From 25a387a3bc59d382d80d2c7d18bf65daa9d1d396 Mon Sep 17 00:00:00 2001 From: "Heine, Matthew" Date: Thu, 11 Jan 2024 09:19:46 -0800 Subject: [PATCH 1/3] open sourcing azure code Still a work in progress, not a finished set of tools! --- azure/az_cli_guide.txt | 42 + azure/blob_access.ipynb | 2756 +++++++++++++++++ azure/dev-env.yml | 18 + azure/documentation/ pv_rooftop.md | 346 +++ azure/documentation/PR100.md | 55 + azure/documentation/sup3rcc.md | 103 + azure/examples/Azure Cloud Costs.docx | Bin 0 -> 234181 bytes azure/examples/PR100.ipynb | 183 ++ azure/examples/pv_rooftop.ipynb | 308 ++ azure/examples/sup3rcc.ipynb | 894 ++++++ azure/examples/wtk.ipynb | 703 +++++ azure/hpc_migration.ipynb | 161 + azure/pipeline/ASL/job.json | 21 + azure/pipeline/ASL/job_definition.json | 31 + azure/pipeline/ASL/kerchunk-1TB.json | 1 + azure/pipeline/ASL/state_machine_input.json | 1 + .../pipeline/ASL/state_machine_template.json | 74 + azure/pipeline/__init__.py | 0 azure/pipeline/aws_glob_patterns.json | 1 + azure/pipeline/aws_tools.py | 360 +++ azure/pipeline/azure_tools.py | 35 + azure/pipeline/blob_access.ipynb | 81 + azure/pipeline/etl_tools.py | 698 +++++ azure/pipeline/hpc_gen_refs.py | 65 + azure/pipeline/hpc_process_file.py | 58 + azure/pipeline/hpc_to_azure.py | 18 + azure/pipeline/hpc_tools.py | 476 +++ azure/pipeline/run_aws_pipeline.ipynb | 441 +++ .../transform_h5_container/Dockerfile | 25 + .../transform_h5_container/entrypoint.sh | 6 + azure/pipeline/transform_h5_container/env.yml | 16 + .../transform_h5_container/gen_ref.py | 63 + .../pipeline/transform_h5_container/hello.py | 3 + .../transform_h5_container/install_azcopy.sh | 17 + .../transform_h5_container/transfer.py | 6 + .../transform_h5_container/transform.py | 59 + azure/pipeline/update_trans_container.sh | 4 + 37 files changed, 8129 insertions(+) create mode 100644 azure/az_cli_guide.txt create mode 100644 azure/blob_access.ipynb create mode 100644 azure/dev-env.yml create mode 100644 azure/documentation/ pv_rooftop.md create mode 100644 azure/documentation/PR100.md create mode 100644 azure/documentation/sup3rcc.md create mode 100644 azure/examples/Azure Cloud Costs.docx create mode 100644 azure/examples/PR100.ipynb create mode 100644 azure/examples/pv_rooftop.ipynb create mode 100644 azure/examples/sup3rcc.ipynb create mode 100644 azure/examples/wtk.ipynb create mode 100644 azure/hpc_migration.ipynb create mode 100644 azure/pipeline/ASL/job.json create mode 100644 azure/pipeline/ASL/job_definition.json create mode 100644 azure/pipeline/ASL/kerchunk-1TB.json create mode 100644 azure/pipeline/ASL/state_machine_input.json create mode 100644 azure/pipeline/ASL/state_machine_template.json create mode 100644 azure/pipeline/__init__.py create mode 100644 azure/pipeline/aws_glob_patterns.json create mode 100644 azure/pipeline/aws_tools.py create mode 100644 azure/pipeline/azure_tools.py create mode 100644 azure/pipeline/blob_access.ipynb create mode 100644 azure/pipeline/etl_tools.py create mode 100644 azure/pipeline/hpc_gen_refs.py create mode 100644 azure/pipeline/hpc_process_file.py create mode 100644 azure/pipeline/hpc_to_azure.py create mode 100644 azure/pipeline/hpc_tools.py create mode 100644 azure/pipeline/run_aws_pipeline.ipynb create mode 100644 azure/pipeline/transform_h5_container/Dockerfile create mode 100644 azure/pipeline/transform_h5_container/entrypoint.sh create mode 100644 azure/pipeline/transform_h5_container/env.yml create mode 100644 azure/pipeline/transform_h5_container/gen_ref.py create mode 100644 azure/pipeline/transform_h5_container/hello.py create mode 100644 azure/pipeline/transform_h5_container/install_azcopy.sh create mode 100644 azure/pipeline/transform_h5_container/transfer.py create mode 100644 azure/pipeline/transform_h5_container/transform.py create mode 100644 azure/pipeline/update_trans_container.sh diff --git a/azure/az_cli_guide.txt b/azure/az_cli_guide.txt new file mode 100644 index 0000000..853c3d6 --- /dev/null +++ b/azure/az_cli_guide.txt @@ -0,0 +1,42 @@ +# OEDI data exist as blobs in Azure. Blobs live in containers. Containers live in storage accounts. For most of our data, the storage account is 'nrel' and the container is 'oedi'. There is a directory structure within the container to organize different data sets. Currently, the datasets present are 'PR100', 'pv-rooftop', and 'sup3rcc'. NSRDB lives in the 'nrel' storage account but in a different container called 'nrel-nsrdb'. + +# In order to access data from the command line, you will need to obtain a temporary SAS token from the planetary computer. You can then use that token as an argument for any commands you make with the CLI. CLI reference for interacting with blobs: https://learn.microsoft.com/en-us/cli/azure/storage/blob?view=azure-cli-latest#az-storage-blob-download + +# Finally, if the goal is to move large amounts of data from blob storage to S3 or local, then the best tool is azcopy: https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10 + +# Obtain a planetary computer temporary access token + +curl https://planetarycomputer.microsoft.com/api/sas/v1/token/nrel/oedi > sas.json + +# View a list of blobs in the PR100 dataset +az storage blob list --account-name nrel --container-name oedi --output table --prefix PR100 --sas-token "" + +# Download a blob from the PR100 dataset +az storage blob download --account-name nrel --container-name oedi --name PR100/Infrastructure/setbacks_runway.parquet --file setbacks_runway.parquet --sas-token "" + + +az storage blob list --account-name nrel --container-name nrel-nsrdb --output table --sas-token "sv=2020-08-04&si=nrel-nsrdb-ro&sr=c&sig=H8GUesZmOXzMomMWdrnXQv2ZPI09hANqHIcVrP7Ejl0%3D" + +"sv=2019-12-12&si=oedi-ro&sr=c&sig=uslpLxKf3%2Foeu79ufIHbJkpI%2FTWDH3lblJMa5KQRPmM%3D" + + +curl https://planetarycomputer.microsoft.com/api/sas/v1/token/nrel/oedi | az storage blob list --account-name nrel --container-name oedi --output table --prefix PR100 + +az storage blob list --account-name nrel --container-name oedi --output table --prefix pv-rooftop --sas-token "st=2023-05-22T16%3A29%3A14Z&se=2023-05-23T17%3A14%3A15Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2023-05-23T16%3A29%3A13Z&ske=2023-05-30T16%3A29%3A13Z&sks=b&skv=2021-06-08&sig=PVT8qk9fOmV1Mv8DCM8QUoR7eVxillRZMi8q1K89%2Bg0%3D" + +# Download a blob from PR100 + +az storage blob download --account-name nrel --container-name oedi --name PR100/Infrastructure/setbacks_runway.parquet --file setbacks_runway.parquet --sas-token "st=2023-05-22T16%3A29%3A14Z&se=2023-05-23T17%3A14%3A15Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2023-05-23T16%3A29%3A13Z&ske=2023-05-30T16%3A29%3A13Z&sks=b&skv=2021-06-08&sig=PVT8qk9fOmV1Mv8DCM8QUoR7eVxillRZMi8q1K89%2Bg0%3D" + +# List blobs in nrel-nsrdb (Note that you need to obtain a different SAS token since it's a different container) + +az storage blob list --account-name nrel --container-name nrel-nsrdb --output table --sas-token "st=2023-05-22T16%3A44%3A49Z&se=2023-05-23T17%3A29%3A50Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2023-05-23T16%3A44%3A48Z&ske=2023-05-30T16%3A44%3A48Z&sks=b&skv=2021-06-08&sig=Sip4D9k3rFo/SeCcGxp5I11X6hL/H2AWt9bREQWZAlY%3D" + + + + +az storage blob list --account-name nrel --container-name oedi --prefix PR100 --sas-token 'sv=2020-08-04&si=oedi-ro&sr=c&sig=O%2BQvKRV9uYuK36WzVRoCJdFO%2BRifXO8aIGqbS%2F3llPs%3D' + +az storage blob list --output table --account-name nrel --container-name nrel-nsrdb --sas-token 'sv=2020-08-04&si=nrel-nsrdb-ro&sr=c&sig=H8GUesZmOXzMomMWdrnXQv2ZPI09hANqHIcVrP7Ejl0%3D' + +az storage blob list --account-name nrel --container-name oedi --prefix PR100 --sas-token 'sv=2020-08-04&si=nrel-nsrdb-ro&sr=c&sig=H8GUesZmOXzMomMWdrnXQv2ZPI09hANqHIcVrP7Ejl0%3D' \ No newline at end of file diff --git a/azure/blob_access.ipynb b/azure/blob_access.ipynb new file mode 100644 index 0000000..7498145 --- /dev/null +++ b/azure/blob_access.ipynb @@ -0,0 +1,2756 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "wtk/Great_Lakes/2000/Great_Lakes_2000_0m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_100m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_10m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_120m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_140m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_160m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_180m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_200m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_20m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_220m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_240m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_260m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_280m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_2m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_300m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_400m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_40m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_500m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_60m.h5\n", + "wtk/Great_Lakes/2000/Great_Lakes_2000_80m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_0m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_100m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_10m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_120m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_140m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_160m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_180m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_200m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_20m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_220m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_240m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_260m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_280m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_2m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_300m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_400m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_40m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_500m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_60m.h5\n", + "wtk/Great_Lakes/2001/Great_Lakes_2001_80m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_0m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_100m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_10m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_120m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_140m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_160m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_180m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_200m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_20m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_220m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_240m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_260m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_280m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_2m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_300m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_400m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_40m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_500m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_60m.h5\n", + "wtk/Great_Lakes/2002/Great_Lakes_2002_80m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_0m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_100m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_10m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_120m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_140m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_160m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_180m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_200m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_20m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_220m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_240m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_260m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_280m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_2m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_300m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_400m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_40m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_500m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_60m.h5\n", + "wtk/Great_Lakes/2003/Great_Lakes_2003_80m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_0m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_100m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_10m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_120m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_140m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_160m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_180m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_200m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_20m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_220m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_240m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_260m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_280m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_2m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_300m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_400m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_40m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_500m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_60m.h5\n", + "wtk/Great_Lakes/2004/Great_Lakes_2004_80m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_0m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_100m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_10m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_120m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_140m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_160m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_180m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_200m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_20m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_220m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_240m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_260m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_280m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_2m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_300m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_400m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_40m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_500m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_60m.h5\n", + "wtk/Great_Lakes/2005/Great_Lakes_2005_80m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_0m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_100m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_10m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_120m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_140m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_160m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_180m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_200m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_20m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_220m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_240m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_260m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_280m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_2m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_300m.h5\n", + "wtk/Great_Lakes/2006/Great_Lakes_2006_400m.h5\n", + 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"wtk/gulf_of_mexico/yearly_hr/gulf_2013_hr.h5\n", + "wtk/gulf_of_mexico/yearly_hr/gulf_2014_hr.h5\n", + "wtk/gulf_of_mexico/yearly_hr/gulf_2015_hr.h5\n", + "wtk/gulf_of_mexico/yearly_hr/gulf_2016_hr.h5\n", + "wtk/gulf_of_mexico/yearly_hr/gulf_2017_hr.h5\n", + "wtk/gulf_of_mexico/yearly_hr/gulf_2018_hr.h5\n", + "wtk/gulf_of_mexico/yearly_hr/gulf_2019_hr.h5\n", + "wtk/gulf_of_mexico/yearly_hr/gulf_2020_hr.h5\n", + "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_0m.h5\n", + "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_100m.h5\n", + "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_10m.h5\n", + "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_120m.h5\n", + "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_140m.h5\n", + "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_160m.h5\n", + "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_200m.h5\n", + "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_2m.h5\n", + "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_40m.h5\n", + "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_60m.h5\n", + "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_80m.h5\n", + "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_0m.h5\n", + "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_100m.h5\n", + "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_10m.h5\n", + "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_120m.h5\n", + "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_140m.h5\n", + "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_160m.h5\n", + "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_200m.h5\n", + "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_2m.h5\n", + "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_40m.h5\n", + "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_60m.h5\n", + "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_80m.h5\n", + "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_0m.h5\n", + "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_100m.h5\n", + "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_10m.h5\n", + "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_120m.h5\n", + "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_140m.h5\n", + "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_160m.h5\n", + "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_200m.h5\n", + "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_2m.h5\n", + "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_40m.h5\n", + "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_60m.h5\n", + "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_80m.h5\n", + "wtk/mexico/v1.0.0/2010/wtk_mexico_2010_0m.h5\n", + "wtk/mexico/v1.0.0/2010/wtk_mexico_2010_100m.h5\n", + "wtk/mexico/v1.0.0/2010/wtk_mexico_2010_10m.h5\n", + "wtk/mexico/v1.0.0/2010/wtk_mexico_2010_120m.h5\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m sas \u001b[39m=\u001b[39m load_oedi_sas()\n\u001b[1;32m 5\u001b[0m client \u001b[39m=\u001b[39m ContainerClient\u001b[39m.\u001b[39mfrom_container_url(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mhttps://nrel.blob.core.windows.net/oedi?\u001b[39m\u001b[39m{\u001b[39;00msas\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[39mfor\u001b[39;00m blob \u001b[39min\u001b[39;00m client\u001b[39m.\u001b[39mlist_blobs():\n\u001b[1;32m 7\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mwtk/\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m blob\u001b[39m.\u001b[39mname:\n\u001b[1;32m 8\u001b[0m \u001b[39mprint\u001b[39m(blob\u001b[39m.\u001b[39mname)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/paging.py:123\u001b[0m, in \u001b[0;36mItemPaged.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_page_iterator \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 122\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_page_iterator \u001b[39m=\u001b[39m itertools\u001b[39m.\u001b[39mchain\u001b[39m.\u001b[39mfrom_iterable(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mby_page())\n\u001b[0;32m--> 123\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mnext\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_page_iterator)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/paging.py:75\u001b[0m, in \u001b[0;36mPageIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mEnd of paging\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 74\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 75\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_next(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcontinuation_token)\n\u001b[1;32m 76\u001b[0m \u001b[39mexcept\u001b[39;00m AzureError \u001b[39mas\u001b[39;00m error:\n\u001b[1;32m 77\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m error\u001b[39m.\u001b[39mcontinuation_token:\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/storage/blob/_list_blobs_helper.py:93\u001b[0m, in \u001b[0;36mBlobPropertiesPaged._get_next_cb\u001b[0;34m(self, continuation_token)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_get_next_cb\u001b[39m(\u001b[39mself\u001b[39m, continuation_token):\n\u001b[1;32m 92\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 93\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_command(\n\u001b[1;32m 94\u001b[0m prefix\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprefix,\n\u001b[1;32m 95\u001b[0m marker\u001b[39m=\u001b[39;49mcontinuation_token \u001b[39mor\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m 96\u001b[0m maxresults\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mresults_per_page,\n\u001b[1;32m 97\u001b[0m \u001b[39mcls\u001b[39;49m\u001b[39m=\u001b[39;49mreturn_context_and_deserialized,\n\u001b[1;32m 98\u001b[0m use_location\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlocation_mode)\n\u001b[1;32m 99\u001b[0m \u001b[39mexcept\u001b[39;00m HttpResponseError \u001b[39mas\u001b[39;00m error:\n\u001b[1;32m 100\u001b[0m process_storage_error(error)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/tracing/decorator.py:78\u001b[0m, in \u001b[0;36mdistributed_trace..decorator..wrapper_use_tracer\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 76\u001b[0m span_impl_type \u001b[39m=\u001b[39m settings\u001b[39m.\u001b[39mtracing_implementation()\n\u001b[1;32m 77\u001b[0m \u001b[39mif\u001b[39;00m span_impl_type \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m---> 78\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 80\u001b[0m \u001b[39m# Merge span is parameter is set, but only if no explicit parent are passed\u001b[39;00m\n\u001b[1;32m 81\u001b[0m \u001b[39mif\u001b[39;00m merge_span \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m passed_in_parent:\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/storage/blob/_generated/operations/_container_operations.py:2488\u001b[0m, in \u001b[0;36mContainerOperations.list_blob_flat_segment\u001b[0;34m(self, prefix, marker, maxresults, include, timeout, request_id_parameter, **kwargs)\u001b[0m\n\u001b[1;32m 2485\u001b[0m request \u001b[39m=\u001b[39m _convert_request(request)\n\u001b[1;32m 2486\u001b[0m request\u001b[39m.\u001b[39murl \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_client\u001b[39m.\u001b[39mformat_url(request\u001b[39m.\u001b[39murl) \u001b[39m# type: ignore\u001b[39;00m\n\u001b[0;32m-> 2488\u001b[0m pipeline_response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_client\u001b[39m.\u001b[39;49m_pipeline\u001b[39m.\u001b[39;49mrun( \u001b[39m# type: ignore # pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m 2489\u001b[0m request, stream\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m 2490\u001b[0m )\n\u001b[1;32m 2492\u001b[0m response \u001b[39m=\u001b[39m pipeline_response\u001b[39m.\u001b[39mhttp_response\n\u001b[1;32m 2494\u001b[0m \u001b[39mif\u001b[39;00m response\u001b[39m.\u001b[39mstatus_code \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m [\u001b[39m200\u001b[39m]:\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:230\u001b[0m, in \u001b[0;36mPipeline.run\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 228\u001b[0m pipeline_request: PipelineRequest[HTTPRequestType] \u001b[39m=\u001b[39m PipelineRequest(request, context)\n\u001b[1;32m 229\u001b[0m first_node \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_impl_policies[\u001b[39m0\u001b[39m] \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_impl_policies \u001b[39melse\u001b[39;00m _TransportRunner(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_transport)\n\u001b[0;32m--> 230\u001b[0m \u001b[39mreturn\u001b[39;00m first_node\u001b[39m.\u001b[39;49msend(pipeline_request)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", + " \u001b[0;31m[... skipping similar frames: _SansIOHTTPPolicyRunner.send at line 86 (2 times)]\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/policies/_redirect.py:197\u001b[0m, in \u001b[0;36mRedirectPolicy.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 195\u001b[0m original_domain \u001b[39m=\u001b[39m get_domain(request\u001b[39m.\u001b[39mhttp_request\u001b[39m.\u001b[39murl) \u001b[39mif\u001b[39;00m redirect_settings[\u001b[39m\"\u001b[39m\u001b[39mallow\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 196\u001b[0m \u001b[39mwhile\u001b[39;00m retryable:\n\u001b[0;32m--> 197\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 198\u001b[0m redirect_location \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_redirect_location(response)\n\u001b[1;32m 199\u001b[0m \u001b[39mif\u001b[39;00m redirect_location \u001b[39mand\u001b[39;00m redirect_settings[\u001b[39m\"\u001b[39m\u001b[39mallow\u001b[39m\u001b[39m\"\u001b[39m]:\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/storage/blob/_shared/policies.py:520\u001b[0m, in \u001b[0;36mStorageRetryPolicy.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 518\u001b[0m \u001b[39mwhile\u001b[39;00m retries_remaining:\n\u001b[1;32m 519\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 520\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 521\u001b[0m \u001b[39mif\u001b[39;00m is_retry(response, retry_settings[\u001b[39m'\u001b[39m\u001b[39mmode\u001b[39m\u001b[39m'\u001b[39m]):\n\u001b[1;32m 522\u001b[0m retries_remaining \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mincrement(\n\u001b[1;32m 523\u001b[0m retry_settings,\n\u001b[1;32m 524\u001b[0m request\u001b[39m=\u001b[39mrequest\u001b[39m.\u001b[39mhttp_request,\n\u001b[1;32m 525\u001b[0m response\u001b[39m=\u001b[39mresponse\u001b[39m.\u001b[39mhttp_response)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/storage/blob/_shared/policies.py:313\u001b[0m, in \u001b[0;36mStorageResponseHook.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 308\u001b[0m upload_stream_current \u001b[39m=\u001b[39m request\u001b[39m.\u001b[39mcontext\u001b[39m.\u001b[39moptions\u001b[39m.\u001b[39mpop(\u001b[39m'\u001b[39m\u001b[39mupload_stream_current\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m 310\u001b[0m response_callback \u001b[39m=\u001b[39m request\u001b[39m.\u001b[39mcontext\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mresponse_callback\u001b[39m\u001b[39m'\u001b[39m) \u001b[39mor\u001b[39;00m \\\n\u001b[1;32m 311\u001b[0m request\u001b[39m.\u001b[39mcontext\u001b[39m.\u001b[39moptions\u001b[39m.\u001b[39mpop(\u001b[39m'\u001b[39m\u001b[39mraw_response_hook\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_response_callback)\n\u001b[0;32m--> 313\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 315\u001b[0m will_retry \u001b[39m=\u001b[39m is_retry(response, request\u001b[39m.\u001b[39mcontext\u001b[39m.\u001b[39moptions\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mmode\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[1;32m 316\u001b[0m \u001b[39m# Auth error could come from Bearer challenge, in which case this request will be made again\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:119\u001b[0m, in \u001b[0;36m_TransportRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"HTTP transport send method.\u001b[39;00m\n\u001b[1;32m 110\u001b[0m \n\u001b[1;32m 111\u001b[0m \u001b[39m:param request: The PipelineRequest object.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[39m:rtype: ~azure.core.pipeline.PipelineResponse\u001b[39;00m\n\u001b[1;32m 115\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 116\u001b[0m cleanup_kwargs_for_transport(request\u001b[39m.\u001b[39mcontext\u001b[39m.\u001b[39moptions)\n\u001b[1;32m 117\u001b[0m \u001b[39mreturn\u001b[39;00m PipelineResponse(\n\u001b[1;32m 118\u001b[0m request\u001b[39m.\u001b[39mhttp_request,\n\u001b[0;32m--> 119\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sender\u001b[39m.\u001b[39;49msend(request\u001b[39m.\u001b[39;49mhttp_request, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mrequest\u001b[39m.\u001b[39;49mcontext\u001b[39m.\u001b[39;49moptions),\n\u001b[1;32m 120\u001b[0m context\u001b[39m=\u001b[39mrequest\u001b[39m.\u001b[39mcontext,\n\u001b[1;32m 121\u001b[0m )\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/transport/_requests_basic.py:339\u001b[0m, in \u001b[0;36mRequestsTransport.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 337\u001b[0m read_timeout \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39mread_timeout\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconnection_config\u001b[39m.\u001b[39mread_timeout)\n\u001b[1;32m 338\u001b[0m timeout \u001b[39m=\u001b[39m (connection_timeout, read_timeout)\n\u001b[0;32m--> 339\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msession\u001b[39m.\u001b[39;49mrequest( \u001b[39m# type: ignore\u001b[39;49;00m\n\u001b[1;32m 340\u001b[0m request\u001b[39m.\u001b[39;49mmethod,\n\u001b[1;32m 341\u001b[0m request\u001b[39m.\u001b[39;49murl,\n\u001b[1;32m 342\u001b[0m headers\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mheaders,\n\u001b[1;32m 343\u001b[0m data\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mdata,\n\u001b[1;32m 344\u001b[0m files\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mfiles,\n\u001b[1;32m 345\u001b[0m verify\u001b[39m=\u001b[39;49mkwargs\u001b[39m.\u001b[39;49mpop(\u001b[39m\"\u001b[39;49m\u001b[39mconnection_verify\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnection_config\u001b[39m.\u001b[39;49mverify),\n\u001b[1;32m 346\u001b[0m timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[1;32m 347\u001b[0m cert\u001b[39m=\u001b[39;49mkwargs\u001b[39m.\u001b[39;49mpop(\u001b[39m\"\u001b[39;49m\u001b[39mconnection_cert\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnection_config\u001b[39m.\u001b[39;49mcert),\n\u001b[1;32m 348\u001b[0m allow_redirects\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 349\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m 350\u001b[0m )\n\u001b[1;32m 351\u001b[0m response\u001b[39m.\u001b[39mraw\u001b[39m.\u001b[39menforce_content_length \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 353\u001b[0m \u001b[39mexcept\u001b[39;00m (\n\u001b[1;32m 354\u001b[0m NewConnectionError,\n\u001b[1;32m 355\u001b[0m ConnectTimeoutError,\n\u001b[1;32m 356\u001b[0m ) \u001b[39mas\u001b[39;00m err:\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 584\u001b[0m send_kwargs \u001b[39m=\u001b[39m {\n\u001b[1;32m 585\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtimeout\u001b[39m\u001b[39m\"\u001b[39m: timeout,\n\u001b[1;32m 586\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mallow_redirects\u001b[39m\u001b[39m\"\u001b[39m: allow_redirects,\n\u001b[1;32m 587\u001b[0m }\n\u001b[1;32m 588\u001b[0m send_kwargs\u001b[39m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msend(prep, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49msend_kwargs)\n\u001b[1;32m 591\u001b[0m \u001b[39mreturn\u001b[39;00m resp\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 700\u001b[0m start \u001b[39m=\u001b[39m preferred_clock()\n\u001b[1;32m 702\u001b[0m \u001b[39m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[39m=\u001b[39m adapter\u001b[39m.\u001b[39;49msend(request, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 705\u001b[0m \u001b[39m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m 706\u001b[0m elapsed \u001b[39m=\u001b[39m preferred_clock() \u001b[39m-\u001b[39m start\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/requests/adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 483\u001b[0m timeout \u001b[39m=\u001b[39m TimeoutSauce(connect\u001b[39m=\u001b[39mtimeout, read\u001b[39m=\u001b[39mtimeout)\n\u001b[1;32m 485\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 486\u001b[0m resp \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49murlopen(\n\u001b[1;32m 487\u001b[0m method\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mmethod,\n\u001b[1;32m 488\u001b[0m url\u001b[39m=\u001b[39;49murl,\n\u001b[1;32m 489\u001b[0m body\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mbody,\n\u001b[1;32m 490\u001b[0m headers\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mheaders,\n\u001b[1;32m 491\u001b[0m redirect\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 492\u001b[0m assert_same_host\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 493\u001b[0m preload_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 494\u001b[0m decode_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 495\u001b[0m retries\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_retries,\n\u001b[1;32m 496\u001b[0m timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[1;32m 497\u001b[0m chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[1;32m 498\u001b[0m )\n\u001b[1;32m 500\u001b[0m \u001b[39mexcept\u001b[39;00m (ProtocolError, \u001b[39mOSError\u001b[39;00m) \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m 501\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mConnectionError\u001b[39;00m(err, request\u001b[39m=\u001b[39mrequest)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/urllib3/connectionpool.py:703\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_prepare_proxy(conn)\n\u001b[1;32m 702\u001b[0m \u001b[39m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m httplib_response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_request(\n\u001b[1;32m 704\u001b[0m conn,\n\u001b[1;32m 705\u001b[0m method,\n\u001b[1;32m 706\u001b[0m url,\n\u001b[1;32m 707\u001b[0m timeout\u001b[39m=\u001b[39;49mtimeout_obj,\n\u001b[1;32m 708\u001b[0m body\u001b[39m=\u001b[39;49mbody,\n\u001b[1;32m 709\u001b[0m headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[1;32m 710\u001b[0m chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[1;32m 711\u001b[0m )\n\u001b[1;32m 713\u001b[0m \u001b[39m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[1;32m 714\u001b[0m \u001b[39m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[1;32m 715\u001b[0m \u001b[39m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[1;32m 716\u001b[0m \u001b[39m# mess.\u001b[39;00m\n\u001b[1;32m 717\u001b[0m response_conn \u001b[39m=\u001b[39m conn \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m release_conn \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/urllib3/connectionpool.py:449\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 444\u001b[0m httplib_response \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39mgetresponse()\n\u001b[1;32m 445\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 446\u001b[0m \u001b[39m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[1;32m 447\u001b[0m \u001b[39m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \u001b[39m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[0;32m--> 449\u001b[0m six\u001b[39m.\u001b[39;49mraise_from(e, \u001b[39mNone\u001b[39;49;00m)\n\u001b[1;32m 450\u001b[0m \u001b[39mexcept\u001b[39;00m (SocketTimeout, BaseSSLError, SocketError) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_raise_timeout(err\u001b[39m=\u001b[39me, url\u001b[39m=\u001b[39murl, timeout_value\u001b[39m=\u001b[39mread_timeout)\n", + "File \u001b[0;32m:3\u001b[0m, in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/urllib3/connectionpool.py:444\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 442\u001b[0m \u001b[39m# Python 3\u001b[39;00m\n\u001b[1;32m 443\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 444\u001b[0m httplib_response \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49mgetresponse()\n\u001b[1;32m 445\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 446\u001b[0m \u001b[39m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[1;32m 447\u001b[0m \u001b[39m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \u001b[39m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[1;32m 449\u001b[0m six\u001b[39m.\u001b[39mraise_from(e, \u001b[39mNone\u001b[39;00m)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/http/client.py:1375\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1373\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1374\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1375\u001b[0m response\u001b[39m.\u001b[39;49mbegin()\n\u001b[1;32m 1376\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mConnectionError\u001b[39;00m:\n\u001b[1;32m 1377\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclose()\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/http/client.py:318\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[39m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m 317\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[0;32m--> 318\u001b[0m version, status, reason \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_read_status()\n\u001b[1;32m 319\u001b[0m \u001b[39mif\u001b[39;00m status \u001b[39m!=\u001b[39m CONTINUE:\n\u001b[1;32m 320\u001b[0m \u001b[39mbreak\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/http/client.py:279\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_read_status\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m--> 279\u001b[0m line \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfp\u001b[39m.\u001b[39;49mreadline(_MAXLINE \u001b[39m+\u001b[39;49m \u001b[39m1\u001b[39;49m), \u001b[39m\"\u001b[39m\u001b[39miso-8859-1\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 280\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(line) \u001b[39m>\u001b[39m _MAXLINE:\n\u001b[1;32m 281\u001b[0m \u001b[39mraise\u001b[39;00m LineTooLong(\u001b[39m\"\u001b[39m\u001b[39mstatus line\u001b[39m\u001b[39m\"\u001b[39m)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/socket.py:705\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m 704\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 705\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sock\u001b[39m.\u001b[39;49mrecv_into(b)\n\u001b[1;32m 706\u001b[0m \u001b[39mexcept\u001b[39;00m timeout:\n\u001b[1;32m 707\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_timeout_occurred \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/ssl.py:1274\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1270\u001b[0m \u001b[39mif\u001b[39;00m flags \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 1271\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 1272\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m\n\u001b[1;32m 1273\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m)\n\u001b[0;32m-> 1274\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mread(nbytes, buffer)\n\u001b[1;32m 1275\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 1276\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mrecv_into(buffer, nbytes, flags)\n", + "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/ssl.py:1130\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m 1128\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1129\u001b[0m \u001b[39mif\u001b[39;00m buffer \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1130\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sslobj\u001b[39m.\u001b[39;49mread(\u001b[39mlen\u001b[39;49m, buffer)\n\u001b[1;32m 1131\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 1132\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sslobj\u001b[39m.\u001b[39mread(\u001b[39mlen\u001b[39m)\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "from azure.storage.blob import ContainerClient\n", + "from pipeline.etl_tools import load_oedi_sas\n", + "\n", + "sas = load_oedi_sas()\n", + "client = ContainerClient.from_container_url(f'https://nrel.blob.core.windows.net/oedi?{sas}')\n", + "for blob in client.list_blobs():\n", + " if \"wtk/\" in blob.name:\n", + " print(blob.name)\n", + " #client.delete_blob(blob)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2001.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2002.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2003.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2004.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2005.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2006.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2007.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2008.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2009.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2010.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2011.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2012.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2013.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2014.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2015.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2016.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2017.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2018.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2019.h5',\n", + " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2020.h5']" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import s3fs\n", + "\n", + "s3 = s3fs.S3FileSystem()\n", + "files = s3.glob('kerchunk-staging/wtk/pr100/hourly/*.h5')\n", + "for file in files:\n", + " with s3.open(file) as f:\n", + " az_path = file.replace('kerchunk-staging', 'oedi')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "blob_client = blob_service_client.get_blob_client(container=container_name, blob=local_file_name)\n", + "\n", + "client.get_blob_client()\n", + "# Upload the created file\n", + "with open(file=upload_file_path, mode=\"rb\") as data:\n", + " blob_client.upload_blob(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "v1.0.0/Alaska/Alaska_wave_1979.h5\n", + "2021-10-26 18:49:07+00:00\n" + ] + } + ], + "source": [ + "from azure.storage.blob import ContainerClient\n", + "from pipeline.etl_tools import load_oedi_sas\n", + "\n", + "sas = load_oedi_sas()\n", + "client = ContainerClient.from_container_url('https://nrel.blob.core.windows.net/nrel-wave?sv=2020-08-04&si=nrel-wave-ro&sr=c&sig=VACZ6rXzsE7l2eFQMvYMQCgy8dT23%2Bs1eDPYa%2FBCnEM%3D')\n", + "for blob in client.list_blobs():\n", + " print(blob.name)\n", + " print(blob.last_modified)\n", + " break\n", + " #client.delete_blob(blob)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "33546.735149671\n" + ] + } + ], + "source": [ + "size = 0\n", + "for blob in client.list_blobs():\n", + " size += blob.size\n", + "print(size * 10 ** -9)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "v1.0.0/Alaska/Alaska_wave_1979.h5\n", + "v1.0.0/Alaska/Alaska_wave_1980.h5\n", + "v1.0.0/Alaska/Alaska_wave_1981.h5\n", + "v1.0.0/Alaska/Alaska_wave_1982.h5\n", + "v1.0.0/Alaska/Alaska_wave_1983.h5\n", + "v1.0.0/Alaska/Alaska_wave_1984.h5\n", + "v1.0.0/Alaska/Alaska_wave_1985.h5\n", + "v1.0.0/Alaska/Alaska_wave_1986.h5\n", + "v1.0.0/Alaska/Alaska_wave_1987.h5\n", + "v1.0.0/Alaska/Alaska_wave_1988.h5\n", + "v1.0.0/Alaska/Alaska_wave_1989.h5\n", + "v1.0.0/Alaska/Alaska_wave_1990.h5\n", + "v1.0.0/Alaska/Alaska_wave_1992.h5\n", + "v1.0.0/Alaska/Alaska_wave_1993.h5\n", + "v1.0.0/Alaska/Alaska_wave_1994.h5\n", + "v1.0.0/Alaska/Alaska_wave_1995.h5\n", + "v1.0.0/Alaska/Alaska_wave_1996.h5\n", + "v1.0.0/Alaska/Alaska_wave_1997.h5\n", + "v1.0.0/Alaska/Alaska_wave_1998.h5\n", + "v1.0.0/Alaska/Alaska_wave_1999.h5\n", + "v1.0.0/Alaska/Alaska_wave_2000.h5\n", + "v1.0.0/Alaska/Alaska_wave_2001.h5\n", + "v1.0.0/Alaska/Alaska_wave_2002.h5\n", + "v1.0.0/Alaska/Alaska_wave_2003.h5\n", + "v1.0.0/Alaska/Alaska_wave_2004.h5\n", + "v1.0.0/Alaska/Alaska_wave_2005.h5\n", + "v1.0.0/Alaska/Alaska_wave_2006.h5\n", + "v1.0.0/Alaska/Alaska_wave_2007.h5\n", + "v1.0.0/Alaska/Alaska_wave_2008.h5\n", + "v1.0.0/Alaska/Alaska_wave_2009.h5\n", + "v1.0.0/Alaska/Alaska_wave_2010.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1979.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1980.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1981.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1982.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1983.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1984.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1985.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1986.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1987.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1988.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1989.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1990.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1991.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1992.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1993.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1994.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1995.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1996.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1997.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1998.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_1999.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_2000.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_2001.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_2002.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_2003.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_2004.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_2005.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_2006.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_2007.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_2008.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_2009.h5\n", + "v1.0.0/Atlantic/Atlantic_wave_2010.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1979.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1980.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1981.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1982.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1983.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1984.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1985.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1986.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1987.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1988.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1989.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1990.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1991.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1992.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1993.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1994.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1995.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1996.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1997.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1998.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_1999.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_2000.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_2001.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_2002.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_2003.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_2004.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_2005.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_2006.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_2007.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_2008.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_2009.h5\n", + "v1.0.0/Hawaii/Hawaii_wave_2010.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1979.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1980.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1981.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1982.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1983.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1984.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1985.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1986.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1987.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1988.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1989.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1990.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1991.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1992.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1993.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1994.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1995.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1996.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1997.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1998.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_1999.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_2000.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_2001.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_2002.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_2003.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_2004.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_2005.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_2006.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_2007.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_2008.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_2009.h5\n", + "v1.0.0/West_Coast/West_Coast_wave_2010.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1979.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1980.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1981.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1982.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1983.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1984.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1985.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1986.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1987.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1988.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1989.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1990.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1991.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1992.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1993.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1994.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1995.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1996.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1997.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1998.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1999.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2000.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2001.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2002.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2003.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2004.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2005.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2006.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2007.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2008.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2009.h5\n", + "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2010.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1979.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1980.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1981.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1982.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1983.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1984.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1985.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1986.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1987.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1988.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1989.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1990.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1991.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1992.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1993.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1994.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1995.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1996.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1997.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1998.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1999.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2000.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2001.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2002.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2003.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2004.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2005.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2006.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2007.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2008.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2009.h5\n", + "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2010.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1979.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1980.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1981.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1982.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1983.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1984.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1985.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1986.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1987.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1988.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1989.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1990.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1991.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1992.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1993.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1994.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1995.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1996.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1997.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1998.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1999.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2000.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2001.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2002.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2003.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2004.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2005.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2006.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2007.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2008.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2009.h5\n", + "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2010.h5\n" + ] + } + ], + "source": [ + "for blob in client.list_blobs():\n", + " print(blob.name)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".env", + "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": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/azure/dev-env.yml b/azure/dev-env.yml new file mode 100644 index 0000000..bdb2dad --- /dev/null +++ b/azure/dev-env.yml @@ -0,0 +1,18 @@ +name: oedi-azure-dev +channels: + - conda-forge + - defaults +dependencies: + - python=3.10.12 + - h5py=3.9.0 + - boto3 + - cftime + - kerchunk + - planetary-computer + - s3fs=2023.6.0 + - pandas + - ujson + - xarray + - zarr + - ipykernel + - adlfs diff --git a/azure/documentation/ pv_rooftop.md b/azure/documentation/ pv_rooftop.md new file mode 100644 index 0000000..2ce34c1 --- /dev/null +++ b/azure/documentation/ pv_rooftop.md @@ -0,0 +1,346 @@ +# PV Rooftop Database + +## Overview + +The National Renewable Energy Laboratory's (NREL) PV Rooftop Database (PVRDB) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for 128 metropolitan regions in the United States. The source lidar data and building footprints were obtained by the U.S. Department of Homeland Security Homeland Security Infrastructure Program for 2006-2014. Using GIS methods, NREL identified suitable roof surfaces based on their size, orientation, and shading parameters Gagnon et al. (2016). Standard 2015 technical potential was then estimated for each plane using NREL's System Advisory Model. + +The PVRDB is down-loadable by city and year of lidar collection. Four geospatial layers are available for each city and year: 1) the raster extent of the lidar collection, 2) buildings identified from the lidar data, 3) suitable developable planes for each building, and 4) aspect values of the developable planes. + +## Storage Resources + +The pv-rooftop Dataset is made available in Parquet format in the following container: + +`https://nrel.blob.core.windows.net/oedi` + +### Data + +The data are located in the `pv-rooftops/` directory. The four main datasets are stored in the following subdirectories: + +Main datasets + - `/aspects` + - `/buildings` + - `/developable_planes` + - `/rasd` + +Each partition is stored in an individual folder within each subdirectory. + +Partitions + +- `/city_year=__` + +e.g. `/city_year=dover_de_09` + +### Data Format + +The PV Rooftops dataset is provided in geoparquet format partitioned by city_year. There are 4 core datasets: + +#### `oedi/pv-rooftop/aspects` +field | data_type | description +-- | -- | -- +`gid` | bigint |   +`city` | string | city of source lidar dataset +`state` | string | state of source lidar dataset +`year` | bigint | year of source lidar dataset +`bldg_fid` | bigint | building id +`aspect` | bigint | aspect value +`the_geom_96703` | string | projected geometry ([US Contiguous Albers Equal Area Conic - SRID 6703](https://spatialreference.org/ref/sr-org/6703/)) +`the_geom_4326` | string | geometry ([WGS 1984 - SRID 4326](https://spatialreference.org/ref/epsg/4326/)) +`region_id` | bigint |   + + +#### `oedi/pv-rooftop/buildings` + +field | data_type | description +-- | -- | -- +`gid` | bigint |   +`bldg_fid` | bigint | the building fid +`the_geom_96703` | string | projected geometry ([US Contiguous Albers Equal Area Conic - SRID 6703](https://spatialreference.org/ref/sr-org/6703/)) +`the_geom_4326` | string | geometry ([WGS 1984 - SRID 4326](https://spatialreference.org/ref/epsg/4326/)) +`city` | string | the city of the source lidar data +`state` | string | the state of the source lidar data +`year` | bigint | the year of the source lidar data +`region_id` | bigint |   + + +#### `oedi/pv-rooftop/developable_planes` + +field | data_type | description +-- | -- | -- +`bldg_fid` | bigint | building ID associated with the developable plane +`footprint_m2` | double | developable plane footprint area (m2) +`slope` | bigint | slope value +`flatarea_m2` | double | flat area of the developable plane (m2) +`slopeconversion` | double | the slope conversion factor used to convert the flat area into the sloped area +`slopearea_m2` | double | sloped area of the developable plane (m2) +`zip` | string | zipcode +`zip_perc` | double |   +`aspect` | bigint | the aspect value of the developable plane +`gid` | bigint | unique developable plane ID +`city` | string | the city of the source lidar data +`state` | string | the state of the source lidar data +`year` | bigint | the year of the source lidar data +`region_id` | bigint |   +`the_geom_96703` | string | projected geometry ([US Contiguous Albers Equal Area Conic - SRID 6703](https://spatialreference.org/ref/sr-org/6703/)) +`the_geom_4326` | string | geometry ([WGS 1984 - SRID 4326](https://spatialreference.org/ref/epsg/4326/)) + + +#### `oedi/pv-rooftop/rasd` + +field | data_type | description +-- | -- | -- +`gid` | bigint | the unique geographic ID of the raster domain +`the_geom_96703` | string | projected geometry ([US Contiguous Albers Equal Area Conic - SRID 6703](https://spatialreference.org/ref/sr-org/6703/)) +`the_geom_4326` | string | geometry ([WGS 1984 - SRID 4326](https://spatialreference.org/ref/epsg/4326/)) +`city` | string | the city of the source lidar data +`state` | string | the state of the source lidar data +`year` | bigint | the year of the source lidar data +`region_id` | bigint |   +`serial_id` | bigint |   +`__index_level_0__` | bigint |   + + +Within each core dataset there are paritions by city_state_year(YY) that can be queried using Apache pyarrow tools or dask, or downloaded as individual geoparquet format data files. + +Aspects Lookup: +``` +1 337.5 - 22.5 north +2 22.5 - 67.5 northeast +3 67.5 - 112.5 east +4 112.5 - 157.5 southeast +5 157.5 - 202.5 south +6 202.5 - 247.5 southwest +7 247.5 - 292.5 west +8 292.5 - 337.5 northwest +0 flat flat +``` + +Regions Lookup: +``` +1 Albany NY 2006-01-01 +2 Albany NY 2013-01-01 +3 Albuquerque NM 2006-01-01 +4 Albuquerque NM 2012-01-01 +5 Allentown PA 2006-01-01 +6 Amarillo TX 2008-01-01 +7 Anaheim CA 2010-01-01 +8 Arnold MO 2006-01-01 +9 Atlanta GA 2008-01-01 +10 Atlanta GA 2013-01-01 +11 Augusta GA 2010-01-01 +12 Augusta ME 2008-01-01 +13 Austin TX 2006-01-01 +14 Austin TX 2012-01-01 +15 Bakersfield CA 2010-01-01 +16 Baltimore MD 2008-01-01 +17 Baltimore MD 2013-01-01 +18 Baton Rouge LA 2006-01-01 +19 Baton Rouge LA 2012-01-01 +20 Birmingham AL 2008-01-01 +21 Bismarck ND 2008-01-01 +22 Boise ID 2007-01-01 +23 Boise ID 2013-01-01 +24 Boulder CO 2014-01-01 +25 Bridgeport CT 2006-01-01 +26 Bridgeport CT 2013-01-01 +27 Buffalo NY 2008-01-01 +28 Carson City NV 2009-01-01 +29 Charleston SC 2010-01-01 +30 Charleston WV 2009-01-01 +31 Charlotte NC 2006-01-01 +32 Charlotte NC 2012-01-01 +33 Cheyenne WY 2008-01-01 +34 Chicago IL 2008-01-01 +35 Chicago IL 2012-01-01 +36 Cincinnati OH 2010-01-01 +37 Cleveland OH 2012-01-01 +38 Colorado Springs CO 2006-01-01 +39 Colorado Springs CO 2013-01-01 +40 Columbia SC 2009-01-01 +41 Columbus GA 2009-01-01 +42 Columbus OH 2006-01-01 +43 Columbus OH 2012-01-01 +44 Concord NH 2009-01-01 +45 Corpus Christi TX 2012-01-01 +46 Dayton OH 2006-01-01 +47 Dayton OH 2012-01-01 +48 Denver CO 2012-01-01 +49 Des Moines IA 2010-01-01 +50 Detroit MI 2012-01-01 +51 Dover DE 2009-01-01 +52 El Paso TX 2007-01-01 +53 Flint MI 2009-01-01 +54 Fort Wayne IN 2008-01-01 +55 Frankfort KY 2012-01-01 +56 Fresno CA 2006-01-01 +57 Fresno CA 2013-01-01 +58 Ft Belvoir DC 2012-01-01 +59 Grand Rapids MI 2013-01-01 +60 Greensboro NC 2009-01-01 +61 Harrisburg PA 2009-01-01 +62 Hartford CT 2006-01-01 +63 Hartford CT 2013-01-01 +64 Helena MT 2007-01-01 +65 Helena MT 2013-01-01 +66 Houston TX 2010-01-01 +67 Huntsville AL 2009-01-01 +68 Indianapolis IN 2006-01-01 +69 Indianapolis IN 2012-01-01 +70 Jackson MS 2007-01-01 +71 Jacksonville FL 2010-01-01 +72 Jefferson City MO 2008-01-01 +73 Kansas City MO 2010-01-01 +74 Kansas City MO 2012-01-01 +75 LaGuardia JFK NY 2012-01-01 +76 Lancaster PA 2010-01-01 +77 Lansing MI 2007-01-01 +78 Lansing MI 2013-01-01 +79 Las Vegas NV 2009-01-01 +80 Lexington KY 2012-01-01 +81 Lincoln NE 2008-01-01 +82 Little Rock AR 2008-01-01 +83 Los Angeles CA 2007-01-01 +84 Louisville KY 2006-01-01 +85 Louisville KY 2012-01-01 +86 Lubbock TX 2008-01-01 +87 Madison WI 2010-01-01 +88 Manhattan NY 2007-01-01 +89 McAllen TX 2008-01-01 +90 Miami FL 2009-01-01 +91 Milwaukee WI 2007-01-01 +92 Milwaukee WI 2013-01-01 +93 Minneapolis MN 2007-01-01 +94 Minneapolis MN 2012-01-01 +95 Mission Viejo CA 2013-01-01 +96 Mobile AL 2010-01-01 +97 Modesto CA 2010-01-01 +98 Montgomery AL 2007-01-01 +99 Montpelier VT 2009-01-01 +100 Newark NJ 2007-01-01 +101 New Haven CT 2007-01-01 +102 New Haven CT 2013-01-01 +103 New Orleans LA 2008-01-01 +104 New Orleans LA 2012-01-01 +105 New York NY 2005-01-01 +106 New York NY 2013-01-01 +107 Norfolk VA 2007-01-01 +108 Oklahoma City OK 2007-01-01 +109 Oklahoma City OK 2013-01-01 +110 Olympia WA 2010-01-01 +111 Omaha NE 2007-01-01 +112 Omaha NE 2013-01-01 +113 Orlando FL 2009-01-01 +114 Oxnard CA 2010-01-01 +115 Palm Bay FL 2010-01-01 +116 Pensacola FL 2009-01-01 +117 Philadelphia PA 2007-01-01 +118 Pierre SD 2008-01-01 +119 Pittsburgh PA 2004-01-01 +120 Pittsburgh PA 2012-01-01 +121 Portland OR 2012-01-01 +122 Poughkeepsie NY 2012-01-01 +123 Providence RI 2004-01-01 +124 Providence RI 2012-01-01 +125 Raleigh-Durham NC 2010-01-01 +126 Reno NV 2007-01-01 +127 Richmond VA 2008-01-01 +128 Richmond VA 2013-01-01 +129 Rochester NY 2008-01-01 +130 Rochester NY 2014-01-01 +131 Sacramento CA 2012-01-01 +132 Salem OR 2008-01-01 +133 Salt Lake City UT 2012-01-01 +134 San Antonio TX 2008-01-01 +135 San Antonio TX 2013-01-01 +137 San Diego CA 2008-01-01 +138 San Diego CA 2013-01-01 +139 San Francisco CA 2013-01-01 +140 Santa Fe NM 2009-01-01 +141 Sarasota FL 2009-01-01 +142 Scranton PA 2008-01-01 +143 Seattle WA 2011-01-01 +144 Shreveport LA 2008-01-01 +145 Spokane WA 2008-01-01 +146 Springfield IL 2009-01-01 +147 Springfield MA 2007-01-01 +148 Springfield MA 2013-01-01 +149 St Louis MO 2008-01-01 +150 St Louis MO 2013-01-01 +151 Stockton CA 2010-01-01 +152 Syracuse NY 2008-01-01 +153 Tallahassee FL 2009-01-01 +154 Tampa FL 2008-01-01 +155 Toledo OH 2006-01-01 +156 Toledo OH 2012-01-01 +157 Topeka KS 2008-01-01 +158 Trenton NJ 2008-01-01 +159 Tucson AZ 2007-01-01 +160 Tulsa OK 2008-01-01 +161 Washington DC 2009-01-01 +162 Washington DC 2012-01-01 +163 Wichita KS 2012-01-01 +164 Winston-Salem NC 2009-01-01 +165 Worcester MA 2009-01-01 +166 Youngstown OH 2008-01-01 +167 Andrews AFB DC 2012-01-01 +136 San Bernardino-Riverside CA 2012-01-01 +168 Tampa FL 2013-01-01 +``` + + +## Sample code + +A complete Python example of accessing and visualizing some of these data is available in the accompanying [sample notebook](https://nbviewer.jupyter.org/github/microsoft/AIforEarthDataSets/blob/main/data/pv_rooftop.ipynb). + +## Mounting the container + +We also provide a read-only SAS (shared access signature) token to allow access via, e.g., [BlobFuse](https://github.com/Azure/azure-storage-fuse), which allows you to mount blob containers as drives: + +`https://nrel.blob.core.windows.net/oedi?sv=2019-12-12&si=oedi-ro&sr=c&sig=uslpLxKf3%2Foeu79ufIHbJkpI%2FTWDH3lblJMa5KQRPmM%3D` + +Mounting instructions for Linux are [here](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-how-to-mount-container-linux). + +## References + +Main References: +1. [Rooftop Solar Photovoltaic Technical Potential in the United States: A Detailed Assessment](https://www.nrel.gov/docs/fy16osti/65298.pdf) + +2. [Using GIS-based methods and lidar data to estimate rooftop solar technical potential in US cities](https://iopscience.iop.org/article/10.1088/1748-9326/aa7225/pdf) + +3. [Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling](https://iopscience.iop.org/article/10.1088/1748-9326/aaa554/pdf) + +4. [Rooftop Photovoltaic Technical Potential in the United States](https://data.nrel.gov/submissions/121) + +5. [U.S. PV-Suitable Rooftop Resources](https://data.nrel.gov/submissions/47) + +Related Reference: + +1. [Rooftop Solar Technical Potential for Low-to-Moderate Income Households in the United States](https://www.nrel.gov/docs/fy18osti/70901.pdf) + +2. [Rooftop Energy Potential of Low Income Communities in America REPLICA](https://data.nrel.gov/submissions/81) + +3. [Puerto Rico Solar-for-All: LMI PV Rooftop Technical Potential and Solar Savings Potential](https://data.nrel.gov/submissions/144) + + +## Disclaimer and Attribution + +Copyright (c) 2020, Alliance for Sustainable Energy LLC, All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +## Contact + +For questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=oedi%20question). + + +## Notices + +Microsoft provides this dataset on an "as is" basis. Microsoft makes no warranties (express or implied), guarantees, or conditions with respect to your use of the dataset. To the extent permitted under your local law, Microsoft disclaims all liability for any damages or losses * including direct, consequential, special, indirect, incidental, or punitive * resulting from your use of this dataset. This dataset is provided under the original terms that Microsoft received source data. \ No newline at end of file diff --git a/azure/documentation/PR100.md b/azure/documentation/PR100.md new file mode 100644 index 0000000..69e9052 --- /dev/null +++ b/azure/documentation/PR100.md @@ -0,0 +1,55 @@ +# NREL Puerto Rico 100 Dataset (PR100) + + +## Overview + +The [Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study](https://www.energy.gov/gdo/puerto-rico-grid-resilience-and-transitions-100-renewable-energy-study-pr100) is a 2-year study by the U.S. Department of Energy’s (DOE’s) Grid Deployment Office and six national laboratories to comprehensively analyze stakeholder-driven pathways to Puerto Rico’s clean energy future. + +The PR100 dataset is a collection of geospasial data that will be useful for renewable energy development in Puerto Rico. The dataset is curated by the National Renewable Energy Laboratory. + + +## Storage resources + +The data are stored in Azure Blob Storage, in the following container: + +`https://nrel.blob.core.windows.net/oedi` + + +### Data + +The data are located in the `pr100/` directory and have been categorized into 5 subdirectories: + +- `Boundaries/` +- `Habitat/` +- `Hazards/` +- `Infrastructure/` +- `Topography/` + + +### Data format + +Vector data are stored in the geoparquet format and rasters are stored as cloud-optimized geotiffs. + + +## Sample code + +A complete Python example of accessing and visualizing some of these data is available in the accompanying [sample notebook](https://nbviewer.jupyter.org/github/microsoft/AIforEarthDataSets/blob/main/data/PR100.ipynb). + + +## Mounting the container + +We also provide a read-only SAS (shared access signature) token to allow access via, e.g., [BlobFuse](https://github.com/Azure/azure-storage-fuse), which allows you to mount blob containers as drives: + +`https://nrel.blob.core.windows.net/oedi?sv=2019-12-12&si=oedi-ro&sr=c&sig=uslpLxKf3%2Foeu79ufIHbJkpI%2FTWDH3lblJMa5KQRPmM%3D` + +Mounting instructions for Linux are [here](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-how-to-mount-container-linux). + + +## Contact + +For questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=oedi%20question). + + +## Notices + +Microsoft provides this dataset on an "as is" basis. Microsoft makes no warranties (express or implied), guarantees, or conditions with respect to your use of the dataset. To the extent permitted under your local law, Microsoft disclaims all liability for any damages or losses * including direct, consequential, special, indirect, incidental, or punitive * resulting from your use of this dataset. This dataset is provided under the original terms that Microsoft received source data. \ No newline at end of file diff --git a/azure/documentation/sup3rcc.md b/azure/documentation/sup3rcc.md new file mode 100644 index 0000000..10679b6 --- /dev/null +++ b/azure/documentation/sup3rcc.md @@ -0,0 +1,103 @@ +# Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) + + +## Overview + +The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under climate change scenarios. + +Sup3rCC is downscaled Global Climate Model (GCM) data. For example, the initial dataset "sup3rcc_conus_mriesm20_ssp585_r1i1p1f1" is downscaled from MRI ESM 2.0 for climate change scenario SSP5 8.5 and variant label r1i1p1f1. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data ([Sup3r GitHub Repo](https://github.com/NREL/sup3r)). The data includes both historical and future weather years, although the historical years represent the historical average climate, not the actual historical weather that we experienced. + +The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the *possible* future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty. + +For more info, view the [OEDI Sup3rcc catalogue entry](https://data.openei.org/submissions/5839). + +## Storage Resources + +The Sup3rcc Dataset is made available in h5 format in the following container: + +`https://nrel.blob.core.windows.net/oedi` + +### Data + +The data are located in the `sup3rcc/` directory. The initial datset is the subdirectory `conus_mriesm20_ssp585_r1i1p1f1/`. + +Each h5 file's name encodes info about the variables it contains and the year. + +e.g. `sup3rcc_conus_mriesm20_ssp585_r1i1p1f1_pressure_2015.h5` + +### Data Format + +The Sup3rcc dataset is provided in h5 format. A kerchunk reference file is also included to facilitate faster access. + +#### `Dimensions:` +field | data_type +-- | -- +`time_index` | int +`latitude` | float +`longitude` | float + +#### `Location Metadata:` + +field | data_type +-- | -- +`country` | string +`state` | string +`county` | string +`timezone` | string +`eez` | string +`elevation` | string +`offshore` | string + +#### `Variables:` + +field | data_type +-- | -- +`dhi` | float +`dni` | float +`ghi` | float +`pressure_0m` | float +`relativehumidity_2m` | float +`temperature_2m` | float +`winddirection_100m` | float +`winddirection_10m` | float +`winddirection_200m` | float +`windspeed_100m` | float +`windspeed_10m` | float +`windspeed_200m` | float +`offshore` | float + +## Sample code + +A complete Python example of accessing and visualizing some of these data is available in the accompanying [sample notebook](https://nbviewer.jupyter.org/github/microsoft/AIforEarthDataSets/blob/main/data/sup3rcc.ipynb). + +## Mounting the container + +We also provide a read-only SAS (shared access signature) token to allow access via, e.g., [BlobFuse](https://github.com/Azure/azure-storage-fuse), which allows you to mount blob containers as drives: + +`https://nrel.blob.core.windows.net/oedi?sv=2019-12-12&si=oedi-ro&sr=c&sig=uslpLxKf3%2Foeu79ufIHbJkpI%2FTWDH3lblJMa5KQRPmM%3D` + +Mounting instructions for Linux are [here](https://docs.microsoft.com/en-us/azure/storage/blobs/storage-how-to-mount-container-linux). + +## Disclaimer and Attribution + +Copyright (c) 2020, Alliance for Sustainable Energy LLC, All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + + +## Contact + +For questions about this dataset, contact [`aiforearthdatasets@microsoft.com`](mailto:aiforearthdatasets@microsoft.com?subject=oedi%20question). + + +## Notices + +Microsoft provides this dataset on an "as is" basis. Microsoft makes no warranties (express or implied), guarantees, or conditions with respect to your use of the dataset. To the extent permitted under your local law, Microsoft disclaims all liability for any damages or losses * including direct, consequential, special, indirect, incidental, or punitive * resulting from your use of this dataset. 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u;4lAa{112K-|hcx#{O!b8}y&{|1xTFk`KTW2L*)+{`i4;@;n5*LH!>90ZsJ) literal 0 HcmV?d00001 diff --git a/azure/examples/PR100.ipynb b/azure/examples/PR100.ipynb new file mode 100644 index 0000000..476eb9f --- /dev/null +++ b/azure/examples/PR100.ipynb @@ -0,0 +1,183 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Demo Notebook for Accessing PR100 Data on Azure\n", + "\n", + "Launched on February 2, 2022, a two-year study entitled Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy (PR100) will perform a comprehensive analysis of stakeholder-driven pathways to Puerto Rico’s clean energy future. For more information, please visit [https://www.energy.gov/gdo/puerto-rico-grid-resilience-and-transitions-100-renewable-energy-study-pr100].\n", + "\n", + "To support the PR100 project, the Open Energy Data Initiative has made an assortment of data sets available for free public access. This notebook will demonstrate how to access the PR100 data located in Azure BLOB storage." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get Access Token\n", + "\n", + "You do not need an Azure account to access public data. Instead, you can obtain a temporary access token via the Planetary Computer's API. This can be accomplished via either the requests or planetary_computer libraries. Both options are shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get a token with requests\n", + "import requests\n", + "\n", + "token = requests.get(\n", + " 'https://planetarycomputer.microsoft.com/api/sas/v1/token/nrel/oedi'\n", + ").json()['token']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get a token with planetary-computer\n", + "import planetary_computer\n", + "\n", + "token = planetary_computer.sas.get_token('nrel', 'oedi').token\n" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explore Container\n", + "\n", + "Use the token to create a PyFileSystem object. You can explore the contents of the container using the get_file_info method. The PR100 data consists of geoparquet and geotiff files that are organized into directories:\n", + "- Boundaries\n", + "- Habitat\n", + "- Hazards\n", + "- Infrastructure\n", + "- Topography" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyarrow.fs import PyFileSystem, FSSpecHandler, FileSelector\n", + "from adlfs import AzureBlobFileSystem\n", + "\n", + "# Create file system\n", + "fs = PyFileSystem(\n", + " FSSpecHandler(\n", + " AzureBlobFileSystem('nrel', credential=token)\n", + " )\n", + ")\n", + "\n", + "# View files in the 'Boundaries' directory\n", + "fs.get_file_info(FileSelector('/oedi/PR100/Boundaries/'))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Vector Data\n", + "\n", + "Let's load one of those files into a geodataframe and visualize it." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import geopandas\n", + "\n", + "df = geopandas.read_parquet('oedi/PR100/Boundaries/land_protected_areas.parquet', filesystem=fs)\n", + "df.explore()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load Raster Data\n", + "\n", + "If we look in the Topography directory, we'll see some tif files. These are cloud optimized GeoTiffs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fs.get_file_info(FileSelector('/oedi/PR100/Topography/'))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can load these files with the rasterio package." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import rasterio\n", + "import rasterio.plot\n", + "\n", + "with fs.open_input_file('oedi/PR100/Topography/elevation.tif') as file:\n", + " raster = rasterio.open(file)\n", + " print(raster.meta)\n", + " rasterio.plot.show(raster, adjust=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pr100-env", + "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": "3.10.9" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "d2e0ca302a5f5f673bd05f1fbb5f2420578af44ff0f5cef95f9f5d4b68b66ae3" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/azure/examples/pv_rooftop.ipynb b/azure/examples/pv_rooftop.ipynb new file mode 100644 index 0000000..1bcb163 --- /dev/null +++ b/azure/examples/pv_rooftop.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Demo Notebook for Accessing PV Rooftop Data on Azure\n", + "\n", + "The National Renewable Energy Laboratory's (NREL) PV Rooftop Database (PVRDB) is a lidar-derived, geospatially-resolved dataset of suitable roof surfaces and their PV technical potential for 128 metropolitan regions in the United States. The source lidar data and building footprints were obtained by the U.S. Department of Homeland Security Homeland Security Infrastructure Program for 2006-2014. Using GIS methods, NREL identified suitable roof surfaces based on their size, orientation, and shading parameters Gagnon et al. (2016). Standard 2015 technical potential was then estimated for each plane using NREL's System Advisory Model.\n", + "\n", + "This notebook will demonstrate how to access the PV Rooftop data located in Azure BLOB storage." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Get Access Token\n", + "\n", + "You do not need an Azure account to access public data. Instead, you can obtain a temporary access token via the Planetary Computer's API. This can be accomplished via either the requests or planetary_computer libraries. Both options are shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get a token with requests\n", + "import requests\n", + "\n", + "token = requests.get(\n", + " 'https://planetarycomputer.microsoft.com/api/sas/v1/token/nrel/oedi'\n", + ").json()['token']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# get a token with planetary-computer\n", + "import planetary_computer\n", + "\n", + "token = planetary_computer.sas.get_token('nrel', 'oedi').token" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Explore Container\n", + "\n", + "First, we use the token to create a PyFileSystem object. We can then use ParquetDataset objects to explore the metadata for each table. pv_rooftop consists of 4 tables:\n", + "- buildings\n", + "- aspects\n", + "- developable-planes\n", + "- rasd\n", + "\n", + "Each table is partitioned by city_year." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from pyarrow.fs import PyFileSystem, FSSpecHandler\n", + "from adlfs import AzureBlobFileSystem\n", + "import pyarrow.parquet as pq\n", + "\n", + "# Create file system using token\n", + "fs = PyFileSystem(\n", + " FSSpecHandler(\n", + " AzureBlobFileSystem('nrel', credential=token)\n", + " )\n", + ")\n", + "\n", + "# Create ParquetDataset for the buildings table\n", + "buildings_dataset = pq.ParquetDataset('oedi/pv-rooftop/buildings', filesystem=fs)\n", + "\n", + "# View the partition keys\n", + "city_years = buildings_dataset.partitioning.dictionaries\n", + "city_years\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# View the schema for the buildings table\n", + "buildings_dataset.schema" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Read Data\n", + "\n", + "pv_rooftop is a large data set. For the purposes of this example, we will read data from a single partition, city_year=albany_ny_13, and take a random sample of 100 buildings. We will read the tables directly into geodataframes. This may take several minutes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import geopandas as gpd\n", + "\n", + "# Read the bldg_fid column from the buildings table and take a random sample of 100 buildings.\n", + "bldg_fid_sample = pd.read_parquet(\n", + " 'oedi/pv-rooftop/buildings',\n", + " filesystem=fs,\n", + " filters=[('city_year', '=', 'albany_ny_13')],\n", + " columns=['bldg_fid']\n", + ").sample(100)['bldg_fid']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read buildings table using bldg_fid_sample as a filter\n", + "buildings = gpd.read_parquet(\n", + " 'oedi/pv-rooftop/buildings',\n", + " filesystem=fs,\n", + " filters=[\n", + " ('city_year', '=', 'albany_ny_13'),\n", + " ('bldg_fid', 'in', bldg_fid_sample)\n", + " ],\n", + " columns=['gid', 'city', 'state', 'year', 'bldg_fid', 'the_geom_4326']\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read aspects table using bldg_fid_sample as a filter\n", + "aspects = gpd.read_parquet(\n", + " 'oedi/pv-rooftop/aspects',\n", + " filesystem=fs,\n", + " filters=[\n", + " ('city_year', '=', 'albany_ny_13'),\n", + " ('bldg_fid', 'in', bldg_fid_sample)\n", + " ],\n", + " columns=['gid', 'city', 'state', 'year', 'bldg_fid', 'aspect', 'the_geom_4326']\n", + ")\n", + "\n", + "# Add a column for the aspect_string\n", + "aspect_lookup = {\n", + " 0: 'flat',\n", + " 1: 'north',\n", + " 2: 'northeast',\n", + " 3: 'east',\n", + " 4: 'southeast',\n", + " 5: 'south',\n", + " 6: 'southwest',\n", + " 7: 'west',\n", + " 8: 'northwest'\n", + "}\n", + "aspects['aspect_string'] = aspects['aspect'].replace(aspect_lookup)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read developable-planes table using bldg_fid_sample as a filter\n", + "developable_planes = gpd.read_parquet(\n", + " 'oedi/pv-rooftop/developable-planes',\n", + " filesystem=fs,\n", + " filters=[\n", + " ('city_year', '=', 'albany_ny_13'),\n", + " ('bldg_fid', 'in', bldg_fid_sample)\n", + " ],\n", + " columns=['gid', 'city', 'state', 'year', 'bldg_fid', 'footprint_m2', 'slope', 'flatarea_m2', 'slopeconversion', 'slopearea_m2', 'aspect', 'the_geom_4326']\n", + ")\n", + "\n", + "# Add a column for the aspect_string\n", + "developable_planes['aspect_string'] = developable_planes['aspect'].replace(aspect_lookup)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Read rasd table\n", + "rasd = gpd.read_parquet(\n", + " 'oedi/pv-rooftop/rasd',\n", + " filesystem=fs,\n", + " filters=[\n", + " ('city_year', '=', 'albany_ny_13')\n", + " ],\n", + " columns=['gid', 'city', 'state', 'year', 'the_geom_4326']\n", + ")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Visualize Data\n", + "\n", + "We are now ready to visualize the data using folium." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import folium\n", + "\n", + "# Dictionary for coloring the polygons based on aspect\n", + "color_dict = {\n", + " 'flat': 'yellow',\n", + " 'north': 'red',\n", + " 'northeast': 'red',\n", + " 'east': 'yellow',\n", + " 'southeast': 'green',\n", + " 'south': 'green',\n", + " 'southwest': 'green',\n", + " 'west': 'yellow',\n", + " 'northwest': 'red'\n", + "}\n", + "color = aspects['aspect_string'].replace(color_dict)\n", + "m = buildings.explore(color='gray', name='buildings')\n", + "m = aspects.explore(m=m, name='aspects', color=color)\n", + "m = developable_planes.explore(m=m, name='developable-planes', color=color)\n", + "m = rasd.explore(m=m, name='rasd')\n", + "folium.LayerControl().add_to(m)\n", + "m" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Export Data\n", + "\n", + "There are many options for exporting the data for use in GIS software. Here, we demonstrate writing a geopackage." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "file_name = 'pv_rooftop_albany_ny_13.gpkg'\n", + "buildings.to_file(file_name, layer='buildings', driver=\"GPKG\")\n", + "aspects.to_file(file_name, layer='aspects', driver=\"GPKG\")\n", + "developable_planes.to_file(file_name, layer='developable-planes', driver=\"GPKG\")\n", + "rasd.to_file(file_name, layer='rasd', driver=\"GPKG\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "oedi-env", + "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": "3.10.9" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "4c7bf1489743dc7ac4eb5d54993539996d2b573f88c885c7af86ecea3199729c" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/azure/examples/sup3rcc.ipynb b/azure/examples/sup3rcc.ipynb new file mode 100644 index 0000000..a1dff55 --- /dev/null +++ b/azure/examples/sup3rcc.ipynb @@ -0,0 +1,894 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Demo Notebook for Accessing Sup3rcc Data on Azure\n", + "\n", + "The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under climate change scenarios.\n", + "\n", + "Sup3rCC is downscaled Global Climate Model (GCM) data. For example, the initial dataset \"sup3rcc_conus_mriesm20_ssp585_r1i1p1f1\" is downscaled from MRI ESM 2.0 for climate change scenario SSP5 8.5 and variant label r1i1p1f1. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as \"Sup3r GitHub Repo\"). The data includes both historical and future weather years, although the historical years represent the historical average climate, not the actual historical weather that we experienced.\n", + "\n", + "The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the *possible* future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.\n", + "\n", + "For more information about Sup3rcc, please refer to its OEDI catalogue entry: https://data.openei.org/submissions/5839\n", + "\n", + "This notebook will demonstrate how to access the Sup3rcc data located in Azure BLOB storage using kerchunk." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Requirements\n", + "\n", + "In order to run this notebook, please ensure you have the following packages installed:\n", + "\n", + "matplotlib==3.7.1 \n", + "numpy==1.24.3 \n", + "planetary_computer==0.5.1 \n", + "scipy==1.10.1 \n", + "ujson==5.7.0 \n", + "xarray==2023.4.2 " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load kerchunk Reference File\n", + "\n", + "You do not need an Azure account to access public data. Instead, you can use the planetary-computer library to obain temporary credentials, as shown below. To access Supe3rcc, we need to load the kerchunk reference file, which is stored in the same container as the rest of the data. Depending on your network, this step may take several minutes as the reference file is a few hundred MB." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import planetary_computer\n", + "import ujson\n", + "\n", + "fs = planetary_computer.get_adlfs_filesystem('nrel', 'oedi')\n", + "with fs.open('oedi/sup3rcc/conus_mriesm20_ssp585_r1i1p1f1/kerchunk_reference.json', 'rb') as ref_file:\n", + " ref = ujson.load(ref_file)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create xarray dataset\n", + "\n", + "Next we use the xarray package to read the reference file and give us an interface to access the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
+       "Dimensions:              (latitude: 650, longitude: 1475, time_index: 175320)\n",
+       "Coordinates:\n",
+       "  * latitude             (latitude) float32 51.57 51.52 51.48 ... 22.5 22.45\n",
+       "  * longitude            (longitude) float32 -129.9 -129.9 ... -63.63 -63.58\n",
+       "  * time_index           (time_index) datetime64[ns] 2015-01-01 ... 2059-12-3...\n",
+       "Data variables: (12/19)\n",
+       "    country              (latitude, longitude) object ...\n",
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+       "    elevation            (latitude, longitude) float32 ...\n",
+       "    ...                   ...\n",
+       "    winddirection_100m   (time_index, latitude, longitude) float32 ...\n",
+       "    winddirection_10m    (time_index, latitude, longitude) float32 ...\n",
+       "    winddirection_200m   (time_index, latitude, longitude) float32 ...\n",
+       "    windspeed_100m       (time_index, latitude, longitude) float32 ...\n",
+       "    windspeed_10m        (time_index, latitude, longitude) float32 ...\n",
+       "    windspeed_200m       (time_index, latitude, longitude) float32 ...\n",
+       "Attributes:\n",
+       "    full_version_record:  {"rex": "0.2.77", "pandas": "1.3.5", "numpy": "1.19...\n",
+       "    gan_meta:             [{"temp_lapse_rate": 0.0065, "s_enhance": 25, "t_en...\n",
+       "    input_features:       ["pressure_0m"]\n",
+       "    input_files:          ["/scratch/gbuster/sup3r/source_gcm_data/pressure_d...\n",
+       "    model_class:          MultiStepSurfaceMetGan\n",
+       "    model_kwargs:         {"temporal_model_kwargs": {"model_dir": "/projects/...\n",
+       "    output_features:      ["pressure_0m"]\n",
+       "    package:              sup3r\n",
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" + ], + "text/plain": [ + "\n", + "Dimensions: (latitude: 650, longitude: 1475, time_index: 175320)\n", + "Coordinates:\n", + " * latitude (latitude) float32 51.57 51.52 51.48 ... 22.5 22.45\n", + " * longitude (longitude) float32 -129.9 -129.9 ... -63.63 -63.58\n", + " * time_index (time_index) datetime64[ns] 2015-01-01 ... 2059-12-3...\n", + "Data variables: (12/19)\n", + " country (latitude, longitude) object ...\n", + " county (latitude, longitude) object ...\n", + " dhi (time_index, latitude, longitude) float32 ...\n", + " dni (time_index, latitude, longitude) float32 ...\n", + " eez (latitude, longitude) float32 ...\n", + " elevation (latitude, longitude) float32 ...\n", + " ... ...\n", + " winddirection_100m (time_index, latitude, longitude) float32 ...\n", + " winddirection_10m (time_index, latitude, longitude) float32 ...\n", + " winddirection_200m (time_index, latitude, longitude) float32 ...\n", + " windspeed_100m (time_index, latitude, longitude) float32 ...\n", + " windspeed_10m (time_index, latitude, longitude) float32 ...\n", + " windspeed_200m (time_index, latitude, longitude) float32 ...\n", + "Attributes:\n", + " full_version_record: {\"rex\": \"0.2.77\", \"pandas\": \"1.3.5\", \"numpy\": \"1.19...\n", + " gan_meta: [{\"temp_lapse_rate\": 0.0065, \"s_enhance\": 25, \"t_en...\n", + " input_features: [\"pressure_0m\"]\n", + " input_files: [\"/scratch/gbuster/sup3r/source_gcm_data/pressure_d...\n", + " model_class: MultiStepSurfaceMetGan\n", + " model_kwargs: {\"temporal_model_kwargs\": {\"model_dir\": \"/projects/...\n", + " output_features: [\"pressure_0m\"]\n", + " package: sup3r\n", + " spatial_enhance: 25\n", + " temporal_enhance: 24\n", + " version: 0.0.9" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import xarray as xr\n", + "\n", + "token = planetary_computer.sas.get_token('nrel', 'oedi').token\n", + "ds = xr.open_dataset(\n", + " \"reference://\", engine=\"zarr\",\n", + " backend_kwargs={\n", + " \"storage_options\": {\n", + " \"fo\": ref,\n", + " \"remote_protocol\": \"abfs\",\n", + " \"remote_options\": {'account_name': 'nrel', \"credential\": token}\n", + " },\n", + " \"consolidated\": False,\n", + " }\n", + ")\n", + "ds" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Data\n", + "\n", + "The sel() and isel() methods make it easy to select the data that you want. We will select some data and then load the results into pandas dataframes.\n", + "\n", + "Here we load data from the 2015 and the 2050 files. Note that:\n", + "\n", + "1. The 2015 year does not represent the actual historical weather in 2015, just the historical climate in 2015\n", + "2. Comparing single years is imprecise because normal variability in the climate can skew the results, we use single years here just for illustrative purposes" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# Coordinates for NREL campus\n", + "coords = {\n", + " 'latitude': 39.741,\n", + " 'longitude': -105.171\n", + " }\n", + "\n", + "# Select the point in the dataset that is nearest to NRELs campus\n", + "ds_nrel = ds.sel(**coords, method='nearest')\n", + "\n", + "# Take time slices for 2015 and 2050\n", + "ds_nrel_2015 = ds_nrel.sel(time_index = slice('2015-01-01', '2015-12-31'))\n", + "ds_nrel_2050 = ds_nrel.sel(time_index = slice('2050-01-01', '2050-12-31'))\n", + "\n", + "# Identify variables\n", + "vars = ['ghi', 'windspeed_100m', 'temperature_2m']\n", + "\n", + "# Note that no data has actually been loaded yet! Next, we load the data into pandas dataframes\n", + "data_2015 = ds_nrel_2015[vars].to_dataframe()[vars]\n", + "data_2050 = ds_nrel_2050[vars].to_dataframe()[vars]\n", + "\n", + "# Rename columns for convenience\n", + "data_2015 = data_2015.rename(columns={'windspeed_100m': 'ws', 'temperature_2m': 'temp'})\n", + "data_2050 = data_2050.rename(columns={'windspeed_100m': 'ws', 'temperature_2m': 'temp'})\n", + "\n", + "# Fill na in ghi with 0\n", + "data_2015 = data_2015.fillna({'ghi': 0})\n", + "data_2050 = data_2050.fillna({'ghi': 0})" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparing Differences in Temperature\n", + "\n", + "Here, we can see that 2050 has an increase in average dry bulb temperature versus 2015, as well as more extreme hot and cold events." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2015 Summary\n", + " ghi ws temp\n", + "count 8760.000000 8760.000000 8758.000000\n", + "mean 209.282654 3.378573 8.702493\n", + "std 286.178650 2.151689 10.412784\n", + "min 0.000000 0.010000 -19.379999\n", + "25% 0.000000 1.860000 0.380000\n", + "50% 0.000000 2.920000 7.610000\n", + "75% 402.000000 4.390000 16.850000\n", + "max 1023.000000 16.480000 34.540001\n", + "2050 Summary\n", + " ghi ws temp\n", + "count 8760.000000 8760.000000 8758.000000\n", + "mean 221.332199 3.564847 9.956614\n", + "std 296.225220 2.364556 12.077272\n", + "min 0.000000 0.010000 -25.789999\n", + "25% 0.000000 1.860000 0.630000\n", + "50% 0.000000 3.020000 9.220000\n", + "75% 439.000000 4.680000 19.449999\n", + "max 1036.000000 15.250000 38.079998\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "print('2015 Summary')\n", + "print(data_2015.describe())\n", + "\n", + "print('2050 Summary')\n", + "print(data_2050.describe())\n", + "\n", + "plt.hist([data_2015['temp'], data_2050['temp']], bins=30)\n", + "plt.xlabel('Dry Bulb Tempeature (C)')\n", + "plt.ylabel('Counts (Number of Hours)')\n", + "plt.legend(['2015', '2050'])" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wind and Solar Resource\n", + "\n", + "You can use joint probability distributions to visualize the distribution of syncronous wind and solar resources. In practice, you would want to use a tool like the System Advisor Model (SAM) or the Renewable Energy Potential Model (reV) to convert these meteorological variables into potential power generation." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '2050 Hourly Windspeed (m/s)')" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np\n", + "from scipy.stats import gaussian_kde\n", + "\n", + "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", + "\n", + "def get_density(x, y):\n", + " xy = np.vstack([x,y])\n", + " z = gaussian_kde(xy)(xy)\n", + " z = (z - z.min()) / (z.max() - z.min())\n", + " return z\n", + "\n", + "c1 = get_density(data_2015['ghi'], data_2015['ws'])\n", + "c2 = get_density(data_2050['ghi'], data_2050['ws'])\n", + "\n", + "ax[0].scatter(data_2015['ghi'], data_2015['ws'], c=c1, vmin=0, vmax=0.5)\n", + "ax[1].scatter(data_2050['ghi'], data_2050['ws'], c=c2, vmin=0, vmax=0.5)\n", + "\n", + "for subax in ax:\n", + " subax.set_xlim(0, 1100)\n", + " subax.set_ylim(0, 18)\n", + " \n", + "ax[0].set_xlabel('2015 Hourly GHI (W/m2)')\n", + "ax[0].set_ylabel('2015 Hourly Windspeed (m/s)')\n", + "ax[1].set_xlabel('2050 Hourly GHI (W/m2)')\n", + "ax[1].set_ylabel('2050 Hourly Windspeed (m/s)')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '2050 Daily Average Windspeed (m/s)')" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", + "\n", + "def get_daily_means(arr):\n", + " arr = np.roll(arr, -7)\n", + " day_slices = [slice(i, i+24) for i in range(0, 8760, 24)]\n", + " arr = [arr[islice].mean() for islice in day_slices]\n", + " return np.array(arr)\n", + "\n", + "ghi_2015 = get_daily_means(data_2015['ghi'])\n", + "ghi_2050 = get_daily_means(data_2050['ghi'])\n", + "ws_2015 = get_daily_means(data_2015['ws'])\n", + "ws_2050 = get_daily_means(data_2050['ws'])\n", + "\n", + "c1 = get_density(ghi_2015, ws_2015)\n", + "c2 = get_density(ghi_2050, ws_2050)\n", + "\n", + "ax[0].scatter(ghi_2015, ws_2015, c=c1, vmin=0, vmax=1)\n", + "ax[1].scatter(ghi_2050, ws_2050, c=c2, vmin=0, vmax=1)\n", + "\n", + "for subax in ax:\n", + " subax.set_xlim(0, 400)\n", + " subax.set_ylim(0, 12)\n", + " \n", + "ax[0].set_xlabel('2015 Daily Average GHI (W/m2)')\n", + "ax[0].set_ylabel('2015 Daily Average Windspeed (m/s)')\n", + "ax[1].set_xlabel('2050 Daily Average GHI (W/m2)')\n", + "ax[1].set_ylabel('2050 Daily Average Windspeed (m/s)')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, '2050 Daily Average Windspeed (m/s)')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", + "\n", + "def get_daily_means(arr):\n", + " arr = np.roll(arr, -7)\n", + " day_slices = [slice(i, i+24) for i in range(0, 8760, 24)]\n", + " arr = [arr[islice].mean() for islice in day_slices]\n", + " return np.array(arr)\n", + "\n", + "ghi_2015 = get_daily_means(data_2015['ghi'])\n", + "ghi_2050 = get_daily_means(data_2050['ghi'])\n", + "ws_2015 = get_daily_means(data_2015['ws'])\n", + "ws_2050 = get_daily_means(data_2050['ws'])\n", + "temp_2015 = get_daily_means(data_2015['temp'])\n", + "temp_2050 = get_daily_means(data_2050['temp'])\n", + "\n", + "a = ax[0].scatter(ghi_2015, ws_2015, c=temp_2015, vmin=-15, vmax=30, cmap='bwr')\n", + "b = ax[1].scatter(ghi_2050, ws_2050, c=temp_2015, vmin=-15, vmax=30, cmap='bwr')\n", + "plt.colorbar(b, ax=ax, label='Daily Average Temperature (C)')\n", + "\n", + "for subax in ax:\n", + " subax.set_xlim(0, 400)\n", + " subax.set_ylim(0, 12)\n", + " \n", + "ax[0].set_xlabel('2015 Daily Average GHI (W/m2)')\n", + "ax[0].set_ylabel('2015 Daily Average Windspeed (m/s)')\n", + "ax[1].set_xlabel('2050 Daily Average GHI (W/m2)')\n", + "ax[1].set_ylabel('2050 Daily Average Windspeed (m/s)')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Azure_workflow", + "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": "3.10.9" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/azure/examples/wtk.ipynb b/azure/examples/wtk.ipynb new file mode 100644 index 0000000..dff1cf6 --- /dev/null +++ b/azure/examples/wtk.ipynb @@ -0,0 +1,703 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Demo Notebook for Accessing the Wind Integration National Dataset (WIND) Toolkit on Azure\n", + "\n", + "The WIND Toolkit is available on Microsoft Azure's Planetary Computer in HDF5 format. Kerchunk reference files are provided to facilitate speedy access. This notebook demonstrates how to access and use this data. " + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, specify which data subset and resolution you are interested in.\n", + "\n", + "datasubset and resolution options:\n", + "\n", + "| datasubset | resolution |\n", + "| --- | --- |\n", + "| Great_Lakes | hourly, 5min |\n", + "| Hawaii | hourly, 5min |\n", + "| bangladesh | hourly |\n", + "| Mid_Atlantic | hourly, 5min |\n", + "| NW_Pacific | hourly, 5min |\n", + "| Offshore_CA | hourly, 5min |\n", + "| canada/v1.0.0 | hourly, 5min* |\n", + "| canada/v1.0.0bc | hourly, 5min |\n", + "| canada/v1.1.0 | hourly, 5min |\n", + "| canada/v1.1.0bc | hourly, 5min |\n", + "| central_asia | 15min* |\n", + "| conus/v1.0.0 | hourly, 5min* |\n", + "| conus/v1.1.0 | hourly, 5min |\n", + "| eastern_wind | 10min* |\n", + "| gulf_of_mexico | hourly, 5min* |\n", + "| india | 5min* |\n", + "| North_Atlantic | hourly, 5min |\n", + "| mexico/v1.0.0 | hourly, 5min* |\n", + "| mexico/v1.1.0 | hourly, 5min |\n", + "| philippines | hourly |\n", + "| philippines_tmy | hourly* |\n", + "| pr100 | hourly, 5min |\n", + "| seasiawind/v1 | 15min* |\n", + "| seasiawind/v2 | 15min* |\n", + "| south_atlantic | hourly, 5min* |\n", + "| vietnam | hourly* |\n", + "| western_wind | 10min* |\n", + "| wtk-us | hourly* |\n", + "\n", + "\\* data migration still in progress" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "datasubset = 'south_atlantic'\n", + "resolution = 'hourly'" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we load the relevant kerchunk reference file from the main wtk directory of the oedi container." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import planetary_computer\n", + "import ujson\n", + "\n", + "fs = planetary_computer.get_adlfs_filesystem('nrel', 'oedi')\n", + "with fs.open(f'oedi/wtk/{datasubset}/kerchunk_{resolution}_ref.json', 'rb') as ref_file:\n", + " ref = ujson.load(ref_file)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we can use the references to create an xarray dataset object. Note that no data is actually downloaded until you query this object. Also, the planetary_computer token expires after about an hour and you will need to rerun this cell to regain access to the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset>\n",
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+       "  * gid                           (gid) float64 0.0 1.0 ... 3.284e+05 3.284e+05\n",
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+       "Data variables: (12/75)\n",
+       "    boundary_layer_height         (time_index, gid) float32 ...\n",
+       "    country                       (gid) object ...\n",
+       "    county                        (gid) object ...\n",
+       "    elevation                     (gid) float32 ...\n",
+       "    friction_velocity_2m          (time_index, gid) float32 ...\n",
+       "    inversemoninobukhovlength_2m  (time_index, gid) float32 ...\n",
+       "    ...                            ...\n",
+       "    windspeed_300m                (time_index, gid) float32 ...\n",
+       "    windspeed_400m                (time_index, gid) float32 ...\n",
+       "    windspeed_40m                 (time_index, gid) float32 ...\n",
+       "    windspeed_500m                (time_index, gid) float32 ...\n",
+       "    windspeed_60m                 (time_index, gid) float32 ...\n",
+       "    windspeed_80m                 (time_index, gid) float32 ...\n",
+       "Attributes:\n",
+       "    identical_dims:  ['gid', 'latitude', 'longitude', 'country', 'state', 'co...
