPUA/fr1O3LFihWhfX5JaRkD7nyHfbu2YZ8urUNLVyQVLNqQx5qtO+nVoRUH1KOKj0gqOuPgHnzv2H1q37AWKjzXXzoUnsO2Nb+Iz5Zkc86hPWvfOIlydhTRqnkGrVuENZJpZWu37mTv9q0qCgnlSsucDKOi0Fhu/Jz1/GHCQt7+yUmVnubHo6S0jM15RXt0s6Op2lZQjHukqne57QXFlJXB379Ywe/GL+BHw/rzy7Orb6v/xdJsjsrqvNvfeE8UFJdy0K/e5YLBvXj8isMrli/csJ2ubVvSqXVz3CEjwxj463fJLypl2YMjcIfthSX88tVZvDtnPTNHn1XpO0Gk0Pqdpz8D4MvlW7j4iN48+u1Qiy2hWb0lnxN/+wH3XTSIq0LIx6KVljl/en8R1564L+1i3IRqKK98uYpfvDaLufcOD+V6k06F59nAEHcvMbP5wPXu/lH5OncfFFKc5Z93HJHq4MOD+dsB3P3BWNuHnUG7O7e+MpMlm/JCS1MklcxcncvgBm5/JZIM5x7Wk+tP7l/vdFR4rr+mWHgWSXe5+cXc8soMHr70MLq0bZmwz12Vk0/39i1p2azmp+8btxewcVshg3rv+g3j7hSWlIXy5D7ZCopLadksY7cbTBKRyLy5vkX9l4APzWwzsBP4GMDMBgC59Uw7li+B/c1sX2ANcDnw3Zp3CY+Z8eh3UvOulIiIiIhIQ+jQujnPXX1Uwj+3b+f4anp2b9eK7u0q1zIws0ZRcAYazfdoDOpVeHb3B8xsEtATeM93PcbOINL2OVTBE+6bgfFAJvC8u88J+3NEREREREREotWr2naqM7NNRHr+rq+uwOYQ0km0dIw7HWMGxZ1I6RgzKO5EasiY93H3mscKkRopb07LuNMxZlDciZSOMYPiTqRGkTc36sJzWMxsWjq2cUvHuNMxZlDciZSOMYPiTqR0jFnqLl3/zukYdzrGDIo7kdIxZlDciZSOMceSkewARERERERERFKdCs8iIiIiIiIitVDhOT5PJzuAPZSOcadjzKC4EykdYwbFnUjpGLPUXbr+ndMx7nSMGRR3IqVjzKC4EykdY96N2jyLiIiIiIiI1EJPnkVERERERERqocKziIiIiIiISC2adOHZzJ43s41mNrua9WZmj5vZYjObZWZHRK0baWaLgmlk4qKOK+4rg3hnmdkUMxsctW65mX1jZjPMbFoKxTzMzHKDuGaY2a+j1p1tZguCv8OoRMUcfHZtcd8WFfNsMys1s87BumQd675m9oGZzTOzOWb2vzG2SblzO864U/HcjifulDq/44w5Fc/tVmY21cxmBnHfE2Oblmb2r+B4fmFmWVHrbg+WLzCz4YmKW+pGebPy5toob1beHFLcKXV+K29Ok7zZ3ZvsBJwMHAHMrmb9COAdwIBjgS+C5Z2BpcFrp+B9pxSK+/jyeIBzyuMO5pcDXVPwWA8D3oqxPBNYAuwHtABmAgNTJe4q254PvJ8Cx7oncETwvh2wsOoxS8VzO864U/HcjifulDq/44m5yvapcm4b0DZ43xz4Aji2yjY3Ak8F7y8H/hW8Hxgc35bAvsFxz0z0d9AU199ZeXPqxJxS1654466ybapcv5Q3p97xTqnzO56Yq2yfKud2k8qbm/STZ3f/CMipYZMLgRc94nOgo5n1BIYDE9w9x923ABOAsxs+4oja4nb3KUFcAJ8DfRISWA3iONbVORpY7O5L3b0IeJnI3yUh6hj3FcBLDRhOXNx9nbt/FbzfDswDelfZLOXO7XjiTtFzO57jXZ2knN97EHOqnNvu7nnBbPNgqtrr5YXAmOD9q8DpZmbB8pfdvdDdlwGLiRx/STHKmxNHeXPiKG9OLOXNidPU8uYmXXiOQ29gVdT86mBZdctT0bVE7mKWc+A9M5tuZtcnKabqHBdU+XjHzA4JlqXFsTaz1kQysteiFif9WAfVYg4nchcwWkoktdZaAAAgAElEQVSf2zXEHS3lzu1a4k7J87u2Y51q57aZZZrZDGAjkR+T1Z7b7l4C5AJdSIFjLaFJ6etXnFLu+lWDlLx2xSPVrl9RcWWhvDlhlDc3vKaUNzdLdgApzmIs8xqWpxQzO5XIRezEqMUnuPtaM+sOTDCz+cEd3GT7CtjH3fPMbATwX2B/0uRYE6k686m7R98JT+qxNrO2RC6qP3X3bVVXx9glJc7tWuIu3yblzu1a4k7J8zueY02KndvuXgoMMbOOwOtmNsjdo9s9puy5LaFJ679xKl6/apCS1646SKnrFyhvTrHjnZLnt/LmlLyWVNCT55qtBvpGzfcB1tawPGWY2WHAs8CF7p5dvtzd1wavG4HXSZGqEe6+rbzKh7uPA5qbWVfS4FgHLqdK1ZlkHmsza07kwvsPd/9PjE1S8tyOI+6UPLdrizsVz+94jnUgpc7tqBi2ApPZvepixTE1s2ZAByLVO9PlWiK1S8nrVzxS8fpVk1S8dtVRSl2/lDen1vFOxfNbeXPKXksqmHtKF+7rpWvXrp6VlZXsMEREpJGYPn36Znfvluw40pnyZhERCVMi8+ZGXW07KyuLadMS1lO7iIg0cma2ItkxpDvlzSIiEqZE5s2qti0iAHy6eDMn/vZ9Ji/YmOxQREREBCgpLePMRz/kvTnrkx2KiBBS4dkivmfB4OJm1s/MUqK9jojE56sVW1i9ZSfTlm+pfWMRERFpcLk7i1m0MY9fvjYr2aGICOE9eX4COI7IeGMA24G/hJS2iIiIiEiTtSW/ONkhiAjhFZ6PcfebgAKAYKD0FiGlLSIiIvVgZp3NbIKZLQpeO1Wz3chgm0VmNjLG+rFmNjtq/m4zW2NmM4JpREN+D5GmZnn2jmSHICJRwio8F5tZJsG4XGbWDSgLKW0RERGpn1HAJHffH5gUzFdiZp2B0cAxRIY4GR1dyDazi4G8GGn/wd2HBNO4BolepIkqKmm8o+KIpKOwCs+PExlPrLuZPQB8AvwmpLRFRESkfi4ExgTvxwAXxdhmODDB3XOCGmQTCMbqNLO2wC3A/QmIVUQCc9dtS3YIIhIllKGq3P0fZjYdOB0w4CJ3nxdG2iIiIlJvPdx9HYC7rzOz7jG26Q2sippfHSwDuA94BMiPsd/NZvZ9YBpwa1DwFpEQuOvJs0gqqXfh2cwygFnuPgiYX/+QREREpK7MbCKwd4xVd8abRIxlbmZDgAHu/jMzy6qy/kkiBWtnVwH7BzFiux64HqBfv35xhiMiLZtpVFmRVFLvwrO7l5nZTDPr5+4rwwhKRERE6sbdz6hunZltMLOewVPnnkCsAd1XA8Oi5vsAk4mMpnGkmS0n8ruhu5lNdvdh7r4h6jOeAd6qJrangacBhg4dqkdpInHav0e7ZIcgIlHCup3VE5hjZpOCnjjHmtnYkNIWERGR+hkLlPeePRJ4I8Y244GzzKxT0FHYWcB4d3/S3Xu5exZwIrDQ3YcBBAXxct8CZiMioWnVPBOAw/p0SHIkIgIhtXkG7gkpHREREQnfQ8ArZnYtsBK4DMDMhgI3uPt17p5jZvcBXwb73OvuObWk+3BQrduB5cAPGyR6kSaqqCQyeE2GxWpVISKJFlaHYR+GkY6IiIiEz92ziXTqWXX5NOC6qPnngedrSGc5MChq/qpQAxWRSp6YvBiAGau2JjkSEYGQqm2b2bFm9qWZ5ZlZkZmVmpn61hcRERER2UPrcwuSHYKIRAmrzfOfgSuARcBeRO5i/zmktEVEREREmpy9O7RKdggiEiWsNs+4+2Izy3T3UuAFM5sSVtoiIiIiIk1N+1bNARjSt2OSIxERCK/wnG9mLYAZZvYwsA5oE1LaIiIiIiJNztiZawGYt06tIUVSQVjVtq8CMoGbgR1AX+CSkNIWEREREWmy1Nu2SGoIq7ftFcHbnWjYKpG05MkOQERERGLKzFDhWSQVhFJ4NrNlxPjt7e77hZG+iIiIiEhTc8KALny6OJtBvdsnOxQRIbw2z0Oj3rcCLgM6h5S2iCSA69GziIhISjl7UE8+XZzNft3aJjsUESGkNs/unh01rXH3PwKnhZG2iIiIiEhTlFdQAkCm2jyLpISwqm0fETWbQeRJdLsw0haRxHC1ehYREUkpv313PgCFJaVJjkREILxq249EvS8BlgPfDiltEREREZEmq0z3t0VSQli9bZ8aRjoikjxq8yzSeJlZZ+BfQBbBDW533xJju5HAXcHs/e4+Jlg+GehJZFQNgLPcfaOZtQReBI4EsoHvuPvyBvsiIk2Uqm2LpIawqm3fUtN6d380xj59iWS4ewNlwNPu/lh1GbyZGfAYMALIB65296/CiF9ERKSRGwVMcveHzGxUMP/L6A2C/Hc0kaZXDkw3s7FRhewr3X1alXSvBba4+wAzuxz4LfCdhvwiIk1RhoaqEkkJoXQYRiSj/RHQO5huAAYSafdcXdvnEuBWdz8YOBa4ycwGsiuD3x+YFMwDnAPsH0zXA0+GFLuIsGusObV9FmmULgTGBO/HABfF2GY4MMHdc4IC8wTg7Dqk+ypwenCzW0RCpLKzSGoIq81zV+AId98OYGZ3A/929+uq28Hd1wHrgvfbzWwekYL3hcCwYLMxwGQid8cvBF50dwc+N7OOZtYzSEdERESq16M8v3T3dWbWPcY2vYFVUfOrg2XlXjCzUuA1IlW6PXofdy8xs1ygC7C5Ab6DSJOVqdKzSEoI68lzP6Aoar6ISLXruJhZFnA48AVVMnigPIOvLVMvT+t6M5tmZtM2bdoU/zcQaeqCRs9q+yySnsxsopnNjjFdGG8SMZaVXxGudPdDgZOC6ao49omOTXmzyB64cEgvADq1bpHkSEQEwnvy/Ddgqpm9TiTT/Ba7qnHVyMzaErmL/VN331ZDba+4Mmh3fxp4GmDo0KEqBoiISJPg7mdUt87MNpTX1jKznsDGGJutZlfNL4A+RGp/4e5rgtftZvZP4Ggi/ZasBvoCq82sGdAByIkRm/JmkT2wT+fWAKgxhEhqCOXJs7s/AFwDbAG2Ate4+4O17WdmzYkUnP/h7v8JFm8IMnaqZPDlGXS5PsDaMOIXkeg2zyLSCI0FRgbvRwJvxNhmPHCWmXUys07AWcB4M2tmZl2hIt8+D5gdI91LgfeD6twiEoLyf6Y/TlyU1DhEJCKUwrOZ9QfmuPtjwEzgJDPrWMs+BjwHzKvSG3d1GfxY4PsWcSyQq/bOIiIicXkIONPMFgFnBvOY2VAzexbA3XOA+4Avg+neYFlLIoXoWcAMYA3wTJDuc0AXM1sM3MKuTj5FREQanbCqbb8GDDWzAcCzwJvAP4kMK1WdE4i0mfrGzGYEy+4gkqG/YmbXAiuBy4J144L0FhMZquqakGIXEXa1ddYzI5HGx92zgdNjLJ8GXBc1/zzwfJVtdhAZxzlWugXsyqdFJGTKk0VSS1iF57Kgl82Lgcfc/U9m9nVNO7j7J8RuxwyxM3gHbqp/qCIiIiIiIiJ1E1Zv28VmdgXwfeCtYFnzkNIWkQQoH99Z4zyLiIikBuXJIqklrMLzNcBxwAPuvszM9gX+HlLaIiIiIiJNTnS17YLi0uQFIiJAeL1tz3X3n7j7S8H8Mnd/KIy0RSQxXN1ti4iIpKxHJyxMdggiTV5YT55FRERERKSBPP3RUjZuL0h2GCJNmgrPIgJonGcRSU+FJaX8fvwCNLy0NEZVz+rLnvosKXGISERY4zzvNkxFrGUiIiIiYTrwrnf58weLOeGh95MdikiDW5Gdn+wQRJq0sJ483x7nMhFJUbvGedbTGxFJP2tzVZ1VGp9YWbLyaZHkqdc4z2Z2DjAC6G1mj0etag+U1CdtEREREZGmLNZQVfvePo659w6ndYt6/YwXkT1Q3yfPa4FpQAEwPWoaCwyvZ9oikkAV4zzrhraIpImyMl2wpJGr5hR/6sOliY1DRIB6Fp7dfSaR8Zw/cfcxUdN/3H1LOCGKiIiI7O7iJ6dUmv940aYkRSLSMBxo1Xz3n+uPT1qU+GBEpP5tnt29FOhiZi1CiEdEksUrvYhII2Jmnc1sgpktCl47VbPdyGCbRWY2Mmr5ZDNbYGYzgql7sPxqM9sUtfy6RH0nd2fGqq2Vll313FTufXMua7fuTFQYIg3K3TEs5rrb//NNgqMRkbAaS6wAPjWzscCO8oXu/mhI6YuIiMieGwVMcveHzGxUMP/L6A3MrDMwGhhK5D7adDMbG1WT7Ep3nxYj7X+5+80NGHtMc9Zui7n8+U+XMXP1Vl770fEJjkgkfO5gBr077sWaKjeFXpq6koG92rNu605uGNafls0yaNksM0mRijQNYfW2vRZ4K0ivXdQkImmiYpxnPXoWaYwuBMYE78cAF8XYZjgwwd1zggLzBODsBMVXZ9t2Fle7bvoKtRyTxsOAj35xasx1v/rvbJ6YvITD7n6PMx/9KLGBiTRBoTx5dvd7AMysXWTW88JIV0RERELRw93XAbj7uvJq11X0BlZFza8OlpV7wcxKgdeA+33XeDmXmNnJwELgZ+4enUaD+d9/zUjEx4gkVfk/WWaGsfyhc8ka9Xa1267M0RjQIg0tlCfPZjbIzL4GZgNzzGy6mR0SRtoikhjlv4NjDYshIqnPzCaa2ewY04XxJhFjWfkF4Up3PxQ4KZiuCpa/CWS5+2HARHY93a4a2/VmNs3Mpm3aFE6nXpu2F9a4/oSH3mfK4s2hfJZIskSqbcdu8xzLymwVoEUaUljVtp8GbnH3fdx9H+BW4JmQ0hYREZFauPsZ7j4oxvQGsMHMegIErxtjJLEa6Bs134dIsyzcfU3wuh34J3B0MJ/t7uWl2GeAI6uJ7Wl3H+ruQ7t161b/LxuHNVt38t1nvyBr1NtMWbKZldn5fPv/PmPGqq3sKCyp2C43v5gvlmYnJCaJT1FJGWOmLKdUQ5HheKW7WjefOqDG7U/+3Qfc+I/pZI16m/W5BQ0WV1FJGdeN+ZIF67fHXL90Ux5/fl89gqerxRvzmDRvQ7LDSElhFZ7buPsH5TPuPhloE1LaIpIA5RUw1eZZpFEaC5T3nj0SeCPGNuOBs8ysU9Ab91nAeDNrZmZdAcysOXAekZpm5QXxchcA8xoo/hq9+IOja1z/3We+4OTffcDUZTlc9JdPOWT0eG7423R+/NLXDL73Pb7z9OfcPXYOADuLSikpLasxvc15hbv19N2UrM8tIDe/+jbn9fHZkmz+OHEho8fO4eUvV1Zat6OwpGJs7/yiElbtYTXl9bkF5AZt5rfsKCJr1Nss3RRfi8MdhSWhF+rdnffnb6g0bnlecIPHnUp1Qn40rH+t6Y37Zj0Axz44iUfeW8CzHy/l/D99UrF+a34R+UUlLN4Yu+Bbm0Gjx3P8Q+8zcd5GfvLS1xXLV+Xks3BDJM3vPvMFv39vIVt2FO3RZ1RVWFJKYUlpnfYZP2c9yzfvqH1DIn/X1Vt2P5+iz7lypWVOftGuG3BLN+VRXMs1o9yarTvZVlDMhLkbmFtNp4dVLd6Yx7I4v0fVz5q2PKdi/sF35vHbd+fHte8Zj37ItWNi9Q8Zn815hXy9snH2PRFWb9tLzexXwN+C+e8By0JKW0REROrnIeAVM7sWWAlcBmBmQ4Eb3P06d88xs/uAL4N97g2WtSFSiG4OZBKpnl1eu+wnZnYBUALkAFcn7BtFOWn/rnXe59056yvN/3XKcv46ZXnF/Pz7zqZV8109F+8oLKFNy8jPpvMe/4T12wpY/tC5exYw8MDbc1mVs5Onrqr8sH7eum102Ks5vTruVec0v1mdy3ef+ZwPbhtG17YtK+Ju3SIzZtXfqusKikvJzDCaZ2awcMN21ucWcPIBu2oKTJq3gZemrmJi8ETq7Z+cSO7OYo7vv+v43/XfbxhxaE96d9yL0WPnMPr8Q/hmTS4XDO5Vbdy5O4spLC7lhSnLeXLykorld74+m/MO7UXrlpmsyN7BGY9+xIhD9+aJK49k4K/HA3BEv45s2FbIeYN7csuZB8TV2/SxD04C4LUfHcclT34GwGmPfMh7PzuZZz9eyg2n9GfKkmwuPbJPpXOgqKSMQ0aPZ+Rx+3DPhYOASPOBox6YyG8vOZTzDuvF9X+bxqeLs/nndcfw3CfL6N6+JQft3Z6Rx2dViuGD+Rv5bGk2Zw/amxkrt3LvW3O5+/yBXH3Cvrzw6TLueXNuxbbtW+36uV5+DsbrT+8vrngfq730GzedwIF7t+NbT0zhvMN68rvxC7hzxMG0bdWMy4/qi5nx+dJsbvj7dD76xam0b9WcvMKSisL9gg3bd0t3+UPnVqrdUR+z1+Ty6eLN/GHiQgqKy/jg58Po3LoFf5y0kOtO2o8THnqfR789mLlrt3H8gC6cdlAPIFLA/eHfpgPw2OVDuHBIpPuGHYUlnPzwBxzerxN/uuJw9moRObeuem4qK3PymXPPcNq0bMbqLfm0btGMI+6bUBHLPl1ak51XxJkDe/D612t46OJD+XjxZt6etY5u7VrSqXVzzhnUk5+deQDLNu+gb6e9aJYZeU65Kief/369hkcmLKz0/c44uDufLN7ME1cewWkH9aC4tIyH3pnPjcP6c9d/Z9OyWQb/nbEWgNdvPJ7D+0VGGSwuLeOzJdmV/j9nr8nluU+W8chlg1mZk8+w308GYMqo0+jVcS/+78OlAPzy7IPI2VHE3WPncOOp/Tn7jx8DcOmRffj9ZYP5KqrQ++zHSzl/cC96tG9FcWkZa7bsJKtr7OejyzbvoE3LTNZs2cnN//yaNVt3MuzAbow+/xD6dW5NUUkZe7WI/D8tWL+dDIP9e6Rf/9LmITxmCu5Q3wOcSNApIHB31PAWSTF06FCfNm3P75qINCX3vTWX5z5ZxtXHZ3H3BeqyQCQWM5vu7kOTHUc6CytvLv/BPvyQHvzfVUNr7EipvkafP5B73pzLE1cewdH7dmbo/RMBuOGU/rw5c23FEEIPX3oYm7YXcsMp/Xn43fn830dL+fgXp9K3c+uKtLbmF1Fa5hwZpFHuwYsP5ZIj+nDAXe9ULPv4F6fSq+Ne9L9jHO1aNuM7R/Xl2U8izyYO6dWenUWlvPvTk5mxaiuDerdn1GvfMHbmWh67fAgvT13FiuwdrM0t4PvH7cPNpw3g7Vnr6NmhFWcP6snctdsY8fjH9Atie/7qoZzx6Ef06tCKd356MoPvea9SfL+95FB++Vr14wq/97OT2VlUyoV/+bTGYzn1jtN57pNlLNqYx/NXHwXELtTtqRtO6c9TH0YK4OU3NwqKS2mWYTTLzODV6av5+b9n1jnd315yKNt2lvDAuEjlig9+PowlG/P42+cr+HBh3drxT7zlFM549MM67VP1Rk2Yx+zQ3h34Zk1urdu1aZHJjqK6Pf0dedw+9O3cmvvfjhy3g/aOFJbmr9/OXecezHtzNjA0qxMH9WxPYXEpt706ix+esh//9+FSXrjmKK554cuako+pU+vmXHR4b174dHnFske/PZhbXqn73z2Rbht+IL8bv6DGba47cd+Ka0C58wf3YtGG7cwPqtCPPG4fxny2omL93u1bsX5b/arwn3xANz4KzvMfnrIflx/Vj8+WZFNSVsb3j8tifW5BxU2pWK44uh8vTV3J/PvO5p435/LS1EitkvrcgIyWyLw5lMJzqlLhWSR+KjyL1E6F5/oLu/Bc/uNrW0Exh939Xk27JEzrFpnkB4WMzAzjlAO6MeqcgxjQrS373TGuwT73jIO7M3FerObslV1xdF9empqQTtElJFULGWNnruX212bVuTArkkp6d9yLT0edVu90Epk3h1Jt28wOAH4OZEWn6e71Pxop4C8fLCYnaLPRolkG1524L12C6lAijcWuNs+N94aaiDRe7Vs1T3YIFfKjCjSlZc778zfy/vzaC7X1FU/BGVDBuRG4YHAvzj+sJz//9yxe+2p1ssMR2SPltXbSSVhtnv8NPAU8CzS6W2BvzVrHqpx8ytzJLyqlf7e2XHpkn2SHJSIi0qR1btMi2SGIJI2Z8ci3BzN1eTarctKvECKSjsLqbbvE3Z9096nuPr18CintpHvnf09i9j3DmXTrKQC19sIpko7Kx3fWc2cRSQd9Ou3FsAMrD3v1r+uPBeBbh/dORkgiSfHu/55Muzp2JCYie6Ze/2lm1jl4+6aZ3Qi8DpSP94i758TcMU1lZkR6oyzRuIMiIiIp55j9ulS0DX396zUVy7u2bcnmvMLqdhNJa21aNuObe4aTV1hCSWkZd4+dU9FDs4iEq75PnqcD04iMGXkbMCVYVr68UckMhnIIe3w/kVSgcZ5FpDF572cnV7x//Ioh3H3+QCAyNI9IY9S2ZTM6tm7BHy8/PLRejEWksno9eXb3fcMKJF5mdjbwGJGxJp9194cS9dnNMiL3GlR4FhERSa6Pf3FqjesP6NGOJb8ZgbvTLDOD4/t35eoTIj9bFj9wDh8s2MT/vDiNUw7oVjHU0E9OG8DAXh244e+NpuWZNAKPXDZ4j/Z77PIhzF6Ty+C+kbGwV2bvqDSE0VkDe/De3A1hhdkojDh0b07avxu3/ycyLNtL/3MsVzzzeZ3Tufr4LL57TD/O+sNHYYfYqAw/pEeyQ6iz+lbbPgpY5e7rg/nvA5cAK4iM8xxqtW0zywT+ApwJrAa+NLOx7j635j3DkZmpJ88iIiKpwILaYDWJNLfafbtmmRmcObBHxdO5ldn5dG3XgtYtKv8sGn3+QCbN28g3a3LJ3VkcStyp6u/XHkPH1s0570+f1Gm/Xh1aMXzQ3rw6bTUnH9CN/t3a8Pj7i/nwtmGR9R33Yv8736k5kcDjVxzOBYN7cd9bcxn3zTrW5RZweL+O9O3UmrEzI9WQ37z5RG5+6SvatmzGnLXbak3z/VtPYfKCTdz7VuSn4os/OJqhWZ3IzDB+P34B/56+mq35xRzRryMjDu1J17YtGdC9Lft0aU3rFs0w4PvPT+WTxZsr0jywRzsWbNheMd+381586/A+PPXhEopKwu8XZ0/b8F84pDcXDtm1747CEibM3cDa3AJ+fNoArjp2n2oLz+/97ORqC36PfnswT324hIUb8oBdY/iWW/KbEcxdu41735rDbcMP4t635jB7zTYG9mzPoo3bKS6N/I7u1Lo5W/Kr/7/6z43Hc/ETUwA4/aDuTJq/kbYtm5FXWFJpOLh47Nu1Dcs27wCgY+vm3HPBIazespPrTtqX179aQ0mZ88aMNTxx5ZEAFYXn4/p3qUjjzZtP5NA+HVizdSfPfryUFz5dzj0XHMLosXO49sR9ee2r1Zw1sAcPX7rrZselR/Zh9prcijGYJ/98GMN+PxmAj247la7tWrB00w56tG/FdS9O44cn78fAnu1ZuGE7P3n5awqKwzufzh/cizdn7qrO/8C3BnHn67Mr5gd0b8vijXmhfV5V5eOE3zniYB4YN4+LhvTiNxcf2mCf11DqNc6zmX0FnOHuOWZ2MvAy8GNgCHCwu18aTpgVn3cckUL58GD+dgB3fzDW9mGP87yzqJSDf/0uo845iBtO6R9auiKp4O6xc/jrlOV879h+3H9R+l3MRBJB4zzXX9h5c0N45ctVHLB3O4b07bjbun98sYIV2fn079aGVs0z+d+XZwDw+o3Hc+u/Z5JfWMr6bQVxf1brFpm89eMT+WjhJibN38i3Du9N/25tufAvn3L7OQdx5bH7cN2YL2nZLJMPF25i7r3D2V5QQoYZJWVlnPnoR+QVlvDU946gRbMMfvDX2o/tWz8+kS5tW/DZkmxOHNCV7u1bVazbWVTKXi0yK+bHzlzL8f27MPT+iRXLrj4+i5wdRfz+ssFkWORmRE0mL9hIv86t6dVxL0rLnENGjwfg61+dSWFJGV8syyY7r4gfnFi5QuO63J102Ks5rVs0Y/nmHSzL3sGpB3YHIg8ySsrKOPCudyu2v+LovvTqsBc/GtafT5dkc/L+XStushSVlMWMdcmmPP40aRG/u2wwzWv5HoUlpWzcVkjfzq0BKCtzikrLaNU8s9J2Ix77mLnrKhfsX7/xeLK6tOHw+yYA8MHPh7E8ewcn9O9a0WFny2aZ7CwqZXtBMRu3F/LCp8u5/uT9OHDvdjXGVR8rs/Pp0rYFM1dt5bvPfsEHPx9Gzw6taNU8kw3bCli6aQctm2ewfPMObnllJrBrzOnFG/NolmFkdW3DzqJSnpy8mG8f1Zc+nVrH9dllZc6nSzZz4oDI36l87PZLjujDa1+tZvED5zAguPEy7icnMeLxj/nF2Qdy47ABADzy3gL+9P5irji6Hw8GhbDyNKLjnLJkM0P36cwBd71TaXlNtuYXUVrmdGnbcrcx5QGKS8uYuiyHEwZ0ZcrizQzN6kyLZvG1hl2zdSc7i0oY0L32v2v5Z7/zvyeR1aUN67cVkGnG9JU5HLdfV7q2bcHWncX84tVZ/O7Sw+jStiU3/eMr3pm9js9vP52H3pnPf75eQ2aGseQ3Iygr84qx5pc/dC5LN+WxZutOVm/ZybmH9WTK4mwO69OBklKnV8dWrMzJ55mPl9IsI4NfnTew4hiOvfkELvjzpwDs160NSzdFbkzs17UNzTMzuGBIL244pT/97xjHU987krMH7R3XsdlTicyb61t4nunug4P3fwE2ufvdwfwMdx8SSpS7Pu9S4Gx3vy6Yvwo4xt1vjrV92Bl0UUkZB9z1DrcNP5CbTh0QWroiqUCFZ5HaqfBcf+lQeK6L16av5oyDe9Ch9a5xpvOLSnAHM9ireSYfL9pM386tGffNOpplGGcdsjc7Ckvo3r4l3du1qiH1XaoW3Mod/+Ak1uYW8MkvT60otGTnFXLk/RN5+NLD+PbQvmwvKOab1bm8On011560L4f06lDn73n1C1OZvGATU+84vVJhu65KSssYcOc73H7OQfwwhAcRG7YV0LpFJpkZtlvNgWTLLyqhdYtmFa8As9fk8vXKLVx1XFZyg9sDD74zj6OzOnP6wUu1FeMAACAASURBVA1T1XZ9bgFtWzWjbVTP4Zc9NYXtBSW8+9OTWbopj6wubcjIqL7WSazCc9V1dW0P/s3qXL5Zk8t3j+lXp/3CcOrvJ7Ns8w6+uON0euzB/11pmXPbv2dW+r/f0+MA8MXSbNq0bMag3h3IzS+msLSU7u1asWVHEcN+P5kv7jh9t5tJiZBOhefZwBB3LzGz+cD17v5R+Tp3HxRSnOWfdxkwvErh+Wh3/3HUNtcD1wP069fvyBUrVsRMa0+Uljn97xhH80yr9e6kSLopKimjpMzJzDBaxnn3VCRdXHlMP+48d2C901Hhuf4aW+E52cbPWc/dY+fw4W2nxv3kS6SxWpWTz/aCEtbl7tytkP+bcfN4+qOladWZ2srsfN6ctZYbh/WPq6lKPOpTeE5V6VR4vhMYAWwG+gFHuLub2QBgjLuH2qVlsqttA7w0dSVLNzVcewCRZFq2OZ99u8ZX3UoknQzN6szwQ+pfbUyF5/pT4VlEJHk+WLCR3h334oAeDdccINESmTfXt7ftB8xsEtATeM93lcQziLR9DtuXwP5mti+wBrgc+G4DfE61rjg68VU2RERERERE6qu83wDZM/VuHOLuu/Xf7u4L65tuNZ9VYmY3A+OJDFX1vLvPaYjPEhERERERESlXr2rbqc7MNhEZNivRuhKpyp5uFHfipGPMkJ5xp2PMoLgTqS4x7+Pu3RoymMZOeXOdpWPc6RgzKO5ESseYQXEnUkrmzY268JwsZjYtHdvEKe7ESceYIT3jTseYQXEnUjrGLHWXrn/ndIw7HWMGxZ1I6RgzKO5EStWY1S2jiIiIiIiISC1UeBYRERERERGphQrPDePpZAewhxR34qRjzJCecadjzKC4EykdY5a6S9e/czrGnY4xg+JOpHSMGRR3IqVkzGrzLCIiIiIiIlILPXkWERERERERqYUKz3VgZq3MbKqZzTSzOWZ2T4xtbjGzuWY2y8wmmdk+UetKzWxGMI1NsbivNrNNUfFdF7VupJktCqaRKRTzH6LiXWhmW6PWJeVYR31+ppl9bWZvxVjX0sz+ZWaLzewLM8uKWnd7sHyBmQ1PoZhT7ryO+vya4k6p8zrOmFP5vF5uZt8Enz8txnozs8eDc3iWmR0RtS4pxzuOmK8MYp1lZlPMbHC8+0pqSMe8OR3z5TrEncrXMOXNCaK8OXGUNyeYu2uKcwIMaBu8bw58ARxbZZtTgdbB+x8B/4pal5fCcV8N/DnGvp2BpcFrp+B9p1SIucr2PwaeT/axjvr8W4B/Am/FWHcj8FTw/vLycwQYCMwEWgL7AkuAzBSJOeXO6zjjTqnzOp6Yq2yXauf1cqBrDetHAO8E/7/HAl8k+3jHEfPx5bEA55THHM++mlJjijOPS6lrWJwxp9z1K564q2yfatcw5c2pEXfKndu1xVxlu1Q7r2vL55Q3hzjpyXMdeEReMNs8mLzKNh+4e34w+znQJ4EhxhRP3DUYDkxw9xx33wJMAM5ugDAr2YOYrwBeaui44mFmfYBzgWer2eRCYEzw/lXgdDOzYPnL7l7o7suAxcDRDR0v1B5zKp7XENexrk5Szmuoc8wpc17H6ULgxeD/93Ogo5n1JInHuzbuPiWICVLo3Jb4pWPenI75MihvVt4cH+XNKUd5c4hUeK6joErHDGAjkRPuixo2v5bInZ5yrcxsmpl9bmYXNWigVcQZ9yVB9YhXzaxvsKw3sCpqm9XBsgYX77EOqintC7wftThpxxr4I/ALoKya9RXH1N1LgFygC0k81tQec7SUOa+JL+6UOq+J81in4HkNkR/J75nZdDO7Psb66o5rMo93bTFHq3pu12VfSaJ0zJvTMV8G5c2kYH4RSInzOqC8ObGUNyeQCs915O6l7j6EyB2Qo81sUKztzOx7wFDgd1GL+7n7UOC7wB/NrH+DBxyII+43gSx3PwyYyK67rxYruYaLNOpD4jzWRKpXverupVHLknKszew8YKO7T69psxjLvIblDSrOmMu3TZnzOs64U+q8rsuxJoXO6ygnuPsRRKpQ3WRmJ1dZn1LndqC2mAEws1OJZNC/rOu+knzpmDenY74Mypujljco5c0VUupYk0LndRTlzQmkwvMecvetwGRiVG8wszOAO4EL3L0wap+1wevSYN/DExFrtOridvfsqFifAY4M3q8G+kZt2gdY28BhVlLTsQ5cTpXqM0k81icAF5jZcuBl4DQz+3uVbSqOqZk1AzoAOSTvWMcTcyqe17XGnYLndVzHOpBK53XVz98IvM7uVRerO65Ju47EETNmdhiRqnoXunt2XfaV1JKOeXM65sugvLmhA0Z5M6TYsQ6k0nld9fOVNyeCJ7GBe7pNQDegY/B+L+Bj4Lwq2xxOpDOJ/ass7wS0DN53BRYBA1Mo7p5R778FfB687wwsC+LvFLzvnAoxB+sOJNJxgKXCsa4S2zBid5RxE5U7JXkleH8IlTslWUoCOyWpJeaUO6/jjDulzut4Yg7Wpdx5DbQB2kW9nwKcXWWbc6ncKcnUZB7vOGPuR6QN4/F13VdTakzx5Bepdg2LM+aUu37FE3ewLuWuYVFxVJdfKG9OXNwpd27XFnOwLuXO63jyKpQ3hzpZ8OGNUteuXT0rKyvZYYiISCMxffr0ze7eLdlxpDPlzSIiEqZE5s3NEvEhyZKVlcW0aRqaUyQe7s7G7YV0b9eSSAejIlKVma1IdgzpTnmzSN0UlpTSIjNDebNINRKZN6vNs4gA8O/pqznmN5P4xxcrkx2KiIiIADuLSjnwrnd5dMLCZIciIoRUeLaI75nZr4P5fmamjlVE0siG3AIA1gevIiIiklzbC4oBeGnqqlq2FJFECOvJ8xPAcUQGDQfYDvwlpLRFRESkHsyss5lNMLNFwWunarYbGWyzyMxGxlg/1sxmR83fbWZrzGxGMI1oyO8h0tSUBn0TNctQlW2RVBBW4fkYd78JKABw9y1Ai5DSFhERkfoZBUxy9/2BScF8JWbWGRgNHENk6I/R0YVsM7sYyIuR9h/cfUgwjWuQ6EWaqG07SwBYv021wkRSQViF52IzyyQYWNvMugFlIaUtIiIi9XMhMCZ4Pwa4KMY2w4EJ7p4T3ASfQDCGr5m1BW4B7k9ArCIS+HjRpmSHICJRwio8P05kkOruZvYA8Anwm5DSFhERkfrp4e7rAILX7jG26Q1EN6xcHSwDuA94BMiPsd/NZjbLzJ6vrjq4iOyZ5pnq21cklYQyVJW7/8PMpgOnExmA+yJ3nxdG2iIiIlI7M5sI7B1j1Z3xJhFjmZvZEGCAu//MzLKqrH+SSMHa2VXA/kGM2K4Hrgfo169fnOGIyOH9OgKwT5fWSY5ERCCEwrOZZQCz3H0QML/+IYmIiEhdufsZ1a0zsw1m1tPd15lZT2BjjM1WA8Oi5vsAk4l0CHqkmS0n8ruhu5lNdvdh7r4h6jOeAd6qJrangacBhg4d6nX5XiJNWVnw39Jxr+bJDUREgBCqbbt7GTDTzHQrWUREJDWNBcp7zx4JvBFjm/HAWWbWKah+fRYw3t2fdPde7p4FnAgsdPdhAEFBvNy3gNmISGgWrt8OwKw1uUmOREQgpGrbQE9gjplNBXaUL3T3C0JKX0RERPbcQ8ArZnYtsBK4DMDMhgI3uPt17p5jZvcBXwb73OvuObWk+3BQrduB5cAPGyR6kSZq4rxI5Q5XfQ2RlBBW4fmekNIRERGRkLl7NpF+SaounwZcFzX/PPB8DeksBwZFzV8VaqAiUknblmH9VBeRMITVYdiHYaQjIiIiIiIR2woi4zx3bK02zyKpIJT+783sWDP70szyzKzIzErNbFsYaYuIiIiINEWfLt4MwNb84iRHIiIQ3jjPfwauABYBexGpAvbnkNIWEREREWlyikvLkh2CiEQJrSGFuy82s0x3LwVeMLMpYaUtIiIiItLUlJSppzCRVBLWk+d8M2sBzDCzh83sZ0CbkNIWEREREWlyjsrqBMCBPdolORIRgfAKz1cBmcDNRIaq6gtcElLaIpIAurctIiKSWr51eB8AhvTtmORIRATC6217RfB2Jxq2SkRERESk3jy4tZ2RYUmOREQgpMKzmS0jxoMrd98vjPRFpOG5Hj2LiIiklKKSSIdhKjuLpIawOgwbGvW+FXAZ0DmktEVEREREmpx73pwLqNdtkVQRSptnd8+Omta4+x+B08JIW0RERESkKVOv2yKpIaxq20dEzWYQeRKtbgFF0oiryzAREZGUlGmqty2SCsKqtv1I1PsSYDnw7ZDSFhERERFpsjJUeBZJCWH1tn1qGOmISPKowzCRxsvMOgP/ArIIbnC7+5YY240E7gpm73f3McHyyUBPIqNqAJzl7hvNrCXwInAkkA18x92XN9gXEWmi1Nu2SGoIq9r2LTWtd/dHY+zTl0iGuzdQBjzt7o9Vl8GbmQGPASOAfOBqd/8qjPhFREQauVHAJHd/yMxGBfO/jN4gyH9HE2l65cB0MxsbVci+0t2nVUn3WmCLuw8ws8uB3wLfacgvItIUZYbSS5GI1FdY/4pDgR8BvYPpBmAgkXbP1bV9LgFudfeDgWOBm8xsILsy+P2BScE8wDnA/sF0PfBkSLGLCLvGmlPbZ5FG6UJgTPB+DHBRjG2GAxPcPScoME8Azq5Duq8Cpwc3u0UkRM1VehZJCWH9J3YFjnD3W939ViLVt/q4+z3ufk+sHdx9XfmTY3ffDswjUvCuLoO/EHjRIz4HOppZz5DiFxERacx6uPs6iOS/QPcY2/QGVkXNrw6WlXvBzGaY2a+iCsgV+7h7CZALdKmasJldb2bTzGzapk2b6v9tRJqIq47dB4D2rZonORIRgfA6DOsHFEXNFxGpdh0XM8sCDge+oEoGb2blGXx1mfq6PQ1aRKIEjZ7V9lkkPZnZRCJNoaq6M94kYiwrvyJc6e5rzKwd8BpwFZGmVzXts2uB+9PA0wBDhw7VVUYkTp1aRwrN2wqKkxyJiEB4T57/Bkw1s7vNbDSRQvCYWvYBwMzaEsmIf+ru22raNMay3TJg3d0WEZGmyN3PcPdBMaY3gA3ltbWC140xklgN9I2a7wOsDdJeE7xuB/4JHF11HzNrBnQAcsL/diJN2wufLk92CCJCSIVnd38AuAbYAmwFrnH3B2vbz8yaEyk4/8Pd/xMsri6DrzZTrxLL0+4+1N2HduvWbU+/kkiT41VeRaRRGQuMDN6PBN6Isc144Cwz62RmnYCzgPFm1szMukJFvn0eMDtGupcC77ur/opIWPTPJJJaQik8m1l/YI67PwbMBE4ys4617GPAc8C8Kr1xV5fBjwW+bxHHArnl1btFRESkRg8BZ5rZIuDMYB4zG2pmzwK4ew5wH/BlMN0bLGtJpBA9C5gBrAGeCdJ9DuhiZouBW9jVyaeIiEijE1ab59eAoWY2AHgWeJNIta4RNexzApE2U9+Y2Yxg2R1EMvRXzOxaYCVwWbBuXJDeYiJDVV0TUuwiwq62znpmJNL4uHs2cHqM5dOA66Lmnweer7LNDiIdgcZKt4Bd+bSIhEx5skhqCavwXObuJWZ2MfCYu//JzL6uaQd3/4TY7ZghdgbvwE31D1VEREREJL2s3bqTXh33SnYYIk1aWB2GFZvZFcD3gbeCZepTXySNlI/vrHGeRUREUk/uzmJKy5RHiyRTWIXna4DjgAfcfZmZ7Qv8PaS0RURERESatHMe+5j+d4xLdhgiTVoo1bbdfS7wk6j5ZQSdkYhIenB1ty0iIpJSVBtMJLWE9eRZREREJOG2FRSTNept1uXuTHYoIqFTh2EiqUWFZxEBNM6ziKSnw+5+D4DjHnw/yZGIhE95skhqCWuc592GqYi1TERERERERCQdhfXk+fY4l4lIito1zrPuc4tIeihTz8PSyMXKkq94+vPEByIiQD07DDOzc4ARQG8zezxqVXugpD5pi4iIiNTkxc+WJzsEkQYVq8Owz5ZmM3ftNgb2ap+EiESatvo+eV4LTAMKgOlR01hgeD3TFpEEqhjnWQ9yRCRN3P3m3ErzxaVlSYpEpIFUkye/P39DYuMQEaCehWd3n0lkPOdP3H1M1PQfd98STogiIiIitdv/zndYvDEv2WGIhKpF5u4/13//3kJ2FKqSp0ii1bvNs7uXAl3MrEUI8YhIsnilFxFpRMyss5lNMLNFwWunarYbGWyzyMxGRi2fbGYLzGxGMHUPll9tZpuill+XqO9UXf8MZzz6Id+szqWoRE+hJf05kFHNr/VDRo9PaCwiEl6HYSuAT83sV2Z2S/kUUtoiIiJSP6OASe6+PzApmK/EzDoDo4FjgKOB0VUK2Ve6+5Bg2hi1/F9Ry59twO9QybQV1VdwO//Pn3DfW3OrXS+SLtwdw/jF2QfGXJ816m3WbNUY5yKJElbheS3wVpBeu6hJRNJExTjPevQs0hhdCIwJ3o8BLoqxzXBggrvnBE2vJgBnJyi+Ovv3tFU1rv9k8eYERSLScNzBDG4cNqDabU54SGOciyRKKIVnd7/H3e8BHgUeiZoXERGR5Ovh7usAgtfuMbbpDUSXSFcHy8q9EFTN/pWZWdTyS8xslpm9amZ9Q4+8Gq9MW13j+mWbdzD6jdlsLyhOUEQiDaP8n+3hSw+rdptnPlqamGCakMKSUgqKS5MdhqSYUArPZjbIzL4GZgNzzGy6mR0SRtoikhjl7QdjDYshIqnPzCaa2ewY04XxJhFjWfkF4Up3PxQ4KZiuCpa/CWS5+2HARHY93a4a2/VmNs3Mpm3atCn+L1VPYz5bwaF3vwdAzo4isvMK67T/mzPX8tHCxMUrUlV0jvztodXfm3pg3DwOv/c97ldzhdAc/cAkDvrVu8kOI6Wt3pLPzqKmdYMhrGrbTwO3uPs+7r4PcCvwTEhpi4iISC3c/Qx3HxRjegPYYGY9AYLXjTGSWA1E/zrvQ6RZFu6+JnjdDvyTSJto3D3b3ctLpM8AR1YT29PuPtTdh3br1q3+X7aOska9zRH3TeDI+ydy+39m8cXS7Lj2+/FLX/P956c2cHQCkJtfzH++itQmKCwppbQsMTdyC4pLKavyWYUluy8Lw5y1uWzaXrcbOJFq27Hua+1uS34xz36yjKxRb/P616uZMHcDq7fk70mo9VJa5hSWJK9ANX1FDnlVeiJfn1vAjf+YTn5RzT2UL920q7f+3J3JrbVSWFLK8Q9OimtYsoLiUn777nxy84sbpA38ZU9N4ZzHPt5t+Ym//YCRLzSta2RYhec27v5B+Yy7TwbahJS2iCRAeVtntXkWaZTGAuW9Z48E3oixzXjgLDPrFHQUdhYw3syamVlXADNrDpxHpKZZeUG83AXAvAaKv1oZBosfOId2rZrFtf1LU1fxnac/Z23wA7OopAx3r7F37kGjx/Pa9EjBLq+whDFTlvPA23MbpEr46i353PLKjDr1Fj5//TamLc8BYPaaXHLzG+5H/wcLNvL4pEUV84s35jFlSeX25QXFpbw6fXW1PaLHcuu/Z3LLKzNZuGE7B971Lv3vGMedr38DRAoRvx+/gG1VjvdHCzdViqUm89ZtI2vU23y9cgtLNuXxyHsLKCwp5aBfvct+d4yrVMPgwLve5af/mlExvzI7nx2FJWSNepsXP1seM/2py3Iq/c3W5xbsVuX33Mc/4dTfT44r3nIlZZXPg5f+59i49vvZv2byPy9O47w/fQLAVyu31FpwDMuP/j6dA++q+xPbbz/1GX9+fxFb84v2+LO37Cjikic/4ycvfV2xrLCklNtencm4b9bz8tRV/GHCQnJ3FvPwu/O5Oqrg98H8jZz2yIc8MXkxJVFjxq/Lrb4wui53Z7X/qyWlZdXeBPrHFyu45Mkp1f6PbM4rZNGGPNbmFvCDv05j47b/Z+++46Oo0weOf55USAglJCA9NKVKi3QQbIANz96x/Ty7d56e6FnABnee59l7vTv76akIKkWwINJ7L6GXkEASEtI2z++PnYRN2BTIZHcDz/v12tfuzHz3O89OJvvdZ+Y7883l+xW7yo3jnV9SeGXmBno89j2DJs4gO6+QBZv3Mf7rFXy9ZAdzNqZR4Cli6bb9/HvOZvZk5ZKZW8D/Fm3nk/lbUVWWbcvgH1PX+q1/Xso+Vu3MJONgAcu2ZZRaNndTeqnpvEIP85zvI1WlwHP49lmzK4t3f9lU7ucJZXIkX2zlViLyBbAQ+Jcz62ogWVX93ZAkYJKTk3X+/PnBDMGYWuOJSSt58+dNXDcwiXHn21UXxvgjIgtUNTnYcRwpEWkMfAK0BrYAl6hquogkA7eo6k1OuRuAB523Pamq74hILPAjEAmE4+2efY+qekRkAt6kuRBIB25V1dUVxeJW25w09hsA1j4xiqiIsFLzqqpDk3qlxoV+5/pT2Lk/l07N4ujYpF5Jl+9iF/RsTlp2Pj+tO5QsznngdBLjonn4y+XM3ZTO3y/pwYLN+1i/5wC3nNqONo2P7FzCmLfnMmttKu9cdwrDOzUhJ7+Q12ZtpEWjusxPSeevF52MiPDKzA1cfkorCouUU56cBsDvh7bjNefa1zVPjCQyLIyVOzNZuSOTxvWi6N6iAU3q1/G73gJPERFhgohQVKR4VMnJ9yACaQfyEbw/6C9+9VcAlo8fQU5eIX2fmg7AJ78fwP6cfFbtzOLjeVvYkZHLPy7twVdLdvD8Fb2IjYpg0tIdtIqP4cKXZ/PQOZ154ptV/PWi7tz/32UVbpOo8DDynR/gT1zQjchwoV1iPS5xYply9xCycgvp2rw+L89cz0s/bGDGn06lXWI9Zq1N5aUf1tOlWX3enZ1S4Xqev6IXm/dm84yTQCx59Cymr9rNPZ8sYfz5XXn0qxUAfHxzfzbtzaZOZDijezZn5c5Mznn+Z9olxPLMpT1YuGU/j09aSfcWDfjqjkEUqXf7FncB3jThbDbuzSauTgRN4rx/j6zcAmas3sPont7bDMxYvZs1uw7w12+9/04pE88pifO8F35m2fbSCUxFhp+UyA9rUhnZ9QRevaYP2/cfpEXDulV+/5Eq/j/c+NTZzN+8j2YN6hAWJpxQvw67MnPZnZnL5rRs/vjxEm4c3JYHz+7M+j0HGPHPH0vqeP2aPuzPKeDSU1qRW+A9eHJRn5ZER4TRolFdMg4WlGy7Ysu2ZfDz+r0l2+yp33WnU7M4Lnx5drU/0wtX9OLODxcxuEMC/dvFc8dpHckt8B58iQgTZt43jAe/WM6qnZnMffB0RISksd/Q6YQ4vv3D0JJ6Nqdl8+PaVB7+0rsv3XlaBwZ1SKBp/Tq0TYhlc1o2hUXK6c/MKrX+6Igw8gqLWP34SKIjwvAUKXsP5BMfG8VN789nb1YeK3dmVvtzAvx55EnkFRTx5eLtvDkmmZ0ZuVzzVumzy/eP7ESRKk9/twaAK/q2Yl92Aat2ZdIoJorFW/cDMLLrCXy7YhfT7jmViDAhvl4UsVERtH9wMgB/u+hkLj2l+rfKCGTb7Fby3AgYDwzGe83Uj8A4526dQWPJszFV9/iklbxlybMxFaqtyXMocTt59k0qzn3hJ5Zvd+cHpFtWPTaSOpFhfLFoO1OW7+LGwW3p364xAAfzPeQXFlG/rvesuYjQ/sHJeIqUt69L5rROTf0eEIiPjSI9++jOzg09MZFfN+ylV6tGXN63FSe3bMDB/CLOe9F7hvLmoe14vZbefCoqIqzUWcBW8XXZmh56wzgVJ7MA48/vyi/r9/L9Sm/X3IfO6cymvdn857ctpd7ju58XFSntnOTjSD1ybhcec66LnvuX00sloHsyc5m0dCfXD0pCRFi2LYPlOzI46YQ4erRsSHiYkJNfyK6MXM5/8Rcm3zWEoU//wHs39OXUExP5w0eL+N/iHSX1FR8gqWkbnzqbsDA54oNn1VG/TgSZueWfya8TGUZugXdfHHdeF8Z97e616Ilx0Ud8CUCo8t23j1atS55DlSXPxlSdJc/GVM6S5+qryeQZ4LVZG5gwpcKT30FXJ9J7FukY/glmXFZ2P7/7o0V86ZOoHq3iM5q+Xr26Dz+tSz0sgY+NCif7OLs5lKl5tS15rtoFQpUQkROBe4Ek3zpV9TQ36g+256evKznCGx0Rxv8NbUdCveggR2WMuw5d82y/5owxtVc/56xuKCs+I2XM0Xru8l48d3kvBk6Yzo6M3KOup2ziDHDLvxf4LWuJszEuJc/Ap8CrwJvAMfef9f3KXWxJy0EVsvIK6dCkHpdUMFyAMcYYY2peVPjh9z09uUUD7hjega7N63PrfxYGISpjAmf2A6czY/VubnjXeloaEwhuJc+FqvqKS3WFnEl3DgG8d9QbMGEGhQEaPsGYQCoe39n2bmNMbdA2IZbuLRocNj8sTLh3xElBiMiY4DitU1NevLIXd3ywqPLCxphqqdZQVSISLyLxwNcicpuINCue58w/poSHecfZC9TYg8YYY4zxz1OkJe1yeVImnsOih88MUETGBM+5JzcnZeI53DCobbBDMeaYVt0zzwvwnqgqbr3u81mmQLtq1h9SIsK8xxoseTbHIhvn2RhTm3iKFKk4dwagUWwUb16bzO6sXP7yxfKaD8yYIHrkvC48cl6XgN552pij1aBuZLBDOGLVOvOsqm1VtZ3zXPZRI4mziIwUkTUisl5ExtbEOsoT7rTS1m3bGGOMCa5Xr+7Dnad1rFLZM7o05ap+bUiZeA5ndmkKwGOjuzK4Q0JNhnjc6dHy8G70R+ua/m3o1bohbRrHANC8gf/xqavrg//rVyP1Btvcv5zOw+d2YWD70L+BXjD97aKTmXhhd24e2o6pfxxaYU+V2KjwAEZ2uJiocM7o3OSo35/k/C/5069tcDoMv3Ft7Ru8olpnnkXkFGCrqu5ypq8FLgI24x3nOb36IZZaXzjwEnAmsA2YJyJfqaq7g6eVIzzcmzwXWfJsjmFqVz0bY2qB7keZqPn+WLt2QBJJY78huU0j/n1TP059+gfCROjXNp6R4IHoFwAAIABJREFU3U4gPCyMlo3qUi86gl2ZuVzy6q8ARIQJNwxuy8bUbG4f3p7l2zOYvSGN7HwPP65NdeXzHY0xA9rwyfxtHCzwEBkuFHgq/z4f0jGBQR0S+Nu3q3lgVGeenHz4uLxT7h5CUuNYXpm1gfd/TWF/TgFjR3Vi4pTVTLywO2M/X0bX5vX57NaBdPzLlHLXldQ4hpS0nJLp6wYmMWnpDvYeyKdzs/pMuXsIGTkFzN6wl1Hdmx32/ktenc01A5I4v0dzMnMLOO3vs9h7II/BHRL4ef1ev+u867QOXNW/DfWiI7j7o8VcmtySgR0S+HVDGp4iZWD7BN6/oS/Xvj2X3x48ndSsPM594Wc6NqnHuj0HiIuOICvPO57vL2NP4/b/LGTx1v30axvPb5sO/cy9cXBb7jq9Iw3qRpKZW8DZz/3EGZ2b8u7sFAAax0aRlp3P6J7NOa1TE/70yRK/J2OGnpjoyj7UJK4ONw5uy42D2+IpUnILPExbtZu7P1pc7brd8u0fhnBC/TrE1YkkTKDtA4ePXZ0YF80/Lu3Boi37+deczbSOj+Hpi0/mtGdmHdU6P7ipH1e++VvJ9KWnHH4D4OKhk+anpDN/8z4mTlnNQ+d05sbBbek+7nsO5BWSGBfNw+d24dWZGzj1pERGdD2BC176pcJ1D+mYQKv4GD4oMwRYWXed1oHnZ6wvNe/3p7Zj7MhOiAjdH/2uZJ8s9vHN/bns9Tkl8b88cz0nNolj8vKdfL5wOzPvHUZSQizb9uVwQn3vgagwEXZl5lKkSstGMbwwfR3PTF3Lhb1b8OPaVH578AzaO+OJP3tZD/748ZJS6+zSrD4rd2aWTM+6bxhtGseyYHM6F73yK/+6sS/REeFc+pr3e/Pn+4fzh48Wk3GwgBeu7MVz09aR3KZRhdsiFFVrnGcRWQicoarpIjIU+Ai4E+gJdFbVi90Js2R9A/Am5SOc6QcAVHWCv/Juj/OcW+Ch08Pfcv/ITtw6rL1r9RoTCsZ9tYJ3Z6dwdf/WPHFB92CHY0xIsnGeq8/ttrm6CjxFhIsQVsn10wD/nrOZfm3j6dg0zu/y2Rv2cuUbv9GmcQwX9W5JdESY3zGnVz8+ksjwMNo/OJnOzerz0pW9KFJlx/5cBndIQHwSiWcv68HCzd7EAbw/ok89MZE5G9JKfmC3TYhl095sXrumD6d3asLnC7dzUZ+WFKkSLkJhkfLM92t47ceNvHPdKezMyOWS5JZ+P/fW9BzGf72SmWv28I/LetKqUV16tT70AzftQB57svLo3Kx+ybwvF29neKcm1IuKoN2Dk+neogEDOzTmtmEd2Jqew4dzt/D46G58u2IXt/ncAT1l4jnkFXpIzcqjWYO6lV7DXlW7M3PJyi2gQxP/f6eqyCv08OKM9Vw3MInRL/3CPy/rSXKS9+zc3E3p9GzVkOU7Mkg/kE+7xFjaJdbzW889Hy/m80Xe5CUqIoyEetFERZTu+JlfWERkuCBOD8d7P13CZwu2AfDc5T05s0tTYqLcucdvoaeImWtSuen9+cTViSArt7DyNwG3DWvPyzM3lLu8R6uGXNCzOdcPalvyf3DH8A68+MN66kVHMP78ruzMOMiKHZls2ptNswZ1eOf6vqXqKPB4xz6PCBPaOUlbRWMA/7QulWvemsvV/VvTo2VD7vts6WFlGsVE8sa1yUxfvYdXZm5g6bizqF8nki6PfEtOvqdKYwzP3rCX/m0bExYmrN+TxVVv/sbXdw6mSVzp3hBdH/mWC3u3ZPz5XSlSJcIZDaDsmPT5hUVERYTx9ZIdDDspkbqR4YgI4WFSsi+c/+IvpOzN5plLe9C8YV26+dwcMSu3gAKPsjH1ABe/+ivvXH8Kw09qwsbUA9SJDKd5w7olZXMLPCzfnlGy71ZEVSnwaKn9s/j7MafAQ7dHvyuZP+HC7lzRtzX7svMRgazcQlrF+z+znZlbwMF8D03r10zvEQhs21zd5HmJqvZwXr8EpKrqOGd6sar2dCXKQ+u7GBipqjc509cA/VT1Dp8yNwM3A7Ru3brP5s2bXVt/fmERJz40hav7t+ay5Nau1WtMKHjtxw1MWrqTUd1O4LZhHYIdjjGuiq8XRQufHxRHy5Ln6gu15NlNRUXKc9PXcXX/NiTGRZfML/QUcbDAO5LnwQLPYT+6/Vm2LYN8j4c+bbw/em/99wLaJcZy34hOJWWemryK13/cyKQ7B/Pt8l388cwTy01ACz1F7NifS+sKum66Yfv+gzSOjaJO5OFdXFWVj+ZtpX1iPU5qGkeDmNp3veORUlXyCov8bo+K5BcWESaUJGBu2pKWw9Cnf+DxC7rRv208sdERNG9YtyTJ+/L2QXRoUo+uTrL0w73DSGocU3JAJ2XiOWTnFbJqZyY5+R6+XbGLJy/oVpL8A2xMPUDbhNhS845ETn4hmQcLOeEIuuv/b9F2Tj0xkQ/nbeG8k5uTkpbNoPYJhIXJYX+HjIMFFHiKSKgXXUmt1fffBdvYl5PPTUPcv6I1t8BzxPtWdda1cMs+rnzjN2b86dRyDxgFQ21KnpcDPVW1UERWAzer6o/Fy1S1m0txFq/vEmBEmeS5r6re6a+82w10UZHS+ZFv/Q4ob4wxJnRdNzCJced3rXY9ljxX37GcPAdaoaeILek5IfUj1tQOB/IKiY0KL5XcFifPa58YRVREGC/9sJ5pq3bzxW2DAPh53V4a14sq1evAmFAQyLa5un1APgRmiche4CDwE4CIdAAyqlm3P9sA34sTWgI7amA9foWFCV/cNogd+w8GapXGBFSRKmFHeZTYmFBWXncyY2qziPAwS5zNUakXfXgKsPiRMzlY4Cnptnv78A7cPvxQT7TBHe0Ge8ZUK3lW1SdFZDrQDPheD53GDsN77bPb5gEdRaQtsB24HLiyBtZTri7N69OluR1xM8YYY4wxx46GMVE0DHYQxoS4at99QFXn+Jm3trr1lrOuQhG5A/gOCAfeVtUVNbEuY4wxxhhjjDGmWLWueQ51IpKKd9isUJcA+B9jIbRZ3IFTG2MGizuQamPMUPvibqOqicEOojaztrnGWdyBUxtjBos7kGpjzFD74g5Y23xMJ8+1hYjMr403oLG4A6c2xgwWdyDVxpih9sZtjn21dd+0uAOnNsYMFncg1caYofbGHQju3//eGGOMMcYYY4w5xljybIwxxhhjjDHGVMKS59DwerADOEoWd+DUxpjB4g6k2hgz1N64zbGvtu6bFnfg1MaYweIOpNoYM9TeuGucXfNsjDHGGGOMMcZUws48G2OMMcYYY4wxlbDkuYaJSIqILBORxSIy38/yYSKS4SxfLCKP+CwbKSJrRGS9iIwNsbjv84l5uYh4RCS+Ku+twZgbishnIrJaRFaJyIAyy0VEnne251IR6e2zbIyIrHMeYwIVcxXjvsqJd6mIzBaRHj7LgrKtqxh3yO3bVYg5FPfrk3xiWiwimSLyhzJlQmrfrmLMIblfm+ODtc3WNrsQd8h9h9XGdrmKcYfifm1t8/FKVe1Rgw8gBUioYPkwYJKf+eHABqAdEAUsAbqEStxlyp4HzDia97oc83vATc7rKKBhmeVnA1MAAfoDvznz44GNznMj53WjEIp7YHE8wKjiuIO5rasYd8jt25XFXKZsSOzXfrbdLrzjGYb8vl1JzCG5X9vj+HhY2xzQbW1tc+jEHKr7tbXN1jbXmoedeQ5dfYH1qrpRVfOBj4DRQY6pPFcAHwYzABGpDwwF3gJQ1XxV3V+m2GjgffWaAzQUkWbACGCqqqar6j5gKjAyVOJW1dlOXABzgJaBiK0iVdze5QnKvn0UMQd9v/bjdGCDqm4uMz/k9m0ffmMOxf3amCqwtvkIWNscOLWxXQZrm61trn0sea55CnwvIgtE5OZyygwQkSUiMkVEujrzWgBbfcpsc+YFSlXiRkRi8P7D//dI3+uydkAq8I6ILBKRN0UktkyZ8rZpMLd1VeL2dSPeo5jFgrGtoepxh9K+XeVtHUL7dVmX4/9HQyju28XKi9lXqOzX5vhhbXNgWNscWtsaQm+/trbZ2uZaxZLnmjdIVXvj7fpwu4gMLbN8Id4uEz2AF4D/OfPFT12BvDV6ZXEXOw/4RVXTj+K9booAegOvqGovIBsoe81Oeds0mNu6KnEDICLD8X6R3e8zOxjbGqoWd6jt21Xe1oTOfl1CRKKA84FP/S32My/Y+3ZlMReXCaX92hw/rG22trkitbFtro3tMljb7Ds/IKxtrh5LnmuYqu5wnvcAX+DtGuO7PFNVDzivJwORIpKA9yhUK5+iLYEdAQmayuP2cdiRqyN4r5u2AdtU9Tdn+jO8X8Zly/jbpsHc1lWJGxE5GXgTGK2qacXzg7StoQpxh+C+XaVt7QiV/drXKGChqu72sywU922oOOZQ3K/NccLaZmubK1Eb2+ba2C6Dtc2+8wPF2uZqOKbHeU5ISNCkpKRgh2GMMeYYsWDBgr2qmhjsOGoza5uNMca4KZBtc0QgVhIsSUlJzJ9//N5J3RhjjLtEpOwNYcwRsrbZGGOMmwLZNlu3bWMMAFm5BXy5eDsZBwuCHYoxxhhjHIu27CM7rzDYYRhjsOTZGOP4eN5W7v5oMf+eYyfWjDHGmFCwLzuf3708m66PfhfsUIwxuJQ8i9fVIvKIM91aRI7Li8iNqa0O5ntKPRtjjDEmuHZn5QY7BGOMD7fOPL8MDMA7cDlAFvCSS3UbY4wxxhhz3PEUHbs39jWmNnLrhmH9VLW3iCwCUNV9zhhixhhjjDHGmKNQVBTsCIwxvtw681wgIuE4A3yLSCJg/+7GGGNMCBCReBGZKiLrnOdG5ZQb45RZJyJj/Cz/SkSW+0yPE5HtIrLYeZxdk5/DmONNZq7dxNOYUOJW8vw83sGym4jIk8DPwFMu1W2MMcaY6hkLTFfVjsB0Z7oUEYkHHgX6AX2BR32TbBG5EDjgp+5nVbWn85hcI9Ebc5zauDc72CEYY3y4kjyr6n+APwMTgJ3ABar6qRt1G2OMMabaRgPvOa/fAy7wU2YEMFVV01V1HzAVGAkgIvWAe4AnAhCrMcaRX2gdOY0JJdW+5llEwoClqtoNWF39kIwxxhjjsqaquhNAVXeKSBM/ZVoAW32mtznzAB4HngFy/LzvDhG5FpgP/MlJvI0xLmjVqG6wQzDG+Kj2mWdVLQKWiEhrF+IxxhhjzFEQkWkistzPY3RVq/AzT0WkJ9BBVb/ws/wVoD3QE2/Ps2fKie1mEZkvIvNTU1OrGI4xJjbarXv7GmPc4NZ/ZDNghYjMBUouzlDV812q3xhjjDEVUNUzylsmIrtFpJlz1rkZsMdPsW3AMJ/plsBMvENR9hGRFLy/G5qIyExVHaaqu33W8QYwqZzYXgdeB0hOTraxd4ypogKPdds2JpS4lTyPd6keY4wxxrjvK2AMMNF5/tJPme+Ap3xuEnYW8ICqpuM9w4yIJAGTVHWYM92suDs48DtgOcYY1xR67FiTMaHEleRZVWe5UY8xxhhjasRE4BMRuRHYAlwCICLJwC2qepOqpovI48A85z2POYlzRf7mdOtWIAX4fY1Eb8xxqtAGejYmpLiSPItIf+AFoDMQBYQD2apa3436jTHGGHP0VDUNON3P/PnATT7TbwNvV1BPCtDNZ/oaVwM1xpRSWGRnno0JJW6N8/wicAWwDqiLtyF+0aW6jTHGGGOMOe54LHk2JqS4dgs/VV0vIuGq6gHeEZHZbtVtjDHGGGPM8abArnk2JqS4lTzniEgUsFhE/oZ3uIpYl+o2xhhjjDHmuOOxa56NCSluddu+Bu91znfgHaqqFXCRS3UbYwLAjm0bY4wxocVGqjImtLh1t+3NzsuD2LBVxhhjjDHGVJtH7dC2MaHErbttb8LPiStVbedG/caYmmftszHGGBNaiuyGYcaEFLeueU72eV0H7/iR8S7VbYwxxhhjzHHHhqoyJrS4cs2zqqb5PLar6j+B09yo2xgTGGpXPRtjjDEhxc48GxNa3Oq23dtnMgzvmeg4N+o2xhhjjDHmeGTXPBsTWtzqtv2Mz+tCIAW41KW6jTEBYO2zMcYYE1o8dubZmJDi1t22h7tRjzHGGGOMMcYrM7cg2CEYY3y41W37noqWq+o/3FiPMabmaMmzHeU25lgjIvHAx0ASTu8wVd3np9wY4CFn8glVfc+ZPxNohndISoCzVHWPiEQD7wN9gDTgMlVNqbEPYsxx5rVZG4MdgjHGhys3DMN7jfOtQAvncQvQBe91z36vfRaRViLyg4isEpEVInK3Mz9eRKaKyDrnuZEzX0TkeRFZLyJLy1xnbYwxxpjyjQWmq2pHYLozXYqTYD8K9AP6Ao8Wt8GOq1S1p/PY48y7Edinqh2AZ4G/1uSHMMYYY4LJreQ5Aeitqn9S1T/hPQLdUlXHq+r4ct5TCPxJVTsD/YHbRaQL5Tfwo4COzuNm4BWXYjfGQMlFz3btszHHpNHAe87r94AL/JQZAUxV1XTnrPRUYOQR1PsZcLqIiAvxGmOMMSHHreS5NZDvM52Pt2tYuVR1p6oudF5nAavwnrUur4EfDbyvXnOAhiLSzKX4jTHGmGNZU1XdCd72F2jip0wLYKvP9DZnXrF3RGSxiDzskyCXvEdVC4EMoLHbwRtzvLppcNtgh2CM8eHW3bb/BcwVkS/wXjr5Ow4lwJUSkSSgF/AbZRp4ESlu4Mtr1HdWN3hjjO81z8aY2khEpgEn+Fn0l6pW4Wde8VfCVaq6XUTigP8C1+C91rmi9/jGdjPeXmO0bt26iuEYY8LDrCOHMaHErbttPykiU4AhzqzrVXVRVd4rIvXwNsR/UNXMCnp7WQNtjDHGlENVzyhvmYjsFpFmzkHpZsAeP8W2AcN8plsCM526tzvPWSLyAd5rot933tMK2CYiEUADIN1PbK8DrwMkJyfbMTpjqsj+WYwJLa502xaR9sAKVX0OWAIMEZGGVXhfJN7E+T+q+rkze3dxd+wyDXxxA12sJbCjbJ2q+rqqJqtqcmJi4lF/JmOON8XXOts1z8Yck74CxjivxwBf+inzHXCWiDRybhR2FvCdiESISAKUtNvnAsv91HsxMEPVvkWMMcYcm9y65vm/gEdEOgBvAm2BDyp6g3O91FvAqjJDWZXXwH8FXOvcdbs/kFHcvdsYY4wxFZoInCki64AznWlEJFlE3gRQ1XTgcWCe83jMmReNN4leCiwGtgNvOPW+BTQWkfXAPfi5i7cx5ujZsShjQotb1zwXqWqhiFwIPKeqL4hIZd22B+G9ZmqZiCx25j2It0H/RERuBLYAlzjLJgNnA+uBHOB6l2I3xhhjjmmqmgac7mf+fOAmn+m3gbfLlMnGO4qGv3pzOdROG2NcZrmzMaHFreS5QESuAK4FznPmRVb0BlX9Gf/XMYP/Bl6B26sTpDGmfOpcWaV2hZUxxhgTEqxFNia0uNVt+3pgAPCkqm4SkbbAv12q2xhjjDHGmONaXqEn2CEYc9xzJXlW1ZWqepeqfuhMb1LViW7UbYwJDLWxqowxxpiQEhl+6Kf6J/O2VlDSGBMIbp15NsYYY4wxxrgowmec54e/XBHESIwxYMmzMcZhJ56NMcaY0GL3ITEmtLg1zvNhd9r0N88YY4wxxk3/nrOZpLHfUOApCnYoxriu7N22d+w/GJxAjDGAe2eeH6jiPGNMiCpuoG1MSWNMbfLQ/5YD8NbPm4IciTE1b+DEGcEOwZjjWrWGqhKRUXjHXm4hIs/7LKoPFFanbmOMMcaYqpo4ZTW3nNo+2GEY4yp/h7P3HsgjoV50wGMxxlT/zPMOYD6QCyzweXwFjKhm3caYACoZ59lOPBtjagkbuscc6/y1yclPTAt8IMYYoJpnnlV1iYgsB85S1fdciskYY4wxplLrdh8IdgjG1Kjybhj27i+buG5Q2wBHY4yp9jXPquoBGotIlAvxGGOCRUs9GWNMyDv3hZ9LTX80dwsZBwuCFI0xNUAhOuLwn+vjvl5JboH1vDAm0Ny6Ydhm4BcReVhE7il+uFS3McYYY6pBROJFZKqIrHOeG5VTboxTZp2IjPGZP1NE1ojIYufRxJl/nYik+sy/KVCfyZ+xny/jT58sCWYIxrhKARF4+uKTD1vW6eFvAx+QMcc5t5LnHcAkp744n4cxppYoGefZTj0bcywaC0xX1Y7AdGe6FBGJBx4F+gF9gUfLJNlXqWpP57HHZ/7HPvPfrMHPUCXTVu0OdgjGuEZVEYSL+7T0u7zLI5ZAGxNI1brmuZiqjgcQkTjvpNpFSMYYY0zoGA0Mc16/B8wE7i9TZgQwVVXTAURkKjAS+DAwIR6ZPVm55S678d15nNmlKZf3bR3AiIxxn6r3zLOI+F2ek++h+7jvmHzXEFrFxwQ4OmOOP66ceRaRbiKyCFgOrBCRBSLS1Y26jTGBUTy+c3k3JzHG1GpNVXUngPPcxE+ZFsBWn+ltzrxi7zhdsx+W0r/kLxKRpSLymYi0cj3ycvR9cnq5y6av3sPYz5cFKhRjaowC/tPmQ7JyCxnytx9IGvsNK3ZkBCIsc5T25+SzM+NgsMOo0KIt+5izMS3YYYQst7ptvw7co6ptVLUN8CfgDZfqNsYYY0wlRGSaiCz38xhd1Sr8zCs+mnaVqnYHhjiPa5z5XwNJqnoyMA3vWW1/sd0sIvNFZH5qamrVP1Q1JY39ht+9/AvvzU6hqMgODJraqfhY1erHR1Za9tVZG9manlPTIdUa01buZt3urFLzVJU3ftzI/pz8UvOXbctgx/6KE9staTms2pmJqvL89HWk7M0ut+w3S3eyfHvpgxk9H5vKgAkz2J2Zy+pdmUf4aarugc+XkjT2m6N67+9ens3lr8+pdgz3fLKYP368uNr1hBpXum0Dsar6Q/GEqs4UkViX6jbGBEDxtc52zbMxtZOqnlHeMhHZLSLNVHWniDQD9vgpto1DXbsBWuLt3o2qbnees0TkA7zXRL+vqr6nJ94A/lpObK/jPdBOcnJyQL9lFm3Zz6It+/ltUxqTl+3i/Rv6MvTERH7dkEbdqHB6tmpY4zHkFxZRWFRETNSR/+zamXGQupHhNIw5ukFNvluxC1UY2e2EKr8nM7eAzIMFtGxk3YABHvh8Gfuy83n1mj7llikqUrLyCmlQN9LVdfu2yXUiwxk7qhMTp6wut/zXS3bw9ZIdzPvLGew9kEfnZvVdjac22Z+Tz03vzwcgTKBI4d839mPFjgwmTFnNk5NX8eKVvTj35Obsy87nvBe9d++fee8wkhJiuf+zpfRJasSlyYc61Ax92pvu/Pbg6fxj6lpenrmed67rC0DLRnVpFR9DXqGHrekHuf2DhQAMaNeY/u0a8+y0tSX1XPH6HDbuzeasLk0ZcmIiV/drjYiwdncWOfkeXv5hPS9f1ZuI8IrPc3qKlJz8QuLqRPLp/K3UjQpnX04BH871diIaNHEGzRrU4bNbB5a8591fNpFT4OG1WRvp1boh717f12/d+YVFRJW503tmbgGxURGEh3kP6KzZlUVEuHBC/TrERIWTmVvIut1ZtEusx+cLtwNw89B2NIqJov+E6bx6dR9GdjuB5dszWLotgwt7t6BOZHiFnzHUuJU8bxSRh4F/OdNXA5tcqtsYY4wx1fMVMAaY6Dx/6afMd8BTPjcJOwt4QEQigIaquldEIoFz8Z5lpjghd8qfD6yqwc9Qricu6MZD/1teYZnJy3YBcO3bc2nRsC7bnTNMF/ZqwTOX9ij3mlKAjakHmLspHRG47JTKr6POziskKiKMSOeH7yWv/cqSrftZ8shZNIgpnVzl5BfS5ZHviIuOICuvkJSJ55RaPmDCDCLDhXVPnl3pev35/b8WABxWb7Fznv+J3AIP0/80rGTeyeO+L/c9a3dnsTU9h9M7Nz2qeAD2ZeeTW+hhwIQZdG5Wnyl3DznquqprweZ9tIqvS3R4eKm/zd4DeaTszSY5KZ4P526ptJ7nZ6zjn9PWseChM1i35wCb07LZnJZDu8R6jOp2ApHhYRSpogp1o6qeLChaqkvILae2rzB5LnbKk9MAuPyUVqzfc4B7zjqRge0TKnzP1vQcGsREUr/Ooe2QX1hEgaeI2GhvypCVW0DdyPBKk7rKLNm6n9Ev/cKXtw+ih88BrIP5HkQgr7Co2gciPp536CqU4o4nV7/1W6kyd3ywiDs+WFRq3rC/z+Tq/q35eP5WPp6/lfd/TeGE+nXp1za+pMzuTO89F3ILirjijUNnaS/s3aIkaSz268Y0fi3TDXqjc8b6+5W7+X7lbh728/31yfxtPPjFMp6/ohfn92jO/px8bvvPQmZvSKNB3Ui+uWswg//6w2Hv87V9/0G27z9I0thvCBNY9fhIxn29smT5zDWp7MrIJSoijHd+2cQLM9aXLDvxoSlMu2cod3ywiNW7smgdH8OW9Byu6Nu6Sv8TxUY991PJ61v+vYDnLu/J3R95z0gv2bqfv/q5k3woE3XhNJPT0I4HBuPt9vUjME5V91W78mpITk7W+fPnBzMEY2qNxyet5K2fN3HdwCTGnW+3LDDGHxFZoKrJwY7jSIlIY+AToDWwBbhEVdNFJBm4RVVvcsrdADzovO1JVX3H6Un2IxAJhONNnO9RVY+ITMCbNBcC6cCtqlrhL3u32mbfLokpE8/hpR/W8/R3a6pV5wOjOnF539b0GO9NHseO6kShp4i/f3/ojFHbhFg+/L/+PPS/5ZzZpQmJcdFEhIWRGBdN52b12ZyWzalPz6RJXDQ/3T+c6IjwUrE+9bvuXH5KK8KcMzcpe7MZ9veZJcvHDGjDe79uJiJMKPTpah4dEcb3fxxK6/iYkjNUZz37I89e1oPerRuRneehRcO6hIcLny/cxjX92yAiJetePn4E0T4JfdnteFW/1jz5u+68+8umkh/X/pLn4vKf3jKAK16fQ9P6dbiodwtuHNKO+nUi2JKeQ2x0BFm5hbRN8HYuBeU2AAAgAElEQVRC3Hsgj6cmr+Lsbs04o0vTw7qTjj+/K2MGJlXpb/TD6j10bVGfJnF1AJizMY3LX5/DC1f04rwezf2+Z+WOTDo3i0NE2Hsgjzd+3MiA9o158PNl7Mg4dOO5v17UnYe/XMGH/9ePez5Zwua0HK7p34Z/zdlcUmbtE6M4WOBh3qZ0Tu/cBBEhv7CIzo98i6dIeXx0Vx7+ckWFnyGhXjQ/3z/c7xm3r5fsoGerhiU3/yreVr5/i4f/t7xUTFV1WqcmzFi9h8axUXzwf/3p0KQe4WHCut1ZhIUJpz8zi6TGMdw/shODOyYQVyeS3o9PJT07n3/d2JcOTeoxYMIMAH7683BioyM49ekfeP7yXmxIPcANg9qW7NfTVu5m+uo9TLiwe8n6X5u1gQlTVrPmiZHc8O48flmfxt2nd+Tyvq14bdZGujSrz5//u7Sk/H9vHUCfNvH4o6r0fGwqreLr8vmtg/j3nM08NulQUvj3S3pw76c2bF1Z557cjElLd1ZeMIDKO7B3JALZNruSPIcqS56NqTpLno2pXG1NnkOJ28lz36R4PrllAOt2Z3Hmsz9Wu97qaNM4hs1ph643Hdi+Mad3bsrjPj/qi02+awib9maXdO2sqq7N63NR75alEgVfJzatx9rd3kFPnr2sB3/8+FAC0Tg2iv7tG/PE6G40ivV2A/dNZB89rwvjv/Zf78PndvH7OSry6tW96dKsAee9+DMZBwsqLFtc/4tX9mJwhwTe+GkjL/2wgbVPjGLZ9gzu+nAR1w9K4olvvJ0bLunTkoEdGjNl2S6+X+kdnuzFK3vRuVl9Ln5lNr8/tT3NG9bl53WpfDJ/Gw+d05nvV+5m7qb0I/oMZcXHRpGenV95wSro1zaek1s24P1fNzPtnlNpEBNZctZ/3HldaNkopqTbsW+C4SlSFm/dz0WvzHYljprWsUk91u3xPxBPcUJfFa9e3ZuzupzAx/O38oDdEPCYcVwmzyJyInAvkIRPV3BVPa3alVeDWw30P75fQ5rzRRkVEcatw9qXHPE05ljx2NcrefuXTYwZ0Ibxo7sFOxxjQpIlz9XndvL88/3DadkoBlWl7QOTq13v8WTyXUM4+/mfKi9ogq6iXgDG1Ga1LXl265rnT4FXgTcBj0t1howf1+1l274cPEXKvpwCOjerX+rmAcYYY4wJjuKbWlV0zbLxzxLn2u3L2wcx+qVfgh2GMccVt5LnQlV9xaW6Qs7/bh8EwK6MXPpPmI7Hhrswx6Di8Z1t7zbG1Aat42NKrqkttmL8CLo++l2QIjImsHq0akjKxHM4kFdIN9vvjQmIat0qT0TiRSQe+FpEbhORZsXznPnHlDBna1nybIwxxgRXRLgQV6f0OYDY6AjWPjGK7/84lCWPnhWkyIwJrHrRESx5xPZ3YwKheveZhwXAfLzDXtwHzHbmFc8/pkQ42bMlz+ZYZOM8G2Nqk0KPEhF2eFftqIgwTmwa5/p4u8aEsgYxkaRMPIe7TusAeG/EZYxxX7W6batqW7cCqSoRGQk8h3e4jDdVdWKg1l08IHihJc/GGGNMUMVEhZeMPVse3+GZ7OZK5njwxzNP5OI+rWjZqC43vDePmWtSgx1SyHj7umRO61R6qLTEuGjuPetE7v/v4XfvPtprym8f3p6OTeL4w8eLeeWq3ozq3qxkWUXfQ4sePpNJS3ewcMt+vli0vdxybqsXHcGBvEK6Nq/PmIFJ/PmzpZW/qRr6t4vng5v6MzclneQ2jWp0XTWhWsmziJwCbFXVXc70tcBFwGa84zxXbzyAw9cXDrwEnAlsA+aJyFeqemRjJxyl4iPcnqKiQKzOmKBQu+rZGFMLfPuHoZWWqeeTXK9+fCTb9h2kQ5N6JeP+frFoO/d8soROJ8Tx/g192ZyewyWv/lqTYVcqoV4Uew+4MxSSOf6ICK0be2+i17xh3SBHU3W/P7Udr83aeNTvb5cYy8bU7MPm/9+Qtny1ZAeT7xpC43rRALxz3Sl8vXQH/7i0Z0m5D+duZfHW/YB3uLcXr+zNiU3jiAoPI99TxH0jTuK8k5uzfEcGt/1nIXcM78CLP6wvta7Jdw2hXWJsyfjdF/RqcVg8Sx45ixU7M+jftjFrdmfR6YQ4tu07yJb0HBrFRnHNgCSu6qd8tWQHJ7dswO96teCMzk1ZvSuT7fsOlowhfsfwDtw74iQA1u3Ook3jWKIiwhj736V8NG8ra54YyaIt+7n89Tkl6378gm6Ei3Blv9bs2H+QgRO9Y3YvefQspq/azZldmiIifm+KvCcrFxSa1K/D379bw8AOjfl+xW6mLN/J7sy8knLLx49AVYkIC+NggYfr3pnL0m0ZjB3VictPaUXDmKiSsv3bNa7oTxqyqjVUlYgsBM5Q1XQRGQp8BNwJ9AQ6q+rF7oRZsr4BeJPyEc70AwCqOsFfebfHec4t8NDp4W/588iTuG1YB9fqNSYUjPtqBe/OTuHq/q154oLuwQ7HmJBkQ1VVn9ttc034ZulO1u3J4p/T1pWaP/78rgzq0Jgz/uEdT/qT3w/g0te8yXZ0RBh5hUd2cD1l4jklvy0AfrxvOM0b1uHmfy3grC5Nubxva7bvP8gg50dug7qR3Di4LWt3ZzFp6c5qfcZPbxnAlrQc/vSpdxzoU09MZNba6p+lHHdeF9o3qUerRjEM+/vMUsvuPetEbhvWgXYPTiYuOoIZ9w7jlCen0bV5fU5u2YAP524tKXvfiJN4+rs1ftfRODaKbi0aVCneq/q15vuVu0nN8v7Af/ayHozs2oy8Qg8NY6L4dvkuDuQV8uzUtbx3wylsTM2mUWwU81LS+du3pdc/7rwuXHpKK2KiIpi+ajez1qayNT2Hrs0b8NG8rXx+60CGPv3DYTGc0bkp01bt5uFzu9C1ef1SCU1VHc1wPgs27+OiV2Yz6c7B/Lohjbd+3sSuzNyS5X+7+GQGd0igft1Irn9nLvNS9nHqiYm8e/0pbEj1jstcvK+vemwknR/59ohjeOGKXtz54aLD5v/+1Hac2jGRfE8RURFhDGyfwIG8QtIP5PPFou08O21tSdkbB7flrZ83lUyXPcDku206P/wtBws8zH3wdDJzC+jQJK5Kca7amcnfvl3NJcmtOLNLUyLDvZdq7svO52CB57ADEQWeIjr+ZQrgTcbbNI6hXWK9Kq3raHmKlN//awFhAq9d08fvCAOeIiU7v5D6dbyXrZzxj1lcmtySQR0S6Nq8QamyP6/bS706EfRs1bBacc3esJcr3/gNOHw/zcgp4OulO7iqX+saHRGh1ozzLCJLVLWH8/olIFVVxznTi1W1Z0XvP4r1XQyMVNWbnOlrgH6qeodPmZuBmwFat27dZ/Pmza6tv/gfZdhJiQzukOBavcaEgu9W7GJeyj56t27I2T5djIw5FnRpXp+B7av/vW3Jc/XVhuS5WHEXy//eOpBfN+zljtM6ArBmVxbtE2OJ8OkOvmL8CDamZtMmIQYtgrTsPE57ZhYA39w1mLYJsWQcLODHtalMmLKaz24ZUPLDfum2/TSuF02LKp4p3JyWzalPzwSgZ6uG3DfiJNbuziI7r5C/f38o6XhgVCdOaFCHuz9aXOr9/7ysZ8lZsaSx35DcphGf3TqwZPnBfA8fz9tCr9aNaJcYyx0fLGLRln1k5haWnOGb/9AZeIqUvIIidmfl0qxBHeKiI2kQU/pac9/xt38ZexotGtYlM7eAmMhwIsIPv/XO7R8s5JulO9k04WwmL9vF7R8sPKzMpglnIyKlusA+dE5nnvhmVcn08vEjSvU82JlxkF83pHFh75aVbt9iBZ4i8gqL2JyWTVx0ZMkZ3Yqs2JHBmz9tKtXtNmXiOezPyS+5Dt93PPIFD53B10t2cM7JzYmNDqfLI4fumv3Y6K78c9o60rPzXRkLt9jeA3mk7M0mOanye/s+8uVy3v91c8n61+3OIikhtiS5zCv08O4vKQw7qQlPTV5VckCjXnQEfzijIzcNacen87eSGBfN7A1pXNO/DTNW7+HaAW3KTaZW7cxk1HM/8fC5XTitUxPaJsSSW+DhL18sZ9z5XUjNymPS0p28/2sKew+U3jblJbumZiWN/YbrByXx6Hldg7L+2pQ8Lwd6qmqhiKwGblbVH4uXqWo3l+IsXt8lwIgyyXNfVb3TX3m3G2hVZdDEGezIyK28sDHGmJBx3cAkxp1f/Ubdkufqq03J856sXAQhMS663DJ/+GgRF/VpyZCOiYctS83KQ1GaxNVxNa7cAg/9nprOXy/qzshupQ92ZucVct6LP/PMJT3o1frQ9YS/bUyjY9M44mOjDisfGR5GVETF95Ate0brSDz93WrmbdrHJ7cMOOL3bk3PoUFMpN/1Tl62k7xCD8u2ZfLnkSeRkpbNg58vY+GW/ax/cpTf5DwQ8go9fPDbFpLbxNMwJpJW8aWT7oP5HnILPGTmFtCmcenh1t74cSMD2jemZaO6NKgbSebBQg7kF1b5wMrxJDO3gIycgsO2rzn+1Kbk+S/A2cBeoDXQW1VVRDoA76nqIHfCLFlfULttg/co5MECj6t1GhMqiq/tMeZYExUeVnIdWnVY8lx9tSl5NsYYE/oC2TZX927bT4rIdKAZ8L0eysTD8F777LZ5QEcRaQtsBy4HrqyB9ZQrMjyspKuKMcciNxIMY4wxxhhjjjXVOvMcDCJyNvBPvENVva2qT1ZQNhXvnb8rkoD3zHltY3EHTm2MGSzuQKqNMYPFfTTaqOrh/XNNlVnbHJJqY9y1MWawuAOpNsYMFvfRCFjbXOuSZ7eJyPza2AXP4g6c2hgzWNyBVBtjBovbhK7a+je2uAOnNsYMFncg1caYweIOddb/2BhjjDHGGGOMqYQlz8YYY4wxxhhjTCUseYbXgx3AUbK4A6c2xgwWdyDVxpjB4jahq7b+jS3uwKmNMYPFHUi1MWawuEPacX/NszHGGGOMMcYYUxk782yMMcYYY4wxxlTCkmdjjDHGGGOMMaYSx2zyLCInichin0emiPyhTJlhIpLhU+YRn2UjRWSNiKwXkbEhFvd9PsuXi4hHROKdZSkissxZNj+Acf9RRFY48XwoInXKLI8WkY+d7fmbiCT5LHvAmb9GREYEKuYqxn2PiKwUkaUiMl1E2vgs8/j8Hb4KoZivE5FUn9hu8lk2RkTWOY8xgYq5inE/6xPzWhHZ77MsKNvaWffdTswryv4vOstFRJ539uGlItLbZ1lQtncVYr7KiXWpiMwWkR4+y4LyHVLFuEPuO9scmSq2cSH3d65i3NY2By5ua5sDF7e1zYGL2drm2kBVj/kHEA7swjuAtu/8YcCkcspvANoBUcASoEuoxF2mzHnADJ/pFCAhwHG2ADYBdZ3pT4DrypS5DXjVeX058LHzuouzfaOBts52Dw+huIcDMc7rW4vjdqYPBGGfqErM1wEv+nlvPLDReW7kvG4UKnGXKX8n8HYwt7Wz3m7AciAGiACmAR3LlDkbmAII0B/4LZjbu4oxDyyOBRhVHLMzHfDvkCOIexgh/J1tjyP+m1vbXLNxWtscWtv6Oqxtditua5tDa1sPI4S/s91+HLNnnss4HdigqpurWL4vsF5VN6pqPvARMLrGoitfVeK+AvgwQPFUJAKoKyIReP/BdpRZPhp4z3n9GXC6iIgz/yNVzVPVTcB6vNs/UCqMW1V/UNUcZ3IO0DKAsZWnsm1dnhHAVFVNV9V9wFRgZA3F6M+RxB0q+3VnYI6q5qhqITAL+F2ZMqOB99VrDtBQRJoRvO1dacyqOtuJCUJnv67Kti5PqHxnmyNjbXPNs7Y5cKxtDhxrmwPH2uYygp48i0i4iCwSkUnOdFun69A6pytRlDO/3K5FVXA55f+zDxCRJSIyRUS6OvNaAFt9ymxz5gVaRXEjIjF4/+H/6zNbge9FZIGI3FzD8XlXqLod+DuwBdgJZKjq92WKlWxT558vA2hMELd1FeP2dSPeo5jF6ojIfBGZIyIX1GCoJY4g5oucbj+fiUgrZ16t2NZO97u2wAyf2QHf1o7lwFARaez8v50NtCpTprztGqztXZWYfZXdrwP+HeKoatyh/J1tjoy1zTXI2mZrmytjbXOp+TXN2uZjpG0OevIM3A2s8pn+K/CsqnYE9uHdeXCe96lqB+BZp1ylnOT7fOBTP4sX4u121QN4Afhf8dv8lA3omF6VxF3sPOAXVU33mTdIVXvj7e5xu4gMrcEwARCRRniPJLUFmgOxInJ12WJ+3qoVzK9xVYy7uOzVQDLwtM/s1qqaDFwJ/FNE2tdwyFWN+WsgSVVPxtu9pvisQq3Y1nh/mH6mqh6feQHf1gCqugrvd81U4Fu8XY4KyxQLqX27ijEDICLD8X633u8zO+DfIVDluEP2O9scGWubrW0uj7XNQAhua6xtrhZrmw9VWUOhBkxQk2cRaQmcA7zpTAtwGt6uQ+D9Yik+klVe16LKjAIWqurusgtUNVNVDzivJwORIpKA98iI71GVllS9+41byo3bx2FHv1V1h/O8B/iCwHSzOgPYpKqpqloAfI73ug1fJdvU6RrUAEgnuNu6KnEjImcAfwHOV9W84vk+23ojMBPoFQoxq2qaT5xvAH2c1yG/rR0V7deB3NbF635LVXur6lC8++y6MkXK265B295ViBkRORnvd+9oVU3zeW8wvkOK111h3CH+nW2OjLXNNc/aZmubK2Nts7XNlbK2uTRRDd4BABH5DJgAxAH34r2Zwhzn7DJOl5YpqtpNRJYDI1V1m7NsA9BPVfeWqfNm4GaA2NjYPp06dQrUxzHGGHOMW7BgwV5VTQx2HLVZQkKCJiUlBTsMY4wxx4hAts0RgViJPyJyLrBHVReIyLDi2X6KahWWHZqh+jrwOkBycrLOnx/Qu7kbY4w5holIVW9uZcqRlJSEtc3GGGPcEsi2OZjdtgcB54tICt67r50G/BPv3fCKk3rf0/vldS0yxrggZW82f/liGRtSDwQ7FGOMMcbgHVL2jR83sisjN9ihGGMIYvKsqg+oaktVTcJ7HcUMVb0K+AG42Ck2BvjSef2VM42zfIYGs8+5MceYb5bt5D+/beHLRduDHYoxxhhjgA2pB3hy8ir6T5ge7FCMMbiUPIvX1SLyiDPdWkSO9kL2+4F7RGQ93uES3nLmvwU0dubfA4ytbtzGmEOKirzHoorskJQxxhgTEnLyPZUXMsYEjFvXPL8MFOHtev0YkIV3fMNTqvJmVZ2J9y59xXfsOyzxVtVc4BJXojXGGGOMMSbExdWJBKBRTGSQIzHGgHvJcz9V7S0iiwBUdZ8zFqIxxhhjjDHmKHiKigCIj7Wf1caEAreueS4QkXCcu1+LSCLeM9HGGGOMMcaYo1DoXEsVERbMe/waY4q59Z/4PN4Bu5uIyJPAz8BTLtVtjDHGmGoQkXgRmSoi65znRuWUG+OUWSciY/ws/0pElvtMjxOR7SKy2HmcXZOfw5jjTaHHmzyHh/kbsdUYE2iudNtW1f+IyALgdLzjMV+gqqvcqNsYY4wx1TYWmK6qE0VkrDN9v28BEYkHHgWS8fYkWyAiX6nqPmf5hYC/seyeVdW/12j0xhynPMVnnsMteTYmFFQ7eRaRMGCpqnYDVlc/JGOMMca4bDQwzHn9Ht6bdN5fpswIYKqqpgOIyFRgJPChiNTDO9LFzcAnAYjXGAOs2pkJwKa92UGOxBgDLnTbVtUiYImItHYhHmOMMca4r6mq7gRwnpv4KdMC2Oozvc2ZB/A48AyQ4+d9d4jIUhF5u4Lu4DeLyHwRmZ+amnrUH8KY480Xi7YDkJVbGORIjDHg3jXPzYAVIjLduR7qKxH5yqW6jTHGGFMJEZkmIsv9PEZXtQo/81REegIdVPULP8tfAdoDPYGdeBPswytRfV1Vk1U1OTExsYrhGGPO6noCAAn1ooMciTEG3BuqarxL9RhjjDHmKKjqGeUtE5HdItJMVXeKSDNgj59i2zjUtRugJd7u3QOAPiKSgvd3QxMRmamqw1R1t8863gAmVfuDGGNKNKjrHd/5xKb1ghyJMQbcu2HYLDfqMcYYY0yN+AoYA0x0nr/0U+Y74CmfrtdnAQ8410C/AiAiScAkVR3mTDcr7g4O/A5YjjHGNQ9+sQyA2RvSghyJMQZc6rYtIv1FZJ6IHBCRfBHxiEimG3UbY4wxptomAmeKyDrgTGcaEUkWkTcBnCT5cWCe83is+OZhFfibiCwTkaXAcOCPNfUBjDke5RcWBTsEY4wPt7ptvwhcDnyKd4iLa4GOLtVtjDHGmGpQ1TS8w0mWnT8fuMln+m3g7QrqSQG6+Uxf42qgxhhjTAhz64ZhqOp6IFxVPar6DqWvmzLGGGOMMcYcgVHdvDcM696iQZAjMf/P3n2HR1WlDxz/vmn00HsLTQGlCBHsUkSavSsqupa17v7UVUFFUVFwd627lsXeewMLXbqAAQHpobcQQgKppL+/P+YmTJKZEMwlM0nez/PMM3PPnHvmnZubnJx7TzEG3Gs8Z4hIBLBSRP4pIvcBdVwq2xhjjDHGmGrn3BM8s9N3aWYThhkTDNxqPN8AhAL3AOlAW+Byl8o2xlQADXQAxhhjjCkiTz21s4ivleSMMRXNrdm2dzgvD2PLVhljjDHGGFNu+c6VbWs7GxMcXGk8i8g2fNy4UtWObpRvjDn+1G49G2OMMUFFC+48BzgOY4yHW7NtR3u9rglcCTRyqWxjjDHGGGOqnUMZOYDdeTYmWLgy5llVE70ee1T1JWCQG2UbYyqG2qhnY4wxJqi8MHMTACHWejYmKLjVbbuP12YInjvR9dwo2xhjjDHGmOrMJgwzJji41W37ea/XucB24CqXyjbGVAAb82yMMcYEK6ukjQkGbs22PdCNcowxxhhjjDHGmGDkVrft+0t7X1VfcONzjDHHjxY+29VtY6oaEWkEfA5E4fQOU9WDPvKNBh5zNieo6vtO+lygJZ4lKQHOV9X9IlID+ADoCyQCV6vq9uP2RYypZq7s24Yvl++mdoRbnUWNMeXhyoRheMY43wm0dh53AN3xjHu2sc/GGGNMYI0BZqtqF2C2s12E08B+AugP9AOeEJGGXllGqWpv57HfSbsFOKiqnYEXgeeO55cwprqpVzMcgLx8u7BtTDBwq/HcBOijqg+o6gN4rkC3UdUnVfVJXzuISFsR+UVE1ovIWhH5u5PeSERmikis89zQSRcReUVENovI6mKTlBljyssZ9Gxjn42pki4G3ndevw9c4iPPUGCmqiY5d6VnAsOOodyvgMFiMxsZ47r3Fm8PdAjGGNxrPLcDsr22s/F0DStNLvCAqnYDTgPuFpHu+L86Phzo4jxuB153KXZjjDGmqmuuqnEAznMzH3laA7u8tnc7aQXeFZGVIjLOq4FcuI+q5gLJQGO3gzfGGGOCgVsDKD4ElonIt3iGTl7KkSvRPjmVd0FFnioi6/FUwhcDA5xs7wNzgYed9A9UVYElItJARFoW/DNgjCkfLfZsjKlcRGQW0MLHW4+WtQgfaQV/Ekap6h4RqQd8DdyAZ6xzaft4x3Y7ngvftGvXrozhGGOMMcHFrdm2nxGRn4GznaSbVfX3su4vIlHAKcBSil0dF5GCq+P+rogXaTxbBW2MMaY6UtXz/L0nIvEFF5xFpCWw30e23Ry5eA3QBs8FbFR1j/OcKiKf4BkT/YGzT1tgt4iEAfWBJB+xTQYmA0RHR9s1OmPKyHsSz/x8JSTERkUYE0iudNsWkU7AWlV9GVgFnC0iDcq4b108V7H/T1VTSsvqI61EBayqk1U1WlWjmzZtWpYQjDEcGetsY56NqZKmAKOd16OB733kmQ6cLyINnflGzgemi0iYiDQBEJFw4AJgjY9yrwDmOD3EjDEu27Q/NdAhGFPtuTXm+WsgT0Q6A28BHYBPjraTUwl/DXysqt84yfHOVXGKXR0vuLpdoA2w153wjTHGmCptEjBERGKBIc42IhItIm8BqGoS8DTwm/N4ykmrgacRvRpYCewB3nTKfRtoLCKbgfvxMYu3McYdccmZgQ7BmGrPrTHP+aqaKyKXAS+r6n9EpNRu285kI28D64utA11wFXsSRa+OTwHuEZHP8CyjkWzjnY1xT0HXMFvn2ZiqR1UTgcE+0mOAW7223wHeKZYnHc8qGr7KzQSudDVYY4xPiWnZR89kjDmu3LrznCMi1wI3Aj84aeFH2edMPBOODHJm71wpIiPwc3Uc+AnYCmzGc8X7LpdiN8YYY0wldSgjmyEvzGOzdWk1Vdw/vlwV6BCMqfbcuvN8M3AH8IyqbhORDsBHpe2gqgvxPY4ZfF8dV+Du8gZqjPFNbbptY0wlNGv9fmL3p/HqL1t48eregQ7HGFfZDALGBBe3ZtteB/zNa3sbR+4YG2OMMcYcFwV34/YcPBzgSIw5/rYmpNGxad1Ah2FMteVWt21jTCVnN56NMZXZsu0lVsgypso574V5gQ7BmGrNGs/GGGOMMcZUAvl2hduYgHJrnecSM236SjPGBK8j6zxbzWyMqRyyc/MDHYIxx12NsKL/rj/z47oARWKMcevO89gyphljjDHGuOKEx34OdAjGHHcRxRrPby7YRkZ2LqmZOQGKyJjqq1wThonIcGAE0FpEXvF6KxLILU/ZxpiKVbjOs914NsZUAruSMkqkXfCfBUy95yxE/C3mYUzlE+LjfO7++HQAtk8aWdHhGFOtlXe27b1ADHARsNwrPRW4r5xlG2OMMcb4NH3tvhJpa/akkJWbT83w0ABEZIwxpqorV7dtVV2FZz3nhar6vtfjG1U96E6IxpgKoUWejDEmqH322y6f6V3HTePr5bvJs5mVTBVyUqtIn+l7Dh0mKzevgqMxpvoq95hnVc0DGotIhAvxGGOMMcYc1eb9aX7fe+DLVXR65CcOZ1ujwlQNT19yss/0MyfN4cTHppGZk0dmjp3vxhxvbk0YtgNYJCLjROT+godLZRtjKj683+cAACAASURBVEDhOs92s8aYKkdEGonITBGJdZ4b+sk32skTKyKjvdLnishGEVnpPJo56TeJSIJX+q0V9Z3Kotvj02xSJVOpFayA0addQ6bcc6bffF3HTaPruGnc/O4ym4XemOPIrcbzXuAHp7x6Xg9jjDHGBN4YYLaqdgFmO9tFiEgj4AmgP9APeKJYI3uUqvZ2Hvu90j/3Sn/rOH6HP6XH+Bn8vvMgv2zYz8H07ECHY8wxK5gvrGebBkfN+8vGBNbuTT7OEVV9SenZdiff+FTeCcMAUNUnAUSknmdT/felMsYYY0xFuxgY4Lx+H5gLPFwsz1BgpqomAYjITGAY8GnFhPjnhYcKOXn+u81c+triItu/jxtCwzo22qyymLYmjn4dGtPIfmY0qB3OoYzSe1PM2bCfiLAQTmpVv4Kiqnr6PD2T3m0b8N3d/u/2m+rJlTvPInKyiPwOrAHWishyETnJjbKNMRWjoGuY2pRhxlRFzVU1DsB5buYjT2vAexau3U5agXedrtnjpOhaUJeLyGoR+UpE2vr6cBG5XURiRCQmISGhnF+lpPkPDTym/OviUrjjw+V+J1qavnYfg56fS8pRunx/vHQHny7bSV6+kp5lK3SWJj0rl89/21lY15TVwfRs7vhoBbe8/1u5Y0hKz+b3nQf5evlu3l647U+Xk5UbuPHFv48bwqz7zy01z3/mbGbkKwtRVT5ZujPo7qBm5+az/UB6oMM4qpW7DgU6hHLZvD/V55J+bknNzGHZtqTjVn6wcuXOMzAZuF9VfwEQkQHAm8AZLpVvjDHGmFKIyCyghY+3Hi1rET7SClo6o1R1j9PD7GvgBuADYCrwqapmicgdeO5qDypRiOpkPP8rEB0d7eoVuicu7E7L+rWOaZ9Rby0F4MTHpnHHuZ3o1rIeK3cdol9UI1Iyc3j46z8A6Dl+BhFhIfz32lPYc+gwfds3pHvLSOKSMwkNER79dg0AP6/Zx/xNCbSIrMm+lEwA3rwxmts+iOHDW/rRp11DQkOEV2bHcsPp7YlLzuSy1xbTo3V9cvLymXxDNM0ia7ArKYP6tcJ5Z9F2GtUJ57azO/LRkh0M7NqMFpE1CQstec/jw1+38/6vO47aoCouNy+fuz9Zwb2DunBya/fvUObnK2c9N4cHh53I0JNacPN7v7FsWxIZ2XncfGaHUvedumov9376O789eh75TmN798HDPvOu2HmQVvVr0aJ+zaPGdOF/FrLn0JFybjnrSBz7nJ9p03o1fO4bNeZHQgS2ThzJKU/NJCM7j5E9WhJZK4zfth9k8/40xl3Qnb7tGxKfksnQk3z9KsL6uBT2JWcysKuv61dHJyJ0bla3THk7jP0JgEe+/YN3bopmUNfmf+oz3fbElDV8umwXyx87j8Z1fR/v4yUxLYvJ87fy0LCuhIaUbT34w9l5KErtiLI1m5LSs2lYOzzg682f98J8oGxrgWfn5jN/UwJRTeqQr8oJzT2jbwsmXawVcWT5v5TMHMJDQugxfgYAq8efT2TNcLfDD1puNZ7rFDScAVR1rojUcalsY0wFKLgZYBOGGVM5qep5/t4TkXgRaamqcSLSEtjvI9tujnTtBmiDp3s3qrrHeU4VkU/wjIn+QFUTvfK/CTxXri/xJ1x6iufm+Nd3ns4Nby8j4xhn2H5j3pbC1+8u2l7i/ezcfG7/cHmpZczf5LmbXtBwBrjtgxgAbnh7WZG8r83dQmRNz79ff+zxjE0951+/4EvL+rUY9/1a+H5tYdqcB85l0PPzeHDoiZzcur7nfSAvX1mzJ5kW9WuyLzmTR7/7g24tIvly+W4eHHoiNcJCuOrUtlzy6iK2JqTz0tW9mb42nm0H0plxn/+G9/uLt5OXrzSuG0FOnnJF3zaAp7fSloQ0WjWoxUdLdhARGsLVp7YjOzefD5dsZ9jJLdibnMl9n68CVhWW9+TUdbRqUMtv4xLgwyU7ANiSkEZUY8+/k2mZuWRk51IjLJQ/9iTTu61n/O9lTpf8TROGEx4qXPzqIrYfSOfugZ2Z+PMGAO4Z2Jn07NwiDWfwNIhH9GhBdPtGPPXDOgD+PrgLw05uwRWvL2Zkz5bk5cMvGz2/Lvnq2afAj3/EFSnvaacMgHdvPpXHvl3DhEtPpnZ4KCJC47oRDH95AeBp0Lw+dwund2pc+F0A9qdk0qRuDUa9tZSOTev4beCte2oo3R+f7vcYFveX92LY8uwIPv9tF5viUxl/ke8OotPWxHHHRyuYes9Z9GhTn6T0bOrUCCUiNMRnYzA9K5fR7yxj0uU96Nys5HRHY7/5g7AQKZwtfEdiOlNXeY5bamZuka74BeXvPXSYtxdu49ER3ViyNZGQEOG0jo2ZPH8LzSNrsmZPMmOHdyOk2LHJzcvnYEaO3wsgAI9/v5Yf/4jj1KhGDO7WjPcXb+fCXq1oVCeC1buTyVOlZ7GLST3GTyc3X5l1/7l8vHQHd5zbieaRNZm1Lp6DGdlcGX2kw833K/fw989WcnHvVvTr0Ii6NcJITMvmL86FmvSsXL6M2cX4qetY+fgQGtQufShCUno2yYdz6NDEd7Nq8ZYDxMan0aB2OCe1qk/nZnWJT8mkmZ9jsC85k9Mmzubbu87glHZHprT4x5ermLJqb+F27DPDST6cQ/SEWQA8d3kPLuzVitoRYfR0Gs0FCv5v/HF1HH3bN6RezTB2Hcyga4tIMnPyuPp/v/Lezf04mJHNoOfn8fboaAZ3C44LOX+GHGv3GZ+FiHwLrAA+dJKuB6JV9ZJyF14O0dHRGhMTE8gQjKk0JvywjrcWbuOmM6L8VqrGVHcislxVowMdx7ESkX8Biao6SUTGAI1U9aFieRoBy4E+TtIKoC+QAjRQ1QMiEo5nDPQsVX2joEHu7H8p8LCqnlZaLG7VzQWNmK3PjijyT7R348aUzYKHBvLQV6v5dWsiA05sytyNCax5cigph3M4Y9Kc4/KZfdo14LzuzRnSrTkpmblc/vpiPrm1P+0a1+as53xfTAA4o1NjFm9J9Pt+ZbF90sjCc/XjW/tz83u/8ddzOvKfOZtL5G1YO5zfHz+/RPrlry9m+Y6Df+rzn774JMZ9v5Zv7jqDuz5awc1nRvHXczsVxnRtv3bcP+QETn3G03g6sXk9/ja4CzuS0rlrQGd2H8wo8nPqF9WIL+44nfSsXN5ZuI07B3Ti3H/NLbxg8ep1fWjbqBYX/XdR4T4vXNWL+784cmHlnoGd+e8vJb+/L1PvOYuuLevx9sJtzFm/n2Xbj3Qf/u3R82harwbPTdtAdm4+t57dgRveXsZro/pw/ovzC7/fp8t2+iy7b/uGx3Rc+7RrwNq9KVzYqxVfLd/tM8/ro/ogAnd8tKJI+svX9KZT07pFen8s25ZE+8a1SUzL5urJv5KamVt49zg3L5//zd9KXPJh7h3Uhf7Pzi5znE9edBKZOXmFF5XqRITy31F9uPnd8g2JqF8rnMhaYexK8t07xJfwUOGdm07l7C5Ny/XZBSqybnar8dwQeBI4C0+3r/nAeFX9c7/RLrHGszFl9/QP63jbGs/GlKoSN54bA18A7YCdwJWqmiQi0cAdqnqrk+8vwCPObs+o6rtOT7L5QDgQCszCM1QrT0QmAhcBuUAScKeqbigtFrcbz8W7JI795g+//xQbU1n56nq7Pi6l8E52MLjx9PbM2bDfbxd7U7riFxOqg7J0KS+Liqyb3Zpt+yDwNzfKMsYYY4y7nO7Vg32kxwC3em2/A7xTLE86njvQvsodC4x1Ndgy6ti0Dt1aRpZIHzO8qzWeTbXQrWUkmyYM57uVe3joq9WBDocPft0R6BAqterWcAbPmGrv8dSVgSuNZxE5AfgHEOVdpqqWmDSkMpr483oOpHrWhowIC+Hvg7uUaWIKYyqTI2OebdCzMSb45ecroT7GYNavFV54N8O6cJuqLiIshKui2wZF49mYY9Xt8Wmu3X2uKG5NGPYl8AbwFhBc8+G7YOXOQ+w+eJi8fGVfSiantGvAVdE+V+MwxhhjTAXIUyXsKLPlfnJrf775fQ/N6tXgcE6ezwnBjDHGmLJyq/Gcq6qvu1RW0Pn8r6cDR2aoy8u3O3Om6ilY39nObmNMZdC4To2jzlR7RucmnNG5SeH2hb1asWV/Gg/aXTpTxcx54Fw2xadxx0fLeWjYifxz2sZAh2RMlVSuxrMzMyfAVBG5C/gWyCp4X1Wr1MrZBcsF5Frj2RhjjAmo7+4+85j36dOuIX3aNeTK6LYkZ+Rw5f8W061lJN+v3Hv0nY0JYh2b1qVj07psnDCMGmGhXNizFWf/0/+s5caYPyeknPsvB2KA0cCDwGInrSDddSIyTEQ2ishmZ7mNClPQPSwvL78iP9aYCmHrPBtjqpP6tcOZcd+5vHzNKWyfNJINTw8r0g38slNaM6S7Zy3SNg1rFaYvfHggn99+GtsmjmBU/3Y+y37j+j5MuqwHAB39rM9a3MPDupZIe/yC7mX+Pqbq2jRheJnz1gjzTL7UtlFtZj9wLt/ffSbbJo7ggp4tj1d4phrxnmbi5NYlJ2w8VudVwvWey3XnWVU7uBVIWYhIKPAqMATYDfwmIlNUdV3pe7ojNNTuPBtjjDFVUc3wUDY/O4L5mxJoUDucnm0aFHl/8eYDdG8VSYPaEbRpWBuACZeczEmt6nNC87q0aVibkBDPBcjmkZ5JRa/p52lcb9yXSlST2sTGp3FSq0hEhKd/WEeP1vW5oGdL0rJyaVA7ggt6tuRgRjY//hHHnPX7+ctZHcjOy2dw12bM25TATWdEsWr3IS5//Veev7IXew4dplGdCB77bg1nd2nC4xd0p1ZEKFm5+Xy6dCdvLdwGeO7SX/LqIto1qs3OpIwS371gwp6pq/ZyVucmbE5IY+O+VP49YyOHMnL8HrO7BnTioWFdCydm2zZxBEnpnglWD6Rlcygjm7jkTOZs2M/6uBRi96cRFiJ/6v+oGmEhZOXmc3735sxYF4/IkYu9fds35Os7z2DVrkNc/Ooi7jvvBPp1aMRJrSPpOX5GYRk/3HsWs9bH89Ks2MLvnZ+vbEtMZ/Dz8wD42+AuvDLb8/7grs14+6ZTGT9lLe8t3s4nt/Vnwg/reema3tSvFc7KXYf4YXUcuw9m8OylPRj+8gKu69+Oqav2UiMshH4dGvHaqL6s2nWIRnUiaFG/JvmqZOXmF4mrQMcmddh6IB2AId2b89qoPoSH/vn7XJ2a1i183b9jY35YHfeny6qunru8Bw9//QdwbOtQFyhtPWk31Y4I5dazOvCKj3XCy6Jg3W+Ai3u34sxOTRhwYlOaRdYkOSOHXk/NoEndCGIeG0JKZg4RoSHUDA9l3qYE/jltA/+6ohfXvbWEF6/qzXPTNrBhX2qR8lvWr0lccmbh9kmtIuneMpKnLzn5z3/pACnXOs8iciqwS1X3Ods3ApcDO/Cs8+xqt20ROd0pd6izPRZAVSf6yu/2Os/pWbmc9MR0HhnRldvP6eRaucYEg4J/Dq4/rR0TLukR6HCMCUqVdZ3nYOJ23WyOzab4VM5/cT4Ar17Xh5Gl3JHMz1d++COOJVsTeXRENyLCQth2IJ0xX69mxc5DbH12BCEhQmZOHiEiRIT5b+jl5ysLNh/gnC5NEBFmr4+nR5v61AwPZc2eZB78cjUz7z+H2hFH7uvk5OVzMCObdxdt556BnVm+4yBnd2lCWlYu9WqGsyk+lZb1a1InIowQP5PHTV21l34dGhVe0DicnUe3x6fRPLIGSx85rzDfxJ/X8795W9k+aSQpmTlE1gw/puMKsDD2ANFRDakZXrald6LG/Mhfz+3I/+ZtBdxb89aXrNw8TnxsGgBT7zmLWhGhPDl1Le/d3I87P1rOjHXxLBoziA8Wb+e7lXuIT8nizRujGdK9Oa/+spl/Td/IvAcHUCMslJrhIfR+aiYA53dvTlpWLuviUopcaDmzc2M+vvU0xn23hg+X7OCT2/rTL6oRnR/9uTBPl2Z1id2fxr+v7MUVfdsAsCMxnXP/NbcwT4vImky99ywiwkI4kJZFfHImM9bFc/1p7Xht7ha+WbGHcRd05+kfjtxHGzO8K+d1a06bhrXoOs7znV+4qheN6kTQt31DeoyfwaTLenBNv3as2ZPMwYxsBKFpvRqMn7KWX7cm8vboaDo1rUuUV8+Rmeviue2DGIad1IJ7B3dm5CsLC9/7x/kn0KJ+Lf7xpWe5qW0TRyDObdrpa/cx8af1vHLtKTStV4MJP67nx9VxfHJbf1bsOMiXy3ezI/HIRa0RPVrw0x/7ALjlrA687VwE83bZKa05kJ7N/E0J3HxmFE9ceBIAew8d5qMlO3ht7hYu79OGr1fsLtznlWtP4aJerXhhxsbCRva6p4aSnZtfOH9EWlYutcNDS/w++Uv3Jys3D1VYui2J0e8sY+JlPQgLER78ajURYSHH1JuiLCqybi5v43kFcJ6qJonIOcBnwL1Ab6Cbql7hTpiFn3cFMExVb3W2bwD6q+o9vvK7XUFn5uTRddw0Hhp2IncN6OxaucYEA2s8G3N01nguP2s8m0B7b9E2BnZtRvvGZetSf7z9smE/m/encds5HY/r52Tn5rM/NbOw50SBrNw8EtOyadXAMzxBnbvjpV0EiEs+TMPaEUXyJKZlkZWbzxcxu7h3UBdCQ4T8fCU770hZBb0U5v5jAC3q12Tqqr1c0bdNYUMT4MNftzPu+7V88Jd+nHNC0zJ9t03xqTSoFU6zyKJLya6PSyGyVjitG9Tys2dRmTl5HMzIpmX9kvlVla+W7+ai3q2oERbKze8u45eNCbx386kMOLEZAH/sTkbREj1XyqL4sfLlYHo2Hy3Zwd0DOxMSIuxMzKBNw1pFGrXFf35RY35k7PCu/PXcwNz4234gnfaNa/PD6jju/fR3RvZoyauj+rj6GZWp8bxKVXs5r18FElR1vLO9UlV7uxLlkc+7EhharPHcT1Xv9cpzO3A7QLt27fru2OHegu25efl0fvRnOjerW+YxTMZUFuv3pbAr6TBtGtaie8vyj2MxJpgM6tqssAtteVjjufys8WxM9VXQeK5sa/v6si85k9fmbubxC7oTVo7u9dXF1FV7PY3nni159brK23gu71JVoSISpqq5wGCcRqtLZfuyG/BeYLkNUGSKTFWdDEwGTwXt5oeHhggX9GzJ5v1pPscMGVOZ1XG6ytWtEWbnt6lykjKyAx2CMcZUexMv60H7RrWPnrESaFG/Jk9dXPnG7AbKoK7NOL1jYx4aemKgQymX8jZwPwXmicgB4DCwAEBEOgPJ5Szbl9+ALiLSAdgDXANcdxw+xycR4b8uXykxxhhjjDGmOrjWhR5ApnKqUyOMT28/LdBhlFt5Z9t+RkRmAy2BGXqkD3gInrHPrlLVXBG5B5gOhALvqOpatz/HGGOMMcYYY4zxVq4xz8FORBLwzPztpibAAZfLrAgWd8WpjDGDxV2RKmPMYHEDtFfVss1gY3yyurkIi7viVMaYweKuSJUxZrC4oQLr5irdeD4eRCSmMk4WY3FXnMoYM1jcFakyxgwWtwlelfVnbHFXnMoYM1jcFakyxgwWd0WzqeGMMcYYY4wxxpijsMazMcYYY4wxxhhzFNZ4PnaTAx3An2RxV5zKGDNY3BWpMsYMFrcJXpX1Z2xxV5zKGDNY3BWpMsYMFneFsjHPxhhjjDHGGGPMUdidZ2OMMcYYY4wx5iis8WyMMcYYY4wxxhyNqlbJB/AOsB9Y45X2NLAaWAnMAFo56Rd7pccAZ3nt809gLbAeeIUjXd2vdvZZC/zTTwyjnDILHvlAb+e9ucBGr/eauRz3c8Aa53G1V3oHYCkQC3wORPiJfSyw2YlxqFf6MCdtMzAmWGIGhgDLgT+c50Fe7wXtsQaigMNesb3h9V5f5/tsxjn3giTmYDyv73GOkwJNSvm7MNr5brHA6NKOdbDEDfQGfsXzt2Z1sf3fA7Z5He/ewRCzky/PK64pRzvHgiFuYCBFz+1M4BJ/x9rfd7eH/4eLP+cKq5sr4Nx0vV6uoN8pq5utbnarvrC62ermSlM3B7wiPW5fDM4B+hT7YUd6vf4bzh9DoK7XL2RPYIPz+gxgERDqPH4FBgCNgZ1AUyff+8Dgo8TTA9jqtT0XiD5OcY8EZgJhQB3nBI503vsCuMZ5/QZwp48YugOrgBrOL9MWr2OwBegIRDh5ugdJzKdw5Jf3ZGBPJTnWUd6fX+y9ZcDpeCrmn4HhwRBzkJ7XpzjHcjv+//g2ArY6zw2d1w39HesgivsEoIvzuhUQBzRwtt8Drgi2Y+3kS/OT7vMcC5a4i50vSUBtf8faHsf+cOnnXKF1cwWcm67Xy0EUt9XNFRTzsZ7XFXSsrW4OomPt5LO6uZyPKtttW1Xn4zm43mkpXpt18FzlQFXT1PkJeKc7zzXxVEg1gHAgHk8ltUlVE5x8s4DLjxLStcCnFRR3d2CequaqajqeynSYiAgwCPjKyfc+cImPMC4GPlPVLFXdhueKUD/nsVlVt6pqNvAZcHEwxKyqv6vqXmdzLVBTRGr4+G7e+wQ8bn9EpCWePyy/Op/7AZ6rbMEWc8DPa2ef31V1+1HCGArMVNUkVT2I5w/5MH/HOljiVtVNqhrrvN6L5wpw01LyBzxmf0o7x4Iw7iuAn1U14xj2MUdRGevmIPm7e0z1crDEbXVzwGK2utnq5jKzuvnYVNnGsz8i8oyI7MLTveVxr/RLRWQD8CPwFwBV/RX4Bc/VpDhguqqux1NpdRWRKBEJw3OCtT3KR19NyT9k74rIShEZ55y4rsSN56QcLiK1RaQJnu4ObfFclT+kqrlOvt1Aax8f1xrY5bVdkM9fejDE7O1y4HdVzfJKC9ZjDdBBRH4XkXkicraT1trZp0CwHutgOK/LqrTzuszHOgBxe39uPzwNhi1eyc+IyGoRebG0f0oDEHNNEYkRkSUiUvCP3jGfY4E61sA1lDy3y3SszbGrjHVzZayXAxC3N6ubj3/MBaxutrrZH6ubyyngjWcRCXX+OP3gbHcQkaUiEisin4tIhJNew9ne7Lwf9Wc+T1UfVdW2wMd4+tkXpH+rql3xVLZPO5/ZGegGtMFzEg0SkXOcK2N34hkTsABPV4Nc/BCR/kCGqq7xSh6lqj2As53HDW7FraozgJ+AxXhOsF+d+Hz9sVQfaf7ylXX/QMTsCVzkJDzjKv7qlRzMxzoOaKeqpwD3A5+ISOQx7B+ImIGgOq/LypXzOgBxe4L3XIX/ELhZVfOd5LFAV+BUPF2ZHg6imNupajRwHfCSiHSich3rHsB0r+QyH2tz7Cpj3VwZ6+UAxO0J3upmq5v9C7Zz+5hY3Vz96uaAN56Bv+OZ8KPAc8CLqtoFOAjc4qTfAhxU1c7Ai06+8vgEH9251NM9oZNzZeRSYIl6uiGk4RlvcZqTb6qq9lfV0/FMwhBbymeVuEqiqnuc51Qnln4uxo2qPqOqvVV1CJ5filjgANDAuSIPnn889hYvC88VJ+8rQgX5/KUHQ8yISBvgW+BGVS28+hfMx1o9XfASndfL8Vy1PAHPsW7jlTWojrUjWM7rsirtvP4zx7qi4sb5p+1H4DFVXeL1OXHqkQW8S9mOd4XErE5XTVXdime83Skc+zlW4XE7rgK+VdUcr8/5M8faHLvKWDdXxnq5ouK2urmCYvZidbPVzX5Z3Vx+BYO6A8L5g/o+8AyeK3sXAglAC1XNFZHTgfGqOlREpjuvf3V+uPvwTAri9ws0adJEo6Kijvv3MMYYUz0sX778AJ4r7ZmqOibQ8VRGVjcbY4xxU0XWzWFHz3JcvQQ8BNRztkvrc184JsJpWCc7+Q/4KlhEPu3bty8xMTHHK3ZjjDHVjIjUBZoAdwQ6lsrI6mZjjDFuq8i6OWDdtkXkAmC/0x2mMNlHVi3De97l3i4iMUCXhIQEH7sYY3z5dUsig5+fy8JYn9ejjDEea1X1eqe7sDlGqnptoGMwpjLJz1f6PD2TaWviAh2KMcGswupmVxrP4nG9iDzubLdzZp4rzZnARSKyHc/SCoPw3In21+e+cEyE8359ik2dDqCqk1U1WlWjmzb1O2O8MaaYmO1JbElIZ8nWxECHYowxxhggLiWTpPRs7vhoRaBDMcbg3p3n1/AsYl5wRTkVeLW0HVR1rKq2UdUoPJMbzFHVUXiWn7jCyTYa+N55PcXZxnl/TmnjnY0xxhhjjKnMDmcf86TExpjjyK3Gc39VvRvIBFDPchERf7Ksh4H7RWQznjHNbzvpbwONnfT7AZuoxRhjjDHGVGGlLslsjKlgbk0YliMioThjkEWkKZBf+i5HqOpcPNOlF0ydXqLLt6pmAle6EKsxxhhjjDGVgHWyNCaYuHXn+RU8a/g1E5FngIXAsy6VbYwxxhhjjDHGBJQrd55V9WMRWQ4MxtO/5BJVXe9G2cYYY4wxxlRHNruPMcGl3I1nEQkBVqvqycCG8odkjDHGGGOMybPWszFBpdzdtlU1H1glIu1ciMcYY4wxLhORRiIyU0RineeGfvKNdvLEishoH+9PEZE1XtvjRWSPiKx0HiOO5/cwprrJzbPGszHBxK0Jw1oCa0VkGZBekKiqF7lUvjHGGGP+vDHAbFWdJCJjnO2HvTOISCPgCSAazyxFy0VkirOCBiJyGZDmo+wXVfXfxzV6Y6qp3HxrPBsTTNxqPD/pUjnGGGOMcd/FwADn9ft4Vrh4uFieocBMVU0CEJGZwDDgUxGpi2eZyNuBLyogXmMMsDA2IdAhGGO8uDVh2Dw3yjHGGGPMcdFcVeMAVDVORJr5yNMa2OW1vdtJA3gaeB7I8LHfPSJyIxADPFBwp9oYU36/7zwU6BCMMV5cWapKRE4Tkd9EJE1EskUkT0RS3CjbGGOMMUcnIrNEZI2Px8VlLcJHmopIb6Czqn7r4/3XgU5AbyAOTwPbV2y3i0iMiMQkJNidNGPK6rr+NqWQMcHErW7bs/OrJwAAIABJREFU/wWuAb7EM1bqRqCLS2UbY4wx5ihU9Tx/74lIvIi0dO46twT2+8i2myNduwHa4OnefTrQV0S24/m/oZmIzFXVAaoa7/UZbwI/+IltMjAZIDo62gZxGlNG/5u/NdAhGGO8uHLnGUBVNwOhqpqnqu9StAI2xhhjTOBMAQpmzx4NfO8jz3TgfBFp6MzGfT4wXVVfV9VWqhoFnAVsUtUBAE5DvMClwBqMMa5Zt9c6choTTNxqPGeISASwUkT+KSL3AXVcKtsYY4wx5TMJGCIiscAQZxsRiRaRtwCcicKeBn5zHk8VTB5Win+KyB8ishoYCNx3vL6AMdXRxb1bBToEY4wXt7pt3wCEAvfgqTjbApe7VLYxxhhjykFVE4HBPtJjgFu9tt8B3imlnO3AyV7bN7gaqDGmiD7tGvLx0p2BDsMY43Brtu0dzsvD2LJVxlRKNgjRGGOMCS55ts6zMUHFlcaziGzDx//eqtrRjfKNMcYYY4ypbvLUGs/GBBO3um1He72uCVwJNHKpbGNMBbD62RhjjAkuuXbn2Zig4sqEYaqa6PXYo6ovAYPcKNsYY4wxxpjqKC0zN9AhGGO8uNVtu4/XZgieO9H13CjbGFMx1EY9G2OMMUHluWkbAh2CMcaLW922n/d6nQtsB65yqWxjjDHGGGOMMSag3Jpte6Ab5RhjAsfGPBtjjDHGGOOfW9227y/tfVV9wY3PMcYYY4wxprq449xOvDFvS6DDMMY43Jxt+1RgirN9ITAf2OVS+caY40wLn+0WtDHGGBMMaoWHBjoEY4wXtxrPTYA+qpoKICLjgS9V9VaXyjfGGGOMMaZasQvaxgQXV5aqAtoB2V7b2UBUaTuISFsR+UVE1ovIWhH5u5PeSERmikis89zQSRcReUVENovI6mIzfBtjyssZ9Gxjn42pevzVrT7yjXbyxIrIaK/0uSKyUURWOo9mTnoNEfncqZuXikhUxXwjY4wxpuK51Xj+EFgmIuNF5AlgKfD+UfbJBR5Q1W7AacDdItIdGAPMVtUuwGxnG2A40MV53A687lLsxhhjTFXnr24tJCKNgCeA/kA/4IlijexRqtrbeex30m4BDqpqZ+BF4Lnj+SWMqW7sgrYxwcWVxrOqPgPcDBwEDgE3q+rEo+wTp6ornNepwHqgNXAxRxre7wOXOK8vBj5QjyVAAxFp6Ub8xhjvMc/GmCrIX93qbSgwU1WTVPUgMBMYdgzlfgUMFhFxIV5jDFYnGxNsXGk8i0gnYK2qvgysAs4WkQbHsH8UcAqeO9bNVTUOPA1soJmTrTVFJyDb7aQZY4wxpnT+6lZvR6tn33W6bI/zaiAX7qOquUAy0Lh4wSJyu4jEiEhMQkJC+b+NMdWF3Xo2Jqi41W37ayBPRDoDbwEdgE/KsqOI1HX2/z9VTSktq4+0En9RrII25s8pqJ+tnjamchKRWSKyxsfj4rIW4SOt4C/CKFXtAZztPG4owz5HElQnq2q0qkY3bdq0jOEYY4wxwcWtxnO+c8X5MuBlVb0POGqXahEJx9Nw/lhVv3GS4wu6YzvPBeOqdgNtvXZvA+wtXqZV0MYYY6ojVT1PVU/28fge/3WrN7/1rKrucZ5T8Vwc71d8HxEJA+oDSe5/O2OqJ7uebUxwcavxnCMi1wI3Aj84aeGl7eB0+XobWK+qL3i9NQUomOFzNPC9V/qNzqzbpwHJBV3QjDHlV7Achi2LYUyV5K9u9TYdOF9EGjoThZ0PTBeRMBFpAoUXvS8A1vgo9wpgjqr1XzHGLfbbZExwcWud55uBO4BnVHWbiHQAPjrKPmfi6fb1h4isdNIeASYBX4jILcBO4ErnvZ+AEcBmIMP5TGOMMcYcnc+6VUSigTtU9VZVTRKRp4HfnH2ectLq4GlEhwOhwCzgTSfP28CHIrIZzx3nayruKxlT9Xlf0N5z6DCtG9QKYDTGGFcaz6q6Dvib1/Y2PBV1afssxPdYKYDBPvIrcHc5wjTGlEJtum1jqixVTcR33RoD3Oq1/Q7wTrE86UBfP+VmcuQitzHmODpz0hy2TxoZ6DCMqdbc6rZtjDHGGFPhVJVpa+LIy7crf6bqsW7bxgQXazwbYwBb59kYUzlNWbWXOz5awTsLtwU6FGNcV7xOzsrNC0gcxhgPt9Z5LtFly1eaMcYYY4yb1u5NcZ6TAxyJMe4rfuf5xMemBSYQYwzg3p3nsWVMM8YEqSPrPNu9Z2NM5TF5/lYAvltZYvVKY6qkfBuiYEzAlGvCMBEZjmcG7NYi8orXW5FAbnnKNsYYY4wxpjrztXxkamYu9WuXuiKsMeY4Ke+d571ADJAJLPd6TAGGlrNsY0wFKlzn2S5oG2MqCespY6o8H6d4r6dmkJOXX/GxGGPKd+dZVVeJyBrgfFV936WYjDHGGGOOqus4G/9pqjYFaoaHkJlTtLHc5dGf2TZxBCL+Vn01xhwP5R7zrKp5QGMRiXAhHmNMoGiRJ2OMCXpZuUUbFMmHcwIUiTHHj+C7gdxh7E8VHIkxxq0Jw3YAi0RknIjcX/BwqWxjjDHGmKPq9eSMQIdgjKsKhibcOaCTz/fXx6WweX9qRYZkTLXmVuN5L/CDU149r4cxppIoXOfZbj0bU+WISCMRmSkisc5zQz/5Rjt5YkVktFf6XBHZKCIrnUczJ/0mEUnwSr+1or7Tkq2JPtOfm7aBh75aRWaOrYdrKj9VEIEHzz/R5/vDX17AeS/Mtxm4jakg5RrzXEBVnwQQkXqeTU1zo1xjjDHGuGIMMFtVJ4nIGGf7Ye8MItIIeAKIxnM9bbmITFHVg06WUaoa46Psz1X1nuMYu0/XTF7iM/31uVsAaBFZk/v9NDiMqSwUECAkpPSxzRN/Xs+Y4d0IPUo+Y0z5uHLnWUROFpHfgTXAWhFZLiInuVG2MaZiFHQN87UshjGm0rsYKJjY833gEh95hgIzVTXJaTDPBIZVUHyue2XOZg6kZbFub0qgQzGmXAomBZv/4EDu8tN9+80F23h51qaKDMuYasmtbtuTgftVtb2qtgceAN50qWxjjDHGlE9zVY0DcJ6b+cjTGtjltb3bSSvwrtM1e5wUneL3chFZLSJfiUhb1yMvh+gJsxjxygLeW7Qt0KFUmPFT1vK/eVsCHYZxifdQqnaNa/PQsK5+874yZzMpmTlsSUjjmxW72ZWUUWRJqxveXsrfPv39eIYbNKav3ceBtKxAh2GqILcaz3VU9ZeCDVWdC9RxqWxjTAUoqKBtzLMxlZOIzBKRNT4eF5e1CB9pBX8RRqlqD+Bs53GDkz4ViFLVnsAsjtzdLh7b7SISIyIxCQkJZf9SLhk/dR2x8amkZgbvbNyf/7aTxVsOlLuc9xZvZ+LPG0jJzCE9K5fkjBwOZ/se/62qPsfKZmTnsmRrIv+bt8VvnqpmS0IaL83adMxrh3sfmxU7D/q9UKOqvL94+zHPCK+on7m2fes5fgaDn5/H/V+s4ux//sL/fb6SFOe8XxB7gCmr9h7T51eEpPRsrn9rKQmpRRu7f/a8S8/K5a8fLmf0O8uKpG87kO7z+3+/cg9L/cyhUB4b96WSnpXr870HvljFwtgDbDuQzqGMbOKSD/Pb9iTXYyjN7zsPlvl837w/rfA8KrA1IY3kjJLnc8HPrar+3XCr8bzVuRId5TweA6rPZV5jjDEmwFT1PFU92cfjeyBeRFoCOM/7fRSxG/C+c9wGz4SgqOoe5zkV+ATo52wnqmrBf7xvAn39xDZZVaNVNbpp06bl/7LFLH1k8FHzDHlxPj3Gz+Cuj5fz4+o4Vu8+RH6+Ehvvmak4OSOHrNy8Ig3NNXuSiRrzIzsS08nMyeOTpTsLG5ObnP2+jNlVolG+cV8q475bU+Kfx5W7DpFY7G5YbHwq8zcl8PDXf3Ddm0sBOJSRzW0fxHAoIxuAvYcOk5KZw3e/72FHYnqZjknP8TOInjCLXk/N4PyX5gGQmZPHtgOe7xI15kc6jP2Jjo/8VKLREj1hFtdMXsLEnzfwwBer6PjIT0XuYC7efIChL84nK7f8k7KN+Xo1g/49F6DEP+JpWbnsSsookqaqrNx1qEg83pZuTSQtK5f+z85i/JS1Rd7bkpDG9gO+j98Nby3lpVmxJKZnk5yRU6RRkZiWRdSYH5mzIZ5nf1rPg1+uAmDKqr10fOSnwjIve20x46eu81n+ip2HeGLKWh7+ajUAy3ckFX7f/Hzllw37i3xmQQxaMOjZy3d3n+nzM3z5cXUcPcfP4OOlOwrT3lu0jVnr4tmfksnB9Gyyc/NZti2Jz5btLMyzKT6VXUkZrI9LYcC/fuGtBVuJ8dG425WUQUb2kQaiqvLLxv1Fzv2Y7UksjPV/YejjJTtYuPkApz4zq3AiwJvfXUbHR35iS8KRaZTW7U0pMlHgur0pxCUfLlLWZ8t28u8ZGwHYmpDOpvhUcvPy+XDJDgb+e27hnXdV5d1F29iSkMbfP1vJ1ZOXcMeHy0nNzCm1If3D6r3sT8nkrQVb2ebnXALIzctn6EvzOemJ6fR4YjpPfL+G056dTXxKJvd9vpKvV+zm+reXMvDfcxny4nwGPz+PK9/4tUgZuw9mMGPtvsLtqDE/ct2bS3hx5qYSf1uem7ahxM8nPiWTNXuSC7cH/XsuY75eTXpWLtPWxHHpa4u555PSeyLsT83kcHYe570wj57jZ3D7BzGs3n3IU97z8xjxygLAOYc37ic2PpWOj/xU+HfjBednkZaVS25ePu8t2sY3K3YX/i2tjMSNwJ1ZO58EzsLzKz4fGO81yUhAREdHa0yMr7lNjDHFPf3DOt5euI2bzohi/EU2ZYExvojIclWNDnQcx0pE/gUkek0Y1khVHyqWpxGwHOjjJK3A0xhOARqo6gERCQc+BWap6hsi0rKgO7iIXAo8rKqnlRaLW3Vz1JgfC19vnzSyyLYbzjmhKfM3lbxL3r9DIw5l5LAxPpWJl/Vg7Dd/MKhrM9656dQSsS18eCCRtcJ5fvpGHh7ele6PTy/MExoiRDWuzZaEkv+AX9uvHZ96NWSK+/jW/vRsU598haycPJpF1mRHYjrn/muu3322TxrJvZ/+ztRVe/n572cz/OUFPvPNe3CAz3LuGdiZK6Pb0LReDU55aiZZuflM/79zUJQWkTVpUDuiSP74lEz6PzubNU8OpW6NMHYmZvDzmjh6t23Ai7M2cctZHakRFsKNzt3B87o1Z9b6eK6KbsOzl/Zg2tp9hf/Y1wgL4bazO/KPoSfy4ZIdjPtuDQAvXNWLHYkZvDw7lnsHdWZ+7AFW7TpEt5aRrI/zjHV/cOiJ3HZ2RyLCQgp/Lp/c2p+DGTkM6d6cl2Zt4rW5R7q516sZRmpmLpf3acNNZ0TRrnFtej81o9ReWRMuOZm6NcL4v89XAnDLWR3YmpDGf67rQ/LhHJ6fsZGRPVpyy/ue837yDX25/cPlALxxfR8OpGXz2HdrGH9hd16ft4X4FM/FjMcv6M7OpAy+WbGb1eOHFn7e6t2HuOi/i/wHdIxqhIUUrpn+4tW9uO/zVX7zjrugO73bNqBv+4YkpmXRd8IsABY8NJB/Td9I7YhQPvttF49f0J06NUKpWyOcuz9ZAcC0/zubYS8t4ITmdZlx37kAvLtoG0/6ueBQoHZEKNf1a8dbCz335bZPGsl/58Ty7xmeMd6/PXoeM9btY93eFD5e6v/35lhcc2pbnr20B30mzOSjW/rTrWUkKYdzOOXpmbSqX5O9yZkALBk7mAv+s4DWDWrxxg192ZmYwbM/rUdEWLnr0DF/7sKHB9Kqfi1CQoRu46ZxOCeP7+8+ExF8/szvHtiJhZsTWeV8VtcW9fj0ttP4YfVexn3vuXg0+4FzaVwngt5PzfT7ue/edCr//WUzV0e35aGvV7Py8SF8vHQn/5q+0Wf+q6Pb8nmMZ5TPtP87m5dmxjLNq6FfFref05FHRnQ7pn38qci62ZXGc7CyxrMxZWeNZ2OOrhI3nhsDXwDtgJ3AlaqaJCLRwB2qequT7y/AI85uz6jquyJSB89F8XAgFE/37PtVNU9EJgIXAblAEnCnqm4oLRa3G8/X9mvHxMt6uN54PlahIUJevtK4TgSJ6dkBjSVQ3rkpmvx86N4qkjMmzQl0OFXK9kkjC1+v2ZPMBf9ZGMBoAuuxkd2Y8OP6QIdhXPDpbadxeqfG5S6nIutmV5aqEpETgH8AUd5lquogN8o3xhx/R8Y8V90LasZUV6qaCJTo2+wsPXWr1/Y7wDvF8qTjvzv2WGCsq8Eeo4eHeZaj+uiW/jz63R/sSMw4yh7HR57TjbK6NpwB/vKe3bCoCI3reu7y33RGFGNHdOXEx6YFOKKKZQ3nquPaN5cUuTBUGbjSeAa+BN4A3gLKPwAmyDz+/ZrC8UARYSE8OPRE2jSsHeCojDHGmOqta4t6hd2Fz+rShHkPDuThr1YXdic0pipqWb8Wsx84l3aNahMeGsKXd5xeYrysMeb4cKvxnKuqr7tUVtDZmZTB3kOHyc1Tth5I58xOTbjqVGs8m6qlYH1nu+9sjKksNuxLLZH23BU96d+xEfd/4X/cpjGVXaemdQtfnxrVKICRGFO9lKvx7EwuAjBVRO4CvgUKp2xU1Yqdc/04ee/mfgDsS87ktImzybNurcYYY0xAPXnRSbRr7PtC9sieLYnZcZC7BnTirOd+8ZnHmKrkp7+dTXZePk98v4ZVu5OPvoMxQaBT08q3snF57zwvx3OjqmAS/Qe93lOgYznLDyqhIZ6vmVtF1y0z1Zut82yMqUxGnxHl970aYaE8e2mPigvGmADr3ioSgM//ejpjvl7N7oOH6dCkDl8u3x3gyIzx74Nb+gc6hGNWrsazqnZwK5CyEpFhwMt4Zvx8S1UnVdRnhzmN5zw/awsaY4wxJric0Lwum+LT6NW2QeFyLsaUlb8lyyrSrPvPLXPemuGhvHTNKYBnAtAFsQfYl5J5vEIzZRQM51GwiW7fkNYNagU6jGNW3m7bpwK7VHWfs30jcDmwA886z6522xaRUOBVYAiwG/hNRKaoaukLxLkkNNTuPBtjjDGVybd3nUlKZg4t69di/qYEnpy6ln4dGtOlWV2e+sHz74OvdZUXPDSQs/9Zssv34xd0Z9K0DWTn5hMeKlzepw2f/RbYCcqa1qtROLHp4K7NmL1hf7nLvO+8E3hx1qbC7Q1PDyMhNcvnMSmPCZecTE5ePmd1bkJ8ShZndm7MkBfns3l/Wqn7dW1Rr8iY94iwELJzj9zcWPDQQPYeOszVk5cU2e/yPm14/qpe5OTlk3w4hyZ1a/hc5qxj0zp0bFKHt0afSkZ2LjPWxhMd1ZD6tcLpMX5GYb5tE0fQYexPJfZ/fVQfurWMZMC/55b1UNC9ZSTrnPWpC3zwl350blbXzx6lExGWPDKYnLx8vlmxm5Na1S/zEld3DejEgtgD/LHH3S7gG54eRs3wUL77fQ87EjOKnGPFndaxEf+7PppeT83wm8eXT27rz5SVewt/L286I4r3Fm8vfH/zM8PJys2nTo0w8vOVL5fvYvfBw/xnzmYu7t2KYSe14M6PV5T6Gf+8vCcPfb26SFpEaAjZzg22qMa1Of+kFlx6Smu6tfT0Cti8P40L/rOA6/u355ER3fhte1KJ8xMosk45wPNX9uKBL/3P4fDXczryv/lbSz8oXq6KbsO2A+n8tv1gmfd5/ILuHMrI5pU5m0u8d+Pp7enbviEiwkW9WpGYlkVkrXBu+yCGuRsTOKNTYx4b2Z2GdcJZtzeFrQnpPPPTel6/3uciDkGvXOs8i8gK4DxnrchzgM+Ae4HeQDdVvcKdMAs/73Q8jfKhzvZYAFWd6Cu/2+s8Z2Tn0v3x6Ywd3pW/ntvJtXKNCQbjp6zlvcXbuf60dky4xLo7GuNLZV3nOZi4XTeXR3ZuPlsPpHFi83qs3ZtChyZ1+GbFbq4/rT0ingvmCalZhAj0nTCLVvVrsnjsYJLSs+nz9Exeva4PI3u2ZNHmAzz01Wou7NWKQxnZhf+0f/HX05mzYT9vzNtS4rMb1g7ns9tPJyxUWLo1if/MiSUu2XOH8LVRfdgUn8pLs2IBGNW/Hc9c2oPzX5zHnQM6ESJC3/YN+WF1HIs2H+DDYl0fkzNyqBURSkRYSGHDcN6DA3joq9Us3ZbEHed24oxOjenVpgG9nprBB3/pxzknNOWP3ck0qB1O20ZHxpLHp2QiQLPImgDsOXSYhNQserdtwO6DGSyMPUDHpnW56n9HZns+u0sTFsQeAODq6LZ8HrOLyTf05fYPl3Pb2R0YO7wbHR/xNDh9LVOzIDaBMV//wc1nRjHhx/UsHjOIA2lZhIYIXVtEFg6j8270bn5mOGGhIZz27Gz2pWTy69hBtKxfqzDP9kkjWR+XQqemdYkICynyed55Nu9PY8a6fdw1oLOfswZen7uFXzbs58FhJ3JqVCOixvzI6R0bM7xHCxbEHmDyDX0Lzx+Acd+tYfWeZN4eHU2ICJviU2lcJ4K2jWpTMzy0MJ+q8sLMTVzQsxU5efmc0LxeiVjdcPnri9mXnMmiMYOYuS6emB1JPDS0K8mHc9h76DA7kzIY0aMlmTl5dB3nWQbrg7/046RWkTSsHUGIj+Nf3A2ntSchNYsTW9Tj5dmx/D5uCA3rRBTJk5Gdy32fryQtK5dFmxNZ8NBAmtStgaJs2JfKKW0bICJM/Hk9cYcyee7ynhxIy2LCj+v4f/buO06q6vzj+OfZTu9N2oIUBQRFROyiKPZuojFGTQw/oyYajUZjb5EUozGJGgtGo1EToxErIgqoiAoqSu8dYaUsLMv25/fH3F1ml22wd2dm2e/79ZrXzr333DPP3p2dM+ee9qPDMlm8IYdTB3ehXbM0Coudv09Zwo+P7EWz9Ejb4OrNuWzaXkBRiXPOI9OY+Muj6dupRaXxujvPTV/B2UO70Tw9hZz8IpLNyEhN4uvV2XRv25QVG7dz9iPTuO/sQVx0aE8enLiQP09aRNfWTVizZQfLx57KN6uzOf2vH3FMvw488+Phtfp7LN6wja9XZ9OpZQb7d2lJ2wrXCeCVL1ZzRJ/2dAr+D6uSk19EkkHTtBRWbcrl3jfncsfpA7n3zbk8cP6BNEmLvN8Wrt/GiQ9O5Ypj9uWxKUvo3aEZD5w/hB2FxVz74lf866eH0jw9lRH3TwJ2/p+W/s3fu+4Yln23nf99uYa//uCgcu/3eIhl2VzXyvMsdx8SPP8bkOXudwbbX7n7gaFEufP1zgNOcvfLg+2LgUPd/erK0oddQOcXFdP/1ne4YXR/rhpZ9YeqSEOkyrNIzVR5rrtEqjzvjnnrtrJPqya0appaY9royhjAD56Yzra8Ik45oAu/e2d+uWPR3J3CYi+rMH2zOpvbx8/m4QsOKleh3R0VY6kP7s4jk5dwwoBO9KuicgKR71FpyUmYGZ8t20THFulktt/zCYP+OGEBS7/L4eyDunHCgE4APDF1Kfe9Na+shfOpj5YxoEtLDtu3XZX5bNiWR4v01LKKxe7asC2Plhmp5SrCjcH0pRuZs3YrFx3ag607Cjn1Lx+RtS2fD28cucfv10S3alMu3do0KVdZ3Ly9gA3BjQJ356mPlnH2QV1p1zw9jpHWzvb8Ii4Z9xljzz2APh13/d8tKCohJcnKbpic/cjHDM9sy82n7B/rUKvVkCrPs4ED3b3IzOYDY9x9aukxdx8UUpylr3c+MLpC5Xm4u/88Ks0YYAxAjx49Dl6xYkVor19c4uz7m7dIT0lqdB+QsvfbUVBMQXEJaclJe/wFQiRRXTi8BzedvF+d81Hlue4aauV5d9w5fg7pqUncfPKuXzC/Xr2FwuISDu4Zm+WFYlF5FoFIq+eW3AK6tdk7K86SuGJZNtd1tu0XgClm9h2wA/gQwMz6APUxT/5qoHvUdjdgbXQCd38ceBwiBXSYL56cZNx95kCWZm0PM1uRhLFowzb6VnLnUaShG9ytVbxDkEbkzjMGVnlscLfWMYwEnrpkGJ1bVd/VUyQMzdNTaJ5e16qFSGKr62zb95nZJKAL8K7vbMZOIjL2OWyfA33NrBewBrgA+EE9vE6VfnRYZixfTkRERGSPHb9/p3iHICKy16jz7SF332WaOHeveuq8ur1WkZldDUwgslTVOHefUx+vJSIiIiIiIlKqTmOeE52ZZRFZNiue2gPfxTmGPaG4Y6chxgwNM+6GGDMo7liqKeae7t4hVsHsjVQ210lDjLshxgyKO5YaYsyguGMpYcrmvbrynAjMbEZDnFxGccdOQ4wZGmbcDTFmUNyx1BBjlt3XUP/ODTHuhhgzKO5Yaogxg+KOpUSKOfzF40RERERERET2Mqo8i4iIiIiIiNRAlef693i8A9hDijt2GmLM0DDjbogxg+KOpYYYs+y+hvp3bohxN8SYQXHHUkOMGRR3LCVMzBrzLCIiIiIiIlIDtTyLiIiIiIiI1ECV5z1kZt3N7AMzm2dmc8zsmkrSmJk9bGaLzexrMxsadewSM1sUPC5JoJgvCmL92symmdmQqGPLzewbM/vKzGbEIubdiPtYM8sOYvvKzG6POnaSmS0I/g43JVjcN0TFPNvMis2sbXAsXtc7w8w+M7NZQdx3VZIm3cxeCq7pp2aWGXXs5mD/AjMbnUAxX2dmc4P39iQz6xl1rDjq7zA+FjHvRtyXmllWVHyXRx2Lx+dIbWJ+MCrehWa2JepYXK511Osnm9mXZvZGJccS6n0tu09ls8rmkOJW2Ry7mFU2xy5mlc1hcHc99uABdAGGBs9bAAuBARXSnAK8DRgwAvg02N8WWBr8bBM8b5MgMR9eGgtwcmnMwfZyoH2CXutjgTcqOTcZWAL0BtKAWRXPjWfcFdKfDryfANfbgObB81TgU2BEhTRXAo8Fzy8AXgqeDwiucTrQK7j2yQkS80igafD8Z6UxB9s5sb7OuxH3pcBfKzk3Xp8jNcZcIf3PgXHxvtZRr38OQCUjAAAgAElEQVQd8K8qPi8S6n2txx79fVU2J9a1PraK/zWVzbsft8rmxLrWl6KyOcz4G0zZrJbnPeTu69z9i+D5NmAe0LVCsjOBZz1iOtDazLoAo4GJ7r7J3TcDE4GTEiFmd58WxAQwHehW33HVpJbXuirDgcXuvtTdC4AXifxd6t0exH0h8EIsYqtO8H7NCTZTg0fFyRHOBJ4Jnr8MHG9mFux/0d3z3X0ZsJjI3yDuMbv7B+6eG2wmynu7Nte6KvH6HNndmBPifQ1gZt2AU4Enq0iSUO9r2X0qm2NHZXNsqWyOHZXNsdXQymZVnkMQdB84iMhdnmhdgVVR26uDfVXtj5lqYo72EyJ350s58K6ZzTSzMfUXXdVqiPuwoLvK22Y2MNgX92sNNV9vM2tK5MP1v1G743a9g+4zXwEbiBQCVb633b0IyAbaEcfrXYuYo1V8b2eY2Qwzm25mZ9VroBXUMu5zgy5tL5tZ92Bfwl/roPtdL+D9qN1xu9bAQ8CNQEkVxxPufV3fzKytmU0MuhdONLM2VaSrthuimY03s9n1H3HtqWyOHZXNsaGyOXZUNsdUgyqbVXmuIzNrTuRD9Vp331rxcCWneDX7Y6KGmEvTjCTyIfbrqN1HuPtQIl3GrjKzo+s92PIxVRf3F0BPdx8C/AX4X+lplWQV0ynma3O9iXQL+9jdN0Xti9v1dvdidz+QyB3g4WY2qEKShHtv1yJmAMzsh8Aw4A9Ru3u4+zDgB8BDZrZvvQccqEXcrwOZ7j4YeI+dd18T/loT6V71srsXR+2Ly7U2s9OADe4+s7pkleyL+2d2PbsJmOTufYFJwXY5FhnreQdwKJG7+ndEV7LN7Bwgp+J58aSyOXZUNqtsro7K5kh29Rdp1IuobC7dX69Uea4DM0sl8sH7vLu/UkmS1UD3qO1uwNpq9te7WsSMmQ0m0nXiTHffWLrf3dcGPzcArxLDbos1xe3uW0u7q7j7W0CqmbUnjtcaane9AxdQoftMPK93VAxbgMns2uWo7LqaWQrQCthEnK83VBszZjYKuAU4w93zo84pvdZLg3MPikWs0aqK2903RsX6BHBw8Dyhr3Wguvd1rK/1EcAZZracSBfR48zsuQppEvZ9XY+iu8M9A1TW4lBlN8SgAnIdcG8MYq0Vlc0qm2uisjmxyguVzeFS2VzPPI6Dw93LJo34kmCAOJFuBJ8Ci4CXgLRgf3qwvTg4nhnnuA14FniomjSnUn5Sks+C/W2BZUQmEmgTPG+bIDH3CK7x4RX2NwNaRD2fBpyUQNe6M5StWz4cWBmcl0JksoZe7JyUZGCixB2kK/0QaJYg17sD0Dp43gT4EDitQpqrKD95w7+D5wMpP3nDUmIzKUltYj6IyGQSfSvsbwOkB8/bB589sZq4pjZxd4l6fjYwPXger8+RGmMOjvUnMrGOJcK1rhDbsVQ+KUlCva9jdC22VNjeXEmaXwG3Rm3fBvwqeP5g8L7MBGYnwO+jslllc53jDtKpbI5NzCqbYxRzcExlcx0fpR9ocWNm1xHpptHS3U8zs38Dr7j7i2b2GDDL3R81syuBwe5+hZldAJzt7t+vLu/27dt7ZmZmvf8OIiLSOMycObMYON3d364xcYIws/eIVGIqugV4xt1bR6Xd7O7lxj2b2Q1EvljdG2zfBuQS6eZ9j7ufHowdfcPdq+qSOQYYA9CsWbOD99tvvzr/XiIiIhDbsjmulWeLzK72DHAfkW5fpwNZQGd3LzKzw4A73X20mU0Inn8SNNl/C3Twan6BYcOG+YwZMVuCT6TB27S9gLbN0uIdhkjCMrOZHhkXtlcwswXAse6+ziIzTk929/4V0lwYpPm/YPvvRLr1tSbSCl1ApCWxIzDN3Y+t7jVVNovsnpISxwzMKhviKSKxLJvjPea54uxq7Yh0ISsKtqNnTatqpjURCcF/Z65m6D0TeeGzlfEORURiZzxQOnv2JcBrlaSZAJxoZm2CicJOBCa4+6Puvo+7ZwJHAgtrqjiLyO4pKCqh92/e4o/vLoh3KCJCSJVni/ihmd0ebPcws2onUKhidrXqZk2r1YxqZjYmmGp9RlZWVi1/AxFZu2UHAGs274hzJCISQ2OBE8xsEXBCsI2ZDTOzJwE8MtPwPcDnweNuLz/7sIjUkx2FkQmRn/1kRZwjERGIdLMKwyNEWo+PA+4GthGZxfCQas4pnV3tFCADaEmkJbq1maUErcvRs6aVzqi2usJMa+W4++PA4xDpGlb3X01ERGTv5JFZm4+vZP8M4PKo7XHAuGryWQ5UtSyKiNSVvtGKJISwum0f6u5XAXkAHlnKotqBk+5+s7t3C7p7XQC87+4XAR8A5wXJoruQRXctOy9Ir48SEREREdkrlQ5z1hdekcQQVuW50MySCf63zawDO8cx765fA9eZ2WIiY5qfCvY/BbQL9l8H3FS3kEVEREREElfpmMXiElWfRRJBWN22HyayUHxHM7uPSMvwrbU92d0nE5m5E48szr3LeGl3zwPODyFWEREREZGEt2JjLrBz7LOIxFcolWd3f97MZhIZN2XAWe4+L4y8RUREREQao3XZefEOQUSi1LnybGZJwNfuPgiYX/eQRERERERE3bVFEkudxzy7ewkwy8x6hBCPiIiIiIgAJZobVyShhDXmuQswx8w+A7aX7nT3M0LKX0RERESkUVHLs0hiCavyfFdI+YiIiIiICGp5Fkk0YU0YNiWMfEREREREJEJ1Z5HEEso6z2Y2wsw+N7McMysws2Iz2xpG3iIiIiIijVGRum2LJJRQKs/AX4ELgUVAE+DyYJ+IiIiIiOyBElWeRRJKWGOecffFZpbs7sXA02Y2Lay8RUREREQam2L12xZJKGFVnnPNLA34ysx+D6wDmoWUt4iIiIhIo6PZtkUSS1jdti8GkoGriSxV1R04N6S8RSQGVDyLiIgkFs22LZJYwppte0XwdAdatkpEREREpM6KilV5FkkkoVSezWwZlTRcuXvvMPIXkfqnm9siIiKJRS3PIoklrDHPw6KeZwDnA21DyltEREREpNHRUlUiiSWUMc/uvjHqscbdHwKOCyNvEREREZHGSBOGiSSWsLptD43aTCLSEt0ijLxFJDZcU4aJiIgkFK3zLJJYwuq2/UDU8yJgOfC9kPIWEREREWl0tM6zSGIJa7btkWHkIyLxo/JZREQksajbtkhiCavb9nXVHXf3P4XxOiIiIiIijYUqzyKJJczZtg8BxgfbpwNTgVUh5S8i9czLfqqgFhERSQQqkUUSS1iV5/bAUHffBmBmdwL/cffLQ8pfRERERKRRsXgHICLlhLJUFdADKIjaLgAyQ8pbRGIhGPSssc8iIiIiIrsKq/L8T+AzM7vTzO4APgWeqe4EM+tuZh+Y2Twzm2Nm1wT725rZRDNbFPxsE+w3M3vYzBab2dcVlscSERERERERqTehVJ7d/T7gMmAzsAW4zN3vr+G0IuB6d98fGAFcZWYDgJuASe7eF5gUbAOcDPQNHmOAR8OIXUQivMJPEdn7VXXDupJ0lwRpFpnZJVH7J5vZAjP7Knh0jF30IiIisRVK5dnM9gXmuPufgVnAUWbWurpz3H2du38RPN8GzAO6Ameys9X6GeCs4PmZwLMeMR1obWZdwohfRESkkarqhnUZM2sL3AEcCgwH7qhQyb7I3Q8MHhtiEbSIiEg8hNVt+79AsZn1AZ4EegH/qu3JZpYJHESku3cnd18HkQo2UHoXuyvlZ+9eHeyrmNcYM5thZjOysrJ2/zcRaaRKxzprzLNIo1LVDetoo4GJ7r7J3TcDE4GTYhSfSKNmmjFMJKGEVXkucfci4Bzgz+7+S6BWrcJm1pxI5ftad99aXdJK9u3yNd/dH3f3Ye4+rEOHDrUJQUREpLGq6oZ1tJpuXj8ddNm+zUxf9UVEZO8V1lJVhWZ2IfAjIms8A6TWdJKZpRKpOD/v7q8Eu9ebWRd3Xxd0yy7tArYa6B51ejdgbSjRi0jZ+s5a51lk72Jm7wGdKzl0S22zqGRf6QfFRe6+xsxaECnPLwaerSSGMUTmK6FHjx61fFkRiVZQVEJaSljtXiKyJ8L6D7wMOAy4z92XmVkv4LnqTgjuTj8FzHP3P0UdGg+UTkZyCfBa1P4fBbNujwCyS++Wi4iISOXcfZS7D6rk8RrBDWuACjeso1V589rd1wQ/txEZrjW8ihjUK0ykjlZs3B7vEEQavbBm257r7r9w9xeC7WXuPraG044gcof6uKhZOk8BxgInmNki4IRgG+AtYCmwGHgCuDKM2EUkwjXdtkhjVNUN62gTgBPNrE0wUdiJwAQzSzGz9lDWk+w0YHYMYhZpNCyq48dz01fEMRIRgfC6be82d/+IyruCARxfSXoHrqrXoERERBqXscC/zewnwErgfAAzGwZc4e6Xu/smM7sH+Dw45+5gXzMilehUIBl4j8jNbREJSfQsAs98soK7zhwUv2BEJH6VZxFJLGp4Fml83H0jld+wngFcHrU9DhhXIc124OD6jlGkMdMKGCKJJax1ns+vzT4REREREamdipN4bsktiFMkIgLhTRh2cy33iUiC2rnOs25zi0jD8uXKzZSU6LNL9j4Vi+QD754Yn0BEBKhjt20zOxk4BehqZg9HHWoJFNUlbxEREZGaPPnhUu59cx492jZl6o0j4x2OSKgquyVUUuIkJWlJdZF4qGvL81pgBpAHzIx6jAdG1zFvEYmhsnWe1XgjIg3IvW/OA2Dlptw4RyISvsrK5N6/eYt/z1gV+2BEpG4tz+4+y8xmAye6+zMhxSQiIiIi0uhVHPNc6saXv+b8g7thphZokViq85hndy8G2plZWgjxiEi8eLkfIiIJb0lWTrxDEKlfDukplX9d/2TpxhgHIyJhTRi2AvjYzG4zs+tKHyHlLSIiIrKL4x+YEu8QROqVE1nreclvT9nl2A+e+JT356+PfVAijVhYlee1wBtBfi2iHiLSQJSt86ymZxFpoB6cuFArBshexd0xjOQqJgj78T9mkJ1bGOOoRBqvOo15LuXudwGYWYvIpqsflYiIiNSbvMLiXfb9edIisnLy+e3ZB8QhIpHwuUdanqsz5O53WT721NgEJNLIhdLybGaDzOxLYDYwx8xmmtnAMPIWkdgoba2panISEZFEcuv/Zle6/1+friTzpjcp1rrPshdwoLTu3L55epXpMm96kyVZOeQVFlNQVBKT2EQao1BanoHHgevc/QMAMzsWeAI4PKT8RURERMq8PHN1tce35Bbw2bJNnHxAlxhFJBK+SMtzpPo849ZRfLFyM+c8Mq3StNFzADxw/hCO7teBDi3SyS0ooklqsmbmFglBWGOem5VWnAHcfTLQLKS8RSQGSocJarigiOwNDr73PX72/Bd8uCgr3qFIlMUbcsi86U2mLfku3qE0CI4TXeUd2qMN153Qr8bzrv/PLA657z0+W7aJAbdP4LEpS9m8vYCZKzZrXgCROgir8rw0mGk7M3jcCiwLKW8RERGRKr16ZdUd3S5+6jP+PmVJXCoMny3bxN+nLIn56yay0krz2998G+dI6ldJiXPX63NYtSm33P4fPvkpw+59b5f0S7JyKKlkqIFH99sO/OL4vrWO43t//wSA370zn4Pumci5j07juekrAPho0XdVDm9wd/4zYxX5RbvOLRBvs9dkszEnv2x7xcbtfLy45psx78xex79nrCrbLiouYdl323dJty2vkL++v6jctfk2O49xH5Wv2mzNK+Tb7Dy25xfxl0mLKCpWd/nqLN6Qs1fcuAmr2/aPgbuAV4j8i08FLgspbxGJgYb/cSYijdVBPdpUe/z+t+dz/9vzOf/gbvzh/CFVptu8vYDfvTOfO88YSEZqcp3jKq24/N8x+9b6nFte/YbnP13J8rGn8m12HptzC9i/S0sAVm3KpV3zNJqmhfX1bafFG3LIbNeUlORd21W25BbQumlauX3ZOwppnp5S5SzQVSksjpQ2RVVU2v76/iJG9G7HsMy2tcqvuMTJyS+iVZPUWsfw7CfLmbduG/efE5lY7tUvVzNq/058sXILRcUlZG3L5/xh3Xf7dxv30TI2bMvnppP3Y/babJ7+eDlTFmbx06N6c0hmG/p0bMFHlVTyFm/Yxqg/TY3E9uPhHN2vAwCFxSX8Y9ry3YqhNm57bQ63vTanbPvqkX0Y0bsdP3zq07J9147qy0PvLeKGl78GYGiP1nyxckutJiYb99EyThjQie5tm9aYNntHIS3SUzjl4Q/5/iHdueyIXmXHFm/I4dqXvuT5y0fQqkkq+UXFFBU7p/3lIzq3zGD6b45n7tqtnPLwhwAsvPdk+t36NgAD92nJtaP6sSQrhw7N03nly9V8vDiyLvYRfdrzwLsL2J5fxIQ56/nwxpFlsW7Ylsfw+yYB8I9pK/jZsftyQNdWZf/LKzflcsnhmRQWl/CDJ6bzXU4BPxzRg+emr6R726acdVDXGn/n+jR1YRZ/n7qEh75/ECs35fL6rLVcf2I/Fm3IoV2zNJZvzOWY4P21fmseLTNSefXLNSz4divPfLKCx344lGP6daRJ2p5//q3dsoP2zdNJi1qffNaqLZz5t4+55ZT9+enRvev8e8aT7Q13AKoybNgwnzFjRrzDEGkQ7nljLk99tIxLD8/kzjM0359IZcxsprsPi3ccDVlYZXPmTW+WPV8+9lQufupTPlxUc+vT+9cfwxtfr+NPExdy7ai+XHN837KxoKUVV4AbRvfnqpF9djl/xvJNvPDZKn5/3mCSk4zfvPoNvdo1o2PLdDq2yGBE77Zl+ZXGeP7B3Tj34G48FbRcPfGj8m+hTdsLaNM0lfyiEva77R0AzhiyD+NnrQXgy9tO4P35G7j+P7No3zydy47I5LucfO44fdfP6pIS528fLOaiET1p22xnhTe3oIg7XpvDoK6tOGPIPmzKLaBZWgofLsoir7CY216bwzlDu3L/OQfwXU4BzdNSWLU5lw8Xfcfv3pnPPWcN4rb/zeaDXx1LhxbpDLpjAucM7corX6zh/nMO4JDMNvRo24wPFmzg//45syzu/KIStuwooF/HFiQlGX+ZtIgHJi4E4OIRPbnnrEFApBK8JCuHEx+MVCKX3X8KZkZ2biGLs3Lo2a4pLTJSKCgqYdxHy/n3jFWMv/oIbn9tDm9+s455d59U9oX/kyUb6dOxOTsKiklPTeKLFZvLjX0v/btMvWEkm3MLOPNvH9O+eTrfRbVm3nbaAH5yZK+yv8+OwmImzvmWtdl5HNS9NWu27ODLlVt4a/Y6rjm+L1ccs2/Z327+PScx8o+TWZedV8W7EF4aM4IN2/L5YP4GBuzTknvfnFdlWmCXSutHi77jhc9X8ubX66o9r75cfmQvjunfgU+WbOSXJ/QjNTmJouISVm7K5bgHptCtTROe+fFwvtuWz9h35vPSmMO4/+15HLdfR976Zh092zXjofcWkle4a2vtcz85lO5tm3DMHyYD8PvzBrN1RyEvfb6KRRvqZ0Gfnx7Viyc+rHun2devPpJNuQUc2ac9yUnGBws2cNnTn3P6kH04Z2hXhvZoQ7O0ZFKSk3B3vlmTzaL1OVz/n1k8etFQlm/M5Ypjepcbn569o5CmacmkVnJj68NFWVz81GeMObo3vzll/3Kfi1WZcO3RjH5oarVp0lKS+MN5g7nmxa/4zSn7cUDX1kxfupFzhnalXfN0Hp+6FAOe+mgZvTs04+vV2TRJTWZH1CoIme2asnxjbqX5337aAO5+Y27Z/3ldxbJsDqXybGb9gF8BmUS1Zrv7cXXOvA5UeRapvbtfn8u4j5dxyWE9uevMQfEORyQhqfJcd2FXnmfcOor2zdNZsXF72ZftsJw2uAs/ObIXG3MKaNMsjXMf3TlR0+BurdieX8SSrF27ff727AMYPbATB1fSPRfgwuE9+HTpRpZW0mV0TxzYvTXpKUks2pDDpu0FZftbZKSwLa8IgEsPz6yXVsy6euj7B9I0LZkxQYV7T73w0xF8uCiL/bq05BcvfBlSdImjqhbfReu3ccKD1VeEJPEct19H3p+/odJjVx67L2cd1JUWGSkkmzH8t5No3zyNohKnsKiE7QWVd6Vv1yyNjVH//w1FGMusNcTK8yzgMWAmUPYXdfe6fRLWUVgF9PX/nsWGbZG7h2nJSdx22gAy22s+NNm7qPIsUjNVnusu7Mpz9Bevheu3lbVciuxNaqpg1KbFUSQRNbTKc1iDZorc/dGQ8ko4eYXFbM8voqC4hNlrtjJ6YGdVnmWvU7q+8947kENE9iadWqazT+sm5fb169QiTtGIxNeHN46kZUYqQ+5+N96hiOzV6jTbtpm1NbO2wOtmdqWZdSndF+zfK/ztoqG8cuURPPmjQ4CqJ7kQERGR2GiRkUqXVhm77P/ithMY0q1VHCISiZ/ubZvSqmkqy8eeypy7Rsc7HJG9Vl1bnmcSaagqHel9Q9QxBxr2dGoVpCRHfs3iEk1FL3sfrfMsIg3J4g05LK5k8qC2zdJ47eojWZe9g8Pufz8OkYnEV7P0nV/vq5u0SSTeejXAnrx1qjy7e6+aU4XLzE4C/gwkA0+6+9hYvXZKsGSBWp5FRETi64pj9mX/LlV30+7SqgkvjhnBBY9Pj2FUIonh9auPJDnJGLBPS1ZtyiU9JYn2zdMpKnGKS5xxHy/jJ0f24oF3F9AyI5XNuYW89PlKfjW6P3e9PrcsHzPdVJf688Gvjo13CLutrt22DzGzzlHbPzKz18zs4frotm1mycDfgJOBAcCFZjYg7NepSul6f1UtKC+yN3CNehaRBuCmk/fjzAOrX1N1RO923HbaAMZffQQ/O3Zfzo7zGqxhKF2jVRqG20+LfE0d1LUlGamVf+1uFywpFr20WLQ/VrM2eVUO6NaKAftE1gfv3rYpHVtmkJRkpKUk0SQtmatG9iEjNZlbTh3Az4/vy+2nD2DO3Sdx/rDuAFxzfF+Wjz2VZfefyvKxpzL1hpH8+IhejNq/027HUp3fnn0Az/54eK3SDq5mOMYBXVtx7ai+lR778MaRnDFkHy45rCcA5wztyjvXHgXARYf2oGvrJuzboRlH9+vAvLtP4rWrjmDe3Sfx7i+PZv49J5X9fQBm3jqKT27euZjQSQM787cfDOWXo/rV6ndIJKdGLd9WsQW46W6s8/y9Yd122XfGkH122bf4vpPLnrdIT+H8g3c9ryGoa7ftvwOjAMzsaGAs8HPgQOBx4Lw65l/RcGCxuy8NXvNF4ExgbrVnhSQlKfKhp5ZnERGRhqF0rd7B3VoD8OqXawB4+tJDuOwfnwMw7+6TGHjHO+xJ8T77rtEMumNCnePs36kFC9ZvK9vu0iqjbJ3g3587mJRk45DMtnRv2xSA9+au5/Jnw1uO89GLhnLyAV3YsC2P4fdNqjH98ft1ZFLUUjsVt6sT1pq6pfp3asHAri3535dryGzXrNolwP51+aF8tXoLm3IKePKj6mM4d2g3bj9tADsKi5m8YANtmqUx7qNl3H76APbt0LxsXecxR/fm8alLGdm/A09fNpwHJy4kIzWZzbkFXHJ4Jgf3bMN+XVqQnpLMlIVZXDLuM248qT+nD96n7O9Z6p+fLGdtdh6PTl5C7/bNeD/GLXPN01NYcO9JpFVYU7hHu6bcfnrkRsAPnpjOtCUbgcg1OrR3W8a+Pb/cMmkA3do04aqRfbj5lW+AyE2Cy4/qze/emQ/AuEuHcdx+kcr48rGnsqOgmO0FRTzw7kJOOaAzR/WN3ChatSmX9VvzGJYZaZfLyS+iaWoyE+et587xc1iXncfYcw9gQJeWjJ+1lv87ujcHdm9D51YZZKQmkZ6SzMMXHgTAzafsT0ZqctlrVmZI98hnRekEhFNvHMnAOybw/WHdadc8vSzdIZlteOzig8u2+3duzl2vz612fe93f3l0pSsCDOnWilmrs6s8r6L0lCQuOTyTK47Zl5Wbcjnrbx8DkfXRX/96HVnb8jl9cBc6tszg+AcmsyRrOx/eOJLiEqdlk1Q2bc+nT8cWDP1oGfe8MZf/XHEYBUUllLjTrU3kPVlYXELfW94GYPzVRzC4W2v+PmUJR/ZtT/9OLRj79nyuHNmHts3SKCp2XvlyDVNvGEnHlulkpCZz7ai+ZKQml5vY8Zs7T2T15h3s36VlrX/XRFOnparMbJa7Dwme/w3Icvc7g+2v3P3AUKLc+XrnASe5++XB9sXAoe5+dVSaMcAYgB49ehy8YsWK0F6/oKiEfre+zaWHZ3Lh8B6h5SuSCB6ZvJjXvlrLqYO78IvjKr97K9JQtW6aSqeWu04utbu0VFXdhbVU1Z467S8f0rFFBuMuPYRvs/Po0CKd5CQjO7eQ/321hpMGdebQ307irAP3Yf3WfC49IjOyykbUUkB/vuDAcq3eV/xzJu/M+bZs+61fHMUpD3+4y2vPvms0BUUl3PfmPC4+rCedWqbzyZKNHNq7HV1bN8Hd6XXzW0Dki/3jU5fw8eKNPFNFy9yW3AJy8ov45ycruGZUX/IKSxh6z8Sy80u/t4zo3ZbnfnIo67Lz6N62KT97biaLNuTw3ysO5/r/zGLsuQfQPqpSMGdtNt3bNqVJajJz127lzL99zM+P68O1o/rx9MfL6NmuGScM6MTWvEIG3/kuHVuk89ktowD4atUW5q/bypSFWTz4/QNJSTJ+NO4zju3fgWGZbTmwW2uSkoyteYWMeXYGt582kMkLN3DBIT1IT0li8oIsjuzTvmzW6JMHdSYjNZlXv1zDnLtGM33pRto1TycjNYk2TdMoKCrZpQIazd15e/a3/GniQh774VD6dNy1q/+Fj0+nqKSEPh1bcNaB+/D9x6fXquL61jfrmLduK9ef2J/3569neK92NE+vuV2qoKiEtJTqO39u2l5ARmoSTdPCWhgnPDn5RSzekENaclJZ6/bv35nPI5OXcMEh3ckvKuHVL9fw7i+PLquAbtiaR9YPuhYAACAASURBVJtmaaQkGf+Ytpznpq/g3V8eU9arc09t3l7Am9+s44cjetb599od3+Xk0zw9pawiXpG7szY7jyPGRuZdePuao+jXqUXZ75u9o5Akg9TkJFKSjJTkJIqKS+gTVFYBDt838n56/EfDyn02ADz74+EcHdULZerCLDq3yqh0xYGteYWsz86jbyXH3J3CYq/y/ZhXWMy8dVs5qEebaq9HSYlT7E5qcp06Ne+xBrPOs5nNBg509yIzmw+McfeppcfcPdTFYs3sfGB0hcrzcHf/eWXpwy6gS0qc/W5/h4IiTRgmItKQXHp4JneeMbDO+ajyXHfxrjzXRnGJk2RgVv6L/b8/X8Xx+3cs1/oEsKOgmJc+X8mwzLZ8l5PPsf07AnDvG3PLWjd/flwfrj+xf42v/f789WRty+f7h+zZTfqSoPk8KfiSvml7AU3Tkqv8kl8bX6/ewsB9WtW5orM7VmzcTsuMVNpU0ZW5Pm3JLSA9JZkmu9F1tbErLC5h0rwNjB7YieISZ966bRygWe+ZMOdb5q3byrW72a27qLiElEoqou7OrNXZHBi0jktEQ6o83wKcAnwH9ACGurubWR/gGXc/Ipwwy17vMOBOdx8dbN8M4O73V5a+Pgror1dvYfXmHaHmKZIoCotL4nbXUKQ+9WzXlIH71P2LnCrPddcQKs9hm70mm/27tIxp5VNEpLGIZdlc19m27zOzSUAX4F3fWRNPIjL2OWyfA33NrBewBrgA+EE9vE6VBndrXTZuSkRERKQmg7qqBU5EZG9Q54EU7r7LGhDuvrCu+VbxWkVmdjUwgchSVePcfU59vJaIiIiIiIhIqTp12050ZpYFhDdjWOy0J9IVviFpiDGD4o6lhhgzKO5Yaggx93R3rRVUByqbY6ohxgyKO5YaYsyguGOpIcQcs7J5r648N1RmNqOhjalriDGD4o6lhhgzKO5YaogxS+PREN+fDTFmUNyx1BBjBsUdSw0x5vqkmYFEREREREREaqDKs4iIiIiIiEgNVHlOTI/HO4A90BBjBsUdSw0xZlDcsdQQY5bGoyG+PxtizKC4Y6khxgyKO5YaYsz1RmOeRURERERERGqglmcRERERERGRGqjyHGNmlmxmX5rZG5Ucu87M5prZ12Y2ycx6Rh0rNrOvgsf42EZdY9yXmllWVHyXRx27xMwWBY9LEijmB6PiXWhmW6KOxe1am9lyM/smeO0ZlRw3M3vYzBYH75OhUcfiea1rivuiIN6vzWyamQ2p7blxjvtYM8uOej/cHnXsJDNbEPwtbkqgmG+Iind28H5uW5tz6znu1mb2spnNN7N5ZnZYheMJ+d6WxkFlc8LErLI5tnGrbI5dzCqb9xburkcMH8B1wL+ANyo5NhJoGjz/GfBS1LGcBI77UuCvlexvCywNfrYJnrdJhJgrpPs5MC4RrjWwHGhfzfFTgLcBA0YAnybIta4p7sNL4wFOLo27NufGOe5jq3jPJwNLgN5AGjALGJAIMVdIezrwfoJc62eAy4PnaUDrCscT8r2tR+N4qGxW2VxDLCqbE+t6q2wOL26Vzbv5UMtzDJlZN+BU4MnKjrv7B+6eG2xOB7rFKrbq1BR3NUYDE919k7tvBiYCJ4UdX2V2M+YLgRfqN6LQnAk86xHTgdZm1oU4XuvacPdpQVyQQO/tOhgOLHb3pe5eALxI5G+TaBLivW1mLYGjgacA3L3A3bdUSNYg39vS8KlsVtkcggb5+aWyOW4S4r2tsnnPqPIcWw8BNwIltUj7EyJ3ekplmNkMM5tuZmfVS3RVq03c5wbdOV42s+7Bvq7Aqqg0q4N9sVCrax10v+sFvB+1O57X2oF3zWymmY2p5HhV1zSe1xpqjjtaxff27pwbttq89mFmNsvM3jazgcG+eF7vWl0vM2tKpCD77+6eWw96A1nA00F3zSfNrFmFNIn63t6rmVlbM5sYdLubaGZtqkhXbfc8MxtvZrPrP+J6obJZZXNNVDbHlsrm2FDZvAdS4h1AY2FmpwEb3H2mmR1bQ9ofAsOAY6J293D3tWbWG3jfzL5x9yX1F3FZLLWJ+3XgBXfPN7MriHQBOY5IF4+K6n1699251sAFwMvuXhy1Ly7XOnBE8NodgYlmNt/dp0Ydr+qaxuVaR6kpbgDMbCSRAvrI3T03TnF/AfR09xwzOwX4H9CX+F7v2l6v04GP3X3THpwbthRgKPBzd//UzP4M3ATcFpUmUd/be7ubgEnuPtYi4wNvAn4dncAi4/LuIFIuOTDTzMaXtliZ2TlATmzDDofK5jIqm6unslllc01UNjeSslktz7FzBHCGmS0n0o3kODN7rmIiMxsF3AKc4e75pfvdfW3wcykwGTgoBjFDLeJ2941RsT4BHBw8Xw10j0raDVhbv+ECtbzWgQuo0HUmjtc6+rU3AK8S6YIUraprGq9rDdQqbsxsMJGueme6+8bdObe+1PTa7r7V3XOC528BqWbWnjhe7924XtW9t2N9rVcDq93902D7ZSIFdsU0CffebgTOJFKpIvhZWYteld3zzKw5kTGs98Yg1vqgslllc41UNqtsrmvMUVQ2N3B79TrP7du398zMzHiHISIie4mZM2d+5+4d4h1HWMxsi7u3jtre7O5tKqT5FZDh7vcG27cBO9z9j2b2IDAV+JLIBD6DanpNlc0iIhKmWJbNe3W37czMTGbMiOmM7yIishczsxXxjmF3mdl7QOdKDt1S2ywq2edmdiDQx91/aWaZNcQwBhgD0KNHD5XNIiISmliWzXHvtm0V1vszs15m9mkwKclLZpYW7E8PthcHxzPjGbfI3mZ7fhHvzF5HTn5RvEMRkRC5+yh3H1TJ4zVgvUVmTiX4uaGSLKrqnncYcHDQDfcjoJ+ZTa4ihsfdfZi7D+vQYa9puBeJidlrstlRUFxzQhGpd3GvPAPXAPOitn8HPOjufYHNRCYwIPi52d37AA8G6UQkJC98tpIrnvuCZz9ZHu9QRCR2xgOls2dfArxWSZoJwIlm1sYis3GfCExw90fdfR93zyQy0dBCdz82BjGLNBpb8wo57S8fcc2LX8Y7FBEhpMqzRfzQzG4PtnuYWY2D3a3Cen9mZkRmgnw5SBI9eUn0pCYvA8cH6UUkBKV3tXPzdXdbpBEZC5xgZouAE4JtzGyYmT0JEMwKew/wefC4u8JMsSJST/IKI2XyFys315BSRGIhrDHPjxBZs+844G5gG5H1yw6p4bzS9f5aBNvtgC3uXtpvNHrNsLL1xNy9yMyyg/TfRWdYcVyViIiIVC6YXff4SvbPAC6P2h4HjKsmn+VAjZOFicjuSQraifbi+X1FGpSwum0f6u5XAXkAwVIWadWdEL3eX/TuSpJ6LY7t3KFxVSIiIiKyFyguiXzV3bi9IM6RiAiE1/JcaGbJBJVZM+tApCW6OqXr/Z0CZAAtibREtzazlKD1OXrNsNIJS1abWQrQClC3MRERERHZKz03vcFN8C+yVwur5flhIot6dzSz+4jMuvnb6k5w95vdvVsw0cgFwPvufhHwAXBekCx68pLoSU3OC9KrE4uIiIiI7JW27iiMdwgiEiWUlmd3f97MZhIZN2XAWe4+r4bTqvJr4EUzuxf4Engq2P8U8E8zW0ykxfmCOoYtIiIiIpKwtmuJKpGEUufKs5klAV+7+yBg/p7k4e6TgcnB86XALjN1u3secP4eByoiIiIi0oBofWeRxFLnbtvuXgLMMjNNbS0iIiIiEpKc/KKaE4lIzIQ1YVgXYI6ZfQZsL93p7meElL+IiIiISKOyXZVnkYQSVuX5rpDyERERERERNOZZJNGENWHYlDDyERERERGRiJx8zbYtkkhCWarKzEaY2edmlmNmBWZWbGZbw8hbRERERKQx6tq6SbxDEJEoYa3z/FfgQmAR0AS4PNgnIiIiIiJ74LTB+8Q7BBGJEtaYZ9x9sZklu3sx8LSZTQsrbxERERGRxqbEPd4hiEiUsCrPuWaWBnxlZr8H1gHNQspbRERERKTRKSlR5VkkkYTVbftiIBm4mshSVd2Bc0PKW0RiQMWziIhIYlHZLJJYwppte0XwdAdatkpEREREpM625Gq2bZFEEkrl2cyWUcnNMXfvHUb+IlL/NKxKREQksfx50qJ4hyAiUcIa8zws6nkGcD7QNqS8RUREREREROIqlDHP7r4x6rHG3R8CjgsjbxGJDdfIKhERERGRKoXVbXto1GYSkZboFmHkLSIiIiIiIhJvYXXbfiDqeRGwHPheSHmLSAxozLOIiIiISNXCmm17ZBj5iIiIiIiIiCSisLptX1fdcXf/UxivIyL1x8t+qglaREQkEZx14D7876u18Q5DRAJhzrZ9CDA+2D4dmAqsCil/EREREZFGZZ/WTeIdgohECavy3B4Y6u7bAMzsTuA/7n55SPmLSH0LBj1r7LOIiEhiMIt3BCISLZSlqoAeQEHUdgGQGVLeIiIiIiIiInEVVuX5n8BnZnanmd0BfAo8U90JZtbdzD4ws3lmNsfMrgn2tzWziWa2KPjZJthvZvawmS02s68rLI8lInXkFX6KiIiIiMhOoVSe3f0+4DJgM7AFuMzd76/htCLgenffHxgBXGVmA4CbgEnu3heYFGwDnAz0DR5jgEfDiF1ERERERESkJqFUns1sX2COu/8ZmAUcZWatqzvH3de5+xfB823APKArcCY7W62fAc4Knp8JPOsR04HWZtYljPhFZOdYZ415Fmk8qurtVUm6S4I0i8zskqj9k81sgZl9FTw6xi56ERGR2Aqr2/Z/gWIz6wM8CfQC/lXbk80sEziISHfvTu6+DiIVbKC0IO5K+dm7Vwf7REREZM9U1durjJm1Be4ADgWGA3dUqGRf5O4HBo8NsQhapLEwNGOYSCIJq/Jc4u5FwDnAn939l0CtWoXNrDmRyve17r61uqSV7NuljczMxpjZDDObkZWVVZsQREREGquqentFGw1MdPdN7r4ZmAicFKP4REREEkZYledCM7sQ+BHwRrAvtaaTzCyVSMX5eXd/Jdi9vrQ7dvCz9C72aqB71OndgF1WjXf3x919mLsP69Chwx79MiKNkQf3olxThok0JlX19opWU8+vp4Mu27eZVb6wjm5si+yZpKSd/1Lb84viGImIQHiV58uAw4D73H2ZmfUCnqvuhKCAfQqY5+5/ijo0HigdT3UJ8FrU/h8Fs26PALJLC3wRERGpnJm9Z2azK3mcWdssKtlXepftInc/ADgqeFxcWQa6sS2yh6ImIjnnkWlxDEREAFLCyMTd5wK/iNpeBoyt4bQjiBSy35jZV8G+3wTn/dvMfgKsBM4Pjr0FnAIsBnKJVNhFJCSutapE9kruPqqqY2a23sy6uPu6Cr29oq0Gjo3a7gZMDvJeE/zcZmb/IjIm+tmQQhdp9KKL5AXrt8UtDhGJCKXyvCfc/SMqv5sNcHwl6R24ql6DEhERaVxKe3uNpXxvr2gTgN9GTRJ2InCzmaUArd39u2AY1mnAezGIWaTR0AoYIoklrG7bItLAqeFZpFEaC5xgZouAE4JtzGyYmT0J4O6bgHuAz4PH3cG+dGCCmX0NfAWsAZ6I/a8g0njMWL4p3iGINGqhtDyb2fnu/p+a9omIiEjicPeNVN7bawZwedT2OGBchTTbgYPrO0aRxqziJJ7nPfYJy8eeGqdoRCSslueba7lPRBJUadcwVx8xEWlASkqcd2Z/S0mJPrtk71NZkXzeo5o4TCRe6tTybGYnE5nEq6uZPRx1qCWg+fRFRESkXvX+zVsAfH9Yd3533uA4RyMSrspuCc1YsZkpC7M4pp9mrheJtbq2PK8FZgB5wMyox3hgdB3zFpEYKlvnWY03ItJArNi4vez5SzNWVZNSpGFyh7TkXb+uXzLuM/KLiuMQkUjjVqeWZ3efZWazgRPd/ZmQYhIRERGp0TF/mBzvEETqleNVrk3T/9Z3mHnrKNo1T49tUCKNWJ3HPLt7MdDOzNJCiEdE4sXL/RARaXA0Z4PsjQy47oR+lR47+F6tDicSS2FNGLYC+NjMbjOz60ofIeUtIiIiUqN/TFse7xBEwhXcD/rF8X2rTHLTf7+OUTAiElbleS3wRpBfi6iHiDQQZes8q+FGRBqou16fy9i358c7DJHQOGBBt+3Zd1U+ndCLn6/irW/WxS4okUYslHWe3f0uADNrEdn0nDDyFREREalMQVFJpfsfm7KECw7pzj6tm5CWElYbgUh8uDsWDHpunl711/Yrn/+Cj349km5tmsYqNJFGKZRSxcwGmdmXwGxgjpnNNLOBYeQtIrFROlbQNepZRBqAX1fTVfXYP06m361vk1eo2YilYXPf2fJckyN/9wGLN2yr34BEGrmwbsk+Dlzn7j3dvSdwPfBESHmLiIiIlPPql2tqTHPuo9M0iZg0eNF15+VjT6027ag/TeXqf31BTn5R/QYl0kiFVXlu5u4flG64+2SgWUh5i0gMlH6/1PdMEdlbzFm7lV43v8ULn61k/rdbmTRvPV+s3FwuzdotOxptC3VBUQkXP/UpX63aEu9QpAp7UiS/8fU6Bt0xgYG3v8O/P1/Fza98zf1vzWN7fhGfL99EQVEJW3ILeGf2t3sU046CYrJ3FLJhW16t0heXeK3/x8bPWkvmTW+yZssOioorH5ohiS+3YO+9eRPKmGdgqZndBvwz2P4hsCykvEVERESqtOz+U+h181tVHr/5lW/Kbd9yyv4M7NqSg3u24fCx73Ns/w7847LhALz21Rr6dWpBr/bNyEhNrte498QXKzfTMiOFPh13f17W175aw36dW9K/cwt2FBTzyOTFfLjoOzZszWfCL48G4PJnZtC5VTr3nnXALufnFhSxcH0Oa7fsoGe7pgzcp1Wdf59EUVBUghmkJifWOPlIt+3y/ba/uO0EmqYls99t71R77vaCYm6MGt7w96lLd0kz/ebj6dwqo9Lz8wqLSU1OIjmp/Ovvf/vO151yw7H0bNeMzJveJC0libd+cRTZOwp5fdZavl69he8N685Nwf/ff392GNOXbqJvx+a0a57GwT3blv+9Vm7mFy98CcARY99nWM82PHXJISzbuJ0Du7cuS1dS4jw9bTnnDe2G46zLzmP/Li3L5bUuewfvzdvAxSN6lu3bUVBMRmpSueu5o6CYFZu206F5epXrZb88czXDM9vSo13V48mre//kFRYz7uNljDmqNynJSUycu55hPdvQplkaxSXO+FlrOHNIV5KSatk/v4LVm3OZuvA7fnBoj3L7s7bl0yQtudqx8ntiXfYOWmSksuDbrbv8DVdtyuXz5Zu47t+zeObHw8lISaJzqwx6tou0qWbnFnLo/e/x9KXDOWzfdqHGFSthXc0fA3cBrxDpXTIVuCykvEUkBtTgLCIN0feGdcPMeHHMCC54fHqtzrnvrXnlticvyGLwnRO4aERPHp28pGz/8rGn8m12Hm2apZKSlERRSQnpKbGpUP/r05X85tVv+PlxfTigaytOHNgZgHMemVYWW7TVm3Pp3DKDohIvV+l3d/KLSli9eQfXvPgVAH+58CB+HlRSALYXFDHyj5O5+8yBvDdvPQDPTV/JzFtH0a55Otf/exbuzvSlG1mbvbO1cfnYUykucbYXFNEyI7XS32Pz9gKSkoymaZGYUpOTWJKVQ8uMVDq0iFRW8gqLSUtOKqs85BUW8/THy/npUb3YlFtA8/QUmqbV7Suru/P58s0M77Xzy35RcQlrtuwgNTmJw8e+T4uMFL65c+eM1jsKivnpszO484yB9OnYnBc+W0nX1k04ul+HSvNftWlHtRWsqjz03kLaNUvj4sMyAfjVf2bx8szVZccrjnlu2ywNgLevOYqT//zhbr9etF+88CWjB3XmnjfmcsfpA9i6o4jB3Vuxo6CYK5//gvbN07n7zIHc9r/ZvHfdMazZsqPc+cf8YXLZ84KiEkb9aUq541+s3Nmr4dxHPyl3bNn9p2Bm3PLqNyz7bjvTlmwsd3zGis0MufvdcvvGXTqMB95dyJy1W3lsyhKytuUD8O4vj2bZd9s5cUAnXp65mhtejtw0+NenK5m3bisnDOjExLnrOW6/jvzf0b158qNlTJy7vlzelx6eCcCdZ0SmbZq9JptxHy/jlS92DhE5ok87Pl68kT99bwi9OzTnwO6t2VFQXO6GQueWGXy7NY9Xrzycg3q04W8fLOYv7y/GMAZ1bclPn53BkO6tOXlQZ1ZszOWFz1YyZUEWg7u15sdH9qr8D1VBaUv++FlruTH4XU8f0oUWUf+Hh9z3Hl1bN+Hjm44r2zd7TTafLNnIT4/uXS6/ouISSpxqJ1ncnl/E9vwiDrv//bJ9L40ZwQVPTOfyI3sxemBnznts59/4kyUbeWxK5PN0+dhTySss5r635pJXWMKfJi7gP/seXqvfNdHY3jwWaNiwYT5jxox4hyHSINzzxlye+mgZlx6eWVZwiEh5ZjbT3YfFO46GLKyyOfOmNwG4emQffjW6Px8uyuLipz6rc741mXv3aC54fDo/Pao3pw/Zh0Xrt/HyzNUsycphR2ExT/xoGOc8Mo2HLjiQ/Tq3ZM7abHq2a8a2vEJaZqTSLD2FvMJiDrp7Iv/66aGc/cg03vj5kVz5/Bes3JTLDw7twcUjeu5SKfr9eYMpLvGyVvSM1CTe/MVRdG3dhA/mb+Bnz39RlvaxHx7MlIUbGP/VWrYXxLZL+rWj+vLQe4u46eT9alw27LIjMjn7oK6c8dePGXN0bzo0T+fz5Zt4N6jU3Hzyftwf5HHFMfvSNC250vWOc/KL+OOEBfzkyF50aJFeaY+BZz9Zzu2vzeH+cw7g+U9XcOKAzrw8czUrN+XukvaEAZ349Un9efubb3lg4kKO7d+BP5w3hEPuew+I9Fz4yZG9SEoySkqc3r95i44t0tmwLZ9/XHYIQ7q15rEpS/jV6P58m53Hdzn5nP3INM4d2o27zxzIwDsmlL3WMf06MGVhVo3Xtaqxztm5hbtUMCV2RvbvwAcLqv/7De3RutxNhOpMuPZo+nduwfSlG3nhs5VcdGhPFqzfxsUjepJfVMyhv51Ebn4xBZV0aT9naFduP20ArZpEKtClvXGm3HAsBUUlJCcZxz0Qublxw+j+nD54Hy4e9ykrNu78H/j9uYM5pFdbPlyUxeuz1mJmnDyoM51aZnBl1GdMWEpv0NVVLMvmUCrPZtYP+BWQSVRr9v+zd9/xVZX3A8c/3+wEQkKYgQTCHoKARBERCwqIYLW1atVaV61t1bbWWovbOulQa2urP9x2uLWioIgyxIUM2UPC3gSyyB73+/vjnsSbcLPIyb03yff9et1X7nnOc57zzclJnjz3PENVz6ztmECwxrMxDXffuxt47rPtXDm2N384f1iwwzEmJFnjuencbjxnPHgOEeFhFJdVcNYji8krKuNoiEyW1DMx9pgndU9fkc5PX3Lnf5PRvTuyYmd2/Rlbqb5d2vHA+cO47Jmlx+xL6RjLjHMGMyIlkX9/uZO84nJe/mpXEKJ0R10ThVX+LpjWYWzfTnyx7Uj9GVuJ+ibBa4hA1s1uddt+HXgKeAZodbNu/PxfK9if6638oiLCeOj7wxnQrfFjjYwxxhjjrghnjGFMZHhV98RQaUzUbDgDrjWcgTbdcAbYllngt+EMsCe7iBv/+7Xffa3Nl7edxYb9uVzzgj0wag3aUsO5JXKr8Vyuqk+6VFbISYiNpLi8gpIyD19sO8LXu3Ks8Wxancr1nVvvQA5jTGsysFt7+nVp73ffjpnTmbf+AD/714oAR2VM4HVPiKF7QgybH5jKur25x4wtNsa4p0lTCopIkogkAe+KyPUiklyZ5qS3Cn+88EReuPoUHr9kJADlHmteGGOMMcFU4VHCas6k5OPsE7qzY+Z0XvvZ2ABGZUzwREeEM7p3Epvun8pTl48OdjjGtEpNffK8Au+Dqsra63c++xToe8wRLVjlVP0VHlt3zrQ+ts6zMaYl8S7hU3++E1Naz3JKxjRETGQ4U4d5PzzalpnPmY8s5s7pQ3hgzsb6DzbG1KlJT55VtY+q9nW+1nw1S8NZRKaKyGYRyRCRGc1xjtpEhHkvV1mFtS6MMcaYYPKoHrP+rD8xkeHsmDmdHTOn89Tlo+newf+atsa0Rn27tGfHzOlcO74vq+6eXJXep3O7ZjvnIxeNaFT+P114YtX7wd39D4ucMrRbk2KqzS/P7F/n/t9OHtgs5zVe8S6vQR0ITYpYRE4GdqvqAWf7CuAHwE7gXlXNanqI1c4XDvwDmAzsAZaJyGxV3eDmeWoTHl755Nkaz6b1Uhv1bIxpASq07m7b/kwd1p2pw7ozf8PBqom7rj29D0OSO5DWuR0d4yKZ9ck2Xlm2G6BqbdjWIDEukpzCMgZ3j+ea0/sw4801+P47s+beKezLKeKyp5fy75+MISoijFvfWM3KXTm8dM0pPLloa9VERueN6MHs1fuqjl10ywQm/GVRgL8j91Uus9Uc7pw+hAO5xTzz6XauGNubl77Y2aDj+nZpx9Fid2aPT4yL4t0bTyelYywdnbWifR3JL2H0Ax/x+CUjOffEHqzclc3urEJufm11VZ4nf3QS5wxPZs2eHM574jP+cN4JVHiU+97bwJmDu/L4JSOJj4nkB6NTAO/awDGR4YSHCWkz5pCcEMMzV6ZTVFrB5oNH6ZEYy8RBXZk+PJmYyHDCBN5auZdZn2zjO4O6MOuTbQDMuqL6RMo3/Gcl5R4Pf7loBP+3eBtPLd7KlgfPQZy/CarKU4u3cXF6Cit2ZrM/t5j8knKG9ujAuH6dGXjn+/Tr0o7fThlEZHgYj87/hsd+OII1e3LplRRHxqF8bj17MAlxkTwy/xvAO5dCWYWHAXe8XxXHWYO7clLvjvx0fF9yikqJiQxn4aZDnDm4K8Pv9S4htuTWiYz/08Jafy6xkeGcOaQrc9bsP54fa1A9d1U6/bq0599f7uTpK3WJvgAAIABJREFUJdtZdsckcgpLmfzYJwCsvGsyMZFhDL17HotumUBcdDi/fW01PxrTm+4JMZzYs+X1DGrSUlUishKYpKpZInIG8ArwS2AkMERVL3QnzKrzjcXbKD/b2b4NQFUf9pff7aWqissqGHzXB/x+6mB+MaGfa+UaEwrunb2eFz7fweWn9uKB7w0PdjjGhCRbqqrp3Kqbx81cwKl9O/HIxY17ylUpt6iMh+Zs5O7vDqVdPU8/dh0ppEKVhNhI9mQXct4TnwHeBufSbVm0iw7ntH6dKSqt4LOMw1z70nJmXjCcGc6azD0SYtiXW8zQ5A5cc3ofxg/ozJiHPmZA1/aMSE3kjRV7qs7VuX00h/NLALjqtDSKyyr49aQB/PrlVUw/MZlO7aPIKyrn9rfXMmVoN7YfLuDkPkn8d+mxyzCN7duJXVmFVbOQ+you8y6O4m9N5NpsOpBHSsc42kdHkF9SzjBnveIdM6eTNmMOKR1jiQoPY9vhApbcOpGcwjJufHklYSIsvGUCqsqkRxfzszP6MWFwF0558ON6zynifzjRVaelcfOUgZx474eMH9CZJVsO11nO5KHdiI4I4/JTexMXFc7yHdlk5pdQVFrBmj053HveCZyYkkhpuYfPth7m/vc2MPdX4/nnoq1MHNSFoT06MOjOD2otf/vD01i4+RDbMgvIKSzjiYUZgHc5sZ9/px+TfZ6cHswrZsxD337vT10+mp//2zu53fo/nM2WQ/kkJ8TQLUR6SczfcJABXdsTHiakJsUds19V2ZqZT/+u7k+mu2DTQXZnFXHlaWmuljtv/QFGpSbS9Tiu8aG8YqIjwkmIi6wz376cIjrERtI+OoI/frCJJxdtBeCDm8YzqFt81TrMG+47m4N5JUz8yyK6dYjmk1snEhkWxi9f/po5a70N6gtHp3DF2N6c98Rn9EqK4/mrT6Z7hxjumb2eN1bsITxMGJGSQERYGDFR4fzgpJ6cP7Inmw8c5dY317B6dw73f28YPz61d9X3/5+lu5g+vDsXjk4lPEz4x8IM/jxvMwBf3HYmXdpHk5lfQvcOMYgIJ90/n6yCUsA7lHXrQ9Nq/d7TZsxhRGoi79wwrtHX93i0mHWeRWS1qo5w3v8DyFTVe53tVao60pUovz3fhcBUVb3W2f4xMEZVb/TJcx1wHUCvXr1G79zZsE/2GqLy06azBnflO4O6uFauMaFgzpr9LN2exclpHfnuiB7BDscYVw3qFs+Yvp2aXI41npvOrcZzxqGjxEZF0DMx1oWoGmfXkUJKKzz07+p/tu9K1/9nBZOGdOOCk1IoLC0nIiyMqIjaR8yt2ZNDj8RYvt6Vw76cxjcY3l29j1W7c7hhYn/KPR66xjdv4+ur7Vm0j45gaI8Ox3X80eIy4qIiUFWKyz3VGuO1WbMnh7IKZXTvjsfsW7Ezm+iIMIb1TOCTbzI5rV+nqqXM3PDh+gMMT0lgyTeHufXNNQCs+8PZZBeUHtOoTJsxhxEpCbxz4+l+y3p39T7G9utEZHgYCbF1N8JM61BYWk54mBAd4f3AKr+knNyiMnomxnI4v4T0Bz7ih+mp/NGnG/vmA0cZ5HRl3364gIl/WUTvTnEs/t1E1+NTVRZsOsQpfZKIj/F/T6oqD83dyAUnpTAkufbf+91ZhSS1i6r3g0m3tKTG8zpgpKqWi8gm4DpV/aRyn6oOcynOyvNdBJxdo/F8iqr+0l9+t588qyqnPvwxB/NKXCvTGGNM87vqtDTuPe+EJpdjjeemc7tuNq3H6t05bM3M54KTUoIdSr3mrt3Py1/t4l8/GeN3f6AbD6bl25qZT2rHuFo/YKvs7XHHtCH89IxWNSdzkwWybm7qb/TLwGIROQwUAUsARKQ/kNvEsv3ZA6T6bKcA+2rJ6zoRYfHvJlJQ4s7YE2NCTUxkeFVXPmNak8Z0TTXGBMeI1ERGpCYGO4wGmTY8mWnDk2vd7697szF1qW3d+krtoyPq7JVhAqNJjWdVfVBEPgaSgQ/128fYYXjHPrttGTBARPoAe4FLgMua4Ty1iokMt3/CTKtmn5IbY4wxxhhzrCZ12w4GEZkG/BUIB55T1QfryJuJd+bvhuoM1D3jROhpiTGDxR1ILTFmsLgDqSXGDMGJu7eq2qQXTWB1c0izuAOnJcYMFncgtcSYoZXXzS2u8dycRGR5SxvL1hJjBos7kFpizGBxB1JLjBlabtymcVriz7klxgwWdyC1xJjB4g6klhgztNy4G8q9KQiNMcYYY4wxxphWyhrPxhhjjDHGGGNMPazxXN2sYAdwHFpizGBxB1JLjBks7kBqiTFDy43bNE5L/Dm3xJjB4g6klhgzWNyB1BJjhpYbd4PYmGdjjDHGGGOMMaYe9uTZGGOMMcYYY4yphzWejTHGGGOMMcaYerSJxrOIJIrIGyKySUQ2isjYGvt/JCJrnNfnIjLCZ98OEVkrIqtEZHmIxT1BRHKd2FaJyN0++6aKyGYRyRCRGSEW9+98Yl4nIhUikuTsC8r1FpFBPjGtEpE8EbmpRh4Rkb8513SNiJzks+9KEdnivK4MoZhD7t5uYNwhdW83MOaQu6+dc/9GRNY7Mb0sIjE19keLyKvO9VwqImk++25z0jeLyNkhFvfNIrLBubc/FpHePvsqfH4WswMZt2m4BtQVIff3q4Fxh9Tfr0bEHVJ/wxr4d1ckhOrlRsQdcvd2A+MOqXu7gTGH1H3tE5fVzS2Vqrb6F/AicK3zPgpIrLH/NKCj8/4cYKnPvh1A5xCNewLwnp/jwoGtQF/nuNXA0FCJu0be7wILQuF617h+B/AuuO6bPg14HxDg1Mr7BEgCtjlfOzrvO4ZIzCF5bzcg7pC8t+uKuUaekLivgZ7AdiDW2X4NuKpGnuuBp5z3lwCvOu+HOtc3GujjXPfwEIp7IhDnvP9FZdzOdn6gr7W9juvnbHWz1c0Njb3F1cv1xB2S93YD4g7Je7uumGvkCYn7uoF1nNXNIfpq9U+eRaQDcAbwLICqlqpqjm8eVf1cVbOdzS+BlMBGeayGxF2HU4AMVd2mqqXAK8D5zRNpdccR96XAy4GIrRHOAraq6s4a6ecDL6nXl0CiiCQDZwPzVTXLuY/mA1MDG7L/mEPx3q6htmtdm6Dd2z4aEnMo3dcRQKyIRABxwL4a+8/H+081wBvAWSIiTvorqlqiqtuBDLzXP1DqjFtVF6pqobMZive2qYPVzVY3N1JLrJfB6uZAsro5MNp83dzqG894PwXLBJ4Xka9F5BkRaVdH/p/g/RSzkgIfisgKEbmuOQOtoaFxjxWR1SLyvoic4KT1BHb75NnjpAVCg6+3iMThrcze9EkO1vX2dQn+/7jWdl2Deb0r1Razr1C5t33VFXeo3duV6rzWoXRfq+pe4C/ALmA/kKuqH9bIVnVNVbUcyAU6EcRr3cC4fdW8t2NEZLmIfCki32vGUM3xs7rZ6ubGaIn1MljdHEhWNzczq5u92kLjOQI4CXhSVUcBBYDfsRgiMhHvD/r3PsnjVPUkvN1qbhCRM5o53koNiXsl3u4pI4C/A/9z0sVPeYFak6zB1xtv95nPVDXLJy1Y1xsAEYkCzgNe97fbT5rWkR4Q9cRcmSeU7u3KmOqKOxTv7QZda0LovhaRjng/pe4D9ADaicjlNbP5OTSo93UD467MezmQDvzZJ7mXqqYDlwF/FZF+zRxyiyUiSSIyX7zjQuc7195fvjrHj4rIbBFZ14hTW93sZXVzPVpivQxWN2N1c62sbm7ZdXNbaDzvAfao6lJn+w28FUg1InIi8AxwvqoeqUxX1X3O10PA2wSua0S9catqnqrmO+/nApEi0tk5NtUnawrHdgdpLg263o5jPiUM4vWudA6wUlUP+tlX23UN5vWGumMOxXu7Uq1xh+i9DfVca0co3deTgO2qmqmqZcBbeMfa+aq6pk43rAQgi+Be64bEjYhMAu4AzlPVksp0n+u9DVgEjApE0C3UDOBjVR0AfIyfBpV4J9e5BxiD9969x7eRLSIXAPmNPK/VzVY3N1RLrJfB6marm2tndXMLrptFNaAfxAVU586dNS0tLdhhGGOMaSVWrFhxWFW7BDsOt4jIZmCCqu53xoouUtVBNfJc6uT5mbP9f06+l0WkPfABcB3wmqoOq++cVjcbY4xxUyDr5ohAnCRY0tLSWL48oDPPG2OMacVEpKET6LQU3VR1P4DTgO7qJ09dY+zuBx4BCmseVBurm40xxrgpkHVzW+i2bYxpgF1HCrl39np2HC4IdijGGBeJyEfiXZOz5quhs+H6HWMnIiOB/qr6dgNiuM6ZKGZ5ZmZmo+I3pq3779Jd7Mlu8OdTxphmFPTGs4iEOzM/vuds9xHvYuBbxLs4eJSTXuti4caYpnt3zT5e+HwHb63cE+xQjDEuUtVJqjrMz+sd4KDTXRvn6yE/RdQ2xm4sMFpEdgCfAgNFZFEtMcxS1XRVTe/SpdX0ejem2eUWlnH722u54tmvgh2KMQaXGs/idbmI3O1s9xKRhg66/zWw0Wf7j8BjzuQl2XhnIcT5mq2q/YHHnHzGGJd4PN75DzytdxoEY8yxZgOVs2dfCbzjJ888YIqIdHQmCpsCzFPVJ1W1h6qmAacD36jqhADEbEybUVrhASC3qCzIkRhjwL0nz//E+wn0pc72UeAf9R0kIinAdLyzDSIiApyJdxZI8C4OXrkOWG2LhRtjjDHm+MwEJovIFmCys42IpIvIMwDO0i73A8uc1301lnsxxjSTco+38RwRbv/yGhMK3JowbIyqniQiXwOoanZld+t6/BW4FYh3tjsBOc5i4FB9UpJqi4WLSOVi4Yd9C3QWOb8OoFevXsf/HRljjDGtnLNEzll+0pcD1/psPwc8V0c5O4B6Z9o2xjROeYW3O1hEWNBHWhpjcO/Jc5mIhOMs0i0iXQBPXQeIyLnAIVVd4ZvsJ6s2YN+3CTauyhhjjDHGtAJlTrftSHvybExIcOvJ89/wLi7eVUQeBC4E7qznmHHAeSIyDYgBOuB9Ep0oIhHO02ffhb8rJyzZU2OxcGOMMcYYY1qdCmcikohwe/JsTChw5TdRVf+Dt/v1w8B+4Huq+no9x9ymqinORCOXAAtU9UfAQryNb6g+eYnvpCYXOvltaiNjjDHGGNMqlVV127Ynz8aEgiY/eRaRMGCNqg4DNjU9JH4PvCIiDwBfA8866c8C/xKRDLxPnC9x4VzGGGOMMcaEJJswzJjQ0uTGs6p6RGS1iPRS1V3HWcYiYJHzfhtwzDJXqloMXNSEUI0xxhhjjGkxymzCMGNCiltjnpOB9SLyFVBQmaiq57lUvjHGGGOMMW1KVkEpAIWl5fXkNMYEgluN5z+4VI4xxhhjjDEGeHrJNgC+OZgf5EiMMeBS41lVF7tRjjHGGGOMMcaruKwi2CEYY3y4MoBCRE4VkWUiki8ipSJSISJ5bpRtjDHGGGNMW1RUao1nY0KJW7MPPAFcCmwBYoFrnTRjjDHGGGPMcSi0xrMxIcWtMc+oaoaIhKtqBfC8iHzuVtnGGGOMMca0NdZt25jQ4lbjuVBEooBVIvInYD/QzqWyjTHGGGOMaXOKrPFsTEhxq9v2j4Fw4Ea8S1WlAj9wqWxjTABosAMwxhhjTDXWbduY0OLWbNs7nbdF2LJVxhhjjDHGuCY6wq3nXcaYpnCl8Swi2/Hz4EpV+7pRvjGm+ak9ejbGGGNCUniYBDsEYwzujXlO93kfA1wEJLlUtjHGGGOMMW3OlWN78+IXO7lwdEqwQzHG4NKYZ1U94vPaq6p/Bc50o2xjTGCojXo2xhhjQkqvTt75d8PEnjwbEwrc6rZ9ks9mGN4n0fFulG2MMcYYY0xbpM6YKms8GxMa3Oq2/YjP+3JgB3CxS2UbYwLAxjwbY4wxoaWybrYhz8aEBrdm257oRjnGGGOMMcYYr8ohVfbg2ZjQ4Fa37Zvr2q+qj7pxHmNM89Gqr/YI2hhjjAkFlU+exVrPxoQEN2fbPhmY7Wx/F/gE2O1S+cYYY4wxxrQpHqfxvC0zP7iBGGMA9xrPnYGTVPUogIjcC7yuqte6VL4xprk5H2/b2GdjjDEmNHicSvmjjYeCHIkxBlxaqgroBZT6bJcCaS6VbYwxxhhjjDHGBJVbT57/BXwlIm/jHTr5feBFl8o2xgSA1vhqjDHGmODyeKxWNiaUuPLkWVUfBK4GsoEc4GpVfbiuY0QkVUQWishGEVkvIr920pNEZL6IbHG+dnTSRUT+JiIZIrKmxtrSxhhjjGmk2upcP/mudPJsEZErfdIXichmEVnlvLoGLnpjWj9rOxsTWlxpPItIP2C9qj4OrAbGi0hiPYeVA79V1SHAqcANIjIUmAF8rKoDgI+dbYBzgAHO6zrgSTdiN8Z4VY51tjHPxrQptdW5VUQkCbgHGAOcAtxTo5H9I1Ud6bxsYKYxLrIVMIwJLW6NeX4TqBCR/sAzQB/gv3UdoKr7VXWl8/4osBHoCZzPt12+XwS+57w/H3hJvb4EEkUk2aX4jTHGmLaotjrX19nAfFXNUtVsYD4wNUDxGdOm2ZNnY0KLW41nj6qWAxcAj6vqb4AGN2xFJA0YBSwFuqnqfvA2sIHKLmA9qb701R4nrWZZ14nIchFZnpmZeRzfijFtU+Wn2/YptzFtSm11rq/66t/nnS7bd4ktRmuMu6w7mDEhxa0Jw8pE5FLgCrxrPANENuRAEWmP98n1TaqaV0e962/HMX9RVHUWMAsgPT3d/uIYY4xp00TkI6C7n113NLQIP2mV9euPVHWviMTjrct/DLzkJ4br8A65olevXg08rTHGnjwbE1rcevJ8NTAWeFBVt4tIH+Df9R0kIpF4K9v/qOpbTvLByu7YztfK8VN7gFSfw1OAfS7Fb0ybpzbdtjGtkqpOUtVhfl7vUHud66vW+ldV9zpfj+IdrnVKLTHMUtV0VU3v0qWLe9+cMa2cx+fJ857swiBGYowB92bb3qCqv1LVl53t7ao6s65jnK5dzwIbVfVRn12zgcqZPK8E3vFJv8KZdftUILeyq5kxxhhjjkttda6vecAUEenoTBQ2BZgnIhEi0hmqPgw/F1gXgJiNaTN8nzz/4t8rgxeIMQZwr9v28RiHt3vXWhFZ5aTdDswEXhORnwC7gIucfXOBaUAGUIj3abcxxiX24NmYNslvnSsi6cDPVfVaVc0SkfuBZc4x9zlp7fA2oiOBcOAj4OnAfwvGtF6R4d+Omli7N5d9OUX0SIwNYkTGtG1Bazyr6qf4H0cFcJaf/Arc0KxBGWOMMW2Iqh7Bf527HLjWZ/s54LkaeQqA0c0dozFtmafGhGEvfbGTGecMDlI0xhi31nm+qCFpxpjQ9e06z/bs2RjTsry1cg8FJeXBDsMY19Wskp9avDU4gRhjAPcmDLutgWnGGGOMMa55ddkubn5tNbe9tTbYoRjjOn+zbVfYFNzGBE2TGs8ico6I/B3oKSJ/83m9ANhHwMa0IFXrPFudbIxpQX7/prfRPHu1LcBhWh9VJTys+ijHC578PEjRGGOa+uR5H7AcKAZW+LxmA2c3sWxjjDHGmFqt2Jkd7BCMaVYKRIVX/3d99e4cdhwuCE5AxrRxTZowTFVXi8g6YIqqvuhSTMaYYNBqX4wxJuS9v9ZWrDStm8ejhPmZXnfCXxax9aFpxzyVNsY0ryaPeVbVCqCTiES5EI8xxhhjTIM88+n2YIdgTLPyKIgI839zxjH7+t0+l00H8oIQlTFtl1sThu0EPhORu0Tk5sqXS2UbY4wxxtRr4J3vU17hCXYYxrhGUURgQLd4v/un/nUJh44WBzgqY9outxrP+4D3nPLifV7GmBaisru2TRhmjGkJPH5mHC4t9/Da8j2UllsD2rQOqlDZMXv6icl+85zy4Md8tT0rcEEZ04Y1acxzJVX9A4CIxHs3Nd+Nco0xxhhj/Hnso2/8pt/+9lpuf3st2x6aRpiNBzUtnKpW3cdPXDqKOWv8j/O/+P++4O5zh5IYF8nQHh0Y3L1DIMM0ps1w5cmziAwTka+BdcB6EVkhIie4UbYxJjDUeeSsNmWYMaYF+PuCjDr39719Lh+uPxCgaIxpHh6FMPE2nkWEu88dWmve+97bwM2vrWbqX5cEKjzTSlV4tOr/QlOdW922ZwE3q2pvVe0N/BZ42qWyjTHGGGMa7bp/rSBtxhyueWEZ4P2QsLC0nAWbDrJ6dw7r9uZytLgsyFGaQGtJP3ePKr79Jy5MT2nQcS3l+zOhJ6ewlH63z+XpJduCHUpIcqvx3E5VF1ZuqOoioJ1LZRtjAqDyA0b7oNEY09os2HSItBlzGHnffIbePY9rXljO+f/4jHP//inD7/2w1uNu+O9KPlh3bDfZ0nJPoxonGYeOcuhocdWTnB2HCyguq/CbN6ew1O947kDKOJRfNW78x88u5a7/rTsmzxdbj/DRhoN1lrNuby7z1h9g15FCsgpKASiv8JBbVMburMKqa5hXXEbajDk8/tGWY8pYsOkgv3z5a745eLQq7bevrWbczAW8vnw3aTPmkDZjDvM3HGR3ViFXPPcVX247wlfbs5j1yVaueO4rKnyup6py7t8/rfpApVJxWQUFJeVV2++s2ss7q/ZWbecWlbEvp6jO77c5KN4nzpU6xEQycVCXeo8bfu+HpM2YwyMfbqaotMK12LMKSo+5Vm54Y8UeNuwL7ZnDVZXXl++mqNT/7262c483l6PFZZSU+z93XbYfLuDfX+6sM4+q8tIXO9h5pIADed4J6J7/bEet+bMKSqs9mS4uq+Dxj7a0ifkmXBnzDGwTkbuAfznblwO2foQxxhhjmt1rPxvLxf/3Rb35cov8N3jTZszh/JE9eGfVvqq01fdMYc6a/cxZs58vbjuT5IRYissq+O7fP2XLIe/ULr86awA3Tx5Ifkk5w+6Zx+3TBnNKn04cyC1icPcO3PjySkrKPFX575g2hKKyCh6d7x2v/bMz+vJ/n9T/dGfjfVN5fcVulu/IZkhyB84c3JWfvrScXVmFnJiSwPUT+hMmkNQuigN5xZzWrzO/eXUVPzm9D8N6JhAfE0GFR3l12W4uTk/Fo8oJ98yrKv+swV25+7tD6d2pHYfyipn06GIA/nLRCJZsOcySLYf58djeCPD+ugOkJsXym1dXA3DpKan8ZvJAusbHAFBUWsGQuz/w+308e2U67687wBsr9gDQJT6a3MIySp0Z0h/76Bs6tY/ilWW7SO+dxAuf76g69t3V+44p73dvrKl6/9OXlle9/+SbzGr5+t0+lxNTEjj7hO78ed5mAJbtyGbFzmyG90zg5Ac/qro3PptxJl3aR/PrV1YBcO6JPQgPE856ZBGH80vZMXP6MXG8umwXc9ce4MVrTjlmX3FZBev35XLprKX07hTH/Ju/4/fa1FRe4SFMxDvmucbQ/eevPoU/z9vEPxZurbecvy/IqBriMO+mM1i3N5dDR0s4tW8SI1IS8agSEd6wZ2lbDh5l8mOfVG37uxa1UVUO5BWTnBDrd/8tr69udJn+VF43f/MdqCrlHiXS5/t9a+Uebn5tNavvmUJxWQXdOnjv40N5xew4UkhyQgwxkeF0iY/ms4wj/O6NNfzujTU8ctEIfvv6an52Rl9umzaEFTuz+cGTn/PEZaOYNKQb0x5fwrbDBY1ai7tmfDuPFHC0uJxdWYUM65HAGX9eyKheibx9/biqY7IKSlm3N5czBno/UOl/+1xGpibyxi9Oq8oz8S+LACir8HDZmF786uWvmbf+INsfnkaf2+YCMGlIVz7aeKhaPPtzixl134e896vxPDR3IzdPHkhSnPdvzDmPL+Ge7w5lygndGTdzAdERYZSUe+gQG8HV4/pUlXHeE5/Sv2t73lq5l6tOS+Pe87yjerccPIoCA2uZRT6UiRv92UWkI/AH4HS8kwJ+AtyrqtlNLrwJ0tPTdfny5fVnNMZw/3sbePbT7dX+uBljqhORFaqaHuw4WjK36ua0GXMA+P6onjz2w5HszSli3MwFTS63NlERYa3+qcqI1ERW7845rmM7xESQV+zu08hQNWlIN07tm8TEwV25ZNaXZB4tqTVvfHQER/08pU3rFMeOI4XV0lbfPYUR933I1BO688H6A8RGhjMyNZEvth05plF5OL+E9Ac+cuX76RIfTebREs4Z1p3pJyZztLicC07qSXREuNNDoJyhPTrw36W7uP3ttVXH1dbQ/dEzX/JZxhHm/mo8mw/mMaBrPOf+/VMAnr/qZCYO7lqVd1tmPp9lHOaud9ZXlZlfUk67qHBEhIxDR9l5pJD4mEj+uSiDxy4eyd8XZLBuby4FpeXMOGcw+cXlTBzclayCUk5z/gY89sMRREeEM65/ZxJiI3n5q13c9pY39o9uPoNuHWL4ansWP3mx+t+it64/jWeXbGfO2uo9TqYPT6Z7QgzPNnJt+aiIMIZ0j2fCoK68tnw3+3OLmXfTGZz9V++HEOeemEyvpDj25xbz9tfeng5PXX4SecXl3Orz4VBD3DJlIH/58NuJFPt1acfWzIJGldFUCbGRzPnV6dz+9jquPi2Nq2v08Lj0lF7sOFzAF9uOALDk1omkJsU1+byBrJtdaTyHKms8G9Nw1ng2pn7WeG46txvPvrNql1d46H/H+00u25hQU1tDtfL3oLmd2jeJL7f5Xw5rbN9OVY2hphrYrT3fHLRFe9qKpHZRrLxrcpPLCWTd7NZs2wNFZJaIfCgiCypfbpRtjAmMb8c8t94P1IwxrY9v98yI8DDm/mo8I1MTgxiRMYHzyEUjAnKe2hrOgGsNZ8Aazm1MVjOPE28Obo15fh14CngGaPxI9hB36awv2etMtBAVEcZffziSYT0TghyVMcYY03b17hTHKD+N5KE9OvDT8X254b8rgxCVMYH1g9EpDOr+bbdoY0zzcqvxXK6qT7pUVsgZ1rMD3RNiKCqt4IP1B9iwL88az6bVqVzf2Z7L6wNQAAAgAElEQVQ7G2NaAo9q1fq3NZ0zrHuAozEmeIb1TGD7w9MoKqtg6N3z6j/AGHPcmtR4FpEk5+27InI98DZQNWuCqtbex6MFuWO6d0H6Q3nFfLD+AGWe1j1hiDHGGBPqPJ7qS/j4CgsTdsycTnFZBbuyCpniM0OwMa2RiBAXFcHKuyazL6fInkQb00yaOuZ5BbAcuBL4HfC5k1aZ7joRmSoim0UkQ0RmNMc5alM51XxFkNdfNKY52DrPxpiW5OpxaUw5oVudeWIiwxnYLZ65vxrPsjsmVdt31WlpnDOsOz87o29zhtlgl5ycyrs3nn7cx184OsXFaEJHfHT15zxDkzsA8OjFI7hj2pBghBTSktpFMaxnAv/80Ul8cNP4YIdjWqD7zw/cpLHbH54WsHO5pUlPnlW1T/253CMi4cA/gMnAHmCZiMxW1Q2BOH9EmPezhvIKa10YY4wxwXTt+IY3eof28Da4bpzYnxGpiUweWr3R/d0RPYiJDKd/1/as3JXNBf/8vNr+SUO68vQV6fzsXyv4cMNBwLtmbmxkOGf8eWFVvp99py8d46IY3D2ekamJJMZFAfDNwaO8sWIPs3zWdL773KF8b1RPVu/J4YTkDnR11pedfeM4BnaLJyYyvCpv5YzKPzm9D786cwAj7vsQgOE9E1i7Nxfwrsk8bXh3rnlhOR3jIvnJ6X2YPLQ72w8XMHVYd47kl/DgnI3cPGUgCzcd4q531ldb2uaacX1YsSubh78/nLlr9xMTGcZfPvyGX501gA4xEVxwUgpREd7/g47kl/CdPy+qdo1W3T2Zbw7mExURxgnO9V68OZPUpDhW7squWiaopspZpNNmzOH2aYNZtiOb/l3bc3JaR07v34WoiLCq7/+Dm8YzqFs8e7KLqpa3OX9UDxZuOkRqxzgy80tYtzeXnomx3Pvuhmrlg3edWwEWbs7k862HuWZcH1KT4tidVUhKx1he+mInw1MSvOvmDuhCXHQ48dGR/GfpTh6Ys7GqnMHd47l16iCuecH7nOjxS0ZWrQvdGI/9cETVetlumzY8GfB+/4Wl5dz1v/Xcfe5QVu/J4YrnvqqW94fpqby6fHezxBEqfjSmF/9ZuqtaWtf4aA7VscyYm05MSWDNntyAnMvfud/8xWkMqGclgg9uGk/3DjEkxkVVWzqscn3vockd+N8N4xCBeesPcON/v652/EvXnEJOURnbMwt47CPv35ULRvXkLWcJrppO79+51t5DoaxJS1WJyMnAblU94GxfAfwA2Il3nWdXu22LyFin3LOd7dsAVPVhf/ndXqqqoKScE+6Zx+3TBnPdGf1cK9eYUHDv7PW88PkOLj+1Fw98b3iwwzEmJNlSVU0X6stIZhzKZ9KjixnVK5G3fnFatX/uyio83P3OOn591kC6J3gbu4fzS7jwyc/5/qgUfj1pQK3lejzKpgNHGZIcD9Te5dyfSY8uJuNQflVDMLuglPYxEUSGh3Egt5iIcKFz+2jAu2JCQ8pevy+XId07sC+3iNyiMk7o0bi5XE57+GP25Rbzxs/HMji5A+2jG/c85v21+8kuLOOyMb3qzVvZeK5tuSZ/vtx2hOU7srjxzNp/Jo3hL4ZR931IdmEZ2x+eVu2ap82Yw5DkDmzcn1d1TF5xGev35jG2X6eqfBUe5fdvrqFDTCR7sgsZ07cT97/37fOgL287i6PFZaQmxVX7MKWpnlq8lUlDuvLvL3exancO/7thHNkFpYy6fz4jUxO5cHQK//5yJ/26tmfu2v219kiruXzVuP6d+Nsloxjts/50fHQEl43pxctf7SKvuJzT+3fm0NFiTumTREmZhwN5xZzatxN/nreZnomx7M0p4oKTenLR6FQy80vo1C6KockdGHX/fAB+/p1+jOmbxNHicv79xU5unjKQMX2SOJBXTG5RGYO6xVf9LHIKSzlaXE5Kx1hEhNmr91Fa7uGW11dzzrDuPHn5aBZuPsTVzy/j1qmDeGJBBoWlFUwb3p1rx/dl0aZD/G1BBgAzLxhOv67tueipLwB4/edjyS0sIzkxhqHJHRj/p4XsyS5i+8PTyDiUz+JvMnlgzkbm/+YMBnTz/s7vOFzA21/vZfyAzqSnJXHNC8tYsOkQd587lPve28Cd04dU+5AmITaSt68/jTMfWcylp/Ti1L5JpHSM5dVlu7n3vBOIi4o4ZqmyW6YMZOn2LJZsOczyOyfxwboDXHpKL8LDhEmPLmbqCd358djehInQJT4aVeWZJds5Z3h3Ujp+u95yVkEpRWUV9EyMBSDzaAmd20f5/dvS7/a5VHi02u9GcVkFe7IL6d/V+717PMrCzYe483/rGN27I09cdlJdt2ijtZh1nkVkJTBJVbNE5AzgFeCXwEhgiKpe6E6YVee7EJiqqtc62z8Gxqjqjf7yu11BF5dVMPiuD7h16iCun9DftXKNCQXWeDamftZ4brpQbzwDzF27n9MHdKZDTGSwQwG8jeW9OUVtdrLSjENHiY+JpJvzdD4YGtOA359bREJsJPtyimkXHU5yQmyDz/OfpTvplRTH+AFdjjvW5nDn/9aScSi/qrFceR0yDuXTPjqCvTmFDE1OIDbK28hftiOLnMIyJg3piojw0NyNzPpkG/NuOoNB3eOPKT+vuKzO37fC0nIiw8OIDG/aiFNV5Z1V+zhneHeiI+r+QEJVeXrJNn6Y3ouEuMiqtPyScuJrxJpbVMauI4UMT2n472huURmfZxzmHKeXAEB+STlxkeFsP1JA387t6v0g7HB+CRFhwr6cYronxJDULqrB53dLbmEZZR5P1Qd4wdCSGs+rVXWE8/4fQKaq3utsr1LVka5E+e35LgLOrtF4PkVVf+mT5zrgOoBevXqN3rlzp2vnL6/w0P+O9xnULZ7+3dq7Vq4xoWD93lx2HCmkV1Jco/74G9MSfGdgFy5OT21yOdZ4brqW0Hg2pqb5Gw6yfl8uN00aGOxQgup4egGAt9fGxv15nJhia7Ab9wWybm7qUlXhIhKhquXAWTiNVpfK9mcP4PvfTwqwzzeDqs4CZoG3gnbz5OFhwpSh3diamc8mpyuOMa1F5YR4keFi97dpdYb4edJhjDENNXlot2PGyrdFvzt7EPkl5Y0+LjI8zBrOplVoagP3ZWCxiBwGioAlACLSH2iOUfHLgAEi0gfYC1wCXNYM5/FLRJh1hT1wMMYYY4wxbc8NE23Yomnbmjrb9oMi8jGQDHyo3/YBD8M79tlVqlouIjcC84Bw4DlVXe/2eYwxxhhjjDHGGF9NGvMc6kQkE+/M382hM3C4mcpuThZ34LTEmMHiDqSWGDO07bh7q2pozeTTwljd7JfFHTgtMWawuAOpJcYMbTvugNXNrbrx3JxEZHlLnDTG4g6clhgzWNyB1BJjBovbhK6W+jO2uAOnJcYMFncgtcSYweIOlKbN926MMcYYY4wxxrQB1ng2xhhjjDHGGGPqYY3n4zcr2AEcJ4s7cFpizGBxB1JLjBksbhO6WurP2OIOnJYYM1jcgdQSYwaLOyBszLMxxhhjjDHGGFMPe/JsjDHGGGOMMcbUwxrPxhhjjDHGGGNMPazxXAcRuUhE1ouIR0RqnUJdRHaIyFoRWSUiy33Sk0Rkvohscb52DJW4RSRVRBaKyEYn76999t0rInud72eViEwLhZidfFNFZLOIZIjIDJ/0PiKy1LnWr4pIVHPH7Jy33p+xiEz0uZarRKRYRL7n7HtBRLb77BsZKnE7+Sp8Ypvtkx7w693Aaz1SRL5w7qU1IvJDn30Bvda13as++6Oda5fhXMs0n323OembReTs5oyzkTHfLCIbnGv7sYj09tnn914JkbivEpFMn/iu9dl3pXNPbRGRKwMZtzk+Vjdb3dyAuK1utrq5tnitbg6duFtm3ayq9qrlBQwBBgGLgPQ68u0AOvtJ/xMww3k/A/hjqMQNJAMnOe/jgW+Aoc72vcAtoXatgXBgK9AXiAJW+8T8GnCJ8/4p4BcBirtRP2MgCcgC4pztF4ALA3mtGxM3kF9LesCvd0NiBgYCA5z3PYD9QGKgr3Vd96pPnuuBp5z3lwCvOu+HOvmjgT5OOeEhEvNEn3v3F5Ux13WvhEjcVwFP+Dk2CdjmfO3ovO8YjO/DXo36mVvdHELXuq7fwWDUFcfzM8bq5maNGaubmztmq5sD+LInz3VQ1Y2qurkJRZwPvOi8fxH4XtOjql9D4lbV/aq60nl/FNgI9AxEfLXE05BrfQqQoarbVLUUeAU4X0QEOBN4w8kXsGtN43/GFwLvq2phs0ZVv+O+N4N4veuNWVW/UdUtzvt9wCGgSwBiq8nvvVojj+/38wZwlnNtzwdeUdUSVd0OZDjlBT1mVV3oc+9+CaQEIK76NORa1+ZsYL6qZqlqNjAfmNpMcRqXWN0cOFY3B5zVzc3L6ubAabV1szWe3aHAhyKyQkSu80nvpqr7wVshAl2DEl09nC4po4ClPsk3Ot0/nqut21AQ9AR2+2zvcdI6ATmqWl4jPRAa+zO+BHi5RtqDzrV+TESimyNIPxoad4yILBeRLyu7sxG8692oay0ip+D9tHOrT3KgrnVt96rfPM61zMV7bRtybHNo7Hl/Arzvs+3vXgmEhsb9A+dn/4aIpDbyWNMyWd0cGFY3u8fqZquba7K6OcTq5ohgBxBsIvIR0N3PrjtU9Z0GFjNOVfeJSFdgvohsUtVP3IvyWC7FjYi0B94EblLVPCf5SeB+vP943A88AlzTtIhdiVn8pGkd6a6oK+5GlpMMDAfm+STfBhzAW5HMAn4P3Hd8kR5zPjfi7uXc232BBSKyFsjzk8+V6+3ytf4XcKWqepzkZrvW/kLwk1bzGgXlfq5Dg88rIpcD6cB3fJKPuVdUdau/413WkLjfBV5W1RIR+TnepwpnNvBYEwRWN1vdXO9JrW62urnxrG62urnJ2nzjWVUnuVDGPufrIRF5G29XhU+AgyKSrKr7nT8Yh5p6Lp9zNjluEYnEWzn/R1Xf8in7oE+ep4H3mnoup9ymxrwHSPXZTgH2AYeBRBGJcD4lrEx3RV1xi0hjfsYXA2+raplP2fudtyUi8jxwiytB407cPvf2NhFZhPcpyJs00/V2I2YR6QDMAe5U1S99ym62a+1Hbfeqvzx7RCQCSMA75q4hxzaHBp1XRCbh/YfpO6paUpley70SiAq63rhV9YjP5tPAH32OnVDj2EWuR2gazepmq5vrY3Wz1c3Hwepmq5ubTFRDpiHvus6dO2taWlqwwzDGGNNKrFix4rCqBmOsXqthdbMxxhg3BbJubtVPntPS0li+fHn9GY0xxpgGEJGdwY6hpbO62RhjjJsCWTfbhGHGGAC+2p7FOY8v4fOth4MdijHGGGOMOQ6qylXPf8WCTQfrz2wazZXGs3hdLiJ3O9u9nNn0jDEtxNJtR9i4P4/PM47Un9kYY4wxxoQcj8KizZlc+6L18GkObj15/icwFrjU2T4K/KMhB4pIuIh8LSLvOdt9RGSpiGwRkVdFJMpJj3a2M5z9aS7FbowxxhhjjDHG1MmtxvMYVb0BKAZwFrSOauCxvwY2+mz/EXhMVQcA2XjXK8P5mq2q/YHH+HZGNmOMMcYYY4wxplm51XguE5FwnDW4RKQL4Kn7EBCRFGA68IyzLXjX93rDyfIiULmg9/nONs7+s5z8xhhjjDHGGGNMs3Kr8fw34G2gq4g8CHwKPNSA4/4K3Mq3De1OQI6zPh141/nq6bzvCewGcPbnOvmNMcYYcxxEJElE5jtDpeaLSMda8l3p5NkiIlf62T9bRNY1f8TGGGNM8LjSeFbV/+BtBD8M7Ae+p6qv13WMiJwLHFLVFb7J/opvwD7fcq8TkeUisjwzM7NB8RtjjDFt1AzgY2eo1MfOdjUikgTcA4wBTgHu8W1ki8gFQH5gwjXGGGOCp8mNZxEJE5F1qrpJVf+hqk+o6sb6j2QccJ6I7ABewdtd+69AoohUrj+dAuxz3u8BUp1zRgAJQFbNQlV1lqqmq2p6ly4BWSvbGGOMaal8h0T5DpXydTYwX1WznDlN5gNTAUSkPXAz8EAAYjXGGGOCqsmNZ1X1AKtFpFcjj7tNVVNUNQ24BFigqj8CFgIXOtmuBN5x3s92tnH2L1DVY548G2OMMabBuqnqfgDna1c/eaqGTTl8h1TdDzwCFDZnkMa0VSXlFaTNmMO9s9cHOxRjDO6NeU4G1ovIx864p9kiMvs4y/o9cLOIZOAd0/ysk/4s0MlJvxk/XcuMMcYYU52IfCQi6/y8zm9oEX7SVERGAv1V9e0GxGBDqow5Dt8c8I6IeOHzHcENxLQ49oSxeUTUn6VB/tCUg1V1EbDIeb8N75iqmnmKgYuach5jjDGmrVHVSbXtE5GDIpKsqvtFJBk45CfbHmCCz3YK3jp7LDDaGX4VgXfS0EWqOqHG8ajqLGAWQHp6uv1PZ0wDFZSW15/JGB+2FFHzcqXxrKqL3SjHGGOMMQFVOSRqJtWHSvmaBzzkM0nYFOA2Vc0CngQQkTTgPX8NZ2PM8Vv8jfXUMCaUuNJtW0ROFZFlIpIvIqUiUiEieW6UbYwxxphmMxOYLCJbgMnONiKSLiLPADiN5PuBZc7rPifNGNPMissqgh2CMcaHW922n8A76dfrQDpwBTDApbKNMcYY0wxU9Qhwlp/05cC1PtvPAc/VUc4OYFgzhGhMm7Y1syDYIRhjfLjVeEZVM0QkXFUrgOdF5HO3yjbGGGOMMaat+cS6bRsTUtxqPBeKSBSwSkT+BOwH2rlUtjHGGGOMMW1O705x7DxiK8EZEyrcWqrqx0A4cCNQAKQCP3CpbGOMMcYYY9ocazibxrLlDJqXW7Nt73TeFtHEZauMMcFhf2yNMcYYY1oHW7Kqebg12/Z2EdlW8+VG2cYYY4wxxrRF54/sEewQTAtlD0Wah1tjntN93scAFwFJLpVtjAkAtb+yxhhjTEjpGBcV7BCMMT5cefKsqkd8XntV9a/AmW6UbYwxxhhjTFsUJt92vi0sLQ9iJMYYcK/b9kk+r3QR+TkQ70bZxpjAUOvgY4wxxoQUn7YzQ++eh1o3MdNAdqs0D7dm237E5/UwMBq42KWyjTHGGGP8em3ZbtJmzKGswhPsUIxxXc0GUJ/b5jJ/w8HgBGOMcW227YlulGOMCR77hNIY09KUVXi49c01AFz01Bf874ZxQY7IGHf56xX205eWA9AjIYYlvz+T8DCbV9mYQHGl8SwiN9e1X1UfdeM8xhhjjDGVBtzxftX7VbtzSJsxh0W3TCCtc7sgRmWMe1QhPiaChNhI9mQXVdu3L7eYfrfP5Z7vDuXqcX2CFKExbYubs22fDMx2tr8LfALsdql8Y0wz06qv9gjaGBP60mbM8Zs+4S+Lqm0/cdkozj3RlvsJtMLScnKLykhOiA12KC2eAJ/+/sxa7/k/vLuBP7y7AYDO7aN5+adjCAsTYiPDSU6IQcSeTBvjFrcaz52Bk1T1KICI3Au8rqrXulS+McYYY0yj3fjfr7nxv19XbXeNj+bQ0RJGpCayencOs28cx4kpieQVl3Eor5jO7aPpEBNJblEZHWIjOZhXTI/E6g3ACo+ybEcWp/btBEB5hYeI8DCKyyoo9ygVHiUuKpzI8LBqx4QJDW7IeDyKOPlzC8uICBcKSstJiI0kOiK8Kt+SLZmkdWpHalJcVVppuYdPMzLZfCCfEakJpHVqR0JsJDuPFBIbFc6Wg0fp26Ud2zILGD+gC4fzS6odX1hazsG8Eo4Wl9EjMZbO7aPJOHSUV77azYmpiUwZ2o2YyHDuf28DqR1jGZLcgTV7cmkXHcGpfZNYsuUw98xeX1Vel/horp/Qj0Hd4xmV2pGXv9rFfe9t4P1fj+fpJdt4a+Verhzbm4tPTuWu/63jx2N7M7ZvZzq3j+LXr65i5c5sLjipJ598c5irTkvjP0t38u9rxxAXFcHHGw/SKymO2KhwsgpKSU6I5Z1VezmcX8qFo1PYmpnPLa+v5s1fnEZsZDh7sosY1rMD7689QPuYCKYNT0ZVmbN2P5OGdCMiTIgID2P74QLaRYfTNT6GsgoPL3+1izMHd2Xe+oOcnNaRdtER9EyMZVtmAb9/cw03Tx7Ix5sOcts5Q4iOCONAXjHx0ZFIGOzNLqJz+2giwoSisgqS2kWxbEcWw3ok0LHdt0tRlTvj9u+ZvZ5JQ7sxcVBXVLXqntkxczr/WJjBn+dtrvW+OZxfwuTHPjkmffqJyZw3ogdDkzvwzcGjnDWkGwCq3vsV4O2v93L+yJ5ERYRRUl5BmAh7souIDBfW7c2lfXQkG/fncfW4NCLCw9iamY8AD83dyMTBXfnRmN5V58s4dJSl27OYekJ3wsOERGfJrZzCUio8SnZhKTmFZaSnJbFiZxZd42NITYqjsLSc2MjwWn9PcgvLaBcdTlFZBe2jI6rlyy0so7TCQ5f4aADW78ulb+f2RIQLFR4lKjyMsDBhf24RuUXeezsiTIiL8t8cKq/wUO5RYiLD/e73lV1Qysb9eYzslVhVnqry6rLdjOrVkX25RezJLuKRDzdzy5RB9OvSnpSOsSzcfIgrxqYB3t/5knIPsVHhlFd4KCitID46gsKyCv70wSbG9OnEqF6JxMdEEBEWRlREGBv355GcEMPmg0eJj47kn4syOJBXXBVXVkEpWzPz6ZEYS3mFh/25xcxZs5/vjerJ7qxCSis8REeE0a9LexLjIjmSX8qI1ERW7Mzm6ue/4vPbziImIoysglIKSivYn1vECOfv5fId2fRIjGFkakfKKjxUeJR20dWvZVFpBcVlFdXu87IKD9mFpXSNj6n3uoYicWPWPhHZBIxQ1RJnOxpYraqDm1x4E6Snp+vy5cuDGYIxLcajH27mbwsyuH5CP26dGtRfXWNCloisUNX0YMfRkrlVN1c+hRs/oDM3TRrAD578osllGhOqdsycXm175vubeGrx1iBFY4w7Xv/5WE5OS2pyOYGsm9168vwv4CsReRtv78/vAy+6VLYxxhhjjF//+skYwNu4OJBbzKkPfxzkiIxpfjPOGcyMc7wfdG/NzOesRxYHOSJjGu+ip7445oOhUOfWbNsPisj7wHgn6WpV/bquY4wxoUVrfDXGmJame0IMO2ZOp6CknLIKDxv25XHZM0uDHZYxzapfl/bsmDmdwtJyNuzLo2+X9vz2tVUs3JwZ7NCMaXXcmm27H7BeVVeKyARgvIhsV9WcOo5JBV4CugMeYJaqPi4iScCrQBqwA7hYVbPFO6jhcWAaUAhcpaor3YjfGGOMaYtqq3P95LsSuNPZfEBVX3TSo4AngAl46/I7VPXNZg+cY7ux+qocd3da/87V8nk8SrlHue2ttfx4bG+yCkooLVeyCkp5dP43HM4vafa4A+GH6am8urz2OVv7dWnH1swCAFKTYtmdVXRMnjF9kli6Pata2p3Th/DBugMkxEbSq1Mcz3+2o2rfqX2T+NsloziYV8K2w/n07dyed9fs47wRPejYLoqnP9nGmYO74lHl5LQk1uzJpU/ndtzx9lo6tY9iW2YBEwZ1YeHmTNpHR3B6/848OHfjMXFNG96dfTnFrNqdw7CeHfh/9u47PI7q3OP491Vzkavce8GmmG6EaYEAppoEkwQSINRASAKppAAhJJRw45ubAgRSHEqAhNACwSE0AzbdBhnb2MaAhQtu2LLlbstq7/1jR/JaWkkra7Q7K/0+z7OPdmbOnHl3NNqjM3PK3v268u6yDSxdv51vHDeSv7y6uDbtLRP359ZnFlJWUV3b173GFw4dxJOzVyZ9TpvSu0uHtF8/nfNyKAyawN536TjWbC6jU142G7aV88pHJbzyYQlllVW8UbyevOwsyhuYG71H51w2bq9o8DjHjOrFZ0b1AeCF9z9l9icbefLKo/nCH98M/0O1AQcP6UGfLh342meG8+S7K3m9eB2rN5Vx1/ljueqh1FRlcrKMympnUI9OrNy4g6EFnfly4WBeXLiWOct3Vde+NHYwa7eU8dqidY3md8ZBAyhaWsqazS275r9z4qgW7Z8OYfV5nkNsxO3hwHPAf4B93H1CI/sMAAYEFe6uwCzgLOASoNTdJ5nZtUBPd7/GzCYA3yFWeT4CuN3dj2gsLvV5Fkneb57/kDunFfPNz+5V2xRMRHbX1vo8m9mvSVDm1klTABQRK+edWHl9WHBj+yYg291/ZmZZQIG7N/pfV6aVzRVV1RiQEzf4Vw13DwbcaXpAoeYq3VZOyZad7NO/a+2xNpdVsqO8im/8fRZ/ueAwtpVXMrhnp1Y5ftS4OyVbd6ZskKEN28pZs6WMfft3S3qfsooqOuRkhTq69YoN2xnYvRNZEZjL2T02oFUyA2gBbNpRQfdOuUCsaXnfrh3o2jG3wfQVVdVkmTVr3uql67axo6KKkX3yWbt5J6XbyjlwUHe2lVfStWMu5ZXVTP9wLafs3z/h54HEg/it3VzGxyXb6JyXzcFDegCwbP02Nu+oZHS/LmzbWcn9by7lsmNH1n7GRPnGD/gWhroxl1VU1ft9VFU7e/30GaDxm4xhKd1WTteOObsNkFjXJ+u3U7K1jL37daVrx9zQz0sm9nmudvdKM/sisUrtH8ys0Wbb7r4aWB2832JmC4FBwERid7Ah1m96OnBNsP4Bj101M8ysh5kNCPIRERGR5muozI13KjDV3UsBzGwqcBrwT+BrwL4A7l4NNP64IgM19g+hmbVaxbUgP4+CuBFqzYzunXLp3imXp646plWOGWVmltLReXvm5+02QnAykq1UNsfgnp2bTpQiZtaszxhfqdyrT5cm0zf2t9aQ+DndhxR0rh01vqaSnpeTlbDiDI2PfN+3W0f6dtv9ehvWa9exOuZmc/Up+zSZb9jThNXNL9Hvozk3H8JQkMTfydBenRnaa9e1nMnTpzX/Kk2swszOAy4Cng7WNXxrqQ4zGw4cCswE+tVUiIOffYNkg/EhH98AACAASURBVNh93ugVwToRCUHN/M6a51mkXWmozI2XsPw1sx7B8i1m9q6ZPWZm/RIdxMyuMLMiMysqKVE/TBERyUxhVZ4vBY4CbnX3JWY2Avh7MjuaWRfgX8D33X1zY0kTrKv3X74KaBERkV3M7EUzm5/gNTHZLBKsc2Kt1wYDb7j7WOAt4DeJMnD3ye5e6O6Fffr02aPPISIikm5hjbb9PvDduOUlwKSm9jOzXGIV53+4+xPB6jU1zbGDftFrg/UrgCFxuw8GViWIZTIwGWL9qvbg44i0S67htkXaJHc/qaFtZtZQmRtvBbuadkOs/J0OrCc2gOeTwfrHgMvCiFlERCSKwnry3GzB6Nn3AAvd/Xdxm6YAFwfvLwaeilt/kcUcCWxSf2cREZEWaajMjfc8cIqZ9TSznsApwPPBGCT/YVfFejzwfuuGKyIikj5hDRi2J44BLgTmBaN1A/yU2BPrR83sMuAT4Jxg2zPERtouJnan+9LUhivStunBs0i7lLDMNbNC4Jvufrm7l5rZLcA7wT431wweRmxwsQfN7DagBJXNIiLShoU1z/M57v5YU+viufvrJO5HBbG713XTO3BViwIVERGRWu6+nsRlbhFwedzyvcC9CdItA45rzRhFRESiIqxm29cluU5EIqqmz3MYc7+LiIiIiLQ1LXrybGanE2tKPcjM7ojb1A2obEneIiIiIiIiIlHR0mbbq4Ai4ExgVtz6LcAPWpi3iKRQ7TzPevAsIiIiIlJPiyrP7j7XzOYDp7j7/SHFJCIiIiIiIhIpLe7z7O5VQC8zywshHhFJF9/th4iIiIiIxAlrqqplwBtmNgXYVrOyzvzNIiIiIiIiIhkprMrzquCVBXQNKU8RSaHaeZ716FlEREREpJ5QKs/ufhOAmXWNLfrWMPIVERERERERiYJQ5nk2swPMbDYwH1hgZrPMbP8w8haR1KiZ39nV61lEREREpJ5QKs/AZOBqdx/m7sOAHwJ/DSlvERERERERkbQKq/Kc7+7TahbcfTqQH1LeIpICNX2d1edZRERERKS+sAYMW2xmNwAPBssXAEtCyltEREREREQkrcJ68vw1oA/wBPBk8P7SkPIWkRTQA2cRERERkYaFNdr2BuC7YeQlIiIiIiIiEjWhVJ7NbG/gR8Dw+Dzd/cQw8heR1rerz7OeQYuIiIiI1BVWn+fHgD8DdwNVIeUpIiIircjMCoBHiN38Xgp8OWhNVjfdxcDPgsVfuvv9wfrzgJ8S6/mxCrjA3de1fuQiIiKpF1bludLd/xRSXpEz4fbXWF66HYC8nCwmX1TIYcN6pjkqkXDVzO+s584i7cq1wEvuPsnMrg2Wr4lPEFSwfwEUEvuKmGVmU4AtwO3AGHdfZ2a/Br4N3JjC+EVERFKmRQOGmVlBUKj+x8yuNLMBNeuC9W3ChAP7c07hECYcOID128opXrsl3SGJiIiEYSJwf/D+fuCsBGlOBaa6e2nwVHoqcBpgwSvfzAzoRuzps4iISJvU0ifPs4jdhbZg+cdx2xwY2cL8I+HbJ44GYO3mMh4pWk5ltZ7NiYhIm9DP3VcDuPtqM+ubIM0gYHnc8gpgkLtXmNm3gHnANmARcFVrBywiIpIuLao8u/uIsAJJlpmdRqyZWDZwt7tPStWxc7JjD+orq1R5lrZn14Bh6Y1DRMJlZi8C/RNsuj7ZLBKsczPLBb4FHAosBv4AXAf8MkEMVwBXAAwdOjTJw4qIiERLS5ttH25m/eOWLzKzp8zsjtZotm1m2cBdwOnAGOA8MxsT9nEakp0V+/9BT55FRCRTuPtJ7n5AgtdTwBozGwAQ/FybIIsVwJC45cHEmmcfEuT/sceG6X8UOLqBGCa7e6G7F/bp0yfETyciIpI6Lao8A38BygHM7DhgEvAAsAmY3MK8ExkHFLv7YncvBx4m1l8rJXJqKs9V1ak6pEjKuYYME2lPpgAXB+8vBp5KkOZ54BQz62lmPYFTgnUrgTFmVlMbPhlY2MrxioiIpE1L+zxnu3tp8P4rwGR3/xfwLzOb08K8E0nU7+qIVjhOQjnZevIsIiJtyiTgUTO7DPgEOAfAzAqBb7r75e5eama3AO8E+9xcU/ab2U3Aq2ZWASwDLkn1BxAREUmVFleezSzH3SuB8QT9mULKO5GE/a52S9CK/apysmIP6u+aVszf3lwaat4i6balrAKAx2et4PkFa9IcjUi4vlI4hB+duk+6w4gcd19PrPyuu74IuDxu+V7g3gTp/gz8uTVjFBGR5uuY29IGxpJISyu4/wReMbN1wA7gNQAzG0Ws6XbYGup3VcvdJxM0GS8sLAz1EXF2lvGzM/bj45JtYWYrEhkffLqZfft3S3cYIqHbu3/XdIcgIiKSEr8552AOG9Yz3WG0SS0dbftWM3sJGAC8EAwYArG+1N9paXAJvAOMNrMRxPpanQuc3wrHadDlx7aJ2bdERERERKQNOvuwwekOoc1qcdNqd5+RYN1HLc23gWNVmtm3iQ1Ukg3c6+4LWuNYIiIiIiIiIjXM2/CkrmZWQmwAk9bUG1jXyscIWybGDIo7lTIxZlDcqZSJMUPL4x7m7pprqQVUNjcoE2MGxZ1KmRgzKO5UysSYIYPK5jZdeU4FMyty98J0x9EcmRgzKO5UysSYQXGnUibGDJkbtzRPJv6eMzFmUNyplIkxg+JOpUyMGTIrbg3DJiIiIiIiItIEVZ5FREREREREmqDKc8tNTncAeyATYwbFnUqZGDMo7lTKxJghc+OW5snE33MmxgyKO5UyMWZQ3KmUiTFDBsWtPs8iIiIiIiIiTdCTZxEREREREZEmqPLcADM7zcw+NLNiM7s2wfbfm9mc4PWRmW2M21YVt21KiuO+18zWmtn8Brabmd0RfK73zGxs3LaLzWxR8Lo4QjF/NYj1PTN708wOjtu21MzmBee6KFUxB8duKu7jzWxT3LXw87htjV5faYz5x3Hxzg+u5YJgWzrP9RAzm2ZmC81sgZl9L0GaSF3bScYcuWs7ybijeG0nE3ckr29JXiaWzZlYLgfHVtkcnZgj+d2lslllc0hxR/L6bpC761XnBWQDHwMjgTxgLjCmkfTfAe6NW96axtiPA8YC8xvYPgF4FjDgSGBmsL4AWBz87Bm87xmRmI+uiQU4vSbmYHkp0Dui5/p44OmWXl+pjLlO2s8DL0fkXA8AxgbvuwIf1T1nUbu2k4w5ctd2knFH8dpuMu466SNzfeuV9O84I8vmJMqKSH13NSPuyH1/JRl3FL+/VDarbA4j7ihe222ubNaT58TGAcXuvtjdy4GHgYmNpD8P+GdKImuCu78KlDaSZCLwgMfMAHqY2QDgVGCqu5e6+wZgKnBa60fcdMzu/mYQE8AMYHAq4mpKEue6Ic29vkLTzJijdF2vdvd3g/dbgIXAoDrJInVtJxNzFK/tJM91Q9J5bTc37shc35K0jCybM7FcBpXNqGxuksrm1FHZHB2qPCc2CFget7yCBn7RZjYMGAG8HLe6o5kVmdkMMzur9cLcIw19tqQ/c5pdRuwOZg0HXjCzWWZ2RZpiasxRZjbXzJ41s/2DdZE/12bWmVgh9q+41ZE412Y2HDgUmFlnU2Sv7UZijhe5a7uJuCN7bTd1vqN8fUuj2mrZHNnvrmaI3PdXEyL7/dWYKH93qWxOHZXN6ZWT7gAiyhKsa2hY8nOBx929Km7dUHdfZWYjgZfNbJ67fxx6lHumoc/WnM+cFmZ2ArEvsc/ErT4mONd9galm9kFwBzcK3gWGuftWM5sA/BsYTQaca2LNZt5w9/g74Wk/12bWhdiX6vfdfXPdzQl2Sfu13UTMNWkid203EXdkr+1kzjcRvb6lSW21bI7kd1eyovj91YTIfn8lIZLfXSqbVTY3pS2VzXrynNgKYEjc8mBgVQNpz6VO8wJ3XxX8XAxMJ3aXJSoa+mzN+cwpZ2YHAXcDE919fc36uHO9FniSWNOUSHD3ze6+NXj/DJBrZr2J+LkONHZdp+Vcm1kusS/ef7j7EwmSRO7aTiLmSF7bTcUd1Ws7mfMdiNz1LUlpq2Vz5L67khXF76+mRPX7K0mR++5S2ayyuSltrmz2CHS8jtqL2BP5xcSafNV0rN8/Qbp9iHVkt7h1PYEOwfvewCJS1Ck/LobhNDxQxhnsPnDD28H6AmBJEH/P4H1BRGIeChQDR9dZnw90jXv/JnBahM51/5prg9gf+yfBeU/q+kpHzMH27sT6XuVH5VwH5+0B4LZG0kTq2k4y5shd20nGHblrO5m4g3SRu77T+Qr+PqYSK6um0sCAPcDFQZpFwMUJtk9p7HslpFgztmxu7Hs3at9dzYg7ct9fScYdue+vpmIOtkfuuyuZ792oXd9Jxhy5azvJuCN3bScTd5Auctd3Q6+aE9wm9e7d24cPH57uMEREpI2YNWvWOnfvk+44wmJmvwZK3X1SMH1JT3e/pk6aAqAIKCTW1G8WcJgHA+qY2ReBs4GD3P2Apo6psllERMKUyrK5Tfd5Hj58OEVF0ZgSTCQTbCmroGvH3HSHIRJZZrYs3TGEbCKx6U0A7ifWnPmaOmlqR8UFMLOaUXH/GfRjuxq4Ang0mQOqbBYRkTClsmxWn2cRAeDfs1dy4I0v8GjR8qYTi0hb0c/dV0NsShGgb4I0jY3UegvwW2B7awYp0l6VVVQx/Nr/8oeXFqU7FBEhpMqzxVxgZj8PloeaWfo7dItI0paXxv73/WS9/gcWaUvM7EUzm5/glew8nwlHajWzQ4BR7v5kEjFcEUwTVVRSUtKs+EXasy1llQDc/9bStMYhIjFhNdv+I1ANnAjcDGwhNqra4SHlLyIiInvA3U9qaJuZrTGzAe6+2swGAGsTJFvBrqbdEBupdTpwFHCYmS0l9v9EXzOb7u7H19kfd58MTAYoLCxsu4OtiIRs19hEie5hiUiqhdVs+wh3vwooAwgGEclLZkczyzaz2Wb2dLA8wsxmmtkiM3vEzPKC9R2C5eJg+/CQYhcREWmvphAbSZvg51MJ0jwPnGJmPc2sJ3AK8Ly7/8ndB7r7cGLzoH6UqOIsInuuKqg8Z6ujpUgkhPWnWGFm2QQTbptZH2JPopPxPWBh3PL/Ar9399HABmKTkxP83ODuo4DfB+lERERkz00CTjazRcDJwTJmVmhmdwMEA4XdArwTvG6uGTxMRFpXZZUaaohESViV5zuITVzd18xuBV4H/qepncxsMLH53+4Olo1Y0+/HgyT3A2cF7ycGywTbxwfpRUREZA+4+3p3H+/uo4OfpcH6Ine/PC7dve4+KnjdlyCfpclMUyUizbNpRwUAazbvTHMkIgIh9Xl293+Y2SxgPLFOGWe5+8ImdgO4DfgJ0DVY7gVsdPfKYDl+RM/a0T7dvdLMNgXp14XxGUREREREoiQ7K/acaGSf/DRHIiIQQuXZzLKA94I7zh80Y7/PAWvdfZaZHV+zOkHSxkZKqNeWxcyuIDbfJEOHDk02HBERERGRSKmqjv2r2yk3O82RiAiE0Gzb3auBuWbW3JrqMcCZwSidDxNrrn0b0MPMair1g4FVwfsVwBCAYHt3oF6fK3ef7O6F7l7Yp0+f5n4cEREREZFIqKiKDSGUk6WeiiJREFaf5wHAAjN7ycym1Lwa28Hdr3P3wcEonecCL7v7V4FpwNlBsviRP+NHBD07SK9RFERERESkTaoMnjznaLhtkUgIa57nm0LKB+Aa4GEz+yUwG7gnWH8P8KCZFRN74nxuiMcUEREREYmUisrYk+fcbD15FomCsAYMe6WF+08HpgfvFwPjEqQpA85pyXFERERERDJFzZPnXD15FomEUP4SzexIM3vHzLaaWbmZVZnZ5jDyFhERERFpjyqrY0+es9XnWSQSwrqNdSdwHrAI6ARcHqwTEREREZE9ULIlNr9zlqnyLBIFYfV5xt2LzSzb3auA+8zszbDyFhERERFpb657Yh4AbxSvS3MkIgLhVZ63m1keMMfMfg2sBjSbu4iIiIjIHgq6PKvZtkhEhNVs+0IgG/g2sI3YfMxfCilvEUkBzfsmIiISTao8i0RDWKNtLwve7iDcaatERERERNo1jbYtEg2hVJ7NbAkJHly5+8gw8hcRERERaa9y9ORZJBLC6vNcGPe+I7H5mAtCyltEUsDVbltERCSSVHkWiYZQ2oC4+/q410p3vw04MYy8RURERETasxw12xaJhLCabY+NW8wi9iS6axh5i0hquIYMExERiSQ9eRaJhrCabf827n0lsBT4ckh5i4iIiIi0WznZqjyLREFYo22fEEY+IpI+6vMsIiISTdlZarYtEgVhNdu+urHt7v67MI4jIiIiItLeqNm2SDSEOdr24cCUYPnzwKvA8pDyF5FW5rU/9QhaREQkSrJVeRaJhLAqz72Bse6+BcDMbgQec/fLQ8pfRERERKRdylWfZ5FICKsDxVCgPG65HBgeUt4ikgpBp2f1fRYREYmWHPV5FomEsJ48Pwi8bWZPEmv9+QXg/pDyFhERERFpt95avD7dIYgI4Y22fauZPQscG6y61N1nh5G3iKSG1/kpIiIiIiK7hNIGxMz2Aha4++3AXOBYM+vRxD5DzGyamS00swVm9r1gfYGZTTWzRcHPnsF6M7M7zKzYzN4zs7FhxC4iIiIiIiLSlLA6UPwLqDKzUcDdwAjgoSb2qQR+6O77AUcCV5nZGOBa4CV3Hw28FCwDnA6MDl5XAH8KKXYRYVdfZ/V5Fmk/GrphnSDdxUGaRWZ2cdz66Wb2oZnNCV59Uxe9iIhIaoVVea5290rgi8Dt7v4DYEBjO7j7and/N3i/BVgIDAImsqu/9P3AWcH7icADHjMD6GFmjR5DREREGtXQDetaZlYA/AI4AhgH/KJOJfur7n5I8FqbiqBFRETSIazKc4WZnQdcBDwdrMtNdmczGw4cCswE+rn7aohVsIGau9iD2H3e6BXBOhEJQc38zprnWaRdaeiGdbxTganuXuruG4CpwGkpik9ERCQywqo8XwocBdzq7kvMbATw92R2NLMuxJp9f9/dNzeWNMG6ev/lm9kVZlZkZkUlJSXJhCAiItJeNXTDOl5TN6/vC5ps32BmmoxWpJW4+lWJpF0olWd3f9/dv+vu/wyWl7j7pKb2M7NcYhXnf7j7E8HqNTXNsYOfNU3AVgBD4nYfDKxKEMtkdy9098I+ffrs+YcSaWdcw22LtElm9qKZzU/wmphsFgnW1XxTfNXdDyQ228axwIUNxKAb2yIt9NbHmq5KJN3SNuN6cHf6HmChu/8ubtMUoGYwkouBp+LWXxSMun0ksKnmbrmIiIgk5u4nufsBCV5P0fAN63gN3rx295XBzy3EBgod10AMurEt0kIlW3emOwSRdi9tlWfgGGJ3qE+MG6VzAjAJONnMFgEnB8sAzwCLgWLgr8CVaYhZpM3Sg2eRdqmhG9bxngdOMbOewUBhpwDPm1mOmfWG2pZknwPmpyBmkXbpew/PSXcIIu1eThiZmNk57v5YU+viufvrJG4KBjA+QXoHrmpRoCIiIhJvEvComV0GfAKcA2BmhcA33f1ydy81s1uAd4J9bg7W5ROrROcC2cCLxG5ui0grqayqJic7nc++RNq3UCrPwHVA3YpyonUiElG75nnWs2eR9sLd15P4hnURcHnc8r3AvXXSbAMOa+0YRWSXUdc/y9JJZ6Q7DJF2q0WVZzM7HZgADDKzO+I2dQMqW5K3iIiIiIjs7uOSrezVp0u6wxBpl1ra7mMVUASUAbPiXlOIzQspIhmidp5nPXgWkQzy3oqNDL/2v/xj5rJ0hyKSEuN/+wrV1SqsRdKhRZVnd59LbD7n1939/rjXE+6+IZwQRURERBI78843ALj+SY1VJu3HyJ8+k+4QRNqlFo844O5VQC8zywshHhFJF9/th4hI5GmMBmnPfvTYXMoqqtIdhki7EtZwfcuAN8zsBjO7uuYVUt4iIiIi9Yy4Tk/fpH3401fH1lv3+KwVPPz2J2mIRqT9CqvyvAp4Osiva9xLRDJE7TzPepAjIhlq2odr0x2CSKs4cHD3hOtv/M/7KY5EpH0LZaoqd78JwMy6xhZ9axj5ioiIiCRSlWDApEvve4fJFx7GKfv3T0NEIq1ncM/O3HHeoXz3n7PrbXv47U/Yq28X+nfrSIfcLLaUVWo0bpFWEkrl2cwOAB4ECoLldcBF7r4gjPxFpPXV9B109XoWkQzwl1c/Trj+igdn8eg3jmLciIIURyTSus48eGDCyvO1T8yrt05zQYu0jrCabU8Grnb3Ye4+DPgh8NeQ8hYRERHZza+f+7DBbV/+y1t89e4ZLFm3rXbduq072bazMhWhibSaey8pTHcIIu1aKE+egXx3n1az4O7TzSw/pLxFJAVq+jqrz7OItAVvFK/nhN9MZ+IhA5m3chOLS2IVaT2Ra7+emrOSG/49n1k3nExudljPj1KrY052UunKK6vJy8nMzygSZWH9VS0ORtoeHrx+BiwJKW8RERGRPfLUnFW1FWeIPYGW1rVw9WZOu+1VyiqqKKuoYmdlFUVLS7n39SVUVTtrNpexvHR7q8excuMOquP6xt/0n/fZXFbJph0Vu6XbXFbBA28tTTj12YZt5Ux69gMqq6oTHqOyqpryyl3bnnh3Be8sLQ3nAyRw1F69kkq37w3Pct8bS/jbG0twd01p1YrcnRUbtlNWUcXzCz5N+fF3VlZp2r4UCuvJ89eAm4AnAANeBS4NKW8RSQF97YpIe1D4yxe56/yxXP/vecy4bjwdc5N7kheWpeu28Y+Zy7ju9P3IyrLdtrk7f3l1MV8pHELP/DwAXljwKT3z8zh8eLT6cFdXO68Xr+PY0b0xMzbtqKBLhxyys4zTb38NgH1veK7efjc/vWt06JpWABu2ldOjcy5mVi99It/952zOPHggJ43pV2/bb57/kDunFXPMqF68UbwegDMOHMBdXx1bO8jcI+8sZ8zAbuw/oBtzlm/k4XeW8/IHa/n5Uwu46/yxnHHQAJ6b/ykvLlxDdbXzxOyVHDy4O6cfOICn5qxkVN8u7D8wNvr15/7wOh98uoV/fesoZn+ykV/+dyEAJ+zTh29+di8GF3SmV34eFVXVONCtY26SZzgxM2PppDMYfu1/G01X7bGbBbBrRO4/XzCW0w4YgLsnfa7T4ZWPSvikdDsXHjmM4rVb6JSXw6AendhRXoUZe/Q3u3VnJXnZWY0+jd9cVkHn3Gxymtkq4dGi5Vzzr3ns278rH3y6hSeuPJqxQ3s2O8Z4sz/ZwMjeXejeufHrpayiin1veI5vfHYk152+X4uOmYw3i9dx35tLmXzhYXt0Db37yQZmLi7lW8fv1QrRpUZYo21vAL4bRl4iIiIiyfr558Zw9KhePD13NXdOK05qn6seehfYVbl7/+ZT+flTC7j1CwfQISebWctKeaN4Pb9/8SMGdu/EQ18/gmG9du+NNnPxekb0yadv144APDNvNQcN7s6A7p2YsXg9fbt24OTfvwrsqiTGV3jOPmwI+/SPzer55sfrOP+vM7noqGE88NYyJj37AUsnncHbS0q54sFZAFxw5FBeXriWTTsqeOHqz/KPGcu4+uS9ycnOYtayUj7dtJPnF3zK/37pIDrlNV65uOk/C7jvjaW8fs0J5GVnUeVOv64dayvzm7ZXMG/lJi64ZyY/nbAvp+7fn0652VQ7/ORf7/HqRyVMPGQgT81ZxZ3nH8r4fftx8E0vADCid/K99pqqANYY2TufQT078dqiddx7SSFT5q5iytxVHL1XL7KzjNcWrau3T03FGeC/81az5PbXap84/9/zDfeXv+qhd7nqofrrS7buZNGaLXzv4TkAdO+Uu9sT7C/96a3d0k/7sIRpH5bUy2fJrybU3kD4xef3B2LX0m+nfkRutvE/XziQLh1y6NIxhw6NNNE+57DBPDZrRYPbE/nm32PX/ZCCTpw3bihHjChg7vJNfO0zI2rTVFZVs35bOas3lfHDR+cw5dufYeOOCrLN6N+94275uTs/+/d8jt+nLyeP6Vd7Prp3ymXN5jKO+J+X+M05B3P2YYMbjOnjkq3srKhmzMBuQKwi+pPH3wPgwiOHcdLvXq23zxEjCnjkG0c167Mf8IvnOXJkAfd/bVy98zrtg7UcPqKAg258gTMOGsBd59efU7sx7yzdAMAHn24BYP3W8mbtX5e784U/vkn/bh358an78IVDB7F8w3aG9cpn9aYd/O2Npfzl1cW88IPjmPzqYgD+8spipr6/hokHDyI3xzCsyQpqze+6X7eO9bZtL6/k8VkruPDIYbjDBffM5MIjh3H1o3PZUVHFU3NWMeHAAc3uGvDFP74JkNGVZwvjMb+Z7Q38CBhOXIXc3U9sceYtUFhY6EVFRekMQSRj3Pyf97n3jSVcfNQwbpp4QLrDEYkkM5vl7hqxpwXCKptrKl5LfjUBM6OyqppR1z/bojxP278/zzXQ7PIrhUMo2bqTlz/YfS7pEb3zdxuYrCHjhhfwdlxz3ge+No7PjOrNyJ8+06KY6/r12Qexd7+uVFRVs9+Ablx4z0xmf7KR/QZ0Y+HqzaEeS1rXLz4/pvbpcd2++p+s385x/zct0W7NNu/GU9hRUcW4W1+qt21Qj06s3LgDgHeuP4mz7nqDy48dwd9nLOPjuO4Qi249ndHXP0tBfh7/+tbRvLdiY+2NhskXHsbBQ3rsVkk77bZXayubAF8aO5jPHzyAS+57p3ZdfOuBZPz2nIMpWlbK6k1lfP3YkTw08xPuPP9QdlZW79YK4srj9+LKE0bRISeLlRt2cPxvpvOZUb15vTh2Eyb+XP9+6kdMfX8NT151NHe/toSvHD6EnCyjQ042C1ZtYuXGHbzw/hr++97q3WK5+6LC2pYRf5+xjJ/9e/5u26f+4DhWbypj/qpNTDhgANvLq8jNNl54fw3/fPsTVmzYkfTnbsjt5x5Ch5xsxo0ooCA/DvantwAAIABJREFUjzWby9i4vaL2pt3Pn5rPA28t4/VrTuClhWv57Qsf8tZ148nvkMMN/57PgzOW8fVjR/DX1xL3xB07tAdnHDSQz+7dh1F960+N9vyCT1lcso1Rfbtw8ph+zFy8nq9MngHAT07bh+NG9+GAQYnnL2+uVJbNYVWe5wJ/BmYBtZ0q3H1WizNvgbAK6PLK6trpe7LMMnaQCZHGqPIs0jRVnlsuzMrz0ILOvPqTE+qtF2lrEg10972HZ9Ozcx4TDhzAl//yVoK9RGJu+NwYbglaPNz4+TE8/d5qipZtqJfu4CE9+ErhEH76ZP3pzxqz34BufP3YETw5eyWvLVpHQX4epduafgJ//YT9+PpxI5t1rERSWTaH1ee50t3/FFJekXPK719h6frYwBbZWcaDl43j6L16pzkqkXDV3CBS32cRyQQje+fXNvWM95XCITxStDwNEYmk1u3nHlr7/uqT9+Z3Uz9KYzQSZbfEjTVQ0wc+kbnLNzJ3+cZm579w9WaufnRu7XIyFWeAW59ZGErlOZVaVHk2s5rRK/5jZlcCTwK1w1i6e+sNN5hClx87kk07KthSVsmfX/k4NkJk5jbVFxERyXiV1Z6wJdhNE/fny4cPoWRLGb+b+hEfrdmahuhEUuuqE0ZxxIiC2maxItI6WvrkeRaxB1U1w639OG6bA5l1K6EBFxw5DIC1W8r48ysfU1GlZ3PS9mieZxHJJDnZRocEg9V0zM3msGGxkW5rRhb+w8vFeionbVp2lnHEyOSmsRKRPdeiyrO7j2g6VbjM7DTgdiAbuNvdJ6Xq2DlZsUK6obn+REREJDVe/uHxSaUzM646YRR3TivmpP368sy81M/DKpIqNX2j75pW3Oio4iKyZ1rabPtwYLm7fxosXwR8CVgG3Bh2s20zywbuAk4GVgDvmNkUd2+48X6IcrJjD9grq/VoTtouV69nEWljsrOMj355OgB/e2MJHXKz+fVzH7Bhe0UTe6bPr754INc90bxBe6JmzIBunDSmH0/OXsE+/boypKAz3/rsXqzdspOqaqey2jlocHeO+tXLrNu6s+kMmyHZAYvC0KdrB0q2hBt/S111wiiuOmEUe/30mdr5rUWk5VrabPsvwEkAZnYcMAn4DnAIMBk4u4X51zUOKHb3xcExHwYmAqmpPGep8iwiIpLJLjkm1mjuvHFDgdgcy2UVVXztb0XsP7AbJ+3Xjx+cvDcAN05ZwN/eXNpofsN6dWZZMKho/24deeu6E7nz5WL2H9SN309dxLyVmxLu16VDDlOvPo7rnpjH9ATzAZ83bij/fW815VXV/OqLBzL7k418sHozd7++hNevOYEl67bxyDvLebrOFDl1/fxzY8jOMn4xZQGw+/Q/r/3kBIYUdGZ7eSU5WVnk5WSxcXs5h9w8FYAfn7oPn927D5/7w+sMLejM5w8ewF3TPt4t/9MP6M+z8z9l6aQzqKp2XvloLTdOeZ9PSrfzzPeOBWKDWcXrW2de2aKfnUR5ZTUVVdX89bXF3Pbiotpt8248hUVrtzJ2aE+em7+aHRVVrCjdwZCCzhw9qhd52Vn06JxHdbWzeN02Nu2oYNayUr5+7Eg+LtlKfocctpRV0jkvm8/8b2xqp6euOoZBPTtR+MsXAZh9w8nMX7WJC+95G4g9vX3wraUM753Phfe8zei+XRjcsxPTPizh9nMPYVTfLpzz57eY/qPjd/ssxWu30Ckvhw45WWSZUVZRRbdOuRzwi+cBePmHn+WVj0pqp59K1m1fOaRZ6ePN/Ol4Xlq4hpv+8z6v/PgEVm3cQc/Oefxj5jLOP2IoA7p3Yu+ftWyKN2l9nfNiU04dNKg7Pzh5by64ZyZvFK/npP368uLCXdPn1fw9Quxvv0fn3N0G8oqa1685oelEEdOiqarMbK67Hxy8vwsocfcbg+U57r7nf+2Jj3c2cJq7Xx4sXwgc4e7fTpQ+7Hmed1ZWsc/PnuOyz4zgq0cMDS1fkSj4w8vFPDl7JZ8/eCA/OGl0usMRCVW3Trn07tKhxfloqqqWC7tsbk3uzl3Tipl4yCCueuhdPlqzhQ9uOb3Z+SxYtYlN2ys4/+6ZtU8p37j2RAb16ATE/r/Iy85i/srNvPnxOvbu35UT9umbVN47K6vokJNNVbVz1K9e4qxDB3H5sSNYXrqdHz32Hk9/5zN0zsvm+n/PZ/y+fRm/X79mx18Tn5lRUVXNA28t49zDh9AhJ4ucBIO2lVVUsW1nJb328G9u5cYd9MrPY+vOylD+bmtc/egcnnh3JXN/cQrdO+XW2/7yB2sY1KNz7Ty48dyd1ZvKGBj8zvZUyZadHH7ri9w8cX9mLdvAywvXcsr+/Tlpv76cdkB/Vm0qY2D3jixYtZlRfbvQMTe7RcdL1sclW8nPy+HIX+2a6/m57x/L/JWbOWqvXqwojd0gqhmQ7PoJ+3HrMwtr0370y9O54d/zeaRoOcW3ns6MxaUcMKgbPTrn1U4f16VDDlt3VjJ+3758d/xonpy9koL8PPbp35Xx+/alosqZtWwDF9wzE4DpPzqeXz27kMuPHckBA7uz38+fY3ivzvzwlH343EEDKKuoZr+fx+ZvPmm/vtx98eGUVVQx8c43OGqvXrvd+Cr62Um1N0sGdu/ISWP68cBby+qdh/h5rRM5ZUw/5izfyAGDunPNafvy51c+5oUFn7KtvIqrTtir3s2lbx2/F8eO6s35d8c+00NfP4KRvbvw6qISfvL4exy/Tx/uOO9QrnigiEuOHs5pBwyoPV+HDu3Bk1ce0+jvrbraWblxB9M/KqFDThZnjx3M7S8t4vaXFtU24Y+fvu+Gz41hxuL1LF23ja8fN5KfPP7ebvl17Ri72ZSM355zMGceMpDR1+9+82Vkn3wWx80BnpNlfG/8aL4zfjRH/s9LfLq5rPZY8248NaljNSVj5nk2s/nAIe5eaWYfAFe4+6s129w91Mlizewc4NQ6ledx7v6duDRXAFcADB069LBly+r/Yeyp6mpn3xueo1x9nkVEMsolRw/nxjP3b3E+qjy3XCZVnlvDxu3lvFG8njMOGpDuUNqdnZVVrNpYxoje+ekOJdJWbdxBl445dOtY/wZDvPG/nc7HQSUp0TzUNa57Yh5Hjizg1P374w6d8hq/IXDsr19meemOenlu2lFBlw45ZGfZbuvfXhKrqHfOq9+gdsO2cnp0zsXMWLJuG6s37uDoUbHpZtdsLmPFhh1cct/bXHzUcO6cVswDXxvH6H5dWLh6Myfuu+tGU1lFFX99dTHf+Oxe5NUZqLCsoor/vreaL44dVHuDacbi9Rw7uk9tmqfmrOSWp99nxnXjE95wirezsorL7y/i2tP3Zf+B3RtNm6zSbeVUVlfTt2vHettqKtd1z/eV/5jFM/M+5bWfnEBltbNtZyV5OVlsKavgsGEFtenmLt9Ilhl79c0ny6z2hs9d04o5ZlRvDhnSI5TP0JhMqjxfD0wA1gFDgbHu7mY2Crjf3Ru/XdL84x1FrC/1qcHydQDu/qtE6VujgJ61bAMrNmwPNU+RqNhZUU2H3Ma/1EUy0Yje+Rw0uOUFuCrPLdfeK88ibUV1tbNheznrtpYnfFq/p7btrGRnZTUF+Xmh5ZmMsoqqlD3pj5LlpdvJy8miX53uFFXVzqeby2pbyERZKsvmlo62fauZvQQMAF7wXTXxLGJ9n8P2DjDazEYAK4FzgfNb4TgNOmxYz9opMERERERE2qOsLKNXlw573Dy/IfkdcsgPN8uktMeKM8CQgs4J12dnWUZUnFOtpQOG4e71ZmN391aZTDFoHv5t4HliU1Xd6+4LWuNYIiIiIiIiIjVa1Gw76syshNi0WVHRm1gT90yjuFMnE2MGxZ1KmRgztJ24h7l7n4YSS9NUNodGcadOJsYMijuVMjFmaDtxp6xsbtOV56gxs6JM7CunuFMnE2MGxZ1KmRgzKG6Jrkz9HSvu1MnEmEFxp1ImxgyKe09oZCARERERERGRJqjyLCIiIiIiItIEVZ5Ta3K6A9hDijt1MjFmUNyplIkxg+KW6MrU37HiTp1MjBkUdyplYsyguJtNfZ5FREREREREmqAnzyIiIiIiIiJNUOU5BGbW0czeNrO5ZrbAzG5KkKaDmT1iZsVmNtPMhsdtuy5Y/6GZnRqxuK82s/fN7D0ze8nMhsVtqzKzOcFrSoRivsTMSuJiuzxu28Vmtih4XZyKmJsR9+/jYv7IzDbGbUv5uY47draZzTazpxNsi9x1HXf8xuKO1HVdJ7bG4o7ctZ1EzFG9rpea2bzg2EUJtpuZ3RFcw++Z2di4bWk715I8lc0qm0OKO6rfYSqbU0hlc8pijn7Z7O56tfAFGNAleJ8LzASOrJPmSuDPwftzgUeC92OAuUAHYATwMZAdobhPADoH779VE3ewvDWi5/oS4M4E+xYAi4OfPYP3PaMSd5303wHuTee5jjv21cBDwNMJtkXuuk4y7khd182IO3LXdlMx10kXpet6KdC7ke0TgGeDv90jgZlRONd6Net3rLI5Wuc6ct9fycRdJ32UvsNUNkcn7shd203FXCddlK7rpUS8bNaT5xB4zNZgMTd41e1MPhG4P3j/ODDezCxY/7C773T3JUAxMC4FYScVt7tPc/ftweIMYHAqYmtIkue6IacCU9291N03AFOB01ohzHr2IO7zgH+2emBNMLPBwBnA3Q0kidx1DU3HHbXrukYS57shabu2mxlzJK7rJE0EHgj+dmcAPcxsAGk819I8KptTR2VzaqlsTi2VzZGS9rJZleeQBE0j5gBrif3yZtZJMghYDuDulcAmoFf8+sCKYF1KJBF3vMuI3e2p0dHMisxshpmd1aqBxkky5i8FzTkeN7MhwbqMONdBM6URwMtxq9NyroHbgJ8A1Q1sj+R1TdNxx4vEdR1IJu6oXdtJneuIXdcQ+wf5BTObZWZXJNje0DlN97UtzaCyWWVzU1Q2q2xOgsrm1Il82azKc0jcvcrdDyF2l2ycmR1QJ4kl2q2R9SmRRNwAmNkFQCHwf3Grh7p7IXA+cJuZ7dXqAZNUzP8Bhrv7QcCL7Lr7mhHnmlgTq8fdvSpuXcrPtZl9Dljr7rMaS5ZgXVqv6yTjrkkbmes6ybgjdW0351wTkes6zjHuPhY4HbjKzI6rsz1y17Y0n8pmlc1NUdmssrmJWFQ2q2zejSrPIXP3jcB06jcVWAEMATCzHKA7UBq/PjAYWNXqgdbRSNyY2UnA9cCZ7r4zbp9Vwc/Fwb6HpiLWuOMnjNnd18fF+VfgsOB95M914FzqNJ9J07k+BjjTzJYCDwMnmtnf66SJ4nWdTNxRvK6bjDuC13ZS5zoQleu67rHXAk9Sv+liQ+c0Et8j0jwqm1NHZXOrU9mssrkpKptb8Vy36Xmee/fu7cOHD093GCIi0kbMmjWrFNgM1Izw+S5wmLuXpi+qzKKyWUREwpTKsjkn7AyjZPjw4RQV1RvlXEREZI+Y2RLgj8A7waqbVXFuHpXNIiISplSWzW268iwiydteXslbH6/nyJG9yO+grwaRhrj7vcC96Y5DRKQt27i9nM07Khnaq3O6Q5EMkKqyWX2eRQSAh99ezmX3F/HgjGXpDkVERETauRN/+wrH/d+0dIchsptQKs8Wc4GZ/TxYHmpmKZs3TkRabtvOSgC2llWmORIRERFp70q3lac7BJF6wnry/EfgKGKTbANsAe4KKW8RERERERGRtAqrY+MR7j7WzGYDuPsGM8sLKW8RERERERGRtArryXOFmWUTTEZtZn2A6mR2NLNsM5ttZk8HyyPMbKaZLTKzR2oq4WbWIVguDrYPDyl2ERERERERkUaFVXm+g9hE1n3N7FbgdeB/ktz3e8DCuOX/BX7v7qOBDcBlwfrLgA3uPgr4fZBOREREREREpNWFUnl2938APwF+BawGznL3x5raz8wGA2cAdwfLBpwIPB4kuR84K3g/MVgm2D4+SC8iIiIiIiLSqlrc59nMsoD33P0A4INm7n4bsUp312C5F7DR3WuG+10BDAreDwKWA7h7pZltCtKva0H4IiIiIiIiIk1q8ZNnd68G5prZ0ObsZ2afA9a6+6z41YkOkcS2+HyvMLMiMysqKSlpTkgiIiIiIiIiCYU12vYAYIGZvQ1sq1np7mc2ss8xwJlmNgHoCHQj9iS6h5nlBE+fBwOrgvQrgCHACjPLAboDpXUzdffJwGSAwsLCepVrERERERERkeYKq/J8U3N3cPfrgOsAzOx44Efu/lUzeww4G3gYuBh4KthlSrD8VrD9ZXdX5VhERERERERaXSiVZ3d/JYx8AtcAD5vZL4HZwD3B+nuAB82smNgT53NDPKaIiIiIiIhIg0KpPJvZkcAfgP2APCAb2Obu3ZLZ392nA9OD94uBcQnSlAHnhBGviIiIiIiISHOENc/zncB5wCKgE3B5sE5EREREREQk44XV5xl3LzazbHevAu4zszfDyltEREREREQkncKqPG83szxgjpn9GlgN5IeUt4iIiIiIiEhahdVs+0Ji/Zy/TWyqqiHAl0LKW0RSQEPXi4iIiIg0LKzRtpcFb3ewB9NWiYiIiIiIiERZWKNtLyHBgyt3HxlG/iLS+jRruoiIiIhIw8Lq81wY974jsSmlCkLKW0RERERERCStQunz7O7r414r3f024MQw8haR1HD1ehaROszsNDP70MyKzezaBNs7mNkjwfaZZjY89VGKiIikRljNtsfGLWYRexLdNYy8RUREJPXMLBu4CzgZWAG8Y2ZT3P39uGSXARvcfZSZnQv8L/CV1EcrIiLS+sJqtv3buPeVwFLgyyHlLSIpoD7PIlLHOKDY3RcDmNnDwEQgvvI8EbgxeP84cKeZmbu+UUREpO0Ja7TtE8LIR0RERCJjELA8bnkFcERDady90sw2Ab2AdSmJUEREJIXCarZ9dWPb3f13YRxHRFqP1/7UAyMRAcASrKv7BZFMGszsCuAKgKFDh7Y8MhERkTQIZcAwYn2cv0XsDvQg4JvAGGL9ntX3WUREJPOsAIbELQ8GVjWUxsxygO5Aad2M3H2yuxe6e2GfPn1aKVwREZHWFVaf597AWHffAmBmNwKPufvlIeUvIq0t6KKonooiEngHGG1mI4CVwLnA+XXSTAEuBt4CzgZeVn9nERFpq8KqPA8FyuOWy4HhIeUtIiIiKRb0Yf428DyQDdzr7gvM7GagyN2nAPcAD5pZMbEnzuemL2IREZHWFVbl+UHgbTN7klhfpy8A94eUt4ikgNf5KSLi7s8Az9RZ9/O492XAOamOS0REJB1C6fPs7rcClwIbgI3Ape7+qzDyFhERERFpr95ftZmdlVXpDkNECKnybGZ7AQvc/XZgLnCsmfUII28RERERkfaoaGkpE+54ja/+dWa6QxERwhtt+19AlZmNAu4GRgAPNbaDmQ0xs2lmttDMFpjZ94L1BWY21cwWBT97BuvNzO4ws2Ize8/MxoYUu4iwa6AwDfUjIpmkutrZsK286YQiGej7j8wBoGjZBg688Xmefm8VVdXtq6CuqKpOdwgitcLq81wdDCzyReB2d/+Dmc1uYp9K4Ifu/q6ZdQVmmdlU4BLgJXefZGbXAtcC1wCnA6OD1xHAn4KfIiIi0s4Mv/a/6Q5BJKW2lFXy7YdmA039i922jL7+2XSHIK3kxauPY1TfzJrVOKwnzxVmdh5wEfB0sC63sR3cfbW7vxu83wIsJDZH9ER2DTZ2P3BW8H4i8IDHzAB6mNmAkOIXafc8GCrMNWSYiERce3vyJiLSFp30u1fTHUKzhfXk+VLgm8Ct7r4kmBPy78nubGbDgUOBmUA/d18NsQq2mfUNkg0ClsfttiJYt7pOXlcAVwAMHTp0Tz6LiIiIRFh2lvHWdSeys6Ka4b3zd9tWWVVNlTvZZpRuK+fVRes4fHhPlq3fzisflbBo7VaO37sPz8xbzYefbqFHfi4Du3eiS4ccFq3dyiel2wEYUtCJTzeV0bNzHmu37Ewqrv0GdGPh6s2hf9624KT9+vHiwjV0yMliZ2XLmuHmZhsVVc2/gWLWcNek/LxstpXv+aBcedlZlDfSvLhTbjY7KpLLf2hB59rrEODFqz/LqL5dGt3H3amockq27mT7zkrWbtlJj865PL9gDZt3VFCydSfLS7fz3opNgK7V1tavWwfWbE7ue6O1HDu6N68tWpdwW6/8PNbX6e7So3MuG7dX0Dkvm+0t+FsA2H9gNxas2kzvLh1Yt3X389ClQw5bd1YCMO/GU1p0nHQIpfLs7u8D341bXgJMSmZfM+tCrM/09919s5k1mDTRoRPEMhmYDFBYWKhb0yJJcs1VJSIZZED3TgnX52Rn1f5z07dbR84+bDAAw3rlc9zefWrTfe0zI1o7RJGUMTPycoxBPWJ/F6P7xZrC7j+wezrDapGarhlLJ52R5khEdgmr2fYeMbNcYhXnf7j7E8HqNTXNsYOfa4P1K4AhcbsPBlalKlYRERERERFpv9JWebbYI+Z7gIXu/ru4TVOAi4P3FwNPxa2/KBh1+0hgU03zbhFpOT14FhERERFpWFjzPJ+TzLo6jgEuBE40sznBawKx5t4nm9ki4GR2Nf9+BlgMFAN/Ba4MI3YRERERERGRpoQ1YNh1wGNJrKvl7q+TuB8zwPgE6R24ak8DFJHG7ZrnWc+eRURERETqalHl2cxOByYAg8zsjrhN3YjN4ywiIiIiIiKS8Vr65HkVUAScCcyKW78F+EEL8xaRFKqd51kPnkVERERE6mlR5dnd55rZfOAUd78/pJhEREREREREIqXFA4a5exXQy8zyQohHRNLFd/shIiIiIiJxwhowbBnwhplNAbbVrKwzBZWIiIiIiIhIRgqr8rwqeGUBXUPKU0RSqHaeZz16FhERERGpJ5TKs7vfBGBmXWOLvjWMfEVERERERESioMV9ngHM7AAzmw3MBxaY2Swz2z+MvEUkNWrmd3b1ehYRERERqSeUyjMwGbja3Ye5+zDgh8BfQ8pbREREREREJK3Cqjznu/u0mgV3nw7kh5S3iKRATV9n9XkWEREREakvrAHDFpvZDcCDwfIFwJKQ8hYRERERERFJq7CePH8N6AM8ATwZvL80pLxFJAX0wFlEREREpGFhjba9AfhuGHmJiIiIiIiIRE0olWcz2xv4ETA8Pk93PzGM/EWk9e3q86xn0CLtnZkVAI8QK9eXAl8ObpTHpzkE+BPQDagCbnX3R1IbqYiISOqE1ef5MeDPwN3ECtA2ZfWmHVRWxSoUHXKy6NutY5ojEhERaVXXAi+5+yQzuzZYvqZOmu3ARe6+yMwGArPM7Hl335jqYEVERFIhrMpzpbv/KaS8Iue8yTNYun577fJ9lxzOCfv2TWNEIuGrmd9Zz51FBJgIHB+8vx+YTp3Ks7t/FPd+lZmtJTbmiSrPIiLSJrWo8hw06wL4j5ld+f/s3Xl8FdXdx/HPLwtBIOyrLAYEQURRiCBaLYoLrrRWrUuVWn3sY21ta2uLdV9L+7RWrd0oarW1al1arTuiqLiAgIBsArIvsoU9Ievv+eNOwk24WSCTuyTf9+t1X7kzc+bML3Mn9+TMnIXIYGGF5dvdPa8++SeLn48ewK7CErYXFHPPKwvZsGNPokMSERFpSF3cfT2Au683sxrvGJvZMKAZ8EU8ghMREUmE+j55nknkQZUFyzdGbXOgTz3zTwpnHtkNgI0793DPKwspLtOzOWl8NM+zSNNiZm8BXWNsunk/8+lGZKrKse5eVk2aa4BrAHr16rWfkYqIiCSHelWe3b13WIHUlZmNBh4E0oGJ7j4+XsfOTIvM7FVaGvN/AxERkZTh7qdWt83MNphZt+CpczdgYzXpWgOvALe4+8c1HGsCMAEgNzdXt+hERCQl1WueZzM71sy6Ri1fYWYvmtlDUU26Q2Nm6cAfgDOBgcAlZjYw7ONUJz098oC9RE+epRFz9XoWEXgJGBu8Hwu8WDWBmTUj0l3rCXd/No6xiYiIJES9Ks/AX4AiADM7CRgPPAFsJ7jDHLJhwFJ3X+buRcDTRAY1iYvyJ8/FpapciIhIozYeOM3MlgCnBcuYWa6ZTQzSXAScBHzbzGYHr6MTE66IiEjDq2+f5/SoQcG+CUxw9+eB581sdj3zjqU7sDpqeQ0wPDpBQ/arSk+LPHn+dNVWnp6+KtS8RRJt8YadACzZsEvXtzQ6/bq0YughoTeIarTcfQswKsb6GcDVwft/AP+Ic2giIiIJU+/Ks5lluHsJkUL2mhDzjsVirKv0GLgh+1VlphsdWzXjzQUbeHPBhjCzFkka05bnMW15oxgoX6TCt4/PUeVZRCSFnDKgM28vijncgkjC1LeC+xTwrpltBgqA9wHMrC+RptthWwP0jFruAaxrgOPEZGa897OT2V5QHK9DisRVi8wM8otLEh2GSOhaNGuI+7kiItJQHhmbqxlAJOnUd7Tte81sMtANeNO94hJPA35Q3+Bi+AToZ2a9gbXAxcClDXCcarVolqF/wqRRa0NmokMQERGRJs7MsFhtTkUSyDzFbumY2VnAA0SmqnrU3e+tIe0mYGWw2BHY3PARhioVYwbFHU+pGDMo7nhKxZgheeM+xN07JTqIVKayOWEUd/ykYsyguOMpFWOG5I07bmVzylWeD5SZzXD33ETHsT9SMWZQ3PGUijGD4o6nVIwZUjdu2T+p+DmnYsyguOMpFWMGxR1PqRgzpG7cYarvVFUiIiIiIiIijZ4qzyIiIiIiIiK1aEqV5wmJDuAApGLMoLjjKRVjBsUdT6kYM6Ru3LJ/UvFzTsWYQXHHUyrGDIo7nlIxZkjduEPTZPo8i4iIiIiIiByopvTkWUREREREROSAqPIsIiIiIiIiUotGUXk2sxVm9pmZzTazGTG2m5k9ZGZLzWyumQ2J2jbWzJYEr7FJFPNlQaxzzexDMxtc130THPdIM9sebJ9tZrdFbRvQEDbmAAAgAElEQVRtZp8Hn8O4JIr5xqh455lZqZm1r8u+DRx3WzN7zswWmdlCMxtRZXvSXdd1jDvpru06xJx013Ud4066a9vM+kfFNNvMdpjZj6qkScprW/ZPHb57k+5zrkPMSff9Vce4k+47rA4xJ933V3Bslc3JE3PSXdd1jDvprm1T2Vx37p7yL2AF0LGG7WcBrwEGHAdMC9a3B5YFP9sF79slSczHl8cCnFkec132TXDcI4GXY6xPB74A+gDNgDnAwGSIuUrac4G3k+RcPw5cHbxvBrStsj3prus6xp1013YdYk6667oucVdJmzTXdpXz9yVwSJX1SXlt67Xfn29t5UXSfc51iDnpvr/qGHfSfYftz/lKpu+vOpQXSXdd1zHupLu26xBz0l3XdYm7StqkubarnD+VzdW8GsWT5zoYAzzhER8Dbc2sG3AGMMnd89x9KzAJGJ3IQMu5+4dBTAAfAz0SGU8IhgFL3X2ZuxcBTxP5XJLNJcBTiQ7CzFoDJwGPALh7kbtvq5Is6a7rusSdbNd2Hc91dRJ2XR9A3ElxbVcxCvjC3VdWWZ9017Y0iJT7nJPt+ysEKpv3g8rm+FHZnFAqm2vQWCrPDrxpZjPN7JoY27sDq6OW1wTrqlsfD7XFHO0qInd6DmTfsNXl2CPMbI6ZvWZmRwTrkv5cm1kLIn/sz+/vvg2gD7AJeMzMPjWziWbWskqaZLyu6xJ3tGS4tusac7Jd13U+10l2bUe7mNj/NCTjtS37T2Vz/Khsjg+VzSqba6OyuZGXzY2l8nyCuw8h0szkOjM7qcp2i7GP17A+HmqLGQAzO5nIl9jP93ffBlLbsWcRaeYxGPg98J9gfdKfayJNZz5w97wD2DdsGcAQ4E/ufgywG6jaZycZr+u6xA0k1bVdl5iT8bqu87kmua5tAMysGXAe8GyszTHWJfralv2nsjl+VDbHh8pmlc21Udm8d32j1Cgqz+6+Lvi5Efg3keYa0dYAPaOWewDraljf4OoQM2Z2FDARGOPuW/Zn34ZS27HdfYe77wrevwpkmllHkvxcB/a505bAc70GWOPu04Ll54h8GVdNk1TXNXWLO9mu7VpjTsbrmjqe60AyXdvlzgRmufuGGNuS8dqW/aSyWWVzfWKOkkzfXyqbVTbXRmVzIy+bzT21bwwETSHS3H1n8H4ScJe7v96xY0fPyclJbIAiItJozJw5c7O7d0p0HMlOZbOIiMRLPMvmjHgcpIF1Af5tZhD5ff7p7q+b2f8OHTqUGTPiOoOBiIg0YmZWdQCVlGaR6VGeAXKIjPJ6UdSgQdHpxgK3BIv3uPvjVba/BPRx90HBKpXNIiISF/Esm1O+2XYwkt7g4HWEu98brP9zomMTSSVrtubzy1cXsjovP9GhiEj8jAMmu3s/YDIx+uYFFezbgeFEmhDebmbtorafD+yK3kdls0h4np6+io079yQ6DBGhEVSeRSQcL85ex1/eW8azM9ckOhQRiZ8xROYkJfj5tRhpqp2GxMxaATcA98QhVpEmZ922Asa98Bn/88TMRIciIoRUebaIb5nZbcFyLzOLdwd3EamHsjKv9FNEmoQu7r4eIPjZOUaamqYhuRv4LVBjkxUzu8bMZpjZjE2bNtU/apEmojQokzfvLExwJCIC4T15/iMwgshE3wA7gT+ElLeIiIgcIDN7y8zmxXiNqWsWMda5mR0N9HX3f9eWgbtPcPdcd8/t1EnjrYnsr+0FxYkOQUQIb8Cw4e4+xMw+BXD3rcE8YSIiIpJA7n5qddvMbIOZdXP39WbWDdgYI9kaYGTUcg9gCpGb5kPNbAWR/yc6m9kUdx+JiIRizpptAOwqLElwJCIC4T15LjazdIIJsc2sE1AWUt4iIiLSMF4CxgbvxwIvxkjzBnC6mbULBgo7HXjD3f/k7ge7ew7wFWCxKs4i4SooKk10CCISJazK80NEJvLubGb3AlOB++qyo5mlm9mnZvZysNzbzKaZ2RIze6b8CbaZZQXLS4PtOSHFLiIi0lSNB04zsyXAacEyZpZrZhMB3D2PSN/mT4LXXcE6EWlgrmFIRJJKKM223f1JM5sJjCLSN+pr7r6wjrv/EFgItA6WfwX8zt2fNrM/A1cBfwp+bnX3vmZ2cZDum2HELyIi0hS5+xYiZXfV9TOAq6OWHwUerSGfFcCg6raLyIEpU+1ZJKnU+8mzmaWZ2Tx3X+Tuf3D3h+tacTazHsDZwMRg2YBTgOeCJNHTZkRPp/EcMCpILyIiIiLS6GgCDJHkUu/Ks7uXAXPMrNcB7P4A8DP29o/uAGxz9/JREaKnw6iYKiPYvj1IX4mmwxARERGRxkBPnkWSS1ijbXcD5pvZdGB3+Up3P6+6HczsHGCju880s5Hlq2Mk9Tps27vCfQIwASA3N1ffOCIiIiKSklyVZ5GkElbl+c4D2OcE4DwzOwtoTqTP8wNAWzPLCJ4u9wDWBenXAD2BNWaWAbQBNGCJiIiIiDRKqjqLJJewBgx79wD2uQm4CSB48vxTd7/MzJ4FLgCepvK0GeXTaXwUbH/bdTtORERERBqpMnV6FkkqoUxVZWbHmdknZrbLzIrMrNTMdhxgdj8HbjCzpUT6ND8SrH8E6BCsvwEYV//IRURERESSk+rOIsklrGbbDwMXA88CucAVQL+67uzuU4ApwftlwLAYafYAF9Y/VBERERGR5LctvyjRIYhIlLAqz7j7UjNLd/dS4DEz+zCsvEVEREREmpoWWaH9qy4iIQjrLzLfzJoBs83s18B6oGVIeYuIiIiINDka3UckuYTS5xm4HEgHvk9kqqqewDdCyltE4kDls4iISHLRPM8iySWs0bZXBm8LOLBpq0RERERERESSViiVZzNbTowHV+7eJ4z8RaTh6ea2iIhIctGsrCLJJaw+z7lR75sTGRW7fUh5i4iIiIg0Oao7iySXUPo8u/uWqNdad38AOCWMvEUkPly9nkVERJKK5nkWSS5hNdseErWYRuRJdHYYeYuIiIiINEW6sS2SXMJqtv3bqPclwArgopDyFpE4UNMwERGR5PL6vC8THYKIRAlrtO2Tw8hHREREREQiFn25M9EhiEiUsJpt31DTdne/P4zjiEjD8YqfegQtIiIiIlJVKAOGEenjfC3QPXj9LzCQSL9n9X0WEREREdlPfTu3SnQIIhIlrD7PHYEh7r4TwMzuAJ5196tDyl9EGlrQ6Vl9n0VERJLDqYd3YenGXYkOQ0QCYT157gUURS0XATkh5S0iIiIi0uSYJToCEYkW1pPnvwPTzezfRLpOfh14PKS8RSQOvMpPERERSSzVnUWSS1ijbd9rZq8BJwarrnT3T8PIW0RERESkKdKTZ5HkEtZo24cC8919lpmNBE40s+Xuvi2M/EWk4ZX3dVafZxERERGRfYXV5/l5oNTM+gITgd7AP2vawcx6mtk7ZrbQzOab2Q+D9e3NbJKZLQl+tgvWm5k9ZGZLzWyumQ0JKXYREZEmqboyN0a6sUGaJWY2Nmr9FDP73MxmB6/O8YtepPEzNdwWSSphVZ7L3L0EOB940N1/DHSrZZ8S4CfufjhwHHCdmQ0ExgGT3b0fMDlYBjgT6Be8rgH+FFLsIsLe+Z01z7NIk1JdmVvBzNoDtwPDgWHA7VUq2Ze5+9HBa2M8ghZpKtJUdxZJKmFVnovN7BLgCuDlYF1mTTu4+3p3nxW83wksJDJH9Bj2Djb2OPC14P0Y4AmP+Bhoa2a1VdBFRESketWVudHOACa5e567bwUmAaPjFJ9I06ZOzyJJJazK85XACOBed19uZr2Bf9R1ZzPLAY4BpgFd3H09RCrYQHkTsO7A6qjd1gTrRCQEruG2RZqi6srcaLWVv48FTbZvNYv9n76ZXWNmM8xsxqZNm8KKXaTxixqIZMXm3QkMREQgpMqzuy9w9+vd/algebm7j6/LvmbWikif6R+5+46aksY6dIz8VECLiIgEzOwtM5sX4zWmrlnEWFde/l7m7kcSmW3jRODyWBm4+wR3z3X33E6dOu3/LyHSREX/o/uvGaurTSci8RHWk+cDYmaZRCrOT7r7C8HqDeXNsYOf5f2n1gA9o3bvAayrmqcKaJEDowfPIo2Tu5/q7oNivF6k+jI3WrXlr7uvDX7uJDJQ6LCG/F1EmproGTD+OOWLxAUiIkACK89B065HgIXufn/UppeA8pE8xwIvRq2/Ihh1+zhge3lTMxERETkg1ZW50d4ATjezdsFAYacDb5hZhpl1hIqb4ecA8+IQs0iToUE8RZJLKJVnM7uwLuuqOIFI865Toqa4OAsYD5xmZkuA04JlgFeBZcBS4K/A98KIXUQi9s7zrIJapAmJWeaaWa6ZTQRw9zzgbuCT4HVXsC6LSCV6LjAbWEukfBaRBnLRXz5KdAgiTVpGSPncBDxbh3UV3H0qsftRAYyKkd6B6w40QBEREanM3bcQu8ydAVwdtfwo8GiVNLuBoQ0do0hTVvV+9vTleYkJRESAelaezexM4Cygu5k9FLWpNZF5nEUkRVTM86wHzyIiIkkhVpH80OQlXD+qX9xjEZH6N9teB8wA9gAzo14vEZkXUkRERKTBTFu2hZxxr/Dl9j2JDkUkdO6QmV65oeb9kxYnKBoRqVfl2d3nEJnPeaq7Px71esHdt4YTooiIiEhs35zwMQDf/cfMBEciEj7HMTP6dGpZaf3W3UUJikikaav3gGHuXgp0MLNmIcQjIonilX6IiCS9DTv2Pm2es3pbAiMRaTgGvP2TkZXWHXP3pITEItLUhTVg2ErgAzN7CdhdvrLKFFQiIiIioSgrc4bfNznRYYg0rBruaL/22XrOPLJb/GIRkdDmeV4HvBzklx31EpEUUV4+a8AwEUkF+qqSpsABq2ZummufnMX8ddvjGo9IUxfKk2d3vxPAzLIji74rjHxFREREYolVn5ixIo/cnPZxj0Wkobg7FlztT19zHBcHffzLnf3QVJpnpvH+z06hU3ZWIkIUaVJCefJsZoPM7FNgHjDfzGaa2RFh5C0i8eHBI2fX8xwRSVEX/Pkjcsa9wtptBSz6ckeiwxGpN/e9T56P69MhZpo9xWUce+9bcYxKpOkKq9n2BOAGdz/E3Q8BfgL8NaS8RURERCrZsae42m0njH+b0Q+8X7G8s4a0IskuupXFj089rNp073y+seGDEWniwqo8t3T3d8oX3H0K0LL65CKSbMr7OqvPs4ikgqPvqn204cNvfZ3/fLqWI+94k9fnfRmHqETCVbVI/uGp/apNe+Vjn3DDM7MbNiCRJi6syvMyM7vVzHKC1y3A8pDyFhEREdlvBcWl/CioTLy7uPJTue35xazdVlBpXd7uIl6Zuz5u8e2P3YUlFBSVVrvd3RtNU/XdhSW8MGtNtdu35Rfx6aqtldZ9siKPqx//hNKy1LwDvD2/mJLSsn3WR5ptV+7h/9YNJ/G9kYfGzOeFT9cybdmWvfkWFFNUsm+++2Ppxp0Ux4gtlcxatZV5azW4mtRfWJXn7wCdgBeAfwfvrwwpbxGJg9T8d0NEBI44uHWtaZ6avpqcca9UvAbf9SYnjH+b6cvzWLM1nxdnr2XI3ZO47p+zmPDeF4x5eGocIo9U+j5cujnmtoKiUu767wJ2F5ZwxO1vMPy+6vu1/mvGakY/8D5T6tB0t6zMeXH2WsoasKI5e/U28otKKq2buTKPnz03h1G/ncLLc9fts8/yzZHZTu94aT43/GsOkxduYNDtb3Dq/e/y+rwvmbpkMy/OXstZD77P1//4YcVnOX/ddi6bOI23Fm5k3bYCCkv23mTILyrhi027KCwpZevuIgBWbN7NWQ++z8L1+3+z4ZW563lzfqQVw6ot+Qy79619bsJAZA7yqr9/tLIyZ3dhScXvPfiuN+l782v8/aMV/Pr1RRXpHN9ncLy+nbP52egB1eb9zQkf773O73yTw255DYDNuwr5yb/msKe4lOnL8ypV1vOLSvjKryJ/D9HWbivg1Pvf495XFlZ7vGjRXSRWbcmntMyZt3Y767cX8Mb8L3ngrcXkjHuFfje/ysfLtrBi827Oe3gqm3YWUlRSxsT3l1FQVMrE95fxz2mrWJ2Xz+q8/GqP98jU5eSMe4V12wpYnZfPyi27K24yrdtWwOZdhcxZvY3z//gh5/x+KsWlZcxbu52cca/wyYo8ht/3Fss21T7OcfTn5e58vGwL7s4nK/Ji3vSYuTKPLzbtYnt+5S4j5Z9L1X32FJdWusnxzqKNzF2zbZ80v5u0uOIG0X2vLmTc83NZu62AVVvyufU/8/hw6WbcnRWbK2YOpri0jNV5+RSWlFb623jgrcX8d07k73D26m387YO6PffcsquQnHGv8ONnZvPczDW8Mf9LfvnaQlZtqfw5PTJ1Of+ctooNO/YwfXkeF0/4iD3Fe48/fXleSt7sMm/EbTRzc3N9xowZiQ5DJCXc/fICHpm6nG8fn8Md52m8P5FYzGymu+cmOo5UFlbZnDPulYr3K8afXWk5bN8+PofbzhlIWpqxdOMunp2xmnFnDqh4IujufPfvM7l25KEccXAbMtNtn6eFEPmnc8rnm+iUnUXn1lkM6Nq6Iu4V48+uSFdUUsaZD77HF5t275NHdLq83UXs2lNCrw4t+J8nZjBpwQZuOnMAI/t3pn/XyjOG/vTZORzZvQ1jj8/h8Q9XcPtL8/nZ6P5cftwhZDfPBGDxhp1MW7aFy0fkVMTRLCONLzbtolN2Fq2DdNF2F5awZmsBj32wnLvGDKJZRhofLt3MpROn0b9LNm0OymT6irx99qv6u4T9+U37xSgenLyEf05bVWO6D8adQqtmkclnlm/ZzY3PzuGKEYdw6fBD2JZfRMusDJpnplNQVMp/Zq/lphc+A+AbQ3qwu7CE1+d/ybUjD6VLdhZDD2nP4g07eWnOOt5dvAmAR7+dyykDugDw5fY9zF69jR8/M5sT+nbkrYUbeOiSY7j+qU/3iWvF+LOZt3Y75/x+KpnpxpJ7z9onzZG3v8HOwuor6LUZ0acDOR1bcv2ovqzaks83J3zMgK7ZnDKgM1d9pTdD73mLti0y2RZVAVx092iaZ6ZTXFrGg28t4ZTDO/PZmu2MHtSVacvzuP6pT/nOCb0pKC7lqek1n/v99dL3T+C8hz9geO/2PHzpENq1yKTvza/VO99vH58DwN8+XFGx7u6vDeK+VxYy4YqhZDfP5I/vLOXNBRt49n9H8Mrc9ZXSAhXnrbi0jL++v28l9OazDmdPcSm/nbQYiJz7krIyPlmxlQuG9uC5mWvIykjjkmG9OKRDC+787wIA+nfJ5qoTe/Oz5+ZWyu/5a0fwjT99VOPvdWinltxyzkBenrOe56Nacnx80yhuemEu73weuUbPP6Y7L3y6FoBHxubSpXVz2hyUyS/+/RlnDupGRppx0bE9gUhF/OfPza1IH8uH406hbYtMBt72Rszt/7hqODNXbuV3by3mxjP6c93JfWv8PeoinmVzKJVnMzsM+CmQQ9T0V+5+Sr0zrwdVnkXq7q7/LuDRD5YzdsQh3DlmUKLDEUlKqjzXX9iV5+k3j6JzdnPmrN7GmD98UO98q/Pdk/rwl/eW7bP+P9edwNeqHPfiY3tyxqCujDysEx9+sYXLJk7j+lP68tDbSyulG9A1m0Vf7gTg7jFH8O7iTWRlpNMpO2uff86jvXvjSA7p0JLDb32dguLSGm8ePPDNo+ne7iAu/HPkH+2cDi1YUeUJUbc2zTnnqG4x/+mPdsvZh5PToSUtmqVz6cRpNaZtLNq2yKRjqyyWbkzsLKzRNxrKlbdISITjD+3Ah19sqT2hSA2OOLg1r1x/Yr3zScXK8xzgz8BMoOJ5vLvPrHfm9RBWAT1v7XYKg6YUWRlpDOzWmrS0amasF0lRqjyL1E6V5/oLu/IcXalwd3rf9Gq98xZJNrEqz+UastWFSEOr6dquq3iWzRm1J6mTEnf/U0h5JZ3v/3NWpbu0f/7WEEYP6pbAiETCVz6/c+PtyCEijZ2Z8c5PR3Lyb6YkOhSRuPn8ntH0v+X1RIch0iTUq/JsZu2Dt/81s+8RGSyssHy7u8fu5JJifn3BYAqKIwNN/OiZ2ZX6foiIiEj89Wh3EMN6t99nfe+OLVkx/myG3fsWG3cWxthTpHHJykhPdAgiTUZ9nzzPJPKgqrwN841R2xzoU8/8k0J54bwpKISLU3BkOJHaaJ5nEUklA7q2plf7FtVun/yTr1JQXEpGWhrn/n5qzBGRRRqLD8adwsotu+nQMovJizbw69c/T3RIIo1SvSrP7t47rEDqysxGAw8C6cBEdx8fr2NnBP2cYw1JLyIiIvEzcWzN3duym2dWjCL9wbhT+OiLLewuLMEMrnpcg4lK49K97UF0b3sQAP27ZnN0z7Z8sXEXt744P8GRiTQu9Zrn2cyONbOuUctXmNmLZvZQVJPu0JhZOvAH4ExgIHCJmQ0M+zjVyUiPVJ5TcU4ykbpy9XoWkUZoxKEdOHVgF0Yd3oUV48/mzR+fxMxbTuU/153AhMuHcvyhHXj2f0dU3CiXxOnTsSVv/+Sr3FnDtIm5h7SreH/+kO7Vprvl7MOr3XbSYZ0OLMA4G3/+kfu9z/GHduTyETl8dsfpvPnjkwDo0LJZ2KHtl5P7d+LO847gi/vO4rcXDm6w47RrkckfLxtSbcuUr4b8uWc3j/0s8i+XD+XcwQdz2zkDyUgzLhzag19/46jQjvvXK3L5eTDf93mDD65Yf/GxPflmbs/QjlOb4/q0p0/HljG3PXn1cADSg+/V84/pzu3n7q26zb/zjIYPMGT1bbb9F+BUADM7CRgP/AA4GpgAXFDP/KsaBix192XBMZ8GxgALQj5OTJnpkXsNxaWqXIiIiKSyw7pE5kHu0CoLgNOPiDwLWHrfWfzrk9Vs2lVYMf/oI1OX06djS6782yeM6NOB4tIyrh156H4/wX7w4qPZtLOQX7/xOX+4dAhPfLSCZulpTF60scb9Lj/uENoclMn8ddsr5matj7OP6sYrc9dXLJdPfVWXUZtPOqwT7wVzGB+b045PVmyttP2rh3WqmOP4G0N6kJWZts9cy9ef0pfTBnYlM8MY/cD7APzruyPo0jqLdi2bVcwn3adTK4b3ac/sVdtYvGEXPzy1H+8t3sRX+nYku3kG+cWlZGdlYGYM792epz9ZzaNjj8WBFs3SMYv0B776xMq9CKct28LGnYWcO/hgPlmRx1PTVvHCp2u549yB3PHfvf9SPjI2l3cXb+K4Ph0AKCgqpV+XVrRolkHfzq3ILyph+ebd/Ojp2SyJmspq7h2ns2pLPk9NX8WT01Zxy9mHc9VXejP+tUVMXrSRpRt3MaBrNv93wWCO7NGGHXuKue+VhZxz1MEc06stpe4x59Q+EOUtMBbcdQaZ6WlkpqexdlsBJ4x/m9vOGUjP9i04bWAXdhWWMOj2Nzi0U0v+duUwSsqck38zhUfG5nL7S/PJSDNWbMnn4mN78tna7cxft6PaY1YdPbmgqJT0NKNZxt5ndt8Y2oPi0jLyi0q56+UF3HDaYfTr3IrtBcVkpKdxcv9OtMzKoKi0rOIzvugvHzF9+b7DKc278wx27ilmxC/fJjsrg09vOx2As46MDO4bPQp/eWzuzt8+XMGZg7px3C8nc+nwXrz46VoGdGvNzJVbmXhFLqcO7EL/W16jsKSMhy89hnOO2ltBPf+PH9C2RTMe/faxlJSWMf61RVw78lC2FxTzzIzVbNxRyBlHdOWM4HvlO1+JNNYtK3O2FxRz5pFdGf/aIn5z4WCaZ6Yzd802OrbK4uCg9UB+UQnN0tPISN97zr7+xw/4dNU2AAZ1b81pA7tw2sAuXHZcL1o3z+S2cweSmZZGmxaRa+fakYcy8jdT+J8Te3P5cTnMW7edo3q0oUe7yE2FRV/uYPQD73PP1wbx+Zc7+fvHKwG49+uDWLJhF3/7cAXHH9qBBy4+ms7ZzXH3irnnWzfPoHPr5pU+h5te+Ix3P9/Iuu17aNksnfl3ja50zldtyadb2+Zkpqdx5Qlxb7wcmnpNVWVmc9x9cPD+D8Amd78jWJ7t7keHEuXe410AjHb3q4Ply4Hh7v79qDTXANcA9OrVa+jKlStDO35xaRn9bn6Nw7u1ZkDX7NDyFUkGc1ZvY9nm3fTu2JKje7ZNdDgioTqxX0fOH9Kj3vloqqr6C2uqqmRR/n9UYUkZs1Zu5dKJ03j+2uMZ0qstuwpLWLBuB/27ZtMyK6PiJnxNVufls2TjTk7u35nSMmfnnhLaVXliuDovnxv+NZs/fWsoxaVlLNmwa5+nqJ+t2c65D0/lrRtOorCkjDEPf0BJmfP4d4ZVPHlbvGEnL8xay89H98ds3yfuL89dx5TPN/HczDUAXHlCDrefewTXPTmLVz5bz/0XDebxj1YyZ/U2Ft41muaZaRX5LN24k57tW5CVkc5Pn53DczPX8NcrcslunlFRGQUqKtphPw08UO8s2siVf/uEu8YcwRUjcuq0T2mZsy2/iMyMNLbnF9MzeOKZX1TChPeWcd3Jfev02ceTu+/zmZdfy7GuBYh8ViP6dKBZRhplZU5amlFYUsqe4jLaHJTJ6rx8dheVMKBr6/2KZdPOQjplZ9WabvLCDVz1+AxevO4EerVvQfPMdIpKyioqi5+t2U6X1ln7VOoArntyFi2z0vn1Bfs+8d66u4js5hmVKqrl8nYXMXXp5kpPdhOlrMzJyy+iY6vaz1W5z7/cyaGdWsb83apanZdPQXEph3XJpri0jDv/O5/rR/Wjc/a+57MmkxduoH/X7IpKejykzDzPZjYPONrdS8xsEXCNu79Xvs3dQ50s1swuBM6oUnke5u4/iJU+7ALa3fnO3z6puOsi0tisysuvcQAekVR1/pDu/OjUw+qdjyrP9dfYKs+pYueeYkrLnLYtDqzpbmFJKZlpaaSlGUs37uS7f5/J89cef8D5JbOZK7cypFfbasND1VMAACAASURBVCuRkjh7iktpnqnRxaWyVJrn+SngXTPbDBQA7wOYWV9gez3zjmUNEN2IvwewrgGOE5OZ8diVw+J1OBEREZFQZNezGXD0dEh9O2cz+Scj6xlR8hoa1Z9akosqzpJo9R1t+14zmwx0A970vY+x04j0fQ7bJ0A/M+sNrAUuBi5tgOOIiIiIiIiIVKhXs+1EMLOzgAeITFX1qLvfW0PaTcCBdHruCGw+sAgTSnHHTyrGDIo7nlIxZlDctTnE3ZOjc2aKUtmcMlIx7lSMGRR3PKVizKC4axO3sjnlKs/xYGYzUrFPm+KOn1SMGRR3PKVizKC4JXml6mesuOMnFWMGxR1PqRgzKO5kklxD/4mIiIiIiIgkIVWeRURERERERGqhynNsExIdwAFS3PGTijGD4o6nVIwZFLckr1T9jBV3/KRizKC44ykVYwbFnTTU51lERERERESkFnryLCIiIiIiIlILVZ5FREREREREatGkKs9m1t/MZke9dpjZj6qkuczM5gavD81scNS2FWb2WbDvjCSLe6SZbY9Kc1vUttFm9rmZLTWzcUkU841R2+eZWamZtQ+2JeRcB8f+sZnND2J6ysyaV9meZWbPBOdzmpnlRG27KVj/uZmdkWRx32BmC4Jre7KZHRK1rTTqs3gpiWL+tpltiort6qhtY81sSfAaG6+Y6xj376JiXmxm26K2JeRcB8f+YRDz/Kp/j8F2M7OHgmt4rpkNidqWkPNdh5iT7jtb9k8dy4uk+5zrGLfK5vBiV9mcPDGrbA43bpXNqcTdm+QLSAe+JDKpdvT644F2wfszgWlR21YAHZM07pHAy9Wk/wLoAzQD5gADkyHmKmnOBd5O9LkGugPLgYOC5X8B366S5nvAn4P3FwPPBO8HBuc3C+gdnPf0JIr7ZKBF8P7a8riD5V1Jeq6/DTwcY9/2wLLgZ7vgfbtkibtK+h8AjybyXAfHHQTMA1oAGcBbQL8qac4CXgMMOK78+y9R57uOMSf1d7Ze+/2Zq2xOcMxV0qhsbvi4VTbHKe4q6VU2N2zMSf2dXZ9Xk3ryXMUo4At3Xxm90t0/dPetweLHQI+4R1azmHHXYBiw1N2XuXsR8DQwpsGii60uMV8CPBWneGqTARxkZhlEvhjWVdk+Bng8eP8cMMrMLFj/tLsXuvtyYCmR8x8vNcbt7u+4e36wmCzXdm3nujpnAJPcPS/4e50EjG6gGGPZn7iT5do+HPjY3fPdvQR4F/h6lTRjgCc84mOgrZl1I3Hnu9aYU+A7W/aPyub4UdkcHyqb40dls8rmBteUK88XU/sfzVVE7vSUc+BNM5tpZtc0WGQ1qynuEWY2x8xeM7MjgnXdgdVRadYE6+KpxnNtZi2I/LE/H7U6Iefa3dcCvwFWAeuB7e7+ZpVkFec0+NLYDnQggee6jnFHq3ptNzezGWb2sZl9rQFDrbAfMX8jaPbznJn1DNalxLkOmt/1Bt6OWh33cx2YB5xkZh2Cv7mzgJ5V0lR3XhN1vusSc7Rk/M6W/aOyOX5UNjcwlc3Jea5VNtdbky6bm2Tl2cyaAecBz9aQ5mQiH/bPo1af4O5DiDQ/uM7MTmrQQPeNqaa4ZxFpejUY+D3wn/LdYqSN2/xkdTnXRJqFfeDueVHrEnKuzawdkTt8vYGDgZZm9q2qyWLs6jWsb3B1jLs87beAXOD/olb3cvdc4FLgATM7tIFDrmvM/wVy3P0oIs2Cyp8qpMS5JvLP6XPuXhq1Lu7nGsDdFwK/InJn+nUizRhLqiRLqmu7jjEDyfmdLftHZbPK5uqobFbZXBuVzZXWN6imXjY3ycozkQ9rlrtviLXRzI4CJgJj3H1L+Xp3Xxf83Aj8m/g2+4Ea4nb3He6+K3j/KpBpZh2J3IWKvhvUg7o3vwlDjec6sM/d7wSe61OB5e6+yd2LgReI9NuIVnFOg6ZBbYA8Enuu6xI3ZnYqcDNwnrsXlq+POt/LgCnAMckQs7tviYrzr8DQ4H3Sn+tATdd2PM91+bEfcfch7n4SkWt2SZUk1Z3XhJ3vOsSczN/Zsn9UNsePyub4UNmcZOc6oLK5nppy2WzucbvRGXcdO3b0nJycRIchIiKNxMyZMze7e6dEx5HKVDaLiEiY4lk2Z8TjIImSk5PDjBmpNwK6iIgkJzOr64BQUg2VzSIiEqZ4ls1Ntdm2iFQxc2UeY/7wAdOWbak9sYg0CmbW3swmWWSO0ElBv8FY6WqcS9TMXjKzeQ0fsUjTUlbm/M8TM5i+PK/2xCLS4EKpPFvEt8zstmC5l5mlVPt1kabuw6VbmLN6G+8v2ZzoUEQkfsYBk929HzA5WK7EzNoDtwPDifRNuz26km1m5wO74hOuSNOyeVchkxZs4HtPzkp0KCJCeE+e/wiMIDJnGsBO4A8h5S0iIiINI3pu3MeBWFO0VDuXqJm1Am4A7olDrCJNTlFpGQBb84sSHImIQHh9noe7+xAz+xTA3bcG0yCIiIhI8uri7usB3H29mXWOkaamuUTvBn4L5DdolCJNVN7uSKW5tKzxDvArkkrCqjwXm1k6wdxiZtYJKAspbxERETlAZvYW0DXGppvrmkWMdW5mRwN93f3HZpZTSwzXANcA9OrVq46HFZFZK7cmOgQRiRJW5fkhIvN0dTaze4ELgFtCyltEREQOkLufWt02M9tgZt2Cp87dgI0xkq0BRkYt9yAyD+oIYKiZrSDy/0RnM5vi7iOr7I+7TwAmAOTm5uoRmkgdrd5akOgQRCRKKJVnd3/SzGYCo4jcof6auy8MI28RERFpMC8BY4Hxwc8XY6R5A7gvapCw04Gb3D0P+BNA8OT55VgVZxE5cEf1aJPoEEQkSr0rz2aWBsx190HAogPYPx2YAax193PMrDfwNNAemAVc7u5FZpYFPAEMBbYA33T3FfWNX0REpAkbD/zLzK4CVgEXAphZLvC/7n61u+eZ2d3AJ8E+dwUVZxFpYC2ahdVIVETCUO/Rtt29DJhjZgfaiemHQPRT6l8BvwumzdgKXBWsvwrY6u59gd8F6UREROQAufsWdx/l7v2Cn3nB+hnufnVUukfdvW/weixGPiuCm+giEqLXPluf6BBEJEpYU1V1A+ab2WQze6n8VdtOZtYDOBuYGCwbcArwXJAketqM6Ok0ngNGBelFRERERBqd8qmqRCQ5hNUW5M4D3O8B4GdAdrDcAdjm7iXBcvR0GBVTZbh7iZltD9Jvjs5QI3qKiIiISGNQrMqzSFIJa8Cwd/d3HzM7B9jo7jPNbGT56ljZ12FbdCwa0VNEREREUt5hXbJ5Y/6GRIchIoFQmm2b2XFm9omZ7TKzIjMrNbMdtex2AnBeMMXF00Saaz8AtDWz8kp9D2Bd8H4N0DM4XgbQBtCAJSIiIiLSKA3u0RaAUQM6JzgSEYHw+jw/DFwCLAEOAq4O1lXL3W9y9x7ungNcDLzt7pcB7xCZJxoqT5tRPp0Gwfa33V1PlkVERESkUSopi/yrm5amYX5EkkFYlWfcfSmQ7u6lwUicIw8wq58DN5jZUiJ9mh8J1j8CdAjW3wCMq2fIIiIiIiJJq6A4MgxQhirPIkkhrAHD8s2sGTDbzH4NrAda1nVnd58CTAneLwOGxUizh2D+SRERERGRxu6+VxcB8MkK9VQUSQZhPXm+HEgHvg/sJtI3+Rsh5S0iIiIi0uSkB7OyZmWkJzgSEYGQKs/uvtLdC9x9h7vf6e43BM24RSRFaAABERGR5HL9qH4AjDi0Q4IjEREIqdm2mS0n9rRRfcLIX0RERESkqSnv6qwuzyLJIaw+z7lR75sT6ZvcPqS8RSQONHa9iIhIcnlzQWSO56KSsgRHIiIQXrPtLVGvte7+AJF5m0VERERE5AC8vWgjAGu2FiQ4EhGB8JptD4laTCPyJDo7jLxFJD5cvZ5FRESSUqvmYTUWFZH6COsv8bdR70uAFcBFIeUtIiIiItLk3HDaYdw/aTF9O7VKdCgiQkiVZ3c/OYx8RCRx1OdZREQkuWRlRHpYpmnEMJGkEFaz7Rtq2u7u94dxHBERERGRpqL8vraqziLJIczRto8FXgqWzwXeA1aHlL+INDCv+KlH0CIiIsnAVXsWSSphVZ47AkPcfSeAmd0BPOvuV4eUv4iIiIhIk1IW1J7TTLVnkWQQylRVQC+gKGq5CMgJKW8RiYeggFbfZxERkeTgQaG8VlNViSSFsCrPfwemm9kdZnY7MA14PKS8RURERESanPIb2i/NWZfYQEQECG+07XvN7DXgxGDVle7+aRh5i0h8eJWfIiIiklhlKpRFkkpYo20fCsx391lmNhI40cyWu/u2MPIXEREREWlqNIinSHIJq9n280CpmfUFJgK9gX+GlLeIxEF50zD1eRYREUkOevIsklzCqjyXuXsJcD7woLv/GOgWUt4iIiLSAMysvZlNMrMlwc921aQbG6RZYmZjo9ZPMbPPzWx28Oocv+hFmgDd0RZJKmFVnovN7BLgCuDlYF1mTTuYWU8ze8fMFprZfDP7YbA+ZkFuEQ+Z2VIzm2tmQ0KKXUTY2zRMTcREmpRxwGR37wdMDpYrMbP2wO3AcGAYcHuVSvZl7n508NoYj6BFmgo9eRZJLmFVnq8ERgD3uvtyM+sN/KOWfUqAn7j74cBxwHVmNpDqC/IzgX7B6xrgTyHFLiIi0lSNYe/sGI8DX4uR5gxgkrvnuftWYBIwOk7xiUhgdV5+okMQafJCqTy7+wJ3v97dnwqWl7v7+Fr2We/us4L3O4GFQHeqL8jHAE94xMdAWzNT03CRkLiG2xZpirq4+3qIlMtArGbX3YHVUctrgnXlHguabN9qZhbrIGZ2jZnNMLMZmzZtCit2kUYvujXYlt1FCYxERCC8J8/1YmY5wDFE5oeuriCvrfAuz0sFtIiISMDM3jKzeTFeY+qaRYx15f/RX+buRxKZqvJE4PJYGbj7BHfPdffcTp067f8vIdJERTfbzi8sSVwgIgIkQeXZzFoRGa37R+6+o6akMdbt84xMBbTIgdGDZ5HGyd1PdfdBMV4vAhvKW3EFP2P1WV4D9Ixa7gGsC/JeG/zcSWSWjWEN+buINDVlUQOGXTpxWgIjEREIqfJsZhfWZV2MNJlEKs5PuvsLwerqCvJqC28RERE5IC8B5aNnjwVejJHmDeB0M2sXDBR2OvCGmWWYWUeoKM/PAebFIWaRpqPKHe09xaWJiUNEgPCePN9Ux3UVgn5RjwAL3f3+qE3VFeQvAVcEo24fB2wvb94tIvW3d55nPXsWaULGA6eZ2RLgtGAZM8s1s4kA7p4H3A18ErzuCtZlEalEzwVmA2uBv8b/VxBpvMqqlMkDbn09QZGICEBGfXY2szOBs4DuZvZQ1KbWREbTrskJRPpGfWZms4N1vyBScP/LzK4CVgHlT7BfDY61FMgnMsK3iIiIHCB33wKMirF+BnB11PKjwKNV0uwGhjZ0jLVxdzbsKKRrm+aJDkUkdJqqSiS51KvyTKTZ9AzgPGBm1PqdwI9r2tHdpxK7HzPELsgduO7AwhSR2lTM86yCWkRSyK0vzuMfH6/ixjP6c93JfRMdjkioqj55Bti6u4h2LZslIBoRqVezbXefQ2Q+56nu/njU64VgLkgRERGRBvOPj1cB8H9vfJ7gSETC5w6tm1d+1nXM3ZO48dk5CYpIpGmrd59ndy8FOpiZboGJpDKv9ENEREQSzN1JSzNOPbxLpfXPzlyjwcNEEiCsAcNWAh+Y2a1mdkP5K6S8RURERPYx8f1liQ5BpEGVeaSP418u33d4gfteXRj/gESauLAqz+uAl4P8sqNeIpIiKuZ51qNnEUkR97yiyoM0bo6TZkZ62r7DBD3x0Uo+XLo5AVGJNF31HTAMAHe/E8DMsiOLviuMfEVERETqaujdk5h562mJDkMkNGUOkdldY7t04jSyszL48KZTyG6eGcfIRJqmUJ48m9kgM/sUmAfMN7OZZnZEGHmLSHyUz+/s6vUsIilg/faCfdZt2V3ELf/5jM/WbE9ARCLhc3fK687z7zwjZpqdhSW8Pu/LOEYl0nSF1Wx7AnCDux/i7ocAPwH+GlLeIiIiIpWc9eD7Mdf/4+NVnPvwVPYUl1KmSXIlxblDeYvtllkZ3H/R4JjpbnxuLsWlZXGMTKRpCqvy3NLd3ylfcPcpQMuQ8haROCjv66w+zyKSCrbmF9e4fcCtr3PTC58xa9VWfvma+kZLairzSJ/ncucP6VFt2n43v8aSDTsrWpKJSPhC6fMMLDOzW4G/B8vfApaHlLeIiIjIfntmxmqembEagJGHdabMnSMObk3zzHR+8uwcbjn7cLq1OSjBUYpUr8ypVHkGMKv+Rvdpv3sPgCevHk528wxWbsnn3MEHN3SYIk1GWJXn7wB3Ai8QGVH/PeDKkPIWkTjQfWoRSVWjBnRm8qKNNaa55K8f77NuW34RN515OJ2zs9i8q4iBB7duqBDjyt3J211Eh1ZZ7NhTzNKNuxjSq12iwwL2jq9R0yBY5fYUl3LGA+/xy/OPZESfDvvss2pLPiVlZZSUOVM+30hOh5ac2K8TZe60zIr8i/vczDV0bNWMkf07VxtPUWkZa7cW0KdTqwb5PeqjLEYt+WdnDOBXry+qcb/LJk6reP+Dpz4F4I5zB9IpuzlnH9WtUtp4/S7xVFbmbCsopn3LZgk5fjKd00TFsnjDTvp2akVajJHiU1lYo21vBa4PIy8RERGR/TH+G0dx7L1v7fd+Hyzdwjm/n1qxfP9FgyuaxRaXlvHo1OWMPT6H5pnp7NxTTJpZRaUsWt7uIlZs2V3nCupT01dxSIcWHNYlm+LSMl6f9yVnDurGwvU7OOLg1nTKzuI/s9dyyoAufPTFFjq0akbLZhkVlft3Pt/Ikd3bkHvPW/zPib35bG1kgLSzj+zGpcMP4clpK7ntxfmVjnn6wC4c3PYgvti0izvOO4LP1mxncM+29Gh3EJnplXvxzViRx+INuygoLuXonm34YOkW8nYXcWxO+30qXgB3v7yAf81Yzc9HD+CW/8zjo5tO4b5XF/HbCweTmW4sWL+DGSu2ctJhnbjtxXm8v2QzK8afXXHuCopLKSwu5eW56/n7xys5sV9HurZuzpTPN7FySz6X/jVSEXzjRyfRtU1z/vzuF3z/5L6c9H/v7BNLufL8f/rsnErLAAVFpewpLqVti0zOemgqC9fvAGDWrafRunkGVz0+g7OP7MYFQ3tU/OM/f912WjTLoO1BmbTISqf/La8zuEcbDuuSzX3nH0m/m1+ryL9Hu4O452uDKirsJaVlbNhZyGNTlzPq8C48OW0lv7/kGMyMjTv20K5lM6Yvz2NIr3Yc1Cy98i/ikFalk+W1Iw+lX+dWXP3EjGp//1ju+O8CACa835YT+3bkx6cdRpk7/W5+jRP6duCWswcCcOaD7zP/zjNiXusbduyhc3YWZsbabQW8Of9L5q7ZTuvmGdw5ZhAAC9btoH/X7ErTa20vKOb/3lhE/66tuXBoD5pnVv49S4PxCWJNyVWdL7fvoVN2Fss372LWym0c0b01yzfvpsxh0fod/HHKF/zz6uFkZqRxbE77Svvm7S4iu3lGxbXv7kx8fzlfO6Y7nbKzKtLtLixh/GuL+PmZA2gVnI8Pv9jMpX+dxns3nkyvDi1ixvbU9NX84t+fMf3mUXTObk5JaRkZ6TX3ll23rYCJ7y/nF2cNoMyhWUYa7s6Jv36HJ74zjPYtm7FzTwk928c+ZvnvVVBcSve2e1vU/OiZ2bw4e13F30BZmVfclMnbXcRBzdJx4J/TVrFzTzE3nNaf9DTjn9NW0a5FJl3aNKd/l2ymfL6JAd2yGfXbd7n/osGcO/hg3l+yiRP7deLk30xhZP9OjBrQhZZZGUxdupmHJi8B4KLcHry7eBMbdhTSu2NLRvbvxGMfrABg8T1n0iwjrF7E8WFh9Isws8OAnwI5RFXI3f2UemdeD7m5uT5jxv59sYg0VXf9dwGPfrCcsSMOqSgARaQyM5vp7rmJjiOVhVU254x7BYhUVKb+/JSK5frq0e4gTu7fmV2FJfz707X7bH9kbC7Tlufx/Mw1bNldVGnb4nvOZFtBEZ2zm1es27mnmCPveJOfnn4YZkb3tgfxo2dmH3B83z+5Lw+/s/SA96+v0wZ2YdKCDQzu0YY5ST6q+VcP68S7izcBkJWRxq8vOIpVW/L57aTFoR6nZbN0dheV7tc+3do0Z/32Pfusv+Xsw/nWcYcw4NbXAejQshnNM9P5YNy+/1Iv+nIHv5u0mDfmbziwwGvx+o9OZP7aHUxfnsfFw3ry9T9+CMCIPh34aNmWfdIP7tkWgDmrt1Vaf1FuD3YUlPD6/MiI4N/M7cl95x9JSVkZ7yzayI6CEn72/Fyym2cw9/bTmb9uB5+t3c4lw3qxYN0O/v3pGn5x1uGs2VrAI1OXM3pQV2asyOM3b9b9c1x092iyMtKYu2Y7R/VoQ++bXqVls3Q+HDeKwXe9WSntJcN68tT01RxxcGuG9W5fUdH76xW5bMsv4sbn5lakbdsik9m3nU5xaeR3ARjUvQ3XPjmLOau3MeHyoXz+5U5+O2kx9180mK8f050/TvmCbx7bk46tspi6ZDO7i0q4/cX5fLlj3+uhJn+6bAjXPjkrOGZrjj+0IxPeWwbA09ccR7/OrRh6z96bin07t2JIr7b8a8aa/TpOQ7p25KH8fPSAeucTz7I5rMrzHODPwEyg4tvD3WfWO/N6UOVZpO5UeRapnSrP9Rd25fmjm06hW5uDWLpxJ7f+Z37Mf+oT5aFLjuH6oMmsSH1EPzmPVlbmjH1sOu8v2RzniKRc+Q2luqjupklTVt21vT/iWTaH1ee5xN3/FFJeSeedzzeSXxi5J9AsI42TDutIVkZ6LXuJiIhIQysf8Ktv52yeuuY4CktKOeK2NyhJgmmqVHGWhpaWZvz9quGhtbyQ/VfXijOginMjUK/Ks5mVdyD4r5l9D/g3UFi+3d3z6pN/srjzpfms2JJfsfzQJcdwnkYulEbGgyHDEv/vpohI7Xq0O4hhVfoxAmRlpLP0vrNUmZAm5cR+HfX0WSQO6vvkeSaR/7XLe/ffGLXNgT71zD8pPHblMIpLy9i8s5BLJ05jd2FJokMSERFp0jz6v48Yxp9/JN3bHcQJh3bklN9OqXQTXKSxefzKYZS6s2DdDh54azHvfL4p0SGJNEr1qjy7e++wAqkrMxsNPAikAxPdfXxDH7N3x5YAtGsRGe6+pLSsoQ8pEnflwx+EMAyCiEiD++X5R1aUy7FcPKxXxfspN55MSWkZ6WlG75teBeDerw/i8G6tOT8YBEkklaWlGWkYg3u25bErhwFw0wuf8dT0VQmOTKR6b/74pESHsN/qNTa4mR1rZl2jlq8wsxfN7KGoJt2hMbN04A/AmcBA4BIzGxj2caqTmR65xZ0M/ahERESaspMO68SRPdrUOX1GehpmxorxZ7Ni/NlcNvwQhvRqx91jjmjAKGsXPS1OmNq1yGyQfNvWMd9TD+9S5zybpafx6a2ncU4wDdYRB7dm7h2nV5v+V984suL9sN6h/7tZycQrcunYqubP6NLhvWrcnjiV/1+t7lz95sLB8QhGUkhWRhqvXn8i3z2pciPiWFPVlWuWnsZlw3vx1g1fZc7tlf9+/3PdCUz+yVcrlmfdehrLf3kWh3XJDjfwOKhvs+2/AKcCmNlJwHjgB8DRwATggnrmX9UwYKm7LwuO+TQwhv9n787jo6ru/4+/PkkIO7LvS0BABFTAiKJiERUQWrFuRavYVqtt3ardXKu2YunyVWu1WrVa9Vd3a6WKIiCuqCwKyqYgBNn3NUDWz++PuQlDmCyQm1nI+/l4zCNzzz33zCc3d+bkzD0LLAj5dWIqWZ+tsEiNZzl0uUY9i0gtcsmgLG57dT5dWjTgL+cfw/kPfwTAsj+M5N2vNrBsYy53/m8BHZvVZ+WW3aG85qxbT6dBZjoNMiP/hg2/9z0a18tgcI9WnNS9BYvW7mDjzjxmL9/CY5dmc/7DH/HY2GyWbsxlzCMfA5Glaf524QC6tmzIszO+oWebRhzbZW/jKL+wmLXb9rBlVz4N66bTrWUjbn7lC77VsxU5m3bxxzcX8cBF/Tn1iNb0uX0SZ/drz3/nrAagbZN6+y2b07pxXU47sjXjzj6KtDQrHVM+9/Zh5BUW7bM8V4mcjbk0qV+H9Tv28MHijQzr3RYzaHtYPbbvLmB3QRF10tNokJlO43p1eOCiAYw7u4Am9TMwM87s25Y35q3lvu/1o1nDTPq0b8Lm3Hx6tmlM15aN+O2r83jqRwP54RMz+WjpJub+dhiHBY37kvgi56UZZ/Ruw9BebXj0vaVs31PA395ewv0X9mf+6m1ckN2Jm17+goVrt/OPS46lbkY6fdo3KV2LeFbvNny5dgd10o1urRqxO7+If36wlPZN65euC/770X257dV5/OKMnjSpX4f8wmL63D6JcwZ04J4L+vHuVxuYMGc1fz7vaD5YspGxj88o9/o4uuNhNGuQyf0X9ufR95YydlCXA7/IgJFHtePZGSv424X9mf71Jsad3Ze0NGPZxlzq1Ulj0B/eBuC8Yzty7oAOpb0yerRuxOL1Oystf8xxnXhu5orS7YqOa9OkLuu2R6ZFandYPa47rQf/mp7DorU79snXoWl97v1ePy74x0cH/Pt2aFqfqb/4FlMWruPqZ1Jzsr5/XHIsVz4dWayoZBbq8uZvaNEwkztH96nS79qkXgbfPqY9r81dzfY9hbRpUpdzBnRkRJ+2fLN5F73bN+HwVo3ILyxm3fY9pWtJ927fhJtGHrlPWX85r4iFa7ezYvMurntuDj84MYt/Tc/hzZ8PplurRqX5csaPYsn6nczM2Uy/YBmzEs0blt9rKNlVa6kqM5vr7scEzx8ENrj7HcH2HHfvF0qUe1/vPGCEu18ebF8CHO/uV8fKH/ZS+MrxlwAAIABJREFUVXsKiuh125v8esQR/GxI99DKFUkGd0yYz7+m53DxCZ256+yjKj9ApBbSUlXVl4zLSG7YkUeDzHQa1q34nsLUhesY2LU5X67dQXYwWVnJP7bTbxzKp99sYe6KrYwdlEXdOmmlDcq35q9lcI9WPDF9Gd8+qj2dWzQ46Fh35hXSqJI4K+Pu7MovKv19d+UXUjcjnelfb2THnkJGHtWOvMIiFq/bSadmDUobpNHenLeGenXSGXJE62rFUpG8wiI27cynfdP6B3zsFyu3Rb40iPpnPpnsKShi664CnvhwGaOObseEOas5Z0BHerdvErcY3pq/lox0Y2iv/XsJ7CkoImdTLlktGlKvTjq784vISDd63PIGV57SjSb16/CzIYezK7+I+nXSSUvbOwHB8k25dG7eALO9X7IsvXvkPnlK5BcWU+xeuq519LJF2XdNYePOPKb9cghFxcUs27iLM3q3YeGa7XRoVp8m9eqUxppmRmbG3g61kxesY/mmXFZu2c2YgZ3o1bYJD05bwp8nfUmvtpG7nROvHbxPTPNWbaNry4Zs3JlHlxYNmbNiK8/P/IaBXZvz3f4dKSwq5t4pX3HpiVkUFXvplw+xXH96T+6dElmHulXjurRsVJeFa7az6PcjMIN3v9zA+h15XHxCF95etI4f/WsW8+4cTqO6GSxZv4O8wmL6tD9sn9/vwyUbWbx+B9/q2Zq2Tert877ctruA9DRj2+4CducXsnjdTg5v3YhXPlvFNUO7l35RB7A7v4jMjDTSY/w9DtTOvMLSdc6r8rn0vX98xCfLNoeyPFW0lFnn2czmAf3cvdDMFgFXuPt7JfvcPdTFYs3sfGB4mcbzQHe/JirPFcAVAJ07dz52+fLlob1+YVEx3W95g8b1MkrfsCKHiq278snNL6JBZnqF4whFUtG5x3bkhjN6VrscNZ6rLxkbz9WRdePrXHXq4fxqeK9EhyKSdHrcMpGCIq+0sTRlwToOb92odJ4hgEnz1/Lrlz7nk5tPK+0FUB0bduRx+ZMzefiSY0uXuKuOB95eTH5hMTcMO6Lc12tcLyOU2A8VewqK2LGnMPThKqm0zvOzwLtmthHYDbwPYGbdgW3VLDuWlUCnqO2OwOroDO7+CJEu42RnZ4fa/zQjPY3fjOjF1xsq78oikormrdpG3w5VH0Mokiq6Rf1DJhKmsO+giBxK3rhuMDNztlSa7/Te+9/9Ht6nLcP7tI2R++C0alyXV68+ObTyrh7ao9LXk33Vq5Oe8l8mVHe27XFmNhVoB7zle29jpxEZ+xy2mUAPM+sKrALGABfVwOuU66dDDo/ny4mIiIiIpKTurRvTvXXqTQolUp7q3nnG3T+OkfZVdcst57UKzexqYBKRpaoed/f5NfFaIiIiIiIiIiWqNeY52ZnZBiC8Qc8VawlsjNNrhSkV407FmEFxx1MqxgyKO54ONuYu7t4q7GBqE9XNVZKKcadizKC44ykVYwbFHU9JXzcf0o3neDKzWak4iUwqxp2KMYPijqdUjBkUdzylYsxy4FL175yKcadizKC44ykVYwbFHU+pEHNa5VlEREREREREajc1nkVEREREREQqocZzeB5JdAAHKRXjTsWYQXHHUyrGDIo7nlIxZjlwqfp3TsW4UzFmUNzxlIoxg+KOp6SPWWOeRURERERERCqhO88iIiIiIiIilXH3WvMAHgfWA/Oi0n4PfA7MAd4C2pc55jigCDgvKu1SYHHwuDQq/XtBWfOBP5UTQxawO3i9OcDDUfuOBb4AlgD3s7dnQDLE/f2omOcAxUC/YN87wJdR+1qHHPebwFbgtTJ5uwKfBL/P80BmObHfFJzTL4HhUekjgrQlwI3JEjNwBjA7uBZmA0Oj9iXtuSax13Z14j6gazsOMV8dnCcHWlbweVbe+zlR57rSuIF+wEdEPms+B74Xte9fwLKoc90vWeIO8hVFxTbhQD+H9Cj/EeLfWHWz6uYaixnVzaqbVTerbnavdY3nU4ABZf7QTaKeX8u+HyrpwNvAxJI/NNAcWBr8bBY8bwa0AL4BWgX5ngROixFDVvTrl9k3AxgEGPAGcGayxF0mzqOApVHb7wDZNXG+g/TTgO/EeGO9AIwJnj8M/DRGDL2BuUDd4I30dfA66cHzbkBmkKd3ksTcn+ADB+gLrEqRc51FAq7t6sZ9oNd2HGLuH5zLHMqv6GK+nxN8rqsSd0+gR/C8PbAGaBps/yv6dZLpfAf5dpaTfkDXmB4xz2HC6zhUN6tuVt2surnimFU3J9n5DvLFvW6uVd223f09YHOZtO1Rmw2JfMNR4hrgZSLfrJQYDkx2983uvgWYTORb0m7AV+6+Icg3BTi3qrGZWTsiF91HHvlLPwWcnaRxXwg8W9nvFFLcuPtUYEd0mpkZMBR4KUh6kuB8lTEaeM7d89x9GZFvsQYGjyXuvtTd84HngNHJELO7f+buq4PN+UA9M6sb43eLPibhcZcnDtd2mHFXem3XZMxB+mfunlNJnDHfz4k611WN292/cvfFwfPVQdmtKjkm4XGXp7rvDYlIwjqulOrmcuNW3ay6WXXz/lQ3xzHu8tR03ZwRVkGpzMzGAWOBbcCpQVoH4LtETv5xUdk7ACuitlcGaW8CvcwsK0g7m8i3prF0NbPPgO3Are7+flDGyhjlJlPcJb5HpOKL9oSZFRF5Y9wVfDiEEXd5WgBb3b2wzO9TVgfg46jt6Hxlz8fxSRJztHOBz9w9LyotWc81JObaDiPuEgd9bYcUc1WV935O1Lk+YGY2kMhnzddRyePM7LfAVCLdNfNiHpyYuOuZ2SygEBjv7v/l4K4xqSLVzaqbUd0cVtyqm1U3V4nq5srVqjvP5XH3W9y9E/BvIn3sAe4DfuPuRWWyW+wifAvwUyL96t8n0s2gMEbeNUBnd+8P3AA8Y2ZNyis3ieKOFGJ2PLDL3edFJX/f3Y8CBgePS0KMu9xQYhV9APkO6HzHOeZIZrM+wB+BK6OSk/lcJ+rarm7ckczVvLZDirmqQrmuIe5xA6V3PJ4GfujuxUHyTUAvIhVrc+A3SRZ3Z3fPBi4C7jOzwzmI8y1Vp7pZdXMFx0d2qG6uStyqm1U3V4nq5qpR43lfz7C3W1Q28JyZ5QDnAX83s7OJfHvRKeqYjsBqAHf/n7sf7+6DiExksLjsC3iki9Km4PlsIt/s9AzK7Rir3GSIO8oYynSdcfdVwc8dQSwDQ4y7PBuBpmZW0nuivPNV3u9d7vlIgpgxs47AK8BYdy/99i+Zz3UCr+1qxR0lrGu7OjFXVUXXdSLOdZUF/7S9TuTuR+mdJ3df4xF5wBPE59quMg+6a7r7UiLj7fpz4NeYHBzVzaqbkyFm1c1xjDuK6mbVzeVKRN1c6xvPZtYjavMsYBGAu3d19yx3zyLSZ/5nHukKMAkYZmbNzKwZMCxIw8xaBz+bAT8DHovxeq3MLD143g3oQWQShDXADjM7wcyMSJeHV5Ml7mB/GnA+kTFIJWkZZtYyeF4H+DYwL9bxBxl3TO7uwDQib0KIzG4Y63xNAMaYWV0z60rkfM8AZgI9zKyrmWUS+XCekAwxm1lTIh9gN7n7h1HpSX2uE3htVyvu4DWrdW2HFfMBiPl+TtS5rqrgvfYK8JS7v1hmX7vgpxHpolrj1/YBxN3MgrGNwTVxErDgQK4xOTCqm1U3q25W3ay6OT5xq24+wNf18odkpLyWLVt6VlZWosMQEZFDxOzZs4uAK939n1D6z+hzRLqzfQZc7BWMBxPVzSIiEq541s2HdOM5OzvbZ82alegwRFLGnoIi6tVJT3QYIknLzGZ7ZHyVHCTVzSIiEqZ41s21vtu2iERMmLuaXre9ycuzV1aeWURERGpcfmExWTe+zoPTliQ6FBEhpMazRVxskWnMMbPOFpnqXERSxPKNuQAsC36KiIhIYm3dnQ/Anyd9meBIRATCu/P8d2AQkUXMIbLY9YMhlS0iIiI1wMyam9lkM1sc/GxWTr5LgzyLzezSGPsnmFm5E8mIyMEpDhYMqpMea/UdEYm3sBrPx7v7VcAeAI+sT5gZUtkiIiJSM24Eprp7D2BqsL0PM2sO3A4cT2SZktujG9lmdg6wMz7hitQuBUWR1nPrxvUSHImIQHiN54JgGnyHyLT4QHHFh4iIiEiCjQaeDJ4/SWQpkrKGA5PdfXPw5fhkYASAmTUCbgDuikOsIrVOUXFkYt8M3XkWSQphNZ7vJ7I+WGszGwd8ANwdUtkiIiJSM9oEa5AS/GwdI08HYEXU9sogDeD3wP8Bu2oySJHaqjBoPKebGs8iySAjjELc/d9mNhs4DTDgbHdfGEbZIiIicvDMbArQNsauW6paRIw0N7N+QHd3v97MsiqJ4QrgCoDOnTtX8WVFpOTOs9rOIsmh2o1nM0sDPnf3vsCi6ockIiIiYXH308vbZ2brzKydu68xs3bA+hjZVgJDorY7Au8QmSj0WDPLIfL/RGsze8fdh5Q5Hnd/BHgEIus8H9xvIlL7TF6wFoCvN2glDJFkUO1u2+5eDMw1s4P6KtnM0s3sMzN7LdjuamafBDN6Pm9mmUF63WB7SbA/q7qxi4iI1HITgJLZsy8FXo2RZxIwzMyaBROFDQMmuftD7t7e3bOAk4GvYjWcReTgLduoEREiySSsMc/tgPlmNjVYrmKCmU2o4rHXAdFdvP8I3BvM/LkFuCxIvwzY4u7dgXuDfCIiInLwxgNnmNli4IxgGzPLNrPHANx9M5GxzTODx++CNBGpYXsKixIdgohECWXMM3DnwRxkZh2BUcA44AYzM2AocFGQ5UngDuAhIjOC3hGkvwQ8YGbm7ur+JSIichDcfROR+UrKps8CLo/afhx4vIJycoC+NRCiSK1WUKjFa0SSSVgThr17kIfeB/waaBxstwC2unthsB09o2fpbJ/uXmhm24L8G6ML1KQkIiIiInIoKFnnWUSSQyjdts3sBDObaWY7zSzfzIrMbHslx3wbWO/us6OTY2T1Kuzbm+D+iLtnu3t2q1atqvw7iIiIiIgkkzP7tgPgiDaNK8kpIvEQVrftB4AxwItANjAW6FHJMScBZ5nZSKAe0ITIneimZpYR3H3uCKwO8q8EOgErzSwDOAzQmCsREREROSQ1qR/5V71LiwYJjkREILwJw3D3JUC6uxe5+xPsu6xFrPw3uXvHYJbOMcDb7v59YBpwXpAteubP6BlBzwvya7yziIiIiBySSnptp6dpoWeRZBDWneddwZJSc8zsT8AaoOFBlvUb4Dkzuwv4DPhnkP5P4GkzW0LkjvOYasYsIiIiIpK0ioL7RGlqPIskhbAaz5cA6cDVwPVEulefW9WD3f0d4J3g+VJgYIw8e4Dzqx+qiIiIiEjyKyqO3HrOUONZJCmENdv28uDpbg5y2SoRSSyNgRAREUku6rYtklxCaTyb2TJiz3zdLYzyRURERERqG915FkkuYXXbzo56Xo9I9+rmIZUtInGg6fdERESSi+48iySXUGbbdvdNUY9V7n4fMDSMskVEREREaqPSCcNMjWeRZBBWt+0BUZtpRO5EazV3kRTiGvUsIiKSVN79cj2gbtsiySKsbtv/F/W8EMgBLgipbBERERGRWmfKwkjjWUtViSSHsGbbPjWMckQkcTTmWUREJDmpjhZJDmF1276hov3ufk8YryMiIiIiUluM6NOWN+evpWmDOokORUQIacIwImOcfwp0CB4/AXoTGfessc8iKcBLf+rrbRERkWTQsVl9AO6bsjjBkYgIhDfmuSUwwN13AJjZHcCL7n55SOWLiIiIiIiIJExYd547A/lR2/lAVkhli0g8BAOqNK5KREQkOahKFkkuYd15fhqYYWavEHmffxd4MqSyRURERERERBIqrNm2x5nZG8DgIOmH7v5ZGGWLSHx4mZ8iIiKSWFqgSiS5hDXb9uHAfHf/1MyGAIPNbJm7bw2jfBERERGR2kZfaIskl7DGPL8MFJlZd+AxoCvwTEhli0gclIx11phnEREREZH9hdV4Lnb3QuAc4K/ufj3QLqSyRURERERERBIqrMZzgZldCIwFXgvSKlzN3cw6mdk0M1toZvPN7LogvbmZTTazxcHPZkG6mdn9ZrbEzD43swEhxS4i7F3fWes8i9Qe5dW5MfJdGuRZbGaXRqW/Y2Zfmtmc4NE6ftGLiIjEV1iN5x8Cg4Bx7r7MzLoC/6+SYwqBX7j7kcAJwFVm1hu4EZjq7j2AqcE2wJlAj+BxBfBQSLGLiIjUVuXVuaXMrDlwO3A8MBC4vUwj+/vu3i94rI9H0CK1RfSEYeu270lYHCISEUrj2d0XuPu17v5ssL3M3cdXcswad/80eL4DWAh0AEazd5mrJ4Gzg+ejgac84mOgqZmpa7hISFzTbYvURuXVudGGA5PdfbO7bwEmAyPiFJ+IBIbd+16iQxCp9cK681wtZpYF9Ac+Adq4+xqINLCBki5gHYAVUYetDNLKlnWFmc0ys1kbNmyoybBFRERSXXl1brTK6t8ngi7bt5mZVtYRCVH099nbdhckLA4RiQhlqarqMLNGRGbr/rm7b6+g3o21Y797ZO7+CPAIQHZ2tu6hiVSRbjyLHJrMbArQNsauW6paRIy0ko+K77v7KjNrTKQuvwR4KkYMVxAZckXnzp2r+LIiIiLJJZQ7z2Z2flXSYuSpQ6Sy/be7/ydIXlfSHTv4WTJ+aiXQKerwjsDq6sQtIiJyqHP30929b4zHq5Rf50Yrt/5191XBzx1ElqgcWE4Mj7h7trtnt2rVKrxfTuQQV3b5yLXbNO5ZJJHC6rZ9UxXTSgVdu/4JLHT3e6J2TQBKZvK8FHg1Kn1sMOv2CcC2kq5mIlJ9e9d51r1nkVqkvDo32iRgmJk1CyYKGwZMMrMMM2sJpV+GfxuYF4eYRWqtE/4wNdEhiNRq1eq2bWZnAiOBDmZ2f9SuJkRm067ISUS6d31hZnOCtJuB8cALZnYZ8A1Qcgd7YvBaS4BdRGb4FhERkYMXs841s2zgJ+5+ubtvNrPfAzODY34XpDUk0oiuA6QDU4BH4/8riNQu7o6mFxBJjOqOeV4NzALOAmZHpe8Arq/oQHf/gNjjqABOi5HfgasOLkwRqUzpOs+68SxSa7j7JmLXubOAy6O2HwceL5MnFzi2pmMUqc08xkwkXW+ayIxbTqN143oJiEikdqtWt213n0tkPecP3P3JqMd/guUsRERERGrMlU/PIuvG1/l85dZEhyJSIxrV3f9e18BxU1m/Q+OfReKt2mOe3b0IaGFmmSHEIyKJ4vv8EBFJCZPmrwPgrAc+THAkIjWjvB7aA8dp/LNIvIU1Ydhy4MNgjccbSh4hlS0iIiKynxWbdyU6BJEaVTKU6rVrTo65f84K9bgQiaew1nleHTzSgMYhlSkicVS6zrNuPYtIihj8p2mJDkGkxhnQt8NhMfed/WCkx8XzV5zA8d1axDEqkdoplMazu98JYGaNI5u+M4xyRURERKrqsn/N5IZhPenTPnZDQySVPTY2m8ufmhVz32ufr1HjWSQOQum2bWZ9zewzIus7zjez2WbWJ4yyRSQ+StZ3jjWzp4hIKpi6aD2j7v8g0WGI1IjTjmxd7r6nP16uSfNE4iCsMc+PADe4exd37wL8Aq31KCIiIjXkpdkry9334qwVzFi2OY7RiNSckjWdzYxlfxhZbr6SSfMWrd3O6Ac+IDevMC7xidQmYTWeG7p76cAjd38HaBhS2SISByVjnTXmWURSwS9fnFvuvl+99DkX/OMjZuZsZsn6HXGMSlLVx0s3kVdYlOgwKmVm5IwfVe7+rBtfZ8R97zN35TY+XrqptFcZRCYX27arIB5hihyywmo8Lw1m2s4KHrcCy0IqW0REROSAnf/wR5x+z3t8uXYHG3bkMWXBun0aE5VZuGY797z1ZQ1GKMlg4ZrtjHnkY8a9vjDRoeznQK7Xsq565lO63jQRgLzCIs5+8EPGPjEjrNCSyuE3T+RPby5KdBiHtOWbctm6Kz/RYSRcWI3nHwGtgP8ArwTPfxhS2SISB7rhLCKHquH3vcdx46Zw+VOzOGn82xx1+yQKioorPe6cv0/n/reXsKeg/DuSa7btJuvG13lr/towQy61dVc+m3P3/sOaV1hUYTyxTF24jsfeXxpz35QF65iyYF3p9rKNuZU22F6YtYKnPsrhzXlreXXOKs5/eDrFxRUf8/LslazbvueA4o6XLcH5/Wpd5b0UcqpwfqpqT0EReYVFZN34OndMmF9uvljrPC/6/YgqlB+5xrNufJ0jbn0TgLkrtpKzMbc0z449BYy6//0q/e5hKS52dlajS/kbX6zZb3x3UbHz93e+ZuPOPD5csrFa8a3fvoesG1/n02+2lJvH3Xnt89UUVXLdV+SrdTtKr71kt3bbHr7153cYft97B3X8jj0Fpe+b2cs388nSTWGGF1dhzba9Bbg2jLJEREREDsRPhxzOQ+98XaW8q7dFGnCzcrZQPzOdzs0b4O7syi9i6sJ1jB2UxeL1O+neuhGFxZHGh1nkH/60tP1bMfNWbQfg+ZkrGNanbWn65U/OJK+wmKcvO56NO/No0TATMyt9vmjtDnq1bYyZ8aN/zWREn7ZccFyn/crv97vJAOSMH8XGnXkMu/c9NufmkzN+FO5O39snkVdYzJK7946FfXXOKq57bg4j+rTl4UuO5bInIzM0Xz64G1t35ZfG2qReRunszW9cN5hlG3P52b8/5a6z+3LxCV3YkptP/99PZuRRbfn794/l6Y9yeOfLDUxdtH6/OLvdPJHWjevy/m9O5aJHP2HRmu3k5hdx6hGtmPblhn3y/mzI4fx6RC+mL9nIza98wYUDOzOib1t2FxRxRJvGpWN8IdLVODevkJO6t+TxD5bxu9cW0LJRXWbdejrFxc6HX2+kR+vG1M1Io1nDTCDSsHGHvMJiCouLaVyvTml5xcXOll351KuTzvY9BfzihbmcdUx7AD5eupk3563lpO4tGH7vewzr05Z/Tc/hpZ8M4okPc/jRyVmc+9BH3DyyF60b16Nvh8Po3roRAJt25lFQ5LRpUpf3F29kV34hHZs1iLnEVG5eIVt3F3DS+LdL0/41PYcxAztx7bOfMeHqk8lIM254YS7rd8T+wqFenXR+M6IXfzyIu61D/vIOLRtlcvt3+nDP5K9YtjGXe976iocuHoCZsWLzLnI25TK4Rysg0sh/58sNnH5kazLS08jZmMuQv7zDL4f15KpTu+NO6Xtjd34RRe40qJOOA398cxFjjutEnfQ0OjVvAMAf3ljIo+8vo0PT+lw+uCvrtudxZLvGjO7XgX9/spxbXpnHraOOZOygLNZu28PvXlvAr0ccwdMfLefSE7vw039/CsD0G4fy4LQlvPvV3usr+64pAIw5rhPjvnsU6THesyXXR1qasSU3n8b1Mti2u4CiYqd1k3pM/zrSsHv0vaX8YlhPPv1mK/NXbeO33+lDeppx1gMfkNWiIRPmrgY+Y+ndI0t//89XbmX11j2M6NuW2cs384sX5vL6tYNpWHdvk+v1z9ewZVc+t/53HgBXfqsbKzfv5pJBXRjzyMcA/O/qk3lp9gpG9+/A/NXbOenwFjz98XKuO60HO/YU0qxhJo2CMu9560t2FxRx7Wk9+Me7S/nBSVm0bFT3gK+LEnNWbOXsBz/k+K7NuWRQF65+5rPSfeu253HuQ9P51fAjGJjVHDP2eb+WuOAfH9EgM52LBnZm+55CfvniXO74Tm9+cFJXzn3oI4AKhx8kMwvj2zMz6wn8EsgiqkHu7kOrXXg1ZGdn+6xZsaf0F5F9/e5/C3j8w2VcOqgLd47um+hwRJKSmc129+xEx5HKwqqbs258HYArT+nGqKPblU6WVJPGn3MUUxet59GxkUtg/Y49fLp8Kz/5f7MBePJHA/lWz1b7xPffq07i7Ac/5Ig2jenephGvf76GwT1a8v7ijdzxnd4M6NKsNPZrT+vBoG4tOLrjYZhB799OKn3tNk3qsm57Xun2wxcPYMayLTz+YWSU3Dn9O/Cfz1bV2O9+zoAO/OfTmis/DN1aNuTcYzvy50n7drXPzEgjv7DyngY16bxjO1Y4yV1lYjU03J2nPlrO7RXcta4Jx3dtzidlJuR74gfHsXDtdv70ZvnDHDLT0zim02HMzIl9R7fkfVFVAzo35dNvKp5h/KdDDueYjk15YdYK3l60nqV3j+TI375JXmExD1zUn6uf+YzvHNOe/81dDUDvdk1YsGZ7lWMocf3pPbl3ylel23d8pzd3/G/BPnmuOKUbj7wXuwfIwchMT+OWUUfG/Pv/dUw/WjWqy0WPfcLQXq35v/OPYe7KrazYvItLBmWRm1fIXa8v5OaRvbh74kKenbGCM3q3Ia+wmOWbclm+aVeV4/jqrjNJM5g0fx3fbN5V5S90Prn5NNo0qVfl16lIPOvmsBrPc4GHgdlAaV8id59d7cKrQY1nkapT41mkcmo8V1/Yjeev7x5Jeprx0deb6NKiASdG3c0TOVRUdJeuuNjpdvPEOEYjEo7fje7D2EFZ1S4nnnVzWGOeC939IXef4e6zSx4hlZ1wL8xawWPvL+Wx95fy9Ec51RqnIZKsStZ31thnEUklJb0yBx3egvZN6/PyTwclNiCROEtLM1675uREhyFywH77anx7TYShWmOezax58PR/ZvYzIpOFlfYpcvdDYpHFv09bQk5U94WGdTM4Z0DHBEYkIiIinZs32G+83bFdmtOrbWMWrdUSVVJ79O1wGMv+MJIbXpjLKzXYfV+ktqvuhGGzidyoKqm5fhW1z4Fu1Sw/Kbx27WCK3Vm/PY/T73m3dPZCkUOJ1nkWkVTSoWl9srOaxdz36tUnMXfFNi74x0dxjkokccyMe7/Xj3u/1w/YO7RBRMJTrcazu3cNK5CqMrMRwF+BdOAxdx9f069ZMptdyWQTJbNvioiISGLc/p3etChzyrxuAAAgAElEQVRnRtm6GekM7NqcBy8aQJpROjuvSG1y27d789k3W7h/TH/emLeWhnXT2bAjj1+99HmiQxNJWdUa82xmx5lZ26jtsWb2qpndH9WlOzRmlg48CJwJ9AYuNLPeYb9OeeqkRU5XYZFuzcmhyzXqWURSwLA+bTm2S+w7zyVGHd2OM49qR874UUy8dvA++y4+oXNNhnfIapiZvs/2OQM68L3sThzTqWlp2rd6tqJjs/oAzLtzOF/ddSaPjc3m8FYNS/MM6tYiPgHH0L11I16/9mQalPldEqlkuaxoT182kGM6NeWaod0PqszLTu7KAxcNIC3NGHV0O4Yc0ZrzszuRM34U8+4czl/H9OOxsfvOsXTWMe25dFAXIDI7tOzvtF6ta6TcV686qUbKlXBVt9v2P4DTAczsFGA8cA3QD3gEOK+a5Zc1EFji7kuD13wOGA0sqPCokGSkR3qn686ziIhIaundvgk540fxh4kL+cd7S7llZG/uOvsobnhhDv/5dBWf3XYGhcVOq8Z1mbxgHT9+au+M4I+NzS5dD7lOulFwAF+iX5DdkRdmrWTMcZ340cld6dmmMQ9OW7LPckqTfn4Kt7zyBUs35rI5Nz9mOc/8+HjaNqnH0P97F4DHf5CNmfG3qYu59du96dy8Adt3F3DZk7NYtjEXgLGDunDnWX2Y/vUmerRuxLiJC/lu/w5s211AepqxeutuRh7VjuYNM8kvLGb28i2la0LfPLIXzRpkcmqv1qSZsWVXPlktGpJfWMyov73Ptl0F/Pn8oxnaq01pjBO/WMPu/CLOPbYja7bt5v3FG0t7753euw1De7XmqDsmcdPII7n4hC786sW5vDh7JS9cOYiXZq/ghVmRpZxe+skgju3SjGUbc0t/35zxo7hvylfcN2UxADed2Ysxx3VmyYYdTFm4fp91vn9/dl+aN8jkzflradEwkwVrtjNj2Wa6tWrIXaP7cmL3lgB8etsZvPzpSr5YuY16ddJpUi+Dnww5nJc/XcVt/53HNUO787e3lzDtl0N45pPlXH9GT1Zv3c3hrRqxu6CI+6cuYVifNgzovPdLnC25+RQUFTPw7qmlae0Oq8erV5/E9x/9hMXrd/LIJcfy4LQl3DKqN1MXruOXw4+gTnoaC9ds5/TebfjNiF6lx5astRy2RnUzGN2vQ+m5zbrxdVo1rsv9F/YHKF114+aRRzJv1TZ+/dLnMZdwuvrU7jwwbQknd2/JB0s2csUp3WiQmV76dwIY1rsNR3U4jDP6tKF7q0a8/sUazjqmPZMXrGNTbj5/nbKYtdv30LxhJpcOymJor9Y8O/MbBmY15+fPzwHghjN60qReBne/sYjOzRvQ7rB6dG3ZkFtGHcmqLbtLrxOAs/u1579zVjPvzuFMmreWX7w4F4DPbjuD0+95l01R77G/nH8MBUXF3PSfL4B939+f3HwaeQXFbN2dzy9fnMtVp3anbkYaI/q2o6jYefT9pazdtofpX2/kxStP5IFpi+nZpjGPvr+UC7I70bBuRmm5Jfq0b8L81bGXwurb4TAeuKg/D077mosGdmJwj1b8a3oOX6zaRs82jejUvEHpUmC/G92Hvh0O45y/T2fSz0/hgWlLSpfbGtStBU9fNpDut7xRpWuhxJs/H0z7pvU5+o63gMh1ccydb7FtdwHD+7Th+jN6kpGWxqadeewqKOKHT8zkxMNb8OBFA2jWMHOfWd9f/ukgNu7M58qn984fPaJPW96cv3af17x11JEHFGMyqNZSVWY2192PCZ4/CGxw9zuC7Tnu3i+UKPe+3nnACHe/PNi+BDje3a+OlT/sparyCos44tY3ufJb3bjkhC6hlSuSDO6dvJiXP13J2f3a88vhRyQ6HJFQNaqbQdMGmdUuR0tVVV+il5EsLnbyCoupX4W7jqu37sYM2h1Wn2uf/Yx6ddL403nHlJv/7okLOaN3G7q0aMC9k79i+aZdPPPjE2Lmzc0rJM2MHXkFtG4ce63T1Vt38+k3W/j20XvvSk5ZsI6slg3p3rpRuXHk5hWyu6CIluV0a6/I7OWb6disQWjrr1bVnoIi7pgwn1+P6EXzhnvfq796cS55hcWljbr8wmJ25xdxWIM6+5Xx5ry1pKcZZ/Rus096YVExhcVOvTrxu9N86l/eYdnGXMYc14lfDT+i3CEGyWLTzjzq1kkv/bKjIkXFzv1TF/Ojk7rG/DscqMKiYtbtyKND0/r77SsZMpmZUXFn2fXb99Ckfp2Yf+NnZ3xDdpdm9GjTmC25+fzv89W0O6z+PteJu3PS+Le5/oyevDh7JTOWba5webCq2pybz9Zd+azZtoc+7ZvQtEEmyzfl0iAzgxYNM1m9bTe5eUWs3rabU4+o/I72rvxCduXHfm8XFhVz/9TFXDyoC60b1+PyJ2exeutuXvzJIL79tw9YtjGXDk3r89il2Zz51/cZ3qcNk+avA+D9X59Kp+YNAJi3ahvb9xRw4uEtueH5Ofzns1W88rMT6d95354+KzbvomOz+vtM2rhi8y7qZqTROvj8+PSbLRzeqhFzV2zllJ4180UQpNA6z2Y2D+jn7oVmtgi4wt3fK9nn7qEuFmtm5wPDyzSeB7r7NVF5rgCuAOjcufOxy5cvD+31i4udI25744C+cRYRkcT7wYlZ3HFWn2qXo8Zz9SW68SwSD5tz88nZlLvPXWlJDcXFkQFs6WlWad5UcdW/P+X1L9Yw45bTaN24HgVFxWSkGau27qZhZgbNGsb+cjk3r5C3Fqzlu/2Te5WheNbN1e22/SzwrpltBHYD7wOYWXdgWzXLjmUl0ClquyOwOjqDuz9CpMs42dnZobZy09KMpy87nm8276o8s0gK2pVXSIMqfOsskmoquksnIhK25g0z97mDLqkj7RBqNJf48/lHc/ngrqW9XOqkR+7kd2zWoMLjGtbNSPqGc7xVd7btcWY2FWgHvOV7b2OnERn7HLaZQA8z6wqsAsYAF9XA65TrhG4tOCGBk1yIiIiIiIhUVYPMjP26XcvBqfYtJnf/OEbaV9Utt5zXKjSzq4FJRJaqetzd59fEa4mIiIiIiIiUqNaY52RnZhuA8AY9h6slsDHRQRyEVIw7FWOG1Iw7FWMGxR1PqRgz7I27i7vX3KwntYDq5tClYsyguOMpFWMGxR1PqRgzJKBuPqQbz8nMzGal4qQzqRh3KsYMqRl3KsYMijueUjFmSN245cCk4t85FWMGxR1PqRgzKO54SsWYITFxVzzvu4iIiIiIiIio8SwiIiIiIiJSGTWeE+eRRAdwkFIx7lSMGVIz7lSMGRR3PKVizJC6ccuBScW/cyrGDIo7nlIxZlDc8ZSKMUMC4taYZxEREREREZFK6M6ziIiIiIiISCXUeA6ZmT1uZuvNbF45+39lZnOCxzwzKzKz5sG+HDP7Itg3K44xdzKzaWa20Mzmm9l1MfKYmd1vZkvM7HMzGxC171IzWxw8Lk2yuL8fxPu5mU03s2Oi9sX9fFcx5iFmti3qOvlt1L4RZvZl8He4MR4xH0DcyXht1zOzGWY2N4j7zhh56prZ88E5/cTMsqL23RSkf2lmw5Mo5hvMbEFwXU81sy5R+4qi/g4T4hHzAcT9AzPbEBXf5VH7EvU5UpW4742K+Ssz2xq1LyHnWw6MqW5W3Vz9mFU3hxe36uY4qWLcqpsPhLvrEeIDOAUYAMyrQt7vAG9HbecALRMQcztgQPC8MfAV0LtMnpHAG4ABJwCfBOnNgaXBz2bB82ZJFPeJJfEAZ5bEnajzXcWYhwCvxTg2Hfga6AZkAnPLHpvIuMvkT5Zr24BGwfM6wCfACWXy/Ax4OHg+Bng+eN47OMd1ga7BuU9PkphPBRoEz39aEnOwvTPe5/kA4v4B8ECMYxP5OVJp3GXyXwM8nujzrccB/51VN6turm7MQ1DdHFbcqpuT61z/ANXNVX7oznPI3P09YHMVs18IPFuD4VSJu69x90+D5zuAhUCHMtlGA095xMdAUzNrBwwHJrv7ZnffAkwGRiRL3O4+PYgL4GOgYzxiK08Vz3V5BgJL3H2pu+cDzxH5u9S4g4g7Wa5td/edwWad4FF2oofRwJPB85eA08zMgvTn3D3P3ZcBS4j8DRIes7tPc/ddwWbCr2uo8rkuTyI/Rw407qS4tuXAqG5W3VwR1c3xpbo5flQ3h0+N5wQxswZELsCXo5IdeMvMZpvZFQmKKwvoT+QbnmgdgBVR2yuDtPLS46qCuKNdRuQb+hIJPd+VxDwo6Kryhpn1CdJS4lwn27VtZulmNgdYT6QSKPfadvdCYBvQggSe7yrEHK3sdV3PzGaZ2cdmdnaNBlpGFeM+N+jS9pKZdQrSEnptV/V8B13wugJvRyUn7HxL+JLt8ysqrixUN8eF6ub4UN0cP6qbwz3fGWEWJgfkO8CH7h79TfhJ7r7azFoDk81sUfBteVyYWSMiH6o/d/ftZXfHOMQrSI+bSuIuyXMqkQ+yk6OSE3a+K4n5U6CLu+80s5HAf4EepMi5JsmubXcvAvqZWVPgFTPr6+7R4x6T7tquQswAmNnFQDbwrajkzsG57ga8bWZfuPvXSRL3/4Bn3T3PzH5C5K7CUBJ8bVf1fBPpOvhSkL9Ews631Iik+vwC1c2qmyunull1czXjVt18AHTnOXHGUKZ7gbuvDn6uB14hDt1QSphZHSIfvP929//EyLIS6BS13RFYXUF6XFQhbszsaOAxYLS7bypJT9T5rixmd99e0lXF3ScCdcysJSlwrgNJdW1HxbAVeIf9uxyVnlczywAOI9K9M6HnGyqMGTM7HbgFOMvd86KOKTnXS4Nj+8cj1mjlxe3um6JifRQ4Nnie8HMNFZ/vQEXXdsLOt4QqqT6/VDerbq6M6mbVzVWlujkch/Q6zy1btvSsrKxEhyEiIoeI2bNnb3T3VgBm1gzYFXxb3xL4iEhDYEFCg0xyqptFRCRM8aybD+lu21lZWcyaFbeZ90VE5BBnZsujNo8E/mFmxUR6co1Xw7lyqptFRCRM8aybD+nGs4hU3Z6CImbmbCa7S3PqZ6YnOhyRpOfu04GjEh2HiIhIdWzYEem13apx3QRHUn01XTdrzLOIAPDsjG+45J8zeOqjnESHIiIiIiJxcty4KRw3bkqiw0gJoTSeLeJiM/ttsN3ZzOI+6YCIHLydewoB2BH8FJHaw8xGmNmXZrbEzG6Msb+umT0f7P8kWBYHM8sys91mNid4PBzv2EVEROIlrDvPfwcGEVmgGmAH8GBIZYuIiEgNMbN0InX2mUBv4EIz610m22XAFnfvDtwL/DFq39fu3i94/CQuQYuIiCRAWI3n4939KmAPgLtvATJDKltERERqzkBgibsvdfd84DlgdJk8o4ms/QnwEnCamcVaA1REROSQFVbjuSD45toBzKwVUBxS2SIiIlJzOgArorZXBmkx87h7IbANaBHs62pmn5nZu2Y2uKaDFRERSZSwZtu+n8ji6q3NbBxwHnBrSGWLiIhIzYl1B9mrmGcN0NndN5nZscB/zayPu2/f52CzK4ArADp37hxCyCIiIvEXSuPZ3f9tZrOB04hUsGe7+8IwyhYREZEatRLoFLXdEVhdTp6VZpYBHAZsdncH8gDcfbaZfQ30BPZZyNndHwEeAcjOzi7bMBcREUkJ1W48m1ka8Lm79wUWVT8kERERiaOZQA8z6wqsAsYAF5XJMwG4FPiISO+yt93dg2Fam929yMy6AT2ApfELXUREJH6qPebZ3YuBuWZ2UP2wzCw9GCv1WrDdNVgGY3GwLEZmkB5zmQwRERE5eMEY5quBScBC4AV3n29mvzOzs4Js/wRamNkS4AagZDmrU4DPzWwukYnEfuLum+P7G4iIiMRHWGOe2wHzzWwGkFuS6O5nlX9IqeuIVNZNgu0/Ave6+3PBepGXAQ8RtUyGmY0J8n0vpPhFRERqLXefCEwsk/bbqOd7gPNjHPcy8HKNBygiIpIEwmo833kwB5lZR2AUMA64IVj2Yih7u4s9CdxBpPE8OngOkW+3HzAzC8ZbiYiIiIiIiNSYsCYMe/cgD70P+DXQONhuAWwNupDBvstl7LNMhpmVLJOx8SBfW0RERERERKRKQlnn2cxOMLOZZrbTzPLNrMjMtldyzLeB9e4+Ozo5Rlavwr7ocq8ws1lmNmvDhg1V/h1EREREREREyhNK4xl4ALgQWAzUBy4P0ipyEnCWmeUAzxHprn0f0DRYBgP2XS6jdCmN6GUyyhbq7o+4e7a7Z7dq1ao6v5OIiIiIiIgIEF7jGXdfAqS7e5G7PwEMqST/Te7e0d2ziCyL8ba7fx+YRmQZDIgsi/Fq8LxkmQyIWiYjrPhFREREREREyhPWhGG7giWl5pjZn4A1QMODLOs3wHNmdhfwGZHlMQh+Ph0sk7GZSINbREREREREpMaF1Xi+BEgnsk7k9US6V59b1YPd/R3gneD5UmBgjDwxl8kQkXCoG4eIiIiISPnCmm17efB0Nwe5bJWIiIiIiIhIsgql8Wxmy4hx48rdu4VRvojUPM0gICIiIiJSvrC6bWdHPa9HpHt185DKFhEREREREUmoUGbbdvdNUY9V7n4fkaWnRCRFuEY9i4iIiIiUK6xu2wOiNtOI3IluHEbZIiIiIiIiIokWVrft/4t6XgjkABeEVLaIxIHGPIuIiIiIlC+s2bZPDaMcERERERERkWQUVrftGyra7+73hPE6IlJzvPSnbkGLiIiIiJQV5mzbxwETgu3vAO8BK0IqX0RERERERCRhwmo8twQGuPsOADO7A3jR3S8PqXwRqWnBoGeNfRYRERER2V8oS1UBnYH8qO18ICukskVEREREREQSKqzG89PADDO7w8xuBz4BngypbBGJAy/zU0RqDzMbYWZfmtkSM7sxxv66ZvZ8sP8TM8uK2ndTkP6lmQ2PZ9wiIiLxFNZs2+PM7A1gcJD0Q3f/LIyyRUREpOaYWTrwIHAGsBKYaWYT3H1BVLbLgC3u3t3MxgB/BL5nZr2BMUAfoD0wxcx6untRfH8LERGRmhfKnWczOxyY7+5/BeYCg82saRhli4iISI0aCCxx96Xung88B4wuk2c0e3uUvQScZmYWpD/n7nnuvgxYEpQnIiJyyAmr2/bLQJGZdQceA7oCz4RUtojEQclEYZowTKTW6cC+q2OsDNJi5nH3QmAb0KKKx2JmV5jZLDObtWHDhhBDFxERiZ+wGs/FQWV6DvBXd78eaBdS2SIiIlJzLEZa2a/RystTlWNx90fcPdvds1u1anUQIYqIiCReWI3nAjO7EBgLvBak1anoADPrZGbTzGyhmc03s+uC9OZmNtnMFgc/mwXpZmb3B5OSfG5mA0KKXUQAD/7fdU0ZJlLbrAQ6RW13BFaXl8fMMoDDgM1VPFZEROSQEFbj+YfAIGCcuy8zs67A/6vkmELgF+5+JHACcFUw8ciNwFR37wFMDbYBzgR6BI8rgIdCil1ERKQ2mwn0MLOuZpZJZAKwCWXyTAAuDZ6fB7zt7h6kjwlm4+5KpI6eEae4RURE4iqs2bYXANdGbS8DxldyzBpgTfB8h5ktJDJOajQwJMj2JPAO8Jsg/amgsv7YzJqaWbugHBGpJtdaVSK1krsXmtnVwCQgHXjc3eeb2e+AWe4+Afgn8LSZLSFyx3lMcOx8M3sBWEDkS/GrNNO2iIgcqkJpPFdXsF5kfyLrQ7cpaRC7+xozax1kK29SEjWeRUREqsHdJwITy6T9Nur5HuD8co4dB4yr0QBFRESSQFjdtg+amTUiMlv3z919e0VZY6Ttd49MM3qKHBzdeBYRERERKV9Y6zzv9210rLQYeeoQaTj/293/EySvM7N2wf52wPogvUqTkmhGTxEREREREQlbWHeeb6piWikzMyJjqBa6+z1Ru6InJbkUeDUqfWww6/YJwDaNdxYJz951nnXvWURERESkrGqNeTazM4GRQAczuz9qVxMiE4dU5CTgEuALM5sTpN1MZKKxF8zsMuAb9o6xmhi81hJgF5EZvkVERERERERqXHUnDFsNzALOAmZHpe8Arq/oQHf/gNjjmAFOi5HfgasOLkwRqUzpOs+68SwiIiIisp9qNZ7dfa6ZzQOGufuTIcUkIiIiIiIiklSqPeY5WM+xhZllhhCPiCSK7/NDRERERESihLXO83LgQzObAOSWJJaZCExERERERESSkLsTmdNZyhNW43l18EgDGodUpojEUek6z7r1LCIiIlLruIPazhULpfHs7ncCmFnjyKbvDKNcERERERERCd+2XQUc87u3SreL3Ukrdz5ngZAaz2bWF3gaaB5sbwTGuvv8MMoXkZpXsr6za9SziIiIyCFrxeZdDP7TtP3S9R9g5cLqtv0IcIO7TwMwsyHAo8CJIZUvIiIiIiIiB+nTb7Zwzt+nl7u/xy1vAPDW9afQs41G4sYSVuO5YUnDGcDd3zGzhiGVLSJxUDLWWWOeRURERA4dk+av5cqnZ8fc9+jYbH781Kx90obd+17p839ems3JPVpSJy2N9TvyaNkok4z02As25RcW8/KnK3nvqw28MW9tpXEN692GR8ZmH8BvknhhNZ6XmtltRLpuA1wMLAupbBERERERETkAI+57j0Vrd8Tc9/gPshnaq02lZVz25KxK8xystxasq7Gya0pYjecfAXcC/wEMeA/4YUhli0gc6IaziIhIcskrLGLoX97lF8N6cs6AjokOR1JAUbFz+M0Ty93/4Y1D6dC0/j5pS8adyXf/Pp0JV5+EO3Sr4PgwvfPLIXF5nTCFNdv2FuDaMMoSERERERHI2biLVVt38+C0JWo8S4XyC4vpeesb5e7/+u6RpKfFnkk7Iz2N/11zMhBZqipn/KjSfTOWbWb5plx+9dLnlcYw5IhWPHDRAOplpLFlVwEtGmaSVs5rpqqwZtvuCfwSyIou092HhlG+iNS8vWOedQ9aREQkGXy9IbL6a5sm9RIciSSzb/15Gss37dovfcoNp9C9dfUm/hrYtTkDuzbn/OxOB3Rcq8Z1q/W6ySqsbtsvAg8DjwFFIZUpIiIiNcjMmgPPE/nyOwe4IOhNVjbfpcCtweZd7v5kkP4O0A7YHewb5u7razZqkdrj/cUbAGjWMDPBkUgy2rGngKPueGu/9A9+cyodmzVIQESHvrAaz4Xu/lBIZSWdB95ezObcAgAyM9L48eCutGh0aH6bIrVXyfrOuu8sUqvcCEx19/FmdmOw/ZvoDEED+3Ygm8hHxGwzmxDVyP6+u9fcjDIitdizM1YA0LhuWP+yy6Fi9vItnPvQvstOPfPj4znx8JYJiqh2iD3PeBWZWfOgUv2fmf3MzNqVpAXph4SJX6zlxVkreH7mNzz87te8vUhfqouIyCFhNPBk8PxJ4OwYeYYDk919c9BgngyMiFN8IrXaMR0PA+C5mSsSHIkkk/Xb9+zXcM4ZP0oN5zio7tdYs4l8C10yEvxXUfsc6FbN8pPCxOsGA7Bu+x6Ov3sq+UXFCY5IJHxa51mkVmrj7msA3H2NmbWOkacDEP2f+8ogrcQTZlYEvEykS7c+RURCMqBLM+au3AZA1o2v89ltZ6gLdy2XX1jMwLunlm6ri3Z8Vavx7O5dwwqkqsxsBPBXIB14zN3Hx+u1M4LZ4oqK9X+BiIikBjObArSNseuWqhYRI62kIvy+u68ys8ZEGs+XAE/FiOEK4AqAzp07V/FlK7crv5Dev50EwK2jjuTywYfEd/Yipcp+FdX/95NLn99wRk+uGdods0NrNmOpWPSM2m/+fLAaznFWrcaz/X/27jtOrrpc/Pjn2c1uym4KyW4gpJDsht4xUqRIUSkWLHABlYtXuFwVy1Wv/sCCiOLFqyIWLAjciyhNEIkU6VVqAgkBQkk2vbDZbNr2Ms/vj3NmdnYz5czMmXPOzDzv1yvZmTntmTNn5pznfJvIu4E1qrrRff6vwCeAVcBlqtpeeIjDtlcNXAO8H+fO94tuu6vX/dxOOqOqnVru/YOWPJvypdbq2ZiyoqrvSzdNRN4RkWluqfM0IFW7pLXA8UnPZwCPu+te5/7dISI3A4eTInlW1WuBawHmzZvny4/M/Us28Pk/v5R4/sN7l/LDe5cOm2f0qCru+Nx7aN3Rw7zZk6mpFsaMqkYh7ZAtJvr6BmJ09g7sVAI7GFMESm5onA3bupk2cWzKaarK+NGj2NE7sNO0qx56i6seeivlco9+/b0MxpRd6mpp6+hln90mANA/GKNapOT2UdQsa+2gubEu8BsXb27ckXg8/4tHJz5XE5xCq23/HngfgIgcB1wJfAk4BOckeUaB6x/pcGCZqra427wVp71WIMlzTbXzBRmwatvGGGPKw3zgPJzz93nA3SnmeQD4kYjs4j7/AHCJiIwCJqlqm4jUAB8CHg4gZoBhiXM6vQMxPvzrpwOIxpjiWnnlB1m+qYOTfvaEp/lP9DifKV0HzZgUdggVqdDkuTqpdPks4FpVvRO4U0QWFbjuVFK1uzoieYZiVQ0DGFXllDwvXruVOxeu9XXdxoRtWaszluTy1k47vk3ZaZ5azyEz7UIjhSuB20XkfGA1cCaAiMwDPqeqF6hqu4j8AHjRXeZy97U64AE3ca7GSZz/EPQbuOaTh9FQX8tZ1z4X9KaNCVRzYz0rr/xg4vlfFqxh/uL1PPV2W4hRmTB885S9ww6hYhWcPIvIKFUdAE7CTVp9WncqmdpdOU+KUDUsrqZamFxXy31LNnLfko1+rtqYyHi2ZTPPtmwOOwxjfPWZ98y25DkFVd2Mc/4e+foC4IKk5zcAN4yYpxN4V7FjTGfp5acwqEq9O4RPclIR19M/yEurt3DPKxuora7i/55ZGXCUxhTPmfNmcua8mWmnr2jr5J/L2nj37Mk8vPQdnmvZbIl2mfjC8XPDDqFiFZrg3gI8ISJtQDfwFICIzAW2FbjuVNYCyb8SM4D1RdhOSiLC4984nq3umM/GlMsQZjsAACAASURBVJuxtdV09w2GHYYxvqsfY2OklpuxtdVZ5xlTU817mhsSw7dc9pH9ix2WMZExp6GOOQ11AOy923guOsESrlIWbzYa74PJhKPQ3ravEJFHgGnAg0nDU1ThtH3224vAniIyB1gHnA18sgjbSWvCmBomjKkJcpPGGGOMMcaYCmZJczRIqQ3HKCKnAVfjtK+6QVWvyDDvJpyev5M1AKVYZ8XiDk4pxgwWd5BKMWawuP2wh6o2hh1EKbNzcySUYtylGDNY3EEqxZjB4vZDYOfmkkueCyUiC1R1Xthx5MriDk4pxgwWd5BKMWawuE10lepnbHEHpxRjBos7SKUYM1jcpcbK/40xxhhjjDHGmCwseTbGGGOMMcYYY7KoxOT52rADyJPFHZxSjBks7iCVYsxgcZvoKtXP2OIOTinGDBZ3kEoxZrC4S0rFtXk2xhhjjDHGGGNyVYklz8YYY4wxxhhjTE4seTbGGGOMMcYYY7Iom+RZRMaIyAsislhEXhOR76eY5+cissj995aIbE2aNpg0bX7AsVeLyMsick+KaaNF5DYRWSYiz4vI7KRpl7ivvykiJwcZs7v9THF/TUReF5FXROQREdkjaVpo+9rdfqa4PyMim5LiuyBp2nki8rb777wIxRzV43qliCxxt70gxXQRkV+6x/ArInJY0rQw93W2uD/lxvuKiDwjIgd7XTbkuI8XkW1Jx8OlSdNOcX9HlonIxRGK+RtJ8b7qHs+TvSxrosHOzXZu9srOzYHFbOfmaMVt5+ZSo6pl8Q8QoN59XAM8DxyZYf4vATckPe8IMfavATcD96SY9gXgd+7js4Hb3Mf7AYuB0cAcYDlQHaG4TwDGuY8/H4877H3tIe7PAL9O8fpkoMX9u4v7eJcoxDxivigd1yuBhgzTTwPud7+7RwLPR2RfZ4v7PfF4gFPjcXtZNuS4j09zzFe7vx9NQK37u7JfFGIeMe+HgUejsK/tX06fsZ2b7dzsR9x2bvYvZjs3R2t/27m5xP6VTcmzOjrcpzXuv0y9oZ0D3FL0wLIQkRnAB4Hr0sxyOnCj+/gO4CQREff1W1W1V1VXAMuAw4sdb1y2uFX1MVXtcp8+B8wIKrZMPOzvdE4GHlLVdlXdAjwEnOJ3fKnkGHMkjmuPTgf+6H53nwMmicg0QtzXXqjqM25cEKFjuwCHA8tUtUVV+4BbcT6bqCmlY9u47Nxs52Yv7NwcKXZujgY7N0dU2STPkKg+swhoxfmCP59mvj1w7gg/mvTyGBFZICLPichHAwg37mrgm0AszfTpwBoAVR0AtgFTkl93rXVfC0q2uJOdj3MXMy6sfQ3e4v6EW+3nDhGZ6b4W5v72tK8jdlyDc4H8oIgsFJELU0xPt0/DPrazxZ1s5LGdy7J+87Lto8SpPnu/iOzvvhbm/va0v0RkHM5F2p25LmvCZ+dmOzd7YOfm4Ni5OVh2bi4zo8IOwE+qOggcIiKTgLtE5ABVfTXFrGcDd7jzx81S1fUi0gQ8KiJLVHV5MeMVkQ8Braq6UESOTzdbitc0w+tF5zHu+LyfBuYB7016OfB97cbiJe6/A7eoaq+IfA6nZOFEQtrfuexrInJcJzna3fZU4CEReUNVn0yaHrlj25UtbgBE5AScE/QxuS4bUtwvAXuoaoeInAb8DdiTcPe31/31YeCfqtqex7ImZHZutnNzlljs3GznZi/s3Gzn5kgoq5LnOFXdCjxO+uokZzOiioGqrnf/trjLHlq8CBOOBj4iIitxqmOcKCJ/GjHPWmAmgIiMAiYC7cmvu2YA64sdsMtL3IjI+4BvAx9R1d746yHta/AQt6puTor1D8C73Mdh7W9P+9oVleN65LZbgbvYuepiun0a5rHtJW5E5CCcqnqnq+rmXJYtlmzbVtXt8eqzqnofUCMiDYS4v3PYX5mO7cD3tcmPnZuLzs7Ndm7Oys7Ndm4uNOYkFXluFtUgbxoVj4g0Av2qulVExgIPTpky5ZjZs2eHHJkxxphysXDhwjZVbQw7jlJh52ZjjDHFFuS5uZyqbU8DbhSRapwS9dtnz559zIIF5dlLujHGmOCJyKqwYygxdm42xhhTVEGem8um2raqvqKqh6rqQap6gKpeHnZMxpSS9Vu7+ekDb7J2S1f2mY0xxgM7NxtTmJ7+QW56diWxWHnUFDWm1JVN8myMKczfFq3j148t4y8L1oYdijHGGGOAnz/0Ft+9+zXuXbIh7FCMMfiUPIvj0yJyqft8loiUXQNxY8rZ4KBzV3vQ7m4bY4wxkbClqw+Azt6BkCMxxoB/Jc+/AY7CGSgbYAdwjU/rNsYYY4wpG3t9+35ueHpF2GHkJBZTvnXXEt7YuD3sUDI69/rnmffDh8IOI6W7F63j5dVbclpG3BGLonpbezCmfOdvS1jTHl6Trw3bunPer365f8kG/rJgTfYZi+ixN1r5xG+fCaVq/6YdvfQNeBlWvnz4lTwfoaoXAT0AqroFqPVp3cYYY4wxZaNvMMbl97wedhg5Wbe1m5ufX80FN0a7s7en3m6jraMv7DBS+sqti/jYb57JaRlJNdpvhLy8egt/em41X7n15dBiOPbHj+W8X/3y+T+/xDfueCWUbcd9+ZaXWbhqCx19wddOePcVD/Oft4X32YfBr+S53+1JUyExNEVl3YYwxhhjSpSInCIib4rIMhG5OMX00SJymzv9eRGZ7b4+W0S6RWSR++93QcduglUmI5yaMjJgzc2A8L6b9y3ZGM6GQ+LXUFW/xBkIe6qIXAGcAXzHp3UbY4wxpkjcm9/XAO8H1gIvish8VU0uGj0f2KKqc0XkbODHwFnutOWqekigQZcwtezT5CBe8hzVwybqJeMVwT6DQPmSPKvqn0VkIXASzkf4UVVd6se6jTHGGFNUhwPLVLUFQERuBU4HkpPn04HL3Md3AL8WsctmY4wjorl9ZQn4Q6jUG4EFJ88iUgW8oqoHAG8UHpIxxhhjAjQdSO7xZi1wRLp5VHVARLYBU9xpc0TkZWA78B1VfarI8ZoQ2K2ScGlk01O3Q7OohlcB4l/N6B4j5aXgNs+qGgMWi8gsH+IxxhhjTLBSpUUjr8LSzbMBmKWqhwJfA24WkQk7bUDkQhFZICILNm3aVHDApcySDJObaN+1sJsq4bNKQMHyq8OwacBrIvKIiMyP//OyoIhUi8jLInKP+3yO2xnJ227nJLXu6yk7KzHGGGNMQdYCM5OezwDWp5tHREYBE4F2Ve1V1c0AqroQWA7sNXIDqnqtqs5T1XmNjY1FeAsmKJVaVTMspZIX2VFReSr1p8CvDsO+X8CyXwGWAvE71T8Gfq6qt7q9dp4P/JbMnZUYY4wxJj8vAnuKyBxgHXA28MkR88wHzgOexekU9FFVVXd0jXZVHRSRJmBPoCW40E1Q4qVbFXq9HLqoJiolkttXhKgeI+XGrw7DnshnORGZAXwQuAL4mtv5yIkMnbRvxOmg5Lek6axE7RaoMcYYkze3DfMXgQeAauAGVX1NRC4HFqjqfOB64CYRWQa04yTYAMcBl4vIADAIfE5V24N/F6XDLlpMLobasxqTWqnUTigXviTPInIk8CtgX6AW5+Tbqao7tXsa4Wrgm8B49/kUYKuqxkf5XovTSQmk76ykzY/3YIwxxlQqVb0PuG/Ea5cmPe4Bzkyx3J3AnUUP0BgTbVaWFbqgP4FK/cT9avP8a+Ac4G1gLHCB+1paIvIhoNVtI5V4OcWs6mFa8nqtUxJjjDHGGB8lSkAr9YrZpGTV+cNnBc/B8it5RlWXAdWqOqiq/wscn2WRo4GPiMhK4Fac6tpXA5PczkhgeKclKTsrSRGHdUpijDHGmEgq1dZmVjU0HBLxuxZ2WERHqf62lBq/kucut1fsRSLyPyLyVaAu0wKqeomqzlDV2Thtpx5V1U8Bj+F0RgJO5yR3u4/jnZVAUmclPsVvjDHGGGOysLFkgyUlkp7aFXl4wir9r9Q0zK/k+Vycds5fBDpxSog/kee6/h9O52HLcNo0X+++fj0wxX39a8DFBUVsjBmmMn8CjTHGmOiL6jnaaiSYSuNXb9ur3Ifd5DFslao+DjzuPm4BDk8xT8rOSowxxhhjSkVUkyATTfHktEIL+YyJHL96215BivOBqjb5sX5jTPHZidkYY0w68erDdq4wqVh1/vCE1Sy+Uj9xX5JnYF7S4zE4JcSTfVq3McYYY4wJkVXPNamUSpvscmbfzWD50uZZVTcn/Vunqlfj9J5tjCkRdtfYGGOKz0puTS6GShWjfeBEPLyKEPR1XKV+5n5V2z4s6WkVTkn0eD/WbYwxxhhjoqFCr5dDE/VxlK1NdhTEP4Rwo6gUflXb/lnS4wFgJfAvPq3bGBMAO/EZY4xJx2qGhivq5+iIh1fWrNp2sPzqbfsEP9ZjjDHGGFPOSr2JTNSTuHIjES9UHCp5jmqE5S9RtT/g7Zb6b1m+/Kq2/bVM01X1Kj+2Y4wpHk38rcwfQ2OMMRkkSrfsHBGkqni17Ygmp1VW7Bm6+EcQi+gxUm787G373cB89/mHgSeBNT6t3xhjjDHGhMW9Lo/Z9Xmgop6aWpvn8NkwcsHyK3luAA5T1R0AInIZ8BdVvcCn9Rtjis391bUfX2OMKZ5S/4210q1gRT05TSRuEaiRoKqJDtYqSVVIJc9RPSaLzZehqoBZQF/S8z5gtk/rNsYYY4wxIYpfJ8es6DlQ8WQwqjcthhK3cOOAyk3mEj2yV+j7D5pfJc83AS+IyF04v68fA270ad3GmADoiL/GGGPMSHaOCFZYnUF5FaUOw8KPIFwR+Agqgl+9bV8hIvcDx7ov/ZuqvuzHuo0xxhhjTDTYBXrAIl5tG6IzDnVMlerItxL3n3UYFiy/ettuBl5T1ZdE5HjgWBFZoapb/Vi/Mab44r+59ttrjDFmJE10GGYniSBFqU1xRhEIr1IPzagPZ1Zu/GrzfCcwKCJzgeuAOcDNPq3bGGOMMcZEQKUmKGGJeodhcVEIr1Jv7FRFvF18ufEreY6p6gDwceAXqvpVYJpP6zbGBCB+Vzvyd7eNMaaElfr1rV2gB6tUKiHbcRGeRLt46207EH4lz/0icg7wr8A97ms1mRYQkZki8piILBWR10TkK+7rk0XkIRF52/27i/u6iMgvRWSZiLwiIof5FLsxxhhjjMkgcYO1Qi+Yw5IoVYxCd9YpRee4qNQE3nrbDpZfyfO/AUcBV6jqChGZA/wpyzIDwNdVdV/gSOAiEdkPuBh4RFX3BB5xnwOcCuzp/rsQ+K1PsRtjSPrRtR9fY4wpmlKv3VOpCUpYSmXY4igc15G9v1Bk8UOkUt9/0HxJnlX1dVX9sqre4j5foapXZllmg6q+5D7eASwFpgOnMzTM1Y3AR93HpwN/VMdzwCQRsarhxhhjTIFE5BQRedOt3XVxiumjReQ2d/rzIjI7adol7utvisjJQcZtgmfJcziivtdjsbAjiMZwWaFIdBgWcLXtyB+VxeFXyXNB3JPwocDzwK6qugGcBBuY6s42HViTtNha9zVjjA+s4NmYyiQi1cA1ODW89gPOcWuCJTsf2KKqc4GfAz92l90POBvYHzgF+I27PlNmhnrbDjeOSiMR7wwqSmFV6rE5VLU/5EAqROjJs4jU4/TW/Z+quj3TrCle2+lrIiIXisgCEVmwadMmv8I0xhhjytXhwDJVbVHVPuBWnNpeyZJrhd0BnCTOVf3pwK2q2quqK4Bl7vpMGlFKNkz0DXUGFWoYWUWi1DcCIYRhqNp2he6AgPmSPIvImV5eSzFPDU7i/GdV/av78jvx6tju31b39bXAzKTFZwDrR65TVa9V1XmqOq+xsTG3N2JMBRsa59l+fI2pMF5qdiXmcUfX2AZM8bis3dg2Jk+lMoZvFOKr1OQxrHbxFbq7fSt5vsTjawnuHevrgaWqelXSpPnAee7j84C7k17/V7fX7SOBbfHq3cYYY4zJm5eaXenm8VQrzG5sl75Su04ulxvBQrQHeo5HFYXENfwIwhE/RqLwGVSCUYUsLCKnAqcB00Xkl0mTJuD0pp3J0cC5wBIRWeS+9i3gSuB2ETkfWA3ES7Dvc7e1DOjC6eHbGOMTG4bEmIrlpWZXfJ61IjIKmAi0e1zWJLGf2GColk5P1ZmUTMlzBAKs1ORRon1/pewUlDzjnCAXAB8BFia9vgP4aqYFVfVp0o/9flKK+RW4KL8wjTHGGJPGi8Ce7jCT63A6APvkiHnitcKeBc4AHlVVFZH5wM0ichWwO85wki8EFrkJTKmV5MZUqUp7mVk6SqbNc9gBEP19VCxhdSpXobu7sORZVReLyKvAB1T1xqwLGGOMMSZSVHVARL4IPABUAzeo6msicjmwQFXn4zSzuklEluGUOJ/tLvuaiNwOvI5T4+wiVR0M5Y0Yk6Rcel6OlypGtVR1qL+UcONwYohAECGwcZ6DVWjJM6o6KCJTRKTW7aXTGFOKdNgfY0wFUdX7cJpHJb92adLjHoaaUY1c9grgiqIGWEYq9QI/aFFNNnMlJVP3PPz9XanJY1WiB6sK3QEBKzh5dq0C/ulW3+qMvziiIzBjjDHGGFOCSi0XLbV4s4nq24n3lxKFxFUju5eKa6jDsGC3W6k3Av1Knte7/6qA8T6t0xgToPhPYIX+FhpjjCkj5VLyXBVSe9ZcRSGRikICHwbrMCxYviTPqvp9ABEZ7zzVDj/Wa4wxxhhTTuz6NhhRTza9StTajvjbiUJ4UUjgwzDU5rky33/QfBnnWUQOEJGXgVeB10RkoYjs78e6jTHBiJ90KrXakzHGmPRK7bq83Eoho/p2otVhWJjbDnHj1tt2oHxJnoFrga+p6h6qugfwdeAPPq3bGGOMMcaEaCAWCzuEnJRLKWRVokputN9PFOILN3kOb9tVJVI7oVz4lTzXqepj8Seq+jhQ59O6jTEBiNLdY2OMKVel+hs7WGJFuSUWblphdQaVqygc12FWW45ClemoHyPlwq8Ow1pE5LvATe7zTwMrfFq3McYYY4wJ0UCJXZlHIZnxQ7wpVVQHrIrv5ijs7zBjCPPdDwyG0+xOS6syim/8Knn+LNAI/BW4y338bz6t2xgTgPBPe8YYY6IqfoFeKqKQzPmhdXsvAFVV0Uyf4zUSorC3w4whzMNtRZszSnDQ97f6S6wph1/86m17C/BlP9ZljDHGGFO2opBl5GHtlq6wQ8hJ1HLn2Rffm9dyty1YA0Bn74Cf4fhiW3c/H/710wB09Q2GEsMvH3k78TjodtcDg0PJY1g3a5KPqyBj6OobYN4PHw5se1HiS/IsInsB/wXMTl6nqp7ox/qNMcU31OY5YlccxhhTRg6+/MGwQ8iJqvL4m5v4/J9fCjuUnESl5Ll1ew+H/+iRvJc/98g9+M3jy6kf41dLS3+MvBlw+OzJgW5/W3c/B39/+HcpyI/8//65gsv+/npwG3SpKgtXbeGM3z2bYmLxt796cxfH/eSx7DOWMb++iX8BfgdcB4Rz68kYY4wxJoKWbtjOS6u38O27Xg07FM/aOnpTliw1NUSnP9j2zj4mjq3h6off4lePLhs2Lawm2qpKR+8AZ/3+OV7fsL3g9W3v6Qfg90+0cMmp+xa8vnzdvmAN37zjlbTTxweU3MdiStO37ks9rcifee/AIHt/5x9ptl28jbd19LJxWw8f+tXTGecrZgwLVranTtiBg2ZMLNp2o8ivI31AVX/r07oi5zt/W5Joc1I7qor/d8o+zJw8LuSojPFXvKOJaNyrN8aY0tU7MMjDr7dy0c3pS2sb6kcHGFF2XktIp9TXBhBNen0DMb43/zVueWF1xvliAWbPr6/fzrJNHXz5lpczzvelE+fyq0eXoaqIeGvDXFtdDcBhsyYVHKdXqsqvHl3GVQ+9lXXeb5y8Nz954E0eeaO1aPFs3NbDyVc/ybbu/pTT/+eMg/jmHa8UJXm88ZmVfG/+a1nn8/twe+C1jfzHTQs9zXv1WYfwn7ct4o6Fazlp31192f7GbT0sWNXOF29Of0zfdP7hXP/0Cto7+3zZZqkoKHkWkXgdjb+LyBdwOgvrjU9X1fZC1h8VG7b2sG5rN/2DMZZv6uS4PRsteTbGGGNMwkurt/Dx3zzjad4JY0YxdXx4yfPAYIzH3tzEv/9xgedllv/oNM674QW6+4OtYLi1q4/zbniBxWu3eZp/TkMdK9o6i1qFd3tPP8f9z2Ns7UqdzI30lZP25Msn7cmv3dLxmEJ1jv1/+ZUUpXLLC6u56qG32LSjN/vMrsf/63hmu7UQfvLAm77G0z8Y44IbF/DEW5syznftue/iA/vvxuNvOom7H+2uVZVnl2/mk9c9n3XeJZd9gAMvc6qOt+3opX50/mnVyrZOfvv48kQbdy9afnQaVVXCM8vbAFi7pTvv7QN09A5wwPceyDrfhcc18a3TnFoQf3puFT0B/yaErdCS54U4BVXxn4BvJE1ToKnA9UfC9Z95NwDvbO/hiB89UrG9y5nyZuM8G2PMcNu6+6mrraa6Sli7pZsHX3+HH9zzOsft1ciTWS7sR5pSV8sL334f1VXC525ayIJV7fT0DzKmproosbdu72Ht1m7PCX0qD3/tvcydWp94LuL/eM+xmFJVJdzw9ApuX7CGNzbuyHkd0yaO4Y7Pv4fpk8Zy18tr+epti30rhdywrZvt3QNc/3QLty9Y63m56/51Hu/bb3jCG6+CvXTDdg6Y7q2qa/+gf9ec3X2DfPtvSxgYVOYvXp/z8vd86Rj2mzYhbc/fR1/5KP+8OLfujmIxZXtPP4dc/pDnZZ78xgnMmjJUiBVPWr98y8s5bx+chPmPz67yVML8lZP25PPHNye+t+ceuQc3PbfK8/HWOzBILAbXPdXCzzyU7Md9/LDpfOH4ucO+j3H7TZsAwJJ127jkr6/w3x8/yNM6r3uqhV8+8jbbe7x1RvfEN45njynDm228vmE7a9q7mX3xvay88oNZ17G5o5dP/PYZVm52OiD8wvHNfPOUfTxtPyoKSp5VdY5fgXglIqcAvwCqgetU9cqgtl1T7YzsVWrDNRhjjDHlKt5x0X1fPpbdJ42hbvSoxPk6k66+AcbWVNPW0cf7f/6E51JEwHPivOyKUxmVIpZ/vLYRgH2++w8e/tpxzJ063vO2U4knRX99aV1B67niYwfw8UNnMLY2dUL/1NtOCdfsi+/lka+/l+bGnS/kM+kbiPHHZ1fy4Ovv8MKKwion3nzBEbxnbsNOr8dzzeN/+jiApwv6uG1d/by4sp0LciiRHxbTvx/BQTMmpS2B/OOzKwESbVf/8rmjeHeWjrY2bu8Bcu/Ms6N3gPaOvrw7d7r9P47i8Dm5dQK2bquTRC3+3geYOLYmcWMEnJsA72zvYWVbF5++Pnup7khnvGsGPz3z4JTT4lXg49v/0olz+foH9k4575r2Lo79n9z2yWUf3o/PHJ065bl7kfOdO/FnTwBw42cP5717NSamDwzG+NF9bzB/8XraOryX7J9z+Cy+efLe7FKXvZnExLE1ice3vLCGW15Yw6Uf2o/PHjOHpRu2owrzF6/nd08s97z9uDd+cErGG3xr2odKu2dffC/7TpvAnZ8/iprqKhav2crEsTWc84fnaOtIXbX7N48vL7nkWQrpWVdE3g2sUdWN7vN/BT4BrAIu87vatohUA28B7wfWAi8C56hqyu7u5s2bpwsW5PcDmMr2nn4OuuxBvvPBfbng2LIoVDcm4bL5r/F/z6zk00fO4ocfPTDscIyJJBFZqKrzwo6jlPl1bn7wtY1c6LFNYFAe/Opx7LVr9kQ41bBFP/7EgfzLvJkp28L2D8Z4ZGkrb27cwc8f9l5alc2DXz2OuY31nscQThX35Lpabjr/cCaMqWHm5HGoKgtWbWHdlm7+87ZFvsR53F6N7DZhND/62IEpb0Yku+6pFn5479KdXr/1wiNpdKvKTxpbQ99gjM7eQf7jpgUs39SZV1xeP++4F1a08y+/T93pklf3f+VYJo2robF+NKOqq1jR1sndi9Zx4PSJnH9j/t+rb5+2L589Zg7VeYwnvXxTBye5yaNfMiWsI/X0D7LPd1N35JWvxd/7AONHj8r63fje3a9y47OrfNnmrhNGc/dFx7DbxDE5L/uzB9/cqdO8fHzrtH34wH67JarkZ/PWOzv4wM+fzHt7I2825CvIc3OhyfNLwPtUtV1EjgNuBb4EHALsq6pn+BNmYntH4STlJ7vPLwFQ1f9ONb/fyXN33yD7XvoPLj51Hz733mbf1mtMFFjybEx2ljwXzq9zc77j5uZrr13ruezD+zMQUw6eMYmJ42qyL5TGmxt3cPLV+V9w5uLZS05k2sSxvqxrYDDG3G/f78u6UvnBRw/g44dOZ1xttecOtUbK1BtzIR79+nuZOLaGKQV29FaMRC8XHz54d/774wdSW11F7ajsNTRyUch38nsf3o9/85gsF2P73/ngvsybPZmDpk/0fDOpkO3us9t4bv73I+nqG2DGLv70o9Q/GGPPHL+fP/rYgXzkkN0Laq8Nue2Dmmrh7StOK2h7I5VS8rxYVQ92H18DbFLVy9zni1T1EF+iHNreGcApqnqB+/xc4AhV/WLSPBcCFwLMmjXrXatW+XM3CIYOygOnT0y0LzCmXCxcvYVlrR00N9Yxb49gx2s0ptjeM3cKpx8yveD1WPJcOL+T5yPmTOYnZxycc/XUE/eZyqNuD8GnHrAb27r76R2I8aOPHcjeu41HVRmMadaSzkJs7erLqa1nJv/ziYM4fu9Gpk7IvdQqV4UmgE9+4wQm1dUwYUz+NyCyUVXmXJJ7Er3PbuP51TmHsmcOJcr5+PItL+fV7jgXDfWj+fMFR7D3bsV9L8myHRv3fflYGsePpqG+Nu8bJJncv2RD1jHJF1/6gYJufqXzzPI2PvmH1FXSX/3+yQUnqF7Fa8om22e38dzg9uG0+yR/bqaNpKo89mYrn/2/od/3/3hvaRY27wAAIABJREFUE9u7B/jUEbM8t/PPRyklz68Ch6jqgIi8AVyoqk/Gp6nqAT7FGd/emcDJI5Lnw1X1S6nm97vkWVX59PXPs7w1v+o9xkTdxu097BbAhZcxQTtz3oy0beByYclz4fw6N9/6wmp2qavl5P13Szl9/dZu6kaPGtYeMIo2buvh4aXv8J2/eRsD+tpz38VxezVSW12VcwmZ3/oGYty2YA3fTRH72e+eyVHNUzhp310ZW1OdV3Vgv6za3Ml7f/L4Tq8fu2cDPz3zYHYN8bwXiykxVaqrBFUSn2nrjh6m1I0ett/WtHfxp+dX8fsnWnZazy/POZRj5jYw2UMb2UqhqsP2qSlfpZQ8fxs4DWgDZgGHqaqKyFzgRlU92p8wE9sLtdq2McaYymbJc+Hs3GyMMcZPQZ6bC+1t+woReQSYBjyoQ5l4FU7bZ7+9COwpInOAdcDZwCeLsB1jjDHGGGOMMSahoJLnMIjIacDVOENV3aCqV2SYdxNOz9+FasApXS8lpRgzWNxBKsWYweIOUinGDMWNew9VLbxr0Apm5+aSixks7iCVYsxgcQepFGOGMjk3l1zyHAYRWVBq1fRKMWawuINUijGDxR2kUowZSjduk5tS/JxLMWawuINUijGDxR2kUowZSjfukYrXhaQxxhhjjDHGGFMmLHk2xhhjjDHGGGOysOTZm2vDDiAPpRgzWNxBKsWYweIOUinGDKUbt8lNKX7OpRgzWNxBKsWYweIOUinGDKUb9zDW5tkYY4wxxhhjjMnCSp6NMcYYY4wxxpgsLHk2xhhjjDHGGGOyqOjkWUQmicgdIvKGiCwVkaNGTBcR+aWILBORV0TksKRp54nI2+6/8yIW96fceF8RkWdE5OCkaStFZImILBKRBRGL+3gR2ebGtkhELk2adoqIvOl+FhdHKOZvJMX7qogMishkd1oo+1pE9k6KaZGIbBeR/xwxT+SObY9xR+rY9hhzFI9rL3FH7th2t/1VEXnNjekWERkzYvpoEbnN3afPi8jspGmXuK+/KSInBxm38c7D727kfr88xh2p368c4o7ib5idm6MVd6SObY8xR/G4tnNzKZybVbVi/wE3Ahe4j2uBSSOmnwbcDwhwJPC8+/pkoMX9u4v7eJcIxf2eeDzAqfG43ecrgYaI7u/jgXtSLFcNLAea3OUWA/tFIeYR834YeDQK+3rEvtuIM3h85I9tD3FH8tjOEnPkjmsvcY+YJxLHNjAdWAGMdZ/fDnxmxDxfAH7nPj4buM19vJ+7j0cDc9x9Xx3W8WL/Mn7Odm6O1v6O3G9YtphHzBuJ368U+87OzeHGHLnj2kvcI+aJxLFNhZ2bK7bkWUQmAMcB1wOoap+qbh0x2+nAH9XxHDBJRKYBJwMPqWq7qm4BHgJOiUrcqvqMGxfAc8CMIGLLxOP+TudwYJmqtqhqH3ArzmdTVHnEfA5wS7HjytFJwHJVXTXi9cgd2yOkjDuKx3aSdPs6nVCO6xS8xB2lY3sUMFZERgHjgPUjpp+Oc2ENcAdwkoiI+/qtqtqrqiuAZTifgYkQOzcHy87NobFzc3Ds3ByMijk3V2zyjHNHaRPwvyLysohcJyJ1I+aZDqxJer7WfS3d60HwEney83HuYsYp8KCILBSRC4sZ6Ahe4z5KRBaLyP0isr/7Wlj72/O+FpFxOCeyO5NeDmtfJzub1D+sUTy2k6WLO1lUju24TDFH6bgeKeO+jtKxrarrgJ8Cq4ENwDZVfXDEbIn9qqoDwDZgCtHZ3yYzOzfbuTkbOzdH9HzhisqxHWfn5iKrtHNzJSfPo4DDgN+q6qFAJzCyXYOkWE4zvB4EL3EDICIn4PyI/b+kl49W1cNwqtVcJCLHFTneOC9xv4RTPeVg4FfA39zXw9rfnvc1TtWZf6pqe9JrYe1rAESkFvgI8JdUk1O8FvaxDWSNOz5PlI7tbDFH7bhO8LKvidCxLSK74NylngPsDtSJyKdHzpZi0Ugc28YTOzfbuTkbOzcPvR4YOzcHx87N0T43V3LyvBZYq6rPu8/vwPkxHjnPzKTnM3CqIaR7PQhe4kZEDgKuA05X1c3x11V1vfu3FbiL4KpGZI1bVberaof7+D6gRkQaCG9/e9rXrp3uEIa4r+NOBV5S1XdSTIvisR2XKe4oHtuQIeYIHtfJMu5rV5SO7fcBK1R1k6r2A3/FaWuXLLFf3epjE4F2orG/TXZ2brZzczZ2bo7g+SKCxzbYudnOzUUgqpFO7gvS0NCgs2fPDjsMY4wxZWLhwoVtqtoYdhylzM7Nxhhj/BTkuXlUEBsJy+zZs1mwINCe2o0xxpQxEfHa6YxJw87Nxhhj/BTkubmSq20bY4wpM7GYsqa9i8ffbOX6p1fwrbuWcNbvn+UPT7aEHZopgn8ua+P19dt9Wde2rn5WtHX6sq5C9A3EeGRpptqa3gwMxugdGCxoHYMx5Uf3LWXTjt68ll/W2sHnblpI30Asr+W3dvXR3Zf7e3h13TY2buvxPP/AYIwXVrRnnzFJLKZ09g54nn9FWyfLWjs8z3/7i2u45rFlnudftGarp/2sqty9aB0Dg9nnfertTdyxcG3W+br7Brn3lQ2e4lzR1skbG719Zxev2Zr3sTdSy6bs+35Nexf/+88Vvmwvk57+QU/Hgl/vfUtnny/ryWRHT3/RtxEVljwbY4wpOV19A7y6bht3L1rHVQ+9xUU3v8QpVz/Jvpf+g2P/5zE+878v8oN7Xue+JRsYiCljaqvDDtkUwaeue57TfvmUL+s67ZdPccJPHy9oHdu6+5l98b3c9Fz+hSA/ffBNzr9xAc8u35x95gzOvvY59v7OPwpax1Nvb+LaJ1v41l1L8lr+kr++wj9e28iiNV5HvxrukMsf4kO/yv3z/dCvnuaYHz/qef6rH36bf/n9syxctSX7zK6fPfQm+3/vAbZ7TBpO+OnjvO+qJzyv/5t3vsJPHnjT07zLWjv46DX/5Ef3Lc06792L1vOVWxfxew83FM+9/gX+6y+Ls853+T2vcdHNL3nafyf89HFOudrbZ3r6Nf/k1F88mXGem59fzeyL76WrL/2NjL8vXs+JP3uCR9/IfFPqU9c9z/f//jpbu9Inm5f//XVmX3xvxvXsd+k/uOjml9JO/6+/LOZ9Vz2RMeF88LWNvPuKh/nnsra083T2DvCTB97IeNPkby+v49AfPMSStdvSzvPdv73Kbx5Pf6NmYDDGjc+sTHvDZfGarRx42YPcvyT1DZTnWjYz91v3BZLEB6Gsq20bY4wpXapK645elrd2sHxTB8s3dTp/WztYn1SqVCUwc/I4mhvrOXbPBpob62meWk9zYz2T62pDfAelQ0ROAX4BVAPXqeqVI6aPBv4IvAvYDJylqitFZDawFIhf5T+nqp8LKm4/rdvaXfA6Nmxz1vGnZ1dx7pF75LWO1Zu7ANiS4QLeiwU5JILpxNx+cfo9lFJmUkj/Oss35VcbYCDmfZtvvbMDyK2k7+5FTp9G27r6mTCmJrfgfNbuJiWvrkufII2c169STYC1W5zjvhilj20dmb8H8aRvc0cf4yanTmteXe/slzc3dnDiPrumXVf8RkimQ+cGDyXTXW5J/DWfTD39uRbnxlhPf4zxY1LPs3C18/19Ze02jp7bkHKeXz76Nr9/ooXdJozh3KNmp5znaTf5XrpxOwfOmJhynvjNvi8cPzfl9D8+u4rL73md/sEYFxzbtNP0V9zj7ullbZx64LSdpv/28eUMxJRFa7dywt5TU26jlFjybIwxJlS9A4Os2ty1U5LcsqmTjqRqkXW11TRPreeIpik0N9YlkuQ9poxj9CgrWc6XiFQD1wDvx+n59EURma+qryfNdj6wRVXnisjZwI+Bs9xpy1X1kECDjjgtYKQVSTVwS0gk5Sgy5Sz3zy0K/e7mcswU4/iSCBy0Xj6HbN/LoN+Fl9+JTPP09js3tfoGPb35vMVvKmzvznxzJAJfhUBY8myMMSYQ7Z19iZLjeJLcsqmD1e1dw+707z5xDM1T6znjXTOGJclTx4+OxEVaGTocWKaqLQAicivOmJ3JyfPpwGXu4zuAX4t9GEUVhaQsLkqxFEP8SM7lfUbx6M/lYyrGaDthHCaJzy7D1uM3gby+5eKPRJQIOsMc2WP2cgzGZynohl6W2wqJbWTbRJn8jljybIwxxjcDgzHWbOlOSpCdEuTlmzrY0jV013r0qCrmNNSx//SJfOSQ6YkkuamxjnG1dmoK2HRgTdLztcAR6eZR1QER2QZMcafNEZGXge3Ad1TVn0bIpuQlEoCQ4yh3EczjA+Nn7YhSvR8Y1LDD9j122BWKMcaYnG3v6XeS4tbhCfLKzZ30J1Uha6gfTXNjHaceOM0pQXaT5N0njaW6qjQvVMpQqg9i5HVSunk2ALNUdbOIvAv4m4jsr6rDutMVkQuBCwFmzZrlQ8jRlGsJlyl9hZToGVOIoJpVlOg9haKJbPIsIiuBHcAgMKCq80RkMnAbMBtYCfyLqhbeI4YxxpidxGLK+m3dThvk1g5a2jpY3uokya1Jnc2MqhL2mOJ02PW+/XZNlCA3N9QzcVy4HekYT9YCM5OezwDWp5lnrYiMAiYC7eoUefQCqOpCEVkO7AUMG8hZVa8FrgWYN2+eZRsZROpCNXvt0rKQTxISxfbgXkogixF1Yp0RPVBy/U4VvdK2h++VX78D+TRJyHcbkT0AfBbZ5Nl1gqom99F+MfCIql4pIhe7z/9fOKEZY0x56O4bdBJjtw1ycrLc0z/Uy+6EMaOYO7We9+7VSPPUepoa6mieWs+syeOoqbaRD0vYi8CeIjIHWAecDYzsJ3Y+cB7wLHAG8Kiqqog04iTRgyLSBOwJ2KDaPohCiWbB1+/Ryy8zymePR6GWQT6Jlp9hR+GGjx+fQ1Bvw8t2/IrFz5s86fZxFG8kFVPUk+eRTgeOdx/fCDyOJc/GGJOVqrJpRy/LkqpYx5Pk5CF6RGDmLuNobqzjPc1TaIpXtZ5az5S62pJtE2bSc9swfxF4AGeoqhtU9TURuRxYoKrzgeuBm0RkGdCOk2ADHAdcLiIDODXFPqeq7cG/i2jw4+sRxarfQbWpDEs+n1sUfwq9fErF/A0P44aPp1LcHNfpqedu1YL3ZaFfqyBKlaHwTse8dOpWSqKcPCvwoIgo8Hu3yteuqroBQFU3iMhOg4VVSrsqY4xJpW8gxqrNnUNjIsd7tW7tYEfSsE/jaqtpbqzn3bN34azGmW6P1nXMnlLHmBob9qnSqOp9wH0jXrs06XEPcGaK5e4E7ix6gKaklXnuHQHRu+FSioK6IRLFGy+ZeI032/FXLiXUUU6ej1bV9W6C/JCIvOFlIWtXZYypBFs6+4a1QY4nyavbuxhMGvdp2sQxNDXW8bHDprsddjlJ8m4TxlgpsjFFYhcfpSmf5LNUP+tKTLT9rEGhGkwS7FfMxfy4PSfXJfttGS6yybOqrnf/torIXTjjUL4jItPcUudpQGuoQRpjTBENxpS1W7rcsZGHJ8ntnX2J+WpHVTFnSh37ThvPhw6alkiS5zTWUT86sj/zxphUInRPq1JusOVVbdv/MAJRjI/U8zi/ReCp/XDOHYZlfyN+vNWMY1N7qJKdy3sP4rNJ3yY6uBiCEMmrKhGpA6pUdYf7+APA5Qx1WHKl+/fu8KI0xhh/dPQOuB11DU+SV7Z10Tc41GFXQ30tTQ31nLz/rkOlyI31TN/Fhn0yptxE4Tqz0F+VUvtVyqdkLArtwfPrMMy/uKNwk8WfzyGY7uW9VF/OpYpzUCW62ZLjdKJwfPgpkskzsCtwl7uzRwE3q+o/RORF4HYROR9YTYr2V8YYE0WxmLJxe4+bIHcMa5P8zvahYZ+qq4Q9Jo+jqbGeE/aZmpQk1zFpXG2I78AYk40fl4jldZnpKJfqmlHnqcOwYm4/jJLnHBKzrG1yc9g5TrIe7rc1sA7D3A3Zt9gRyeRZVVuAg1O8vhk4KfiIjDHGm57+QVa0de5UityyqZPu/sHEfOPHjKK5sZ5j5jbSPLUukSTPmjyO2lE27JMxpSwKpZFRkEhGbHcU1dAwu7ajUylGR1W+VNv20qt3gdsIsqfrSrlJFsnkOWp+/I832NzRy6jqKi46YS7TJ40NOyRjTIhUlbaOvqE2yG6S3NLWwdot3YkToghMnzSW5sZ6jpgzJZEkNzXW0Vg/uuyqMhlj/BOlBDxCoRRFPsODlfrvd7l9pl7eTlTespdDx7/Dq/jHqdcS8HI55ix59mDBynZWbe6idUcv++42nnOPmh12SMaYAPQPxli1uWtYkuz0cN3B9p6hYZ/G1lTT1FjHoTN34YzDZtI8tY6mhnrmNNQxttaGfTLGeBelpKzcxmdNq4BdXnJ7pgjHV5gVDIrRGZWnRNyH7RW6naCrU+f7nsutAoolzx785XPvYXNHL+/64cNl88EbY4Zs6+pnWaIna6eK9fJNHaze3MVA0rBPu04YTXNjPacfMp3mxjqaGutpnlrPtAljqLIOu4wxriglwIUotLprokTXj2ACkEucUepBOOw2qaEe7j5uO5fPtJAbSn6FHNRuz/b5lsv4zV5Z8myMqQiDMWXdlu5hwz05iXIHbR1Jwz5VVzG7YRx77zqe0w6YRlPjUFXr8WNqQnwHxpio87G/37JQJvcQIi+f3VyMRDtKTQ2Sea1WXM7Hqz8l5dH8fIMWSPIsIrvhjNOswIuqujGI7fopcVfPjhtjIq2zdyBRctySnCS3ddI3MDTs0+S6Wpob63jfvrsmEuTmxnpm7DKWUdXWYZcxxpjSYteoqRUjJ/YlGc2wkqGqzoVtaKjpRQHr8LgH021j6OZFeRygRU+eReQC4FLgUZxj4Vcicrmq3lDsbRdDuXzwxpQyVXfYp6Q2yPEkecO2nsR8VQJ7TKmjubGO4/ZqpDlRilzP5Dob9skY4y8/L9KjdLkRpViKIa/PLUKllLmUmEapurm//HtDxS5h9dSsw8chuIoua8l+hL4sPgii5PkbwKHuMFOIyBTgGaCkkufy+tiNKQ09/YOs3NzplCS3DlW3btnUQWff0LBP9aNH0dxYx1FNU2ieWp9IkmdNGcfoUdZhlzEmGL5U247QBYdf48iGfnHvUX4FJCXy5ooqvDbXft4MCLrtbsExB92hX6G/A/5EEbogkue1wI6k5zuANQFs11d+VHswxuxMVdnc2Zeoap2cJK/Z0jXs5DJ90liaGus4c97MRJI8t7GexvE27JMxJjrK5deo0PdRKr1153P+iOJnHNZ+LpXTr9f946nDsGgf0gl+jAFuOdBwQSTP64DnReRunP1+OvCCiHwNQFWvCiAGY0zI+gdjrG7vSpkkb+vuT8w3pqaKOQ31HDRjIh87dHoiSZ7TUMe4Wuvj0BhTGaKecHpRab3whiWX/Vwqia6vPPZbFMV9U2iSHsR78qt9dqkI4kp0ufsv7m737/gAtu2bxHALlXFcGJO3bd39wzrqiifJq0YM+9Q4fjTNjXV86KBpTmddbpK8+8SxNuyTMaZi2a9faSnd68KSDXyYsGqdFb1ddGI7mebxnpsUM9psn4FfzT+ioujJs6p+v9jbCIRVWTAmIRZT1m3tHjbkU7zTrraO3sR8NdXCHlPqmDu1npP33y2RJDc11jHBhn0yxpi0onShWe4lSvmkX1FsKuTlmClmYVCYx6yfm/ayrkLeq1/JpJdDML6NQo7WgptvFLh81ATR2/Y84NvAHsnbU9WDir1tY0xhuvqGhn1KTpJXtHXSmzTs08SxNcydWs+J+zQmhnxqaqxj5uRx1NiwT8YY41mkkrIK6zAsH1F4a2EfMkObD35veHnrXnePp3VJaR3PiVB9OEiydqiXdb+U0I7LIIhq23/G6XF7CRDLMm9kldsYZcbEqSqtO3qHtUF2xkjuZN3W7sR8VQIzJ4+jubGeY/dsSAz51NxYx+S62mhd8BljjAmddTQUrLAuUUvl9O9193i51i/2rk7sUw9jQXtanw+xpAsl27pL5fjwKojkeZOqzg9gO8aYDHoHBlm1uStlktzRO5CYr662muap9Rw+ZzLNjXVuglzPHlPGMabGhn0yxph0/Exe7F598Ep9n3sJv9wSmbhMn53X9yweOhYTnP1cSGGal17o/epsL8hjusS/Pp4FkTx/T0SuAx4BEo0hVfWvqWYWkZnAH4HdcEqqr1XVX4jIZcC/A5vcWb+lqvcVM/BhcQW1IWMK1N7ZN6yjrni169XtXST118XuE8fQPLWeM941gyZ3XOTmxnp2nWDDPhljTCEK+QmN0q9vpfSWnc+QWn6OLxyGUo17pFIZDi1ZLt8rb+2vs89VzMs6r+sul2MuiOT534B9gBqGqm0rkDJ5BgaAr6vqSyIyHlgoIg+5036uqj8tarRpeLkbZUxQBgZjrNnSnVSKPJQkb+kaGvapdlQVTQ117D99Ih85eHe3R+t65jTUUTfahn0yxphi8ONaIUqXG4XGEvUmb/l1GOZ7GIEoZthhfMw53eDxMUA/1lQKw1BBUidzIS0fNUFcPR+sqgd6nVlVNwAb3Mc7RGQpML1YwRkTZdt7+p2keEQp8srNnfQPDv0MNdSPpqmxjlMOmEZzYx3NU+uZ21jP7pPGUm3DPhljjMnDULvLfJe3808Qwt7NUUiOMle19m8HidtjWMTvByVxAi1kH3gvWU69U8I+Pv0WRPL8nIjsp6qv57qgiMwGDgWeB44Gvigi/woswCmd3pJimQuBCwFmzZpVQNgj1uv+LaVqIaY0xGLK+m3dThvk1g5a2jpY3uokya07hoZ9GlUlzJridNh10r67JpLk5oZ6Jo6zYZ+MMSYqCrpYLMMOSsvnnUTT0PBT3ve0n9ezYSZHuWw72zv2MoSUH2/VS0d6uQxnlWmexFBVPgSebjtVEv7NkyAFkTwfA5wnIitw2jwLoNmGqhKReuBO4D9VdbuI/Bb4Ac5n8wPgZ8BnRy6nqtcC1wLMmzfPt8+x3Ab4NsHr7ht0EuNNnbTEO+xyk+We/qGO6CeMGUXz1HqO2ys+7JOTJM+yYZ+MMSbS/EhIolCKV2niJeSxHMaEiX9OsQhcGOZyjVpVps0QM30OXvePp468fLi5NfQZFNaTdi5Ja2HjUse3k7lkOVZmx1Q6QSTPp+S6gIjU4CTOf453LKaq7yRN/wNwj28RGuMTVWXTjl6WJVWxjifJycM+icCMXcbS3FjPUc1ThiXJU2zYJ2NMwETkFOAXQDVwnapeOWL6aJzOPN8FbAbOUtWV7rRLgPOBQeDLqvpAgKGXnaqhqm6hq5Qz0dDFfw4dhkWoUCWnTrOKmOiEsS+83Ayo8rh/hkrws2+3oGTU/evlM8jcI3d8PcXd8VUej/VsNxSi8F3xQ9GTZ1VdBSAiU4Ex2eYXJ2u4HliqqlclvT7NbQ8N8DHg1SKEmz4uuxNskvQNxFi1uTMx3FM8SW5p7WBH0rBPY2uqaZ5ax7zZu3BW40wnSZ5ax+wpdTbskzEmEkSkGrgGeD+wFnhRROaPaG51PrBFVeeKyNnAj4GzRGQ/4Gxgf2B34GER2UtVB4N9F9GQy8V32nXkkcgVS6JENs9YEsl3+G8lo0JKY6PQnC+X464YzRCjXm176Dj2ti5PJbke5km/oexr8XRzxsvQWj70Rp4tSc9WAl6KPaJnUvTkWUQ+glPFenegFdgDWIpzok3laOBcYImILHJf+xZwjogcgvPZrAT+o4hhGwPAls6+YW2Q40ny6vYuBpN+hXebMIbmqXV87LDpiSGfmhrr2G3CGKqswy5jTLQdDixT1RYAEbkVOB1ITp5PBy5zH98B/Nq92X06cKuq9gIrRGSZu75nA4rdd6oaau2fKN2sr8ohmUilVCpRVeVxwyJKo7Dk8jlV5ZIh5iiM5MhTO2WPJafeqlM7Iz0XcnPLyzBn4qFKtrfPvfDjNH4dm24d2aqy+3FTMUqCqLb9A+BI4GFVPVRETgDOSTezqj5N6ppCgY3pnEqUqucYfw3GlLVbutyxkYcnye2dfYn5aqurmNNQx77TxvOhg6YlEuSmxnrqbdgnY0zpmg6sSXq+Fjgi3TyqOiAi24Ap7uvPjVi2pEfIUM0/6YsnD4VcKkTpeiN+AT9YYB3fqJc4xUelyOVtRum+eC61FaqqvM/refshVvD30vZ86IaUlzQzy3HgQ80QL22VqzzU+vDSUZwf90qylTyXW3KcTRBX/P2qullEqkSkSlUfE5EfB7Ddooj6CcCk19E74HbUNTxJXtnWRd/gUC8hU+pqaW6s5+T9d00kyM2N9czYZZwN+2SMKUepfthGnuzSzeNl2aKNhFEMMVWqCkwGCulMKFvnPEGqrsp+cZ5JqZwxEzcJ8mjzHKXq9d6aPOd+o8CrcNo8u9v2Mo/HattedmRBJbleEmMPMXt5X0NvyY/fpHTTC95ESQkied7q9pz9JPBnEWkFBrIsY0xeVJUN23rcBLljWJvkd7YPDftUXSXsMXkcTY31nLDPVJobnLbITQ317FJXG+I7MMaYwK0FZiY9nwGsTzPPWhEZBUwE2j0uW7SRMIqhkODiF4/+tHkuIBCfxC/Oc0kqS1G1h+q6I0Wx12ovoRSj1nYRa4J73riX0tdYli+Vl7bRfnTSlcuQWJ7eV4Z5qjy8p2yyVWfP1iFblG40+SGI5Pl0oBv4KvApnBPu5QFs11eVdlcl6nr6B1nR1rlTKfKKtk66+ob6qRk/2hn26Zi5jYkS5LlT65g1uY7aUTbskzHGAC8Ce4rIHGAdTgdgnxwxz3zgPJy2zGcAj6qqish84GYRuQqnb5M9gRcCi7wICrnASyTPBWzfj5KiZIW04a5KVNsuNIbCli+2Ko/JVbKgejrOhZfkX/K4UZCNl7bCxeKlc3qv/Qh4a4vs/PXj5lbmUmUvnYFlf1/+DK3l/E0/lFvmBL0qz9+fqAqit+1O92EMuNHt1fNs4M/F3rYpbapKW0cpuQ7LAAAgAElEQVTfUBtkN0luaetg7ZbuYQO/T5/kDPt0+JzJiQ67mqfW0Vg/2oZ9MsaYDNw2zF8EHsAZquoGVX1NRC4HFqjqfJxRMG5yOwRrxzmP4853O07nYgPARaXe03Yh1/++jPPscyleIW24C02KotSpViZee2MesRAQjY7d4rzE77UKcy7CLFn0khzm3GGYh081lxstO8fjvdp2xlLwXEqwc4gvfSyZS5bTxZFPh3xRVrTkWUQmABfhdBwyH3jIff4NYBElljyH2RlCuesfjLFqc9ewJNnp4bqD7T1DNfzH1FTR1FDPITN34ROHzUgkyXMa6hhba8M+GWNMvlT1PkZ0zKmqlyY97gHOTLPsFcAVRQ0wQH5c3xVWyuNvwulHG+5yuehNx0sb1J2Xcf5Gadd4Sfq8dLCVKy8ltsWSSwlttvcsWUtY/ekcy0v3OZ4SbE8dhhUeb7YS7qGS5XTJtfteCqzBEhXFLHm+CdiCU8XrApykuRY4XVUXZVowivyo9lDptnX1syzRk3UHLW575NWbuxhIurW264TRNDXU85FDdk8qRa5nmg37ZIwxpsj8qLZdSJVOv6sD+7GWQnvbjrq8hqpy/0bputBLclKMZohhtv/20kN2rpeOXm5CFFLLxFtP2l7WE48lu+L2th2fnmb5MNvEF0Exk+cmVT0QQESuA9qAWaq6o4jbNCEbjCnrtnQPG+7JSZQ7aOsYGvapplqYPaWOvaaO59QDdhs2NvL4MTUhvgNjjDGVzI+ktZALa7+rOvvyfvJchR9VRoNQlcdQVV7amwYll88nW+dO+fBaslsMnqptu3+zlzx7/+4VdIMsp560C+sIzY/26NnGBo8Pf5a+Q7Hwjo9iKGby3B9/oKqDIrKilBPnMKukRFFn70Ci5LglOUlu66RvYOjW5y7jamhurOekfXaleWpdIkmesctYRlVbh13GGGOixY/TvB+9bfvZ5rlQ+fa2XSpdjuTTZjeK14XeEiT/h6oKswq7l87AvCbF3qpTO38L6m3b/ZtpDV5u6HhpUupHTYNsY4NnG/4snw75oqyYyfPBIrLdfSzAWPe5AKqqE4q4bd9F6Q5jUFSVjdt7hrVBjifJG7b1JOarEpg1eRzNjfUct1cjzW6v1k2N9Uy2YZ+MMcaUEC2gXZ4vQ1V5aMeYCz86QMs/USiNEqfqPEr7w+xhOh0vkRQj0Q21ZDGXUtwse8hLYuxLdf0cqm172acZbxwk5vGjRk26150p6W6WVedRsyPKipY8q6r14FQievoHWbm50ylJbh2qbt2yqYPOpGGf6kePormxjqOaptA8tZ7mxjqaGuvZY8o4Ro+yj9sYY0zpK6jNs3sRWciFtd9tUv1IaPLt6MdL1dMoGBqSK5d6286fKCUEXj7rfIaqyjbcWZhjk3s6xjz2pp5L2+3Cxk0m+3ZyGoaqsHmySYxuk+f0UrmJ5lUQ4zyXhShWz8mFqrK5sy9R1To5SV6zpWvY+5o+aSxNjXWcOW+mkyQ31NE8tZ6p423YJ2OMMeWtkNO8H+M8+90m1ZfkOc91xEucCh0nutiGqsh6f5/FaDtcKC/h59MOPdtwZxLivvBUbdv9m23/eCnt9aN9t5fSa79qCPhRc3ZoaNjUB0G26bl0bFYKLHnOUZR+JFPpH4yxpr0rUb06OUne1p1ohs7oUVU0NdZz0IyJfOzQ6TRPraepoY6mxjrG1dphYYwxpjKF2cEW5DnmcAZ+rCff3rbjSWm+baaDUlABSSTemib9n1k+ZSDZhjvLp5O7bKXZucq0pmxDKTFiqpe3UciwS14S2lyab2TKTfwo/IsvmrbkOcv0KDZxKIRlSR5FrcB1W3f/sI664knyqhHDPjWOH01zYx0fOmgaTY31ifbI0yeNtWGfjDHGmBEKq7Zd+Dp850N1zbxLnhPju0Zof6SQuPjP4bIoW2lbkPoHnWCqc4glp2rbWabnUz0/plAd0K7zMn4zJPdZkP19FPsmWz7Dp6UylKjnv554DOlLnjO3ebYOwyqU30NHeBGLKeu2dg8b8ineaVdbR29ivlFVwuyGOuZOrecD+8eHfXLaI08ca8M+GWOMMZ4VlGx6LwFMJ/n8nq/kUS8Kufhu3dHjriO/5Tdud5aP+jjR8f3lpffiuCi9o+1uzcJqD4Uiy1o7AOjLoS59tmOoKo/aEjFVqjPsb6+JVnunMwyqH51mbdrR68aWfp6O3gFPcWXS0z/obif9hrzU1nhkaSuQOTd5fsXmrPNk88Rbm4D08Q69mvrz9Ls2TdhKLnkWkVOAXwDVwHWqemXIIRWsq29o2KfkJHlFWye9SSfAiWNrmDu1nhP2bnQ77HKS5JmTx1Fjwz4ZY4ypIMWqApjvBZ6qMn/xemDoIjxXPf2D/PWldQDDmlrlGsde37k/8Tzf5Pmd7T189v8WAEMJSi7++/6lvLCiHYDaUblfo3z/76/lvAzAn59flfMyv3tiOQADOdTFjb+3YjTnS7754cVZ1z4HwLqt3RnnGxiM8d/3vwHAP5dt9rz+bIfQui3d7nyZZ3z7naERa7Mdl1+9fVHWuHb09PPmO9lHwb3kriUAvPVOR8b54jd70oWW/P7Sxd+d1NFuOq+t355xOwDfvuvVrOt5tsX5DFvaOlNOj8WUl1dvBdLfwDr4+w9m3c69r2wAhm68jPSXBWsB6O5PfWPh/55ZCUTrhlMhSip5FpFq4Brg/cBa4EURma+qrwcVQ74fvKrSuqN3WBtkZ4zkzmE/dlUCMyePo6mhjmPmNgxLkifX1UaiepAxxhgTtqZv3efLelSVU65+KvE832TzoO8/yI6e/EulXlzZzpm/ezbxfM6UupzXoarMuWT4fsnn3ezo6eeIHz2Sx5KO1u09/P6JlsTzmhzr586++N68tvvn51d5SjrSbctrbb3/uGlB4rGXw+X6p1fkFFPyzY9sbYNz2Vdzvz203qbGzMfXKVc/mRRD+vlUlUfecEpAM914GhiM8f6fe1/n3YvWZ4xvMKYceNlQ4pdufQODscS0tzMk2sn7Md1vQPJ3K9173ffSf6TdxsjtpLvZkDxPuptoyfPsNy316L/Jv5Fjana+gdXTP5j1Jl3ydk7cZ+pO09dv7ebhpe8A8OLKLRmXL5fasCWVPAOHA8tUtQVARG4FTgcCSZ7FGaHa8/xLN2znD0+2JJLl5KoedbXVNDXW8+7Zu3B248xEkrzHlHGMqbFhn4wxxphMCi14nr94PV++5eWd15vjem54egWX31PYZcghlz/I1q7hF7FequDG9Q4Msvd3Ul+053IzYGVbJ8f/9PGdXj/9kN09LR+LKRf8cQGPuslUnNcawj39g+zz3czJRzoLVrbnnDg3XTI88fRSQHH531/ngdfeSTzPtnsffeMdfpDD8TEyGc7UNvimZ1cOe37O4TNTzheL6U43mz50UPrP9JllbbyxMXsp8cj1TqmvTTlf6/YeDh9xMybTcTnyBlAqzSPeT6r19Q/G+P/snXeYFEX6x78vIElAMgICCwIiIIIEA0ZQouHuzPoz3annnfn0zhVzIOiZPc+cMHtGdJEcVdKS0wILLJkNLJvj7NTvj+me6emu7umZrp7pma3P8+yzM93Vb4Xu6aq33rfe6qOZMLhzVG9DmhqfP2yyAuDfU+N9MSaKNJHxt09X62REzmdYj7YR03Rs2SRiGl5e2t9aI8775sYPVoZ9b6D7fejfF7ef0yvs/KYDxWHfU8X8l2zKc1cA+zTf9wM4XZuAiG4HcDsAdO/ePX4l4/B15j78sO4AzjqxPS4/ravGitwCnVrJbZ8kEolEInHK8a2aRpX+l42H8LfP1piet7vWkmflVbGrbFopi3aV3tunZ2LOllzT83bE/LT+IO7mTCQAQFq75rZkmCnegL31m1N/2RpmrVY5MYKFFOArLT3bW1/HuyaS2zHvGrP7xFPKrKio8aH/47NNymQcL/IVNWM6s4kVs7r+Z8EOvDBne9gxXh0XbcvDzR+u0sk0yvtm9X48+L/1huO8n9mBokqMnLYg7Nip3VqHX8eZCACMk16lVbVhlmmAH1CNd4/0bWPnWdGn6aBTaD9dvge/bDqsK3O4jN6ceunb3s4zyEujddvmvXdaNw+3Cs/bkoslylpnXj7bc0sxRuNJAITXJzuvDBe//qvp9fO25OLW6Zlo0/wYrH18jKG8XibZFsrytM2wJ4Yx9g5jbBhjbFiHDh2EZx7NjLSvjqF188b49NbT8dRlA3HjmWkY2bs9jj+uqVScJRKJRCIRgN31tCVVtUhLzzBVnAd2Dbg+RtLzGGNIS8/gKs5XDD0BvdofG3HddG5JFdLSM7iK86IHzwcQGJya4fcz/LjuANLSM7iK89z7zw1OKizfxV/bWuPzY+G2PKSlZ5gqzrunTkBuSTUOFVuvpf337Cyu4rzggfMAAOUWQZbu/mIt0tIzDIrzSZ1aAgB25vPXcwIBazNPUbjk1C6mSu29X641XKO6o/LWhdbW+dHz4QxTqyIvnx25pVEpzr/vLDAozg9c1FeRH562vNpnqqh9sXJv2PGqWqPivPif56NhAzI852WKXL3iDBjHvo/+sNGgOAfShad89uctBsX5HsX6q2+3M6bMNyjOA7u2woGjFcHv5dU+g+J8z+g+AIDtGkt51uESg+IcyDP0+UBRpaEdByuKuhpMze9nhjS9lEkZVZb6PtAytEcblGmWcKSlZ+DRH0JeEQ+OMd7btPSMsN1ynrikP4DQOuycgnJDPnPuPxdAKMaC+k7Qor5PVGU/63CJ4b1zbOOGKCgLxDXw1fmRlp6BW6eHliaM7N0OAIJxmL5fu9+gOAMhF/OX527HhS8tNpxXb/mbi3YG5R+tiC22QyJJNsvzfgBan5QTAFgvihBMNG5iPr+f6wYhkUgkEonEGeMGHI9Zmw9jb2GFaRq/n2HF7kJc++5y0zTrHr8IrZs3xr1frsWmAyXILa1C93bNDel4liwtm54aixZNGiEtPQO7Csrx+rVDDGkKyqox7Nl53Ot/uHMkBndrjd+zCwAA7y7djUcm9jekW7ojHze8v9JwHAB+vvtsDOx6HIDQoPveL9fhssFdg2kYY5j0/UZ8sXIfV4a2LgBQWVuHVTlHUVvnDwtOyhjDc7O2BYNtaXnz+tMw/pTOmKso9g9/txHXjugedu17S3dj8syt3PzfvXEYLurfKagIVNXWBZe0Vfvq8MWKvXjyJ74rdM60iRj4xGyUVfvg9zM0aEBgjOGrVfuQ/t1GQ/qf7jobfTq1QL/HZoUpM5sOFBssZypdWzfDa9cOxuVvLkOu0s5A5GcEQLBMQGAShbe2/LnLT8GUmYHAXpW1dYHI1A0ozAVZy6pHLjQoTd+u3o8HdIrr1D+dgh7tjkWdn+E/C7Px4NiTwBjD0z9vwYe/5RjkXjuiO75YuTfojbFke77BlRcAZt93Lsa+siSoxJl5ZWx7dhz++knAdXnNnqM488R2+MdX65Gx8ZAhbdYz44JK3o/rDuDeL40BxF65ejA27A+4Bk+euRVjBnTCef9eZEi3ctJojJgyH9+u2Y+LB3XGOc8vNKT5+e6zsXZfEdbtK+JODKg8NL4f/vrJ6rD4BFr+dFrXYNA/3oTL8LQ2aKT8jm76YCVOaNMM+4+GT069cvVgjBt4PJ76KXBfePfmpjN7IE2Ji/D2kl14e4nRa+OeUb3RrW3gXfbCnO3ciZH1j4/BqU/PMS0vAHx26xlIS8/AW4t3cn/vOdMmIi09A58u34tPl+81nF/wwHkY9eJi/OvbDfjXtxu4eSQTyaY8rwLQh4h6AjgA4BoA18Ur82itxb46JpVniUQikUhc4K0bhgYHe4//uAlPXzYweM7vZ/hmzX786xvzgdqOyePDlMFVSvTkK99ahs1PjcWxTUJDpAtfWmwaafaWkWl44pIBhuPXvLMMH90yAg0bELIOleKS//AVMSBg4VXHGH2Pbxk8npaege3PjsfavUeDEZXNWP/4GByncb1UFR9Vzjs3DMXtn6w2uxzHt2qKWfedg9bN+etW+zzyC1648lS0bnZMmFVKj7ZdtS7XaekZWP7waHyyPAdvLDQOwFVypk00HOv32Cyc06c9lu4oML3umcsG4IYz0wCEthOKFFRuxaTR6NSqKap9gQjJz83KwnOzsiyvUScW7vsyYK1/6NuNeOhbo1KusvKR0ejYsmnwWY1Upl8fugAntGkelGkVDfntG4Zi7IDjw46ZKUDf/f0snNa9ja20QEghAoDBT881Tbf16XFYuzcQKGrKzKyg0s+TBwCLtgVcga0UVP1vk6c4//f60zDhlM7YcqgkeIynOG9/dnzQyp2dV8ZVnBc8cB56dWhhWNOr5eph3fDIxSdbrqtXy6Qqzzz+d8dZ+GpVSMHUK85/Oq0r/jCkq/6yMFY/eiHatTCuc9by4Ji+uGtUH8ulCDunTIgYW2HXlAmW53m/V/35HJNo4EDg3ZdsJJXyzBjzEdFdAGYjsFXVB4yx2PYyiIGA27Z907PPz9AwXjvASyQSiURST5m+bA+mL9uDD28ejls+Mh+UA+HWWS3a3n3AE8a1p3puP7cXJk042fT88l2FEYNfaS28Ku2ODVdeI7n/7poyIWjJ1DL1T6eEufGaKc4j0tri89tOD1rDrOCtXVVp0aQRNj01NuxY97bhFvwzpvIjeHdo2QTL0kdZlsFKcc56ZlzUwVa1g34rpUnl3tF9cL/iSg0Afz67J36IEBF6+7Pjo9qmSzuJEonF/zwfPWxGZI9G7v/uOBPD04xBqvQ8f/kgXDU84Ax6Wo82pumGdG+Nb+84K/h90AnHBa3FeiYO6ow3rjstYt7zHzgPJ3ZoAQC484LeeIdjdQUCSmak9tc+Bz04HicA8OKVp+LyoScAAG44owd+Wm+87/rJKz0f/3kEzusbWE46lBMEDAA+u/V0jOzd3lRG744tMPf+cyPeS+3vwSyt9r3Rsmkjw04B2jrzaN64IbY8Pc6yHGrb6t8DgPl7OBkgt/ZJ9ALDhg1jmZnms6PR0nvSTPz1vF7459h+ttLf/cVabDpQjIXKegOJRCKRJDdEtJoxNizR5UhmRPbNvKBFZiz91wVBF0YeVgHAtAzt0Qbf/u0s0/P3fbk2olK17dlxaNLIXNk7a+p8HCyuMj3fp2MLzP3HeRHLOublxaZ72958VhoenXhyRKVZH7FYz6MTT8atuii7Wia+tjS4r62eYxoSNj451lTxLa/2WU5kmCmFVu7T2ZPHc+tsZoWdec85OLlzS24+ZtfwFAMrd/uVk0ajoy7wXX5pNYZP5rv486x9vSfNDFszq8KbXPl5w0Hc9blxnfvWp8ehWePQvTD7ff1458iwQF5mv52FD57PDdzGazeeNbWypi5s66cHLuqLv51/ouH+Xffucvy+M7S2/64LeuPBsSdZ5vn6tUNwyanGwH5Dn5mLI5p9zX9LH4WurZuFpZn0/UZ8viIwMXXr2T3x6MXhyyu0QdpO7dYan916umGS7JLXf8VGJRr1WSe2w+e3nWEoS3ZeWXDtsNkkGWMML8zZhkNFVXjuikFhFnuVI2XVuPGDldhXWIHF/7wAbXQTdIwxrNtXhD/+93cse3gUOh/XzCCDMYZ3l+7CFyv34clLBwQnAlR2F5Tj7i/W4JEJ/XHmie0M1+8uKMcvmw7hnSW7sM6FAGHx7Jul8hwFfR/5Bc0aN0TbY/kuTXpyS6rQtXUzWx2cRCKRSLyPVJ6dI7pvjrQ9TDQWOisFWmtpi8Rr83fgpbnG9YXv3TgMF/bvFPF6v5/hhg9W4LfskELQ+bimeOP60wyut5HYkVsatr/urPvOQb/j+fvCmsFrlw9vHo6Rvdvbsqz+uqMA//f+iuD3m89KwxOX9LdtDT19yjzkllQHv9txWwWAR77fiKpaPwrLq/Hy1YNNXdKBQKCkx2dsDipFz/xhIG44o0fEPBZvz8dNyjrg9U+MsdzLts7P0O+xX1Bbx9Cm+TGYcdfZlhM61b46TPslK7jmNZIlO+twCf71zQZs2F+MjU+OQcum5mXJL61GaVUteikWXCu+Wb0fXVo3xZm92lnes5KqWhSUVqNFk0aGyQAnVPvqLCebAKBQUXjtjtG9wOo9hRjQ5Ti5Ra0ApPIsCNEd9H8XZSPrkHn0Sx7n9GmPK4fZ62wlEolE4m2k8uwc0X0zYFRWM+45GwO6xO4SuK+wAmv2HsX4gZ2jcrvVUuPzgyiwf6rcYUMikUjcI559c1KteU40fz/fuLm6RCKRSCSSxHLP6D7BLWtE0K1tc0uLoB1iVbolEolE4l3km10ikUgkEolEIpFIJJIIpLTbNhHlA9hjcro9APPQjamFrGtqIuuautSn+iZbXXswxjpETiYxQ/bNQWRdUxNZ19SlPtU32eoat745pZVnK4gos76sW5N1TU1kXVOX+lTf+lRXSWTq0/Mg65qayLqmLvWpvvWprtEi3bYlEolEIpFIJBKJRCKJgFSeJRKJRCKRSCQSiUQiiUB9Vp7fSXQB4oisa2oi65q61Kf61qe6SiJTn54HWdfURNY1dalP9a1PdY2KervmWSKRSCQSiUQikUgkErvUZ8uzRCKRSCQSiUQikUgktkhp5ZmIPiCiPCLaZHL+fCIqJqJ1yt/j8S6jKIioGxEtJKKtRLSZiO7lpCEieo2IsoloAxGdloiyOsVmXVPi3hJRUyJaSUTrlbo+xUnThIi+Uu7rCiJKi39JnWOzrjcTUb7mvt6aiLKKgogaEtFaIvqZcy4l7qtKhLqm1H2VWCP7ZkMa2TcnGbJvNqRJqXe47JuD51LqvoqiUaIL4DIfAfgPgOkWaZYyxi6OT3FcxQfgAcbYGiJqCWA1Ec1ljG3RpBkPoI/ydzqAN5X/yYadugKpcW+rAYxijJUR0TEAfiWiXxhjyzVp/gLgKGOsNxFdA+A5AFcnorAOsVNXAPiKMXZXAsrnBvcC2AqgFedcqtxXFau6Aql1XyXWfATZN8u+ObmRfbPsm5P9vqrIvjlKUtryzBhbAqAw0eWIB4yxQ4yxNcrnUgR+CF11yS4DMJ0FWA6gNRF1jnNRHWOzrimBcq/KlK/HKH/6QAWXAfhY+fwNgNFERHEqojBs1jVlIKITAEwE8J5JkpS4r4CtukrqEbJvln1zsiP7Ztk3K5+T9r4Csm+OlZRWnm1ypuKK8gsRDUh0YUSguJAMAbBCd6orgH2a7/uR5B2bRV2BFLm3ikvNOgB5AOYyxkzvK2PMB6AYQLv4llIMNuoKAJcrro3fEFG3OBdRJK8A+BcAv8n5lLmviFxXIHXuq0QMKfH+1iL75iApcW9l32wgVd7hsm8OJ1XuqzDqu/K8BkAPxtipAF4H8EOCy+MYImoB4FsA9zHGSvSnOZck7exhhLqmzL1ljNUxxgYDOAHACCIaqEuSMvfVRl1/ApDGGBsEYB5Cs79JBRFdDCCPMbbaKhnnWNLdV5t1TYn7KhFGyry/VWTfHCRl7q3sm8NIiXe47JsNpMR9FU29Vp4ZYyWqKwpjbCaAY4iofYKLFTPKWpRvAXzGGPuOk2Q/AO2s0QkADsajbKKJVNdUu7cAwBgrArAIwDjdqeB9JaJGAI5DkrtEmtWVMXaEMVatfH0XwNA4F00UIwFcSkQ5AL4EMIqIPtWlSZX7GrGuKXRfJQJItfe37JtDpNq9BWTfrBxPlXe47Js1pNB9FUrClGcyicpIRG2JaC4R7VD+t1GOEwmORklEx6vrFIhoBALtccSp3ESg1ON9AFsZYy+ZJJsB4EalLc8AUMwYOxS3QgrCTl1T5d4SUQciaq18bgbgQgBZumQzANykfL4CwALGkm8Ddzt11a0DvBSBNXVJB2PsYcbYCYyxNADXIHDP/k+XLCXuq526psp9lYghVd7fgOybOWlS4t7Kvln2zcrnpL2vsm+OHUrU/VZuSGemicoI4A8AbgZQyBibRkTpANowxh4iogkA7gYwAYEolK8yxiyjUbZv356lpaW5WQ2JRCKR1CNWr15dwBjrkOhyJDOyb5ZIJBKJSOLZNydsqyplVvWQ8rmUiNSojJcBOF9J9jEC7iEPQRONEsByImpNRJ2tZmfT0tKQmZnpXiUkEolEUq8goj2JLkOyI/tmiUQikYgknn2zJ9Y866IydlIVYuV/RyWZ56NRpqVnYOpMZx4NjDGkpWfgPwt2OJJTVVuHtPQMfPx7jiM5RRU1SEvPwHdr9juSc7i4CmnpGZiz+bAjObsLypGWnoHfdxY4krPpQDHS0jOwfl+RIzkrdxciLT0D2XmljuQszMpDWnoGDhRVOpLz47oDSEvPQGF5jSM5ny7fg7T0DFTU+BzJeXPRTqSlZ6DO78zD5flZWUhLz3AkAwAe/3GTEDn3f7UOAx6f5VjOrR9n4owp8x3LufrtZbjopcWO5Ux4dSn++N/fHMs55/kFuOmDlY7lDH56Du76fI1jOZLU5M7P12Dw03OEyiyurOUeL62qRW5JldC8AGDvkQrU1lkFuo0ev5+h2lcnVCYAbDtcCp63ImMMS3fkw895z9f5Gfeaw8VVWLnbuEw0t6QKF760GAc5fWFVLb9OuwvKufX11fnxW7ZxrMAYv32qausc98H6/OduyeXW/9vV+/E7p2yxMG9LLjYdKBYiywpfnR8FZdXcc9sO88dAZdU+lFU7G0eoZOeV4qjDsU0i8dX5hbWFHWrr/PAJfrdIAiRceY4QlTEsKeeY4Y1ERLcTUSYRZebn54sqpm3eXrLL0fVq3/PS3O2O5KjK01uLdzqSs+dIBQDgI4dKuPpi/2rVvggprVm+K7A0asY6Z7FUFmTlAQDmbc11JOen9YFy/L7T2ZIttV02OFTmpy8LTLztyi+LkNIa9bk5Uuaso3plXuA5djo4/O8iZ8+xito+Tvl+7QGU1zgfnM7bmovDAgbkK3YXYkees3sOAFsOlWDtXmfPIADsK6zE4u3O379FFVUNbVEAACAASURBVLX4eUPSLf2UxImMDYdQVGFUdo+W1+DJGZtR4zO+d7LzyrBiF/99nXW4BKc+NQf/yzT2UxNeW4rTLSa6Xpm3navAVNYEJrJfn2+cEC8oq8a5/16Ip37abDjHGMO7S3aZKguzNx/GJ8v577NnMrbgpEdncQfO5dU+XP32Muzk9BGLt+djiokBYEFWLsa+sgTfrz1gOPfzhkO44f2V+GxFeHl8dX6cOGkmV+aYlxfjqreXGY5/vWofsvPK8PmKvWHHv1uzH/0em2Uod3FFLS54YREe/m6jQdZrC7Jx/XsrsEzXP0+blYWTHp1lUMZvm56JkdMWGOQAQN9HfsGTM4z36VBxJW54fwVKqozP4X8WZuO26ZlYuC3PcO6B/63Hde+F7/b047oDGPPyYoOynV9ajb9+kmmqeN06PRMXv/4r99zMjYe4z+XszYdx8etLDRMe23NLkZaegT1Hyg3XPJuxFcOenWeo66xNhzD2lSXB8ZCWgU/MxsAnZnPLNm9LrmGscrS8Bo98v5E7uXHhS0sw5pUlXFmfr9iLD3/bzT23MCsPbyzM5p4rrqzF5IwthnfFde8uR+9JMw3psw6X4L4v15oaBJbvOoKN+/kTGfd9tc60LVbvOYqr3lrGfWdd9fYyrgHrSFk10tIzsIjzfAHAgMdn40zO8/zt6v0Y/+pS7jUHiirx1E+bufWrrfPjpg9WYu3eo4ZzD3+3AeM492ZfYQXu/GyN4bdW7avDoz9sdGzoSRQJVZ5NojLmqgvUlf/qU2ErGiVj7B3G2DDG2LAOHZJ3WZqoleiilrQLkyNGjPfq5bX2ESXHY+0jkUjcgYjGEdE2JShnOud8EyL6Sjm/QvEYAxGlEVElEa1T/t6Kd9n1TJm5FR/9noOMjcbB/IUvLcbV7yznXqdaz5bsMFoE9xVaWyRfmbcDl/zHqMColuxPVxgVXfXc79lGZT5zz1FMnrkV6d9t4Ob3109W47EfNnHPqYqnjzMAXrqjACt2F+K5X/SxrYCbPliJd0wMANnK5NzWQ0Ybh2ol3nc0vI3U/HmTliVV0VngZiteaztywy2c5Yp3lF5BBhAc5Os9Br5WJqsrdBOgSzn3XaWmzs81Irw2fweW7ijAz+uNk3zqM2N3EvreL9dhe65xUuO1+Tswe3NuTB6Af/9sDVexvvfLtdh0oATVOmXtm9WBPGZtMnoJqvegTHfv1DJvz43OA+/W6ZkY9WK4x9Rzs7Lw2Yq9+HEt30CSX8q3fE/6fiOe+mkL99wtH63Cv2dv4557YfY2vLt0N37QTQr9vvMI9/dz1+dr8cO6g6YGimveWc59DwCwnAhO/3YDVuYUIoczabFydyH+8fV6w/GNyqTIB7/lcGXW1Pm57fXA/9Zzf8cAcN+Xa/HhbzlcBXnPkXIs3p6PB/9nLMsXK/chi+N98OSMzcjYeAi/6n5bP60/hE+X73XsrZsobCvPRNSMiE4SlbFFVEZtFLubAPyoOZ700SgjoZrXnSobpAhiDtUo4XIcVizYPk7LI0qOqHoF5TgSk7LPj0QicQ8iagjgDQDjAfQHcC0R9dcl+wuAo4yx3gBeBvCc5txOxthg5e+OuBTaAtVq4o+zx6LISULVAlUapZLpFqT0LlZ1FBGANtY+g5e1qgzPdehhFm2+wXNKHYh4jpOJxcmtcrM3d7rEK1pUr7i6JJzhV0ss8ulS21/UI2vWquq7Is63Wxi2lGciugTAOgCzlO+DiWiGw7xHArgBgX3F1BnrCQCmAbiIiHYAuEj5DgAzAewCkI3AXmN/d5h/SmOno4urnGCHKEiOqPKIUladifHgJIW3nh+JROIqIwBkM8Z2McZqENjz8zJdmssQCOIJAN8AGE1e1AqA4Is51teOG7uQWCtZsV3nJM9oserDIz0F0RRDfaTslt3OE6h3Xw/l4byBVBHccqjnYpRp93gsBBUvk8Jx73MkmQLKl6ihgu3nTU2f4HJoCyHyLRzK3lxoLHXXlzH4G0xSw4rdaNtPItC5LgIAxtg61WUrVhhjv8L87ozmpGcA7nSSZzIg6jHynLIqdG5M4jainx+JROJpeAE59VtBBtMwxnxEVAygnXKuJxGtBVAC4FHGGH9BXZwITdpF9wZzYy7ASqRVbk5KYqcaXh+y6ssXupX2W6ZJowao9vkx6ITWYcdFKkBB6zL3nJKfzSIT8cdarvSjJpWPJSuRxQvWP05jh2jbVtSY2Gk5AOtnL1YsJ4NiyMnsHRyU5PUXkQl23bZ9jDH3Q/lJJBKJRCKJN3YCcpqlOQSgO2NsCIB/APiciFoZMkhwMM9EE7MV3GOjS2u3bbMT9uWbKSeRLaXGTC48uRMAoHvb5vxrRFhKLZQNP4vNBTaed9y0PV12zzeV7YJCGE2+iSaacoSePXGtZWvuwhtNlVDsKs+biOg6AA2JqA8RvQ7gdxfLJUlRhLkBOyxHyGVEkByPuDeLah/ReK08EokkDDsBOYNpiKgRgOMAFDLGqhljRwCAMbYawE4AffUZxDOYp1PPGZHvq5iHtS5rD9GKt3KzNPMsi4fnkaVXm8kpkbE4QsqGeTnset7FU2EMKal6f1rza+K5SiN+eUWXT2iMJXZUE4t3ZlB5FlmQ4ISPUarIWyLKuzFR2FWe7wYwAEA1gM8BFAO4z61CSZwjKnCUimMxon4ogl1mZPtYihEX4E0uepZIvMwqAH2IqCcRNQZwDQJBOrVog3leAWABY4wRUQcl4BiIqBeAPgjEJ0kYsboEenGVibPATuYXx74e3Co/55gpJ5HaIbp2Emips7A8W7vAJpZIZbN8dtzszt1QCAXiltt2LES7LCAqmeJEBuQZ1jwr+XmhIWPA1ppnxlgFgEeUP0kyIGhBgego2U4RJkeQIC92iiIQbZmXSCTeRVnDfBeA2QAaAviAMbaZiJ4GkMkYm4HA7hifEFE2gEIEFGwAOBfA00TkA1AH4A7GWGH8ayEQF8Zz0XahTuKEWFpBYxRruT47YsAw+5UPyrK5JjeW+kTKIxqsImo7C1jnbt9ppiQlOj6NGwqhrXw9osNFU47QuDw+k0HBNA7kipDlBWwpz0Q0F8CVjLEi5XsbAF8yxsa6Wbj6SLLOwthFVP1EucwIk+MRC21oTOCt58hbpZFIJHoYYzMR2NVCe+xxzecqAFdyrvsWwLeuFzAKYnXLdWXAbid4l8V73yvvzlgm0mNRxMz1Wut8uWcjKOBC2tbCUsosXGBjykrg+FB02QBBbvAxrhOPlVjz8cJQ3R3Ls3nFRN6SZN+Jxa7bdntVcQYAxthRAB3dKZJEBKLX0DpFWiCTC1Fu/8KXDyTrm1YikcSNWPu/WF8v9t5LnLXCFv2iiC5T6FZVqkyX84ukpOvbzEkzidxaieu2rZ6LUaZKHINth87HEORNBHbWkLuB3ep6aYslN9Y8h2SaSxW5n3uyYld59hNRd/ULEfWAdyZEJRy8t1WVGEQp4cImBTw2uSDshSTI7d+zC5ckEokkDjgehDp4BYsN8JNgd95Y1jzHI2CYlaU0yjXP8WxjM8XLcms12Z+7RmxtK95KL3qdfqRnOlkVSbv7PD8C4FciWqx8PxfA7e4USSIxR5hOJ8hvy7OdSbK+kSLAmIfbXCKReIJYJ4/deLfEqqB59TXntvNPJK8BEe0i0qppZSk1jWjtIdxQvETI8Go/L9qbzglulEG0yEgThMnqTWg3YNgsIjoNwBkIPDv3M8YKXC2ZJCXxTHRr0XI84t7s1a2qRBXIc/WSSCSew0sRca1wy9LohkupE2ttLC7A+ksiiYilKUUqe1bnot7n2ePPrZvEu+rRPjZuK/VRBQxT/rsxOcOrp8j3VaI9WZxi1/IMAE0QiLDZCEB/IgJjbIk7xaq/iHpxCAtAJci9SbicFA3QJS6AmbPrha95FhqYLblfuhKJxNuIVD6dDmydlEXkoDreFjdTy7NJlUQo6LFgueY5yjWpiejZYlFior030cmuxzMHMSLWeyA+3hJCg/YlALvRtp8DcDWAzQD8ymEGQCrPHkXYVkMeCzwmiQ9yqyqJRJK8xBbUJ1H9FHe5roB3p1ALtI0+wSy/aEphFpDJVKmKoZnEBlgyj1od2sYqSpkC2tENIm5JJjQAW5x/izYL77ZXSzS/WTdd3EVvVWUqP9EPdYzYtTz/AcBJjLFqNwsjEYcwy6HgwGNOkXLiJEf5L+z5kW7bEokkTjh977iylpAjM5mmFq3KGk9FxzyrWNzJBQQMU/5z44UFxdtrH7O6xbV9Y7nGhXDb8apxtEWnGCfmIpcjBg8AF9bUW/0k3Hg2vBC1PBbsRtveBeAYNwsiqR94TYkSt4WSs+vFT3Z464XktfslkUhSHy8EDHPbgmOFnfW4XpCpxawvNBMd017SqnVboN+21VZVUYvU1z0BHZ/13uPulyfuhud45sVp21jucfASoXMXkb0lolqXbbblXJLv82zX8lwBYB0RzQcQtD4zxu5xpVSSlEVYQCyPBNYS5d6s4hVl3qt4bVJAIpF4j9CuDN55X4hSpJxeE+s4OzSYNgqPJDMaxSDSmmYzZTlRt9rK+hetW21ARmKfWcutqiLcaTFzEfFZc6sSbT5eCkbogu5s+TsTu/WdOFmJwK7yPEP5k7iMqB+kVwNrOcWr7s2O5Xht/2pRAd4ElEUikUiiwenrVOTA2NLd2eqciOBLNo/ZIV6WoohjDl27xNJOIhWgGl9ACL+vVBVBe5j1t4mIHeK2h0Ek2V5Vrtxaqhtb4Dbz9faxYhUAL5QmmsmwCBMuHpiEiAW7W1V97HZBJO4gaq2y4+fboy9CCR9hFv6ge5yYN2SyvmglEkn8iPeWfa4qE+6JjgorhdPUWhxD6UV7c/HzECdr3tZcAMD3aw7g4kFdws6pbdXAq5ogB6sJ+EjVEFHLhHkQxDFfxiJ7WESDKwHwuPmIyyk0CeGVN1x02I223QfAVAD9ATRVjzPGerlULkmKIm5LJzF4xU1a1GymyC04ROK18kgkktTFqWXRDV3Hcg1pHIOJxey2rfyP12DX7ppnu+f514irS0lVLUd+AFFu216fPBbrth0fov6tCzYIBMXGcI0bVvpon9lY8ZL7eyzYDRj2IYA3AfgAXABgOoBP3CqU17j141X4039/w94jFYkuSvSIMhkLWmOccqRqtQS92FK0eSQSSQoj1G3b4SjUySBd5AA/XsZTsyVDZhaxmCIAu+CCzl/zHDn4Eg8vKBRWRYhH8bxqrBdh6BBm/HFjoiFYOLdvgEdvsE3sKs/NGGPzARBjbA9j7EkAo9wrlrcoqfRhzd4ibDpYnOii2EZ9+XrFkilMGRM062e2l2TUckRtWyBojbqKMDlO20fw7KIXBhUSicTbxBqrI1HDOZ6i4Jby4PQVGlMAsyjSRqq22WRELEHJhHYnFtG2bRsPPKBPJFpplX28fUKWZ/E3LV7PQbLebrvKcxURNQCwg4juIqI/AujoYrk8xZQ/DQQA1Pndv83J6v9vF3EzboLkeEyp81r7eI1U/31IJBLnxGPdrEgsgzM5kevgWj121pELmbSNchupWBQHN/SCBjzl2Y1wyB5GxO0PiYhvo9l9dt1yN45FWQ1FxhaHnWoJ/Jkn7WSJXeX5PgDNAdwDYCiA/wNwo1uF8hoNGwSaKR7Ks2i8ZsmUJBfiJgVkwDCJRJIcxGuSznrAHPuQ2Kr0MUu1ChgWqTxRNKdb0Yx5CHVr57ltB89FRzwniStqfLbTxkOdjXe07aiXPAc/JX45hRse1pYBwxzko69yss8n2VWe0xhjZYyx/YyxWxhjlwPo7mbBvERD5YlJRuXZKcK2YkpVOYIEidtiyltykv8VKZFIkpVox6huDtiTffQQr4BhZhYpJ1Y2fYnd8EzgPTvJsGygvLou6mvM6iV0H2BxolIW9T6UVtmfAIkoU/lvNQYUa8RIzjejXeX5YZvHXIeIxhHRNiLKJqL0eOTZsGHyKs/O1yqL6WRI0CIjCvXgYuQ4RLgSLipAl0cCfYmql4o4d/3k+y1LJBJ7OO1u4rXPczA/lyIsW18bpWJno9MU0WwUwfYsou8OrYl3Liso06Jc0U5Gm5Urnr0Wf0sy63qIaU81yFqc3bZtpqMolxU4ySsSmw4E4jDN3ZIrSKI7ruCA8fchoh0TieVWVUQ0HsAEAF2J6DXNqVYIRN6OK0TUEMAbAC4CsB/AKiKawRjb4ma+quXZl4zKsyAlynmgL0WORwJ0xRpQxiBHUKAvUbP6wtpZcLRtEQHnGBO5DCHxgVEkEonXcO+l4CUruCNiUKriQVSu6i4Ulx9t27XsXCOm6OVC3YZjL0csRPvsur2sIJr3REVN9F4DEfMPTl4Ilmvitp18WlWASPs8HwSQCeBSAKs1x0sB3O9WoSwYASCbMbYLAIjoSwCXAXBXeVYiQZRV16K4wriXnx6rNAzM0kW3ui70Y3AiR91zsMpX50iO6g5SWetMTpkip6LGmZzyakFylJdOuePyBOSUVTuTU1EbkFNa5XMkp7JGjJyqYHmsn/mIz7PPDwAoqaxF00YNY5ajvniLK2vh95smiyhHpaiyNjgp5kROpPeBCDmh7SikHCdyGjdqgGaNzZ9BSeoQ2ooo8UMzO4NQy3efk8wtLxavNLgZSMhsQtjZGF/gmmee23aUikik0sRTCbf0hnA138SQ6FdFTAHDxBdDM3nh7tMmyvCUKCyVZ8bYegDriegzxljcLc0cugLYp/m+H8DpbmfauFHAu33KzCxMmZkVMf2pT88Rkq8IOYyJkZNfWi1EzuaDJULkLNqWL0TON6v345vV+x3LeWvxTry1eKdjOc9mbMWzGVsdy7n3y3WOZQDAde+tECLnopeXCJFz9nMLhcg57Zm5QuR46bcu5Vhz81lpePLSAQJKI/E68TaEOh3+8RQVR1WwVaBo3baVqziDXbP2jmVcbObtFmlQH1NQMpfH7bEqImbFiouaYfHjiRgYTkAJY90bO16I8Mqzjq6fWGVy/9FKAMDhkip0b9c87JzQNe0evb92ieS2/TVj7CoAa4nIcEcZY4NcK5lJkTjHwspFRLcDuB0AuncXE9PsuGbH4M3rT8Oh4irLdGv2HkW3ts3RoUUT7nkG4JmfA0byxy/ubypnVU4hendsgTbNG3PPV/v8eG5WVkQ5y3YdwYAurdCq6THc86VVPrw8b3tEOb9mF2BIt9Y4tgn/cTlQVIn3f90dUc7i7fk4vVdbUwvkpoPF+G7NATRu2ADp4/uZylm4LQ9n926PYxryl+wv3ZGPhdvy0bFlE9xx3oncNAzAgqxcnN+3Y9CzQM9PGw5i7d4i9O3UAtcM5z9LfsawICsPo0/uZNqxTF+Wg5wjFRjRsy3GDTiem8bn92Px9nyM6mcu59X5O1BcWYuxAzrh9J7tuGlq6vz4LbsAF5xkvpPc08ozeM3wbujbqSU3TWVtHVblFOLcPh0iyrnjvBPRsSX/mS+r9mH9viKM7N0+opwHx/RF88b8Z6yoshZZh0pwRi9+vbVyJk3oh0YN+M9GYXkNdhWUYViPthHlWD3LeaXVOFBUiSHdWjuSc7CoEkfKa3BK1+McydlbWIHyah9O7tzKkZxdBWWo8zP06ch/LuzK2ZFXikYNGqBn+2MdydlyqAQtmzZCtzbNTdPYkQPAsm0kErfhKp12lCrBGl6sLtau7I1smY+9Nc92qhOfgGHGgtTW+ZV8xCyjiieWCp7BBVdcAUMBq4SJjCrfSLjl1RJLG/pdnP3JOlyCET3546So9lSPcD457c6R3bbvVf5f7HZBbLIfQDfN9xMQcC0Pwhh7B8A7ADBs2DBh92X8KZ0jpvkzelqeZ4wFlec/n22e1uocEFBIVOXZiZy8kiq8PG872rdo7EjO5oPFeP/X3Ti5cytHchZty8N3aw7gzBPbOZLTomkjLNyWj3P7drBM+5cIcipr67B2bxFGn9zJUs6t5/SylLO3sAIf/Z6DcQOOt5Rz+7l8RV9l9Z6jyNh4CBcP6oJLTu1ims5swkBl1qbDWJlTiD8M6WqpjEbik+V7sLugHFcOOwEndmgRs5yX5m5HWbUP153eA22P5U8Y2UFVom48Mw1Nj4ndPfdpG79RKSf15EhSh3hZFu3gtvujFTylLdZBv516iLCahZST6K4TOaCPBZ7MVTlHAQDfrTmAcywmovUk0pXVsm3MPAxcUIHi9buJVUl3UmNh7eXiY8Jrllgm3syKGOvv3CtYRttmjB3SpMtljO1hjO0BkIfExEBYBaAPEfUkosYArgEwIwHlSDEcrhoSFOBB2Iyj4IATjosjyA1JVCCH0BpRh3Is9gNMhByVZHcHkkgSQaSdLIioCRF9pZxfQURpmnMPK8e3EdHYeJabh9PAia6sJYz1QtdeaFGuebZwV43HK9dskG1HyTJLYXfgbkeZNXFgAxCIHSIC76wPda8c8a5i1PkJcNu2Ihq5rjaV2wOpJB+n2d2q6n8AtGF66pRjcUVZd30XgNkAtgL4mjG2Od7lSBW88hoOImwyTlREZkFyhEgR97IWJkeMGO89hxJJPUOzk8V4AP0BXEtEej/4vwA4yhjrDeBlAM8p1/ZHYCJ7AIBxAP6ryEsYsW6D4sp40aFly0mRRG5V5eyqKDBxD4806RtNuSJNrqju1tFgZZWLequqqHMXD68MZrVww23bq8qVl4rl5mRKvIxPXnjWY8Gu8tyIMVajflE+x+5f6QDG2EzGWF/G2ImMscmJKEOqIWzPY1FyxIhJ3XoJkiRqexFxcoSISaibpESSpAR3slD6d3UnCy2XAfhY+fwNgNEU+PFfBuBLxlg1Y2w3gGxFXsIIum3HeL3QMWmMsoIeXTFF4TXP1On72m3rZ0SXewGv90h9hH4tqZ0qW0kUN6Zxv2+zyiKeik68evHY3bZjbw1RPyE3d89t4PKzFvqdJ6f6bFd5zieiS9UvRHQZgAJ3iiSJF8LcrR108mFyknYOqn4i/PkRNikgRIxEUp/g7WTR1SyN4gVWDKCdzWtBRLcTUSYRZebn5wssujjceHXYWSbDHz8KiFzsWEIIN4JsWecTvQJrhjFgWHQy7SSz6ndE9UlxVTRiyUtA8ULL3OLbkdttW3I6MxdJbhS4+TS4PYnihb3hnWBXeb4DwCQi2ktE+wA8BOCv7hUrNRFmoQvKEyLOczifGVfkCFvLLUiOqHo5nqQQI0c4HrPwSyT1CN7PxqBzmKSxcy0YY+8wxoYxxoZ16GA/eFJMuLwuMRqsymBvD2hvYKccQvZ5DgozOx9ekqCSHYuuZ3KN6OcmWiuem9GsbefNKbOp27YLxYub5Tnq/c7dLVk0j1683bYdLSERKMsLRIq2DQBgjO0EcAYRtQBAjLFSd4sliQfC1gYLW2MsRIxcixsnvPb8SCSSmIm4k4UmzX4iagTgOACFNq+NK8FIrrE7bgsrS0gXcaZAJRpLi1scZmLNmsPOpLL5Oml7jWxHSbFe82wrm4RguiWY1TXM+ruj8njsuTdDdDFjeUQS1VRCJsk8NMEZC7aUZwAgookIBARpGgrGwZ52qVwSG3hubbAot1shUryz5jlVo2SreKWdQ3I8PFKRSLxJcCcLAAcQCAB2nS7NDAA3AVgG4AoACxhjjIhmAPiciF4C0AVAHwAr41Zygbjx7oh1t4VYlW5e3rFcp8/XzoSEGHdOa1n65vDH4OYbKaCcYc2zHZmW52K8+fFArwhbJI1H3ypqvOQWXlL6nFqeq2rrTLf15LW/k3vidALLa9hSnonoLQDNAVwA4D0EOs6k7BxTCc+5NztVDr0qx5kYD64tFyRHuHu8GDza50oknoUx5iMidSeLhgA+YIxtJqKnAWQyxmYAeB/AJ0SUjYDF+Rrl2s1E9DWALQB8AO5kjNUlpCIKsQ5wnSibpudikhgiJouUjUxjjUTu9lZVoXzsaXX+GPrFSEljeQysXLO9qggC3PUVAKJsT4H1yy+tBgAUV4rZ3isS0ZZdRF2tnq9o3kFOFXj9JJEW0e7pRrdt6wksr2PX8nwWY2wQEW1gjD1FRC8C+M7NgkncR5gS5bgkYuVI4oNoZV4ikSQOxthMADN1xx7XfK4CcKXJtZMBeGb3Cy/pKrFODop4v3I9rGOUFYqOa5GfkDXP1oHJ9OW3FZAtyjLo09uKtm0ZMMxmqyegM0xQXDBTtueWAQC+ztyPiwd1cTGncKKeaItTxDDLiTmHRbBUkC0Dhjmvu5cnlOxgN2BYpfK/goi6AKgF0NOdIkkiITpyo1fcgMXtq+wtOZIICAqoppLsL2WJRJIYErXkg9f1hfpVdy1AdstjhchmM7Nwm/XHarpognJF8kywsshFksk9F6WseI49oskrosXeWVHCaBCnn2K02YiwmHpljGp1Pe/3JPJdZGcyzsvYtTz/TEStAfwbwBoEfiPvulYqiS08pyMI60E9Jsepe7xoOYLcpL32BIlbg+2tekkkkvhi6vqbAGId4Lpd8ljdVRM1qWy2a4XfBTdju4p7uEz33Lbd7NLMfiJWipIxGrh4GsZ7qyqbz7WXhhdOX2+WOwG4mK82g2Q1UtmNtv2M8vFbIvoZQFPGWLF7xZLYwUs/YpHINc8R5HhkzbNovFYeiUSSnDi1DgnVuZ161jhx247RxZp/yk6bims4uwHDYpkMjhj8TK88O6yW062q3MTgom6R1qwabhTX6+MBL6h8TstgueZZcPt7/HZGjd2AYU0B/B3A2Qjcr1+J6E1lDZQkSRG2/7CAsqSynFQl1iiyBjmypSUSiQdwY4AX69vNmVuoOTGveY4QBVsUmt1cbKVXFQArN1/9KVfctmM8l2j07Wxnkj4efXa8PMjUgHN2JziifT55RD9xFb0cO/gtrrdewx99XvU1YNh0AKUAXle+XwvgE5gED5HEB7c3a7eL1yy0KuLcgD0mR4wY5/dLleORqO8SiUQCOFf0hBqeHfZrTt6KPCXHGRBgcAAAIABJREFU3ppnZsjZ3hpF5+/wYD4209tT9qJD+Hjedryw+GsS5m7bvGP8irjRc8drzXMsW525gVnurnp6WLptG0sk8vmM12ScW9hVnk9ijJ2q+b6QiNa7USBJZLyqZHjdzSZxeMwW7tGpPvn8SCQSEcQajMaNd1Dsa569+Z7mIWpJUZgMw7pj5bwufVD5icptm5tFKC8Ta6wVlltVRTlmS+Sdt3ruYmnrWInW1T1WmA3PBS3RTu5w87Qsj305VpZje9dH57YtDR0h7EbbXktEZ6hfiOh0AL+5UySJbTyyNlh9FTj/OYlxA45ln0JLOcL2MRYkR5CbvTiLsTOSZ4gokUgk0RFzPyuif+a8XO2I472TyUyrhWYJj+2CWeTN1Fz0Ciy/X4+pfSO43hrXAUfupSzdxgXpGvGc9+aVWVW2GppoDiID9MVLeY7ebVv5IPhexOQK7bC9Y71aRHC+4OEkHQDatTyfDuBGItqrfO8OYCsRbQTAGGODXCmdJC6kmsXPY3belCfVnh+JRJLkqMpRlG9xdSBdWVMX1XWWlqRQoQznqn2BfPJKjeFjQpZWd16wRytqokpvx5ofqwKgnci9+4u1AICZGw+Hpauq9QMAfHXhBQiteRZnedZb5OxY+CyjbdsrVhC9UrQrP7D3cZ0L2rMhsrhFFj4/38XZjTFAvMYV6jsiXm7iZtTFYEZ2N2AYz207QGmVz3YeZlms2VsEANh9pNy2LC9h1/I8DoF9nc9T/noCmADgYgCXuFM0SSScW/zEvIhFvc+FW2iFWXodiREfJduZGIEWY1HPjyBPAYlEIgGwr7ACAODXDUhr6/yW1902PRMAsGzXkajy6/PIL6bnfl5/EABQUFZtOHf79NVKuYzv0uvfW2Falqpaa+XeaiB+q1JHdfBql5KqWgD8gfP+o5VKucLb93CxuJiyWw6VAACuU9pF5d2luwEAB4oqw46v3F2IjA2HABj7uk0HApvFmI5dNMdnbTqMgU/Mjlg+3v01O2d2f3jPAQAs3JYPAFi6I99w7pV5203z3Xa41PScir4fn7c117Qse44EflfaiYrSqlp8ujxgVysoi25Cxgq98uaL8Ns1I9J1atntKq+LlHtR7TPKVd87keBZjDfsDzyTeoV2V0FZxLLwWJVTyD3+x/+GnIYtlWfOsZHTFgAAyqrD3wHqJCCP+Vl53OPPzcoCAOSXmv9uvIwt5ZkxtocxtgdAJQKvFRY4HDwukSQ84IIkuZHraCQSiQi+X3sAALB2X7iCaKXkWrlAWlmif99ZYFmWqb9kGY69vXgniipqsKuAb3WJNODv99gs03PaCQN9jVbv4Q+oK2p8eP/X3aHrOE3xj68DYW5Wcgblby/ZBQD4ZvX+sOPnv7CIm59qSY2FDi2ahH3/YuVebrqr3l4W/LxXp9SoFlTtPdcqBNrq3/HpatOyaNu6d8cWYee0soeltQ079/6vu0xl6vPX5jGm//Fh6cqqfXhl3g5TOWNfWWKZT6Cc4d+35wbuzS+bDoUdv12ZdAHCFaur314ebN+F28IVJbPnDQDeWJhtWa5ynYLW2+K3a/YMRLpOnUQBgKMVtZblAcLvqb58jDGc8/xCwzV+P4PfzzD+1aWmcrW/92bHNAw7d+FLke+hnqraOlz51jLuubXaSTPdvZ+3Jde0HFac9Cj/faSd5NM+M6v3HLUt26vY3arqUgAvAugCIA9ADwBbAQxwr2gStxFmoRVQFpFyJPHBa8+PRCKRTJm5Nfi5WrGETv1lK/YUmFuF/H6GXpNmBr9fPKhz2PmTH+cPDnfkluK6d0OW0IY6389+j4UG7u2ObQwgYBGa+ktWmFLdokn4UEw74L/5rLSwc3d+tsa0HgDC6qGlrNqHy98MDaivHtYNQMA6GUnJSkvPMD13RGNVHdClVfBz1uESbnrGGEa9uFjzXbt1FL83eGvxzuDn7u2aBz+f92+jssKjh+YarRKotR5qrct2t6rStvXpPUMKMmMMPR8OnevV/tiw66bMNE6omLWxNo8e7ZqjrNqHrEMlOLZJI0uFzEpxVS2IQHj/qy3DtSO6Bz9X1tRhjkax0qJ6BADA1cO7heQyFva8aWGM4d+zt4UdW7bzCK59d3nw+3Wa/HebTDIBwIpdR/Dwdxv5ZTvIfwZVLn791+Dnkzu3DH7eeoh/nfaeqp4YvHNazn5uAQ7qPDC0bV7nZ2G/98aNQjbNLy0mBe74JDSpM6pfx7BzVpNrWrTG9k+W5eCxHzcHvzdvEq48a5+NfseH2spqYlFbDrVegefid1vl8zJ21zw/A+AMAPMYY0OI6AIEtquSJADVzUaYG7Aot2RnYgS6SYtqHzFBUITJEV0vrz0/0vAskUgcUOPz450lIate08aBAeDbi60tfXqF86ROocGhXqnx1fnRqGEDZOeV4qKXw5XOM3u1C36urKkLc2NW35e8tZVaq+dnK8Kd+Tq1ahr8nF9ajYyN4RbBGp8/ODDVl1V9t/rq/AbX4z6dApZSrfUtVNbQqHqUifUYAJbvOoJr3gkpPLed0wtAQCEY90pIsbty6AnBz3olw88YGii9o5kCMk0z0aBas/JKq4JuxADw13N7BT9n54W7K7dtHpi48PtZmBKoylq7N9wSpm03Lad2ax38bNbWvHr4GZBXUoXv1x5AU45Fz8wN/7fscK+Gg0VV+Nunq7F0h7W3w77CClPFddvh0jAXd3Vcobfedj6uWfCzfvLIbGrh1BNC7WN2L83OaRVnAGig8Yu9wOQZrKqtw9XvhF+XV1qFsioferY/FhNeM59c0N+/Jpr7wpuU+OsnmWHftZZnvSztJJJecQbCn5UTTSa7CsqqkW4yKfDSnG2YtTkUE2DQCceZlkXLrR+vCvuunSTSKs76Mk7VTEgCwJj+nVBSVYvGDRuYTiwafh/Kf6vnIpmwu+a5ljF2BEADImrAGFsIYLCL5ZJYIG5Nb4ordc7EeHfNs7DJDm9E21aRurNEInFC30fDXTQ37C8ytWaq7rAjJs8znHtxbmAdKW8g+ugPm8AY47pT/qooO746v2FQWVgeWA86lWN1VKn21eGR7zeFHZuzJTBILq/2YTinrP2VfKwGzTzX1TWKwvjA//i7jjLGkJaeYepaXuPzhynOAPD6gh0oKKs2KAT/U9y5eWVUg2CZlV9/vLKmDowxjJg8P+z48t0BS2theY2pq6t+kiSvtBp1foY//jfcEqY+Mfp2a64oWK9yXKWrlHWfvHqU1/hw+yerMfWXLDwxI6SkqMq43lLIWGBS43rd+u6X522PqDhX1tRx3YeBQNvovQwYAvdSb73NVzwKePVR16nqz81WFLoBume/6TEhVePD33aHnbuofyduWdWfrdlzUePzG9qtfYvGGDF5Pka9uNhSSePJLKmsNT23/2gFZm8Ot7xfpLjQ89acb45g8d5zpDz4+9KTmRP4XQ571vhb/2rVXrwwexteWxDu8q621evzw5/Lvp1CSwnyS6sxb2u4W736nPPKocrcV1gRXJah8tqCbAx6co6phVvvvh+Qx5BXYpxIUD1ykg27ynMREbUAsATAZ0T0KgD74dYkrpCqljpRa19FrcEWJkdUvYS1jxAxwpBr5iUSSSy8PHc7dwD40lWnmg6i9xRWIC09A3kmAWtOfWoO9/gP6w4YZO6aMiHsu9k6y+/X7kemyXo/X52fu3Zw7d4i1PkZBpgErfL5+YNwANiZXxbmpgwAmY9eCCAQzXrO5sO8y7D9cJlpfkBgIKyfqAjkV84d9APGAfpgRXGs87MwN2ItvEBE5dU+7j1dv68I17+3HKc9M9dw7rMVe7lt1LtjC67l73BxFTf9sl1HUFxZi5c5CtP9X63nRk0HgH99swHr9hkDtB0urjRRoArDXIpVeMGVvv7rmWHfzSyBfj/jtg1jxkknAHjsh02mz9XHy3K459o0b4w3F+1EucaVd3C31kFFbMn2fDz105awa+ZuyeXK2pFXZjkhpC9zyyaNIgYs89X5TWW+PHc7xry82HC82leHs58zTkYcKq7E16v2Wa451+d1Tp/2AALric3eS7M3HzYt40PfbsR/OGvF3/91N35afzA46QcA55/UIbh+3Vfn50685RSUG/K68cweAAJLJYoqakwnYszI2HAIt3y4ynD85g9XYcSU0ITXpAn9AABHysUFmYsnlsozEfUmopEALgNQAeB+ALMAHAFwt/vFk0gkEolEIjHnVZ3F5ZJTuwAA/vxRuOKoVXLNXEFViiv5AYT0EaWn/ukUNND4YusHozs1ed7/VcjK26lVE+RMmxj8bhXYSK/gLXt4lEXJQ1z/3oowN+XTe7ZF62bHBL/frlk3eUrXkOvnJf/5FRUWaxn1A/+/nN2Tm+7lq081laEqk/0fn22IlK3Cm0zQu8FmTx4f/PxbdnhkctX9Xu/qrmIWVEm/JnPd4xcFP+snVf459qTgZ701/PPbTufKV8kt4U/c/E23rv22c/jte3bv9hihWWutf/YeHt8v+NlsLfyFL4UrjB/eMpyb7rGL+6N9i4CVUI1iruej33OCUZRV1u0rQrXPj2HPzsWNH6zkXqdFXb87TRdo71LlNw0Y6znrvnNQWs235z068eTgZ95v7Ic7RwIAco5UBJVNLfpnUHXJvvnDVfjXtxvCzml/z/oyLnjgvKDXgH65h5bF28MjaG94coxpWvXdUlbtC27xpvLRLSOCn83eLXrPhv9cNwTTlwWWjfyaXYDBT4dPtuzUTRLyuPNz65gMKn8eGXqmr39vuUVKbxLJ8vwKgFLGWDljzM8Y8zHGPgYwE8CTrpdOwkV0gC6nFj9R+1GK3vLKsRwxYsTJ8Vj7BGsmyGAs7c4SicQpDQgY1a+D4fi9o/uEKbl6+nZqgVev4a9G0w6Ktbx01alhgZX0PHf5KYYgYiorJl1oet23fzvLNM8Zd41E5+Oa4fLTTuCeN7sOAL7665lo1JA/7Jtx10jT63KmTcSqR/jlffWawVzlufNxTfHHIfwy3jIyzTSvJf+8gHv8wTF9ucfN6rPxyTG48awehuP64F0q7Vs0RiOTe9W6Od+1dPfUCZhwSmfuuZxpEzGkWxvuubWPXcQ9zmPO/efi1nN6GY4/fdkAfHqrtXJ+1bBu3OOqwqjntWuHYLBm3bKWv5zdE8/+YSD33IpJo7nHd08NKVt6q7D2nJZxA4/nHn/t2iHc4387/0T0O74V99x/rhvCbTuVnGkT0fm4pqbn9WQ+eiGaN+ZHobb63QFArw4tLM/zGNO/E1o1PYZ7LmfaRNN3i1VZlv6L//t65g8DcfGgLvjprrO557OeGRd1fv+4qC/e+r+hhuO7pkwI+93qJ72SgUjKcxpjbIP+IGMsE0BarJkS0b+JKIuINhDR90TUWnPuYSLKJqJtRDRWc3ycciybiNJjzTsVELdWGULlOBUU1OlEyXGIcGU1RdsnVd3IJRKJt9FbQoZ0b41dUyfiD4O7GtLef1FA+dr+7Piw4w9c1Bc50yZizv3n4TLOderAUK9wnNOnPf5kosACASXt6uEBxfpUTUAfrUwe5/btgKE9+ErXh7cMxyBFuXnhykGmZeVZpq2sRjnTJoKIsP5xo5VLVXI6tGxiODemfydcNrgrurRuZji37GG+QgUEIjn/nm4sY860iejW1ihr27PjcNeoPobjL1zJt2z/cOdItGx6DK4/3ag8L3jwfKx+1DgRkPnoRdimezbUMvHY9NRYEBF6cpTxHYo1XLvWV2XnlAlow1nnmTNtIlbqlNC3bxiKvp1ahgWNU7nxzLTg54kmCnwTTv6vXjMYg7u1xvwHzgs7fkavtrj01C6mZQNC63z153jlU58pHlnPjOOey5k2kavwm92DAV1a4aFxAeu6/jfzxW1n4OJBAWu11WSFWdn1fP/3s9C+RRN8efuZhnNa74drR5iXf+7953LP5UybGCZD5Z0bh3HLHElRV1HbRuWL285At7bNDe+C9i2a4IYzAr+VU3TvKgDY/NRYbqA7IPR+WKB7nub94zzcM7qPYTLk07+cbjmJmSxEUp6tpmSMbzj7zAUwkDE2CMB2AA8DABH1B3ANAltgjQPwXyJqSEQNAbwBYDyA/gCuVdLWa7y2RtRrgaNENY8wOWLEeK59ROG151kikXgbrSWkV/tj8f3fAwouEeFFRbF647rTwgabjRs1wPOXBxTPFZNG4+7R4UqZmrZX+2PDrhvcrTWevCQw7Hjt2iH45C/hVj+9JW3Bg+cHP/+osebo02kV1t/TR2H6n0PultoB9eJ/no8LTgptSUNEeP+mwOB6winHh7mkayMlAwHFSNtWm54K2iWQ9cy44OfjmodbuXZNmRD2Xv7mjpDiMP3PI8IG99efHpgoOL5V07B209Z399QJyJk2EX07tUSX1s2C656H9mgTvEbfD3x4y3A0aRQYuL9x3WkAAutbAaBXh2MNdciePD4oFwDaapRBtY3atWiC8/qGvBPe+r+A3IYNCD8qkyS3nt0zrB7aSZe1j11k2F5MZevT43CMYlUjojDL5ke3DA/eB+29VduoY6umwTq9es1gjB0QUjy07tT6Z+iN608Lfk5r1xw7Jo/Hykmj0bxxo7B85j8QmiA6UWcJ1SqFZq7CDRsQPrg5cM/vu7BPWPuM0OxlrX0W9ZbOnVMmBBUxtWyj+3UMk5WucTc3UxT7Hd8SGfecE/z+7d/OCn6+Zng3nHliKPJ9m2MbB6OxTxzUGU9c0j/MFf78k0LPgtq22nxXThqNId3bBNvgr+eFW7O1VtSpfwqf1NLK6aOJ4q8/16hhA7Rs2oh7Tv3cp2MLw73XTpR1bNkkLOL/HZpyLnrw/GCbNGxAeOrSwE7DT1zSPxgHgceKSaNxbBN+ueb947zg77VXhxb44rYzMLJ3O+yeOiFs3/OVj4zGRf07IXvyeJytrPtWZc25/1xb7uBeI9JWVauI6DbG2Lvag0T0FwDmO8dHgDGmXTSyHMAVyufLAHzJGKsGsJuIsgGoPUk2Y2yXkv+XStrwyAP1BHH74ooxPTNBJTKLipowOaLqlbLtE0DqvBKJJFGYDa4vH3oCLh/KtwxfNbwbrhrOd2m1knnzyJ64WbNWTwsRYfE/z8d5/14Ulczjmh9jeq5RwwaWVqbRJ3cyPZ8zbSK2HirB+FeX4pw+4W7sLZo0srzOjGFpbU3PT/7jKZj8x1MMx4nI9Boz9+HdUydgR14Z+uqUjYmDOmPioICsg0WVQYt302MamuaxxsTq+PGfR2D6shw8/uPmMEXy1G6tubIaN7K+F0BgjXcznVuvmQXe7N4ueOB8bvoLTupomb/+XEfFomr1DO2eOgGl1T6Da3CrpoFn8pvV+/Hq/PDgaKP68Z+5r+8wWmQBoFvb0B7b+uvMynbHeSfijvNONBxvdkxDVNbWYdGD5yONY/G3ap+HJ5yMhyeczD2nXR9sR97D40/Gw+NPxm3TM1FcYYyNsHvqBFNjQM60iWCMcc9vfHIs5wrrsnQ+rpnpOavf3k1npeEm3R7ydvM0O37mie3CJi1UOrZsindNrOj633iyEEl5vg/A90R0PULK8jAAjQH8UVAZ/gzgK+VzVwSUaZX9yjEA2Kc7br3Yw8Oc5PBhOUbZAG9gV/46D7uos0knm6wXsUsbZT1Qn07Rr+nQorqFma1JsovaoXbXvLRjoVubwPVdjnPiZAH0bBeoT0eOe1A09O7YAkt3FITNpMdCv+NbIutwqelaGrsM6NIKS3cUBPcYjZWBXVth0wHrrR3s0LV1M9PAM9GgdtCS5MFsLZZEEk86tWqKFk0a4fkrjC7VieDkzq0w7x/nold7Z31zvCGiiINqnqt4tNxwRg+MH9iZ65IeDc9fPgj/+nZDmGdAMkBEluOAK4aegCtMJqCioX2LxhEjYdth6UMXoKiilqs4JwIzhTCSF53Xvew+v/V0tHf4m0h1yI4VioguAKBGCtjMGOPvKxB+zTwAvJX/jzDGflTSPIKAMv4nxhgjojcALGOMfaqcfx+B4GQNAIxljN2qHL8BwAjGmCHiNxHdDuB2AOjevfvQPXv2RKxfPFm3rwhp7ZqbBqCwy+o9hejTqaVjBWjl7kIM7NoKzRtHmkex5vedBRjao03QvSpWlu7Ix5m92pkGAbEDYwxLdhTgnN7tHa2tUOWc26e9o5ddnZ/h950Fhtn/aKnx+ZGZU4izerePnNiCypo6rN9fhDN6GWcIo6G0qhbbDpdimMZdKxaKK2qxq6As6BYVK0fKqnGgqDK4JjBW8kqrkF9ajQFdjGt/ouFQcSWKK2tNg5nYZV9hBap9dejd0dmk254j5fAzcNfoRcPO/DI0btggzKIQC9tzS3Fsk0bo6nAQvPVQCdoe25i7ds0NiGg1Y4w/apLYYtiwYSwzMzNyQolEkvTkl1Yjt6QKA7s661MlEivi2TfbUp5dyZjoJgB3ABjNGKtQjj0MAIyxqcr32QhF9X6SMTaWl84M2UFLJBKJRCRSeXaO7JslEolEIpJ49s3O/C1jhIjGAXgIwKWq4qwwA8A1RNSEiHoC6ANgJYBVAPoQUU8iaoxAULEZ8S63RCKRSCQSiUQikUjqJwmxPCuBwJoAUDf3Ws4Yu0M59wgC66B9AO5jjP2iHJ+AwL7TDQF8wBibbCOffADe8ttODO0BFCS6EPUA2c7xQbZzfJDtzKcHY8zZGox6juybg8jfWHyQ7RwfZDvHB9nOfOLWNyfMbVsSP4goU7oZuo9s5/gg2zk+yHaWSNxF/sbig2zn+CDbOT7Idk48CXHblkgkEolEIpFIJBKJJJmQyrNEIpFIJBKJRCKRSCQRkMpz/eCdRBegniDbOT7Ido4Psp0lEneRv7H4INs5Psh2jg+ynROMXPMskUgkEolEIpFIJBJJBKTlWSKRSCQSiUQikUgkkghI5TmJIKIPiCiPiDZpjj1DRBuIaB0RzSGiLrprhhNRHRFdoXwfTETLiGizct3VmrRERJOJaDsRbSWie+JXO28QhzYeTURrFFm/ElHv+NXOOwhq5x5EtFpJv5mI7tCkHUpEG4kom4heIyKKX+28g5vtTETNiSiDiLKU49PiWzuJxBvIvtl9ZN8cH2TfHB9k35zkMMbkX5L8ATgXwGkANmmOtdJ8vgfAW5rvDQEsADATwBXKsb4A+iifuwA4BKC18v0WANMBNFC+d0x0nVOwjbcDOFn5/HcAHyW6zknczo0BNFE+twCQA6CL8n0lgDMBEIBfAIxPdJ1TrZ0BNAdwgSbN0vrazvKvfv/Jvjkl2lj2zeLaWfbNCWxn2Te7/yctz0kEY2wJgELdsRLN12MBaBex3w3gWwB5mvTbGWM7lM8HlXPqpuJ/A/A0Y8yvnM9DPSMObcwAtFI+HwfgoMjyJwuC2rmGMVatfG0CxZOGiDoj0AktY4HeYzqAPwivRBLgZjszxioYYwvVNADWADhBdB0kEq8j+2b3kX1zfJB9c3yQfXNy0yjRBZA4h4gmA7gRQDGAC5RjXQH8EcAoAMNNrhuBwKzUTuXQiQCuJqI/AsgHcI/a0dR3BLbxrQBmElElgBIAZ7hb8uQi2nYmom4AMgD0BvBPxthBIhoGYL8m2X4AXd0vffIgop1151sDuATAq64XXiJJEmTf7D6yb44Psm+OD7JvTg6k5TkFYIw9whjrBuAzAHcph18B8BBjrI53jTID+AmAW9TZbARmrqoYY8MAvAvgA3dLnjwIbOP7AUxgjJ0A4EMAL7lb8uQi2nZmjO1jjA1CoOO4iYg6IeAOZkjqVpmTEUHtDAAgokYAvgDwGmNsl/ull0iSA9k3u4/sm+OD7Jvjg+ybk4R4+4nLP2d/ANKgWSOhO9dDPQdgNwLrH3IAlCHg6vEH5VwrBNw4rtRdnwUgTflMAIoTXd9UamME3MN2ar53B7Al0fVN5nbWXfMhgCsAdAaQpTl+LYC3E13fVGtnzfcPEOicE15X+Sf/EvUn++bkbWPZN4tvZ901sm+OYztrvsu+2aW/hO3zrLgaTAdwPAA/gHcYY68SUVsAXyHwUOUAuIoxdlSJyPcqgAkAKgDczBhbY5VH+/btWVpammt1kEgkEkn9YvXq1QWMsQ6RU0rMkH2zRCKRSEQSz745kWuefQAeYIytIaKWAFYT0VwANwOYzxibRkTpANIBPARgPIA+yt/pAN5U/puSlpaGzMxMF6sgkUgkkvoEEe1JdBmSHdk3SyQSiUQk8eybE7bmmTF2SLUcM8ZKAWxFIHDAZQA+VpJ9jFAkvssATGcBlgNoraxb8QwHiypRWcNdYhMV+49WoKrWuZx9hRWo8fkjJ4xATkE5fHXO5ezKL4Pf79zTYWd+GUR4TOzML3MsgzEmTM4uAXLq/Aw5BeWO5dT4/Nh7pMKxnKraOuwrdC6nsqYOB4oqHcspr/bhULFzOaVVtcgrqXIsp7iyFvml1ZETRqCoogZHypzLKSyvwdHyGsdy8kurUVxR61hOXkkVSqqcy5FIRFDnZ0L6HolEIomGqto6IXqBqL65vuOJgGFElAZgCIAVADoxxg4BAQUbQEclWVcA+zSXcaP0EdHtRJRJRJn5+fluFtvAWdMW4Ib3VziS4fcznP3cQtz1uaVHekSKK2txzvML8cj3Gx3JOVBUifNfWITnZmU5krP1UAlGvbgYby7eGTmxBSt2HcHoFxfj85V7HcmZs/kwRr+4GBkbDjmS87/V+zH6xcX4LbvAkZx3l+7CqBcXY+P+YkdyXpyzDee/sAh7jjhToJ+YsQnn/nuhY0Xq/q/W4ZznFzqexPnzR6swctoCRzIA4PI3f8eZU53LGfPyEoyYMt+xnDOmzMfwyfMcyxn89FwMfda5nNOemYshz8x1LGf45HkY8swcx3JGTJmPC/69yLEcSf1h7d6jyM4rDTuWW1LFHXhW1dZhy8ESw3EAmL8113DNiZNmYtL3mwxpfXV+00nCw8VVmLXJ2M/syC2aRpHWAAAgAElEQVTFun1FhuN/+WgVnrfob2esP4hNB+z3E4wx/OWjVVi83Tge+nHdAZz93ALDpHZ+abXlpN7SHfmo011zsKgSaekZWLbziCF9XkkVcgVMNgKB+ny6fA8qanyGc5sPFuPh7zZyJ+mz88pM75Hfz7h9VF5JFU55YrbhGamo8eGuz9cgr9RYp2pfXVQTfpU1dfhuzX7upExFjc9W31lSVcudNP961T7u2GRBVq7tydbSqloUcNLuKzQaeWrr/NxJ7k0HinH3F2sNz8y+wgrDJH1VbR2254b/fqPhaHkNdts0IBRV1OCtxTvD2t7vZ/jg190GQ9ih4kqs2BX+bFfV1uGpnzajvNr4LIqm32OzMORpY99sZtxijHHv8fDJ8zD0WaOclbsLw5RqX50fS3TvjIKyasxYH76DW7Wvjvs70OP3M+77LllJuPJMRC0Q2LvsPha+x5khKeeY4W3DGHuHMTaMMTasQ4f4L0vL3HPU0fVqheb/P3vnHWdFdf7/z7NLB0ERbCBiwSh2Jagx9oZYY4xGY0li4lcTUzS/GIzGELEbNbHHWGIvsUQUxIIUKdJh6bD0hYXdZdlle7vn98fM3DvlzMyZmTP3zr2c9+u1r7137swzZ86cOXOecp6zItoyjsbD/M3qaEpdlf4C/XZttc+e3mzeoXWo8yLWz1q9UyzZFE3JXLFV65yXl3s1OX8MZbe0IprXeP4GrVPZtCOal3bWOu0+VUT0Zhrtpq452kvha70d21+aQZm51jkgC4Nx36NSXitnINgkwZKcVCQEmQAAtkvwhCv8IaIRRLSSiEr1KVP237sS0bv677N0ozeIaDARNRHRQv3v+WyX3cwPnp2Bcx6fatl24gMTccPLsx373v7eQox88hvUNlmVnfkbd+DGV+fi/nHLHce8zTHcPvjZCpz6yCRuNMoVz8/AzW/MdyhH5z4xFZc9M92x/8QVFXh2sruR+bdvL8BFT02zbOtIMZz92GRMWLLVsX9LewoTV1Tgl685w+T/9EEJynY0ocWmoH33/q9cjXpTV1Xiupdm45lJpZbts9ZpffS7c5z1M/yBiTjRxdg4fnE5fs1xFkxaUYFh933pUNCmrq7C3f9bgjGfOu/NDS/PwduzN6Kqwfn+O+fxKTj1kUncMtz+3kIcevdnzjKsrEBdSzv+M2OdZfvYhVvwaUk5Hvt8leOYK56biaNHOw2Hre0prnH8vnHLcPt7i7jvuKH3fI6rXphp2VZR1+yo4x88Mx1n/H2y4/g7PijBT160OnQaW9vx8//MxQ2vZJ6HtZX1GDxqHFe5OePRyRhmM8y2d6Rw6iOT8Nu3F1i23/XRYpzy0NeosxkPbnlzHj5ZtCU9DjQ49ZFJOO1R6z35f/9dhPOemOp4Ji966hv85MVvHeW7+fV5eH9eZvWrM/4+GWdy6mLUByX42ydLLdvu/HAxHvpshWV8O2HpVtz76TKHw+jMv0/GVS9Yz//qjPV4Zfp6PG9zCp0w5ks88WWmbTS3deDgP4/HpyVWxXNzTROemVTq6Bs+XrgZqzkGBPt4oWxHo6tz681ZG3HCfV9xDRHttpdza3sKV/5rpqVNPDNpDa5/eTa+WZ1RoH/52lz89u0FFsParW8twPD7rc/2xu2NGDxqnEXR/tfUtbjsmekOA0S+klPlmYg6Q1Oc32SMfahv3maEY+v/DS2yDMD+psMHogAXsedZCELJkSQocXLkiCncepYjJnHXpVAo4oOIigE8Ay23yFAAVxPRUNtuNwLYwRg7BMATAB42/baGMXas/ndzVgodEMOwaGbOes2Y22IblNY0agabMkFjpjHA3MEJhyzTFQaKsTPc2dSGNZUNGPVhSWznMDA8yBskTOsBgF+9OZ8bAXbfuGWoqm913ING3TEgY3qJwf8WyhtKLnaJChjz6TJc/PQ0h4fYqM+GFr4hdcFGq0L7y1fn4k8fLMZWkwF3TaV4pJmhOG2oytTrpJVa+/144WbH/jzjpSFjss0zacixe22DzHQwnlP7M7lk805ML3UqXhOWbsX/+++i9He70m3wzpxNeGX6ess2wznQnsoYjxr1stujB5rbnN5dox7szoHtDa3458TMMuxbaprQkWJ47AurseWm1+bi0c9XYr3tWfrdOwtx7hNWAyCPqnrt3szm9G2G13itQNtI6TfI7Exar0cvmhXlLXpUgfl6v1y2zSFvWbn2DIwzGQtWbNVky3I85Bph5ZmIuhPRd2SdWM+e/RKA5Ywx83p6YwHcoH++AcDHpu3Xk8ZJ0JZqiBZzm2BkTatikpbQkyZH0oUV7HXJuu8F2n4UCkUsDAdQyhhbyxhrBfAOtDwjZsz5SN4HcDbFqRHmEKP/LNDLi4VsTwXnv1O0bSTNjKxLtZ3KaBZB3mslZZoSXOOi3IliKExmhS9fSOrjxG27ArfWGPf5XZchyr6bYWRIJSSPgmgp/Nq91+UUylhQSHkmoosBLAQwQf9+LBGNjXjuUwBcB+AsU7jXSAAPATiXiFYDOFf/DgDjAawFUArg3wB+FfH8BY3x8oj6TEqTk37ZSJIjqzyR60eXE01MqJexp5yIF5a09qNQKGJFJKdIeh/GWDuAWgB76r8dSEQLiGgKEZ0ad2FlY++e0sqz6PEJ6d+8ysGd9xai3GlFwKVyZBsc3BRXr33lRXLxBYV5ryWkiaQxlyetBAY0Orjt7fY8BSEb9cVrJ0FqINNPeB/FfDqUsP0HE2rw4YTzxpDB2weZPhcWoktVjYZmmZ4MAIyxhcZ8p7AwxqbBvT7P5uzPAPw6yjnzAVkdRuKUVUmPjjQ5kt6uheqZkN5+IspRKBSxIpJTxG2fcgCDGGPbiegEAP8joiPsOUyI6CYANwHAoEGDJBQ5PlKCHiU7uXodhFUo/RRhvkD9mACHhCHMu9XNwxcVGe8vN/0pqKHGflxQvM4jWuVhjRQi++fiEQp7f0WfH9d7HPFivdp7qL5IsCL82l6ofiXPEA3bbmeMRcvQpFAoFAqFIomI5BRJ70NEnQD0AVDNGGthjG0HAMbYPABrABxqP0Guk3l64VBoXH/hk2vjYDY9327RUWGVQP/zuWzn/JAJo405iUgEo7C9aLlSNMyexaDtxy9CzmkgCF5TuY7mEDm9aJvP3GNRX708ROqR64HnbJRhXMn1fZWFqPK8hIiuAVBMREOI6CkAM2Isl6JAkTcXN2FyEhLenJYTTYxCodi1mANgCBEdSERdAPwYWp4RM+Z8JFcA+Joxxoiov55wDER0EIAh0KZY5Q1uYaZFQT1rUkojF09FJ0w4rcvAOC0q9uSX7r9k672Xnq4VKGxbbunCKts8pcgoW9BpCoHDeAUKnWtvZZDzp++pz0FuSnbUS5U1TSG48cTvd07Id65vrGREleffADgCQAuAt6DNdfp9XIVSRCdM5+5FZDGywnclhZEbqPrxFCNh7rQcOQqFIj70Ocy3AvgcwHIA7zHGlhLRvUR0ib7bSwD2JKJSALcDMJazOg1ACREtgpZI7GbGWLS1DbOEqx8oYNh2rvs3obBtL3U0xLhWdlKuoHBzPMXl/XbMuzaM1OL3PazCKVom8ePcD5QVhu0wRhn7BxOfU0SeaXHPc7hpIP74Gz1knjOoKF5bL5ShoNCcZ8ZYI4C79D9FPpBus0lJQCUHaXJkWcdjt7LnSE56cCBHjkKhSDaMsfHQEnOat91j+twM4Eec4z6AtuRk3uEaEqz/D+5Zi1ScWOHPeQ4RTusiLy4DQpCEYUH2EcEnajvQC9LVY58AbUK259FNfiivbpYJVkaxYzKRLPwd42wCYcO2ZZ8zwV1jKESzbX9JRLubvu9BRJ/HV6xdF1kdqeww4KgoJSq/kBW5kJYTTYxCoVDEhlsSpyLB2Lwk929eZQvjDfVThOSNGcTKYd1meOLkjjfiVOSyPeeZH7bt/huPTD27nMMhX7z+chHRwBt3C5VYsL25LUUVdVzstaRemHoUvU9+eorsKRxJRDRsux9jLL1SO2NsB4C94imSopBR6zxnSU7ChnPywuyTdV0KhSL/sfcqqaBKWB6MFrMV2ikb+7vMq6jZug1hVpHw2zeXYfCBl2bT/7spf/nylo6qdIpeZ1zrxssKh+eHV0eft5zgbiUyospziojSa0sQ0QHIn+djl6Rgw5IlrX2U7iwKzDOfFhM53jpqSSTL0VG6s0KhkIVv7ltJc0BziWyDI3MZssv2oLq9Wz2vRlICJb+yhJnO5jaPPlfvNO5pIybIc88hYPwePMIhG4Q9VdBw9KAJCGUQyMAjuLPvUlW7wEBNdJ3nuwBMI6Ip+vfToK/XqEg2sppwUp6FQp3znFwScuMVCoVCMq5znn3CUvOJoCG5vvLcFIaYknUFIa7EVE7vd/icIK4KZLbCtjnbMtm2RcO2Xba77W+cO2EPVLZ8BLKyYrvJ5RH1XDL6C68pAvmOaMKwCUR0PICToLWT2xhjVbGWTBGJzJxVSQnDpCUeiyQmVLgUVw4ky5E1N1iSJzxp1yWrxyyUjlehUCQftwQ/dpLSL3lmUubt7/Gb6zmCFCgGPMO2JVv53RSIKOOZpCmQQAjlzqfhiM6FzndEs2j7GSfCe77Fzu8rR/JTzZ0GUGA3X9TzDABdAVTrxwwlIjDGpsZTrF2XXL+YFLmh0D3hcueWJ/QiFQpFXuHWk6QCDkpz7an28hh6KXiZwXeQFMP8a81+ng33JE/SE5RKuDTf9bEFSdI73nfag/E96QNbbvI5gcME5zK73Xt5nm/5jUKGISpJbVU2QsozET0M4CoASwGk9M0MgFKeE4q0pYYkZ+1WCbqyJCeyh19S+yE57ccg6e9ghUKR/4RdLzhXq0p4vjdiimV2zbYdc2iqVx3LXufZNxxXwjmCGl7iVETFHc98o4vHnRE+Qa4VrjBLVfnRntLUppKyWr6ckPc0qeMhr/6oUOZDi3qeLwPwHcZYS5yFUchDWtitrDDphHlWkyYnachuPwqFQpFU7P2c37qs+YhX2HYQ3OeJhxDmQZiqFw2jDS7XSsYoHCBhmE/obrYNL+aiBw3/DepFD5UwTHjP3CEa7r61lq86RQ63FjCMBfGgm+FmIg/aPszHFljEoGi27bUAOsdZEEU8JM5Dm5g5vZI86pI8q/KNHQnzhEuRkgfhXwqFIu9JdzOig8W4CiJI2IFpGE+tmyKUGcfLHSS7JqfySpYU80A9TM4UWUmj4jg+W+9VkbJnU8XyKo9IlYgmWot7SgPv7NEV8/Bl9gpnz3VfKQtRz3MjgIVENBFA2oTCGPttLKVSFCzy1vuVJEeOmMSEN8syCiSVpK1frVAo8h/n8kFig2KHHFkFkohbiK2ZMAPtbIVtO+R7/JYt73eYJSGT/OYKavhwc3j6zYFO4vMRCUGDiO/STiFbh/REX5xtUYwtFs9zgd18UeV5rP6niBlZnX/i1meWJSdh4dYF1h+kUfWjUCh2FdzDtsMdnyt4xZC9TI6bRyrbdcC9Vv2//HnX0S/ObW6z7HnaovAUr8AJ8mwHuIdt+xtw3I7JFSLnFzUKpFxkRY6QkPxsc09hKrpw+4inKIlCdKmqV+MuiCIeZM1VjvwwKC0qr5Af1p6MsH+FQqEwcBsMBp07G9dc2yTipzDIn29s7fQ95WfL8xwhAWquPPbp83DuXND3atCrDrJ/rpLuhTm/6FzxuMct2Z5P7NfuWa4sQllENNv2EAAPAhgKoJuxnTF2UEzlShQNLe0oLiJ061yc66IEJylxyWkxhTmHVtqc54jlkGbs0ElaeLxCoVDIwm8+bfCw7RwP/LNwDtHleaISRn62jBhh1nnOh3dg0IzfQau50HQp0fsf13SzbLQp87MUPKy/0O54BtGEYa8AeA5AO4AzAbwG4PW4CpU0znpsMobeMwELN9Xkuig5QE7jlz0XNzFLTMlK0JV+GScjwZvssO2kGCkUCsWuhUhfaO/vUkbYtugIKSF4hjJ7HCdDEY5NQQiioIY0eoQl0PsoaBa6mODdp/S2gO1AdN3iMFMHcv2uF0sYpuGbMCymaxGp1zieS+E5z7xyFcgYTvTV0J0xNhEAMcY2MMZGAzgrvmIli4uO3g8pBmze0ZTroghjNO6keDLlzVWWpMzLklPAljUgese7K4QxKhSK5BJmqRYmpHIGO0eseA2eQ8w39cJ9XqtAYQIQRkpcc57tZCLFoodti2ZstiOzzQX1JLtdt++c54SOl8zXE6aEkROGRZ4ex9kWZFkwj/OHmfPMawjJvPPhEVWem4moCMBqIrqViH4AYK8Yy5Uorh4+CADQkYU3ZKFnE05YFHliPKLSluAiOXKSSqE/HwqFQi5eCpXrnOeAnjLZSbmSjN88z2zVAS+iwC0pV+Rz2b6HW4PaZXtet51gYbyFhui4zzVhWOR8YQJJzWKofD+RGYNQ4SKqPP8eQA8AvwVwAoBrAVwfV6GSRrGecrMjlcpxSYKTlDBgRX4ib86zShimUCiyj/Hu4vUd7nOeC2fwF8yHLo7bOs9xI7RWcOyRZcGnocluU9HX8TV9DirT5bp9D0/oAxV6nXSBZeC0/XzkhHx4ZE9T4I3zrXOeReU4j03/ViCmFFHleTBjrJ4xVsYY+xlj7IcABsVZsCTRKa0857ggOUBaR298kJRYS5qciEi3FifENS8vGYzcDGbyIg4KowNXKBTehHnSsxX+mw2y7dnMVpV5ze+Wfi6b4DDT2TJtSpaiI0WMRVjghFABle28ep4CTPfwvay45jzr/73qVaaRJSjm9pRX914AUeX5TsFtsUNEI4hoJRGVEtGobJwzrz3PEY+XFQYsb66yFDGJW8c4ceWRJafAOkyFQpFfeA3+/MK2i1QH5iDXYcZeCp7sFXLiuEa7yKDKibT1ul0+B8FxLT7nyoenKUz9+h3jFrZtEKc3VijnQ8Dz+zkfvH4tFL+F51JVRHQBgJEABhDRk6afekPLvJ1ViKgYwDMAzgVQBmAOEY1ljC2L87zFeex5lraEkqSEBpETUEVYX9EqRy+PpKzUkeUY5ZGUoEuaHFntJ5oYEGllkbletBoXKxSFT5i+MMUJsfXqe0TXe40dXmh6TANzx6VKHhVHqcusJQwLFLbtsj3g/NBY5rDGPMc/yHs7F89Q2GdENBTfTXpUp5JnvcbgaBItb5uuLJXVNJqKk+vOUS5+6zxvATAXwCUA5pm21wG4La5CeTAcQCljbC0AENE7AC4FkBXlecmWWnyxdKvv/l77bNjeiL16d0V3lzWj21OZh8FLzrqqBuy7e3d068QPHqhv0WwbTW0dnnLWVDZg/77d0aWYL2dbXQsAYGN1o6ec1RX1OLBfz3SIu53l5XUAgBXldZ5yVm2rwyF79XK1+M9etx0AsHTLTk85K7fWYcjeu8GlOJi3UVt2bFGZ9z1dsbUO39lnN9fHftGmWgDA/I01rnKYXp7D9tnN9TyLN2ty5qzfgT16dOHuk2LA6m1aedxYXr4TAPDt2mq0d/A71o4Uw9qqBgzZq5ernNXb6gEA00qrUF7LzzLfnmJY5yOnQm8/U1ZWYvcenbn7tHaksKm6CQf37+kqx3hHfL2iwvXZaWrrwNbaZhzYz12OwRfLtrm2jcbWDlTVt2BQ3x7+cjzaTl1zO2qa2rD/Ht0jyalpakNjSzv22z2anOqGVrR1pLB3726R5FTWt4BA6NeL305F5Wzb2YwunYpc27uonC01TejZtRP6dOe3L4P9+/bA4fv29j2XorAIo2yEXcs47rWP3c/r8WP6+mVl285uwjC/tbj5ZZBbCGfCsOBmYd81qLOWaI2zLagC75Ih3O/4XD0fYRBKxqX/953z7JNbISyypwIEPa8bs9ZVAwCml26PvzA5wlN5ZowtArCIiN5kjGXd08xhAIBNpu9lAE4070BENwG4CQAGDZIzLbtHl2J06VSEt2ZtxFuzNvruf9Pr83z3EaEQ5dS1tEuRs2F7oxQ5CzfVSJHz9YoKfL2iIrKc9+eV4f15ZZHlvDB1LV6ILAV4eMIKCVKAOz4okSLnd+8slCLn5jeS80woOdmR89PvDcboS46QUBpFoRN0TmeuIxFFxuDSwn1dDAuy6yAXSiZjTEgRiRKRF9UDF6eeFJfsMGHbuQrvDbPEU9Sw7agI2c689vHYifdbruZ4Jwm/sO33GGNXAlhARI7qYIwdHVvJXIrE2WYpF2PsBUDTG4YNGyblFvbo0glT/ngGtte3eu5X19yOnl2LPedJXfTUNADAp7/5vus+O5vbsFvXzq4PZFNbB370/Ex/OU1t6O3hjaluaMX1L8+OLGdNZT1+985C7NGjM16/8UTX/Wqb2jy9Q7PWVWPMp8swZK9eeOKqY0PLGb+4HM9OXoOTDuqLuy8cGlrO6zM34N25m3Dh0fviltMPDi3niS9XYeKKClx/8gG4ctj+oeXc/b8lWLipBredcyjOPtx9pTg/Obe8OQ+bqptw32VH4tj9dw8t58p/zURjawee+8nx2N/DS+vXfoxn4q1fnOi6H2NAXUsbenfzl/Phr77nGkmRYgz1Le1CcryeiY4UQ1NbB3p1de9CReS0pxha2jrQM6Kc1o4U2jsYenThe+VF5bS0p8AYQzcX776onOa2DhARurpExojKaWztQOdiQmeX+ykqBwD2FPCUKwqPcJ7nYAmUDJLoV5M9jnWd1yo582/YcsRyLlsjCpUwzEXRSsIc0KBliDPMO5tOVE+lM8BcYd+wbdelqqJm83L/Kc5q9KuaQsmo7YVf2Pbv9P8XxV0QQcoAmLWPgdBCy2Nn3z7dsW8f/7BJL8wP0JED+oSWY4RkR5VTsbMZANCvV5dIcoznf58+3SPJqarXQnz32z2anGV66PLAPXpEkjNoT00hHNQ3mhxDsRy8Z89Icgbs3h0LN9XgoP7R5Ozbuzs2VTfhkL16RZKzd+9uWFfVgEP32Q0H93cP3fajV9dOqG9px2H79kbfntEVnKH79vZU/kSJUjdKTv7KURQWYuGX1n3CKgdJRtZg2i8pl/Sw7SwOxOPMheHXRoIaHWTWS9Dw36Ce5KQqU7xSBbn/os+9r7IZsnp8pwIEluck1JrmXp7s4OISiWe2bcZYuWm/bYyxDYyxDQAqkBsj6xwAQ4joQCLqAuDHAMbmoBwFRtSkBTKkBA+V8xOUlMzTspLJyOooZS1gL2vdyqStf6lQ7Ir4rWRBRF2J6F3991lENNj025369pVEdH42y+1HKM8zp4/MZmi0TGJT7B0e1IjzN+3eXZ83Qjar2q5UykoUCuRQmYhw4qDXHSYqIakKt5lMOoFwc56TMkbNBknsG6MgulTVfwGYc0136Nuyij7v+lYAnwNYDuA9xtjSbJejUJCmrMqSI00Jl6RkSlN6NaIrmYYcScaOqJkeIVtOJDFpCi2ro0IRN6aVLC4AMBTA1URkn/NyI4AdjLFDADwB4GH92KHQDNlHABgB4FldXiIQGYLb+4zAnmeBs6RS8SsDvDPI9055X4fs3tc9S3X2cFPsw5RBVqI1me+58GHY/APs9yzIOz6b7+/IYduCY1ZZq4TEhWjpcpkRPmmIKs+dGGPpCb/655xMIGOMjWeMHcoYO5gxdn8uylAoyGrg0uRIeh0m7boKFVXPCkXBkF7JQn+/GytZmLkUwKv65/cBnE3a6PlSAO8wxloYY+sAlOryEkGYgWtDawcAYPb6HYGO8xr41za1BS6HMCJecVnZtmOa22y/Ta5KptSz2spgP5dPIYItVWUYMewue3EZ1sNkhm2Hm+MfB5X6Ch1iyqv8wUPQ4HnA3ygQ1xBHxBEis45EJXm1zUIZ74kqz5VEdInxhYguBVAVT5EU2UaFyyqiIC/0SI4k1Q4VisDwVrIY4LaPHgVWC2BPwWNBRDcR0VwimltZWSmx6N4IZZu17bVCz5uxaFNNDCWKD25G1ZgGq246YNhIpKDFzKY3z6FUu2wPIsOOaK1FjjyLdLQYjiYQ4KT9d+sqvG+uFTFjOdDGlg7P/dyCTqJHR+pyuLJzNxDiZugusHGZqPJ8M4A/E9FGItoE4E8A/i++YhUmshqzIaXQGqOBvHpKhhxpYdIBQ6tc5UCOHOlIC9tWKBQB8V3JwmMfkWPBGHuBMTaMMTasf//+IYoYDs/1gF16i6AR1iJ9czb6W37YdnbOH1WREVWGs6kUyDyTm6KTiLm9ocO2xfZv7dBmfba0p3z2lD/NQISwbfejBZsBAI9/ucpHvvcJcm0EEH72ZJwrCe1dAn7ZtgEAjLE1AE4iol4AiDFWF2+xFNlAdgKqqMgL/06aHEn1I62e5ZYnKe1HoVCERmQlC2OfMiLqBKAPgGrBY3OHR/fi1vfw+kivXkpWXos4kZdtW26Cx7RcyfLCoF2b/5UZCnyYd6n7nOfsth5z2w/afv3e/W7VsqJ8J048aE+hc4g4LXLdZtp1K1tLu5/nmV/SgnWEcMmLQgojpDwDABFdCC0hSDdTx3FvTOVSCCArAVVUpCeyklUeWSExSUmoZsiRIyZ57UdaPRdWJ61QZIH0ShYANkNLAHaNbZ+xAG4AMBPAFQC+ZowxIhoL4C0iehzAfgCGAJidtZL7EMY415FrV1AeIGnqri9h5Da1dqC7x5r3UQsR5g3jbqgJIUwyhhGgPWDIRRxzpMPMI48Kb8gQpN8o8hlzuFWrvGgNufkMIsuRIybRCIVtE9HzAK4C8Btod+lHAA6IsVwKARIX3pw0JTMhymrijAKQI8dAXni8HJTqrFAEw20lCyK615Tv5CUAexJRKYDbAYzSj10K4D0AywBMAPBrxpi3KyaLeOkDssO2c4V3gh65hTPqprGVf4vDvlccCcNc9hMRv2lHY7hC2M/lkxk7mKKnH+tyBbm0+U5ZpeUgeHLiaqH9fdesjnAtQcY5cTx2Ycrurzx7l7TQ7P257g+zgajn+XuMsaOJqIQx9jciegzAh3EWTBE/spQoWbavXeB5KyjkK/PJMog+mh4AACAASURBVOIoFLsSjLHxAMbbtt1j+twMzXDOO/Z+AIlc/cJv4MojtMLp0ffkakApK+eGwax12wEAr0xfj79efIQUmUCwEHptu7ussFfqJtJetsxSVeI3VbbROrrXMvO5ql5bTEdkTjLgfy2u6xpLfjnH+UwFke2bbTsuz7PH+YPUtBp7B0c0YViT/r+RiPYD0AbgwHiKpPBDXuebsDm0kufiJkWOwgdVzQqFIibCKM8dgddk9t+/ULq5+uZ27vbMXOiQ2bYd6enCe2iTFlVllWklCe0i7NjLfi1+9S7bsB1Pvp3ghfS7rrjWeJcV1Rj2vB57RDg2PxD1PH9KRLsDeBTAfGg18+/YSqUQonAdbHLmTkuTEzVLtvFBlpyE1I9spIVtK9ezQqHQCTNY4yncIgqGl7IVRokPSjaWb/LTA7LV/cZh3LZXn9s5ooRtO7eHS8Ams54Dm4pCRAPEQaye5wD7hp7zzNsm+aJyoazuCktViWbbHqN//ICIPgXQjTFWG1+xFCIkZU5vRlCywm6TUj+FPudZFkkrj0KhyH/CKK1xzHnOtcdFVv+aDSOAGfvZxBKUyk2gZD8X2X4XIZ2R2WceddxwM8kHbe/6fzdDdT6/y8OU3e8Qt2cmbTgJXV/uxweRyStelKc8231ELhBNGNaNiG4nog8BvAXg50TULd6iKeJG2vrD+v+kzHlOmpzElUhymL2spariCJFTKBS7NiKKsMPjGDaM1XPOc3wDSi/RslcziG3+pmDCsGwaf/2CtIJ4v+v0cPf5G3ZEK5RxbonNKayy0+oyR9pNXJFIErAA44qoSpqsKDU/z7Pfs28e+8iIZggqRxgJ1VUoarVo2PZrAOoAPKV/vxrA63BJHqLYtZC9ZJEiP5CV6Evdd4VCERdh5hsG9jxL2idOUmK5oPzl+CoC4RBdyimjXLmfKXTGb1sZXEOqQ4RtG+y0zRkPKiIWo0HISIvNNU2W7TLLJjKuiDqV2Eup9Qqzd5TNp6hxLX1X36K1pc+XbpMum3tJES6j0FwjognDvsMYu5ExNkn/uwnAoXEWTOGOYaWStq5y5FYtxwOZlhNZjGQ5BRZGLkuOtHB0/X8+h3opFIpkIuKdsvc9QT1aIvNWcxXKaCiEdkUnLG7XUdXQAgCYsHRrKLkOz7Ort1f/3UOW7FeJXVmKMvbqZHO/Bs1BEsdavMFFJmPOcy6eKe58Xp9j4soTsKOxVY5MCdVY29QmJq5AvCWiyvMCIjrJ+EJEJwKYHk+RFMIkTNmQl/ApYXJkGSmkGTskhdkrbVWhUBQ4YcK2w2bH9epTc71UlSzcqmZ5eR0AoGxHOCXdwwfI/ZrN15dbVukwVVvsErucT6/j8G3K/yKDiGYRoyl4z6ufU4mnsBf5xKO7KfntnIcpyPXzjnfKC3ezuEd5XabpgF0hYZio8nwigBlEtJ6I1gOYCeB0IlpMRCWxlU6RFQqtUSdshnHBU2jtR6FQFA5e3qmtO5tdjnFu83of7GjUvC5m70uQ40VxzXKs/29o7XD8JuKda2zlLz/Fwy/5kSyM5cLs80mZQIRa2JI4Q8S1/65RuiFOZL+e5jbnPfMijqVKg3pw2zr4+8som7HWtEh7isPzbJzXTR/m9Q3+c57520sr6h3b2jrELQLb6909z27z0XnIDivn9YNba7XIFLe2k2+IznkeEWspFKGIHi4rK3GUFDHyPbQJCWuXniU7mhiJ4day2o/c8HiFQrFrws8kHLyfWrw53GIi8zfswIH9enJ/M3uzy3Y0cvfxK+vOJr6Se/yYL12PueujJZ4yAaCyrgUH7Ck2HFxb2SC0X1Ds115Spt2Dbp2LLdsnr6wE4GOosMkKG0mwapvmTW9psyoiDbqxodojbNYNu6K1Yqt2jnWVDTh+0B7p7W5tQVQpcju+Wb8Wc5U0cowuXqQzh9vYtrPF8ziRd3x1g7NO3RTKnc3ubcDMl8v4c4Knrqp0bDPqwu1R5HqeTdf167fmO35fusW7PzHL/GjBZs99zbw0bZ3rb58t0aZPGEor4N4mfvnaXNfjqxsydZx59p1yzAr4wk01jt+/Wq7dg6p6axsRyWGQRESXqtoAAES0F4Bupu0bYyqXQgBpSl1UJTNdHllyIolJU2hzjNNyElI/0ttP0uYhKBSKvOLAO8c7trnpTeMXl6c/l+1own67d/eUzfMS2eUM2rOH5bePFpQ59q+qb8H3H57ElXXO41M8y3DMvV94/m5nRmkVZq7d7rtfp+JMEOLgUeNc9/PyULspGyVlzoG0wevfbsgcr//f2dyGB8Ytz5TN5gJ8baZ2zPyN7nLt9/ygPzvbhYHX9T47eQ0A5zzum9/QFCR7m6ht5CtzZqXF7b27odpqUPnu/V9x9yuv1aIl/MwB95nq0MyTE1f7HJnBrW6emVTq2NbQkmkbZmXXTdG2K8lbappcDTND7vqMu/30Rydzt9vhKYdAJuzZXN4//HcRAOALk8Jtvn+8dt5kMj6MKym3/Hb06M8dSeIA4J6PM0atrp0yBqI7P1zs2Jen9G5ziZoBrPfC/Pzw+sfW9hTmcTLAG0aa0oo6x29GdZmjJjoEDVR79uqa/rxqWx3Oe2IqAGD9QxcKHZ8UhJRnIroEwGMA9gNQAeAAAMsBHBFf0RRuyPL4yUapPnxke2gjy0lq+1ENSKFQhIQ3oAeAC5/8xrGtuqEVv3oz4yHq1TUzFLrj/UXpzz84bkD6s+GFNNPekbLI6dklI4cxhtvezcgyvEt2z4uZNR5eXZ4is3RLbdpDa6e1PYVrXpzlKu/Fb9Y6tnm9Y5paOzD0ns/T30cetY/l92mlVdzjLnmanx6HMYa//G+J6bv2/+mvS/HOnE3cY8yD9b49u1h+G3rPBIcsgH+dBl6K89rKjGL8/UP6WX5z8/66GTfMSsvAPTJGmmtN9+eUg/dMf95a24wqTkiu+byGUjRvQ3V6mzkTtJtX0mywALQ2bNB/t4xi41U3Xy2vcGw74q+ZtmFWmL9zd+a+mL3u9miJUx7+2nLfjM9rKvlGK57X2IxRF+0uXuv352UMW317atdtbl+7dcs8y+b7ZzzH5melVT/HLI6hiqc4AxkjkF5at8twnN/gxAcmpj9fe9Igy2/me3HoPrsBsCrUZg6922mYMN+/Ew7oC8DaHrp0KsK9nyzDy9Mzbcyol6e/zhhnrh6ulevTki3pbQf1z0TmGIpzPiIatj0GwEkAvmKMHUdEZ0JbrkqRx8jy9CZtqapCVzKTQtLaj0Kh2DXZVN2IRz9f6dieSjHuHDv7wL1rZ83z2tzWgffmZgbV+/fNeJJ/985Ch5xDbB4xs/fFPuA1+jk3xWvEP/gDScYYd/C8prIeFz45zbLt0mP3S3/mDYoNZqypsngmO/Q64p0H0OrlcJNyCgAnHphR9irr+AYBLwXMcS69fozwTttmAMBhf8mU4abTDkp/XrSpxhJ6nGIMXy7b5vA49uiS8fB5L1PEcNZjmSiAC4/eN/3Z7Zp4xhUAmLO+2vLdfFqzwcGc/OmkByeCB++e/vC5mRbZRO5l5G03t+Hfnj0EAHCE7V6bsXtWp6yqxFuzrAp5pyLteZphM6gYCv8iTliv2+04+zFnNAZjDNe/PDv9/dQhmnHDfH0pBhSBOZ5Rg//334xhy0jiZm5ffxpxGACn8c1QEs3t9/ZzvwMAuOqFb9Pb+nTv7Djnz04ZDAA4yqTcAkBrO3OU//RD+wPgT/G46l8zLd/Nnmv7PW7T+5sjbOcEgH98tcry/YaTDwBgNXjs2bOL41lhjFkUZ0Dr+xhj+PsXGZkH6JE4t761IL2tix7lciSnPPmEaMKwNsbYdgBFRFTEGJsE4NgYy6XwQN6cXv/EG4HkRJ7TK0uOhrS5wUmb85yUcGtJc6cNlONZoVAE4du12zF41Dic+gg/DJoXrstTINp15dE8eAYy77YrnpvhOObB8c6w2HZ9MWXeOYz+m+eJLa2oS899BYAj9uud/uym0G6rdYZtGt6yA+90V1pTKYZr/m31SN/46hxMW21VdIxoz1SKOeoFyCRbq6xr4YYYmz17dtyUu5b2Dkfo7k59bvNNNkXYUMTWVTXg0mesdZpizOFhBaxze3n12tjawTVW3PnhYtQ2tbmWe9GmGocXjTGGxtZ2/Oh5q6JjKF92WYbybN9ueNjt4bPVDa047C9WxbA9xTDkLmvZjx7YBwDwi1fnOMrtuB7G8PHCzdzEc4DWFuxzem94ebZjneH2VEprZ5zIB8aY5X7t3sOpZALAzLXbXadK2O9Pa3vKETacYgw/5Dy3AK/9MfzgWWsbuvt/SzB41Dgs3bLTJhc467HJlm19und2yKxtasP8jdZw6GIiVDe0os7mBR7z6TLHvkWk1ZV9igdjDLPWWQ0yxjPCa5+bdjRyQ6+b2zrwj6+sIfwTV1Q4ImM6UnzjnZ0U57lhDPhk0RbLNi1MvDq9RjUALL83/9JqiXqea4ioF4CpAN4kogoA4ukZFVKRPTdYFrLmrEqTI6mCpM3lLrBEaLLJt4QRCoUit/zY5OmxYw5nBYALj9rXofCec/he+Gp5BV6dud6hPALAN6ur8NrMDY7kVHPXV+NfUzPhwJcfPwAfzt+MSSsruV41AJiysgKfLHIOm3Y0tOKcx62Kl7Ems5fn9htOmPQb327EjsY2Vy8eY4xrUFhdUY9rX7IqOoYuYt//mhMH4a1ZG/Hc5DW44/zvOBTnu0YejrrmNotnz4ybx2nWuu246fV5ju0TlmxFe4pZ5qAC2pzey44bgDP/PtlxTFsH8wzrvdLmuevXqwuq6ltR19zmmnzNba5wR4o5lHdjuznM3WBLTTN+8Kzzvja1tnPvd3VDKzZVNzraSFtHKp38y6C+pd0RaVFV14KyHY3cUGs7ja0d+MvHS7m/ba1tdvWIO8vchnMed7azhpZ2h4JV08g3Stzz8RLH9QHWcHqDWeuqcbCtnd710WLuvHjeuT5ZVI4FHnPozUxYUu4w8BhGMzuXP2tV3l+ctg4vcsLpZ67d7th30spKR10N2L07V5H977wy/NfFWPXwhBX42yfO8vEMYmU7mjDsPuvz/AvOfPGzONEAvDwOD09Y4dj20rR1jike3bsUO/ZLOp6eZyI6hIhOAXApgEYAtwGYAGA7gN/EXzyFQrGroFRnhUIRhRmjzkp/NoezAsC4xeUWhffq4fvjG11hfmvWRmw0JWxa8rfzAWhZY3lZna+weRNX6l7jJyeuxuhPlnHLNvqTZXj8y1WO7cdxlDU3hcKcVOc5PZkVAHz2u1PTn+1htecN3Tv92T7w/sX3D+SW1YBXhrtGHu4qDwBenr4OR43mz/0dPGqcxeN07P67pz/zFGdAUzjcjCT2gf6he/cCAFxmU2Y/uOV7ljLMtnnujPnF9gRUvz9nSPqz2xxiu9Jm4BYubPfcGp5XIwmZwR4mjywvssLeBgF+1vUttc2uCersPPiZVdn5y0VD059FFWfAaZzo0klTNa7yMHbZ4SnOgFVxM+TyME+/8OM/M9YL7/unD5wJve4xGRyuHDZQWJb52RTBMKoZeCXZOk0P+3arR1E5YXj+2hNcf7MrzvmWKMzAL2z7HwDqGGMNjLEUY6ydMfYqgPEARsdeOkWsyM6SHZXEzZ2WI6aAkRy3rVAoFCGZd/c5vhmzzTx4+dEW5cDgvKF7WxKI+bH+oQtx3UkHcH9b+8BIjLrgMGFZi0ef53keNw7ftzd3+/qHLsQL1w9zPY7nBTMMBzwuP34AerrUjaGgltvCyUvvv8BV3v9+fYojGRcAvH7jcPzweL4SsuaBkdztK+8bgVXb+GG+JxywB3c7ALzys+9yt587dG9c63JfV4zhh5kac1rtvPd/J+NcF0Xprxc72yAALLjHvS1EgdeOeGVb9+BI3OhiXNmN0wY+uOVk7r7v/d/J3Hn+Qzlt9sphA7ntwa3tr7rPvW2Zcbtft597qGNbCecZ/PoPp3OPH/fb7zu2PXLFMY5tvDb2+o3D8dQ1xwmd3wi9N/PU1c5jDdY9OBL3X3akY/v8v5zr2Dbapf3xcAuxt3P+Ec729CSnvNNNxs58w095HswYK7FvZIzNBTA47EmJ6FEiWkFEJUT0ERHtbvrtTiIqJaKVRHS+afsIfVspEY0Ke+5CQN5cZUiVE1VQWlmVJSci0pX5Aq0feWH2UsQoFIpdjPUPXWhZAsXMWo6yte5BbRtPOXJTNo1jzBiK5lXf3Z9bpqIiws9PcSogPFm/O3sIduvGH5x6Kc5uyinvuu0yzdnEAeCfPz4Wvbp2siTWMuPmKevdrZNlnrbBR7/6nmUZLPv5Aadi8cAPjsKpQ/rjwcuPchyz7N7z08md7OXu2qkYC+9xKgfGeQZwDCtrHhiJM7+zFyZylKN/Xz8Me9oyegNavdrXnja4/LiBDm9ov15dMPzAvlxlZ/1DF+KyYwc4tk/+f2dw5X9wy8m4+fSDLdu+uO00x35T/3gmnrjKqcQt4ijkFxy5j8NTePXwQWmnyty7z3Ecs5hjYDnhgL5Y9Fer/IP698TwA/ti9p/Pduw/3hQtYfDIFcfgtZ8Pt2ybpNeFPbs7T8nksXj0eejWuZhr2PjNWYdYvs+9+xz05jyDB/XvhRFHWM+/6K/nOQwAbgaEM7+zl2PbqUP6WxJ9AZpCzDv/2FudSvrFx+zn2GZARJZkhwAw5rIjHRnqAeCnnP6Jx5oHRmLmKOt9XPfgSCy1tYX1D13ocMhN+eMZuMRW3hmjzuI+k/mCn/LczeO3KFf9JYAjGWNHA1gF4E4AIKKhAH4MbQmsEQCeJaJiIioG8AyACwAMBXC1vu8uiSyPcZpkiSm89ZAhy9iRrARvBvLkKO1ZoVCI8/1D+lmyLgNWxXTan85EkU3ZWjFmhKWvOemgvlzZb9x4YvrzmgdGOvqn5feOSHuo7b+Zl/yxK1Mlo88DEVm8Yeccvhdu071gdm/MDNt3syL91e2np5VTs7yV942wXLddGTNkPHFVJu/rrWcegkt1Ra7EpgStf+hCrLxvBEYcqWWdNrIyA1pW4pLR5zsUysuO3Q/HDdI8vqttCr7ZU9/ZpFwPP7AvrjlRW97GXm+r778APbo4PZ4zRp2VLvfuPazKwcr7MnUy+Y9nWH5b9+DItCJ+cP9ejusFtPtqLodhELFz9fBBGHbAHhiydy+LN/S0Q/tj7t2aQm+vH6Od8t57hnf/hesySm3J6PNwwgF9LZEMT19zHA7dezdLm/j39cMwaM8e+MFxA/HNHWemt3/9h9PRR/ccGksIAcBz156A4iLCkL20OjiwX0+L4aKfzShlnMvsSZynK9h9undOt/1fnXEwvv7DGQCAvXpbVQnj2s0GHmNbURFhjO41nf3ns3FgP21po2d/kqmLn50yOK1kGjKuP/kArH/oQot39ds7z04bpO4xRZkcN0jz1xERHr/ymPQ1GNdq1PFrPx+evt7nTffig1u+hz7dO4OI8MpPNePPx78+Jb2sk1Gm4YP7po83jBD/vn6Y5X69/NOMsc5QiNc8MBIDdu+OK4cNTO87566MEcPcrg3l9dqTBmHA7t1xzuEZRX3MpZnVhI1nbv1DF+LiY/bDXSMPt5TD6D+OHtgHX9x2Gr6982y8/NNhOHzf3lj/0IUoLiLL3OTS+y8AEaFn1054+5cn4affG2yRZ/58wJ7aPZz957PRq2snlIw+L1CUUBLxi02aQ0S/ZIz927yRiG4EwJ+gIgBjzDwh5lsAV+ifLwXwDmOsBcA6IioFYJihShlja/Xzv6Pvy59cpBAkWUs6SVtiSi1VlRVU7SgUilzyxi9OdGwjIoen1stz+85Nmrdo8KhxuPz4jBfw+0P6ceU0tLSjUzE5vEbrH7oQ23Y248QHJmLfPlZlYfX9F6CyrgX9d+uaVha7dS7mlmvA7t0x9tZTcMnT03HkgN7cQSbvODd5ADC4X098c8eZ2NnchiP2s4aA8o7pVFzk2G6+3tvPPRRXHD8Q3ToXWRSjJX87H0f+9XPM/8u5Fi9XZ13ehCXluPmN+Tj7cKsnzq3c6x4ciSWbd+LwfXfjerAP22c3R/2svv8CNLd1OLz4nYuLsGLMCJRW1OPIAc4w2KChwWseGInl5TtxxH69HQqwmyy/7Y2t7Vi2ZWdaAT3viH24x6x5YCTaUynLPeHtt3/fHtztD15+lMOz/+Xt/NBkN9mXHLOfw5sIWJU8PxlFRc5nFdAUPbepEADw14szSqFdRt+eXbgyef0CAFx+/EBcbpsicPPpBzs8/G7XcOZhezm2866rX6+u3OPPOswZzVFcRA4jWv/d+Mf37NrJtV1dd/JgXHfyYMd2XhQEr//Yp083R/l45zr54D1xsmmdcjODTB7wvXp385wWkk/4eZ5/D+BnRDSZiB7T/6YA+AWA30kqw88BGJkVBgDYZPqtTN/mtt0BEd1ERHOJaG5lpfci6rnC/IIOQ1fdEvqjAIkJeBhWtkuPiVYew6o04sh9fPb0xpi3ddZhzhCXIBjW7lMOds6dCcLJB2mdwbAD+J4JUc7QQ3bsg5agnK+HDR3Ur5fPnt4Y1s29e3sFlvjzQ70d9+R4A4Jw1TBnyGMY7GFVYTHue1R4c7rCICu0abeundJrLEbF7FmLwiF7RWvLBl5zGhUKERaPPg+P/PBo3/16du3kUJwN9u7dDQ//8Ci8eIM19LtzcRH22727xcvqxb59tGd+5FH7+uwpzv59e0R+B5kZtGcPh0exlz6Q54WHAsCII/fF3LvPSb8T/SAiHDWwj0NxfuuXmtHkac6c0c7FRa7h7906F3MV5zAUFxGOHNBHasRUjy6dMGyw/3ijuMhpvNkVuO+yI13nwuczC/5yLnfKQb4z5Y9n4BNOyHkhQCJeOiI6E4Ax+3wpY+xrgWO+AsAbzd7FGPtY3+cuAMMAXM4YY0T0DICZjLE39N9fgpacrAjA+YyxX+jbrwMwnDHmmfF72LBhbO5cZ5r1XNLU2oEunYq483aC0Njajm6dirkhREHldO9cHPkF0NDSjh5d5MhxS0iyq8vR1o3skCKnqa2DGwIXhFSKobk9upyOFENreyrycgUdKYa2jpTrfDRR2jtSaE+xyHLaOrS1J6PKaW1PgYFFHiwZSVu8MpSK0NLeAQJFltPc1oHiIhJWKLzkdCoi17mVsiGieYwx9yxMCl+S+G5OGrLeqQqFQrErkM13s9ColzE2CYBYrvvMMfzYDR0iugHARQDOZhkNvgyA2Q01EICxwrbb9rxC1npmURUW2XJkKJhKjjfG/BIZcmTc96IiOXLsc2miyCkuii6nU3ERZBj1OxcXIaLeDCC6sitbjiyPR1Sjgmw5CkWSkPXuUSgUCoVcsmOqt0FEIwD8CcAljLFG009jAfyYiLoS0YEAhgCYDWAOgCFEdCARdYGWVGxstsutUCgUCoVCoVAoFIpdE6Gwbekn1RKBdQWwXd/0LWPsZv23u6DNg24H8HvG2Gf69pHQ1p0uBvAyY+x+gfNUAtgg/wpipx+AKt+9FFFR9Rw/qo6zg6rn7NAPQE/GWP9cFySfUe9mhQ+qnuNH1XF2UPWcHbL6bs6J8qzwhojmqjl18aPqOX5UHWcHVc/ZQdXzro26/9lB1XP8qDrODqqes0O26zknYdsKhUKhUCgUCoVCoVDkE0p5VigUCoVCoVAoFAqFwgelPCeTF3JdgF0EVc/xo+o4O6h6zg6qnndt1P3PDqqe40fVcXZQ9ZwdslrPas6zQqFQKBQKhUKhUCgUPijPs0KhUCgUCoVCoVAoFD4o5TkmiOhlIqogoiWmbWOIqISIFhLRF0S0n+2Y7xJRBxFdoX8/gIjm6fsvJaKbOecZaz7Hrkbc9UxEXYjoBSJaRUQriOiH2bu65JCFer6aiBbr8iYQUb/sXV1ykFHPpu29iWgzET1t2naCXs+lRPQkEVH8V5Us4qxjIupBROP0vmIpET2UnatSiKLezdlBvZuzg3o3Zwf1bo6fvHo3M8bUXwx/AE4DcDyAJaZtvU2ffwvgedP3YgBfAxgP4Ap9WxcAXfXPvQCsB7Cf6ZjLAbxlPseu9hd3PQP4G4D79M9FAPrl+poLrZ4BdAJQYdQtgEcAjM71NedrPZt++6fePzxt2jYbwMkACMBnAC7I9TUXUh0D6AHgTP1zFwDf7Ip1nOS/uN8Z+jb1blbv5ryvZ6h3s9R6Nv2m3s1ZrmNIfjcrz3NMMMamAqi2bdtp+toTgHnC+W8AfACtozL2b2WMtehfu8IUKUBEvQDcDuA+uSXPL+KuZwA/B/Cgvl+KMbZLLnYfcz2T/tdTt7b2BrBF6gXkCTLqGdCs2AD2BvCFadu+0F5EM5n2BnkNwGVSLyAPiLOOGWONjLFJ+udWAPMBDJRZfkU01Ls5O6h3c3ZQ7+bsoN7N8ZNP7+ZOYQ9UhIOI7gdwPYBaAGfq2wYA+AGAswB817b//gDGATgEwB8ZY0bHNQbAYwAas1Py/EJGPRPR7vrPY4joDABrANzKGNuWlYvIA2S1ZyK6BcBiAA0AVgP4dZYuIS8IUs9EVAStb7gOwNkmMQMAlJm+l+nbFJBWx2Z5uwO4GJoFXJFw1Ls5O6h3c3ZQ7+bsoN7N8ZPEd7PyPGcZxthdjLH9AbwJ4FZ98z8A/Ikx1sHZfxNj7GhoHdoNRLQ3ER0L4BDG2EdZK3ieIaOeoRmXBgKYzhg7HsBMAH/PygXkCZLac2cAtwA4DlqoWAmAO7NyAXlCwHr+FYDxjLFNtu28OVRquQUdSXUMACCiTgDeBvAkY2xtXGVWyEO9m7ODejdnB/Vuzg7q3Rw/iXw3h433Vn9C8fuD4TLnCcABxm8A1kGbY7IeQD20EITLOMe8AuAKaJ3ZFn3/MgCtACbn+noLsJ4JmrW1SN++P4Club7eAqzn7wKYaNp+GrTOL+fXnI/1DO0Fs1HfXgVgJ4CHAOwLYIVJ1tUA/pXr6y2kOjbJeBnaVgnp6QAAIABJREFUyznn16r+5N9/zjHq3Zzdelbv5uzUs3o3S6xnt/cG1Ls59jo2yZDybs7ZOs96iMhrAPYBkALwAmPsn0TUF8C70CpwPYArGWM79PkW/wQwElo41E8ZY/O9ztGvXz82ePDg2K5BoVAoFLsW8+bNq2KM9Te+E9FPAQxjjN2qf78PwOEAfsQYS+WmlMlGvZsVCoVCIZNsvptzOee5HcAfGGPziWg3APOI6EsAP4Vm6XqIiEYBGAXgTwAuADBE/zsRwHP6f1cGDx6MuXPnxngJCoVCodiVIKINHr8NBHAXgBUA5uurjTzNGHsxS8XLC9S7WaFQKBQyyea7OWdznhlj5YbnmDFWB2A5tAnylwJ4Vd/tVWQyzl0K4DWm8S2A3fUMdYnhq2XbsKk6eo6QCUvKsW1nc2Q5nyzagu31Lf47+vDRgjLUNrVFksEYw3tzN6GxtT2ynHdmb0RLu2PKTiA6UgxvzdqI9o5ojqHW9hTenr0RqVS0CI6m1g68N2cTokaC7Gxuwwfzyvx39KG6oRVjF0VPqlmxsxmfLS6PLGdzTRO+XBY9F8yG7Q2YtLLCf0cfSivqMW119OSuK7buxLdrt0eWs2RzLeZtqPbf0Yf5G3dg0aaayHJmr6vGsi07/Xf0YUZpFVZvq4ssJ04YY/8xLNuMsTLGGDHGDmeMHav/KcVZUfCkUgxvS3g3KxQKhQzifDcnImEYEQ2GlpBgFoC9GWPlgKZgA9hL320AAPMEcG42OiK6iYjmEtHcysrKOIvt4BevzcUF//wmkoxUiuHmN+bjR8/PjCSnsq4Fv3l7AX75WjTr/qptdbjt3UX4w3uLIsn5dm017ni/BH8buyySnHGLyzHqw8X451erI8l5c9YG/PmjxfjPjPWR5DwzqRR3frgYHy/aHEnOA+OX444PSjA1okI26oMS/OG/i7Bkc20kOTe/Pg+/fXsBttZGM+Jc/e9vccub89HcFm1AddGT30RuywBw+qOT8bNX5kSWc87jU3DtS7Miyxnxj2/w4xe+jSznoqem4YfPReszAODyZ2fg0memR5Zz5b9mYuST0fpCALjmxVk494mpkeUoChvGGJ6auBqba5os26eXVmF6qbVPXb2tDu/NseaSqaxrwQ+fm4GKukx/19qewlmPTcZkAWNbbVMbOmwG1K21zfjrx0scBtpxJeWO/nBLTRPem8vNb4Oq+haHcTaVYtw+dfTYpRhvM1aur2rAS9PWWbZ1pBgmr6ywGGtLymrw6OcrHDInLCnHyq1OA9am6kaLMfzTxeW488PFeGpiqWW/irpm7GhotWybUVqFwaPGObbXNbehtKLeca7SijqHUs4YQ2u70/j91qyNDifGN6srsa6qIf29pb0D33twIr5eYTXIjl9cjvWm/QDNsF3TaC3nmsp6LNi4w7KttrFNyPC4uKzWYRCsbmjFJxxj9abqRjS1+r87O1IMK7ZajZWNre14dnKpo12u3laH2karM2Tl1jpMWGJtN7WNbbjj/UWWe7yuqgGvTLe2pSBs29nsKE97R8qxDQC217fgpWnrhB0K1Q2twuOMptYORxvhbXOjI8UiOV4q6pod9Q1o7T8s1Q2tvmVqbuvAF0u3hj6HF6UVdZGdSPlEzpVnfU3EDwD8nlnX83LsytnmuFOMsRcYY8MYY8P69+/POSRe6lsielb1/5t2RPNgt+kP0ZaaaMqP0XFH9YQ36PVSGdETXtesydle3+qzpzc1+sujpjGaR71af/nvbIp23yvrtHppiNh+tu3U5DRFVFa31GqDUN7gJAhlOzQ5UVMr7Ih4nxQKhTdENIKIVhJRqT5lyv57VyJ6V/99lm70BhENJqImIlqo/z2f7bKvq2rAY1+uwk02A9tPXpyFn7xoNXKd+8RU3PFBiWXbm7M2YN6GHXjj243pbeW1TVhb2YB7Pl5q2XfwqHF46LOMktnansIxf/sCf/l4iWW/UR+W4NWZGzB9TSayZO76avz6rfm491OrEfnqf3+LO94vcURmba5pwrD7vsKzk60K6b2fLsNhf5ngGCz/Z8Z6/OpNayqYq16YiTGfLrO8W56fsgY/fWUOJi7PGAYueXo6npm0BnZufmM+zv+H04B16iOTcN1Ls9Pfd+rRadU2RXP4/RNx3JgvLduem6Kdp8Rm5L32xVk45/Eplm01ja045/GpuPODxZbtr85Yj0Pv/gwVprFJU2sH/vzRYodB8rqXZuPMv09Of99S04wttc249xPrffjVm/Md57/46Wk49l5r+c9+bAp+8OwMy7afvPStw/A4aWUFbnh5tkUBvPjpaQ6D4M2vz8Nv3l7gGGed+sgk/OI1q7H39+8swBmPTrJse+rr1Rjxj2+wdEumPh/9fCUembASn5ZYlfJzn5iKS5+ZZtl2/j+m4uY3rO3mnxNX4725ZXhrVuaZuOK5GfjbJ8sshowrnpuBE2z398y/T8bv3llg2ba1thknPjART3y5yrL9kLs+w2Ucg+1t7y3CmE+XYaktgmnj9kYMHjUO823Gi+PHfIlr/u00RA8eNQ6jbM/7L16bg1Mfsdbhz/4z27Ft6qpKXPfSLIdSOPKf3+CQuz7jnus3b1uve9W2Oocz49oXZ+HmN6xOhVlrt+Oo0V9g0orMM1nb1IbBo8bhc5PC+/WKbRg8ahxKKzIGmOa2Dhw/5kvcMzbTV01eWYHBo8ZhTWXGGDV67FLc9Po8lJRljDwlZTUYPGqcI1Jse30L16D35qwNuPNDa32u3FqHcx6fiie/tjq1ymub8MD45Zb621zThGPv/QJrK51GsnxCWHkmou5E9B2ZJ9fT4H8A4E3G2If65m1GOLb+32hJZdAyKhoMRAEu1m5YCKIqG6QLYhGz3UuXE/HC0vUTtTyy5Mi6rrScSGIKtv0oFIr4IKJiAM9Ayy0yFMDVRDTUttuNAHYwxg4B8ASAh02/rTGFwt2clUKbMMZmUY2Gojw/JaNkGorE2IXW4QjPm7ZT9yyV2zzkhvHU3m8b+329wur9fnu2ptC0C3h6DIOzGcPDFtWYPW/DDv+dArCozBkxZTgkZq2zTkv5WPfUbtqRqcuUXoE7GsMb1+11yvOE81iy2en7+eWrczFlVaXvfTIiJnjG6uml1mk9/1u4Beu3W50rhsfbrHzX6/e9pc0p0348D6MuiTJ+K15bmrthB7bbIgjWVTXgY9vzYLTxyauckRyLOZFyxlTBVpuBaMpqLaKUNz1t/ka+5/8dW6SJvU4BLTrSzi1vzMM3q6vQaOtXVnpMJbJHEJz3xFRc9JTVWLGpWrvfKdMDb5T923WZshkKsrm/Gb94q2V/IOPkMkedfFqifTY/oxv0+15vuo+GYm6Pwrjlzfm44/0Shzf+ro+W4O3Z1vos150t9vq//d1FeGHqWswzGTo+WbQFNY1teHcOP9ImXxBSnonoYgALAUzQvx9LRGOjnFjPnv0SgOWMscdNP40FcIP++QYAH5u2X08aJwGoNcK7FU6I66jPoRw5YpInR46YxJG09qNQKGJlOIBSxthaxlgrgHeg5RkxY85H8j6As4lk9aTRoIw1NBocq6Of4c/41a0iohhWZdRuthZUSYJ51O9euO2fa+J4itLXFlE2N+QzixWXiA4mm3DqVrg9+9yXIE4MI6KjLUB4ur2vMwwfZNlHWFyiEfU8j4b2cq0BAMbYQmhLSUXhFADXATjLFO41EtqaZ+cS0WoA5+rfAWA8gLUASgH8G9pC2AofZDVUaXLkiCm460qah98gafWsUChiQSSnSHofxlg7gFoAe+q/HUhEC4hoChGdGndh7cjSnc1ajKjhz1jwpM425cbLrmAvZ9j+MchxvOJI7ZcNT6VEkYInNhXB6S3lkTSFTFbkmfl443PYa83UpWljDow5brtnc0iRjSV9vZos9+ymjZ7NndMmePfRfonpXQUsO2778NpQeoybtIcwIKJLVbUzxmplGpkZY9PgXn1nc/ZnAH4trQAJRdYjmlHGJMmJHE6cLE+mrLYsTU7i6kf7L88ooFAoEoxIThG3fcoBDGKMbSeiEwD8j4iOsOcwIaKbANwEAIMGDZJQZItsrTAytQ/3TRaChIq79c/GgFL0dRLktcMzyMYZL5CtWASvBivLUycL0fOENZ7zxiGZNhXuhhglKZI2VpIiJqs6V+4Da5znFy2R137mfsi1T9IbQJHACV2jbjh7ZIw6ua7baIh6npcQ0TUAioloCBE9BWCG30GK3JG4Zpm4Aim8kHW71G1XKPICkZwi6X2IqBOAPgCqGWMtjLHtAMAYmwdgDYBD7SeIM5lnVM8zbyAnOm7u8NGMeL+6HeKuXLvJ9r9ir8GqzFwUuTKQ8rytfi+enOtENox7I9X2E9HBZ4Tr5jqKzX06ROii5B28a+U9u7x7Zd7Pq8oc0TCG8UWgBbk52Yyp/rznLWnPYFBEleffADgCQAuAt6CFa/0+rkIpCpfIHZ5kT6YKI1coFArMATCEiA4koi4Afgwtz4gZcz6SKwB8zRhjRNRfTzgGIjoIwBBoU6yyTmTlI4RMt+VZgowN3c/BlxLGa5M9j7D4iaLNB+fFnhplSBZ+dR/13ggtRRMQIynUhwsyy3Dmol4d4S85KESuxlOiSqew8Y+jzLrVJ/NQfN3O71DoOVM5GGdbPiIUts0YawRwl/6nyCOSpowpZdUFWUYBSWH2BvKszkqdVyiSCmOsnYhuBfA5gGIALzPGlhLRvQDmMsbGQkvw+ToRlQKohqZgA8BpAO4lonYAHQBuZow5U9fGSNScEbEOyHkJgFzO5zeQlY3M0Mkgg20553NWCi9DtKeMGNQixljgcN+okRN8o0+wqQBu8JbzDNMe89HTmARjget+XG+02H4i1xXmHrvNmzaH/me7n4gLIeWZiL4E8CPGWI3+fQ8A7zDGzo+zcIoIpBtmMhJQpV8OSVmqKmEJupJXP4YlMRqy5CgUinhhjI2HlpjTvO0e0+dmAD/iHPcBtCUnc0Yu58+5hmB7JQByGWTKOreobKlh2yE8SrLnlGaSHPmcN8vtxe8+SZuzbz5nWnY0OblWcvJcxxJGfC4z1/UstF+YefBCnmcXpw1PUc7MrMjvOysatt3PUJwBgDG2A8Be8RRp10ZW5ylrDk3SEnQpskNGmZcjR6FQKOIijmzFwscEUUBlJU0KFBPOOyYZPbPssO2khYQGHfaEn7PPkSUpMRPvaDWci49IOoB5/j93TrT2X+T+BYnicE8Y5pw3HSQRWZIRVZ5TRJROj0lEB0A5kxQKhSBJCyNXKBSFh8zuQVRBkLK2agjvddIIUvVxGdMz3lbBsO0YXifcBGY+yDJWc/KFSfA884wU0WTKIRGFSARxrAcfaKkzl0jOlL6MH3+pqjzq3DiILlV1F4BpRDRF/34a9CUnFMlE2rIAsuTIEZPpyGV51AvMM58WIykxW2Qk94+M5X2fq1AoJBNnn+BnsHP71atIrplt3eY8e5bAGy/lXaYSlPFq+d+MMEZQt2MsCmOeJgyLWmD+Gt6yohi9z5NtchHum01jAX/ZsRByuGHbxm/m/fgEWT7PLdLVaw52AppSJEQThk0gouMBnATtmm9jjFXFWjKFFAotQZc03VBSgi5562DLkiN7jnFSMqpJFaNQKAqIqHNG+aGpot5Lf6VOvBxk+x6dJK+rGsVo7HVkLpU80aRNZkxBrdLLEdkwH4MBPG/IQTsy9ydBDHAi24IkkQvTbziMgroQS8Iw/X8SDDFREPU8A0BXaBk2OwEYSkRgjE2Np1iKqEhLQCU5sVZUEucJlyQnaag5zwqFIt+IOi7nzhP0OcZlpSpP7P1i2H5W5DCvwarMAWyYSwgyPhHyaHN9azxZwqeNhOh5MsafaOfj1WfUS+XOp06AGTuvlHABPNuKWaEWblMcMV6nsP0aRMl1i3jkzrEukBsnmm37YQBXAVgKQI9iBwOglGfJyG5WSfM8K/gkzbhgIK39SJKkDQ6USq5QKDJENfbx104Vw00BDOo1citHUNlBjonjvS422I6pD8/z6ZQ8L6EUYREw36skRC/k5N7GNP4VzcIe5ljvY0xyXc4RbJ1nl/NxQr8z5q3ct6UoiHqeLwPwHcZYS5yFUchD2hxaWXN63RZRD0nS1h+Wdl3S6ifa8fLmYMuxqBsoG45CobAjK0IqDH6eZ3Pf5z7I9MEtNDxsmHoM49YgZZE559myj/5f3PARuBgCMjMG3mwnDDO/t4PMWfWUydmWBGdKEsoQN9xIgoCGPu46z9w58o4tumz/BpRZktTmveaEfhfKOs+i2bbXAugcZ0EUcpHXGetyoolJnGc1aXKShuz2o1AoFHEha2lG3vF+MlMuOwQxQLotseQmI5RsSYmI/MjlGspJHZj7GXVkGX/Mypa0paoSVpc5KU5MJxVTYsWxzJcWDNt2NegF8Ty75P7hhX4bbTzfl6oS9Tw3AlhIRBMBpL3PjLHfxlIqRcGSlIRY0tbBluRZlW/sKEyz7K5gbVYoFMGIauSNEmkTLnwymMIdpdvjeWPjHLfGFbZtP4avHIh7y+LCfK/Ew/CjjiM4hhFJhgReXSZNoY6dLIw7+PXsEbYNFng/cIx0bpcWNIqDJyuTMMxRhPimbmQJUeV5rP6nyDMSF94sK3w3YWHA0T3zspRw2WHSssLR5VCoRgGFQhEeWcbHMLh5ng0sYdsBB4x+exdqb8gYCzW4FlUYkzZu9/JABimrZc40nIpSGGR7R/OJpDQTa3VHL1WQ5eRE9s30v/ywbXOZC6XpiC5V9WrcBVFoyOqUCjUrddLCrZPSucpGdv0kzfiiUCgKiBg6YtE+0K1P4s8VdZm7LHaqUGSrz8zV+q2WUGXkpgx+iN4DSwi6/l/kWmQp32HOsyuQK6N90LnMfqUMEradCvA8+61PbwnbTujUiqCIZtseAuBBAEMBdDO2M8YOiqlcCknImqscuevI8wdlV0N2WLtCoVDET8Q5o9xtPp5l398FzpsjxVMmshJUicILcw3iLTPvLxNrgjhCkDYZj5IWdc5zdubKByUbCm2+jF9cMiM4tgTpZ4KsCZ0+xk2GZZ/cT62QgWjCsFcAPAegHcCZAF4D8HpchUoaM9ZUYUZpFTrCLOiYa2RpvdLmKicsDDj65GkpctIe2mhipCd4S8p1KRQKhRuyp6uYZZrhKVtu4wL+usreA0ZXD47tFGGGnVLXdI7ZnR163WvB47KlFImOd3jTtqLWMc/rFwbZc+VljU12JcJnzLb97mHgcii+6fMI+Z655ze+FplkVO7U0matq6oXkJtcRJXn7oyxiQCIMbaBMTYawFnxFStZ/N/r83DNi7PwzerKXBclMEmZ05uWI23OsywlPCFy0spqwuaoJ2TpLNlyFApF4SAtQioEcdrUY1cUJHaooRKnSTu7FeGQ+5jOHxSv4oZV9JMWzRD4MgLkEshXomXWznz2qluu4m1qFa7HBgivdut/Uxzv9fgl5QCA9+aW+QtOMKLKczMRFQFYTUS3EtEPAOwVY7kSxX2XHQkAaGjpyHFJxEnc3GlpHlHDgy1JTuISdCVLTlRkZ/9WCcMUCoUdWbkVwixV5dfbWpaPCV4k/Qwuc6VDXm7U97rXfEsZXt2wdzEJ8ynDvKN4kV5h6sB6X4KFsLvB93AGL520MWlizAHxwJ2XTPzPzmOZ536e98D2Y11LuybH45D0uVxk8JZLy8cAXh6iyvPvAfQA8FsAJwC4FsD1cRUqaRw5oA8AoD2Viv1cSjnwpmATmEkqUb7M0VEoFApZRDWq8kOsxY6VORgU7r8ldfO5nmErOww46HzKuN+WwuXw2C2Q8mJClufZHHKb1fHFLjaWCeo9Fn12Lc8Hx7jk10bXVjb4nsOt/02CMSsuRJXnwYyxesZYGWPsZ4yxHwIYFGfBkkSxfufzcc5zVEu8LIt+AT47BY0sz7zsO18IoVoKhUIumfdUNDk847WfyJTPuCBWhSPA9cosB++0MvtmofGGhAzTcbxOIoXimkoUvS1rRJ7zbMmUHL5QssO2ZeD37BrEVRQ/sVyjnsf+zW0pof2sZfAuRVObf8RtoFtbIGM4UeX5TsFtBUlxUR4rzwmTk7wCJYN0JykpQVfi6idpYe1KC1coCoZM2Ku851p88OnzexSFQziPblj5MRAhO29U0gqjz37ZMuYLJwzzmP4VTOk0K9/uyaGCUMiOjw6fZzMpHlNuOzKVva1D0AgQ4txFASrBkTBM31BUlJFRKNG1nktVEdEFAEYCGEBET5p+6g0t83bWIaIRAP4JoBjAi4yxh+I+Z6fi/FWeoyLLWi1PjhQxap1nH5IW1q5QKBRuxDkP0k/3TbnuINHTmyPPVzBZuRkfycxQHRd+xeLlhAlSnyLZk0OT0LDtbN7pXLUqXr/GqxbDwdetM8cfyrwNKn59Z7CEYdaa4q0VndBHNDB+6zxvATAXwCUA5pm21wG4La5CuUFExQCeAXAugDIAc4hoLGNsWZznTYdt5+Fdl7bUUFQ5khJHyVrySlo4uiSPR8bxLKk8suTIaj/RxIBIK4u8bORKsVcoCo3Qc555A0jR0F+fk0YJcZbVDzvkRlTsef0wLzmQfzlc5Ic8NmjSsriHdKLrPPPGI2Hq00zU4w286jlW3G6OxPe2r1FD3qmEkd4mOc8Cdxk+l8OLRJRn16WqnMp6/mlRfDyVZ8bYIgCLiOhNxlhOPM02hgMoZYytBQAiegfApQDiVZ711vPponKhyfNjPnUvzoQlWzF0v94Y1LcH93ezd9tLzthFW/DdwXtg3z7dub836/MUmto6POV8OL8Mpx3aH/16deX+XtPYBgDYWN3oKee9OZtw7hF7Y48eXbi/b9iu1duc9Ttc5TAGvD17Iy45Zj/06sZvmiVlNQCA8Yu3esp549sNuGLYQHTvXMzdZ+oqbdmxD+dvRqdi/uyFjhTDG99uwDUnDkJnl30+WbQFAPD27E3YodeVndb2FN6evRHXnnRAui3Z+ViX8+qMDVi1jb/+XWNrBz6YX4ZrTzzAVfGbvFK7rhe/WYfppdu5+9Q1t+HTknJcPdw9bcHSLTsBAM9OLsV+u/Pb2I6GVny1fBt+NGx/VzkVddqafv/4ahV6dOHf04q6FsworcJlxw1wlWN0yo9MWIkunfj3YktNE+Zv3IGLjt7PVY7BmHHLXMORNmxvwPLyOow4ch9/OR7PRGlFPTZWN+Ksw/wXJvCSs7x8JyrrWnDaof0jySkpq0FDSwdOPnjPSHLmbtgBAnDCAXtEkjNjzXbs1rUTjhrYJ5KcKasqsXfvrjhsn96eMr47uK/QPVXkIbKsdCFw8zxHSUKWzwS5xii3y8so4Ru2HcN9MAy8oQ92/S2cyHRRJM55LrTm6+dkiLs7ieIMsCYCCyZHNHu3voewPGfYtvbfPNZKanRIUPzCtt9jjF0JYAEROa6YMXZ0bCXjMwDAJtP3MgAnmncgopsA3AQAgwbJyWm2W7fOGLJXLyzeXIvFm2t99393zibudsYYGlo7sLmmCb26+jn9/eWMX7w1kpwUY2hs7cDHC7e4yjE3dDc5ja3tSDFNEXWT09aRSWTgJqdeT43/7txNrnKaTckL/OS8NWujqxxjn7qWdl85r83c4Ctnc02Tr5z/zFjvKqe1XaufZeU7sbG60VPOy9PX+d73OeurUVLGb6uGnJem+cuZtLICnYr4yqoh5/WZG1wVWoNPFpW7/mbIeXv2Rt/5NR8t2Owr553ZG309D//1WGMw3Q5d7qcZr31kyymvbZYix619BZVTWsE38gSVs6x8Z2Q5pRX1WLTJu28uLiKlPBco0tZ55grwlprL2VxRI4zCjmMjRxL5yedO8RQ5q6D2nN5b/s2LljBMHukQ3YhycqYw+4VtS6isAtHjTHP9xeosiPFIxPPsej79P3G25Tt+mtfv9P8XxV0QQXwjSBhjLwB4AQCGDRsm5T516VSEL28/PbIcxhgOvHM8AGDJ384PLae+pR1H/vXzyHIqdjZj+AMT0a9XF8y9+9zQcpZuqcWFT07D4fv2xme/OzW0nMkrK/DTV+bg9EP749WfDw8t5725m3DH+yW44oSB+PuPjgkt55lJpXj085W45YyD8acRh4WWM3rsUvxnxnrcc9FQ/Pz7B4aW8+s352Pc4nI8dfVxuPgYf++qG1c+PxOz11fjnZtOwkkH+Xsh3Tjz75OxrqoBn/3+VBzcv1doOUf+9XPUt7Rj2p/OQt+e/MgFEQaPGgcAmPeXc9HNJeIgiJwoz5aSk39yFPlL1Gk4/Ky2YqNG9znPGnEM0JO8JKFIyWRUCXcwKOp5zrJK6Keky5oex084Jv9aIxkIRA92i+gIf+rAJOUpk9WHeLVD1yj5IAnD3GQSZ1ue4xe2bbiLigCUM8aaAYCIugPYO+ay8SgDYI4RHQhtXrYih8h7sCVRIA9n0pE3/1jdMIVCEZ6o6zwb+M1P5nZV8U/NjO2VFlav8vIMy9DVeIN8t0G8ZXkn/X91Q2v0QmSRzKVxrlvkeI+9Inuecxy3LXLfw5LEoYf5ukSNeqLrQPPmwfvdUqE5zy65fzLRD0kxQ8hDdKmq/wJImb536NuyzRwA/5+98w6Tokj/+Ld2yVkEUUAEAUVQMHCCWc+EoqJnOPVOzzvv55k99c7DHDDg6amnYsAznRkzJygqEgTJOecFlhyWZdnE7k79/ugw3dVV3TXT1TM9Q32eZ5+drq5+u7q6urveet96qzshpAshpAGAKwGMzEI58gxFkbCVSIlfJOywclR1KnjBF0LJCSfGJnZy8u89rdFEDiFkACFkGSFkJSFkMGd/Q0LIJ+b+aYSQzo5995rpywghGXcBCGu5470yZCMYB1mewxD0LovCUpkJVLyiuffHvB5R/BFRfpWko9Tx2m86LrZOrCmGa7YHx+nxg3v6EPWWqiU8ysF12XsVVRlCzfmXDTrIS0vhFsgovqI8yQB+8ufLFWTXJNRGAAAgAElEQVSV53qUUnsoz/ydvn9lmphBy24FMAbAEgAjKKWLMl2OfEF1ww4tJ7XpSj5iFCmZypReg/BKuCUnbKRUU07IC0s1smmwnFBibPJxlFOjiRLHShbnAegJ4CpCSE8m2/UASiil3QA8D+Bp89ieMAayewEYAOAVU17GiLJzFhSYJ3jOszdDqgoWe97Qa/cqGoB1pUWsiMsoMNLrKkcRMCzMsYoK5Lx6K9jrT0u3hpKpuq5UKaIqvvPBS4hlvi8RZFnmFknWEz6d8qRgefacz1rnmRfxO8e7abLK8zZCyEXWBiFkEIDt0RTJH0rpaErpYZTSrpTSJ7JRhnxBdn6QrJyw5Mvi6fsKcWs/Go0mbeyVLMzBcWslCyeDALxr/v4MwJnE6F0OAvAxpbSaUroGwEpTXsZQtYShE9n3W0rRthUP7IV2U1f48pUNWuTMK9yfSrEceRMJcbZ0ypEOfJd2/2PCBov3U0TCBHwC1LXZdOVE6ratOJ8K0rkuWYNROkHkUlrnmSm6n+Eox3XnwIBhFjcC+IAQ8jKMa14P4NrISpWnqBrFimuji8tIkjJlXit1GUXZRzom7VCjySECV7Jw5qGU1hJCSgHsb6ZPZY71rDsXxUoYSdnGf5XvbNk1SgPXeY4iYFiWj/dTEKN6/8r0n1J1oVdZVBJirSpe+01OrwpXyqAVLALhTl/IXOfI63Wh7q5lO95KRgKvceBPSREFaEslYBg751l8vjgHPZRBSnmmlK4C0J8Q0gwAoZSWRVssjQw53vaEqBtkiIccZW7SijonsZ2HErO57hrNPoSMM6Aoj5QjYRQrYQSeNNXj04icHaSw+c3NDUu2O/885AJcRXPCOsl1w2JYbQDc91PV977AYXpOp70Qwe+so+AeyoqI03VzX7aS3g6OUGTJXwEXJ2V5tjx/mHOWVhpTB3jPZZzqNB1kLc8ghAyEMaepkR3ZktLHIiqXRoLYzA1WNEKqzA3Y/B8XJTNugb5Urf+o/LqUzb3P9deyRpNxZFaysPIUE0LqAWgJYKfksZkh3YBhkq8MnnVGpDzHVUFTQTauTaj4Od22Y1DpXO+EgGOS0w7UE9ptO8ufU/Z7rrI40qtmKTyniJQDqQl+e+V6M6oe0Asq+oTl2/CbYzumdEzckZrzTAh5DcBvAdwGo+1eDuCQCMulyQCqA1CFJV/l5Ctxaz8ajSZtZFayGAngD+bvywD8RA2NZiSAK81o3F0AdAcwPUPlBuBwew3ZzeW/i4jv/ijfX3EMaCSC+vXMU5UlUae8704clGcXslXBdaNNHd7lh3Xbll0aKSoi9a6QnIueSWSXnVKFindI0Jx9bsCwHLc9ywYMO5FSei2MSJuPAjgB7pFmTRZQFeU4LNrSGyBHcWCt2NSzqkEK87+qV2luv5I1mswjWsmCEPKYI1jomwD2J4SsBHAXgMHmsYsAjACwGMB3AG6hlNZlsvwit0E/nJ1ybkdOUlYqCpvKdZWN9Owoi37RtqN6/4oDRyWpSzFgWBSEcY3mHSrlBu8bMMx/8CcV2fEarAlP1gdbAgfHxIe4DMqS15FOXyu1gGGCedN5GDFM1m270vxfQQhpD2AHgC7RFEmTaeK2rrImt4ibu3WMvu8aTc5AKR0NYDST9pDjdxUMrzPesU8AyNrqF+k885R6j+O5Qubz+yRd1SHyZakkSsa7L7JznqOA20wki8PznEhHCefVWxTLmmWylqNU2GWvI9s6Ng9evQTVVHL5Vfk6TS1gGJ9CztyBXH+tyirP3xBCWgF4BsBsGHX0RmSl0vhitfvYzH1V9GZJyomZDVKVhV+Zp4AqN+mYvL4UWyziNDqu0WiiJ8htkEdQXlkX7XSsV6q9dlIlCq+1tMqSpqdAmDxOMvWpCCqXraDwLM8ShfRTcFyW50BJqcnOdXj3paYugfqFhlNuJrsSfqfiBZJz7ecfFbJEqSBuvwBQyKnI0FHgs4xstO0h5s/PCSHfAGhEKS2NrlgaTfroOc8ajUaz7+AIYip9TIJSFDJdVndsHe/gMjdgWKCrMOH8kkPlN2hbWTXaNm+oTiCLbdWK7hRBZNHwbOMqgmRdEI7ukdpAkDh3rgcMY1FZHl6t1dZR1C8098egPcmQauAzXhWGudSge1LAszzHrF2lipTyTAhpBOBmACfDqONJhJBXTTcuTZaIXeOLmdttXNzR83XOs2riVh6NRpMbpGV5DrDg8N63YS3PabtJs+unpiGjcq+6aejK5loL3vncub8SH4i6XNF2UiDsZ9E951m1l6A8qr/vKq4l280lk+tlA+r6kCxB71+e5TnXkQ0Y9l8Yy1S9BOBlAEcAeC+qQmkyg7L1h83/oZXDkMfHVU7sSqT4AxqXpc40Gs2+STJgjfwxTqVXdl4nNy3C/m9Q5zqVc0dtsZu4YjsA4IUfV6g7kYMNuyoD8yRiYHp23pO9tXIRzNJpv67jfb6dKqcxxUEHUtuOOYHv4P9eyCT+9zX52+894ZQRVUyAIM+fAo6mGYOmFApZ5flwSun1lNJx5t8NAA6LsmCa3EG7Se+bJC3YquZgazQaTfYIehfxOofCdZ45aXHrMKp8985dvyuNAgQnB31fnNeQ1YBhPsWsqZOb8+wOGGbvDEWhbC9fAK/+8+aTzfUk4WWLzxVzyyJpCLMGoF4Zt8orIsJL5C5Vle2RiZDIPlZzCCH9rQ1CSD8Ak6MpkiYI60Wran3d8G1Y1VwnNQHM7LlXquTkmRu5KjnK3NHN/zn+LtVoNFkinY5YkLu1rFtolPpaoDKfpU59Jgc8remSB7Zo5Ern3fIwSw9FsexX/UKjkEFuq1zLcxrf16B1ntMLGMb/nW1U3C2eDHcbivaKnaeSfYXJ9rv8mvPUNTukzysV+d7Oy2dfjrbdD8C1hJB15nYnAEsIIQsAUEpp70hKp/EnZq1PXbTkmMlRNUihbLBDkZu91lY1Gs0+Cn/Os9fy53xv8125U+/Gi46prq1Dw3qF3vxM9nTe3LygPekS/Zxnf3daUbqs8uy3TnVoHHLqFRSgpq5Ouo3wcu2uqg0+0OfWRrFUVRhSDW7FktLSSYEDZJy0DKwVbj2KQXP0fes+xKCI00PDioVQJ4h8KHO/gqYdcAePcrz7Kas8D4i0FJq0yPG2JyS0kqlaTtg5vaoDfYUTo8xirBqty2s0mnSoqkk9GFaQa3BJxV4AwObdybiom0u9MVKDLM9O0Us3lwEApq7eyc27aVcVOrdpam9b11VT5+7YllTU+J+UVw7Hb8t9s6o2vSBiyqztEnLKTOVxy+5qV/rOcm8d1GVA8RFh9DfcF1Rp3r+g+rLur1PZqwkO425Tax3PK5crYJi0yOTxjt9Wu0tHzswio80v21KW+sEOxi3bCgCYuHxbYN6geudGz3dc3J7qGitjJDjn6FsKrWj5LAsVRXGe96WfVgIAflyyFfcP7OnJm8q9Fr2H01mTOu5IuW1TStdSStcCqIRx76iRbKdrchA7UJMi5TAs+SonX1HXfnRFazSa9Ll4mHcWWZBC7ewkvz7BmAO4aONuO+3fTOCrir21OOf5iR4501bv8KQBwA+LtwBwf0ce+2axb5ka1HN3yX47fCoAoGhHBf8Ah+x1ojzerHbZZqzxKvHOjvW5nOsFgB+XbPE9V1CZtpZVc9N58OocAJZsMu5VaWVSiX7+x+WefLx50DW1Rlp5dbKNVKQxAONkr63AihUy0bfOGkxxWplven+W9LnHLDLuxxbO4E6ZQ+ZGiaBrLKmuxyu6xge/XuRJK/UZBJpfbKyGy1pEv567EUByYMKPGUX8QSoLv+j5c9aVoKpGfgDDUuplKLesvY4CPD5qCQBggmNQ4Lx//wwAWLWt3E77cJrhAFxSvtdOm2Y+x3uqk/f64xnrARjvLZZazjPRs30Lblnd3jj8e/v3z+YDED/XPLdtKY+KGCOlPBNCLiKErACwBsAEAEUAvo2wXJoMoMyd2Pyvyp1Ykxsobz85Pxap0WiygWXRddLjwe88aVscVmRnP9Dq9DVwRFcau9TdGXYqWk6+mb/Jt2ys1Zjl67kbkmVypIuCXznzOzn1mXGeNOfyVLxI1Ed2aInte6rRefAoO23xpuQAgshKeI/ZWQ6CVyYgqfjurkoqTwtMZQkI7gs4B0Y6tW5i/165dY8n79uT1wjL5VQ2jnx4jCffVM7AyLilckqS8/5Ralheu9w72pNvzroS+3eto63MKCrx5A2ytjq9Fiw67tfY/n36s+M9+8cGDITsqrSszd67wivPiUN/8pXnpM9j33PTnxy9xP4tUmCd6SIl+Upz8MmJU5m0rsj1nJiJl7zyC1fmpa/y0//49gxPWpBhoJYTSE44UOaRnfx9+0dzhPm4Vl8zqdrhedK+ZTKmwOOOQT7nebrdz1f7pnMG4b6cU+w5n5O+h+wnLHMuIOu2PQRAfwA/UkqPIYScAeCq6Iql8UOVxc8idoGs8uy6kksxhZSjemmouNWz1p01Gk2KfDXHq0yKlizq9+RY+7f1Pi13KFB9OxsdOqdSZ+G0cAadx6mMdjCVlxGmJQgAbjmjKwBDWbrj47lceV3vSypa7Vo0tH8781u5RZ30Ix5KDiBYWf7w1nQ7rVPrJvh5hVsBshRK5zU44SlMPCv/CoHivX1P0jqVdMmuwoUvT/LkfWbMUvv3GYe3tX87B0YsqxarBO7YU41//bDcttT5Iao/nvL1x3e8ShKvrpz3j1KKwV8s4J7DqaRZCreozNc67p2F8362aGR06Xs57rulpLCDMUs27cb4Zdvw9HdLwVK0PWnpbGh6QzgVf6sPwSvPJo71++YP5K3oADB84mr799TVO3BStzYA3PX85CVH2b8vf22KR8ZpgoGbng8lB0ms++5+Tnjz7Q1q6hKYtdY7qCGyoPMGS5x1YVm5nUua3XhaV0++S4/tiDnrSvDfKUlH385tjEGjoGeva1tjQGXAC0kPjg6tjHfS4Q8kr/uojq0AAOt3VuA/k7wDTntrE9wBPVGU/Ts/mWf/tqr005nJd+D5Rx3EPS5XkFWeayilOwghBYSQAkrpOELI05GWTBOIKotfWJTN6VUmR5GymudKZuzajyJ5Go1m32D4xFV4crS383/ofe5O6+PfLMbhBzZ3pVnvnV4Oi+OhbZsBAHo/krSI/b5/JwDAWc9NsNOu6NvRc55BR7cHAOx0uFNa50kkKO75PGmtHTZuFf52zuEeS47VOWWVsQt6G7JZxXVzaRXaNmvoKkcv0/2SdTunoCivrnW5hbZq0sDl/gkYCq3TEue+FupSmM46oh0AvpX/bIG7dd/Hf7R/H3GQUVbnoAZgWHcv6tMewxxL6hx+oJH3uR/crtmz1+1Cr/Ytcf27M+20C3ofhKe/W4oRM4vBcui9ybo9sev+AICr35hmp3U7wGgDL41119+W3VVct+dqZt44pW7LKWBYM3lWcfY+W67f933pVbTv+Wyea7u2LoF6hQWu+1mvkGBPda3tFgwklVmnMt+pdRNc8NIkoXeD00J95fGduHlWbfNez9EcS3LF3lqMXrDZk/7Ht5PtqG3zhrj9ozkYOW+jtyyHHwDA68ZuKZ6PjHS7g38xuxhz1u3CWocF12qn7KAYpe7BM0OueDClu8Dy6rSgH9+lNQBwn6GauoSrLiz36cMeSMo9/ECj/TnztWvR0GMJb9WkAQD3s/enk7oAcD97hBBQSl3eObUJ6vGIuf2jObioT3uc8k/+oIOzjIAxXeWglo25U2bYdm3V598dHiv16xWAUoou947GNf0PwZCLj+SeN67IKs+7CCHNAEwE8AEhZCuA3HZYz2HUzelVaxENi6plN1QrdbGRo0aM8uvSSq9Go8kGPMV50ortru2DWzfmWlISlHqsU5RSLGcsprsra7Grwq1g1isssAMgWTRpUA+z1u7Epa+6rWAv/bQC45d5rbU8q1RtgrqsfslyGf9ZS9+gYZPRuL47OvcBzRsikaCe+b91CeoaKACAF35c7prnDQBlVTUuS5xF5d46T3q9AuKymllc8brXEgh4O9W1dQmu1faOj+e6LOyAofiUlO/Fi4xSu1+T+hg+0b1ubYvG9bGMceU/9bC2mLV2pyuI1PIte1BTl8AUh3v2EQe1QFlVDf7FKOmrtu1xKdnWNE6n9Q4w5rE6LaeAV8E+rF0zruK6tzbhUQYBI8AbOxCwtaza4yK9ZXc1Ln3VfY/bt2rkqeMeBzbHup1e92BKqasuAOP7zh4/f30p/vzf5GBF0waFWL+zArscFthe7VtgZ/leHDvkB895dlXsxTjHM3FQy0ZcxRkwjAWfzSrGtwvdCvi/x67AoKPb451fiuy0c3u1w10j5oHFmqPvHBSzy8k8EwlToXOydkeFZ4DH8nhg66Zx/ULUJSj3GWKV70SCchRN47l28sp477rMc9aV4NsF7ikjizaWugZTDHne65lfXModCOA9i1U1dbjrk7me9N9yvDIA7+AhAKzbWYF/M88tHOV6b+ra/FKeCSHdALQDMAhGsLA7AfwOwCEAbou8dBouyTnGWS1GZOTbkk6q5vSqX/IqlBjl6KWzNBpNWH7/5jTX9vqd/CBJ3y/e4rJOAcDrE1a75vwCwMh5Gz0d+w+nrfO41n40fR0+mu51t+Upziy9O7bE/OJSDP12KTcY11uT1+AtztxdwBs4adyybR7LOwD8+l8TPGms4gyAq3wA4CoD3y3a7LFIAfw5kLyOeVAANSe8OgeAT2asx8/MgAkvX2nFXs/AxvY91R4l4n/zNuJ/HEXutg/d80oTFHiLMyjjtKxbnPbMeNf28i17XJbg/ZrUR0lFDb6cs8EOlOXkJM484n9y3K1v/mC2J+2m971p3y/mz3P+ZdUO/O4/7ueH16adijNgBMBiLZaLNu7mKs4AcPRj7nTeNR9xUAss2bQbL47lDz5tK6vGUYwybAVO48ELwMazsn7imF5hMfhz7xz/ugTFL6u2e9InLN/murcWvPbPuw+i54/lBcazBDACh01b4x5gu44zFzsVLuO4xPvBu+d/58RI4AWQyyWCAoa9AKCMUlpOKU1QSmsppe8CGA3gkchLp8kIqpQWHfBpHyUmc8s1Gs2+yYJHzkn5mHs5c1BZxTkMx3Zq5Ukbffsp3LyW8iAbxTpTvPPHX3HTf7zrVCXyeco7y9/PPdx3P6s4i5jHUdBSYQfHopaK8u9HfTNQnVOJvOSYDr7HfDU3qeA3qi/uyldzPANEsIozYCjUmeaq4zvZQeVkBp9ETPz7GfZv1nItwmMhBbCa4w0CuN397xng306dtGxcHwDwzJhlvvku7NPek9awnlScZy4HNG/oSet/aOu05Vk89ZujuOnDrj5W6vjp950ZugyZJugudKaUeoYMKKUzAXSOpESajKHMDViVHB1uO8fQN0yj0WSf5o3qe9JWPHGeJ+2OM7tzj//p7tM8aauePN+TtujRcz1pyx/3ngcAerVv6UnjLQdTNHQgXvv9cVwZfzypsyft2hMOwW+O9SpWRUMHetL+ds5hXLn/ubYvN53Fmm/qpEFhAbod0NyT/trvvR3lNU956xBIBrZyUjR0IHp39NbZLWd086Tde14P3Hd+D0/6as4945WBdx+vO7GzJ43XLmRpUFiAq/vx5ws7GX37KRh6qVf5eP63R3vSfr7nDE8aALz1B+8gxwMDj/Ck8doIr84A4NnL+3jSmjUMnunJe+5E/HAnfxDmqd8cxVXInr28jz2v18lH/9ffk/bGtX3Raf8mnnQRVmCtIHh1CAA3n+5tp0MG9fKk9enYEq2aeN9XPF666hhP2ijOAByvTM9c1tuTdtPpXT1pH99wgidt/iPnYNI/vG1NdO1XcebFr3ryfJx/1IGedN7zeECLRp60uBOkPPtdUWOffb4QQp4hhCwlhMwnhHxJCGnl2HcvIWQlIWQZIeRcR/oAM20lIWRwuufOB9TNVYZSOWEF2aqYKjkhUT7nOU/rR5XHgfba1mg0qTDo6Pb4xwCvEjWw90GoX1jgWsYIAO48+zDMfvBsV9r4v52Ojvu5871/fT/P2qSTB/8aTRnl4dYzunnWZgaAQ/ZvgkcvcneceZ1GK23Ake5O5p1nHYbp952JBwf29Bzz2KAj8cTFbsXi2zv4Fu1bf93dowAWDR2Is3q2c6VNvfdMjPjLCZ58gBHMyclygXI04Eh39Nwvbj4RhBD04SjED1zgvq6lQwYA8FqqeAMTV/frhL+c1hU3nOpWBGbcfxYKmHv2/G/7eDzrrj+5i+c+AsAjF3kVnUPbNsMXN5/oSisaOtCOlu5MY63xy584z6PAsspHvQKCnu1b4AxmkOINzuDGcYfsh4NbN/EolUVDB+JEMxq1M+3PpxzqSuO1vzMOb4uCAuLxMFj2+ABcfLTb6rnqyfMx/2G3h0fR0IG4/LiO9vb/ndLFtqKz+XiKV/d23kEYKx+rkE36xxm47LiOeOhCd9u597weOMEM/ObkbKaNO+WzgydFQwd66nzNU+d78ll1+DBThpWCZ+KaEzp70r6+9WS8dZ27vodfcxw+v8n9/P3v1pM9xy569Fw7oJ0F7xm58lcH4zLHfQGMa7yWKQ/vniwdMgAtGtX3vBMXP2bUxbLHBwTKeOu6vigsIJ5nb9WT53vSRAp53AlSnmcQQv6PTSSEXA8gtdjzbn4AcCSltDeA5QDuNeX2BHAlgF4ABgB4hRBSSAgpBDAMwHkAegK4ysy7T6JqLq4tSdXSR2FLoziAWVyWdMrf+oESORZ6zrNGo0mFf195jG1NsVy337qur62Ejfvb6QCMOaVWJ6110wb28aNvPwWd2zR1Kcond2uDk7sbyojVeR5792n28i5rnjofRx/cCp/fdAL+ZroUT7/fcDt8/OIjcfrhbfHcFUejoIDg85tOxGHtmmG1o9O4dMgA/PPS3igaOtD1zpv38Dn49MYTUDR0IO44qzsOaNEIBQUEX99yEgBjDqh1DY0bJIOEzbj/LDtqtWVF7H9oazvvoW2b2VZES0kFgE9uMKx1E/5+Og5s2ciOEmylOeVbOK2zlmX+3vN62OfqYUY0f+33x+LYTsYSSV/fejKOPriVXXcf39Aflx/XEd/feSp6d2yJpUMGoJEZ9Ozg1k0w7OpjceWvDkbR0IH2wMSSx4xyf3xDf9cSRd/cZigY0+8701byFzxyDn7VeT+89vvjcMkxHV31cmaPA/Cgqbg7r8uyxD/iUIqsazq20362bEt5+vu5PTz5uh3Q3G5HVr4mDeph1O0n49nL+9j5ioYOxLGdWuHhC3tipVkuQghevybpfdDdVJCKhg7EV7echJ/uPg2f32Qo8Vcd38m20DuVtmFXH4vG9Qsx96Hk4JBlkZ1235l2WysaOhBn9jgAH/65H97+4/EADA+Da084BIChHDWsV4h6DiX4gz8bg0kFBQTj/nY6xvz1VPt6nrm8D96/vh8WPnou7jcHe6xyPfWbo1zKkXUff77nDDvdeQ2sIvXWdX3RvGE9zHv4HJcyZ1m37zu/B/5iLu0064Hk/XR6jVhKH2BYVAGgacN6djuwnolD2zbDX041BhwWPXouCCFo2rAehlx8JNq1aIg1TyWf4T8y1m+rroqGDkQBAfoc3Mp1vy2sdtG1bTNc0PsgdN6/CdY8dT7O6XUgjjukNX686zT0OLA51jx1Po4y7/F3fzUGxsbefZo96GO1+6VDBtjPiLMeh17aG4QQfHrjCa76KiwgWPnEefjmtpM95br9190w96Gz7WcRMO5T84b1sPixc9GkgXHuhvWMNvb6Nce5ZMx7+By0a9EQo28/Bb/u0c6Vbr0jrOejaOhAzLj/LKFnSi5A/CIlE0LaAfgSwF4kleW+ABoAuIRSKjeJwK8AhFwC4DJK6e8IIfcCAKX0KXPfGCTnVj9CKT3XTHflE9G3b186c+ZMvywZxwoaEGa0ZXdVDXo/8j2aN6yHBRz3I1lWbi3DWc9NRNe2TTH27tPTlvPLqu24+o1p6H9oa64LiCxfz92AOz6eiwv7tOe6q8jyn59X4/FRS/Cnk7p4RilT4alvl+D1CavxjwE9uO4ustw1Yi6+mL0Bz17exzMamArXvDkNP6/Yjnf/dDxOO6xt8AECBrwwEUs3l2H07adw3QhlOXbID9hZvhezHjgL+zfzzqWRRcUzoeVoOZkawSaEzKKUyvm9arjE7dtMKcXZz0/ELWd0tRUuTZIv5xTjzk/m4cbTumLweV5Lf64ybNxKPDNmGb694xR7ACKbfDhtHe77cgEWPXou1zqeaTL9bs01utw7CpTGp3729fuVyW+z79NJKd0C4ERCyBkArDjioyil3tB/6fMnAJ+YvzsAcMY/LzbTAGA9k96PJ4wQcgOAGwCgU6fg+SaZpmvbprjh1EODM/rQtEE9dGjVGP8I+RHruF8TtGnWEPdz5sakwpEdjDkcfz2LP79KlpO6tUGzhvXwf6d457SkwnlHHYTnfliOq44/OJScK/oejPemrMUFvcMt5v6nk7pgzMLNOPWwNsGZfbj9zO6Yt34Xju7oDUSTCoPP64G7R8xDlzZyc3xEPH7xkRjyzWI7+EW6PHNZbwwbtzKUDAB49KJe+JgTKTNV/jGgB8YqCNxz26+7hQ5SAxjtZ1MpP2JwKlz5q4NRUxfeaf/CPu2xn+ScLT/O7HEA120vVfof2hondQ33bGn2bQgh+PGu9Oe35juD+nTAzvIa/E5iDm8ucfPpXTHgyAPRtW2z4MwZ4Op+naTmSWeKJy85Cv+dUpTtYsSW+Q+fA8FS2VlhxF9OQJMGhcEZNaHxtTyHEkzIjwC8s8WB+ymlX5t57odhyf4NpZQSQoYBmEIpfd/c/yaMyN4FAM6llP7ZTL8GwPGUUt/lsuI2uq3RaDSa3EZbnsOjv80ajUajUUlsLM9hoJSe5befEPIHABcAOJMmNfhiAE5zYUcAVjx+UbpGo9FoNBqNRqPRaDSRErC7If0AACAASURBVJnl2fekhAwA8ByA0yil2xzpvQB8COB4AO0BjAXQHUaMpOUAzgSwAcAMAFdTSn1X2SaEbAOwNopr4NAGgNyCg5ow6HqOHl3HmUHXc2ZQXc+HUErTDzqg0d/m/ETXc/ToOs4Mup4zQ85+m7MVkeBlAA0B/GBGr5tKKb2RUrqIEDICwGIAtQBuoZTWAQAh5FYAYwAUAngrSHEGgEx2cAghM7UrX/Toeo4eXceZQddzZtD1HD/0tzn/0PUcPbqOM4Ou58yQy/WcFeWZUupdTTy57wkAT3DSR8OY/6zRaDQajUaj0Wg0Gk1GCVrnWaPRaDQajUaj0Wg0mn0erTyrY3i2C7CPoOs5enQdZwZdz5lB1/O+jb7/mUHXc/ToOs4Mup4zQ87Wc1YChmk0Go1Go9FoNBqNRpNLaMuzRqPRaDQajUaj0Wg0AWjlWaPRaDQajUaj0Wg0mgC08uyAEPIWIWQrIWShI20IIWQ+IWQuIeR7Qkh75phfEULqCCGXMektCCEbCCEvm9tNCCGjCCFLCSGLCCFDM3NV8SPKemb2jXSeY18j6nomhDQghAwnhCw32/Wl0V9V/MhAPV9FCFlgyvuOENIm+quKF6rq2Nyea/6NdKR3IYRMI4SsIIR8QghpkJkr08igv82ZQX+bM4P+NmcG/W2Onn3126yVZzfvABjApD1DKe1NKT0awDcAHrJ2EEIKATwNY/1pliEAJjBpz1JKewA4BsBJhJDzVBU8x3gH0dYzCCG/AbBHVYFzlHcQbT3fD2ArpfQwAD05+/cV3kFE9UwIqQfg3wDOoJT2BjAfwK1KS58bvAM1dVxJKT3a/LvIkf40gOcppd0BlAC4XvUFaELxDvS3ORO8A/1tzgTvQH+bM8E70N/mqHkH++C3WSvPDiilEwHsZNJ2OzabAnBGWLsNwOcAtjqPIYQcB6AdgO8dcioopePM33sBzAbQUWX5c4Uo69lMbwbgLgCPqyt17hF1PQP4E4CnTLkJSul2NSXPLSKuZ2L+NSWEEAAtAGxUVvgcQVUd8zDr9dcAPjOT3gVwcZjyatSiv82ZQX+bM4P+NmcG/W2Onn3126yVZwkIIU8QQtYD+B3MERRCSAcAlwB4jclbAOBfAP7uI68VgAsBjI2qzLmIwnoeYu6riLTAOYqKejbbMAAMIYTMJoR8SghpF3nhcwgV9UwprQFwE4AFMD7MPQG8GXnhc4RU6tikESFkJiFkKiHE+gjvD2AXpbTW3C4G0CHiomsUoL/NmUF/mzOD/jZnBv1tjp58/zZr5VkCSun9lNKDAXyApFvGCwD+QSmtY7LfDGA0pXQ9T5bp6vERgBcppaujKnMuoqKeCSFHA+hGKf0y8gLnKIracz0Y1pnJlNJjAUwB8GyExc45FLXn+jA+0McAaA/DNezeSAueQ6RYxwDQiVLaF8DVAF4ghHSFYT3wiI6kwBql6G9zZtDf5sygv82ZQX+boyfvv82U0qz8ATgYwDgASwAsAnCHmd4awA8AVpj/9zPTCYAXAayE0UiPjahcnQEsFOw7xNoHYA2AIvNvDwwXhIthNJR1Zvp2ALsBDHXIeAvGxzlrdR+Hv6jqGcbLbKOZXgxgL4Dx2b7ePKxnAqAcQIF5/MEAFmX7evOwnn8FYKxD1qkwPuRZv+Zcq2POMe8AuMxsy9sB1DPTTwAwJtvXq//U3n/RM+aQob/NEdYz9Lc5U/Wsv82ZqWf9bVZUx5xj3kHMv83ELFDGIYQcBOAgSulsQkhzALNgNNTrAOyklA4lhAyGoTz/gxByPgxf+fMB9APwb0ppP79ztGnThnbu3DnKy9BoNBrNPsSsWbO2U0rbZrscuYz+Nms0Go1GJZn8NtfLxEl4UEo3Adhk/i4jhCyB4cs+CMDpZrZ3AYwH8A8z/b/U0PanEkJaEUIOMuVw6dy5M2bOnBndRWg0Go1mn4IQsjbbZch19LdZo9FoNCrJ5Lc5FnOeCSGdYcwbmAagnaUQm/8PMLN1AOCccxCbieMWT3+3FFNW7Qglg1KKR/+3CHPWlYSSk0hQ3PflAizeuDs4sw+1dQn8/dN5WLUt3MoS1bV1uPOTuSguCRcnpHJvHW7/aA62lVWHkrO7qga3fjgbpRU1oeTs2FON2z6ag/Lq2uDMPmwqrcRfP56DqhreVBB5iraX4+4R81BblwglZ+nm3bj3i/lIJMJ5psxbvwsPf70QYT1cpq/ZiSdHLwklAwB+XrENz32/LLScHxdvwbBxK0PLGTV/E/7zc/jplV/MLsZ7U4pCy/lw2jqMmMGdEpoSb09eg6/nbggt57UJq/Ddws2h5Wjyk+8WbsJrE1a50ir2pv4uXr+zArPWugLGorikAlt3V6UsK5GgWL/T/Z3bXFqFPY5vxOgFm7BjT7hvWBA79lRj5dYye7suQX2/d2t3lGNBcam0/OrautDfGU1uUlK+N/Q3XaPJZbKuPJtLF3wO4K/UHd7ck5WT5nl6CSE3mBHbZm7btk1VMaV4dfwqXPXG1FAyEhR4e3IRLn31l1ByNu+uwofT1uH6d2eEkrNo4258OqsYd34yN5Scn5dvx5dzNuDhrxeFkvPV3A0YOW8j/hVSAXpnchG+mb8J/5kUTnF54ccV+N+8jfh8dnEoOY+OXIyv5m7EuKWB0ft9ufvTefh8djHmrt8VSs6f352Jj6avx4ZdlaHkXPH6FLw7ZS2qa8N1sq54fQqGTwyvZF7z5nS8+FN4pffP/52JZ8aEV8Jv+XA2Hh8VflDgrhHz8GDIZwsA7vtyAe75fH5oOY/+bzHu+DjcOwMAhn67FDe+Pyu0HE0whJABhJBlhJCV5pQpdn9DQsgn5v5p5qA3CCGdCSGVhJC55h8vkmok3Pj+bAz9dqm9/fmsYvR8aIytNCYSFJ0Hj8JT3yafsdLKGrw5aY2r83/KP8fh0lenuGSf/PQ4HP9kMuh2nSnr5Z9W2GlVNXW47NVfsHBDUul8dcIqnPLPcS7Ftf9TYzHo5UkAgJ3le3HzB7Nx/btJy/trE1ah8+BRqDGV0XHLtuKUf/6E6trkYOqVw6fgb5/Os7cHvTwJzzreQb+s2o53fymyt898bgLOem6ivT3km8Xo89j39uDCnupavDJ+pT1Aetoz43GhWUYARr05BixHzd+EWWuTg/qHP/AdBr6YzP/GxNWuAbOqmjqUVdW4jh85L7mSz9Bvl7oGPm7/aA4mLjf6bIs2luLFscl63rK7Ck99u8Qu6xezi3HS0J/s7RVbyjC/eJcr/5rt5fb2pBXbMc/xTfxyTjHGLEoOyi3dvBvTVicNHws3lNr3tKqmDl/OKbbby87yvfjnd0tRl0huO2XxmLt+F5ZsSnZv6xIUH09f5xp8eG/qWswscg/gbNxVaZ8HABZv3O0yiFBKMWnFdldb/nxWMUrK99rba7aXo3Kv0Y52V9Xg+Cd+dA0UbS6twootybb6zuQ16Dx4lF23G3dVoseD32LZZiNP0fZyHDPkB7w1uQgA8PXcDa7+anVtHb6Ynayvqpo6DJ+4yr6OmroErho+FTPMay2rqnFdo8X0NTvx6cz19jFfz91gy1y5tQybS42BrbU7yvHAVwtsGVNW7cDWsir7uCJHO5hfvAvT1ySvfdHGUuw222htXQLbHQNaq7btsftRtXUJdB48CsMnrrLP8fH0dcI6dN6DS16ZjL6P/2jv21Raad/3mrqEq53WJaj9DrDqcndVagaeKat22DJKK2vw2oRVdr0lEtQzIMgabEbMWO9rnFq5tQwrt4qNaRt2VWL0gqRDcF2C2m3HYvyyra66zkWyqjyb0eo+B/ABpfQLM3mLOR/amhdtaRPFMAIfWHQEZ001SulwSmlfSmnftm1zd1qaqjE9VYODyuSoERO/64pb/aiSE7P60Wg06iGEFAIYBuA8GEuuXEUI6clkux5ACaW0G4DnATzt2LeKUnq0+XdjRgrNYezSLQCAZZuNzl2d+eJ58+c1dp6Hvl6IId8sTtlLzOqQvuQYfJtfXIqZa0vw6P+SA1fTzM55cYl74HHVNqOTbHWcnQOTw0yZlWZH9uGvF2H9zkpbQQCAqat34rNZyUHaecWleNnh/XL1G9Pw8MhkOXYxVuZv5hvdpfJq4xxPjFqCf363DN8vFit+rzsGLG/5cLZnUH+ZQ2F4YvQS14DZxcMm46hHvncdf/tHc+zt1yascg18jJy3Ede+NR0AcOFLk/DcD8vtfXePmIfXJ6zGTFN5/8fn87FhVyVqTYXp7Ocn4qKXJ9v5+z05Fmc8O97e/v2b0zBoWHL/nZ/Mw1/eSw7KDXjhZ/x2eNLwccFLk3DBS8bAwLNjluHOT+Zh/DJDsX/wq4V4ZfwqjF9mdE2vf3cG/vLeLFtZmrOuBJ0Hj3IpyxcPm4zz/v2zvf3xjHUY/MUCvDkp2S4f/GohLnstOYCzqbQSJw79yWUoOP/Fn3H+i0k5n8xYj9+/OQ1fmYMWRdvLcfen83DrR7MBGIrLGc+Ox00fGNc6b/0ubC2rxvM/JAcm+j81Fmc/nxxksQZzrWdnzKLNqKpJ4CNTWVxnelVY13/Hx3NdgyrPfb8cd42Yh7FLjP0v/LgCT45eii/nGGUsLqnElNU78PdP56Gqpg5HPfK9/fy8P3UtppqDGFe8PgV//8wYyH1l3Crc8fFcfGt6IZ313ET0f8oY2Lrtozl4f+o6LDAHO656YyoGmW3h4ZGLcPqz421F7aKXJ+OK15N1PPDFSbj2zel23r6P/2h7EZ75rwm42GwzVebg/79/XGGfY/AXC7h1yN6DOet22effsacaJzz1E54wB6WeGLUEZzw7HptKjXfBdW9PR/f7v7XlXjV8Kno7nqH/zduI/04psretgQ6rzHPX78JVb0y1B9Ue+nohhn67FD+vMJb5fnXCKhz/5Fis22Hcw5+WbkGPB7+zBwmKSypwz+fzcZNjwPqSVybjiVGL7e2znpuIs56bYG8/9/0ydB48yh7ou2TYZNz8wWx7/4tjV+DcFybaz0NNXQLXvT0Dv//PNOQy0sozIaQxIeRwVSc2F79+E8ASSulzjl0jAfzB/P0HAF870q8lBv0BlPrNd85VLPN6WGWDmIJoSDVKuZyQF2bXT9jyqJKj6rpsOaHE5G370Wg0kXI8gJWU0tWU0r0APoYRZ8TJIBhxSADgMwBnmt/x2OH3vrGUyuosuxxna0DRqhurwx3WK0jEUsbalAqsIXKvWcZsuApvMa1wlgXQstRZRbFc9C1F3ppmMmG52POxtNKQVeLjRm9Z/yzFh4elyG7cZQyyVJkKjHWsVV9+MlSzlakvy/ugkjMlrbrGuK9fmYr1A18txJXDvd6bm01raUnFXs8+C+eLaJM56PTLyu1mGcRTOSzF0bpvvHKmwl7zvbJ1t9eyat1vq21YAwUl5UY6e59mr3N7EN720Rw85PAw+485+LLTHLjZbta9ZRm2rtsa+LPOu9FU1icuN85nTROtqTPai9MqPGfdLrzhGHxkedv0dqky7+VWxmpt1a91DxNmm1y9rRy5jJTyTAi5EMBcAN+Z20cTQkaGPPdJAK4B8GuHu9f5MELAn00IWQHgbHMbAEYDWA1jqao3YKy9phFAzFdJeGVMkRxFXazYyVEjJobXpbb9aDSaWCMTU8TOQymtBVAKYH9zXxdCyBxCyARCyCm8E2RiSpXofaNS5eLJcr4niU8+VwYfOZZStCdkLA2/E6sasM0E7GBINstsnZr91lpltPdLyRJfiHWNft909mj7GObs7KCDzGC2dQhb17LtxrPfUwb+75RkpiEj6Hju/NA0Ghz/CCo8h0qsc1vl9rRVyuwXHK8RIxtt+xEYI9PjAYBSOtea75QulNJJELehMzn5KYBbwpxTk33y1Z1Ynbu1GklxC+YRs+JoNBo3MjFFRHk2AehEKd1BCDkOwFeEkF5sDBNK6XAAwwGgb9++GXkj+HZSFZSAp9iQQO2ZJ4ifvHV3NXq1T7VUqZ0yF7yCkoqkOagLazv6c7On+MmMS7LJdqnnK6p+ZbOVWwll0O8SWQWb3fbUl4Ta5h0UYGRaA+uCwrNnYOUR4YZ8mVIVIdMfStj3LVyj8jva8qgoYM4R9hkUDbSw950tW4J5rtK6colBHme+fOkLyrpt11JK5cMwatJGVbsiwe/m1OTExAKpTI6ir64yObGrH+O/svYTUo5Go4kUmZgidh5CSD0ALQHspJRWU0p3AACldBaAVQAOi7zEPvh9r0K/sjmynUnySmmAoKjIwZexSFnOpl+TM0icE5H114lMG0zP2uk+Jkz92JZ0phzSlueAbVX4yU2nf8Y7QlU/TzS4kS7pHp70kvC3TMvJMssSlDHPnBBlleeFhJCrARQSQroTQl4CEC4ctCZSYtdOFbuIqbP0KpKTZ5ZwZe7oiuRoNJpImQGgOyGkCyGkAYArYcQZceKMR3IZgJ8opZQQ0tYMOAZCyKEAusOYYpV5BC8cFZ44XCszN5//lBeeUiV8T0bxAmWtkjFWpj2KmyA9M2Vxb3vag7k/QQX7nce6D+GfT3giZx7L7dZ9L21LoqDf5Tu4JGh0rPVSKMMzwCGYSpGG33aqdz2VduLnJp/WQAbnGPZ+sedOF++0Bmvgw0ywn3m2jK7daSnj0nUja6HOEWSV59sA9AJQDeBDGHOd/hpVoTTqiIsypow8efByhbi5f2s0GvWYc5hvBTAGwBIAIyiliwghjxFCLjKzvQlgf0LISgB3AbCWszoVwHxCyDwYgcRupJS619zJMLnw1uK9WjPpQh2oBMUI75xdg0zEqxOdglVQLWTuoUygUVax4WfilzGpDKVfP4GDBYHHC9y6Cf+3H37ZVLUA223b12Mgdbd31zkS7jxhy86WVey2zUc0QJPKeyjVZzEX3jcySM15ppRWALjf/NNo0kZbegWocm9WbOFXhVbCNZp4QykdDSMwpzPtIcfvKgCXc477HMaSk1knE2OrvI6l8/0WpJTmi+Ulk8TBbTvIJdray1p/w5zRkBOUw9vmgk7tuz/Ihdfuq4jmPEtYmpnjpXsHKfYjUroHEv7HKVmyuWnR9IO8dWulC+ZxM/OP7cGWEE+U53mg1H3ePHvvyUbb/oEQ0sqxvR8hZEx0xdKExm6o4R5W5UtMxWWpqoAPQMpy8q5+TDewUFLUydFoNBpZZN6jqb4jue7WPFduhXEeIh10jFE8CtF1eiM+Z97VXNTnD7Ie+uunwdchY3lmg5OJ5rCKZMuQdDW2yiN3D0QDHukQ1Rxx1/E+50ptIMTHzV7ZwIolhz2zyOVetN8oEBvALBWkq30fddtuQym1FxyjlJYAOCCaIu3bqPpYxm2pIR2gK7dQ5dKn2jVQW7A1Go0IKbfKkOfgu1uHQ1TuaHVnq4+QO+9Uj7KQxc+vJ2KyregHK7CpDLD4tWnRXGahEpXWnFb3sUFlF53DO39dvjDh5wTLnCNYkZQrRnCuAsEcZFWIlhmz95v/WTdy9nipc6UYdCyHXje+yCrPCUJIJ2uDEHII4jFgqREQv2jbmpxCkeeCvvEajSbbRNdZ4b3g1CmlUSqHubQSQhzLKAqyJOH9K4WUwmefy+3hFUW78SxnFFqhpUq7B2ENK373TZURICExsKISVrEVzc8XtWWpcwQM2Niwu3O8byi7zvP9ACYRQiaY26cCuCGaImk0EiibPK1ITkiUu/HE5LpUQ2n+uP1oNJr8I1eUUlVKUJTYRaPuTr5FJgKGiRBFK0/FNTes23ayLNYx/HpK5byevPJZfUkq397SBQ00hXXblqlDdcvbSbhtR6w5Brlzs5bpMI+RaMDGU51sQozfOzLIBgz7jhByLID+MFrGnZTS7ZGWTJOXKHMDDlmOuFrmVbnZx+69pMptW40YjUajySr6XZYkqBOf7KBnT1mWX5HHcv8V50nGAhELlXH/FgWKClNPnlmxbFApibLz8ERMp6mXk3tGmUEKKdnRW4Uty3NxSYVSuR69VOC2LfaSiO6i89XYIWt5BoCGAHaax/QkhIBSOjGaYmnCklTGwn2iVQfWCosyOWrE5LrniRDVc541Go0mW2QjwJR4nWcxqazHmzrMnFNF33YVyLo6Z+N7IlLwhK6winynZRSbpMLntoJHorCwc56Dmo3Acp5O2XyVu5AWZ1aMTH3LCfQmbSqtAgCUVNRwz50qnpIK2qLIXVu4P5XyCOc2U14Jk+R451BKeSaEPA3gtwAWATCnmIMC0MqzYlS7yIS2rMYs8Fi+ErvBBeXRttW07MAXskaj2WfxWM188qb7TeMd5pSVfAf7n8C1vJXgldalTdOUypYKqr7tUWK97yNVCqXL4t72BAyzFBJz26+oMoPTVEKQ93h/t+10lEnREl2ioovO4fssplCuqPAb80jlGfFd55kNmhZRe6b2f//2wA6+pHeuzM7jjguylueLARxOKa2OsjCa/EetEhUjOaquS1n9KBGjjNisp63RaPIeP3dFla6/3GVtApRS3+jJ5v9OrZtg3c6KUEvI8EqWqySVgMxfQ5AiKppPKhNt248gxYdXFhVLIYmskNZ1yi7PGaSYUZp6i+SeUtUzInHfws4VT4istCF7NqK57oEDMALvgNTObcnI3fdLOshG214NoH6UBdGoRZnbbdzmBse1PHGZ86zKzV55edQQt0EBjUYTH8TL5Dh/q+moutI4ZQhzlkwEHYtTYLNcWi7LQqxkBSv6qUyrk9ENU72XMv0Dj8U5KCCUIJ+dX8bKnqJMlfgN0KTSOv2nZbBW/HAXJh6Y4J/HM9BjywlVDK6M3HuiU0PW8lwBYC4hZCwA2/pMKb09klJpQpOvc4Pjtj5zNgOYREnc2o9Go9HIkql5vKEsNj7yMvHeVDXwqxL2e2q7n8agjGwRPGWl7v/+lmeJaVEyc3A5QbgAn2BlMoq4IBNhfgXdE1Wuzm6ZYqGpNhFWVkLivsWgGXJJKsH850d4HBOULq2lqlI/JC+QVZ5Hmn+aHCN27s1xsfSqsmBDkRzBshcpy1E8jy0OwWScxK08Go0mPki5uEY84Bn0jfK1TCkvjR9q41FESVbnPAvOGRQwLGxRZayCrFdu0n1XZP5NoxwpKlhRuO+GrstUFHlfQdGUQ1nAMI9gM5+gjdoDBp6pB/IFCnQZl0zPNWSXqno36oJoDPKlYWk0TuIy+KLRaDRh4L6CnMG/0lBKhVMSI3zhxcnyHGghy0gpUkPkAptglE3usRJ1LzNgwM5xtsVFOMggazBg94e5hzLHci85hXrwtWrHLDZOqucLmPLsGBgJM1c+OSc+Du+UqJGNtt0dwFMAegJoZKVTSg+NqFwaRSibGxy2IIrm4lrEbQ5t3AJihbeoK7KEK4rardFoNLLIvLdUvLOjsmJn+72ZrTnIQdarOM2SshUOJj1pDZaY8+xzh2WCpLGRjhMCK6DnxCngDRiW2qhLptyg+YNaPvmZfZYVtqo2gWaF/HBQqfRfubERMvxYBQWQEz3nqRST9X5Ipud3r082YNjbAF4FUAvgDAD/BfBeVIWKG6+OX4XnfliOsqqa4MxxQ53WG1JKjL56KonZ3GDVS17pdZ41Gk2uIJqH6sqTpmx/Rcd7gjDvzozMeTb/84qZaTfp4Pmz8euIi+YVyywxJUMqA0DsqVTct+RSVXyZ6d6R9NZ5Tm9fOmzdXSXcJ3NPfKPpZ3gwyGtZFu23AoqlcQ7mXSG2clPX/1xHVnluTCkdC4BQStdSSh8B8OvoihUvho1biRfHrsDklTuyXZSUiYvlWd1cZcsiGk6QqvWH03HREwgy5MRtjnrY+lHsGhjDPpRGo4kZ2XxPSLu1cjKIAkBFgd2RjtFLNRvzaWWRjZQstcSURIwTmTnPrDyZIGNB5xXNj7X3B8hIRSFMtZ+ScktNR1HnXEBcPAKFcgWDKBYi7wjrhzDAXITkumFFVnmuIoQUAFhBCLmVEHIJgAMiLFes+PLmEwEAtYlElksiT4y+hy7i9hKKi1KXVMJDyslzN+l8GbXUaDTRESf3Xhbu2tBsYpbLr8h4GhrbWpVhS7gTYfTpgImkfhZImQEWGcVSNK9YdOpUBh+o/d89GCAfGZvdZgeG5L/lUUyPEJ3d70zpuDO70hS4SbsIqJagOk66+Yev3yCvH1GMgFxFVnn+K4AmAG4HcByA3wO4NqpCxY1Cc1imTrTCuUK0cuCPMvdmVXKy3bvQaDQajYsoB4+dsnmv/3S8o7Lhluw3YJuMsByvD1w2i8PWk1dZoK58YYvKutRy8zAWRxWDDOL50pZrr7+3nXccKA5tKHMefako3+pqhu+xEjSYkslBKXYgLNeRVZ47U0r3UEqLKaV/pJReCqBTlAWLE5lUnlUT2r1ZsRxNbqBq6SwEfGhTJV9evBqNRj2pfGeiepXIlkHum6qylIzbsc+UmkxbnoOCY8kEz4oKsRXX2s9XosNG2wajGPtl8gYMU6iWCebpBs5Tl2i7qbbubH3/lZ1XICfd/nXQXPek2NSmGGSinnNdJ5BVnu+VTIscQsgAQsgyQshKQsjgTJzTUp5rc1F5Dnm8KjfgZITGsHKgVk5IlFuw1UydVicnJKquy0Kdu37uPcsajUYOv6c7CiuLyxodoFzwFBtWAcvEdGS/gfGsBQwTdP6zus6zhUCJtHeb+yevMmLjTFy+TSgq2QYkLJk+18xGwpY5RpYgS7q4bfqfPCpvBv9nhTOPWXCAquLxB6UCGpEiksG5+Kex7y3zXKkoTty8VaLCd6kqQsh5AM4H0IEQ8qJjVwsYkbczCiGkEMAwAGcDKAYwgxAyklK6OMrz5rblOWZylC1VlTk3nIzKiV39KBETO6WXUu1yr9HkHRl+prnzl83/Qe9grru0LTeKC2EVrNyJj5HNMoqDMPH3bCurBgDMXFsSKNs/YBjfKsgtS4q2Cf/BJf6oQKrRtlNZp1qW9NuBqn5VMH7XG5UKIRpEsRB6SXhyWOnhCyoKYpYLyudxyAAAIABJREFU7xsZgtZ53ghgJoCLAMxypJcBuDOqQvlwPICVlNLVAEAI+RjAIABaeY6IuM0NVuWOFL/rUiRHVXlieF3aWKzRaFIhUzFEUjqPj8IdF7LpJu1HNkojurOBkcFDum0HrdHLO9522xYck1IkbPZYe51n/+OC5teK5KcjMyiPsvaSQmH97mlCsDPdt5R4/Wb/wrC7rWjbSgKHCc6Vb/gqz5TSeQDmEUI+oJRm3NLMoQOA9Y7tYgD9oj5pvQLDu/2VcSvxyYz1AbmBC1+aJNy3YEMpAOCoDi25+50PVxg5lot5ZU1doJx6BQRHHNSCu7+ypg4AsG5nhVAOBcXCDbvRtEEhDm3bjJtna5mxdt6MohKhnNoExZJNu9G6aQN0aNWYm2f1tj0AgNELNgvl7K1NYNmWMhzYohHaNm/IzWPV37tT1mL2ul3cPOV7a7F6Wzk6tW6Clo3r+8p59vvlGLNoCzdPScVeFJdUomvbpmjSgP/IWXLu/WIB3p+6jptny+4qbC2rRo8Dm6N+IX/GhSXnrx/PxSH7N+XmWV9SgV0VNeh5UAt7YIhl0cbdAIAb35+F1k0bcPOs3LoHlTV1OLJDC+FLd6s5Cv+Ht6YLy7xoYykSVNyWgeRL/revTxWWOeiZcHLRsEnCMqciJ8wzquVkVs4FvQ/CX07rGnguTe6TqWkZfi7YwiKk0hFPo0ypwl8yy/wRE91ZZh5xprEVDkGZCiSibfsRtPwQwLFOS82TTg2REi37jHnm5foJD0EmrNhSa2+b/3m3P2r7W9A6zixbzDWtt+2pdh+noJz5Hvw4yG17BKX0CgBzCCGemqCU9o6sZIIicdJc5SKE3ADgBgDo1ElNTLP9mtTH7/p1wqZS8eLpFq2bNhAqbM6XjSgPYCgufoqfc+61n5wlm3bjkP2bCPNU19bZ8vzkrNy6B90PaCbMs6N8LwCgfG+dME+bZg2wZfc29DyohTBP0fZyAMDO8r04+uBW3DytmzbAhOXb0OfgVthfoNTNLzaU4c27q9CzPX9Q4JTubfDziu34Vef90LwRXzFetrIMgDFw8Ose/JXZ+h/aGlNX78SJXfdHo/qF3DxW535zaRX6Hbo/N89xh+yHWWtLcFK3NoGK8a6KGuF19e7YEvOLS3HsIfsJP96WnNpEAge2bMLN0+PA5li6uQyHtWsmfPFachrVK0QLweDCoW2bYvW2crQXDIYAyQ9Ky8b10aAe/9o77tcYxSWVOLBlI6Eci9ZNGwgV7AOaN8TWsmoc0DxYjt8z0apJfeyqqPHNIyOncf1CVNaInxtZOYUFBHUBz7GMnDjmqVdAlMhp1ijI0UqT68TBUqpivnKkc54Zd1pfF94ITu+H6Hwi99JMIHTbDtDk/fYm615c+35KmJ3Hcy8lBxnSsKSGnRfLXquqtq16QCXsO8Tq5/P6X5mOtSIagLG2rT7ciBnr8bt+h0QyOOWdK++ej52rBPUm7jD/XxB1QSQpBnCwY7sjDNdyG0rpcADDAaBv375K7g8hBE9cclRoOZRSdLl3NADgret+lbacPdW1OPLhMaHlbN1dheOfHIs2zRqEkrNoYykGvjgJRxzUIpSc8cu24rq3Z+C0w9qGkjNi5nrc89l8XHZcRzx7eZ+05QwbtxLPjFmGm07vin8M6JG2nEdGLsI7vxTh7nMOx59O7pK2nFs+mI1RCzbh/oFH4MI+7dOWc8VrUzC9aCceG3Qk+guUeRnOeHY81mwvx9OX9UZXgceBDEc+PAZ7qmvx4lXHCK3cMnQePAoA8Po1xwkHMlKRE6YNajm5J0eTP8h8+FV2ZFMSlaYLaibJWsAwYYaMFCOgCAFz2Jnd/pbnAO8EyLXPpIOAW56K2+Zdp9d/vwjP0lWO7VyxTqZSTj+njQRjelb1eImdXCTnqwvnQodHOO1B4TmyQZDb9ibzZwGATZTSKgAghDQG0C7isvGYAaA7IaQLgA0ArgRwdRbKoXEQt6BScXsf528wsXjJ0Wg0+zZyyl563Tbea4p7PtngTX7u0pHAdtxTc7/NBOKldvj7M0E6btnGgeHOm5K1nZlDXV2bkMovs4tVwGSs5sZ+d5kg2E6FrA14KZKr2m1btonZ89UF+5NzntURo9dKJMguVfUpAOfTWGemZRRz3vWtAMYAWAJgBKV0UabLkS+odoUKLUXRaHcqUSp95VA1cizyLZiXqmlxqqfXZdtqo9HkIkHLQBJCGhJCPjH3TyOEdHbsu9dMX0YIOTeT5ebi23FT36tzLVWlUCmNwjIn5Q6c4YBh7HWmGtk5G4TSnWUHWALOw1qad1XWAAB+MZfLEh8ocWLKnINYClj67uqeU8har0O7Uoc6PNWzGf+4c54FAbwUl8+SV1Fdxz+vwEsiimrK1wBisspzPUrpXmvD/J2+f2UIKKWjKaWHUUq7UkqfyEYZ8o18U+psOTG5LmVKpv1OVvMhUbUcijo5SsTEYt6jRpNLOJaBPA9ATwBXEUJ6MtmuB1BCKe0G4HkAT5vH9oThBdYLwAAAr5jyMk6+PPkybr3pSOXhFzAs0wORone36oHsdBC5MYvqyO+7KDOnPaWAYea5Kvf6x/VV4ZghEyncmcHPbTtqVPVNUnkO/e4bq8SqXlOaFbfajCP07cLNvueL4pbki5IsQlZ53kYIucjaIIQMArA9miJpMoWq+TGqPrS5Mv9FY6C8/ajygMiXHrRGkznsZSDNwXFrGUgngwC8a/7+DMCZxOidDgLwMaW0mlK6BsBKU17WsL4l/E5vtC8I2TV3nfszqWCw723edzcuX2KvRTrzL3fh2rkBRRHErEwBieBfzI1S6RZsP0NMerLd8JFfqkq+sHK3ndOOfbTeqPqbllSeW79yt21RXTPXZgUHFlFgexVEVyaLfHHnlg0/eiOADwghL8Oo3/UAro2sVHmKMgudEin5i7I52PkqR40YjUaTP8gsA2nnoZTWEkJKAexvpk9lju3AniCKlTDiiP19TuNFm8kBZH934Oxbep2ontoTpgwWrHLkUfR9SmsvZ+YXbVtiUJmtF9mpAn7n9bjZCowjQedKTg9wH+i8HhWtPS5t1MLPmCSusnA1Yd9PRfPzlSxVFSAk140cUsozpXQVgP6EkGYACKW0LNpiaWTI9cYnQt0gQ8zkxM29OW7tJ2Zu/xrNPgTvsREZndg8MsdGshKGCJl3dlQFkA6oJBNNWWkh+VZcv3Nkw9LLI1tu5DLnTMcyLTPAIjM3fU+14aZdUmHMqlRh2WRFiOajs/n21ibQoF5BpNOmuFMMfPIrc9tOKa84XoDHbTtMoQTn4Odzw95T20siHo97TiC98CUhZCCMOU2Nki9d+lhE5dJIENpdVnZNQFk5qubihiuO1Ecns3JU13NIOYqsCsqvS9mggP4CaDQpErgMpCNPMSGkHoCWAHZKHptRfC1rEb8eopmvrA72O8IrZqYtvWLrplWe7FWm6D6y3xlvZPBwAzgy7eeHxVsAAK+MX4U/n3KodC1JDS5ZwjwWcP6xdYzmHhCjCpTKW8qjIKpTJ8zQynzLM/+kUQUMs7AjpQvyey3T4QsUkZE9NkjNeSaEvAbgtwBug1H/lwM4JMJyaSQIqySoD0AVSkx85YQTE8O55YrkqG4/4Ypjo1VnjSZl7GUgCSENYAQAG8nkGQngD+bvywD8RI3e4EgAV5rRuLsA6A5geobK7SInxs18NSZ2U2VPU75y7M53huqTPZ3otNkIBikatLaX9xG6yoplJoNuie9vVY0xT9UK9uRHgYQ8J6m4bdvp7PWKFDR2/r5AjipkxIV9ilJR8v3qti5gBbGwiL0gggZ6/I9XUYZ8QzZg2ImU0mthRNp8FMAJcI80a3IQ1RbasOTJgNQ+g2plXqPRZAfRMpCEkMccwULfBLA/IWQlgLsADDaPXQRgBIDFAL4DcAul1D9CTcRk0+orGzCMOYqREZ31mlWM42gh51kpgewMjojOHWSr83XblriOrWXVwZmY0gTdy1QMLozhmTmT+B4J5XEs0fKW8swRdsDKbzUT0VJVqtt1qlfgmb8f5XsnT5B12640/1cQQtoD2AGgSzRF0gQR19H1mBZLkyPEbU64RrMvQSkdDWA0k/aQ43cVDK8z3rFPAMj60pFxsHpIL+XDIZMGX9u9nNfVNpPKqvyXPooaW4GLQcebbVvJdY/l8vPwuyyRosXDtjxLqk2pzHMXTfNirbHCc2f/kUypCKraGjcQRERu28GDJv7bQenpICpTvqyqI2t5/oYQ0grAMwBmAygC8FFUhdLIkb9uriHdgBV1QZIRDMPKsYqjSI4iN+m43Xll7VlrzxrNPo1M9ywOCllmke+4Z7qDyyoV7Bt8wy7DfrO1rCpDJUoiqomgpah83bYlLMWpuApbn7yggGHpfBpZy3syUjg/X3LbX7kmnGMCy5Jmuwzttp1KXp9uI3t/wvZVZNdtZgdy2HoPjMat8SAbbXuI+fNzQsg3ABpRSkujK5YmE6gK1hA/OUrE7IOdqyyh61mj0ShApg8YfTfRx6LrgLc3E98cmela2fr2iayeFrPWluC4Q1rb23UJisLwCyr7Ipz/HVBWvwqWaaepRM6W9bhI5b56BzT8Le0JgYVaKF++KGnbHdI5LLyibdYD5+R1Istz2HNS97lZ2IEPluSceTXlcQoRnVP1mteZRjZgWCNCyF2EkC8AfAjgT4SQRtEWTROEusBa8bKsxiYgliUnnJg8Dhgm/kikVh41cjQajQbI7py9dNy2RW6VUSqxfq/bTPdrvfOFRZ18d/qm0kpuPpWIAoaxc3/ZsoUNZJWO23Yqx4hgq35TqWHtn7B8m2t/YDRtVi5H+U7VkuxvqVdjgAkrwy+Qqmd8JdSZeCc35TKC2fElUWR4636EGo/ynIvxQKD89FxD1m37vzCWqXoJwMsAjgDwXlSF0sgRh/ldTuLmRq7ODThmctSIiVnriV971mg0uUWUA3B+Hf10uoH+806Dz5ku7KBnnDqxtkIqWaaorc4AhBY0wiisQQqL+1jLbVt8nSlZnk15iYCDUnk+LEmz1pYAACat3G7IEHhWBBlj0onwbe/3teKn1wbEc3Ll8/odn8pSVapJtS+VfO7YFHmKtpe7toPXuU/5FLFCNmDY4ZTSPo7tcYSQeVEUSBNM3JQM1UtD5R+qLixe/ujJj4SaOdja8qzRaOIK77XJe2exFsmUzhGpzZe1jIrn3WZaoZZVZFjrbibnaormkVpl91ieQ38X1c95lpLFbAtdjQPmOCflCZRpR/YwxVbdVsMH7xK7r7NrYSs7p1mDIjFBOoM9+ELTtzzXCq5N7Lad2x1+WcvzHEJIf2uDENIPwORoiqTJFOqUlvxUDvNYm1eCMnf00CXRaDSaJNmM6Eq8ZpxYIVOsrM1HDJib6bXuRq88iyyJQa71fiWTGWBJRbmwyiJSYFj8cln7rNOz1myh27bstttvOyUy0yxDum1bP7hLVbm3wzbfir38FQFFXhCi07GDL+mUS+QF4g0c5z5XriJree4H4FpCyDpzuxOAJYSQBQAopbR3JKXT+BMzS11c3KTVz3lWZFlVJUfZ3PKYNSCNRqMJRSrvtBTnW3LSikuMObfLt5Q5SkCE+Z1U1ng7vuy3Iq3lriiVerfvrqoBwO/ExsmV20k2vlii2B7JIEv8uvJT7C3F2M/Nui4hX0brXEH3Tab+auvcMsIq5Gw1VNcYF7Z9j/w61os37gYA7K6s4cgPvqpUIsqHjT6f9ETw7mMHRMI+Zut2VgBIWrSF8sw62ms2KjZbIeP2n86glCVjb23CVZbdWV7uLipklecBkZZCkxb5qvqocktXJkeRO3q+BTBTjdblNRpNGKwOudWZrK71aiBFO4y5eal2XHkuly/9tMKzb6O5rJJIMdpVudeTtq3MrUgs2JD+YiYJChRy3qU7yt3n+O+UtQCACcu34o6zurv2sQrTjhQUnbQwT1dWVYu563cJlYGgaNwRFs1D0CCJ3/fsjo/nAgC+XbgZALB1t3cJrtoUtOftZvthlbPNpW65VvCv2etKhLL2MuctZRTWhWbbtJYPs7DOPWbRZvNcxv6Scnd7X7l1j+MY43/F3jpb6eLRtnlD+z97z61rSVCgrNqtqK1wnIvF73wsW3en0v7Fbtts2Wea88kb1At2Al66ebdwX7OGhionmn9vbf68wpi3zg5ClFXXmOVz50+lDYqw5szvqjDOsYaZG52ryC5VtRYACCEHAGjkSF8nPEgTe1RFOVZmEY3nYLdGgLr2Y31stPas0WjS54fFWwAkO8ZPf7fUk2f5FqNDzSoFTqat3uFJG/jiz560hRu8HdqxS7cCABZv4nd2G9UvBAA0Nv87YT+B1nzTPdXy1psEpSh0vEstS7To+7q1zKsYsFbB4x7/0fecC1NU9lkl4sclW+zfoxdsEh7HKvW8S2IHLdiI3DvLvYMXfog8xywlc1Mpf+3pLaZCPKNoZ+A5jn9yrCftP5PWuLbLfdpAuem+O2zcKlf6hS9Pcm2/P9UYMKEUWL+zAuOXbfXIsi2HgmGBmz6YzU236smqD0u5/njGegDJNvLHd2bYx1z1xlT79+WvT+HKBYAv52wAYCjilrcHe94fFm/GFT4yqmvdnh4Pfb2Ifx3wts/bPpojlFvJuE5/PXcjAGDJpjJP3nfNASuWRvWDlecBL3jfPxZW3/vln1YCACavdL+/dlW42zw7QFKvwDj/e1OLAAAbzXt4+rPjXfn8lGl2bjzbeho3MN53l776i1BGLiG7VNVFhJAVANYAmACgCMC3EZZLkwGUuRMrKItSOXrKc0aIW/vRaDQaAGhoKqYfThOP73c7oBkAvhL92+FTPWlsp92ppHU3ZTmx3o8PfrXQlW4pgA3NDjPb+XZ23BuaFqkjHx7jylPFuHzf9+WCZLkoRVlV8prYjjJL9wOaYaK5DJHFRS/7h7RZyVj0LnjJraSxyj47GLFoo3tg4e5Pk/Fny5xuntRdHwe1bOTaTlCKYx773iWr+wPurukJT/3k2j52yA+u7W/mb3RtP/f9Mtf2ss2GEsQOEu/ftIGv23XnNk0BAJe/5lboZKzlPCWlF9MGnNz+627cdNajYdqapCJ/yj/H4UGBAsnjmE6tfPezynbn/Zu6tvsc7D3eaYWct36X/dtpMXfWV9sWjXDKP8dx913Up4O7PI59e6prcPgD37n2j5yXvO/WoJtxHHD9uzOF5Zy+xj0Y8sr4la5ty6ODNy3DifMZJiD4yhwgsLjdR2EHgBfHrnCU2bjW6YKBmkP2b+qqj8MPbO6q7/OOPBAA8MbP7gEb9p3X7f7kszVu2Vb0eihZpwlKPe8lZztu1yK/VjeWDRg2BEB/AMsppV0AnAkdMCxrKLP4mf+VWZ7DiVEYtVtV/Yjdb7IiJ2brKitvP9rwrNFo0sRpXTl4v8ao4SggFzqUPCvATZ9Hv/fkY2EVOAA46pGkMjPo6PYAgM6DR9lplivme1Pd1qYz/zXeLK+h4B7h6IBSStHl3tGObX55ejzoVgScgwSUAkc9krym6toELnjpZ9f+AS9MtLd/XrEd1741nX8iAMUlFZ60s56b4CozC6vss4MRrLLt5KPpyWvZUlblOlerJvU99VNS4R78cLrQPzl6iWsfTym99UO3kvLiT25laJk5n72sqhaP/i+pbDZpWA+H3ucuyxezi+3t8486yHOub+ZvdJUfcFvG2zQz3JOd9693x5YeOQAwaFiyC95p/6YuhbNZw3oexV4U6dmJs/2CAut2JO/9Jcd08Fhvne78lALXvZ1sR93bNXNZgru1beaq/wt6e+vHov9TSUu804Oj436NXfmcSi7rsu4cNGnWsL7wXADwf/9NyqGg+Glp0iLPtm/Wuv2So7048zZvWM9Vn0438aYNCvEnhwW+pi6Bv34y1yXXqdzzBmme+2G5/bukogaP/W+xvX1mjwNcbbVT6yaudkfgbj+NGxS6BtwA76ASyx/fnmF7PFhldL6XKKW45cOkl8LaHeWuAYHD2hkDjp0Hj8LPK9yDd7mArPJcQyndAaCAEFJAKR0H4OgIy6XxQZXFTzV5ux5yTJRD9YMdMWs/2S6ARqPJWY5+LGlRbFBYgO73uy2QlXvrXHOJl2wqs4NmOXF2Wi2cHc9zerYDAFfHkRDi6eAW7SjHPZ95V/Ssqkl2op3KlgiXQgOvAvgm497L6rLVNQmXezkFxdLNSZdS1hV6jmM+bN9D9sPJT49z7Wfn557/olsRZi35W5j8rBK3whFsbUCvA1379mvSAKu2Ja1+7LWNdbh7A8BbTF0Mn7jatd2NaROsFZGdB7t+Z1J5bNG4Pt6eXAQ/7hqRvN+FhHjuHauo/+bYDi4lr1f7FkgkqMtqeXK3Nrh7hLsddR48ymU5TFDqUjh/+6uDXYo9AHR1bPfr0tr3OixOfcZ971nrrdOdn1Jg/LKkErRfkwau+i3aUe6q/2/mJ93zz+3VTliGgY72NcacI27hVHKdyiAAbHa0u3GMe7rzvlzUp71r3+j57mkDTtf3C5m87PvjxKHJe8nOvz7M4RFx/lEH4ZdVSW+M75jrmmyuqW1x8Svua3t2jNs7YsOuSrw1Odn215dUuNpqAaPpsa7T7IDboW2betrqiJnr4Qcb+ZsCGLMo+Xze89l817v1qA6t7PtwzZviwbu4IhswbBchpBmAiQA+IIRsBZCfIdT2QfJNaYmb+3e+k2/tR6PR5Bas4rqL44rttPAChhWz9yNuq3MiQT3ukqyy+u6UtdjBzJt9ZswyPMN0aBvXr4cRM5PK8amHtXV12vdv2sClbAHwKKo8109WARzyzWLXNnudD33tdhs/7ZnxHplOLnnFf04iOz93CTO3m7Xk92Pyd2WUurOfT1rB2fnIVw53W/l+959prm3W7fgxpi6CYK2IhzEu304X4b996r5Xf2Cs9ayCI1of2cncdbtc2xOWb/MovSUVNfg8YJDlKcbCzgaEOqpDS9fAURszAJeTU//pbntnOiz+gP8UCMBtLQaA696e4dp+eZzbou/EqWT5Yc2fTpWh33pjH1iwg2Ws54Fz4Ol/TF72/SGa/87y6Sz3/WTrhm3n84vdMQXY/Ow7y4rrYPGnd9xu6Kwhm21fq7d5g3rd89l8T5oTdvCiLxMngR2kC2rTccdXeSaEdAPQDsAgAJUA7gTwOwCHALgt8tJpfFHl3hwWZXJs92ZVUbJjJidPo4irQi+dpdFo0oFVOG4WBDUKysPKAbzKKuC2mon4kbGKsvOKWQWcBztfNh2+ZaxaqWBFA84U7JzNVZxOfLYIis7MKv5BygYArJaIPOx0YxfBuq6zyhkbvX0Up/2u2+l2zy9jlhhyeiukw64KcYA+TXaxonBni7OOOCCr50+HILftFwCUUUrLKaUJSmktpfRdAKMBPBJ56TRcVAfoUrZusKJ1jMOiTI4aMbGzhKtTehVNdodSMRqNRqMJoI9gLq0mep65rHdgHtZFmMdRHfQ93Jf58M/9Usp/LBP0bcb9Z4Uuwy1ndE0p/+/6dfKk/ecPvwpdjkwTpDx3ppR6hs8opTMBdE73pISQZwghSwkh8wkhXxJCWjn23UsIWUkIWUYIOdeRPsBMW0kIGZzuufMJZXN6FcmJX4FiRp7WT1ws4RqNZt/maqZjNvya4zx5vrnt5EA5j17Uy5M26wFvR7No6EBP2stXH+PafvbyPr7naly/EDed7u6AnnZYW9f23IfO9pXRqXUT3/1AMkCaxf+zd+ZhUhTnH/++u1xyidyKwoKAiFdQVLxFUAGNR2LiFY9EY0w0GjU/AfGKJ8Z4H4mKJp4RFY0IeHGDcoPc17Jcy33uAsve7++PPma6Znq6Z7pnumf2/TwPz9I91W+9XV3dVW+9b1V98aezHK8xWPH4gIS/P3jJsa5luaFPF+uc3OmD+7q+tlWTBpbjyX8933I84sbeKesFAPMfsj6L4zs0txwvf8xaVsseuxgqv+p9lOV47dODYtK8cm2vmHMqo++0PsN4cqKZeN95luOipwZh0AnWeeZndGmVUIZTXViglM+MoRdYjmc/0C/h9Qa3nt3Z/L9a5hZ5wyLyXrzauhST+q7nJehjTLvfvo6p3xWVF66OvOOJ5m+r34alf7PWjWGDrO/RvRd2t+Rx85kFlt/PONr6rNTvkXpPn95+pvn/Iw87xNw/20Ctu+qzjNYHAB66tCf+etExlnNOAzpPXnmC5XjkbX0Spg8rTsZzorXFD0nwmxPfAziemU8EsArAUAAgop4ArgFwHIABAF4nonwiygfwGoCBAHoCuFZPWyfxL9zaFzHi6c2UHH/EhK58BEEQUuHvv7R68O6/2NqRu0hZgGr64L44XuncrRt+CQYP6GE5d9OZBTHGZqum1o7mqD+eCZWVTwzApSdaPYZXnXJkTJpolj8+wJL/lP87H789q8CSpkXjBua2VUCsQTj1/r4Wo2bNU4NiDI7ojvHapwchL+r+Xrm2F57+RaRTq3bCG9XPx5Kojr76+63ndMHt50UGANY+PchiNK56YiBuiTKGrj+9Ixrk51nSGwMApxYchjduiBi4Y/58No48LDI4MPX/+lrKtOipQZZ7n/fQhSh6SjMiT+vcEgWtm2BClNHYv2c7y70A1q2elj82AD8OiRh80cbZ69efjJZRxvnMof3w1Z0RA+2GPp3M/WwB4Og2TdC4QT3MHBqRoZbdmzecAiKyGJkfxzEo1g2/xOIpLHpqkCVq8O+/PDEminD64L74w3ldzOMubSJbqh3dpgny8givXx8ZYJox9AJ8GOXNfOjSnpaBjFeu7WXuUw4AQwf2wBNXHG8ezx7WD4dFlc8dfY/G4YdGTIWzurZC2+aN8Np1J+Pq3kdhzVODMHtYP9zTvzvWPj3INPaW/O1iPHhpT1x20hFmmb8cNZhgGOBv3dgbbZs1wpCBPTDixt64olcHzH/oQjTIz8O0+7VIw+7oAAAgAElEQVR3/Zh2zVAvj7Bu+CVYHlVPiICv7jwb53Zvg9VPDsRRLRubA/jrhl+CwicHmmmfuvIE852bdn9fLHr0Iks5X9nrSLTXt2J66ZpeGDpQe59XPznQMhAX/W2Ydn9fNGlonTl76zmRd2Td8EtwV79uALRF467sdSQeurSn5XciwtT/08psjV7njdd6zJ/PxlFRg2qFTw5Efh7hspOOQMeWjTF9sFbfjG/N/IcutNRdADisSQOzHM4/pg3u6tfNUi63nN0ZRGR6tIueGoQv74gM6Fx3ekf86zeR+mUM7jwVZUCf7jBYE1qY2fYfgP8C+H2c87cAGJnoWrf/AFwJ4EP9/0MBDI367VsAZ+j/vo06b0ln9++UU07hsNFp8BjuNHiMJxklByu50+AxfNzD33iSs3pbKXcaPIb7/mOSJzk/FO7gToPH8K//9aMnOf9bUMydBo/hOz6c50nOW1PXcKfBY/jR0Us8yXlq3DLuNHgMvzZptSc594xcwJ0Gj+FP5mzwJOc3I2Zyp8FjeNKKbZ7kXPzCFO40eAwv2bTXk5xej33HnQaP4R37yj3J8eOdEDkiJ1MAmMs+tH11+Z+fbbP6/EfO3sC9n/iey6uqmZm5uqaWX5242jxmZv5ifjGf88xErq6pZWbmg5XVMXKqqmti2tna2lr+5es/8Oa9Zea5LXsPcqfBY3hvWaV57vulW/mekQu4tlaTX15VzVNXbTd/37DrAN/ynznm7ypGm9pp8BhLmse/Wspz1+0yddleav321tTUck1NJP3kldu50+Ax/JsRM817OlhZzXZ8tXATbys5aMo/UFFlm7aquoZXbS21/T0eT4xZyp0Gj+F/fLuCmbVytysDlZqaWi6r0HR/5Msl3GnwGH5nelFS+cdjf7l2j9tLyy1lF83L41fx+zPW2cq48rXp3GnwGJ6zVns2VdU1ju2i+qyYI3V54+4D5rmd+8pdlxGzVt8NOYYOZRXVZl2vra3lquoa1/LcsGlPmeX9UvHrG723rJKL95Q5J8wA5VXV/PXiLfzF/GLPsk578nvu++wk70r5RKbbVL/IZNvstNr2XwB8QUTXA5inn+sNoIFu9PrB7wCM1P/fAUD0hoDF+jkA2KicTy7YP4cwxhajRwBTkqMPszWq501Ovi6noUd96unr6Teo53YHtfjU10e0o0e2U8G4vr66zn+SGB6Devne4pIb6s9J9Ygki1Fv8jzGSR+iy/Eabd2gXp7jYiyCIAjxUD15vz71KPz61EhYbH4e4Y6+XS1prujVAVf06oBE1MvPi5FNRPhM8Ti3P7RRTLr+Pduhf89I+GbDevk4p1skFPuolo0x4ib78OG2ejjltad1tHgTH4zyPBFRTNhlntI2/OyoFjiscX3crXuw6uXnIVFzH+0ZIyI0bmDfRayXn4du7ZrZC4vDL04+Em9NW4uL9YiAZPoweXlkesb+0r8byiqrcfWpRzlc5Yzh/VPLMpo/6+Vnx2UnHYH5G/aig74Pcb38PHPPZjvUZwVontRHRy9F22aRoE814sGJ/DzCtacdhf/O3ojDGmte4GiPIhF57ouoHNEicSBqfh652mfaiUMPqY9DD0m8Z3OmaFgvHwOOb++c0AWzHvA+99hvTul0WNAqhBrSjHWHRER9ARixGUuZeWKi9Po14wHEq1nDmPlLPc0waMb4L5iZieg1ADOY+QP997ehLU6WB+BiZr5VP38DgNOYOWbFbyK6DcBtANCxY8dT1q9f73h/mWTEtCKc3a01erRv7pw4Aa9PLsSA49pbwnCShZnx0oTVuOqUIy1hUclSW8t47vuVuOnMAstHP1mqa2rx7Hcr8cfzjkaLxg2cL7ChoroGz323Cnf164amDd3uxhZLWWU1Xhy/Gvde2N3TQEXJwSq8PrkQf73oGNOwT4Wd+yvwzvS1+OtFx8RteN2yae9BjJyzEff07+Zpsbi1Ow9g3OItMR3TZFm5dR+mrtqB35/bxTlxApZsKsG89XtwkzIvKFnmrd+DVdv24drTEs9zcmLGml3YtPdgTNhmskxZtQOlB6tcLSCTiO+XbUNNLXtu8Mcu2oLGDfPR9xhvK2R+saAYbZs1wlldW3uSM3LOBnRp0xSnFrjbu9QrRDSPmb1NnKzj9O7dm+fOneucMIMMeHEqhgzsgfM91mu/mFm0Cz87qoXnQXIh/TAzKqpr5VnZULynDBt2l+HMo7196wUhEZlsm10Zz2nJmOgmALcD6MfMZfq5oQDAzE/rx98isqr3o8x8cbx0doSxgRYEQRCyFzGevSNtsyAIguAnmWybvcWjpggRDQAwGMBlhuGsMxrANUTUkIg6A+gGYDaAOQC6EVFnImoAbVGx0ZnWWxAEQRAEQRAEQaibBOJ5JqJCAA0B7NJPzWTm2/XfhkGbB10N4C/M/LV+fhC0fafzAbzDzE+6yGcHADVuuzWAYHcErxtIOWcGKef0I2WcGbKlnDsxcxvnZIId0jYHipRz+pEyzgxSzpkhW8o5Y21zYGHbQUFEcyXkLv1IOWcGKef0I2WcGaSc6zby/DODlHP6kTLODFLOmUHKOZZAwrYFQRAEQRAEQRAEIZsQ41kQBEEQBEEQBEEQHKiLxvObQStQR5ByzgxSzulHyjgzSDnXbeT5ZwYp5/QjZZwZpJwzg5SzQp2b8ywIgiAIgiAIgiAIyVIXPc+CIAiCIAiCIAiCkBR1xngmogFEtJKIColoSND65CJEdBQRTSKi5US0lIjuDlqnXIaI8oloARGNCVqXXIWIWhDRZ0S0Qq/XZwStUy5CRPfo34wlRPRfImoUtE5CZpC2Of1I25xZpG1OP9I2ZwZpm+NTJ4xnIsoH8BqAgQB6AriWiHoGq1VOUg3gPmY+FkAfAHdIOaeVuwEsD1qJHOclAN8wcw8AJ0HK23eIqAOAuwD0ZubjAeQDuCZYrYRMIG1zxpC2ObNI25x+pG1OM9I221MnjGcApwEoZOYiZq4E8DGAywPWKedg5i3MPF///z5oH7MOwWqVmxDRkQAuATAiaF1yFSJqDuBcAG8DADNXMvPeYLXKWeoBOISI6gFoDGBzwPoImUHa5gwgbXPmkLY5/UjbnFGkbY5DXTGeOwDYGHVcDGk40goRFQDoBWBWsJrkLC8CuB9AbdCK5DBdAOwA8G89BG8EETUJWqlcg5k3AfgHgA0AtgAoYebvgtVKyBDSNmcYaZvTjrTN6Ufa5gwgbbM9dcV4pjjnZJnxNEFETQGMAvAXZi4NWp9cg4guBbCdmecFrUuOUw/AyQD+ycy9ABwAIHMyfYaIDoPmbewM4AgATYjoN8FqJWQIaZsziLTN6UXa5owhbXMGkLbZnrpiPBcDOCrq+EhI6EFaIKL60BrnD5n586D1yVHOAnAZEa2DFuZ4ARF9EKxKOUkxgGJmNjw0n0FrsAV/6Q9gLTPvYOYqAJ8DODNgnYTMIG1zhpC2OSNI25wZpG3ODNI22xCY8Wy3+iMRtSSi74lotf73MP08EdHL+oqci4gomRdlDoBuRNSZiBpAm/A+2v+7qtsQEUGbg7KcmZ8PWp9chZmHMvORzFwArS5PZGYZDfQZZt4KYCMRHaOf6gdgWYAq5SobAPQhosb6N6QfZPGXuoK0zRlA2ubMIG1zZpC2OWNI22wDMQcTIUVEhwM4nJnnE1EzAPMAXAHgZgC7mXm4vm3FYcw8mIgGAfgzgEEATgfwEjOfniiP1q1bc0FBQTpvQxAEQahDzJs3bycztwlaj2xG2mZBEATBTzLZNtfLRCbxYOYt0Cagg5n3EZGx+uPlAM7Xk70LYDKAwfr591iz9mfqe7wdrsuJS0FBAebOnZu+mxAEQRDqFES0Pmgdsh1pmwVBEAQ/yWTbHIo5z8rqj+0Mg1j/21ZPFvpVOQuGjMVz3630JIOZUTBkLN6YssaTnPKqGhQMGYuPZm3wJKfkYBUKhozF6IXepqFtLy1HwZCxmLhimyc563cdQMGQsZi9drcnOcu3lKJgyFgs2VTiSc689XtQMGQsinbs9yRn6qodKBgyFltKDnqSM27xFhQMGYs9Byo9yRk5ZwMKhozFwcoaT3JGTCtCwZCxqKn1FuHy4vhVKBgy1pMMAHhizDL0eOhrz3KGjFqE3k+M9yznjo/mo+8/JnuWc9M7s/HzV6Z7lvPLf/6Ia9+c6VnORS9MwW3veTeOznx6Au795CfPcgRniGgAEa3Up0bFLL5DRA2JaKT++yy93QYRFRDRQSL6Sf/3r0zp/NEs7TtVXqV9p8oqq1G43fotfnPqGmzcXeZbntU1tVi/64Dr9As37sWiYvtddJgZk1duR3QUYG0tw21UIDNjjdL+VFa7X+h59bZ9eGtqkev0KjW1jOoabwtLb9hVZt5vvPLIJCVlVZ7bq3jsr6jGV0n0o8qralCbBj2E7OT571biPz+sDVoN3/h+2TZsLSkPWo2UCdx4TmL1R1erchLRbUQ0l4jm7tixwy81XfPKxEJP1xvfyme+WeFJzm7deHpl4mpPctbt1DoJI6al3rgCwKJizUj9cKY3Y/7HNbsAAJ/PL/Yk5/tlmhH/7dKtnuT8b8EmAMD0wp2e5BiDHD9t8LZV4dvTtY+r2plKFqMe79xf4UnOs99qg0lVHjtXL473Vo8NRkxfi/Iq7zuIfDxno+eyAYCxi7Zg7U73HXE7pqzagcUeB4IAbTBoRtEuz3JWbduP75Z5GygDgM0l5fh8/ibPcoTEEFE+gNcADATQE8C1RNRTSXYLgD3M3BXACwCeifptDTP/TP93e0aUBvDShFUAgL1lVQCA296bh/7PTzENr+2l5Xhq3Arc9M5s85pFxXtRvCdiTD85dhm+/ClSx7aUHMRT45abhsuL41fh5Me/j6QftxznPTvZ7PiVV9Vg1LxiM89lm0tx2pPjzTb48td+wGWv/mBeP3LOBvR/fkrU8Ubc/O85+GxepE3r8sA4PDp6KQDgYGUNbnpntmWANnqA/dN5xej33BT8oLdBG3aVofuDX+PTuRtN/U545Ft8s0Rr6/ZXVKPXY9/hxzU7Tf2ejLrf8qoavD9jnXl8+/vzLAOXyzaX4rVJkX7ORS9MQddhkQHJWUW7zHYRAJZsKjF1A4BV2/ahYMhYzN+wBwCwYMMenPvsJHwwc715Pzf/ew4+0fVnZnz50yZzQKC2lnHPyJ/Mge/K6loMfGkafozKY8mmEtMAHvzZIpz46LeW8p+2eocpe8CLUzFmkWbUlpZX4aTHvsPwr5ebunV9YBx27NO+9TW1jBVbI93U75ZuRcGQsebgzIhpRfjzfxeYv49dtMUc6B/2xWL8+b8LTL1/LNyJ76L6H/eO/Al/1/t9ZZXV6PHQN3g2yhkzal6x2ebU1rJlQGbj7jLLANEPhTst7dPWknLLQHjRjv1mHwgAFheXYFRU/ftiQbFlUGrZ5lI8/fVys47PXbcbFz4/xZRZVVOLcYu3WAY8Fm7cazl+98d1FifMngOV2Lw34ixYu/MA5q7bbbk+2rlx3ycLccyDkXpWvKfMMlC2bHMptu+LGGPTVu9AaXmVeTxh+TbzuQNaPV26OSJ//oY9Zp3ZUnIQv3j9B/Md3rm/wqL78i2luOAfk1FyUJM/ffVOPP/9KvP3pZtL8ELU8daScsyKalffn7EOZz8z0TxmZsvzqqiuwXVvzbTc/8sTC/HoV5Fp3aPmFeObJZHA25fGr8aHsyKO10krtlvq1/sz1+PxMZHrP5i5Hgv0dxAAZhbtwvszI9fPKtqFAS9ONevA42OWocvQyHfgrOETMWTUIos8Q9/dBypRMGSspX/9+fxiyz3+/r25+OU/f0S2EqjxbLP64zZ9PrQxL3q7ft7VqpzM/CYz92bm3m3aZO+0NL/GG/0avPVNjj9iwndfYSsfv+SErHwEQUgLpwEoZOYiZq6EtlLw5Uqay6FNpQK01W376YvIhAZ1ENMYjD5QWW2eu+zVH3D2M5PM47emrcXdH0eiG+4Z+RPenFqEBRu1juWL41ebnWgAmKEP4O4p0849++1K3PfpQkxeqXXM/zVlDbbvq7B01KMZPGqxpdO/STcgtihemHdnaB3ZHwp3YsqqHXhyrHWdnqe/1gytxfrAtDFgumrbPgAwjeXNew9iX0W1OSC/bHMp9pRVmZ37Mt0AMp7kc9+txENfLsXX+vXfKAPMl7wyzRwU1fK1Dv5d/eZM/GVkpDwvfWU6rh8R2VJ68kqtS/f1Yq3jbwweztcHjjftOajrrZXHt0u34e6PfzIdAZv2HsQXCzbh9g+03aA27inD8i2lePB/SwBohvOlr0zHSxO09CPnbkRpeeT5Dx61GDe8rQ2mVNbUYsXWfbh35EIAQKluDI1dpOn29vS1qK5lc0DxpQmrMeDFaVi+RTOgjYE9w2h4Yuxyi3f5jo/m49dvzLDc10HdGLluxCzc9n5kR6vPF2zC65O1AZH9ur7GgMrmvQdx36cLcbue/s1pRbjs1R9Mw/ycv0/COX+P1OnrR8wy8wWAPk9PwI3vRJ7BBc9Nwe+jIoN+/up03PfpQvP4ue+0umEMGvz6jRl4Y0oR9ldoej0+ZhlWb9+PlXpde21SIf704Xx8u1QzyL9evAWXv/YDRkUNfD4yeinuihpY6PP0BJw5PGJA9v3HZFz1r4jOl7/2Ay6NiqYaNb8YFVERFWc/M8kyCDXo5Wm44B9TTL1veHs27vwokt8t7841nzug1dNLXtbkz1u/G794/Ue8qjsN3pq6FvM37DWdNLe8Oxd3/XcBdunG30vjV6No5wFzwOY3b8/CyxMiA/yXv/oDXpqw2hyAuvD5Kbg6KqLroS+XonhPZODg4zkb0fuJ8Vi2WatXyzaX4sc1uzBMr9PxuO/Thbj9g/nm8QvjV2HYF5H0v/3PHEv9euh/S0znCgA8+L8luPL1iPF6zZsz8VBUfo+MXooVW/ehSH+/356+FtGBEJv2HsTHczZa5BnPa+VWrV68o+e3rbQc936yELe+a41G27TXW6RlkLg2nonokKiV7TyTYPXH0QBu0v9/E4Avo87fqK+63QfaZt22852zFaMn4tXYMBpC9mhG+S7H442Z5eNVH7/k+HVfphxPYnK2/giCkFbcTIsy0zBzNYASAK303zoT0QIimkJE56RbWRX1+6J+/5L5HlbVGOHD7tJv1w2MaC9XKjjlV5FEKLZFrv7XaZTDyH/3Ae0+yqIGHOKl8xu7NtQYpDAMOSeMQYhlm1OPxLEbE1q4UTPwt5ZaBzrS3boZkVtGXTOMrETTvIqUQY056/bYpHTGeDZGuaj3a0RhGM+qSB8QSRQBl2p9ToRh3Bve0jXb3UXgbS3RynXltvjBr4aH3G04f3WtUV7a8b6K+O+SgTHo5zViMCwY32Pj/o366/YdzgZcGc9E9HMAPwH4Rj/+GRF53U7iLAA3QNsHz5grNQjAcAAXEtFqABfqxwAwDkARgEIAbwH4k8f8cxrSm0rvxphPckwjyic5funjl7HqTUwIBynCVX8EQUgrbqZF2aXZAqAjM/cCcC+Aj4ioeUwGGZhSpdo8Xvzidp8s41tmJzvZtsVJRcNgSnZqkJE921jPdvcR6fimJ6ggki/Z5G/Vy1ZPdYDEi06IP2BiGos2mSVTRG4HMSzXxGTLFlnpJnYQynrC7YBMrqDejtPtJXv/YSyulPqkxvuh1JCg1jFIB25X234UWljXZABg5p+MxUJShZmnw/7d6xcnPQO4w0ue2YBfVSt0xmpSTYYQNH7XH0EQQo2baVFGmmIiqgfgUGjbSjKACgBg5nlEtAZAdwCWGD1mfhPAmwDQu3fvrO1Fmcal3qapnzi/opoM2jRrCADo16OtQ0o9/5hvrqGv3e/RqRDV8U0PjkYkW8uXHa5Qz7LdaEHcaxOniXm2XgZjTO+ti8TqIJCNEZKuAQ77gSHrYLjdgEbQ7X6qetj1c5OtB0TJ9Zmd3oUg8FK31AEwVVYuGNFuw7armdn7ajSCIAiCIISNOQC6EVFnImoA4BpoU6WiiZ5SdRWAiczMRNRGX3AMRNQFQDdoUWJpx874SdZD5CeqgeEWO2M7KaMrznWqpzeSn34+Rg8jfXL5udfL5rxDeid9zPuFu/TxsItccHqW6bMF4k9HyJjnOSZ/tpx3GnwIarqWGjbshNt0ueBJTpZU6rbTOxuWQRYvuPU8LyGi6wDkE1E3AHcByN5l0oTA8C0M2KMednN3UpYTkvBmv8rHb8KmjyAIEZi5mojuBPAtgHwA7zDzUiJ6DMBcZh4NbY2S94moEMBuaAY2AJwL4DEiqgZQA+B2Zva2l6BbvZUvC8GHb7pTnk5eN+XYOUN/e5C2YdAur69N0Vh3i/nMDK+U6lXV/6rRT3ae81ivVgo6sfWvKdvUSTEiU6hlkSuS94jHPAtdWF6anlFs/u7KmGyeaVC41cOpziT7LqT6HYrxyAZoXXrJ2W5whZW/4aglqeHWeP4zgGHQQrM+gtbAPpEupQTv+LVwlIFnMT6FAZty/LovKZ9EYvxb4C0HwnQEIZdh5nHQ1haJPvdw1P/LAfwqznWjoO2aERq0702ka5ZKJ83JQ+oUBu1Xfl5Dc5Pz3EaXWZrnPMeEIqu/R+vlQn/l2FV0dAZ7735629x6fj3nYz6T+B5wp3noQZFtnk3bdzwsBZoktUr5202tCNlmDUnhynhm5jJoxvOw9Koj+IZZJz0aP6Yx5o8R5RXf5PgkKIvf/YT47ZkXBEHwm7gesjhtVUa7oEkOhDp7ulPTPuLlcZijrZRZMsa2l+97ZD6kItcmVNl23m+sZknrYneFU2RZups3u1Xj020cJj1QYZc+INsvWc+m24UGk404UAek3JKr/SZ12kE236Xb1ba/J6IWUceHEdG3ia4RUiPXPXR+3Z9fc2l8kxMSD61dmFnQhEsbQRByAdt5wvpfL50ze2dQYk9oZApOkh1tRX48yW6w8+jGegnVUGTr33Rja28p+hp65pnGtsM82yQMS6ckagSfvY3oXGrJ1EnngQv3srwQCaGPn5PTivPZSmz4fvwb8itaJMuLKwYnz3K2RQbEw+2CYa2Zea9xwMx7ALhb+lEIBL/n0HolV0fSchW/wv59nz6Q44NLgiCkjpNB5gfJzF11Q7pDX5OdRuk2pDJVPd1+w438IyGgyenjS9/FIYogqTx8DFVN9+C4kyPZ7TMMqrVONSzY9fZyLsXW1d6K3UCRX1uyhgG3xnMtEXU0DoioE+puvcgKwrdVlT/4ZYT7NiiQc2OGOj6F/edq8QiCEDyOHqF0fH9s50JbPdJuv5zpDgm227dZzTZTIZWOK/E6HDvKT1Yh2BuDdlEEduHU6SLTW0K5n3Jg45H2T5WU8FqH3RvHdvWmjuMwJ95uKkY24XbBsGEAphPRFP34XAC3pUclQbDHN5suEt/sjxyP+DYo4NN9hRXm7A71EQQhfditSuwnkVBW/a+qg8e8nS5LOhzcIXnM59TtnOektIi9znYl3hjDNH6YvF3+yRiWyd6DL3vfpiwhVlamzbSYMPaQtsVBB6jlaPfLNZF1FjRs60lI648b3C4Y9g0RnQygD7TbvYeZd6ZVMyEnCc3q1n7LCUl4c1i3qvJLodDdlyAIoSGTnWbb/mCSoYn2c6e9oZaFuq2R0/zddHuFnAwv9fc8/YRdKGiy8q1pE8/p9aNapWJw+jFAkApu1/9yWuwtOJKbFO62GJOdGpD0vtDqwFFyl/uKl7plv7e89Y6y2HZ27XkGgIbQ9nasB6AnEYGZp6ZHrbqLXy+LbwtQ+TRHwXc5ObpAl38LmHm73vc5z74uzJbNn1xBEPzCfsGw9K2oZNdmqKGISX879Qs8r+vgMsQ3NjyaLenThe0WYMb9K5qZ+067lZ/KatuKUjFRBT6USTJ6Od9rcmWSKrZ1PWaAxW76RDDmn98LqiW7+nay+YbVgw+k9j2oVcrf7SKG2YQr45mIngFwNYClAGr10wxAjOeQ4ttWQyFbeEzIDLJVlSAI2YY/e+i6+934tnn9xtmFL3v9Bpth0A6GTqrzaVMdyIwN6Yxv1Rv6rt62HwDw9ZKtePDSnjF6qnKDmE/pKkTcB71ijfz03KPXFc3D0uwnvWCYw9tmN+UgVXlO6YMsRm+e5/hTLczfMxTdkk7cep6vAHAMM1ekUxnBP3zzHPoUthS2fZVFjoMc/a9v9UfCtgVB8Bn3W8gk/+Vw+pQ6h217w214stN19ltVxT9WjVu/iSzmljgH4+fFm0oAAJv2Hox7mW0H3IfwaLuIOS8LhiUVtm2zanTGt6qyOR9W/J6Oly5PsrFXutsw+WzBbk5+pvYpzwRuV9suAlA/nYoIdYOwGVH+baHk7Xr/BzvC9dkN2/MSBCH3ULuh6fBsOC7AleKAoekptg1rTk6eKtdu8Z5UQyq9esTtDDK3cp0MWj9QI++81KbkDGybQSH9b62DZ88rTmJNo94xnS/qJE3KRq/jHOb03lCmBkXSjdsV9bP5Pt16nssA/EREEwCY3mdmvistWgk5i28LYvklx5sY38KbDcJizIeVsA0KCIJQt1C3pIqFLOmcsFt0yathZM4htunJ2mln5zXyG7vVylXDrFZpzGIGSGzmcvuhvV0UQaZaIaeBhqA8d3b5h2ULolTfoZiwaaOOpljQTv0wY/eQ3JvelnhwJdV9uMOEW+N5tP5PSDN+GT1hXVjLK2ENb/YsJ2z7V/vkwc7eT6MgCFlHBj84Th5bryvtOhmJruWa8pLTw3nOc2r6wDSObaxeWPN3m08kJDT5jrnTXtu2OvgQGp6M2Ezvk+tc9g6eWt80SQ235WPrIU3xBrR83V8c1MJqbkhtAT7tb2TwQZWpn09drcBxu1XVu+lWREgPfs1V9vxqZ/NbUgfxzcNveOZ9ahxC3MYIglAHiDVyvZH2eatJzy9M7DUi8vYddvKahmm7HjtSeWZOiyglzM/2YaSgiKsM4582n43DYFD7BUkAACAASURBVEPQDkWvc2qTHWhKlTDWbT+IGMcOa1JksV3gdrXtbgCeBtATQCPjPDN3SZNeQo7i35ZO/hCWMGnTw+9RDz/3pfSTsOkjCELu4mVBJ7eynfZn9uiYTbljGetxtRrDrr2aAVlAasdbDdt2vN7DvNHYrOKH4HupTn54izNne8QvTKewbbvjTJF2o9ap4JN8MNkcvhwPo/zzlFW1WPlPNt+12wXD/g3gnwCqAfQF8B6A99OlVNj44wfzcM2bM7Bxd1nQqiSPXy5jn+YY5xy5elsphh7GyPGuiiAIQkLsDKZU+qSpRsnEGBS+TZnyJMbeGLYpNLeGWaoD4Wq2jtsd2WTjduG2VFA9qGFZHTiyHVeG54zGGMXKgEyONvSOgwMe5avfmrAMPgDe+uy7D2hLY41bvFWXZSUyJz17K45b4/kQZp4AgJh5PTM/CuCC9KkVLraWlmNm0W5zy4RsILLthDf83qoqLGHAkYW+vHZw/JFjjlH4Ft7sl4ffaznrckLi4RcEIffJ6MKCpB4abUJas3GNagzbr+Qcf7Ekv3FazMnJQHFeBTyZuaaJz9vZ76m0r14McDtDJmjTw3E/6IBizvwyzvwauHIi6OeYiFTKYIONo1GNbslm3BrP5USUB2A1Ed1JRFcCaJtGvULFs1edCACoqU3/E8/11YR9C7f2S07IjLqwlU/YyPX3QxCE5HGaa+nnV8Np7mrqW1UZXkWrnMjv7uTGeHlchpkbTFyxHQDw8ZyNCfNJeessG0+4ef8xW1DFz8guez+3+4mNIrAbeEhBpgscjfs0WV0xYlOMTggq4jDVOpDsdV7vLmZQRpUfZqs6ATH11mXYfzbh1nj+C4DGAO4CcAqA3wC4MV1KhY08/QlnwngWhFzEt7nu8goKguCAH98Jp46dbzskxHQsjZBYf7xmkRBbBy+hUmbfL9sWN53fBoP9quXGnOfE+dsPYqSuqf2lqVesZNpAP73rqWArPWZefFrVyCCJbyTVQTnXz8llFEYQpDT1xel3l/uEhxm3xnMBM+9n5mJm/i0z/xJAx3QqFibq6bPes9F4zrmtqsImxydB/m0x5a8c7x/vbP48CoIgWHH6JKbaZibrUbZNZ+PlsRMQFgPIbhVzuwXDnLyyKemghq77GIKfyvZSdnNig95POdU9zDNFqivMu8Xn9cIiWnhYkT1dpPI+Oe5vrf+tC3Oeh7o8l3aIaAARrSSiQiIakok88/Oz1/Psfa6yP42H+ZJ4nvMMf+V4xHcj3J+p0/7J8Yhf92XgX7h+9r3LgiC4w21YaUqyHUI7k20zY8KXfftWKmHgMb/7m59bnPaAtUtv4vKh+hm2HdHBKjWVPNz0PZz2+M542LaCnfEeVJ1ScZpXn7LcZKdiOBmRAQ+CJMJL2bkeXEk9i8BJuFUVEQ0EMAhAByJ6Oeqn5tBW3s4oRJQP4DUAFwIoBjCHiEYz87J05puv16LqbDSeQzen16fwXd/CgMMhJ2JjepRjjlGEK0w6bEYvc7hGdwVB8E4mO6GxBo23bxMrf/36Phme2xVb92lyHfJ3gjxu9OwUlu7V++blKcQYrYpOah7J5OXrvPs0GYdu882WsG3334PUwrb9Sh/mtVxS0cz+85Bd9ScRTvs8bwYwF8BlAOZFnd8H4J50KZWA0wAUMnMRABDRxwAuB5Be4zlPe8LlVTU4WFnjmN5NGjsqq2t9kXOwSru2qqbWk5yKan/kGPdVWR0OOVU1mpyKkMkprwqHHCPKorzaXZ13knOw0pscg4NVNeY0Ci+UVdWYg2Ke9PHhnkROZuTk5xEa1PNed4TsIxnDNtl5jPZGoOss4+L162TotetAJQCg5GCV5fdMrSQce6E7eU5h27biPawYFrviuDWKwJM3LoV5nuqdG8cVRj8xQzZXphbQ8gu/jLP0zy0Pr9HsJ7ERCumMDcoMCY1nZl4IYCERfcjMGfc0x6EDgOglIIsBnJ7uTI0O12NjluGxMc52+rEPf+NLvn7Iqa5lX+RsKSn3Rc7C4hJf5Ixfvt0XOR/P2ei4qqgbXplYiFcmFnqW88jopXhk9FLPcv704XzPMgDgV/+a4Yuc8/8x2Rc5pz05wRc5xz/yrS9ywvSui5zE3HxmAR697DgftBHCRuz+qIZnzv/OmVPIqtcZSrGGfmJJdh1wdU9gA/stojITaulkiMbcfYoF6SUKoaKq1jlRiriqkpTwEAs27AUAfPnTZpzZtbUvesXDad9h2/n1xu++a+SOVE0zv01Z19EcWWxExsNxwCvXPc9E9Akz/xrAAiKKKQ1mPjFtmtmoFOecRS8iug3AbQDQsaM/a5odekh9vHTNz7ClpDxhullFu9CpVRO0P7RR3N+ZgWe+WQEAGDKwh62cHwp34ph2zdC6WcO4v1dU1eKF8asc5UxdtQMnHtkCLRrXj/v7/vJqvDqp0FHOxBXbcVpBSzRtFL+6bN57EO/NWO8oZ/yybTiza2s0bpAf9/elm0vx1cLNyCPg/gH2cr5ZshV9j2mLhvXje5F+KNyJaat3omWTBrjt3C5x0zAD3yzZggt7tkO9/Phyxi7agsWbStClTRP8uvdRcdPU1DK+XboVA45vb67KrvL+jPXYtPcgTu7YAhcd1z5umuqaWny/bBsGHH+47Qfl1YmF2F9RjX492uLUzi3jpimvqsGUVTtwsU0+ADD8a60O/uLkDujerlncNGUV1ZhRtAv9jm3nKOd3Z3VG2+bx62rpwSrMW78HfXvY72xnyLnrgq5o3DB+HdtzoBJLNpfgnG5tHOX89aLuts90x74KrN6+H2ce3cpRTqK6vLWkHBt3l9k+B7dyNu4uw459FTi502Ge5KzdcQD7K6pxwpGHepKzats+1NQyjj28uSc5yzaXon5+Hrq1a+pJzqLivWjeqD4KWjfxJAcAjj/CvmyE3MDvUOp4qMafmqe5yFSKC4Z5XUzHbvVq23xTyiV1r5mTBzZ2tW27fJRQaj2dFy+xkdX2fRVxz3shKREuvfP7K2N9WplYz0MdnIqdTx+sR9WvMrD3mCbOx60x7LSneZB4sWudFvPLfr+zc9j23frfS9OtiEuKAURbMUdCCy03YeY3AbwJAL179/atLl7+sw6OaW4/7+iEvzOzaTwnSuskZ39FtWk8e5GzvbQcr04qROumDTzJWbq5BO/NWI9jD2/uSc7kldvx1cLNOKdbG09yWjZpgGmrd+KCHm0Tpv3j+Ynl1NQyFm8qwcXHtU8o546+XRPK2VpSjv/8uA6XnngEfnd2Z9t0d17QLaGcxcUlGLt4C67o1QE/P+kI23R/6d89oZyJy7dj9rrd+HXvo9Cni70R6cTIORuxducBXN+nI45uY28kOWEMCtx8Vme0bNIgZTmGEXXrOV3QqH78AZpk5DjVM5GTW3KE7MXJA5ai1JR+zfRilE5h427Dnt3PeXaZ0KWcZMsxdjGt+A/fy2Jeh+sOEKcogqTyMIW4v4pi/uMimwxYYJt1R9Ku/RXo0OKQ9GeYJMb6RAuLSzzJsZuf73Zgy3U0h20URvDmtB8DEZle8C4TJJwAxsxbotJtY+b1zLwewHYEM2gwB0A3IupMRA0AXANgdAB65BjeHqWHKUZWOfpfzy+UT/oYeFbHp+0H/FokJNIgeJTj0159fu/5l80fZEEICqedLIioIRGN1H+fRUQFUb8N1c+vJKKLM6m3RUf12MePQW2tdbGbGLzOcY5Z2Tn+727lGPhtTKVrrrQZZq569xzk+OlpNYwkY50buygCL1m6eY72RpM63z492L03qlZLNpWmSQNvlCW5RkZM9IjHgk35+uBtZV9wMvr92ss+SNyunvIpgOhJIDX6uYyiz7u+E8C3AJYD+ISZvU8QraOE7j31SaHQrcTtixT/OkGhW0HbJzmCIKRG1E4WAwH0BHAtEfVUkt0CYA8zdwXwAoBn9Gt7QhvIPg7AAACv6/ICIx3hkFW1Rhco8ehsZIVmjxn6ZATb7pNs3EeKclPVJxKyqRuodl4p89jdnO2I/NQHmZ2MKE+hrMmkVULX7YyMeFPF/Kjrnj2mWe5Z9LqHeLLvhm30RQiMy1QGIN1u0VVdm761BdKNW+O5HjNXGgf6/1OPr/QAM49j5u7MfDQzPxmEDrmG32FYnuX4IyZ378snSX55ZfyT44uYUDQ4gpBlmDtZ6O27sZNFNJcDeFf//2cA+pH28l8O4GNmrmDmtQAKdXkZQzU2DPz8EtQoW1XGfGeUOdBuB3FjIm98HiSNXcQpcZh3unFtWJlznp2SxZ/j7aYdsF1sLcmycGNsJhNh5SWEPp1znlXZeQ43k44F+9KJ16iPGHkp5he5PnjXQir1yfG+9b8791cmTBdm3BrPO4joMuOAiC4HsDM9KgmZwrdwa59GGcPwoRDc43v98W1QwBcxglCXiLeThbrQh5lGjwIrAdDK5bVZg11fUTWeYy/U/pjbGyXppVKN7tgQdEs2tsQs3mTnefYafp3ydYn1UX+11T/F/BNh52mOVSH13N0YlMY9Oxmn6SJmEMpG55g2O1MjMA54HUBwuxJ8stc7EapwZg8dKfupGcbv4agnXnBaMMzgdgAfEtGr0L4nGwHcmDatchTfPHSmPF/EhQ6v5eSXMea7HL/uy/MghT9yfCdkHn5BqEPEe21inZbx07i5Ni07YURkx8/dzy5adY11zrPdglemweVSrhrGbMqxMcLVMGznraoU/ew+kBnuz0baQzvPsX6sXucg18+w5ZgBDQ+NSzJ61brsM7h68Xwg24ydVLW1X6Qu8bGtHur0Eea4zzR2zbvwlHdqmiSem59l1SkuroxnZl4DoA8RNQVAzLwvvWoJmcC3ucG+zTH2RYzMxc0QYas/giCkjONOFlFpiomoHoBDAex2eW3adsLQZGt/0zlwZqzgGzF2FR2S/I7V1LK5MBXgvGCYmU+SJefsME+xe5xig+3klTIwyqPW6QaU670MMpsDFuqxD7U1mTrqNpw87pznNDansSNi6jHFT5dpUlQgsgid3e/uBLs2roMvqaSxGwCwpsmQMgHi1vMMIroE2oIgjSIfFH4sTXoJLgjd3GC/wm59kRKeOc+5ukq2QVjKOSJHfM+CkCTmThYANkFbAOw6Jc1oADcBmAHgKgATmZmJaDSAj4joeQBHAOgGYHbGNI9DOjqldovbxHxuXBrBFdU1aNygnu3iZnafMdXzbNfumitFu5177TqdP9hH0CmedYcMbRdbSqEZiFmcLK5G8fJyn5mrpIqhbXtNmps6dRDBMXub8ss0jlMsFMLWYwhF2LYNzHE85S5Xozf3Ys8B49qV8UxE/wLQGEBfACOgNZyBNo5CCMObvRqHYZXjTUwI55b7JMf38Hh/CG+zIwjhhJmricjYySIfwDvMvJSIHgMwl5lHA3gbwPtEVAjN43yNfu1SIvoEwDIA1QDuYObk9orx6z7szvvQWYuEbRuDod6M1pgw7ZjfE6d3wsjfzo5w8rKlG7tVttV2xf6Zctzf/Rg4ScuCYUnoZXvPynG8OpFOb2bsFADS/6Yty5Rwu7e5Hbbh28neaEzYtlV2bFh3cuLTiZfZHWodjFnULws97ipuPc9nMvOJRLSImf9GRM8B+DydignpxzcjyrMm/soRMoPfxrwgCMHBzOMAjFPOPRz1/3IAv7K59kkAge1+EWNA+fBRUb9rNU5h28r30PWCYbBeaL9AlmEMJ+eKTdd81VSlqpFPdu1IpByTyymS3MOCR4rr1w8jJ6lVwM0yoWg1Yoi3oJifj9tJU1sDK+BGPUnHs2vcb+GVXN0LegX8ZNDKIP46BXbH5nn9b7qeTyZxu9r2Qf1vGREdAaAKQOf0qCQ44dcon1/hu37L8YrMxc0ylI6CV8I2Ci4IQmawX1na+7e82mWPL9n5jrVWO80xVDbZVbztQljTMeDgBvX+jGeTbzO/2DFk2uZ8eVXywQ8xnlUlisCPcFo37ZMxQBJ0W+Ycru6UwC9NksPvAaNU30W1DxkbJZF9uPM8K8dKQXmNDAgDbo3nMUTUAsCzAOYDWAfgv+lSSnBH6GwE3770IZPjNTzebzk+hUmHrQb5Nwc7XPclCEJ6sQvhTUmWzXljzrOTp9Qtqk1rt01S5Jj065ILB4/Zn1r1TCeUZk+q/d9d+t6uSzeXAIheWVr7O3vdbgDAqHnF+u/ujH+Dwu37AQBfLNiUmoJw9qQ5DXB4xSlU3cBN9n4akrHhuO7SZZq0eZ71v07lnmqtiAnLD2FXxk11sktj3I7bRQDDjCvjmZkfZ+a9zDwKQCcAPaLDuYRgCOOL5Qcy59lBTkjmPPtN2PQRBCE7Mb5xfhoOMXOe7bZYchlubP7ulM7h2IlkQyqd5Xkr0ymrdgAAPplbrOeryTNWjl678wAAYGHxXj2/5OSXHqzypJ+mk4afbVIy9+E2ms/Natt+9Bvst3AKZ6OduvEe/zr7+b/JTinguMe2gxAhsDFjB45ilYr9RiUe8KoJw415xO2CYY0A/AnA2dDufzoR/VOfAyVkKeq8mpTl+KBLLsvJVZw+/K7lSEkLguAD6eyTGR7c0vJqAMDOAxU4tHH9OAt/xQ8/VonxPCvX7TpQGfc6jr/od2w6Q+90FUqKYlVPslkOMZ58pwXD4h/7cb9OA95emjx33uL4aTPVUtqq6BAtEZaWPOnVtj1OkYi5LsXr/XKQeMHYisqTQe+QJtnnE0bchm2/B22bqlcAvArgWADvp0spwR1hGfULm4fWwL8w4JDJ8UeM9+dlyAnJqu+CINRNYg0p/ztn6pzn5VtKrXmqOjkJZMufqNNKaKzD7yrq5zhmayvbTnFmOrRqx9nIV/WimothOagVe7/udbG/ZevAcIzH0H0WJgcqqx3yTJ54bW9Sq3r7pIzTdmmZxutq6W6Nabv7c73OgJmfk4aZw3Eed5zf3b4fRjKbXf+yCrerbR/DzCdFHU8iooXpUEhwJqxGRpg+AOEiZL7wkIbMSP0RBMEL6YxiUTvExgJX9qtEJ5ZnGLVO3iZDjGF8qf1y23s2O6o2HXkz/8y2B6oxbxyqK0cbxrTtnGel/NTzbog15K2/q9EAXtqovWVaOPmkldtx4xkFCdOqA8p2nsx4+tjNz07nY45ZGCvgLobX70BMHUpybre6Mr9zfi4ThgA3Zev0DuZC2LZbz/MCIupjHBDR6QB+SI9KgmtCMjfYHKX1KkYZ7U1djM9yfBqs8E2Ob6tS++Ux9kb2f0YFQQgTSXuB46B+16prrO4S+32e3WEaNWr7qSi7a38FAGDa6p3az6qXTDHw1JWa7cLDbbJzTaoGiqqPqa+Szs54NjreRvh8jF5JqGXMY7fbc9oOL33/ympnt5v6DA1jrH5+8q1tIkMm2ftUk4d1cc6aJD2b7u/DXV/Xzji0K+8qvR7WS+H5+k2siskPjDhFf+RC2LZbz/PpAG4kog36cUcAy4loMQBm5hPTop2QEUL6/UuZkPl5fRtV9E2OP2JMcq3+CIKQHTgZPYaBtc/G0EoGNWw7zzROrR7QKtPrlPhLa+d5VvuV7Zo3AgCc1bVV3N/zdUWMDqkREml0xO06qjFe14TaJp/ODnvPc/ywbTU/4/5aNWkAIHIfW0vLLcduqNKFRSLErdfOLNoFAKjUrbGvl2wFAOyvSH1RMjeGmhnOq/81DO76+VZ/V9x9npVjt1uspYJZbiGzhfzeCsmoU/Xy3PkbTc+z46KB2t+9Zdr6Bg2U5xuEkWns47xhV1n8312cc4oWyYWtqtwazwPSqoWQEt49fv5UYL+NQ68eWt/lhMTDrzaoKcvxzWPsV/3xKVJAEIQ6hdr2VFRbt5M6UGE1mtUQ5nid24Ub98bNq1qZqGcYrcaq0Zv3HgQA/HPyGgCxc6LtdC/eo11nqKbms003Cg0DSpU7aeV2AMAevQO+W/97sFLb53jjbmsneMHGPQCA1fqWTsnicpFwx+sNduieddWorDYNEGv6Kt2QNZLPXb/b8vs3S7e61uXt6WsBRMreeBbGYm1z12tl9cncjbjvomPM68r0snV6xvFw41yMtIla4p16GZUoK4nHM67Uc0bEQtx8bM4bdcMo4yrlvTIwfv9wluZXW75lHwDgpfGrAQA79lXY5p1ODGPULT8UamU0VS+rj2dvBACs2Krdz3rdkDQGeP7302YAwLZS5/szVo8H7PtME1Zo77BRt1+bVGjJ34lkjexE6Y1fjHdArXPxvplO0x8MWcZgph8r4geN262q1jPzegAHoZUta6fN84IQ2hAeITsI61x+QRDCieFttDPkLn1luuX47GcmWo6XJWH8qKGgap4dWza2HP+4ZleMjOjQb6MTOnqh1hE3jOAHvlhiueahL5cCAOas0wy5+z61LjdjdOwPVGgG3V3/XQAAmL9BGwT4XNnveJgi360xHK/TbBjoBss2W8vTTYjy8K9XAABGzS+2nC9TZBuc8/dJAICd+n7R94x0v/zOyDkbLMfGQIfB9SNmxb3usMYNLMdHtDgEQGRP6U36wEk0hTaDE/n5eTFlqU4JMMJ4dx3QjLMrX/8RgFYmxv7YANDz8OYx8t+YYr2n3783N64ebjA8hAdsnkWD/Dw8PmaZebx4k1bnDE+98UzVQSz1WC2PeOWZCPX6p8atsBx/qwyolJZbjbf//LgOQKS+7tP1s3uGBu2aN0yoBwD0/cdkewFK8iYNNX+mEeFgMGeddYBo4optluOjHxhnOS6rTFy+d3403/Z39RbaNmsUo3JFtbU+TNa/XQZjF2+xHF/0wlTLcXSdyVZcGc9EdBkRrQawFsAUAOsAfJ1GvYQM4JuH1gdd/JQjZIaw1R9BEOouaoc8HptLrLtrXvKy1bhWjb1xUZ3A8qoaFAwZax4f1sRqUFUpHuPu7ZrF5N/z4W/N/+seCPP40hOPAABM1fdBtsPOm9e1bVPLcb14Mb0OqB4ki9eMgRVbI8YxA/jDB/PM4xlrdmHQy9Ms13d/0NpNjJZ3R9+jLb+deXQry/FvzypISneVvse0wZJNJZZzg0ctdn19dFm0bd4I8zfsMY9bNmlgMSB6Ht485rn1f35KXLnMHFMuXYdZjy9+UTM2yqtiBx+ufmOmRa/vFMPwdWVAQOXB/1nL4Olxyy3H63dFnlG9PMK+KEOTwXh14mrzuGWTBqb3HgB+f04Xy4DK/12seeuPeyRS7+Mddx5qNf7OGm4d5PpgptVH9+jopZbjv30VMcY+mbsRKn94f57l+MRHv4tJE49BJxweY3hGP/ctJeUxhqwRJQDE79uodfLJsRHdmzaMHwz8q3/NsBz/7j+JB0SivzMAcOu7kfQTlm+LMc7V72D0NyY/j2KM62Me/MaS/vYPrMa4E+tsQsKzCbdh248D6ANgPDP3IqK+AK5Nn1pCIswFRvwKA/YrLNmbGB/DpP0qH3eLQ2RMjt/3Fbb6I45nQRBSgMGWDjmDTY+uQbRRYEe0UZNHhD99GOkUrttpvZ4IeCrK8OjcqonFuG6Qn4dr3rR2eiujPIy1zBajoX1zq4cHsHbE/3pR95jfo/Nr0bg+Vm+LhHnecnZnDP5skXk88Pj2FoPw7K6tMXut1aOlerCivWa1zBjwotU4jjYYZ62N9bSrRMs7/NBDLB6rQSccbvFcd2/XzFK+N/TphGvfjBiOjernYWVUWOsvTu5gMUwmrdyBSSvtByK2lFi9m9HeunO7t7GUxbLNJfiF7v0FNE9gtEHTID8P19l4rQHrc2zbrKHpWXZD9FSDIw87xAwtB7S2M3oAQyXac3pu9zYAgA9mRrzvpQer8MbUIss15z07OeqIcIJiaP7ju1WR/JX8GtbLx7EPRwyrDi0OifHyOhHPe/vg/yLRErW1bHqKDaKP74+q85eeeHhMNISKOhj1eVQEROfWjdHn6QnmcWl5Fc59dpJ5/P6M9dgSNSDHDPR+YrxtXszWaBgG461pkcGHkoNVOOXx783jX/TqEBPdoaJORVGjGIBIWDgA3PJurOEdHYHDYMt3ixk4M3owQ3k86rSQaH0GndAePynTYKLvR53jnU24NZ6rmHkXEeURUR4zTyKiZ9KqmWCLf3N6c9yo8yYmvHOefRvsCMdq2wZiOwuCkAqx2xVFwpcNoo2CglaNLb93bt0EJWXWTv5+xZOtektqaxlvRhke45dbQxdnFFmNyTU7rCGgI+dYPWSfzN2IO6LCKQed0N7SEScii7GsUlPLuDAqPFI1ig4/9BCLQXjcEc3x6zcineQlm60eMZUKxSv/4xrrXNp3ojyQQGyI9GYlFLeWGTf/e455PGnFdouRlE9kKd9OrRrj/SgPZHlVremhBYDP52/C5/OtIerRqB7XM562ejejvXWdWzVGdKCpESJucNKRLSzHq7ZZ56ZOUsJYo5/jHqWeFW63Xjsvag5380b10CXqmUUbzppeVsNP9YJGe7/LK2vw3HcrLb/3ijLUAGDPAet9quG56hxf9b1T5+uXllfFeHlPVvJUB6XU0Pmhn1ufmzodQq2H0TRrVC8mGmLCcmsZnfqk1di995PINICXJhRa7lk12g9pkG85nl5o1UX1MhfvsX5DZhZZB6++XrLFnB8MaIMl0YMRrZs2iPkGRNePji0bx0QxqF56lf/Otr6npQersWZH5JnM27DHMkAwb4NVZ2MaBaAtahitz1EtG+OK16wbM0Xfz8XHt4+Z358tuDWe9xJRUwBTAXxIRNsBeF++UvBEltU11+Talk6mnNBteeWLGN/Ito+nIAjh4HxlXuHpT02In1Bn3a4yizHcqkkDnPSYtZOvduLVOblXKaGU7/xgNR5V+j1nDeN9cfxqy/FcZV7juMXW0Moqh/13Xhi/KuHv//7Rqp9qXP82ypCNhxpqe+dH1sEJdesoNUT6TCUU9+EvrZ36XYrhdv+oRZZjdX5yskR7XJ14d0bipXzUuvHoV9Y5nNFl2b1dU6zaFhk4UY3B/s9b54P+8p+Rjz4CUQAAIABJREFUemW3HZdB9GBD22YNE4bzzl63G7OVOqaiGtNqOK9qCEUPvgCxdUJ9xgCwW3nO6rurGseqcfebt61lf91b9h7/hRtjB4TieV7tUBeE+2ye9RtQtMNq+N/4zmzL8c3KO6U+69uVqAH1nX95YqHl2MkLvWF3bDi0avCrqPVRHUx46H/WNRIS1bEfCq3P7o0pRTYpNb5auBlfLdyMLm2aYOJ95ydMGzYSGs9E1BVAOwCXQ1ss7B4A1wPoBODPaddOEARBEAQhjRirKgeJ3aJMBqqxraJ25FXCvjuMGt6pohrX2UK04ZxOtge0snWYSWZBwGzA6RuRrTh9u8KIU8D5iwD2MfMBZq5l5mpmfhfAOACPpl07IS5+L9Dl1eMXCZP2JwzYK2HbD9k3OSErH/POfHIYi99ZEAQ/adbIbXBdcPz5gq5Bq2DBWOQpUzRWQl/DzNW9j7Icd2/X1CZlbvPqdb0sx384r4vl+NmrTsykOhnn8cuP81XetPv7+ipPSD9OxnMBMy9STzLzXAAFqWZKRM8S0QoiWkREXxBRi6jfhhJRIRGtJKKLo84P0M8VEtGQVPPOBfybqwxf5XgVZNp0fsnxiO/Gao6WT66GkQuCEG6OO8K6Vc+TVx5vOV786MWW47VPD7IcP3xpzxiZ3/zlHMuxugr0d/ecazkufHKg5Xi1cqzyr9+cbDmO3j8YACbed57l+J/XW9Orxu1L1/zMcqze41ldrfqr8lspq4bf0TexMf93B8OoV8fIfOA2zRqiiWIcrxt+ieV42WMDLMc/DLnAcrzmKev9qNcv+Zv1Ga96InH5R/PmDadYjtVnt1zR7Rnl3sfeZa0r0VzYs53l+JGfx9Y1t3zxpzMtx0VKmSRi1gP9LMcX9GiLq0450jzuf2zbmGv+9Rtrubx9U2/Lcb8e1nsbOvBYy/GvlEEG9Zncd2F3XHycVUY09/S3Lo538XHt0Ki++8Wlhg7sYTlW34kbz+hkOV748EWWY7XOvXytdbDg+tOt19+kyFPf2c9uP8Ny/GhUXbiyVwccpWxzp9bxxY9a9fvqzrMtx+PvPdeybdbwX5xg+f2uC7rilE6Hmcfq4oP3DzgGT1xh/Xaq9D/W/nlN+uv5lmOnd1Z9z9T02YBTbYxd/jHCIR7y/R7A8cx8IoBVAIYCABH1BHANgOMADADwOhHlE1E+gNcADATQE8C1eto6TdjmiIZt4Si/isc3Of6ICV35+EXY6rMgCOFm2KBIp/1P5x+Na0/taB7/9PCFAIBf6YbCmzecAiKyGH+/O7sz/v7LyHEeAT3aRwzyF64+Ce/97jTz+KNbT7dsQTXt/r6oF7Vi7IT7zkP9qGN1O6a1Tw/CgOMPtxxHc9wRzdGljdWbOfCEwy3Hd/TtivOPaWMeX/6zDub/j+/Q3PId7dDiEHx4ax8MOqE9AG2wIFp+j/bN8OPQiLGqGjlzhvW3GGq/7n0kfh1lGK0bfgmWRhmv64Zfgs9u1wy9glaNMWdYf8sAhtGJNsr8R8VQNnQ2mHjfeciP2nLrP789FUBkT+0e7ZuhacN6+OCW0wEAk/96PhrUy8Ndujf/yzvOshgu64ZfgjF/1gyPM49uhYuOa29e+9p1J6N+fh5uP097Zv+74yzLglCG7kbZL3joQsuzBqwDK2/d2Buf/CGS981nFpj/X/3kQMx/6EKL7Oh6tiDqtzOPboVeHQ9Db934GXfXOcjLI0y7vy/+dP7Replb7zGads0bYcr/nW8ev3PzqXgmqs6PuOlUzB4WMbBXPzkQA45vb5HR95iIgT1nWH8c0iAfF/TQzqmGvPHeGQwd2AMN6uXho9+fbp77c79uFgN95RMDLINQd/fvhrkP9jeP37ihN5b9LTKQsW74JXglyqBdN/wSRO/M9ofzjkafLi0BaF5yIsLapwehe7umeOOGU/DY5cebZdKxZWMc2rg+mutRKp/efgby8wiHHlIfgGbYXXbSEda88sgy6PC3y4+3/B79zi5+9CL0LmhpHhc9NQg3n9UZAHBaQUu8cLU2+GU8f+N9+vDWSJ1u1qg+Vj4Ruf8TjjzUHDT6/Tmd0bVtM8x6IFJe15zW0awHV51yJO696BiM+uOZeGBQD9zTvzvuvKCb+a4/e9WJ+NP5XfGbPtoAQNtmDTHqj2dYBtl+evhCvHVj5HmtG36JOYjXqkkDdG7dBOPv1er+2zf1Rn4exWyVZxz369EW9fPzzOe74vEBlnc8W6B4y8KbPxL9F8BEZn5LOX8LgIuY+WrPChBdCeAqZr6eiIYCADM/rf/2LSLh4Y8y88X6eUs6O3r37s1z56a+QXw6MFbKUz9wyWCsYNi0Yb2YUddkKNy+D/2fn+p5sv6Pa3biurdm4fTOLTHyD2c4X2DDlz9twt0f/4RLTzwcr153svMFNoyYVoQnxi7Hb88qwCM/Tz285umvl+ONKUW4f8Ax+NP5qYfW3fvJT/h8/iY8e9WJMSOyyXDD27MwbfVO/Oe3p+L8Y2JHi90y4MWpWLF1H8bedTaOO+LQlOWc/Pj32H2gEnMf7I/WTRs6X2CDH++EyBE5XuW4hYjmMXNv55SCHX62zQVDxqJd84aWzqMTa3bsR5fWTUxDs7aW0eWBcbinf3fc3b8byqtqUFFda3agS8urUFVdi1Y237mqmlps31dhMfwMmBmj5m/Cxce1Q7NG9V3p9/z3q/DyBG2O87rhl4CZsbW0HG2bNfKlo1lby6hhjjH+7Bg5ZwMGj1qMa0/riKcVr1YQPPDFYnw0awMev+J43NCnk2P67aXlaNaofszqyH5RU8vYsLsMnVs38STn1CfHY8e+Csx6oB/axdm6zC3dho1DVQ1j+WMDPN3ztNU78PWSrXjqSn+eeU0te6q/6re+sroWDeoFt93R0Q+MQ00tZ6ztcSLTbaETudw2O00I+guAL4joegDGsnC9ATQAcKVPOvwOwEj9/x0AzIz6rVg/BwAblfOnI0s5uo23D2z9PO1jcezhzRxSJqZxA+3xd2/rTY7RwVBHzJPFMMA6tWrskDIxRqMTryOTDMb18fbfTAZjlLx1s9QNTADo0roJpq3eicMaN3BOnICubZtixdZ9aNrQ23zAHu2b4cc1uzw3Xj3aN8OKrfucEzrQtllDXxZNqZ9PSe3DKQhCcKTSMTtaaavy8sgip1H9fDSqHzE6mjsYvfXz82zbGyKyhMm64cQO1kFNIsLhh3prz6LJyyPkJRHDdEWvDli8qQT3XZjZ+dB2XHbSEfho1gac0aWlc2IAbT224U7k55FnwxnQIhtGztmIth77Chcf1x5jFm1BvXxvAy3ndGuDc7q1cU7oEq8DP3f27WrZmipIwxkAFj5yUdx9qYMkTPPw37jhFF/eizCS0PNsJiLqC8CIS1jKzBMTpdevGQ+gfZyfhjHzl3qaYdCM8V8wMxPRawBmMPMH+u9vQ1ucLA/Axcx8q37+BgCnMXPMit9EdBuA2wCgY8eOp6xfn3jLgUwzd91udGnTFC2beDOAZqzZhZ5HNDcN11SZvnonenVsgSYeDanJK7ejT5dWlg5HKkxcsQ3ndGvjekQ8HsyMCcu344IebZHn4WNdW8uYuGI7+h3b1lNIcXVNLaas2oF+CeaMuKGiugYz1uzy5HUGgLLKasxbv8dzo1haXoWlm0pxhjInMFn2HKjE6u37cVpndx0hO3bsq8CG3WWWuT2psLWkHNtKy3HSUS2cEyegeE8Z9pZV4fgOqXv3AWD9rgM4WFVjCSdNhTU79oOZ0dXjYNmqbftQPz/Pc6O4bHMpmjWqFzPfK1kWF5egVdMGOMLjYJlbxPPsnTBGhYWN92eux5W9Onge5BTqHpXVtdh1oMLXARch/FTX1CKPyFO/N5vJZNvsynhOS8ZENwG4HUA/Zi7Tz+V82LYgCIKQvYjx7B1pmwVBEAQ/yWTbHEjMAxENADAYwGWG4awzGsA1RNSQiDoD6AZgNoA5ALoRUWciagBtUbHRmdZbEARBEARBEARBqJsE4nkmokIADQHs0k/NZObb9d+GQZsHXQ3gL8z8tX5+ELR9p/MBvMPMT7rIZweAcMVth5vWAHY6phK8IGWcGaScM0NdLOdOzOzfRMA6iLTNSVMX37NMI2WcGaScM0NdLOeMtc2BhW0L4YOI5ko4YnqRMs4MUs6ZQcpZENKPvGfpR8o4M0g5ZwYp5/QS7FJ1giAIgiAIgiAIgpAFiPEsCIIgCIIgCIIgCA6I8SxE82bQCtQBpIwzg5RzZpByFoT0I+9Z+pEyzgxSzplByjmNyJxnQRAEQRAEQRAEQXBAPM+CIAiCIAiCIAiC4IAYzwKIaAARrSSiQiIaErQ+uQgRHUVEk4hoOREtJaK7g9YplyGifCJaQERjgtYlVyGiFkT0GRGt0Ov1GUHrJAi5hLTN6Ufa5swibXP6kbY5/UjYdh2HiPIBrAJwIYBiAHMAXMvMywJVLMcgosMBHM7M84moGYB5AK6Qck4PRHQvgN4AmjPzpUHrk4sQ0bsApjHzCCJqAKAxM+8NWi9ByAWkbc4M0jZnFmmb04+0zelHPM/CaQAKmbmImSsBfAzg8oB1yjmYeQszz9f/vw/AcgAdgtUqNyGiIwFcAmBE0LrkKkTUHMC5AN4GAGaulMZZEHxF2uYMIG1z5pC2Of1I25wZxHgWOgDYGHVcDGk40goRFQDoBWBWsJrkLC8CuB9AbdCK5DBdAOwA8G89BG8EETUJWilByCGkbc4w0janHWmb04+0zRlAjGeB4pyTWP40QURNAYwC8BdmLg1an1yDiC4FsJ2Z5wWtS45TD8DJAP7JzL0AHAAgczIFwT+kbc4g0janF2mbM4a0zRlAjGehGMBRUcdHAtgckC45DRHVh9Y4f8jMnwetT45yFoDLiGgdtDDHC4jog2BVykmKARQzs+Gh+Qxagy0Igj9I25whpG3OCNI2ZwZpmzNATi8Y1rp1ay4oKAhaDUEQBCFHmDdv3k5mbhO0HtmMtM2CIAiCn2Syba6XiUyCoqCgAHPnzg1aDUEQBCFHIKL1QeuQ7UjbLAiCIPhJJtvmnDae/WL+hj0or6qJOd+2WSN0bds0AI0EQRAEwT+IaACAlwDkAxjBzMOV3xsCeA/AKQB2AbiamdfpiywtB7BSTzqTmW/PlN5C8JRVVqOqmnFo4/qB5M/MKN5zEEe1bBxI/mGluqYW9fJldqYg+E1o3yp1I3Ui6kxEs4hoNRGN1Pcuywj3jvwJ1701K+bfwJemxjWqBUEQBCFb0PcUfg3AQAA9AVxLRD2VZLcA2MPMXQG8AOCZqN/WMPPP9H85aThv2nsQ3y/bFrQaJrOKduFfU9YErQYA4LxnJ+Okx74LLP9P5xXjnL9PwqyiXYHpAGhG/BNjlqFox/5A9QCAb5duRddhX2Pl1n0Zz7u8qgYLN4Zrd6T9FdWYtGJ70Gpg2eZSfDx7Q9BqYOnmklDU02wltMYzgLuhjWYbPAPgBWbuBmAPtIY8I7x4TS98fFsfy78b+nRCVQ2jskZW3BcEQRCyGjd7Cl8O4F39/58B6EdE8VaEzkl+/sp0/P698ISaX/3mTAz/ekXQagAAduyrCDT/n3RDbdX2YI2BdbvKMGL6WtwagnoyXh/oCcKIfeh/S3D5az9g096DGc/bjv/7dCF++585WL/rQKB6DHp5GoZ8vjhQHQDgkpen44LnpgStRtYSSuNZ3Uhdb6AvgNZgA1oDfkWm9PnZUS3Qp0sry79OrbTwoBxeb00QBEGoG7jZU9hMw8zVAEoAtNJ/66xHik0honPiZUBEtxHRXCKau2PHDn+1zwC7D1QGrYJggzmCE3CHzNCjpjb4jmGePq5VG0CZLN5UAgAoKavKeN52rN2pGc0HKiRaVPBOKI1nxG6k3grAXr3BBuI37IIgCIIgJI+bPYXt0mwB0FHfU/ReAB8RUfOYhMxvMnNvZu7dpo0sVi74h2EoBm2yGnEYQRisKhFdgtOBA38isYRRJyH7CJ3xbLORupuG3bg+I6PbZrSavIeCIAhCduNmT2EzDRHVA3AogN3MXMHMuwBAb7fXAOiedo0DIpe398xWTEMxYI+v6e0NwWy+SBc182ViDmaE6FUJo05C9hI64xlxNlKH5oluoTfYQPyGHYCMbguCIAhCkswB0E1fmLMBgGsAjFbSjAZwk/7/qwBMZGYmojb6gmMgoi4AugEoypDeGScMIbmCFcO7EvSTMQ3WUFhowRmLkXLIfN52hFEnIXsJnfHMzEOZ+UhmLoDWgE9k5usBTILWYANaA/5lQCoCiP5Yy5soCIIgZC/6lKg7AXwLbaHOT5h5KRE9RkSX6cneBtCKiAqhhWcP0c+fC2ARES2Eti7J7cy8O7N3kDlqpPcdOigkXsWwhI8DQF6AhnykHMJQEhpBeuLjEY4BFiFVsmmf58EAPiaiJwAsgNaQC4IgCILgEWYeB2Cccu7hqP+XA/hVnOtGARiVdgVDQhhCcgUrZZXacjh7Dwa7QFWY5jxHFgzLfN5h9PJSgJ74eDBHyknIPtLqeSaiu92cs4OZJzPzpfr/i5j5NGbuysy/YuZA90YI48dBEARBEIT0ERbPlRBh8aZSAMDMgPd5DtJgVQkyhJxC5IE3yAvRwAYgESzZTrrDtm+Kc+7mNOcpCIIgCILgO2EwjKKR8E/grKO1HdP6H9s2YE00wvBMgpwHbuQdFkMViBj0YXl/w1Q2QvKkJWybiK4FcB20vR+jFx1pDiDYoUGfkGgLQRAEQahbhK3TW8tAfh3vkIQl/NWoGmGoIhRgoYQxMjNSHOFQKkxlIyRPuuY8/wht78fWAJ6LOr8PwKI05RkIUv8FQRAEoW7AIZvzXMuMfBnODwVGSH+Y+oWBrLZt5h2ekojoFKgaJmHRQ0iNtBjPzLwewHoi6g/gIDPXElF3AD0ALE5HnpkmyFE9QRAEQRAyT9g8zyFTJ1DCUhZhqCNBLl4WplXHDcKmUxjqiJA66Z7zPBVAIyLqAGDC/7N33+FtVffjx99H8kxsx048kthJ7AxnD4LJAkIgYY+wScv6UspooZRR2rA6aEsLhba0QPlRVoEWCDuQQBgZZEASZ+/EiZPYjmcc7ynp/P6QLMuOLMtDvtf25/U8fiRdnatzfK6kq889C7gFeD3AeXYpM11ZE0IIIUTgmO1Hr9nKYwSzTFDVcCgcJhhYqwzsjeCeOM0E9dDAfTHBJGWSz233FujgWWmtq4ArgX9qra8AxgU4zy4hDc9CCCFE72K2n7zyI9w8S0TpZrdGMnLcceOayubhXqrK4HI0MEkML9op4MGzUmomcD2wxLWtO60t3Sp5/wshhBC9g9EBWnMmK44h3F1yDa6Lhp6IRpcDPGfbNq7btqk+KyabxEx6rXZvgQ6e7wUeAj7SWu9SSg0HVgQ4TyGEEEKITmPGGYRB1osF801QZYZyWCzGXVCwuCILE1SDm5EXE7yRlufuLaCtwFrrVcAqpVRf1+NDwD2BzLOrmG3mPiGEEEIEhkUp7FqbqzUN883+bQTztDw7b80QGDWutdz1eTccD7sZKsLFYrK+5Gb7HhFtE9CWZ6XUTKXUbmCP6/FkpdQLgcxTCCGEEKIzGRmM+CI/wj3HPBtbjgamOCbuWFG6bYO8R0TnCnS37b8D5wPHAbTW24DZAc6za7hnd5QPgBBCCNGTmXEGYZAf4WCeLrkNh8IMR8TI1niLCYc4KAMvJnhjproRbRfo4BmtdVazTfZA5ymEEEII0WlMGBCAeVrSwLixvsrd0mlI9icxw5hnI8eBm7LlGXN07W9gproRbRfoma+zlFKzAK2UCsE53nlPgPPsEu6VquT9L4QQQvRojd22zXXSN0Og1sChwWrAMp6NraxGL1XlzN8MQbyxS1WZb8yzyYY8m+I9Itov0C3PdwJ3AYlANjAF+GmA8xRCCCGE6DRmbE0Dc/0IN6puzNJN2N1t2+iCYOy6xhaTjS/2ZPSxcY+9NmPlCL8FuuV5tNb6es8NSqnTgbUBzjfgzHYVSwghhBCBYbYJhxqYKZg3qiyNx8YcdWGG94iRLc9m6QngSSnjLiY0KYerDCaqGtEOgW55/qef24QQQgghTM5cv3rNEjCCcQGBWQIjo/P3pAzsKdGwzrMZLiI0MMtQSyOPi+g8AWl5VkrNBGYBcUqp+z2eigKsfuw/BHgDGAg4gJe01s8qpfoD7wLJwGHgWq31ic4tvX/MNvmAEEIIIQLDYrJJqRqY6TeIUWUxS5d6M7W0NnZl7/oymTFAdNeHwdGzWedOEG0TqJbnECACZ3Ae6fFXBlztx/424AGt9VhgBnCXUmocsBD4Rms9CvjG9VgIIYQQImDM+qPXTOUxutu20VVhniNh9Jhn8wXP7oDeYXQ5nLfmqRnRHgFpedZarwJWKaVe11ofacf+uUCu6365UmoPzknH5gNzXMn+A6wEftUZZW4rs60ZJ4QQQojAaJzox9hyNGemGY0NC55dt0a3/JooVjT0goLVJBczPDWuBW4s5Rr1bPR7VXRMQMc8tydwbk4plQycAqwHElyBdUOAHd/R1xdCCCGE8MWMXVHBXN3IjSqLWbvUG8nIbsrmbHl23hodtJp14kHRNoGeMKxDlFIRwAfAvVrrMj/3uV0pla6USi8sLAxc2QL2ykIIIYQwE7N0DW7O6GDAk1FlMc9s20bn30gZeEHByLxbYpZJ5czzXhUdYdrgWSkVjDNw/q/W+kPX5nyl1CDX84OAgub7aa1f0lqnaa3T4uLiAl5Oef8LIYQQPVtDa5rdZCd9MwUoRrc8G31ojM7fG2OWqnLemmktY7N07W8Yi2624R+ibQIaPCulhiulPlVKFSmlCpRSnyilhvuxnwJeAfZorf/q8dRi4GbX/ZuBTzq/1P5R0vQshBBC9AoWk7YYmak8xk8YZvCYZ0Nzb8rIeXmsFvNdaDJLzxFpee4ZAt3y/D9gEc4lpwYD7wFv+7Hf6cCNwDlKqa2uv4uAPwPnKqUOAOe6HhtK3v5CCCFEz9Y4W685zvpm/BFu+IRhhuRuTka2xjd8Vsw0mZ27xdfo4Nl1a6KPrWiHgMy27UFprd/0ePyWUuru1nbSWq+h5WHFczulZB2kZNSzEEII0Ss0tDybJSCwKIVda3P9CDeq27bFHBNUmelYGNl12owXdsxSJmXS4R+ibQIdPK9QSi0E3sH5tXodsEQp1R9Aa10c4PwDzuhuQkIIIYQILLONeTbjutNGXVcwywRVZlq61Nh1np23ZrnQBOaZAbzhc2umuhFtF+hu29cBdwArcK7J/BPgR8AmID3AeQshhBDCD0qpC5RS+5RSGa6L3s2fD1VKvet6fr1rGcmG5x5ybd+nlDq/K8vdVcwyKVUDMy55Y3RgYvSxMTp/T0aO8bWYsNs2MubZK7MMQ+luAtbyrJSyADdordcGKg9DmeSDKIQQQnSEUsoKPI9zLpFsYKNSarHWerdHsluBE1rrkUqpBcCTwHVKqXHAAmA8zrlNvlZKpWqt7V37XwSWxdXUYJaAwNnaqk3zIxwMDAjc+ZqnLsxC1nl2MktPjYZeEja7OerGoTUWGYbaZgFredZaO4CnA/X6QgghhOgU04AMrfUhrXUdzqFW85ulmQ/8x3X/fWCua2WM+cA7WutarXUmkOF6vR7FbN22zbgckNHL7xidv0neGoCxPSUaW567Pu+WNMwAbjP482K2WftN9PXRrQS62/aXSqmrlOp5Czv1uH9ICCFEb5UIZHk8znZt85pGa20DSoEBfu7b7VnMNtu2SWYP9mRUQNCQq9EBianGPBu4fJfZAkTwCJ4NbvF1tzyb5INrpmPUnQR6wrD7gb6ATSlVgzPm1FrrqADnK4QQQgj/eLse3PxXVUtp/NkXpdTtwO0AQ4cObWv5DNcQjJil27YZAxSjAoKGKjD60JjoULgZMmGYxXxjnoNd4y5sBndPMFuPETN9f3QnAW151lpHaq0tWusQrXWU63GPCJyVySYPEUIIIdopGxji8TgJONZSGqVUENAPKPZzX7TWL2mt07TWaXFxcZ1Y9K5hljGTDZQJx5UaFSw1tK6aqeXXaA1vC2PWeXbemil4tlqdhao3fKyx2VqejS5B9xSQ4FkpNcZ1O9XbXyDyFEIIIUS7bARGKaVSlFIhOCcAW9wszWLgZtf9q4Hl2hm1LAYWuGbjTgFGARu6qNxdzizjOBuCeRPFzoa16umT7ogGRl5cMdOFnSB3t21ztDyb5cKCmY5RdxKobtv34+ye9YyX5zRwToDy7TLuE5d8WwshhOjGtNY2pdTdwDLACryqtd6llHocSNdaLwZeAd5USmXgbHFe4Np3l1JqEbAbsAF39bSZtqGxpdcsE4aZsXXP6LIYHQiY5K0BeI4DNy5zo4+HJ7NMGGa2z61Zuo93NwEJnrXWt7tuzw7E6wshhBCi82itlwJLm237tcf9GuCaFvb9I/DHgBbQYO5u2yb5sekeOmZwOTz1+jHPJjoajROFdX2ZGnI0Sy8NaGx5rrMZ3fJsrotwRn9muqtATxiGUmoWkOyZl9b6jUDnG2hGLkAvhBBCiK5nlhYjZcIJwwwb89zsVpjjfWGGMjRn/IRhDZOpmePKghmPUXcQ0OBZKfUmMALYCjR049JAtw+ehRBCCNG7mOXHptmWzgLjlgFqaGU1+tiY5K0BNLb6GnFBo+F4mOVCEzQeG+OXqnLemqVV3ujPTHcV6JbnNGCcNmKhuQBztzwbWwwhuhWb3cH+/Ap2Hislr7SG4so6gq2KfuHBjIyPJC05htiI0C4vV3FlHZ9szSH7RDWj4iO4bMpg+oR0/OvRZnfwydZj7MsvZ/aoOM4YFduu18kqruLdjVkkxYRz9alJBFn9n+vxUGEF723KJm1YDHPHJvi1T0WtjdfXZpIQFcbVpya5u4i2pLrOzqtrM0mMDufyU3wv8WuzO3h93WH6hQe3+tqrDxSy9WgJt5yRQkQMA6LZAAAgAElEQVSo9+OhtebtDVkEWxXXpA3xmkaIjjJbS29DN1TjZw9uZHSwZPRPTfMcicb3qRHBYsNhMPr94KmhJEZ/XhqDZ3NEzyYpRrcT6OB5JzAQyA1wPkIIk8orreHrPfl8vSef7w8dp6a+8ds6IjQIu0NTXe/smGJRMC2lP7ecnsK5YxPc60UG0qfbjvHwhzsor7URGmSh1ubgn8szePGGU5mY1K/dr1tTb+f2Nzfx7f5CLApe+vYQ981L5efzRrXpdQ4VVnDlv9ZRUlUPwIbDxTxzzeRWA1qA7BNVXP3idxRX1gHw7IIpzJ/iO7jVWvOTtzax+kARAGU1Nm49I8XnPgs/3M4nW52rEymFzzye/eYA/1yeAUCw1dJisJ1RUM6PXt9IvV1zqKiSv103xWu69zdl8/BHOwCICg/m/PEDfZZViPZQNHS3NLggLsGuC2j1ZikQxneJNToQMDp499TQI6HeiJbnhjKYqD7cLc8Gv0nM9j1ipmPUnQQkeFZKfYrz8xMJ7FZKbQBqG57XWl8WiHy7kiLwP+qF6K6yT1SxZHsuS3bksj27FICh/fuw4LShnDI0momJ/UiMCSc0yApAZa2NffnlrNxXyEdbsrnjzU1MSIziiSsmMikpOmDl/O/6Izzy0U5OHRbDE1dMJDUhgvWZxTywaBs/fPl73r19JuMGt29p+qeX7ePb/YX84fIJXJOWxEMf7uBvX+9nQmKU3y3ADofmvkXbAFj5izl8uDmbfyzP4LxxCVwwYVCr+/9p6V6q6+x8ed9sfvXBdn7/2W7mjk1osRUXYPneAlYfKOI3l47j2/2F/P2r/VyblkRkWLDX9Htyy/hk6zF+OmcEazKKePrLfVw6abDXCx+lVfW8uiaTiycO4khxJf/45gDzpwz2eiHgvU3ZAFx9ahIfbM7mVxeMYWC/sJPSvbMxi1HxEdTZHbyyJtNr8FxcWUdMn2C/LjgI4YtZJvppnD3YJL/CMe6HuHtNY1O1/RrL7u46bdz7w1wtz66LCaZZqsq4cnhe5JHguX0Css4z8DTOZap+C1wOPOF63PDXY5jpSqMQRsovq+HVNZlc+cJaznhyBX/6fC8AD54/mq/um82qB+fw28vGM39KIsPjItyBM0Df0CCmDo3h/nNTWfHAHP567WQKymq5/Pm1/P3r/QEZ1/fFzjwe+Wgn54yJ53+3TWf0wEiUUswYPoBFd84kIjSI299Mp6ymvs2vfbCwgtfXHWbBaUO4YcYwQoOs/OnKiYwdFMXDH+2gus6/lXxW7CtgW1YJj1w0luTYvtwzdxSjEyJ56ot9rdZJRkEFS3bkctvs4aQmRPLYJeMoqqjjvfQsn/u9vu4widHh3DBjGPedm0p5rY330rNbTP/uxixCgyzcMXsEPz5zOFnF1aw9WOQ17bLdeVTW2bl99nBumpHMoaJKtrkurjS3fE8B01MG8NM5I9AaPtt+7KQ0pVX1bDl6gvPHD+TyKYlsPFxMYXltkzRaay74+7c8+vFOn/+3EL64u22bJCAIspqv27ZhY55dgZHRP8fMcyQa36d1NuO6bZsxMDP68+Je8s7A7xHPOjDTBY7uJFDBcw5g01qv8vzD+d3S8q8wPyilLlBK7VNKZSilFnZKadtVDqNyFsIctNbsyS3j+RUZXP2vdcz40zc8/tluqusdPHj+aFY9OIfFd5/BXWePZFRCpN+tfkFWC1dOTeLrB87i8imJ/P3rA9zy+sZ2BbEtOVRYwS/e28bkpH68cP3UJoE8QGJ0OM/9cCq5pTU8+lHbg64/LtlDWLCVB84b7d4WGmTld5eNJ7+sllfXZvr1Ov/v20NNxhEHWS3cfc5IDhVV8uXufJ/7vrPhKEEWxY0zhgEwdWgMk4dE89/1R1u86JdbWs2ajCKuPjWJYKuFSUnRTEiM4pOtOV7TOxyaz3fmMmd0HP36BHP+eGer9pLt3kfqfLU7n8H9wpiU1I/zJwwk2Kr4fMfJaY8er+JAQQXnjIlneFwEYwZGsnxvwUnp1h4swqHhrNFxnDsuAa1hTUZhkzSHj1dRUF7L2EHt60EghCez/NhsGPNs9ARIngybbdskwZqZYsWGQ1FnQEtrw8UMs3xWwHPCMIO7bTe0PBtYNZ6t77UGL93VXQUqeP47UO5le5XruXZRSlmB54ELgXHAD5RS49r7ep3BPF8NQgRWTb2dHdml/GfdYe55ewuz/rycC59dzV+W7aPGZufnc0fx9f1n8fnPz+Sus0cybEDfDuUXFRbMM9dO5o9XTGBtRhHX/b/vKSir6fD/UVVn4ydvbSbYqnjhhlMJC7Z6TXfqsBjuOWcUi7cdY8W+kwO3lqzaX8jyvQX87JyRxEU2nfxsWkp/5o1N4MWVBympqvP5OpuPnmBDZjE/OiPFPb4R4MIJAxnSP5yXVx9qcd+aejsfbM7mvPEJTcpw/fShZBRUsPHwCa/7fbzlGFrDlVMbxyFfOmkw27JLOXK88qT0W7JOkF9Wy0UTnV3IQ4OsnDU6jq/3FJzUQlddZ2f1gULmjUtAKeckcWnD+rNqf+FJr/vNXueFgblj4wE4c1Qs6YdPnNRiv2pfIZFhQZwyJJpxg6LoFx7MdwePN0mzIdP5eMbw/t4rSwg/7M1z/qQxOkBrEGQx45hnY+rmQEEFYHyrYmWtzdD8PeWWOs+VdTb/ejl1psYJw7o86xalHykGjBkD7qnGdQ4zstu251rX/vaCE00FKnhO1lpvb75Ra52Oc83n9poGZGitD2mt64B3gPkdeD0hRDNHj1fx6ppMnl62j4c+3MH/vbaB2U+tYNyvv+DS59bwm8W7WJ95nKlDY3jyqolseHgun/3sTO6dl8rI+IhOLYtSiuunD+PV/zuNI8crufJf68gsOjmI85fWmkc+2sn+gnKeXXAKidHhPtPfOWc4I+L68tjHO/06ydjsDv7w2W6GDejD/52e7DXNL85PpaLOxr99BL8AL606RL/wYBac1nQG6SCrhVtmpZB+5ATbskq87rtsVx4nqur5wbShTbZfMmkQEaFBLGqh6/bHW3JIGxbT5MLHJZMHA/CZl9bkpTvyCAmycM6YePe288YlUFRRy5ZmZVuTUURNvYNzxzWO956dGsfevHLym10U+WZPASPjI9zlOH1kLHV2BxsPF7vTaK359kAhZ4yMJchqwWJRTE/pz/eHipu81vpDxcRGhDAirnPfm6J3WJtRRPLCJe7HZmip+ec3B9idWwYYHzx7ti4a0dJYVWfjfdf8CEZe2KiotXHTqxsMy7+5DzY766SuC9+vdofmZ29v4fV1hwHzXGgC2Jnj/LwY2fL82Mc7Oea6qGHEhSa7Q5O8cAmn/P4r97aGyVqN8ov3trEzx/vQLTMLVPB88qwujXz/WvUtEfD81Zft2uamlLpdKZWulEovLDy5RaOzmei7QYhO8cxX+3j8s928sDKDr3bnkV9Wy6SkfvzsnFE898NTWLvwHL5/aC7PXz+V604bSnyUr49755idGsfbt82gqs7ONS+ua/eX7X/XH+WjLTncNy+V2alxraYPDbLyxysmkn2imme/OdBq+v9tOMqBggoevmjsSV3BG4wZGMUlkwbz2trDFFXUek1zqLCCZbvzuHHGMPp6mdzrmrQkIkKDWuz+/c6GLIb0D+f0EU2XxuoTEsSlkwexZHsu5c26we/PL2dffjmXTRncZHtidDinDovh021NxxxrrfliZx6zR8U2mUxszuh4giyKL3fnNUm/fG8+EaFBTE8Z4N42O9VZvm89Wp/La+pZn3mcuR4B+fSUAYRYLaw+0JjuQEEFuaU1nOVxHGcMH8DR4iqyT1S5t63PLGZaSn+ZLEy0yZ7cMlIf/ZzrX17fZHtVnXGtiw0/fp/5ar97m1GtveActjHi4aWGlMVmd7DmQBHjfr2ssTwG/SD7eEsOE36zrPWEAWZ3aIoqaptc7OmKbttaO9+XIx5e2uQ8sT/fWwfUrlVdZ29SH0b0TiipqmP0o5/z5vdH3Nue+mJfl5Yhr7SmyWe1QVcHz3aHpqrOhs3uIHnhEt7flM0l/1zTpWXoDIFaqmqjUuo2rfW/PTcqpW4FNnXgdb39+mnySdBavwS8BJCWlhawT4n8EBM9VXWdndEJkXz+8zO7ZKkof00eEs2iO2Zy0yvr+cFL3/PyzWlMHz6g9R1dtmeX8Pinu5kzOo67zx7p934zhg/gmlOT+PfqQ1w6eRDjB3tfvqqkqo6/frWfWSMGcN4437Np3ztvFEu2H+PFlQd59JKTR578e3UmwVYLN89K9rp/ZFgw16YN4Y3vDvPQhWObzEJ9uKiS7w4d58HzR3s9ftemDeHtDVl8tj23Scv0Z9uOYVFwoZdZvC+dNIjffrqbjIJyRsZHArAjp5Sckmrubbb0Vr/wYGaOGMCXu/JZeMEYlFJorVmxt5AzR8USEtR4zXbcoCjiIkNZtb/QvUbzmgNF1Nt1k9bs8BAr01L68+3+Ih652Llt1T5nIO15EeT0kc5gfF3Gca49rQ9ZxVXklFRz25m+l9oSosHNr27wOpSgQWVt17fU/GXZXp5fcdDrc/UGtITX2RykPvq51+1dYfZTKzhaXHXS9uwT1V2SPzhb/N/ZcJTHPtnl9XmHQ3fp+XPF3gJueX3jSdsDdUy01vxl2T5eWOn9fQmw61hZQPJuybqMImwOTWF5LQ+8t81rmq7qqVFaVc/kx79s8fkxAyMDXoYTlXXc/NoG94on3tR0Ybftlt6j3VGggud7gY+UUtfTGCynASHAFR143WzAsw9jEnDyFKxdSpqeRc/i0JogqzJV4NxgZHwE7/9kFje+sp6bXt3A8z+cyrxWAlVwzgT+k7c2ExsRwt+undLm/+2Ri8eyYl8hCz/YwUc/nUWQ9eROO898uZ+y6noeu2RcqxfXRsRFcNXUJN78/gg/PnN4k+A3v6yGDzZlc01a0kljpj3dcnoyr6/L5I3vDvPLC8a4ty9Kz8Ki4KqpSV73mzIkmtSECN7dmOUOnrXWfLY9lxnDB3jN86KJg/jdZ7v5dFsu953rPOkv25WH1aKY52XZrfPGD+Sxj3dyoKCC1IRI9uSWk1dWw9keATE4L0KePTqOz3fkUWdzEBJk4es9BfQLD+bUYTFN0s5OjeWJpXvJLa1mUL9wVu4vYFR8BIM9ut6nJkQQFxnK6owirj1tiHv884wR/l9kEb3P1qwSXliR4XMSvk2PzuPMp1ZQ2EJvkc6ktWbxtmP8/J2tPtMFWxWVXfDj12Z3uJcdfGVNy5MdNu/N0pnySmuY8advfKY5cvzkgDoQnl62j+dWZHh97pcXjOapL5zzgPQJCdRPbKe80hr25JVxy2veA5ILxg9kb17nBbAZBRXM++uqVtPdO28Uf/+69Z5aHbF42zHueXtLm/YZFR9BQVngPr9f7c7ntjfSfaa5cmoiH27Occ+hEAg5JdUs31vAYy2sMLH9t+dRXFHHnKdXBrzl2bPVvyWHnrgooGUIhIB8srXW+cAspdTZwATX5iVa6+UdfOmNwCilVArOGb0XAD/s4GsKITzYHdq9hqgZDY4O5707Z3HLaxu4461NPHXVJK461XugCM4W4Ztf3UBJVR3v3jGTmL4hbc4zuk8Iv7tsPHf9bzP/Xp3JT+aMaPL89uwS3lp/hJtnJvs9q/M9c0fx8dYcnltxgD9cPtG9/aVvD2HXmjtmj/CxNwzp34dzxyXwvw1H+dk5owgPsVJdZ2dRehZnj473uiYyOAPWa9OG8Icle9ifX05qQiQbD5/gUFEld5w13Os+8VFhTE/pz6fbj7lbmpdsz2V6Sn+v9XneuAQe+3gny3bmkZoQyZe781AK5njpKn/++IEsSs/m+0PHmTliAN/szWfumPiTLlCclRrPE0v38u3+Qi6aOMg5mdrpTVuUlVKcMTKWVfsLcTicY6LjI0MZnRD4q/yi+zhWUs2sP7f+cyQ+MpRnF5zCTNfFl6o6O0u25/J8J//qqKm3sz+/nMueW+tX+m8fPJuhA/qQvHAJR4vbPweELztzSvn71/v5ek/rkyWuXXgOZz65vFNXRNBac6Cggr155T6DpINPXITVopj7zEoOFgamLvwJimaNGMB/fzydt1xdcytrAxM819rsjH70C59p7j83lXvmjuIX722jpr5jLa3f7i/0ayz3rBEDePX/TnNPwBmI4Lmy1sb4dnSP/9UFY7jl9GTGPOa73trjz5/v5cVVLbe+N9j5u/Ox2R1E9wnhw83eV69oj5KqOpbsyOURP1YF+exnZzB2UBRWi3LP4XK4A3PI+HL582vZ2sKcLA2euGIiP5w+1GcaswroZTGt9QpgRSe+nk0pdTewDLACr2qtvfeZCbCG0ELGPIuexq7BYvJhCf37hvDf22Zwx5vpPPDeNjYfPcHDF409aXxwZlElt72RztHjVbx8cxoTEr13ufbHRRMHcuGEgTz95T7GDY5yj7UtrarnZ29vISEyjPvPS/X79Yb078N1pw3h3Y1Z3H7mCIYO6MP+/HL+s+4wV09NYuiAPq2+xo9OT2HZrnw+3JLN9dOH8d/1RyiqqOOOs3wH3lecksiTX+zl3Y1ZPHbJOF5bm0lUWBCXTU5scZ9LJw/mkY92svloCTX1dg4fr+KeuaO8pk2ICuOUodF8su0YPz17JB9uzmHWiAFex8efPjKWPiFWlu7IRQMlVfWcP2HgSelSEyIYGBXGV7sLCA8Jot6umeul1fuMkbF8tCWHTUdPsPpAEfPGJsgwm17A4dAMbzam7+GLxlBZa8dqUfzVY5ywPzL/dFGL75vb3kjn3zeltbmMJyrr0Di/vz7ZmtNqy3Jzh/988Unblu7I85Ky7XZkl3Lpc20be7jh4bnuz7RDw/MrDvLg+WNa2atlVXU2cktrmPtM662bWx47t8mFu4bAubLW5nWeCH/YHZqaeuf75X/rj/L4Z7tb3eeRi8Zy2+zGi46xEc6eO4XltT57DvljZ05pm8eDHnriInfPqoaJ1Nrq2a8P8Lev/f+87P39BS2uWlFTb2/xOX9U19kZ++u2B71v3TqdxJhwUmJPXvWjvKa+yTwdbbU9u4Qnv9jL2ozjPtNFhAax8sE57vdEc+3p2m+zOxj5yMlDJlrT/Luj4avtH8szuN9jSc22lCPIauG7g8f5wb+/JzIsiPKa1ueDWP3Ls3lr/RF+ef4YUzfStCawfUoCQGu9FDh51LsQolPYHY5u8aUWERrEq/93Gk8v28fLazJZtiuP66cPY3pKf+xas2pfIW9+f4SwYCtv3DqNGW0YH+2NUoqnr5nMVf9ax21vpPPgeaMZHteXvyzbR25JDW/fPp2oNp6Q7z57FB9tzuG2N9K5/7xU/vz5XqLCg/nlBf6dzKal9GfykGj+smwfQa4A4cxRsUxL8b0s04CIUC6cMIi3vj9CVFgwn+/M4565ztbrlsyfkshfv9zP7z7dhc2uiYsMdS9R5c3NM5O5992t3PqfjRwtruLB873/T2HBVi6bPJgPN+ew4bBzZuyzvLRQK6W4cmoi/1p1kG3ZJSTFhJ/UtRtg3rgEwoOt3PDyemptjpMmQBM9i8Ohufifa9iTe3L31CeW7m3TazUPyFry1e58khcu4cAfL2yyjFxznuuppzzUvp8t7985k+FxEfT3Ua6jx6v8utgGkH2iiug+Ifz720N+TYLY3O/nj+fGmcktPr83r4wxA1vvfVNvd5BRUMGFz65uU/5f33+Wz1Udxv9mmdeLDC3RWnOstIYXVx5sMqFTax48fzR3eZk7I9YVMF/0j9XuVnF/2R2aDzdn8+D7Jy1W49OmR+cxoIUADZxdZ3f97vwmFxVqbXbsDo3WcNf/NrNyn/8T7H581+lMGRLtV9oxj31B+qPzWgwgW+JPT4LTRw7gutOGcumkQW26QDrxt1/y87mjuO9c/y52v78pm6iwIF769hDpR7wv89ggNiKU7x46x+f3QoPhDy/lppnDeHz+BJ/pam12duaU8cTSPWxqJX9Pzy6YwukjY73WfZzHtuSFS/z6zDgcGptDM++vq06ac6C1wNnz9R+6cGyreZmd0j246TQtLU2np/vuatNen20/xt3/28KX980mVboEih5kwUvf4dCw6I6ZRhfFb5uOnODvX+9n9YEi97Ygi+LiSYN4+KKxJHTijOAnKuv4+btb3TNEx0aE8LfrpnDmqNZn7/ZmzYEifvLWJsprbUT3Ceblm9JIS/Z/TeLDRZVc8/++o7C8lqSYcBbdMbPJOOCWFJTXcMXz68gpqWZCYhTv3THLZ/AMuFvLlIIXfjiVC30Ezza7gwUvfU/6kRNMS+nPO7fNaPEqe1ZxFZf8cw2l1fX86cqJJy2x5S5zWQ0XPrua45V1PH3NZK5uobv+U1/s5YWVB5kyJJoPfjKrUy8GKaU2aa3b3uwo3Drr3HzDy+tZk1HUesIWvHJzmtfeCy15bW0mv/u09dbI9oqNCGHtwnNanKm/uebjCZ+6ahJnjIp1f/611jz04Q6+2p3P8Urf68q35Ov7z2J4bN9WW8ial+WGGUO5b14q/1p5kJfXZHLnWSP86t7qzfIHzmJ4K0vN3fZGOl81G7O+9/cXEBpkcQdWWcVVnPlU+zpD9g2x8vLNp7m78Lcks6iSs59e6X782CXjmJbcn+p6O2nDYsgvryEqLJisE1UMienTrm7I16UN4fHLx7f6PvFnvKm/fPXE8ObW1zfyzd6mXf5X//JsBvULY2tWCccr6wgPtnLmqFgOH69qUme+DIwKY93Cc9o1J0tn1kdz2359Hv36+Hfx3Fc5ZqfGNVl9oi2uTUviT1dO8vt8F8j6gNYv6nS2rjw3S/DcThI8i57q2he/w2pRvH37DKOL0mZFFbXsyytHKedMztF92j6+2V9788oorqxjypDoDo9tK66sY09uGRMG9/P7BOyprKaendmlTB4S3aYui+U19ezIKWXq0Bi/u9ZlFJQDyq81vWvq7WzLKmHykOhWXz+/rIbC8tpWu9YXlDvTtTTrOTivkG/NLmF0QmS7u3C2RILnjuuMc7O3btoAL914Ks+tyGgyw+w954wk2GrhplnJ9Atvf3dNgFfXZPrVndcfExP78eKNp7a63nxLfvyfdL7e0/IkZ+1x/fShPD5/QpsvOKUfLubqF7/rlDI8/8OpXDRxYJuCNa11u1v3W/LMNZO5dPLgJisEGFGO88Yl8MB5oxk2oE+bukD7O7bfm8fnj+fKqUn0DbG2a9hLZ9dDe1qvvenMgHH9w3PbfWG+M8qx+bFzffZIac2i9Cx+2cZeDr48c81kzhwV2yXLl3ojwXMnCWTwvGR7Lnf9bzO/vmScX608QnQXf1m2l0H9wnnrx9ONLooQpiPBc8d1xrm5+Y9zX+MuA6GtP377hQfzjx+cwmnJMZ06kVRHfoTfdmYKCy8c22k9M65/+ftWx4F68+at0xgS04e4yNAOXezKKanm9HYGi49e7OyhdOnkjg/zWJtRdNL64G3Rli7FrTleUcupf/i61XQDo8L45oGzOv1i45UvrGXzUd8TR7Vk6T1nMm6wfxNwtoU/E8B5c/mUwTx19WSUwq9u2a158L1tvNfGcembHzuXsGBLp32HtPcixwc/meV12JSRJHjuJIEMnjv65SiEmV0yaRDP/XCq0cUQwnQkeO64zjo3ZxRUEBZsISnGv/G+gbD+0HGCgyx8viOXYyU1LNmRy7qF59A3NIggi+r0YMQbrTWbj5Zw1b/WtZhmwyNzsShFaJClQ5Mlteb9Tdn8wssau4/PH8/IuAhOVNW3uVW5rVpbq/vlm9KYOzY+4BMJ+jO506WTB/PYJWOJiwgNaHmq6+zYtaZviJWiijqiwoP8Hh7QUfV2By+vzuTJL3zPQfDenTM5ZUg0VovqkkketdbszSsnJbYvoUEWHBqsFkWdzUGQpWuX69yZU8r6zGLq7Q76hFj5tWv98K/vn01KbARaa69LZHaW8pp6nvxiLx9tzuHdO2bSLzyYH/8nnX355fzm0nHszCkjLTmGq09N6pQLB4EgwXMnCWTwrLUms6iyw8sACGFGw+P6dmkrjhDdhQTPHRfIc7MQwtyq6pyTSwV6HWzRu3TluVneue2klGp1AgshhBBCCCGEkwTNorszZ9u7EEIIIYQQQghhIj2627ZSqhDwd+G+WKD9a170DlJHvkn9+Cb10zqpI9/MUD/DtNbtW5dMAHJuDgCpI9+kfnyT+mmd1JFvZqifLjs39+jguS2UUukyjs03qSPfpH58k/ppndSRb1I/vY8c89ZJHfkm9eOb1E/rpI586231I922hRBCCCGEEEKIVkjwLIQQQgghhBBCtEKC50YvGV2AbkDqyDepH9+kflondeSb1E/vI8e8dVJHvkn9+Cb10zqpI996Vf3ImGchhBBCCCGEEKIV0vIshBBCCCGEEEK0olcFz0qp0UqprR5/ZUqpe5uluV4ptd31t04pNdmo8hrBnzrySHuaUsqulLq6q8tpFH/rRyk1x/X8LqXUKiPKagQ/P2P9lFKfKqW2uernFqPKawSl1H2u/3unUuptpVRYs+dDlVLvKqUylFLrlVLJxpTUOH7U0f1Kqd2u7+lvlFLDjCqr6Dg5N/sm5+XWybnZNzk3t07Oza2Tc7NTr+22rZSyAjnAdK31EY/ts4A9WusTSqkLgd9qracbVU4jtVRHHs99BdQAr2qt3zegiIby8R6KBtYBF2itjyql4rXWBUaV0yg+6udhoJ/W+ldKqThgHzBQa11nUFG7jFIqEVgDjNNaVyulFgFLtdave6T5KTBJa32nUmoBcIXW+jpjStz1/Kyjs4H1WusqpdRPgDm9qY56Mjk3+ybn5dbJudk3OTefTM7NrZNzc6Ne1fLczFzgYPOTj9Z6ndb6hOvh90BSl5fMPLzWkcvPgA+AXnfi8dBS/fwQ+FBrfRSgN56cXVqqHw1EKqUUEAEUA7auLpyBgoBwpRk0FywAACAASURBVFQQ0Ac41uz5+cB/XPffB+a66qo38VlHWusVWusq18Pe/j3d08i52Tc5L7dOzs2+ybnZOzk3t07OzfTu4HkB8HYraW4FPu+CspiV1zpyXX26Anixy0tkLi29h1KBGKXUSqXUJqXUTV1cLrNoqX6eA8bi/NLdAfxca+3oyoIZRWudAzwNHAVygVKt9ZfNkiUCWa70NqAUGNCV5TSSn3Xkqbd/T/c0cm72Tc7LrZNzs29ybm5Gzs2tk3Nzo14ZPCulQoDLgPd8pDkb54H/VVeVy0xaqaO/A7/SWtu7tlTm0Ur9BAGnAhcD5wOPKaVSu7B4hmulfs4HtgKDgSnAc0qpqC4snmGUUjE4r16n4Pz/+yqlbmiezMuuvWZ8jZ911JD2BiAN+EvXlVAEipybfZPzcuvk3OybnJu9k3Nz6+Tc3KhXBs/AhcBmrXW+tyeVUpOAl4H5WuvjXVoy8/BVR2nAO0qpw8DVwAtKqcu7snAm4Kt+soEvtNaVWusi4Fug10xu4+Krfm7B2XVOa60zgExgTJeWzjjzgEytdaHWuh74EJjVLE02MATA1TWqH87uc72FP3WEUmoe8Ahwmda6tovLKAJDzs2+yXm5dXJu9k3Ozd7Jubl1cm526dEThsXGxurk5GSjiyGEEKKH2LRpU5HWOs7ocnRncm4WQgjRmbry3BzUFZkYJTk5mfT0dKOLIYQQoodQSnmbqEm0gZybhRBCdKauPDf31m7bbfL62kw2ZPamnhlCCCF6E6XUBUqpfa41TBd6ed7rGqdKqWSlVLVqXD+2yyasOlxUyaHCiq7KrlNsyyphT26Z0cUQQgjRTqYNnpVSVqXUFqXUZ67HKa4T9gHXCTykq8ry20938+fP93RVdkIIIUSXca37+jzO8ZDjgB8opcY1S3YrcEJrPRL4G/Ckx3MHtdZTXH93dkmhgTlPr+ScZ1axN6+MPbllvLAyg1+9v50tR0/w5a487nt3K9/uL6S0up4tR09wsLCCWpudPbll7Mwp5VhJNYvSsygoq2Hj4WLmP7eG5IVLKKqoRWvNp9uO8eWuPGx2B6VV9by+NpOGoW6HCisoKK/B4dDsOlZKYXktNfV2duaUusu3NqOIvXll2OwOVuwrIKekmvnPr+XCZ1fz2fZjFJbXsi2rBACHQ6O1pqLWxvK9+eSWVrNsVx5788rYkFlMTkk1h4sq2ZPrfPzW90eoqW+cG8zh0Jz3t1V8viOX8pp69uaVceR4JQ99uIP30rOw2RsnTdZas+5gEdknqtz5LkrPoqLWRkZBOeU19eSX1VBvd5BXWgNARkEF1XV2DhVWsO5gEasPFJJfVsOflu7hUGEFhwor+NtX+91lyimpptZmp87m4FBhBWszitiXV051nZ16u38TOGutWb43H4fDWed1tsb9Sqvq3fX9xneHsdkdaK3JPlFFZa1/qytlFlVSWF7bpDwHCyuwO5oOZ6y12SmvqffrNYUQPZ9pxzwrpe7HOQFGlNb6Etdi3B9qrd9xXdneprX+l6/XSEtL053RNeymVzdQWlXHJ3ef0eHXEkII0X0ppTZprdOMLkdnUkrNBH6rtT7f9fghAK31nzzSLHOl+c41WU4eEAcMAz7TWk/wN7/OOjcnL1zS4dfoqD4hVqrqOjbBdb/wYEqruyY4S4wOJyzYwsHCyi7Jz5c5o+OoqrOTUVBBnc1BhY+gNyI0yOfzrfE8Tg9fNIatWSUs3ZHnNe3M4QMoq6ln17EyXrh+Kj/97+aT0gzt34cnrpjIaSkxhAZZ2ZZVQlxkKFuzShgZH0FqQqRf5aq3O/ju4HHOHBXLY5/s5NJJg5k+vPXVj2rq7ZRV1xMfFeZXPkL0dF15bjblmGelVBLOpQT+CNzvWoT8HJwL3INzkfLfAj6D504rT1dkIoQQQhjDvX6pSzYwvaU0WmubUspzjdMUpdQWoAx4VGu9unkGSqnbgdsBhg4d2rmlN1BHA2egywJncLYIm8XKfYV+p+1I4AxNj9MTS/f6TPvdocaJ3L0FzgBHi6u44ZX1HSqTp8jQIMprbbz1/VGG9A8nNT6Sb/YWAHDmqFhWHygiPjKUP181kTUHjvPl7jyyT1Tz6MVjGdQvnNUHCokIDeKmmcl8uCWbSUn9GBLThw2HiwmxWrhyahKVdTYqa230DQ1i97EyZjQL0uvtDixKYbUo3t+UzcTEfqTE9iUkyNlJNaOggoSoUCLDgrE7NFaL71/H+WU1xEWEYnGlq7XZCQ2yek2rtabW5uzpkBzbt6PVKURAmTJ4xrle4S+Bhkt3A4AS16Lk4DyxJ3ZVYZREz0IIIXouf9YvbSlNLjBUa31cKXUq8LFSarzWusnAXq31S8BL4Gx57oQyC9FjlHtcHMgqriaruPEix+oDRQAUlNfyo9eb9tj4w5KmQwpfXpPp9fUffH97i3lHhgVRXtOxixOtSR7Qh8PHq/xKO2vEAOaMjmNUQiS3vLaRqLAgfjd/PPU2zbHSamaNiKW63k50eDBHi6v42dtb+MV5qcyfksiWrBKGxIQTZLEQ3SeYIf37AM6W+mCrxR3wl9XU88KKgzxwXirBVgtFFbUUVdQyOiGSyjo71XV24iJDO/Q/a635fGceZ4+Op6ymnoSoMPewD+UjsKiqs1Fb7yCmb+Po1EOFFWhgRFwEVXU2QqwWgqydO/I2r7QGi4L4VspZVWfD7tBEhgV3av7diemCZ6XUJUCB1nqTUmpOw2YvSb2efAN1dVvO9EIIIXoo9/qlLknAsRbSZHuucaqdv7JqAVzn7YNAKiDTaQvRDQQ6cAb8DpwB1h08zrqDja3/ZTU27nt3m/vx378+cNI+T3+5n6e/3N/mcr246mCb9/Hm0YvHcryyjtfWZlJT3/qY/nvmjmJA3xB+s3gXAFFhQZS18Tj8YNoQ3t7g7DAUGRZEQlQYV5+aRLDVwnPLD/DjM4djUYonv3D2tAgPtlJd37SnzAPnpjJr5AAOF1XxwHvbmjx35dREFm89RnJsXzIKnBMzDu3fh6PFzmM5OzWO2aNiGdK/D3e8uYmZwwdww4xhHCmupKbewT++OcBpyTH85tLxJMWE8+m2Y0wfPoCU2L7YHZpX12YyJSmaWSNj2/R/m4Hpxjwrpf4E3AjYgDAgCvgIOB8Y6Oou1mR8Vks6a1zVLa9t4HhlHYtlzLMQQvRqPXTMcxCwH5gL5AAbgR9qrXd5pLkLmKi1vlMptQC4Umt9rVIqDmcQbVdKDQdWu9K1uERFTxrzLIQQov32/+FC99CAjujKc7PpZtvWWj+ktU7SWicDC4DlWuvrgRXA1a5kNwOfdG25ujI3IYQQomu4hkTdDSwD9gCLtNa7lFKPK6UucyV7BRiglMoA7gcalrOaDWxXSm0D3gfu9BU4CyGEEA0c3TDAMl23bR9+BbyjlPoDsAXnibxL+BqbIIQQQnR3WuulwNJm237tcb8GuMbLfh8AHwS8gEIIIXqc7hhimTp41lqvBFa67h8CphlWFhn1LIQQQgghhBCdQnXDNY1M121bCCGEEEIIIUTP1h1bniV49kM3PK5CCCGEEEIIYVrdMcaS4NlP3XA8uxBCCCGEEEKITiLBsx+6Y5cCIYQQQgghhDCr7jgpswTPfpKWZyGEEEIIIYToHN0vdJbg2U/d8dAKIYQQQgghhDl1w4ZnCZ79JQ3PQgghhBBCCNE5pNt2D9UNj6sQQgghhBBCiE4kwbOftAx6FkIIIYQQQoheS4JnP0jDsxBCCCGEEEL0bhI8CyGEEEIIIYQQrZDgWQghhBBCCCGEaEVAg2el1HCl1KdKqSKlVIFS6hOl1PBA5hkIMmGYEEIIIYQQQvRugW55/h+wCBgIDAbeA94OcJ4BIfOFCSGEEEIIIUTvFejgWWmt39Ra21x/b9ENl0xWMmWYEEIIIYQQQvRqQQF+/RVKqYXAOziD5uuAJUqp/gBa6+IA599pdPeL+YUQQgghhBBCdJJAB8/XuW7vaLb9RziDaa/jn5VSQ4A3cHb3dgAvaa2fdQXd7wLJwGHgWq31ic4vdvPyBDoHIYQQQgghhBBmFrDgWSllAW7QWq9tx+424AGt9WalVCSwSSn1FfB/wDda6z+7WrQXAr/qtEL7IGOehRBCCCGEEKL3CtiYZ621A3i6nfvmaq03u+6XA3uARGA+8B9Xsv8Al3dCUVslLc9CCCGEEEII0bsFesKwL5VSVynV/vBTKZUMnAKsBxK01rngDLCBeC/pb1dKpSul0gsLC9ub7Umk4VkIIYQQQggheq9Aj3m+H+gL2JRSNYACtNY6yp+dlVIRwAfAvVrrMn9icK31S8BLAGlpaRLzCiGEEEIIIYTosIAGz1rryPbuq5QKxhk4/1dr/aFrc75SapDWOlcpNQgo6IxytloWFFoGPQshhBBCCCFErxWQ4FkpNUZrvVcpNdXb8w3jmX3sr4BXgD1a6796PLUYuBn4s+v2k04qshBCCCGEEEII0aJAtTzfD9wOPOPlOQ2c08r+pwM3AjuUUltd2x7GGTQvUkrdChwFrumc4rZCJgwTQgghhBBCiF4tIMGz1vp21+3Z7dx/DS2HrHPbW66OkE7bQgghhBBCCNF7BXrCMJRSs4Bkz7y01m8EOt/OJA3PQgghhBBCCNG7BXSpKqXUmzjXej4DOM31lxbIPANGmp6FEEL0UEqpC5RS+5RSGUqphV6eD1VKvet6fr1rGcmG5x5ybd+nlDq/K8sthBBCdKVAtzynAeN0N5+qugPLVAshhBCmppSyAs8D5wLZwEal1GKt9W6PZLcCJ7TWI5VSC4AngeuUUuOABcB4YDDwtVIqVWtt79r/QgghhAi8gLY8AzuBgQHOo0t06+hfCCGEaNk0IENrfUhrXQe8A8xvlmY+8B/X/feBua6VMeYD72ita7XWmUCG6/WEEEKIHidQS1V9ijPejAR2K6U2ALUNz2utLwtEvoEi7c5CCCF6sEQgy+NxNjC9pTRaa5tSqhQY4Nr+fbN9E5tnoJS6HecqHAwdOrTTCi6EEEJ0pUB1214MJACrm20/C8gJUJ4B1c17ngshhBAt8XaNuPlJr6U0/uyL1vol4CWAtLQ0OaEKIYTolgIVPM8HHtZab/fcqJSqBH4DvBKgfIUQQgjRNtnAEI/HScCxFtJkK6WCgH5AsZ/7CiGEED1CoMY8JzcPnAG01uk4l63qVpSSMc9CCCF6rI3AKKVUilIqBOcEYIubpVkM3Oy6fzWw3DUZ6GJggWs27hRgFLChi8othBBCdKlAtTyH+XguPEB5CiGEEKKNXGOY7waWAVbgVa31LqXU40C61noxzh5jbyqlMnC2OC9w7btLKbUI2A3YgLtkpm0hhBA9VaCC541Kqdu01v/23KiUuhXYFKA8A0YBMuRZCCFET6W1Xgosbbbt1x73a4BrWtj3j8AfA1pAIYQQwgQCFTzfC3yklLqexmA5DQgBrghQnkIIIYQQQgghREAEJHjWWucDs5RSZwMTXJuXaK2XByK/QHMuZSmEEEIIIYQQorcKVMszAFrrFcCKQObRVbSPKcM+2pLNf78/yi2np3DxpEEnPf/kF3uprrPzm0vHuQPxhz7cwbjBUdw4YxgOh+aB97Yxb2wCF08axKKNWWzLLuGPV0zkze+PkFlYyWOXjOUX721n3th4+oUH89b6Izw+fwKPfLSDe+aO4tU1hzl3XALrM48zMbEf27NLOWVoNF/szGPe2AQ+3prDJZMG8eWufCYk9sPmcLDrWBmTk6LJKKwgNMhCfGQYx0qqcWhNnxArFbU2SqvriQwNBqDO7qDe7sDu0ARZLdgdDmrqHViVoqLWRliwBbtDU2931pVSzu7udXYHNrsDm0PjcGgcGuxao7VGa+dkbA6tsds11fV2lIJ6u8aiwCHd5YUQneD388dz48xko4shhBBCiG4soMFzT+Gr3XnTkWLuX7QNgO05pUwdFs2gfo1zouWUVPOvlQcBuDZtCOMGR3GwsIK3NxwF4PppQ9mWXcJHW3L4aEsOF0+6mD8s2U1ZjY0HzhvNYx/vBOAH04bwweZsPt12jJHxEezOdQa+y3blU13v4Nv9hXy6/Rh1Noc779fXOW8/35kHwOoDRQB8s7fAnWblvsKOVU4ASeAshOgsj32yS4JnIYQQQnRIoJaq6nG8TRimtebJL/YxoG8on//8TOpsDt5Pz26SZmdOqfv+hszjAOzNLXdvyz5RzZHjVU1es6zGBsCR45Xu7VuOlgDOVtzsE870+/MrADhY4Lz1DJyFEEIIIYQQQnQeCZ790ULT8+oDRWzILOZn54xkzMAoTkuOYamrlbfB7mNlWBSEBVvYl+8Mmg97BMVZJ6rIKal2P24InAEyXEEx4N4XGgP5ffllAByvrG3f/yWEEEIIIYQQwi/dLnhWSl2glNqnlMpQSi3sqnybtzxrrXn6y30kRoezYNoQAOaMjmdPbhknKuvc6XbnlpES25cpQ6LZ42pxzixqDJ6zT1RxzCN49gyY93sEzJ737a7CNKStqZcWZyGEEEIIIYQIpG4VPCulrMDzwIXAOOAHSqlxRpTlq935bM8u5edzRxEaZAVgekp/ANZnFrvT7T5WxrjB/UhNiCSjoAKtNYeLKjl1WAxWiyKruLpJy/P27BL3/b155V7vV9XZAQmahRBCCCGEEN1TVnFV64lMplsFz8A0IENrfUhrXQe8A8wPdKbKS7/tfyw/QEpsX66cmujeNikpmrBgC+tdY5tLq+rJKalm3KAoRsVHUFFrI6+shqwTVaTE9mVgVBg5JdUcK6kmJbYvANuzG8dI7/MImAvLpWu2EEIIIYQQomc486nutyhTdwueE4Esj8fZrm1uSqnblVLpSqn0wsLAzSR9sKCSuWPiCbI2VmFIkIWpQ2PY4Gp53p3rHJM8bnAUI+MjAdiVU0Z+WS1JMeEkxoSTfaKK/LJaJib2A2Cbq+W5b4iVAgmYhRBCCCGEEMIUulvw7G3qriajkbXWL2mt07TWaXFxcZ2TqXKOcfZk1xqr9eTiTEvpz+7cMkqr6xuD50FRjEqIAODbA86APimmD0kx4WQUVFBaXc/ogZGEWC0cKqzEalGMHhjpfs2GVmkhhBBCCCGEEMbobsFzNjDE43EScMyIgjgcGqvyHjxr7Vz/eVdOKfGRocRFhhIbEUr/viGs2OdcYzkxOpykmD6cqKoHICEqjEHRYQDER4YSH+m8H9MnmISoUABGxke48xnS37mWtJciCCGEEEIIIYToZN0teN4IjFJKpSilQoAFwOJAZ+otPrVrjdVy8jNTh8YQbFWsP1TMjpxSd3dscAa/WcXOycGSYsJJig53P5cQFcqgfs6AeVC/MAa67g+ICGVAhCt4jmsMnofHRrhupVVaCCGEEEIIIQKtWwXPWmsbcDewDNgDLNJa7+qSvD3uOxwarfEaPIcFW5mcFM1Xu/M5WFjBBI/geZSr5Tg82Mrg6HCSYhqD58TocAa7gumkmD4kuu6HWC3E9g0BYOiAPu70w1z34yJDO+cfFEIIIYQQQoguMjyu+zUCdqvgGUBrvVRrnaq1HqG1/mNX5Nm8a3TDOsveum0DnD4ylkNFlTg0zE5tHHfdEDwnRIVitSiSYhqD4aH9+7i7ag+ICHEH0sFWhdXiPEyxESHu9PGuoLmhVVoIIYQQQgghuovfXDre6CK0WZDRBeguPOcLszucDyxeWp4Bbpw5jA82Z5MS25epQ6Pd288YFUtkWBC3zx4BOMctnzI0mqH9+xBktXDRxIG8u/EoF04YRFJMONF9gvnRGSkkRofz7sajnDEyjuvSKskuqWLO6HheXHWIW2Ylsy6jiHvnpbIoPYtzxsSzNqOIaSkD+HxnLnPHJPD+piyuOCWRRenZzB0bz57cMjSQEBnG1qwSRg+MpM7mIKekmrjIUOpsDoor67A7NEo515UOC7Y0WVc62Kqot+vm/7oQQpjSz84ZaXQRhBBCBMDj88czpH8ffv/pbo4WVzEiLoKrTk1k6Y48UhMiWJSezYzh/blpZjLHSqqxOzRPLduH3aF58YZT+c3ineSXNa5w88aPpmHXmlte28i05P5sPFJMdHgwT18zmdvf3ITdoblhxlBSEyL59Se7uP/cVG6elUxEaBAr9hbw4zfSeeH6qShg5ogBlFXbcGhNkFVx9HgVs0bGAlBaXc/e3DK+O3SckfERbDlawpvfHaHO7vy9nTYshvQjJ5r8r//8wSmcNTqO/NIazv3btwAsvvt0Kmpt/PL97Xz009M5UFDOyPgIjh6v4uoXvwNg/x8u5LnlB/h46zEevmgMg6PD2ZZdyqTEfiQP6MtzKw4QZLUQGxHKlqMnmDIkmme/OcBP54xkZ04pc0bH8eD72/nFeal8uDmHj+8+nfIaG8dKqrnltY189NNZ1NocrMko4gfThnLF82v50RkpnDEylpi+IfQLD27yf2itKa2uJ7pPCN2Naj6LdE+Slpam09PTO/w6v3p/O6v2F/L9w3MBqKqzMe7Xy1h44RjuPGtEm17L4dBNgm6tNcqjBdvzec/7DekajpdSyv28t1vnDOF43e58Pe/PeebRkE/DxQJrC2XxpLWzi7tnWl9vsYbyqGat+J714vk/CyGEkZRSm7TWaUaXozvrrHNz8sIlnVAaYUbTU/qz3rXsp9ktOG0Id5w1AouCs/6y0r1966/P5ZU1mfxzeQYAj148lm8PFPHt/sZlVKcl92fD/2fvvsPbKs/Gj39veWXvvXBCAiRsEvamlJUWSpldL29LSwd0/GjfEqAtUKCMDigtUFaBUlbYIwlJIAkJ2c7eieM4jkcc770kPb8/zpEsyVp2JFky9+e6dFnn6IxbR7LOuc+z8iu5dvo4cg/Vs/GANVzp8/8zg79/toc2l5tXbj6dWe9s5rOdh3j/1rP51Rsb+OYp4/jbwt38z5lHcLCmmetnjKe6qY10h/DEoj3klTVw5qSh/PLiKTy7NI9vnTaBl1bsY3luBX+86lj+8ME2Xvvh6fTOTGP+tlLcxvCVY0ZQ3dTGsj1lpDscjBvcmwfn7uC7px/Bd86YQPbQvrS63DS3uvj1W5toc7m5ZNooMtKEqsY2bjlvEsbA/XO2c9SIfszIHkJ+RQOLd5ZxwdHDmTZmAA/N3cF1M8azvbiWt9cVct5Rw+mV4eCSaaMY0Dudxxbu4aTxAympaeYn5x/JwdpmFm4v5a4rpib0M1WpJ5HnZk2eo3DH25v5aHMxN546AcD6MVu1n7uvmMqPzpt02NtXSimVGjR5Pnxf1uR5WL8syutbIi/YSadmD2ZtvlU6dc7kYXyRWx5y2V4ZDs6cNJTFu6wE7vyjhvO5ncydd9Rwv8Tu1189ihnZQ/jWc6u88343cyrHjx3IqrxK+mal8cCcHQBcftwoLj9+NMePHUi6Q9hRUsvkEf04YmhfXG7Dir3lDOydgdsYxg7q4+0UFWB5bjnTjxhMr4y0kHE3tjppbHWRV9ZAbVMbzyzdy9r8Kgb3yWDq6AGs2FsBWP3HFFU3edf76QVHcsOM8Ywa2IuPN5fw+Ke7Gd4/i5f+9zTKG1qoa3aS7hCOGdWfZqebFbnlDOufxQljB1LZ2Iox1mgowbQ4XaSJkJ7W3gKyvsWJy2UY2Ccj6DrB1Da3MaBX9MsrpTrS5DlGYnWCfmXVfh79ZKdfr2HpacLfbzzZr02zUkqpnk2T58MXq3PzL17fwIebisl/eKZ3XlVDK0XVTX6ddYLV3MohcKCyidrmNrLSHUwZ2R+AhhYnzy7N47aLJpNhJ0JLd5cxrF8WU0f399a0ClZDqrbJGTFRcrn9R+dwB2n65YkpKz10AmmMobKh1dvXidttWJNfyRmThobdv1JK9XSaPMdIrE7QSimlFGjyHAt6blZKKRVLiTw3p1xv20oppZRSSimlVKL16JJnESkD9ndjCMOA0I2PkpfGnVgad2Jp3ImVqnFD8NiPMMZoe53DoOfmLtO4E0vjTiyNO7F6WtwJOzf36OS5u4lITipW79O4E0vjTiyNO7FSNW5I7dhVaKn6uWrciaVxJ5bGnVgad9dptW2llFJKKaWUUioCTZ6VUkoppZRSSqkINHmOr2e7O4Au0rgTS+NOLI07sVI1bkjt2FVoqfq5atyJpXEnlsadWBp3F2mbZ6WUUkoppZRSKgIteVZKKaWUUkoppSLQ5FkppZRSSimllIpAk+cIROTfInJIRLb6zLtfRDaLyEYRWSAiYwLWOVVEXCJyrT19ob2s59EsIt+wX5soIqtFZI+IvCkimSkS90siss/ntZOSJW573qMisk1EdojIEyIi9vzpIrJFRHJ956dA3EtEZJfP8R6RZHE/IiJb7ccNPvOT9vsdIe64fL87G7uIXCAiNT5x/MFnncvs70SuiMzymd/tx7yLcXf7b0qEuDtsx54/REQW2sd7oYgMjkXcKrxY/AaInpsTGrc9T8/NiY1bz81xiF303JxMcSf+3GyM0UeYB3AecAqw1WfeAJ/nvwD+5TOdBiwC5gLXBtneEKAS6GNPzwZutJ//C/hpisT9UrDlkiFu4Cxguf1aGrASuMB+bQ1wJiDAPODyFIl7CTAjSY/3TGAhkA70BXI820jm73eEuOPy/e5s7MAFwMdBtpEG7AUmAZnAJmBashzzLsYdl2Mei7hDbcee/ygwy34+C3gkHt8bfXT9c7Wn9dzczXGj5+ZEx63n5jjFjp6bkyLuUNux58ft3KwlzxEYY5ZinZh859X6TPYFfHtd+znwDnAoxCavBeYZYxrtO5cXAW/br70MfCPZ445FfKHEKG4D9ML6AcgCMoBSERmN9Y+50lj/Tf8huY530LhjEV8oMYp7GvC5McZpjGnA+tG9LAW+30HjjkV84XQh9mBOA3KNMXnGmFbgDeCqJDvmUccdi/hCiVHcQbdjuwrrOEMMj7cKT8/NHeOORXyh6LlZz83dHXcs4gtHz816bo6WJs9dKG/tOQAAIABJREFUJCIPisgB4DvAH+x5Y4Grse4ohXIj8Lr9fChQbYxx2tOFwNj4RGyJUdweD9rVKx4Tkay4BGzrTNzGmJXAYqDEfsw3xuzAOraFPosm1fEOE7fHi3Z1ld/bP8RJETfWie1yEekjIsOAC4HxJP/3O1TcHgn7foeK3XamiGwSkXkicqw9byxwwGcZz7FNimPehbg9uvU3JUzc4Yw0xpQA2H9jUm0z0UJV2fN5PcuuaphrVz3Mtudni0iTT3W6cOeRuNNzM6Dn5kTF7aHn5sTE7aHn5sTF7aHnZh+aPHeRMeZuY8x44FXgNnv248AdxhhXsHXsu6vHA/M9s4JtOtax+m08NnED3AkcA5yKVW3sjrgFTefiFpHJwFRgHNY//0Uich5JfrzDxA3wHWPM8cC59uN7yRK3MWYBVpWrFVgXcSsBJ0l+vMPEDQn+foeJfT1whDHmROAfwPv2/FDHNlmOeWfjhuT4TQkVd48mImnAk8DlWKU+3xKRaQGL3QxUGWMmA48Bj/i8ttcYc5L9+ElCgg5Bz81J8X+k5+YkiFvPzXGNXc/NcZIK52ZNng/fa8A19vMZwBsiko9VleopsTvxsF0PvGeMabOny4FBIpJuT48DiuMfMnB4cWOMKTGWFuBFrCofiRBN3FcDq4wx9caYeqz2U2dg3Ukb57OtZDveoeLGGFNk/62zt5VMxxtjzIP2hfNXsX6E95AC3+8QcXfn99svdmNMrf1dwBgzF8iw78QX4n8n3nNsk+KYdyHupPhNCRN3OJ5qp55kJlT12mQWTZU93ypwbwNfiXcp22HSc7Oem+Mdt56bExu3npsTH3dS/KYk27lZjInrjY9uNWzYMJOdnd3dYSillOoh1q1b5zLGeC6AEJE/AxXGmIft6s5DjDG/7b4IO0+sXm4vM8b80J7+HnC6MeY2n2W22ssU2tN7gdOBfsA2YDdQC/zOGLMs3P703KyUUiqWEnluTo+8SOrKzs4mJyenu8NQSinVQ4iIQ0QKgXuMMS8ADwOzReRmoAC4rlsD7JpoqhSGWqYEmGCMqRCR6cD7InKs8e/4BRG5BbgFYMKECXpuVkopFTOJPDf36OQ5Vm5/cyMnTRjE/5yZ3d2hKKWU6l7rjTEzPBPGmArgK90YTyyErLIXZJlCu+rhQKDSWNXXWgCMMevsEumjsIaX8TLGPAs8CzBjxoyYVHl7Z10haQ7hGyfHtd8dpZRSyS9h5+akTZ7tDkxygCJjzNdEZCJWO6whWA3Hv2e3zYq7dzcUsbesXpNnpZRSPdFaYIp9ni3C6sH52wHLfAjchNV5z7XAImOMEZHhWEm0S0QmAVOAvEQE/eu3NgFw3NiBZKY5KKlpYndpHddMH0efzHQe+WQnLrfhhHEDWZ5bwT1fn8a764sY0T+L/ZWNTBs9gIG9M5g4rC+9M9MAOFDZyKurC/jaCaOpaGjliCF9aGx1MaxfJlc8sYxzpwznT1cfz2/f2cz3z87mlAmDOVTXzPB+WYgIlQ2tZKY72F1ax4QhfRjWL4unl+zlkU928sUdF1JW18Ke0nqOGNoHlzFsLqzhomNGkJNfxTdPGUtmmoOc/VVMHNaXjDRhW3EtpbXNXHniGNLTrG5qthbVsGZfJXvL6umTmcbdM6fhdLn5aHMxM48fg0PAbSAzvb1bmxani3SHgzSH4HIbWpwuHCJsOlDN/spGLjpmBP2y0umVkUZdcxv9stJxuQ3paQ7qW5z0yUhDBDYcqOaUCYMBWJVXQYvTzflHDQfAGEOL002vDOtYbimsYdLwvtZrQL+srl1yNrQ42XSgmlMnDiEjzUFzm4uCykbGDupNr4w0HAI1TW08tyyPwX0yaXW5OXHcIAoqGzlqZH/qmtsY0jeT8voWVuRW8OtLjsZtDJnpDjLSHBysaWZov0zSHcKeQ9Znk+Fw4HAIh2qbGTGgF1uLapgwtA+9M9J4e10h188YT5pDKKhoZMfBWs6YNJQnPtvDKRMGM/OE0ewrb2DisL5+76OyoZUhfTO7dAyUUskjads8i8jtWJ0JDLCT59nAu8aYN8QaCmOTMebpcNuYMWOGiUXVsJv+vYbqpjY+uPXsw96WUkqp1CUi63zvbvcUInIFVu+3acC/jTEPisgfgRxjzIci0gt4BTgZa0zNG40xeSJyDfBHrN5wXVhV5j4Kt69YnZuzZ8057G0crguPHs7iXWUA9M5Io6nNv2PsScP7klfWEPc4BvfJoKqxLfKCEfzwnIk8/8W+iMv9z5lH8J+V+4O+NrRvJhUNHcs28h+eCUBjq5Nnl+bx+Kd7APjO6RO4+ZyJLNlVRlF1Ey/47P8XX5nCE5/t6cpbiejE8YPon5XOF7nlYZf737OyeWlFPtD+GYc7TqdmD2ZtfhXnThnG6IG9mJ1TyCXTRrJgeylfO2E0DhGOGzuAW847MtZvSakvrUSem5MyeRaRcVi9ej4I3A58HSgDRhljnCJyJnCvMebScNuJafLc2MoHt51z2NtSSimVunpq8pxIPSl5Vqqrnv3edC45dlR3h6FUj5DIc3OyDlX1OPBbwG1PRz24uIjcIiI5IpJTVlYWk2CSejAOpZRSSimVUm55ZR0FFY3dHYZSqpOSLnkWka8Bh4wx63xnB1k0aJG5MeZZY8wMY8yM4cOHxyyu5CufV0oppZRSqeq8Py/mjrc30+ZyR15YKZUUkrHDsLOBK+32V72AAVgl0YNEJN0ufU7k4OJBM3ellFJKKaUOx5s5B3gz5wB3XzGVMYN6M/OE0d0dklIqjKQreTbG3GmMGWeMycbq8XORMeY7wGKsHj7B6vHzg8TGlci9KaWUUkqpL4sH5+7g1tfWs/NgLcYYNh2oxhhDMvZNpNSXWTKWPIdyB/CGiDwAbABeSNSORRs9K6WUUkqpOLvs8WUd5u390xWkOfRaVKlkkHQlz76MMUuMMV+zn+cZY04zxkw2xlxnjGlJaCza6lkppZRSSiXYkXfN7e4QlFK2pE6ek4Xe61NKKaWUUt0le9YcsmfN4ePNCevyRykVRCpV2+5W2uREKaWUUkp1p9te20D20L5sLqyhb1YaV50UdORWpVScaPKslFJKKaVUivjaP77wPp8wpA9D+mZyxNC+3RiRUl8emjxHQfsLU0oppZRSyebqp1Z4n//4/El874wjGDe4TzdGpFTPpslzlLTatlJKKaWUSlbPfJ7HM5/n8ePzJrG7tI6/XX8SbmMY3CcTh/bWrVRMaPIcFf3BUUoppZRSye+ZpXkAPPLJTt5Ye4AzJg3hnMnDcDiEn10wuZujUyq1afIcJS14VkoppZRSqWJtfiUAq/IqWZVnPb/w6BFMHT0AgEN1zdQ0tjFlZP9ui1GpVKNDVUVB2zwrpZRSSqlUsresocO8y/++jA83WcNdnf3wIr762FK/12ua2vjrgl20udzeeW0uNw/N3UFNU1t8A1YqBWjyHCWjjZ6VUkoppVSK+8XrG6hqaKXN5X9tu6Okll++sYF/LMplyt3zWLzzEADvrS/imaV5PPrJTr/ltxXXcKCyMeL+XltdwObC6ti9AaW6kSbPUdCCZ6WUUkop1VPM3Vriff7r2Ztocbq4/O/LWLKrzDv/+y+tBeC372wGoKnNxaHaZsAqVJr5xBec++hiAK7/10ru+2ib3z7+9flefvbqOu56bwtX/nN5p2MsrW3muaV5WoClkoq2eVZKKaWUUupL5O73tnqfv7O+kAe+cVzQ5faW1Xufv7u+iHfXF7Hl3kv46t/8q3uvya9kTX4l93z9WL73wmqW7SnvsK36Fif9sqJPPU7/02fW30lDOGHcoKDLvLR8Hw2tLm69MLYdodU1t9EvKx3RtpsqgJY8R0H/b5RSSimlVE/1m7c2BZ3/lb9+3mHewu2lHLRLoMEqIfYVLHEGOO6e+QHLlXH535cx653NbC2qYUNBFW63YcmuQxhjGNQnA4BeGWkh4773o+38ef4ujDHM3VLibatdXt/S5ariB2uaOf7eBd5ey5XyFdfkWUQmichHIlIuIodE5AMRmRTPfcaL1hhRSimllFI90ZwtJZEXst0+2z/R9pQQA3z3+dVh152/7SAAi3aW8r0X1rCjpJY31h7ga//4gqufWsGLK/L53xfX8sHGYqobrQ7KPGVYxhj2lbd3gnbra+u9zz/bcYifvbqeJz7bQ1Ori3MeWcSV/1xOm8vN62sKcLmjv5AvqWkCYN7Wg1Gvo7484l3y/BowGxgFjAHeAl6P8z6VUkoppZRSCfZFbvBSZ48fv7IOgB+8lBP0dU8HZMFKwt9eV8iFf1nCCnsfcza3J/yVja0AlNQ0c84ji2hus0qgn1uWx53vbuGtnANRvweHXeW0rll7F1cdxTt5FmPMK8YYp/34Lyk4ZLJol2FKKaWUUkodtr8u2BXytQV2ybQzoKS4uc3F/71tdVyW69MOO5iKhlbv88p663lds9ParstNTVMbGwqqeGpJLpU+y3rsLq0DIC/IUF9KxbvDsMUiMgt4AytpvgGYIyJDAIwxlXHef8yY1Mv5lVJKKaWUSir/WJQb8rXimuYO80TgxeX5XdqXJwf39F/0u/e38sba9lLo9fureP6mU/3WaWx1dWlf6ssh3snzDfbfHwfM/wFWMh20/bOIjAf+g1Xd2w08a4z5u510vwlkA/nA9caYqtiHHRhPvPeglFJKKaWUCrRmXxV5PqXNja0upt+/MKp1Awu/3l1f5Dfd0NIxUXbodb8KI27Js4g4gO8aYzo/sBs4gV8bY9aLSH9gnYgsBP4X+MwY87Bdoj0LuCNmQYehHYYppZRSSimVWHe9t8Vv+uF5O6Ne13P97mnH7A64oG9xujDGICK43IbmNpdfqdnv39/KtDED+NZpE7oYvepp4tbm2RjjBv7SxXVLjDHr7ed1wA5gLHAV8LK92MvAN2IQakRa8qyUUkoppVRyCyzsMvYMz7V8YPK8vqCap5bs5aXl+zjyrrkce8983D7trV9ZtZ873/VP3jvD7TZc9vhS5naiN3OV3OLdYdgCEblGDmOEcRHJBk4GVgMjjTElYCXYwIggy98iIjkiklNWVtbV3XagBc9KKaWUUkolr33l/p2Jea7f20ueO67z4cZi7v1ou3e6M8NaRdLidLPzYB3/782NMdum6l7xTp5vxxqeqkVEakWkTkRqo11ZRPoB7wC/MsZEtZ4x5lljzAxjzIzhw4d3LerAOLS3baWUUkoppZLa+oJqv2l3QMlzMIGvpQVp9Hzt0ysOO7buUlbXwuKdh7o7jB4jrsmzMaa/McZhjMk0xgywpwdEs66IZGAlzq8aY961Z5eKyGj79dFAwr4JRhs9K6WUUkoplXR+aw9jFch4e9sOnT3vPFjnN+0Ikjzn7O9a/8TJ0PTzO8+v4vsvraXN5e7uUHqEuCTPInKM/feUYI8o1hfgBWCHMeZvPi99CNxkP78J+CDWsQcPKCF7UUoppZRSSsWId6gq4G9hxpf2lRYm43W63Dy5OJemFBnO6sXl+9hdGn5cbNU58ept+3bgFuCvQV4zwEUR1j8b+B6wRUQ8jQTuAh4GZovIzUABcF1swo1My52VUkoppZRKHZ7Ov0TgiTDjS/tKC1G0+M9Fe/jLgt0A1Da1cecVU2MSYzzd59OWW8VGXJJnY8wt9t8Lu7j+F4Qu7/1KV+NSSimllFJKfTm8mXMAgDZn9FWWc/KDV9H2JM5gdQQWjWhbfeaXN7B6XwU3nNr1IbGKq5uoaWpje3Et3zh5LE63VtOOh7iN8+whImcB2b77Msb8J977jSUBLXpWSimllFIqBd3biRLYt9YVRlwm2rbMJkIC8c66Qn791ibvdFZ6Go9+spMh/TL5+OfnRtz+ntI6/r08nwe/cRxnPbzIO/9vC3dTVN3kH4sdSl1zGw0tLkYN7MWmA9UcPao/AK+vKeCsI4fx/RfXMPeX5zKoT2bI/X62o5RzpwwnM91Bc5uLl1bk86NzJ/GDl9Zy/lHD+cE5EyPGnqri2mGYiLyCNdbzOcCp9mNGPPeplFJKqc4RkctEZJeI5IrIrCCvZ4nIm/brq+1hJD2v3WnP3yUilyYybqWU6g4vLs9nzubgYzfvK2/guaV5gG+HZR2XO/Ohz/wSZ4BfvbmR4ppmthZZgwxd/6+VZM+aEzKOH7+yjtfXFLCvosFvfmDiDO2J/PH3LuCMhz6jsKqRq55czh8+2Mrd723lvo+2c+njSymuaeZt+wZCc5uLLYU1/PDlHG+HYyv2lnPzyzn8daHVhvyxT3fz8LydvLehiM93l/HHj4PfqPh8dxk7Sqz3dee7W7jx2ZUh31cyi3fJ8wxgmknxrqoPY5hqpZRSKqmJSBrwJPBVoBBYKyIfGmN8r4BuBqqMMZNF5EbgEeAGEZkG3AgcC4wBPhWRo4wxqdGbjlJKddGtr61n5gkzAXhuaR5D+mZyzfRx3PjsSkprW7h2+jgy0q1yyua2jlWoS2qaw25/a1ENa/IrAThQ2cjzy/I4ZvQA7nx3C1vvu5S+mWmU17dEHe+SXWVceuwo73RRlZVgz87pWNL+8eYS1uyrZMH2Uu+8nSV1HD9uIK+uKgDg2aV53Hn5VCrrWwFocbb/7Lc63TS1uRjYO8M776Z/rwEg/+GZvL6mIOq4k028k+etwCgg+K2ZFJLS2b9SSikV2mlArjEmD0BE3gCuAnyT56uAe+3nbwP/tEfGuAp4wxjTAuwTkVx7e6lZpKCUUl3w4NwdAFwzfRyltVZCO3drCVeeOCbo8s1tke8vfu0fX3if//DlHHaVtg+pddw98/2WrW92Rtzeo5/sZOOB9nGwqxpbQy7ru5yHZ8zszUXWa8bAR5uKvdXc9/j06v2d51exNr+K/IdnRowr1cQleRaRj7Dyzf7AdhFZA3hvjRhjrozHfuNFy52VUkr1YGOBAz7ThcDpoZYxxjhFpAYYas9fFbDu2MAdiMgtWKNwMGFC1zvEUUqpVOFb6hqotZNjLvsmzsEM758VcRt7yxp4esle73Rna9auyqvgxPGDaGptj/3nr2/wPvdNxtfana653SbouNmpLF4lzx8CI4FlAfPPB4ritM+4SvGa50oppVQowa5sAk96oZaJZl2MMc8CzwLMmDFDT6hKqR4he9YcLp460m/a47bXNvgte/bDi7h+xnh+efEUWoJU4z4cXflRfdZulx2t0YN6A6FLzU/NHsIHG4v95rW53WQ50roQXfKKV4dhVwEfGmM+930Ac4FvxGmfcaNNnpVSSvVghcB4n+lxQHGoZUQkHRgIVEa5rlJK9Vif7iiNvBBWJ16PfWoNdxVNte3O8Ixn3Rnr9gcfkiuUScP6AtAUIvblueUd5jldVlytnRgqLNnFK3nONsZsDpxpjMnBGrYq5ehtcqWUUj3UWmCKiEwUkUysDsA+DFjmQ+Am+/m1wCK7M9APgRvt3rgnAlOANQmKWymlUo4xhobWyG2Uo5GZZqVy7gTUkL3/4+1kz5qDK0SiPm/rwQ7zPEnzir0dE+tUFa/kuVeY13rHaZ9KKaWU6iRjjBO4DZgP7ABmG2O2icgfRcTTR8kLwFC7Q7DbgVn2utuA2Vidi30C3Ko9bSulVGhOt4mqg69oeNpO7zwYvk10LKzeV9mFdSoAaGrtOaeFeLV5XisiPzLGPOc7U0RuBtbFaZ9xI7SP06aUUkr1NMaYuVhNq3zn/cHneTNwXYh1HwQejGuASinVQzS1ubqUiIbTLyveAyh1TVZGGrPXHuA/q/K7O5SYideR/hXwnoh8h/ZkeQaQCVwdp30qpZRSSimlVNI64d4FMd/m4D6ZMd9mLHz/xbXdHULMxSV5NsaUAmeJyIXAcfbsOcaYRfHYX7yJCCZCq+c2l5uMtOC14D2N+H27ane7DSLt3cQbY/y6jPd07e7p5VtE/Lp7d7kNaQ4J+tch4DaEnBasTtDcBhyCvYzgcIg3Lm8c9tv2hB5q2hhDuqfdhc82jPFvLx6s7zXf/XW223yllFJKKaW+zGqa2ro7hC4JzH9SQVzL+I0xi4HF8dxHdzPGcPf7W3l9TQE/PGcid8+c5ve622244dmVNLa6eP/Ws8lIc+ByGy59fCknjx/En687keY2F1/56+dcc8pYbr/kaB74eDsLd5Sy5DcXcNd7W9hWXMtbPzmTr/5tKddNH8fQflk89ulu/nvz6XzruVU8es0J/PHj7dxw6ngWbC/ljIlDWL63nLMnD+OddUXMPH4Ub6w9wJUnjuGTbQc5fuxAWp1uthbXcOyYgRRUNpImwqA+GZTXt9DmshLxmqY20hxCm8ut1daVUiltyoh+LLz9/O4OQymllIq5N9cWdHcIUfMdzqvNZchM1+S5xwn3kX6wsZjXVhdwxNA+PLdsH1edNJbjxg70vr7nUL13oPC1+ZWcdeQwthTVkHuontxD9Tx49fGs219FUXUTTyzK5fZLjub5L/YBUFzTzOtrDgCwtaiWgspG/rpwNyP6Z1FW18KczcVUNrTyt4W7Kahs5M/zdwGw6UC1dx2Al1fuB+CtdYUArNhb4Y3Pt5v6g7XNHd5fqB71lFIqlew5VN/dISillFJx8f7G1BwhsKi6iYn2EFipIl69bfc4wUpe21xu/rZwN1NHD+DD284hM93B23aC6rG9pMb7fNMB6/len4u4/IoGCqsavdO+47QVVLTP31rUvh3P+Gq5ZdZ2yutbuvKWlFJKKaWUUqpb/Gnuju4OodNSLnkWkctEZJeI5IrIrMTsNPjs2TkHKKhs5P8uPYqBvTO44KjhLNxe6m2nDLC9uJbMdAcjB2Sxo8QqCc6vaPC+XljVSFF1e4lveUN7Ipx7qL3b+V2lPl3Q25vfZXdLX9nQ2uW3ppRSSimllFKJtnB7aXeH0GkplTyLSBrwJHA5MA34lohMC79WbASWPDe3ufjHZ7mcMmEQFx49AoBzpgyjqLqJwqom73LbS2o5ZlR/jh0z0Jvs7itvoHdGGgCFVU0UV7cvv7OkPUn2HbNtl8/zNrc1ptveMisJd2rVaqWUUkoppVLCv747vbtDUF2UUskzcBqQa4zJM8a0Am8AV8V7pxKk6Pm11QUcrG3mN5ce7e0l7vSJQwFYlWe1KTbGsL24lmmjBzBlRD/2lTfgdLnZX9HIjOzBZKY7OFDZ6Jc8by6s9j7fFSJ5bm5zx/YNKqWUUkoppRLisuNGdXcICZX/8Ey/6V9+ZUo3RXL4Ui15Hgsc8JkutOcl3Atf7OOMSUM468hh3nlTRvRjcJ8MVuVZA5+X1rZQ1djGtDEDmDyiH60uNwWVjRyoamTCkD6MG9SbouomSmqaOW7sAAA2Fba3bfZNmOtbnAl6Z0oppZRSSqlI3r/1bB699oTD2sb4Ib15/IaTOsy/6cwjGD2w12FtO1q//upRUS238s6Lolrucp+bA986bUKH13918RS+fuIYlv7fhdEFmERSrbftYK2P/eosi8gtwC0AEyZ0/LBipaKhhZknjPab53AIp00cwpp8q+TZ01nYtNEDvGMgbzxQTXVjG+MG92Hs4EYKq5o4WNPMN04ey9aiWm/J83C7R22AjDShzaVVs5VSSimllOUXF00mM93BpceO4qNNxZxx5FCOHTOQgb0zAPhoUzFnTx5GSU0TRw7vRy+7ySBAY6uT+hYnK3IrOHXiEMYO6s1D83ZQWd/Kn755PA4RKhpaOO3Bz8LGsPD/ncdXH1vKaROH8K/vTqeuuY2PN5dw8zkTuenfa1i9zypQevkHp/H8sjzuu/JYthXX8vPXN/DNU8ayoaCafeXtfQFdffJYlu0po7y+lXSH8Oi1J3DGpKE8uTiXvlnpXHXSGGY+8YVfDDNPGM2czSUdYvvb9ScydlBvbnh2lXfe4zecxAVHDyf3UD1pDmHs4N5sL67l6SV7+eVXprB8bzlPLt4LdCwtXbq7jMkj+jFmUG+/+SeNH8TFU0dyyv0LWXv3xczbWsL/nJkNQGltM9uLa/n+S2vZet+lHHfPfP7fxVai+sA3juN3729lyW8uJM0hON2Gy44bRd/MNFpdbrLS07jvquPYX9HA+MF9OOmPCzh5wmD+fuNJZKQ5eHH5Pob2y+LOd7cAsPP+y/j56xs4Z/Iwxg3uzc0v5wBw46njeWPtAY4Z1d+vSehbPzmT6/61EoDbLprMXxfuBuD/Lj2aMyYN5ZqnV5Bux/X6j87guLED6N8rg/yHZ7K9uJb8igbu+XAbZXUtTD9iMA0tTu/2T584hHlbDwLw0DeP9zteN546HhHhH986Ocg3KvmJSaEBfEXkTOBeY8yl9vSdAMaYh4ItP2PGDJOTk3PY+/3NW5tYkVvOiju/4p131N3z+ME5E5l1+TF+y77wxT7u/3g7K2ZdxDvrCvnrwt1sufcSRITj7pnPlSeO4cNNxTzxrZNZubeCd9YV0upyc/cVU3n80900tLrITHdwyoRBrMqrJM0hTBnRj50H6+iV4fBW2U5ziA4jpZRSnRB4IdQVIrLOGDMjBuF8acXq3Ow7VqhSPdk5k4eRV1ZPcU17B7M777/MLxmOl5V7K9hdWsdNZ2VTVN1Ev6x0+mam8ZcFu/m/S48mzRGbMXrbXG4qG1oZ0T/L2xwylMZWJ+V1rQzqm0H/rHS/5Y0xVDa0MrRflnfeobpmemWkMaBXRlSx1Lc4aXW6GdI3s2tvJkkUVTcxakAv0hxCm8tNup07OERw2J9bq9ONy23onZlGXXMbGWmOw/pe/WnuDp5dmsemey7B7Takpwn97eNe09TGa6sL+Mn5kyJ+xp2VyHNzqpU8rwWmiMhEoAi4Efh2dwTiMoa0IJXeT584BLDGdN5SVMPEYX29X5qxg3qzeNchAMYN7s24wb1pdVnJ8IgBWYwZ1Js9h+oZPbAXw+x/+qF9MxneP4udB+uYMqI/W+whqyYN68ueQ/X0zkjzDl2llFJKKaVS08DeGXz883Moqm4izSEcqm1h1MAsph/YceWmAAAgAElEQVRhXVu63IY2lzshSbPHmUcO5cwjrT59xvqUuAYWHh2ujDQHIwdEV0W5T2Y6E4YGT2FExC9xBhjRv3NVn/tlpUNW5OWSne/nlWEnLelp/klrZnp7MtM/ypsL4dx1xVTuumJq0NcG9s7gpxccedj76G4plTwbY5wichswH0gD/m2M2Rbv/QoBdcOxfsDSgtw1mTp6AP2z0lmVV8nWohqmZw/xvjZ5RD8+310GtCfPHiMH9GK0T/LsqRIypG+mN5GePKKfN3meaCfPE4f1Zbs9BJZSSimllEodd11xDD88ZxIieEvjxg/pE3TZNIeQ5khc4qyU6iilkmcAY8xcYG53xuC2q0s7glRVSXMIM7IH8876Qlqdbm4eP8j72hQ7eR7YO4Ph/bL8kucjhvZh7CDrzti4wX28d4tExNt2Zcyg9jtnE+wf1qH9UrtKiVJKKaXUl822+y6lb1bKXYYr9aWXar1td4vAAmaX3U48WMkzwEVTR9LqtKpjX3TMCO/8KSP7AZA9rC8iwoQhfb2vjRrQi9EDrYR55ID2xHpE/yz6ZFp3GYf7VEPxlEx7euEbOaAH1C9RSimllPoS0MRZqdSk/7lR8u1XzRWm5Bnguunj2FBQxaRhfZk4rD1BvnjqSC46ppQfnjsRsHrU/vF5kxgxoBciwnUzxrGtuIbrpo9naL9MvjptJD85/0iG98tiR0ktlx8/GodDKK9r4dLjRvFFbjn/76tHUd3Yxv+cmc3C7QeZkT2EzYXVHDd2IMv2lHPWkUOZu+UgM08Yxfsbirnw6OHsKq0HYMzAXqzdX8WJ4wbS6nKTV9ZA9tA+tLkMBZWN9M1KJ8MhVDe14XIb+mWl43IbnG43xkCL040IOF3Ge0PBGENjq4s0hzU6dovTTZvLjdtYx81tDMbg/9fnuBpjdWTgdBscYq3f3OYisG807TBNKdUZs398ZneHoJRSACz69fndHYJSqotSqrftzopVj553vL2Zz3eXseouq7fthhYnx94znzsvP4Yfn5/6Dd+VUkpFR3vbPnza23bPMuvyY3h43s6Qr/9u5lSunT6OQX0yMcbQ4rQ6vCqqbqJvptXp6cDeGfTJtMpzdh2sY+OBKpxuw7XTx/H+hiLueGdLot5OQsSi53+lVDvtbTsJlde3MPOJZUB7yXOsuudXSimlVNdcffJYjh0zgM93l7FsTzk/v2gyU0b2Z9nuMq4+eSynTxrKi8v38cCcHX7r/W7mVN5eV+g37unauy+mxenisYV7eGd9oXf+MaP6870zj2BfWQOvrSngiuNH88A3jmNbcS1D+mbS0OIkPU1obHWxZFcZVxw/imNGDeDDTcX84vUNnDtlGKW1zTx8zQneMVuvtcdXPWpkP+b/6jz2VzRywV+W8LuZU2lqdXHC+EG43G7mbD5I9tA+HD2qPw/P28k/vn0yf56/i8nD+3HxtJGcMWkorU43O0pqeX9jESeNH8TRo/pTWd/K5BH9aHG62Xigmq+fOAaAv8zfxTXTxzG0Xya90tPYc8h6/zVNbRw3diBv5RRywdHDGTWgF32z0qlqaKVXRhqFVY043YapowfgdhueXJzLD8+dRO/MNL516gSKa5o4emR/HA5hc2E1Ywf17tDrsYh4e4r29O0yCH9Hj+rP0aP6e6dvOHUCN5w6wTvd5nKTkebA6XJT3+JkUB+r7xe321DR0MrHm4vZUVLL7JxCv+3275XOCeMGsjy3ArCGespMc/Do/F1sLqzmtIlD+NXFR7GtuIbtxbWcd9RwhvXLYm1+JdPGDOCEexfw0W3n8Oj8ndx/1XFkD+uLMYZzHlnM0aP6c+uFR/LfVQU89M3j2VtmdejaJzOdplYX+ysbeHd9EddOH8dRI/ujlEpdWvIcheW55by4PB/fPrfTHQ5+fclRTNEfQaWU+tLQkufDF6tz81/m72L1vgre+slZMYhKKaVUqtKS5yRz9uRhnD15WHeHoZRSSinbby49urtDUEop9SWjvW0rpZRSSimllFIR9Ohq2yJSBuxP4C6HAeUJ3F+spGrckLqxa9yJpXEnVk+O+whjzPBEBNNT6bk5ahp34qVq7Bp3YmnciZVU5+YenTwnmojkpGJbuFSNG1I3do07sTTuxNK4VTJJ1c9V4068VI1d404sjTuxki1urbatlFJKKaWUUkpFoMmzUkoppZRSSikVgSbPsfVsdwfQRakaN6Ru7Bp3YmnciaVxq2SSqp+rxp14qRq7xp1YGndiJVXc2uZZKaWUUkoppZSKQEuelVJKKaWUUkqpCDR5DkJE/i0ih0Rkq8+8+0Vks4hsFJEFIjLGnn+BiNTY8zeKyB/CbceeP0REForIHvvv4BSJ+14RKfJZ54pkiVtExovIYhHZISLbROSXPttK2uMdIe5kPt69RGSNiGyy477PZ1sTRWS1fbzfFJHMFIn7JRHZ57POSckSt896aSKyQUQ+9pmXtMc7QtxxOd6xjF1E8kVkiz0/x2d+XH5TVHgx/Fz13JyguEXPzYmOW8/NCYzbZz09Nycwdunuc7MxRh8BD+A84BRgq8+8AT7PfwH8y35+AfBxtNux5z8KzLKfzwIeSZG47wV+k4zHGxgNnGI/7w/sBqYl+/GOEHcyH28B+tnPM4DVwBn29GzgRvv5v4CfpkjcLwHXJuPx9ln2duA132WS+XhHiDsuxzuWsQP5wLAg8+Pym6KPhH2uem5OUNzouTnRceu5OYFx+yyr5+YExk43n5u15DkIY8xSoDJgXq3PZF8gYmPxYNuxXQW8bD9/GfhG1yKNvL8Yxx0XsYjbGFNijFlvP68DdgBj7ZeT9nhHiDsuYhS3McbU25MZ9sOIiAAXAW/bryXb8Q4adyziC7PPmPxfisg4YCbwvM+8pD7edowd4o63WMUeRlx+U1R4em7Wc3M09Nys5+Zo6LlZz81dpclzJ4jIgyJyAPgO4Fv14Uy7qsk8ETk2ik2NNMaUgPUDDYyIQ7heMYwb4Da7esW/41YdwtbVuEUkGzgZ684lpMjxDhI3JPHxtqv7bAQOAQuNMauBoUC1McZpL1ZInC84YhS3x4P28X5MRLKSKW7gceC3gNtnXtIf7xBxeyTseEOXYjfAAhFZJyK3+MxP6G+KCk/PzUASnyt81stGz81xj1vPzYmNGz03H7aUOzfHozi7JzyAbAKqRvm8didwn/18AO1VTa4A9kTaDtY/lO90VYrEPRJIw7rp8iDw7ySMux+wDvhmih3vYHEn/fG25w8CFgPHAcOBXJ/XxgNbkj1ue3o0VtWxLKw7ln9IlriBrwFP2c8vwK7KlOzHO1Tc8T7esfquAGPsvyOATcB59nTcflP0Ef/PNdR24vm5xjnupD9XoOfmhMZtz9dzc5zjRs/N3fJdoZvPzVry3DWvAdeAVd3A2FVNjDFzgQwRGRZh/VIRGQ1g/z0Uz2B9HFbcxphSY4zLGOMGngNOi3fAtqjiFpEM4B3gVWPMuz7rJ/XxDhV3sh9vnzirgSXAZUA5MEhE0u2XxwHFKRA3xqqmZ4wxLcCLJNfxPhu4UkTygTeAi0TkvyT/8Q4Vd3ce72hjxxhTbP89BLznE2N3/aao8PTcnITnCj03JzZunzj13Bz/uPXcnPjYu/3crMlzlERkis/klcBOe/4oERH7+WlYx7QiwuY+BG6yn98EfBDbaNvFMm7PF9J2NbA11LKHq7Nx2/NeAHYYY/4WsLmkPd7h4k7y4z1cRAbZ83sDFwM7jXWrbzFwrb2tZDveQeO2pz0/uILVTiZpjrcx5k5jzDhjTDZwI7DIGPPdZD/eoeK2l0vY8e5K7CLSV0T62/P7Apf4xJiw3xQVnp6bk/5coefmxMat5+YExq3n5sTHngznZrE+355p2LBhJjs7u7vDUEop1UOsW7euHDgGqyfVCUABcJ0xJmEdOaU6PTcrpZSKpUSem9MjL5K6srOzycnJibygUkopFQUR2W+MqQC+0t2xpCo9NyullIqlRJ6btdp2FM55ZBF3vL25u8NQSimllFJKpaDPdpSSPWsOVQ2t3R2KOgxJmzyL1WX9BhH52J6eKCKrRWSPiLwpIpmJiqWwqomdB2sjL6iUUkoppZRSAZ75PA+AXaV13RxJ532xpxyXu+c29e2MpE2egV9iDUrv8QjwmDFmClAF3JyoQM4/aniidqWUUkoppZTqYQxW8indHEdnLdtTxndfWM2Ti3O7O5SkkJTJs4iMA2YCz9vTAlwEvG0v8jJWD3AJiidRe1JKKaWUUkp9Wew6WEf2rDks21PW4bUF2w4yb0tJN0TV7lBtCwD55Q3dGkeySMrkGXgc+C3gtqeHYg187bSnC4GxwVYUkVtEJEdEcsrKOn4JlVJKKaWUUiqUdfureHlFPgCvrNrP3DgmsGv2WaPRfbL1YIfXbnllHT99dX2XtltR30L2rDnsKOnY9PSheTv476r9UW3HU4jYmUrbxhgaWpyRF0xBSZc8i8jXgEPGmHW+s4MsGvQzNMY8a4yZYYyZMXx47Kpbay1/pZRSSqmeq6qhlaZWV3eHoZLANU+v4J4PtwHw+/e38rOABHZ7cS2bDlT7zSutbeb2NzfS3Bb+OySBVVrtaU+u8djC3SzedShijDVNbdSHSVA98V/+92UdXnvm8zx+9350Qzg77PjcnRje+JVV+zn2nvkcqGyMep1UkXTJM3A2cKWI5ANvYFXXfhwYJCKeobXGAcWJCkhrbSullFJKJd6+8gYe/WQnphMX7l118v0L+fo/vwj6WlVDa4fSx/c3FLGhoMpvXmltM08uzg0Z79wtJazKq+gwf93+SrJnzWHXwdTrTArgUG0zC7eXJnSf9320jZz89mF8V+6tIHvWHPLK6uO2z9dWF7DzYC1XPLGMq55cDoDbbTDGcP/H23l3QxHztx3kgY+3kz1rjt/3oMBOJNtcbr9tOjwlu/aif/9sD99/cW3EWE68bwHH3TO/w/zdpXWU17fw8Wb/72vuoXpanJ2/OSQB8UVj/jarFH1/hSbPcWeMudMYM84Ykw3cCCwyxnwHWAxcay92E/BBYuNK5N6UUkoppaJXXt/CwZrmmG6z1elOaNXLBdsO0uq0EotPth4ke9YcLnt8KU8t2UtRdRP/WZnPbrunYmMMNY1tUW33QGUjBVFexOceCp54/fi/6/jZq+sprW0/xr96cyNXP7XCb7mfvbqeP8/fxe7S4Nv52avrufHZVR3mexKdYO1eE6HF6fJ7b9GYv+2gd53rn1nJj/6Tg9ttKKtrSUgJ/ovL87n2Xyu907f8xxo//rlleXHb513vbeGyx/1LcifdNZef/re9ZFpEeP6LfR3WLbXbDgfePPGU7MbqBtEljy1lxgOf+s2rbGjl4r99zl3vBi9trqhv4ZWV+d44Xlq+j+pG/yG1DNDY6qSsznof24trqWxopaaxjd+/v9WvxF3wlKb3vAQq6ZLnMO4AbheRXKw20C8kascdqlcopZRSSiWJ9QVVzHjgU8546LOYbvfbz63i2Hvmc83TK7rc0272rDn8evYmv3mBJW8AK3LLueWVdfxlwS4A3lhbAECLs33ZP3ywjSvsKqgPzdvJiX9cwIHKRvZX+Hdk5Nn+roN1HKpt5txHF3PenxcHjc8Yw+y1B2hs9b9J8Mzne8meNcdbUldc3QTgTe5D8dxsCKziuiK33Jt0ANw+eyPuIEP/RHPNefvsjcx8oj2B21FSy9wtJWwvruUXr2+IOKRQ9qw5/PKNDdQ0tvH8sjyMMfzi9Q2c/ifr+9PidPH0kr1BP6ern1rO+X9ejMtt+PEr67jhGSt5zfe5OXHqg59yzdMrOqwbLZfb8Mznezt8Jr6CJZp19rHfVpz44WU/2XYwaJoYLB9ucbq58p9fsG6/VWru+cTDVYsuq2vh5pfWUtMU3Q2jQJ7v5ep9HWs9APz89Q38/oNt7CmtY2VeBfd+tN1b3Vt8kvtj75nPqQ9aifkVTyxj5hPL+OvCXbyyaj9vrSv0bs/zNe6Jo1sldfJsjFlijPma/TzPGHOaMWayMeY6Y0xLpPVjGksPvHOilFJKqdRW19zGN58Knqi0udyHNTZrzn6rSvK6/VX8ef6uLm/nnfXtF9Vvri1gyt3zOrSFLG+wSrmK7CQ1FKf9fp5dapUuXviXJZz/5yXMsUtuP9tRypS757G1qIZLH1/KWQ8vCru9lXsr+O07m7n/4+1+85/+fC8AjS1W8lxYZcXVGKFENTD/uffDbXy2o5RvP7+a659pLyV9d32RXyIUuN5Dc3dw0h8XeKc3Haj2Jozvri/ySxAv//syfvbqem59bT0fbiom376ZsHJvBcv2lOF2G15dvd8v8f9gYzF3vLOZB+bsIGd/FfO3tVe5fm5pHo98stPboVSL0+X9XDYUVLO/otGb6HmOi/d92H+3B+mkKlofby7moXk7+cv83SGXScYaoZ7v4E6f9x4szF0H69hcWMMfPrDaJEdTLfrpJXv5bOch3so50KXYPNsuDvH/VW3X4mhxulmzz0rqS+yaLJ7k3gSJsaSmmTaXsffR/qJ0oZ10qkjq5DlZaLmzUkoppcBq0/ri8o5VMj2MMawvqIp5G91WZ/BEOFwyN+Xuedz07zUxjeNweaon5wUMe+M5XmmdrO3nSaZX7C0H4LOdVkdLG+3OnJwRbh54Olwqq2sNu5xHYFXWQN6xfO238dKKfG5+2apOvC/gPTvCvNdnluZ5E5rX1xRw1ZPLeTTKGxierX7ruVV874U1vL+xiLvf28o/A2oPVNnvxelqP0bGGG9SX99sHZvb39zE2Q8v8iuJ7kxSdOtr67l99sawyzS2Opn6+0/4dHup9zsdrsmAK8z+uztf870BFOx3wHPs6uzjKwEdhgXjWSfN0bWsZJfd3CHUv4PDzgiDHTvxzZ6DeH2NVUvk0U/av5/eKHte7qzJc7S6+x9RKaWUUh09tSSX55Z2bOP4/LI8thbVBF1n3paSqNvABvrRf3K476PtIXuRfXd9Ed98agVzOjG0ze2zN7J4Z/jedY/63Tx++HLHToR8qwIH80VuecT9P/P5Xm57LfJwOK+s2k9BRSMtThdOl5v6Fif/XbW/UzcKPIsKMOOBhd5q2J7kwJMbBKYIkaozv7q6wN6+f/IavejeQ6SC/Pb3FzkA8bkKDxfv00v2+v3tLE+S1qENqx2rQ9qPu9tYnzNYiT/g/S77Js+eEvnAmxPBvgtzNpfw7vqioLFlz5rDne9upqCykaY2F3+ev6v9GIY5JuFqVHRXaedpE4cAcNExI8Iut86u0eHpQCya3qydbuvYdzV5DlYF35dvDIHf3WjbL/v2/O3tBK0HZs/pkRdR2uRZKaWUSk6e0o4fnTfJb/4Dc3YAkP/wzA7r+I6bGuz1cGrtUrmVeRWU1jbTKyONwX0zGTuoNwB55VZHUfnlDbyyMp9zpwwne1hfAJwuN063oVdGmt82311fxLvri7yxNLQ4eSvnAP979kS/5RbvsjqTqm9x8t76Qr57xhGU10fXim3h9lIe+WQnn/zyXNLTrKxteW45/bLSeWjeTgD++e3w2/i9z9A2p0wYxMRh/XhnfSGThvflrCOHMXdLCSMHZDH9iCEht+FbMlte30p5vZXQea7tPRfxnvfqXS/KhKgzyasVh7Vcm8t/+y5X8P1FmwxEc+3oW8oe7u31yoiurKuztR08pbcOh1jHwVi9RnuORWtAwtXc1j5d2xy87W1XUqXX1xzg+/Z33W2Mz3ck9EEM91a7q8BrWL9MANId7Z9XsFACa4t4C3bDxO35KLqaPEeqgeFbuBxiJC3c4fPvgHWk0+ukCk2eo6Qlz0oppVTy8LTL8zjzoc84cdwg/vW96WHXOTV7sN88t9vgiHBB+sHGIn75xka+uONC70Xhb9/e7LdMYBLe6jL8/oNtDOuXyX9+cDpjB/fmhy+vZW1+VcSE/Vh7+JkJQ/tw0TEjO7z+x4+2MTunkCOG9o04pqzHb97aRE1TG3XNTgb3tS7yv/P86qjWDWZ9QTUDe2cAeGPwjIUbzQ2JwOTW7ZPIBRPtdZhvaWo0PNW9P9/tn6x7Op+qaWrzHi9rBxH2H+Y1Oz/1ClZtO1jY3zxlHA/P28m3T58Q1b5DJZ2Bx7C9tF/8kqf/PSubF77Yxw8Cbt74ykyPbeXV9pJv4y3dD3cDwpkiWVk031tPO+T8gI7vfHk6l+tsswYPV6Tj5dMpWIdaH/bfzpQiR6jpndK02rZSSimlUs71z6z064CppKaZT+yxRYP5dHsp1z+zkpftqqgengTiqSW5nOzTQVNNUxtf7LESK0+bvj2l9SHLM9ftr+SeD3yGgbG3W9PUxhVPLOPE+xawNr/KfslQWBW52rinemcgT5vKxlZXhxLTq59azo4gnTV5Lr4j3SjojPZeeKNfJ1SVXONN5Kzpc6cM61JMgW2OI/GUfIfSJzMt7Osd9u+pNh7ktcDExxHlVXi6fVB6Z0QXS8cq73ZsAalMrj2cVmOr06d3ZEOGXTMhK6DE2zf+PplW+VtgSWhXC5v8vkthjqFHuGrb8UrYgvWO7rffIC9Hk3B+sKkYsDpjC8UV4eZSJIG/E4F8q+13LHnu/P+5bw/dPY0mz1HRettKKaVUKqhrbqOyoWNC5ElWAztt8lyUPvrJLqp8xg3+2avr+O4Lq6lqaGVVnlXKvau0rkNHVx7XPL2Sl1fujyrG/6zczzmPLA7ZJtvjQGXwnnGX51rDzWwurO7QTnJDQTUP2lXWfXneZ1erfQaz1i79D1WFNxjfNs++Aqttd3WY0M5W2z5h7ECg43EZ1McqVfdUcfduP8L29pZZ349geVZgSXOwUsRgbzvUMQu1XIdthljeU7r+0aZinx6TQyd8vqXNnqSoT0BC39U2rr5tbttL0EMvHy4ZjFfCFliNveN+rb+d/epGs7jrsEueIyXPPiXPAftov/kSvYO11m+XDlX1JdYDP3ullFKqxznr4UWccv/CqJcPrM3odhucLje5h6xSuWZne7XocL3/enjGu/UMvRTs4nFNvpV0BibygSIlui1Ot19PyR7BOh6K5uL71tfWc/qfPg27z/692lv8ecfVLYp+WKJQyVVgte3ABCjafMh7vKPMMcYNttqqXzzVv5OnUAlrtJ1R1QW5oRB46H2T6SW7rA7j9nexIzuAJrv6fKiPOFToqwOaQHh4bkBk2jcQfD+7UNvqat7qW/I52x6O6YMNxSGXD1vyHKeL9pYIY3x7+B7+aGKJJh/2tFnucpvniB2GWX+DHdZo2mQH2mr/Jny+O3xHiKlIk+coaIdhSimlejIRuUxEdolIrojMCvJ6loi8ab++WkSyfV67056/S0QuTWTcwXh6FoYQpXgB04FtJ3/66jom3z2P0lqrIy7f5HTEgF4R9+8Z6/WTrVYV8mAX+Z6w3HYHTR7Zs+awaGf7eLvhhjIC2FtWH3T7weZFM9TNnM0l3vcdSrC1u1SVNEK17UDRtnH1jCkdajzbQO25dnTvIdoEIrDEGjp+nk0+7dU9N108Q2z5xxhdVXRPz+sdelaOsKJvXFa16eD7933v3qcxukb2LXnOs0vv68LcrAr3fYhXb9utUSbP8fCRXbU73DB54USqtu1pUhLsuLaXRPvePInuGNc2Rb7hmGo0eY5ST6yzr5RSSolIGvAkcDkwDfiWiEwLWOxmoMoYMxl4DHjEXncacCNwLHAZ8JS9vaQQrJQ1MLEMvFacv63Ub7qx1eXtZOxIu9fsaIS7gPetFhmY5368qX2Iq0g5aW1TW9BedIOVJHounmNZbfuokf0AOHPS0KjX8VSBDzzuns8l1A0D3yr10Sisii559ghZWhswHW58YWiv2jzI7kzNV+CxL61t7rDMiP5ZHWPwVgeO7rMLVSgbKnLfzfqWLndol+4X0+FfF/t2duetGmwIWpsiULhl4nXFHukGTrCbHNEcpkg3yXxtKgzf1COUSNW2PUqqmzt87p7PqaGl/fPyjAUeSSx/b5KFJs9R6Hkfu1JKKeV1GpBrjMkzxrQCbwBXBSxzFfCy/fxt4CtiXclfBbxhjGkxxuwDcu3tJYVgF6WBSW2kZKjF6WrfTicuCHyrN3eMy/prTMckZMfBOu/zSBeeaQ6J3ItugEN1HRO2rhrRv5c3js5qaPUvkZprl9R7StgCRRqn1sPT0djZk6NL6CMlN4GfT6ghrDxGDrCS32D5UFNAz+i+x+266eMAOP/o4eEDikJWQE/Y+XbzgNfssbADOQJu5oR6h77HIlTt+M7k1E0+QzZ5Ymh1uWmL4jsdbuileBV4RUrq23ebfJlDpKGqPFxBxnn2lHavzKvwzotUku2RnpZ8x+JwafKslFJKfbmNBQ74TBfa84IuY4xxAjXA0CjX7TbBOvgJvACOpiMd4/M8Wi1toROAarsU1XdYHg/fnrIjVYdOdzhCXhSHSiB2+STnXeFX+tjJnq19BR5Lz9BjnhLmwPCjTZ4H9ckMuv1QPO8hcHlPyVpnS559OyzrkHgHfFa+ybPnebDq49E24x5qD6kVOIzU4l3h2536jzftU/LsnecfB7TfhAosDe9Mh2G+n+lnO6waH2V1Ld79hbspE21JaixF2+bZVzRVyLvaOV5nRGrz7JEm0uH/OdhvTLRDlaVryXPniMgkEflIRMpF5JCIfCAik+K5z3jQNs9KKaV6sGBnucCrpVDLRLMuInKLiOSISE5ZWVmQVeLv+S+s0pO31hX6zY90cetbOtyZy4FDdaHbDnvGFM4rawibbETqWTcj3cH6EMPbhMotoh0XOhTfNuWd7dnaV0Fl+I6xAo9LtO1NQyV1oZe3n0T5FqIdrkgkcmmfb3LY2fGpg2nvbM1/vqcNcSj+1bbxDtFW2ejfa73fdn3eZ1f5JqNL7X36Cpd4hbuZEq+0uittnqujqN4cyzQj1E2zaEueRdpvZHgEHY88yqDToh2PLYXE+x29BswGRgFjgLFLg5MAACAASURBVLeA1+O8z7jQJs9KKaV6qEJgvM/0OCCw7qx3GRFJBwYClVGuizHmWWPMDGPMjOHDD79aaleEagMb6aLS6XZ3us1ptMrrW8KWZrVX7w4eY7pDqG4MPk5x6JK52L0HTwIT6rCESzQ97aVD2RrQg/f6guBjXnfgTeg7J9TygYe+MwWeVSE+G480v+rSodt8R3sNGotrVWNgu137wTOUmvd/JFiHYYcRg+93P9h3PCNIp2vB1j2cGDojWMLuW6K7q7RjrY4BYZpvePi3kT684Btag98ci7bDPWPocEPucG7oaMlz54kx5hVjjNN+/JcUHPWpK3dUlVJKqRSxFpgiIhNFJBOrA7APA5b5ELjJfn4tsMhYV3kfAjfavXFPBKYAaxIUd0xEKkl0uo33Qj3W14EVDa38//bOOz6O6trjv7NFvVdLsmXJRu4duQHugDGmhh56MSShBEgCpoROIJSX8ngJIdRAIBAghN5tMNUYsI17k9yrbMuWZavs3vfHzOzemZ2ZnZV2V7vifD8ffTTlzszZO3d35tzT5qywdqm1siQG9hNhnEWyLivlOZqfIZih1/xadrGrdm7tQGhCIqcJw+yUUDPawlgTjRbw8J4Kwf0HLRQZM+ws4NsalYmf8HWGlZMYJdTKcRnl02iW5ZR2G/vQSamqcG7tMi1SGTizIeS1iZf93mYypb7B3tLeUUzDQCTBtTJj8nfPSW+EZDvvBFaeJU6SsAGKkm0XXqDhPOt899OhYq08zyGi2URURUS9iegGAG8RUQERFcT42lGlo0XfGYZhGCaRUWOYrwLwHoDlAF4SQiwloruI6CS12RMAColoDYDrAcxWj10KxcNsGYB3AVwphOicX3CcCRc72e4T+EG1wEX7TeCDZdt1btBGNMuklcLmcREm9TO35FtZmmJRxsdKAbVzc7VLqGbGV1KyIjuCyrOz8/7PB6sAAPNM3IYBhNx0p/0nRHiFX+cFbaP0P/PlegDAU5/XOzqfUUEe1jNXJ5cRnfu4JFWI8qyzPJuHMogIPJvl8WE2AWNW7kvDLmFVrCzPcj4CDbPxYOaOb4echyCSyQczmix+T+T+tXN5N+vXZVtCP7dTC3l3tDxH9ssVOWep/68wbL8EynfcNP6ZiHoB+AcUd28/gMeEEH9SFe4XAVQBqAdwphDCoR8PwzAMwzBmCCHeBvC2Ydtt0vIhAGdYHHsvgHtjKmAMCfeyKr8cO7XeRIJdDLKmu1i5Ynrcocl9NKwmBZxmyY0GdtcyJhwaWJZjqpxo9C7ICBu7C+hjjp2wWa0HbVV6JyRhmNqvVh4LZkm1rJB3b21UsqB3RtcIWJ4Nl5U9KM2U1PwML+rUZb8ApvQvxpyVO3HUYUX680vL/kA/6wXeuMc+ll1GVp6bTWo623Vf78IMx9eJFhsaQj+b2TDQ3UMHXzfZot3Zya1D7ea/FVr9ecB+UsvMHd7s98dp+ALHPEcAEbkAnCeEqLb4s0sc1g7gV0KIgQDGAbhSrSU5G8BHQogaAB+p6zGHiGOeGYZhGCbZcFJCyYnbtkYsEogu3Gie8AsIum3vajJPPuZxkeX7iZUrdSyyFOdlhNY0Buxf0o1WzYE9sm2v4TTT8eodTQCAXU328cYdRes/K3k0JVhAhHUdl628Whmg7fusE82Fw8rS3SQppmYeCVVS/XK/EOiZryimqV69miBbG+fXKfLuPqDv5y17ndfXlpVGs1FpNe4BoDqCmuvRotlkosvM60JOEhap52pn9Q2rCT55cshuWGplzcLRFdnOE4WYKc9CCD+Ahzp47FYhxHfq8n4obmQV0NeZfAbAKVEQlWEYhmGYboiTF7ywCcOkF/ys1Og67PUqSA9YPs3QFHurZGd2Vh2rz+40624kuF2EBhNFp8XCCgaYvMCHmZj4Yq0zt+069eX/kY/XOGofjtCEYZryHD46IVzMs5midMDEAqvRUYVRy+4OmCu38gSS3y8sFT5561uLt5m2iaSck9N7akaa1w3AWUIuK3Y1tTgugQaY3xuzzyB/9yL9unVWKZVriadJkx9/v6AWAFCWm4b6XdbeAaer9cbD8eTndeEbIXY1t7uSWNvS3yei06gT6SmJqArASABfAygVQmwFFAUbQEk0hAwvQzyuwjAMwzBMvAkb89xBy7OTl/rRvQtsX96bWnzYc6DV1KUVANwua0uVteU58nI74RAA1puUnrKzPBuTsTY6TAjmlGMGlUblPEZFUrtdB8OU/BICeG+puYIZPHcodknWynLTbM+nsXhTo+U+s1dy2VjpEwL/mq+Ubg+JZ5bafWgoZ6Th1HIJ6K2h366PLArz+a+VOPB9JjG+PXLC91Nrux+193yIm179wfE1zZTnfYdCx60+5jky5bGzbtty8reJNcF8CFvVpHM5aV5bi356itvRdf46d62jdt3RQh1r5fl6KOWpWohoHxHtJyLrgBYDRJQF4BUA1wohHB0Xq1qS3e/WMwzDMAwT7mU1li9/xTmpmNxPbweQE2m9MH8DRt79ATIli7f8su4isrQSWrmjxyLmWQjgjUUhFcpsrZBGJfEjm6zjzuUIfrYeDhXN8OfUr2sx8k4yadtZka0Y2CPHcp9d6SaZ3QeslaMUk3PIY8XnF4GJF+P9c+KC7IogaFtOlGWGnRL893nWlk+vJ7wM2qTVy4a673akekMVy1e/Cz2+VJI70m9bgxRuYFWGDrCebPrHF/Wm2x96fyUApZzWtn2HLM9r93tojIF3Qiw8XbqamCrPQohsIYRLCJEihMhR161/FSSIyAtFcf6nEOJVdfN2IipT95cBCPmljUUtSS5VxTAMwzDdk0gsz5EYhcwsYkam9i8JSVhWnJ0a0q5BUoaG3fF+YJnI2i00XMxzNN0pv1u/xzQTtF1pJbMMvp2l+qa3UZGnlGWqLIhOQiljL2mK5qEwpbYEwrswm92DNIOCJrcxxhdbMaJXvuU+Y6I2QP8d8Ok8LQzvvw6GjDGG326c7Tex2spUdjApmDuKLqNyErsME+VZK9cmI09QRPo102L2AfsxtmO/uQIsT0LJ91KOpb/h5cWW57WTV7ZKlzucnDLe/8bmNsvkfMlCTJRnIhqg/h9l9ufgeIJSFmO5EOJ/pF1ynckLAfw32rJb0R199hmGYRjmx044y3N7BDGRkSIADKvI1W2bObQspN3O/eaWRL/f+v3ESm5NqY6mQeizNeZlnppbrK2zt762JHoCSGhJrmJRkks+74L1u23b/Wv+hrDW76N+PwdVs9/SbUsxWE3lj1GUleJIRjvX28fnrQvZJk/gyPH1xrHlpEfn1wX75fqXFmKDiTu/xqrtTSHb5NJGxokEK1rb/VixLajkOokWdZK5HdBP8vw7Aiu1RqQJw+SwCrMayVocs5OJlI54c9h9beSJl/xMZ2Nx7iq9F/Dwu97H8Dvft2idHMSqVNX1AC4H8LDJPgFgapjjjwRwPoAfiGihuu1mAPcDeImILgWwARZlM6IOG54ZhmEYplsSTjf+zKr+bxTwC4Eig6XZ7MW/ziKO1C+sX83DxTxHU7k0O1fjwTZc/uwCvUw2nX1E38JOJZDS2LRbUf4idbcfWpEbqOctY1QgtfPuCJMVOyfdi7994iwuVOa9Jdvx2KdBBVe+upYFOxxCCPx34WbTfd9vCM3uLveVrLB2ZIjIVsVXv9uMV78zl8MKedyO6Jlr0zLIXW8uxXNfbQisOzE83/a6s8mb5tbIXe9f/CYoi9aHizeZZ9U3xoi3SmEVZi7hmm79vWThf2PRFlz9wvdYdtf0iGUNOb/NTS+T3NGXOvQcWW9S3ivZiYnyLIS4XP0/pYPHfwZrlXVaR+XqDGx3ZhiGYZjuRzglSy7zE3VjpgCWGhS2vPTQsk8vqAmcjPj8wlIm+XNtawy6eGoxz9GM5T5hWBnmGSYZjNalE/53HpZstn7hLjFxV7difYO11VBzFb/+pUWOzwdYx+paZdt+ZI59Nm8hhCPXfQDYLsWgvrhAf6+FEOiRk4Zt+w6hpjTLkSekAPDcV+tN9zWbxGrL15fvQ66hBFm8nTDTUzxYuqURh5Vk4S9z1mJy/2KMrAx1SZcVZzPeXbINP3vuW8yaUI2/z6tDdqoH+x3Go3+yKvLJs9cWBuP/hQDmrNyBi5/6xrTt5Ifm6tbTJOuuPImioXkJ1EmW86tf+B4AMOi29yKWFQgtCWZVotepJ4DMyMq8DsmUyMS8cjURHUFEPyWiC7S/WF8z2rDhmWEYhmG6J+EssIVZQWUiUhfMcBxo9eH3767UbYvkCsqLdOgRVYUZOuV43H0fBZbfXaJkgI6mIlSuxhnbYac4A/qMz+GY9OBc540dssii3rbRPbvJRCH+Yu0u9L35bV2Cp0gSs9lNZAggkOAp3eu2jSPX8AthGosLAMu2ht4HeXLlque/CyzvMbgGR3v8mzFAqve9/1AbZv75M/S/9V386aPVOPUvXzg6h5wAbc+BVvzsuW8BBJOMOVWcAaDxYOfqhQsI02R6VsilxMxqlWtj5dXvI3chN+O5r9aj9p4PA+tC6H8bTnrks8ByTrq9zdVs/umcMZWmbet3HcDanaFu+8lATJVnInoWSq3nowCMVv9qY3nNmMGmZ4ZhGIbpdoSzwJ49ulfMrj3rHwtCSh5FUnfWL8xjlw8rycZei6Q8Wl3paLptR8OKLSsY/5pvb0mMNTe9GkyoNL9Or4R+uDw0jvSnf/8aPr/Ao58ELYWR9IldrW/jbVpgUIrNLNFmscR2NEhK8iKpzNV976zQnT8elue+xVmBZbOa1Mb4f1nZ1pC7fuTdH0Qsg/wdrLOpieyEhqbWiFzX73hjWdg2ry/aErWs+cbcA8bfBbnsWbgh/ZvpAwDox2QfqTa5vH3yQ3Mx7eFPsCEJ3bpjFfOsUQtgkOBsWwzDMAzDRJF+pVmOlIRdTS22L/3GbNdGtARUgPM4v85w/zsrHLf1C2FaMqlvSaZlvd02nx9CiLCfOxL2O3RPdsrsCGrvRkLtPR8GXFTr759p2c7KTR4AxvYpsNz3qBTjbFev2Ui7jSJktPae+/jXunWzOO05USj7pSEradEYMVqCNKv+90pJsmT3Z42JD8zBv382PrBekpOGFYaSV34hcKCl3XF2cplv1+/BaX8NWrjtaiI7waoedme4RnXTjgUvLbAe+3bhEgBQnqfERMuZvWWF28xrYuKDc2y/i4lIrJXnJQB6ANga4+vEFCJiwzPDMAzDJBAjeuU5Up5ll0QzLn7qG5w4vNxyv6wD3RQjpa6j+HwCj3wcGnub6nFjYJl5ZdDRVQWovult1Pa2LmdkzAAdDqO1ykkN5K5AVoSaW9uRkeLkNVj/2c6sdeaJYKcQG1m13bresdy1Zmc0y3Rulf28I8gKqJnLeiTMXRlU6v9j4XbsCVPL+mCbDyf8b9CVuDgrNFbe7xeY/sdPdZnDw9Hc2g6Py6VTnKNBopkPh1Tk2IZQaGEdZizaGDpRI6N91qelWtPyb4NV1YBkIybKMxG9AeU7ng1gGRHNBxDoMSHESbG4LsMwDMMwPw5cUazlaheTGKuSR9Gg1efHPpNauS4CKvLN45A/Vq2SC9abx8R2hNWGSYxpD8+N6PjOWvc6gtPkSi/M36hT0JwOh0jcts0SQ5lh5si5dkeoNTCaSsqFT84PLL/1Q6gtbOqAEsfnukhKmnXdi8GEbiN65QXqQ+eaJMyzw0zX9gtgSwSK84aGZkx8cE5E13VKIvx6VOSlB0IDwuUeaLCx1q+0meQBlEmbNK8LL0hhF9rv5yerdurGUjITK8vz6wBKAcwzbJ8EILKc9QkAJwxjGIZhmMQiirqzLQmsOwcUYSNC6JMmxRpj5uktUgIqJ4TzDuhq/ixZ9/1CYOU2eyUCiCx23S7mecBv3w0s3/hKqOfDKybljKKJrDD9+aPVIftLc1I75B4ts7Ux+PnDuQY7Ydu+yMZfrBRnwLpEVTyxG1+REG5C6OVvN+FlQy3sm179AcN65kWUNC3RiZXyfDKAm4UQi+WNRHQAwO0AnojRdWMGh20zDMMwTCIRH+25KYLMvNHg/HG98axFmSGnfLdhD8py08I3ZCJGdhm2IxLluaO8Y2IJjjdtPoFRHUjKJSPHyJolZLMjEtfsrmDuyp1dLUKXsr6hudvVeo6V8lxlVJwBQAixgIiqYnTNmBGv2W2GYRiGYZxhUZY36sxbHd+X34LMlE6fY97qXSF1l5n48tKC2FqEAeDn//wufKMYY7Q0xhu7eHGGiQWxKlVlN90ZvhhgAsJ2Z4ZhGIZJHOI1sf2XuWvDN4oiyf6+IZemYZhYs+9gfD1DGCZWyvM3RDTLuJGILgXwbYyuGTPY8MwwDMMwiUU0E4YlEp+sSm43z2RX/pnkwqz8EZM8vHTF+PCNEoxYuW1fC+A/RHQugspyLYAUAKfG6JoxhUOeGYZhGCZx6J6qM7BoY9cnGOoMiZydnGG6E1mpnrjnZIg2Y6qt66YnKjGxPAshtgshjgBwJ4B69e9OIcR4IYR1ATGGYRiGYRgHUDe1PCc73S05UDJzzdTDuloEJoaMsqnVngwM65nb1SJ0iFhZngEAQog5AGKX/z1OEBGEjSPSwVYfvt+4B6Mq85HmdYfs37RHeZD0zM8IbFu3swmFmanIzVDq2a3d2YSS7FRkp3mx+0Ar9h9qQ+/CTOxqakFziw+VhRlYu7MJxdmpSPO4sb7hAGpKs7Fq+370Lc5C3a4DKM9LQ0NTK3IzvGhsbkNBZgo27G5GZUEGVm3fj4FlOVizowmFWSkQQqmrWFmQga2Nh5DicaEwMwW7mlrh8wvkpnvR3NqOPep5CEC734+mFh8yUtxwq5lamlt8aPf74XW7kOpR5mK0TPZ+IeDzK39EABnsBMY+FSLohicg4BfBLOcCRuu/CByjrfn9QrlX6kafX8DtIggoWS+9bheEUFx8vG4C1OUUjwst7X54XS4ICLT7BFI8Lhxs9SHN60ZzazsyUjw40NqOjBQ3DrT4kOZVjnERwUVAa7sfaSluHGr1weUiuIlwqN0Hr9sFFxEOtfngcRHcLkKrzw+/ALwuAhHQ6hPwq7Jq74I+vwjM3gf6RCj9IoT+cwth6EkhL6rtdX0V7Dvten61gXweIfRZ5rVz6Nvo76F2r+XNPiHgIoLPr/z3C6WNdr+0+9TuE3C7gHa1nRDKsR4XoV11y3Kp7YigHuuHEFDus1COVfoseG3tc2rjT0D5ELJ1ROj6TPr8hnEmf+aQvjLpawCB/hCSLHJ7bd2vkyFUNuN15TFgvl+AQLrP6TfdFhwHwrBdCBFQUPyq/DLCcIwwfIag7KF9KSRh5X3GzxO8jnaM4XfDsMl4XwWAqsIMPHTGcGSmxvSRx8QZ1p0Tj6MHluLD5du7Woyo0B2seqsM9beZ2HB473x8G6Zu+q0zB6J/j2yc/0R0ah3fdfJgvLc0+eyRlQUZ2LBb0YsGl7Py/KOkubUdZ/7tSyzZvA9jqgvwr1nj4JJSgB5q8+HUv3yBpkPtmH/LNGSneXGw1YeZf/4MlQUZeO+6iWhoasG0hz/BhJoiPHvpWPz2tSV464etWH3vDFz9/Pf4qq4Bi24/FtMe/gTHDCpFRV46nv6iHk9dPBoXP/UNbjiuPx54dyVOGl6O1xdtwdjqAnxdtxvDe+Vh0ca9qCrMQH1Dc7d4EDAMw0TK8q378M6Sbai/f2ZXi8JEke4a85zM5KkGge7AC7PG4cRHnJWlSlTeTULlKhkJpzgDwIyhZdjTyXrYMheMr8Jt/10atfNN7FeMTy3yLSy49eio1WJ/7cojA6XNLjqiKirnjDexShjWrSBYxzw/Ma8OSzbvw8xhZZhftxtzVurr0y3fug8797fgYJsPn69pAAB8v2EPDrb5sHL7fjS1tGOhGt+klZV4S63bt3F3M75c1wAhgO83KG0+WLYdr36nlAX4cq1yvlfUMgGvqwXIv67bDSAYN1WvulCx4swwDMN0F1h1TjzieU8eO//wmJ4/PSXUk5DpWtb97nis+93xpvt6F2Zgrcm+16860tG5bz9xUGD5f88Z2TEBbSjJTk1ob5lsG8+s3PTIJ8X6l2aHbKu/f6auFF//HqFtkgFWnjvB3uZWPPbpOhwzqBR/PGsE8jO8eG3hFl2bZVv3BZaXbmkEANQ1HAhsq9t5AFv2Bgu8t0lZA9fvDsYNLdsSPI+mx2u17fY0t3X+wzAMwzBMEvH4Z3VdLQJj4N9xrPl79MDSkG2/PWGQSUs9dfcdj7r7zBUwGZ/fwmrSBZwyoryrRUgIXC7SeXfKHDe4RyCkUOPjX03CsJ55+OhXkwLK8fe/Pcb0+BOHK3386HmjAsvRRAvjMzKkIgeLbj+20+cfVZkHABhUlhO2rVFRfvri0YHQSwB4+5oJuv1ed2TqYv39M3HOmF66bacf3jOicyQySac8E9FxRLSSiNYQ0ez4XNR8898+XYem1nb86th+8LpdOGZQKT5ZuUP3g7tsyz7kpHnQtzgTy1VFun5XUHnetKcZm/ceCqxvawwur9oWLPy+cpukPKunX63GsuyOohsIwzAMwzBMLDAqN53BTImaObQsbHgGETlKNvfF2l269ecvGxuZgJ0kX3KBv2xCn8Dy5RP7mDVPGu4+ZQg+nz0V826Y4jiUZmRlHlbcfVxgfcmd0zHvhim6NscPLQs5rk9xFgCgb3EWLj6yGvX3z0R+Zgpy0kKtrEVZqai/fyaOGxJ6no5QKFlY3/mlooxqRi+ZMw7vpbPsXjC+d9hz/9nEMj5QVZrPGVtpeszqe2cElktz03T7ehdmIlvqk4woeF3USJbna4+uwUNnDA+sH9G3EFlJnIMkqZRnInID+D8AMwAMAnAOEYWfZowCRrftnftb8PTn9ThxWDkG9FAG7Pi+hdh3qB0rJEV32dZ9GFSegwE9crB6h6Ls1u1qRmlOKgBg056DOsvzks2NgeWVsvIsJX1oafcBADZLxzEMwzAMw3QVhzvI/PvSFeNiKkNxdmrExxitbBqyMeOyo6od+6TfedLgwLLR+mbHF7OnBpZHVebpvAr7SYrIfDU0LxxXTunr+NrhuGnGgLBtJtQUOTrXuWMqUZGXjl4FGeEbq/znF0fqEvJmpXpCjh/eK8/x+RbfMR3198/E2BiWSbpySjDTuabYmt07o7I8saY47LmPMfG6GNenEADQryTL9BjZ6v3704bq9lUXZeq+O1VFmYHlu08ejI7Qpzh4jjNq9d+D52eNw5I7p3fovIlAUinPAMYAWCOEWCeEaAXwLwAnx/qixizRAPDXuWvR6vPj2qNrAtvGVisD96t1ypfD5xdYsXU/BpXl4rCSLGzY3YxDbT6sbziAYT3zkJ3qwcY9zdiy9yDS1R+FRZuCyvMKC8tzmy9xXIkYhmEYhokvJR1QEo18e+vRUZAkiNHyN3VACS45slq37fDeBajISw97ruG98vDqL46IWIZILNsf/2oSHjv/cAwqN3dzlevPej0uLN8afCe79ugaeCyuleYNvlpfPtG5AivHgvYuzNTtS5Fcap3mrzltVGRuspWSMmp0f29p9xubh/D5ml2m2+8yKF9WbtdWXHZUdfhGHaQjsbxOSfWGqlhmb+9GLwgnE0Bm8fgnDi/H/JunYayqRAPALyYHx5/c7f17hI75fGn8yUwf0iOsPJdK9+i+nyiKeYrk6u3kO59MJJvyXAFgo7S+Sd0Wd179fhOOH1oWcAkBgPK8dFQWZGB+nZLIq77hAA62+TCoPAf9SrMhBLBmRxM27TmIXvkZqMhPx+Y9B7G18VCg1tniTXsD51spuXckUOgNwzAMw/yokK0vwxOgNumbVx8V8TFvXaM/pjDL/iXdzLXVjrNG661LT140GredGOocWFtlbqE+om/wpf+1XxyBUZXWlmyzeOdI6VOchWMHWysGZbnBF/6KvPRAklYAuOSoaqwxJKfS3FDTU4L9Jiu9VpxZ2xNFWak6y+BlE6wVxouPrNKtHz2wxLSd/H4q8/cLak23b5Dy7BRl6RWpfibJn4xkp5kroheMrwp7rB1Orm3GuD7hrcozhlrf/1NHKuqF1SSJGXKisfLcUIWxOMx3DtAn0frZJP3kyxezp+K9aydaHluSo3fHLpXWZSU9zWRcjrCw3Jdkp5lul5Gt/ueMUdzGu3NpyGRTns1GsE6tJKLLiWgBES3YudM85XrEFzW56qE2H8pzQwfUmOoCzK/bDb9fBJJ8DSzLRk2p8iP2Tf1uHGzzoSI/HT3z07FxTzN27D+EIRWa8qxYnnsXZgRip7MjfIAxDMMwDBPkg+smYuFt5omCnHC+pAAYX1DjQYHBKuSJMIEPoLy3GLGLbTx7jHnspJF5N0zB4xfUOo5hlJVkmednBV26w8UkOy2JteTO6RhSET6Bkhny55ncv1hn7csxURTn3TAFn/5mis5T0InF7YHTh2PBrUfrrOZ29W+NWYwfv3B02GvIyMp23+JM0zbTDZMKvQpCP8dPRuptV05coEdWWrtWP3peMHv6uVLc7qT+4d2Y+5h8jn9eFj5E4JQRFfj1sf1Mk4hpnhQXSuWUwinShdKkg5ky6uSzyO7pZQY9ozwv3TRDtRwqIGNlxTb7/XBac7lPUWhfC5OSRGleNx48fRj+0wEPkkQn2ZTnTQDkqc2eAHTprYUQjwkhaoUQtcXF4QepU4wDw+83dz0ZW12APc1tWLOzCUu37IPXTagpyUZVYSbcLsKclYpC3zM/HT3zM7BqexPafAK98tORn+FFU0s7slM9AZedVI8L1epAlR+eyRxozzAMwzDxojg7FTWl2cjLMHdLjJTeEcRqOsHKEqix5t4Z+M7wcn/QRBE2suwufUxhU0vwGM21eNHtx+IqKTZT5pppNabbjfQqyMDRg5xbgmurIo8zfeLCWsz99eTA+rQB5tZWI1mpHrx5dTCm2akVcUhFjs5wUZqThvF9zJV+AlIP/QAAFMhJREFUjfzMFFQWZiAjpWPvZ0bRrJRRKwuvU+SJCaus0rIC98F1E5EpfSbNnV0+dnB5jul4qTXEwaeaWDxfu/JIXDmlL46T3INnSzHWpQ4mq16/KtQTw4kLPxHhqqk1pi7LRw8swe9PG4rfTO8f2PbHs0eEtJs5tAz/uGQM8jK8AW+JzBS3qY5QIP0GHT+0R9jEXLLV3TiBJmPMN/DSFeORm+7FkYfp49DN4tIfOG2Ybl1T+mcOLTOdANCSlZXnpgUyfFebKNSAEus80saDJFlJNg3sGwA1RFQNYDOAswH8tCsEaff74TaZGdXinr9e14AlmxvRv0d2wG2nqjAjUIC8p2p51uiRm4byvHTsaW5Dj9y0gMtMUVYqilQ3j8OKszD/gBJP3ac4E4s3NaIgM4WzbTMMwzCMgXSvG31LMsMqp5Fy9dSaqJbJMnuh/c30/njwvZUAzK1EWZIy88lvJmPSg3ND2shKXL/SLGRKL+oLb1NK43jdLvx6en88MmeN7tjSnNSYTdJndkC5nGZw0x6vWq//dPYIPPLxGrx/3URHGbTNMjLLpLhdaPX5ccmR1ToF0ut2Oa7R29F+M8o/olcevjZJMNUzPx3HDirF+8u2hw0hmNivGJ+u2onpg0vx3tLtIftlxea8cZV47qsNIW20rMmzJlTjmEE9UNs7Hyu27dfFiu/Y3xLwoJS5/ph+uvWLjwx1Rx/RKy+gpK26ZwZ2H2iNeIIgFmOViHDWaL33RVFWKh44bRheX7QFn6kx3pP7F2Niv+LAd2rxHcciM8Vjqrz3zE/H2OoCXH9MP11sMgD8cMexaFdzGj136Vhc9o9vMK5PAcpz07Cl8RBeumJ8yPl6FaRj4+6DIZ9/THVBoPzVKSPKUZCp6BHPXhrMGP/Jbybj+a836EpIrf3d8YFJnP87d5RpvwypyMXvTh2KoweWwON24c3FWzDU5N53Z5LK8iyEaAdwFYD3ACwH8JIQYmmsr0vQ+4YLIeAX5pbnXgXp6JGThq/qdmPJlkYMkdwgakqypXYZOuW5JEdRngGgLC894OqTm+4NpLvvWxKc2alSLdORZCtkGIZhmB8LD54xDG9ePUEXuxoNcjO8jkvsOMHsJfsiyVXUSgZAycpsTC5lRmVBpk65kRVDIJh996UrxmNMdQHev24SAH3CIZmzas2zSD+nvpzPtsnOLFt0b1MTU2kv8C/MGocPr59k+1mAoKJ58ogKfHD9pBDF8+5ThgTODQAnDFOUZmNctpHldx+HV34+Hj8Z1VOX+AsAtu87ZHGU3q25xiLbcaScN07Jwmy01mamenDPqUMwrGcuHlMnhrTM2tMH6ycZtCzZs2cMNL1GQUYKqosy8ddzR8HjslcJbpk5CGOqC+ByUUiStcsn6MtnfX3zNMyaUI0jVMunZq0cHcbrIMXjQg+TkEgrZpgks7rkyGocG4EnhBM0a3u/0mycOboXnrtsLH6pWtqPMFh3c9K8ge/0M5eMwWc3BktqedwuvHjF+BDFGVA8CjQL+FE1RVhx9wwQEZ66eAzOHt3L1F1aiy+2SzL2x7NHmuYe6F2YiZuOH6jTZdwu6zJu546txHnjlOv9dGwlSnLSUJCZggvGV4GIsOj2Y/HDHZ2vV50MJJvlGUKItwG83ZUyaMm7zCzPRISxfQrw34WKN7nsSlFTmoV3lyqDPCfNi4q8oOJbXZgZiKHulR9Unv1CIEt90PTICf44a4p3vsO4H4ZhGIb5MfDURaNRW5Vva7266+TB6JmfjkueXgAA+Hz2VMxduQO3/GdJSNuqQuVZPf+WaTjQYu0urVmB8jO8gTJDa+6dgTafwHF/+hTrG5pNj/OaWJadJNuxU+DTDcrxqSMrTF1mNc6s7YWJ/YpRlpuus3DdcNwA/GZ6f9Te8yEaJC+3W04YiI9WbMeLBmvYUTVFIXLV3Xc8au/5EHeqSdcyUz246+TBmFBTjOqiTFwiZeodb4iHnn/zNFPLe3aY/jl/nL78zx/OGoGfjKoIcWM14nYRDu+tKEqaEqHF3xonHADgsfMPx+XPfov3rw0q/FbZuyOlV0EG3rjqqEA87x/OGh6ItS7JTtO5Kv/qmP7oU5SFk0eU4+EPVuFMdXJjYFlO4H58cN3EEKOP10OYo7rD9y7MxNNf1OMOE0UrHFqCs1d+Ph69CzNRlJWKW2YGz3PS8HKcZOEibsUCB9ng/yrFSWuYKYqdxczq+8tpNfjp2Epbt/JJ/TofPtq/RzbuN7hWa/xi8mH4+aS+jrwuOsu9pw613R/LzOWJRtIpz10Bkb7Os5bIyypfx4nDygPK81QpLkerBz1Mnf2tKgoqz/mZKYG6ahX56YGSAYPKcgKZ7srygl9QzfI8uDwHc1fuRG3vfCxYvwfF2anYub8FKR4XWh2UFmAYhmGYZCQ71YP9UtmevsWZuOMkRSkLR//SbBymWggvGN8bFXnpOHF4Of72yTr0yEnD/HrFXfapi0YHLLYl2WmAReLfkZV5eP6ycYGkUrP+sQBjqwvgcbvgcSsxu89+uR5frG3A6h1Njj7fw2cM13moyZ87HMvvPg4AMLoqH9/U78ERfQttX7CJyNJCT0SYd+MU7D7QitP/+iW27TuEnDQvFtzqLAEbEeFbQ8y20wzMVsnZIi135HW7MHWAtTVy4W3HoNUX+s4kTwQYE3UBwLGDe4RMFuSmezFrQjVOHqFPqDW8Zy4WbWrEN7ccjcWb9uLSZxbg5Z/plbJZE6rRQ7oPQyW37FNHWpeecrkIp6nW+xuPM7f610jyv3XNUbj3reW6zzSoPAdL75wemLgpzUnFkX3tJxvevXYCtjYeCowtbeIhGhQ5yEzdlbhc5CgeO9bEQ3Fm9LDy3AH8qiZt9eM9TU0yUJ6XrisFccygUtw0Y0Ag5iY7zYtHzxuF3HTFTePs0ZXwC+CM2p7ITPHg1pkDcfzQsoCL04nDylFdlIm9zW0Y37cQu5tbcdERVSjITMXUASVYvGkvDivJwvqGZlQWZGDplkYMLs/Fl2sbMKFfET5avgPj+hSgflczBJQsfgs37sWQilz4/H6s23kA1UWZ8PkF1u06gIKMFHg9hMbmNjS3+QI/ZO0+gaaWNnjdLnjcLrT7/PALwO8XaPX50druR5rXDa+b0OYTaGn3wS80d3cBIfRu8AQliYdQ+7bdL3STFa3tfvj8Aj4h4KKgxd8nBJpbFStAqscNIqClzY9D7T6ke93wuAj7Dimz/5kpHjS3+tDS7kNOuhf7DrYDEMhK9WDH/hbkpntxsM0Hn18gI8WN3QdakZ+Rgp1NLSjMTMHOplYUZqZgV5PSdm9zGzJS3Gj1KbKled3Y29yKzFQPWtv9aPP5kZ7iwcHWdvj8AqkeN9p8frT6/AErQ0u7D34/4HEThFAmZXx+AQEBAsGvhgcE+g1KHwsAUPtQ60/tv7JLPi7S0c0w3ZNo17TtLhBRAYAXAVQBqAdwphBij0m7CwHcqq7eI4R4hogyAPwbQF8APgBvCCFmx0NuAPjsxqn45/z18PkEHv5gFZ69dGwg/CkcmtvkiruPC1hkc9K8+PSGKZhftxtn/u1LAMAUB4mpJtQU6WIJgdAkYIeVZOPOk4dg6kNzTc/x/KyxeOTjNRhVmY8DrcqEwGmH65WlweU5WLplH/5wVmjSopNHBCftn710TGD7UxePwdLNjQF30HPHVlqWirIjI8WDjBQPPrtxCnwmmXXjxUnDy/HluobwDSPESTK5fmqWY6vkSBpEpLO6rrpnBg60tCM9xY1dTS0ozk7FtIGlpp4D8nGxZHB5ri67uYbs8fD1zeF/Mwf0yAkYhhjmxwKZpRfvLtTW1ooFCxZ0+jw3vrwYc1buCLi2NLf6MPreD3HTjAG4YpJ5PBDDMAzT/SCib4UQ0c1A1YUQ0QMAdgsh7iei2QDyhRA3GtoUAFgAoBbKvN23AA4H0AJgrBBiDhGlAPgIwO+EEO/YXTNaz+aOUDX7LQD2Ls+NzW0Yftf7Ydu9uXgLrnr+e3wxe6pjpX1XUwtq7/kQ95wyBLe+tiTsNWSEENjSeMiy/FG7z4/lW/frrJVMdPl6XQOG9sztcEZthmFiQzyfzfztd4DHTdixvwWDb39Ptz3FJn6IYRiGYZKAkwFMVpefATAXwI2GNtMBfCCE2A0ARPQBgOOEEC8AmAMAQohWIvoOSgnJpCY3w4tzxvTCScMrbNudMKwcJwyLLI6zKCs1oCxryrNTiMi2brDH7WLFOcaYJXpiGObHBSvPDrhiYl9UFWZCSM7GHpcLp4ywf7AyDMMwTIJTKoTYCgBCiK1EZOanXAFgo7S+Sd0WgIjyAJwI4E9mFyGiywFcDgCVlZVmTeLCvBumoN1BPMt9PzFP0BNNHj1vVELETDIMwzDOYeXZAZWFGZg1sU/4hgzDMAyTYBDRhwBCa7oAtzg9hcm2gAZKRB4ALwD4sxBindkJhBCPAXgMUNy2HV436iRSecfjhtjXHGYYhmESD1aeGYZhGKYbI4SwzPxDRNuJqEy1OpcB2GHSbBOCrt2A4po9V1p/DMBqIcQfoyAuwzAMwyQs3TphGBHtBLA+xpcpArArxteIBSx3fGG540uyyg0kr+w/Frl7CyE6X7wzQSCiBwE0SAnDCoQQNxjaFEBJEjZK3fQdgMOFELuJ6B4AAwGcIYRwVB+Rn822sNzxheWOL8kqN5C8sv9Y5I7bs7lbK8/xgIgWJGPmVZY7vrDc8SVZ5QaSV3aWOzkhokIALwGoBLABihK8m4hqAfxMCHGZ2u4SADerh90rhHiKiHpCiYVeASXzNgA8IoR4PK4fwoRkva8sd3xhueNLssoNJK/sLHf0YbdthmEYhvmRIoRoADDNZPsCAJdJ608CeNLQZhPM46EZhmEYplvCtZYYhmEYhmEYhmEYJgysPHeex7pagA7CcscXlju+JKvcQPLKznIziUSy3leWO76w3PElWeUGkld2ljvKcMwzwzAMwzAMwzAMw4SBLc8MwzAMwzAMwzAMEwZWngEQ0ZNEtIOIlkjb7iaixUS0kIjeJ6JydftkImpUty8kotukY+qJ6Ad1+wJpewERfUBEq9X/+Uki9x1EtFk65vgEkzuPiF4mohVEtJyIxqvbE72/reSOSX9HS3Yi6i9tW0hE+4joWnVfwvZ5GLkTfYxfR0RLiWgJEb1ARGnq9moi+lrt7xeJKCVJ5H6aiOqkY0YkmNy/VGVeqo0RdXtMxjdjTxTvKz+b4ys3P5vjKDvxszmucqv7+NkcX7kT69kshPjR/wGYCKV+5RJpW460fA2AR9XlyQDetDhPPYAik+0PAJitLs8G8PskkfsOAL9O4P5+BsBl6nIKgLwk6W8ruWPS39GUXWrvBrANSl29hO9zG7kTdowDqABQByBdXX8JwEXS8tnq8qMAfp4kcj8N4PQE7e8hAJYAyIBSieJDADWxHN/8F/v7qu6rBz+b4yk3P5vjLLvUnp/NMZYb/GyOt9wJ92xmyzMAIcSnAHYbtu2TVjMBiE5c4mQoP8pQ/5/SiXMFiIPcMSEachNRDpQv5RPq8a1CiL3q7oTt7zByx4wYjJVpANYKIdar6wnb5waMcseEKMrtAZBORB4oD44tREQApgJ4WW2TiP0dInc05LMiSnIPBPCVEKJZCNEO4BMAp6r7YjK+GXv42Rxf+NnMz2an8LOZn81O6K7PZlaebSCie4loI4BzAdwm7RpPRIuI6B0iGixtFwDeJ6JviehyaXupEGIrAKj/S5JEbgC4SnWveDLW7hARyt0HwE4ATxHR90T0OBFlqvsSub/t5Abi2N8dkF3mbAAvSOuJ3OcyRrmBBB3jQojNAB4CsAHAVgCNQoj3ARQC2Ks+RABgE5QZ5USXW+Netb//QESpiSI3lJntiURUSEQZAI4H0EvdF9fxzdjDz2YACfq7BX42d4XsMvxs7gD8bAbAz2bnxMO8nQx/AKoguRUY9t0E4E51OQdAlrp8PIDVUrty9X8JgEUAJqrrew3n25MkcpdCcaVxAbgXwJOJIjeAWgDtAMaq638CcHei93cYuWPW39EaK+q2FAC7oPxoadsSts/DyJ3IYzwfwMcAigF4AbwG4Dx1fY10rl4Afkh0udV9ZQAIQCqUWeLbEkVudf1SAN8B+BSKy90fYj2++S8u95WfzXGSG/xs7pKxom7jZ3N8xgo/m+M8TpBgz2a2PDvjeQCnAYq7gRCiSV1+G4CXiIrU9S3q/x0A/gNgjHr8diIqAwD1/45kkFsIsV0I4RNC+AH8Xfo8iSD3JgCbhBBfq8e8DCWuAkjs/raUuwv726nsGjMAfCeE2C5tS+Q+1wiRO8HH+NEA6oQQO4UQbQBeBXAElJeMPFLcrgCgJ2LsehUluSGE2CoUWgA8hcTqbwghnhBCjBJCTITiarZaPb6rxjdjDz+bE+93i5/N8Zddg5/N8ZGbn83xlTvhns2sPFtARDXS6kkAVqjbexARqctjoPRhAxFlElG2uj0TwLFQXA0A4HUAF6rLFwL4bzLIrQ1IlVMR/DxdLrcQYhuAjUTUXz1mGoBl6nLC9red3PHs747ILrU9B6HuVQnb51LbELkTeYxDca0aR0QZ6v5pAJYLIQSAOQBOV8+VaP1tKrfaTnvIEZTYpETqbxBRifq/EsBPEBwvcRvfjD38bE7s3y1+NsdfdqktP5s7AD+b+dkcMSIO5u1E/1NvwlYAbVBmHy8F8AqUwbMYwBsAKtS2VwFYCsWF6isAR6jb+6jbFqn7b5HOXwjgIygzJR8BKEgSuZ8F8IN6rtcBlCWK3Oq+EQAWqMe8BiA/0fs7jNwx6e8oy54B5ccs13D+RO9zK7kTfYzfCeXBskSVNVX63s4HsAbAv7XtSSD3x2p/LwHwHFQXrQSSex6UF+ZFAKbFenzzX+zvK/jZ3BXfI342x192fjbHV25+NsdX7oR6NpN6cYZhGIZhGIZhGIZhLGC3bYZhGIZhGIZhGIYJAyvPDMMwDMMwDMMwDBMGVp4ZhmEYhmEYhmEYJgysPDMMwzAMwzAMwzBMGFh5ZhiGYRiGYRiGYZgwsPLMMAzDMAzDMAzDMGFg5ZlhGIZhGIZhGIZhwsDKM8MwDMMwDMMwDMOE4f8BKzsgX3cwJWcAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(16.0, n * 1.5))\n", - "for i, (stim, sig) in enumerate(zip(stimuli, acq_signals)):\n", - " sig = sig.load()\n", - " stim = stim.load()\n", - " plt.subplot(n, 2, 2 * i + 1)\n", - " plt.plot(stim.times, stim)\n", - " plt.ylabel(stim.annotations[\"series_label\"])\n", - " plt.subplot(n, 2, 2 * i + 2)\n", - " plt.plot(sig.times, sig)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Load data using pynwb\n", - "\n", - "This is to check what metadata is currently not being loaded by Neo (Neo-NWB support is a work in progress)." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "import pynwb" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "root pynwb.file.NWBFile at 0x140364012163368\n", - "Fields:\n", - " acquisition: {\n", - " index_000 ,\n", - " index_001 ,\n", - " index_002 ,\n", - " index_003 ,\n", - " index_004 ,\n", - " index_005 ,\n", - " index_006 ,\n", - " index_007 ,\n", - " index_008 ,\n", - " index_009 ,\n", - " index_010 ,\n", - " index_011 ,\n", - " index_012 ,\n", - " index_013 ,\n", - " index_014 ,\n", - " index_015 ,\n", - " index_016 ,\n", - " index_017 ,\n", - " index_018 ,\n", - " index_019 ,\n", - " index_020 ,\n", - " index_021 ,\n", - " index_022 ,\n", - " index_023 ,\n", - " index_024 ,\n", - " index_025 ,\n", - " index_026 ,\n", - " index_027 ,\n", - " index_028 ,\n", - " index_029 ,\n", - " index_030 ,\n", - " index_031 ,\n", - " index_032 ,\n", - " index_033 ,\n", - " index_034 ,\n", - " index_035 ,\n", - " index_036 ,\n", - " index_037 ,\n", - " index_038 ,\n", - " index_039 ,\n", - " index_040 ,\n", - " index_041 ,\n", - " index_042 ,\n", - " index_043 ,\n", - " index_044 ,\n", - " index_045 ,\n", - " index_046 ,\n", - " index_047 ,\n", - " index_048 ,\n", - " index_049 ,\n", - " index_050 ,\n", - " index_051 ,\n", - " index_052 ,\n", - " index_053 ,\n", - " index_054 ,\n", - " index_055 ,\n", - " index_056 ,\n", - " index_057 ,\n", - " index_058 ,\n", - " index_059 ,\n", - " index_060 ,\n", - " index_061 ,\n", - " index_062 \n", - " }\n", - " devices: {\n", - " Unknown (value: 5)-4-1 with Unknown (value: 3) \n", - " }\n", - " experiment_description: PatchMaster v2x90.3, 19-Mar-2018\n", - " file_create_date: [datetime.datetime(2019, 5, 13, 10, 51, 31, 69049, tzinfo=tzoffset(None, -25200))]\n", - " ic_electrodes: {\n", - " Electrode 0 \n", - " }\n", - " identifier: 1ed51563e8f0218c0270ee9fb6c27b0b1558c4b821c10be2756797a697b35ff3\n", - " session_description: PLACEHOLDER\n", - " session_id: PLACEHOLDER\n", - " session_start_time: 2019-04-18 03:41:56.136000-07:00\n", - " source_script: {\n", - " \"git_revision\": \"() \",\n", - " \"package_version\": \"0.16.2\",\n", - " \"repo\": \"https://github.com/AllenInstitute/ipfx\"\n", - "}\n", - " source_script_file_name: run_x_to_nwb_conversion.py\n", - " stimulus: {\n", - " index_000 ,\n", - " index_001 ,\n", - " index_002 ,\n", - " index_003 ,\n", - " index_004 ,\n", - " index_005 ,\n", - " index_006 ,\n", - " index_007 ,\n", - " index_008 ,\n", - " index_009 ,\n", - " index_010 ,\n", - " index_011 ,\n", - " index_012 ,\n", - " index_013 ,\n", - " index_014 ,\n", - " index_015 ,\n", - " index_016 ,\n", - " index_017 ,\n", - " index_018 ,\n", - " index_019 ,\n", - " index_020 ,\n", - " index_021 ,\n", - " index_022 ,\n", - " index_023 ,\n", - " index_024 ,\n", - " index_025 ,\n", - " index_026 ,\n", - " index_027 ,\n", - " index_028 ,\n", - " index_029 ,\n", - " index_030 ,\n", - " index_031 ,\n", - " index_032 ,\n", - " index_033 ,\n", - " index_034 ,\n", - " index_035 ,\n", - " index_036 ,\n", - " index_037 ,\n", - " index_038 ,\n", - " index_039 ,\n", - " index_040 ,\n", - " index_041 ,\n", - " index_042 ,\n", - " index_043 ,\n", - " index_044 ,\n", - " index_045 ,\n", - " index_046 ,\n", - " index_047 ,\n", - " index_048 ,\n", - " index_049 ,\n", - " index_050 ,\n", - " index_051 ,\n", - " index_052 ,\n", - " index_053 ,\n", - " index_054 ,\n", - " index_055 ,\n", - " index_056 ,\n", - " index_057 ,\n", - " index_058 ,\n", - " index_059 ,\n", - " index_060 ,\n", - " index_061 ,\n", - " index_062 \n", - " }\n", - " sweep_table: sweep_table \n", - " timestamps_reference_time: 2019-04-18 03:41:56.136000-07:00" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "io._file" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "index_000 pynwb.icephys.VoltageClampSeries at 0x140363981660792\n", - "Fields:\n", - " capacitance_fast: 0.0\n", - " capacitance_slow: nan\n", - " comments: no comments\n", - " conversion: 1.0\n", - " data: \n", - " description: {\n", - " \"cycle_id\": 2001001,\n", - " \"file\": \"H19.28.012.11.05.dat\",\n", - " \"group_label\": \"PGS4_190418_701_A01\",\n", - " \"series_label\": \"extpinbath\",\n", - " \"sweep_label\": \"\"\n", - "}\n", - " electrode: Electrode 0 pynwb.icephys.IntracellularElectrode at 0x140362621608176\n", - "Fields:\n", - " description: PLACEHOLDER\n", - " device: Unknown (value: 5)-4-1 with Unknown (value: 3) pynwb.device.Device at 0x140362621608568\n", - "\n", - " gain: 5000000.0\n", - " rate: 199999.99999999997\n", - " resistance_comp_bandwidth: nan\n", - " resistance_comp_correction: nan\n", - " resistance_comp_prediction: nan\n", - " resolution: nan\n", - " starting_time: 13008.059839\n", - " starting_time_unit: seconds\n", - " stimulus_description: extpinbath\n", - " sweep_number: 2001001\n", - " unit: amperes\n", - " whole_cell_capacitance_comp: nan\n", - " whole_cell_series_resistance_comp: nan" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "io._file.acquisition['index_000']" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} From ac266933ca5bbbb5c20497494124d7561968d53b Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Thu, 8 Jul 2021 13:48:00 +0200 Subject: [PATCH 69/79] Apply suggestions from code review Co-authored-by: Ben Dichter --- neo/io/nwbio.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index a5a04f929..088a98d5f 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -250,7 +250,7 @@ def read_all_blocks(self, lazy=False, **kwargs): """ assert self.nwb_file_mode in ('r',) - io = pynwb.NWBHDF5IO(self.filename, mode=self.nwb_file_mode) # Open a file with NWBHDF5IO + io = pynwb.NWBHDF5IO(self.filename, mode=self.nwb_file_mode, load_namespaces=True) # Open a file with NWBHDF5IO self._file = io.read() self.global_block_metadata = {} @@ -372,7 +372,7 @@ def _read_timeseries_group(self, group_name, lazy): def _read_units(self, lazy): if self._file.units: - for id in self._file.units.id[:]: + for id in range(len(self._file.units)): try: # NWB files created by Neo store the segment and block names as extra columns segment_name = self._file.units.segment[id] @@ -457,11 +457,10 @@ def write_all_blocks(self, blocks, **kwargs): io_nwb.write(nwbfile) io_nwb.close() - io_validate = pynwb.NWBHDF5IO(self.filename, "r") - errors = pynwb.validate(io_validate, namespace="core") - if errors: - raise Exception(f"Errors found when validating {self.filename}") - io_validate.close() + with pynwb.NWBHDF5IO(self.filename, "r") as io_validate: + errors = pynwb.validate(io_validate, namespace="core") + if errors: + raise Exception(f"Errors found when validating {self.filename}") def write_block(self, nwbfile, block, **kwargs): """ @@ -755,7 +754,7 @@ def __init__(self, epochs_table, epoch_name=None, index=None): self.shape = (index.sum(),) else: self._index = slice(None) - self.shape = epochs_table.n_rows # untested, just guessed that n_rows exists + self.shape = (len(epochs_table),) self.name = epoch_name def load(self, time_slice=None, strict_slicing=True): From 5b73c97b45eaf31ce52e7147cbaf30485853f6f6 Mon Sep 17 00:00:00 2001 From: legouee Date: Thu, 8 Jul 2021 15:27:06 +0200 Subject: [PATCH 70/79] PR suggestions --- neo/io/nwbio.py | 83 +++++++++++++++++++++++++++---------------------- 1 file changed, 46 insertions(+), 37 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 088a98d5f..a7e7a17f7 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -20,10 +20,7 @@ from itertools import chain from datetime import datetime import json -try: - from json.decoder import JSONDecodeError -except ImportError: # Python 2 - JSONDecodeError = ValueError +from json.decoder import JSONDecodeError from collections import defaultdict import numpy as np @@ -55,8 +52,6 @@ have_pynwb = True except ImportError: have_pynwb = False -except SyntaxError: # pynwb doesn't support Python 2.7 - have_pynwb = False # hdmf imports try: @@ -247,7 +242,7 @@ def __init__(self, filename, mode='r'): def read_all_blocks(self, lazy=False, **kwargs): """ - + Load all blocks in the file. """ assert self.nwb_file_mode in ('r',) io = pynwb.NWBHDF5IO(self.filename, mode=self.nwb_file_mode, load_namespaces=True) # Open a file with NWBHDF5IO @@ -372,7 +367,7 @@ def _read_timeseries_group(self, group_name, lazy): def _read_units(self, lazy): if self._file.units: - for id in range(len(self._file.units)): + for id in self._file.units.id[:]: try: # NWB files created by Neo store the segment and block names as extra columns segment_name = self._file.units.segment[id] @@ -435,7 +430,7 @@ def write_all_blocks(self, blocks, **kwargs): assert self.nwb_file_mode in ('w',) # possibly expand to 'a'ppend later if self.nwb_file_mode == "w" and os.path.exists(self.filename): os.remove(self.filename) - io_nwb = pynwb.NWBHDF5IO(self.filename, manager=get_manager(), mode=self.nwb_file_mode) + io_nwb = pynwb.NWBHDF5IO(self.filename, mode=self.nwb_file_mode) if sum(statistics(block)["SpikeTrain"]["count"] for block in blocks) > 0: nwbfile.add_unit_column('_name', 'the name attribute of the SpikeTrain') @@ -457,15 +452,17 @@ def write_all_blocks(self, blocks, **kwargs): io_nwb.write(nwbfile) io_nwb.close() - with pynwb.NWBHDF5IO(self.filename, "r") as io_validate: - errors = pynwb.validate(io_validate, namespace="core") - if errors: - raise Exception(f"Errors found when validating {self.filename}") + io_validate = pynwb.NWBHDF5IO(self.filename, "r") + errors = pynwb.validate(io_validate, namespace="core") + if errors: + raise Exception(f"Errors found when validating {self.filename}") + io_validate.close() def write_block(self, nwbfile, block, **kwargs): """ Write a Block to the file :param block: Block to be written + :param nwbfile: Representation of an NWB file """ electrodes = self._write_electrodes(nwbfile, block) if not block.name: @@ -673,10 +670,10 @@ def __init__(self, timeseries, nwb_group): def load(self, time_slice=None, strict_slicing=True): """ - *Args*: - :time_slice: None or tuple of the time slice expressed with quantities. + Load AnalogSignalProxy args: + :param time_slice: None or tuple of the time slice expressed with quantities. None is the entire signal. - :strict_slicing: True by default. + :param strict_slicing: True by default. Control if an error is raised or not when one of the time_slice members (t_start or t_stop) is outside the real time range of the segment. """ @@ -726,10 +723,10 @@ def __init__(self, timeseries, nwb_group): def load(self, time_slice=None, strict_slicing=True): """ - *Args*: - :time_slice: None or tuple of the time slice expressed with quantities. + Load EventProxy args: + :param time_slice: None or tuple of the time slice expressed with quantities. None is the entire signal. - :strict_slicing: True by default. + :param strict_slicing: True by default. Control if an error is raised or not when one of the time_slice members (t_start or t_stop) is outside the real time range of the segment. """ @@ -747,29 +744,36 @@ def load(self, time_slice=None, strict_slicing=True): class EpochProxy(BaseEpochProxy): - def __init__(self, epochs_table, epoch_name=None, index=None): - self._epochs_table = epochs_table + def __init__(self, time_intervals, epoch_name=None, index=None): + """ + :param time_intervals: An epochs table, + which is a specific TimeIntervals table that stores info about long periods + """ + self._time_intervals = time_intervals if index is not None: self._index = index self.shape = (index.sum(),) else: self._index = slice(None) - self.shape = (len(epochs_table),) + self.shape = time_intervals.n_rows # untested, just guessed that n_rows exists self.name = epoch_name def load(self, time_slice=None, strict_slicing=True): """ - *Args*: - :time_slice: None or tuple of the time slice expressed with quantities. + Load EpochProxy args: + :param time_slice: None or tuple of the time slice expressed with quantities. None is all of the intervals. - :strict_slicing: True by default. + :param strict_slicing: True by default. Control if an error is raised or not when one of the time_slice members (t_start or t_stop) is outside the real time range of the segment. """ - start_times = self._epochs_table.start_time[self._index] - stop_times = self._epochs_table.stop_time[self._index] - durations = stop_times - start_times - labels = self._epochs_table.tags[self._index] + if time_slice: + raise NotImplementedError("todo") + else: + start_times = self._time_intervals.start_time[self._index] + stop_times = self._time_intervals.stop_time[self._index] + durations = stop_times - start_times + labels = self._time_intervals.tags[self._index] return Epoch(times=start_times * pq.s, durations=durations * pq.s, @@ -779,34 +783,39 @@ def load(self, time_slice=None, strict_slicing=True): class SpikeTrainProxy(BaseSpikeTrainProxy): - def __init__(self, units_table, id): - self._units_table = units_table + def __init__(self, time_intervals, id): + """ + :param time_intervals: A trials table, + which is a specific TimeIntervals table that stores info about short repeated segments. + There can only be one trials table. + """ + self._time_intervals = time_intervals self.id = id self.units = pq.s - t_start, t_stop = units_table.get_unit_obs_intervals(id)[0] + t_start, t_stop = time_intervals.obs_intervals[id] # Only handle the case where there are no observation intervals, as that is the most common case. self.t_start = t_start * pq.s self.t_stop = t_stop * pq.s self.annotations = {"nwb_group": "acquisition"} try: # NWB files created by Neo store the name as an extra column - self.name = units_table._name[id] + self.name = time_intervals._name[id] except AttributeError: self.name = None self.shape = None # no way to get this without reading the data def load(self, time_slice=None, strict_slicing=True): """ - *Args*: - :time_slice: None or tuple of the time slice expressed with quantities. + Load SpikeTrainProxy args: + :param time_slice: None or tuple of the time slice expressed with quantities. None is the entire spike train. - :strict_slicing: True by default. + :param strict_slicing: True by default. Control if an error is raised or not when one of the time_slice members (t_start or t_stop) is outside the real time range of the segment. """ interval = None if time_slice: interval = (float(t) for t in time_slice) # convert from quantities - spike_times = self._units_table.get_unit_spike_times(self.id, in_interval=interval) + spike_times = self._time_intervals.get_unit_spike_times(self.id, in_interval=interval) return SpikeTrain( spike_times * self.units, self.t_stop, From 73582d1924bb41d26f121a4812aed278e297c604 Mon Sep 17 00:00:00 2001 From: legouee Date: Thu, 8 Jul 2021 17:25:00 +0200 Subject: [PATCH 71/79] Apply suggestions from code review Co-authored-by: Andrew Davison --- neo/io/nwbio.py | 34 +++++++++++++++++++--------------- 1 file changed, 19 insertions(+), 15 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index a7e7a17f7..b8874fe45 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -367,7 +367,7 @@ def _read_timeseries_group(self, group_name, lazy): def _read_units(self, lazy): if self._file.units: - for id in self._file.units.id[:]: + for id in range(len(self._file.units)): try: # NWB files created by Neo store the segment and block names as extra columns segment_name = self._file.units.segment[id] @@ -452,11 +452,10 @@ def write_all_blocks(self, blocks, **kwargs): io_nwb.write(nwbfile) io_nwb.close() - io_validate = pynwb.NWBHDF5IO(self.filename, "r") - errors = pynwb.validate(io_validate, namespace="core") - if errors: - raise Exception(f"Errors found when validating {self.filename}") - io_validate.close() + with pynwb.NWBHDF5IO(self.filename, "r") as io_validate: + errors = pynwb.validate(io_validate, namespace="core") + if errors: + raise Exception(f"Errors found when validating {self.filename}") def write_block(self, nwbfile, block, **kwargs): """ @@ -755,7 +754,7 @@ def __init__(self, time_intervals, epoch_name=None, index=None): self.shape = (index.sum(),) else: self._index = slice(None) - self.shape = time_intervals.n_rows # untested, just guessed that n_rows exists + self.shape = (len(epochs_table),) self.name = epoch_name def load(self, time_slice=None, strict_slicing=True): @@ -783,22 +782,27 @@ def load(self, time_slice=None, strict_slicing=True): class SpikeTrainProxy(BaseSpikeTrainProxy): - def __init__(self, time_intervals, id): + def __init__(self, units_table, id): """ - :param time_intervals: A trials table, - which is a specific TimeIntervals table that stores info about short repeated segments. - There can only be one trials table. + :param units_table: A Units table (see https://pynwb.readthedocs.io/en/stable/pynwb.misc.html#pynwb.misc.Units) + :param id: the cell/unit ID (integer) """ - self._time_intervals = time_intervals + self._units_table = units_table self.id = id self.units = pq.s - t_start, t_stop = time_intervals.obs_intervals[id] # Only handle the case where there are no observation intervals, as that is the most common case. + obs_intervals = units_table.get_unit_obs_intervals(id) + if len(obs_intervals) == 0: + t_start, t_stop = None, None + elif len(obs_intervals) == 1: + t_start, t_stop = obs_intervals[0] + else: + raise NotImplementedError("Can't yet handle multiple observation intervals") self.t_start = t_start * pq.s self.t_stop = t_stop * pq.s self.annotations = {"nwb_group": "acquisition"} try: # NWB files created by Neo store the name as an extra column - self.name = time_intervals._name[id] + self.name = units_table._name[id] except AttributeError: self.name = None self.shape = None # no way to get this without reading the data @@ -815,7 +819,7 @@ def load(self, time_slice=None, strict_slicing=True): interval = None if time_slice: interval = (float(t) for t in time_slice) # convert from quantities - spike_times = self._time_intervals.get_unit_spike_times(self.id, in_interval=interval) + spike_times = self._units_table.get_unit_spike_times(self.id, in_interval=interval) return SpikeTrain( spike_times * self.units, self.t_stop, From 0e277572f713e7f0c6cb2c13e2116912efea3638 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 9 Jul 2021 15:21:57 +0200 Subject: [PATCH 72/79] handle differences in float precision when obtaining unit prefixes --- neo/io/nwbio.py | 10 ++++++++-- 1 file changed, 8 insertions(+), 2 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index b8874fe45..4910493e8 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -193,9 +193,15 @@ def _recompose_unit(base_unit_name, conversion): UnitCurrent('nanoampere', 0.001 * uA, 'nA') """ - if conversion not in prefix_map: + unit_name = None + for cf in prefix_map: + # conversion may have a different float precision to the keys in + # prefix_map, so we can't just use `prefix_map[conversion]` + if abs(conversion - cf)/cf < 1e-6: + unit_name = prefix_map[cf] + base_unit_name + if unit_name is None: raise ValueError(f"Can't handle this conversion factor: {conversion}") - unit_name = prefix_map[conversion] + base_unit_name + if unit_name[-1] == "s": # strip trailing 's', e.g. "volts" --> "volt" unit_name = unit_name[:-1] try: From 2cca447981d85524bf5e38d96a6cdf0f80b00401 Mon Sep 17 00:00:00 2001 From: Andrew Davison Date: Fri, 9 Jul 2021 15:32:56 +0200 Subject: [PATCH 73/79] Switch testing of NWB to use BaseTestIO --- neo/test/iotest/test_nwbio.py | 23 +++++------------------ 1 file changed, 5 insertions(+), 18 deletions(-) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 03eb30514..22cc338b7 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -28,27 +28,14 @@ @unittest.skipUnless(HAVE_PYNWB, "requires pynwb") -class TestNWBIO(unittest.TestCase): +class TestNWBIO(BaseTestIO, unittest.TestCase): ioclass = NWBIO - files_to_download = [ - # Files from Allen Institute : - # "http://download.alleninstitute.org/informatics-archive/prerelease/H19.28.012.11.05-2.nwb", # 64 MB - "http://download.alleninstitute.org/informatics-archive/prerelease/H19.29.141.11.21.01.nwb", # 7 MB + entities_to_download = ["nwb"] + entities_to_test = [ + # Files from Allen Institute: + "nwb/H19.29.141.11.21.01.nwb", # 7 MB ] - def test_read(self): - self.local_test_dir = get_local_testing_data_folder() / "nwb" - os.makedirs(self.local_test_dir, exist_ok=True) - for url in self.files_to_download: - local_filename = os.path.join(self.local_test_dir, url.split("/")[-1]) - if not os.path.exists(local_filename): - try: - urlretrieve(url, local_filename) - except IOError as exc: - raise unittest.TestCase.failureException(exc) - io = NWBIO(local_filename, 'r') - blocks = io.read() - def test_roundtrip(self): annotations = { From f4e6e2b6202222774abb2dea7b2db3bdaee5cef7 Mon Sep 17 00:00:00 2001 From: sprenger Date: Thu, 22 Jul 2021 11:11:06 +0200 Subject: [PATCH 74/79] pep8 reformatting --- neo/io/nwbio.py | 123 ++++++++++++++++++---------------- neo/test/iotest/test_nwbio.py | 19 ++++-- 2 files changed, 77 insertions(+), 65 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 4910493e8..b84882bed 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -13,18 +13,19 @@ """ from __future__ import absolute_import, division -from neo.core import baseneo +import json import logging import os +from collections import defaultdict from itertools import chain -from datetime import datetime -import json from json.decoder import JSONDecodeError -from collections import defaultdict import numpy as np import quantities as pq + +from neo.core import (Segment, SpikeTrain, Epoch, Event, AnalogSignal, + IrregularlySampledSignal, Block, ImageSequence) from neo.io.baseio import BaseIO from neo.io.proxyobjects import ( AnalogSignalProxy as BaseAnalogSignalProxy, @@ -32,8 +33,6 @@ EpochProxy as BaseEpochProxy, SpikeTrainProxy as BaseSpikeTrainProxy ) -from neo.core import (Segment, SpikeTrain, Epoch, Event, AnalogSignal, - IrregularlySampledSignal, Block, ImageSequence) # PyNWB imports try: @@ -45,10 +44,12 @@ from pynwb.misc import AnnotationSeries from pynwb import image from pynwb.image import ImageSeries - from pynwb.spec import NWBAttributeSpec, NWBDatasetSpec, NWBGroupSpec, NWBNamespace, NWBNamespaceBuilder + from pynwb.spec import NWBAttributeSpec, NWBDatasetSpec, NWBGroupSpec, NWBNamespace, \ + NWBNamespaceBuilder from pynwb.device import Device # For calcium imaging data from pynwb.ophys import TwoPhotonSeries, OpticalChannel, ImageSegmentation, Fluorescence + have_pynwb = True except ImportError: have_pynwb = False @@ -57,16 +58,15 @@ try: from hdmf.spec import (LinkSpec, GroupSpec, DatasetSpec, SpecNamespace, NamespaceBuilder, AttributeSpec, DtypeSpec, RefSpec) + have_hdmf = True except ImportError: have_hdmf = False except SyntaxError: have_hdmf = False - logger = logging.getLogger("Neo") - GLOBAL_ANNOTATIONS = ( "session_start_time", "identifier", "timestamps_reference_time", "experimenter", "experiment_description", "session_id", "institution", "keywords", "notes", @@ -146,7 +146,7 @@ def get_units_conversion(signal, timeseries_class): else: # todo: warn that we don't handle this subclass yet expected_units = signal.units - return float((signal.units/expected_units).simplified.magnitude), expected_units + return float((signal.units / expected_units).simplified.magnitude), expected_units def time_in_seconds(t): @@ -175,6 +175,7 @@ def _decompose(unit): raise NotImplementedError("Compound units not yet supported") # e.g. volt^2 uq_def = uq.definition return float(uq_def.magnitude), uq_def + conv, unit2 = _decompose(unit) while conv != 1: conversion *= conv @@ -197,7 +198,7 @@ def _recompose_unit(base_unit_name, conversion): for cf in prefix_map: # conversion may have a different float precision to the keys in # prefix_map, so we can't just use `prefix_map[conversion]` - if abs(conversion - cf)/cf < 1e-6: + if abs(conversion - cf) / cf < 1e-6: unit_name = prefix_map[cf] + base_unit_name if unit_name is None: raise ValueError(f"Can't handle this conversion factor: {conversion}") @@ -251,7 +252,8 @@ def read_all_blocks(self, lazy=False, **kwargs): Load all blocks in the file. """ assert self.nwb_file_mode in ('r',) - io = pynwb.NWBHDF5IO(self.filename, mode=self.nwb_file_mode, load_namespaces=True) # Open a file with NWBHDF5IO + io = pynwb.NWBHDF5IO(self.filename, mode=self.nwb_file_mode, + load_namespaces=True) # Open a file with NWBHDF5IO self._file = io.read() self.global_block_metadata = {} @@ -262,12 +264,15 @@ def read_all_blocks(self, lazy=False, **kwargs): value = try_json_field(value) self.global_block_metadata[annotation_name] = value if "session_description" in self.global_block_metadata: - self.global_block_metadata["description"] = self.global_block_metadata["session_description"] + self.global_block_metadata["description"] = self.global_block_metadata[ + "session_description"] self.global_block_metadata["file_origin"] = self.filename if "session_start_time" in self.global_block_metadata: - self.global_block_metadata["rec_datetime"] = self.global_block_metadata["session_start_time"] + self.global_block_metadata["rec_datetime"] = self.global_block_metadata[ + "session_start_time"] if "file_create_date" in self.global_block_metadata: - self.global_block_metadata["file_datetime"] = self.global_block_metadata["file_create_date"] + self.global_block_metadata["file_datetime"] = self.global_block_metadata[ + "file_create_date"] self._blocks = {} self._read_acquisition_group(lazy=lazy) @@ -440,7 +445,7 @@ def write_all_blocks(self, blocks, **kwargs): if sum(statistics(block)["SpikeTrain"]["count"] for block in blocks) > 0: nwbfile.add_unit_column('_name', 'the name attribute of the SpikeTrain') - #nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') + # nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') nwbfile.add_unit_column( 'segment', 'the name of the Neo Segment to which the SpikeTrain belongs') nwbfile.add_unit_column( @@ -448,10 +453,11 @@ def write_all_blocks(self, blocks, **kwargs): if sum(statistics(block)["Epoch"]["count"] for block in blocks) > 0: nwbfile.add_epoch_column('_name', 'the name attribute of the Epoch') - #nwbfile.add_epoch_column('_description', 'the description attribute of the Epoch') + # nwbfile.add_epoch_column('_description', 'the description attribute of the Epoch') nwbfile.add_epoch_column( 'segment', 'the name of the Neo Segment to which the Epoch belongs') - nwbfile.add_epoch_column('block', 'the name of the Neo Block to which the Epoch belongs') + nwbfile.add_epoch_column('block', + 'the name of the Neo Block to which the Epoch belongs') for i, block in enumerate(blocks): self.write_block(nwbfile, block) @@ -552,7 +558,7 @@ def _write_signal(self, nwbfile, signal, electrodes): rate=float(sampling_rate), comments=json.dumps(hierarchy), **additional_metadata) - # todo: try to add array_annotations via "control" attribute + # todo: try to add array_annotations via "control" attribute elif isinstance(signal, IrregularlySampledSignal): tS = timeseries_class( name=signal.name, @@ -562,8 +568,9 @@ def _write_signal(self, nwbfile, signal, electrodes): comments=json.dumps(hierarchy), **additional_metadata) else: - raise TypeError("signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format( - signal.__class__.__name__)) + raise TypeError( + "signal has type {0}, should be AnalogSignal or IrregularlySampledSignal".format( + signal.__class__.__name__)) nwb_group = signal.annotations.get("nwb_group", "acquisition") add_method_map = { "acquisition": nwbfile.add_acquisition, @@ -592,11 +599,11 @@ def _write_spiketrain(self, nwbfile, spiketrain): def _write_event(self, nwbfile, event): hierarchy = {'block': event.segment.block.name, 'segment': event.segment.name} tS_evt = AnnotationSeries( - name=event.name, - data=event.labels, - timestamps=event.times.rescale('second').magnitude, - description=event.description or "", - comments=json.dumps(hierarchy)) + name=event.name, + data=event.labels, + timestamps=event.times.rescale('second').magnitude, + description=event.description or "", + comments=json.dumps(hierarchy)) nwbfile.add_acquisition(tS_evt) return tS_evt @@ -692,26 +699,26 @@ def load(self, time_slice=None, strict_slicing=True): signal = self._timeseries.data[i_start: i_stop] if self.sampling_rate is None: return IrregularlySampledSignal( - self._timeseries.timestamps[i_start:i_stop] * pq.s, - signal, - units=self.units, - t_start=sig_t_start, - sampling_rate=self.sampling_rate, - name=self.name, - description=self.description, - array_annotations=None, - **self.annotations) # todo: timeseries.control / control_description + self._timeseries.timestamps[i_start:i_stop] * pq.s, + signal, + units=self.units, + t_start=sig_t_start, + sampling_rate=self.sampling_rate, + name=self.name, + description=self.description, + array_annotations=None, + **self.annotations) # todo: timeseries.control / control_description else: return AnalogSignal( - signal, - units=self.units, - t_start=sig_t_start, - sampling_rate=self.sampling_rate, - name=self.name, - description=self.description, - array_annotations=None, - **self.annotations) # todo: timeseries.control / control_description + signal, + units=self.units, + t_start=sig_t_start, + sampling_rate=self.sampling_rate, + name=self.name, + description=self.description, + array_annotations=None, + **self.annotations) # todo: timeseries.control / control_description class EventProxy(BaseEventProxy): @@ -776,7 +783,7 @@ def load(self, time_slice=None, strict_slicing=True): raise NotImplementedError("todo") else: start_times = self._time_intervals.start_time[self._index] - stop_times = self._time_intervals.stop_time[self._index] + stop_times = self._time_intervals.stop_time[self._index] durations = stop_times - start_times labels = self._time_intervals.tags[self._index] @@ -788,7 +795,7 @@ def load(self, time_slice=None, strict_slicing=True): class SpikeTrainProxy(BaseSpikeTrainProxy): - def __init__(self, units_table, id): + def __init__(self, units_table, id): """ :param units_table: A Units table (see https://pynwb.readthedocs.io/en/stable/pynwb.misc.html#pynwb.misc.Units) :param id: the cell/unit ID (integer) @@ -811,7 +818,7 @@ def __init__(self, units_table, id): self.name = units_table._name[id] except AttributeError: self.name = None - self.shape = None # no way to get this without reading the data + self.shape = None # no way to get this without reading the data def load(self, time_slice=None, strict_slicing=True): """ @@ -827,15 +834,15 @@ def load(self, time_slice=None, strict_slicing=True): interval = (float(t) for t in time_slice) # convert from quantities spike_times = self._units_table.get_unit_spike_times(self.id, in_interval=interval) return SpikeTrain( - spike_times * self.units, - self.t_stop, - units=self.units, - #sampling_rate=array(1.) * Hz, - t_start=self.t_start, - #waveforms=None, - #left_sweep=None, - name=self.name, - #file_origin=None, - #description=None, - #array_annotations=None, - **self.annotations) + spike_times * self.units, + self.t_stop, + units=self.units, + # sampling_rate=array(1.) * Hz, + t_start=self.t_start, + # waveforms=None, + # left_sweep=None, + name=self.name, + # file_origin=None, + # description=None, + # array_annotations=None, + **self.annotations) diff --git a/neo/test/iotest/test_nwbio.py b/neo/test/iotest/test_nwbio.py index 22cc338b7..5e8bc1f2c 100644 --- a/neo/test/iotest/test_nwbio.py +++ b/neo/test/iotest/test_nwbio.py @@ -4,8 +4,9 @@ """ from __future__ import unicode_literals, print_function, division, absolute_import -import unittest + import os +import unittest from datetime import datetime try: @@ -13,11 +14,13 @@ except ImportError: from urllib import urlretrieve from neo.test.iotest.common_io_test import BaseTestIO -from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, Block, ImageSequence -from neo.utils import get_local_testing_data_folder +from neo.core import AnalogSignal, SpikeTrain, Event, Epoch, IrregularlySampledSignal, Segment, \ + Block + try: import pynwb from neo.io.nwbio import NWBIO + HAVE_PYNWB = True except (ImportError, SyntaxError): NWBIO = None @@ -71,8 +74,8 @@ def test_roundtrip(self): t_start=120 * pq.ms) # 2 Neo IrregularlySampledSignals - d = IrregularlySampledSignal(np.arange(7.0)*pq.ms, - np.random.randn(7, num_chan)*pq.mV) + d = IrregularlySampledSignal(np.arange(7.0) * pq.ms, + np.random.randn(7, num_chan) * pq.mV) # 2 Neo SpikeTrains train = SpikeTrain(times=[1, 2, 3] * pq.s, t_start=1.0, t_stop=10.0) @@ -226,8 +229,10 @@ def test_roundtrip_with_annotations(self): nwbfile = pynwb.NWBHDF5IO(test_file_name, mode="r").read() self.assertIsInstance(nwbfile.acquisition["response"], pynwb.icephys.CurrentClampSeries) - self.assertIsInstance(nwbfile.stimulus["stimulus"], pynwb.icephys.CurrentClampStimulusSeries) - self.assertEqual(nwbfile.acquisition["response"].bridge_balance, response_annotations["nwb:bridge_balance"]) + self.assertIsInstance(nwbfile.stimulus["stimulus"], + pynwb.icephys.CurrentClampStimulusSeries) + self.assertEqual(nwbfile.acquisition["response"].bridge_balance, + response_annotations["nwb:bridge_balance"]) ior = NWBIO(filename=test_file_name, mode='r') retrieved_block = ior.read_all_blocks()[0] From c5a898344d809e3719b6fe4caef1d2f18242f6ba Mon Sep 17 00:00:00 2001 From: sprenger Date: Thu, 22 Jul 2021 11:14:23 +0200 Subject: [PATCH 75/79] more pep8 reformatting --- neo/io/nwbio.py | 15 +++++++++------ 1 file changed, 9 insertions(+), 6 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index b84882bed..96956ec85 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -9,7 +9,8 @@ Supported: Read, Write Python API - https://pynwb.readthedocs.io Sample datasets from CRCNS - https://crcns.org/NWB -Sample datasets from Allen Institute - http://alleninstitute.github.io/AllenSDK/cell_types.html#neurodata-without-borders +Sample datasets from Allen Institute +- http://alleninstitute.github.io/AllenSDK/cell_types.html#neurodata-without-borders """ from __future__ import absolute_import, division @@ -312,7 +313,8 @@ def _get_segment(self, block_name, segment_name): def _read_epochs_group(self, lazy): if self._file.epochs is not None: try: - # NWB files created by Neo store the segment, block and epoch names as extra columns + # NWB files created by Neo store the segment, block and epoch names as extra + # columns segment_names = self._file.epochs.segment[:] block_names = self._file.epochs.block[:] epoch_names = self._file.epochs._name[:] @@ -464,7 +466,7 @@ def write_all_blocks(self, blocks, **kwargs): io_nwb.write(nwbfile) io_nwb.close() - with pynwb.NWBHDF5IO(self.filename, "r") as io_validate: + with pynwb.NWBHDF5IO(self.filename, "r") as io_validate: errors = pynwb.validate(io_validate, namespace="core") if errors: raise Exception(f"Errors found when validating {self.filename}") @@ -508,7 +510,8 @@ def _write_electrodes(self, nwbfile, block): def _write_segment(self, nwbfile, segment, electrodes): # maybe use NWB trials to store Segment metadata? - for i, signal in enumerate(chain(segment.analogsignals, segment.irregularlysampledsignals)): + for i, signal in enumerate( + chain(segment.analogsignals, segment.irregularlysampledsignals)): assert signal.segment is segment if not signal.name: signal.name = "%s : analogsignal%d" % (segment.name, i) @@ -758,8 +761,8 @@ class EpochProxy(BaseEpochProxy): def __init__(self, time_intervals, epoch_name=None, index=None): """ - :param time_intervals: An epochs table, - which is a specific TimeIntervals table that stores info about long periods + :param time_intervals: An epochs table, + which is a specific TimeIntervals table that stores info about long periods """ self._time_intervals = time_intervals if index is not None: From 0f61b0b28fc2a9a39aa36059e6d8851df3fef78d Mon Sep 17 00:00:00 2001 From: sprenger Date: Thu, 22 Jul 2021 11:22:59 +0200 Subject: [PATCH 76/79] [nwbio] guess fix for typo --- neo/io/nwbio.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 96956ec85..29981b2d4 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -770,7 +770,7 @@ def __init__(self, time_intervals, epoch_name=None, index=None): self.shape = (index.sum(),) else: self._index = slice(None) - self.shape = (len(epochs_table),) + self.shape = (len(time_intervals),) self.name = epoch_name def load(self, time_slice=None, strict_slicing=True): From 4b25e7c206c9a5130255302a009f1f0572405b31 Mon Sep 17 00:00:00 2001 From: sprenger Date: Thu, 22 Jul 2021 11:23:15 +0200 Subject: [PATCH 77/79] [nwbio] add missing docstrings --- neo/io/nwbio.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index 29981b2d4..c423a38f1 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -763,6 +763,11 @@ def __init__(self, time_intervals, epoch_name=None, index=None): """ :param time_intervals: An epochs table, which is a specific TimeIntervals table that stores info about long periods + :param epoch_name: (str) + Name of the epoch object + :param index: (np.array, slice) + Slice object or array of bool values masking time_intervals to be used. In case of + an array it has to have the same shape as `time_intervals`. """ self._time_intervals = time_intervals if index is not None: From b968ab80fd70d8e5f34abbf5061eccfb932d7ab5 Mon Sep 17 00:00:00 2001 From: sprenger Date: Thu, 22 Jul 2021 11:49:08 +0200 Subject: [PATCH 78/79] [nwbio] more pep8 --- neo/io/nwbio.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index c423a38f1..a06c98576 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -447,7 +447,8 @@ def write_all_blocks(self, blocks, **kwargs): if sum(statistics(block)["SpikeTrain"]["count"] for block in blocks) > 0: nwbfile.add_unit_column('_name', 'the name attribute of the SpikeTrain') - # nwbfile.add_unit_column('_description', 'the description attribute of the SpikeTrain') + # nwbfile.add_unit_column('_description', + # 'the description attribute of the SpikeTrain') nwbfile.add_unit_column( 'segment', 'the name of the Neo Segment to which the SpikeTrain belongs') nwbfile.add_unit_column( @@ -805,7 +806,8 @@ class SpikeTrainProxy(BaseSpikeTrainProxy): def __init__(self, units_table, id): """ - :param units_table: A Units table (see https://pynwb.readthedocs.io/en/stable/pynwb.misc.html#pynwb.misc.Units) + :param units_table: A Units table + (see https://pynwb.readthedocs.io/en/stable/pynwb.misc.html#pynwb.misc.Units) :param id: the cell/unit ID (integer) """ self._units_table = units_table From 16e9f7dfcb0d3cb397bcb80fd386a1932e79c5ee Mon Sep 17 00:00:00 2001 From: sprenger Date: Thu, 22 Jul 2021 12:04:46 +0200 Subject: [PATCH 79/79] [nwbio] remove unused import --- neo/io/nwbio.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neo/io/nwbio.py b/neo/io/nwbio.py index a06c98576..91889273d 100644 --- a/neo/io/nwbio.py +++ b/neo/io/nwbio.py @@ -38,7 +38,7 @@ # PyNWB imports try: import pynwb - from pynwb import NWBFile, TimeSeries, get_manager + from pynwb import NWBFile, TimeSeries from pynwb.base import ProcessingModule from pynwb.ecephys import ElectricalSeries, Device, EventDetection from pynwb.behavior import SpatialSeries