" + ], + "text/plain": [ + "\n", + "Dimensions: (time_index: 184104, gid: 328443)\n", + "Coordinates:\n", + " * gid (gid) float64 0.0 1.0 ... 3.284e+05 3.284e+05\n", + " * time_index (time_index) datetime64[ns] 2000-01-01 ... ...\n", + "Data variables: (12/75)\n", + " boundary_layer_height (time_index, gid) float32 ...\n", + " country (gid) object ...\n", + " county (gid) object ...\n", + " elevation (gid) float32 ...\n", + " friction_velocity_2m (time_index, gid) float32 ...\n", + " inversemoninobukhovlength_2m (time_index, gid) float32 ...\n", + " ... ...\n", + " windspeed_300m (time_index, gid) float32 ...\n", + " windspeed_400m (time_index, gid) float32 ...\n", + " windspeed_40m (time_index, gid) float32 ...\n", + " windspeed_500m (time_index, gid) float32 ...\n", + " windspeed_60m (time_index, gid) float32 ...\n", + " windspeed_80m (time_index, gid) float32 ...\n", + "Attributes:\n", + " identical_dims: ['gid', 'latitude', 'longitude', 'country', 'state', 'co..." + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import xarray as xr\n", + "\n", + "token = planetary_computer.sas.get_token('nrel', 'oedi').token\n", + "ds = xr.open_dataset(\n", + " \"reference://\", engine=\"zarr\",\n", + " backend_kwargs={\n", + " \"storage_options\": {\n", + " \"fo\": ref,\n", + " \"remote_protocol\": \"abfs\",\n", + " \"remote_options\": {'account_name': 'nrel', \"credential\": token}\n", + " },\n", + " \"consolidated\": False,\n", + " }\n", + ")\n", + "ds" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output in the cell above lets you browse the schema for the data set. Let's pick one of the variables and a snapshot in time and generate a map." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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xDAaDwWz5ysoKT37yk49b/2/+5m94ylOewv79+/HeU1UVP/ADP0BKiU9+8pN3eLx3vvOd7Nmzhyc/+cm77vGDHvQgTj/99DudiXasPelJT9r1/T73uQ8iwhOe8ITZshAC55133q7w3Dvf+U7ud7/78aAHPWhXux73uMfd6Qy5yWTCM57xDD73uc9x2WWXsby8fNw6t/UcHbv8zjxvJ7NuSum49/BLadfJWM5517FSSne6vV8t+0pfV4Cf/dmfJcbIhRde+FU/1j8FW4CZu4n1oaMrr7ySP/uzP6PrOv7lv/yXPOYxj5mFXq688koe/vCHMxwO73B/+/fv3/W9aRqAmdv+1ltvBeD0008/bttjl33/938/r371q/nc5z7Hd37nd3Lqqafy0Ic+dAaybm/bfll/vBtvvBGAf/Wv/hVVVe36/Mqv/AqqyqFDh7j11luJMfIbv/Ebx633xCc+EWAGqm699daTOo+vpO3duxcRmZ3XvB06dAiAffv27Vp+7Pe6rm93+R2FAp71rGfxP//n/+S5z30uf/RHf8SHP/xhrrnmGk455ZTZfb6zdvjwYXLOJ3U9P//5z/Mv/sW/4Itf/CIve9nL+NM//VOuueaaGafpZNpw4403cuTIEeq6Pu4+33DDDbcLnE/GTnRtR6PRLqDWL5+/3jfeeCN/93d/d1ybVlZWUNWTbtd0OuXpT386f/Znf8bb3/52HvrQh+76vX9Pb+s5mm///v37T+p5u6N9igh79uwB4Ju/+Zt3nd9//a//9U6362Ttv/7X/7rrWN/8zd8MfGnv0lfS9u/fz2QyYXt7+4TH/1LuwdfDsb7RLNzxKgv7erCzzjqLe93rXlx55ZWce+65PPjBD2bPnj08+tGP5kd+5Ef4i7/4Cz70oQ/t4op8OdZ3VjfccMNxv51o2aWXXsqll17K1tYWf/Inf8ILX/hCnvSkJ/HJT35yF2nvtvZ33nnnAXDgwAHAdBa+/du//YRtO+2004gx4r3n+7//+/l3/+7fnXC9e9zjHrNzOdnz+EpZrx3ykY985LjfPvKRjzAcDrnnPe/5VTv+0aNHeec738kLX/hCfvInf3K2fDqdzjq7L8X6geVkrufv//7vs7W1xVvf+tZdz8Df/u3fnvTxDhw4wP79+3nPe95zwt9XVlZOel9fSTtw4ADD4ZBXv/rVt/n7Hdl0OuVpT3saV111FX/wB3/Aox/96OPW6bkTH/nIR7jvfe87Wx5j5BOf+ATf8z3fs2vd3/3d3yXGuIs30z+D97vf/QADKMPh8DafzfPOO28G5t7xjncwnU5nvx88ePBOt+tk7fnPf/4uT1k/wbqr36X5c50Hmz2Y7q9rv+5ttRPYte5dfaxvNFt4Zu5G9pjHPIb3ve99XHHFFVx88cUA3Ote9+Kbvumb+Nmf/Vm6rjsp8u/J2Pnnn88ZZ5zB7/7u7+4iM37uc5/jAx/4wG1ut7S0xBOe8AT+83/+z7RtexwJ7U1vetOu7x/4wAf43Oc+N8u4evjDH86ePXv42Mc+xoMf/OATfvrZ86Me9Sj+5m/+hgc84AEnXK8HZI961KP46Ec/yv/9v/9317Hf/OY3fzmX6A7t6U9/Ou973/t2ZadsbGzw1re+lac85SnHETW/kiYiqOpsQOjtVa961XHu+2O9crdnS0tLPOQhD+Gtb33rLk/FxsYG73jHO45rw/z+AVSVV77ylcftt2maEx7/SU96ErfeeisppRPe4/PPP/8O9/HVsCc96Ul8+tOfZv/+/Sds1x2JSvYemfe9731cfvnlPO5xjzvheg996EM544wzjssu+73f+z02Nzd5xjOeMVv29Kc/nc3NTS6//PJd677uda/j4MGDs8ExhMCTn/xk3vrWt+7K6Pv85z/PVVddtWuf97///XedVw9m7ky7TtYOHjy461jzJNi78l16/OMfz2AwOO5c+wzNpz3tabva+YlPfGKWOQYG8N74xjfy0Ic+dHb9vh6O9Q1ndyljZ2F3yi6//HIFFNCrr756tvzSSy9VQPfu3buLQKp62wTgF7/4xcftn2OIea961asU0Kc+9an6zne+U9/4xjfqeeedp2efffYuUt1zn/tc/Q//4T/oW97yFr366qv1f//v/60PetCDdG1tbSbA1ROAzz77bH3Oc56j73nPe/SVr3ylnnrqqXrmmWfqrbfeOtvfG97wBnXO6b/+1/9aL7vsMr366qv1937v9/S//Jf/oj/8wz88W++jH/2o7t27Vx/ykIfoa17zGr3qqqv07W9/u/76r/+6PupRj5qtd/311+spp5yiZ555pr7mNa/Rd7/73fq93/u9evbZZ39JBOD3v//9etlll+lll12mg8FAL7rootn3ecGxm266Sc844wy9//3vr29729v03e9+tz7ykY/UlZUV/fjHP36H96TXwbnssst2Le+v5bHk02PtkY98pO7bt09f+cpX6hVXXKE/8zM/o2eccYbu2bNnF2n5M5/5jAL6tKc9Tf/0T/9Ur7nmGr3llltuc7/vfe971Tmnj3jEI/Rtb3ub/t7v/Z5+27d92+x69vbxj39c67rWiy66SN/97nfrW9/6Vr344ov1W77lW4677j0h9xWveIX+xV/8xezcYoz6hCc8Qfft26cvetGL9A//8A/1yiuv1Ne+9rX67Gc/exdR99nPfrY2TaNvectb9MMf/rD+3d/93a7reyIC8M0337zr3G6LCHrhhRfqBRdcMPu+ubmp/+yf/TM966yz9Nd+7df0iiuu0D/6oz/SV77ylfrMZz5TP/ShD93uvXnSk56kgP7n//yfjxNqO1bk8g1veIMC+vznP1+vuuoq/Z3f+R3ds2fPbYrm7d27V3/nd35H3/e+9+nznvc8BfSNb3zjrvU+/vGP6/Lysj7ykY+c3Zv73e9+d0o07860693vfrdedtll+upXv1oBfeYznzl7Z7a2tu7wWCf7Lqlav9Dv+1u/9Vv1lFNOmX2/LQHR2yMAq+4I2f30T/+0vv/979cXv/jF2jTNCYXsLrjgAj377LP1TW96k15xxRX69Kc//YRCdiciAH+1jvVPwRZg5m5khw8fVuecLi0tadu2s+W9UNV8tkVvXw6YUTVA8y3f8i1a17Xe61730le/+tXHiea97nWv00c96lF62mmnaV3XevDgQf2u7/qu2WCiujMAv/e979Xv//7v1z179uhwONQnPvGJ+v/+3/87ri1XX321XnLJJbpv3z6tqkrPPPNMveSSS44b2D/72c/qv/k3/0bPPPNMrapKTznlFP2O7/iOWXZUbx/72Mf04osv1sFgoPv27dPnPOc5+gd/8AdfEpi58MILZ6Dy2M+x+/rUpz6lT3va03R1dVVHo5E++tGP1r/6q7867hy+GmDmH//xH/U7v/M7de/evbqysqKPf/zj9e///u/1nHPOOS4D66Uvfane4x73UO/9cQP/ieztb3+7PuABD9C6rvWbvumb9Jd/+ZdPKJr3jne8Qx/4wAfqYDDQM888U3/8x39c//AP//C4a3Xo0CH9V//qX+mePXtURHbtp+s6fclLXjLbz/Lyst773vfWH/qhH9r17Fx77bX62Mc+VldWVhSYPaNfDTCjaoDmZ37mZ/T888/Xuq51bW1N73//++uP/diPHafie6zd1vMDnDAj681vfvPsep9++un6ghe8YFfWU28bGxv6ghe8QE8//XSt61of8IAHnDBrSVX1L//yL/XRj360jkYjXV1d1ac97Wn6qU996nbb/aW265xzzrnN8/3sZz97Usc6mXdJdefenuhzW1lUdwRmVFVf9rKX6b3uda/ZM//CF75wVz/c2w033KA/8AM/oPv27dPBYKDf/u3frldcccVttvNE/c9X+lj/FExUvwRBhIUt7E5ar1lzzTXX8OAHP/iubs7CFrawhS3sG8gWnJmFLWxhC1vYwhZ2t7ZFNtPCFoYRU48lxh5r3vt/ctoNC1vYwhZ2d7BFmGlhC2MnDHZ7dtVVV+2qc7WwhS1sYQv7+rAFmFnYwjDxr89+9rO3u875559/l+maLGxhC1vYwm7bFmBmYQtb2MIWtrCF3a1tQQBe2MIWtrCFLWxhd2v7hicA55y57rrrWFlZWZA3F7awhS1sYQu7m5iqsrGxwcGDB3cV4T2RfcODmeuuu46zzz77rm7Gwha2sIUtbGEL+xLsC1/4AmedddbtrvMND2Z6wuYXvvAFVldX7+LWLGxhC1vYwha2sJOx9fV1zj777JNKvPiGBzN9aGl1dXUBZha2sIUtbGELu5vZyVBEFgTghS1sYQtb2MIWdre2BZhZ2MIWtrCFLWxhd2tbgJmFLWxhC1vYwhZ2t7YFmFnYwha2sIUtbGF3a1uAmYUtbGELW9jCFna3tgWYWdjCFrawhS1sYXdrW4CZhS1sYQtb2MIWdre2BZhZ2MIWtrCFLWxhd2tbgJmFLWxhC1vYwhZ2t7ZveAXghX19mKqiCjFGcla6NpG6TBcjKWZiSrRdoouJrkvELhI7pesiXcq0muk002aly4mYE13KdCkTUyamSBeVlBIxKzEl8jQTRWmrTJvte1IlaYapkmOmQ0koSey33GVkO+E2Ev5oC5NEFKWrHZNVz2QFkge3mSEp7UiIQcmquLESNhLqlHbJ0dWKJBh9sWP4yUOEw9vQ1LSnrbJ1+pDpPkcOgAJx7mIJZAfav50JfKv4FiQqCuRgipguZarNRHNrBAeTvRXT/Z44dDtTFQXJ5ThJCZNMvZGp1hPV0ZZwaBM5ugmqsDRCVWFrDMtD0r4VurWGrTNqxmd4JCmDw5nqaKY5msgDYbKvYrrkoBJQcFEJE7X9byb8NOGmGcmKqAL2LMCcsmdWJCvqBa08ioIIKiCdbZudoE0gDzzZO/B2rcCui5tm/DQiMeHGLdJlNCvUHioPGYgZ9Y40qkhLFXnoUazNonO3ICtuEpFJxJVt1Hv70Ykdt7QZlFw7ch3Q2oNmXKdIzEgCQUlO0MqBF1BFuoxLICmj/T5VkS7hkqIi5NqTKw9ekJjxk47shFwFtHJocOAEnb/PO5fE9nsC4VSZ+6vH/C5qP4jIzs6cIE6s7U4QwHdKmGZr6ygw3Rvo9nq6vY60AloLUvbjnCAefHA4Z999EJwXvHN4X5aJ4MXh3c7fgMd7oXIO7xzBOYIXgtjfyns8Qu09lfME76i9rVeJp/KOynlq522Z8zRl3co5Gh+onKN2FUHs98o7gniCeCrxdg7iFoWKv85tAWa+iqaq5KzEmGhjop1E2mnHdNzSjjvaaaSbtkwmkek0Mp10tG2knXRMp5FuYuvFSUvbRiZR6ZzagJ6gS4mYM7HLxBjtb9uRJ5E46cgxk7KSgSSgSdGY0C6jbUS7iOZsHfx0ik5amLaQsw1oqogI0jToaARNBc5BztBFdDKFrgPvkLqGQUMe1GgdSI0nNR6tBHVig0Oby8CWkJjI3tnAVTnUOzueAqpoELL35IEjNUJqbOBXJ2S/+zr3HbCWASaMlTQU2mVBK6g2QT1kD9UWSIJU2zItoECSUm1mqm2lPtIRjk6JqwO0EqZrnu1VT66F5oaMn8J4v6A1EAW3kRkcVaSDo6c6ciNAYnh9x56/OYy//hB50LB15j7GZy0xPuDRgUAGPwVFbWBBSpvUgEgWpIMwVnxUUEgVqBdcZ9+XvzDFTTLTfTXj0wLdiuyMVnnu+qiBgmZDaY4m/GakvmUbObwOW9uwNILlJXR9A9qOfOYppJUB0301G+cE2j3C8hftHg5vTrhJZHxqw3SvJ9d2/e0eQ9jONEczYRzx04R0PYjp34sykHqBVACBKloGbwM1HskZN012b2tPHlZo5cgChLnzKs+Vn0aICTfuICYQB6PGwMCkQ50jN4G00tANPdSCdIpPPbKy/7ku4yYRN40GqOoA2CCuoTynMSOqqIc0qMi1B2fgxLUZSRlJijohlWccUQNdrW1LVhRFK2fvRGc3LFcerTy5tmP57SkkSE2w9yU4e07c3OCqx4CYueUqu9fpf88yt115VlQUcvlbQFsu75VdayVstuROGQ8Dk1Mrpmsw2Rfp9kYbUSJoBByos2fZ9gfMvpcHcx597TqJuZY6wO08zFK2kR59lmXidLZcsO9C+U1s/yLgJONc/1fxLuNE8ZLxLuNFcdL/tU/fLoc9dF507vdM6PdFnm3jSzvA9hUk41CCK+uLrV+5jCdRudIGIIjiMfzoRQiiBIRKhCBQiSOIGPgSb+DOhfJvT5CAl4CTiuBqAhVOapxrcDQE1+BkgMgALw1Ohjg3xEkDNDg3AAY4NwKGiNSINEBAvk6B3QLM3EmLMfGeP/kYr3zjn3HkhnXCeoffnOLaBCi58qRhRVyu6FYqcEoYZ8J6JGxOceMWUkKcQ53N1JT5AUiRnK0zTvYCa1OhywNS7a0DrcJsNiaZMkvMuC4jbUKwGa16Rw62jSQDLRINSEjWGShhPIG2g2juARWBnJG6gqUldDhAy36ICZ22MJ2gKSNNDVWFjgZoY516HgRS7QzIAK5TfJuQacZNO5uNVt5mzMHboIYNhoigwZOCIw+FWDnywACROmZARrEOSjKzztEAidKuOrqRoLVSHzEAkCuoNwzI5BoIYp4PLdttGZCpjkbC4Qlx7xActKue7dNswG6OZsJUGe9zaG3HDONMc1TxE9g8w5Fru5fNkciej6zjrz+MNjXp9D1MDy4x3e/Rxo7r23ImNilHnQ2OMyATIUwVl8qjURn48lMbBFe+2OK3E+3emvGBQLfcP0cyGwdm/X3K1FtQryf8dqY+PEbWt2BrCwYNrCyh4wm6sYl800F0qSGuVoxPD3RrwujGjGthcNi8H5NTGto980AGXAd+mqnXM75N9jxGLSdXzrMHbsEhSe3+pYwG82xIyuQyiEos21SONAjkytn5BZmBXunsWH4aoXhTSBnBPBsEB9Nu9lzlpZo09FA7XFRcnvdgCC6Wd2hq70L2s5EQvA1lvYcp++I9mQEZa08PZBAxj04o55PUrknu77kBODeNBmSUGVjJldj5tPZbHtYGYoK9B/NAZt6jtNvMw3Oidcy/1z94/Y7YAT89kOm9UKgBme2E65Q09ExPqWnXHNP90O3FPGX97m4PyPQNOhbIzP7MoSxRAyn0/2anPf3zNLcf19+u8teV3Qm2nx58iGMOuBig8S7j6YGMzdukfy5K+5woYQ7EOMkEZyDFzYOgfn0UL6mAmURwiiPjJVNJxkmiEvvegxsvGSGXm5Zw2HchIWTzJWtGiaRs6yiKSiKjROmPofjyvkkPwGDue3md6K+VzG6JPQIyeyz6W6Nqk+OMkgz37jw+sszayr9jdfn5BQR9bW0BZu6ETaYdl/74G/jCtbdQbXbUG525s2NGnUOHFWlY0a7WxJHDZXO1h80OvzGxDlIzeG9AxsnuWVTOSDTQQc724te1AZmBx6VMrkN52sQ6TCidXsZ1CXog4wzIoGXmmwwkSQFIYMCEabsbyDgHMSJVBcMhOmhsQCjtI0aka9GsuEGDOg+DBg1lxlg5chDzrIjgklo7sw2osw679qj31lmWgcmOX7ZthFSJARnfd+DHTNzy7ExQwHeQBkJqhNzojkdmaN4Z30IcGCDS0qkK4KINxK5T/Lij29ugjZAGnsl+G7AlQ7WuxJEjDSggiBL6gXZFZoOWa5XVz7aEGw7bAUZD4r4lupEjNdhRU2m5ljm/qAHU2fUAlxTXlY44l4GlhDV8meHnxtGteNJwbtA9BsiQ1c6xzTaAtxFpU/GqebvPKRuQOXU/aWVAXPK0q4HJPvMW+Ynt2E1zCXWIDfT9OWQb5KttA18u2uC9a7JdzkuFHYCdki1zgrTmCcEb0CBlA/s98O9PrffyZMxD0mWbBEy62SRAvYMqzN4prexZy8GRpW93/ynXKytExXUJEXvuEDfz/OXifbLQkjVVg9txd2S1532nd7dnog8t9V4ozCsjTgzj5d4DYeuqFzs3VfxWRx5UNiQFZ967HsjsXP5dA46Uf82aoTsgcnYNDQnMJj6S+36Bub0VNFDAqu8UP00WrRsE4khItb1TPZDZ1Zj5v+VzQo9M76rrF+jOQyM9Lp97vOeP4ZztT+bQmv1s90F774j0A7R5m30PWOaAjEMRyebZKQDIrpHOvD1+BmIMvBg44YRARuaAUehBEOaVCQUI9UDGPDJlfZK9BuwAnTD7d8SjVJJm21RoAUOZgBYAZX/7c/boLOosBdQ4SuSQ3UDGzU2uXXnIMjtAJs0BmXlT3eTI+q+wvvm/OPP0P8G7vXwtbUEAvhP2X17yDgMyW5Gw2eEmnXWUImjtSXUgDgJ5aA9GGGf8dsJvdzZ45FzePuwpmsWly4NU3O3WwWdwHhoL2bg2WfxcZKdDz9m2zzoDKYqCs3i6AE4LiMnZBodsHSmxDCQpgZo7vA/1ELyFlJoaCQXL927xGNGU7Jydg0GFNsG4DCVMYB284FSNixAzUsCW0B+rgJVgH5x5HWzmKTseq97LoDt9pe5cth2QoxBaJQ0EDQYMXNe7yG1w1b5jLte971d7voSLip9ExHvcVM21760NYQJaQbfMbID2U/u4jIV3nIKzjtBPMrSRLFhoo8GATOmRXZnSmIdNy2yYWUcvqXzKSSdv64VWkTYzuLmDrMSlijgyT9Fcb7Rzwco/XWvPo5tm3FaHtq2FGJsaHTSIZpi2SFXZM9MxGzSqDfP6VVsZddDurWiXxAawvsllIulSPwgUgKFa7tlsNDFvhTILcaJ55p0r/n9blu3ZVmegVPsZdwaS4tq0A+K1DMglpqBuDvlSPBnBoRWIB4k64xH1809Rnb2HkjJawNhs9ik7OMrAsMw8GKLs9n6IkKWcdwE69O835bxEcG0s12muT/AG6HA2gKh3qEDqryE7++nbPPvO7jbM/rlrzl3AVdmPqLLj3tg5px3OVXmWUuFrDRxxyZEriMuQG46Zos8dZhaimUe1x3yOXTYHZGbvabm4s036/kClgGRB6Hk6OttGRMpf++z8Z4/orFnlerj+9GcuzR5QyQ5o6p8D7a9N2ZbdQMbLXMjK7YAk44PtnPDO5TGw41ACiVoylaQCZAy8OCxctXM9DIBB7nGntUMLqMEATii3M7BDNes9M8Ixk+rZ7ZfSNpk921pu9bFAZt5yvoUbb/7Xt7PGV8cWnpmTtI2tCR/4q8/MEL91eFpmcG4WNkkjjzrBtWqflJEY6fknfaeuPbgA63xnHpSyzPvi7fAFOs/PWowQiAjEAhJiAUpl9mOd6NwT2L+0XkpfKBDLm+qkTDsFsoXA5vpLw+ZqHSveoU0NdUB9gEFdOt6d7rI3LQOBFwvpZJyROOvCk3EO+usZ7UiSC7+ndGo9WMn9VIICZvq3rwcosMtTI3nndxftN+1xWQEPMpsJsnNPy31VLDy1M+u2GWiqd/Ytt9GB03sdRKCpias1cejI9c7q8327zO1mtrQMILnsMwdbx00ViQnflU417DxTO2c/t0NV/DQTJsblIBXwUAZN8a7vrXdtqhiAkjZTH7bQlIsWRotDZyG6XSPnMVa8hqmckgNy7+FzAl0umEvLA9sfWGennz1oY2GcHPqO1a6LpDlPx+xh2X3eBtqjgX7VGaeKlHF57hnpB9BdPXq59g7wFPAuOGfHV3Fz93v+uHM76NsgClHLBKIMdlln7/wOCJLdf1OJL2oyBLbrYWPnvG/rPsyui+68M8rxXpRjdjs7/9yDHJBWSx+D8ZdqOe69nO2j/8xfX4+R3Pt1Z+Ei2JnUHbMf1cLdwwbUHuiXbaQAAHpPm+xs2oMGzRRAUwCKk5nHYodLY32B6z0ys+W6cwtLm7WE9Q1z7wAgmTuNWStE6P1jWftuXPFqYCNINo4NOgsz9efW7ynveiayTRisJy0emmz7E8p56awdinndcg+Yym/9nMn+Ck5k57bsAsB2DbNY35+wfen8M34b1nYfoYvXUYWDd7zyV8gWYOYk7Y//7BO4NuHHEbfZmUs7FZKht08/QKgT6ylVbTaTFUmpzAaKN6VkL9jT1RNgZPa99x4oQJd3ZgL9YKnmOBbYPSOE2Xs9618dSFXc9WXAEEBSnAsy9yvPIZjy5CcH4g20ia/QbC586c+371vnOqR+nPcOUiU4VzJ3nBQCow0GUmZWvddkNqPDBpNMD86gnwHP2nfseffrcMwMLrEzs+5XVWYuaPUGFlJlbUu1kFNxraaMxNIeh82iywDhLKpng+4c6KHvRCohDj1xIMTAXOZICSml+YbO3by5W5FK+3DmzZGUceOIZrEMmsp+136DfvPi1UBBxopMtXg51O7HMIAb2Hql46apwDty7emGjpwzMnZIp2jMxFpIQ0c3tGvReyP6EBO6c465z7KRcn1FzGvnBJfLLHasEM1bJ16MR6YecUJyDvXW1lwLUrwULhaeTDIPlbQR6ZI9q6mEZ5MiOdkzlpIBmon9JlLhqoB4bw8nfRhUCo9MUeeIlXkOjYVp77b48k5IIQEnW19mYau5W5hSAW0KsYQM2zR3f8pz1Ide54fBVPqOqXlOdSp2TpotK0u8Pb9h5907Fs9oAcJ9l9HzdOyBnwdDdmyXdSd81d9Hnfuw8x5qP8KrlHvPTkZVf/557kOZixVwaKFkLSCRHWDTN5a540Zm/ZmIA1++uML6cf12cx2CzPCrbTsDQg7ntRCDjTfjjr1wpQm5OLE176zg1c3IuKoZ8YrDgeTiVdlpvlEStWD1OYSkpX9XxUt/8qncmp3QENijMUv2E0p4zCaFPRFZSoN7IDSjOAFZc+mryj1AZvfB9x7icgHmwU7/VChiUe0e/M3dmpOxzc3Xs3fPT57k2l++LcDMSdon/+F63DjitjvcpN3phG4vUJczSgEiBaVLLli9j707xy6XQnmLVEGTQ3OChHkxVHc9Vn1GxOzN7b02zIGAfmapDhcsdCOacU5QClkRNS9NVuNQzFz19snOFd+kKzMNheCRLManKd4pvCPP8VvABmNxNrCrBMSJDYRzL7doJpe0VxHjYuTKQFTBXrv5MdKDwJ0ONntIjcwm+TPHVAda7wy+PRdB1QYnBWJdOlg8rmtoV41rFGvLbKGEXGSaqaIYUVVtMM61s0FPQboykZ9Yx5mrQBpVdAMhNaBeUZdnGJZknhb1NijMurG+j8NIpvgdsBcbh2sVXXaklUA3EuMszM6t7EINOEsEyYlcObqhg5Ej7w2ESYIMfpJwMeM3E86tEUeBdq0irng0WIp1HHhYNv5SGpYZccJCatF4Pa6QeXcAtJC9s3E/2P3PA0cuHBRRT+0UqTBA4tRSu4OglRgBnN4DZDN0H9VSryfJgMy0tUlFF8sIpDvvAeW98M4A07TDTTvc2NuzujwkrQ3Jldg5RJtsiAq5MU+QwixzSMRmyeqV3NlZ+phmzyTKHBHdQoxunjxbkEXPGxMF7UqYF/OQqSokQTqZAaLsHdJFGLd4ZyFLGTbWbzQlpFsGrBlY7p99mA36O56Y0g5V8xS5EmToDFAi2AQll39LAalzMySZRPyWx9dC2JSSZWj3WR2IL5e+A+mM2zd7Lr2Qgt3rXIFWzMLC9P2GM/BGJ7h+RpPZCel5Rb1CBRpyCctp+dhFSFEhOkhlsqQe5xRxGeeVUGVCyNb2LJYdVzwbWRS7hYEuerKWRAaByil1SNQh0Sg0ffKCU1QtLqxZiUlIBLoss8hhLYnKJwa+Y+Bkx7NTZmD98TUpUSERyCrglJpM7RID6Rh4m11Z95lnI8IMTGUlZmgJxUvjLFMKpVbwGKemf0TmRpQC5LTMg/JsrttjxtsLLx1rbft3d2LtL98WYOYkba2pcdstsjWF7Q6cn0OwZipYxyggqX8JtbykhuCzlM42ZzTmGbIWmfOalFCTOIFo6ZhoRnvWqjPXn0SFnKwzt9fABsR+oEcK4LGHPjlHroykay76gEwHuLazAWUa7W/KSFXtpKFqLoAKqBypCUYunk+R7jvBPmW0zMZxrkwxei9IGSSc7HReOKhKv1m2VW+UodRHQWYTJJ0RMvsevO8ou5Il5KbGl5m5Wzvr5KyDtwHelUm8vcBCJtONhFTVNgunhFlStk65zbhxhzjP9BRPXIVuaIObS2LcqIniozD8xw1Ijm7PiG6lol3xpNpm6JIU8c4IldNM2M50Q0/qvQ+CXbvC6zAgapcoedAVgbXRTmSmMvDo+llcDzQUiIofJ1wHMSi+Kh390JOHYeYpQ6E+OmRw04TcTcnBSNR0CWmdtbcAQERwW8lAby3IVE1zpNtpZ6wEr47cx+FKWoiFKQF1ZKe0B4awd2CDb+lRRc0zkgKQEn4qFkbtM5c2O2R7ikwTLicjr7dlciGUqXrZVxvRQW0eSRHjjqknRzWicFZUHFpbyDM7A2VKCeOG4kV0WjipPQDPkC0l202SzcxL9lKuBNcCMdHzeqUAFqpgE/PiAfJgg3ZO6NQGZcHcBZrVJgq1Q5wzXtDGBDmakZVlGNYkrctzozMi8nxf1AO83vsx8xA4kFgydPpEgj6ES5kTZQM6muamTypImwhdNI9q1ZjXIwqIFuJ/CUUkeyYLjQzNJUzYT5JC7w0tMgyBHW9NBlXB9ShM3SyrqAdV4qwfVO+LnEG2PiSkMuLmwqeR4lIVEjvn2QbAZ7xXfJUIIVJVincRTyJlSOqJ2ZGzI5Vn37wjiSoolY80oaMOiWHoGIZIRQRNxByYaiCqbStYiDJIpnaRUYgMfMvQR5Z8y8C1DF3Ea0dWpdOyPZ6cHc6pgRmXGKUJQ9cych0rats2EmkkGddLPWMqxlqR1MLQVVYqiQzIDHxikJUBmSyU7KwSCk5C0kxE0UIUrgoN2NEDrpPzzjg3PIm1vnK2ADMnad/2rffgza//E1zOaNuS15ZLxwTWs5mXw2/bjEzyDskTBdVkg3VdGXdGdQY4LBDrLOyRFekSsjFG2g6cwyGoM5ehxTCdzeZRtK7J3tyo2k8fSkdh+i4OjQqTjE8RHx20gsQEzlkq+SiQKm+u8EkL02gDkHeFOAxMo83y24QfJ3xdARmZTmfeEh1V5NGAvNzYjEIKMKkEsswGO8QG3NzzNvqMHe09LbITWupnnMVNbu0RtO7nEjoXKjHvkp8jVKfenethFhrQjKjD97O92UjKbPaXnNjMrwV3NOIyxEHRQAmZbjXjNyCMhfqmluENE0ILzgXk5g22738q7fKIrrGZnosQJpYtlZuKXAnJC2nNsqPsfikulhFJYLCZmQwcuiKkYDFxis5Org385WDfNSphjInTTYv7OyVoU0FG4NtsIRlflzCfDb6WMixUGy355sN4P8BNKnIX6ZY8eSVYhlIWcoLRP26QRgOmpw2pJhk/zuS6DCoZXBC64BGXIBaRte2I20poU++EGVVn4m9a28DpErNUXpkqbpwIbbZ2ZruHrkswHhvnpA/RQvFyzne15lWg9rNnRKvayOrLtekBFRdeDt6OMc24NpnXBA85QuUQ3RmwESnp8vasuO1oqdp1MO+E2LPX+zFnEwQBKpNrKE+uARYFspsN9vYOz01IeqKqOHzbmqCh9+SR8ScUZ1yu8q7IHAdJogn44QQduJIZaO0ucn1FEE9n8gdKLtpA2bLSomXBSTZpB4egk0gY1+Ay2ZbseEr7iQ8YX4USDpNs/UQ5R/X2rJjnrnh3eg5VP9GZpWbLjtdGxACPKJRIP62BTtTPvDwz3Qrpr2vfV1ifq8kRnRJbz9RXZTTP4CybTUo7BHa8bgLROdoEzlV4V+M8VKaASEW08NXsuP3hFTenZ7MRzdNSOaV2kcpHgiY8Hc73AM46TBHrtypnaeA1IyqXGLjIQFpGvqORlkZbFEvgTpbkjaLlccrUdNQuUefEwLXUJBqXqSUzIBvILl5vEaUCRhiY6SeRSQQvOgPqt2dN9bA7XukraAswc5J23wvOxiWxl9sH4sCTVmojUrWWkskkGg9AZl2VdYDDALUjp0IAnHaWOeIEmsYG2srCF7kS8AG3OsJNI65NpCAQAlkjtK64+E2RVAFdqgxkHN6yAacx3oEGZxo1XqBSy4jxzjpaBTeN+C5DF5Al6/CrW8YwaS3zw1lbCNYJJgEfsY6oFhM2C0ObFXcdiIWepAVChlC4PyrW8cz81NZROfOqIw4kQmxKJ+v7fkB26DydOTnDxFz2nXclPICBlBI+y7XMPFUSPAQxLYbiyfFbEd2e4Jq6eIdMi0RSJlUOpwJJyXs93TKELc/wyJRu4GAYaEcJ304ZXq8MP9cR2gpSR71hAm9SweSc/aRKCGOIA0euLV04BcGraZlocLR7nJGMMaG/+oj1zKnx+Kh0K1K4EYqblJh8IZ7OtDuAsK3U65ZtVG9k86iInZNsteShY7p/gJ9kZLMF8bT7bcYmUQnb5sFJlUPPOkAalVTgTqjXO7qSWVNvJlx0hPXE4NAmOjAJgn4Q9VPTlcmVIMGeMz8u4ZuY8W1HjpCbGgIGGryj3VNZyKstujRiINyNoVrv8DGRq4R40yTKoxo3bU3sbziEqgZaA7zBQd3Y81gyA7NWBnoEqE1CITdhRpzOo3Kt8LPQKoBMEiEreaUyfordqgK4bFutzRMjXcZ1rZH9u4R0lsmmVTAwlDN0GXyfcVU8VsHNvCe9V8pm8ZBlJ+MK781b2nbIpEXDBFluoPKzMKUhK2EO3duhWptIpeBKuKaAmCyFu2MrKoJ0EReNOOuLQrGbWiq/xGhe0LpCKo+LGdcKwRfv74wcbcCj14TR4l0to7z1WXkHqOWSuQdq7JGdfGFmCsf9vucnRD3BI5dlqSCH/nt/nbHvOkuDxEgoc8ewcH3ZBuv7Zmnkrgzy/Xk4AxjqsfCVKp1ae9py/3oA5SgCfU5nQCZ4JeZIdJ7gMq0XaEPJeBrapHGmO0MR81MqTYScqSRQa2SqFRMX2NYEOZFUiCUereoQr1Rkgo8MJDOQloF21BIZEGjoCJrNw0nGiSBFvG9Ex4rPLHnBI8RCInfmO8ShtxtycsBgcOHtrPGVtwWYOUkbDCuc+FkGhG5N0OW6kEBLTHVq7m9po3VqbUeOrXXOzYA0apCmxLulDDrjMbQdquArbx3bsMGtLtlxyqA9wwLSoWNLjXZllpeDIw8CrA1xh6f4WzcNtDiPjhrSqMEf7WDSooOKNAiWLh1LPMJXxgkYBRyOvLIEXYdsT3CaSjzfQmJ5WKNDRwpCGlW4lEtnaiJzucyCq0miW7HOSxQLU3k1rR0R05qJxmnQMnHeIQTuaOKqM0CjHty2iY7FYUByJkQL3fgum5cpKNWWMvjCBrmp2D5nGYlQtUquyyArgowj1TZISshgSFyuoOtwwROXA65VYg3TA0q1Lcj6FHEBweE6x8qntiB4m4n3noWBR7Y78Jmw3ZElMjlnwPSekLrE8mcTftOYvLFW4lLGTxW/lam2lPooNEc7lEwcOqZ7a7qhkpZrm31GQQegWkKQ5g4jbCvVGKQzXZxpcPjW0xyO1J8/hE460ul7SYNMGnpCrkqY0a6bBmdep1VPt2cZFYefKmG9JdfQLgfissNvWxkEF6E9YwV305hmPQKhcKQUvx0J662FxAYBnbRUG/bM5abId5XZbQ7Gi5EkFvJrFVcyrnLjbV7ZRVxJk/ZRUV+elyqgq0sl/FjCJFKV+IghPHUOnU6gqsha5qtJEa1RZ2E9445k1HkLa0xz4YYJGm0AypWNl6kyD5PbTriopOXKOoKpqfmKF+gSfhJhe2LCmEGMtwSFuGseG+0H2lkCgD3j2Zf+JaWdEHUBJQRviQTB276nLS7mki0mBgY8xoMBCz1hgNWFUmJBQWMujj8jPIuRgcwDktU8MkVd2ifT6HHbnfF2UjbZBihg2YCdK92I9kTgMpabk6V3F+3uT+2rtcRlkI7Zu947u/sQq/UEMutLNM9u84wKRML8YPPOuR7wOHbI9j03p+ATsqCSC8O23I/Zxzxats0cR7JsnFXJRZlYsHBgkoyVP9g5vlPw2fg6SWxy5Z0QnakBb3eO4DI+K76Xduh1Z4oqsFMlqsdh3pmQK8uG0gqnJpmQsiPhLLwmDhdzCWslGtfRuETjI0NpaVLHyHVU2pEKhxJRahJDOpI4as1MVfEkIjpjFagqQSxBr79VvfV+0iAQqm/ma2kLMHMnLA89sjbEtTYzTBJIVcanws6nJmhnM5iY0K6QhVURCegpa+TK4zanpiEyjUjhzhAjkoJ1VG2EUEPbkre2kaWRDcLTqZFsl2rroJOibcSnRBBITUXas4xowt9wyMiPRwXucQbifVEttfxk6evfIIgT/PYUDUJaapCcafev4rol3Lgjty2hTWTnSYNAaFu08+RUldlqUTnNRghNjdB5b0g+KiSlmma6gMXgFeuMxVnWUDC0L51azR0n5Lpkqzhnsu6VgzqRE8ahmALibVYRbUbuphaKmx4YkJaCpfNmJY+EpJZamrIyig6NkbzWkAaOehzpho6Nb7I6LNVW6T4TTFehPW3AZCTkxjqIdtnjgon+iQjt6pA8jCxvJWIjbJ3lWT+/wdWJ+nBLc61j9IXEdFWZHhxAyix9MaLOEysTIwtJzMXdTqi2IdyyQbtnwMYFDdM9Gb/lqDcTvnj+dGK9pYuW7ZMHrvTZSovSLgf2Xe9hfRu6RBr4ouFj18yPKTwLG8RSZTV/wjgirVWsGp8xZLoEYVNpNluYKL4yIJCXa9w0M7xxaqGYzgZ5N26RcUtA0NjBcECqHLIZ8RsTqANpvyMPPdl7fC58kyKUaCEcu/duOvMvlNm9ed0kFsJ7XaOa0VGANiLj1jwgWsIVVTDl6uBM4qCUIEAsK8pNs3lu3IBclfEqGSk076nRthTMKrN7UQiThHTJnkkR/DjaMzYMNvB1lpliHpSIGw3JKeKcR+oGnC9hi+LZ6b2GJSHLEgCwUg7FzSDBl0xJZ55EMBXumGwy0WL8nl5SQcqAX2ZAWlmMRIuTYcY/60X7yrjtY56RyPvsNEkmBUAqoedSTmI+FaHHDrPIQx9uKl6YY4EMx6w/wx5lgc82eemjHlooR5J3cNF80mX/D0c5nxmPsfyaKCEk21CLF8U8nCAzNU6dXT+kB8blQP3J6I5nWbWASNFCEQARb0CnTMr6pFV1WrCRkouDLCm00epTRTVNGkkGaEIfklL7txF2M8LOupoySLBEbbU8qFzi8+L6kguJ4KBykUoStTcSceM6fO5M7M+BkGkk07iOVcaodzSaGOiUTCYiDMs9z0BFyeo65vYaVjQahZsPAX8NbAFm7oSpE/KogioYL0ZsQNCYTU+mLaRe59C6stlUE0wwrzFXetzT4KtA6BKik1n2kGYL1Yj35pre2LSObTy1DqOvibTVmdt3OkWWRkVN2FJUObIBriKetmqd0w2HzG28OaVnw9rsztngM2mNVNwEUyu+eQtdqiFlwvqUbv8SeVCRBku0xe2cBXIbC2nPkYaeamwzajcxVVlpMBe2F3RY7+iEVMYL0hjx2wldaWZkwKhQH24J3tHtqRlcP8Ud2YaVIe3+Ad2Sp9qE6uYJGgLt3kAeFJd2I4RJxreZNAy0pw/A2Uw7tEpsSkG7cYts20ycaUs44tE9kEotqW5PQKXwRzYj7XYAyeSBQ5qKahLpRo5u35DhzVN8jqTgCAhhI5IrZXzuMkfuX5O9cuaVW6Q6gPe4rTFuNCAcjoRxJg+FNPJU6x314Qk6aJgeqJmuDciVmIejbqgmEfc5wfmEZsG3iTCxfjYH6zijuJ1UWjGvRLcmbN9zH0tdAo34aSRT2bkk8wpFr0aGbjNp2dSJ1TnapYo8HJC9UB9tqQ51NLcUBeuqwteVZcNlcJpx6xPY2IJcymRMWxgO0KWGVCs6niJtZ6TRPsy5HRFXMpu8pY6bHo7tQxu79ubFCyX0kwsol53aS9tjRIRu3xKuG1i5kPEUHXcQwswBosNAbioSDjy4sRXA1MreCz/FgFguHKRaLB0aRXDktm9L0ZDqCbveITkRG49UzjwzVYCpm4WIvHMwmaLTDli2EiCaZzN37f/26sVSBPw0lcSAbCDGi+07VbC+iYynyMAYDVmdJRcUHak+C8kV4T+yhWF3WPFa6pLZebpJJhUeHk7mhPQMWECAJpAHFYSStTjXNyaLzpQEAGacl56O1gtbzss39NZH0mBn3X6QnA2WurPyDND03o9+/bJBT+CHncF25uHRco49kBHbcJb9VYBdvzMjENv57AppqU3eFFdqQs2LQ6rxXNQyNw0D6UytuIwmxHK7cqZwI80T47LgXTKui8uEbAA5eMVJMhCRrWCuinkV+yeVHpzmEtqigCMJFuJKmZqIk2jfXa9QbATioY+WcYijlkQtieQiXaEKeDFsaArDdpXnIYuUc53OPxxfI1uAmTth5rL0IEaCc52i2c1Ii7l2SFfUAsSDr0kre/CTDn94G7e+hawOiEsBDixTxYRsj23fJXzQ974aS2e/Z8WO2bbkzW3caIhoqYk0niBNPXtZ/GBAzgmJkPet4G88hFYe33Y26MeEbG3jJ0JeGpJXBqZ0W8h4dEZk1tpbh11m9H0xPg3O3r4QrCKxV9zUlE7xigRwG1OqWya47NDVIToVUmMdv5uaTo8eWUeXh7TLirQRl41AE7Y7pEvmYVqu6EbLSGUAo7m1NcQ/rElOSKNg5LYs+DajHqb7KxtsIgbIxplqmkguUE2U6rqjtKcvkwYO6hHdkmeyv4JKbDCKxl1xnTLZ5xFRRl/MDI4k4p6atnZIFqs9tJWhrsijoouyEthcqWnvKfjcMfyConiqzUy3ZrL69Q2buO2GuC8gnac6GkmVY3JwCd9m4kBo1wJhnKgnDn+4RYNjureyVM0hdE1F9olQZtB4wW9mZGDTPZeUbllMLdY7JufsI0clZzGC8RRkmmcqrpKVFCCOrASAH5uXQRWWbs7UX9g2AmkqpMaUS+ZQ4T/0HoXtsYVHBg26MiKfupe4UqHjMTK2bCTGCTm6hZt0uOUh3SnLxCUD8/WGvSMS1QQZYym9EXVW/oLsTHG71ysaBCQIrG/hp1Py2oi0OoSlIZKiKfiW9GdpM3nJkWrzUIVOYNwhGx2hMeBR3bJtpOKVmuwrI/tmC9X4jNUlE2chJsU8NFUBkq6IInrzDFAH82akBEsDAzbjCUymRgIOMpvpSxlpLesnG3Ar2VZGWs1QlWHZORgMbL/rm0jwyMoQV2HcOi8zl4aUtGbpie9e+sQem5UkZoV5MtaWnmSbnIUfswriB0hS4mpDT0CnLzviSsH3XpLBHwNoYEdLZh7MyM73nq+8Q/qd8x6dqB9mN9g5dlnBcfQ6XPNCXAaMpKw7txeZ/Uq5FDv7nWUm9LMFiuZEGRN6Jb1ZgwoQKihNs86yKbMr4Twx0nQunjZXPGlS/jqx/qdXEBaxsF+QbNzLnhw8hyQMp/YqxnYcL2rebwdeHFGVThwe4+j4nAkuUYknuUQWb54wnIEdSWQmJHVkWoSMd0IjSiW9t8muWy+o1wGb+XZu4FfJ7lIw85u/+Zv85m/+Jtdeey0AF1xwAT/7sz/LE57wBAA2Nzf5yZ/8SX7/93+fW2+9lXPPPZcXvOAF/Nt/+2/vmgZr+V8pC5CWCh1Ks+m0TKegEIeBPBjiuogc2ZzpOSCOsNUSfW0zrKWhpZU669ClK7VpKislwMD4NdJ20AIpoj2xsZ91dh2zF6nsw20ZT4cQrAONCR3WSF+LZzDExQytIl1HrmtT5R3VpOWAi+DVKiP7SUf2ztzoYCRXsHAS1pHHRvCx1GYa7UHaFj9uYTDEt0rYmoI4tPFMTh8iByp0rFQ3bRBPXbGXPUXaAwNL72xAcXYdOqXZsHRFFehOrUkK0prmiA5CIbKai9w0YYx46tuEdoqPUG1nmhu2qDYy4z2evDagq1MREnRIcoQNmYUe/MQxvMW8RdIJK58e040cWZXkhbjWEEdig2xMpCUhDUb4dWV0A7gOaALTNUf24FcCablm84wBS5/bwgVHtyrkoSMFJTaO0GaGN0aqTUVrx3S/0K5U4DPjNY8koT6cEV8GrpJxUY8TXe2IS6ATodpQBm3ETZW80pDIxAoE8zh1a4E4dMSBgIeVT7cMb8xsnLtC9kK1XkTAJoWzs5lxuTXAPOmgTeSlQXkeChdLFRkOYG0FXR0iKVPduI4c2bJwqJRBte1IG2N71vc15OCNVJ7sfjJtYRpsYK0ry5ArMv9aMsB6oa9cO2QQ8GQ4soU7vG2yCWBhxWFNXqqNd6NqZRwmHTqqS/XsQLhlSrjuiIUpNyZoVlSWbbIyV5qDjKWFp2RSCZWfKQSrd4UgWzyFgwo3bYvg4sRCu6vL5oXqSv2o4C000cdb+npPSlEML4Nn4a1oF42M32ffra7YZObwOk4FXRsgPpCKboyl5+ucqCHGbVPL1PPZQK1x2ZyBiGD7zoB4K2lIqI2wDuSBn9Xj0srS3WelSHog06f7916ZnttyLEARCqen/N6Dn3mw03e5JzEunsgRsAN2dntqdq9xO1Z+Fp3z0qCFPDwPh3qkxhxw6o9YQEsBMJbhpoWWozNRvlw0Z3YoO7YfV9CZF/stZ3s3xRXvSPHAWPdvoSk7bh+mNGDrFKIa/0bFkTD5hCDZ9JNcNjHUclY2JTdBy+g9pMzECRHPCh21TBk4SpmEckw1YDtROJKb27+2XwW7S8HMWWedxS//8i9z3nnnAfC6172Opz71qfzN3/wNF1xwAT/2Yz/GVVddxRvf+EbOPfdc3vve9/IjP/IjHDx4kKc+9alf8/aaGJRxTNKwIo08bmqI12lCB55YObQR/DTjbtnGbW6jTSCtLiMIbtrht41rIdsTtO1g6JEQjDcjoF1EcrK6OVWw7Jy2K+mCReK762ywB+MEFD0ZWpO6TqUcAimh65sw2IOuLMHRDWQDdGVo3p3gZy5TUJimoq1hAEGcsyrbdaBdAXAW5qjF6gFlyyjKUtzYOOLagG7fwAh7SQlHFecCsbGUvyyOSjsmZ62Sa0fYshl4qh1xJZj4FWVm2MCkDqRQMsmcFfDUmIhLwbqTChj6kj5q/I+eEeQ2p7DkmOwNRTzVU6WKjX2NKXiOLc02rYCfJpav3cavT0hLTeEoOCMKJ0dcCtapZ6UNmTwK1NuZ5osTRDLj0wY2g9pO5lbuPNo42n0149P2EsaJZj3jcvGS4WeeHpcMOKk3gNSVzn66V8gN1Icz2nq0gsmSpdH7aGCmy+YVyMFBDU4TbksI2x3tsOgUSUYmmW7gSE1f3dui8H5L0cb4K7kSmusmVk+szEItTVntWnRT6+pWB+TKIylZ/ar9K0gI9kxlcBsTZDzBZP8zEjsLv3oLczGeUv/jUWSSyMsD4kpAWCasT+1lUyV3yUI9dZ9ZYoAmO8FPrS5TGgXimWvIKcv4UrHabVkleJt7GKEYLL3atx1MjbujtaPd2+DHAbc1Ndn3rLC+RcgKjV27XAfzViLIpk1YqDyuNu9g763E90x2KWVIHEw73MaWfV8emQer62ZqyzOCML13whVFY4oishoXLVv/Yx4RjzqPeMFtt+jGFt0gWK2NIhzpei6Htxpps9BSqcau9mgbmOlLqPTIos8iyrpD1i4codxX7S7E5ewNJBlHRmZelZmHZZ43UxIlerDSZ2HTg8LeyzAPfG7DS3OyUYx5783JLN+1wjELZMYU6huux688A2G9BGZ/gsblykn6OBZSwqjl0hRQUnxKKrMQaR+S6n1PFooy+ZydIpIljNUDEbGJV9/ClMRUAEQMVIkWWOVIpYCdujQDwCRTG5YIXXZUrqXRiqwO58bUOaN0piBQHFZZLel0KwcOpZXbvS9fDbtLwcyTn/zkXd9/4Rd+gd/8zd/kQx/6EBdccAEf/OAHefazn81FF10EwPOf/3x++7d/m7/8y7+8S8BMM6rY3uzIjZRO2uGSVcJ2bUaOTkipQ1eW0ODIqzUMKoPX3tvsbTy2tNLBAHMbCzKZoDFZzNx7JGCdXzZ3Yl4e4DSX4KrNcDWWB29QQ1XZwDEeW1hma4qMBuRT9+AnUysOeeMhG2iWhtbuabSK3M4VF3QwLZmqISOkJZutOYQcjLNgarX2uqTK4cTCU9kJqRbLVhIjo/qJkp2px7quRnG4NlIfaoHi/t/boLUQ1UJ2aVA6iCJylhy4tmRktOat6MPZuuJhnJGpEWl1IFTrLX47EZcq8rLQNR490lLdmJh88x4Of/tp1K2nG3mmq84UYWMuRRXNE1HdPCnFNT05ZvKyqez6VnHZ0a6a4JafCoMjHeDpmYmSwY07BtdvMz11mTiA6R6PtJHl61tcrkxLJUM4tM3UVeiBMlgseepbpyjJ0v4rIVeZar0ljBWhZrpsCsDWueuMzDkNDpdlVjxRRWjXHC57ZBKpxtZRxpHdN0k2O7O4utCe2lBtROpbW9Q7K2aJWLpxm+iCIw5rfEx0OcKgMtLxKBCHgp46ICSHm0Ry7QzIT5yFGGOyNG28CdQtD+zZRmBQE8YJaSe0exvLslOsnMDI4zY6XLIMJhKm5OvLiCcgrYnoUXt0FJismSaTHytsDRFNM0AKRUelyCmQsskQbG2Sl2ri/iXc2giZRNgYm6s/Q95u8ZmiQ+PwwxoZt1h9K5DO0t/xDt2zbO3qVYj7gpE5I5tjGGUDPDDzzlipBCPZzjCHdzMgKaWOVp+GPCuguTW2kBWF37K5DUsVDOx81SULM4GFVXx5ZmLxBpQsPFLCJaXPcdL58iaFz6PeSMQGtmRGXu7VvilClzNAUm5vL4zZ3y/dac5usNCDn/kQU48V5jw1/cB8LAjZ9V12/9W5tebXO26bY9pz3G8ndOLsABotYOdEvwFoPuZXARVfODsA2SbKUjTD6G+D9S06t09VITslqN01TUWuKBf+lbfNLOwkRpJXoUsOhye4RHCmf5WlDxMlckGTrvzPp0DOA5IXVrAAgiJs5CFBMjEbr8aXfKaEo1XPptZs6vKJLthX1b5uODMpJS677DK2trZ42MNMbOcRj3gEb3/72/k3/+bfcPDgQd7//vfzyU9+kpe97GW3uZ/pdMp0Op19X19f/4q1cc/qiO3to0YgFUPGMsmWjt0lXJdxbUc8pWL7QMXwHxNydL10AKbXAh4/btHtKYwaZGUZHU+BiPhgz7UT6+jGU+OwNAFdHaHDGta3TbxqPN15XzJIMzAS8fo6zpmira6toPv3wI23mo7G5jZMJrC8bPo2qlAHhFw6rAolo40nDgOguK2E2+zIywPS0PRP3LQjLQ3IycS0XARVJY88fqvDbZqbuz4cybVYinYQqgjj02rz1owTPgt+I5FSLHWHOpw6JqcPLVwk2bw7RbE3OnATpb5xgzSqwHtStlhyXHakYSAcmtB0SpcrJgdqtu53ANFYQFhAW0t/daqmgtpaWySBj0JeHRixL4NvE46WbrUmDlwpVCkMDieT56+NBC0Hlqk2p7hJRgcV7bJnfEbNdH9FNVZGnxnjJx1xraJbBjcRulEgLTvakoUkCSb7K7QxvSG/3ZqmSytoCMRBHz7YmdJqcU2rl1l18Gpq5N5UCdM9gdHnNvGTxGTfEi4kIx9XrqQkO7Qp6bjTRDUFbQJxVKGhsnt0/QY+Z9r9DYrHVaZjlIbevHbJxPJyEVcDRxo40mkruNUBcmRsQNvXFibau0perpAs+E5x04TrEtWtE7Rx5MbAvwB52SMb5iaXXlyxK0KAKZuAZXGBu6MRV0N3+irtqsc7O1fX5qIo7cvkWAr3y6GjCjep8FsRp2MbkLwY56dyllGlRra0EIqDXOOaQK5MDkERiNFKQrTZRhbVuXGsvKQxwhioa/PiQEl1NkVu9Y5ZaZJSVFJjnmVn2eBXfl/fsgSAUl8qekeuKztOUSK2kHMy0jWmdIyWXUNJEZdSADMaSOzra/UHLAJ7uepjH9AX3JxlK815YuZ5MjPycH8JTvDZFULquTxz4GfXv+WYfx8DWnbZcb/pLk/PcR+O+e2Epif85y6QJSfYvNyyGTDDLk1ORhyeJVIVAnAv0teHm1JyxUNTso6ckLLgNJNweKck7Bl1IgSXSYWso2p6RV6k991Yxn8WFKvkbb4ZV+57RnLJtpNMUEHEswzULjKgA3Fs5CFeMsl1VBrxJbQVVZhqYD0Pqfnaqv/C1wGY+chHPsLDHvYwJpMJy8vLvO1tb+O+970vAP/jf/wPnve853HWWWcRQsA5x6te9Soe8YhH3Ob+fumXfokXvehFX5W2ftOZ+7ju+iP2RWxWkoY22Ggj6EoFfg95GKg3Iz6C1JW5ZLUQJ8E4Im1b6rLkQlIsULoq5Ygnnc3ovEOmigZPXK5hbVCKXbbItEUnE9OqcQ4XAhoqEyjbGJOWR+TlIW5rZLO3wvURpdRWAk1qGjJHx8hogN/q0ODxKdqAEjxp2VL9JCnd0BE0QFLCOFo4KlgmRU4gKD5lshdCitRHlbAeafctEabK1n7P4IZty4SqwHlBQ03XRuqtPBMnc5JxExuoEZM9l2htqG/exktFtxRQEpM9DUzEQmtlNtPuCXTL0C07cDXD6zpGn9tGRw3dWsBNhebWFjYTISZyELoDI6b7KkSV+lBHCo64FuhGFheWNjK4LlJfv8nkm1aZ7HGENqFemK4F2r2V8VC+qSYPPH4MzaHE+KwhkgaMboqEWzKynUA9cZqoNwScx21PqA9PIUF3+jJpqaE+NMVNEnGlIlcVvhXjYYiSK2dpuSpIDVoJfqJUGxFRRyrhqu2zR4RNqynW3BSJo4Cow7VCXjMNm1wJ7f5mrhfFCL+dEldqMg6KurWsbyPDChcH4IwsnJ0nDpQUKstI2ujIA8/kjCX8UkW4NeCnnakcNxa2ITjY7qyOVc5W52g7oXVFWh2QS4Gf5K0MAU1AxOG7Ul04Fk9lVis3sT2hWt/Cb7ZMzjsAKLQJmSQbxJva0vu7MviUZ0lXhiY8J4I/OkbXtwFFVobmvUJMMDKIeWYENPnCEfFGhm0a05iZTpBxtLBw7GY8GNWMTlMBJjuqxbO5u3M73JJkkvR9X0Es+i6lxhSChYqHAwNFdUUeNejeIXnZap9B4VJEC1FRuTmPh9sBSMm0pqz0iqI5FzVxsVR5B7maeyZKmEjFSmv03hc9FnS4uX/PL789EDF/QdztrH/sPk/072IGlvT2gc8Jlve+ll3LTxBROpGraH41LRPNnQU9wLMGay6pWYYkSBTxw6JV01MHxGMe7yz4InSW1O6jc4KQS/0o89aKRe3NKVmy0voj5wJqdrLVTc4iq2WkRXUm7aHBQI9UDCQylMiSa+3xSyM20pCogbpkRSkQ1TPJgbE2fPNg6TYu9lfP7nIwc/755/O3f/u3HDlyhMsvv5xnP/vZXH311dz3vvflf/yP/8GHPvQh3v72t3POOefwJ3/yJ/zIj/wIZ5xxBo95zGNOuL+f+qmf4j/+x/84+76+vs7ZZ5/9FWnrA+5zFh/60KcAi9vjII0CtJk0MoWnsK2Ew1N8VOtYVKHtcGLp2urESIB9ummiKAzZMbQvQLm2bJ1cF3FdRNfH+C6i+1eIKw2VONOf0IxMphZ798E6uZiQFPE3HyUd3Ice2APrljpLFhPw6isGdwmmUyvaN2hIdZiloGdnoR0XLf6Ks3TObtVDEuKwsiyPpHRLHiWTsv2ttiNuaoXy0rDGTztiXVvqa2XptWErWpp25XA50Q4cNI6wbYWVcrDZu59m06JJpsuz8YADDG9qGf31F2DQ4G50pDP2MDljxOTMJdy4pRtBamxAro9mlq5PVBro2kR1VHHbHYPrNmn3LxOHxkVpl8HFQLMe6VYDaWCZatV6xCV79WV7wvicZcanB5obOup1JQ+Fdq22oo8rpvxbHU1WSsIrflupNzPhaEnFdw6dRvwk0e2twYO/aWIF5vY0xKHxg8JmR3YW7vNTm3XrQKGULvDbCQ1CXPakoZBqC2v5iUIjdANwVcXkQEWqMs3NyYTtJsW7tlKTaiWtGZ8kTAAsqyk7qDZaS3XGEYqmjtbOqj/LFK2C8XSWPCoe1yX8NOK3p/j1hMoyceTo9o/Qzc7I4yUUlhobDfN2wo+z1ViqCsk8ZsIE8iRZcc5ReSYHDiohNY7KRZh0SJ8r25inzt9wiMHKiK4JVJuteXGCB59NwwisZIFY6MTE2gqBd1rqPAmEjcJBagYzL0WunJGVY8JNMzFocUkYxyGpELpoPDgx0UpqQaID6WbAxDgqGXFLM6AB7IjkiYVWS49gWVEYuJMi46BdhywNiaMBabVBhzYJUrV0e5KaXEQh9GpPVM7mRlGx9PJeBLT3JolgXqheoXj2m+yEgY4FG3MhIjPZ/fuxIGIeBBwLZIQTAwdOsM2J1tfjt7v9sNSx4afj1945zG0GqpiJLls3MUtF37m3svtcwVbOGD+pcFV6x5zdf3u2+yCTRS/7HUNSwasWJ9jOzrO6sq7OtHp6TRivCcU4hTKDVf325rHpdXoUYZp3NGuqklm1lFs2tSFmTyBS8DNW1crR0LKv+ia+1naXg5m6rmcE4Ac/+MFcc801vOxlL+OlL30pP/3TP83b3vY2LrnkEgAe8IAH8Ld/+7e85CUvuU0w0zQNTfPVYVI/5MHn8ju/XVImNUA2+fPsnRXtA8JWJGx1yGZRAc2lSjIZNJPFmatZsdiokwIsnGUxTDrrvAYNeVjhbzxifJpgdYFkY0JaqojDgAuCrxy6GZDxxN6votIpqrA1MUKvE6gDuWogGHlUh5UpBQ8adGmAVgHBajZVX7iVfN5pSO1wW5E89KTGIyXFDxWqkg6dGsfw5ink2uTyj2wTa0UHNd1KRZgo1aaydfbA0rOz0u1pUA9rn9jAX3cLjEaA0rlM3L9khEtR8qiXTQfEZprSZVJd0+6tGfoy4zh1L5NTaiy1VIijColKfThRH4k0t04JyVvW1dEJnLJM3DvAXb+Nc6Vsg2bqwxGHh9b0R1Lt6SrH4EhEULZPGZAGVml5+fNTms9auCuuDMlDIS5bwcmVT7eESSYNqlLLyYir22dbHLk5klAH3SgQR44wVuLpK7DRouKo1hOiFspKtXGNqqNTBMf41Mb6zZxmlbb9lg1EubZBVfOU7IaoM4DTjQDvGX7qqJW5WKrIyw2IEjolYWnp6sSAytaUEGrqm7bQ6ZR4xh6mewJumhlE8GMLV2qXcMsNk+E+8lDINcRhRQrgt4oa8HbhFAnkYcAhhImdmzpHHASk9bjWPJG5CZZ2H5yBkLZDOkvHyUObDBDUeFaV1eORpPiJQ/YKHN2wd+aUFdNW6WJRCc5I2yJVYynFWXF1IA3snP3WBD1y1CYIe1aM2L49Rn1l3XybcUXQEQcapGj9YNce8INAHtaWcJQyUueZlg5b29CWVO2ugyOdEav3rMIQdnT65wZJMa+v9Doofexh2JSSHaBFxwjF0toDFu4unO3sylDY1zTyxVmRSvhMHBZm9jMQZTwLMe9VLnybMlbOPDGFN7MLxMxGRp21/7jf5oDM/Nd5LZdeN2YXXsg7+zhWr2ZGlL0tz8vOIecAljVgVzTwWIBSjjM73u49ncDEQAu6k1Axk0A/wf2dHbi4RbyhSdPC2TkfndtEoRQGtwBRzongtdQz7uNZurNhf9KGx81zTsb1GjMUTRyxKIE3NSrL2C8cGC3FMpONEOxxW+QkjK3a087tRhnQccBvsDZ8+G3dhq+a3eVg5lhTVabTKV3X0XXdcSqC3ntyzrex9VfX7nmv03GthVZMe0NnGglGdlPisqM+UsBFSla1d98qMmltZhaCXfVkeirG0chItk7VsQ2d2LIu2oyzruzBzhnWtwmbSjxtD3F1YGmgweFVrabSdIoMBqXSdoVvI7FxOHGw3SKj2q6fr4mrQwSrSmzvVIZBDSuDkhKebUbqi3T5JJfwhoV/qqMRWVKrQ+NrXAAZVYStthQ0NEKsz9CsV0z3VJa7VwupgfHBASGsWUrpoEJSItYVuYJwdEJ1OELTkJeCzVwwnkpza4s2ga37nEpzqGV6xpC0Igy+OEUHjnbZE7YyzeHOZPTbFvyQdn9NPK2iah31zdsGNjGvh2xM6E4d0R7w4Dx+OxKXA5ISflJqKw0HpIFQH0oMP79FWq6Iy6FUuM7UtwrDGyYMbjBtlbC6RLe3IgeHiqNbMS5EtZ1xt0bYG0wTyINvheaGbbyr6JZrwpalt/d6MZIy1DVNEJIztdx2pcLFTG4cGmy/Kqaplhsl1X3KptDc2DE4OmXzHnvRwm2qp2rKscs2aPlJov78Yer1CXnvCnlUk/cPISv1rR0+lkKVqrPiidy8Tj1qmJy9AjgrGzFqmJ46pNpKuI0I4xYCCKbC69pEfTSTGoc2jm7fEiEIbmwicWDk4xwcuaqLdgsW1kKKICNobWEQK5Ka8dsVofG4QxvI9YfJa0u42jR+aDvcdkTbBCs1aVgZ2JFCnI4JTQkqy8jSmEy1t+1IpdAi4wROSENHrI0w4vq4S4ZUOfKeEX5SmcCg6ozMzqDBddE8P5tjk3HYHts6k4G95yGUiQ12wpVnptIbC6IXkDqgUyuBQiwVxbeNWJ99CX3mAliWG+N2lbRsxJprvxuhOveCfPawGOFeKcUcrYmzYbIQfXeFgvSYv7Azps6DgMTOuc39s9fn2wVITgCCZN4rNG/9b3OH5gT/3mXHeIyO98jsLDM8MA8y+78nQFBzoAzXN66sq+Wv9JlKuy+EqiA+m86Ls/Rr6QX4iohf70VxTsk5k7IQk1XVhh4/KkJRFO6Hz4LKTDsmUznLenWiVhNKE06USjKV64timpenU8dUayYaCUSWXMd+2WJbO6a5KrrESi2dlUpwHXv/qYGZn/7pn+YJT3gCZ599NhsbG7zlLW/h/e9/P+95z3tYXV3lwgsv5Md//McZDoecc845XH311bz+9a/n13/91++S9nrvqb54CNaWyAPFbXvywNugX9nMpleo1NiZcNdWQkKwZ8lZiCGnouzpxVKaq4BMzPuS9i7ht1tTVB2PTTK/bmB1CSVZDDSayz6u2iwprtTEZpn6sAEmHdSmOtuZi93FjAwqK3ZZW3aNJtDlBr85NQ6PZgsbDALdSgWNkIKfkQKtmqxaxkrlLB16yaTnu4Hgx4lqPSLe0+4fodMpeVBRO5kVkpNsDi3TMIE4CrTn7Wdw65SwPjUtjUFFDtAtB5rtjJZ4bHV4QkglbRVlevoS7enLdKdb2GJ4a8LfvEleW0KGgVR7UpVo91awdw97P7HJ4EbYOHdEF2DpaEfuOhPKC7B17kqRfTci5ORAhVaw8rFNqBs0Q3PTmMnpQ5aumzDdH5gcXCKpMPzHo6gMSHsr0x8aVlTZ0uLdpsLUQeNxowHdkmPzzIqltiIPhDSwWVhzawJnYcPq6MQI3s4jQ08uNbtw4MYGcNsVIUyNwKfB0S0Vb0GXiQeGoMLwJkv7nezz1EczeXWENjaaifOkRnCTCYPrI5Id9aGJpbKPBjZQR8UdmiLTSAweRoG0VNs93ABfynGEG47QNJ64NiBsRaTLTE8ZkGpPXhNk1bxmYZLRSbSQZVKqI62Br9WGdt8I11oNJpfLAJciUgl5EMjOipYKWPkAh6XpjxNu3BJHgelekxpwzlm2TwkPUVsIWCKE9W3y0S1kdQgrQ9Q5KzkSo4VvwHSh+pIJ0xa2x7ggsGRZeSp+NlWXVLKkxLwi+EIKrkJJu7ZR1qXiCWhqpK6sMn3qTP5gaxtNQxgKSCkNIgXBuVJ7isBMSr8Ktv9UMtpa0/qhi6YfM5kgVWP9zqBCfSj6NSWOUcJROvClAKfMyO3ozrgLzMi92RlPZpdXZt568HJMuMUuUr+C7HbHyK4/Oz/lnS/Wb7LLi3KbAOXY33q+zPznmH3Zv28byMz2MwNvurNsHrj1wEaOuYC+oBuP5S07ioTuHBCSctIRZGB8JudLjSenM8+M870ujV3s4DMxBVK2OlESFEgFoBhIQQqmEtOlCaVopXMlpVusEGYwP8+sMrfHhPpETVtmkisqqanEM/RWdbvSSBJHxuHIVkNKMm0OLNVfGWrHnbG7FMzceOONfP/3fz/XX389a2trPOABD+A973kPF198MQBvectb+Kmf+im+93u/l0OHDnHOOefwC7/wC/zwD//wXdZm2bRaNzIaUC1VTAelBtF2soc4O7rVinDmfis70Lu61zehMhn4NLK0X9lqcaOGbskTsmUAaR0MfLSdzVKbQemghW55BMNkaZixxR/ZwrWJvDJg++xVpJoQjkysfkqoUJdwKeHXt61OTgjIqEYHAaZTfGcFJ/PagFQH0x4BsssmVZ8c9BkaKC4KYTKxdy9YNe5uJOTVmjywNN3B0czwxg7NSvYdk32BaprRmPBTj3b2Eg+2EzKZMD64TLdsIMAdbhlsdMR1cJsT4gDqCciRiBZNE9ncRoOnudERiGx98xqhtUF7+4K9qCbcdoTGOpD6qJUgYKsl4KiPRHJlHWQ6sEoeOdoVj1RWgJJKiWvBZkmlCkTXOMI0Mrg54lJL9Y+HaM9ao7m1Zf28IUf2r+A6QXJEhxU6Ftq9gssZP+6QSSYzsChgDa4Txmctm2KsA6LxgmQa8aXOFKWsRR5UTPc1JhCXFJ8EOTqFFGjXHHlQSJooYTMz+uwGW9+yh+wF35nXB4E4tNlgONzCshVl9Bsdgy8csTCeD8bNWlsijWrS0IM4XKe4oQnFpQCoolKZ8uy0w21OcdNE9cWjiFgWXCidbxwK9aaYEq+oaQUVt3dc8VQK7sgYP03E1caIwdk8fXirOJwFaB2UiupkLdfLpvmuS/j1Mf66CfG0VRPzW6mhKQT3XLwUvb5GUtx4glx/CNYH6MrSDuE2BCPzFm+IWtoJsjG28KNz5EGFdJmQjBTtopKWPDnY4GaCvWLhS7Hwj5ZwMOPOipEmRZoKfGP8mWlrntRSLVqRWVhIwIBSib+odyXOUJXaUYpsd8jWVqn1phaCajKytlJCqJhoYDIQGZ3VGaMXBCxpOPb2FyUVKQCtKPqmOTG8XaY73pVZH1k8P7s9FHPek2O8LzNdmtuw+WjJiWDHTqt3L5sd4xgvj857S45ty7G/9SUiXI8o5kCZYJ5mZ8stHNWTd225eEVLvaW+qHevvbRT7VIQn9DsETIuGJih6MFAyUwSy37qBfS8y6i2RTFYcKQSQgLnlMoZwEjl4hZpIIJkAgmRVG6p4rDSCZVLeMmF2KuE8mtLYKI1URNj1zGkK+AozjnmhE49G3lQPElfW7tLwcz/+l//63Z/P/3003nNa17zNWrNSdpwCO0Uv2XFE6fe0obDZodmy5zIw4r2jFWkS4TNjurweCZBLuMpbjOBF9KwNnVeaituN7AOy9cev2e5FMur7EWVUj4hBANFR7dgbNLuPipuX8N0jxX/kzaRho40dPhtAW/ZNCY6p0Yyy7mEt0pa37RFopQCmC25qdCmwikkUeLQI11n4IySBbGVCDe36FJNt9qQGmG6t4IUqW/pcMMh1UZGa9DKWZps8RXnWvDbEKaWcRRXany1ynRkpNu8XROrSM6B5rpNdFQjR1tke4osLaGTKbLUMLh5yuS0hvGyqTGPPh8ZfHGDrXPWoFOrgZUz3d4KJ4KftPiJo11tkE4Zn1oRR4HhjVMi3mosCVSDQFoV1u+9imuVIRDaZKErlOpQy/TMugw43nhDU1Npne6pcN6ZCrEYOXd8SsP4VMsuWPnUNu3exlLOh2LhD7XKxNrlGSnVqk1bZe7cBNBMdf02/uYNunNOp1sRQmvcIK2EelOppuCmwADaVUccmFdg+TMb5jVcWgbNuM2p6ay0EarGwhjR+DJ+0lqWmXPGz2gqqA28SDQZgj7TiKUBUnUIAb/dldCQlArVzMQXrc5RtrAIVvgxroiV9JgmwlZHbtU8EdNolYBjQpcqxHuilM64FBp1nfGn6BJoxm9NcNd2pD0jU9cdVUbSTyXd2tvAnd0QaTxyaAOZdugwz/ReZGmILg3NG9qAqoV3Z0VeN6Uo//oZ78RNM27cWjG+5caAshdyPyUWwDliqQFVTWLp6AX1RSAyBPPGeAtxzg+wpjdV/hYl5B6cEPyM7C+58IJy2d/yiLRs6bFuWpSune0niqIDNwcyircOLZmXGIgRt1Ph2/VqvTuDlM79Q3qHgexEl8QcvsbNmV2LnXOb/T0R76Z4ZTIF7ITdv59orNRj/q+9t0h21tCyP5nnzOwCMr0XRnfCaf3yXrengAkE85S4jLhsz5oz5GUCh4rzWqp/9GlJ/TUvqtgCEM0DUyVLbPMGfrKaF0XL+pXPJTnNspdELPRYeyP1NiGVekyY99vZflxOVpMMLanaCeeSgSDVWdgpSKKShHdKJQkRaMRU6Tv1THNFEmEzW029ikjxQ5IROnVMtGIjfe3TsuHrkDPz9W7OO/Lqis3SRsGUGnMm14JrHbI+QbpE3D8iLldQedMr6SxzR1Rh3wrSRsKhdVJS3LBCa28zK1HiUiCGkcmUVwHfRqvAuz2BLpFXRuSDNTqNJtXeJUZf2GJ6yoBuraJaV8Itm6Q9IysRUA3x08zgcIfbHBdNmRo2pzDtLC02JmQyJR08QHtwmVg7cuVp1hMybvFHN/FTIdeWxqq1IC4go4rYOPxUaTYz+YjVWI1DR25AkqcbOarNbF7nwEwFOO4dWtHBZF4piWK1ngaeEKBbqZhUwvA6R97umO5rcKNgtZRqR9zblLADhI2ET5nlazfoVgcgmbji8ZtKXKqs7lBnKc1+nJmcbkCEZDyD7TMG1IcTbS0lm2Mm5kqYWhvjINCuCG7tALn2xpepwE1MxTjVnm7N41vz6hinKhNHA9q9nhxg6XMt9eEppgYd2T59YKEhZ54GCz0qNB51ViQzLnkbJKMw2Svk0/YR10ys3HWWpuxb67W7PUNCzHTRKo+7BM1NHe2+IVoJ3WpAtjuGmybJb6QT48AwmSIpQdMg2Zk+T3amJ9R1dq2TVe9149bCH42VBnAu4GIyBd8q4LcjflyRlo0jJrqjKSPO0uxnng7zhpuidqf4nOw4k5Z6PCGmDMsDpE34lHCTDu2yhZMqb6U6/B6rRn94y8JFq8vIihUc1VyydgQDNCtDGDRWKX4G2s0jpN4Gee0KWKgKjyWrcV42J1A7qwvlzdslUfGTiESIq3XxnJQHqOqTA0pGVe3xnYW9CEX/Q9XCvz3Bt9d0odTwcWoeqXkxO82l3o+BaUbmwaWL5jVdHZl3azviamfetkpI3sprSJjzLhQAkCnn3qOT2fHYJWS3K0QjzC04JhRU+CHKDmiY4Yv57XovDjvHAMMO2THzDs17WebqYM4BIp3tbtaG2QF7fqPOnFGzbenPr4CW2bF0B/AVUMGMv1Ksz4WWvMM7KgeuagMC6hRxiqtMGJFUCsfiENeZx9QrUvWp1or3SkoO73PvlMP3lbcdBOlwpZp17TPemWfF9e3UUu6ggJpArzRtbRWgJuF9ppZEIFFJpnbGjmrE+qPKR8C8O2McDYFxrlGEWiqCGO8wqQGeiVaM9a6BFQswc2fNFVEtJ3TLNqNz2UGX8dNscexpxq935LXKQgujCmJjsd8QiGumghqCM+n0I2Pa05eg8G98m3HZClemJU+74qnWPa4zkqwTE9oKGxOYRrJ3+O2W+mYl7h8xPWVITTatiS7jWik8A8FVFWFrQlYhOXsxpWRGoFoqANsAF7ZNdKzajJAcqaiZOjGxurhS46Oay7pW8mYirHf4W9dJ7Tbp4H7yUkPoatJIUWfKwOot/dbi9hmyuf/jsieMLXW1W/FUrdI2MFkLDK9dJ1crTE8dlgwhj28z1a1TqqmJvuGchT66mnCoI48Cfr3Fi0Mq0OIdCFuRPPC0K85CeWU2Ot3v8WNB24SKhX40mLrx+LTKrnNSWBLSMNAtKWmghE3zqtSHO5rrMt2BEbpUMT6lpt7OlgUWIIyVwS2Jbq2m3d8wXW4QlKXPJGRsxTNzbcAlDwNx74B2T2W8jgjUSlpdto7Xm9x8XBJcK4TDHS55/DjhVPCDDJ1VgW5u2iYuedKeIXlqoAjNNqhVgTiA4AdWFmM4sMrUUMilGXdo2wqdeodrGsuwSeZOZ5rQ5NCRGIeltWw/dUIzSUxlSBo6ZGzCcsmBCyWduS33bdJCUxHXTFDRxYTbmNBLq/vtiPOFa9MlpDWeDL13IjioKvLQst+sJ7fyFjaQuFn4yoi0FWllYHWbYrbyHdKgbbBxKWXICW1bpK5JI+PiSBtxk45qfUpXO1IQpBFcNP6ba+3Z0kpmPLnkKBW/zQ0vGctoKpkD2hduDM6I9rviMDBjxhY3Qy9YJyolTbc8C76CpQGus+uMYtmPOUNntarw3gSrKzF+FXMgo/fI9C6HOeAwW1F3QMBMKK//vfdUyO7mm2fFQlZZbFDuQ0q9yNyxdZlELXpjzzmlGCa7AE+fLT5LFJrzuth11R1uj/SieXPemH6LWVvzjifG7XhnxJVL7+bCTuwckwpEOnCCczshpqxCXSfrZ6OJ20kwb0gn4IOFCINXxCvBC15s/R6leac4KWJ5fWhJMEFIl4zA652Fk1ymKiAlq3FtqlKryRSFBacd3uIAeMlUPtFIYuBseZBE7aweU+ViqWqR8ZqIGlA1usNUKzQ7Osklg8ouR1THVCumWnFX2ALM3EnTM/cyHVVIgm5kblhJmRCN+CkosjnBH07I2DQ2utUKmSZTCPWecPMmabmmO2MN2bdkPI+JZVO4VBRa8YQjU2Q70p46Iq5WuJSJS0PCRqK5cQt361GrcnrGPmTSWWHF1kiMbprI2qHVAB3Wlh7sPVXOhC1wkzHxlDXSMmjb4VSQQYWq0twytdRYBzhHu9bgxlbRWrMSV53p0DTWOUu0TA4XMjmAVA6/qbiNSPI1YXNCihFdrsij2mZHnRA2W2vXaSbfr8GxfQBryySBg7AtdGs14fTlQrI0wFff0lKtT/EbRdLdeUhT8iBANyVsCtPVFbq9NaHNxrmbRAvDbU4IjdCuDkl9Fr+H5oZtqtYzGTmka0n7GuKwCLV1Sh4I4zXFt0K9EamOKoObYHBjS3OoxY8juQ6kaSZri6+GtMtlAElKcyQzOa1C8RYenGaadaU6bHoq3aojjSrSSkMaWF0n1wpu2hKKoCBOyU1tKrtZcZppjmTC4RapgvGrpplqms2DgaM9dUgaBtwkUW1MkI2ItJm01pBckaov1cK19vY8bxZuR9eZ92zaWohzYKDHCpWVvj2YNH5WrBBjyogYZyVMEtmX8IeCDgIxuBmHw6X+vEqGYFZLrQ+O6lAmb0/NY9ImpPJGnkUMaLURE4GzASBXAYae4gPc5emQntCIw00SaEtaqu161w7XBSNXx4S0QGvPoU6mSB1I+yry6hJuqyVsdfijLXn/gFh5QqNINiDkS2kAzclCDRKs6GhxPyn2vsmgIQlkVwCZF6u5VAZJG87EgCEFBPSjWR++8QZmtOrLIgg0wbg5IjvCe8lCzwysJIMFIQrAKmGkeW9Lr0JrLdmxGS9Xdtab92zMlhdAkQ0z76zrmGVD7fLglOWm0j3DeTPwMvOw9MtmAKSEr+aA1gyguJ3fFd29nWB9ydx65vIoXpkSIhK176KA1/KclbhVOS/nO6qQSdkRQt4pCaAGRoJkKyEiELx5O6SOBN8RnCn0uqLjElw2rZa5cBbCrJCkHdm4LiJqoaZyJz2G3cwbU0jABel5UTIZL4naZRofqZ2FlCqXaVwklHTt2iUcSuNMX6qWjrF687xQ4TTRKoUfY2Cr98xYqMnTas1dYQswcydNv3kv4zgltFilaSANjCdgnbqpKfqpDf7h0Ji8UqN1ycZoS4XhaZmdhYrq8BStlOlaTecS1aQlDQfoWo3bbGlumRDXrJxAco54wOHigOZIYwXFmopWlDCJ0EaqicAtR3HbU+qjy+iBFeLaENdl6xwHFdJ1uJsPo0sDC9dUgbCdkfEE39S0S4F2xWr+uFZI+xrCJJEaoV1yhBunpTaTWKmBoZhGyNATVmrqOpBXhrRrFdVmQqqGyT6PTDJumumWhex84exk2rVgM6hgegarnxqTRxVpGMgxU98yJpHRyhNHFWjGt8bfkFTKMQxq4oFluoGYZ2MIum3H09rRVjVhmqgmU9wtm9ReaE8bkCu7FX4qJuFfOcanNDYYb5mnzHfWsTTJUa+Dy2K8kYFnfGag29ew8tlN2JyWKs0ef9OEyWkNtDbzag61AOTGUr6r7Y5wNKNtR1xzJOetFtVYDZgGoTo6IRzaxncF5G5N6M49lfHpNSkotJk4ckhr/KY89PgW3LizHq7ypGFF2M40n7kV5zwpK92BEXGpsc7aWWhLtjKaSwmA7SlsT+yhFzFy7Jn7SU2Fn0QjJ7vi6w/eUuBrh64EJAnVdoTWQHpw3pSGg5U6cFkgWSZet1qjboDvkpUVGHfQduRhRbc6xGkybkwqdZYE7EbUVmzRWSgWJ5aBlLNVhq6t0KoUXRRJUibd5oWULuE3piRpkGCeilzboEPKOOehNjkAN+7g8Jh8eiDuadDaG8DdTvZcDT3emYfTJ0ptpmx1i4ton7Ra3r1gGXcIqbKQ6S4wMjeOUTwwqNVf6gXuNOVSTw3j93hXlLLFslpy4ROBXYOYLBkhBmjcLIxaDnGcd2XeswI2cGmfSdWHjnrwMh8ecjsgROdIsCf66Nxmu9K8SxbUTGtmDj/MANAso2juk7HwbJ+tVOSNZh4VZ94aUQv72LKyfgmt4At4mSP/SgFDfSaRlIJoBjqj8UuqZJlBzlTQEUXV4bwB+ODTbm+Ldlj5N52lRoNQ+QRJjLC78yjM/U0FxWqJ4BXl3tL0XJxRlCwoNxMEjFSiBBLDkBn4joFEvCSC6CxN27gzEU+mcQmY4lBiqpmqJ6tQOevbFCnU4BLaUyGXUFNkAWbuFrZ6cIVD13ckzD0sCcJ2hJjJSxVpxZPWlqhvnuI3xkZWPDI1El82Amzcu4SOGhuIvZCWaqRNDA53dHFi+h4Cohbf1sa0Q6r1Du8T3VrFdG9NOm+/SeAL5KUBGks6qlis3YlAl4yAhqneojA5dYmqdvjNCZMDK8TVCt8loiRkOELEmSS9r4grARfF3PFOrOihKnE1EJ1QTYzv4sa5V3Mysbb9y2iMuC0DZnHZ0sJdzHRDR6o9YSuVzjDhJxAmCp25e/PqgOwV32WqcSYOHVrXSJeo15Ww3pr0e2XZZDlHGI8J6xXJ1aRGqbYSzXVT0p7alIgrsbd+/8B6Rqel7lNG2kxeqWkHGR0UgDYSU3zNGT/OVkdqKZCWnCnMTxQlIRFk0pngWFaqQ2NwwQY1MY6Jb4dU66UieUrEUWC8f0BTRaRzxMbInJMlj/OewU0TxJlIVdozIHojdLPpmJ4ZmOw3r1XO0C05JFUm0Fh7NChSV2QXCJ0SjrY0h6f47Zbu9DVTjd7ujP80KgUns1IdncDm2ID41gQmk5LyGWB5iTwYGKcGm8nnQWVp042zEB+lOGXxCOSqqPYGh2y20GakGRZlbMo9lSIYJoRxxHcJ2fz/2fuXWNu2LT0L/FrrvY8xH2utvfc5594T9xmEHRA4gjQoMaRCsrBQCiFTsaECFUTBdSSQAJkCkiVLYUQJUQhXSTIlBGlRQJaikBJhJYmEyJSNKIRsMDjsiLiP89p7PeacY4zeW8tC62PMuc+9Qco30/eQ0hk6++y155qP8Zq9//1v//+3C/Z8ZjmM+GEXPYp6nyCxddTWaKpYuxOpFCiKLJXWrcY2hKuI6lsIIilylbznV6VLOMhagtb1CtG5XGFOUabB0ecL+bPM/K1jAOyuR/JumbcS7KBUogw2GWmucf1Ve9pwbxq5H6ljCj1pZ6W2tN2NJfH39ChqHvfiVAMUpWAyo4FkzHyWpHfLFiwHw0bWYCYceKn4XYoGkd1mLRDg4Wb2XCsy4Fu+jKhcGY732JGbf2u8ehX7CtdykOtNF2jjOvPcUDTi4D1OZ8vbl/eftpaKrjsaD1nfNy8d+fRGiu/t21YyIj4srcfTPzwMfB13BFMTTiE2zYxo2JnNW7AgIqQUtuY1C0b7SRUiBT6pI9pbj0hjhZBRRqKXguI22OVKM+2aoGg/ENsSp1p6VTX3FgamOAGAtF+v1NuGxvMjG2ZUp6TGTiv7FczQOviBJJFLk8QoBNApLMxWuPjIuUXH7DFtUuI45m5jMwQLTojmr/gqtq/BzN/n9ovf+ZD/5QefX78QIrRdIr8z0rmFeHen6JsBnyvpdI7ShseKQqqhnzzRvj2gz2f0srB85w3n7xde/Y9PjG9n7Pe/oH73Q+w4hC30VGn7gbpX8vlaFyVnlm8UlrtEOTfs04rXaB/AfbindNW5vDuTPj3B3Ui9v2N+k9l9OoZ+IcF0KAyPiXSpWI4O0TjozNY3Kp8duSxoy6QkzDvCbjsZ5eTkOah/2w9MrwumBUQYXiy0NF9M5NlYxkw9RnKrvjuTnjM7zTFOpRBEeoL6jZHzq0SaMjurpFlIj2fs4RCraItSjOeEHQsyN0wgPU/Rl0dg+WCglUwrUI9CfrLu4MnU+4RrX0ErnD5S8pxwr7RDUPzpnVGmAJJLduxVYjmEc2T/w5nhx5Xh7OgXz2gTfMjYOLAcFHElXQK46GnG+0Cmlxq27UFYRmH5eI/TqCmhDYZnx7LAqMhkkd48ZKY3meX7O+pRGN8t7P/OCTmBHwuRUQOSQzScG+RPXhhqwpcFquHHPRx2TK+inYLtiMm0RqsILOrlvh+i3PR5Bwqq+N0+BvnHS6yMDzva3RA6rBtx7ap9sCJYLvFaA99nIhNHMFG0NXR22hC5QqiS5rAet1eHYBqQSJ5WiWaYqccdaTTWVAO5nJDTGdd7/NWeNiitda1Ia9igRFZL7F47DJj3ruEeuUkQ4B8Ew9Ek2G6AA+gcgFzOE/r5IyULy0f73kW6T2BDt587tMFJJeMpdD3li4X2SpnfjCz3Cb0EA+ips7hlRRASZgLoLMQNsjBYa0vWgZqkSJV2leiWnNj6mEkJlmddhJDinICFs0yjzLnmx6z6VfrHrBWXSJhmAz7cgJSr04eboDh5j9nZSkZyBU56A1h81cL0MtFtgO17/Z4SrA6i98pT28d7AK3cn7uCrl4u2t5IvZeHejmpAxnZGJoAL9xoVLY/naERNbKGHTkwaOS0eGcp1g7nt/ppVcjJsF4yXMXLwcr0VgN+dTeFkymaRi6dAVKcIQWLkzREuwg0SSHt3SpkoZ1JUlEahcoux2Olu5XyWr5aj209lvVMiSNuiDqTjzzXkYuF/vNoBdFKoY+b67X2KDXNriT55k/Mmz+P7Wsw8/e5/dN/5Dv89n/7tyJ/Zcx9VaYsrwd07uLEotFV97tH6nxk+OSF/PlL6ECsxeDsRnuzI/3ehfzjZ/jWhwGKzudIM33u/V0IUWB5O7N8ODLfpahDZ492B+eGLsTK/ruvMMAGxfI9uSTyy4Q8T+SlIvOCvd6He+YuIuPz44zpiOcAELZXZHF8jJJAmo303LAxRQ5NCYCizzOHt8b0wUgblLz0EkVz5FzJFu9DX9nrpZE+fYqSzi99yOnjTD4nHv5muKukGTrXGFSnmeXbryL/xYX9p5XxswU9Lyx3ij5N6KnGJHbY4x/e0Xa5B/0pOmsX0iY8hfC5HmJSq8dIRlaD9PYCHx4oT73uPEWpwXMmz0Jrhu2VOTssifZQEBfSKfJedp9MWA5GQSbD+gRfh0TLHtSvCO0+00ahtUx5WWgZlodgRNpOsDFWVMMXxuHvvbB8tI+u1DgcFRtjhtDFGZ6EwyeN49+9II8Xln3vr1SWABVz9GbSxUJfYg53e2pSyrsz6dNnDn9wYR4LLLB8fIjJj3DR+ZtoPok76X4f5Z8vXuDpjGTBHka8xoCfnhdkzHHNq3WHV8KOCSzE1Vuqq5Y+gQajqUtP3p0NGaKlRB0E3UV/gHqM3mDSJMqjObp8r+UHF+2sUUJq5Kss9zlKNXOAMNHoQL5OjlohnZYYeXvHc8udTSwJCyEKVEdbw0alMpD3BT2NyMuF9OkTslSWb9zRhugivzVz7AdrBr4D1zFacAjBZu4TWSTo3LNh+xyarT4zrqWkuNqy6TJi8RITT9yfGsdXguT3HG1FBMGrkVogiDb249MAM56ujVvZ2BmP4M6ezisdxFBvAMMmwL06k3T94Ut/JBGBnKugd50huSlt5evvpQOYW4nPBpT6/l0BXn+f9d+rQ2m1WWe/lo7W98kdwLT+87K+fzDAsn2WdyYl2BfoWpVe0oqMlzVkzjcgoJ2daR6vS/1aXVFdHFySKdLrXZDknQXpwlxxVFu0FaHnx5gwpEqhoki3ZoeoF5wxhbi3ueHIVmJKYiStjDpRegkpi5O1kfv+rVs0lQTtwuF2g/dmMskaLzZwtoHJwoR9sWg2adp6f6fYotylnFvhe4c/8pMT589h+xrM/H1u/4c//ovo/zm+C2vT63XgqIdO+zpBN3tkqLR85LgYaXmJctPdiDydI7jt1R6toBfn9At77v/eiBz22HffYEUZ/t47/OWC7gbKFzNYo94N+Jiph4QdlXL2aOynQnuVacmhCvU+Y4dCPlXSBWQYcIfx0wsuI8s+0XLBU2L3yRnXhNbac2oKSSPiPk+NZZdYeodwcUd2O/afzOTnFs0EM+ix4GPoI1ILK246TVjJTN/YoeVIkhK6jnUZMo6x+m2EQyX3DsaT9bp/nNf2Rx8Yfv+J8lQxlrDX7kvQ+G4wzaRzI682eCO0EBL6mZfvHJmPimWN1gFfTHCeqD4iGCZOOxZmFdpdrGzFFZsa41OLoMEGqZcKvBDA1Abq3YBOl6CklxZBeV4iQj7BMgAizB9nnofIFEmzM7yNPk3DZ8/hOjst1P2IT5Xdp2dsSLT7Eit5EbTnswzPFV2M+s17po9Gmi3kdzPpYtguYQ+F+UHJUyH/4BS6jb1i+6Gn/gopFeZDiMplqbB4TH5jQhdBa8Puhk7LJ9Jnj6S3J6Qt2N0Oz0Nco+bIkHpmUZ8ZhG0SsLEzDq31EDogCbUopTV07nqnJNg+sbhQnibK48ySJLpsC91iGq/3dBWx2q6guwHOF1juqK+HSMHtOUo0sH3sQxviT55ahJA1jzYFOC2nAEu9M58VpZU1/0XQMSP3O9LTCX28MJ4n5l94RT3msN4Aa3NIEXroodBy6G6YHQawHNEDq3+4DXJlVIh8nlXCIT2HUwKHB0iyXsZJSstd/Vp0Kx1p1qjUzuHSoqwFD8cL+NDzYqIe0WfAHopHfB5B3uBVroSLX8HJmve2brL94L3c0LfO/IRehevnriCClRlay1lyZVSSb4Jg+j7QGR7vgCUYnwAZ62s2IRAEeMl+RV9KB2adfro9AOlgpgt01vs3rkscUUoNwchdKxZgJ9yYeW0nsOpyfIWkhkijKCStXFpmp6GZ8Q4AIvuFrWWLirHLjSFPDN2NlAATZdTasWgvdyE0U0ykm7cWBl3YaZSWpAOclXmRjgybawdTcQsFP9m6GyvQa8I42cDsmYpiLpzagAAF7SnBcdzNleqJl1b4h+/+GF/F9jWY+fvcvvf9jygvTi0eJYASHYa9ZwBQBS9OHSMQbhOffrRHzjO6zPD2BAlyGbH7PZ6EdF6YXw/Mf/QbSBOmNyPjFwt+OePeIh328RT04LtzdMr98EC7G4EWsehJKG8XuE9cPhpJd4X8Ev2g7H6kvJ3DAZJDJFp3IdrNz43yw0fa6/ve7yah5uRz70GwVNLbhqUD7a7Xjl1Yjl0I6UZLiptFUFkRZHY0R2qtUGl3CSsD5QcXhtaQaYjePENCzpdwygCIsHx8pI3K8Q9mWnGWD3bUsWDlNcN//wny+Rf4mKh3BZ0dvVyw+3uWh8LwboGnhg8D6WWOMt43j7R9DErijg2Z+cGRNyOizjJE12zLMYilKY4xnyqca7A8GcpktL2Q3kVXdDOnPeTIE3qd0OeZ9jAikqjmtGM4lvJLw0tj+Nx7ewmnvERpLtwskXybvnhCmmBN8MtMemwMnyZIGVkilNFz5LmQBzQJ4jtUNTQNSVD3CLPrTSPd+zRRG8vrkenhiC5GPtcIpDsvlMdLAJNSqHeRa2JD2MFlbiE8/uAefZnQz07Ieca+/REo6BIOLtv3klIhRukKWj1SiHOwLmKxct8W6x4D8FKIVXhT/A6kZcrTQnqesQVsP0ZoXLXorl0NhoLtM/YwktIr9PHC8MNHxB+wMfQikabsVwCAQ1Zqjq7R0iBdevbNOTq1s4teZFr7anUIEYVrQ0xoxwfS3Uj67MTwB4/omz3LocCi10aUTrfjd50PUXrQXopSgfqQqBls7OWWvpJXA2uhx5E+0YvF981biMIdoCh17NeZFRAF4ql7IeW02ahXpsBLlP+2shH9cztIcQ1QgQW4kXkjm4K5WVkMjyX8mi4tfi1HtRV0dFdQBPVdP+rqlIoT4T3e150tT8aTB4vX61KrzsW3/STATgchq3B3E/v2t9+0Mh3MyMri+JfYG/EeTucbEL+WmVZdS3yPkkYmTJBpEfu/iG46mZVtAadaoqSJ2hJjbuTUcGCXWg/AExZLVE+oOoPV/jOMOnFIIcxNKVoPVFLYsDu4KhK92EyEhiI0MgFkhuRRToLN1XQbzBM6m84ddTu+uFA99YA9IamxeMZYM4KFU7d/7rySxLbTXV25WOaljfzaq3/sp86d/6C3r8HM3+emquw+PTO/3rMsjfZGaWOvyTeHkQAWHpRumh2m6BFkuw84/o7hlwk/FFxjQF4eouFgepm5vBkY3zXKqYY48htv8Lmi1qJT9nlBz1NYqC+V6XuvuDxkdhaTpM6N3Y8WdBlZjkq9L5CUcorJ7Hr3GbrA8C6st/bBHclhHkPfoaeG3yvLXbmmuCpgkTgcK91wcdkYFvVWEpUQHstLxS+ylZB2f+8UA/O8kC8z5WnCXu+Z94rd70IoPcbt2EZlep0ZHp3d5xOpXbh8NNKy8PJLR8oYM6IMhfQykT95Jlnm/AuRaTLfHWmHTHmbMV+YvrejjXD3t06hyRiV+rAPxmruQ49LJCBPznCO3AgQ2n6kDjGpOML4yUT5/S8iVM4crwtuGYYhMnNqw8Vorw60ZJQvQhflxx1pMtp9CmdUL321sWeEOKAfMPz+O3ZnwQ4DklosInsDQqmVpo7nQnuIZo6twPBJpfz4BRuE9jp3stBpB5i+ucdzL2mNISL2osEOTLa5H9bMeJHuzpO45nJpW3YMpfRePl1sOjfacaQeo8SRLkZ69nDuVTYxsB9752nW5b1Djo7ealH+CR2t07KyvCpx/g+ZWiSs+tVw6Xbu0wznBc9H2jFj44G0L8ilkk4VN0EvvSnmYYBlLSHE8tQE0mzBBD2k6F00Gem0lqeCLZLJ0M6cmKbeI0nwhxEbC/ndBfnizDC1WFRo6ItEgQbqXXTsHlb6XtKyBZad0kbHi/coBrpJAKQK3vuZaYVU+yQ8anwHe3sIBt26Yke3a5AXox41AISzml8ia6Tny/gaZPfliV/kCkBagBBtca+qB/jDg60xIZgev5ac2sqWbHoUNm3O7Vxq3Y0TpaIOgOhlol4HCceVbzod6O+jfi1f9ZLj1U59fS6rUPiGJVytzrLaf2Im7+4l38SwwdCwPSZiqIYTNPVSUyOyY5IYrVlnQJyiQaclcWZbGLRiKRxEqkBeGFMjd0FwUmNucX+OudK8US0s1LvcXVLrvliUoLTDpbQCGwGks0bE/q4iZu2iGL0NQIyZLNg6d1IP0zFJoYPp12pymLe0wvjM6sq5jSyeI9dGotXC4spiicWVj/cf/3+aRv+BbF+DmZ9hU5NIZFyuK5GwhMQk4IOTTv0WUBieK2KJ5VVm+tYD6fc/g6VSi4TV+XWhPBu7/+lzZBct1dXD6qmnmfpHP2Iahd3vvcPzjKHU+4yIkp4mjAi606JwSAxfhJ1bp8J8TDiOZaXeZfLc3SglhagSojSRM8MXF4Y/eMJGiZ4xbUCnGnqDqaKnRCsa4XMaq1bbRUkoz3EuTELU2JIgO4XXO9LzTHo6x8qn5BgYVWmiDM8Nq4bNDZtm1JXy2YUmEg0WEfK7hSElTt8aWV6PKIU5hV7CMKgH0unC+IOK70aG0zkC6O52TN++Ryvc/d6F8ZOJNijnj3a4Gulxor3aRTdrc3Q2dr/3QrostLs98zcOSOlkcXKWozB8Gs0D1QyzSvndz0gfvaa+OWDjAKdzNLl8JTDB5Tt70rQDILeY8PPLgqDUhyE0DX3Amb6xg3MjP08xuAwpup0jQO7jdMWPe5YPj9SDcvifv2D4/MJUYHl4iIyfc0VOjVShzMLlw0I9xmfUEWSANAdI4CIoCS+G3SWWnYT+w7qGalQEJ52jczgpwbyQHy9YEmx3wEsifzGhl4bm1Ff/CTt05JAlQE3ntH3tjK0KGoO0zL04cZ864ExbPyivENorxdMILqSlkh6nEDaPoYuxfcyEdkx4HvCpYTkm8bR4aGlUST3gbg2mrL0nWRslBODmmzBSZmAPuGwCTRMlLbC82cMuRQZTDQ0LtQVzlARq15L1iaS+ydRdRA+4R+dzHztwMo8wQ+klJQVJEWrJvE7G3YJNjalF2EL0oiwKttPQ1UQ7td5Eligf5AChqyD3p27reNbFtOay9l6NIW5ZQUW3OHe9Bnjv8M77LMoKMvwGVMkKpJy2MSgBZFaGZmNgNjLBN8Hw1gRSeJ+V6SzLCs7WktH6+jiFtj1P+hOjjOe9oWWUeaLgs76+e3VSBxAaLQpSb9o4EMm+u1yjAzUepSWBXTaqG/s8Y6RI2e15M4srgxsqFTNhTJFPpTk8TOMKXCSoKe//7uRf1+tA8or2Pkv0vBf3HreAd5NpHP/6fweqS1fpdHGWRzmyetiuzUvPzXFKB3iTZ2ZXmilz3xfzOENtzb3ZGKCf7/Y1mPlZtv4lS3r9osSKxNBz/30W5BSK8/mYyC/G7gcX6n3m8r//NsPvvo0Ow7XB7AEM7oawWs9LiDdVI7sD0HMNTcTTGfJAfqowZOTzdxSc9vFrlo/vQmCrEUWfTpXRhWWEOnTdxSkCzNoA7KIpZhvoTMSOPERInrttQlIrSrsbsR71n5ZY3cia5eCxqmrHHIMn0FqwHPZqIGXBFyGfra/uhfw8Iec5ygEExd32CZlD6FpOIZbWZWHZgb5MlCdoY+HyUSSKLtkZdGR4V6l2wg8j7dWI7qLdgd0NpItjo4e7Kgl6bjhh+fal9hwPZfjkRJor6fHC/O07po8PtF1meNcYFqMmCdHkoEgZIDvLN16hT2eGi1ET1LtM++gBV6U8VdJnl1htFwI8kmPiJkou1gdtqR5i6SRIVppF5/P6and1jS5O/nQinc54GqM9xWzoqTJ/64HLN3bYkNG5MTxWhk8eERf07i4YJIkJTWu39Sog4eoJN1GGbi1nit5b6d1EfrogkmBYl/pEyJtKXMvueNHJ0GmhHcNdN7xYpAMj6LxgKUpNgYzX7023eCdCRzQ5+blCkRCfn53cGZk2apTCxLFDAetiydngfEFeztAa7k77/jfgEAyVycqsREbI2tbAxoReKtkisduaBwiTYB6jyaKiZrS6ujYkzpVHpolLJEHLIZMvrU+0YeeXlxntuTJtP0SEw9moY44k5xSfR29j4NbR03LVMawZvUKk55JiGlILhoUaTpxWdNO1LIde2uqTevQO6uNWP/1reWh1GPk6rvnNn3R9TTAgEj2WMptYeF3mi0NbQcNNeWkDNTd4wr70mEOwLavcytlyYDYH1fritfSEXdmklThYJ+wVwKT1qDqI6Y4h6Vatle0Qwqmp2bvOJRJ3Wwe0MMdwrmsmjJNSI5mQNezZOUXc/z5XVt1Nwyj9+eowamPxuBY7XQIsWaJJQjzuHdWJ7MKgtjEvSrA83XOH0tlU70wn6znp1vl+Mqxf2Z6Etp2LLhkPN6yHA0mdYI06CImSlXOx247YcfyLhYi59nN1xY4BZEapfFXb12DmZ9hsDbjKV7qUBdwsKDvzmDgXJ789014fqPeJ/ScVP8H8kDn/ox8wfL6QXnrOxSAsHx5RUfS536Z1wV4f4XxGxsL8ZkT3HzAPhjbFjyOpQDovJFPaueEk6n3C9tGlOS0eYtdpod7vaYOgs5PcmUuUwdI5Bgcxj4aRx0R+FmSZYtV4mcO62grZKyrCfKfRc6r2wDEhwuuKYkvQnC2Bpsj6iMWF95VaiDrTZ09IDR1IXhZ86N2+B4XThawl3CgYvotO1jYI+RIllNwSw3mOJp2v98igpOc5BvahoM1Jnz9hhxx5KBK0vlZjfjOS05F8adFTazfSDgPLhwdspwEuk7AchTIllrvQQrmM6FRJp8iVcZWw4s+GVqKfyuIcflApPzoxHzLnX3wIELSATZH9kSq9q3Nf7edgq6aPD/hDJjVo+4TXnsZbHXt1xD64Y/6g0HaZtDiXP/pBlIVSUMXR4wokl2idUQiNzwnakNFdOKvKqZFOLUDYfekTJ4CSJ6N8dkafLngJkboPmaE5Ms/RyNCBrOjpgqrFBDRkXHtZaGrk87wlBNuYwTJeYiJfJynf6G/Bs6NnRy4Ge/AiUDvA6gCEGgN85GuwpaNKytHI1SUE7PdhV99YAZHoJ1aj+m85EoOl9rW3BzPk6rQCisRnzSBTjXJRiXBA1FnuEy0J3hLJQjuXLk6aavRcOg5wmtHHCVk8Wn+kQjkbVo28U6oIpiHMZQWYRFaMOtGzaRXPOmHHdo/jIK6zirMcbsamVYBt4aqKk7TNU11A28esldW5HdxuMcCKdFZw023hawkLjzLYinFl1cfcfMZWyhKuwXvp/c9Y9TVIB20eP69W/620tNquV/Di17LUej8JwtWOvX522JlEY9KWFDuvq4PptgTT20aEoLei4uTsDMk2DU3CGVJjSNafa8wtx3uvihM3hhwlqepKUkLzpoqmKLUVWmjcULLOqBhZ6K3A4jVKlK/M/ZrADN0FHw447596FWXLdnr15g8I1YP1WUwx1w7mIqZAYMu2cXdqv4ghIO5skVQWUgevsn1alLjaxjp9FdvXYOZn2HwUWCrSrW1YYjxNzEOI4tIpap0tR6klTRblBHPKJyekOfOHY6SzHojV3hIzW1VDHgakGrlFjwsTEDMkZaQ4pVnIzy9GPe6wuzFAw+KI1RAOzsGceBI0Z9qYaAel7jL51N0+50DR1t0k3ojBtTouFnkWrTE8LkyvS/SccWe6T9RDZKjo8yVATBcMX37pA+reaEUo1XAi3Vf2O9QcvRjDU6UeB2Q/UH70jvZmB3KgtgVpoSux44hVp1zA9hkfQvcznA11YZ5T1wYodcioCKYKh4y4Ub6YKI8TPJ7AojTjY8GHzHIszFlJF0inGXY5HD8lmocOX1wYftx4+YeOLK+UNkXNOazaidM3R4YnRVLChgNLTnhPtxUJlkJqBNjZcY9sS1SHQWDRLrB0skUZRLpIte6Vts/k596AkxhgnYj5b/sUrqMi4YKbDa1GHSWs8QTQEzmQeh+hlhTfF1CoSUjNyCfrYEaQQSKJNmsPXQM7ZNrxjvow4KoMLy3Kfsc9Ps1R3ni5oGYMT5ce+Z7RJJHwuBICtUVnaxVkTHgz0szVIuxEKSf1lefipOcz6Zypr3e0ddlovXSTEy6NRpSO3AU7FOo+gRyR5rQx4gO89+aJHk2Ry0KRWHVDuH3EoyHofY7STrNwig2RTp3O0Ssqz9bBdLfK9wnQDgEarBCJrq7IuQVQLTlcRUB+ifTncanISDA6e6fdaWhNhs70ZMFq4LA0xTlZEDhHOc77ecKJFiBd0EtnPSCuXz8s+qI9Hl6Bi/R/9Pnodup3+Ilu00KA7pWR3iZNi7HLuzh4ZRpXIe57QGadhW+1LtafuwKgzpZsomP8J8tNK4BZ51KNvb5Oq359bv9ZNIwMSYlU3n7trmF1ccDNQqid1FBZAriokVW28pEKlM7IDNqonUnJElO8deYkdRGxqqMWi0XV3iML39oeFG2IV4Y1tI51/+y93qIJ5yq3vbJ23o/eNs5lw6nxOTcQyAzchSbCbEq1HMdvTk3RxqBIZfKMa+XSysbprO+ZtSHuNNct0E+Itg1JWj+XX832NZj5Gbb5F0baBMNzWzVloMLx0wXzaJKojxekOWlqcIQsJRrVzRV/mSkqpBZajFYEW1qkdWpockipo5hEfneC/UjyKN3kl5nFDbt/1VdolTqmLURUTw0vkaPRBlh2hfGLhfHtBHdCfpnCZZUEaRVSCv1Ebw3AkJHLEpH2AONAetewXWF5PUTewty/JpdLRKUPO2xfyBcjP074VPFdiR41s5AnOH9zgEEYfucZPVfm770K9qEklleF+aAMn0+kqdEU6jFcRtGTRiELNcVkEsJZWF4XUlFsbqEXeYmMnvT5C/p8QvIQAtvLglWPZNQxM7w0xBptV5AalH90djb0xRh/9IS68vRLd9RjDLR6Aq/O8maAQSMs8FIYnmp0uK4aWTzA/CYjD3d4Cj2OVO/Og1iJp+qoh9OGk0WA3F6j63ZN2K4iM8hzb8dghueCXozxXJk/3EMCfemi0wMbI5gvGplCOdMeUrSFGLT3QILhxzPaYH4z4IPQcHJ3cIVA1vFjjjKnR0ls2StuhfLBHf73PomJUcMBoVOFaQ7XVVbWLgdecgBICVrbm8HQz4cF02Cpyx2sl0B2gtYSJQ25Tnra2DpFg0JSfAnRY93nSBr2AC3SIltoTSf1RKxgVwt1iu9dsEZyLX3oOjsGnWNZ8XtHlyt4keakS78fWi+RKXhRluRYKeQcvbuwCMeTGpk3+d0l9m83YJeK6EAzWHAWFRjpVmM6EBNscZZBSFnCTdf7oVnuJa+1mSJXgLhVi1ZQ0Ce2eFI/ji8Djf6r9bUr2/Lets2U1wdc+2KMm8dXtkVuXnMLZtYJT9mAyXuZNGl9jmxlqNWpte3I+vwbR81WatroKECjR5YIpHxtBLkBDbkyMyLWmzQGIzN0fYz2vxO2Pd68O6A6SFGP5o3NokyVevp3Fse6hVr7fq932NKPT9dJ5Ep2dE2+UP16aFHSkbCDr/3HNhZGuKqAYDXkrxDHm/X+SonaMtUTs5fYf3GKGdJ7M1VLWFJebKRI7feDkLP3bJnVtbXuw9XWvgmNv4LtazDzM2zlYWR+mqnL9bGoxQv5tCAITTLQaPcDKglLMN0l5P4QjoynE/ZwoJbIJmmDk5FoS9Ba2BxJkFKsFJrA44n6zQfqhwOkyMCQ2aj3GU+99YF3a/XF0PNCegr7Xt0PWE7UfcaHXVi95+7gkLR1/VXppZ69wHGH4zFZXc6RhLpThgWsGPV+oH73IUTFeWR60IjFf0rY60I+O/vfe2Z5sye6UEdk/Pk7B4bPZ+ZjguOO4bFXe5NgY8HR3utHqBqfr1Ol7UowMIC2aEa5e1tJjwt1UNoh2I12P8Z8Ny9BGw+l14U1ekN9ckHvCvMYjIheKpbzlgTqdwM2HUmPc1hl+7dkfAHmyrJTTJXhbGH/bgEc80vFZ+lNOrXrB0Jg6E26ULTGSFYNdgPtYYgQsajCIItRTkZ6rKH5KIK9jo7rdZfC7m96TW/dZyzRg+hAn0MT5UOKbtVbUllMrDqHDopBo/2BJqTFGi/VAALppYU1WaPtRH2VaUnJjzXKAN/+IPZ/6mWdHnfqpzNaG/5wgP0IvY7u9yPLqyHAR2TDB60929YXyY1wTTm0Y7i7pMUAGaGBkE41GB6NMq+VtRrRbekLXcuoW3kpWAoNkJCASpStCLYTka18s/H3rInAfZJYQ976e+jMlpib5v64EMLbvdPGEHbnyRnw3lvKSEtjUUV2bdOGqBnjY8xicyIATXfyuELK0GoALhskGKzcw+88vgd9BNoYDFOu5ZpthA+B8Kp3uQUqK2Oz9jd8jwn5ie36yi075pbduZmQYwevYGtjglZ2Z9PV+JX1uWV2VlQlAD10Z9XWbKWy9XPWXVufH6Jd6WBkzbURCRZH1UlbuavfB50ZSVIjY0ujq3XujR1zB0BJfKuCrdyFanxeUUGlkfvCJfBasCPaBWZr9svaAkC2Qo1fcWJHoe7X3B4Xwdy2AGPpx7NCGd9O8npq4vFWhWaJsxdmK8ymmKebVgmQuz6nphpgj0bzSDFfGb4h1c215b5a0DuY6dfY/KuDFF+DmZ9h++iw5+Vppu1SUNTERLE8FPx1IU3O+MN3cJpY7odgKPYjPk+xCtztsWL4XqJmXyvDqeJakKTIXJFpgbsdthfmb+zBnDw69YPQlKROmgiN8vkzvhuY3wzo0xRZJbu0jVKeiACvY0Y9JgGtSnqZYiAp0HYZ9UaaI/0XL3gSptcDtheY7kMdXx2qkx8r+SLI6cJwdurBSM+JdszkF+PyzcLpAyWd9hEe11fM6XmhjoV0KMQUGkFm0TVeaHcJOUho/FxwMdSE9HxBl66byJl8MfR5QWaLEkruFGoCyYqVEZmOpHPDDmO0aThE+UGWSjovDKdGHXP0T/EukBajjcr5mwdsCKG0KyGSTkY+XWi7feR+FCBDHUKnlC5OfjuDSJRnElcxJg4DG/fvu0Q7JmwEvRhlctoilGejfHFBmnD+qFCPKcCHStiqR7bMD3EBjXJLmhvMFqWqISZorQTo6LZdnY00RSNJebxQNOHfOdKKkF4a+XwJJqmDAVfBx7i3SdEaYvrGQDob5d2MPk/407lbx1MIgucFvniE54IcdgFczjO5vObyJkS92lskgJNPldbPBZqiyeNikVFT11V2xrKgWeOxZj09OVHH3h5Cc5Q+F0IYC/3e73S8BqOna56Ir3EK12ui+YbK3xjXa4lEPITENgQwxyIPxghmycSxAdpeqGMhz1wXOLP1NgxB/beU0RrielwY3xriyuWNwC4+13LcX22JPBozRSSSkIlKBF77fcANsFgz+W4s0rH/vrFc27YdXC/WrADjPUDzk6WDjc1Zn3fD7myqUNbJliti4soAruF3m6X69t/cfP56MYWbWXzdJ0e2WSyOb2tS2RkYkRDwijruvbR4w4Bs30+BxExJAVqKBBOTdbVCe0/rheQhbs894TDR+zOphGVZu+4JiZwXCe3JdkBiZDcqeSvWXBHZFZz4zQkxD/dXhBprZ3nCdxVlH7reRaJnlMSYOzvRY6kVaqfMnNBLradaLcpj1RXDad0oYGQmi7iNg0WKcI+lWXmzbe+jbdrdT9wrP6/tazDzM2y//M0P+d0fvOvfmphA5jvdxLDp5YLvB5DI9Fhej9Rjpn10j86OSCI/Rg7G/I0d42f9+7UfOH9YyE9GejoDQjotyFRZDkp7vaf86Jn20R1tF6vzJBr1VDPSu0p6unQ3RYFao1xUFGTB9gMUxeeJdHFojeVN9Hyyo6LN0OywCxFkfTMwPxRE4Pi7xvjZmfowsOyF3Rw6k0RGfCE/T6RLwtKEPYzsPmv4pzNSG+VdDSfNIfJSzkdY7lK4SIrRiiKmwSb11aM8hSAY9wjjc8MOSt31FfUcA8ZyEIaLM3wxkcyoxwG7z1hWpm/dUV4abZ9JPfxOq5E+vVA+O5GnSkmKvTlie6IMUwbmfQwsdRSsN4BNF9j/7Ud2P3wm/1Jhuc9hpa2AGjr1RpYPQ4CfQ7w+zU45O1YBETQpdZdin2Zn/NGFtEgfGIU0twi0c0e/MBJC26cuEL32JALfnERo6EIoiRqzVgxx7lHWHPI2iWnv/q1LWInzJaLT0zwjl4pI7amzgu+VTVuRIuUagfLjGUxor3bx+6VGGbA5XCbkNIE1ZApmTFUon51o+RCAsYWGzHOUYNK5dnCitGNCWoTe5Uu4jGLl3uP6cwiC1cDnFgPuLkUrkZ1Sd0J+6S4LJwLxmkcfqs5A4lHe8kTkQDlh+KiQUohu1/lS3DHVmPA2Qa1i2Vjt2t5zWPLF8SnC8tpRme4FvLD7QkmnSrWMJ4cGuTrytmE7ox4y4rD/1IDE5QPBdzHTNPHeAmIFWp2RMbkyL20jI4DIe9ls0WteSwc/4qxxr3EO+4RkKxOSbl7L1R9zU7+ITb7kTFpFuv13t/jH6UBKwG5LRF92IvV7ORTat58ZIEfSRjJed2NladbP6/a/yBViJTjCycZaGlnfYC2NxM+JJfoepRAiqxoqsjWazBKAxZGtH1OS6AwVzE2IhAepNCJIb/Zg1ptruKHw/g7BDrm1tT8v4Qi6ltKkO4mcqLCZB1Mzk2Khh2xfzxXMrPckDlbjtp49s7REde1OpZ5r1Et0K1OkEqJfIb4DgzVmz0wt5oCHMiHeNnv47fU1D1Gx8hFf1fY1mPkZtn/8V7/L/+1v/M+dV5BgZxTqIWyuRStLvVDGHbLMiI+R03FpMJawIy9d57Eo4onhJDQ3DmbU7CRP+DTRjgN6biEcPhzIn77EBP16j0nYVusHd+THCX06Ux9G/GUm10Y7FuT1AX2ZyS8z6XnBs9DuR+pdlJVcGz6EIFUPvcSl1ss94YZCYHqTsP0d6Wnh8LsvvUNwQ0J6HwLlrjlIn08M7QzTAiUzvSrMr8boi4MzfjZDSpSnBX18CVrz4Y7pw3EDD3aXsUzoXxZDxoJ89kLWEmxzrXCpIcLdD7R9puUUGqBLQ9IS5ZgUo2P0OhLm4wh3GfvxwPD7b5GXM3IqcBy3lf4yKFacthcQJ50jzTldWuiLniuaw7aLSiQHzw1Bw+EzG/puQY7lalMdwWdBn40yg7gyfnYhvT3hb47UQ4leQHfC9PoOTxJiwRRMWpoqufaRvueZpHPDx4FWhHrolG+LJOY++vWJuy/BRDATdFewGsLh9LywfJBpRVFV8qViS4jIyQXTzna1Xi4RiQ7QRBf1+mqHTkuwQCrIvsDrIyyNdO6J0z1sLz/OoaHpOhnX6CmmLYLYmJ00V1yF5SFzPibyk3XHWwDEtADNo3Gixj1MM7xIJC0rwXb0OUtaABZdWs/F6eGSRuTZlDVh18PO69LlGgFK1rJMKyFOXkW4EJotsZ5I0kFDWqKZqU1G+7iwHIMJSPsMFlk7Oju5l/vyWyjPFp3MRZBlQJfE+Rvam50aOkUGjh36dWxEEKBHxouoR5luZT9yFzz3/JUr4LiCX2ldghKYjNUZHmzJmiET2ybevWKA/pdvTNAVZ3lfta/PiReHiWGbta/ZMOv737A718235689lDbcs5aNtF+zDsaClekqdgkgHSLfztJ4sDMq6+Md0PQO0kk7A8P7+SxrKSX6IfX3IgSvTgCZ0kXCJVAiA8bS2wY0D+AQriABGoZG36Sea+MS4GWlPsQj06q2SOttKIsVmieaC+7a9z3E4yEpCvTWqrOYUEmYJ6oFkMG7RmhjZQKIuxKf7dIzZUJVPrXMuYOZUysB2vDNmdbJMMyVc8t8b/zuly/iz237Gsz8DNs//U/+EeT/8tt0eE55atRB8X2sfi/f2sM3BnR26uFIOyrMkF6WAAEI8gLDH7ylvTkgU0OfzmBjdKWvlabRMyddYjXpr+7wpNTvvaE8LpR3C21U9DRBbtHFd4L8WaMtC+31PrQjAjrskHlCcPT5BT++YXozonN0s/bU80LUI4Z+apR3M5Id21t0mSYo9fLFGUSZ7hOkIco3Q/R00tmiRGbxhbH9QP1gz+k7A+7GwxcTTVuU1pJGeF4OhqItFWHAUkKWRmqRHKxVkOeZtDTkMqODRV+mMiBEKS09nvGs+Mf3mCbK84QPCeY5xMweqx5Xx4+CpMT8zQOUzPCjZzwLNTvLHdQ9ESA3KDo38knIF6MeEudfeo19Nr8nRk4zyGlGT0v0TznuSLMhHmFp9T6AgkPYMO9yRPOXFGnBY6YecjR5LMpyCH1EH4vh4qTTRH43oyTY5cgRapH23HIMeDZ23YnF6i1Fu934/J32NFdIKEsRZNyTphZulEGRJ0GWFrED+310dR5CVJBm23qNiRttn2JAXsHcvkSqK9IzWsBV8cOO9HxCNEVAoEdXbnHFrcVSG1//iwyTGm5AvTTmDwo2BGUuNUS9JA9d10gwUlnQSwAErREmV0fHd6lnP2l0sK7G8AhTDuuzz047pGDjWme5dAXmsJHoGm0IPHVtj2wkx+bqsY0d0AASIqSzsf+8MR9CeD3fK16d8V0PXWwgTSDTk7uDqU0XY3irlOeB6XXXUSzBWr18P57vGuyLVg/NV5/crbdL2NJzV3HyLfNBZO9IVDoi+2UFJ6tmrDMhP1Hm2f7RpZ8bKLlhf/rjN1Boe8zUrm+h7z8/dugny1lbSWj9fXyRNg3MyrzcPn8V1Gov76y/15jLu/26P11AurA3gEwHOBhJegDdBs6sAw9FWTNl4lDCzROlmiINQ0jSKG7sZAnA6D0WINYoNAsA44Q7KPand5nv6G6pREidZxZXqgXDYt7F+R2EOLJdx9aU5qGot34h12Tf9UjW/19PXVBht1U/EWHxxNQySZyXOiLAoJFgvF6u5sJsiadlx6984x/+yWv4c9q+BjM/w/aL33oDs9AOEUKmp5nxHKg5zYZeKvWYo4PwLsXgvDR0Bp0n7DAw3wvpF+6QWrER5odC/WgX9tEWA2Tb32GEIHT4fEGnKVJTO+8oqSClwHnCconuw/MCY+hqdKpYycxvCqePXpMulfSU0KS4V2wQ6ptCPThVlZHM7vGCnlvPuYjJJ72bSO9e8MMu9Ckt9CrsvHenFlx6TP/SOe/aQrhZnPLSel1f0JcZGwTxFJbOrNS9UocUojIxfK80FdJFYArRJwaMQziurNs3a1DKts+k6qRPnuBhiNXK8xT78niBMcF+FzoiDCxcONNHO+pdYfhswvcZcaE8NXwGS8bwtKAX8CF6WWGRydKOKT77HGmv7kSX8aLomKk7i1GzzqR3PaMnZUyVZI61hjWlvhmxJLRDBtGwYs+CrO0xKnir8Xjvt2RJoYR2g2FAlt7Hp0bduxWNfk8ewYKer6UVqQ0s2k/Y0AWx7gzvKvrpCxSlHYewFJuDGzo1XPJVoNnTr62ALL3NgMZErybRVwfCWTUqng+kS0VrOK1cQZaGzo4NFduX0OxcDJ0FGxJkI79U0o+N84djLzvoJrLVKfKNbIzcIZ09AGiJezEi/vskJk7bZ7iEVii9GD4q0cmms2bTDTNxOznWmAxxejuI+MW6KN0m7dRfqFCzkm2dQIXy4gyPC+IWk9niW6PMYHQUl+hpJjnuAXfY/3Dh8HshuLZ9YX4YqHcwvwHfE+BFAqDG+Y5dts54bboX7dfjhvnw5L3vEz2Pxjf24z0L9XtgYx39rivyTfOyBex1MLWenJXOWTUpfOl91/dan74ClLXScpvquz6+kjvr39oBTgcWom1rSxDVKn8P7Kxhec0CBCRCOJX06moKQGKY6PbRuop5V2eSrGbo4FlyL9koAZq0u5qyNwat1CZUEyZRFhOKKrMlzCMjqhHBiXltHunR6XwxZWqFRgp2xhPuGswMveS6ugWdXn7qLAvr7evk3vbAfSN+AozB1jV7bY6+xvs4vVeTK47xsgwIzpKMYm373OrC1DJPy8A/+eGv8FVtX4OZn2FLKVFf9Rq+OzYm8snIlxZN9V4Xal8BpinunrwozA09Obgzf5BZ9kfy84IkYf5gxHaQqpK/mLAipBqdhmXy0AyUYC2kErT0ZQlKPUV5RiRhx0xrFc/Rk6ntQ7vRxgFS6T1znN1nUwSB5czDjxZMnXoco1/UAZrGclNU0DFhxyjl2LGQz40CXIaInVczDMcHR8eC97YE5MRyX5hfhWNm/KKgz2daTtSsyE6DwelZItKcfDLEhfkho4uz+3SG5wt6itbzJIVxiYaNlwl/eoFf+pjpmNn9T5/GxN8MWypcFrRFKYZFSC/hgFo+2rHcRUbN9Erj5zncEnJulBfwkiKB9lTx00J7tWN+FVocsRhn215p+wEYEDNsVMoFytuKvlyQz1+QnGAcqG+OtNcjzXJ0qTY2G3Gee8PJFIMgLehebcC5oZMjVfGh0+RduOqioQUB8uzIEuxGk1jJc6m93BQDrjbpQLADI1XyVKM30uvDVSBt0UTTs+AlRMqWIc2CpHWFGUvSnmgfGTsV/LmFg6s1WByGzHI3hMj57QUvShsyMi2kzyfkYWT+aIfdpy6qDZG3VIveRKcWmTpdx+AqWNHIjulZMu1gSMs9CE9CFEuwBfG3shwhJSHPRi36Xv8gOsZdmRjzEJ1rSFjCeWWEvb671NapOoCix3dUwO8SVoj7BOmNVkEvfUJPvYQA12C5nndjg4a2KgcrKUs8XWjk0Tj8SBCUSwIficaRRLlp3R9zv+a2fBmQdJBL1+FED1KLv9cJTvpTNhIlUN6mh+nnFW7OnwLclLN6ma4/+/2/15LUusmKfvxLv7h5j1tS6Ja9ee994olrPMUqPBZ8m+BbCwF4XkPrUqOkRm29VONXFicpUeaR6/XSNTnYeq/TVavT15fVYiwbOhO8YjVzoeLMFOaWqabM5lSLTtMLNRYfsJWclqpMLcDE4jnAysoPuXYWJgThq40brMfwOJquwIQ1+bifS5duE9/OU5y3W7s+rEAomkwaMLWEsGOxRu6gMbQyiaklLjXxy8dv8VVtX4OZn3VbU29NWY6J/FLRybE8QBUYBZkquaVoJ7BT8qJoSt2FYD1XRmlJsN3AKkycPz5g+xQuoNYoj9BwyBkZMq23GhABGwvSGlobslSkZNSjXDLvw4qbnxvScqz2hx1g+JCRFMF3aemDwNlCX+EaOoQBMO2r5UPYfQdZuVjaMTMnJ3vDJHJXqPQ+O47rELbWEgN+W1eEpxrpm7mECPSQWY4JWZOCq5F6Z2fbF2wH46fAJQSl/nhCXt9Fp+27PXKp+F2hfnAkfX5C3EjT1F043Z68VLw5WRV7qiyHfeT7FJgGIV0SnBfKonAGJVinOYOPibZPwTQAw7saolzv0fc10E1cD2H6YEQfMvJRlGsovddQSiRztEeopnOU5bwKmiFGyYafzjERjrsAHy/hMvIyRrjimhCbomTDaldfB/AqaG1og9p7S0WMvcc1Fcin0BXMr0sE6D1BH01pu5iEo7TSJ9s+aLdEtLNIwRLFPoSYt4mTVNCpIY9nBMf2AylFEq/tMjo1cp2DWZwq/nsn9ItC/fgBu9uhfXKd34yYhJ1dW6QDS1fmOtBGuhZIqCmFlmYVTnRdifa4BPG4j1EHa/GcBtRI0yWDzLLpRVYiwVOn4bsEQwlGCrw7g2LUF4/JIT9bXG8NUAXBdlw+KOQvAqRKirKTt7hO0gUdLfumrRKXzlylsGP3YLw8w+7TACKXD4Ed+OA0dXSJMpa7XxN14ScZlo1W6udpzeJZgUx/nq8C41sg8RNMSi9LvcfgfKlUtL5u3Ze1vHSLXdayj3PzZu3mc9Y3Wyfg68vXn9dcRTe6dq+D9vfw0Yo+Wtj5icm4BwhcyzSdvUhf1stg1BpAqHrBLMazjCGeI4XXAmwoTpXC7ImpOY/1wOw5AE/vjN1MaJ5YIgmRMAFUalWqdXZmM1hrL4n5dkpE7Hoegmfsup/OUkmAlSuTKNdzjW9OrzUMMIS9V5Aj14san+DKpUnXAaXtMjbv+4vGd/0r2r4GMz/j5ilWq2rQDimACQChL8ifV4Yfv2BvjrQPC0lgeZXxvaJPxt1njafvRvPH4aVRZu/uk1C4+4vGStmd1EAnJ73MkRD6MHTBrTLfJ9SimSQpHDURniWkS6Q11pK2fjxaDWmx4g7rHSwU9FJDfLkvaAWfG9YM32eWMXQ0MkcmjXUb+urYmaUwvCyYVEDIS6zGUUg/emI4R5uA5+9n+N4HvReRkE+NfFlIU2PQHDV1jbA8KuS3l2gmOCbkUuEyRTDbseCXGbsb8YexO2KM6eMjOcPuD56QxfCe5EqJa1Rfj9hxDAFr/45ahlSF8YuKfnoiLyBJqcfC8pCx0gWrRJlDLcCZDYAITeL5LiAl0aRbMVVodyVWzS1KKCEYVhhjArKHglqUt8SDxZPTgp0rsovk3eVVZPaISc9b6aUcDU+CdybFV/H17MHkOLQxxXF2piedDGkNH0p3E3XbtXov+RGi165bEYiu0sMaAhb3ldQQ3Eana0WW1fUiUIOytrsxxMs5Ren1ZcbdQq+wdWFXyIVyrqTffYvvEjZmfD8i7YDfhyhaX1oAmhITlS5OO+imH0ECSGncfttk6RYD9Vreiv1PkZTdU6tNU0+77udSCIZrLaU1tlh57y6ytf/Y5ui9LoFJcyQ/u65YIMBh/Sg0dGtsPCqkqUXKb4kModDkBFORqmJL79t0s2pOM4xvY/yZPugMzeCYWteteDih1v3bzsftxN4zQlbLV3/O2t260wnxmtVVRH/Oe8Ckf8h7YOZmkPwyi7ICmZvHA1+t+8Lq792A0upgumWObsHQOjcvm0Vdw2G02tVdQqTbwcJ6/t0D/MzW39RiUk5qWEvUVMME6j0fBmGpAT6MxNSUrBF6OaZGJdMIIDNbpvQ03HkRqo+c2kA17dqWuKDhlFeWzpyaC6IhdNpA5fq1W1U0KwMlt7+3DXykGxATgKUH6QnRm0pjjmIrMcV7s349+ofclubiMwTrFKaZU3t5zddz+T7q/Eq2r8HMz7r1m6llUElc3hTKuxrUeooVpd0X6lGxYyG9GOXZWO4SfDPjj2FHXevAbSewgA6KWwhM1yaTPih+X0jPsRqwXUaOjdQ1CzYm6iFBp5st94aCBSAhU6QLb6tugB7413bKcizgJYBOFSw77X5HPQi2i0692qLeH+6MoOnbTkA1BoWaYA+tQfrUGT6dyC+N9MUzfhg5//LI6ZsRXjecGvJUw1k1ZKxAuRjD0wRZkTqQJiNNLQDAZy+hnUm9hYA1fGn4otRXA2gAAhuF+RfuKTOkP5gwi+aVyzf3tDc76j7DoDHx98FY+grddoLuC3UIl5JUJ5/DNSMIlq0n6IZDSmYolwZzYzkkEoY1SKWzBylKAhB9t7RK5MU4WLO4zioB4CSEo/N9XAf0Dm1CeWkMz40FkCyYKIwxaLj2GrkQsy1xPHUP7BQWkBqTq26qVdv6QfmgPQAujtV3aeu5I2tWR74JprvEBOM5UUe6QDuOKeaiuD+IFknBYiSJtgh3kI45rN/nGuzImMP5pB0AXRZ0qehnJ6iP+HFH+vAV9c3Qhc0OFCCOgYmwi0Mv59BBHWylFKczSzEym0ZukYuTl2iPkRZjvktXRw6wIRS53idrywDm630T4WF9wu85QKJ9ali45rx4sFuma9halGRaSaQx5KSeBLJ09kh6uUiDxU0BOjwJJMhnyE9xjNMH4DvwYb0XPIJIOjbg1kHEl4+RjXHbGjOuYGbVZltnoG5n1/fKS9ex8P33/yk/b5RC/AlM45ttm/V+Xu3V/Zrcvs2X39h61g7Qk8K7oHlLOo5zuZaQRAI0mIXWI6JKlUqUlLJEHs1UMy01lpY55IVm0CyxxKDK3BIl2WaRXpey1ROLJYo25kVYu0nPVjqAXPftesKaRxZVgK3b31wdVFcia82t6gAF67dpF+9ugmlumKtbUHNFp7oVrmKMu82N4Us/r7eKdev2Cnbc1z39adfp57t9DWZ+xq28OCZOqhZOpjHR7hyZel+Ww8C8C0FiOlu3tTrjY2V6PdB2yu4xQtlakaCcuy4gVY9O2hKiQJmM6cOBscLwBy8sr2F6U+ClkkMVFt2Be9R5Ojm+F+ZjtzE/W2RtZIlBN61JpjFoOzEpNTc4ZFQy+d1Eeqq0+4HpTYnQMXFco8WALEZ5MuZjgyFhD4lUFT8Zl2/fc/y9p1gFDSXKRueZ/R9U2n1mfkgUEnIOx4+44BjpskTuQu45CFNoS6R1Krw1bCjU+5HaXUHzm7JNuFHDgtN3j5S9ou/O0Gp8TTtrbSWFVsWASzR2yycjXSA/zzEgjmPvQ0XksACaoi+Pj7EvqcagoVmj03M1Ug42zXbBerTSV4sicY2GmBx97om0GTwnxCSEvHjvviwsD4W6D9GpS9TIxRpm3eHV+iqzOqToIxTAWtHWaA5o78xcPVJz99E3KC2VagVMGJ6WsCjvU2hsUoCQ0AaE7qMJFIuJTVvPBRIiLyYROqWLbS4gPS3oecZf7WlD6LY8W9TxkwTCyNo1LZFvJFP0I5PzgD6dwu79trvUEJI71jpLoYpeomkfQx9mVwuyEf+LkT3uG+0TnQIlgGXrACU9N8qLseyDfvESgB/YJt7m8fq+KI7S0E2rBG0h2hTR/vneQ/RugMR7K92YaXwQaglHkli3p6+/W2ci8XC8qNHGfqiVyLQpwvAWptcOIwFABuvZM/1Dt9gSf5+duaWVbhmTbQ7r5zXRWZr+N/IeIOH26V/+++b3G6PyZeCzzpJ9n6Q3/OTLQGYFUzcPuIdodntwez+NCAKNfkRmKRJ6Jc59XeKc5hQfkgSWlhGElIKyEUAtc1lykEXVmLysWY8slnFpwbLUANSIUS2RxDl159EKYNzSDYC7AodVKB4/+3Zs+qV/y/bc239Hxk0WOjvzPqPy3rnHkZ4zEDKxuOZhVw84Evf4FeCAUOuV2RHYGK4bQ9j11H/FaOZrMPMzbkMFf2oMTwv1GJqPcnJYPPJmiHh7XLDBqHdhf05TaFTqoCGkfYkuulZCU5AWQy/h2JEpOs61YwJxljuBbwy4KJhEd+xD1+AUwGOwr8cQZuZTsAl2J32lFpOfThYDb6NrHnpWB0vU3neJVjPmieWDQr1TylND54V0TuiLsPtsgeeZ3UGp94X54wNLaIxpCZY3e4bzGZ8a7kb+5EQeR2qLrsp1p0h3bi1JYGxQR8bfe0v+8RPtm3fRgPJc0bog1iAXfJqwcgg9wa7QiqEeYWTRwTfYo/n+iPVmi1SBUjYtRb4EiFptxOJCG5z6i/eki5EfW09sXcWyQQen2WFuyGIsrzJiCTPDs2Mp+HDFYQnNibpHJ/AkMMZkrEZMqOclJmeJslA+10i+XQL41l23VKeoV8jS8JdKORniKQSq04JeFqZfuKfto0mkzhbuoRwgJE0WLqdjiHvTqTfAHKK3T57jM9oO1FKcZxHaEJkuMlWS9ywdJ9hDczwrywi5CjoRQLyDch2UNu5YPth1JjC0IWqdXSrBXHkS0hSiWt9HwCS2Jz8cSM9THN+7M+wzthvQGspLT0Tp0SOtOOboXvpzD1ZkXSoKgUYGAtz074hLiHRNQ2Af5Z6eh+K9VKt9/t7LlqYvDrIyAQqm3jVEbGSHdIeRl3iOLv1xg2RXsiREutoBYdybW/mEAKsu0AZHWgClNgT7ND765uYaHpVlcNgLvuMmw8UD1KxBMnZdtW+lHjG2XkjrfvmX/l5ZqZ4g/NOrCT8duH1ZVPre0+Xm9zeT45oXs4Xh9VqL94yUzVptcJtgvOo93OlWL8f78W9disz7xVo1KIKkUHo7RB5LC+tzlIky1sFNuBFj182VaqulOvd73OJxwH3lPeLzv4zbvnTW1oPZtCvcsB8ruBARrqF6rYfwxXdPWFshXN8xHl3BScOBjF87zUvPSdsAlW+gRiUSyVtbwcuNDoc/ZPvywf2ct6/BzM+4lX3mvFT8JQYvbREpL08XOBGdng9D3KKDhkZFnLnQ2wn0IC5X/JCoOXQxdSc4ieHtzO7TSzgxHgosxvymsLwZukiXLtLzcDjNDp1xQUMMOJyttwpYozMdqUTpSNicFeOPTig5UPl5ZvloZPl4xFuD5pS3jf0PzuTTgrWF9u3XIELxBLsd8zHSfLMLVKecPLJVNIShLoIfBtqhIAjlsQUTXpR5J+QW6abt1Yif7tFPn+DzE5RMfbWPdOLPhaQD9qPPkQnsg4yYMLwITkzA6iBzo97FKig1w3LGDlFyyDUAT5qA1sIirsGS0G2YIRqOUoTUCFsTBx0TnoP6X/vehG5GkUnCfj+Hjdyzki7BKk3fiJYU0mJiMiVKRYuj54ouCVktxSmhQ+TkpMWi1Egwc8FzE8FtnYWQHoyXLpU2BI1dj0RmjQJTwwehnLXn3oTbre41XHAaQtqYaAVLjWT0z4oSWH5x0uctWg4cevT6YviOaKCXuoOI64BtuxwD4mLkc4BbfVnIz5UpC+QcYliN58hphv3QO18DzxLC+Ls9LEvsjxGT/qDoZUEez+D3ERY29FXv7FfyYZ2bLb6bIQyNrBof+pTQM+dFrJepiOyWIDtjEmqOj6HT2SIC8Bg5K50F8c4Qxc+iRL+uVVvT1gnQr2m9OVa3kW9DBzBbdST0S3FbB9CcQy+jNT4/zc7w6Mz3gjZhOAlVjbqXKDOldbXtV/HzTR+nDdAIP8G0BMt5g7puAUTy66y87qyv//tDprnbh/2nPL7N9NcJc3vKtl/+3mvj6f38roLX9ZjfQ1S3QiABDLdwUXo/L61F4Fzk0AQkWGqwk3MD1dLvkUhi3nQmnvqn6E+wFf6lc/GTZ+YW2tR4B4lmlgE0Ori4BRpyfX4i+inFMN5dVx6Aai39wKplCT3DGgSoN2B0PV/CVUzsCJgxewiYkQ1P/qHbl5mar2L7Gsz8jNubVwee55npgxBTlucAAqZGbsKSSq+lRiS5eU+rzWlL5rSitL1vHXKdil6I1efQV+VLJZ2UtEzkBS7fGKBEiBhGuIFaT6cltB9ewrK63MXftBigNTuW467MFyPNjpWwxLajdD2AU75YyE+N8tKwaWH6OKzMuijlyZBPL1y+f4g+PrNRniOG30tiPijlMeL46wjDs8HjC/JyIX37DfXVLjQSMbpTJkLDkcMS2/YFPV8Qq9irO5b7zPkbO3S6ozwtyEcFuz+w3Cn5JbpNp9qi51RSdp+emOpIvR9wolt3+sES57k7kMQ6SNGETy2YGiUyTFpMRDKHWFWXGFBa05gMu3YhnRvWBEQZ3i5RAjuE5Vgfz+SXJeYDGiPBvvgyQwlXE80j96ZGqwbtQmGa07KSn2f0rIBtGT1h7b/gY/T7qvuEJgFr5BeLLs+L41ZJHg4fQ8ifX/Cx0O57f6eSYn96x/bQo3gEAQok6xOtJiTFJJJfQuNV7zXyVyZjd2rRmqEo6RzdvaX2YMAEw2cTqSTqMZx0roIfcpSNZg+Luiq2T6gGSyUSKb1tHENIWwe0J2e7RjlVSyJ7Lwvuw83nHRx46pPK6hkXgRTW6rQEW2aLIEPogbbqhcWUFCBRtkZ6amATbHEm0pmZfkm0twbA4j0EYA6wkyfpbECUo24JEK+xko7k1UhhBaJthfWZI/VVsTht339d2QTe2vrCqMsg9JLC7ZZDEEqOt3ENAXgPXunbysxc/3xZXLsd9CocvfFnbz/6zZ9rvegPGTX7m69P67sluk6WVwbg9iXvsTc3+hroTEFaQdDK3MSJ3rphp9V7bvzkJptYeE1FMJNesgkRvBmkyOfsScIdEPQSzfV4txOxmcm2xzfcd/tcgIVE9H9ae0FJP65bAGGr5icSaSi9k/fqRApwJRvwWAGr97Lddpm1E5TrXngvLd18nlPBnZclR7NJQnckfwigcY9zNzD+lPP789u+BjM/4/aL3/mAv/vZY0zCA7RdY/7mkbrTni0TE4VcHHcjGXhnT9LZr8yIx8oiTdZvMMME2q5gHyvpEiJOcMZ3CzYmLt+IVXBqvZ7fiZfyssBjgzFR96VT4q03O5TNKdDUIjFYHK1GfRgjZfVSyZ/P5MUwb8zfeeD0Rx8wN3Y/CIai3Q3INJHeZeqxsPu0Uj4/4/tCfdBwXjnIJUo1NiT8w4cYeFVwb+HGGUqwET31Vab4Etedkl7fY3cD5+8eWV7HsCAo5Sz4uA+IeIJ06WI4h7rvpbCXmdEcGzPLfYhVSx6v6cbm0fDyqYZG49IoTzNSMvVuDI1JBl8kHFHEikxUWHbdhZBBz0Y+G+2YafuojftcyU8T6bREp/HTxPj5O6Tk0A4RjSLtPlKJ/dMJOV+CsdKETku4fBDYDTGCThPuho47vC0wVZrOpOPYJ7ClA2GP7J2lYU8v0RH7wzcsr/ZYjrRly9HUU4iJ3YxIoJ27O2qyKMl1izI1ZrWWFd0lbEy0EYbF0E/OpJcF/+BIezPio+NztE7wHPZj28Xw4lmou4JdekU+d/uqO/Mh47mEEBlBThX3KIut7ig89GmUHIuA3kYhvyyhRdv3lGftjMo6WotFSVbCQm4a4CMtkBbr58OvgIM+OdqVhLHSS4Pdsh3pxjfAxTpAWZxVfyYtxPe3WXGb09j6hLAKVzW0VGufrSvbQOh8gHSGtof5FegSAJ1n38peYcjpE/cSac++OlYy1ypCFx732f6GYbllQNjKSJs0aP2z2aOuZAn6pef8BKj5aUt2uT7Pb37ss7jb+wzNuv/bhItvhwBE2USuE/J7Oyg9dkIqa0nKjd7m4Lpv6/G7S/8TYGHMHqAh+Y1gdt032UpbsVkP1nO0LwLWU7AKj9ctgEMlizOk+KxIEV6dZmwY6PoJsWAtGiW1AD6+7besAH67vn4F5f2kvFcmkvfhnQQhgyanWebiBXONKqN3QMPN69dL7vH7+3L8Kdf657d9DWZ+xu0f+0e+w//9v/9f+uAWTfjCrh1uoPJcY7U8pBhQXTbHhy6Rjmo5blVdwErPfLkYSxGWwcknJS8z8jLHoPWwI1+c/e9dYpKVjNxnag79R36p6NMpmItjoe4kxKGzoFMNl05bUArTMZMmY3huwRBlRfcDfjRmDUfP6Xt7WlF2bw2ZnfmoUT4TxY8lNAejcv8/LnBasPlE5hVahXxucI5VuiBwNuS0MD7OIZwbCvXNjnqM5aMQZRTLgt3vemBbdw1NBkmxQXpQWp8gj9HXJhwfEk6rDwa0KelcWe6jNu5DH5wmj8Tm2pB5orQEnz6hzeH+CDqG4LZCmRRLsSSzqOmh5y72XPsyiZA/nyIYLSkmDZuXYH36wBEr4oztwxZu+4IPmfS8oLlCjk7m0hkjmWakFLzFZ4smeHeOOP+SIIXA0GoL4a2B3w947hO6LKSdwemMnWb48BCszNK7LXeLuQ9E+aODSFeinFK7sDJF/yUD2GlviaBIdconZ/T3PkFTYnl1ZElEyciDCbQstAz2JgB1CIYFmRO+tE2oaEloWSAnXJV8atG/ywOMBqKQDjrrNc04x31gSyQ9WwnGCY1AQ+uagNXdAp1t6f2KRDpTMxks9IC73otJ+kq3Xw/P0V5Ce2dtViuvxMJAJPQ1t8SFumGSo81BjalIBVZta1j8OwvVwYUsBiU0GWawJvfKALnrlGwQbISGUAfYPcLuCzi/YStVRfPT7oLqom0p/j77YvGZV9DXB7V1ZpPrfm6P/5QV+Qpu3mN0/Eqe/HQg894bcH2ab69H5L1gvvXj3wM4G3DpGhC9AsGUfGNbNK+JwL1M4xGaJyJoBBFtotbVgh0ApZLVezicb9kt7+13P43x2oUkUfrJ/bPWe+JagpIrCPNKwik5GlimHn63MSirU279KA/qL8DS++cirtXGtQRI8V7WvD4at28HVhsYvTmaRpzr6IUqNIte4PR2Cb6CwS9dxWBmhO/sX/8h1/nns30NZn7G7df/sV/kN/+v/48IIpOIKPfmoUPIYXfWxdm9bSQV5iLQnOwxQNZDOG+09cCmIRJwd79/Zni5MH2wo350z5wHcgk3Q301Mjw3dj8+oy8nbChc/qEH2puBto9bLklPJpZ4T1cN7cgglOcWGRpjn9SnFhT20KnGosxvBuoomy1XPTIwhl1GTg3KADjj5xVX5fILO07f2rP/g1NoZZ4W7DB2V1fDztFuwEclVUdqgKo0VzzPeOsTv6ZoHpigHXIk4z41NDuiQi1C3fdwNIhjrPEltr2G8PousRzvQhvTQBYocyVNvY9IDdDTkmw9jvzjOxYMGUbm14qnFKLswbAyoNMck+mpkrsINDfDpyVaIFh3oyRFLxO8nIKd34/4YR8iYAmnSzQlpKdyOV4bWMW9oHONCc0cX2rYdCkxQbvDyzlaV2jPyDnPUZaqDfYjHDJOECuSQDpo8pzQ1lk/XWmBcJCJWXeSybaPXlvYwLNECbJPxq5R2tx9eo6u8HcH2qEEUOklpDrIRmnkKUTWVoQ1IbXtBYaMN8NrB3Au0Ix8bogJ9fXQk6kDtGop+NDTkDdwECO5jYl0mim//xY+ONA+2AeDY4BFk0EjgO8a1W99whchrOizR21B6NqRsNPq0vtRraJZ4vtLXkENmxjW85W0sESIdaEfdAcNzdHUr2V359laWutuIen9tCR1t50Ge6P948e3HkymC5cPlMkdWUAvHgngdKfWRNRBapxbN4fiWz+jrVLzHkiRK+PRJ971d3JbGro+/fbF18c6ztte8FNJGvnSi37Km3vMyN5F0KLXCfz6x3tZqpdb9PbgbpmQhqqTxMndgr4CgTUd2CzKyBHcXxm0UbIxaLAlGwDYXrdiLwEaCWPM1gW83VlE16z0z/DteCtCOClTTxXewur6+b4t+0TkT7xDwO7NqLdterNvZgHsts/nfdP1alrbMOiX3isabOr10mn82/pB37I7KzMDwh979X2+yu1rMPMzbv/w9795pQOlC2/p9WyCZvQkTPfK8OLkL2qnHtfsjr4qEGj3cefaUXj+I6+Qdhelh0sMVvN9xnZdJzO3SNTNidSM/e+fKI+VNiiaFXLG28LwVPCi4aTaAblQ76LHULoY5RT6g3pXkHlhOaYoi0lhekg9FbbiKRKF252SznB+E32QxucFvRjp3Egt8l5s3MfEVoCcegM/Y35dUBTOFm0JPATSkgQfM3KucT4GjYRhEvlUSedKQpCUYoBsRE5MIgLkcgRdyWwkd4SEZUDDfl0mI50qMlV0AVdner2L99JILF5e7aj92LUpah5duheLZOJcyG8r6emFJCW+xPNCejnTPq+oZnzMkHJ0LT7PdAsADOXah+c8B0ngAlpJlwX5/Bl7eYGHOxhHGDKUHLZtiK7kU0dv4xAsjTVYWnQNtxjo9TJhx5G2T9TXOyztiGycyFUpzwlqin1pxEQNsXpftSPikbkz9LqBx/mtBWhOstAJ6Wy048jyMNLuCrWkTZ6yzjJr40QX7RqWDh5U0cWoSQOkGtFB/mzhshoTy31mbeCK9WyVIXdLOmEzz31wzordj+TPK+Pvv2OZG/W+YDnFMYoiuXef7qO992aVMWESdvkVnPTR3fXqbF4DDV0IXVa9lmqU3hS0twYIB1Lc/9ICUK/NKbfRX64iXtS37CEf4pgbdBeT3JSvuq5m7gGAPcyvHoV0dsbPnHrv1L3GxLj0z01C6+fMR4PiVzfS+veqj72dDVc0sM1mPw1w/GGbX9/mvZ/7+9wCne3E/6+9Xd/Bfq9vAiECnKWbEpPgvdRyy9xE09eUOmuypePGPWAWTw5Q40jv1zTkRk7GqLYF1F1LbdEccitHYeR0ZWRkO7Z4b1tLV/T94apzSStIW9++v2zVb7nfnnldw6i7JX1lxlZKLD7X3G+A1petZ70kvQGc6wesYO1qG3/vSLaf3t+n6+N/4sNf/l+/lv+At6/BzM+4lZJ6N13Hq1x7jkRYBOXJEa89hl7wV4kpR0O9eMY6iPbSSaY3z0sIXSU/QE0p2BSPL+r8Qcb2iXwo4IZWI7+9UE4XZMjUnPAcbQzK5xOXj3cxqM9xA+eTsf/hJcSUJTG9iQhuvbSNptcqHH9UKT984vL9Vzz/YkFaId2laKA2O8sojIszfLGQL90yq4rkhD4v+JA75dxIL2EPtjF3NiGsvfKyMC6hS0Aq2QttF+WkpgURxbTrG5KjPU9HTJDZw/Z76CufqZK/qAF07oZYyQOWoR0HZLFIZ83B2JAybWwgGoF2M1F+MsM6k4XHislzCltmM3wowXSoomQ4HvDDEKWiyxwdsS06nnsfVDQl3AzmmVSGCNRrXZh3f4/e7bGxRIxpSviu4KWHHU5Lz3+QCMezfmB9aSalAGHTdozcQnDb9pGLE+m/ju161bzSrflxz7bmvYQSWpd6VFLtEyoSOqNu+abA/OGI7TOtW7vdgKmvLDuvHhk6PWixenddNaRHCtC1UnohRNwWqbVRqg1GJso86+pUiLDCPkT3ycVU8H3CP7yjJKU8ThH6eCTKSWawBPNhveu1DV331FfOcVKkRxQEixIi0jhfnnr5wns/p+axvx30uF4ngdViHTlGPWumOlr66li7Q2idh5wQQlenpiir0jr70sMae34crcZz2Unvvg3zXRxBnvpbuqM1gFtY30FKOBvbIjBaUK3mnerwyCFc57v3VugrBXHzb3l/CvvSE/hJlmXlI+T9p7y3rrdtQXjLEm2//vJjdPfZqhkRtjKS3oKa9W8qKTlFvZeL7H3c1j/HkO35Y4ryUllZmZv9vrql4oVOjRiM9CU8uBJTopuOJ0o1N8yQ/5Rzc0toyZehXtxH9t6/2aiVn8x+kfee9v77XJmf97aNprlF4D/52p/cnH/89dfMzP/fbnluLLvodBtiVonVrQj12EBy0M6iEbnf4ktoJn1CD62LXLoA02Dzlq6oXLu12jxKHSeDITG9HnrzP+fu83NoEZrBh/csbw5xZZ8W1PbBaldnOMWAbUVIjzOpTiz3hemu4Kl24WAfQKeKunL5MNwn+08a4ycX2GWmRIhi58g98ZK6lgb0eSJ/foKSonwwjngTLq8zUhL5zZ782UusykumPgzIeY52CacK+UBrhvTlo2ZBZ2OYHXk5RYdpTZRLi6yaJLSUuugaJOVwOJ0W0lRpOP5qjKj8LJEBNkR7BBsUrLMCIpSnBX37FG0TVFFJodXJErqlacZfPQTo240BUJKiS4O5wukSrMwqyLUWo1Ft8fg8g17iOpUcvbbGAXdBL7Wv4AMYrkFlvt/BcQztw2WGyxx6ll5uMiHYm1bJHoAkH3a0uwEfNfrHuEYOTp+/fFydPxFOKEuwCdZXiaYerpmXFsxBCmfdclCsZNoAeQlgbtrdeC2YF4QIgJsiqFEF2j71SbtFv7EULJGnRmqRxRO2bLkO5qmDAvdeuhGiebduI3xoYKKJabvfXR1oteGWesBeMJG5N+5p+0zdF3zs7E8PjJMuJvC+TF71DiYB7j13+j6FCNdTgA5SsCSt63HeYzN6Cckb3QYTIKNJlCYB6lGDBSQ+y0poibJHoCaXcDBJbx6ptZeTZ2H3hTGPvXp48UhErs6aWOwJNAs2C2lw2iU+W9eaWHIYiMiB4luDys2qvSXr0lfsK6D50iS8bVfWhJX9WL8LrOem01wOQROGxmUT6rI+VXCTGwAQC56kRk5OTu2mqWSfmHUFJCuYWSjqlGTdxuxb6N1WqrqSdAjBng7ZUYkykG5MSxfb3hy305Fmx2EqN+WcfgOJ3OADbIv6iR2//t0hTjTs7ROAr2D+BpT4ey99v3xk3JxrYG1AKtwCshX4Xi/H9b2iC3ZUadcPkw563v/k2yu1bsdhz1e5faVg5jd/8zf5zd/8Tf7O3/k7APzar/0a/96/9+/xp//0n96e8zu/8zv8O//Ov8Nf+2t/DTPj137t1/jP/rP/jO9//6tFgQDjJzN8OGBFo38d9BYFoUWwIZiO8lIZf/iCXmZ8KCz3Iy/fHoNB7XQ0pb/Wg6Jvg5A8GIjlXrHc0whygA2vleHziIe3osjDPvopvT5gh0TdKzJ2S3hV9p/OYIJWo2WjfhQ9iuY30asm//4Lvh+iVPGgXD4aIwp/ccqLM7xdwhbMyOFpxnsuyHCKwUanFiWa5wnNCc+ZNip0R8vwdqHtjXockacLqYYIQufG/HpHfpzJ7ybKZxN2p9hdgBLMaYOGBuIwkh8n9PQC00xpBjljr460MUfuS+pMz+sRPyfS84X86TPJFVka7duvubwKp5BOFZnbds7Ty0x6vsQA3Bbk8oyUEn+8SytqCFGlGZzPMC+RVTPNRE6GRouCpBHUVxturc9UFpoU68nHwwB0wWjJYTE+z/B0JokG4BkHfCxxjXeFNPd2ANotvDn38187LVywqcbkXBsyZKZXu5iJa+9sLQJjd96sYtPOLAigcwNT8tTQqdGOJQTDFkvF0HmxlVBkCWfNxlQIW94R2XtZUBnOFSd0OG0XwIvaJ7fueRXzq0NmnRthYw889Ry4HO00xPrqVzMyOl6D6vc+0wVLYmCKLJCfFtLszD1BWtTD6Wb0VgRR4rGun4EY5m3TxghNe6lLrpOVaLBU1gGY0M/z9gQ6mAFaB30tGEpUAxgbfcKV3iG8t6e49KlGQdw3djf0RrBahPNkWzNO9NqTynJ3ZamQVqpH6K4zx0bHBofRg2Fe/3yZZFlBjvClX65bR5kpHEaxk3Yt6/iNiLSDmZwgpc60aF9M4Ti9M7SvE6uRtDFkI6dG1ijR2HqFViqDNTclOjsP2ktAGrb8rYyzskEuvTRkCNZdQmw6lk2UuwGBOK62XmNgtYOvQclrQvStG8u8V/ZuMYGs5yzeaYUeQjfxvfcoGwi7PdJVQ7NsQLH/1pXVH7uBof6cFcysF3UNztNeQjNfe0hdX/Y+hPkpj/y02+HnvH2lYOa73/0uf+kv/SV++Zej1vYf/8f/MX/mz/wZ/vpf/+v82q/9Gn/7b/9t/uSf/JP8uT/35/gLf+Ev8OrVK37nd36H3W73Ve72tqXFGZ6MNjruaUsNdVlDueI5+SUiQNuhUD/Y0/YZTZHp4RouJOaES5RU8nOs0pfXYV/Wc0NF0JOhcw1LNktoXJJy+ugOWSrpeQGbkSVj9zvKDM1esG8eqbuGnqO/EdMC+x3SDCtKfXCmh3v0NEc/ouzYHqpGdL6n3nJBID2eY4IDdM54NXSq+Ki0+xG1EXOn3Y/UMcCXzE5+e8Fmp5VMerVDT5HLkl5mdF+wu0LL8e/xsxOL5hiW1hJJTixHpR0zet51wBisV+rMhjzPGL05X1HaoURmSVHSqYWV99LgdfSkKhVYlsgfuYRVm6GEJdo9gg6HATvs4t/TFILZsQSyOQPTjO/HcOQMJQabyxz7nhOyG3pbhktMVuFz7MCmIqrB2tSGtGDXfGlIimPGDKbaxajA/QHMsLqAgR3GmOdr6yuo0Il40VDyNY/X5+4Vdo/fO2gLsEpz8uToSyQQp3Pt1v4YHHVyxMI9tRwzzfQ6OQ2CW5D0pqCdgVjZGBZIHknUvs0S9BKShGD3ZsW5skertUJaZ6j6vrjGPR/NNuPJNvRjtQAzWn1bdXsSnGBvNAnUAEDDc6MeiJYTNc7bpojN8TrxzrqsicXrBCWxbz4GwPN+DGnuLSq0R5BtDThXcBYMj0gAjLSCEeJYDAL4rGVOEbwIVSLsMfdzkRpwdlyNtEjvwRZAk7YyMzFJR9sSojGoARr2eST2FY0U4bpzbA4hcYRDrmCk/8krS7Bu25H15/VsG+1ApgOYrefQaiPetopIOItyurp51rdcp/fVLeS+MOTVxmzkNShu3ZvOKND/FpZoR6cRFLdlstD3z0G3gDl/b3p2v+Jo4dp3ad290O+vjqv1tytNdHt+Ymv2k++rV4gBRNNYJPaj2vXxFT6tTIp07Y3efMKqkQlQ1w3kEizVT7I5Tu0+gPWolXVoiEeiCzaYxT1jSOg93zuu99GLX+mnr2wT9/8tYKrr9sEHH/Af/Af/AX/uz/05/pV/5V+hlMJ/8p/8Jz/z+z0+PvLq1SvevXvHw8PD/w/3FP6Pf+ovsowJnY3lobDcpRjwGpsmQjtT45mg61XQhU3QJpORni+RmpoTaano04X0eNmyRnSq2Os7Lvfxhal7pe4tmiPOPdZ8lOjvMzm0yvLhAT0Haalm5FON1eWQ0LpSkIm6T7gIeTY4N3Y/PlF3Snu9C+uqZHQyxk+eafscJaom0O3HbjC+nfDWYiV6HKM0UzLt0AMCXcjPC7ZU6iFTXhppWfDDGLqHEs0mTQXUSHOftC6RqusQZSuLoDnPGj2Mug5HF0jnBaZGOyZcosRAUlILZsd2EZ2fnxaWY8bFSecZWUCqhb6j97kS6XkpCSDSc+XxHA0iux5Czj3tr2T84Q4IRxsiUVYCvD+fuUWJaJ6DmQGoFVsquh+Rsact99YFvoQ9WYYSg5HHBE4pkBPtsENSF/oVjZh/c8gaeS3NQhB8KLhb12NIZ88iMXebrGuUg1J19Lx05shZ7gZsn+Ialz70ej+XRSmnijssbwrVG6nnm6w9kbQSzTrXRpalp073yhupr1Kbw9r36mYy2+hvC9bBuK50/abE5imYyo2/nzyuFzf6lfV9LMTP0Usp7ktLBPOnimK0HDZ0oDNFnYFKwUCx+Ea7t6NGW4ml98Bq3YXSAV0kNxPAq9u326BodVpPFU4VmB0fldrdfJ4NG7twXDrwE2d8Bm9OuVhY2ls4/axTB6t+xzqz0nI/jrSWyAJwSi/heepC0Z5K3kanjWDFo3yYvTeulAA3BSR3ZmfTukDADQs3UALRG6Cx2bd7jhDdIi3BruS06lg6I7IKeFnBhIMY7r2PWrfW5/58+vtd5SfBKLivDRiJUqN01quDrCDeYoxutrIsuvE6q5BX+vNkO84OZt6jqlZB7/ssjopHCJ9fWZbt6G5YlvVnEaet/a9cuhNKtze+npPrFgzOeqbW38Tfq4bnVhvTOni7VvRkRUlbuW5IjZdl4EfPRxYrlJ6D836mTt/1zgRVg//Xv/AbP/H7/2+3v5/5+38zmpnWGv/5f/6f8/Lywq//+q9jZvzVv/pX+bf/7X+bf/6f/+f563/9r/NLv/RL/Pk//+f5s3/2z/6h7zNNE9M0bf9+fHz8B7bPqUH1cBnpHMvu1OKOcCfSY5e4hX2OTJG0dOZm11mcgXCgsGA7YfEEb/boPMagrUpaejJuC9Gs6TV1dpjAp8ry8R1tn/Es5ItQfn+GVLi8jok5TQvDs6PnmewZfZmZv3UPrzpVngTJiu1L9DO6RP7N2qtHZmFYGnKZYjWaQydDNSxFSUTMt1WEXhb08SUG9t0Yn2sgZmHpvd/3lb+QThcoJQayeaaNA0mDYeCphvblzR5KChZLNEDV7KSnc0ze+wJDb5jZ9RBqhjcnTY5JikrPEE4pLxmpCZkrUmv0fcogHh25ZenMT63BlqSElgBSTDUGl2HAj/uwG996JXPqgujegHAM7Y2UhDy9REnKDOoSK2zr4W7t6txyc7iEM0rWcsV+j7y+w+5H8IZM0THdcnRrtrGXzi4VKesgmGBw3Bt6WaKP0JI2JoOUoIQjLJihFiW9JdxFPaHr2jfIw+1FBVEnv4RbRHI4Z0Sg7RXLUTaoqsH+EN8Hp6/YG0BYpq2XT9a8oG1I7kyMd4GydSbBJb570uglpSvjw84xi8abpgFMtTfabCVAhS0pPk+kN/CMyTkcSH1ZLGBZe1uAWIV7YJ5Izs4gTa51AyFAw8os9ed771VGil5ZViDPwrIHHULjohlSc2yka3jW2XA9EfFv6+83p2i9wczV+dTPCYSg2HIXrK/BnEhPC1ndVd6F8IJ1wxxGlCGHaEbbikOJ+L0tKMdjH2V9D3WcAOgpOymtwEa2mfc9YIKjElbprLfW5F7W6QaKje/o9uuYlH2bPNsKNgQit6hbka0LuYmSVoMA0CuIEQ/XIY6KXsFDh1rrzafum4tp1baDb6WXUPSttIdc44fwXsVzFmdjlgwNUbmshaP1fHQGxYOgpcOp9c8GUUyusUD9hPbUhJ8AMeuT2lZ2orOb12c1X9kb6e8g6y2Au7M0ZalKcwnHlcOqAbra2uPtAzx+9czMVw5m/of/4X/g13/917lcLtzd3fFf/Bf/Bb/6q7/KD3/4Q56fn/lLf+kv8Rf/4l/k3//3/31+67d+i3/pX/qX+K/+q/+KP/Wn/tRPfb/f+I3f4C/8hb/wc9n3MiQuCrbvE0LnEH2FxOZBYbuR3s3Y3oPub8bu777Qdik0H8uCPJ5oHz1AFhoLsh9Z7gptl5hLNEfc/4+fYx/eU0Ui34MGl8rwo2fKxZk/2lPvRnY/nnGBlz864DslPc3RjycLZZlhdvLjxOX7D+w+ndHnOYDV3Q6dFFpCPzsHO3DchyjtYcAb+JDRuWIlh5W4Nnh7AY3H9PGClowlRV+WmLD3Bk8vyHGPPi/w4QM2NJRgErQaDLAMSnk2yidfREbKw4HlPtN2idQICn/MyNLQyYL5uUuUT07o8wX2Y9SypxY5JSkaGKaLUd5d8H3h/KaQF6WcPTo4J7AyxiylIEOk6ErtJsglBWM0ZFginVaWFuTsbsBLjhYFPVlYrA/4xKDpTpSgUl9WDhleLpvWZtXRROpuCrfWboBaYQn2CfpK0x0OO0hh0VQiC4Zdp7yTIpZ6WcqCcSKAqLUZfZzRpfZ+VIrWYIPsbh+iXBHI2nluC20PEZS3Zs5r7cxUEZZdQg3Ko+HS0H3B6eFzAJ2KXwGKG2vWXJyrdeIysGpxviyu2zohvTdOJ7o9N/oeqfQJvk848Ty9PrdrMFbNqnSRsu+InWjhvFoH/2tpmH6s0eaiHSQyinovLFbbdM9A8dz3oRK6ogRy6StmJ6IUUjA8bSCEvRosSRsk7MLPUYpbS2trJJD3Y9GuJ/Ik0fKgeYiCm1+dS3Pswypeziej7pQtsj53LU66umRW3QXduEDtmHUIoNZWl5d3K3LXlEjkDPTTHexKSqA9KVd73yDvbMmmIVmx2s2k2G+VTVOyvk4lhpeI6Ukg0UcJEZIYtZexUi9hJXVac6LLfL8POhpZAUbqwEhZSzOdcVhD9tZJfQVRgHT9TkT6x3dhWSd2OjjvhNUqPnac2liVOFuJiFsw1bVU2tmklfHxm5PjG2C6/l42poftdxt9c/udueFwrqUuZxVErb+NcWp9X2FpwnmBxVYIJ1vD9LX0vF23W8HwV7x95WDmV37lV/gbf+Nv8PbtW/7KX/kr/Gv/2r/GX/trf43Xr18D8Gf+zJ/h3/g3/g0A/ol/4p/gv/lv/hv+8l/+y38omPnzf/7P82/+m//m9u/Hx0e+973v/QPZ98PHd7x7d0JEaBqpp0liNSkLMDtpqqELyIrUhfnVGHXw/YE2hgOqHgrYIeLkc0ZfQD0ayHlz8tlA4fwLA/ZBZM40S5QfzVGaeHWP1kp+nrECp+8dAEcvzvG5kU9gU7AO+V1k4EirDI8VuTS8ReKqjcrwdsbOE2ks+NyoA0h2zh8NjE+NNGfamCPNeO7JkIcQQQt9NVtr/PzmGJ2UEWQXbIDvjtRDZInUBMNzJT1OsFTaNw604wPl1Z7xswv62Smo+dJTfHcH6pjRIZxH+bEyfzhw+cVXlHfB4LhbnP+5RZ+gozC/LqTn6Ew9Pgr1mKl7I0mInyMlygPQSKeDc0bc8CFf59MapZpYlDb8NCG7kZYUHyVGjBauobRYNBo0X5fqATZ0CDeUg7c7OE/hcqKP3uMQtvUlAXM0WkwJxgK5BDPy9oyMJVanyaFalNPuogu0joX0dopS1FiCwXqe0ZczLFEOlLwOaBrvf9zhu8i+98UibK0vu0InBO2Qwql06YyTK2sTSHKoMUUI/c0SoMazXBmAsbOYSwRNChK5MWZR5iIm/OWubJ2tWfUA0ld/CmtNoQ2+jdfSPEpAPc03xPUx4bShl3R62Sk0JRplhjXHRIlEbokO2is4chydws7bsvXaVgcvhPujDn0fbsbzwCWxoElLiLWtEQC6H4O0eKINwdTQnFxD4yK9X5cLLHuFMXQ225y1LrW7xZ4keInj0ClYMK3RkNKga4f6ZLmJndc5PH6zNdp2oiS3Bsqxzl8rixE/i4JqY0tN1pX56HrufmrX5F2RXu7iyhStzIVYAC5b38vjVFfvIADdWBkcmsiW1bJlu1jsqXQmSq43Ti85Oa0jAZdo5yESZSZFrmwTbAhZ+j0Q0VHREDc+ZmUpooR2JdECzNXaAxt7KcqlMy2+eZX6OLKe1/fPrtz8//YR7+zQTwKZ6/0n/v5Le9Wbtgmw+5N9BYq+8lJUBzXhPA9YdwSYdQYV/5JOKT47KryFr3r7ysHMMAybAPhP/Ik/wX/33/13/If/4X/If/Qf/UfknPnVX/3V957/x/7YH+O//q//6z/0/cZxZBx/Pg2vfuHbr/mD04maY7XDupLqVKeuKaZT3E1y8XAn3SXaqxGdnOHtBWXANLH/3SeWu0ZuQhsMSouOwKNiyYE9Wp3yaSM/N4Y/OGFk7JjwQbl8Y6DeZ9LUojP1xZnfjLS9srwKwa8rDI8L8zfuI6BuX6jjjrYXhndRq2+vHmBppCdIjyeWjw6kyZEp7KJpEfRce1y8oEmwZjAkvAmMYZ+WaqyZOUIinRba4jFI3e9YKVUMyhcT00f7ABYpc/n4QH7JpJcoCXkGmWLFZzthel0o50Z5uzB/EKUNmpCeKy0nGBJMRvn8gg8H2qsRKUZ+N5MujeWhhIC0NxuULsplBaPumGhcS/rgDtFGl1j9+RLnwJVwtB1LsEbNaT3gTprj0xL2aQdPOQSsLqG32e9w+oq/Vnxq0XRwjN5EPi8wFGQ3xiBVK/pyCZfXq10MqNVwTdCiIWQ6LbGKL2GBlqWhjy9hHXfvwuIE4w5Kjqj88xyD4VCwQ6Fh0XizEuLiovjaWTyvq78utM3aMUGAN71Y17F0HVS6Ygbpk5P0B2wQdCJCD5tFa48hRalwfU1chq2/Ec5VEEA8rn3lWYnv3pbyuw7sKqEB6c9f3T6y0jExX8Y5z8HakIJpkTVoMPVygjvdAEURj8TsxJaRozUcUmFx76vwKZKjW42Z3gm2tR60xwJ4d0J1K3bXQqgGU9QGaPGVIZ8t2l14ZPlsJbbe/sFyt3oX4toSYErsei5CYxWvs1UByvsS3W27nVP7RKq95LYFIjrReLXnDJn0a9pn+fUjr4wQ2z1QOyZbJ3hdG1/Fk6747T3mwfskalzdOrIdwXqYEO0JfBtt4ve1pzD7Rjn0BaTb1cW0OZ1CExKlIukW6DjoYFn02uzx5kStWhbZJga2MMRwza1PjZMau+vvgeL1NKz/76a37Tq8h4C4ViZXBqx2pq1FLvH1if0UN66gzfqXZa6ZSxtpLaEaDFWzEPfLjUsrCLsAM3fy1ZtyvnIw8+XN3ZmmiWEY+Kf+qX+Kv/k3/+Z7v/9bf+tv8Yu/+Itf0d69v/3KP/It/tu/+0Mk90HbQKpHUuoSAgIBPKdgYR4y2hbKc6xExh+eGD+dYWi0Q5QtGI05pZ5UKuQXI/UO1/nTR/y4i3LSfaa8ukffPdPI4XiaIb9AfrqQJ4Gc4ks5g47CshPkrlDezfjdGGWgsUT9N8H+77yNFe5H930FIejnZ9p+hBT9mbQRE/x5QUrCJBEq4BysjLWeyyFQNBxUKvghY9NM+sHn6A8dvvsN5u8eme8zUgfy44Xh7cxyn9HZMIX5dYE3I2qCL5V0auhpQVqUdWxMtEHJj+HSqvcD9X4IoevLHOW9SyN/emH+TrBENibSy8L4aTSJ9JzjGz+HMNSF0K1U2/Qja5mCaUFytA1A+0rRLcBqC4ZEbV0REmWN2pDTBXk5w+FAez3E8NZzT7A+qHeWQNISK6G5xmBdCj7N8XyR6Nc0DOgqFra+yh01rOlFO0vicIm0ZXWLDuGRc05QRz1tOCVoLez+x2gl4D0dec0rIXVAujIBfVUXGhfCAeOERqWXHdZwOHWuuo2+4gfe14MUaCmhi0SJZenZMnYdwDUTYXxdlLu+H8bWcmCj5NeY1G2lGhNPgDF/b/B3ArzqJgAAkG1xEvNm179Y965IBzkeDMgwX1moVoTy0gKsDBoLCHRrTKknEGuhh3PwE5E/tXSrcZ+9RNjO4ba7vQSVZgl3fl80JXOsXg9oe6n2+6B577ezTq6shAWrSjbIj/jg7X5fz0cj2B/r19zAatcHAW5xf3i3V4fNOliQtQmkSlQvZduDq/4iJszO4LBqMNiYhwAX13Mh2wy+odpgQW8s0Ruokas2fMUtrd8/1lkT8yvjoKroDdsjQLUuMr7xpq+C4MCSsglr++3C6py6xV/r16bflfH6K+3V8bT0v68Prq9dgQkEOFlZre3AOjB57737m3gHVGtxScSvMNB7vowbKspiidYSbtFGp1noZmyVk91+f1xoBh8dXvFVb18pmPl3/91/lz/9p/803/ve93h6euI//U//U377t3+b3/qt3wLg3/q3/i3+5X/5X+af+Wf+Gf7Zf/af5bd+67f4L//L/5Lf/u3f/ip3e9v+yX/6l/g//dX/Z6zwE7EUWAd3SZSnhfK8MCePVNyHPWkJtwANbF+wh6DFl1clElj3Cb1UhrPTVMhzDSFxi+7Zcj7hi1Ffj8hlJp0W5jcH5jcj548zFGW52yMIuQdprSLLMgnjY7iQmIw2CiQntUZ6UvTxhGrCqlOPCRmV9JzJ7y54IXJnCNeJWqxq5GWKgeB+pA7RCdlTrxKLQA7gtuwS7fv3HF5O6NOF/acv1A/3tEOmHTKedngS8jnyXsolVIn1WLBd6cuIFlHtfdDSyah38fp0NspjpT4U/FCi+eYUVuPyPGNPI7bL/bsu3TkUX/7UDLfo8o0SoQ0tBLMyzQFocg4r9RBx+QEIuvthqqSTkcYc9mwzfEgsrwpygTwlmAvtbqAeC+ViyGlCThM+L0hdYqSvwfrIUNZlb7dt13A47XeR+Ntr3OpGk9DZtEHxUfGz4dME3aLsS2ifKCVs5yuwSQnxXjYZoo+TaXfbmUeZzohU3p32UlxP7q1sehxfU9aqIYVYHfe4fimK3Y7gN2Pu2qpARDb3j3VRsrSY4Nd1tHRmAesgo7cLkJ5Z0gkcgK3/1Qp8kLhPldjNq8W1TwTrrH07QK9zgndGyYh73Ky3HehTggpWrmBXDJIRLFYL5kBX1aax0g/QzzEupJ6SvHbIXjUjazZNG6X3xaK7nzo4Wa5lChNIa8hiP17p591ho0Q291jpzODqhBkCUL53OlZA0XucufLeCTJbabN1cgsWw9JafrIANnZ1AnkH0quuJEBPV4mswFNA0OskLzHRroDoVme/grJt0l6BqMf1imiH2/eNH5a6HmrABrpmZgXsG0u0lqTwaz4MsgXvXe+Z7e4LZ1S/t3zVx9wYhoXr61oHJ9b1QPFWqzPqfU0O7pvgl54Fs/Z9uqJf720O5MahtcK/FULSgazcXO5VcBz/rt7BTLcXxrW2K6hk21XMhdaEX777Jl/19pWCmR/96Ef8q//qv8oPfvADXr16xR//43+c3/qt3+Kf++f+OQD+xX/xX+Qv/+W/zG/8xm/wr//r/zq/8iu/wl/5K3+FP/kn/+RXudvb9r/7R79DnrzX0hWGviIaE3aA5T6RlrHbgI2apdP3CRuh6oDnxv7zOSzKWRkeG/lU0YvhDyVslgapRtmgHRJ1H8mhS0nIt95gDwesNfInL7QPjl0V6dA0+kCNwvA4k54W8uSk58rl20M4PZ5m1BQfCvXjN9cF8zlYGzC0NfwSKbaewrnTdmO3Xg+gEUVvY7A06e0MKNLrA54UnYLWX773EfndmXQxdl/MnHYpSg0eAlRPQn0YSUNCTwvDuxmeFtphgEs0Y2yi2CGFYHDhKkqcGuXzRtsnUMWGFO/1tJCnxpJC+BrlAg8XTgW9GMyG4bR9jr5XSyTSSm3QZvx8gnnBXwT54CHEv6oEFWGxCkaiJCOh86mHRHaHxZBaQ39kjkwLOtVoHjlN16Woe4CYObQ/1zFS4txPU5zTnk2TlxF7vQ/mA0JM/bQgP3oboKwMoY1J8cdLDmajd98OH20IbknR2Vrd8UrMECVKnG3NrInlN5a7pdiIUkwf2G2IdaWel5hkNb4TVzqdPjG3bmP2KI+tK0rV/zd7fxNry7KlhWLfGBGZc8611t77/Nx767qoWwUPnsuAXLyHLVtXzx3LIIQwwh03rxDVoYNoINGhYYkGAnrgTrWqQQ8JIWhYorD0bJBlCTqARAfZso0RvPq59/ztvdaaMzMjxnDjGxEZc+51qgrXoc4BndTZZ841Z87MiMjMGF+M8Y1vhPBeI50i7GUbn5izk6BpjYmENXMaIk20eG7OTJvwPJiHMQf/9vAwUIQyQjFyrRWn1bveHakyTMGmtyuAlWk39FIRIaxID1Z6bCXIu82IMXPNu75HVQnyOPvQM80QQGJ2yAaklYMoFp2PV90owqjNQ9VCahESScH3aO2TYnAT1GBPixEzmwDIYKFS4a2tEvpBKyim1sRSGsFGw4cQ1w1t3FtoUYOXEde7y0x3ng26mJ8oDaY3z5sEVA1QYJGNBBGk2EHFUfvQNuZH+32M4QBWO3+1XVgneIodQdAFCCwUiPl7ww6orAEebxW2+dNYi8CE9ZgEzow3aZwcD/jPNlu0mFyaBjo8Pt/vd3HvmU4WLHhv80IcA0AQjIFq0gXz9tVDANe459o9fQ3KpAPVMbjn7TPZgR7QUuDZ1//5934OX/f2tYKZX/7lX/4t9/nFX/xF/OIv/uLvQmv+47eH0xH1FWgQq6Mqb6G0OfJasUY1a6mO9GkB7iZcXiccHjcUy/BJsD0kiBww/+SM7aM7aDHYnJFKRT5X1BSFACeBzQ+cNCZBfZiwvjqgTkC+CHQR5O2IooK8OUNOi0GejSGtTxbWDkrUfsmPNLqyboCmXThpTtDnFem8wY8F+vmZuhzHDDvOKK8PsPsZ+ZECdP6K6crpsSCdK8HWx0ekszPE5ACqIW0VUhJl4d/cwY8VaS04fHLBdmL9GHEB5gRLAnuYIKeM6d2C9FSQ3p4B5wSjlwLAWQcpOS2QCMNaW4W822DHCXZU2DFRL2er0MLwnTugjwtkY3hKcg7jFwCiGCdU1SgdEKIoxwRcLpBPPod/8Jo1l+6PbXbsoEKqQ9aC+dOK/MUC/eKRXJetkqeUhFlQH72BvHuEPF32zImcATj1SaqFsAboSZLdW6Mzs+L0vMAeDjTkF/Jt5OEesErA0kjIB4XnTBB3OjCV3gU+E/hpMWZBXRQyxzkNwEqGADQBpwRTElub3o47qLECxkhairouJYpEKjBJ9xqwk5zCfQN60cdha2UBWn0i8pf4HTWInAtqLlv3/cP40JNGo9KNm7RQBgiac9Mm2TM5IFFYE2DV9Vij+BSFBY+UPoALPJbJ7XiWGRqQaHerWuzO/kS9UUhlOM4PcWkSwQhq+Akq1Yap8uuortTLEQSIJe9OBF1nxlNbwbPNvhmwIcpVeIxpgG7n+Ux099wgxi+TKuRxHKnhIGy6OR2UJriEq6fFUtR5LcWpX2ORKSaRHt0GH2F8BVGrChCNMguqEc4Z/AkBfCKTGCYWKcr0AJUItTEVPNrZoY33zglIZB1MOkGpjPbcd5DTFIh7awgWTBwiCgnSicZlcYsUduweDXg7nvfr08BSH/nYt6WBo3savXORaquy3hLE24PUPFCDN2nPKhpfrz9rbJ7u1Wkg31uzI40cDe/J3lwfj92+E/x33/uv8HVv3zjOzH9uW76Q38CsEKAcE7w4Tr/2HB4MhZhj+ShDRJAXID067KHCTYFDIk/heyccf7wivbvAXh2R3q1ItWCdFNv3DlEVG9juM2w21Htgfc0VUL0L9P7TE/K7ivmJxefSQqMqrpDLFobvBJSK+XHF5aMZsoDZMaoMQ0SVZjUHnhbIYYbNGfjiifWbrMDAdGmuxgGoQWtkGD2uWO8SdWiyQpcN+lTgpUBPE709S2Eq8JSQiwOPhdpQa4U+hqhbpK1vH5xQHyry5wv03cIyA4zpQSrTpOEOnzPqKcHuMwXwNsf0XLAlQc2CtAH6tMHmTN2dKZHX5ILtbgJkgn/B8Iy4kZcR+iYULzxxKT9n+PMFsha4VtSHE/yOVZq5WHXI8wX5k4V+ZIuVcHXkkrHcJ0xPygnrNMEPH0DTO8i7J9hlIbA6HAhG4PA8E/yokn+C0WNDAy6XilypxAwT4P5Egb61AMvSuS711QNcC/zuSB6MGESpD4SnFWlZaehXZVjiTYZtRoHCOaH6DLzK9MTNFMZzIZhhGYMwUklgh7ynZIdHppMXJkFXqzUEe5czt0T4g1L9XP1Tw4OgpnGEmOYd6cwaKc+gd6rnWA8xBssUq5RGxgjyOmw3Vi1ixnU5gMl35eJWwiGBNYv2hX1/41kJihGGtAr5MAsIRFsF7Yoguns3L6wwT+9J48LIFsTwzSFbZcgVFh6cRgzi5glwC4Kmg7XfigEzvZ2QaJ8D0xJjMDnUYgxqcDUS+9S4wjBeHlbQYPu9DVYrBQHZGb7JecGjRAbHJkJNymvRMsHcWE4C5j0VWkLDxuGh0CuRuhzlDlwD2HDMWlZRI4dLNKUBAZFujoNaMhriALg+gMGGE7wfIB41CYfUDrVizYGeotUeSkg/jrQHIF5FJMJi3snTAjBTKP42jzIFQi4Q92s+nb397dbpQOTKG9M8O9H+kegy3jcRwmtkXgr3vX8OF+mhqeHw/ZQ/uP8AX/f2LZj5HW7zpxcgVo8w6r94AuyomL9YUd86ZMrwsqF854R6UJx/ioq301unOqpLFJRksUl5XKBfPENUcJgn+GPF8/cz/KjUizloFJk05M9X5M8K5DRjezNh+swxvzUg9EHS24XaJgKGju4P8PsjbKs4fHKBbeRr+CyQrTCMsbHek641JvWJBL6cYPPEyXEz5Lcr7C35PLIVKGjoZptRUgmybNSdmSnIZ3PCVDhZ23mFPl0w5QQcJxQF9LxBI4OnfnyP8uEJ211GnRTTaYaeV+CyQZ9YDFIckK2yyrQcm0QoHJx8s1lMWgYIlWMpDrjzZ+wYpFcwK0ouheUWjolu4cqVM6qRt3KcgU/fwWuBfOcBNWWKzj0z9VkvK/zpmfursAZQUujTM/T5nmOigGVBPc2YAKRSWWl721h88niEWIj7BaAyFdZNWtYIJSTgRJ15WUIbRwZNm62QDJwyPW73M7bjiTowSTtdQAzQ8wQsK6REKYVSUC8X+OEOkibIVjD5xhBegIFyL5Aia+td3AABAABJREFUnf/iYTg4hqBx3oz3lPguLNiAioD8ojZ5CgAhP8FV4DPgrZZUrMTp1eG+WgFbHQKDaSLBGwHENwCz7KRLCYO/sl2epYMX3Xgt2sqcZUmYXQQJ+X8QbKAZudaHKE8hGxiiAXivwZBWhngIJkmkbCAIBmb+BVcrFa744WDh0uCbaaWxz08beU4aEZ7VI8TnrIo9hx7QgLIcMY6RZdW8WPWUkJ4rrCbynL5k6+t5Z1jNzIESQDSchE07BRg9Bg0IAIPdD4S0e3MYBlLsVFQCmkYGtmbc0cCJh5eD/axxD+lA6pYGUATdAyQQlAbBOm6WYZ6gp0JBL0fjyYsAsAgTgm3yuKFGcNCwTPNghDOph7mado2DJF72dNCeCS8MJAANgNrDbdE3ppzxN7KDpbZdeXvawuBmn3FrZN6xDQ5B9eY5Gi4+BZu4T0fw7TV6M1YJ/Zq2b8HM73BbP57gFbj/8QYrCllZuG358IjjT1akp2c8/vwbeDrBZsH0yQWzCWTl6mV7YBFBDZl0E0qVywcT6sM9/KRYHyaUVwn5qWJeDfVAEbn5nWH6lD5oSYbyznH49QtjmccJLqHEeV4gSwHcoJ89Uel1YfqDJCWX4jjDDwmWJ1iaYHczlXG3wlpEH3yE+nCiGz6DwmMIr9SyhdEq2H7qNWvlbFEM8ZDh20TvwmECsqJ8EEJ0cOTfIA/GzLkiLRW6bMDTBbJukNVYAfqYo3jmEbImyBPDTyhtIiDpUhFlFoqzn6HzYjPJ0eoOLJWTZMj0N66CVmf9q0rBOJsz/IMT8uMC/eyRfTrMwGGGvLoH3j4CnzzRED1fKIa3rPDEEsuuAply8CAS8HTB/KuP8I/usH10hM2ZbZ+URSVfv9oZjqpAnglE4ASEeYIdI/10Wdm/jQKBAiMvBsKwyMMEfHCCuJGwG6xHnxKJwgANeeVcVe8nyDpDzgtBUY5Mp2aQJqYQp9V6gUSfEzyzbtCu585jF6/QTbqDxJtRV1DobU79GRJD1GpCpPLSk9KMsNdYlevOTwEYxpAgqE7vKmwSVFSgUkNGNQUxP8BNH9s2BYcXobOCYxVrbdIm4JHmvXGWZkgWZiXarC1JrHh4KAxpC5Ju9aiQXeE1ITVvlFukzVsYc0TdJuq9WBgxEp15H4sx3dnRSjiEByOqEmp12KRUqW59ax6RJiIognKnAczQvSV9Px9sVbxpWWMqwcWIsREfEmnai+2GcXd5xIB3dePBWvZx33VfuhGOEEoDZ3RoSIAjnphejcBHYcQbltq9E7Fg9AaM9vsSDXcF4BJIN/Q28LSCetU9OC0kU0rcr9Ao7bWHlBqY0gArY3ZRTFpxO0ZvfOdoeXCmOgiE9Ha3C9PGqScpYg+shdpSf3yHy8SCuftwxXnC8yI6hKqGG6CHzjBshHDdA/o1b9+Cmd/hpk/GKtdZoZth/nzD+mGGFINNgnk16NmwfTxBV2D+7Ax/8wBPdBOntwv0MEGfyWdZvnPAdgTyplBwwt/eKOodJ3epNL5eKam+fTCTW3OaoNXpVbjL2FIQGN2pKvzqRCKfAfKTzxlWUoXcH7FNSmvjgnpMuHxvojJsmmFK7RmuwJS1pM5MfdZzYabU/Qn+6g6+FRIbnzfI+cKQlit825CejGq1dzNTuDM9Ntvv+SgIn4r842f09MFw1+tn76DrCX5ilW/ZjJoty0YewJRQU4rCkqzUrcbqyPLujAzFOiVAC9SoxaKFOh2igKhCnwHPTNs2NVSdIbpxVStAPU6wD07M4AkOCTPYFOkwoYgDViLDyEnUDZKuTBOaroiIYHpesB2Z2eSJtbVQHEg5Jj8huClO8IQAIFMKT4pQd0YVKBVpKcDZeS7luMoG+Krw00Qw5HGs2iiHUaNHpGf91PsM4AQ5TUhLlFCYg1czKTylQY/EoVuFmsISghTLydM1snimhJqBturUDeGpAo3UFHod3oiw3mfcvhhUkg59aoYMXfsFAlh42iwZ8upM7U8OPyXYlDqnR8LD4WBmX50kwjItDTwm4zDmLXtJ0EBYnNwBLeH5CbJFLu36hTHYKpWAl8g88m4Zee+48H5wp0d0M2gCya1AKO8K1WFBwINq9LyYEeQBveaUgBlUzX3RfAA1PGWewuuk4HnDMpeThLcRrKqt4bmKRL1+8HELfLQ7AFqFa46Fd6MmuyltocXRqjbPjvE6NxHmEdigH2EHNE3JuHrzThAAqfRE7PZfbwfQQjVDs32/L5t9boDFIoTYyM4OHr+DFKXicANf1H0hib2FaXisHdQYQuiue0t44nbOwblFrD9GrSIFWwIUNa/QnnXVyiCg68gIIjTbQK+g77+LFqIDFN9HJkJMGuNTwyM2IL/xZui3wbdg5r+ITWuU9y0COWQk5erVIdSWuTtienZs3yWR9+m/eoNcBLCEdDG6Uh3AUqHGyV4/uSBbhiLB7w+wX10hH2X4lKDPsZ9zFQZJqM6aS3oxpE++gNcHlLZCQQuTrIAq1u/eQU8aNXwq/IN7Zq188RZVMrVCqkAvFUlo4H1tJMyKegTKCdAzMNkGfVcgSeCvTrDMopuybhSHk8hOeTiyeObjijJlCsKZQy+FD+AhQZKjvp5YndlZLVymKcIpDF95olHSYsClcJV4N8MPuQMY15lVi08TtlcfRqooK44j+DWoCe4RcqoEC+IGWQuzJNzglwKtG43WlIHTETXq60hxpKcVkh5QH45IzytgCkkZOAiwblTuPcxkfjYXbFLIsiH/5C3gr0jcNbBvE70gsqzwp5Up4EnoGYNDVnpgcBelF5YFeCYpOiGA7uHAiWmKENrzBjvNJIwfT1HHSbrmCTK9AaZAWkAgeT/DcqVWjwI27yEKZjQJ7ADIZsy4myTkbq2vxAXUW2EKv9LjhkhvLQgVVgFmggJm+XLiFePi3lr2FML4dxvnITUg3ZthWeHZkc5KUCnYNWliHpYwJnAEuYbHk2qd2AkVenqaYR7c+e5GPkuhh8RTAzd89cjs0gJgbXEHowck7t1mDFwRXicJoy5o5TRc0TOhdI0w2ayoU6uDJt37Jo3PksBwnSplBSQ4PwdBadenjWFbzR9YeBVAL7n00uK7YRcXXP2+f4Gb19Hedb2fuHihnozQlWlhziYR04xte1z2UI5gbBW9CABT5L03pZ1moLqE10J2L0QPqSjDS7J7TqxiyALS6y4FHoQ56gBO2Lzdyxis8i78x+7vXid46w8/1xbqjefGENVEwgPn49gMx2tgqJGDuwaOEJ40jxKzNtGcab1nHfyxtX0Mu/MI/XBo5PmXtq6+/A3YvgUzv8MtfXDCWYG7X1+Y1TAL5neVE7IVbEfFds/U5PmzDS4VejFsr2fYnHD6tQI8PaNOgjIL6sdH1PkOx88M80+eYGaYf7zCZEb58MiJaUNfVaE60ruC6ZMz0rszrBas3z0BtVDk7rwCTxt0phIsTpk1izJ1ZOTxgvrxPerrA/xuIk+kAOndRuLqIcFmFj+cvyiQyxn14YDlpDAoNHMiTs8F9S4KQF4M6UL5dj/NiOIdEfYBYAZ9Lkg/eQesBTgFALmbUY+UUxZntpJFBolugE9AmSZkCOyDO8jjAqkVTTNDt4rps6WnGdurmUUei1PZeFLMz4VZFGBGUU00YNOnF+gnX0BEUT56TYLz44WpzscDHEwHrQ8BGLLCp2NkEW0ERYLIRgKwxfjZCuRMYHaYATgLa35CoThmbynkOLN44ZNCtxW+rMx4ukvA6QATYYbZ5tQFETATzWw3RBBgBTBN8PsDw04qqEd6rDzLTpRsYfDwMEglMPFJSZBWUBL/YsCUYLP3EIIWsJL7YUJyZg3BsJNbFVEbKzi8RUh2bbmg3ZceACnAknW1ZYozdo8MCC4Eg6FufW61ityxvErwLUpLJNkzXIoHkdXhmfwPXxzu9KJoqVDlqpQRkWhfhEW8RrioShTIjL4EkIF73NcEuq5OwBJhKk8aJFyBlLDWAbIIZNiPoEUwdTsJpJadt4GWLeMkUAvDGuZOLk2ALAVYJV3BSvQHkvR9mOk7LxqsGl5SfJ9370xLq4Zcv0oAritQ09jCfvOvbTYcA0ADCv3qBhj0OIe3XVqorR+IB43at/FJhBs7gNnTpd13KX808uzuAiKABXrozLslH1Ebuq5VG3/BCGTQeTUN+HSeT/y+RDX18cMGXrx6BysC39O70STluRgTgOToBkPEO5FYmtReu6c6Vyf6KoRvPayGEYDc5nxFGEpY60qi70235/rC89e/GTfnd3P7Fsz8DrePfrLgx7OgHBMuH2aU+4T5neL4Kfkc5aThpmf65vQFM3csGcqHM9I24fT2CT7PVNV9t8FfzVg/TNju75E8w92wfZCC9MvsFSiJg/l5w/TJE4vjZcHl93yI8mbG/IXDHzL03SU0JwCcFxz+/QrMM3ye4IeM+uEJ608dkdYj8o/P8Lcb7PUBdqeQTaHnFdMjSbnpTD5L+mJFnhSoBeX1Efb6SDLmIaPeJdj9jOmLFTV7rAoT8KxIbpBlRSpMD5U5A5cF+I3PkB8e4Cv1MmxS+HEKPQ2waOVS6FE5aDfM9e6E9FQIfBbKzWcDfN3gokh3E7AGCTVSJ51PKnkFmRlC5J0k+MMpCjfSmLg5s7s0QeYJ8rwgXzbYcmH6c0oQUEMGHk7eFCIdKcHWBaIJUgpF+U5RmDJnyLtnaFL4mzsCoEqhvfK9V0inGemLM7CsnOjhsNMMSawxBDMCwHJH0rATXNlxooJzNeD5DJlmknk3ypJbTNLeXODOFFoFxfE6CXPiPZvfbSQcq8CrRsYSOD9uDjmip/EivB8S2TASk7QnCe+OwBOzZ7yFBTTCU60YUIRFGimZ8SqgCfFJSPA7aHw76Ihj2V0O3grConibyzmxF+c1bYbQACnkt7iyuCNayYJJIwzCmk4WQoBo2jXArorbml8Nblz1k/CMXi5BguRvh0QO04X3bQQtWZ0+RcHSCItoeFuairAfBC2Hl1xYR1PlHa9rzQz91UwgY7OwGObg4VIn2CnRD5s4hh3EtOX7S2DF9uscJpnv4royhCYdXHgKNNNcBENYw+HMBweBEmoQf80hKbobACciqxwbATObAli28E4HKQLUrjKnaOHrRrQd2+1weICe3cwPGUlXf3vn1MTDiQYIiM89vEMRArUR1emeRt01WxoRGlHBe/+tN+ZLRQf5DfT0EZRQmA7Q0QT3er+Gx8H7iO99v91K2cFKSvuz0ro7/tEfg2/BzH8Z20cfHPDZf3iL8tE9DfmseP4pQTkB+aIobsifPmO9f4P6UxPWV456mrnCzIK3P5ex3X2A+fMKf1vIXdBEwbG7BFkd0+eO6Ytn1DsCkPxUUTcWmUznSs7GIcNnQKcjjv/hKeL1G3U5vlhY1RsCn2ZyLiaqvqoD8+eFqcXVsbw54PKdGWrA/JMV6VzpsTmcYIeCVGYYBPl5gWzCUMYHR0An6KXCDgnbvUDWjHReqalxSlgPr5DfLYBX1s15ZBaYfecN/M19VBeP1eXbM3wjAVex0dAHZ2Q7AlkS9OzItdIgNQOvCRbVp7UYDYZm1IfEcg+LwY4UHkznVr5AKdpXEvzhwLTwxDCbXjaG6Eqhd2rdIG7A6zts37mDQJDfrpy8I3MJIkxxPx56OQTqtWzA4zNwPNBAlQr97B0LPx6PwJSQngvkeaWVnacg4Bq1XnQFWZy1pwbj4QRTesP8spKndWJYy0GPA0qFnje4s0qhBxjgDujuaNHQpKn0LkhU1Abqnjbs3j0evShlauEk9PIDfaVWADuCHqEkQQxv5MTWiNgCDCE4OLoCrX5MI+myKnSEWoJAjizXnoD2OdgXRAZSSwdOGwg4cqR6hxUSxPnWimQEdHXS8JSAxHE0LwqCwE7vjLaK0x4hk2ShCcJsRVQel6RuIDTww9CF9yfax3Cbw03Jk3ECkZqEHLdEQ4hKgKRmPL7v+ikmSk/LFOBHCf4C8/Df4qiHkOGP6vHeasy1rXs/9muEKugyxd1r01wCbSylk2EhoHAcL2Efvw6W4hajEyIOaiBoNMDUdkDjVDsQAUrLsGnnGMABIjvJWtGnVnch+qQa4bv2tclNGKWhGL/6s7WxPz8NIbS33TPCHbxiT6ve89Sv2ouAFk0Dh8dq/JsAV/00wQHr++Fmu/aa7JetQTTHCNeukQrQUCrbJlwAOXCd1r0fu4Gwu5zwTdi+BTO/w+2n/1f/Nf7N/+lfwhPTM+0V4/3n701IF4dUw/pmhh2D76ET4+oZNLwCPH8/4fyRQv1jpHfA9Fwis8epkKqAaUyuGVheK+AzZOPDo9M97FxgD0fU44Ttgwn52TF/vsGrwws9FHY/A6BHxA4ZfpogDqSFxML1OyfYUTG9A+AEN3reIO9W+IevsH14B6sOT46KOwrnrRX11YHAZq1ITxvSowOzYntzgJ5pfOWQsH54wvS8MqxSCtLnz9DPHfb6hHI/IX9+hqwrNIVGjTkrUy8J9YMTAHBMzWFHQFyp2XMUYCXxl6BH4GuFz6y2XZNQCK6CYa1L5QRbHH6kV6IeFfOvL9DPVqS3Z678g+/ibx+pSQMAU0Z9faKq8RcX4PMnApVqLGWQlEBE7zjbbNsOdEqlJ0qVxOBS4OcFelnghwmSMtJlhV8urL/04WtUVeD5jHQR4HBoy1e0ophMu8+wV0fg8UI9oTkxm+1wZDFFUYYH0jDhtvm96WbE5331echwM6ilmNMjRGLK4pyi9HqFcJrFsQlAaNW5sBOS1wX0LC2RehEel6a1wppLDllAYUFtAAlwJwctWTP2QIVBnPuy/pOCa1QOTyN3yjD5N2DSPAB08dNj6knCIxQctgYqikNUdyAQbZCZ6eg2hVWOmZQhBgWU950nZpPpqvSauSIthaUjghdkwWmRWFObBX8mJWikXTfOklsDVuyLDQLKYuEokqGz3aUSgCXs0vTWcfme7p9NQItsdOPZQN6wSZzYg6uBAKD9vQ/vO+AR9OrrnaxMYLwDkrYzArgEYAoOFVpYpdv88D9oBD90MPAiVLEeLzoCcQNRJZr7iXB9cO2BkB2o3WzW+g10T0+7Lbr3I8a/eWXcZB94aeBkBzOtnddE2uG6fcnG20B/EwLu9QV53xvz0t/cr4W+FLsQ3zV4atDZ8cH09ReZBL4FM7/j7Y/80d+Hf/zf/0uUDwQ+AXBHeuaNaCfA5oT5YvC3G2BAWonEy13GvDjymTdPOQrVfA0kIV4qsxfuFcubhHrMUBMcfrJh/tW30Kcz/HSiHkspQBJUVXqHVIB7R74YpE4oP/guZCmQdxfAqeYrm0HOBfX1jHpUpAKGforDZsXyoPB5hpY75LehP2KAZ+XfEPIGzpUGaGJmR3paYfcTigHbRxP89QRx75LqpSbk58JyAB9l1jVaKmqe4KcZ+nhuQVuy9hWw7NBn9lGXWOVNyoyYQwIuUYZgrUB4WnBIwWMRgr7V6HF5t8AOJ2wPCq2C9Fwxv12BOaF8fIf84JgeL3AoORwlyLzzDJwO8LVA/91PkI8zPRHrRt6OCIFFVoaY7mfg/kBAVkguJieAvnK7hKdlZdaUrORHuFlLGOKYO7VnfNmobnx/IvBZeW4vFfruAklMP3cY/N0GfSRo0qzA3QR7OMHB6uPYhsVmC7s0e9KICxpueSi5G3BIVoYBoOEVcGA1hqAyZfmRQUBTGaPXiwOwIL3SK5LWIG0eg+0KNBRAQmtwXBqBtzkwtOwhQg1tmrQwPFROAZC2nfAokL64bvwaB0Ndsnl4IsIahb6MZyNPNYXXKe8G2BMIDB0EcsEvsdwcAPScqbUVuvCaTA5XQz4Hu1ile01YyFN72jTEScyNPpPcrL0PjdzZeAwU43PylhBjnwOwGXZuXdhFT8D06PBDpFn3CujDJsM/DOOHwfQ6RspLeCV9dNCED2A31v13VSIc03aW4ZyDEW86NO07G47VPCANMATg8av92vFomaVqP07fv+/a3ES98fvbOIRV7Pv3FPHByA9/98KfAAQ67OQ3SLF5arB7YxowclwBiNu/MYR6hisRm/W/NUB+C1Hx7AyXUuUneDduKBRz2q9aAJoxG6xtrNXk+On7D/FN2L4FM7/D7X/2R34O2wd3URnYWfnX6VWZn4EyG8oDoIsQQFwMqWh31abiUVsl7eS7A4X3vLDqrF6AdKnYXmf4zKwZTROzczaDPRywvsqcrCvFumxSLG8ypuTQc4XdHxj+qBsgAp3AhzoB5S5Bzo7p0wVSDXY3wRJ9wZ4SDTRigq70uqACetlg9xNwoA/Yk6PeTVg/nFFekXyaCpCeQnfDqKlhkwBpAsSgc6a68RrCa8cZ8vk78uvnGXaYGYJhDXqS/xw05C30MSWsIQNPd3mrsOOQYpguhulpg2XB+QcPqDOQz/Qe+CSQt8ZU7oeZ9a9eH+HHI2PvKTEVvBTgDJYSiNRZvz/CH06Qn3xOfktOzHyypvoKeM6Qmd4Rb6nJy8ZslZThx0Mn4XopQCl7KvC6Qh7fAQi14nrm7HI8wGZAhmraUh3y2ROkFJizVpQkVsqWLwR+tyB98IDt4zumTBsBngDMgtn11GlT1sr7NUjbUiyydBwphcZMgwwqAZCM922OcEoQNFIB9Fw5WT5kgs2W/BSGtknyA+iFLEk21a4V1Az8uF4lWdiRn5oWDTgezR4GSRJZdyARXBh6CrxnaUEixKVAA3Ro2UgtO2Uw9F3zxkGy/SGEAcXhVTqJGSJAVtQDvbfelIcVcZAw2k3NGIAMYUuLWlOAM8QVYxd2EE0F14WEXl7DyOKrbBttL8Hm4XPH+XvaPXXA/tr7056gG1Czv9/hyv6zEby0cRqM+NVhpJ/UxwvbTjqmOIlfM2pH99GOXPf3VzwVaRa5C971tniArx19XQO5uh/XO3hr55Y+UOMYCrBzauJm2VvTfG/R1n3gelN3wDI2au/11RjGfu1V++/QvTVRqqxHwK95QAQjHHNDUmCpe0isyWOZxX43V7u14Q+/+T34Jmzfgpnf4fYz3/sA8AxMsmeLxP2Xnw2nX1vgcGyvFVUM0yNr6OD1iV6UBLLhNyNPwIHpkwX1VUb1BD8XYE7ABuSzYXmd4D9zh/x4gK4F+bFAni6Q04lhiqcN2RM2YQbPohP0PkOLsLaQUXp/VVCXZq2YPwUAZuVQVdcxPTnq0VFOE9NXhRNqvhQqAx8n1PuJVb5dYGaoxwSJiVo3wBJf0XgVz45UFelxQ81sn0NhU4ZsK/SLC9390wR7fUc597XyuT8I5doFbEuSfq7tIbN4ZwJSMehCTRVdKfPviboj25uE8uEEebdBLyxBYQfF9vER8ukF6oLtlHC5fwN1YPrkDDyeI2y1QaYMvLpnCnYpJDYfEgHPYaYWzjxD3KDLBj+fqRNyPAClMrtpXfvkinkG5gSfppiXw9uysY4TV0OJXreJ5F41Ay4rOVJ3B/icwpO38LdgaJNGOO3T52VFerwwDf/VjGrBCRIWGW1S+2KAXqgmrBu5JZ4oQufB1dDzCiwKu5sgM3lKNnES1GLw1XZuTUgP+ARIiEp6EoZ5FMHF4X2/r3CFKd8FVwbHJe7ZytQXr6HlooBMCb0EQuNPdDsgUfuoeSmaMQvQUIWq0FH50DWAcWTvoIUWdDhe8yZ48GUdsM0DeI3La+wpWaKQZAQw1fYMuIIuasc0fVBIswmy+f4MIUUCbwDBVkOqncMaMTdC2eoOX4GpOGxzoCrW10Bt3JXWr1tL+WXbEHrpl8tB71WUFetg4IUwzdXWvR+yZ8NxmHZtEwF2FbwBcbSb5aq4UnMf+NA42c/Vwc9+XfotYtivW3TO0QCr7PdA86g0lCcBV/ppvmQgiVcHsCI3nha/eX3hAFdgZf/XtCA7UOn8of02bVucuv+mSSbt4od8ZpmuzyzBlirvft22Nlz/i+/+/i9p8+/u9i2Y+R1uqgo/hXE1xsibVoVNIIl1rXj4fz1D3z6iThl+PLFApUVBw60iVwBCo5Y/ecJ0PmK9T9juyR9JcOjiyCmky+8V2+sZ24cGeXeGPm4oc4KdZtTFCHIgSM8bqjq1R5KgniZYZTgJSWCnDIcgP2+w7QLc3cNFyZVZBcuHM1bNyAv65OSJK0CfMmxW2Azok3UCqaoDCz0xXHXH/geBb4JcI7ivQpEwJzE3PSrkvNK7sXFZJAD8MAdhE1z2JN2fRqXwnFrFMsfT5UB6KtDnBagb9PUDnr97QDkppi9WTG9LhJ0QZFLF9uEJaa1BsOPkun10wrS8Bn7jc4gUghJN9DfHStlV4ccoDf3qnryYrQBrYXmIqj0c5aE4Jnd3YBQgZvwoTeCw0HkRAsvnDQZDyhIKxpkG3ECQlTP8MENqofrweaEH4njkGEa2EC4LcL4AtUDhmLYTU5BrgbqSXyBOwbfNug6NqMIkceUsIC/llIBJkc6FxTrXhDQn1CmhTgyLaDFIieylKfXwWj2Axjh4Oi0M6BIaNEHYVQnAE84Uir85alZ6h+IyJzJzQ8AQPbuHfCC/+q2DIn2YIvtlM8jmXTvEByJpN1ICLjRC/6lrmzR714xT3AeorM9VM0OgkHDA9r6he//NJRAATyRAiMfRE2PqsARoy8Kboy+RHdjCg1JJQJZCcFMFkJjVmzaMAJFtJTi+dSwfheesqTY3MHNjR5sR7DgC6Eb5pf1b2v/IvekH+jIbDXQgIQMAbPlB3sZubIC0rCTsfJd23Roo6UhJ2sBGX/c5YpcIiOM6sN90Q3979lK044Z567u/BVdA6nZrYKq/b22wK2/jS9tOFh5ByzWQGT8fjzUeuyUIQrCXlBLyvDQQpV/92y/cLeHYncf4bz782d+k5b9727dg5ivY8hbVdiPjIi0x4UqslCbAvQApQQ8zyizIP36Cf3Qg+LhLKCq9+vPTH/wA01NoV0Tem4kguZETUMkVmN4+wuaE7XsP2L4HeK3ITwX6VJA0UfBOBPntBeIXyFahr06wVzNqUhJxN4MGmCg/+wHKMSGvgFhGfo6V+Ap4NYYPjgpoZraLRxjnuSI9bsBSYPczrBm7wlmgSaeLg8JtxwS9VAqrAXtNocNE7ZRphpbC802sj4Q1rEBSPkEFzBYqFWnOTOW+m2DOtG+fBP4wo6YJdp+BpDj9xoL86TNkc1gSKvtmAaaoeTUp0hcXpHMBDjPqw4zy3VdIbkifPgHbBl8LPUPB4HcA9uaBBq/9c2YDuTs1X455r5X09Aw8Xwg0DlFEchFgqywlcHdE+ZnX2D48EqQtr7AuG/K5hPhdBlZDeutAMei6ccxK4bk5lPASiPp4ZOFJBYHC4xnydIHmxHT0AzPrpDhwiUriBwoR9hlQWZ/IjokW3JrWy8QRMEF+LpS7n53ZYSZRfVyAOUIoAPlBFpiwSerH7NtKGUiAYoRXpEeHcmRECY/jBmilfogFSGpAqRNNleCXtqC5d+LKhaHqwl8lQJs3DgH3d8P1Krppg2zeLb1NHqAoLElkWXkjREc3ncPHn0UKvKt3u8bxRi/eWRDRjuYlih0tR7o1do5QbXyhNqvHHNRAi0Xae4+WANfA4yVr2j0acT3GcM+wv4AN7QVF26bDDg04vARuRnDRBgvouj59H2m8lgY+27ElgE0DE/GlDNdchvutA5TWEN1/N/aqAbTbsNDgmbsehNvtpqNDeCry61/60c1v/eqTL8VKDb+BCyVt4TGJZG4HCe/uqHVvgio6ydf8Oszk0d7RaTWez1zw5nD6Tfrwu7d9C2a+gi2fK8oxo6eSmtPAPxcayruEy899gPzpBdPjivz2GXJ3wHI3UTumAum5MHMpCfykqEeEfoIBmyE5WCqgVGYZXcjzqB9m1HcrvSBJsc4KORfo6hAJHRVVeC3wuwNkTpi+WJDXivU7d/CJM6aeC2SeUI+KehRAEuqJq8GSE9KFRgibAVmQP7vQK3F/QDkKto9miM0wZ6w/PW7QpXJ1eEhhhLiCqfczkAuRv9JDhNCEqA8neAL0Wej1mqPOkRHMtNWbQGL1ziKO5ZggQnKtT4B5Qk2s3OxJIAs9DvVu6sbAurtZQlzOUQ9HzD85Iy182utdhtyfkN4u8HWlBokq03Gfz9B141SThMBAW9ZPZaHNnGB3B4jH5ynRgxIhJckTkKN6canAF4/Qhxnb77mHmkOfgRYO0+rAxQlCphSZUiRG4/Ud/JJICo2Vu28FWDbIcYbc3fXMKqkVcin03jhiouMEJjmzAvk9lYYbJ6dJ4fewiwQBOwch1pSGx0jIbdlS4t7DTpiCewXsarzNIBnYNpce/vAMejhWANgn5m74EL/LYJgm7qEUaco7HyAcCi5UyIUzk81BQcYQMhFHVPoOr04FBBZEZN+9Mwmd5AuJEKoDtcOWwaq01+DzdEOR4ngTw3gtnKEdH5EcXJOgpvDwREiuh30zs/VacpBNYeCd4w4EuID087eyBVdeiVtw4vtX6P3c920OjKvw1GB3pe52uq3833MVvGTDbz8X3HhldrzRMvDGY+8hvjbm0tt41Y5Ba2gMF+2AS677dTtOX7bd7vvb+Y1cvbzn/WjHbYcavS/j1ng6JMzzfvV2MwBdBVmAVow+9o0yGaYwsaaCEMBFdk/ObZMC6PTiot+A7Vsw8xVsaTFMTzSW24mr3PRuQdoMWBJ0CV0LhCDcq1dYPzhCanhNlDwWHAGrCVOpNMJzCrEwkOy6VaQVaOqY/uYOUg2n/+EJ+u4J9uoe6cMDyibdxV4PCSoTcDH4rNBwxaoHd8dYq8aOGXlzTIszNdZBV/xaoAs5HOXVkSvJDNRThlZHOQlq6OHk84bp0zPsGOkdCdQvEQFyhm6GdN4ornc/weBI5xLVfgFUgz1M2B4y5IMDjj++0MBFSMcmgj1JCXVWavKYY3qs0NUxXRZYohXVM8UJu2ENt77NyqyjJoongGwFeQM5HlNCeXNCCbe3T4Ll+weYUyfHxYIjINCtECgeFHaYSRR+PlNoT5SEawfk7ZnAISWWOGjqwDmFx0LoJbs/QZ4vmH79C9ghoXx4RDaDfvIId4MemVpPIrcz/AVnJe/XD8CbV/BtpTetVgAhxvd8Id8HwmWYNtIp7yNZaxjsQBZ1ABolPGKadm8JeC+65j0bJso5i4YGTAIauTWFEJo1D4jwWquQBwXxIBk38BBhEOHvJbwfAkAqr5tmJx/HHNika9x0Yxgr5y5HgmFB7cJzCEFWzQwRS4kMJ0gHD2jNAIF305TxjB3cTLHS35yoA1wgiHkQnGNZ25iY8AiPBUjMrHHFoprR0Lh8zTtrEwGnVEAjC6op9TZs52BYsY2zRMilGXLWu5LdOjXwYBhE8IZ+7QkxnIPi9mmeqs5ridtpBBrSXUAIcDoAvRGwDAa/B2v85l/fzeM8AwKIfnXtpba1C96MuPtetd2GfcbXl4Dd1QHH7SU09lttN78Zz9VAPbB7mzqwHYDhzWZAj1byUHxDRxSfR77yejEtXYZnQnt5hmoSHhrp56Yn5xqP7rhV3vPWfJ3bt2DmK9jmTxde2ElRDwLJe0w+lY0KmMbg7nafsX33BK1cQZamxRKTdLJCgHFMwMZJc/68AFbpNa/MlEAIxunTQoVZN+jTBYe1YHp6QjkdoN9V2GEi3+JELoZBkVTh6YDy5kCOyurMkoAAmw3xV0fajCGji8HSBp8S0pmk3HrKkM0xfVqQP3uCPhOYleMEc4M+GvxeqHobwErMqVxcDDUlSNTm0eLMAtoM6bHAp8Sq1g64O/xpYwphmmMC4xin5wJ93qhNM5HPUcEsrbQW8kDSBDeL1FoEGRXI7qhCzk3aSszWPHedE/yYIM/kQNndkYtEK8A0QaYMq460GMdwKdDHC+z5QtCizCwTr8DKuliYJ4Zw7u4gWwCeABiSBcgTAIFeLph/9S0gwOWnDpjMkD55ouZJEor9zRPk1QnyeKaxX1Z6esx7GKwpssKth7ZGheI+LRmF8ZASJCcCjecVljLEKjOKkiBNCitg5lQxqBB0I8kQSqTR5qotPC0IrAOE2u9+6lbeQjZmJbWVccuM0ai651X2iEX8vk/+TjE8s8iLmdqTKUBUm/YpdlaCWkiAgRyiZirAgd87JArkxDYUlgRApV2R4PvEedRRZ97HUgjAtHj0f6803ouINiNQHZicfKRM0KIl+ueArAbNiioCJIdPEXYaPRAiPbOqe3U8CN0RZiF5GLtVulluu/Ea+Xht9rcxziDwGSI9Hr9tSJGH9w5S21G8k6l/E6+H79eW495a0W+LvS39ewygqzNtYmzDKMcYdMCLdt2+xIXSP7pCUv/x22/1mxcASucrjzwd38eyCeCJB7AMXkvcBrsQoAsLZIZGU0MkfTHR3VUAnJmJBkWrLu6tOKf3H7zX1v37b8b2LZj5CrZsjrXNlQCmsyOfK7wUOJTGJ9EtL6JU5VWm007VoY8LAc1qFF2DI00ZOLJ6dX63AgBXXIWeE0qGMxTicwaOb6Bvn4FlRcozyroyg2bKVL6dyGuorw5UDl4q/O1KXsMkDOOYIJlC323BO2ENJxxmyOMj8hdA+fjAisnVuxaOiCA9r/CUsf7UAwHP58/AFNWZi5Fw+5BhlpAjxVcjNJCqQ5ycHF0q5GljWCIn2IneFxwmoKysG1SM3pRikIWZN348wO5mlKNCamXRy0sFNMGOCptn2B2VlUWYBWI5o5YCXWhMBaxzg+TIjysrTx8m6OZYRcm9uDiwXpgefZrgecL06SNTycsG1QR/vtAT0zYHl9zPhUTcnIHTATJlhuoAiCiN4ylD9IS0bjj8xjOgCdv374A5Iz1e4Jp66CB9/IYZUc3FDE7cNTOTSIpT0M99uF+UgCVCMfDSZ0GpFdADw2jnFZIrdArQY45aHAp668hrCSPdOC/Obooaq8UY6IFAO35AlO7G59JbK0iaB416n3SNxGDzMLSGLrIH86vJVQBgwl7/qa1QY2IGwhhWMASngr3w4WAyHRS0jHRn2QyCXVwtpE/iePTWeGK7fa2AI0pOONJqgFInJW0Gqx4VnJ36HpLCo4PwRKFbcxHnpTlX6ESS/e7xGkGH7N6JNqTDmDik10G8cXZ0Y9v/vjXaAaiujG783Wp7SaS6d09C4z01l4IhMtp8J/OOuOHW4N9mgI1NHcM//fNBK6aDu+H7NkYtWyr+7bAAeO+gu3sPVwN61Zjfegt8d32oNubN43JzOAKJYdf+ZdyhcX84rkNLnS9z1bb9Zg0m2Qt9IgEfkM6XsY0HatlMI0jqbW9t/TbM9F/WNk1KuXL3IMw68lphP/4E/vFHDLvMuWetiBtcWRxOLwZZHLJs5KzMmat9B+RcgJyYPrpsJPVmpTcDjiKKnBLS4wpHQb0/wt2QRJHv3mB9NbE9jgh50XA1F/p0rpDqsKeKekywKUifSeAroAvb6YlGXY4TayJNwtTdCqqaLhcayvlI478Y9O0KoCK9UwrkfZxg5rH6VKR3K1IFV7zLBtcES1RdlXDF+7YBANV7xSHHmZlc6wZUjoXfz3w8I9wTlQIhMCAD5Tih3lHMTjzKFIRRVQGmSwmV4grMFB2sd7mTOVUEdSYg2h5n5HVD+uQJ8vmZXpbvvEb9+B75339CDwxwNaPs2QDhu69lj+2n6JcbfAP8mFHvDtCDAe8ALYb5cQOSwg4JgiNDQonAymeFnA6smL5sBHU1zpUinLZtBFBBTAYAr8GpSczAgQWIDlVipEQQ0NIl4ncalZ0bV8Tm1Ffpe4XqDb4JOsmyyfA3z4Y1lzfg4SFBcMMsJXJJwsctLXRg5GGpaA91SRjQVp6hZdyagpo32bohE4n9O1k3NDSgNAjuQTSm96RVwhY0JEVPg6hEaQdE2Nihq8HdYRPbmy8EKrxGEuDGyLMpzHYy9Vg5e3ixwoBtTu5P4mcVDn9IXYgQsWruBTCB9w344PVwoIOC9/Ri2r6DEv3V8dr7Bl5i/2YKWy0jgGN7BaaaGyXUoTug6L/xa2M9WuB2ggGgtzfjbn7zOvJ3do7tCFiu9r5592UGuSEEvDzGct3kF4/QQEADZ+26tH57a2V4FW08WiwSJEBFoxcMJ94xUgv58Lf7WF237P3eNpDDd1tBH0A36ZUgZBwxj1952/ebsX0LZr6C7dX3XuP87z8DVFCnO9SjYPnePXIW2GGGrgwDSRVISZDnClHD1DwupwPyucDOG3Q1Fj6cEyXvK3keujhj5/cZ8rwilQ2yVIYuEom+NhM0WWkB7sT076xMFZ60G1Kb6DGCGdtXDLgHDURSSI6Fq9DVbwfOqNNnlWq2k8ImQHKmvbssAID0tEKWCt22kHtHVKkGiYEq8Flhp9xj+hLLepvJrJTqsNOEOgvSSjl42ZyEWGdxPo1q0RCgHibyVpokfQ2DEBWsdQN0JelVl0oCLII70kIFOeTlC0NdNisLA8ZErheWd0hGtWSsG/D2GVgK/LtvgFcPwE8+Y/HK0wxsKz0CpTKzKPRzcDyG8ayQpwBE8ww5eE/dtTlBTzP8zLBRXg0VoWGisSTeomFZqTeTNCTmOX4w48peEyylq7RXGPVuXGQ32J4ixTnqQVlYWBUIAlBbhWnwfFpb2qwZwm7a2hVpxS4KUe/ekcAp7X/gKh4kodfaybDS0lUF9FJsDskg12WzMFYszulZOvkYBoKJVjCvgckGuhR9KeuI8KZbtJs1ckQBfSaQZraR7DahxD0YoRtdjfWiJBSmDZ3j1QiorRBnE+wYPVO0G96tjCuztlxCRl4Unhk23Y3qsKQf7N6VpWp/62g8b76P64Eh4tg+ax6ZDmYG0NTfF3SM3sBli5x6eNLavleEYEj3F7zYroGr815/xv1v9xlLBDmuxrYL8wmuM6xuX3s21M053tt2kH81vn2AhqbUq1/tx49pugMRAbrgi6A7onbiO3liGry1K6AmDXCMF/ZLW44mtzCCkVIEVuPEjaw+jPEYUmpaNNJR7Ne/fQtmvoLt+z/7IX787z6FXjbM/0NB/e4Dwzk5spU+u0CWhdkUdxnlYUJeSYb15wvkO29YFXrZYB/dww8J6WKw80JvghO0eFZWYc6ZfJW8QCsfBKkVUhTwwtXjJFGIWCJjiWmtWrFnjQhghxwprSCZ0wB1gzytNGB3R4bHVIBzIWcHkRUxZxLEDop0yEjnDXousFPG9jMfclJ6WiMdNYBTpOwikUisbaV72ehxmiO0FTWF4I70fAEuG70+x5mprFGoRcypztoMaxAj5ZRRkgNFkAoJq7pGlhHCqM0CV2rqGBy+AXlx1M2gSaFrYRhlEnjKtNFLgZYKXzcCkvMF/gmviwvoBZknAqllIx/FgmhQDaJB4JUweqVCssGLQFCQnmNV34i3awGyQGdyWVqYBNLAIdA8HvtEKNTvsV08qxOhLKxOKQS6qhDmZnL1N85YZsBWIImVv7G0FOYa4ZENAIX5vPJayFZ5TyAFX6UOgmphylQIcCLkoJUV4JtKqwN7irWyb9Lr+IS9WSpBRUUoULNdIpGar8J+hVXzSi6Yz3vWlQTwYD2myMKDo6pAN4aMqjlkFgA5sEFo4gQg6zoyVuGS2vADwM57cMR9pDB3OEKMcI4ioVOAnuDhoBF1w654inEYbNeVR+J2e8EYd2N5S/4l/r3+/OoEwzYSTaOfvdp0+zsAFDGXRAaXX3Nhom1UAd7/7qCgnWf8jd383Y8TH7Y+j+MRgLH3M2H34FzZ/FtU9PJH743FVTtujjt+YXHAfmO0fQMMNYDa9pf9OWxApvlvRFi1W9pCpKMjH15funj8XNtr8Js6WTw8MY1nBBDYM2o4tL0dPbyEJxy+ZIB+97dvwcxXsP38f/0/wr/+v/wbYNmQPzsDr04wVBIAN0DmzIKHq6OG9gu5Fxvki7fUIPniHfTVA3CYoVuFOYmo4gI5V+Bpgc+V3olSkT7fKH8vQmCQmpAWBcekGPLnF/iB5QLsFHrxWZHO5C+4sraRvZpixdwefEM6Tay4vLHKtZ0m2MPMzKa3F8i60Wsx5X31ZTQiNiuNzdMKfP4IOwg0UQfCk3YJexZwYlVAOSR4yvQiVWdad3BVUZwaLHDowxHbncLrXp/HQ7jPZ0ALkJ4q9LzChBwYrzSG25sZPh+RHwvS22fg80sQVwW4P9BLUiure28bjd2UYUWB5OGlAHCYIAurZXspkHWD2xIcGCEZFxxHpNyL4EEVqBUowqwmEODhsgByIM9iKTBhzRQpDP+4g56z4B1II/wFeZaAwKmq27RukoaU/2ADRNiGbdvfG1OPgQC07R4I7gpSCg8Vr4s8LRRqA4C1RJovkIyeM6ZK0+vHlTg5Jz4F6GmTb1aYEWCg7uDULTIwjG2QxMndJTxPyWmUJgI+VocPlehamDRkAKbE6w/pRZ5RnVXgFSwu6VSRZgZV3GwBbuoDU/jFQkgxSmf0xao4PURGsNGyrDoBGiCwBlBFkJ3AsyIykY4kuNc5UrNzANPmSelGV/Y06BeAS7PF0v/AtQfjyxbODcS0BboP52zP5ws28crpEECujYkDe2p59LcDab+5F7sDwvuxrl5HQDZggP1EwzFk+K4BszTs17LwWv9uPDj7e7/u90vgqX1+C65GMDMCrNsillexskDmI6CS9vkO3q+ByojCvqyBY+fIv5IAMiTiS29KRCwJkq5IRbEIMEQo1K84M80z8/H93W9y/t/d7Vsw8xVs/+0f/Tn8vVphlws0Z9SjIj/SA6DnAnlaaOgzoMng9bBniRxm6LsnFiecUnA0FEjMPuLqznYDA3QAgxo8glJptIdVh9dK4nFWuCSYJVQhJ8CPCXkxggkBLEcYonpkXSlMDZoSw0wrsxHqKaEcFemOFZWtGPJSkZ43gi0I5PmM9Hwmh6PSqNrxAKSE9LwGZ0KplZKon9LSrbVYf/Y1UpfLIUFeHUiCPFIIMC0VXiIUdEqw4I94cBrSpUI/fUJeNtj9CfUuox5n1BOzjGxSyvpPCTJlpK1pmDBtUTQMPTgeshgwOZCbWCAIRlTIM6kVOJ95Dbx5OCKbJ7RmpF2/nNFCay7hSQtyrHvtq/1dnhNIW4FZkEmBYcLkip9AwMO48EdiFuJukXXjvmvGxD/JmTNT+7zS++JblLWYJ0A582s46hFkbSBUirfCscqZTWqGvFAlmQDH4Cs9QAxlCYAcU3FjZvK3erNaj+VjhJ12y+xJYKLQjRllqPSAubYVKwvntbpiNJxgIdVw02uzauGZkvhnFu6GSWiRLca4GgUQA8ho84S5XC3UG+G5d2BWlMQkAEsCOwRvJ1Hsz6dQyG6pw20bV/KjdwFAV7UFx+fKwLbfyAv/xsPbsOvY5KEzV4TS0W6OXhrsgOjK/tp+7I5Z2nXoYGU/wQjI+rHafCaDMRVAygD6eLl3QNY2ve4LKjh+Ddzcgr2hOGQHRq1hI47YcDPO40A0ZDl8Nvx4DBkRM8gAYPYBHMHDzvYeQFL7ygERv24fgD1WF//Eb5DouJ9G5mMDMqCKO/bMJvJ29j57DP7ve/XxC8f8erZvwcxXsP3hP/JzdN9Vhx9YkqDeJWhx5JUF//SyoHpGerfBjgeUgwIfnpCzAu+egdMR9uaOOihV4Bvd0XaYCHKMBk8LQw0+Z0rfbxX67gyUArub4FMIqt1NnNyzEpHDgTmxwq8nSthnIZBY+RTnxSClouQUehWAJs4cAgBGTZ28GMmb75YQNsuwg7Hi8YHcE787MLw1zyhHAZYKebLwIBTIMwtRyaTw0wTMXIHqUuP5d4Y+cvBrRLC9YWZRXipKpjYOvVKyu3Lh8FmBV3cQOQNPZ8jhDpYBVEd+V5GXDZgPqMcAVAHktBhMDV4TklPvxLSFwhy+0FvjEOA4RyZHgIEU2jrbyvpLytnS3QGZaWTbbDDlWL0OpFwzAocgzMJsn/g00ofhDFWOk94Uy99q+7gJs9M6sS84OgZAIzXbjRlhMrFMAkS5fyP8RhitLxiV33sQtAVgJlWAKORmZQMUe/BuYkbWuovPebCGxQlk3EMELgk5QDG/t0wgUYdlko/RUvid4+LiSLUCzysMgjSHJ8sq0qTU95u0j6UnAiy5WBBUOX7dQwdmV7nGMxi2gJpFzuy5atRPgkcBS4YrvWEj8BJZUmo4TYLpzDBcPdAbYxN6YUnXeH8DOPrb4FJ59zYMRukFm9V+PHLP+8FuCL8CdA2ZKwB0u1Nr0Ev2sJ37pf1saEe7ru0ct16joS8dIDX7GiErBBHWa5yv6f7EAoIXX3Yv8xiyakA5jnM1NgOvhW2IxYLsWJttbA9E/x/2HW/608dhQD59nG+Q5TjQnTX80mDzex6WXpe9PcOgI5KtxZECCOoAZhp0MRnRXr6+9u3AbTyvOsbtv/3ON6PIJPAtmPlKtvvXJyATSNhBgLVS5VaM2TsfHCGvJpIrawWyw5RFFn3KwKs72CGjnDJrFC0GKQo/ZPiBlY+9AGnZ4FVQDhmYE8r9RMG+89aBix1Ccl4FWElQhFCgLBVFjeKMJmCtppnhg/RMQOOidDlmgUBZkA4CWQpSK8Ea/AOdM8r5Aj8pZErAskGRUQ4TyZ9RioFaMPTKWE7BCdqQnlb4F0885sMd6ollCZDCP79s0En6gy8F9KoIWN5gc9SDAJNCqiMtFfpYgKcNgKG+uYPfHyHnM/KvVfirE1Khdk6dtKfQUoAswihVmF21bnxkJ3I/UAyyLKDyLYnWIgpcVoizdIFkVsEWVWBZ4VY4EdcCsbZ0jAm3GUNjNgzBgDIsFZlOrmCqeAsrNe0aA9uUCXz8oOR4VHJzHEAjbPC9754LM0gi/8bqRn7U6dSNOnJk3gHAeeW5e7rNuIKPCW2eed4mCFcKxJvQIXYxvhSemaT9XJyzhW3uq27ZARWAXj/IQf0WDPN2ACvLCXqcIgMNEOvqfiy0WKyHiQgkaycLe+aqUwyRZciwE5B2mzYRsNQpShys0SZDSCwYbBbYTNJ5WgL85gBoENSJYGSbwZDoAYNBl5e5HIP9UADWCLdRlsAVIffPTYCdnIvdvno43jq/qpGJvd0fA3i4ndxGgNJ+P6zQb/cTDOCltaneAII42QhyIOiekW5fZcAI7fwq8NV7arii9at1VnobvezHFjQvJfZQlGA30lfhoJhvAvxIv+GGTrRHuQ9i+/pmn74auPmuu6Dab78EvDgGhLq/ttCQXO3YOuJIYkjqUDWCmTjF/huHSNOBaqKZ47H4DOzbeygHgOOH3/vZFz7/erZvwcxXtElxeK101cNQJoEWRT1qz/yQ6kjPFfLjt6gfv0Y9ClQnyErDpsWAhSs5TDOJgcEdsOxwnTCVDeLGtORJyS89zZDP3wHvzhC56zd/KhVQRbnLLF5ogCxOLwVkl3dPgB01whgMK2GhOLufMmwS6CJU1T0kwI1p43Dgo1cwOMMNWZGeC9KyQlyQSoHMc9ToMfImsqAayI85CknNhTyP9GufI3/wCttH9+FC1ihAyKJ7duB6YvnwAHGqVlrIz+tmmD5dkD956qtdmWfUD0+oHx6gS4GDBFUHOUQ+pU5AFo9sIavAQeHzEeYkaeOywGplNpIIDX7K1DzJiSnZzusvOQNzTJKrd1Kup0hjnhLF8XJwmErmODcvT2FWjPQlodNb0ybhcbVZLDgkjZwRX2QBJMEv6zWht6v/gt6YeWI/cuOLWFQDF353nBkuAnbLU6zzJEQV3b+/bvs5ml66Kg1CpuXxZpUacAnzyRICiDBLWIkWXh32BxiiFBWuSivoIVSBzxlWK1J4sEigDUJtgGpsDm8ifJq4qk3hgRSga2ZoIggRoLuJFAyRmaM8BEiLa2sO1Cyod/EM5aapw6KRSMB2ZPafASRnJ1wBNSCAXJtPhtfmiFNgVwiO26fVI9rtrex2V9EJ4M2uert/8nCSwR6/t/mNGb8BHrf7orW3nXcENmNb2v082sgAPe9FQ0akVcGwmu44sM0FuPonu55QO8xQWLMTle2mHeM1B647fnP/8q8AouMIjuP5EkYZAeu4XQGX2/PvXhfRBkqItNp7ghWCl5QcKYCMDv94SIeDC9LkggrtHF/RWBAo9gzEF4EM2/QL33pm/svb7JSB+QPUDJi2pRN5H/K8Ia00ClqoSFsU8Dyj+gY7TEjFoJcS0u8J9ZTpcbgYM4dUgBODwNOnBXIpkDk8Kx8dkcsGgcIrC0cKBLJV1PuEcs+lmC6M8/vq3XAoBLZS5dbDVvsk8LsJZbWYlCnzLiIEOecN6bJFkccZfpdhaYY8rQwHPJ2Bad5DBirAIQMeugWtNlAF5LIBdQOgkOORxveysZ5hYoaQGtPaewXjWVEmDeVRgyxAWqgg28S66gf3NHATPRu6rTQ+SSmQt5JnZImqzRAgnQVpq1AXbPcsEGmbQ0olYMskL6fNgVKgppBVEFKczG5CGLqUgMOBZFsnXwKHQ3h0WjhJIhPHB46M9zpVAgBaO89EREIXZl8eC3RPdTYnmF6dBs4qx0cVPk1Qsy7mJ6KQnMmLSUoVaWMf/LJAcoLnzH6sGwFcI0G0yuXNMlq0SSU8QL4DiCToSz4NT4XKvlIXxLFikq1OCxNgRlo4IVNUsGexBVlXHbDgnGDK8MXo1UoKZNlrE2V6CHRjuI0k0aaGTM8JMGQ2NW+gx6ReDRLsXFfeMy10WFNmllL2PXTbxsMQITGQYJlJtO7AqRF8x+3aLr4HbBBJch5Z9N3UtPhWtL3L3Ad3pPNKxoX3LaB5AdWIv/z5b7bd0jiaTe+f+W63+6GH81z9Ln60Kw0PfRPuReJxG1sAJZ7Ddp5GYm2AOUCf3wKqOKYEaPH3BqV/eN2xkR3bPn4JA9wer3fQvxQzSFxk0SDzNv5QgBcBwY1KgYpjyo6s9LyQx08PTa+w3bw3AMG2ARUMS7OgqUd4zV/oxN52UcecvjkQ4pvTkv/MN3t1IBFVhTWVlIW7UuFqsp7oonYz+N0d7IOJXhJhBoxDIOsCr476eqLHYXU02XIxhyVHvc9wAQ6fLUg/KchzQn11wPqzH2D+tWd6SCoipMUU63RI1Ikp1Ixxo0psbd4CBVfHzTMAIK1AVUCiBIJ45cSZBHZiMcJ8KX2VI145eecM//gNyimFp0egpcCOE9VUzxv0vCKWVPzdEiTYo8A9I28GQ4RTSgmOQuYYTII6xySlMXero5wAWqQH1pO68PFEVmgxEgZr1ENyQ367YMsZ60eB4ESgWxjazMKY9ZgxqROQWoU8b9TmEd3j88A+kcVGEEJSnaXEsNTpwBBU0siCacvtBhDiWGNM3ozGFdaNnrRJu4ZIY5Cpu9elVha/NAsvIdspkbouMizX4xxohGclSMO6wS6XqDt1hD7cMQRjJBEToKYdhFiAtRaDcovVc4A0TehF/5SZd00ssFvoEuEgAbxqkKXDym/Ucum1o9pqOLg/w6hTP6lp3QShFwjw5MG7CK+NhUCdhCeoE5glOGMAeoXs6l1gUCWjzsy8qyEJLMmJ6xzh+STQpLdPAlB7BxFM0Q5L2IDfYP6/DD90+11B70h4GACwb5m/pobQ4IlphiwO3D+P5/89b8FLoGb4fCx78OJ+N9uXaav1/oz73hyng58va1qPKsqwQ7sH0AGHaBvb4HxpI+oPDRlBXcfhIWn3EinoqjF+hWuue/QlAzCqAw7lH647FyBDmlcGuycGHmTdEvkFhiyOFGBGheKM/TeoECGQkUaQFocqkDSkIxJCxTsSGjqtZr+Jmhfnm7R9C2a+qi3k2NWBVB0FQD1Qq8RDvwUqqG+OTD/eYhIrgJQCU0F9OEBLRf5iJecmeAiaAa+srGwZsEPC8vEJshbkdxvyj59RPj5i/f498r/7BDUlZC4Fkc5nklx/illW01t6D6pGxV1xutwdFJablSEzFXpxjMsgEWFYqAprHhmVcXHZkLKG4xJQGFAk+A2hAZMS0ru1Jxe4k2jM7KkweECEhyQ4AcrU8NDfwd0JUiZIoofHwYlI1FHdqbzqYGmHrKwn9bjC54zlTcZBBDhvsYKP75cCkRmleWYuCO+GQYugJEHNigRq+IgH2XhKwFrJYWl/20yvBLCDnJygorA5saL2ITMduFSmpNfWb40yFtj1JSwAi3l4ZnQvpldqLM15LkmJnpPGacgJsoVQXaz6JNL3ea+Gvsw80VPTrkEDOUAHJrKuwJoBmdk2Y2jN3SO8ZXBT8myad6iwlhhrEEU8o2XgbQHCIvXbVSAW5GZ4L4FgZtS8SLrb12Z4kwzlajRAHvlEe1W83bBpAxgzM8gMiPATgMjTkmb4ajyXAdI9jIdGppwGVtOViwucohxFWOtUOfmzgCjvaQCASZD42QcpBDQ2x5h374x34NWN6wvAoX/kMfeMhjgj0sh3QDNGn0akNBruqwOP9vpLUMSVtsxwiFs8dKuRM1JA/mPNYccpcuMvaX1qkUkh/d2vEBi6kN84xo1E20DNS1hEmrfrqmfXbWJD2nHGUXgJrrVBa/freLK2D11RmgKwNM8M0AEKAKgyQWNSQ1aD6k76FYnnSnYgM4kFnncIFObAZuTY5EQOX06Ozbggk8RBvcqwgiOnkTX99W/fgpmvaCtvJmCjxP90Lqh3GuqhBA4QR1q5wktbYbpsxOW3A1fX9WBIKytoa6Exs0Ni2naOMMpGV6odSKRd7mccPl2QPz1j+/iE9Wc/Rv71d3Qbniak9YJ0PsM+56qcq0PDVCvWw5ErTgeQlTL+oOiYSCTjZoUvtevBYALq6xl4KgwBGKCXAnk8oyaBH2aIscieZ1bF9lIo5b4Uiv6dn+EzC2DqfE9V3OdnCs6tK/CokOMB+uoOJg59jM8LPRtpAw3LWqFrZQHGyofdDjNBh2ToUqGXDXkGesbJJKiZBGuqAlfY/QytDpsBO01Im1HZtXFv1krdnEtk+EwTBE6dFQfrLOUcZRYKIGkPxeRE/onqbqCaF6RZgUaKrZUgBs0BIXvIonFXaiU3qwEEt53r0jwXrVK5CPcHuqdoFMmjdnnbhIBnnjmflsIJPEcZDnMK56Xo27Kyb00xuIVzxNGysQDf29ZWdylBMsGZR2HQxm8AwDEJ4T0WxozMjUnRKkxDWt1fdGOO1MYRETrYDQhrKkkfk5Yh08GRKO+z7VoQrwbfRURgqoCh6+eIgPW9ijO0BPSQhjhY0uGmsGNf/FdefhVgE7+uZN36hOG3gWgcVIBFX2igG1+P5KzGfX4xOjAQXzGc4kVA0drTvhwznkbDPYKfvak7UJF9vx5W2hf473lC+ji+0KTezivj/35f3m9SE517+bgc2wHUfNm5fW/Yy8e6QXVfeqS4SRrXRkcUtQMGkUqnZoAZTh3e92n7kRsD5FSZXNfADBphPjgzqOG14elYOoYLh6V7c3gOioFXVNtvpp0Ize90vGe/Adu3YOYr2vwho2xczmnhpFhN4IfMVbyQwCqbA4XgxmZO6mktsK0wGylRoC49G/JqEFN4YZVpXw0pKWoCwzOTArNi+/CAnBXT2w1yB9Tvv4K/vUBN4B89IH3yiPyTJ8jpADvNwJyhqyFfmOIMcRajnAm+fE7hhiYHRbLANANWuw6IzUoVyc+oJ+OvjnCwvo5USr7DADtOcJ1QxTF9cUH67Bm2seBhMz6YEvy7HwJPF1aBvmyQzXoUBebwx2eoCvw7r5GegfS8caxmBQ4J1QrF0hoImBLKXYZeDPlxAyTBZ2ZnaUlwNXgy6KWG/D8AY4jQTizOKedKL/x5Q7qQ4OxHFoeEO8QOBHmhN+OHmd6U8EIQPFHDxxH9qJUp3iU8M809nsJv1YBGAy8d2XDC66vtWkg2bllIFnwBY50pbxlB7R8AeAqAEiAk2oSmq6JtJZY6/wfNK6RKgnOJquEAfFlJZnZje5WZdCTH2l7YUpr7fSyfYEGLicm5G0jyYsRBsO/OUB3aipdj4VtwBdYY/wM9Qz1bKkZMAVxpsiAO1MNe2ENWoqitDlJ8Ri9QhHsTNYq8rc+bgW6WTWk4+5hHaQSO876bAJ37MhlQ3GFH2WfjcWV/BWj2V2sAyXcba2Efrezt+VJU0MDNAEJe3PclPk/71zyBg4HvIa/22fD7DrCGyGBvQuP1DGCodR+4vnwdRLVjDu2+uswC8j5u+uXvvec+Lw2Bj3vfgB2/2bPv066XYr9J2oGl8rqlADGCCNfw9zLsp0HkVQ0A0kDM8KpSkJRAhHQ0A2tgezj2473WkOjo3UUv9yDSIq/hfWEjeO5rQAMhsZhg6kVo+LVt34KZr2g73M9YHxeICsoRvJero85K13OET7S5sovRzVdXpAJsEyj8JgAgqCcGwNNmSFulwNZdIgVhITG3isA0XIV3E+pxz87xhwPKZlCZ4EmRnzamek+sZeM50XNRI7V3NYKSmcqkUgCSiC1EvThz1nDzy0GRKrD99AP0vEE3AIcjTB1YDelxBS4Ffn/k/PswY80Jx6VCTjONghlqniCZJQz89AopJejTGU20zb3C7w8UFztSOyc9M+W5fnBk6rsK0pMDZQOU/I165EoeLtANkI3XQ44J9ejw48w+Q5CeKxWSFSQ8q0KkEgRNzKhyFXI4cnBFzIG7GVjrznNR27kr4Dj1kLgZXdxdyTcIusVYsmDLe+iFSzB0rZYaKd5RRBMNxDSSqRK8AQDMGG4B0MNGDQlY1D9wXIEHrqZjopPw0OS0h/8gkfYd2UFmFD10J6epFODiTNM+ztGOOGcpcJnYCNFOfG7VsWGFxy4NMKQOvgTawzzNYHsJnZuYSLV5W1p4iB/uXoHafri7E5pezijd3sdRQOXeyHTqZNoGuIKz08oMXGWvtvcWx/RrT0FPOR9smxqQL0B1px5TW7FHGjZGr83oLXGgKbS2DBQYbxnFfqlbf/rWhmGkGt0CljjWlQfkvXMPfY6/x1uqnyqOcRUq66BAOsjrjsbx2K2NIxbV4XjD8RsYgTh6SYghM2gEPX77/2bhd39ffLcDlP18gbbG8yO4Xymu3Y2npW9SAXFIpvejCQFKoFtpd4qQjJuSI6UWUmrPZwMxgKPSealAUscURVrbo9Uznlpfg6dlwtAuxFFd4KDWVilt/NmO1ja50qJp1/ebBWSAb8HMV7Z99ME93p2X+EsinVggCSQ0bi1eLjAY0uMFejzAzVFeT7C7xImpctWZopaLV0c5ke+SKqDPhZOVoAt/torddkqomaUM0kWAymymOh/hkyIVgTWhLgHqlAm4LoVlBi70VGim+98mpTfJg1ngABZW/i33GT4BcKGybqyqUgFMCzabML1bgGWFHDJ8Y9jFH46QlVokvpXQlIgQzd2M7XsPSI8ThcuOE2wpSFaj+vMG04x6P0GKAwbkJ2NauQn0uUA2h51m+JxYoHI1yGqsF3WKUFuxUDTO0GrIi6MkQ50FehToBcBmSCKoKrCs5EwYMCrz9hkYBKsSXgkg5utlgzbF4MFYek7kndQcSr0EOFTTFXoZ0rRnLvVsp31SoQdFBhc9Q0iYJh6nhDKvA3tBzeDrmLFcQwAHj5CQH9iuTrRtZN7aJvXoQ+PoNI9RFN70ZQG2Q4Sl6O3xahAtAaCiXT30Jns6fDMQid6Vpjfj0+DBA13jfBMgI42WLMYCvnNVnNeS7yVmPGNNM3VmczggoSWzGykhD6cb4StLuO83hm4achmVfB09e6ofqnlpBpuXVkDMURQR2tqHvGbsYKKBjxoHb6dqWFYQUgMg922S3q6xia1tjYPdeTUynMNxDXpGSz/aMsXV+Ntg6FuBzN7fuAxX6sDx2gtT3oLA4by94OZVO3w/rkhUXr8BPOP+DZw0YBTjKO91sO03os8YrA484vzJO/m6hY96tesGeOCQiSGcTp6V3RMiTR1R6GWhZ4Soko9DgHxxUBDPMCk9Ja34pIoFBnYoDNU9mizwKE4VVZ7g7jBTVAiWLXOKcYl/Y7hrHMC973qFkr/+7Vsw8xVtP/f9j/D//fefAM68fy2ARaE/Krh6X1HYQYD7iWGLif9QwToxG0Xd4IBNCesbAZRS/7pF1ogbC+C1FT5YXbpmD0OisNkgxgrd9mpCeTUDFwuyJY2AzZFVsgjqZkF/EGqyeIZkhalAVqZ0lxQP4Uq+ime2Sy7eq0xzJa2wg6NeFOnxDF8yw273meRTd4gkJGGdKiwb/ItHaFLg41ewuwlwJcA7CWpVyBMAZfaVS4KfQI8RgPx2hZ0mbN870YtbwXRu4wTls6LcKT1XxUku1jASJSGtwPRUsZ1YPiEfHL4ow2pGL4I1r01tE04YgAZUQksIUSGcFaxlT1VWhR+oLyMpqnH7sOTskvkRNknKsI1Vzh3rRq5MO587rJMmImXZsYOf5iFKiVNPiown8e5JcoCApnFcVIDm/dHIvhOlrH81oDLUhJxIHI6UcU/hrVo24LygShTGvMqyMoKsdetFLiVlTqyyZxn53FSsab0l+tS4Ygw9jd4vxejtaGBhd+UwDZ+p+ASK6tS20cajmUleb7ycdl7vRuvGHgr4IDfvRx9MXId25PqHrny+WtiLhlo6JlRj1YyW6NaNScFeTyqcXL1+0tg4pTHiNY7ut9BNa1e094qEG3Srfs4AJx3YjMDtpQX5uN/YphbCi7934P3CMVpzAsy0Nl8BmgGz9k40jNm+U2/Ox97JkfwcTydB0QBmBhZWfFY7uL7tx76jEzkqgYx00lTs2D0jRJgi9LSIMjsJjgjB+oCj6YXJyXcamnhEPRufjhpgSTx0ZDjPtbVGG6daqaFUXUPuKu41ozfGnZXTShVsriiusCD9mglSB3G3F40L24OcvvxCfg3bt2DmK9p+4ff+FP5v//f/B29eFRJ9I6OoPdh8wJjp43OCzVT71bVAVVCPmWq0hWRfeFvWOfRizBCK1bgai9bBmZkCAHkzVDNgzvCUUD5Q6BLhDU3UUzFHqmCF4ZaiesiwI4ClwItBRZFCCA4WAmLFkSmsCxiQzgY7UvU0VSA9FfikJHcKM53K65mTwWWDPp8heuC08ZPPCUzmzCKW6wZZV8haoecL9HCgZshPfwR7NQUxU4Glwk8Z8MqHbjNmg4nTE/aQkJ6YPmyO0HYR1CixoIsjFRp9+g2atSD5M58rvVFJoXPiSibAqItQa6fIACIBQFkOotRBwC5WaUqdGykFDgnF3gAMmbMZU+IRJQCwk2UbkbZxNxoPI7g2LgJzp7pwSruyL7BnVTW+CwIcNV2VdiwNNWPEecF7DZF27qOHBgh1YFb89pz3MIwq9XOO1LVpQMIRnDFNTGm/rJDLwn6mxPtWg5qZrld5UqIAZoC+5hmUClg1IIW17mMZv+sqwIhsrjDQCSTNLsF9MhrLLA5fJAjgTMlH5nPbCI9XYnYiV1N70zexZkFGUOO49tq0vt54ksYQTDzufb5ghiSBk1YCD9O4RcIAdi9DM9Ae4efhHNIAUgNRtrernV5GQDICs2YgRy8NcB16GkHPFfJ74e/htz6ef/AG7et/dIG7fdiGrJr4zFobE7onZUgI468CuEABZL/y3FxzY+KkeQAyLXTUt2goHJgCaOgwjg1NAhAwvMS1hQfIwQ4uBu+NBEFXdBe503iWGliRdo4Avq26tanA0FKxjdlIiOcQnLcab42V7QXFFMUF8FCkD3BtHvIX2J+D3nMnUPr47h7fpO02Wvq7uv3SL/0SfuEXfgGvX7/G69ev8cMf/hD/6B/9oxf3/fN//s9DRPC3/tbf+t1t5G9z+6O/8LPQakgbybSyUhtDq5NKYSGZvkWJg8QJShwMe0wCn7x5QQEIcKnIz+SIKFpdJj52ftIQXGNBPct8IHXzvkrPz4bpzDQ8UwTvhca9vJpQT5yhpDqQBXZMKKcJ5SHTk5MAfbciPW2c4Cfqq6RzofhfDZ2GScg5yaEtmSS8MxnlYUb5+B711QF4XukBeDhBnp+BL95BtsKU1QB9Oh+g0wQVRf78gsOvPmL6YoFW0Ai1WjqbIT0xXFVzgqwFx08LZKGCrmcKm5WjxsQmaIRJMVD/Z71a50FX6gLZBJT7CX7ILL+QBL1mUmqrd0XPTQ3AAyDCQb4b+dCYwXFmCChngqwSqcubB5Bx9PTorga8RRp1gJEQuIMDvhVmeLXXlUrF2Oj5wGWBXxj2bLWUiKMasNG9vQ3otJCT1R3cJFD1OQAyUoSIJr73KcFPE+w0w+6PsIcT/OPXqB++hr++gx9n+N0MP83wV3fwV/fAXZRP0BjXw9SNnjiNsGysEM+Jezf+HitXdQL6Ps8quldK4pX2LxYXE/liItizkYB+/cQBWVmtXZbg8ISWC8UYr01Za1A7f+fr+n6PdXs3Ug6a/AAf3f1f4jMKxe41iFuseR0EBHOygSHnOK6280V2YbN2IwhrYeAWgup/B0Dsnxl6lmJ7hXEceviq7r/tAG4fkuus5PZ+BEc3IOlqa9838CL7GFE8lABjHLvGT/XsEaaMMRzeewJT6bPDZ2coKv6+ek3g4M5ON9nkQLb43MJT0/Y1YDakbNBskGTQxNRoUY+soApNjpSZ9tzE6xj5jXs59kuJ3vHcybzoGUZZWGcJbo0/T5AiBCGGBHNBdUUxxbopzBXFMqoleICVgCeo7caK+arXeIqL4CaoRg+NGUNPZoJa6Sk3U/y+h4/xTdq+Vs/Mz/zMz+Bv/I2/gT/wB/4AAODv/J2/gz/zZ/4M/uW//Jf4w3/4D/f9/uE//If45//8n+Onf/qnv66m/pbbH/gD30e+8IFJAGQ1Ug1uCtDZBHhVkvYyUCZFvjhkNSRnurayBiNUDH5gGQQXRZ2VNWAOvGyeFLquDGfN4aqHRnyeWVXpUnnzz0qw0RbAlStMOyVO2FtLgU0os0KWinoQyCna10jMx4SaDsyY8QAEiqjODZZrUEM9ZogJSy0slSm1H00oxaDHjASBfPFI8iucYYhpgr25Z7ZVYRjNooBjNwBigCk9VEpDJ87QnDxXgg9XyCyQlAm4RIAMCu6ZRO2rCoUCOdHWJJAXAwIfm7Vr5vRVdtQngsfqpoVrkoTha7yWNlvFWIuE8QfaUlKU3KarNGLHHmNwp5hgZEd5C1cF0BA3+LqFRyNAlcc4tvNGurZPkaEUnhbyVIIEu217inZn2kYadymAEkB5FOqkodHdaxQqzT7JVVgGAHrdG3PKC6hA0gF6KSQqG8edkvAxuQLh7mgk3tamWFliMNItJBRDd724HhYGGirJiDToBowUkRYdgKaFYIpRABPODLoGRsZQQw8K7Oe7tcxX6/h2zm7ER59BHKEdIqpTNA9B9zS1/rT9WhimhSwcO7H5Big0Z0PzxEDQHQsQ9PpJzTvTlXH9+tXH3wLdG4Hb/RsoeQnQvAR24vu2DgDAZIImdNgaru+fyoEOXHYQ1Dq6n4BSE76Dpealubp0gdiUx5Srz+MCeAyYAJp8v28GRCu9Hw2w7JHRtl8PC8VAcLExIuN4NtoetoetqtAbQ1UED1zCC1RDIGkn8HJxxDWYd548cxcUtccgx5tGQvtSbm7kdi8L/ug3qJQB8DWDmT/9p//01d9/7a/9NfzSL/0S/tk/+2cdzPyH//Af8Bf+wl/AP/7H/xh/6k/9qa+jmb+tbZ4zZK1Ic0LVAO4rg+C1UgHVRCBVIMuG8mqi57KQ4CuFhRI9uCL53cbSAKtRtfcYsvtNabXNapMiL5UCXHd5n6wquSJWFAggIzWMQtygnoi4kRxpZRirWAUOCeWoQVCc4FMTr4jV8JxQBcxqihtdgiQq5jsRd1ZYAjRCKXJhWmJ5NaMeXyOfJqTPniBPZ8ANPmV4zrC7GfK4QR6fgfsj7EiirlXWd4KSy5OWGgJy6CTWdEjYjgmuGeJUz5Ut2P9N3yQ5dAXSZvBjXwJ1ICHFI1uFXq8rUTDHXhW6gQeXHYQg3seFUAG8pWHPOVaV0i+fAOhZLm0uoR+X/8L74rV2Uq/Uyv62U6rS89BuCzPyXMa6SlclCGJGamTgUsC0atDrMlq84PJw7IaJFSBQEnRg08JC6VIidTgm0Ror2R5Wiv5G+QSsZT+oArxpwnPTgZ7vf+9WgJuF5TDbsU9raNtae5pxEIoR9sKW/XUIP8Rp3/MeDJv/Jp83Q3u1j+zfA7JnvMr+wx42avtgACzYo5BX4Er2t+21tf0WT/RqDf1+G16jDVffx3eO4fPW1tjX0w6IGjDsfJ3bhn2J1tp+er7byci+c5PiwjZv1c6VAT0z1/YYjuFmEEQozm+AaXvfHkonyB5B1Dgmg2qvdJDi17yjTqjDtSemnUriyt6AWoY/A8K4c55t18cakOf8YZEN1TSLRGI6ilE00+bI5bC7wirL41glV6d7a9pgXt0I0qeLl7Yffvf3vfzF17R9YzgztVb8vb/39/D09IQf/vCHAAAzw49+9CP85b/8l688Nd/Uze8ISihjDyRzYAMkhVcjJlM5sD4QUuIDWEF13SRIZ4KXegwJeDeGpNQhBUzprqCnRcAUWmyAKV2wEMgKhpqUmUZ1ZtqxbZUp1GB64K6BwBIB1AgjacyV3B4AYEpWMwDxcGiCNT4Q0EM4HhkoYhHy2hwWPAONiUwuFKSrH9xB8oS0bkwbdAeeLxAYoBP8OIeb22CqqG8Ou1HeDPUQ4EQBpBnpEtWaHUhbRVF6LdQtXOgO2Qz5HSt2WxLUdIRPHLdmZGk3JEDLMJNHBknzyvRVsrW1YlTAbgudxp8J74MUg6mwEnqoQkuMXU+zFeVyqxn6GplcTfUX6JW0OZvGTNmIvE3nxowZOmboqdQS0v0tc2obikO2Lao9D0vSfRtDFxLgpRu8AAORxUfOSQMOADVgmMfraFoyHDcD6yHJVsIgCHCah1BYnBu7MaCNiQy7tloeUsl3Q8HfSmtH/D2Coqu5ulX0bvunfY5/j4Mx/KwtbkfDbcMQXtmEtu8AlPzmt+18tye0sR/DZXsJb412cGxvP+TwwUv2SoZ9rjBdN67XJ7/CRAGYehZz7N8rXL93ojjCcCwAkSKPAfT51Ti1aEkn6go6iHgvm2kk844Ap7cjPB+K9zLKenbP6DXRlr7sHeg0j2CbHBrXZbz1EN4aPqZtcbHzXUqNJJLh8SqFP24eH5d9rrK4u5p+ZptUHNIj100R2KHYSrtKRMTm+j7ofm+T9/b4n3z4/d/0F7/b29cOZv71v/7X+OEPf4jL5YKHhwf8g3/wD/CH/tAfAgD8zb/5N5Fzxl/8i3/xt328ZVmwLEv/++3bt195m79s89BccHH4MbHoY2UICUkitADY3cz4eBjLBGpneA6kvxjqpJ0giXeVGNqpAKpZUNEIfo56lyBnQC4On0EP0cLVtD1E+m93KYdhXxAeiWiHkszrLRTSvBQgAbLF5UUAmKNOStf3OGEYyC8xRc0CKJA2fubFUWYlVyUBUgR62fizDx6A54UA6shKzp4VOJ7gwvPYQaBOVVaxyvEMReV6oNaMHBLyUwHxoUHmBJvInxEHUBzzFyv03QWyFWBmvSgX5SpMdtLkvgrzkct3/Tx344lu3JtCLcILZCLQrXa9k056ldTr+VBojma9MzvHlGyARtbqblSU5D6J2kySUpRVSFTnRcyEU3hatC01fQ8Rme0ekObpaLygVkagVY9u3b/ykIyDEsRoBauRbxagV3YhtBaS0NC5MBKgfZ5YNkEVbqwfZv0i+M5DCs9iB08Aehp5kMAB8rgazutL15fAWUIPMV2Lu8kOZFpqcRzrKu243Q5hrd/L/hkMc+NsXYGUF70DEm24PY4PYEmu8EAn9w6HuQJaN6DqFvn85kZsb8KLgKeduxtRXPV7BETN6XGr+XIFUgeQAmBXaW79vx3/ADsvApR+PXwf7zR8N7y2bFMAV6CoAbF2m6H/zU51BQC/ui17n6QDleh4YnFfuKBalBwRCQ/JfoCWHj2OpBiTPjrQCYDvoIe99DS4CDHFHEC+v/VHx+MCGABn7H4Xdxy25uXc23C9TTek/a97+9rBzM///M/jX/2rf4XPP/8cf//v/3382T/7Z/FP/+k/xfl8xt/+238b/+Jf/Av8xygN/vW//tfxV//qX/1P2OIv33T1XqIAUYTOs7CEQNzsHvFwbamVhl4VOzlgmXyV/AVrKtWZGUJbkCXTucDOjlQS9WQ2hodsUlhVeAHTuKujTg48b5gKz++hmSLnDeKOaU3wTFG4ckzwA9nsKr7XQdzI7XHw/DbEmC0rReAGY2BZSHZW/sYy0L0H7oxTucCyA6cEebdBjkcWYjwvTBfPBH4anAoAWO8zAU1x+BZ8E/XIrKrQGtk1ICDzQ6gYV0q/61bRtJ/8MMEeJhr4JJ072rWufDDaPb3E91XmPgTcRKKAZWikRPvJl1KUQ+b54xr0H6vCm89fwfaUCLs0jowIcJj5eYSfOscEAiSGmJguPZMXdTxAzkEGZmpEBwQuCBJwpigQOxCAJvRjouq0JyFYGrdm1dwQ+ahRZyqARIQx+9TX0KGDnCIB9w3PIftgsEOmonSxENELD1N4JxHk9z3LSPZsqnZOq/2qdJDT81txHaYajVmAuQ4UFLhaFqf4HDe/a+24Wu1Hd18ANVdA52a7MhU3+3k7z7jjwD15yd7s4//+OV8EN+2f79+/B45uf/dC+29fx2bcfs5j+nvH99bGRvYdD3AFZgaQ0l7HfRoIAq73awAnPpf3PDQDcNH9+4G+xUf46jf7jEBvyJ65Rgd43F8VXR+MTlQZLogzMykyo0QUZgZz3UNU0rgsHlPIfk7vQ6Boi+o2JC4aslE76nTfwU7qg9wguuzh9fG6/3aQ79e0ifs3q3l/7I/9Mfz+3//78Qf/4B/EX/pLfwk6TKa1VqgqfvCDH+Df/tt/++LvX/LM/OAHP8AXX3yB169f/ydt+//6f/PXKUonkQ7sUYF6c3oXZq7aaxYko4vci6PO6KsGjSyK/LhherdCvzgDpwMuHx7hOSEvG3QzwEIA7ZBRTgn1LsNhwOqYnzZIEbhbVKh2yOEAnxPqnGHYoE8bAVUL6CqrRNc7Gnh3PnRpCwPU6t4APRzsk0AvlSuDCQAIHAic2Kc9M6Kp3saKJtqfn8OgqqAI4EoSr0CQqwGV4mXlIChvZrgo5t94hilYHTrKD0hicUU1AoqaleGyiTWY8rkOHBqDTQy9eVKggyY+2bsuB524zDrb2y8l0uSr7aRUxNioM91ZW2iD3hpTcqgkVklQdNVlnp+6LnLZ6FlZN/JlGgip5LY4EGUMYlZWgZyO5LqEMrC3VOW1lU1gCjcnOJKBvZCE28NShznIvMKQVE70LvVQjEcmGT0wQACzVtqh7DEHz+HtacvVofijOIClRN0roRzATMAlW4U+r5BtY+2uGMcuGNhChd5PhfChh+eqXbdmjQfwkmQg+wahPEnPfLPmoYl7sXko233McMYOZrrnoLVDmwGO38Xl8dipf6dyDXTasa76hBADjPc3OObW8PYv7IXPBgN+y/95D8jI8Jnf7APsyQPjPjKAD8FOwo3PZPyue1cEPX365lg8Z4CY4VjXIAboLqEh84v77MDkijjcAEx7r7vXZQc83seMt1YDMdHWuI8FiJVPu532c6p4KCOEFz3WEoIdCPCeGM2uXN2yLdylAuqJgQvDK+9QtPnWeyLjfoIrob1WLg0AU6/jt+6ClBxbyVhXjajzeJO9tDn+n//7/8Nvsc/vfHv79i3evHnz27LfX7tn5nZzdyzLgh/96Ef4Y3/sj1199yf+xJ/Aj370I/y5P/fnvvT3h8MBh8PhP3UzX95iourAXxQuTsE1c5JyIwvVDXxoEmJCDadFTMbbBzP8OCGfJlawroBbQZkUyRxJBLLt/BZW9BVM7zY+dGDoyu5Y7dgmpbzC00Lj81wYo50bSPGeQVTvMyDMPER7mDJnGy3Own1AX+V7S+F0pqW7gvWWhjEZ0yNEHGVS6BSriY1VuSULlmkGMh9kqwR3THkH0qcrypE1oTyWSOntBekpoX5wRD1NwIWAI1V6xXrbAHBi4zXRywZAWLBz0n251NCaCTMkIMw8q75L46cEF6MHpgbAAXbvSqCjPj9XFu60Q4KulYq5xtUSlGPO2kb7bCUBML2FmTKD+OpO0JL37CTyZVJkOxVms0xpL72wVngpHYTADUiJpGEHB7rxYTK9dS1M9p43w3cDvSNbfs/0eusu87YfjUazIn51TJ9T76dkhR9TgGBHqgbfBqPXjJzvdolEZgDadGl2wHG7NfvX/zVQ042ddGDy8jHiijavSOh2eDMSo2uvh8PQJRh8VO0VXHt//OY0N+Dm5Q7dvOoL32EHQ1eABTefjed5AWR0TtB4LL/+7DqbaDz3fsbGb7kFdO+1rake3/TxPR7M7djIC/9e+g7XQ9w/eG+s484zGn247/e10Ks+NnPd4vkNEmEtAWYEnVDskercTyhNAya8jpFWtkVyQfNEOoZbqz+LBCkEMbI/J7i+rWptkIjfNB0Zd8ChLLfSMJ0GcPvSG89fDEt93dvXCmb+yl/5K/iTf/JP4gc/+AHevXuHv/t3/y7+yT/5J/iVX/kVfPzxx/j44+s89mma8P3vfx8///M//zW1+DffZhFc1rqLeLVVvgCYYqXeVxoObJTi90M8xZtfrSzrJNA5o5wiNXZz6LnAjpnAKLHCdf5i5Y08U/cjPRts2yAu0K1AaolqyQmyVQqXmQOvjnCbIC27F2DdowA0nsPIrxY8leBpCOO3Flwb0Qo3Ai69UJLflBoITVnWEwnFvehiEmzHBDkqUjE+4FnoKQFl3dPGmLK5Ij1TwyedGW6QZSPnJGfIVpF/coZ8wFRzvTgAozQ/OP42KTQ0eiTTYmmhl6allff50cAspohaiAi8hhZDlFGg7kSs7JvHAHHtHOFNkL7K021jJpbEfVAGow7fywy0ma8dq3NDlKJ7DvQq2xMF6XrDo7iKlMpxTBk4DB4WM3TNliRdbHFcNvZ6Nm5A1R0BJP6OHqoddPdzZyGoiCm0rSe9ZS81j46DSruRteGqEGsKxgLXDD9lSKn8TIAu3pd2A+AtzSJAdDegrbxB227m444TOhcD1/s2ufyO2PaXTpj36HuEIMf7xtV3z4O23+yXs9dcEgQgj79tGEtBF//jzXvTj98K3Ax9fdGgD5/3jKf4u90e74GLl8I9w+/amF7zhm6F6TB4r273vWnjbejuJfDyWwEbH16/zPja0MfWlhfGu9Vi3dE0r7HHvdCJye05aoPTQIfv6dq17gClgRIEELG47wh89crz0m6HDubFQzZAeoabxEXk+QJse2uyw6CdttESvT0uUimAW8xHyr741UXH/t03cPtawcyv//qv40c/+hF+9Vd/FW/evMEv/MIv4Fd+5Vfwx//4H/86m/X/93a4n7F8dibQaPdyu6ErxdxQwQBloeeD3geBJWMG1AaIcJXpCagzeS52UIo3OZCWAsuKehTkpSI9F8hG7RS7m0l4XcOYODNiZC29Ho5r8AvOCzopJSWmLDsgjyvSZtgepj7h6FKYJj4parj/1YWlGaBRg4gznq6VaeWZBSthFWhFNB38LHlXxPWUKLyXOVZ+4VPvIcLH35D467NwCXiXYVth2u8xU6xwY9ZOWy33xbEwu6wt3FWVaq9RIgKFbdmdM2GgwjuASGNEqxlkFVgj/DYNtYwCMKBy4gCiv8IsItsI7MgrwUDwi2yvYthn11hdNY9JAx7uPSwIyA1BN0JhZYPIDNSVOjk5oZOOMRqR+GGkbPtxDgMS44cKz3lPFUV818aiG2vOsD5bJ0vTuDXPDvoEi+aZiUmYSsq2c2FS3CeamXHViNB9Scp+k1KpEexvqe7D8Ztlln4Z9lflmO4OkBsrOLjjW2dE0D0vzTB4xfWvwxCKxuo7DEIzkhLntuG43di1g4THth83QhvjNbjyrLzQ/Be9Ee13498jIInvRsPut6/NI3UDHq6OMwKaYevO0dvQ0bjv7et7rpPhc8F71+iqbe07G74fOHh934HDT9Tie1/jWLZhoMTsDW5hp6oRam4cNwFaLYZWrgAie7Ih0EHQVaduvIoeXhwJ3o4Pfexho/bRCFDCq9KBUh+M2zjh3p9SNTxGO1Zrj9B4x3VwZbcX5evfvlYw88u//Mv/Uft/GU/mm7J9+P1X+OLx0mP3Hh4MsTBcFXFjcpVapgD4IQ4ghZwPy2RhmAr8jqq7gEOrAHPCNgV4UMU6CaakkNWQL+RH1FNCqhmyrrDTgSnQAKtmJwEsuC1roUrxUriaV41ClAlijvzEejYeHgnupwxjGCu74n5iNtBS4clZ3uCdIS0V2x3LM0zPBqyG8jChZqasqzm8cJImmADg0sMHjnDhVoYByoFPl+UATNXheY7CeoYaWVq5r9Qlxj8yxWI8IQqTUAY9KtLK0Ji1zJ5mOAwMAQpJsLoV2MaZiKRpZil5zPYehhiKQYU1VGRp4a4yoL1lTomEl4dk4eaNGJS40MsQBK+mT5plI3iaJxraaWK4ap4BCT2aVg9JJICJdp6U9HBPTIgztWqY/i5ANY532sN6nE8ZqkPWPgFz1Z27gm+feJWCjxIAw6fwbDTuy0agIkIOlRfykCAIxWG2x9swRs5v8/b09jfPgcc1wwDABn7R1SZyxSnpF3IEkz20IHFdB3BiNB4dM7WfewAaDgrv0cFoN0pClzmwNn64AjkjaBl51Fe90P3z97YGMsa/2/3XfjMCnRiSEcQ40L1VHue78t5EuzuQGYHYcBzE91cesVsw0xsnaJlEneczvo6eE8UOAMdBGEFKAy2RbdrbMFTwQEebDYyEhw0eIeHb/YZetlIkt6I67ULHA2EVV7yu5qXmoXcU5rGqalinCWuq7PvXqpCWThpZio2gvgvtSQDxKyj/woAbVSBCdM9j7uu9bO3w/Z/YN46h8s3jzPznvP2+n/sY/+//z4+ZUSKDoQbvNwvjIZXgIK+GOgn8BKQq0AKSxhpJNiHAtEMiFlsngW7t2EaWvCbgoChKQm56LhBlxW0pG58pVYgV+Jzp5XFAzgt0LXTtZ7o0bSYJNG2GtBVYq83kYDp0qcyWuhTAKmQ7wd4cUY+px5TLQ+SYOw2eZwrc6WJdgwSXCoFCM0MOeq58aA8CSRITCI2oFsAnRUkAwJW7V4NugDmrt1pmGnbdHPm5IhXsRF4ALeTTV6FBBq1HAjBsvCajqrAYgYRlB6YM2TwMJV+rovOM1MPbpQieET1gomC2D4zcHJE9DNkmTTgkBU+lWgcuSClAI3qGUffKlI03Rys+ac7wzZz52xLHaYbcgpOT4/cisDR4lQJ871aDbfCcdoMzGNmRTCsunRxKA+jdwdRSUzuQCEAkqbmwDXVTJFTOy1Ep3Ft6WZKooo0IdUSok0VpdsPWPFhBwCcmiWl9AD1QDb0nxMws+ytiHG2witKwpcf9jStbcDVkQKsheVXHaLSzrcyBJ+yrfdvP00Cbx5wxLuD7innAXHLTnltQ8x7HpF2jBnQGsCE3+zfwci1Yd71P9zKlm9+O57u1nV+2jQAkQIdg7y8csTho92p8OYwhBgBy1aHxOjWVudqvCjp6kOF4jcQ+buM+zevWMpUGANLO1z0wDdNEP0X3nQiIeR6z/dBw6dW1BQQbHRRL47hIB0PDiFwNTctQvR7cCI01kOzooc5aBKN2zjgE7oBVwWuZ8E3bvgUzX+H2P/0f/x789//Xf9P/bpOGKENJeqkox8xFQkLI6jvW48waI4k3pFSHCaAFBEMK1hIxQDshDDQ2HvdgBXRSknrDg7D91Ctmh8SMmhYDPjsjhTHVn7yDuKAeMvzhCJcWiwEcDK/o+QI/TV0tuE4MVVgVKGYSVdcK3E3ATIVJ8kv2VVU97sabwAoQF6TCR9kSDeJ0ZgFJywrTUI5tfS1OI20gCVaZQaVbaDi0CE0S1LsMnCvkQlBnc9M6iRklwBJl9iVCNc6nYTN4BmrXnGE4jattZhZZIn9FXRmqWo0kX+e16xNbLG0bXwiXjSDTOYY9nzJ4NF3jpVXgnvI+u4mQNyP0lGA+ErC0FVJbMiXyqXgchtBcnNlK1dBVtKLWlik9T46YvFpWklsvJrlnsbR7IzbtH+2AofvBY3wHVzq7Osz2ApgkSDbYMbHMR6t23hWVPVLe0x6WlPDgBaCRyqrXrrjKwNqNYBiEBnIE/Xume6OHPxqnSK4azXureSRGo9FCCOM22rP3xW7D7NhwaNnHtB/fce2BaIblveMNPxpBRHs/XJaO19rPbjgp7TDNrgH7uLR7+raj3Zt05eG6eR0b3UJ44/eK64FqoKTuoKOFiAfxXfah8ZgwtKHt0/rUrk9w4Dphuw3MEI58b0AGULr35QYgNVR5C5zGjvvuOUF81aqC8x4nLOk8quE4DonwOJ8Fq23Q9nlWxqZfbXsb9sN2jeX+CcdIoWqwoqhipONdgSCK8NVN8L0P71462de6fQtmvsLtv/mjv/f6IQlyaCM7qoY+yqwwd/hmSLUiz4J6Cp2U0ISR8GBodXhUywbQCYQiCjxWEogTjy+Vr/UA5I0G0g4ZetngmmD3E1QceamsSfT9j8izAVeKah5GWZDeLsCBxRaxFWglV8Yiy8AfJqgJUvHwOIATp4Keoup9lQ0RlDuGN7TwoTUNj8PWPCAKqFPFNzs0kefSeBnaQnVKLoLU+Ocs5OkZVDcOQ7W+zpgfC9JzZd8jVVuNTz+bKzsBUqlajNDF8TmhCVJZpccmJQWWCMGAhlhVYVKhCzh2TQSruandea2U+jACp6cmuDeOMLzF90nZnVlMIpBp2mercUIXJxem1B04uLD0QQyDlwL/4h3MCvThHnJ3x3DatgA1yMinGZ2L0rwSxvvNjX3qYa4hlLOfZFylNiAj3UPVDLLHSrRzaJ3XDkF09SnB5qClBJhhGjYrUrewVg9XBaqSGs9FWwVPOmj5NNJxXBMM7Zbr91KDTNm4OWjfDZb35m2za+N0P3ohRtt3lQ3kQxN8X/SMYKL9Pm6HHYDI9XfjdtUm2btxC0QcfF46kLhq7N6eFo4ZPTPjeVul6iswg5tXwR7OaV/FrXplgO2m3wL4jkb4ufnVWO8YRPq4+niR4ra+dm/xuW+Dtddw2vvf/6itIbfjNLS0x1P3Xa+uTe/fALqkeUWadhUbbzcinPDoF+gVdJMrLw6fgyYM0Z7NBtz34Ud4WcYwt2LoBoAmnscQk8OKks+mQwq3IUJgjt/78B1807ZvwcxXuP3cz3wcuioVtWYgPHFUnwXDGd0DoyhvMnRLFIe7VPicyJ8RgbUyAKGH0OLqWip8ZghJFfDNmGbXDKIKkBKqOPInF9hRUU8Z6WLAJLBXM2qu8MyClVL5OFh16EJuhR0Efs4BhhLq6YDp7crJZJ5halADbAIfzKUira1+j6KmZjyEKdJOBrwdCLSSCLAoaxbNwW5w6piI256ivgE1R7+d3KLqDEPRycL+SjVIaatEEuAkEdDInREwbILkHit/2Sen6qFTEsbYOGlII5aKwCfOUp5jfI37iguqOtSZToxNyStCZA300ArCsxBqymsBYqybDXEP8KfhBjFjMU8AktP+eeOINDCg9MAwO8rQakZ5ISG8p8QvG4BngiMAOK/wZQPWplHDOIGUWL4qUZNuFRXCWk+iO6G3eTQCIQgc3gochhu/LVZbLbDm1XFt7RdqzLihhhhH86SIt7R30PMm6F4ZxKVixk8zKDHQ6vusLRhcDGFmzPYw0sihacbfQU2fEKkct9vQiQMvhmHa2Xr0KpSE2/3Zww2xWRwH6bpGZAMhfM9nygI4dFAwnLa248Q4Dsk0HZN17PcSf8X3PjpwTdYd+jVghR0g9jAP9s4JG/USVhg5RB5SBQ3EXIGcETBddYim/ApRagu27Gfsi5WxfS2jDHFv3oaSgJ7h05HnVeNf+LvtKsCVl2Yci36DBSjQ6MOgGdCTRvrYynAP8O/xvpDWh34z7IyZdt59rHeg05zC1nky7aIKVBkZ8KKd5N43NWhy/JGPvnlFn78FM1/hlnNitk111l6a2zdExo0zg0yvAyseG2xiKIMGDBAYkzTmBBOl3oY57CAQ481cDwQdUrzH4QGJys00Op4V+fNn6DTB7iYABrn4vlwxwCYqvboKIAyZuE7YPjhAzxv5PQ6K8rUVd4oHsYK6OabQpzXCHxk+C9yVJDIF0upwZzigzonnOhJgEMRwPzFQ/r8asBFIpJC8l42/15zgx3hgU4x1rMh6uqIIsAD1oNjuMupJkN5V4ClW/vC+UqJOzj4pOiLrzCNduHkEWp0qbUs9D2KnoibAq0Amh88pBORC6M4BV42QGa+/zZmZS2sh6RcEM3CH18ryE30SFGqw8AZrH5KL1LgWIj29HKbQZUXPAjoeqayblJlecVySnB14fKbmzDwD08RjDSk1AmrbeAkXeFvRow9D3wiMw9PSSI5jvHHUVdFmQ5RMb2kAuPU5Dmzt/o4wGKS7KTz+7iGpZjC6AQ5wMxq4yqvsZQdIo56Oh2Hxtm+AjB6zCO9UL3mgDTgM4d/YLIBO019ponsd5HkLMTFkGovj/Thh54Doct6PN/Jt2ntDtLV5NFuz/QZQvOBxGbd+SW9DR23f4dz942aMx+ON1x43uMiBJgMg/Ue75+XKITbeYyPIGdxUtNUynm7vTAMZzYfR0+tx3WDZT8bFYwzcS8Sktu84HjeorU01gpv99gHYOSvtyxtA026WMeKIvprB9dYeL3kZa7WtVs43e8UUJaiJ31oVpMnhIbnRZBY0alHVTfDD7/9efNO2b8HMV7zJRpCgcBYRaytYcy6LZqDVhPGLwmbQwKnAwhswPVWkhYZBRAholhUqGTYrSXCdE+BIpcKjLI/k+N4AOyX4dI98rtBzpEuDKc76VKBa6Hm5n4BjzLi28QY/Kmyaqe2yGOyQUB4o2a+LAxvDJpYV24NA709IzwW6OfICFC2wFLOvhCieAxIk4FbHqhsuiYc+g0DIQ5OmMgVWl8J+HQkcGomze1y7Hx4Q9SBRO+CKtAnqnLC9LpB35C8Bw8SoYP2szIfaJ4NWIccIDssOFUUTF+Rpwuo0Y9lCJini2BaKuRHSqjPfayVAsIcD9EngUZOoc04cPFaN8gUieylctR1MmXdhQmaFMdyIJLDjBFk24LJyIrMIFamixeVFWiGx+NcqbE9TpF3vSzrF4OCIMFpfTPZQzo6R2/zvAAHXQA7mwLfXsACpfRtjKbJ7RRS9jEEnwKY24zcwLN3b0mmQzf3f7oO2BVDUZkTEgcL6XI2T0ykVaQcd3oBMADIDWOYjgAObMpgNoafEM4BMYIMEauoMtqgijhEgsQOVdi9HV0xBle0YssH+8yUSWzDhCqjs4Zdh62Dz+npc7TNu0ferHW45Lt6bth+rsYNfAiNAl63w/gO5ujUk5jeJ7/ewk3fphas2jwBFbt73ffx9LtLt7w3XoKh7hOJsOuw/dsrb7DCEgkYiceeU7feKSJMsQP+7Hzoacd2MflI0lBYU4t6ol/CcOGB8+vt04qZwZwkbGG/qljlVN5KPNe3Pj1Uh6BHHH/qGFZkEvgUzX/k2XSrl0beKugVJq8295tAngywV9T4FqRboyqxh0DwrsFSkpwq7zz2c0ifmINBagKK8OL0cTTWzBscmJP5ZgJBKum0yFwF02aDrhrRU2ClHzQ8BzJCfLB6RKPAoHgQ6Ccl+dM6KeJQOeJhYUqA4JeuXCr8TFpiMFbaCXUANgw9E+i3Yv3ABN6MhBSEQyNV7E4FqZRHapKWOnkEilfuhAmmtMBWkg2M7xkMLh08sK9HSjlOAFUvC4pRBLtYaSshRU0v6ROf8zEFPCoaMnkrgYu5AVDen40FQMzPTxAF/OMAXhUZWjovSE1aj3tKy7qEm91C1AjxI2F4MmBMJ2g56VFToHTxl6HaEnBfIZYVsFOWjd426M1dBdGCYrHdPxRiGGBeVbej3a9Cs7P6lI+7H5sVo+7rsC+s+88puNF7ahlAVIDvXYTAKvV2NbNkIKYbOnbmlJeyJKXHvwVEn3heWcQXEWnYR4jbyLAQ0Kt3Yd6ASj2sDMx4lEkY+ZXukkNAzIKFsuw9tMyfI6bP1CFRurslV0cVb4z7awS/juPgL/8bjt609awP+7JexHfIGR74ILobwUjuPj20d9EzGj7szxfvP3jt+U1m/6r/f/H27z9hXGf4BA8IadxpPGBMRfCAZ7/cD/9TeaD7D4zkGDoygN+zKCXPlahsG++Za9i769fhtzavpQNcDvnIpyq6TZIo6PC9twAXAIX/zoMM3r0X/uW9Bok3O8IqlXU+EWRmC6bEgP1cs3zlwVVcAbOSEWONEZKZoJzhBS04Uyyu+C6UlVpSGhS5NThC34GowU8onAZbapkd4ezhqoP5SgW2BHDLqqxwuc4eduQCWUsghKQ24kGiMABlomjFtNZ+FnKDgQtDIO2row6TV+9PdH6Hi3R6KOcyle69sBuszFYFcNq5CgjTaH0Jt7lrvc4wALNhWecxkTAVvacO6OPkxU4DNiVXD6yTwWuEVyJNGeIzHbpoifSKxlqrdJlOmEcNJYkakgxMAgpYrUoPVDG6JXiqrIabIkIkulTpC04nCccsGa4UnbQVAgSJJqfN3PCtQhVwZEPzalKD5BBxngsvLCt+2+D4ATfBgujGAdHE0Zi13CcErI3Rt4/hFD/sMk/91raTr375oKJuBGT+4MaRx58YxvbeVk/e1XRrn+T7BZ3AhMTwVY8jCE/ldmJTezhYSGwBTVQGmOJYArbZXJ/jKwIVp++jurLjqTwND7V4Z2tuzrxzXHJfb41xZ+thuQY3h/TG/HagbECMv7QdcZxW1Y4/em1sgcwuAb78azjNGdUb6yXhh3ztOO760a7XfD629LYQ4jsnVsI3gZgQyV8A5ftGuxxjqkqbI2wDLMADNCxXhZonF0N4A3Xl7rb3A8CwNfe2DPgxadHRcFw8ICA6PWrXtHpZhJ4cko6cmSFXS2zKAIeFi4H/5vd97O/rfiO1bMPMVb2I0JBSjq/Cj9gfEJ4ZHtpQwf1Fw/I0LtrsUwAVwKJIRqruSTa5PBX6XQ7wu+CTVAWH9JCkMCUkZ0vsC2QuEqcZdk8Z7faE6p6gmrVCjxotszhViJnDBZiGL71B32Oa0o9UJwBC4pDg9PkNWk01hXMNQ0E0vsIOTFySIsgE+TIQEFlqN7P1gQ0rewVuTr+/CdAHsXGOCaMJU2B9DBT9LF+9JPwJHulTIOYi8k1AQ8Kj02DSvkQOWpRcmFKOxkxhHjxIEpkAz5F2xNz4bU0dbVo9ZYgZNVlRPkQUFGrNcgaWwjtPpAEwZflk5h4aCMEqFuAGYWKAxJxgMshWCvlohWVlJe4oaTcfIXDK7LiLp0d7GW4m2j1oyV8ZpBCWCXRm2DbZj0AHZ93vRkg3beIj3Prx+O2xx/buL/9p+9/TkuM18AuyQQxwyJu5xCe2CMoG8r8x/3EmGJTbJ+VchnaF/o73opQ2GwpHt1ce/O5i56cDQzfeAx/j9CABvQcRtWKQ9O7ceCUHEvfYm3Rp/AO/nm7e/b+6B97bxWGO/XtjltivvB09e/tHVmLa37TvHe/114P3xaJf59jw9Ntler38D8FqLGLzIfo1sWCxoA1kCL3HvjQReAM0D1D6+BjQeu4wPBllu8NHZOiRgG7hA7FhuH6weThMQtEcGV/dy6w6S3AQqgv/jf/e/ux2Zb8T2LZj5ijdRoEyCvFEF14Ir4WGIGtyuJ0V+W3BcDOVArRdJTFPWpqY7haclbkKfWRJeK42qLt7DLT4lxvibloIK7AjophA49WBMkLygigAzUEM+U6oAa8X0dAFEoK8PNKRLBU5R0sA2iBvcM9JmkK2iTmnnIECZwuwA3CCg9L8WMCOregAC5UPTknPaRGIedW1Az0nLaHKw2KDTIEgr+OhgmCc8GghlY7T6S+GB6atGIQBJbfYKK6eNr7M4fDWkNaEcBVaNGiyZBrCcOEuoAyhArgA2C92cNsFgF8oLASoCJw0DT6DrDckBaFWnq1dIr0iHHlZkhpnCjzOB04GVsfG8AZcFshR66ELY0LcNWLcoL+GAXnjt5wzMM5q8umwbs5iQdiPa6mmpxqpRd68M3reRPVPnZvk42qye4tv2uGHJ3h7zJTs+hh3s5jvp+wT3IO0fqvHHTPEWlPDE+ZFaRlciitHMCt53PsU92gBJ9y41z0mEFW/E4trrFYAbPVMjeLkaqJvBGMNANry2342v4z7NAMYxRm2WEfAo0EMcvnetA5MxYxm3/fOhfzdfte+/dLsFxLdA5KXd3fd+vgCU3gu3DaByx+EDQhv6eQViWir26MEZwEFPT2vtuR0XgM/9+F1rXLu5BEEH8AhZN2HHvQOdXnPT0S+BcnvbsOMsAqHoC9CSHAHEojiOL+F90cSwuANMZogwvw9hvlNK+D//b/88vnt6+NKWfJ3bt2DmK97kzQFUbplY8RnSH0hxRZ0cugCuCfWeVa7zuUI3R72fGHbIYFYRKDjnGYGilSvMiRlAGsUNZTW40qPQ7l/x4H8cKDZGYrLD7jJQKrXTcoAgp6clWyWP5tcX2HGC5wSvrGasayWp9DhBLxV+SNAoEyBbJT8HMV/HqqNpgbSJQs33tNfYPMhx1I6RPoEYsAONhj3APshm9DQ074g3HZ7B6sWptTTwY9Ckg3dn8OPGz9QBWSpQmMnEFbcSRDnIhQJ5ONIF6egLU4+xkKb+i94OwAcgC0D2v5mm/P9j7++DbduuulD011ofY8y51v445+TjJOT7iwAJSQiBSMBAoiQY4V4FuYCAhI+LT99TL1Kg4EUeUPBMGQmCV5BXKsT74Fqg5SuxFH0lEd+z9AkSEKzrVzDUfRgBSc7H3mvNOcforb0/fq310efc+8TkcA7kHFav2nutNeeYY/bRRx+9/Xprv/ZrFMTzCJd5YcaXTVuU2wwlWnA+REuUUyC41Yd3kEvWA1Nu+ciNSVLuwhRw7HYU1EsV4aFwcZ18rdlVZL03aSgjk+c00zSN2eqVaP4N9Lc9h/hUMKz79U7PTWDXng/RPPy9ge8AgAHwCfSmdB4XsZWgXEGujW+CExTH5bW1zCL4yoVJS9P9bOAtwUJYzGZ6esByN4OXHe6Ah3v3Wv8ZRz+rV4PYA6ETr0w7Pkiya+ZY97l8D2jRBBZMXQ9rl+PHgDKHo6NH3QlsTkDL6Tnv2uLa7zjudOz6edeDQL3z2Ayb9+Odx4ofk2+POp4gMJ4Dga+1tvrj++/O72yp39qef26+ONC+xMnbJoIna89J85+sIC4jQnJ6ffmsNHDIz9eQshKR4MbLHZ/LlG53LnM60MvsSQRuxU8dr3vm8/C/feYfwkdyuwIzj3E7v7bF5UOX5JbEayqhH5FVpgFAQI7GtmC4YPVm21XuvEUY2jEHDhEaSe+DStsdpHaFRIaLcBMPgQEH/o6JQX8vDAM5AEwCDwJu8lRwjUZgDOG3sltgWuGbEXWYCIpqGgBHPaOrXg58KHWpFAVUentsBj1FTmPiHtwaB7LadBKeU2cGiDUnnk7P3xFhuMpK1jJXXpfHmEikVweQa7u9WIQ1Xz8YXKwBGImaPz4V3i+nB0mcXBmmKzIlfKiG5aw09WDdV4KMgigsiQB3QFuMcqFKa1mymGW3cHmE8lzpmVKSgH1UYHHUm1vAgv+0xFi7YL7JgqLl2ojh1wT64C1gP8NL6RY8R8ukcADzDMwg18ZHfv8hwN5m5A0rscJ2YRFPPkjpQEln0Hv2p6+X3wyNd96LZuN6IqKgq/8TQCQI4+LCHWOGcwZpdATer7A7DtiY5O0EM7z2DGeaB8gsjuMdN9pabwJ6px5J+VbyWnIccHceSwfMevBxJBTb/Z7HNmDRf677urt+R3cYutvSznMC/IDOKHZARezO9++KxU5BzMm9vcNzgpO/T859xxj2re9AChr2Y4tH+Gw3/9pmqP9cAykSYxDPSJdx1Ai/ub7k+OQzFYkBcAmvjnfjIS10zsU+1gQFXGPQHQzjdPMvq4+kJyVPdeRxycvo7ukxd17jfYIauIeab/e5WH6TIOzGMH0p1jZYOYjf8Krfha/+2E+72935iGpXYOYxbk+7eY4H3n8bMtfYWQZZNcImxWJXXun6tklRZxowUUGxyihIZMroxQwVQd1GTSWVlpaI4GsAAZREIHunynAJAHBY4MPANMg5jkmV2uRFGKF5vS7w6RzDrQON9Ux9GzkwQ0sGxoZ8ZJaUqEIXg+wt9GgEGJ1hjwOtjAQ3w6s1oyKVFbKZleNNKyY3MBIB4HWxi4XAGdQlR2chMPSsHaVtByrhsVoNRe7WnSJ+BnjkIWoFU+iLMlMK9IoxMchRC6IWlWG4HJjFtQBlt7Bekq5emLZDMgJKC2CQehprX7y5o9Nz4I4IEXrb7YsgQl0KaJxPHbWQrOxFMd8YUK6P2PzKBP21B9tOUwScB8a0eEYUifQsJ5CC4FPAopT7uAEj62x5hpk8gEQW4gQiJBULY8yjdG2vgC7sRvJBGksXnefKyTcKUJSp3gSXnAs1v6MgirAigIHEWHPR9hEEMqkZA3TnCWJ5fFeClKYGm89oTKU+XHXkCWmD293SfO9ux/sjHNMdeuTFSPuJY2N1B5iR7vR+8l73s/2Zhvjku5r97t+7o4OP3HqD219zD9Lad5yc667hodMx7P9oSDkMcTxr7fOKNRRnOAppJvE/Tyc9Wsvz9IPYc/kMoWkVF+xo4eF2THFu9vq4WX6uIY7sV3fRDSDFqfXOqXU0CnmqnBt5cMWKYGNuWFTB7lItWqKEu8DMj4vfduOt6jgrA/7xW/4Ynn52A0+EdgVmHuP2/Gffh/f8x1+FVm87fR+UYL5WFgsbacx1RqtDo5d7wAx2PsLnBTKGYN4cXBIPMTIDaxMZd6UZLlEA1S2cHcoSCF4BLYBVytOnmjCED9mBfmVRIieDwifBfC/JqeVyhl4swQFJfg6Ng+4Whlq6RWFdgD00RwQ+GxV6NQBFABWd6XHKrUgGKbhb5aLgETpJIAKAHgpE0vhszN4Zh1Z1e919edvlIonPuj7oYpF5YE5+kBjDSYh4sdNIcsPGFG3MM1pNnyTGmlOfBKDIXrI/FS3rqjV3ro2xwDUjEPF0MRBwjkwLTjGxNMoGoJ4JlrOCegYSxxegXNvAzgdsz0bYQxehwxIE590BcqiNYCyqsHEErpFYrCKAaLiaYzGuFjoqApew6h1XJIGHhRMnM8uOdr/RbwMa9+RoWW0bVmlApQntIa7d+LGa/KkJAR4le9KMtIWnss8IartuA6SwrkwDVgJkVcjm6YnksDvIuK2/j2DZ/S5/WFzvipUACT5XGntbz3k6Lq2FV0XQ9Sk/n4av5390fe7taiP25nPaAaJHJOyego/ccNwFmPSgzf9bw3T6vnTvZZ/t+H3ERqEHeFlYvg+hycnfjS8HAdPegfXC45hTxHAKqBxreC87YVjXgni2vEcYDqwuLxzdu+PRkO51j+QFWc+j67X3fc4wckPlETJroSPICnwiCynlrBDvuQuqeSxluWbz03/wha/Bt3zi7+X68ARpV2DmMW4v//jn4Cfe9W8DbBi9GgDS3VnEWGp9ovKuKGBTAW4Z5NceQDk/Y0jn9oxyNoWasEEmkjyz+KIJ4NVRlpDBL4oyUHzPixC8VNBQO3fYEhlSACAHZ+ppPIBc2/PJV7galvMRU3gu0iCVhWCDqsEK3yj8bIBczCiz8cFuWTABOkzghwoZo0pz6LKU6ukUogHKbV4uGsLddFt8C4BNIVfFmfKt6WNeqKEiERJBfEYWJ4kafHgbAJltNZoAAAOqUqyuCKtFG+tUZYq5Zu0iFdQSniChqF+/u9U4jITKsJYBigTevHNrqMmhWXelOtwSxAVBL4hQthHUCahbqj+jCOrILtdJsGxvYHj/CPMFGOnJGm7tMTw0Q2/vIbsZstDDYzcm+LUtVFiTSubQIcrRKEKO0IgTg9zZrTD8qWzbxjxPIgIT53jqeh6C1riHAEy9qds20NMZmazNJK0afRyD9ZhMs05dmGb0gaYNZAmqUjsmgFPj5+TxOeRdOYqjZokKOivo62He/9cbIsOxUbL1AtoYdieRnHp57rSTMY2PnBW9QU6eRYJNO3k/rhFdv9Y3Ts7Zcb8eEe90IMlO3u9sbfeBk2NyjLq/AbTxamE97721HbDpB166EFv2O0IocFk3WXJyNQnoOk9J+yLBqgicoDYvNgHNAeugtg0P0HRyDARTDd8I7gqO2+D3ICfHY93QJplXoo9ZfPVISbibFy4ABkAjWSGqoEAgTf0XALZF8f9801fjo+95+p19+whvV2DmMW6vevXzKf/uNJh6e6Y3YeIKqvvKjCedInYKuArq065BzyeUBy9Q3vcgcO2cgEULSjXY7Zm6LzXAxxQuxNmhlwe6/ccBMil8U9b02oFS+jLH1mIqTEuNUgCaq4KCXhRh6q87+2pFI7TCkEXdFJQDAGeqt+yN5xuiAnMabwvQMBXUrUBGXWX604gEZ6e5alWoBtysi9DrUZ3fAS5GUpQEXS+sjZQL3WJwaHB1EAtyEIYhLDeQO38RbvnDKmQJAghY48rCwFiUdGgqxcrwWHil9HykbauVi2XJsgLrSsSU8ZQgjNBRIYoTjzCQrCA1xdksdGd8ZOr4ovxpkYSURh0CLAOBzv78DLJfGIsvCjxjizID5WLBcGEolwvqfgbORmAzYBkKi8lVhyyOsrDOVQULovqk4fqOcerc8Q60jJ5WdBDHNt5ECFSyJEAeEwuo5zEFXfYQGB5Krwv4jLTQTx5j63e1Y2Q9hobWO+8NAmivyCPxc6aith21ru8de0t8/dmTeNO4xmX28gBp0I926NHpHpABBLB9hlLOmabVlt6IOE+vA9Nsenhq2t6kBy5H19JdUm84e+DRG9O72V45eb8DGKek7VOujPefQzd+JyCtYZn+Gk+ARx7Tf79Yf7myAruSYMKPgFObNx5jeBR6ys8cj5kb1g+ax04G6z1MTk13PW0O5/V1Y3R0IcdYmRufzOSMDzVs1aalrOduYTWezyPUKl32JYCoxg181rM+Fv/Lp33+HRmHT5R2BWYe4/bClzwjDBdaxpHPFsqyDBvp5QFiguXGyLDqQkOCUlCffhM6FOh/fQgKh283cFGUpcJueWTRkGxr2wmAQw4R8hkqbBngszXviCVnwkBexGLQqZBgWvgUSIqtpax8DRXIScknQQjhGTk7NiXzDdCF5Q4cHpyOAFxpeMzW1GkpQYBIEBeDFkKDGFbyqkXBTlT2XSsinZarWmb+YBDuvNzhJWoYNc6NtzVBzOFzxVqEMHY1WXjwsLBfEgUipca4VSoVL7VpCOFQuEalnlBhWQdmMRkwKjRY322nGjszxNBwIavIgoae69TI8I2o4HCd4UiIw4aBHCegqyXEU2dpCC8hnnc2xmIevKwNQ1PLTYb3cAAkCCI0lJG+vAmnmQePCDUKTMqqo+OxaCOKFg7SvDNtne8WZgeo1dK4QPw+K+A9y/DV0M0HSZCzGiR65uI8eZDETjQ8PEchJKRBDG9d7lQFR2OXQEaSG9E+2N23/NmBkDSyPVgRoIEgBNASrMYqd+UJxJrRdRDwCkIZez22HY8wXekFdGkRzTbmaZzytQ64NHBwQpZtx5wY7qNj7vb6yT2+4+/TMN3JOfqkoNa/OM6cz3sLjZGvzvcy7BL3RSy8mXLynTF9pb/uvPaa8ynmT+teflAaiGyho+jH0dxwPqfpMUFwuBrPptOXamqJPXA78ez1Pzmma/i9rZkWi0CooSdSvQNve6Ky/FuDquCNR73OF8Hf+swvxyc87dl4IrcrMPMYN1UWDrRBWAMo06UXh84zZFmgHlwPA2yigVCXUNZ1+D3X4KVA3/8wK2ePA2sFxQyU3QGYD5DzcxaQNPJSgIpSBF4KTIQhlsXbTpS7b0M1h8/KMMKGOiO5G5GGBgIkNI9EGBYLQBAuVg/L7FKoauvgtWU16mWhDS+6ZsiYE61YvBbBXF2MO3wziFWUJNwJgRkWZRZT+EilMlSxZnfF6+z9utsu2rxgUgkGsdhq1IInQitCa0LgEv2bl8blkcWhQ4AeF/jlnhlIY4EXhYrAvDD1WUFQg1hPKqujZLaWgCAhjQW9C0zHRwFs1PCieWR/5UGy7vBypw4A4VHLE65aPg6tDA2WwpChmEDA/lC3SFbBR49FXhRe8v4HOM4vjmq76VFLF3ovlpcGl5yYXHiBrJp9pGHTAZHeOCYRHpDVu5PXCyB1Ohpo7Im7cWDaDT8Zs5PDmuE7tTP5ZSyTgNWjgnz9+GTtu04NXwIowVrF2dcp2AOs5rGMA/JeNmOdx6ZRTxCV86kHLo67ple3Y06BjJ+834OiU9DQ9cn7z8rJOdH93Y/LCbiCgqKaC5id6V14MOtXxXhpRfAGA4wqmpqyB5hpGlBxT6S7douQ/dGcyYcxr9uB5lHMC7yLi6mFw47GUejlvRv/ClgnBLCucznH5C4D38fwWrwq349TtPHM97x7WNjJVKUAgD/w4o/H/+1T3oJNeeJDgSf+FXwENnEHxkI9mKa0CggUVUfYdoSNBdgvwNmAOhbu7I11jfRg8M0Ev+8GcLGHGwvhZZoyigB7h17uILYwXNMqBwu8VGCg7oyCRFkCFgcWo6DaUOAWNX6m0pV696ZuK6liG3goeUBt9Q4OiZhBpgE+KmReWM5hrqyA7aDYW1H4ZoiUaqPCbe6oSoGMlNcv7rxeM2CurC4NULG2UCPHh7J+PwSyVKiRCyIqQA0PTUmBOv5zAc8pwv6EVaG3a0Er3GNG8TlVyDDwvYhJUzHXIdMIOL9b1OGedY8UxSpsGJgKP/C6RBDVuWMxVI9K1+STWOFCzNRg6gY1S9csBVbD0y9sXSVr6dau1dhESnghV0cjnTxdPXUAdAmScwvJdJZXUkMH8FwtG4iNWRPZTY0Xk+ONDpz0vJA7jN2JgUgD2Z8Dx587tS2tX3lc7nL7XW8PPHqjCjQi9tGxHZAB0DwqR6Jy8QFPMFk7QKMntzA/1IXImFV2PAx9vwVoYpi98ZfumPTYtO/oQlV5cA5JP0dWDafO5uW54zN99lcfOmrAoPvsHdo6J0CvH9M7vtTBMikLItEBLaOtgfiUd4jx1QUE6nHNmcIvOQaWxHRvgM6BqJMlzTMDoHkt2mbgbvO0BwgZSo/+i4CgHd6RqrtntyHlDzI2OXBd2Gudy4Ljwev+vqP0QMwNOb1BbK97xvPxQ2/6IuhpfbYncLsCM49DswgDeGElYx8ZgqDOTO40lGq1AmCg/ouaY9k6sBQMl4KiEhkyFXVUqgMDgAyQa2c02rsZUitkmgAAvlQgMqN0HOBjATZCpdgaPJe5xqLgQKWXyEfl7wA0H2abyZ8ZpBllPTCbiI+ABEkWwMFg1RjyOiwMhQli509w4hcKbEa6iqvTaxPFKxGCdlKYWYPl0Lw86g4cBCoHyDjANhtykNxoFMyAalR8ncqRh0CN/U09GhQl+EuRvYWfRTVgPgC1wmsF9gfyd6YBGEaOB4CmEzQvka7O95JQDBFgoOy3DyNkMxC4bgbOCaxlJ9aTcox14PkB7p5sCcMzBJBQCePj6zqWFid3l/2OsjfeQXwk18ZZmNSkAVcv5PKkIb7jsy2bCR3QIGm5N3Lea4EcLcT5QXS7UKxG9xT4nH6sM5D9DvgoYyd/pkFNo30KBO/W8nrremxesZ4e0/1ruCgNZnecutA4R5/TwLquQEayXwkqul18zx1pWobWjUFDJOuQpzfSDUdhu6PxRHdbvHtPTn7i+GcjaMccO3JQ3GVc273pwWMAZunu1VFmVV2Bog9rH6WFhgS6MHyqhsabSpBpiGMzJJTg1qUDvb6mMuf7jecSWZ8JVvox7K9H4wJT/j9RZYLOuDZuPLqRPwVHJyBjfTEnzfE7AlnBXCjGM1Eg7lFF0AUAkfBc5/cCOCsD/tff/QfxSc94zt2++AndrsDM49CWa9QfKUVgs60GA1j1HkZAtlGCQIDmXlwEGBTL9QgvjQV+uQfg8GmAikNnQHcztWy0AIcDDbAAUg26F8h+Dr2MAb6dwm1dgbk2IIE9oIVCaT4WKv5qABRRyEDDr5cVkIWlCABqyGhElZnjRwXJRji0SLHx8I7E5+aFIZvkuyCMuoC1hjKkBmc/M3RUCsFKgDB1wJ2qtTYk2BoZvivdaiFCrstS4TNCvwRB6I3VL8NOKsA40AosQKtSPi+xKMbKVEmwcDNgZ8AwMKQ0FH4+wJi4Qw4zAeQ0wBYDVKnAPArW2k1p6AStCJwDYoISNB73MICsxUCJ8Rae8iCvoIVqkmcCxEKfRrF5c1br0gBSLM4xFY82fEe1l9JoAsgw0bF7XY7W5wSAx4uyHB3QDGZnRI9Ikf3H5OTvu20sT41o/ux4KndY6/jjNLykp8cdeWb8CNC0UEOGPvI6OsDQZyJl346iDWm0+34Djedwt/To7nYdbdDF1mjgkYPgZKgMWMsy9J6V+FzjuHZAy7t+97g3P7N+lx91QCogM1pWpisodNhboi7c6CMfyVIBPeRclCgg293mAErqTEr0vJceACAGralKd7ynrO9GQBRJCRmiCpDTQoMJiAu652mdBA1s1AA6HWn9kZrH546a3HnEHe9VhoETTBK8aHjZPcaR419E8IO/6wvx+me96JE78gRvV2Dm8WguVHt1oYGNxSufafF8mJy7Y8/pyr/dnN6aYvBtgZ2d0yiLYynKqNVhQrl1gB4qdBB6EnLOHw7AngBDHPBbKWPP7qV2ioQhQ/ztCCE9i+2OguEdZSFL3dPouyrgleCmIh7ckPh3oxcovCVwIUlU4vuD60JgQbAiZSBwEV4bSmwtlspjIsMp0w8Z2omsLBECCgW8ptdFQ3OBGUGMrTs/ZwavDlnCU7VU4DCjbkZgS08KDhVSK0HOjl4uLDW4N0oACax8EXPIvJDbfK1AzrbsMxAlAphW78F/se3QMm+4Lq0AQIDkCXM9WgLMlAQGQFbLzKyn4//RsmCaQUW34DuOd4p9E1m/PH+cbhLTkOb35clPwyR3MciP8K13OG5Oj73jc0cXFT/t5H3DXfsAgJyMPHG7qO4cRmDeg441WwRIDaM0pnd8Z77ecryP78Ed1xb9kP5nvi/rz36OAJHaHvcmyeHSfaYBgtTf6V5rgDTu3bH+Tndv+452494A2Imd7efGafYzYsx0BsqeY+wFsMlhQ4RaixwDhRhL18DEhzhOnE6VmhIHfN7rIsDYKH2Rfdd5zIIa125tDmyMUZZiWeN36yTIzZtXPvOnAA/odaXiRhqOwsB3m5NH01Ma9ALunPltLvH+0F4gjhcVVr/urwuOz3r+R+MvfdofwJTr6pO0XYGZx6ENlwtsF0x0UVRNPgUfPMxh6Es5yjxwc3JEhrJmNGTmyiBwz20CYBtHnbYYdgvKZWHs87BQcE8V4hWp+CoWK4AIwz3pdVCFT1OAGqG0PSKVtdCA22akIuwAAHTx5u5TzODiJLk6+CAarxPV6S1Kt71GppKuD5Rbhma0I5LGylDKanhzVYz0bKgihfNgqXJbuGi5E4yIE2QodyqmgGyH8Ow4MCsXmloZ+spQkBtED1AfmK49jsBuD59nmCq5QWfXWPMolh3xGMuxMKyXVaqLstCoULOlDoIKp2em6LoqiRxxF9riFnjSo2akxaLbvCW2Hm8IHJKGKLwsLWTUgRHphvS0OeTIK7KGcfxuS+vxZ08BRru+7qAOIDziZ4/6c5cX7mZIe7DR8Q2k9cXpcYOs1++rJ0YAvm/dmPVWP1vtPtO93l7r7+PpteexffdXvPOI195zghK8IO1YD0T6Y2WlmTRPxwnIaWAnf4/j2v3Pa89Fqi1W69i0cJcc34I27vmar7dB3VGcpHQDWmgmwbqnSJPEnM4Nl4DrUHiRloHeGg3uloXooQ0goAlvsWRiQ3gs299F4Iszu0nRZAaoGk2F3IYOMwyVj63hOLTbPDB+57zJn6eA37vxOn1GTo97RE5MhJIsX8g+Ol57//Pw1974Bbg+bfDboV2BmcehTS6Y9wu1UIYwalJhm4I6CEolV0GN0vQtg+/gUGSRMIcPQsa9czdjBroVI9SBjaJeG+FFUQalKu/lAasvGsiq1vy90nhnOnLvt84sIAsLWgsXoP2B9X62I1AUdeKKJzDYbNB9uA60dDs3rohmRg/IspBIOxR6UVqAl7wdsQWu6eeOBzY1YHKnNo0kmRrLPthY4NPQQhgtxdIQYS5eltQKqwbfKOoZSbliQdxdKuTgkFqpRL4d4GdjiB3ObTzLcoD7ALs+AudbyDCh2LSSuxeWWHAR+NkGdq2w1EER+MisMS6W3bZKgSaU5rKGctr4cRiOMEHcuuRctJ9YHTaN5Ni50XM9bmukH587213X4O6eHvFT0B3cGz90P5uxP/5g43Lg5Pi7neeRDEN/XAIYR+NQNA+ErCAseRfa8WHaeCTAifNozM/+u1vIzu8chqPL6S73kQDg6et3/H3y+VMyatt4J5iJ93r+Sb7fgMpd+B/tmLucA+ju1clFtmP89O6u70teQF6EgWrmg8Pn6Gfcs0aaNifno3jLfuR9iTIkKkzjj366etOEqQX8rAbokQA2FeTmIe19APaQLkA8L24guGmi17zAnuLFDWZ3L3rw3I9VR7xe0SeQte3azT0duPzY3SaYP8Ix0h8o+Lznfzz+/Ke+BcOT3BNz2q7AzOPQtje3mN9/iw8KwOJ4JXQLFFjOFHowCpSBxlmEHhhZyA2xPicyGHHDbK3iqswLKkYq4o7MRqLgGisqY48I9dBwe8pGOpgRBKxAR0LEzdNjpMCWDDrZL9C6g90SyFAg0wCMExwOPSz0WlgNMT/QQyESBSf5XbAZriyCqNMYYSE+eBQXrHCJkgebCRgHLiSRUcSLBzBy1bXUZinMAIMZPSIOirz5wDGCxFbNUBUkB48lQMgYyrDcadtugYozvLcZIGcTlqczFXl63xl0f0A9zMA1KufabNDLmcdfP2ftH0TNpE2BjRou88iaghxzZPgbX+jd6p7/dcY/fuY6ysQ1x1rVdsWTmV6fRNCWdRM7yrt6RrrF0mQFO0eGVI8PPdqZ5xtycszpBfQgRrASmQE0rkG2JKscEZrDuAV5k13oPYVp/Hz9qHTXXfneHXYigcyMBmROPVe9Yc6uHdmQu/x+VywnJ8P/CMYMOB73o/OKNB5LH0I6Ahgd+EmwE486mx7f3waCehAlxyD4aCIeXeDd329gOIHmgXw/GEGGgyBDFrB8SqXz1QfwORoRWUfooj4eBW9X4GKF58imBtQFfFgKgCE8s712DWIwunGSGDiv/AyHUgD3o82Bt4v0Ni8lM/t6zlH/rBVdj4fgSIfmtPX9679Uus8fDTQn2P/0yk/F13zC73zCit79RtsVmHkc2r3PvIEHH7qghwAMMWDQlasgQsO8M5RdZWp0MWCktgsWytiLeWQTKTOjBqHoWTVgDjXXINiyKTACPo2QurQVypeFwMARAMbpoQGA/Y7aMeNI70lXcFGAKIGg0FohlwtwsSNvZpz48NcKD2IvLOO1wW8J3RkRAMsMHA6o+wNkGqGbiSEcd9iS2zRF8xSVWIFym6kcFx8QpODVMgrQhALdJLR9SqRpanu4c7eGElk4CobDigCDYlanqGF4IVx5z+wZ92BRgx0c8AX1rMAHgcwz9HLGeDCoF4golvOC+VqUbRAgReY0Opp4MhdEcayZFbloPdJ2vrveo81dZrA4WopvTwKGxEJ+Cjh649MZKO9/9gYVj2Dc2gnvfPmOVz/Ygu8BToBjzksSd9OgCFb16ARfSd4MsHHUz/ylJ+Ymjs/xyS6GxIH48Wdbd7vL+GC3yRuC+uCtjfEJIOqGJECInHjK1n8OHI+pHmG/FXSeACkHVo/NaSjqv9Ha9efY5+/5GJ/c57IDxtvAsEMAGm9cElNBqQjCuwPKkh31DKgTz5M1s7xwjmT4yIYARiMze2AENuXAEgY28LmnE5oTQ7O/nbdKACpRJ8jIgQ+14ATePsdnW4hpHWRpBH6OsqebVHH8jN9ljNtc9NP3Y9GQ06P5cxDF2z/1s/G5L37FB79hvw3aFZh5HNqzn/cU/OIv/VeUgwHLDJkZjlAHbHG4VjiogqomkINRk0T44HCl6KTjAxH4WOCIVGhU8ld2lURhBQCH7FlbCOME1AU2SmQHzfDDgR1Mn6lFPaNlWXdtJVKN8zsH5bkg5OMcDlzs54h7ASth13zVa6m1rXYOiUKXwlDX3gmAxgIfhgh95WJRmPU0jTxvarQsS1P6lM0EP5s4ZqPCSgEqdW2gChvJwREL4AOsgmtmwOxN7ZgBMxZw9OqQHRchUYVgTXmEEYAum4J6JqgboG4G2OYcuhiGC8d4G8ABWM4RYogk8JYqrN0SRrI5VLqN15FbGsdA5G6LX77c8HHG/XMx7Ne/zoI1DY7+9f78vfekd593IOuob83adn/37/fXln11tPnWgAvWwqAtK0i6vi5xTBzfPAh5/LKCFGC1Ma1LwnNkFlLSLFvYSNCKfQpOwEyO5+k5H6ElUM3vPd2AN1PUAcUeePVcF+8NZl5zelW6EFMP3o7StvuQSL6eP/9bACYB0KktPRkEAeffUXkBiWcu+HO6ALoHdMfMJKmA7JhNtwxROd0cg3GJcAUsqB6W5TKG9foynJgVzl2cmdSRWVkshCAXAp8G+uMZbEUVbR3rDEPmuhvLLK+xrKCyATjhzW1lNHKnEmt4Txpva0h6I9sCgPWgfhL0IkOtM+tEujFu8Nfe+Pl47TOf9wg377dfuwIzj0P7uJc9C//vf/4fYQUoDx1QZoNtC2QaI5TC2kJZZ6M9IIpmcCnTHd4GV7hTMRgqUWagoLhDDga1Cp8G2JZZQXpwYONUnxwK6vkGZVTg/Q8Dux0A0OMSRiRTiUn+VHgJ2dVIi/YsXrkdgXkTmUugt2W3ZyhlO4X4nEU21YGeqTLGw0tBu3U3Fx6dfVSiztTneY7XS3iKIuUZgIWqLvvMmks+KEHL4sDEatPMIFotOhd+DT0ZsKDmHIvYoFB31InnIa+Wst++CGQUVPGI3wcgWyKTgkWLUDcD6n2O/X0A9kCZfSULmjARauTrGoJgHnF/zcyZbqFt6bTZ5C5rHzgOGkbTgSOyL3KcT8/TkWP9Lu+nkWq7/bZwY9Xk6M/dvCYni/RpHxzr7rQzpC0k5uFR6YFRD2pO0p1PNU6kO8eR1yp/E7RQRMBbZvvl4akmC/Slm45tyAmYu2MTjSPMd0dYsR3fXVf/uTvOJWi8qnaO/nPdTr8fEwAr1+X0xJ0T10/O0Y7P13rNlP79U/Aa/9JbkgDDWgZelMPIx7dQqBHCMFPyUGxiboRWRCamA8piqj3w0wAjHmCPU9SBwUMRWlppNEk+GpwH1+NraWC589R5bITyuql7FR/rACTxixyPx2mT9dgcvru2DuAf/XJ0/wT3n13DD7/pi/CSe594hSAf73YFZh6H9upXPZ8PzaB8KC8PkB2AzQKbBox1gC0L3Z0qrEkEhwl3/boU6MxMIakOmRHhH8RiI8B2gB0q1AHdG2ALalGGrGCQObZkhcUCl6fcRCkD8NAtApA504wInnxZGGoCmm6KR1q0jwONuQO+KRAZYBBgGXmsV/g0RWjJIcsW2B+AeYZWgznDWCiFladVIgVyZngqQl6e4AXg9syM/w4HQAR+33XYjTNgGkj+dbR+QQfM5+y7OMGMDVxgOwRF0t+uUrMmMsx8HKLsA5gZldWxR4WDISW6q0Pl1rgojpeOYc9FuG6AZSvwLXUu9ACGBAuBSy3k0+ji8AMXahEu6uZoKatq6ybvEY1dv7vud4Dw40XTcWzhGkDwDtjI8ckza6P/uxms+KI7dGOcRiy/I1FHgvPQFFFbDQHfj/HM3a91i/7dwAziWiXViGV9sa4IUN2PLkniOnqvSxsfOXZGHQGhBDUn96E3SKeZSBleOiI5r11rxreNVWdQDWhelzTwCTgEaMU8j8jA/d89EOm++25aPOv86N8Pj1XOgQSyPX0PWHWI+qmVc9jRvB3S3W8f6bHUGRhmoi8bgGVYkSOlC4AlExknRFgp+rsE+E9+zETvTR0qsy+Ta7NXlFlaZjTvOTsvBSt3Zlifs9ruRXrr8gUEiJd189l7SqzrX39fcPd7kdWW7thJxHPo7T4APWL/mHufiv/Hm78ITz+/fufNvGoArsDM49I++iUfxYwbVXg1KKLcwH4mGfewMMvCKnCgvL6fbVCmATgMwNnAtMIa9XIyjitYeQNF4Rul6m5lhpNcLgQKlqs0Fw2IANMIu+8GdDtBLg7AxQXk4dv0giTJdl7gw0yp/s1Eb0kNv69SGyU1agAQoIwD6n6GZu0lF3itkA1rRlFcbiZg2Y6N2wBVqG75nYcDsCywzQQZB+q4mJEX4R5enJlVu7cKuzbAhwKBMvRUuEQsIy3TuO+MSiGo8BoLz2Iot/bQiwMETgA1jajXJ5KH4cDBISBHxrYOmUpbpBk/j3sQwzzMDtsLhktn+EnCC1PBApcBaLwIbIwAhwk9BQbWXpIoZeB+7LJPJdP1x53N/QiT9GGB48VxNdIA2nzyPp1UccxX6YBGM5ottoWVh5KGLA2TdueucWqPyGRb9Nc5LS7ptV+NW2gded+PvCjl5wXxnQv71OObHAARkBjdvxb3s/ds9GOSv/e4sY3niYFq0WA5PuaIHB39yFM3AvPpcXmerO7dn6sj7bbv6duJ8TwCOXJybAx9FtnsvREtjTkuLBl0qwgdVomTbhzaeeP+JAClXhV5MAdz4IJgn14cem1Yno3hJi8Mwfu4jo06IAvDVOOO3hsrwOEeoN4EfMPvEJOGu9MT17SSgjTcz7NGoM5sp3wGJAfC2/ut1lkOqHfFKHtg2I/76dinN/BIOfH0ZkoLff6u574Y/8sb/nucjyOu2gdvjxrMvOc978EP/MAP4D3veQ+++7u/G/fffz9+/Md/HM997nPx8pe//LHs4xOujSMFD2xQ+DjAdxfULHEjF0MFErV8PDwjujvARVBU4feco9444xOnYPgmiK25PZKFareuEqEWpnr7wirZ3NF0fnQBC0te2wLbCXrjDDJNkA88ACwLfBj40O8PaJow51vWTHKHI7ZZ7SEHvTuDQq4TlKCE/osGD6YMfEavnYWSLsNQUo3eHhFgG26NmXK3tp0IiozhIINGfNthcCznG8w3Rko7LIIiDhtZYXo+F5QLwzDzO3ROInIMmwgwVwgM4oaoXMWI2p4p3FBh5pMDagbZcTdYRwXSQwPEbi0HlrvFxpEBGhFVljRaDht8DYMtNB5MS5XVYxEArNWZ63fOp5YVWHd4HYBph3gsvGlcTuZphhlb9lIPJvJfgBVdv+bofQ+D0Yi38f6R7H4ayRi2I8Aj3SY1r6HnC+TON7kwXd8lXpD+O/JaeyNia9SxjZ3E23ZyTUAjFnt/zd3Pdu3t7zVt3RPQ6foZOT0+x0COEtJWr00a3j7ElAclSDydEz1oSW9K9rX/3vwlN0XJs3IclVnIzC4P1WmOVWwIcg3yfjjXCSgua/ZPBYFmoFA7ExwGEoJlHxhiTDqJruOUISSihTaOEuGoPlwkFkJ3JkceuhQlhaNlNcEDJKEDNN24tuvqn7PeO3mXcV3Du6k0nG/Eybq5dezh6hHg+usnPePZ+JZP+d34+Kc947dtZtKjaY8KzPzkT/4k3vKWt+DTPu3T8E//6T/Fd3zHd+D+++/Hv/7X/xp/9a/+Vfytv/W3Hut+PuGamsOKws4n6MWeD1aN1W4JTZdlBkqJHY5AjWq0st9D9jPsxhk9EJlCOFNNWILUWqx72AcCAIwC7GmorUSF5m4n7QGmIAJ7+r3Q7QR//4NoujPLAlxewvY7yvFvt0HGBfMdRaPoI0GGLyHXvx3X0E1Wx07D7EIvjlW4Rxp3rax5VMIaDQUOZxHOs8JaUcJVXaJwjTtgW0XdEgTqhbe4OYTKxDYYbKmYHp6h+/B6QRh6UGEphgrq2pS4lrEwfR5RUqAa3dWThupvqPf2BsWBtWBOvFUjTJTrmR//rQtCb4ZGwUTWZIj0YiRYPN2Ci+OUCHq08zvZBbaNX0++FRwVNEyPRbb2tXcDM7aephnYuOZ+jdd8vztPz4MRdEakM0jeG0Osx7ZLsqPLW4eguUXW14+uKb0Zth5z5EXIseqHtL/P3di1648/jkCGrGAkvQD5gZ7f04xkm7fdWAEM3wYYMl3HoqdmIK9HsFbT7q+v9371F5bX0N0HD0DtQbJNAN7qGxUce20Q73WAScPr1Yw64vglHEwH0EM5EkjYKLDRgfP4rhyn+GnpLUlgNrAP3AgAOq5jWanCgHLJ9UJysDKseMDq0Qopqww3NtDp6zgnryvo4a22FoBVMV27+9g/i6et3dhucp7ilzhmKwX/p1d9Mr7mk377plb/RtujAjPf8A3fgG//9m/H137t1+LGjRvt9Te+8Y347u/+7sesc0/klrsrOxuBswl+4BZIqq1p0sNAr0yu9tNIgbplgXzgFmRfYfddZ7Vpd4hVuld9YZhpZGFGGpUwdsIyCOIOTArTstY5kgA3NS2DwG6eQ8YRcvuCypvzDBwW1nt66GHI/gC5dk7CcHvGhNyWgZ4NXByAs9JAE3kQ5Kdg5sqZdZ5M6VOWyhXTRyV3ogiqKBe67UCSc2Z4+UBPS4hfDZcGV4NcOuTgKNuC/b1gYT93lP2M8oFb0IsZIrpWho3aUxZaMkyZL23c6O4RGKh47EOJuk+yCt61VbBr0sfmc5EK7Ynjw2hAYoFcPc1HW9zVDWL9a3HcHZbXj+McvbXvDVw7HkfHHO2uuwW2hSg6XY62Fiewic/nqdedeXSn34WeLP7tmK67PY2gz3hqoKM7xZGd7j53x/uG1XOTx3Xk46NO3PnnenxeA+Q45RndNeZr/T1QHHttwngecYf6L9YwogXHFxnve97zFf2s19fdl3buLnTUKkoHZ8RKN+4VDMG4Q3sGevMYrqPSploAmHIIwIIAHOMK5iRKF5Q9YCOwXANDQipB2hXAHMW6a0n9zA4le77GJRM6c4OiCyA1wj2QdSMVZOSc8304DcpnMDk90kT4urIHFU2n5hS3oB+OuOkpKZCFH49BjqyTtNtMDKL4v7z6d+BrPvnTrgDMY9AeFZj5+Z//efzwD//wHa8//elPx6//+q//hjv1ZGg2URsGc4EVhUbBQhdhLaJcRRJcmLEI48gCirRbrHDtKX2/sAZQyRTlIsw8KnwwbWFdIrqCw8gVAbYDfDFqqsCPHk4XATYj4GfhOdkAuz1kmeD7A/xyB9/toGfnzC6aBgIuq3Qfa4EeZqZ3Cxg+Um3OhFJrCONFX6CwTWwDtxPqjQ0XtgiZVQC+jZRwgEAnCL1U+zQMD8/Qyz3GhxfIXKGqKA9sUa8zdFVuLbFLZG2n3H6TdKuwQVHPB9QzVhUnyJAV4AXhz4u3lVvi3rFPaGGP3iA1g57hI6zYo2VHrIcHt+mRjX7vuVjTpH310IQmSu9haCanAyl9k/69DqScHnOkw4Ljc7X+n4CA3gvRH5dz4W4pwP3rd+sH7C6voZu/p306fe3kWtuHe9Rzgg9PD+e5jgto5o7+Dp5Mvpcd6P+174yaShFO9NBWabj25GKkP0caaIk3dD2tBiCoYbBzTrWQ0gDIhMbHEKBl1cEJEGT240FYYsodqANjce5Mi9bgSolzo4HKr6tlBQ4ifK/MAajGE+5QEdgSw5Z9BxrXKkNWCUAlngOfOeBq5Al6rHlecsz4fhOSXDgGCMAk6J6xeL2VCenGoAGgGPZ83tbQ8DpDGq8u7ukRygXBzhd87Mvxxz75dXjuzXtw1R679qjAzL333ov3ve99eOELX3j0+rvf/W48+9nPfkw69qRoEiUJplDl3e0pMufGrJsh/KVNryWYcIUKuT4NLSOKmT2VxRGdOygsBtQDMBQWVIQD4YlwAV2sAqrjDoF43LgjM7CkQmY1jQNJqQKGouYFcv0cPs+Qh26Hx+YAyBkrbQsVeDEUArQo2mg2w8expWcCABOI01gbScPbDeyc4amMTbg4LDMVkggLAIO311QE2FcMDx0gFzOGPVeq6daB4aKJwM0GRb3nGmsulShhUCuF7YaCGnWUfCqdbkdaqMAJqo27Irnz7QzLHUY4f2lckCRN+prOeRejKp21PfWUNGeLS/P2JZcqJeDbd/eGDzjyPHh/XN/nD9KaTT3t911at14fAY0eXPxG2ilIcXTAr/uu02MBrGEtv/O1/oOJce4AqPld+Voa3JNznIKd9OQcoUbHHSRRSYOLFdhmDa5euyU7JAgqyIgozkiw4DMg89rXfjomMMmwYJvzgpVjxOUjNH0CpESOsxqgM58D297F6KfKbq5NFeSYjdzzuADjJSAV0J3DNoFKStfJzrvUMo6CO6RKwUxxkNi78FrbZyVE67rx8ugXsII3hFI6ImzVvEB3AZzZUosox6q9ntylNjfk+J5l/xV43XOehz/zOz8dL7//Gbhqj097VGDmi7/4i/Gn//Sfxo/+6I9ChDV4/tk/+2f4uq/7OnzZl33ZY93HJ2Qb33+Juikouwq5feD2IlKN3UhQxULDTgTh9NBMQBYtRFkNsRtYGmCOdGbztrNHKSiFWjQ2TrDRoaKALfEUK+POiN89MmxKVK+eqVPjw0CvwsByB1Ir5PwMPm2A25cEUxD2QxxSF2YJbYa2AGg1QA11GiliFwuAD4Wk5VT+nBQ2EoT5EIuACLAFsK/ADOiBadJpu2WO3x/eA7cPLMoJAMNATZtqwOWenpxpgN2YUK9v4Bv67DN9FGCqNAX5uIITxKWcGuA1xwhNUr1P7zVgBS1xz482+gl4HIBJ51Fo39BtxbNfRCMEfVjJrwBUvDnygG4BPkIQHRJC915n2O6287+j9UinP6U8wiFpzO+CkI7AgZy8/mG0Uwx4hFxO+4Lje3L02S7M03fpFMjcoQPTX5+ux/T3+o6LcjBrMd9P/tQRiEUTdYPGvuaEjJpZUM3zVoW1izrvS4K7Iy9OgtvuUlpR9DDEtgAY1+90JcBw74CD8BmoZwjiemxPPCKi+ewo+1QjXMtn31HPHHUrmK9FWMjAorZbQOa1gw0gBScn+Tvt/QRpTi8PlhizBBl53QEk8xqOntMIsyWvKrOZJD+HFdg18BIhqyxG2UeEjoBON2c2peD3f9zH4Y//jtfh2Tdv4qo9/u1RgZnv+I7vwJd/+Zfj2c9+NtwdL3vZy1BrxRd/8Rfjm77pmz7k83zf930fvu/7vg/vfe97AQAvf/nL8c3f/M14y1vegnme8U3f9E34+3//7+MXf/EXcc899+AzP/Mz8ba3vQ3PetazHk23f1Pb9P4HMW/OIHOF/PoHIhOgtgXPw2gl+ZVaHPHEpXZKhp/cUUThC8MkMk0EBbVG7SOjkB2AslzCloGaMVG7yMzgNVbvrKSd4ZtSwgCwnpLDI+17JN8FApkKgC2zkNyZbj0vQWReGH7ROL87+yOAb2IVKSTnkh+j8MUi9GSR8iyscWSAl4iF7w3l9oyyX6C7Ba7KBdQcemvHVO6BnhhsRmZ2FaGqsAPzKLDzCbZhKMkLV7dMITbk4kjSYI8NLFOpU+RNQdCV3x/AxmInm4tbhp5aZKubDyudJV7NXW3uRDPkBA9XujR+RDNEzYOVJ/0gRn595fTFO186AS93vHcXQqm3/7AaGj15/26tRw+PBKbucvxpt+7W0qD3HUjvzSmPBcAq2Nqf+wQkHu3a+/PnIX5yKZ0H5Y4O1/XFo8zcJTwriFIaqXQbnI2KY10dj6wcRNhEwuOSRNz0/giwzrMeUEXfs8BjgoHmfez6ztdkTbsvHplDWCUEckyTkwL2u5GTY0/lg2AZQY9KggzLMgZYCzyCz17ycnQBuWzF45zSyNHxCNPL0t+6fl5F6OkOoJMcnbvOyW5yhDyCWgCXLozXbxSuDQO+/DWvwZd9wifgadeu4ar95jZxv0MH9ENu73nPe/Dud78bZoZXv/rV+OiP/ugP6/M/9mM/hlIKXvKSlwAA3vnOd+Ltb3873v3ud+M5z3kOPv/zPx9f/dVfjVe96lX4wAc+gK/5mq/Bsiz46Z/+6Q/5Ox566CHcc889ePDBB3HzNxEhf97LvwG3XKCHBf7gw+Q57PbkpcQuXMcRONuSCBxqlxhGhks0jWelBwcAK1vHqlJiG9eBGYYzlBoxGnKb2wl2NsKjkCUU8Mg6Esgq6x2GGuqRWmlMCadbgEUiDwtkdyCXJopLigMyrF4kAMBSUc9G2PkZZIrq0RnfV6UnKEGVcdWyiSGrOipKNfhuQXn4EuViBm5dQuCQcWImlgPmhnpjAy0jfDvCNgNsEPjAzKQFJBz6hn23kFYX97b4O7xV4JUICbWda2GKtucOOIBEn36ZkurNZuVOMN6LTzTQwt9XSX7UKFnhDq1r+qnnCilognPisi7I0qVyn3pO8j2s7zeQ3L1+mhV6tAicAILW/OS97oOnRv8RwYqc/Dx940jGvTv/XU592rVHaomx2x8dz+RuH2w6LycApumq3CXEJMLHNHfujeCs3ffMPNhHGnZ6iZwy/zN/NwV8FG4ABsAHb2J5cIHOK1hOVd3kzGVY1u/St7znHgY854hLEIHjbzmsQF/DIyk1+8fPWdLZLECYsQ8+hjezRsbSFnB16OzQfYjYxXVXde5/DNCD8DwdoOmpUhpk3LoBfATDqwvgwefBFM96XFcbiwR3ef2ZnZW3/dSjEiHclvEUy5/34KV9mOP46S94Pr76Uz4Zr37WR2F7pQXzmLcPx37/hsDM49Ge8pSn4O1vfzu+6qu+6o73fuqnfgqvfe1r8Uu/9Et43vOe9yGd77cKzLz1c74T/7//8iCGh/bAgw9RiG5/gB/mFkuX8MrINIXqboCawq2AD3zypHarlDuBSaYwVIMclgA9C7wawcxmgpcRsolyBAYWYNxMkHmGuVCNF3w9v7OR5gSAs6wCRLjAOoClQh7eA/MMUYfsjXWairL/UQrBpwHL+QbSqlQjFsMIrO8XFstcFuBQI7Sm8IkcGtkdgNs76KEyRVxALtGgkMOCqoL6lOvQcWARzmlk9tPIDKQ6EKxYAcUFRxKMrQhBmCGKFWZtFYItWQKgmVAVtAgX2B4IpJHoBOOS99JsdYKaXPwc5BKk18ZA8LhQ5Vm7rCH0fWogh8a1kRFbPZiYGs3Qyp3Aojf9Eds44lVY1/OMa/QkmYxx9O0EWRyBhaPvxrFluhuYeSTA0x9yl2M6e338vtwF8Pj6el5fqsKuXxDesMxGaSCAx7UQ0AkQzJPPoLEVxVqiIo8zNL0VGwU+BTh1VpIuB8CqB49DGsj2KAuA4LIk6G5jphzeXkfHwbkZEVRWoU414Q5ktbmT1xTf4W2uMiQqoMCjIrg6ZQU0Ygz5UEQvwJgCtgFsI6hnDldHWYDhlkBmEuxtiGt3XhMFJtmPpU8JFzTJA1OClhzPo9uXniAlmIKxBtQRYIlzNjmHR5qz6utYxUYkx0kL8EWf8Er8n1/3O/BRXSbvVXt82odjvz/kMNPXfu3XfsgdeMc73vEhH5ut1oof/dEfxe3bt/G6173ursc8+OCDEBHce++9j3ie/X6P/X7f/n7ooYc+7L48Fu0ZL7of/8f7b8OuO+S2cnEAIMMAc6N2yzC03XqKzEEVq/ot4NsR7kbRPQGNfjxpXgpkMxKgpCifGc9fShSvFLAuEms72X4GFkNxI7CAMAYvFPFrRhexmLRtYGQYFdZvwm1hqGlTuIs0B0ZWjuaKZCj7Gb5UkoRLppGHoQYzs7BfoIc5wl8Gc4dsN1xw9wuzFKYR2G5YXBKA7PaQorDthLpRlMWYtm5AjVVZRFAlspCCa+OTQIrDR4I7r8GZ6WLqMqz8FvixkWz8g34h7FqvMMsXsLrtU/gL+VoAm1AmbgtnvM/F2tv3tVPaamwawVTWr8tq0hlCu8P90O/au5CIwLsFvr9oHH+235Ym+D1taWzv8tZdXSwJdPvTdWPYenNyvqNbcHpNp9/tBCXujlRwdfGVZB0X3zGa4nNxouQ0JaBxRFowACcIkUlaim/v7WseHu26lfc8+xYAIb0HBLxOvlV8Zw6/6Yq3gOZQYNjTE9g4SuU8twA06SGEIBSTZZ0eBdzwGFCqQwPUewxB0xmKECxC8yXDLsOBQnioDpyBelcO2AHACNQRsJvA+HCEjZbu1idIqSe3LedxADF1QPbhIQsPjnfvtWctgJwDRwTjdiv6edfPlXzduC441vsBB/6HV7wcb/u9n4Wr9pHZPmQw8+53v/vo73/1r/4Vaq34mI/5GADAv//3/x6lFLzmNa/5sDrw8z//83jd616H3W6H69ev4+/8nb+Dl73sZXcct9vt8A3f8A344i/+4g+K0P7cn/tz+NZv/dYPqw+PR3vpi56On/6X76Gn4JlPAS4P9AjsDhSfC00ZRGYQs5Sc3JFKTwWJvpTuz4cMaaTcCGyUAAMbBMemq/Scu9EiEBmAxaAJkpJ0KPROrKk5nTF0Lvha6Zt2IITpBHK+ge8LdD8zNCagwFz2bwHkck9bq+FxGSOtW6Lm1FJZhXuu5Py4Q7Mq9/mW9JsiwLUNbDuRR7IZYfdM8FIwT4J6TaD7irKr5NdcGvVhrEALWL06BkRnh5nANx7aMQAC0Ljm9VGM8G67tv4lxGUeeR16QAIcARnuJtM6IACLryrAd8EPvRJpini5rNkpPFVuteX4s/le7r5jLkgAIMnXcp5Ef6UT7mvgxrqD0snWCkuGwY0pyRBGTrzWkZPWW/iuf1h/7wFl++nxfQni4seRV6Y/vh3nFKPz4+MFd54+X0ziaI6bSXrsQtwgwIwevI2RFoeLBTm2M8t5Dg3wEOhDAZJYs18afBmJas8LyNESRMgpCjaKo4YY5ZGYYf5ifjQPUg/GgMhW4ncnWIKsZHcYvSS6jyUnycEhI5XCdS1bS4G6Zf8KANuvtwYGaA1v18z7ZiAvqFS0OSYB0HwDYF4Bvziah8qSG9QBssybkBIvJSAGOkC//us1iwAceafaYLVxjHtVgWfdcw0/9MVfiOfeew+u2kdu+5DBzLve9a72+zve8Q7cuHED73znO3HfffcBAD7wgQ/gK77iK/D617/+w+rAx3zMx+Bnf/Zn8cADD+Bv/+2/jbe+9a34yZ/8ySNAM88zvuiLvghmhu/93u/9oOf7xm/8xiMv0kMPPYTnPve5H1afHov2ilc+F/LX/gkBx/kGNeoSld0MmRcCFmgo34LhmODGSJBkUY1ZRGcjXAv5Isr3rcrq9y58Ur1oJ6oF6FJDEMubgq0sgWSqURE3iMDikR0lYOirKNwqfGGFagrt0e/ro0BQyH0ZC/VxDguwi8B3gBcp9Ej5TD0YDAMrb0PolTkssdPVAF0B7EqJ8gYRez8fUO/bwItgPi/wLX3rfhAAFfVGweXTR5QFGB+uTAG12LztHDYBMiDS1xHE3gA0wKrymY6qjLsnUOl3cZ0hBO78HcBxNkQDMJFqnjwj+BHhUpJLc+SaEBz/te4s08V+BEpOW+KF5OukVwGxfU+NjcrXpP9cuvjT4ud35mdOxqOxaQPgSbgkPD/o3cEdUXjtZxwfY3OcMoIV0PVjK93vpyAmr/2R3js5LvcJrut9yarNBoQqboDSjmya+oZLHDMe6NlwBeoE+Bh1hmIYknpFwx4C2EF2TQ+FuEP2wHjhkIW1x5YNNZgkCzOmZwJY0/5nrMTcnM8hgmfLeu816lhpjrdxKfFubFgBm99dzwgmlng/uf4NGATYmrdC2pyCyQVLOrGCH5SerFaTLD6bt7pXbs77HAq+LbyXYDr70JNy8x5n63kuSQKOz2WI+I40+/yoAm/66Jfg//qmN+L+61fFHZ8I7VFxZp797GfjH/2jf3RHDaZf+IVfwJvf/Gb85//8nx91hz7zMz8TL37xi/H93//9AAhkvuALvgC/+Iu/iJ/4iZ/AU5/61A/rfL9VnJmHHriNP/CJf5aWZBxhT7kWD38YKPOoGB3p0RmzL7H7Xhy43EMOM3wzkjhrBt+MkIFFH83INxEJUDKU4JVwhZDDwvIERVk2QQW61GZ4qnuEiZQx9qhonSUGrFZIhHDEkmSnzZOgi8Gd2i2YF8g+xPOWymtTYeq5KrDZsO9Kf69EmrkvC93G7gRRqsBm5A5YFXMx2D3nsLMRtlXMNwr2NxR1ywVPL7ngMyuM6aFlJ5huRabUEmUMEHWVRiEJMd36kJb+TY6C0M3fGXHJLXv4rns+TAsF5O/9ex7b4ST5LiT6HgGYPB5pGFKcLVbX3ijL+jfC+GXr7XXzJDVxPawhqziwvadYOQHwlrWSPJG2/c3ztibNgDSc0vWtBwsewGA9JxpYyR10vwzdDdR5ZKK1Fp603sHTMJH248D+pzelZ2xrZ8Crh00L3RFdCAJcgboRahvtwAKyQWJVA+cegCXm1LgzlJ3Bi2I5V8zXBbaJLGLj8685LwL4qYfXYyM4XGOR0/G2YvuAYbjNuT1vBMt5CjoSZNh2vQ+uAPbAMAO6j3tdhKBd6BVyADobybwA/IzZRQBJtvMAFBeUPY+DOGxULNc4BhZfJ9aH2DjEFkBXUi8nVICbcq+CHqNFVrCElYPjUZagjUvcujZfT4BbD0qlu28tSwlYx6WfJz1GjknTzgvgxU+9Fz/0pV+Ip10/x1X7rW+PC2fm9At+5Vd+5Q4w86u/+qt4+OGHH80pW3P3xnlJIPMf/sN/wLve9a4PG8j8Vrab916DXuxgY4EsFfp+0Ltx/YzpxAAAAeY9CbBmgBS6gbcTlWltoFcjCcCHBbjcA2dbyDRArSILVfqhcrFQpdheeDkEBE2+GHAW6sLKrCEoYHONRZwrh6Aj96lCbAYuZ3itBEIh8CfDANlMqGMEpYfSFiIpBRhH7sqVQAobempQHS1F3Qx+7YweoJGeJSsKLeTeiADz+Yh6PsA3gIiiPOy4dlExXwtDMejKIXBwsVQu/MNtwzgzcwJFoIs36XYfwhirN29WUXoSMpxztIs/wfxNDzaBWDomkljabxGN1orVzdGIwMCRbb2z9W+0/O/+vHK8oJ8clhLrayYV1qyVDJcAkVkWY5i731zoFWvRwAQnsl52jkWTi89ue3pZeL70Aklhf1wTsmUfEsTg+Dpz7FNe/nR8/Bhv9e+lEXUHsvZY7sRTrVaDdCsl+S583yZpHBQaYQOMVc+XqNC8GIudwkO3yJ21gjY0rhmSqVngUCQAQIaRYt7EdS0C2JmjRu1VK5RwGnZoqciac0cdWAiUamYzYdW0UQ+AmPdQeL1J2AUc8yTwc2DZ8HWvgByCcxMoUwz0So0n9zc8lzIH6FixPm9LxZFzjaEmad5Bj+VC4/wwNLDUvCXAyqNxNA+MZ65CD1wzxJnE4WU9plfqbaHbnBfRXv3sZ+IHvuhzcX27xVV7YrZHBWY+93M/F1/xFV+B7/zO78SnfMqnAAD+xb/4F/j6r/96fN7nfd6HfJ4/82f+DN7ylrfguc99Lh5++GH8zb/5N/FP/sk/wY//+I9jWRZ8/ud/Pn7mZ34Gf+/v/T3UWvFf/st/AcCMp2maHk3Xf1ObmNM7MhV4NdYjutyvVZzBWL5Zhe7nEH4D5PYlMA4EJrUCB+NiC0CVIMXB3aMfdlGwsQRLT2KhrmiUU3GIG+TiED1z6DTBtwN0VNi+QheDbQc+7BkeUsDHAr08MItpXgBjVW4olX+pHByEx00AkaiALaEObG5Qs05bR4OQOJDU++DD8PNrsGsT9vdvmXFkFcPtBb4ZsYw83jaAxs6xXPpa7yWVPJVAxsMr4ZPClhquaHIQ3BOwrTwPixCQNPYk1to44TkBsIZljgxtvJe8knRTdCADEcJLb01L207Ga7cbhXecpQ4QHenhSfcjF3ln/5PjoSIrhyUAgUSsI4nDKcTHc8cXGAjG1NfwowN5sgyVeBTdo1BbfEnyHKLDmYGVeiYo3ow6gJVgHrvp1dKtu+0GKJPj1QyZt0wTgaxCb22nHeUvEIbWwjNUWKbCRYDF1+wwz3sv632McdaDo1zy/VoQxRIJxDy8ERXxRarh3RPYht7AFsZY1mmjxjBS2VW4ALsbCr9Z1tpGcCymTEm+7fBFoGbk6GT5jwBKPoAikzmFQnW3ZVTlOJYIJ0UaMwZ+vp4F+Ag9GIv7qAEIdAH8EvAJ0BgbJr0JMKCbz+v45z13eKdmLM2rYwMBUgrlNc2asoKmzOZq+jno5oF0ACzAtwW4QXh6+vTsXmcm540L8KaXvgjf9Xmfjc3wqEzhVfsIao/qDv6Vv/JX8HVf93X40i/9UswzfZbDMOCrvuqr8Pa3v/1DPs+v/Mqv4A/9oT+E973vfbjnnnvwyle+Ej/+4z+ON73pTXjve9+Lv/t3/y4A4BM+4ROOPveud70Lb3jDGx5N139Tm18/B6Iytl8bUTfXIEUwXCyQiz13fJc7+KARUxaILcxs2u24ay3KitPJW1EHZm3hINlsVjKmFh5TK9DIhgI/cBXVBYAIuS77GT5vCGBsBvYGPRwYvx8GSIld5G4PubykwRCBbzZwcdjZBn59w8VjBgCCKVduvWU/M+1YqPtiKiRDjyE7DgFkw6rV169huXcDmwpsDIIxFPstK4YvmR4tjiqKunHoIi39VJ2LfUu9HBzLKKgjUEeF3HIUd8rozEbvjXH37SNBpYDpsXWQ1fMANEOaBvSuZNMIIzHkhGMXQy78huahaZ4SoANGxy6WJprX+pCukTS00q3wcR7vKgh17nWBcK4F4DrysEiAusrPiQAe1b2ZmMbv9LxGDSOWF5q73k4PRyIM4rENlpoesS7UlN6a7GuKEXa76BziJm7mYbwMUPNVrXYdzNBBybpaYVkrx92Lx3vhNRFg2LG/WvmaKVBmek2ojUTwLAuYZpy6RBE6a88YwDCnOLMDjbyXwcj39yqNzNqMqgDl4CiXC+pBsFyfsOx1LR2wEDQdbgj0Ehgedoy3iVwXU9Qi0CFw9MySAzIHKEsAlWOcQnyqsGLsd3UWbJzRShHYuHJjctxTt6Vk+CrATqZOW/+MSHf7Mj08QUddp6VGCriPaCCrzYm8dSX7jBaOq5nphQAr6XUqcc6g4fnIz0soDmvU9oUCL3/2/fif3/IGfOJznoWrAo9PnvYb0pm5ffs23vOe98Dd8ZKXvATXPgJVD3+rODMA8Ls/5VsZYrpcUDcK246wswE6G8rDO+itA3B5CZsXyGZiKMbBzKZqrIUUoR0ZQ/gOHmnXA4tSjoUclUhtRhJrgycjQ6w4tTJrKIjC4tb4JKltQ8GpMMpjpI1f7shtGQb4NEA2W/jZiHpty2upBrnFcg02KsQFw/tvQQ4L/Mb5alwgTDM/K1xsQ81Y9wvsYNg/dcByvWC5McImLjC5qJsBdaOxw3OgMuNI00i6w4I3RNGxACrBLyh7QHYGXRxldhIro8p43RA8GXhO1tIS1DSUYWjT66A1N+1ccSW+HwsNovSgIIEMANkbtJIzI/m5rqUjwHN7ixPxu9yRtnhP955IgJ3oK1ZshPBWyWIMoFm8ltycFFJcfAVCYZHSkwGghaLSayRJjI3jrFqEsKJaeAJ0JZhJwrmnR8zRjnd4MGi9VSmXIEC0Hb45+5I7azjv24gA6AiwD4YskyPiQLnFa1/OFbZV2EgArAdguHCUvWNRwM4FdRKMtx3jhZEnMjDcWReDbwDbUgCy7IXAWIBlJEHXJuH8uqQQXoKWOgg1WM4UdWL/y94x7A3DBdWu9yNgNwvmGwrbOPWdFvA7RwCXwLX/WjHdImhZtorDDcVyTk0lOTjGC6AsUQ+u48LAwA3BFMvHwTBccq76IJi3wOFafKYEuJuB4WIFLZnWnl4SJrHJyktJb7PgSNCxn48OWUOZ4HE1NWWIe1ECgKUXzgpYzTv4NC700NrIv8slz2URBmueGEWrvZTtD7ziY/Ed/91noWhHNrtqH9HtcefMZLt27Rpe+cpX/kZO8eRuRbmoi1BNt3oUYFP4tS3qZgTqGeRQ4fs9U4q3EwSCcjFDLwoN8lLh8wIbBorvhdscGZYaCnyOLUmtwGEPJOESgIwDBe1yd7+JopJ1IVjxKGEgwvMkYErxPnd4rfBxy4rXZyNsE8q+FVBRLO7AMMAKUIITZNsBPihsKgyVKVBDIMtHVq8GCnwbNZqKxs6Mi+SyAfyMoMaW1V0sBbAZkEoejC7OzfAEFAh8D+jByHsYQ0l1Q8+RTY5lS4MzXgDDpTFbZNRGRHYP+fZ0fefO0zqvQFjK3CX6wlCV1uMwEkGKQOZ4MRf4xCN5j1LAkB8Moy3tI0gvgAMtfpRARiLjrEu7V4AeA/PwfBikCgQGj1SQFFpLr4nCVxVW5HUHiDB6BMRDK8jRdrWtblTl7/QEoXlsxGJ8EowsvKeuvGfN8GWIMC60ARrhWNfM5ktjWMLrVMHSGMFHckuibFzLKCwrFpWRdQGG24ZhZyizQA+OZQR8W9pu30Zp4atlAjADVQmyRNZ+cD6EVyAKoqZXi0J5HAgfABscdbN+zmd6GX3UUPoV6C48qPF5S17KDNggmM94z32KbKniLTSq6QGsfEY8wkjpVQMI8JdNEuXjM5XnL5nen4AjvTsJVBKYx9/eh9DyRwdmpNKTl9M9AYaECF77Clu9PB73SXLTEFwp19XLo6E14xOwXAeG2/SqQlaeEpzcoI976tPwp3/PZ+B3vPi5V16YJ3l7VGDmjW984wedGD/xEz/xqDv0ZGrl9gEWZQe0GqwU6L5yB7xfaORie+vXzgl8Bm5vlrGgiEEeuM2Qgxb4ZkS9RrCjM3ku9OQwBdtVSA4e19xRdwf2c/BYEABFgKnQU6NC709kZUj+nynSmzHUchfyDSaFbZTlB8C+17MCV3JnmAUhqPdew3Jz4qKyp6enwqASqaoj1a5sUOyfMmCJLCNxQA+xaJbgJkzCdNglMpcGQd1yMdcFKLOyYjDtLalDzsVUl8hGEScXB9z+HTbAfO6YLowLYQClxi9AkijjZoY4GhfiCLuEEW4u78Wp42Noabw0Ibam9QKNXLu6KNC+qBFv3en778M40VLUMK2Ba4CNvP7GQ0mBQsBmb6CD9ifmTuFJEgy1a44inOnhcfcmCFcyOyiNd3hjpP2k181FmgwPwxEBsAfeY4C8Lwkl5MRdHgx0Sc+YOwt/qkMinIAZUDfUSammG6HCrAlkE+9JqSwH4FtlbR91lEUwPVgx3l5Qb05YrhHZaYRDXFePj2fIBhSG6+v82ChdPSSGPhOkLROwedgwvX8Pq4aLZ0zAjbGFRahIHc9PnJ8BWg9LHca703xx0GsDAXk5QWKVAz0wFtFnQbg5BB1ilTb/1uvjtKZ3JADRDAyXMbZDeOYcUAXcGEbzAIUYIlMw05xxJwjSHeDqsFGAkQDIC+Azny8keE4QmyAwAA9wHDaCsI/TbS5bdQMsN2LeR4bVJz33WfiG3/PpeMVzPwpX7bdPe1Rg5pTDMs8zfvZnfxa/8Au/gLe+9a2PRb+eFE1rDdl+pkMXA3wzcJ3ZHajzsBkB4W7PReCR0WSjws5uQs8n6AO3oJczTBU2FNSbE8Qdw8WCclGZOg2HHfbw7QScnTPlOsshHBaUh3fArQuWHrhtJA0vEbhWBYYBvlRIpnIPA1V3NxMXqEI3M8XwKkQqK18PgAyRrlFptJebZ6jXIiw1VxrCIpAaqquVCxyGZKsod+3uWAqg6vS6xILmM3e9ZisvwEYwnDAplgGQyVsBu6yYoAuAPT03y7RqaiAAjY+K/b0SxjFCHbFgJkdD4CsfYOkMDEC13bTWyyqAR++Mtx1rZuNkGCezMFZ0I42zEr6GeN9WD0jurJE7YxooRyfm1nbWfI9eijwW4X0BCdkB2IzuqDW8aPS+ZC0rhKEWi3sYyqhi3rxoAkPtLmm9LjTCqCUXQkOgMEMHwYFJZ5PuONg+KHwkx2l8eMYyDpDrFD2UxVFauI/y9yViYDXq9CD4EuNDFXKo2F8fUFU5FO6oN6jbVDeK+Vp4laL4kgtQt2H8BVGyA41n4uDcaVooDohxfmkNwK0ZJhRgrhhmw7xzDC7RtwglYfVk0OPapZxbhDUTtI7hJIk5l3WaSO7mvaGWkjeeCjud3j8chfcStCfATql/FaDsBHqIrKr07uQz0bxnfC2zH/P3/EoPMrPuARk6PJ4k3ayInX2Ka/QKYGIWlys9LOZoAn4+cWyGHYGXK/DM+67jT/53n4bPefXHXXlgfpu2RwVmvuu7vuuur3/Lt3wLbt269Rvq0JOpjde3ODy0i1iuM+tnT/6MX9tG7Z9gtCU3oQBGZiZsUvhTzlHvOSMHpcZxpcAGweF8ZKZFKODaMtEoDOSP6G5hmvZ2Qh0HaFHg4Qv6rptwX1juUOBtVi7AVU86FQVMlTyZixlmA/szBIKIVHGLEgdpgOvEHW7J71sM2EZIYZtWFUAsXF5CR2NAAzTYB1CIw8sl4HugDs7FLRew8CaYCDAyxq8zMF2Q/JnODizhIRpkTc0N3ogkBwBtvY/dLloopy8ymQXpaOBX8KNp2J39OlILDk9Pxo6ygCbWy2gGrYVhctcdp21ASxChsbBWLsyMAw1bem0aEMkvMUexylCgGYGlyroqpLHOPxz0iFQCEhONVGuBKuvy6J6eIVPeLIIHWXfdCbxWHyD7r0JO0YHg07bAPMQ8Mon5P3K8o2Aj5xgJrBriag5Zs1gqBRMVhY+YgTwUrPopYoKyd06/dGCUUNqFQ+YoBBmcHsR79Ig4hp0FH0XhStL5MPO6D9cFdTPBDhP8nCGqzcMBNJJvIgEiijR8kfo3DjRPhQfCyYy4zNxKL2TySaCA5dh2QCjnXnreXBFZg3wvAYoPwHyN5yyHCPEKOCccDFUmDybItU0nJh9DW7+vbtF0m9S59KR3JnloGbaCRDQzvms557Nbh/DA7rq+KPCij7oXX//Zn4HXf8wLrwDMVfuNcWZO25d+6Zfita99Lf7CX/gLj+Vpn7Dt/KnXsb88ADLAVVGvTxguZqSIWRoVhgK40sscuhfVmLUkFI/z6xNlRRaGOcwBLBH2EYFtBoKR2eBLpEKrxgIVJMZ7zjHMC3CILd1hz+rYCOBiRmN3vuXisFS4LiQMHxaef1yAceRO8JLsXBMBBkUxBxYKhpkLvRUH4y5WSZBEpUdlPmN66bBgDZsUoAqL0pWDQQbAR22LmYKAI7kPMMdwIB/Chy4kkpkRIi2GrjMNf6lcEAs4lh5ZLNDMDNEVfABNgKstlWEM1H1V801MUlfggOAa9aJ47RRxuW1HDKzGKLlOcvK9+Yv3JwpgIAQqmbkiEUtKr01uxx3RZzG4FJapqBZeBGkDJw7eu+IkwAJMDa4WwmjGOlxByJY5BmUbAGOm56FczLzW7ciQBQA5GMpsBOpTcKRE2CekkXdgdpRLQz0XHJ46QS4WYASWLcGW7DkA4kHwPlT4tkBdwttDuOSDMowSBT0dLbubmkYBSoqDYZDGnObou/qagVPBNH4RFpM8ANNDC2yjkLMYNwh8JD8JSE4MX1cDysz3fEATjIOu4Eaqs69AzPMV9CDmiRw4/xheyzkVHCNFE0gkUZvzqZHJe4CaHpacjzkHhVo6NngLIQGd5ybAlVpglvQs6Tq3s3AkswslKo47L9qwptI3jxffLobYZPCzqdFjI7AZFJ/xkhfgf3z9J+ETnneViXTVjttjCmb++T//59heiQ619tT7b+AD73uQBnAzoE4F5TYFIWyjsJGLr22YBVSnCHkc6lE6rxSjez1i2G6xyFSjOFSkoNogTKtejITdooCy6KLsKfzlN84hD92md+TGdRqwuQKHmQtqpIC7sJSBDAq7voHrBnprB+xmLrjDQG+CCHS/QA4HOBx1ewa5vkESIFht2yGzoW4KpFbYSPJwPVd47HyH24YykD/goD3TCtjisImZKxgA2SHldMJp5FAQ0Ihizbqp4C48uQWDNEIlEJnEHl6acBg1ldj8Zzj+231Vb01uTOqrBMqRJOKi4aE7MpekO+Vpy904Tt+PeyxA6PV4O6AVBpX4L906EgAl0q8NrJQuOwO2GiA3DIIqcGlQqYCW8PrQs2MiKJUgWUQbt0sMkL2hHCpsKqhJ6jSH7EK7qAjVdQeBCdVxhwd28M2Iw80Nd+1BjtfDAjgziCAkYVt6AqpHGQpm8pSDYbio0D2zpKxpAAW4XGKz4EDZVd43KQQWiqgYTw8mEEY47zfWEBAAfsYimyfwKgDUc+BghQBYGRLj51bPSJOlbTeepPUankfLApDppQGQNZe8kp9iEZZpE7aE1yXKBWT5jSY21wMLT4ByMs9inNy7eRZeHwiv2SRkCpIDEyntNQjhWgP/AqsHEen1QSMXcwylvZ+eTKRwXoSbNMONcW0Q4CnXz/BVb3gNvvRTPxHjcKqaeNWu2toeFZg5FcZzd7zvfe/DT//0T+PP/tk/+5h07MnQXvDC+/EffuaXmv7I4d4RZTfT1TobsBlgouSoOF38vuGuRHYOrbUZWHJHKHUOD9EspYiWKD0rujPUVBdOAmAuSObQXXBktiNwseMOdjMB5wWoC2P9hVsmj0KWdjbCNgP8bIQPAp0nmBn0kh4mGQb4+YbeFwPKdkCFQw/pJqDXhsbdgaKwwWGgR2E5E8iWLuSy8J9pW99RouCeDyQR2hDOhOCnJAdCSxj5CPNouDa8MryE2AH3QOEOddtcfBPoOGi5cncaKdDJm0kBPOqoxG56WTOcWqX0NTgUY7KeL7VYEke1tOg1GNTuI9BbHqx/d84kT69QeqcQYDJS8KWwcGg9VJLAnd6zIoI6RF/UkcRR3VcU+nTC4Fd4GeBggU+Nau5i1Eyhq0vW+78ZiCAXQKtBLxdquBSed94IZBGUw0KpgqFg2Yyo1wpVcs3Jo9lVEoB15PNhnO9aHcu1AXUszJqJsGTeOxpt3reycD5gkKZPw3EiYGqGGet4JoGlbpyAObwcJMIq9vcAMocYnKN5ZySzw8DvyErP5KBEan5lhpflN4YXhuKCBDT8zhU0ZEiqAaqcbCFDQPAuTS03gXTzzNTg1+QDFliL+kHerh1A48kJ1nT6Bo5KpzwcXT/y/gwIhWUga3/ZJCvg7K7HggOjIdr3GS97Ab7x970Rz37qPVfel6v2IbdHBWZu3rx5NMlUFR/zMR+Db/u2b8Ob3/zmx6xzT/T28a94Nv5ff/NfsG4RANsKlhsbuvQPBjsToEbxQ3WoCzxSmK2sonDcPvm6i1yMi8Go8EL9FJ80CIEMDVShW13myl03nOTewwzXXMUWYA8CmGkTlbdjQQt9eTsbUbfKLImDQR66YL2luUJVyY2QAmypg+NFUCCoRkMEc7gV2NnIqtVFYKNyBzZH6YAiWK4LajXoPkJJCWiW0H858PcWSkrPDMJVfQCgtvIZcjOsK3YAcFSTyIWcCu6W0fgI8I5LgAQmWFPrbd3StuydDC9ZwpBuC59gJHeuAI1pdjAJEIKVV9N20yeLebrrj1w7aWyC/FCddbNGjbIYDtnNkEHgCNK2BlnTKaKIuXI8awU2U3j9vIXBfFTAF+jBggo1kBh7eQCmgUY0wyhKb6POHBuX4Fkt9FzZdkQ9G/j9RqAtc4TElgrdCZZrA5ZzxXAA9CDAUKCiwMFTbpe7fRBQKsCwoUcKeIyP1kClwU1L9VfLTDkEQTVIMxJ4TFuqTXjEREI5l3dXAxh5yVprAXAFqCOgBZC9Q2encCMAD7Dh4VkpFbA9H5865JwEOUKVro58/NttdyGwT4VbifuYjjhFE5uTDEc3gOyZMtUuDUDTcZEk5KYDREFvcEoUACEtwedozbzrgF/E8FyD+6eOcgDGHb2sSxTfzAehCPCxz3o63vo7X4PPetVLMZYr78tVe3TtUYGZH/zBH3yMu/HkbK/4hOeTd7KfUa9tUAeFjbSumlu1wYE9V0EZYkE/+LrLsSiYeIhA9VSoUwMw5APuUmH8jF7M5MsUkKAp9PTYVCBjgR6ianfUv/LtEKS9dE+AO7sI0ciuQoV8BYYPWPySfZngA+s9iYROTBr56qijAIUGKTOaMAi5Lwr43jEcGEKqG2Go4ka462fyE2zk7lQPaGnPLpLRAAAdTqgstOcALPQ3mgIpOi5tLugqbZH2ws+LpqclDFwCFO8dJHmN1owmDZp3LpK8f0BTCutcLS408tIDlggPdJQN9Be68ms6S5SfawaKHgYVh2XIcAjvWK1wpaqyFY2UaIZ44DXAsjCFeVRMtdKrUpSFQQ8W5M8KVao5mw4QUdTMUDoAso8QZ4SH0ivEHX18b/CJkrwrDmAzQJYK0YLhMoBFEczXFEOoBurMEKspULdklgp4L6SG8Q8SeMODAV44twOAzkKjPArqaCsJNebReMHMPBuk1TGDCTBSx0YEjXRsZQXPAHVxli0go7A4ZXhB0lvmwbnJEhIeeMvAJcHG8J5kn4Kr0tLF4ny2IfDKOZBThaAkXTJYf6YXMsKghvAEmTdAnyneVkAOUVmndAvJzvSmeM69KBKZGketzxMFA+uEJpOwheDjP+qj8CVv/ER8+itehM34mDIdrtpv4/aoZtKLXvQi/NRP/dQdhR8feOABfOInfiJ+8Rd/8THp3BO9Peu5T+HiWRTzUzcwdczndHHrThuRV9Ko7ln0Mb0CSVqEO8qByr5+GOHnAzAo+SXVYK6QxUhoFMqjy34BNgVVFT4V+Fhg1+gV0os9UBf4XOmpSJVgF2AcsJxxWvhSwwiSV2PbAn3avXCrwOUeZTfTI7MBeQ6DQi8XrrdDAJuhYCmh1rphOEw8vDITF3adQR7BYqhboUfBrBXxsyF2uxUoe0RGCpAkWcTvSFd3ApF8LbaQTOoIoAJp1Zyb1UsgkQs3mAreMo8SkzhaGjo/Grv3xIM9MOxbGpbOU9O6mbvnZpHy+FOjxF9IIF/P62FQvdbQdBEWNFWQqC2UAZBDZeZagIqyX2CTws4n+LJAd8FDiewcisgR/GiAN4m6R3JZmeq/3UAnxYIgx1YnD2aIMMVcKUUfXi/AUS4rMCrqhlt5ekwErvTooC7Qg1JHZpDwVK4VpzVE/Dx1UBpadWa4ZShlsfC0GD2HAYQVgB9A67tEv3KMFdSQ8eCFKFpNT4uyGTaENygI0a7CelixCZGZH6jDSpgVEOxltp4PnIPe5i3nqQ9A1QBtQZZtZRtyXnQ/JdFGALseSHvMGc26SECrvcR7iTZ387nRAF2txMDIYfLKcfY5HDzjOkUb6ElQXUnYfc2Ln4PPf+3L8dJn3o9n33fzCrxctcetPaqZ9d73vhc1Kzl3bb/f45d/+Zd/w516srRSSmhoOMwq5MBFUxa0CIJmFWrQPR/rGXRvLVwAq9zhDsr02pmcFd3v4OMIGQf4dqI+xlmBD1uUWwfIbCRhmlFhCgKowq+fAWcb+MO3yWEZB5YvmCsXxwA2JiEvfz4FKc9JKN6HP/qS5RiwLMDZFnbPFn4+MPsJAocxM0GVKa1jYYaEg7VqFgMGpuFKFQwHZjHZRrBkmCmIvDQgArtOGXrskZzYI25D22l2tikGeMUSsQLTq9VsyPG/4MMkqVeD9MuF2ldPSBjV5p05baf96Fso3rbP9gf6aiiOzuurYfRAV1nigR4fHi1LdHB2hoYEkCFkAFwg8wIpBVaURh6Csnh8rrZUYFeNXXdo0RwWuBeIOHQ3Qx66gF03+PURNgF1Fuh2QPJo9GKGPnAbfr6FXR9pxE04ry+5hrRQjTh8GKLsRIUfDMOeVdGrCnwT4VKLz3TcHAvOh6NEyCjGjnGbJsTXQIBTUK+FldzD6xUhuyhU2sRRnIAjlH5gwTFSgNlbFWvFZl9J6hYikQkYXNDCx/SeomU0Hd3o5M/ka96m5dFrWDgXU/G3qe/mPElgD19DqZHhl+fLvniJsVjodarmsCoMxUZfJXkwM71qGdaaoHjh/U/Bp3/cC/E5r/k4vPiZxxvdq3bVHu/2YYGZLPwIAP/wH/5D3HPPPe3vWiv+8T/+x3jBC17wmHXuydC0Oup+B93fgGpwKytJjYyL0zshi3O3H9lHPsTu0+gVcQBycHg9kMxbyVmBVdQsIIkRUEXdKurZGcaHZ8jDu7YA2lSgURFbdKBXZamoW5J7C6hZk/o0cjai6qoU68JMBikDpv9yAd8dgPMbkHGA7md4ZeFIGxW6OHfaohS3E8F8To+NHtDSftO74qPANkA5CPTSMQpl12shH7IsxHQ+ZQ0Zb8XrKFaGBk7SWLkGfyWcTr3QV4q16ZG7JEIh6RkDDbhiNQoehOD8nhXwdKfJlm6cu9aCOX7PU7SuN1T5Mz1G1rbacX0tuR+RrhTnoLfNM8SjAV5qdLqAmXTkeK81uSrotTFHSMnSO+jeqmTLwgGXQm+OFkCgwc8yYGGqv6lCzCCjArVAFoNm+GkQmAtkXzFUZ9p2qBXboBCvJLZHyAaqWJLrFGBSJDwkk6xZZQJ6hPZCMoYyfIklb2+6V9BCXQphRXsHUJRDPaBxoTzuocQYJ1jMkhZSe7I11vBlzqP4WnQgI1vvYOnDignOvV3yWgOM/Lo432IolZsjTx6QILhPYHp7Ap3QsUlCbu1k/zlusQGI69CZ55SygscEaz4Az7znGr7k01+NT3np8/Gxz3n6FVH3qv2Wtw8LzPz+3//7AXDnfqr0O44jXvCCF+A7v/M7H7POPRmanU8AroX6bcQRlig4OIdREoMvC8pl5XqmClUSY32jUFPyarxClyXKD1TIdos6UhBMZoNIRRVAtKCOisM9E8ZDhV7ssZQR9eYAMcV460AOyzgAe0EdBXa9wCaGHliSoEJVoBu67H2J2jdCQ7jcPMPw0MPw/QJXRVHuxmuUY7BJYOaok2DZSoQm+F1149C9QHeRRZI7UAfmDYAzFg6UWKDngWJ6wwz4Hqx+PARIiZTVOgQ4WfgvPRvWK/p2BkdCkZekzdWT04xN7mLTIxOGSYFj+kt8pv2dpN8g9TpALR9Na5Ufiv/CA4XO0emRubTimhDBO/Li0Ap5oYpyar2Qt2TkXCSJdCrI4pJwMKxYAmwYoPvKHb4KMBZ6zIwAUwTQucI2A2wQaIYdjVbSthMjdPuK6XKhsfTwAoVR9/OJY36oUDHY+UQvY0jAljDU1QlUbTNgBMmzAOgdKRwHB0UNyR8ShjMBlmtYOIgSxjmBU1Yg90AIXqIOUvI8JMZYPbhGcV/S45eaNeFtkQpuPhDifyqwMbxk3ZxoQB35X4CNBGDojgkQlL9r4FaR4wnmce+lCvwQ1ePz9XkFQBLPwVBXihWLbzqfrSQbKyDG59AUDOnBgSWetxnhXeUFvOIFz8Bf/vrPx/XzDa7aVftIah8WmLEIibzwhS/ET/3UT+FpT3va49KpJ1Oz8wk6FBJ+Fy5mg2EVOnHG2jE7pFZ6NDTk/Q1B0GRcykqBnBeIG3eTs0Fv7Vi36YzbLk2dCOVCX+/dsE7NgaGDeqYol4LhgYjTVEPdbgBjUct6VpgqDkDmBWMVVDFAFMWdO++Z2RnYbGHbCTibUItGZWuuzjYSkGA2+HmBbfi6zNzl1QnksswAg1KxZs+MxdctdUkypFK3NHTDjrtGCXE5k9VweCF51cb0oqAV/WvNU7cEzbNCUjHTu49cI+GhaVlliZCU96/bTKP/JNYzpDXqvv5EwKzGBWoY1Hwrwhokils7l0t4JZrYoMYYeaTdGjCUliru6Y1IcFGFRN4ogOqZ1r/ngLhVVl6fBizXNuzjAwwvYRsiLVZZpdwdslsomFgAP4QXpwg1jaYsp1GBoTSvkcNRzxXlYkBZOLdwqKH8OgAiqJsgm4THDVkB2UPgLzxiAFr9JBcFisM2FItMdVxfhJOhaOi18JqtcA4JBYqY+h/viznKwvtdB4dXavQk+JB4bxmCiDuipS+rhRxAkn7zM9a6fCSCByQXnXWkJPgxMsc9DHkFV75W9o5yMFR31K0DY2RaNRdieGHCc6SgfpOMOacC8JeoWxUdLDPJxxZkbhu96cS85mOfje/545+Hs82Iq3bVPhLbo+LM/Kf/9J8e6348aZttKWI3zBV6WVltGuAiv6vQQ4VWgy8VbgYvpenG0A7SxWwuwCSo51HrPrgh5faenAgAshgN3B4scOngDr0MLPQGAaqzD0EGFTMK6hWm3creSAI+H+il2Rv5C8u8CsKZtYW93DjH4d4NdE9fvodmx7IpUDXMm4J6Fgt7xx2QGeyPRqG9UJPNYnFl75BB6A7PhX+kl2bYU4I+RdT6NFGIkJ9TYiee3o903+dhCoYfPMijFcDijb9At75FVryvHpq4dz1fRo5gyHFL/0oDI+naiXOkdH3LWGkeJWa8mMXOGWAoKu67ALBMEdcQdJPQ4MmyEQz4ACDo43cJT7oLkm8oTNuNEXqo8H3wSMzhUNi5Qg8V46/sOA/GAoSXsW4LxtlIMA7CNx1IGhL3iloEZV4g+wPBRBjbOijqfQXj7YWVol3WEFdFkIFppE0jXIkggjtYumHh/MbI8QJATljoChnooWCKvTR5f5lirKGxSeDnUKQRiiGR7XNwXu/EcW4ZOyVQdAGf14ZY2o1HpvaH4wmIMLPHJEw+tOT8kxPgbcycSo+igrWIxocrxouK3QYU4xyVxPk4l3YZTm2+xz3NzCOtgOz5rNnIa7PByecLb+n2bMAXffqr8Ec/59Owma6Iu1ftI7t9yDP0e77ne/CH//Afxna7xfd8z/d80GP/xJ/4E7/hjj1Zmo+KehaARgT7QoNpG0Aqs3Ywk+hbq8GHAh8Vqko9mVJIwh0Lj10c2CBItcBy3zVW4q4GvZiB/Qy9AEqtsGsb7siLQnaVRSlHg1wsMDHg+gAfJtTrE2xZIutC6C26rGu9GMY1gMWaDhsc8PMtDfFiJPdG+qsXweE6WEMqQFUSBZsbPIiEPsRirzQKuRtMOXQ90PXdx/frhkat7MBwiLa3srNHPvz2XveTh5Km7B72wriTdXFmcQW3Qh1N+FDMggoTQKKlY8e1N3UxrMat/5c1mHJ+BBDyDG0gTtmNpUZerCyVWToCeOab1/j8IDADNC3mHAZqIn9Ja3hAgh8iS8Vyppjv3WDYVQgEy42CYT/T+6NU/cUe9LBMCtnNzIybRtYiGgt8MpSLPSxS7FG03Yc02na+oRyAAyj06JS9waYCGxTDMsPh8LMJkvyi8Bi4KpZzgVeGZhOcIrJzvHiDktSAiRBOKs82EE2uWDkw+89DwI1cNRLdWzmBKIZZS35XnoveHIi0QqiABN+om2gW2kgIYrLG84N8BsJDFYAnQ4eN0L4Hhpnq3i7UlEmPJzcFgmUq8DOEeGZ4qvJZAlqaNVxCzBCoIq2iuB5i02AEfF7WcO8rXvRM/N//p8/H2XbCVbtqT5T2IYOZ7/qu78KXfMmXYLvdPmKhSYAG4grMrG00YSJRKUwtPsSOzLlQ27UJNhWUwxhu6FiOUhXuwKrbak41XhUu5HOlnkySPccCnxburG5foNy+hNx7A3Z9C2wHyPmEaoZye0Z5aAfZ74BphN84x2CAHhbovtIjsADFKhfIIpChkFjpFhVtlenTZyNsUxitmmKXO4a7X3Stx9Lq5YALbsi4e2d02kIO7jAtvdnm3IGHF0aCAwMV2BkoXGZonJhGzO1iP00FFSdE3Uz5ULRwRu6SvdqqCFsJZtq5M5uplQFG85AACFYtmvFJANOOPnHipNFvZMwgWzdwIxHiGDT6VhsQg1NNmSq3FuJ2DgwBWuaIc8wGjIJ6VlBu02UgFl6sbcH44AFYFDaOvBcjvXjjxR4yG+xsar0VD8L6HF6dqARvhfPEnS4iXSxUmcM7NAnDTQC057hUgx0qM+HCsOdYiiT521D2UW9sq7Bt8IRAkOLVW7FWpuUnyAguSIJyC/Ku8Wan2KEA8CoNpDo4Vy3jNRF6hYcXpgQZfum8IRWr0nTOu5yL5u1YUwAjyxAcoe0EsSMBDQ4OjTIAJgRm8xkowrdP3SY9QUJoISkrsWFwQA8E7T5FVuAGLYSa3KE3f+JL8c1vfTO2mysQc9WeeO1DBjN9aOkqzPSht+meDeZlgUU2SCrGZ3gCrsA4MM1z9kjZiZBCLlLjwNVmX6Hi8LOhLZR6WICHyOiTCEn5ZqJE/cMXKIuh3jxDvTZBJoqcqZ4B80Sv0TSgLKCKsGjsjGvwRAw+G0QjCyWqJdfzDWyrQHqNSpCbDxYVciMLSASYsy4QaJzJY6YXJ4CNRLin6bQAa+ozpKm2NiXSBmhifGru0PmeRiigVRTOzwPNcB1VsA57f4dbvu9HxAqSY9O8L33r9WWagfGG53rtmdNPO47s0Qp+4hoN9FyJOsQU7gEawpuUfBKdWQ+pcXtEILtOVdqowixD4dR7/56hGXOUZaGHbXsWKq0CKQK9NJgU+I0z1g0zah6VoQTaUIKUwpvic8zd6lCrDGcWAWQguJkr58ZQArgqoKyIbZExAwOKC72BVSBLJZgZHXUqBEaFJRRkthWwhoclhRHLLAGqfAWWQLzGeb+AsgeNaxL1oCyIN2bCEFNkfTlWcOQxV8TWNG+fInQK0JtqFKQbL6jWDVAheXeNar4AAgCyiy5APZc2xYlmcs5Tr0kKvUHN6Rf8O6r8ss6bIIEbK5KPO4elFyb4Ya9+ybPwjq/7XFw/v6qpd9We2O1RBUK/7du+DV/3dV+H8/Pzo9cvLy/x9re/Hd/8zd/8mHTuydBu3jjDww/vWvYNwsjq3qjpoRKqwAIvBkEB9nS7QxS+KWFYCSQoL+qQ/UyJ+lqp+3KYIduJUvSisG0hMCnKxd6olSL7CofBpwE+FdQbA+TCMbz/NrCb4dc3ASyMRGTEznrUljlC5dQBqZPizorKHtLnooDu6c5upEgE/0PCBR5ZEqkyiuA2NLCRO9vMOMnXI0VbQv5dFBRn61ww3mGK5MUceWv682N9P9Oxc5e80heyxs/6mfZZQ39gc7DkaaFrOi8PkAaaGh7yZNZI+2AaShJeuZvWCAlIaKxAaKQkwi+ePI7Zg4NDBqoPAp9B8DDSuDPFXqGLQcRxuG8DqY7y0Ax4hUuhQ0kKxPbQBy/hN65hGYXZdUuFHhbU6yNcCIgBIWif8kp8TZveG7AZ2a9RQ4qfF+pTaRlLMGc6tQhk59SCkfWeZopwjYrSLCsgcFSIFXoO07AXYRX2gwAWNb6E5Q4opJegR7q0fUHdWBN3RHw/vTWy3vvwAnp4chiKDG/j4limEPozUHzPCUaTM9NAMXL+EPw3BV2wXIP5qj7N6RAjGyEldTA7SRAE6uDODMCi7EM5GCUPFgLf591/L77mS96I1736hSilm7xX7ao9gZu4322L+cFbKQXve9/7cP/99x+9/uu//uu4//777yqo91vVHnroIdxzzz148MEHcfPmzd/07//6r/th/MufeS9sUPhGYaNguDToxR6yq5AywDeFYRoY0ykfOsAXA0aKk8k+xMXG8IbUClweUB6+hFzugP2eXoFphFy/BjvfAGMJAuUAmwbUM4aDsDdgWaBS4JuC+Zxu/82vXqI8cAGfBpg7dGZqtptDop6TF5bOq/deQ702QlQpLBaLvqnA1Cnct1V6CBrpNfiyHdFVgFX/RbrFOadPEoYzMykiOeJ0u2eGS6q6NgE6oQGV0KFJ976kYQjgoqGt4qGloi2cxANxqekAAQAASURBVEcivQsrX8ZbuKmRofOaosvUJZFw98fP9CDF+3ndtFveHSvr9WTIIHRiLLYdUh3DwaIAIftQFlZrtjHOvTDTTQJsSmRM+aAEOBeUcPWpYNjTCzdfHyGDQB86MFQ0lgAQgrI7QP7rQ8A4Yn76DY6BMmPIxwIMgnJ7gc8LfBw4TzURCMdNLxcsUVKDcyLrGkV22eywxeCTYLl3gk2CYWfQvQcYqiizoU4KOx9aWFMMkEsjuBoCcBeG5lq2U4Btjew0KurqGo5RSgrIyf2SxYC9QYSSAhjWe5d8Ml8oSqjhRYwMZsr4D5ysmnqV5vTMOO/FvCHBPcF7SwcXaSW7dIn5n6J2pcuGyzIbFrox49ovoPP+OfCS++/FN/yBN+KTXvI8DFcA5qo9QdqHY78flWfG3e8qkvRzP/dzeMpTnvJoTvmkbS956Ufhp37mP9EzMXNh1F3FcGtm1CTvgAhsGuh2tgnl12/T41AUGML1XQFYXY22O1384xRppwO3m6AuTNbGIQIYKEhmURsJRjf8Jc9n2wFyT4QR9geg1lD8JfkYhmbAxBEhsYVbRIDeBRHgnLoy9QzM3mhpzVhFtwqajgaw7jr70Ioa1roz3dprvdfE1uNTlt3hXYgKzUiw/tD6PU1cT9ZzpdpvchzoSfPVcxMnaP1d7eXaOs9LDMvKiWmApns/j2+umPw7+olVmRYF8FGwqMBngYhBnFk4w47lFTzrdpUIKzk4J6q1woT1nPKveqCrjN6amCuFoodehAKKi8GHAfaM+wCzUIuVAEasam7uqNsS94DjZinXr1FGoijnkxugBXWjsEFRZrC6tFZoAT0REc5ctorBneT2RWFBdtaZVco1yMxLEUiAarF4TwVyIOF3HhXYSLs/AsD3FcUIqGwbITLh81gnctvKwYGDs2Za0ZD3X704WT1bgaY67Plv5PPZUuwDpEBlnZreCmQ3ThlJ0uu8t8K5nSCpicYoQjcm+hAhWNMg8qrg45/7dPzPX/iZePnzn3ElanfVnvTtwwIz9913H3kZInjpS1969IDUWnHr1i38kT/yRx7zTj6R26te+Wz8bz9MI6sHw3AJ5l5oQXngYSyjwmWAuDG7ScAMlBsbZi4dKo2O8jM0KEyPllwYzzbAMHCRHgdmQC0zcLmH3DKUwzXYdmRlbQd3nG7skwp0t0D3Mw3fUvm+gAqyqTo7LzT2w4AyFfhEVVefK70NkcWyHwv1OwbKoNdwp5eop9Q4EcBKuD3dSQYOK+FBaQqq4E9TtPqNTH+NJOysx5Pn7c599AUZ1nE/4s40yf78bKi8tg8HuDg+X7iJFKv6ates8TSCsEsiFLNUJDFMeCjajhtNDwUR3FAP8cIIS/oAYNZVIVeAEqFLHxVWA91Fho6CXBmYMQV7KpxXi8FuTgSYe2csUFlCQIuQJzUqLCqvs45RZLoVhVfKC3h6RZIUHYJ5aoDMhrKvnOtDFJHSErWNjJ8ZBAZFqQ4/UPPFB2X/Z9CTM0XYM7xkttEw4IFIF9aa0h3F+KQEuSo8ay5U1pbKuaWzw6oD2yDpBIp0VdTJUfYC7DiJHCA3K7wyLFFgBPUSac3psVEQyC0GEVaWZ20parnojCOPowqagi9/9zU0mV7L8Cplaj7fS08SQ28iwCue9wx80xd/Jj72ucde86t21Z7s7cMCM3/xL/5FuDu+8iu/Et/6rd96VM5gmia84AUvwOte97rHvJNP5Pbylz8nFjVAbx+gwxDKvgW4voWZQWDAXjBckivTdtOlkhtRKwszjnTvo9rqfTjbwEsWWgF/1oWhAtA4aa0ou5kLXkUTXVNbgFLIu1nqWj5BaDixGeDKVVJuXwIPPgwdIgX72iZ0Spjm6lB4IS/Hi6KOscCWwAwTF28zAIOgIrgzlnjDm7+d7nQnITK5NBFWalLu2lFcglcj8TfQgZkck7azXT1F6QBpoCaPrxmOoqHyLLDYp2J3/1oBQOmgTrxmQZAmIFkNVIaVWOGZXpnmnIn+r+AueB55wSnIFhlfNVKP4SS0JpfDNyFcmJ3cL/BrDOEAgmUYMVxWek1Ghe5n6OWCeSrAOAKl8D6qRM2hEGeL2kwOXp8Mw8ptggWwAfu5D69BcId8BAQFEhW46eGLGmDCemByMAwQLOf08JCTHu6JCFuy6jQBsw8ShSUrhgUou8oCrNcEdm2CSzxHAmCUGCeD7g+wIQsqFaZ+G/V6fBLM14FiwwpCM9SjMd92DBtxExHZWi4R3gwPlaKF01DAkGaAKw3vCiLUNCzr3Kl5z0LOwIdunns4wMSx3Qz4gjd8At76WZ+E+24ccxiv2lX77dQ+LDCTJQxe+MIX4lM/9VMxjldqkP+tduPGOd3N8OAweKRwCupTr5E060LBOkPUNHJyXqTQg2IO3+2Zpl0KsMxoIm0GoB6QRFGRAWIVvjtA6gLfjtS6MKA8sIcutannknSs8M3Anf++AvPM3bIqd+GTk7h5/QxymGE3rsHGQsMzZTZL6MBAUYy1dizUVhOciXFBTq+LKJjGXb2JfFE/JYiWRVCVGhu5o7XYXWfWSCAENC2QE1G7lhqbryc8OD1OYu9tIJAJdd8MM0nGihLI9E1WTxN39fFiSMe3YsyFGUitCKZq21UjrplZ+aHyCxpE57AGhmGGTUQlkGnsLgBG0HNXmN1D8b+4LzuGU5YbG86bfY2MH4WP9BjWUVHPhwjDsN9VDTIlKosBc85di3ANhkLQHOMjCzhv4l5DQMHGoTSgiBKE2DkUg+M7HAQm4kIvkzvqJqp3D97GywNYwAOca4IMZgm5bmCjYDkbWOPsolKEsbB4Yt4nK6V5TPSSNaVUgGkx2CSoqaob95NeMD53sgDDzlHmBG/SajSJAWXHCbicOWqhR8hd4BNYm20GypzjHF65us7ZYuGNssCpAYC1CF7y7KfgrW/+ZLz+VS++Kitw1a5atEfFmfmMz/iM9vvl5SXmeT56/7eCaPuR3PQDt2E3z2HbgYq/C/VatBZgW2jU4XAYM1XgwFy5SKsSkFiUOqgLcHFJb8E4QKaJJQP2+zA0Cty4Drm2hR0OzQvgRaCXM+T2Bb0dY0HdDvCzQtLvpjDDqm6gu0MUjmSaOA4VPgzAPdfg17bMhIoUpSx2mNwPWRxl56jb8J+XKN4Y4QGG+0Pnw0FDHtwO52H8RTPAEiKDsTNNzkDyWlZQE4OdYaMIAWXUJzVGJLN/mgcnSyk44LYSgDN9JpFDOye9HtoDF+l0YQrLLrjGCwE2RBxVKWAne6a7I7PDFB0gQEai2H8Hs23i+0Up6qei5JfEVfpIrwYdXAJfwiMkgJznvWVopuwAnw04G1DPdB3XitBsQfA2lGTriYReXwzmQUxHjku4vpzhIjew5EUNMDuw/y3Tx53ZVtH3Ru4+V4ZaQDJzmY0AxLCm/zvDTgThNPwJ/m0bjNmoewZVaqwYUPZMWWcsjsUn/XoBzkqQ1B1lZyiHNaRW9o4yAkvRAIrh+jPEMwrADbrns+yhP+CRIg8hX6zsojxACj+CRO0slTAeeI11S9CXpTMy20kBvOx5z8BXfN7r8KmveiGLy161q3bV7miPCsxcXFzgT/2pP4Uf+ZEfwa//+q/f8f5HUjbTR0LTG1t4hFbK3riILwb3BVJH+PnIXWkz6AI1g8wVdjZh0RFFgRopp2UsFBqbqM0hl1FnaaDXBgD8xjl8ugm5uGT9nN2M5Snn0PMB8sCOHJldBbasw2Mj0YLOhjoV4CGBP3Sb+huDNBIxlgo7nxhiQngxJHbYg0IPjvPbhuVmge+B1Pzw0JTRCmABSgjPOXjaI0CSOu8BaFzoKDJ4M1BJiOyJvH3ohwClew1B7jWErk0K6K3emgwPyJLa8lj/dd6cFpqKmyaxw/cx+Sw0vjYwHTZrmpFrISgw6MzMF6tgxpiHYFv2R4VgTVIcL99aw03pPRKhp4qgWFBEWZ8qhtEirV5mQ9kZ/EzgC3kxBYWZQM5SGnUqMHP4lhWkrTr5K4XEcTkk6TS8I1FjzEcJ1dsggi8emVRhnNOTUynIKFCCHneoKaoLoA43DRAv5PosoXGjmaJOMGEBZoZDZBNpAL2BoILcG2+quFQFrtBRYOcD5rMADgcCIFlYYNUdkC09T1oBKYaq7JNW8mxqhnsWj3uY4TLAthEmjXS1cnCIGw5Q2BnfW6L0wHjBcwwHx7CQvKuT4qUvfSb+4Ge/Bm98zUdjGJINfNWu2lX7YO1RgZmv//qvx7ve9S587/d+L77sy74Mf/kv/2X88i//Mr7/+78fb3vb2x7rPj7hmxwsNDaowIoDK1AraMgUEVufFDW4F+W2sfr1maKWAb6TAA4CmUboQ5c8340txkMF7AAfRqBsqTnzkEPORqKAzUBjfTBgmuD3FuDWAVoEdRhDSp+GoGY21H3n0DpDdguBzlioSXLrAsOhYrn/Bgms+xlaKxTMxpKJhqccaHj0EDhDHXUCOQsSO+gDjRwOHhkyaNyA5qkIRdVMVc7QhcNXQAN00vBhwYOz0AT3HE10T9JD02VUoTrJ1QtBZIYWULSFFuAdITixjSpPq6AI4ZaZQFljygaB1+CCgLtvGwaUOfgWM4GOmkTGUXiEWh66EEwmocYS53jzukGDfFopwmYqrdqxOitESxXIAOjBoTu08J0PIH8rwmVqClySGOxToZpzxyeShR5EbAaGL7PsgrJ4aBFE8Ugw/AiELH+EgxCeGTGGhBIYzPwMhfcY/pRagZklNGwswATYtrSQZqQIQS/ifs4ZeotQkDtMFLYhQGNNJ4QXTSFRRkT3oWeT3j4DgWkB79vivAczMF4ukE0BRsVgCiwLigBeGMbS2VvtpsyK0urY3DIsi2A55/yoZ4K6dUxe8HHPfjq+9Pd8Mj71E194VULgql21R9keFZj5sR/7MfyNv/E38IY3vAFf+ZVfide//vV4yUteguc///n4oR/6IXzJl3zJY93PJ3Sz85HlCEywjIVpr6mS64DsK6sYL5VGaVLUawON7chSBZbCW4vBzWDXt1zAdwswDgz9FHIesN8DD91CuRzgpZAgvKkYIv6emSYIES+Yww6VPJ1BATPYqLBn3YfhVx7ksYPAhgI5DJBbFyjbETgfoPuZUvkqKIvBMeFwYwOZY3ec4YBLx3jbuciP0jKfdaGBMQV8oRfHgvSYngvJiskAssAm0KJUABCMC2nqvKnKijBgq4owQxxNY6arfo0YW/JmavBcEFL2jlT4pWcCyFRbVUrbe6bLDpT7H27NWLYFy6hQ4c5fDWCVZvYazh2/FxJPNbwafcVl9rnG95FcW1RgFbASAm9CkGYSmTZwCgwqsw9F6QFxNXowDCzQGF43MdYCQo05MkaRyACGGtdu19PVsV4/y7HTaGMAFXNLyAqAHoccNJ3ryj0p4UGqKzD1HGMR1K1CJo2wICtXMyyprVSBFwFGJYhoRF1m/enscKnwbcEiBWVAy5BKrlEX0WNId6SmDzWTCIh0Z9CiHEsIysLaYDYpyo4Vx+kQKsxcmgRuCgygEq8TOI47R9lX/Pe/5+PxpZ/7KXj6fTewGa8KOF61q/ZYtEf1JL3//e/HC1/4QgDkx7z//e8HAPzO3/k78Uf/6B997Hr3JGlNrGxSVPFQBQ0SrSFqrMQifHEgtwUApsIs3cVgKphHQYFg2gMI9WANEi82G/jZCB+p99F4NwCkFNQw0Lqv5MJIAbYjpARX4AD4BGBbuFO9fcEQz2bibvxQqTNzY8u03ocvgX2BbybY2QC7NgKy8mL8HMAOgAsMDpwBsheUnWO4cFQRLBuwcJ9JAyNp3DN0MgQdwyJcBUnPShtdvpgEVaAJ5mkY2aPQUwrfBXdG6/oZRDZLhpCyptDqjImwT5JQBSFixntgCRJFUAsgI9WbpbBKtIYKsyuNnDkwLcICjtuBhFyzRkbmfTRYZUo9NMbSCCBadpSHZwvhtQEC9ND15AMgTpAkI71vHoRxyvwL74FEqGoTHpkkRScPCDxPK+cQ6eWazqQY35aarPQ4ycFWT1NyZ0rEBW29hgZG1RtY9IH33VP48MDbXfbUkEEKO5oDRVFHsKaRB68m5oaHXpKN/FsW3tVaSC72zCQqSiA2MEZGr826CaibCH8dmG69nBdM1Vi53gGdIitKDDZpSxsXB77wcz4Rf+yrfteV5stVu2qPQ3tUYOZFL3oR3vve9+L5z38+Xvayl+FHfuRH8NrXvhY/9mM/dpSufdXY1BzzSECjcNSJYQg9UF23eOxuzUnihEN3FfLABeXGaWtgT72B5XwIQ+axg08Bs4nhhakAm+soZwPkYm5qv7QgiF18ZFXNC3QsMBWUCmA5wOpAcLIZUR64AFwgg5JDsAQp+GwDKYrh9h7LbPB7N1Q4ngp0dowPVszXh+A+IGTbBcvWYZNAD6vWhg1MLS4GAo/4DMKbkkRIKWGkM9QEtJpOSKIs4u8llFYtvTJr2CFDTEhAE+q/AOiRWRjqyQwmiRiLy5qijEwjLvRA2BBqskUJAEFgOEfFcEh4H8yhh0BWKPR+jUo9Fw8vCUIA71CBoaAWhQ6RKp88mQWhDOxHhGfpJO6TdO2F4IKFChWuoR00CxWEw9MkherNvOIYL9DTk7gmTXAr2qgBFnJsHTCyYVayNbCG6WQdP6TKcek0XFpoLe599dC3kfW5CE9iZg1JNQ6BAV4MNg68Z+FysUGbZ8STYCQ5b2LgQvDHB+mu3AFXqFWGzcyJiwuRiTpg5rBRMd8YSbCeDTgsnHsKDA/TG3d2PuIP//E343f93ldeAZmrdtUep/aowMxXfMVX4Od+7ufwGZ/xGfjGb/xGfPZnfzb+0l/6S1iWBe94xzse6z4+8dvTzjB7hVbAJsEycEGbbIFYIS/gcgH2ey7OWwrfqYSmDGgI7cE9NVwKgOCm2FgYAqhRUXhUpnD6FlLGRh9hFtQeWgp8M3LhTp6AKmTm95fDAvgG9dqAenYPxtsz5PaOC/R+puqqRfXseYZoFKcM4yPOcIM74BtAd2j1dARAFUG9BoIrRzMuFQijj0YUbfWQAuioomX6eOys1dZwUgNAM4FOel0aCIjBkOCmSLXQ1gHzvgGGW9xWD5NFGErTiUAQQ80U6ulETnkARoVEhooUAgh1JwcKCDVYQbkkYMUg0EOFLRH38SDODhoCbOGxILWW19GJpqVdXjVqmArcFGM7IrMXeozKXMIDxDNnv+DaNFDQUpJXEKkBnDJNmPcpQGHkoEtkRBFsSAAKgiaZHQJtKtDoPFwI7wvyHjuO6hAZBMUB2VXorK1ERnrC3A0+lBATFMihUqBviJEz+tjcuTnIquOSGkPh2XEAOBiyhKNUb2E/EWo9JUDVA2BFMJ8VyFnBeGvB9lcvg7zM8F89G/HQVPC2v/EufPvf/klgULzoOU/BWz/ntXj9a16C8yuOzFW7ao9Je1Rg5k/+yT/Zfn/jG9+If/tv/y1++qd/Gk9/+tPxAz/wA49Z554sTT/qHPX9t0nKHGMhFaBG+MkmQ5kU2JNki1qb2xwuwNkEGwpsInDxEBpDpIjapgAoEAhF+IRqr7o34GIPbCf42QRAYIfKnfmiPIfNkIXyvCICnyuGizkk5T0yPSrchKnEbvCLPY/dH1DOzrCIAFF/p1wsKA8BdTzHxUeNLUwAQbjcJVR2ddVgiZ0/gKZPUoTGrXFrjP8ysqKOxtM40pNxhhBkyVpLQKsRhAQsAWgWgsC+zpLPkY6enpnicI06RQhSqCorF2ukCisvpA7UKWHWiwGDYjnzlppNngpJ3lKBclGBfYTVqsOKsT+zRZmKEFNphZriOsM75cIMMog3rRsPcGcBHtPgZyAPoFotEWRkisWNSI+LhDgcnCEcfkxQizdgmYCDirSp20IvoQQY8OqQQQhgKsEEAoR4EquFANAD0KD1OdwzhceKct6XCgL39BhFWnwvmii7Cr04wG5sG/Ba6wZgBWDmkIMn2wq+1KiHxqKe8wjKF2xqy2Qqe7SCqj4IhQmVOlGbX9sRyERF7vneLZbzgv19AzlRM6C7il/6t7+Kb//XP4ayN5RquH59i1e+6vn4H77wd+DjX/4caHqyrtpVu2ofcntM2GfPe97z8LznPQ8/93M/h3e+853463/9rz8Wp33StPuefg3vf/iSYmgeO1wHReeagqhCNgPMAqjcnqG2pzrvvHBxN4Umb0LCKM9Gp8JIoxbCubRVmwG6LNBbl6zVNC80lmNpWioSoRUPoykO+H5GOVTu3ueZnphSqAiMMcTcHJhG1t4RpcE31p6Sw4KzX51x+fQRWmkUWNwPjQshNYivMUaSO20AUEcVemJyt261I/IiQQp/akXrewsPVRCgmDVvT5hwVpCuAWTiH4GCrWGMTJP20FgZ6YHyiIF4oWdM58p05oneJzOBukF3M3wagGlg0c5fv4QMBX428ZThHdIIFSJDQkNhaCcAGEsGMP09W1/bSVKEz0AxOpCHxXhTTIQG5tqn2r04yuhyoNUeks7jk8Alyja4IDgnwgjN4E2vx+GwQm9UC8vF/ZVQc/ZI9SY5vMR9AvoyDpYALDV8ggeFGH8Z8tvQgKcjPrsRYKehKYOMsLZLl5lE3DKHB3EgKT3nERpQ8hV4RaHKVSAxHDoiUHMMt5f2PAFAvb7BclZwuDeE+/ZM0y4HR9lVlL0xdDwqHtwd8E//v/8B//Rf/sf23fc95Rp+9xtfhi/4/Z+E+5928y6rylW7aletb1dU+t+E9sJ7buI9+DXUiQvxsEfIqgvcK8SYe0w9lnBtTwKrI0RC61wYAvDFKGKnQiG7QdpOHSDvQN2ZXmxOr4xoUxi24Et4nSFjZD+l5ooEarjcAWWAbEdg2jYDjsW4G7153qpyt/DNskBUWcQQA9TohbKhAzBAuFki4pNCeEm+TLRxZGwTvYC7+0ivhsTnMnspM2LCM9G8L/3r8QWaSrz5L0JOq0XMJgzdBcnaxkJibRppBbCrUAHmayPqICh7itrBQP0To4qtXSsoC++zGiCXJGdjVHJgqiPSZ6IqNTklKfDn6b0Ib0TjnwAtvdkBoKycmaQve5BqAdAz5mieJipTh+qyJ3lYmlFPd0ejehi5NhaZcAIEWZjXWzLEGH0E0JWGwOqFyxT6CAGuvJYMQcW1pWcpACdLGfAewBk6MwfEtGm+LFuFjRuUSI/3BIJRaZwgMsKhws2BF0WdsCpMF4WIQfasSq5zzjtpUzVS0wAHyp7Zb1hmZmltByzXCupI9eoyE8jo3hqQwYYeP3cPr2c8L4PgVy4v8cP/8Gfww//wZwAH7rt5hle//Ll486d9LD71k158pT9z1a7aSbsCM78J7RXPeSZ+4l/8R2qATNzlseifkweyr/TQRPo1HJCDwSsLAmIcyIXR1N8YILsDsEQusyrTl0GPB+bKcNG8BM/CgSXAzTAARaC7HRfRDGU1136ET9xoYIPbgFKAwYMAKYwDhYH1QTHsDct1QDaF1ZB3Bl0ch5tCQ1AdqLLyMXL3z9O0zOMMpTRgI4DXLNKICItkthJateGSnpoWbvBVzbeCqdbhnhCEcazWeWBijDRcQojxSKKOSCtBAHiUehDUmywY1cnAkG8xKmQByu3KUMswAhZcnAL4+ciMtEJ+hfoCBwEiJl1VhyUdDx7fn4rL9OgIwrsi9I64BbdGHGISFe7Rxr1J7gtaaK3VAEp+SV3HEHk9vbemEa87L1Fwi9yFCtcjU5uR3+0SY0TUpYZWAT1b7yGSDA+Bn9eoMWWDskjkvjYCNgzQ/QKZFePBWEpj4HeW2VZvC2J+aNyDwLQ6OyrIubHUQQJpVIMBcoiwIcDwamXKfmbZJTjPYarKtHYvEVLN8TKeh+UnGOZ1gIToKJxpBS19vK/+/oEHL/Gu/8+/w0/+xP8OnR2DCl72cc/G//jlr8crX/m8K2LxVftt3/S/fcjj177v+74Pr3zlK3Hz5k3cvHkTr3vd6/AP/sE/aO+7O77lW74Fz3rWs3B2doY3vOEN+Df/5t/8Fvb40bVXvfw5jJcfAL2kkJaEngbAhXCYDeX2DL2YoTM5ByJCVd/qkMuFejRzbS5+SBQHvH0Abs3AYWHByOrQeYEdFsjFnv8OM3CYqZexpeKwHGb4fqYuzW4PHGZgnlk5uxpkXiAHFqGUuUaaM3kaVkqEABR1o6j3jJhvTI0L4WPB2a9Z7HLR1GB1Bgm6QdKVUARuYnYtA4nhgCaZ3xZ2b4YVCrT8IevAi4UBTm5E5ZjIXGn09pVp60uI5AHtnL0R9zBI9MQoRAUYWFwzK3oz48VRLhzjHpjmNF78bFkc460Fw21+p/WelSEI3uZwKAGfCkwVNcM9Wasr2NCtqGX1Ne28hWnyWPZNEVyjmeFImYMUnSUgwivigo5QG8Amry9bgsvAfhnBSiVciYwfDQIzjGnvyU0BvKU3e9xP6Z0/QAO5CWo1+EN6qJCFadsa4EgOISmwOOs2TQUaqsHFIyUcAGaq85YdM8lYMV4i8wwc/4sd9GLhcyXCkCpirKLEBcNN4akLgUdTwAdgOQOW84GjaQ5zRx0cdUB4gOI+eEeOD2Dee8y4WZEjIJMq1WU2jA/OKDsOwqzAz/+b/wN/8k/8r/jM138H3vK734b/+c/8CP73f/efUWt/467aVfvt0T4sz8znfd7nfdD3H3jggQ/ry5/znOfgbW97G17ykpcAAN75znfi9/2+34d3v/vdePnLX44//+f/PN7xjnfgB3/wB/HSl74U3/7t3443velN+Hf/7t/hxo0bH9Z3/Va2l7z4GdBdZagBmV4bnpglCJ/VoNWAfXBUzibgfOLKvizQyxk+LywvMI6xXXdYNWiQRHwvkJELsewiO2pZ0AIsQ6FBvbGBbgf4AxcAvGXsQLWFGFw1YxHwMtIAFS70dSys9FvJi6hbWiEi49AKgaP8/9n723BbtqwsEHzHmDNirbX3Pufce/MbMk2zFdFU8QtFFMFCwLYQUPxGAaUFbJJUM21QoNsqKCWlH6WEtqQobRMpQNSWbLKUTqG1MpHGrBJaHikL0X606bIgv+89Z3+sFRFzjtE/3jFnxD6ZSAJybybs+Tz3nnP2XitWrIgZc7zzHe94x+IYHnpPBXh0f04tJbLxgPHQWPRA5t6htpQ4lVZpA/QSboIk794yvVmkOwPVXJkNqBa+MwxQXqxfQ3rVy63drTeDukg19cqbdo7g+bRuzyy9ZnPnJkxtIElDu1N3GT5S0yItZTGzDNvHjBqtEEQC9MXOXYvDKlB3QD3w+uYprkfQJd7TNZE2aTRKtd5tGa3E2yP1VIGeK5JNaqlH0XY9vDMsgtZ2Id5TG5jhLGNKLu6l9nej6XHIBHkAoc15xmeYAtpcd2dDNmpwLBpQ2sL3IRNQIEfZ+SjsBN/nVNR+VSBPlfN/IBA1AHUnqPsMualIVwXJBBgF5UwwnxN965VgPAbgUyUoD1M9L8Y+Thmoe8F8P2N8zww/TWtjSKDrfdpolWHSgGcI4ss+kGGrDPT2fkcqjnxZWCywp8A4X7FXmydFOU+Ydoo3/9CP4C1/5kcAc1wMIz7hYz4cv/d3/lr8vJc///1Ype7G3fjgHj8pMPMTecg8ePAAn/VZn/V+H+9TPuVTbv37z//5P4+v+7qvw1vf+la88pWvxF/+y38ZX/ZlX9ZB1N/8m38TL3rRi/At3/It+PzP//yfzKk/p2O/H6M+mkGnjmRpuDNVaPSyclXokIDFITczHX0HiV14RZoL/Ibl0BgGthgIUS+SrmWmAyubvFayLc1VtfA1ehyAcQTskr8rBZ4zg7nxs9DchJdga1QhZ3uI7Bg8RVDOU1SkKImSJRbt2aBLBRbF4WnBctZ8WJh6cKDHyVZjo4gUCLCKXVsMCJ2JFl97MW1yEr18NtgIdm8OYa8RuJCRCI+VBt5KXLuc+F8DcG0EE4Mk/TwABCBqIt0AIBXI1xU1fIFcJdJ0AhsVWkqktqSDJ8BhZwN8VAIxpddMipSGq0LcqD0CDduWc1bapAqkI+eNqZNpSinM3lqPgcZ+OdQMZgrk6H0EBlGmCeN6Cp2CeVD0dAu/83q/JKiFzhIFgJMF7NSeFNihNxBtZe7wlo7RtQKpVZvFEBOYGFJtRI10+thEOiNUdxrHF+TG3ECYNjWw51ICn625IkFoFHiIOWP0B6pngF7sMDyakS4dQ1Is9xOmJwRJCRo5J3lPUQw6E2TUxOvowvsyPTFgN+34ukowbgpYoSN01zElXl9aB0iviLNt24yWHq4s4W9skFTqbrTS46ac5+gUzudPT4Z8U7DUCf+Pv/v9+I5v+3/Dk+DJ55/jUz/1I/Gpv/1X4KknL3A37sbPtvGTAjM/k2XXtVb83b/7d3F9fY2P/uiPxr/7d/8Ob3vb2/BJn/RJ/TW73Q4f93Efh+/93u/9oAIzAFDu5UgFcGVPczAyCupiqrPDtGb42cjAl8LDRWmyYlmgldVCmBa03j3e9B7ucKvQlIBhgM9zD+hwB2aHzTMUApzvuUJb7FpLhVmkbDwC1sCaaplmlovnjDQO8OoowdJ4WgW2Xe9jABZHPi2wQ0bZ82dVwKDYGktuhqMxDVj1IgDP20Ama2Efq7bl1Yq25KM1ikR4xzDtAZrPGeBLDbAXQgk3ipad98QB6oIaM9F0C5u0FgXYBFsewdaCCeh2Ke68Hy11A3AH/2Af1TihU2mlzwmAZHrblAotynRjIdDBXJCuFvhZQn2wByJ4AjVSFpFeO03QlGmff75DM4ITc+hcOmPhDmCIY4T5nC5xrurorS4am9M6cTcyxrGmqR4jbwgSAIGRAWrVR1Ft1IRFJCs8QHA7fgR7D48XxRrcJUCmOTUrWTpDIgXQoxDop0T2IszvpFW5lZa2BXwWiCaE3Qx1RecjSo4NwC5BZkc6seO1xnflHAPSbNCbgrITaDOwXFh9NT2VIX4BuV4ApRlkPQeWPTBeCwYHdCFEy9UgpxDlD4HwrVEyvLgOasF08fX5nw1pqjBVlH0YL6aolLqpyDdcB2xMvekpADz97mt809f/Y3zzX/1HEFX88l/18/DaP/3J+NCXPe/HXbPuxt34YBrPuQD4B3/wB/HRH/3ROJ1OuLi4wBve8Aa88pWvxPd+7/cCAF70ohfdev2LXvQi/MiP/MiPe7xpmjBNU//3o0ePfmZO/Cc5/IyaiHQTpZlXFelUIIkusC2NIU0P0QWT3I0hOex8Dz/sqJM5zitrAjAgLywLciO4ETN4KdyxS6RRlgpcXrFMGGCQSLmLSeNs18CXBHLYAQ8vecwmmF0MOljs8NfyZ4sg7geFm1I3AcDGsPpApDqi5LqLgQMQSUX3i/EARi0tp4v3lInourNvbQm0ufe2/0qN1FOAuWBiJFIqvhReX1XIAvS+PqmxNPQQgcc11agUCsGtO9hteojI6GQKUGswNi2ggvoYAJgX6iI8VLhRGSVT0BdOpsgUdBbOI3fh1TFMDrNAHpHKAyKtlRM/JwE2Cmyn4bRsZCngUKdw1SaHpBqiYGH6DVFKLQQMLI/2LjBGS48ivI3MISnEwlEpxvue2GOsGGCJQmQneGECjDeOFVcBOGv7TO8sDqu55BbobXHeE4GOq8DH0ANdFVZZDQPsoLAkGK8M+nCC1wIfmLLt30cQrSEIlj1TuA4THN5dcHiHE1hlRRmlp8jSwo72qBTwaqGAO58cw6MF+XJGgcGUbcs9DBbrSJ8ZjJwrpSpkAtKpQCUH4NLIwHk4I0t3Vq4txRqpXfZsY+qruYmnI/2pbBdWAZl9rdLNQqY0gL/D8c9/8H/BH/yc/wbI7NL9R/7Qx+DXf+T/5k5IfDc+aMdzDmY+/MM/HD/wAz+AZ555Bn/v7/09fPZnfzbe8pa39N8//nCxOuPHf+Be97rX4cu//Mt/xs73pzpY2cCgKLNxtzyznFksd++N5lMCd+BU6AmTE83wADr/DgM7VB9nyOkEzFHaMw6AJgbTWhi8IQx0AVhQK3CaYJdXPJ5VAiFVSNo4rQ0JWBa4Vcg4AvcvCJyO7LadMMKHNUXRzMk04oOLkk6fDXpTUZ/MQInAF0GypZW6oDcYGCzRVLOJIAFWlJRINTmYLgptioY+pnXCbr9v2g5pTExjZnzjT1IrK7xqXJuoZGJaYRU/tEaVHlkBi6og1ErDu5HVZuqIcmAAQwSnEMd6ijRIMBGtQ7YNCjsLnUYxCp+T9rLyejZwbsCRH4U/Sk70IoKjDokMlAN+f4+yE9QzhY6hZUn0CfLMgJyPFCHZkHojUPYvAhmpHKAm0kkSVVg90KqwMmvmhbIkqKFp0dDi9KoyYNXluPJaBGPSmTwEAMaGwGl4LwJ6l/M0MBy0kCPSM+ZIbrDZIeegBsUV+VKhxjJpFcUWssMcqVY6MYPn7yMI8E4E7vNBUM8VamxmqRVwH3u5dyoEdOPDinxVIccCjGT90jzAj2wTIQE4dHH4SDamHDLElmgYa9CDoErkotyphbP2bHmv3PNMIENDQc4ZLZUu4cFoWaaXVDoGkBGmvuwso+wTQZWx3cK/+cH/FV/6RX8bAPALfuEL8IWv+kT88o/4eWylcjfuxgfJeM7BzDiOXQD8kR/5kfhn/+yf4Wu+5mvwp//0nwYAvO1tb8NLXvKS/vp3vOMd78XWbMeXfMmX4LWvfW3/96NHj/Cyl73sZ+js3//hCRQoQqHLEnoZoWZDKqTz3pE6yqCD71y5AzvfwatBpgKBUZx6cQD2O/rCXB0JQC7OGHWOrE4SOPUU5Pb5marAzRGuKSqmcv9siR16H6HXweFAocblFXSegScuUA+ZokqN79KAjBLYYKpIFdg9BMpBqQ1xgguRACuP1+cWMji6oHusANQXSICh5IgKqAZYKMB12zSOjMM14LuVf3gT/vIf7JQNQIpGx2VB76AYr12DoPeS4YbBEI1AMUZ6IFIuDtAHiIk9OuSOBJsCAgJEX6a6Swx4N8bS5tZ1Or6rVodeL8jHJX4fbtFmgOxg1ZCuZ+hcka4XlKcOsJyQJrIovhcga78+unAOmTDVQvCnDIbtyxWHVouz31zDBjAXAh4fFDas7Q20Mn1kws9AMI50x13v6e1UY6AVjQxZku5RJCZIC+C1EqyeyDLVJBBR5OvK75OE1W2LI584n+3JA9yMuq2Rx8kngSzWy/kB4ftdUDWF7QCrkmxQTE8muDqGaz47Sdl4NU2VUyQlIAH1PMMvMswqUhXoZUWaHXVQapxmGu9ZAZAMvlMUDPw5AC9MU1o0BpXmDZUUDlYGmoK93LB5bEIz5pEa9oEwjyCHNJSYox4y6j4DYwjWp8rKOwB1z+fzX//oe/CaL/5bgAPPe94FvuRP/3b8ml/9CtyNu/GBPp5zMPP4cHdM04RXvOIVePGLX4zv+q7vwq/6Vb8KADDPM97ylrfgq77qq37c9+92O+x2u2frdN/vkS8L8/xnCnmaC38dGJB6dUjTF0gr03TuqiaDLmQO1qqe8A9JCpwfgJwIdlR7F22UAikFMrEs25uviupauRS6HFeF7wYAAplnLpTzQjpeBJIXioSHzJLtqxPkfAfJQ6RQQv/htP+XQlGJ3hhkNpy9U3DzwgE2SlS+cGjTyQS7I2GMpxuDPEEs7mheJpGhikAvXmPXGsDDrL+vD0eUQRvBWQMxhYyG5AiWJbqDO9C8aqAESg14tEIfRzjVxg63lXJDNAAS2RrL8X2cAl86ylKw2iz+xQWenZVARvZCNK+tAxobEiyKlgVIiczeqUCDYdPLa4w3J+QJWB7skW4mlAHhuwLOrb2iDgFR3NdeVBbCp6gblkLhdUdtDXfWlgOktw2W9XVSmq9NsBjNus8iHdbvZ7BXcU0pRHdgJ+FmDbar2Nw+uEKmgrxU2My0UD3wudHKNJeUsDg4SohqHTUrbJdQzhSLOPKNYf9Qka4LLAmbn4r276inAnn6BHkwkrkyoA6KugfKyZCuK9JpofbGFPXMMd/PLCMvACZDOhrScYEPCXVPBkwXbgzU2fKhWwhsDAbbP11AXc2xwIcESSvb5U30vkk9tb5oreEpmSZ08b856H2Tec9Z7s7PK/eo/Uk3wTJmXrt3PX2N1/6Zv42UFJ/w8a/EF3zex+OJB2c/6bXvbtyNZ2M8p2DmS7/0S/Hbfttvw8te9jJcXl7iW7/1W/HmN78Zb3rTmyAi+JN/8k/iK7/yK/FhH/Zh+LAP+zB85Vd+Jc7OzvAZn/EZz+Vp/5TG/X9/wnxImO8PsL1Cbhw+hJalWHRBBmDRpznSD+x5tFCb0eJ+VMJgLpEmSYAkIA/AkEO+IcBuiB22AVjCOK/0nTUa6MnxngZwFoHPMwHPNAUIiKga4mJ1IJ0W2HmKPksBwprup3Bl9SA40k3F/qHi+ILcgUtrZ9AFxODXcufuvFludPHput6vI9JT7ZL08u2mk2keLZFm8lrXn1mAG7Q4L7H7T2giaDemsJh+ihoTbyexliA3hoaMToddvYdQA6Gs2tp8EZc4Ne/BVJYFOBpUFXYYCHaKAVNFTUCuLAkWiU7sS4Wf4v7kBCkGvTxCntrj9KIRiyqSKtICYHEEeoqqL++pMgigxrSN5NR7IBE0ev/+0Ois3REpul9PTwGLhvgcvcmoNCPGAIJNfN20LK13VxPckhiLyiMJMJQEXnlOw2ywPau8mFYx6KmQ3aoGN0cZFXhyj1oydGYbDjGBi6GqwAaBpwxRnr8WY5XYaYbvEoYbaqLylWP3aMHwcIHeTATko6BkzhHLCeVckK8qdleO1HRE1ZFOtgL91lojaaTvHL2FBBDgBEDma9JS4Iuhng3QaBdCl2IyUEkrbQ1a6nT7XMT890RhM0ZCQ51sBTJnrHxLJ56LDau7ctOzVQH+4T/+n/EP//G/xDhm/P7f8+vwWb/vozEMH3B74bvxc3g8p7Px7W9/Oz7zMz8TP/ZjP4YHDx7gIz7iI/CmN70Jn/iJnwgA+OIv/mIcj0d8wRd8AZ5++ml81Ed9FL7zO7/zg8pjpo18NsIvZzIPMw3UEC0JvER7gu6zwgWo7jN0TMji0KspLPeFfZDGBN+NwDxxxxfWrp4EKMZCpZzgGKCn1kzSuEgl5X+qZFAAYL/jQpmUaad54WI4DpEqCMGqVfjkkJyQzFBmY2WONV0DQY3GR9kgNGtzdv5m+W6UrDbdrKG71LZ0hHVwtGkYuYmfEARQ8A6A5LFrTvqdjAVKJfBrpegBZjwADcGc8Do1F+Du0KahLYpS9EireaBCE+lpKNryG9sfGKCzraXd5qEB4T1ufXw0/ufO39mY4U6jQ99n2KjI0eLAhujNpRlYmilhYg8nBZDZlLQJysv9HaoX6CmYn8WjeWiUrkuA2OMCXQpcDLkY/DDAKuAjetuKJgSOzEy/14gScmnzrv0+3iPg9ej3p+EpENS0CrLeFLJ5BrX7nYGqdDX2nDAE85OuCw5Xc7BgzhRXEzJDIccj9HJBFopitSTocUG+KRAk1JxRB4EMAjOaH7K8G/AnzuECDJcLu5zPFfnHHkEvj3A34PwAG4deVs1O4AobapjjURyvUf7lxvsmMadtzx5cww1TeexLxespDmCi/mV58hDdxjuC7qlgAeABQqTlbaM9h+dWGca3WQrGNgTZrfybjuLUaTXdF9eI9TlrDKUuBrta8M1/7bvxzf/Xf4JX/rIPxRf9Hz4ZL7/zsbkbHwBD3LfGGj/7xqNHj/DgwQM8fPgQ9+/ff87O43f/jv8SD3/kPXA49NEN+xydn8Hu7bjr3mcWqWRWPRmcuylhQMzXBXozQeeCmqPpY2Z5tC6RJggQwCDpPSDLzQx9dAV/dMV01DCElqZAcubrhgF2tgNUoccJuD6SnTHuznG2B4aBKRwR4PwAHzLqLqM8L6jnat3FViGwSn8MAVmYcpZwfMEANFJkRK8QksqGfRK/gzs36ZWi3+ai2nQE3WG2lSYXh8yFmhCraF4zKJXptmUh01AqsMxMuTUwE746MmTIOFLwPA5MuyWF50TH2F2m+LIZ6EmrqmEc6WDUmFxBohalDimqYZqxn3V2zBW9TN8zGRczQz1EoEkJYoLhusCmAhWW3QqEpdxz3O+JVW0+ZNhZgg+K+QE1EnJaaDg3UjNzeEdBfudDOAT1fId6b0A+GdJSo4JKUcfM65VAI0Yjs8LUGVCUTTJTBXxMrJqZeN1rlLVreP+IEvA1xgXu1O8ECEUiTrR4gdbt9VFgVCyDwN2gJhhOBemaqULMM2w2+EUGUmJX7WDRsBgwzQRJ989QzjLy9YT07mtgyJieOkO5T71YLrQ9ADiP0rEApwWSE8r5DgIgv+cSejnB3IGLPexshJ3tsNzPWC4Slp0gX844e7chPTpBpgLsh5g/AhOmYm2XUC4oyB8fLdCbSs3NSFNFiEALIrUJov3SUlIBeMKSwaPsXcyQTwYsBt8l2D5FsUFBcmGp9iGzc8Vs8NloOHhGxgrGflsrs4a+O5BiSDcVviMgsiSoo/b+aVkFX/T5n4jf/om/4md0Db0bP/fGTyZ+3/GEz9J44ZDw8JlLyFy4437eA2oDqnHD39sbyOoDEpHAB0U9z7BDhp4KfC7czd8sEJ8YLYZgUAxkAmplKfW8AMcFPi9cAFWBfaCIeg2fZqYmVAA9wMfM7tduwBAMTa2sdsoJqBpls1GqezMD5wM80iFqwpTEEMFYQJCVGdzyVcX0/EQBrzAHZSJcWE2QCsLcbpNrSJGKiZJV8dBlKG570rTxGDwXUVZ5leD4VaMvE9aUU1kIRmqFlwWSBFKiSzbife39ktaFvtEMCl4XOKuBWjAWMgp1AIWsvUIoTjJRSELNg8GvZ8i9ATYklHMCmbN3LMjPTCgXA2poqupekY6O9OgUzsMJthf4fkA9T6ijopwphhtDetsJMiSUixESKQp1QVUEAzcAKYUrchgguocGmqkS7upTN7lrrsaoShdjgCXSZgz2OagXbfeAeiEprVwZzQAXFg26XB3pVJCuF4hQ5wIBU1OD9nsuJtDriYaD+xG+B5YzhZ0rtEo3ugMEclmhlwurB88GLE8dUA5KAJkAvV5Y5ZQHAqpBkTRDr8taTedAOSTUF99DvndglWHMbZkr0jXNIGVRasSmEqCg3avEuRDgLk01mC1BvlyQrieUwwhJCl9YBdjSs75EqjM0cmROpYMYnaICL1iXVB0WTS9rCjAYqVKZ2dxUKuBe4Z5CkO3hPE3zR++sTJSdHyvqnoC87JTav9AADSfO3b/0F74Df+kvfAd+/s9/Pv7s//HT8IpXvPAnt0Dejbvx0xx3YOZZGi97/jn+zc0RulTYi55CeeF9DI8mpoTcABRIypCR1SGyj67U1dhLKLo32/kAOx9hpxn5eoKcTlw49zu47qmzgMGPC/TmGB4wRpt1IIzhlDvG3QPI9RE4TfDTDOymXjrsrZw52AE72xEwlWBqouFkGjNKaH4sN5ol/FZE4dV6p2M1x3hVsVxQX6ALwl6/XSVS69SVMIigNekTR+u43PQtW9TCdIRS8wJgbXugkRhJEB8gkWISCFxa6k0I4JoTcjA4IoUpFNFgnKKKTFbtQivdbhb23XQuAV4Q6Tcaq/VUSpRIJ6OGwzKiJYVAoZDLGYMo8smh04zhmQKVxEt7s8ANqPcPuH5+xngh2L+rAC0ImVOQK47x3QuGk8MPA6oI0lSj9UABxgGSFXaRUfcJelOhpUDF4EWRJoMmhbvDspM5zCytTnOl10rXEUXaLSqwGrsmQABrh0wFWsj6dBQjkRJN4GtA4CJzhYhBc0LZRaoy9CeyRLMGBfTpawBAffIcdm/AfJFgAzDeAOMzBfl6hj6aaTJ52BFAZYU/ucesArmaMD6qSE6DO8+CchCUXUJ6WiAJKPsE35HVslFR7g0Yn56hJz5XaSm9zN4SINNCAbEJsBuZ9s2KZZ9CIGyQyTA+nGFDCgPFSPXMFakS8CDT44il4MEyzgW+yzAZOLWb6BchC4vWIk2gbgLomODXC9LNwp5su8SMtAuZKwgbXW7tLjz+175XpH49SQcy+ZoWAa2DvSWBZcH/551P4zNf8w0Yc8Yf/N2/Dn/o0z8Ku3H4aa6ed+Nu/MTjDsw8S+MX//KX4b//734A2CvmD30CnhXDMyfokGDN7A0VOFbgBOiSUXaJ3h2XMwPDxZ6576yoMhCXZIUPI1MNVyfg4sB2BqMCU2xll2huVOmdIeBC54cRfr7vvZ8YoLngy25gSkqB3m1bmb6QLPBh6E0Y7WJsCIOOo7GwupClaL4rlgCHYnxkqAcNF1pQV1Al6HL0ih04ut/MLRFwkmAR2muDXQlNx3uNVi3SBMyNmlfqgzzSPlsfmvZ3WQpchui+7XDU0GnrSvurdwH39k9JtjJMDfD0k4oS7kiLeF0gNcOe2MMneoboMwvy258hm/GCB0xV7Ed4FtQELOeK05M7TE8O2L/HkJ6ZoFPBeFxg+wR5NHGnfhhR72XUUZm1ODrLxJVl4+lUocfC+QNA7p3BUgiPJ2MrgFOB5UwGT7Rf56bxggiZFDhLugupKQ/iSkR6FR4BD0Ef0xUtkAYCap5M1SjGDj1IOlWkmxIuwAky5jCKdLhF6wQV2BAGdv/LuyHzBDs7sMO2gC7OAUCHiaklz2Q1PLRSnoB6MQCVrUC8fc94PmrsB8wFqZKRyl5oDXCcCbYkKgYrIKcK1WhsuRgE9ENKxwIMCbYbIKUiFTIo6g6rIWqGE1gvBlxNZHBD76anOL+Rc8h2AnimcD3sFTwLbJ9ZmeZAiV5WMktvtGo5kTWr29lJBkeKww5MxdUDS/fTycmgMusY1WJCgOq8xstU8Df/2+/F3/zG78WD+3u86o9+PH7rb/mld6Z8d+NnbNyBmWdp/NLf+OGA/newww4laOd6GLkruq5rWTGof5CbCbvdgPneyMXPwQA+JgbL84xyf4fhIQENbekrF/fdnhVKuxG4PvFnKoArfF5CHzNSx9H6PHm0SZgrvVAOe2BQilFzBK85ql5CfGgj2x0omLIQi3QRAPUQKLYLIPE/B/KpYv9ux+mJHCAOvQpJI8BLVPhQNuArcAG2R33s7+1zECxOgA0PIKPS46U31bA7U1AoXT8jOfUqJ4DpFDeyMAKyVrIYd7h5g1CazqABQMOmf5H0VEEzoGsCVxsE5YXnGGbv6UU9Op2Az/dINzPktCAnQYHDNEGWinREdC1PKGeCdClMf7hjuTdC9gl2U5Bnh1wvkGGH0wsGlPOM3aPQFw3KUns47N4eAGAPRtRdQpodeS5kyapD08osqSnMGusm0XVb+n0SkMGTEJkyBWohDo6ZocLeVTnuSAGS0YtFlMyENMGxgdU710sAngQ77FCfPIMf2CpkuDH4UZBnR76coNMMt0r/pUyGqFXyZAPSsQm/vaccZTYyNDuBDgPSsUBtxgIg1TAyPBag+SAVo5t2CrdoYXduP0RKV4KBqw5bTtC4D7ZPyFcL/PoE3w2r95GBc3DI1Nc4mROThHSIVDICMCeFWDyTMJZwd0ND40uVaWo7ZPri3BgsXIillcXHfxYp3GZo2CwjaPi5+idpa9IKoKYNkDE+x80NvKVaHz1zxFf+xX+A1331P8An/CevxBd+7sfjySfO/4Pr5d24Gz/ZcQdmnqXxild+CNwcPg6xm28mcsLAX2MxGjOFpk4mYLhm/XLf9VaDpYw6AIMIcGKPJndAMmlrcRCAuENOM7yUKKlmkPZSyJzsEwTKXXTbOTqocdnxd13HI5FC6qtfOLOmRmvz3+bSS03V0Ks3HBHMPXay18AuVyw73TjGUsvhAQhSBYgIAqP0Tn1AL7eO1gd9qHbU461MCBZMwPvYFbqjl523CqfCpo1kaUL04ejMQvNiEXNYeMIgjNNEwPJbYhLit6h8ag0GvZUYlwCvrj0I5csTnZeHhOV8h/rUkxguZwyXM71o3IHFsHtmwXhZqGVwYHz6BJkqPGUs9wcs9zPKPmO8HJD//RGSBHUgC1EueG9271mQjifUrEw/OdOYdZdQD0wLNr8dhDCWW/MQskKh7eI35iwmUauc6Wk3kX6faSDTdGHe768eC914rb2fuRKtkX40i9YNGXUXFztT4JHnCswCXWhUBwFF6+7AYQfLOdyJawhdmX6UasAESDKkk0AnIzN54lxUALie0Vo8yLSwj1keWF0Vwd2HtLa1gMF2CfUwUFsUDU5lcehUoMhsEDkm6DNXkOsTsB/JsAypa7o0SvBdqM+p93Z8puDU3wQD2kwovVBELgbIVCFZUFXZ821UeKVvlbrDD5nnvjBdJqYsB5c1dcXGp1hBuntP+7oDPkj0WQsg44j+YwFkWrl4FpSBfj7/4Pt/GP/g+34YZ0PGH/6dvx6/77f/Gux2d2mou/HTH3dg5lkaw0BzubrP7JWzB+niapCk9ISJ9AeGTM1FNQr05qgSgkNvZuR5waCC/PQJkhRVmD/n69mE0g8D7HwPXDM4Sk6Q3cj8uybmy0srNWFOxx2AVcgE7qh32kt4fTfAdpnixtipIwvKKKhj4Axpek/vmKMNqRaAhWxQKoBfGauvYuUkeeNoTrAeVvFb+hsgs4HmDhyCVN98nieJ9E2IlBsg8wRfSsRW7U6+PE5DHlhBTaWBHpLdNtKLXalhw7KYRyfNCCwmKxvVwGs7Dea4oB31UEwp1zPyM9cwFZQhoe4V01OK6ak9dk+P2D9TIEtlw0EAcmLfHXFgePsVzCrKix+gnOWoLlLoEkDZDVIydDZWjk1kQKoQXKI620LcFAyLI+9TTyOYKiTREqBZ1Hh8L4uO6axY4p+SWnDbsA0BUD1yT5w/YOVbAGgRBURho4Qm63YclRpVPtl7hRCUoDCFNkxng5wKP+tsT7DS+o4VQ7qeuKk4H2HO9I1aRStT1mjgWqNCDarAGJV2pUKOSwiCFBgyfFQ+S8cJkB0ECk8UzNsuwSuQrxdgqsizQyVFelVQzjKGJ86Am5li6pkgD0MiKHEAM1iy3fyLojoxRcdzpugokMewpnk4QQ06N7ExXZoVof9yg6fMNWM2SHZWkzWfJpCp8Z1EV3h+hkb6t5ekt/RdewbieWpAxkfBslP4AOjcHkPBjVV83d/5Hnzd3/4evOJlT+H/8md/L5734K6b9934qY87MPNsjvPz3vzNg6JGse5MK+7wKVYST9x1zlyYKVgFZDYMD0PbIEIh5xLsTGbAlFKhN06KYMjw8wNZn90IjCOxwpBRE6ufbIic+S7HomqtcW/sEA01K3zMMBfICXQSzYDlhLoPI68ldm5GQauEFIVpCGeVk0TWB4J8NCAllMy0RBeFtl4+ALqOZiHl1ItEHGQTWs+qRr27MAC5RzBiQG36HFZ+hG6mfV7T0nSDvRaEPfpcre/pu82u8+DQiAGt35SKrxxWlNFCwpAOPP+6Dwdo4izqn9yBmwnYZehkyFeKugNspyhnCenSkJ65Ac531EZBSfuPGW4JdcgM/AuQC5CPRqB0f4QPGfmKJdT5SJrE7u3gpUTlDDVVyRwe7EYNl2jLdI8VpJ4SbdccAVBaZ24xh8+2VndF9VJ3F4ySegOwHNZ05DiB9yopAT7QkkwADGKVF3gGZKjALjRdMLg78myQyxPk6tjnvdQZciqQNAN7plPzzQSfK6uXco42DzTZ08Upqh8SS/PjvD3KlvkcKivblM+MuUJvFuqKVIDdiNp6dS0WrRIC0A60YJC5QgaFne84p+eYB01YHvozmRvgQmjX2KuNhpkG00zwmKT7Bkn7PAO9gACyM6J8Vt3pTaRAPcto3dFbXzUx9JYaBoJGbwypNYO/DePWAM5mz8FnBKhZbgOZQUKETobSVfBvf/RpfPIf+3qkJPgD/+mvwRf8/t8EjR5pd+NuvL/jDsw8i6M+/4zVMhqBeCpcaEsldRzVHYhO19gNkCVy/gJWGO0STPc0wjstQNPT7DP8sCPdHQLKPCs8zahPZvjFGfICUtqagCEWPRXYkCGaIMeFKRbj7h0Lz0OiL5DM9CGxUUJLI/QIGRmkczM6c0TXatrc2651pQa6WDSASTrSY4PN88BUjYX9fYg6Dc5NfHR/RugTpAl3gTWoNlQRGg53IfsVv0Ziq4Em/pVq0UmbfBK7ZLMcVlqaqYGaxxdY1a4ViezK+jlEMT2oaFQ3iTBI1AGAC9w1dtXA/GCA1PtIVyf42QgU4OwdM9MH+wyIoJxnqIwQU7JWbnCrMER/KK8MRCoQMC1TDwP0umAMfU8+sWeWw5EfRSQyOlJLwynWRKTSWRv3BGTvYFJFuit18/6Bd+9jxrdujyxs1QDAXWFD3Edwd880l7KHV04Yp3BrFgeOjoRKvU7TcClZghppDSlRBBTmegJQ0yPhcxQeOrZPZKiOC8TZ3BFjBqKPETVk9NiRUik0rkZ9kUY/qGajUIKqGLSnhtlV3Vn6rbULoaU2l28yUV7q2iIDYBpIIoXX5k3rfVWFjGm0MLCzEb7fQY6nkIQJn5VCwCfFKHhOTME1PY9U44aiGRC2zxew/1drkbLdNAAUvltj5KKST32bgV2Z0cbShKan7pVVT0JtmC5kgK3Ze7dhTJ19y7f9M3zz//2f4VM/8SPw6j/4cbg4+8BrTXM3PjDHHZh5FoeNDEgSCx4AlnaeZor+dgM7XU8Lo19KwP0z5vCdPiSwBB8z6n6A30wsTy0FdjYy+N4YdEj0sDFnz6XWlPDITt3YB4VvFajh1jsqfUKO7MskItRuKBdEMYfsR+Aw0tMkAd7KxydAxqCyY4cHYTdrAbjIhm2/SIC2HN4zTkBjg8NMaRSYELtg7yClRiI/zWS0CGaw9vxp6SFlawVRj15UgLiuUS+Ymbb77WCotW8ANmmnzdimTNq/BV0f5NuXbv6uzCgw7dV2sg5oYXWXhUSi929oXbWnBWmgD0g6VQKALMjusGOAroGpiHJvh+X+QIbvVDBcLrADQjcFBvDWk2cy6HEhOzJm+JjQM2KhmxJBD7y8GbHLb2KJ4MKiOUDHkVBEIOZ9oTYIkeYAEP21PEfFnBCgmjjSQnalHgbYTjAsFenhiSmRMdEpOwE+JFruDwoTiocrHOlmBkxRz3esBCtsQ+DeArZAj4Xi9t0QlWQUMOtcgMWgKvAxoZ7v2aQ1WjQgAVgirbMbWB3YBOVhQ8Aya6NeKvpapVOlEeKgnADFOjspKe7JXOklMyqZNml2DCxB9xDxCoSpvkHDuBHs8WQO9QobMvueIdPnJ8AXcjCVleaSBnQQIUBnVbrYFzR+rDvONxsjdTVzDaApdgB9U7oh92sRx5ItUeOtPVSfO00Tx0VxZfTqQOYGInjDP/lBvOEtP4gnLg74C3/yU/Crf/Fz3yz4bnxgjzsw8ywOmWIXBwZ1qUa6+HhiIIrWAlIqHIW71icvYPuM9MyRO9Wz1KOlne/p5nuaQ9zrSNMMTCXaGoQfCPg7KZU58uPECpZoOOl7gU3squv3n+BO9GaBPlOoHdmx8skudjRz2xMs2cAFTI1pDS1O/UDbbYbglRVQYJBzMgYU+kQwGARp4e63REoDQGgsYuepNFWDOvJps1hLq5DZZKcizdRSGgBYuqshYk6InkpcUcUZSFHKCmKE320N3TG2KShvq/T6+nW32RCCBHe/EjY9mETQD00tA1hcO209m0S46w+9K0J75E2H49yJy6ioFxkibCpoGbBD6EmOWEXJShahnWITrfL7VwqlFYDSobbjlwREe/R4PQMfNAClgd+uMTHx78701Bo9uJzzRvmldabzcDoVGgIedgAkfH6Uc2UxmBUyKINA5wo/FfhuIENlFXIsSMXhZzvYEwf4FH5DyQg0hL2QdDFqUsbMZy+n8LZZIJOHL0w4NodOijqv4JuUrBCW6ImWyCBpa08CrBodR/RFQ+jM0LVBnlYg5G7sq6Zke5oDcqvEcwmX6eaobM7Pt7hv1cg47lhC7VFtJA1F8jAQEeKaJtoH1rRpw+lpw6Z5sH+2/tfBisiqr9HN8+qca3Qj3zwiirBo2LBBBgIZAGUEN2CIVF/8/NEzR3zBl/8d3Dvf4a9/xR/Ayz/kebgbd+N9jTsw8yyONBWYJ6jQJ8RLhR+P8NOJYKMWAODidu8ieiQV6ClRnDkX2N64U3PS83aWgQM7WfvMiCXmkOsj/OyA5cX3uDN85sgSZxAsaTUgJ9TdQD3Ejrt0luQa5NSYDIHtBtT7Iys0zjJsJ+w7OJAy1xJApiLcgcEFVINR0jWIN7q62fpLiCG9OBIcPlcsF7HzNNLnVgmQzJl/tyEhHSOdUsDu0WBpb6dF4rhNQeqZ+f/mUwOnoV9ju6R1FPcGkjYgBYjdcRy+ElS1qg9pu1JgLcUG+vaU/4x+U9GRuvUmMqdnh6tABqBogp5G4HqG1wI/G9mbZ6lMUx1nuBWUe2fQrPDFUC4StADje1jqa/d2sASUAZBBMD505EcT6vlIY8Exk72ananKxEo5Cd2VgW0PUJyl5ymRg2nbbY80xPaad5daus82PyKA+gsJpsYHAdyQrytBRk5QhPh3LzBVpNmh1xM8JTIWBliwAV6cItYSYFwEelNC6EqgYz2IBxQVZcCdjRuEQprMwwzRdwme95AbbgSYhhUCh4Rb4m2mFMOTpZZw1Ga7Cwl9W2MexQCEDqmlPsUb0gvgkYTpuw0RyHQT5yIbbfL6SfQXU1uVRKICpMzjZJ6bToWAJymsgs96S4duemT1Kd40L9sp39ia8hjtGDqX7qe0XeCicrEGcHL1lZFEMNIN+LTjO1Az1xLAkaZ4DBXU5cV4dJrwe7/4G/AhL3iA13/FH8AT9+5Ku+/G7XEHZp7FMdzMKDZ0NsTdaCd/mhhQwlHVw9wOsYOSqaIeRvi9PatxtjujlLiztwypIQQ2alX8Ys8KjTkqiURCjJtYIp42QbsGGFlCy1No+OZ1AaxyJxtAxSHAIDD3Xt1APQPY36ZR8iDroFvvemWungwE0wCpslGeHBckA/JZwvSA34s7Wu39pqom2GCouwRdFHpFvRH38CzjBbCCCG/BJVGzMdKjBUuNsvFY3C20Hu169PuBlTc3A6ztpr3REysjs4kSTUvSdsQeDsZ98Y/3xYYfXhEpQUc5HwIwhFA8wIKEI7MvvE9wY9roTDA9jy0MhusJMlUMD4HhqkTFnIdmwuHRZsJGQLIjnQguK4AEDfYg+l9V425631g070JmCSQnEgRCVDHpzCowHyJdZt5F7jJmWHOYDu3FchDIPiFnsK1FWPl7tBYoDyimHR8tkMuFzEoWpntygpt1xwA/G1EHAn96wRBseCaToXMBhgwbc4hQK7/fifPPdyOwAyRHV3AjsGksGEvz2RPKB4UgKtegFEcXiQay6D3KKIIONjL0R5grZGbfMqQUlWKypmDbVCoSWqV1DVF3aqiCBXIXqAXrhRppyrBNcDIxdPiNo/o6TflPgQj1Vt7KrB1RRr/ZiDTq07GykCu853rVNFZw2jEUMDWZQggu/ZNX0XBsNIBodeF8ljz3Q6/O2u74X9/1EJ/0Bf81clJ81m//dfi8T//oO7Hw3QBwB2ae1bGbZ9RpAXYjZNyHu20EiN0OuHdO6r1UPtSFnhsKR01kT2y/Q5qjE7RE+qiRB0vs5MYEvyzAqSA/cwncP6fY92biYjkOrH4ZaGW6diqu0EEpIs4VuH+ALQM1AKcKP2SkIxfleiG38uDsnB2MgxmqCNef1EAXF6yWOmkiQagw4wSNVEvB+IxiOQgbJm6BQhV2yTYaktWdAc5yXUwhFm36mfcaDTnE54tQCLxUuOzYcHOaIfMcwsgQCPeFO0b42jB94ivVnoC+Ostm4e6YJ9iC7fkZICUOnw3iTEHW8wzZJWqDCnUTvs+wgf4uKLtgMKhFytcVyz7Bdgp/3jnnwFKRb3hNfFSU+zumALfMFTj/JNKSrqxUEuO5+pB7ioMaq7jnUcEi2PjQCANyHYG0WBSJ8dr5Lnp6FRrWiYDaFRGCszgOS6uBeqZYnn+GVATlYsB8LjB17KeKNPGeAQmW+H3MKnDYRRqR7I5Uh96c4MNAlmRr4igK3wHwCpkqp0XzdgIoeG5BfWLqzRsgsGB3BoIc7yDHoyKRTEirnvNMsNImQ8cCHnMSMTcqn6s+1TxaGLgxDdboEpB5rZniaXGCQrIoDWxhTf0Y0Dqq25YVaWxJezYMgbz7H/xNbFTiwQnmCdg+EytGCTDWqtgsvKIk9DDtWW5vd2rGWoduLXEqaX1GViZnQx0BKOb4G2/8H/A33vg/4H/3aR+Fz/9dvxF34+f2uIO0z+LYP3kGnSbo9TXSM+zTxP4tCZgmPsDne/gLnoA/7z6DyEJBcJoWVjDVyNsnim+3aRQ/GxmEo/pJnn4EfXSD9PZnuCtNwv4s+8zd+ZBghwRkZZm2g14XxmDjiWZuNXOxlMpO1elUmNfuQTm29gVchJMwZZTargtoFT/Na6ZLSsIIT2usmmZIlzN2z1SglanfAhMSVD1o9BUlvJY23ayBlTFpfhfts0TIjOUU1yqzpcGY4WcH4PwMcthBhoE/z6mv1j3+RADvOgKzaHeAFdwAfLoSNiCq/Vx6mqn114HJesrCLsdlkK5bgdLrIx0dWpXM2yCwPSPB7lHB8LASDA1MP9rZwCqZJLBDhkVFFGplg8WrGXrDKjUAvR8Q2wWEHV7CZicOoHgIunkx+FogqAFAm+AVIXInC2c7VtnpsQBTpE1AhklvHMO7r5EeHsnAeczTnCAnw3BpoWfRaILokOPE8z+V0I+QMdGpQq8ngoP9bsO0ETDIaYYsETVTgtTC77+0JqJhIFg8tDIIfBr1WcViPnmkmywas4Lgcymha2rO1vx5L5VWsFJRQ9Dr3jE4GUZ0oz2HA0qmSWpF81Ti4yQ812BbUAhgERsdX2FKf/7WvNLmeQqAIdLmcnt2OBdswziy4hId0Bg2zxYn7nroCqT2XIchI0ux4zu051Jvn0/TvwEbIIPNWtPJHw+3Ycff+La34td/5l/Cd/yTf4m78XN33DEzz+K496tfhnf9vxak6xl6feRCKALZ74BagJsbRvvdyDdMJcSjSmHgYvCFbqNhsQlAV+FhTsy3G4A8UOTrgNwc4RbNIM931NhIC2wgG2SFyRqLclADVBSmCg97T/Z8YRTOi8ONrqIwIBWDj0IDrtlI7ysgoS3BbNyhxg7UUlTCxMLvUcoZ1l3I1wW7UTHfa6wPyFKFkPLWutzy9yph0Lua5a0kzQoWuIBHsLYaPh0h5lSJdgaNA48S5RYMGqXdduIGiKXOoMQ6G7vN2OkCKyOi8Y8APK3jghpY7uoSnYvbffDYLQcArAwQnli1Vc4S0gnQm5ltJUKH0iOMSGhNLPxFKK7UYw19SADXMKHzJKtgOwSqrfGhJ6yGbP3iAwKl2HyeORcH5ghkCQ2XSLTGUB62sVpTeMOoAEPuATUdK/ISvZhsDfpe41kwsiXNgRqHHSsA6xK6HqfIdkiAWzRpNOpsovzaVeBegZSBADRejCDDV/2JxBzAYtBTgCRlNZwkan1EN89kY6ra/BFb5Ttt3rS5tFjXjXiIjnugdl8r7hqTI+t199gZSBMHt+kqElVxjPqtukz6OfF5aSXrqB7P9ub5aCNSZTxuo3PWV3jM357l8QBEoY0jGItnob148yh0lieu2a36wU73eH92+7AV3JjE91Hgz/6NN+HPfeN34a9+0e/Gr/xFL8Xd+Lk17sDMszhe8ktfgh/6d2/HcL1DejRB5wJ9VIGzAwFDAxOXN5HPr/D9jgtKo2KrUXcABI0d1LXHCncxwpYCTcK0RS3wsx1k3EFOE3Cc4Bd7QARa2ItJ9gPN0YxaB1Z0RHrhLEcV0VrJoh5FP3FOSEwT1IxudiaVzIGLR8B26MmCRuYCSzMuMICLwwaFHEEGyAzDFXfbyzlLVruuJ+QHAvQCm9YiACBtb8GckLnxlYP0DaABKHCt4LVTEOQUshcNEEij7WVzEOd5epQk6eDspRRBghtVD48P9KDSVuUupk3amS0JW3wIOjuADFSR3sDTBvr+UFtAoW3JjowBWtjpuqZ2DSjM1oXHcxgBqwI+KNQJYGzMcS/D4l9bEGuBMPQsUYkiwbhIq7YKfa0sZChqSiwoE1CvESZ6HfxYhRh1QRoGjR6dlaU6hvBgSseZ4tb9EBVuCssOQQZOSwCt5gdkUNBo0nYsN08L/ZLo6TQB08zXnwQ6KCwloCwRHB0a5egEn9FiwgkGxR0yLdwsKOcne5Hx9ToV+LJB2c19zhVYPEBSgApvWhsyLA7OoZbeY6pMQqMV8ziAKQFn65sRfkhoKRwJ8LAB332uSweiHWT4Ck6pS47JmQRoJdfxfu1PFp9XoIF2vw36gFX0GwxPY7a0BvjgVFvBG9bnoh93g94asPOmoWuYUWnv0HRWUMUJjs/5S38HL3riAt/4JZ+BFzxx5yr8c2XcgZlncfySD3sh/uHFSNfNJ3ZQAMM7bjC865qVRJk6CD0tEMxsfFgqMMaONFEPAGwpWKA99NZcgAcgXY/wmxPwxAXq/QMUCn2owC6h7geIAEV3kOo0vdtn4FSQDKx4UQnhLSjgq8ZgBYTxmANeY4HxAFgsx5RRkW8qGZpBu5agjk23wCDR0ietwEeqAIcBfmJJeb4pSNcGfWKHcpECdLT/fF1swz6/d6VWhcJiUfXN9jFWwdrMwWQ9TkM4Elqkxuo0BmYrXmxxSqgBUYBCYl2DFG8INSaeNhx5r+6I9T4hTM/CgdUaYAB0ATAb7CLDlRVGYrTytxZ4RGBJsDwh9OCBrGxEV2E7tITgOQe7lhWWCaQ8J5g39g2AROsGpS8M6Xxb9T41EGGzsq9kr+wsOjaPMV88ql5SsE1K8TGMbSwwSG+N0UuVzYNsZNDWVjrcWA5DzKlEEbTxebHQBok7cFrYriAn6DRFh3BAxzGqszJarzHXBJGyGvg1LVEL7rXGvd+CWYEMMUcaw7EQ/IpIaN2MaawhhyDYIpI38BDHbCwi2rFk1YhpYuVViHEjcdUZQPBWhU5FVwaxUYF9jpP1a6xQA9ISL7EGHjTYN07o/toNBu+MIqu80EW/3rCq0xxx+9lSwe8Agy0Aho0GqT2zwmP2VOxm9Cvk7TrwWbeMSHHGAfpJCt7+9BV+6xf/NfyXr/pUfNyv+AXvfdC78bNu3IGZZ3H8yl/4ofAB8ErAUQWYf8F97C9GjO+8AZKi3BugpwV6maDHaHa4FDaLrGmzioGLVFu4QIMrGxRqGTI/hA0JOOygE63fl5fchw2ZO7oE7vCi9DNfV+BmYaCIVJIHq9EWEXH2vgEccsjwQkEuBXut7SAX6LrXHrgtylvrKNAaOKYJX5s4uLb0BvUi7kJgNRl275mhNqLsWIIt4aHhGn+isTNCC3UnuNDi0VPGezm2GnfBrXrpNn/tPcghB9AJYXNbrG+9fHMrgNAHCFkhAEAjd3RlOLbiSQd6R2IEm8KdL7qWwFujPwgkTA/tMLCcWFvMkd4AUMKnpFV1UY9DcKMOlOYy2/RFQwM80pkXVKa9zK3vwrWxR7Fh78EYwSxAogKM90FCP8UddYr2Ft5TBGqsdJPi0JuJrTLOCdoI8hQ4DJQhZZqzSaQubMyQMQOXE+S0wOcFfr6H7xLSTYGcTkil6dGUTRUPmWLeOD6mJcB03Ijwg0FL0cXNbhgXYCVcS72082xtBeBOoNTuR6Keq1Vis4cXJ5FgFVWvXi0bcN4mWQDLrkeK+9Z8i7onUmoCLL6HbZzkNuif48GLflgAn0tRgQcY5fMTrtYBWPvy0vQygYCsscVN5M0ZipYuoo9TM9Bs8znAvGJNt7aHoc2H7UPl7TJsXgf0Ngj9mJtzXNXL/Plr/uob8fIXPoFv+rLPwPn+zk34Z/O4AzPP4viFL30Rn7noeCs77qbm5+2gR2fAEIHd26PeGzG84wp6eYIlBnh1OptapvDPtPlQIBZY7hZlXrg4n43AoyMkJSxPncMOmc97SxNUWzUQIix3rQbczFyEQvugzYKlUcanysA8JujCtFFrNinqLFHNgIxroPP4jKYRiGKPtfw6qoTY/DLKhxVQyUCwNNQDRLWNtlU7zj+kQmQTQpOjxlLxxiQ4F2GFxhnZyvbYepzO1AiPBxO6xW62qI/vIAlCIl0B6VoXRLCAx7lDevlpT30pL4aH2W77ar5LsNMmsg0KWcKpedS+o40wgta5WIuFzkUZsCQBwsokVFt35+2zRSBeY25E/HOPPjy8WW7eUw0ewZwXlJoQG/i90szOzCiVQGJomq6Y3wjRLIQNTxH3czH2XBqxMg8tlRlaKZQCQCFDuw0CPU6ocKaWzgY22JQ9Rbk3E2oC858NPETjSU+JgLU64GvzSN7HuBnByqFZCwhWdsZ5LJkWprlyVH4JIrUZLElKvBbt2ieJdgw8pkN6/JUOUlrV0xqcGyvT51RgT/aNis9supNI397WNwmfkcQ0o7Q5EIwMovy8z+bQnwm8i4IJMqU/vBrn3h/ofp5xffpzJLGpEUgKUNRYqvacbbAc2r2X9Ti3MEoDbqk/eI99XnsuOH7kHc/gN/2Jv4qP+iUvw1959acjtTT93fhZNe7AzLM4xjFBKpDCYK5GDxTPgulDdji8bWJlRVXYIWN54X1kCPDOp4GzEeV8h1SMu8Eh95JT12A/ANLlN0ukXmJ3tUtdaOdZSAmVKHE1sMlkdCr2yZBuCktgwTJlNrTMqDv6hIhwR+21MMANCTWFnXtaU0lcK5157cjl+0KHTwgrJRjAhdb+AwDLnUVoWgMYOZ9Ufa0gbfKEphoUHgtJqN2pgpQiSBYG04QIXlGh3Kqi+r6vlbc2EWUDLimtaQcw4PfguFk0ZXusXgUdzBZ1q7x2afWh6efeGAKATrVlGzz5i7pTeBpo9NZMfItDYAGESb2L8T2eBOrC/l/qZLwyz43sSjuBmCeikVIxamTAoNdKlltXdIKojdJTBN2YTgvkSDbRzka0zJ4uzo7TujJ/EKEb71l8/13MIaFmJFUDCpBAiwJfHCIGK/Q/oWdLYQolZ5rEHQaaxF2egJsjcO8cIgUila06zID9SBCQBwiMoGYRuBUAOVK4YSIIpYu2gmmfplOpZHYkRWXcmIMltFVfFsiw2xMMZH06GPSypkhuTSSJdgGBLgPEeAAhbyLilINhJBvowAYgY61McjbR9FFpBp6b+Bdd+OuNZQF6zyWUtXpJw/ng8bSTxp7AWTEAKL1tmvzHt98rUtaI50OM52FbgLIBN42T6YC6PzOP7SS2P9teynaiUTH2P/5P/z/8us//y/gj/+mvw6t+52+8nTq+Gx/04w7MPMtDzXtTNyzGaqEdKz3KIWEIZkKqQ2aDPXkOubeHLYVUfPbbu2oH3Iy9kqLfTXlyT4GnOXB5gt3fo5xR0GgJkMTgovNCVkRzK2pAWioMDjkMUVlSKEQGgUkdeRzxADQRy1J1VI3FKcCLVEcdHFCgHtj9eRgVw2Xl4igCL85gBEBnwAdAcoJVWxMYlUyDLAzyNXa2zYUUaGCEoM5jwUYBTBOSGryGyR8iZdRcVGWzdgI9LQNE2XEshggbe++pJ+duOJgNmuhJ6Id4YIlrZsHDq4PppLbIymZZbuXbDroCawSCgxJEtOoYpc4kTUBV7pwV6GXhLYa24UrWpJWAt7SRbYIXu483j5CGdMK0T7GyVxLpg3Yc5Ql7Ad8XonHfK0TD9czJ1GiJCqO2i+bZ9YodKQa5Wbo3kTYNiMc8QQ1NSLBqJmy6+sQ5r2Fm+qyeJcBGDFcnIGfofoANgKQBPmZ66gwpWlfEd4vqMIx05W3GczYOACw6V0dvpTAdpC8Tnzkk7eled84TifnA/mjoQAa1soFrTuxAL8DqMCwdobfA3bMorW+W+VrynnOAV85bcwDqG7G8o4uN27FD1NzAgyw1WjToCqzM6QSdNnoefiKf77ZxkhW8sx9VAI8sZM8CWMjtW97n/IYHWtOvDWQ31Ny0gbpiE+lvigN2agvrwQ0dxGyfbwB4/Xf8j3jDd/8L/P2v+qM4tMrRu/FBP+7AzLM80gSkhfoTAfsN+UCxpy4enYvjCRyUue4hww8jaewwj7LMnbQ63TuDJYcURz0olsMBqRh21emSujikLMEMKEvBo2RVTzOSgV4Wpxl1UNTn3YPsE2xUpBMBjRRh2iCHWCNH1IxVxlvqJBZOnSu0MB1mg8PPFGUE5KBI0cuJulNqWwDEDjJWwPiYukvIp0qr+jER0OxDRLhhjK2LArm4lUF5TZNCFmf34ZkmhF1rkhLcwhrfjDqc2G3S8VTW1Xabo49Ajka1RzWZN2GztBAgkO1hYgFuWp8+2jFCt2IuTBcVMjmtNB0qKGNrIxGipNAQSWMBBiH7BtAXJUk0K430lwIS/Xe4m2dprlYKGiRASIsO0rbkXUwaMWOuvJ4pKu5nBn6ygUxpaqHJnASaEw9xcANP5vRmKc4d/ZABiSqxnHg/GKUjZRZ4ywJ0DQNZASDYMGHzzAcX0BwNKodwc64OGbynfFTokOz7kRsBKMuzlxkQh+hIV2DMTBWZwWVgP7TWkKrv/tGfWZbNS9cq0YVY2ZIkXK/JhsjKDrRUzKCcyL6QEQzwKuaxGZGGeCNdxrnRnJs9h0/OEt2tE98jiJR02QCccCd2tLkdc7ECWmv4zEh/bXOR2c5bi3SPGLobeExJrBNs/Ypdty+3H63OTm5BSTw3/fkIFLMFRiu62YwGBP3WP1eNjQBPnyb8xj/xX+HLPuPj8bs+9lfgbnzwjzsw8yyPFD4hLTil5DB32Aj2OJoLalZ4FtRzxeBgE70dUMKlVIIpYFdbJVtdC8wVeqzQaUE5tHJboWHesUBmhx4nihyHRDnAIXNn6Awqfv/AZo8HgSZOj3JvQFqYfoJSZCxBb6MtYsESmAO6sJJFHUxniaDMjnzdykwjzy8WDunWUzECoayhpYLCKr6OfUkKO35hWip2k1znojJKNzvaFELX6EXkg6M6kDy0LKE38PAmAUAtRKSUOrDkv9YDA+gVUpvx+C4wTnmjAXgfq++tw0jXMLjKmi3IUSJs7HIOAcQUbmxwyEq30Au1CrK6AhI0zUbs3h0EFtJM++I/Ark2x+J8LHb5oflh851odWDNXyeAVHWk40K36l3qrBS9UMItN8qO00bLJAKyF4lVYY3tkhPbEJgDfsZ56zOgFjqUIbGJJoQGfA24u0UJc8y3HCXaNwt7oJUK7MDUW7hhe1xTTM55GxoUyxk6GFwNNiZgVNoJxFzxnODN9TcASAMMVdv9lGC1AtxGObxF+gYJBN0OSDXUwvtqOVEbvATDE0yFOGBm0RTTAyDJei1BFtFzAkvy+Ty6B8sX7GNRRBTQDhxECI4cK6vTZ6yv96t1RWdjSm4QmmVD69EmmweiEymb9ztiHxBMYX+8fH39LUykm/Po59SeUVnfuGUnb7E26/PnAP7cN/9jfP3ffyve8BV/BOf7O5bmg3ncgZlneZC8YIBwcWABMDtwpijnisMlad8au6G6E+QjYLNBs8EkNm6xKNqg7GpgGTIbS7ufOSG/B/DLR9DzM5SLPfR6YjAZcnfN1eLwzNRR3Scg7cPPxFFj16yFIKMmZfl2o+TDRA8SOgEAMlVgDBUrBHDjTjIJrfeF1SR95xQVN+yc6z3gR5clppdgLBXPicyRaDiVerBa3AWrba9ybPksjhs/4i42xMVg8NG5eWSAFVVRSuSMFo+vf7dHL+ve/PKx122XzhZsbgUErP9ua7EBTAVuHFW5Vq8NH1tpqlgLQh4djivSoix9bpciUn7dXjUacIqvr2lBwtyj6bismg1p5yrh8RNAr++mpe+i2bkb4YjcQJGsTrjtjUIhMLLCxsR2G43Lii27AEw/TQWaEkoiiEilkFUYwrRR2aMpRXWPRApSomxYFTAoJDnB0mxwq91BFongxatF4BT4YSSDKQgQaNHfi8yLOgXm7AQdWpa41s2dFiLs8zQmNKM6WGFlVzFUUWhz2U0tfcW/SxpgKUGyhnkwWRdzC2wUAt5Kpok6XmfHagA+Kp+9UWELN1B9O5AC/jewK1jPDyzflz450Um5zjA2xqQ95wGkrem8uv45hLuhoW5kTX8e258NaLz33mB9zCzmmm2Ogc3rfZ2X2PCe3ueb9/nVT8P5DLzr6Wt87B//K/j6P/V78JEf/rL3PoG78UEx7sDMczQEDOKeHKk4ijkBhQjSzYK6a/wt8/NqBvdtr5zG7khvWOeloOwF+f4Bw7uuaCwmE9JuR9AzVwa/nGAj/3Nw98boCMAlPGMsOu56eESAFHYAGagDcwSr6iwsMECmCdr0DJnGfXAgzw4LAWeLjiKh4Wmmde1XUwRaM/4XVLlaSy0RcOkElg/vyFJtgQKZllic0f7urGaKFB0csJE9pBALsVsEi+jG7X17+R+6kRHowc8WNHr8NtC5vXn0x1b3zYvaV8nSc/+3Go+4hHgzAos7LFpIwIVpuubeGik1uESWzGGNwYoKJQ+tT6uG6oFl0Nj0ChkgR6/6QQrthMX9KI48VXYn39MwRBZWvUnvCeVhOsfPFhcyG1HxhGAoEEAaAIH3YYBlhVK0RHALsKt2BlsY2Mqm4DDAxgwVXc8bBLO+H3gDFhrrNRExIEhO8W7Zj/A9fZ8EgCwKHCOYRvm1SzCUHcjF3EyJ3a2XAEa7qCLL0VZiGJCmyMdKtB1o/jvt/kfFGUbhOmAEZNrcmFW7zoxYxG+DA/A8bZDQwpD9lcawqHYBOauv2iyUmGdEFU0H9jizgtCltT5MvE/oRQaIQzQgAzQ9D3qKqU/7bep2+zhsQU38rIOohon11h6Ao65v7o+XRPpXNi82Vtel2ZGP1OS9+v/0rfjYj/twfNWrPhV344Nv3IGZZ3vo5j9jryOtTgMwFZSzAcMzR9qvn0WOeyrcZVZS514rBGxgJ9vdUBLIiQuSPTgAZ8NasVNYzdIz3yqk1qMkmikhh3tYxQvQOrzIwsXBMgOYwZEW0vmYSzj2xgbdK6QY0rVEc8TYfQsi2AJNxOkjVzxXpTlZ2OmnxVeR7sKVyw9sGEgfE+7eqwJqAj066kjgZaMiWlDzOoTOohXfNDfV1gkZ8BDMUnSpVeBiG1aGwOnHI2e2i25f7A1r2mU7Ngtpu2dbqHMrA9XNBCPot18oOrhsJ9UCkY+gf48TXKFQRC3tg4TApG9gPUCFxamZAB7tBaCRXiBIgDnvT3we3ZsFitpuKdM4J4JyHwWwlcnpe2WNINhK7114zptA03b/0jVJBGcaYMnTmhJBgAqd4rybaDiud+8uXwL5CQBV1MPIaxHiXTWDS2hmYHDJwSbRhRpj7uk+D30VlKwotVaIBq3sTaU5Ooi3e1q9B3ZAu1eSZUZ4iYabcNCJuAtJBGoemrfKSq2WWjUL0AoYyAKtnavZgNXBQO29d1nMAQisUy+hm5EmyI75Y2GauJmXXR8Wa1gXyDdQi/j3BrR0RmWriQlw0x+Lxx+uLajZfvZjP+sv93Yu60v6cdqcjS/SGLnDu+vaXw0AJsc//Y5/hU///n+Pv/V1n4vdbsDd+OAZd2DmWR5jUhSvt55FqSAwCOocwhRQrd6rIaRUyFyRk2DJwkU3J3Yanrx7S+hikFPhg70faO61sIKC5aikytmtVtHzM87PNKNoE0kgbrCc2CuxGrwo7KCoSaCTAceCdDPRr6ZSc6Oq8DEFCDJoFvg4QA9DmMk5LAubJDYtATb+K0KgIa3/ighbBBBTIF0voXERyPMTSaLZAVPk2bAI4LPBDwofvC/unsNAT8BdrQsdR8FqH4kyYxOhLkW0d5mmBkdur6ERLW/tVldkEatnA2+bu93iR0Wvur0lvtzsbPk7WdNl9tjhQqPSu5BHWkoMTINIAIEApx4LeUtxbTfJbYfe/eajgoxVZJVBUTKBlYIMS6lsU5CdQlyhWDhVQ7XWFJJ+MuYeiCfARTAA7sYqpw7JwqK+rtfRkQkqKroHD3Lc1/AB8jBm9HBPZgVcfNGh3adgNHbDWjFTGVWpP8t08m3AowG0cEmm94pA3cKjiednUVXnA4W+yIqKBD1V6CkE0SoBBiVSXOiNWNnyQticcY5mkcLvocXjfsa9HxSWyf5INDg1hMi3gU9ZAZ04UJSXvl2zdl/dwQjQkHHlQyatzUikfVZyUjpR43J7vrfpzn8QrXQQZJvfbxkXxwqA3sdOYQtyNrr69xr9kLb5hzz+Gv6CXeEdh/cYu3S3z9D1Te981xX+t7/va/Ctf+1z8aIXPHgfn3g3PhCH/sQvuRv/Mcf5foySyfVn4qEbKc6cPgS5VMYz46ouZpCbGZi4O9MwImNvEunVDg6DXN1AjicAijok1MMIf+IMdthx95hidQuLdYpdKyzs+NUc6VTDVbb2FE66mpEvF+wujSwOWqrAIZfXwOU10s0J6dEN9FSgpUBuZuhSgHkBYNC50iunAsjSS6k9GAOPKhcBqIc4VchUgMUIgEYiEN0LajP5UnTH23Q0qNcIflzdmmNoW7jbv6sGw6DSd/w2RBoutBzcibONhIus66WDAktpeXhZmYHWZBJrnADQUyfdTj7SOvwzUnY9CK27207fe3PveWzEit8BIIDmOtuM2Frgqc1obDsaaNL1u7GxYmEbhCyQnCGinIel9rYDaBU57RjhzyNLhSxAs8hvJcwE0EBrGQFtfwawbStSQvdjWQXKQeHEfSDqD8YhKrZsTLBdzG+JZ0Izu4eHBYKNmemfTCbFE+BDhqTEjvKHIa69QrJSKzMkIErGmboJUGaI1FsLyhF1IRQnCyAzn1kXrGnSAGctxSTOjQi1ODy+BGPbLAqCZOHtHRPKgd5PfpbghxQbGn4fG+gIXgZAcjuvAIDtcsbU8U2aVA10rg7mxLPQtTxt5kdkpTvDLJsDxvxuQuftHGsmx7enn3fmRDbH8NuH5Mu4TN0GLZuNwOPPVX/r5udwIE2tQq59jw1dZIY0VfjVjN/7h/4qvv+f/39xNz44xh0z8yyP590/w6Mfu9yIXcGKoMrg2ESSOlf2rKkGOc3wpUJ9hswL9P4By2BkFoYMGx06C2A0T/NaoTrw3yXEkLHI9d09wicD8UC3nikK1L0gHx0pC+qg3Hm6YLgB8uUM3yXkd1/zsxXQw45f5uoaQC/E5f9Hiob16WtoTqzOCH+OCsB2AknRhA6gILOJFbPQSG1jMmd7RTVgvp9QzwTpIbUAejJanZgBU0UZK+YnR1aTRHrEAGgzw0veWaCuPIxyDxOhB1w0XPRwjUVoS3zjrOrA7S2B9P9tXkAm6LHt6wbprMDHg4FhTPT1eL6pOmoCXCe7I41p2hyuVZF4q9CKWNYaknbg1AIQPLpS898an2cisCHD3dhJPQCsLJUBfpe7rw0FrAYtFakCJRqWrtv59oGCbj7Stv0GamvEWdXU2ChxYKlseTAkIDtkqVAkrB1PPTyHGHDNFZoNsiAANzUTza6/CZ/roJDEFhtiTu2MCAGMcA5aThBppeTrBbYxQVUoek8bhq7TXhJOwh5tLozgRhXlAAwWD2JPpzpSNKr0MUV6MS5dEvg+RS9QDwdh3jWaTSpgDqtN0ByVfrFh8RLp4gCgLkwZk1VSxvJYG3hbvINQTlPlPGubsAYOWgWVrF97ewluwW5HL+l3YC3Pfm8K5faztGV+8NjvHv97dRrxCVZjy6454xF0AcZLsmo062vPjSFfF+Qj11322nL8mT/6enzWq/4TfObn/GbcjQ/scQdmnuXxoR/yJP7dv3p7DzrrzsN7oGP6BdTDLBZiST6Q/UEVpQB3dHqIDI78rhn56RumR+qJ9vcOyC6zMWHobsQMMnHXpSnx8+BkWxJ3ovM9vrcOCaIO94QKYZ8nALIbuKMbhzATS5DDAThNNCQTAYY9v5MZdMzw6sg3E2yoiLgCR+7NBiW0FW0TKRXcWYdGohmL9aaElYtyDt2QjYBXxe6mAlcVtqshckakAagTUAct9NFswOLaFAAQeHKW6GqAmNanyQnTemn9Bpd0SPL4fV1fcnv0N2y4lkgnEHi0SBYvbimfzdtbf6mWrrpltCYEFoJVB7E9KfdoL9B+VrCWIodgFQCa2BoOeo/UOLQgyp+VniuhAYFGhV1LocW8kz7HN/O9RTyLVFNLL7X3isAyhc12vu8Oz2liNRLN75jysUF4k8Xhg6BoRrK6gmSLy5kTTC02DREtU4vQ1G6ZAZoonG0ACFONwi5nynafmVZaQk8kce4LqxRtx+con5SMa5wDJBq7noTP72KAhwdNSwuCwMzbzFHOOxvY/qG79YbvjOVocSFA75jdaT0+QxLGhx0cJelg2eL4vVKpXY7HZm3veG2gHkdiLjyWJt0UFfU/2zPtj53eLcDim7m9HRuahY+FrC9sn1EAr+vzI9GmoT0nTeAvNVhwB/2bAKCyXcpwrJDriXPkbOii7G/4G9+D73/rv8VX/9d/hHqmu/EBOe7AzLM8ftEvejH+yT/+VwBiHQ/gAol8OZzlzVPB8PQxgAFg+4GN80ZS3jLSc0NM6J+iBhkUWgobx3kYguUI/LFzdZde7ioAMGZoCBmtBfec4OcZ5aCwPYO8zhQ51kOkBVwhZYFPlZ8XDQwlJ6AWyEw/G0nK7t8ukMMIAV2G5UQnVq2kzMU8dkoRfDOpcngjIxxmyk7LTuPBulOk4pAbamTKGUWPacmwYzPA80jvbLZ4IrBIQdxahPO683TR0Oq0HxhYwmNMxzkizdAWzVUc20d7TQMELX2GLbbYrsjr51OzEQsw5BaQAVYyqO9ygSiF5TG2pamtyaFg/SgxUJsB6a0vJMBR73kT9LtMNQSyLKd2lx40W2orbOCCIYm+UU392gLM5uu2vj+WQ+NSrDMriKDsMW/ZXiC8UsL9Vhdjhc6QeKzqBFtHpyfRsKaamvBXIlp780aJ68FdPBkbllG38+d/lhTYg+XfCzuQe0lY7mdgVKSrEg04DbJIT5U6uCHxIcT2CsjsvTu0hqC96Vw8tzwO2g2l11AwtO5xb6JpKsFsVAh2UCB9mlvMCcA7E9MmD4HF5np3NS/vW980pe0U5fzSxuBsQLu3jUibmMbL39JishX/9u+3+XMLtB//WwPp7dnYbha2oNnbD7BqccwhJXqGeQMy/EJtXYE4gczVhHo2sOJNNsdIgh/812/D7/89X4u/9Xdefdfb6QN03IGZZ3n8sld+KIP2DpHnRwg5mR6RhQuhmkOPM1sVKGjsdTawUiSndRcFRzJW5FDcmyCprA97Sms/FDiQAXeF68A1pOXnS/T3cQcW+mBIHYOOFchskGPshlu7AkOvRII7GwfuBRhHYJq4GFcyM+IAjlOsRBVycugg8HyACVDO2y5bOmMgsTNfmyM6MUEF8rVj2dGjQ9xRs8CrQI/UJlgIjsXoQNts/LmjjZU4QrCohDOudnGqABSWNuVxIBWHdoGpVKGgtFWXxCXeLs49KLZVXR97TezWAdxiexpYapBENkipf852+OYDWxqsrfoSC7kDttXVRGWRJwUGRJWHda2NewSnEPyKRGBvzrPtuzRvk77TDqFrZdTrhm0gOPRtukkVdQQwhVuut4uiZN6EXkLtonhB9Acjk+fm0aOMgEbMkY6Vt0xj/kXDaCA0KOYB2OK7DAJZLKq54uKL0JXYgRrXyBNBcAN5Wiw8mgS58P5IcepjspEpzAJA6RnjjlSM51A3ouXwpRFzVAiQQzfW4nEWelOF2LsjQ6dAGPFdW+Vdv+/a9GcEnp6ki+rfa2zxfgrRPbC2xtgAjy4M1vWt7zXaXHgMq9/6/WPMz/ala15584vA3kjrewjUg2Vs2eAw7ONaBujsGCam4OgpJagJof9y5JsFXupauh8g5pagWYF3PX2D3/Xpfxn/t7/3J5Dz5iTuxgfEuAMzz/L48A//kPCcAOooFCZCgDFBH4HCwKXClwWyFJZfptR3iW0hk7arMoNMpNohCru3B3BguWjWzULifXE2x+prEYugC8u8HQASkBaDLYY8CUwMOlWkucCPE3vcpIT8cEJtXjXCRQUa7Al2MI/8c5iRIeUIfg64QY8z6mEETCGLROUIhb6upOd1jmqSbrkTQcaAw7sXzGeJDI6wDYIvgnlM8IHtDERs9cxqQ7EujEDk8hkIWBkUFHzTDDD3Egs0d/kWbJaUBhzQvSzaOu395xsA4m2Nb4AKXfjcKlh65QW22Gd9/TYGtOO2e6CtbDYW81vxxB2p7ajDHLB/J2MqwsOnY1MZzJRT9H7qYt0mgnWE1iZ+LvG5FoW/2yCrDUNK1zRYgAobI4ZszfOEvbQsJQL8uQaIaZqWmG9Rfde+S3PgdQfBLpgx9AzYGC7Ei/X+Qa7UhqXQlvQghgD5cJgmnrM5kBOPO1UM17FxEKUvTw4QaADmNU3VStW9i//jmsXc1EiX0XtJwAlvaEJpVwkTQnTnat4i3oOmgeqTw9pcWz9nMxHQS+UbBt48I9vD3MIcbU4At+cHsH7G9vUBMD3xDU3H1T6PaAzrs4X10jR2sr2s4TMH4tn1nnaXEs1rHV0bw2eArFY+VaRTfOdgfN3Be3s9I10XyGIoTxxu46444fToSNPR6jhmxaf85q/Et/791+LBE+e4Gx844w7MPMvjcBhhTVzXqHoBZDbkiQI011iw3YGk0LMRNg58VmvTRkb5JjwEkjOw28HOR5aDtuoQp39Gp5JD30JNChkYLLaWsjZlvzv0VHs/xaZXEQhwM0MDTEkx2F5QcwaOLdZ77Egz3BaIFS6f8wxo7GhOJ8gkkN0A2edguyXs74VpBhVYs/CfiAI8U4TqqtDZkAfqE2q8vu6B5bwpDQXu3OELIp8ObFIz0iPXLQ8LxO+AvrvtIAXBxjTTM2nVYHG4rbZF0BdEv33UdYHexpoWfNqf8epYl1fCpwcE/kSCzJDtyi/oupDmFNvbEjSWXEjBs6eTbkBdCwiCJmZdS0ICyIis+opAi508iiCGSBeyq7tE8CNYtCjJbufqCPYj3obwXWnpuxbcmn5KF+7EPUUrEGP8b9VPrWcR4rhwh8+GlKJybUhkqdr1a2DAPRxtAVENvQ1FtBj4mc3Z2BP9kqSsN9Pz+p10pqjah3B2qRWYjFg6U3zc20koATLc2APNDaIER8gCg63zIK47WbUVKd/q8wT0Cr9WXdfn3gaodH1MIJkOZJonzwZluGzmfzM53IKk/gGI68QHQavcbjECdFNHqVjZTWnzuzFtcZ663h5+T76mP6rmLAAoTk+rtAIWrYbhulDjs8/Rq0phVpGuF6RHU7BiGgz25guVgvFtl5uHlODW336JP/Ab/zy+7fv+M+wPO9yND4xxB2aeg9HLT+cAIuYYH87I18HEQHsXXxGBD0OkgtjqoJnLaQFQHSUr/GygNXpWIJNJMaH2RsypuUlR6orQxySBToBGnkAAeLGofgJ0XtjMsFaWIZtD9iNwPtLbwpzNHyMw1fOB7Q+qwWWhngeAjwMksRS2iT1hDplO0Ic30LMRdVSeOxxuwuAhQvATpeMSJoOtFwxGRTKgDIiFcAMZWkwOBsAdUY2D2NH5BtTEfYlt8u2S0hXUAHG8FlEaHQ3pFTJIwVLVzdoeO1O+9vaazzjk8XfvQbv/Xm+/TmQ9z3WrHM63j6cEekUQAUvXxLQApgIM6KXNvVoqDOFu7dD7526jV/u8TTBqvw+X4AZ8PFI33XQtinmQsHrXgGDAY2ctoqyEWyxcjiPVNQCeE+0DFqOEJAtq+LWs7S4kehdFyXe0LUBFdI/XFdA7wisIa9VO8m74pgCsgIBhrlGyrrDDCB9CBByl1hQjgyAmJV6yhX5QMhsEhqoD+0UForDmNlw4UbWyLYgW0JdmaeASK7AWhM+OxWd7lLPHbQlQ3ViwPq/TCo7b791XNqOlkTpydsAQgCQAr286qd8GMvwhncFjumwFwkBPn3JKrCxRtyDYzCsJMG5xQgJ+Z9t8Zj4a8pHzhPqhVpau0GpIJzpR1zGhjiAwXBzDbAR80wTfHTqOcQiggvFHVyDjtUJKIUBLirrM+H2/9j/H3/uBP3eXcvoAGXdg5jkYdhBIAfR6IQV6s0CmGSopNBrgwrUbmW5JtE2nWp/NHD0Ueun6RMHhWe5VPnWvAJijt9khlxPybPB9YldiJ+BAYu8n2+0CeDBlZZFmkAqKetvudYjdfQEwcsdbD7nT7zJXoC4EMznTyGuaIDcFogbsdyzVNgBLYergNEHfcwXfUQ+EqDJSUBxt/Pp9F8t+UkBzPYUI0lRRVDFcAflEX5x6WEV6vSRUA2TYds3csButmkzWhf/W5tQ7Ob9iiQh2t7adLbA38NDAR9vN+rr/6+/5iYZv//R1Ny1YfYvaObe8FicKWnlyZ5Ag1DmZhPi3fR8Gxu2laY6x3bOlbdG3lFJruNlSTUAHLhLMoyH8aDTE11X7RXSAx5awD+j0jJOdaOaFDWgWge8S6ijUO1TO12RsQMpu1E6NSfXu8ivOYC8VgNBN2wTQJYKvtfSisjlrDUFwyvx5NJTEqGgWR1Jt9SpZ2DWcPn4SFTTSMmz8plnoe5OlzyuprU4YFNu3Cdbu3UQtk40hYLoFHND9U7qVv3rvv3brXgK3gXo7grIBrhfE9UcX1t6qTNINCGo/N94XAqEtyJXHPmz9nTWPns32o7/EN39RpiNdAMwVaXFWm+8EOgbAWixaexiyO3wm26aTwPe6tpEYo6BhUOBUkU6VwG8/0HMo0X9LipF5m2sw1QosC2RZYPfPgXuHuNYVx2r4zN/yOnzLm79sZcXuxnM2nlNZ9ute9zr82l/7a3Hv3j288IUvxO/4Hb8DP/zDP3zrNVdXV/jCL/xCvPSlL8XhcMAv+SW/BF/3dV/3HJ3xf5xhmc1QRAC9maFXR6SyilFb0zdvAclYSipzpRdG2Mu7sMpJzkYagZlBb2bkdx8hVyfIoxP0+hg7fmMH4qsZaSosq50Lu2yfKl2Cw/8FYIVRucgo9weUezvUeyN3uSCg6sZUhp6nFjD3j6sT9N1XSO++ghaDJmUH47IAp5kVWkMGlILldFogJ1ZtdXFfcTYYnAsXmFaGvEl5tZ2quiNdF+hsSCfDcElBsrcKjgRs20i0BRzWjtchR19HbwGNbRn29uftZ48/RQ2Mhki2H72lPbb6ghiy+UsLFvJeH7p5rQeD0I3bHjtkpC8E3ltVoLbr2MACmYBkFK2yOWT7zsYy/mIBfHzdpQbC65rjYGTavXGwEqx9Zbv1fR0dD7Vjtf/avWgGba0kP1NH4gmwHMCgGDyz/UcdJFIWBp2NTEe460rWnhZqz5aNCg+E6zmFUaJ0XxUbANun7l1Td5FaiYoiQZzbmOIZNbQWFk2PowDbCwDddbmnoDQ6yreLsjj0VMi+xNxuVXi3cYLcnhOG7lLdjsYO2/x5n6INAzW8vZ3P7Wcb9qRnq7b3N17joXPqr290XrXbhnZ9bWCpum/PvMT98M13cV4HqbbOxYYCAeSrBeOjgvGyYrwqSCejnu6mIp+WPtekM5FkXdQBPx9RzthM19WRH50wPJyYTjwb4LsBvg+gOhXItCA/feyMDOYF9qKngMMIfdcl9MfeA33b09B3PIOn/+078cd/31/B3Xjux3MKZt7ylrfgVa96Fd761rfiu77ru1BKwSd90ifh+vq6v+Y1r3kN3vSmN+Gbvumb8EM/9EN4zWteg1e/+tX49m//9ufwzH+aQ8GGjY0RSMqKjRzppSSk+TMXUVkqXVdjh4KlQE4VUitsSGwfEDS1Fj7EaSoESRNtzy2CayuR1MX52qXSlXfmnzrXtRLAhTn+mWkrmWukUAyYay/L7S6zKqi7zPSYVejNCfLwiqXZ+wH2xAV8P3J3PA7w/Q62H2EXu7VyYxCmHqpjuJoxPDMjXc7QY+lGggjmqIwIVkGxf6ZiuF6gBgzPLBgfGQN4Y07afzF8A3I80Rm329KvBMq65sd7HydI2jG75qZ/QFyTH2fH1lyO23/Y/l2xsh7v9cZ2b+Lal5Ug6tKD6Ld060sEAIE7TdRChOAA78/ivKe+OR+nFkXC3n+9HtJTUN37p5Vkt0qncPBdUyPSz/99fq3NSuTCjs9Na2NZKQpPtPK3QcLYjJVOdZfDHTqeAfPbNzGG5dBLKNhTqaX+mr4sGECmihSI3mG6eHfj1Xi9S3SzToBrQquA8ix8jnuwR/dRQsxdzJWtPiKla2eZeo5Rb0+GYpHyw9rnq9KbRuYKmQvvf/U1XWp0EZYmcNfNBYjz6XP31s+lNyBtG6110wB2HG+vbfNJAVSCCi1oTgK8pg0x+WZySiOg4lpOxlL1xfmsmkFPBjk60o1BjxUyGdLlhHRZkGZW9aXFkSZHKkA+FqQj+8PVkddfFue6dqyQU402EUxHje+YMT49IV2emIofEucPFLoYcFyg1xMLMULDZE9cAHDoOx8BxxNwPAKnE/+7vsa/+Uf/An/hC7/hfczqu/Fsjuc0zfSmN73p1r9f//rX44UvfCG+//u/Hx/7sR8LAPin//Sf4rM/+7Pxm3/zbwYAfN7nfR6+/uu/Ht/3fd+HT/u0T3u2T/k/yhAXiBk0XFsphIzVXENrENoQGSRs+dsu36FzgcMgbkBOqIeBQjqh7T7ORsAH+MUODoMu6KZavkuo+wQpgvyemYFxPxAUZXpd0AysQJcKgZAdmQqp7kxqVoFoUunhXhyL7phRnziDDomtDG6O8HGEpwykBDtLSFMBSoUVwM538EOGnaVuCUKPES6eGmk3cXaGbuyGGJCaeLX1twmvEgU74eadog6xwpYN86Irq9DBg1IDJFX6jtgcvS/ONgJvc/tbRqUFiS3D03e7/S2PHWwzbsUXfewHALqXhgQLALkV+x7bbK9+HC22KzpYiTohbBsTSrALa4fhYBa2YKixJwFeeT1k/f7tWsj6g1tAr0XdtHl9+0uksaQpXSMYNqM0aZUqKcGcaQcvjpoAd2UjVAOrksLN2EExvSUyM6bOlOri4UPEDUPdVDnpYvCRXd9blYwDPRXVOse7KDVcXnspPSz0WCK9VFw3d8UTzQG1Ojx6MHlsXmAWdxV9/ouBpo0pmBrzx1IarP5D06d5XOKersMt0M3rvLnuLdUn6P43YStN8EGZGzFNmM/xkDwHXUKvU6PDvDoFy9Wx3kZZU2DuyHOFTg51gaXK5pwJdHauFNQPx5nsUk5Ij47AUmEPDiiHEXUU6AwMDxf4VABXpJvWzIvnI4shD6FDDu1UXgz54QIvAHKid9bJWDhWqb/SuUBOc2i9wDTixQH69qfJKE8Tr2toZzCwGeWbv/V78bJXPA9/8E99Cu7GczM+oDQzDx8+BAA89dRT/Wcf8zEfgze+8Y34nM/5HHzIh3wI3vzmN+Nf/+t/ja/5mq95n8eYpgnTNPV/P3r06Gf2pH8KIy8eFUQRLMJrAiUeyHC+pSebsx+M0EtGF4cpgMwyTjGHF4OlzO7bAj5opULmGX42og4AskJvChmeMaGejdzlPH1NcZ0Kyr0BdeRKl26MjFA4BmOXKdKNKo7mVusAoICasulg5Ov9fA+MA+z8AJsmiFWYDLBDJpM0F/j1ROHmPrPcW8Bu4WF4ZjloEooIuOuNHbUD3BG60/0zhM2eAHd2LE6FQSWgIEFJ31VySe5l08oF2hV0B462EkgUfmrcq9aTR+LPdrjHIcoWXHSvmsdGf28va4p3CsKLZeNdE2xMB1GPA6wNlFl/1c4ZWFWdcez2i84ACedYe3NUdmyridZUndw6V37Hdl3XC9DxTFTT+OZirSW9WK12HAzu/dTXc5IqHTDDHXZIsB2CeURUB9Kzxktoq2oLud7njCA2B+3znGDHgUizGWQxOmbnyDstlaXTrH3n/Wo6IY+Gk+EULa28P+4JGTJbL0Zr9ggKVuGAj4lsZNcuhShW0VtxtO7OsgGZQIic+5xBaJLiO4cOR7b3qE2lFJe4lUVvwbOBomKPZ5FW3XymZn5nFzo/ewULDxIBGuZIfyuo3RvW+ZMK4HNBfrgglbZ+eKT6HOaJr88E2/mZm94+xA6ZjBcE5aDAaNg97RgmdOfo/OgIOc7Uv+wyfJdW00E1mj+WgrrLqGNGKlw/fDbgtKyashwpxgbKI+WKOTZ/5sD9e5Ccbj3W/+3X/iO89MNego/71I/E3Xj2xwcMmHF3vPa1r8XHfMzH4Jf9sl/Wf/61X/u1+NzP/Vy89KUvRc50q/3rf/2v42M+5mPe53Fe97rX4cu//MufrdP+KY2z64r5OCOdrNus98W1RMl1cS4kDrIugwJjgoPpFgZeoQcNAIDW8m4AhsQqJDekE5mcMvKDZJmgD5lS8dhOy1JhqUKWBJXYoUfw9iwwU4Kr3UC63x2YjE0gi7PMNVNYKU10quxarUmBDNTTAjnOSBa73kOGn49cDMYUHZZjxOJqu0TTvqzQxVfRpNCsLM1Yq8n3Civ8B1syUGSdBJCotpI1jvTAyS8ZK1KIHx0IO/9YuDIX52YJvy78rTw5QEILNL6CHKwv/XH4mM13/vGGP/7f+4BQj4Obdo6RAmypK2Km9YXt3IkdNgt4ANz+9zi+t8C5+T5bYLLhhfi+zWk2IfaW9Vnr5QPEbPpe9dd5q77TaCVBdsXCj0huWL5vAQi0kkWogf9a8Rsa6AnQIeDOm4BZ1jL7CLzWOiOOGgDIyZiG55GERqQZtzG9FGguOskjrrdvfFa8RGfuWle/I8k812ba6OgVYEBonVqVWTPD62VLcV9SuBurrLqU9p0brHNWMfrm8ouH7rvSJVoKkE4FelyoHTof4Tul+dyjmX25sqKmSP9ltjzBAuTrAi8Vdj9jvq+oZw3oCHB0DA9ZXZRNWKl2c4S7oI4Zdn+Hshsx3xfUw4iz5QB5+yXsXLA8bw8/H1D3IKiEAr6wN5YklPOEdCXQ6yNw/5wawiywnfL1i0GnGVhqpPjjkkzU21D0u7BNy47FC3pagEOGXJ+6MSiqAU886NdcBNTVOBmz133hN+EFL30+Xvmrfz7uxrM7PmDAzBd+4RfiX/yLf4Hv+Z7vufXzr/3ar8Vb3/pWvPGNb8TLX/5yfPd3fze+4Au+AC95yUvwCZ/wCe91nC/5ki/Ba1/72v7vR48e4WUve9nP+Pn/ZMYZBHY507TJEUyHow7s+Otjokbl4Yl59HlhJNgNbGVg1kEDqkFPM6SyzUEdogvwmOEX9yDVkK8X+LGijopcCuTRFfJcgHt7iITlw80EwCHnO64TWeAzWAlwb+wMjVT2OvJqkHkBTgbfj5CcoqwUcK8U+CYa6klxiDpszEjVoc8caTx2NqLuBpjFtrLpWjTAW9OdaugKU9NKgCXFA+lmVlPoWl4cVUNSyFTR/b1RGrfoDDROg5vZtkvdBFiiofgntQjStBbiUX4qaxBOgFuAnFt3Xd4b1LTddPvHrZff+uUKHBqgaUBh+7YGdAIxuPv66waA2nuablTad7mFSNCFvg19aDvE9ue3j9H+vWaYNqLPzg489j3b/W4EQ8EKFm0tRRcPZqZdv5k+LJYUMoIiWmMqshwEOitkik9v4ldbHaTbyYp5/162E6RZCOS3bEbSXkJOkTOfPQp+yftJgDXEXGjHFQS4iOvHaqrwqRGlcV9obNTipCIlQxKKZek9PegEmZLazAV6GbxL/26+VHZ/HhKwS/BG69QKzxl1D6QqZEId0KWy0i9K25M7ysUIHwV1FEgmQGQ3dLKW1gwBk674NAkqKKpuE6F10JZq0KlAPYCjxjN6fYN6sUd94R7LmaCcJ8yDQOcddpc3sD1NB+tA8CQGpOuKfFOhR26S8LxzLC88h1yfIGMGYPA89CpFrdTnoBpfc2LbCi1ksX1MZKJCLGTnI+YHGcM7jsg3S+QvE9NKEte8VgKZWuHzwr8DeM1vfR2+8Z9/JV700ufhbjx74wMCzLz61a/GG9/4Rnz3d383XvrSl/afH49HfOmXfine8IY34JM/+ZMBAB/xER+BH/iBH8Bf/It/8X2Cmd1uh93uA9vI6MG9Pa5/9BlqPGIhaAufKAWN5XxAGhV6NTMnv1RW/8DRtpyr8R6iBUGFjo6yI6Pjg8L2I+ohI51YEVWyQE8D5DRD3vmQC+DAHkpaDCbg4pEFIgPzygBsyOGNxl2thb4hXc/cwe0G7nhyhsXOnfbvgGaFnRz1oKh5wKiC9PAEmWbgwQV8l7gzNIeKRONECQ2QR1NI6c3igGCucuzGXWOnvakAS4K0MDXgAqba0m22oF33Pno8b+mmBmKka1i8BxvQ76cBmibQjAoM946B3v+xjfPts9FSWus533qt9/91EgR18+G3wE4oMh4/ry2OeV8gRdadfKtCWl+/ATKxYX6fOTcQ9DVAd+sctkZ+wPpdo7pKdE3dsNdO2NcDkFH7Ne+eNKOijgr1GmlZZTsAcyT3VS/lDZTwvVKiijDAQ89PLlHVZUQsdH2uXRjdgZ4hmlaii7AF3o0UAVZctUojVYWNygaSbd5aU808dksattXNvBUA4l1sLJWid4XDr06QU4GOA+xsJMuZBNUFy7lgeSBIE7B/RvraoPHgagAtdWBykJnMLImWB7u43gITJSMjQCsjryN/7gMbgqZFAaWTd360IM+gUFsC8D/vHJqYWrIMLPcU5cAKspsXZeD6HMOjGfmyQqwQKI3S2Sl1gZwmjO9wlCfPUJ9/gXxdoFMBIPAxQ28WwCo3iObAvBAY5wGaE6g/zGyaOxfYxQ56qvAHmSn9GiX9UOCwDw1jgJjjCVgWOrYLgJyBk+GzPvw1+Or/55fhl370h+NuPDvjOQUz7o5Xv/rVeMMb3oA3v/nNeMUrXnHr98uyYFkWakY2I6UE2/phfJCNl7zgHn7sf/7RDU0fu7zK8unkAtkl2MUO9TwB71Kk00LGQYV6jkJ+2pNCNEHE2a1YhIBAwVLrAaRNB4NOBssJclYhZQ89zvTTOFXoUmCXBs0CxwhJaaVSDSG01fD6CD1PGI+pGdLNCRgG2A6AJu52UobtBKcnz2B1j8hcwLNiSIJ0dUI6LZjLiHqWmE4CwrGXkbGxUGJOwadzt14GduEG0FMqOm93x/wjXzMy1gsl2NlSzBHzm4YA2MRhiR11q9SSYGBEVkCJCNAhHupeLRFt3Ddmepux/VFLPfRf9A/fngwe+7fc+nnXm7Tv1QKebd4r62Fvf1Fg7dC8OSn396rQaoJfvofHaEG1gxSsYOdW+mnFRZvPcjRDOm8Ga9YYJO+C8P4ZKz0BqY4kjqrxggJAKCrFdYFOul6S+C6i/BxYpFNa1Vecr06R/oGHM3JdBbctnynBoJh3hqV/2WJwZ0p2e09u63X5ZaQ6GcD4U7o2I65Lc19ub1MwjZV11UvVzT0yh94skOPEYy4VOi2ACGTJ8B3L5csFcHoKKBeC4Uow3hjyI7YD4DWKvFccP19XalKMiMnTKgzu3w+hI8qRXjamm8RB3ZEDaTLotMAGRTnPgAB5dkhWLON9yLwg3xjyZYGNmWApOdwL9OqEVAXD1Qi7GrHcc5g4S+x3CVoG9lh6+sj2MEuhmPi0kGk2hVyeAoQ1YW9j1RLXuFqBXYbWChxn1Is9hnef6AEmTkZmKbwnqpBSYKcTfJogtUIOe8g48j7GnPhTn/rV+LPf8Hn4DZ/8a3A3fubHcwpmXvWqV+FbvuVb8O3f/u24d+8e3va2twEAHjx4gMPhgPv37+PjPu7j8EVf9EU4HA54+ctfjre85S34xm/8Rnz1V3/1c3nqP63x8z/sxfj+N/8wUDf5W8Ti6uzKKw7oXGD7Acv9AbZL0TtGsNzPkFrhc+HCqWCu3ugSrN50fSzTtGXtnu2DA5Wgwe7t+XwvBn/PFWShgZ9OBb7fx64vKg1iQfdd28k4tS8XCq/GCiVNId5t9u0snfVBUJ4aITcF6T0zGZezEX4g89PiuWe2MxBF1z60YN1Ko1N1NpA8Vsgu3IFTE2NwNK2BANGLRakFWoyN/EasgaIDCawBf91sIroWdF2TAGs1VLAikoSB5RZzsgZ0eV+4O47X8M9q2vLYy3zzu3ZugvcCGu08O1vi4CLcP6sBoM0XaefQf765hk0A2XiCrqdBP8423dQ/Zwts+mesAO/WF9k4EkuA1OgfgGYNYILuWItK0M/yfWFVUokb0VK2WSFZo8IOZErm9ToIJNylN2BGgwFr6Zw4r2QCR7Ss8Jj/MVGkhE3Axta/4apWdSaC254urRqrjcrnE4bof+XrPd42w2z3JO6thyBeaolnnvqcdDUB11NoawS2bx2gDXo1QZJifnJEOUc3noQEKbY4NLVL78CgKOcJxYF0WqCnhFTjJNxhIlAxqEQZ+0D2ouYEVKaAxpsFngcKneeZabXzDMsZ5UKxmGC8dsixIBVFejQjzY7lkdHrxwzjO4/IlyeKeAvd0jEbysUAOySU8xFpYj87Pc3wmwqPDZA64HMB9juus8eJ1zBnsiq7AVIL6/q6j5JCHt5AF0Myg5TKC5UEGPZofktuBl8KK5wuziE5o4vXmy+TCL7ij/w1fO13PR+/6Fe8HHfjZ3aIr6UNz/6HP54/j/H6178ef/gP/2EAwNve9jZ8yZd8Cb7zO78T73nPe/Dyl78cn/d5n4fXvOY175fr4qNHj/DgwQM8fPgQ9+/f/495+j/l8T3//b/Ef/6F34y0FD5YuppkuYJ9mHbKMmsVVDPYTpEc0GIohwH1kDA8Cg8WpNB2oDemrIK1yV0Ku3RRmNPKO81tKxyBZC6Q6xk+MGWDlGG71Oljdyr/7TDCdhHYLo8Ynj5BoWTPUoLtEkwTmiDZdgnlfEDZCwwFux+7Rn44Ie0O8F0GkmIZFMtFgh0o1mtC4mSIbsZsYyAOsjMZWMI3RwtdWltPPgfQ4FGLAw5wcQ+B43JB0NVjsdFnRuKaOdA1Lw1M9GoSa+9p+plNQO5NBDn6z+2xR6xN24jD7XgbJISuIYLjcUzyPgEP4jwRwQir4PQ2FXT7L37rZ3GYjTi3FYC7rMG0ear0UwXWf7dqn+3PWs5toxr20FdplDJJnIi7dXDT2DVXYVrTrAP6di1s1PBbcfYACtDOMmKmTbUhwGgVIOGHg+rQoK88ha6ktnsd72miaQ+AkAjWtVYa9zVRVv/+Icwv1ueOdxG2oBvstTRZaE74jJHlaHoatA7lYZBJEG3ruQUaFuP6keYFNUWloAB+yCj3qEMBDMUd04sHzE/SaXu8Ag7POHZPF4pd47lTY8VkucioVpBuFmTLFAqfDXyuzSHTjGROJ+VDQs2CZVDUe4J8VXF4zwLLit27jjSeG1ZR7vzUDssDMrLpUcXZO2eM77gCILCLkWm9h5e8RjNbsshuBHYjSlaUp85R7g/QYti/7QZ6deK1PU28lmZ9nlDn4gQ25pD9SK8sFYgG4zQk3vtxgE8TGgODAD+ewujw5gQvheafpxMcQLp3sYKYaeZ7dyM1NmZAKfimH/wqPP9D1irdu/H+jZ9M/H7O00w/0Xjxi1+M17/+9c/C2Tx745Uf8TL4yIdfPRakmTSEg1G1jgn1bOgN08wq0sMZcrUgzQapI+xAelVOFTIbHOEP4QaBs8RwAGlRQxe2Sth1SxhIaPX+cCNnpqsWmuQhTNC0Rh8TLZCFSv9lZ7AEpGPpC0YL7qZKweBk0PnE4iAF9LpAbxboyYEnzlAudpAhqktCTCvqK02vGucJQByWgb6drGBlSQQs96btiIUK7RhAqzbQpSIfg83vaZM1MPdg2/7XdsNbBNFSE+qrfgZYOxu/X7Ngw5Bs2CEG6Q162VA4nUHpXyz+/fhztMnlkPl5/87ovfQvkF7qe6sUvKXS2udsL+NWTwOgl5NvLmgD7h3MNSajv9Z7Hy6K0QlyzdC1Mr0kuYHOKEPWakx1KE9am15FIn1kYFf6AQx1TdciAXRCjN6BIwDpzohMTbC9Q6R4lwrvNs9OpkR1/Uxvz4Sw7UINDVS7ho2tCTZIwihPtMItrxWNp4XPJAI0p5iTogy6I920damoSVAvBtiBjG65p5juK8pAD53x6QotguEEpBsnu+ACH8D+UdcnpOMMfZhQxwR1hUqJcygoT53DkmO8maCa4YcEc1Y12Y7MmZ0l2AmAGW0XLm9iSiQkM+zeKcgTcP2CHeoea2HDtAAwiGbqfvY7+Djy8u8GQBNbv1yeWMjZbtJSgFp4D/MA3wV4FY9KpJiLSWhbkaPUey48r5y4ZtZKIDLN8FIgOa2MdIlrtVRqZMyg52eMY+4EMoc9sNtxfuQUZe2OP/Qb/gt8+w99FXaH8f17Fu/GT3p8QAiAf66Np553H/UsQwyoicZu+x8lhepmNAHTBB8EdafsNWSOPAnSSZCPBV5YHQQ3VhbBIJqYborFUpy9dls3Yi0UQErQp3KcY5OtkKQ0l4o+UDBn3yeRMLujsZ5OBXKcIMcZOk9AHqKHVDABxSHJe8kyWZ9KCrxpEKYCn4/ANEGne/AnDkiagWTRgmBNYXis+55AXUTod7RXwLCO1QFqjlLstFpqxyKNptrb1YxXhklpXiZZeu/ILaihkDiO4YhrugEZLaUTARHC8/IGLnoAvz1u4YrHKpbaR91+Q1yDrsAFeo6r7c6b6PZxDLQ5JmNte8GGlQFuiXa312F1sV0FwO/FHv0HgVIDLSu71FNzwWLpJr3WSB/3qJxxB6rE/bXVtK2BTongu8QxGyhCpYC9daT2OLBIuD4HyAGBDBx8HoAAPXGenS1riFNuzev+fSo9nxo4tlZG3dilQKkNBPXWEG1CVrIcXriLb1V4PmSylwDk+kiGIiU+b0OONDN6ahe1Qk4L6plieuIARK8zMTal1QLs37NgvCLoUydQQjG4CVPZxwJ95hKYC+ojh77gAWSIXlXFoJdHyPkOKg69moBMY808V0jJMEsQFZrfTYbh6WNUdlm4Q4PnWbQzVOmqkIEVgSwEF36WgPsXwFK5Jnnor40brHR1QpqNIOR4ArxSR1hZKCHjGbBXuvXOhYxfqeyzleNZNQJYTDPflxIrRxUQKBkYEaDOkOnEdhYzQUzvYZZiN7UsTGftdvCB5d3YDX1OuDs+7Tf8F/iGf/BavPhDnvwPPDN346c67sDMczT8bGRVwyA4viBjuDHoO68YGIsBVoEjF1ndZwiAkjP0XJDGSBUt0e16rtwBDcJST4kdQVvxHUzBKKOVFmeOPicuFq0BzDiE1wbCxp4LgF6ckbkxVlXJtDSiBD7NwGHH3XBWvr/USE2EWHhQyNkIOU3QUlhCnRNwc4TOC+TmHPr8C0wP9vDz6Doc2gD1qCQIwKBtdz07zA3YAXVI0Nyca6lzoLdHuwJr8HYTaGHZqklmbG3+Ms2PZHvpYnPHmEfWyFtKq8qmjJu/Z6J+ZVNkRUoct2q223vfF+rBLe1FRxuCLj5Gb8gkK8jZAqTGALTg+eNORtwCP/3z39fP/kNvaiwjNgZ5rUfUxr0Y8N4csKfmBAC0s1IOzsHm0itmiMpspkEW74yTVDZHFTThrcPDuZfl3c7WBw2UBmvSQScQ4u7NjzS+R1IGLgdaN/F+aVp7ggZsQ2DKkuPoodY+MyXOK4v31AiIxqohnxZgmgnAhgyMAyRnfv4UrrTTzOalwwDsR2CXUA/UnvmgyALocWZn6FHgoyJPFN8OLlCvOLyrIB+ta9VMQD+dPdkEGzOw2xE0JUU9z+wpdVmguwTf7WGjQB4eoyLJYTC+D0C6oQ8WRr5fZgqB/bCH7zPKE3u4V6STIc2G8T0n6M0J6Rj3ICcWI8wze9gZCyNQWQGGLGwHAYcsR6bp5wV+PPF+WbAnNyc2tU099xxAh9pCqLDPXTNQnGauhzkD0xJ+QUqg6jwHX2ZOlmao2MCoO4HSxQVB5n4A9sNq17AUAioAn/3b/iK+/tv+OH7+L3jR+3oS78ZPY9yBmedo2KjUDgZ/Xy4G6FViZdJADUi+WjBMC7DL8CHzgbYKU4XsEksObyagFBpXxc7VhQv4LX2FS2gPgKTWdyI+ZGpS3KNzMaJSqnlytAiikLJwgd4TvOB8B1zekN6VET1VkLlAW6mAFchALU2SHVNDCuAiw2slZXyaIO8WjEkxp5Gfl1kVYVvWIQnMmJrTJUR4SQBTWDBD3UW1WpA2zfiNxxAF6qAYZsMy0Jckqi/fe7wP0sFb+irMzXyTBhJrb9oE+iYutfayJqgFerfq9tnbXE777MYQxLE2Z9KFxV3o2wDNe517/MzaMQh6+vRoH7tlX2xznFu4ZcMQbfs8NLao8ov11FYzufN2qGA+amhHqodZm7AEOBEQalD3UmIHnTan1sBttOgQj5YBje5HYDoId95xnRuO8m5MF+ffgIpgU0Ukt+9BS41ZU/QSZHkxiFXqcHKOa+DAVIDT1AEOxiG8l5TX4yY0Hjl6MJgFaySwiz18P5CNq4XHzAEyagGmAr++gd17gPJghN0bUceE2Rz5wCCdryvkxqFRhaSjAIXC3DTVrhVBJqtgA9NDqSrsiXPItKAMQHmwRznPKPcShhvEhseQrieocv3AcYEmxXI2Il2foFcG7AeUBzvYvT3yOx9BDplrlAvqxQ71gSDdAMO7HyE9c420GAsIBCyRLqsRHZvgVuiQgZuZepg2PWuwLeNIcOLRDX6egZsbsmjDwKrDlNf7Yb5WJw2Z1Ze1QlLia6sDUunDo0wViVLHSHAeBQ/unDPR1sCTArvM49cKecdDAqFE+wu/Bv73H/86fPP3fQWeesEHhobzZ8u4AzPP1Ujay0mlOMp5grzwHtLExRdzRSqVdOiJD6hlYUVAqYCApcxZITcLzAy+y/TdiPJkgQNFgBwhtDElowCHkeZXStdTC9DCQMvP8Ys95Jq7PQlA42Ms/kOCDyNw2FE8aKHTKQavFC/qzQxfZgaeIUMuzphHnkPZeX5AffIe9LQQ0FxOGOBIx4RyMZBCNlBrEEG19WfSJNCUsGRlozr10Gtu2JkFDLht4RZ0h1VUsAndGGCnsx/xxzZ9sg32HRBIBzSIVJOrR+XThiEQhEbCu77mvUDSLRCyoiqxUOqEeRyBKlbGpqVBpDEw8UbdHLcdzzZ/9h3l5nyi9LhVSknoP/rfW6AHOntyO+AHaOnePBv2ogmo4z29aqmlksKjxwP4YbE4VpxkcaQqsAB7Etdcm6jYVnC2HdJzjdhQLgHiokR6LbkPsOLSmTnaEjhTG5XH0A3g9FqZxigMbLLfwXOCqgDHE9mCSh2bnB3gS6WINSnTSXOBp8rNCAz+4B7sMMIPO/ieICc9s7D0VwV270BgVhbU0wzbDahnA20NBhrvlTxifFqRbgryUuH7AeU8wcWh08K2DOHDBKXHlCsZmbpTthwAYBd71HOhMHpMuH7xBfLJsH/aMPx7VhjJfh86nQI8ZJGB3hSkxWCaUIvD9sF+3swQTVAzDDcKOx+gN+xOLZc38HkB8gAZB2C/p74tfLj6/QymGKWSuR7YfIkeL33xig7ett7nSAmJhzbQJ4LDHFWhImRx2lzN3DgiB/iMztlQpaP5OACFa1OzyMBhBwwpjsP1Td7xNBmui3OeWlvPRfCZH/0V+Lb/6Sux299paP5jjTsw81yNTf2vGqAnZ+8kj+XWnWCjWsTiAgG7UbeSYRsTbDxA9zn6HCWkcML1zKaTTQNREz040k3seJYIvCGk9Mi9W1LI0eGjwIcd0mGEX56Qjgtp3x0XJ2neP8rUEj0vBFIK9GqBo3IxQAK8QK9uuBO6dwYcBsaW6tx1jRm2z0xPTQX58oR8s0N5Ys+mmEm6FwzZIyOjUhE79iiPdul5eAlzNTiAHAG8RVhzwIA8G6w6fAdAwhdmvUHxp6x/3d4+IcvTLfIBikeT97SDQ1ZRay8t2tz/9meQGtJTFtLBgQIrmGln1/xN1im0YW1kPWYDYJsqq56FavMvjr8tMb7V0NJxuyqsaVOwEUULIm2EaPngKwBqlv7x/XoZumGlw5pjcw2w4B4pGgbcpq/VKMMWjXSUedyAFdN1YCogq9PvXwOejVXxwGLbLxmsS59XWAFwXFNeuwDjwSz69YlVRvsdxZ8qwBwBNudg8kJcXCt375qAZF0DA3E+F2cjWwgIkCdjlc5S6J3yxAUZy32Gnu1RL0YG0KiwEgs/F3NgTKjq8FFRzpiKybG++G5gFZgDthjsXFHPEupOUEwwLhV+WiCHM3ji9ZAKzPcU6QTsjhMdg30CDmNsFixSPJWVPgDGuaDtQqQsSFcCXIPfZzdARKHzwvSwGXUnOSqAGrB2j1SRw6eZepUhAafC9WQ3AMfCGzNkvr8QkTkQTUFjDmg8KIW6Gk3sFN4qQHvqMSWCU48HuTFFKbEfkwEiUa69LIAM8X5dWZxnLrlG7ne8d9bSZExlVRF8+q/5z/B//qY/hlf+yp/3/ov078aPO+7AzHM0tpkBOOCj0KAphIHiBimFD3oLLKhIAJs9ipNVSIqyS8D5LhaYYCaS0kBKQiAZJabpaoYatQr0Y0hrBa2Bi/iYYkFXbnpziiqLCs/jCmBS6FoKS7Gpuxm5i3l4AwwZftgB+xGeFXqagUc3wMWBD29ixYpVunWKIFozRPO+nGBDQjmEiNGM8UvJSGEyBsQMfu9NBYtULoRaEVUNxu682emWvBhkKUjqqPv9aufRsgga16Lt1t8HoLmVfomKKQI9rNrY9r6Whtn8u0EnaVGyg5Z2Lt5TNIh1uPvObJxiFbEDVSKVZizobZ4JGEwbqGrBGOg+K4CzNNnX9JNsS7Db922aD2xZkQZiECArzs2x6koa8OrAJYTrArgmBq+osnuvSx1eRxrzwp3Ozv3MeplyABNHvzHSH54AOR6Ml3tnzFxCCNslSE4WrF27QkUOr7NBlhl+WoBljoakvLZSKjBfrUEthzi0IcjCVgJw6mJ8iOV3yHzmRKBHViZKqdBHN8DNiemJaYI/vIKcH/hcDpmxea7sNTXwXuzeOUOmBXY+YrmfUM8UIuwWna8LBMLrHcyimWE5U8znYOsUAPPzDtCHJ6TrGXJtwP2KnEbInCA3od8Rjef8GvUlT2A5J8DI1yf4zYRUHX7iecMcmBbIeSZD64A/uuEaMGRei3S29p4yu+3NFEwWdTMBRochWBen/i4YEs6nACwxt6QL5VO3b2jTmRuumCKRknaN+dBAljtwtud3MPZ585w5h1Lqz13/T5W6m/MzpsGMBQg47FhhmlhtV8eEP/nHvgFiwP0He3zcJ/1y/Kbf8kr84l/6odjvh8efgrvxE4w7MPMcjRrSEFkYW+ZzxXiWMLz9Eu6OlDMX3hQOszUCS9IVfDR7/yTciSUwhQNQTxD5XHEHCl0XUrVojQBgzBsaNsy3wifDoZFWcuB8hA4ENjYy9wsH+yclNteTy6l32NUdXyNL4YM/EtT4OEKnhRUXKoDR0Is9nyoXnVJhWeFQ6GzINwUeFQhw4U5RhZKLc6bqmsDUW8mq8k8JG+TWXRxzhRSWGrs70s2C8VTgg8Ce3HXBciNBrBmLbViKPn4c0W7XwNz6dfyjAaWmG0EETw+24//P3t/G2rY1Z4HYUzXmnGvtfc65976vX7/+xrE74JgGN/lhCWjHgCJBh47AoEh0ou4EGSlSZEyCJZAsSwmWUAxI+Rnhf063FJQfoWmDkKyOWhiEaOUHkKAEN3QILdQNtvHHe+895+y915yjKj+eqhpjrXNe4xDM/ciZ0r1n77XnmnPMMceoeqrqqariqcTf0yOQSraPrC0ivwmszOeX1p7ucXj9Ijp5MIJTUl0go7li/il5MYN3FONJwDSlwNOzNPFX0guTnphYiw5ALKvtRkuImKOsNs0aK/lc0bE6J9inZ/BpzjEAnuc9Z3AYU3PFJUtwlmn6PgBdGRyhwEjk3Akw9p0AYztBlAzy9Ey4H5BnzwYQTAWXFbzd2Jz1vFGBHxFSM2b1ILJ6sB/wbS3lKq+fgMeda/uD9wA3bJcGe7hQaTuwfPgI33fox09YfplZiNg7vb7B2+jZQmFz2NGj04JD3NDvBA9ft2FdHOt/8yHWl0/YfmlF/9mGfmpYXu3QX/44ZFGDvHoN/XmBfvke/tETSf1PBE14/wWkHcDlie/2OOCtASdyWPDIz6Es4un3Z77TnQAARxSla6zFheOI98m0alkDtAADrAJRRyhenk5gI8+7OwdvxqBpmAEjZBR/K+J3WgatMSsKzp8XJjs4AOzHCJkGSBWAYS0RApltgYkw00n5XXQWH/3w9QU/+ZN/Bz/5l/8OXIB1W/Bvf+9vwL//H3wPvu3bv4x3x7/4eAdmPqHD3l/gLw96UE3gi+Lxy3eQhwuWr7wmolcdAl8bLUQBfG20JgFWOj0sLEx6LjQUjDxZZA8pfG1Q6xSEB2jRyVAYtjX2SFpb8ARauO0VdgJ8a6UjK0u4NYKHc3ALxCEaZN+lRSGuTjKk8HlsoftWQA6QQaAH61cgLGbfTvCNZeHV2MYgi48CAluoRNsRqmun1lKziSyrMOnQJdLSQcCjkb3lW0N/74T2eGB9Zehnem76ilLOLQp5jgwllLujdKS/8QOKA5P/xmfiGAISGEikyKWpQKdrHeN3Sc9HXrAq0MpwoEzfD/Ufn3v82SGuI0g0OTJu9X6BsgkUzQ6miujkc1oCo+lLSQAuT06cf8micgFmo+IwvTcY3gyJ9Z8DK+LyDAdvgKXf1voZ76BCaHOLlHncPbKfZsTjIBH39SOfeVu57iNUUs8e5Hf2aIrwRn4u3MPuBtaiCa+NAXi6oN+tLMS3H8C2we83+Lbw+btH80QpDwUeHiEvDW07jWKTeweedqg4/PEReGXQlyfI045+v8BP9+SSdAPUWczyYlheXlhEc1tYWXdlRo++fIC8vEDvAX19QXt4AB4eKTeCyAw49MNXOL1+gtkBXMhBkeQTDTcfM7ZECE6SeJsg72CWZHruAL4jbwsBsIOerlgDAEG9G2WOtyTjEojIFpmV+Z4zlGQkp8vSmI3YeyxiDTJ2gPDWItOJ5F2oxrsOY9E6pCvftzYS342A0++cHpgEMkH8dhHK0QAy3h04R+XgSL7wWDd7N/z0f/Yz+On/7GcgAJZF8W/8hq/Hv/c//m347d/zG9AmLtG7g8c7MPMJHXq34LLTGlITdAX6ecHTN30AP69YPrpAD4MfLG4nTuHJXkzX1qeawXeDwtGXSIWGhPIT6APTAv3Yw6WvkBaZQCAwwKKwraGfG2twdAuCMqJOTVSC9SGcq15JE/QXUbac2h9+Xun13xXoWTviFNV6HXLJFBOFLWfIejC8dBhd8RurhYo5m75BWdo+yMRorCUj4a2oSEIMD1EQ0IXK0VcqfT06fCeQ688b7NkGF8GyG46mzKKZehWlYwaeBtpNzZRS5PNnPki6Pp0HjLBRklshyL48WU7/KhRVPch43hUOSoCiwS+BXHkjJAcd90URk21k7fgEUia8FlMaRNmciDw/crjiSy7hLUrSbo57toidYFJUIgymgIfiAyL13qc6SeD7C1/LFVabs9NyQ008oyRZcpC5aNKDlCBuzI3k/3aLLBZUYTtvUmPPAmjsnrzQDui01PkcvKd3Vh4GDCpaCtJWpXJ155544vl9VezPGvTRsD506LED25meTwEEBwH7tsHuTpysywH98CW0LbD7e+DSYeLs+O0COd+RG/OcWT7+6hHy6pFy4vEJeLojcHt8hH74Gu1g3Rk7Nxzv32H5hZfQr7yuInJyx1CsP10CgPA5fN8hLXpduYcyVljvkJevy4PDNaRML8cOWRtrVDVWHPdlIWAMowZL8IAEBD+5KlsLj3PuTJ1+DuSkJE/r0oLPF1Wf57GIxFoMj6R5gBajZyiqDeNyCY9agKOdAsa3E9wsQFAAKQ+eYDfY+TTqXm1rVHkGK6wfxro6EVr0BDMt97CPfRNL+GKOn/kH/wz/6x/9S4AA57sV/87v+c349/8nvx1f8zUv8O54B2Y+seO98wm/8PGF8e7IvoSwNsT+5Wfw04rll19DXzvsOCKM0iE9yqU3KoRE8+ogycw1YrlBAu4CDb6JP/Xhrj0MiE7SpglY6EBh3RUFJOq1CGhZeACVEPgMSwR5WAVuwjTVx2O4Zh8PSHfY3QnHexuwLmiPrMCK3nlP00jbzLBDKAYDhdJu0F3QF0FvodTvmYXhOunrJRSTgZaSO8wkultzPH1V6BIIJ3WtCguV7Z3VZvPZJtc14CPcE4eE4so2B7MnI1sypMJN8JHnaYY3AixIkmHD+5J6eE4cylRVukbyfB/hqzi5spxytPn34KgkWOM9JAi2xtBigiyREXpJio0EkEmLXBSWaUqdHgcgPAmZ0j/PWHg+kC6YCJuiRSrs9EijSGG2VEARTd+4ZrGa+afCZT7N09VLs2vysYBjN6uyQfR6hQdCotbRtg6ipkiA4lBoWYBN6GW1ZytEFb0b5OkIMjIKzNi6ACs7QPfe0J8v2L/2Dtga1p97Cfmlj6DP7tDfO9OpcDyScCzMmIKFh+XVa8jHK/y8cR4vexDfHTgtsAbsHyxY9IT1n38M//g1mjbWYflwhTaFPlyAfYdKg/ziI+SfKjOy3PleP/q4OEZyf0ceTJSKKG6QspK4qVJWHUHKPZ9K2YsZfFmR5Ok0iHJvMnxInp8vawCZFiRbekE4//zXPXh6Iqz6Cw+uTBhZkMhYuhAkZ3uBpfF2UTOowkqiNPZ8EMR93QJ0g8DEAgB1I9jEQlB0d4I8Xnjd80Zg8rSPfR2ABR4GWBOSuQE+Q1a0zgy/ab3SIMv9SBny9GrHT/7Hfwf/yX/8dyAq+Pb/9tfif/Yf/Pfwb//2X8939/+Hxzsw8wkdX//8GX7xn7H3CBczMlwPV8XxYoMvDcuHT5APXwII2/JwoEftGaGitoiDj/CPICrBc8N1hzxcgndjTBG9XCB3p/IkuDizhYJfc+V+MDAGLVI8nEEoyDCYsInl7mjdWYcBCnWDvXpgKndT2JlkzrYDcgmhFtavR7ooMxc65PU+QhDRp0YV0O7Ym1Toy1cWyxJXgjGAHBuAVlKG3aJJXhL7xKmE9PFAf75ieTL0RSKTJUDD5AWYQzgFWgLIyOEVnuJ9fXgjUp6Vt8YrQ6Z8A3krxwA4KiPTaD401kF6W6KmUHpuKi08B+q4rrPSAxQJ4DDAlBlZQ+5e1/lL/JQF7Twq6EajQTcCGBiLJeo0fXC7GhsOCyUIhl3C6s1KvIiqhQw1acKY4K3Eheva+UqmbK/J43LlEZs9iuFFShK155pOkA6lAlUFbFSWFW3MpAmCr7szDGJOr4J18lzuN/h2h/78TEXeO+T1hU0gn6LqtgN2v+F4seJAhzfB8ayhf9v7uDut2H7uJeTDl5CFe8LuTtCHJ+CffwWyNOj5xHsdB/RnD/j7z9BP7OYse4csG+SxYz1eY4nqu7J3+OUC3N0TcH7lY8iLexJon3b40+vIunL4tkJbY6hLlR6KDPC1Rg9JhnCaMk1ao35NApzLhecurFQOsGWBqwBb8FYSuPYIA6kWiZfv3oB1JYhWJUBJZV+hwiDtKiJk6AR8uYaz/s+2krSt4fWJOj/eGmTf4/4hk3JfqsOEgKVcwBGyEggL5Jkzg6s1uPUKifnSgJdPAZbZ1iHtEBG9AnPpobw6AshgBjKTMeD5+IfhH/8XP4c/9SP/Z0CA996/w//yf/Xv4Hf+ru+8veLn+ngHZj6h41u/9AX8/f/iZyO+Dq5PDes/mun5qeF4sUH3M+PpTSvDp4xe82EdtpEfQ/NfeO7uWF4+oq8LSa2nxvDP5TIspyNcomteF6E0WXyO5NrYPbNSAYrvwToh0csHfJ5+t0LsWbh+A7QsDQbDcgDm6U722twEHBfI0xM74ZpX5gM5CbR+jy9E3Z0AQCY+wMNEYhWP+UllvCqfK4queRPIU4evivZ6R9c16nAASWSVTDGNZy2vjQF6ePSVKp9AeJUw8VxkFLlLKzTBzQ2IqSN/n8mL5YlJ5T+9EsS9slDc5NaZWCeBGRKUDuBQ7QtqCXllOuXzMPxoVZHZRYj9zFkQsUzsuFCU+q+QpoCeMtBzmBwBD30j2V8rXEHlnSovWU4WxkArFIdx35o3z2BEfOSj03aAUymPUTynBuDa9youKa2xV49ItA/gOrJ1g4KK0Y+DBGBVoAv0wiwWWxfg/UbS70dPaDsJwN4EcnE0GI73TrBtwXHv8HaPZor2+onVfwOg4OkJ8hjl9u/O8HWBwAlQfvYRSyMHDdsGvDhBRCGXC/w125LIhV2svUeLgGesEwMRyP0ZZkalDic42VZ6Ywyw16/5HtYlmuNGSng3SFt4z36E5ynew7pOVXcRIGAZnyUHJl+nMovT4z3y3yRO61i73aYU/GHEYVsipZ/Ph9M6qjALwgPYwgjMhroKrAp3gifPz62TfC3BC8wMqbbCHy619PzEFjLL5YAdnb9HeJx8wpUGFbyaAacT5vbw+eM0KCq0PEoiFJBxsFULhiHoAH755SP+N//bnwT+3F/GN3zDB/if/nu/Db/nv/+bPvc8m3dg5hM6vvPf+Hr81N/4GVpBKtGBOf6Lzr0QML36gzv0R9at0A66JyU3HdObfT/YCVpDuXcgU33tpLAvPONmXZW1FdoC7Af04Qm63JFTEhsDW2MUJg2UqGuSZRckrFKal7S6pXskvjiOZwtEma4lh6M9O6O9ujC08tqAlRuvLwq0cL9nBok5leVh9D5cDkikd6oAvm1YWgOebeiPDr9LSkIApdjkoTqp2w4AEplaoUhFwHt0R4s0bTs1yGmDrx1+J/BDCDQzRdu86qgAnItM4dUKKXnp28IFwQcRoN5ZgpHRAHQmFXsBl2qlkHVP+FgBdGTwQ+bvZqjIZmIwU5C9rh0f62QVRl0imfknPfk49Mb4YeVFkQKKMf8JgrP+iwDZr8qdesm3aNxnFO7pPpcYkwOTNyqvOXlUagJSmaF6dlUISvIsr+es+Z44PFdHkFDRDX7e6FEI8ESwE4R568DugAWZd00PKWuT4Oysl3TZIZcDeDqhnxdoNxwXckX8tOG4W9mfDQAeBHrpaI8CORTrVzraV15XwUXpxnosbQGW4CK1xqJ193fMpPnKRwBaGPDOTKFtBdYNsNcE4yrQhU0UcXTodhfTxt5Juq7w0zaSD9Y1+hDtkVVErhCCQAsRAobU0B5/z/5Fpy3GGuG6fQ8AvRRny1VQXcQFldhQdoEI/Nij71wrg4SeMZTHFkeHQBniidYGHKsBx1N4RJzNKmPz+on3sqZMD98ZNvP7Bn99AR524MUaTTyFYa5LZ4ZntGRxFdhpRTuRp+Nn1v7xkOe2KWWzBnBXgXljCw5PwFKLdFqQsYjTfnRc7VnAyVGUmIs0JBtYL2wBjpPgHz9+jD/1H/2n+FP/0f8Fv/7XfQl/6n/xP8Cv/5bPZ3bUOzDzCR3/1nd8I5JD4d3ZmM0lGPoMpdCaUPiZG9P3HfrRBYLYNIHeDYAsWoarBjfGUympor8400oRAfYdvins+T30lx+gLx9h+wq5W4DdYIdFb5X4/qVDL1G9UgV+v7Jfi6Os3AZALx29d+CusRpxPmyGTfbIptrB3bkovS42Kvwy48iqzo1vC/vc2B5eK6bHyt7RfMURAMYF0AMjHBZ1TwgvIrX3CKs8wlrWCEIkUGR7PGhlPTXsW4CnIyY1M12MIE8QoZ30WFjJXpQr4Bj8EkTWZwKRCvvkce1oGR/PnpL5EB0k2xnMxDuvawXA5I8W7vcAQPndshQjBTmkpriVZ6tOEfJbroZlfN4SyU2QxXbY7DTe0cL+ROzCzeKHbKeOIiALUK0OgAQ1eWEf//o0afm5TWBSZHi2hM6qAQ59AEYAlVHT+wg1qQB7h2wbQ1zZSynnM7gNBIFSqfW+s1qs9B5h3gUiC+dkbXz4zqw6kwWX5w0NHfrYcfpFhhG2n3+F9vEj9iaAOPR0YhG7bQWe3cM/eglA4PdnriMR+CnqPzVWIsZxwD9+yRTxbeW4txXy+pHE1dbgGbbYO3DsEf4T4PmzkD3RasEZ6vG7E2TdQqZIhH8wss+aEtStXtwUZvEE12VpBFLh3SkPXK6BaE0Aj7ISiYCXBmsKVWX7heQENRpn4gIJDh7B1RmQWLtN2eqgODCM6Us3fj9q1PgaY+wdWDe2dPjoFQCDtzWWUci/M9PlRQF56FDpOJ6fIAtBmy8NsoBp1yqwE7kxsgQ5GuHtLtb9NYE5Hb9jk6XgyGVvrKOVoeIAMt4AbIpjE/RTeD6VAMdW4P/5lV/C/+jP/R/hAL7jG78G/7s/8j/Er/vyF/F5Od6BmU/o+LZv+RK9LBFC8T5CJJp8Co8E2iYsDGcLfOvQjx+Z9ROABhD0jWBGXaCxIYapC7gq+jNlaKWDdSnCapLDmM4ZbQHa4ZCXO62KcBurewEte1ToWWCZTpug6pQhoMZebhneOCgwZVFyLNJ50YOoHBZ8eVYUUIkKxs/vIaeVROIgAhoCGHWDa4OtUqBQopBbWXzxjFUzxZKg6kyJXxuOrUF8o0UdjTRbE9i9QiGw3VnIuAPahyGV4HEo9jC14oNUcBVWqgd/y4K4IrHegJO3fiEOIxDL7JLba4xb5qDAeH6F9sZJYgJPAmWEEDQlqyYvCeM6+bNSsGaxvgQHLrR6uwA4JR8i3vNMzElANYfO8to9vC3JhwiAOmdUeXpu4mtFgA7w4sJ9lOE+hxMU572XRhBzGBXqpVPBtqXK4rMUfi+PFQSQrUWNFs6veIc8XshhOq2wo0Men2Bfc8+U/0uD7R3toUMfdiyHwfcN9vCA5eMDKsrWAF95xaJsW4OGDBAVVsA9sclkJq/haWftm20bvA4VJgeY02tD84ffPRiuqmq5D5doyKijorEIK9fCIR6emMtOwHQ6UeEfwQE5+vC+pOesNY4/PTge8yMCX1dAHJZViyMN3hd6F6Rptf1wCIHGthHQJMfpzHCSR/Vjd4zvHAacVragSE7KCZDDYGu8U4miiR6eIDVA2wh1Hky79hf3sb4D/EDQmwa3UIrLopcDti585hbhZA0g0dkAuOpxIX7ONR6GVR5e62lsszSA8m+Ixqvl2QqZ6k3QG66AzLECtgG+SOxTTss/+Oe/iH/3x/5D/Lbf8Ovwv/+ffx/WNrt+P5vHOzDzCR2n01rKhyRSGf1rQj/RTY+R3bEq+j1dv/r6gOCAnZYgU4Y1fHQCghaW6XGQ0NsAFZZJ104U7wCtsjVSqfOGTYBLujFpWfeNRD6PDA8FaNXlhmoUmKlUrgrMAfTCuMEUSLglDvjhlWqeWSN9a6MZoAC2LpCTVdqmBykyvS+ugDdAdwxSp4TF2ghapEXaKJz8HnMWFgRKIBK8rXADmhvktcNOLVJwARxD10o8Iw319BDky8NAOMhH88mjMMkvvT6VAovApvrNZEG9VPjVSiLOK7IvhXaSBk25rvLC2fbANTgfEyhLeowY4GB16HTNk3gJVCYVmLWi6ajIW7SI75fHR+Fq7FlVJMb8F1SK6bkSVJRojhZVKZ4e722e72m95YN4CHsqAKNyc6bmisezA1S8lye4Lqzh4ulNWKKQHUrBu0YGX2aD9YPp16c79AWQuwa/MLUZAPy04PjgDgDQXj1FuYHGDD5t0N2h+4HlsUMfX8FwQB8PSMB/uTvDLzva5QDkCLJ3EGR3NkP0prD7E9phwOvO0ND5RC/n085nXMODkJ6UBNp3d5AtPCOLMrTqgPQjWpZslRrt3QEs8Ls7eoaWxrIJyUU5rZznZYG0ADNGrxGmkC6CKO6aYbmYq6xX04XXVaF3qTMsbOJVp0WOdH+2Qfhd2xWYTaqfb2tkdwp8WWAazU+V68ODX+Pu9JjlukxbpHdASPjO+lYCD45NhGa7MfSnxrW4NlSRydzPyWWMPVSbXWNudBgcBWLC0+WxHwErz2XKg7ETx3e8CewkxZ/pK9iWZqGMrCrn0375z//Lf4Lf92P/B/zlH/7Dn3lA8w7MfIIH+TGhFBcvzoymOzxV5wxoloZ+t8E2FpPiBgoltqIEn4S2Egitxad9VJxcGvoXw5Xce23wClGsbIUgB2iR7MbwQFrXZQmlBoytG1kCLIQWfxegnxXezqTxqEQyVKSQ92scoI2kXjsvUdjKygKXEzMHLLtlC5hho0ql0oTOlyX4HeDGFmDiciDi1xRstkdmTinbfITwUFmHn9sUWhkegDfeZ/yb3KI83EMohWdjCLrpQglcr79a96rPypsR1m6k7CPqWIQdTyXhHEy2nshnp9ckEp5z8oMAe0Uhn4GXKq17OLI6dFfypDzcE6zOrLEOcrw65rYeJJRPgqmkwmQ4EuEJkfG5mBXWqc8TtKR1KrjiD8mO4VmJbte+H1Q11gnk+wWwE8oDtp2mujmGoxvwbIGCFXZJED3Bjh324g7Hiw04r2g74CvrOdm9wu439LOivX/G9vGO9ouvYPdRW8Wd4RYRkseTIL6xfhIOI+cmQqLSGrCB7/qyM4yUc3paGV56eGCjy3XlCxFw/zhYbkCVihknSH+AQ9gSwaK2z6JwX2AnZw2YBA9AcV8SuDJ01tkoU4RhrCZwIbDQPcLcSwBI5PoTdroHqmYPQS5gSr6KAMCS6c9cK7ZN2UTh/bMk/ocX0RduHofDsiXH0pD1nmyhl5UePgwjpiH6QTm9wYsEHU3KaIIKfAM828IEcdlCXmQdLgSRno1sY7EvY/PWsgXgUegy/03DwVUgLfC8S3ge41sZ2rfA3gJWfc8wn6I8MF349+rbptdjmH/+r3/pI/z5n/rP8cf+3e/BZ/l4B2Y+wUNAoSmu9Mykp0bDjZiZHRiK1BcFTEfdl3DXau/wnWmVerDfkUT1W1sXLN2hHz/SIr07o3cHwDRJvYSAD0sqXaRMFxd6hIT3YoqtwMyAJaz/MokpTHJ3Cjw2uaKf44kVwyuQz8ehACIwZ0NMrFGS/EiOBa54JrYIjlVgK12rrkC/EyyvAVuFXhQB1KTSjjU4DllsDx7WVln6AnUPJwhDEe0CdhSPWho2CYYslBdJM3G/Mc6sK+HinCeNcGLdLorYxYS4e2WOS3FOvFBPFgAUIEjeoRhcCrghFLhrbm2tsVQ9j3pHcY3KmhpAofgMU1ye76/BW1bsVZhEg053ZoTEOiksHiGoJKrzRUzILedtfrdfbbdojEOVkC2L9JnXeiosYxFKuLqEACdlXyUzZqzsO0MS5zM0Sa0AvS9NSaAVYcjhiFYgAgAb/MUZx3sbfBX0Q9BWodW+CvyOnLO+AP2pQV4L2ssOVSdIWFfg5WsgUrtlId+FoCLqNC3KDs7hbcTWgCOq5O4HCaru4Sk585pwehSiLgv2aGwJcO/enTjhmX58OgUYWCD7DjlFscrs37SH9+y0shJR5zz2uzU8ljRoPIjQGmupK0NMVX8pQIEZlTTCGKku9MqMIYZPwCzMwyDpnk3QAkQ1cAfhiZfHzxpGlDcLeDblnhUg62RhQckwgpoIgcVesyUAiQYJXFn36thiH7bYt1FXyYp/lrIQgIYcCbJuwNXxzLH9JluG5zeHhbvWdNgAKTfLDlA63G3lvtCMdyJkgYCgsE3g/1c4/sOf/tvvwMy741/+kCM8MFHyvUq9W2o7/kvuTOnbUTTLUK5XMuaJ3C2scIl0WXG6bvHijuW2t4UE3aMzRfThArx8BD64K9KwR00H0VmJhnvVQE9ICJqqMwJQMJSlD3qNLLk/FKgGj6HGOCNcZYgQ0pNHdpOjN1Y0VsXI8nLw8xPH4S3+g+C4o0Lt6WXZQ4js9CtklV2JrJ/yogRPKVOBDWGFd8P60QXHixPTLuMd5LNRuEjVYDHFdeaB0FrS6Mcg8GiFRSHsDmi1rAjrV2O+OvifhoJvIdSDzOoAi5QlYUA4Zou6Igg+QLrJB4hEgQipv/kAjWnlTRyMGpeDYCLmW1NAp2QuQhGViwRgKq+Lg5keybPJdgWxXgQo78p18TD+YwJmw4HrTMwhT17vAirkhb1+IhjMqr1Hh5832P1CL2USz3unt+bo6B+8B4luybYt8PMZ9v4ZeP1ED9TdBsXOMv2nE72gTWBbg8LhXeDnNvgN3dGewoy+X4M7FshdFXJ3h65gem0xPgn+/S6K40HoUVUrw8WXBoFDLzvnVRvw/nOGY45jXMfDmo/2BOjGarTPzyQqqxR3BBFixdMOnM70ZMQ+8eSgLQvMjOvV6X3yzqa1mUXpLTpwNIJrs5BDywC4vkzgKkjmHk1r+9qgCRrB9ieVKBBLwc4Mrdth5LDpCOswWwhMmAoOjy9zdqbXXpB1Gd5H9eElFyHvpwUYsZB9QJSnSFQBlIcxwH6BeGAkJqQHMYFMyA4PY4YexbEnK8ykIWct3mWUl3ABExMamMixAHIJjJq3XxL4j73z1Q4BcOkWnLdfBfL5lB7vwMwneOhuAVYclnl6ufkj24KcmTRfUZZC+ny1G/B0QB8usLuV2T8Ylrj2DouN6ssCLCzspTuLyfnaSDR8/cRKwx88i4wXo1s6rB5pdBHnUveFsWQJzgs9Hc4MHqBIuOnJEMEIJ6Tk0bTSIzYO0NpfOoHaorDDYSdBXwnU1AA1R3eGr2wFbI1NaAQO3h2ycu7MIowEhL6lWPJQ1rJTiTcD044XJRiMMIOG16A9dRwnvgCfhFhmYrESswR4owUrnR4keICcUOJivCckwmIS10EoeRV4ELm9+QiPIdznDbQGl3Dbt6hCCoYTvKFSnuktkTHtXlMBAEzlLAKuJH5GpmmTq5HxfikQhQCjPs3tmOQELl6vOY83rEQRGqrhTIATYDqA5gR0tilrnUisswsRqEisa3N68iKzBQBkbexzlNwMERoAv/yKjRAtAMRpizRZo8I7N/iyws4r7NzgULRD6fVUoD/fgJOw/stTx/LKcNw14HDoq50eS1G0yF5bXne0jx7RTxpeGIvaI+QduQfIrtTkUPKnhZwXY9YNgrODbQmOByJt2gu0ePQ6EniBjHyXOK3AJTpPn9ZSluheJQpsWxLXcu+uQd49OmXC2litWyPsHL3ZcGQoz+Fwdt8OMO3NI9Qn8Cj3PzwbDluVvDkJUGUBdlQ51iqkN4pAMmyV6QUVfIzQi0S0WNGjrQuTAQbI9lzKUWIiJBwNksysbFl3iRNSVQHCYeXplck13SIbNfaHx/w6pAy9xDRpdJQsBdCzOa7xOr6GPLHY84m6FkGHQNSC5MuxHiefjDDKFEt8/Ks8PstABngHZj7RQ3YvVrocYS1EyEM6AKXSZWqQTz5HxlFlN+CgpaRRu0N2LvJMbfWF3asN4bh57x5ydLQe5oQo/P17urKPjuXnP0b/mmeQQ2GIuHGEacTC2lDw/EVK0JJnEMAGQGazAIBEvNzCHVrF40ymlFxuevP0Ozv6ptjPUWwqC5U5PRu9O44F7D4e1hMyQQXpcojvmiNL4XtIn9TfLMJn6AuK/yHpFr70cFcDejmApwZ7rqhO0QmOxBk/b7SefHGm2x8erap4s3x2OQKMyCDpijnUFN1iDh0EoZl1cTikOXpylnIABxW2Bbm5S4N7FvoaHoxYDSVMC1xmsbzwX5elHqBGEtTICFNlLxnEGAnIp+8gbNTwIA1LNevJZO2ZtG7DugwFbAF+uyk0gRklOnxV6DHWu+4HegN7EsEDTCJCpo3husOYLv10gR5HcFC2MBacCtYM/vyE/t4J/dmGflL4JrAHRzufsLy6YHl1QKTBXtyh37OXWHvs0GOBralmWbdI95jHy0Fy7gWQc2QuGhW+nVms0i4H9BJZQdFnyNEi/KHVtFD24Khk65G9Ax7p9muABASov1thu0WBudgjwWHJNUnw7QTvuVmXRm9fdO/2U2OdFQHBovng9AUgQFP4pRdQoB0WwH8J4HE56H1bOA6v9Hsh+Ok96lcNY85iz6MB7oImWh5eSHpzCHTRYy1msWFhSN6CI0OPR2Q7CjklyD2cewnJQYkNkh6fkHdZoE8kAEYihZR1ZRCkPEAAG6+9UZyuFvOUGzKLdMYydiV/Ry/hMU0PkHCvWuwZb1qGi4mjb+AcpGcmjunHNw7/Ff72WTregZlP8FhCSUEcsje2A5hd62G9AED115mO4pFEhot0h6tB9lD+oXD0SP4AiaEW9RBEQOHSQYF5WqGvnqAfPcLOC3vvKLMemBmAUJKxp2JMtZFvPLBAKOIe4Z1F5szlq8eJPcrniRg6mgDnMDHSjdvI04AQyHhThpIybTo8G9rHDVwA2cj1bOHBIYku/TRhUYUV5kZgJQUuZQDGLQR2ArXgAFR8XD28KgJTh3WHNABoMPcAfBiCPUxhEaeXxDzc7vGnI9LOkUX/aE1qDMCCrIkITWUV4myQCJFIaQ1PSnJaxGvsBJRe68iTIOnhwUrOAjBK2Ot4fyOsRjTooz1n2swosAQUoDFJ7xOCXxJAbe+AN4Y4e0CwZSEPTBTWnJ6Qo8MvDnNlnaODC5KkcoFIoHAzhmqimSczUhZgXSJdmFVb7b0z+vMV+zNFPzPM4JvjkBVPX2porwzLRbDs7F6Ak2D96MD2i0/Y399w+ZoT9PHA+gs7n6NFSGRtAdzoiSG/KCBfkHutMbxCpatUdmtkLwXvzFtcJ1OFlXEMCy4bMuusR2wy9luC2tpnPeY8jYCmtPIh0TyRLzj5SKZS1aFt4zuSS3R9XhLoeHhV2mjWOXlfTFfI417cKwHBiGlQABHPFiA86wIl2RYL0IWGTBpHyb0ylQjVc0489qBk1p56hGq1intWJhFkhIsRYGmhkBrhnRhTGBlewjf2lI79IAcCKNIrK+LongVOJfg1uW8CnVi8C429kqClB5BJ2kB4eMwFWB2+CmwBLOoAdhUgspfSI14cuFnOToff/PtZPt6BmU/wWODoQeKUo1OwZRpuHMWh6U7GV6a1hmVaeiQ3dAOqD026MsOagoB1RJZUSGG27AeADn9xxv7l99A+fAV93Nn5FShyLu9pUYFYKhuLgkVR3ZhzUCIjC0HwJiATLy+SxD00iLAQicwshg5IPqQLH01wLMJN7AheDG9i4cKuHnRJ0hWBr4ruhrbzWdJIkvB+yc7qth59VeT1zjofQmtRL+SmWJsEYBQlpQMikViEb0yiwjEtdngIRHNyO9xRqZySLmoCorL8FtDdHt/1RuuYjjdaaGnRU0FQmVOIx7+rxLqKsF9wb6SI0Pl+CTR8QWWSJNBJwrDl65WMr0tiGAjYIgKLMyU1LfWnDiV5ginBeTkPgOwEkKqCvgrgHe3wAsoRGJxqZTTY5pBDoVuDHx3mwhBkZAGRg5AcqGnRbSzT75lVJMI+Z/cryxM0jTopgC1spOkN8POCy3vA8iTQ3dFeKpZXHfpqx/LRjkYmPLow006C7yQi6PdMT/GDxRsZAtYRPmga2Xk9QpAxb0rvkDwdFfqp8FDE5MR89CESsF5LN3rs1mUQwd0J2srCT4Dk5SGhhyCvFbVpugVdQwjyQZDVSzjlVwkiLIFYeF6yMaSps/6Lxmd7CkGuX2tG4J7AObh6OSw2wOXn5U3xAa4zK4lD0rF20wArQE4DAxIKX5L8iwKc5TUJQNYNBIbgnkpZlfWLsFCWSpST6B4e4SU4bU5e3AhHh6zr+S7GM5WXeWrm6zEeBaJYHuARerclxhGhpapYrhlCDoMiMeqNCM7ffzUk4U/78Q7MfILH3brg5esD6o7lcOyrX/Fl6gjQIBdmJkgqIgDpbmX/k1Ca5tU9VnTEZhkfP4CulerNQmDsTI2jw56tsPMHWH/hJeSyA2dW9XQV+DasiiTKJrHOASCqh+fmoEsdJRyB6edUMEcKjSkVN5W7Acujw1aBPyE8T9zrtvE/OeKSQSKWmC9IXLsKvUl5TWxhJ/Hi+RiVvXa2UTBIpLxLZYJ4N+hTh3bgWAJkJJfJJsunXgoAcYbSupTAypMkxgJH9Wiq1y306lTxrXSvK+jdivFKKqUQWsjnbBTMzGQID1wQVypdXRnuUA6zcHKCiAy1IQsMnsOd7WEBh1WeRG+CbtZ1kR1Q2eHbUu52idoqlq16NmUJekNk9zQSvZO0/sSsIcl0/8MLQCKqPLNImJIv08GsM29QWNwr+o2VgiRQQVsKkLPqL8mivnfIk0CXDtXwAIlDD8AuDpxD0R2c/3YJ99+6QC8W3J4bJQwASYBPEqyMECxDtCyv0BeB7j0KPHLN+Krwg54Yi30uEkpS6XWFOwm7CXbOK1spBPEWxIccyxKVf5E2ihYadwl0HsqTwKkVKIRFKFbpEaz+Q/AwpgjoXYSd6buHwo8jgAy/r+Exzr81lqcIQy4zhGTh2ve4t1T4WgaPpAQLBp+tQLYXeCBvJ8aYpQomX4UDBPJxfUvghAAU4VXRqItjyP2ZYWeGeur85K0lwdkD+LS8Ga6yOifxN9qytXG+Q9DhwRMU+BaGlQBdQY9SesfDMxMYm2I7eT/TkbLaPwdI4HPwCJ/d44Mv3OHlh49Ahh96EPcMqM7NPpB1M6NVoVKZBoyFN3pMKvzTI0ziZUGkJSwGyN6psINgKe7Mbvo4sjbOkaL6+gmyNfiJ2yG5KN4DPCR5FIgifbTOZNqgRYpBAhyUYEyCc4YpJDIRyuUaG1zMGQ1bIlxliGQghk1cAXlw4Oxjw+Z1DcUf4rUAmKOvgHpWVQmFoKAQfjroYQjElv1ifGvBscCwphLQlVTAlVTyaB5a3qq0uvL1yrD23EGXfky/l4UYHrSWDxUhAaRABfKU8kSlMkqhCkQ4jONPsJlY0zKr6AHMrgmc1hdHy1lKS/bJGNrIqqsCyCV4Qhq1U17vkA+fgONAP63A8xOgCn2KTuiPjd6aJbwYcqAtDdYEZgdL17vwmTUA/MrS+BrvEE7rM4sjCmRUhN0d0g9ANvLGFiVRIjgk1jTAhUIOhz7RMyqPHerA8vLA/nzB5aRYjg49HP2+FVeoPZLUas/vAEityfwZ3UggXjSKq0nwGjTMbA0ADtgCKEjcPlbyJCoBYCdI8sacNRFBi0wkW9lI0frB3kUhLBh2WukdyQw4Tc8OGAZM2SGILLrcxxjKOZS+h3Gh3QgyYu1aG+CiLxyr115XehiFeyXT/yW5KKvCxaZsoiC+ZiXGkAGkdSnBXnhmE4wNDiFurhMyUxAabkKWKkx9DvnKkF/IHoBp6cK9kh4s7jEvOUrOGNcfyb/j/l3GZwx5x/ymJzjJK9GNl06xFBrDqHEF64ZFSC1rznQBkGBmkfK+1HWToyZjDuf5idc6fpQcIz7zxzsw8wkeX/vl9/Ff/1e/RMXbDWrhXckGj0sIjhnQHF4xYCrHsIokdlS4nWWPcrV7Zx2KJtADtaq1R+l2EJiYdbT9gH7lIPHwcQ/BL5DTBf7iDr4u12Q0B12p6ZFRRBr2MI5i+AVkACmrIb02TAAJAZp8kTxnAjXNhZa9AO2BMfQOR9vJS/GcNwnrC+WV5hwaYMGrIVEXYxBNYEuDuzI19CldRjEWIWfB8p2UlRPhlpRFEl2iTcZ7jDkosCm8bWYbuCp5LZVxgaqmqx6Cs4UQD8ErOqzgiaICxHMbQAWvvBbBRxDHlaBJGoCN37XOGiGMt0+cm86Qnqtg2Q2+KPTB4fe0EMUJrNU7+hH8iLNAT4r26gK5tHDECaspHx3yYPQyHiyR37pFo9SVvXRWZT+b7HWjKfjJPegBHjPKwQcKb1H3aPB4sMCcKHw7sSLs0emxWlt1rWaRRWFKMjJ7hgrx9OEOPQn0yZiC/sqLg8J1rgSGLoDS0NDsb9QEcmBknpWSE5JZD4dlWFEkSpZQC/bN2DPMBJAO2cMDsoVnp6HqvfjW4AdBvSTPxUMmaON7FpSXz4QGTIIrNomNekUBTNLgkA5ylhYCE1Z9FniUT6xnisri2ITGmCSgSSWexsm4PkTQowVHdWtP8i5kkL4rTkJvFH+dZIwP+VJe3+Tb5DPFHGQUydObmW4XcC+683ODVBuPHvOd4b30GGc1BLQAaZ7wbwAiwt149ibkEjnXVmGPAB2Ca8PIItzrkSWlPeQiZHjFxYdnLIVcUgImo6qAis5ya/zNZzD0GT7egZlP8PjWb/sy/m//139MRXt0SGehKCQxE6jUUmDysofQTs9GOT8yzo2G7NUj5sB+sC6BCzV+B2AhoJ+eWDCrd27UZYE8Hmxmt21QnICPH+HdYV/zgpk3SXYzXs6jcFPGZTMDBwCGFJk2WIEaWkUevBMSlL2EpHS60W2T2pi5Z9Uc64PQ8HwClotD7EDfWJdGwuOUyl0BwJ0p2C0vEhapAnI0WoqNO1sPZbZIui5AcLDfSYFIyZh3IaZoaBnCOZWIOa5i7tpDuEboqIclqME/cox5Mh/eKw//M/VeEpHH3FT4RVgF1RfyTDSvp7TuZHfgPoRYtBl3cdghbJAHrfTZHqRMiEQJgIPhJBdmkkGA3dFXQQ/SqS7MhJP7E+TORrinCQnljQCBmUcEIfLLHwEfvAe532CboD9bCGiCcyCGaDAoyArGiNcIc8iuwOLQPfeLkD8S3Y77ekKDwPcLrDGTyDam/i+7oa/Rh0yi+NqiEDTIw4VrZqMil0fWczpWhkCMOAZuVPjktCl6Y18rNmId4QB3gTRBV4M8AaIBLpaxd7oKfA1PR6MGyppUtgqsLdFZPt5rhkLLwLEIbXHd5RivwpBZtM4jsyo4w+WTyXom4dVMFELQoAOcT4Rxj/IAHlMh4dzkWo19FutYBPQ8BPCq8FB2w87f45QUGWnkpBc0gXvtuZgHS8+wJjjL830YTGVlRXZTnp8AKVaSJwE6w2QJotJTGfsSjsGVSXkmARjA96SOAJNeRYLDUcbrhGjPEJIIKozdTeArIOZYXxvw2nF53tE3XiSNKovv4Ih5WeL+Xo9bR/J4tOEzf7wDM5/g8Z2/+Zvwn/yf+LMaIJdOr4BhWBPhlgWAZKRlSl82LsyQArVWCJrzBnm8hLckQI22MmfECXJwGND3SmwQB+tVvHgG7DvJh9sC++g15G6DvDgHBSG3hI9U2MZrZw0GAFMFUFToQhzIdOUyjlJ6aJrd/LsEr8PzOQGCvZ1PKt0p1HfH+mRYXzr6WdFPUYsjevokLyUtKwrP9Gz4uHcUfuF9w+q57BzP3cJ7RX2PBg++DYoDkUNMAl6F7x3cbcb4doaV0vXsAHp4T6p6XQj9OiyuGwDLm6S3GmmqstowKzbbKsCqMHe0C69nK6AXjBTsg+TwvgpsQ1isNNfaQb5BvxMqxW1BezLoM8W+OPxEPlY/CZY1FpA5sANLj1BnlsSfrcJlgYMFG3F0SH+iR+a04Hi24Xi+wM6KHn3HxADdYz3F/2Tyi0tyPlzRzw4XFpwzJdlXliXmWeGPkf3l3GP2fIFdOtqHR9VJ0Zc7h3u3knflwhT9KOAmnWujK4G8WXS9j1IDLnz5HpygVHouCpU0RDDqiAgG380RPBZmqkh4KTRDNa7l7WlgfarKyAlOjuyg96QJsm1ArjFLZLCyUaz2MI6Cm2EBwLN7eu25XGciV+DBBEHyD09ItgnJMFaER2ZwMrwpkepc6T0YRo9iANb8Y9biCc8ZPYq4SkMuAyLHGOApRCO6C7AhPCAp9GI/6fgZUUCPhPkxtjRI0AIU5voEhhdHB9BKSZjgLx0nFfKtsg1eAM2EYKY3embbwf1qDQVM5FFw+oUnrP9sx+M3PIPdxftrfJ8JGFUBO6HkL+YpBQrwnT8Hrpl3YOYTPL7zN39z/ewA9NIHYdUFvpMV4cH4T5Y/JATE1MGZ/ygyGxXqbNC405KW7DhsoVEipo3GaqJ0yQsJkkuDvX/HAng7+83gvQX6Cx/ymvdrWScSPAl6h0OoitMyD9CFZQg68XSJA2UjxEOk67qAkjA1sarqYgIARqUMCK3WBTjADAG16HQtiLRtK4sTy3SNKZUb4SVBAhjnzcQNuHSCv+cnrC+Dr5BYTEJwxPiseXFUSjjLJGyjM683ijr3fHIpROQ+wCyAUUMl/i+Nc5JVU93BJptKwqg1XtEXhCXH2jd6AXAR7GeDnxiqWC9O5aeC/XlkDIFKbnlg+MnC3X28B8Aa77UbloPckX5W2EkglxjjCpgJBIpl71geLujPTpCV2sIU7BsUxF+5PwHbFyHbCl8b/9ZohXKtI1z1FPrptRRIeSixRFXopbHj+SpA24CVlWlld8iGSgfXC/eD78DxrEFWFo6UpdGw6A6xHkrIoQfDK9Yi5OJg1lRkInHNxEBjrCI6Ql9pQASxN8nzfPMKNKMXrifQDi9A7NW+hubOUCVIUPYN5CqlF2QRqCv7COnYkx5eHI/yAawmHIstjKcKpySBnKMO+yferSUQwWge2idF2QK5Zzq4A1lawdNICV6HhwExOT95eoAKnRRs9hnidhqk4t4SiE1h2TD4srmimFQF4Zzb5AKlkWPhwai1FZhzDnXHtKNKYQXQcQdJ/hjnDW9qAKsgFmeoixmXMgBXWJNdAFtZdgJtiOt8YAlP6X4HtPsG+cqO7aFjbyS06xqyIf9rgB0AVhSyusIzIUvfkw2f9eMdmPkEj6/7+g+qHL0cYLy9eyj9sEKy0WLUEqFPMCw0QXU3rlAIgOyDIsF9kK7MLzzYkBDHlOnQQ6N3Vr8tKROdfiErFboI5HyH5ec/xPHNX2DmxtVNU7GjYuLwEPoeDefKTT0sq/IqpNxKrAWeL2kt5SFUHIJxDZeIp9+FRRh8Aux0f2T9GbijJ4xKYrCEKQRQcuwU7hIlP6Ub5xgU8ksUsfI1lCmiNUO6tg4Ai7ERXoKzBGIh26XiPqg06tm1Q1KzREjqZl5SEim5Hr4QsFljozwK2fDqNa9wgnSFrY6jCaQ5jmeAr4LLk2B9cDaw3sgnEaGbvm+O9hgxBHPok8DOJHu23aCPhu0rO+y0wM5DCfgiOFQgHiEHFbTLjr5ulYF1lGvc4KctiN30tOnF2OwQAerSHy+xtlOxxrwIyPOBKPwMHLag7dxLejiOk2KJNW1Lg5hB0dFeXxgi+RjA04Vcm/MGe/9MnszhLKIWBPtabzrWLbOYgpehqAwbSTCJ4b2gJ8CRxdjSWidtiwReb5FOHooo4wAmBtcGFY1rAJlJA9hoHeEEnzCJ7JbwEqweiXtC71Ku3hYl8BNA567OvRphpmxcWp6GJJEngFEvzlpy6pL/kUo13ZTplaCBM8BJ2A81ubk9axhtrLFcDglC2BcqwIJPpN14L9IZggdAAm2+Q+FYJDxKCcpyr3p6Zup9zWiltnG9qzTyCsc5gsQeY3apyhkZ6hJEPZkeIaYzYBsq/O4LoG7QpzBaFu7dpy+u0FNjeYhYSxmSkgPQMNx0x0jbnmVp7p8L8OUPnuGzfrwDM5/goVkjYVEKy9lEAUY2Tkrs2RWYVTuTWBbWRkLv9LK4O7MlutDlf3T+pz4sKHPIeYsQQYfIhft0C6XKuBL82QY8AMvPfQj7uhcMW2XZ72qgGEpdfQhrATOgUgiXa1eGRZdKPXFFAoAW52fcOoRjpYnKZDWlFMnfo4ZK/b2A1ABdcjiwBhh5dLTXezSL9Aj5MFTi5xXQFnUd+CxZfr/i5YuEMhKkN8YthEqCkRyvz+83xi4ZY6cihI6Ikwb4qpi3odzcaGF9ZuhIYqlEmnNeN2vI9K0FcBH0s2A/eyjv8K6sIYnDS7W8Jhl1fXT0TXB5X6HO4mNYlcTggwrF1qjcCwQI4frwZUU/NXhTArSm6PdOL0J4K/SIcKsQxKkBx+Ko3leBY8hRCcAfIQVJL0QA456eTATXRIX8m97hp4auQHODfvga+vIBrOLc4A877P4EXyINdmHWiPTIohKMlxJAUY8AoEARV7P+km3cn5ktk54XyXR7H6CAmFFqzyDJprUmYgJyP6SVHfWk3EL5NYHdtwLO4qyvlEAyORzFU6vXLaWExae9KwnbEXwUFEhOLk4+T1HkYsP1yDZM/ke2FpmLx/HW/EYPD6lWXBoVnh7nczQcT3TMFnoR1YGecqbSrxl+yYkeWV25F6Uum3vTJPZslumRfA85jlhbIW9uQcxsYF6F1BMQTn+3XDMLWAE9PE3pAXbpaA8d6wOgYCNMOwHYQVL4MgBeZkCiTbffx70TVCV6lIMe7m/+mg/wWT/egZlP+iim/MTQn45KHTysSIJ+tUlkfImFGCgASuCGB8ctwEz4m7Nvjaey4ObBcQCvH0uR47QWeVEg8GdnyNOO7edf4vL+PXvFxH1gdHMSh8TOSeHRQMCTHJobYVaudUzfFxmhFKBqzADCMvdJgFwl3PkYzSgxhSRyiipug6gHMgQRwzoMBegTHezeBNqpKI4t4kNgxo4UUJgELWNt5H8ABdosLDvpALIf6GQlyQxogAqF5bsZyg1I8qsA1egQ8/0N9C4tLECInaGn7BSsCZCCHOjqtAS7Y/sYaAdgq7OvjQI4OpYHxu31cLRHJ6jxtBobDOSGNKNLu8ELjCRXJQv/2cYsn3St9w3oG2unrK9p3XsCyQsCaIJejUtwUCIAkeR3yxAeEuh4FTZDI2Clp4ZVlE0FujT0M7DsJ4gI+i9+BfLFM+z5FkRlIWk7QkEQJSjp8VIzHJOpwI4BzBuzhorQreHNUzAVPUG4TSAh9oUtgcmUigvgPaUaNwoajCHg4EwxnTrGkI1lM8TQKBb0aOTXNH6e1W0ZyE50LTHXowbStSGA8rSkt6II0AFwOI7xxQS4yRvq4TntACQ4IbARSq42AB4hIx+gQ9KwcfqPLJVzCo40GtLwyXIDsbVmwJYcoooD5f4qj3IiTBmeOBnzUYbbvHMnL3KFRvMEHd+r9TrNrTewi/eTA0+xLlbeoX104O6fX7DswrDc/YpdlHwuBRMkbAJqWdoibx1tXtAnMBbPLQf//uu/6Uv4rB/vwMwnfHgUtaISyp0yIRr3kfZ82OBjGKg9MvwUVmi6K5L7QG4M4NbgS9RPyYJ6Rw+WPzk1rhF2MgcuF7pe16UUpgPkCDw7w23F8upCgWINuAu3eng1bAd0CaE4WXiQSQREqXQJgS/AtPv5nd4A3zKtPIRJC5ex0IJPT4saFa4YorotALdykdMDZAUKKI4ClPQOOQy6A2qsAkjwEUjDqQz1kZ/bNoQkmpbANge9FkGq8fSmhLJTgEXavDz4HHtaVkJFI0Jic4VYNJ/Zi+TYLmyx0DP8OFlbEm0R1ID2YDjOgn4X9VoeDWu0a3Bl9s7y2iA7QdrpFQDtsCbQR0EL/kWGDcScafKpXAJw8Ucqp8oIKVeURe8lTb0JX6lFRABbFMedY3n0kS2/cx1ZcF2WTg8SzoLeSOqsbLjFgS717q8s48zUUGUht/BqSBP43UaP1xdf4PjiPfqLFRlqsexLFc+OY4AlS8J7XEvdC5DAQM+O0tskAeq8xb6MWnvZg0oNwYMawEJiyVWIg5oYEKAvg5qSyjb/JjD03CepgYX7K8G2h/LUBFPGPZXhT3cZxf90ujdYXBHNg5snZVz1qKMiAN9DTPnRAN+43yQGpVF12JuPYn4C7Gvo5LAbumMq9eAlGvL9Tg6VUZwzx5t7PEFmgVJHGmZ5wVGwcwqxAQUKro68VqLQ8U+dW9OV4ChuVSDWvQBSNWF1QHZgeQTaYbEnWHfs9POP2D5i9ikrPAOGBT1bPeRcdIlqxBgGVqgENmRNUBn3SznpwH/3278Jn/XjHZj5pI8lBObu5c4dLQFiJ6bl1zCV5x9ESEr+2ElVd0aoxCWJfIg07c4+NcfBImGVrgA2pDQj0dAtqoWCqYZIQqrTtXtaybnZOzQa3vW7FYJWrQSqUFQK0bwPn4DPNcWpoaG8Y8P1hspU8EUi5o8K8XgQ5HQHQYrPbu8BIEqYAOW2NwVL6L/emarMAReYE/fwJDms91D8VPZyYZXbvkg08uRz9qgQawcilBLPBAy3PcY71BJy15IxElkhh2PpMd/pKTj43sUiQrgjqpZ6XSJx7dqFXBcD1pe08PdnimaO9WPH+pTZYB3LywOPX3OC3dOU10PQdo+y89S2ntWknSX30aLjePSjOTYp71joWXI5goul2fFalQrRA7DtHXbnsKWhrw65CEQ5ttNHTIe3jZPVdoeLAScNkm88tKG8c6W3c5INwUEih8sWib5PBPu+LegvXmB/vgLnhTU9RMgBkWjEqAJXepew02vH3jtEFWbRgblCfYLjFCT+R4zwYHjzzEDuhkiApDBaMhsGAXYMVQqh1nFjA0VHNOUsryCQvbWygaQgwQjKK2MLP+8YIbIspJhhoK5S4NXTWxRTnL2CNHZz9hhK0mnufZfgdSmyfyZgJGqn5rEciAXYqLBOeoxzWyQ8GlE+9GlPxd6h40Zi34UgyPtUeAhDpjoGwXfcIoymsS2B8Q7K6Mgt59P7kXGZuua14KO3aqEtisP5/jubk7ZOj976yrA8HsDrJ7SHHSINdlogqlwz3YGNhH0TqdDsIOaNMWSY2oHR5R64Sh75zl/3ZXzWj3dg5pM+oqjT8MljABlc/ysuU2iIf5Ig8lbthvxOcF1K0K0NXQXNA5S8fgI8Qi15pERIsrAoZH2ErPdRZCxIre5V88M3umPkAJaPLujPVuDELr/kKUel2HTJ25uCINOUIZOFZB4N32hJBx6qLCRcUnEkwY/PWT0pPerTRC0YejyQBiyv0RxoivZ4sLYOwGfvRFcSKdjuzM5i1U2WYS/hLUAWC1we2bXWQ6J5eglaVlKNIyv7xvM7UN6MEqBZs293qHUWo9t01BIJkKgKoNM7U8kiKUBDGUHoKVoeWNQt5x0ARAWqDb55FSpGrBuxAEo+GhEaJkANodJcGToa3id6lhK0+FkYnhFyGnA45JGZe75GQbBHw/EMbHMQBACNe62vDP4IhqBAXk87DLIRQHkD8OTlaUACgGJzBkjowhYFi+AQhTYSmbE17C8WdrFeSJSGswcTQqmRu8WsLW/sTO8rvQ3VCyuLPRoCEMf7WirJn3MeCjTDQw5EHagEFvSOqOUaCfgvCF4cCxxapff4lQenawIQqc/SY5Zhhp5AJzMP4zKmUr2/8lwVnp9ctfzX0hjpqBRmAX9HjAMruI8zXjQpWUCiB5ZXPaYMC2kicpmATN4P0Y2+jf2cSrqIrkgjAeWhqGz+fBGK8n6lTMqqEnM4JrdTgZm8Xl4uP5Oc86pRPPH5AsvldXPuNUDNU8f6kkkY/SQ47oVctYdI1jgpyyycG/ZN0Z8p+plp+gLK1t28eHTJJXQd7wOYPEHzVAjw/vM7fNaPWyfav9bjx37sx/Dd3/3dePHiBb785S/j+77v+/AP/sE/eOO8n/mZn8Hv+32/D++//z5evHiB3/pbfyv+yT/5J5/AiH8NjgwRLQAmSwIAiuxnATA6ybskp4bbfu/876kzJHJ04BL1YwRl0acksLsF/b1n8Bf3wLZV92AsC9O0y4RC8GwO6B7dhkM5sOIrhkt2W+EnSr320SPaxxemvl5IntXDy5V+pbinqrciTkIbUApbbJIucUMxQC7MUmkP/A/dIaIjc6IssOwnlIoAGAW6xr0R1rSa08tkVtlMAzjyXFuFXZVPoeAXphAnB6btiFCGl2cGhspK8iTtpkBTei9sErR9A3CmVbvfU7gJAH0iaHSZzo+5nDdyZTyJ0FJXKnwJz55YVL5t9N5ZU/RnC2Rr9FRI9C1yQESZKebOdxQCEhKgBZwH2zD6hU2Hq5K3leHU8HaJhTdip1bQw7F8fGB53eN9EYw7IlR2SWCoVYZfOoAn9hXT3Zm9hqGY+B6dJO8DkaGnxd1yFRzPFhzPFvipRfaPk1S/ZEdiemCKv7WEQZH1VCJt3RvQN0HfFP2ECsE5oghfNDuMhJ7AotybtgqLPZ7yHqjK2rYQFJiO3+0kOM5BLl7iPaowLNYUfRHY1iL0KfX+WXdGRviosc+YL8LvJEco6pnkf5ZEWgG7xicQa6g58hUxZ1wLtgwvCT3DisxmHPePsanCVmX9l1TGcUODvAkk8pk0n3uMPTt8u6CaoiYwK9mgYw/R8xvvV8d+nPfp/N18j8lVLMK+jvWKHHOOt93cvwHMtIr7wLE+GtZXhvXDA+3J0U+C/XlDf7ah32/oW4M3ekdNWSXbG3DccS24gj2b2pirun56iBLgyDQ/wnf1eTg+0cf463/9r+MHfuAH8N3f/d04jgM/8iM/gt/9u383/v7f//t49oypYv/oH/0jfM/3fA/+yB/5I/jRH/1RvP/++/iZn/kZnM/nT3Lo/+qO2HCyLPB2ocfj6vCqEeOhWK9cqwA5Kt0hpuXBwMG2BtgU2axQLEzIhbwXrAvTUdPMeLrQ/310YGVhAncHjgPLE3CctzArw2cptB47AF8b08ybsPv0U4ffr5BHhgQcQm8DgCywpRBapJlCaJGhAJ+IlR5kVV4XYPhDOmhVi5Dwuml52l2F84HAhhGsrno1gggjgYXIhNcnAAy+AKeehOBtg9+vITCjT06YcaOoVpqIAQRTiKUHZHLvJn8oy9swdRcMvXRAeigMpcrrjYXZGPeWyLwh4mtT6nZ2zp3j8MUVcAzPDUDuE7imdKE569llXcqQI55uAVzCGvZwcxdmgiKz44psiEkBgaCKqc2hmiTmcqESJBdHoE8OrFbdwEsBOKawJefaFwc2B55i7UT4JcMcbGLJooBycB9J4zpV58I1EVYDbunVEYbKAhE6wnOaPCCVqhkS0Tccd/G3HdU+gs+DyiajRyu4OuGWo1NNKgTDbtkEXhLgo08eAm+IjDkpHk6NK66ZNUbS0BheBBLHE+enl6fWf6wdXMilqdTh+L52hGcMQdgZe8nDleHhEekJ0A3VLiO9OfCJuCtDhllt1ljL5SbEGx6V5J5krRfH8CoVvym+b+H1LXAS14mpq/1wZUOmPIgNUFyXGLDkOTGvc4ZQ8XXymad7OjA2pznkYGHK9vrA8sBK1NJJVt9fEA3a85X8sB5eq5IrHF96Tw8HK7HnnGrcS7lmLBvyXk/lAK+fg+MTBTM/9VM/dfX7T/zET+DLX/4y/vbf/tv43u/9XgDAj/zIj+D3/t7fiz/35/5cnfft3/7t/1rH+Wt5tPOC46kjUzGHgzIXnsTGDB6HMx2GZcRjx3Yn2dac/R5BYcwaKY2FvtyZzbGH+awKbG0QYgFAT5GV5HAYq6cKgMcL0A3NDP3ZmWGF7JbcnX127ngfVcCerext1Ds9Jq87HAINBVoNMBHZBlnDA7TQ/QD8rOSUdId0gZhhec2aHoDwWS4W1UsVR5bsjt44UgzJEDyWMSijN8EBF6/+J3ZIgB+pmhgSGThoGnwOEn0hYHjGQA/M1GkXHpVhQ5C0MAuvMlqijlB3GR4boTKT6JYtHu8xNQ8otMSi2V9wIvTJC9lSWMavcV2C25iLVGKKsCT5vBbhOg/pLmE1JvgjYTYKD6YS6UP6y0AtqVnjZSK1AMsHwAeY4jLjeFcNge3FKSmFIaAlujjw5Mz2iEJ5asCyC1yM2TmgV8Ua1yeJj4b2ugdoB3BuUTCONXMQIHs5QA5QAqsnku37igISWSEhMw8VHoUtJTLGDJk2bUpQ1MQZ3soGrB2jDYhHdWtIcXsKpGShsybcjy0qAjfOcfHnSlgMYm9lJhoq861XzRouLD3APk2Sy1b4/BvoIY1rZ6f0nttp8igmoElC9VDcvKjPHaLzT6nsY50WxyTHEGvIdKzh9AQmaBljQ4VSHahyUclTy31hEzArnqF6rcMCRznUJP4mj2feWxNYkbyFDnCToCVBuEzPJzZq8bQLCfzro6F9ZFiewnttTg/ipaPfN9j9yhIKArQElLFGtQO48Pnm8FrN9xz+jM+r9YSMMX5ejk+Vg+nDDz8EAHzxi18EAJgZ/upf/av4k3/yT+L3/J7fg7/7d/8uvu3bvg0//MM/jO/7vu976zWenp7w9PRUv3/00Ue/5uP+/+U4vzjh8XjNDbquwH55+4keK9HAFW0Z+gGADjeFNlrXzRnz1sNYdK+RnIgge1YZytniWRS+NPi6QJYG7Dv8fIKfVwIWBXzb6nSJtNeyXAEqWCePwE/MI/cnVi3VJ4NvDrTGPZZVecOoJ4OW7gsF0HsQog8vQSAGtIfOUMHRSZoDYIiUaqUljqx+GlU/0R0SxcjSq9B2YyqksG4DCys3Vubt7FWEpuhndkruyvAP+0QF96cnEVZSo4c3InlQtFIlrSpDdBrmvGWMvrJSIk6mR8yJhXJN/JnCNW8U1p90sDQ6ZNTr8XEaAryw2SCFoScxXGJscS0CyOBazYBD2eSTSoJgKgt0+Xj8SXHFeNJKFbIhPMCnr0CiK6++TU4gleEYLm16JuO9afQkchWoC/YWa1Eo3O0xquLG1sjSjmoOWxR+1+oZxIG+cvB6xHtSZti1KEaoi6MHOHAheL/K8hFAI3sPjuyGEdWLE1j4CG2IsIjZCiDqo+Sin3Q+AFT6fDg06hrsoBwKKbPbctbzHaei6rE+ghRjiqhwzOs7ELwLL65Xb2EMpXwIoGQA0EJsJADwcY0Jdw9vRYCCIsAn8A6Qk4rV8jvg8yFkGDDATK6xrBA8f148pETLDQXcMgOyuLEOWJLrZIDKuHtdt1KY/Xp8ubzzWWwe3wxmCu2gvGgaQLJdgPbo0FcHW4RUyDvfZQDY6DWHRnmD9I4FQlIn+wCRAUavHx80PdpMoMBItBiPyrF9FZXzWTs+NWDG3fFDP/RD+J7v+R78pt/0mwAAP//zP4+XL1/iz/yZP4M//af/NP7sn/2z+Kmf+in8wT/4B/HX/tpfw+/4Hb/jjev82I/9GH70R3/0X/fw/6WP9772GX7x1QOAdOeH4K8sI6eGCa5BVpPLlGqaHUYJ1JyZI06rsbwL0QnYj2NwFsLDA4DCKsnFTUj2vay0NqPjtuyxSVRYWyRrX+RnUKaOT94OMbDiqgPSOuRiJAk2Kpfkc4iRRKoJ1oRWOl3zRjd2a6V0XJ2l8KuoXVi4i4ZHCCRdRolv6TxfRNDMAA3wckQ6etacydg5vMIpD19/T46CCAtVrSgysXdmvCRwSQ1cgizfYLYwyOyLtJTSsk6pnfyeJRxJADR4CRAQTIQiomJFZDN4pV2qT8Aij1RuHoowLG5vQ7hnFljiSpfIOtGRyqyBXMoIDcvTHaguwnE9ttowFMFdhCGyFNoyabYQxp5FJKcMOyigF9D7d+lMvRetNhfZHsAji08dkIuFI4rAFMcBqGD/YGP4KgjREmUPLNOoY35JXJcJ7HN9kOiNCmHkc0koQ4u/aXbLXsacl8NKCWS6ALIMRZzgQHRQhdP67u700lSWI6p9Qfb7mrP2Skm3IPp2HyRgDa5OhATnEMzsNUsAU5lUiPE1XJHAY3q4PiMtuBQ9eE8pNDABnwQKqG1T3p4EBp57Ip8rZiaBNxL0GoFYhZ7qRlJZTOnlk/n7kycpnyk9FjM4sfn8+KzWfQKfvKlk2HgC8o7wuDDzjw106YXWI0LFCQ7j/dv9gn6/wjYaUxIoubxRBY7zYQOgDkxEMNM42Br/PD+GyCbE5+L41ICZP/pH/yj+3t/7e/ibf/Nv1mcWjSl+/+///fjjf/yPAwB+y2/5Lfhbf+tv4cd//MffCmZ++Id/GD/0Qz9Uv3/00Uf4lm/5ll/j0f/LH9/wTR/g//VPfwkQw/IgY2dLhF/iPK5ZH0FgOFd07/Ae1dhaSFDVQdhV5UZoCpgxbJMXnJVGitDghPi2MNS0LlT6wSkwD+/CGlk9YfVgEsAO3hedCoZ1FRpEDMvLneesIYWjF5VkqfLXO1QVhgXSGvQJkMcLbG1grxtgeezwCKq7EkTNi54uAABwWklEQVS1V8ZxHGG67LT41QHfWlimoVguHQKCrS4WACG1yZBUCXD2Z20Q6zJ11MONfgnhHPOTc+vxDoEx31mJvoRhQ6WXTjJpWOmp2IRXEURGWwjo3iKrY00uBl9plwF60rOS3iBLZZOCX/gslm71qJ46kklI4IQzE2hO8xwPPK6FqSGkOAg6lwDJInC0G9kfnASTSFVGdCP3eL8CrI72JBS6qXwXQT8xnCMuzJCKxpjZYTotVXkk0ur3KzJ0YtMcAWD9jgBFCdr6GplaRawArWX3olTky7T6e8xREJa7EpCig1wUMJ02ibUaY01P0sjm46UNCFSbQHN40VIyeHTrZoNF51zPgCHG79P7EhOWPlAw4yzeVz6W1/vnd4Y3ZNSWkdl7EvfLcVWX6lhDvGd4taKDfIWHMABETqnZCJtdDyrmKjWX8PnSU1VhrZzTBGQyAZAESukdmua+wGBeO9/BFKqp8+M9SFkBKVJl8p7E5ztDmXrxcvrQoBpj8UUgxky7476REJ6JITmnLQYQOzNflF4wstcEVeBd9ggntkKE9Rxwrv/lMv/hs3t8KsDMD/7gD+Iv/+W/jL/xN/4GvvmbR/PFL33pS1iWBb/xN/7Gq/O/8zu/8wr0zMfpdMLpdPo1He+/yuPb/ltfi7/+d/8RAEHfGtZpQ9e/6WZPgHKESRWAx4+jwknaV2YnhSCn8Fio4VRpLZf/+OaIAmpQYShASYxsTzvkcPT370goBhW9rcI+OofVdzNEkOGhlKWlPbcGfTzQ+wEsjVZKdFfuG9DMoI87QVo7oV068PoCWRpEG7A2Fvy6RProqvCtob0+oB89wq3D7jZAW3AwgtOzsXKmiaE1QV8alqfOTC2JVOKUQKEcSpEJlY8Ao3Q+4pcz/52zBlKR8vVJCf5RqMsxvDHxq03YIJWVo4h+QEzk1ANnaKaQuwqGHYK3kXV4pE2nJ+4UDG8DMOpPCD09WcumnnPhBdyd13SJ+iCpkby8hvyM/ykCYDkwio3EmEWQdXBgjn4IZO75kxqvKVwtPEYSDQ41Supr8L5khC7Ck1mLb1XI04722GEnZoKQDxThwlie3nhtC4B9iEAmT5w4qpqzGflQxakQjEaFNs2pjsdA8I6sT/MZ3JpEDgNkhqcBHllPKCUsudaKMyMjJR8ygOX0vlm8MVOwGWbMvkZV1yZA9hwaSoWd2VD0MgwPcoW/IpQ2jLEBiAoMWYRSJ8BxBaxzzWP6TmqoXFq5jicvTlKB0uMpdSKuCeQyninDVOUZi+evLSXTeQGSch1UO7zarCGLAcqbMIyywrgcqFo6AgyCvkWdGM13yJsem6LfN2QTz0kCTCCE8y0r0IJXCOHasBZp8UIPEMN7o41DXkfMITvmrg+f6eMTBTPujh/8wR/EX/pLfwk//dM/jW/7tm+7+vu2bfju7/7uN9K1/+E//If41m/91n+dQ/01O77rO74hUj8xVRTlaq815pwrhpliN/WeZgDBzWUnwNgPyLZVSX1RRZKGvUUhKgCZWggA1fiuMcME0aXbm8LFcHzhjPb6oEW7CcMrDZVZIknyzO97bOhspRDSQgWwrpBnGz0ronRN74BeDOsDBbc3hb66EIyoor08sH9Ay1leP5EHsTQqKed3+n0DjgV4ycq1fregXVjZWJ1ZI9y10a0ZgDX26iE4GzUbaGkb+qnhWIdQrvYH8XKKPDg1khvW2cCjAoKSQR7kBSWVTwhuDZCT1wVQ9CYOeFy30qM7Ko7O/jcBxiBBGfAwykKSB4Bhxti43qzEkGnFqTAmxVGTMYUZ8qtptcr0cz6E2CBWF1jJ6nZxSXWHZV0gICo4AxCC52PRaMac6zYzssJwDmXrUYNFApT1Zxtsa9CHDpyXSsv2ZeIsAejCMJiGpc8MH68G0DkuW7jupHuF/DK0YIjv51RJrhsqOInxJkCswo8evJbJwyH5YiLMJ9P7AnBdryhfhATgavwgeVkDAKRCkwIQtyTQIuYLQ1zJR6nCc+FhLCCY2XzTEvEYX4GVCoUNzVneRGDsnwl41DXyoXNP5LmxJgugJMjz8S8wAZNc3z6u4R7Pkus7wdF8XR3jcAXB6hHrOoVBxBn1EZy7DcwgcgRYDSCXILRzvdmJa7oF2OlwXN5T9DtB6+TWIGqY1hY27nsC1OGFRIzNhA56a0B7BHTHFJpDTZB0/m15/vlIZ/pEwcwP/MAP4C/8hb+An/zJn8SLFy/wsz/7swCA999/H3d3LOLzJ/7En8Af+kN/CN/7vd+L3/W7fhd+6qd+Cn/lr/wV/PRP//QnOPJ/dce/+a3fMIGJkCprA/rxxrmzcmTtGRKBcRzhrcEIQ61r8Uwwfy9/SO9LgBpyKCSIwBESaACENUKOFwI9hFkWQUxFU+DE4mIW1V8hrOWRQk4EJJSawSHoJ5qqbgLtBhFFP1OxtEeasg5AzyuiNwD8zP5Q+xc3LE/A+jKq9gqAjYqgnxr6l+6gz9YIVRgQqY5y6XxWSzAn0AuZssMLEIAkZskW1nS4cn/P7+LKyBknCN5yfgrWSSCjDTyQlmSPcFDqngqBwKkoI4Mls6J8DQ/Bzlh81iMRRHE6B+uYWKaXynXoAbjmOIQyEc/zElXkM4d36RplE2T7CLNViDPn1SwyW7S8AIFRhhL0MRxWeI0Al6HAjG+CfSVASf3bA4Bm9VMXhzmbHnoDfBUsi8DvWgC+MbceHrc5rOAa/YMMkEVQ3J6cA0NUXL4GlR4gonUpxYPgjiX2K+6Uj2vAA3zk9SrrZqyXcs5N7yKoZVPaOn+3eIby1iUouPFOIMY+c0dKzyXZGUOhzynIMwiZuzFfhWtiLU1DHp4fGc9X4aF5DDJ9ni3RJk4XJq5XjSHHEafPYMfjw+LmOEb4SgIopAdk2u+eE50XqlAbP2+Pse7CMHDQE9K6Y18jRBReR8k00yDodwG9uqpoHZBHoJnDVmB/DhqNl7imARa1uhbHqPHlGKUQ5lCakpdlK+CXCAtfUOFEAMGb5PXPH3x2Ihm/0vGJgpk//+f/PADgd/7O33n1+U/8xE/gD//hPwwA+AN/4A/gx3/8x/FjP/Zj+GN/7I/hO77jO/AX/+JfxPd8z/f8ax7tr83xxfefoQhab1Oa88fBe0lrjQVeJqXhoMSInkskEUct8skiSi4NghhMaysG0ALk6EQUFZD0uwAtmxtiFBWTFWwKFxUocxwWXBJcDO2xQ7vDzy04AyQN6yU2ehAzs8mfPB5RbK0B7wE9Cq8dDejnDeurjva6w3t0H4bAF8HxwRm+H9h+8TX01SUKrTWs7rADQHBkNIEfGKorT4JwbgzMdPEJtZQrfxJqDlQ4oV5YHvLmZyPeLiWArIEZWC34D4brEE9eQ4HjBHh0wYWA3JGQbDMgyZZOEACHlIWZXJg5elUCPBWMcY0UtovfJbTCDFrUON5UGADCY0Ht4eDa6BGOmnGUx3wzFCelyAp/58/BK+qrwtdYd07rE851dCzBfYr3ZYoqu28rWynYoqXIZAajOoE5CZAzvat5fhLAiAo8yJwVgsHQfRLaM7uL0IsU69Sna8Z/liScXFeCQTaOm9crk+m8ut/gR1Wxt1DgtT7zHScfR6fPc7lNa2i4l3BFjIUHMMnP02aaQdIEUOr7ce3CGRNxOQFMPW2CwfwgvTzAVapxraP0aGGEcXLqBGM/WboQdYRYyxuD8Xt5arrXfBXwDY+hOAFNCy/N3hBcqLjmMsLS4pkYQW+IbQQbAsWyO6w5lifg4ZngOGtMHeVBj70RpZH4XkNmzGN2Ye8sW+P6UcAQF68MtnofMcd9FXzwhc9+9V/gUxBm+tUc3//934/v//7v/zUezSd4JBHUB3KGRvaHIKy7DCEQhHDDC3BBpK5qZVcAk6D2EIL5N9XBXRCUh6Y6Z4eC53lSgoYETsexcCwpMK0JcCYPYFHAumN5ONCeOpbLgR49dKQLmwSGYLVVI43bR2+kkDwiQrJmE/S7BmtbhNkE66uDQmtRHO816MMRsV9D64K+GbNIJObztMK3lV2key9rk5MDZLsCkeimHMDQzorLM0F/hqzRhprYENQVRkirMkBCCUIftxmKaboOUHH0UhTBVZhDSlkun5JaqvqqxPehFFxF8Azhb2EZat5/FtipUOcjgUsqzgR45pMUBSrtJXjnkppNACcFtdoX+KY4MnSa340nE3MaqnMYLec2XTSxfuUw+IleNRJ9uQZ7M2iXaliqWUq/AaLsr30EmMmmhVfN+OJp06od79gLBFiQa7WjPAtoYAfrnBsFidiZOWSS9fLKITeTt2cFzv05KWod85QEcKAcn2Pv57oJROO5PhRXwAMY9yweiE33mk6dScLpgSngk96MBEPTs/h0fgE+Gbd/w1OSYczp77M6KBAzGQkFtBLYTPssOWtJSq6MpPR6W3gootN0Kv+8Te2bCZBlWvsM1Or5JQBDeHTcBL467C48IitBtMXcKxDAV6CN69POLCR5dIF2egQR8lUvDHlKsAlyPvM96J4tQzAy51qA/BOi9YrjOAkWCD3qPgAx15rgOAPf9OX38Xk4PhUE4HcHhtVRNVuEhfG8E4ZnGiICBIa3RTRCRRpem2W5lgrlX40MAg3goplj6TRloomgm0cWEjeKpCLqiCwJKStLDhJ3YQrfgP1OoK8BfXXB8qpDHh6xPV5g5wX+/B4CcmiO5wt8U2A3+EGtKvBB4Nt5f1sb77cqxxrl6pevPEGfLrAX9+gwPlN4A+Sx0+F0ALKt8NPGhoTaIIcNfVptFDg3dlLYqeEAFbHvlGCmgLdkEYz3VEcKwSJjYoSgQgjOIZT8ex4KjM7BAJDpy8kXAa64EdqzczFGqjDi3ZRrBMiUZYIt/lzjTyWQY86bR2ipuCqpoQyc24qLeYGZis6lckolEa54gHUyLEBqcUccQdI1+LoMKznGV9fwfNaRKkxeQsdhCj/JUOCC6lHFzKkcs7JnVhR6VHh4QmS8zwmUchjDUwQJZRmehKhHB1mmKIRO/8ZyLdc/MgQ1vVMEIJkNmHx5E6jKkFHW6ckQS/Ym4p4FKqNq9lokMEyFH4DNE+xleCiB7rw28jXnOpg8H5muPXu5ar3Y9OzARExGAZJyFE9zj+m+5Yxq47sVlpqAYBoNFovwKgMr3mGF7BDP/xQAYQVkCa/g5GnJW+dYvRzbXBHdwOxQyRYQDg1jjunzGO0gNADfFkvjALADe3P4GewmDjB87wF2TdAe+ewW30sR7lEdPHvTuTr6ppXdByGZ2M4EUmgEVocgUsFHWQVriL5YwHd809fi83C8AzOfhiMErwk9HQpyWKRFmMNBILB4gBqhSdAa07dFgKOxOqy2EBSTKVGZEZMsTZKxAd6HVqPrt4fwYJ8egQMPnbyNTdn0rDHFeXtg9xRfGhsh2lGWoWwsn9q6o+8GLAJ5OtDg6FtjZlQQTo9TFodjvRk5HPL6gFoLoSjA3tEuHW3vkNcX+PvPgGXhJo8aNWqAvLpAH1lbBG6QnYQ8aqGQ6sX1ANwNl+cn9icS1uZpr6ICZ9b3iMnLfi9X1hwwlPdkfc6ZElS04/dbAQ5Hud3TTS4O9GjGyA7VI2V3VlTu83UH0dAh1x2Vp+/lWDOVvkiSEeKSzAZyMN3aY27zedyRadVAhDSPRCEkT2uk4nr0zUnNVdlBMYHWhIK3j2lxZdhSNc+JAoKhHNpFsD4acCj6MwswH9/P1GQTdDg0Q3rxYvrUbU/i3ZSCTOU5ebLyyH5LA2yQFyHRESTgE4v/JTia1kIle2V4J9d17NfMjMqBWS7X9MIVKpnePVC0Hs91KNN7ivvlep35InO2EeK7FQLDWDd53cwWyw99Gkd5cHIub0BVgaLcStNc599nTITpnSQQu64NE125EVw/8VrrdY18X3FtORA9wTCqca81hMiqy/XPefN7QT/xXtod8hSD1zC+VkQfKqd8AzOLBOyOghPgS4x1FchGki82UIZGmFI8xndBhafKy26AWFSmPgjQvYGp22vwHRXYFfAT0FfAFgeaQNcBDi3qGuV6t0ZQ92/9um/E5+F4B2Y+LYci+tRopOkGkGkt9AN3oEgPtC5j1zaF7A3oHa01EnDNR08nkSADy5v3Ta+Ld0iYl2oCPwx+UdixAItDL0xjhipsawwfuUEfDyyPJPKm5Yy9E4idNhy+RYo0NaED0JcXZlvdbyG0BWIaoMxZev7xgvZwYFnYbI21YgA87JCnA/78jpt4I6DTXdAuB6R3SGsQTYIvJblcDDitcFiEfBw4jOcrWDJ8oYdJjwY5gH5aApjdeGUmRVAl5eNvZaXmefPniqrnIrM3Jq1QDyEKVOJJCvIiN6aFXAWyKMjpyZBrb8s8LkxKZxrjVXNIAd3wITAJxnyEvuo0H96YaYyl0BamTSdfABESSodiIevGNgZpVZbjJp8zelJlXxqoRr0TtrVQi8q7KP1ytZ/Ss5GZz6VgZbxDwQhfpMKXVNLhueR5AbhAnpCGgZCckQKvsQccCTQTSEWl3vRglQcpxpDvK8c6r7NYG/kSe85jgOMEChn2mTBCKbLyjoShcdXvaAp/JmjIuaj1lwaKj2ctrxlwFZKaQ3gzsCogg0mp+hhLbaUJdM8grN7rfI77qP2UYzRcle0XxLvc411tg79SMqtP+zSApDRUM8b0NNnGPSFwZF2XfY0xbJwAORD91ZwNOKMlhpqXVzWpjJwrp/tEUK1E8p62xNpzplGLRSmtM5h0Mb3PvgK+Af1MjwwaELblAOL5HuI9983xXd/49fg8HO/AzKflWCQQdcT9M+20qh+xKZ82oQlxZADfCWaWBTgOysElTS9HcWCaRiPBG0BjXtUlcYDXS6+FKnTvrAi872iPO+ACXRqWleaid5bi1igDLxbZHCmNFiHRLXgtWcVUAdjR6fnxHfpE97+IsgP4yyfIZYdcOvSjBVgV/fkd5DAKmNNCF3xWzY3N2Q5n6KopcGH+om/L8BB0ogmVCaAI6+nYBtYgOTFkVgAhcWMqgBvFMwvNPOZUz1L2CSJSiE1AI4+sbFregy0vrBXCuIoFALhisyYKKvM07h/cmfosPTxhieahu5f1msqp3O/+xqPzUun6hwDm1b2ZDBdUiCjLDkimZ7eoXrzyqgKuE7NJMYFNQ5MLAwH6xnIAJoCdvQR6bwzDlUclig7OXopZS0t4i2ZvhPRp6oKngJjSNLfl4tFlGwVECzSmRlQf6cIa9xTUZBZ5VzjmDIkE9hnvUnJNYNL203hkvKfiqeTz+3hH1bH+RjmPNTNCV1eelLzG/Dmmv81rHNNx89lMeh4ZUhhrerpsAZzbzZHnyM3n07jrXnkhpTxSDw9VyCPfYr7CWzNKTRBcmPIcWQZmYt0bL+CewzA4cEYV8pMVkIOkXuigB1h6tmTMxRX4E4xwZouo+sbva48eap2hJD8JgVJc/KjPHLYGiGoO2wMkTdWBcw69OfoKfHD3jgD87vhXfLC4GZsScnErwUYPl6ZHqCHJucAQGr1D+sKWBetSlqVEE0cKsOhejUkWhGeGirYDopD9gO+dDPymwNLgbsBlhx7k5fi2QNY1WiRYtA9YWAgPIM/mCL6PhvmTEmCNbKm1wbtDHzvaq4fq1q2qwMNTeCoMYhfg5cHid6oQFXScKQiOlMLgfbKZ5qJMV3/l/PlMU0jE6f0K14IsC44vnmCnDI5TyNmiVbq9hO2tEMUAMbcytzwVwPCqyCSfhyTk8RaPyrjn9K4Rik9w9Vn9O7nVkWmb/uZ/STIuoZx/mz972yP7uEfWT2F1aKlnNSUoz/YCdS4A9hWil8LCe4MlpX2UBEjPQXpCVBlmMkTbgyACx3WzqBt8/JvvTaYXQWXOMFwET1Hk3+YjpTpeUnrKJPfeQj7T8uSsQ/RMYQnUnC94gFoZVvPVZMqoKBtDM+AK+PQJQJbSUyrDObsrPXQODG9QhqRkPEOCu6vPU6GGHSMBeKtpYk6DXYO9K28QUAB/cGDiTjIBQZ/GKBjh3fzilPJf+na+57x2EcAslL7sA/hozI1nk854XotwEENDMrKSwitiwg72rhOvTAhOPPgxBVo2rzVGz2WEUDdMxGQfADRAbZU7CH5TpYNhjDPrHtUUKIAWY9oJ4HusE1sBX7zmuEfIzFbATgQqEMA2B3YwfI752sw2tdMVBP1MH+/AzKfhyFW2LFN4hAW4JMpX08Jkw0jWZxFyXTTCOxZScAlCcHaNjqpcrLGipfhLVybrL2WQdAKZ42AzR6C4Jp51bWSHPjV4dtU2AIdAFgN6gy+NVVgt+sIsiiIoh8en/LmrAK8677X30X/qcQ/OEMr9K/tBQdEE+HiFbAsx3RGcifAF6L7Do0S3PF2gv+zoL06Q0wo3g50WyNawP1uZTfBioXH35MCToZ8E/qwV8ANwZT1+1fcXR3JCSmEA16n3YXmWcpm4Ivn96sMz3yI1UiokDIXBdzlfBFeVRofwxQA7ean82xxuwM2Y3jINDhS4mK9pQV5Ek8oIiTJsUTpIiuhe/BYPve1DKWcxtuwuXinHkyfE4tya52ncOceSwCfPxejFkwAiMwezuzUzSIJ/ZCTvOlAhhOUlQ7j2olUYCaFAkpvjkBHaAsJIkeGRiHddJNnpvSYgS29LvqwMi9T+jbnoMs5NcDIDs/IcxXnzGOpd3n4eSh3mqHL4ggH6MP4lMPECPpw2nwCU1O/VjR2gV9jH8+Q9q2p1jn/ny66QSTZW7EJvbHgT21NwXaK/WQKCbOOR5JyrjMHoaN+C0G7O9WsnH+5MA2yx6GQ+5pMVD4U1XLQgYwFFrr0E61ezPf4RjjHjyw5hOCqAMfelD5B1ZqZSAubyqCkirOUEMwAsYqiyBIhMEB/7y9Zp03/Gj3dg5lNwrCfF5Sl3lsfmCGHvEgvdywIxbRAN70OYXq7ggu1aKZriTDuGCdDtilchVcGK1/RuJNG6s7M2QqIDwM5dLj2b/XRg3yF7kGyXBVgXAqbu9H5k8T4DZGHYAQK2OdAeNXOc7QmWBbI24HJgLgbobYFvDdUJ1oxg5MUzyKoMcTUFROFHlGJVgZ8WyOMBf3oC1gV6dMiHD/ATWyhcmjKDZlX0E9gTRaK098XRnhyPi0LOMT83YOXqmKzeKxsnrbDZhYxhwSKGmwJqNkKvEEOeMwMZYNTzmYTyzbDeOnSfLv/Wxyom8KT8MJaChTLNn8urAo5DkEKSRHWfxmjRw6v6UwHX/IBQMhZu9qsqtpAq0DZntJTnLDg3vHTcY0Htmbzu7BGZ+UJ5TWY5+ShCBwntFs/nrHOjcJw+PAARXO6l0n3n5ygFH3uRQCOUi445LdBwA2iqbsyS947TZ5wb9ywwkt+fAUyelyBtPmde4j6uM//O9xPjns7NL15xjwKRXhF6nd+vPeLpoJjCgDnQfg2qoSgPo8Y6Wp5q2qq5pXRerzfev4j6QRSe10HdL+cigJFRlOBwh58AP40q2fAk/Pr1xskHSsJUfmgIg8RrfkZOhtfGspzIJrUuffHrWkHRqsAFHNfqBZbK+9YCpmiMceGY3LIPWeiB3HgKWDPgfCW1PtPHOzDzKTienzb84tMjAJS7l6TJ3BxGT4cbnAUuaLpkD5w0t6LBT9XvSU+MhCcn6tEANsWHY4E3iUq9Y6OVNwXgvz2kv9BDJP3gBglPC4vfccwSxfvweOFDRQjKI41SVKEPSpLn0tDvNnhTtIed4OX+DGsKnNhPQI6DPaguF/j9Rk/L0oj9NHZnN/StQVXohVqo8U0bpAlkbVRW8Xy6MwvLmwMHHeT7PUisa6BVPkuuSVbVMQuU6aMZuNSf9Oa7+ZnEO5+U1ZVVbddycr7OzQjfPHIp3HwsN+O9AmNlxXIwhW8kGxLKABJhQatRmZhgVKEFSqCbBEDxoRBTsXo2h9HwRiToSO+Fx1LO+8k0b0CsvfAExMPkuZm5w6w9jFTb4AqpjShleXICCVTIKJQsvUOOy/sCb4r1QbE8GNpHjuP9Be3JYWdhZ/XU4jnOYXeUEi0ll3MfE5PeFQDDS5HfTyXcUO0EcvtftVybgVHcN8PNHgbTCIlFN+oAhbkoJN73bX+kmR/mgWzyea6yqfzqa+WxLG+OTQ07cyKiEKFgzEN5DTOdWIC2k0MCAUZzoYnYm+snANC8Xuo1OCock69LLHolrT6qNEvIgrladIIYn3+ffm7Tx+pjfyfm0XgPM8IDkL3YKnMr952MDuDVm2u6XQFMjXsnmDk5vVr9aofTS7g48M4z8+74V3l84b17/MLHA8zAjLVVciOB2UllJihBCgGKl9VEsgKQvlLya4D0+7oOm9wOg7qFUItAewuvzNGj0SN3hhwjDuKqbLfgoAcFIQqbQjJjKvfNfsAfn4DOtG7ZVvTWcHzpHmiN1YQNkN5hdyv6sw04H2hB/nUBa8UI4MfCrBdV2H1Dv18jlEUlJQfNU1Ggb41dvR/p6fGoHuynhfyObBvhgHRjgSplQcB+31iAbipCV8CEU1ffvfp9/liuT0G8AgCDX5H/TuGnsubn78Q52QiyhFYeqchuZNItyHnbcOsyMkL5xHnBK0nF6bMrW+qL7gEOfCiDAhpOQJj0Ds/lmwoqbpyelasaL7MHZRqrO+htmUFGen9yLnLuFZBITy1XfA5GAvcbqlYOEErCUXVb4AQ7CO+MC3BsEkRLgd0Bx6PDHsinaXDoY6Rnn3idzIzK93k1R/meJjAsMXUJanz67kA98azheVIb81pAouYnur87kHXwff45Qc7VgvHhkZnvnd5GoXKufVGgMp8r0sznNZEKPD0k6Yg+CBIlU9OTfA6CmsxNwDrWD6J2CoQk2IFMAkgH12XworyywgZIRwG1GWx5MuUXjDjtNP43eDzTnH01mTCAj7zlfLnyrM490wDQUxWF//JdvLVa/FzDKOWJAr4aw6XHCHdVs8/FvmrP4c/i8Q7MfAqOb/ry+/gv/5tfGmsxF38Cg7Q+QmgV+W5SPhRMAhELUlouXGFqtIUASkJbUy7mvQf3Ji0CITA5oi9RAqfjAJZO7845vSJh1ooMl2oQjgl2DL42aAPBlTncdhybwL5wB3XWIhED+mEsOuwKg0AfCFywNTiUDP2msNOC48wCd77Gsz05tHeGi9ISOy/oS4PsnYS51iCLFg+hhG9kyogZe0S90Kqm6e1acJQB9TarCLgSOFcf+PS5TfdO5RNjqr/lPePilcLrb4KUOibD63ZMMmvLeVgp9OLLBTbiU3o6+F1zYLR0l3GdvLQAlcGjEUaJeehCizeBFxBF61KJpuch52QCax4TUEq+TfdOhRDKMYnA2bw0wcuVUkaEE0I5WaNCRdb6yDmP71h4Co5nXFtdNYBWgKhzlIxXYD8BegjaI8e7r6HlzdGeON6+CjNiYjylW1M5JxjJudExHXOxulnBWs47ZFj89YKzxs+kbFMpx1xfZQeV9w1XIIXz4QOM4/rvBSQLUIW3JkJznMzpvoIIS0YxwFTozndS9JMc3+QlmYEcFq9rO1BkX9cI4bhUyHF+qJrD4jT5WHt1Lq7X5fXyH8f8+ww4a/DjXc3ye/SDupnQeR/UJYY+kGnOrwj/0axyrnclzYs35DUWp307u2c/B8c7MPMpOH7Dt3wt/tr//R9fA+vu5CPkJgglkZLGmxTJlALFAY9aLRF+SrekL1kam0qJVYOdVlHT6OWkEGFan+ydISE469woIMfCho29s0XA0pC1MsQcuOywyw65HHBj0T0/DvgHL2CnDb42piqao58bxBS+NhyNfUPQG7sdW4fLwmsLC2KZCknE8TvDWpwrb4p+oqDWJyNYW0B3tQPZTNNOCjVldlentdrFkHk4roKjKZVbQ5XLvwpn5DHrCkzgIYVvnpdAJT4vF3tDNZwDcBUpnC3kq7TgvO4sLG8U//zZLLdSWSamkQxbpbIMJa7Tesq0aIkvO1A1RQbACoJs3MPsek06wBCJyRWgKKCd0zkr13zwGSS5vF2JAqO8f3QYdnXgFCdcUKGsrO8DYFijua+26T3YNH8G9FMMqxRLkMxjbBaAl+Rhrp++Ohv7OViskVsTp48cj3dgKvoZtbc9X/IEJjOL6NYKn0nBcuPKS9JnrbvScpNCzq+0sT6ugJOOz66U9/yZz2uBH6aHBoJrgvI08HqFKcZ0rHmG1x1Yg6wdfLskYg8w4fUdW8cegQyvDFYvsnW2KynC8PQuK6NpnsTmV6TtK6+MvOXztx23wqLWfr6fAVoq1JR/z18TqGhW9uaG4CsfZSXm2yRmJYgXeHMWj/f5wafxA7hKn/qMH+/AzKfg+E3f/g1l2byhNNNiCbJcaccUHOmujQ3SI7REpeLA2mASxFxH1GAB67k0Zd2MJnC3CjfJ2qC7wYwkY28KsQVyItBhfF6HdlTQC3McUYDK4ZauakO/W2DPTzieNfRzY72csF5F2IBPo6dN3wTaGJxuTzvN+qbomwa4S86FjH5BQPAjQhNEw0YVADsrzNragB6l+qP2hC+C414hAvRNYauy8FQbiqWUAVBK/OqYwEOeU54ZG+/z6vupxFKgpnUV9yvFP8m5+iyEnEyPXsorf9fxWlJhe5r8czhqVk4uxQ+0aGXhQFUeZTgoBhTxe4nrZkaKFekcyPxuB/ksbmOsCaxyCK4TgTaVa459nteOcqcrMAqkTYq0PQHLI0OGvgLWHXgUtIbBFTneBFA1FZrPhSIgIzwHV9ltV3szdYKMsMUSv2vsgwZ6lZ5YcNCfgmy8YJB20v3vOoBrKtw4JftFFUDOOc17yABCtW5mQHPz3q/+nmtFwgOTSjO2eelDDSKz5rAdPdOrXSI0guEBBa5SvscYmMnGcHh69UJ1d9puRdqOqsrxKqIL++C01r7JTB4N+ae5WHjz4jLli7uqtOi/OtDib/ls/sOM2srdN+Z8CAsf159kep07XdZbbAqLatthYNwOr3hVHYNwdPtMtZYEJ/v8QIDPz5N8ho/v+pav52ZURM0OQLMexXzMFqvHps9KokCkhkoJXguLxKVBluhkvAAe/l5pTq/N7nBTZBND7c4aMIcFTyUARDfo2oD9oOeoR76qGXw/YNsCf343FKhEVd1nJ9i5wU8N/UxBLatBL7HzJFINmwLHtNd3blxrwHHfIBkywxCu+tCZCg56CUSjoiyUTQgRbvgmUWyv0bPz1LG/UBzPG/oCoAkOSM1BpsACQ+5M/wzZMCuEScgVGTK/NIUUytvtN+ekV+M2Lh/PW2BoDjfEecXtzvFgWPhl5Uu49CfwU/yM+VzIdbXcUO4F8qbHRffwggXwXYREcQvl1uJ6NS8yLpBKuo15vZrbWbDXDQNTmbOiq0TRPY30WwBtjzmJUvOSc+5j3dTvudbSS5OemduBKK4GadOcJXdokF9l8qgMJbo/A+TE7+gFaK8cRygxzeq0dzJ5DKZnF9DrlGss5y6L/M3fmQFZAsJJx8r8vNM8IEGMJ7BmNsw8DST6+hXX5AqxJ+jcA8CkhpkVaj2D169XYD3+NUswMwof1hgyzJRgJN+l+iCJ5wNf7adpI98iAcEV8Tyfi1hjWoDzM9diuv7z1TVv75GI43ZOEpXmZ7dyID27Pt1qGpaXUJEKN1XYch7HdL3ncnrLoD+bxzsw8yk4Pnh+zx8EdKm+xXqeBRsFVoRH0gNiqWxI9Mp05go1BXhJdM/0SaXSUQCHhYBQciU6oF2ikJYM/sEK0HLMnlAOcYOYwbXheH+Fn1ZmE4W3xVepImpAXCfSdKsHTvTWMQkrugtsUehTx3EC9mcKccH22qIRotOLk6Chx9ilg00zBSINvqHaMWGV6kLbo+R+X8AeJ1XvxKfsiOmQa9BSsmxSNiP74M3z4QNs1OudhUzOAwaYKcU7eR88fk8eTY6tPDx5bnpn4ntVsj/Bb3jQ5tBCXveqH9CCKg9/a+GJAHJQEW8PDkjHfq/oamgXh4Cp71jH2G2ZwHesBWAoW/hbPN+Twq2xp8LNxb9ENeHI0jEEd2aNUzLsOL2b0is65j1paukREGB4wqZ9mDilQoNtem/1v+lcAFljRgDY4lgfgNOHjv0ESHcsu+PiQH8mgzQyI5B5Xc3ANfhCM8L2HPDsfZLxndKBM6iYvGMDzHlxWwoYTMrWw5OSKdUESpEBl9yNNn0n5ybfgV5/XGOdvW4364B9kDBqNcV7TI9MHZP3qGK5NZ9S76f+lbf8/jaAcnvM3ylhMw0YmMbl49ozgM7FeFU4Kv7tN+fPACbncL5GGDbeeQG+11Fgz1MHdOCLz+5/FQ/42TjegZlPy5GgenLNppIrayg3I7hAbeoZVIJNQdd/CJ9i8SM+j43sklYRvR2iig6v9GpvPB8X49+zm3YQdo9F4acGiW7Uuhs6HHZaWR04x3mE23eJmhI9/4Bo3cBr627oS4OdSDzVJpDO9BITR98AuxPYJmgPDrkI+gmwLSoOv+a1XKW8WxLkQ3d6Z6QpWjcq+e54PCsL5GXIYS4M5kNu5BS/jdh7JWzzPea5N4K4FCZwlco9C6jiSYQwL4szFcl87gQwHIhaGxi1YHz8nNn8xZnJeHyENiS/b1GBdg3F617donmSFPDh0KOp4kJPw+nSo4CbYzkM/prvGQAeXwhwkqjlgsHnSaWUz4iQzToAi1iAwMYCkirCVOv0Jh0ObECP/+QxFFm0BZEF7Gw8F7G7eXU1iKxxE4vY4+U5Yk9ozEvy1nJuZsU8LZwkpWe9HBJXBbtGMcxH1jXR18DdP3e8bo5+Py0zmSrTKt7wmg1PhNc9gWnNlGL262e6UtaCq+ydUpbTZwhSeIHI2F/Nau0SZMbDT/euvTQbatO6lnmseflBmhrYMPlTbYTCuEfifc+p+2/Zr9MbuhrHG0DmjbP8jb+9FezUtXz8LQFjnm+O7PQ+vp8bHeO/2zELBqfp9lGEIM8LOArkyHXIE64wlgFyCL7lxXv4vBzvwMyn5TAAcIaGWrR81Kz7ggh9pCBCWB1MZ7xKr7wCLrGINcSBSikyk6EougrTWCOEw7omsSubowe40YPhAyY4Gex+ZXE0LKzXEQqT8eowvVYjgTeUg2YqrNML4w2Q7AgtIL/lBHhT7M8V7dLQreO4A3wF+vuK5eTwS3APlijzvTnDVl0qq6kdBGlujsszwO8EcgRn5wCOE70zaCP18TZzwb+asLoRMnlcZ4ZggJsUUPPfJ7RUgCGP5BhMWR4VfppBTir8vN8EECCoDsA+We/i/EzDE1Zp0FGt15zKn2m/cv19hAzugOx8YFscBziH2+vgJeW6XQRqgF4crTn6i1bde5EARa5wzPhMpylKb8xM3lgEsht0B9pDdAvenJ42By3aZcyRx++YLXqd3plPn03vNb0M3F9Umh0gqb7Aq8TcTkorPjNQl10XAwRsFezvAX7P6rOHOe5/Hlhes95R8T3y/c9rKqch9Z94jbdCCxifXQGa66U35nNem/ki4j2wmCUBhsRDS891IOPr0xgGwCvK6vV98965llPpz59PoEsE6OkmKxB/Axqmv/0Lj9vn/dUct3tYxxzXEppCX1fnFODMFyhvznd+LzfBnHKtCI/L9DWb/g5UFWpeh4Amva/12AYqAAP+O1/62v8vJ+DTe7wDM5+WY9rQ3qJ+DOZ1mlpokk5TXH4mf+a3bPbOLPFzNPsbNc/BE+J360Gc7STlMpPEcJwVUC4XcYEdzvb2TSv7QFzgRwqurJcQ6dBR4ZJ8B47ZJm+TnZhhZS4MDS1KACMKayu5GKEE9nuBbA7rYzpsUbTF4Y9seunK0JYagN1hi2G/F4YdEmil9A0BOCuwedqvBNik+G4Vyqw4rgyr/I5df1bvNu7/Bpcm/7Xp5/x+njspY+FLZyXmmG93RCNRVBaRAJCF88cwJIGkKFh8zjBCQwlQo52AIP4e49H0EiyCroKLOtaXBBip9OnpY0NTawaowMAiiz55SwgQQjlPRc/KE+QALkDbHeujw5pgb1xv7eJYX3E99RPwdEcyeba/MMXomixXr3Eo7PzAx++OoZvoGZq/OL3Hmc+CSTnHvWyZ3muGiPLmDXBjPZB+yuac430zU+rN9cTO0DGQGwVeXrtpieW48kJvAJp5nc8/RyjNF4sCL/k+0jMs9Z5mTyH/G16lOZSazzXG5FWoMoGPJUDzUOI52q/iiRoV+G6OaV++4UkZMzZOyL311U4Fps1/c4t879PkJv9oACBM7x/Xe3zOu8/Pypt1A35m3IJY25nCnmOMUsjScXMyf/yur/u6X+kpP1PHOzDzKTmyS7GtiLow/JybQ64ESx0pla8+iy/NBTNyk5TAJhDyPQXN2LamOQbAO0M3vTO8Y1sbGzTCDwjrve1jlzGCEU0DBVWvhT1wxrNC2SCNHaxZG8c0uto2v+bZJPBAPG+SPgPk6QIci0Cbox+C9uRoDtb12IBj05FRMqVGFti7PW4E0hWQmX6fEyPyX3/b+bhRmLPSmIXbPBbF2z1D8QrL+m5SoajKLEpFGh6Sce2obJtxdSAIuB69QDPTC8klhx6g9wxS1XPT2nMBidoHLXbbBPvJcdpJNpYensFV0Z6MXocXCC5LpM/O86Kx9Keqs1lDxgBgA5bwLp5es8Lp8QLw58D6inOiByDiDEMuUk0k/T7mWjzI0dH7K4FTLov5nWrwjYKPdEVWvn3/07tyTO8VPoUI5WpduALy6Ng+BJZHVrDuUbfmqlO1TNcspeihr3x4QCYSiksSZ+VqzLzmSPPFJC6Ke3G75spD5LAj9ux8XoDQeW0gnjsTFqRihtMkTWBwXv6cMx/jrYnwNwm+Xw11zClfc6+z+f7zUPJlvu35ry/8lnPyYbILGc8rIHP97XFdu/0wfx2gLm/2trIwVyJjWscQhCXho87P7fqE47u+/PVvXvQzerwDM5+SQwBgUZbSfwCt6ETsuTAFqCJ1QhHl2YSkLD4f50ZKI9MlfTovrn2AbsgCKABc0BfH8axBoqCcW5ApI900a4zk5rBOpdmih0rJ/iwrnjVbIkXXs6FbhJl6KEX1UFqKypypGhM1UWP70ssEbvp0nzfF0QzHXcivrFVTmTgTOALeCCtdHSnAbxTVW1/em/LizetOwGUeQ93Lr89NGZRdmx3xDOkFiHOyvkiF6R3M6sh7RIikzov/ZedhX/jFrEnhyaeZmwBajCEz6hCKK/hXkqmi8cymBK04nCErAWQXbB8btCsevxS8LPEri9pT+Qd4FYRSDs9QPyleP+to7wm2j4D+JLCz4+m54PFLUmG13gR+jnpKPvo2QX2kX2f2Tu6HWflazGEqeh1zW40Gb5X/NC+OASzr3SeISAs7ct7XJ2D92LE+GV59Y8PlA1wrpreB6nn9ZAbP1fKxARSCO5bDqLR5x1WaL3IvwUcbAQlPLjBS46d759CuqlAnwMgxZe2mIKfWi859PAEU4h4f3y/l7MNTcfOsV/Mxz/fth2/s4/QbjaKMVwDt5t1d3ev2ZwDXhKD44gwE81q3Yzwwnk1uhiiI8UndQlIOpKDNo119s/RHAStkzRrUuL7h+TvOzLvj1+poATwEGOks8XMADwlFlJJ2KLHYCdF5WoI/wloW4fqvwnOA7xjN+0JAHEpr1jcAokUKRYYbgIk5D8Co9Poaw4+/WXAVMkvIUjmlhR9KqydIkhEmJhCTawsMiHlJC28aQwhm3Rle0QszQmyVAn48/0b63Aq3t4Eaect5wJvenLf8fiWz5uu8DeRM1/Dp51SA/HyshyLP5lhkulaAQgB8zz3OV48QDwVbZSkt0xiS1yGjEqyB796jfkiuhxSIAgAHw5NigD5ZPYQCsMPInWkEvqevGPbniuM5ynNXzzUeAYBjcjpwPS18ht4ED1sUp1vBFgMygRYMMFLk2wAlClRGVM6rzHNY++st76kAxM17nLktMv2by88TPARgzLCRBfBaAd+FRPcTRsZRKlgfczOvIRMbWTxJ8Pfp2VMNehSPyzHPE3WMd3+11uf9Hnu9piMzpcB18dZ9koo8DSrBIBTfAoXbjCdMn2P6203ItT67utjNOCpVbr6H1xoDJpA+7aN6nK8mE954ENS8isYemD3LjqsebHXxA6g6CROBuSJZtwbQrQyx6W/5+zynAQ6vgEycqzpP5Gf7eAdmPi1HAxXJukCcfYm4yEEFbpNbuMVq1mFV8Uev77UsKCcOW1kUT3eGhJKY6CroS4QBFnovPNzoWTfDV5LICtCE63JsyNhJGgTMCCX5EoovNZNM4GaNUJNlZgdu3Od5D7kWJA56kgL8eJzrQKSM819fBdhkkB1mwT2GM45bxZQn5b+TIPmq2Uu/wu//Qqvu9pp53ynMVPyCCldMw8zQxzzOufeOAu7ZdTqViZfHhmn+EYqRkTrNKqqxZiLUI1NdE09BGZluy5OXZ4T1bKS4HtnbCK5QdywPjv09GQ/YpMKGV+E2D176MoB3ViYWZ2p9hhzz/Bp7AK1KXc05vZ1nwagb81Xeda3j9CbldRF7Um/Om99RKpsLoE9RFyckr+y81nEnaIdE2QHQg5QoJpTeW9dVKq0Mnx1WoR4CAYMfGt42R5Z0KAJq8rjy4TPnP7ssB3F/JDUN5ezT2ACU7MiJYGg5QtkBjuf3WmDibfvDETHO6Tnnv92sEf4+nXSr4OcvC67HAoz1k5/fDvbq5pgWCqItwIR+chy3HqR8t2m15Zrs8fstQAowWmCmyMA+7pHj0Km20e0afBvIBAaI/Zwc78DMp+RYNsVlt/JgVI0QYGSTSKzhIPTPaXeDG+wVxmmGSnsWZzgHQHEQMqOkhG2GoCQEWfIIFlxVXsUR90rrIbkES4R0uhNcRG+a7E68J3DB8FYPoTw9MMKrJGOsda6BgnaZNjk4GZb1n5acqKFkgKE3Idc/v7HRr7TGW/7+tj/F7b4qsHkbYLm556w8Zy8MHJnsxnlOgebhNi6lOgZUqapCAFmWeVlrUlZidkeuCqsZYonrmgxPTraKqFonsQb0cP6XXpsKScVFVWHu0KPDTbB9ZDjOguMZgEdHf6ZRiG0omyLqhmfFgVo/VYE41t6wQEOxZlhTUPwQcl7iueHFD8IUTstMHeqyUrdXpM6r+iYBvCujcFK8WRZAcr0JWy7IxXE8E+6ri2P7yLDsgstzhW03gOBKdw7FX7pWc37j9zbtowiHYYn9HKY+x6ojBDGFJAdAxSB/x3qSMgp8GiSK25JrqNZxjnXGBFchl+kiqayv9okML3WuK0w/y/SdisneXuMa9VyDGL/5jl99+arNQZ4v43mvrlEyyq+fK0/JuTYUKTdbzF+PTsZankHVLXCbUr2dpgiN2dQHwFVj1bcCxc/Z8Q7MfEqO+/sNT4+PoyeQD4FNfMMMJ1MKldT/wLSPY5Hm3u9B5NVjUggnDOu+pbCS0RpgA2ThBbIBX1l/Pcm9/FmOGIBFOu7isE3p8k/XqZIU2ktwvkWvJ7Ipa5LfFUPVjKnNv3oIeXnzQjL9UCAnPr/lpMjtd3BtzbzlmDDE1XffSMeehzArn/z1BuTMsrgeVm6um/pjqppa7uNZ4bgMAFiW4VRBtfkAPansPBX4OI/hHIrFUvi7wKLMfJarzwJmlaGW3YpFoObMbgursXWHmEFd0HbH9tLx0ZcW+Fmg7tgbwqOGKjNQryQVWJJLPZ4/AHjNX5YXmN9hm66R1w1AczVPDlhmEWa6et7fx5jG/I81HbN3s5zGYpLYQ8cdsL4G1g+B4xlrHi0X7tPHF4LLBz71PRr3quq/aZBg3vfBa0nenE1zoHznnJcQJlOtpxIkLlX4Ljk23uZz4isxEXOqdd1nBjJ57s3vI4NphMQGppAC6j65dKXqr0yqOvsY1GIe7RQqLJsetLqFX/PgEM9+69GY3mCdnHOQNX9uDgdGAUAbn41xgZlgdrux8/rORaKUb1jGdSj7fICgBCOSa8yv3hGkw12hYFmKq7/V6QL7nCGad2DmU3J88OIOv/yVR9gCcj0u4SZvFHQAuHgXwDUa3aV1IvGjjA1lENiZi1YPR9uNqbgrmzZWuqsIrIEZTAa0KDZ22VCelZQdKqBLNdz2mTni8bM8OezCSsL7WSAXbsy+tEmxhsdllhrRBBOR2UIw5lW4r4RRQ1gys9Uy5mYWELd/vjpm4JDIDxiC22/Omw7/Kp9/1fPkehxvG9M17vIhU0Pu8dFCNL4lxD17UYZS84m0KtPffFjzGT5KwmURVsd7B7yypbAP3ZeDdICVlE8KdYuu1QJ9AKQHh8ocAhZV1APQblWv5P4XOp6+1KCd93z6AMAdxo3yfcReqJBOgguEQM+51jG2+u7EBSnOhiYW9AHqAMyVYZle7eVhqbnNE3p8P8BMZQ5dKegAChbAbwP2BmwfAff/DOxpdAD9XrA/B3yVAgHFucnnnMjG+WoARPh3fA8dkIvAT7xfJQJkWKE5PDwe1cNNwAylLjyvRb8k4EZxjrkUTM8vuIJy+Xm9Gy7Aiask47yc9lKyk4Ku610r3gIucXaBzvhuhc1K0hg9VDKuUGG1+Z3GOK+NnxxPrjO5GlvVfTKMDEHneRxX5jfJACQZSs9sPmB4WyYQmd7pNPgq3OdAzzG1aTwJaIW1uApI3QgehwOnryohP5PHOzDzKTm++Wvex//7n/4yMzbWqGa7ASZ0z4sxs8iiloBheFwc7O7rC9gY0BzdAKyALQqBQw+FXNjnqN8JRByyk1tjAvQFOFoYcjugO+8FdaBfW5xlc4hQMDog5rATIIdj/cigT4pjA2yTEZLILT3FigVOo3GTiD1HyOw1eO3zDYBogrfbRnFMRues1K6uMX/dv8q/IeveMKKm+1yJglsuxgxkfkXwU+J8+hmTMhuucQdG+Gge//QZHznQbRbQSsWfYabkkMyWZAKEHFN36C4lHAv8BgiSzvUjr8HU58a6PrY2LA82iI5Gz4w8dFhjQ0nbBPv9Am+O7gJ9Apbdcf7YIdbw+I2oEBEEFVodumUo1TnlvIgvGM/BS0h9f5B98+V4rc0CdBbAYC5+VvM8FB6n1DnPRb6/UeAy3qu3jJCE9xUCfc06Tu2JxfL6HUZrhOlZKm1YBXBSuJH1ocJrmuE9eS3YXgLHM8HxngMZfh1aNq6FK+VZIaZsR1CPEGtQp7k30DtajRxxHRnR6Tv5fq4sjFzXAdwyJAenVbWEoo8QO+ClrdyEHCy38Bw7PCsdeuyRDLclUIdP4aEJ5bYofHhFkEo3dhDDcvwJFOY5zGvZdN4enysGkfzqmBBphvTnuQrZOB8+/T+hERsJx3MlITsz29TplTny/Pm7zppBt9lPn/HjHZj5lBy/4Ru/hL/x//ivmG68CPxO0SW8NIhwURdW413Bmh6dPAUzhpT6meeKK9Sc4eRGQNQ3QE+AmcE2lvb3O8GeIaKwEljnhQLA9+wyTeu0PRrTqS8pFyIUtDuO5wBE0dRhXaEXwO8AOyNOlqFYU0YoaO3vFBS+kUcgQGWnXFmiVz/gGj1IGUNvsUKm7958Z7ZaZJZR88+YQE3+N1tubwNJcvOdFEeTcLwFiFdAJgY0e2KuSrbfPqPOD+BUBvnZ9Iwce4REJs8eLbxQ9hwoT7/EmjJAHxy6O3BSWAN0AeQMVl1uHqEl4/eVa1KMaxWiDJWGgO93iqcvCHx3bC/BTDQHzl8xPH5R6Z2ZFIFPP830iFScCEXMKIgME3aeo4kzchVeaKgilUWOSR7IfJlUFrXQYmQLJ8i7jvTl8pTNczyAiZ2Apy8IcAc0F5w+Epx/iVya/fn1dwqM5v10YCsBCji2XbB9BcAeOu21wFaFrTMSnJjKWXuljI2Rxo7JC5Vf9QyB1D1tUsAyhVcm4FB1dSaAl+eWpzDuYkKg0mJOmzEk1uNFJBDvxvN6fNY8iF5RbFQDzIgOrpBavKd4YCcolPDWiEdIxoxhUQHoTpNpTeRrHyiz+IbRpCvT+otzWPseA2gcuYBzgU1AKb/fr6yLAQ6T0yWxx1cfxkm+3vxvNfa8m+qWWc5N85Gd+jk53oGZT8nxXb/uG/mDsHJpB2A74JszLAQQuEQ2D8NCGrVgHMcSBFgRaAiF7KQs3ekhAWC9wVaP8GyADAXMWC1XdioLX5lGLReQ+5Bl2A3w5jiaoB3OlNsjxrwBooJ2BtpB+WKpwHKjpgZSiZ5NAM6U2sOKGSDm1urLw4HrJnUp9G/m9a11ZHz6dzKobg+P/70BZKZhfrXfr4HJdPG0RKeTPJ8zrxFWbFWhzTh9Kp35GnmFiR9T9ylF42Pu84szoTKnwqavixJYuqA90aLr6jh/DPTu6M/4vt0F8iBoC3CcHctFWM12ERx3gvWRnr5qERDvkVWCBV0I0iXSvtsTsL0CHu+HMp29BtWSQcLadJR1DgSQMa8smlQukmmvc/XYaY7TA+kO1odRjHBPZf6k4giAl3O+xKQ1Zg5V2MWmsWuMIa6X9U28AaaCfg+cfwmQS4xlXli5rpLsmnwKn9aWECTaqkA32BlYPxacfw542BS++jUg8HGNfDEuelWVtxIMCuwGmEklbNO6i+t5ztG0TuuoTLqcc5veB3g995HdCAxgkmN3ABoxwp4ARcazdBtAvsCI0SBrcQ3j90U7WoABM9bsst6hLQp+msSaYEHJYjf7/BC4/rcEUYCI5MEA9GAmQnaMa+W7SA9bhFyz4W3WDMKCaKsR72FzVuoul5hwTnN/NBBk31ZGDo/tcluq4jN+vAMzn5Lj3/zWryulZi2AwQJ6acLlaqsCxuixr8C+hCJwhR0OWaKmi9OT4wf31AIKul6CR3i9MFiSrOsKyAGsDwaB43ISEjIPELg4ex1ZA/wMdBG6pJ8rOgRYmHbazzKa/4HPAIz0zhEakKoKSv5oCvsbgDEptVIOv9Kh03lf7dwZ0Pjb/1THJOBnC7tk13wOMBQrxvfyNFpoKOAl0zmzN6GAzJRizfN8KIrb3OH6VYaCrhvkIJ2WYQpQhLBv0zgrZIHypMgl+C8Ato+B3dm1nK50J5cLgsvqaJtCHhx+UvhK4NKeADkMaLSW24OhPTAM6isocDvQukF3rb5KpXgdkbnkY75AZT7qLmEADhkenMq4m7M7ElwVdyFPTGUR96q1NylTYIS9ZkvYwPLE8Q5c5Jq4HN6fLHpm25jj3oD9haN6O916lgTACogwCSCBjGSIDA5XweWLVvWAjmeO888plq8Y9ufR4kKnpRArMDlBeVyFyXKJIEBFemc8QcKkkBUj+6jN94h/30h/9msNlGGsAFwSL/q2vlKF2ZYAnh7GFpuJMYEh7u0BZjQJ7Qq4RbBPDbrGcFuHmcCXDo1W870LTLUIiRnq8zGB15u4fpebP0zyINfVnvMWmztDaW+RR0UIFsS+jOun9yrDVAHaOM+tSOeu1xdMDPNsWfF5Ot6BmU/J8bXvPwc8Eg2Ca6KCqoabqdQCIcdkRXloAKBSbUO49MZzvDv2VSI2PIgdDlTDSQiY/u3MtoAC60vg/Mrx4I5+r+gA2qEcT6ek8RVAY58dHlL/eAM7Zcv4fDZoromaMoThrLxSeM3HJDjeCjomoPGG4fSWL95e/6rGyTzEm3MqTX5SClnnR6b7+jymK4A1ao8mCbWGMgOXqZhYPdcUVql/5+Ji6bpO4DML2H49GZX5FAr+6hpBEEYH9BHQ1xJWH3D6JeB4BvT7cPJE+MsXDSXtaKvAuwDPAVsMy6sG8SR7g2EpJ28m5007Sejlvk8AoiGUdcqIybU8e1umNXgFgtXL0p2BTs1TKmCPC2dISWLuKgw1vyAMXtL8XgQFCDn1MffRlb16ZzmA7qMPk+ar82l15Hk5v7gJlySgi/MagBP9NbY5niBYXin6A+CbUW4kn6TPajYX6c06yLlKr8YUOsm5vPLy5Ly/AcYGMBREtlLM3xXmib0g48zygBV3x/kzPP6WoADpgZnuG+tfInwt4XHLZAnBcPRIfL4UIG2A2JRargRHtcfiD8k5u52DK2QiN3+b5j3PSK7WfDS7XlsKVMZFzksRtRn2k+ZXnJuSUzeXfv/uhM/T8Q7MfIqOuUswyYKpIKX6G01nozaCXv9aaZIOZiSZD3CQizrBQ7g3vSoPC45FcNxHuArjmseawlOmTRmIQ8a5IwMH1+Biyiq5+luOZf7OhCJmq/EWl8yzcXWSvOWPX+3L0ylXXpT43W/Oy+eZw0f2No5GfB/wa4Bz5ZmZCL4hAGuYNUeTkiyl4NdgbVYgjqGgU5gjFGQW3oqb+qyMMM73gFlQJWA9A+aOLo7DgPVjoF2iw/kC+EEQYiuLNCILMp4Bf+DCbo827i9sTSC7s+v2naLt9C6cftmwfSD0DGYafiqo1cswrfTcBGM5H1PWT81PTcvtYpqA4Kx74IPjkPdIrdYDAdY7md6LxQBEkBUD/cjbhRKf+wRNXoQCJwm+5iU1lwFOcNHTaxGhEBdW401gKcDxPK7z5FAXAs31zbXKqwe00TF95THMZICpCN4b+7Oy4lBgi3o/PEiT/Lrug+STXPC38N44t3TcZlFFnqDRn+pq6WusXUiE9GbwIqXcJf7e+wxoeJHWACwd0h2uCjcmYpT37laQ2c14EzjnoDJMNWUmDQkwzX/xC+OBFJO3L48AbVXHCRA/yKVsHp21b4TeG6/c8c3vP7/98DN9vAMzn6LDBVHplHwVboIACWl96nTyV1POgimDCFHTQYawnoWFTj+ntRgmS1m+JlfXTlWHWSjksG7+re8I3iCuvhEumuRDgqFr9/fNz7cgDm/5+a1I5Ktc8+a7BUbKoeV13jz9dZsMb1Q6+SDwppfJganUfF4sPsusknqO5IjML21SaLfPGd+XYobO547KvlffK+DIQoUlaA0E0M0AVdjC8akE13IRrC8BuQD+DNifM2S5vgLaxbGvQN8cWBSHOGxV6CNTs9uRao6l/J/ecxzvA3oBQ1A7sP0isL8PZshher7kx2QGR7rdk2eSQKDe142iyb0xg8+aI4/rWXkiqnVGzJHPE27Xl3BQt4wmiwFqkoSZgMjBkNO0H8dyCB6JJrAJUJA3qFCZ8PrlEQhgevOsWAzHc0CaoL0C2oeCvRFkXoH83OszgK6BjXUkt6EixBxFVs51+nr+HdfXmf82Xy/Wa37G8CAHN/Pj0tOV65y9rgaaSYDDc+k31isektS8E/AIujncHWYtwowdS4SwBB3WgH3X4lHV9hLcZEIlCMn1eCPE5vPyIuZw2ARs48i9rwF5ag77tJZiMM3RVocoCczHni7Xtx0OiOHXf+GLX+Xvn83jHZj5FB2exL5o6OiZnspfhwVaDHpca9SUu28DCXH+vyh0ww0m15dPBZACIr5QIZPJKprvP6nfq3HK9Nmc+Xj9hesx3WKw22euS0yy43Ya3nZcgYy3zV9wRuap9lRYE2+l6pWEokwjLhsb5hfLy5IgZw4V6ZuPfuWJmZ95llPT+71K75TpP8ebMvXGLe5T6KKuswJ2MtYe2QTmJOt6AzOastZKE1zeE7g6Th+xeJ61yeGmgn0TtIUhJIFAdl5nvxc8fhEV2pEu0JfxUtrwXEFQ4ZZ8N9wPNuagOVNjq0nk9FIzZDDPXy6++j3vi1Cg08rLtXIburvaPwQUsmBEItQAzXCFMtSXVnp9V3DVOyk8M1J8iohZHTf3FNx4lmalF+9yAfzecayC9SvA9ouKpy8Yw4YrYNlNPL8/h4AxjWcC4fM+K62ufp0ROK/maa/eghu5WtsTcEn2tuLKW+Phth3tJHxcNz6p0B4cOu+1q+MaxXkATevAjgbdOgQGVUGLPh4XksMGbybG/FbjcJYa87sWjJo37iOza+oUX2ReTfJxrHHpaA1Q5QW7KcQF2nYsje/BIeirwXdgxJCnMYlDN8N3ffB1+Dwd78DMp+jwfBvRk+nWNftGue+3/Oxv+9vVH8bf51AOcCOgcA06gDGWWfHfAp2ZdDvOiU0mY/86GD5LsPTGECfQlbye2eircd3okgQa5QX/FcDNnHhQ47/ZEdWIcxLshvgsQxAh3Dwsq9nFnS5/YBamE/BxDMtufteOAWRmITj/nAoLuAK4DlQ/oivdcv30McHxhQTRJZz9ekyLECgcDtNpEB04IvunLbyxt6DmLAJ9BLaPndanCHwVHKLoJ0APx34SHGdAdmB9ZB8wPQRdO3QnR+sqVOYyUnWTO5IZNrVgLMihILCxQKNJXlUZ85bfj8UpV8roZq5iMkfLA39j/UHBdg+xV2XiQhHgWHhq8GYp/nk8imgnHyBTDdWZHBO3IkF1kUDzGuMVVRxmaTieA6dfUmw/B4g4WgeevgjYc1B51nemeZAEFBNomNd53Ti+XBlP0+czkKk5zfm83l+MnrDkRCrzPMfh096WKzkBZIj2+gUOT2XO0S2y4B702IsCR++O1w8IICPYTh1LM3QzcryS4CRRXDCF4gzapjVyZeElOJzmJXlvtfU634e0nIP/T3tnH2NVcf//98yccx8WFthlXR5kwYb6RYRYW4PRYqyoYEEbqE3aGp8oYh8UNCGtD7URtVLTJv6i1WjblNBq1PqHEU1jFLGuIKJSLCgiKEbkaa2iuICw994z8/n9MTPnnnN3kZXu7r0Ln5e5snfuOXPmfOacmffMfGbGizeNTMZAuTLJGAmiCPms7YHSbt2LKFSIt9HzvZeC3Ew9ggoinNo0GkcTLGZqCPLLwvspekD54U9UVl4AEFB2PkvWWoTUS1XuMinXgXF44rxODQqRDvNj6XGYv3ZFesqzflwxRz6s/AL7WT1xeZcUFj7uZLwJgZIsH1ItNm8vJOJD4nslAmkB4a/nRVcsyih20I3rnbAi/S6NlUNScXicThde2ZoGEsMjyfR4QZO0fFyapzMo4UNi6y8BRNS5ceYFmjeoptgBPHb0BKwQ0CI9qwiws9YCgg4IpijiPXyMtLuu6xAQJQEZiTjTVEQoBgI6L0HCrXar7PRXUq6+LBGye411DIZNi8kJV9EisaaHzyRT4YALWyErYytAaYBAgQqwrdTA2KFU3xJOCiBYkQCiuEdFwFaEfhgu5XmsylowNio5EePXGHFDDEIAFNqpwQLOlUYIxFvMk3JrgTgFqqic3z5P3TYe7uTyvUobYfxu2B1c7dT6OLPdEEZEQGj9aMTnBpSTUF8I1O0i7B9FgO+hUU5l+TVWAjet2Qs5wMYZ188JCVE5fNclvhwo+4eJRP7GHT3S+bQmetl8VsSvRFIcJQvHRCNDwAtAl/LK10zY3hhyrSaCnblkSIC0goHdADWb0ZDSWP9CQfHOGfDvmiEQZFqw+fcU7nmAHSISqXfS5ZRz5LXrP0WABGTi+ZYSCAKNbGBsDycJEBkoUUI+jEBCohQpGBIoRoF97gzBmNgCEJIgpUEmjDB6AA8zMb2Ff8C9kCGkx8B9g0JUhHkxkGzBJN9rH1fyIxLxmXLB4H344hGURAEgEzNLvMgQiWunejmSMzvctMPkNWyF6/bt8eVQ4rfU7ACUr+Ebmb7CoeT9AelGrgtPll3J+ihuLLl4YrEiE/H4NIvy37Ew8cfI9LGp7qPK85Kk4qMujqHyMxG3LtM/l/WNM6CfgST88JZIC2MA5X1ebHc8ESALsLMpAkDnCFAC8qBbFDELu6BhcsVQ509DGQAd0q40KgVMUO4AIQOUcnZ1amEENASEW5XVhHZtFVGAXW1aAsXBQFRvnamDL4D8p/aevhgFIJe2qTWZy7SEL5bwGSoMBARkRsMoA1OStpUsZDnzE4LZnphoscfviYBQdisQuOfVPtOJB9PnHwkrDAHEDqLGDr0Jl4dSAUJpN+XXLdBGBogkRGT9NmTW2MpUO8VUMhCZxHNSci+Hf84kwW6cJoHAQIbGijItbVhIdnE4RYBQ0C6pStvZi7ndEnUfaRxoAahOQ2aMnexUdDZTcI6lBmQStXD8HtgIK7V1JallTUTyD0oPAZHNW5n0nfHZmnh/01B8De9Ub4wVl5oEpBMQWlM6Hf5UX9a4a5MQAEX2XyGhI6AoAKUiBIHtEREC0FrYJShc4kQ8tqitWHOC3W5k5te6SRbk7ll1w0bCDdcRDMLAxENkhiSEKCEbWCESSiu2NBFyQYRsoCEhUHAiq6QDCE3QWrqp5u5RkQQlNfJhCaHiqdlMLyGRaH0iIRaSFaf/3pVvV/IFT4qcckMl7sX28Zj4woh7h+My3qQLID/70fsNUOI4GFdPCNgxen8dF79fOM8LLIHy8JHvdTHJylkg3uguKVxi/xbXK5TszU6O15OzQarMRKIQTNolKVxkxfE+LClUkqIjcU6qMAc651GlEKqMBxVhhyM5hQOiQvB4EeMyFSKe1QaCXaQusmsUwRi7j1KHdb4lJRDVEYy0Qz4o2lWndRa2RwoEFN0NCthK0gAoivKzEbg1ViIBE0gY90yoDsRDVqQEkLUVvyxYYSW1hHQzWigAggN2VdviYJQFTVJUJx/uCHa9GmWnNBvYykwIQIRumMavFOuHZwjwGzXGTu3uFsktpS/8ZqedHGwTlbp/qQwgCjacvK1S+U8IlAEpY3eXN3ZBTFJkF0ErOYfVkBBkSzAkoUtOuMGlQ2qIyD1Gbq0RQQC0gRAGUhGE0lCQ0JG0W1NIAmUFkCGIUNr1oA5IqA6b1txnCiYLdIzRUKHBgHwRxZJER0fGLl8SGISBhjEGUUnB96n4SjgeyohxL5ZIhXTS6gCl3xn/k/F6Mb0GTlx+iOS7bPNRyorjABBJmEhCg6CUgZ3qnU6JSSxXEPcVuW04pDIJJ3DbY6gCDRhCJICS0G7fOis+dQT4ce4gtAWQ1tI9SwYqNJDKQJOy1yVAKQMVaBiSIFjfnWxYQjawCwYRCUTGQEmNXBghVNou10QCAUUIpUFG2ll+OVECQaCoIwhJiKTtqfE2kdIgoyIMyBa7sHr/pqsqsc+46667MGnSJNTX16O5uRmzZs3C5s2bD3n8z372MwghcM899/RdIvuQASrouvclWWb6r64xBv+3/zfZwvBxJBZBM66xlmoIKdgNLn1XL9mKRGcSx2rXuMjaY2FshUjCDisYtxu3dK0hEwI6545NXEdn7G/G+5GA4lGDVK+PEzPxrsY+XDqRE5SPi4d3kquJVp7vDZgUI5WiIhYC5WtVhqVL48SO3kiHd0lSrFV+T8adDJMVxybvwR+jYFvenbqhkBAysD4Yzvk2bklqQHYIqKJ03fh2r6TspwLZzxSCg9JuaRHBHlMUtgfHCWDZAagDAqKUFgggcqKmfG9CAqZOwISuvC+6JIcA5dyKwkIg3C+Qb5PIf0TIfaSRbRcIvhAQkYjFEwHuuXaG8EMzRrixLul8a0TcsoeA9UFQ7vadkShle3JxJh5ELUCRAGlZ0SNAqawWAhAZgAZSSmgL96P9z0aglEEYEsKssb01blhM5gCV8S11ghAGKkMIsnbp/diPLgRE6KSAa6XInEaQ1xB+dUNhEIQRwry2i8bBOaYrQNYZ0MAI0QCDUp19H4PPJYJPA5iC7UWoy0bI5CO38qw9XymDIKOtD4ef2RM7B1cuK1ChQiobZRXap3wsuTVlOqv92LHe5lz51a0QTUR2UVBj7HR1U5IoFQLoKEB5jLDcUIqvIw2E0vGwmpRAEGpksyWEQQQpCIEkZMMI+WwB+UwJQRDZ/MxGCLMRVCaCVHZIKgwiZLMlZPIlBDkNFWqEoUYuW0Q+X0IQRlBKIxNo5HIl5HMlZDMlhMogExrkM0XkM0XUZSPkwwgZpRFKg0AYhFIjVAQp7b0KsmtShtKgLlNENtDIhSXkwgi5MEI+LLlPhAFBCUcbVe2Zeemll3Dttddi0qRJiKIIt9xyC6ZNm4aNGzdiwIABqWOXLl2K1157DSNHjqxSanufSWNa0Lrpg/KLnRxqqawfqfwC+56J5HTsVK+ISMQDJxSyNiwethGuJRkiFkoCNkxnXHwl2BahEzWAPd/7DZoAoIyL08VNyooduDSIyP5t3KJ/wnXflwsTl27X25MK8+HJSr18W6kyKSnohLcDKspXL4YoYbdkmL+2SGzVVlkg+x6RRJ7El0iKpfJVE5EfgkphI7oIj9Nb9ukQKp2WeEq9t2NqMS1X0UPY3ZINoIpwwz62BaoDu4KqKLmVpbP2OkYIxGulGLcxqHFxGir7n3rbk1sROrDDCSIQselIAogIwgiERRE7CkMKmFAgExH0pwQdSlAoEdVrK6YAO0TmuuXtu+KEiBIgbWwXv7YPOPlK1xswnvLswozvARAJ/yDp/CBcmHug7LUr89blqxeXMrHPEZzAh4CQEnanZ4KSTswr79RqezhICig3NGUdYe2QghIEo20XqO/BldI6AwvlBIBwjtig+FkkEvEWDSANUyKgKOyemlm7uWV4EFBaQJWs4C0YhWxQQMaUbM+CkSBYJ1iCHQIyRrqhILJT9l2Gi7Kpyo+6M7MVluXnNBaavoBw6U1CCadAEUfuTU7uPfB56R55A8D3dBgB4fOSBEqCEGZEnG32nU94/ghABa6g8e8XgFAZKKXj3igJWDGjJEpaIIoCSGWda11uxmVHKDXCsByXFAQlI4QBEBl7XxKEQGnIkGCIIIWBFIRQaQhZsuvhCEBJghJuKJBEvNWCd2EUgjAgKEGCUNABiMqVQkZFyKsIQeUWB0cBVRUzzz77bOr7kiVL0NzcjLVr1+Lss8+Ow3fu3Il58+bhueeew4UXXtjXyewzbrjwbLy46YP01Olki0OmK+OY5NCIf8n9LAo/VJQ4Jm5dJns7KutYVe49ibv1g86TJgDnfpHsxRCwG1b65eB9OeXP9+9WcraNSdxbQlykcPfVSQuI8n3FcfiKhpIFJsoVUVfXSoaLzkE+sQJI+eoIV4p4G9rf/cUr0ukrTDgD+uN9Ad9VQitbsIlpp/Bf464GSsdvfMUh4mtbgee2r5Ao72YtBQJhC1oZAUILGA3I0O7zZdNijWX8BncSdld1Tc5xsZxY4ZxpBYTd8R0iXszNHyc0xauyGiWgIrt9R5STkCWCLBlk2yNQGEJAoIMU9AA7ZARpxTAVFZDR5XzUAjAKBDcbxD0c5MRLOX9cRqaep1gBlPOKAMT3RmVTWmum8if5bJRXrLV9MiDARNLltt3rSgrrxxAPsUorMg1JKKsQ7fAJAeS6bUkQDOzwQ1ynO6djCWtLX1cJlw5D1pnUaOED7aWkQbHe9rqpg0C4R8AMCmCyGoVMgLpsCcZIdBQz0FEApUoIlHbOp34vK7iF6pxPjXtG40c52YCI/+f+8M+8gHVk9vsYCWM72tx4NvmuQOEWBxRUFkVUvk78agg7JdvaSjlBY/OASnAbpboCKM5gWc5DlxVS2vdBuGnSJAAlrLD04skAkFJCyQiaJEqRHTpUyv4qBeKhHukW9BN+6r0AQgkrXNysKCt0DAJpIIWBcPkpAyDwG34Jd4/Cvl9WOJdFlhGEfBAhF2hoJ/6lgEu3wbebTsXRRk35zLS3twMAGhsb4zBjDC6//HL86le/woQJEw4bR6FQQKFQiL/v3bu35xPaS5xwXCP+r7kR7330WfkFFamysqLC9IEod2v7ggFIi5quKNdv9uN6glJxAOWpyQmBEIui5LUScfqKMt5jqGIIKD5fouzb4SqWVF3dVQPiS8K8X00q3Asab9NEuEC5sPU29WF+XN6LDQEq++qYhIBzN5OqyCghUKh8vWSakoG2d0wgNWXTCxe/35AnKYh8QMom9ruQrj8pFm7uToTLH7KCU5Jt4QHlGRpEgCzaBe5MiYCMFQ5G2/VhohzsdhkKMBmyvTtFlHtoXHb6KeKAdSAng3iNmliEeVu7YUiQAPKEolIwoUB40CD/aYTgoETwhURxsEShAc4nBfb5NMqu3eH3DzICKCmQtrODvOMxuRlM5R60sppNPgupbk+fsVROb9yzIJ1NveAQQGo9Fnesy2WQAXRJwWgJFRiQNFCSIN2wikkMb2ktbUXmV7SVTuu4+E0k4mNt3AQNY5eo8ZWwgGv1u2QrQEJbJ2AtICKCDAmFBkKoBFSHQGZ7iBIIJUlAnUF9voAg0Nh/IIdCIUQmU0ImjCClQakUWKEgCVIRSHhBk1T+Fc++r4B9oD9M+veuLNIlDEiU4yM/XTsxTT0p4snNHpMAjCTIgGCMAbR076OAMBJRgRA4h+x4pqSx8fp8J9crQ0JY3xsykP45cD1nREAgjUu3hNQGIgS0EbEIAWxe+IdC+HRLintS/EKbUtpWnYpFnYCUVhC5DphYoEoB6yjujEUkYZwDcNzrBytik2VDIICfj/0xjjZqRswQERYsWICzzjoLEydOjMN///vfIwgCXHfddd2K56677sLtt9/eW8nsdR655seY+f8ewkd79netQpJCI1leJAsL/2dSrKDiPCTOTf6e9CmpqC+T8VQOcSXTFl+GykVWfJ0uPuSmopNv3ZvykuOd7v1QJhGAc6WEhHRLiQgo2Bk0yrUUlZBQkPZfKaCEPTaQ0rWuBJSSUFJCSIFACQSBgpQCgZKQqvx74P4N3b9KCQRSIlAKoRSpY7LKxS8kMlJCKYVA2LgCuHOUtK0+qaCEvV7GxSdg06ikROB+U1BQUkCSgFL+XgSktFN1pXS7rSd7SzpN5WBqneRMH0Nupg4RDBkQEbT7W5MVM8YQDAxK2iCCdt8JkdEokUHJaPs9MtAaKEQGpmRgIoKOCLpkoEsGRRFB5zV0tgChImhoFDShoyhQ1AYkNAgRiobQUTR2Fg1pGGiUDBBFBpGx19VE0MYgMhoRGRjYdEdkYMjAwP4bmciNMBsYsjOCNPnz7Uq9xrUwjDAQsHEhUdL43jIFQCoDmbVrIZlIgbTrxdABogMSIowQhFaI2c5HN+XZdTcbaXuItHFiCAJOMkI50WF7VOwMNCkkhLHO7MbIuEfECh8/7d8JapenAq7h53vwYHukpDDOR8i1nlzDqSxk4O6T3OixLcA1ABiDwAmaZLEfCIE7Jy7A4Ex9rz2v1aJmxMy8efPw5ptv4uWXX47D1q5di3vvvRdvvPFGtwvhm2++GQsWLIi/7927Fy0tLT2e3t5iYC6L5268Cs+ufxf3PbcKuz7bGwuGSgtIYT8QsC+RtBVYoARUoOKKNwwkQqUQhAEySiKQyoZJhWygkAkDZJRCJpTIBiFyGXtcNhMiGwTIhQHyYeD+DpELAmSVQjYToC4IkA1DZF0cobLXkkJwxckwPUDyPVKpd0p1PpjpE4yxAswKRI2iiVDUJXSYCAVdRFFrdJgIHTrCAV2KwwqmiI4oQocp2XNMCUVTQslEKLk4SmQQUQklaGhTBEFDUwkGEYhK7rsVkuSlH1k/rAiAgYSBQMmQE1wCg4KB+O7wyfje8edhQJCvtvl6BUGd13juc+bPn4+lS5dixYoV+NrXvhaH33PPPViwYAFkYs6d1hpSSrS0tGDr1q2HjXvv3r0YPHgw2tvbMWjQoN5IPsMwDMMwPcxXqb+r2jNDRJg/fz6efPJJtLa2poQMAFx++eU4//zzU2EXXHABLr/8cvzkJz/py6QyDMMwDFOjVFXMXHvttXj00Ufx1FNPob6+Hh999BEAYPDgwcjn8xg6dCiGDh2aOicMQwwfPhzjxo2rRpIZhmEYhqkxupoA22c8+OCDaG9vxznnnIMRI0bEn8cff7yayWIYhmEYph9R9WGmr0p3/GQYhmEYhjl2qGrPDMMwDMMwzP8KixmGYRiGYfo1LGYYhmEYhunXsJhhGIZhGKZfw2KGYRiGYZh+DYsZhmEYhmH6NTWzN1Nv4ad/96fdsxmGYRjmWMfX291ZxuWoFzP79u0DgH612STDMAzDMJZ9+/Zh8ODBX3pMTWw02ZsYY7Br1y7U19fzLs6Hwe8wvn37dt6U8yvAdjty2HZHBtvtyGHbHRnVsBsRYd++fRg5cmRqw+muOOp7ZqSUGDVqVLWT0a8YNGgQv+RHANvtyGHbHRlstyOHbXdk9LXdDtcj42EHYIZhGIZh+jUsZhiGYRiG6dewmGFistksFi5ciGw2W+2k9CvYbkcO2+7IYLsdOWy7I6PW7XbUOwAzDMMwDHN0wz0zDMMwDMP0a1jMMAzDMAzTr2ExwzAMwzBMv4bFDMMwDMMw/RoWMwzeffddzJw5E01NTRg0aBAmT56MF198Mf59/fr1uOSSS9DS0oJ8Po/x48fj3nvvrWKKa4fD2Q4Atm3bhu9973sYMGAAmpqacN1116FYLFYpxbVBa2srhBBdftasWRMft2bNGpx33nkYMmQIGhoaMG3aNKxbt656Ca8y3bUbAPztb3/DKaecglwuh+HDh2PevHlVSnVt8FVsBwCffvopRo0aBSEEPv/8875PcI3QHbvVRB1BzDHP17/+dZoxYwatX7+e3n33Xbrmmmuorq6O2traiIho8eLFNH/+fGptbaX333+fHn74Ycrn83TfffdVOeXV53C2i6KIJk6cSFOmTKE33niDnn/+eRo5ciTNmzevyimvLoVCgdra2lKfuXPn0gknnEDGGCIi2rt3LzU0NNDs2bNp06ZNtGHDBvrBD35Azc3NVCwWq3wH1aE7diMiuvvuu2nkyJH0yCOP0JYtW2jDhg309NNPVzHl1ae7tvPMnDmTpk+fTgBoz549fZ/gGqE7dquFOoLFzDHOJ598QgBoxYoVcdjevXsJAC1fvvyQ511zzTU0ZcqUvkhizdId2z3zzDMkpaSdO3fGxzz22GOUzWapvb29z9NcqxSLRWpubqY77rgjDluzZg0BoG3btsVhb775JgGgLVu2VCOZNUdXdvvss88on89/6fvLdG07zwMPPEDf+c536IUXXjjmxUwlX2a3JH1dR/Aw0zHO0KFDMX78eDz00EP44osvEEUR/vznP2PYsGE47bTTDnlee3s7Ghsb+zCltUd3bLd69WpMnDgRI0eOjM+74IILUCgUsHbt2molveZ4+umnsXv3bsyePTsOGzduHJqamrB48WIUi0UcPHgQixcvxoQJEzBmzJjqJbaG6Mpuzz//PIwx2LlzJ8aPH49Ro0bhhz/8IbZv3169hNYgXdkOADZu3Ig77rgDDz300GE3NzwWOZTdKunzOqLPZBNTs+zYsYNOO+00EkKQUopGjhxJ//nPfw55/CuvvEJhGNKyZcv6LpE1yuFsd/XVV9PUqVM7nZfJZOjRRx/tw5TWNtOnT6fp06d3Ct+wYQONHTuWpJQkpaSTTjqJPvzwwyqksDbpym533XUXhWFI48aNo2effZZWr15N5513Ho0bN44KhUKVUlp7dGW7jo4OOuWUU+jhhx8mIqIXX3yRe2YqONS7mqQadQTLzqOU22677ZBOW/7z73//G0SEa665Bs3NzVi5ciVef/11zJw5ExdddBHa2to6xfv2229j5syZuPXWWzF16tQq3Fnv09O2E0J0ugYRdRne3+mu7ZLs2LEDzz33HK666qpU+MGDBzFnzhxMnjwZr776KlatWoUJEyZgxowZOHjwYF/eVq/Tk3YzxqBUKuGPf/wjLrjgApxxxhl47LHH8N5773VyTj8a6Enb3XzzzRg/fjwuu+yyvryFqtCTdktSrTqCtzM4Stm9ezd27979pceccMIJWLVqFaZNm4Y9e/aktnU/8cQTcdVVV+Gmm26KwzZu3IgpU6Zg7ty5WLRoUa+lvdr0pO1uvfVWPPXUU1i/fn38+549e9DY2Ih//etfmDJlSq/dRzXoru1yuVz8/be//S3uu+8+7Ny5E2EYxuGLFy/Gr3/9a7S1tcXd/cViEQ0NDVi8eDF+/OMf985NVIGetNuSJUswZ84cbN++HaNGjYrDhw0bhjvvvBNXX311z99AFelJ25166ql466234oYGEcEYA6UUbrnlFtx+++29cxNVoCft5qlmHRH06dWYPqOpqQlNTU2HPe7AgQMA0GlsWEoJY0z8/e2338a5556LK6+88qgWMkDP2u7MM8/EokWL0NbWhhEjRgAAli1bhmw2+6U+Sf2V7trOQ0RYsmQJrrjiik6F44EDByClTPVg+e/JZ/NooCftNnnyZADA5s2bYzHz2WefYffu3Uelr1FP2u6JJ55I9fqtWbMGc+bMwcqVKzF27NgeS3Mt0JN2A2qgjuizAS2mJvnkk09o6NChdPHFF9O6deto8+bN9Mtf/pLCMKR169YRkfVbOO644+jSSy9NTc/7+OOPq5z66tId2/mp2eeddx698cYbtHz5cho1atQxPzXbs3z5cgJAGzdu7PTbO++8Q9lsln7xi1/Qxo0bacOGDXTZZZfR4MGDadeuXVVIbe3wZXYjstOKJ0yYQKtWraK33nqLLrroIjr55JOP2SntSQ5nuyTsM1Pmy+xWC3UEixmG1qxZQ9OmTaPGxkaqr6+nM844g5555pn494ULFxKATp8xY8ZUL9E1wuFsR0T04Ycf0oUXXkj5fJ4aGxtp3rx51NHRUaUU1xaXXHIJffvb3z7k78uWLaPJkyfT4MGDqaGhgc4991xavXp1H6awNjmc3drb22nOnDk0ZMgQamxspO9///upKe7HMoezXRIWM2W+zG61UEewzwzDMAzDMP0ans3EMAzDMEy/hsUMwzAMwzD9GhYzDMMwDMP0a1jMMAzDMAzTr2ExwzAMwzBMv4bFDMMwDMMw/RoWMwzDMAzD9GtYzDAM02/ZunUrhBBYt25dr8QvhMDSpUt7JW6GYXoOFjMMwxwxs2fPxqxZs6p2/ZaWFrS1tWHixIkAgNbWVggh8Pnnn1ctTQzD9D280STDMP0WpRSGDx9e7WQwDFNluGeGYZhe4aWXXsLpp5+ObDaLESNG4KabbkIURfHv55xzDq677jrccMMNaGxsxPDhw3Hbbbel4ti0aRPOOuss5HI5nHzyyVi+fHlq6Cc5zLR161ZMmTIFANDQ0AAhBGbPng0AOOGEE3DPPfek4j711FNT13vvvfdw9tlnx9d6/vnnO93Tzp078aMf/QgNDQ0YOnQoZs6cia1bt/6vpmIY5n+ExQzDMD3Ozp07MWPGDEyaNAnr16/Hgw8+iMWLF+POO+9MHff3v/8dAwYMwGuvvYY//OEPuOOOO2IRYYzBrFmzUFdXh9deew1/+ctfcMsttxzymi0tLXjiiScAAJs3b0ZbWxvuvffebqXXGIOLL74YSim8+uqr+NOf/oQbb7wxdcyBAwcwZcoUDBw4ECtWrMDLL7+MgQMH4rvf/S6KxeJXMQ/DMD0MDzMxDNPjPPDAA2hpacH9998PIQROOukk7Nq1CzfeeCNuvfVWSGnbUaeccgoWLlwIADjxxBNx//3344UXXsDUqVOxbNkyvP/++2htbY2HkhYtWoSpU6d2eU2lFBobGwEAzc3NGDJkSLfTu3z5crzzzjvYunUrRo0aBQD43e9+h+nTp8fH/OMf/4CUEn/9618hhAAALFmyBEOGDEFrayumTZv21YzEMEyPwWKGYZge55133sGZZ54ZV/oAMHnyZOzfvx87duzA6NGjAVgxk2TEiBH4+OOPAdjelZaWlpRPzOmnn95r6R09enQsZADgzDPPTB2zdu1abNmyBfX19anwjo4OvP/++72SLoZhugeLGYZhehwiSgkZHwYgFR6GYeoYIQSMMYeM40iRUsbX95RKpU5pq0xLEmMMTjvtNDzyyCOdjj3uuON6JJ0MwxwZLGYYhulxTj75ZDzxxBMpQfLKK6+gvr4exx9/fLfiOOmkk7Bt2zb897//xbBhwwAAa9as+dJzMpkMAEBrnQo/7rjj0NbWFn/fu3cvPvjgg1R6t23bhl27dmHkyJEAgNWrV6fi+Na3voXHH38czc3NGDRoULfugWGYvoEdgBmG+Z9ob2/HunXrUp+f/vSn2L59O+bPn49NmzbhqaeewsKFC7FgwYLYX+ZwTJ06FWPHjsWVV16JN998E6tWrYodgA/VYzNmzBgIIfDPf/4Tn3zyCfbv3w8AOPfcc/Hwww9j5cqV2LBhA6688koopeLzzj//fIwbNw5XXHEF1q9fj5UrV3ZyNr700kvR1NSEmTNnYuXKlfjggw/w0ksv4frrr8eOHTuOxHQMw/QQLGYYhvmfaG1txTe/+c3UZ+HChXjmmWfw+uuv4xvf+AZ+/vOf46qrrsJvfvObbserlMLSpUuxf/9+TJo0CXPnzo3Pz+VyXZ5z/PHH4/bbb8dNN92EYcOGYd68eQCAm2++GWeffTYuuugizJgxA7NmzcLYsWPj86SUePLJJ1EoFHD66adj7ty5WLRoUSruuro6rFixAqNHj8bFF1+M8ePHY86cOTh48CD31DBMlRHU1WAxwzBMDbJq1SqcddZZ2LJlS0qMMAxzbMNihmGYmuXJJ5/EwIEDceKJJ2LLli24/vrr0dDQgJdffrnaSWMYpoZgB2CGYWqWffv24YYbbsD27dvR1NSE888/H3fffXe1k8UwTI3BPTMMwzAMw/Rr2AGYYRiGYZh+DYsZhmEYhmH6NSxmGIZhGIbp17CYYRiGYRimX8NihmEYhmGYfg2LGYZhGIZh+jUsZhiGYRiG6dewmGEYhmEYpl/DYoZhGIZhmH7N/wcTW7P1S1Kf4gAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "var = 'windspeed_10m'\n", + "\n", + "time_indx = 0\n", + "dt = ds['time_index'][time_indx].values\n", + "dt = np.datetime_as_string(dt,unit='m')\n", + "lon = ds['longitude'][:].values\n", + "lat = ds['latitude'][:].values\n", + "data = ds[var][time_indx,:].values\n", + "\n", + "plt.scatter(lon, lat, c=data)\n", + "plt.xlabel('Longitude')\n", + "plt.ylabel('Latitude')\n", + "plt.title(f'{var} at datetime {dt}')\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also choose a particular location and view a time series graph." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gid = 0\n", + "var = 'temperature_100m'\n", + "lon = ds['longitude'][gid].values\n", + "lat = ds['latitude'][gid].values\n", + "ds[var][:, gid].plot()\n", + "plt.title(f'{var} at coordinates ({lon:.1f}, {lat:.1f})')\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally, we show how to find the location closest to a given set of coordinates and plot some data." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'sklearn'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmath\u001b[39;00m \u001b[39mimport\u001b[39;00m radians\n\u001b[0;32m----> 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mneighbors\u001b[39;00m \u001b[39mimport\u001b[39;00m BallTree\n\u001b[1;32m 3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m 5\u001b[0m my_coords \u001b[39m=\u001b[39m [\u001b[39m-\u001b[39m\u001b[39m66\u001b[39m, \u001b[39m17\u001b[39m] \u001b[39m# Coordinates of interest\u001b[39;00m\n", + "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'" + ] + } + ], + "source": [ + "from math import radians\n", + "from sklearn.neighbors import BallTree\n", + "import numpy as np\n", + "\n", + "my_coords = [-66, 17] # Coordinates of interest\n", + "time_sel = '2017-07' # Make sure your selection is in range for the data set selected!\n", + "\n", + "my_coords_rad = [radians(my_coords[0]), radians(my_coords[1])]\n", + "all_coords_rad = np.array([[radians(lon), radians(lat)] for lon, lat in zip(ds['longitude'].values, ds['latitude'].values)])\n", + "tree = BallTree(all_coords_rad, metric='haversine')\n", + "my_gid = tree.query([my_coords_rad])[1][0][0]\n", + "\n", + "var = 'windspeed_10m'\n", + "lon = ds['longitude'][my_gid].values\n", + "lat = ds['latitude'][my_gid].values\n", + "ds[var].sel(time_index='2017-07', gid=my_gid).plot() # Time slice for July 2008\n", + "plt.title(f'{var} at coordinates ({lon:.1f}, {lat:.1f})')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".env", + "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": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/azure/hpc_migration.ipynb b/azure/hpc_migration.ipynb new file mode 100644 index 0000000..32106e1 --- /dev/null +++ b/azure/hpc_migration.ipynb @@ -0,0 +1,161 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On 9/5, 700 jobs were scheduled (sq | wc -l returned 701)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this notebook, we demonstrate how to use this package to migrate h5 data from Eagle to Azure." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we need to identify the files that we want to migrate. On Eagle, data are located in the `/datasets` directory." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of files: 2\n" + ] + } + ], + "source": [ + "from pipeline.hpc_tools import get_dataset\n", + "\n", + "dataset = 'WIND/kazakhstan'\n", + "resolution = '15min'\n", + "\n", + "files = get_dataset(dataset, resolution=resolution)\n", + "\n", + "print(f'Number of files: {len(files)}')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we launch a series of jobs to copy and transform each file in the set." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting 1 transformation jobs.\n", + "Starting job to copy dataset to Azure.\n", + "Starting job to combine references.\n", + "All jobs scheduled!\n" + ] + } + ], + "source": [ + "from pipeline.hpc_tools import process_h5_dataset\n", + "\n", + "comb_ref_file = f'/scratch/mheine/{dataset}/kerchunk_{resolution}_ref.json'\n", + "job_ids = process_h5_dataset(files, comb_ref_file=comb_ref_file)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After a run, use scan_err to identify any file transformation jobs that failed." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Timeouts: 1\n", + "Other errors: 0\n", + "Total Files: 1\n", + "The smallest file that timed out in WIND/conus/v1.1.0 was 1537 GB.\n" + ] + } + ], + "source": [ + "from pipeline.hpc_tools import scan_err\n", + "dataset = 'WIND/conus/v1.1.0'\n", + "resolution = 'hourly'\n", + "files, timeout_redos, other_redos = scan_err(dataset=dataset, resolution=resolution)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "process_h5_redos allows you to launch a partial job. `files` should be all files in the dataset, and `redos` should be a subset of them that you want to reprocess." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Starting 1 transformation jobs.\n", + "Starting job to copy dataset to Azure.\n", + "Starting job to combine references.\n", + "All jobs scheduled!\n" + ] + } + ], + "source": [ + "from pipeline.hpc_tools import process_h5_redos\n", + "\n", + "comb_ref_file = f'/scratch/mheine/{dataset}/kerchunk_{resolution}_ref.json'\n", + "job_ids = process_h5_redos(files, timeout_redos + other_redos, comb_ref_file=comb_ref_file)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".env", + "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": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/azure/pipeline/ASL/job.json b/azure/pipeline/ASL/job.json new file mode 100644 index 0000000..dee78c2 --- /dev/null +++ b/azure/pipeline/ASL/job.json @@ -0,0 +1,21 @@ +{ + "jobName": "test3", + "jobDefinition": "arn:aws:batch:us-west-2:351672045885:job-definition/kerchunk-h5-new:1", + "jobQueue": "arn:aws:batch:us-west-2:351672045885:job-queue/kerchunk-h5", + "dependsOn": [], + "arrayProperties": {}, + "parameters": {}, + "containerOverrides": { + "resourceRequirements": [], + "environment": [ + { + "name": "staging_bucket", + "value": "kerchunk-staging" + }, + { + "name": "s3_file", + "value": "nrel-pds-wtk/south_atlantic/yearly_hr/v1.0.0/satlantic_2000_hr.h5" + } + ] + } + } \ No newline at end of file diff --git a/azure/pipeline/ASL/job_definition.json b/azure/pipeline/ASL/job_definition.json new file mode 100644 index 0000000..f831696 --- /dev/null +++ b/azure/pipeline/ASL/job_definition.json @@ -0,0 +1,31 @@ +{ + "jobDefinitionName": "kerchunk-h5", + "type": "container", + "containerProperties": { + "image": "351672045885.dkr.ecr.us-west-2.amazonaws.com/transform_h5_container", + "jobRoleArn": "arn:aws:iam::351672045885:role/ecsTaskExecutionRole", + "executionRoleArn": "arn:aws:iam::351672045885:role/ecsTaskExecutionRole", + "resourceRequirements": [ + { + "value": "1", + "type": "VCPU" + }, + { + "value": "100000", + "type": "MEMORY" + } + ], + "environment": [], + "secrets": [], + "linuxParameters": { + "tmpfs": [], + "devices": [] + }, + "mountPoints": [], + "ulimits": [] + }, + "platformCapabilities": [ + "EC2" + ], + "parameters": {} +} \ No newline at end of file diff --git a/azure/pipeline/ASL/kerchunk-1TB.json b/azure/pipeline/ASL/kerchunk-1TB.json new file mode 100644 index 0000000..c88cb75 --- /dev/null +++ b/azure/pipeline/ASL/kerchunk-1TB.json @@ -0,0 +1 @@ +{"EbsOptimized":true,"IamInstanceProfile":{"Arn":"arn:aws:iam::351672045885:instance-profile\/ec2-base-role"},"BlockDeviceMappings":[{"DeviceName":"\/dev\/xvda","Ebs":{"Encrypted":true,"DeleteOnTermination":true,"Iops":3000,"KmsKeyId":"arn:aws:kms:us-west-2:351672045885:key\/a14e1832-d4ca-4667-a986-631341c44db8","SnapshotId":"snap-0b98405d74debf232","VolumeSize":1000,"VolumeType":"gp3","Throughput":125}}],"NetworkInterfaces":[{"AssociatePublicIpAddress":false,"DeleteOnTermination":true,"Description":"","DeviceIndex":0,"Groups":["sg-0dd899f63f3874c77"],"InterfaceType":"interface","Ipv6Addresses":[],"PrivateIpAddresses":[{"Primary":true,"PrivateIpAddress":"172.18.37.24"}],"SubnetId":"subnet-002fd73ee4a6c6baf","NetworkCardIndex":0}],"ImageId":"ami-038c0c1c6c6b1fb07","InstanceType":"x2iedn.xlarge","KeyName":"matt-key","Monitoring":{"Enabled":false},"Placement":{"AvailabilityZone":"us-west-2b","GroupName":"","Tenancy":"default"},"DisableApiTermination":false,"InstanceInitiatedShutdownBehavior":"stop","TagSpecifications":[{"ResourceType":"instance","Tags":[{"Key":"Name","Value":"kerchunk-1TiB"}]}],"CpuOptions":{"CoreCount":2,"ThreadsPerCore":2},"CapacityReservationSpecification":{"CapacityReservationPreference":"open"},"HibernationOptions":{"Configured":false},"MetadataOptions":{"HttpTokens":"required","HttpPutResponseHopLimit":2,"HttpEndpoint":"enabled","HttpProtocolIpv6":"disabled","InstanceMetadataTags":"disabled"},"EnclaveOptions":{"Enabled":false},"PrivateDnsNameOptions":{"HostnameType":"ip-name","EnableResourceNameDnsARecord":false,"EnableResourceNameDnsAAAARecord":false},"MaintenanceOptions":{"AutoRecovery":"default"},"DisableApiStop":false} \ No newline at end of file diff --git a/azure/pipeline/ASL/state_machine_input.json b/azure/pipeline/ASL/state_machine_input.json new file mode 100644 index 0000000..79be9b9 --- /dev/null +++ b/azure/pipeline/ASL/state_machine_input.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/azure/pipeline/ASL/state_machine_template.json b/azure/pipeline/ASL/state_machine_template.json new file mode 100644 index 0000000..8db9ed2 --- /dev/null +++ b/azure/pipeline/ASL/state_machine_template.json @@ -0,0 +1,74 @@ +{ + "Comment": "Takes a set of s3 paths to h5 files as input and transforms them to be compatible with kerchunk. Kerchunk reference files are generated for each file for both s3 and Azure,then the combined reference files are generated. All files are uploaded to an s3 staging bucket for testing.", + "StartAt": "Map", + "States": { + "Map": { + "Type": "Map", + "Parameters": { + "ContainerOverrides": { + "Command": ["python", "transform.py"], + "Environment": [ + { + "Name": "s3_file", + "Value.$": "$$.Map.Item.Value" + }, + { + "Name": "staging_bucket", + "Value.$": "$.staging_bucket" + } + ] + } + }, + "ItemProcessor": { + "ProcessorConfig": { + "Mode": "INLINE" + }, + "StartAt": "transform-h5-files", + "States": { + "transform-h5-files": { + "Type": "Task", + "Resource": "arn:aws:states:::batch:submitJob.sync", + "Parameters": { + "JobName": "transform-file", + "JobDefinition": "arn:aws:batch:us-west-2:351672045885:job-definition/kerchunk-h5", + "JobQueue": "arn:aws:batch:us-west-2:351672045885:job-queue/kerchunk-h5", + "ContainerOverrides.$": "$.ContainerOverrides" + }, + "End": true + } + } + }, + "ItemsPath": "$.s3_files", + "MaxConcurrency": 20, + "Next": "generate-references", + "ResultPath": null + }, + "generate-references": { + "Type": "Task", + "Resource": "arn:aws:states:::batch:submitJob.sync", + "Parameters": { + "JobName": "refjob", + "JobDefinition": "arn:aws:batch:us-west-2:351672045885:job-definition/kerchunk-h5", + "JobQueue": "arn:aws:batch:us-west-2:351672045885:job-queue/kerchunk-h5", + "ContainerOverrides": { + "Command": ["python", "gen_ref.py"], + "Environment": [ + { + "Name": "staging_bucket", + "Value.$": "$.staging_bucket" + }, + { + "Name": "run_name", + "Value.$": "$.run_name" + }, + { + "Name": "s3_comb_ref_file", + "Value.$": "$.s3_comb_ref_file" + } + ] + } + }, + "End": true + } + } +} diff --git a/azure/pipeline/__init__.py b/azure/pipeline/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/azure/pipeline/aws_glob_patterns.json b/azure/pipeline/aws_glob_patterns.json new file mode 100644 index 0000000..0deedfc --- /dev/null +++ b/azure/pipeline/aws_glob_patterns.json @@ -0,0 +1 @@ +{"nrel-pds-wtk":{"pr100":{"hourly":"nrel-pds-wtk\/pr100\/hourly\/*.h5","5min":"nrel-pds-wtk\/pr100\/5min\/*.h5"},"south_atlantic":{"hourly":"nrel-pds-wtk\/south_atlantic\/yearly_hr\/v1.0.0\/*.h5","5min":"nrel-pds-wtk\/south_atlantic\/monthly\/v1.0.0\/*.h5"}}} \ No newline at end of file diff --git a/azure/pipeline/aws_tools.py b/azure/pipeline/aws_tools.py new file mode 100644 index 0000000..57995ed --- /dev/null +++ b/azure/pipeline/aws_tools.py @@ -0,0 +1,360 @@ +import boto3 +import ujson +import s3fs +from etl_tools import load_oedi_sas, gen_ref_comb +from azure.storage.blob import ContainerClient +from dotenv import load_dotenv +import subprocess +import h5py + +def get_tags(): + tags = [ + { + 'key': 'org', + 'value': 'oedi' + }, + { + 'key': 'billingid', + 'value': '210090' + }, + { + 'key': 'task', + 'value': 'kerchunk' + }, + { + 'key': 'owner', + 'value': 'mheine' + } + ] + return tags + +def get_dataset(bucket, prefix=None, extension='.h5', resolution=None): + """ + This is a convenience function that generates a list of s3 bucket+key paths for a given dataset + + Parameters + ---------- + bucket : str (required) + Bucket in which the dataset lives (e.g. 'nrel-pds-wtk') + prefix : str + Prefix of all files in the dataset (e.g. 'Great_Lakes') + extension : str + File extension for all files in the dataset (e.g. '.h5') + resolution : str + For WIND data only. Options are 'hourly' or '5min' + + Returns + ------- + files : list + List of all bucket+key paths to all files in the bucket subject to the provided options. + """ + s3 = s3fs.S3FileSystem(anon=True) + with open('aws_glob_patterns.json') as f: + aws_glob_patterns = ujson.load(f) + + if prefix and resolution: + files = s3.glob(aws_glob_patterns[bucket][prefix][resolution]) + elif prefix: + files = s3.glob(aws_glob_patterns[bucket][prefix]) + else: + files = s3.glob(aws_glob_patterns[bucket]) + + files = [file for file in files if file.endswith(extension)] + + return files + # if not resolution: + # if not extension: + # files = s3.glob(f'{bucket}/{prefix}/*.*') + # else: + # files = s3.glob(f'{bucket}/{prefix}/*{extension}') + # elif bucket == 'nrel-pds-wtk': + # subsets_1 = ['North_Atlantic', 'gulf_of_mexico'] # yearly and yearly_hr + # subsets_2 = ['pr100'] # hourly and 5min + # subsets_3 = ['south_atlantic'] + # if any([subset in prefix for subset in subsets_1]): + # if resolution == 'hourly': + # files = s3.glob(f'{bucket}/{prefix}/yearly_hr/*.h5') + # elif resolution == '5min': + # files = s3.glob(f'{bucket}/{prefix}/yearly/*.h5') + # if any([subset in prefix for subset in subsets_2]): + # if resolution == 'hourly': + # files = s3.glob(f'{bucket}/{prefix}/hourly/*.h5') + # elif resolution == '5min': + # files = s3.glob(f'{bucket}/{prefix}/5min/*.h5') + # else: + # if resolution == 'hourly': + # files = s3.glob(f'{bucket}/{prefix}/*.h5') + # elif resolution == '5min': + # files = s3.glob(f'{bucket}/{prefix}/*/*.h5') + # return files + +def get_AWSServiceRoleForBatch(): + # Not used + iam = boto3.client('iam') + try: + ARN = iam.get_role(RoleName='AWSServiceRoleForBatch')['Role']['Arn'] + except: + ARN = iam.create_service_linked_role( + AWSServiceName='batch.amazonaws.com' + )['Role']['Arn'] + return ARN + +def get_StepFunctionRole(): + # TODO: Add code that creates the role if it doesn't exist + """ + This function obtains the StepFunctionRole to be used when creating a step function. + + Parameters + ---------- + None + + Returns + ------- + roleArn : str + Amazon resource number of the StepFunctionRole + """ + iam = boto3.client('iam') + roleArn = iam.get_role(RoleName='StepFunctionRole')['Role']['Arn'] + return roleArn + +def create_state_machine(name, definition='./ASL/state_machine_template.json', tags=None, region_name='us-west-2'): + """ + This is a convenience function that creates or updates a state machine in AWS from the definition. + + Parameters + ---------- + name : str (required) + The name given to the state machine in AWS. + definition : str + Path to the json file that contains the ASL definition of the state machine. + tags : dict + key-value pairs for tracking aws resources. Defaults will be used if none are provided (see get_tags). + Returns + ------- + stateMachineArn : str + The amazon resource number of the state machine. + """ + + sf = boto3.client('stepfunctions', region_name=region_name) + + sms = sf.list_state_machines()['stateMachines'] + stateMachineArn = '' + for sm in sms: + if sm['name'] == name: + stateMachineArn = sm['stateMachineArn'] + break + + if not tags: + tags = get_tags() + + with open(definition) as f: + if stateMachineArn: + sf.update_state_machine(stateMachineArn=stateMachineArn, definition=f.read()) + else: + roleArn = get_StepFunctionRole() + stateMachineArn = sf.create_state_machine(name=name, definition=f.read(), roleArn=roleArn, tags=tags)['stateMachineArn'] + return stateMachineArn + +def get_state_machine(name, region_name='us-west-2'): + """ + This function gets the ARN for a state machine by name. + + Parameters + ---------- + name : str (required) + The name of the state machine in AWS. + + Returns + ------- + stateMachineArn : str + The amazon resource number of the state machine. + """ + sf = boto3.client('stepfunctions', region_name=region_name) + sms = sf.list_state_machines()['stateMachines'] + stateMachineArn = '' + for sm in sms: + if sm['name'] == name: + stateMachineArn = sm['stateMachineArn'] + break + if not stateMachineArn: + raise Exception(f'State machine {name} not found.') + return stateMachineArn + +def create_state_machine_input(files, staging_bucket, s3_comb_ref_file, az_comb_ref_file, run_name=None, input_file='ASL/state_machine_input.json'): + # TODO: Check access/existence to/of staging bucket + """ + This function generates the state machine input to process a dataset. + + Parameters + ---------- + files : list (required) + A list of bucket+key paths to the files of the dataset + staging_bucket : str + Name of the bucket where transformed files and json references will be written + s3_comb_ref_file : str + Key for the combined kerchunk reference file that points to the dataset in staging + az_comb_ref_file : str + Key for the combined kerchunk reference file that points to the dataset in azure + run_name : str + The name of the run. This will be used to create a json file in S3 containing the inputs needed for the run + input_file : str + A path in which to store a local copy of the json inputs needed for the run. + Returns + ------- + input_data : str + A serialized copy of the input data + """ + smi = { + 's3_files': files, + 'staging_bucket': staging_bucket, + 's3_comb_ref_file': s3_comb_ref_file, + 'az_comb_ref_file': az_comb_ref_file, + 'run_name' : run_name + } + with open(input_file, 'w') as f: + ujson.dump(smi, f) + + s3 = s3fs.S3FileSystem() + s3.put_file(input_file, f'{staging_bucket}/{run_name}.json') + + input_data = ujson.dumps(smi) + return input_data + +def run_state_machine(name, run_name = 'sm_run', input_file='ASL/state_machine_input.json', region_name='us-west-2'): + sf = boto3.client('stepfunctions', region_name=region_name) + stateMachineArn = get_state_machine(name) + with open(input_file) as f: + input = f.read() + response = sf.start_execution(stateMachineArn=stateMachineArn, name=run_name, input=input) + return response + +def create_job_def(job_def_file='./ASL/job_definition.json', region_name='us-west-2'): + with open(job_def_file) as f: + job_def = ujson.load(f) + tags = get_tags() + job_def['tags'] = {} + for tag in tags: + job_def['tags'][tag['key']] = tag['value'] + job_def['propagateTags'] = True + batch = boto3.client('batch', region_name=region_name) + response = batch.register_job_definition(**job_def) + return response + +def create_launch_templates(): + # TODO: Need to add the 2TB and 3TB versions + ec2 = boto3.client('ec2') + LaunchTemplateNames = ['kerchunk-1TB'] + for LaunchTemplateName in LaunchTemplateNames: + with open(f'./ASL/{LaunchTemplateName}.json') as f: + LaunchTemplateData = ujson.load(f) + existing_template = ec2.describe_launch_templates(Filters=[{'Name': 'launch-template-name', 'Values': [LaunchTemplateName]}]) + if existing_template: + ec2.create_launch_template_version(LaunchTemplateName=LaunchTemplateName, LaunchTemplateData=LaunchTemplateData) + else: + ec2.create_launch_template(LaunchTemplateName=LaunchTemplateName, LaunchTemplateData=LaunchTemplateData) + +def create_cluster(): + batch = boto3.client('batch') + +# def update_container(): +# # TODO: Modify this to use the docker sdk +# subprocess.run(['sh', 'update_trans_container.sh']) + +def create_aws_resources(): + create_state_machine('kerchunk-h5') + # create_compute_environment() + # create_job_queue() + create_job_def() + # update_container() + +def process_h5_dataset(files, staging_bucket, s3_comb_ref_file, az_comb_ref_file, state_machine_name='kerchunk_h5', region_name='us-west-2'): + smi = create_state_machine_input(files, staging_bucket, s3_comb_ref_file, az_comb_ref_file) + stateMachineArn = get_state_machine(state_machine_name) + sf = boto3.client('stepfunctions', region_name=region_name) + sf.start_execution(stateMachineArn=stateMachineArn, input=smi) + +def copy_s3_dataset_to_azure(files, staging_bucket, dry_run=False): + #batch = boto3.client('batch', region_name=region_name) + CONTAINER_NAME = 'oedi' + sas = load_oedi_sas() + load_dotenv() # Store AWS credentials in .env file + cmd = [ + 'azcopy', + 'copy', + f'https://s3.us-west-2.amazonaws.com/{staging_bucket}', + f'https://nrel.blob.core.windows.net/{CONTAINER_NAME}?{sas}', + # '--exclude-pattern', + # '*' + '--include-path', + ';'.join(files) + ] + # for file in files: + # #cmd.append('--include-pattern') + # cmd.append(file) + if dry_run: + cmd.append('--dry-run') + # source = f'https://s3.us-west-2.amazonaws.com/{staging_bucket}/{file}' + # dest = f'https://nrel.blob.core.windows.net/{CONTAINER_NAME}/{file}?{sas}' + # print(source) + # print(dest) + #os.system(f'azcopy cp "{source}" "{dest}" --dry-run') + # subprocess.run(['azcopy', 'cp', source, dest, '--dry-run']) + print(cmd) + subprocess.run(cmd) + +def create_combined_ref(files, staging_bucket, comb_ref_file=None, remote_protocol='s3'): + s3 = s3fs.S3FileSystem() + f = h5py.File(s3.open(f'{staging_bucket}/{files[0]}')) + identical_dims = list(f.attrs['identical_dims']) + if remote_protocol == 's3': + ref_files = [file.replace('.h5', '_s3.json') for file in files] + elif remote_protocol == 'abfs': + ref_files = [file.replace('.h5', '.json') for file in files] + else: + raise Exception('remote_protocol must be "s3" or "abfs"') + refs = [] + for ref_file in ref_files: + with s3.open(f'{staging_bucket}/{ref_file}', 'rb') as f: + refs.append(ujson.load(f)) + ref_comb = gen_ref_comb(refs, identical_dims=identical_dims, remote_protocol=remote_protocol) + temp_file = 'temp.json' + with open(temp_file, 'wb') as f: + f.write(ujson.dumps(ref_comb).encode()) + s3.put_file(temp_file, f's3://{staging_bucket}/{comb_ref_file}') + if remote_protocol=='abfs': + sas = load_oedi_sas() + CONTAINER_NAME = 'oedi' + dest = f'https://nrel.blob.core.windows.net/{CONTAINER_NAME}/{comb_ref_file}?{sas}' + subprocess.run(['azcopy', 'copy', temp_file, dest]) + +def copy_s3_file_to_azure(source, dest, sas=None, container='oedi'): + s3 = s3fs.S3FileSystem() + if not sas: + sas = load_oedi_sas() + client = ContainerClient.from_container_url(f'https://nrel.blob.core.windows.net/{container}?{sas}') + blob = client.get_blob_client(dest) + with s3.open(source, 'rb') as f: + blob.upload_blob(f.read()) + +def copy_local_file_to_azure(source, dest, sas=None, container='oedi'): + if not sas: + sas = load_oedi_sas() + client = ContainerClient.from_container_url(f'https://nrel.blob.core.windows.net/{container}?{sas}') + blob = client.get_blob_client(dest) + with open(source, 'rb') as f: + blob.upload_blob(f.read()) + +# We need: +#X A container to hold the code that will perform etl and ref generation +#X A container to combine the references +# A function to build the AWS resources including: +# A function that will load the container into ECR +# A function that will initialize a cluster and job queue +# A function to create a state machine (started) +# A function to process a dataset including: +#X A get_dataset function to glob all of the files of a particular data set +# A function to run the state machine +#X A function to create state machine input +# A way to test the results in s3 staging +# A function to copy data to Azure +# Logging functionality to track progress throughout diff --git a/azure/pipeline/azure_tools.py b/azure/pipeline/azure_tools.py new file mode 100644 index 0000000..b1ef66e --- /dev/null +++ b/azure/pipeline/azure_tools.py @@ -0,0 +1,35 @@ +import planetary_computer + +def get_fs(account='nrel', container='oedi'): + return planetary_computer.get_adlfs_filesystem(account, container) + +def get_size(path, units='B'): + fs = get_fs() + size = fs.du(path, total=True) + match units: + case 'B': + pass + case 'kB': + size = size * 10 ** -3 + case 'MB': + size = size * 10 ** -6 + case 'GB': + size = size * 10 ** -9 + case 'TB': + size = size * 10 ** -12 + case 'PB': + size = size * 10 ** -15 + case 'kiB': + size = size * 2 ** -10 + case 'MiB': + size = size * 2 ** -20 + case 'GiB': + size = size * 2 ** -30 + case 'TiB': + size = size * 2 ** -40 + case 'PiB': + size = size * 2 ** -50 + case _: + raise NotImplementedError(f'Units "{units}" not recognized.') + + return size diff --git a/azure/pipeline/blob_access.ipynb b/azure/pipeline/blob_access.ipynb new file mode 100644 index 0000000..89f2fe0 --- /dev/null +++ b/azure/pipeline/blob_access.ipynb @@ -0,0 +1,81 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Deleting blobs\n", + "\n", + "from azure.storage.blob import ContainerClient\n", + "from etl_tools import load_oedi_sas\n", + "\n", + "sas_token = load_oedi_sas()\n", + "client = ContainerClient.from_container_url(f'https://nrel.blob.core.windows.net/oedi?{sas_token}')\n", + "for blob in client.list_blobs():\n", + " if \"wtk\" in blob.name and 'test' in blob.name:\n", + " print(blob.name)\n", + " #client.delete_blob(blob)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Copying blobs\n", + "\n", + "source_blob = client.get_blob_client('wtk/wtk_bangladesh_hourly_ref.json')\n", + "dest_blob = client.get_blob_client('wtk/bangladesh/kerchunk_hourly_ref.json')\n", + "dest_blob.start_copy_from_url(source_blob.url)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import s3fs\n", + "from azure.storage.blob import ContainerClient\n", + "from etl_tools import load_oedi_sas\n", + "\n", + "def copy_file_to_azure(source, dest, sas=None, container='oedi'):\n", + " s3 = s3fs.S3FileSystem()\n", + " if not sas:\n", + " sas = load_oedi_sas()\n", + "\n", + " client = ContainerClient.from_container_url(f'https://nrel.blob.core.windows.net/{container}?{sas}')\n", + " blob = client.get_blob_client(dest)\n", + " with s3.open(source, 'rb') as f:\n", + " blob.upload_blob(f.read())\n", + "\n", + "copy_file_to_azure('s3://kerchunk-staging/test.txt', 'wtk/test/test.txt')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".env", + "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": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/azure/pipeline/etl_tools.py b/azure/pipeline/etl_tools.py new file mode 100644 index 0000000..5ba70f8 --- /dev/null +++ b/azure/pipeline/etl_tools.py @@ -0,0 +1,698 @@ +import h5py +import pandas as pd +import numpy as np +from kerchunk.hdf import SingleHdf5ToZarr +from kerchunk.combine import MultiZarrToZarr +import ujson +import subprocess +import planetary_computer +import os +from time import time +import s3fs + +def time_index_bytestring_to_float(dset): + t = pd.Series(dset) + t = t.str.decode('utf8') + t = t.str.split('+', expand=True)[0] + t = np.array(t,dtype=np.datetime64) + t = t.astype('int') + return t + +def copy_attrs(obj1, obj2): + # Copy the attributes from obj1 to obj2, which may be h5 File objects or h5 dataset objects + for key in obj1.attrs.keys(): + obj2.attrs[key] = obj1.attrs[key] + +def copy_dataset(f_in, f_out, var, mem_limit_GB=80): + # Determine sizes of slices to read + dtype_size = f_in[var].dtype.itemsize + max_read_size = mem_limit_GB * 10 ** 9 # Read 80 GB at a time + time_index_read_size = f_in[var].shape[0] # Read all time values + gid_index_read_size = int(max_read_size / time_index_read_size // dtype_size) # Number of sites to read at a time + + # Create slices + end = f_in[var].shape[1] + starts = np.arange(0, end, gid_index_read_size) + stops = starts[1:] + stops = np.append(stops, end) + + # Copy slices + for start, stop in zip(starts, stops): + f_out[var][:, start:stop] = f_in[var][:, start:stop] + +def elapsed_time(st): + return f'{(time() - st) / 60:.2f} min' + +def load_oedi_sas(): + # read/write sas token must be stored in a plain text file located at $HOME/.sas or oedi_azure/.sas + home = os.path.expanduser('~') + if os.path.isfile(f'{home}/.sas'): + path = f'{home}/.sas' + elif os.path.isfile('./.sas'): + path = './.sas' + elif os.path.isfile('../.sas'): + path = '../.sas' + else: + path = None + + if path: + with open(path) as f: + sas = f.read() + else: + raise Exception('.sas file not found. Please save your read/write .sas token to a file called .sas located in the oedi_azure directory.') + + return sas + +def transform_wtk_h5_file_old(h5_file, chunk_size=2, weeks_per_chunk=8): + # This is an updated version of transform_h5_file, designed for wtk. wtk does not have a nice rectangular coordinate grid, + # so the data will be left in 2 dims rather than be converted to 3 dims. + + # h5_file should be a path to a local h5 file. The file will be opened in write-mode, transformed and then closed. + # chunk_size is the desired size of each chunk in MiB + # weeks_per_chunk determines the length of chunks in the time_index dimension + + # Summary of data transformations: + + # 1. time_index is converted from byte-string to int (when read by xarray, this will automatically convert to np.datetime64) + # 2. A gid dataset is created to index the locations + # 3. time_index and gid are converted to dimension scales + # 4. Each variable is rechunked so that we will have consistent chunk sizes accross all files + # 5. The dimension scales are attached to each variable's dimensions + # 6. The scale_factor metadata is inverted (new_sf = 1 / old_sf) + # 7. The meta variable is unpacked + + # Notes: + # Once again, the download/upload steps are what will take all of the time here. To scale up to wtk, this transformation + # should either happen on Eagle (where the data are already local) or the transformation should be containerized for use + # with AWS batch. + + f = h5py.File(h5_file, 'r+') + file_name = h5_file.split('/')[-1] + + # Get the length of time_index + time_len = f['time_index'].len() + + # Convert time_index from bytes to float. + t = pd.Series(f['time_index']) + t = t.str.decode('utf8') + t = t.str.split('+', expand=True)[0] + t = np.array(t,dtype=np.datetime64) + t = t.astype('int') + + # Determine time_index chunksize + time_step = t[1] - t[0] + time_index_chunk_len = min(weeks_per_chunk * 7 * 24 * 60 * 60 / time_step, time_len) + + # Replace time_index variable with int. 'units' metadata required for xarray to interpret as datetime. + attrs = {} + for key in f['time_index'].attrs.keys(): + attrs[key] = f['time_index'].attrs[key] + del f['time_index'] + f.create_dataset('time_index', data=t) + for key, val in attrs.items(): + f['time_index'].attrs[key] = val + f['time_index'].attrs['units'] = b'seconds since 1970-01-01' + del t + del attrs + + print(f'{file_name}: time_index processed.') + # Create lon/lat datasets from meta + # f.create_dataset('latitude', data=f['meta']['latitude']) + # f.create_dataset('longitude', data=f['meta']['longitude']) + # del f['coordinates'] + + # Create gid variable + nloc = len(f['coordinates']) + f.create_dataset('gid', data=np.arange(nloc, dtype=np.int32), fillvalue=-1) + print(f'{file_name}: gid created.') + + # Start tracking identical_dims (anything with only a gid dimension) + identical_dims = ['gid'] + + # Convert to dimension scales + f['time_index'].make_scale() + f['gid'].make_scale() + + # Attach dim scales to lon/lat + # f['latitude'].dims[0].attach_scale(f['gid']) + # f['longitude'].dims[0].attach_scale(f['gid']) + + # Get var names + vars = [var for var in f.keys() if var not in ['meta', 'time_index', 'latitude', 'longitude', 'gid', 'coordinates']] + + # Loop over the variables and rechunk and add scales + for var in vars: + # Check dims + if not f[var].shape[0] == time_len: + raise Exception(f'Dim 0 of {var} has different length than time_index.') + if not f[var].shape[1] == nloc: + raise Exception(f'Dim 1 of {var} has different length than gid.') + + print(f'{file_name}: Processing {var}...') + + # Copy attrs + temp_attrs = {} + for attr in f[var].attrs.keys(): + temp_attrs[attr] = f[var].attrs[attr] + + # Copy data + data = f[var][:] + + # Determine location chunk size + element_size = f[var].dtype.itemsize # size of single element in bytes + gid_chunk_len = min(chunk_size * 2 ** 20 / time_index_chunk_len // element_size, nloc) + + # Delete original dataset + del f[var] + + # Create new rechunked dataset + chunks = (time_index_chunk_len, gid_chunk_len) + f.create_dataset(var, data=data, chunks=chunks) + for key, val in temp_attrs.items(): + f[var].attrs[key] = val + f[var].attrs['chunks'] = chunks + + # Fix scale_factor + if 'scale_factor' in f[var].attrs.keys(): + f[var].attrs['scale_factor'] = 1 / f[var].attrs['scale_factor'] + + # Attach scales to the dims + f[var].dims[0].attach_scale(f['time_index']) + f[var].dims[1].attach_scale(f['gid']) + + # Progress report + # print(f'{file_name}: {var} processed.') + print(f'{file_name}: Complete.') + + # Unpack metadata variables + #chunks = f['meta'].chunks + for var in f['meta'].dtype.names: + data = f['meta'][var] + element_size = data.dtype.itemsize + gid_chunk_len = min(chunk_size * 2 ** 20 // element_size, nloc) + chunks = (gid_chunk_len,) + f.create_dataset(var, data=data, chunks=chunks) + + # Add chunks attribute + f[var].attrs['chunks'] = chunks + + # Attach dimension scales to the dimensions + f[var].dims[0].attach_scale(f['gid']) + + # Append to identical_dims + identical_dims.append(var) + del f['meta'] + print(f'{file_name}: meta unpacked.') + + # Delete coordinates, since we have lat/lon + del f['coordinates'] + + # if 'coordinates' in f.keys(): + # data = f['coordinates'] # No attrs + # element_size = data.dtype.itemsize + # gid_chunk_len = min(chunk_size * 2 ** 20 // element_size, nloc) + # chunks = (gid_chunk_len,) + # del f['coordinates'] + # f.create_dataset('coordinates', data=data, chunks=chunks) + + # # Add chunks attribute + # f[var].attrs['chunks'] = chunks + + # # Attach dimension scales to the dimensions + # f[var].dims[0].attach_scale(f['gid']) + # print(f'{file_name}: coordinates rechunked.') + + # Add identical_dims to file metadata so we can pass to kerchunk later + f.attrs['identical_dims'] = identical_dims + + # Close the dataset to ensure changes are written + f.close() + + print(f'{file_name}: Done with transormations!') + return + +def transform_wtk_h5_file_with_h5repack(in_file, out_file, chunk_size=2, weeks_per_chunk=8): + # This is an updated version of transform_h5_file, designed for wtk. wtk does not have a nice rectangular coordinate grid, + # so the data will be left in 2 dims rather than be converted to 3 dims. + + # h5_file should be a path to a local h5 file. The file will be opened in write-mode, transformed and then closed. + # chunk_size is the desired size of each chunk in MiB + # weeks_per_chunk determines the length of chunks in the time_index dimension + + # Summary of data transformations: + + # 1. time_index is converted from byte-string to int (when read by xarray, this will automatically convert to np.datetime64) + # 2. A gid dataset is created to index the locations + # 3. time_index and gid are converted to dimension scales + # 4. Each variable is rechunked so that we will have consistent chunk sizes accross all files + # 5. The dimension scales are attached to each variable's dimensions + # 6. The scale_factor metadata is inverted (new_sf = 1 / old_sf) + # 7. The meta variable is unpacked + + # Notes: + # Once again, the download/upload steps are what will take all of the time here. To scale up to wtk, this transformation + # should either happen on Eagle (where the data are already local) or the transformation should be containerized for use + # with AWS batch. + + f_in = h5py.File(in_file, 'r') + file_name = out_file.split('/')[-1] + + # Get the length of time_index and coordinates + time_len = f_in['time_index'].len() + nloc = len(f_in['coordinates']) + + # Convert time_index from bytes to float. + t = pd.Series(f_in['time_index']) + t = t.str.decode('utf8') + t = t.str.split('+', expand=True)[0] + t = np.array(t,dtype=np.datetime64) + t = t.astype('int') + + # Determine time_index chunksize + time_step = t[1] - t[0] + time_index_chunk_len = min(weeks_per_chunk * 7 * 24 * 60 * 60 / time_step, time_len) + + # Get var names + vars = [var for var in f_in.keys() if var not in ['meta', 'time_index', 'latitude', 'longitude', 'gid', 'coordinates']] + + layouts = [] + # Loop over vars to determine chunk sizes + for var in vars: + # Check dims + if not f_in[var].shape[0] == time_len: + raise Exception(f'Dim 0 of {var} has different length than time_index.') + if not f_in[var].shape[1] == nloc: + raise Exception(f'Dim 1 of {var} has different length than gid.') + + # Determine location chunk size + element_size = f_in[var].dtype.itemsize # size of single element in bytes + gid_chunk_len = min(chunk_size * 2 ** 20 / time_index_chunk_len // element_size, nloc) + + layouts.append('-l') + layouts.append(f'{var}:CHUNK={int(time_index_chunk_len)}x{int(gid_chunk_len)}') + f_in.close() + + # Copy the h5 file to scratch while rechunking datasets + print(f'Repacking {in_file} to {out_file}.') + subprocess.run(['h5repack', '-i', in_file, '-o', out_file] + layouts) + print(f'Repack complete.') + + # Open out_file + f_out = h5py.File(out_file, 'a') + + # Replace time_index variable with int. 'units' metadata required for xarray to interpret as datetime. + # Original dataset must be deleted and replaced because the data type is changing + attrs = {} + for key in f_out['time_index'].attrs.keys(): + attrs[key] = f_out['time_index'].attrs[key] + del f_out['time_index'] # Must be deleted because you can't create a dataset with the same name as an existing dataset + f_out.create_dataset('time_index', data=t) + for key, val in attrs.items(): + f_out['time_index'].attrs[key] = val + f_out['time_index'].attrs['units'] = b'seconds since 1970-01-01' + del t + del attrs + + # Create gid variable + nloc = len(f_out['coordinates']) + f_out.create_dataset('gid', data=np.arange(nloc, dtype=np.int32), fillvalue=-1) + print(f'{file_name}: gid created.') + + # Convert to dimension scales + f_out['time_index'].make_scale() + f_out['gid'].make_scale() + + # Start tracking identical_dims (anything with only a gid dimension) + identical_dims = ['gid'] + + # Loop over the variables and add scales + for var in vars: + + print(f'{file_name}: Processing {var}...') + + # Fix scale_factor + if 'scale_factor' in f_out[var].attrs.keys(): + f_out[var].attrs['scale_factor'] = 1 / f_out[var].attrs['scale_factor'] + + # Attach scales to the dims + f_out[var].dims[0].attach_scale(f_out['time_index']) + f_out[var].dims[1].attach_scale(f_out['gid']) + + # Progress report + print(f'{file_name}: Complete.') + + # Unpack metadata variables + #chunks = f['meta'].chunks + for var in f_out['meta'].dtype.names: + # data = f_out['meta'][var] + element_size = f_out['meta'][var].dtype.itemsize + gid_chunk_len = min(chunk_size * 2 ** 20 // element_size, nloc) + chunks = (gid_chunk_len,) + f_out.create_dataset(var, data=f_out['meta'][var], chunks=chunks) + + # Add chunks attribute + f_out[var].attrs['chunks'] = chunks + + # Attach dimension scales to the dimensions + f_out[var].dims[0].attach_scale(f_out['gid']) + + # Append to identical_dims + identical_dims.append(var) + del f_out['meta'] + print(f'{file_name}: meta unpacked.') + + # Delete coordinates, since we have lat/lon + del f_out['coordinates'] + + # Add identical_dims to file metadata so we can pass to kerchunk later + f_out.attrs['identical_dims'] = identical_dims + + # Close the dataset to ensure changes are written + f_out.close() + + print(f'{file_name}: Done with transormations!') + return + +def transform_wtk_h5_file(in_file, out_file, chunk_size=2, weeks_per_chunk=None, in_file_on_s3=False): + # This is an updated version of transform_h5_file, designed for wtk. wtk does not have a nice rectangular coordinate grid, + # so the data will be left in 2 dims rather than be converted to 3 dims. + + # h5_file should be a path to a local h5 file. The file will be opened in write-mode, transformed and then closed. + # chunk_size is the desired size of each chunk in MiB + # weeks_per_chunk determines the length of chunks in the time_index dimension + + # Summary of data transformations: + + # 1. time_index is converted from byte-string to int (when read by xarray, this will automatically convert to np.datetime64) + # 2. A gid dataset is created to index the locations + # 3. time_index and gid are converted to dimension scales + # 4. Each variable is rechunked so that we will have consistent chunk sizes accross all files + # 5. The dimension scales are attached to each variable's dimensions + # 6. The scale_factor metadata is inverted (new_sf = 1 / old_sf) + # 7. The meta variable is unpacked + + # Notes: + # Once again, the download/upload steps are what will take all of the time here. To scale up to wtk, this transformation + # should either happen on Eagle (where the data are already local) or the transformation should be containerized for use + # with AWS batch. + + # Begin logging + st = time() + file_name = out_file.split('/')[-1] + print(f'{elapsed_time(st)} - {file_name}: Starting transformation.') + + # Open input file + if in_file_on_s3: + s3 = s3fs.S3FileSystem() + f_in = h5py.File(s3.open(in_file)) + else: + f_in = h5py.File(in_file, 'r') + + # Delete output file if it exists, and then create it (note that 'w' mode for h5py would be better, but is unreliable) + if os.path.exists(out_file): + os.remove(out_file) + f_out = h5py.File(out_file, 'a') + + # Copy file attrs + copy_attrs(f_in, f_out) + print(f'{elapsed_time(st)} - {file_name}: File attrs copied!') + + # Get the length of time_index and coordinates + time_len = f_in['time_index'].len() + nloc = len(f_in['coordinates']) + + # Convert time_index from bytes to float. + t = time_index_bytestring_to_float(f_in['time_index']) + + # Create time_index variable in new file. 'units' metadata required for xarray to interpret as datetime. + f_out.create_dataset('time_index', data=t) + copy_attrs(f_in['time_index'], f_out['time_index']) + f_out['time_index'].attrs['units'] = b'seconds since 1970-01-01' + + # Create gid variable + f_out.create_dataset('gid', data=np.arange(nloc, dtype=np.int32), fillvalue=-1) + print(f'{elapsed_time(st)} - {file_name}: gid created.') + + # Convert to dimension scales + f_out['time_index'].make_scale() + f_out['gid'].make_scale() + + # Determine time_index chunksize + time_step = t[1] - t[0] + if not weeks_per_chunk: + if time_step == 5 * 60: # 5min data + weeks_per_chunk = 1 + elif time_step == 10 * 60: # 10min data + weeks_per_chunk = 2 + elif time_step == 15 * 60: # 15min data + weeks_per_chunk = 3 + elif time_step == 60 * 60: # hourly data + weeks_per_chunk = 12 + else: + weeks_per_chunk = 8 # other resolution + print(f'Warning: Non-standard resolution of {time_step / 60} min detected.') + + time_index_chunk_len = int(min(weeks_per_chunk * 7 * 24 * 60 * 60 / time_step, time_len)) + + print(f'{elapsed_time(st)} - {file_name}: time_index and gid created') + + # Get var names + vars = [var for var in f_in.keys() if var not in ['meta', 'time_index', 'latitude', 'longitude', 'gid', 'coordinates']] + + # Loop over vars copying them to the new file + for var in vars: + print(f'{elapsed_time(st)} - {file_name}: Processing {var}...') + + # Check dims + if not f_in[var].shape[0] == time_len: + raise Exception(f'Dim 0 of {var} has different length than time_index.') + if not f_in[var].shape[1] == nloc: + raise Exception(f'Dim 1 of {var} has different length than gid.') + + # Determine location chunk size + element_size = f_in[var].dtype.itemsize # size of single element in bytes + gid_chunk_len = int(min(chunk_size * 2 ** 20 / time_index_chunk_len // element_size, nloc)) + + # Create dataset in new file + chunks=(time_index_chunk_len, gid_chunk_len) + + # f_out.create_dataset(var, data=f_in[var], chunks=chunks) + # copy_attrs(f_in[var], f_out[var]) + + # --- New code + f_out.create_dataset(var, shape=f_in[var].shape, dtype=f_in[var].dtype, chunks=chunks) + copy_dataset(f_in, f_out, var) + copy_attrs(f_in[var], f_out[var]) + # --- + + # Add chunks attribute + f_out[var].attrs['chunks'] = chunks + + # Fix scale_factor + if 'scale_factor' in f_out[var].attrs.keys(): + f_out[var].attrs['scale_factor'] = 1 / f_out[var].attrs['scale_factor'] + + # Attach scales to the dims + f_out[var].dims[0].attach_scale(f_out['time_index']) + f_out[var].dims[1].attach_scale(f_out['gid']) + + # Progress report + print(f'{elapsed_time(st)} - {file_name}: Done!') + + print(f'{elapsed_time(st)} - {file_name}: All variables transformed!') + + # Start tracking identical_dims (anything with only a gid dimension) + identical_dims = ['gid'] + + # Unpack metadata variables + for var in f_in['meta'].dtype.names: + print(f'{elapsed_time(st)} - {file_name}: Unpacking {var} from meta...') + element_size = f_in['meta'][var].dtype.itemsize + gid_chunk_len = min(chunk_size * 2 ** 20 // element_size, nloc) + chunks = (gid_chunk_len,) + f_out.create_dataset(var, data=f_in['meta'][var], chunks=chunks) + + # Add chunks attribute + f_out[var].attrs['chunks'] = chunks + + # Attach dimension scales to the dimensions + f_out[var].dims[0].attach_scale(f_out['gid']) + + # Append to identical_dims + identical_dims.append(var) + + print(f'{elapsed_time(st)} - {file_name}: Done!') + + print(f'{elapsed_time(st)} - {file_name}: meta unpacked!') + + # Add identical_dims to file metadata so we can pass to kerchunk later + f_out.attrs['identical_dims'] = identical_dims + + # Close the datasets to ensure changes are written + f_in.close() + f_out.close() + + print(f'{elapsed_time(st)} - {file_name}: Done with transormations!') + + return + +def transform_sup3rcc_h5_file(infile, outfile): + # This function is designed to transform h5 files for the Sup3rcc dataset, to prepare them for use with Kerchunk. + # infile and outfile should both be local file paths. infile is the original Sup3rcc h5 file. outfile will be created + # by copying and transforming the data from infile. + + # The Sup3rcc data uses a nice rectangular, evenly-spaced grid of lon/lat coordinates. This allowed for easy transformation + # from 2 dimensions to 3 dimensions, which results in improved user experience when loading the data with xarray. + + # Summary of data transformations: + + # 1. time_index is converted from byte-string to int (when read by xarray, this will automatically convert to np.datetime64) + # 2. latitude and longitude are given their own datsets + # 3. time_index, latitude and longitude are converted to dimension scales + # 4. Each variable is reshaped from 2 dims (time_index, location) to 3 dims (time_index, latitude, longitude) + # 5. Each variable is rechunked, resulting in about 1.8 MB per chunk + # 6. The dimension scales are attached to each variable's dimensions + # 7. The scale_factor metadata is inverted (new_sf = 1 / old_sf) + + # TODO + # 1. Future iterations of this transformation should modify the original h5 file, rather than copying the contents to a new file + # 2. Rechunking should be automated (currently the choice of chunk size is specific to the Sup3rcc dataset) + + # Open infile, create outfile + f1 = h5py.File(infile) + f2 = h5py.File(outfile, 'a') + + # Copy attributes + for attr in f1.attrs.keys(): + f2.attrs[attr] = f1.attrs[attr] + + # Get the length of time_index + time_len = f1['time_index'].len() + + # Convert time_index from bytes to float. + t = pd.Series(f1['time_index']) + t = t.str.decode('utf8') + t = t.str.split('+', expand=True)[0] + t = np.array(t,dtype=np.datetime64) + t = t.astype('int') + + # Grab the lat and lon coordinates from meta + lat = f1['meta']['latitude'].reshape(650, 1475)[:, 0] + lon = f1['meta']['longitude'].reshape(650, 1475)[0, :] + + # Add time_index dimension to the temp dataset. 'units' metadata required for xarray to interpret as datetime. + f2.create_dataset('time_index', data=t) + f2['time_index'].attrs['units'] = b'seconds since 1970-01-01' + + # Add lon/lat dimensions to temp dataset + f2.create_dataset('latitude', data=lat) + f2.create_dataset('longitude', data=lon) + + # Convert them to dimension scales + f2['time_index'].make_scale() + f2['latitude'].make_scale() + f2['longitude'].make_scale() + + print('Dimension scales created.') + + # Get var names + vars = [var for var in f1.keys() if var not in ['meta', 'time_index']] + + # Loop over the variables and transfer them to the temp data set + for var in vars: + # Check dimensions + time_len = f1['time_index'].len() + assert f1[var].shape[0] == time_len + assert f1[var].shape[1] == 650 * 1475 + + # Copy data, reshape it and rechunk it. Now we have 3 dims, time, lat, lon + # Note that chunks=True will result in auto-chunking. This doesn't really work when + # data sets have different lengths for the time_index (as is the case for Sup3rcc) + chunks = (24, 130, 295) + f2.create_dataset(var, data=f1[var][:].reshape(time_len, 650, 1475), chunks=chunks) # Results in 1.8 MB chunks for pressure data + print(f'{var} reshaped and transferred to new dataset.') + + # Add attributes + for attr in f1[var].attrs.keys(): + if attr == 'scale_factor': + f2[var].attrs[attr] = 1 / f1[var].attrs[attr] + elif attr != 'chunks': + f2[var].attrs[attr] = f1[var].attrs[attr] + f2[var].attrs['chunks'] = chunks + + # Label the dimensions of the main variable + f2[var].dims[0].label = 'time_index' + f2[var].dims[1].label = 'latitude' + f2[var].dims[2].label = 'longitude' + + # Attach dimension scales to the dimensions + f2[var].dims[0].attach_scale(f2['time_index']) + f2[var].dims[1].attach_scale(f2['latitude']) + f2[var].dims[2].attach_scale(f2['longitude']) + + print(f'Dimension scales attached to {var}.') + + # Add metadata variables + for var in f1['meta'].dtype.names: + if var not in ['latitude', 'longitude']: + chunks = (130, 295) + f2.create_dataset(var, data=f1['meta'][var].reshape(650, 1475), chunks=chunks) + + # Add chunks attribute + f2[var].attrs['chunks'] = chunks + + # Label the dimensions of the main variable + f2[var].dims[0].label = 'latitude' + f2[var].dims[1].label = 'longitude' + + # Attach dimension scales to the dimensions + f2[var].dims[0].attach_scale(f2['latitude']) + f2[var].dims[1].attach_scale(f2['longitude']) + + #print(f'Dimension scales attached to {var}.') + + # Close the new dataset to ensure changes are written + f1.close() + f2.close() + + return + +def gen_ref(local_path, storage_path, ref_file=None): + # local_path is the file to be analyzed. storage_path is the path to the same file in cloud storage. ref_file is + # an optional argument that can be used to save the kerchunk references as a json.\ + + with open(local_path, 'rb') as f: + ref = SingleHdf5ToZarr(f, storage_path, inline_threshold=300).translate() + + if ref_file: + with open(ref_file, 'wb') as f: + f.write(ujson.dumps(ref).encode()) + + return ref + +def gen_ref_comb(refs, ref_file=None, concat_dims=['time_index'], identical_dims=None, remote_protocol='abfs'): + # This function takes a list of kerchunk references and combines them into a single reference. + # For sup3rcc, we used identical_dims=['country', 'county', 'eez', 'elevation', 'latitude', 'longitude', 'offshore', 'state', 'timezone'], + # however, None would probably have been fine... + # Generate combo reference + + if remote_protocol not in ['s3', 'abfs']: + raise NotImplementedError() + + kwargs = { + 'remote_protocol': remote_protocol, + 'concat_dims': concat_dims, + 'identical_dims': identical_dims + } + if remote_protocol == 'abfs': + token = planetary_computer.sas.get_token('nrel', 'oedi').token + kwargs['remote_options'] = {'account_name': 'nrel', "credential": token} + + ref_comb = MultiZarrToZarr(refs, **kwargs).translate() + + # Write to json file + if ref_file: + with open(ref_file, 'wb') as f: + f.write(ujson.dumps(ref_comb).encode()) + + return ref_comb diff --git a/azure/pipeline/hpc_gen_refs.py b/azure/pipeline/hpc_gen_refs.py new file mode 100644 index 0000000..06b6b7c --- /dev/null +++ b/azure/pipeline/hpc_gen_refs.py @@ -0,0 +1,65 @@ +import ujson +from etl_tools import gen_ref_comb, load_oedi_sas +import xarray as xr +import planetary_computer +import os +import sys + +# Get input +# First arg should be the path for the combined ref file +# Next should be any number of paths to individual ref files + +args = sys.argv +comb_ref_file = args[1] +ref_paths = args[2:] + +USER = os.getenv('USER') +CONTAINER_NAME = 'oedi' + +az_path = comb_ref_file.replace(f'/scratch/{USER}/', '') + +if 'sup3rcc' in ref_paths[0]: + DATASET = 'sup3rcc' + identical_dims = ['country', 'county', 'eez', 'elevation', 'latitude', 'longitude', 'offshore', 'state', 'timezone'] +elif 'WIND' in ref_paths[0]: + DATASET = 'wtk' + az_path = az_path.replace('WIND/', 'wtk/') + # Open one dataset to get the identical_dims attribute + token = planetary_computer.sas.get_token('nrel', CONTAINER_NAME).token + ds = xr.open_dataset( + "reference://", engine="zarr", + backend_kwargs={ + "storage_options": { + "fo": ref_paths[0], + "remote_protocol": "abfs", + "remote_options": {'account_name': 'nrel', "credential": token} + }, + "consolidated": False, + } + ) + identical_dims = ds.attrs['identical_dims'] +else: + raise NotImplementedError('The only implemented Eagle datasets are sup3rcc and WIND.') + +print(f'Identical dims: {identical_dims}') + +# Open all reference files +refs = [] +for rp in ref_paths: + with open(rp, 'rb') as f: + refs.append(ujson.load(f)) + +# Generate the combined reference file +gen_ref_comb(refs, ref_file=comb_ref_file, identical_dims=identical_dims) + +# Send to Azure +sas_token = load_oedi_sas() +blob_address = f'https://nrel.blob.core.windows.net/{CONTAINER_NAME}' +# comb_ref_file_name = comb_ref_file.split('/')[-1] +# az_path = {DATASET}/{comb_ref_file_name} +# fs = planetary_computer.get_adlfs_filesystem('nrel', 'oedi') +# if fs.exists(az_path): +# raise Exception(f'Path "{az_path}" already exists in Azure.') +# else: +dest = f'{blob_address}/{az_path}?{sas_token}' +os.system(f'azcopy cp "{comb_ref_file}" "{dest}"') diff --git a/azure/pipeline/hpc_process_file.py b/azure/pipeline/hpc_process_file.py new file mode 100644 index 0000000..5570791 --- /dev/null +++ b/azure/pipeline/hpc_process_file.py @@ -0,0 +1,58 @@ +import sys +import os +from etl_tools import transform_wtk_h5_file, transform_sup3rcc_h5_file, gen_ref +from hpc_tools import construct_paths +from time import time + +# Start timer +start_time = time() + +CONTAINER_NAME = 'oedi' +USER = os.getenv('USER') + +# Get input +args = sys.argv +if len(args) != 2: + raise Exception('Must provide exactly one file path.') +source_path = args[1] +# if 'WIND' in source_path: +# DATASET_NAME = 'wtk' +# file_path = source_path.replace('/datasets/WIND/', 'wtk/') +# elif 'sup3rcc' in source_path: +# DATASET_NAME = 'sup3rcc' +# file_path = source_path.replace('/datasets/', '') +# else: +# raise NotImplementedError(f'Dataset for {source_path} not implemented yet.') + +# Copy h5 file to scratch and transform +#scratch_path = f'/scratch/{USER}/{file_path}' + +# Construct paths +file_name, job_name, job_dir, ref_file, az_path = construct_paths(source_path) +scratch_path = f'{job_dir}{file_name}' +#scratch_path = source_path.replace('/datasets', f'/scratch/{USER}') +#file_name = scratch_path.split('/')[-1] +# scratch_dir = scratch_path.replace(file_name, '') +# os.makedirs(scratch_dir, exist_ok=True) +# if DATASET_NAME == 'wtk': +if 'WIND' in source_path: + # shutil.copy(source_path, scratch_path) + # print(f'{(time() - start_time) / 60:.2f} min: {file_name} copied.') + transform_wtk_h5_file(source_path, scratch_path) +elif 'sup3rcc' in source_path: + transform_sup3rcc_h5_file(source_path, scratch_path) +else: + raise NotImplementedError(f'The only Eagle datasets that have been implemented are WIND and sup3rcc.') + +print(f'{(time() - start_time) / 60:.2f} min: {file_name} transformed.') + +# with open(f'/home/{USER}/Azure_workflow/temp_files_{DATASET_NAME}.txt', 'a') as f: +# f.write(f'{scratch_path}\n') + +# Generate a kerchunk reference file for the h5 file +# ref_file = scratch_path.replace('.h5', '_ref.json') +# file_name = ref_file.split('/')[-1] +#ref_dir = ref_file.replace(file_name, '') +#os.makedirs(ref_dir, exist_ok=True) # Need to create the dir if it doesn't exist +gen_ref(scratch_path, f'abfs://{CONTAINER_NAME}/{az_path}', ref_file=ref_file) +print(f'{(time() - start_time) / 60:.2f} min: {job_name} references generated.') diff --git a/azure/pipeline/hpc_to_azure.py b/azure/pipeline/hpc_to_azure.py new file mode 100644 index 0000000..9af93f4 --- /dev/null +++ b/azure/pipeline/hpc_to_azure.py @@ -0,0 +1,18 @@ +import planetary_computer +import sys +import subprocess +from etl_tools import load_oedi_sas +import os + +args = sys.argv + +blob_address = 'https://nrel.blob.core.windows.net/oedi' +sas_token = load_oedi_sas() + +for arg in args[1:]: + source, dest = arg.split(':') + source = f"'{source}'" + dest = f"'{blob_address}/{dest}?{sas_token}'" + + os.system(f'azcopy copy {source} {dest} --overwrite ifSourceNewer') + #subprocess.run(['azcopy', 'copy', source, dest]) diff --git a/azure/pipeline/hpc_tools.py b/azure/pipeline/hpc_tools.py new file mode 100644 index 0000000..1d4e73f --- /dev/null +++ b/azure/pipeline/hpc_tools.py @@ -0,0 +1,476 @@ +import os +import h5py +import subprocess +import math +from glob import glob +import re + +def run_job(job_file): + job_submission = subprocess.run(['sbatch', job_file], capture_output=True) + output = job_submission.stdout.decode() + if 'Submitted batch job ' in output: + jobid = output.split()[3] + else: + jobid = 0 + print(f'Job submission failure: {job_submission.stderr.decode()}') + return jobid + +def cancel_jobs(job_ids): + for job_id in job_ids: + subprocess.run(['scancel', job_id]) + +def construct_paths(file): + # Need username to access scratch + user = os.getenv('USER') + + file_name = file.split('/')[-1] + job_name = file_name.replace('.h5', '') + job_dir = file.replace('/datasets', f'/scratch/{user}').replace(file_name, '') + ref_file = f'{job_dir}{job_name}.json' + + if 'WIND' in file: + az_path = file.replace('/datasets/WIND', 'wtk') + elif 'sup3rcc' in file: + az_path = file.replace('/datasets/', '') + else: + raise NotImplementedError(f'The only Eagle datasets that have been implemented are WIND and sup3rcc.') + + return file_name, job_name, job_dir, ref_file, az_path + +def get_dep_str(dependency): + if not isinstance(dependency, (list, tuple)): + dependency = [dependency, ] + + return '#SBATCH --dependency=afterok:' + ':'.join([str(id) for id in dependency]) + +def get_dataset(dataset, resolution=None): + files = [] + if 'WIND' in dataset: + subsets = ['North_Atlantic', 'gulf_of_mexico'] + subsets2 = ['india'] + if any([subset in dataset for subset in subsets]): + if resolution == 'hourly': + files = glob(f'/datasets/{dataset}/yearly_hr/*.h5') + elif resolution == '5min': + files = glob(f'/datasets/{dataset}/yearly/*.h5') + elif any([subset in dataset for subset in subsets2]): + if resolution == '5min': + files = glob(f'/datasets/{dataset}/*.h5') + else: + files = [] + else: + if resolution == 'hourly': + files = glob(f'/datasets/{dataset}/*.h5') + elif resolution == '5min': + files = glob(f'/datasets/{dataset}/*/*.h5') + else: # 10min and 15min resolutions + files = glob(f'/datasets/{dataset}/*.h5') + return files + +def gen_hpc_single_job(file, job_dir, job_name, mem_GB=None, time_limit_hrs=4, debug=False): + + # Get bash path + bash_path = os.popen('which bash').read().replace('\n', '') + + # Construct paths + file_name, job_name, job_dir, ref_file, az_path = construct_paths(file) + + # Get user + user = os.getenv('USER') + + # Set parameters + nodes = 1 + ntasks = 1 + + # Create job file paths + job_file = f'{job_dir}{job_name}.sh' + output_file = f'{job_dir}{job_name}_out' + error_file = f'{job_dir}{job_name}_err' + + # Add debug partition if desired + if debug: + add_debug = '#SBATCH --partition=debug' + time_limit_hrs = 1 + else: + add_debug = '' + + if mem_GB: + add_mem = f'#SBATCH --mem={mem_GB}GB' + else: + add_mem = '' + + with open(job_file, 'w') as f: + # Write SBATCH inputs + f.write( +f"""#!{bash_path} +#SBATCH --job-name='{job_name}' +#SBATCH --nodes={nodes} +#SBATCH --ntasks={ntasks} +#SBATCH --time={time_limit_hrs:.0f}:00:00 +#SBATCH -o {output_file} +#SBATCH -e {error_file} +#SBATCH --export=ALL +#SBATCH --account=oedi +{add_mem} +{add_debug} + +#------------------ + +cd /scratch/$USER +module load conda +conda activate .env2 +srun python /home/{user}/oedi_azure/pipeline/hpc_process_file.py {file} +""" + ) + + return job_file + +def gen_hpc_combine_refs_job(comb_ref_file, ref_files, time_limit_hrs=4, dependency=None, debug=False, py_file='/home/mheine/oedi_azure/pipeline/hpc_gen_refs.py'): + bash_path = os.popen('which bash').read().replace('\n', '') + + comb_ref_file_name = comb_ref_file.split('/')[-1] + job_dir = comb_ref_file.replace(comb_ref_file_name, '') + job_name = comb_ref_file_name.replace('.json', '') + job_file = f'{job_dir}{job_name}.sh' + + # Add dependency if any + if dependency: + add_dependency = get_dep_str(dependency) + else: + add_dependency = '' + + # Add debug partition if desired + if debug: + add_debug = '#SBATCH --partition=debug' + time_limit_hrs = 1 + else: + add_debug = '' + + # Create job file + with open(job_file, 'w') as f: + # Write SBATCH inputs + f.write( +f"""#!{bash_path} +#SBATCH --job-name='{job_name}' +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --time={time_limit_hrs:.0f}:00:00 +#SBATCH -o {job_dir}{job_name}_out +#SBATCH -e {job_dir}{job_name}_err +#SBATCH --export=ALL +#SBATCH --account=oedi +{add_dependency} +{add_debug} + +#------------------ +cd /scratch/$USER +module load conda +conda activate .env2 +srun python {py_file} {comb_ref_file} {' '.join(ref_files)} + +""" + ) + return job_file + +def gen_hpc_to_azure_job(files, transformed_files, az_paths, dependency=None, transfer_speed=1500, debug=False, py_file='/home/mheine/oedi_azure/pipeline/hpc_to_azure.py'): + # Transfer speed in Mb/s + bash_path = os.popen('which bash').read().replace('\n', '') + + first_file_name = transformed_files[0].split('/')[-1] + job_dir = transformed_files[0].replace(first_file_name, '') + match = re.search(r'/\d\d\d\d/$', job_dir) + if match: + year = match.group(0) + job_dir = job_dir.replace(year, '/') + job_name = 'hpc_to_azure' + existing_job_files = glob(job_dir + 'hpc_to_azure*.sh') + if existing_job_files: + job_name += f'_{len(existing_job_files) + 1}' + job_file = f'{job_dir}{job_name}.sh' + + # Estimate time requirements + + total_bytes = 0 + for file in files: + total_bytes += os.stat(file).st_size + + time_factor = 1.5 # Provide extra time in case things move a little slower than usual + time_required_hrs = math.ceil(time_factor * total_bytes * 8 * 10 ** -6 / transfer_speed / 60 / 60) + if time_required_hrs > 240: + print('Warning: Transfer job is estimated to take longer than the maximum of 240 hrs.') + time_required_hrs = 240 + + # Create transfer args + # : + transfer_args = [f'{transformed_file}:{az_path}' for transformed_file, az_path in zip(transformed_files, az_paths)] + + # Add dependency if any + if dependency: + add_dependency = get_dep_str(dependency) + else: + add_dependency = '' + + # Add debug partition if desired + if debug: + add_debug = '#SBATCH --partition=debug' + time_required_hrs = 1 + else: + add_debug = '' + + # Create job file + with open(job_file, 'w') as f: + # Write SBATCH inputs + f.write( +f"""#!{bash_path} +#SBATCH --job-name='{job_name}' +#SBATCH --nodes=1 +#SBATCH --ntasks=1 +#SBATCH --time={time_required_hrs:.0f}:00:00 +#SBATCH -o {job_dir}{job_name}_out +#SBATCH -e {job_dir}{job_name}_err +#SBATCH --export=ALL +#SBATCH --account=oedi +{add_dependency} +{add_debug} + +#------------------ +cd /scratch/$USER +module load conda +conda activate .env2 +srun python {py_file} {' '.join(transfer_args)} + +""" + ) + return job_file + +def process_h5_dataset(files, comb_ref_file=None, time_limit_hrs=None, mem_factor=1.2, debug=False, skip_transformation=False, skip_transfer_to_azure=False): + # For each file in files, we generate a job script and submit to sbatch + # files should a be a list of absolute file paths to files in the /datasets directory. + + # Make lists to track jobs + job_ids = [] + ref_files = [] + transformed_files = [] + az_paths = [] + # Loop over files + print(f'Starting {len(files)} transformation jobs.') + for file in files: + # Get max dataset size to determine memory allocation + # f = h5py.File(file) + # max_dataset_size = 0 + # for key in f.keys(): + # max_dataset_size = max(max_dataset_size, f[key].nbytes * 10 ** -9) + # mem_GB = int(max_dataset_size * mem_factor) + + # It was found that files as small as 415 GB timed out when only given 4 hours. + # In practice, there is a lot of variablity in the lengths of job runs. This may + # be due to network limitations when running many jobs concurrently. We're bumping + # up the time limit to 48 (the limit for the standard partition) for all files larger + # than 400 GB. + if not time_limit_hrs: + # Get file size to adjust time limit + file_size_GB = os.stat(file).st_size * 10 ** -9 + if file_size_GB < 400: + time_limit_hrs = 4 + else: + time_limit_hrs = 48 + + # Construct paths and create directory + file_name, job_name, job_dir, ref_file, az_path = construct_paths(file) + os.makedirs(job_dir, exist_ok=True) + ref_files.append(ref_file) + transformed_files.append(f'{job_dir}{file_name}') + az_paths.append(az_path) + + if not skip_transformation: + # Generate job file to transform and generate references for a single h5 file + job_file = gen_hpc_single_job(file, job_dir, job_name, time_limit_hrs=time_limit_hrs, debug=debug) + + # Run job file + job_id = run_job(job_file) + if job_id == 0: + cancel_jobs(job_ids) + raise Exception('Job submission failure') + else: + job_ids.append(job_id) + + # Generate job file to copy dataset to Azure + if not skip_transfer_to_azure: + print('Starting job to copy dataset to Azure.') + copy_job_file = gen_hpc_to_azure_job(files, transformed_files, az_paths, dependency=job_ids, debug=debug) + copy_job_id = run_job(copy_job_file) + if copy_job_id == 0: + cancel_jobs(job_ids) + raise Exception('Copy job submission failure') + else: + job_ids.append(copy_job_id) + else: + copy_job_id = None + + # Generate job file to combine references + # NOTE THAT DEBUG IS CURRENTLY SET TO TRUE TO EXPEDITE JOBS WHILE ACCOUNT IN STANDBY + print('Starting job to combine references.') + if comb_ref_file: + ref_job_file = gen_hpc_combine_refs_job(comb_ref_file, ref_files, dependency=copy_job_id, debug=True) + ref_job_id = run_job(ref_job_file) + if ref_job_id == 0: + cancel_jobs(job_ids) + raise Exception('Gen combined ref job submission failure') + else: + job_ids.append(ref_job_id) + + print('All jobs scheduled!') + + comb_ref_file_name = comb_ref_file.split('/')[-1] + if 'hourly' in comb_ref_file_name: + job_id_file = comb_ref_file.replace(comb_ref_file_name, 'job_ids_hourly.txt') + elif '5min' in comb_ref_file_name: + job_id_file = comb_ref_file.replace(comb_ref_file_name, 'job_ids_5min.txt') + else: + job_id_file = comb_ref_file.replace(comb_ref_file_name, 'job_ids.txt') + with open(job_id_file, 'w') as f: + f.writelines([job_id + '\n' for job_id in job_ids ]) + + return job_ids + +def process_h5_redos(files, redos, comb_ref_file=None, time_limit_hrs=None, debug=False, skip_transfer_to_azure=False): + """ + Process an h5 datset where some of the transformations failed. + + Parameters + ---------- + files : list + Paths to source h5 files for entire dataset (must be in /datasets on Eagle) + redos: list + Paths to source h5 files that failed (must be in /datasets on Eagle) + comb_ref_file: str + Path to where the combined kerchunk reference file will be + stored. If None, then no combined reference will be generated. + time_limit_hrs: int + Override the default time limit for the file transformation tasks. + debug: bool + Submit all jobs to the debug partition + skip_transfer_to_azure: bool + If true, then no files will be transferred to Azure. + + Returns + ------- + job_ids : list + List of all job_ids submitted to sbatch. + """ + + # For each file in redos, we generate a job script and submit to sbatch. + + # Make lists to track jobs + job_ids = [] + ref_files = [] + transformed_files = [] + az_paths = [] + # Loop over files + print(f'Starting {len(redos)} transformation jobs.') + for file in files: + + # It was found that files as small as 415 GB timed out when only given 4 hours. + # In practice, there is a lot of variablity in the lengths of job runs. This may + # be due to network limitations when running many jobs concurrently. We're bumping + # up the time limit to 48 (the limit for the standard partition) for all files larger + # than 400 GB. + if not time_limit_hrs: + # Get file size to adjust time limit + file_size_GB = os.stat(file).st_size * 10 ** -9 + if file_size_GB < 400: + time_limit_hrs = 4 + else: + time_limit_hrs = 48 + + # Construct paths and create directory + file_name, job_name, job_dir, ref_file, az_path = construct_paths(file) + os.makedirs(job_dir, exist_ok=True) + ref_files.append(ref_file) + transformed_files.append(f'{job_dir}{file_name}') + az_paths.append(az_path) + + if file in redos: + # Generate job file to transform and generate references for a single h5 file + job_file = gen_hpc_single_job(file, job_dir, job_name, time_limit_hrs=time_limit_hrs, debug=debug) + + # Run job file + job_id = run_job(job_file) + if job_id == 0: + cancel_jobs(job_ids) + raise Exception('Job submission failure') + else: + job_ids.append(job_id) + + # Generate job file to copy dataset to Azure + if not skip_transfer_to_azure: + print('Starting job to copy dataset to Azure.') + copy_job_file = gen_hpc_to_azure_job(files, transformed_files, az_paths, dependency=job_ids, debug=debug) + copy_job_id = run_job(copy_job_file) + if copy_job_id == 0: + cancel_jobs(job_ids) + raise Exception('Copy job submission failure') + else: + job_ids.append(copy_job_id) + else: + copy_job_id = None + + # Generate job file to combine references + if comb_ref_file: + print('Starting job to combine references.') + ref_job_file = gen_hpc_combine_refs_job(comb_ref_file, ref_files, dependency=copy_job_id, debug=debug) + ref_job_id = run_job(ref_job_file) + if ref_job_id == 0: + cancel_jobs(job_ids) + raise Exception('Gen combined ref job submission failure') + else: + job_ids.append(ref_job_id) + + print('All jobs scheduled!') + + comb_ref_file_name = comb_ref_file.split('/')[-1] + if 'hourly' in comb_ref_file_name: + job_id_file = comb_ref_file.replace(comb_ref_file_name, 'job_ids_hourly.txt') + elif '5min' in comb_ref_file_name: + job_id_file = comb_ref_file.replace(comb_ref_file_name, 'job_ids_5min.txt') + else: + job_id_file = comb_ref_file.replace(comb_ref_file_name, 'job_ids.txt') + with open(job_id_file, 'w') as f: + f.writelines([job_id + '\n' for job_id in job_ids ]) + + return job_ids + +def scan_err(dataset='WIND/Great_Lakes', resolution='5min'): + if resolution == 'hourly': + files = glob(f'/datasets/{dataset}/*.h5') + elif resolution == '5min': + files = glob(f'/datasets/{dataset}/*/*.h5') + + if len(files) == 0: + raise Exception('No output files found. Dataset/resolution does not exists or has not been processed.') + + timeouts = [] + other_errors = [] + timeout_redos = [] + other_redos = [] + for file in files: + err = file.replace('/datasets', '/scratch/mheine').replace('.h5', '_err') + with open(err) as f: + text = f.read() + if 'TIME LIMIT' in text: + timeouts.append(err) + timeout_redos.append(file) + elif len(text) > 0: + other_errors.append(err) + other_redos.append(file) + print(f'Timeouts: {len(timeouts)}') + print(f'Other errors: {len(other_errors)}') + print(f'Total Files: {len(files)}') + + sizes = [] + for redo in timeout_redos: + sizes.append(os.stat(redo).st_size * 10 ** -9) + if len(sizes) > 0: + print(f'The smallest file that timed out in {dataset} was {min(sizes):.0f} GB.') + + return files, timeout_redos, other_redos \ No newline at end of file diff --git a/azure/pipeline/run_aws_pipeline.ipynb b/azure/pipeline/run_aws_pipeline.ipynb new file mode 100644 index 0000000..efc5580 --- /dev/null +++ b/azure/pipeline/run_aws_pipeline.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from aws_tools import *\n", + "\n", + "# Update state machine and job def\n", + "\n", + "create_aws_resources()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2000-01.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2000-02.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2000-03.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2000-04.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2000-05.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2000-06.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2000-07.h5',\n", + " 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'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2017-12.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-01.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-02.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-03.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-04.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-05.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-06.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-07.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-08.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-09.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-10.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-11.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2018-12.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-01.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-02.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-03.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-04.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-05.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-06.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-07.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-08.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-09.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-10.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-11.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2019-12.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-01.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-02.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-03.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-04.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-05.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-06.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-07.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-08.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-09.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-10.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-11.h5',\n", + " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-12.h5']" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Get the s3 addresses for the dataset\n", + "\n", + "prefix = 'south_atlantic'\n", + "resolution = '5min'\n", + "staging_bucket = 'kerchunk-staging'\n", + "run_name = 'south_atlantic-5min-2'\n", + "\n", + "files = get_dataset('nrel-pds-wtk', prefix=prefix, resolution=resolution)\n", + "files" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + 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+ ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Generate the state machine input for this dataset\n", + "\n", + "s3_comb_ref_file = f'wtk/{prefix}/kerchunk_{resolution}_ref_s3.json'\n", + "az_comb_ref_file = f'wtk/{prefix}/kerchunk_{resolution}_ref.json'\n", + "create_state_machine_input(files, staging_bucket, s3_comb_ref_file, az_comb_ref_file, run_name=run_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'executionArn': 'arn:aws:states:us-west-2:351672045885:execution:kerchunk-h5:south_atlantic-5min-2',\n", + " 'startDate': datetime.datetime(2023, 9, 7, 16, 18, 29, 364000, tzinfo=tzlocal()),\n", + " 'ResponseMetadata': {'RequestId': 'e9ced2eb-9fdb-40f2-9e2a-83c369b986ee',\n", + " 'HTTPStatusCode': 200,\n", + " 'HTTPHeaders': {'x-amzn-requestid': 'e9ced2eb-9fdb-40f2-9e2a-83c369b986ee',\n", + " 'date': 'Thu, 07 Sep 2023 16:18:29 GMT',\n", + " 'content-type': 'application/x-amz-json-1.0',\n", + " 'content-length': '129',\n", + " 'connection': 'keep-alive'},\n", + " 'RetryAttempts': 0}}" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Run the state machine\n", + "\n", + "run_state_machine('kerchunk-h5', run_name=run_name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If the state machine fully executed without error, then there should now be a set of transformed h5 files, s3 refs and az refs, as well as a combined s3 ref file in the staging bucket. Use the test_staging.ipynb notebook to verify that the transformation was successful by loading the combined s3 ref file.\n", + "\n", + "Once you are satisfied, continue to the next cell to copy the data to Azure and generate the combined az ref file.\n", + "\n", + "Make sure to update the .env file with AWS credentials!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "copy_s3_dataset_to_azure(files, staging_bucket, dry_run=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO: Scanning...\n", + "INFO: Any empty folders will not be processed, because source and/or destination doesn't have full folder support\n", + "\n", + "Job d5998f8f-4aad-1649-4924-6aa7e8fe7c7e has started\n", + "Log file is located at: /home/ec2-user/.azcopy/d5998f8f-4aad-1649-4924-6aa7e8fe7c7e.log\n", + "\n", + "100.0 %, 1 Done, 0 Failed, 0 Pending, 0 Skipped, 1 Total, \n", + "\n", + "\n", + "Job d5998f8f-4aad-1649-4924-6aa7e8fe7c7e summary\n", + "Elapsed Time (Minutes): 0.0667\n", + "Number of File Transfers: 1\n", + "Number of Folder Property Transfers: 0\n", + "Number of Symlink Transfers: 0\n", + "Total Number of Transfers: 1\n", + "Number of File Transfers Completed: 1\n", + "Number of Folder Transfers Completed: 0\n", + "Number of File Transfers Failed: 0\n", + "Number of Folder Transfers Failed: 0\n", + "Number of File Transfers Skipped: 0\n", + "Number of Folder Transfers Skipped: 0\n", + "TotalBytesTransferred: 507255254\n", + "Final Job Status: Completed\n", + "\n" + ] + } + ], + "source": [ + "comb_ref_file = f'wtk/{prefix}/kerchunk_{resolution}_ref.json'\n", + "create_combined_ref(files, staging_bucket, comb_ref_file=comb_ref_file, remote_protocol='abfs')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once these tasks have finished, you can open the wtk example notebook and verify that the dataset can now be loaded from Azure." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "oedi-azure-dev", + "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": "3.10.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/azure/pipeline/transform_h5_container/Dockerfile b/azure/pipeline/transform_h5_container/Dockerfile new file mode 100644 index 0000000..b536919 --- /dev/null +++ b/azure/pipeline/transform_h5_container/Dockerfile @@ -0,0 +1,25 @@ +FROM continuumio/miniconda3 + +# make docker use bash instead of sh +SHELL ["/bin/bash", "--login", "-c"] + +# create environment +COPY ./pipeline/transform_h5_container/env.yml . +RUN conda env create -f env.yml + +# install azcopy +COPY ./pipeline/transform_h5_container/install_azcopy.sh . +RUN sh install_azcopy.sh + +# copy all necessary files +COPY ./.sas . +COPY ./pipeline/transform_h5_container/* ./ +COPY ./pipeline/etl_tools.py . +COPY ./pipeline/aws_tools.py . + +# make entrypoint script executable +RUN chmod u+x entrypoint.sh + +# activate environment and run container +ENTRYPOINT ["./entrypoint.sh"] +CMD ["python", "transform.py"] diff --git a/azure/pipeline/transform_h5_container/entrypoint.sh b/azure/pipeline/transform_h5_container/entrypoint.sh new file mode 100644 index 0000000..67ef690 --- /dev/null +++ b/azure/pipeline/transform_h5_container/entrypoint.sh @@ -0,0 +1,6 @@ +#!/bin/bash --login +set -e + +# activate conda environment and let the following process take over +conda activate oedi-azure-container +exec "$@" diff --git a/azure/pipeline/transform_h5_container/env.yml b/azure/pipeline/transform_h5_container/env.yml new file mode 100644 index 0000000..5dbc52f --- /dev/null +++ b/azure/pipeline/transform_h5_container/env.yml @@ -0,0 +1,16 @@ +name: oedi-azure-container +channels: + - conda-forge + - defaults + - hcc +dependencies: + - python=3.10.12 + - h5py=3.9.0 + - boto3 + - cftime + - kerchunk + - planetary-computer + - s3fs=2023.6.0 + - pandas + - adlfs + - xarray diff --git a/azure/pipeline/transform_h5_container/gen_ref.py b/azure/pipeline/transform_h5_container/gen_ref.py new file mode 100644 index 0000000..41a0834 --- /dev/null +++ b/azure/pipeline/transform_h5_container/gen_ref.py @@ -0,0 +1,63 @@ +import ujson +from etl_tools import gen_ref_comb, load_oedi_sas +from aws_tools import copy_local_file_to_azure +import xarray as xr +import os +import s3fs +import h5py + +# TODO: Remove all az stuff. The az combined ref file gets created at a later step, after data moves to Azure + +# Azure container name +CONTAINER_NAME = 'oedi' + +# Access S3 +s3 = s3fs.S3FileSystem() + +# Get input from container environment +# s3_source_files = ujson.loads(os.getenv('s3_files')) +s3_comb_ref_file = os.getenv('s3_comb_ref_file') +# az_comb_ref_file = os.getenv('az_comb_ref_file') +staging_bucket = os.getenv('staging_bucket') +run_name = os.getenv('run_name') + +# Get s3 file list from input file on s3 (This list was too long to be an env variable.) +with s3.open(f'{staging_bucket}/{run_name}.json') as f: + input_data = ujson.load(f) +s3_source_files = input_data['s3_files'] + +# Get paths to references and list of identical dims +test_file = s3_source_files[0] +if 'nrel-pds-wtk' in test_file: + s3_ref_paths = [f"{staging_bucket}/{f.replace('nrel-pds-wtk', 'wtk').replace('.h5', '_s3.json')}" for f in s3_source_files] + az_ref_paths = [f"{staging_bucket}/{f.replace('nrel-pds-wtk', 'wtk').replace('.h5', '.json')}" for f in s3_source_files] + test_file = test_file.replace('nrel-pds-wtk', 'wtk') + with s3.open(f'{staging_bucket}/{test_file}') as f: + h5 = h5py.File(f) + identical_dims = list(h5.attrs['identical_dims']) +elif 'sup3rcc' in test_file: + identical_dims = ['country', 'county', 'eez', 'elevation', 'latitude', 'longitude', 'offshore', 'state', 'timezone'] + raise NotImplementedError() +else: + NotImplementedError() + +# Open all reference files +s3_refs = [] +az_refs = [] +for s3_rp, az_rp in zip(s3_ref_paths, az_ref_paths): + with s3.open(s3_rp, 'rb') as f: + s3_refs.append(ujson.load(f)) + with s3.open(az_rp, 'rb') as f: + az_refs.append(ujson.load(f)) + +# Generate the combined reference files +if s3_comb_ref_file: + local_s3_ref = 's3_ref.json' + gen_ref_comb(s3_refs, ref_file=local_s3_ref, identical_dims=identical_dims, remote_protocol='s3') + s3.put_file(local_s3_ref, f's3://{staging_bucket}/{s3_comb_ref_file}') + +# if az_comb_ref_file: +# local_az_ref = 'az_ref.json' +# gen_ref_comb(az_refs, ref_file=local_az_ref, identical_dims=identical_dims, remote_protocol='abfs') +# s3.put_file(local_az_ref, f's3://{staging_bucket}/{az_comb_ref_file}') +# copy_local_file_to_azure(local_az_ref, az_comb_ref_file) diff --git a/azure/pipeline/transform_h5_container/hello.py b/azure/pipeline/transform_h5_container/hello.py new file mode 100644 index 0000000..b862a78 --- /dev/null +++ b/azure/pipeline/transform_h5_container/hello.py @@ -0,0 +1,3 @@ +from etl_tools import load_oedi_sas + +print(load_oedi_sas()) diff --git a/azure/pipeline/transform_h5_container/install_azcopy.sh b/azure/pipeline/transform_h5_container/install_azcopy.sh new file mode 100644 index 0000000..56eb716 --- /dev/null +++ b/azure/pipeline/transform_h5_container/install_azcopy.sh @@ -0,0 +1,17 @@ + +#!/bin/bash + +# Install AzCopy on Linux + +# Download and extract +wget https://aka.ms/downloadazcopy-v10-linux +tar -xvf downloadazcopy-v10-linux + +# Move AzCopy +rm -f /usr/bin/azcopy +cp ./azcopy_linux_amd64_*/azcopy /usr/bin/ +chmod 755 /usr/bin/azcopy + +# Clean the kitchen +rm -f downloadazcopy-v10-linux +rm -rf ./azcopy_linux_amd64_*/ diff --git a/azure/pipeline/transform_h5_container/transfer.py b/azure/pipeline/transform_h5_container/transfer.py new file mode 100644 index 0000000..b95a599 --- /dev/null +++ b/azure/pipeline/transform_h5_container/transfer.py @@ -0,0 +1,6 @@ +import subprocess +import sys + +args = sys.argv +print(args[1]) +subprocess.run(['azcopy', '--version']) diff --git a/azure/pipeline/transform_h5_container/transform.py b/azure/pipeline/transform_h5_container/transform.py new file mode 100644 index 0000000..50621c9 --- /dev/null +++ b/azure/pipeline/transform_h5_container/transform.py @@ -0,0 +1,59 @@ +import os +from etl_tools import transform_wtk_h5_file, transform_sup3rcc_h5_file, gen_ref +from time import time +import boto3 + +# Download h5 to local and then build out the rechunked copy + +# Start timer +start_time = time() + +# Get input from container environment overrides +container_name = 'oedi' +staging_bucket = os.getenv('staging_bucket') +source_path = os.getenv('s3_file') +file_name = source_path.split('/')[-1] + +# Download file to local +s3 = boto3.client('s3') +Bucket = source_path.split('/')[0] +Key = source_path.replace(f'{Bucket}/', '') +local_path = f'/data/{source_path}' +os.makedirs(local_path.replace(file_name, '')) +s3.download_file(Bucket=Bucket, Key=Key, Filename=local_path) + +# Transform dataset +print(f'{(time() - start_time) / 60:.2f} min: {file_name} - Starting transformation.') +if 'nrel-pds-wtk' in source_path: + #DATASET_NAME = 'wtk' + az_path = source_path.replace('nrel-pds-wtk/', 'wtk/') + scratch_path = f'/data/{az_path}' + os.makedirs(scratch_path.replace(file_name, ''), exist_ok=True) # Need to create the dir if it doesn't exist + transform_wtk_h5_file(local_path, scratch_path, in_file_on_s3=False) +elif 'sup3rcc' in source_path: + DATASET_NAME = 'sup3rcc' + az_path = source_path.replace('/nrel-pds-sup3rcc/', 'sup3rcc/') + scratch_path = f'/data/{az_path}' + os.makedirs(scratch_path.replace(file_name, ''), exist_ok=True) # Need to create the dir if it doesn't exist + transform_sup3rcc_h5_file(source_path, scratch_path) +else: + raise NotImplementedError(f'Dataset for {source_path} not implemented yet.') +print(f'{(time() - start_time) / 60:.2f} min: {file_name} - Transformed.') + +ref_file = scratch_path.replace('.h5', '.json') +ref_file_s3 = scratch_path.replace('.h5', '_s3.json') +gen_ref(scratch_path, f'abfs://{container_name}/{az_path}', ref_file=ref_file) +print(f'{(time() - start_time) / 60:.2f} min: {file_name} - Azure reference generated.') + +s3_staging_path = f's3://{staging_bucket}/{az_path}' +gen_ref(scratch_path, s3_staging_path, ref_file=ref_file_s3) +print(f'{(time() - start_time) / 60:.2f} min: {file_name} - S3 reference generated.') + +# Upload to staging +s3 = boto3.client('s3') +s3.upload_file(ref_file, staging_bucket, ref_file.replace('/data/', '')) +print(f'{(time() - start_time) / 60:.2f} min: {file_name} - Azure reference uploaded to staging.') +s3.upload_file(ref_file_s3, staging_bucket, ref_file_s3.replace('/data/', '')) +print(f'{(time() - start_time) / 60:.2f} min: {file_name} - S3 reference uploaded to staging.') +s3.upload_file(scratch_path, staging_bucket, az_path) +print(f'{(time() - start_time) / 60:.2f} min: {file_name} - h5 file uploaded to staging.') diff --git a/azure/pipeline/update_trans_container.sh b/azure/pipeline/update_trans_container.sh new file mode 100644 index 0000000..9f1eeea --- /dev/null +++ b/azure/pipeline/update_trans_container.sh @@ -0,0 +1,4 @@ +docker build -t transform_h5_container -f ./pipeline/transform_h5_container/Dockerfile . +docker tag transform_h5_container:latest 351672045885.dkr.ecr.us-west-2.amazonaws.com/transform_h5_container:latest +aws ecr get-login-password --region us-west-2 | docker login --username AWS --password-stdin 351672045885.dkr.ecr.us-west-2.amazonaws.com +docker push 351672045885.dkr.ecr.us-west-2.amazonaws.com/transform_h5_container:latest From abe0aa9700294b220f977d69994c1af11540ade5 Mon Sep 17 00:00:00 2001 From: "Heine, Matthew" Date: Thu, 18 Jan 2024 14:25:45 -0800 Subject: [PATCH 2/3] Changes to address PR comments --- LICENSE | 2 +- azure/az_cli_guide.txt | 42 - azure/blob_access.ipynb | 2756 ----------------- azure/documentation/az_cli_guide.md | 19 + azure/examples/Azure Cloud Costs.docx | Bin 234181 -> 0 bytes azure/examples/sup3rcc.ipynb | 894 ------ azure/examples/wtk.ipynb | 703 ----- azure/pipeline/aws_tools.py | 83 +- azure/pipeline/azure_tools.py | 49 +- ...access.ipynb => blob_access_example.ipynb} | 6 +- azure/pipeline/etl_tools.py | 353 +-- azure/pipeline/hpc_gen_refs.py | 9 +- azure/{ => pipeline}/hpc_migration.ipynb | 7 - azure/pipeline/hpc_process_file.py | 33 +- azure/pipeline/hpc_to_azure.py | 1 - azure/pipeline/hpc_tools.py | 38 +- azure/pipeline/run_aws_pipeline.ipynb | 342 +- .../transform_h5_container/gen_ref.py | 11 +- .../pipeline/transform_h5_container/hello.py | 3 - .../transform_h5_container/transfer.py | 1 - .../transform_h5_container/transform.py | 15 +- 21 files changed, 113 insertions(+), 5254 deletions(-) delete mode 100644 azure/az_cli_guide.txt delete mode 100644 azure/blob_access.ipynb create mode 100644 azure/documentation/az_cli_guide.md delete mode 100644 azure/examples/Azure Cloud Costs.docx delete mode 100644 azure/examples/sup3rcc.ipynb delete mode 100644 azure/examples/wtk.ipynb rename azure/pipeline/{blob_access.ipynb => blob_access_example.ipynb} (92%) rename azure/{ => pipeline}/hpc_migration.ipynb (96%) delete mode 100644 azure/pipeline/transform_h5_container/hello.py diff --git a/LICENSE b/LICENSE index d716797..da7e522 100644 --- a/LICENSE +++ b/LICENSE @@ -1,6 +1,6 @@ BSD 3-Clause License -Copyright (c) 2023 Alliance for Sustainable Energy, LLC and Skye Analytics, Inc. +Copyright (c) 2024 Alliance for Sustainable Energy, LLC and Skye Analytics, Inc. All rights reserved. Redistribution and use in source and binary forms, with or without diff --git a/azure/az_cli_guide.txt b/azure/az_cli_guide.txt deleted file mode 100644 index 853c3d6..0000000 --- a/azure/az_cli_guide.txt +++ /dev/null @@ -1,42 +0,0 @@ -# OEDI data exist as blobs in Azure. Blobs live in containers. Containers live in storage accounts. For most of our data, the storage account is 'nrel' and the container is 'oedi'. There is a directory structure within the container to organize different data sets. Currently, the datasets present are 'PR100', 'pv-rooftop', and 'sup3rcc'. NSRDB lives in the 'nrel' storage account but in a different container called 'nrel-nsrdb'. - -# In order to access data from the command line, you will need to obtain a temporary SAS token from the planetary computer. You can then use that token as an argument for any commands you make with the CLI. CLI reference for interacting with blobs: https://learn.microsoft.com/en-us/cli/azure/storage/blob?view=azure-cli-latest#az-storage-blob-download - -# Finally, if the goal is to move large amounts of data from blob storage to S3 or local, then the best tool is azcopy: https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10 - -# Obtain a planetary computer temporary access token - -curl https://planetarycomputer.microsoft.com/api/sas/v1/token/nrel/oedi > sas.json - -# View a list of blobs in the PR100 dataset -az storage blob list --account-name nrel --container-name oedi --output table --prefix PR100 --sas-token "" - -# Download a blob from the PR100 dataset -az storage blob download --account-name nrel --container-name oedi --name PR100/Infrastructure/setbacks_runway.parquet --file setbacks_runway.parquet --sas-token "" - - -az storage blob list --account-name nrel --container-name nrel-nsrdb --output table --sas-token "sv=2020-08-04&si=nrel-nsrdb-ro&sr=c&sig=H8GUesZmOXzMomMWdrnXQv2ZPI09hANqHIcVrP7Ejl0%3D" - -"sv=2019-12-12&si=oedi-ro&sr=c&sig=uslpLxKf3%2Foeu79ufIHbJkpI%2FTWDH3lblJMa5KQRPmM%3D" - - -curl https://planetarycomputer.microsoft.com/api/sas/v1/token/nrel/oedi | az storage blob list --account-name nrel --container-name oedi --output table --prefix PR100 - -az storage blob list --account-name nrel --container-name oedi --output table --prefix pv-rooftop --sas-token "st=2023-05-22T16%3A29%3A14Z&se=2023-05-23T17%3A14%3A15Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2023-05-23T16%3A29%3A13Z&ske=2023-05-30T16%3A29%3A13Z&sks=b&skv=2021-06-08&sig=PVT8qk9fOmV1Mv8DCM8QUoR7eVxillRZMi8q1K89%2Bg0%3D" - -# Download a blob from PR100 - -az storage blob download --account-name nrel --container-name oedi --name PR100/Infrastructure/setbacks_runway.parquet --file setbacks_runway.parquet --sas-token "st=2023-05-22T16%3A29%3A14Z&se=2023-05-23T17%3A14%3A15Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2023-05-23T16%3A29%3A13Z&ske=2023-05-30T16%3A29%3A13Z&sks=b&skv=2021-06-08&sig=PVT8qk9fOmV1Mv8DCM8QUoR7eVxillRZMi8q1K89%2Bg0%3D" - -# List blobs in nrel-nsrdb (Note that you need to obtain a different SAS token since it's a different container) - -az storage blob list --account-name nrel --container-name nrel-nsrdb --output table --sas-token "st=2023-05-22T16%3A44%3A49Z&se=2023-05-23T17%3A29%3A50Z&sp=rl&sv=2021-06-08&sr=c&skoid=c85c15d6-d1ae-42d4-af60-e2ca0f81359b&sktid=72f988bf-86f1-41af-91ab-2d7cd011db47&skt=2023-05-23T16%3A44%3A48Z&ske=2023-05-30T16%3A44%3A48Z&sks=b&skv=2021-06-08&sig=Sip4D9k3rFo/SeCcGxp5I11X6hL/H2AWt9bREQWZAlY%3D" - - - - -az storage blob list --account-name nrel --container-name oedi --prefix PR100 --sas-token 'sv=2020-08-04&si=oedi-ro&sr=c&sig=O%2BQvKRV9uYuK36WzVRoCJdFO%2BRifXO8aIGqbS%2F3llPs%3D' - -az storage blob list --output table --account-name nrel --container-name nrel-nsrdb --sas-token 'sv=2020-08-04&si=nrel-nsrdb-ro&sr=c&sig=H8GUesZmOXzMomMWdrnXQv2ZPI09hANqHIcVrP7Ejl0%3D' - -az storage blob list --account-name nrel --container-name oedi --prefix PR100 --sas-token 'sv=2020-08-04&si=nrel-nsrdb-ro&sr=c&sig=H8GUesZmOXzMomMWdrnXQv2ZPI09hANqHIcVrP7Ejl0%3D' \ No newline at end of file diff --git a/azure/blob_access.ipynb b/azure/blob_access.ipynb deleted file mode 100644 index 7498145..0000000 --- a/azure/blob_access.ipynb +++ /dev/null @@ -1,2756 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "wtk/Great_Lakes/2000/Great_Lakes_2000_0m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_100m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_10m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_120m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_140m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_160m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_180m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_200m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_20m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_220m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_240m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_260m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_280m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_2m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_300m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_400m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_40m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_500m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_60m.h5\n", - "wtk/Great_Lakes/2000/Great_Lakes_2000_80m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_0m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_100m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_10m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_120m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_140m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_160m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_180m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_200m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_20m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_220m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_240m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_260m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_280m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_2m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_300m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_400m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_40m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_500m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_60m.h5\n", - "wtk/Great_Lakes/2001/Great_Lakes_2001_80m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_0m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_100m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_10m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_120m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_140m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_160m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_180m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_200m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_20m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_220m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_240m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_260m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_280m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_2m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_300m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_400m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_40m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_500m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_60m.h5\n", - "wtk/Great_Lakes/2002/Great_Lakes_2002_80m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_0m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_100m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_10m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_120m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_140m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_160m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_180m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_200m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_20m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_220m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_240m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_260m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_280m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_2m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_300m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_400m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_40m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_500m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_60m.h5\n", - "wtk/Great_Lakes/2003/Great_Lakes_2003_80m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_0m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_100m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_10m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_120m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_140m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_160m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_180m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_200m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_20m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_220m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_240m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_260m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_280m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_2m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_300m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_400m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_40m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_500m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_60m.h5\n", - "wtk/Great_Lakes/2004/Great_Lakes_2004_80m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_0m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_100m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_10m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_120m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_140m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_160m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_180m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_200m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_20m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_220m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_240m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_260m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_280m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_2m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_300m.h5\n", - "wtk/Great_Lakes/2005/Great_Lakes_2005_400m.h5\n", - 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"wtk/conus/v1.0.0/wtk_conus_2012.h5\n", - "wtk/conus/v1.0.0/wtk_conus_2013.h5\n", - "wtk/conus/v1.1.0/2014/wtk_conus_2014_0m.h5\n", - "wtk/conus/v1.1.0/2014/wtk_conus_2014_100m.h5\n", - "wtk/conus/v1.1.0/2014/wtk_conus_2014_10m.h5\n", - "wtk/conus/v1.1.0/2014/wtk_conus_2014_120m.h5\n", - "wtk/conus/v1.1.0/2014/wtk_conus_2014_140m.h5\n", - "wtk/conus/v1.1.0/2014/wtk_conus_2014_160m.h5\n", - "wtk/conus/v1.1.0/2014/wtk_conus_2014_200m.h5\n", - "wtk/conus/v1.1.0/2014/wtk_conus_2014_2m.h5\n", - "wtk/conus/v1.1.0/2014/wtk_conus_2014_40m.h5\n", - "wtk/conus/v1.1.0/2014/wtk_conus_2014_60m.h5\n", - "wtk/conus/v1.1.0/2014/wtk_conus_2014_80m.h5\n", - "wtk/conus/v1.1.0/kerchunk_5min_ref.json\n", - "wtk/conus/v1.1.0/kerchunk_hourly_ref.json\n", - "wtk/conus/v1.1.0/wtk_conus_2014.h5\n", - "wtk/gulf_of_mexico/kerchunk_hourly_ref.json\n", - "wtk/gulf_of_mexico/yearly/gulf_2000.h5\n", - "wtk/gulf_of_mexico/yearly/gulf_2001.h5\n", - "wtk/gulf_of_mexico/yearly/gulf_2002.h5\n", - "wtk/gulf_of_mexico/yearly/gulf_2003.h5\n", - "wtk/gulf_of_mexico/yearly/gulf_2004.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2000_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2001_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2002_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2003_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2004_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2005_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2006_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2007_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2008_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2009_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2010_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2011_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2012_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2013_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2014_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2015_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2016_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2017_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2018_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2019_hr.h5\n", - "wtk/gulf_of_mexico/yearly_hr/gulf_2020_hr.h5\n", - "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_0m.h5\n", - "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_100m.h5\n", - "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_10m.h5\n", - "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_120m.h5\n", - "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_140m.h5\n", - "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_160m.h5\n", - "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_200m.h5\n", - "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_2m.h5\n", - "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_40m.h5\n", - "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_60m.h5\n", - "wtk/mexico/v1.0.0/2007/wtk_mexico_2007_80m.h5\n", - "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_0m.h5\n", - "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_100m.h5\n", - "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_10m.h5\n", - "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_120m.h5\n", - "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_140m.h5\n", - "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_160m.h5\n", - "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_200m.h5\n", - "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_2m.h5\n", - "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_40m.h5\n", - "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_60m.h5\n", - "wtk/mexico/v1.0.0/2008/wtk_mexico_2008_80m.h5\n", - "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_0m.h5\n", - "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_100m.h5\n", - "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_10m.h5\n", - "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_120m.h5\n", - "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_140m.h5\n", - "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_160m.h5\n", - "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_200m.h5\n", - "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_2m.h5\n", - "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_40m.h5\n", - "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_60m.h5\n", - "wtk/mexico/v1.0.0/2009/wtk_mexico_2009_80m.h5\n", - "wtk/mexico/v1.0.0/2010/wtk_mexico_2010_0m.h5\n", - "wtk/mexico/v1.0.0/2010/wtk_mexico_2010_100m.h5\n", - "wtk/mexico/v1.0.0/2010/wtk_mexico_2010_10m.h5\n", - "wtk/mexico/v1.0.0/2010/wtk_mexico_2010_120m.h5\n" - ] - }, - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[2], line 6\u001b[0m\n\u001b[1;32m 4\u001b[0m sas \u001b[39m=\u001b[39m load_oedi_sas()\n\u001b[1;32m 5\u001b[0m client \u001b[39m=\u001b[39m ContainerClient\u001b[39m.\u001b[39mfrom_container_url(\u001b[39mf\u001b[39m\u001b[39m'\u001b[39m\u001b[39mhttps://nrel.blob.core.windows.net/oedi?\u001b[39m\u001b[39m{\u001b[39;00msas\u001b[39m}\u001b[39;00m\u001b[39m'\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m \u001b[39mfor\u001b[39;00m blob \u001b[39min\u001b[39;00m client\u001b[39m.\u001b[39mlist_blobs():\n\u001b[1;32m 7\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mwtk/\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m blob\u001b[39m.\u001b[39mname:\n\u001b[1;32m 8\u001b[0m \u001b[39mprint\u001b[39m(blob\u001b[39m.\u001b[39mname)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/paging.py:123\u001b[0m, in \u001b[0;36mItemPaged.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 121\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_page_iterator \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 122\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_page_iterator \u001b[39m=\u001b[39m itertools\u001b[39m.\u001b[39mchain\u001b[39m.\u001b[39mfrom_iterable(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mby_page())\n\u001b[0;32m--> 123\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mnext\u001b[39;49m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_page_iterator)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/paging.py:75\u001b[0m, in \u001b[0;36mPageIterator.__next__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 73\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mStopIteration\u001b[39;00m(\u001b[39m\"\u001b[39m\u001b[39mEnd of paging\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 74\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 75\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_get_next(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mcontinuation_token)\n\u001b[1;32m 76\u001b[0m \u001b[39mexcept\u001b[39;00m AzureError \u001b[39mas\u001b[39;00m error:\n\u001b[1;32m 77\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m error\u001b[39m.\u001b[39mcontinuation_token:\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/storage/blob/_list_blobs_helper.py:93\u001b[0m, in \u001b[0;36mBlobPropertiesPaged._get_next_cb\u001b[0;34m(self, continuation_token)\u001b[0m\n\u001b[1;32m 91\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_get_next_cb\u001b[39m(\u001b[39mself\u001b[39m, continuation_token):\n\u001b[1;32m 92\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 93\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_command(\n\u001b[1;32m 94\u001b[0m prefix\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprefix,\n\u001b[1;32m 95\u001b[0m marker\u001b[39m=\u001b[39;49mcontinuation_token \u001b[39mor\u001b[39;49;00m \u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m 96\u001b[0m maxresults\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mresults_per_page,\n\u001b[1;32m 97\u001b[0m \u001b[39mcls\u001b[39;49m\u001b[39m=\u001b[39;49mreturn_context_and_deserialized,\n\u001b[1;32m 98\u001b[0m use_location\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mlocation_mode)\n\u001b[1;32m 99\u001b[0m \u001b[39mexcept\u001b[39;00m HttpResponseError \u001b[39mas\u001b[39;00m error:\n\u001b[1;32m 100\u001b[0m process_storage_error(error)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/tracing/decorator.py:78\u001b[0m, in \u001b[0;36mdistributed_trace..decorator..wrapper_use_tracer\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 76\u001b[0m span_impl_type \u001b[39m=\u001b[39m settings\u001b[39m.\u001b[39mtracing_implementation()\n\u001b[1;32m 77\u001b[0m \u001b[39mif\u001b[39;00m span_impl_type \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m---> 78\u001b[0m \u001b[39mreturn\u001b[39;00m func(\u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 80\u001b[0m \u001b[39m# Merge span is parameter is set, but only if no explicit parent are passed\u001b[39;00m\n\u001b[1;32m 81\u001b[0m \u001b[39mif\u001b[39;00m merge_span \u001b[39mand\u001b[39;00m \u001b[39mnot\u001b[39;00m passed_in_parent:\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/storage/blob/_generated/operations/_container_operations.py:2488\u001b[0m, in \u001b[0;36mContainerOperations.list_blob_flat_segment\u001b[0;34m(self, prefix, marker, maxresults, include, timeout, request_id_parameter, **kwargs)\u001b[0m\n\u001b[1;32m 2485\u001b[0m request \u001b[39m=\u001b[39m _convert_request(request)\n\u001b[1;32m 2486\u001b[0m request\u001b[39m.\u001b[39murl \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_client\u001b[39m.\u001b[39mformat_url(request\u001b[39m.\u001b[39murl) \u001b[39m# type: ignore\u001b[39;00m\n\u001b[0;32m-> 2488\u001b[0m pipeline_response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_client\u001b[39m.\u001b[39;49m_pipeline\u001b[39m.\u001b[39;49mrun( \u001b[39m# type: ignore # pylint: disable=protected-access\u001b[39;49;00m\n\u001b[1;32m 2489\u001b[0m request, stream\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m 2490\u001b[0m )\n\u001b[1;32m 2492\u001b[0m response \u001b[39m=\u001b[39m pipeline_response\u001b[39m.\u001b[39mhttp_response\n\u001b[1;32m 2494\u001b[0m \u001b[39mif\u001b[39;00m response\u001b[39m.\u001b[39mstatus_code \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m [\u001b[39m200\u001b[39m]:\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:230\u001b[0m, in \u001b[0;36mPipeline.run\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 228\u001b[0m pipeline_request: PipelineRequest[HTTPRequestType] \u001b[39m=\u001b[39m PipelineRequest(request, context)\n\u001b[1;32m 229\u001b[0m first_node \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_impl_policies[\u001b[39m0\u001b[39m] \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_impl_policies \u001b[39melse\u001b[39;00m _TransportRunner(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_transport)\n\u001b[0;32m--> 230\u001b[0m \u001b[39mreturn\u001b[39;00m first_node\u001b[39m.\u001b[39;49msend(pipeline_request)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", - " \u001b[0;31m[... skipping similar frames: _SansIOHTTPPolicyRunner.send at line 86 (2 times)]\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/policies/_redirect.py:197\u001b[0m, in \u001b[0;36mRedirectPolicy.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 195\u001b[0m original_domain \u001b[39m=\u001b[39m get_domain(request\u001b[39m.\u001b[39mhttp_request\u001b[39m.\u001b[39murl) \u001b[39mif\u001b[39;00m redirect_settings[\u001b[39m\"\u001b[39m\u001b[39mallow\u001b[39m\u001b[39m\"\u001b[39m] \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[1;32m 196\u001b[0m \u001b[39mwhile\u001b[39;00m retryable:\n\u001b[0;32m--> 197\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 198\u001b[0m redirect_location \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mget_redirect_location(response)\n\u001b[1;32m 199\u001b[0m \u001b[39mif\u001b[39;00m redirect_location \u001b[39mand\u001b[39;00m redirect_settings[\u001b[39m\"\u001b[39m\u001b[39mallow\u001b[39m\u001b[39m\"\u001b[39m]:\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/storage/blob/_shared/policies.py:520\u001b[0m, in \u001b[0;36mStorageRetryPolicy.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 518\u001b[0m \u001b[39mwhile\u001b[39;00m retries_remaining:\n\u001b[1;32m 519\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 520\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 521\u001b[0m \u001b[39mif\u001b[39;00m is_retry(response, retry_settings[\u001b[39m'\u001b[39m\u001b[39mmode\u001b[39m\u001b[39m'\u001b[39m]):\n\u001b[1;32m 522\u001b[0m retries_remaining \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mincrement(\n\u001b[1;32m 523\u001b[0m retry_settings,\n\u001b[1;32m 524\u001b[0m request\u001b[39m=\u001b[39mrequest\u001b[39m.\u001b[39mhttp_request,\n\u001b[1;32m 525\u001b[0m response\u001b[39m=\u001b[39mresponse\u001b[39m.\u001b[39mhttp_response)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/storage/blob/_shared/policies.py:313\u001b[0m, in \u001b[0;36mStorageResponseHook.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 308\u001b[0m upload_stream_current \u001b[39m=\u001b[39m request\u001b[39m.\u001b[39mcontext\u001b[39m.\u001b[39moptions\u001b[39m.\u001b[39mpop(\u001b[39m'\u001b[39m\u001b[39mupload_stream_current\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m 310\u001b[0m response_callback \u001b[39m=\u001b[39m request\u001b[39m.\u001b[39mcontext\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mresponse_callback\u001b[39m\u001b[39m'\u001b[39m) \u001b[39mor\u001b[39;00m \\\n\u001b[1;32m 311\u001b[0m request\u001b[39m.\u001b[39mcontext\u001b[39m.\u001b[39moptions\u001b[39m.\u001b[39mpop(\u001b[39m'\u001b[39m\u001b[39mraw_response_hook\u001b[39m\u001b[39m'\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_response_callback)\n\u001b[0;32m--> 313\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 315\u001b[0m will_retry \u001b[39m=\u001b[39m is_retry(response, request\u001b[39m.\u001b[39mcontext\u001b[39m.\u001b[39moptions\u001b[39m.\u001b[39mget(\u001b[39m'\u001b[39m\u001b[39mmode\u001b[39m\u001b[39m'\u001b[39m))\n\u001b[1;32m 316\u001b[0m \u001b[39m# Auth error could come from Bearer challenge, in which case this request will be made again\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:86\u001b[0m, in \u001b[0;36m_SansIOHTTPPolicyRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 84\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_request, request)\n\u001b[1;32m 85\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m---> 86\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mnext\u001b[39m.\u001b[39;49msend(request)\n\u001b[1;32m 87\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m: \u001b[39m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m 88\u001b[0m _await_result(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_policy\u001b[39m.\u001b[39mon_exception, request)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/_base.py:119\u001b[0m, in \u001b[0;36m_TransportRunner.send\u001b[0;34m(self, request)\u001b[0m\n\u001b[1;32m 109\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"HTTP transport send method.\u001b[39;00m\n\u001b[1;32m 110\u001b[0m \n\u001b[1;32m 111\u001b[0m \u001b[39m:param request: The PipelineRequest object.\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 114\u001b[0m \u001b[39m:rtype: ~azure.core.pipeline.PipelineResponse\u001b[39;00m\n\u001b[1;32m 115\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m 116\u001b[0m cleanup_kwargs_for_transport(request\u001b[39m.\u001b[39mcontext\u001b[39m.\u001b[39moptions)\n\u001b[1;32m 117\u001b[0m \u001b[39mreturn\u001b[39;00m PipelineResponse(\n\u001b[1;32m 118\u001b[0m request\u001b[39m.\u001b[39mhttp_request,\n\u001b[0;32m--> 119\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sender\u001b[39m.\u001b[39;49msend(request\u001b[39m.\u001b[39;49mhttp_request, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mrequest\u001b[39m.\u001b[39;49mcontext\u001b[39m.\u001b[39;49moptions),\n\u001b[1;32m 120\u001b[0m context\u001b[39m=\u001b[39mrequest\u001b[39m.\u001b[39mcontext,\n\u001b[1;32m 121\u001b[0m )\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/azure/core/pipeline/transport/_requests_basic.py:339\u001b[0m, in \u001b[0;36mRequestsTransport.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 337\u001b[0m read_timeout \u001b[39m=\u001b[39m kwargs\u001b[39m.\u001b[39mpop(\u001b[39m\"\u001b[39m\u001b[39mread_timeout\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconnection_config\u001b[39m.\u001b[39mread_timeout)\n\u001b[1;32m 338\u001b[0m timeout \u001b[39m=\u001b[39m (connection_timeout, read_timeout)\n\u001b[0;32m--> 339\u001b[0m response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msession\u001b[39m.\u001b[39;49mrequest( \u001b[39m# type: ignore\u001b[39;49;00m\n\u001b[1;32m 340\u001b[0m request\u001b[39m.\u001b[39;49mmethod,\n\u001b[1;32m 341\u001b[0m request\u001b[39m.\u001b[39;49murl,\n\u001b[1;32m 342\u001b[0m headers\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mheaders,\n\u001b[1;32m 343\u001b[0m data\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mdata,\n\u001b[1;32m 344\u001b[0m files\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mfiles,\n\u001b[1;32m 345\u001b[0m verify\u001b[39m=\u001b[39;49mkwargs\u001b[39m.\u001b[39;49mpop(\u001b[39m\"\u001b[39;49m\u001b[39mconnection_verify\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnection_config\u001b[39m.\u001b[39;49mverify),\n\u001b[1;32m 346\u001b[0m timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[1;32m 347\u001b[0m cert\u001b[39m=\u001b[39;49mkwargs\u001b[39m.\u001b[39;49mpop(\u001b[39m\"\u001b[39;49m\u001b[39mconnection_cert\u001b[39;49m\u001b[39m\"\u001b[39;49m, \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconnection_config\u001b[39m.\u001b[39;49mcert),\n\u001b[1;32m 348\u001b[0m allow_redirects\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 349\u001b[0m \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs\n\u001b[1;32m 350\u001b[0m )\n\u001b[1;32m 351\u001b[0m response\u001b[39m.\u001b[39mraw\u001b[39m.\u001b[39menforce_content_length \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m 353\u001b[0m \u001b[39mexcept\u001b[39;00m (\n\u001b[1;32m 354\u001b[0m NewConnectionError,\n\u001b[1;32m 355\u001b[0m ConnectTimeoutError,\n\u001b[1;32m 356\u001b[0m ) \u001b[39mas\u001b[39;00m err:\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/requests/sessions.py:589\u001b[0m, in \u001b[0;36mSession.request\u001b[0;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001b[0m\n\u001b[1;32m 584\u001b[0m send_kwargs \u001b[39m=\u001b[39m {\n\u001b[1;32m 585\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtimeout\u001b[39m\u001b[39m\"\u001b[39m: timeout,\n\u001b[1;32m 586\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mallow_redirects\u001b[39m\u001b[39m\"\u001b[39m: allow_redirects,\n\u001b[1;32m 587\u001b[0m }\n\u001b[1;32m 588\u001b[0m send_kwargs\u001b[39m.\u001b[39mupdate(settings)\n\u001b[0;32m--> 589\u001b[0m resp \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49msend(prep, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49msend_kwargs)\n\u001b[1;32m 591\u001b[0m \u001b[39mreturn\u001b[39;00m resp\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/requests/sessions.py:703\u001b[0m, in \u001b[0;36mSession.send\u001b[0;34m(self, request, **kwargs)\u001b[0m\n\u001b[1;32m 700\u001b[0m start \u001b[39m=\u001b[39m preferred_clock()\n\u001b[1;32m 702\u001b[0m \u001b[39m# Send the request\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m r \u001b[39m=\u001b[39m adapter\u001b[39m.\u001b[39;49msend(request, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m 705\u001b[0m \u001b[39m# Total elapsed time of the request (approximately)\u001b[39;00m\n\u001b[1;32m 706\u001b[0m elapsed \u001b[39m=\u001b[39m preferred_clock() \u001b[39m-\u001b[39m start\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/requests/adapters.py:486\u001b[0m, in \u001b[0;36mHTTPAdapter.send\u001b[0;34m(self, request, stream, timeout, verify, cert, proxies)\u001b[0m\n\u001b[1;32m 483\u001b[0m timeout \u001b[39m=\u001b[39m TimeoutSauce(connect\u001b[39m=\u001b[39mtimeout, read\u001b[39m=\u001b[39mtimeout)\n\u001b[1;32m 485\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 486\u001b[0m resp \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49murlopen(\n\u001b[1;32m 487\u001b[0m method\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mmethod,\n\u001b[1;32m 488\u001b[0m url\u001b[39m=\u001b[39;49murl,\n\u001b[1;32m 489\u001b[0m body\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mbody,\n\u001b[1;32m 490\u001b[0m headers\u001b[39m=\u001b[39;49mrequest\u001b[39m.\u001b[39;49mheaders,\n\u001b[1;32m 491\u001b[0m redirect\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 492\u001b[0m assert_same_host\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 493\u001b[0m preload_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 494\u001b[0m decode_content\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m 495\u001b[0m retries\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mmax_retries,\n\u001b[1;32m 496\u001b[0m timeout\u001b[39m=\u001b[39;49mtimeout,\n\u001b[1;32m 497\u001b[0m chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[1;32m 498\u001b[0m )\n\u001b[1;32m 500\u001b[0m \u001b[39mexcept\u001b[39;00m (ProtocolError, \u001b[39mOSError\u001b[39;00m) \u001b[39mas\u001b[39;00m err:\n\u001b[1;32m 501\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mConnectionError\u001b[39;00m(err, request\u001b[39m=\u001b[39mrequest)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/urllib3/connectionpool.py:703\u001b[0m, in \u001b[0;36mHTTPConnectionPool.urlopen\u001b[0;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, **response_kw)\u001b[0m\n\u001b[1;32m 700\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_prepare_proxy(conn)\n\u001b[1;32m 702\u001b[0m \u001b[39m# Make the request on the httplib connection object.\u001b[39;00m\n\u001b[0;32m--> 703\u001b[0m httplib_response \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_make_request(\n\u001b[1;32m 704\u001b[0m conn,\n\u001b[1;32m 705\u001b[0m method,\n\u001b[1;32m 706\u001b[0m url,\n\u001b[1;32m 707\u001b[0m timeout\u001b[39m=\u001b[39;49mtimeout_obj,\n\u001b[1;32m 708\u001b[0m body\u001b[39m=\u001b[39;49mbody,\n\u001b[1;32m 709\u001b[0m headers\u001b[39m=\u001b[39;49mheaders,\n\u001b[1;32m 710\u001b[0m chunked\u001b[39m=\u001b[39;49mchunked,\n\u001b[1;32m 711\u001b[0m )\n\u001b[1;32m 713\u001b[0m \u001b[39m# If we're going to release the connection in ``finally:``, then\u001b[39;00m\n\u001b[1;32m 714\u001b[0m \u001b[39m# the response doesn't need to know about the connection. Otherwise\u001b[39;00m\n\u001b[1;32m 715\u001b[0m \u001b[39m# it will also try to release it and we'll have a double-release\u001b[39;00m\n\u001b[1;32m 716\u001b[0m \u001b[39m# mess.\u001b[39;00m\n\u001b[1;32m 717\u001b[0m response_conn \u001b[39m=\u001b[39m conn \u001b[39mif\u001b[39;00m \u001b[39mnot\u001b[39;00m release_conn \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/urllib3/connectionpool.py:449\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 444\u001b[0m httplib_response \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39mgetresponse()\n\u001b[1;32m 445\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 446\u001b[0m \u001b[39m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[1;32m 447\u001b[0m \u001b[39m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \u001b[39m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[0;32m--> 449\u001b[0m six\u001b[39m.\u001b[39;49mraise_from(e, \u001b[39mNone\u001b[39;49;00m)\n\u001b[1;32m 450\u001b[0m \u001b[39mexcept\u001b[39;00m (SocketTimeout, BaseSSLError, SocketError) \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 451\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_raise_timeout(err\u001b[39m=\u001b[39me, url\u001b[39m=\u001b[39murl, timeout_value\u001b[39m=\u001b[39mread_timeout)\n", - "File \u001b[0;32m:3\u001b[0m, in \u001b[0;36mraise_from\u001b[0;34m(value, from_value)\u001b[0m\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/site-packages/urllib3/connectionpool.py:444\u001b[0m, in \u001b[0;36mHTTPConnectionPool._make_request\u001b[0;34m(self, conn, method, url, timeout, chunked, **httplib_request_kw)\u001b[0m\n\u001b[1;32m 441\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mTypeError\u001b[39;00m:\n\u001b[1;32m 442\u001b[0m \u001b[39m# Python 3\u001b[39;00m\n\u001b[1;32m 443\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 444\u001b[0m httplib_response \u001b[39m=\u001b[39m conn\u001b[39m.\u001b[39;49mgetresponse()\n\u001b[1;32m 445\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mBaseException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[1;32m 446\u001b[0m \u001b[39m# Remove the TypeError from the exception chain in\u001b[39;00m\n\u001b[1;32m 447\u001b[0m \u001b[39m# Python 3 (including for exceptions like SystemExit).\u001b[39;00m\n\u001b[1;32m 448\u001b[0m \u001b[39m# Otherwise it looks like a bug in the code.\u001b[39;00m\n\u001b[1;32m 449\u001b[0m six\u001b[39m.\u001b[39mraise_from(e, \u001b[39mNone\u001b[39;00m)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/http/client.py:1375\u001b[0m, in \u001b[0;36mHTTPConnection.getresponse\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1373\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1374\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1375\u001b[0m response\u001b[39m.\u001b[39;49mbegin()\n\u001b[1;32m 1376\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mConnectionError\u001b[39;00m:\n\u001b[1;32m 1377\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mclose()\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/http/client.py:318\u001b[0m, in \u001b[0;36mHTTPResponse.begin\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 316\u001b[0m \u001b[39m# read until we get a non-100 response\u001b[39;00m\n\u001b[1;32m 317\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[0;32m--> 318\u001b[0m version, status, reason \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_read_status()\n\u001b[1;32m 319\u001b[0m \u001b[39mif\u001b[39;00m status \u001b[39m!=\u001b[39m CONTINUE:\n\u001b[1;32m 320\u001b[0m \u001b[39mbreak\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/http/client.py:279\u001b[0m, in \u001b[0;36mHTTPResponse._read_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 278\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_read_status\u001b[39m(\u001b[39mself\u001b[39m):\n\u001b[0;32m--> 279\u001b[0m line \u001b[39m=\u001b[39m \u001b[39mstr\u001b[39m(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mfp\u001b[39m.\u001b[39;49mreadline(_MAXLINE \u001b[39m+\u001b[39;49m \u001b[39m1\u001b[39;49m), \u001b[39m\"\u001b[39m\u001b[39miso-8859-1\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m 280\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(line) \u001b[39m>\u001b[39m _MAXLINE:\n\u001b[1;32m 281\u001b[0m \u001b[39mraise\u001b[39;00m LineTooLong(\u001b[39m\"\u001b[39m\u001b[39mstatus line\u001b[39m\u001b[39m\"\u001b[39m)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/socket.py:705\u001b[0m, in \u001b[0;36mSocketIO.readinto\u001b[0;34m(self, b)\u001b[0m\n\u001b[1;32m 703\u001b[0m \u001b[39mwhile\u001b[39;00m \u001b[39mTrue\u001b[39;00m:\n\u001b[1;32m 704\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 705\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sock\u001b[39m.\u001b[39;49mrecv_into(b)\n\u001b[1;32m 706\u001b[0m \u001b[39mexcept\u001b[39;00m timeout:\n\u001b[1;32m 707\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_timeout_occurred \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/ssl.py:1274\u001b[0m, in \u001b[0;36mSSLSocket.recv_into\u001b[0;34m(self, buffer, nbytes, flags)\u001b[0m\n\u001b[1;32m 1270\u001b[0m \u001b[39mif\u001b[39;00m flags \u001b[39m!=\u001b[39m \u001b[39m0\u001b[39m:\n\u001b[1;32m 1271\u001b[0m \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[1;32m 1272\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mnon-zero flags not allowed in calls to recv_into() on \u001b[39m\u001b[39m%s\u001b[39;00m\u001b[39m\"\u001b[39m \u001b[39m%\u001b[39m\n\u001b[1;32m 1273\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m\u001b[39m__class__\u001b[39m)\n\u001b[0;32m-> 1274\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mread(nbytes, buffer)\n\u001b[1;32m 1275\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 1276\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39m()\u001b[39m.\u001b[39mrecv_into(buffer, nbytes, flags)\n", - "File \u001b[0;32m~/miniconda3/envs/oedi-azure-dev/lib/python3.10/ssl.py:1130\u001b[0m, in \u001b[0;36mSSLSocket.read\u001b[0;34m(self, len, buffer)\u001b[0m\n\u001b[1;32m 1128\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m 1129\u001b[0m \u001b[39mif\u001b[39;00m buffer \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1130\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_sslobj\u001b[39m.\u001b[39;49mread(\u001b[39mlen\u001b[39;49m, buffer)\n\u001b[1;32m 1131\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 1132\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_sslobj\u001b[39m.\u001b[39mread(\u001b[39mlen\u001b[39m)\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "from azure.storage.blob import ContainerClient\n", - "from pipeline.etl_tools import load_oedi_sas\n", - "\n", - "sas = load_oedi_sas()\n", - "client = ContainerClient.from_container_url(f'https://nrel.blob.core.windows.net/oedi?{sas}')\n", - "for blob in client.list_blobs():\n", - " if \"wtk/\" in blob.name:\n", - " print(blob.name)\n", - " #client.delete_blob(blob)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2001.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2002.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2003.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2004.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2005.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2006.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2007.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2008.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2009.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2010.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2011.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2012.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2013.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2014.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2015.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2016.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2017.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2018.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2019.h5',\n", - " 'kerchunk-staging/wtk/pr100/hourly/puerto_rico_wind_hourly_2020.h5']" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import s3fs\n", - "\n", - "s3 = s3fs.S3FileSystem()\n", - "files = s3.glob('kerchunk-staging/wtk/pr100/hourly/*.h5')\n", - "for file in files:\n", - " with s3.open(file) as f:\n", - " az_path = file.replace('kerchunk-staging', 'oedi')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "blob_client = blob_service_client.get_blob_client(container=container_name, blob=local_file_name)\n", - "\n", - "client.get_blob_client()\n", - "# Upload the created file\n", - "with open(file=upload_file_path, mode=\"rb\") as data:\n", - " blob_client.upload_blob(data)" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "v1.0.0/Alaska/Alaska_wave_1979.h5\n", - "2021-10-26 18:49:07+00:00\n" - ] - } - ], - "source": [ - "from azure.storage.blob import ContainerClient\n", - "from pipeline.etl_tools import load_oedi_sas\n", - "\n", - "sas = load_oedi_sas()\n", - "client = ContainerClient.from_container_url('https://nrel.blob.core.windows.net/nrel-wave?sv=2020-08-04&si=nrel-wave-ro&sr=c&sig=VACZ6rXzsE7l2eFQMvYMQCgy8dT23%2Bs1eDPYa%2FBCnEM%3D')\n", - "for blob in client.list_blobs():\n", - " print(blob.name)\n", - " print(blob.last_modified)\n", - " break\n", - " #client.delete_blob(blob)" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "33546.735149671\n" - ] - } - ], - "source": [ - "size = 0\n", - "for blob in client.list_blobs():\n", - " size += blob.size\n", - "print(size * 10 ** -9)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "v1.0.0/Alaska/Alaska_wave_1979.h5\n", - "v1.0.0/Alaska/Alaska_wave_1980.h5\n", - "v1.0.0/Alaska/Alaska_wave_1981.h5\n", - "v1.0.0/Alaska/Alaska_wave_1982.h5\n", - "v1.0.0/Alaska/Alaska_wave_1983.h5\n", - "v1.0.0/Alaska/Alaska_wave_1984.h5\n", - "v1.0.0/Alaska/Alaska_wave_1985.h5\n", - "v1.0.0/Alaska/Alaska_wave_1986.h5\n", - "v1.0.0/Alaska/Alaska_wave_1987.h5\n", - "v1.0.0/Alaska/Alaska_wave_1988.h5\n", - "v1.0.0/Alaska/Alaska_wave_1989.h5\n", - "v1.0.0/Alaska/Alaska_wave_1990.h5\n", - "v1.0.0/Alaska/Alaska_wave_1992.h5\n", - "v1.0.0/Alaska/Alaska_wave_1993.h5\n", - "v1.0.0/Alaska/Alaska_wave_1994.h5\n", - "v1.0.0/Alaska/Alaska_wave_1995.h5\n", - "v1.0.0/Alaska/Alaska_wave_1996.h5\n", - "v1.0.0/Alaska/Alaska_wave_1997.h5\n", - "v1.0.0/Alaska/Alaska_wave_1998.h5\n", - "v1.0.0/Alaska/Alaska_wave_1999.h5\n", - "v1.0.0/Alaska/Alaska_wave_2000.h5\n", - "v1.0.0/Alaska/Alaska_wave_2001.h5\n", - "v1.0.0/Alaska/Alaska_wave_2002.h5\n", - "v1.0.0/Alaska/Alaska_wave_2003.h5\n", - "v1.0.0/Alaska/Alaska_wave_2004.h5\n", - "v1.0.0/Alaska/Alaska_wave_2005.h5\n", - "v1.0.0/Alaska/Alaska_wave_2006.h5\n", - "v1.0.0/Alaska/Alaska_wave_2007.h5\n", - "v1.0.0/Alaska/Alaska_wave_2008.h5\n", - "v1.0.0/Alaska/Alaska_wave_2009.h5\n", - "v1.0.0/Alaska/Alaska_wave_2010.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1979.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1980.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1981.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1982.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1983.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1984.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1985.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1986.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1987.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1988.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1989.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1990.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1991.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1992.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1993.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1994.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1995.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1996.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1997.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1998.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_1999.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_2000.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_2001.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_2002.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_2003.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_2004.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_2005.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_2006.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_2007.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_2008.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_2009.h5\n", - "v1.0.0/Atlantic/Atlantic_wave_2010.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1979.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1980.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1981.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1982.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1983.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1984.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1985.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1986.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1987.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1988.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1989.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1990.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1991.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1992.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1993.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1994.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1995.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1996.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1997.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1998.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_1999.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_2000.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_2001.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_2002.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_2003.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_2004.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_2005.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_2006.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_2007.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_2008.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_2009.h5\n", - "v1.0.0/Hawaii/Hawaii_wave_2010.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1979.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1980.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1981.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1982.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1983.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1984.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1985.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1986.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1987.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1988.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1989.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1990.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1991.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1992.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1993.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1994.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1995.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1996.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1997.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1998.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_1999.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_2000.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_2001.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_2002.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_2003.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_2004.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_2005.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_2006.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_2007.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_2008.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_2009.h5\n", - "v1.0.0/West_Coast/West_Coast_wave_2010.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1979.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1980.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1981.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1982.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1983.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1984.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1985.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1986.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1987.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1988.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1989.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1990.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1991.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1992.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1993.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1994.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1995.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1996.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1997.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1998.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_1999.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2000.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2001.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2002.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2003.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2004.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2005.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2006.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2007.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2008.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2009.h5\n", - "v1.0.0/virtual_buoy/Atlantic/Atlantic_virtual_buoy_2010.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1979.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1980.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1981.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1982.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1983.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1984.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1985.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1986.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1987.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1988.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1989.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1990.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1991.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1992.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1993.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1994.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1995.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1996.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1997.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1998.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_1999.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2000.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2001.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2002.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2003.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2004.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2005.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2006.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2007.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2008.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2009.h5\n", - "v1.0.0/virtual_buoy/Hawaii/Hawaii_virtual_buoy_2010.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1979.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1980.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1981.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1982.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1983.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1984.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1985.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1986.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1987.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1988.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1989.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1990.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1991.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1992.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1993.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1994.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1995.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1996.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1997.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1998.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_1999.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2000.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2001.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2002.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2003.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2004.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2005.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2006.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2007.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2008.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2009.h5\n", - "v1.0.0/virtual_buoy/West_Coast/West_Coast_virtual_buoy_2010.h5\n" - ] - } - ], - "source": [ - "for blob in client.list_blobs():\n", - " print(blob.name)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".env", - "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": "3.10.12" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/azure/documentation/az_cli_guide.md b/azure/documentation/az_cli_guide.md new file mode 100644 index 0000000..5e153be --- /dev/null +++ b/azure/documentation/az_cli_guide.md @@ -0,0 +1,19 @@ +## Azure CLI Guide + +OEDI data exist as blobs in Azure. Blobs live in containers. Containers live in storage accounts. For most of our data, the storage account is 'nrel' and the container is 'oedi'. There is a directory structure within the container to organize different data sets. Currently, the datasets present are 'PR100', 'pv-rooftop' and part of 'sup3rcc'. NSRDB lives in the 'nrel' storage account but in a different container called 'nrel-nsrdb'. + +In order to access data from the command line, you will need to obtain a temporary SAS token from the planetary computer. You can then use that token as an argument for any commands you make with the CLI. CLI reference for interacting with blobs: https://learn.microsoft.com/en-us/cli/azure/storage/blob?view=azure-cli-latest#az-storage-blob-download + +Finally, if the goal is to move large amounts of data from blob storage to S3 or local, then the best tool is azcopy: https://learn.microsoft.com/en-us/azure/storage/common/storage-use-azcopy-v10 + +Obtain a planetary computer temporary access token: + +`curl https://planetarycomputer.microsoft.com/api/sas/v1/token/nrel/oedi > sas.json` + +View a list of blobs in the PR100 dataset: + +`az storage blob list --account-name nrel --container-name oedi --output table --prefix PR100 --sas-token ""` + +Download a blob from the PR100 dataset: + +`az storage blob download --account-name nrel --container-name oedi --name PR100/Infrastructure/setbacks_runway.parquet --file setbacks_runway.parquet --sas-token ""` diff --git a/azure/examples/Azure Cloud Costs.docx b/azure/examples/Azure Cloud Costs.docx deleted file mode 100644 index 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For example, the initial dataset \"sup3rcc_conus_mriesm20_ssp585_r1i1p1f1\" is downscaled from MRI ESM 2.0 for climate change scenario SSP5 8.5 and variant label r1i1p1f1. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as \"Sup3r GitHub Repo\"). The data includes both historical and future weather years, although the historical years represent the historical average climate, not the actual historical weather that we experienced.\n", - "\n", - "The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the *possible* future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.\n", - "\n", - "For more information about Sup3rcc, please refer to its OEDI catalogue entry: https://data.openei.org/submissions/5839\n", - "\n", - "This notebook will demonstrate how to access the Sup3rcc data located in Azure BLOB storage using kerchunk." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Requirements\n", - "\n", - "In order to run this notebook, please ensure you have the following packages installed:\n", - "\n", - "matplotlib==3.7.1 \n", - "numpy==1.24.3 \n", - "planetary_computer==0.5.1 \n", - "scipy==1.10.1 \n", - "ujson==5.7.0 \n", - "xarray==2023.4.2 " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Load kerchunk Reference File\n", - "\n", - "You do not need an Azure account to access public data. Instead, you can use the planetary-computer library to obain temporary credentials, as shown below. To access Supe3rcc, we need to load the kerchunk reference file, which is stored in the same container as the rest of the data. Depending on your network, this step may take several minutes as the reference file is a few hundred MB." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import planetary_computer\n", - "import ujson\n", - "\n", - "fs = planetary_computer.get_adlfs_filesystem('nrel', 'oedi')\n", - "with fs.open('oedi/sup3rcc/conus_mriesm20_ssp585_r1i1p1f1/kerchunk_reference.json', 'rb') as ref_file:\n", - " ref = ujson.load(ref_file)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Create xarray dataset\n", - "\n", - "Next we use the xarray package to read the reference file and give us an interface to access the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset>\n",
-       "Dimensions:              (latitude: 650, longitude: 1475, time_index: 175320)\n",
-       "Coordinates:\n",
-       "  * latitude             (latitude) float32 51.57 51.52 51.48 ... 22.5 22.45\n",
-       "  * longitude            (longitude) float32 -129.9 -129.9 ... -63.63 -63.58\n",
-       "  * time_index           (time_index) datetime64[ns] 2015-01-01 ... 2059-12-3...\n",
-       "Data variables: (12/19)\n",
-       "    country              (latitude, longitude) object ...\n",
-       "    county               (latitude, longitude) object ...\n",
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-       "    ...                   ...\n",
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-       "    windspeed_10m        (time_index, latitude, longitude) float32 ...\n",
-       "    windspeed_200m       (time_index, latitude, longitude) float32 ...\n",
-       "Attributes:\n",
-       "    full_version_record:  {"rex": "0.2.77", "pandas": "1.3.5", "numpy": "1.19...\n",
-       "    gan_meta:             [{"temp_lapse_rate": 0.0065, "s_enhance": 25, "t_en...\n",
-       "    input_features:       ["pressure_0m"]\n",
-       "    input_files:          ["/scratch/gbuster/sup3r/source_gcm_data/pressure_d...\n",
-       "    model_class:          MultiStepSurfaceMetGan\n",
-       "    model_kwargs:         {"temporal_model_kwargs": {"model_dir": "/projects/...\n",
-       "    output_features:      ["pressure_0m"]\n",
-       "    package:              sup3r\n",
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-       "    temporal_enhance:     24\n",
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" - ], - "text/plain": [ - "\n", - "Dimensions: (latitude: 650, longitude: 1475, time_index: 175320)\n", - "Coordinates:\n", - " * latitude (latitude) float32 51.57 51.52 51.48 ... 22.5 22.45\n", - " * longitude (longitude) float32 -129.9 -129.9 ... -63.63 -63.58\n", - " * time_index (time_index) datetime64[ns] 2015-01-01 ... 2059-12-3...\n", - "Data variables: (12/19)\n", - " country (latitude, longitude) object ...\n", - " county (latitude, longitude) object ...\n", - " dhi (time_index, latitude, longitude) float32 ...\n", - " dni (time_index, latitude, longitude) float32 ...\n", - " eez (latitude, longitude) float32 ...\n", - " elevation (latitude, longitude) float32 ...\n", - " ... ...\n", - " winddirection_100m (time_index, latitude, longitude) float32 ...\n", - " winddirection_10m (time_index, latitude, longitude) float32 ...\n", - " winddirection_200m (time_index, latitude, longitude) float32 ...\n", - " windspeed_100m (time_index, latitude, longitude) float32 ...\n", - " windspeed_10m (time_index, latitude, longitude) float32 ...\n", - " windspeed_200m (time_index, latitude, longitude) float32 ...\n", - "Attributes:\n", - " full_version_record: {\"rex\": \"0.2.77\", \"pandas\": \"1.3.5\", \"numpy\": \"1.19...\n", - " gan_meta: [{\"temp_lapse_rate\": 0.0065, \"s_enhance\": 25, \"t_en...\n", - " input_features: [\"pressure_0m\"]\n", - " input_files: [\"/scratch/gbuster/sup3r/source_gcm_data/pressure_d...\n", - " model_class: MultiStepSurfaceMetGan\n", - " model_kwargs: {\"temporal_model_kwargs\": {\"model_dir\": \"/projects/...\n", - " output_features: [\"pressure_0m\"]\n", - " package: sup3r\n", - " spatial_enhance: 25\n", - " temporal_enhance: 24\n", - " version: 0.0.9" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import xarray as xr\n", - "\n", - "token = planetary_computer.sas.get_token('nrel', 'oedi').token\n", - "ds = xr.open_dataset(\n", - " \"reference://\", engine=\"zarr\",\n", - " backend_kwargs={\n", - " \"storage_options\": {\n", - " \"fo\": ref,\n", - " \"remote_protocol\": \"abfs\",\n", - " \"remote_options\": {'account_name': 'nrel', \"credential\": token}\n", - " },\n", - " \"consolidated\": False,\n", - " }\n", - ")\n", - "ds" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Import Data\n", - "\n", - "The sel() and isel() methods make it easy to select the data that you want. We will select some data and then load the results into pandas dataframes.\n", - "\n", - "Here we load data from the 2015 and the 2050 files. Note that:\n", - "\n", - "1. The 2015 year does not represent the actual historical weather in 2015, just the historical climate in 2015\n", - "2. Comparing single years is imprecise because normal variability in the climate can skew the results, we use single years here just for illustrative purposes" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Coordinates for NREL campus\n", - "coords = {\n", - " 'latitude': 39.741,\n", - " 'longitude': -105.171\n", - " }\n", - "\n", - "# Select the point in the dataset that is nearest to NRELs campus\n", - "ds_nrel = ds.sel(**coords, method='nearest')\n", - "\n", - "# Take time slices for 2015 and 2050\n", - "ds_nrel_2015 = ds_nrel.sel(time_index = slice('2015-01-01', '2015-12-31'))\n", - "ds_nrel_2050 = ds_nrel.sel(time_index = slice('2050-01-01', '2050-12-31'))\n", - "\n", - "# Identify variables\n", - "vars = ['ghi', 'windspeed_100m', 'temperature_2m']\n", - "\n", - "# Note that no data has actually been loaded yet! Next, we load the data into pandas dataframes\n", - "data_2015 = ds_nrel_2015[vars].to_dataframe()[vars]\n", - "data_2050 = ds_nrel_2050[vars].to_dataframe()[vars]\n", - "\n", - "# Rename columns for convenience\n", - "data_2015 = data_2015.rename(columns={'windspeed_100m': 'ws', 'temperature_2m': 'temp'})\n", - "data_2050 = data_2050.rename(columns={'windspeed_100m': 'ws', 'temperature_2m': 'temp'})\n", - "\n", - "# Fill na in ghi with 0\n", - "data_2015 = data_2015.fillna({'ghi': 0})\n", - "data_2050 = data_2050.fillna({'ghi': 0})" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Comparing Differences in Temperature\n", - "\n", - "Here, we can see that 2050 has an increase in average dry bulb temperature versus 2015, as well as more extreme hot and cold events." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2015 Summary\n", - " ghi ws temp\n", - "count 8760.000000 8760.000000 8758.000000\n", - "mean 209.282654 3.378573 8.702493\n", - "std 286.178650 2.151689 10.412784\n", - "min 0.000000 0.010000 -19.379999\n", - "25% 0.000000 1.860000 0.380000\n", - "50% 0.000000 2.920000 7.610000\n", - "75% 402.000000 4.390000 16.850000\n", - "max 1023.000000 16.480000 34.540001\n", - "2050 Summary\n", - " ghi ws temp\n", - "count 8760.000000 8760.000000 8758.000000\n", - "mean 221.332199 3.564847 9.956614\n", - "std 296.225220 2.364556 12.077272\n", - "min 0.000000 0.010000 -25.789999\n", - "25% 0.000000 1.860000 0.630000\n", - "50% 0.000000 3.020000 9.220000\n", - "75% 439.000000 4.680000 19.449999\n", - "max 1036.000000 15.250000 38.079998\n" - ] - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "print('2015 Summary')\n", - "print(data_2015.describe())\n", - "\n", - "print('2050 Summary')\n", - "print(data_2050.describe())\n", - "\n", - "plt.hist([data_2015['temp'], data_2050['temp']], bins=30)\n", - "plt.xlabel('Dry Bulb Tempeature (C)')\n", - "plt.ylabel('Counts (Number of Hours)')\n", - "plt.legend(['2015', '2050'])" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Wind and Solar Resource\n", - "\n", - "You can use joint probability distributions to visualize the distribution of syncronous wind and solar resources. In practice, you would want to use a tool like the System Advisor Model (SAM) or the Renewable Energy Potential Model (reV) to convert these meteorological variables into potential power generation." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '2050 Hourly Windspeed (m/s)')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import numpy as np\n", - "from scipy.stats import gaussian_kde\n", - "\n", - "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", - "\n", - "def get_density(x, y):\n", - " xy = np.vstack([x,y])\n", - " z = gaussian_kde(xy)(xy)\n", - " z = (z - z.min()) / (z.max() - z.min())\n", - " return z\n", - "\n", - "c1 = get_density(data_2015['ghi'], data_2015['ws'])\n", - "c2 = get_density(data_2050['ghi'], data_2050['ws'])\n", - "\n", - "ax[0].scatter(data_2015['ghi'], data_2015['ws'], c=c1, vmin=0, vmax=0.5)\n", - "ax[1].scatter(data_2050['ghi'], data_2050['ws'], c=c2, vmin=0, vmax=0.5)\n", - "\n", - "for subax in ax:\n", - " subax.set_xlim(0, 1100)\n", - " subax.set_ylim(0, 18)\n", - " \n", - "ax[0].set_xlabel('2015 Hourly GHI (W/m2)')\n", - "ax[0].set_ylabel('2015 Hourly Windspeed (m/s)')\n", - "ax[1].set_xlabel('2050 Hourly GHI (W/m2)')\n", - "ax[1].set_ylabel('2050 Hourly Windspeed (m/s)')" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '2050 Daily Average Windspeed (m/s)')" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 2, figsize=(10, 4))\n", - "\n", - "def get_daily_means(arr):\n", - " arr = np.roll(arr, -7)\n", - " day_slices = [slice(i, i+24) for i in range(0, 8760, 24)]\n", - " arr = [arr[islice].mean() for islice in day_slices]\n", - " return np.array(arr)\n", - "\n", - "ghi_2015 = get_daily_means(data_2015['ghi'])\n", - "ghi_2050 = get_daily_means(data_2050['ghi'])\n", - "ws_2015 = get_daily_means(data_2015['ws'])\n", - "ws_2050 = get_daily_means(data_2050['ws'])\n", - "\n", - "c1 = get_density(ghi_2015, ws_2015)\n", - "c2 = get_density(ghi_2050, ws_2050)\n", - "\n", - "ax[0].scatter(ghi_2015, ws_2015, c=c1, vmin=0, vmax=1)\n", - "ax[1].scatter(ghi_2050, ws_2050, c=c2, vmin=0, vmax=1)\n", - "\n", - "for subax in ax:\n", - " subax.set_xlim(0, 400)\n", - " subax.set_ylim(0, 12)\n", - " \n", - "ax[0].set_xlabel('2015 Daily Average GHI (W/m2)')\n", - "ax[0].set_ylabel('2015 Daily Average Windspeed (m/s)')\n", - "ax[1].set_xlabel('2050 Daily Average GHI (W/m2)')\n", - "ax[1].set_ylabel('2050 Daily Average Windspeed (m/s)')" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0, 0.5, '2050 Daily Average Windspeed (m/s)')" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "fig, ax = plt.subplots(1, 2, figsize=(12, 4))\n", - "\n", - "def get_daily_means(arr):\n", - " arr = np.roll(arr, -7)\n", - " day_slices = [slice(i, i+24) for i in range(0, 8760, 24)]\n", - " arr = [arr[islice].mean() for islice in day_slices]\n", - " return np.array(arr)\n", - "\n", - "ghi_2015 = get_daily_means(data_2015['ghi'])\n", - "ghi_2050 = get_daily_means(data_2050['ghi'])\n", - "ws_2015 = get_daily_means(data_2015['ws'])\n", - "ws_2050 = get_daily_means(data_2050['ws'])\n", - "temp_2015 = get_daily_means(data_2015['temp'])\n", - "temp_2050 = get_daily_means(data_2050['temp'])\n", - "\n", - "a = ax[0].scatter(ghi_2015, ws_2015, c=temp_2015, vmin=-15, vmax=30, cmap='bwr')\n", - "b = ax[1].scatter(ghi_2050, ws_2050, c=temp_2015, vmin=-15, vmax=30, cmap='bwr')\n", - "plt.colorbar(b, ax=ax, label='Daily Average Temperature (C)')\n", - "\n", - "for subax in ax:\n", - " subax.set_xlim(0, 400)\n", - " subax.set_ylim(0, 12)\n", - " \n", - "ax[0].set_xlabel('2015 Daily Average GHI (W/m2)')\n", - "ax[0].set_ylabel('2015 Daily Average Windspeed (m/s)')\n", - "ax[1].set_xlabel('2050 Daily Average GHI (W/m2)')\n", - "ax[1].set_ylabel('2050 Daily Average Windspeed (m/s)')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Azure_workflow", - "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": "3.10.9" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/azure/examples/wtk.ipynb b/azure/examples/wtk.ipynb deleted file mode 100644 index dff1cf6..0000000 --- a/azure/examples/wtk.ipynb +++ /dev/null @@ -1,703 +0,0 @@ -{ - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Demo Notebook for Accessing the Wind Integration National Dataset (WIND) Toolkit on Azure\n", - "\n", - "The WIND Toolkit is available on Microsoft Azure's Planetary Computer in HDF5 format. Kerchunk reference files are provided to facilitate speedy access. This notebook demonstrates how to access and use this data. " - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, specify which data subset and resolution you are interested in.\n", - "\n", - "datasubset and resolution options:\n", - "\n", - "| datasubset | resolution |\n", - "| --- | --- |\n", - "| Great_Lakes | hourly, 5min |\n", - "| Hawaii | hourly, 5min |\n", - "| bangladesh | hourly |\n", - "| Mid_Atlantic | hourly, 5min |\n", - "| NW_Pacific | hourly, 5min |\n", - "| Offshore_CA | hourly, 5min |\n", - "| canada/v1.0.0 | hourly, 5min* |\n", - "| canada/v1.0.0bc | hourly, 5min |\n", - "| canada/v1.1.0 | hourly, 5min |\n", - "| canada/v1.1.0bc | hourly, 5min |\n", - "| central_asia | 15min* |\n", - "| conus/v1.0.0 | hourly, 5min* |\n", - "| conus/v1.1.0 | hourly, 5min |\n", - "| eastern_wind | 10min* |\n", - "| gulf_of_mexico | hourly, 5min* |\n", - "| india | 5min* |\n", - "| North_Atlantic | hourly, 5min |\n", - "| mexico/v1.0.0 | hourly, 5min* |\n", - "| mexico/v1.1.0 | hourly, 5min |\n", - "| philippines | hourly |\n", - "| philippines_tmy | hourly* |\n", - "| pr100 | hourly, 5min |\n", - "| seasiawind/v1 | 15min* |\n", - "| seasiawind/v2 | 15min* |\n", - "| south_atlantic | hourly, 5min* |\n", - "| vietnam | hourly* |\n", - "| western_wind | 10min* |\n", - "| wtk-us | hourly* |\n", - "\n", - "\\* data migration still in progress" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "datasubset = 'south_atlantic'\n", - "resolution = 'hourly'" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next, we load the relevant kerchunk reference file from the main wtk directory of the oedi container." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "import planetary_computer\n", - "import ujson\n", - "\n", - "fs = planetary_computer.get_adlfs_filesystem('nrel', 'oedi')\n", - "with fs.open(f'oedi/wtk/{datasubset}/kerchunk_{resolution}_ref.json', 'rb') as ref_file:\n", - " ref = ujson.load(ref_file)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now we can use the references to create an xarray dataset object. Note that no data is actually downloaded until you query this object. Also, the planetary_computer token expires after about an hour and you will need to rerun this cell to regain access to the dataset." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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<xarray.Dataset>\n",
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-       "    ...                            ...\n",
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-       "    windspeed_400m                (time_index, gid) float32 ...\n",
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-       "    windspeed_60m                 (time_index, gid) float32 ...\n",
-       "    windspeed_80m                 (time_index, gid) float32 ...\n",
-       "Attributes:\n",
-       "    identical_dims:  ['gid', 'latitude', 'longitude', 'country', 'state', 'co...
" - ], - "text/plain": [ - "\n", - "Dimensions: (time_index: 184104, gid: 328443)\n", - "Coordinates:\n", - " * gid (gid) float64 0.0 1.0 ... 3.284e+05 3.284e+05\n", - " * time_index (time_index) datetime64[ns] 2000-01-01 ... ...\n", - "Data variables: (12/75)\n", - " boundary_layer_height (time_index, gid) float32 ...\n", - " country (gid) object ...\n", - " county (gid) object ...\n", - " elevation (gid) float32 ...\n", - " friction_velocity_2m (time_index, gid) float32 ...\n", - " inversemoninobukhovlength_2m (time_index, gid) float32 ...\n", - " ... ...\n", - " windspeed_300m (time_index, gid) float32 ...\n", - " windspeed_400m (time_index, gid) float32 ...\n", - " windspeed_40m (time_index, gid) float32 ...\n", - " windspeed_500m (time_index, gid) float32 ...\n", - " windspeed_60m (time_index, gid) float32 ...\n", - " windspeed_80m (time_index, gid) float32 ...\n", - "Attributes:\n", - " identical_dims: ['gid', 'latitude', 'longitude', 'country', 'state', 'co..." - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import xarray as xr\n", - "\n", - "token = planetary_computer.sas.get_token('nrel', 'oedi').token\n", - "ds = xr.open_dataset(\n", - " \"reference://\", engine=\"zarr\",\n", - " backend_kwargs={\n", - " \"storage_options\": {\n", - " \"fo\": ref,\n", - " \"remote_protocol\": \"abfs\",\n", - " \"remote_options\": {'account_name': 'nrel', \"credential\": token}\n", - " },\n", - " \"consolidated\": False,\n", - " }\n", - ")\n", - "ds" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The output in the cell above lets you browse the schema for the data set. Let's pick one of the variables and a snapshot in time and generate a map." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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xDAaDwWz5ysoKT37yk49b/2/+5m94ylOewv79+/HeU1UVP/ADP0BKiU9+8pN3eLx3vvOd7Nmzhyc/+cm77vGDHvQgTj/99DudiXasPelJT9r1/T73uQ8iwhOe8ITZshAC55133q7w3Dvf+U7ud7/78aAHPWhXux73uMfd6Qy5yWTCM57xDD73uc9x2WWXsby8fNw6t/UcHbv8zjxvJ7NuSum49/BLadfJWM5517FSSne6vV8t+0pfV4Cf/dmfJcbIhRde+FU/1j8FW4CZu4n1oaMrr7ySP/uzP6PrOv7lv/yXPOYxj5mFXq688koe/vCHMxwO73B/+/fv3/W9aRqAmdv+1ltvBeD0008/bttjl33/938/r371q/nc5z7Hd37nd3Lqqafy0Ic+dAaybm/bfll/vBtvvBGAf/Wv/hVVVe36/Mqv/AqqyqFDh7j11luJMfIbv/Ebx633xCc+EWAGqm699daTOo+vpO3duxcRmZ3XvB06dAiAffv27Vp+7Pe6rm93+R2FAp71rGfxP//n/+S5z30uf/RHf8SHP/xhrrnmGk455ZTZfb6zdvjwYXLOJ3U9P//5z/Mv/sW/4Itf/CIve9nL+NM//VOuueaaGafpZNpw4403cuTIEeq6Pu4+33DDDbcLnE/GTnRtR6PRLqDWL5+/3jfeeCN/93d/d1ybVlZWUNWTbtd0OuXpT386f/Znf8bb3/52HvrQh+76vX9Pb+s5mm///v37T+p5u6N9igh79uwB4Ju/+Zt3nd9//a//9U6362Ttv/7X/7rrWN/8zd8MfGnv0lfS9u/fz2QyYXt7+4TH/1LuwdfDsb7RLNzxKgv7erCzzjqLe93rXlx55ZWce+65PPjBD2bPnj08+tGP5kd+5Ef4i7/4Cz70oQ/t4op8OdZ3VjfccMNxv51o2aWXXsqll17K1tYWf/Inf8ILX/hCnvSkJ/HJT35yF2nvtvZ33nnnAXDgwAHAdBa+/du//YRtO+2004gx4r3n+7//+/l3/+7fnXC9e9zjHrNzOdnz+EpZrx3ykY985LjfPvKRjzAcDrnnPe/5VTv+0aNHeec738kLX/hCfvInf3K2fDqdzjq7L8X6geVkrufv//7vs7W1xVvf+tZdz8Df/u3fnvTxDhw4wP79+3nPe95zwt9XVlZOel9fSTtw4ADD4ZBXv/rVt/n7Hdl0OuVpT3saV111FX/wB3/Aox/96OPW6bkTH/nIR7jvfe87Wx5j5BOf+ATf8z3fs2vd3/3d3yXGuIs30z+D97vf/QADKMPh8DafzfPOO28G5t7xjncwnU5nvx88ePBOt+tk7fnPf/4uT1k/wbqr36X5c50Hmz2Y7q9rv+5ttRPYte5dfaxvNFt4Zu5G9pjHPIb3ve99XHHFFVx88cUA3Ote9+Kbvumb+Nmf/Vm6rjsp8u/J2Pnnn88ZZ5zB7/7u7+4iM37uc5/jAx/4wG1ut7S0xBOe8AT+83/+z7RtexwJ7U1vetOu7x/4wAf43Oc+N8u4evjDH86ePXv42Mc+xoMf/OATfvrZ86Me9Sj+5m/+hgc84AEnXK8HZI961KP46Ec/yv/9v/9317Hf/OY3fzmX6A7t6U9/Ou973/t2ZadsbGzw1re+lac85SnHETW/kiYiqOpsQOjtVa961XHu+2O9crdnS0tLPOQhD+Gtb33rLk/FxsYG73jHO45rw/z+AVSVV77ylcftt2maEx7/SU96ErfeeisppRPe4/PPP/8O9/HVsCc96Ul8+tOfZv/+/Sds1x2JSvYemfe9731cfvnlPO5xjzvheg996EM544wzjssu+73f+z02Nzd5xjOeMVv29Kc/nc3NTS6//PJd677uda/j4MGDs8ExhMCTn/xk3vrWt+7K6Pv85z/PVVddtWuf97///XedVw9m7ky7TtYOHjy461jzJNi78l16/OMfz2AwOO5c+wzNpz3tabva+YlPfGKWOQYG8N74xjfy0Ic+dHb9vh6O9Q1ndyljZ2F3yi6//HIFFNCrr756tvzSSy9VQPfu3buLQKp62wTgF7/4xcftn2OIea961asU0Kc+9an6zne+U9/4xjfqeeedp2efffYuUt1zn/tc/Q//4T/oW97yFr366qv1f//v/60PetCDdG1tbSbA1ROAzz77bH3Oc56j73nPe/SVr3ylnnrqqXrmmWfqrbfeOtvfG97wBnXO6b/+1/9aL7vsMr366qv1937v9/S//Jf/oj/8wz88W++jH/2o7t27Vx/ykIfoa17zGr3qqqv07W9/u/76r/+6PupRj5qtd/311+spp5yiZ555pr7mNa/Rd7/73fq93/u9evbZZ39JBOD3v//9etlll+lll12mg8FAL7rootn3ecGxm266Sc844wy9//3vr29729v03e9+tz7ykY/UlZUV/fjHP36H96TXwbnssst2Le+v5bHk02PtkY98pO7bt09f+cpX6hVXXKE/8zM/o2eccYbu2bNnF2n5M5/5jAL6tKc9Tf/0T/9Ur7nmGr3llltuc7/vfe971Tmnj3jEI/Rtb3ub/t7v/Z5+27d92+x69vbxj39c67rWiy66SN/97nfrW9/6Vr344ov1W77lW4677j0h9xWveIX+xV/8xezcYoz6hCc8Qfft26cvetGL9A//8A/1yiuv1Ne+9rX67Gc/exdR99nPfrY2TaNvectb9MMf/rD+3d/93a7reyIC8M0337zr3G6LCHrhhRfqBRdcMPu+ubmp/+yf/TM966yz9Nd+7df0iiuu0D/6oz/SV77ylfrMZz5TP/ShD93uvXnSk56kgP7n//yfjxNqO1bk8g1veIMC+vznP1+vuuoq/Z3f+R3ds2fPbYrm7d27V3/nd35H3/e+9+nznvc8BfSNb3zjrvU+/vGP6/Lysj7ykY+c3Zv73e9+d0o07860693vfrdedtll+upXv1oBfeYznzl7Z7a2tu7wWCf7Lqlav9Dv+1u/9Vv1lFNOmX2/LQHR2yMAq+4I2f30T/+0vv/979cXv/jF2jTNCYXsLrjgAj377LP1TW96k15xxRX69Kc//YRCdiciAH+1jvVPwRZg5m5khw8fVuecLi0tadu2s+W9UNV8tkVvXw6YUTVA8y3f8i1a17Xe61730le/+tXHiea97nWv00c96lF62mmnaV3XevDgQf2u7/qu2WCiujMAv/e979Xv//7v1z179uhwONQnPvGJ+v/+3/87ri1XX321XnLJJbpv3z6tqkrPPPNMveSSS44b2D/72c/qv/k3/0bPPPNMrapKTznlFP2O7/iOWXZUbx/72Mf04osv1sFgoPv27dPnPOc5+gd/8AdfEpi58MILZ6Dy2M+x+/rUpz6lT3va03R1dVVHo5E++tGP1r/6q7867hy+GmDmH//xH/U7v/M7de/evbqysqKPf/zj9e///u/1nHPOOS4D66Uvfane4x73UO/9cQP/ieztb3+7PuABD9C6rvWbvumb9Jd/+ZdPKJr3jne8Qx/4wAfqYDDQM888U3/8x39c//AP//C4a3Xo0CH9V//qX+mePXtURHbtp+s6fclLXjLbz/Lyst773vfWH/qhH9r17Fx77bX62Mc+VldWVhSYPaNfDTCjaoDmZ37mZ/T888/Xuq51bW1N73//++uP/diPHafie6zd1vMDnDAj681vfvPsep9++un6ghe8YFfWU28bGxv6ghe8QE8//XSt61of8IAHnDBrSVX1L//yL/XRj360jkYjXV1d1ac97Wn6qU996nbb/aW265xzzrnN8/3sZz97Usc6mXdJdefenuhzW1lUdwRmVFVf9rKX6b3uda/ZM//CF75wVz/c2w033KA/8AM/oPv27dPBYKDf/u3frldcccVttvNE/c9X+lj/FExUvwRBhIUt7E5ar1lzzTXX8OAHP/iubs7CFrawhS3sG8gWnJmFLWxhC1vYwhZ2t7ZFNtPCFoYRU48lxh5r3vt/ctoNC1vYwhZ2d7BFmGlhC2MnDHZ7dtVVV+2qc7WwhS1sYQv7+rAFmFnYwjDxr89+9rO3u875559/l+maLGxhC1vYwm7bFmBmYQtb2MIWtrCF3a1tQQBe2MIWtrCFLWxhd2v7hicA55y57rrrWFlZWZA3F7awhS1sYQu7m5iqsrGxwcGDB3cV4T2RfcODmeuuu46zzz77rm7Gwha2sIUtbGEL+xLsC1/4AmedddbtrvMND2Z6wuYXvvAFVldX7+LWLGxhC1vYwha2sJOx9fV1zj777JNKvPiGBzN9aGl1dXUBZha2sIUtbGELu5vZyVBEFgTghS1sYQtb2MIWdre2BZhZ2MIWtrCFLWxhd2tbgJmFLWxhC1vYwhZ2t7YFmFnYwha2sIUtbGF3a1uAmYUtbGELW9jCFna3tgWYWdjCFrawhS1sYXdrW4CZhS1sYQtb2MIWdre2BZhZ2MIWtrCFLWxhd2tbgJmFLWxhC1vYwhZ2t7ZveAXghX19mKqiCjFGcla6NpG6TBcjKWZiSrRdoouJrkvELhI7pesiXcq0muk002aly4mYE13KdCkTUyamSBeVlBIxKzEl8jQTRWmrTJvte1IlaYapkmOmQ0koSey33GVkO+E2Ev5oC5NEFKWrHZNVz2QFkge3mSEp7UiIQcmquLESNhLqlHbJ0dWKJBh9sWP4yUOEw9vQ1LSnrbJ1+pDpPkcOgAJx7mIJZAfav50JfKv4FiQqCuRgipguZarNRHNrBAeTvRXT/Z44dDtTFQXJ5ThJCZNMvZGp1hPV0ZZwaBM5ugmqsDRCVWFrDMtD0r4VurWGrTNqxmd4JCmDw5nqaKY5msgDYbKvYrrkoBJQcFEJE7X9byb8NOGmGcmKqAL2LMCcsmdWJCvqBa08ioIIKiCdbZudoE0gDzzZO/B2rcCui5tm/DQiMeHGLdJlNCvUHioPGYgZ9Y40qkhLFXnoUazNonO3ICtuEpFJxJVt1Hv70Ykdt7QZlFw7ch3Q2oNmXKdIzEgCQUlO0MqBF1BFuoxLICmj/T5VkS7hkqIi5NqTKw9ekJjxk47shFwFtHJocOAEnb/PO5fE9nsC4VSZ+6vH/C5qP4jIzs6cIE6s7U4QwHdKmGZr6ygw3Rvo9nq6vY60AloLUvbjnCAefHA4Z999EJwXvHN4X5aJ4MXh3c7fgMd7oXIO7xzBOYIXgtjfyns8Qu09lfME76i9rVeJp/KOynlq522Z8zRl3co5Gh+onKN2FUHs98o7gniCeCrxdg7iFoWKv85tAWa+iqaq5KzEmGhjop1E2mnHdNzSjjvaaaSbtkwmkek0Mp10tG2knXRMp5FuYuvFSUvbRiZR6ZzagJ6gS4mYM7HLxBjtb9uRJ5E46cgxk7KSgSSgSdGY0C6jbUS7iOZsHfx0ik5amLaQsw1oqogI0jToaARNBc5BztBFdDKFrgPvkLqGQUMe1GgdSI0nNR6tBHVig0Oby8CWkJjI3tnAVTnUOzueAqpoELL35IEjNUJqbOBXJ2S/+zr3HbCWASaMlTQU2mVBK6g2QT1kD9UWSIJU2zItoECSUm1mqm2lPtIRjk6JqwO0EqZrnu1VT66F5oaMn8J4v6A1EAW3kRkcVaSDo6c6ciNAYnh9x56/OYy//hB50LB15j7GZy0xPuDRgUAGPwVFbWBBSpvUgEgWpIMwVnxUUEgVqBdcZ9+XvzDFTTLTfTXj0wLdiuyMVnnu+qiBgmZDaY4m/GakvmUbObwOW9uwNILlJXR9A9qOfOYppJUB0301G+cE2j3C8hftHg5vTrhJZHxqw3SvJ9d2/e0eQ9jONEczYRzx04R0PYjp34sykHqBVACBKloGbwM1HskZN012b2tPHlZo5cgChLnzKs+Vn0aICTfuICYQB6PGwMCkQ50jN4G00tANPdSCdIpPPbKy/7ku4yYRN40GqOoA2CCuoTynMSOqqIc0qMi1B2fgxLUZSRlJijohlWccUQNdrW1LVhRFK2fvRGc3LFcerTy5tmP57SkkSE2w9yU4e07c3OCqx4CYueUqu9fpf88yt115VlQUcvlbQFsu75VdayVstuROGQ8Dk1Mrpmsw2Rfp9kYbUSJoBByos2fZ9gfMvpcHcx597TqJuZY6wO08zFK2kR59lmXidLZcsO9C+U1s/yLgJONc/1fxLuNE8ZLxLuNFcdL/tU/fLoc9dF507vdM6PdFnm3jSzvA9hUk41CCK+uLrV+5jCdRudIGIIjiMfzoRQiiBIRKhCBQiSOIGPgSb+DOhfJvT5CAl4CTiuBqAhVOapxrcDQE1+BkgMgALw1Ohjg3xEkDNDg3AAY4NwKGiNSINEBAvk6B3QLM3EmLMfGeP/kYr3zjn3HkhnXCeoffnOLaBCi58qRhRVyu6FYqcEoYZ8J6JGxOceMWUkKcQ53N1JT5AUiRnK0zTvYCa1OhywNS7a0DrcJsNiaZMkvMuC4jbUKwGa16Rw62jSQDLRINSEjWGShhPIG2g2juARWBnJG6gqUldDhAy36ICZ22MJ2gKSNNDVWFjgZoY516HgRS7QzIAK5TfJuQacZNO5uNVt5mzMHboIYNhoigwZOCIw+FWDnywACROmZARrEOSjKzztEAidKuOrqRoLVSHzEAkCuoNwzI5BoIYp4PLdttGZCpjkbC4Qlx7xActKue7dNswG6OZsJUGe9zaG3HDONMc1TxE9g8w5Fru5fNkciej6zjrz+MNjXp9D1MDy4x3e/Rxo7r23ImNilHnQ2OMyATIUwVl8qjURn48lMbBFe+2OK3E+3emvGBQLfcP0cyGwdm/X3K1FtQryf8dqY+PEbWt2BrCwYNrCyh4wm6sYl800F0qSGuVoxPD3RrwujGjGthcNi8H5NTGto980AGXAd+mqnXM75N9jxGLSdXzrMHbsEhSe3+pYwG82xIyuQyiEos21SONAjkytn5BZmBXunsWH4aoXhTSBnBPBsEB9Nu9lzlpZo09FA7XFRcnvdgCC6Wd2hq70L2s5EQvA1lvYcp++I9mQEZa08PZBAxj04o55PUrknu77kBODeNBmSUGVjJldj5tPZbHtYGYoK9B/NAZt6jtNvMw3Oidcy/1z94/Y7YAT89kOm9UKgBme2E65Q09ExPqWnXHNP90O3FPGX97m4PyPQNOhbIzP7MoSxRAyn0/2anPf3zNLcf19+u8teV3Qm2nx58iGMOuBig8S7j6YGMzdukfy5K+5woYQ7EOMkEZyDFzYOgfn0UL6mAmURwiiPjJVNJxkmiEvvegxsvGSGXm5Zw2HchIWTzJWtGiaRs6yiKSiKjROmPofjyvkkPwGDue3md6K+VzG6JPQIyeyz6W6Nqk+OMkgz37jw+sszayr9jdfn5BQR9bW0BZu6ETaYdl/74G/jCtbdQbXbUG525s2NGnUOHFWlY0a7WxJHDZXO1h80OvzGxDlIzeG9AxsnuWVTOSDTQQc724te1AZmBx6VMrkN52sQ6TCidXsZ1CXog4wzIoGXmmwwkSQFIYMCEabsbyDgHMSJVBcMhOmhsQCjtI0aka9GsuEGDOg+DBg1lxlg5chDzrIjgklo7sw2osw679qj31lmWgcmOX7ZthFSJARnfd+DHTNzy7ExQwHeQBkJqhNzojkdmaN4Z30IcGCDS0qkK4KINxK5T/Lij29ugjZAGnsl+G7AlQ7WuxJEjDSggiBL6gXZFZoOWa5XVz7aEGw7bAUZD4r4lupEjNdhRU2m5ljm/qAHU2fUAlxTXlY44l4GlhDV8meHnxtGteNJwbtA9BsiQ1c6xzTaAtxFpU/GqebvPKRuQOXU/aWVAXPK0q4HJPvMW+Ynt2E1zCXWIDfT9OWQb5KttA18u2uC9a7JdzkuFHYCdki1zgrTmCcEb0CBlA/s98O9PrffyZMxD0mWbBEy62SRAvYMqzN4prexZy8GRpW93/ynXKytExXUJEXvuEDfz/OXifbLQkjVVg9txd2S1532nd7dnog8t9V4ozCsjTgzj5d4DYeuqFzs3VfxWRx5UNiQFZ967HsjsXP5dA46Uf82aoTsgcnYNDQnMJj6S+36Bub0VNFDAqu8UP00WrRsE4khItb1TPZDZ1Zj5v+VzQo9M76rrF+jOQyM9Lp97vOeP4ZztT+bQmv1s90F774j0A7R5m30PWOaAjEMRyebZKQDIrpHOvD1+BmIMvBg44YRARuaAUehBEOaVCQUI9UDGPDJlfZK9BuwAnTD7d8SjVJJm21RoAUOZgBYAZX/7c/boLOosBdQ4SuSQ3UDGzU2uXXnIMjtAJs0BmXlT3eTI+q+wvvm/OPP0P8G7vXwtbUEAvhP2X17yDgMyW5Gw2eEmnXWUImjtSXUgDgJ5aA9GGGf8dsJvdzZ45FzePuwpmsWly4NU3O3WwWdwHhoL2bg2WfxcZKdDz9m2zzoDKYqCs3i6AE4LiMnZBodsHSmxDCQpgZo7vA/1ELyFlJoaCQXL927xGNGU7Jydg0GFNsG4DCVMYB284FSNixAzUsCW0B+rgJVgH5x5HWzmKTseq97LoDt9pe5cth2QoxBaJQ0EDQYMXNe7yG1w1b5jLte971d7voSLip9ExHvcVM21760NYQJaQbfMbID2U/u4jIV3nIKzjtBPMrSRLFhoo8GATOmRXZnSmIdNy2yYWUcvqXzKSSdv64VWkTYzuLmDrMSlijgyT9Fcb7Rzwco/XWvPo5tm3FaHtq2FGJsaHTSIZpi2SFXZM9MxGzSqDfP6VVsZddDurWiXxAawvsllIulSPwgUgKFa7tlsNDFvhTILcaJ55p0r/n9blu3ZVmegVPsZdwaS4tq0A+K1DMglpqBuDvlSPBnBoRWIB4k64xH1809Rnb2HkjJawNhs9ik7OMrAsMw8GKLs9n6IkKWcdwE69O835bxEcG0s12muT/AG6HA2gKh3qEDqryE7++nbPPvO7jbM/rlrzl3AVdmPqLLj3tg5px3OVXmWUuFrDRxxyZEriMuQG46Zos8dZhaimUe1x3yOXTYHZGbvabm4s036/kClgGRB6Hk6OttGRMpf++z8Z4/orFnlerj+9GcuzR5QyQ5o6p8D7a9N2ZbdQMbLXMjK7YAk44PtnPDO5TGw41ACiVoylaQCZAy8OCxctXM9DIBB7nGntUMLqMEATii3M7BDNes9M8Ixk+rZ7ZfSNpk921pu9bFAZt5yvoUbb/7Xt7PGV8cWnpmTtI2tCR/4q8/MEL91eFpmcG4WNkkjjzrBtWqflJEY6fknfaeuPbgA63xnHpSyzPvi7fAFOs/PWowQiAjEAhJiAUpl9mOd6NwT2L+0XkpfKBDLm+qkTDsFsoXA5vpLw+ZqHSveoU0NdUB9gEFdOt6d7rI3LQOBFwvpZJyROOvCk3EO+usZ7UiSC7+ndGo9WMn9VIICZvq3rwcosMtTI3nndxftN+1xWQEPMpsJsnNPy31VLDy1M+u2GWiqd/Ytt9GB03sdRKCpias1cejI9c7q8327zO1mtrQMILnsMwdbx00ViQnflU417DxTO2c/t0NV/DQTJsblIBXwUAZN8a7vrXdtqhiAkjZTH7bQlIsWRotDZyG6XSPnMVa8hqmckgNy7+FzAl0umEvLA9sfWGennz1oY2GcHPqO1a6LpDlPx+xh2X3eBtqjgX7VGaeKlHF57hnpB9BdPXq59g7wFPAuOGfHV3Fz93v+uHM76NsgClHLBKIMdlln7/wOCJLdf1OJL2oyBLbrYWPnvG/rPsyui+68M8rxXpRjdjs7/9yDHJBWSx+D8ZdqOe69nO2j/8xfX4+R3Pt1Z+Ei2JnUHbMf1cLdwwbUHuiXbaQAAHpPm+xs2oMGzRRAUwCKk5nHYodLY32B6z0ys+W6cwtLm7WE9Q1z7wAgmTuNWStE6P1jWftuXPFqYCNINo4NOgsz9efW7ynveiayTRisJy0emmz7E8p56awdinndcg+Yym/9nMn+Ck5k57bsAsB2DbNY35+wfen8M34b1nYfoYvXUYWDd7zyV8gWYOYk7Y//7BO4NuHHEbfZmUs7FZKht08/QKgT6ylVbTaTFUmpzAaKN6VkL9jT1RNgZPa99x4oQJd3ZgL9YKnmOBbYPSOE2Xs9618dSFXc9WXAEEBSnAsy9yvPIZjy5CcH4g20ia/QbC586c+371vnOqR+nPcOUiU4VzJ3nBQCow0GUmZWvddkNqPDBpNMD86gnwHP2nfseffrcMwMLrEzs+5XVWYuaPUGFlJlbUu1kFNxraaMxNIeh82iywDhLKpng+4c6KHvRCohDj1xIMTAXOZICSml+YbO3by5W5FK+3DmzZGUceOIZrEMmsp+136DfvPi1UBBxopMtXg51O7HMIAb2Hql46apwDty7emGjpwzMnZIp2jMxFpIQ0c3tGvReyP6EBO6c465z7KRcn1FzGvnBJfLLHasEM1bJ16MR6YecUJyDvXW1lwLUrwULhaeTDIPlbQR6ZI9q6mEZ5MiOdkzlpIBmon9JlLhqoB4bw8nfRhUCo9MUeeIlXkOjYVp77b48k5IIQEnW19mYau5W5hSAW0KsYQM2zR3f8pz1Ide54fBVPqOqXlOdSp2TpotK0u8Pb9h5907Fs9oAcJ9l9HzdOyBnwdDdmyXdSd81d9Hnfuw8x5qP8KrlHvPTkZVf/557kOZixVwaKFkLSCRHWDTN5a540Zm/ZmIA1++uML6cf12cx2CzPCrbTsDQg7ntRCDjTfjjr1wpQm5OLE176zg1c3IuKoZ8YrDgeTiVdlpvlEStWD1OYSkpX9XxUt/8qncmp3QENijMUv2E0p4zCaFPRFZSoN7IDSjOAFZc+mryj1AZvfB9x7icgHmwU7/VChiUe0e/M3dmpOxzc3Xs3fPT57k2l++LcDMSdon/+F63DjitjvcpN3phG4vUJczSgEiBaVLLli9j707xy6XQnmLVEGTQ3OChHkxVHc9Vn1GxOzN7b02zIGAfmapDhcsdCOacU5QClkRNS9NVuNQzFz19snOFd+kKzMNheCRLManKd4pvCPP8VvABmNxNrCrBMSJDYRzL7doJpe0VxHjYuTKQFTBXrv5MdKDwJ0ONntIjcwm+TPHVAda7wy+PRdB1QYnBWJdOlg8rmtoV41rFGvLbKGEXGSaqaIYUVVtMM61s0FPQboykZ9Yx5mrQBpVdAMhNaBeUZdnGJZknhb1NijMurG+j8NIpvgdsBcbh2sVXXaklUA3EuMszM6t7EINOEsEyYlcObqhg5Ej7w2ESYIMfpJwMeM3E86tEUeBdq0irng0WIp1HHhYNv5SGpYZccJCatF4Pa6QeXcAtJC9s3E/2P3PA0cuHBRRT+0UqTBA4tRSu4OglRgBnN4DZDN0H9VSryfJgMy0tUlFF8sIpDvvAeW98M4A07TDTTvc2NuzujwkrQ3Jldg5RJtsiAq5MU+QwixzSMRmyeqV3NlZ+phmzyTKHBHdQoxunjxbkEXPGxMF7UqYF/OQqSokQTqZAaLsHdJFGLd4ZyFLGTbWbzQlpFsGrBlY7p99mA36O56Y0g5V8xS5EmToDFAi2AQll39LAalzMySZRPyWx9dC2JSSZWj3WR2IL5e+A+mM2zd7Lr2Qgt3rXIFWzMLC9P2GM/BGJ7h+RpPZCel5Rb1CBRpyCctp+dhFSFEhOkhlsqQe5xRxGeeVUGVCyNb2LJYdVzwbWRS7hYEuerKWRAaByil1SNQh0Sg0ffKCU1QtLqxZiUlIBLoss8hhLYnKJwa+Y+Bkx7NTZmD98TUpUSERyCrglJpM7RID6Rh4m11Z95lnI8IMTGUlZmgJxUvjLFMKpVbwGKemf0TmRpQC5LTMg/JsrttjxtsLLx1rbft3d2LtL98WYOYkba2pcdstsjWF7Q6cn0OwZipYxyggqX8JtbykhuCzlM42ZzTmGbIWmfOalFCTOIFo6ZhoRnvWqjPXn0SFnKwzt9fABsR+oEcK4LGHPjlHroykay76gEwHuLazAWUa7W/KSFXtpKFqLoAKqBypCUYunk+R7jvBPmW0zMZxrkwxei9IGSSc7HReOKhKv1m2VW+UodRHQWYTJJ0RMvsevO8ou5Il5KbGl5m5Wzvr5KyDtwHelUm8vcBCJtONhFTVNgunhFlStk65zbhxhzjP9BRPXIVuaIObS2LcqIniozD8xw1Ijm7PiG6lol3xpNpm6JIU8c4IldNM2M50Q0/qvQ+CXbvC6zAgapcoedAVgbXRTmSmMvDo+llcDzQUiIofJ1wHMSi+Kh390JOHYeYpQ6E+OmRw04TcTcnBSNR0CWmdtbcAQERwW8lAby3IVE1zpNtpZ6wEr47cx+FKWoiFKQF1ZKe0B4awd2CDb+lRRc0zkgKQEn4qFkbtM5c2O2R7ikwTLicjr7dlciGUqXrZVxvRQW0eSRHjjqknRzWicFZUHFpbyDM7A2VKCeOG4kV0WjipPQDPkC0l202SzcxL9lKuBNcCMdHzeqUAFqpgE/PiAfJgg3ZO6NQGZcHcBZrVJgq1Q5wzXtDGBDmakZVlGNYkrctzozMi8nxf1AO83vsx8xA4kFgydPpEgj6ES5kTZQM6muamTypImwhdNI9q1ZjXIwqIFuJ/CUUkeyYLjQzNJUzYT5JC7w0tMgyBHW9NBlXB9ShM3SyrqAdV4qwfVO+LnEG2PiSkMuLmwqeR4lIVEjvn2QbAZ7xXfJUIIVJVincRTyJlSOqJ2ZGzI5Vn37wjiSoolY80oaMOiWHoGIZIRQRNxByYaiCqbStYiDJIpnaRUYgMfMvQR5Z8y8C1DF3Ea0dWpdOyPZ6cHc6pgRmXGKUJQ9cych0rats2EmkkGddLPWMqxlqR1MLQVVYqiQzIDHxikJUBmSyU7KwSCk5C0kxE0UIUrgoN2NEDrpPzzjg3PIm1vnK2ADMnad/2rffgza//E1zOaNuS15ZLxwTWs5mXw2/bjEzyDskTBdVkg3VdGXdGdQY4LBDrLOyRFekSsjFG2g6cwyGoM5ehxTCdzeZRtK7J3tyo2k8fSkdh+i4OjQqTjE8RHx20gsQEzlkq+SiQKm+u8EkL02gDkHeFOAxMo83y24QfJ3xdARmZTmfeEh1V5NGAvNzYjEIKMKkEsswGO8QG3NzzNvqMHe09LbITWupnnMVNbu0RtO7nEjoXKjHvkp8jVKfenethFhrQjKjD97O92UjKbPaXnNjMrwV3NOIyxEHRQAmZbjXjNyCMhfqmluENE0ILzgXk5g22738q7fKIrrGZnosQJpYtlZuKXAnJC2nNsqPsfikulhFJYLCZmQwcuiKkYDFxis5Org385WDfNSphjInTTYv7OyVoU0FG4NtsIRlflzCfDb6WMixUGy355sN4P8BNKnIX6ZY8eSVYhlIWcoLRP26QRgOmpw2pJhk/zuS6DCoZXBC64BGXIBaRte2I20poU++EGVVn4m9a28DpErNUXpkqbpwIbbZ2ZruHrkswHhvnpA/RQvFyzne15lWg9rNnRKvayOrLtekBFRdeDt6OMc24NpnXBA85QuUQ3RmwESnp8vasuO1oqdp1MO+E2LPX+zFnEwQBKpNrKE+uARYFspsN9vYOz01IeqKqOHzbmqCh9+SR8ScUZ1yu8q7IHAdJogn44QQduJIZaO0ucn1FEE9n8gdKLtpA2bLSomXBSTZpB4egk0gY1+Ay2ZbseEr7iQ8YX4USDpNs/UQ5R/X2rJjnrnh3eg5VP9GZpWbLjtdGxACPKJRIP62BTtTPvDwz3Qrpr2vfV1ifq8kRnRJbz9RXZTTP4CybTUo7BHa8bgLROdoEzlV4V+M8VKaASEW08NXsuP3hFTenZ7MRzdNSOaV2kcpHgiY8Hc73AM46TBHrtypnaeA1IyqXGLjIQFpGvqORlkZbFEvgTpbkjaLlccrUdNQuUefEwLXUJBqXqSUzIBvILl5vEaUCRhiY6SeRSQQvOgPqt2dN9bA7XukraAswc5J23wvOxiWxl9sH4sCTVmojUrWWkskkGg9AZl2VdYDDALUjp0IAnHaWOeIEmsYG2srCF7kS8AG3OsJNI65NpCAQAlkjtK64+E2RVAFdqgxkHN6yAacx3oEGZxo1XqBSy4jxzjpaBTeN+C5DF5Al6/CrW8YwaS3zw1lbCNYJJgEfsY6oFhM2C0ObFXcdiIWepAVChlC4PyrW8cz81NZROfOqIw4kQmxKJ+v7fkB26DydOTnDxFz2nXclPICBlBI+y7XMPFUSPAQxLYbiyfFbEd2e4Jq6eIdMi0RSJlUOpwJJyXs93TKELc/wyJRu4GAYaEcJ304ZXq8MP9cR2gpSR71hAm9SweSc/aRKCGOIA0euLV04BcGraZlocLR7nJGMMaG/+oj1zKnx+Kh0K1K4EYqblJh8IZ7OtDuAsK3U65ZtVG9k86iInZNsteShY7p/gJ9kZLMF8bT7bcYmUQnb5sFJlUPPOkAalVTgTqjXO7qSWVNvJlx0hPXE4NAmOjAJgn4Q9VPTlcmVIMGeMz8u4ZuY8W1HjpCbGgIGGryj3VNZyKstujRiINyNoVrv8DGRq4R40yTKoxo3bU3sbziEqgZaA7zBQd3Y81gyA7NWBnoEqE1CITdhRpzOo3Kt8LPQKoBMEiEreaUyfordqgK4bFutzRMjXcZ1rZH9u4R0lsmmVTAwlDN0GXyfcVU8VsHNvCe9V8pm8ZBlJ+MK781b2nbIpEXDBFluoPKzMKUhK2EO3duhWptIpeBKuKaAmCyFu2MrKoJ0EReNOOuLQrGbWiq/xGhe0LpCKo+LGdcKwRfv74wcbcCj14TR4l0to7z1WXkHqOWSuQdq7JGdfGFmCsf9vucnRD3BI5dlqSCH/nt/nbHvOkuDxEgoc8ewcH3ZBuv7Zmnkrgzy/Xk4AxjqsfCVKp1ae9py/3oA5SgCfU5nQCZ4JeZIdJ7gMq0XaEPJeBrapHGmO0MR81MqTYScqSRQa2SqFRMX2NYEOZFUiCUereoQr1Rkgo8MJDOQloF21BIZEGjoCJrNw0nGiSBFvG9Ex4rPLHnBI8RCInfmO8ShtxtycsBgcOHtrPGVtwWYOUkbDCuc+FkGhG5N0OW6kEBLTHVq7m9po3VqbUeOrXXOzYA0apCmxLulDDrjMbQdquArbx3bsMGtLtlxyqA9wwLSoWNLjXZllpeDIw8CrA1xh6f4WzcNtDiPjhrSqMEf7WDSooOKNAiWLh1LPMJXxgkYBRyOvLIEXYdsT3CaSjzfQmJ5WKNDRwpCGlW4lEtnaiJzucyCq0miW7HOSxQLU3k1rR0R05qJxmnQMnHeIQTuaOKqM0CjHty2iY7FYUByJkQL3fgum5cpKNWWMvjCBrmp2D5nGYlQtUquyyArgowj1TZISshgSFyuoOtwwROXA65VYg3TA0q1Lcj6FHEBweE6x8qntiB4m4n3noWBR7Y78Jmw3ZElMjlnwPSekLrE8mcTftOYvLFW4lLGTxW/lam2lPooNEc7lEwcOqZ7a7qhkpZrm31GQQegWkKQ5g4jbCvVGKQzXZxpcPjW0xyO1J8/hE460ul7SYNMGnpCrkqY0a6bBmdep1VPt2cZFYefKmG9JdfQLgfissNvWxkEF6E9YwV305hmPQKhcKQUvx0J662FxAYBnbRUG/bM5abId5XZbQ7Gi5EkFvJrFVcyrnLjbV7ZRVxJk/ZRUV+elyqgq0sl/FjCJFKV+IghPHUOnU6gqsha5qtJEa1RZ2E9445k1HkLa0xz4YYJGm0AypWNl6kyD5PbTriopOXKOoKpqfmKF+gSfhJhe2LCmEGMtwSFuGseG+0H2lkCgD3j2Zf+JaWdEHUBJQRviQTB276nLS7mki0mBgY8xoMBCz1hgNWFUmJBQWMujj8jPIuRgcwDktU8MkVd2ifT6HHbnfF2UjbZBihg2YCdK92I9kTgMpabk6V3F+3uT+2rtcRlkI7Zu947u/sQq/UEMutLNM9u84wKRML8YPPOuR7wOHbI9j03p+ATsqCSC8O23I/Zxzxats0cR7JsnFXJRZlYsHBgkoyVP9g5vlPw2fg6SWxy5Z0QnakBb3eO4DI+K76Xduh1Z4oqsFMlqsdh3pmQK8uG0gqnJpmQsiPhLLwmDhdzCWslGtfRuETjI0NpaVLHyHVU2pEKhxJRahJDOpI4as1MVfEkIjpjFagqQSxBr79VvfV+0iAQqm/ma2kLMHMnLA89sjbEtTYzTBJIVcanws6nJmhnM5iY0K6QhVURCegpa+TK4zanpiEyjUjhzhAjkoJ1VG2EUEPbkre2kaWRDcLTqZFsl2rroJOibcSnRBBITUXas4xowt9wyMiPRwXucQbifVEttfxk6evfIIgT/PYUDUJaapCcafev4rol3Lgjty2hTWTnSYNAaFu08+RUldlqUTnNRghNjdB5b0g+KiSlmma6gMXgFeuMxVnWUDC0L51azR0n5Lpkqzhnsu6VgzqRE8ahmALibVYRbUbuphaKmx4YkJaCpfNmJY+EpJZamrIyig6NkbzWkAaOehzpho6Nb7I6LNVW6T4TTFehPW3AZCTkxjqIdtnjgon+iQjt6pA8jCxvJWIjbJ3lWT+/wdWJ+nBLc61j9IXEdFWZHhxAyix9MaLOEysTIwtJzMXdTqi2IdyyQbtnwMYFDdM9Gb/lqDcTvnj+dGK9pYuW7ZMHrvTZSovSLgf2Xe9hfRu6RBr4ouFj18yPKTwLG8RSZTV/wjgirVWsGp8xZLoEYVNpNluYKL4yIJCXa9w0M7xxaqGYzgZ5N26RcUtA0NjBcECqHLIZ8RsTqANpvyMPPdl7fC58kyKUaCEcu/duOvMvlNm9ed0kFsJ7XaOa0VGANiLj1jwgWsIVVTDl6uBM4qCUIEAsK8pNs3lu3IBclfEqGSk076nRthTMKrN7UQiThHTJnkkR/DjaMzYMNvB1lpliHpSIGw3JKeKcR+oGnC9hi+LZ6b2GJSHLEgCwUg7FzSDBl0xJZ55EMBXumGwy0WL8nl5SQcqAX2ZAWlmMRIuTYcY/60X7yrjtY56RyPvsNEkmBUAqoedSTmI+FaHHDrPIQx9uKl6YY4EMx6w/wx5lgc82eemjHlooR5J3cNF80mX/D0c5nxmPsfyaKCEk21CLF8U8nCAzNU6dXT+kB8blQP3J6I5nWbWASNFCEQARb0CnTMr6pFV1WrCRkouDLCm00epTRTVNGkkGaEIfklL7txF2M8LOupoySLBEbbU8qFzi8+L6kguJ4KBykUoStTcSceM6fO5M7M+BkGkk07iOVcaodzSaGOiUTCYiDMs9z0BFyeo65vYaVjQahZsPAX8NbAFm7oSpE/KogioYL0ZsQNCYTU+mLaRe59C6stlUE0wwrzFXetzT4KtA6BKik1n2kGYL1Yj35pre2LSObTy1DqOvibTVmdt3OkWWRkVN2FJUObIBriKetmqd0w2HzG28OaVnw9rsztngM2mNVNwEUyu+eQtdqiFlwvqUbv8SeVCRBku0xe2cBXIbC2nPkYaeamwzajcxVVlpMBe2F3RY7+iEVMYL0hjx2wldaWZkwKhQH24J3tHtqRlcP8Ud2YaVIe3+Ad2Sp9qE6uYJGgLt3kAeFJd2I4RJxreZNAy0pw/A2Uw7tEpsSkG7cYts20ycaUs44tE9kEotqW5PQKXwRzYj7XYAyeSBQ5qKahLpRo5u35DhzVN8jqTgCAhhI5IrZXzuMkfuX5O9cuaVW6Q6gPe4rTFuNCAcjoRxJg+FNPJU6x314Qk6aJgeqJmuDciVmIejbqgmEfc5wfmEZsG3iTCxfjYH6zijuJ1UWjGvRLcmbN9zH0tdAo34aSRT2bkk8wpFr0aGbjNp2dSJ1TnapYo8HJC9UB9tqQ51NLcUBeuqwteVZcNlcJpx6xPY2IJcymRMWxgO0KWGVCs6niJtZ6TRPsy5HRFXMpu8pY6bHo7tQxu79ubFCyX0kwsol53aS9tjRIRu3xKuG1i5kPEUHXcQwswBosNAbioSDjy4sRXA1MreCz/FgFguHKRaLB0aRXDktm9L0ZDqCbveITkRG49UzjwzVYCpm4WIvHMwmaLTDli2EiCaZzN37f/26sVSBPw0lcSAbCDGi+07VbC+iYynyMAYDVmdJRcUHak+C8kV4T+yhWF3WPFa6pLZebpJJhUeHk7mhPQMWECAJpAHFYSStTjXNyaLzpQEAGacl56O1gtbzss39NZH0mBn3X6QnA2WurPyDND03o9+/bJBT+CHncF25uHRco49kBHbcJb9VYBdvzMjENv57AppqU3eFFdqQs2LQ6rxXNQyNw0D6UytuIwmxHK7cqZwI80T47LgXTKui8uEbAA5eMVJMhCRrWCuinkV+yeVHpzmEtqigCMJFuJKmZqIk2jfXa9QbATioY+WcYijlkQtieQiXaEKeDFsaArDdpXnIYuUc53OPxxfI1uAmTth5rL0IEaCc52i2c1Ii7l2SFfUAsSDr0kre/CTDn94G7e+hawOiEsBDixTxYRsj23fJXzQ974aS2e/Z8WO2bbkzW3caIhoqYk0niBNPXtZ/GBAzgmJkPet4G88hFYe33Y26MeEbG3jJ0JeGpJXBqZ0W8h4dEZk1tpbh11m9H0xPg3O3r4QrCKxV9zUlE7xigRwG1OqWya47NDVIToVUmMdv5uaTo8eWUeXh7TLirQRl41AE7Y7pEvmYVqu6EbLSGUAo7m1NcQ/rElOSKNg5LYs+DajHqb7KxtsIgbIxplqmkguUE2U6rqjtKcvkwYO6hHdkmeyv4JKbDCKxl1xnTLZ5xFRRl/MDI4k4p6atnZIFqs9tJWhrsijoouyEthcqWnvKfjcMfyConiqzUy3ZrL69Q2buO2GuC8gnac6GkmVY3JwCd9m4kBo1wJhnKgnDn+4RYNjureyVM0hdE1F9olQZtB4wW9mZGDTPZeUbllMLdY7JufsI0clZzGC8RRkmmcqrpKVFCCOrASAH5uXQRWWbs7UX9g2AmkqpMaUS+ZQ4T/0HoXtsYVHBg26MiKfupe4UqHjMTK2bCTGCTm6hZt0uOUh3SnLxCUD8/WGvSMS1QQZYym9EXVW/oLsTHG71ysaBCQIrG/hp1Py2oi0OoSlIZKiKfiW9GdpM3nJkWrzUIVOYNwhGx2hMeBR3bJtpOKVmuwrI/tmC9X4jNUlE2chJsU8NFUBkq6IInrzDFAH82akBEsDAzbjCUymRgIOMpvpSxlpLesnG3Ar2VZGWs1QlWHZORgMbL/rm0jwyMoQV2HcOi8zl4aUtGbpie9e+sQem5UkZoV5MtaWnmSbnIUfswriB0hS4mpDT0CnLzviSsH3XpLBHwNoYEdLZh7MyM73nq+8Q/qd8x6dqB9mN9g5dlnBcfQ6XPNCXAaMpKw7txeZ/Uq5FDv7nWUm9LMFiuZEGRN6Jb1ZgwoQKihNs86yKbMr4Twx0nQunjZXPGlS/jqx/qdXEBaxsF+QbNzLnhw8hyQMp/YqxnYcL2rebwdeHFGVThwe4+j4nAkuUYknuUQWb54wnIEdSWQmJHVkWoSMd0IjSiW9t8muWy+o1wGb+XZu4FfJ7lIw85u/+Zv85m/+Jtdeey0AF1xwAT/7sz/LE57wBAA2Nzf5yZ/8SX7/93+fW2+9lXPPPZcXvOAF/Nt/+2/vmgZr+V8pC5CWCh1Ks+m0TKegEIeBPBjiuogc2ZzpOSCOsNUSfW0zrKWhpZU669ClK7VpKislwMD4NdJ20AIpoj2xsZ91dh2zF6nsw20ZT4cQrAONCR3WSF+LZzDExQytIl1HrmtT5R3VpOWAi+DVKiP7SUf2ztzoYCRXsHAS1pHHRvCx1GYa7UHaFj9uYTDEt0rYmoI4tPFMTh8iByp0rFQ3bRBPXbGXPUXaAwNL72xAcXYdOqXZsHRFFehOrUkK0prmiA5CIbKai9w0YYx46tuEdoqPUG1nmhu2qDYy4z2evDagq1MREnRIcoQNmYUe/MQxvMW8RdIJK58e040cWZXkhbjWEEdig2xMpCUhDUb4dWV0A7gOaALTNUf24FcCablm84wBS5/bwgVHtyrkoSMFJTaO0GaGN0aqTUVrx3S/0K5U4DPjNY8koT6cEV8GrpJxUY8TXe2IS6ATodpQBm3ETZW80pDIxAoE8zh1a4E4dMSBgIeVT7cMb8xsnLtC9kK1XkTAJoWzs5lxuTXAPOmgTeSlQXkeChdLFRkOYG0FXR0iKVPduI4c2bJwqJRBte1IG2N71vc15OCNVJ7sfjJtYRpsYK0ry5ArMv9aMsB6oa9cO2QQ8GQ4soU7vG2yCWBhxWFNXqqNd6NqZRwmHTqqS/XsQLhlSrjuiIUpNyZoVlSWbbIyV5qDjKWFp2RSCZWfKQSrd4UgWzyFgwo3bYvg4sRCu6vL5oXqSv2o4C000cdb+npPSlEML4Nn4a1oF42M32ffra7YZObwOk4FXRsgPpCKboyl5+ucqCHGbVPL1PPZQK1x2ZyBiGD7zoB4K2lIqI2wDuSBn9Xj0srS3WelSHog06f7916ZnttyLEARCqen/N6Dn3mw03e5JzEunsgRsAN2dntqdq9xO1Z+Fp3z0qCFPDwPh3qkxhxw6o9YQEsBMJbhpoWWozNRvlw0Z3YoO7YfV9CZF/stZ3s3xRXvSPHAWPdvoSk7bh+mNGDrFKIa/0bFkTD5hCDZ9JNcNjHUclY2JTdBy+g9pMzECRHPCh21TBk4SpmEckw1YDtROJKb27+2XwW7S8HMWWedxS//8i9z3nnnAfC6172Opz71qfzN3/wNF1xwAT/2Yz/GVVddxRvf+EbOPfdc3vve9/IjP/IjHDx4kKc+9alf8/aaGJRxTNKwIo08bmqI12lCB55YObQR/DTjbtnGbW6jTSCtLiMIbtrht41rIdsTtO1g6JEQjDcjoF1EcrK6OVWw7Jy2K+mCReK762ywB+MEFD0ZWpO6TqUcAimh65sw2IOuLMHRDWQDdGVo3p3gZy5TUJimoq1hAEGcsyrbdaBdAXAW5qjF6gFlyyjKUtzYOOLagG7fwAh7SQlHFecCsbGUvyyOSjsmZ62Sa0fYshl4qh1xJZj4FWVm2MCkDqRQMsmcFfDUmIhLwbqTChj6kj5q/I+eEeQ2p7DkmOwNRTzVU6WKjX2NKXiOLc02rYCfJpav3cavT0hLTeEoOCMKJ0dcCtapZ6UNmTwK1NuZ5osTRDLj0wY2g9pO5lbuPNo42n0149P2EsaJZj3jcvGS4WeeHpcMOKk3gNSVzn66V8gN1Icz2nq0gsmSpdH7aGCmy+YVyMFBDU4TbksI2x3tsOgUSUYmmW7gSE1f3dui8H5L0cb4K7kSmusmVk+szEItTVntWnRT6+pWB+TKIylZ/ar9K0gI9kxlcBsTZDzBZP8zEjsLv3oLczGeUv/jUWSSyMsD4kpAWCasT+1lUyV3yUI9dZ9ZYoAmO8FPrS5TGgXimWvIKcv4UrHabVkleJt7GKEYLL3atx1MjbujtaPd2+DHAbc1Ndn3rLC+RcgKjV27XAfzViLIpk1YqDyuNu9g763E90x2KWVIHEw73MaWfV8emQer62ZqyzOCML13whVFY4oishoXLVv/Yx4RjzqPeMFtt+jGFt0gWK2NIhzpei6Htxpps9BSqcau9mgbmOlLqPTIos8iyrpD1i4codxX7S7E5ewNJBlHRmZelZmHZZ43UxIlerDSZ2HTg8LeyzAPfG7DS3OyUYx5783JLN+1wjELZMYU6huux688A2G9BGZ/gsblykn6OBZSwqjl0hRQUnxKKrMQaR+S6n1PFooy+ZydIpIljNUDEbGJV9/ClMRUAEQMVIkWWOVIpYCdujQDwCRTG5YIXXZUrqXRiqwO58bUOaN0piBQHFZZLel0KwcOpZXbvS9fDbtLwcyTn/zkXd9/4Rd+gd/8zd/kQx/6EBdccAEf/OAHefazn81FF10EwPOf/3x++7d/m7/8y7+8S8BMM6rY3uzIjZRO2uGSVcJ2bUaOTkipQ1eW0ODIqzUMKoPX3tvsbTy2tNLBAHMbCzKZoDFZzNx7JGCdXzZ3Yl4e4DSX4KrNcDWWB29QQ1XZwDEeW1hma4qMBuRT9+AnUysOeeMhG2iWhtbuabSK3M4VF3QwLZmqISOkJZutOYQcjLNgarX2uqTK4cTCU9kJqRbLVhIjo/qJkp2px7quRnG4NlIfaoHi/t/boLUQ1UJ2aVA6iCJylhy4tmRktOat6MPZuuJhnJGpEWl1IFTrLX47EZcq8rLQNR490lLdmJh88x4Of/tp1K2nG3mmq84UYWMuRRXNE1HdPCnFNT05ZvKyqez6VnHZ0a6a4JafCoMjHeDpmYmSwY07BtdvMz11mTiA6R6PtJHl61tcrkxLJUM4tM3UVeiBMlgseepbpyjJ0v4rIVeZar0ljBWhZrpsCsDWueuMzDkNDpdlVjxRRWjXHC57ZBKpxtZRxpHdN0k2O7O4utCe2lBtROpbW9Q7K2aJWLpxm+iCIw5rfEx0OcKgMtLxKBCHgp46ICSHm0Ry7QzIT5yFGGOyNG28CdQtD+zZRmBQE8YJaSe0exvLslOsnMDI4zY6XLIMJhKm5OvLiCcgrYnoUXt0FJismSaTHytsDRFNM0AKRUelyCmQsskQbG2Sl2ri/iXc2giZRNgYm6s/Q95u8ZmiQ+PwwxoZt1h9K5DO0t/xDt2zbO3qVYj7gpE5I5tjGGUDPDDzzlipBCPZzjCHdzMgKaWOVp+GPCuguTW2kBWF37K5DUsVDOx81SULM4GFVXx5ZmLxBpQsPFLCJaXPcdL58iaFz6PeSMQGtmRGXu7VvilClzNAUm5vL4zZ3y/dac5usNCDn/kQU48V5jw1/cB8LAjZ9V12/9W5tebXO26bY9pz3G8ndOLsABotYOdEvwFoPuZXARVfODsA2SbKUjTD6G+D9S06t09VITslqN01TUWuKBf+lbfNLOwkRpJXoUsOhye4RHCmf5WlDxMlckGTrvzPp0DOA5IXVrAAgiJs5CFBMjEbr8aXfKaEo1XPptZs6vKJLthX1b5uODMpJS677DK2trZ42MNMbOcRj3gEb3/72/k3/+bfcPDgQd7//vfzyU9+kpe97GW3uZ/pdMp0Op19X19f/4q1cc/qiO3to0YgFUPGMsmWjt0lXJdxbUc8pWL7QMXwHxNydL10AKbXAh4/btHtKYwaZGUZHU+BiPhgz7UT6+jGU+OwNAFdHaHDGta3TbxqPN15XzJIMzAS8fo6zpmira6toPv3wI23mo7G5jZMJrC8bPo2qlAHhFw6rAolo40nDgOguK2E2+zIywPS0PRP3LQjLQ3IycS0XARVJY88fqvDbZqbuz4cybVYinYQqgjj02rz1owTPgt+I5FSLHWHOpw6JqcPLVwk2bw7RbE3OnATpb5xgzSqwHtStlhyXHakYSAcmtB0SpcrJgdqtu53ANFYQFhAW0t/daqmgtpaWySBj0JeHRixL4NvE46WbrUmDlwpVCkMDieT56+NBC0Hlqk2p7hJRgcV7bJnfEbNdH9FNVZGnxnjJx1xraJbBjcRulEgLTvakoUkCSb7K7QxvSG/3ZqmSytoCMRBHz7YmdJqcU2rl1l18Gpq5N5UCdM9gdHnNvGTxGTfEi4kIx9XrqQkO7Qp6bjTRDUFbQJxVKGhsnt0/QY+Z9r9DYrHVaZjlIbevHbJxPJyEVcDRxo40mkruNUBcmRsQNvXFibau0perpAs+E5x04TrEtWtE7Rx5MbAvwB52SMb5iaXXlyxK0KAKZuAZXGBu6MRV0N3+irtqsc7O1fX5qIo7cvkWAr3y6GjCjep8FsRp2MbkLwY56dyllGlRra0EIqDXOOaQK5MDkERiNFKQrTZRhbVuXGsvKQxwhioa/PiQEl1NkVu9Y5ZaZJSVFJjnmVn2eBXfl/fsgSAUl8qekeuKztOUSK2kHMy0jWmdIyWXUNJEZdSADMaSOzra/UHLAJ7uepjH9AX3JxlK815YuZ5MjPycH8JTvDZFULquTxz4GfXv+WYfx8DWnbZcb/pLk/PcR+O+e2Epif85y6QJSfYvNyyGTDDLk1ORhyeJVIVAnAv0teHm1JyxUNTso6ckLLgNJNweKck7Bl1IgSXSYWso2p6RV6k991Yxn8WFKvkbb4ZV+57RnLJtpNMUEHEswzULjKgA3Fs5CFeMsl1VBrxJbQVVZhqYD0Pqfnaqv/C1wGY+chHPsLDHvYwJpMJy8vLvO1tb+O+970vAP/jf/wPnve853HWWWcRQsA5x6te9Soe8YhH3Ob+fumXfokXvehFX5W2ftOZ+7ju+iP2RWxWkoY22Ggj6EoFfg95GKg3Iz6C1JW5ZLUQJ8E4Im1b6rLkQlIsULoq5Ygnnc3ovEOmigZPXK5hbVCKXbbItEUnE9OqcQ4XAhoqEyjbGJOWR+TlIW5rZLO3wvURpdRWAk1qGjJHx8hogN/q0ODxKdqAEjxp2VL9JCnd0BE0QFLCOFo4KlgmRU4gKD5lshdCitRHlbAeafctEabK1n7P4IZty4SqwHlBQ03XRuqtPBMnc5JxExuoEZM9l2htqG/exktFtxRQEpM9DUzEQmtlNtPuCXTL0C07cDXD6zpGn9tGRw3dWsBNhebWFjYTISZyELoDI6b7KkSV+lBHCo64FuhGFheWNjK4LlJfv8nkm1aZ7HGENqFemK4F2r2V8VC+qSYPPH4MzaHE+KwhkgaMboqEWzKynUA9cZqoNwScx21PqA9PIUF3+jJpqaE+NMVNEnGlIlcVvhXjYYiSK2dpuSpIDVoJfqJUGxFRRyrhqu2zR4RNqynW3BSJo4Cow7VCXjMNm1wJ7f5mrhfFCL+dEldqMg6KurWsbyPDChcH4IwsnJ0nDpQUKstI2ujIA8/kjCX8UkW4NeCnnakcNxa2ITjY7qyOVc5W52g7oXVFWh2QS4Gf5K0MAU1AxOG7Ul04Fk9lVis3sT2hWt/Cb7ZMzjsAKLQJmSQbxJva0vu7MviUZ0lXhiY8J4I/OkbXtwFFVobmvUJMMDKIeWYENPnCEfFGhm0a05iZTpBxtLBw7GY8GNWMTlMBJjuqxbO5u3M73JJkkvR9X0Es+i6lxhSChYqHAwNFdUUeNejeIXnZap9B4VJEC1FRuTmPh9sBSMm0pqz0iqI5FzVxsVR5B7maeyZKmEjFSmv03hc9FnS4uX/PL789EDF/QdztrH/sPk/072IGlvT2gc8Jlve+ll3LTxBROpGraH41LRPNnQU9wLMGay6pWYYkSBTxw6JV01MHxGMe7yz4InSW1O6jc4KQS/0o89aKRe3NKVmy0voj5wJqdrLVTc4iq2WkRXUm7aHBQI9UDCQylMiSa+3xSyM20pCogbpkRSkQ1TPJgbE2fPNg6TYu9lfP7nIwc/755/O3f/u3HDlyhMsvv5xnP/vZXH311dz3vvflf/yP/8GHPvQh3v72t3POOefwJ3/yJ/zIj/wIZ5xxBo95zGNOuL+f+qmf4j/+x/84+76+vs7ZZ5/9FWnrA+5zFh/60KcAi9vjII0CtJk0MoWnsK2Ew1N8VOtYVKHtcGLp2urESIB9ummiKAzZMbQvQLm2bJ1cF3FdRNfH+C6i+1eIKw2VONOf0IxMphZ798E6uZiQFPE3HyUd3Ice2APrljpLFhPw6isGdwmmUyvaN2hIdZiloGdnoR0XLf6Ks3TObtVDEuKwsiyPpHRLHiWTsv2ttiNuaoXy0rDGTztiXVvqa2XptWErWpp25XA50Q4cNI6wbYWVcrDZu59m06JJpsuz8YADDG9qGf31F2DQ4G50pDP2MDljxOTMJdy4pRtBamxAro9mlq5PVBro2kR1VHHbHYPrNmn3LxOHxkVpl8HFQLMe6VYDaWCZatV6xCV79WV7wvicZcanB5obOup1JQ+Fdq22oo8rpvxbHU1WSsIrflupNzPhaEnFdw6dRvwk0e2twYO/aWIF5vY0xKHxg8JmR3YW7vNTm3XrQKGULvDbCQ1CXPakoZBqC2v5iUIjdANwVcXkQEWqMs3NyYTtJsW7tlKTaiWtGZ8kTAAsqyk7qDZaS3XGEYqmjtbOqj/LFK2C8XSWPCoe1yX8NOK3p/j1hMoyceTo9o/Qzc7I4yUUlhobDfN2wo+z1ViqCsk8ZsIE8iRZcc5ReSYHDiohNY7KRZh0SJ8r25inzt9wiMHKiK4JVJuteXGCB59NwwisZIFY6MTE2gqBd1rqPAmEjcJBagYzL0WunJGVY8JNMzFocUkYxyGpELpoPDgx0UpqQaID6WbAxDgqGXFLM6AB7IjkiYVWS49gWVEYuJMi46BdhywNiaMBabVBhzYJUrV0e5KaXEQh9GpPVM7mRlGx9PJeBLT3JolgXqheoXj2m+yEgY4FG3MhIjPZ/fuxIGIeBBwLZIQTAwdOsM2J1tfjt7v9sNSx4afj1945zG0GqpiJLls3MUtF37m3svtcwVbOGD+pcFV6x5zdf3u2+yCTRS/7HUNSwasWJ9jOzrO6sq7OtHp6TRivCcU4hTKDVf325rHpdXoUYZp3NGuqklm1lFs2tSFmTyBS8DNW1crR0LKv+ia+1naXg5m6rmcE4Ac/+MFcc801vOxlL+OlL30pP/3TP83b3vY2LrnkEgAe8IAH8Ld/+7e85CUvuU0w0zQNTfPVYVI/5MHn8ju/XVImNUA2+fPsnRXtA8JWJGx1yGZRAc2lSjIZNJPFmatZsdiokwIsnGUxTDrrvAYNeVjhbzxifJpgdYFkY0JaqojDgAuCrxy6GZDxxN6votIpqrA1MUKvE6gDuWogGHlUh5UpBQ8adGmAVgHBajZVX7iVfN5pSO1wW5E89KTGIyXFDxWqkg6dGsfw5ink2uTyj2wTa0UHNd1KRZgo1aaydfbA0rOz0u1pUA9rn9jAX3cLjEaA0rlM3L9khEtR8qiXTQfEZprSZVJd0+6tGfoy4zh1L5NTaiy1VIijColKfThRH4k0t04JyVvW1dEJnLJM3DvAXb+Nc6Vsg2bqwxGHh9b0R1Lt6SrH4EhEULZPGZAGVml5+fNTms9auCuuDMlDIS5bwcmVT7eESSYNqlLLyYir22dbHLk5klAH3SgQR44wVuLpK7DRouKo1hOiFspKtXGNqqNTBMf41Mb6zZxmlbb9lg1EubZBVfOU7IaoM4DTjQDvGX7qqJW5WKrIyw2IEjolYWnp6sSAytaUEGrqm7bQ6ZR4xh6mewJumhlE8GMLV2qXcMsNk+E+8lDINcRhRQrgt4oa8HbhFAnkYcAhhImdmzpHHASk9bjWPJG5CZZ2H5yBkLZDOkvHyUObDBDUeFaV1eORpPiJQ/YKHN2wd+aUFdNW6WJRCc5I2yJVYynFWXF1IA3snP3WBD1y1CYIe1aM2L49Rn1l3XybcUXQEQcapGj9YNce8INAHtaWcJQyUueZlg5b29CWVO2ugyOdEav3rMIQdnT65wZJMa+v9Doofexh2JSSHaBFxwjF0toDFu4unO3sylDY1zTyxVmRSvhMHBZm9jMQZTwLMe9VLnybMlbOPDGFN7MLxMxGRp21/7jf5oDM/Nd5LZdeN2YXXsg7+zhWr2ZGlL0tz8vOIecAljVgVzTwWIBSjjM73u49ncDEQAu6k1Axk0A/wf2dHbi4RbyhSdPC2TkfndtEoRQGtwBRzongtdQz7uNZurNhf9KGx81zTsb1GjMUTRyxKIE3NSrL2C8cGC3FMpONEOxxW+QkjK3a087tRhnQccBvsDZ8+G3dhq+a3eVg5lhTVabTKV3X0XXdcSqC3ntyzrex9VfX7nmv03GthVZMe0NnGglGdlPisqM+UsBFSla1d98qMmltZhaCXfVkeirG0chItk7VsQ2d2LIu2oyzruzBzhnWtwmbSjxtD3F1YGmgweFVrabSdIoMBqXSdoVvI7FxOHGw3SKj2q6fr4mrQwSrSmzvVIZBDSuDkhKebUbqi3T5JJfwhoV/qqMRWVKrQ+NrXAAZVYStthQ0NEKsz9CsV0z3VJa7VwupgfHBASGsWUrpoEJSItYVuYJwdEJ1OELTkJeCzVwwnkpza4s2ga37nEpzqGV6xpC0Igy+OEUHjnbZE7YyzeHOZPTbFvyQdn9NPK2iah31zdsGNjGvh2xM6E4d0R7w4Dx+OxKXA5ISflJqKw0HpIFQH0oMP79FWq6Iy6FUuM7UtwrDGyYMbjBtlbC6RLe3IgeHiqNbMS5EtZ1xt0bYG0wTyINvheaGbbyr6JZrwpalt/d6MZIy1DVNEJIztdx2pcLFTG4cGmy/Kqaplhsl1X3KptDc2DE4OmXzHnvRwm2qp2rKscs2aPlJov78Yer1CXnvCnlUk/cPISv1rR0+lkKVqrPiidy8Tj1qmJy9AjgrGzFqmJ46pNpKuI0I4xYCCKbC69pEfTSTGoc2jm7fEiEIbmwicWDk4xwcuaqLdgsW1kKKICNobWEQK5Ka8dsVofG4QxvI9YfJa0u42jR+aDvcdkTbBCs1aVgZ2JFCnI4JTQkqy8jSmEy1t+1IpdAi4wROSENHrI0w4vq4S4ZUOfKeEX5SmcCg6ozMzqDBddE8P5tjk3HYHts6k4G95yGUiQ12wpVnptIbC6IXkDqgUyuBQiwVxbeNWJ99CX3mAliWG+N2lbRsxJprvxuhOveCfPawGOFeKcUcrYmzYbIQfXeFgvSYv7Azps6DgMTOuc39s9fn2wVITgCCZN4rNG/9b3OH5gT/3mXHeIyO98jsLDM8MA8y+78nQFBzoAzXN66sq+Wv9JlKuy+EqiA+m86Ls/Rr6QX4iohf70VxTsk5k7IQk1XVhh4/KkJRFO6Hz4LKTDsmUznLenWiVhNKE06USjKV64timpenU8dUayYaCUSWXMd+2WJbO6a5KrrESi2dlUpwHXv/qYGZn/7pn+YJT3gCZ599NhsbG7zlLW/h/e9/P+95z3tYXV3lwgsv5Md//McZDoecc845XH311bz+9a/n13/91++S9nrvqb54CNaWyAPFbXvywNugX9nMpleo1NiZcNdWQkKwZ8lZiCGnouzpxVKaq4BMzPuS9i7ht1tTVB2PTTK/bmB1CSVZDDSayz6u2iwprtTEZpn6sAEmHdSmOtuZi93FjAwqK3ZZW3aNJtDlBr85NQ6PZgsbDALdSgWNkIKfkQKtmqxaxkrlLB16yaTnu4Hgx4lqPSLe0+4fodMpeVBRO5kVkpNsDi3TMIE4CrTn7Wdw65SwPjUtjUFFDtAtB5rtjJZ4bHV4QkglbRVlevoS7enLdKdb2GJ4a8LfvEleW0KGgVR7UpVo91awdw97P7HJ4EbYOHdEF2DpaEfuOhPKC7B17kqRfTci5ORAhVaw8rFNqBs0Q3PTmMnpQ5aumzDdH5gcXCKpMPzHo6gMSHsr0x8aVlTZ0uLdpsLUQeNxowHdkmPzzIqltiIPhDSwWVhzawJnYcPq6MQI3s4jQ08uNbtw4MYGcNsVIUyNwKfB0S0Vb0GXiQeGoMLwJkv7nezz1EczeXWENjaaifOkRnCTCYPrI5Id9aGJpbKPBjZQR8UdmiLTSAweRoG0VNs93ABfynGEG47QNJ64NiBsRaTLTE8ZkGpPXhNk1bxmYZLRSbSQZVKqI62Br9WGdt8I11oNJpfLAJciUgl5EMjOipYKWPkAh6XpjxNu3BJHgelekxpwzlm2TwkPUVsIWCKE9W3y0S1kdQgrQ9Q5KzkSo4VvwHSh+pIJ0xa2x7ggsGRZeSp+NlWXVLKkxLwi+EIKrkJJu7ZR1qXiCWhqpK6sMn3qTP5gaxtNQxgKSCkNIgXBuVJ7isBMSr8Ktv9UMtpa0/qhi6YfM5kgVWP9zqBCfSj6NSWOUcJROvClAKfMyO3ozrgLzMi92RlPZpdXZt568HJMuMUuUr+C7HbHyK4/Oz/lnS/Wb7LLi3KbAOXY33q+zPznmH3Zv28byMz2MwNvurNsHrj1wEaOuYC+oBuP5S07ioTuHBCSctIRZGB8JudLjSenM8+M870ujV3s4DMxBVK2OlESFEgFoBhIQQqmEtOlCaVopXMlpVusEGYwP8+sMrfHhPpETVtmkisqqanEM/RWdbvSSBJHxuHIVkNKMm0OLNVfGWrHnbG7FMzceOONfP/3fz/XX389a2trPOABD+A973kPF198MQBvectb+Kmf+im+93u/l0OHDnHOOefwC7/wC/zwD//wXdZm2bRaNzIaUC1VTAelBtF2soc4O7rVinDmfis70Lu61zehMhn4NLK0X9lqcaOGbskTsmUAaR0MfLSdzVKbQemghW55BMNkaZixxR/ZwrWJvDJg++xVpJoQjkysfkqoUJdwKeHXt61OTgjIqEYHAaZTfGcFJ/PagFQH0x4BsssmVZ8c9BkaKC4KYTKxdy9YNe5uJOTVmjywNN3B0czwxg7NSvYdk32BaprRmPBTj3b2Eg+2EzKZMD64TLdsIMAdbhlsdMR1cJsT4gDqCciRiBZNE9ncRoOnudERiGx98xqhtUF7+4K9qCbcdoTGOpD6qJUgYKsl4KiPRHJlHWQ6sEoeOdoVj1RWgJJKiWvBZkmlCkTXOMI0Mrg54lJL9Y+HaM9ao7m1Zf28IUf2r+A6QXJEhxU6Ftq9gssZP+6QSSYzsChgDa4Txmctm2KsA6LxgmQa8aXOFKWsRR5UTPc1JhCXFJ8EOTqFFGjXHHlQSJooYTMz+uwGW9+yh+wF35nXB4E4tNlgONzCshVl9Bsdgy8csTCeD8bNWlsijWrS0IM4XKe4oQnFpQCoolKZ8uy0w21OcdNE9cWjiFgWXCidbxwK9aaYEq+oaQUVt3dc8VQK7sgYP03E1caIwdk8fXirOJwFaB2UiupkLdfLpvmuS/j1Mf66CfG0VRPzW6mhKQT3XLwUvb5GUtx4glx/CNYH6MrSDuE2BCPzFm+IWtoJsjG28KNz5EGFdJmQjBTtopKWPDnY4GaCvWLhS7Hwj5ZwMOPOipEmRZoKfGP8mWlrntRSLVqRWVhIwIBSib+odyXOUJXaUYpsd8jWVqn1phaCajKytlJCqJhoYDIQGZ3VGaMXBCxpOPb2FyUVKQCtKPqmOTG8XaY73pVZH1k8P7s9FHPek2O8LzNdmtuw+WjJiWDHTqt3L5sd4xgvj857S45ty7G/9SUiXI8o5kCZYJ5mZ8stHNWTd225eEVLvaW+qHevvbRT7VIQn9DsETIuGJih6MFAyUwSy37qBfS8y6i2RTFYcKQSQgLnlMoZwEjl4hZpIIJkAgmRVG6p4rDSCZVLeMmF2KuE8mtLYKI1URNj1zGkK+AozjnmhE49G3lQPElfW7tLwcz/+l//63Z/P/3003nNa17zNWrNSdpwCO0Uv2XFE6fe0obDZodmy5zIw4r2jFWkS4TNjurweCZBLuMpbjOBF9KwNnVeaituN7AOy9cev2e5FMur7EWVUj4hBANFR7dgbNLuPipuX8N0jxX/kzaRho40dPhtAW/ZNCY6p0Yyy7mEt0pa37RFopQCmC25qdCmwikkUeLQI11n4IySBbGVCDe36FJNt9qQGmG6t4IUqW/pcMMh1UZGa9DKWZps8RXnWvDbEKaWcRRXany1ynRkpNu8XROrSM6B5rpNdFQjR1tke4osLaGTKbLUMLh5yuS0hvGyqTGPPh8ZfHGDrXPWoFOrgZUz3d4KJ4KftPiJo11tkE4Zn1oRR4HhjVMi3mosCVSDQFoV1u+9imuVIRDaZKErlOpQy/TMugw43nhDU1Npne6pcN6ZCrEYOXd8SsP4VMsuWPnUNu3exlLOh2LhD7XKxNrlGSnVqk1bZe7cBNBMdf02/uYNunNOp1sRQmvcIK2EelOppuCmwADaVUccmFdg+TMb5jVcWgbNuM2p6ay0EarGwhjR+DJ+0lqWmXPGz2gqqA28SDQZgj7TiKUBUnUIAb/dldCQlArVzMQXrc5RtrAIVvgxroiV9JgmwlZHbtU8EdNolYBjQpcqxHuilM64FBp1nfGn6BJoxm9NcNd2pD0jU9cdVUbSTyXd2tvAnd0QaTxyaAOZdugwz/ReZGmILg3NG9qAqoV3Z0VeN6Uo//oZ78RNM27cWjG+5caAshdyPyUWwDliqQFVTWLp6AX1RSAyBPPGeAtxzg+wpjdV/hYl5B6cEPyM7C+58IJy2d/yiLRs6bFuWpSune0niqIDNwcyircOLZmXGIgRt1Ph2/VqvTuDlM79Q3qHgexEl8QcvsbNmV2LnXOb/T0R76Z4ZTIF7ITdv59orNRj/q+9t0h21tCyP5nnzOwCMr0XRnfCaf3yXrengAkE85S4jLhsz5oz5GUCh4rzWqp/9GlJ/TUvqtgCEM0DUyVLbPMGfrKaF0XL+pXPJTnNspdELPRYeyP1NiGVekyY99vZflxOVpMMLanaCeeSgSDVWdgpSKKShHdKJQkRaMRU6Tv1THNFEmEzW029ikjxQ5IROnVMtGIjfe3TsuHrkDPz9W7OO/Lqis3SRsGUGnMm14JrHbI+QbpE3D8iLldQedMr6SxzR1Rh3wrSRsKhdVJS3LBCa28zK1HiUiCGkcmUVwHfRqvAuz2BLpFXRuSDNTqNJtXeJUZf2GJ6yoBuraJaV8Itm6Q9IysRUA3x08zgcIfbHBdNmRo2pzDtLC02JmQyJR08QHtwmVg7cuVp1hMybvFHN/FTIdeWxqq1IC4go4rYOPxUaTYz+YjVWI1DR25AkqcbOarNbF7nwEwFOO4dWtHBZF4piWK1ngaeEKBbqZhUwvA6R97umO5rcKNgtZRqR9zblLADhI2ET5nlazfoVgcgmbji8ZtKXKqs7lBnKc1+nJmcbkCEZDyD7TMG1IcTbS0lm2Mm5kqYWhvjINCuCG7tALn2xpepwE1MxTjVnm7N41vz6hinKhNHA9q9nhxg6XMt9eEppgYd2T59YKEhZ54GCz0qNB51ViQzLnkbJKMw2Svk0/YR10ys3HWWpuxb67W7PUNCzHTRKo+7BM1NHe2+IVoJ3WpAtjuGmybJb6QT48AwmSIpQdMg2Zk+T3amJ9R1dq2TVe9149bCH42VBnAu4GIyBd8q4LcjflyRlo0jJrqjKSPO0uxnng7zhpuidqf4nOw4k5Z6PCGmDMsDpE34lHCTDu2yhZMqb6U6/B6rRn94y8JFq8vIihUc1VyydgQDNCtDGDRWKX4G2s0jpN4Gee0KWKgKjyWrcV42J1A7qwvlzdslUfGTiESIq3XxnJQHqOqTA0pGVe3xnYW9CEX/Q9XCvz3Bt9d0odTwcWoeqXkxO82l3o+BaUbmwaWL5jVdHZl3azviamfetkpI3sprSJjzLhQAkCnn3qOT2fHYJWS3K0QjzC04JhRU+CHKDmiY4Yv57XovDjvHAMMO2THzDs17WebqYM4BIp3tbtaG2QF7fqPOnFGzbenPr4CW2bF0B/AVUMGMv1Ksz4WWvMM7KgeuagMC6hRxiqtMGJFUCsfiENeZx9QrUvWp1or3SkoO73PvlMP3lbcdBOlwpZp17TPemWfF9e3UUu6ggJpArzRtbRWgJuF9ppZEIFFJpnbGjmrE+qPKR8C8O2McDYFxrlGEWiqCGO8wqQGeiVaM9a6BFQswc2fNFVEtJ3TLNqNz2UGX8dNscexpxq935LXKQgujCmJjsd8QiGumghqCM+n0I2Pa05eg8G98m3HZClemJU+74qnWPa4zkqwTE9oKGxOYRrJ3+O2W+mYl7h8xPWVITTatiS7jWik8A8FVFWFrQlYhOXsxpWRGoFoqANsAF7ZNdKzajJAcqaiZOjGxurhS46Oay7pW8mYirHf4W9dJ7Tbp4H7yUkPoatJIUWfKwOot/dbi9hmyuf/jsieMLXW1W/FUrdI2MFkLDK9dJ1crTE8dlgwhj28z1a1TqqmJvuGchT66mnCoI48Cfr3Fi0Mq0OIdCFuRPPC0K85CeWU2Ot3v8WNB24SKhX40mLrx+LTKrnNSWBLSMNAtKWmghE3zqtSHO5rrMt2BEbpUMT6lpt7OlgUWIIyVwS2Jbq2m3d8wXW4QlKXPJGRsxTNzbcAlDwNx74B2T2W8jgjUSlpdto7Xm9x8XBJcK4TDHS55/DjhVPCDDJ1VgW5u2iYuedKeIXlqoAjNNqhVgTiA4AdWFmM4sMrUUMilGXdo2wqdeodrGsuwSeZOZ5rQ5NCRGIeltWw/dUIzSUxlSBo6ZGzCcsmBCyWduS33bdJCUxHXTFDRxYTbmNBLq/vtiPOFa9MlpDWeDL13IjioKvLQst+sJ7fyFjaQuFn4yoi0FWllYHWbYrbyHdKgbbBxKWXICW1bpK5JI+PiSBtxk45qfUpXO1IQpBFcNP6ba+3Z0kpmPLnkKBW/zQ0vGctoKpkD2hduDM6I9rviMDBjxhY3Qy9YJyolTbc8C76CpQGus+uMYtmPOUNntarw3gSrKzF+FXMgo/fI9C6HOeAwW1F3QMBMKK//vfdUyO7mm2fFQlZZbFDuQ0q9yNyxdZlELXpjzzmlGCa7AE+fLT5LFJrzuth11R1uj/SieXPemH6LWVvzjifG7XhnxJVL7+bCTuwckwpEOnCCczshpqxCXSfrZ6OJ20kwb0gn4IOFCINXxCvBC15s/R6leac4KWJ5fWhJMEFIl4zA652Fk1ymKiAlq3FtqlKryRSFBacd3uIAeMlUPtFIYuBseZBE7aweU+ViqWqR8ZqIGlA1usNUKzQ7Osklg8ouR1THVCumWnFX2ALM3EnTM/cyHVVIgm5kblhJmRCN+CkosjnBH07I2DQ2utUKmSZTCPWecPMmabmmO2MN2bdkPI+JZVO4VBRa8YQjU2Q70p46Iq5WuJSJS0PCRqK5cQt361GrcnrGPmTSWWHF1kiMbprI2qHVAB3Wlh7sPVXOhC1wkzHxlDXSMmjb4VSQQYWq0twytdRYBzhHu9bgxlbRWrMSV53p0DTWOUu0TA4XMjmAVA6/qbiNSPI1YXNCihFdrsij2mZHnRA2W2vXaSbfr8GxfQBryySBg7AtdGs14fTlQrI0wFff0lKtT/EbRdLdeUhT8iBANyVsCtPVFbq9NaHNxrmbRAvDbU4IjdCuDkl9Fr+H5oZtqtYzGTmka0n7GuKwCLV1Sh4I4zXFt0K9EamOKoObYHBjS3OoxY8juQ6kaSZri6+GtMtlAElKcyQzOa1C8RYenGaadaU6bHoq3aojjSrSSkMaWF0n1wpu2hKKoCBOyU1tKrtZcZppjmTC4RapgvGrpplqms2DgaM9dUgaBtwkUW1MkI2ItJm01pBckaov1cK19vY8bxZuR9eZ92zaWohzYKDHCpWVvj2YNH5WrBBjyogYZyVMEtmX8IeCDgIxuBmHw6X+vEqGYFZLrQ+O6lAmb0/NY9ImpPJGnkUMaLURE4GzASBXAYae4gPc5emQntCIw00SaEtaqu161w7XBSNXx4S0QGvPoU6mSB1I+yry6hJuqyVsdfijLXn/gFh5QqNINiDkS2kAzclCDRKs6GhxPyn2vsmgIQlkVwCZF6u5VAZJG87EgCEFBPSjWR++8QZmtOrLIgg0wbg5IjvCe8lCzwysJIMFIQrAKmGkeW9Lr0JrLdmxGS9Xdtab92zMlhdAkQ0z76zrmGVD7fLglOWm0j3DeTPwMvOw9MtmAKSEr+aA1gyguJ3fFd29nWB9ydx65vIoXpkSIhK176KA1/KclbhVOS/nO6qQSdkRQt4pCaAGRoJkKyEiELx5O6SOBN8RnCn0uqLjElw2rZa5cBbCrJCkHdm4LiJqoaZyJz2G3cwbU0jABel5UTIZL4naZRofqZ2FlCqXaVwklHTt2iUcSuNMX6qWjrF687xQ4TTRKoUfY2Cr98xYqMnTas1dYQswcydNv3kv4zgltFilaSANjCdgnbqpKfqpDf7h0Ji8UqN1ycZoS4XhaZmdhYrq8BStlOlaTecS1aQlDQfoWo3bbGlumRDXrJxAco54wOHigOZIYwXFmopWlDCJ0EaqicAtR3HbU+qjy+iBFeLaENdl6xwHFdJ1uJsPo0sDC9dUgbCdkfEE39S0S4F2xWr+uFZI+xrCJJEaoV1yhBunpTaTWKmBoZhGyNATVmrqOpBXhrRrFdVmQqqGyT6PTDJumumWhex84exk2rVgM6hgegarnxqTRxVpGMgxU98yJpHRyhNHFWjGt8bfkFTKMQxq4oFluoGYZ2MIum3H09rRVjVhmqgmU9wtm9ReaE8bkCu7FX4qJuFfOcanNDYYb5mnzHfWsTTJUa+Dy2K8kYFnfGag29ew8tlN2JyWKs0ef9OEyWkNtDbzag61AOTGUr6r7Y5wNKNtR1xzJOetFtVYDZgGoTo6IRzaxncF5G5N6M49lfHpNSkotJk4ckhr/KY89PgW3LizHq7ypGFF2M40n7kV5zwpK92BEXGpsc7aWWhLtjKaSwmA7SlsT+yhFzFy7Jn7SU2Fn0QjJ7vi6w/eUuBrh64EJAnVdoTWQHpw3pSGg5U6cFkgWSZet1qjboDvkpUVGHfQduRhRbc6xGkybkwqdZYE7EbUVmzRWSgWJ5aBlLNVhq6t0KoUXRRJUibd5oWULuE3piRpkGCeilzboEPKOOehNjkAN+7g8Jh8eiDuadDaG8DdTvZcDT3emYfTJ0ptpmx1i4ton7Ra3r1gGXcIqbKQ6S4wMjeOUTwwqNVf6gXuNOVSTw3j93hXlLLFslpy4ROBXYOYLBkhBmjcLIxaDnGcd2XeswI2cGmfSdWHjnrwMh8ecjsgROdIsCf66Nxmu9K8SxbUTGtmDj/MANAso2juk7HwbJ+tVOSNZh4VZ94aUQv72LKyfgmt4At4mSP/SgFDfSaRlIJoBjqj8UuqZJlBzlTQEUXV4bwB+ODTbm+Ldlj5N52lRoNQ+QRJjLC78yjM/U0FxWqJ4BXl3tL0XJxRlCwoNxMEjFSiBBLDkBn4joFEvCSC6CxN27gzEU+mcQmY4lBiqpmqJ6tQOevbFCnU4BLaUyGXUFNkAWbuFrZ6cIVD13ckzD0sCcJ2hJjJSxVpxZPWlqhvnuI3xkZWPDI1El82Amzcu4SOGhuIvZCWaqRNDA53dHFi+h4Cohbf1sa0Q6r1Du8T3VrFdG9NOm+/SeAL5KUBGks6qlis3YlAl4yAhqneojA5dYmqdvjNCZMDK8TVCt8loiRkOELEmSS9r4grARfF3PFOrOihKnE1EJ1QTYzv4sa5V3Mysbb9y2iMuC0DZnHZ0sJdzHRDR6o9YSuVzjDhJxAmCp25e/PqgOwV32WqcSYOHVrXSJeo15Ww3pr0e2XZZDlHGI8J6xXJ1aRGqbYSzXVT0p7alIgrsbd+/8B6Rqel7lNG2kxeqWkHGR0UgDYSU3zNGT/OVkdqKZCWnCnMTxQlIRFk0pngWFaqQ2NwwQY1MY6Jb4dU66UieUrEUWC8f0BTRaRzxMbInJMlj/OewU0TxJlIVdozIHojdLPpmJ4ZmOw3r1XO0C05JFUm0Fh7NChSV2QXCJ0SjrY0h6f47Zbu9DVTjd7ujP80KgUns1IdncDm2ID41gQmk5LyGWB5iTwYGKcGm8nnQWVp042zEB+lOGXxCOSqqPYGh2y20GakGRZlbMo9lSIYJoRxxHcJ2fz/2fuXWNu2LT0L/FrrvY8xH2utvfc5594T9xmEHRA4gjQoMaRCsrBQCiFTsaECFUTBdSSQAJkCkiVLYUQJUQhXSTIlBGlRQJaikBJhJYmEyJSNKIRsMDjsiLiP89p7PeacY4zeW8tC62PMuc+9Qco30/eQ0hk6++y155qP8Zq9//1v//+3C/Z8ZjmM+GEXPYp6nyCxddTWaKpYuxOpFCiKLJXWrcY2hKuI6lsIIilylbznV6VLOMhagtb1CtG5XGFOUabB0ecL+bPM/K1jAOyuR/JumbcS7KBUogw2GWmucf1Ve9pwbxq5H6ljCj1pZ6W2tN2NJfH39ChqHvfiVAMUpWAyo4FkzHyWpHfLFiwHw0bWYCYceKn4XYoGkd1mLRDg4Wb2XCsy4Fu+jKhcGY732JGbf2u8ehX7CtdykOtNF2jjOvPcUDTi4D1OZ8vbl/eftpaKrjsaD1nfNy8d+fRGiu/t21YyIj4srcfTPzwMfB13BFMTTiE2zYxo2JnNW7AgIqQUtuY1C0b7SRUiBT6pI9pbj0hjhZBRRqKXguI22OVKM+2aoGg/ENsSp1p6VTX3FgamOAGAtF+v1NuGxvMjG2ZUp6TGTiv7FczQOviBJJFLk8QoBNApLMxWuPjIuUXH7DFtUuI45m5jMwQLTojmr/gqtq/BzN/n9ovf+ZD/5QefX78QIrRdIr8z0rmFeHen6JsBnyvpdI7ShseKQqqhnzzRvj2gz2f0srB85w3n7xde/Y9PjG9n7Pe/oH73Q+w4hC30VGn7gbpX8vlaFyVnlm8UlrtEOTfs04rXaB/AfbindNW5vDuTPj3B3Ui9v2N+k9l9OoZ+IcF0KAyPiXSpWI4O0TjozNY3Kp8duSxoy6QkzDvCbjsZ5eTkOah/2w9MrwumBUQYXiy0NF9M5NlYxkw9RnKrvjuTnjM7zTFOpRBEeoL6jZHzq0SaMjurpFlIj2fs4RCraItSjOeEHQsyN0wgPU/Rl0dg+WCglUwrUI9CfrLu4MnU+4RrX0ErnD5S8pxwr7RDUPzpnVGmAJJLduxVYjmEc2T/w5nhx5Xh7OgXz2gTfMjYOLAcFHElXQK46GnG+0Cmlxq27UFYRmH5eI/TqCmhDYZnx7LAqMhkkd48ZKY3meX7O+pRGN8t7P/OCTmBHwuRUQOSQzScG+RPXhhqwpcFquHHPRx2TK+inYLtiMm0RqsILOrlvh+i3PR5Bwqq+N0+BvnHS6yMDzva3RA6rBtx7ap9sCJYLvFaA99nIhNHMFG0NXR22hC5QqiS5rAet1eHYBqQSJ5WiWaYqccdaTTWVAO5nJDTGdd7/NWeNiitda1Ia9igRFZL7F47DJj3ruEeuUkQ4B8Ew9Ek2G6AA+gcgFzOE/r5IyULy0f73kW6T2BDt587tMFJJeMpdD3li4X2SpnfjCz3Cb0EA+ips7hlRRASZgLoLMQNsjBYa0vWgZqkSJV2leiWnNj6mEkJlmddhJDinICFs0yjzLnmx6z6VfrHrBWXSJhmAz7cgJSr04eboDh5j9nZSkZyBU56A1h81cL0MtFtgO17/Z4SrA6i98pT28d7AK3cn7uCrl4u2t5IvZeHejmpAxnZGJoAL9xoVLY/naERNbKGHTkwaOS0eGcp1g7nt/ppVcjJsF4yXMXLwcr0VgN+dTeFkymaRi6dAVKcIQWLkzREuwg0SSHt3SpkoZ1JUlEahcoux2Olu5XyWr5aj209lvVMiSNuiDqTjzzXkYuF/vNoBdFKoY+b67X2KDXNriT55k/Mmz+P7Wsw8/e5/dN/5Dv89n/7tyJ/Zcx9VaYsrwd07uLEotFV97tH6nxk+OSF/PlL6ECsxeDsRnuzI/3ehfzjZ/jWhwGKzudIM33u/V0IUWB5O7N8ODLfpahDZ492B+eGLsTK/ruvMMAGxfI9uSTyy4Q8T+SlIvOCvd6He+YuIuPz44zpiOcAELZXZHF8jJJAmo303LAxRQ5NCYCizzOHt8b0wUgblLz0EkVz5FzJFu9DX9nrpZE+fYqSzi99yOnjTD4nHv5muKukGTrXGFSnmeXbryL/xYX9p5XxswU9Lyx3ij5N6KnGJHbY4x/e0Xa5B/0pOmsX0iY8hfC5HmJSq8dIRlaD9PYCHx4oT73uPEWpwXMmz0Jrhu2VOTssifZQEBfSKfJedp9MWA5GQSbD+gRfh0TLHtSvCO0+00ahtUx5WWgZlodgRNpOsDFWVMMXxuHvvbB8tI+u1DgcFRtjhtDFGZ6EwyeN49+9II8Xln3vr1SWABVz9GbSxUJfYg53e2pSyrsz6dNnDn9wYR4LLLB8fIjJj3DR+ZtoPok76X4f5Z8vXuDpjGTBHka8xoCfnhdkzHHNq3WHV8KOCSzE1Vuqq5Y+gQajqUtP3p0NGaKlRB0E3UV/gHqM3mDSJMqjObp8r+UHF+2sUUJq5Kss9zlKNXOAMNHoQL5OjlohnZYYeXvHc8udTSwJCyEKVEdbw0alMpD3BT2NyMuF9OkTslSWb9zRhugivzVz7AdrBr4D1zFacAjBZu4TWSTo3LNh+xyarT4zrqWkuNqy6TJi8RITT9yfGsdXguT3HG1FBMGrkVogiDb249MAM56ujVvZ2BmP4M6ezisdxFBvAMMmwL06k3T94Ut/JBGBnKugd50huSlt5evvpQOYW4nPBpT6/l0BXn+f9d+rQ2m1WWe/lo7W98kdwLT+87K+fzDAsn2WdyYl2BfoWpVe0oqMlzVkzjcgoJ2daR6vS/1aXVFdHFySKdLrXZDknQXpwlxxVFu0FaHnx5gwpEqhoki3ZoeoF5wxhbi3ueHIVmJKYiStjDpRegkpi5O1kfv+rVs0lQTtwuF2g/dmMskaLzZwtoHJwoR9sWg2adp6f6fYotylnFvhe4c/8pMT589h+xrM/H1u/4c//ovo/zm+C2vT63XgqIdO+zpBN3tkqLR85LgYaXmJctPdiDydI7jt1R6toBfn9At77v/eiBz22HffYEUZ/t47/OWC7gbKFzNYo94N+Jiph4QdlXL2aOynQnuVacmhCvU+Y4dCPlXSBWQYcIfx0wsuI8s+0XLBU2L3yRnXhNbac2oKSSPiPk+NZZdYeodwcUd2O/afzOTnFs0EM+ix4GPoI1ILK246TVjJTN/YoeVIkhK6jnUZMo6x+m2EQyX3DsaT9bp/nNf2Rx8Yfv+J8lQxlrDX7kvQ+G4wzaRzI682eCO0EBL6mZfvHJmPimWN1gFfTHCeqD4iGCZOOxZmFdpdrGzFFZsa41OLoMEGqZcKvBDA1Abq3YBOl6CklxZBeV4iQj7BMgAizB9nnofIFEmzM7yNPk3DZ8/hOjst1P2IT5Xdp2dsSLT7Eit5EbTnswzPFV2M+s17po9Gmi3kdzPpYtguYQ+F+UHJUyH/4BS6jb1i+6Gn/gopFeZDiMplqbB4TH5jQhdBa8Puhk7LJ9Jnj6S3J6Qt2N0Oz0Nco+bIkHpmUZ8ZhG0SsLEzDq31EDogCbUopTV07nqnJNg+sbhQnibK48ySJLpsC91iGq/3dBWx2q6guwHOF1juqK+HSMHtOUo0sH3sQxviT55ahJA1jzYFOC2nAEu9M58VpZU1/0XQMSP3O9LTCX28MJ4n5l94RT3msN4Aa3NIEXroodBy6G6YHQawHNEDq3+4DXJlVIh8nlXCIT2HUwKHB0iyXsZJSstd/Vp0Kx1p1qjUzuHSoqwFD8cL+NDzYqIe0WfAHopHfB5B3uBVroSLX8HJmve2brL94L3c0LfO/IRehevnriCClRlay1lyZVSSb4Jg+j7QGR7vgCUYnwAZ62s2IRAEeMl+RV9KB2adfro9AOlgpgt01vs3rkscUUoNwchdKxZgJ9yYeW0nsOpyfIWkhkijKCStXFpmp6GZ8Q4AIvuFrWWLirHLjSFPDN2NlAATZdTasWgvdyE0U0ykm7cWBl3YaZSWpAOclXmRjgybawdTcQsFP9m6GyvQa8I42cDsmYpiLpzagAAF7SnBcdzNleqJl1b4h+/+GF/F9jWY+fvcvvf9jygvTi0eJYASHYa9ZwBQBS9OHSMQbhOffrRHzjO6zPD2BAlyGbH7PZ6EdF6YXw/Mf/QbSBOmNyPjFwt+OePeIh328RT04LtzdMr98EC7G4EWsehJKG8XuE9cPhpJd4X8Ev2g7H6kvJ3DAZJDJFp3IdrNz43yw0fa6/ve7yah5uRz70GwVNLbhqUD7a7Xjl1Yjl0I6UZLiptFUFkRZHY0R2qtUGl3CSsD5QcXhtaQaYjePENCzpdwygCIsHx8pI3K8Q9mWnGWD3bUsWDlNcN//wny+Rf4mKh3BZ0dvVyw+3uWh8LwboGnhg8D6WWOMt43j7R9DErijg2Z+cGRNyOizjJE12zLMYilKY4xnyqca7A8GcpktL2Q3kVXdDOnPeTIE3qd0OeZ9jAikqjmtGM4lvJLw0tj+Nx7ewmnvERpLtwskXybvnhCmmBN8MtMemwMnyZIGVkilNFz5LmQBzQJ4jtUNTQNSVD3CLPrTSPd+zRRG8vrkenhiC5GPtcIpDsvlMdLAJNSqHeRa2JD2MFlbiE8/uAefZnQz07Ieca+/REo6BIOLtv3klIhRukKWj1SiHOwLmKxct8W6x4D8FKIVXhT/A6kZcrTQnqesQVsP0ZoXLXorl0NhoLtM/YwktIr9PHC8MNHxB+wMfQikabsVwCAQ1Zqjq7R0iBdevbNOTq1s4teZFr7anUIEYVrQ0xoxwfS3Uj67MTwB4/omz3LocCi10aUTrfjd50PUXrQXopSgfqQqBls7OWWvpJXA2uhx5E+0YvF981biMIdoCh17NeZFRAF4ql7IeW02ahXpsBLlP+2shH9cztIcQ1QgQW4kXkjm4K5WVkMjyX8mi4tfi1HtRV0dFdQBPVdP+rqlIoT4T3e150tT8aTB4vX61KrzsW3/STATgchq3B3E/v2t9+0Mh3MyMri+JfYG/EeTucbEL+WmVZdS3yPkkYmTJBpEfu/iG46mZVtAadaoqSJ2hJjbuTUcGCXWg/AExZLVE+oOoPV/jOMOnFIIcxNKVoPVFLYsDu4KhK92EyEhiI0MgFkhuRRToLN1XQbzBM6m84ddTu+uFA99YA9IamxeMZYM4KFU7d/7rySxLbTXV25WOaljfzaq3/sp86d/6C3r8HM3+emquw+PTO/3rMsjfZGaWOvyTeHkQAWHpRumh2m6BFkuw84/o7hlwk/FFxjQF4eouFgepm5vBkY3zXKqYY48htv8Lmi1qJT9nlBz1NYqC+V6XuvuDxkdhaTpM6N3Y8WdBlZjkq9L5CUcorJ7Hr3GbrA8C6st/bBHclhHkPfoaeG3yvLXbmmuCpgkTgcK91wcdkYFvVWEpUQHstLxS+ylZB2f+8UA/O8kC8z5WnCXu+Z94rd70IoPcbt2EZlep0ZHp3d5xOpXbh8NNKy8PJLR8oYM6IMhfQykT95Jlnm/AuRaTLfHWmHTHmbMV+YvrejjXD3t06hyRiV+rAPxmruQ49LJCBPznCO3AgQ2n6kDjGpOML4yUT5/S8iVM4crwtuGYYhMnNqw8Vorw60ZJQvQhflxx1pMtp9CmdUL321sWeEOKAfMPz+O3ZnwQ4DklosInsDQqmVpo7nQnuIZo6twPBJpfz4BRuE9jp3stBpB5i+ucdzL2mNISL2osEOTLa5H9bMeJHuzpO45nJpW3YMpfRePl1sOjfacaQeo8SRLkZ69nDuVTYxsB9752nW5b1Djo7ealH+CR2t07KyvCpx/g+ZWiSs+tVw6Xbu0wznBc9H2jFj44G0L8ilkk4VN0EvvSnmYYBlLSHE8tQE0mzBBD2k6F00Gem0lqeCLZLJ0M6cmKbeI0nwhxEbC/ndBfnizDC1WFRo6ItEgQbqXXTsHlb6XtKyBZad0kbHi/coBrpJAKQK3vuZaYVU+yQ8anwHe3sIBt26Yke3a5AXox41AISzml8ia6Tny/gaZPfliV/kCkBagBBtca+qB/jDg60xIZgev5ac2sqWbHoUNm3O7Vxq3Y0TpaIOgOhlol4HCceVbzod6O+jfi1f9ZLj1U59fS6rUPiGJVytzrLaf2Im7+4l38SwwdCwPSZiqIYTNPVSUyOyY5IYrVlnQJyiQaclcWZbGLRiKRxEqkBeGFMjd0FwUmNucX+OudK8US0s1LvcXVLrvliUoLTDpbQCGwGks0bE/q4iZu2iGL0NQIyZLNg6d1IP0zFJoYPp12pymLe0wvjM6sq5jSyeI9dGotXC4spiicWVj/cf/3+aRv+BbF+DmZ9hU5NIZFyuK5GwhMQk4IOTTv0WUBieK2KJ5VVm+tYD6fc/g6VSi4TV+XWhPBu7/+lzZBct1dXD6qmnmfpHP2Iahd3vvcPzjKHU+4yIkp4mjAi606JwSAxfhJ1bp8J8TDiOZaXeZfLc3SglhagSojSRM8MXF4Y/eMJGiZ4xbUCnGnqDqaKnRCsa4XMaq1bbRUkoz3EuTELU2JIgO4XXO9LzTHo6x8qn5BgYVWmiDM8Nq4bNDZtm1JXy2YUmEg0WEfK7hSElTt8aWV6PKIU5hV7CMKgH0unC+IOK70aG0zkC6O52TN++Ryvc/d6F8ZOJNijnj3a4Gulxor3aRTdrc3Q2dr/3QrostLs98zcOSOlkcXKWozB8Gs0D1QyzSvndz0gfvaa+OWDjAKdzNLl8JTDB5Tt70rQDILeY8PPLgqDUhyE0DX3Amb6xg3MjP08xuAwpup0jQO7jdMWPe5YPj9SDcvifv2D4/MJUYHl4iIyfc0VOjVShzMLlw0I9xmfUEWSANAdI4CIoCS+G3SWWnYT+w7qGalQEJ52jczgpwbyQHy9YEmx3wEsifzGhl4bm1Ff/CTt05JAlQE3ntH3tjK0KGoO0zL04cZ864ExbPyivENorxdMILqSlkh6nEDaPoYuxfcyEdkx4HvCpYTkm8bR4aGlUST3gbg2mrL0nWRslBODmmzBSZmAPuGwCTRMlLbC82cMuRQZTDQ0LtQVzlARq15L1iaS+ydRdRA+4R+dzHztwMo8wQ+klJQVJEWrJvE7G3YJNjalF2EL0oiwKttPQ1UQ7td5Eligf5AChqyD3p27reNbFtOay9l6NIW5ZQUW3OHe9Bnjv8M77LMoKMvwGVMkKpJy2MSgBZFaGZmNgNjLBN8Hw1gRSeJ+V6SzLCs7WktH6+jiFtj1P+hOjjOe9oWWUeaLgs76+e3VSBxAaLQpSb9o4EMm+u1yjAzUepSWBXTaqG/s8Y6RI2e15M4srgxsqFTNhTJFPpTk8TOMKXCSoKe//7uRf1+tA8or2Pkv0vBf3HreAd5NpHP/6fweqS1fpdHGWRzmyetiuzUvPzXFKB3iTZ2ZXmilz3xfzOENtzb3ZGKCf7/Y1mPlZtv4lS3r9osSKxNBz/30W5BSK8/mYyC/G7gcX6n3m8r//NsPvvo0Ow7XB7AEM7oawWs9LiDdVI7sD0HMNTcTTGfJAfqowZOTzdxSc9vFrlo/vQmCrEUWfTpXRhWWEOnTdxSkCzNoA7KIpZhvoTMSOPERInrttQlIrSrsbsR71n5ZY3cia5eCxqmrHHIMn0FqwHPZqIGXBFyGfra/uhfw8Iec5ygEExd32CZlD6FpOIZbWZWHZgb5MlCdoY+HyUSSKLtkZdGR4V6l2wg8j7dWI7qLdgd0NpItjo4e7Kgl6bjhh+fal9hwPZfjkRJor6fHC/O07po8PtF1meNcYFqMmCdHkoEgZIDvLN16hT2eGi1ET1LtM++gBV6U8VdJnl1htFwI8kmPiJkou1gdtqR5i6SRIVppF5/P6and1jS5O/nQinc54GqM9xWzoqTJ/64HLN3bYkNG5MTxWhk8eERf07i4YJIkJTWu39Sog4eoJN1GGbi1nit5b6d1EfrogkmBYl/pEyJtKXMvueNHJ0GmhHcNdN7xYpAMj6LxgKUpNgYzX7023eCdCRzQ5+blCkRCfn53cGZk2apTCxLFDAetiydngfEFeztAa7k77/jfgEAyVycqsREbI2tbAxoReKtkisduaBwiTYB6jyaKiZrS6ujYkzpVHpolLJEHLIZMvrU+0YeeXlxntuTJtP0SEw9moY44k5xSfR29j4NbR03LVMawZvUKk55JiGlILhoUaTpxWdNO1LIde2uqTevQO6uNWP/1reWh1GPk6rvnNn3R9TTAgEj2WMptYeF3mi0NbQcNNeWkDNTd4wr70mEOwLavcytlyYDYH1fritfSEXdmklThYJ+wVwKT1qDqI6Y4h6Vatle0Qwqmp2bvOJRJ3Wwe0MMdwrmsmjJNSI5mQNezZOUXc/z5XVt1Nwyj9+eowamPxuBY7XQIsWaJJQjzuHdWJ7MKgtjEvSrA83XOH0tlU70wn6znp1vl+Mqxf2Z6Etp2LLhkPN6yHA0mdYI06CImSlXOx247YcfyLhYi59nN1xY4BZEapfFXb12DmZ9hsDbjKV7qUBdwsKDvzmDgXJ789014fqPeJ/ScVP8H8kDn/ox8wfL6QXnrOxSAsHx5RUfS536Z1wV4f4XxGxsL8ZkT3HzAPhjbFjyOpQDovJFPaueEk6n3C9tGlOS0eYtdpod7vaYOgs5PcmUuUwdI5Bgcxj4aRx0R+FmSZYtV4mcO62grZKyrCfKfRc6r2wDEhwuuKYkvQnC2Bpsj6iMWF95VaiDrTZ09IDR1IXhZ86N2+B4XThawl3CgYvotO1jYI+RIllNwSw3mOJp2v98igpOc5BvahoM1Jnz9hhxx5KBK0vlZjfjOS05F8adFTazfSDgPLhwdspwEuk7AchTIllrvQQrmM6FRJp8iVcZWw4s+GVqKfyuIcflApPzoxHzLnX3wIELSATZH9kSq9q3Nf7edgq6aPD/hDJjVo+4TXnsZbHXt1xD64Y/6g0HaZtDiXP/pBlIVSUMXR4wokl2idUQiNzwnakNFdOKvKqZFOLUDYfekTJ4CSJ6N8dkafLngJkboPmaE5Ms/RyNCBrOjpgqrFBDRkXHtZaGrk87wlBNuYwTJeYiJfJynf6G/Bs6NnRy4Ge/AiUDvA6gCEGgN85GuwpaNKytHI1SUE7PdhV99YAZHoJ1aj+m85EoOl9rW3BzPk6rQCisRnzSBTjXJRiXBA1FnuEy0J3hLJQjuXLk6aavRcOg5wmtHHCVk8Wn+kQjkbVo28U6oIpiHMZQWYRFaMOtGzaRXPOmHHdo/jIK6zirMcbsamVYBt4aqKk7TNU11A28esldW5HdxuMcCKdFZw023hawkLjzLYinFl1cfcfMZWyhKuwXvp/c9Y9TVIB20eP69W/620tNquV/Di17LUej8JwtWOvX522JlEY9KWFDuvq4PptgTT20aEoLei4uTsDMk2DU3CGVJjSNafa8wtx3uvihM3hhwlqepKUkLzpoqmKLUVWmjcULLOqBhZ6K3A4jVKlK/M/ZrADN0FHw447596FWXLdnr15g8I1YP1WUwx1w7mIqZAYMu2cXdqv4ghIO5skVQWUgevsn1alLjaxjp9FdvXYOZn2HwUWCrSrW1YYjxNzEOI4tIpap0tR6klTRblBHPKJyekOfOHY6SzHojV3hIzW1VDHgakGrlFjwsTEDMkZaQ4pVnIzy9GPe6wuzFAw+KI1RAOzsGceBI0Z9qYaAel7jL51N0+50DR1t0k3ojBtTouFnkWrTE8LkyvS/SccWe6T9RDZKjo8yVATBcMX37pA+reaEUo1XAi3Vf2O9QcvRjDU6UeB2Q/UH70jvZmB3KgtgVpoSux44hVp1zA9hkfQvcznA11YZ5T1wYodcioCKYKh4y4Ub6YKI8TPJ7AojTjY8GHzHIszFlJF0inGXY5HD8lmocOX1wYftx4+YeOLK+UNkXNOazaidM3R4YnRVLChgNLTnhPtxUJlkJqBNjZcY9sS1SHQWDRLrB0skUZRLpIte6Vts/k596AkxhgnYj5b/sUrqMi4YKbDa1GHSWs8QTQEzmQeh+hlhTfF1CoSUjNyCfrYEaQQSKJNmsPXQM7ZNrxjvow4KoMLy3Kfsc9Ps1R3ni5oGYMT5ce+Z7RJJHwuBICtUVnaxVkTHgz0szVIuxEKSf1lefipOcz6Zypr3e0ddlovXSTEy6NRpSO3AU7FOo+gRyR5rQx4gO89+aJHk2Ry0KRWHVDuH3EoyHofY7STrNwig2RTp3O0Ssqz9bBdLfK9wnQDgEarBCJrq7IuQVQLTlcRUB+ifTncanISDA6e6fdaWhNhs70ZMFq4LA0xTlZEDhHOc77ecKJFiBd0EtnPSCuXz8s+qI9Hl6Bi/R/9Pnodup3+Ilu00KA7pWR3iZNi7HLuzh4ZRpXIe57QGadhW+1LtafuwKgzpZsomP8J8tNK4BZ51KNvb5Oq359bv9ZNIwMSYlU3n7trmF1ccDNQqid1FBZAriokVW28pEKlM7IDNqonUnJElO8deYkdRGxqqMWi0XV3iML39oeFG2IV4Y1tI51/+y93qIJ5yq3vbJ23o/eNs5lw6nxOTcQyAzchSbCbEq1HMdvTk3RxqBIZfKMa+XSysbprO+ZtSHuNNct0E+Itg1JWj+XX832NZj5Gbb5F0baBMNzWzVloMLx0wXzaJKojxekOWlqcIQsJRrVzRV/mSkqpBZajFYEW1qkdWpockipo5hEfneC/UjyKN3kl5nFDbt/1VdolTqmLURUTw0vkaPRBlh2hfGLhfHtBHdCfpnCZZUEaRVSCv1Ebw3AkJHLEpH2AONAetewXWF5PUTewty/JpdLRKUPO2xfyBcjP074VPFdiR41s5AnOH9zgEEYfucZPVfm770K9qEklleF+aAMn0+kqdEU6jFcRtGTRiELNcVkEsJZWF4XUlFsbqEXeYmMnvT5C/p8QvIQAtvLglWPZNQxM7w0xBptV5AalH90djb0xRh/9IS68vRLd9RjDLR6Aq/O8maAQSMs8FIYnmp0uK4aWTzA/CYjD3d4Cj2OVO/Og1iJp+qoh9OGk0WA3F6j63ZN2K4iM8hzb8dghueCXozxXJk/3EMCfemi0wMbI5gvGplCOdMeUrSFGLT3QILhxzPaYH4z4IPQcHJ3cIVA1vFjjjKnR0ls2StuhfLBHf73PomJUcMBoVOFaQ7XVVbWLgdecgBICVrbm8HQz4cF02Cpyx2sl0B2gtYSJQ25Tnra2DpFg0JSfAnRY93nSBr2AC3SIltoTSf1RKxgVwt1iu9dsEZyLX3oOjsGnWNZ8XtHlyt4keakS78fWi+RKXhRluRYKeQcvbuwCMeTGpk3+d0l9m83YJeK6EAzWHAWFRjpVmM6EBNscZZBSFnCTdf7oVnuJa+1mSJXgLhVi1ZQ0Ce2eFI/ji8Djf6r9bUr2/Lets2U1wdc+2KMm8dXtkVuXnMLZtYJT9mAyXuZNGl9jmxlqNWpte3I+vwbR81WatroKECjR5YIpHxtBLkBDbkyMyLWmzQGIzN0fYz2vxO2Pd68O6A6SFGP5o3NokyVevp3Fse6hVr7fq932NKPT9dJ5Ep2dE2+UP16aFHSkbCDr/3HNhZGuKqAYDXkrxDHm/X+SonaMtUTs5fYf3GKGdJ7M1VLWFJebKRI7feDkLP3bJnVtbXuw9XWvgmNv4LtazDzM2zlYWR+mqnL9bGoxQv5tCAITTLQaPcDKglLMN0l5P4QjoynE/ZwoJbIJmmDk5FoS9Ba2BxJkFKsFJrA44n6zQfqhwOkyMCQ2aj3GU+99YF3a/XF0PNCegr7Xt0PWE7UfcaHXVi95+7gkLR1/VXppZ69wHGH4zFZXc6RhLpThgWsGPV+oH73IUTFeWR60IjFf0rY60I+O/vfe2Z5sye6UEdk/Pk7B4bPZ+ZjguOO4bFXe5NgY8HR3utHqBqfr1Ol7UowMIC2aEa5e1tJjwt1UNoh2I12P8Z8Ny9BGw+l14U1ekN9ckHvCvMYjIheKpbzlgTqdwM2HUmPc1hl+7dkfAHmyrJTTJXhbGH/bgEc80vFZ+lNOrXrB0Jg6E26ULTGSFYNdgPtYYgQsajCIItRTkZ6rKH5KIK9jo7rdZfC7m96TW/dZyzRg+hAn0MT5UOKbtVbUllMrDqHDopBo/2BJqTFGi/VAALppYU1WaPtRH2VaUnJjzXKAN/+IPZ/6mWdHnfqpzNaG/5wgP0IvY7u9yPLqyHAR2TDB60929YXyY1wTTm0Y7i7pMUAGaGBkE41GB6NMq+VtRrRbekLXcuoW3kpWAoNkJCASpStCLYTka18s/H3rInAfZJYQ976e+jMlpib5v64EMLbvdPGEHbnyRnw3lvKSEtjUUV2bdOGqBnjY8xicyIATXfyuELK0GoALhskGKzcw+88vgd9BNoYDFOu5ZpthA+B8Kp3uQUqK2Oz9jd8jwn5ie36yi075pbduZmQYwevYGtjglZ2Z9PV+JX1uWV2VlQlAD10Z9XWbKWy9XPWXVufH6Jd6WBkzbURCRZH1UlbuavfB50ZSVIjY0ujq3XujR1zB0BJfKuCrdyFanxeUUGlkfvCJfBasCPaBWZr9svaAkC2Qo1fcWJHoe7X3B4Xwdy2AGPpx7NCGd9O8npq4vFWhWaJsxdmK8ymmKebVgmQuz6nphpgj0bzSDFfGb4h1c215b5a0DuY6dfY/KuDFF+DmZ9h++iw5+Vppu1SUNTERLE8FPx1IU3O+MN3cJpY7odgKPYjPk+xCtztsWL4XqJmXyvDqeJakKTIXJFpgbsdthfmb+zBnDw69YPQlKROmgiN8vkzvhuY3wzo0xRZJbu0jVKeiACvY0Y9JgGtSnqZYiAp0HYZ9UaaI/0XL3gSptcDtheY7kMdXx2qkx8r+SLI6cJwdurBSM+JdszkF+PyzcLpAyWd9hEe11fM6XmhjoV0KMQUGkFm0TVeaHcJOUho/FxwMdSE9HxBl66byJl8MfR5QWaLEkruFGoCyYqVEZmOpHPDDmO0aThE+UGWSjovDKdGHXP0T/EukBajjcr5mwdsCKG0KyGSTkY+XWi7feR+FCBDHUKnlC5OfjuDSJRnElcxJg4DG/fvu0Q7JmwEvRhlctoilGejfHFBmnD+qFCPKcCHStiqR7bMD3EBjXJLmhvMFqWqISZorQTo6LZdnY00RSNJebxQNOHfOdKKkF4a+XwJJqmDAVfBx7i3SdEaYvrGQDob5d2MPk/407lbx1MIgucFvniE54IcdgFczjO5vObyJkS92lskgJNPldbPBZqiyeNikVFT11V2xrKgWeOxZj09OVHH3h5Cc5Q+F0IYC/3e73S8BqOna56Ir3EK12ui+YbK3xjXa4lEPITENgQwxyIPxghmycSxAdpeqGMhz1wXOLP1NgxB/beU0RrielwY3xriyuWNwC4+13LcX22JPBozRSSSkIlKBF77fcANsFgz+W4s0rH/vrFc27YdXC/WrADjPUDzk6WDjc1Zn3fD7myqUNbJliti4soAruF3m6X69t/cfP56MYWbWXzdJ0e2WSyOb2tS2RkYkRDwijruvbR4w4Bs30+BxExJAVqKBBOTdbVCe0/rheQhbs894TDR+zOphGVZu+4JiZwXCe3JdkBiZDcqeSvWXBHZFZz4zQkxD/dXhBprZ3nCdxVlH7reRaJnlMSYOzvRY6kVaqfMnNBLradaLcpj1RXDad0oYGQmi7iNg0WKcI+lWXmzbe+jbdrdT9wrP6/tazDzM2y//M0P+d0fvOvfmphA5jvdxLDp5YLvB5DI9Fhej9Rjpn10j86OSCI/Rg7G/I0d42f9+7UfOH9YyE9GejoDQjotyFRZDkp7vaf86Jn20R1tF6vzJBr1VDPSu0p6unQ3RYFao1xUFGTB9gMUxeeJdHFojeVN9Hyyo6LN0OywCxFkfTMwPxRE4Pi7xvjZmfowsOyF3Rw6k0RGfCE/T6RLwtKEPYzsPmv4pzNSG+VdDSfNIfJSzkdY7lK4SIrRiiKmwSb11aM8hSAY9wjjc8MOSt31FfUcA8ZyEIaLM3wxkcyoxwG7z1hWpm/dUV4abZ9JPfxOq5E+vVA+O5GnSkmKvTlie6IMUwbmfQwsdRSsN4BNF9j/7Ud2P3wm/1Jhuc9hpa2AGjr1RpYPQ4CfQ7w+zU45O1YBETQpdZdin2Zn/NGFtEgfGIU0twi0c0e/MBJC26cuEL32JALfnERo6EIoiRqzVgxx7lHWHPI2iWnv/q1LWInzJaLT0zwjl4pI7amzgu+VTVuRIuUagfLjGUxor3bx+6VGGbA5XCbkNIE1ZApmTFUon51o+RCAsYWGzHOUYNK5dnCitGNCWoTe5Uu4jGLl3uP6cwiC1cDnFgPuLkUrkZ1Sd0J+6S4LJwLxmkcfqs5A4lHe8kTkQDlh+KiQUohu1/lS3DHVmPA2Qa1i2Vjt2t5zWPLF8SnC8tpRme4FvLD7QkmnSrWMJ4cGuTrytmE7ox4y4rD/1IDE5QPBdzHTNPHeAmIFWp2RMbkyL20jI4DIe9ls0WteSwc/4qxxr3EO+4RkKxOSbl7L1R9zU7+ITb7kTFpFuv13t/jH6UBKwG5LRF92IvV7ORTat58ZIEfSRjJed2NladbP6/a/yBViJTjCycZaGlnfYC2NxM+JJfoepRAiqxoqsjWazBKAxZGtH1OS6AwVzE2IhAepNCJIb/Zg1ptruKHw/g7BDrm1tT8v4Qi6ltKkO4mcqLCZB1Mzk2Khh2xfzxXMrPckDlbjtp49s7REde1OpZ5r1Et0K1OkEqJfIb4DgzVmz0wt5oCHMiHeNnv47fU1D1Gx8hFf1fY1mPkZtn/8V7/L/+1v/M+dV5BgZxTqIWyuRStLvVDGHbLMiI+R03FpMJawIy9d57Eo4onhJDQ3DmbU7CRP+DTRjgN6biEcPhzIn77EBP16j0nYVusHd+THCX06Ux9G/GUm10Y7FuT1AX2ZyS8z6XnBs9DuR+pdlJVcGz6EIFUPvcSl1ss94YZCYHqTsP0d6Wnh8LsvvUNwQ0J6HwLlrjlIn08M7QzTAiUzvSrMr8boi4MzfjZDSpSnBX18CVrz4Y7pw3EDD3aXsUzoXxZDxoJ89kLWEmxzrXCpIcLdD7R9puUUGqBLQ9IS5ZgUo2P0OhLm4wh3GfvxwPD7b5GXM3IqcBy3lf4yKFacthcQJ50jzTldWuiLniuaw7aLSiQHzw1Bw+EzG/puQY7lalMdwWdBn40yg7gyfnYhvT3hb47UQ4leQHfC9PoOTxJiwRRMWpoqufaRvueZpHPDx4FWhHrolG+LJOY++vWJuy/BRDATdFewGsLh9LywfJBpRVFV8qViS4jIyQXTzna1Xi4RiQ7QRBf1+mqHTkuwQCrIvsDrIyyNdO6J0z1sLz/OoaHpOhnX6CmmLYLYmJ00V1yF5SFzPibyk3XHWwDEtADNo3Gixj1MM7xIJC0rwXb0OUtaABZdWs/F6eGSRuTZlDVh18PO69LlGgFK1rJMKyFOXkW4EJotsZ5I0kFDWqKZqU1G+7iwHIMJSPsMFlk7Oju5l/vyWyjPFp3MRZBlQJfE+Rvam50aOkUGjh36dWxEEKBHxouoR5luZT9yFzz3/JUr4LiCX2ldghKYjNUZHmzJmiET2ybevWKA/pdvTNAVZ3lfta/PiReHiWGbta/ZMOv737A718235689lDbcs5aNtF+zDsaClekqdgkgHSLfztJ4sDMq6+Md0PQO0kk7A8P7+SxrKSX6IfX3IgSvTgCZ0kXCJVAiA8bS2wY0D+AQriABGoZG36Sea+MS4GWlPsQj06q2SOttKIsVmieaC+7a9z3E4yEpCvTWqrOYUEmYJ6oFkMG7RmhjZQKIuxKf7dIzZUJVPrXMuYOZUysB2vDNmdbJMMyVc8t8b/zuly/iz237Gsz8DNs//U/+EeT/8tt0eE55atRB8X2sfi/f2sM3BnR26uFIOyrMkF6WAAEI8gLDH7ylvTkgU0OfzmBjdKWvlabRMyddYjXpr+7wpNTvvaE8LpR3C21U9DRBbtHFd4L8WaMtC+31PrQjAjrskHlCcPT5BT++YXozonN0s/bU80LUI4Z+apR3M5Id21t0mSYo9fLFGUSZ7hOkIco3Q/R00tmiRGbxhbH9QP1gz+k7A+7GwxcTTVuU1pJGeF4OhqItFWHAUkKWRmqRHKxVkOeZtDTkMqODRV+mMiBEKS09nvGs+Mf3mCbK84QPCeY5xMweqx5Xx4+CpMT8zQOUzPCjZzwLNTvLHdQ9ESA3KDo38knIF6MeEudfeo19Nr8nRk4zyGlGT0v0TznuSLMhHmFp9T6AgkPYMO9yRPOXFGnBY6YecjR5LMpyCH1EH4vh4qTTRH43oyTY5cgRapH23HIMeDZ23YnF6i1Fu934/J32NFdIKEsRZNyTphZulEGRJ0GWFrED+310dR5CVJBm23qNiRttn2JAXsHcvkSqK9IzWsBV8cOO9HxCNEVAoEdXbnHFrcVSG1//iwyTGm5AvTTmDwo2BGUuNUS9JA9d10gwUlnQSwAErREmV0fHd6lnP2l0sK7G8AhTDuuzz047pGDjWme5dAXmsJHoGm0IPHVtj2wkx+bqsY0d0AASIqSzsf+8MR9CeD3fK16d8V0PXWwgTSDTk7uDqU0XY3irlOeB6XXXUSzBWr18P57vGuyLVg/NV5/crbdL2NJzV3HyLfNBZO9IVDoi+2UFJ6tmrDMhP1Hm2f7RpZ8bKLlhf/rjN1Boe8zUrm+h7z8/dugny1lbSWj9fXyRNg3MyrzcPn8V1Gov76y/15jLu/26P11AurA3gEwHOBhJegDdBs6sAw9FWTNl4lDCzROlmiINQ0jSKG7sZAnA6D0WINYoNAsA44Q7KPand5nv6G6pREidZxZXqgXDYt7F+R2EOLJdx9aU5qGot34h12Tf9UjW/19PXVBht1U/EWHxxNQySZyXOiLAoJFgvF6u5sJsiadlx6984x/+yWv4c9q+BjM/w/aL33oDs9AOEUKmp5nxHKg5zYZeKvWYo4PwLsXgvDR0Bp0n7DAw3wvpF+6QWrER5odC/WgX9tEWA2Tb32GEIHT4fEGnKVJTO+8oqSClwHnCconuw/MCY+hqdKpYycxvCqePXpMulfSU0KS4V2wQ6ptCPThVlZHM7vGCnlvPuYjJJ72bSO9e8MMu9Ckt9CrsvHenFlx6TP/SOe/aQrhZnPLSel1f0JcZGwTxFJbOrNS9UocUojIxfK80FdJFYArRJwaMQziurNs3a1DKts+k6qRPnuBhiNXK8xT78niBMcF+FzoiDCxcONNHO+pdYfhswvcZcaE8NXwGS8bwtKAX8CF6WWGRydKOKT77HGmv7kSX8aLomKk7i1GzzqR3PaMnZUyVZI61hjWlvhmxJLRDBtGwYs+CrO0xKnir8Xjvt2RJoYR2g2FAlt7Hp0bduxWNfk8ewYKer6UVqQ0s2k/Y0AWx7gzvKvrpCxSlHYewFJuDGzo1XPJVoNnTr62ALL3NgMZErybRVwfCWTUqng+kS0VrOK1cQZaGzo4NFduX0OxcDJ0FGxJkI79U0o+N84djLzvoJrLVKfKNbIzcIZ09AGiJezEi/vskJk7bZ7iEVii9GD4q0cmms2bTDTNxOznWmAxxejuI+MW6KN0m7dRfqFCzkm2dQIXy4gyPC+IWk9niW6PMYHQUl+hpJjnuAXfY/3Dh8HshuLZ9YX4YqHcwvwHfE+BFAqDG+Y5dts54bboX7dfjhvnw5L3vEz2Pxjf24z0L9XtgYx39rivyTfOyBex1MLWenJXOWTUpfOl91/dan74ClLXScpvquz6+kjvr39oBTgcWom1rSxDVKn8P7Kxhec0CBCRCOJX06moKQGKY6PbRuop5V2eSrGbo4FlyL9koAZq0u5qyNwat1CZUEyZRFhOKKrMlzCMjqhHBiXltHunR6XwxZWqFRgp2xhPuGswMveS6ugWdXn7qLAvr7evk3vbAfSN+AozB1jV7bY6+xvs4vVeTK47xsgwIzpKMYm373OrC1DJPy8A/+eGv8FVtX4OZn2FLKVFf9Rq+OzYm8snIlxZN9V4Xal8BpinunrwozA09Obgzf5BZ9kfy84IkYf5gxHaQqpK/mLAipBqdhmXy0AyUYC2kErT0ZQlKPUV5RiRhx0xrFc/Rk6ntQ7vRxgFS6T1znN1nUwSB5czDjxZMnXoco1/UAZrGclNU0DFhxyjl2LGQz40CXIaInVczDMcHR8eC97YE5MRyX5hfhWNm/KKgz2daTtSsyE6DwelZItKcfDLEhfkho4uz+3SG5wt6itbzJIVxiYaNlwl/eoFf+pjpmNn9T5/GxN8MWypcFrRFKYZFSC/hgFo+2rHcRUbN9Erj5zncEnJulBfwkiKB9lTx00J7tWN+FVocsRhn215p+wEYEDNsVMoFytuKvlyQz1+QnGAcqG+OtNcjzXJ0qTY2G3Gee8PJFIMgLehebcC5oZMjVfGh0+RduOqioQUB8uzIEuxGk1jJc6m93BQDrjbpQLADI1XyVKM30uvDVSBt0UTTs+AlRMqWIc2CpHWFGUvSnmgfGTsV/LmFg6s1WByGzHI3hMj57QUvShsyMi2kzyfkYWT+aIfdpy6qDZG3VIveRKcWmTpdx+AqWNHIjulZMu1gSMs9CE9CFEuwBfG3shwhJSHPRi36Xv8gOsZdmRjzEJ1rSFjCeWWEvb671NapOoCix3dUwO8SVoj7BOmNVkEvfUJPvYQA12C5nndjg4a2KgcrKUs8XWjk0Tj8SBCUSwIficaRRLlp3R9zv+a2fBmQdJBL1+FED1KLv9cJTvpTNhIlUN6mh+nnFW7OnwLclLN6ma4/+/2/15LUusmKfvxLv7h5j1tS6Ja9ee994olrPMUqPBZ8m+BbCwF4XkPrUqOkRm29VONXFicpUeaR6/XSNTnYeq/TVavT15fVYiwbOhO8YjVzoeLMFOaWqabM5lSLTtMLNRYfsJWclqpMLcDE4jnAysoPuXYWJgThq40brMfwOJquwIQ1+bifS5duE9/OU5y3W7s+rEAomkwaMLWEsGOxRu6gMbQyiaklLjXxy8dv8VVtX4OZn3VbU29NWY6J/FLRybE8QBUYBZkquaVoJ7BT8qJoSt2FYD1XRmlJsN3AKkycPz5g+xQuoNYoj9BwyBkZMq23GhABGwvSGlobslSkZNSjXDLvw4qbnxvScqz2hx1g+JCRFMF3aemDwNlCX+EaOoQBMO2r5UPYfQdZuVjaMTMnJ3vDJHJXqPQ+O47rELbWEgN+W1eEpxrpm7mECPSQWY4JWZOCq5F6Z2fbF2wH46fAJQSl/nhCXt9Fp+27PXKp+F2hfnAkfX5C3EjT1F043Z68VLw5WRV7qiyHfeT7FJgGIV0SnBfKonAGJVinOYOPibZPwTQAw7saolzv0fc10E1cD2H6YEQfMvJRlGsovddQSiRztEeopnOU5bwKmiFGyYafzjERjrsAHy/hMvIyRrjimhCbomTDaldfB/AqaG1og9p7S0WMvcc1Fcin0BXMr0sE6D1BH01pu5iEo7TSJ9s+aLdEtLNIwRLFPoSYt4mTVNCpIY9nBMf2AylFEq/tMjo1cp2DWZwq/nsn9ItC/fgBu9uhfXKd34yYhJ1dW6QDS1fmOtBGuhZIqCmFlmYVTnRdifa4BPG4j1EHa/GcBtRI0yWDzLLpRVYiwVOn4bsEQwlGCrw7g2LUF4/JIT9bXG8NUAXBdlw+KOQvAqRKirKTt7hO0gUdLfumrRKXzlylsGP3YLw8w+7TACKXD4Ed+OA0dXSJMpa7XxN14ScZlo1W6udpzeJZgUx/nq8C41sg8RNMSi9LvcfgfKlUtL5u3Ze1vHSLXdayj3PzZu3mc9Y3Wyfg68vXn9dcRTe6dq+D9vfw0Yo+Wtj5icm4BwhcyzSdvUhf1stg1BpAqHrBLMazjCGeI4XXAmwoTpXC7ImpOY/1wOw5AE/vjN1MaJ5YIgmRMAFUalWqdXZmM1hrL4n5dkpE7Hoegmfsup/OUkmAlSuTKNdzjW9OrzUMMIS9V5Aj14san+DKpUnXAaXtMjbv+4vGd/0r2r4GMz/j5ilWq2rQDimACQChL8ifV4Yfv2BvjrQPC0lgeZXxvaJPxt1njafvRvPH4aVRZu/uk1C4+4vGStmd1EAnJ73MkRD6MHTBrTLfJ9SimSQpHDURniWkS6Q11pK2fjxaDWmx4g7rHSwU9FJDfLkvaAWfG9YM32eWMXQ0MkcmjXUb+urYmaUwvCyYVEDIS6zGUUg/emI4R5uA5+9n+N4HvReRkE+NfFlIU2PQHDV1jbA8KuS3l2gmOCbkUuEyRTDbseCXGbsb8YexO2KM6eMjOcPuD56QxfCe5EqJa1Rfj9hxDAFr/45ahlSF8YuKfnoiLyBJqcfC8pCx0gWrRJlDLcCZDYAITeL5LiAl0aRbMVVodyVWzS1KKCEYVhhjArKHglqUt8SDxZPTgp0rsovk3eVVZPaISc9b6aUcDU+CdybFV/H17MHkOLQxxXF2piedDGkNH0p3E3XbtXov+RGi165bEYiu0sMaAhb3ldQQ3Eana0WW1fUiUIOytrsxxMs5Ren1ZcbdQq+wdWFXyIVyrqTffYvvEjZmfD8i7YDfhyhaX1oAmhITlS5OO+imH0ECSGncfttk6RYD9Vreiv1PkZTdU6tNU0+77udSCIZrLaU1tlh57y6ytf/Y5ui9LoFJcyQ/u65YIMBh/Sg0dGtsPCqkqUXKb4kModDkBFORqmJL79t0s2pOM4xvY/yZPugMzeCYWteteDih1v3bzsftxN4zQlbLV3/O2t260wnxmtVVRH/Oe8Ckf8h7YOZmkPwyi7ICmZvHA1+t+8Lq792A0upgumWObsHQOjcvm0Vdw2G02tVdQqTbwcJ6/t0D/MzW39RiUk5qWEvUVMME6j0fBmGpAT6MxNSUrBF6OaZGJdMIIDNbpvQ03HkRqo+c2kA17dqWuKDhlFeWzpyaC6IhdNpA5fq1W1U0KwMlt7+3DXykGxATgKUH6QnRm0pjjmIrMcV7s349+ofclubiMwTrFKaZU3t5zddz+T7q/Eq2r8HMz7r1m6llUElc3hTKuxrUeooVpd0X6lGxYyG9GOXZWO4SfDPjj2FHXevAbSewgA6KWwhM1yaTPih+X0jPsRqwXUaOjdQ1CzYm6iFBp5st94aCBSAhU6QLb6tugB7413bKcizgJYBOFSw77X5HPQi2i0692qLeH+6MoOnbTkA1BoWaYA+tQfrUGT6dyC+N9MUzfhg5//LI6ZsRXjecGvJUw1k1ZKxAuRjD0wRZkTqQJiNNLQDAZy+hnUm9hYA1fGn4otRXA2gAAhuF+RfuKTOkP5gwi+aVyzf3tDc76j7DoDHx98FY+grddoLuC3UIl5JUJ5/DNSMIlq0n6IZDSmYolwZzYzkkEoY1SKWzBylKAhB9t7RK5MU4WLO4zioB4CSEo/N9XAf0Dm1CeWkMz40FkCyYKIwxaLj2GrkQsy1xPHUP7BQWkBqTq26qVdv6QfmgPQAujtV3aeu5I2tWR74JprvEBOM5UUe6QDuOKeaiuD+IFknBYiSJtgh3kI45rN/nGuzImMP5pB0AXRZ0qehnJ6iP+HFH+vAV9c3Qhc0OFCCOgYmwi0Mv59BBHWylFKczSzEym0ZukYuTl2iPkRZjvktXRw6wIRS53idrywDm630T4WF9wu85QKJ9ali45rx4sFuma9halGRaSaQx5KSeBLJ09kh6uUiDxU0BOjwJJMhnyE9xjNMH4DvwYb0XPIJIOjbg1kHEl4+RjXHbGjOuYGbVZltnoG5n1/fKS9ex8P33/yk/b5RC/AlM45ttm/V+Xu3V/Zrcvs2X39h61g7Qk8K7oHlLOo5zuZaQRAI0mIXWI6JKlUqUlLJEHs1UMy01lpY55IVm0CyxxKDK3BIl2WaRXpey1ROLJYo25kVYu0nPVjqAXPftesKaRxZVgK3b31wdVFcia82t6gAF67dpF+9ugmlumKtbUHNFp7oVrmKMu82N4Us/r7eKdev2Cnbc1z39adfp57t9DWZ+xq28OCZOqhZOpjHR7hyZel+Ww8C8C0FiOlu3tTrjY2V6PdB2yu4xQtlakaCcuy4gVY9O2hKiQJmM6cOBscLwBy8sr2F6U+ClkkMVFt2Be9R5Ojm+F+ZjtzE/W2RtZIlBN61JpjFoOzEpNTc4ZFQy+d1Eeqq0+4HpTYnQMXFco8WALEZ5MuZjgyFhD4lUFT8Zl2/fc/y9p1gFDSXKRueZ/R9U2n1mfkgUEnIOx4+44BjpskTuQu45CFNoS6R1Krw1bCjU+5HaXUHzm7JNuFHDgtN3j5S9ou/O0Gp8TTtrbSWFVsWASzR2yycjXSA/zzEgjmPvQ0XksACaoi+Pj7EvqcagoVmj03M1Ug42zXbBerTSV4sicY2GmBx97om0GTwnxCSEvHjvviwsD4W6D9GpS9TIxRpm3eHV+iqzOqToIxTAWtHWaA5o78xcPVJz99E3KC2VagVMGJ6WsCjvU2hsUoCQ0AaE7qMJFIuJTVvPBRIiLyYROqWLbS4gPS3oecZf7WlD6LY8W9TxkwTCyNo1LZFvJFP0I5PzgD6dwu79trvUEJI71jpLoYpeomkfQx9mVwuyEf+LkT3uG+0TnQIlgGXrACU9N8qLseyDfvESgB/YJt7m8fq+KI7S0E2rBG0h2hTR/vneQ/RugMR7K92YaXwQaglHkli3p6+/W2ci8XC8qNHGfqiVyLQpwvAWptcOIwFABuvZM/1Dt9gSf5+duaWVbhmTbQ7r5zXRWZr+N/IeIOH26V/+++b3G6PyZeCzzpJ9n6Q3/OTLQGYFUzcPuIdodntwez+NCAKNfkRmKRJ6Jc59XeKc5hQfkgSWlhGElIKyEUAtc1lykEXVmLysWY8slnFpwbLUANSIUS2RxDl159EKYNzSDYC7AodVKB4/+3Zs+qV/y/bc239Hxk0WOjvzPqPy3rnHkZ4zEDKxuOZhVw84Evf4FeCAUOuV2RHYGK4bQ9j11H/FaOZrMPMzbkMFf2oMTwv1GJqPcnJYPPJmiHh7XLDBqHdhf05TaFTqoCGkfYkuulZCU5AWQy/h2JEpOs61YwJxljuBbwy4KJhEd+xD1+AUwGOwr8cQZuZTsAl2J32lFpOfThYDb6NrHnpWB0vU3neJVjPmieWDQr1TylND54V0TuiLsPtsgeeZ3UGp94X54wNLaIxpCZY3e4bzGZ8a7kb+5EQeR2qLrsp1p0h3bi1JYGxQR8bfe0v+8RPtm3fRgPJc0bog1iAXfJqwcgg9wa7QiqEeYWTRwTfYo/n+iPVmi1SBUjYtRb4EiFptxOJCG5z6i/eki5EfW09sXcWyQQen2WFuyGIsrzJiCTPDs2Mp+HDFYQnNibpHJ/AkMMZkrEZMqOclJmeJslA+10i+XQL41l23VKeoV8jS8JdKORniKQSq04JeFqZfuKfto0mkzhbuoRwgJE0WLqdjiHvTqTfAHKK3T57jM9oO1FKcZxHaEJkuMlWS9ywdJ9hDczwrywi5CjoRQLyDch2UNu5YPth1JjC0IWqdXSrBXHkS0hSiWt9HwCS2Jz8cSM9THN+7M+wzthvQGspLT0Tp0SOtOOboXvpzD1ZkXSoKgUYGAtz074hLiHRNQ2Af5Z6eh+K9VKt9/t7LlqYvDrIyAQqm3jVEbGSHdIeRl3iOLv1xg2RXsiREutoBYdybW/mEAKsu0AZHWgClNgT7ND765uYaHpVlcNgLvuMmw8UD1KxBMnZdtW+lHjG2XkjrfvmX/l5ZqZ4g/NOrCT8duH1ZVPre0+Xm9zeT45oXs4Xh9VqL94yUzVptcJtgvOo93OlWL8f78W9disz7xVo1KIKkUHo7RB5LC+tzlIky1sFNuBFj182VaqulOvd73OJxwH3lPeLzv4zbvnTW1oPZtCvcsB8ruBARrqF6rYfwxXdPWFshXN8xHl3BScOBjF87zUvPSdsAlW+gRiUSyVtbwcuNDoc/ZPvywf2ct6/BzM+4lX3mvFT8JQYvbREpL08XOBGdng9D3KKDhkZFnLnQ2wn0IC5X/JCoOXQxdSc4ieHtzO7TSzgxHgosxvymsLwZukiXLtLzcDjNDp1xQUMMOJyttwpYozMdqUTpSNicFeOPTig5UPl5ZvloZPl4xFuD5pS3jf0PzuTTgrWF9u3XIELxBLsd8zHSfLMLVKecPLJVNIShLoIfBtqhIAjlsQUTXpR5J+QW6abt1Yif7tFPn+DzE5RMfbWPdOLPhaQD9qPPkQnsg4yYMLwITkzA6iBzo97FKig1w3LGDlFyyDUAT5qA1sIirsGS0G2YIRqOUoTUCFsTBx0TnoP6X/vehG5GkUnCfj+Hjdyzki7BKk3fiJYU0mJiMiVKRYuj54ouCVktxSmhQ+TkpMWi1Egwc8FzE8FtnYWQHoyXLpU2BI1dj0RmjQJTwwehnLXn3oTbre41XHAaQtqYaAVLjWT0z4oSWH5x0uctWg4cevT6YviOaKCXuoOI64BtuxwD4mLkc4BbfVnIz5UpC+QcYliN58hphv3QO18DzxLC+Ls9LEvsjxGT/qDoZUEez+D3ERY29FXv7FfyYZ2bLb6bIQyNrBof+pTQM+dFrJepiOyWIDtjEmqOj6HT2SIC8Bg5K50F8c4Qxc+iRL+uVVvT1gnQr2m9OVa3kW9DBzBbdST0S3FbB9CcQy+jNT4/zc7w6Mz3gjZhOAlVjbqXKDOldbXtV/HzTR+nDdAIP8G0BMt5g7puAUTy66y87qyv//tDprnbh/2nPL7N9NcJc3vKtl/+3mvj6f38roLX9ZjfQ1S3QiABDLdwUXo/L61F4Fzk0AQkWGqwk3MD1dLvkUhi3nQmnvqn6E+wFf6lc/GTZ+YW2tR4B4lmlgE0Ori4BRpyfX4i+inFMN5dVx6Aai39wKplCT3DGgSoN2B0PV/CVUzsCJgxewiYkQ1P/qHbl5mar2L7Gsz8jNubVwee55npgxBTlucAAqZGbsKSSq+lRiS5eU+rzWlL5rSitL1vHXKdil6I1efQV+VLJZ2UtEzkBS7fGKBEiBhGuIFaT6cltB9ewrK63MXftBigNTuW467MFyPNjpWwxLajdD2AU75YyE+N8tKwaWH6OKzMuijlyZBPL1y+f4g+PrNRniOG30tiPijlMeL46wjDs8HjC/JyIX37DfXVLjQSMbpTJkLDkcMS2/YFPV8Qq9irO5b7zPkbO3S6ozwtyEcFuz+w3Cn5JbpNp9qi51RSdp+emOpIvR9wolt3+sES57k7kMQ6SNGETy2YGiUyTFpMRDKHWFWXGFBa05gMu3YhnRvWBEQZ3i5RAjuE5Vgfz+SXJeYDGiPBvvgyQwlXE80j96ZGqwbtQmGa07KSn2f0rIBtGT1h7b/gY/T7qvuEJgFr5BeLLs+L41ZJHg4fQ8ifX/Cx0O57f6eSYn96x/bQo3gEAQok6xOtJiTFJJJfQuNV7zXyVyZjd2rRmqEo6RzdvaX2YMAEw2cTqSTqMZx0roIfcpSNZg+Luiq2T6gGSyUSKb1tHENIWwe0J2e7RjlVSyJ7Lwvuw83nHRx46pPK6hkXgRTW6rQEW2aLIEPogbbqhcWUFCBRtkZ6amATbHEm0pmZfkm0twbA4j0EYA6wkyfpbECUo24JEK+xko7k1UhhBaJthfWZI/VVsTht339d2QTe2vrCqMsg9JLC7ZZDEEqOt3ENAXgPXunbysxc/3xZXLsd9CocvfFnbz/6zZ9rvegPGTX7m69P67sluk6WVwbg9iXvsTc3+hroTEFaQdDK3MSJ3rphp9V7bvzkJptYeE1FMJNesgkRvBmkyOfsScIdEPQSzfV4txOxmcm2xzfcd/tcgIVE9H9ae0FJP65bAGGr5icSaSi9k/fqRApwJRvwWAGr97Lddpm1E5TrXngvLd18nlPBnZclR7NJQnckfwigcY9zNzD+lPP789u+BjM/4/aL3/mAv/vZY0zCA7RdY/7mkbrTni0TE4VcHHcjGXhnT9LZr8yIx8oiTdZvMMME2q5gHyvpEiJOcMZ3CzYmLt+IVXBqvZ7fiZfyssBjgzFR96VT4q03O5TNKdDUIjFYHK1GfRgjZfVSyZ/P5MUwb8zfeeD0Rx8wN3Y/CIai3Q3INJHeZeqxsPu0Uj4/4/tCfdBwXjnIJUo1NiT8w4cYeFVwb+HGGUqwET31Vab4Etedkl7fY3cD5+8eWV7HsCAo5Sz4uA+IeIJ06WI4h7rvpbCXmdEcGzPLfYhVSx6v6cbm0fDyqYZG49IoTzNSMvVuDI1JBl8kHFHEikxUWHbdhZBBz0Y+G+2YafuojftcyU8T6bREp/HTxPj5O6Tk0A4RjSLtPlKJ/dMJOV+CsdKETku4fBDYDTGCThPuho47vC0wVZrOpOPYJ7ClA2GP7J2lYU8v0RH7wzcsr/ZYjrRly9HUU4iJ3YxIoJ27O2qyKMl1izI1ZrWWFd0lbEy0EYbF0E/OpJcF/+BIezPio+NztE7wHPZj28Xw4lmou4JdekU+d/uqO/Mh47mEEBlBThX3KIut7ig89GmUHIuA3kYhvyyhRdv3lGftjMo6WotFSVbCQm4a4CMtkBbr58OvgIM+OdqVhLHSS4Pdsh3pxjfAxTpAWZxVfyYtxPe3WXGb09j6hLAKVzW0VGufrSvbQOh8gHSGtof5FegSAJ1n38peYcjpE/cSac++OlYy1ypCFx732f6GYbllQNjKSJs0aP2z2aOuZAn6pef8BKj5aUt2uT7Pb37ss7jb+wzNuv/bhItvhwBE2USuE/J7Oyg9dkIqa0nKjd7m4Lpv6/G7S/8TYGHMHqAh+Y1gdt032UpbsVkP1nO0LwLWU7AKj9ctgEMlizOk+KxIEV6dZmwY6PoJsWAtGiW1AD6+7besAH67vn4F5f2kvFcmkvfhnQQhgyanWebiBXONKqN3QMPN69dL7vH7+3L8Kdf657d9DWZ+xu0f+0e+w//9v/9f+uAWTfjCrh1uoPJcY7U8pBhQXTbHhy6Rjmo5blVdwErPfLkYSxGWwcknJS8z8jLHoPWwI1+c/e9dYpKVjNxnag79R36p6NMpmItjoe4kxKGzoFMNl05bUArTMZMmY3huwRBlRfcDfjRmDUfP6Xt7WlF2bw2ZnfmoUT4TxY8lNAejcv8/LnBasPlE5hVahXxucI5VuiBwNuS0MD7OIZwbCvXNjnqM5aMQZRTLgt3vemBbdw1NBkmxQXpQWp8gj9HXJhwfEk6rDwa0KelcWe6jNu5DH5wmj8Tm2pB5orQEnz6hzeH+CDqG4LZCmRRLsSSzqOmh5y72XPsyiZA/nyIYLSkmDZuXYH36wBEr4oztwxZu+4IPmfS8oLlCjk7m0hkjmWakFLzFZ4smeHeOOP+SIIXA0GoL4a2B3w947hO6LKSdwemMnWb48BCszNK7LXeLuQ9E+aODSFeinFK7sDJF/yUD2GlviaBIdconZ/T3PkFTYnl1ZElEyciDCbQstAz2JgB1CIYFmRO+tE2oaEloWSAnXJV8atG/ywOMBqKQDjrrNc04x31gSyQ9WwnGCY1AQ+uagNXdAp1t6f2KRDpTMxks9IC73otJ+kq3Xw/P0V5Ce2dtViuvxMJAJPQ1t8SFumGSo81BjalIBVZta1j8OwvVwYUsBiU0GWawJvfKALnrlGwQbISGUAfYPcLuCzi/YStVRfPT7oLqom0p/j77YvGZV9DXB7V1ZpPrfm6P/5QV+Qpu3mN0/Eqe/HQg894bcH2ab69H5L1gvvXj3wM4G3DpGhC9AsGUfGNbNK+JwL1M4xGaJyJoBBFtotbVgh0ApZLVezicb9kt7+13P43x2oUkUfrJ/bPWe+JagpIrCPNKwik5GlimHn63MSirU279KA/qL8DS++cirtXGtQRI8V7WvD4at28HVhsYvTmaRpzr6IUqNIte4PR2Cb6CwS9dxWBmhO/sX/8h1/nns30NZn7G7df/sV/kN/+v/48IIpOIKPfmoUPIYXfWxdm9bSQV5iLQnOwxQNZDOG+09cCmIRJwd79/Zni5MH2wo350z5wHcgk3Q301Mjw3dj8+oy8nbChc/qEH2puBto9bLklPJpZ4T1cN7cgglOcWGRpjn9SnFhT20KnGosxvBuoomy1XPTIwhl1GTg3KADjj5xVX5fILO07f2rP/g1NoZZ4W7DB2V1fDztFuwEclVUdqgKo0VzzPeOsTv6ZoHpigHXIk4z41NDuiQi1C3fdwNIhjrPEltr2G8PousRzvQhvTQBYocyVNvY9IDdDTkmw9jvzjOxYMGUbm14qnFKLswbAyoNMck+mpkrsINDfDpyVaIFh3oyRFLxO8nIKd34/4YR8iYAmnSzQlpKdyOV4bWMW9oHONCc0cX2rYdCkxQbvDyzlaV2jPyDnPUZaqDfYjHDJOECuSQDpo8pzQ1lk/XWmBcJCJWXeSybaPXlvYwLNECbJPxq5R2tx9eo6u8HcH2qEEUOklpDrIRmnkKUTWVoQ1IbXtBYaMN8NrB3Au0Ix8bogJ9fXQk6kDtGop+NDTkDdwECO5jYl0mim//xY+ONA+2AeDY4BFk0EjgO8a1W99whchrOizR21B6NqRsNPq0vtRraJZ4vtLXkENmxjW85W0sESIdaEfdAcNzdHUr2V359laWutuIen9tCR1t50Ge6P948e3HkymC5cPlMkdWUAvHgngdKfWRNRBapxbN4fiWz+jrVLzHkiRK+PRJ971d3JbGro+/fbF18c6ztte8FNJGvnSi37Km3vMyN5F0KLXCfz6x3tZqpdb9PbgbpmQhqqTxMndgr4CgTUd2CzKyBHcXxm0UbIxaLAlGwDYXrdiLwEaCWPM1gW83VlE16z0z/DteCtCOClTTxXewur6+b4t+0TkT7xDwO7NqLdterNvZgHsts/nfdP1alrbMOiX3isabOr10mn82/pB37I7KzMDwh979X2+yu1rMPMzbv/w9795pQOlC2/p9WyCZvQkTPfK8OLkL2qnHtfsjr4qEGj3cefaUXj+I6+Qdhelh0sMVvN9xnZdJzO3SNTNidSM/e+fKI+VNiiaFXLG28LwVPCi4aTaAblQ76LHULoY5RT6g3pXkHlhOaYoi0lhekg9FbbiKRKF252SznB+E32QxucFvRjp3Egt8l5s3MfEVoCcegM/Y35dUBTOFm0JPATSkgQfM3KucT4GjYRhEvlUSedKQpCUYoBsRE5MIgLkcgRdyWwkd4SEZUDDfl0mI50qMlV0AVdner2L99JILF5e7aj92LUpah5duheLZOJcyG8r6emFJCW+xPNCejnTPq+oZnzMkHJ0LT7PdAsADOXah+c8B0ngAlpJlwX5/Bl7eYGHOxhHGDKUHLZtiK7kU0dv4xAsjTVYWnQNtxjo9TJhx5G2T9TXOyztiGycyFUpzwlqin1pxEQNsXpftSPikbkz9LqBx/mtBWhOstAJ6Wy048jyMNLuCrWkTZ6yzjJr40QX7RqWDh5U0cWoSQOkGtFB/mzhshoTy31mbeCK9WyVIXdLOmEzz31wzordj+TPK+Pvv2OZG/W+YDnFMYoiuXef7qO992aVMWESdvkVnPTR3fXqbF4DDV0IXVa9lmqU3hS0twYIB1Lc/9ICUK/NKbfRX64iXtS37CEf4pgbdBeT3JSvuq5m7gGAPcyvHoV0dsbPnHrv1L3GxLj0z01C6+fMR4PiVzfS+veqj72dDVc0sM1mPw1w/GGbX9/mvZ/7+9wCne3E/6+9Xd/Bfq9vAiECnKWbEpPgvdRyy9xE09eUOmuypePGPWAWTw5Q40jv1zTkRk7GqLYF1F1LbdEccitHYeR0ZWRkO7Z4b1tLV/T94apzSStIW9++v2zVb7nfnnldw6i7JX1lxlZKLD7X3G+A1petZ70kvQGc6wesYO1qG3/vSLaf3t+n6+N/4sNf/l+/lv+At6/BzM+4lZJ6N13Hq1x7jkRYBOXJEa89hl7wV4kpR0O9eMY6iPbSSaY3z0sIXSU/QE0p2BSPL+r8Qcb2iXwo4IZWI7+9UE4XZMjUnPAcbQzK5xOXj3cxqM9xA+eTsf/hJcSUJTG9iQhuvbSNptcqHH9UKT984vL9Vzz/YkFaId2laKA2O8sojIszfLGQL90yq4rkhD4v+JA75dxIL2EPtjF3NiGsvfKyMC6hS0Aq2QttF+WkpgURxbTrG5KjPU9HTJDZw/Z76CufqZK/qAF07oZYyQOWoR0HZLFIZ83B2JAybWwgGoF2M1F+MsM6k4XHislzCltmM3wowXSoomQ4HvDDEKWiyxwdsS06nnsfVDQl3AzmmVSGCNRrXZh3f4/e7bGxRIxpSviu4KWHHU5Lz3+QCMezfmB9aSalAGHTdozcQnDb9pGLE+m/ju161bzSrflxz7bmvYQSWpd6VFLtEyoSOqNu+abA/OGI7TOtW7vdgKmvLDuvHhk6PWixenddNaRHCtC1UnohRNwWqbVRqg1GJso86+pUiLDCPkT3ycVU8H3CP7yjJKU8ThH6eCTKSWawBPNhveu1DV331FfOcVKkRxQEixIi0jhfnnr5wns/p+axvx30uF4ngdViHTlGPWumOlr66li7Q2idh5wQQlenpiir0jr70sMae34crcZz2Unvvg3zXRxBnvpbuqM1gFtY30FKOBvbIjBaUK3mnerwyCFc57v3VugrBXHzb3l/CvvSE/hJlmXlI+T9p7y3rrdtQXjLEm2//vJjdPfZqhkRtjKS3oKa9W8qKTlFvZeL7H3c1j/HkO35Y4ryUllZmZv9vrql4oVOjRiM9CU8uBJTopuOJ0o1N8yQ/5Rzc0toyZehXtxH9t6/2aiVn8x+kfee9v77XJmf97aNprlF4D/52p/cnH/89dfMzP/fbnluLLvodBtiVonVrQj12EBy0M6iEbnf4ktoJn1CD62LXLoA02Dzlq6oXLu12jxKHSeDITG9HnrzP+fu83NoEZrBh/csbw5xZZ8W1PbBaldnOMWAbUVIjzOpTiz3hemu4Kl24WAfQKeKunL5MNwn+08a4ycX2GWmRIhi58g98ZK6lgb0eSJ/foKSonwwjngTLq8zUhL5zZ782UusykumPgzIeY52CacK+UBrhvTlo2ZBZ2OYHXk5RYdpTZRLi6yaJLSUuugaJOVwOJ0W0lRpOP5qjKj8LJEBNkR7BBsUrLMCIpSnBX37FG0TVFFJodXJErqlacZfPQTo240BUJKiS4O5wukSrMwqyLUWo1Ft8fg8g17iOpUcvbbGAXdBL7Wv4AMYrkFlvt/BcQztw2WGyxx6ll5uMiHYm1bJHoAkH3a0uwEfNfrHuEYOTp+/fFydPxFOKEuwCdZXiaYerpmXFsxBCmfdclCsZNoAeQlgbtrdeC2YF4QIgJsiqFEF2j71SbtFv7EULJGnRmqRxRO2bLkO5qmDAvdeuhGiebduI3xoYKKJabvfXR1oteGWesBeMJG5N+5p+0zdF3zs7E8PjJMuJvC+TF71DiYB7j13+j6FCNdTgA5SsCSt63HeYzN6Cckb3QYTIKNJlCYB6lGDBSQ+y0poibJHoCaXcDBJbx6ptZeTZ2H3hTGPvXp48UhErs6aWOwJNAs2C2lw2iU+W9eaWHIYiMiB4luDys2qvSXr0lfsK6D50iS8bVfWhJX9WL8LrOem01wOQROGxmUT6rI+VXCTGwAQC56kRk5OTu2mqWSfmHUFJCuYWSjqlGTdxuxb6N1WqrqSdAjBng7ZUYkykG5MSxfb3hy305Fmx2EqN+WcfgOJ3OADbIv6iR2//t0hTjTs7ROAr2D+BpT4ey99v3xk3JxrYG1AKtwCshX4Xi/H9b2iC3ZUadcPkw563v/k2yu1bsdhz1e5faVg5jd/8zf5zd/8Tf7O3/k7APzar/0a/96/9+/xp//0n96e8zu/8zv8O//Ov8Nf+2t/DTPj137t1/jP/rP/jO9//6tFgQDjJzN8OGBFo38d9BYFoUWwIZiO8lIZf/iCXmZ8KCz3Iy/fHoNB7XQ0pb/Wg6Jvg5A8GIjlXrHc0whygA2vleHziIe3osjDPvopvT5gh0TdKzJ2S3hV9p/OYIJWo2WjfhQ9iuY30asm//4Lvh+iVPGgXD4aIwp/ccqLM7xdwhbMyOFpxnsuyHCKwUanFiWa5wnNCc+ZNip0R8vwdqHtjXockacLqYYIQufG/HpHfpzJ7ybKZxN2p9hdgBLMaYOGBuIwkh8n9PQC00xpBjljr460MUfuS+pMz+sRPyfS84X86TPJFVka7duvubwKp5BOFZnbds7Ty0x6vsQA3Bbk8oyUEn+8SytqCFGlGZzPMC+RVTPNRE6GRouCpBHUVxturc9UFpoU68nHwwB0wWjJYTE+z/B0JokG4BkHfCxxjXeFNPd2ANotvDn38187LVywqcbkXBsyZKZXu5iJa+9sLQJjd96sYtPOLAigcwNT8tTQqdGOJQTDFkvF0HmxlVBkCWfNxlQIW94R2XtZUBnOFSd0OG0XwIvaJ7fueRXzq0NmnRthYw889Ry4HO00xPrqVzMyOl6D6vc+0wVLYmCKLJCfFtLszD1BWtTD6Wb0VgRR4rGun4EY5m3TxghNe6lLrpOVaLBU1gGY0M/z9gQ6mAFaB30tGEpUAxgbfcKV3iG8t6e49KlGQdw3djf0RrBahPNkWzNO9NqTynJ3ZamQVqpH6K4zx0bHBofRg2Fe/3yZZFlBjvClX65bR5kpHEaxk3Yt6/iNiLSDmZwgpc60aF9M4Ti9M7SvE6uRtDFkI6dG1ijR2HqFViqDNTclOjsP2ktAGrb8rYyzskEuvTRkCNZdQmw6lk2UuwGBOK62XmNgtYOvQclrQvStG8u8V/ZuMYGs5yzeaYUeQjfxvfcoGwi7PdJVQ7NsQLH/1pXVH7uBof6cFcysF3UNztNeQjNfe0hdX/Y+hPkpj/y02+HnvH2lYOa73/0uf+kv/SV++Zej1vYf/8f/MX/mz/wZ/vpf/+v82q/9Gn/7b/9t/uSf/JP8uT/35/gLf+Ev8OrVK37nd36H3W73Ve72tqXFGZ6MNjruaUsNdVlDueI5+SUiQNuhUD/Y0/YZTZHp4RouJOaES5RU8nOs0pfXYV/Wc0NF0JOhcw1LNktoXJJy+ugOWSrpeQGbkSVj9zvKDM1esG8eqbuGnqO/EdMC+x3SDCtKfXCmh3v0NEc/ouzYHqpGdL6n3nJBID2eY4IDdM54NXSq+Ki0+xG1EXOn3Y/UMcCXzE5+e8Fmp5VMerVDT5HLkl5mdF+wu0LL8e/xsxOL5hiW1hJJTixHpR0zet51wBisV+rMhjzPGL05X1HaoURmSVHSqYWV99LgdfSkKhVYlsgfuYRVm6GEJdo9gg6HATvs4t/TFILZsQSyOQPTjO/HcOQMJQabyxz7nhOyG3pbhktMVuFz7MCmIqrB2tSGtGDXfGlIimPGDKbaxajA/QHMsLqAgR3GmOdr6yuo0Il40VDyNY/X5+4Vdo/fO2gLsEpz8uToSyQQp3Pt1v4YHHVyxMI9tRwzzfQ6OQ2CW5D0pqCdgVjZGBZIHknUvs0S9BKShGD3ZsW5skertUJaZ6j6vrjGPR/NNuPJNvRjtQAzWn1bdXsSnGBvNAnUAEDDc6MeiJYTNc7bpojN8TrxzrqsicXrBCWxbz4GwPN+DGnuLSq0R5BtDThXcBYMj0gAjLSCEeJYDAL4rGVOEbwIVSLsMfdzkRpwdlyNtEjvwRZAk7YyMzFJR9sSojGoARr2eST2FY0U4bpzbA4hcYRDrmCk/8krS7Bu25H15/VsG+1ApgOYrefQaiPetopIOItyurp51rdcp/fVLeS+MOTVxmzkNShu3ZvOKND/FpZoR6cRFLdlstD3z0G3gDl/b3p2v+Jo4dp3ad290O+vjqv1tytNdHt+Ymv2k++rV4gBRNNYJPaj2vXxFT6tTIp07Y3efMKqkQlQ1w3kEizVT7I5Tu0+gPWolXVoiEeiCzaYxT1jSOg93zuu99GLX+mnr2wT9/8tYKrr9sEHH/Af/Af/AX/uz/05/pV/5V+hlMJ/8p/8Jz/z+z0+PvLq1SvevXvHw8PD/w/3FP6Pf+ovsowJnY3lobDcpRjwGpsmQjtT45mg61XQhU3QJpORni+RmpoTaano04X0eNmyRnSq2Os7Lvfxhal7pe4tmiPOPdZ8lOjvMzm0yvLhAT0Haalm5FON1eWQ0LpSkIm6T7gIeTY4N3Y/PlF3Snu9C+uqZHQyxk+eafscJaom0O3HbjC+nfDWYiV6HKM0UzLt0AMCXcjPC7ZU6iFTXhppWfDDGLqHEs0mTQXUSHOftC6RqusQZSuLoDnPGj2Mug5HF0jnBaZGOyZcosRAUlILZsd2EZ2fnxaWY8bFSecZWUCqhb6j97kS6XkpCSDSc+XxHA0iux5Czj3tr2T84Q4IRxsiUVYCvD+fuUWJaJ6DmQGoFVsquh+Rsact99YFvoQ9WYYSg5HHBE4pkBPtsENSF/oVjZh/c8gaeS3NQhB8KLhb12NIZ88iMXebrGuUg1J19Lx05shZ7gZsn+Ialz70ej+XRSmnijssbwrVG6nnm6w9kbQSzTrXRpalp073yhupr1Kbw9r36mYy2+hvC9bBuK50/abE5imYyo2/nzyuFzf6lfV9LMTP0Usp7ktLBPOnimK0HDZ0oDNFnYFKwUCx+Ea7t6NGW4ml98Bq3YXSAV0kNxPAq9u326BodVpPFU4VmB0fldrdfJ4NG7twXDrwE2d8Bm9OuVhY2ls4/axTB6t+xzqz0nI/jrSWyAJwSi/heepC0Z5K3kanjWDFo3yYvTeulAA3BSR3ZmfTukDADQs3UALRG6Cx2bd7jhDdIi3BruS06lg6I7IKeFnBhIMY7r2PWrfW5/58+vtd5SfBKLivDRiJUqN01quDrCDeYoxutrIsuvE6q5BX+vNkO84OZt6jqlZB7/ssjopHCJ9fWZbt6G5YlvVnEaet/a9cuhNKtze+npPrFgzOeqbW38Tfq4bnVhvTOni7VvRkRUlbuW5IjZdl4EfPRxYrlJ6D836mTt/1zgRVg//Xv/AbP/H7/2+3v5/5+38zmpnWGv/5f/6f8/Lywq//+q9jZvzVv/pX+bf/7X+bf/6f/+f563/9r/NLv/RL/Pk//+f5s3/2z/6h7zNNE9M0bf9+fHz8B7bPqUH1cBnpHMvu1OKOcCfSY5e4hX2OTJG0dOZm11mcgXCgsGA7YfEEb/boPMagrUpaejJuC9Gs6TV1dpjAp8ry8R1tn/Es5ItQfn+GVLi8jok5TQvDs6PnmewZfZmZv3UPrzpVngTJiu1L9DO6RP7N2qtHZmFYGnKZYjWaQydDNSxFSUTMt1WEXhb08SUG9t0Yn2sgZmHpvd/3lb+QThcoJQayeaaNA0mDYeCphvblzR5KChZLNEDV7KSnc0ze+wJDb5jZ9RBqhjcnTY5JikrPEE4pLxmpCZkrUmv0fcogHh25ZenMT63BlqSElgBSTDUGl2HAj/uwG996JXPqgujegHAM7Y2UhDy9REnKDOoSK2zr4W7t6txyc7iEM0rWcsV+j7y+w+5H8IZM0THdcnRrtrGXzi4VKesgmGBw3Bt6WaKP0JI2JoOUoIQjLJihFiW9JdxFPaHr2jfIw+1FBVEnv4RbRHI4Z0Sg7RXLUTaoqsH+EN8Hp6/YG0BYpq2XT9a8oG1I7kyMd4GydSbBJb570uglpSvjw84xi8abpgFMtTfabCVAhS0pPk+kN/CMyTkcSH1ZLGBZe1uAWIV7YJ5Izs4gTa51AyFAw8os9ed771VGil5ZViDPwrIHHULjohlSc2yka3jW2XA9EfFv6+83p2i9wczV+dTPCYSg2HIXrK/BnEhPC1ndVd6F8IJ1wxxGlCGHaEbbikOJ+L0tKMdjH2V9D3WcAOgpOymtwEa2mfc9YIKjElbprLfW5F7W6QaKje/o9uuYlH2bPNsKNgQit6hbka0LuYmSVoMA0CuIEQ/XIY6KXsFDh1rrzafum4tp1baDb6WXUPSttIdc44fwXsVzFmdjlgwNUbmshaP1fHQGxYOgpcOp9c8GUUyusUD9hPbUhJ8AMeuT2lZ2orOb12c1X9kb6e8g6y2Au7M0ZalKcwnHlcOqAbra2uPtAzx+9czMVw5m/of/4X/g13/917lcLtzd3fFf/Bf/Bb/6q7/KD3/4Q56fn/lLf+kv8Rf/4l/k3//3/31+67d+i3/pX/qX+K/+q/+KP/Wn/tRPfb/f+I3f4C/8hb/wc9n3MiQuCrbvE0LnEH2FxOZBYbuR3s3Y3oPub8bu777Qdik0H8uCPJ5oHz1AFhoLsh9Z7gptl5hLNEfc/4+fYx/eU0Ui34MGl8rwo2fKxZk/2lPvRnY/nnGBlz864DslPc3RjycLZZlhdvLjxOX7D+w+ndHnOYDV3Q6dFFpCPzsHO3DchyjtYcAb+JDRuWIlh5W4Nnh7AY3H9PGClowlRV+WmLD3Bk8vyHGPPi/w4QM2NJRgErQaDLAMSnk2yidfREbKw4HlPtN2idQICn/MyNLQyYL5uUuUT07o8wX2Y9SypxY5JSkaGKaLUd5d8H3h/KaQF6WcPTo4J7AyxiylIEOk6ErtJsglBWM0ZFginVaWFuTsbsBLjhYFPVlYrA/4xKDpTpSgUl9WDhleLpvWZtXRROpuCrfWboBaYQn2CfpK0x0OO0hh0VQiC4Zdp7yTIpZ6WcqCcSKAqLUZfZzRpfZ+VIrWYIPsbh+iXBHI2nluC20PEZS3Zs5r7cxUEZZdQg3Ko+HS0H3B6eFzAJ2KXwGKG2vWXJyrdeIysGpxviyu2zohvTdOJ7o9N/oeqfQJvk848Ty9PrdrMFbNqnSRsu+InWjhvFoH/2tpmH6s0eaiHSQyinovLFbbdM9A8dz3oRK6ogRy6StmJ6IUUjA8bSCEvRosSRsk7MLPUYpbS2trJJD3Y9GuJ/Ik0fKgeYiCm1+dS3Pswypeziej7pQtsj53LU66umRW3QXduEDtmHUIoNZWl5d3K3LXlEjkDPTTHexKSqA9KVd73yDvbMmmIVmx2s2k2G+VTVOyvk4lhpeI6Ukg0UcJEZIYtZexUi9hJXVac6LLfL8POhpZAUbqwEhZSzOdcVhD9tZJfQVRgHT9TkT6x3dhWSd2OjjvhNUqPnac2liVOFuJiFsw1bVU2tmklfHxm5PjG2C6/l42poftdxt9c/udueFwrqUuZxVErb+NcWp9X2FpwnmBxVYIJ1vD9LX0vF23W8HwV7x95WDmV37lV/gbf+Nv8PbtW/7KX/kr/Gv/2r/GX/trf43Xr18D8Gf+zJ/h3/g3/g0A/ol/4p/gv/lv/hv+8l/+y38omPnzf/7P82/+m//m9u/Hx0e+973v/QPZ98PHd7x7d0JEaBqpp0liNSkLMDtpqqELyIrUhfnVGHXw/YE2hgOqHgrYIeLkc0ZfQD0ayHlz8tlA4fwLA/ZBZM40S5QfzVGaeHWP1kp+nrECp+8dAEcvzvG5kU9gU7AO+V1k4EirDI8VuTS8ReKqjcrwdsbOE2ks+NyoA0h2zh8NjE+NNGfamCPNeO7JkIcQQQt9NVtr/PzmGJ2UEWQXbIDvjtRDZInUBMNzJT1OsFTaNw604wPl1Z7xswv62Smo+dJTfHcH6pjRIZxH+bEyfzhw+cVXlHfB4LhbnP+5RZ+gozC/LqTn6Ew9Pgr1mKl7I0mInyMlygPQSKeDc0bc8CFf59MapZpYlDb8NCG7kZYUHyVGjBauobRYNBo0X5fqATZ0CDeUg7c7OE/hcqKP3uMQtvUlAXM0WkwJxgK5BDPy9oyMJVanyaFalNPuogu0joX0dopS1FiCwXqe0ZczLFEOlLwOaBrvf9zhu8i+98UibK0vu0InBO2Qwql06YyTK2sTSHKoMUUI/c0SoMazXBmAsbOYSwRNChK5MWZR5iIm/OWubJ2tWfUA0ld/CmtNoQ2+jdfSPEpAPc03xPUx4bShl3R62Sk0JRplhjXHRIlEbokO2is4chydws7bsvXaVgcvhPujDn0fbsbzwCWxoElLiLWtEQC6H4O0eKINwdTQnFxD4yK9X5cLLHuFMXQ225y1LrW7xZ4keInj0ClYMK3RkNKga4f6ZLmJndc5PH6zNdp2oiS3Bsqxzl8rixE/i4JqY0tN1pX56HrufmrX5F2RXu7iyhStzIVYAC5b38vjVFfvIADdWBkcmsiW1bJlu1jsqXQmSq43Ti85Oa0jAZdo5yESZSZFrmwTbAhZ+j0Q0VHREDc+ZmUpooR2JdECzNXaAxt7KcqlMy2+eZX6OLKe1/fPrtz8//YR7+zQTwKZ6/0n/v5Le9Wbtgmw+5N9BYq+8lJUBzXhPA9YdwSYdQYV/5JOKT47KryFr3r7ysHMMAybAPhP/Ik/wX/33/13/If/4X/If/Qf/UfknPnVX/3V957/x/7YH+O//q//6z/0/cZxZBx/Pg2vfuHbr/mD04maY7XDupLqVKeuKaZT3E1y8XAn3SXaqxGdnOHtBWXANLH/3SeWu0ZuQhsMSouOwKNiyYE9Wp3yaSM/N4Y/OGFk7JjwQbl8Y6DeZ9LUojP1xZnfjLS9srwKwa8rDI8L8zfuI6BuX6jjjrYXhndRq2+vHmBppCdIjyeWjw6kyZEp7KJpEfRce1y8oEmwZjAkvAmMYZ+WaqyZOUIinRba4jFI3e9YKVUMyhcT00f7ABYpc/n4QH7JpJcoCXkGmWLFZzthel0o50Z5uzB/EKUNmpCeKy0nGBJMRvn8gg8H2qsRKUZ+N5MujeWhhIC0NxuULsplBaPumGhcS/rgDtFGl1j9+RLnwJVwtB1LsEbNaT3gTprj0xL2aQdPOQSsLqG32e9w+oq/Vnxq0XRwjN5EPi8wFGQ3xiBVK/pyCZfXq10MqNVwTdCiIWQ6LbGKL2GBlqWhjy9hHXfvwuIE4w5Kjqj88xyD4VCwQ6Fh0XizEuLiovjaWTyvq78utM3aMUGAN71Y17F0HVS6Ygbpk5P0B2wQdCJCD5tFa48hRalwfU1chq2/Ec5VEEA8rn3lWYnv3pbyuw7sKqEB6c9f3T6y0jExX8Y5z8HakIJpkTVoMPVygjvdAEURj8TsxJaRozUcUmFx76vwKZKjW42Z3gm2tR60xwJ4d0J1K3bXQqgGU9QGaPGVIZ8t2l14ZPlsJbbe/sFyt3oX4toSYErsei5CYxWvs1UByvsS3W27nVP7RKq95LYFIjrReLXnDJn0a9pn+fUjr4wQ2z1QOyZbJ3hdG1/Fk6747T3mwfskalzdOrIdwXqYEO0JfBtt4ve1pzD7Rjn0BaTb1cW0OZ1CExKlIukW6DjoYFn02uzx5kStWhbZJga2MMRwza1PjZMau+vvgeL1NKz/76a37Tq8h4C4ViZXBqx2pq1FLvH1if0UN66gzfqXZa6ZSxtpLaEaDFWzEPfLjUsrCLsAM3fy1ZtyvnIw8+XN3ZmmiWEY+Kf+qX+Kv/k3/+Z7v/9bf+tv8Yu/+Itf0d69v/3KP/It/tu/+0Mk90HbQKpHUuoSAgIBPKdgYR4y2hbKc6xExh+eGD+dYWi0Q5QtGI05pZ5UKuQXI/UO1/nTR/y4i3LSfaa8ukffPdPI4XiaIb9AfrqQJ4Gc4ks5g47CshPkrlDezfjdGGWgsUT9N8H+77yNFe5H930FIejnZ9p+hBT9mbQRE/x5QUrCJBEq4BysjLWeyyFQNBxUKvghY9NM+sHn6A8dvvsN5u8eme8zUgfy44Xh7cxyn9HZMIX5dYE3I2qCL5V0auhpQVqUdWxMtEHJj+HSqvcD9X4IoevLHOW9SyN/emH+TrBENibSy8L4aTSJ9JzjGz+HMNSF0K1U2/Qja5mCaUFytA1A+0rRLcBqC4ZEbV0REmWN2pDTBXk5w+FAez3E8NZzT7A+qHeWQNISK6G5xmBdCj7N8XyR6Nc0DOgqFra+yh01rOlFO0vicIm0ZXWLDuGRc05QRz1tOCVoLez+x2gl4D0dec0rIXVAujIBfVUXGhfCAeOERqWXHdZwOHWuuo2+4gfe14MUaCmhi0SJZenZMnYdwDUTYXxdlLu+H8bWcmCj5NeY1G2lGhNPgDF/b/B3ArzqJgAAkG1xEvNm179Y965IBzkeDMgwX1moVoTy0gKsDBoLCHRrTKknEGuhh3PwE5E/tXSrcZ+9RNjO4ba7vQSVZgl3fl80JXOsXg9oe6n2+6B577ezTq6shAWrSjbIj/jg7X5fz0cj2B/r19zAatcHAW5xf3i3V4fNOliQtQmkSlQvZduDq/4iJszO4LBqMNiYhwAX13Mh2wy+odpgQW8s0Ruokas2fMUtrd8/1lkT8yvjoKroDdsjQLUuMr7xpq+C4MCSsglr++3C6py6xV/r16bflfH6K+3V8bT0v68Prq9dgQkEOFlZre3AOjB57737m3gHVGtxScSvMNB7vowbKspiidYSbtFGp1noZmyVk91+f1xoBh8dXvFVb18pmPl3/91/lz/9p/803/ve93h6euI//U//U377t3+b3/qt3wLg3/q3/i3+5X/5X+af+Wf+Gf7Zf/af5bd+67f4L//L/5Lf/u3f/ip3e9v+yX/6l/g//dX/Z6zwE7EUWAd3SZSnhfK8MCePVNyHPWkJtwANbF+wh6DFl1clElj3Cb1UhrPTVMhzDSFxi+7Zcj7hi1Ffj8hlJp0W5jcH5jcj548zFGW52yMIuQdprSLLMgnjY7iQmIw2CiQntUZ6UvTxhGrCqlOPCRmV9JzJ7y54IXJnCNeJWqxq5GWKgeB+pA7RCdlTrxKLQA7gtuwS7fv3HF5O6NOF/acv1A/3tEOmHTKedngS8jnyXsolVIn1WLBd6cuIFlHtfdDSyah38fp0NspjpT4U/FCi+eYUVuPyPGNPI7bL/bsu3TkUX/7UDLfo8o0SoQ0tBLMyzQFocg4r9RBx+QEIuvthqqSTkcYc9mwzfEgsrwpygTwlmAvtbqAeC+ViyGlCThM+L0hdYqSvwfrIUNZlb7dt13A47XeR+Ntr3OpGk9DZtEHxUfGz4dME3aLsS2ifKCVs5yuwSQnxXjYZoo+TaXfbmUeZzohU3p32UlxP7q1sehxfU9aqIYVYHfe4fimK3Y7gN2Pu2qpARDb3j3VRsrSY4Nd1tHRmAesgo7cLkJ5Z0gkcgK3/1Qp8kLhPldjNq8W1TwTrrH07QK9zgndGyYh73Ky3HehTggpWrmBXDJIRLFYL5kBX1aax0g/QzzEupJ6SvHbIXjUjazZNG6X3xaK7nzo4Wa5lChNIa8hiP17p591ho0Q291jpzODqhBkCUL53OlZA0XucufLeCTJbabN1cgsWw9JafrIANnZ1AnkH0quuJEBPV4mswFNA0OskLzHRroDoVme/grJt0l6BqMf1imiH2/eNH5a6HmrABrpmZgXsG0u0lqTwaz4MsgXvXe+Z7e4LZ1S/t3zVx9wYhoXr61oHJ9b1QPFWqzPqfU0O7pvgl54Fs/Z9uqJf720O5MahtcK/FULSgazcXO5VcBz/rt7BTLcXxrW2K6hk21XMhdaEX777Jl/19pWCmR/96Ef8q//qv8oPfvADXr16xR//43+c3/qt3+Kf++f+OQD+xX/xX+Qv/+W/zG/8xm/wr//r/zq/8iu/wl/5K3+FP/kn/+RXudvb9r/7R79DnrzX0hWGviIaE3aA5T6RlrHbgI2apdP3CRuh6oDnxv7zOSzKWRkeG/lU0YvhDyVslgapRtmgHRJ1H8mhS0nIt95gDwesNfInL7QPjl0V6dA0+kCNwvA4k54W8uSk58rl20M4PZ5m1BQfCvXjN9cF8zlYGzC0NfwSKbaewrnTdmO3Xg+gEUVvY7A06e0MKNLrA54UnYLWX773EfndmXQxdl/MnHYpSg0eAlRPQn0YSUNCTwvDuxmeFtphgEs0Y2yi2CGFYHDhKkqcGuXzRtsnUMWGFO/1tJCnxpJC+BrlAg8XTgW9GMyG4bR9jr5XSyTSSm3QZvx8gnnBXwT54CHEv6oEFWGxCkaiJCOh86mHRHaHxZBaQ39kjkwLOtVoHjlN16Woe4CYObQ/1zFS4txPU5zTnk2TlxF7vQ/mA0JM/bQgP3oboKwMoY1J8cdLDmajd98OH20IbknR2Vrd8UrMECVKnG3NrInlN5a7pdiIUkwf2G2IdaWel5hkNb4TVzqdPjG3bmP2KI+tK0rV/zd7fxNry7KlhWLfGBGZc8611t77/Nx767qoWwUPnsuAXLyHLVtXzx3LIIQwwh03rxDVoYNoINGhYYkGAnrgTrWqQQ8JIWhYorD0bJBlCTqARAfZso0RvPq59/ztvdaaMzMjxnDjGxEZc+51qgrXoc4BndTZZ841Z87MiMjMGF+M8Y1vhPBeI50i7GUbn5izk6BpjYmENXMaIk20eG7OTJvwPJiHMQf/9vAwUIQyQjFyrRWn1bveHakyTMGmtyuAlWk39FIRIaxID1Z6bCXIu82IMXPNu75HVQnyOPvQM80QQGJ2yAaklYMoFp2PV90owqjNQ9VCahESScH3aO2TYnAT1GBPixEzmwDIYKFS4a2tEvpBKyim1sRSGsFGw4cQ1w1t3FtoUYOXEde7y0x3ng26mJ8oDaY3z5sEVA1QYJGNBBGk2EHFUfvQNuZH+32M4QBWO3+1XVgneIodQdAFCCwUiPl7ww6orAEebxW2+dNYi8CE9ZgEzow3aZwcD/jPNlu0mFyaBjo8Pt/vd3HvmU4WLHhv80IcA0AQjIFq0gXz9tVDANe459o9fQ3KpAPVMbjn7TPZgR7QUuDZ1//5934OX/f2tYKZX/7lX/4t9/nFX/xF/OIv/uLvQmv+47eH0xH1FWgQq6Mqb6G0OfJasUY1a6mO9GkB7iZcXiccHjcUy/BJsD0kiBww/+SM7aM7aDHYnJFKRT5X1BSFACeBzQ+cNCZBfZiwvjqgTkC+CHQR5O2IooK8OUNOi0GejSGtTxbWDkrUfsmPNLqyboCmXThpTtDnFem8wY8F+vmZuhzHDDvOKK8PsPsZ+ZECdP6K6crpsSCdK8HWx0ekszPE5ACqIW0VUhJl4d/cwY8VaS04fHLBdmL9GHEB5gRLAnuYIKeM6d2C9FSQ3p4B5wSjlwLAWQcpOS2QCMNaW4W822DHCXZU2DFRL2er0MLwnTugjwtkY3hKcg7jFwCiGCdU1SgdEKIoxwRcLpBPPod/8Jo1l+6PbXbsoEKqQ9aC+dOK/MUC/eKRXJetkqeUhFlQH72BvHuEPF32zImcATj1SaqFsAboSZLdW6Mzs+L0vMAeDjTkF/Jt5OEesErA0kjIB4XnTBB3OjCV3gU+E/hpMWZBXRQyxzkNwEqGADQBpwRTElub3o47qLECxkhairouJYpEKjBJ9xqwk5zCfQN60cdha2UBWn0i8pf4HTWInAtqLlv3/cP40JNGo9KNm7RQBgiac9Mm2TM5IFFYE2DV9Vij+BSFBY+UPoALPJbJ7XiWGRqQaHerWuzO/kS9UUhlOM4PcWkSwQhq+Akq1Yap8uuortTLEQSIJe9OBF1nxlNbwbPNvhmwIcpVeIxpgG7n+Ux099wgxi+TKuRxHKnhIGy6OR2UJriEq6fFUtR5LcWpX2ORKSaRHt0GH2F8BVGrChCNMguqEc4Z/AkBfCKTGCYWKcr0AJUItTEVPNrZoY33zglIZB1MOkGpjPbcd5DTFIh7awgWTBwiCgnSicZlcYsUduweDXg7nvfr08BSH/nYt6WBo3savXORaquy3hLE24PUPFCDN2nPKhpfrz9rbJ7u1Wkg31uzI40cDe/J3lwfj92+E/x33/uv8HVv3zjOzH9uW76Q38CsEKAcE7w4Tr/2HB4MhZhj+ShDRJAXID067KHCTYFDIk/heyccf7wivbvAXh2R3q1ItWCdFNv3DlEVG9juM2w21Htgfc0VUL0L9P7TE/K7ivmJxefSQqMqrpDLFobvBJSK+XHF5aMZsoDZMaoMQ0SVZjUHnhbIYYbNGfjiifWbrMDAdGmuxgGoQWtkGD2uWO8SdWiyQpcN+lTgpUBPE709S2Eq8JSQiwOPhdpQa4U+hqhbpK1vH5xQHyry5wv03cIyA4zpQSrTpOEOnzPqKcHuMwXwNsf0XLAlQc2CtAH6tMHmTN2dKZHX5ILtbgJkgn/B8Iy4kZcR+iYULzxxKT9n+PMFsha4VtSHE/yOVZq5WHXI8wX5k4V+ZIuVcHXkkrHcJ0xPygnrNMEPH0DTO8i7J9hlIbA6HAhG4PA8E/yokn+C0WNDAy6XilypxAwT4P5Egb61AMvSuS711QNcC/zuSB6MGESpD4SnFWlZaehXZVjiTYZtRoHCOaH6DLzK9MTNFMZzIZhhGYMwUklgh7ynZIdHppMXJkFXqzUEe5czt0T4g1L9XP1Tw4OgpnGEmOYd6cwaKc+gd6rnWA8xBssUq5RGxgjyOmw3Vi1ixnU5gMl35eJWwiGBNYv2hX1/41kJihGGtAr5MAsIRFsF7Yoguns3L6wwT+9J48LIFsTwzSFbZcgVFh6cRgzi5glwC4Kmg7XfigEzvZ2QaJ8D0xJjMDnUYgxqcDUS+9S4wjBeHlbQYPu9DVYrBQHZGb7JecGjRAbHJkJNymvRMsHcWE4C5j0VWkLDxuGh0CuRuhzlDlwD2HDMWlZRI4dLNKUBAZFujoNaMhriALg+gMGGE7wfIB41CYfUDrVizYGeotUeSkg/jrQHIF5FJMJi3snTAjBTKP42jzIFQi4Q92s+nb397dbpQOTKG9M8O9H+kegy3jcRwmtkXgr3vX8OF+mhqeHw/ZQ/uP8AX/f2LZj5HW7zpxcgVo8w6r94AuyomL9YUd86ZMrwsqF854R6UJx/ioq301unOqpLFJRksUl5XKBfPENUcJgn+GPF8/cz/KjUizloFJk05M9X5M8K5DRjezNh+swxvzUg9EHS24XaJgKGju4P8PsjbKs4fHKBbeRr+CyQrTCMsbHek641JvWJBL6cYPPEyXEz5Lcr7C35PLIVKGjoZptRUgmybNSdmSnIZ3PCVDhZ23mFPl0w5QQcJxQF9LxBI4OnfnyP8uEJ211GnRTTaYaeV+CyQZ9YDFIckK2yyrQcm0QoHJx8s1lMWgYIlWMpDrjzZ+wYpFcwK0ouheUWjolu4cqVM6qRt3KcgU/fwWuBfOcBNWWKzj0z9VkvK/zpmfursAZQUujTM/T5nmOigGVBPc2YAKRSWWl721h88niEWIj7BaAyFdZNWtYIJSTgRJ15WUIbRwZNm62QDJwyPW73M7bjiTowSTtdQAzQ8wQsK6REKYVSUC8X+OEOkibIVjD5xhBegIFyL5Aia+td3AABAABJREFUnf/iYTg4hqBx3oz3lPguLNiAioD8ojZ5CgAhP8FV4DPgrZZUrMTp1eG+WgFbHQKDaSLBGwHENwCz7KRLCYO/sl2epYMX3Xgt2sqcZUmYXQQJ+X8QbKAZudaHKE8hGxiiAXivwZBWhngIJkmkbCAIBmb+BVcrFa744WDh0uCbaaWxz08beU4aEZ7VI8TnrIo9hx7QgLIcMY6RZdW8WPWUkJ4rrCbynL5k6+t5Z1jNzIESQDSchE07BRg9Bg0IAIPdD4S0e3MYBlLsVFQCmkYGtmbc0cCJh5eD/axxD+lA6pYGUATdAyQQlAbBOm6WYZ6gp0JBL0fjyYsAsAgTgm3yuKFGcNCwTPNghDOph7mado2DJF72dNCeCS8MJAANgNrDbdE3ppzxN7KDpbZdeXvawuBmn3FrZN6xDQ5B9eY5Gi4+BZu4T0fw7TV6M1YJ/Zq2b8HM73BbP57gFbj/8QYrCllZuG358IjjT1akp2c8/vwbeDrBZsH0yQWzCWTl6mV7YBFBDZl0E0qVywcT6sM9/KRYHyaUVwn5qWJeDfVAEbn5nWH6lD5oSYbyznH49QtjmccJLqHEeV4gSwHcoJ89Uel1YfqDJCWX4jjDDwmWJ1iaYHczlXG3wlpEH3yE+nCiGz6DwmMIr9SyhdEq2H7qNWvlbFEM8ZDh20TvwmECsqJ8EEJ0cOTfIA/GzLkiLRW6bMDTBbJukNVYAfqYo3jmEbImyBPDTyhtIiDpUhFlFoqzn6HzYjPJ0eoOLJWTZMj0N66CVmf9q0rBOJsz/IMT8uMC/eyRfTrMwGGGvLoH3j4CnzzRED1fKIa3rPDEEsuuAply8CAS8HTB/KuP8I/usH10hM2ZbZ+URSVfv9oZjqpAnglE4ASEeYIdI/10Wdm/jQKBAiMvBsKwyMMEfHCCuJGwG6xHnxKJwgANeeVcVe8nyDpDzgtBUY5Mp2aQJqYQp9V6gUSfEzyzbtCu585jF6/QTbqDxJtRV1DobU79GRJD1GpCpPLSk9KMsNdYlevOTwEYxpAgqE7vKmwSVFSgUkNGNQUxP8BNH9s2BYcXobOCYxVrbdIm4JHmvXGWZkgWZiXarC1JrHh4KAxpC5Ju9aiQXeE1ITVvlFukzVsYc0TdJuq9WBgxEp15H4sx3dnRSjiEByOqEmp12KRUqW59ax6RJiIognKnAczQvSV9Px9sVbxpWWMqwcWIsREfEmnai+2GcXd5xIB3dePBWvZx33VfuhGOEEoDZ3RoSIAjnphejcBHYcQbltq9E7Fg9AaM9vsSDXcF4BJIN/Q28LSCetU9OC0kU0rcr9Ao7bWHlBqY0gArY3ZRTFpxO0ZvfOdoeXCmOgiE9Ha3C9PGqScpYg+shdpSf3yHy8SCuftwxXnC8yI6hKqGG6CHzjBshHDdA/o1b9+Cmd/hpk/GKtdZoZth/nzD+mGGFINNgnk16NmwfTxBV2D+7Ax/8wBPdBOntwv0MEGfyWdZvnPAdgTyplBwwt/eKOodJ3epNL5eKam+fTCTW3OaoNXpVbjL2FIQGN2pKvzqRCKfAfKTzxlWUoXcH7FNSmvjgnpMuHxvojJsmmFK7RmuwJS1pM5MfdZzYabU/Qn+6g6+FRIbnzfI+cKQlit825CejGq1dzNTuDM9Ntvv+SgIn4r842f09MFw1+tn76DrCX5ilW/ZjJoty0YewJRQU4rCkqzUrcbqyPLujAzFOiVAC9SoxaKFOh2igKhCnwHPTNs2NVSdIbpxVStAPU6wD07M4AkOCTPYFOkwoYgDViLDyEnUDZKuTBOaroiIYHpesB2Z2eSJtbVQHEg5Jj8huClO8IQAIFMKT4pQd0YVKBVpKcDZeS7luMoG+Krw00Qw5HGs2iiHUaNHpGf91PsM4AQ5TUhLlFCYg1czKTylQY/EoVuFmsISghTLydM1snimhJqBturUDeGpAo3UFHod3oiw3mfcvhhUkg59aoYMXfsFAlh42iwZ8upM7U8OPyXYlDqnR8LD4WBmX50kwjItDTwm4zDmLXtJ0EBYnNwBLeH5CbJFLu36hTHYKpWAl8g88m4Zee+48H5wp0d0M2gCya1AKO8K1WFBwINq9LyYEeQBveaUgBlUzX3RfAA1PGWewuuk4HnDMpeThLcRrKqt4bmKRL1+8HELfLQ7AFqFa46Fd6MmuyltocXRqjbPjvE6NxHmEdigH2EHNE3JuHrzThAAqfRE7PZfbwfQQjVDs32/L5t9boDFIoTYyM4OHr+DFKXicANf1H0hib2FaXisHdQYQuiue0t44nbOwblFrD9GrSIFWwIUNa/QnnXVyiCg68gIIjTbQK+g77+LFqIDFN9HJkJMGuNTwyM2IL/xZui3wbdg5r+ITWuU9y0COWQk5erVIdSWuTtienZs3yWR9+m/eoNcBLCEdDG6Uh3AUqHGyV4/uSBbhiLB7w+wX10hH2X4lKDPsZ9zFQZJqM6aS3oxpE++gNcHlLZCQQuTrIAq1u/eQU8aNXwq/IN7Zq188RZVMrVCqkAvFUlo4H1tJMyKegTKCdAzMNkGfVcgSeCvTrDMopuybhSHk8hOeTiyeObjijJlCsKZQy+FD+AhQZKjvp5YndlZLVymKcIpDF95olHSYsClcJV4N8MPuQMY15lVi08TtlcfRqooK44j+DWoCe4RcqoEC+IGWQuzJNzglwKtG43WlIHTETXq60hxpKcVkh5QH45IzytgCkkZOAiwblTuPcxkfjYXbFLIsiH/5C3gr0jcNbBvE70gsqzwp5Up4EnoGYNDVnpgcBelF5YFeCYpOiGA7uHAiWmKENrzBjvNJIwfT1HHSbrmCTK9AaZAWkAgeT/DcqVWjwI27yEKZjQJ7ADIZsy4myTkbq2vxAXUW2EKv9LjhkhvLQgVVgFmggJm+XLiFePi3lr2FML4dxvnITUg3ZthWeHZkc5KUCnYNWliHpYwJnAEuYbHk2qd2AkVenqaYR7c+e5GPkuhh8RTAzd89cjs0gJgbXEHowck7t1mDFwRXicJoy5o5TRc0TOhdI0w2ayoU6uDJt37Jo3PksBwnSplBSQ4PwdBadenjWFbzR9YeBVAL7n00uK7YRcXXP2+f4Gb19Hedb2fuHihnozQlWlhziYR04xte1z2UI5gbBW9CABT5L03pZ1moLqE10J2L0QPqSjDS7J7TqxiyALS6y4FHoQ56gBO2Lzdyxis8i78x+7vXid46w8/1xbqjefGENVEwgPn49gMx2tgqJGDuwaOEJ40jxKzNtGcab1nHfyxtX0Mu/MI/XBo5PmXtq6+/A3YvgUzv8MtfXDCWYG7X1+Y1TAL5neVE7IVbEfFds/U5PmzDS4VejFsr2fYnHD6tQI8PaNOgjIL6sdH1PkOx88M80+eYGaYf7zCZEb58MiJaUNfVaE60ruC6ZMz0rszrBas3z0BtVDk7rwCTxt0phIsTpk1izJ1ZOTxgvrxPerrA/xuIk+kAOndRuLqIcFmFj+cvyiQyxn14YDlpDAoNHMiTs8F9S4KQF4M6UL5dj/NiOIdEfYBYAZ9Lkg/eQesBTgFALmbUY+UUxZntpJFBolugE9AmSZkCOyDO8jjAqkVTTNDt4rps6WnGdurmUUei1PZeFLMz4VZFGBGUU00YNOnF+gnX0BEUT56TYLz44WpzscDHEwHrQ8BGLLCp2NkEW0ERYLIRgKwxfjZCuRMYHaYATgLa35CoThmbynkOLN44ZNCtxW+rMx4ukvA6QATYYbZ5tQFETATzWw3RBBgBTBN8PsDw04qqEd6rDzLTpRsYfDwMEglMPFJSZBWUBL/YsCUYLP3EIIWsJL7YUJyZg3BsJNbFVEbKzi8RUh2bbmg3ZceACnAknW1ZYozdo8MCC4Eg6FufW61ityxvErwLUpLJNkzXIoHkdXhmfwPXxzu9KJoqVDlqpQRkWhfhEW8RrioShTIjL4EkIF73NcEuq5OwBJhKk8aJFyBlLDWAbIIZNiPoEUwdTsJpJadt4GWLeMkUAvDGuZOLk2ALAVYJV3BSvQHkvR9mOk7LxqsGl5SfJ9370xLq4Zcv0oAritQ09jCfvOvbTYcA0ADCv3qBhj0OIe3XVqorR+IB43at/FJhBs7gNnTpd13KX808uzuAiKABXrozLslH1Ebuq5VG3/BCGTQeTUN+HSeT/y+RDX18cMGXrx6BysC39O70STluRgTgOToBkPEO5FYmtReu6c6Vyf6KoRvPayGEYDc5nxFGEpY60qi70235/rC89e/GTfnd3P7Fsz8DrePfrLgx7OgHBMuH2aU+4T5neL4Kfkc5aThpmf65vQFM3csGcqHM9I24fT2CT7PVNV9t8FfzVg/TNju75E8w92wfZCC9MvsFSiJg/l5w/TJE4vjZcHl93yI8mbG/IXDHzL03SU0JwCcFxz+/QrMM3ye4IeM+uEJ608dkdYj8o/P8Lcb7PUBdqeQTaHnFdMjSbnpTD5L+mJFnhSoBeX1Efb6SDLmIaPeJdj9jOmLFTV7rAoT8KxIbpBlRSpMD5U5A5cF+I3PkB8e4Cv1MmxS+HEKPQ2waOVS6FE5aDfM9e6E9FQIfBbKzWcDfN3gokh3E7AGCTVSJ51PKnkFmRlC5J0k+MMpCjfSmLg5s7s0QeYJ8rwgXzbYcmH6c0oQUEMGHk7eFCIdKcHWBaIJUgpF+U5RmDJnyLtnaFL4mzsCoEqhvfK9V0inGemLM7CsnOjhsNMMSawxBDMCwHJH0rATXNlxooJzNeD5DJlmknk3ypJbTNLeXODOFFoFxfE6CXPiPZvfbSQcq8CrRsYSOD9uDjmip/EivB8S2TASk7QnCe+OwBOzZ7yFBTTCU60YUIRFGimZ8SqgCfFJSPA7aHw76Ihj2V0O3grConibyzmxF+c1bYbQACnkt7iyuCNayYJJIwzCmk4WQoBo2jXArorbml8Nblz1k/CMXi5BguRvh0QO04X3bQQtWZ0+RcHSCItoeFuairAfBC2Hl1xYR1PlHa9rzQz91UwgY7OwGObg4VIn2CnRD5s4hh3EtOX7S2DF9uscJpnv4royhCYdXHgKNNNcBENYw+HMBweBEmoQf80hKbobACciqxwbATObAli28E4HKQLUrjKnaOHrRrQd2+1weICe3cwPGUlXf3vn1MTDiQYIiM89vEMRArUR1emeRt01WxoRGlHBe/+tN+ZLRQf5DfT0EZRQmA7Q0QT3er+Gx8H7iO99v91K2cFKSvuz0ro7/tEfg2/BzH8Z20cfHPDZf3iL8tE9DfmseP4pQTkB+aIobsifPmO9f4P6UxPWV456mrnCzIK3P5ex3X2A+fMKf1vIXdBEwbG7BFkd0+eO6Ytn1DsCkPxUUTcWmUznSs7GIcNnQKcjjv/hKeL1G3U5vlhY1RsCn2ZyLiaqvqoD8+eFqcXVsbw54PKdGWrA/JMV6VzpsTmcYIeCVGYYBPl5gWzCUMYHR0An6KXCDgnbvUDWjHReqalxSlgPr5DfLYBX1s15ZBaYfecN/M19VBeP1eXbM3wjAVex0dAHZ2Q7AlkS9OzItdIgNQOvCRbVp7UYDYZm1IfEcg+LwY4UHkznVr5AKdpXEvzhwLTwxDCbXjaG6Eqhd2rdIG7A6zts37mDQJDfrpy8I3MJIkxxPx56OQTqtWzA4zNwPNBAlQr97B0LPx6PwJSQngvkeaWVnacg4Bq1XnQFWZy1pwbj4QRTesP8spKndWJYy0GPA0qFnje4s0qhBxjgDujuaNHQpKn0LkhU1Abqnjbs3j0evShlauEk9PIDfaVWADuCHqEkQQxv5MTWiNgCDCE4OLoCrX5MI+myKnSEWoJAjizXnoD2OdgXRAZSSwdOGwg4cqR6hxUSxPnWimQEdHXS8JSAxHE0LwqCwE7vjLaK0x4hk2ShCcJsRVQel6RuIDTww9CF9yfax3Cbw03Jk3ECkZqEHLdEQ4hKgKRmPL7v+ikmSk/LFOBHCf4C8/Df4qiHkOGP6vHeasy1rXs/9muEKugyxd1r01wCbSylk2EhoHAcL2Efvw6W4hajEyIOaiBoNMDUdkDjVDsQAUrLsGnnGMABIjvJWtGnVnch+qQa4bv2tclNGKWhGL/6s7WxPz8NIbS33TPCHbxiT6ve89Sv2ouAFk0Dh8dq/JsAV/00wQHr++Fmu/aa7JetQTTHCNeukQrQUCrbJlwAOXCd1r0fu4Gwu5zwTdi+BTO/w+2n/1f/Nf7N/+lfwhPTM+0V4/3n701IF4dUw/pmhh2D76ET4+oZNLwCPH8/4fyRQv1jpHfA9Fwis8epkKqAaUyuGVheK+AzZOPDo9M97FxgD0fU44Ttgwn52TF/vsGrwws9FHY/A6BHxA4ZfpogDqSFxML1OyfYUTG9A+AEN3reIO9W+IevsH14B6sOT46KOwrnrRX11YHAZq1ITxvSowOzYntzgJ5pfOWQsH54wvS8MqxSCtLnz9DPHfb6hHI/IX9+hqwrNIVGjTkrUy8J9YMTAHBMzWFHQFyp2XMUYCXxl6BH4GuFz6y2XZNQCK6CYa1L5QRbHH6kV6IeFfOvL9DPVqS3Z678g+/ibx+pSQMAU0Z9faKq8RcX4PMnApVqLGWQlEBE7zjbbNsOdEqlJ0qVxOBS4OcFelnghwmSMtJlhV8urL/04WtUVeD5jHQR4HBoy1e0ophMu8+wV0fg8UI9oTkxm+1wZDFFUYYH0jDhtvm96WbE5331echwM6ilmNMjRGLK4pyi9HqFcJrFsQlAaNW5sBOS1wX0LC2RehEel6a1wppLDllAYUFtAAlwJwctWTP2QIVBnPuy/pOCa1QOTyN3yjD5N2DSPAB08dNj6knCIxQctgYqikNUdyAQbZCZ6eg2hVWOmZQhBgWU950nZpPpqvSauSIthaUjghdkwWmRWFObBX8mJWikXTfOklsDVuyLDQLKYuEokqGz3aUSgCXs0vTWcfme7p9NQItsdOPZQN6wSZzYg6uBAKD9vQ/vO+AR9OrrnaxMYLwDkrYzArgEYAoOFVpYpdv88D9oBD90MPAiVLEeLzoCcQNRJZr7iXB9cO2BkB2o3WzW+g10T0+7Lbr3I8a/eWXcZB94aeBkBzOtnddE2uG6fcnG20B/EwLu9QV53xvz0t/cr4W+FLsQ3zV4atDZ8cH09ReZBL4FM7/j7Y/80d+Hf/zf/0uUDwQ+AXBHeuaNaCfA5oT5YvC3G2BAWonEy13GvDjymTdPOQrVfA0kIV4qsxfuFcubhHrMUBMcfrJh/tW30Kcz/HSiHkspQBJUVXqHVIB7R74YpE4oP/guZCmQdxfAqeYrm0HOBfX1jHpUpAKGforDZsXyoPB5hpY75LehP2KAZ+XfEPIGzpUGaGJmR3paYfcTigHbRxP89QRx75LqpSbk58JyAB9l1jVaKmqe4KcZ+nhuQVuy9hWw7NBn9lGXWOVNyoyYQwIuUYZgrUB4WnBIwWMRgr7V6HF5t8AOJ2wPCq2C9Fwxv12BOaF8fIf84JgeL3AoORwlyLzzDJwO8LVA/91PkI8zPRHrRt6OCIFFVoaY7mfg/kBAVkguJieAvnK7hKdlZdaUrORHuFlLGOKYO7VnfNmobnx/IvBZeW4vFfruAklMP3cY/N0GfSRo0qzA3QR7OMHB6uPYhsVmC7s0e9KICxpueSi5G3BIVoYBoOEVcGA1hqAyZfmRQUBTGaPXiwOwIL3SK5LWIG0eg+0KNBRAQmtwXBqBtzkwtOwhQg1tmrQwPFROAZC2nfAokL64bvwaB0Ndsnl4IsIahb6MZyNPNYXXKe8G2BMIDB0EcsEvsdwcAPScqbUVuvCaTA5XQz4Hu1ile01YyFN72jTEScyNPpPcrL0PjdzZeAwU43PylhBjnwOwGXZuXdhFT8D06PBDpFn3CujDJsM/DOOHwfQ6RspLeCV9dNCED2A31v13VSIc03aW4ZyDEW86NO07G47VPCANMATg8av92vFomaVqP07fv+/a3ES98fvbOIRV7Pv3FPHByA9/98KfAAQ67OQ3SLF5arB7YxowclwBiNu/MYR6hisRm/W/NUB+C1Hx7AyXUuUneDduKBRz2q9aAJoxG6xtrNXk+On7D/FN2L4FM7/D7X/2R34O2wd3URnYWfnX6VWZn4EyG8oDoIsQQFwMqWh31abiUVsl7eS7A4X3vLDqrF6AdKnYXmf4zKwZTROzczaDPRywvsqcrCvFumxSLG8ypuTQc4XdHxj+qBsgAp3AhzoB5S5Bzo7p0wVSDXY3wRJ9wZ4SDTRigq70uqACetlg9xNwoA/Yk6PeTVg/nFFekXyaCpCeQnfDqKlhkwBpAsSgc6a68RrCa8cZ8vk78uvnGXaYGYJhDXqS/xw05C30MSWsIQNPd3mrsOOQYpguhulpg2XB+QcPqDOQz/Qe+CSQt8ZU7oeZ9a9eH+HHI2PvKTEVvBTgDJYSiNRZvz/CH06Qn3xOfktOzHyypvoKeM6Qmd4Rb6nJy8ZslZThx0Mn4XopQCl7KvC6Qh7fAQi14nrm7HI8wGZAhmraUh3y2ROkFJizVpQkVsqWLwR+tyB98IDt4zumTBsBngDMgtn11GlT1sr7NUjbUiyydBwphcZMgwwqAZCM922OcEoQNFIB9Fw5WT5kgs2W/BSGtknyA+iFLEk21a4V1Az8uF4lWdiRn5oWDTgezR4GSRJZdyARXBh6CrxnaUEixKVAA3Ro2UgtO2Uw9F3zxkGy/SGEAcXhVTqJGSJAVtQDvbfelIcVcZAw2k3NGIAMYUuLWlOAM8QVYxd2EE0F14WEXl7DyOKrbBttL8Hm4XPH+XvaPXXA/tr7056gG1Czv9/hyv6zEby0cRqM+NVhpJ/UxwvbTjqmOIlfM2pH99GOXPf3VzwVaRa5C971tniArx19XQO5uh/XO3hr55Y+UOMYCrBzauJm2VvTfG/R1n3gelN3wDI2au/11RjGfu1V++/QvTVRqqxHwK95QAQjHHNDUmCpe0isyWOZxX43V7u14Q+/+T34Jmzfgpnf4fYz3/sA8AxMsmeLxP2Xnw2nX1vgcGyvFVUM0yNr6OD1iV6UBLLhNyNPwIHpkwX1VUb1BD8XYE7ABuSzYXmd4D9zh/x4gK4F+bFAni6Q04lhiqcN2RM2YQbPohP0PkOLsLaQUXp/VVCXZq2YPwUAZuVQVdcxPTnq0VFOE9NXhRNqvhQqAx8n1PuJVb5dYGaoxwSJiVo3wBJf0XgVz45UFelxQ81sn0NhU4ZsK/SLC9390wR7fUc597XyuT8I5doFbEuSfq7tIbN4ZwJSMehCTRVdKfPviboj25uE8uEEebdBLyxBYQfF9vER8ukF6oLtlHC5fwN1YPrkDDyeI2y1QaYMvLpnCnYpJDYfEgHPYaYWzjxD3KDLBj+fqRNyPAClMrtpXfvkinkG5gSfppiXw9uysY4TV0OJXreJ5F41Ay4rOVJ3B/icwpO38LdgaJNGOO3T52VFerwwDf/VjGrBCRIWGW1S+2KAXqgmrBu5JZ4oQufB1dDzCiwKu5sgM3lKNnES1GLw1XZuTUgP+ARIiEp6EoZ5FMHF4X2/r3CFKd8FVwbHJe7ZytQXr6HlooBMCb0EQuNPdDsgUfuoeSmaMQvQUIWq0FH50DWAcWTvoIUWdDhe8yZ48GUdsM0DeI3La+wpWaKQZAQw1fYMuIIuasc0fVBIswmy+f4MIUUCbwDBVkOqncMaMTdC2eoOX4GpOGxzoCrW10Bt3JXWr1tL+WXbEHrpl8tB71WUFetg4IUwzdXWvR+yZ8NxmHZtEwF2FbwBcbSb5aq4UnMf+NA42c/Vwc9+XfotYtivW3TO0QCr7PdA86g0lCcBV/ppvmQgiVcHsCI3nha/eX3hAFdgZf/XtCA7UOn8of02bVucuv+mSSbt4od8ZpmuzyzBlirvft22Nlz/i+/+/i9p8+/u9i2Y+R1uqgo/hXE1xsibVoVNIIl1rXj4fz1D3z6iThl+PLFApUVBw60iVwBCo5Y/ecJ0PmK9T9juyR9JcOjiyCmky+8V2+sZ24cGeXeGPm4oc4KdZtTFCHIgSM8bqjq1R5KgniZYZTgJSWCnDIcgP2+w7QLc3cNFyZVZBcuHM1bNyAv65OSJK0CfMmxW2Azok3UCqaoDCz0xXHXH/geBb4JcI7ivQpEwJzE3PSrkvNK7sXFZJAD8MAdhE1z2JN2fRqXwnFrFMsfT5UB6KtDnBagb9PUDnr97QDkppi9WTG9LhJ0QZFLF9uEJaa1BsOPkun10wrS8Bn7jc4gUghJN9DfHStlV4ccoDf3qnryYrQBrYXmIqj0c5aE4Jnd3YBQgZvwoTeCw0HkRAsvnDQZDyhIKxpkG3ECQlTP8MENqofrweaEH4njkGEa2EC4LcL4AtUDhmLYTU5BrgbqSXyBOwbfNug6NqMIkceUsIC/llIBJkc6FxTrXhDQn1CmhTgyLaDFIieylKfXwWj2Axjh4Oi0M6BIaNEHYVQnAE84Uir85alZ6h+IyJzJzQ8AQPbuHfCC/+q2DIn2YIvtlM8jmXTvEByJpN1ICLjRC/6lrmzR714xT3AeorM9VM0OgkHDA9r6he//NJRAATyRAiMfRE2PqsARoy8Kboy+RHdjCg1JJQJZCcFMFkJjVmzaMAJFtJTi+dSwfheesqTY3MHNjR5sR7DgC6Eb5pf1b2v/IvekH+jIbDXQgIQMAbPlB3sZubIC0rCTsfJd23Roo6UhJ2sBGX/c5YpcIiOM6sN90Q3979lK044Z567u/BVdA6nZrYKq/b22wK2/jS9tOFh5ByzWQGT8fjzUeuyUIQrCXlBLyvDQQpV/92y/cLeHYncf4bz782d+k5b9727dg5ivY8hbVdiPjIi0x4UqslCbAvQApQQ8zyizIP36Cf3Qg+LhLKCq9+vPTH/wA01NoV0Tem4kguZETUMkVmN4+wuaE7XsP2L4HeK3ITwX6VJA0UfBOBPntBeIXyFahr06wVzNqUhJxN4MGmCg/+wHKMSGvgFhGfo6V+Ap4NYYPjgpoZraLRxjnuSI9bsBSYPczrBm7wlmgSaeLg8JtxwS9VAqrAXtNocNE7ZRphpbC802sj4Q1rEBSPkEFzBYqFWnOTOW+m2DOtG+fBP4wo6YJdp+BpDj9xoL86TNkc1gSKvtmAaaoeTUp0hcXpHMBDjPqw4zy3VdIbkifPgHbBl8LPUPB4HcA9uaBBq/9c2YDuTs1X455r5X09Aw8Xwg0DlFEchFgqywlcHdE+ZnX2D48EqQtr7AuG/K5hPhdBlZDeutAMei6ccxK4bk5lPASiPp4ZOFJBYHC4xnydIHmxHT0AzPrpDhwiUriBwoR9hlQWZ/IjokW3JrWy8QRMEF+LpS7n53ZYSZRfVyAOUIoAPlBFpiwSerH7NtKGUiAYoRXpEeHcmRECY/jBmilfogFSGpAqRNNleCXtqC5d+LKhaHqwl8lQJs3DgH3d8P1Krppg2zeLb1NHqAoLElkWXkjREc3ncPHn0UKvKt3u8bxRi/eWRDRjuYlih0tR7o1do5QbXyhNqvHHNRAi0Xae4+WANfA4yVr2j0acT3GcM+wv4AN7QVF26bDDg04vARuRnDRBgvouj59H2m8lgY+27ElgE0DE/GlDNdchvutA5TWEN1/N/aqAbTbsNDgmbsehNvtpqNDeCry61/60c1v/eqTL8VKDb+BCyVt4TGJZG4HCe/uqHVvgio6ydf8Oszk0d7RaTWez1zw5nD6Tfrwu7d9C2a+gi2fK8oxo6eSmtPAPxcayruEy899gPzpBdPjivz2GXJ3wHI3UTumAum5MHMpCfykqEeEfoIBmyE5WCqgVGYZXcjzqB9m1HcrvSBJsc4KORfo6hAJHRVVeC3wuwNkTpi+WJDXivU7d/CJM6aeC2SeUI+KehRAEuqJq8GSE9KFRgibAVmQP7vQK3F/QDkKto9miM0wZ6w/PW7QpXJ1eEhhhLiCqfczkAuRv9JDhNCEqA8neAL0Wej1mqPOkRHMtNWbQGL1ziKO5ZggQnKtT4B5Qk2s3OxJIAs9DvVu6sbAurtZQlzOUQ9HzD85Iy182utdhtyfkN4u8HWlBokq03Gfz9B141SThMBAW9ZPZaHNnGB3B4jH5ynRgxIhJckTkKN6canAF4/Qhxnb77mHmkOfgRYO0+rAxQlCphSZUiRG4/Ud/JJICo2Vu28FWDbIcYbc3fXMKqkVcin03jhiouMEJjmzAvk9lYYbJ6dJ4fewiwQBOwch1pSGx0jIbdlS4t7DTpiCewXsarzNIBnYNpce/vAMejhWANgn5m74EL/LYJgm7qEUaco7HyAcCi5UyIUzk81BQcYQMhFHVPoOr04FBBZEZN+9Mwmd5AuJEKoDtcOWwaq01+DzdEOR4ngTw3gtnKEdH5EcXJOgpvDwREiuh30zs/VacpBNYeCd4w4EuID087eyBVdeiVtw4vtX6P3c920OjKvw1GB3pe52uq3833MVvGTDbz8X3HhldrzRMvDGY+8hvjbm0tt41Y5Ba2gMF+2AS677dTtOX7bd7vvb+Y1cvbzn/WjHbYcavS/j1ng6JMzzfvV2MwBdBVmAVow+9o0yGaYwsaaCEMBFdk/ObZMC6PTiot+A7Vsw8xVsaTFMTzSW24mr3PRuQdoMWBJ0CV0LhCDcq1dYPzhCanhNlDwWHAGrCVOpNMJzCrEwkOy6VaQVaOqY/uYOUg2n/+EJ+u4J9uoe6cMDyibdxV4PCSoTcDH4rNBwxaoHd8dYq8aOGXlzTIszNdZBV/xaoAs5HOXVkSvJDNRThlZHOQlq6OHk84bp0zPsGOkdCdQvEQFyhm6GdN4ornc/weBI5xLVfgFUgz1M2B4y5IMDjj++0MBFSMcmgj1JCXVWavKYY3qs0NUxXRZYohXVM8UJu2ENt77NyqyjJoongGwFeQM5HlNCeXNCCbe3T4Ll+weYUyfHxYIjINCtECgeFHaYSRR+PlNoT5SEawfk7ZnAISWWOGjqwDmFx0LoJbs/QZ4vmH79C9ghoXx4RDaDfvIId4MemVpPIrcz/AVnJe/XD8CbV/BtpTetVgAhxvd8Id8HwmWYNtIp7yNZaxjsQBZ1ABolPGKadm8JeC+65j0bJso5i4YGTAIauTWFEJo1D4jwWquQBwXxIBk38BBhEOHvJbwfAkAqr5tmJx/HHNika9x0Yxgr5y5HgmFB7cJzCEFWzQwRS4kMJ0gHD2jNAIF305TxjB3cTLHS35yoA1wgiHkQnGNZ25iY8AiPBUjMrHHFoprR0Lh8zTtrEwGnVEAjC6op9TZs52BYsY2zRMilGXLWu5LdOjXwYBhE8IZ+7QkxnIPi9mmeqs5ridtpBBrSXUAIcDoAvRGwDAa/B2v85l/fzeM8AwKIfnXtpba1C96MuPtetd2GfcbXl4Dd1QHH7SU09lttN78Zz9VAPbB7mzqwHYDhzWZAj1byUHxDRxSfR77yejEtXYZnQnt5hmoSHhrp56Yn5xqP7rhV3vPWfJ3bt2DmK9jmTxde2ElRDwLJe0w+lY0KmMbg7nafsX33BK1cQZamxRKTdLJCgHFMwMZJc/68AFbpNa/MlEAIxunTQoVZN+jTBYe1YHp6QjkdoN9V2GEi3+JELoZBkVTh6YDy5kCOyurMkoAAmw3xV0fajCGji8HSBp8S0pmk3HrKkM0xfVqQP3uCPhOYleMEc4M+GvxeqHobwErMqVxcDDUlSNTm0eLMAtoM6bHAp8Sq1g64O/xpYwphmmMC4xin5wJ93qhNM5HPUcEsrbQW8kDSBDeL1FoEGRXI7qhCzk3aSszWPHedE/yYIM/kQNndkYtEK8A0QaYMq460GMdwKdDHC+z5QtCizCwTr8DKuliYJ4Zw7u4gWwCeABiSBcgTAIFeLph/9S0gwOWnDpjMkD55ouZJEor9zRPk1QnyeKaxX1Z6esx7GKwpssKth7ZGheI+LRmF8ZASJCcCjecVljLEKjOKkiBNCitg5lQxqBB0I8kQSqTR5qotPC0IrAOE2u9+6lbeQjZmJbWVccuM0ai651X2iEX8vk/+TjE8s8iLmdqTKUBUm/YpdlaCWkiAgRyiZirAgd87JArkxDYUlgRApV2R4PvEedRRZ97HUgjAtHj0f6803ouINiNQHZicfKRM0KIl+ueArAbNiioCJIdPEXYaPRAiPbOqe3U8CN0RZiF5GLtVulluu/Ea+Xht9rcxziDwGSI9Hr9tSJGH9w5S21G8k6l/E6+H79eW495a0W+LvS39ewygqzNtYmzDKMcYdMCLdt2+xIXSP7pCUv/x22/1mxcASucrjzwd38eyCeCJB7AMXkvcBrsQoAsLZIZGU0MkfTHR3VUAnJmJBkWrLu6tOKf3H7zX1v37b8b2LZj5CrZsjrXNlQCmsyOfK7wUOJTGJ9EtL6JU5VWm007VoY8LAc1qFF2DI00ZOLJ6dX63AgBXXIWeE0qGMxTicwaOb6Bvn4FlRcozyroyg2bKVL6dyGuorw5UDl4q/O1KXsMkDOOYIJlC323BO2ENJxxmyOMj8hdA+fjAisnVuxaOiCA9r/CUsf7UAwHP58/AFNWZi5Fw+5BhlpAjxVcjNJCqQ5ycHF0q5GljWCIn2IneFxwmoKysG1SM3pRikIWZN348wO5mlKNCamXRy0sFNMGOCptn2B2VlUWYBWI5o5YCXWhMBaxzg+TIjysrTx8m6OZYRcm9uDiwXpgefZrgecL06SNTycsG1QR/vtAT0zYHl9zPhUTcnIHTATJlhuoAiCiN4ylD9IS0bjj8xjOgCdv374A5Iz1e4Jp66CB9/IYZUc3FDE7cNTOTSIpT0M99uF+UgCVCMfDSZ0GpFdADw2jnFZIrdArQY45aHAp668hrCSPdOC/Obooaq8UY6IFAO35AlO7G59JbK0iaB416n3SNxGDzMLSGLrIH86vJVQBgwl7/qa1QY2IGwhhWMASngr3w4WAyHRS0jHRn2QyCXVwtpE/iePTWeGK7fa2AI0pOONJqgFInJW0Gqx4VnJ36HpLCo4PwRKFbcxHnpTlX6ESS/e7xGkGH7N6JNqTDmDik10G8cXZ0Y9v/vjXaAaiujG783Wp7SaS6d09C4z01l4IhMtp8J/OOuOHW4N9mgI1NHcM//fNBK6aDu+H7NkYtWyr+7bAAeO+gu3sPVwN61Zjfegt8d32oNubN43JzOAKJYdf+ZdyhcX84rkNLnS9z1bb9Zg0m2Qt9IgEfkM6XsY0HatlMI0jqbW9t/TbM9F/WNk1KuXL3IMw68lphP/4E/vFHDLvMuWetiBtcWRxOLwZZHLJs5KzMmat9B+RcgJyYPrpsJPVmpTcDjiKKnBLS4wpHQb0/wt2QRJHv3mB9NbE9jgh50XA1F/p0rpDqsKeKekywKUifSeAroAvb6YlGXY4TayJNwtTdCqqaLhcayvlI478Y9O0KoCK9UwrkfZxg5rH6VKR3K1IFV7zLBtcES1RdlXDF+7YBANV7xSHHmZlc6wZUjoXfz3w8I9wTlQIhMCAD5Tih3lHMTjzKFIRRVQGmSwmV4grMFB2sd7mTOVUEdSYg2h5n5HVD+uQJ8vmZXpbvvEb9+B75339CDwxwNaPs2QDhu69lj+2n6JcbfAP8mFHvDtCDAe8ALYb5cQOSwg4JgiNDQonAymeFnA6smL5sBHU1zpUinLZtBFBBTAYAr8GpSczAgQWIDlVipEQQ0NIl4ncalZ0bV8Tm1Ffpe4XqDb4JOsmyyfA3z4Y1lzfg4SFBcMMsJXJJwsctLXRg5GGpaA91SRjQVp6hZdyagpo32bohE4n9O1k3NDSgNAjuQTSm96RVwhY0JEVPg6hEaQdE2Nihq8HdYRPbmy8EKrxGEuDGyLMpzHYy9Vg5e3ixwoBtTu5P4mcVDn9IXYgQsWruBTCB9w344PVwoIOC9/Ri2r6DEv3V8dr7Bl5i/2YKWy0jgGN7BaaaGyXUoTug6L/xa2M9WuB2ggGgtzfjbn7zOvJ3do7tCFiu9r5592UGuSEEvDzGct3kF4/QQEADZ+26tH57a2V4FW08WiwSJEBFoxcMJ94xUgv58Lf7WF237P3eNpDDd1tBH0A36ZUgZBwxj1952/ebsX0LZr6C7dX3XuP87z8DVFCnO9SjYPnePXIW2GGGrgwDSRVISZDnClHD1DwupwPyucDOG3Q1Fj6cEyXvK3keujhj5/cZ8rwilQ2yVIYuEom+NhM0WWkB7sT076xMFZ60G1Kb6DGCGdtXDLgHDURSSI6Fq9DVbwfOqNNnlWq2k8ImQHKmvbssAID0tEKWCt22kHtHVKkGiYEq8Flhp9xj+hLLepvJrJTqsNOEOgvSSjl42ZyEWGdxPo1q0RCgHibyVpokfQ2DEBWsdQN0JelVl0oCLII70kIFOeTlC0NdNisLA8ZErheWd0hGtWSsG/D2GVgK/LtvgFcPwE8+Y/HK0wxsKz0CpTKzKPRzcDyG8ayQpwBE8ww5eE/dtTlBTzP8zLBRXg0VoWGisSTeomFZqTeTNCTmOX4w48peEyylq7RXGPVuXGQ32J4ixTnqQVlYWBUIAlBbhWnwfFpb2qwZwm7a2hVpxS4KUe/ekcAp7X/gKh4kodfaybDS0lUF9FJsDskg12WzMFYszulZOvkYBoKJVjCvgckGuhR9KeuI8KZbtJs1ckQBfSaQZraR7DahxD0YoRtdjfWiJBSmDZ3j1QiorRBnE+wYPVO0G96tjCuztlxCRl4Unhk23Y3qsKQf7N6VpWp/62g8b76P64Eh4tg+ax6ZDmYG0NTfF3SM3sBli5x6eNLavleEYEj3F7zYroGr815/xv1v9xlLBDmuxrYL8wmuM6xuX3s21M053tt2kH81vn2AhqbUq1/tx49pugMRAbrgi6A7onbiO3liGry1K6AmDXCMF/ZLW44mtzCCkVIEVuPEjaw+jPEYUmpaNNJR7Ne/fQtmvoLt+z/7IX787z6FXjbM/0NB/e4Dwzk5spU+u0CWhdkUdxnlYUJeSYb15wvkO29YFXrZYB/dww8J6WKw80JvghO0eFZWYc6ZfJW8QCsfBKkVUhTwwtXjJFGIWCJjiWmtWrFnjQhghxwprSCZ0wB1gzytNGB3R4bHVIBzIWcHkRUxZxLEDop0yEjnDXousFPG9jMfclJ6WiMdNYBTpOwikUisbaV72ehxmiO0FTWF4I70fAEuG70+x5mprFGoRcypztoMaxAj5ZRRkgNFkAoJq7pGlhHCqM0CV2rqGBy+AXlx1M2gSaFrYRhlEnjKtNFLgZYKXzcCkvMF/gmviwvoBZknAqllIx/FgmhQDaJB4JUweqVCssGLQFCQnmNV34i3awGyQGdyWVqYBNLAIdA8HvtEKNTvsV08qxOhLKxOKQS6qhDmZnL1N85YZsBWIImVv7G0FOYa4ZENAIX5vPJayFZ5TyAFX6UOgmphylQIcCLkoJUV4JtKqwN7irWyb9Lr+IS9WSpBRUUoULNdIpGar8J+hVXzSi6Yz3vWlQTwYD2myMKDo6pAN4aMqjlkFgA5sEFo4gQg6zoyVuGS2vADwM57cMR9pDB3OEKMcI4ioVOAnuDhoBF1w654inEYbNeVR+J2e8EYd2N5S/4l/r3+/OoEwzYSTaOfvdp0+zsAFDGXRAaXX3Nhom1UAd7/7qCgnWf8jd383Y8TH7Y+j+MRgLH3M2H34FzZ/FtU9PJH743FVTtujjt+YXHAfmO0fQMMNYDa9pf9OWxApvlvRFi1W9pCpKMjH15funj8XNtr8Js6WTw8MY1nBBDYM2o4tL0dPbyEJxy+ZIB+97dvwcxXsP38f/0/wr/+v/wbYNmQPzsDr04wVBIAN0DmzIKHq6OG9gu5Fxvki7fUIPniHfTVA3CYoVuFOYmo4gI5V+Bpgc+V3olSkT7fKH8vQmCQmpAWBcekGPLnF/iB5QLsFHrxWZHO5C+4sraRvZpixdwefEM6Tay4vLHKtZ0m2MPMzKa3F8i60Wsx5X31ZTQiNiuNzdMKfP4IOwg0UQfCk3YJexZwYlVAOSR4yvQiVWdad3BVUZwaLHDowxHbncLrXp/HQ7jPZ0ALkJ4q9LzChBwYrzSG25sZPh+RHwvS22fg80sQVwW4P9BLUiure28bjd2UYUWB5OGlAHCYIAurZXspkHWD2xIcGCEZFxxHpNyL4EEVqBUowqwmEODhsgByIM9iKTBhzRQpDP+4g56z4B1II/wFeZaAwKmq27RukoaU/2ADRNiGbdvfG1OPgQC07R4I7gpSCg8Vr4s8LRRqA4C1RJovkIyeM6ZK0+vHlTg5Jz4F6GmTb1aYEWCg7uDULTIwjG2QxMndJTxPyWmUJgI+VocPlehamDRkAKbE6w/pRZ5RnVXgFSwu6VSRZgZV3GwBbuoDU/jFQkgxSmf0xao4PURGsNGyrDoBGiCwBlBFkJ3AsyIykY4kuNc5UrNzANPmSelGV/Y06BeAS7PF0v/AtQfjyxbODcS0BboP52zP5ws28crpEECujYkDe2p59LcDab+5F7sDwvuxrl5HQDZggP1EwzFk+K4BszTs17LwWv9uPDj7e7/u90vgqX1+C65GMDMCrNsillexskDmI6CS9vkO3q+ByojCvqyBY+fIv5IAMiTiS29KRCwJkq5IRbEIMEQo1K84M80z8/H93W9y/t/d7Vsw8xVs/+0f/Tn8vVphlws0Z9SjIj/SA6DnAnlaaOgzoMng9bBniRxm6LsnFiecUnA0FEjMPuLqznYDA3QAgxo8glJptIdVh9dK4nFWuCSYJVQhJ8CPCXkxggkBLEcYonpkXSlMDZoSw0wrsxHqKaEcFemOFZWtGPJSkZ43gi0I5PmM9Hwmh6PSqNrxAKSE9LwGZ0KplZKon9LSrbVYf/Y1UpfLIUFeHUiCPFIIMC0VXiIUdEqw4I94cBrSpUI/fUJeNtj9CfUuox5n1BOzjGxSyvpPCTJlpK1pmDBtUTQMPTgeshgwOZCbWCAIRlTIM6kVOJ95Dbx5OCKbJ7RmpF2/nNFCay7hSQtyrHvtq/1dnhNIW4FZkEmBYcLkip9AwMO48EdiFuJukXXjvmvGxD/JmTNT+7zS++JblLWYJ0A582s46hFkbSBUirfCscqZTWqGvFAlmQDH4Cs9QAxlCYAcU3FjZvK3erNaj+VjhJ12y+xJYKLQjRllqPSAubYVKwvntbpiNJxgIdVw02uzauGZkvhnFu6GSWiRLca4GgUQA8ho84S5XC3UG+G5d2BWlMQkAEsCOwRvJ1Hsz6dQyG6pw20bV/KjdwFAV7UFx+fKwLbfyAv/xsPbsOvY5KEzV4TS0W6OXhrsgOjK/tp+7I5Z2nXoYGU/wQjI+rHafCaDMRVAygD6eLl3QNY2ve4LKjh+Ddzcgr2hOGQHRq1hI47YcDPO40A0ZDl8Nvx4DBkRM8gAYPYBHMHDzvYeQFL7ygERv24fgD1WF//Eb5DouJ9G5mMDMqCKO/bMJvJ29j57DP7ve/XxC8f8erZvwcxXsP3hP/JzdN9Vhx9YkqDeJWhx5JUF//SyoHpGerfBjgeUgwIfnpCzAu+egdMR9uaOOihV4Bvd0XaYCHKMBk8LQw0+Z0rfbxX67gyUArub4FMIqt1NnNyzEpHDgTmxwq8nSthnIZBY+RTnxSClouQUehWAJs4cAgBGTZ28GMmb75YQNsuwg7Hi8YHcE787MLw1zyhHAZYKebLwIBTIMwtRyaTw0wTMXIHqUuP5d4Y+cvBrRLC9YWZRXipKpjYOvVKyu3Lh8FmBV3cQOQNPZ8jhDpYBVEd+V5GXDZgPqMcAVAHktBhMDV4TklPvxLSFwhy+0FvjEOA4RyZHgIEU2jrbyvpLytnS3QGZaWTbbDDlWL0OpFwzAocgzMJsn/g00ofhDFWOk94Uy99q+7gJs9M6sS84OgZAIzXbjRlhMrFMAkS5fyP8RhitLxiV33sQtAVgJlWAKORmZQMUe/BuYkbWuovPebCGxQlk3EMELgk5QDG/t0wgUYdlko/RUvid4+LiSLUCzysMgjSHJ8sq0qTU95u0j6UnAiy5WBBUOX7dQwdmV7nGMxi2gJpFzuy5atRPgkcBS4YrvWEj8BJZUmo4TYLpzDBcPdAbYxN6YUnXeH8DOPrb4FJ59zYMRukFm9V+PHLP+8FuCL8CdA2ZKwB0u1Nr0Ev2sJ37pf1saEe7ru0ct16joS8dIDX7GiErBBHWa5yv6f7EAoIXX3Yv8xiyakA5jnM1NgOvhW2IxYLsWJttbA9E/x/2HW/608dhQD59nG+Q5TjQnTX80mDzex6WXpe9PcOgI5KtxZECCOoAZhp0MRnRXr6+9u3AbTyvOsbtv/3ON6PIJPAtmPlKtvvXJyATSNhBgLVS5VaM2TsfHCGvJpIrawWyw5RFFn3KwKs72CGjnDJrFC0GKQo/ZPiBlY+9AGnZ4FVQDhmYE8r9RMG+89aBix1Ccl4FWElQhFCgLBVFjeKMJmCtppnhg/RMQOOidDlmgUBZkA4CWQpSK8Ea/AOdM8r5Aj8pZErAskGRUQ4TyZ9RioFaMPTKWE7BCdqQnlb4F0885sMd6ollCZDCP79s0En6gy8F9KoIWN5gc9SDAJNCqiMtFfpYgKcNgKG+uYPfHyHnM/KvVfirE1Khdk6dtKfQUoAswihVmF21bnxkJ3I/UAyyLKDyLYnWIgpcVoizdIFkVsEWVWBZ4VY4EdcCsbZ0jAm3GUNjNgzBgDIsFZlOrmCqeAsrNe0aA9uUCXz8oOR4VHJzHEAjbPC9754LM0gi/8bqRn7U6dSNOnJk3gHAeeW5e7rNuIKPCW2eed4mCFcKxJvQIXYxvhSemaT9XJyzhW3uq27ZARWAXj/IQf0WDPN2ACvLCXqcIgMNEOvqfiy0WKyHiQgkaycLe+aqUwyRZciwE5B2mzYRsNQpShys0SZDSCwYbBbYTNJ5WgL85gBoENSJYGSbwZDoAYNBl5e5HIP9UADWCLdRlsAVIffPTYCdnIvdvno43jq/qpGJvd0fA3i4ndxGgNJ+P6zQb/cTDOCltaneAII42QhyIOiekW5fZcAI7fwq8NV7arii9at1VnobvezHFjQvJfZQlGA30lfhoJhvAvxIv+GGTrRHuQ9i+/pmn74auPmuu6Dab78EvDgGhLq/ttCQXO3YOuJIYkjqUDWCmTjF/huHSNOBaqKZ47H4DOzbeygHgOOH3/vZFz7/erZvwcxXtElxeK101cNQJoEWRT1qz/yQ6kjPFfLjt6gfv0Y9ClQnyErDpsWAhSs5TDOJgcEdsOxwnTCVDeLGtORJyS89zZDP3wHvzhC56zd/KhVQRbnLLF5ogCxOLwVkl3dPgB01whgMK2GhOLufMmwS6CJU1T0kwI1p43Dgo1cwOMMNWZGeC9KyQlyQSoHMc9ToMfImsqAayI85CknNhTyP9GufI3/wCttH9+FC1ihAyKJ7duB6YvnwAHGqVlrIz+tmmD5dkD956qtdmWfUD0+oHx6gS4GDBFUHOUQ+pU5AFo9sIavAQeHzEeYkaeOywGplNpIIDX7K1DzJiSnZzusvOQNzTJKrd1Kup0hjnhLF8XJwmErmODcvT2FWjPQlodNb0ybhcbVZLDgkjZwRX2QBJMEv6zWht6v/gt6YeWI/cuOLWFQDF353nBkuAnbLU6zzJEQV3b+/bvs5ml66Kg1CpuXxZpUacAnzyRICiDBLWIkWXh32BxiiFBWuSivoIVSBzxlWK1J4sEigDUJtgGpsDm8ifJq4qk3hgRSga2ZoIggRoLuJFAyRmaM8BEiLa2sO1Cyod/EM5aapw6KRSMB2ZPafASRnJ1wBNSCAXJtPhtfmiFNgVwiO26fVI9rtrex2V9EJ4M2uert/8nCSwR6/t/mNGb8BHrf7orW3nXcENmNb2v082sgAPe9FQ0akVcGwmu44sM0FuPonu55QO8xQWLMTle2mHeM1B647fnP/8q8AouMIjuP5EkYZAeu4XQGX2/PvXhfRBkqItNp7ghWCl5QcKYCMDv94SIeDC9LkggrtHF/RWBAo9gzEF4EM2/QL33pm/svb7JSB+QPUDJi2pRN5H/K8Ia00ClqoSFsU8Dyj+gY7TEjFoJcS0u8J9ZTpcbgYM4dUgBODwNOnBXIpkDk8Kx8dkcsGgcIrC0cKBLJV1PuEcs+lmC6M8/vq3XAoBLZS5dbDVvsk8LsJZbWYlCnzLiIEOecN6bJFkccZfpdhaYY8rQwHPJ2Bad5DBirAIQMeugWtNlAF5LIBdQOgkOORxveysZ5hYoaQGtPaewXjWVEmDeVRgyxAWqgg28S66gf3NHATPRu6rTQ+SSmQt5JnZImqzRAgnQVpq1AXbPcsEGmbQ0olYMskL6fNgVKgppBVEFKczG5CGLqUgMOBZFsnXwKHQ3h0WjhJIhPHB46M9zpVAgBaO89EREIXZl8eC3RPdTYnmF6dBs4qx0cVPk1Qsy7mJ6KQnMmLSUoVaWMf/LJAcoLnzH6sGwFcI0G0yuXNMlq0SSU8QL4DiCToSz4NT4XKvlIXxLFikq1OCxNgRlo4IVNUsGexBVlXHbDgnGDK8MXo1UoKZNlrE2V6CHRjuI0k0aaGTM8JMGQ2NW+gx6ReDRLsXFfeMy10WFNmllL2PXTbxsMQITGQYJlJtO7AqRF8x+3aLr4HbBBJch5Z9N3UtPhWtL3L3Ad3pPNKxoX3LaB5AdWIv/z5b7bd0jiaTe+f+W63+6GH81z9Ln60Kw0PfRPuReJxG1sAJZ7Ddp5GYm2AOUCf3wKqOKYEaPH3BqV/eN2xkR3bPn4JA9wer3fQvxQzSFxk0SDzNv5QgBcBwY1KgYpjyo6s9LyQx08PTa+w3bw3AMG2ARUMS7OgqUd4zV/oxN52UcecvjkQ4pvTkv/MN3t1IBFVhTWVlIW7UuFqsp7oonYz+N0d7IOJXhJhBoxDIOsCr476eqLHYXU02XIxhyVHvc9wAQ6fLUg/KchzQn11wPqzH2D+tWd6SCoipMUU63RI1Ikp1Ixxo0psbd4CBVfHzTMAIK1AVUCiBIJ45cSZBHZiMcJ8KX2VI145eecM//gNyimFp0egpcCOE9VUzxv0vCKWVPzdEiTYo8A9I28GQ4RTSgmOQuYYTII6xySlMXero5wAWqQH1pO68PFEVmgxEgZr1ENyQ367YMsZ60eB4ESgWxjazMKY9ZgxqROQWoU8b9TmEd3j88A+kcVGEEJSnaXEsNTpwBBU0siCacvtBhDiWGNM3ozGFdaNnrRJu4ZIY5Cpu9elVha/NAsvIdspkbouMizX4xxohGclSMO6wS6XqDt1hD7cMQRjJBEToKYdhFiAtRaDcovVc4A0TehF/5SZd00ssFvoEuEgAbxqkKXDym/Ucum1o9pqOLg/w6hTP6lp3QShFwjw5MG7CK+NhUCdhCeoE5glOGMAeoXs6l1gUCWjzsy8qyEJLMmJ6xzh+STQpLdPAlB7BxFM0Q5L2IDfYP6/DD90+11B70h4GACwb5m/pobQ4IlphiwO3D+P5/89b8FLoGb4fCx78OJ+N9uXaav1/oz73hyng58va1qPKsqwQ7sH0AGHaBvb4HxpI+oPDRlBXcfhIWn3EinoqjF+hWuue/QlAzCqAw7lH647FyBDmlcGuycGHmTdEvkFhiyOFGBGheKM/TeoECGQkUaQFocqkDSkIxJCxTsSGjqtZr+Jmhfnm7R9C2a+qi3k2NWBVB0FQD1Qq8RDvwUqqG+OTD/eYhIrgJQCU0F9OEBLRf5iJecmeAiaAa+srGwZsEPC8vEJshbkdxvyj59RPj5i/f498r/7BDUlZC4Fkc5nklx/illW01t6D6pGxV1xutwdFJablSEzFXpxjMsgEWFYqAprHhmVcXHZkLKG4xJQGFAk+A2hAZMS0ru1Jxe4k2jM7KkweECEhyQ4AcrU8NDfwd0JUiZIoofHwYlI1FHdqbzqYGmHrKwn9bjC54zlTcZBBDhvsYKP75cCkRmleWYuCO+GQYugJEHNigRq+IgH2XhKwFrJYWl/20yvBLCDnJygorA5saL2ITMduFSmpNfWb40yFtj1JSwAi3l4ZnQvpldqLM15LkmJnpPGacgJsoVQXaz6JNL3ea+Gvsw80VPTrkEDOUAHJrKuwJoBmdk2Y2jN3SO8ZXBT8myad6iwlhhrEEU8o2XgbQHCIvXbVSAW5GZ4L4FgZtS8SLrb12Z4kwzlajRAHvlEe1W83bBpAxgzM8gMiPATgMjTkmb4ajyXAdI9jIdGppwGVtOViwucohxFWOtUOfmzgCjvaQCASZD42QcpBDQ2x5h374x34NWN6wvAoX/kMfeMhjgj0sh3QDNGn0akNBruqwOP9vpLUMSVtsxwiFs8dKuRM1JA/mPNYccpcuMvaX1qkUkh/d2vEBi6kN84xo1E20DNS1hEmrfrqmfXbWJD2nHGUXgJrrVBa/freLK2D11RmgKwNM8M0AEKAKgyQWNSQ1aD6k76FYnnSnYgM4kFnncIFObAZuTY5EQOX06Ozbggk8RBvcqwgiOnkTX99W/fgpmvaCtvJmCjxP90Lqh3GuqhBA4QR1q5wktbYbpsxOW3A1fX9WBIKytoa6Exs0Ni2naOMMpGV6odSKRd7mccPl2QPz1j+/iE9Wc/Rv71d3Qbniak9YJ0PsM+56qcq0PDVCvWw5ErTgeQlTL+oOiYSCTjZoUvtevBYALq6xl4KgwBGKCXAnk8oyaBH2aIscieZ1bF9lIo5b4Uiv6dn+EzC2DqfE9V3OdnCs6tK/CokOMB+uoOJg59jM8LPRtpAw3LWqFrZQHGyofdDjNBh2ToUqGXDXkGesbJJKiZBGuqAlfY/QytDpsBO01Im1HZtXFv1krdnEtk+EwTBE6dFQfrLOUcZRYKIGkPxeRE/onqbqCaF6RZgUaKrZUgBs0BIXvIonFXaiU3qwEEt53r0jwXrVK5CPcHuqdoFMmjdnnbhIBnnjmflsIJPEcZDnMK56Xo27Kyb00xuIVzxNGysQDf29ZWdylBMsGZR2HQxm8AwDEJ4T0WxozMjUnRKkxDWt1fdGOO1MYRETrYDQhrKkkfk5Yh08GRKO+z7VoQrwbfRURgqoCh6+eIgPW9ijO0BPSQhjhY0uGmsGNf/FdefhVgE7+uZN36hOG3gWgcVIBFX2igG1+P5KzGfX4xOjAQXzGc4kVA0drTvhwznkbDPYKfvak7UJF9vx5W2hf473lC+ji+0KTezivj/35f3m9SE517+bgc2wHUfNm5fW/Yy8e6QXVfeqS4SRrXRkcUtQMGkUqnZoAZTh3e92n7kRsD5FSZXNfADBphPjgzqOG14elYOoYLh6V7c3gOioFXVNtvpp0Ize90vGe/Adu3YOYr2vwho2xczmnhpFhN4IfMVbyQwCqbA4XgxmZO6mktsK0wGylRoC49G/JqEFN4YZVpXw0pKWoCwzOTArNi+/CAnBXT2w1yB9Tvv4K/vUBN4B89IH3yiPyTJ8jpADvNwJyhqyFfmOIMcRajnAm+fE7hhiYHRbLANANWuw6IzUoVyc+oJ+OvjnCwvo5USr7DADtOcJ1QxTF9cUH67Bm2seBhMz6YEvy7HwJPF1aBvmyQzXoUBebwx2eoCvw7r5GegfS8caxmBQ4J1QrF0hoImBLKXYZeDPlxAyTBZ2ZnaUlwNXgy6KWG/D8AY4jQTizOKedKL/x5Q7qQ4OxHFoeEO8QOBHmhN+OHmd6U8EIQPFHDxxH9qJUp3iU8M809nsJv1YBGAy8d2XDC66vtWkg2bllIFnwBY50pbxlB7R8AeAqAEiAk2oSmq6JtJZY6/wfNK6RKgnOJquEAfFlJZnZje5WZdCTH2l7YUpr7fSyfYEGLicm5G0jyYsRBsO/OUB3aipdj4VtwBdYY/wM9Qz1bKkZMAVxpsiAO1MNe2ENWoqitDlJ8Ri9QhHsTNYq8rc+bgW6WTWk4+5hHaQSO876bAJ37MhlQ3GFH2WfjcWV/BWj2V2sAyXcba2Efrezt+VJU0MDNAEJe3PclPk/71zyBg4HvIa/22fD7DrCGyGBvQuP1DGCodR+4vnwdRLVjDu2+uswC8j5u+uXvvec+Lw2Bj3vfgB2/2bPv066XYr9J2oGl8rqlADGCCNfw9zLsp0HkVQ0A0kDM8KpSkJRAhHQ0A2tgezj2473WkOjo3UUv9yDSIq/hfWEjeO5rQAMhsZhg6kVo+LVt34KZr2g73M9YHxeICsoRvJero85K13OET7S5sovRzVdXpAJsEyj8JgAgqCcGwNNmSFulwNZdIgVhITG3isA0XIV3E+pxz87xhwPKZlCZ4EmRnzamek+sZeM50XNRI7V3NYKSmcqkUgCSiC1EvThz1nDzy0GRKrD99AP0vEE3AIcjTB1YDelxBS4Ffn/k/PswY80Jx6VCTjONghlqniCZJQz89AopJejTGU20zb3C7w8UFztSOyc9M+W5fnBk6rsK0pMDZQOU/I165EoeLtANkI3XQ44J9ejw48w+Q5CeKxWSFSQ8q0KkEgRNzKhyFXI4cnBFzIG7GVjrznNR27kr4Dj1kLgZXdxdyTcIusVYsmDLe+iFSzB0rZYaKd5RRBMNxDSSqRK8AQDMGG4B0MNGDQlY1D9wXIEHrqZjopPw0OS0h/8gkfYd2UFmFD10J6epFODiTNM+ztGOOGcpcJnYCNFOfG7VsWGFxy4NMKQOvgTawzzNYHsJnZuYSLV5W1p4iB/uXoHafri7E5pezijd3sdRQOXeyHTqZNoGuIKz08oMXGWvtvcWx/RrT0FPOR9smxqQL0B1px5TW7FHGjZGr83oLXGgKbS2DBQYbxnFfqlbf/rWhmGkGt0CljjWlQfkvXMPfY6/x1uqnyqOcRUq66BAOsjrjsbx2K2NIxbV4XjD8RsYgTh6SYghM2gEPX77/2bhd39ffLcDlP18gbbG8yO4Xymu3Y2npW9SAXFIpvejCQFKoFtpd4qQjJuSI6UWUmrPZwMxgKPSealAUscURVrbo9Uznlpfg6dlwtAuxFFd4KDWVilt/NmO1ja50qJp1/ebBWSAb8HMV7Z99ME93p2X+EsinVggCSQ0bi1eLjAY0uMFejzAzVFeT7C7xImpctWZopaLV0c5ke+SKqDPhZOVoAt/torddkqomaUM0kWAymymOh/hkyIVgTWhLgHqlAm4LoVlBi70VGim+98mpTfJg1ngABZW/i33GT4BcKGybqyqUgFMCzabML1bgGWFHDJ8Y9jFH46QlVokvpXQlIgQzd2M7XsPSI8ThcuOE2wpSFaj+vMG04x6P0GKAwbkJ2NauQn0uUA2h51m+JxYoHI1yGqsF3WKUFuxUDTO0GrIi6MkQ50FehToBcBmSCKoKrCs5EwYMCrz9hkYBKsSXgkg5utlgzbF4MFYek7kndQcSr0EOFTTFXoZ0rRnLvVsp31SoQdFBhc9Q0iYJh6nhDKvA3tBzeDrmLFcQwAHj5CQH9iuTrRtZN7aJvXoQ+PoNI9RFN70ZQG2Q4Sl6O3xahAtAaCiXT30Jns6fDMQid6Vpjfj0+DBA13jfBMgI42WLMYCvnNVnNeS7yVmPGNNM3VmczggoSWzGykhD6cb4StLuO83hm4achmVfB09e6ofqnlpBpuXVkDMURQR2tqHvGbsYKKBjxoHb6dqWFYQUgMg922S3q6xia1tjYPdeTUynMNxDXpGSz/aMsXV+Ntg6FuBzN7fuAxX6sDx2gtT3oLA4by94OZVO3w/rkhUXr8BPOP+DZw0YBTjKO91sO03os8YrA484vzJO/m6hY96tesGeOCQiSGcTp6V3RMiTR1R6GWhZ4Soko9DgHxxUBDPMCk9Ja34pIoFBnYoDNU9mizwKE4VVZ7g7jBTVAiWLXOKcYl/Y7hrHMC973qFkr/+7Vsw8xVtP/f9j/D//fefAM68fy2ARaE/Krh6X1HYQYD7iWGLif9QwToxG0Xd4IBNCesbAZRS/7pF1ogbC+C1FT5YXbpmD0OisNkgxgrd9mpCeTUDFwuyJY2AzZFVsgjqZkF/EGqyeIZkhalAVqZ0lxQP4Uq+ime2Sy7eq0xzJa2wg6NeFOnxDF8yw273meRTd4gkJGGdKiwb/ItHaFLg41ewuwlwJcA7CWpVyBMAZfaVS4KfQI8RgPx2hZ0mbN870YtbwXRu4wTls6LcKT1XxUku1jASJSGtwPRUsZ1YPiEfHL4ow2pGL4I1r01tE04YgAZUQksIUSGcFaxlT1VWhR+oLyMpqnH7sOTskvkRNknKsI1Vzh3rRq5MO587rJMmImXZsYOf5iFKiVNPiown8e5JcoCApnFcVIDm/dHIvhOlrH81oDLUhJxIHI6UcU/hrVo24LygShTGvMqyMoKsdetFLiVlTqyyZxn53FSsab0l+tS4Ygw9jd4vxejtaGBhd+UwDZ+p+ASK6tS20cajmUleb7ycdl7vRuvGHgr4IDfvRx9MXId25PqHrny+WtiLhlo6JlRj1YyW6NaNScFeTyqcXL1+0tg4pTHiNY7ut9BNa1e094qEG3Srfs4AJx3YjMDtpQX5uN/YphbCi7934P3CMVpzAsy0Nl8BmgGz9k40jNm+U2/Ox97JkfwcTydB0QBmBhZWfFY7uL7tx76jEzkqgYx00lTs2D0jRJgi9LSIMjsJjgjB+oCj6YXJyXcamnhEPRufjhpgSTx0ZDjPtbVGG6daqaFUXUPuKu41ozfGnZXTShVsriiusCD9mglSB3G3F40L24OcvvxCfg3bt2DmK9p+4ff+FP5v//f/B29eFRJ9I6OoPdh8wJjp43OCzVT71bVAVVCPmWq0hWRfeFvWOfRizBCK1bgai9bBmZkCAHkzVDNgzvCUUD5Q6BLhDU3UUzFHqmCF4ZaiesiwI4ClwItBRZFCCA4WAmLFkSmsCxiQzgY7UvU0VSA9FfikJHcKM53K65mTwWWDPp8heuC08ZPPCUzmzCKW6wZZV8haoecL9HCgZshPfwR7NQUxU4Glwk8Z8MqHbjNmg4nTE/aQkJ6YPmyO0HYR1CixoIsjFRp9+g2atSD5M58rvVFJoXPiSibAqItQa6fIACIBQFkOotRBwC5WaUqdGykFDgnF3gAMmbMZU+IRJQCwk2UbkbZxNxoPI7g2LgJzp7pwSruyL7BnVTW+CwIcNV2VdiwNNWPEecF7DZF27qOHBgh1YFb89pz3MIwq9XOO1LVpQMIRnDFNTGm/rJDLwn6mxPtWg5qZrld5UqIAZoC+5hmUClg1IIW17mMZv+sqwIhsrjDQCSTNLsF9MhrLLA5fJAjgTMlH5nPbCI9XYnYiV1N70zexZkFGUOO49tq0vt54ksYQTDzufb5ghiSBk1YCD9O4RcIAdi9DM9Ae4efhHNIAUgNRtrernV5GQDICs2YgRy8NcB16GkHPFfJ74e/htz6ef/AG7et/dIG7fdiGrJr4zFobE7onZUgI468CuEABZL/y3FxzY+KkeQAyLXTUt2goHJgCaOgwjg1NAhAwvMS1hQfIwQ4uBu+NBEFXdBe503iWGliRdo4Avq26tanA0FKxjdlIiOcQnLcab42V7QXFFMUF8FCkD3BtHvIX2J+D3nMnUPr47h7fpO02Wvq7uv3SL/0SfuEXfgGvX7/G69ev8cMf/hD/6B/9oxf3/fN//s9DRPC3/tbf+t1t5G9z+6O/8LPQakgbybSyUhtDq5NKYSGZvkWJg8QJShwMe0wCn7x5QQEIcKnIz+SIKFpdJj52ftIQXGNBPct8IHXzvkrPz4bpzDQ8UwTvhca9vJpQT5yhpDqQBXZMKKcJ5SHTk5MAfbciPW2c4Cfqq6RzofhfDZ2GScg5yaEtmSS8MxnlYUb5+B711QF4XukBeDhBnp+BL95BtsKU1QB9Oh+g0wQVRf78gsOvPmL6YoFW0Ai1WjqbIT0xXFVzgqwFx08LZKGCrmcKm5WjxsQmaIRJMVD/Z71a50FX6gLZBJT7CX7ILL+QBL1mUmqrd0XPTQ3AAyDCQb4b+dCYwXFmCChngqwSqcubB5Bx9PTorga8RRp1gJEQuIMDvhVmeLXXlUrF2Oj5wGWBXxj2bLWUiKMasNG9vQ3otJCT1R3cJFD1OQAyUoSIJr73KcFPE+w0w+6PsIcT/OPXqB++hr++gx9n+N0MP83wV3fwV/fAXZRP0BjXw9SNnjiNsGysEM+Jezf+HitXdQL6Ps8quldK4pX2LxYXE/liItizkYB+/cQBWVmtXZbg8ISWC8UYr01Za1A7f+fr+n6PdXs3Ug6a/AAf3f1f4jMKxe41iFuseR0EBHOygSHnOK6280V2YbN2IwhrYeAWgup/B0Dsnxl6lmJ7hXEceviq7r/tAG4fkuus5PZ+BEc3IOlqa9838CL7GFE8lABjHLvGT/XsEaaMMRzeewJT6bPDZ2coKv6+ek3g4M5ON9nkQLb43MJT0/Y1YDakbNBskGTQxNRoUY+soApNjpSZ9tzE6xj5jXs59kuJ3vHcybzoGUZZWGcJbo0/T5AiBCGGBHNBdUUxxbopzBXFMqoleICVgCeo7caK+arXeIqL4CaoRg+NGUNPZoJa6Sk3U/y+h4/xTdq+Vs/Mz/zMz+Bv/I2/gT/wB/4AAODv/J2/gz/zZ/4M/uW//Jf4w3/4D/f9/uE//If45//8n+Onf/qnv66m/pbbH/gD30e+8IFJAGQ1Ug1uCtDZBHhVkvYyUCZFvjhkNSRnurayBiNUDH5gGQQXRZ2VNWAOvGyeFLquDGfN4aqHRnyeWVXpUnnzz0qw0RbAlStMOyVO2FtLgU0os0KWinoQyCna10jMx4SaDsyY8QAEiqjODZZrUEM9ZogJSy0slSm1H00oxaDHjASBfPFI8iucYYhpgr25Z7ZVYRjNooBjNwBigCk9VEpDJ87QnDxXgg9XyCyQlAm4RIAMCu6ZRO2rCoUCOdHWJJAXAwIfm7Vr5vRVdtQngsfqpoVrkoTha7yWNlvFWIuE8QfaUlKU3KarNGLHHmNwp5hgZEd5C1cF0BA3+LqFRyNAlcc4tvNGurZPkaEUnhbyVIIEu217inZn2kYadymAEkB5FOqkodHdaxQqzT7JVVgGAHrdG3PKC6hA0gF6KSQqG8edkvAxuQLh7mgk3tamWFliMNItJBRDd724HhYGGirJiDToBowUkRYdgKaFYIpRABPODLoGRsZQQw8K7Oe7tcxX6/h2zm7ER59BHKEdIqpTNA9B9zS1/rT9WhimhSwcO7H5Big0Z0PzxEDQHQsQ9PpJzTvTlXH9+tXH3wLdG4Hb/RsoeQnQvAR24vu2DgDAZIImdNgaru+fyoEOXHYQ1Dq6n4BSE76Dpealubp0gdiUx5Srz+MCeAyYAJp8v28GRCu9Hw2w7JHRtl8PC8VAcLExIuN4NtoetoetqtAbQ1UED1zCC1RDIGkn8HJxxDWYd548cxcUtccgx5tGQvtSbm7kdi8L/ug3qJQB8DWDmT/9p//01d9/7a/9NfzSL/0S/tk/+2cdzPyH//Af8Bf+wl/AP/7H/xh/6k/9qa+jmb+tbZ4zZK1Ic0LVAO4rg+C1UgHVRCBVIMuG8mqi57KQ4CuFhRI9uCL53cbSAKtRtfcYsvtNabXNapMiL5UCXHd5n6wquSJWFAggIzWMQtygnoi4kRxpZRirWAUOCeWoQVCc4FMTr4jV8JxQBcxqihtdgiQq5jsRd1ZYAjRCKXJhWmJ5NaMeXyOfJqTPniBPZ8ANPmV4zrC7GfK4QR6fgfsj7EiirlXWd4KSy5OWGgJy6CTWdEjYjgmuGeJUz5Ut2P9N3yQ5dAXSZvBjXwJ1ICHFI1uFXq8rUTDHXhW6gQeXHYQg3seFUAG8pWHPOVaV0i+fAOhZLm0uoR+X/8L74rV2Uq/Uyv62U6rS89BuCzPyXMa6SlclCGJGamTgUsC0atDrMlq84PJw7IaJFSBQEnRg08JC6VIidTgm0Ror2R5Wiv5G+QSsZT+oArxpwnPTgZ7vf+9WgJuF5TDbsU9raNtae5pxEIoR9sKW/XUIP8Rp3/MeDJv/Jp83Q3u1j+zfA7JnvMr+wx42avtgACzYo5BX4Er2t+21tf0WT/RqDf1+G16jDVffx3eO4fPW1tjX0w6IGjDsfJ3bhn2J1tp+er7byci+c5PiwjZv1c6VAT0z1/YYjuFmEEQozm+AaXvfHkonyB5B1Dgmg2qvdJDi17yjTqjDtSemnUriyt6AWoY/A8K4c55t18cakOf8YZEN1TSLRGI6ilE00+bI5bC7wirL41glV6d7a9pgXt0I0qeLl7Yffvf3vfzF17R9YzgztVb8vb/39/D09IQf/vCHAAAzw49+9CP85b/8l688Nd/Uze8ISihjDyRzYAMkhVcjJlM5sD4QUuIDWEF13SRIZ4KXegwJeDeGpNQhBUzprqCnRcAUWmyAKV2wEMgKhpqUmUZ1ZtqxbZUp1GB64K6BwBIB1AgjacyV3B4AYEpWMwDxcGiCNT4Q0EM4HhkoYhHy2hwWPAONiUwuFKSrH9xB8oS0bkwbdAeeLxAYoBP8OIeb22CqqG8Ou1HeDPUQ4EQBpBnpEtWaHUhbRVF6LdQtXOgO2Qz5HSt2WxLUdIRPHLdmZGk3JEDLMJNHBknzyvRVsrW1YlTAbgudxp8J74MUg6mwEnqoQkuMXU+zFeVyqxn6GplcTfUX6JW0OZvGTNmIvE3nxowZOmboqdQS0v0tc2obikO2Lao9D0vSfRtDFxLgpRu8AAORxUfOSQMOADVgmMfraFoyHDcD6yHJVsIgCHCah1BYnBu7MaCNiQy7tloeUsl3Q8HfSmtH/D2Coqu5ulX0bvunfY5/j4Mx/KwtbkfDbcMQXtmEtu8AlPzmt+18tye0sR/DZXsJb412cGxvP+TwwUv2SoZ9rjBdN67XJ7/CRAGYehZz7N8rXL93ojjCcCwAkSKPAfT51Ti1aEkn6go6iHgvm2kk844Ap7cjPB+K9zLKenbP6DXRlr7sHeg0j2CbHBrXZbz1EN4aPqZtcbHzXUqNJJLh8SqFP24eH5d9rrK4u5p+ZptUHNIj100R2KHYSrtKRMTm+j7ofm+T9/b4n3z4/d/0F7/b29cOZv71v/7X+OEPf4jL5YKHhwf8g3/wD/CH/tAfAgD8zb/5N5Fzxl/8i3/xt328ZVmwLEv/++3bt195m79s89BccHH4MbHoY2UICUkitADY3cz4eBjLBGpneA6kvxjqpJ0giXeVGNqpAKpZUNEIfo56lyBnQC4On0EP0cLVtD1E+m93KYdhXxAeiWiHkszrLRTSvBQgAbLF5UUAmKNOStf3OGEYyC8xRc0CKJA2fubFUWYlVyUBUgR62fizDx6A54UA6shKzp4VOJ7gwvPYQaBOVVaxyvEMReV6oNaMHBLyUwHxoUHmBJvInxEHUBzzFyv03QWyFWBmvSgX5SpMdtLkvgrzkct3/Tx344lu3JtCLcILZCLQrXa9k056ldTr+VBojma9MzvHlGyARtbqblSU5D6J2kySUpRVSFTnRcyEU3hatC01fQ8Rme0ekObpaLygVkagVY9u3b/ykIyDEsRoBauRbxagV3YhtBaS0NC5MBKgfZ5YNkEVbqwfZv0i+M5DCs9iB08Aehp5kMAB8rgazutL15fAWUIPMV2Lu8kOZFpqcRzrKu243Q5hrd/L/hkMc+NsXYGUF70DEm24PY4PYEmu8EAn9w6HuQJaN6DqFvn85kZsb8KLgKeduxtRXPV7BETN6XGr+XIFUgeQAmBXaW79vx3/ADsvApR+PXwf7zR8N7y2bFMAV6CoAbF2m6H/zU51BQC/ui17n6QDleh4YnFfuKBalBwRCQ/JfoCWHj2OpBiTPjrQCYDvoIe99DS4CDHFHEC+v/VHx+MCGABn7H4Xdxy25uXc23C9TTek/a97+9rBzM///M/jX/2rf4XPP/8cf//v/3382T/7Z/FP/+k/xfl8xt/+238b/+Jf/Av8xygN/vW//tfxV//qX/1P2OIv33T1XqIAUYTOs7CEQNzsHvFwbamVhl4VOzlgmXyV/AVrKtWZGUJbkCXTucDOjlQS9WQ2hodsUlhVeAHTuKujTg48b5gKz++hmSLnDeKOaU3wTFG4ckzwA9nsKr7XQdzI7XHw/DbEmC0rReAGY2BZSHZW/sYy0L0H7oxTucCyA6cEebdBjkcWYjwvTBfPBH4anAoAWO8zAU1x+BZ8E/XIrKrQGtk1ICDzQ6gYV0q/61bRtJ/8MMEeJhr4JJ072rWufDDaPb3E91XmPgTcRKKAZWikRPvJl1KUQ+b54xr0H6vCm89fwfaUCLs0jowIcJj5eYSfOscEAiSGmJguPZMXdTxAzkEGZmpEBwQuCBJwpigQOxCAJvRjouq0JyFYGrdm1dwQ+ahRZyqARIQx+9TX0KGDnCIB9w3PIftgsEOmonSxENELD1N4JxHk9z3LSPZsqnZOq/2qdJDT81txHaYajVmAuQ4UFLhaFqf4HDe/a+24Wu1Hd18ANVdA52a7MhU3+3k7z7jjwD15yd7s4//+OV8EN+2f79+/B45uf/dC+29fx2bcfs5j+nvH99bGRvYdD3AFZgaQ0l7HfRoIAq73awAnPpf3PDQDcNH9+4G+xUf46jf7jEBvyJ65Rgd43F8VXR+MTlQZLogzMykyo0QUZgZz3UNU0rgsHlPIfk7vQ6Boi+o2JC4aslE76nTfwU7qg9wguuzh9fG6/3aQ79e0ifs3q3l/7I/9Mfz+3//78Qf/4B/EX/pLfwk6TKa1VqgqfvCDH+Df/tt/++LvX/LM/OAHP8AXX3yB169f/ydt+//6f/PXKUonkQ7sUYF6c3oXZq7aaxYko4vci6PO6KsGjSyK/LhherdCvzgDpwMuHx7hOSEvG3QzwEIA7ZBRTgn1LsNhwOqYnzZIEbhbVKh2yOEAnxPqnGHYoE8bAVUL6CqrRNc7Gnh3PnRpCwPU6t4APRzsk0AvlSuDCQAIHAic2Kc9M6Kp3saKJtqfn8OgqqAI4EoSr0CQqwGV4mXlIChvZrgo5t94hilYHTrKD0hicUU1AoqaleGyiTWY8rkOHBqDTQy9eVKggyY+2bsuB524zDrb2y8l0uSr7aRUxNioM91ZW2iD3hpTcqgkVklQdNVlnp+6LnLZ6FlZN/JlGgip5LY4EGUMYlZWgZyO5LqEMrC3VOW1lU1gCjcnOJKBvZCE28NShznIvMKQVE70LvVQjEcmGT0wQACzVtqh7DEHz+HtacvVofijOIClRN0roRzATMAlW4U+r5BtY+2uGMcuGNhChd5PhfChh+eqXbdmjQfwkmQg+wahPEnPfLPmoYl7sXko233McMYOZrrnoLVDmwGO38Xl8dipf6dyDXTasa76hBADjPc3OObW8PYv7IXPBgN+y/95D8jI8Jnf7APsyQPjPjKAD8FOwo3PZPyue1cEPX365lg8Z4CY4VjXIAboLqEh84v77MDkijjcAEx7r7vXZQc83seMt1YDMdHWuI8FiJVPu532c6p4KCOEFz3WEoIdCPCeGM2uXN2yLdylAuqJgQvDK+9QtPnWeyLjfoIrob1WLg0AU6/jt+6ClBxbyVhXjajzeJO9tDn+n//7/8Nvsc/vfHv79i3evHnz27LfX7tn5nZzdyzLgh/96Ef4Y3/sj1199yf+xJ/Aj370I/y5P/fnvvT3h8MBh8PhP3UzX95iourAXxQuTsE1c5JyIwvVDXxoEmJCDadFTMbbBzP8OCGfJlawroBbQZkUyRxJBLLt/BZW9BVM7zY+dGDoyu5Y7dgmpbzC00Lj81wYo50bSPGeQVTvMyDMPER7mDJnGy3Own1AX+V7S+F0pqW7gvWWhjEZ0yNEHGVS6BSriY1VuSULlmkGMh9kqwR3THkH0qcrypE1oTyWSOntBekpoX5wRD1NwIWAI1V6xXrbAHBi4zXRywZAWLBz0n251NCaCTMkIMw8q75L46cEF6MHpgbAAXbvSqCjPj9XFu60Q4KulYq5xtUSlGPO2kb7bCUBML2FmTKD+OpO0JL37CTyZVJkOxVms0xpL72wVngpHYTADUiJpGEHB7rxYTK9dS1M9p43w3cDvSNbfs/0eusu87YfjUazIn51TJ9T76dkhR9TgGBHqgbfBqPXjJzvdolEZgDadGl2wHG7NfvX/zVQ042ddGDy8jHiijavSOh2eDMSo2uvh8PQJRh8VO0VXHt//OY0N+Dm5Q7dvOoL32EHQ1eABTefjed5AWR0TtB4LL/+7DqbaDz3fsbGb7kFdO+1rake3/TxPR7M7djIC/9e+g7XQ9w/eG+s484zGn247/e10Ks+NnPd4vkNEmEtAWYEnVDskercTyhNAya8jpFWtkVyQfNEOoZbqz+LBCkEMbI/J7i+rWptkIjfNB0Zd8ChLLfSMJ0GcPvSG89fDEt93dvXCmb+yl/5K/iTf/JP4gc/+AHevXuHv/t3/y7+yT/5J/iVX/kVfPzxx/j44+s89mma8P3vfx8///M//zW1+DffZhFc1rqLeLVVvgCYYqXeVxoObJTi90M8xZtfrSzrJNA5o5wiNXZz6LnAjpnAKLHCdf5i5Y08U/cjPRts2yAu0K1AaolqyQmyVQqXmQOvjnCbIC27F2DdowA0nsPIrxY8leBpCOO3Flwb0Qo3Ai69UJLflBoITVnWEwnFvehiEmzHBDkqUjE+4FnoKQFl3dPGmLK5Ij1TwyedGW6QZSPnJGfIVpF/coZ8wFRzvTgAozQ/OP42KTQ0eiTTYmmhl6allff50cAspohaiAi8hhZDlFGg7kSs7JvHAHHtHOFNkL7K021jJpbEfVAGow7fywy0ma8dq3NDlKJ7DvQq2xMF6XrDo7iKlMpxTBk4DB4WM3TNliRdbHFcNvZ6Nm5A1R0BJP6OHqoddPdzZyGoiCm0rSe9ZS81j46DSruRteGqEGsKxgLXDD9lSKn8TIAu3pd2A+AtzSJAdDegrbxB227m444TOhcD1/s2ufyO2PaXTpj36HuEIMf7xtV3z4O23+yXs9dcEgQgj79tGEtBF//jzXvTj98K3Ax9fdGgD5/3jKf4u90e74GLl8I9w+/amF7zhm6F6TB4r273vWnjbejuJfDyWwEbH16/zPja0MfWlhfGu9Vi3dE0r7HHvdCJye05aoPTQIfv6dq17gClgRIEELG47wh89crz0m6HDubFQzZAeoabxEXk+QJse2uyw6CdttESvT0uUimAW8xHyr741UXH/t03cPtawcyv//qv40c/+hF+9Vd/FW/evMEv/MIv4Fd+5Vfwx//4H/86m/X/93a4n7F8dibQaPdyu6ErxdxQwQBloeeD3geBJWMG1AaIcJXpCagzeS52UIo3OZCWAsuKehTkpSI9F8hG7RS7m0l4XcOYODNiZC29Ho5r8AvOCzopJSWmLDsgjyvSZtgepj7h6FKYJj4parj/1YWlGaBRg4gznq6VaeWZBSthFWhFNB38LHlXxPWUKLyXOVZ+4VPvIcLH35D467NwCXiXYVth2u8xU6xwY9ZOWy33xbEwu6wt3FWVaq9RIgKFbdmdM2GgwjuASGNEqxlkFVgj/DYNtYwCMKBy4gCiv8IsItsI7MgrwUDwi2yvYthn11hdNY9JAx7uPSwIyA1BN0JhZYPIDNSVOjk5oZOOMRqR+GGkbPtxDgMS44cKz3lPFUV818aiG2vOsD5bJ0vTuDXPDvoEi+aZiUmYSsq2c2FS3CeamXHViNB9Scp+k1KpEexvqe7D8Ztlln4Z9lflmO4OkBsrOLjjW2dE0D0vzTB4xfWvwxCKxuo7DEIzkhLntuG43di1g4THth83QhvjNbjyrLzQ/Be9Ee13498jIInvRsPut6/NI3UDHq6OMwKaYevO0dvQ0bjv7et7rpPhc8F71+iqbe07G74fOHh934HDT9Tie1/jWLZhoMTsDW5hp6oRam4cNwFaLYZWrgAie7Ih0EHQVaduvIoeXhwJ3o4Pfexho/bRCFDCq9KBUh+M2zjh3p9SNTxGO1Zrj9B4x3VwZbcX5evfvlYw88u//Mv/Uft/GU/mm7J9+P1X+OLx0mP3Hh4MsTBcFXFjcpVapgD4IQ4ghZwPy2RhmAr8jqq7gEOrAHPCNgV4UMU6CaakkNWQL+RH1FNCqhmyrrDTgSnQAKtmJwEsuC1roUrxUriaV41ClAlijvzEejYeHgnupwxjGCu74n5iNtBS4clZ3uCdIS0V2x3LM0zPBqyG8jChZqasqzm8cJImmADg0sMHjnDhVoYByoFPl+UATNXheY7CeoYaWVq5r9Qlxj8yxWI8IQqTUAY9KtLK0Ji1zJ5mOAwMAQpJsLoV2MaZiKRpZil5zPYehhiKQYU1VGRp4a4yoL1lTomEl4dk4eaNGJS40MsQBK+mT5plI3iaJxraaWK4ap4BCT2aVg9JJICJdp6U9HBPTIgztWqY/i5ANY532sN6nE8ZqkPWPgFz1Z27gm+feJWCjxIAw6fwbDTuy0agIkIOlRfykCAIxWG2x9swRs5v8/b09jfPgcc1wwDABn7R1SZyxSnpF3IEkz20IHFdB3BiNB4dM7WfewAaDgrv0cFoN0pClzmwNn64AjkjaBl51Fe90P3z97YGMsa/2/3XfjMCnRiSEcQ40L1VHue78t5EuzuQGYHYcBzE91cesVsw0xsnaJlEneczvo6eE8UOAMdBGEFKAy2RbdrbMFTwQEebDYyEhw0eIeHb/YZetlIkt6I67ULHA2EVV7yu5qXmoXcU5rGqalinCWuq7PvXqpCWThpZio2gvgvtSQDxKyj/woAbVSBCdM9j7uu9bO3w/Z/YN46h8s3jzPznvP2+n/sY/+//z4+ZUSKDoQbvNwvjIZXgIK+GOgn8BKQq0AKSxhpJNiHAtEMiFlsngW7t2EaWvCbgoChKQm56LhBlxW0pG58pVYgV+Jzp5XFAzgt0LXTtZ7o0bSYJNG2GtBVYq83kYDp0qcyWuhTAKmQ7wd4cUY+px5TLQ+SYOw2eZwrc6WJdgwSXCoFCM0MOeq58aA8CSRITCI2oFsAnRUkAwJW7V4NugDmrt1pmGnbdHPm5IhXsRF4ALeTTV6FBBq1HAjBsvCajqrAYgYRlB6YM2TwMJV+rovOM1MPbpQieET1gomC2D4zcHJE9DNkmTTgkBU+lWgcuSClAI3qGUffKlI03Rys+ac7wzZz52xLHaYbcgpOT4/cisDR4lQJ871aDbfCcdoMzGNmRTCsunRxKA+jdwdRSUzuQCEAkqbmwDXVTJFTOy1Ep3Ft6WZKooo0IdUSok0VpdsPWPFhBwCcmiWl9AD1QDb0nxMws+ytiHG2witKwpcf9jStbcDVkQKsheVXHaLSzrcyBJ+yrfdvP00Cbx5wxLuD7innAXHLTnltQ8x7HpF2jBnQGsCE3+zfwci1Yd71P9zKlm9+O57u1nV+2jQAkQIdg7y8csTho92p8OYwhBgBy1aHxOjWVudqvCjp6kOF4jcQ+buM+zevWMpUGANLO1z0wDdNEP0X3nQiIeR6z/dBw6dW1BQQbHRRL47hIB0PDiFwNTctQvR7cCI01kOzooc5aBKN2zjgE7oBVwWuZ8E3bvgUzX+H2P/0f/x789//Xf9P/bpOGKENJeqkox8xFQkLI6jvW48waI4k3pFSHCaAFBEMK1hIxQDshDDQ2HvdgBXRSknrDg7D91Ctmh8SMmhYDPjsjhTHVn7yDuKAeMvzhCJcWiwEcDK/o+QI/TV0tuE4MVVgVKGYSVdcK3E3ATIVJ8kv2VVU97sabwAoQF6TCR9kSDeJ0ZgFJywrTUI5tfS1OI20gCVaZQaVbaDi0CE0S1LsMnCvkQlBnc9M6iRklwBJl9iVCNc6nYTN4BmrXnGE4jattZhZZIn9FXRmqWo0kX+e16xNbLG0bXwiXjSDTOYY9nzJ4NF3jpVXgnvI+u4mQNyP0lGA+ErC0FVJbMiXyqXgchtBcnNlK1dBVtKLWlik9T46YvFpWklsvJrlnsbR7IzbtH+2AofvBY3wHVzq7Osz2ApgkSDbYMbHMR6t23hWVPVLe0x6WlPDgBaCRyqrXrrjKwNqNYBiEBnIE/Xume6OHPxqnSK4azXureSRGo9FCCOM22rP3xW7D7NhwaNnHtB/fce2BaIblveMNPxpBRHs/XJaO19rPbjgp7TDNrgH7uLR7+raj3Zt05eG6eR0b3UJ44/eK64FqoKTuoKOFiAfxXfah8ZgwtKHt0/rUrk9w4Dphuw3MEI58b0AGULr35QYgNVR5C5zGjvvuOUF81aqC8x4nLOk8quE4DonwOJ8Fq23Q9nlWxqZfbXsb9sN2jeX+CcdIoWqwoqhipONdgSCK8NVN8L0P71462de6fQtmvsLtv/mjv/f6IQlyaCM7qoY+yqwwd/hmSLUiz4J6Cp2U0ISR8GBodXhUywbQCYQiCjxWEogTjy+Vr/UA5I0G0g4ZetngmmD3E1QceamsSfT9j8izAVeKah5GWZDeLsCBxRaxFWglV8Yiy8AfJqgJUvHwOIATp4Keoup9lQ0RlDuGN7TwoTUNj8PWPCAKqFPFNzs0kefSeBnaQnVKLoLU+Ocs5OkZVDcOQ7W+zpgfC9JzZd8jVVuNTz+bKzsBUqlajNDF8TmhCVJZpccmJQWWCMGAhlhVYVKhCzh2TQSruandea2U+jACp6cmuDeOMLzF90nZnVlMIpBp2mercUIXJxem1B04uLD0QQyDlwL/4h3MCvThHnJ3x3DatgA1yMinGZ2L0rwSxvvNjX3qYa4hlLOfZFylNiAj3UPVDLLHSrRzaJ3XDkF09SnB5qClBJhhGjYrUrewVg9XBaqSGs9FWwVPOmj5NNJxXBMM7Zbr91KDTNm4OWjfDZb35m2za+N0P3ohRtt3lQ3kQxN8X/SMYKL9Pm6HHYDI9XfjdtUm2btxC0QcfF46kLhq7N6eFo4ZPTPjeVul6iswg5tXwR7OaV/FrXplgO2m3wL4jkb4ufnVWO8YRPq4+niR4ra+dm/xuW+Dtddw2vvf/6itIbfjNLS0x1P3Xa+uTe/fALqkeUWadhUbbzcinPDoF+gVdJMrLw6fgyYM0Z7NBtz34Ud4WcYwt2LoBoAmnscQk8OKks+mQwq3IUJgjt/78B1807ZvwcxXuP3cz3wcuioVtWYgPHFUnwXDGd0DoyhvMnRLFIe7VPicyJ8RgbUyAKGH0OLqWip8ZghJFfDNmGbXDKIKkBKqOPInF9hRUU8Z6WLAJLBXM2qu8MyClVL5OFh16EJuhR0Efs4BhhLq6YDp7crJZJ5halADbAIfzKUira1+j6KmZjyEKdJOBrwdCLSSCLAoaxbNwW5w6piI256ivgE1R7+d3KLqDEPRycL+SjVIaatEEuAkEdDInREwbILkHit/2Sen6qFTEsbYOGlII5aKwCfOUp5jfI37iguqOtSZToxNyStCZA300ArCsxBqymsBYqybDXEP8KfhBjFjMU8AktP+eeOINDCg9MAwO8rQakZ5ISG8p8QvG4BngiMAOK/wZQPWplHDOIGUWL4qUZNuFRXCWk+iO6G3eTQCIQgc3gochhu/LVZbLbDm1XFt7RdqzLihhhhH86SIt7R30PMm6F4ZxKVixk8zKDHQ6vusLRhcDGFmzPYw0sihacbfQU2fEKkct9vQiQMvhmHa2Xr0KpSE2/3Zww2xWRwH6bpGZAMhfM9nygI4dFAwnLa248Q4Dsk0HZN17PcSf8X3PjpwTdYd+jVghR0g9jAP9s4JG/USVhg5RB5SBQ3EXIGcETBddYim/ApRagu27Gfsi5WxfS2jDHFv3oaSgJ7h05HnVeNf+LvtKsCVl2Yci36DBSjQ6MOgGdCTRvrYynAP8O/xvpDWh34z7IyZdt59rHeg05zC1nky7aIKVBkZ8KKd5N43NWhy/JGPvnlFn78FM1/hlnNitk111l6a2zdExo0zg0yvAyseG2xiKIMGDBAYkzTmBBOl3oY57CAQ481cDwQdUrzH4QGJys00Op4V+fNn6DTB7iYABrn4vlwxwCYqvboKIAyZuE7YPjhAzxv5PQ6K8rUVd4oHsYK6OabQpzXCHxk+C9yVJDIF0upwZzigzonnOhJgEMRwPzFQ/r8asBFIpJC8l42/15zgx3hgU4x1rMh6uqIIsAD1oNjuMupJkN5V4ClW/vC+UqJOzj4pOiLrzCNduHkEWp0qbUs9D2KnoibAq0Amh88pBORC6M4BV42QGa+/zZmZS2sh6RcEM3CH18ryE30SFGqw8AZrH5KL1LgWIj29HKbQZUXPAjoeqayblJlecVySnB14fKbmzDwD08RjDSk1AmrbeAkXeFvRow9D3wiMw9PSSI5jvHHUVdFmQ5RMb2kAuPU5Dmzt/o4wGKS7KTz+7iGpZjC6AQ5wMxq4yqvsZQdIo56Oh2Hxtm+AjB6zCO9UL3mgDTgM4d/YLIBO019ponsd5HkLMTFkGovj/Thh54Doct6PN/Jt2ntDtLV5NFuz/QZQvOBxGbd+SW9DR23f4dz942aMx+ON1x43uMiBJgMg/Ue75+XKITbeYyPIGdxUtNUynm7vTAMZzYfR0+tx3WDZT8bFYwzcS8Sktu84HjeorU01gpv99gHYOSvtyxtA026WMeKIvprB9dYeL3kZa7WtVs43e8UUJaiJ31oVpMnhIbnRZBY0alHVTfDD7/9efNO2b8HMV7zJRpCgcBYRaytYcy6LZqDVhPGLwmbQwKnAwhswPVWkhYZBRAholhUqGTYrSXCdE+BIpcKjLI/k+N4AOyX4dI98rtBzpEuDKc76VKBa6Hm5n4BjzLi28QY/Kmyaqe2yGOyQUB4o2a+LAxvDJpYV24NA709IzwW6OfICFC2wFLOvhCieAxIk4FbHqhsuiYc+g0DIQ5OmMgVWl8J+HQkcGomze1y7Hx4Q9SBRO+CKtAnqnLC9LpB35C8Bw8SoYP2szIfaJ4NWIccIDssOFUUTF+Rpwuo0Y9lCJini2BaKuRHSqjPfayVAsIcD9EngUZOoc04cPFaN8gUieylctR1MmXdhQmaFMdyIJLDjBFk24LJyIrMIFamixeVFWiGx+NcqbE9TpF3vSzrF4OCIMFpfTPZQzo6R2/zvAAHXQA7mwLfXsACpfRtjKbJ7RRS9jEEnwKY24zcwLN3b0mmQzf3f7oO2BVDUZkTEgcL6XI2T0ykVaQcd3oBMADIDWOYjgAObMpgNoafEM4BMYIMEauoMtqgijhEgsQOVdi9HV0xBle0YssH+8yUSWzDhCqjs4Zdh62Dz+npc7TNu0ferHW45Lt6bth+rsYNfAiNAl63w/gO5ujUk5jeJ7/ewk3fphas2jwBFbt73ffx9LtLt7w3XoKh7hOJsOuw/dsrb7DCEgkYiceeU7feKSJMsQP+7Hzoacd2MflI0lBYU4t6ol/CcOGB8+vt04qZwZwkbGG/qljlVN5KPNe3Pj1Uh6BHHH/qGFZkEvgUzX/k2XSrl0beKugVJq8295tAngywV9T4FqRboyqxh0DwrsFSkpwq7zz2c0ifmINBagKK8OL0cTTWzBscmJP5ZgJBKum0yFwF02aDrhrRU2ClHzQ8BzJCfLB6RKPAoHgQ6Ccl+dM6KeJQOeJhYUqA4JeuXCr8TFpiMFbaCXUANgw9E+i3Yv3ABN6MhBSEQyNV7E4FqZRHapKWOnkEilfuhAmmtMBWkg2M7xkMLh08sK9HSjlOAFUvC4pRBLtYaSshRU0v6ROf8zEFPCoaMnkrgYu5AVDen40FQMzPTxAF/OMAXhUZWjovSE1aj3tKy7qEm91C1AjxI2F4MmBMJ2g56VFToHTxl6HaEnBfIZYVsFOWjd426M1dBdGCYrHdPxRiGGBeVbej3a9Cs7P6lI+7H5sVo+7rsC+s+88puNF7ahlAVIDvXYTAKvV2NbNkIKYbOnbmlJeyJKXHvwVEn3heWcQXEWnYR4jbyLAQ0Kt3Yd6ASj2sDMx4lEkY+ZXukkNAzIKFsuw9tMyfI6bP1CFRurslV0cVb4z7awS/juPgL/8bjt609awP+7JexHfIGR74ILobwUjuPj20d9EzGj7szxfvP3jt+U1m/6r/f/H27z9hXGf4BA8IadxpPGBMRfCAZ7/cD/9TeaD7D4zkGDoygN+zKCXPlahsG++Za9i769fhtzavpQNcDvnIpyq6TZIo6PC9twAXAIX/zoMM3r0X/uW9Bok3O8IqlXU+EWRmC6bEgP1cs3zlwVVcAbOSEWONEZKZoJzhBS04Uyyu+C6UlVpSGhS5NThC34GowU8onAZbapkd4ezhqoP5SgW2BHDLqqxwuc4eduQCWUsghKQ24kGiMABlomjFtNZ+FnKDgQtDIO2row6TV+9PdH6Hi3R6KOcyle69sBuszFYFcNq5CgjTaH0Jt7lrvc4wALNhWecxkTAVvacO6OPkxU4DNiVXD6yTwWuEVyJNGeIzHbpoifSKxlqrdJlOmEcNJYkakgxMAgpYrUoPVDG6JXiqrIabIkIkulTpC04nCccsGa4UnbQVAgSJJqfN3PCtQhVwZEPzalKD5BBxngsvLCt+2+D4ATfBgujGAdHE0Zi13CcErI3Rt4/hFD/sMk/91raTr375oKJuBGT+4MaRx58YxvbeVk/e1XRrn+T7BZ3AhMTwVY8jCE/ldmJTezhYSGwBTVQGmOJYArbZXJ/jKwIVp++jurLjqTwND7V4Z2tuzrxzXHJfb41xZ+thuQY3h/TG/HagbECMv7QdcZxW1Y4/em1sgcwuAb78azjNGdUb6yXhh3ztOO760a7XfD629LYQ4jsnVsI3gZgQyV8A5ftGuxxjqkqbI2wDLMADNCxXhZonF0N4A3Xl7rb3A8CwNfe2DPgxadHRcFw8ICA6PWrXtHpZhJ4cko6cmSFXS2zKAIeFi4H/5vd97O/rfiO1bMPMVb2I0JBSjq/Cj9gfEJ4ZHtpQwf1Fw/I0LtrsUwAVwKJIRqruSTa5PBX6XQ7wu+CTVAWH9JCkMCUkZ0vsC2QuEqcZdk8Z7faE6p6gmrVCjxotszhViJnDBZiGL71B32Oa0o9UJwBC4pDg9PkNWk01hXMNQ0E0vsIOTFySIsgE+TIQEFlqN7P1gQ0rewVuTr+/CdAHsXGOCaMJU2B9DBT9LF+9JPwJHulTIOYi8k1AQ8Kj02DSvkQOWpRcmFKOxkxhHjxIEpkAz5F2xNz4bU0dbVo9ZYgZNVlRPkQUFGrNcgaWwjtPpAEwZflk5h4aCMEqFuAGYWKAxJxgMshWCvlohWVlJe4oaTcfIXDK7LiLp0d7GW4m2j1oyV8ZpBCWCXRm2DbZj0AHZ93vRkg3beIj3Prx+O2xx/buL/9p+9/TkuM18AuyQQxwyJu5xCe2CMoG8r8x/3EmGJTbJ+VchnaF/o73opQ2GwpHt1ce/O5i56cDQzfeAx/j9CABvQcRtWKQ9O7ceCUHEvfYm3Rp/AO/nm7e/b+6B97bxWGO/XtjltivvB09e/tHVmLa37TvHe/114P3xaJf59jw9Ntler38D8FqLGLzIfo1sWCxoA1kCL3HvjQReAM0D1D6+BjQeu4wPBllu8NHZOiRgG7hA7FhuH6weThMQtEcGV/dy6w6S3AQqgv/jf/e/ux2Zb8T2LZj5ijdRoEyCvFEF14Ir4WGIGtyuJ0V+W3BcDOVArRdJTFPWpqY7haclbkKfWRJeK42qLt7DLT4lxvibloIK7AjophA49WBMkLygigAzUEM+U6oAa8X0dAFEoK8PNKRLBU5R0sA2iBvcM9JmkK2iTmnnIECZwuwA3CCg9L8WMCOregAC5UPTknPaRGIedW1Az0nLaHKw2KDTIEgr+OhgmCc8GghlY7T6S+GB6atGIQBJbfYKK6eNr7M4fDWkNaEcBVaNGiyZBrCcOEuoAyhArgA2C92cNsFgF8oLASoCJw0DT6DrDckBaFWnq1dIr0iHHlZkhpnCjzOB04GVsfG8AZcFshR66ELY0LcNWLcoL+GAXnjt5wzMM5q8umwbs5iQdiPa6mmpxqpRd68M3reRPVPnZvk42qye4tv2uGHJ3h7zJTs+hh3s5jvp+wT3IO0fqvHHTPEWlPDE+ZFaRlciitHMCt53PsU92gBJ9y41z0mEFW/E4trrFYAbPVMjeLkaqJvBGMNANry2342v4z7NAMYxRm2WEfAo0EMcvnetA5MxYxm3/fOhfzdfte+/dLsFxLdA5KXd3fd+vgCU3gu3DaByx+EDQhv6eQViWir26MEZwEFPT2vtuR0XgM/9+F1rXLu5BEEH8AhZN2HHvQOdXnPT0S+BcnvbsOMsAqHoC9CSHAHEojiOL+F90cSwuANMZogwvw9hvlNK+D//b/88vnt6+NKWfJ3bt2DmK97kzQFUbplY8RnSH0hxRZ0cugCuCfWeVa7zuUI3R72fGHbIYFYRKDjnGYGilSvMiRlAGsUNZTW40qPQ7l/x4H8cKDZGYrLD7jJQKrXTcoAgp6clWyWP5tcX2HGC5wSvrGasayWp9DhBLxV+SNAoEyBbJT8HMV/HqqNpgbSJQs33tNfYPMhx1I6RPoEYsAONhj3APshm9DQ074g3HZ7B6sWptTTwY9Ckg3dn8OPGz9QBWSpQmMnEFbcSRDnIhQJ5ONIF6egLU4+xkKb+i94OwAcgC0D2v5mm/P9j7++DbduuulD011ofY8y51v445+TjJOT7iwAJSQiBSMBAoiQY4V4FuYCAhI+LT99TL1Kg4EUeUPBMGQmCV5BXKsT74Fqg5SuxFH0lEd+z9AkSEKzrVzDUfRgBSc7H3mvNOcforb0/fq310efc+8TkcA7kHFav2nutNeeYY/bRRx+9/Xprv/ZrFMTzCJd5YcaXTVuU2wwlWnA+REuUUyC41Yd3kEvWA1Nu+ciNSVLuwhRw7HYU1EsV4aFwcZ18rdlVZL03aSgjk+c00zSN2eqVaP4N9Lc9h/hUMKz79U7PTWDXng/RPPy9ge8AgAHwCfSmdB4XsZWgXEGujW+CExTH5bW1zCL4yoVJS9P9bOAtwUJYzGZ6esByN4OXHe6Ah3v3Wv8ZRz+rV4PYA6ETr0w7Pkiya+ZY97l8D2jRBBZMXQ9rl+PHgDKHo6NH3QlsTkDL6Tnv2uLa7zjudOz6edeDQL3z2Ayb9+Odx4ofk2+POp4gMJ4Dga+1tvrj++/O72yp39qef26+ONC+xMnbJoIna89J85+sIC4jQnJ6ffmsNHDIz9eQshKR4MbLHZ/LlG53LnM60MvsSQRuxU8dr3vm8/C/feYfwkdyuwIzj3E7v7bF5UOX5JbEayqhH5FVpgFAQI7GtmC4YPVm21XuvEUY2jEHDhEaSe+DStsdpHaFRIaLcBMPgQEH/o6JQX8vDAM5AEwCDwJu8lRwjUZgDOG3sltgWuGbEXWYCIpqGgBHPaOrXg58KHWpFAVUentsBj1FTmPiHtwaB7LadBKeU2cGiDUnnk7P3xFhuMpK1jJXXpfHmEikVweQa7u9WIQ1Xz8YXKwBGImaPz4V3i+nB0mcXBmmKzIlfKiG5aw09WDdV4KMgigsiQB3QFuMcqFKa1mymGW3cHmE8lzpmVKSgH1UYHHUm1vAgv+0xFi7YL7JgqLl2ojh1wT64C1gP8NL6RY8R8ukcADzDMwg18ZHfv8hwN5m5A0rscJ2YRFPPkjpQEln0Hv2p6+X3wyNd96LZuN6IqKgq/8TQCQI4+LCHWOGcwZpdATer7A7DtiY5O0EM7z2DGeaB8gsjuMdN9pabwJ6px5J+VbyWnIccHceSwfMevBxJBTb/Z7HNmDRf677urt+R3cYutvSznMC/IDOKHZARezO9++KxU5BzMm9vcNzgpO/T859xxj2re9AChr2Y4tH+Gw3/9pmqP9cAykSYxDPSJdx1Ai/ub7k+OQzFYkBcAmvjnfjIS10zsU+1gQFXGPQHQzjdPMvq4+kJyVPdeRxycvo7ukxd17jfYIauIeab/e5WH6TIOzGMH0p1jZYOYjf8Krfha/+2E+72935iGpXYOYxbk+7eY4H3n8bMtfYWQZZNcImxWJXXun6tklRZxowUUGxyihIZMroxQwVQd1GTSWVlpaI4GsAAZREIHunynAJAHBY4MPANMg5jkmV2uRFGKF5vS7w6RzDrQON9Ux9GzkwQ0sGxoZ8ZJaUqEIXg+wt9GgEGJ1hjwOtjAQ3w6s1oyKVFbKZleNNKyY3MBIB4HWxi4XAGdQlR2chMPSsHaVtByrhsVoNRe7WnSJ+BnjkIWoFU+iLMlMK9IoxMchRC6IWlWG4HJjFtQBlt7Bekq5emLZDMgJKC2CQehprX7y5o9Nz4I4IEXrb7YsgQl0KaJxPHbWQrOxFMd8YUK6P2PzKBP21B9tOUwScB8a0eEYUifQsJ5CC4FPAopT7uAEj62x5hpk8gEQW4gQiJBULY8yjdG2vgC7sRvJBGksXnefKyTcKUJSp3gSXnAs1v6MgirAigIHEWHPR9hEEMqkZA3TnCWJ5fFeClKYGm89oTKU+XHXkCWmD293SfO9ux/sjHNMdeuTFSPuJY2N1B5iR7vR+8l73s/2Zhvjku5r97t+7o4OP3HqD219zD9Lad5yc667hodMx7P9oSDkMcTxr7fOKNRRnOAppJvE/Tyc9Wsvz9IPYc/kMoWkVF+xo4eF2THFu9vq4WX6uIY7sV3fRDSDFqfXOqXU0CnmqnBt5cMWKYGNuWFTB7lItWqKEu8DMj4vfduOt6jgrA/7xW/4Ynn52A0+EdgVmHuP2/Gffh/f8x1+FVm87fR+UYL5WFgsbacx1RqtDo5d7wAx2PsLnBTKGYN4cXBIPMTIDaxMZd6UZLlEA1S2cHcoSCF4BLYBVytOnmjCED9mBfmVRIieDwifBfC/JqeVyhl4swQFJfg6Ng+4Whlq6RWFdgD00RwQ+GxV6NQBFABWd6XHKrUgGKbhb5aLgETpJIAKAHgpE0vhszN4Zh1Z1e919edvlIonPuj7oYpF5YE5+kBjDSYh4sdNIcsPGFG3MM1pNnyTGmlOfBKDIXrI/FS3rqjV3ro2xwDUjEPF0MRBwjkwLTjGxNMoGoJ4JlrOCegYSxxegXNvAzgdsz0bYQxehwxIE590BcqiNYCyqsHEErpFYrCKAaLiaYzGuFjoqApew6h1XJIGHhRMnM8uOdr/RbwMa9+RoWW0bVmlApQntIa7d+LGa/KkJAR4le9KMtIWnss8IartuA6SwrkwDVgJkVcjm6YnksDvIuK2/j2DZ/S5/WFzvipUACT5XGntbz3k6Lq2FV0XQ9Sk/n4av5390fe7taiP25nPaAaJHJOyego/ccNwFmPSgzf9bw3T6vnTvZZ/t+H3ERqEHeFlYvg+hycnfjS8HAdPegfXC45hTxHAKqBxreC87YVjXgni2vEcYDqwuLxzdu+PRkO51j+QFWc+j67X3fc4wckPlETJroSPICnwiCynlrBDvuQuqeSxluWbz03/wha/Bt3zi7+X68ARpV2DmMW4v//jn4Cfe9W8DbBi9GgDS3VnEWGp9ovKuKGBTAW4Z5NceQDk/Y0jn9oxyNoWasEEmkjyz+KIJ4NVRlpDBL4oyUHzPixC8VNBQO3fYEhlSACAHZ+ppPIBc2/PJV7galvMRU3gu0iCVhWCDqsEK3yj8bIBczCiz8cFuWTABOkzghwoZo0pz6LKU6ukUogHKbV4uGsLddFt8C4BNIVfFmfKt6WNeqKEiERJBfEYWJ4kafHgbAJltNZoAAAOqUqyuCKtFG+tUZYq5Zu0iFdQSniChqF+/u9U4jITKsJYBigTevHNrqMmhWXelOtwSxAVBL4hQthHUCahbqj+jCOrILtdJsGxvYHj/CPMFGOnJGm7tMTw0Q2/vIbsZstDDYzcm+LUtVFiTSubQIcrRKEKO0IgTg9zZrTD8qWzbxjxPIgIT53jqeh6C1riHAEy9qds20NMZmazNJK0afRyD9ZhMs05dmGb0gaYNZAmqUjsmgFPj5+TxOeRdOYqjZokKOivo62He/9cbIsOxUbL1AtoYdieRnHp57rSTMY2PnBW9QU6eRYJNO3k/rhFdv9Y3Ts7Zcb8eEe90IMlO3u9sbfeBk2NyjLq/AbTxamE97721HbDpB166EFv2O0IocFk3WXJyNQnoOk9J+yLBqgicoDYvNgHNAeugtg0P0HRyDARTDd8I7gqO2+D3ICfHY93QJplXoo9ZfPVISbibFy4ABkAjWSGqoEAgTf0XALZF8f9801fjo+95+p19+whvV2DmMW6vevXzKf/uNJh6e6Y3YeIKqvvKjCedInYKuArq065BzyeUBy9Q3vcgcO2cgEULSjXY7Zm6LzXAxxQuxNmhlwe6/ccBMil8U9b02oFS+jLH1mIqTEuNUgCaq4KCXhRh6q87+2pFI7TCkEXdFJQDAGeqt+yN5xuiAnMabwvQMBXUrUBGXWX604gEZ6e5alWoBtysi9DrUZ3fAS5GUpQEXS+sjZQL3WJwaHB1EAtyEIYhLDeQO38RbvnDKmQJAghY48rCwFiUdGgqxcrwWHil9HykbauVi2XJsgLrSsSU8ZQgjNBRIYoTjzCQrCA1xdksdGd8ZOr4ovxpkYSURh0CLAOBzv78DLJfGIsvCjxjizID5WLBcGEolwvqfgbORmAzYBkKi8lVhyyOsrDOVQULovqk4fqOcerc8Q60jJ5WdBDHNt5ECFSyJEAeEwuo5zEFXfYQGB5Krwv4jLTQTx5j63e1Y2Q9hobWO+8NAmivyCPxc6aith21ru8de0t8/dmTeNO4xmX28gBp0I926NHpHpABBLB9hlLOmabVlt6IOE+vA9Nsenhq2t6kBy5H19JdUm84e+DRG9O72V45eb8DGKek7VOujPefQzd+JyCtYZn+Gk+ARx7Tf79Yf7myAruSYMKPgFObNx5jeBR6ys8cj5kb1g+ax04G6z1MTk13PW0O5/V1Y3R0IcdYmRufzOSMDzVs1aalrOduYTWezyPUKl32JYCoxg181rM+Fv/Lp33+HRmHT5R2BWYe4/bClzwjDBdaxpHPFsqyDBvp5QFiguXGyLDqQkOCUlCffhM6FOh/fQgKh283cFGUpcJueWTRkGxr2wmAQw4R8hkqbBngszXviCVnwkBexGLQqZBgWvgUSIqtpax8DRXIScknQQjhGTk7NiXzDdCF5Q4cHpyOAFxpeMzW1GkpQYBIEBeDFkKDGFbyqkXBTlT2XSsinZarWmb+YBDuvNzhJWoYNc6NtzVBzOFzxVqEMHY1WXjwsLBfEgUipca4VSoVL7VpCOFQuEalnlBhWQdmMRkwKjRY322nGjszxNBwIavIgoae69TI8I2o4HCd4UiIw4aBHCegqyXEU2dpCC8hnnc2xmIevKwNQ1PLTYb3cAAkCCI0lJG+vAmnmQePCDUKTMqqo+OxaCOKFg7SvDNtne8WZgeo1dK4QPw+K+A9y/DV0M0HSZCzGiR65uI8eZDETjQ8PEchJKRBDG9d7lQFR2OXQEaSG9E+2N23/NmBkDSyPVgRoIEgBNASrMYqd+UJxJrRdRDwCkIZez22HY8wXekFdGkRzTbmaZzytQ64NHBwQpZtx5wY7qNj7vb6yT2+4+/TMN3JOfqkoNa/OM6cz3sLjZGvzvcy7BL3RSy8mXLynTF9pb/uvPaa8ynmT+teflAaiGyho+jH0dxwPqfpMUFwuBrPptOXamqJPXA78ez1Pzmma/i9rZkWi0CooSdSvQNve6Ky/FuDquCNR73OF8Hf+swvxyc87dl4IrcrMPMYN1UWDrRBWAMo06UXh84zZFmgHlwPA2yigVCXUNZ1+D3X4KVA3/8wK2ePA2sFxQyU3QGYD5DzcxaQNPJSgIpSBF4KTIQhlsXbTpS7b0M1h8/KMMKGOiO5G5GGBgIkNI9EGBYLQBAuVg/L7FKoauvgtWU16mWhDS+6ZsiYE61YvBbBXF2MO3wziFWUJNwJgRkWZRZT+EilMlSxZnfF6+z9utsu2rxgUgkGsdhq1IInQitCa0LgEv2bl8blkcWhQ4AeF/jlnhlIY4EXhYrAvDD1WUFQg1hPKqujZLaWgCAhjQW9C0zHRwFs1PCieWR/5UGy7vBypw4A4VHLE65aPg6tDA2WwpChmEDA/lC3SFbBR49FXhRe8v4HOM4vjmq76VFLF3ovlpcGl5yYXHiBrJp9pGHTAZHeOCYRHpDVu5PXCyB1Ohpo7Im7cWDaDT8Zs5PDmuE7tTP5ZSyTgNWjgnz9+GTtu04NXwIowVrF2dcp2AOs5rGMA/JeNmOdx6ZRTxCV86kHLo67ple3Y06BjJ+834OiU9DQ9cn7z8rJOdH93Y/LCbiCgqKaC5id6V14MOtXxXhpRfAGA4wqmpqyB5hpGlBxT6S7douQ/dGcyYcxr9uB5lHMC7yLi6mFw47GUejlvRv/ClgnBLCucznH5C4D38fwWrwq349TtPHM97x7WNjJVKUAgD/w4o/H/+1T3oJNeeJDgSf+FXwENnEHxkI9mKa0CggUVUfYdoSNBdgvwNmAOhbu7I11jfRg8M0Ev+8GcLGHGwvhZZoyigB7h17uILYwXNMqBwu8VGCg7oyCRFkCFgcWo6DaUOAWNX6m0pV696ZuK6liG3goeUBt9Q4OiZhBpgE+KmReWM5hrqyA7aDYW1H4ZoiUaqPCbe6oSoGMlNcv7rxeM2CurC4NULG2UCPHh7J+PwSyVKiRCyIqQA0PTUmBOv5zAc8pwv6EVaG3a0Er3GNG8TlVyDDwvYhJUzHXIdMIOL9b1OGedY8UxSpsGJgKP/C6RBDVuWMxVI9K1+STWOFCzNRg6gY1S9csBVbD0y9sXSVr6dau1dhESnghV0cjnTxdPXUAdAmScwvJdJZXUkMH8FwtG4iNWRPZTY0Xk+ONDpz0vJA7jN2JgUgD2Z8Dx587tS2tX3lc7nL7XW8PPHqjCjQi9tGxHZAB0DwqR6Jy8QFPMFk7QKMntzA/1IXImFV2PAx9vwVoYpi98ZfumPTYtO/oQlV5cA5JP0dWDafO5uW54zN99lcfOmrAoPvsHdo6J0CvH9M7vtTBMikLItEBLaOtgfiUd4jx1QUE6nHNmcIvOQaWxHRvgM6BqJMlzTMDoHkt2mbgbvO0BwgZSo/+i4CgHd6RqrtntyHlDzI2OXBd2Gudy4Ljwev+vqP0QMwNOb1BbK97xvPxQ2/6IuhpfbYncLsCM49DswgDeGElYx8ZgqDOTO40lGq1AmCg/ouaY9k6sBQMl4KiEhkyFXVUqgMDgAyQa2c02rsZUitkmgAAvlQgMqN0HOBjATZCpdgaPJe5xqLgQKWXyEfl7wA0H2abyZ8ZpBllPTCbiI+ABEkWwMFg1RjyOiwMhQli509w4hcKbEa6iqvTaxPFKxGCdlKYWYPl0Lw86g4cBCoHyDjANhtykNxoFMyAalR8ncqRh0CN/U09GhQl+EuRvYWfRTVgPgC1wmsF9gfyd6YBGEaOB4CmEzQvka7O95JQDBFgoOy3DyNkMxC4bgbOCaxlJ9aTcox14PkB7p5sCcMzBJBQCePj6zqWFid3l/2OsjfeQXwk18ZZmNSkAVcv5PKkIb7jsy2bCR3QIGm5N3Lea4EcLcT5QXS7UKxG9xT4nH6sM5D9DvgoYyd/pkFNo30KBO/W8nrremxesZ4e0/1ruCgNZnecutA4R5/TwLquQEayXwkqul18zx1pWobWjUFDJOuQpzfSDUdhu6PxRHdbvHtPTn7i+GcjaMccO3JQ3GVc273pwWMAZunu1VFmVV2Bog9rH6WFhgS6MHyqhsabSpBpiGMzJJTg1qUDvb6mMuf7jecSWZ8JVvox7K9H4wJT/j9RZYLOuDZuPLqRPwVHJyBjfTEnzfE7AlnBXCjGM1Eg7lFF0AUAkfBc5/cCOCsD/tff/QfxSc94zt2++AndrsDM49CWa9QfKUVgs60GA1j1HkZAtlGCQIDmXlwEGBTL9QgvjQV+uQfg8GmAikNnQHcztWy0AIcDDbAAUg26F8h+Dr2MAb6dwm1dgbk2IIE9oIVCaT4WKv5qABRRyEDDr5cVkIWlCABqyGhElZnjRwXJRji0SLHx8I7E5+aFIZvkuyCMuoC1hjKkBmc/M3RUCsFKgDB1wJ2qtTYk2BoZvivdaiFCrstS4TNCvwRB6I3VL8NOKsA40AosQKtSPi+xKMbKVEmwcDNgZ8AwMKQ0FH4+wJi4Qw4zAeQ0wBYDVKnAPArW2k1p6AStCJwDYoISNB73MICsxUCJ8Rae8iCvoIVqkmcCxEKfRrF5c1br0gBSLM4xFY82fEe1l9JoAsgw0bF7XY7W5wSAx4uyHB3QDGZnRI9Ikf3H5OTvu20sT41o/ux4KndY6/jjNLykp8cdeWb8CNC0UEOGPvI6OsDQZyJl346iDWm0+34Djedwt/To7nYdbdDF1mjgkYPgZKgMWMsy9J6V+FzjuHZAy7t+97g3P7N+lx91QCogM1pWpisodNhboi7c6CMfyVIBPeRclCgg293mAErqTEr0vJceACAGralKd7ynrO9GQBRJCRmiCpDTQoMJiAu652mdBA1s1AA6HWn9kZrH546a3HnEHe9VhoETTBK8aHjZPcaR419E8IO/6wvx+me96JE78gRvV2Dm8WguVHt1oYGNxSufafF8mJy7Y8/pyr/dnN6aYvBtgZ2d0yiLYynKqNVhQrl1gB4qdBB6EnLOHw7AngBDHPBbKWPP7qV2ioQhQ/ztCCE9i+2OguEdZSFL3dPouyrgleCmIh7ckPh3oxcovCVwIUlU4vuD60JgQbAiZSBwEV4bSmwtlspjIsMp0w8Z2omsLBECCgW8ptdFQ3OBGUGMrTs/ZwavDlnCU7VU4DCjbkZgS08KDhVSK0HOjl4uLDW4N0oACax8EXPIvJDbfK1AzrbsMxAlAphW78F/se3QMm+4Lq0AQIDkCXM9WgLMlAQGQFbLzKyn4//RsmCaQUW34DuOd4p9E1m/PH+cbhLTkOb35clPwyR3MciP8K13OG5Oj73jc0cXFT/t5H3DXfsAgJyMPHG7qO4cRmDeg441WwRIDaM0pnd8Z77ecryP78Ed1xb9kP5nvi/rz36OAJHaHvcmyeHSfaYBgtTf6V5rgDTu3bH+Tndv+452494A2Imd7efGafYzYsx0BsqeY+wFsMlhQ4RaixwDhRhL18DEhzhOnE6VmhIHfN7rIsDYKH2Rfdd5zIIa125tDmyMUZZiWeN36yTIzZtXPvOnAA/odaXiRhqOwsB3m5NH01Ma9ALunPltLvH+0F4gjhcVVr/urwuOz3r+R+MvfdofwJTr6pO0XYGZx6ENlwtsF0x0UVRNPgUfPMxh6Es5yjxwc3JEhrJmNGTmyiBwz20CYBtHnbYYdgvKZWHs87BQcE8V4hWp+CoWK4AIwz3pdVCFT1OAGqG0PSKVtdCA22akIuwAAHTx5u5TzODiJLk6+CAarxPV6S1Kt71GppKuD5Rbhma0I5LGylDKanhzVYz0bKgihfNgqXJbuGi5E4yIE2QodyqmgGyH8Ow4MCsXmloZ+spQkBtED1AfmK49jsBuD59nmCq5QWfXWPMolh3xGMuxMKyXVaqLstCoULOlDoIKp2em6LoqiRxxF9riFnjSo2akxaLbvCW2Hm8IHJKGKLwsLWTUgRHphvS0OeTIK7KGcfxuS+vxZ08BRru+7qAOIDziZ4/6c5cX7mZIe7DR8Q2k9cXpcYOs1++rJ0YAvm/dmPVWP1vtPtO93l7r7+PpteexffdXvPOI195zghK8IO1YD0T6Y2WlmTRPxwnIaWAnf4/j2v3Pa89Fqi1W69i0cJcc34I27vmar7dB3VGcpHQDWmgmwbqnSJPEnM4Nl4DrUHiRloHeGg3uloXooQ0goAlvsWRiQ3gs299F4Iszu0nRZAaoGk2F3IYOMwyVj63hOLTbPDB+57zJn6eA37vxOn1GTo97RE5MhJIsX8g+Ol57//Pw1974Bbg+bfDboV2BmcehTS6Y9wu1UIYwalJhm4I6CEolV0GN0vQtg+/gUGSRMIcPQsa9czdjBroVI9SBjaJeG+FFUQalKu/lAasvGsiq1vy90nhnOnLvt84sIAsLWgsXoP2B9X62I1AUdeKKJzDYbNB9uA60dDs3rohmRg/IspBIOxR6UVqAl7wdsQWu6eeOBzY1YHKnNo0kmRrLPthY4NPQQhgtxdIQYS5eltQKqwbfKOoZSbliQdxdKuTgkFqpRL4d4GdjiB3ObTzLcoD7ALs+AudbyDCh2LSSuxeWWHAR+NkGdq2w1EER+MisMS6W3bZKgSaU5rKGctr4cRiOMEHcuuRctJ9YHTaN5Ni50XM9bmukH587213X4O6eHvFT0B3cGz90P5uxP/5g43Lg5Pi7neeRDEN/XAIYR+NQNA+ErCAseRfa8WHaeCTAifNozM/+u1vIzu8chqPL6S73kQDg6et3/H3y+VMyatt4J5iJ93r+Sb7fgMpd+B/tmLucA+ju1clFtmP89O6u70teQF6EgWrmg8Pn6Gfcs0aaNifno3jLfuR9iTIkKkzjj366etOEqQX8rAbokQA2FeTmIe19APaQLkA8L24guGmi17zAnuLFDWZ3L3rw3I9VR7xe0SeQte3azT0duPzY3SaYP8Ix0h8o+Lznfzz+/Ke+BcOT3BNz2q7AzOPQtje3mN9/iw8KwOJ4JXQLFFjOFHowCpSBxlmEHhhZyA2xPicyGHHDbK3iqswLKkYq4o7MRqLgGisqY48I9dBwe8pGOpgRBKxAR0LEzdNjpMCWDDrZL9C6g90SyFAg0wCMExwOPSz0WlgNMT/QQyESBSf5XbAZriyCqNMYYSE+eBQXrHCJkgebCRgHLiSRUcSLBzBy1bXUZinMAIMZPSIOirz5wDGCxFbNUBUkB48lQMgYyrDcadtugYozvLcZIGcTlqczFXl63xl0f0A9zMA1KufabNDLmcdfP2ftH0TNpE2BjRou88iaghxzZPgbX+jd6p7/dcY/fuY6ysQ1x1rVdsWTmV6fRNCWdRM7yrt6RrrF0mQFO0eGVI8PPdqZ5xtycszpBfQgRrASmQE0rkG2JKscEZrDuAV5k13oPYVp/Hz9qHTXXfneHXYigcyMBmROPVe9Yc6uHdmQu/x+VywnJ8P/CMYMOB73o/OKNB5LH0I6Ahgd+EmwE486mx7f3waCehAlxyD4aCIeXeDd329gOIHmgXw/GEGGgyBDFrB8SqXz1QfwORoRWUfooj4eBW9X4GKF58imBtQFfFgKgCE8s712DWIwunGSGDiv/AyHUgD3o82Bt4v0Ni8lM/t6zlH/rBVdj4fgSIfmtPX9679Uus8fDTQn2P/0yk/F13zC73zCit79RtsVmHkc2r3PvIEHH7qghwAMMWDQlasgQsO8M5RdZWp0MWCktgsWytiLeWQTKTOjBqHoWTVgDjXXINiyKTACPo2QurQVypeFwMARAMbpoQGA/Y7aMeNI70lXcFGAKIGg0FohlwtwsSNvZpz48NcKD2IvLOO1wW8J3RkRAMsMHA6o+wNkGqGbiSEcd9iS2zRF8xSVWIFym6kcFx8QpODVMgrQhALdJLR9SqRpanu4c7eGElk4CobDigCDYlanqGF4IVx5z+wZ92BRgx0c8AX1rMAHgcwz9HLGeDCoF4golvOC+VqUbRAgReY0Opp4MhdEcayZFbloPdJ2vrveo81dZrA4WopvTwKGxEJ+Cjh649MZKO9/9gYVj2Dc2gnvfPmOVz/Ygu8BToBjzksSd9OgCFb16ARfSd4MsHHUz/ylJ+Ymjs/xyS6GxIH48Wdbd7vL+GC3yRuC+uCtjfEJIOqGJECInHjK1n8OHI+pHmG/FXSeACkHVo/NaSjqv9Ha9efY5+/5GJ/c57IDxtvAsEMAGm9cElNBqQjCuwPKkh31DKgTz5M1s7xwjmT4yIYARiMze2AENuXAEgY28LmnE5oTQ7O/nbdKACpRJ8jIgQ+14ATePsdnW4hpHWRpBH6OsqebVHH8jN9ljNtc9NP3Y9GQ06P5cxDF2z/1s/G5L37FB79hvw3aFZh5HNqzn/cU/OIv/VeUgwHLDJkZjlAHbHG4VjiogqomkINRk0T44HCl6KTjAxH4WOCIVGhU8ld2lURhBQCH7FlbCOME1AU2SmQHzfDDgR1Mn6lFPaNlWXdtJVKN8zsH5bkg5OMcDlzs54h7ASth13zVa6m1rXYOiUKXwlDX3gmAxgIfhgh95WJRmPU0jTxvarQsS1P6lM0EP5s4ZqPCSgEqdW2gChvJwREL4AOsgmtmwOxN7ZgBMxZw9OqQHRchUYVgTXmEEYAum4J6JqgboG4G2OYcuhiGC8d4G8ABWM4RYogk8JYqrN0SRrI5VLqN15FbGsdA5G6LX77c8HHG/XMx7Ne/zoI1DY7+9f78vfekd593IOuob83adn/37/fXln11tPnWgAvWwqAtK0i6vi5xTBzfPAh5/LKCFGC1Ma1LwnNkFlLSLFvYSNCKfQpOwEyO5+k5H6ElUM3vPd2AN1PUAcUeePVcF+8NZl5zelW6EFMP3o7StvuQSL6eP/9bACYB0KktPRkEAeffUXkBiWcu+HO6ALoHdMfMJKmA7JhNtwxROd0cg3GJcAUsqB6W5TKG9foynJgVzl2cmdSRWVkshCAXAp8G+uMZbEUVbR3rDEPmuhvLLK+xrKCyATjhzW1lNHKnEmt4Txpva0h6I9sCgPWgfhL0IkOtM+tEujFu8Nfe+Pl47TOf9wg377dfuwIzj0P7uJc9C//vf/4fYQUoDx1QZoNtC2QaI5TC2kJZZ6M9IIpmcCnTHd4GV7hTMRgqUWagoLhDDga1Cp8G2JZZQXpwYONUnxwK6vkGZVTg/Q8Dux0A0OMSRiRTiUn+VHgJ2dVIi/YsXrkdgXkTmUugt2W3ZyhlO4X4nEU21YGeqTLGw0tBu3U3Fx6dfVSiztTneY7XS3iKIuUZgIWqLvvMmks+KEHL4sDEatPMIFotOhd+DT0ZsKDmHIvYoFB31InnIa+Wst++CGQUVPGI3wcgWyKTgkWLUDcD6n2O/X0A9kCZfSULmjARauTrGoJgHnF/zcyZbqFt6bTZ5C5rHzgOGkbTgSOyL3KcT8/TkWP9Lu+nkWq7/bZwY9Xk6M/dvCYni/RpHxzr7rQzpC0k5uFR6YFRD2pO0p1PNU6kO8eR1yp/E7RQRMBbZvvl4akmC/Slm45tyAmYu2MTjSPMd0dYsR3fXVf/uTvOJWi8qnaO/nPdTr8fEwAr1+X0xJ0T10/O0Y7P13rNlP79U/Aa/9JbkgDDWgZelMPIx7dQqBHCMFPyUGxiboRWRCamA8piqj3w0wAjHmCPU9SBwUMRWlppNEk+GpwH1+NraWC589R5bITyuql7FR/rACTxixyPx2mT9dgcvru2DuAf/XJ0/wT3n13DD7/pi/CSe594hSAf73YFZh6H9upXPZ8PzaB8KC8PkB2AzQKbBox1gC0L3Z0qrEkEhwl3/boU6MxMIakOmRHhH8RiI8B2gB0q1AHdG2ALalGGrGCQObZkhcUCl6fcRCkD8NAtApA504wInnxZGGoCmm6KR1q0jwONuQO+KRAZYBBgGXmsV/g0RWjJIcsW2B+AeYZWgznDWCiFladVIgVyZngqQl6e4AXg9syM/w4HQAR+33XYjTNgGkj+dbR+QQfM5+y7OMGMDVxgOwRF0t+uUrMmMsx8HKLsA5gZldWxR4WDISW6q0Pl1rgojpeOYc9FuG6AZSvwLXUu9ACGBAuBSy3k0+ji8AMXahEu6uZoKatq6ybvEY1dv7vud4Dw40XTcWzhGkDwDtjI8ckza6P/uxms+KI7dGOcRiy/I1FHgvPQFFFbDQHfj/HM3a91i/7dwAziWiXViGV9sa4IUN2PLkniOnqvSxsfOXZGHQGhBDUn96E3SKeZSBleOiI5r11rxreNVWdQDWhelzTwCTgEaMU8j8jA/d89EOm++25aPOv86N8Pj1XOgQSyPX0PWHWI+qmVc9jRvB3S3W8f6bHUGRhmoi8bgGVYkSOlC4AlExknRFgp+rsE+E9+zETvTR0qsy+Ta7NXlFlaZjTvOTsvBSt3Zlifs9ruRXrr8gUEiJd189l7SqzrX39fcPd7kdWW7thJxHPo7T4APWL/mHufiv/Hm78ITz+/fufNvGoArsDM49I++iUfxYwbVXg1KKLcwH4mGfewMMvCKnCgvL6fbVCmATgMwNnAtMIa9XIyjitYeQNF4Rul6m5lhpNcLgQKlqs0Fw2IANMIu+8GdDtBLg7AxQXk4dv0giTJdl7gw0yp/s1Eb0kNv69SGyU1agAQoIwD6n6GZu0lF3itkA1rRlFcbiZg2Y6N2wBVqG75nYcDsCywzQQZB+q4mJEX4R5enJlVu7cKuzbAhwKBMvRUuEQsIy3TuO+MSiGo8BoLz2Iot/bQiwMETgA1jajXJ5KH4cDBISBHxrYOmUpbpBk/j3sQwzzMDtsLhktn+EnCC1PBApcBaLwIbIwAhwk9BQbWXpIoZeB+7LJPJdP1x53N/QiT9GGB48VxNdIA2nzyPp1UccxX6YBGM5ottoWVh5KGLA2TdueucWqPyGRb9Nc5LS7ptV+NW2gded+PvCjl5wXxnQv71OObHAARkBjdvxb3s/ds9GOSv/e4sY3niYFq0WA5PuaIHB39yFM3AvPpcXmerO7dn6sj7bbv6duJ8TwCOXJybAx9FtnsvREtjTkuLBl0qwgdVomTbhzaeeP+JAClXhV5MAdz4IJgn14cem1Yno3hJi8Mwfu4jo06IAvDVOOO3hsrwOEeoN4EfMPvEJOGu9MT17SSgjTcz7NGoM5sp3wGJAfC2/ut1lkOqHfFKHtg2I/76dinN/BIOfH0ZkoLff6u574Y/8sb/nucjyOu2gdvjxrMvOc978EP/MAP4D3veQ+++7u/G/fffz9+/Md/HM997nPx8pe//LHs4xOujSMFD2xQ+DjAdxfULHEjF0MFErV8PDwjujvARVBU4feco9444xOnYPgmiK25PZKFareuEqEWpnr7wirZ3NF0fnQBC0te2wLbCXrjDDJNkA88ACwLfBj40O8PaJow51vWTHKHI7ZZ7SEHvTuDQq4TlKCE/osGD6YMfEavnYWSLsNQUo3eHhFgG26NmXK3tp0IiozhIINGfNthcCznG8w3Rko7LIIiDhtZYXo+F5QLwzDzO3ROInIMmwgwVwgM4oaoXMWI2p4p3FBh5pMDagbZcTdYRwXSQwPEbi0HlrvFxpEBGhFVljRaDht8DYMtNB5MS5XVYxEArNWZ63fOp5YVWHd4HYBph3gsvGlcTuZphhlb9lIPJvJfgBVdv+bofQ+D0Yi38f6R7H4ayRi2I8Aj3SY1r6HnC+TON7kwXd8lXpD+O/JaeyNia9SxjZ3E23ZyTUAjFnt/zd3Pdu3t7zVt3RPQ6foZOT0+x0COEtJWr00a3j7ElAclSDydEz1oSW9K9rX/3vwlN0XJs3IclVnIzC4P1WmOVWwIcg3yfjjXCSgua/ZPBYFmoFA7ExwGEoJlHxhiTDqJruOUISSihTaOEuGoPlwkFkJ3JkceuhQlhaNlNcEDJKEDNN24tuvqn7PeO3mXcV3Du6k0nG/Eybq5dezh6hHg+usnPePZ+JZP+d34+Kc947dtZtKjaY8KzPzkT/4k3vKWt+DTPu3T8E//6T/Fd3zHd+D+++/Hv/7X/xp/9a/+Vfytv/W3Hut+PuGamsOKws4n6MWeD1aN1W4JTZdlBkqJHY5AjWq0st9D9jPsxhk9EJlCOFNNWILUWqx72AcCAIwC7GmorUSF5m4n7QGmIAJ7+r3Q7QR//4NoujPLAlxewvY7yvFvt0HGBfMdRaPoI0GGLyHXvx3X0E1Wx07D7EIvjlW4Rxp3rax5VMIaDQUOZxHOs8JaUcJVXaJwjTtgW0XdEgTqhbe4OYTKxDYYbKmYHp6h+/B6QRh6UGEphgrq2pS4lrEwfR5RUqAa3dWThupvqPf2BsWBtWBOvFUjTJTrmR//rQtCb4ZGwUTWZIj0YiRYPN2Ci+OUCHq08zvZBbaNX0++FRwVNEyPRbb2tXcDM7aephnYuOZ+jdd8vztPz4MRdEakM0jeG0Osx7ZLsqPLW4eguUXW14+uKb0Zth5z5EXIseqHtL/P3di1648/jkCGrGAkvQD5gZ7f04xkm7fdWAEM3wYYMl3HoqdmIK9HsFbT7q+v9371F5bX0N0HD0DtQbJNAN7qGxUce20Q73WAScPr1Yw64vglHEwH0EM5EkjYKLDRgfP4rhyn+GnpLUlgNrAP3AgAOq5jWanCgHLJ9UJysDKseMDq0Qopqww3NtDp6zgnryvo4a22FoBVMV27+9g/i6et3dhucp7ilzhmKwX/p1d9Mr7mk377plb/RtujAjPf8A3fgG//9m/H137t1+LGjRvt9Te+8Y347u/+7sesc0/klrsrOxuBswl+4BZIqq1p0sNAr0yu9tNIgbplgXzgFmRfYfddZ7Vpd4hVuld9YZhpZGFGGpUwdsIyCOIOTArTstY5kgA3NS2DwG6eQ8YRcvuCypvzDBwW1nt66GHI/gC5dk7CcHvGhNyWgZ4NXByAs9JAE3kQ5Kdg5sqZdZ5M6VOWyhXTRyV3ogiqKBe67UCSc2Z4+UBPS4hfDZcGV4NcOuTgKNuC/b1gYT93lP2M8oFb0IsZIrpWho3aUxZaMkyZL23c6O4RGKh47EOJuk+yCt61VbBr0sfmc5EK7Ynjw2hAYoFcPc1HW9zVDWL9a3HcHZbXj+McvbXvDVw7HkfHHO2uuwW2hSg6XY62Fiewic/nqdedeXSn34WeLP7tmK67PY2gz3hqoKM7xZGd7j53x/uG1XOTx3Xk46NO3PnnenxeA+Q45RndNeZr/T1QHHttwngecYf6L9YwogXHFxnve97zFf2s19fdl3buLnTUKkoHZ8RKN+4VDMG4Q3sGevMYrqPSploAmHIIwIIAHOMK5iRKF5Q9YCOwXANDQipB2hXAHMW6a0n9zA4le77GJRM6c4OiCyA1wj2QdSMVZOSc8304DcpnMDk90kT4urIHFU2n5hS3oB+OuOkpKZCFH49BjqyTtNtMDKL4v7z6d+BrPvnTrgDMY9AeFZj5+Z//efzwD//wHa8//elPx6//+q//hjv1ZGg2URsGc4EVhUbBQhdhLaJcRRJcmLEI48gCirRbrHDtKX2/sAZQyRTlIsw8KnwwbWFdIrqCw8gVAbYDfDFqqsCPHk4XATYj4GfhOdkAuz1kmeD7A/xyB9/toGfnzC6aBgIuq3Qfa4EeZqZ3Cxg+Um3OhFJrCONFX6CwTWwDtxPqjQ0XtgiZVQC+jZRwgEAnCL1U+zQMD8/Qyz3GhxfIXKGqKA9sUa8zdFVuLbFLZG2n3H6TdKuwQVHPB9QzVhUnyJAV4AXhz4u3lVvi3rFPaGGP3iA1g57hI6zYo2VHrIcHt+mRjX7vuVjTpH310IQmSu9haCanAyl9k/69DqScHnOkw4Ljc7X+n4CA3gvRH5dz4W4pwP3rd+sH7C6voZu/p306fe3kWtuHe9Rzgg9PD+e5jgto5o7+Dp5Mvpcd6P+174yaShFO9NBWabj25GKkP0caaIk3dD2tBiCoYbBzTrWQ0gDIhMbHEKBl1cEJEGT240FYYsodqANjce5Mi9bgSolzo4HKr6tlBQ4ifK/MAajGE+5QEdgSw5Z9BxrXKkNWCUAlngOfOeBq5Al6rHlecsz4fhOSXDgGCMAk6J6xeL2VCenGoAGgGPZ83tbQ8DpDGq8u7ukRygXBzhd87Mvxxz75dXjuzXtw1R679qjAzL333ov3ve99eOELX3j0+rvf/W48+9nPfkw69qRoEiUJplDl3e0pMufGrJsh/KVNryWYcIUKuT4NLSOKmT2VxRGdOygsBtQDMBQWVIQD4YlwAV2sAqrjDoF43LgjM7CkQmY1jQNJqQKGouYFcv0cPs+Qh26Hx+YAyBkrbQsVeDEUArQo2mg2w8expWcCABOI01gbScPbDeyc4amMTbg4LDMVkggLAIO311QE2FcMDx0gFzOGPVeq6daB4aKJwM0GRb3nGmsulShhUCuF7YaCGnWUfCqdbkdaqMAJqo27Irnz7QzLHUY4f2lckCRN+prOeRejKp21PfWUNGeLS/P2JZcqJeDbd/eGDzjyPHh/XN/nD9KaTT3t911at14fAY0eXPxG2ilIcXTAr/uu02MBrGEtv/O1/oOJce4AqPld+Voa3JNznIKd9OQcoUbHHSRRSYOLFdhmDa5euyU7JAgqyIgozkiw4DMg89rXfjomMMmwYJvzgpVjxOUjNH0CpESOsxqgM58D297F6KfKbq5NFeSYjdzzuADjJSAV0J3DNoFKStfJzrvUMo6CO6RKwUxxkNi78FrbZyVE67rx8ugXsII3hFI6ImzVvEB3AZzZUosox6q9ntylNjfk+J5l/xV43XOehz/zOz8dL7//Gbhqj097VGDmi7/4i/Gn//Sfxo/+6I9ChDV4/tk/+2f4uq/7OnzZl33ZY93HJ2Qb33+Juikouwq5feD2IlKN3UhQxULDTgTh9NBMQBYtRFkNsRtYGmCOdGbztrNHKSiFWjQ2TrDRoaKALfEUK+POiN89MmxKVK+eqVPjw0CvwsByB1Ir5PwMPm2A25cEUxD2QxxSF2YJbYa2AGg1QA11GiliFwuAD4Wk5VT+nBQ2EoT5EIuACLAFsK/ADOiBadJpu2WO3x/eA7cPLMoJAMNATZtqwOWenpxpgN2YUK9v4Bv67DN9FGCqNAX5uIITxKWcGuA1xwhNUr1P7zVgBS1xz482+gl4HIBJ51Fo39BtxbNfRCMEfVjJrwBUvDnygG4BPkIQHRJC915n2O6287+j9UinP6U8wiFpzO+CkI7AgZy8/mG0Uwx4hFxO+4Lje3L02S7M03fpFMjcoQPTX5+ux/T3+o6LcjBrMd9P/tQRiEUTdYPGvuaEjJpZUM3zVoW1izrvS4K7Iy9OgtvuUlpR9DDEtgAY1+90JcBw74CD8BmoZwjiemxPPCKi+ewo+1QjXMtn31HPHHUrmK9FWMjAorZbQOa1gw0gBScn+Tvt/QRpTi8PlhizBBl53QEk8xqOntMIsyWvKrOZJD+HFdg18BIhqyxG2UeEjoBON2c2peD3f9zH4Y//jtfh2Tdv4qo9/u1RgZnv+I7vwJd/+Zfj2c9+NtwdL3vZy1BrxRd/8Rfjm77pmz7k83zf930fvu/7vg/vfe97AQAvf/nL8c3f/M14y1vegnme8U3f9E34+3//7+MXf/EXcc899+AzP/Mz8ba3vQ3PetazHk23f1Pb9P4HMW/OIHOF/PoHIhOgtgXPw2gl+ZVaHPHEpXZKhp/cUUThC8MkMk0EBbVG7SOjkB2AslzCloGaMVG7yMzgNVbvrKSd4ZtSwgCwnpLDI+17JN8FApkKgC2zkNyZbj0vQWReGH7ROL87+yOAb2IVKSTnkh+j8MUi9GSR8iyscWSAl4iF7w3l9oyyX6C7Ba7KBdQcemvHVO6BnhhsRmZ2FaGqsAPzKLDzCbZhKMkLV7dMITbk4kjSYI8NLFOpU+RNQdCV3x/AxmInm4tbhp5aZKubDyudJV7NXW3uRDPkBA9XujR+RDNEzYOVJ/0gRn595fTFO186AS93vHcXQqm3/7AaGj15/26tRw+PBKbucvxpt+7W0qD3HUjvzSmPBcAq2Nqf+wQkHu3a+/PnIX5yKZ0H5Y4O1/XFo8zcJTwriFIaqXQbnI2KY10dj6wcRNhEwuOSRNz0/giwzrMeUEXfs8BjgoHmfez6ztdkTbsvHplDWCUEckyTkwL2u5GTY0/lg2AZQY9KggzLMgZYCzyCz17ycnQBuWzF45zSyNHxCNPL0t+6fl5F6OkOoJMcnbvOyW5yhDyCWgCXLozXbxSuDQO+/DWvwZd9wifgadeu4ar95jZxv0MH9ENu73nPe/Dud78bZoZXv/rV+OiP/ugP6/M/9mM/hlIKXvKSlwAA3vnOd+Ltb3873v3ud+M5z3kOPv/zPx9f/dVfjVe96lX4wAc+gK/5mq/Bsiz46Z/+6Q/5Ox566CHcc889ePDBB3HzNxEhf97LvwG3XKCHBf7gw+Q57PbkpcQuXMcRONuSCBxqlxhGhks0jWelBwcAK1vHqlJiG9eBGYYzlBoxGnKb2wl2NsKjkCUU8Mg6Esgq6x2GGuqRWmlMCadbgEUiDwtkdyCXJopLigMyrF4kAMBSUc9G2PkZZIrq0RnfV6UnKEGVcdWyiSGrOipKNfhuQXn4EuViBm5dQuCQcWImlgPmhnpjAy0jfDvCNgNsEPjAzKQFJBz6hn23kFYX97b4O7xV4JUICbWda2GKtucOOIBEn36ZkurNZuVOMN6LTzTQwt9XSX7UKFnhDq1r+qnnCilognPisi7I0qVyn3pO8j2s7zeQ3L1+mhV6tAicAILW/OS97oOnRv8RwYqc/Dx940jGvTv/XU592rVHaomx2x8dz+RuH2w6LycApumq3CXEJMLHNHfujeCs3ffMPNhHGnZ6iZwy/zN/NwV8FG4ABsAHb2J5cIHOK1hOVd3kzGVY1u/St7znHgY854hLEIHjbzmsQF/DIyk1+8fPWdLZLECYsQ8+hjezRsbSFnB16OzQfYjYxXVXde5/DNCD8DwdoOmpUhpk3LoBfATDqwvgwefBFM96XFcbiwR3ef2ZnZW3/dSjEiHclvEUy5/34KV9mOP46S94Pr76Uz4Zr37WR2F7pQXzmLcPx37/hsDM49Ge8pSn4O1vfzu+6qu+6o73fuqnfgqvfe1r8Uu/9Et43vOe9yGd77cKzLz1c74T/7//8iCGh/bAgw9RiG5/gB/mFkuX8MrINIXqboCawq2AD3zypHarlDuBSaYwVIMclgA9C7wawcxmgpcRsolyBAYWYNxMkHmGuVCNF3w9v7OR5gSAs6wCRLjAOoClQh7eA/MMUYfsjXWairL/UQrBpwHL+QbSqlQjFsMIrO8XFstcFuBQI7Sm8IkcGtkdgNs76KEyRVxALtGgkMOCqoL6lOvQcWARzmlk9tPIDKQ6EKxYAcUFRxKMrQhBmCGKFWZtFYItWQKgmVAVtAgX2B4IpJHoBOOS99JsdYKaXPwc5BKk18ZA8LhQ5Vm7rCH0fWogh8a1kRFbPZiYGs3Qyp3Aojf9Eds44lVY1/OMa/QkmYxx9O0EWRyBhaPvxrFluhuYeSTA0x9yl2M6e338vtwF8Pj6el5fqsKuXxDesMxGaSCAx7UQ0AkQzJPPoLEVxVqiIo8zNL0VGwU+BTh1VpIuB8CqB49DGsj2KAuA4LIk6G5jphzeXkfHwbkZEVRWoU414Q5ktbmT1xTf4W2uMiQqoMCjIrg6ZQU0Ygz5UEQvwJgCtgFsI6hnDldHWYDhlkBmEuxtiGt3XhMFJtmPpU8JFzTJA1OClhzPo9uXniAlmIKxBtQRYIlzNjmHR5qz6utYxUYkx0kL8EWf8Er8n1/3O/BRXSbvVXt82odjvz/kMNPXfu3XfsgdeMc73vEhH5ut1oof/dEfxe3bt/G6173ursc8+OCDEBHce++9j3ie/X6P/X7f/n7ooYc+7L48Fu0ZL7of/8f7b8OuO+S2cnEAIMMAc6N2yzC03XqKzEEVq/ot4NsR7kbRPQGNfjxpXgpkMxKgpCifGc9fShSvFLAuEms72X4GFkNxI7CAMAYvFPFrRhexmLRtYGQYFdZvwm1hqGlTuIs0B0ZWjuaKZCj7Gb5UkoRLppGHoQYzs7BfoIc5wl8Gc4dsN1xw9wuzFKYR2G5YXBKA7PaQorDthLpRlMWYtm5AjVVZRFAlspCCa+OTQIrDR4I7r8GZ6WLqMqz8FvixkWz8g34h7FqvMMsXsLrtU/gL+VoAm1AmbgtnvM/F2tv3tVPaamwawVTWr8tq0hlCu8P90O/au5CIwLsFvr9oHH+235Ym+D1taWzv8tZdXSwJdPvTdWPYenNyvqNbcHpNp9/tBCXujlRwdfGVZB0X3zGa4nNxouQ0JaBxRFowACcIkUlaim/v7WseHu26lfc8+xYAIb0HBLxOvlV8Zw6/6Yq3gOZQYNjTE9g4SuU8twA06SGEIBSTZZ0eBdzwGFCqQwPUewxB0xmKECxC8yXDLsOBQnioDpyBelcO2AHACNQRsJvA+HCEjZbu1idIqSe3LedxADF1QPbhIQsPjnfvtWctgJwDRwTjdiv6edfPlXzduC441vsBB/6HV7wcb/u9n4Wr9pHZPmQw8+53v/vo73/1r/4Vaq34mI/5GADAv//3/x6lFLzmNa/5sDrw8z//83jd616H3W6H69ev4+/8nb+Dl73sZXcct9vt8A3f8A344i/+4g+K0P7cn/tz+NZv/dYPqw+PR3vpi56On/6X76Gn4JlPAS4P9AjsDhSfC00ZRGYQs5Sc3JFKTwWJvpTuz4cMaaTcCGyUAAMbBMemq/Scu9EiEBmAxaAJkpJ0KPROrKk5nTF0Lvha6Zt2IITpBHK+ge8LdD8zNCagwFz2bwHkck9bq+FxGSOtW6Lm1FJZhXuu5Py4Q7Mq9/mW9JsiwLUNbDuRR7IZYfdM8FIwT4J6TaD7irKr5NdcGvVhrEALWL06BkRnh5nANx7aMQAC0Ljm9VGM8G67tv4lxGUeeR16QAIcARnuJtM6IACLryrAd8EPvRJpini5rNkpPFVuteX4s/le7r5jLkgAIMnXcp5Ef6UT7mvgxrqD0snWCkuGwY0pyRBGTrzWkZPWW/iuf1h/7wFl++nxfQni4seRV6Y/vh3nFKPz4+MFd54+X0ziaI6bSXrsQtwgwIwevI2RFoeLBTm2M8t5Dg3wEOhDAZJYs18afBmJas8LyNESRMgpCjaKo4YY5ZGYYf5ifjQPUg/GgMhW4ncnWIKsZHcYvSS6jyUnycEhI5XCdS1bS4G6Zf8KANuvtwYGaA1v18z7ZiAvqFS0OSYB0HwDYF4Bvziah8qSG9QBssybkBIvJSAGOkC//us1iwAceafaYLVxjHtVgWfdcw0/9MVfiOfeew+u2kdu+5DBzLve9a72+zve8Q7cuHED73znO3HfffcBAD7wgQ/gK77iK/D617/+w+rAx3zMx+Bnf/Zn8cADD+Bv/+2/jbe+9a34yZ/8ySNAM88zvuiLvghmhu/93u/9oOf7xm/8xiMv0kMPPYTnPve5H1afHov2ilc+F/LX/gkBx/kGNeoSld0MmRcCFmgo34LhmODGSJBkUY1ZRGcjXAv5Isr3rcrq9y58Ur1oJ6oF6FJDEMubgq0sgWSqURE3iMDikR0lYOirKNwqfGGFagrt0e/ro0BQyH0ZC/VxDguwi8B3gBcp9Ej5TD0YDAMrb0PolTkssdPVAF0B7EqJ8gYRez8fUO/bwItgPi/wLX3rfhAAFfVGweXTR5QFGB+uTAG12LztHDYBMiDS1xHE3gA0wKrymY6qjLsnUOl3cZ0hBO78HcBxNkQDMJFqnjwj+BHhUpJLc+SaEBz/te4s08V+BEpOW+KF5OukVwGxfU+NjcrXpP9cuvjT4ud35mdOxqOxaQPgSbgkPD/o3cEdUXjtZxwfY3OcMoIV0PVjK93vpyAmr/2R3js5LvcJrut9yarNBoQqboDSjmya+oZLHDMe6NlwBeoE+Bh1hmIYknpFwx4C2EF2TQ+FuEP2wHjhkIW1x5YNNZgkCzOmZwJY0/5nrMTcnM8hgmfLeu816lhpjrdxKfFubFgBm99dzwgmlng/uf4NGATYmrdC2pyCyQVLOrGCH5SerFaTLD6bt7pXbs77HAq+LbyXYDr70JNy8x5n63kuSQKOz2WI+I40+/yoAm/66Jfg//qmN+L+61fFHZ8I7VFxZp797GfjH/2jf3RHDaZf+IVfwJvf/Gb85//8nx91hz7zMz8TL37xi/H93//9AAhkvuALvgC/+Iu/iJ/4iZ/AU5/61A/rfL9VnJmHHriNP/CJf5aWZBxhT7kWD38YKPOoGB3p0RmzL7H7Xhy43EMOM3wzkjhrBt+MkIFFH83INxEJUDKU4JVwhZDDwvIERVk2QQW61GZ4qnuEiZQx9qhonSUGrFZIhHDEkmSnzZOgi8Gd2i2YF8g+xPOWymtTYeq5KrDZsO9Kf69EmrkvC93G7gRRqsBm5A5YFXMx2D3nsLMRtlXMNwr2NxR1ywVPL7ngMyuM6aFlJ5huRabUEmUMEHWVRiEJMd36kJb+TY6C0M3fGXHJLXv4rns+TAsF5O/9ex7b4ST5LiT6HgGYPB5pGFKcLVbX3ijL+jfC+GXr7XXzJDVxPawhqziwvadYOQHwlrWSPJG2/c3ztibNgDSc0vWtBwsewGA9JxpYyR10vwzdDdR5ZKK1Fp603sHTMJH248D+pzelZ2xrZ8Crh00L3RFdCAJcgboRahvtwAKyQWJVA+cegCXm1LgzlJ3Bi2I5V8zXBbaJLGLj8685LwL4qYfXYyM4XGOR0/G2YvuAYbjNuT1vBMt5CjoSZNh2vQ+uAPbAMAO6j3tdhKBd6BVyADobybwA/IzZRQBJtvMAFBeUPY+DOGxULNc4BhZfJ9aH2DjEFkBXUi8nVICbcq+CHqNFVrCElYPjUZagjUvcujZfT4BbD0qlu28tSwlYx6WfJz1GjknTzgvgxU+9Fz/0pV+Ip10/x1X7rW+PC2fm9At+5Vd+5Q4w86u/+qt4+OGHH80pW3P3xnlJIPMf/sN/wLve9a4PG8j8Vrab916DXuxgY4EsFfp+0Ltx/YzpxAAAAeY9CbBmgBS6gbcTlWltoFcjCcCHBbjcA2dbyDRArSILVfqhcrFQpdheeDkEBE2+GHAW6sLKrCEoYHONRZwrh6Aj96lCbAYuZ3itBEIh8CfDANlMqGMEpYfSFiIpBRhH7sqVQAobempQHS1F3Qx+7YweoJGeJSsKLeTeiADz+Yh6PsA3gIiiPOy4dlExXwtDMejKIXBwsVQu/MNtwzgzcwJFoIs36XYfwhirN29WUXoSMpxztIs/wfxNDzaBWDomkljabxGN1orVzdGIwMCRbb2z9W+0/O/+vHK8oJ8clhLrayYV1qyVDJcAkVkWY5i731zoFWvRwAQnsl52jkWTi89ue3pZeL70Aklhf1wTsmUfEsTg+Dpz7FNe/nR8/Bhv9e+lEXUHsvZY7sRTrVaDdCsl+S583yZpHBQaYQOMVc+XqNC8GIudwkO3yJ21gjY0rhmSqVngUCQAQIaRYt7EdS0C2JmjRu1VK5RwGnZoqciac0cdWAiUamYzYdW0UQ+AmPdQeL1J2AUc8yTwc2DZ8HWvgByCcxMoUwz0So0n9zc8lzIH6FixPm9LxZFzjaEmad5Bj+VC4/wwNLDUvCXAyqNxNA+MZ65CD1wzxJnE4WU9plfqbaHbnBfRXv3sZ+IHvuhzcX27xVV7YrZHBWY+93M/F1/xFV+B7/zO78SnfMqnAAD+xb/4F/j6r/96fN7nfd6HfJ4/82f+DN7ylrfguc99Lh5++GH8zb/5N/FP/sk/wY//+I9jWRZ8/ud/Pn7mZ34Gf+/v/T3UWvFf/st/AcCMp2maHk3Xf1ObmNM7MhV4NdYjutyvVZzBWL5Zhe7nEH4D5PYlMA4EJrUCB+NiC0CVIMXB3aMfdlGwsQRLT2KhrmiUU3GIG+TiED1z6DTBtwN0VNi+QheDbQc+7BkeUsDHAr08MItpXgBjVW4olX+pHByEx00AkaiALaEObG5Qs05bR4OQOJDU++DD8PNrsGsT9vdvmXFkFcPtBb4ZsYw83jaAxs6xXPpa7yWVPJVAxsMr4ZPClhquaHIQ3BOwrTwPixCQNPYk1to44TkBsIZljgxtvJe8knRTdCADEcJLb01L207Ga7cbhXecpQ4QHenhSfcjF3ln/5PjoSIrhyUAgUSsI4nDKcTHc8cXGAjG1NfwowN5sgyVeBTdo1BbfEnyHKLDmYGVeiYo3ow6gJVgHrvp1dKtu+0GKJPj1QyZt0wTgaxCb22nHeUvEIbWwjNUWKbCRYDF1+wwz3sv632McdaDo1zy/VoQxRIJxDy8ERXxRarh3RPYht7AFsZY1mmjxjBS2VW4ALsbCr9Z1tpGcCymTEm+7fBFoGbk6GT5jwBKPoAikzmFQnW3ZVTlOJYIJ0UaMwZ+vp4F+Ag9GIv7qAEIdAH8EvAJ0BgbJr0JMKCbz+v45z13eKdmLM2rYwMBUgrlNc2asoKmzOZq+jno5oF0ACzAtwW4QXh6+vTsXmcm540L8KaXvgjf9Xmfjc3wqEzhVfsIao/qDv6Vv/JX8HVf93X40i/9UswzfZbDMOCrvuqr8Pa3v/1DPs+v/Mqv4A/9oT+E973vfbjnnnvwyle+Ej/+4z+ON73pTXjve9+Lv/t3/y4A4BM+4ROOPveud70Lb3jDGx5N139Tm18/B6Iytl8bUTfXIEUwXCyQiz13fJc7+KARUxaILcxs2u24ay3KitPJW1EHZm3hINlsVjKmFh5TK9DIhgI/cBXVBYAIuS77GT5vCGBsBvYGPRwYvx8GSIld5G4PubykwRCBbzZwcdjZBn59w8VjBgCCKVduvWU/M+1YqPtiKiRDjyE7DgFkw6rV169huXcDmwpsDIIxFPstK4YvmR4tjiqKunHoIi39VJ2LfUu9HBzLKKgjUEeF3HIUd8rozEbvjXH37SNBpYDpsXWQ1fMANEOaBvSuZNMIIzHkhGMXQy78huahaZ4SoANGxy6WJprX+pCukTS00q3wcR7vKgh17nWBcK4F4DrysEiAusrPiQAe1b2ZmMbv9LxGDSOWF5q73k4PRyIM4rENlpoesS7UlN6a7GuKEXa76BziJm7mYbwMUPNVrXYdzNBBybpaYVkrx92Lx3vhNRFg2LG/WvmaKVBmek2ojUTwLAuYZpy6RBE6a88YwDCnOLMDjbyXwcj39yqNzNqMqgDl4CiXC+pBsFyfsOx1LR2wEDQdbgj0Ehgedoy3iVwXU9Qi0CFw9MySAzIHKEsAlWOcQnyqsGLsd3UWbJzRShHYuHJjctxTt6Vk+CrATqZOW/+MSHf7Mj08QUddp6VGCriPaCCrzYm8dSX7jBaOq5nphQAr6XUqcc6g4fnIz0soDmvU9oUCL3/2/fif3/IGfOJznoWrAo9PnvYb0pm5ffs23vOe98Dd8ZKXvATXPgJVD3+rODMA8Ls/5VsZYrpcUDcK246wswE6G8rDO+itA3B5CZsXyGZiKMbBzKZqrIUUoR0ZQ/gOHmnXA4tSjoUclUhtRhJrgycjQ6w4tTJrKIjC4tb4JKltQ8GpMMpjpI1f7shtGQb4NEA2W/jZiHpty2upBrnFcg02KsQFw/tvQQ4L/Mb5alwgTDM/K1xsQ81Y9wvsYNg/dcByvWC5McImLjC5qJsBdaOxw3OgMuNI00i6w4I3RNGxACrBLyh7QHYGXRxldhIro8p43RA8GXhO1tIS1DSUYWjT66A1N+1ccSW+HwsNovSgIIEMANkbtJIzI/m5rqUjwHN7ixPxu9yRtnhP955IgJ3oK1ZshPBWyWIMoFm8ltycFFJcfAVCYZHSkwGghaLSayRJjI3jrFqEsKJaeAJ0JZhJwrmnR8zRjnd4MGi9VSmXIEC0Hb45+5I7azjv24gA6AiwD4YskyPiQLnFa1/OFbZV2EgArAdguHCUvWNRwM4FdRKMtx3jhZEnMjDcWReDbwDbUgCy7IXAWIBlJEHXJuH8uqQQXoKWOgg1WM4UdWL/y94x7A3DBdWu9yNgNwvmGwrbOPWdFvA7RwCXwLX/WjHdImhZtorDDcVyTk0lOTjGC6AsUQ+u48LAwA3BFMvHwTBccq76IJi3wOFafKYEuJuB4WIFLZnWnl4SJrHJyktJb7PgSNCxn48OWUOZ4HE1NWWIe1ECgKUXzgpYzTv4NC700NrIv8slz2URBmueGEWrvZTtD7ziY/Ed/91noWhHNrtqH9HtcefMZLt27Rpe+cpX/kZO8eRuRbmoi1BNt3oUYFP4tS3qZgTqGeRQ4fs9U4q3EwSCcjFDLwoN8lLh8wIbBorvhdscGZYaCnyOLUmtwGEPJOESgIwDBe1yd7+JopJ1IVjxKGEgwvMkYErxPnd4rfBxy4rXZyNsE8q+FVBRLO7AMMAKUIITZNsBPihsKgyVKVBDIMtHVq8GCnwbNZqKxs6Mi+SyAfyMoMaW1V0sBbAZkEoejC7OzfAEFAh8D+jByHsYQ0l1Q8+RTY5lS4MzXgDDpTFbZNRGRHYP+fZ0fefO0zqvQFjK3CX6wlCV1uMwEkGKQOZ4MRf4xCN5j1LAkB8Moy3tI0gvgAMtfpRARiLjrEu7V4AeA/PwfBikCgQGj1SQFFpLr4nCVxVW5HUHiDB6BMRDK8jRdrWtblTl7/QEoXlsxGJ8EowsvKeuvGfN8GWIMC60ARrhWNfM5ktjWMLrVMHSGMFHckuibFzLKCwrFpWRdQGG24ZhZyizQA+OZQR8W9pu30Zp4atlAjADVQmyRNZ+cD6EVyAKoqZXi0J5HAgfABscdbN+zmd6GX3UUPoV6C48qPF5S17KDNggmM94z32KbKniLTSq6QGsfEY8wkjpVQMI8JdNEuXjM5XnL5nen4AjvTsJVBKYx9/eh9DyRwdmpNKTl9M9AYaECF77Clu9PB73SXLTEFwp19XLo6E14xOwXAeG2/SqQlaeEpzcoI976tPwp3/PZ+B3vPi5V16YJ3l7VGDmjW984wedGD/xEz/xqDv0ZGrl9gEWZQe0GqwU6L5yB7xfaORie+vXzgl8Bm5vlrGgiEEeuM2Qgxb4ZkS9RrCjM3ku9OQwBdtVSA4e19xRdwf2c/BYEABFgKnQU6NC709kZUj+nynSmzHUchfyDSaFbZTlB8C+17MCV3JnmAUhqPdew3Jz4qKyp6enwqASqaoj1a5sUOyfMmCJLCNxQA+xaJbgJkzCdNglMpcGQd1yMdcFKLOyYjDtLalDzsVUl8hGEScXB9z+HTbAfO6YLowLYQClxi9AkijjZoY4GhfiCLuEEW4u78Wp42Noabw0Ibam9QKNXLu6KNC+qBFv3en778M40VLUMK2Ba4CNvP7GQ0mBQsBmb6CD9ifmTuFJEgy1a44inOnhcfcmCFcyOyiNd3hjpP2k181FmgwPwxEBsAfeY4C8Lwkl5MRdHgx0Sc+YOwt/qkMinIAZUDfUSammG6HCrAlkE+9JqSwH4FtlbR91lEUwPVgx3l5Qb05YrhHZaYRDXFePj2fIBhSG6+v82ChdPSSGPhOkLROwedgwvX8Pq4aLZ0zAjbGFRahIHc9PnJ8BWg9LHca703xx0GsDAXk5QWKVAz0wFtFnQbg5BB1ilTb/1uvjtKZ3JADRDAyXMbZDeOYcUAXcGEbzAIUYIlMw05xxJwjSHeDqsFGAkQDIC+Azny8keE4QmyAwAA9wHDaCsI/TbS5bdQMsN2LeR4bVJz33WfiG3/PpeMVzPwpX7bdPe1Rg5pTDMs8zfvZnfxa/8Au/gLe+9a2PRb+eFE1rDdl+pkMXA3wzcJ3ZHajzsBkB4W7PReCR0WSjws5uQs8n6AO3oJczTBU2FNSbE8Qdw8WCclGZOg2HHfbw7QScnTPlOsshHBaUh3fArQuWHrhtJA0vEbhWBYYBvlRIpnIPA1V3NxMXqEI3M8XwKkQqK18PgAyRrlFptJebZ6jXIiw1VxrCIpAaqquVCxyGZKsod+3uWAqg6vS6xILmM3e9ZisvwEYwnDAplgGQyVsBu6yYoAuAPT03y7RqaiAAjY+K/b0SxjFCHbFgJkdD4CsfYOkMDEC13bTWyyqAR++Mtx1rZuNkGCezMFZ0I42zEr6GeN9WD0jurJE7YxooRyfm1nbWfI9eijwW4X0BCdkB2IzuqDW8aPS+ZC0rhKEWi3sYyqhi3rxoAkPtLmm9LjTCqCUXQkOgMEMHwYFJZ5PuONg+KHwkx2l8eMYyDpDrFD2UxVFauI/y9yViYDXq9CD4EuNDFXKo2F8fUFU5FO6oN6jbVDeK+Vp4laL4kgtQt2H8BVGyA41n4uDcaVooDohxfmkNwK0ZJhRgrhhmw7xzDC7RtwglYfVk0OPapZxbhDUTtI7hJIk5l3WaSO7mvaGWkjeeCjud3j8chfcStCfATql/FaDsBHqIrKr07uQz0bxnfC2zH/P3/EoPMrPuARk6PJ4k3ayInX2Ka/QKYGIWlys9LOZoAn4+cWyGHYGXK/DM+67jT/53n4bPefXHXXlgfpu2RwVmvuu7vuuur3/Lt3wLbt269Rvq0JOpjde3ODy0i1iuM+tnT/6MX9tG7Z9gtCU3oQBGZiZsUvhTzlHvOSMHpcZxpcAGweF8ZKZFKODaMtEoDOSP6G5hmvZ2Qh0HaFHg4Qv6rptwX1juUOBtVi7AVU86FQVMlTyZixlmA/szBIKIVHGLEgdpgOvEHW7J71sM2EZIYZtWFUAsXF5CR2NAAzTYB1CIw8sl4HugDs7FLRew8CaYCDAyxq8zMF2Q/JnODizhIRpkTc0N3ogkBwBtvY/dLloopy8ymQXpaOBX8KNp2J39OlILDk9Pxo6ygCbWy2gGrYVhctcdp21ASxChsbBWLsyMAw1bem0aEMkvMUexylCgGYGlyroqpLHOPxz0iFQCEhONVGuBKuvy6J6eIVPeLIIHWXfdCbxWHyD7r0JO0YHg07bAPMQ8Mon5P3K8o2Aj5xgJrBriag5Zs1gqBRMVhY+YgTwUrPopYoKyd06/dGCUUNqFQ+YoBBmcHsR79Ig4hp0FH0XhStL5MPO6D9cFdTPBDhP8nCGqzcMBNJJvIgEiijR8kfo3DjRPhQfCyYy4zNxKL2TySaCA5dh2QCjnXnreXBFZg3wvAYoPwHyN5yyHCPEKOCccDFUmDybItU0nJh9DW7+vbtF0m9S59KR3JnloGbaCRDQzvms557Nbh/DA7rq+KPCij7oXX//Zn4HXf8wLrwDMVfuNcWZO25d+6Zfita99Lf7CX/gLj+Vpn7Dt/KnXsb88ADLAVVGvTxguZqSIWRoVhgK40sscuhfVmLUkFI/z6xNlRRaGOcwBLBH2EYFtBoKR2eBLpEKrxgIVJMZ7zjHMC3CILd1hz+rYCOBiRmN3vuXisFS4LiQMHxaef1yAceRO8JLsXBMBBkUxBxYKhpkLvRUH4y5WSZBEpUdlPmN66bBgDZsUoAqL0pWDQQbAR22LmYKAI7kPMMdwIB/Chy4kkpkRIi2GrjMNf6lcEAs4lh5ZLNDMDNEVfABNgKstlWEM1H1V801MUlfggOAa9aJ47RRxuW1HDKzGKLlOcvK9+Yv3JwpgIAQqmbkiEUtKr01uxx3RZzG4FJapqBZeBGkDJw7eu+IkwAJMDa4WwmjGOlxByJY5BmUbAGOm56FczLzW7ciQBQA5GMpsBOpTcKRE2CekkXdgdpRLQz0XHJ46QS4WYASWLcGW7DkA4kHwPlT4tkBdwttDuOSDMowSBT0dLbubmkYBSoqDYZDGnObou/qagVPBNH4RFpM8ANNDC2yjkLMYNwh8JD8JSE4MX1cDysz3fEATjIOu4Eaqs69AzPMV9CDmiRw4/xheyzkVHCNFE0gkUZvzqZHJe4CaHpacjzkHhVo6NngLIQGd5ybAlVpglvQs6Tq3s3AkswslKo47L9qwptI3jxffLobYZPCzqdFjI7AZFJ/xkhfgf3z9J+ETnneViXTVjttjCmb++T//59heiQ619tT7b+AD73uQBnAzoE4F5TYFIWyjsJGLr22YBVSnCHkc6lE6rxSjez1i2G6xyFSjOFSkoNogTKtejITdooCy6KLsKfzlN84hD92md+TGdRqwuQKHmQtqpIC7sJSBDAq7voHrBnprB+xmLrjDQG+CCHS/QA4HOBx1ewa5vkESIFht2yGzoW4KpFbYSPJwPVd47HyH24YykD/goD3TCtjisImZKxgA2SHldMJp5FAQ0Ihizbqp4C48uQWDNEIlEJnEHl6acBg1ldj8Zzj+231Vb01uTOqrBMqRJOKi4aE7MpekO+Vpy904Tt+PeyxA6PV4O6AVBpX4L906EgAl0q8NrJQuOwO2GiA3DIIqcGlQqYCW8PrQs2MiKJUgWUQbt0sMkL2hHCpsKqhJ6jSH7EK7qAjVdQeBCdVxhwd28M2Iw80Nd+1BjtfDAjgziCAkYVt6AqpHGQpm8pSDYbio0D2zpKxpAAW4XGKz4EDZVd43KQQWiqgYTw8mEEY47zfWEBAAfsYimyfwKgDUc+BghQBYGRLj51bPSJOlbTeepPUankfLApDppQGQNZe8kp9iEZZpE7aE1yXKBWT5jSY21wMLT4ByMs9inNy7eRZeHwiv2SRkCpIDEyntNQjhWgP/AqsHEen1QSMXcwylvZ+eTKRwXoSbNMONcW0Q4CnXz/BVb3gNvvRTPxHjcKqaeNWu2toeFZg5FcZzd7zvfe/DT//0T+PP/tk/+5h07MnQXvDC+/EffuaXmv7I4d4RZTfT1TobsBlgouSoOF38vuGuRHYOrbUZWHJHKHUOD9EspYiWKD0rujPUVBdOAmAuSObQXXBktiNwseMOdjMB5wWoC2P9hVsmj0KWdjbCNgP8bIQPAp0nmBn0kh4mGQb4+YbeFwPKdkCFQw/pJqDXhsbdgaKwwWGgR2E5E8iWLuSy8J9pW99RouCeDyQR2hDOhOCnJAdCSxj5CPNouDa8MryE2AH3QOEOddtcfBPoOGi5cncaKdDJm0kBPOqoxG56WTOcWqX0NTgUY7KeL7VYEke1tOg1GNTuI9BbHqx/d84kT69QeqcQYDJS8KWwcGg9VJLAnd6zIoI6RF/UkcRR3VcU+nTC4Fd4GeBggU+Nau5i1Eyhq0vW+78ZiCAXQKtBLxdquBSed94IZBGUw0KpgqFg2Yyo1wpVcs3Jo9lVEoB15PNhnO9aHcu1AXUszJqJsGTeOxpt3reycD5gkKZPw3EiYGqGGet4JoGlbpyAObwcJMIq9vcAMocYnKN5ZySzw8DvyErP5KBEan5lhpflN4YXhuKCBDT8zhU0ZEiqAaqcbCFDQPAuTS03gXTzzNTg1+QDFliL+kHerh1A48kJ1nT6Bo5KpzwcXT/y/gwIhWUga3/ZJCvg7K7HggOjIdr3GS97Ab7x970Rz37qPVfel6v2IbdHBWZu3rx5NMlUFR/zMR+Db/u2b8Ob3/zmx6xzT/T28a94Nv5ff/NfsG4RANsKlhsbuvQPBjsToEbxQ3WoCzxSmK2sonDcPvm6i1yMi8Go8EL9FJ80CIEMDVShW13myl03nOTewwzXXMUWYA8CmGkTlbdjQQt9eTsbUbfKLImDQR66YL2luUJVyY2QAmypg+NFUCCoRkMEc7gV2NnIqtVFYKNyBzZH6YAiWK4LajXoPkJJCWiW0H858PcWSkrPDMJVfQCgtvIZcjOsK3YAcFSTyIWcCu6W0fgI8I5LgAQmWFPrbd3StuydDC9ZwpBuC59gJHeuAI1pdjAJEIKVV9N20yeLebrrj1w7aWyC/FCddbNGjbIYDtnNkEHgCNK2BlnTKaKIuXI8awU2U3j9vIXBfFTAF+jBggo1kBh7eQCmgUY0wyhKb6POHBuX4Fkt9FzZdkQ9G/j9RqAtc4TElgrdCZZrA5ZzxXAA9CDAUKCiwMFTbpe7fRBQKsCwoUcKeIyP1kClwU1L9VfLTDkEQTVIMxJ4TFuqTXjEREI5l3dXAxh5yVprAXAFqCOgBZC9Q2encCMAD7Dh4VkpFbA9H5865JwEOUKVro58/NttdyGwT4VbifuYjjhFE5uTDEc3gOyZMtUuDUDTcZEk5KYDREFvcEoUACEtwedozbzrgF/E8FyD+6eOcgDGHb2sSxTfzAehCPCxz3o63vo7X4PPetVLMZYr78tVe3TtUYGZH/zBH3yMu/HkbK/4hOeTd7KfUa9tUAeFjbSumlu1wYE9V0EZYkE/+LrLsSiYeIhA9VSoUwMw5APuUmH8jF7M5MsUkKAp9PTYVCBjgR6ianfUv/LtEKS9dE+AO7sI0ciuQoV8BYYPWPySfZngA+s9iYROTBr56qijAIUGKTOaMAi5Lwr43jEcGEKqG2Go4ka462fyE2zk7lQPaGnPLpLRAAAdTqgstOcALPQ3mgIpOi5tLugqbZH2ws+LpqclDFwCFO8dJHmN1owmDZp3LpK8f0BTCutcLS408tIDlggPdJQN9Be68ms6S5SfawaKHgYVh2XIcAjvWK1wpaqyFY2UaIZ44DXAsjCFeVRMtdKrUpSFQQ8W5M8KVao5mw4QUdTMUDoAso8QZ4SH0ivEHX18b/CJkrwrDmAzQJYK0YLhMoBFEczXFEOoBurMEKspULdklgp4L6SG8Q8SeMODAV44twOAzkKjPArqaCsJNebReMHMPBuk1TGDCTBSx0YEjXRsZQXPAHVxli0go7A4ZXhB0lvmwbnJEhIeeMvAJcHG8J5kn4Kr0tLF4ny2IfDKOZBThaAkXTJYf6YXMsKghvAEmTdAnyneVkAOUVmndAvJzvSmeM69KBKZGketzxMFA+uEJpOwheDjP+qj8CVv/ER8+itehM34mDIdrtpv4/aoZtKLXvQi/NRP/dQdhR8feOABfOInfiJ+8Rd/8THp3BO9Peu5T+HiWRTzUzcwdczndHHrThuRV9Ko7ln0Mb0CSVqEO8qByr5+GOHnAzAo+SXVYK6QxUhoFMqjy34BNgVVFT4V+Fhg1+gV0os9UBf4XOmpSJVgF2AcsJxxWvhSwwiSV2PbAn3avXCrwOUeZTfTI7MBeQ6DQi8XrrdDAJuhYCmh1rphOEw8vDITF3adQR7BYqhboUfBrBXxsyF2uxUoe0RGCpAkWcTvSFd3ApF8LbaQTOoIoAJp1Zyb1UsgkQs3mAreMo8SkzhaGjo/Grv3xIM9MOxbGpbOU9O6mbvnZpHy+FOjxF9IIF/P62FQvdbQdBEWNFWQqC2UAZBDZeZagIqyX2CTws4n+LJAd8FDiewcisgR/GiAN4m6R3JZmeq/3UAnxYIgx1YnD2aIMMVcKUUfXi/AUS4rMCrqhlt5ekwErvTooC7Qg1JHZpDwVK4VpzVE/Dx1UBpadWa4ZShlsfC0GD2HAYQVgB9A67tEv3KMFdSQ8eCFKFpNT4uyGTaENygI0a7CelixCZGZH6jDSpgVEOxltp4PnIPe5i3nqQ9A1QBtQZZtZRtyXnQ/JdFGALseSHvMGc26SECrvcR7iTZ387nRAF2txMDIYfLKcfY5HDzjOkUb6ElQXUnYfc2Ln4PPf+3L8dJn3o9n33fzCrxctcetPaqZ9d73vhc1Kzl3bb/f45d/+Zd/w516srRSSmhoOMwq5MBFUxa0CIJmFWrQPR/rGXRvLVwAq9zhDsr02pmcFd3v4OMIGQf4dqI+xlmBD1uUWwfIbCRhmlFhCgKowq+fAWcb+MO3yWEZB5YvmCsXxwA2JiEvfz4FKc9JKN6HP/qS5RiwLMDZFnbPFn4+MPsJAocxM0GVKa1jYYaEg7VqFgMGpuFKFQwHZjHZRrBkmCmIvDQgArtOGXrskZzYI25D22l2tikGeMUSsQLTq9VsyPG/4MMkqVeD9MuF2ldPSBjV5p05baf96Fso3rbP9gf6aiiOzuurYfRAV1nigR4fHi1LdHB2hoYEkCFkAFwg8wIpBVaURh6Csnh8rrZUYFeNXXdo0RwWuBeIOHQ3Qx66gF03+PURNgF1Fuh2QPJo9GKGPnAbfr6FXR9pxE04ry+5hrRQjTh8GKLsRIUfDMOeVdGrCnwT4VKLz3TcHAvOh6NEyCjGjnGbJsTXQIBTUK+FldzD6xUhuyhU2sRRnIAjlH5gwTFSgNlbFWvFZl9J6hYikQkYXNDCx/SeomU0Hd3o5M/ka96m5dFrWDgXU/G3qe/mPElgD19DqZHhl+fLvniJsVjodarmsCoMxUZfJXkwM71qGdaaoHjh/U/Bp3/cC/E5r/k4vPiZxxvdq3bVHu/2YYGZLPwIAP/wH/5D3HPPPe3vWiv+8T/+x3jBC17wmHXuydC0Oup+B93fgGpwKytJjYyL0zshi3O3H9lHPsTu0+gVcQBycHg9kMxbyVmBVdQsIIkRUEXdKurZGcaHZ8jDu7YA2lSgURFbdKBXZamoW5J7C6hZk/o0cjai6qoU68JMBikDpv9yAd8dgPMbkHGA7md4ZeFIGxW6OHfaohS3E8F8To+NHtDSftO74qPANkA5CPTSMQpl12shH7IsxHQ+ZQ0Zb8XrKFaGBk7SWLkGfyWcTr3QV4q16ZG7JEIh6RkDDbhiNQoehOD8nhXwdKfJlm6cu9aCOX7PU7SuN1T5Mz1G1rbacX0tuR+RrhTnoLfNM8SjAV5qdLqAmXTkeK81uSrotTFHSMnSO+jeqmTLwgGXQm+OFkCgwc8yYGGqv6lCzCCjArVAFoNm+GkQmAtkXzFUZ9p2qBXboBCvJLZHyAaqWJLrFGBSJDwkk6xZZQJ6hPZCMoYyfIklb2+6V9BCXQphRXsHUJRDPaBxoTzuocQYJ1jMkhZSe7I11vBlzqP4WnQgI1vvYOnDignOvV3yWgOM/Lo432IolZsjTx6QILhPYHp7Ap3QsUlCbu1k/zlusQGI69CZ55SygscEaz4Az7znGr7k01+NT3np8/Gxz3n6FVH3qv2Wtw8LzPz+3//7AXDnfqr0O44jXvCCF+A7v/M7H7POPRmanU8AroX6bcQRlig4OIdREoMvC8pl5XqmClUSY32jUFPyarxClyXKD1TIdos6UhBMZoNIRRVAtKCOisM9E8ZDhV7ssZQR9eYAMcV460AOyzgAe0EdBXa9wCaGHliSoEJVoBu67H2J2jdCQ7jcPMPw0MPw/QJXRVHuxmuUY7BJYOaok2DZSoQm+F1149C9QHeRRZI7UAfmDYAzFg6UWKDngWJ6wwz4Hqx+PARIiZTVOgQ4WfgvPRvWK/p2BkdCkZekzdWT04xN7mLTIxOGSYFj+kt8pv2dpN8g9TpALR9Na5Ufiv/CA4XO0emRubTimhDBO/Li0Ap5oYpyar2Qt2TkXCSJdCrI4pJwMKxYAmwYoPvKHb4KMBZ6zIwAUwTQucI2A2wQaIYdjVbSthMjdPuK6XKhsfTwAoVR9/OJY36oUDHY+UQvY0jAljDU1QlUbTNgBMmzAOgdKRwHB0UNyR8ShjMBlmtYOIgSxjmBU1Yg90AIXqIOUvI8JMZYPbhGcV/S45eaNeFtkQpuPhDifyqwMbxk3ZxoQB35X4CNBGDojgkQlL9r4FaR4wnmce+lCvwQ1ePz9XkFQBLPwVBXihWLbzqfrSQbKyDG59AUDOnBgSWetxnhXeUFvOIFz8Bf/vrPx/XzDa7aVftIah8WmLEIibzwhS/ET/3UT+FpT3va49KpJ1Oz8wk6FBJ+Fy5mg2EVOnHG2jE7pFZ6NDTk/Q1B0GRcykqBnBeIG3eTs0Fv7Vi36YzbLk2dCOVCX+/dsE7NgaGDeqYol4LhgYjTVEPdbgBjUct6VpgqDkDmBWMVVDFAFMWdO++Z2RnYbGHbCTibUItGZWuuzjYSkGA2+HmBbfi6zNzl1QnksswAg1KxZs+MxdctdUkypFK3NHTDjrtGCXE5k9VweCF51cb0oqAV/WvNU7cEzbNCUjHTu49cI+GhaVlliZCU96/bTKP/JNYzpDXqvv5EwKzGBWoY1Hwrwhokils7l0t4JZrYoMYYeaTdGjCUliru6Y1IcFGFRN4ogOqZ1r/ngLhVVl6fBizXNuzjAwwvYRsiLVZZpdwdslsomFgAP4QXpwg1jaYsp1GBoTSvkcNRzxXlYkBZOLdwqKH8OgAiqJsgm4THDVkB2UPgLzxiAFr9JBcFisM2FItMdVxfhJOhaOi18JqtcA4JBYqY+h/viznKwvtdB4dXavQk+JB4bxmCiDuipS+rhRxAkn7zM9a6fCSCByQXnXWkJPgxMsc9DHkFV75W9o5yMFR31K0DY2RaNRdieGHCc6SgfpOMOacC8JeoWxUdLDPJxxZkbhu96cS85mOfje/545+Hs82Iq3bVPhLbo+LM/Kf/9J8e6348aZttKWI3zBV6WVltGuAiv6vQQ4VWgy8VbgYvpenG0A7SxWwuwCSo51HrPrgh5faenAgAshgN3B4scOngDr0MLPQGAaqzD0EGFTMK6hWm3creSAI+H+il2Rv5C8u8CsKZtYW93DjH4d4NdE9fvodmx7IpUDXMm4J6Fgt7xx2QGeyPRqG9UJPNYnFl75BB6A7PhX+kl2bYU4I+RdT6NFGIkJ9TYiee3o903+dhCoYfPMijFcDijb9At75FVryvHpq4dz1fRo5gyHFL/0oDI+naiXOkdH3LWGkeJWa8mMXOGWAoKu67ALBMEdcQdJPQ4MmyEQz4ACDo43cJT7oLkm8oTNuNEXqo8H3wSMzhUNi5Qg8V46/sOA/GAoSXsW4LxtlIMA7CNx1IGhL3iloEZV4g+wPBRBjbOijqfQXj7YWVol3WEFdFkIFppE0jXIkggjtYumHh/MbI8QJATljoChnooWCKvTR5f5lirKGxSeDnUKQRiiGR7XNwXu/EcW4ZOyVQdAGf14ZY2o1HpvaH4wmIMLPHJEw+tOT8kxPgbcycSo+igrWIxocrxouK3QYU4xyVxPk4l3YZTm2+xz3NzCOtgOz5rNnIa7PByecLb+n2bMAXffqr8Ec/59Owma6Iu1ftI7t9yDP0e77ne/CH//Afxna7xfd8z/d80GP/xJ/4E7/hjj1Zmo+KehaARgT7QoNpG0Aqs3Ywk+hbq8GHAh8Vqko9mVJIwh0Lj10c2CBItcBy3zVW4q4GvZiB/Qy9AEqtsGsb7siLQnaVRSlHg1wsMDHg+gAfJtTrE2xZIutC6C26rGu9GMY1gMWaDhsc8PMtDfFiJPdG+qsXweE6WEMqQFUSBZsbPIiEPsRirzQKuRtMOXQ90PXdx/frhkat7MBwiLa3srNHPvz2XveTh5Km7B72wriTdXFmcQW3Qh1N+FDMggoTQKKlY8e1N3UxrMat/5c1mHJ+BBDyDG0gTtmNpUZerCyVWToCeOab1/j8IDADNC3mHAZqIn9Ja3hAgh8iS8Vyppjv3WDYVQgEy42CYT/T+6NU/cUe9LBMCtnNzIybRtYiGgt8MpSLPSxS7FG03Yc02na+oRyAAyj06JS9waYCGxTDMsPh8LMJkvyi8Bi4KpZzgVeGZhOcIrJzvHiDktSAiRBOKs82EE2uWDkw+89DwI1cNRLdWzmBKIZZS35XnoveHIi0QqiABN+om2gW2kgIYrLG84N8BsJDFYAnQ4eN0L4Hhpnq3i7UlEmPJzcFgmUq8DOEeGZ4qvJZAlqaNVxCzBCoIq2iuB5i02AEfF7WcO8rXvRM/N//p8/H2XbCVbtqT5T2IYOZ7/qu78KXfMmXYLvdPmKhSYAG4grMrG00YSJRKUwtPsSOzLlQ27UJNhWUwxhu6FiOUhXuwKrbak41XhUu5HOlnkySPccCnxburG5foNy+hNx7A3Z9C2wHyPmEaoZye0Z5aAfZ74BphN84x2CAHhbovtIjsADFKhfIIpChkFjpFhVtlenTZyNsUxitmmKXO4a7X3Stx9Lq5YALbsi4e2d02kIO7jAtvdnm3IGHF0aCAwMV2BkoXGZonJhGzO1iP00FFSdE3Uz5ULRwRu6SvdqqCFsJZtq5M5uplQFG85AACFYtmvFJANOOPnHipNFvZMwgWzdwIxHiGDT6VhsQg1NNmSq3FuJ2DgwBWuaIc8wGjIJ6VlBu02UgFl6sbcH44AFYFDaOvBcjvXjjxR4yG+xsar0VD8L6HF6dqARvhfPEnS4iXSxUmcM7NAnDTQC057hUgx0qM+HCsOdYiiT521D2UW9sq7Bt8IRAkOLVW7FWpuUnyAguSIJyC/Ku8Wan2KEA8CoNpDo4Vy3jNRF6hYcXpgQZfum8IRWr0nTOu5yL5u1YUwAjyxAcoe0EsSMBDQ4OjTIAJgRm8xkowrdP3SY9QUJoISkrsWFwQA8E7T5FVuAGLYSa3KE3f+JL8c1vfTO2mysQc9WeeO1DBjN9aOkqzPSht+meDeZlgUU2SCrGZ3gCrsA4MM1z9kjZiZBCLlLjwNVmX6Hi8LOhLZR6WICHyOiTCEn5ZqJE/cMXKIuh3jxDvTZBJoqcqZ4B80Sv0TSgLKCKsGjsjGvwRAw+G0QjCyWqJdfzDWyrQHqNSpCbDxYVciMLSASYsy4QaJzJY6YXJ4CNRLin6bQAa+ozpKm2NiXSBmhifGru0PmeRiigVRTOzwPNcB1VsA57f4dbvu9HxAqSY9O8L33r9WWagfGG53rtmdNPO47s0Qp+4hoN9FyJOsQU7gEawpuUfBKdWQ+pcXtEILtOVdqowixD4dR7/56hGXOUZaGHbXsWKq0CKQK9NJgU+I0z1g0zah6VoQTaUIKUwpvic8zd6lCrDGcWAWQguJkr58ZQArgqoKyIbZExAwOKC72BVSBLJZgZHXUqBEaFJRRkthWwhoclhRHLLAGqfAWWQLzGeb+AsgeNaxL1oCyIN2bCEFNkfTlWcOQxV8TWNG+fInQK0JtqFKQbL6jWDVAheXeNar4AAgCyiy5APZc2xYlmcs5Tr0kKvUHN6Rf8O6r8ss6bIIEbK5KPO4elFyb4Ya9+ybPwjq/7XFw/v6qpd9We2O1RBUK/7du+DV/3dV+H8/Pzo9cvLy/x9re/Hd/8zd/8mHTuydBu3jjDww/vWvYNwsjq3qjpoRKqwAIvBkEB9nS7QxS+KWFYCSQoL+qQ/UyJ+lqp+3KYIduJUvSisG0hMCnKxd6olSL7CofBpwE+FdQbA+TCMbz/NrCb4dc3ASyMRGTEznrUljlC5dQBqZPizorKHtLnooDu6c5upEgE/0PCBR5ZEqkyiuA2NLCRO9vMOMnXI0VbQv5dFBRn61ww3mGK5MUceWv682N9P9Oxc5e80heyxs/6mfZZQ39gc7DkaaFrOi8PkAaaGh7yZNZI+2AaShJeuZvWCAlIaKxAaKQkwi+ePI7Zg4NDBqoPAp9B8DDSuDPFXqGLQcRxuG8DqY7y0Ax4hUuhQ0kKxPbQBy/hN65hGYXZdUuFHhbU6yNcCIgBIWif8kp8TZveG7AZ2a9RQ4qfF+pTaRlLMGc6tQhk59SCkfWeZopwjYrSLCsgcFSIFXoO07AXYRX2gwAWNb6E5Q4opJegR7q0fUHdWBN3RHw/vTWy3vvwAnp4chiKDG/j4limEPozUHzPCUaTM9NAMXL+EPw3BV2wXIP5qj7N6RAjGyEldTA7SRAE6uDODMCi7EM5GCUPFgLf591/L77mS96I1736hSilm7xX7ao9gZu4322L+cFbKQXve9/7cP/99x+9/uu//uu4//777yqo91vVHnroIdxzzz148MEHcfPmzd/07//6r/th/MufeS9sUPhGYaNguDToxR6yq5AywDeFYRoY0ykfOsAXA0aKk8k+xMXG8IbUClweUB6+hFzugP2eXoFphFy/BjvfAGMJAuUAmwbUM4aDsDdgWaBS4JuC+Zxu/82vXqI8cAGfBpg7dGZqtptDop6TF5bOq/deQ702QlQpLBaLvqnA1Cnct1V6CBrpNfiyHdFVgFX/RbrFOadPEoYzMykiOeJ0u2eGS6q6NgE6oQGV0KFJ976kYQjgoqGt4qGloi2cxANxqekAAQAASURBVEcivQsrX8ZbuKmRofOaosvUJZFw98fP9CDF+3ndtFveHSvr9WTIIHRiLLYdUh3DwaIAIftQFlZrtjHOvTDTTQJsSmRM+aAEOBeUcPWpYNjTCzdfHyGDQB86MFQ0lgAQgrI7QP7rQ8A4Yn76DY6BMmPIxwIMgnJ7gc8LfBw4TzURCMdNLxcsUVKDcyLrGkV22eywxeCTYLl3gk2CYWfQvQcYqiizoU4KOx9aWFMMkEsjuBoCcBeG5lq2U4Btjew0KurqGo5RSgrIyf2SxYC9QYSSAhjWe5d8Ml8oSqjhRYwMZsr4D5ysmnqV5vTMOO/FvCHBPcF7SwcXaSW7dIn5n6J2pcuGyzIbFrox49ovoPP+OfCS++/FN/yBN+KTXvI8DFcA5qo9QdqHY78flWfG3e8qkvRzP/dzeMpTnvJoTvmkbS956Ufhp37mP9EzMXNh1F3FcGtm1CTvgAhsGuh2tgnl12/T41AUGML1XQFYXY22O1384xRppwO3m6AuTNbGIQIYKEhmURsJRjf8Jc9n2wFyT4QR9geg1lD8JfkYhmbAxBEhsYVbRIDeBRHgnLoy9QzM3mhpzVhFtwqajgaw7jr70Ioa1roz3dprvdfE1uNTlt3hXYgKzUiw/tD6PU1cT9ZzpdpvchzoSfPVcxMnaP1d7eXaOs9LDMvKiWmApns/j2+umPw7+olVmRYF8FGwqMBngYhBnFk4w47lFTzrdpUIKzk4J6q1woT1nPKveqCrjN6amCuFoodehAKKi8GHAfaM+wCzUIuVAEasam7uqNsS94DjZinXr1FGoijnkxugBXWjsEFRZrC6tFZoAT0REc5ctorBneT2RWFBdtaZVco1yMxLEUiAarF4TwVyIOF3HhXYSLs/AsD3FcUIqGwbITLh81gnctvKwYGDs2Za0ZD3X704WT1bgaY67Plv5PPZUuwDpEBlnZreCmQ3ThlJ0uu8t8K5nSCpicYoQjcm+hAhWNMg8qrg45/7dPzPX/iZePnzn3ElanfVnvTtwwIz9913H3kZInjpS1969IDUWnHr1i38kT/yRx7zTj6R26te+Wz8bz9MI6sHw3AJ5l5oQXngYSyjwmWAuDG7ScAMlBsbZi4dKo2O8jM0KEyPllwYzzbAMHCRHgdmQC0zcLmH3DKUwzXYdmRlbQd3nG7skwp0t0D3Mw3fUvm+gAqyqTo7LzT2w4AyFfhEVVefK70NkcWyHwv1OwbKoNdwp5eop9Q4EcBKuD3dSQYOK+FBaQqq4E9TtPqNTH+NJOysx5Pn7c599AUZ1nE/4s40yf78bKi8tg8HuDg+X7iJFKv6ates8TSCsEsiFLNUJDFMeCjajhtNDwUR3FAP8cIIS/oAYNZVIVeAEqFLHxVWA91Fho6CXBmYMQV7KpxXi8FuTgSYe2csUFlCQIuQJzUqLCqvs45RZLoVhVfKC3h6RZIUHYJ5aoDMhrKvnOtDFJHSErWNjJ8ZBAZFqQ4/UPPFB2X/Z9CTM0XYM7xkttEw4IFIF9aa0h3F+KQEuSo8ay5U1pbKuaWzw6oD2yDpBIp0VdTJUfYC7DiJHCA3K7wyLFFgBPUSac3psVEQyC0GEVaWZ20parnojCOPowqagi9/9zU0mV7L8Cplaj7fS08SQ28iwCue9wx80xd/Jj72ucde86t21Z7s7cMCM3/xL/5FuDu+8iu/Et/6rd96VM5gmia84AUvwOte97rHvJNP5Pbylz8nFjVAbx+gwxDKvgW4voWZQWDAXjBckivTdtOlkhtRKwszjnTvo9rqfTjbwEsWWgF/1oWhAtA4aa0ou5kLXkUTXVNbgFLIu1nqWj5BaDixGeDKVVJuXwIPPgwdIgX72iZ0Spjm6lB4IS/Hi6KOscCWwAwTF28zAIOgIrgzlnjDm7+d7nQnITK5NBFWalLu2lFcglcj8TfQgZkck7azXT1F6QBpoCaPrxmOoqHyLLDYp2J3/1oBQOmgTrxmQZAmIFkNVIaVWOGZXpnmnIn+r+AueB55wSnIFhlfNVKP4SS0JpfDNyFcmJ3cL/BrDOEAgmUYMVxWek1Ghe5n6OWCeSrAOAKl8D6qRM2hEGeL2kwOXp8Mw8ptggWwAfu5D69BcId8BAQFEhW46eGLGmDCemByMAwQLOf08JCTHu6JCFuy6jQBsw8ShSUrhgUou8oCrNcEdm2CSzxHAmCUGCeD7g+wIQsqFaZ+G/V6fBLM14FiwwpCM9SjMd92DBtxExHZWi4R3gwPlaKF01DAkGaAKw3vCiLUNCzr3Kl5z0LOwIdunns4wMSx3Qz4gjd8At76WZ+E+24ccxiv2lX77dQ+LDCTJQxe+MIX4lM/9VMxjldqkP+tduPGOd3N8OAweKRwCupTr5E060LBOkPUNHJyXqTQg2IO3+2Zpl0KsMxoIm0GoB6QRFGRAWIVvjtA6gLfjtS6MKA8sIcutannknSs8M3Anf++AvPM3bIqd+GTk7h5/QxymGE3rsHGQsMzZTZL6MBAUYy1dizUVhOciXFBTq+LKJjGXb2JfFE/JYiWRVCVGhu5o7XYXWfWSCAENC2QE1G7lhqbryc8OD1OYu9tIJAJdd8MM0nGihLI9E1WTxN39fFiSMe3YsyFGUitCKZq21UjrplZ+aHyCxpE57AGhmGGTUQlkGnsLgBG0HNXmN1D8b+4LzuGU5YbG86bfY2MH4WP9BjWUVHPhwjDsN9VDTIlKosBc85di3ANhkLQHOMjCzhv4l5DQMHGoTSgiBKE2DkUg+M7HAQm4kIvkzvqJqp3D97GywNYwAOca4IMZgm5bmCjYDkbWOPsolKEsbB4Yt4nK6V5TPSSNaVUgGkx2CSoqaob95NeMD53sgDDzlHmBG/SajSJAWXHCbicOWqhR8hd4BNYm20GypzjHF65us7ZYuGNssCpAYC1CF7y7KfgrW/+ZLz+VS++Kitw1a5atEfFmfmMz/iM9vvl5SXmeT56/7eCaPuR3PQDt2E3z2HbgYq/C/VatBZgW2jU4XAYM1XgwFy5SKsSkFiUOqgLcHFJb8E4QKaJJQP2+zA0Cty4Drm2hR0OzQvgRaCXM+T2Bb0dY0HdDvCzQtLvpjDDqm6gu0MUjmSaOA4VPgzAPdfg17bMhIoUpSx2mNwPWRxl56jb8J+XKN4Y4QGG+0Pnw0FDHtwO52H8RTPAEiKDsTNNzkDyWlZQE4OdYaMIAWXUJzVGJLN/mgcnSyk44LYSgDN9JpFDOye9HtoDF+l0YQrLLrjGCwE2RBxVKWAne6a7I7PDFB0gQEai2H8Hs23i+0Up6qei5JfEVfpIrwYdXAJfwiMkgJznvWVopuwAnw04G1DPdB3XitBsQfA2lGTriYReXwzmQUxHjku4vpzhIjew5EUNMDuw/y3Tx53ZVtH3Ru4+V4ZaQDJzmY0AxLCm/zvDTgThNPwJ/m0bjNmoewZVaqwYUPZMWWcsjsUn/XoBzkqQ1B1lZyiHNaRW9o4yAkvRAIrh+jPEMwrADbrns+yhP+CRIg8hX6zsojxACj+CRO0slTAeeI11S9CXpTMy20kBvOx5z8BXfN7r8KmveiGLy161q3bV7miPCsxcXFzgT/2pP4Uf+ZEfwa//+q/f8f5HUjbTR0LTG1t4hFbK3riILwb3BVJH+PnIXWkz6AI1g8wVdjZh0RFFgRopp2UsFBqbqM0hl1FnaaDXBgD8xjl8ugm5uGT9nN2M5Snn0PMB8sCOHJldBbasw2Mj0YLOhjoV4CGBP3Sb+huDNBIxlgo7nxhiQngxJHbYg0IPjvPbhuVmge+B1Pzw0JTRCmABSgjPOXjaI0CSOu8BaFzoKDJ4M1BJiOyJvH3ohwClew1B7jWErk0K6K3emgwPyJLa8lj/dd6cFpqKmyaxw/cx+Sw0vjYwHTZrmpFrISgw6MzMF6tgxpiHYFv2R4VgTVIcL99aw03pPRKhp4qgWFBEWZ8qhtEirV5mQ9kZ/EzgC3kxBYWZQM5SGnUqMHP4lhWkrTr5K4XEcTkk6TS8I1FjzEcJ1dsggi8emVRhnNOTUynIKFCCHneoKaoLoA43DRAv5PosoXGjmaJOMGEBZoZDZBNpAL2BoILcG2+quFQFrtBRYOcD5rMADgcCIFlYYNUdkC09T1oBKYaq7JNW8mxqhnsWj3uY4TLAthEmjXS1cnCIGw5Q2BnfW6L0wHjBcwwHx7CQvKuT4qUvfSb+4Ge/Bm98zUdjGJINfNWu2lX7YO1RgZmv//qvx7ve9S587/d+L77sy74Mf/kv/2X88i//Mr7/+78fb3vb2x7rPj7hmxwsNDaowIoDK1AraMgUEVufFDW4F+W2sfr1maKWAb6TAA4CmUboQ5c8340txkMF7AAfRqBsqTnzkEPORqKAzUBjfTBgmuD3FuDWAVoEdRhDSp+GoGY21H3n0DpDdguBzlioSXLrAsOhYrn/Bgms+xlaKxTMxpKJhqccaHj0EDhDHXUCOQsSO+gDjRwOHhkyaNyA5qkIRdVMVc7QhcNXQAN00vBhwYOz0AT3HE10T9JD02VUoTrJ1QtBZIYWULSFFuAdITixjSpPq6AI4ZaZQFljygaB1+CCgLtvGwaUOfgWM4GOmkTGUXiEWh66EEwmocYS53jzukGDfFopwmYqrdqxOitESxXIAOjBoTu08J0PIH8rwmVqClySGOxToZpzxyeShR5EbAaGL7PsgrJ4aBFE8Ugw/AiELH+EgxCeGTGGhBIYzPwMhfcY/pRagZklNGwswATYtrSQZqQIQS/ifs4ZeotQkDtMFLYhQGNNJ4QXTSFRRkT3oWeT3j4DgWkB79vivAczMF4ukE0BRsVgCiwLigBeGMbS2VvtpsyK0urY3DIsi2A55/yoZ4K6dUxe8HHPfjq+9Pd8Mj71E194VULgql21R9keFZj5sR/7MfyNv/E38IY3vAFf+ZVfide//vV4yUteguc///n4oR/6IXzJl3zJY93PJ3Sz85HlCEywjIVpr6mS64DsK6sYL5VGaVLUawON7chSBZbCW4vBzWDXt1zAdwswDgz9FHIesN8DD91CuRzgpZAgvKkYIv6emSYIES+Yww6VPJ1BATPYqLBn3YfhVx7ksYPAhgI5DJBbFyjbETgfoPuZUvkqKIvBMeFwYwOZY3ec4YBLx3jbuciP0jKfdaGBMQV8oRfHgvSYngvJiskAssAm0KJUABCMC2nqvKnKijBgq4owQxxNY6arfo0YW/JmavBcEFL2jlT4pWcCyFRbVUrbe6bLDpT7H27NWLYFy6hQ4c5fDWCVZvYazh2/FxJPNbwafcVl9rnG95FcW1RgFbASAm9CkGYSmTZwCgwqsw9F6QFxNXowDCzQGF43MdYCQo05MkaRyACGGtdu19PVsV4/y7HTaGMAFXNLyAqAHoccNJ3ryj0p4UGqKzD1HGMR1K1CJo2wICtXMyyprVSBFwFGJYhoRF1m/enscKnwbcEiBWVAy5BKrlEX0WNId6SmDzWTCIh0Z9CiHEsIysLaYDYpyo4Vx+kQKsxcmgRuCgygEq8TOI47R9lX/Pe/5+PxpZ/7KXj6fTewGa8KOF61q/ZYtEf1JL3//e/HC1/4QgDkx7z//e8HAPzO3/k78Uf/6B997Hr3JGlNrGxSVPFQBQ0SrSFqrMQifHEgtwUApsIs3cVgKphHQYFg2gMI9WANEi82G/jZCB+p99F4NwCkFNQw0Lqv5MJIAbYjpARX4AD4BGBbuFO9fcEQz2bibvxQqTNzY8u03ocvgX2BbybY2QC7NgKy8mL8HMAOgAsMDpwBsheUnWO4cFQRLBuwcJ9JAyNp3DN0MgQdwyJcBUnPShtdvpgEVaAJ5mkY2aPQUwrfBXdG6/oZRDZLhpCyptDqjImwT5JQBSFixntgCRJFUAsgI9WbpbBKtIYKsyuNnDkwLcICjtuBhFyzRkbmfTRYZUo9NMbSCCBadpSHZwvhtQEC9ND15AMgTpAkI71vHoRxyvwL74FEqGoTHpkkRScPCDxPK+cQ6eWazqQY35aarPQ4ycFWT1NyZ0rEBW29hgZG1RtY9IH33VP48MDbXfbUkEEKO5oDRVFHsKaRB68m5oaHXpKN/FsW3tVaSC72zCQqSiA2MEZGr826CaibCH8dmG69nBdM1Vi53gGdIitKDDZpSxsXB77wcz4Rf+yrfteV5stVu2qPQ3tUYOZFL3oR3vve9+L5z38+Xvayl+FHfuRH8NrXvhY/9mM/dpSufdXY1BzzSECjcNSJYQg9UF23eOxuzUnihEN3FfLABeXGaWtgT72B5XwIQ+axg08Bs4nhhakAm+soZwPkYm5qv7QgiF18ZFXNC3QsMBWUCmA5wOpAcLIZUR64AFwgg5JDsAQp+GwDKYrh9h7LbPB7N1Q4ngp0dowPVszXh+A+IGTbBcvWYZNAD6vWhg1MLS4GAo/4DMKbkkRIKWGkM9QEtJpOSKIs4u8llFYtvTJr2CFDTEhAE+q/AOiRWRjqyQwmiRiLy5qijEwjLvRA2BBqskUJAEFgOEfFcEh4H8yhh0BWKPR+jUo9Fw8vCUIA71CBoaAWhQ6RKp88mQWhDOxHhGfpJO6TdO2F4IKFChWuoR00CxWEw9MkherNvOIYL9DTk7gmTXAr2qgBFnJsHTCyYVayNbCG6WQdP6TKcek0XFpoLe599dC3kfW5CE9iZg1JNQ6BAV4MNg68Z+FysUGbZ8STYCQ5b2LgQvDHB+mu3AFXqFWGzcyJiwuRiTpg5rBRMd8YSbCeDTgsnHsKDA/TG3d2PuIP//E343f93ldeAZmrdtUep/aowMxXfMVX4Od+7ufwGZ/xGfjGb/xGfPZnfzb+0l/6S1iWBe94xzse6z4+8dvTzjB7hVbAJsEycEGbbIFYIS/gcgH2ey7OWwrfqYSmDGgI7cE9NVwKgOCm2FgYAqhRUXhUpnD6FlLGRh9hFtQeWgp8M3LhTp6AKmTm95fDAvgG9dqAenYPxtsz5PaOC/R+puqqRfXseYZoFKcM4yPOcIM74BtAd2j1dARAFUG9BoIrRzMuFQijj0YUbfWQAuioomX6eOys1dZwUgNAM4FOel0aCIjBkOCmSLXQ1gHzvgGGW9xWD5NFGErTiUAQQ80U6ulETnkARoVEhooUAgh1JwcKCDVYQbkkYMUg0EOFLRH38SDODhoCbOGxILWW19GJpqVdXjVqmArcFGM7IrMXeozKXMIDxDNnv+DaNFDQUpJXEKkBnDJNmPcpQGHkoEtkRBFsSAAKgiaZHQJtKtDoPFwI7wvyHjuO6hAZBMUB2VXorK1ERnrC3A0+lBATFMihUqBviJEz+tjcuTnIquOSGkPh2XEAOBiyhKNUb2E/EWo9JUDVA2BFMJ8VyFnBeGvB9lcvg7zM8F89G/HQVPC2v/EufPvf/klgULzoOU/BWz/ntXj9a16C8yuOzFW7ao9Je1Rg5k/+yT/Zfn/jG9+If/tv/y1++qd/Gk9/+tPxAz/wA49Z554sTT/qHPX9t0nKHGMhFaBG+MkmQ5kU2JNki1qb2xwuwNkEGwpsInDxEBpDpIjapgAoEAhF+IRqr7o34GIPbCf42QRAYIfKnfmiPIfNkIXyvCICnyuGizkk5T0yPSrchKnEbvCLPY/dH1DOzrCIAFF/p1wsKA8BdTzHxUeNLUwAQbjcJVR2ddVgiZ0/gKZPUoTGrXFrjP8ysqKOxtM40pNxhhBkyVpLQKsRhAQsAWgWgsC+zpLPkY6enpnicI06RQhSqCorF2ukCisvpA7UKWHWiwGDYjnzlppNngpJ3lKBclGBfYTVqsOKsT+zRZmKEFNphZriOsM75cIMMog3rRsPcGcBHtPgZyAPoFotEWRkisWNSI+LhDgcnCEcfkxQizdgmYCDirSp20IvoQQY8OqQQQhgKsEEAoR4EquFANAD0KD1OdwzhceKct6XCgL39BhFWnwvmii7Cr04wG5sG/Ba6wZgBWDmkIMn2wq+1KiHxqKe8wjKF2xqy2Qqe7SCqj4IhQmVOlGbX9sRyERF7vneLZbzgv19AzlRM6C7il/6t7+Kb//XP4ayN5RquH59i1e+6vn4H77wd+DjX/4caHqyrtpVu2ofcntM2GfPe97z8LznPQ8/93M/h3e+853463/9rz8Wp33StPuefg3vf/iSYmgeO1wHReeagqhCNgPMAqjcnqG2pzrvvHBxN4Umb0LCKM9Gp8JIoxbCubRVmwG6LNBbl6zVNC80lmNpWioSoRUPoykO+H5GOVTu3ueZnphSqAiMMcTcHJhG1t4RpcE31p6Sw4KzX51x+fQRWmkUWNwPjQshNYivMUaSO20AUEcVemJyt261I/IiQQp/akXrewsPVRCgmDVvT5hwVpCuAWTiH4GCrWGMTJP20FgZ6YHyiIF4oWdM58p05oneJzOBukF3M3wagGlg0c5fv4QMBX428ZThHdIIFSJDQkNhaCcAGEsGMP09W1/bSVKEz0AxOpCHxXhTTIQG5tqn2r04yuhyoNUeks7jk8Alyja4IDgnwgjN4E2vx+GwQm9UC8vF/ZVQc/ZI9SY5vMR9AvoyDpYALDV8ggeFGH8Z8tvQgKcjPrsRYKehKYOMsLZLl5lE3DKHB3EgKT3nERpQ8hV4RaHKVSAxHDoiUHMMt5f2PAFAvb7BclZwuDeE+/ZM0y4HR9lVlL0xdDwqHtwd8E//v/8B//Rf/sf23fc95Rp+9xtfhi/4/Z+E+5928y6rylW7aletb1dU+t+E9sJ7buI9+DXUiQvxsEfIqgvcK8SYe0w9lnBtTwKrI0RC61wYAvDFKGKnQiG7QdpOHSDvQN2ZXmxOr4xoUxi24Et4nSFjZD+l5ooEarjcAWWAbEdg2jYDjsW4G7153qpyt/DNskBUWcQQA9TohbKhAzBAuFki4pNCeEm+TLRxZGwTvYC7+0ivhsTnMnspM2LCM9G8L/3r8QWaSrz5L0JOq0XMJgzdBcnaxkJibRppBbCrUAHmayPqICh7itrBQP0To4qtXSsoC++zGiCXJGdjVHJgqiPSZ6IqNTklKfDn6b0Ib0TjnwAtvdkBoKycmaQve5BqAdAz5mieJipTh+qyJ3lYmlFPd0ejehi5NhaZcAIEWZjXWzLEGH0E0JWGwOqFyxT6CAGuvJYMQcW1pWcpACdLGfAewBk6MwfEtGm+LFuFjRuUSI/3BIJRaZwgMsKhws2BF0WdsCpMF4WIQfasSq5zzjtpUzVS0wAHyp7Zb1hmZmltByzXCupI9eoyE8jo3hqQwYYeP3cPr2c8L4PgVy4v8cP/8Gfww//wZwAH7rt5hle//Ll486d9LD71k158pT9z1a7aSbsCM78J7RXPeSZ+4l/8R2qATNzlseifkweyr/TQRPo1HJCDwSsLAmIcyIXR1N8YILsDsEQusyrTl0GPB+bKcNG8BM/CgSXAzTAARaC7HRfRDGU1136ET9xoYIPbgFKAwYMAKYwDhYH1QTHsDct1QDaF1ZB3Bl0ch5tCQ1AdqLLyMXL3z9O0zOMMpTRgI4DXLNKICItkthJateGSnpoWbvBVzbeCqdbhnhCEcazWeWBijDRcQojxSKKOSCtBAHiUehDUmywY1cnAkG8xKmQByu3KUMswAhZcnAL4+ciMtEJ+hfoCBwEiJl1VhyUdDx7fn4rL9OgIwrsi9I64BbdGHGISFe7Rxr1J7gtaaK3VAEp+SV3HEHk9vbemEa87L1Fwi9yFCtcjU5uR3+0SY0TUpYZWAT1b7yGSDA+Bn9eoMWWDskjkvjYCNgzQ/QKZFePBWEpj4HeW2VZvC2J+aNyDwLQ6OyrIubHUQQJpVIMBcoiwIcDwamXKfmbZJTjPYarKtHYvEVLN8TKeh+UnGOZ1gIToKJxpBS19vK/+/oEHL/Gu/8+/w0/+xP8OnR2DCl72cc/G//jlr8crX/m8K2LxVftt3/S/fcjj177v+74Pr3zlK3Hz5k3cvHkTr3vd6/AP/sE/aO+7O77lW74Fz3rWs3B2doY3vOEN+Df/5t/8Fvb40bVXvfw5jJcfAL2kkJaEngbAhXCYDeX2DL2YoTM5ByJCVd/qkMuFejRzbS5+SBQHvH0Abs3AYWHByOrQeYEdFsjFnv8OM3CYqZexpeKwHGb4fqYuzW4PHGZgnlk5uxpkXiAHFqGUuUaaM3kaVkqEABR1o6j3jJhvTI0L4WPB2a9Z7HLR1GB1Bgm6QdKVUARuYnYtA4nhgCaZ3xZ2b4YVCrT8IevAi4UBTm5E5ZjIXGn09pVp60uI5AHtnL0R9zBI9MQoRAUYWFwzK3oz48VRLhzjHpjmNF78bFkc460Fw21+p/WelSEI3uZwKAGfCkwVNcM9Wasr2NCtqGX1Ne28hWnyWPZNEVyjmeFImYMUnSUgwivigo5QG8Amry9bgsvAfhnBSiVciYwfDQIzjGnvyU0BvKU3e9xP6Z0/QAO5CWo1+EN6qJCFadsa4EgOISmwOOs2TQUaqsHFIyUcAGaq85YdM8lYMV4i8wwc/4sd9GLhcyXCkCpirKLEBcNN4akLgUdTwAdgOQOW84GjaQ5zRx0cdUB4gOI+eEeOD2Dee8y4WZEjIJMq1WU2jA/OKDsOwqzAz/+b/wN/8k/8r/jM138H3vK734b/+c/8CP73f/efUWt/467aVfvt0T4sz8znfd7nfdD3H3jggQ/ry5/znOfgbW97G17ykpcAAN75znfi9/2+34d3v/vdePnLX44//+f/PN7xjnfgB3/wB/HSl74U3/7t3443velN+Hf/7t/hxo0bH9Z3/Va2l7z4GdBdZagBmV4bnpglCJ/VoNWAfXBUzibgfOLKvizQyxk+LywvMI6xXXdYNWiQRHwvkJELsewiO2pZ0AIsQ6FBvbGBbgf4AxcAvGXsQLWFGFw1YxHwMtIAFS70dSys9FvJi6hbWiEi49AKgaP8/9n723BbtqwsEHzHmDNirbX3Pufce/MbMk2zFdFU8QtFFMFCwLYQUPxGAaUFbJJUM21QoNsqKCWlH6WEtqQobRMpQNSWbLKUTqG1MpHGrBJaHikL0X606bIgv+89Z3+sFRFzjtE/3jFnxD6ZSAJybybs+Tz3nnP2XitWrIgZc7zzHe94x+IYHnpPBXh0f04tJbLxgPHQWPRA5t6htpQ4lVZpA/QSboIk794yvVmkOwPVXJkNqBa+MwxQXqxfQ3rVy63drTeDukg19cqbdo7g+bRuzyy9ZnPnJkxtIElDu1N3GT5S0yItZTGzDNvHjBqtEEQC9MXOXYvDKlB3QD3w+uYprkfQJd7TNZE2aTRKtd5tGa3E2yP1VIGeK5JNaqlH0XY9vDMsgtZ2Id5TG5jhLGNKLu6l9nej6XHIBHkAoc15xmeYAtpcd2dDNmpwLBpQ2sL3IRNQIEfZ+SjsBN/nVNR+VSBPlfN/IBA1AHUnqPsMualIVwXJBBgF5UwwnxN965VgPAbgUyUoD1M9L8Y+Thmoe8F8P2N8zww/TWtjSKDrfdpolWHSgGcI4ss+kGGrDPT2fkcqjnxZWCywp8A4X7FXmydFOU+Ydoo3/9CP4C1/5kcAc1wMIz7hYz4cv/d3/lr8vJc///1Ype7G3fjgHj8pMPMTecg8ePAAn/VZn/V+H+9TPuVTbv37z//5P4+v+7qvw1vf+la88pWvxF/+y38ZX/ZlX9ZB1N/8m38TL3rRi/At3/It+PzP//yfzKk/p2O/H6M+mkGnjmRpuDNVaPSyclXokIDFITczHX0HiV14RZoL/Ibl0BgGthgIUS+SrmWmAyubvFayLc1VtfA1ehyAcQTskr8rBZ4zg7nxs9DchJdga1QhZ3uI7Bg8RVDOU1SkKImSJRbt2aBLBRbF4WnBctZ8WJh6cKDHyVZjo4gUCLCKXVsMCJ2JFl97MW1yEr18NtgIdm8OYa8RuJCRCI+VBt5KXLuc+F8DcG0EE4Mk/TwABCBqIt0AIBXI1xU1fIFcJdJ0AhsVWkqktqSDJ8BhZwN8VAIxpddMipSGq0LcqD0CDduWc1bapAqkI+eNqZNpSinM3lqPgcZ+OdQMZgrk6H0EBlGmCeN6Cp2CeVD0dAu/83q/JKiFzhIFgJMF7NSeFNihNxBtZe7wlo7RtQKpVZvFEBOYGFJtRI10+thEOiNUdxrHF+TG3ECYNjWw51ICn625IkFoFHiIOWP0B6pngF7sMDyakS4dQ1Is9xOmJwRJCRo5J3lPUQw6E2TUxOvowvsyPTFgN+34ukowbgpYoSN01zElXl9aB0iviLNt24yWHq4s4W9skFTqbrTS46ac5+gUzudPT4Z8U7DUCf+Pv/v9+I5v+3/Dk+DJ55/jUz/1I/Gpv/1X4KknL3A37sbPtvGTAjM/k2XXtVb83b/7d3F9fY2P/uiPxr/7d/8Ob3vb2/BJn/RJ/TW73Q4f93Efh+/93u/9oAIzAFDu5UgFcGVPczAyCupiqrPDtGb42cjAl8LDRWmyYlmgldVCmBa03j3e9B7ucKvQlIBhgM9zD+hwB2aHzTMUApzvuUJb7FpLhVmkbDwC1sCaaplmlovnjDQO8OoowdJ4WgW2Xe9jABZHPi2wQ0bZ82dVwKDYGktuhqMxDVj1IgDP20Ama2Efq7bl1Yq25KM1ikR4xzDtAZrPGeBLDbAXQgk3ipad98QB6oIaM9F0C5u0FgXYBFsewdaCCeh2Ke68Hy11A3AH/2Af1TihU2mlzwmAZHrblAotynRjIdDBXJCuFvhZQn2wByJ4AjVSFpFeO03QlGmff75DM4ITc+hcOmPhDmCIY4T5nC5xrurorS4am9M6cTcyxrGmqR4jbwgSAIGRAWrVR1Ft1IRFJCs8QHA7fgR7D48XxRrcJUCmOTUrWTpDIgXQoxDop0T2IszvpFW5lZa2BXwWiCaE3Qx1RecjSo4NwC5BZkc6seO1xnflHAPSbNCbgrITaDOwXFh9NT2VIX4BuV4ApRlkPQeWPTBeCwYHdCFEy9UgpxDlD4HwrVEyvLgOasF08fX5nw1pqjBVlH0YL6aolLqpyDdcB2xMvekpADz97mt809f/Y3zzX/1HEFX88l/18/DaP/3J+NCXPe/HXbPuxt34YBrPuQD4B3/wB/HRH/3ROJ1OuLi4wBve8Aa88pWvxPd+7/cCAF70ohfdev2LXvQi/MiP/MiPe7xpmjBNU//3o0ePfmZO/Cc5/IyaiHQTpZlXFelUIIkusC2NIU0P0QWT3I0hOex8Dz/sqJM5zitrAjAgLywLciO4ETN4KdyxS6RRlgpcXrFMGGCQSLmLSeNs18CXBHLYAQ8vecwmmF0MOljs8NfyZ4sg7geFm1I3AcDGsPpApDqi5LqLgQMQSUX3i/EARi0tp4v3lInourNvbQm0ufe2/0qN1FOAuWBiJFIqvhReX1XIAvS+PqmxNPQQgcc11agUCsGtO9hteojI6GQKUGswNi2ggvoYAJgX6iI8VLhRGSVT0BdOpsgUdBbOI3fh1TFMDrNAHpHKAyKtlRM/JwE2Cmyn4bRsZCngUKdw1SaHpBqiYGH6DVFKLQQMLI/2LjBGS48ivI3MISnEwlEpxvue2GOsGGCJQmQneGECjDeOFVcBOGv7TO8sDqu55BbobXHeE4GOq8DH0ANdFVZZDQPsoLAkGK8M+nCC1wIfmLLt30cQrSEIlj1TuA4THN5dcHiHE1hlRRmlp8jSwo72qBTwaqGAO58cw6MF+XJGgcGUbcs9DBbrSJ8ZjJwrpSpkAtKpQCUH4NLIwHk4I0t3Vq4txRqpXfZsY+qruYmnI/2pbBdWAZl9rdLNQqY0gL/D8c9/8H/BH/yc/wbI7NL9R/7Qx+DXf+T/5k5IfDc+aMdzDmY+/MM/HD/wAz+AZ555Bn/v7/09fPZnfzbe8pa39N8//nCxOuPHf+Be97rX4cu//Mt/xs73pzpY2cCgKLNxtzyznFksd++N5lMCd+BU6AmTE83wADr/DgM7VB9nyOkEzFHaMw6AJgbTWhi8IQx0AVhQK3CaYJdXPJ5VAiFVSNo4rQ0JWBa4Vcg4AvcvCJyO7LadMMKHNUXRzMk04oOLkk6fDXpTUZ/MQInAF0GypZW6oDcYGCzRVLOJIAFWlJRINTmYLgptioY+pnXCbr9v2g5pTExjZnzjT1IrK7xqXJuoZGJaYRU/tEaVHlkBi6og1ErDu5HVZuqIcmAAQwSnEMd6ijRIMBGtQ7YNCjsLnUYxCp+T9rLyejZwbsCRH4U/Sk70IoKjDokMlAN+f4+yE9QzhY6hZUn0CfLMgJyPFCHZkHojUPYvAhmpHKAm0kkSVVg90KqwMmvmhbIkqKFp0dDi9KoyYNXluPJaBGPSmTwEAMaGwGl4LwJ6l/M0MBy0kCPSM+ZIbrDZIeegBsUV+VKhxjJpFcUWssMcqVY6MYPn7yMI8E4E7vNBUM8VamxmqRVwH3u5dyoEdOPDinxVIccCjGT90jzAj2wTIQE4dHH4SDamHDLElmgYa9CDoErkotyphbP2bHmv3PNMIENDQc4ZLZUu4cFoWaaXVDoGkBGmvuwso+wTQZWx3cK/+cH/FV/6RX8bAPALfuEL8IWv+kT88o/4eWylcjfuxgfJeM7BzDiOXQD8kR/5kfhn/+yf4Wu+5mvwp//0nwYAvO1tb8NLXvKS/vp3vOMd78XWbMeXfMmX4LWvfW3/96NHj/Cyl73sZ+js3//hCRQoQqHLEnoZoWZDKqTz3pE6yqCD71y5AzvfwatBpgKBUZx6cQD2O/rCXB0JQC7OGHWOrE4SOPUU5Pb5marAzRGuKSqmcv9siR16H6HXweFAocblFXSegScuUA+ZokqN79KAjBLYYKpIFdg9BMpBqQ1xgguRACuP1+cWMji6oHusANQXSICh5IgKqAZYKMB12zSOjMM14LuVf3gT/vIf7JQNQIpGx2VB76AYr12DoPeS4YbBEI1AMUZ6IFIuDtAHiIk9OuSOBJsCAgJEX6a6Swx4N8bS5tZ1Or6rVodeL8jHJX4fbtFmgOxg1ZCuZ+hcka4XlKcOsJyQJrIovhcga78+unAOmTDVQvCnDIbtyxWHVouz31zDBjAXAh4fFDas7Q20Mn1kws9AMI50x13v6e1UY6AVjQxZku5RJCZIC+C1EqyeyDLVJBBR5OvK75OE1W2LI584n+3JA9yMuq2Rx8kngSzWy/kB4ftdUDWF7QCrkmxQTE8muDqGaz47Sdl4NU2VUyQlIAH1PMMvMswqUhXoZUWaHXVQapxmGu9ZAZAMvlMUDPw5AC9MU1o0BpXmDZUUDlYGmoK93LB5bEIz5pEa9oEwjyCHNJSYox4y6j4DYwjWp8rKOwB1z+fzX//oe/CaL/5bgAPPe94FvuRP/3b8ml/9CtyNu/GBPp5zMPP4cHdM04RXvOIVePGLX4zv+q7vwq/6Vb8KADDPM97ylrfgq77qq37c9+92O+x2u2frdN/vkS8L8/xnCnmaC38dGJB6dUjTF0gr03TuqiaDLmQO1qqe8A9JCpwfgJwIdlR7F22UAikFMrEs25uviupauRS6HFeF7wYAAplnLpTzQjpeBJIXioSHzJLtqxPkfAfJQ6RQQv/htP+XQlGJ3hhkNpy9U3DzwgE2SlS+cGjTyQS7I2GMpxuDPEEs7mheJpGhikAvXmPXGsDDrL+vD0eUQRvBWQMxhYyG5AiWJbqDO9C8aqAESg14tEIfRzjVxg63lXJDNAAS2RrL8X2cAl86ylKw2iz+xQWenZVARvZCNK+tAxobEiyKlgVIiczeqUCDYdPLa4w3J+QJWB7skW4mlAHhuwLOrb2iDgFR3NdeVBbCp6gblkLhdUdtDXfWlgOktw2W9XVSmq9NsBjNus8iHdbvZ7BXcU0pRHdgJ+FmDbar2Nw+uEKmgrxU2My0UD3wudHKNJeUsDg4SohqHTUrbJdQzhSLOPKNYf9Qka4LLAmbn4r276inAnn6BHkwkrkyoA6KugfKyZCuK9JpofbGFPXMMd/PLCMvACZDOhrScYEPCXVPBkwXbgzU2fKhWwhsDAbbP11AXc2xwIcESSvb5U30vkk9tb5oreEpmSZ08b856H2Tec9Z7s7PK/eo/Uk3wTJmXrt3PX2N1/6Zv42UFJ/w8a/EF3zex+OJB2c/6bXvbtyNZ2M8p2DmS7/0S/Hbfttvw8te9jJcXl7iW7/1W/HmN78Zb3rTmyAi+JN/8k/iK7/yK/FhH/Zh+LAP+zB85Vd+Jc7OzvAZn/EZz+Vp/5TG/X9/wnxImO8PsL1Cbhw+hJalWHRBBmDRpznSD+x5tFCb0eJ+VMJgLpEmSYAkIA/AkEO+IcBuiB22AVjCOK/0nTUa6MnxngZwFoHPMwHPNAUIiKga4mJ1IJ0W2HmKPksBwprup3Bl9SA40k3F/qHi+ILcgUtrZ9AFxODXcufuvFludPHput6vI9JT7ZL08u2mk2keLZFm8lrXn1mAG7Q4L7H7T2giaDemsJh+ihoTbyexliA3hoaMToddvYdQA6Gs2tp8EZc4Ne/BVJYFOBpUFXYYCHaKAVNFTUCuLAkWiU7sS4Wf4v7kBCkGvTxCntrj9KIRiyqSKtICYHEEeoqqL++pMgigxrSN5NR7IBE0ev/+0Ois3REpul9PTwGLhvgcvcmoNCPGAIJNfN20LK13VxPckhiLyiMJMJQEXnlOw2ywPau8mFYx6KmQ3aoGN0cZFXhyj1oydGYbDjGBi6GqwAaBpwxRnr8WY5XYaYbvEoYbaqLylWP3aMHwcIHeTATko6BkzhHLCeVckK8qdleO1HRE1ZFOtgL91lojaaTvHL2FBBDgBEDma9JS4Iuhng3QaBdCl2IyUEkrbQ1a6nT7XMT890RhM0ZCQ51sBTJnrHxLJ56LDau7ctOzVQH+4T/+n/EP//G/xDhm/P7f8+vwWb/vozEMH3B74bvxc3g8p7Px7W9/Oz7zMz8TP/ZjP4YHDx7gIz7iI/CmN70Jn/iJnwgA+OIv/mIcj0d8wRd8AZ5++ml81Ed9FL7zO7/zg8pjpo18NsIvZzIPMw3UEC0JvER7gu6zwgWo7jN0TMji0KspLPeFfZDGBN+NwDxxxxfWrp4EKMZCpZzgGKCn1kzSuEgl5X+qZFAAYL/jQpmUaad54WI4DpEqCMGqVfjkkJyQzFBmY2WONV0DQY3GR9kgNGtzdv5m+W6UrDbdrKG71LZ0hHVwtGkYuYmfEARQ8A6A5LFrTvqdjAVKJfBrpegBZjwADcGc8Do1F+Du0KahLYpS9EireaBCE+lpKNryG9sfGKCzraXd5qEB4T1ufXw0/ufO39mY4U6jQ99n2KjI0eLAhujNpRlYmilhYg8nBZDZlLQJysv9HaoX6CmYn8WjeWiUrkuA2OMCXQpcDLkY/DDAKuAjetuKJgSOzEy/14gScmnzrv0+3iPg9ej3p+EpENS0CrLeFLJ5BrX7nYGqdDX2nDAE85OuCw5Xc7BgzhRXEzJDIccj9HJBFopitSTocUG+KRAk1JxRB4EMAjOaH7K8G/AnzuECDJcLu5zPFfnHHkEvj3A34PwAG4deVs1O4AobapjjURyvUf7lxvsmMadtzx5cww1TeexLxespDmCi/mV58hDdxjuC7qlgAeABQqTlbaM9h+dWGca3WQrGNgTZrfybjuLUaTXdF9eI9TlrDKUuBrta8M1/7bvxzf/Xf4JX/rIPxRf9Hz4ZL7/zsbkbHwBD3LfGGj/7xqNHj/DgwQM8fPgQ9+/ff87O43f/jv8SD3/kPXA49NEN+xydn8Hu7bjr3mcWqWRWPRmcuylhQMzXBXozQeeCmqPpY2Z5tC6RJggQwCDpPSDLzQx9dAV/dMV01DCElqZAcubrhgF2tgNUoccJuD6SnTHuznG2B4aBKRwR4PwAHzLqLqM8L6jnat3FViGwSn8MAVmYcpZwfMEANFJkRK8QksqGfRK/gzs36ZWi3+ai2nQE3WG2lSYXh8yFmhCraF4zKJXptmUh01AqsMxMuTUwE746MmTIOFLwPA5MuyWF50TH2F2m+LIZ6EmrqmEc6WDUmFxBohalDimqYZqxn3V2zBW9TN8zGRczQz1EoEkJYoLhusCmAhWW3QqEpdxz3O+JVW0+ZNhZgg+K+QE1EnJaaDg3UjNzeEdBfudDOAT1fId6b0A+GdJSo4JKUcfM65VAI0Yjs8LUGVCUTTJTBXxMrJqZeN1rlLVreP+IEvA1xgXu1O8ECEUiTrR4gdbt9VFgVCyDwN2gJhhOBemaqULMM2w2+EUGUmJX7WDRsBgwzQRJ989QzjLy9YT07mtgyJieOkO5T71YLrQ9ADiP0rEApwWSE8r5DgIgv+cSejnB3IGLPexshJ3tsNzPWC4Slp0gX844e7chPTpBpgLsh5g/AhOmYm2XUC4oyB8fLdCbSs3NSFNFiEALIrUJov3SUlIBeMKSwaPsXcyQTwYsBt8l2D5FsUFBcmGp9iGzc8Vs8NloOHhGxgrGflsrs4a+O5BiSDcVviMgsiSoo/b+aVkFX/T5n4jf/om/4md0Db0bP/fGTyZ+3/GEz9J44ZDw8JlLyFy4437eA2oDqnHD39sbyOoDEpHAB0U9z7BDhp4KfC7czd8sEJ8YLYZgUAxkAmplKfW8AMcFPi9cAFWBfaCIeg2fZqYmVAA9wMfM7tduwBAMTa2sdsoJqBpls1GqezMD5wM80iFqwpTEEMFYQJCVGdzyVcX0/EQBrzAHZSJcWE2QCsLcbpNrSJGKiZJV8dBlKG570rTxGDwXUVZ5leD4VaMvE9aUU1kIRmqFlwWSBFKiSzbife39ktaFvtEMCl4XOKuBWjAWMgp1AIWsvUIoTjJRSELNg8GvZ8i9ATYklHMCmbN3LMjPTCgXA2poqupekY6O9OgUzsMJthf4fkA9T6ijopwphhtDetsJMiSUixESKQp1QVUEAzcAKYUrchgguocGmqkS7upTN7lrrsaoShdjgCXSZgz2OagXbfeAeiEprVwZzQAXFg26XB3pVJCuF4hQ5wIBU1OD9nsuJtDriYaD+xG+B5YzhZ0rtEo3ugMEclmhlwurB88GLE8dUA5KAJkAvV5Y5ZQHAqpBkTRDr8taTedAOSTUF99DvndglWHMbZkr0jXNIGVRasSmEqCg3avEuRDgLk01mC1BvlyQrieUwwhJCl9YBdjSs75EqjM0cmROpYMYnaICL1iXVB0WTS9rCjAYqVKZ2dxUKuBe4Z5CkO3hPE3zR++sTJSdHyvqnoC87JTav9AADSfO3b/0F74Df+kvfAd+/s9/Pv7s//HT8IpXvPAnt0Dejbvx0xx3YOZZGi97/jn+zc0RulTYi55CeeF9DI8mpoTcABRIypCR1SGyj67U1dhLKLo32/kAOx9hpxn5eoKcTlw49zu47qmzgMGPC/TmGB4wRpt1IIzhlDvG3QPI9RE4TfDTDOymXjrsrZw52AE72xEwlWBqouFkGjNKaH4sN5ol/FZE4dV6p2M1x3hVsVxQX6ALwl6/XSVS69SVMIigNekTR+u43PQtW9TCdIRS8wJgbXugkRhJEB8gkWISCFxa6k0I4JoTcjA4IoUpFNFgnKKKTFbtQivdbhb23XQuAV4Q6Tcaq/VUSpRIJ6OGwzKiJYVAoZDLGYMo8smh04zhmQKVxEt7s8ANqPcPuH5+xngh2L+rAC0ImVOQK47x3QuGk8MPA6oI0lSj9UABxgGSFXaRUfcJelOhpUDF4EWRJoMmhbvDspM5zCytTnOl10rXEUXaLSqwGrsmQABrh0wFWsj6dBQjkRJN4GtA4CJzhYhBc0LZRaoy9CeyRLMGBfTpawBAffIcdm/AfJFgAzDeAOMzBfl6hj6aaTJ52BFAZYU/ucesArmaMD6qSE6DO8+CchCUXUJ6WiAJKPsE35HVslFR7g0Yn56hJz5XaSm9zN4SINNCAbEJsBuZ9s2KZZ9CIGyQyTA+nGFDCgPFSPXMFakS8CDT44il4MEyzgW+yzAZOLWb6BchC4vWIk2gbgLomODXC9LNwp5su8SMtAuZKwgbXW7tLjz+175XpH49SQcy+ZoWAa2DvSWBZcH/551P4zNf8w0Yc8Yf/N2/Dn/o0z8Ku3H4aa6ed+Nu/MTjDsw8S+MX//KX4b//734A2CvmD30CnhXDMyfokGDN7A0VOFbgBOiSUXaJ3h2XMwPDxZ6576yoMhCXZIUPI1MNVyfg4sB2BqMCU2xll2huVOmdIeBC54cRfr7vvZ8YoLngy25gSkqB3m1bmb6QLPBh6E0Y7WJsCIOOo7GwupClaL4rlgCHYnxkqAcNF1pQV1Al6HL0ih04ut/MLRFwkmAR2muDXQlNx3uNVi3SBMyNmlfqgzzSPlsfmvZ3WQpchui+7XDU0GnrSvurdwH39k9JtjJMDfD0k4oS7kiLeF0gNcOe2MMneoboMwvy258hm/GCB0xV7Ed4FtQELOeK05M7TE8O2L/HkJ6ZoFPBeFxg+wR5NHGnfhhR72XUUZm1ODrLxJVl4+lUocfC+QNA7p3BUgiPJ2MrgFOB5UwGT7Rf56bxggiZFDhLugupKQ/iSkR6FR4BD0Ef0xUtkAYCap5M1SjGDj1IOlWkmxIuwAky5jCKdLhF6wQV2BAGdv/LuyHzBDs7sMO2gC7OAUCHiaklz2Q1PLRSnoB6MQCVrUC8fc94PmrsB8wFqZKRyl5oDXCcCbYkKgYrIKcK1WhsuRgE9ENKxwIMCbYbIKUiFTIo6g6rIWqGE1gvBlxNZHBD76anOL+Rc8h2AnimcD3sFTwLbJ9ZmeZAiV5WMktvtGo5kTWr29lJBkeKww5MxdUDS/fTycmgMusY1WJCgOq8xstU8Df/2+/F3/zG78WD+3u86o9+PH7rb/mld6Z8d+NnbNyBmWdp/NLf+OGA/newww4laOd6GLkruq5rWTGof5CbCbvdgPneyMXPwQA+JgbL84xyf4fhIQENbekrF/fdnhVKuxG4PvFnKoArfF5CHzNSx9H6PHm0SZgrvVAOe2BQilFzBK85ql5CfGgj2x0omLIQi3QRAPUQKLYLIPE/B/KpYv9ux+mJHCAOvQpJI8BLVPhQNuArcAG2R33s7+1zECxOgA0PIKPS46U31bA7U1AoXT8jOfUqJ4DpFDeyMAKyVrIYd7h5g1CazqABQMOmf5H0VEEzoGsCVxsE5YXnGGbv6UU9Op2Az/dINzPktCAnQYHDNEGWinREdC1PKGeCdClMf7hjuTdC9gl2U5Bnh1wvkGGH0wsGlPOM3aPQFw3KUns47N4eAGAPRtRdQpodeS5kyapD08osqSnMGusm0XVb+n0SkMGTEJkyBWohDo6ZocLeVTnuSAGS0YtFlMyENMGxgdU710sAngQ77FCfPIMf2CpkuDH4UZBnR76coNMMt0r/pUyGqFXyZAPSsQm/vaccZTYyNDuBDgPSsUBtxgIg1TAyPBag+SAVo5t2CrdoYXduP0RKV4KBqw5bTtC4D7ZPyFcL/PoE3w2r95GBc3DI1Nc4mROThHSIVDICMCeFWDyTMJZwd0ND40uVaWo7ZPri3BgsXIillcXHfxYp3GZo2CwjaPi5+idpa9IKoKYNkDE+x80NvKVaHz1zxFf+xX+A1331P8An/CevxBd+7sfjySfO/4Pr5d24Gz/ZcQdmnqXxild+CNwcPg6xm28mcsLAX2MxGjOFpk4mYLhm/XLf9VaDpYw6AIMIcGKPJndAMmlrcRCAuENOM7yUKKlmkPZSyJzsEwTKXXTbOTqocdnxd13HI5FC6qtfOLOmRmvz3+bSS03V0Ks3HBHMPXay18AuVyw73TjGUsvhAQhSBYgIAqP0Tn1AL7eO1gd9qHbU461MCBZMwPvYFbqjl523CqfCpo1kaUL04ejMQvNiEXNYeMIgjNNEwPJbYhLit6h8ag0GvZUYlwCvrj0I5csTnZeHhOV8h/rUkxguZwyXM71o3IHFsHtmwXhZqGVwYHz6BJkqPGUs9wcs9zPKPmO8HJD//RGSBHUgC1EueG9271mQjifUrEw/OdOYdZdQD0wLNr8dhDCWW/MQskKh7eI35iwmUauc6Wk3kX6faSDTdGHe768eC914rb2fuRKtkX40i9YNGXUXFztT4JHnCswCXWhUBwFF6+7AYQfLOdyJawhdmX6UasAESDKkk0AnIzN54lxUALie0Vo8yLSwj1keWF0Vwd2HtLa1gMF2CfUwUFsUDU5lcehUoMhsEDkm6DNXkOsTsB/JsAypa7o0SvBdqM+p93Z8puDU3wQD2kwovVBELgbIVCFZUFXZ821UeKVvlbrDD5nnvjBdJqYsB5c1dcXGp1hBuntP+7oDPkj0WQsg44j+YwFkWrl4FpSBfj7/4Pt/GP/g+34YZ0PGH/6dvx6/77f/Gux2d2mou/HTH3dg5lkaw0BzubrP7JWzB+niapCk9ISJ9AeGTM1FNQr05qgSgkNvZuR5waCC/PQJkhRVmD/n69mE0g8D7HwPXDM4Sk6Q3cj8uybmy0srNWFOxx2AVcgE7qh32kt4fTfAdpnixtipIwvKKKhj4Axpek/vmKMNqRaAhWxQKoBfGauvYuUkeeNoTrAeVvFb+hsgs4HmDhyCVN98nieJ9E2IlBsg8wRfSsRW7U6+PE5DHlhBTaWBHpLdNtKLXalhw7KYRyfNCCwmKxvVwGs7Dea4oB31UEwp1zPyM9cwFZQhoe4V01OK6ak9dk+P2D9TIEtlw0EAcmLfHXFgePsVzCrKix+gnOWoLlLoEkDZDVIydDZWjk1kQKoQXKI620LcFAyLI+9TTyOYKiTREqBZ1Hh8L4uO6axY4p+SWnDbsA0BUD1yT5w/YOVbAGgRBURho4Qm63YclRpVPtl7hRCUoDCFNkxng5wKP+tsT7DS+o4VQ7qeuKk4H2HO9I1aRStT1mjgWqNCDarAGJV2pUKOSwiCFBgyfFQ+S8cJkB0ECk8UzNsuwSuQrxdgqsizQyVFelVQzjKGJ86Am5li6pkgD0MiKHEAM1iy3fyLojoxRcdzpugokMewpnk4QQ06N7ExXZoVof9yg6fMNWM2SHZWkzWfJpCp8Z1EV3h+hkb6t5ekt/RdewbieWpAxkfBslP4AOjcHkPBjVV83d/5Hnzd3/4evOJlT+H/8md/L5734K6b9934qY87MPNsjvPz3vzNg6JGse5MK+7wKVYST9x1zlyYKVgFZDYMD0PbIEIh5xLsTGbAlFKhN06KYMjw8wNZn90IjCOxwpBRE6ufbIic+S7HomqtcW/sEA01K3zMMBfICXQSzYDlhLoPI68ldm5GQauEFIVpCGeVk0TWB4J8NCAllMy0RBeFtl4+ALqOZiHl1ItEHGQTWs+qRr27MAC5RzBiQG36HFZ+hG6mfV7T0nSDvRaEPfpcre/pu82u8+DQiAGt35SKrxxWlNFCwpAOPP+6Dwdo4izqn9yBmwnYZehkyFeKugNspyhnCenSkJ65Ac531EZBSfuPGW4JdcgM/AuQC5CPRqB0f4QPGfmKJdT5SJrE7u3gpUTlDDVVyRwe7EYNl2jLdI8VpJ4SbdccAVBaZ24xh8+2VndF9VJ3F4ySegOwHNZ05DiB9yopAT7QkkwADGKVF3gGZKjALjRdMLg78myQyxPk6tjnvdQZciqQNAN7plPzzQSfK6uXco42DzTZ08Upqh8SS/PjvD3KlvkcKivblM+MuUJvFuqKVIDdiNp6dS0WrRIC0A60YJC5QgaFne84p+eYB01YHvozmRvgQmjX2KuNhpkG00zwmKT7Bkn7PAO9gACyM6J8Vt3pTaRAPcto3dFbXzUx9JYaBoJGbwypNYO/DePWAM5mz8FnBKhZbgOZQUKETobSVfBvf/RpfPIf+3qkJPgD/+mvwRf8/t8EjR5pd+NuvL/jDsw8i6M+/4zVMhqBeCpcaEsldRzVHYhO19gNkCVy/gJWGO0STPc0wjstQNPT7DP8sCPdHQLKPCs8zahPZvjFGfICUtqagCEWPRXYkCGaIMeFKRbj7h0Lz0OiL5DM9CGxUUJLI/QIGRmkczM6c0TXatrc2651pQa6WDSASTrSY4PN88BUjYX9fYg6Dc5NfHR/RugTpAl3gTWoNlQRGg53IfsVv0Ziq4Em/pVq0UmbfBK7ZLMcVlqaqYGaxxdY1a4ViezK+jlEMT2oaFQ3iTBI1AGAC9w1dtXA/GCA1PtIVyf42QgU4OwdM9MH+wyIoJxnqIwQU7JWbnCrMER/KK8MRCoQMC1TDwP0umAMfU8+sWeWw5EfRSQyOlJLwynWRKTSWRv3BGTvYFJFuit18/6Bd+9jxrdujyxs1QDAXWFD3Edwd880l7KHV04Yp3BrFgeOjoRKvU7TcClZghppDSlRBBTmegJQ0yPhcxQeOrZPZKiOC8TZ3BFjBqKPETVk9NiRUik0rkZ9kUY/qGajUIKqGLSnhtlV3Vn6rbULoaU2l28yUV7q2iIDYBpIIoXX5k3rfVWFjGm0MLCzEb7fQY6nkIQJn5VCwCfFKHhOTME1PY9U44aiGRC2zxew/1drkbLdNAAUvltj5KKST32bgV2Z0cbShKan7pVVT0JtmC5kgK3Ze7dhTJ19y7f9M3zz//2f4VM/8SPw6j/4cbg4+8BrTXM3PjDHHZh5FoeNDEgSCx4AlnaeZor+dgM7XU8Lo19KwP0z5vCdPiSwBB8z6n6A30wsTy0FdjYy+N4YdEj0sDFnz6XWlPDITt3YB4VvFajh1jsqfUKO7MskItRuKBdEMYfsR+Aw0tMkAd7KxydAxqCyY4cHYTdrAbjIhm2/SIC2HN4zTkBjg8NMaRSYELtg7yClRiI/zWS0CGaw9vxp6SFlawVRj15UgLiuUS+Ymbb77WCotW8ANmmnzdimTNq/BV0f5NuXbv6uzCgw7dV2sg5oYXWXhUSi929oXbWnBWmgD0g6VQKALMjusGOAroGpiHJvh+X+QIbvVDBcLrADQjcFBvDWk2cy6HEhOzJm+JjQM2KhmxJBD7y8GbHLb2KJ4MKiOUDHkVBEIOZ9oTYIkeYAEP21PEfFnBCgmjjSQnalHgbYTjAsFenhiSmRMdEpOwE+JFruDwoTiocrHOlmBkxRz3esBCtsQ+DeArZAj4Xi9t0QlWQUMOtcgMWgKvAxoZ7v2aQ1WjQgAVgirbMbWB3YBOVhQ8Aya6NeKvpapVOlEeKgnADFOjspKe7JXOklMyqZNml2DCxB9xDxCoSpvkHDuBHs8WQO9QobMvueIdPnJ8AXcjCVleaSBnQQIUBnVbrYFzR+rDvONxsjdTVzDaApdgB9U7oh92sRx5ItUeOtPVSfO00Tx0VxZfTqQOYGInjDP/lBvOEtP4gnLg74C3/yU/Crf/Fz3yz4bnxgjzsw8ywOmWIXBwZ1qUa6+HhiIIrWAlIqHIW71icvYPuM9MyRO9Wz1KOlne/p5nuaQ9zrSNMMTCXaGoQfCPg7KZU58uPECpZoOOl7gU3squv3n+BO9GaBPlOoHdmx8skudjRz2xMs2cAFTI1pDS1O/UDbbYbglRVQYJBzMgYU+kQwGARp4e63REoDQGgsYuepNFWDOvJps1hLq5DZZKcizdRSGgBYuqshYk6InkpcUcUZSFHKCmKE320N3TG2KShvq/T6+nW32RCCBHe/EjY9mETQD00tA1hcO209m0S46w+9K0J75E2H49yJy6ioFxkibCpoGbBD6EmOWEXJShahnWITrfL7VwqlFYDSobbjlwREe/R4PQMfNAClgd+uMTHx78701Bo9uJzzRvmldabzcDoVGgIedgAkfH6Uc2UxmBUyKINA5wo/FfhuIENlFXIsSMXhZzvYEwf4FH5DyQg0hL2QdDFqUsbMZy+n8LZZIJOHL0w4NodOijqv4JuUrBCW6ImWyCBpa08CrBodR/RFQ+jM0LVBnlYg5G7sq6Zke5oDcqvEcwmX6eaobM7Pt7hv1cg47lhC7VFtJA1F8jAQEeKaJtoH1rRpw+lpw6Z5sH+2/tfBisiqr9HN8+qca3Qj3zwiirBo2LBBBgIZAGUEN2CIVF/8/NEzR3zBl/8d3Dvf4a9/xR/Ayz/kebgbd+N9jTsw8yyONBWYJ6jQJ8RLhR+P8NOJYKMWAODidu8ieiQV6ClRnDkX2N64U3PS83aWgQM7WfvMiCXmkOsj/OyA5cX3uDN85sgSZxAsaTUgJ9TdQD3Ejrt0luQa5NSYDIHtBtT7Iys0zjJsJ+w7OJAy1xJApiLcgcEFVINR0jWIN7q62fpLiCG9OBIcPlcsF7HzNNLnVgmQzJl/tyEhHSOdUsDu0WBpb6dF4rhNQeqZ+f/mUwOnoV9ju6R1FPcGkjYgBYjdcRy+ElS1qg9pu1JgLcUG+vaU/4x+U9GRuvUmMqdnh6tABqBogp5G4HqG1wI/G9mbZ6lMUx1nuBWUe2fQrPDFUC4StADje1jqa/d2sASUAZBBMD505EcT6vlIY8Exk72ananKxEo5Cd2VgW0PUJyl5ymRg2nbbY80xPaad5daus82PyKA+gsJpsYHAdyQrytBRk5QhPh3LzBVpNmh1xM8JTIWBliwAV6cItYSYFwEelNC6EqgYz2IBxQVZcCdjRuEQprMwwzRdwme95AbbgSYhhUCh4Rb4m2mFMOTpZZw1Ga7Cwl9W2MexQCEDqmlPsUb0gvgkYTpuw0RyHQT5yIbbfL6SfQXU1uVRKICpMzjZJ6bToWAJymsgs96S4duemT1Kd40L9sp39ia8hjtGDqX7qe0XeCicrEGcHL1lZFEMNIN+LTjO1Az1xLAkaZ4DBXU5cV4dJrwe7/4G/AhL3iA13/FH8AT9+5Ku+/G7XEHZp7FMdzMKDZ0NsTdaCd/mhhQwlHVw9wOsYOSqaIeRvi9PatxtjujlLiztwypIQQ2alX8Ys8KjTkqiURCjJtYIp42QbsGGFlCy1No+OZ1AaxyJxtAxSHAIDD3Xt1APQPY36ZR8iDroFvvemWungwE0wCpslGeHBckA/JZwvSA34s7Wu39pqom2GCouwRdFHpFvRH38CzjBbCCCG/BJVGzMdKjBUuNsvFY3C20Hu169PuBlTc3A6ztpr3REysjs4kSTUvSdsQeDsZ98Y/3xYYfXhEpQUc5HwIwhFA8wIKEI7MvvE9wY9roTDA9jy0MhusJMlUMD4HhqkTFnIdmwuHRZsJGQLIjnQguK4AEDfYg+l9V425631g070JmCSQnEgRCVDHpzCowHyJdZt5F7jJmWHOYDu3FchDIPiFnsK1FWPl7tBYoDyimHR8tkMuFzEoWpntygpt1xwA/G1EHAn96wRBseCaToXMBhgwbc4hQK7/fifPPdyOwAyRHV3AjsGksGEvz2RPKB4UgKtegFEcXiQay6D3KKIIONjL0R5grZGbfMqQUlWKypmDbVCoSWqV1DVF3aqiCBXIXqAXrhRppyrBNcDIxdPiNo/o6TflPgQj1Vt7KrB1RRr/ZiDTq07GykCu853rVNFZw2jEUMDWZQggu/ZNX0XBsNIBodeF8ljz3Q6/O2u74X9/1EJ/0Bf81clJ81m//dfi8T//oO7Hw3QBwB2ae1bGbZ9RpAXYjZNyHu20EiN0OuHdO6r1UPtSFnhsKR01kT2y/Q5qjE7RE+qiRB0vs5MYEvyzAqSA/cwncP6fY92biYjkOrH4ZaGW6diqu0EEpIs4VuH+ALQM1AKcKP2SkIxfleiG38uDsnB2MgxmqCNef1EAXF6yWOmkiQagw4wSNVEvB+IxiOQgbJm6BQhV2yTYaktWdAc5yXUwhFm36mfcaDTnE54tQCLxUuOzYcHOaIfMcwsgQCPeFO0b42jB94ivVnoC+Ostm4e6YJ9iC7fkZICUOnw3iTEHW8wzZJWqDCnUTvs+wgf4uKLtgMKhFytcVyz7Bdgp/3jnnwFKRb3hNfFSU+zumALfMFTj/JNKSrqxUEuO5+pB7ioMaq7jnUcEi2PjQCANyHYG0WBSJ8dr5Lnp6FRrWiYDaFRGCszgOS6uBeqZYnn+GVATlYsB8LjB17KeKNPGeAQmW+H3MKnDYRRqR7I5Uh96c4MNAlmRr4igK3wHwCpkqp0XzdgIoeG5BfWLqzRsgsGB3BoIc7yDHoyKRTEirnvNMsNImQ8cCHnMSMTcqn6s+1TxaGLgxDdboEpB5rZniaXGCQrIoDWxhTf0Y0Dqq25YVaWxJezYMgbz7H/xNbFTiwQnmCdg+EytGCTDWqtgsvKIk9DDtWW5vd2rGWoduLXEqaX1GViZnQx0BKOb4G2/8H/A33vg/4H/3aR+Fz/9dvxF34+f2uIO0z+LYP3kGnSbo9TXSM+zTxP4tCZgmPsDne/gLnoA/7z6DyEJBcJoWVjDVyNsnim+3aRQ/GxmEo/pJnn4EfXSD9PZnuCtNwv4s+8zd+ZBghwRkZZm2g14XxmDjiWZuNXOxlMpO1elUmNfuQTm29gVchJMwZZTargtoFT/Na6ZLSsIIT2usmmZIlzN2z1SglanfAhMSVD1o9BUlvJY23ayBlTFpfhfts0TIjOUU1yqzpcGY4WcH4PwMcthBhoE/z6mv1j3+RADvOgKzaHeAFdwAfLoSNiCq/Vx6mqn114HJesrCLsdlkK5bgdLrIx0dWpXM2yCwPSPB7lHB8LASDA1MP9rZwCqZJLBDhkVFFGplg8WrGXrDKjUAvR8Q2wWEHV7CZicOoHgIunkx+FogqAFAm+AVIXInC2c7VtnpsQBTpE1AhklvHMO7r5EeHsnAeczTnCAnw3BpoWfRaILokOPE8z+V0I+QMdGpQq8ngoP9bsO0ETDIaYYsETVTgtTC77+0JqJhIFg8tDIIfBr1WcViPnmkmywas4Lgcymha2rO1vx5L5VWsFJRQ9Dr3jE4GUZ0oz2HA0qmSWpF81Ti4yQ812BbUAhgERsdX2FKf/7WvNLmeQqAIdLmcnt2OBdswziy4hId0Bg2zxYn7nroCqT2XIchI0ux4zu051Jvn0/TvwEbIIPNWtPJHw+3Ycff+La34td/5l/Cd/yTf4m78XN33DEzz+K496tfhnf9vxak6xl6feRCKALZ74BagJsbRvvdyDdMJcSjSmHgYvCFbqNhsQlAV+FhTsy3G4A8UOTrgNwc4RbNIM931NhIC2wgG2SFyRqLclADVBSmCg97T/Z8YRTOi8ONrqIwIBWDj0IDrtlI7ysgoS3BbNyhxg7UUlTCxMLvUcoZ1l3I1wW7UTHfa6wPyFKFkPLWutzy9yph0Lua5a0kzQoWuIBHsLYaPh0h5lSJdgaNA48S5RYMGqXdduIGiKXOoMQ6G7vN2OkCKyOi8Y8APK3jghpY7uoSnYvbffDYLQcArAwQnli1Vc4S0gnQm5ltJUKH0iOMSGhNLPxFKK7UYw19SADXMKHzJKtgOwSqrfGhJ6yGbP3iAwKl2HyeORcH5ghkCQ2XSLTGUB62sVpTeMOoAEPuATUdK/ISvZhsDfpe41kwsiXNgRqHHSsA6xK6HqfIdkiAWzRpNOpsovzaVeBegZSBADRejCDDV/2JxBzAYtBTgCRlNZwkan1EN89kY6ra/BFb5Ttt3rS5tFjXjXiIjnugdl8r7hqTI+t199gZSBMHt+kqElVxjPqtukz6OfF5aSXrqB7P9ub5aCNSZTxuo3PWV3jM357l8QBEoY0jGItnob148yh0lieu2a36wU73eH92+7AV3JjE91Hgz/6NN+HPfeN34a9+0e/Gr/xFL8Xd+Lk17sDMszhe8ktfgh/6d2/HcL1DejRB5wJ9VIGzAwFDAxOXN5HPr/D9jgtKo2KrUXcABI0d1LXHCncxwpYCTcK0RS3wsx1k3EFOE3Cc4Bd7QARa2ItJ9gPN0YxaB1Z0RHrhLEcV0VrJoh5FP3FOSEwT1IxudiaVzIGLR8B26MmCRuYCSzMuMICLwwaFHEEGyAzDFXfbyzlLVruuJ+QHAvQCm9YiACBtb8GckLnxlYP0DaABKHCt4LVTEOQUshcNEEij7WVzEOd5epQk6eDspRRBghtVD48P9KDSVuUupk3amS0JW3wIOjuADFSR3sDTBvr+UFtAoW3JjowBWtjpuqZ2DSjM1oXHcxgBqwI+KNQJYGzMcS/D4l9bEGuBMPQsUYkiwbhIq7YKfa0sZChqSiwoE1CvESZ6HfxYhRh1QRoGjR6dlaU6hvBgSseZ4tb9EBVuCssOQQZOSwCt5gdkUNBo0nYsN08L/ZLo6TQB08zXnwQ6KCwloCwRHB0a5egEn9FiwgkGxR0yLdwsKOcne5Hx9ToV+LJB2c19zhVYPEBSgApvWhsyLA7OoZbeY6pMQqMV8ziAKQFn65sRfkhoKRwJ8LAB332uSweiHWT4Ck6pS47JmQRoJdfxfu1PFp9XoIF2vw36gFX0GwxPY7a0BvjgVFvBG9bnoh93g94asPOmoWuYUWnv0HRWUMUJjs/5S38HL3riAt/4JZ+BFzxx5yr8c2XcgZlncfySD3sh/uHFSNfNJ3ZQAMM7bjC865qVRJk6CD0tEMxsfFgqMMaONFEPAGwpWKA99NZcgAcgXY/wmxPwxAXq/QMUCn2owC6h7geIAEV3kOo0vdtn4FSQDKx4UQnhLSjgq8ZgBYTxmANeY4HxAFgsx5RRkW8qGZpBu5agjk23wCDR0ietwEeqAIcBfmJJeb4pSNcGfWKHcpECdLT/fF1swz6/d6VWhcJiUfXN9jFWwdrMwWQ9TkM4Elqkxuo0BmYrXmxxSqgBUYBCYl2DFG8INSaeNhx5r+6I9T4hTM/CgdUaYAB0ATAb7CLDlRVGYrTytxZ4RGBJsDwh9OCBrGxEV2E7tITgOQe7lhWWCaQ8J5g39g2AROsGpS8M6Xxb9T41EGGzsq9kr+wsOjaPMV88ql5SsE1K8TGMbSwwSG+N0UuVzYNsZNDWVjrcWA5DzKlEEbTxebHQBok7cFrYriAn6DRFh3BAxzGqszJarzHXBJGyGvg1LVEL7rXGvd+CWYEMMUcaw7EQ/IpIaN2MaawhhyDYIpI38BDHbCwi2rFk1YhpYuVViHEjcdUZQPBWhU5FVwaxUYF9jpP1a6xQA9ISL7EGHjTYN07o/toNBu+MIqu80EW/3rCq0xxx+9lSwe8Agy0Aho0GqT2zwmP2VOxm9Cvk7TrwWbeMSHHGAfpJCt7+9BV+6xf/NfyXr/pUfNyv+AXvfdC78bNu3IGZZ3H8yl/4ofAB8ErAUQWYf8F97C9GjO+8AZKi3BugpwV6maDHaHa4FDaLrGmzioGLVFu4QIMrGxRqGTI/hA0JOOygE63fl5fchw2ZO7oE7vCi9DNfV+BmYaCIVJIHq9EWEXH2vgEccsjwQkEuBXut7SAX6LrXHrgtylvrKNAaOKYJX5s4uLb0BvUi7kJgNRl275mhNqLsWIIt4aHhGn+isTNCC3UnuNDi0VPGezm2GnfBrXrpNn/tPcghB9AJYXNbrG+9fHMrgNAHCFkhAEAjd3RlOLbiSQd6R2IEm8KdL7qWwFujPwgkTA/tMLCcWFvMkd4AUMKnpFV1UY9DcKMOlOYy2/RFQwM80pkXVKa9zK3vwrWxR7Fh78EYwSxAogKM90FCP8UddYr2Ft5TBGqsdJPi0JuJrTLOCdoI8hQ4DJQhZZqzSaQubMyQMQOXE+S0wOcFfr6H7xLSTYGcTkil6dGUTRUPmWLeOD6mJcB03Ijwg0FL0cXNbhgXYCVcS72082xtBeBOoNTuR6Keq1Vis4cXJ5FgFVWvXi0bcN4mWQDLrkeK+9Z8i7onUmoCLL6HbZzkNuif48GLflgAn0tRgQcY5fMTrtYBWPvy0vQygYCsscVN5M0ZipYuoo9TM9Bs8znAvGJNt7aHoc2H7UPl7TJsXgf0Ngj9mJtzXNXL/Plr/uob8fIXPoFv+rLPwPn+zk34Z/O4AzPP4viFL30Rn7noeCs77qbm5+2gR2fAEIHd26PeGzG84wp6eYIlBnh1OptapvDPtPlQIBZY7hZlXrg4n43AoyMkJSxPncMOmc97SxNUWzUQIix3rQbczFyEQvugzYKlUcanysA8JujCtFFrNinqLFHNgIxroPP4jKYRiGKPtfw6qoTY/DLKhxVQyUCwNNQDRLWNtlU7zj+kQmQTQpOjxlLxxiQ4F2GFxhnZyvbYepzO1AiPBxO6xW62qI/vIAlCIl0B6VoXRLCAx7lDevlpT30pL4aH2W77ar5LsNMmsg0KWcKpedS+o40wgta5WIuFzkUZsCQBwsokVFt35+2zRSBeY25E/HOPPjy8WW7eUw0ewZwXlJoQG/i90szOzCiVQGJomq6Y3wjRLIQNTxH3czH2XBqxMg8tlRlaKZQCQCFDuw0CPU6ocKaWzgY22JQ9Rbk3E2oC858NPETjSU+JgLU64GvzSN7HuBnByqFZCwhWdsZ5LJkWprlyVH4JIrUZLElKvBbt2ieJdgw8pkN6/JUOUlrV0xqcGyvT51RgT/aNis9supNI397WNwmfkcQ0o7Q5EIwMovy8z+bQnwm8i4IJMqU/vBrn3h/ofp5xffpzJLGpEUgKUNRYqvacbbAc2r2X9Ti3MEoDbqk/eI99XnsuOH7kHc/gN/2Jv4qP+iUvw1959acjtTT93fhZNe7AzLM4xjFBKpDCYK5GDxTPgulDdji8bWJlRVXYIWN54X1kCPDOp4GzEeV8h1SMu8Eh95JT12A/ANLlN0ukXmJ3tUtdaOdZSAmVKHE1sMlkdCr2yZBuCktgwTJlNrTMqDv6hIhwR+21MMANCTWFnXtaU0lcK5157cjl+0KHTwgrJRjAhdb+AwDLnUVoWgMYOZ9Ufa0gbfKEphoUHgtJqN2pgpQiSBYG04QIXlGh3Kqi+r6vlbc2EWUDLimtaQcw4PfguFk0ZXusXgUdzBZ1q7x2afWh6efeGAKATrVlGzz5i7pTeBpo9NZMfItDYAGESb2L8T2eBOrC/l/qZLwyz43sSjuBmCeikVIxamTAoNdKlltXdIKojdJTBN2YTgvkSDbRzka0zJ4uzo7TujJ/EKEb71l8/13MIaFmJFUDCpBAiwJfHCIGK/Q/oWdLYQolZ5rEHQaaxF2egJsjcO8cIgUila06zID9SBCQBwiMoGYRuBUAOVK4YSIIpYu2gmmfplOpZHYkRWXcmIMltFVfFsiw2xMMZH06GPSypkhuTSSJdgGBLgPEeAAhbyLilINhJBvowAYgY61McjbR9FFpBp6b+Bdd+OuNZQF6zyWUtXpJw/ng8bSTxp7AWTEAKL1tmvzHt98rUtaI50OM52FbgLIBN42T6YC6PzOP7SS2P9teynaiUTH2P/5P/z/8us//y/gj/+mvw6t+52+8nTq+Gx/04w7MPMtDzXtTNyzGaqEdKz3KIWEIZkKqQ2aDPXkOubeHLYVUfPbbu2oH3Iy9kqLfTXlyT4GnOXB5gt3fo5xR0GgJkMTgovNCVkRzK2pAWioMDjkMUVlSKEQGgUkdeRzxADQRy1J1VI3FKcCLVEcdHFCgHtj9eRgVw2Xl4igCL85gBEBnwAdAcoJVWxMYlUyDLAzyNXa2zYUUaGCEoM5jwUYBTBOSGryGyR8iZdRcVGWzdgI9LQNE2XEshggbe++pJ+duOJgNmuhJ6Id4YIlrZsHDq4PppLbIymZZbuXbDroCawSCgxJEtOoYpc4kTUBV7pwV6GXhLYa24UrWpJWAt7SRbYIXu483j5CGdMK0T7GyVxLpg3Yc5Ql7Ad8XonHfK0TD9czJ1GiJCqO2i+bZ9YodKQa5Wbo3kTYNiMc8QQ1NSLBqJmy6+sQ5r2Fm+qyeJcBGDFcnIGfofoANgKQBPmZ66gwpWlfEd4vqMIx05W3GczYOACw6V0dvpTAdpC8Tnzkk7eled84TifnA/mjoQAa1soFrTuxAL8DqMCwdobfA3bMorW+W+VrynnOAV85bcwDqG7G8o4uN27FD1NzAgyw1WjToCqzM6QSdNnoefiKf77ZxkhW8sx9VAI8sZM8CWMjtW97n/IYHWtOvDWQ31Ny0gbpiE+lvigN2agvrwQ0dxGyfbwB4/Xf8j3jDd/8L/P2v+qM4tMrRu/FBP+7AzLM80gSkhfoTAfsN+UCxpy4enYvjCRyUue4hww8jaewwj7LMnbQ63TuDJYcURz0olsMBqRh21emSujikLMEMKEvBo2RVTzOSgV4Wpxl1UNTn3YPsE2xUpBMBjRRh2iCHWCNH1IxVxlvqJBZOnSu0MB1mg8PPFGUE5KBI0cuJulNqWwDEDjJWwPiYukvIp0qr+jER0OxDRLhhjK2LArm4lUF5TZNCFmf34ZkmhF1rkhLcwhrfjDqc2G3S8VTW1Xabo49Ajka1RzWZN2GztBAgkO1hYgFuWp8+2jFCt2IuTBcVMjmtNB0qKGNrIxGipNAQSWMBBiH7BtAXJUk0K430lwIS/Xe4m2dprlYKGiRASIsO0rbkXUwaMWOuvJ4pKu5nBn6ygUxpaqHJnASaEw9xcANP5vRmKc4d/ZABiSqxnHg/GKUjZRZ4ywJ0DQNZASDYMGHzzAcX0BwNKodwc64OGbynfFTokOz7kRsBKMuzlxkQh+hIV2DMTBWZwWVgP7TWkKrv/tGfWZbNS9cq0YVY2ZIkXK/JhsjKDrRUzKCcyL6QEQzwKuaxGZGGeCNdxrnRnJs9h0/OEt2tE98jiJR02QCccCd2tLkdc7ECWmv4zEh/bXOR2c5bi3SPGLobeExJrBNs/Ypdty+3H63OTm5BSTw3/fkIFLMFRiu62YwGBP3WP1eNjQBPnyb8xj/xX+HLPuPj8bs+9lfgbnzwjzsw8yyPFD4hLTil5DB32Aj2OJoLalZ4FtRzxeBgE70dUMKlVIIpYFdbJVtdC8wVeqzQaUE5tHJboWHesUBmhx4nihyHRDnAIXNn6Awqfv/AZo8HgSZOj3JvQFqYfoJSZCxBb6MtYsESmAO6sJJFHUxniaDMjnzdykwjzy8WDunWUzECoayhpYLCKr6OfUkKO35hWip2k1znojJKNzvaFELX6EXkg6M6kDy0LKE38PAmAUAtRKSUOrDkv9YDA+gVUpvx+C4wTnmjAXgfq++tw0jXMLjKmi3IUSJs7HIOAcQUbmxwyEq30Au1CrK6AhI0zUbs3h0EFtJM++I/Ark2x+J8LHb5oflh851odWDNXyeAVHWk40K36l3qrBS9UMItN8qO00bLJAKyF4lVYY3tkhPbEJgDfsZ56zOgFjqUIbGJJoQGfA24u0UJc8y3HCXaNwt7oJUK7MDUW7hhe1xTTM55GxoUyxk6GFwNNiZgVNoJxFzxnODN9TcASAMMVdv9lGC1AtxGObxF+gYJBN0OSDXUwvtqOVEbvATDE0yFOGBm0RTTAyDJei1BFtFzAkvy+Ty6B8sX7GNRRBTQDhxECI4cK6vTZ6yv96t1RWdjSm4QmmVD69EmmweiEymb9ztiHxBMYX+8fH39LUykm/Po59SeUVnfuGUnb7E26/PnAP7cN/9jfP3ffyve8BV/BOf7O5bmg3ncgZlneZC8YIBwcWABMDtwpijnisMlad8au6G6E+QjYLNBs8EkNm6xKNqg7GpgGTIbS7ufOSG/B/DLR9DzM5SLPfR6YjAZcnfN1eLwzNRR3Scg7cPPxFFj16yFIKMmZfl2o+TDRA8SOgEAMlVgDBUrBHDjTjIJrfeF1SR95xQVN+yc6z3gR5clppdgLBXPicyRaDiVerBa3AWrba9ybPksjhs/4i42xMVg8NG5eWSAFVVRSuSMFo+vf7dHL+ve/PKx122XzhZsbgUErP9ua7EBTAVuHFW5Vq8NH1tpqlgLQh4djivSoix9bpciUn7dXjUacIqvr2lBwtyj6bismg1p5yrh8RNAr++mpe+i2bkb4YjcQJGsTrjtjUIhMLLCxsR2G43Lii27AEw/TQWaEkoiiEilkFUYwrRR2aMpRXWPRApSomxYFTAoJDnB0mxwq91BFongxatF4BT4YSSDKQgQaNHfi8yLOgXm7AQdWpa41s2dFiLs8zQmNKM6WGFlVzFUUWhz2U0tfcW/SxpgKUGyhnkwWRdzC2wUAt5Kpok6XmfHagA+Kp+9UWELN1B9O5AC/jewK1jPDyzflz450Um5zjA2xqQ95wGkrem8uv45hLuhoW5kTX8e258NaLz33mB9zCzmmm2Ogc3rfZ2X2PCe3ueb9/nVT8P5DLzr6Wt87B//K/j6P/V78JEf/rL3PoG78UEx7sDMczQEDOKeHKk4ijkBhQjSzYK6a/wt8/NqBvdtr5zG7khvWOeloOwF+f4Bw7uuaCwmE9JuR9AzVwa/nGAj/3Nw98boCMAlPGMsOu56eESAFHYAGagDcwSr6iwsMECmCdr0DJnGfXAgzw4LAWeLjiKh4Wmmde1XUwRaM/4XVLlaSy0RcOkElg/vyFJtgQKZllic0f7urGaKFB0csJE9pBALsVsEi+jG7X17+R+6kRHowc8WNHr8NtC5vXn0x1b3zYvaV8nSc/+3Go+4hHgzAos7LFpIwIVpuubeGik1uESWzGGNwYoKJQ+tT6uG6oFl0Nj0ChkgR6/6QQrthMX9KI48VXYn39MwRBZWvUnvCeVhOsfPFhcyG1HxhGAoEEAaAIH3YYBlhVK0RHALsKt2BlsY2Mqm4DDAxgwVXc8bBLO+H3gDFhrrNRExIEhO8W7Zj/A9fZ8EgCwKHCOYRvm1SzCUHcjF3EyJ3a2XAEa7qCLL0VZiGJCmyMdKtB1o/jvt/kfFGUbhOmAEZNrcmFW7zoxYxG+DA/A8bZDQwpD9lcawqHYBOauv2iyUmGdEFU0H9jizgtCltT5MvE/oRQaIQzQgAzQ9D3qKqU/7bep2+zhsQU38rIOohon11h6Ao65v7o+XRPpXNi82Vtel2ZGP1OS9+v/0rfjYj/twfNWrPhV344Nv3IGZZ3vo5j9jryOtTgMwFZSzAcMzR9qvn0WOeyrcZVZS514rBGxgJ9vdUBLIiQuSPTgAZ8NasVNYzdIz3yqk1qMkmikhh3tYxQvQOrzIwsXBMgOYwZEW0vmYSzj2xgbdK6QY0rVEc8TYfQsi2AJNxOkjVzxXpTlZ2OmnxVeR7sKVyw9sGEgfE+7eqwJqAj066kjgZaMiWlDzOoTOohXfNDfV1gkZ8BDMUnSpVeBiG1aGwOnHI2e2i25f7A1r2mU7Ngtpu2dbqHMrA9XNBCPot18oOrhsJ9UCkY+gf48TXKFQRC3tg4TApG9gPUCFxamZAB7tBaCRXiBIgDnvT3we3ZsFitpuKdM4J4JyHwWwlcnpe2WNINhK7114zptA03b/0jVJBGcaYMnTmhJBgAqd4rybaDiud+8uXwL5CQBV1MPIaxHiXTWDS2hmYHDJwSbRhRpj7uk+D30VlKwotVaIBq3sTaU5Ooi3e1q9B3ZAu1eSZUZ4iYabcNCJuAtJBGoemrfKSq2WWjUL0AoYyAKtnavZgNXBQO29d1nMAQisUy+hm5EmyI75Y2GauJmXXR8Wa1gXyDdQi/j3BrR0RmWriQlw0x+Lxx+uLajZfvZjP+sv93Yu60v6cdqcjS/SGLnDu+vaXw0AJsc//Y5/hU///n+Pv/V1n4vdbsDd+OAZd2DmWR5jUhSvt55FqSAwCOocwhRQrd6rIaRUyFyRk2DJwkU3J3Yanrx7S+hikFPhg70faO61sIKC5aikytmtVtHzM87PNKNoE0kgbrCc2CuxGrwo7KCoSaCTAceCdDPRr6ZSc6Oq8DEFCDJoFvg4QA9DmMk5LAubJDYtATb+K0KgIa3/ighbBBBTIF0voXERyPMTSaLZAVPk2bAI4LPBDwofvC/unsNAT8BdrQsdR8FqH4kyYxOhLkW0d5mmBkdur6ERLW/tVldkEatnA2+bu93iR0Wvur0lvtzsbPk7WdNl9tjhQqPSu5BHWkoMTINIAIEApx4LeUtxbTfJbYfe/eajgoxVZJVBUTKBlYIMS6lsU5CdQlyhWDhVQ7XWFJJ+MuYeiCfARTAA7sYqpw7JwqK+rtfRkQkqKroHD3Lc1/AB8jBm9HBPZgVcfNGh3adgNHbDWjFTGVWpP8t08m3AowG0cEmm94pA3cKjiednUVXnA4W+yIqKBD1V6CkE0SoBBiVSXOiNWNnyQticcY5mkcLvocXjfsa9HxSWyf5INDg1hMi3gU9ZAZ04UJSXvl2zdl/dwQjQkHHlQyatzUikfVZyUjpR43J7vrfpzn8QrXQQZJvfbxkXxwqA3sdOYQtyNrr69xr9kLb5hzz+Gv6CXeEdh/cYu3S3z9D1Te981xX+t7/va/Ctf+1z8aIXPHgfn3g3PhCH/sQvuRv/Mcf5foySyfVn4qEbKc6cPgS5VMYz46ouZpCbGZi4O9MwImNvEunVDg6DXN1AjicAijok1MMIf+IMdthx95hidQuLdYpdKyzs+NUc6VTDVbb2FE66mpEvF+wujSwOWqrAIZfXwOU10s0J6dEN9FSgpUBuZuhSgHkBYNC50iunAsjSS6k9GAOPKhcBqIc4VchUgMUIgEYiEN0LajP5UnTH23Q0qNcIflzdmmNoW7jbv6sGw6DSd/w2RBoutBzcibONhIus66WDAktpeXhZmYHWZBJrnADQUyfdTj7SOvwzUnY9CK27207fe3PveWzEit8BIIDmOtuM2Frgqc1obDsaaNL1u7GxYmEbhCyQnCGinIel9rYDaBU57RjhzyNLhSxAs8hvJcwE0EBrGQFtfwawbStSQvdjWQXKQeHEfSDqD8YhKrZsTLBdzG+JZ0Izu4eHBYKNmemfTCbFE+BDhqTEjvKHIa69QrJSKzMkIErGmboJUGaI1FsLyhF1IRQnCyAzn1kXrGnSAGctxSTOjQi1ODy+BGPbLAqCZOHtHRPKgd5PfpbghxQbGn4fG+gIXgZAcjuvAIDtcsbU8U2aVA10rg7mxLPQtTxt5kdkpTvDLJsDxvxuQuftHGsmx7enn3fmRDbH8NuH5Mu4TN0GLZuNwOPPVX/r5udwIE2tQq59jw1dZIY0VfjVjN/7h/4qvv+f/39xNz44xh0z8yyP590/w6Mfu9yIXcGKoMrg2ESSOlf2rKkGOc3wpUJ9hswL9P4By2BkFoYMGx06C2A0T/NaoTrw3yXEkLHI9d09wicD8UC3nikK1L0gHx0pC+qg3Hm6YLgB8uUM3yXkd1/zsxXQw45f5uoaQC/E5f9Hiob16WtoTqzOCH+OCsB2AknRhA6gILOJFbPQSG1jMmd7RTVgvp9QzwTpIbUAejJanZgBU0UZK+YnR1aTRHrEAGgzw0veWaCuPIxyDxOhB1w0XPRwjUVoS3zjrOrA7S2B9P9tXkAm6LHt6wbprMDHg4FhTPT1eL6pOmoCXCe7I41p2hyuVZF4q9CKWNYaknbg1AIQPLpS898an2cisCHD3dhJPQCsLJUBfpe7rw0FrAYtFakCJRqWrtv59oGCbj7Stv0GamvEWdXU2ChxYKlseTAkIDtkqVAkrB1PPTyHGHDNFZoNsiAANzUTza6/CZ/roJDEFhtiTu2MCAGMcA5aThBppeTrBbYxQVUoek8bhq7TXhJOwh5tLozgRhXlAAwWD2JPpzpSNKr0MUV6MS5dEvg+RS9QDwdh3jWaTSpgDqtN0ByVfrFh8RLp4gCgLkwZk1VSxvJYG3hbvINQTlPlPGubsAYOWgWVrF97ewluwW5HL+l3YC3Pfm8K5faztGV+8NjvHv97dRrxCVZjy6454xF0AcZLsmo062vPjSFfF+Qj11322nL8mT/6enzWq/4TfObn/GbcjQ/scQdmnuXxoR/yJP7dv3p7DzrrzsN7oGP6BdTDLBZiST6Q/UEVpQB3dHqIDI78rhn56RumR+qJ9vcOyC6zMWHobsQMMnHXpSnx8+BkWxJ3ovM9vrcOCaIO94QKYZ8nALIbuKMbhzATS5DDAThNNCQTAYY9v5MZdMzw6sg3E2yoiLgCR+7NBiW0FW0TKRXcWYdGohmL9aaElYtyDt2QjYBXxe6mAlcVtqshckakAagTUAct9NFswOLaFAAQeHKW6GqAmNanyQnTemn9Bpd0SPL4fV1fcnv0N2y4lkgnEHi0SBYvbimfzdtbf6mWrrpltCYEFoJVB7E9KfdoL9B+VrCWIodgFQCa2BoOeo/UOLQgyp+VniuhAYFGhV1LocW8kz7HN/O9RTyLVFNLL7X3isAyhc12vu8Oz2liNRLN75jysUF4k8Xhg6BoRrK6gmSLy5kTTC02DREtU4vQ1G6ZAZoonG0ACFONwi5nynafmVZaQk8kce4LqxRtx+con5SMa5wDJBq7noTP72KAhwdNSwuCwMzbzFHOOxvY/qG79YbvjOVocSFA75jdaT0+QxLGhx0cJelg2eL4vVKpXY7HZm3veG2gHkdiLjyWJt0UFfU/2zPtj53eLcDim7m9HRuahY+FrC9sn1EAr+vzI9GmoT0nTeAvNVhwB/2bAKCyXcpwrJDriXPkbOii7G/4G9+D73/rv8VX/9d/hHqmu/EBOe7AzLM8ftEvejH+yT/+VwBiHQ/gAol8OZzlzVPB8PQxgAFg+4GN80ZS3jLSc0NM6J+iBhkUWgobx3kYguUI/LFzdZde7ioAMGZoCBmtBfec4OcZ5aCwPYO8zhQ51kOkBVwhZYFPlZ8XDQwlJ6AWyEw/G0nK7t8ukMMIAV2G5UQnVq2kzMU8dkoRfDOpcngjIxxmyk7LTuPBulOk4pAbamTKGUWPacmwYzPA80jvbLZ4IrBIQdxahPO683TR0Oq0HxhYwmNMxzkizdAWzVUc20d7TQMELX2GLbbYrsjr51OzEQsw5BaQAVYyqO9ygSiF5TG2pamtyaFg/SgxUJsB6a0vJMBR73kT9LtMNQSyLKd2lx40W2orbOCCIYm+UU392gLM5uu2vj+WQ+NSrDMriKDsMW/ZXiC8UsL9Vhdjhc6QeKzqBFtHpyfRsKaamvBXIlp780aJ68FdPBkbllG38+d/lhTYg+XfCzuQe0lY7mdgVKSrEg04DbJIT5U6uCHxIcT2CsjsvTu0hqC96Vw8tzwO2g2l11AwtO5xb6JpKsFsVAh2UCB9mlvMCcA7E9MmD4HF5np3NS/vW980pe0U5fzSxuBsQLu3jUibmMbL39JishX/9u+3+XMLtB//WwPp7dnYbha2oNnbD7BqccwhJXqGeQMy/EJtXYE4gczVhHo2sOJNNsdIgh/812/D7/89X4u/9Xdefdfb6QN03IGZZ3n8sld+KIP2DpHnRwg5mR6RhQuhmkOPM1sVKGjsdTawUiSndRcFRzJW5FDcmyCprA97Sms/FDiQAXeF68A1pOXnS/T3cQcW+mBIHYOOFchskGPshlu7AkOvRII7GwfuBRhHYJq4GFcyM+IAjlOsRBVycugg8HyACVDO2y5bOmMgsTNfmyM6MUEF8rVj2dGjQ9xRs8CrQI/UJlgIjsXoQNts/LmjjZU4QrCohDOudnGqABSWNuVxIBWHdoGpVKGgtFWXxCXeLs49KLZVXR97TezWAdxiexpYapBENkipf852+OYDWxqsrfoSC7kDttXVRGWRJwUGRJWHda2NewSnEPyKRGBvzrPtuzRvk77TDqFrZdTrhm0gOPRtukkVdQQwhVuut4uiZN6EXkLtonhB9Acjk+fm0aOMgEbMkY6Vt0xj/kXDaCA0KOYB2OK7DAJZLKq54uKL0JXYgRrXyBNBcAN5Wiw8mgS58P5IcepjspEpzAJA6RnjjlSM51A3ouXwpRFzVAiQQzfW4nEWelOF2LsjQ6dAGPFdW+Vdv+/a9GcEnp6ki+rfa2zxfgrRPbC2xtgAjy4M1vWt7zXaXHgMq9/6/WPMz/ala15584vA3kjrewjUg2Vs2eAw7ONaBujsGCam4OgpJagJof9y5JsFXupauh8g5pagWYF3PX2D3/Xpfxn/t7/3J5Dz5iTuxgfEuAMzz/L48A//kPCcAOooFCZCgDFBH4HCwKXClwWyFJZfptR3iW0hk7arMoNMpNohCru3B3BguWjWzULifXE2x+prEYugC8u8HQASkBaDLYY8CUwMOlWkucCPE3vcpIT8cEJtXjXCRQUa7Al2MI/8c5iRIeUIfg64QY8z6mEETCGLROUIhb6upOd1jmqSbrkTQcaAw7sXzGeJDI6wDYIvgnlM8IHtDERs9cxqQ7EujEDk8hkIWBkUFHzTDDD3Egs0d/kWbJaUBhzQvSzaOu395xsA4m2Nb4AKXfjcKlh65QW22Gd9/TYGtOO2e6CtbDYW81vxxB2p7ajDHLB/J2MqwsOnY1MZzJRT9H7qYt0mgnWE1iZ+LvG5FoW/2yCrDUNK1zRYgAobI4ZszfOEvbQsJQL8uQaIaZqWmG9Rfde+S3PgdQfBLpgx9AzYGC7Ei/X+Qa7UhqXQlvQghgD5cJgmnrM5kBOPO1UM17FxEKUvTw4QaADmNU3VStW9i//jmsXc1EiX0XtJwAlvaEJpVwkTQnTnat4i3oOmgeqTw9pcWz9nMxHQS+UbBt48I9vD3MIcbU4At+cHsH7G9vUBMD3xDU3H1T6PaAzrs4X10jR2sr2s4TMH4tn1nnaXEs1rHV0bw2eArFY+VaRTfOdgfN3Be3s9I10XyGIoTxxu46444fToSNPR6jhmxaf85q/Et/791+LBE+e4Gx844w7MPMvjcBhhTVzXqHoBZDbkiQI011iw3YGk0LMRNg58VmvTRkb5JjwEkjOw28HOR5aDtuoQp39Gp5JD30JNChkYLLaWsjZlvzv0VHs/xaZXEQhwM0MDTEkx2F5QcwaOLdZ77Egz3BaIFS6f8wxo7GhOJ8gkkN0A2edguyXs74VpBhVYs/CfiAI8U4TqqtDZkAfqE2q8vu6B5bwpDQXu3OELIp8ObFIz0iPXLQ8LxO+AvrvtIAXBxjTTM2nVYHG4rbZF0BdEv33UdYHexpoWfNqf8epYl1fCpwcE/kSCzJDtyi/oupDmFNvbEjSWXEjBs6eTbkBdCwiCJmZdS0ICyIis+opAi508iiCGSBeyq7tE8CNYtCjJbufqCPYj3obwXWnpuxbcmn5KF+7EPUUrEGP8b9VPrWcR4rhwh8+GlKJybUhkqdr1a2DAPRxtAVENvQ1FtBj4mc3Z2BP9kqSsN9Pz+p10pqjah3B2qRWYjFg6U3zc20koATLc2APNDaIER8gCg63zIK47WbUVKd/q8wT0Cr9WXdfn3gaodH1MIJkOZJonzwZluGzmfzM53IKk/gGI68QHQavcbjECdFNHqVjZTWnzuzFtcZ663h5+T76mP6rmLAAoTk+rtAIWrYbhulDjs8/Rq0phVpGuF6RHU7BiGgz25guVgvFtl5uHlODW336JP/Ab/zy+7fv+M+wPO9yND4xxB2aeg9HLT+cAIuYYH87I18HEQHsXXxGBD0OkgtjqoJnLaQFQHSUr/GygNXpWIJNJMaH2RsypuUlR6orQxySBToBGnkAAeLGofgJ0XtjMsFaWIZtD9iNwPtLbwpzNHyMw1fOB7Q+qwWWhngeAjwMksRS2iT1hDplO0Ic30LMRdVSeOxxuwuAhQvATpeMSJoOtFwxGRTKgDIiFcAMZWkwOBsAdUY2D2NH5BtTEfYlt8u2S0hXUAHG8FlEaHQ3pFTJIwVLVzdoeO1O+9vaazzjk8XfvQbv/Xm+/TmQ9z3WrHM63j6cEekUQAUvXxLQApgIM6KXNvVoqDOFu7dD7526jV/u8TTBqvw+X4AZ8PFI33XQtinmQsHrXgGDAY2ctoqyEWyxcjiPVNQCeE+0DFqOEJAtq+LWs7S4kehdFyXe0LUBFdI/XFdA7wisIa9VO8m74pgCsgIBhrlGyrrDDCB9CBByl1hQjgyAmJV6yhX5QMhsEhqoD+0UForDmNlw4UbWyLYgW0JdmaeASK7AWhM+OxWd7lLPHbQlQ3ViwPq/TCo7b791XNqOlkTpydsAQgCQAr286qd8GMvwhncFjumwFwkBPn3JKrCxRtyDYzCsJMG5xQgJ+Z9t8Zj4a8pHzhPqhVpau0GpIJzpR1zGhjiAwXBzDbAR80wTfHTqOcQiggvFHVyDjtUJKIUBLirrM+H2/9j/H3/uBP3eXcvoAGXdg5jkYdhBIAfR6IQV6s0CmGSopNBrgwrUbmW5JtE2nWp/NHD0Ueun6RMHhWe5VPnWvAJijt9khlxPybPB9YldiJ+BAYu8n2+0CeDBlZZFmkAqKetvudYjdfQEwcsdbD7nT7zJXoC4EMznTyGuaIDcFogbsdyzVNgBLYergNEHfcwXfUQ+EqDJSUBxt/Pp9F8t+UkBzPYUI0lRRVDFcAflEX5x6WEV6vSRUA2TYds3csButmkzWhf/W5tQ7Ob9iiQh2t7adLbA38NDAR9vN+rr/6+/5iYZv//R1Ny1YfYvaObe8FicKWnlyZ5Ag1DmZhPi3fR8Gxu2laY6x3bOlbdG3lFJruNlSTUAHLhLMoyH8aDTE11X7RXSAx5awD+j0jJOdaOaFDWgWge8S6ijUO1TO12RsQMpu1E6NSfXu8ivOYC8VgNBN2wTQJYKvtfSisjlrDUFwyvx5NJTEqGgWR1Jt9SpZ2DWcPn4SFTTSMmz8plnoe5OlzyuprU4YFNu3Cdbu3UQtk40hYLoFHND9U7qVv3rvv3brXgK3gXo7grIBrhfE9UcX1t6qTNINCGo/N94XAqEtyJXHPmz9nTWPns32o7/EN39RpiNdAMwVaXFWm+8EOgbAWixaexiyO3wm26aTwPe6tpEYo6BhUOBUkU6VwG8/0HMo0X9LipF5m2sw1QosC2RZYPfPgXuHuNYVx2r4zN/yOnzLm79sZcXuxnM2nlNZ9ute9zr82l/7a3Hv3j288IUvxO/4Hb8DP/zDP3zrNVdXV/jCL/xCvPSlL8XhcMAv+SW/BF/3dV/3HJ3xf5xhmc1QRAC9maFXR6SyilFb0zdvAclYSipzpRdG2Mu7sMpJzkYagZlBb2bkdx8hVyfIoxP0+hg7fmMH4qsZaSosq50Lu2yfKl2Cw/8FYIVRucgo9weUezvUeyN3uSCg6sZUhp6nFjD3j6sT9N1XSO++ghaDJmUH47IAp5kVWkMGlILldFogJ1ZtdXFfcTYYnAsXmFaGvEl5tZ2quiNdF+hsSCfDcElBsrcKjgRs20i0BRzWjtchR19HbwGNbRn29uftZ48/RQ2Mhki2H72lPbb6ghiy+UsLFvJeH7p5rQeD0I3bHjtkpC8E3ltVoLbr2MACmYBkFK2yOWT7zsYy/mIBfHzdpQbC65rjYGTavXGwEqx9Zbv1fR0dD7Vjtf/avWgGba0kP1NH4gmwHMCgGDyz/UcdJFIWBp2NTEe460rWnhZqz5aNCg+E6zmFUaJ0XxUbANun7l1Td5FaiYoiQZzbmOIZNbQWFk2PowDbCwDddbmnoDQ6yreLsjj0VMi+xNxuVXi3cYLcnhOG7lLdjsYO2/x5n6INAzW8vZ3P7Wcb9qRnq7b3N17joXPqr290XrXbhnZ9bWCpum/PvMT98M13cV4HqbbOxYYCAeSrBeOjgvGyYrwqSCejnu6mIp+WPtekM5FkXdQBPx9RzthM19WRH50wPJyYTjwb4LsBvg+gOhXItCA/feyMDOYF9qKngMMIfdcl9MfeA33b09B3PIOn/+078cd/31/B3Xjux3MKZt7ylrfgVa96Fd761rfiu77ru1BKwSd90ifh+vq6v+Y1r3kN3vSmN+Gbvumb8EM/9EN4zWteg1e/+tX49m//9ufwzH+aQ8GGjY0RSMqKjRzppSSk+TMXUVkqXVdjh4KlQE4VUitsSGwfEDS1Fj7EaSoESRNtzy2CayuR1MX52qXSlXfmnzrXtRLAhTn+mWkrmWukUAyYay/L7S6zKqi7zPSYVejNCfLwiqXZ+wH2xAV8P3J3PA7w/Q62H2EXu7VyYxCmHqpjuJoxPDMjXc7QY+lGggjmqIwIVkGxf6ZiuF6gBgzPLBgfGQN4Y07afzF8A3I80Rm329KvBMq65sd7HydI2jG75qZ/QFyTH2fH1lyO23/Y/l2xsh7v9cZ2b+Lal5Ug6tKD6Ld060sEAIE7TdRChOAA78/ivKe+OR+nFkXC3n+9HtJTUN37p5Vkt0qncPBdUyPSz/99fq3NSuTCjs9Na2NZKQpPtPK3QcLYjJVOdZfDHTqeAfPbNzGG5dBLKNhTqaX+mr4sGECmihSI3mG6eHfj1Xi9S3SzToBrQquA8ix8jnuwR/dRQsxdzJWtPiKla2eZeo5Rb0+GYpHyw9rnq9KbRuYKmQvvf/U1XWp0EZYmcNfNBYjz6XP31s+lNyBtG6110wB2HG+vbfNJAVSCCi1oTgK8pg0x+WZySiOg4lpOxlL1xfmsmkFPBjk60o1BjxUyGdLlhHRZkGZW9aXFkSZHKkA+FqQj+8PVkddfFue6dqyQU402EUxHje+YMT49IV2emIofEucPFLoYcFyg1xMLMULDZE9cAHDoOx8BxxNwPAKnE/+7vsa/+Uf/An/hC7/hfczqu/Fsjuc0zfSmN73p1r9f//rX44UvfCG+//u/Hx/7sR8LAPin//Sf4rM/+7Pxm3/zbwYAfN7nfR6+/uu/Ht/3fd+HT/u0T3u2T/k/yhAXiBk0XFsphIzVXENrENoQGSRs+dsu36FzgcMgbkBOqIeBQjqh7T7ORsAH+MUODoMu6KZavkuo+wQpgvyemYFxPxAUZXpd0AysQJcKgZAdmQqp7kxqVoFoUunhXhyL7phRnziDDomtDG6O8HGEpwykBDtLSFMBSoUVwM538EOGnaVuCUKPES6eGmk3cXaGbuyGGJCaeLX1twmvEgU74eadog6xwpYN86Irq9DBg1IDJFX6jtgcvS/ONgJvc/tbRqUFiS3D03e7/S2PHWwzbsUXfewHALqXhgQLALkV+x7bbK9+HC22KzpYiTohbBsTSrALa4fhYBa2YKixJwFeeT1k/f7tWsj6g1tAr0XdtHl9+0uksaQpXSMYNqM0aZUqKcGcaQcvjpoAd2UjVAOrksLN2EExvSUyM6bOlOri4UPEDUPdVDnpYvCRXd9blYwDPRXVOse7KDVcXnspPSz0WCK9VFw3d8UTzQG1Ojx6MHlsXmAWdxV9/ouBpo0pmBrzx1IarP5D06d5XOKersMt0M3rvLnuLdUn6P43YStN8EGZGzFNmM/xkDwHXUKvU6PDvDoFy9Wx3kZZU2DuyHOFTg51gaXK5pwJdHauFNQPx5nsUk5Ij47AUmEPDiiHEXUU6AwMDxf4VABXpJvWzIvnI4shD6FDDu1UXgz54QIvAHKid9bJWDhWqb/SuUBOc2i9wDTixQH69qfJKE8Tr2toZzCwGeWbv/V78bJXPA9/8E99Cu7GczM+oDQzDx8+BAA89dRT/Wcf8zEfgze+8Y34nM/5HHzIh3wI3vzmN+Nf/+t/ja/5mq95n8eYpgnTNPV/P3r06Gf2pH8KIy8eFUQRLMJrAiUeyHC+pSebsx+M0EtGF4cpgMwyTjGHF4OlzO7bAj5opULmGX42og4AskJvChmeMaGejdzlPH1NcZ0Kyr0BdeRKl26MjFA4BmOXKdKNKo7mVusAoICasulg5Ov9fA+MA+z8AJsmiFWYDLBDJpM0F/j1ROHmPrPcW8Bu4WF4ZjloEooIuOuNHbUD3BG60/0zhM2eAHd2LE6FQSWgIEFJ31VySe5l08oF2hV0B462EkgUfmrcq9aTR+LPdrjHIcoWXHSvmsdGf28va4p3CsKLZeNdE2xMB1GPA6wNlFl/1c4ZWFWdcez2i84ACedYe3NUdmyridZUndw6V37Hdl3XC9DxTFTT+OZirSW9WK12HAzu/dTXc5IqHTDDHXZIsB2CeURUB9Kzxktoq2oLud7njCA2B+3znGDHgUizGWQxOmbnyDstlaXTrH3n/Wo6IY+Gk+EULa28P+4JGTJbL0Zr9ggKVuGAj4lsZNcuhShW0VtxtO7OsgGZQIic+5xBaJLiO4cOR7b3qE2lFJe4lUVvwbOBomKPZ5FW3XymZn5nFzo/ewULDxIBGuZIfyuo3RvW+ZMK4HNBfrgglbZ+eKT6HOaJr88E2/mZm94+xA6ZjBcE5aDAaNg97RgmdOfo/OgIOc7Uv+wyfJdW00E1mj+WgrrLqGNGKlw/fDbgtKyashwpxgbKI+WKOTZ/5sD9e5Ccbj3W/+3X/iO89MNego/71I/E3Xj2xwcMmHF3vPa1r8XHfMzH4Jf9sl/Wf/61X/u1+NzP/Vy89KUvRc50q/3rf/2v42M+5mPe53Fe97rX4cu//MufrdP+KY2z64r5OCOdrNus98W1RMl1cS4kDrIugwJjgoPpFgZeoQcNAIDW8m4AhsQqJDekE5mcMvKDZJmgD5lS8dhOy1JhqUKWBJXYoUfw9iwwU4Kr3UC63x2YjE0gi7PMNVNYKU10quxarUmBDNTTAjnOSBa73kOGn49cDMYUHZZjxOJqu0TTvqzQxVfRpNCsLM1Yq8n3Civ8B1syUGSdBJCotpI1jvTAyS8ZK1KIHx0IO/9YuDIX52YJvy78rTw5QEILNL6CHKwv/XH4mM13/vGGP/7f+4BQj4Obdo6RAmypK2Km9YXt3IkdNgt4ANz+9zi+t8C5+T5bYLLhhfi+zWk2IfaW9Vnr5QPEbPpe9dd5q77TaCVBdsXCj0huWL5vAQi0kkWogf9a8Rsa6AnQIeDOm4BZ1jL7CLzWOiOOGgDIyZiG55GERqQZtzG9FGguOskjrrdvfFa8RGfuWle/I8k812ba6OgVYEBonVqVWTPD62VLcV9SuBurrLqU9p0brHNWMfrm8ouH7rvSJVoKkE4FelyoHTof4Tul+dyjmX25sqKmSP9ltjzBAuTrAi8Vdj9jvq+oZw3oCHB0DA9ZXZRNWKl2c4S7oI4Zdn+Hshsx3xfUw4iz5QB5+yXsXLA8bw8/H1D3IKiEAr6wN5YklPOEdCXQ6yNw/5wawiywnfL1i0GnGVhqpPjjkkzU21D0u7BNy47FC3pagEOGXJ+6MSiqAU886NdcBNTVOBmz133hN+EFL30+Xvmrfz7uxrM7PmDAzBd+4RfiX/yLf4Hv+Z7vufXzr/3ar8Vb3/pWvPGNb8TLX/5yfPd3fze+4Au+AC95yUvwCZ/wCe91nC/5ki/Ba1/72v7vR48e4WUve9nP+Pn/ZMYZBHY507TJEUyHow7s+Otjokbl4Yl59HlhJNgNbGVg1kEDqkFPM6SyzUEdogvwmOEX9yDVkK8X+LGijopcCuTRFfJcgHt7iITlw80EwCHnO64TWeAzWAlwb+wMjVT2OvJqkHkBTgbfj5CcoqwUcK8U+CYa6klxiDpszEjVoc8caTx2NqLuBpjFtrLpWjTAW9OdaugKU9NKgCXFA+lmVlPoWl4cVUNSyFTR/b1RGrfoDDROg5vZtkvdBFiiofgntQjStBbiUX4qaxBOgFuAnFt3Xd4b1LTddPvHrZff+uUKHBqgaUBh+7YGdAIxuPv66waA2nuablTad7mFSNCFvg19aDvE9ue3j9H+vWaYNqLPzg489j3b/W4EQ8EKFm0tRRcPZqZdv5k+LJYUMoIiWmMqshwEOitkik9v4ldbHaTbyYp5/162E6RZCOS3bEbSXkJOkTOfPQp+yftJgDXEXGjHFQS4iOvHaqrwqRGlcV9obNTipCIlQxKKZek9PegEmZLazAV6GbxL/26+VHZ/HhKwS/BG69QKzxl1D6QqZEId0KWy0i9K25M7ysUIHwV1FEgmQGQ3dLKW1gwBk674NAkqKKpuE6F10JZq0KlAPYCjxjN6fYN6sUd94R7LmaCcJ8yDQOcddpc3sD1NB+tA8CQGpOuKfFOhR26S8LxzLC88h1yfIGMGYPA89CpFrdTnoBpfc2LbCi1ksX1MZKJCLGTnI+YHGcM7jsg3S+QvE9NKEte8VgKZWuHzwr8DeM1vfR2+8Z9/JV700ufhbjx74wMCzLz61a/GG9/4Rnz3d383XvrSl/afH49HfOmXfine8IY34JM/+ZMBAB/xER+BH/iBH8Bf/It/8X2Cmd1uh93uA9vI6MG9Pa5/9BlqPGIhaAufKAWN5XxAGhV6NTMnv1RW/8DRtpyr8R6iBUGFjo6yI6Pjg8L2I+ohI51YEVWyQE8D5DRD3vmQC+DAHkpaDCbg4pEFIgPzygBsyOGNxl2thb4hXc/cwe0G7nhyhsXOnfbvgGaFnRz1oKh5wKiC9PAEmWbgwQV8l7gzNIeKRONECQ2QR1NI6c3igGCucuzGXWOnvakAS4K0MDXgAqba0m22oF33Pno8b+mmBmKka1i8BxvQ76cBmibQjAoM946B3v+xjfPts9FSWus533qt9/91EgR18+G3wE4oMh4/ry2OeV8gRdadfKtCWl+/ATKxYX6fOTcQ9DVAd+sctkZ+wPpdo7pKdE3dsNdO2NcDkFH7Ne+eNKOijgr1GmlZZTsAcyT3VS/lDZTwvVKiijDAQ89PLlHVZUQsdH2uXRjdgZ4hmlaii7AF3o0UAVZctUojVYWNygaSbd5aU808dksattXNvBUA4l1sLJWid4XDr06QU4GOA+xsJMuZBNUFy7lgeSBIE7B/RvraoPHgagAtdWBykJnMLImWB7u43gITJSMjQCsjryN/7gMbgqZFAaWTd360IM+gUFsC8D/vHJqYWrIMLPcU5cAKspsXZeD6HMOjGfmyQqwQKI3S2Sl1gZwmjO9wlCfPUJ9/gXxdoFMBIPAxQ28WwCo3iObAvBAY5wGaE6g/zGyaOxfYxQ56qvAHmSn9GiX9UOCwDw1jgJjjCVgWOrYLgJyBk+GzPvw1+Or/55fhl370h+NuPDvjOQUz7o5Xv/rVeMMb3oA3v/nNeMUrXnHr98uyYFkWakY2I6UE2/phfJCNl7zgHn7sf/7RDU0fu7zK8unkAtkl2MUO9TwB71Kk00LGQYV6jkJ+2pNCNEHE2a1YhIBAwVLrAaRNB4NOBssJclYhZQ89zvTTOFXoUmCXBs0CxwhJaaVSDSG01fD6CD1PGI+pGdLNCRgG2A6AJu52UobtBKcnz2B1j8hcwLNiSIJ0dUI6LZjLiHqWmE4CwrGXkbGxUGJOwadzt14GduEG0FMqOm93x/wjXzMy1gsl2NlSzBHzm4YA2MRhiR11q9SSYGBEVkCJCNAhHupeLRFt3Ddmepux/VFLPfRf9A/fngwe+7fc+nnXm7Tv1QKebd4r62Fvf1Fg7dC8OSn396rQaoJfvofHaEG1gxSsYOdW+mnFRZvPcjRDOm8Ga9YYJO+C8P4ZKz0BqY4kjqrxggJAKCrFdYFOul6S+C6i/BxYpFNa1Vecr06R/oGHM3JdBbctnynBoJh3hqV/2WJwZ0p2e09u63X5ZaQ6GcD4U7o2I65Lc19ub1MwjZV11UvVzT0yh94skOPEYy4VOi2ACGTJ8B3L5csFcHoKKBeC4Uow3hjyI7YD4DWKvFccP19XalKMiMnTKgzu3w+hI8qRXjamm8RB3ZEDaTLotMAGRTnPgAB5dkhWLON9yLwg3xjyZYGNmWApOdwL9OqEVAXD1Qi7GrHcc5g4S+x3CVoG9lh6+sj2MEuhmPi0kGk2hVyeAoQ1YW9j1RLXuFqBXYbWChxn1Is9hnef6AEmTkZmKbwnqpBSYKcTfJogtUIOe8g48j7GnPhTn/rV+LPf8Hn4DZ/8a3A3fubHcwpmXvWqV+FbvuVb8O3f/u24d+8e3va2twEAHjx4gMPhgPv37+PjPu7j8EVf9EU4HA54+ctfjre85S34xm/8Rnz1V3/1c3nqP63x8z/sxfj+N/8wUDf5W8Ti6uzKKw7oXGD7Acv9AbZL0TtGsNzPkFrhc+HCqWCu3ugSrN50fSzTtGXtnu2DA5Wgwe7t+XwvBn/PFWShgZ9OBb7fx64vKg1iQfdd28k4tS8XCq/GCiVNId5t9u0snfVBUJ4aITcF6T0zGZezEX4g89PiuWe2MxBF1z60YN1Ko1N1NpA8Vsgu3IFTE2NwNK2BANGLRakFWoyN/EasgaIDCawBf91sIroWdF2TAGs1VLAikoSB5RZzsgZ0eV+4O47X8M9q2vLYy3zzu3ZugvcCGu08O1vi4CLcP6sBoM0XaefQf765hk0A2XiCrqdBP8423dQ/Zwts+mesAO/WF9k4EkuA1OgfgGYNYILuWItK0M/yfWFVUokb0VK2WSFZo8IOZErm9ToIJNylN2BGgwFr6Zw4r2QCR7Ss8Jj/MVGkhE3Axta/4apWdSaC254urRqrjcrnE4bof+XrPd42w2z3JO6thyBeaolnnvqcdDUB11NoawS2bx2gDXo1QZJifnJEOUc3noQEKbY4NLVL78CgKOcJxYF0WqCnhFTjJNxhIlAxqEQZ+0D2ouYEVKaAxpsFngcKneeZabXzDMsZ5UKxmGC8dsixIBVFejQjzY7lkdHrxwzjO4/IlyeKeAvd0jEbysUAOySU8xFpYj87Pc3wmwqPDZA64HMB9juus8eJ1zBnsiq7AVIL6/q6j5JCHt5AF0Myg5TKC5UEGPZofktuBl8KK5wuziE5o4vXmy+TCL7ij/w1fO13PR+/6Fe8HHfjZ3aIr6UNz/6HP54/j/H6178ef/gP/2EAwNve9jZ8yZd8Cb7zO78T73nPe/Dyl78cn/d5n4fXvOY175fr4qNHj/DgwQM8fPgQ9+/f/495+j/l8T3//b/Ef/6F34y0FD5YuppkuYJ9mHbKMmsVVDPYTpEc0GIohwH1kDA8Cg8WpNB2oDemrIK1yV0Ku3RRmNPKO81tKxyBZC6Q6xk+MGWDlGG71Oljdyr/7TDCdhHYLo8Ynj5BoWTPUoLtEkwTmiDZdgnlfEDZCwwFux+7Rn44Ie0O8F0GkmIZFMtFgh0o1mtC4mSIbsZsYyAOsjMZWMI3RwtdWltPPgfQ4FGLAw5wcQ+B43JB0NVjsdFnRuKaOdA1Lw1M9GoSa+9p+plNQO5NBDn6z+2xR6xN24jD7XgbJISuIYLjcUzyPgEP4jwRwQir4PQ2FXT7L37rZ3GYjTi3FYC7rMG0ear0UwXWf7dqn+3PWs5toxr20FdplDJJnIi7dXDT2DVXYVrTrAP6di1s1PBbcfYACtDOMmKmTbUhwGgVIOGHg+rQoK88ha6ktnsd72miaQ+AkAjWtVYa9zVRVv/+Icwv1ueOdxG2oBvstTRZaE74jJHlaHoatA7lYZBJEG3ruQUaFuP6keYFNUWloAB+yCj3qEMBDMUd04sHzE/SaXu8Ag7POHZPF4pd47lTY8VkucioVpBuFmTLFAqfDXyuzSHTjGROJ+VDQs2CZVDUe4J8VXF4zwLLit27jjSeG1ZR7vzUDssDMrLpUcXZO2eM77gCILCLkWm9h5e8RjNbsshuBHYjSlaUp85R7g/QYti/7QZ6deK1PU28lmZ9nlDn4gQ25pD9SK8sFYgG4zQk3vtxgE8TGgODAD+ewujw5gQvheafpxMcQLp3sYKYaeZ7dyM1NmZAKfimH/wqPP9D1irdu/H+jZ9M/H7O00w/0Xjxi1+M17/+9c/C2Tx745Uf8TL4yIdfPRakmTSEg1G1jgn1bOgN08wq0sMZcrUgzQapI+xAelVOFTIbHOEP4QaBs8RwAGlRQxe2Sth1SxhIaPX+cCNnpqsWmuQhTNC0Rh8TLZCFSv9lZ7AEpGPpC0YL7qZKweBk0PnE4iAF9LpAbxboyYEnzlAudpAhqktCTCvqK02vGucJQByWgb6drGBlSQQs96btiIUK7RhAqzbQpSIfg83vaZM1MPdg2/7XdsNbBNFSE+qrfgZYOxu/X7Ngw5Bs2CEG6Q162VA4nUHpXyz+/fhztMnlkPl5/87ovfQvkF7qe6sUvKXS2udsL+NWTwOgl5NvLmgD7h3MNSajv9Z7Hy6K0QlyzdC1Mr0kuYHOKEPWakx1KE9am15FIn1kYFf6AQx1TdciAXRCjN6BIwDpzohMTbC9Q6R4lwrvNs9OpkR1/Uxvz4Sw7UINDVS7ho2tCTZIwihPtMItrxWNp4XPJAI0p5iTogy6I920damoSVAvBtiBjG65p5juK8pAD53x6QotguEEpBsnu+ACH8D+UdcnpOMMfZhQxwR1hUqJcygoT53DkmO8maCa4YcEc1Y12Y7MmZ0l2AmAGW0XLm9iSiQkM+zeKcgTcP2CHeoea2HDtAAwiGbqfvY7+Djy8u8GQBNbv1yeWMjZbtJSgFp4D/MA3wV4FY9KpJiLSWhbkaPUey48r5y4ZtZKIDLN8FIgOa2MdIlrtVRqZMyg52eMY+4EMoc9sNtxfuQUZe2OP/Qb/gt8+w99FXaH8f17Fu/GT3p8QAiAf66Np553H/UsQwyoicZu+x8lhepmNAHTBB8EdafsNWSOPAnSSZCPBV5YHQQ3VhbBIJqYborFUpy9dls3Yi0UQErQp3KcY5OtkKQ0l4o+UDBn3yeRMLujsZ5OBXKcIMcZOk9AHqKHVDABxSHJe8kyWZ9KCrxpEKYCn4/ANEGne/AnDkiagWTRgmBNYXis+55AXUTod7RXwLCO1QFqjlLstFpqxyKNptrb1YxXhklpXiZZeu/ILaihkDiO4YhrugEZLaUTARHC8/IGLnoAvz1u4YrHKpbaR91+Q1yDrsAFeo6r7c6b6PZxDLQ5JmNte8GGlQFuiXa312F1sV0FwO/FHv0HgVIDLSu71FNzwWLpJr3WSB/3qJxxB6rE/bXVtK2BTongu8QxGyhCpYC9daT2OLBIuD4HyAGBDBx8HoAAPXGenS1riFNuzev+fSo9nxo4tlZG3dilQKkNBPXWEG1CVrIcXriLb1V4PmSylwDk+kiGIiU+b0OONDN6ahe1Qk4L6plieuIARK8zMTal1QLs37NgvCLoUydQQjG4CVPZxwJ95hKYC+ojh77gAWSIXlXFoJdHyPkOKg69moBMY808V0jJMEsQFZrfTYbh6WNUdlm4Q4PnWbQzVOmqkIEVgSwEF36WgPsXwFK5Jnnor40brHR1QpqNIOR4ArxSR1hZKCHjGbBXuvXOhYxfqeyzleNZNQJYTDPflxIrRxUQKBkYEaDOkOnEdhYzQUzvYZZiN7UsTGftdvCB5d3YDX1OuDs+7Tf8F/iGf/BavPhDnvwPPDN346c67sDMczT8bGRVwyA4viBjuDHoO68YGIsBVoEjF1ndZwiAkjP0XJDGSBUt0e16rtwBDcJST4kdQVvxHUzBKKOVFmeOPicuFq0BzDiE1wbCxp4LgF6ckbkxVlXJtDSiBD7NwGHH3XBWvr/USE2EWHhQyNkIOU3QUlhCnRNwc4TOC+TmHPr8C0wP9vDz6Doc2gD1qCQIwKBtdz07zA3YAXVI0Nyca6lzoLdHuwJr8HYTaGHZqklmbG3+Ms2PZHvpYnPHmEfWyFtKq8qmjJu/Z6J+ZVNkRUoct2q223vfF+rBLe1FRxuCLj5Gb8gkK8jZAqTGALTg+eNORtwCP/3z39fP/kNvaiwjNgZ5rUfUxr0Y8N4csKfmBAC0s1IOzsHm0itmiMpspkEW74yTVDZHFTThrcPDuZfl3c7WBw2UBmvSQScQ4u7NjzS+R1IGLgdaN/F+aVp7ggZsQ2DKkuPoodY+MyXOK4v31AiIxqohnxZgmgnAhgyMAyRnfv4UrrTTzOalwwDsR2CXUA/UnvmgyALocWZn6FHgoyJPFN8OLlCvOLyrIB+ta9VMQD+dPdkEGzOw2xE0JUU9z+wpdVmguwTf7WGjQB4eoyLJYTC+D0C6oQ8WRr5fZgqB/bCH7zPKE3u4V6STIc2G8T0n6M0J6Rj3ICcWI8wze9gZCyNQWQGGLGwHAYcsR6bp5wV+PPF+WbAnNyc2tU099xxAh9pCqLDPXTNQnGauhzkD0xJ+QUqg6jwHX2ZOlmao2MCoO4HSxQVB5n4A9sNq17AUAioAn/3b/iK+/tv+OH7+L3jR+3oS78ZPY9yBmedo2KjUDgZ/Xy4G6FViZdJADUi+WjBMC7DL8CHzgbYKU4XsEksObyagFBpXxc7VhQv4LX2FS2gPgKTWdyI+ZGpS3KNzMaJSqnlytAiikLJwgd4TvOB8B1zekN6VET1VkLlAW6mAFchALU2SHVNDCuAiw2slZXyaIO8WjEkxp5Gfl1kVYVvWIQnMmJrTJUR4SQBTWDBD3UW1WpA2zfiNxxAF6qAYZsMy0Jckqi/fe7wP0sFb+irMzXyTBhJrb9oE+iYutfayJqgFerfq9tnbXE777MYQxLE2Z9KFxV3o2wDNe517/MzaMQh6+vRoH7tlX2xznFu4ZcMQbfs8NLao8ov11FYzufN2qGA+amhHqodZm7AEOBEQalD3UmIHnTan1sBttOgQj5YBje5HYDoId95xnRuO8m5MF+ffgIpgU0Ukt+9BS41ZU/QSZHkxiFXqcHKOa+DAVIDT1AEOxiG8l5TX4yY0Hjl6MJgFaySwiz18P5CNq4XHzAEyagGmAr++gd17gPJghN0bUceE2Rz5wCCdryvkxqFRhaSjAIXC3DTVrhVBJqtgA9NDqSrsiXPItKAMQHmwRznPKPcShhvEhseQrieocv3AcYEmxXI2Il2foFcG7AeUBzvYvT3yOx9BDplrlAvqxQ71gSDdAMO7HyE9c420GAsIBCyRLqsRHZvgVuiQgZuZepg2PWuwLeNIcOLRDX6egZsbsmjDwKrDlNf7Yb5WJw2Z1Ze1QlLia6sDUunDo0wViVLHSHAeBQ/unDPR1sCTArvM49cKecdDAqFE+wu/Bv73H/86fPP3fQWeesEHhobzZ8u4AzPP1Ujay0mlOMp5grzwHtLExRdzRSqVdOiJD6hlYUVAqYCApcxZITcLzAy+y/TdiPJkgQNFgBwhtDElowCHkeZXStdTC9DCQMvP8Ys95Jq7PQlA42Ms/kOCDyNw2FE8aKHTKQavFC/qzQxfZgaeIUMuzphHnkPZeX5AffIe9LQQ0FxOGOBIx4RyMZBCNlBrEEG19WfSJNCUsGRlozr10Gtu2JkFDLht4RZ0h1VUsAndGGCnsx/xxzZ9sg32HRBIBzSIVJOrR+XThiEQhEbCu77mvUDSLRCyoiqxUOqEeRyBKlbGpqVBpDEw8UbdHLcdzzZ/9h3l5nyi9LhVSknoP/rfW6AHOntyO+AHaOnePBv2ogmo4z29aqmlksKjxwP4YbE4VpxkcaQqsAB7Etdcm6jYVnC2HdJzjdhQLgHiokR6LbkPsOLSmTnaEjhTG5XH0A3g9FqZxigMbLLfwXOCqgDHE9mCSh2bnB3gS6WINSnTSXOBp8rNCAz+4B7sMMIPO/ieICc9s7D0VwV270BgVhbU0wzbDahnA20NBhrvlTxifFqRbgryUuH7AeU8wcWh08K2DOHDBKXHlCsZmbpTthwAYBd71HOhMHpMuH7xBfLJsH/aMPx7VhjJfh86nQI8ZJGB3hSkxWCaUIvD9sF+3swQTVAzDDcKOx+gN+xOLZc38HkB8gAZB2C/p74tfLj6/QymGKWSuR7YfIkeL33xig7ett7nSAmJhzbQJ4LDHFWhImRx2lzN3DgiB/iMztlQpaP5OACFa1OzyMBhBwwpjsP1Td7xNBmui3OeWlvPRfCZH/0V+Lb/6Sux299paP5jjTsw81yNTf2vGqAnZ+8kj+XWnWCjWsTiAgG7UbeSYRsTbDxA9zn6HCWkcML1zKaTTQNREz040k3seJYIvCGk9Mi9W1LI0eGjwIcd0mGEX56Qjgtp3x0XJ2neP8rUEj0vBFIK9GqBo3IxQAK8QK9uuBO6dwYcBsaW6tx1jRm2z0xPTQX58oR8s0N5Ys+mmEm6FwzZIyOjUhE79iiPdul5eAlzNTiAHAG8RVhzwIA8G6w6fAdAwhdmvUHxp6x/3d4+IcvTLfIBikeT97SDQ1ZRay8t2tz/9meQGtJTFtLBgQIrmGln1/xN1im0YW1kPWYDYJsqq56FavMvjr8tMb7V0NJxuyqsaVOwEUULIm2EaPngKwBqlv7x/XoZumGlw5pjcw2w4B4pGgbcpq/VKMMWjXSUedyAFdN1YCogq9PvXwOejVXxwGLbLxmsS59XWAFwXFNeuwDjwSz69YlVRvsdxZ8qwBwBNudg8kJcXCt375qAZF0DA3E+F2cjWwgIkCdjlc5S6J3yxAUZy32Gnu1RL0YG0KiwEgs/F3NgTKjq8FFRzpiKybG++G5gFZgDthjsXFHPEupOUEwwLhV+WiCHM3ji9ZAKzPcU6QTsjhMdg30CDmNsFixSPJWVPgDGuaDtQqQsSFcCXIPfZzdARKHzwvSwGXUnOSqAGrB2j1SRw6eZepUhAafC9WQ3AMfCGzNkvr8QkTkQTUFjDmg8KIW6Gk3sFN4qQHvqMSWCU48HuTFFKbEfkwEiUa69LIAM8X5dWZxnLrlG7ne8d9bSZExlVRF8+q/5z/B//qY/hlf+yp/3/ov078aPO+7AzHM0tpkBOOCj0KAphIHiBimFD3oLLKhIAJs9ipNVSIqyS8D5LhaYYCaS0kBKQiAZJabpaoYatQr0Y0hrBa2Bi/iYYkFXbnpziiqLCs/jCmBS6FoKS7Gpuxm5i3l4AwwZftgB+xGeFXqagUc3wMWBD29ixYpVunWKIFozRPO+nGBDQjmEiNGM8UvJSGEyBsQMfu9NBYtULoRaEVUNxu682emWvBhkKUjqqPv9aufRsgga16Lt1t8HoLmVfomKKQI9rNrY9r6Whtn8u0EnaVGyg5Z2Lt5TNIh1uPvObJxiFbEDVSKVZizobZ4JGEwbqGrBGOg+K4CzNNnX9JNsS7Db922aD2xZkQZiECArzs2x6koa8OrAJYTrArgmBq+osnuvSx1eRxrzwp3Ozv3MeplyABNHvzHSH54AOR6Ml3tnzFxCCNslSE4WrF27QkUOr7NBlhl+WoBljoakvLZSKjBfrUEthzi0IcjCVgJw6mJ8iOV3yHzmRKBHViZKqdBHN8DNiemJaYI/vIKcH/hcDpmxea7sNTXwXuzeOUOmBXY+YrmfUM8UIuwWna8LBMLrHcyimWE5U8znYOsUAPPzDtCHJ6TrGXJtwP2KnEbInCA3od8Rjef8GvUlT2A5J8DI1yf4zYRUHX7iecMcmBbIeSZD64A/uuEaMGRei3S29p4yu+3NFEwWdTMBRochWBen/i4YEs6nACwxt6QL5VO3b2jTmRuumCKRknaN+dBAljtwtud3MPZ585w5h1Lqz13/T5W6m/MzpsGMBQg47FhhmlhtV8eEP/nHvgFiwP0He3zcJ/1y/Kbf8kr84l/6odjvh8efgrvxE4w7MPMcjRrSEFkYW+ZzxXiWMLz9Eu6OlDMX3hQOszUCS9IVfDR7/yTciSUwhQNQTxD5XHEHCl0XUrVojQBgzBsaNsy3wifDoZFWcuB8hA4ENjYy9wsH+yclNteTy6l32NUdXyNL4YM/EtT4OEKnhRUXKoDR0Is9nyoXnVJhWeFQ6GzINwUeFQhw4U5RhZKLc6bqmsDUW8mq8k8JG+TWXRxzhRSWGrs70s2C8VTgg8Ce3HXBciNBrBmLbViKPn4c0W7XwNz6dfyjAaWmG0EETw+24//P3t/G2rY1Z4HYUzXmnGvtfc65976vX7/+xrE74JgGN/lhCWjHgCJBh47AoEh0ou4EGSlSZEyCJZAsSwmWUAxI+Rnhf063FJQfoWmDkKyOWhiEaOUHkKAEN3QILdQNtvHHe+895+y915yjKj+eqhpjrXNe4xDM/ciZ0r1n77XnmnPMMceoeqrqqariqcTf0yOQSraPrC0ivwmszOeX1p7ucXj9Ijp5MIJTUl0go7li/il5MYN3FONJwDSlwNOzNPFX0guTnphYiw5ALKvtRkuImKOsNs0aK/lc0bE6J9inZ/BpzjEAnuc9Z3AYU3PFJUtwlmn6PgBdGRyhwEjk3Akw9p0AYztBlAzy9Ey4H5BnzwYQTAWXFbzd2Jz1vFGBHxFSM2b1ILJ6sB/wbS3lKq+fgMeda/uD9wA3bJcGe7hQaTuwfPgI33fox09YfplZiNg7vb7B2+jZQmFz2NGj04JD3NDvBA9ft2FdHOt/8yHWl0/YfmlF/9mGfmpYXu3QX/44ZFGDvHoN/XmBfvke/tETSf1PBE14/wWkHcDlie/2OOCtASdyWPDIz6Es4un3Z77TnQAARxSla6zFheOI98m0alkDtAADrAJRRyhenk5gI8+7OwdvxqBpmAEjZBR/K+J3WgatMSsKzp8XJjs4AOzHCJkGSBWAYS0RApltgYkw00n5XXQWH/3w9QU/+ZN/Bz/5l/8OXIB1W/Bvf+9vwL//H3wPvu3bv4x3x7/4eAdmPqHD3l/gLw96UE3gi+Lxy3eQhwuWr7wmolcdAl8bLUQBfG20JgFWOj0sLEx6LjQUjDxZZA8pfG1Q6xSEB2jRyVAYtjX2SFpb8ARauO0VdgJ8a6UjK0u4NYKHc3ALxCEaZN+lRSGuTjKk8HlsoftWQA6QQaAH61cgLGbfTvCNZeHV2MYgi48CAluoRNsRqmun1lKziSyrMOnQJdLSQcCjkb3lW0N/74T2eGB9Zehnem76ilLOLQp5jgwllLujdKS/8QOKA5P/xmfiGAISGEikyKWpQKdrHeN3Sc9HXrAq0MpwoEzfD/Ufn3v82SGuI0g0OTJu9X6BsgkUzQ6miujkc1oCo+lLSQAuT06cf8micgFmo+IwvTcY3gyJ9Z8DK+LyDAdvgKXf1voZ76BCaHOLlHncPbKfZsTjIBH39SOfeVu57iNUUs8e5Hf2aIrwRn4u3MPuBtaiCa+NAXi6oN+tLMS3H8C2we83+Lbw+btH80QpDwUeHiEvDW07jWKTeweedqg4/PEReGXQlyfI045+v8BP9+SSdAPUWczyYlheXlhEc1tYWXdlRo++fIC8vEDvAX19QXt4AB4eKTeCyAw49MNXOL1+gtkBXMhBkeQTDTcfM7ZECE6SeJsg72CWZHruAL4jbwsBsIOerlgDAEG9G2WOtyTjEojIFpmV+Z4zlGQkp8vSmI3YeyxiDTJ2gPDWItOJ5F2oxrsOY9E6pCvftzYS342A0++cHpgEMkH8dhHK0QAy3h04R+XgSL7wWDd7N/z0f/Yz+On/7GcgAJZF8W/8hq/Hv/c//m347d/zG9AmLtG7g8c7MPMJHXq34LLTGlITdAX6ecHTN30AP69YPrpAD4MfLG4nTuHJXkzX1qeawXeDwtGXSIWGhPIT6APTAv3Yw6WvkBaZQCAwwKKwraGfG2twdAuCMqJOTVSC9SGcq15JE/QXUbac2h9+Xun13xXoWTviFNV6HXLJFBOFLWfIejC8dBhd8RurhYo5m75BWdo+yMRorCUj4a2oSEIMD1EQ0IXK0VcqfT06fCeQ688b7NkGF8GyG46mzKKZehWlYwaeBtpNzZRS5PNnPki6Pp0HjLBRklshyL48WU7/KhRVPch43hUOSoCiwS+BXHkjJAcd90URk21k7fgEUia8FlMaRNmciDw/crjiSy7hLUrSbo57toidYFJUIgymgIfiAyL13qc6SeD7C1/LFVabs9NyQ008oyRZcpC5aNKDlCBuzI3k/3aLLBZUYTtvUmPPAmjsnrzQDui01PkcvKd3Vh4GDCpaCtJWpXJ155544vl9VezPGvTRsD506LED25meTwEEBwH7tsHuTpysywH98CW0LbD7e+DSYeLs+O0COd+RG/OcWT7+6hHy6pFy4vEJeLojcHt8hH74Gu1g3Rk7Nxzv32H5hZfQr7yuInJyx1CsP10CgPA5fN8hLXpduYcyVljvkJevy4PDNaRML8cOWRtrVDVWHPdlIWAMowZL8IAEBD+5KlsLj3PuTJ1+DuSkJE/r0oLPF1Wf57GIxFoMj6R5gBajZyiqDeNyCY9agKOdAsa3E9wsQFAAKQ+eYDfY+TTqXm1rVHkGK6wfxro6EVr0BDMt97CPfRNL+GKOn/kH/wz/6x/9S4AA57sV/87v+c349/8nvx1f8zUv8O54B2Y+seO98wm/8PGF8e7IvoSwNsT+5Wfw04rll19DXzvsOCKM0iE9yqU3KoRE8+ogycw1YrlBAu4CDb6JP/Xhrj0MiE7SpglY6EBh3RUFJOq1CGhZeACVEPgMSwR5WAVuwjTVx2O4Zh8PSHfY3QnHexuwLmiPrMCK3nlP00jbzLBDKAYDhdJu0F3QF0FvodTvmYXhOunrJRSTgZaSO8wkultzPH1V6BIIJ3WtCguV7Z3VZvPZJtc14CPcE4eE4so2B7MnI1sypMJN8JHnaYY3AixIkmHD+5J6eE4cylRVukbyfB/hqzi5spxytPn34KgkWOM9JAi2xtBigiyREXpJio0EkEmLXBSWaUqdHgcgPAmZ0j/PWHg+kC6YCJuiRSrs9EijSGG2VEARTd+4ZrGa+afCZT7N09VLs2vysYBjN6uyQfR6hQdCotbRtg6ipkiA4lBoWYBN6GW1ZytEFb0b5OkIMjIKzNi6ACs7QPfe0J8v2L/2Dtga1p97Cfmlj6DP7tDfO9OpcDyScCzMmIKFh+XVa8jHK/y8cR4vexDfHTgtsAbsHyxY9IT1n38M//g1mjbWYflwhTaFPlyAfYdKg/ziI+SfKjOy3PleP/q4OEZyf0ceTJSKKG6QspK4qVJWHUHKPZ9K2YsZfFmR5Ok0iHJvMnxInp8vawCZFiRbekE4//zXPXh6Iqz6Cw+uTBhZkMhYuhAkZ3uBpfF2UTOowkqiNPZ8EMR93QJ0g8DEAgB1I9jEQlB0d4I8Xnjd80Zg8rSPfR2ABR4GWBOSuQE+Q1a0zgy/ab3SIMv9SBny9GrHT/7Hfwf/yX/8dyAq+Pb/9tfif/Yf/Pfwb//2X8939/+Hxzsw8wkdX//8GX7xn7H3CBczMlwPV8XxYoMvDcuHT5APXwII2/JwoEftGaGitoiDj/CPICrBc8N1hzxcgndjTBG9XCB3p/IkuDizhYJfc+V+MDAGLVI8nEEoyDCYsInl7mjdWYcBCnWDvXpgKndT2JlkzrYDcgmhFtavR7ooMxc65PU+QhDRp0YV0O7Ym1Toy1cWyxJXgjGAHBuAVlKG3aJJXhL7xKmE9PFAf75ieTL0RSKTJUDD5AWYQzgFWgLIyOEVnuJ9fXgjUp6Vt8YrQ6Z8A3krxwA4KiPTaD401kF6W6KmUHpuKi08B+q4rrPSAxQJ4DDAlBlZQ+5e1/lL/JQF7Twq6EajQTcCGBiLJeo0fXC7GhsOCyUIhl3C6s1KvIiqhQw1acKY4K3Eheva+UqmbK/J43LlEZs9iuFFShK155pOkA6lAlUFbFSWFW3MpAmCr7szDGJOr4J18lzuN/h2h/78TEXeO+T1hU0gn6LqtgN2v+F4seJAhzfB8ayhf9v7uDut2H7uJeTDl5CFe8LuTtCHJ+CffwWyNOj5xHsdB/RnD/j7z9BP7OYse4csG+SxYz1eY4nqu7J3+OUC3N0TcH7lY8iLexJon3b40+vIunL4tkJbY6hLlR6KDPC1Rg9JhnCaMk1ao35NApzLhecurFQOsGWBqwBb8FYSuPYIA6kWiZfv3oB1JYhWJUBJZV+hwiDtKiJk6AR8uYaz/s+2krSt4fWJOj/eGmTf4/4hk3JfqsOEgKVcwBGyEggL5Jkzg6s1uPUKifnSgJdPAZbZ1iHtEBG9AnPpobw6AshgBjKTMeD5+IfhH/8XP4c/9SP/Z0CA996/w//yf/Xv4Hf+ru+8veLn+ngHZj6h41u/9AX8/f/iZyO+Dq5PDes/mun5qeF4sUH3M+PpTSvDp4xe82EdtpEfQ/NfeO7uWF4+oq8LSa2nxvDP5TIspyNcomteF6E0WXyO5NrYPbNSAYrvwToh0csHfJ5+t0LsWbh+A7QsDQbDcgDm6U722twEHBfI0xM74ZpX5gM5CbR+jy9E3Z0AQCY+wMNEYhWP+UllvCqfK4queRPIU4evivZ6R9c16nAASWSVTDGNZy2vjQF6ePSVKp9AeJUw8VxkFLlLKzTBzQ2IqSN/n8mL5YlJ5T+9EsS9slDc5NaZWCeBGRKUDuBQ7QtqCXllOuXzMPxoVZHZRYj9zFkQsUzsuFCU+q+QpoCeMtBzmBwBD30j2V8rXEHlnSovWU4WxkArFIdx35o3z2BEfOSj03aAUymPUTynBuDa9youKa2xV49ItA/gOrJ1g4KK0Y+DBGBVoAv0wiwWWxfg/UbS70dPaDsJwN4EcnE0GI73TrBtwXHv8HaPZor2+onVfwOg4OkJ8hjl9u/O8HWBwAlQfvYRSyMHDdsGvDhBRCGXC/w125LIhV2svUeLgGesEwMRyP0ZZkalDic42VZ6Ywyw16/5HtYlmuNGSng3SFt4z36E5ynew7pOVXcRIGAZnyUHJl+nMovT4z3y3yRO61i73aYU/GHEYVsipZ/Ph9M6qjALwgPYwgjMhroKrAp3gifPz62TfC3BC8wMqbbCHy619PzEFjLL5YAdnb9HeJx8wpUGFbyaAacT5vbw+eM0KCq0PEoiFJBxsFULhiHoAH755SP+N//bnwT+3F/GN3zDB/if/nu/Db/nv/+bPvc8m3dg5hM6vvPf+Hr81N/4GVpBKtGBOf6Lzr0QML36gzv0R9at0A66JyU3HdObfT/YCVpDuXcgU33tpLAvPONmXZW1FdoC7Af04Qm63JFTEhsDW2MUJg2UqGuSZRckrFKal7S6pXskvjiOZwtEma4lh6M9O6O9ujC08tqAlRuvLwq0cL9nBok5leVh9D5cDkikd6oAvm1YWgOebeiPDr9LSkIApdjkoTqp2w4AEplaoUhFwHt0R4s0bTs1yGmDrx1+J/BDCDQzRdu86qgAnItM4dUKKXnp28IFwQcRoN5ZgpHRAHQmFXsBl2qlkHVP+FgBdGTwQ+bvZqjIZmIwU5C9rh0f62QVRl0imfknPfk49Mb4YeVFkQKKMf8JgrP+iwDZr8qdesm3aNxnFO7pPpcYkwOTNyqvOXlUagJSmaF6dlUISvIsr+es+Z44PFdHkFDRDX7e6FEI8ESwE4R568DugAWZd00PKWuT4Oysl3TZIZcDeDqhnxdoNxwXckX8tOG4W9mfDQAeBHrpaI8CORTrVzraV15XwUXpxnosbQGW4CK1xqJ193fMpPnKRwBaGPDOTKFtBdYNsNcE4yrQhU0UcXTodhfTxt5Juq7w0zaSD9Y1+hDtkVVErhCCQAsRAobU0B5/z/5Fpy3GGuG6fQ8AvRRny1VQXcQFldhQdoEI/Nij71wrg4SeMZTHFkeHQBniidYGHKsBx1N4RJzNKmPz+on3sqZMD98ZNvP7Bn99AR524MUaTTyFYa5LZ4ZntGRxFdhpRTuRp+Nn1v7xkOe2KWWzBnBXgXljCw5PwFKLdFqQsYjTfnRc7VnAyVGUmIs0JBtYL2wBjpPgHz9+jD/1H/2n+FP/0f8Fv/7XfQl/6n/xP8Cv/5bPZ3bUOzDzCR3/1nd8I5JD4d3ZmM0lGPoMpdCaUPiZG9P3HfrRBYLYNIHeDYAsWoarBjfGUympor8400oRAfYdvins+T30lx+gLx9h+wq5W4DdYIdFb5X4/qVDL1G9UgV+v7Jfi6Os3AZALx29d+CusRpxPmyGTfbIptrB3bkovS42Kvwy48iqzo1vC/vc2B5eK6bHyt7RfMURAMYF0AMjHBZ1TwgvIrX3CKs8wlrWCEIkUGR7PGhlPTXsW4CnIyY1M12MIE8QoZ30WFjJXpQr4Bj8EkTWZwKRCvvkce1oGR/PnpL5EB0k2xnMxDuvawXA5I8W7vcAQPndshQjBTmkpriVZ6tOEfJbroZlfN4SyU2QxXbY7DTe0cL+ROzCzeKHbKeOIiALUK0OgAQ1eWEf//o0afm5TWBSZHi2hM6qAQ59AEYAlVHT+wg1qQB7h2wbQ1zZSynnM7gNBIFSqfW+s1qs9B5h3gUiC+dkbXz4zqw6kwWX5w0NHfrYcfpFhhG2n3+F9vEj9iaAOPR0YhG7bQWe3cM/eglA4PdnriMR+CnqPzVWIsZxwD9+yRTxbeW4txXy+pHE1dbgGbbYO3DsEf4T4PmzkD3RasEZ6vG7E2TdQqZIhH8wss+aEtStXtwUZvEE12VpBFLh3SkPXK6BaE0Aj7ISiYCXBmsKVWX7heQENRpn4gIJDh7B1RmQWLtN2eqgODCM6Us3fj9q1PgaY+wdWDe2dPjoFQCDtzWWUci/M9PlRQF56FDpOJ6fIAtBmy8NsoBp1yqwE7kxsgQ5GuHtLtb9NYE5Hb9jk6XgyGVvrKOVoeIAMt4AbIpjE/RTeD6VAMdW4P/5lV/C/+jP/R/hAL7jG78G/7s/8j/Er/vyF/F5Od6BmU/o+LZv+RK9LBFC8T5CJJp8Co8E2iYsDGcLfOvQjx+Z9ROABhD0jWBGXaCxIYapC7gq+jNlaKWDdSnCapLDmM4ZbQHa4ZCXO62KcBurewEte1ToWWCZTpug6pQhoMZebhneOCgwZVFyLNJ50YOoHBZ8eVYUUIkKxs/vIaeVROIgAhoCGHWDa4OtUqBQopBbWXzxjFUzxZKg6kyJXxuOrUF8o0UdjTRbE9i9QiGw3VnIuAPahyGV4HEo9jC14oNUcBVWqgd/y4K4IrHegJO3fiEOIxDL7JLba4xb5qDAeH6F9sZJYgJPAmWEEDQlqyYvCeM6+bNSsGaxvgQHLrR6uwA4JR8i3vNMzElANYfO8to9vC3JhwiAOmdUeXpu4mtFgA7w4sJ9lOE+hxMU572XRhBzGBXqpVPBtqXK4rMUfi+PFQSQrUWNFs6veIc8XshhOq2wo0Men2Bfc8+U/0uD7R3toUMfdiyHwfcN9vCA5eMDKsrWAF95xaJsW4OGDBAVVsA9sclkJq/haWftm20bvA4VJgeY02tD84ffPRiuqmq5D5doyKijorEIK9fCIR6emMtOwHQ6UeEfwQE5+vC+pOesNY4/PTge8yMCX1dAHJZViyMN3hd6F6Rptf1wCIHGthHQJMfpzHCSR/Vjd4zvHAacVragSE7KCZDDYGu8U4miiR6eIDVA2wh1Hky79hf3sb4D/EDQmwa3UIrLopcDti585hbhZA0g0dkAuOpxIX7ONR6GVR5e62lsszSA8m+Ixqvl2QqZ6k3QG66AzLECtgG+SOxTTss/+Oe/iH/3x/5D/Lbf8Ovwv/+ffx/WNrt+P5vHOzDzCR2n01rKhyRSGf1rQj/RTY+R3bEq+j1dv/r6gOCAnZYgU4Y1fHQCghaW6XGQ0NsAFZZJ104U7wCtsjVSqfOGTYBLujFpWfeNRD6PDA8FaNXlhmoUmKlUrgrMAfTCuMEUSLglDvjhlWqeWSN9a6MZoAC2LpCTVdqmBykyvS+ugDdAdwxSp4TF2ghapEXaKJz8HnMWFgRKIBK8rXADmhvktcNOLVJwARxD10o8Iw319BDky8NAOMhH88mjMMkvvT6VAovApvrNZEG9VPjVSiLOK7IvhXaSBk25rvLC2fbANTgfEyhLeowY4GB16HTNk3gJVCYVmLWi6ajIW7SI75fHR+Fq7FlVJMb8F1SK6bkSVJRojhZVKZ4e722e72m95YN4CHsqAKNyc6bmisezA1S8lye4Lqzh4ulNWKKQHUrBu0YGX2aD9YPp16c79AWQuwa/MLUZAPy04PjgDgDQXj1FuYHGDD5t0N2h+4HlsUMfX8FwQB8PSMB/uTvDLzva5QDkCLJ3EGR3NkP0prD7E9phwOvO0ND5RC/n085nXMODkJ6UBNp3d5AtPCOLMrTqgPQjWpZslRrt3QEs8Ls7eoaWxrIJyUU5rZznZYG0ADNGrxGmkC6CKO6aYbmYq6xX04XXVaF3qTMsbOJVp0WOdH+2Qfhd2xWYTaqfb2tkdwp8WWAazU+V68ODX+Pu9JjlukxbpHdASPjO+lYCD45NhGa7MfSnxrW4NlSRydzPyWWMPVSbXWNudBgcBWLC0+WxHwErz2XKg7ETx3e8CewkxZ/pK9iWZqGMrCrn0375z//Lf4Lf92P/B/zlH/7Dn3lA8w7MfIIH+TGhFBcvzoymOzxV5wxoloZ+t8E2FpPiBgoltqIEn4S2Egitxad9VJxcGvoXw5Xce23wClGsbIUgB2iR7MbwQFrXZQmlBoytG1kCLIQWfxegnxXezqTxqEQyVKSQ92scoI2kXjsvUdjKygKXEzMHLLtlC5hho0ql0oTOlyX4HeDGFmDiciDi1xRstkdmTinbfITwUFmHn9sUWhkegDfeZ/yb3KI83EMohWdjCLrpQglcr79a96rPypsR1m6k7CPqWIQdTyXhHEy2nshnp9ckEp5z8oMAe0Uhn4GXKq17OLI6dFfypDzcE6zOrLEOcrw65rYeJJRPgqmkwmQ4EuEJkfG5mBXWqc8TtKR1KrjiD8mO4VmJbte+H1Q11gnk+wWwE8oDtp2mujmGoxvwbIGCFXZJED3Bjh324g7Hiw04r2g74CvrOdm9wu439LOivX/G9vGO9ouvYPdRW8Wd4RYRkseTIL6xfhIOI+cmQqLSGrCB7/qyM4yUc3paGV56eGCjy3XlCxFw/zhYbkCVihknSH+AQ9gSwaK2z6JwX2AnZw2YBA9AcV8SuDJ01tkoU4RhrCZwIbDQPcLcSwBI5PoTdroHqmYPQS5gSr6KAMCS6c9cK7ZN2UTh/bMk/ocX0RduHofDsiXH0pD1nmyhl5UePgwjpiH6QTm9wYsEHU3KaIIKfAM828IEcdlCXmQdLgSRno1sY7EvY/PWsgXgUegy/03DwVUgLfC8S3ge41sZ2rfA3gJWfc8wn6I8MF349+rbptdjmH/+r3/pI/z5n/rP8cf+3e/BZ/l4B2Y+wUNAoSmu9Mykp0bDjZiZHRiK1BcFTEfdl3DXau/wnWmVerDfkUT1W1sXLN2hHz/SIr07o3cHwDRJvYSAD0sqXaRMFxd6hIT3YoqtwMyAJaz/MokpTHJ3Cjw2uaKf44kVwyuQz8ehACIwZ0NMrFGS/EiOBa54JrYIjlVgK12rrkC/EyyvAVuFXhQB1KTSjjU4DllsDx7WVln6AnUPJwhDEe0CdhSPWho2CYYslBdJM3G/Mc6sK+HinCeNcGLdLorYxYS4e2WOS3FOvFBPFgAUIEjeoRhcCrghFLhrbm2tsVQ9j3pHcY3KmhpAofgMU1ye76/BW1bsVZhEg053ZoTEOiksHiGoJKrzRUzILedtfrdfbbdojEOVkC2L9JnXeiosYxFKuLqEACdlXyUzZqzsO0MS5zM0Sa0AvS9NSaAVYcjhiFYgAgAb/MUZx3sbfBX0Q9BWodW+CvyOnLO+AP2pQV4L2ssOVSdIWFfg5WsgUrtlId+FoCLqNC3KDs7hbcTWgCOq5O4HCaru4Sk585pwehSiLgv2aGwJcO/enTjhmX58OgUYWCD7DjlFscrs37SH9+y0shJR5zz2uzU8ljRoPIjQGmupK0NMVX8pQIEZlTTCGKku9MqMIYZPwCzMwyDpnk3QAkQ1cAfhiZfHzxpGlDcLeDblnhUg62RhQckwgpoIgcVesyUAiQYJXFn36thiH7bYt1FXyYp/lrIQgIYcCbJuwNXxzLH9JluG5zeHhbvWdNgAKTfLDlA63G3lvtCMdyJkgYCgsE3g/1c4/sOf/tvvwMy741/+kCM8MFHyvUq9W2o7/kvuTOnbUTTLUK5XMuaJ3C2scIl0WXG6bvHijuW2t4UE3aMzRfThArx8BD64K9KwR00H0VmJhnvVQE9ICJqqMwJQMJSlD3qNLLk/FKgGj6HGOCNcZYgQ0pNHdpOjN1Y0VsXI8nLw8xPH4S3+g+C4o0Lt6WXZQ4js9CtklV2JrJ/yogRPKVOBDWGFd8P60QXHixPTLuMd5LNRuEjVYDHFdeaB0FrS6Mcg8GiFRSHsDmi1rAjrV2O+OvifhoJvIdSDzOoAi5QlYUA4Zou6Igg+QLrJB4hEgQipv/kAjWnlTRyMGpeDYCLmW1NAp2QuQhGViwRgKq+Lg5keybPJdgWxXgQo78p18TD+YwJmw4HrTMwhT17vAirkhb1+IhjMqr1Hh5832P1CL2USz3unt+bo6B+8B4luybYt8PMZ9v4ZeP1ED9TdBsXOMv2nE72gTWBbg8LhXeDnNvgN3dGewoy+X4M7FshdFXJ3h65gem0xPgn+/S6K40HoUVUrw8WXBoFDLzvnVRvw/nOGY45jXMfDmo/2BOjGarTPzyQqqxR3BBFixdMOnM70ZMQ+8eSgLQvMjOvV6X3yzqa1mUXpLTpwNIJrs5BDywC4vkzgKkjmHk1r+9qgCRrB9ieVKBBLwc4Mrdth5LDpCOswWwhMmAoOjy9zdqbXXpB1Gd5H9eElFyHvpwUYsZB9QJSnSFQBlIcxwH6BeGAkJqQHMYFMyA4PY4YexbEnK8ykIWct3mWUl3ABExMamMixAHIJjJq3XxL4j73z1Q4BcOkWnLdfBfL5lB7vwMwneOhuAVYclnl6ufkj24KcmTRfUZZC+ny1G/B0QB8usLuV2T8Ylrj2DouN6ssCLCzspTuLyfnaSDR8/cRKwx88i4wXo1s6rB5pdBHnUveFsWQJzgs9Hc4MHqBIuOnJEMEIJ6Tk0bTSIzYO0NpfOoHaorDDYSdBXwnU1AA1R3eGr2wFbI1NaAQO3h2ycu7MIowEhL6lWPJQ1rJTiTcD044XJRiMMIOG16A9dRwnvgCfhFhmYrESswR4owUrnR4keICcUOJivCckwmIS10EoeRV4ELm9+QiPIdznDbQGl3Dbt6hCCoYTvKFSnuktkTHtXlMBAEzlLAKuJH5GpmmTq5HxfikQhQCjPs3tmOQELl6vOY83rEQRGqrhTIATYDqA5gR0tilrnUisswsRqEisa3N68iKzBQBkbexzlNwMERoAv/yKjRAtAMRpizRZo8I7N/iyws4r7NzgULRD6fVUoD/fgJOw/stTx/LKcNw14HDoq50eS1G0yF5bXne0jx7RTxpeGIvaI+QduQfIrtTkUPKnhZwXY9YNgrODbQmOByJt2gu0ePQ6EniBjHyXOK3AJTpPn9ZSluheJQpsWxLXcu+uQd49OmXC2litWyPsHL3ZcGQoz+Fwdt8OMO3NI9Qn8Cj3PzwbDluVvDkJUGUBdlQ51iqkN4pAMmyV6QUVfIzQi0S0WNGjrQuTAQbI9lzKUWIiJBwNksysbFl3iRNSVQHCYeXplck13SIbNfaHx/w6pAy9xDRpdJQsBdCzOa7xOr6GPLHY84m6FkGHQNSC5MuxHiefjDDKFEt8/Ks8PstABngHZj7RQ3YvVrocYS1EyEM6AKXSZWqQTz5HxlFlN+CgpaRRu0N2LvJMbfWF3asN4bh57x5ydLQe5oQo/P17urKPjuXnP0b/mmeQQ2GIuHGEacTC2lDw/EVK0JJnEMAGQGazAIBEvNzCHVrF40ymlFxuevP0Ozv6ptjPUWwqC5U5PRu9O44F7D4e1hMyQQXpcojvmiNL4XtIn9TfLMJn6AuK/yHpFr70cFcDejmApwZ7rqhO0QmOxBk/b7SefHGm2x8erap4s3x2OQKMyCDpijnUFN1iDh0EoZl1cTikOXpylnIABxW2Bbm5S4N7FvoaHoxYDSVMC1xmsbzwX5elHqBGEtTICFNlLxnEGAnIp+8gbNTwIA1LNevJZO2ZtG7DugwFbAF+uyk0gRklOnxV6DHWu+4HegN7EsEDTCJCpo3husOYLv10gR5HcFC2MBacCtYM/vyE/t4J/dmGflL4JrAHRzufsLy6YHl1QKTBXtyh37OXWHvs0GOBralmWbdI95jHy0Fy7gWQc2QuGhW+nVms0i4H9BJZQdFnyNEi/KHVtFD24Khk65G9Ax7p9muABASov1thu0WBudgjwWHJNUnw7QTvuVmXRm9fdO/2U2OdFQHBovng9AUgQFP4pRdQoB0WwH8J4HE56H1bOA6v9Hsh+Ok96lcNY85iz6MB7oImWh5eSHpzCHTRYy1msWFhSN6CI0OPR2Q7CjklyD2cewnJQYkNkh6fkHdZoE8kAEYihZR1ZRCkPEAAG6+9UZyuFvOUGzKLdMYydiV/Ry/hMU0PkHCvWuwZb1qGi4mjb+AcpGcmjunHNw7/Ff72WTregZlP8FhCSUEcsje2A5hd62G9AED115mO4pFEhot0h6tB9lD+oXD0SP4AiaEW9RBEQOHSQYF5WqGvnqAfPcLOC3vvKLMemBmAUJKxp2JMtZFvPLBAKOIe4Z1F5szlq8eJPcrniRg6mgDnMDHSjdvI04AQyHhThpIybTo8G9rHDVwA2cj1bOHBIYku/TRhUYUV5kZgJQUuZQDGLQR2ArXgAFR8XD28KgJTh3WHNABoMPcAfBiCPUxhEaeXxDzc7vGnI9LOkUX/aE1qDMCCrIkITWUV4myQCJFIaQ1PSnJaxGvsBJRe68iTIOnhwUrOAjBK2Ot4fyOsRjTooz1n2swosAQUoDFJ7xOCXxJAbe+AN4Y4e0CwZSEPTBTWnJ6Qo8MvDnNlnaODC5KkcoFIoHAzhmqimSczUhZgXSJdmFVb7b0z+vMV+zNFPzPM4JvjkBVPX2porwzLRbDs7F6Ak2D96MD2i0/Y399w+ZoT9PHA+gs7n6NFSGRtAdzoiSG/KCBfkHutMbxCpatUdmtkLwXvzFtcJ1OFlXEMCy4bMuusR2wy9luC2tpnPeY8jYCmtPIh0TyRLzj5SKZS1aFt4zuSS3R9XhLoeHhV2mjWOXlfTFfI417cKwHBiGlQABHPFiA86wIl2RYL0IWGTBpHyb0ylQjVc0489qBk1p56hGq1intWJhFkhIsRYGmhkBrhnRhTGBlewjf2lI79IAcCKNIrK+LongVOJfg1uW8CnVi8C429kqClB5BJ2kB4eMwFWB2+CmwBLOoAdhUgspfSI14cuFnOToff/PtZPt6BmU/wWODoQeKUo1OwZRpuHMWh6U7GV6a1hmVaeiQ3dAOqD026MsOagoB1RJZUSGG27AeADn9xxv7l99A+fAV93Nn5FShyLu9pUYFYKhuLgkVR3ZhzUCIjC0HwJiATLy+SxD00iLAQicwshg5IPqQLH01wLMJN7AheDG9i4cKuHnRJ0hWBr4ruhrbzWdJIkvB+yc7qth59VeT1zjofQmtRL+SmWJsEYBQlpQMikViEb0yiwjEtdngIRHNyO9xRqZySLmoCorL8FtDdHt/1RuuYjjdaaGnRU0FQmVOIx7+rxLqKsF9wb6SI0Pl+CTR8QWWSJNBJwrDl65WMr0tiGAjYIgKLMyU1LfWnDiV5ginBeTkPgOwEkKqCvgrgHe3wAsoRGJxqZTTY5pBDoVuDHx3mwhBkZAGRg5AcqGnRbSzT75lVJMI+Z/cryxM0jTopgC1spOkN8POCy3vA8iTQ3dFeKpZXHfpqx/LRjkYmPLow006C7yQi6PdMT/GDxRsZAtYRPmga2Xk9QpAxb0rvkDwdFfqp8FDE5MR89CESsF5LN3rs1mUQwd0J2srCT4Dk5SGhhyCvFbVpugVdQwjyQZDVSzjlVwkiLIFYeF6yMaSps/6Lxmd7CkGuX2tG4J7AObh6OSw2wOXn5U3xAa4zK4lD0rF20wArQE4DAxIKX5L8iwKc5TUJQNYNBIbgnkpZlfWLsFCWSpST6B4e4SU4bU5e3AhHh6zr+S7GM5WXeWrm6zEeBaJYHuARerclxhGhpapYrhlCDoMiMeqNCM7ffzUk4U/78Q7MfILH3brg5esD6o7lcOyrX/Fl6gjQIBdmJkgqIgDpbmX/k1Ca5tU9VnTEZhkfP4CulerNQmDsTI2jw56tsPMHWH/hJeSyA2dW9XQV+DasiiTKJrHOASCqh+fmoEsdJRyB6edUMEcKjSkVN5W7Acujw1aBPyE8T9zrtvE/OeKSQSKWmC9IXLsKvUl5TWxhJ/Hi+RiVvXa2UTBIpLxLZYJ4N+hTh3bgWAJkJJfJJsunXgoAcYbSupTAypMkxgJH9Wiq1y306lTxrXSvK+jdivFKKqUQWsjnbBTMzGQID1wQVypdXRnuUA6zcHKCiAy1IQsMnsOd7WEBh1WeRG+CbtZ1kR1Q2eHbUu52idoqlq16NmUJekNk9zQSvZO0/sSsIcl0/8MLQCKqPLNImJIv08GsM29QWNwr+o2VgiRQQVsKkLPqL8mivnfIk0CXDtXwAIlDD8AuDpxD0R2c/3YJ99+6QC8W3J4bJQwASYBPEqyMECxDtCyv0BeB7j0KPHLN+Krwg54Yi30uEkpS6XWFOwm7CXbOK1spBPEWxIccyxKVf5E2ihYadwl0HsqTwKkVKIRFKFbpEaz+Q/AwpgjoXYSd6buHwo8jgAy/r+Exzr81lqcIQy4zhGTh2ve4t1T4WgaPpAQLBp+tQLYXeCBvJ8aYpQomX4UDBPJxfUvghAAU4VXRqItjyP2ZYWeGeur85K0lwdkD+LS8Ga6yOifxN9qytXG+Q9DhwRMU+BaGlQBdQY9SesfDMxMYm2I7eT/TkbLaPwdI4HPwCJ/d44Mv3OHlh49Ahh96EPcMqM7NPpB1M6NVoVKZBoyFN3pMKvzTI0ziZUGkJSwGyN6psINgKe7Mbvo4sjbOkaL6+gmyNfiJ2yG5KN4DPCR5FIgifbTOZNqgRYpBAhyUYEyCc4YpJDIRyuUaG1zMGQ1bIlxliGQghk1cAXlw4Oxjw+Z1DcUf4rUAmKOvgHpWVQmFoKAQfjroYQjElv1ifGvBscCwphLQlVTAlVTyaB5a3qq0uvL1yrD23EGXfky/l4UYHrSWDxUhAaRABfKU8kSlMkqhCkQ4jONPsJlY0zKr6AHMrgmc1hdHy1lKS/bJGNrIqqsCyCV4Qhq1U17vkA+fgONAP63A8xOgCn2KTuiPjd6aJbwYcqAtDdYEZgdL17vwmTUA/MrS+BrvEE7rM4sjCmRUhN0d0g9ANvLGFiVRIjgk1jTAhUIOhz7RMyqPHerA8vLA/nzB5aRYjg49HP2+FVeoPZLUas/vAEityfwZ3UggXjSKq0nwGjTMbA0ADtgCKEjcPlbyJCoBYCdI8sacNRFBi0wkW9lI0frB3kUhLBh2WukdyQw4Tc8OGAZM2SGILLrcxxjKOZS+h3Gh3QgyYu1aG+CiLxyr115XehiFeyXT/yW5KKvCxaZsoiC+ZiXGkAGkdSnBXnhmE4wNDiFurhMyUxAabkKWKkx9DvnKkF/IHoBp6cK9kh4s7jEvOUrOGNcfyb/j/l3GZwx5x/ymJzjJK9GNl06xFBrDqHEF64ZFSC1rznQBkGBmkfK+1HWToyZjDuf5idc6fpQcIz7zxzsw8wkeX/vl9/Ff/1e/RMXbDWrhXckGj0sIjhnQHF4xYCrHsIokdlS4nWWPcrV7Zx2KJtADtaq1R+l2EJiYdbT9gH7lIPHwcQ/BL5DTBf7iDr4u12Q0B12p6ZFRRBr2MI5i+AVkACmrIb02TAAJAZp8kTxnAjXNhZa9AO2BMfQOR9vJS/GcNwnrC+WV5hwaYMGrIVEXYxBNYEuDuzI19CldRjEWIWfB8p2UlRPhlpRFEl2iTcZ7jDkosCm8bWYbuCp5LZVxgaqmqx6Cs4UQD8ErOqzgiaICxHMbQAWvvBbBRxDHlaBJGoCN37XOGiGMt0+cm86Qnqtg2Q2+KPTB4fe0EMUJrNU7+hH8iLNAT4r26gK5tHDECaspHx3yYPQyHiyR37pFo9SVvXRWZT+b7HWjKfjJPegBHjPKwQcKb1H3aPB4sMCcKHw7sSLs0emxWlt1rWaRRWFKMjJ7hgrx9OEOPQn0yZiC/sqLg8J1rgSGLoDS0NDsb9QEcmBknpWSE5JZD4dlWFEkSpZQC/bN2DPMBJAO2cMDsoVnp6HqvfjW4AdBvSTPxUMmaON7FpSXz4QGTIIrNomNekUBTNLgkA5ylhYCE1Z9FniUT6xnisri2ITGmCSgSSWexsm4PkTQowVHdWtP8i5kkL4rTkJvFH+dZIwP+VJe3+Tb5DPFHGQUydObmW4XcC+683ODVBuPHvOd4b30GGc1BLQAaZ7wbwAiwt149ibkEjnXVmGPAB2Ca8PIItzrkSWlPeQiZHjFxYdnLIVcUgImo6qAis5ya/zNZzD0GT7egZlP8PjWb/sy/m//139MRXt0SGehKCQxE6jUUmDysofQTs9GOT8yzo2G7NUj5sB+sC6BCzV+B2AhoJ+eWDCrd27UZYE8Hmxmt21QnICPH+HdYV/zgpk3SXYzXs6jcFPGZTMDBwCGFJk2WIEaWkUevBMSlL2EpHS60W2T2pi5Z9Uc64PQ8HwClotD7EDfWJdGwuOUyl0BwJ0p2C0vEhapAnI0WoqNO1sPZbZIui5AcLDfSYFIyZh3IaZoaBnCOZWIOa5i7tpDuEboqIclqME/cox5Mh/eKw//M/VeEpHH3FT4RVgF1RfyTDSvp7TuZHfgPoRYtBl3cdghbJAHrfTZHqRMiEQJgIPhJBdmkkGA3dFXQQ/SqS7MhJP7E+TORrinCQnljQCBmUcEIfLLHwEfvAe532CboD9bCGiCcyCGaDAoyArGiNcIc8iuwOLQPfeLkD8S3Y77ekKDwPcLrDGTyDam/i+7oa/Rh0yi+NqiEDTIw4VrZqMil0fWczpWhkCMOAZuVPjktCl6Y18rNmId4QB3gTRBV4M8AaIBLpaxd7oKfA1PR6MGyppUtgqsLdFZPt5rhkLLwLEIbXHd5RivwpBZtM4jsyo4w+WTyXom4dVMFELQoAOcT4Rxj/IAHlMh4dzkWo19FutYBPQ8BPCq8FB2w87f45QUGWnkpBc0gXvtuZgHS8+wJjjL830YTGVlRXZTnp8AKVaSJwE6w2QJotJTGfsSjsGVSXkmARjA96SOAJNeRYLDUcbrhGjPEJIIKozdTeArIOZYXxvw2nF53tE3XiSNKovv4Ih5WeL+Xo9bR/J4tOEzf7wDM5/g8Z2/+Zvwn/yf+LMaIJdOr4BhWBPhlgWAZKRlSl82LsyQArVWCJrzBnm8hLckQI22MmfECXJwGND3SmwQB+tVvHgG7DvJh9sC++g15G6DvDgHBSG3hI9U2MZrZw0GAFMFUFToQhzIdOUyjlJ6aJrd/LsEr8PzOQGCvZ1PKt0p1HfH+mRYXzr6WdFPUYsjevokLyUtKwrP9Gz4uHcUfuF9w+q57BzP3cJ7RX2PBg++DYoDkUNMAl6F7x3cbcb4doaV0vXsAHp4T6p6XQj9OiyuGwDLm6S3GmmqstowKzbbKsCqMHe0C69nK6AXjBTsg+TwvgpsQ1isNNfaQb5BvxMqxW1BezLoM8W+OPxEPlY/CZY1FpA5sANLj1BnlsSfrcJlgYMFG3F0SH+iR+a04Hi24Xi+wM6KHn3HxADdYz3F/2Tyi0tyPlzRzw4XFpwzJdlXliXmWeGPkf3l3GP2fIFdOtqHR9VJ0Zc7h3u3knflwhT9KOAmnWujK4G8WXS9j1IDLnz5HpygVHouCpU0RDDqiAgG380RPBZmqkh4KTRDNa7l7WlgfarKyAlOjuyg96QJsm1ArjFLZLCyUaz2MI6Cm2EBwLN7eu25XGciV+DBBEHyD09ItgnJMFaER2ZwMrwpkepc6T0YRo9iANb8Y9biCc8ZPYq4SkMuAyLHGOApRCO6C7AhPCAp9GI/6fgZUUCPhPkxtjRI0AIU5voEhhdHB9BKSZjgLx0nFfKtsg1eAM2EYKY3embbwf1qDQVM5FFw+oUnrP9sx+M3PIPdxftrfJ8JGFUBO6HkL+YpBQrwnT8Hrpl3YOYTPL7zN39z/ewA9NIHYdUFvpMV4cH4T5Y/JATE1MGZ/ygyGxXqbNC405KW7DhsoVEipo3GaqJ0yQsJkkuDvX/HAng7+83gvQX6Cx/ymvdrWScSPAl6h0OoitMyD9CFZQg68XSJA2UjxEOk67qAkjA1sarqYgIARqUMCK3WBTjADAG16HQtiLRtK4sTy3SNKZUb4SVBAhjnzcQNuHSCv+cnrC+Dr5BYTEJwxPiseXFUSjjLJGyjM683ijr3fHIpROQ+wCyAUUMl/i+Nc5JVU93BJptKwqg1XtEXhCXH2jd6AXAR7GeDnxiqWC9O5aeC/XlkDIFKbnlg+MnC3X28B8Aa77UbloPckX5W2EkglxjjCpgJBIpl71geLujPTpCV2sIU7BsUxF+5PwHbFyHbCl8b/9ZohXKtI1z1FPrptRRIeSixRFXopbHj+SpA24CVlWlld8iGSgfXC/eD78DxrEFWFo6UpdGw6A6xHkrIoQfDK9Yi5OJg1lRkInHNxEBjrCI6Ql9pQASxN8nzfPMKNKMXrifQDi9A7NW+hubOUCVIUPYN5CqlF2QRqCv7COnYkx5eHI/yAawmHIstjKcKpySBnKMO+yferSUQwWge2idF2QK5Zzq4A1lawdNICV6HhwExOT95eoAKnRRs9hnidhqk4t4SiE1h2TD4srmimFQF4Zzb5AKlkWPhwai1FZhzDnXHtKNKYQXQcQdJ/hjnDW9qAKsgFmeoixmXMgBXWJNdAFtZdgJtiOt8YAlP6X4HtPsG+cqO7aFjbyS06xqyIf9rgB0AVhSyusIzIUvfkw2f9eMdmPkEj6/7+g+qHL0cYLy9eyj9sEKy0WLUEqFPMCw0QXU3rlAIgOyDIsF9kK7MLzzYkBDHlOnQQ6N3Vr8tKROdfiErFboI5HyH5ec/xPHNX2DmxtVNU7GjYuLwEPoeDefKTT0sq/IqpNxKrAWeL2kt5SFUHIJxDZeIp9+FRRh8Aux0f2T9GbijJ4xKYrCEKQRQcuwU7hIlP6Ub5xgU8ksUsfI1lCmiNUO6tg4Ai7ERXoKzBGIh26XiPqg06tm1Q1KzREjqZl5SEim5Hr4QsFljozwK2fDqNa9wgnSFrY6jCaQ5jmeAr4LLk2B9cDaw3sgnEaGbvm+O9hgxBHPok8DOJHu23aCPhu0rO+y0wM5DCfgiOFQgHiEHFbTLjr5ulYF1lGvc4KctiN30tOnF2OwQAerSHy+xtlOxxrwIyPOBKPwMHLag7dxLejiOk2KJNW1Lg5hB0dFeXxgi+RjA04Vcm/MGe/9MnszhLKIWBPtabzrWLbOYgpehqAwbSTCJ4b2gJ8CRxdjSWidtiwReb5FOHooo4wAmBtcGFY1rAJlJA9hoHeEEnzCJ7JbwEqweiXtC71Ku3hYl8BNA567OvRphpmxcWp6GJJEngFEvzlpy6pL/kUo13ZTplaCBM8BJ2A81ubk9axhtrLFcDglC2BcqwIJPpN14L9IZggdAAm2+Q+FYJDxKCcpyr3p6Zup9zWiltnG9qzTyCsc5gsQeY3apyhkZ6hJEPZkeIaYzYBsq/O4LoG7QpzBaFu7dpy+u0FNjeYhYSxmSkgPQMNx0x0jbnmVp7p8L8OUPnuGzfrwDM5/goVkjYVEKy9lEAUY2Tkrs2RWYVTuTWBbWRkLv9LK4O7MlutDlf3T+pz4sKHPIeYsQQYfIhft0C6XKuBL82QY8AMvPfQj7uhcMW2XZ72qgGEpdfQhrATOgUgiXa1eGRZdKPXFFAoAW52fcOoRjpYnKZDWlFMnfo4ZK/b2A1ABdcjiwBhh5dLTXezSL9Aj5MFTi5xXQFnUd+CxZfr/i5YuEMhKkN8YthEqCkRyvz+83xi4ZY6cihI6Ikwb4qpi3odzcaGF9ZuhIYqlEmnNeN2vI9K0FcBH0s2A/eyjv8K6sIYnDS7W8Jhl1fXT0TXB5X6HO4mNYlcTggwrF1qjcCwQI4frwZUU/NXhTArSm6PdOL0J4K/SIcKsQxKkBx+Ko3leBY8hRCcAfIQVJL0QA456eTATXRIX8m97hp4auQHODfvga+vIBrOLc4A877P4EXyINdmHWiPTIohKMlxJAUY8AoEARV7P+km3cn5ktk54XyXR7H6CAmFFqzyDJprUmYgJyP6SVHfWk3EL5NYHdtwLO4qyvlEAyORzFU6vXLaWExae9KwnbEXwUFEhOLk4+T1HkYsP1yDZM/ke2FpmLx/HW/EYPD6lWXBoVnh7nczQcT3TMFnoR1YGecqbSrxl+yYkeWV25F6Uum3vTJPZslumRfA85jlhbIW9uQcxsYF6F1BMQTn+3XDMLWAE9PE3pAXbpaA8d6wOgYCNMOwHYQVL4MgBeZkCiTbffx70TVCV6lIMe7m/+mg/wWT/egZlP+iim/MTQn45KHTysSIJ+tUlkfImFGCgASuCGB8ctwEz4m7Nvjaey4ObBcQCvH0uR47QWeVEg8GdnyNOO7edf4vL+PXvFxH1gdHMSh8TOSeHRQMCTHJobYVaudUzfFxmhFKBqzADCMvdJgFwl3PkYzSgxhSRyiipug6gHMgQRwzoMBegTHezeBNqpKI4t4kNgxo4UUJgELWNt5H8ABdosLDvpALIf6GQlyQxogAqF5bsZyg1I8qsA1egQ8/0N9C4tLECInaGn7BSsCZCCHOjqtAS7Y/sYaAdgq7OvjQI4OpYHxu31cLRHJ6jxtBobDOSGNKNLu8ELjCRXJQv/2cYsn3St9w3oG2unrK9p3XsCyQsCaIJejUtwUCIAkeR3yxAeEuh4FTZDI2Clp4ZVlE0FujT0M7DsJ4gI+i9+BfLFM+z5FkRlIWk7QkEQJSjp8VIzHJOpwI4BzBuzhorQreHNUzAVPUG4TSAh9oUtgcmUigvgPaUaNwoajCHg4EwxnTrGkI1lM8TQKBb0aOTXNH6e1W0ZyE50LTHXowbStSGA8rSkt6II0AFwOI7xxQS4yRvq4TntACQ4IbARSq42AB4hIx+gQ9KwcfqPLJVzCo40GtLwyXIDsbVmwJYcoooD5f4qj3IiTBmeOBnzUYbbvHMnL3KFRvMEHd+r9TrNrTewi/eTA0+xLlbeoX104O6fX7DswrDc/YpdlHwuBRMkbAJqWdoibx1tXtAnMBbPLQf//uu/6Uv4rB/vwMwnfHgUtaISyp0yIRr3kfZ82OBjGKg9MvwUVmi6K5L7QG4M4NbgS9RPyYJ6Rw+WPzk1rhF2MgcuF7pe16UUpgPkCDw7w23F8upCgWINuAu3eng1bAd0CaE4WXiQSQREqXQJgS/AtPv5nd4A3zKtPIRJC5ex0IJPT4saFa4YorotALdykdMDZAUKKI4ClPQOOQy6A2qsAkjwEUjDqQz1kZ/bNoQkmpbANge9FkGq8fSmhLJTgEXavDz4HHtaVkJFI0Jic4VYNJ/Zi+TYLmyx0DP8OFlbEm0R1ID2YDjOgn4X9VoeDWu0a3Bl9s7y2iA7QdrpFQDtsCbQR0EL/kWGDcScafKpXAJw8Ucqp8oIKVeURe8lTb0JX6lFRABbFMedY3n0kS2/cx1ZcF2WTg8SzoLeSOqsbLjFgS717q8s48zUUGUht/BqSBP43UaP1xdf4PjiPfqLFRlqsexLFc+OY4AlS8J7XEvdC5DAQM+O0tskAeq8xb6MWnvZg0oNwYMawEJiyVWIg5oYEKAvg5qSyjb/JjD03CepgYX7K8G2h/LUBFPGPZXhT3cZxf90ujdYXBHNg5snZVz1qKMiAN9DTPnRAN+43yQGpVF12JuPYn4C7Gvo5LAbumMq9eAlGvL9Tg6VUZwzx5t7PEFmgVJHGmZ5wVGwcwqxAQUKro68VqLQ8U+dW9OV4ChuVSDWvQBSNWF1QHZgeQTaYbEnWHfs9POP2D5i9ikrPAOGBT1bPeRcdIlqxBgGVqgENmRNUBn3SznpwH/3278Jn/XjHZj5pI8lBObu5c4dLQFiJ6bl1zCV5x9ESEr+2ElVd0aoxCWJfIg07c4+NcfBImGVrgA2pDQj0dAtqoWCqYZIQqrTtXtaybnZOzQa3vW7FYJWrQSqUFQK0bwPn4DPNcWpoaG8Y8P1hspU8EUi5o8K8XgQ5HQHQYrPbu8BIEqYAOW2NwVL6L/emarMAReYE/fwJDms91D8VPZyYZXbvkg08uRz9qgQawcilBLPBAy3PcY71BJy15IxElkhh2PpMd/pKTj43sUiQrgjqpZ6XSJx7dqFXBcD1pe08PdnimaO9WPH+pTZYB3LywOPX3OC3dOU10PQdo+y89S2ntWknSX30aLjePSjOTYp71joWXI5goul2fFalQrRA7DtHXbnsKWhrw65CEQ5ttNHTIe3jZPVdoeLAScNkm88tKG8c6W3c5INwUEih8sWib5PBPu+LegvXmB/vgLnhTU9RMgBkWjEqAJXepew02vH3jtEFWbRgblCfYLjFCT+R4zwYHjzzEDuhkiApDBaMhsGAXYMVQqh1nFjA0VHNOUsryCQvbWygaQgwQjKK2MLP+8YIbIspJhhoK5S4NXTWxRTnL2CNHZz9hhK0mnufZfgdSmyfyZgJGqn5rEciAXYqLBOeoxzWyQ8GlE+9GlPxd6h40Zi34UgyPtUeAhDpjoGwXfcIoymsS2B8Q7K6Mgt59P7kXGZuua14KO3aqEtisP5/jubk7ZOj976yrA8HsDrJ7SHHSINdlogqlwz3YGNhH0TqdDsIOaNMWSY2oHR5R64Sh75zl/3ZXzWj3dg5pM+oqjT8MljABlc/ysuU2iIf5Ig8lbthvxOcF1K0K0NXQXNA5S8fgI8Qi15pERIsrAoZH2ErPdRZCxIre5V88M3umPkAJaPLujPVuDELr/kKUel2HTJ25uCINOUIZOFZB4N32hJBx6qLCRcUnEkwY/PWT0pPerTRC0YejyQBiyv0RxoivZ4sLYOwGfvRFcSKdjuzM5i1U2WYS/hLUAWC1we2bXWQ6J5eglaVlKNIyv7xvM7UN6MEqBZs293qHUWo9t01BIJkKgKoNM7U8kiKUBDGUHoKVoeWNQt5x0ARAWqDb55FSpGrBuxAEo+GhEaJkANodJcGToa3id6lhK0+FkYnhFyGnA45JGZe75GQbBHw/EMbHMQBACNe62vDP4IhqBAXk87DLIRQHkD8OTlaUACgGJzBkjowhYFi+AQhTYSmbE17C8WdrFeSJSGswcTQqmRu8WsLW/sTO8rvQ3VCyuLPRoCEMf7WirJn3MeCjTDQw5EHagEFvSOqOUaCfgvCF4cCxxapff4lQenawIQqc/SY5Zhhp5AJzMP4zKmUr2/8lwVnp9ctfzX0hjpqBRmAX9HjAMruI8zXjQpWUCiB5ZXPaYMC2kicpmATN4P0Y2+jf2cSrqIrkgjAeWhqGz+fBGK8n6lTMqqEnM4JrdTgZm8Xl4uP5Oc86pRPPH5AsvldXPuNUDNU8f6kkkY/SQ47oVctYdI1jgpyyycG/ZN0Z8p+plp+gLK1t28eHTJJXQd7wOYPEHzVAjw/vM7fNaPWyfav9bjx37sx/Dd3/3dePHiBb785S/j+77v+/AP/sE/eOO8n/mZn8Hv+32/D++//z5evHiB3/pbfyv+yT/5J5/AiH8NjgwRLQAmSwIAiuxnATA6ybskp4bbfu/876kzJHJ04BL1YwRl0acksLsF/b1n8Bf3wLZV92AsC9O0y4RC8GwO6B7dhkM5sOIrhkt2W+EnSr320SPaxxemvl5IntXDy5V+pbinqrciTkIbUApbbJIucUMxQC7MUmkP/A/dIaIjc6IssOwnlIoAGAW6xr0R1rSa08tkVtlMAzjyXFuFXZVPoeAXphAnB6btiFCGl2cGhspK8iTtpkBTei9sErR9A3CmVbvfU7gJAH0iaHSZzo+5nDdyZTyJ0FJXKnwJz55YVL5t9N5ZU/RnC2Rr9FRI9C1yQESZKebOdxQCEhKgBZwH2zD6hU2Hq5K3leHU8HaJhTdip1bQw7F8fGB53eN9EYw7IlR2SWCoVYZfOoAn9hXT3Zm9hqGY+B6dJO8DkaGnxd1yFRzPFhzPFvipRfaPk1S/ZEdiemCKv7WEQZH1VCJt3RvQN0HfFP2ECsE5oghfNDuMhJ7AotybtgqLPZ7yHqjK2rYQFJiO3+0kOM5BLl7iPaowLNYUfRHY1iL0KfX+WXdGRviosc+YL8LvJEco6pnkf5ZEWgG7xicQa6g58hUxZ1wLtgwvCT3DisxmHPePsanCVmX9l1TGcUODvAkk8pk0n3uMPTt8u6CaoiYwK9mgYw/R8xvvV8d+nPfp/N18j8lVLMK+jvWKHHOOt93cvwHMtIr7wLE+GtZXhvXDA+3J0U+C/XlDf7ah32/oW4M3ekdNWSXbG3DccS24gj2b2pirun56iBLgyDQ/wnf1eTg+0cf463/9r+MHfuAH8N3f/d04jgM/8iM/gt/9u383/v7f//t49oypYv/oH/0jfM/3fA/+yB/5I/jRH/1RvP/++/iZn/kZnM/nT3Lo/+qO2HCyLPB2ocfj6vCqEeOhWK9cqwA5Kt0hpuXBwMG2BtgU2axQLEzIhbwXrAvTUdPMeLrQ/310YGVhAncHjgPLE3CctzArw2cptB47AF8b08ybsPv0U4ffr5BHhgQcQm8DgCywpRBapJlCaJGhAJ+IlR5kVV4XYPhDOmhVi5Dwuml52l2F84HAhhGsrno1gggjgYXIhNcnAAy+AKeehOBtg9+vITCjT06YcaOoVpqIAQRTiKUHZHLvJn8oy9swdRcMvXRAeigMpcrrjYXZGPeWyLwh4mtT6nZ2zp3j8MUVcAzPDUDuE7imdKE569llXcqQI55uAVzCGvZwcxdmgiKz44psiEkBgaCKqc2hmiTmcqESJBdHoE8OrFbdwEsBOKawJefaFwc2B55i7UT4JcMcbGLJooBycB9J4zpV58I1EVYDbunVEYbKAhE6wnOaPCCVqhkS0Tccd/G3HdU+gs+DyiajRyu4OuGWo1NNKgTDbtkEXhLgo08eAm+IjDkpHk6NK66ZNUbS0BheBBLHE+enl6fWf6wdXMilqdTh+L52hGcMQdgZe8nDleHhEekJ0A3VLiO9OfCJuCtDhllt1ljL5SbEGx6V5J5krRfH8CoVvym+b+H1LXAS14mpq/1wZUOmPIgNUFyXGLDkOTGvc4ZQ8XXymad7OjA2pznkYGHK9vrA8sBK1NJJVt9fEA3a85X8sB5eq5IrHF96Tw8HK7HnnGrcS7lmLBvyXk/lAK+fg+MTBTM/9VM/dfX7T/zET+DLX/4y/vbf/tv43u/9XgDAj/zIj+D3/t7fiz/35/5cnfft3/7t/1rH+Wt5tPOC46kjUzGHgzIXnsTGDB6HMx2GZcRjx3Yn2dac/R5BYcwaKY2FvtyZzbGH+awKbG0QYgFAT5GV5HAYq6cKgMcL0A3NDP3ZmWGF7JbcnX127ngfVcCerext1Ds9Jq87HAINBVoNMBHZBlnDA7TQ/QD8rOSUdId0gZhhec2aHoDwWS4W1UsVR5bsjt44UgzJEDyWMSijN8EBF6/+J3ZIgB+pmhgSGThoGnwOEn0hYHjGQA/M1GkXHpVhQ5C0MAuvMlqijlB3GR4boTKT6JYtHu8xNQ8otMSi2V9wIvTJC9lSWMavcV2C25iLVGKKsCT5vBbhOg/pLmE1JvgjYTYKD6YS6UP6y0AtqVnjZSK1AMsHwAeY4jLjeFcNge3FKSmFIaAlujjw5Mz2iEJ5asCyC1yM2TmgV8Ua1yeJj4b2ugdoB3BuUTCONXMQIHs5QA5QAqsnku37igISWSEhMw8VHoUtJTLGDJk2bUpQ1MQZ3soGrB2jDYhHdWtIcXsKpGShsybcjy0qAjfOcfHnSlgMYm9lJhoq861XzRouLD3APk2Sy1b4/BvoIY1rZ6f0nttp8igmoElC9VDcvKjPHaLzT6nsY50WxyTHEGvIdKzh9AQmaBljQ4VSHahyUclTy31hEzArnqF6rcMCRznUJP4mj2feWxNYkbyFDnCToCVBuEzPJzZq8bQLCfzro6F9ZFiewnttTg/ipaPfN9j9yhIKArQElLFGtQO48Pnm8FrN9xz+jM+r9YSMMX5ejk+Vg+nDDz8EAHzxi18EAJgZ/upf/av4k3/yT+L3/J7fg7/7d/8uvu3bvg0//MM/jO/7vu976zWenp7w9PRUv3/00Ue/5uP+/+U4vzjh8XjNDbquwH55+4keK9HAFW0Z+gGADjeFNlrXzRnz1sNYdK+RnIgge1YZytniWRS+NPi6QJYG7Dv8fIKfVwIWBXzb6nSJtNeyXAEqWCePwE/MI/cnVi3VJ4NvDrTGPZZVecOoJ4OW7gsF0HsQog8vQSAGtIfOUMHRSZoDYIiUaqUljqx+GlU/0R0SxcjSq9B2YyqksG4DCys3Vubt7FWEpuhndkruyvAP+0QF96cnEVZSo4c3InlQtFIlrSpDdBrmvGWMvrJSIk6mR8yJhXJN/JnCNW8U1p90sDQ6ZNTr8XEaAryw2SCFoScxXGJscS0CyOBazYBD2eSTSoJgKgt0+Xj8SXHFeNJKFbIhPMCnr0CiK6++TU4gleEYLm16JuO9afQkchWoC/YWa1Eo3O0xquLG1sjSjmoOWxR+1+oZxIG+cvB6xHtSZti1KEaoi6MHOHAheL/K8hFAI3sPjuyGEdWLE1j4CG2IsIjZCiDqo+Sin3Q+AFT6fDg06hrsoBwKKbPbctbzHaei6rE+ghRjiqhwzOs7ELwLL65Xb2EMpXwIoGQA0EJsJADwcY0Jdw9vRYCCIsAn8A6Qk4rV8jvg8yFkGDDATK6xrBA8f148pETLDQXcMgOyuLEOWJLrZIDKuHtdt1KY/Xp8ubzzWWwe3wxmCu2gvGgaQLJdgPbo0FcHW4RUyDvfZQDY6DWHRnmD9I4FQlIn+wCRAUavHx80PdpMoMBItBiPyrF9FZXzWTs+NWDG3fFDP/RD+J7v+R78pt/0mwAAP//zP4+XL1/iz/yZP4M//af/NP7sn/2z+Kmf+in8wT/4B/HX/tpfw+/4Hb/jjev82I/9GH70R3/0X/fw/6WP9772GX7x1QOAdOeH4K8sI6eGCa5BVpPLlGqaHUYJ1JyZI06rsbwL0QnYj2NwFsLDA4DCKsnFTUj2vay0NqPjtuyxSVRYWyRrX+RnUKaOT94OMbDiqgPSOuRiJAk2Kpfkc4iRRKoJ1oRWOl3zRjd2a6V0XJ2l8KuoXVi4i4ZHCCRdRolv6TxfRNDMAA3wckQ6etacydg5vMIpD19/T46CCAtVrSgysXdmvCRwSQ1cgizfYLYwyOyLtJTSsk6pnfyeJRxJADR4CRAQTIQiomJFZDN4pV2qT8Aij1RuHoowLG5vQ7hnFljiSpfIOtGRyqyBXMoIDcvTHaguwnE9ttowFMFdhCGyFNoyabYQxp5FJKcMOyigF9D7d+lMvRetNhfZHsAji08dkIuFI4rAFMcBqGD/YGP4KgjREmUPLNOoY35JXJcJ7HN9kOiNCmHkc0koQ4u/aXbLXsacl8NKCWS6ALIMRZzgQHRQhdP67u700lSWI6p9Qfb7mrP2Skm3IPp2HyRgDa5OhATnEMzsNUsAU5lUiPE1XJHAY3q4PiMtuBQ9eE8pNDABnwQKqG1T3p4EBp57Ip8rZiaBNxL0GoFYhZ7qRlJZTOnlk/n7kycpnyk9FjM4sfn8+KzWfQKfvKlk2HgC8o7wuDDzjw106YXWI0LFCQ7j/dv9gn6/wjYaUxIoubxRBY7zYQOgDkxEMNM42Br/PD+GyCbE5+L41ICZP/pH/yj+3t/7e/ibf/Nv1mcWjSl+/+///fjjf/yPAwB+y2/5Lfhbf+tv4cd//MffCmZ++Id/GD/0Qz9Uv3/00Uf4lm/5ll/j0f/LH9/wTR/g//VPfwkQw/IgY2dLhF/iPK5ZH0FgOFd07/Ae1dhaSFDVQdhV5UZoCpgxbJMXnJVGitDghPi2MNS0LlT6wSkwD+/CGlk9YfVgEsAO3hedCoZ1FRpEDMvLneesIYWjF5VkqfLXO1QVhgXSGvQJkMcLbG1grxtgeezwCKq7EkTNi54uAABwWklEQVS1V8ZxHGG67LT41QHfWlimoVguHQKCrS4WACG1yZBUCXD2Z20Q6zJ11MONfgnhHPOTc+vxDoEx31mJvoRhQ6WXTjJpWOmp2IRXEURGWwjo3iKrY00uBl9plwF60rOS3iBLZZOCX/gslm71qJ46kklI4IQzE2hO8xwPPK6FqSGkOAg6lwDJInC0G9kfnASTSFVGdCP3eL8CrI72JBS6qXwXQT8xnCMuzJCKxpjZYTotVXkk0ur3KzJ0YtMcAWD9jgBFCdr6GplaRawArWX3olTky7T6e8xREJa7EpCig1wUMJ02ibUaY01P0sjm46UNCFSbQHN40VIyeHTrZoNF51zPgCHG79P7EhOWPlAw4yzeVz6W1/vnd4Y3ZNSWkdl7EvfLcVWX6lhDvGd4taKDfIWHMABETqnZCJtdDyrmKjWX8PnSU1VhrZzTBGQyAZAESukdmua+wGBeO9/BFKqp8+M9SFkBKVJl8p7E5ztDmXrxcvrQoBpj8UUgxky7476REJ6JITmnLQYQOzNflF4wstcEVeBd9ggntkKE9Rxwrv/lMv/hs3t8KsDMD/7gD+Iv/+W/jL/xN/4GvvmbR/PFL33pS1iWBb/xN/7Gq/O/8zu/8wr0zMfpdMLpdPo1He+/yuPb/ltfi7/+d/8RAEHfGtZpQ9e/6WZPgHKESRWAx4+jwknaV2YnhSCn8Fio4VRpLZf/+OaIAmpQYShASYxsTzvkcPT370goBhW9rcI+OofVdzNEkOGhlKWlPbcGfTzQ+wEsjVZKdFfuG9DMoI87QVo7oV068PoCWRpEG7A2Fvy6RProqvCtob0+oB89wq3D7jZAW3AwgtOzsXKmiaE1QV8alqfOTC2JVOKUQKEcSpEJlY8Ao3Q+4pcz/52zBlKR8vVJCf5RqMsxvDHxq03YIJWVo4h+QEzk1ANnaKaQuwqGHYK3kXV4pE2nJ+4UDG8DMOpPCD09WcumnnPhBdyd13SJ+iCpkby8hvyM/ykCYDkwio3EmEWQdXBgjn4IZO75kxqvKVwtPEYSDQ41Supr8L5khC7Ck1mLb1XI04722GEnZoKQDxThwlie3nhtC4B9iEAmT5w4qpqzGflQxakQjEaFNs2pjsdA8I6sT/MZ3JpEDgNkhqcBHllPKCUsudaKMyMjJR8ygOX0vlm8MVOwGWbMvkZV1yZA9hwaSoWd2VD0MgwPcoW/IpQ2jLEBiAoMWYRSJ8BxBaxzzWP6TmqoXFq5jicvTlKB0uMpdSKuCeQyninDVOUZi+evLSXTeQGSch1UO7zarCGLAcqbMIyywrgcqFo6AgyCvkWdGM13yJsem6LfN2QTz0kCTCCE8y0r0IJXCOHasBZp8UIPEMN7o41DXkfMITvmrg+f6eMTBTPujh/8wR/EX/pLfwk//dM/jW/7tm+7+vu2bfju7/7uN9K1/+E//If41m/91n+dQ/01O77rO74hUj8xVRTlaq815pwrhpliN/WeZgDBzWUnwNgPyLZVSX1RRZKGvUUhKgCZWggA1fiuMcME0aXbm8LFcHzhjPb6oEW7CcMrDZVZIknyzO97bOhspRDSQgWwrpBnGz0ronRN74BeDOsDBbc3hb66EIyoor08sH9Ay1leP5EHsTQqKed3+n0DjgV4ycq1fregXVjZWJ1ZI9y10a0ZgDX26iE4GzUbaGkb+qnhWIdQrvYH8XKKPDg1khvW2cCjAoKSQR7kBSWVTwhuDZCT1wVQ9CYOeFy30qM7Ko7O/jcBxiBBGfAwykKSB4Bhxti43qzEkGnFqTAmxVGTMYUZ8qtptcr0cz6E2CBWF1jJ6nZxSXWHZV0gICo4AxCC52PRaMac6zYzssJwDmXrUYNFApT1Zxtsa9CHDpyXSsv2ZeIsAejCMJiGpc8MH68G0DkuW7jupHuF/DK0YIjv51RJrhsqOInxJkCswo8evJbJwyH5YiLMJ9P7AnBdryhfhATgavwgeVkDAKRCkwIQtyTQIuYLQ1zJR6nCc+FhLCCY2XzTEvEYX4GVCoUNzVneRGDsnwl41DXyoXNP5LmxJgugJMjz8S8wAZNc3z6u4R7Pkus7wdF8XR3jcAXB6hHrOoVBxBn1EZy7DcwgcgRYDSCXILRzvdmJa7oF2OlwXN5T9DtB6+TWIGqY1hY27nsC1OGFRIzNhA56a0B7BHTHFJpDTZB0/m15/vlIZ/pEwcwP/MAP4C/8hb+An/zJn8SLFy/wsz/7swCA999/H3d3LOLzJ/7En8Af+kN/CN/7vd+L3/W7fhd+6qd+Cn/lr/wV/PRP//QnOPJ/dce/+a3fMIGJkCprA/rxxrmzcmTtGRKBcRzhrcEIQ61r8Uwwfy9/SO9LgBpyKCSIwBESaACENUKOFwI9hFkWQUxFU+DE4mIW1V8hrOWRQk4EJJSawSHoJ5qqbgLtBhFFP1OxtEeasg5AzyuiNwD8zP5Q+xc3LE/A+jKq9gqAjYqgnxr6l+6gz9YIVRgQqY5y6XxWSzAn0AuZssMLEIAkZskW1nS4cn/P7+LKyBknCN5yfgrWSSCjDTyQlmSPcFDqngqBwKkoI4Mls6J8DQ/Bzlh81iMRRHE6B+uYWKaXynXoAbjmOIQyEc/zElXkM4d36RplE2T7CLNViDPn1SwyW7S8AIFRhhL0MRxWeI0Al6HAjG+CfSVASf3bA4Bm9VMXhzmbHnoDfBUsi8DvWgC+MbceHrc5rOAa/YMMkEVQ3J6cA0NUXL4GlR4gonUpxYPgjiX2K+6Uj2vAA3zk9SrrZqyXcs5N7yKoZVPaOn+3eIby1iUouPFOIMY+c0dKzyXZGUOhzynIMwiZuzFfhWtiLU1DHp4fGc9X4aF5DDJ9ni3RJk4XJq5XjSHHEafPYMfjw+LmOEb4SgIopAdk2u+eE50XqlAbP2+Pse7CMHDQE9K6Y18jRBReR8k00yDodwG9uqpoHZBHoJnDVmB/DhqNl7imARa1uhbHqPHlGKUQ5lCakpdlK+CXCAtfUOFEAMGb5PXPH3x2Ihm/0vGJgpk//+f/PADgd/7O33n1+U/8xE/gD//hPwwA+AN/4A/gx3/8x/FjP/Zj+GN/7I/hO77jO/AX/+JfxPd8z/f8ax7tr83xxfefoQhab1Oa88fBe0lrjQVeJqXhoMSInkskEUct8skiSi4NghhMaysG0ALk6EQUFZD0uwAtmxtiFBWTFWwKFxUocxwWXBJcDO2xQ7vDzy04AyQN6yU2ehAzs8mfPB5RbK0B7wE9Cq8dDejnDeurjva6w3t0H4bAF8HxwRm+H9h+8TX01SUKrTWs7rADQHBkNIEfGKorT4JwbgzMdPEJtZQrfxJqDlQ4oV5YHvLmZyPeLiWArIEZWC34D4brEE9eQ4HjBHh0wYWA3JGQbDMgyZZOEACHlIWZXJg5elUCPBWMcY0UtovfJbTCDFrUON5UGADCY0Ht4eDa6BGOmnGUx3wzFCelyAp/58/BK+qrwtdYd07rE851dCzBfYr3ZYoqu28rWynYoqXIZAajOoE5CZAzvat5fhLAiAo8yJwVgsHQfRLaM7uL0IsU69Sna8Z/liScXFeCQTaOm9crk+m8ut/gR1Wxt1DgtT7zHScfR6fPc7lNa2i4l3BFjIUHMMnP02aaQdIEUOr7ce3CGRNxOQFMPW2CwfwgvTzAVapxraP0aGGEcXLqBGM/WboQdYRYyxuD8Xt5arrXfBXwDY+hOAFNCy/N3hBcqLjmMsLS4pkYQW+IbQQbAsWyO6w5lifg4ZngOGtMHeVBj70RpZH4XkNmzGN2Ye8sW+P6UcAQF68MtnofMcd9FXzwhc9+9V/gUxBm+tUc3//934/v//7v/zUezSd4JBHUB3KGRvaHIKy7DCEQhHDDC3BBpK5qZVcAk6D2EIL5N9XBXRCUh6Y6Z4eC53lSgoYETsexcCwpMK0JcCYPYFHAumN5ONCeOpbLgR49dKQLmwSGYLVVI43bR2+kkDwiQrJmE/S7BmtbhNkE66uDQmtRHO816MMRsV9D64K+GbNIJObztMK3lV2key9rk5MDZLsCkeimHMDQzorLM0F/hqzRhprYENQVRkirMkBCCUIftxmKaboOUHH0UhTBVZhDSlkun5JaqvqqxPehFFxF8Azhb2EZat5/FtipUOcjgUsqzgR45pMUBSrtJXjnkppNACcFtdoX+KY4MnSa340nE3MaqnMYLec2XTSxfuUw+IleNRJ9uQZ7M2iXaliqWUq/AaLsr30EmMmmhVfN+OJp06od79gLBFiQa7WjPAtoYAfrnBsFidiZOWSS9fLKITeTt2cFzv05KWod85QEcKAcn2Pv57oJROO5PhRXwAMY9yweiE33mk6dScLpgSngk96MBEPTs/h0fgE+Gbd/w1OSYczp77M6KBAzGQkFtBLYTPssOWtJSq6MpPR6W3gootN0Kv+8Te2bCZBlWvsM1Or5JQBDeHTcBL467C48IitBtMXcKxDAV6CN69POLCR5dIF2egQR8lUvDHlKsAlyPvM96J4tQzAy51qA/BOi9YrjOAkWCD3qPgAx15rgOAPf9OX38Xk4PhUE4HcHhtVRNVuEhfG8E4ZnGiICBIa3RTRCRRpem2W5lgrlX40MAg3goplj6TRloomgm0cWEjeKpCLqiCwJKStLDhJ3YQrfgP1OoK8BfXXB8qpDHh6xPV5g5wX+/B4CcmiO5wt8U2A3+EGtKvBB4Nt5f1sb77cqxxrl6pevPEGfLrAX9+gwPlN4A+Sx0+F0ALKt8NPGhoTaIIcNfVptFDg3dlLYqeEAFbHvlGCmgLdkEYz3VEcKwSJjYoSgQgjOIZT8ex4KjM7BAJDpy8kXAa64EdqzczFGqjDi3ZRrBMiUZYIt/lzjTyWQY86bR2ipuCqpoQyc24qLeYGZis6lckolEa54gHUyLEBqcUccQdI1+LoMKznGV9fwfNaRKkxeQsdhCj/JUOCC6lHFzKkcs7JnVhR6VHh4QmS8zwmUchjDUwQJZRmehKhHB1mmKIRO/8ZyLdc/MgQ1vVMEIJkNmHx5E6jKkFHW6ckQS/Ym4p4FKqNq9lokMEyFH4DNE+xleCiB7rw28jXnOpg8H5muPXu5ar3Y9OzARExGAZJyFE9zj+m+5Yxq47sVlpqAYBoNFovwKgMr3mGF7BDP/xQAYQVkCa/g5GnJW+dYvRzbXBHdwOxQyRYQDg1jjunzGO0gNADfFkvjALADe3P4GewmDjB87wF2TdAe+ewW30sR7lEdPHvTuTr6ppXdByGZ2M4EUmgEVocgUsFHWQVriL5YwHd809fi83C8AzOfhiMErwk9HQpyWKRFmMNBILB4gBqhSdAa07dFgKOxOqy2EBSTKVGZEZMsTZKxAd6HVqPrt4fwYJ8egQMPnbyNTdn0rDHFeXtg9xRfGhsh2lGWoWwsn9q6o+8GLAJ5OtDg6FtjZlQQTo9TFodjvRk5HPL6gFoLoSjA3tEuHW3vkNcX+PvPgGXhJo8aNWqAvLpAH1lbBG6QnYQ8aqGQ6sX1ANwNl+cn9icS1uZpr6ICZ9b3iMnLfi9X1hwwlPdkfc6ZElS04/dbAQ5Hud3TTS4O9GjGyA7VI2V3VlTu83UH0dAh1x2Vp+/lWDOVvkiSEeKSzAZyMN3aY27zedyRadVAhDSPRCEkT2uk4nr0zUnNVdlBMYHWhIK3j2lxZdhSNc+JAoKhHNpFsD4acCj6MwswH9/P1GQTdDg0Q3rxYvrUbU/i3ZSCTOU5ebLyyH5LA2yQFyHRESTgE4v/JTia1kIle2V4J9d17NfMjMqBWS7X9MIVKpnePVC0Hs91KNN7ivvlep35InO2EeK7FQLDWDd53cwWyw99Gkd5cHIub0BVgaLcStNc599nTITpnSQQu64NE125EVw/8VrrdY18X3FtORA9wTCqca81hMiqy/XPefN7QT/xXtod8hSD1zC+VkQfKqd8AzOLBOyOghPgS4x1FchGki82UIZGmFI8xndBhafKy26AWFSmPgjQvYGp22vwHRXYFfAT0FfAFgeaQNcBDi3qGuV6t0ZQ92/9um/E5+F4B2Y+LYci+tRopOkGkGkt9AN3oEgPtC5j1zaF7A3oHa01EnDNR08nkSADy5v3Ta+Ld0iYl2oCPwx+UdixAItDL0xjhipsawwfuUEfDyyPJPKm5Yy9E4idNhy+RYo0NaED0JcXZlvdbyG0BWIaoMxZev7xgvZwYFnYbI21YgA87JCnA/78jpt4I6DTXdAuB6R3SGsQTYIvJblcDDitcFiEfBw4jOcrWDJ8oYdJjwY5gH5aApjdeGUmRVAl5eNvZaXmefPniqrnIrM3Jq1QDyEKVOJJCvIiN6aFXAWyKMjpyZBrb8s8LkxKZxrjVXNIAd3wITAJxnyEvuo0H96YaYyl0BamTSdfABESSodiIevGNgZpVZbjJp8zelJlXxqoRr0TtrVQi8q7KP1ytZ/Ss5GZz6VgZbxDwQhfpMKXVNLhueR5AbhAnpCGgZCckQKvsQccCTQTSEWl3vRglQcpxpDvK8c6r7NYG/kSe85jgOMEChn2mTBCKbLyjoShcdXvaAp/JmjIuaj1lwaKj2ctrxlwFZKaQ3gzsCogg0mp+hhLbaUJdM8grN7rfI77qP2UYzRcle0XxLvc411tg79SMqtP+zSApDRUM8b0NNnGPSFwZF2XfY0xbJwAORD91ZwNOKMlhpqXVzWpjJwrp/tEUK1E8p62xNpzplGLRSmtM5h0Mb3PvgK+Af1MjwwaELblAOL5HuI9983xXd/49fg8HO/AzKflWCQQdcT9M+20qh+xKZ82oQlxZADfCWaWBTgOysElTS9HcWCaRiPBG0BjXtUlcYDXS6+FKnTvrAi872iPO+ACXRqWleaid5bi1igDLxbZHCmNFiHRLXgtWcVUAdjR6fnxHfpE97+IsgP4yyfIZYdcOvSjBVgV/fkd5DAKmNNCF3xWzY3N2Q5n6KopcGH+om/L8BB0ogmVCaAI6+nYBtYgOTFkVgAhcWMqgBvFMwvNPOZUz1L2CSJSiE1AI4+sbFregy0vrBXCuIoFALhisyYKKvM07h/cmfosPTxhieahu5f1msqp3O/+xqPzUun6hwDm1b2ZDBdUiCjLDkimZ7eoXrzyqgKuE7NJMYFNQ5MLAwH6xnIAJoCdvQR6bwzDlUclig7OXopZS0t4i2ZvhPRp6oKngJjSNLfl4tFlGwVECzSmRlQf6cIa9xTUZBZ5VzjmDIkE9hnvUnJNYNL203hkvKfiqeTz+3hH1bH+RjmPNTNCV1eelLzG/Dmmv81rHNNx89lMeh4ZUhhrerpsAZzbzZHnyM3n07jrXnkhpTxSDw9VyCPfYr7CWzNKTRBcmPIcWQZmYt0bL+CewzA4cEYV8pMVkIOkXuigB1h6tmTMxRX4E4xwZouo+sbva48eap2hJD8JgVJc/KjPHLYGiGoO2wMkTdWBcw69OfoKfHD3jgD87vhXfLC4GZsScnErwUYPl6ZHqCHJucAQGr1D+sKWBetSlqVEE0cKsOhejUkWhGeGirYDopD9gO+dDPymwNLgbsBlhx7k5fi2QNY1WiRYtA9YWAgPIM/mCL6PhvmTEmCNbKm1wbtDHzvaq4fq1q2qwMNTeCoMYhfg5cHid6oQFXScKQiOlMLgfbKZ5qJMV3/l/PlMU0jE6f0K14IsC44vnmCnDI5TyNmiVbq9hO2tEMUAMbcytzwVwPCqyCSfhyTk8RaPyrjn9K4Rik9w9Vn9O7nVkWmb/uZ/STIuoZx/mz972yP7uEfWT2F1aKlnNSUoz/YCdS4A9hWil8LCe4MlpX2UBEjPQXpCVBlmMkTbgyACx3WzqBt8/JvvTaYXQWXOMFwET1Hk3+YjpTpeUnrKJPfeQj7T8uSsQ/RMYQnUnC94gFoZVvPVZMqoKBtDM+AK+PQJQJbSUyrDObsrPXQODG9QhqRkPEOCu6vPU6GGHSMBeKtpYk6DXYO9K28QUAB/cGDiTjIBQZ/GKBjh3fzilPJf+na+57x2EcAslL7sA/hozI1nk854XotwEENDMrKSwitiwg72rhOvTAhOPPgxBVo2rzVGz2WEUDdMxGQfADRAbZU7CH5TpYNhjDPrHtUUKIAWY9oJ4HusE1sBX7zmuEfIzFbATgQqEMA2B3YwfI752sw2tdMVBP1MH+/AzKfhyFW2LFN4hAW4JMpX08Jkw0jWZxFyXTTCOxZScAlCcHaNjqpcrLGipfhLVybrL2WQdAKZ42AzR6C4Jp51bWSHPjV4dtU2AIdAFgN6gy+NVVgt+sIsiiIoh8en/LmrAK8677X30X/qcQ/OEMr9K/tBQdEE+HiFbAsx3RGcifAF6L7Do0S3PF2gv+zoL06Q0wo3g50WyNawP1uZTfBioXH35MCToZ8E/qwV8ANwZT1+1fcXR3JCSmEA16n3YXmWcpm4Ivn96sMz3yI1UiokDIXBdzlfBFeVRofwxQA7ean82xxuwM2Y3jINDhS4mK9pQV5Ek8oIiTJsUTpIiuhe/BYPve1DKWcxtuwuXinHkyfE4tya52ncOceSwCfPxejFkwAiMwezuzUzSIJ/ZCTvOlAhhOUlQ7j2olUYCaFAkpvjkBHaAsJIkeGRiHddJNnpvSYgS29LvqwMi9T+jbnoMs5NcDIDs/IcxXnzGOpd3n4eSh3mqHL4ggH6MP4lMPECPpw2nwCU1O/VjR2gV9jH8+Q9q2p1jn/ny66QSTZW7EJvbHgT21NwXaK/WQKCbOOR5JyrjMHoaN+C0G7O9WsnH+5MA2yx6GQ+5pMVD4U1XLQgYwFFrr0E61ezPf4RjjHjyw5hOCqAMfelD5B1ZqZSAubyqCkirOUEMwAsYqiyBIhMEB/7y9Zp03/Gj3dg5lNwrCfF5Sl3lsfmCGHvEgvdywIxbRAN70OYXq7ggu1aKZriTDuGCdDtilchVcGK1/RuJNG6s7M2QqIDwM5dLj2b/XRg3yF7kGyXBVgXAqbu9H5k8T4DZGHYAQK2OdAeNXOc7QmWBbI24HJgLgbobYFvDdUJ1oxg5MUzyKoMcTUFROFHlGJVgZ8WyOMBf3oC1gV6dMiHD/ATWyhcmjKDZlX0E9gTRaK098XRnhyPi0LOMT83YOXqmKzeKxsnrbDZhYxhwSKGmwJqNkKvEEOeMwMZYNTzmYTyzbDeOnSfLv/Wxyom8KT8MJaChTLNn8urAo5DkEKSRHWfxmjRw6v6UwHX/IBQMhZu9qsqtpAq0DZntJTnLDg3vHTcY0Htmbzu7BGZ+UJ5TWY5+ShCBwntFs/nrHOjcJw+PAARXO6l0n3n5ygFH3uRQCOUi445LdBwA2iqbsyS947TZ5wb9ywwkt+fAUyelyBtPmde4j6uM//O9xPjns7NL15xjwKRXhF6nd+vPeLpoJjCgDnQfg2qoSgPo8Y6Wp5q2qq5pXRerzfev4j6QRSe10HdL+cigJFRlOBwh58AP40q2fAk/Pr1xskHSsJUfmgIg8RrfkZOhtfGspzIJrUuffHrWkHRqsAFHNfqBZbK+9YCpmiMceGY3LIPWeiB3HgKWDPgfCW1PtPHOzDzKTienzb84tMjAJS7l6TJ3BxGT4cbnAUuaLpkD5w0t6LBT9XvSU+MhCcn6tEANsWHY4E3iUq9Y6OVNwXgvz2kv9BDJP3gBglPC4vfccwSxfvweOFDRQjKI41SVKEPSpLn0tDvNnhTtIed4OX+DGsKnNhPQI6DPaguF/j9Rk/L0oj9NHZnN/StQVXohVqo8U0bpAlkbVRW8Xy6MwvLmwMHHeT7PUisa6BVPkuuSVbVMQuU6aMZuNSf9Oa7+ZnEO5+U1ZVVbddycr7OzQjfPHIp3HwsN+O9AmNlxXIwhW8kGxLKABJhQatRmZhgVKEFSqCbBEDxoRBTsXo2h9HwRiToSO+Fx1LO+8k0b0CsvfAExMPkuZm5w6w9jFTb4AqpjShleXICCVTIKJQsvUOOy/sCb4r1QbE8GNpHjuP9Be3JYWdhZ/XU4jnOYXeUEi0ll3MfE5PeFQDDS5HfTyXcUO0EcvtftVybgVHcN8PNHgbTCIlFN+oAhbkoJN73bX+kmR/mgWzyea6yqfzqa+WxLG+OTQ07cyKiEKFgzEN5DTOdWIC2k0MCAUZzoYnYm+snANC8Xuo1OCock69LLHolrT6qNEvIgrladIIYn3+ffm7Tx+pjfyfm0XgPM8IDkL3YKnMr952MDuDVm2u6XQFMjXsnmDk5vVr9aofTS7g48M4z8+74V3l84b17/MLHA8zAjLVVciOB2UllJihBCgGKl9VEsgKQvlLya4D0+7oOm9wOg7qFUItAewuvzNGj0SN3hhwjDuKqbLfgoAcFIQqbQjJjKvfNfsAfn4DOtG7ZVvTWcHzpHmiN1YQNkN5hdyv6sw04H2hB/nUBa8UI4MfCrBdV2H1Dv18jlEUlJQfNU1Ggb41dvR/p6fGoHuynhfyObBvhgHRjgSplQcB+31iAbipCV8CEU1ffvfp9/liuT0G8AgCDX5H/TuGnsubn78Q52QiyhFYeqchuZNItyHnbcOsyMkL5xHnBK0nF6bMrW+qL7gEOfCiDAhpOQJj0Ds/lmwoqbpyelasaL7MHZRqrO+htmUFGen9yLnLuFZBITy1XfA5GAvcbqlYOEErCUXVb4AQ7CO+MC3BsEkRLgd0Bx6PDHsinaXDoY6Rnn3idzIzK93k1R/meJjAsMXUJanz67kA98azheVIb81pAouYnur87kHXwff45Qc7VgvHhkZnvnd5GoXKufVGgMp8r0sznNZEKPD0k6Yg+CBIlU9OTfA6CmsxNwDrWD6J2CoQk2IFMAkgH12XworyywgZIRwG1GWx5MuUXjDjtNP43eDzTnH01mTCAj7zlfLnyrM490wDQUxWF//JdvLVa/FzDKOWJAr4aw6XHCHdVs8/FvmrP4c/i8Q7MfAqOb/ry+/gv/5tfGmsxF38Cg7Q+QmgV+W5SPhRMAhELUlouXGFqtIUASkJbUy7mvQf3Ji0CITA5oi9RAqfjAJZO7845vSJh1ooMl2oQjgl2DL42aAPBlTncdhybwL5wB3XWIhED+mEsOuwKg0AfCFywNTiUDP2msNOC48wCd77Gsz05tHeGi9ISOy/oS4PsnYS51iCLFg+hhG9kyogZe0S90Kqm6e1acJQB9TarCLgSOFcf+PS5TfdO5RNjqr/lPePilcLrb4KUOibD63ZMMmvLeVgp9OLLBTbiU3o6+F1zYLR0l3GdvLQAlcGjEUaJeehCizeBFxBF61KJpuch52QCax4TUEq+TfdOhRDKMYnA2bw0wcuVUkaEE0I5WaNCRdb6yDmP71h4Co5nXFtdNYBWgKhzlIxXYD8BegjaI8e7r6HlzdGeON6+CjNiYjylW1M5JxjJudExHXOxulnBWs47ZFj89YKzxs+kbFMpx1xfZQeV9w1XIIXz4QOM4/rvBSQLUIW3JkJznMzpvoIIS0YxwFTozndS9JMc3+QlmYEcFq9rO1BkX9cI4bhUyHF+qJrD4jT5WHt1Lq7X5fXyH8f8+ww4a/DjXc3ye/SDupnQeR/UJYY+kGnOrwj/0axyrnclzYs35DUWp307u2c/B8c7MPMpOH7Dt3wt/tr//R9fA+vu5CPkJgglkZLGmxTJlALFAY9aLRF+SrekL1kam0qJVYOdVlHT6OWkEGFan+ydISE469woIMfCho29s0XA0pC1MsQcuOywyw65HHBj0T0/DvgHL2CnDb42piqao58bxBS+NhyNfUPQG7sdW4fLwmsLC2KZCknE8TvDWpwrb4p+oqDWJyNYW0B3tQPZTNNOCjVldlentdrFkHk4roKjKZVbQ5XLvwpn5DHrCkzgIYVvnpdAJT4vF3tDNZwDcBUpnC3kq7TgvO4sLG8U//zZLLdSWSamkQxbpbIMJa7Tesq0aIkvO1A1RQbACoJs3MPsek06wBCJyRWgKKCd0zkr13zwGSS5vF2JAqO8f3QYdnXgFCdcUKGsrO8DYFijua+26T3YNH8G9FMMqxRLkMxjbBaAl+Rhrp++Ohv7OViskVsTp48cj3dgKvoZtbc9X/IEJjOL6NYKn0nBcuPKS9JnrbvScpNCzq+0sT6ugJOOz66U9/yZz2uBH6aHBoJrgvI08HqFKcZ0rHmG1x1Yg6wdfLskYg8w4fUdW8cegQyvDFYvsnW2KynC8PQuK6NpnsTmV6TtK6+MvOXztx23wqLWfr6fAVoq1JR/z18TqGhW9uaG4CsfZSXm2yRmJYgXeHMWj/f5wafxA7hKn/qMH+/AzKfg+E3f/g1l2byhNNNiCbJcaccUHOmujQ3SI7REpeLA2mASxFxH1GAB67k0Zd2MJnC3CjfJ2qC7wYwkY28KsQVyItBhfF6HdlTQC3McUYDK4ZauakO/W2DPTzieNfRzY72csF5F2IBPo6dN3wTaGJxuTzvN+qbomwa4S86FjH5BQPAjQhNEw0YVADsrzNragB6l+qP2hC+C414hAvRNYauy8FQbiqWUAVBK/OqYwEOeU54ZG+/z6vupxFKgpnUV9yvFP8m5+iyEnEyPXsorf9fxWlJhe5r8czhqVk4uxQ+0aGXhQFUeZTgoBhTxe4nrZkaKFekcyPxuB/ksbmOsCaxyCK4TgTaVa459nteOcqcrMAqkTYq0PQHLI0OGvgLWHXgUtIbBFTneBFA1FZrPhSIgIzwHV9ltV3szdYKMsMUSv2vsgwZ6lZ5YcNCfgmy8YJB20v3vOoBrKtw4JftFFUDOOc17yABCtW5mQHPz3q/+nmtFwgOTSjO2eelDDSKz5rAdPdOrXSI0guEBBa5SvscYmMnGcHh69UJ1d9puRdqOqsrxKqIL++C01r7JTB4N+ae5WHjz4jLli7uqtOi/OtDib/ls/sOM2srdN+Z8CAsf159kep07XdZbbAqLatthYNwOr3hVHYNwdPtMtZYEJ/v8QIDPz5N8ho/v+pav52ZURM0OQLMexXzMFqvHps9KokCkhkoJXguLxKVBluhkvAAe/l5pTq/N7nBTZBND7c4aMIcFTyUARDfo2oD9oOeoR76qGXw/YNsCf343FKhEVd1nJ9i5wU8N/UxBLatBL7HzJFINmwLHtNd3blxrwHHfIBkywxCu+tCZCg56CUSjoiyUTQgRbvgmUWyv0bPz1LG/UBzPG/oCoAkOSM1BpsACQ+5M/wzZMCuEScgVGTK/NIUUytvtN+ekV+M2Lh/PW2BoDjfEecXtzvFgWPhl5Uu49CfwU/yM+VzIdbXcUO4F8qbHRffwggXwXYREcQvl1uJ6NS8yLpBKuo15vZrbWbDXDQNTmbOiq0TRPY30WwBtjzmJUvOSc+5j3dTvudbSS5OemduBKK4GadOcJXdokF9l8qgMJbo/A+TE7+gFaK8cRygxzeq0dzJ5DKZnF9DrlGss5y6L/M3fmQFZAsJJx8r8vNM8IEGMJ7BmNsw8DST6+hXX5AqxJ+jcA8CkhpkVaj2D169XYD3+NUswMwof1hgyzJRgJN+l+iCJ5wNf7adpI98iAcEV8Tyfi1hjWoDzM9diuv7z1TVv75GI43ZOEpXmZ7dyID27Pt1qGpaXUJEKN1XYch7HdL3ncnrLoD+bxzsw8yk4Pnh+zx8EdKm+xXqeBRsFVoRH0gNiqWxI9Mp05go1BXhJdM/0SaXSUQCHhYBQciU6oF2ikJYM/sEK0HLMnlAOcYOYwbXheH+Fn1ZmE4W3xVepImpAXCfSdKsHTvTWMQkrugtsUehTx3EC9mcKccH22qIRotOLk6Chx9ilg00zBSINvqHaMWGV6kLbo+R+X8AeJ1XvxKfsiOmQa9BSsmxSNiP74M3z4QNs1OudhUzOAwaYKcU7eR88fk8eTY6tPDx5bnpn4ntVsj/Bb3jQ5tBCXveqH9CCKg9/a+GJAHJQEW8PDkjHfq/oamgXh4Cp71jH2G2ZwHesBWAoW/hbPN+Twq2xp8LNxb9ENeHI0jEEd2aNUzLsOL2b0is65j1paukREGB4wqZ9mDilQoNtem/1v+lcAFljRgDY4lgfgNOHjv0ESHcsu+PiQH8mgzQyI5B5Xc3ANfhCM8L2HPDsfZLxndKBM6iYvGMDzHlxWwoYTMrWw5OSKdUESpEBl9yNNn0n5ybfgV5/XGOdvW4364B9kDBqNcV7TI9MHZP3qGK5NZ9S76f+lbf8/jaAcnvM3ylhMw0YmMbl49ozgM7FeFU4Kv7tN+fPACbncL5GGDbeeQG+11Fgz1MHdOCLz+5/FQ/42TjegZlPy5GgenLNppIrayg3I7hAbeoZVIJNQdd/CJ9i8SM+j43sklYRvR2iig6v9GpvPB8X49+zm3YQdo9F4acGiW7Uuhs6HHZaWR04x3mE23eJmhI9/4Bo3cBr627oS4OdSDzVJpDO9BITR98AuxPYJmgPDrkI+gmwLSoOv+a1XKW8WxLkQ3d6Z6QpWjcq+e54PCsL5GXIYS4M5kNu5BS/jdh7JWzzPea5N4K4FCZwlco9C6jiSYQwL4szFcl87gQwHIhaGxi1YHz8nNn8xZnJeHyENiS/b1GBdg3F617donmSFPDh0KOp4kJPw+nSo4CbYzkM/prvGQAeXwhwkqjlgsHnSaWUz4iQzToAi1iAwMYCkirCVOv0Jh0ObECP/+QxFFm0BZEF7Gw8F7G7eXU1iKxxE4vY4+U5Yk9ozEvy1nJuZsU8LZwkpWe9HBJXBbtGMcxH1jXR18DdP3e8bo5+Py0zmSrTKt7wmg1PhNc9gWnNlGL262e6UtaCq+ydUpbTZwhSeIHI2F/Nau0SZMbDT/euvTQbatO6lnmseflBmhrYMPlTbYTCuEfifc+p+2/Zr9MbuhrHG0DmjbP8jb+9FezUtXz8LQFjnm+O7PQ+vp8bHeO/2zELBqfp9lGEIM8LOArkyHXIE64wlgFyCL7lxXv4vBzvwMyn5TAAcIaGWrR81Kz7ggh9pCBCWB1MZ7xKr7wCLrGINcSBSikyk6EougrTWCOEw7omsSubowe40YPhAyY4Gex+ZXE0LKzXEQqT8eowvVYjgTeUg2YqrNML4w2Q7AgtIL/lBHhT7M8V7dLQreO4A3wF+vuK5eTwS3APlijzvTnDVl0qq6kdBGlujsszwO8EcgRn5wCOE70zaCP18TZzwb+asLoRMnlcZ4ZggJsUUPPfJ7RUgCGP5BhMWR4VfppBTir8vN8EECCoDsA+We/i/EzDE1Zp0FGt15zKn2m/cv19hAzugOx8YFscBziH2+vgJeW6XQRqgF4crTn6i1bde5EARa5wzPhMpylKb8xM3lgEsht0B9pDdAvenJ42By3aZcyRx++YLXqd3plPn03vNb0M3F9Umh0gqb7Aq8TcTkorPjNQl10XAwRsFezvAX7P6rOHOe5/Hlhes95R8T3y/c9rKqch9Z94jbdCCxifXQGa66U35nNem/ki4j2wmCUBhsRDS891IOPr0xgGwCvK6vV98965llPpz59PoEsE6OkmKxB/Axqmv/0Lj9vn/dUct3tYxxzXEppCX1fnFODMFyhvznd+LzfBnHKtCI/L9DWb/g5UFWpeh4Amva/12AYqAAP+O1/62v8vJ+DTe7wDM5+WY9rQ3qJ+DOZ1mlpokk5TXH4mf+a3bPbOLPFzNPsbNc/BE+J360Gc7STlMpPEcJwVUC4XcYEdzvb2TSv7QFzgRwqurJcQ6dBR4ZJ8B47ZJm+TnZhhZS4MDS1KACMKayu5GKEE9nuBbA7rYzpsUbTF4Y9seunK0JYagN1hi2G/F4YdEmil9A0BOCuwedqvBNik+G4Vyqw4rgyr/I5df1bvNu7/Bpcm/7Xp5/x+njspY+FLZyXmmG93RCNRVBaRAJCF88cwJIGkKFh8zjBCQwlQo52AIP4e49H0EiyCroKLOtaXBBip9OnpY0NTawaowMAiiz55SwgQQjlPRc/KE+QALkDbHeujw5pgb1xv7eJYX3E99RPwdEcyeba/MMXomixXr3Eo7PzAx++OoZvoGZq/OL3Hmc+CSTnHvWyZ3muGiPLmDXBjPZB+yuac430zU+rN9cTO0DGQGwVeXrtpieW48kJvAJp5nc8/RyjNF4sCL/k+0jMs9Z5mTyH/G16lOZSazzXG5FWoMoGPJUDzUOI52q/iiRoV+G6OaV++4UkZMzZOyL311U4Fps1/c4t879PkJv9oACBM7x/Xe3zOu8/Pypt1A35m3IJY25nCnmOMUsjScXMyf/yur/u6X+kpP1PHOzDzKTmyS7GtiLow/JybQ64ESx0pla8+iy/NBTNyk5TAJhDyPQXN2LamOQbAO0M3vTO8Y1sbGzTCDwjrve1jlzGCEU0DBVWvhT1wxrNC2SCNHaxZG8c0uto2v+bZJPBAPG+SPgPk6QIci0Cbox+C9uRoDtb12IBj05FRMqVGFti7PW4E0hWQmX6fEyPyX3/b+bhRmLPSmIXbPBbF2z1D8QrL+m5SoajKLEpFGh6Sce2obJtxdSAIuB69QDPTC8klhx6g9wxS1XPT2nMBidoHLXbbBPvJcdpJNpYensFV0Z6MXocXCC5LpM/O86Kx9Keqs1lDxgBgA5bwLp5es8Lp8QLw58D6inOiByDiDEMuUk0k/T7mWjzI0dH7K4FTLov5nWrwjYKPdEVWvn3/07tyTO8VPoUI5WpduALy6Ng+BJZHVrDuUbfmqlO1TNcspeihr3x4QCYSiksSZ+VqzLzmSPPFJC6Ke3G75spD5LAj9ux8XoDQeW0gnjsTFqRihtMkTWBwXv6cMx/jrYnwNwm+Xw11zClfc6+z+f7zUPJlvu35ry/8lnPyYbILGc8rIHP97XFdu/0wfx2gLm/2trIwVyJjWscQhCXho87P7fqE47u+/PVvXvQzerwDM5+SQwBgUZbSfwCt6ETsuTAFqCJ1QhHl2YSkLD4f50ZKI9MlfTovrn2AbsgCKABc0BfH8axBoqCcW5ApI900a4zk5rBOpdmih0rJ/iwrnjVbIkXXs6FbhJl6KEX1UFqKypypGhM1UWP70ssEbvp0nzfF0QzHXcivrFVTmTgTOALeCCtdHSnAbxTVW1/em/LizetOwGUeQ93Lr89NGZRdmx3xDOkFiHOyvkiF6R3M6sh7RIikzov/ZedhX/jFrEnhyaeZmwBajCEz6hCKK/hXkqmi8cymBK04nCErAWQXbB8btCsevxS8LPEri9pT+Qd4FYRSDs9QPyleP+to7wm2j4D+JLCz4+m54PFLUmG13gR+jnpKPvo2QX2kX2f2Tu6HWflazGEqeh1zW40Gb5X/NC+OASzr3SeISAs7ct7XJ2D92LE+GV59Y8PlA1wrpreB6nn9ZAbP1fKxARSCO5bDqLR5x1WaL3IvwUcbAQlPLjBS46d759CuqlAnwMgxZe2mIKfWi859PAEU4h4f3y/l7MNTcfOsV/Mxz/fth2/s4/QbjaKMVwDt5t1d3ev2ZwDXhKD44gwE81q3Yzwwnk1uhiiI8UndQlIOpKDNo119s/RHAStkzRrUuL7h+TvOzLvj1+poATwEGOks8XMADwlFlJJ2KLHYCdF5WoI/wloW4fqvwnOA7xjN+0JAHEpr1jcAokUKRYYbgIk5D8Co9Poaw4+/WXAVMkvIUjmlhR9KqydIkhEmJhCTawsMiHlJC28aQwhm3Rle0QszQmyVAn48/0b63Aq3t4Eaect5wJvenLf8fiWz5uu8DeRM1/Dp51SA/HyshyLP5lhkulaAQgB8zz3OV48QDwVbZSkt0xiS1yGjEqyB796jfkiuhxSIAgAHw5NigD5ZPYQCsMPInWkEvqevGPbniuM5ynNXzzUeAYBjcjpwPS18ht4ED1sUp1vBFgMygRYMMFLk2wAlClRGVM6rzHNY++st76kAxM17nLktMv2by88TPARgzLCRBfBaAd+FRPcTRsZRKlgfczOvIRMbWTxJ8Pfp2VMNehSPyzHPE3WMd3+11uf9Hnu9piMzpcB18dZ9koo8DSrBIBTfAoXbjCdMn2P6203ItT67utjNOCpVbr6H1xoDJpA+7aN6nK8mE954ENS8isYemD3LjqsebHXxA6g6CROBuSJZtwbQrQyx6W/5+zynAQ6vgEycqzpP5Gf7eAdmPi1HAxXJukCcfYm4yEEFbpNbuMVq1mFV8Uev77UsKCcOW1kUT3eGhJKY6CroS4QBFnovPNzoWTfDV5LICtCE63JsyNhJGgTMCCX5EoovNZNM4GaNUJNlZgdu3Od5D7kWJA56kgL8eJzrQKSM819fBdhkkB1mwT2GM45bxZQn5b+TIPmq2Uu/wu//Qqvu9pp53ynMVPyCCldMw8zQxzzOufeOAu7ZdTqViZfHhmn+EYqRkTrNKqqxZiLUI1NdE09BGZluy5OXZ4T1bKS4HtnbCK5QdywPjv09GQ/YpMKGV+E2D176MoB3ViYWZ2p9hhzz/Bp7AK1KXc05vZ1nwagb81Xeda3j9CbldRF7Um/Om99RKpsLoE9RFyckr+y81nEnaIdE2QHQg5QoJpTeW9dVKq0Mnx1WoR4CAYMfGt42R5Z0KAJq8rjy4TPnP7ssB3F/JDUN5ezT2ACU7MiJYGg5QtkBjuf3WmDibfvDETHO6Tnnv92sEf4+nXSr4OcvC67HAoz1k5/fDvbq5pgWCqItwIR+chy3HqR8t2m15Zrs8fstQAowWmCmyMA+7pHj0Km20e0afBvIBAaI/Zwc78DMp+RYNsVlt/JgVI0QYGSTSKzhIPTPaXeDG+wVxmmGSnsWZzgHQHEQMqOkhG2GoCQEWfIIFlxVXsUR90rrIbkES4R0uhNcRG+a7E68J3DB8FYPoTw9MMKrJGOsda6BgnaZNjk4GZb1n5acqKFkgKE3Idc/v7HRr7TGW/7+tj/F7b4qsHkbYLm556w8Zy8MHJnsxnlOgebhNi6lOgZUqapCAFmWeVlrUlZidkeuCqsZYonrmgxPTraKqFonsQb0cP6XXpsKScVFVWHu0KPDTbB9ZDjOguMZgEdHf6ZRiG0omyLqhmfFgVo/VYE41t6wQEOxZlhTUPwQcl7iueHFD8IUTstMHeqyUrdXpM6r+iYBvCujcFK8WRZAcr0JWy7IxXE8E+6ri2P7yLDsgstzhW03gOBKdw7FX7pWc37j9zbtowiHYYn9HKY+x6ojBDGFJAdAxSB/x3qSMgp8GiSK25JrqNZxjnXGBFchl+kiqayv9okML3WuK0w/y/SdisneXuMa9VyDGL/5jl99+arNQZ4v43mvrlEyyq+fK0/JuTYUKTdbzF+PTsZankHVLXCbUr2dpgiN2dQHwFVj1bcCxc/Z8Q7MfEqO+/sNT4+PoyeQD4FNfMMMJ1MKldT/wLSPY5Hm3u9B5NVjUggnDOu+pbCS0RpgA2ThBbIBX1l/Pcm9/FmOGIBFOu7isE3p8k/XqZIU2ktwvkWvJ7Ipa5LfFUPVjKnNv3oIeXnzQjL9UCAnPr/lpMjtd3BtzbzlmDDE1XffSMeehzArn/z1BuTMsrgeVm6um/pjqppa7uNZ4bgMAFiW4VRBtfkAPansPBX4OI/hHIrFUvi7wKLMfJarzwJmlaGW3YpFoObMbgursXWHmEFd0HbH9tLx0ZcW+Fmg7tgbwqOGKjNQryQVWJJLPZ4/AHjNX5YXmN9hm66R1w1AczVPDlhmEWa6et7fx5jG/I81HbN3s5zGYpLYQ8cdsL4G1g+B4xlrHi0X7tPHF4LLBz71PRr3quq/aZBg3vfBa0nenE1zoHznnJcQJlOtpxIkLlX4Ljk23uZz4isxEXOqdd1nBjJ57s3vI4NphMQGppAC6j65dKXqr0yqOvsY1GIe7RQqLJsetLqFX/PgEM9+69GY3mCdnHOQNX9uDgdGAUAbn41xgZlgdrux8/rORaKUb1jGdSj7fICgBCOSa8yv3hGkw12hYFmKq7/V6QL7nCGad2DmU3J88OIOv/yVR9gCcj0u4SZvFHQAuHgXwDUa3aV1IvGjjA1lENiZi1YPR9uNqbgrmzZWuqsIrIEZTAa0KDZ22VCelZQdKqBLNdz2mTni8bM8OezCSsL7WSAXbsy+tEmxhsdllhrRBBOR2UIw5lW4r4RRQ1gys9Uy5mYWELd/vjpm4JDIDxiC22/Omw7/Kp9/1fPkehxvG9M17vIhU0Pu8dFCNL4lxD17UYZS84m0KtPffFjzGT5KwmURVsd7B7yypbAP3ZeDdICVlE8KdYuu1QJ9AKQHh8ocAhZV1APQblWv5P4XOp6+1KCd93z6AMAdxo3yfcReqJBOgguEQM+51jG2+u7EBSnOhiYW9AHqAMyVYZle7eVhqbnNE3p8P8BMZQ5dKegAChbAbwP2BmwfAff/DOxpdAD9XrA/B3yVAgHFucnnnMjG+WoARPh3fA8dkIvAT7xfJQJkWKE5PDwe1cNNwAylLjyvRb8k4EZxjrkUTM8vuIJy+Xm9Gy7Aiask47yc9lKyk4Ku610r3gIucXaBzvhuhc1K0hg9VDKuUGG1+Z3GOK+NnxxPrjO5GlvVfTKMDEHneRxX5jfJACQZSs9sPmB4WyYQmd7pNPgq3OdAzzG1aTwJaIW1uApI3QgehwOnryohP5PHOzDzKTm++Wvex//7n/4yMzbWqGa7ASZ0z4sxs8iiloBheFwc7O7rC9gY0BzdAKyALQqBQw+FXNjnqN8JRByyk1tjAvQFOFoYcjugO+8FdaBfW5xlc4hQMDog5rATIIdj/cigT4pjA2yTEZLILT3FigVOo3GTiD1HyOw1eO3zDYBogrfbRnFMRues1K6uMX/dv8q/IeveMKKm+1yJglsuxgxkfkXwU+J8+hmTMhuucQdG+Gge//QZHznQbRbQSsWfYabkkMyWZAKEHFN36C4lHAv8BgiSzvUjr8HU58a6PrY2LA82iI5Gz4w8dFhjQ0nbBPv9Am+O7gJ9Apbdcf7YIdbw+I2oEBEEFVodumUo1TnlvIgvGM/BS0h9f5B98+V4rc0CdBbAYC5+VvM8FB6n1DnPRb6/UeAy3qu3jJCE9xUCfc06Tu2JxfL6HUZrhOlZKm1YBXBSuJH1ocJrmuE9eS3YXgLHM8HxngMZfh1aNq6FK+VZIaZsR1CPEGtQp7k30DtajRxxHRnR6Tv5fq4sjFzXAdwyJAenVbWEoo8QO+ClrdyEHCy38Bw7PCsdeuyRDLclUIdP4aEJ5bYofHhFkEo3dhDDcvwJFOY5zGvZdN4enysGkfzqmBBphvTnuQrZOB8+/T+hERsJx3MlITsz29TplTny/Pm7zppBt9lPn/HjHZj5lBy/4Ru/hL/x//ivmG68CPxO0SW8NIhwURdW413Bmh6dPAUzhpT6meeKK9Sc4eRGQNQ3QE+AmcE2lvb3O8GeIaKwEljnhQLA9+wyTeu0PRrTqS8pFyIUtDuO5wBE0dRhXaEXwO8AOyNOlqFYU0YoaO3vFBS+kUcgQGWnXFmiVz/gGj1IGUNvsUKm7958Z7ZaZJZR88+YQE3+N1tubwNJcvOdFEeTcLwFiFdAJgY0e2KuSrbfPqPOD+BUBvnZ9Iwce4REJs8eLbxQ9hwoT7/EmjJAHxy6O3BSWAN0AeQMVl1uHqEl4/eVa1KMaxWiDJWGgO93iqcvCHx3bC/BTDQHzl8xPH5R6Z2ZFIFPP830iFScCEXMKIgME3aeo4kzchVeaKgilUWOSR7IfJlUFrXQYmQLJ8i7jvTl8pTNczyAiZ2Apy8IcAc0F5w+Epx/iVya/fn1dwqM5v10YCsBCji2XbB9BcAeOu21wFaFrTMSnJjKWXuljI2Rxo7JC5Vf9QyB1D1tUsAyhVcm4FB1dSaAl+eWpzDuYkKg0mJOmzEk1uNFJBDvxvN6fNY8iF5RbFQDzIgOrpBavKd4YCcolPDWiEdIxoxhUQHoTpNpTeRrHyiz+IbRpCvT+otzWPseA2gcuYBzgU1AKb/fr6yLAQ6T0yWxx1cfxkm+3vxvNfa8m+qWWc5N85Gd+jk53oGZT8nxXb/uG/mDsHJpB2A74JszLAQQuEQ2D8NCGrVgHMcSBFgRaAiF7KQs3ekhAWC9wVaP8GyADAXMWC1XdioLX5lGLReQ+5Bl2A3w5jiaoB3OlNsjxrwBooJ2BtpB+WKpwHKjpgZSiZ5NAM6U2sOKGSDm1urLw4HrJnUp9G/m9a11ZHz6dzKobg+P/70BZKZhfrXfr4HJdPG0RKeTPJ8zrxFWbFWhzTh9Kp35GnmFiR9T9ylF42Pu84szoTKnwqavixJYuqA90aLr6jh/DPTu6M/4vt0F8iBoC3CcHctFWM12ERx3gvWRnr5qERDvkVWCBV0I0iXSvtsTsL0CHu+HMp29BtWSQcLadJR1DgSQMa8smlQukmmvc/XYaY7TA+kO1odRjHBPZf6k4giAl3O+xKQ1Zg5V2MWmsWuMIa6X9U28AaaCfg+cfwmQS4xlXli5rpLsmnwKn9aWECTaqkA32BlYPxacfw542BS++jUg8HGNfDEuelWVtxIMCuwGmEklbNO6i+t5ztG0TuuoTLqcc5veB3g995HdCAxgkmN3ABoxwp4ARcazdBtAvsCI0SBrcQ3j90U7WoABM9bsst6hLQp+msSaYEHJYjf7/BC4/rcEUYCI5MEA9GAmQnaMa+W7SA9bhFyz4W3WDMKCaKsR72FzVuoul5hwTnN/NBBk31ZGDo/tcluq4jN+vAMzn5Lj3/zWryulZi2AwQJ6acLlaqsCxuixr8C+hCJwhR0OWaKmi9OT4wf31AIKul6CR3i9MFiSrOsKyAGsDwaB43ISEjIPELg4ex1ZA/wMdBG6pJ8rOgRYmHbazzKa/4HPAIz0zhEakKoKSv5oCvsbgDEptVIOv9Kh03lf7dwZ0Pjb/1THJOBnC7tk13wOMBQrxvfyNFpoKOAl0zmzN6GAzJRizfN8KIrb3OH6VYaCrhvkIJ2WYQpQhLBv0zgrZIHypMgl+C8Ato+B3dm1nK50J5cLgsvqaJtCHhx+UvhK4NKeADkMaLSW24OhPTAM6isocDvQukF3rb5KpXgdkbnkY75AZT7qLmEADhkenMq4m7M7ElwVdyFPTGUR96q1NylTYIS9ZkvYwPLE8Q5c5Jq4HN6fLHpm25jj3oD9haN6O916lgTACogwCSCBjGSIDA5XweWLVvWAjmeO888plq8Y9ufR4kKnpRArMDlBeVyFyXKJIEBFemc8QcKkkBUj+6jN94h/30h/9msNlGGsAFwSL/q2vlKF2ZYAnh7GFpuJMYEh7u0BZjQJ7Qq4RbBPDbrGcFuHmcCXDo1W870LTLUIiRnq8zGB15u4fpebP0zyINfVnvMWmztDaW+RR0UIFsS+jOun9yrDVAHaOM+tSOeu1xdMDPNsWfF5Ot6BmU/J8bXvPwc8Eg2Ca6KCqoabqdQCIcdkRXloAKBSbUO49MZzvDv2VSI2PIgdDlTDSQiY/u3MtoAC60vg/Mrx4I5+r+gA2qEcT6ek8RVAY58dHlL/eAM7Zcv4fDZoromaMoThrLxSeM3HJDjeCjomoPGG4fSWL95e/6rGyTzEm3MqTX5SClnnR6b7+jymK4A1ao8mCbWGMgOXqZhYPdcUVql/5+Ji6bpO4DML2H49GZX5FAr+6hpBEEYH9BHQ1xJWH3D6JeB4BvT7cPJE+MsXDSXtaKvAuwDPAVsMy6sG8SR7g2EpJ28m5007Sejlvk8AoiGUdcqIybU8e1umNXgFgtXL0p2BTs1TKmCPC2dISWLuKgw1vyAMXtL8XgQFCDn1MffRlb16ZzmA7qMPk+ar82l15Hk5v7gJlySgi/MagBP9NbY5niBYXin6A+CbUW4kn6TPajYX6c06yLlKr8YUOsm5vPLy5Ly/AcYGMBREtlLM3xXmib0g48zygBV3x/kzPP6WoADpgZnuG+tfInwt4XHLZAnBcPRIfL4UIG2A2JRargRHtcfiD8k5u52DK2QiN3+b5j3PSK7WfDS7XlsKVMZFzksRtRn2k+ZXnJuSUzeXfv/uhM/T8Q7MfIqOuUswyYKpIKX6G01nozaCXv9aaZIOZiSZD3CQizrBQ7g3vSoPC45FcNxHuArjmseawlOmTRmIQ8a5IwMH1+Biyiq5+luOZf7OhCJmq/EWl8yzcXWSvOWPX+3L0ylXXpT43W/Oy+eZw0f2No5GfB/wa4Bz5ZmZCL4hAGuYNUeTkiyl4NdgbVYgjqGgU5gjFGQW3oqb+qyMMM73gFlQJWA9A+aOLo7DgPVjoF2iw/kC+EEQYiuLNCILMp4Bf+DCbo827i9sTSC7s+v2naLt9C6cftmwfSD0DGYafiqo1cswrfTcBGM5H1PWT81PTcvtYpqA4Kx74IPjkPdIrdYDAdY7md6LxQBEkBUD/cjbhRKf+wRNXoQCJwm+5iU1lwFOcNHTaxGhEBdW401gKcDxPK7z5FAXAs31zbXKqwe00TF95THMZICpCN4b+7Oy4lBgi3o/PEiT/Lrug+STXPC38N44t3TcZlFFnqDRn+pq6WusXUiE9GbwIqXcJf7e+wxoeJHWACwd0h2uCjcmYpT37laQ2c14EzjnoDJMNWUmDQkwzX/xC+OBFJO3L48AbVXHCRA/yKVsHp21b4TeG6/c8c3vP7/98DN9vAMzn6LDBVHplHwVboIACWl96nTyV1POgimDCFHTQYawnoWFTj+ntRgmS1m+JlfXTlWHWSjksG7+re8I3iCuvhEumuRDgqFr9/fNz7cgDm/5+a1I5Ktc8+a7BUbKoeV13jz9dZsMb1Q6+SDwppfJganUfF4sPsusknqO5IjML21SaLfPGd+XYobO547KvlffK+DIQoUlaA0E0M0AVdjC8akE13IRrC8BuQD+DNifM2S5vgLaxbGvQN8cWBSHOGxV6CNTs9uRao6l/J/ecxzvA3oBQ1A7sP0isL8PZshher7kx2QGR7rdk2eSQKDe142iyb0xg8+aI4/rWXkiqnVGzJHPE27Xl3BQt4wmiwFqkoSZgMjBkNO0H8dyCB6JJrAJUJA3qFCZ8PrlEQhgevOsWAzHc0CaoL0C2oeCvRFkXoH83OszgK6BjXUkt6EixBxFVs51+nr+HdfXmf82Xy/Wa37G8CAHN/Pj0tOV65y9rgaaSYDDc+k31isektS8E/AIujncHWYtwowdS4SwBB3WgH3X4lHV9hLcZEIlCMn1eCPE5vPyIuZw2ARs48i9rwF5ag77tJZiMM3RVocoCczHni7Xtx0OiOHXf+GLX+Xvn83jHZj5FB2exL5o6OiZnspfhwVaDHpca9SUu28DCXH+vyh0ww0m15dPBZACIr5QIZPJKprvP6nfq3HK9Nmc+Xj9hesx3WKw22euS0yy43Ya3nZcgYy3zV9wRuap9lRYE2+l6pWEokwjLhsb5hfLy5IgZw4V6ZuPfuWJmZ95llPT+71K75TpP8ebMvXGLe5T6KKuswJ2MtYe2QTmJOt6AzOastZKE1zeE7g6Th+xeJ61yeGmgn0TtIUhJIFAdl5nvxc8fhEV2pEu0JfxUtrwXEFQ4ZZ8N9wPNuagOVNjq0nk9FIzZDDPXy6++j3vi1Cg08rLtXIburvaPwQUsmBEItQAzXCFMtSXVnp9V3DVOyk8M1J8iohZHTf3FNx4lmalF+9yAfzecayC9SvA9ouKpy8Yw4YrYNlNPL8/h4AxjWcC4fM+K62ufp0ROK/maa/eghu5WtsTcEn2tuLKW+Phth3tJHxcNz6p0B4cOu+1q+MaxXkATevAjgbdOgQGVUGLPh4XksMGbybG/FbjcJYa87sWjJo37iOza+oUX2ReTfJxrHHpaA1Q5QW7KcQF2nYsje/BIeirwXdgxJCnMYlDN8N3ffB1+Dwd78DMp+jwfBvRk+nWNftGue+3/Oxv+9vVH8bf51AOcCOgcA06gDGWWfHfAp2ZdDvOiU0mY/86GD5LsPTGECfQlbye2eircd3okgQa5QX/FcDNnHhQ47/ZEdWIcxLshvgsQxAh3Dwsq9nFnS5/YBamE/BxDMtufteOAWRmITj/nAoLuAK4DlQ/oivdcv30McHxhQTRJZz9ekyLECgcDtNpEB04IvunLbyxt6DmLAJ9BLaPndanCHwVHKLoJ0APx34SHGdAdmB9ZB8wPQRdO3QnR+sqVOYyUnWTO5IZNrVgLMihILCxQKNJXlUZ85bfj8UpV8roZq5iMkfLA39j/UHBdg+xV2XiQhHgWHhq8GYp/nk8imgnHyBTDdWZHBO3IkF1kUDzGuMVVRxmaTieA6dfUmw/B4g4WgeevgjYc1B51nemeZAEFBNomNd53Ti+XBlP0+czkKk5zfm83l+MnrDkRCrzPMfh096WKzkBZIj2+gUOT2XO0S2y4B702IsCR++O1w8IICPYTh1LM3QzcryS4CRRXDCF4gzapjVyZeElOJzmJXlvtfU634e0nIP/T3tnH2NVcf//98yccx8WFthlXR5kwYb6RYRYW4PRYqyoYEEbqE3aGp8oYh8UNCGtD7URtVLTJv6i1WjblNBq1PqHEU1jFLGuIKJSLCgiKEbkaa2iuICw994z8/n9MTPnnnN3kZXu7r0Ln5e5snfuOXPmfOacmffMfGbGizeNTMZAuTLJGAmiCPms7YHSbt2LKFSIt9HzvZeC3Ew9ggoinNo0GkcTLGZqCPLLwvspekD54U9UVl4AEFB2PkvWWoTUS1XuMinXgXF44rxODQqRDvNj6XGYv3ZFesqzflwxRz6s/AL7WT1xeZcUFj7uZLwJgZIsH1ItNm8vJOJD4nslAmkB4a/nRVcsyih20I3rnbAi/S6NlUNScXicThde2ZoGEsMjyfR4QZO0fFyapzMo4UNi6y8BRNS5ceYFmjeoptgBPHb0BKwQ0CI9qwiws9YCgg4IpijiPXyMtLuu6xAQJQEZiTjTVEQoBgI6L0HCrXar7PRXUq6+LBGye411DIZNi8kJV9EisaaHzyRT4YALWyErYytAaYBAgQqwrdTA2KFU3xJOCiBYkQCiuEdFwFaEfhgu5XmsylowNio5EePXGHFDDEIAFNqpwQLOlUYIxFvMk3JrgTgFqqic3z5P3TYe7uTyvUobYfxu2B1c7dT6OLPdEEZEQGj9aMTnBpSTUF8I1O0i7B9FgO+hUU5l+TVWAjet2Qs5wMYZ188JCVE5fNclvhwo+4eJRP7GHT3S+bQmetl8VsSvRFIcJQvHRCNDwAtAl/LK10zY3hhyrSaCnblkSIC0goHdADWb0ZDSWP9CQfHOGfDvmiEQZFqw+fcU7nmAHSISqXfS5ZRz5LXrP0WABGTi+ZYSCAKNbGBsDycJEBkoUUI+jEBCohQpGBIoRoF97gzBmNgCEJIgpUEmjDB6AA8zMb2Ff8C9kCGkx8B9g0JUhHkxkGzBJN9rH1fyIxLxmXLB4H344hGURAEgEzNLvMgQiWunejmSMzvctMPkNWyF6/bt8eVQ4rfU7ACUr+Ebmb7CoeT9AelGrgtPll3J+ihuLLl4YrEiE/H4NIvy37Ew8cfI9LGp7qPK85Kk4qMujqHyMxG3LtM/l/WNM6CfgST88JZIC2MA5X1ebHc8ESALsLMpAkDnCFAC8qBbFDELu6BhcsVQ509DGQAd0q40KgVMUO4AIQOUcnZ1amEENASEW5XVhHZtFVGAXW1aAsXBQFRvnamDL4D8p/aevhgFIJe2qTWZy7SEL5bwGSoMBARkRsMoA1OStpUsZDnzE4LZnphoscfviYBQdisQuOfVPtOJB9PnHwkrDAHEDqLGDr0Jl4dSAUJpN+XXLdBGBogkRGT9NmTW2MpUO8VUMhCZxHNSci+Hf84kwW6cJoHAQIbGijItbVhIdnE4RYBQ0C6pStvZi7ndEnUfaRxoAahOQ2aMnexUdDZTcI6lBmQStXD8HtgIK7V1JallTUTyD0oPAZHNW5n0nfHZmnh/01B8De9Ub4wVl5oEpBMQWlM6Hf5UX9a4a5MQAEX2XyGhI6AoAKUiBIHtEREC0FrYJShc4kQ8tqitWHOC3W5k5te6SRbk7ll1w0bCDdcRDMLAxENkhiSEKCEbWCESSiu2NBFyQYRsoCEhUHAiq6QDCE3QWrqp5u5RkQQlNfJhCaHiqdlMLyGRaH0iIRaSFaf/3pVvV/IFT4qcckMl7sX28Zj4woh7h+My3qQLID/70fsNUOI4GFdPCNgxen8dF79fOM8LLIHy8JHvdTHJylkg3uguKVxi/xbXK5TszU6O15OzQarMRKIQTNolKVxkxfE+LClUkqIjcU6qMAc651GlEKqMBxVhhyM5hQOiQvB4EeMyFSKe1QaCXaQusmsUwRi7j1KHdb4lJRDVEYy0Qz4o2lWndRa2RwoEFN0NCthK0gAoivKzEbg1ViIBE0gY90yoDsRDVqQEkLUVvyxYYSW1hHQzWigAggN2VdviYJQFTVJUJx/uCHa9GmWnNBvYykwIQIRumMavFOuHZwjwGzXGTu3uFsktpS/8ZqedHGwTlbp/qQwgCjacvK1S+U8IlAEpY3eXN3ZBTFJkF0ErOYfVkBBkSzAkoUtOuMGlQ2qIyD1Gbq0RQQC0gRAGUhGE0lCQ0JG0W1NIAmUFkCGIUNr1oA5IqA6b1txnCiYLdIzRUKHBgHwRxZJER0fGLl8SGISBhjEGUUnB96n4SjgeyohxL5ZIhXTS6gCl3xn/k/F6Mb0GTlx+iOS7bPNRyorjABBJmEhCg6CUgZ3qnU6JSSxXEPcVuW04pDIJJ3DbY6gCDRhCJICS0G7fOis+dQT4ce4gtAWQ1tI9SwYqNJDKQJOy1yVAKQMVaBiSIFjfnWxYQjawCwYRCUTGQEmNXBghVNou10QCAUUIpUFG2ll+OVECQaCoIwhJiKTtqfE2kdIgoyIMyBa7sHr/pqsqsc+46667MGnSJNTX16O5uRmzZs3C5s2bD3n8z372MwghcM899/RdIvuQASrouvclWWb6r64xBv+3/zfZwvBxJBZBM66xlmoIKdgNLn1XL9mKRGcSx2rXuMjaY2FshUjCDisYtxu3dK0hEwI6545NXEdn7G/G+5GA4lGDVK+PEzPxrsY+XDqRE5SPi4d3kquJVp7vDZgUI5WiIhYC5WtVhqVL48SO3kiHd0lSrFV+T8adDJMVxybvwR+jYFvenbqhkBAysD4Yzvk2bklqQHYIqKJ03fh2r6TspwLZzxSCg9JuaRHBHlMUtgfHCWDZAagDAqKUFgggcqKmfG9CAqZOwISuvC+6JIcA5dyKwkIg3C+Qb5PIf0TIfaSRbRcIvhAQkYjFEwHuuXaG8EMzRrixLul8a0TcsoeA9UFQ7vadkShle3JxJh5ELUCRAGlZ0SNAqawWAhAZgAZSSmgL96P9z0aglEEYEsKssb01blhM5gCV8S11ghAGKkMIsnbp/diPLgRE6KSAa6XInEaQ1xB+dUNhEIQRwry2i8bBOaYrQNYZ0MAI0QCDUp19H4PPJYJPA5iC7UWoy0bI5CO38qw9XymDIKOtD4ef2RM7B1cuK1ChQiobZRXap3wsuTVlOqv92LHe5lz51a0QTUR2UVBj7HR1U5IoFQLoKEB5jLDcUIqvIw2E0vGwmpRAEGpksyWEQQQpCIEkZMMI+WwB+UwJQRDZ/MxGCLMRVCaCVHZIKgwiZLMlZPIlBDkNFWqEoUYuW0Q+X0IQRlBKIxNo5HIl5HMlZDMlhMogExrkM0XkM0XUZSPkwwgZpRFKg0AYhFIjVAQp7b0KsmtShtKgLlNENtDIhSXkwgi5MEI+LLlPhAFBCUcbVe2Zeemll3Dttddi0qRJiKIIt9xyC6ZNm4aNGzdiwIABqWOXLl2K1157DSNHjqxSanufSWNa0Lrpg/KLnRxqqawfqfwC+56J5HTsVK+ISMQDJxSyNiwethGuJRkiFkoCNkxnXHwl2BahEzWAPd/7DZoAoIyL08VNyooduDSIyP5t3KJ/wnXflwsTl27X25MK8+HJSr18W6kyKSnohLcDKspXL4YoYbdkmL+2SGzVVlkg+x6RRJ7El0iKpfJVE5EfgkphI7oIj9Nb9ukQKp2WeEq9t2NqMS1X0UPY3ZINoIpwwz62BaoDu4KqKLmVpbP2OkYIxGulGLcxqHFxGir7n3rbk1sROrDDCSIQselIAogIwgiERRE7CkMKmFAgExH0pwQdSlAoEdVrK6YAO0TmuuXtu+KEiBIgbWwXv7YPOPlK1xswnvLswozvARAJ/yDp/CBcmHug7LUr89blqxeXMrHPEZzAh4CQEnanZ4KSTswr79RqezhICig3NGUdYe2QghIEo20XqO/BldI6AwvlBIBwjtig+FkkEvEWDSANUyKgKOyemlm7uWV4EFBaQJWs4C0YhWxQQMaUbM+CkSBYJ1iCHQIyRrqhILJT9l2Gi7Kpyo+6M7MVluXnNBaavoBw6U1CCadAEUfuTU7uPfB56R55A8D3dBgB4fOSBEqCEGZEnG32nU94/ghABa6g8e8XgFAZKKXj3igJWDGjJEpaIIoCSGWda11uxmVHKDXCsByXFAQlI4QBEBl7XxKEQGnIkGCIIIWBFIRQaQhZsuvhCEBJghJuKJBEvNWCd2EUgjAgKEGCUNABiMqVQkZFyKsIQeUWB0cBVRUzzz77bOr7kiVL0NzcjLVr1+Lss8+Ow3fu3Il58+bhueeew4UXXtjXyewzbrjwbLy46YP01Olki0OmK+OY5NCIf8n9LAo/VJQ4Jm5dJns7KutYVe49ibv1g86TJgDnfpHsxRCwG1b65eB9OeXP9+9WcraNSdxbQlykcPfVSQuI8n3FcfiKhpIFJsoVUVfXSoaLzkE+sQJI+eoIV4p4G9rf/cUr0ukrTDgD+uN9Ad9VQitbsIlpp/Bf464GSsdvfMUh4mtbgee2r5Ao72YtBQJhC1oZAUILGA3I0O7zZdNijWX8BncSdld1Tc5xsZxY4ZxpBYTd8R0iXszNHyc0xauyGiWgIrt9R5STkCWCLBlk2yNQGEJAoIMU9AA7ZARpxTAVFZDR5XzUAjAKBDcbxD0c5MRLOX9cRqaep1gBlPOKAMT3RmVTWmum8if5bJRXrLV9MiDARNLltt3rSgrrxxAPsUorMg1JKKsQ7fAJAeS6bUkQDOzwQ1ynO6djCWtLX1cJlw5D1pnUaOED7aWkQbHe9rqpg0C4R8AMCmCyGoVMgLpsCcZIdBQz0FEApUoIlHbOp34vK7iF6pxPjXtG40c52YCI/+f+8M+8gHVk9vsYCWM72tx4NvmuQOEWBxRUFkVUvk78agg7JdvaSjlBY/OASnAbpboCKM5gWc5DlxVS2vdBuGnSJAAlrLD04skAkFJCyQiaJEqRHTpUyv4qBeKhHukW9BN+6r0AQgkrXNysKCt0DAJpIIWBcPkpAyDwG34Jd4/Cvl9WOJdFlhGEfBAhF2hoJ/6lgEu3wbebTsXRRk35zLS3twMAGhsb4zBjDC6//HL86le/woQJEw4bR6FQQKFQiL/v3bu35xPaS5xwXCP+r7kR7330WfkFFamysqLC9IEod2v7ggFIi5quKNdv9uN6glJxAOWpyQmBEIui5LUScfqKMt5jqGIIKD5fouzb4SqWVF3dVQPiS8K8X00q3Asab9NEuEC5sPU29WF+XN6LDQEq++qYhIBzN5OqyCghUKh8vWSakoG2d0wgNWXTCxe/35AnKYh8QMom9ruQrj8pFm7uToTLH7KCU5Jt4QHlGRpEgCzaBe5MiYCMFQ5G2/VhohzsdhkKMBmyvTtFlHtoXHb6KeKAdSAng3iNmliEeVu7YUiQAPKEolIwoUB40CD/aYTgoETwhURxsEShAc4nBfb5NMqu3eH3DzICKCmQtrODvOMxuRlM5R60sppNPgupbk+fsVROb9yzIJ1NveAQQGo9Fnesy2WQAXRJwWgJFRiQNFCSIN2wikkMb2ktbUXmV7SVTuu4+E0k4mNt3AQNY5eo8ZWwgGv1u2QrQEJbJ2AtICKCDAmFBkKoBFSHQGZ7iBIIJUlAnUF9voAg0Nh/IIdCIUQmU0ImjCClQakUWKEgCVIRSHhBk1T+Fc++r4B9oD9M+veuLNIlDEiU4yM/XTsxTT0p4snNHpMAjCTIgGCMAbR076OAMBJRgRA4h+x4pqSx8fp8J9crQ0JY3xsykP45cD1nREAgjUu3hNQGIgS0EbEIAWxe+IdC+HRLintS/EKbUtpWnYpFnYCUVhC5DphYoEoB6yjujEUkYZwDcNzrBytik2VDIICfj/0xjjZqRswQERYsWICzzjoLEydOjMN///vfIwgCXHfddd2K56677sLtt9/eW8nsdR655seY+f8ewkd79netQpJCI1leJAsL/2dSrKDiPCTOTf6e9CmpqC+T8VQOcSXTFl+GykVWfJ0uPuSmopNv3ZvykuOd7v1QJhGAc6WEhHRLiQgo2Bk0yrUUlZBQkPZfKaCEPTaQ0rWuBJSSUFJCSIFACQSBgpQCgZKQqvx74P4N3b9KCQRSIlAKoRSpY7LKxS8kMlJCKYVA2LgCuHOUtK0+qaCEvV7GxSdg06ikROB+U1BQUkCSgFL+XgSktFN1pXS7rSd7SzpN5WBqneRMH0Nupg4RDBkQEbT7W5MVM8YQDAxK2iCCdt8JkdEokUHJaPs9MtAaKEQGpmRgIoKOCLpkoEsGRRFB5zV0tgChImhoFDShoyhQ1AYkNAgRiobQUTR2Fg1pGGiUDBBFBpGx19VE0MYgMhoRGRjYdEdkYMjAwP4bmciNMBsYsjOCNPnz7Uq9xrUwjDAQsHEhUdL43jIFQCoDmbVrIZlIgbTrxdABogMSIowQhFaI2c5HN+XZdTcbaXuItHFiCAJOMkI50WF7VOwMNCkkhLHO7MbIuEfECh8/7d8JapenAq7h53vwYHukpDDOR8i1nlzDqSxk4O6T3OixLcA1ABiDwAmaZLEfCIE7Jy7A4Ex9rz2v1aJmxMy8efPw5ptv4uWXX47D1q5di3vvvRdvvPFGtwvhm2++GQsWLIi/7927Fy0tLT2e3t5iYC6L5268Cs+ufxf3PbcKuz7bGwuGSgtIYT8QsC+RtBVYoARUoOKKNwwkQqUQhAEySiKQyoZJhWygkAkDZJRCJpTIBiFyGXtcNhMiGwTIhQHyYeD+DpELAmSVQjYToC4IkA1DZF0cobLXkkJwxckwPUDyPVKpd0p1PpjpE4yxAswKRI2iiVDUJXSYCAVdRFFrdJgIHTrCAV2KwwqmiI4oQocp2XNMCUVTQslEKLk4SmQQUQklaGhTBEFDUwkGEYhK7rsVkuSlH1k/rAiAgYSBQMmQE1wCg4KB+O7wyfje8edhQJCvtvl6BUGd13juc+bPn4+lS5dixYoV+NrXvhaH33PPPViwYAFkYs6d1hpSSrS0tGDr1q2HjXvv3r0YPHgw2tvbMWjQoN5IPsMwDMMwPcxXqb+r2jNDRJg/fz6efPJJtLa2poQMAFx++eU4//zzU2EXXHABLr/8cvzkJz/py6QyDMMwDFOjVFXMXHvttXj00Ufx1FNPob6+Hh999BEAYPDgwcjn8xg6dCiGDh2aOicMQwwfPhzjxo2rRpIZhmEYhqkxupoA22c8+OCDaG9vxznnnIMRI0bEn8cff7yayWIYhmEYph9R9WGmr0p3/GQYhmEYhjl2qGrPDMMwDMMwzP8KixmGYRiGYfo1LGYYhmEYhunXsJhhGIZhGKZfw2KGYRiGYZh+DYsZhmEYhmH6NTWzN1Nv4ad/96fdsxmGYRjmWMfX291ZxuWoFzP79u0DgH612STDMAzDMJZ9+/Zh8ODBX3pMTWw02ZsYY7Br1y7U19fzLs6Hwe8wvn37dt6U8yvAdjty2HZHBtvtyGHbHRnVsBsRYd++fRg5cmRqw+muOOp7ZqSUGDVqVLWT0a8YNGgQv+RHANvtyGHbHRlstyOHbXdk9LXdDtcj42EHYIZhGIZh+jUsZhiGYRiG6dewmGFistksFi5ciGw2W+2k9CvYbkcO2+7IYLsdOWy7I6PW7XbUOwAzDMMwDHN0wz0zDMMwDMP0a1jMMAzDMAzTr2ExwzAMwzBMv4bFDMMwDMMw/RoWMwzeffddzJw5E01NTRg0aBAmT56MF198Mf59/fr1uOSSS9DS0oJ8Po/x48fj3nvvrWKKa4fD2Q4Atm3bhu9973sYMGAAmpqacN1116FYLFYpxbVBa2srhBBdftasWRMft2bNGpx33nkYMmQIGhoaMG3aNKxbt656Ca8y3bUbAPztb3/DKaecglwuh+HDh2PevHlVSnVt8FVsBwCffvopRo0aBSEEPv/8875PcI3QHbvVRB1BzDHP17/+dZoxYwatX7+e3n33Xbrmmmuorq6O2traiIho8eLFNH/+fGptbaX333+fHn74Ycrn83TfffdVOeXV53C2i6KIJk6cSFOmTKE33niDnn/+eRo5ciTNmzevyimvLoVCgdra2lKfuXPn0gknnEDGGCIi2rt3LzU0NNDs2bNp06ZNtGHDBvrBD35Azc3NVCwWq3wH1aE7diMiuvvuu2nkyJH0yCOP0JYtW2jDhg309NNPVzHl1ae7tvPMnDmTpk+fTgBoz549fZ/gGqE7dquFOoLFzDHOJ598QgBoxYoVcdjevXsJAC1fvvyQ511zzTU0ZcqUvkhizdId2z3zzDMkpaSdO3fGxzz22GOUzWapvb29z9NcqxSLRWpubqY77rgjDluzZg0BoG3btsVhb775JgGgLVu2VCOZNUdXdvvss88on89/6fvLdG07zwMPPEDf+c536IUXXjjmxUwlX2a3JH1dR/Aw0zHO0KFDMX78eDz00EP44osvEEUR/vznP2PYsGE47bTTDnlee3s7Ghsb+zCltUd3bLd69WpMnDgRI0eOjM+74IILUCgUsHbt2molveZ4+umnsXv3bsyePTsOGzduHJqamrB48WIUi0UcPHgQixcvxoQJEzBmzJjqJbaG6Mpuzz//PIwx2LlzJ8aPH49Ro0bhhz/8IbZv3169hNYgXdkOADZu3Ig77rgDDz300GE3NzwWOZTdKunzOqLPZBNTs+zYsYNOO+00EkKQUopGjhxJ//nPfw55/CuvvEJhGNKyZcv6LpE1yuFsd/XVV9PUqVM7nZfJZOjRRx/tw5TWNtOnT6fp06d3Ct+wYQONHTuWpJQkpaSTTjqJPvzwwyqksDbpym533XUXhWFI48aNo2effZZWr15N5513Ho0bN44KhUKVUlp7dGW7jo4OOuWUU+jhhx8mIqIXX3yRe2YqONS7mqQadQTLzqOU22677ZBOW/7z73//G0SEa665Bs3NzVi5ciVef/11zJw5ExdddBHa2to6xfv2229j5syZuPXWWzF16tQq3Fnv09O2E0J0ugYRdRne3+mu7ZLs2LEDzz33HK666qpU+MGDBzFnzhxMnjwZr776KlatWoUJEyZgxowZOHjwYF/eVq/Tk3YzxqBUKuGPf/wjLrjgApxxxhl47LHH8N5773VyTj8a6Enb3XzzzRg/fjwuu+yyvryFqtCTdktSrTqCtzM4Stm9ezd27979pceccMIJWLVqFaZNm4Y9e/aktnU/8cQTcdVVV+Gmm26KwzZu3IgpU6Zg7ty5WLRoUa+lvdr0pO1uvfVWPPXUU1i/fn38+549e9DY2Ih//etfmDJlSq/dRzXoru1yuVz8/be//S3uu+8+7Ny5E2EYxuGLFy/Gr3/9a7S1tcXd/cViEQ0NDVi8eDF+/OMf985NVIGetNuSJUswZ84cbN++HaNGjYrDhw0bhjvvvBNXX311z99AFelJ25166ql466234oYGEcEYA6UUbrnlFtx+++29cxNVoCft5qlmHRH06dWYPqOpqQlNTU2HPe7AgQMA0GlsWEoJY0z8/e2338a5556LK6+88qgWMkDP2u7MM8/EokWL0NbWhhEjRgAAli1bhmw2+6U+Sf2V7trOQ0RYsmQJrrjiik6F44EDByClTPVg+e/JZ/NooCftNnnyZADA5s2bYzHz2WefYffu3Uelr1FP2u6JJ55I9fqtWbMGc+bMwcqVKzF27NgeS3Mt0JN2A2qgjuizAS2mJvnkk09o6NChdPHFF9O6deto8+bN9Mtf/pLCMKR169YRkfVbOO644+jSSy9NTc/7+OOPq5z66tId2/mp2eeddx698cYbtHz5cho1atQxPzXbs3z5cgJAGzdu7PTbO++8Q9lsln7xi1/Qxo0bacOGDXTZZZfR4MGDadeuXVVIbe3wZXYjstOKJ0yYQKtWraK33nqLLrroIjr55JOP2SntSQ5nuyTsM1Pmy+xWC3UEixmG1qxZQ9OmTaPGxkaqr6+nM844g5555pn494ULFxKATp8xY8ZUL9E1wuFsR0T04Ycf0oUXXkj5fJ4aGxtp3rx51NHRUaUU1xaXXHIJffvb3z7k78uWLaPJkyfT4MGDqaGhgc4991xavXp1H6awNjmc3drb22nOnDk0ZMgQamxspO9///upKe7HMoezXRIWM2W+zG61UEewzwzDMAzDMP0ans3EMAzDMEy/hsUMwzAMwzD9GhYzDMMwDMP0a1jMMAzDMAzTr2ExwzAMwzBMv4bFDMMwDMMw/RoWMwzDMAzD9GtYzDAM02/ZunUrhBBYt25dr8QvhMDSpUt7JW6GYXoOFjMMwxwxs2fPxqxZs6p2/ZaWFrS1tWHixIkAgNbWVggh8Pnnn1ctTQzD9D280STDMP0WpRSGDx9e7WQwDFNluGeGYZhe4aWXXsLpp5+ObDaLESNG4KabbkIURfHv55xzDq677jrccMMNaGxsxPDhw3Hbbbel4ti0aRPOOuss5HI5nHzyyVi+fHlq6Cc5zLR161ZMmTIFANDQ0AAhBGbPng0AOOGEE3DPPfek4j711FNT13vvvfdw9tlnx9d6/vnnO93Tzp078aMf/QgNDQ0YOnQoZs6cia1bt/6vpmIY5n+ExQzDMD3Ozp07MWPGDEyaNAnr16/Hgw8+iMWLF+POO+9MHff3v/8dAwYMwGuvvYY//OEPuOOOO2IRYYzBrFmzUFdXh9deew1/+ctfcMsttxzymi0tLXjiiScAAJs3b0ZbWxvuvffebqXXGIOLL74YSim8+uqr+NOf/oQbb7wxdcyBAwcwZcoUDBw4ECtWrMDLL7+MgQMH4rvf/S6KxeJXMQ/DMD0MDzMxDNPjPPDAA2hpacH9998PIQROOukk7Nq1CzfeeCNuvfVWSGnbUaeccgoWLlwIADjxxBNx//3344UXXsDUqVOxbNkyvP/++2htbY2HkhYtWoSpU6d2eU2lFBobGwEAzc3NGDJkSLfTu3z5crzzzjvYunUrRo0aBQD43e9+h+nTp8fH/OMf/4CUEn/9618hhAAALFmyBEOGDEFrayumTZv21YzEMEyPwWKGYZge55133sGZZ54ZV/oAMHnyZOzfvx87duzA6NGjAVgxk2TEiBH4+OOPAdjelZaWlpRPzOmnn95r6R09enQsZADgzDPPTB2zdu1abNmyBfX19anwjo4OvP/++72SLoZhugeLGYZhehwiSgkZHwYgFR6GYeoYIQSMMYeM40iRUsbX95RKpU5pq0xLEmMMTjvtNDzyyCOdjj3uuON6JJ0MwxwZLGYYhulxTj75ZDzxxBMpQfLKK6+gvr4exx9/fLfiOOmkk7Bt2zb897//xbBhwwAAa9as+dJzMpkMAEBrnQo/7rjj0NbWFn/fu3cvPvjgg1R6t23bhl27dmHkyJEAgNWrV6fi+Na3voXHH38czc3NGDRoULfugWGYvoEdgBmG+Z9ob2/HunXrUp+f/vSn2L59O+bPn49NmzbhqaeewsKFC7FgwYLYX+ZwTJ06FWPHjsWVV16JN998E6tWrYodgA/VYzNmzBgIIfDPf/4Tn3zyCfbv3w8AOPfcc/Hwww9j5cqV2LBhA6688koopeLzzj//fIwbNw5XXHEF1q9fj5UrV3ZyNr700kvR1NSEmTNnYuXKlfjggw/w0ksv4frrr8eOHTuOxHQMw/QQLGYYhvmfaG1txTe/+c3UZ+HChXjmmWfw+uuv4xvf+AZ+/vOf46qrrsJvfvObbserlMLSpUuxf/9+TJo0CXPnzo3Pz+VyXZ5z/PHH4/bbb8dNN92EYcOGYd68eQCAm2++GWeffTYuuugizJgxA7NmzcLYsWPj86SUePLJJ1EoFHD66adj7ty5WLRoUSruuro6rFixAqNHj8bFF1+M8ePHY86cOTh48CD31DBMlRHU1WAxwzBMDbJq1SqcddZZ2LJlS0qMMAxzbMNihmGYmuXJJ5/EwIEDceKJJ2LLli24/vrr0dDQgJdffrnaSWMYpoZgB2CGYWqWffv24YYbbsD27dvR1NSE888/H3fffXe1k8UwTI3BPTMMwzAMw/Rr2AGYYRiGYZh+DYsZhmEYhmH6NSxmGIZhGIbp17CYYRiGYRimX8NihmEYhmGYfg2LGYZhGIZh+jUsZhiGYRiG6dewmGEYhmEYpl/DYoZhGIZhmH7N/wcTW7P1S1Kf4gAAAABJRU5ErkJggg==", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "import numpy as np\n", - "\n", - "var = 'windspeed_10m'\n", - "\n", - "time_indx = 0\n", - "dt = ds['time_index'][time_indx].values\n", - "dt = np.datetime_as_string(dt,unit='m')\n", - "lon = ds['longitude'][:].values\n", - "lat = ds['latitude'][:].values\n", - "data = ds[var][time_indx,:].values\n", - "\n", - "plt.scatter(lon, lat, c=data)\n", - "plt.xlabel('Longitude')\n", - "plt.ylabel('Latitude')\n", - "plt.title(f'{var} at datetime {dt}')\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "You can also choose a particular location and view a time series graph." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "gid = 0\n", - "var = 'temperature_100m'\n", - "lon = ds['longitude'][gid].values\n", - "lat = ds['latitude'][gid].values\n", - "ds[var][:, gid].plot()\n", - "plt.title(f'{var} at coordinates ({lon:.1f}, {lat:.1f})')\n", - "plt.show()" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally, we show how to find the location closest to a given set of coordinates and plot some data." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'sklearn'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mmath\u001b[39;00m \u001b[39mimport\u001b[39;00m radians\n\u001b[0;32m----> 2\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msklearn\u001b[39;00m\u001b[39m.\u001b[39;00m\u001b[39mneighbors\u001b[39;00m \u001b[39mimport\u001b[39;00m BallTree\n\u001b[1;32m 3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mnumpy\u001b[39;00m \u001b[39mas\u001b[39;00m \u001b[39mnp\u001b[39;00m\n\u001b[1;32m 5\u001b[0m my_coords \u001b[39m=\u001b[39m [\u001b[39m-\u001b[39m\u001b[39m66\u001b[39m, \u001b[39m17\u001b[39m] \u001b[39m# Coordinates of interest\u001b[39;00m\n", - "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'sklearn'" - ] - } - ], - "source": [ - "from math import radians\n", - "from sklearn.neighbors import BallTree\n", - "import numpy as np\n", - "\n", - "my_coords = [-66, 17] # Coordinates of interest\n", - "time_sel = '2017-07' # Make sure your selection is in range for the data set selected!\n", - "\n", - "my_coords_rad = [radians(my_coords[0]), radians(my_coords[1])]\n", - "all_coords_rad = np.array([[radians(lon), radians(lat)] for lon, lat in zip(ds['longitude'].values, ds['latitude'].values)])\n", - "tree = BallTree(all_coords_rad, metric='haversine')\n", - "my_gid = tree.query([my_coords_rad])[1][0][0]\n", - "\n", - "var = 'windspeed_10m'\n", - "lon = ds['longitude'][my_gid].values\n", - "lat = ds['latitude'][my_gid].values\n", - "ds[var].sel(time_index='2017-07', gid=my_gid).plot() # Time slice for July 2008\n", - "plt.title(f'{var} at coordinates ({lon:.1f}, {lat:.1f})')\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": ".env", - "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": "3.10.12" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/azure/pipeline/aws_tools.py b/azure/pipeline/aws_tools.py index 57995ed..5b7f465 100644 --- a/azure/pipeline/aws_tools.py +++ b/azure/pipeline/aws_tools.py @@ -7,23 +7,23 @@ import subprocess import h5py -def get_tags(): +def get_tags(org, billingid, task, owner): tags = [ { 'key': 'org', - 'value': 'oedi' + 'value': org }, { 'key': 'billingid', - 'value': '210090' + 'value': billingid }, { 'key': 'task', - 'value': 'kerchunk' + 'value': task }, { 'key': 'owner', - 'value': 'mheine' + 'value': owner } ] return tags @@ -62,42 +62,6 @@ def get_dataset(bucket, prefix=None, extension='.h5', resolution=None): files = [file for file in files if file.endswith(extension)] return files - # if not resolution: - # if not extension: - # files = s3.glob(f'{bucket}/{prefix}/*.*') - # else: - # files = s3.glob(f'{bucket}/{prefix}/*{extension}') - # elif bucket == 'nrel-pds-wtk': - # subsets_1 = ['North_Atlantic', 'gulf_of_mexico'] # yearly and yearly_hr - # subsets_2 = ['pr100'] # hourly and 5min - # subsets_3 = ['south_atlantic'] - # if any([subset in prefix for subset in subsets_1]): - # if resolution == 'hourly': - # files = s3.glob(f'{bucket}/{prefix}/yearly_hr/*.h5') - # elif resolution == '5min': - # files = s3.glob(f'{bucket}/{prefix}/yearly/*.h5') - # if any([subset in prefix for subset in subsets_2]): - # if resolution == 'hourly': - # files = s3.glob(f'{bucket}/{prefix}/hourly/*.h5') - # elif resolution == '5min': - # files = s3.glob(f'{bucket}/{prefix}/5min/*.h5') - # else: - # if resolution == 'hourly': - # files = s3.glob(f'{bucket}/{prefix}/*.h5') - # elif resolution == '5min': - # files = s3.glob(f'{bucket}/{prefix}/*/*.h5') - # return files - -def get_AWSServiceRoleForBatch(): - # Not used - iam = boto3.client('iam') - try: - ARN = iam.get_role(RoleName='AWSServiceRoleForBatch')['Role']['Arn'] - except: - ARN = iam.create_service_linked_role( - AWSServiceName='batch.amazonaws.com' - )['Role']['Arn'] - return ARN def get_StepFunctionRole(): # TODO: Add code that creates the role if it doesn't exist @@ -256,16 +220,9 @@ def create_launch_templates(): def create_cluster(): batch = boto3.client('batch') -# def update_container(): -# # TODO: Modify this to use the docker sdk -# subprocess.run(['sh', 'update_trans_container.sh']) - def create_aws_resources(): create_state_machine('kerchunk-h5') - # create_compute_environment() - # create_job_queue() create_job_def() - # update_container() def process_h5_dataset(files, staging_bucket, s3_comb_ref_file, az_comb_ref_file, state_machine_name='kerchunk_h5', region_name='us-west-2'): smi = create_state_machine_input(files, staging_bucket, s3_comb_ref_file, az_comb_ref_file) @@ -274,7 +231,6 @@ def process_h5_dataset(files, staging_bucket, s3_comb_ref_file, az_comb_ref_file sf.start_execution(stateMachineArn=stateMachineArn, input=smi) def copy_s3_dataset_to_azure(files, staging_bucket, dry_run=False): - #batch = boto3.client('batch', region_name=region_name) CONTAINER_NAME = 'oedi' sas = load_oedi_sas() load_dotenv() # Store AWS credentials in .env file @@ -283,23 +239,13 @@ def copy_s3_dataset_to_azure(files, staging_bucket, dry_run=False): 'copy', f'https://s3.us-west-2.amazonaws.com/{staging_bucket}', f'https://nrel.blob.core.windows.net/{CONTAINER_NAME}?{sas}', - # '--exclude-pattern', - # '*' '--include-path', ';'.join(files) ] - # for file in files: - # #cmd.append('--include-pattern') - # cmd.append(file) + if dry_run: cmd.append('--dry-run') - # source = f'https://s3.us-west-2.amazonaws.com/{staging_bucket}/{file}' - # dest = f'https://nrel.blob.core.windows.net/{CONTAINER_NAME}/{file}?{sas}' - # print(source) - # print(dest) - #os.system(f'azcopy cp "{source}" "{dest}" --dry-run') - # subprocess.run(['azcopy', 'cp', source, dest, '--dry-run']) - print(cmd) + subprocess.run(cmd) def create_combined_ref(files, staging_bucket, comb_ref_file=None, remote_protocol='s3'): @@ -343,18 +289,3 @@ def copy_local_file_to_azure(source, dest, sas=None, container='oedi'): blob = client.get_blob_client(dest) with open(source, 'rb') as f: blob.upload_blob(f.read()) - -# We need: -#X A container to hold the code that will perform etl and ref generation -#X A container to combine the references -# A function to build the AWS resources including: -# A function that will load the container into ECR -# A function that will initialize a cluster and job queue -# A function to create a state machine (started) -# A function to process a dataset including: -#X A get_dataset function to glob all of the files of a particular data set -# A function to run the state machine -#X A function to create state machine input -# A way to test the results in s3 staging -# A function to copy data to Azure -# Logging functionality to track progress throughout diff --git a/azure/pipeline/azure_tools.py b/azure/pipeline/azure_tools.py index b1ef66e..c8568f1 100644 --- a/azure/pipeline/azure_tools.py +++ b/azure/pipeline/azure_tools.py @@ -6,30 +6,29 @@ def get_fs(account='nrel', container='oedi'): def get_size(path, units='B'): fs = get_fs() size = fs.du(path, total=True) - match units: - case 'B': - pass - case 'kB': - size = size * 10 ** -3 - case 'MB': - size = size * 10 ** -6 - case 'GB': - size = size * 10 ** -9 - case 'TB': - size = size * 10 ** -12 - case 'PB': - size = size * 10 ** -15 - case 'kiB': - size = size * 2 ** -10 - case 'MiB': - size = size * 2 ** -20 - case 'GiB': - size = size * 2 ** -30 - case 'TiB': - size = size * 2 ** -40 - case 'PiB': - size = size * 2 ** -50 - case _: - raise NotImplementedError(f'Units "{units}" not recognized.') + if units=='B': + pass + elif units=='kB': + size = size * 10 ** -3 + elif units=='MB': + size = size * 10 ** -6 + elif units=='GB': + size = size * 10 ** -9 + elif units=='TB': + size = size * 10 ** -12 + elif units=='PB': + size = size * 10 ** -15 + elif units=='kiB': + size = size * 2 ** -10 + elif units=='MiB': + size = size * 2 ** -20 + elif units=='GiB': + size = size * 2 ** -30 + elif units=='TiB': + size = size * 2 ** -40 + elif units=='PiB': + size = size * 2 ** -50 + else: + raise NotImplementedError(f'Units "{units}" not recognized.') return size diff --git a/azure/pipeline/blob_access.ipynb b/azure/pipeline/blob_access_example.ipynb similarity index 92% rename from azure/pipeline/blob_access.ipynb rename to azure/pipeline/blob_access_example.ipynb index 89f2fe0..ba24a9c 100644 --- a/azure/pipeline/blob_access.ipynb +++ b/azure/pipeline/blob_access_example.ipynb @@ -11,7 +11,7 @@ "from azure.storage.blob import ContainerClient\n", "from etl_tools import load_oedi_sas\n", "\n", - "sas_token = load_oedi_sas()\n", + "sas_token = load_oedi_sas() # Loads oedi rw sas token\n", "client = ContainerClient.from_container_url(f'https://nrel.blob.core.windows.net/oedi?{sas_token}')\n", "for blob in client.list_blobs():\n", " if \"wtk\" in blob.name and 'test' in blob.name:\n", @@ -25,7 +25,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Copying blobs\n", + "# Copying blobs within Azure\n", "\n", "source_blob = client.get_blob_client('wtk/wtk_bangladesh_hourly_ref.json')\n", "dest_blob = client.get_blob_client('wtk/bangladesh/kerchunk_hourly_ref.json')\n", @@ -38,6 +38,8 @@ "metadata": {}, "outputs": [], "source": [ + "# Copy objects from S3 into Azure BLOB storage\n", + "\n", "import s3fs\n", "from azure.storage.blob import ContainerClient\n", "from etl_tools import load_oedi_sas\n", diff --git a/azure/pipeline/etl_tools.py b/azure/pipeline/etl_tools.py index 5ba70f8..61e86bb 100644 --- a/azure/pipeline/etl_tools.py +++ b/azure/pipeline/etl_tools.py @@ -9,6 +9,7 @@ import os from time import time import s3fs +import logging def time_index_bytestring_to_float(dset): t = pd.Series(dset) @@ -63,316 +64,6 @@ def load_oedi_sas(): return sas -def transform_wtk_h5_file_old(h5_file, chunk_size=2, weeks_per_chunk=8): - # This is an updated version of transform_h5_file, designed for wtk. wtk does not have a nice rectangular coordinate grid, - # so the data will be left in 2 dims rather than be converted to 3 dims. - - # h5_file should be a path to a local h5 file. The file will be opened in write-mode, transformed and then closed. - # chunk_size is the desired size of each chunk in MiB - # weeks_per_chunk determines the length of chunks in the time_index dimension - - # Summary of data transformations: - - # 1. time_index is converted from byte-string to int (when read by xarray, this will automatically convert to np.datetime64) - # 2. A gid dataset is created to index the locations - # 3. time_index and gid are converted to dimension scales - # 4. Each variable is rechunked so that we will have consistent chunk sizes accross all files - # 5. The dimension scales are attached to each variable's dimensions - # 6. The scale_factor metadata is inverted (new_sf = 1 / old_sf) - # 7. The meta variable is unpacked - - # Notes: - # Once again, the download/upload steps are what will take all of the time here. To scale up to wtk, this transformation - # should either happen on Eagle (where the data are already local) or the transformation should be containerized for use - # with AWS batch. - - f = h5py.File(h5_file, 'r+') - file_name = h5_file.split('/')[-1] - - # Get the length of time_index - time_len = f['time_index'].len() - - # Convert time_index from bytes to float. - t = pd.Series(f['time_index']) - t = t.str.decode('utf8') - t = t.str.split('+', expand=True)[0] - t = np.array(t,dtype=np.datetime64) - t = t.astype('int') - - # Determine time_index chunksize - time_step = t[1] - t[0] - time_index_chunk_len = min(weeks_per_chunk * 7 * 24 * 60 * 60 / time_step, time_len) - - # Replace time_index variable with int. 'units' metadata required for xarray to interpret as datetime. - attrs = {} - for key in f['time_index'].attrs.keys(): - attrs[key] = f['time_index'].attrs[key] - del f['time_index'] - f.create_dataset('time_index', data=t) - for key, val in attrs.items(): - f['time_index'].attrs[key] = val - f['time_index'].attrs['units'] = b'seconds since 1970-01-01' - del t - del attrs - - print(f'{file_name}: time_index processed.') - # Create lon/lat datasets from meta - # f.create_dataset('latitude', data=f['meta']['latitude']) - # f.create_dataset('longitude', data=f['meta']['longitude']) - # del f['coordinates'] - - # Create gid variable - nloc = len(f['coordinates']) - f.create_dataset('gid', data=np.arange(nloc, dtype=np.int32), fillvalue=-1) - print(f'{file_name}: gid created.') - - # Start tracking identical_dims (anything with only a gid dimension) - identical_dims = ['gid'] - - # Convert to dimension scales - f['time_index'].make_scale() - f['gid'].make_scale() - - # Attach dim scales to lon/lat - # f['latitude'].dims[0].attach_scale(f['gid']) - # f['longitude'].dims[0].attach_scale(f['gid']) - - # Get var names - vars = [var for var in f.keys() if var not in ['meta', 'time_index', 'latitude', 'longitude', 'gid', 'coordinates']] - - # Loop over the variables and rechunk and add scales - for var in vars: - # Check dims - if not f[var].shape[0] == time_len: - raise Exception(f'Dim 0 of {var} has different length than time_index.') - if not f[var].shape[1] == nloc: - raise Exception(f'Dim 1 of {var} has different length than gid.') - - print(f'{file_name}: Processing {var}...') - - # Copy attrs - temp_attrs = {} - for attr in f[var].attrs.keys(): - temp_attrs[attr] = f[var].attrs[attr] - - # Copy data - data = f[var][:] - - # Determine location chunk size - element_size = f[var].dtype.itemsize # size of single element in bytes - gid_chunk_len = min(chunk_size * 2 ** 20 / time_index_chunk_len // element_size, nloc) - - # Delete original dataset - del f[var] - - # Create new rechunked dataset - chunks = (time_index_chunk_len, gid_chunk_len) - f.create_dataset(var, data=data, chunks=chunks) - for key, val in temp_attrs.items(): - f[var].attrs[key] = val - f[var].attrs['chunks'] = chunks - - # Fix scale_factor - if 'scale_factor' in f[var].attrs.keys(): - f[var].attrs['scale_factor'] = 1 / f[var].attrs['scale_factor'] - - # Attach scales to the dims - f[var].dims[0].attach_scale(f['time_index']) - f[var].dims[1].attach_scale(f['gid']) - - # Progress report - # print(f'{file_name}: {var} processed.') - print(f'{file_name}: Complete.') - - # Unpack metadata variables - #chunks = f['meta'].chunks - for var in f['meta'].dtype.names: - data = f['meta'][var] - element_size = data.dtype.itemsize - gid_chunk_len = min(chunk_size * 2 ** 20 // element_size, nloc) - chunks = (gid_chunk_len,) - f.create_dataset(var, data=data, chunks=chunks) - - # Add chunks attribute - f[var].attrs['chunks'] = chunks - - # Attach dimension scales to the dimensions - f[var].dims[0].attach_scale(f['gid']) - - # Append to identical_dims - identical_dims.append(var) - del f['meta'] - print(f'{file_name}: meta unpacked.') - - # Delete coordinates, since we have lat/lon - del f['coordinates'] - - # if 'coordinates' in f.keys(): - # data = f['coordinates'] # No attrs - # element_size = data.dtype.itemsize - # gid_chunk_len = min(chunk_size * 2 ** 20 // element_size, nloc) - # chunks = (gid_chunk_len,) - # del f['coordinates'] - # f.create_dataset('coordinates', data=data, chunks=chunks) - - # # Add chunks attribute - # f[var].attrs['chunks'] = chunks - - # # Attach dimension scales to the dimensions - # f[var].dims[0].attach_scale(f['gid']) - # print(f'{file_name}: coordinates rechunked.') - - # Add identical_dims to file metadata so we can pass to kerchunk later - f.attrs['identical_dims'] = identical_dims - - # Close the dataset to ensure changes are written - f.close() - - print(f'{file_name}: Done with transormations!') - return - -def transform_wtk_h5_file_with_h5repack(in_file, out_file, chunk_size=2, weeks_per_chunk=8): - # This is an updated version of transform_h5_file, designed for wtk. wtk does not have a nice rectangular coordinate grid, - # so the data will be left in 2 dims rather than be converted to 3 dims. - - # h5_file should be a path to a local h5 file. The file will be opened in write-mode, transformed and then closed. - # chunk_size is the desired size of each chunk in MiB - # weeks_per_chunk determines the length of chunks in the time_index dimension - - # Summary of data transformations: - - # 1. time_index is converted from byte-string to int (when read by xarray, this will automatically convert to np.datetime64) - # 2. A gid dataset is created to index the locations - # 3. time_index and gid are converted to dimension scales - # 4. Each variable is rechunked so that we will have consistent chunk sizes accross all files - # 5. The dimension scales are attached to each variable's dimensions - # 6. The scale_factor metadata is inverted (new_sf = 1 / old_sf) - # 7. The meta variable is unpacked - - # Notes: - # Once again, the download/upload steps are what will take all of the time here. To scale up to wtk, this transformation - # should either happen on Eagle (where the data are already local) or the transformation should be containerized for use - # with AWS batch. - - f_in = h5py.File(in_file, 'r') - file_name = out_file.split('/')[-1] - - # Get the length of time_index and coordinates - time_len = f_in['time_index'].len() - nloc = len(f_in['coordinates']) - - # Convert time_index from bytes to float. - t = pd.Series(f_in['time_index']) - t = t.str.decode('utf8') - t = t.str.split('+', expand=True)[0] - t = np.array(t,dtype=np.datetime64) - t = t.astype('int') - - # Determine time_index chunksize - time_step = t[1] - t[0] - time_index_chunk_len = min(weeks_per_chunk * 7 * 24 * 60 * 60 / time_step, time_len) - - # Get var names - vars = [var for var in f_in.keys() if var not in ['meta', 'time_index', 'latitude', 'longitude', 'gid', 'coordinates']] - - layouts = [] - # Loop over vars to determine chunk sizes - for var in vars: - # Check dims - if not f_in[var].shape[0] == time_len: - raise Exception(f'Dim 0 of {var} has different length than time_index.') - if not f_in[var].shape[1] == nloc: - raise Exception(f'Dim 1 of {var} has different length than gid.') - - # Determine location chunk size - element_size = f_in[var].dtype.itemsize # size of single element in bytes - gid_chunk_len = min(chunk_size * 2 ** 20 / time_index_chunk_len // element_size, nloc) - - layouts.append('-l') - layouts.append(f'{var}:CHUNK={int(time_index_chunk_len)}x{int(gid_chunk_len)}') - f_in.close() - - # Copy the h5 file to scratch while rechunking datasets - print(f'Repacking {in_file} to {out_file}.') - subprocess.run(['h5repack', '-i', in_file, '-o', out_file] + layouts) - print(f'Repack complete.') - - # Open out_file - f_out = h5py.File(out_file, 'a') - - # Replace time_index variable with int. 'units' metadata required for xarray to interpret as datetime. - # Original dataset must be deleted and replaced because the data type is changing - attrs = {} - for key in f_out['time_index'].attrs.keys(): - attrs[key] = f_out['time_index'].attrs[key] - del f_out['time_index'] # Must be deleted because you can't create a dataset with the same name as an existing dataset - f_out.create_dataset('time_index', data=t) - for key, val in attrs.items(): - f_out['time_index'].attrs[key] = val - f_out['time_index'].attrs['units'] = b'seconds since 1970-01-01' - del t - del attrs - - # Create gid variable - nloc = len(f_out['coordinates']) - f_out.create_dataset('gid', data=np.arange(nloc, dtype=np.int32), fillvalue=-1) - print(f'{file_name}: gid created.') - - # Convert to dimension scales - f_out['time_index'].make_scale() - f_out['gid'].make_scale() - - # Start tracking identical_dims (anything with only a gid dimension) - identical_dims = ['gid'] - - # Loop over the variables and add scales - for var in vars: - - print(f'{file_name}: Processing {var}...') - - # Fix scale_factor - if 'scale_factor' in f_out[var].attrs.keys(): - f_out[var].attrs['scale_factor'] = 1 / f_out[var].attrs['scale_factor'] - - # Attach scales to the dims - f_out[var].dims[0].attach_scale(f_out['time_index']) - f_out[var].dims[1].attach_scale(f_out['gid']) - - # Progress report - print(f'{file_name}: Complete.') - - # Unpack metadata variables - #chunks = f['meta'].chunks - for var in f_out['meta'].dtype.names: - # data = f_out['meta'][var] - element_size = f_out['meta'][var].dtype.itemsize - gid_chunk_len = min(chunk_size * 2 ** 20 // element_size, nloc) - chunks = (gid_chunk_len,) - f_out.create_dataset(var, data=f_out['meta'][var], chunks=chunks) - - # Add chunks attribute - f_out[var].attrs['chunks'] = chunks - - # Attach dimension scales to the dimensions - f_out[var].dims[0].attach_scale(f_out['gid']) - - # Append to identical_dims - identical_dims.append(var) - del f_out['meta'] - print(f'{file_name}: meta unpacked.') - - # Delete coordinates, since we have lat/lon - del f_out['coordinates'] - - # Add identical_dims to file metadata so we can pass to kerchunk later - f_out.attrs['identical_dims'] = identical_dims - - # Close the dataset to ensure changes are written - f_out.close() - - print(f'{file_name}: Done with transormations!') - return - def transform_wtk_h5_file(in_file, out_file, chunk_size=2, weeks_per_chunk=None, in_file_on_s3=False): # This is an updated version of transform_h5_file, designed for wtk. wtk does not have a nice rectangular coordinate grid, # so the data will be left in 2 dims rather than be converted to 3 dims. @@ -399,7 +90,7 @@ def transform_wtk_h5_file(in_file, out_file, chunk_size=2, weeks_per_chunk=None, # Begin logging st = time() file_name = out_file.split('/')[-1] - print(f'{elapsed_time(st)} - {file_name}: Starting transformation.') + logging.info(f'{elapsed_time(st)} - {file_name}: Starting transformation.') # Open input file if in_file_on_s3: @@ -415,7 +106,7 @@ def transform_wtk_h5_file(in_file, out_file, chunk_size=2, weeks_per_chunk=None, # Copy file attrs copy_attrs(f_in, f_out) - print(f'{elapsed_time(st)} - {file_name}: File attrs copied!') + logging.info(f'{elapsed_time(st)} - {file_name}: File attrs copied!') # Get the length of time_index and coordinates time_len = f_in['time_index'].len() @@ -431,7 +122,7 @@ def transform_wtk_h5_file(in_file, out_file, chunk_size=2, weeks_per_chunk=None, # Create gid variable f_out.create_dataset('gid', data=np.arange(nloc, dtype=np.int32), fillvalue=-1) - print(f'{elapsed_time(st)} - {file_name}: gid created.') + logging.info(f'{elapsed_time(st)} - {file_name}: gid created.') # Convert to dimension scales f_out['time_index'].make_scale() @@ -450,18 +141,18 @@ def transform_wtk_h5_file(in_file, out_file, chunk_size=2, weeks_per_chunk=None, weeks_per_chunk = 12 else: weeks_per_chunk = 8 # other resolution - print(f'Warning: Non-standard resolution of {time_step / 60} min detected.') + logging.info(f'Warning: Non-standard resolution of {time_step / 60} min detected.') time_index_chunk_len = int(min(weeks_per_chunk * 7 * 24 * 60 * 60 / time_step, time_len)) - print(f'{elapsed_time(st)} - {file_name}: time_index and gid created') + logging.info(f'{elapsed_time(st)} - {file_name}: time_index and gid created') # Get var names vars = [var for var in f_in.keys() if var not in ['meta', 'time_index', 'latitude', 'longitude', 'gid', 'coordinates']] # Loop over vars copying them to the new file for var in vars: - print(f'{elapsed_time(st)} - {file_name}: Processing {var}...') + logging.info(f'{elapsed_time(st)} - {file_name}: Processing {var}...') # Check dims if not f_in[var].shape[0] == time_len: @@ -475,16 +166,10 @@ def transform_wtk_h5_file(in_file, out_file, chunk_size=2, weeks_per_chunk=None, # Create dataset in new file chunks=(time_index_chunk_len, gid_chunk_len) - - # f_out.create_dataset(var, data=f_in[var], chunks=chunks) - # copy_attrs(f_in[var], f_out[var]) - - # --- New code f_out.create_dataset(var, shape=f_in[var].shape, dtype=f_in[var].dtype, chunks=chunks) copy_dataset(f_in, f_out, var) copy_attrs(f_in[var], f_out[var]) - # --- - + # Add chunks attribute f_out[var].attrs['chunks'] = chunks @@ -497,16 +182,16 @@ def transform_wtk_h5_file(in_file, out_file, chunk_size=2, weeks_per_chunk=None, f_out[var].dims[1].attach_scale(f_out['gid']) # Progress report - print(f'{elapsed_time(st)} - {file_name}: Done!') - - print(f'{elapsed_time(st)} - {file_name}: All variables transformed!') + logging.info(f'{elapsed_time(st)} - {file_name}: Done!') + + logging.info(f'{elapsed_time(st)} - {file_name}: All variables transformed!') # Start tracking identical_dims (anything with only a gid dimension) identical_dims = ['gid'] # Unpack metadata variables for var in f_in['meta'].dtype.names: - print(f'{elapsed_time(st)} - {file_name}: Unpacking {var} from meta...') + logging.info(f'{elapsed_time(st)} - {file_name}: Unpacking {var} from meta...') element_size = f_in['meta'][var].dtype.itemsize gid_chunk_len = min(chunk_size * 2 ** 20 // element_size, nloc) chunks = (gid_chunk_len,) @@ -521,9 +206,9 @@ def transform_wtk_h5_file(in_file, out_file, chunk_size=2, weeks_per_chunk=None, # Append to identical_dims identical_dims.append(var) - print(f'{elapsed_time(st)} - {file_name}: Done!') + logging.info(f'{elapsed_time(st)} - {file_name}: Done!') - print(f'{elapsed_time(st)} - {file_name}: meta unpacked!') + logging.info(f'{elapsed_time(st)} - {file_name}: meta unpacked!') # Add identical_dims to file metadata so we can pass to kerchunk later f_out.attrs['identical_dims'] = identical_dims @@ -532,7 +217,7 @@ def transform_wtk_h5_file(in_file, out_file, chunk_size=2, weeks_per_chunk=None, f_in.close() f_out.close() - print(f'{elapsed_time(st)} - {file_name}: Done with transormations!') + logging.info(f'{elapsed_time(st)} - {file_name}: Done with transormations!') return @@ -593,7 +278,7 @@ def transform_sup3rcc_h5_file(infile, outfile): f2['latitude'].make_scale() f2['longitude'].make_scale() - print('Dimension scales created.') + logging.info('Dimension scales created.') # Get var names vars = [var for var in f1.keys() if var not in ['meta', 'time_index']] @@ -610,7 +295,7 @@ def transform_sup3rcc_h5_file(infile, outfile): # data sets have different lengths for the time_index (as is the case for Sup3rcc) chunks = (24, 130, 295) f2.create_dataset(var, data=f1[var][:].reshape(time_len, 650, 1475), chunks=chunks) # Results in 1.8 MB chunks for pressure data - print(f'{var} reshaped and transferred to new dataset.') + logging.info(f'{var} reshaped and transferred to new dataset.') # Add attributes for attr in f1[var].attrs.keys(): @@ -630,7 +315,7 @@ def transform_sup3rcc_h5_file(infile, outfile): f2[var].dims[1].attach_scale(f2['latitude']) f2[var].dims[2].attach_scale(f2['longitude']) - print(f'Dimension scales attached to {var}.') + logging.info(f'Dimension scales attached to {var}.') # Add metadata variables for var in f1['meta'].dtype.names: @@ -649,8 +334,6 @@ def transform_sup3rcc_h5_file(infile, outfile): f2[var].dims[0].attach_scale(f2['latitude']) f2[var].dims[1].attach_scale(f2['longitude']) - #print(f'Dimension scales attached to {var}.') - # Close the new dataset to ensure changes are written f1.close() f2.close() diff --git a/azure/pipeline/hpc_gen_refs.py b/azure/pipeline/hpc_gen_refs.py index 06b6b7c..c629023 100644 --- a/azure/pipeline/hpc_gen_refs.py +++ b/azure/pipeline/hpc_gen_refs.py @@ -4,6 +4,7 @@ import planetary_computer import os import sys +import logging # Get input # First arg should be the path for the combined ref file @@ -41,7 +42,7 @@ else: raise NotImplementedError('The only implemented Eagle datasets are sup3rcc and WIND.') -print(f'Identical dims: {identical_dims}') +logging.info(f'Identical dims: {identical_dims}') # Open all reference files refs = [] @@ -55,11 +56,5 @@ # Send to Azure sas_token = load_oedi_sas() blob_address = f'https://nrel.blob.core.windows.net/{CONTAINER_NAME}' -# comb_ref_file_name = comb_ref_file.split('/')[-1] -# az_path = {DATASET}/{comb_ref_file_name} -# fs = planetary_computer.get_adlfs_filesystem('nrel', 'oedi') -# if fs.exists(az_path): -# raise Exception(f'Path "{az_path}" already exists in Azure.') -# else: dest = f'{blob_address}/{az_path}?{sas_token}' os.system(f'azcopy cp "{comb_ref_file}" "{dest}"') diff --git a/azure/hpc_migration.ipynb b/azure/pipeline/hpc_migration.ipynb similarity index 96% rename from azure/hpc_migration.ipynb rename to azure/pipeline/hpc_migration.ipynb index 32106e1..7e2ca2c 100644 --- a/azure/hpc_migration.ipynb +++ b/azure/pipeline/hpc_migration.ipynb @@ -1,12 +1,5 @@ { "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "On 9/5, 700 jobs were scheduled (sq | wc -l returned 701)" - ] - }, { "cell_type": "markdown", "metadata": {}, diff --git a/azure/pipeline/hpc_process_file.py b/azure/pipeline/hpc_process_file.py index 5570791..0aa948f 100644 --- a/azure/pipeline/hpc_process_file.py +++ b/azure/pipeline/hpc_process_file.py @@ -3,6 +3,7 @@ from etl_tools import transform_wtk_h5_file, transform_sup3rcc_h5_file, gen_ref from hpc_tools import construct_paths from time import time +import logging # Start timer start_time = time() @@ -15,44 +16,18 @@ if len(args) != 2: raise Exception('Must provide exactly one file path.') source_path = args[1] -# if 'WIND' in source_path: -# DATASET_NAME = 'wtk' -# file_path = source_path.replace('/datasets/WIND/', 'wtk/') -# elif 'sup3rcc' in source_path: -# DATASET_NAME = 'sup3rcc' -# file_path = source_path.replace('/datasets/', '') -# else: -# raise NotImplementedError(f'Dataset for {source_path} not implemented yet.') - -# Copy h5 file to scratch and transform -#scratch_path = f'/scratch/{USER}/{file_path}' # Construct paths file_name, job_name, job_dir, ref_file, az_path = construct_paths(source_path) scratch_path = f'{job_dir}{file_name}' -#scratch_path = source_path.replace('/datasets', f'/scratch/{USER}') -#file_name = scratch_path.split('/')[-1] -# scratch_dir = scratch_path.replace(file_name, '') -# os.makedirs(scratch_dir, exist_ok=True) -# if DATASET_NAME == 'wtk': if 'WIND' in source_path: - # shutil.copy(source_path, scratch_path) - # print(f'{(time() - start_time) / 60:.2f} min: {file_name} copied.') transform_wtk_h5_file(source_path, scratch_path) elif 'sup3rcc' in source_path: transform_sup3rcc_h5_file(source_path, scratch_path) else: raise NotImplementedError(f'The only Eagle datasets that have been implemented are WIND and sup3rcc.') +logging.info(f'{(time() - start_time) / 60:.2f} min: {file_name} transformed.') -print(f'{(time() - start_time) / 60:.2f} min: {file_name} transformed.') - -# with open(f'/home/{USER}/Azure_workflow/temp_files_{DATASET_NAME}.txt', 'a') as f: -# f.write(f'{scratch_path}\n') - -# Generate a kerchunk reference file for the h5 file -# ref_file = scratch_path.replace('.h5', '_ref.json') -# file_name = ref_file.split('/')[-1] -#ref_dir = ref_file.replace(file_name, '') -#os.makedirs(ref_dir, exist_ok=True) # Need to create the dir if it doesn't exist +# Generate references gen_ref(scratch_path, f'abfs://{CONTAINER_NAME}/{az_path}', ref_file=ref_file) -print(f'{(time() - start_time) / 60:.2f} min: {job_name} references generated.') +logging.info(f'{(time() - start_time) / 60:.2f} min: {job_name} references generated.') diff --git a/azure/pipeline/hpc_to_azure.py b/azure/pipeline/hpc_to_azure.py index 9af93f4..7d5a1a1 100644 --- a/azure/pipeline/hpc_to_azure.py +++ b/azure/pipeline/hpc_to_azure.py @@ -15,4 +15,3 @@ dest = f"'{blob_address}/{dest}?{sas_token}'" os.system(f'azcopy copy {source} {dest} --overwrite ifSourceNewer') - #subprocess.run(['azcopy', 'copy', source, dest]) diff --git a/azure/pipeline/hpc_tools.py b/azure/pipeline/hpc_tools.py index 1d4e73f..996c513 100644 --- a/azure/pipeline/hpc_tools.py +++ b/azure/pipeline/hpc_tools.py @@ -4,6 +4,7 @@ import math from glob import glob import re +import logging def run_job(job_file): job_submission = subprocess.run(['sbatch', job_file], capture_output=True) @@ -12,7 +13,7 @@ def run_job(job_file): jobid = output.split()[3] else: jobid = 0 - print(f'Job submission failure: {job_submission.stderr.decode()}') + logging.error(f'Job submission failure: {job_submission.stderr.decode()}') return jobid def cancel_jobs(job_ids): @@ -189,7 +190,6 @@ def gen_hpc_to_azure_job(files, transformed_files, az_paths, dependency=None, tr job_file = f'{job_dir}{job_name}.sh' # Estimate time requirements - total_bytes = 0 for file in files: total_bytes += os.stat(file).st_size @@ -197,7 +197,7 @@ def gen_hpc_to_azure_job(files, transformed_files, az_paths, dependency=None, tr time_factor = 1.5 # Provide extra time in case things move a little slower than usual time_required_hrs = math.ceil(time_factor * total_bytes * 8 * 10 ** -6 / transfer_speed / 60 / 60) if time_required_hrs > 240: - print('Warning: Transfer job is estimated to take longer than the maximum of 240 hrs.') + logging.info('Warning: Transfer job is estimated to take longer than the maximum of 240 hrs.') time_required_hrs = 240 # Create transfer args @@ -253,14 +253,8 @@ def process_h5_dataset(files, comb_ref_file=None, time_limit_hrs=None, mem_facto transformed_files = [] az_paths = [] # Loop over files - print(f'Starting {len(files)} transformation jobs.') + logging.info(f'Starting {len(files)} transformation jobs.') for file in files: - # Get max dataset size to determine memory allocation - # f = h5py.File(file) - # max_dataset_size = 0 - # for key in f.keys(): - # max_dataset_size = max(max_dataset_size, f[key].nbytes * 10 ** -9) - # mem_GB = int(max_dataset_size * mem_factor) # It was found that files as small as 415 GB timed out when only given 4 hours. # In practice, there is a lot of variablity in the lengths of job runs. This may @@ -296,7 +290,7 @@ def process_h5_dataset(files, comb_ref_file=None, time_limit_hrs=None, mem_facto # Generate job file to copy dataset to Azure if not skip_transfer_to_azure: - print('Starting job to copy dataset to Azure.') + logging.info('Starting job to copy dataset to Azure.') copy_job_file = gen_hpc_to_azure_job(files, transformed_files, az_paths, dependency=job_ids, debug=debug) copy_job_id = run_job(copy_job_file) if copy_job_id == 0: @@ -309,7 +303,7 @@ def process_h5_dataset(files, comb_ref_file=None, time_limit_hrs=None, mem_facto # Generate job file to combine references # NOTE THAT DEBUG IS CURRENTLY SET TO TRUE TO EXPEDITE JOBS WHILE ACCOUNT IN STANDBY - print('Starting job to combine references.') + logging.info('Starting job to combine references.') if comb_ref_file: ref_job_file = gen_hpc_combine_refs_job(comb_ref_file, ref_files, dependency=copy_job_id, debug=True) ref_job_id = run_job(ref_job_file) @@ -319,7 +313,7 @@ def process_h5_dataset(files, comb_ref_file=None, time_limit_hrs=None, mem_facto else: job_ids.append(ref_job_id) - print('All jobs scheduled!') + logging.info('All jobs scheduled!') comb_ref_file_name = comb_ref_file.split('/')[-1] if 'hourly' in comb_ref_file_name: @@ -367,7 +361,7 @@ def process_h5_redos(files, redos, comb_ref_file=None, time_limit_hrs=None, debu transformed_files = [] az_paths = [] # Loop over files - print(f'Starting {len(redos)} transformation jobs.') + logging.info(f'Starting {len(redos)} transformation jobs.') for file in files: # It was found that files as small as 415 GB timed out when only given 4 hours. @@ -404,7 +398,7 @@ def process_h5_redos(files, redos, comb_ref_file=None, time_limit_hrs=None, debu # Generate job file to copy dataset to Azure if not skip_transfer_to_azure: - print('Starting job to copy dataset to Azure.') + logging.info('Starting job to copy dataset to Azure.') copy_job_file = gen_hpc_to_azure_job(files, transformed_files, az_paths, dependency=job_ids, debug=debug) copy_job_id = run_job(copy_job_file) if copy_job_id == 0: @@ -417,7 +411,7 @@ def process_h5_redos(files, redos, comb_ref_file=None, time_limit_hrs=None, debu # Generate job file to combine references if comb_ref_file: - print('Starting job to combine references.') + logging.info('Starting job to combine references.') ref_job_file = gen_hpc_combine_refs_job(comb_ref_file, ref_files, dependency=copy_job_id, debug=debug) ref_job_id = run_job(ref_job_file) if ref_job_id == 0: @@ -426,7 +420,7 @@ def process_h5_redos(files, redos, comb_ref_file=None, time_limit_hrs=None, debu else: job_ids.append(ref_job_id) - print('All jobs scheduled!') + logging.info('All jobs scheduled!') comb_ref_file_name = comb_ref_file.split('/')[-1] if 'hourly' in comb_ref_file_name: @@ -463,14 +457,14 @@ def scan_err(dataset='WIND/Great_Lakes', resolution='5min'): elif len(text) > 0: other_errors.append(err) other_redos.append(file) - print(f'Timeouts: {len(timeouts)}') - print(f'Other errors: {len(other_errors)}') - print(f'Total Files: {len(files)}') + logging.info(f'Timeouts: {len(timeouts)}') + logging.info(f'Other errors: {len(other_errors)}') + logging.info(f'Total Files: {len(files)}') sizes = [] for redo in timeout_redos: sizes.append(os.stat(redo).st_size * 10 ** -9) if len(sizes) > 0: - print(f'The smallest file that timed out in {dataset} was {min(sizes):.0f} GB.') + logging.info(f'The smallest file that timed out in {dataset} was {min(sizes):.0f} GB.') - return files, timeout_redos, other_redos \ No newline at end of file + return files, timeout_redos, other_redos diff --git a/azure/pipeline/run_aws_pipeline.ipynb b/azure/pipeline/run_aws_pipeline.ipynb index efc5580..ce9cbf9 100644 --- a/azure/pipeline/run_aws_pipeline.ipynb +++ b/azure/pipeline/run_aws_pipeline.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -15,271 +15,9 @@ }, { "cell_type": "code", - 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'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-06.h5',\n", - " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-07.h5',\n", - " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-08.h5',\n", - " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-09.h5',\n", - " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-10.h5',\n", - " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-11.h5',\n", - " 'nrel-pds-wtk/south_atlantic/monthly/v1.0.0/satlantic_2020-12.h5']" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Get the s3 addresses for the dataset\n", "\n", @@ -294,20 +32,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - 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- ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Generate the state machine input for this dataset\n", "\n", @@ -318,29 +45,9 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'executionArn': 'arn:aws:states:us-west-2:351672045885:execution:kerchunk-h5:south_atlantic-5min-2',\n", - " 'startDate': datetime.datetime(2023, 9, 7, 16, 18, 29, 364000, tzinfo=tzlocal()),\n", - " 'ResponseMetadata': {'RequestId': 'e9ced2eb-9fdb-40f2-9e2a-83c369b986ee',\n", - " 'HTTPStatusCode': 200,\n", - " 'HTTPHeaders': {'x-amzn-requestid': 'e9ced2eb-9fdb-40f2-9e2a-83c369b986ee',\n", - " 'date': 'Thu, 07 Sep 2023 16:18:29 GMT',\n", - " 'content-type': 'application/x-amz-json-1.0',\n", - " 'content-length': '129',\n", - " 'connection': 'keep-alive'},\n", - " 'RetryAttempts': 0}}" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Run the state machine\n", "\n", @@ -369,40 +76,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "INFO: Scanning...\n", - "INFO: Any empty folders will not be processed, because source and/or destination doesn't have full folder support\n", - "\n", - "Job d5998f8f-4aad-1649-4924-6aa7e8fe7c7e has started\n", - "Log file is located at: /home/ec2-user/.azcopy/d5998f8f-4aad-1649-4924-6aa7e8fe7c7e.log\n", - "\n", - "100.0 %, 1 Done, 0 Failed, 0 Pending, 0 Skipped, 1 Total, \n", - "\n", - "\n", - "Job d5998f8f-4aad-1649-4924-6aa7e8fe7c7e summary\n", - "Elapsed Time (Minutes): 0.0667\n", - "Number of File Transfers: 1\n", - "Number of Folder Property Transfers: 0\n", - "Number of Symlink Transfers: 0\n", - "Total Number of Transfers: 1\n", - "Number of File Transfers Completed: 1\n", - "Number of Folder Transfers Completed: 0\n", - "Number of File Transfers Failed: 0\n", - "Number of Folder Transfers Failed: 0\n", - "Number of File Transfers Skipped: 0\n", - "Number of Folder Transfers Skipped: 0\n", - "TotalBytesTransferred: 507255254\n", - "Final Job Status: Completed\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "comb_ref_file = f'wtk/{prefix}/kerchunk_{resolution}_ref.json'\n", "create_combined_ref(files, staging_bucket, comb_ref_file=comb_ref_file, remote_protocol='abfs')" diff --git a/azure/pipeline/transform_h5_container/gen_ref.py b/azure/pipeline/transform_h5_container/gen_ref.py index 41a0834..8ea40c4 100644 --- a/azure/pipeline/transform_h5_container/gen_ref.py +++ b/azure/pipeline/transform_h5_container/gen_ref.py @@ -1,6 +1,5 @@ import ujson -from etl_tools import gen_ref_comb, load_oedi_sas -from aws_tools import copy_local_file_to_azure +from etl_tools import gen_ref_comb import xarray as xr import os import s3fs @@ -15,9 +14,7 @@ s3 = s3fs.S3FileSystem() # Get input from container environment -# s3_source_files = ujson.loads(os.getenv('s3_files')) s3_comb_ref_file = os.getenv('s3_comb_ref_file') -# az_comb_ref_file = os.getenv('az_comb_ref_file') staging_bucket = os.getenv('staging_bucket') run_name = os.getenv('run_name') @@ -55,9 +52,3 @@ local_s3_ref = 's3_ref.json' gen_ref_comb(s3_refs, ref_file=local_s3_ref, identical_dims=identical_dims, remote_protocol='s3') s3.put_file(local_s3_ref, f's3://{staging_bucket}/{s3_comb_ref_file}') - -# if az_comb_ref_file: -# local_az_ref = 'az_ref.json' -# gen_ref_comb(az_refs, ref_file=local_az_ref, identical_dims=identical_dims, remote_protocol='abfs') -# s3.put_file(local_az_ref, f's3://{staging_bucket}/{az_comb_ref_file}') -# copy_local_file_to_azure(local_az_ref, az_comb_ref_file) diff --git a/azure/pipeline/transform_h5_container/hello.py b/azure/pipeline/transform_h5_container/hello.py deleted file mode 100644 index b862a78..0000000 --- a/azure/pipeline/transform_h5_container/hello.py +++ /dev/null @@ -1,3 +0,0 @@ -from etl_tools import load_oedi_sas - -print(load_oedi_sas()) diff --git a/azure/pipeline/transform_h5_container/transfer.py b/azure/pipeline/transform_h5_container/transfer.py index b95a599..c43ae6f 100644 --- a/azure/pipeline/transform_h5_container/transfer.py +++ b/azure/pipeline/transform_h5_container/transfer.py @@ -2,5 +2,4 @@ import sys args = sys.argv -print(args[1]) subprocess.run(['azcopy', '--version']) diff --git a/azure/pipeline/transform_h5_container/transform.py b/azure/pipeline/transform_h5_container/transform.py index 50621c9..515f15f 100644 --- a/azure/pipeline/transform_h5_container/transform.py +++ b/azure/pipeline/transform_h5_container/transform.py @@ -2,6 +2,7 @@ from etl_tools import transform_wtk_h5_file, transform_sup3rcc_h5_file, gen_ref from time import time import boto3 +import logging # Download h5 to local and then build out the rechunked copy @@ -23,7 +24,7 @@ s3.download_file(Bucket=Bucket, Key=Key, Filename=local_path) # Transform dataset -print(f'{(time() - start_time) / 60:.2f} min: {file_name} - Starting transformation.') +logging.info(f'{(time() - start_time) / 60:.2f} min: {file_name} - Starting transformation.') if 'nrel-pds-wtk' in source_path: #DATASET_NAME = 'wtk' az_path = source_path.replace('nrel-pds-wtk/', 'wtk/') @@ -38,22 +39,22 @@ transform_sup3rcc_h5_file(source_path, scratch_path) else: raise NotImplementedError(f'Dataset for {source_path} not implemented yet.') -print(f'{(time() - start_time) / 60:.2f} min: {file_name} - Transformed.') +logging.info(f'{(time() - start_time) / 60:.2f} min: {file_name} - Transformed.') ref_file = scratch_path.replace('.h5', '.json') ref_file_s3 = scratch_path.replace('.h5', '_s3.json') gen_ref(scratch_path, f'abfs://{container_name}/{az_path}', ref_file=ref_file) -print(f'{(time() - start_time) / 60:.2f} min: {file_name} - Azure reference generated.') +logging.info(f'{(time() - start_time) / 60:.2f} min: {file_name} - Azure reference generated.') s3_staging_path = f's3://{staging_bucket}/{az_path}' gen_ref(scratch_path, s3_staging_path, ref_file=ref_file_s3) -print(f'{(time() - start_time) / 60:.2f} min: {file_name} - S3 reference generated.') +logging.info(f'{(time() - start_time) / 60:.2f} min: {file_name} - S3 reference generated.') # Upload to staging s3 = boto3.client('s3') s3.upload_file(ref_file, staging_bucket, ref_file.replace('/data/', '')) -print(f'{(time() - start_time) / 60:.2f} min: {file_name} - Azure reference uploaded to staging.') +logging.info(f'{(time() - start_time) / 60:.2f} min: {file_name} - Azure reference uploaded to staging.') s3.upload_file(ref_file_s3, staging_bucket, ref_file_s3.replace('/data/', '')) -print(f'{(time() - start_time) / 60:.2f} min: {file_name} - S3 reference uploaded to staging.') +logging.info(f'{(time() - start_time) / 60:.2f} min: {file_name} - S3 reference uploaded to staging.') s3.upload_file(scratch_path, staging_bucket, az_path) -print(f'{(time() - start_time) / 60:.2f} min: {file_name} - h5 file uploaded to staging.') +logging.info(f'{(time() - start_time) / 60:.2f} min: {file_name} - h5 file uploaded to staging.') From 4f551e595dae5409fdb62336a4485eaf05da0f11 Mon Sep 17 00:00:00 2001 From: jgu2 Date: Wed, 24 Jan 2024 12:56:39 -0700 Subject: [PATCH 3/3] Update the year --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7c7ee54..29ef781 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,7 @@ # Open Data Access Tools The Open Energy Data Initiative (OEDI) provides a number of tools to enable the use of the open data published through this initiative. The source is largely written in Python, including Jupyter notebooks. -Copyright (c) 2023 Alliance for Sustainable Energy, LLC and Skye Analytics, Inc. +Copyright (c) 2024 Alliance for Sustainable Energy, LLC and Skye Analytics, Inc. Open Data Access Tools: NREL SWR-20-57. Azure Data Tools: SWR-23-92.